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  • 颜荻：秘索思与逻各斯的动力学：古希腊文明精神溯源

    引言 

    古往今来，任何一部文明史都是不同文明互鉴的历史。深刻认识文明互鉴的实践，是一种特有的文明自觉。文明研究有三个关键议题：其一，文明的起源性构造及根源性影响；其二，文明发展的动力原则及生成逻辑；其三，文明对自身历史的认识及系统化表达。无论在中国还是西方，三个议题都贯穿于文明发展的历程之中。可以说，任何一个角度的文明研究都应怀有这三个部分的问题意识并予以展开。 

    就西方文明而言，几乎所有起源性问题都可追溯至古希腊。古希腊作为开端，其始源性构造奠定了西方文明的最初样态。在始源性构造中，有一个议题十分重要，即“秘索思（mythos）与逻各斯（logos）”。它不仅深刻关涉上述三个关键的文明研究内容，且对古希腊文明乃至整个西方文明形成奠基性影响。 

     Mythos一般指“语词”“神话”“故事”与“虚构的言辞”，logos则指“理性”“秩序”“逻辑”和“规则”。二者首先从古希腊历史的发端处，以语言这一最基本的文明形式塑造了古希腊人对自身、社会、世界乃至宇宙的根本想象，同时作为两种不同的思维模式，其动态互动构成古希腊文明乃至西方文明的基本生成逻辑。传统研究将此互动过程经典地描述为“从mythos到logos的转变”，其发展路向通常被认为最终打开了西方理性主义与逻各斯中心主义（logocentrism）的大门，因而对近代以来的启蒙运动与科学主义兴起，乃至现代性的产生与发展形成深远影响。与之相应，在这一过程中西方文明所逐渐形成的对自身历史的认识与系统表达，可称之为历史书写。在logos成为一种权威表达方式时，西方的历史叙事乃至历史观也随之逻各斯化。历史越来越被看作一个理性发展的过程，以至到近代，这一观念进一步与进化论和目的论关联，发展出一系列西方文明对自身价值的评估与判断。 

    因此，mythos与logos一向是西方古典学与相关学科研究的经典课题。无论是围绕mythos与logos的词源学经典讨论，还是从文学、哲学、史学等出发的文本意义考察，均成果丰硕。基于“从mythos到logos转向”的基本框架，相关研究从不同侧面不断巩固“logos对mythos的胜利”这一主流观点，从而形成对mythos与logos关系及其奠基性意义的网络式理解。 

     然而，“logos的胜利”却无法涵盖所有现象。在人类似乎进入由理性、秩序、逻辑与规则构成的科学、中立、通约化的普遍历史世界时，mythos一直作为动力隐隐存在着。自19世纪开始，从“原始思维”到“理性文明”的表述，同时受到不同学科的严厉批评与审查。其中结构主义人类学强调神话作为“深层心智”绝非“野蛮的初级思维”，仪式/功能主义社会学对神话进行了社会功能阐释，神话哲学则努力在哲学中直接复兴神话的意义。这表明，mythos与logos的内在蕴涵显然比既有的线性阐释模式复杂得多。究其根本，在于mythos与logos间相互勾连、冲突与纠缠的状态，在其出现之初便已开始。二者在起源时所构成的此消彼长的动力学原则对西方文明发挥着根本而持续的作用。因此，要厘清整个西方文明在思想史层面的复杂发展脉络，就需回到始源，重新探讨mythos与logos的发生史。从这一视角出发，不仅能观察到西方文明所深含的内在力量，还能在此力量所具有的开放性与包容性中理解西方文明不断塑造与再造的过程，直至通解当下现代性所面临的复杂问题。 

    一、“颠倒”的秘索思与逻各斯 

    从最早的古希腊文献来看，mythos与logos最初即一组有关“言辞”的对立统一的概念。不过，在古希腊早期历史中，mythos与logos的意涵与现在所熟知的意义恰恰相反。早有学者如布鲁斯·林肯指出，logos在古风时期的语境中，所指涉的绝非后人所理解的“理性”与“真实性”，而是与“欺骗”“错误”和“谎言”相关联；反而，现在看似表达“虚构”与“假象”之意的mythos被认为具有更高的真理性甚至神圣的权威性，从而，在mythos与logos的起源之初，两者所表之意，实际正是后来意义的颠倒。 

    赫西俄德与荷马为此提供了经典的例证。例如，在赫西俄德《劳作与时日》中，几乎所有的logos都与虚构和谎言相关，诗人不仅以logos而非我们通常认为的mythos来指代“五代神话”这个虚构的故事，而且特地选用形容词haimulios（欺骗的）来对不同语境中的logos进行修饰。而在《奥德赛》中，足智多谋的奥德修斯，在与佩涅罗佩相认前夕，也讲述了（legein）（<logos）许多谎言（polla pseudea），那些谎言就像真的一样，令王后信任与哭泣。 

    在布鲁斯·林肯所列举的所有相关例证中，可以发现，“秘索思与逻各斯之争”正是始于这两者所包含的积极与消极意义的对立。而伴随着两极的分化，这两个词汇又被进一步赋予相应的性别化特质，从而，在譬喻性的层面上被完全对立。由于logos总带有欺骗与谎言的负面性质，因此，在古希腊整体的厌女（misogyny）语境下，自然与“女性化”的特性相关联。潘多拉“迷人的logoi，以及诡诈的性格”就是典型。而与logos相反，mythos则具有“男性化”的特质。一位英雄的理想就是成为一位“实践的行动者与mythos的言说者”，由此，mythos被显现为一种与英雄精神相关的特质，并时刻与这一男性化的、公共的、强大的力量正向关联。 

    Mythos与logos性别化的对立所反映的不仅是两性本身的问题，而且是在一个更广泛的社会文化意义上，将两者带向了不同的存在之域。与“男性”相关的mythos，其背后意味着“权力”“权威”以及由此而建立的“神圣性”与“真理性”，而logos则恰恰相反。在《荷马史诗》中，当阿伽门农面对克律塞斯（Chryses）的祈求要在集会中力排众议严词拒绝时，他必须使用mythos。因为，越是男性化的、越强大的人，越拥有言说mythos的资格与能力，反之，则被认为应当在mythos的领域保持沉默。与logos相关联的女人便更没有言说mythos的权利。特勒马库斯就明确告诉母亲：“你还是回到里屋，操持你自己的事……mythos是男人关心的事——所有男人，尤其是我，因为我是家中的掌权者。” 

     正如理查德·马丁所指出的，mythos总是一种力量之语，它是一个拥有权力或权威的人所说出的强权化的甚至粗暴的言辞。这种极端男性化的特质与史诗尤其荷马精神高度契合。战争作为英雄荣誉的来源，成为史诗必然歌颂的对象，而正是此“强有力”的话语，不仅标志着英雄取得胜利的强势力量，而且，连同英雄的行动一起，构成了诗歌中那些值得传颂的语言与故事。英雄之诗，从根本上而言，就是力量之诗。换言之，关于英雄的mythos，就是力量的mythos。它光明、正大、直接、不加掩饰，与欺骗、阴暗、迂回的logos形成强烈反差，由此，前者在英雄世界的价值体系中，在对伟大的英雄精神的渴望与追求下，被崇尚为一种揭示英雄本质的、本真性的语言形式，一种与“真实”所关联的“动人”的话语结构。在这个近乎二元对立的价值判别中，mythos——无论是言辞本身，还是其所构成的叙事——便拥有了绝对的权威性与崇高性，甚至与神圣世界关联起来。 

    在此，我们必然会想起赫西俄德《神谱》中缪斯女神谈论mythos的经典段落： 

    女神们首先向我讲出这些话语（mythos）， 

    那些奥林波斯的缪斯，持大盾的宙斯的女儿们： 

    “荒野的牧人啊，你这可鄙的家伙，只知吃喝， 

    我们知道如何讲述（legein）谎言如真实一般， 

    也知道如何如我们所愿唱诵（gēruein）真实（alēthēs）。” 

    神圣之音，mythos，在缪斯作为神明的神圣权威中展开。她们对诗人说话，诗人聆听她们的语词。她们告诫诗人，女神可以讲述谎言，也能够唱颂真实，她们凭自己的意愿，在谎言与真话之间作出选择。若是谎言，则是将其讲出（legein），而若是真理，她们则会为之唱颂（gēruein）。“说”与“唱”标定了谎言与真实的界限，而女神们赐给赫西俄德的是一首“动人的歌”，所以诗人笃信，他从缪斯处所继承的，必然是神明们所歌颂的真实。在神圣的启示下，诗歌作为一种唱颂/言说形式，便接近了最高的真实性与永恒性，它从神圣世界获得了权威的力量，从而在世俗世界中，自然而然成为一种富有权威的真实性表达。 

    在神圣世界的关照下，诗人通过诗歌所唱颂的史诗、故事和神明谱系便与“真实”和“真理”深度勾连。Mythos成为一种罗伯特·福勒所谓的元诗学（metapoetic），一种先验的、不可辩驳的真理，而其所关联的所有语词、言说与话语都与虚假的、错误的、荒谬的logos世界相区离。而当这些“真实的”叙说在世代吟游诗人的口耳相传中成为古希腊的记忆时，mythos所构成的具有“真实性”的“历史”出现了。而这种深嵌于神圣权威之“真理”的真实性，已经超越了历史实证主义意义上的真实，在一种超历史的意义上成为最本真的存在。荷马与赫西俄德，也由此成为所有古希腊人的先师，其mythos之言说，构成了古希腊共同体“真知”的基底，从而塑造着古希腊人对其自身精神与历史意义的根本认识。 

    Mythos与logos在“真”与“假”的二元对立中展开了最初的对话：mythos表达真实的、男性化的、阳刚的、权威性的、公共的、动人的话语体系，logos则表达虚假的、女性化的、阴柔的、边缘性的、私人的、充满冲突的言说。从荷马与赫西俄德到公元前6世纪晚期，这一两极化的表达占据着古希腊世界的主流，mythos也因其所拥有的真理性与权威地位而一直被奉为圭臬。而当mythos的真实性开始受到质疑时，这一图景便开始转变。从希罗多德与前苏格拉底哲人，到修昔底德与智术师群体，最终到柏拉图，mythos逐渐被质疑为不可知的、不真实的、非权威性的话语，而logos则越来越被尊崇为可知的、可控的乃至权威的言说。如此转变使得mythos与logos两者发生结构性倒转，此倒转将影响西方文明对两者意义与关系的根本判摄。而mythos与logos之变是一个逐步发生的漫长过程。 

    二、被“悬置”的秘索思 

    对传统mythos意义的“反叛”，现存文献最早可以追溯至公元前6世纪晚期爱奥尼亚（Ionian）的阿那克里翁（Anakreon）。尽管阿那克里翁本人是一位抒情诗人，但他对mythos的使用却已颇为大胆与前卫。在其残篇中，最具代表性的例子是他在谈及人们反抗萨摩斯的（Samos）僭主波吕克拉特斯（Polykrates）时，用复数mythiētai（说mythos之人）来指涉那些反叛的领袖们。由此，mythos被阿那克里翁纳入政治行动的语境，在动乱的煽动性言辞下，政治领袖所言之mythos就不再是拥有神圣权威的史诗式话语，而是俗化为被世俗政治所利用的“工具与武器”。 

    无论阿那克里翁是否受同时代爱奥尼亚学派（Ionian School）的影响，他作为抒情诗人对mythos意义的创新性用法都可以被视作一个具有标志性意义的节点：当mythos不再与神圣世界确切关联而可以被人事所利用时，这样的言说本身是否还具有美德与权威就被打上了一个问号。这意味着，mythos从前所具有的天然的真理性受到质疑，进而受到优劣评判。在批评与赞扬的表述下，“好的”mythos就变成了一个被竞相争夺的对象，而“坏的”mythos则受到贬斥。这正是阿那克里翁之后的几十年所蔓延开来的景象。 

    诗人品达就常对mythos进行优劣之分，他会批评“有些人所说的mythoi……隐含着谎言和欺骗”而捍卫自己mythos的优越性，将其诗歌视为一种aretai（美德）的表达。在对自我与他人的扬抑之中，品达不断为自身的诗歌立法，以赢得诗人的“桂冠”。前苏格拉底哲学家也参与进了对mythos话语权的争夺中。克洛丰的色诺芬尼（Xenophanes）就曾批评“荷马与赫西俄德将人类中所有有害的、应当受到责难的东西都归因于了神明的力量”，而自己重提一套“好的”mythos的标准。巴门尼德更是明确强调要“听我的mythos！”这与思培多克勒捍卫自己的mythos的方式如出一辙。 

    诗人与哲人同时对自我mythos地位的捍卫，从某种程度上显现出后世所谓“哲学与诗歌之争”的雏形。但此时，哲学仍借用诗歌mythos的权威为自我正名，尚未求诸logos。然而，一旦人人都有权利声称自己的mythos才是更好的言说，mythos原本凌驾于一切的权威便决定性地让位于评判者自身。缪斯不再在场，“人的时代”悄然降临。而伴随着mythos本身超越性的下降，一个必然的问题便是：mythos一词还能否完全承担起其权威性的功能？或者说，mythos是否还具有不可置疑的真理性与说服性来作为人们认识与理解世界的基础？ 

    从阿那克里翁到品达，再到前苏格拉底的哲学家，这些言说者尽管各有其立场与态度，但在面对上述问题时，他们对mythos一词的表达都越来越收缩与谨慎。若在公元前6世纪晚期至公元前5世纪早期，mythos还被部分作为一个正面、积极的词汇来使用，那么，到了希罗多德之时，他已不再能，或不再愿意用mythos来指代其自我表达。他将mythos束之高阁，转身求诸logos，赋予logos以更高的力量与权威。这可以说是logos之变的一个重要转折。 

    希罗多德的写作代表了神话（或mythos）时代对理性（或logos）时代的退让，从他开始，可以明显看到作家对传统mythos整体性的保留态度。在《历史》开篇，希罗多德点明：他希望去探究希腊人与波斯人纷争的原因，于是，详细记述了两者关于同一神话/故事的富有争议的说法。然而，在包括腓尼基人的说法被一一陈列后，这位历史学家以一句总结摒弃了对前述几种mythos的考察：“这两种说法，哪一种更合乎事实，我不想去讨论。下面，我将指出我本人确切知道的那个最先向希腊人发难的人，继而继续我的叙述（logos）。”由此，希罗多德转向了吕底亚国王克洛伊所斯（Kroisos）的故事，并借此将其历史探索追溯到公元前6世纪中叶这个可知的历史时代——它成为希罗多德历史叙述的真正起点，一个“不去论述神话”的历史性开端。 

    有一种历时化（chronological）的意识，清楚表明了希罗多德的记述愿意开展的范围与界限：在对历史“时间”的反复强调下，“历史”停留在“不可知其时”的神话叙事的边缘。对他而言，“神话”过于久远，无法验真与证伪，于是，选择将其悬置——只有那些可以客观知道并验真的时期与事件才是他本人希望去讲述（legein）的对象。这便意味着，在某种程度上，希罗多德将远古的“神话”与故事搁置在了其历史叙述框架之外，或至少，他本人的logos将不会包含传统意义上的mythos，而力图成为一种新的关于过去的叙事。 

    这并不是说希罗多德就此将神话直接贬损为欺骗性、虚假的叙事，而是在“悬置”的方法论原则中，对“神话”或我们称之为mythos的话语体系作出了一个不同于史诗传统的界定。福勒曾敏锐地指出，希罗多德在谈论公元前6世纪中叶的一起历史事件时，引人注目地使用了上文提到的“人的时代”（tēs anthrōpēiēs geneēs）这个不同寻常的短语：“波律克拉铁斯，据我们所知，是在希腊人中第一个想取得海洋统治权的人……不过，在我们所谓的‘人的时代’, 波律克拉铁斯就是第一人。”人类时代的“第一”要从头开始计算，它与神话人物所存在的“前人类时代”或“神话时代”相分离。这意味着，荷马与赫西俄德笔下的英雄与诸神，包括缪斯，都被修昔底德悬置在人类历史周期之外，将其归之于经验事实“不可知晓”“不可确信”或“不可触及”的领域。 

     这是希罗多德在他所处的“人的时代”对mythos作出的 “评判”，但其“悬置”方法使得这一评判相对温和，因为它将史诗传统与希罗多德自身的历史立场之间的张力模糊化了。不过，对于希罗多德而言，仍有一个他必须面对的问题，即，如何解释那些“不可确信”的神话人物所拥有的确定无疑的、流传至今的名字与故事。对此，希罗多德用一句几乎惊世骇俗的评论作出了解释：“每一个神从什么地方生产出来，或者他们是不是都一直存在，他们的外形是怎样的，这一切都可以说是希腊人在不久之前才知道的。因为我认为，赫西俄德与荷马的时代比我的时代不会早过四百年，是他们，把诸神的家世教给了希腊人，把他们的名字、尊荣和记忆教给了所有人并且说出了他们的外形。”希罗多德并不否认神明的存在，但他在可知与不可知的边界上，重新界定了赫西俄德与荷马的位置。这两位诗人“创造”了神灵的名字，正如荷马也同样“创造”了希罗多德本人未曾见过的传说中的欧凯阿诺斯（Ocean）河流一样。他们作为“人”本身，并不一定受到所谓的缪斯的神启，毋宁说，大多数神话故事与人物，不过是诗人自身的创造，它们即便很难证伪，也很难证实。由此，诗人所赋予希腊人的mythos，在希罗多德看来，就应当被排除在人类历史的考察范畴之外，而换个角度来说，书写人类历史的历史学家，也应当自觉地将mythos之言说与内容束之高阁，以确保其可知历史的可确证的真实性。 

     希罗多德在此将诗人的mythos与神圣世界作出了区分，神圣世界仍具有崇高的权威与神圣性，但诗人作为传统中讲述与唱颂mythos之人，却受到实质性质疑。在此意义上，我们或许可以理解，为何希罗多德特别有意识地将自己的叙述指涉为logos，并刻意避免使用mythos一词：他的logos是排除对传统mythos讲述的言辞，而他本人，则是区别于传统诗人的历史学家，是能够给希腊人带去一种新的（也更真切的）记忆的言说者。由此，希罗多德便能够从“可知性”与“真实性”出发为其自身的“历史的logos”赋予更高的位置。于是，当他拒绝采信关于居鲁士（Kyros）出生的三种说法时，他宣称将要告诉我们一个“真正的故事”（ton eonta logon）；而那些希罗多德称之为logioi andres的人，则被认为是具有学养的权威人士，他们不仅通晓过去的故事，而且知道哪些才是值得聆听的。所有这一系列对logos的使用都表明传统诗人权威在明显下降。 

    从古典时期早期的诗人阿那克里翁与品达，到前苏格拉底哲学家，再到希罗多德，可以看到，mythos整体的权威性与神圣性越来越低，随之而来的，是logos以及与之相匹配的 historia(历史)的兴起。尽管，在这一阶段，mythos仍处于某种“中间状态”，希罗多德也仍在书中收集了大量传统神话故事，但mythos还是在historia的判断性“悬置”中受到了无形的挤压与价值重估。这恰恰是希罗多德在其“历史与诗歌之争”的框架下为mythos与logos之变所带来的一个具有深远意义的方向性影响，该影响到智术师与修昔底德之时，将会开展出全部的力量。 

    三、智术主义与逻各斯势力的兴起 

     随着启蒙运动与社会变迁的发展，普罗塔格拉 “人是万物的尺度”的宣言打破了mythos与logos最后微妙的平衡。当把人作为宇宙的中心来度量世界时，诸神便隐退天际，传统中神圣的mythos随之黯然失色。智术师是一个彻底转向logos之言说的群体。当mythos的真理性与说服性一再受到质疑，以“人”为万物中心的智者们，最终选择了彻底摈弃将mythos作为人们理解世界的基础，转而在logos处建立其认识论的根基。对智术师而言，logos之所以被认为是可靠的，是因为它是纯粹的人事：它更多与人类的语言和修辞相关联，与遥远的神话无涉。如此介乎人类现实行动之间的言说，在智术师看来，最能呈现真实的人类社会。高尔吉亚将logos与真实性（reality）联系起来，并在其《海伦颂》（ Encomium of Helen ）中，用logos的修辞学力量为海伦传统的mythos开脱，便是这一观念的典型体现。 

     对logos作为言辞力量的强调，是智术师处理与理解logos的一个显著特征。虽然荷马时代已有katalegein(准确地说)一类将logos作为言说之意的词汇，但公元前5世纪，logos在智术师运动下成为一种社会现象、方法论乃至世界观。就社会与政治背景而言，古希腊城邦对公共辩论的强调强化了logos的重要性，但更重要的是，在人本主义的思想逐渐兴起、传统mythos愈受质疑的大趋势下，logos所进入古希腊社会视野之中的意义。当人们返诸己身，以期对人类自身的行动作出自我解释时，logos作为影响政治行动乃至广泛人类行动的推动力，便获得了作为真实性基础的权威。换言之，通过理解logos在人类社会中所展现的力量，便能够理解人类社会最根本的真实性，而这种真实性又将成为指导人们行动的基础，它足够聚焦当下，不再需要神圣世界与遥远历史的参与。由此，logos与mythos彻底分离。而这一步，智术师们走得要比希罗多德激进许多，在他们对logos的强势追随下，mythos及其背后的整个传统世界与之渐行渐远，甚至隐没。 

    或许并不令人意外的是，在现存智术师的残篇中，mythos出现的情况少之又少。在讨论logos的诡辩与欺骗力量时，高尔吉亚未将该词与mythos相对比，而在《海伦颂》中，他所对比的却是poiēsis（诗歌）。这似乎显示出智术师试图超越既有“mythos与logos”之传统并重新界定两者关系的“野心”。 

    这一野心在智术主义的语境下是可以理解的。因为对智术师（例如高尔吉亚与普罗塔格拉）而言，logos总被认为拥有双重力量：既是一种说理的话语方式，也是一种欺骗性的话术。无论之前人们认为logos与mythos何者真实、何者虚假，在智术师这里，logos囊括了这两个方面，从而在“真实性”问题上不再与传统意义上的mythos相对。两者的关系因而需要被纳入一个新的框架。新的框架是什么？柏拉图的《普罗塔格拉》提供了一个可能是主流的智术师的回答。在这篇被认为很大程度上忠实于智术师本身作品的文本中，mythos被指涉为“给孩子们讲述的虚构的故事”，而logos则为“逻辑论辩”。这意味着，mythos与logos的对立不再是欺骗与真诚、谎言与真理之间的对立，而是“现实”与“虚构”之间的对立。 

    在智术师的现实主义关怀下，mythos被整体文本化（textualization）地处理几乎是一个必然的结果。由于被理解为虚构的，mythos只可能是一种人为的文学现象，而不再是来自缪斯的神启。在公元前5世纪日渐发达的书写体系下，随着口头传播的mythos被越来越多地记录下来当作文本资料和参考资料扩散流通，真实性本就受到质疑的mythos愈加丧失其传统宗教与社会的意义。从而，无论是在智术师群体中，还是在其他领域，mythos都越来越被排除在历史与现实的追问之外。 

    修昔底德无疑深受这一思潮的影响。与希罗多德相比，这位更年轻的历史学家除了精通智术师的作品以外，也更加坚决地将mythos排除在其文本写作之外，从而，在许多人（尤其实证史学家）看来，修昔底德是真正的“历史”书写的开端。尽管就对待mythos的立场而言，希罗多德与修昔底德之间是程度而非性质的差异，但在同时代智术师传统的强烈影响下，修昔底德对mythos与logos的争判更加毫无保留地偏向了logos，即其所代表的“非虚构”的、“现实理性”的一面。 

     修昔底德明确宣称，mythos，连同那些久远的传统记忆都不应当被纳入历史，因为记忆是脆弱、模糊的，甚至是具有欺骗性的——它永远是对历史的挑选、解释与重构。因此，“在这样的领域，很难去相信它们所呈现出来的信息”。作为一位史学家，修昔底德呼吁每一个人仔细甄别所有信息，去觉察那些记忆或传统说法中无法证实甚至不真实的成分，并识别出它们在经年累月后最终与mythōdes(神话)相结盟并倾泻出的那些不可信的言说。对修昔底德而言，现实与记忆之间存在着一个明显的“可信”与“不可轻信”的对立关系，而后者在诗人与故事记录者的笔下又更加严重。因为当诗人“夸大其词地为事件赋予流光溢彩”或当故事记录者“为了听者的愉悦而非为了事实”将未经证实的东西拼凑在一起时，那些令人怀疑的说法就彻底令人难以相信了。为此，修昔底德坚决提出，“如果我们希望能够看清过去的事实，借以预知未来”，就不应当像诗人和故事记录者那样为迎合人们的兴趣而写作，而是应当彻底地回到可信且可证实的“现实”之中。 

    那么，如何确保“现实历史”的真实性？修昔底德走得比智术师更远。他从logos（言辞）转向了ergon（行动），将所有历史书写都建立在现实行动事件的基础上。在《伯罗奔尼撒战争史》中，伯里克利有一个著名说法，即“真理寓于行动之中”，这可以说正是修昔底德的立场。如果说logos还有欺骗的可能性，那么，现实中“当下”的ergon则既不虚构也不虚假。《伯罗奔尼撒战争史》几乎不关注过去与传统，它处理古代（ta palaia），最多是为了通过看似逼真的证据来构建权力逐渐发展的模型。修昔底德所要创建的，是基于权力与战争概念的“行动的理论”，他将关注的视野聚焦于当下，以至于所有远离当下行动的诉说，都被谨慎地悬置甚至排除在外。这位理性主义与实证主义的历史学家不同于那些讲述故事的诗人，他就此将“神话”（mythos）与历史隔绝开来。 

    从智术师对logos的推崇，到其对mythos的文本化理解，再到修昔底德对mythos的排除，在公元前5世纪至公元前4世纪一系列启蒙运动思潮的推动下，mythos已被赋予完全不同于古风时期的位置与地位。一定程度上，mythos在修昔底德的笔下受到了最为激烈的挑战，这也是其在整个古希腊思想历史中所遭受的最为严峻的一次重击。在知识论层面，修昔底德对mythos的处理尤其具有颠覆性，几乎完全否认了mythos之于现实世界的意义，否认了mythos存在的正当性。这使得mythos几乎被驱逐出历史舞台，或至少被足够地边缘化。 

    但修昔底德的观念代表较极端化的立场，甚至，他是与大多数同代人充满分歧的少数派。与修昔底德同时期，存在mythos的另一个面向，且在希腊民间社会更加流行。这一面向在最大程度上保留了对mythos的敬意与推崇，其首要特点正是非历史性以及对神话的演绎，即悲剧。从悲剧中可以看到，尽管mythos无可辩驳地受到了冲击，但它对古希腊社会的影响力仍然强大。由此，历史学家、智术师与悲剧作家之间构成了一种对抗与竞争关系，这显示了秘索思与逻各斯之争在当时更加复杂且充满互动的动力学图景，而这种竞争最终对柏拉图关于mythos/logos问题的判摄形成了重要影响。 

    四、悲剧意识与秘索思逻各斯的此消彼长 

    与修昔底德的历史书写相比，悲剧是一种更加大众化与平民化的文体。虽然，悲剧作家是一群具有高度自觉性的知识精英，但由于悲剧演绎在古希腊尤其雅典城邦是一项面向公民、竞赛性的公共活动，因此，悲剧的受众决定了其与大众阶层更广泛的连接，也由此在一定意义上，可以被视为与陶瓶、壁画、建筑等艺术形式相似的大众文化的代表。尽管以精英与大众、贵族与平民、少数人与多数人等二元架构来与 “秘索思与逻各斯之争”相对应过于粗糙与简略，但悲剧对mythos的敬意与推崇在很大程度上反映了当时社会大众对mythos及其所代表的传统神话的态度与立场。 

    一个有趣的现象是，事实上悲剧经历了一个从历史剧到神话剧的转变。这一转变发生在普利尼克斯（Phrynichus）因其历史剧《米利都的陷落》（The Capture of Miletus）被罚之后。该剧以历史事件为题材，由于其生动呈演了前一年米利都被波斯人攻陷的悲惨遭遇而引得在场希腊观众动容痛哭，所以城邦重金惩罚了普利尼克斯。自此之后，几乎所有悲剧都改为神话题材，不再触碰现实历史，以此避免“悲剧”过于令人悲伤。就这样，现实历史题材在悲剧这个文体刚出现时就被禁止，所有故事又回到神话之中。 

    这是“虚构”的mythos在悲剧领域得到高度肯定的一刻，它在此后成为界定悲剧之所以为悲剧的一个核心要素。在悲剧舞台上，“虚构”是一个被刻意强化的特质。不仅演员会戴上面具、穿上戏服，运用大量台词、“假扮”成剧中人物，而且整个悲剧剧场也与外界隔离开来，被有意制造为一个独立于历史社会的虚构空间。而正是在此空间中，神话的故事被改编、演绎与观看，由此，观众对此“虚构性”形成高度的自觉。“有距离地观看”恰恰构成了虚构之于悲剧的价值，而正是在这多重的距离之下，悲剧及其mythos成为一个被凝视、审查与思考的对象。 

    当然，这里的mythos已不是古风时期意义上的高贵而神圣的话语。虽然同样属于“诗歌”与“神话”范畴，但悲剧特别强调作者对传统神话的独创性改编，这意味着悲剧的mythos是一个极具作者性与创造性的话语表达，而非来自缪斯的神启。就此而言，悲剧的mythos接续的仍是古典时期“去神圣化”的批评传统，它在本质上完全属于智术师意义下“虚构的、非真实的故事”序列。不过，与智术师和历史学家不同，悲剧作家不仅承认并且大大突出了虚构的价值与力量，还试图在“虚构”中，恢复mythos的“真理性”。 

     对悲剧作家而言，真知寓于虚构的故事情节之中。正如亚里士多德所言，悲剧是“对一系列行为的模仿”。戏剧如同镜像一般，通过对故事人物的悲剧性命运的“模仿”，展开了对真实世界中的人性与生命本质的深刻探讨。在一系列无解的悲剧冲突中，世界和人都被展现为充满问题、矛盾与含混性的存在，而恰恰借由“虚构”所带来的距离，那些本被现实世界所掩盖或回避的问题、黑暗与矛盾被充分而安全地暴露出来供观众审视。索福克勒斯的《僭主俄狄浦斯》是以虚构的mythos传达真理的典型，通过对俄狄浦斯悲剧命运的揭示，索福克勒斯表明了理性知识之于真理的局限性。埃斯库罗斯的“奥瑞斯提亚”(Oresteia)对“正义”的根基发出了诘问，在阿伽门农家庭悲剧的演绎中，揭露了绝对正义达成的困难与悖论。欧里庇得斯《美狄亚》《埃勒克特拉》和《希波吕托斯》同样如此，这些剧目都从不同侧面探讨了人与人之间最根本的关系纽带如何可能以及如何不可能。 

    对在场观众而言，这些深植于人性与社会的根本问题指向了他们所身处的真实世界，而恰恰是在这虚构的时空中，真理得以以一种超历史乃至于超人的方式显现出来。它向人们表明，舞台上的mythos，以一种historia和logos所不能达到的方式揭露了真相，此真相不仅比现实历史世界所显现出来的更加深刻，而且也比理性思辨所触及的更加复杂。我们在悲剧中不断看到诸如此类忠告：“你有视力，但你却没有看到你所陷入的困境”，“你根本不知道你过的是什么生活，不知道你在做什么，不知道你是什么人”。对于人们日常所熟悉的知识样态、伦理道德、社会结构乃至于人们自身，悲剧都重新发问，并以一种毁灭性的方式呈现出人类世界中被小心翼翼回避、保护与掩盖起来的难以承受的真相。由此，悲剧作为一种虚构的文学形式，重新给予了mythos最高的真理性。 

     那么logos呢？Logos作为悲剧中的对话与言辞被纳入了mythos的表意系统之中，成为一种工具性的——尽管十分强势的——存在。Logos对悲剧而言不可或缺，它贯穿于整个戏剧演绎，是人物思想表达与交锋最直接的通道。悲剧情节的推进，都在语言的诉说、往来、游戏与较量中达成。而语言的误解、诱惑、欺骗与劝说又构成了悲剧情节中最重要的反转与高潮。可以说，在悲剧中，是logos成就了mythos，这恰恰是悲剧作为一种对话式诗歌文体与史诗或抒情诗最大的区别。在此意义上悲剧充分吸收并利用了公元前5世纪理性主义与修辞学传统，为mythos注入了当代最前沿的活力。然而，悲剧对logos作为言辞乃至逻辑思辨的力量又始终保持谨慎。无论是“奥瑞斯提亚”中对克吕泰莫涅斯特拉修辞术的尖锐批评，还是《僭主俄狄浦斯》中俄狄浦斯诘问判案的反讽性演绎，三大悲剧作家的作品都一再表明，logos是危险的。在如此种种对人物语言的危险性的揭示下，悲剧的mythos毋宁将公元前5世纪的logos整体纳入了其对人性之真理的探讨之中，由此，mythos拥有了对logos进行审查与盘问的权力，进而，前者对后者建立起一种“真理”意义的权威。 

    这是悲剧对公元前5世纪智术师传统、理性主义和实证主义历史观向mythos发起的多重挑战的回应。从悲剧在雅典乃至泛希腊世界的受欢迎程度来看，这一回应无疑十分强劲有力，并且得到了民间社会的大力支持。在每年举行的酒神节中，悲剧在循环往复的宗教与仪式的时空中不断强化着其对古希腊社会的整体性影响。而这一影响首先发生在公民教育上。通过集体的排演与观看，城邦公民不仅形成了个体层面的对悲剧问题的反思，而且通过共同的投票，形成了对悲剧意义的共同意见，从而建立起一种公共的、政治的、社会性的思想基础。这恰恰是自荷马以来mythos对古希腊社会而言最重要的意义，正是悲剧将其延续下来。 

    从mythos所面临的败退之势来说，悲剧在启蒙运动的大背景下，对mythos精神的重新强化是相当不容易的事，但这也表明，mythos在希腊世界中拥有强劲且充满韧性的生命力，使得古希腊的根本特质深深扎根于mythos传统之中，即便深受启蒙运动的冲击，mythos也没有被新兴的思想浪潮所湮灭。比三大悲剧作家再晚一辈的柏拉图目睹了这一切，恰因如此，这位哲学家也显现出了最深的忧虑，他不仅明确发起了“诗歌与哲学之争”，并且还要从根源处对mythos与logos的关系进行彻底的哲学改造。 

    五、柏拉图对秘索思与逻各斯关系的哲学改造 

     柏拉图对传统mythos的批评几乎人所共知。在《理想国》第二、三卷中，他指出，传统诗人所编造的mythos都是虚假的故事，因为他们把伟大的神描写得丑陋不堪、把英雄塑造为无恶不作的恶棍，这样的mythos既不虔诚、也不真实，需要被排除在理想的城邦之外。从上文讨论中可以看出，柏拉图此处所针对的正是史诗与抒情诗传统之下的诗歌，尤其那些将英雄特质极端化的悲剧。对柏拉图而言，诗歌尤其悲剧以虚构的形式所展露出的引以为傲的“悲剧性真理”恰是最糟糕的，因为这些故事对不幸与罪恶“不加拣选地”模仿，并且夸大了欲望、痛苦、快乐这些灵魂中最低劣的部分，因此，这样的诗作极容易将mythos置于伦理的险境。倘若城邦中普通的公民无法分辨模仿的真伪与高下却跟随这些故事行事，那么人们的灵魂不仅不会变得更优秀，还将处于道德败坏的危险之中。因此，最好的办法，就是将那些“讲不道德的故事的”诗人驱逐出去，“至于我们，为了对自己有益，要任用较为严肃和正派的诗人或讲故事的人，模仿好人的语言，按照我们开始立法时所定的规范来说唱故事以教育战士们”。 

     柏拉图之所以对诗歌如此警惕，不完全是因为“虚构”本身对真理形成了威胁，尽管，它的确因其作为对真相的模仿而多少远离真实。他最深的忧虑在于——正如他所目睹的——传统mythos不仅道德含混，而且对公民的影响巨大。这正是柏拉图在“古已有之”的“诗歌与哲学之争”中看到的最大问题。柏拉图深知，在一座城邦中，要彻底驱逐诗歌与故事（mythos）是一件多么困难的事：“故事的制造者”（muthopoioi）在城邦中无处不在。她们首先是母亲和保姆，然后是老男人和老女人，还有忙着照顾新生儿的那些不知疲倦地喋喋不休的人，她们“向他们的耳朵里灌输迷人的话语”，为他们讲述口传的以假乱真的故事。由神话和美丽的故事所承载的整个模仿的情感结构吸引了年轻人的眼睛和耳朵，他们会被那些自发的“神话家”迷住，最终“变成身体、声音和思想的性格和第二本性”。从孩子的睡前故事，到所有公民都热衷于观看的戏剧演出，以情动人的文教无处不在，mythos强劲的生命力令其教育如此深入人心，若其真的道德败坏，那么它将对公民及社会形成毁灭性影响。因此，既然深知无法驱逐mythos本身，那么，至少应当将那些对城邦有害的mythos及其制造者排除在城邦之外，方能对城邦形成最大的保护。这正是柏拉图所谓“驱逐诗人”的真正原因。 

    需要指出的是，柏拉图并未驱逐所有诗人与mythos。在其哲学建构中，更重要的是用新的mythos去替代那些传统的、被驱逐的mythos。“任用较为严肃和正派的诗人或讲故事的人，模仿好人的语言”正是柏拉图在驱逐传统诗人之后，立即给出的一个替代性方案。那么，为何柏拉图要使用这样一个“不彻底的”方案？ 

     从知识论的角度来看，这是因为，mythos仍是柏拉图哲学思辨与教育不可或缺的存在。正如柏拉图笔下的苏格拉底在《理想国》中所承认的，尽管知识最终通过logos获得，但在获取知识的哲学式的辩证法中，人们却必须“不使用任何感觉的对象，而只是通过纯粹的观念来推动达致观念的结果”，这种用非物理术语来对抽象概念和形式进行理解的方法无疑是困难甚至难以自证的。因此，logos的局限性本身就要求mythos作为一种语词性的、哲学的形象，作为“认知的桥梁”，承担起对真理的“可见和可感知的表达”。由此，mythos不仅要成为哲学上的“发言人”，甚至还要成为哲学论证尤其辩证法开始之前真理交流的第一原则（即起点或公理），去完成那些logos或辩证法难以达成的事情。《理想国》中的洞穴神话与厄尔神话等都是典型的例子，由此可以看出，神话对于哲学认知过程的开始和结束都是必要的。从某种程度上而言，它也可以解释为何mythos本身在公众世界中具有（比logos更加）普遍性的吸引力与知识传播的能力，无论结果好坏。 

     在此意义上，便可以理解柏拉图既要“驱逐诗人”又要“留下诗人”的看似矛盾的态度，而我们看到，这一态度远比被动的妥协要积极得多。那么，他所谓“正派的故事”和“好人的语言”是什么？在柏拉图的论证框架下，这两者自然就是由哲学/logos所引领的语言，而这正是柏拉图认为mythos本身所无法达成的东西。哲学之所以比mythos更加权威，是因为其思辨的logos包含了经由理性而得来的“理相”（eidos）。这些“理相”构成了真正的现实，且在那个“真理”的世界中永恒不变。因此，这些具有绝对稳定性的存在可以指明什么是真正的善，并引导人们走向德性。当然，柏拉图哲学“真理性”的自我辩护是一个相当复杂的体系性问题，无法在此展开，但倘若柏拉图假设了他的辩护是成功的，那么，在其理想的城邦建设中，哲学，logos，就成为包括诗歌在内的一切教育与立法的先导与模型，从而使得mythos必然处于一个从属地位。 

     基于此，城邦便可以容纳mythos，并且对其不可或缺的辅助力以及不可抗拒的影响力加以利用。于是，柏拉图提出：“logoi分为两种：一种真实，另一种虚假。必须让人在这两方面都得到教育，而且，首先得在虚假的方面……要首先对孩子们讲神话故事，因为总的来说，这些故事说的是假话，但其中也有真实的东西。”真实的logos，柏拉图指的是哲学的理性辩证；而虚假的logos，即智术师/历史学家意义上的mythos。在哲学向那“虚假的logos”注入“真实的东西”（即哲学真理）后，mythos便得以作为构成城邦logoi（复数）整体的一部分，继续对公民施加“第二本性”般的影响，并作为哲学教育的起点对公民实施真正的知识教育。当mythos成为logos，神话/诗歌成为哲学的一部分时，logos不仅实现了对mythos最好的规训，而且，哲学反过来也成为诗歌，成为“最伟大的一种缪斯的艺术”。 

    某种意义上，柏拉图对logos至高地位的赋予显现出其从智术师处接续而来的批评传统，在mythos与logos问题的整体框架下，柏拉图无疑是作为一位革新的思想家站在了启蒙运动的风口浪尖。然而，这位苏格拉底的学生对智术师传统是有所保留的。他不仅通过对“德性”的强调，用一个完全道德化的“善”的logos取代了智术师笔下“可善可恶”的logos，而且也在其对mythos的处理中，修正了智术师（以及修昔底德）彻底背离mythos传统的进路，将mythos在其理想的城邦中保留下来。这意味着，mythos在柏拉图的哲学中不仅获得了一席之地，而且，还在一个显性的“秘索思与逻各斯之争”中被一位哲学家重新赋予存在的根本价值。柏拉图本人以戏剧对话（mythos）的方式来呈现其哲学，便是最好的例证。 

     柏拉图之所以将mythos纳入其哲学体系，不单是因为其知识论上的前驱性意义及其对公民教育的影响力。《蒂迈欧》中梭伦的故事暗示，这一切或许还与mythos在古希腊的本质相关。这个故事讲述了梭伦前往埃及的见闻。梭伦在与当地最有经验的祭司谈话时发现，“不论他自己还是其他希腊人，可以说都对古老的事物一无所知”。对此，一位年迈的祭司道出了一句箴言：希腊人之所以不知过往，不是因为无知，而是因为“希腊人永远都是孩子”。.祭司的意思是，由于古希腊人总是用口头的方式传播故事，因此，并没有像古埃及那样的书写传统将一切记录下来。在古埃及的对比下，柏拉图指明，古希腊的历史实际总是留存于口头的记忆之上的，神话记忆而非历史书写构成了古希腊之所以为古希腊的本质。就此而言，mythos直抵古希腊精神的核心。它不仅不可能被驱逐，而且还在存在论意义上，牢固地锚定在了古希腊的内核之中。在如此社会里，神话就是历史，它为历史的起源不断输送能量，并塑造着古希腊人的历史与文明意识。我们看到，在此，虚构的故事就不仅是在知识与教育的意义上被需要，而且是在整个古希腊文明的意义上被需要。 

    恰是在这一点上，柏拉图有意识地将mythos融汇进了自己的理想城邦的建构之中，并且，以一种相当积极的方式对其本质进行了最大程度的利用。《理想国》中著名的“高贵的谎言”就是一个典型例证：这个被哲学规训的具有真理性的起源神话成为整个理想的文明城邦建立与教育的起点。这个看似“荒唐的”的传说，“虽然那些听故事的人未必会相信，但后代的后代，子子孙孙迟早会相信的”。在世代的流传下，高贵的谎言成为历史的起源，成为城邦立法最根本的、先验的无可辩驳的基础，从而，mythos也在这个对logos而言最理想的城邦之中成为一个最不可或缺的存在。 

    从柏拉图对mythos的批评来看，他一方面明显继承了智术师与理性主义传统对logos的尊崇，另一方面，也对mythos强韧的力量有着充分的自觉。因此，尽管在柏拉图的理论体系中，logos是绝对高于mythos的存在，后者必须受前者所指导，但无论是在教育意义上，还是在存在论与知识论意义上，柏拉图都承认，mythos对古希腊而言绝不可或缺。 

    由此，虽然在柏拉图这里，我们看到“logos对mythos的胜利”，但我们也看到，这一胜利建立在对mythos的承认、接纳甚至为己所用的基础上。就此而言，柏拉图可以说是从智术师和修昔底德的极端立场的某种后退。在人类的城邦与社会中，这位哲学家试图找到一种mythos与logos间平衡与共存乃至互补的关系，令其各司其职。这一后退，不仅是战略性的，而且深植于其对哲学思辨的理性认识以及古希腊文明本质的深刻理解之中。古希腊人，或人类，对mythos和logos两种精神的需求表明其任何一方都不能，也不可能，被完全否定与排除。恰因如此，可以看到，无论是在柏拉图之前，还是自柏拉图之后跌宕起伏的历史中，mythos与logos总是相互勾连牵延、此消彼长，时而彼此竞争，时而互为补充，直至今日。 

    结语 

    Mythos与logos自古希腊文明伊始，就在口述传统催生下，深深扎根于其文明精神的核心。二者在普遍的二元思维架构中，构成古希腊内在精神的两个面向，一同推动该文明向前发展。 

    Mythos与logos不单是两个词汇与概念，背后隐含的是认知世界及自身处境的表达方式与路径。二者的关系不仅关涉话语体系的构建方式，还包含对“真实与虚假”“神圣与世俗”“诗与思”等一系列问题的思考。因此，该议题既指向神话与历史、神话与哲学、历史与哲学，也在形而上层面与认识论、存在论乃至宇宙论问题关联在一起。正是在多层面的勾连与张力中，mythos与logos开启了一个极为丰富的希腊世界。 

    二者“二元辩证统一”的动力学关系构成古希腊极为关键的文明特质。两者之所以不断此消彼长，是由古希腊开放的宇宙论、世界观与不同时期的社会和思想共同造就的。对变迁动力追根溯源，除却神话与思想、诗歌与哲学、感性与理性这些对立概念本身内在的冲突与竞争，社会文化自身的发展、古希腊民间风俗的变化、传统宗教与世俗生活的抗衡，乃至外来文化与新兴思想的渗透等，也均是推动两者变化的重要因素。 

    进一步看，恰恰是这一相互制约又互相定义的动态特质，对此后西方文明的展开产生了本源性影响。古希腊之后，不仅秘索思与逻各斯之争一直根植于西方思想发展脉络之中，而且两者地位在不同时期的变化也持续影响着西方感性与理性演变的周期以及西方哲学在认识论上的几次重大转型。现代社会兴起后，这一动力学原则更进一步与理性主义、科学主义结合，强化了西方科学与宗教并立的辩证传统，至今仍是西方文明体系的核心结构，对现代性及其内在复杂性的形成产生了深远影响。 

    在mythos与logos经历地位反转与意义更迭后，西方对自身整体文明传统的自我认识与系统性表达也随之形成。Mythos被驱逐出历史叙事的范畴，logos与实证精神合流成为现代西方历史观的真正开端。尽管在古希腊时期，这一历史认识论仍属激进、并非主流，然而经由漫长的中世纪、文艺复兴而进入现代世界之后，它在现代社会发挥出巨大能量。实证主义的理性化书写、古史研究对虚构叙事的全盘否定都显现出自古希腊时期便已在神话、传说与历史之间划出的巨大鸿沟。尽管对神话的复兴一直若隐若现，但整体而言，理性的历史观仍占据上风。这两种历史观的反复纠缠是系统性的，而这正是在现代世界中不断显现的问题。 

    本文节编自《中国社会科学》2024年第10期

  • Zack Savitsky：熵是什么

    生命是一本关于破坏的文集。你构建的一切最终都会崩溃。每个你爱的人都会死去。任何秩序或稳定感都不可避免地湮灭。整个宇宙都沿着一段惨淡的跋涉走向一种沉闷的终极动荡状态。

    为了跟踪这种宇宙衰变，物理学家使用了一种称为熵的概念。熵是无序性的度量标准，而熵总是在上升的宣言——被称为热力学第二定律——是自然界最不可避免的宿命之一。

    长期以来，我一直被这种普遍的混乱倾向所困扰。秩序是脆弱的。制作一个花瓶需要艺术性和几个月的精心策划，但用足球破坏它只需要一瞬间。我们一生都在努力理解一个混乱和不可预测的世界，在这个世界里，任何建立控制的尝试似乎都只会适得其反。热力学第二定律断言机器永远不可能达到完美效率，这意味着无论宇宙中结构何时涌现，它最终都只会进一步耗散能量——无论是最终爆炸的恒星，还是将食物转化为热量的生物体。尽管我们的意图是好的，但我们是熵的代理人。

    “除了死亡、税收和热力学第二定律之外，生活中没有什么是确定的”，麻省理工学院的物理学家Seth Lloyd写道。这个指示是无法回避的。熵的增长与我们最基本的经历深深交织在一起，解释了为什么时间向前发展，以及为什么世界看起来是确定性的，而不是量子力学上的不确定性。

    尽管具有根本的重要性，熵却可能是物理学中最具争议的概念。“熵一直是个问题，”Lloyd告诉我。这种困惑，部分源于这个词在学科之间“辗转反侧”的方式——从物理学到信息论再到生态学，它在各个领域都有相似但不同的含义。但这也正是为何，要真正理解熵，就需要实现一些令人深感不适的哲学飞跃。

    在过去的一个世纪里，随着物理学家努力将迥异的领域整合起来，他们用新的视角看待熵——将显微镜重新对准先知，将无序的概念转变为无知的概念。熵不被视为系统固有的属性，而是相对于与该系统交互的观察者的属性。这种现代观点阐明了信息和能量之间的深层联系，现在他正在帮助引领最小尺度上一场微型工业革命。

    在熵的种子被首次播下200年后，关于这个量的理解从一种虚无主义转为机会主义。观念上的进化正在颠覆旧的思维方式，不仅仅是关于熵，还是关于科学的目的和我们在宇宙中的角色。

    熵的概念源于工业革命期间对双面印刷机的尝试。一位名叫萨迪·卡诺（Sadi Carnot）的28岁法国军事工程师着手计算蒸汽动力发动机的最终效率。1824年，他出版了一本118页的书，标题为《对火的原动力的反思》，他在塞纳河畔以3法郎的价格出售。卡诺的书在很大程度上被科学界所忽视，几年后他死于霍乱。他的尸体被烧毁，他的许多文件也被烧毁了。但他的书的一些副本留存了下来，其中藏着一门新科学“热力学”的余烬——火的原动力。

    卡诺意识到，蒸汽机的核心是一台利用热量从热物体流向冷物体的趋势的机器。他描绘了可以想象到的最高效的发动机，对可以转化为功的热量比例建构了一个界限，这个结果现在被称为卡诺定理。他最重要的声明是这本书最后一页的警告：“我们不应该期望在实践中利用可燃物的所有动力”。一些能量总是会通过摩擦、振动或其他不需要的运动形式来耗散。完美是无法实现的。

    几十年后，也就是1865年，德国物理学家鲁道夫·克劳修斯（Rudolf Clausius）通读了卡诺的书，他创造了一个术语，用于描述被锁在能量中无法利用的比例。他称之为“熵”（entropy），以希腊语中的转换一词命名。然后，他提出了后来被称为热力学第二定律的东西：“宇宙的熵趋于最大”。

    那个时代的物理学家错误地认为热是一种流体[称为“热质”（caloric）]。在接下来的几十年里，他们意识到热量是单个分子碰撞的副产品。这种视角的转变使奥地利物理学家路德维希·玻尔兹曼（Ludwig Boltzmann）能够使用概率重新构建并深化熵的概念。

    玻尔兹曼将分子的微观特性（例如它们的各自位置和速度）与气体的宏观特性（如温度和压力）区分开来。考虑一下，不是气体，而是棋盘上的一组相同的游戏棋子。所有棋子的精确坐标列表就是玻尔兹曼所说的“微观状态”，而它们的整体配置——比如说，无论它们形成一个星形，还是全部聚集在一起——都是一个“宏观态”。玻尔兹曼根据产生给定宏观状态的可能微观状态的数量，来定义该宏观状态的熵。高熵宏观状态是具有许多相容的微观状态的宏观状态——许多可能的棋盘格排列，产生相同的整体模式。

    棋子可以呈现看起来有序的特定形状的方式只有这么多，而它们看起来随机散布在棋盘上的方式要多得多。因此，熵可以被视为无序的度量。第二定律变成了一个直观的概率陈述：让某物看起来混乱的方式比干净的方式更多，因此，当系统的各个部分随机地在不同可能的配置之间切换时，它们往往会呈现出看起来越来越凌乱的排列。

    卡诺发动机中的热量从热流向冷，是因为气体颗粒更有可能全部混合在一起，而不是按速度分离——一侧是快速移动的热颗粒，另一侧则是移动缓慢的冷颗粒。同样的道理也适用于玻璃碎裂、冰融化、液体混合和树叶腐烂分解。事实上，系统从低熵状态移动到高熵状态的自然趋势似乎是唯一可靠地赋予宇宙一致时间方向的东西。熵为那些本可以反向发生的进程刻下了时间箭头。

    熵的概念最终将远远超出热力学的范围。艾克斯-马赛大学的物理学家Carlo Rovelli说，“当卡诺写他的论文时……我认为没有人想象过它会带来什么”。

    扩展熵

    熵在第二次世界大战期间经历了重生。美国数学家克劳德·香农（Claude Shannon）正在努力加密通信通道，包括连接富兰克林·罗斯福（Franklin D. Roosevelt）和温斯顿·丘吉尔（Winston Churchill）的通信通道。那次经历使他在接下来的几年里深入思考了通信的基本原理。香农试图测量消息中包含的信息量。他以一种迂回的方式做到这一点，将知识视为不确定性的减少。

    乍一看，香农想出的方程式与蒸汽机无关。给定信息中的一组可能字符，香农公式将接下来出现哪个字符的不确定性定义为每个字符出现的概率之和乘以该概率的对数。但是，如果任何字符的概率相等，则香农公式会得到简化，并变得与玻尔兹曼的熵公式完全相同。据说物理学家约翰·冯·诺伊曼（John von Neumann）敦促香农将他的量称为“熵”——部分原因是它与玻尔兹曼的量非常一致，也因为“没有人知道熵到底是什么，所以在辩论中你总是占优势”。

    正如热力学熵描述发动机的效率一样，信息熵捕捉到通信的效率。它与弄清楚消息内容所需的是或否问题的数量相对应。高熵消息是无模式的消息；由于无法猜测下一个角色，这条信息需要许多问题才能完全揭示。具有大量模式的消息包含的信息较少，并且更容易被猜到。“这是一幅非常漂亮的信息和熵环环相扣的画面，”Lloyd说。“熵是我们不知道的信息；信息是我们所知道的信息”。

    在1957年的两篇具有里程碑意义的论文中，美国物理学家E.T.Jaynes通过信息论的视角来观察热力学，巩固了这一联系。他认为热力学是一门从粒子的不完整测量中做出统计推断的科学。Jaynes提议，当知道有关系统的部分信息时，我们应该为与这些已知约束相容的每个配置分配相等的可能性。他的“最大熵原理”为对任何有限数据集进行预测提供了偏差最小的方法，现在应用于从统计力学到机器学习和生态学的任何地方。

    因此，不同背景下发展起来的熵的概念巧妙地结合在一起。熵的增加对应于有关微观细节的信息的损失。例如，在统计力学中，当盒子中的粒子混合在一起，我们失去了它们的位置和动量时，“吉布斯熵”会增加。在量子力学中，当粒子与环境纠缠在一起，从而扰乱它们的量子态时，“冯·诺伊曼熵”就会增加。当物质落入黑洞，有关它的信息丢失到外部世界时，“贝肯斯坦-霍金熵”就会增加。

    熵始终衡量的是无知：缺乏关于粒子运动、一串代码中的下一个数字或量子系统的确切状态的知识。“尽管引入熵的动机各不相同，但今天我们可以将它们都与不确定性的概念联系起来，”瑞士苏黎世联邦理工学院的物理学家Renato Renner说。

    然而，这种对熵的统一理解引发了一个令人不安的担忧：我们在谈论谁的无知？

    一点主观性

    作为意大利北部的一名物理学本科生，Carlo Rovelli从他的教授那里了解了熵和无序的增长。有些事情不对劲。他回到家，在一个罐子里装满油和水，看着液体在他摇晃时分离——这似乎与所描述的第二定律背道而驰。“他们告诉我的东西都是胡说八道，”他回忆起当时的想法。“很明显，教学方式有问题。”

    Rovelli的经历抓住了熵如此令人困惑的一个关键原因。在很多情况下，秩序似乎会增加，从孩子打扫卧室到冰箱给火鸡降温。

    Rovelli明白，他对第二定律的表面胜利不过是海市蜃楼。具有强大热视觉能力的超人观察者会看到油和水的分离如何向分子释放动能，从而留下更加热无序的状态。“真正发生的事情是，宏观秩序的形成是以微观无序为代价的，”Rovelli说。第二定律始终成立；有时只是看不见。

    在Gibbs提出这个悖论一个多世纪后，Jaynes提出了解决方法（他坚称吉布斯已经理解了，但未能清楚地表达出来）。想象一下，盒子里的气体是两种不同类型的氩气，它们相同，只是其中一种可溶于一种称为whifnium的尚未发现的元素中。在发现whifnium之前，没有办法区分这两种气体，因此抬起分流器不会引发明显的熵变化。然而，在whifnium被发现后，一位聪明的科学家可以使用它来区分两种氩物种，计算出熵随着两种类型的混合而增加。此外，科学家可以设计一种基于whifnium的活塞，利用以前无法从气体的自然混合中获得的能量。

    Jaynes 明确指出，系统的“有序性”——以及从中提取有用能量的潜力——取决于代理人的相对知识和资源。如果实验者无法区分气体A和B，那么它们实际上是相同的气体。一旦科学家们有办法区分它们，他们就可以通过开发气体混合的趋势来利用功。熵不取决于气体之间的差异，而是取决于它们的可区分性。无序在旁观者的眼中。

    “我们可以从任何系统中提取的有用功，显然也必然取决于我们拥有多少关于其微观状态的’主观’信息，”Jaynes写道。

    吉布斯悖论强调需要将熵视为一种观察属性，而不是系统固有的属性。然而，熵的主观视图是难以被物理学家接受的。正如科学哲学家肯尼斯·登比（Kenneth Denbigh）1985年在书中写道，“这样的观点，如果它是有效的，将产生一些深刻的哲学问题，并往往会破坏科学事业的客观性”。

    接受熵的这个有条件的定义需要重新思考科学的根本目的。这意味着物理学比某些客观现实更准确地描述了个人经验。通过这种方式，熵被卷入了一个更大的趋势，即科学家们意识到许多物理量只有在与观察者有关时才有意义（甚至时间本身也被爱因斯坦的相对论所重新渲染）。“物理学家不喜欢主观性——他们对它过敏”，加州大学圣克鲁斯分校的物理学家Anthony Aguirre 说，“但没有绝对的——这一直都是一种幻觉。”

    现在人们已经接受了这种认知，一些物理学家正在探索将主观性融入熵的数学定义的方法。

    Aguirre和合作者设计了一种新度量，称之为观测熵（observational entropy）。它提供了一种方法，通过调整这些属性如何模糊或粗粒度化观察者对现实的看法，来指定观察者可以访问哪些属性。然后，它为与这些观察到的特性相容的所有微观状态赋予相等的概率，就像 Jaynes 所提出的那样。该方程将热力学熵（描述广泛的宏观特征）和信息熵（捕获微观细节）连接起来。“这种粗粒化的、部分主观的观点是我们有意义的与现实互动的方式，”Aguirre说。

    许多独立团体使用 Aguirre 的公式来寻求第二定律更严格的证明。就Aguirre而言，他希望用他的度量来解释为什么宇宙一开始是低熵状态（以及为什么时间向前流动）并更清楚地了解黑洞中熵的含义。“观测熵框架提供了更清晰的信息”，巴塞罗那自治大学的物理学家Philipp Strasberg说，他最近将其纳入了不同微观熵定义的比较。“它真正将玻尔兹曼和冯·诺伊曼的思想与当今人们的工作联系起来。”

    与此同时，量子信息理论家采取了不同的方法处理主观性。他们将信息视为一种资源，观察者可以使用它来跟日益与环境融合在一起的系统进行交互。对于一台可以跟踪宇宙中每个粒子的确切状态的具有无限能力的超级计算机来说，熵将始终保持不变——因为不会丢失任何信息——时间将停止流动。但是，像我们这样拥有有限计算资源的观察者总是不得不与粗略的现实图景作斗争。我们无法跟踪房间内所有空气分子的运动，因此我们以温度和压力的形式取平均值。随着系统演变成更可能的状态，我们逐渐失去了对微观细节的跟踪，而这种持续的趋势随着时间的流逝而成为现实。“物理学的时间，归根结底，是我们对世界无知的表现”，Rovelli写道。无知构成了我们的现实。

    “外面有一个宇宙，每个观察者都带着一个宇宙——他们对世界的理解和模型”，Aguirre说。熵提供了我们内部模型中缺点的度量。他说，这些模型“使我们能够做出良好的预测，并在一个经常充满敌意但总是困难的物理世界中明智地采取行动。

    以知识为驱动

    2023年夏天，通过Aguirre于2006年共同创立的一个名为Foundational Questions Institute（FQxI）的非营利研究组织，在英国约克郡一座历史悠久的豪宅庄园连绵起伏的山脚下，Aguirre主持了一次闭门研讨会（retreat）。来自世界各地的物理学家齐聚一堂，参加为期一周的智力安睡派对，并有机会进行瑜伽、冥想和野外游泳。该活动召集了获得FQxI资助的研究人员，以探讨如何使用信息作为燃料。

    对于这些物理学家中的许多人来说，对发动机和计算机的研究已经变得模糊不清。他们已经学会了将信息视为真实的、可量化的物理资源，即一种可以诊断从系统中提取多少功的手段。他们意识到，知识就是力量（power）。现在，他们开始着手利用这种力量。

    一天早上，在庄园的蒙古包里参加了一次可选的瑜伽课程后，这群人聆听了Susanne Still（夏威夷大学马诺阿分校的物理学家）。她首先讨论了一项新工作，针对可以追溯到一个世纪前，由匈牙利出生的物理学家利奥·西拉德（Leo Szilard）所提出的思想实验：

    想象一个带有垂直分隔线的盒子，该分隔线可以在盒子的左右壁之间来回滑动。盒子中只有一个粒子，位于分隔线的左侧。当粒子从壁上弹开时，它会将分隔器向右推。一个聪明的小妖可以装配一根绳子和滑轮，这样，当分隔器被粒子推动时，它会拉动绳子并在盒子外举起一个重物。此时，小妖可以偷偷地重新插入分隔器并重新启动该过程——实现明显的无限能量源。

    然而，为了始终如一地开箱即用，恶魔必须知道粒子在盒子的哪一侧。西拉德的引擎由信息提供动力。

    原则上，信息引擎有点像帆船。在海洋上，利用你对风向的了解来调整你的帆，推动船向前行进。

    但就像热机一样，信息引擎也从来都不是完美的。他们也必须以熵生产的形式纳税。正如西拉德和其他人所指出的，我们不能将信息引擎用作永动机的原因是，它平均会产生至少同样多的熵来测量和存储这些信息。知识产生能量，但获得并记住知识会消耗能量。

    在西拉德构思他的引擎几年后，阿道夫·希特勒成为德国总理。出生于犹太家庭并一直居住在德国的西拉德逃离了。他的著作几十年来一直被忽视，直到最终被翻译成英文，正如Still在最近的一篇信息引擎历史回顾中所述。

    最近，通过研究信息处理的基本要素，Still成功地扩展并泛化了西拉德的信息引擎概念。

    十多年来，她一直在研究如何将观察者本身视为物理系统，受其自身物理限制的约束。趋近这些限制的程度不仅取决于观察者可以访问的数据，还取决于他们的数据处理策略。毕竟，他们必须决定要测量哪些属性以及如何将这些细节存储在有限的内存中。

    在研究这个决策过程时，Still发现，收集无助于观察者做出有用预测的信息会降低他们的能量效率。她建议观察者遵循她所说的“最小自我障碍原则”——选择尽可能接近他们物理限制的信息处理策略，以提高他们决策的速度和准确性。她还意识到，这些想法可以通过将它们应用于修改后的信息引擎来进一步探索。

    在西拉德的原始设计中，小妖的测量完美地揭示了粒子的位置。然而，在现实中，我们从来没有对系统有完美地了解，因为我们的测量总是有缺陷的——传感器会受到噪声的影响，显示器的分辨率有限，计算机的存储空间有限。Still展示了如何通过对西拉德的引擎进行轻微修改来引入实际测量中固有的“部分可观测性”——基本方法是通过更改分隔线的形状。

    想象一下，分隔线在盒子内以一定角度倾斜，并且用户只能看到粒子的水平位置（也许他们看到它的阴影投射到盒子的底部边缘）。如果阴影完全位于分隔线的左侧或右侧，则可以确定粒子位于哪一侧。但是，如果阴影位于中间区域的任何位置，则粒子可能位于倾斜分隔线的上方或下方，因此位于盒子的左侧或右侧。

    使用部分可观测的信息引擎，Still计算了测量粒子位置并在内存中对其进行编码的最有效策略。这导致了一种纯粹基于物理的算法推导，该算法目前也用于机器学习，称为信息瓶颈算法（information bottleneck algorithm）。它提供了一种通过仅保留相关信息来有效压缩数据的方法。

    从那时起，和她的研究生Dorian Daimer一起，Still研究了改进的西拉德引擎的多种不同设计，并探索了各种情况下的最佳编码策略。这些理论设备是“在不确定性下做出决策的基本组成部分”，拥有认知科学和物理学背景的Daimer说。“这就是为什么研究信息处理的物理学对我来说如此有趣，因为在某种意义上，这是一种完整的循环，最终回归到对科学家的描述。”

    重新工业化

    尽管如此，他并不是约克郡唯一一个梦想西拉德引擎的人。近年来，许多FQxI受资助者在实验室中开发了有功能性的引擎，其中信息用于为机械设备提供动力。与卡诺的时代不同，没有人期望这些微型发动机为火车提供动力或赢得战争；相反，它们正在充当探测基础物理学的试验台。但就像上次一样，信息引擎正在迫使物理学家重新构想能量、信息和熵的含义。

    在Still的帮助下，John Bechhoefer已经用漂浮在水浴中的比尘埃还小的二氧化硅珠重新创建了西拉德的引擎。他和加拿大西蒙弗雷泽大学的同事用激光捕获硅珠并监测其随机热波动。当硅珠碰巧向上晃动时，它们会迅速抬起激光阱以利用其运动。正如西拉德所想象的那样，他们通过利用信息的力量成功地提起了重量。

    在调查从他们的真实世界信息引擎中提取功的限制时，Bechhoefer和Still发现，在某些状态下，它可以显著跑赢传统发动机。受到Still理论工作的启发，他们还追踪了接收部分低效信息的硅珠的状态。

    在牛津大学物理学家Natalia Ares的帮助下，信息引擎现在正在缩小到量子尺度，她曾与Still一同参加了闭门研讨会。在与杯垫大小相当的硅芯片上，Ares将单个电子困在一根细碳纳米线内，该纳米线悬挂在两根支柱之间。这个“纳米管”被冷却至接近绝对零度的千分之一，像吉他弦一样振动，其振荡频率由内部电子的状态决定。通过追踪纳米管的微小振动，Ares和她的同事计划诊断不同量子现象的功输出。

    Ares在走廊的黑板上写满了许多实验计划，旨在探测量子热力学。“这基本上就是整个工业革命的缩影，但尺度是纳米级的，”她说。一个计划中的实验灵感来源于Still的想法。实验内容涉及调整纳米管的振动与电子（相对于其他未知因素）的依赖程度，本质上为调整观察者的无知提供了一个“旋钮”。

    Ares和她的团队正在探索热力学在最小尺度上的极限——某种意义上，是量子火焰（quantum fire）的驱动力。经典物理中，粒子运动转化为有用功的效率限制由卡诺定理设定。但在量子领域，由于有多种熵可供选择，确定哪个熵将设定相关界限变得更加复杂——甚至如何定义功输出也是一个问题。“如果我们像实验中那样只有一个电子，那熵意味着什么？”Ares说道。“根据我的经验，我们仍然在这方面非常迷茫。”

    最近一项由美国国家标准与技术研究院（NIST）的物理学家Nicole Yunger Halpern主导的研究表明，通常被视为同义的熵生成的常见定义，在量子领域中可能会出现不一致，这再次出于不确定性和观察者依赖性。在这个微小的尺度上，不可能同时知道某些属性。而你测量某些量的顺序也会影响测量结果。Yunger Halpern认为，我们可以利用这种量子奇异性来获取优势。“在量子世界中，有一些经典世界中没有的额外资源，所以我们可以绕过卡诺定理，”她说道。

    Ares正在实验室中推动这些新的边界，希望为更高效的能源收集、设备充电或计算开辟道路。这些实验也可能为我们所知道的最有效的信息处理系统——我们自己——的机制提供一些洞见。科学家们不确定人脑是如何在仅仅消耗20瓦电力的情况下，执行极其复杂的脑力运动的。也许，生物学计算效率的秘诀也在于利用小尺度上的随机波动，而这些实验旨在探测任何可能的优势。“如果在这方面有某些收获，自然界也许实际上利用了它，”与Ares合作的埃克塞特大学理论学家Janet Anders说道。“我们现在正在发展的这种基础理解，或许能帮助我们未来更好地理解生物是如何运作的。”

    Ares的下一轮实验将在她位于牛津实验室的一个粉色的制冷室中进行。几年前，她开玩笑地向制造商提出了这个外观改造的建议，但他们警告说，金属涂料颗粒会干扰她的实验。然后，公司偷偷将冰箱送到汽车修理厂，给它覆盖了一层闪亮的粉色薄膜。Ares将她的新实验场地视为时代变革的象征，反映了她对这场新的工业革命将与上一场不同的希望——更加有意识、环保和包容。

    “感觉就像我们正站在一个伟大而美好的事物的起点，”她说道。

    拥抱不确定性

    2024年9月，几百名研究人员聚集在法国帕莱佐，为纪念卡诺（Carnot）其著作出版200周年而举行的会议上。来自各个学科的参与者讨论了熵在各自研究领域中的应用，从太阳能电池到黑洞。在欢迎辞中，法国国家科学研究中心的一位主任代表她的国家向卡诺道歉，承认忽视了卡诺工作的重要影响。当天晚上，研究人员们在一个奢华的金色餐厅集合，聆听了一首由卡诺的父亲创作、由一支四重奏演奏的交响乐，其中包括这位作曲家的远亲后代。

    卡诺的深远见解源于试图对时钟般精确的世界施加极致控制的努力，这曾是理性时代的圣杯。但随着熵的概念在自然科学中逐渐扩展，它的意义发生了变化。熵的精细理解抛弃了对完全效率和完美预测的虚妄梦想，反而承认了世界中不可减少的不确定性。“在某种程度上，我们正朝着几个方向远离启蒙时期，”Rovelli说——远离决定论和绝对主义，转向不确定性和主观性。

    无论你愿不愿意接受，我们都是第二定律的奴隶；我们无法避免地推动宇宙走向终极无序的命运。但我们对熵的精细理解让我们对未来有了更为积极的展望。走向混乱的趋势是驱动所有机器运作的动力。虽然有用能量的衰减限制了我们的能力，但有时候换个角度可以揭示隐藏在混沌中的秩序储备。此外，一个无序的宇宙正是充满了更多的可能性。我们不能规避不确定性，但我们可以学会管理它——甚至或许能拥抱它。毕竟，正是无知激励我们去追求知识并构建关于我们经验的故事。换句话说，熵正是让我们成为人类的原因。

    你可以对无法避免的秩序崩溃感到悲叹，或者你可以将不确定性视为学习、感知、推理、做出更好选择的机会，并利用你身上蕴藏的动力。

  • 胡宝国：魏西晋时期的九品中正制

    魏晋南北朝时期的九品中正制度由于存在时间很久，各个时期多有变化。因此，有必要对这一制度进行分阶段的考察。在这篇文章中，只讨论魏西晋时期的九品中正制。

    一、释“上品无寒门，下品无势族”

    创立于曹魏时期的九品中正制在西晋一朝遭到了大规模的抨击。当时许多人批评中正制度，其中尤以刘毅“上品无寒门，下品无势族”(1)一语最具代表性。涉及到九品中正制度的论著，大都据此得出结论：当时世家大族垄断了上品。本文认为，这一结论仍有值得商榷之处。(2)

    晋武帝时，段灼上表称：“今台阁选举，涂塞耳目，九品访人，唯问中正。故据上品者，非公侯之子孙，则当涂之昆弟也。”(3)段灼与刘毅都指出一部分人垄断了上品。刘毅称为“势族”，段灼称为“公侯之子孙”、“当涂之昆弟”，二者应该是相等的。只不过段灼说得更具体些。所谓“公侯”，即指封爵，“当涂”是指高官要位。当时也有一些人并未直接批评中正制度，而是指斥高官子弟垄断了某些官位。刘颂对晋武帝说：“泰始之初，陛下践阼，其所服乘皆先代功臣之胤，非其子孙，则其曾玄。”(4)愍怀太子被废，阎缵上疏为之申冤，更具体指出，东宫官属如太子洗马、舍人以及“诸王师友文学”等职任人不当，“皆豪族力能得者”(5)。刘毅与段灼，刘颂与阎缵所选择的批评角度虽然不同，但却有相通之处。九品之品与具体官职存在着一定的关系。

    《晋书》卷九○《邓攸传》：邓攸“尝诣镇军贾混。……混奇之，以女妻焉。举灼然二品，为吴王文学”。《晋书》卷五二《华谭传》：“太康中，刺史稽喜举谭秀才。……寻除郎中，迁太子舍人，本国中正。”《晋书》卷四六《李重传》：“李重……弱冠为本国中正，逊让不行，后为始平王文学。”《晋书》卷六一《周浚传》：“（周馥）起家为诸王文学，累迁司徒左西属。司徒王浑表‘馥理识清正，兼有才干，主定九品，检括精详’。”

    担任中正者，本人必须是二品。司徒左西属是司徒府的官吏，“主定九品”，有时还可兼中正，自然也应是二品。(6)我们看到，被中正定为二品的人往往可以任太子舍人、诸王文学，这些职务正是阎缵所提到的。因此，阎缵批评“豪族”垄断这些职务与刘毅、段灼批评他们垄断上品当是一回事。换言之，正是因为他们垄断了上品，所以才能位居上述职务。

    但是，“势族”、“公侯之子孙”、“当涂之昆弟”究竟是些什么人呢？按通常的解释，这不过是世家大族的代名词而已，世族垄断上品的结论就是由此得出的。但考察一下上述批评中正制度的人的家世，事情就会复杂起来。《晋书》卷四五《刘毅传》：“刘毅字仲雄，东莱掖人，汉阳城景王章之后，父喈，承相属。”《晋书》卷四六《刘颂传》：“刘颂字子雅，广陵人，汉广陵厉王胥之后也。世为名族。同郡有雷、蒋、谷、鲁四姓，皆出其下。时人为之语曰：‘雷、蒋、谷、鲁，刘最为祖。’”《晋书》卷四八《段灼传》：“段灼字休然。敦煌人也，世为西土著姓。”同卷《阎缵传》：“阎缵字续伯，巴西安汉人也。”《华阳国志》卷一《巴志》：“安汉县号出人士，大姓陈、范、阎、赵。”以上四人，刘毅为“汉阳城景王章之后”，其父曾任丞相属，究竟属于哪一阶层，难以确定。其他三人或曰“名族”，或称“著姓”，或为“大姓”，当是世族。

    所谓世族，通常是指累世做官的家族。由于在一个地区长久不衰地任官，即被当地人目之为“著姓”、“大姓”、“名族”，或者也可称作地方郡姓。汉代以来，有一些著姓、名族的政治势力及影响并未局限在本地区，如汝南袁氏、弘农杨氏，这些家族世代在中央居高位，在全国范围内都有影响，这样的世族，可以称之为高等世族，以别于地方世族、地方郡姓。

    身为世族的刘颂、段灼、阎缵为什么要攻击世族垄断上品呢？其实，“世族”并不等于“势族”。我们可以通过元康年间举寒素一事加以推断。

    《晋书》卷九四《范粲传》：“元康中，诏求廉让冲退履道寒素者，不计资。”何谓寒素？何谓不计资？据《晋书》卷四六《李重传》载，诏令下达后，“燕国中正刘沈举霍原为寒素”，但司徒府未通过。司徒左长史荀组认为，“寒素者，当谓门寒、身素、无世祚之资。原为列侯，显佩金紫，先为人间流通之事，晚乃务学……草野之誉未洽，德礼无闻。不应寒素之目。”与荀组不同，李重则积极为霍原辩护：“如诏书之旨，以二品系资，或失廉退之士，故开寒素，以明尚德之举……沈为中正，亲执铨衡，陈原隐居求志，笃古好学……如诏书所求之旨，应为二品。”据此，可以得出如下认识：一、此诏是为了解决九品中正制实施中所出现的问题而发的。具体说，就是要冲破某些人仅凭“资”独占二品这种局面，其措施就是举寒素。按此传先云举霍原为寒素，后又云“应为二品”，可知举寒素意即举寒素者为二品。(7)二、前引刘毅说，势族垄断了二品，此传又称“二品系资”，可知势族获得二品即是凭借“资”。因此，有资者即为势族，反之则是寒素，势族是与寒素相对而言的。三、按荀组的说法，寒素应包括两项内容：门寒、身素，又可概括地称之为“无世祚之资”。门寒一词较空洞，留待下面讨论。所谓身素当是指本人无官无爵。荀组正是从此出发反对举霍原为寒素的。其理由主要有二：第一，“原为列侯”，第二，德行不够。德行较抽象，很难说清，所以第一条理由才是重要的。霍原为列侯，不符合“身素”一项，此外，霍原家世虽不可考，但本人未出仕却有封爵，应该说是从祖先那里袭来的，因此，霍原属于“公侯之子孙”，也即是势族，自然也就不能算“门寒”了。可见，荀组虽然仅指出“原为列侯”，但实际意味着霍原二项条件均不符合，所以才反对举他为寒素。

    《晋书》中明言被举为寒素者还有二人。《晋书》卷六八《纪瞻传》：“祖亮，吴尚书令。父陟，光禄大夫……永（元？）康初，州又举（瞻）寒素，大司马辟东阁祭酒。”《晋书》卷九四《范粲传》：“元康中，诏求廉让冲退履道寒素者，不计资，以参选叙，尚书郎王琨乃荐（范）乔曰：‘乔禀德真粹，立操高洁……诚当今之寒素。著历俗之清彦。’时张华领司徒，天下所举凡十七人，于乔特发优论。”(8)据此，当时被举为寒素者共十七人，由于史料缺乏，已无法全部了解他们的情况。但《李重传》却为我们透露了一点消息。元康年间，李重任尚书吏部郎，“务抑华竞，不通私谒，特留心隐逸。由是群才毕举，拔用北海西郭汤、琅邪刘珩、燕国霍原、冯翊吉谋等为秘书郎及诸王文学”(9)。霍原被举为寒素后并未出仕，此处误记。但我们怀疑其他三人均系被举为寒素者，因为他们被“拔用”的时间也是在元康年间，且既称“拔用”，显然地位不高，又与霍原相提并论，最后又被任命为“诸王文学”之类。如前所述，这些职务往往是由二品人士担任的。

    至此，我们知道被举为寒素者除霍原外还有五人。其中西郭汤、刘珩事迹不详，范乔情况较为特殊。其父范粲在魏末官至侍中，但始终不与司马氏合作，“阳狂不言”三十六载。(10)范乔被举为寒素前未出仕。纪瞻父祖均为吴国高官，纪瞻本人为“江南之望”。(11)吉谋家世也略有可考。《三国志》卷二二《魏书·裴潜传》注引《魏略》云：“冯翊甲族桓、田、吉、郭。”同书卷二三《常林传》注引《魏略》云：“吉茂字叔畅，冯翊池阳人也，世为著姓。”

    由此可见，被举为寒素者中起码有两名世族，即纪瞻与吉谋，他们被推举没有引起争论，看来是符合“门寒、身素、无世祚之资”这些条件的。换言之，他们并非势族。所以，世族并不等于势族。势族垄断上品不意味着世族垄断上品。所谓势族，乃是指现实有势力的家族，即那些魏晋政权中的公侯与当涂者。这些人中固然也有两汉以来的著姓、大族，如琅琊王氏、太原王氏、河内司马氏、河东裴氏等等，但也有像石苞、邓艾、石鉴这样一些起自寒微者。(12)他们显然不能以世族目之。固然势族只要稳定地、一代一代地延续下去，终有一天会演变为世族，但那毕竟是以后的事。在魏晋时期，势族不等于世族。势族的地位也并不十分稳固。在瞬息万变的政治斗争中，一些势族衰落了，一些人又上升为势族，虽然势族垄断了上品，但他们当中具体的家族由于现实政治地位不稳定，品也不稳定。《晋书》卷三三《何曾传附子何劭传》：

    劭初亡，袁粲吊岐（何劭子），岐辞以疾。粲独哭而出曰：“今年决下婢子品！”王诠谓之曰：“知死吊死，何必见生！岐前多罪，尔时不下，何公新亡，便下岐品，人谓中正畏强易弱。”粲乃止。

    何岐虽最终未被降品，但可看出其品并不稳定。《晋书》卷四三《王戎传》：“（戎）自经典选，未尝进寒素，退虚名，但与时浮沉，户调门选而已。”按“户调门选”，须“与时浮沉”，说明门户地位常有浮沉。刘毅云：“今之中正……高下逐强弱，是非由爱憎，随世兴衰，不顾才实，衰则削下，兴则扶上，一人之身，旬日异状。”(13)这是对现实政治的真实描述。另一方面，原有的著姓大族只要未跻身于公侯、当涂者之列，就不能算作势族。所以纪瞻、吉谋可以被举为寒素，而安汉大姓阎缵在势族面前只能自称“臣素寒门”。(14)

    稍后的例子也可以证明此点。东晋初年，王敦叛乱中刁协被杀，事后左光禄大夫蔡谟为刁协争追赠官位，在致庾冰的信中说：“又闻谈者亦多谓宜赠。凡事不允当而得众助者，若以善柔得众，而刁令粗刚多怨；若以贵也，刁氏今贱；若以富也，刁氏今贫。人士何故反助寒门而为此言之，足下宜深察此意。”(15)渤海刁氏是很显赫的家族，刁协父刁攸“武帝时御史中丞”，但一旦官场失意却被称为寒门，因此，这一时期寒门一词的含义与宋齐以后不同。地方郡姓在本地虽然绝对不属于寒门，但与“势族”相比，却只能处于寒门的地位。

    西晋时期，人们批评九品中正制度的另一个方面是，九品评定全由中正，不遵乡里舆论。刘毅在论九品疏中一开始就指斥说：“今立中正，定九品，高下任意，荣辱在手”，在以后所论中正制度的“八损”中，他不厌其烦地屡次指出这一点，批评中正不听乡里舆论，“采誉于台府，纳毁于流言”，以私情定品。前引段灼上疏也指斥：“今九品访人，唯问中正。”所以，许多反对九品中正制度的人都主张废除中正制，在土断的基础上行乡举里选。

    综上所述，西晋一朝，人们对中正制度的批评主要集中在两点。第一，势族凭资垄断上品。第二，中正不遵乡论。晋武帝时，卫瓘与汝南王亮的上疏可以说是对中正制度弊端的总结：

    魏氏承颠覆之运，起丧乱之后，人士流移，考详无地，故立九品之制，粗且为一时选用之本耳。其始造也，乡邑清议，不拘爵位，褒贬所加，足为劝励，犹有乡论馀风。中间渐染，遂计资定品，使天下观望，唯以居位为贵。(16)

    按卫瓘的说法，中正制度两方面的弊端是有联系的。正是由于中正不遵乡论，才导致“计资定品”。值得注意的是，中正制度初建时并非如此，只是“中间渐染”。这说明九品中正制度在魏晋时期曾经有过重大变化。

    二、魏、西晋中正制度的演变

    《通典》卷一四选举二历代制中载：“晋依魏氏九品之制，内官吏部尚书，司徒左长史。外官州有大中正，郡国有小中正，皆掌选举。”按此则魏晋时期的九品中正制没有任何变化。这是不准确的。赵翼《廿二史劄记》卷八中正条：“魏文帝初定九品中正之法，郡邑设小中正，州设大中正，由小中正品第人才，以上大中正，大中正核实以上司徒，司徒再核，然后付尚书选用，此陈群所建白也。”这个说法虽然系统化，但比《通典》更不准确。魏晋时期的九品中正制是有变化的。郡中正与州中正之设置并非同时。对此，唐长孺已有精确的考证。按他的意见，中正制度刚建立时，只有郡中正，州中正的设立“至迟不出嘉平二年（250），至早不出正始元年（240），也即是说在曹芳时”(17)。唐先生的这一论断是完全正确的。但是《晋书》卷四四《郑袤传附郑默传》还有须要解释的史料：

    初，帝以贵公子当品，乡里莫敢与为辈。求之州内，于是十二郡中正佥共举默……及武帝出祀南郊，诏使默骖乘。因谓默曰：“卿知何以得骖乘乎？昔州里举卿相辈，常愧有累清谈。”

    晋武帝当品事发生于魏末，但究竟在哪一年，史无明文。《晋书》卷三《武帝纪》：“武皇帝……魏嘉平中（249—254），封北平亭侯，历给事中，奉车都尉。”既云“嘉平中”，则武帝出仕年代肯定在公元250年以后。一般来说，获得中正品第之后即可出仕，尤其是晋武帝这样的贵公子，不大可能已经得到中正品第无官做，也不大可能出仕后尚无中正品第。因此，他出仕与获得中正品第应该大致同时，即都是在“嘉平中”。按《郑默传》载，晋武帝与郑默是由“州内”推举的。但“求之州内”却没有州中正推举，反而由一州之内的全体郡中正“佥共举默”，(18)当时似乎并没有州中正。《晋书》的记载疑有错误。汤球所辑王隐《晋书》卷六亦载此事：“默为散骑常侍。世祖出祀南郊。侍中已陪乘，诏曰：‘使郑常侍参乘。’谓默曰：‘卿知何以得参乘？昔州内举卿，十二郡中正举以相辈，常愧有累清谈。’”汤球注明此段文字辑自《艺文类聚》卷四八、《初学纪》卷一二所引王隐《晋书》。查此二书，《艺文类聚》引作：“郑默为散骑常侍，世祖祠南郊，侍中已陪乘。诏曰：‘使郑常侍默。’曰：‘卿知何以得参乘？昔州内举卿相辈，常愧有累清谈。’”《初学纪》引作：“郑默，字思元，为散骑常侍，武帝出南郊，侍中以陪乘。诏曰：‘使郑常侍参乘。’”二书均无“十二郡中正举以”七字。汤球可能是从其他地方辑出而在注出处时疏忽了。如此推测无大错，则王隐《晋书》与唐修《晋书》记载此事有所不同。即王隐《晋书》在“十二郡中正”诸字之后无“佥共”二字。虽只差二字，但却是非常重要的。因为有时史籍中说若干郡中正只不过是某州中正的代名词。《世说新语·贤媛》篇注引王隐《晋书》云：“后（羊）晫为十郡中正，举陶侃为鄱阳小中正，始得上品也。”羊晫举陶侃在西晋后期。《晋书》卷一五《地理志》下：“惠帝元康元年……割扬州之豫章、鄱阳、庐陵、临川、南康、建安、晋安、荆州之武昌、桂阳、安成十郡，因江水之名而置江州。”羊晫所任“十郡中正”即指任此十郡的中正。其中包括鄱阳郡，所以羊晫可以推举鄱阳人陶侃为郡中正。“十郡中正”，实际就是江州大中正。《太平御览》卷二六五中正条引《晋书》云：“杨晫、陶侃共载诣顾荣。州大中正温雅责晫与小人共载。晫曰：‘江州名少风俗，卿己不能养进寒儁，且可不毁之。’杨晫代雅为州大中正，举侃为鄱阳小中正。”杨晫当为羊晫，此处明言为江州大中正。据此推论前述“十二郡中正”实际当是司州中正的异称。唐修《晋书》记载此事大概是参考了王隐《晋书》，又觉得“十二郡中正举以相辈”费解，故增“佥共”二字，但意思就大不相同了。由以上的分析可知，唐先生关于州中正建立时间的考证还是不可动摇的。

    下面讨论另一个问题。据前引杜佑语，似乎不仅州中正与郡中正是在制度初创时就已同时存在，而且司徒府参预九品评定工作也是从那时开始的。赵翼更明言“此陈群所建白也。”这一说法也是不正确的。首先，史料中从未发现曹魏时司徒府参预品评工作。魏明帝时，傅嘏在难刘劭考课法时说：“方今九州之民，爰及京城，未有六乡之举，其选才之职，专任吏部”。(19)可见，当时选举工作在中央是由吏部一手包办的。其次，杜佑自己在《通典》卷二○职官二中也说：西晋“太始三年……司徒加置左长史。掌差次九品，铨衡人伦”。既然说“加置”，时间又如此具体，在这之前当无左长史。杜氏自相矛盾。《晋书》卷二四《职官志》也有明确记载：“司徒加置左右长史各一人。”《艺文类聚》卷三一引潘尼《答傅咸诗序》：“司徒左长史傅长虞，会定九品，左长史宜得其才。屈为此职，此职执天下清议，宰割百国，而长虞性直而行，或有不堪。余与之亲，作诗从规焉。”诗中有句云：“悠悠群吏，非子不整，嗷嗷众议，非子不靖。”这是西晋司徒左长史参预评定九品的例子。

    综合上文，魏晋之际州中正的建立与司徒府参预九品评定工作是九品中正制的一大变化。这一变化的出现是有原因的。《太平御览》卷二六五中正条引晋宣帝除九品州置大中正议曰：“案九品之状，诸中正既未能料究人才，以为可除九制（品？），州置大中正。”同卷又引《曹羲集》九品议：“伏见明论欲除九品而置州中正，欲检虚实。一州阔远，略不相识，访不得知，会复转访本郡先达者耳，此为问中正而实决于郡人。”据此，置州中正的建议是由司马懿提出的，而曹羲则持不同意见。据同卷引应璩《新论》，应璩也反对建立州中正。他说：“百郡立中正，九州置都士，州闾与郡县希疏，如马齿不相识面，何缘别义理？”应璩的观点与曹羲的观点在某些方面是一致的。他们都认为不必设州中正，因为一州之地过于辽阔，州中正对郡县的情况不了解。所谓“略不相识”、“如马齿不相识面”都是这个意思。但应璩仅仅担心义理难辩，而曹羲所担心的是，由于州中正不清楚下属郡县的情况，结果还得回去访问“本郡先达”，名曰州中正负责，但“实决于郡人”，这样就失去了建立州中正的意义。曹羲的担心是有道理的。中正制初创时就规定“各使诸郡选置中正”(20)。既然中正的推举权在“诸郡”，推举出来的中正当然是最能体现“诸郡”意志的人。九品评定最终“决于郡人”，“决于本郡先达”就不可避免。所以，如果州中正建立后也落得同样下场就等于毫无意义了。由此可以看到，司马懿的本意原是想不理会“本郡先达”的意见，改变中正品评“决于郡人”的现状。曹羲所提出的问题在魏末究竟是如何解决的，由于史料缺乏，还不清楚。但西晋“诸郡”推举中正的权力终于被剥夺而转交给司徒府。中正品评人物必须由司徒府最终核实，“决于郡人”的局面一去不复返了。(21)

    在此，须着重指出，所谓“郡人”、“本郡先达”绝不包括一郡内的所有人，只能是那些地方上的郡姓、著姓、大族。司马懿所要打击的正是他们。明乎此，我们终于可以理解西晋时期一批地方郡姓为何要攻击中正制度了。但是，魏末作为皇权的实际执行者司马懿、曹爽兄弟等人为何要打击地方郡姓呢？由此为何又导致了势族垄断上品？

    如前所述，势族中有不少人就是两汉以来的著姓、大族，就此而论，他们与地方郡姓似乎并无区别。过去的研究往往将他们视为一体。这是不无道理的，但又不完全对。固然，自汉代以来，郡姓、大族一般都是在本地发展起来的，但是其中一部分郡姓并没有就此止步，而是跨出州郡，走向中央，累世公卿，如汝南袁氏“四世五公”，弘农杨氏“四世三公”。这些人的利益已经不仅仅是与地方州郡相联系了，更多的则是与中央政权联系在一起。没有统一的东汉帝国，“四世三公”就只能是一场空幻的梦。因此，董卓之乱以后，他们都企图重建统一国家。建安元年（196），曹操“挟天子”后，许多人纷纷归附到他的旗帜下，就是由于他们认为曹操“乃心王室”。(22)地方郡姓与中央政权联系并不密切，他们的力量在于州郡、在于宗族乡里。因此，董卓之乱爆发后，大量的地方郡姓并没有离开本土。这一方面使他们以后难以上升，另一方面又使他们能够有效地控制宗族乡里，并进而建立自己的武装。在各个地区，他们往往是不安定的因素。西晋时期，地方郡姓依然垄断着州郡僚佐的职务，操纵着乡里舆论。(23)虽然与势族相比，他们处于寒门的地位，但在本地仍不失为著姓、大族。他们的一切特权也就是来源于此。愈是依靠门第过活，便愈要排挤那些没有门第的人。因此，轻视寒人的风气在地方州郡中自汉末历魏晋而不衰。(24)

    总之，地方郡姓由于远离政治斗争中心，所以在汉末以来的历次动乱中都没有受到重大损失，这个阶层基本上没有什么变化。

    与此不同，汉末的高等世族既然寄生在东汉中央政权的躯体上，当统一帝国崩溃后，他们便四散逃亡了。虽然他们都希望重建统一国家，但究竟借助于哪一种力量、哪一派军阀来实现其目的，每个人的选择并不一样，有人投靠曹操，有人追随刘表，有人与孙氏父子共患难，也有人跟着刘备辗转他乡。尽管他们的主观动机一致，但客观行动却使本阶层陷入了分裂中，今天的史家虽然可以根据血统把他们集合在自己的笔下，但在现实斗争中，血统并没有使他们团结在一起。高等世族能否存在下去，也不在于他们的血统。袁绍凭借“四世三公”的地位当了讨伐董卓的盟主，但当大族一旦发现他并非救世主，便又纷纷离开了他。随着官渡之战的结束，这个家族终于迎来了自己的末日。在动乱的年代里，他们能否存在下去，关键在于自身的能力。荀彧帮助曹操艰苦创业，几度难关；司马懿战诸葛、平辽东，战功赫赫，因此他们的家族才能延续下去，成为魏晋政治舞台上的重要角色。也正是由于他们并非依靠门第过活，所以对于那些卑微之士也并不特别压抑。颍川戏志才、郭嘉先世无闻，有“负俗之讥”，但荀彧“取士不以一揆”(25)，大胆拔用了他们。司马懿“知人拔善，显扬侧陋，王基、邓艾、周泰、贾越之徒皆起自寒门而著绩于朝”(26)。司马师为了任用石苞公开提倡曹操当年唯才是举的方针：“苞虽细行不足而有经国才略。夫贞廉之士，未必能经济世务，是以齐桓忘管仲之奢僭而录其匡合之大谋；汉高舍陈平之污行而取其六奇之妙算。苞虽未可以上俦二子，亦今日之选也。”(27)魏晋政权的势族基本就是由战火中锻炼出来的高等世族与这些有“经国才略”的卑微之士组成的。此时，他们的利益又与魏晋中央政权紧密相连了。

    由以上分析可以看到，汉末以来，地方郡姓与中央高等世族经历了不同的道路，不能把二者混为一谈。正始之初，司马懿与曹爽等人同受托孤之任，双方斗争尚未展开。此时，他们事实上行使的是皇权，加强中央对地方的控制是当务之急，而地方郡姓操纵选举显然是与之背道而驰的。因此必须予以打击。

    打击地方郡姓的措施是成功的，但由此导致势族垄断上品却是司马懿始料不及的。正如西晋刘毅所说：“置州都者，取州里清议，咸所归服。将以镇异同，一言议，不谓一人之身了一州之才，一人不审便坐之。”(28)州中正一人说了算是不符合司马懿本意的。司马懿反对地方郡姓操纵选举，但并不反对乡里清议，他所要做的正是要使乡里清议摆脱地方郡姓的控制。然而，这一时期势族正处于向上发展的阶段，加强中央集权的措施在很大程度上被他们改造成一项特权制度。西晋皇权无力根本扭转这一局面，只能在一定意义上加以限制，试图使中正制度不至于完全背离当初创建它的目的。

    与东晋相此，西晋中正主持清议的事例还是不少的。《廿二史劄记》卷八“九品中正”条所载中正清议事例，基本属于西晋时期。这反映出当时皇权还是比较强大的，仅仅根据势族地位而不顾才德定品，在理论上是不能成立的。正是在这样的背景下，才会有前述元康年间举寒素事发生。也是在元康年间，西晋王朝曾发动了一场清议活动。此事《晋书》失载，有幸《通典》保存了这段材料。《通典》卷六○礼二○嘉五周丧不可嫁女娶妇议：

    惠帝元康二年，司徒王浑奏云：“前以冒丧婚娶，伤化悖礼，下十六州推举，今本州中正各有言上。太子家令虞濬有弟丧，嫁女拜时；镇东司马陈湛有弟丧，嫁女拜时；上庸太守王崇有兄丧，嫁女拜时；夏侯俊有弟子丧，为息恒纳妇，恒无服；国子祭酒邹湛有弟妇丧，为息蒙娶妇拜时，蒙有周服；给事中王琛有兄丧，为息稜娶妇拜时；并州刺史羊暨有兄丧，为息明娶妇拜时；征西长史牵昌有弟丧，为息彦娶妇拜时。湛职儒官，身虽无服，据为婚主。按《礼》‘大功之末可以嫁子，小功之末可以娶妇’。无齐缞嫁娶之文，亏违典宪，宜加贬黜，以肃王法。请台免官，以正清议。”……诏曰：“下殇小功，可以嫁娶，俊等简忽丧纪，轻违《礼经》，皆宜如所正。”

    按清议工作，本应由中正主动进行，而此次大规模的清议活动却是在司徒“下十六州推举”的情况下才发生的。这说明中正对清议事不够负责，但也还不能违抗朝廷的命令。清议当否最终由皇帝审批，说明皇权还是有一定力量的。

    综上所述，西晋皇权对势族垄断上品的特权虽不得不认可，但另一方面，皇权还是企图对势族加以限制，这个目的在一定程度上实现了。中正制度在执行中所起的互相矛盾的作用反映出时代的矛盾性。西晋是以后高门世族形成的时期。势族的力量在发展，中正“计资定品”是发展趋势，但势族还不能彻底超越皇权的限制。皇权也还可以有限度地利用中正制度来维护统治秩序。

    三、九品中正制度的作用

    以往的研究者认为，此制度在客观上保证了世家大族的世袭特权，东晋南朝以后，流于形式。根据本文第一节所论述的观点，西晋时，它仅仅是保证了当时的高官显贵的世袭特权，从而在势族的形成以及势族向世族（或称士族）的演变过程中起了重要作用。但只是这样泛泛而论是不够的。因为，单从保障某些高级官吏的世袭特权这一点看，九品中正制并非创举，大家所熟知的汉代的任子制也具有同样的作用。过去，人们在研究九品中正制时，大都将其与汉代的察举制联系起来考虑，这对于探讨中正制度建立的原因无疑是有益的。但是，中正制度在实际运行中既然已经在相当大程度上转化成一种特权制度，它就不再是仅仅与察举制相联系了，而更多的则是与汉代的任子制存在某种继承关系。只有对这两个制度进行比较，才可以更清楚九品中正制的作用。

    任子制与九品中正制虽有相同之处，但也还存在某些差异。首先，在人数上，任子制有严格限制。西汉初年，二千石以上的官吏可以送弟或子到京师为郎官，这叫作任子为郎。《汉书》卷一一《哀帝纪》颜师古注引应劭曰：“任子令者，《汉仪注》：吏二千石以上视事满三年，得任同产若子一人为郎。”东汉安帝在建光元年（121）又下诏发展了西汉的任子制，申明“以公卿、校尉、尚书子弟一人为郎、舍人”(29)。不仅可以任子为郎，而且也可以任子为舍人，这是一个变化。但任子弟一人为官的规定还是一循西汉。在这种制度下，有任子特权的官吏不可能使其后代全部由任子一途入仕。东汉高门世族袁安位至司徒，其子袁敞“以父任为太子舍人”(30)，但另一子袁赏直到袁安死尚未入仕。袁安本传称：袁安死后“数月，窦氏败，帝始亲万机，迫思前议者邪正之节，乃除安子赏为郎”。袁安孙袁汤“桓帝初为司空”，据袁安本传注引《风俗通》云：汤“有子十二人”，但见于记载的只有四人：“长子平，平弟成，左中郎将，并早卒。成弟逢，逢弟隗，皆为公。”(31)袁汤数子入仕，但并不能据此认为他们都是凭借着任子特权。弘农杨氏家族与袁氏家族情况相似，延光三年（124）杨震“因饮酖而卒，时年七十馀……岁馀，顺帝即位，樊丰、周广等诛死，震门生虞放、陈翼诣阙追讼震事，朝廷咸称其忠，乃下诏除二子为郎。”(32)由以上袁、杨家族任子情况看，任子有限额的规定还是执行得比较认真。袁安子袁赏、杨震二子都是在其父死后，按特殊情况授予郎官的。袁、杨家族尚且如此，一般官吏的任子数量也很难超过制度的规定。虽然高官子弟除去任子制度外，还可以从其他途径入仕，如察举、征辟等等，但这毕竟不属于特权制度，其他人士如一般的地方郡姓也可由此途上升。

    与任子制不同，九品中正制建立时并不是一项特权制度，因此也不可能规定高官子弟可以获得上品的人数。没有人数限制而在实际执行中又确实成为特权制度，这就构成了九品中正制度的一大特点。在此情况下，高官子弟大都可以获得上品，步入清途。说得明确些，高官子弟是以族的规模进入政治舞台的，官之为族终于实现了。这在汉代是缺乏保障的。汉代某些高官家族后来演化为累世相承地做官的世家大族，与其说是靠任子制，倒不如说是靠累世通经，察举入仕更为接近事实。魏晋时期，察举制依然存在，但正如严耕望所说：“晋世公卿另有捷径，故即在西晋，汉代经制之秀孝两途已渐不见重视，东晋以下更无论矣。”严氏更引日本学者宫崎市定所述王谢大族不应秀才之举以为佐证。(33)晋代高官子弟对秀、孝两途的不重视正是由于保障其世袭特权的九品中正制没有人数限制。他们不必再以察举制作为入仕的补充手段了。

    制度是对现实的反映，任子制与九品中正制的上述差异表明，汉代高门世族与魏晋以降的高门世族在保障整个宗族的世袭特权方面所具有的能力是不同的。汉代高门世族在皇权、外戚、宦官的限制下还不可能把任子制发展为九品中正制。宗族政治力量有限，在复杂激烈的斗争中要想壮大力量，就必须到本宗族以外寻求支持。史称袁绍能“折节下士”，其目的不过是为了争取“士多附之”而已。不仅袁绍如此，袁氏家族“自安以下，皆博爱宾客，无所拣择，宾客入其门，无贤愚皆得所欲，为天下所归”(34)。汉末袁绍被认为是最有力量的，但这并不是由于自身“四世五公”的空名，而是在于“树恩四世，门生故吏遍于天下”(35)。建安年间，在袁绍家乡汝南“拥兵拒守”，反抗曹操的并不是袁绍的宗族成员，而是“布在诸县”的“门生宾客”。(36)众所周知，汉代的门生故吏与其宗师举主存在着一种类似父子的关系，宗师举主有势，门生故吏可因此飞黄腾达；宗师举主被贬，他们亦同时被贬，宗师举主死后，他们要为之服丧。非血缘关系被罩上了一层宗法面纱。这表明，社会中宗法观念在发展，世族可以借此壮大自己的势力。但另一方面，宗法观念、宗族力量还不够十分发展，盘踞中央的高门世族还不可能使自己的整个家族都不受限制地进入政治舞台。

    魏晋南朝，门生、故吏、宾客依然存在，但他们参加政治活动的记载则不多见了，地位明显下降。(37)高门世族也并不以广召门生、宾客为重要任务，也从来没有人认为高门世族的政治力量是体现在他所控制的门生、故吏、宾客方面。这些变化说明世族自身的宗族力量大大加强了，因此，在政治斗争中，高门世族靠的是本宗族成员占据高官要职，靠的是世族与世族的政治联盟，而联盟的手段则是婚姻。

    以上讨论了任子制与九品中正制不同的一个方面，以及这种不同产生的历史原因。除此之外，任子制与九品中正制还存在另一个不同的方面。汉代的任子制不具有垄断性，除去任子为郎外，拥有赀产十万钱而又非商人者，也可凭赀产为郎，叫作赀选。在察举制下，被举为秀才、孝廉者也多除郎中。此外，还有献策为郎等多种途径。所以，汉代高官子弟不可能垄断郎官。而在九品中正制度下，“上品无寒门，下品无势族”，低等世族很难进入上品之列。高门世族在很大程度上切断了低等世族上升之路。垄断的特征，一方面造成了高低两等世族长期较为稳定的并存局面，另一方面，随着时间的推移，随着门阀政治理论的确立，又必然地出现了族之为官的转变，即某些家族的子弟理所当然地居高位。从依据现实的政治地位以培植本宗族的力量，到依靠族姓地位以巩固自己的力量——官之为族，族之为官，这就是魏晋南朝高门世族所走过的历程。

    综上所述，没有人数限制、封闭性是九品中正制度区别于任子制的关键所在。在此制度下，高门世族的宗族政治力量必然呈现出日益扩张的趋势。毫无疑问，在不断扩大基础上的世袭特权具有更稳固的特征，因为某一分支的衰落不会影响整个宗族政治权力的继续传袭。南朝一些高门世族的家世，往往可以追溯到晋代，其原因必定是复杂多样的，但九品中正制的实行显然是原因之一。

    本文转自《北京大学学报》1987年第1期

  • 韩国河：武王墩墓与东周王陵历史变迁

    武王墩墓墓位于今安徽省淮南市田家庵区三和镇徐洼村，是一处战国晚期楚国的高等级大型墓地。武王墩墓曾多次被盗掘，基于此，2019年国家文物局组织相关单位对武王墩墓展开考古工作。2024年4月16日，国家文物局公布了武王墩墓的相关发现。这些发现为了解战国晚期楚王陵制度、楚人东迁后的历史、秦汉大一统国家的形成等提供了重要材料。

    一

    武王墩墓为一处以主墓为核心的独立陵园，由主墓（一号墓）、车马坑、陪葬墓、祭祀坑等遗迹组成，四周以一圈环壕为界。主墓为一座大型“甲字形”竖穴土坑墓，由封土、墓道、墓圹及椁室组成，为单一东向斜坡墓道。墓圹底部为亚字形的椁室，由长方形枋木搭建而成，棺室位于椁室正中，内置有三重棺。一号墓墓室由一个棺室和八个侧室组成，在椁室盖板及各侧室内壁发现有众多墨书文字，有表示方位的“东”“南”“西”“北”，展现出楚国棺椁制度中典型的主、边箱形式——“井椁”，这一特点对西汉葬制有所影响。

    随葬品中，东一室内出土器物以铜礼器为主，其中出土的大鼎为迄今所见最大的楚国大鼎。西侧两个椁室出土器物以漆木俑为主，同时出土木车、乐器和少量遣策类竹简。北侧的两个侧室受到盗扰，主要是琴、瑟以及编钟架等。南侧的两个侧室中出土有漆盒、耳杯、盘、豆、鼓、玉璧、璜、佩以及大量铜箭矢。从以上随葬品来看，其“事死如生”的功能一目了然。

    关于墓主身份，出土的一件青铜簠口部刻有“楚王酓前作铸金簠以供岁尝”的铭文，其中“酓前”可释为“熊完”。《史记》载，楚考烈王名为熊元，亦称作熊完。据专家考证，“酓”与“熊”、“前”与“完”在上古音中发音相近，均属于音近通假。自考烈王迁都寿春后，又历经幽王、哀王、负刍，后二王在位时间较短，且负刍为亡国之君，幽王墓一般认为是寿春以东的李三孤堆，故武王墩为楚考烈王墓的可能性最大。

    二

    “陵随城移”，楚王陵墓见证了楚国历史的发展。楚文王时，“始都郢”，郢为楚国对都城的统称。纪南城遗址位于今湖北省荆州市，发掘者认为其为楚国郢都，城墙始建年代约为春秋晚期，废都于公元前278年。在纪南城周围发现多处楚国最高等级的墓葬，熊家冢墓地位于荆州市川店镇的山岗上，东南距纪南城遗址约26公里。熊家冢墓地的年代可能为战国早期偏晚。

    冯家冢位于荆州区（原江陵县）八岭山林场中部的一处岗地上，该墓地为战国时期的某位楚王及其夫人异穴合葬的陵园。大薛家洼墓地亦被认为是楚王墓地，位于今纪山国有林场，南距楚都纪南城约13公里，年代为战国中晚期。除上述墓葬外，纪南城周边的谢家冢、平头冢也可能是楚王墓葬。

    公元前278年，楚顷襄王迁都陈城，即今河南淮阳。马鞍冢位于今河南淮阳东南5公里处，一般认为南冢为楚顷襄王之墓，北冢为其王后之墓。寿春为楚国最后的都城，位于寿春以东的朱家集李三孤堆，多被认为是楚幽王墓，为带封土的单一东向墓道竖穴土坑木椁墓，其周边未发现陪葬墓及车马坑。武王墩大墓位于李三孤堆以北。

    纵观之，从楚都纪南城时期一直到都寿郢时期，楚国王陵都具有很强的传承性。表现在墓地地势较高，多位于南北向的岗地之上，基本由主墓、车马坑、殉葬墓、祭祀坑等要素构成等。具体布局方面，结构较为清楚的一般为主冢居中心，其北为陪葬墓（副冢），二者西侧存在车马坑，而殉葬墓多分布于主墓、陪葬墓南北两侧。在墓葬形制方面，皆为带有较大规模封土的竖穴土坑墓，多为单一东向斜坡墓道，仅马鞍冢南冢采用两条墓道。当然，各楚王陵也存在一些差异性，主要表现在是否有殉葬墓、车马坑的数量以及封土形状等。相较其他楚王墓，武王墩是由一条近方形的环壕确定了明显界限的独立陵园。陵园规模达150万平方米，是其他楚王陵的数十倍以上。此时，楚国国力虽然没落，而埋葬礼俗却在强化，这一现象值得我们进一步思考。

    三

    诸侯争霸，秦、楚两个大国，一个并吞天下，一个失败灭国。两国王陵的一些特征对比亦能带给我们一些启示。秦人迁都次数严格意义上说共有8次：西犬丘（西垂）—秦—汧—汧渭之会—平阳—雍—泾阳—栎阳—咸阳。秦国国君墓葬与楚国一样，遵循“城陵相依，陵随城移”的特点，目前秦国国君墓的发现包括西垂陵区、雍城秦公陵区、咸阳周陵镇秦陵、临潼秦东陵、神禾原秦陵、韩森寨秦陵。秦国都城及陵墓的迁徙呈现由西向东的趋势，在此过程中，秦国不断发展，最终统一六国。也就是说“陵随城移”之于秦人是主动的结果，之于楚人显然是被动的过程。

    秦人雍城陵区的十四处陵园多有“兆沟”为界，呈现出一定的独立性，但陵区西侧及南侧发现的“兆沟”将十四处陵园划入一个大的陵区内，各陵园之间的距离也较近，中字形大墓应为秦公级别的墓葬，具有集中公墓制的特征。至咸阳周边诸秦陵，大多相隔较远，以垣墙或兆沟为界。从雍城陵区到咸阳周边的多个陵区，秦国国君墓完成了从“集中公墓制”到“独立陵园制”的转变。

    战国时期的楚王陵却基本均位于独立岗地之上，具有一定程度“独立陵园”的特征，并拥有车马坑、陪葬墓、祭祀坑等秦国国君墓具备的要素。楚王陵直至战国晚期武王墩墓确认出现了由环壕构成的陵园界限。在墓葬形制方面，秦国国君墓通过“独立陵园制”完成了由中字形大墓到亚字型大墓的转变。楚国于春秋时期已经称王，但陵墓形制却体现出一种“守旧”传统，战国时期的楚国基本采用“甲字形”竖穴土坑墓作为王陵之制，仅马鞍冢出现带有两条墓道的中字形大墓，到寿郢时期的李三孤堆和武王墩，墓葬形制又回归“甲字形”墓。

    究其原因，春秋战国时期是一个大变革的时期，各诸侯国由血缘政治逐渐转向地缘政治。秦楚两国虽同样设县较早，但商鞅变法普遍推行郡县制和系列经济社会改革，有力强化了秦国的中央集权，完成了国家对全国资源的有效整合。四条墓道的“独立陵园”成为秦人陵墓制度的核心要素，某种意义上正是其国家集权的象征之一。

    四

    如果我们再把视野拓展到整个春秋战国时期，也会有一些新的发现。这一时期，周王室衰微，诸侯国崛起，形成多元化的文化格局。齐、韩、赵、魏等国的王侯墓均位于城外的一个大的区域内，各国国君墓具有一定的独立性，燕下都外也发现了独立的国君墓地，战国时期的国君墓普遍已使用高大的封土。同时，各诸侯国王陵也存在一定的差异性。武王墩墓周边设置一圈环壕作为陵园界限，陵园整体近方形。秦国王陵多为南北向的长方形陵园，设置有二重至三重的兆沟或垣墙。胡庄韩王陵周围发现了3条隍壕类的近长方形半封闭环状壕沟。魏国固围村大墓为呈“回字形”的陵园。齐国王陵多是在方形台基之上构筑圆形封土，未发现有壕沟、垣墙等陵园界限。

    墓葬形制方面，秦国迁都咸阳后王陵普遍采用四条墓道的“亚字型”大墓，而其他诸侯国王陵多采用两条墓道的“中字形大墓”，武王墩墓则为单一东向墓道的“甲字形大墓”。葬具方面，武王墩墓使用多重棺椁，且椁室采用枋木构筑，与其他诸国国君墓具有相似性。神禾原秦陵亦采用枋木构筑，使用二棺一椁。韩国王陵中的胡庄大墓采用重棺重椁，枋木构建。燕国王陵中亦使用重棺重椁。武王墩墓中为棺室，四周各带有两个侧室的亚字型结构则不见于其他诸侯王陵。

    在陪葬坑与祭祀坑方面，武王墩主墓西侧发现有一座大型车马坑，这是战国以来楚王陵的传统，主墓南侧的祭祀坑遗存，于其他楚王陵内也多有发现。其他诸国王陵也多存在陪葬坑与祭祀坑，其中陪葬品以车马为主。随葬品方面，礼乐器随葬在战国王陵中比较普遍，但也存在一定差异，如燕国使用大量仿铜陶礼器随葬，魏国王陵中亦出土有九种陶鼎。此外，武王墩墓中出土的众多漆木器、木俑也是一大特点。

    周平王东迁以后，诸侯国日渐强大，同时形成了各具特色的地域文化。各国王陵虽多采用封土，但存在“覆斗形”与“方基圆坟”等不同形式；墓葬形制有“中字形”“甲字形”“亚字型”之分；各国虽多采用木质棺椁，但棺椁形制也存在一定差异，如楚国采用独特的“井椁”。在多元文化并存发展的同时，诸国王陵亦呈现出统一的发展趋势，一是“周制”在各国王陵制度中仍有所体现，如采用棺椁制度、流行礼乐器及车马陪葬等。二是伴随着宗法制的解体，独立陵园制逐步形成与发展。

    武王墩墓的发掘不仅为研究战国晚期楚王陵特征提供了直接材料，也是楚国晚期历史变迁的重要物证。同战国时期秦国王陵的巨大变革不同，楚国王陵呈现“守旧”的态势，结合地方行政组织与社会结构演变形态等，可以看出楚国深受血缘政治以及传统习俗的影响。通过考察各诸侯国王陵的变迁，可以发现战国时期各国王陵形成了多元化格局，同时也存在一体化的趋势。秦汉陵寝文化统一性的形成正是多种文化融合发展的结果，其构成大致可以总结为“承周制”“袭秦制”“融楚俗”的进程。

    本文转自《光明日报》（ 2024年12月16日）

  • 阿克顿：论民族主义

    每当一个时代并存着思想的巨大发展和人们境况的普遍变化所必然造成的苦难，那些善于思辨或长于想像的人们，便会设计一个理想的社会，从中寻求一个救世良方或至少是一点精神安慰，以反抗他们实际上无力涤荡的邪恶。诗歌中总是包含着这样的理想：很久以前，或在某个遥远的地方，在西方岛国或世外桃源，天真而知足的人们远离文明社会的堕落和约束，过着传说中黄金时代的生活。此类诗作几乎千篇一律，理想世界也相差无几。然而当哲学家们构造一个想像中的国家以喻诫或改造人类时，他们的动机更明确和更迫切，他们的国家既是一个楷模，又是一种讽刺。

    柏拉图和柏罗丁，摩尔和康帕内拉，是用被现实的社会结构清除出去的素材来建构他们幻想中的社会，他们的灵感是来自现实社会的弊端。《理想国》、《乌托邦》和《太阳城》，是作者们对自己身历之境况的谴责和抗议，也是他们逃避现实、在对立的极端中寻求慰藉的避难所。它们一直没有影响力，从未从文学史变为政治史，因为一种政治思想要想获得支配芸芸众生的力量，除了对现实的不满和思辨才能之外，还需要一些别的东西。一个哲学家的设计只能调动狂热分子的政治热忱，但是无法唤起全体国民的行动；虽然压迫激起一次次激烈的反抗，就像痛苦的人发出的阵阵痉挛，然而它不能孕育成熟一个坚定的目标和复兴社会的方案，除非某种新的幸福观和当时的邪恶力量携起手来。

    宗教史提供了一个很好的例证。中世纪晚期的教派和新教之间存在着一个重大差别，它的重要性大于在那些被认为是宗教改革之先兆的学说中发现的相似之处，它也足以说明为什么后者和其他改革相比具有如此强大的生命力。威克里夫和胡斯仅反对天主教教义的某些细枝末节，而路德则抛弃教会的权威，赋予个体良知一种独立性，它必然使人持续不断地反抗。同样，在尼德兰革命、英国革命、美国独立战争或布拉班特起义(therising of Brabant)与法国大革命之间，也有类似的差别。

    1789年之前的反抗起因于具体的错误，其正当理由是一些范围明确的不满和一些公认的原则。斗争的过程中有时会提出一些新理论，但这是偶然现象，反抗暴政的重大理由是忠实于古老的法律。自从法国大革命以来，这种情况改变了，渴望铲除社会邪恶和弊端的抱负，逐渐成为遍及文明世界的持久而强大的行动力量。它们我行我素，咄咄逼人，无需先知鼓吹，无需勇士捍卫，深人民心，毫无理性，而且几乎不可阻挡。法国大革命促成了这种变化，一是因为它的理论，二是因为事件的间接影响。它教导人民说：你们的愿望和需要即是最高的公正准则。在走马灯式的权力更替中，各党派纷纷求助于民众，把他们的支持视为裁决成功之神，使得他们不仅惯于反抗而且易于专横。多个政府的垮台和领土划分的频繁变更，使永恒的尊严失去了一切立身之地。传统和惯例不再是权威的保护，革命、战争胜利以及和平协定后所产生的制度安排，一概无视既定的权利。义务和权利是分不开的，各国拒绝受制于没有保障的法律。

    在这种世界形势下，理论和行动紧密相随，现实的邪恶很容易产生反抗的学说。在自由意志的领域，自然进程之节律，受着极端行为之冲突的支配。造反的冲动促使人们从一个极端趋于另一个极端。一个遥远的理想目标以其美妙唤起人们的想像，以其单纯迷惑了人们的理性。对它的追求所激发的力量，远远超过一个理性的、可能的目的所激发的力量，因为后者受到许多对立要求的制约，只能是一个合理的、可行的和适当的目的。一种极端或过分的行为，是对另一个同类行为的纠正；在民众中间，一种谬误通过和另一种谬误的对峙，促进了真理的产生。少数人不靠别人帮助无力实现重大的变革，多数人则缺乏接受纯粹真理的智慧。既然疾病多种多样，也就不存在包治百病的药方。对于那些寻求一个惩治各种具体罪恶的统一方案、一个对众多不同情况一概适用的共同计划的大众来说，只有一个抽象观念或一个理想国家的吸引力能让他们采取共同的行动。因此，既迎合人类善良愿望又迎合他们邪恶目的的虚假学说，就成了各民族社会生活中一个正常而又必要的因素。

    就其反对某些公认而明显的罪恶并承担着破坏的使命来说，这些理论是正当的。作为一种警告，或一种改变现状的威胁，它们的反对是有益的，它们能使人对错误保持清醒。不能把它们当作重建世俗社会的基础，就如同不能把药品当作食物一样；但是它们可以对社会产生有利的影响，因为它们尽管没有指明改革的措施，却指出了改革的方向。它们反对统治阶级由于自私、肆意地滥用权力而造成的事物秩序，反对人为地限制世界的自然进程而造成的事物秩序。这样的秩序缺乏理想因素和道德目的。实践中的极端不同于它所导致的理论上的极端，因为前者既专断又残暴，而后者虽然也是革命性的，同时又是有益的。前者的邪恶带有任意性，后者的邪恶带有必然性。这是发生在现存秩序与否定其合法性的颠覆性理论之间的斗争的一般特征。这样的理论主要有三种，它们分别谴责权力、财产和领土当前的分配状况，分别攻击贵族政治、中产阶级和国家政权。它们是平等主义、共产主义和民族主义，虽然来自同一个根源，反对同样的邪恶，彼此也关联甚多，但是它们并不是同时产生的。第一种理论的正式宣告者是卢梭，第二个是巴贝夫，第三个是马志尼。第三个出现的最晚，目前最有吸引力，得势的前景也最看好。

    在欧洲的旧制度中，民族的权利既不为政府所承认，也不为人民所要求。王室而非民族的利益调整着边界，政府的行为一般不考虑民众的愿望。只要一切自由权利受到压制，民族独立的要求也必遭忽视。费奈隆曾言，一个君主国可能就是某位公主的一份嫁妆。欧洲大陆在18世纪对这种集体权利受到遗忘状况一言不发，因为专制主义者仅关心国家，自由主义者仅关心个人。教会、贵族、民族在那个时代的时髦理论中没有一席之地；因为它们未受到公开的攻击，它们也就没有创立什么理论来维护自己。贵族阶层保有其特权，教会保有其财产；王室利益压倒了了民族的自然倾向，消解了它们的独立性，然而又维持着它们的完整。

    民族情绪最敏感的部分并没有受到伤害。废黜君主世代相传的王位，或者吞并他的领地，被认为是侵害了所有的君主国，被认为因其亵渎了王权的神圣性质而给臣民提供了一个危险的范例。在战争中，由于战事无关乎民族，所以无需唤起民族感情。统治者之间的彬彬有礼与他们对于下层的傲慢和蔑视是相一致的。敌我两军的指挥官互相致词，没有憎恨，没有激情，战斗以壮观而高傲的队列形式展开。战争艺术成为一种优雅、博学的游戏。各君主国不仅通过一种自然的利益共同体，而且通过家族关系联结在一起。有时候，一份婚姻契约可能开启一场持久战，而更多的时候，家族关系阻止了侵略野心的萌生。当宗教战争于1648年结束之后，所有的战争都是为了获得一项继承权或某块属地，或为了反对某些国家，它们的政治制度使自身被排除在王朝国家的公法的管辖范围之外，被置于不但不再受到保护，而且令人生厌的地位。这些国家是英国和荷兰。这种情况一直持续到荷兰不再是一个共和国，英国的詹姆斯二世党人在共和45年的失败结束了王位之争。然而，有个国家仍是例外，有一个君主，其地位并不为国王们的礼法所承认。

    只要王位是通过婚姻或继承获得的，王国的关系网和正统观念就可保证它的稳定，而当时的波兰无此保证。在王朝专制主义时代，没有王室血统的君主，人民所授予的王位，都被视为反常和暴乱。波兰的制度由于有这种性质，它便被排斥在欧洲体系之外。它刺激了一种无法满足的贪欲，它使欧洲的统治家族不能够通过与它的统治者联姻以求江山永固，或不能够通过请求或继承以获得它。

    哈布斯堡家族曾与法国波旁家族争夺西班牙和印度群岛的统治权，与西班牙波旁家族争夺意大利的统治权，与维特尔斯巴赫家族争夺帝国的统治权，与霍亨索伦家族争夺西里西亚的统治权。为了获得半个意大利和德意志，对立的王室曾经发动过战争。但是对于一个不能凭借婚姻或继承对之提出要求的国家，任何王室都无望捞回损失或增长权势。由于它们不能永久性地继承，它们便用阴谋取得每次选举的胜利。在同意支持站在它们这一边的候选人之后，波兰的邻国终于制造了一个最终毁灭波兰国的傀儡工具。在此之前，尚未有任何一个民族被基督教强国剥夺其政治存在的权利；不论怎么忽视民族利益和愿望，它们仍注意掩饰蓄意歪曲法律所造成的不公。但是瓜分波兰是一次不负责任的暴行，不仅公然践踏民心，而且违背公法。在近代史上第一次出现一个大国被控制，整个民族被它的敌人瓜分的局面。

    这个著名的事件，老专制主义的这次最具革命性的行径，唤醒了欧洲的民族主义思潮，沉睡的权利转化为迫切的愿望，模糊的情绪上升为明确的政治要求。埃德蒙·柏克写道：“任何一个明智或正直的人都不会赞同那次瓜分，或在思考此事时不会预见到它将给所有国家带来巨大的灾难。”此后，便有一个民族要求统一在一个国家之内——就如同一个灵魂四处找寻一个肉体，藉以开始新的生命。人们第一次听到这样的呐喊：各国的这种安排是不公正的，它们的限制是违背自然的，一个完整的民族被剥夺了组成一个独立共同体的权利。在这一权利要求能够有力地对抗其敌人的压倒性势力之前，在它于最后一次瓜分之后获得了力量克服长期的被奴役习惯和消除由于先前的混乱人们对波兰的轻视之前，古老的欧洲体系逐渐崩溃，一个新的世界兴起了。

    把波兰人变成赃物的旧专制政策有两个敌人——英国的自由精神和以其自身的武器摧毁了法国君主制的革命理论；它们以相反的方式反对民族没有集体权利的观点。当前，民族主义理论不仅是革命最强大的助手，而且是近三年来各种运动的真实本质。然而，这是一个不为法国大革命所知的新生的联盟。近代民族主义思潮的兴起部分是个顺理成章的结果，部分是对这场革命的反叛。正像忽视民族分裂的理论受到英法两种自由主义的反对一样，坚持这种做法的理论显然也来自两处不同的泉源，分别体现着1688年或1789年的特征。当法国人民推翻他们头上的种种权威，成为自己的主人时，法国面临着解体的危险：因为众意难以确知，不易取得一致。

    维尔尼奥在就审判国王展开的辩论中说：“唯有大体上体现着人民意志的法律才具有约束力，人民享有批准或废除它们的权利。人民一旦表示他们的愿望，什么国民代表机构，什么法律，都必须让路。”这种观点将社会消解为自然的因素，有可能使国家分裂，造成一种有多少共同体便有多少个共和国的局面。因为真正的共和主义，就是在整体和所有部分中实行自治的原则。在一个幅员辽阔的国度，例如希腊，瑞士、尼德兰和美国，只有通过将若干独立的共同体结合为单一的联邦，才能实现真正的共和主义。因此，一个庞大的共和国若不建立在联邦制的基础之上，必定导致一个城市的统治，如罗马和巴黎，以及程度相对较轻的雅典、伯尔尼和阿姆斯特丹。换言之，一个庞大的民主国家必定或是为了统一而牺牲自治，或是用联邦制来维持统一。

    历史上的法兰西随着在数百年中形成的法兰西国家一起衰落了。旧政权被摧毁了，人们以厌恶和警惕的目光看待地方权威。新的中央权威需要按照新的统一原则去建立。作为一种社会理想的自然状态，成了民族的基础。血统代替了传统；法兰西民族被视为一个自然的产物，一个人种学而非历史学上的单位。有人以为，统一体的存在无需代议制和政府，它完全独立于过去，能够随时表示或改变它的意愿。用西哀士的话说，它不再是法兰西，这个民族蜕变成了一个陌生的国家。中央权力所以拥有权威，是因为它服从全体。任何分离都违背民意。这种具有意志的权力，体现为“统一、不可分割的共和国”——在国家之上存在着一个更高的权力，它有别于并独立于它的成员；在历史上它第一次表达了抽象的民族的概念。

    就这样，不受历史约束的人民主权的概念，孕育产生了独立于历史之政治影响的民族的概念。它的形成源于对两种权威——国家的权威和传统的权威——的舍弃。从政治上和地理上说，法兰西王国是漫长历史的一系列事件的产物，缔造了国家的力量，也形成了疆域。大革命对于形成了法国边界的因素和形成了其政府的因素，却一概予以否定。民族史的每一处可被除去的痕迹和遗物——政府体制、国土的自然区划、各社会阶层、团体、度量衡和历法，皆被仔细清除。对法兰西有限制作用的历史影响受到谴责，它不再受这种限制的约束；它只承认大自然所设的限制。民族的定义是从物质世界借来的，为了避免疆域的损失，它不仅变成一种抽象定义，而且成了一个虚构定义。

    在这场运动的人种学特征中包含着一条民族原则，它是一种共同看法的来源，即革命更频繁地发生在天主教国定而非新教国家。事实上，革命多发生在拉丁族而非条顿族，因为它在一定程度上依赖一种民族冲动。只有当需要排除外来因素和推翻外来统治时，才能唤醒这种冲动。西欧经历了两次征服，一次是罗马人，一次是日耳曼人，也两次从侵略者那里接受了法律。每一次它都与征服民族相抗争。尽管两次伟大的反抗因为两次征服的特征不同而各异，但都有帝国制度的现象发生。

    罗马共和国竭力压制被征服的各个民族，使它们成为一个单一而顺从的整体。但是在此过程中，行省总督权威的增长颠覆了共和政体，各省对罗马的反抗帮助建立了帝国。恺撒的制度给予附属地以史无前例的自由权和平等的公民权，结束了民族对民族、阶级对阶级的统治。君主制受到欢迎，被当作抵制罗马民族的傲慢和贪婪的保护伞。对平等的热爱，对贵族的憎恨和对罗马所输入的专制制度的容忍，至少在高卢人那里，形成了民族性格的主要特征。但是有些民族的生命力已被残酷的共和国所扼杀，它们无一具有享受独立或开创新历史的必要素质。

    根据一种道德秩序来组织国家并建立社会的政治能力已经衰竭。在一片废墟之上，基督教领袖们找不到一个民族可以帮助教会度过罗马帝国的崩溃时期。给那个日益衰落的世界带来新的民族生命的，是毁灭这个世界的敌人。蛮族像季节性洪水一样把它淹没，然后又退去。当文明的标志再次浮出水面时，人们发现，土壤变得深厚而肥沃，洪水播下了未来国家和新社会的种子。新鲜血液带来了政治意识和能量，它体现在年轻民族支配衰老民族的能力之中，体现在有等级的自由权的确立之中。与普遍的平等权利不同，对这种自由的实际享有，必然是与权力相伴随，而且就等同于权力，人民的权利取决于多种条件，而其首要条件就是财产的分配状况。世俗社会成为一个分层组织，而非诸多原子无固定形态的结合。封建制度逐渐兴起了。

    自恺撒至克洛维的五个世纪中，罗马帝国的高卢人彻底接受了绝对权威和无差别平等的观念，以致他们无法再接受新的制度。封建制被视为外来物，封建贵族被视为一个异邦的种族，法兰西人民普遍反对它们，到罗马法和国王的权力中寻求保护。绝对君主制借助民众的支持向前发展，这构成法国历史的一个持久特征。中央权力起初是封建性的，受到臣属的豁免权和大领主的制约，但是专制愈深，就愈被民众所接受。镇压贵族和清除中间权威，成为国民的特别目的，这个目的在王冠落地之后得到了更有力的推进。13世纪以来一直努力限制贵族势力的君主制度，最终却被民众推翻。因为它的步伐过于缓慢，而且无法否定自己的根源，不能有效地摧毁它所起源的那一阶层。

    所有这些事情构成了法国大革命的独有特征——渴求平等，憎恨贵族、封建制以及与之相关的教会，不断追随罗马异教范例，镇压君主势力，颁行新法典，与传统决裂，以理想制度取代各种族在相互作用下共同形成的一切制度——所有这些都表现出反抗法兰克人入侵的一种共同类型。憎恨贵族甚于憎恨国王，厌恶特权甚于厌恶暴政；王权倾覆更多是因为它的根源而非它的腐败。没有贵族关系的君主制，即使在最不受控制的时候，在法国也深受欢迎；然而，重建王权，并以贵族力量限制和约束它的努力没有成功，因为它赖以存在的古老的条顿人传统——世袭贵族制、长子继承制和特权，已不再被容忍。

    1789年思想的实质并不是限制最高权力，而是废除中间权力。在拉丁族的欧洲人中，这些中间权力，以及享有这些权力的阶层，源自蛮族。那场自称自由主义的运动，实质上是民族主义的。倘若自由是它的目标，它的方式应当是建立独立于国家的强大权威，它的蓝本应当是英格兰。然而它的目标是平等，如1789年的法国所示，它致力于摒弃源自条顿族中的不平等因素。这是意大利、西班牙与法国共奉的目标，由此形成了拉丁国家的天然联盟。

    革命领袖们并没有意识到这场运动中的民族主义因素。起初，他们的理论似乎完全与民族主义观念相对立。他们教导说，某些普遍的政治原则放之四海而皆准；他们的理论主张不受限制的个人自由，主张意志超越于任何外在制约或义务之上。这种观点明显与民族主义理论不合，因为后者主张某些自然因素决定着国家的性格、形式和政策，于是某种命运便取代了自由。因此当解放变成镇压、共和国变成帝国的时候，民族感情并不是直接从包含着它的那场革命中发展而来的，而是首先表现为反对那场革命。

    拿破仑通过攻击俄国的民族主义、鼓励意大利的民族主义、压制德国和西班牙的民族主义而创造了权力。这些国家的君主或是被废或是被贬，一种具有法国根源、法国精神和作为法国工具的行政体系建立起来了。但人民抵制这种变革。抵抗运动受到民众支持，而且是自发产生的，因为统治者们疏于镇压或无力镇压。这场运动是民族主义性质的，因为它直接反对的对象是外来的制度。在提罗尔、西班牙，以及随后在普鲁士，人民并没有受到政府的鼓动，而是自发地行动起来，努力将革命法国的军队和观念驱除出国土之外。人们意识到那场革命中的民族主义因素，并不是由于它的兴起，而是由于它的征服。

    法兰西帝国公然竭力反对的三种事物——宗教、民族独立和政治自由——结成了一个短暂的联盟，它所掀起的强大反叛导致了拿破仑的覆灭。在这个值得纪念的联盟的影响下，一种政治精神在欧洲大陆觉醒，它坚持自由，憎恶革命，致力于恢复、发展和改良衰落的国家制度。这些思想的鼓吹者是施泰因和格雷斯，洪堡、缪勒和德·迈斯特尔。他们既痛恨旧政府的专制统治，也痛恨波拿巴主义。他们所坚持的民族权利受到二者同样的侵害。他们希望通过推翻法国的统治恢复这些民族权利。

    法国大革命的同情者并不支持在滑铁卢之役中胜利的那派势力。因为他们已经懂得把他们的学说和法国的事业联系在一起了。在英国的荷兰王室辉格党人(TheHollandHouse Whigs)、西班牙的亲法分子、意大利的缪拉党人(theMumtists)以及莱茵联盟(the Confederation of Rhine)的支持者们，将他们的爱国主义融化在他们的革命激情中，为法国势力的衰落感到惋惜。他们惊恐地看着解放战争(theWar of Deliverance)所产生的陌生的新势力，因为它们既威胁着法国的统治，也威胁着法国的自由主义。

    但是在复辟时代，要求民族和民众权利的新希望破灭了。那个时代的自由主义者所关心的并不是民族独立形式的自由，而是法国制度模式的自由。他们一致反对要求建立政府的民族。他们为了实现自己的理想，乐于牺牲民族权利，就如同神圣同盟为了专制主义的利益乐于镇压民族权利一样。

    阿克顿 | 论民族主义

    不错，塔列朗曾在维也纳声明，在所有的问题中应当优先考虑波兰问题，因为瓜分波兰是欧洲所经历的第一位的最大恶行，但是王朝利益取得了胜利。所有出席维也纳会议的政权都恢复了属地，唯独萨克森国王例外，他因忠诚于拿破仑而受到惩罚，然而在统治家族中没有代表的那些国家——波兰、威尼斯和热那亚——没有得到恢复，甚至教皇为摆脱奥地利的控制而恢复公使权也颇费周折。为旧制度所忽视的民族主义，为法国革命和拿破仑帝国所压制的民族主义，刚刚登上历史舞台，就在维也纳会议上遭到重创。这个萌发于波兰第一次被瓜分、由法国革命为其奠定理论基础、拿破仑帝国促使它短暂发作的原则，终于由于复辟时代长期的谬误，成熟为一种严密的思想体系，一种由欧洲的局势所培育并为其提供了正当理由的思想体系。

    神圣同盟中的各国政府既致力于镇压威胁着它们的革命精神，同样也致力于镇压使它们得以恢复的民族主义精神。奥地利没有从民族运动中捞到任何好处，1809年后便一直阻止它的复兴，自然充当了镇压的先锋。对1815年最后协定的任何不满，有关改良或变革的任何愿望，都被定为叛乱罪。这种制度用时代的邪恶势力来镇压良善的力量，它所招致的反抗，先是起于复辟时代，至梅特涅下台而消失，后又兴起于施瓦尔岑堡的反动统治，至巴赫和曼陀菲尔统治而结束。这种反抗源于全然不同的各种形式的自由主义的结合。在持续不断的斗争中，民族权利高于一切权利的思想逐渐获得了统治地位，成为现在革命中的主要动力。

    第一场自由主义运动，即南欧烧炭党人所发起的运动，没有特定的民族特征，但是受到西班牙和意大利的波拿巴党人的支持。其后的几年中，1813年的各种对立思想登场亮相，一场在很多方面反对革命原则的革命运动，开始为自由、宗教和民族权利而斗争。这三个方面的结合体现在爱尔兰的骚乱中，也体现在希腊、比利时和波兰革命者的身上。这些曾为拿破仑所亵渎并起来反抗过他的力量，又开始反抗复辟时代的政府。它们一直受着刀剑的压制，后来又受到条约的压制。民族主义原则给这场运动增添的是力量，而不是正义。除了在波兰之外，这场运动在各地都取得了胜利。再后来，当解放之后出现了废除协定的呼声，当泛斯拉夫主义和大希腊主义在东正教会的支持下兴盛起来的时候，它蜕化为一个纯粹的民族主义概念。这是针对维也纳协定的抵抗运动的第三阶段。这协定的脆弱性在于它没有能够根据民众的正义观或至少是一条道德准则满足民族主义的或立宪的愿望。这两种愿望本来是互相对立的，其中一种可以用作对抗另一种的屏障。

    在1813年，人民最初是为了保护他们的合法统治者起而反抗征服者，他们不愿受篡位者的统治。在1825年至1831年的期间里，他们决心不受异族的不当统治。法国的制度常常优于它所取代的制度，但是对法国人所先行使的权力，还有一些更重要的要求，民族主义的斗争首先表现为争夺合法性的斗争。在第二阶段，这种因素就不存在了。没有一个流亡君主领导着希腊人、比利时人或波兰人。土耳其人、荷兰人和俄国人并不是作为篡权者而是压迫者受到攻击——是因为他们统治不当，而非因为民族不同。

    随后就是这样一个时期，它的说法很简单：民族不应当受到异族统治。权力即使是合法获得的，行使的方式也很有节制，仍被宣布为非法。民族权利就像宗教一样，在过去的联盟中发挥着部分作用，曾经支持过争取自由的斗争，现在民族却成为一个至高无上的要求，它只为自己说话，它提到统治者的权利、人民的各种自由和保护宗教，只是拿它们当借口。如果它不能和它们结合在一起，它为了获胜就不惜让民族牺牲其他事业。

    梅特涅是促成这一理论的一个主要人物，他在这方面的作用仅次于拿破仑；因为复辟时代的反民族主义特征在奥地利最为显著，民族主义发展成一种理论，有悖于奥地利的统治。拿破仑只相信自己的军队，鄙视政治道德的力量，却被这种力量打倒。奥地利在统治它的意大利属地时犯下了同样的错误。意大利王国亚平宁半岛的整个北部统一在了一个国家之下。法国人在别处压制民族感情，但他们为了保护在意大利和波兰的势力，却鼓励这种感情。当胜负之数转变的时候，奥地利便借助法国人培养的这种新情绪反对法国人。纽金特在向意大利人民的声明中宣布，他们应当成为一种独立的民族。这种精神服务于不同的主人，起初帮助摧毁了那些旧式国家，后来帮助将法国人逐出国土，再后来被查理·阿尔贝特利用来掀起一场新的革命。它服务于截然对立的政治原则和一系列各式各样的党派，它可以和一切事物相结合。它最早反对民族对民族的统治，这是它最温和、最低级的形式。后来它谴责任何包含着不同民族的国家，最终发展成为一种完善而严谨学说，即国家和民族必须共存共荣。密尔说：“政府的边界应当与民族的边界保持大体一致。一般而言，这是自由制度的必要条件。”

    我们可以从一个人的经历中，追寻到这种思想从一个模糊的愿望发展为一种政治学说的基石的外在历史进程。这个给予它生命力的人就是居塞伯·马志尼。他感到烧炭党运动不足以对抗政府的措施，便果断地把自由主义运动的基础换成了民族主义，以此赋予它新的生命。正如压迫是自由主义的学校一样，流放是民族主义的摇篮；在避难马赛时，马志尼就想到了“青年意大利”这个主意。波兰的流亡者也以同样的方式成为每一场民族运动的斗士。因为对他们而言，所有的政治权利都包含在独立的思想之中。无论他们之间有多大分歧，独立是他们共同的愿望。

    1830年以前的文学作品也促进了民族主义思想。马志尼说：“这是浪漫主义和古典主义两大流派之间激烈冲突的时代，这场冲突同样可以真实地视为自由的拥护者与权威的拥护者之间的冲突。”浪漫派在意大利为不信教者，在德国为天主教徒，但是他们对两地的民族主义史学和文学都起到了相同的促进作用。但丁在意大利的民主派那里和在维也纳、慕尼黑及柏林的中世纪复兴运动的领袖们那里，都被视为伟大的权威。但是无论是流放者，还是新派诗人和评论家的影响，都没有扩展到民众之中。它是一个没有获得民众同情和支持的宗派，是一种建立在学说而非苦难基础之上的密谋。1834年，他们在萨伏依举起造反的旗帜，提出“统一、独立、上帝和人道”的口号；人民对这些目标感到迷惑不解，对其失败也漠不关心。但是马志尼坚持不懈地进行宣传和鼓动，把他的“青年意大利”“青年欧洲”(GiodneEuropa)，并于1847年建立了国际民族联盟。他在联盟成立的致词中说：“人民只明白一种观念，即统一和民族独立的观念……政府形式绝不是个国际问题，它仅仅是个民族问题。”

    1848年的革命虽然没有成功地实现民族目的，却在两个方面为日后民族主义的胜利做好了准备。第一个方面是，奥地利恢复了在意大利的权力，实行一种新的、更严格的集权统治，没有给自由留下任何希望。当这种制度确立之时，正义便站在了民族的愿望一边。在马宁的努力下，这些民族愿望以一种更完善和更高级的形式复兴了，在十年的反动时期，奥地利政府未能把依靠武力的占有转变为根据权利的占有，也没有用自由制度来创造让人忠诚的条件。它的政策从反面刺激了民族主义理论的发展。

    1859年，这种政策使法兰西斯·约瑟夫失去了所有的积极支持和同情，因为他在行动上犯下的错误，要比他的敌人的理论错误更加明显。然而，民族主义理论获得力量的真正原因在于第二个方面，即民主原则在法国的胜利以及欧洲大国对它的认可。民族主义理论包含在主张公意至高无上的民主理论中。“人类中的任何一群人，如果没有决定他们应和哪一个群体结合在一起，任何人都无法知道他们还应当自由地做什么了。”一个民族就是这样形成的。为了形成集体意志，统一是必需的；为了表达集体意志，独立是不可缺少的。对于人民主权的概念而言，统一和民族独立比罢黜君主和废除法律的权利更加重要。因为人民的幸福或国王的民意基础可以防止这类专制行为的发生。但是具有民主精神的民族不可能一直允许它的一部分属于外国，或者整个民族被分裂为同一血统的若干国家。因此，民族主义理论的出发点是划分政治世界的两条原则：否定民族权利的正统统治和肯定民族权利的革命行动；基于同一理由，它成为后者反对前者的主要武器。

    在探索民族主义理论现实可见的发展过程时，我们也打算观察它的政治特征，评价它的政治价值。促成这种理论的专制统治既否定民族统一的绝对权利，又否定民族自由的权利要求。前者是民主理论的产物，后者则属于自由理论。这两种民族主义的观点分别对应着法国和英国的学说，实际上代表着政治思想中对立的两极，它们仅有名称上的联系。

    在民主理论中，民族主义的基础是集体意志永恒至上，民族统一是这种意志的必要条件，其他任何势力都必须服从这种意志，对抗这种意志的任何义务都不享有权威，针对这种意志的一切反抗都是暴政。在这里，民族是一个以种族为基础的理想单位，无视外部因素、传统和既存权利不断变化着的影响。它凌驾于居民的权利和愿望之上，把他们形形色色的利益全都纳入一个虚幻的统一体；它为了满足更高的民族要求，牺牲他们的个人习惯和义务，为了维护自己的存在，压制一切自然权利和一切既定的自由。无论何时，只要某个单一的明确目标成为国家的最高目的，无论该目标是某个阶级的优势地位、国家的安全或权力、最大多数人的最大幸福，还是对一个抽象观念的支持，此时国家走向专制就是不可避免的。惟有自由要求实现对公共权威的限制，因为自由是惟一有利于所有人们的目标，惟一不会招致真心实意反抗的目标。为了支持民族统一的要求，即使一个在资格上无可指摘、政策宽厚而公平的政府，也必须加以颠覆，臣民必须转而效忠于一个与他们没有情感联系、可能实际上受外来控制的权威。

    另一种理论除了在反对专制国家这一点上，与这种理论没有任何共同之处，它将民族利益视为决定国家形式的一种重要因素，但不是至高无上的因素。它有别于前一种理论，因为它倾向于多姿多彩而不是千人一面，倾向于和谐而不是统一；因为它不想随心所欲地进行变革，而是谨慎地尊重政治生活的现存条件；因为它服从历史的规律和结果，而不是服从有关一个理想未来的各种渴望。统一论使民族成为专制和革命之源，而自由论却把民族视为自治的保障和对国家权力过大的最终限制。被民族统一牺牲了私人权利，却受着各民族联合体的保护。

    任何力量都不可能像一个共同体那样有效地抵制集权、腐败和专制的趋势，因为它是在一个国家中所能存在的最大群体；它加强成员之间在性格、利益和舆论上一贯的共性，它以分别存在的爱国主义影响和牵制着统治者的行动。同一主权之下若干不同民族的共存，其作用相当于国家中教会的独立。它可以维护势力平衡，增进结社，形成共同意见给予臣民以约束和支持，藉此避免出现在单一权威的笼罩下四处蔓延的奴役状态。同样，它可以形成一定的公共舆论集团，形成并集中起强大的政治意见和非主权者意志的义务观念，以促进独立的发展。自由鼓励多样性，而多样性又提供了保护自由的组织手段。所有那些支配人际关系、调整社会生活的法律，皆是民族习惯多样化的结果，是私人社会的创造物。

    因此，在这些事情上不同的民族各不相同，因为是各民族自己创造了这些法律，而不是统治着他们的国家。在同一个国家中这种多样性是一道牢固的屏障，它抵制政府超出共同的政治领域侵入受制于自发规律而非立法的社会领域。这种入侵是专制政府的特征，它势必招致反抗并最终产生一种救治手段。对社会自由的不宽容是专制统治的本性，其最有效的救治手段必定是而且只能是民族的多样性，同一国家之下若干民族的共存不仅是自由的最佳保障，而且是对自由的一个验证。它也是文明的一个主要促进因素，它本身即是自然的、上帝规定的秩序，比作为近代自由主义理想的民族统一体现着更高的进步状态。

    不同的民族结合在一个国家之内，就像人们结合在一个社会中一样，是文明生活的必要条件。生活在政治联合体中较次的种族，可得到智力上更优秀的种族的提高。力竭而衰的种族通过和更年轻的生命交往而得以复兴。在一个更强大、更少腐败的种族的纪律之下，由于专制主义败坏道德的影响或民主制度破坏社会整合的作用而失去组织要素和统治能力的民族，能够得到恢复并重新受到教育。只有生活在一个政府之下，才能够产生这种富有成效的再生过程。国家就像个促进融合的大熔炉，它能够把一部分人的活力、知识和能力传递给另一部分人。如果政治边界和民族边界重合，社会就会停滞不前，民族就会陷入这样一种境地，它同不和同胞交往的人的处境没什么两样。两个人之间的差别把人类联合在一起，不仅是因为这种差别为共同生活的人提供了好处，而且因为它用一条社会或民族的纽带使社会结合在一起。使每个人都可以从他人中找到自己的利益。这或是因为他们生活在同一个政府之下，或是因为他们属于同一种族。人道、文明和宗教的利益由此得到了促进。

    异教以自己的独特性来肯定自身，而基督教以民族混合为乐事，因为真理是普遍的，而谬误却千差万别各有特点。在古代世界，偶像崇拜与民族特性形影不离，圣经中用同一词来表示这两种现象。教会的使命就是消除民族差别。在它享有无可争议的最高权威的时代，整个西欧遵从着相同的法律，所有的著述使用着相同的语言，基督之国的政体表现为一个单一的权威，它的思想统一体现在每一个大学。古罗马人扫除被征服民族的众神而完成征服，查理大帝仅凭强行废除萨克森人的异教仪式，便打败了他们的民族反抗。在中世纪，从日耳曼族和教会的共同作用中，诞生了一个新的民族体系和新的民族概念。

    民族和个人的自然属性皆被改造。在异教和未开化时代，民族之间不仅在宗教方面，而且在风俗、语言、性格上都存在着巨大差异。而根据新的法律，它们拥有着许多共同的事物，使它们彼此隔阂的古老屏障被清除了，基督教所教导的新的自治原则，使他们能够生活在共同的权威之下，且不必失却他们所珍视的习惯、风俗或法律。新的自由观使不同民族共存于同一国家之内成为可能。民族不再是古代的那种民族——同属于一个祖先的后裔，或繁衍于一个特定地域的土著，仅仅是自然和物质的存在物，是一个道德的或政治的共同体，它不是地理学或生理学意义上的单位，而是在国家的影响下，在历史进程中发展。它源于国家，而非位于国家之上。一个国家可能在时间的进程中创造一个民族，然而一个民族应当构成一个国家则有悖于近代文明的性质。一个民族是从先前独立的历史中，获得了它的权利与权力。

    在这个方面，教会赞同政治进步的趋势，尽力消除民族之间的隔阂，提醒它们彼此之间的义务，把征服和封地赐爵看作提升落后和沉沦民族的自然手段。但是，尽管它承认根据封建法律、世袭权利和遗嘱安排产生的偶然性结果，因而对民族独立毫无贡献，但是它怀着建设完善的利益共同体的热情去保护民族自由免受统一和集权之害。因为同一个敌人对双方都构成威胁：不愿容忍差别、不愿公正对待不同民族之独特个性的国家，必定出于相同的原因干涉宗教的内部事务。宗教自由与波兰和爱尔兰的解放事业发生联系，并不仅仅是当地境况的偶然结果。政教协定(theConcordot)没有使奥地利的各族臣民团结起来，乃是一种政策的自然后果，这种政策并不想保护其领地的差别和自治，而且通过给予好处来贿赂教会，而非通过给予独立来巩固教会。从宗教在近代史的这种影响中，产生了一种爱国主义的新定义。

    民族和国家之间的区别体现在爱国情感的性质中。我们与种族的联系仅仅是出于自然，我们对政治民族的义务却是伦理的。一个是用爱与本能联结起来的共同体，这种爱与本能在原始生活中极其重要和强大，但是更多地与动物性而非文明的人相联系；另一个是一种权威，它依法实行统治，制定义务，赋予社会自然关系一种道德的力量和特征。爱国主义之于政治生活，一如信仰之于宗教，它防范着家庭观念和乡土情结，如同信仰防范着狂热和迷信。它有源于私人生活和自然的一面，因为它是家庭情感的延伸，如同部落是家庭的延伸一样。

    但是就爱国主义真正的政治特征而言，它是从自我保存的本能向可能包含着自我奉献的道德义务的发展。自我保存既是一种本能，又是一种义务，从一个方面说它是自然的和无意识的，同时它又是一种道德义务。本能产生了家庭，义务产生了国家。如果民族可以不要国家而存在，只听命于自我保存的本能，它将无法自我否定、自我控制和自我牺牲，它将只把自己作为目的和尺度。但是在政治秩序中，个人利益甚至个人存在都必须牺牲给所要实现的道德目的和所要追求的政治利益。

    真正的爱国主义，即自私向奉献的发展，其显著标志在于它是政治生活的产物。种族所引起的义务感并不完全脱离它的自私和本能的基础；而对祖国的爱，如同婚姻之爱，既有物质基础也有道德基础。爱国者必须区分开他所献身的两种目的或目标。惟对祖国(country)才产生的依恋，如同惟对国家(state)才表示的服从——一种对物质强制力的服从。一个将献身祖国看作最高义务的人，与一个让所有权利都屈从于国家的人，在精神上是息息相通的，他们都否认权利高于权威。

    柏克曾言，道德和政治上的国家不同于地理上的国家，二者可能是不一致的。武装反抗制宪会议(theCovention)的法国人同武装反抗国王查理的英国人一样都是爱国者，因为他们认为有一种比服从实际统治者更高的义务。柏克说：

    “在谈及法国时，在试图对付它时，或在考虑任何和它有关的计划时，我们不可能只想到一个地理上的国家，它必定是指一个道德上和政治上的国家……事实上，法兰西大于它自身——道德之法兰西不同于地理之法兰西。这所房子的主人已被赶走，强盗霸占了它。如果我们寻找作为一个共同体存在的法兰西人，即从公法的角度看，作为一个团体而存在的法兰西人(我所谓的共同体，意指有思考和决定的自由以及讨论和缔约能力的人们)，我们在弗兰德尔、德国、瑞典、西班牙、意大利和英国也可发现他们。它们都有世袭君主，都有国家典章制度，都有议会。……可以肯定，如果把这些东西的半数从英国拿走，那么我也很难把剩下的东西再称为英国民族了。”

    在我们所属的国家与对我们行使政治职能的国家之间，卢梭做了类似的区分。《爱弥儿》中有一句话，很难把它的意思翻译过来，(没有国家的人，哪来的祖国)。他在一篇论述政治经济学的论文中写道：“如果国家对于国民的意义就像对于陌生人的意义，如果它仅仅给与他们对任何人都可给与的东西，人们还怎么爱自己的国家呢”也在是同样的意义上，他继续说：“(没有自由，祖国又从何说起)”。

    可见，我们只对因国家而形成的民族承担着义务，因此，也只有这种民族拥有政治权利。从人种学上说，瑞士人是法兰西族、意大利族，或日耳曼族，但是除了瑞士这个纯粹的政治民族外，没有任何民族能对他们提出哪怕是微不足道的权利要求。托斯卡纳人(theTuscan)和那不勒斯人共同的国家形成了一个民族，而佛罗伦萨和那不勒斯两地的公民彼此并不拥有一个政治共同体。还有一些国家，或是没有成功地将不同的种族凝聚为一个政治民族，或是未能摆脱一个更大的民族的控制而自成一体。奥地利和墨西哥属于前者，帕尔马和巴登属于后者。

    文明的进步几乎与这种国家无缘。为了保持民族的完整性，它们不得不以联盟或家族联姻的方式依附于某些强国，因此丧失了自己的某些独立性。它们的倾向是维持小国寡民的封闭状态，缩小居民的视野，使他们变得孤陋寡闻。在如此狭隘的地域内，政治舆论无法保持其自由与纯洁，来自更大的共同体的潮流泛滥于一个局促之地。人口较少，成分单纯，几乎无以产生对政府权力构成限制的社会自然分层或内部利益集团。政府和臣民用借来的武器抗争。政府的力量和臣民的渴望皆源于外部世界。结果，国土成为于己无益的斗争工具和战场。这些国家就像中世纪的小型共同体一样，处在大国之中，在保障自治方面发挥着一定的作用，但是它们有碍社会进步，因为社会进步依靠同一政府下不同民族的共存。

    墨西哥出现了一些狂妄和危险的民族权利要求：它们的依据不是政治传统，而仅仅是种族。在那里，依据血统划分种族，各种族并不共同聚居在不同的地区。因而，不可能将它们结合成一个国家，或改造为组成国家的成分。它们是流动的、无形的和互不关联的，无法凝成一体，或形成一个政治制度的基础。因其不可为国家所用，便得不到国家的认可。它们独特的禀性、能力、激情和情感无助于国家，因而不被重视。它们必定受到忽视，因而长久遭到虐待。东方世界实行种姓制度，避免了那些有政治要求而无政治地位的种族产生的难题。哪里仅有两个种族，哪里便是奴隶制之源。但是，如果在一个由若干小国组成的帝国里，不同种族居住于不同地域，这种结合形式最有可能建立一种高度发达的自由制度。

    在奥地利，两种情况增加了这个问题的难度，但是也增加了它的重要性。几个民族的发展极不平衡，任何单一民族的力量都不足以征服或同化其他的民族。这是一些政府所能得到的最高度组织的必要条件。它们提供着最丰富多样的智力资源，提供着前进的永恒动力。提供这些动力的不仅仅是竞争，而且还是一个更进步的民族令人羡慕的成就；它们提供着最充足的自治因素，从而使国家不可能凭一己意志统治全体；它们提供着维护地方风俗和传统权利的最充分的保障。在这样的国度，自由可以取得最辉煌的成果，而集权和专制将一败涂地。

    和英国政府所解决的问题相比，奥地利政府面临的问题更棘手，因为它必须承认各民族的权利要求。由于议会制以人民的统一性为前提，所以它无法给予这些权利。因此，在不同民族混居的国家里，议会制没有满足它们的要求，因此被认为是一种不完善的自由形式。它把不为它承认的民族差别较过去更明显地呈现出来，于是它继续着旧专制主义的营生，以集权的新面目出现。因此，在那些国家，对帝国议会的权力必须像对皇帝的权力一样严加限制，而它的诸多职能必须转由地方议会和日趋衰落的地方机构承担。

    民族因素在国家中的巨大重要性，存在于这样一个事实之中：它是政治能力的基础。一个民族的性格在很大程度上决定着国家的形式和生命力。有些政治习惯和观念属于某些特定的民族，并随着民族历史的进程而发展变化。刚刚走出野蛮状态的民族，因文明的过度发展而精疲力竭的民族，皆不能拥有自我统治的手段；信奉平等或绝对君主制的民族，不可能建立一个贵族政体；厌恶私有制的民族，也缺少自由的第一要素。只有依靠与一个先进种族的接触交往，才能够把这些民族中的每一个成员转变成自由社会的有效因子，国家的前途寓含于这个先进民族的力量之中。忽视这些事实、并且不从人民的性格和资质中寻求支持的制度，也不会想到应当让他们自治，而是只想使他们服从最高的命令。因此，否定民族性，意味着否定政治自由。

    民族权利的最大敌人是近代民族主义理论。这种理论在国家与民族之间划等号，实际上将处于国界之内的所有其他民族置于一种臣服的境地。它不承认这些民族与构成国家的统治民族地位平等，因为若是那样，国家就不再是民族国家了，这有悖于它的生存原则。因此，这些弱势民族或是被灭绝，或是遭受奴役，或是被驱逐，或是被置于一种依附地位，一切取决于那个总揽社会所有权利的优势民族的人道和文明程度。

    如果我们把为履行道德义务而建立自由制度视为世俗社会之鹄的，我们就必须承认，那些包容明显不同的民族而不压迫它们的国家，例如英帝国和奥地利帝国，实质上是最完善的国家。那些无民族共存现象的国家是不完善的，那些丧失了民族共存之效用的国家是衰朽的。一个无力满足不同民族需要的国家是在自毁其誉；一个竭力统一、同化或驱逐不同民族的国家是在自我戕害；一个不包含不同民族的国家缺乏自治的主要基础。因此，这种民族主义理论是历史的倒退。它是最高形式的革命思想，在它宣布已经进人的革命时代，它必定始终保持着力量。它的重要历史意义取决于以下两个主要因素：

    首先，它是一个喀迈拉(希腊神话中狮头、羊身、蛇尾的喷火女怪)。它所寻求的结果是不可能实现的。因为它从不满足，从不停歇，总是不断提出自己的要求，这使得政府甚至难以退回到促使它产生的那种状态。它所具有的严重危害和控制人们思想的巨大力量，使得为民族反抗申辩的制度也难以容忍。因此，它必须致力于实现它在理论中所谴责的东西，即作为一个主权共同体之组成部分的各不同民族的自由权利。这是其他力量起不到的一种作用；因为不仅对绝对君主制、民主制和立宪政制所共有的集权制，而且对这三种制度本身，它都有矫正作用。无论是君主制、革命政体，还是议会制度，都做不到这一点；过去所有曾经激发热情的思想都无力实现这种目的，惟民族主义可独善其功。

    其次，民族主义理论标志着革命理论及其逻辑穷竭的终点。民主的平等学说宣布民族权利至高无上，这样就越过了它本身的极限，落人自相矛盾的境地。在革命的民主阶段和民族主义阶段之间，社会主义曾经介入，并且把该学说的结论推行到荒谬的地步。但是这个阶段已经过去了。革命比它的子女更长命，它造成了进一步的后果。民族主义比社会主义更先进，因为它是一种更加独断的学说。社会主义理论致力于在近代社会施加给劳工的可怕重负下的个人生存提供帮助。它不仅是平等观念的发展，而且是一个逃避现实的不幸和饥馑的途径。不论这种解决方式多么虚假，应当拯救穷人于危难之中总是个合情合理的要求；只要为了个人安全而牺牲国家的自由，至少从理论上说便达到这个更迫切的目标。但是民族主义的目标既非自由，亦非繁荣，它把自由与繁荣都牺牲给了使民族成为国家之模型和尺度这个强制性的需要。它的进程将是以物质和道德的毁灭为标志，它的目的是使一项新发明压倒上帝的作品和人类的利益。任何变革的原则，任何可以想像的政治理论，都不可能比它更全面、更具颠覆性和更独断。它是对民主的否定，因为它对民意的表达施加限制，并用一个更高的原则取而代之。它既反对国家分裂，亦反对国家扩张；它既不许以征服结束战争，亦不许为和平寻求保障。这样，在使个人意志屈服于集体意志之后，这种革命理论使集体意志服从于它所不能掌握的条件；它毫无理性，仅仅受制于偶然的事变。

    因此，民族主义理论虽比社会主义理论更荒唐和更可恶，它在世间却有一个重要使命，它标志着两种势力，即绝对君权和革命这两个世俗自由最险恶的敌人之间的决斗，因此也标志着它们的终结。

    本文摘自阿克顿所著《自由与权力》（Essays on Freedom and Power）

  • 安格斯·迪顿：美国人从医疗制度中得到了什么？

    美国人在医疗保健方面开销巨大，这些花费几乎影响经济的各个方面。医疗保健在世界各地都很昂贵，富裕国家在延长其公民生命和减少痛苦方面花费大量资金也是十分必要的，但美国的做法简直是要多糟糕就有多糟糕。

    医疗支出和健康成果

    美国的医疗费用居全球之首，但是美国的医疗制度在富裕国家中则是最差的，在近期出现的死亡流行病和预期寿命下降之前很久，这一点就已经是一个事实。提供医疗服务耗费的成本严重拖累了经济，导致工资长期停滞，这也是劫贫济富式再分配的一个典型例子，我们曾将这种现象称为“诺丁汉郡治安官式”再分配。

    美国的医疗行业并不擅长增进人民的健康，但它擅长增进医疗服务提供者的财富，其中也包括一些成功的私人医生，他们经营着极其有利可图的业务。它还向制药公司、医疗器械制造商、保险公司（包括“非营利性”保险公司）以及更具垄断性的大型医院的所有者和高管输送了巨额资金。

    这张图显示了其他国家与美国之间的差异，以及随着时间的推移，这种差异是如何扩大的。我们选择英国、澳大利亚、法国、加拿大和瑞士为参照国，代表其他富裕国家。图中的纵轴和横轴分别为预期寿命和人均医疗支出，每条曲线是由1970—2017年，这两个数字在当年的交汇点连接而成的（人均医疗支出以国际元计算，因此2017年美国的数字与此前所述的10739美元有所不同）。

    美国显然是异类。它的人均预期寿命比其他国家要低，但人均医疗支出却高了很多。1970年，即曲线开始的第一年，美国和其他国家之间的差距并不明显，美国的预期寿命并没有落后多少，医疗支出也没有高出许多，但在此之后，其他国家做得更好，推动了健康状况更快改善，并更好地控制了医疗费用的增长。瑞士是图中和美国最相近的国家，其他国家的曲线则彼此十分贴近。如果图中再加上其他富裕国家，它们的曲线看起来也会更接近那些人均支出较低的国家，而不是美国。

    另一种计算医疗费用浪费的方法是直接确定医疗支出中对美国人健康没有贡献的部分。最近的计算是，浪费的部分大约占总支出的25%，与美国和瑞士的差额大致相当。

    这个极其巨大的数字是浪费额，而不是总费用。近半个世纪以来，这种浪费一点点侵蚀着人们的生活水平。如果美国的劳工阶层不必支付这笔贡金，他们今天的生活将会好很多。

    美国人花费那么多，到底得到了什么

    考虑到如此高昂的费用，我们无疑希望美国人拥有更好的健康状况，但事实并非如此。正如我们所看到的，美国在预期寿命方面的表现并不算好，而预期寿命是衡量健康的重要指标之一。虽然除了医疗之外，还有许多因素影响预期寿命，但医疗水平在近年来已经变得越来越重要。

    2017年，美国人的预期寿命为78.6岁，西班牙语裔人口显著高于全国平均水平（81.8岁），非洲裔黑人显著低于全国平均水平（74.9岁）。这些数字低于经济合作与发展组织其他25个成员国的预期寿命。在其他成员国中，德国的预期寿命最低，为81.1岁，比美国长2.5岁，日本的预期寿命最高，为84.2岁。无论美国人从医疗制度中得到了什么，他们显然没有得到更长的寿命。

    或许美国人有别的收获？美国是一个非常富裕的国家，美国人为了获得更好的医疗服务而支付更多费用也很合理。然而，美国人并没有比其他国家更多地使用医疗服务，尽管医疗领域的工作岗位大幅增加。2007-2017年，医疗行业新增280万个就业岗位，占美国新增就业岗位的1/3，这些新增就业岗位的资金主要来自非营利部门的“利润”。

    事实上，美国的人均医生数量有所减少——美国医学会通过限制医学院的入学名额有效地确保了医生的高薪——人均护士数量的情况也基本相同。医学院的学费昂贵，这一点常常被用作说明医生有正当理由获得高薪，但如果医学院在没有名额限制的情况下接受竞争，费用无疑会降低。如果不是有体系地把合格的外国医生排除在外，医生的工资和医学院的学费都会下降。

    在实施某些治疗措施方面，美国和其他富裕国家的数字大致相同，尽管美国似乎更侧重于营利性的治疗措施。美国人似乎拥有一个更豪华的体系（像是商务舱而不是经济舱），但无论乘坐商务舱还是经济舱，乘客总是会在同样的时间到达同一目的地（在我们现在所说的情况下，如果他们的目的地是来世，那么商务舱的乘客可能更快）。与其他一些国家的病人相比，美国人等待手术（例如髋关节或膝关节置换术）或检查（例如乳房X光检查）的时间较短。等待时间较短的部分原因，可能是有很多昂贵的机械设备没有得到大量使用。美国的病房大多为单人病房或双人病房，而其他国家的病房更常见的是多人病房。

    发病率比死亡率或手术次数更难衡量，但有人曾经做过一项研究，在英国和美国进行了完全相同的健康状况调查，结果发现一系列健康状况指标（部分源于自我报告，部分来自通过化验血液得到的“硬”生化指标）表明，英国人在中年后的健康状况好于美国人。英国人在医疗上的支出不到其GDP的10%，人均医疗支出大约是美国的1/3。

    美国人对其医疗制度并不满意。2005—2010年的盖洛普世界民意调查中，只有19%的美国人对下面这个问题做出肯定答复，即“你对医疗制度或医疗体系有信心吗？”。盖洛普还询问人们是否对他们所居住的“城市或地区提供优质医疗服务的能力”感到满意。美国在这个更具体、更地方性的问题上表现得更好，77%的人给出了肯定答复，与加拿大和日本的比例大致相当，但差于其他富裕国家，也不如一些更贫穷的亚洲国家或地区，如柬埔寨、中国台湾、菲律宾、马来西亚和泰国。在瑞士，94%的人对本地提供优质医疗服务的能力表示满意，58%的人认为国家医疗制度或医疗体系运作良好。

    美国人的不满主要集中在医疗服务的不公平。根据联邦基金于2007年发布的一份报告，在“获得医疗服务、患者安全、协调、效率和公平”方面，美国在7个富裕国家中排名垫底。

    钱去哪儿了

    美国人付出了这么多，获益却这么少，这怎么可能？这些钱肯定花在了什么地方。病人花的冤枉钱变成医疗服务提供者的收入。在这里再次和其他富裕国家进行比较依然会很有用。

    医疗费用的差异在很大程度上是因为美国医疗服务价格更高，以及医疗服务提供者的工资更高。美国医生的工资几乎是其他经济合作与发展组织成员国医生平均工资的两倍。

    不过，由于医生人数相对于总人口数量下降，他们在高昂的医疗费用中所占份额有限。应医生团体和国会的要求，医学院的招生人数受到严格控制，同时外国医生难以在美国执业。2005年，美国收入最高的1%人口中，医生占16%。在这1%的前10%中，有6%是医生。美国护士的收入也相对较高，但与其他国家的差距不大。在美国，药物的价格大约是其他国家的3倍。

    在美国，服用降胆固醇药物瑞舒伐他汀每月需要花费86美元（打折后），该药在德国的月度花费是41美元，在澳大利亚只有9美元。如果你患有类风湿关节炎，你的修美乐（阿达木单抗）在美国每月需要花费2505美元，在德国是1749美元，在澳大利亚是1243美元。美国的手术费用更高。在美国，髋关节置换术的平均费用超过4万美元，而在法国，同样手术的花费大约为1.1万美元。在美国，即使同一制造商生产的相同设备，髋关节和膝关节置换的费用也比其他国家高出3倍以上。磁共振成像检查在美国要花费1100美元，但在英国只需要300美元。

    美国医生需要支付的医疗事故保险费用也更高，尽管与医院费用（33%）、医生费用（20%）和处方药费用（10%）相比，它只占医疗费用总额的2.4%，这并不算多。相对于其他富裕国家，美国的医院和医生更多地使用“高利润率和高金额”的治疗措施，如影像学检查、关节置换、冠状动脉搭桥术、血管成形术和剖宫产。

    2006年，我们两人中的一位更换了髋关节。当时，纽约一家著名的医院对一间（双人）病房的收费高达每天一万美元。病人在这间病房中能够饱览东河上船只如梭的美景，但电视节目是额外收费的，更不用说药物和治疗了。

    除了价格，还有其他应该考虑的因素。新药、新仪器和新的治疗手段不断涌现。其中有些可以拯救生命、减少痛苦，但很多并没有什么效果，但它们依然被推给病人并收取费用。这就是所谓的“过度医疗”，即投入更多资金并未带来更大程度的健康增长。

    医疗保险公司经常受到媒体的批判，尤其是当他们拒绝支付治疗费用，或者向那些认为自己已有全额保险的病人寄去令其费解的账单时。这里存在的一个大问题是，在一个私营系统中，保险公司、医生诊所和医院在管理、谈判费率和试图限制开支方面花费了巨额资金。而一个单一付款人系统，尽管根据设计不同可能存在各自的优点和缺点，但至少会节省一半以上的类似费用。导致问题出现的根源不仅在于保险公司追求利润，如果医疗制度的运行方式不同，保险公司就可以省去现在所做的大部分工作。

    最后（但并非最不重要）一点是，医院提高价格并不是因为成本上升，而是因为它们正在进行整合，从而减少或消除了竞争，并利用强大的市场势力提高价格。它们正在稳步赢得与保险公司（和公众）的战争。与面临竞争的医院相比，地方垄断性医院的收费要高出12%。此外，当一家医院与5英里内的另一家医院合并后，医院之间的竞争会减弱，而医疗服务价格会平均上涨6%。

    患者在出现急症的情况下最容易处于弱势地位，而医疗急症也越来越多地被视为和作为盈利机会。救护车服务和急诊室已经外包给医生与救护车服务公司，这些医生和救护车每天都在发送“出人意料”的医疗账单。这些服务中的许多项目并不在医保范围之内，因此即使患者被送往自己的医疗保险覆盖的医院，也需要自己支付各种急诊费用。2016年，很大一部分急诊室就诊病人支付了“意外”的救护车费用。

    随着农村地区医院的关闭，空中救护车变得越来越普遍，它们可能会带来数万美元的意外费用。当有人陷入困境，甚至失去意识时，他们没有能力就收费高低讨价还价，同时，由于不存在能够抑制价格的竞争，在这种情况下，即使病人大脑很清醒，也得乖乖按要求付钱。

    提供这些服务的公司许多由私人股权公司所有，它们非常清楚这正是漫天要价的最好时机。现在，那些追在救护车后面寻找获利机会的事故官司律师已经摇身一变，成为救护车的拥有者，交通事故的受害者在医院醒来时，会一眼看到他们的病床上贴着2000美元的账单。

    这种掠夺是一个典型例子，表明一个向上转移收入的系统是如何运作的。在这种情况下，金钱从身处困境中的病人手中转移到私人股权公司及其投资者手中。这也说明了为什么尽管资本主义在多数情况下拥有诸多优点，但却不能以一种可被社会接受的方式提供医疗服务。在医疗急症情况下，人们无法做出竞争所依赖的知情选择，正如人们在陷入对阿片类药物的依赖时，无法做出知情选择一样。

    过去由医生管理的医院现在已经改由企业高管管理，其中有些人是脱下白大褂并换上西服套装的医生，他们领着首席执行官的薪水，追求的是建立商业帝国和提高价格的最终目标。

    一个很好的例子是纽约长老会医院，它现在已经成为一个由多家曾经独立的医院组成的庞大医院集团。长老会医院是一家非营利性机构，其首席执行官史蒂文·科温博士在2014年的薪酬高达450万美元，而纽约北岸大学医院首席执行官的薪酬是其薪酬的两倍。纽约长老会医院推出了一系列制作精美的视频故事广告，这些广告在大受欢迎的《唐顿庄园》系列剧集播出之前在公共电视上播放，每一个广告都记录了一个只有在纽约长老会医院才能发生的非同寻常的康复故事。

    这些广告的目的是诱导员工要求将这家医院纳入他们的保险计划，使医院增加与保险公司谈判的能力，这有助于它提高价格，从而使科温的高薪获得保证。其他医院很快效仿，推出了类似的广告。2017年，美国医院在广告上花费了4.5亿美元。很难看出这些策略能怎样改善患者的健康。医生、医院、制药厂商和设备制造商通力合作，共同推高价格。

    高科技医用扫描设备的制造商向医生、牙医和医院提供具有吸引力的租赁和定价条款，后者使用设备，为各方带来源源不断的现金流，但并不会给病人带来明显的效果改善。或许，扫描设备（scanner）和骗子（scammer）的英文名难以区分并不是巧合。

    制药厂商也会与医院和医生合作，帮助它们开发新产品，并提高需求。2018年，著名乳腺癌研究专家何塞·贝塞尔加被迫辞去纽约纪念斯隆—凯特林癌症中心的首席医疗官一职，该医院自称是世界上最古老、最大的私人癌症治疗中心。贝塞尔加被迫辞职的原因是他未能在已发表的论文中披露潜在利益冲突，这种利益冲突来自他与生物技术初创公司和制药公司千丝万缕的财务联系。在他辞职后，这些利益冲突方中的一家—阿斯利康公司立即任命他为公司的研发主管。

    正如医院管理层所说（他们说得完全正确），医院在为病人提供新药试验，或者医生尝试帮助传播关于有效新产品的信息时，存在潜在的利益共生关系。事实上，新的癌症药物近年在降低癌症死亡率方面发挥了良好的作用。

    然而，由于患者的最大利益并不总是与制药厂商的利益相一致，因此他们自然可能想知道他们的医生到底是在为谁的利益服务，并需要确信他们的医院不仅仅是制药公司的一个分支机构。

    制药公司首席执行官们的薪水都颇为丰厚。根据《华尔街日报》2018年的一份报告，2017年，在薪酬收入排名前十的CEO中，收入最高的是艾瑞·鲍斯比，他的年薪为3800万美元，他是艾昆纬公司的CEO，该公司是一家为制药公司、保险公司和为政府提供患者信息分析服务的数据公司。排名第十的是默克公司的CEO肯尼斯·弗雷泽，年薪1800万美元。352014年，美国收入最高的部分是来自小型私营企业的利润，远远超过大公司首席执行官的薪酬，其中最具代表性的是那些私人诊所的医生。

    美国医疗服务的超额费用流向了医院、医生、设备制造商和制药厂商。从健康的角度来看，这些高达上万亿美元的费用是一种浪费和滥用，从医疗服务提供者的角度来看，它则是一笔丰厚的收入。

    本文选自安妮·凯斯和安格斯·迪顿曾的《美国怎么了：绝望的死亡与资本主义的未来》

  • 刘屹：道荒宏雪岭——重识横跨葱岭的三条古道

    一、问题的提出

    尽管“丝绸之路”的概念，目前看来并非像人们一直以为的是由李希霍芬（Ferdinand von Richthofen, 1833—1905）首创，但李希霍芬仍是最早将“丝绸之路”所经的线路标识在地图上，从而给人以“丝绸之路”确实以某种交通路线状态存在的直观印象之人。李希霍芬主要根据《汉书》的记载，标画出公元前128年至公元150年间的中亚交通路线。在其中，西域南道和北道，分别对应了西越葱岭的南北两条道路：西域北道从疏勒向西，可沿阿赖山脉，进入费尔干纳盆地，再向西抵达撒马尔罕；西域南道则从莎车出发，向西南方向登葱岭，再横穿葱岭上的瓦罕走廊，西去昆都士（Kunduz）和巴尔赫（Balkh）。这很可能是第一张标绘了葱岭东西两侧交通路线的地图。但是，由于李希霍芬本人没有来中国的甘肃和新疆进行过实地考察，他在画这幅中亚彩图时，明显缺乏对葱岭地区实际道路交通状况的充分了解，以至于有的路段画得有些想当然。而李希霍芬这一最早的“丝绸之路”路线图，对后来的“丝绸之路”地图产生了不小的影响。很多由此衍生的“丝绸之路”地图，在涉及葱岭地区的交通路线时，基本上都沿用李希霍芬这一并不准确的描绘。换言之，迄今我们所能看到的“丝绸之路”路线图，在葱岭路段的线路都有很大改进的必要。

    李希霍芬的这幅《中亚地图》还用红线勾勒出一条从地中海东岸一路到中国内地的路线，这是依据托勒密（Claudius Ptolemaeus，约100—168）《地理志》（Geography）所转载的叙利亚商主马厄斯·提提阿努斯（Maes Titianus）所属商队一路东行所留下的记录。这个商队活动于公元前1世纪末或是公元2世纪初，堪称从西方角度关于“丝绸之路”实际道路情况的最早和最重要的记录。1941年，日本学者白鸟库吉（1865—1942）也专门分析了这条商队通行葱岭的道路。白鸟氏受限于当时的条件，对葱岭地区道路的考订也有需要订正的地方。马厄斯商队的记录，对研究“丝绸之路”具有重要的价值，至今仍然受到西方学者的关注。

    此后，关注东西交通、丝绸之路的学者日益增多，但对于葱岭地区道路的考察，仍然是整个“丝绸之路”地理交通研究方面最为欠缺的一环。以笔者有限的知见，只有日本学者桑山正进在研究迦毕试和犍陀罗的历史时，对中国史书记载的求法僧西行求法经行葱岭时的路线，做过一些有益的探索。但葱岭地区的道路并非其研究的重点，因而在整体上相较前人的研究突破性不大。

    虽然中国学者对“丝绸之路”的研究热情经久不衰，但受限于国境线，出国实地考察又极为不便，所以大多数关注葱岭古代交通的中国学者，主要依据的是传世文献记载，只有少数人能够实地考察葱岭地区，但通常也仅限于中国国境线以内的部分。由于人为地截断了葱岭古道的贯通性，对域外的道路交通情况缺乏必要的了解，所以借助这些成果，很难窥见整个葱岭交通道路的全豹。

    近年来，也有一些国内学者努力将视野扩展到国境线以外的地理交通状况，他们的成果极大地弥补了国内学者对葱岭地区境外地理状况和相关研究信息的缺憾。但由于这一领域对国内学者来说长期缺乏必要的前期积累，所以仍留下一些不太准确的描述，或是未能解决的关键性问题。近年来还有勇敢践行域外葱岭古道的中国学者，也为葱岭古道的研究提供了重要的实地考察经验。还有西方学者如傅鹤里（Harry Falk），虽然未曾亲履其境，但善于利用谷歌地图（Google Earth）等现代科技手段，也在探索葱岭古道方面做出了重要推进。本文在利用卫星地图，认定葱岭古道除了传统的南道、北道之外，还有更重要的“中道”等方面，都可说是直接得益于傅鹤里研究的启发。

    总之，对于葱岭这一“丝绸之路”上重要路段的研究，国内外学者一百多年间断断续续地一直在努力推进。但国内学者通常受阻于国境线，对葱岭古道的认识难窥全豹。国外学者往往对汉文史料的理解和掌握存在明显的不足。两方面的研究亟须互为补充，才有可能真正取得对葱岭古道研究的突破性进展。这种突破性一是要建立在对葱岭古道上个别重要地点的重新比定。如对“悬度”位置的重新确认如果可以成立，就会极大强化关于瓦罕走廊在古代葱岭东西两侧交通上重要地位的认识。二是要有对整个葱岭古道的全新认识。以往的研究受李希霍芬的影响很深，以至于似乎横越葱岭的道路只有南北两条，实际上还有一条“中道”更值得重视。而这条“中道”在李希霍芬以降直到今天的各种“丝绸之路”路线图中，却很少得到体现。

    当然，即便能够取得突破性进展，也只是阶段性的推进。毕竟关于葱岭地区道路的研究，将牵涉历史、地理、地质、民族、语言、宗教、国际关系等方方面面。真正综合性的研究仍然有待未来条件具备时才能展开。

    二、“葱岭”与“葱岭古道”

    关于“葱岭”的得名，郦道元《水经注》引佚名的《西河旧事》云：“葱岭在敦煌西八千里，其山高大，上生葱，故曰葱岭。”葱岭上生野葱之说，还见于《水经注》引郭义恭《广志》的记载。葱岭生葱的景象，已得到现代亲履其境者的证实。但葱岭上能够生长野葱的景象，与人们想象中葱岭是终年积雪和寒风凛冽之地，形成鲜明的反差。这不由得令人想到葱岭的地理范围究竟应该如何界定？文献中的记载来自玄奘《大唐西域记》云：

    葱岭者，据赡部洲中。南接大雪山，北至热海、千泉，西至活国，东至乌铩国。东西南北，各数千里。崖岭数百重，幽谷险峻。恒积冰雪，寒风劲烈。多出葱，故谓葱岭。又以山崖葱翠，遂以名焉。

    玄奘所说的葱岭“四至”相当于：北起今吉尔吉斯斯坦的伊塞克湖、塔拉斯一线，南至瓦罕走廊南端的兴都库什山，东起今新疆莎车，西至今阿富汗的昆都士一带。历来谈及古代葱岭的地理范围，都要引述玄奘这一说，并将古之“葱岭”与今之“帕米尔高原”相对应。然玄奘所说的“葱岭”范围，与现代地理概念上的“帕米尔高原”并不完全重合。玄奘之所以用乌铩、活国、热海、千泉、大雪山来界定葱岭的四至，是因为这些地方都是他经行过的。玄奘是历史记录中为数不多的，几乎绕着葱岭走过一圈的旅行者。但对于没有这样旅行经验的人来说，未必也能想象得到，或是都认同玄奘关于葱岭四至的说法。所以，虽然玄奘给我们留下了关于葱岭四至的宝贵记录，但这一记录具有他强烈的个人色彩，需要我们谨慎看待。

    例如，乌铩和活国这两个地点，一东一西，都在帕米尔高原以下的地势平缓、海拔较低地区，理论上就不应属于帕米尔高原。现代地理概念上的帕米尔高原，北部应以外阿赖山脉（Trans-Alay Range）为界，以北就进入费尔干纳盆地（Fergana Valley）了，属于另一个地理区域。东部一般以公格尔峰（Kongur Tagh）一带的西昆仑山脉为界。西部一般以喷赤河（Panj River）自南向北流的河段为界。这三个地理方位上的界线，都与玄奘所言不符。只有玄奘所谓“南接大雪山”，即葱岭的南界应在兴都库什山与喀喇昆仑山之间的连接山脉，与现代地理学概念上的帕米尔高原的南界是符合的。“大雪山”以南就是印度河流域的上印度河谷地带（即印巴争议的克什米尔地区，巴控的吉尔吉特—巴尔蒂斯坦地区，Gilgit-Baltistan），属于另一个地理区域。但现在无论是学界还是社会公众认知，往往把上印度河谷地带也算作葱岭或帕米尔高原的范围。这是需要澄清的。况且，“帕米尔高原”的得名，是由于高原上有所谓“八帕”。这“八帕”的地理范围也不包括上印度河谷地区。因此，如果把“葱岭”界定为今天的帕米尔高原，则其东缘为西昆仑山，西缘为南北流向的喷赤河，北缘是外阿赖山，南缘是兴都库什山。本文讨论的“葱岭”，也主要是指这个地理范围之内。

    所谓“葱岭古道”，本指所有跨越葱岭地区的道路。这些道路既有东西向，也有南北向，而且彼此间犬牙交错，并非呈规则性的直线分布。就“丝绸之路”研究的关注点而言，本文主要讨论东西方向上横跨葱岭的道路。最早李希霍芬标示出了南、北两条路线，现在则应该按照方位，进一步将葱岭古道分为“北、中、南”三条道路。

    葱岭北道，即从今新疆伊尔克什坦口岸西行，进入今塔吉克斯坦境内的阿赖山谷。这条道路早在《汉书·西域传》就有体现：

    休循国，王治鸟飞谷，在葱岭西，去长安万二百一十里。户三百五十八人，口千三十，胜兵四百八十人。东至都护治所三千一百二十里，至捐毒衍敦谷二百六十里，西北至大宛国九百二十里，西至大月氏千六百一十里。民俗衣服类乌孙，因畜随水草，本故塞种也。

    “休循”是从伊犁河流域迁来的塞人所建立的国家。“鸟飞谷”或是指阿赖山谷。如果要说个更具体的地点，应在阿赖山谷的萨雷塔什（Sary Tash），这里也是北上进入费尔干纳盆地的重要岔路口。“捐毒”也是见于《汉书·西域传》的塞人小国，位于与休循接壤的东边，应该在今新疆境内。可见，捐毒和休循就扼守了这条东西方向上横穿阿赖山谷的葱岭古道“北道”。阿赖山谷非常宽阔，水草也多。走这条路既可北上进入费尔干纳盆地的塔吉克斯坦奥什（Osh），也可西行至杜尚别（Dushanbe）。《汉书》既然说从休循“西至大月氏千六百一十里”，说明当时通过这条路是可以通往已经迁徙至阿姆河以北地区的大月氏，自然也包括中亚传统的粟特地区。因而这条“葱岭北道”，在古代主要是从西域北道向西的天然延伸，从西域经此路可去往费尔干纳盆地和阿姆河北岸的粟特地区、阿姆河南岸的巴克特里亚地区（吐火罗）。

    李希霍芬标示出的这条路线，此后也成为丝路交通路线图上关于葱岭地区道路最有代表性的一条。近代以来的外国探险家，如斯文赫定、斯坦因、伯希和等人，也都曾经由这条道路进出中国。但是傅鹤里对这条道路的实际利用率提出质疑，认为在古代很难见到有通行这条道路的记载，这是因为这条道路的降水（雪）量大，又盗匪横行，所以不应作为横穿葱岭的主要道路来看待。他这个意见是有偏颇之处的。

    葱岭中道，即从今塔什库尔干出发，向西南行，而非向南行，越过纳兹塔什山口（Nezatash Pass），进入今塔吉克斯坦的穆尔加布（Murghab）地区，西行至霍罗格（Khorog），再前往阿富汗的法扎巴德（Fayzabad）、昆都士一带。这条路古代主要是从西域通往古代的吐火罗地区，即今天阿富汗北部地区。因这条路的西段有衮特河（Gunt River），故又被称为“衮特路”。衮特河发源于雅什库里湖（Yashilkul，汉文史籍中葱岭上的“三池”之一），自东向西流，与喷赤河交汇处，即霍罗格。这一地带在历史上被称作“识匿”或“赤匿”，即今天的舒格楠（Shighan）地区。这条路以往几乎不被学界所重视，讨论到与这条路相关的历史记录，也大都没有意识到这是一条完全可以单独列出的重要通路。直到近年，傅鹤里才强调了这条路的重要性。在沙俄和苏联时期，从杜尚别经霍罗格到穆尔加布，最终到奥什，修筑了今天帕米尔高原上唯一一条连续贯通的高原公路（原M41）。这条路部分修建于19世纪末沙俄与英国对中亚展开争夺的“大博弈”时期，部分修建于1930年代，居然沿用至今，成为一条几乎横贯帕米尔高原的公路（不含中国境内的塔克敦巴什帕米尔）。通常情况下，现代公路往往就是沿着古代交通路线而修建的。由于这条公路并未连接到中国的边境线，所以国内学者一般对这条路没有给予充分重视。

    葱岭南道，也是李希霍芬根据《汉书》的记载大致勾勒而出的。或从塔什库尔干出发，或从于阗的皮山出发，皆可行至瓦罕走廊的东端入口，再自东向西，横穿大部分属于今天阿富汗境内的瓦罕走廊。到瓦罕走廊的西端，既可沿兴都库什山继续西行抵达阿富汗的喀布尔、巴米扬、贾拉拉巴德地区；也可从兴都库什山的几个山口南下，经巴基斯坦的奇特拉尔(Chitral)，前往斯瓦特、白沙瓦一带。这条路古代主要是从西域通往巴米扬、迦毕试、犍陀罗等佛教圣地，因而在历史记录中出现的频率较高。也是以往学者们关注度最高的一条道路。

    古代的行旅不是今日的旅游，尤其是翻越葱岭这样的高寒高原地区，一定要充分准备，精选线路。除非有必须要绕远才能到达的特定目的地，否则一般不会选择绕远的道路。葱岭古道上这三条道路的选择，主要是根据旅行者的出发地和目的地来定。例如，从中亚粟特地区出发的商人和商队，大概率会选择“葱岭北道”进入西域。这对于他们是最便捷的道路。但中国的求法僧西行求法，却基本上不会选择这条“北道”。因为求法僧要去兴都库什山以南的犍陀罗和印度，选择“葱岭南道”或上印度河谷的道路，才是近便的道路。求法僧如果走“葱岭北道”去犍陀罗和印度，就要先到粟特和吐火罗地区，再南下兴都库什山，这样的选择与从“葱岭南道”西出瓦罕走廊后就从兴都库什山口南下相比，无疑是费时和绕远的。历史上只有个别的求法僧为了去被誉为“小王舍城”的巴尔赫去参礼，才会选择这条路。

    此外，民间商人和商队的活动一般是很难进入古代历史记录的。“葱岭北道”与“葱岭南道”相比，的确很少见到有经行此路的历史记录。求法僧主要选择“葱岭南道”西去东归，因而对于“南道”留下较多的记录。商人和商队则有强烈和明确的逐利意识，在安全有保证的前提下，他们不会选择需要绕远、增加运输和时间成本的道路。粟特商人当然不会只走“葱岭北道”，他们也曾在上印度河谷地区道路的岩刻中留下过踪迹。既然如此，就不能排除粟特商人也会经行过“葱岭中道”和“南道”的可能性。这完全要看他们商业活动的目的地是哪里。像葱岭这样特殊地理环境下的道路，自古至今一直都在那里存在，很多路段甚至千百年来也几乎没什么变化。不能因为没有，或很少见到某条道路的历史记录，就认为这条道路的利用率不如那些频繁见诸记载的道路要低。也不能因为某条道路的记载在某个特定时期明显多于另一条道路，就认为两条道路之间存在此消彼长的兴衰轮替。

    三、中国古人对“葱岭古道”的经行

    笔者已尝试按照朝代先后的顺序，梳理了中国古人经行葱岭古道所留下的历史记录。在此则按照葱岭上三条古道的地理方位，重新爬梳一下这些记录，以期加深对这三条葱岭古道在历史上分别被使用情况的认识。

    有记录的、最早通行“葱岭北道”的中国人，应是张骞。他在第一次出使时，被匈奴扣押十多年后逃脱，继续西行，就是经鸟飞谷至大宛。也就是从疏勒向西，进入阿赖山谷，从萨雷塔什转而向北，进入费尔干纳盆地。然后张骞应从盆地的西侧进入康居所在的索格底亚那地区，再南下到阿姆河北岸的大月氏王庭，进而渡河到阿姆河南岸的蓝市城（巴克特拉，Bactra）。当时大月氏已经征服“希腊—巴克特里亚王国”，但王庭尚未迁至蓝市城。在张骞返国后，大月氏王庭才南迁到巴克特拉。当张骞返国时，特意要避开匈奴在西域的势力范围，所以他不会再走经行鸟飞谷的来时路，既可能走“葱岭中道”也可能走“葱岭南道”。总之东归途中下了葱岭，就选择走经过于阗的西域南道，一直到羌中地区才又被匈奴捕获。

    此外，李广利伐大宛，史书虽未记载其具体的出征路线，但从西域进入费尔干纳盆地，十有八九是要从葱岭北道的衍敦谷、鸟飞谷进兵。这是中国历史上第一次派遣远征军经行葱岭北道，尽管只是走了半途，就转而北上费尔干纳盆地。陈汤攻伐郅支单于时，有“三校从南道逾葱岭径大宛”，也应走的是阿赖山谷这条道路。不过，这些军事行动都不能算是横穿“葱岭北道”。其他可能有大量的粟特商胡是通过“葱岭北道”进入西域乃至中原内地的，只是我们现在看不到直接的文献记录而已。

    2.葱岭中道

    至于“葱岭中道”，前述叙利亚商主马厄斯所属下属的商队，从巴克特拉出发，经葱岭的Komedoi地区，即汉文的“识匿”地区，抵达“石堡”，即塔什库尔干。如果是走“葱岭北道”就无需在“石堡”停留。所以应该是走的“巴克特拉—霍罗格—雅什库里”一线，再通过纳兹塔什山口，抵达塔什库尔干。这虽然不是古代中国人经行的记录，但可以证明这条“葱岭中道”在当时的确是商队经常会选择的一条道路。此外，《汉书·西域传》记载西汉末年时，从皮山出发，经“悬度”到罽宾的途中，将会经过葱岭上的“三池”。这“三池”就是帕米尔高原上六大湖泊中比较靠南的三个，即最南的切克马廷库里（Chaqmaqtin-kul）、中间的佐库里（Zorkul，又名萨雷库里，Sirikul，汉文史籍称“大龙池”）和靠北的雅什库里。雅什库里也是前述衮特河的发源地。可见早在西汉时，汉使已有经行雅什库里的经验。汉使走雅什库里这条路，不仅仅是为了就近水源，因为另两个淡水湖就在“葱岭南道”的途中，完全没必要为了取水而绕远走到雅什库里。而选择经过雅什库里的道路，就意味着前行是要去往霍罗格一带。从霍罗格可以选择向北去阿姆河北岸的粟特地区，还可以南下至伊什卡申，继而向西去往吐火罗的法扎巴德、昆都士；或向南越过兴都库什山，南下犍陀罗。因此，所谓“三池”的记录，实际上就暗示了葱岭的“中道”和“南道”都已有汉使经过。

    在公元1世纪末，贵霜新继位的君主因向东汉求娶公主，被拒，遂由“副王谢”率七万大军进攻西域，围攻疏勒未成而退兵。要动用7万大军穿行葱岭，需要尽可能在葱岭西部（霍罗格和伊什卡申一带都是从西向东横穿葱岭前必要休整、准备的据点）获得足够的给养，再选择相对比较适合大军通行的大路。考虑到当时贵霜都城不是在巴克特拉，就是在犍陀罗的弗楼沙（白沙瓦前身），贵霜军不太可能先北上到阿姆河，再通行阿赖山谷进入西域。他们应是先进入葱岭，到霍罗格和伊什卡申一带，再沿“葱岭中道”至塔什库尔干，这是最有可能的线路。至于“葱岭南道”虽然也可以通行，但要让7万大军鱼贯穿行瓦罕走廊的狭长地带，在军事上恐非明智之选。

    到6世纪初，宋云、惠生出使的去程中，葱岭一段的路程，走的是“汉盘陀—钵和—嚈哒王庭（昆都士）”。以往的研究，包括经常被引用的桑山正进所画的宋云使团的行程路线图，也没有体现出宋云等人的去程走的应该是“葱岭中道”。本文想强调的是：宋云使团很可能是经过“葱岭中道”，而非走“葱岭南道”的瓦罕走廊后，抵达嚈哒王庭所在的昆都士。首先，在宋云使团的记录中，也明确提到了“三池”。如果只走横穿葱岭的单程，这“三池”是没必要都要走到的。宋云很可能去程经过雅什库里，回程则经过佐库里。其次，“汉盘陀”即“渴槃陀”，亦即塔什库尔干。宋云等人从塔什库尔干出发，也是经过纳兹塔什山口，从塔克敦巴什帕米尔进入到小帕米尔。这时既可以向北走“中道”，也可以向南走“南道”。由于“宋云行记”中记载了“波知国，境土甚狭，七日行过。”这应是指瓦罕走廊的狭长地带，七天就可走完。而且波知国只有“二池”，应是指佐库里和切克马廷库里。如果“波知国”指的是瓦罕走廊，则“钵和国”就不可能还在瓦罕走廊上。所以，“钵和国”合理的位置应该在“葱岭中道”上。宋云使团出使的首要目的地是位于昆都士的嚈哒王庭，走“葱岭中道”不仅路途最短，而且路况也比较好走。

    此后，明确走“葱岭中道”的，还有8世纪中期至末期的车朝奉（730—812）。他于751—790年间也游历葱岭东西，并在“罽宾”出家，“悟空”是其法号。回国后，将其经历口述，由圆照于795年记录，作为贞元新译《十力经》《十地经》等经的序，收入大藏，亦名《悟空入竺记》。《悟空入竺记》记载其去程经过葱岭时的路线是：疏勒—葱山—杨兴岭—播蜜川—五赤匿国—护密。这其中，“葱山”应即唐朝在葱岭东部的重要据点——葱岭守捉，或曰“葱岭镇”。亦即说车朝奉一行是从疏勒西登葱岭，到达葱岭镇（塔什库尔干）。“杨兴岭”很可能是纳兹塔什一带的山口，因为“播蜜川”是佐库里湖所在的峡谷，从塔什库尔干到佐库里之间，相对有标识度的山岭，就是纳兹塔什山口。不只是车朝奉，玄奘和慧超也都是经过播蜜川后抵达塔什库尔干的。这说明经行佐库里的道路相对于经行切克马廷库里的道路要更经常被使用。其实这与“石山悬度”的位置有关。因为走切克马廷库里向西通行瓦罕走廊，就一定要经过“石山悬度”；反之，若选择走石山“悬度”东去塔什库尔干，也一定会通过切克马廷库里。最初汉使通罽宾时，之所以走石山“悬度”这条险路，是因为距离最近。后来随着对葱岭地区道路认识的加深，可以替代“悬度”的道路也会出现。但如果像玄奘那样由大象驮着经书，是肯定不会选择“石山悬度”，也就不可能走切克马廷库里之路。车朝奉一行在经过播蜜川后，经过“五赤匿国”。“五赤匿”就是“五识匿”，即是今塔吉克斯坦的舒格楠一带，属于“葱岭中道”的西段。然后从“五识匿”南下到“护密”，亦即“胡蜜”，这是瓦罕走廊西端，今伊什卡申一带。关于“五识匿”和“护密”的位置关系，还可通过慧超《往五天竺国传》得到清晰的理解（详见下文）。这也说明“葱岭中道”与“葱岭南道”之间并非截然分隔，车朝奉一行就是先走了“南道”的东段，然后又走“中道”的西段，再从“中道”回到“南道”的西段。因为他们的目的地是去罽宾犍陀罗地区，所以最终要从瓦罕走廊西端南下。

    至于车朝奉在返程经过葱岭时，他走的是“拘密支—若瑟知国—式匿国—疏勒”一线。其中“拘密支”，Komidai，玄奘记作“拘谜陀”，又作“居密”“俱蜜”，位于葱岭的西部，五识匿地区之北。可见车朝奉还是由葱岭西部向东，经过式匿国，抵达疏勒。其中省略了从式匿到葱岭镇的路段，应该是与去程相差不大，所以没什么特别可记的。车朝奉之所以来去都选择了“葱岭中道”，很可能是因为吐蕃势力已经浸染到上印度河谷的大、小勃律，乃至瓦罕走廊有时也被吐蕃所控制。这种情况下，走“中道”比走“南道”会安全一些。

    此后，清乾隆年间平定大小和卓之乱时，清军追击叛军在葱岭北部的喀拉湖、穆尔加布和雅什库里，与叛军激战，三战三捷。平定叛乱后，乾隆命人在雅什库里湖边树立《平定回部伊西洱库尔淖尔勒铭碑》。这也是最远的一座“乾隆纪功碑”。雅什库里一带可以作为战场，双方投入万人以上规模的部队作战，也说明这一地带相对瓦罕走廊更为开阔，更适合展开大规模的军事行动。

    3.葱岭南道

    以往的研究，有一种从整体上忽视葱岭古道在丝绸之路东西交通上的重要性的倾向。如桑山正进认为：原本上印度河谷道路是中印之间交通的主要通道；由于种种原因，上印度河谷道路被通行瓦罕走廊、走兴都库什山北麓的道路所取代，导致巴米扬地区开始建造大佛像。其实如果梳理历史记录就会发现，葱岭古道很可能较之上印度河谷道路更重要，持续发挥作用的时间也更长。见于历史记载的选择走“葱岭南道”的行者，似乎远多于上述“北道”和“中道”。因而“葱岭南道”一直是西域通往中亚和印度的主干道，甚至上印度河谷道路最兴盛之时，也无法与“葱岭南道”分庭抗礼。

    早在公元前130年左右，从伊犁河流域被大月氏赶出故地的塞人，在“塞王”的带领下，“南越悬度”“南君罽宾”。因为“悬度”已经可以比定为在瓦罕走廊上的一段石山险路，所以塞人就是从塔里木盆地西缘出发，通过瓦罕走廊，实现横穿葱岭，再南下去攻占犍陀罗地区。这也是从葱岭东侧的西域出发，去往葱岭西侧的犍陀罗地区最短的一条路径。因为塞人骑兵要对犍陀罗的希腊人政权发动突袭，所以不可能选择上印度河谷地区那种“悬絙而度”的绳索桥，也不可能在河谷山坳中绕来绕去浪费时间。石山“悬度”虽然凶险，但不是不可逾越。所以塞人进占罽宾，就是通过快速穿越“葱岭南道”的瓦罕走廊而实现的。

    塞人占领犍陀罗地区，建立起塞人的罽宾王国。到西汉末，大批的西汉国使和护送所谓“罽宾使者”回国的汉军将士，都是经历“悬度”险路完成使命的。这其中，只有文忠和赵德等极少数人在历史上留下了姓名。汉使和汉军的马匹不适合葱岭上的高原险路，通行“悬度”时损失较大。所以杜钦建议汉朝放任罽宾，不再参与其国政事；罽宾再有来使，汉朝只负责将其护送到皮山即止，不要再冒着危险将所谓的“罽宾使者”护送回罽宾。这样就避免了在通行“悬度”时的无谓牺牲。

    公元97年，甘英从龟兹出发，“逾悬度，乌弋山离”，去往大秦。既然“逾悬度”，显然也是走了“南道”的瓦罕走廊。因为这样走，出了瓦罕走廊，再沿着兴都库什山西行，就可到达乌弋山离。可以说是最近的道路。甘英无需去往兴都库什山以南的地区，所以南北朝的求法僧才说甘英不曾走过上印度河谷的绳索桥和傍梯险路。

    根据僧传的记载，公元4—5世纪，法显、智猛、昙无竭等大部分求法僧，都是从塔什库尔干南下，不去横穿瓦罕走廊，而是在瓦罕走廊东段的山口，就南下到上印度河谷地区。选择这样的路途，主要是为了去陀历国（Darel,达丽尔山谷）参拜陀历大像。而且通过口耳相传，使得这条路成为南北朝时期大多数求法僧都会选择的道路。但这条路并不能一直保持畅通，如果发生地震，形成堰塞湖，就会破坏道路交通，乃至有的路段会断路两三百年之久。这也就是为何还会有个别求法僧，如北魏的道荣，仍然会在去程和回程都选择走“葱岭南道”。

    大魏使者谷巍龙的题字出现在乌秅，而其出使的目的地是粟特地区的“迷密”（米国）。这并不意味着谷巍龙接下去会沿印度河谷道路一路到犍陀罗，之后北上兴都库什山，经过吐火罗地区，再到粟特地区。前述《汉书·西域传》就有这样的道路，即从乌秅西行会经过“石山悬度”。而要从乌秅西行到“悬度”，就要通过乌秅西北的山口进入到瓦罕走廊东端，再向西经过悬度，横穿瓦罕走廊。到走廊的西端，或者继续西行，就是当年甘英去往乌弋山离的路线，只不过谷巍龙还要继续从乌弋山离北上粟特地区。或者从瓦罕走廊西端沿喷赤河北上，再转西，都可以抵达粟特地区。谷巍龙之所以没选择“葱岭北道”，或是直接从西域北道或南道西上葱岭，大概是因为与北魏敌对的柔然势力控制着西域北道，所以谷巍龙走了西域南道，且从于阗南下到拉达克地区，再转向乌秅。

    520年左右，宋云完成了觐见嚈哒王的使命，带着北魏使团，携带170部佛经，回国复命。因为他是从乾陀罗，即罽宾犍陀罗之地返国，自然会走从犍陀罗去西域的传统道路，那就是“葱岭南道”。宋云带那么多佛经，驮畜行走“悬度”不易，故其返程很可能也是从帕米尔河与瓦罕河交汇处的Gaz Khun村就转而向北，绕开“悬度”，经行佐库里所在的波谜罗川，再抵达汉盘陀（塔什库尔干）。

    大约100年后，玄奘的回程，也是从瓦罕走廊西端开始横穿走廊，经过达摩悉铁帝国（瓦罕走廊西部，汉杜德）、波罗蜜川（播蜜川、大帕米尔）。即从帕米尔河与瓦罕河交汇处的Gaz Khun村以东，就选择相对好走一些的经行“大龙池”（佐库里）道路，大体上走的是“南道”。

    距玄奘经行葱岭差不多一百年，723—727年间，新罗僧慧超，也在从天竺返回唐朝的路途中，走了葱岭古道。他具体的路线是：胡蜜—识匿—葱岭镇。《往五天竺国传》云：

    又从吐火罗国东行七日，至胡蜜王住城。当来于吐火罗国。逢汉使入蕃。略题四韵取辞。五言：君恨西蕃远，余嗟东路长。道荒宏雪岭，险涧贼途倡。鸟飞惊峭嶷，人去难偏梁。平生不扪泪，今日洒千行。

    “胡蜜”又称“护密”或“休密”，本是贵霜时期的五翕侯之一，应该镇守的就是瓦罕走廊西端的伊什卡申一带。慧超到胡蜜时，恰逢“汉使”即唐朝的官使经行胡蜜去往“西蕃”。具体是谁，要出使哪国，都已不可知。就在这域外雪岭之地，两个从东土大唐来的旅人，一个西去，一个东归，意外相遇，而又都喜好汉语诗文，遂以诗相酬，共同抒发在域外偶遇知音、怀念故乡的悲情愁绪。此后，慧超记载了他没有亲履其地，而是听闻传说的“识匿国”：

    又胡蜜国北山里，有九个识匿国。九个王各领兵马而住。有一个王，属胡蜜王。自外各并自住，不属余国。近有两个王，来投于汉国，使命安西，往来〔不〕绝。……彼王常遣三二百人，于大播蜜川，劫彼兴胡，及于使命。纵劫得绢，积在库中，听从坏烂，亦不解作衣著也。此识匿等国，无有佛法也。

    通常认为这里的“九个识匿国”“九个王”应该是“五个识匿国”“五个王”之误。五识匿地区就是今天的舒格楠地区。五识匿中，有的归属胡蜜，有的归顺唐朝，与安西都护府来往频密。但当时唐朝最西境，就是下文提及的“葱岭镇”，亦即“葱岭守捉”，今天的塔什库尔干。从“葱岭守捉”向西，就是识匿地区。应该是比较靠东的两个识匿王更乐于与唐朝往来。“大播蜜川”即玄奘东归时经过葱岭的“波谜罗川”，亦即佐库里湖。这是说五识匿国经常派人劫掠来往的“兴胡”，即通过经商兴利的胡商，主要是指粟特商人。说明粟特商人显然也是经常通行佐库里所在的“葱岭南道”。不仅劫掠胡商，包括来往的国使，也不放过。故前文有诗云：“险涧贼途倡。”这种劫掠行为属于识匿国的“国家行为”，他们劫得大量的“绢”，也不会用来制作衣服，还是习惯穿他们传统的皮裘之衣。实际上在葱岭这样苦寒之地，丝绸、绫绢之类的原料不可能被用于制作当地人的衣服。由此可见，丝绸的确是胡商冒险经行此路运营的主要货品。而识匿国不信佛法，故慧超也不会选择“中道”。慧超选择的道路是：

    又从胡蜜国东行十五日，过播蜜川，即至葱岭镇。此即属汉。

    慧超从瓦罕走廊西端的胡蜜，一路东行，就是走瓦罕走廊，经播蜜川（佐库里），抵达葱岭守捉所在的塔什库尔干。这也是开元时期唐朝西境的极限了。

    公元747年，高仙芝征讨小勃律之役，其大军从龟兹出发，上葱岭后，《旧唐书》记云：

    又二十余日至葱岭守捉，又行二十余日至播密川，又二十余日至特勒满川，即五识匿国也。仙芝乃分为三军：使疏勒守捉使赵崇玭统三千骑趣吐蕃连云堡，自北谷入；使拨换守捉使贾崇瓘自赤佛堂路入；仙芝与中使边令诚自护密国入，约七月十三日辰时会于吐蕃连云堡。堡中有兵千人，又城南十五里，因山为栅，有兵八九千人。城下有婆勒川，水涨不可渡。

    小勃律即吉尔吉特。此前唐军曾三度征讨，都未获胜。天宝六载，高仙芝率一万大军从安西都护府（龟兹）一路西行百日，登上葱岭。在葱岭守捉休整后出发，并未直接从瓦罕走廊东端南下巴罗吉尔山口和达尔科特山口去进攻吉尔吉特，而是直接挥师西进到“葱岭中道”西段的五识匿国地区。“特勒满川”一般认为是帕米尔河。此前唐朝已使位于瓦罕走廊西段的护密国归降，故高仙芝此行并非去攻占五识匿和护密，当地应有亲唐势力接应唐军。他也无需带领一万大军全数西进五识匿地区，应该早在葱岭守捉休整时，就定好分进合击的战术：赵崇玭从“北谷”进军吐蕃占领的连云堡（即萨尔哈德）。所谓“北谷”应即从佐库里一带穿行山谷能够抵达萨尔哈德的道路。今天从萨尔哈德出发，如果不想走石山“悬度”之路，就要向北绕远穿行山谷，也可去往佐库里或切克马廷库里。贾崇瓘则走“赤佛堂路”，有说是在瓦罕走廊东段从帕米尔去往贾帕尔桑河谷的道路。“赤佛堂”的地名或许和《汉书·西域传》所记的“赤土身热之阪”有关。亦即说贾崇瓘这路唐军负责从切克马廷库里这一路夹击连云堡。无论“赤佛堂路”具体地点何在，都不影响学者们认为贾崇瓘这一路实际上是唐军攻击连云堡的“东路军”。赵崇玭和贾崇瓘这两路，不可能是唐军到了五识匿后再回过头去走“北谷”和“赤佛堂路”，应是高仙芝率军绕行到五识匿和护密去实施战略迂回，留下另外两军分别从北面和东面，约定日期，合击连云堡。连云堡南十五里还有吐蕃的一座城寨，下有“婆勒川”。姚大力认为“婆勒”就是Baroghil的音译。在萨尔哈德向南翻越巴罗吉尔山口时，当时吐蕃也派重兵把守。唐军攻下连云堡后继续南下吉尔吉特，就不在本文讨论的葱岭道路范围。总之，高仙芝打小勃律之前，先要拔掉从“葱岭南道”南下小勃律的必经之地连云堡。但如果直接走瓦罕走廊，从东向西进军连云堡，一旦被吐蕃扼守住石山“悬度”，大军就无法前进。高仙芝采取的是通过“葱岭中道”迂回到连云堡的北方和西方，再实现三面合击的战术安排。由此也可见“中道”与“南道”之间存在紧密的关联性。

    此后，随着“安史之乱”的爆发，吐蕃不仅反攻夺占了葱岭古道上的“中道”和“南道”，而且唐朝连西域、河西诸地也逐渐丧失。中原人出于各种政治、军事或是信仰的目的，艰难跋涉于雪岭葱外的时代，遂暂告一段落。

    四、结语

    以上通过将“葱岭古道”细分为“北道”“中道”和“南道”，并将历史上与葱岭有关的每个历史事件和每个具体的旅行者事迹，还原到“葱岭古道”具体的每一条道路上去。这样做希望可以加深我们对这些人物和事件的理解。

    例如，玄奘返程中经过的“大龙池”到底是佐库里，还是切克马廷库里？只要考虑到“石山悬度”的位置，就不难确认“大龙池”一定是指佐库里，因为这条路相对于经过石山“悬度”才能抵达的切克马廷库里之路，要好走得多。再如高仙芝征伐小勃律之战，按照以往的看法，唐军似乎是从瓦罕走廊东端直接进军连云堡，再南下坦驹岭的。但这样一来，唐军必须要经过石山“悬度”才能抵达连云堡。这对上万人的远征部队而言，肯定是危险的选择。高仙芝之所以能够成功，与此前夫蒙灵詧替他打通了护密道路有很大的关系。这使得高仙芝的军队可以得到瓦罕走廊西端护密国的支持，甚至五识匿地区也不会给唐军制造麻烦。所以高仙芝能够采取迂回到连云堡以西，从东、北、西三面合击连云堡的战术。这一点似乎是以往研究高仙芝征伐小勃律的学者都没有意识到的。

    此外，还可得出以下几点关于“葱岭古道”的全新认识：

    其一，从地理上说，葱岭的四至应以今帕米尔高原为界，玄奘的记录并不符合葱岭的实际情况。上印度河谷地区在地质板块上属于兴都库什山以南的印度板块，不属于帕米尔高原的范围，应排除在“葱岭古道”之外，单独作为一个研究对象。

    其二，“葱岭古道”进一步应划分出“北、中、南”三条道路。这其中，“北道”与“中道”和“南道”相比，具有一定的独立性。或者说从“中道”和“南道”较难在东西横向通道上与“北道”产生关联性。但“中道”和“南道”之间，则往往可以根据需要进行穿插经行。实际上，葱岭上的道路组合是多样化的，不是简单的三条线能够涵盖的。古人根据出发地和目的地的不同，可以灵活选择自己要走的道路。但基本的原则是会选择保证安全和距离短、耗时少的路程。玄奘之所以在回程中选择走葱岭而非去程时走天山以北再到中亚粟特地区的道路，就是因为正常情况下从西域到印度去的道路就应该走葱岭古道。

    其三，所谓“瓦罕走廊”，只是葱岭上的“南道”而已，不应被视作通行葱岭南部地区的唯一选择。与之相比，霍罗格与塔什库尔干之间的葱岭“中道”在历史上所起的作用，可能更值得我们关注。今后的“丝绸之路”路线图在经过葱岭地段时，至少应该画出三条东西横贯的路线，而不是只有两条。

    本文转自《中华民族共同体研究》2024年第4期

  • 王颖：揭开自由心证的面纱：德国意涵与中国叙事

    一、引言：自由心证理论研究之迷思

    自由心证的迷雾一直笼罩在我国刑事诉讼理论与实务之上，似有似无，似虚似实。在传统客观主义真实观与对法官自由裁量的质疑之下，《中华人民共和国刑事诉讼法》（以下简称“《刑事诉讼法》”）及司法解释设立了大量证据规范限缩法官自由裁量权，以期实现刑事审判之客观化。然而，纯粹客观的司法裁判仅是一种理想的乌托邦，所谓“良法善治”，良法经由法官运用才能善治，实然司法之中不仅无法回避自由心证，亦需要法官自由心证回应个案特性与现实需求。

    揆诸现实，自由心证原则是否存在于我国刑事诉讼规范之中仍存有争议，但与之相关的理论研究却方兴未艾。有学者将我国刑事证据立法与现实定义为“新法定证据主义”；还有学者肯定了我国自由心证的存在，并提出我国刑事诉讼证据证明模式属于自由心证之亚类型“印证证明”；亦有学者认为我国证据制度实属自由心证制度。不论观点差异，在此之中“自由心证”至少在三个维度使用：作为证据制度的自由心证，与法定证据制度相呼应；作为司法证明模式的自由心证，与印证、拼图、综合证明等相比较；作为证明标准的自由心证，与证据确实充分、事实清楚抑或排除合理怀疑相对照。自由心证似乎飘渺无形，却又无处不在。这不禁让人困惑，自由心证的内涵与外延究竟如何？自由心证是证据制度、司法证明模式抑或证明标准？自由心证与印证证明的关系又如何厘清？经验法则、逻辑法则是否属于自由心证范畴？自由心证与证据确实充分、排除合理怀疑究竟是何种关系？此看似涉及自由心证原则与我国刑事证据核心理论之逻辑关联，实则关涉自由心证内涵、法律性质、适用场域等基本问题之厘清，最终直指自由心证原则在我国刑事诉讼理论中的体系地位。

    事实上，由于长期的语言隔阂与对大陆法系职权主义之偏见，我国对自由心证原则的历史嬗变与当代意涵仍存有不少理论误读与研究缺位。虞于此种现状，学界不仅对自由心证原则存有迷思与混沌，亦导致相关理论研究无法从当代理论与司法实践中汲取灵感，容易偏离法学规范视角而走向虚无主义，在未厘清法学问题的教义学内涵与边界之时，却又将其异化成哲学或心理学问题，容易导致研究之根基不稳与立论偏离。当然，随着学界对德、法等国刑事诉讼理论的直接引介、深入研究与审慎反思，误解得到一定澄清，偏见得以部分破除。然而，对自由心证理论沿革的研究似乎起于神明裁判、止于法国大革命；对自由心证原则内涵之理解亦止于“法官根据自己的理性、经验和良心，对证据的证明力大小强弱进行自由判断，法律不作任何限制性的规定”，而对与我国刑事法基础理论能够有效承接，与证据属性理论、证明责任理论等证据基础理论深度融合的德国自由心证原则之深入研究甚少。

    不容置喙，近代意义的自由心证原则萌芽于18世纪的法国，在法国大革命中由杜波尔提出并确立。然而，在此后相当长的时间里，法国学术界并不承认存在刑事证据一般理论，直至20世纪初才日渐出现体系性的刑事证据一般理论。与之形成鲜明对比，自由心证原则自法国大革命后传入德国，1846年由时任普鲁士立法部长、著名法学家萨维尼（Savigny）提出并确立。此后近两百年间，在德国哲学思辨传统与严谨的刑事法理论影响之下，自由心证原则得以独立发展，在改革的浪潮中不断与证据属性理论、严格证明理论、证据责任理论等德国证据理论深度融合，并在司法实践中伴随法官裁判呈现出历久弥新的强大生机与活力。

    作为一个以职权主义为底色的国家，无论是刑事证据理论之完善抑或刑事程序之革新均应建立在深入理解职权主义传统及传统职权主义理论之上。而鉴于德国刑事法对大陆法系国家之深远影响，德国自由心证原则对我国刑事诉讼亦具有重要学理价值。事实上，我国学界出现的前述概念混用、术语误解和研究迷思均与德国自由心证原则的理论研究缺位存有一定关联。有鉴于此，有必要对德国自由心证原则的历史嬗变与当代意涵进行系统梳理与阐述。只有在明晰自身机理的前提下，才能对自由心证原则的中国叙事脉络予以准确勾勒与检视，以揭开自由心证原则之面纱，澄清学界之误解，明晰其中国意象，并为厘清晚近新法定证据主义、印证证明、经验法则与逻辑法则、刑事证明标准等相关证据核心理论奠定基础。

    二、自由心证原则的德国嬗变

    从18世纪直至19世纪上半叶，随着法国自由心证思潮的涌入、刑讯逼供的废除与陪审团制度的引入，德国围绕新的刑事司法理念与制度展开了论战。由于法国自由心证原则与人民民主理论和陪审团制度直接相关，因此，德国首先探讨的并非“是否应当引入自由心证原则”，而是“是否应当确立陪审团制度”。在对陪审团制度的质疑中，怀疑论者继而展开了针对刑事证据理论与自由心证原则的反思。

    （一）消极证据理论

    1813年费尔巴哈（Feuerbach）对以陪审团制度与自由心证原则为核心的法式哲学展开批判：“陪审团靠直觉判案与教会裁判并无本质区别，前者漫不经心地等待自然启示的光芒，而后者则是等待上帝的灵感。”他并不认为在一种“清醒梦境状态下”的裁判能够比基于理性的权衡更加公正，而认为化解证据体系危机的方法并非是放弃证据规则，而是通过法定规则与法官心证结合互补，以“消极替代积极”。

    所谓消极证据理论（Negative Beweistheorie），是相对于法定证据主义下的积极证据理论（Positive Beweistheorie）而言的。积极证据理论指立法者之积极，由立法预先规定法官证据裁判的规则与证明力大小；消极证据理论则主张，立法者不应预先规定应当在何处找寻心证，而仅能规定在何处不可找寻心证，证据规则应当仅最低限度干涉裁判过程，不能代替法官探寻实质真实的权利和义务。因此，消极证据理论的核心并非完全消除传统法定证据，而是通过改良而承认法官自由心证的可能性。消极证据理论一度受到学界认同并获得立法确认，但最终伴随自由心证原则的确立而在德国法律演变的历史长河中消亡。

    （二）自由心证与陪审团一体化的整体印象论

    与此同时，伴随着法国大革命的民主思潮，德国兴起了一种将内心确信视为整体印象的理论（Totaleindruck）。整体印象论也与法国民主至上思想相承接、与陪审团制度相呼应，其秉承的基本立场是：职业法官受法定证据规则的约束展开证据评判，而陪审团则直接基于不受规则约束的总体印象判案。陪审团作为普通公民，能在审判中直观地体验犯罪呈现在法庭上的过程，并基于良知与理性形成犯罪与否的整体印象。故此，整体印象论认为职业法官是传统法定证据主义之产物，而陪审团则是新兴自由心证理论之成果。

    （三）自由心证与陪审团分离的思想启蒙

    然而，完全受证据约束的职业法官审判与只受良心约束的外行群众审判，这种非此即彼的替代关系受到越来越多的质疑。米特麦耶（Mittermaier）认为康德所定义的真实才是自由心证最好的诠释：真实是认识主体与被认识客体之统一。因此，他认为对法律确定力的寻求是一项专业工作，法定证据制度保证了“决策理由之价值”，仅在预先告知适用证据规则并给出判决理由的前提下，陪审团才具有正当性，没有证据规则制约的心证只会形成不合理、无依据的主观揣测。在这种理念之下，陪审团事实上类似于德国传统法官的角色，这也意味着，米特麦耶重新定义了自由心证的内涵，自由心证并非单纯的猜测，而是基于理性的权衡，主观的心证塑造是一种理性的思维过程。

    基于此，自由心证的认识论基础从法国强调主观的绝对理性转向了德国康德主义哲学的主客体一致性，自由心证并不必然与陪审团制度如影相随，而具有独立的价值与意义。与此同时，对职业法官的过度限制亦受到了越来越多的批判。实际上，赋予法官根据个案审查证据的权利与过度限制法官证据评估的标准相矛盾。至此，自由心证理念最终得以脱离陪审团制度而独立存在，适用范围进而拓展到职业法官的范畴。

    （四）自由心证原则的正式确立

    1846年，时任普鲁士立法部长的萨维尼在《刑事程序基本问题备忘录》中提出应当完全摒弃法定证据理论，主张法官应当根据理由和法规推导判决，探寻和适用证据规则的权力亦完全属于法官，这样才能充分顾及思维规律、经验与洞察力。萨维尼并不怀疑塑造内心理性确信的必要性，但是怀疑抽象的证据规则不能穷尽所有个案特殊性。与此同时，他并不赞同陪审团，因为证据裁判是根据法定证据规则或者没有此种规则时需要持续不断训练的专业工作。总而言之，萨维尼所认同的自由心证概念更多地植根于德国传统，而非借鉴法国经验。

    在此种理念的倡导下，1846年《普鲁士法》第19条正式确立自由心证原则。该法废除了所有针对柏林刑事法院的证据规则，赋予了职业法官根据证据自由心证达到内心确信而判决的权力。伴随此种自由权，立法者亦规定法官阐明判决理由的义务，即法官享有自由心证的权力，但亦应当在判决中述明推导此判决的理由。相比于法式的内心确信（l’intime conviction），萨维尼所理解的自由心证（freie Beweiswürdigung）更接近康德的理解，即内心确信需要每一个具有理智之人的认同。因此，德国自由心证原则更多地受到德国哲学与法学传统思想中自由主义与理性主义的影响，而非法国的天赋人权与陪审团制度，并具有更强的内驱动力与司法目的导向，旨在消除传统法定证据规则之桎梏。至此，自由心证原则成为德国刑事诉讼中法官证据裁判的一项基本原则，并随着司法理念的变迁与司法实践的发展而获得新的意涵与生命力。

    三、自由心证原则的理论困境与突破

    （一）理论困境

    从法定证据演变到自由心证是一种历史进步，法定证据的教条与僵化得以克服，刑讯逼供在立法上得以废除，但接踵而来的是新的理论困境与实践难题。德国自由心证原则的核心条款为《德国刑事诉讼法典》第261条，即“法官根据审判所建立起来的内心确信判定证据调查之结果”。条款如此简明扼要，如此主观抽象，如何替代原本法定证据制度中的证明标准？不同法官所抵达的自由心证亦存在差异，如何保障类案正义？

    即便在理想条件下，法庭所呈现的事实与客观发生的事实不可避免地存在距离，法官通过理性推理判断很难作出完全一致的重构，仅能获得一种概率意义上的认知与见解。因此，自由心证原则虽避免了法定证据主义之弊端，却又产生了两个理论难题：首先，自由心证法律属性与构成要件之澄清。自由心证到底是一种纯粹主观判定抑或存在客观性？这涉及主观主义与客观主义之争。其次，自由心证到底需要在待证事实与证据间建立何种程度的确证？这关涉证明标准之确立。这两个根本问题随着联邦最高法院的判例而在实践中逐渐明晰。

    （二）主观主义与客观主义之争

    1.帝国法院观点之对峙

    主观主义与客观主义之争，是一个发轫于德国刑事司法实务的理论问题。从帝国法院到联邦最高法院，均致力于在个案中明晰自由心证原则的内涵与边界，其中，帝国法院的两份判决对于现今自由心证内涵之明晰与基本范畴之厘定有着重大意义，分别代表着客观主义与主观主义的基本立场。

    （1）客观主义：高度盖然性论（RGSt 61, 202）

    自由心证原则之确立以人类理性觉醒与确定性认知为前提，在法官内心确信中体现为对心证盖然性的承认和盖然性程度确证的理解差异。1927年，帝国法院首次在RGSt 61, 202判决中确立了刑事案件定罪意义上的内心确信，并提出以高度盖然性（die hohe Wahrscheinlichkeit）作为自由心证内心确信的标准。此种观点的核心要旨在于，在纯粹主观主义的自由心证原则之中引入客观主义底色的概率判断，即高度盖然性，与此同时强调不通过主观因素过分夸大“高度盖然性”，而是通过推定降低真相所要求的盖然性程度。虽然判决中并未涉及证明标准的维度，但至少明确确立了证明标准是基于诉讼材料所获得的高度盖然性，此种高度盖然性由法官基于理性推理而来，代表着自由心证的客观化。

    （2）主观主义：内心确信论（RGSt 66, 163）

    然而，1932年帝国法院在RGSt 66, 163判决中认为，基于盖然性的内心确信并不足以支撑起定罪量刑，法官必须达到完全的内心确信（ volle überzeugung ），但又强调人类认知能力具有边界，将事实与最高程度的盖然性相提并论实则属于概念上的不精确。由于此种客观真相（ objektive Wahrheit ）实质上无法获得，因此，法官必须尽其所能达到一种基于司法良知有效的确信，以避免可能的错误与误判，尽可能地消除每一项怀疑。但是这其中的逻辑悖论是，如何通过人类去避免所有基于人类认知可能导致的错误？在案件审理中逐一排除怀疑是一种无法实现的乌托邦。

    事实上，高度盖然性论与内心确信论完全针锋相对，代表着客观主义与主观主义的两个维度。高度盖然性论实则降低了真相查明的标准，法官基于理性所能获得的信息与认知作出裁判，其标准在于达到高度可能性或高概率的内心标准，而并不需要排除所有怀疑；而内心确信论要求法官即便是严格审理了诉讼材料，亦需要逐一排除可能的怀疑。

    2.联邦最高法院观点之争鸣

    在早期阶段，联邦最高法院的判决总体带有明显的客观主义色彩，认为证据评判标准在于法官内心所确信的高度盖然性，然而仍旧回避确定“盖然性”的概念与具体标准。但是1957年的判决发生重大转折，联邦最高法院的主流观点从客观高度盖然性论走向了主观内心确信论。

    （1）主观主义论（Subjektive Theorie, BGHSt 10, 208）

    联邦最高法院在BGHSt 10, 208判决中阐明：“法官必须在主观上排除客观可能存在的怀疑，才可达到内心确信并作出判决。此种个人内心确证（ pers?nliche Gewissheit ）是判决的必要条件，亦是充分条件。”这意味着对于最终判决起决定作用的不是客观可能的怀疑，而仅是法官自己主观产生的怀疑。主观主义论由此判决确立，即只要法官对案件事实产生主观确信，认为事实与法律认定不存在错误，并在判决理由中阐明自己的主观确信即可。但是主观主义招致了众多的反对意见，判决所需要的内心确信是否真的应当完全取决于初审法官的个人内心确证？若存在对被告人更有利的结论，法官是否仍可以定罪？此种推论是否与无罪推定原则冲突？由于主观主义存在这一明显缺陷，联邦最高法院此后事实上放弃了绝对的主观主义，认为即便是遵循主观主义判案，法官亦不能违背类似于法律规范的逻辑法则或已确证的科学知识；并且，为了验证法官是否遵循了逻辑法则，法官有义务在判决中全面列出证据评估的内容。

    （2）生活经验论（Theorie der Lebenserfahrung）

    联邦最高法院在后续的判决中实际上采用了一个相对折中的客观化视角，即生活经验论。联邦最高法院重申了一个不言而喻的基本原则，即当同时存在多种可能性时，法官必须说明他优先认同和选择某种可能性的理由，法官基于实际生活经验所能获得的确证即视为高度盖然性。但生活经验论的反对者认为，“生活经验”本身并未得到有效定义，且不清楚应当根据一般法官抑或理想法官的生活经验进行案件审理，并不具有普遍适用的可能性。

    因此，黑德根（Herdegen）对生活经验论进行了论证，明确了生活经验论背后的推理逻辑。判决由法官作出并负责，因此不可能放弃法官个人确信，但是人类的认知很大程度取决于个人经历、认知局限、偏见及欲望，因此个人确信并非充分的判定标准。故此，人类无法完全获得案件真相，司法认定仅是一种盖然性判决。但是此种盖然性判决必须达到极高程度，却又不可能达到绝对确定。总而言之，应然层面能够达到的盖然性程度取决于人类理性推理的水平，而理性推理必须符合经验与智识要求并考量生活经验与公认价值原则。

    3.主客观自由心证理论之确立（Objektive-subjektive Beweiswürdigungstheorie）

    直到20世纪50年代，无论是“高度盖然性”抑或“内心确信”均不能在联邦最高法院的判决中占据通说地位，学界亦未深入探讨证据评判方法与标准问题。但当联邦最高法院日渐走向主观主义，放宽法官的自由裁判权并放弃了对法官心证塑造之限制时，学界却出现了反对的声音，认为联邦最高法院打开了非理性而无法控制的潘多拉之门。

    以彼特斯（Peters）为代表的学者认为，基于事实评估的客观因素才应当是定罪量刑的决定性因素，在此基础上可以建立基于内心确证的主观因素；并且，法官基于自由心证之判决应可由其他法官理解和推导。心证的塑造不仅与高度盖然性紧密相连，更与案件真实息息相关，法官的自由心证不仅涉及个人内心确证的实现，亦涉及到盖然性之确定。并且，与法国相反，德国的判例和文献从未确认法官内心确信能够免除法官对案件理性审查之义务，故此，兼具主客观的自由心证原则并不存在理论障碍。彼特斯最大的贡献在于，他并未如此前的判例与学说般仅对“理性”进行概括性阐述，而是提出了具体的标准。在方法论层面，他提出法官必须首先审查单一证据的效力范围与可靠性，然后再经由逐步评估全面审查证据，形成证据链；在内容层面，法官在心证塑造过程中应当基于专业知识与经验，采取统一标准进行证据评估；在后果层面，为了防止自由专断，法官应当对自由心证之裁判负责。

    彼特斯致力于刑事程序的理性化，他的核心观点是个人内心确证必须基于合理的基础，受到学界的普遍认可。此后，此种客观化的自由心证理论得到联邦最高法院的广泛认同，以客观理性与事实基础作为个人内心确证之前提的主客观自由心证理论获得通说地位：“司法定罪所需要的法官个人内心确证以客观事实基础为前提，必须基于理性论证得出已确定的事实与客观现实高概率相符合之结论。”具言之，法官定罪量刑的前提是获得内心确信，此种内心确信并非主观臆断或恣意评判，而是经过客观事实认定与理性论证过程，认为已查明的案件情况与客观发生的犯罪事实符合具有高度盖然性。

    从以上对德国重要判例与学者观点之梳理可见，判例观点一直在自由评估证据与受约束评估证据之间摇摆。旧的帝国法院更为关注客观性与高度盖然性，联邦最高法院早期的判决以主观主义为基础，更倾向于追求主观个人确证；从20世纪80年代以来，基于合理性和主观确证性的客观化趋势越来越明显；但从20世纪90年代以来，又开始强调法官自身认知在判决确定中的重要地位。事实上，完全基于法官自由心证而不考量客观基础的绝对主观主义已无人支持；而对被告人罪责之完全确信亦是不可达到，纯粹的客观主义仅存于乌托邦之中。故此，现代德国的自由心证理论始终以主客观论为基本立场，形成一种动态博弈的平衡：当一段时间客观主义占上风时，判例便开始强调主观主义的功能，使得整体理论趋势归于平衡。

    四、自由心证原则的德国当代意涵

    从萨维尼确立德国自由心证原则到帝国法院提出内心确信论与高度盖然性论，从联邦最高法院不断探索到自由心证主客观理论的最终证立，德国自由心证原则在理性主义的光芒与主观主义的摇篮中成长，继而探索心证形成之过程并将其规范化。当代自由心证原则由法官内心确信（Richterliche überzeugung ）与证据自由评判（ Freie Beweiswürdigung ）两大核心要素构成，同时亦涵括实质性庭审、判决理由的书面阐述与自由心证之限制三大要旨。其中，法官内心确信、证据自由评判归于自由心证原则的积极实质要件；自由心证原则的限制为消极实质要件；实质性庭审、裁判说理与心证公开则属于自由心证原则的程序保障。

    （一）法官内心确信

    在证据使用禁止的基础上，具有证据能力之证据乃为法官自由心证之对象，而自由心证的终点需要抵达“法官内心确信”。那么，到底何为内心确信？经由前述判决的梳理与诠释，现代意义上的“法官内心确信”包含个人内心确证、客观事实基础、高度盖然性与高度个人化的判决四个维度的要件，并且强调法官心证塑造过程之公开。

    首先，法官内心确信毋庸置疑具有主观性，它是法官的个体化主观确证。法官自由判断证据的依据是经验、理性与良知，并不可避免地受到法官主观感受与情感因素的影响，最终达到内心确信的程度。与此同时，由于刑事案件个案的偶发性与特殊性，既不存在亦不需要绝对的或概率的确定性，法官根据整体证据情况确定特定事实为真即可，因此对于法官内心确信不应当设立过高的、无法满足的要求。

    其次，法官的主观确信必须以客观事实为基础，并非天马行空之恣意。内心确信建立在对犯罪主客观情况的全面审查与理性判断之上。虽然并不存在系统的刑事证明理论来确认证明力，但法官认定案件事实必须基于合法收集、具有证据能力、符合法定证据种类之证据。法官有义务全面收集证据并进行审查，最后基于专业知识、逻辑与经验法则获得高度盖然性的判决结论，并将此种心证过程公开。因此，法官内心确信并非打开了主观专断之大门，而是构建起了刑事诉讼规范与主观沟通的桥梁。

    再次，内心确信并非某种必然性结论，亦可给予符合思维规律或生活经验的司法权衡，以高度盖然性结论的形式出现。必须承认，心证与犯罪事实无法完全印合，要求绝对与犯罪事实一致的心证结论并不现实。故此，司法裁判只能退而求其次，将高度盖然性视为真实，将法官对此种高度盖然性存在的认知视为对真实之确信。原则上，当法官已尽其所能评估现有证据后，认为犯罪事实存在具有高度盖然性则可判决有罪。若在内心确信过程中，对被告人的犯罪或罪责有所怀疑，则缺乏定罪所需的内心确信。

    最后，自由心证原则要求法官作出高度个人化之判决，具有独立性与不可替代性。一方面，法官不可采用他人未经审验的观点或意见，比如排除证人的意见性证言；另一方面，原则上法官亦不受其他无罪释放或生效判决中事实认定之约束。判决之确立需要法官的内心确信，但是立法不得规定，在何种条件下法官才能达到此种内心确信。被告人是否有罪、有何种罪，是法官需要单独完成的任务，不受法定证据规则之约束。

    （二）证据自由评判

    自由心证原则的另一核心要素为“证据自由评判”。通常而言，法官不受成文法所确立的证明力规则约束，根据每种证据的个案价值，基于自身专业知识、审判经验、生活逻辑与常识、良心与正义感评判全案证据。法官不仅自由认定案件事实确证所需的条件、证据的证明力大小，而且自由确定多种证据的评判顺序、证据之间的相互关联。间接证据的评判亦应符合此基本原则。在基本原则之下，司法判例给证据自由评判设定了外部框架与评判标准。

    首先，证据自由评判建立在理性客观基础之上。这里的客观基础，一方面指证据材料的客观性，即证据材料必须是客观、详尽、完整且不存在矛盾；另一方面亦包含法官证据评判的理性基础，即基于专业知识与逻辑基础进行案件事实的论理与论证。根据统计学分析发展而来的与“证据链/间接证据链”相对应的“证据闭环/间接证据闭环”理论，为证据评判的可控性提供了可能。

    其次，证据自由评判要求全面审查合法证据。证据审查以法官在个案中审查单一证据展开，在确定单一证据并未因程序违法或基本权侵犯而禁止使用后，根据单一证据的性质、与案件的关联，确立证据在案件中的功能。在此基础上，法官综合评估全案证据，获得对案件事实之整体印象。

    最后，证据自由评判必须详尽完整地评估全案证据。法官有义务详尽评估每个证据事实，考虑所有可能影响判决的细节，并在确证单个证据证明力大小的基础上，再基于一般逻辑法则、经验法则与专业知识综合评估全案证据。此种评估并非单个证据的孤立评价，而是对全案证据与案件事实的联系进行整体性评判，确立其中的逻辑关系与因果关系。因此，缺少整体评判的单一证据之孤立评判存在缺陷；而在法官竭其所能详尽评估全案证据后，仍无法对案件事实与罪责问题达成内心确信的情况下，则基于无罪推定原则作出最终判决。

    （三）自由心证原则的限制

    德国在长期的司法实践中亦通过判例设立了自由心证之边界，以防止司法专横与法官主观擅断。虽然判决是基于法官自身确信而获得的主观确定性，但它必须是基于合法取得之证据、客观可靠之事实基础和符合逻辑之结论。

    首先，自由心证原则受到证据禁止制度的限制。自由心证原则不仅是证据证明力评判原则，亦是法官审理案件之综合原则。从证据能力与证明力评判角度，证据使用禁止的立法规制了证据资格问题，法官仅能对具有证据能力之证据展开证明力评判；而从刑事审判程序角度，证据使用禁止的确认实则无法跳脱自由心证，在绝对证据使用禁止的情形下，根据立法即可否定证据之证据能力，但在更多裁量证据使用禁止的情形下亦需要法官之自由裁量。

    其次，自由心证原则受到逻辑法则、经验法则、证据综合评判规则及联邦最高法院判例等的引导与制约。在证据评判中最为重要的逻辑法则为司法三段论，在此涵摄过程中事实与规范交融，事实涵摄于法律，将具体的案件事实置于法律规范的构成要件之下，并据此判决。经验法则系基于一般生活经验、科学知识，以经验归纳或逻辑抽象等方式而获得的关于事实因果关系或属性状态的概括性结论与规则。证据综合评判规则是指在刑事证据审查中应当综合、详尽评估已查证属实之证据，证据需能够形成证据闭环或证据链。与此同时，联邦法院的判例赋予了证据调查与事实认定的具体规则，无论是嫌疑人/被告人的供述、辩解与沉默的审查，证人资格之确立，传闻证言、矛盾证言、利益相关证言、同案犯证言的审查，书证与勘验的审查，证据链证据环的形成、详尽评估与综合审查，直至存疑有利于被告人原则的适用，均存在着判例体系，因此证据评估受到《德国刑事诉讼法典》第337条法律事实审查的规范，经由此将抽象的法官自由心证过程规范化与具体化，最终达到对自由心证展开实质性约束之目的。

    再次，在严格证明程序中，法官自由心证受到证据法定种类、法定证明程序的限制。具体而言，对于涉及被告人定罪量刑事实之认定必须遵循严格证明程序，采用法定种类的证据、严格遵循法定取证程序；可能的程序障碍亦会限制法官的自由心证，例如诉讼时效、有瑕疵的起诉、欠缺行为能力等。

    最后，自由心证原则亦受到少量积极法定规则的限制，例如，在刑事程序中受到《德国刑法典》第190条侮辱罪中真相证明规则和《德国刑事诉讼法典》第274条庭审记录证明力条款的限制。

    （四）自由心证原则的程序保障

    1.实质性庭审

    实质性庭审是自由心证原则践行的制度保障。法官应以实质性庭审中获取的信息与证据材料展开内心确信之塑造，在庭审中展开证据调查，得出不利于或有利于被告人的证据结论，并展开详尽评判。此种意义上的自由心证原则与直接言词原则相辅相成，即法官对案件事实之认定原则上均源自于庭审中呈现的证据材料，并对证据材料的提出均以言词陈述的方式进行，证据调查亦以口头方式展开。因此，用于案件判决的证据必须呈现于庭审中，并经过法庭质证程序认定。

    一方面，法官在实质性庭审中应当遵循证据的用尽原则与证据绝对使用禁止原则。具言之，法官在庭审中应当充分利用审判程序中所有的证据材料，与此同时，判决的作出不可建立在法律规定应当禁止获取的证据上，包括《德国刑事诉讼法典》第136a条规定的禁止强迫自证其罪与第100d条规定的禁止侵犯公民绝对隐私权条款。另一方面，实质性庭审的内容原则上包括法官在审判中及通过审判知悉的一切信息，例如被告人供述、证人证言、鉴定人的询问、文件的宣读等。其中，涉及对被告人定罪量刑的证据则必须采用严格证明程序审理。但是，联邦最高法院与联邦宪法法院亦通过判例认定了一系列不能成为庭审对象的内容，主要包括：案卷内容，例如被告人在此前庭审中的陈述内容；法官在庭审外了解到的与案件相关的业务知识。

    2.裁判说理与心证公开

    防止法官恣意裁判、确保自由心证正当性的一个重要衡量标准是心证的可重复性。在应然层面，法官自由心证虽然是主观的内心认知活动，却是基于客观证据材料之理性判定。案件的实质真实虽无法百分百确证抵达，但可经由证据材料与法官心证尽可能接近，相似的理性人审查亦应可获得相同或相似的心证结论。

    故此，除前述心证经验与理性、客观证据材料的指引外，自由心证原则的重要程序性保证即为裁判说理与心证公开，法官需要在判决中释明自由心证所凭据之事实和内心确信确立之理由，并阐明证据评估的事实基础。判决理由的书面阐明，一方面能够让普通公民获悉法官的心证塑造过程与判决理由，防止裁判之恣意；另一方面亦为上诉法院对判决内容进行审查提供基础，为救济错误的证据评判提供现实可能。具体而言，法官在判决中不能仅简单叙明心证结果，而应当清晰阐明案件犯罪事实与法律基础、所收集证据与逻辑推理裁判过程及因果关系；法官亦不能仅在判决中简单列举待证事实与证据，而应当对证据的合法性展开逐一审查，证据评判必须包括对单个证据证明力之确定并结合案件事实之权衡；此外，法官的释理应当结合案件事实与逻辑关联，对全案证据进行系统性综合评估。

    判决的书面理由必须以谨慎和有条理的方式阐明，其标准在于上诉法院能够准确理解裁判过程和结论，有效审查裁判内容，判断是否存在证据裁判错误。而所谓无救济则无权利，在存在证据裁判错误时，即裁判存在矛盾、缺陷或不明确，或违反逻辑法则、经验法则，或对法官内心确信建立和定罪提出过高要求时，可以通过上告（Revision）实现法律救济。

    五、自由心证原则的中国叙事

    经由新中国初始的批判和20世纪末的反思，到21世纪刑事证据理论研究之勃兴、新法定证据主义的提出，到印证证明理论对自由心证原则的承认，直至经验法则、逻辑法则等研究的展开，自由心证原则在我国经历了一段曲折前进的历程。在此之中，自由心证原则的中国叙事图景不仅展现于自由心证之本，亦显露在新法定证据主义、印证证明等本土理论之中，其中掺杂不少混沌与误读。故此，有必要在澄清本土理论对自由心证误读的基础上，明晰中国自由心证的未来之路。

    （一）现实主义的悲观：与自由心证悖离的新法定证据主义

    我国立法者出于对法官自由裁量权之不信任、对客观裁判的心之向往、对证据真实性之优先考量，倾向于限制法官自由裁量权。故此，刑事诉讼立法中存在对单个证据证明力的普遍限制与对案件事实认定的一般规则。有学者将此种现象称之为新法定证据主义，并总结了典型特征：首先，立法区分证据证明力大小与强弱，并确立一系列证明力规则；其次，立法确认证据相互印证规则；再次，基于客观主义法定化证据裁判的证明标准；最后，法定化间接证据的证明体系。毋庸置疑，新法定证据主义旗帜鲜明地描述了我国刑事证据立法的部分特征，但却建立在对自由心证原则“传统但不系统”的理解之上。

    首先，新法定证据主义论所描述的部分特征，并非对自由心证原则之否定，而是对纯粹主观主义自由心证原则之修正。例如，严格证明程序与刑事证据禁止制度均与当代自由心证原则共存，限制法官自由心证之边界。严格证明程序所包含的证据法定与程序法定属于传统法定证据主义之基本特征。而体系庞杂、覆盖全面的证据禁止制度亦是传统法定证据主义之体现，属于对自由心证原则之限制。并且，相比于我国非法证据排除规则狭窄的适用范围，德国刑事证据禁止所涉范围广泛，立法规定与司法裁判共存，实则涵盖我国部分“证明力规则”，其“法定证据”之特征甚至强于我国，但这并不构成对德国自由心证原则的否定。

    其次，证据相互印证规则并非（新）法定证据主义之特征，证据相互印证是一种证明方法，是在逻辑层面对证据与事实间因果关系建立之确证。证据印证亦是德国自由心证原则下的一项非常重要的证据审查规则。因此，以我国刑事证据审查具有证据相互印证特征而去论证新法定证据主义，存在论据属性错误之嫌。事实上，在刑事证明中通过证据印证推理案件事实并无过错，需要反思的只是过度强调证据印证与证据印证的僵化适用问题。

    再次，我国刑事诉讼法确立的证明标准“证据确实充分”并不能代表证明标准的法定化。立法仅是从主观与客观角度描述证明标准，并未确立精确刻度，实然层面导致了刑事证明标准的模糊与恣意。与此形成鲜明对比的是，《德国刑事诉讼法典》并未明确高度盖然性与排除合理怀疑之证明标准，但无论是判例、评注均认可主客观主义之证明标准，并明晰了证明标准之维度。此种精细化教义学解释与判例指引比我国的模糊化立法更具规范属性。

    又次，间接证据证明的体系化亦非法定证据主义独有。虽然《德国刑事诉讼法典》并未明确列举间接证据审查之标准，但联邦最高法院之判例与评注均就自由心证、间接证据的综合评判作出详述，并形成立法之外的判例体系与规则。只是由于我国学界对德国法的比较研究与学术引介相对滞后，尚未对德国刑事诉讼法评注与判例给予足够的关注，并不了解德国刑事司法中基于判例产生、具有影响力的证据规则。

    最后，中德刑事证据立法的差异并非全然源于传统法定证据与自由心证之立场对立与理念鸿沟，相反，其一定程度上是两国立法例与司法差异所致。中德虽均以法典为基础，然而，德国联邦最高法院之判例具有极高约束力，但并不存在类似于我国的司法解释；我国法院的判决并不具有此种高约束力，却存在独特的司法解释体系。很难断定究竟是德国最高法院的“禁止性”判例，还是我国司法解释中“原则性”规则更具约束力。故此，当视线经由简明扼要的法规延申至背后的判例与评注，便会发现自由心证原则并非想象般自由，仍保留了传统法定证据主义合理的成分，并在判例与教义学理论之下展开了一场暗流涌动的客观化变革。

    综上所述，当代自由心证原则早已不是那种基于康德式的、纯粹依靠人类理性与经验的自由心证原则，它具有浓厚的主客观统一色彩，虽然在路线上摒弃了传统法定证据主义，但实质上保留了其中合理的部分。不仅如此，在当今的自由心证之理论研究中，一个明显的趋势即是通过判例与教义学理论将自由心证原则客观化。概言之，刑事证据之审查判断无法避免由法官主导、审查与评判，即自由心证原则的适用具有客观必然性与必要性；并且，当代自由心证原则并非康德主义的人类理性与经验，而是一种基于客观证据、证明规则而抵达的法官主观确信，它亦吸收了传统法定证据主义中的合理部分；最后，我国新法定证据主义实则并非与当代自由心证原则完全对立。与其用“新法定证据主义”来定义我国刑事证据立法，不如更深层次地探究自由心证主义，将特定证据规则作为自由心证主义之限制，限缩自由心证主义之边界，并将视角转向过去、现在、将来均无法避免的法官心证内容、过程与方法之中，似乎更具有现实意义与价值。

    事实上，新法定证据主义之提出是基于一种现实主义的悲观，甚至带有几分戏谑，我国法定证据理念的存在是一个“问题”，而不应当成为一种“定性”或“主义”，更不可能成为证据裁判一劳永逸的公式，亦容易导致我国立法与司法受之束缚，更难以革新前行。与此同时，新法定证据主义论者亦认为，自由心证是一项美妙的证据评判原则，是对法定证据主义的合理扬弃，但自由心证之确立需要现实制度保障。故此，在刑事司法不断前进、以审判为中心的诉讼体制改革不断深入的今日，不应当再以新法定证据主义去界定、限制、束缚我国刑事证据理论之发展，而应当看到日渐明亮而清晰的自由心证之曙光。

    （二）理想主义的偏差：作为“自由心证亚类型”的印证证明

    新法定证据主义直面法定证据主义与自由心证主义之争，而在另外一个维度——刑事证明领域，作为“自由心证亚类型”的印证证明亦声势浩大地占领我国刑事证据理论研究之阵地。印证证明论者认为，我国刑事司法以印证证明的方式证明案件事实，证据的证明力判断及证据的综合判断主要依靠法官根据个案情况作出。印证证明兼具证明力审查不受法定限制、允许法官在个案中基于具体情况审查证明力等基本属性，因此“印证证明模式”属于自由心证的一种亚类型。此后，印证证明论者又提出修正理论，例如印证证明是以“印证”为核心，但同时包含“心证”“追证”“验证”共同作用的刑事证明模式，并引发了印证证明理论的研究、批判与反思热潮，带来了“原子主义”与“整体主义”之争等。

    跳出争论、回归原点，便会发现争议与迷思实则源自印证证明理论自身对自由心证原则的体系定位偏差与范畴理解错位。早期的印证证明理论将自由心证与印证证明作为同一维度的概念进行参照性研究，并认为印证证明模式属于自由心证的一种亚类型；而修正后的印证证明理论甚至将自由心证纳入印证证明体系，将自由心证变为印证证明的下位概念，尝试构建体系化的印证证明理论，而此种尝试遭致学界的多方批判与反思。相比于新法定证据主义将印证证明作为司法僵化的象征而系统批判，印证证明理论将印证证明划归于自由心证无疑更具说服力。但是，将两者定性为同一位阶却实为不妥。

    首先，自由心证原则与法定证据主义相对立，并承载着相对明确的基本内涵：在审判中，法官通过审查证据获得对案件的认知，并达到内心确信进行裁判。而此种心证既包括主客观统一的内心确信之证明标准，亦包含理性、经验、逻辑、智识综合而成的一系列思维过程，例如归纳与演绎、推论、经验法则与逻辑法则、印证分析与综合分析等。故此，印证证明既非一种“自由心证”的亚类型，亦不涵盖证明标准，而属于法官自由心证的一种证明方法。即便再退一步，按照印证证明理论所述的自由心证指那种康德式的、基于理性主义、主观主义的自由心证概念，那它更不属于“典型的、通行的自由心证原则”，而属于在当代德国几乎没有学者支持的、萌芽期的自由心证原则。此种体系定位错误导致了印证证明的概念泛化与研究迷思，带来了对印证证明诸如无视经验法则、逻辑法则、全案证据综合分析等的批判，或引出了“印证为主，心证为辅” 的改良路径。如此种种缺陷与随之而来的批判实质上并不源于印证证明本身，而存在于被赋予过多内涵与期待的印证证明之外。印证证明理论的过度膨胀反而使自己进退维谷：毕竟要求几种位于同一层次、基于不同逻辑的心证方法又彼此融合，是一种逻辑悖论。

    其次，印证证明理论混淆了印证证明与证明标准的作用场域。修正后的印证证明理论认为“心证既为证明方法，亦为证据标准，其特点是主观的内省性——事实判断者基于自身经验进行证据感知和思维，从而建立内心确信”。不可否认，无论是萌芽期的主观主义自由心证原则抑或现代的主客观统一的自由心证原则，均是涵括证明标准的内容的。但是，这并不意味着印证证明亦涵括证明标准。无论在大陆法系刑事诉讼语境还是我国刑事诉讼语境，印证证明与证明标准均分属于两个范畴：在德国，印证证明属于实现法官自由心证的一种证明方法，刑事证明标准则包括客观高度盖然性与主观排除合理怀疑双重意涵；在我国，虽然刑事证明标准仍有待明确和具体化，但不会跳脱犯罪事实清楚、证据确实充分、排除合理怀疑等基本范畴。故而，印证证明理论出于“修正”之目的对“印证证明”的广义解读与扩张适用，试图将证明标准纳入自身场域的尝试，实质上导致了证明方法与证明标准概念之混乱，继而进一步模糊了印证证明理论的基本定位与核心要旨。

    再次，“以证据裁判为主，自由心证为辅”的证明模式亦存在一个基本范畴理解与定位的偏差：证据裁判原则与自由心证原则亦并非同一范畴内对等的概念。证据裁判是自由心证的前提和基础，两者并非是谁“主”谁“次”的关系，证据裁判对应的应属神明裁判，而自由心证所对应的却是传统法定证据主义。自由心证并非法官的凭空恣意裁判，自由心证必须以收集到的证据材料及可抵达的客观事实为基础。换言之，此观点实质上与前述对“自由心证（主义）”的理解存在相同的概念与范畴时空错位的问题，即在当代的讨论中仍在使用一百多年前的自由心证的概念与范畴。

    此外，印证证明理论中对印证证明与直接言词原则的关系亦存在理解偏差。不可否认，自由心证原则实施的核心保障即为直接言词原则，直接言词原则与自由心证原则相伴相生，但是，并不代表着自由心证原则与直接言词原则下所获取之刑事证据证明案件事实不需要印证证明。由于刑事案件的特殊性，能够获取的直接证据极为有限，在大概率仅存在间接证据的情况下，无论是直接、言词审理还是间接、书面审理，证据间的相互印证对案件主要事实的判定均至关重要。庭审中审查的证据证明力的确证亦需要其他证据进行印证和补强，各证据之间亦需要形成证据链或证据环。在此维度上，自由心证原则与我国的印证证明具有契合之处。

    事实上，在刑事诉讼领域，包括德国在内的大陆法系国家并没有孤证定案的传统，印证证明是大陆法系国家刑事审判的一种基本的，但并非唯一的证明方法或证据审查方法。自由心证原则的两大核心内容为自由裁判与内心确信，而基于印证证明的核心内容与基本属性，它属于自由裁判中的一种证明方法，一如经验法则与逻辑法则、证实与证伪、系统分析等。

    六、回归与展望：中国自由心证原则的未来之路

    在证据裁判原则下，传统法定证据主义与传统自由心证主义均属具有高度概括性的证据裁判类型，代表了证据与事实认定模式的两端。但是，两种证据审查模式并非泾渭分明、水火不容，既不存在法官毫无法律约束之绝对自由，亦不存在不考虑司法裁判特殊性、对证明力及事实认定标准的完全强制性规定。在传统法定证据主义与传统自由心证主义处于两端的光谱上，现代法治国家多依据司法传统与法律文化寻找合适本国的位置。

    自由心证原则早已摒弃法国大革命时期的纯粹主观主义和康德式的纯粹理性主义，转而寻求一种主客观之平衡。德国自由心证原则的客观化趋势日渐明显，刑事程序中对自由心证原则的释明、限制与制约亦呈现增多的趋势。首先，在证据审查维度，自由心证必须在客观证据的基础之上、在证据裁判原则的框架之内展开；其次，在程序保障维度，以审判为中心的诉讼结构是自由心证原则运行的基础；再次，在司法制度维度，审判独立与法官职业化是自由心证原则的基本保障。故此，当代自由心证原则与我国法官的证据审查模式并无实质差异。

    自由心证的德国迷雾已然散去，而中国面纱依旧若隐若现。事实上，自由心证原则在我国不是“是否存在的问题”，而是“是否承认它存在”的问题。一方面，法官裁判无法避免自由心证，亦不自觉地心证裁判；另一方面，立法者拒不承认自由心证的存在，导致立法上缺乏必要规范对“心证”予以保障和限制。这样一种悖论导致我国法官自由心证保障制度与限制规则之阙如。具言之，刑事司法中法官裁量必要性与立法者试图否认此种裁量必要性之间存在悖论与冲突。我国法官受到不合理证据规则的重重束缚而缺乏实质性事实认定权，但个案偶发性与证据多样性又决定了审判中法官自由裁量居于核心地位，证据裁判与案件认定必然要借助其专业知识、生活经验与逻辑推理。与此同时，我国刑事立法基于限制法官自由裁判的基本立场，对法官自由心证必然存在的客观司法现象视而不见，在客观主义“证据确实充分”的外衣之下，实际上隐藏着法官心证过度自由之风险。故此，在裁判中普遍存在套用法条程式化办案、对事实认定说理不充分、不公开阐述心证过程、不详细论证证据、法律与事实的逻辑关系与推理细节等问题，最终难以以理服人，司法公正性与权威性受到不断的质疑与挑战。

    否认或回避自由心证在我国刑事司法的现实存在并非有效解决现有刑事证据审查与案件裁判难题的路径，掩耳盗铃只会导致在客观主义外衣之下纵容更多司法潜规则与恣意裁判。在承认自由心证之现实存在与自由心证原则确立之必要性的前提下，深入研究并领悟自由心证原则，结合我国刑事立法与司法语境，建构符合我国法治文化与立法背景的自由心证原则，才是真正促进我国刑事证据理论发展、推动庭审实质化、保障刑事裁判的必由之路。然而，我国自由心证原则之构建不仅需要传统法定证据理念与自由心证理念之融合，亦无法回避证明标准客观主义与主观主义之角力，更存在刑事立法回应司法现实关切之考验。故此，在厘清当代自由心证原则内涵、廓清我国新法定证据主义与印证证明理论之后，思路便回归到核心问题：基于我国刑事司法传统与立法构架，我国自由心证原则之轮廓何如？

    我国自由心证原则在宏观维度应当包括基本内涵、原则下的具体规则、法规限制及程序性保障；微观维度则涉及法官自由心证的过程及影响因素、心证确信之标准即证明标准。此外，亦需要思考：自由心证原则如何平衡自由与约束的关系，如何防止法官自由裁量权之滥用，又如何适应和面对现代科技的发展及随之带来的挑战？尤其是在我国立法已经确立了众多证明力规则的现实面前，如何平衡法官自由心证与证明力规则的关系？囿于篇幅，本文无法细致描绘我国自由心证原则之全然面貌，而仅能基于自由心证原则之本源，勾勒其在我国刑事诉讼理论体系中的基本轮廓与应然地位。

    首先，自由心证原则为刑事证据审查与司法裁判之基本原则。自由心证原则并不能归于纯粹的主观主义，而是主客观之融合，是基于客观合法证据的主观裁判。在我国证据理论研究中，无论是印证证明理论的研究，最佳解释推理、叙事或拼图综合证明模式的提出、原子主义与整体主义之争，还是对经验法则、逻辑法则之探讨，均属于自由心证研究的基本范畴。因此，可以说自由心证理念早已为我国学界所认可，静水深流的自由心证研究早已展开，然而对自由心证原则内涵之研究仍有必要围绕证据自由评判与法官内心确信两大核心要素深入展开：一方面，证据自由评判之客观基础为合法证据。故此，对取证程序合法之保障、对违法证据之有效排除实则是自由评判之前提，证据能力之评判为自由心证的前置性审查。在此基础上，法官应当主动行使证据调查权，对获取的合法证据进行单独审查与全案审查。另一方面，法官内心确信是证据自由评判旨在抵达的终点，是司法裁判的前提。“法官内心确信”既不是一种主观黑洞，亦非精确的刑事证明标准。它包含个人内心确证、客观事实基础、高度盖然性与高度个人化的判决四个维度的要件，并且强调法官心证的塑造过程，此四个要件保证法官内心确信的可信赖、可追溯、可救济。

    其次，自由心证原则本身并非刑事证明标准，但却蕴含着刑事证明标准，即法官内心确信。自由心证要求法官基于证据之自由裁判达到内心确信的程度，而何为“内心确信”成为界定刑事证明标准之关键。自由心证主客观主义之争的核心议题即关涉证明标准之明晰。诚然，内心确信归于主观证明标准，但却融合了基于客观主义立场的高度盖然性与基于主观主义立场的排除合理怀疑。我国学界对刑事证明标准、证据确实充分与排除合理怀疑的研究可与之接轨，而其中所暗含的客观主义与主观主义之争或许可从“内心确信”的明晰之路中汲取灵感。事实上，若拨茧抽丝般地真正厘清了自由心证原则之内涵，即能明确刑事证明之标准。最后，在综合评判全案证据仍无法确保内心确信之时，法官则依据存疑有利于被告人之基本原则作出判决。

    再次，自由心证原则并非无边界，而应通过立法与司法予以限制。此种心证之限制围绕实体定罪量刑程序（抑或称之“严格证明程序”）展开，以限制证据之证据能力、规范法定程序为核心，亦不全然排斥少量证明力规则。回归我国司法语境，法官自由心证似乎从未有过主观主义阶段，而是在与客观主义的博弈中才渐渐获得些许话语权，故此，相比于德国自由心证原则，我国自由心证原则存在更广维度的客观限制。而我国刑事证据研究对传统法定证据主义与自由心证原则的思维禁锢亦始于斯，自由心证原则建构难点亦显于斯。如前所述，相比于德国重视证据能力之审查而给予证明力审查之自由，我国明显是轻证据能力之审查而重证明力之规制，无论是取证程序规范还是证据排除规范均存在不少缺漏。因此，在德国大量程序违法之证据因不具有证据能力而被禁止进入证明力审查阶段，而在我国仅排除极少数非法获取之证据，从而导致大量程序违法证据进入证明力审查阶段。而司法解释所确立的众多证明力规则，其合法性、合理性与必要性暂且不论，实质多归属于经验法则之法定化，不具强制性规范属性。故此，我国自由心证原则的建构必须建立在全面审视、反思与重塑我国证据能力、证明力理论与规范的基础之上。在立法层面，有必要基于公民基本权保护理念，适当扩充证据能力限制条款，扩大非法证据排除范围；与此同时，全面梳理、缩减司法解释中的证明力规则，剔除不具强制性之规则，合并具有类似功能之规则。在司法层面，通过教义学理论与典型判例设立法官自由裁判与证明力规则之边界。

    最后，职业法官的专业素养与正义良知固然是自由心证原则历久弥新、长久发展的基础，但程序保障才是其行稳致远的核心。实质性庭审的落实、判决说理与心证公开、法律救济途径的保障是自由心证原则从应然走向实然之需，以维护自由心证原则之正当性，并防止法官心证之恣意，以平衡自由心证原则的主观性与自由特性所带来的任意性与不确定性。

    第一，无证据则无心证，无庭审则无裁判，实质性庭审是刑事诉讼的应然之意。“司法的根本特性是判断性，司法判断的前提是亲历性。”一方面，法院审判阶段应当成为刑事诉讼的中心，被告人的刑事责任应当在审判阶段而非在侦查、审查起诉或其他阶段认定；另一方面，法院庭审活动决定被告人的罪与罚的问题，即“审判案件应当以庭审为中心，事实证据调查在法庭，定罪量刑辩护在法庭，裁判结果形成于法庭”。故此，实质性庭审应以直接言词原则之落实、证人出庭义务之强化、被告人质证权之保障与法官证据调查权之赋予为核心，强调被告人、证人、鉴定人等亲自出庭陈述、接受质证与调查，并坚守主审法官与裁判法官合一，将主审法官庭审作为心证的主要来源渠道，继而从根本上确保法官能够获得足够的、真实的证据以支撑心证的形成，为以客观证据为基础构建自由心证提供可能。

    第二，判决说理与心证公开是自由心证原则的防护墙，对心证的公正性起到实质性保障作用。首先，法官有义务分析证据之证明力大小及有无与案件待证事实的关联，阐明证据与事实认定、法律适用间的逻辑关系与推理过程，并释明得出有罪或无罪判决的理由。在此过程之中，法官必须重新考量心证过程、斟酌判决的逻辑推导过程，进一步保证判决的合理性与合法性。其次，自由心证的书面化能够监督、迫使法官在证据裁判与案件审理中更为谨慎、缜密，约束心证、防止恣意裁判。再次，判决理由的书面阐明能够保证被告人、上级法官及公众能够了解自由心证形成的过程，认可判决的合情合理，并藉此保障判决心证的可重复性，保证心证的正当化。又次，判决理由的书面阐述亦为被告人获得法律救济提供前提性基础，被告人有受保障的路径探知法官判决之理由，若其认为法官判决理由存在错误或不合法，则可有针对性地提起上诉。最后，上级法官亦能够通过书面说理的判决知晓下级法官审查证据与认定事实的思路与过程，亦为上诉审提供重要证据，从而对下级法官的心证起到间接的监督作用。

    第三，无救济之权利非权利，无后果之义务非义务，法律救济途径的保障从内源倒逼法官合理、合法自由心证，避免恣意裁判。自由心证原则的有效执行亦需要保障被告人之上诉权，即若被告人发现法官自由心证存在特定法律错误，可以基于违反自由心证原则提起上诉。此类法律错误应当至少包括：法官未阐明判决理由或判决理由存在矛盾；法官未全面评估全案证据或遗漏重要证据；法官违反经验法则或逻辑法则进行事实认定或法律适用。故此，相比于印证证明理论、经验法则与逻辑法则等研究，自由心证原则的程序保障实质更具法学视角与规范属性，理应获得更多理论研究之重视。

    七、结语

    在主观主义与客观主义之间，德国自由心证原则在教义学理论与司法判例的影响中日益客观化。而在我国传统客观化的证据审查规则之下，自由心证理念亦早已静水深流地影响着我国司法实践。无论是新法定证据主义抑或印证证明理论，均属于对本土化自由心证理论的探索与尝试。在反思与纠误之间，本土化自由心证原则的轮廓亦逐渐明晰：作为刑事裁判之基本原则，自由心证原则并非证明标准，却蕴含证明标准；心证自由而非恣意，受到证据能力与证明力规则之限制；实质性庭审、判决说理与公开、法律救济途径是自由心证原则由应然走向实然之基本程序保障。

    本文转自《比较法研究》2024年第6期

  • 杨联陞：传统中国对城市商人的统制

    本文主旨，在就传统中国政府对城市商人之统制（包括控制与利用），提出若干看法，以供讨论。所谓商人，系用广义，一切行商坐贾、铺户店号，乃至当铺钱业牙行，均在讨论之列。所谓城市，亦取广义，兼指城镇，不论大小。所谓传统中国，时限可长可短。在本文多指帝国时代末期，自清初至鸦片战争一段，但亦有时兼及前后。

    中国传统，远自二千余年以前，早已以农为本，视工商为末业，政府对四民之待遇，因有重轻。然就全帝国时代而言，亦不可一概而论。如《史记》、《汉书》所载，政府对商人之统制，包括贾人有市籍，不得为吏，不得名田，重其租税，乃至其车马服饰，亦受限制。此种政策，虽起于汉初（或更早），至武帝时，因财政关系，已有孔仅、桑弘羊等，由市井跃登朝列。其他限制，似亦渐成具文。此后在理论上，虽仍轻商，实则对于商人之控制与利用，力图兼顾。唐、宋以来，此种情形，更为显著，议论亦略有改变。读史者当就各时代分别观之，始能得其真象。如就清初至中叶一段论之，则对商人之控制，已不甚严，租税负担，亦非特重，政府且颇以恤商自许。利用则积前代之经验，特重“保”（如保商、保结、连环保）“包”（如包办、包额）诸术，颇有成效。

    在清代商人入仕，远较前代为易。在隋、唐与辽代，工商及其子弟，均不得应科举。但此限制至北宋已见宽弛。据《宋会要·选举》，庆历四年（1044年）定“诸科举人，每三人为一保，所保之事有七”，其七为“身是工商杂类及曾为僧道者”并不得取应。细玩“身是”与“曾为”字样，则不但工商子孙可以应举，即曾为工商而今已改儒业者，似亦可以应举。更早者为淳化三年（992年）所定，“如工商杂类人内有奇才异行卓然不群者，亦并解送”。虽属特例，已开商贾应举之门矣。

    金元时代，对商人应科举，似乎已无限制。明清更有所谓“商籍”，专为盐商子弟在本籍之外盐商营业之地报考生员，而且特为保留名额。据何炳棣教授之计数，盐商子弟，成进士者，明代近一百九十人，举人三百四十人。清代进士至乾隆之末，已达四百二十余人，举人八百二十余人，其中在18世纪，人数尤众。按明清商籍，盖仿元代河东之运学运籍。当异族入主之世，商人往往特受优待，亦可注意也。

    科举之外，尚有捐纳一途，为富商入仕之捷径。清代捐纳制度，近人已有专书详论。在清代主要自为财政关系，然如雍正上谕所言，捐纳进身，可救偏重科举之弊，则其中亦不无政治意味也。

    宋、元以降，商人入仕之途渐广，此与一般社会经济之发展，关联自极密切，在思想上，亦有反映。如宋元儒者，已不讳言治生，明末黄梨洲，已有工商皆本之论，清代沈垚（《落帆楼文集》）更谓“古者四民分，后世四民不分。古者士之子恒为士，后世商之子方能为士。此宋明以来变迁之大较也”。其言虽近于偏激，亦有相当根据。

    秦汉所谓市籍，至少延至唐代。中唐以后，政府对于市场之管制，大见松弛，对商人之特别注籍，似亦不及以前之注意。明代户籍，分军民匠灶四大类。商人似亦属于民户。清代《嘉庆会典》有“军民商灶”之别，然此所谓商，即上文商籍之商，专指盐商而言，不得误解为一般商人。惟以商人当行及纳税（如门摊、铺税等）之故，政府对于孰为商人，及各商资力之大小，亦当有相当了解。保甲调查，亦分住户铺户，此在19世纪之纪录特为显著，京师所在，固不待言，如《津门保甲图说》（1846年）所记天津各区人户，分类详细，数目似亦相当可信也。

    政府就商人收取关卡通过税及落地税等，几于无代无之。关卡之弊，记述议论，亦复多有。工商当行，在政府视为应尽之义务。然行户采买，名为给值，实多白取。所谓和买、坐办等，皆是此类，深为商民之患。就一般税役而论，明清虽有以货币代实物之趋势，实际负担，仍属不小。惟清代在未创设厘金之前，税额较之前代，似为稍轻。

    牙行中之官牙，领有牙帖（纳费），实只相当于唐代之市司，除介绍买卖外，并可评定物价，有时且可为商人之居停主人。在水路则有埠头，亦称船埠头，其作用与牙行同。牙行之作用，与同业商人自组之行，有时相辅，有时相竞，其关系殊为微妙。在政府用为统制之工具，则无甚异同。政府对物价与币值之控制，普通最重视米粮价格与银钱比价，对米粮与货币之流通，有时亦加管制。惟自宋元以后，亦不时有人论及过分统制之恶果，提倡自由流通，此亦经济发展之反映也。

    政府利用商人之一常法，为发商生息。此在若干情形之下，对商人可能有利。但商人须负责偿还本息，往往为难。至于盐商洋商等之捐输报效，名曰情愿，号为踊跃，实际则多出强迫，不过政府与商人分利之美名而已。

    一般言之，清政府对商人，尚属宽大。商人之苦于苛虐者，罢市、请愿，乃至短期暴动，虽有其例，大规模之变乱，则未有商人为领袖者。此中因素，虽甚复杂，与政府对都市商人统制之和缓，似不无关系也。

    一、导论

    这篇关于政府对城市商人之统制的文章并不是一篇研究论文，文中所提出的数点建议只是一个社会史学者所做的一般性的观察，希望或可作为进一步讨论的基础。文中“商人”一词是用的广义，包括各种商人与生意人，固定的与流动的，甚至牙人（经纪人），经营当铺、钱庄的人，以及投资于传统手工业的生意人。这样使用的理由是中国传统上把这些人都称做“商”。“铺户”一词，是登记职业用的，差不多包括所有从事各行生意的人。“店”这个字或指商店或指旅店。因此商人一词必须使用广义才能把一些有意味与相关的事实包括在内。“城市”一词也是用的广义，兼指城、镇与郊区，而不限于城墙以内的地区。事实上，通称为“镇”的市场中心，大抵是没有城墙的。商人只要是在城市做生意的都可称为城市商人，虽然他并不一定住在城里。“统制”这一词包括与商人的地位、活动以及税役等有关的规定与限制。

    本文的讨论集中于清初到鸦片战争（1644—1840年）这一段时期，换言之，即是传统中国开始受到西方势力的空前冲击以前的两个世纪。这段时期特别令人感觉兴趣的理由，其中之一是这段时期内，中国的统治者是几位相当开明而且非常能干的异族皇帝；这段时期中国正经验到社会与经济方面重要的变迁，即是中国大陆学者称为“资本主义萌芽”或初期成分者。[1]此外，中国在这段时期仍保留有许多传统的面貌。

    一般对传统中国只有初步了解的研究者，可能认为旧社会商人的地位是这样的：农人所从事的职业是“本业”，相对的，商人与工匠的职业被视为次等的、非基本性的“末业”。此外，商人多被视为奸狡、惟利是图，因而受到轻视。他们的投机、操纵物价、屯积货财，都被认为不但害及消费者（特别是无助的农民），也对整个经济有害。商人的这些活动有违于公正与安定的原则，因而各种规限与税役必须加在商人身上，对于他们的地位必须加以降抑。但是，像这种一般性的说法至多不过是粗略的说明罢了。

    这种一般性的说法所以流行的一个原因，是受到古代中国某些时期的史籍的影响。差不多三十年前，如果中国学生曾读过一点点中国的正史，很可能不是《史记》，便是《汉书》；前者的范围是从中国古代至西元前100年左右，后者则从西元前206年到西元23年。上述的说法大部分便取材自这两部史书中谈到食货与商人的篇章。[2]那时候大学里中国通史的课程仍然只着重于古代史方面。比如就制度史来说，教授们认为只要说明与讨论汉代的制度史就可以，因为后代差不多都是因袭汉代的模式，只有很少的修改与出入。

    当然，中国古代史与中国第一个官僚帝国确有许多值得研究之处。简单地说，在战国时代（西元前403—西元前221年），政治、社会与经济上巨大的动乱与变迁中，游士、游侠与行商坐贾这些人变得非常流动而活跃。他们成为各独立邦国以及后来帝国的政治资本。因此，他们可能是艾森斯塔教授（S.N.Eisenstadt）所称的“自由浮动资源”的最好的例子，对于他所谓“历史性官僚帝国”之成立，有过重要作用。[3]

    到西元前221年秦统一各国，这个中国史上第一个帝国要面对的问题是如何处理这些自由浮动分子。明显的办法是统制，包括操纵与利用——为了政府的利益，绝对不能让他们自由集附到另一个政治中心，或是自己形成一个有影响力的集团。秦朝只是短短的十几年（西元前221—西元前207年），未能完成这项工作。它的失败也许由于过分注重法家思想，过分独裁。汉朝从这里学到教训，成绩较好。当温驯的儒家学者（借用顾里雅教授H.G.Creel的定义：儒乃懦弱者也）成群地协助或加入汉朝的统治集团，中国官僚帝国的模式便开始形成了。

    汉代是否真正采用压制商人的政策是值得讨论的。支持这方面看法的人会说，商人得缴纳额外的重税，他们不准拥有土地，不准穿着丝绸，他们的子孙不得做官，他们的活动在政府有专卖权的一些基本货物上受到限制。事实上，上面这些说法，除了有关纳税那一项之外，大多数是不难修改的。一个富裕的商人可以很容易放弃他登录的商人身份，变成一个地主，而仍然做谷物、丝帛或其他生意。汉高祖命商人不得衣帛，这道命令恐怕当时并未认真执行过，以后更是完全被忽略了。雄心勃勃的汉武帝即位后，即打破政府不任用商人为官的规定，两个在盐铁买卖上非常成功的商人成为他的主要参谋。把盐铁收归国有的建议，就是他们提出来的。他们主管专卖事业之后，就引进更多的生意人担任政府官职以协助他们办事。桑弘羊，贾人之子，精明而有谋略，深得武帝信任，由侍中官升御史大夫（副相）。由此看来，中国第一个持久的帝制朝代——汉朝，对商人的态度就已经是模棱两可的，至少在一段相当时期内，政府是有意兼用一种对商人限制、征税而又加以利用的政策。

    在后来的朝代里，商人的命运也走着一条曲折的路途。为了解某一段时期商人的地位，一般历史背景的知识是需要的，因为只有与其他时期商人的地位相比较，才可能对某一时期的情形得到一个有意义的评价。

    二、政府对城市商人的统制

    如果回顾一下清代最初两百年间政府对城市商人的统制，很明显的是，这段时期我们见不到什么特别的障碍妨害商人改善他们的地位；政府对商业活动的控制是有限的，加于他们身上的课税与勒索，相对来说较轻（或至少不特别重），另外是，在统制的执行上，往往都离不开“保”与“包”这两个古老而特别重要的观念。我们可以先从最后一点谈起，以作为了解的背景。

    “保”与“包”这两个观念与“报”不可相混。关于“报”我已有另文谈及。这三个观念都是传统中国盛行的观念，而且还继续到现代。在“保”与“包”这两个观念中，“包”流行较晚，大致是自宋代以降，这点也许可以反映出中国从宋代以来就对有限而可确保的利益或结果越来越感到兴趣。

    保的观念几乎在政治、社会与经济生活的每一方面都可以发现。参加科举考试、进入官场、担保贷款、申请护照等等，都需要某种地位的人或某级以上的店铺担保。几个人或店铺联合起来担保的称为“连环保”，执行地方警卫与地方统制的保甲制度，是中国史上最为人熟知的制度之一。包的观念最常见的是包税（常与另一个字“额”连用）此外还用于包车、包船、包工乃至包饭等等。

    我们可以就商业活动范围之内举出更多的例子：政府核准的牙行的一个作用是保证某种程度内的公平交易。政府要求商人行会的领袖负责保证会员的行会，而且要供应清廷官方所需要的应用物品（这些往往牵制到所谓规费以及类似的勒索）。有引票经营盐运的商人首领称做“总商”，责任重大。经营出入口贸易的“公行”，有时称为“保商”，必须负责一个港口的对外贸易。大规模的商业组织，政府往往要他们成为多头制，以便维持制衡。这种预防办法，类似政治圈内所使用的，例如数名省级的高级官员并列。这是中国统治者从历史上得到的经验，知道倚重惟主管首领是问与联合负责的原则。

    （一）地位与登记

    在清朝统治下，阻止商人爬上政治阶梯的障碍，显然很少。中国帝制早期的几百年内，统治阶级经常妒忌地守住他们的政治权力，商人即使想占一席地位都极端困难。隋代（581—618年）所建立的进士制度，一直成为学者经由考试进入官场的最佳途径。但是这项考试，在隋唐（618—907年），以及辽代（907—1125年），对商人、工匠及其子孙是不开放的。[4]这种歧视政策到宋代（960—1279年）似乎减轻了不少。1444年颁布的规定要进士级的考生之间组成相互担保的团体，每一组三人（首都区开封府内五人）。担保的条例有一项是“身是工商杂类，及曾为僧道者”不得取应。条文中所用的“身是”与“曾为”两词似乎指出，出于商人家庭而自己不是商人，或甚至曾为商人而目下已非商人，都准许参加考试。如果我的解释正确，这点值得研究中国历史的人记在心里。同时要注意的是，在金（1115—1234年）、元（1206—1368年）两个异族入主的朝代，似乎没有禁止商人或工匠参加考试的规定。因此我们可以说，最近数百年中商人已经得到了政治解放。

    事实上，在明清两代，盐商还有一项特权，可以令其子弟注册入“商籍”，参加生员考试，以进入商人居住地与经商地的学府，而不必如一般人须返回本籍才能参加考试[5]。此外，学府中特别为商籍学生保留名额，这些生员以后多半在省城参加考试。这种特权无疑地为清代盐商的后代造就了几百位进士，与更多的举人。何炳棣教授在他的研究中，曾举有数字。[6]把这些资料大略地再检查一遍，可以发现这些举人进士大多数是在18世纪通过考试的。

    令人感兴趣的是，为盐商家庭子弟设置学校的制度，可以追溯到元代。1299年，一位蒙古籍的盐政在河东为盐商家子弟设立了一个学校，称为“运学”。注册的学生称为“运籍”，这名词是“商籍”的前身。这件事以后在16世纪末，被人提出来当作在别处成立类似设施的前例。[7]也许，就元朝来说，给予商人特权是很自然的事，因为蒙古的统治阶级十分依赖维吾尔商人与中国商人给他们带来的巨大利润。

    除了考试以外，商人获得荣耀乃至官位的另一途径是“捐纳”，这是一种花钱买头衔、职位的制度。卖官鬻爵自然不是新事，它甚至可以追溯到汉代，但清代的制度无疑地是最完备，而且是最被倚重的一项主要收入。在18世纪早期更是重要。这个制度显然也包含有政治动机。正如雍正皇帝曾公开承认，有才能的人不由正途，而借着捐纳等非正途出身，可以平衡由科举出身者造成的过分影响力。在理论上，正规的捐纳，虽然本身不是正途，却是让生员得官或小官取得晋升的主要台阶，当然也有例外的情形。实际上，所有的富人都能为他们的父母买一个荣衔，并有不少替自己捐买监生、荣衔甚至官职者。富有的商人任意利用这种机会不难想像得出，18世纪的盐商就可以举出很多例来。商人捐官这件事，在19世纪下半叶曾经遭到章奏强烈的反对，但是清政府不能也不肯放弃这笔每年给国库带来几百万两银子的财源。有人曾说，这一大笔收入使得清代早期统治者不必重视商税，结果是商人得利。此外让人感兴趣的一点是，大约从1851年开始，旧式银行称做“银号”者，为人办理捐纳而大赚其钱。[8]

    在结束我们对商人地位的讨论之前，我们需要注意到明清两代社会系统的流动性，这点何炳棣教授已有畅论。[9]其中很有趣的是，我们可以看到家庭分工的例子，父亲或兄弟经营家中的田产或生意，而让儿子或另一个兄弟去读书、参加考试。清代学者沈垚（1798—1840年）曾上溯到宋代，认为这种经济基础是帮助考生成功的重要因素。沈垚认为，从那时候起，所谓四民的士、农、工、商已有了结合与混合的现象。[10]另一位清代学者钱大昕（1728—1804年）也注意到，宋元时代的儒家学者已经鼓励学生首先应获得适当的生活方式（谋生方法），这样才可以使他们在进入官场前专心读书，日后在任位上才能维持正直与清廉。[11]农夫的职业当然是基本的，一个诚实的商人或制造有用而非奢侈品的工匠，他们的职业也可视为基本的，黄宗羲（1610—1695年）曾强调过这一点。[12]这种态度上的改变，无疑地反映出当时的社会环境。在一个较为流动的社会里，不只富商成为有威势、有影响力的人物，就是普通商人也发现他们的地位改善了。另一方面，我们也不能说，古老的轻商观念，此时已经归于消灭。举例来说，乾隆皇帝在1742年下诏免除米与豆在国内所有的通过税，诏令中他依然提出“重本抑末”的老调作为理由。[13]

    与商人地位密切关连的问题是他们在人民中如何登记。中国历史上，登记（著籍）一直是政府统制人民的一项重要手段。从帝制中国开始，正规商人就得登记在“市籍”项下。秦汉时代由于用兵频繁，有时那些名字登记在市籍下的人是第一批被征召入伍的，然后是那些以前曾入市籍的人，再其次是那些父亲或祖父入市籍的人。[14]

    市籍的登记至少继续到唐代，那时候由政府密切统制与监督的城内集中市场颇为繁荣。关于唐代的市场制度，杜希德教授（Denis Twitchett）曾有精辟的论述。[15]但是到了唐代后期，这种市场制度开始衰落，大多数城市市场的规定都被忽略或遗忘，很可能不久以后市籍登记便终止了。

    在明代，户口的登记主要分为四大项：军、民、匠、灶（制盐者）。[16]工匠有专籍，因为他们必须轮班应差。明中叶以降，班匠可以纳银代差，渐渐得到解放。

    军民工匠四种户籍在名义上延至清初。《嘉庆会典》列举“军、民、商、灶”[17]，这一条很容易引致误解，因为此处之商即上述之“商籍”，单指盐商而言，而非指一般的商人。

    户口的登记从1772年正式成为保甲制度的一部分。然而，保甲制度起初并未认真执行，直到1813年冬天，国内发生一连串暴动事件，特别是这年秋天“天理教”的一次暴动，震动了北京皇城，以后保甲制度才比较认真。清代的保甲制度并不是划一的，大致来说是“门牌”的登录以及登记入籍。登记的事项包括户长的“生理”或“行业”。这分为“住户”或“民户”，与“铺户”两个主要项目。有趣的是，铺户的登记只包括那些不与家人同住的店家（我们可以称为离家商人）。店主与家人同住的则归入民户。我们需记住，在中国帝制时代，远离家乡的老百姓很可能引起别人的猜疑，他们得随身携带执照或护照之类的文件以证明他们的身份。

    根据1851年秋天的官方报告，北京的内城（西洋文献称之为“鞑靼城”，因为大多数居民均为旗人）住户七六四四三户，铺户一五三三三户。[18]在北京的外城或所谓“中国城”，铺户的数目可能更多些。另外从天津在1846年施行保甲制度下登记的民众，我们可以发现某些有趣的项目与细数。[19]生意人分成三个项目：“盐商”、“铺户”与“负贩”。在天津城围内登记的九九一四户中，盐商一五九户，铺户三一三二户，负贩一九三五户。在东郊，即东城门外，登记有七○七七户，其中一一○户为盐商，二九七五户为铺户，一三三○户为负贩。在北部的六六三五户中，盐商五二户，铺户三一九六户，负贩七九九户。其他西郊、南郊、东北郊与西北郊四个郊区，登记的户数较少。但在这些区中，生意人三项登记的总数仍超过总户数的三分之一或接近半数。这些显然相当可靠的数字，很可指出在我们所讨论的这段时期的末期，天津市的商业化程度。

    （二）限制、征税与利用

    唐代的各种民法与刑法包括许多关于市场的详细规定，但清代的《大清会典事例》与明代的会典相似，对于贸易与商业方面较少提及。会典中的“市廛”即市场统制一节，仅包括短短的五项：经纪业务、公平价格、市场的独占（把持行市）、度量衡，以及市场上出售的衣料与用具的品质标准。除了第一款内规定私营经纪业务为非法（私充牙行埠头），这点是从《明会典》中抄袭而来，其他各款都依照唐代标准而制定。[20]关于上述最后两项事务的规定，其起源最为古老，也可能最不受人重视。晚清的法律专家薛允升氏（1820—1901年）曾特别感慨这方面执行的松懈，他强调维持货物品质与统一度量衡的重要性，但并未引起作用。[21]

    根据禁止私充经纪的一款，在城镇乡村的各行业的经纪人（诸色牙行），以及类似泊船地方（船埠头）的经理人，应从殷实人中选出来担任。政府发给他们盖有官方印记的登记簿，让他们记录来往商人或船主的姓名、固定住址、通行证号码，以及货物的数量。登记簿每月要送交政府当局检查。那些未经官方核准而营经纪业务的人应受杖刑六十大板，他们所收取的佣金（牙钱）应予没收，如果官方认可的经纪人或埠头（官牙埠头）有掩饰藏匿，应受杖刑五十大板，然后免职。关于物价一款，将制定公平价格的责任给予经纪人（行人，即牙行），而非唐律上所规定的市场官员（市司）。[22]

    经纪人的作用是在买者与卖者中间协调商定一个合理的价格，除此之外，许多经纪人也充当店家，招待来往商人的食住与寄放货物，当然也照章收费。这些费用是在交易时所收的佣金（牙钱、用钱、或称行用）之外的。经纪人也可能充任商人买卖的代理人，为他们接洽贷款，安排他们的交通与货物运输问题。因此经纪人在贸易商业上能担任不少职务。[23]政府要借着经纪人以钳制商人是很自然的事。

    在理论上，只有有执照的经纪人才准许担任这些职务。根据规定，这种执照（称做“牙帖”）只有省级当局才能发给，并有固定的名额，这个执照每隔五年检查一遍，并重新发给（北京从1725年开始），同时，名额亦可能变更。[24]实际上，省区与地方官员常常不顾名额而自行发给执照，因为这项业务是州县政府收入相当可观的一个来源。对省府与清朝政府而言，从经纪人的执照所收取的费用只是非常小的数目，但是，自太平天国叛乱以来，情况有了重大改变，从那时起，特别捐也由经纪人收取，并与厘金合在一起。在湖北与湖南，从经纪人处收取的年度捐税估计有他们的牙帖费的一百倍之多。[25]

    这些经纪人，特别是那些私营的，带给商人的麻烦实多于帮助。当某一行业的商人组成一个行会后，通常都会被与他们这一行打交道的经纪人控制住。通常借着使官准牙人或为本行会员而达到目的。有关这类做法的例子我们在北京18世纪时组成的行会记录上可以看到。[26]

    在这里要强调的一点是，“行”这个字在中文里经常是表示“行业”而非“行会”，除非我们将行会的意思扩大到包括那些没有会馆或公所，甚至没有行规的原始行会。政府热衷于让商人按行业组织起来的主要理由是配合它对各种物资的需要，这种要求可能来自清廷当局或任何大小衙门。商人有义务应付这种要求，称之为“当行”，意思是“本行的当值”。理论上，政府需要的物品应该用“时价”或“实价”买进。事实上，真正照办的很少，即使政府付给相当的价钱，经办人在中间索取的陋规也成为当值商行的一个沉重的负担。1738年，清廷诏令全国各大小衙门纠正这种陋习。[27]在雍正皇帝名义下发布的《州县须知》，警告地方政府官员，不得向商人与百姓强索物品。[28]然而这些命令与警告实际上完全没效。举例来说，为了供应清廷光禄寺所需用的猪肉与鸡，北京城内宛平与大兴两县特别从这两行里挑选了殷实的商人来负责供给，结果害他们从1752年到1756年之间，每年都赔上两三千或三四千两银子，直到这两行在1756年被废除为止。[29]

    在明代末叶以后，这种“当行”制度照规定本可纳银替代。16世纪时，北京城的铺户分为九等，每户每年要付一钱至九钱的银子称做“行银”，以免当行。到1582年冬天，政府批准一项奏折，免除最下三等的铺户缴纳这笔行银。中间三等的铺户，其资金从三百两银到五百两以上的，以及上三等的铺户，其资金多至数千两银者，则需继续缴纳。同一年早期，政府也批准北京城内两县中一三二家官方认可的行业中，三二家小号得以免除缴纳这笔银钱。[30]

    到清代，北京城内的两县获准从内城以外的铺户收取这笔银钱。上等的铺户每年缴付五两银，中等每年二两五钱，下等的铺户则免缴。北京内城九门内的铺户得以免缴的理由是他们得负责整理街道，特别是填土、洒水的工作。

    大多数城市中对商店开设的地点都没有严格规定，只要不太靠近衙门损其尊严就行。但是暂时性的货摊与浮摊不准见于大街上。在皇都里的规定就比较严格，举例来说，北京的内城不准开设戏院与旅店。1756年所做的调查，显示城里有十五家旅店，其中有好几家“关东店”，显然是为在满洲做生意的商人开设的。[31]还有四四家店铺，夜间也经营旅馆业。所有这些店铺都得迁到外城去。另外七二家经营猪肉、酒、鸡、水果与烟草的店铺则准许留在内城。[32]叫卖的负贩有时不准喊出某些被认为是忌讳的字眼。在1648年与1649年，北京城内的负贩曾被禁止叫卖，因为多尔衮嫌他们的声音太吵。[33]

    有关这方面我们可以再加上一点是北京城内一般都实行宵禁，特别是在内城。为了便利警卫，许多较小的街道，特别是通往大道的傍道都树立起栅栏，夜晚关闭，禁止通行。根据《金吾事例》，1729年北京外城有四四○个官准的栅栏；1763年内城有一○九九个栅栏，皇城内有一九六个。这些栅栏似乎一直维持到19世纪初年。[34]栅栏与宵禁令人想起唐代首都长安城内坊门夜闭的严格规定。

    北京城内的九个城门的征收货物税都是在恶名昭著的崇文门税关管制之下。这从明代起就如此，一直继续到民国时。更早的朝代当然也有类似的税。记得南唐时代曾有官吏幽默地对皇帝说，首都不下雨的原因是雨恐怕在城门要缴税。结果，皇帝下令减轻这些税捐。[35]

    像清朝其他的税关，崇文门的税关也有年度的定额。在本文讨论的这段时期内一般定额是十万两银多一点，这笔数目不算大，留给税吏足够的余地去充实他们自己的腰包。[36]税关的主管者照规定都是旗人，他们在这位置做了几年后，大概都得到类似的下场：借某一个罪名免职，其大部分财产充公，但也罕见完全破产之例。清朝皇帝与这些权贵税吏之间的关系正像渔夫与他豢养的鱼鹰之间的关系。

    州县政府的一个重要收入来源称做“落地税”的，是对所有进入其管辖的地方市场的货品所征收的税。这些税通常都是包给衙门的衙役或牙子，自然有滥用职权与腐败的情事。1735年清廷曾下令废止所有乡、镇、近郊的落地税，仅保留县城与州城的。[37]这道命令是否曾广泛执行以及行之多久却是值得怀疑的事。

    总结来说，清代最初两百年内对地区间以及地方贸易的税收并不特别重，尤其当我们比较一下明代万历朝（1573—1619年）的下半期，朝廷的宦官在征收全国商业税那种无情的勒索时，或是比较一下从1850年代加之于各省的厘金，给朝廷从1869年到1908年每年都带来一千四百万至二千一百万两银子的收入时，就可明白。[38]

    在物价管制方面，政府关心的主要是谷价的稳定，以及铜钱与银两的兑换率。为了防止大量囤积铜钱与米谷，政府曾试用各种方式，下令禁止这种事情发生。当谷价太高的时候，最有效的办法显然就是抛售政府所存积的米谷。在北京，官方的米局特别用来供给旗人。由于北京城人口众多，因而有严格的规定管制米谷运出京城。原则上只有少量的米，村民买来供自己食用的才准许运出京城。此外，不论米或谷都不准运出城或甚至京畿地区。[39]清廷对未去壳的谷子管制更为严格，原因是谷子能保存得更长久。

    银与钱的兑换是钱铺的主要生意。通常，北京城的钱铺得五家一组连合互保。18世纪有一段时期清廷依靠官方认可的钱币经纪人（称做钱行）来稳定兑换率。[40]大体来说，雍正与乾隆两朝在北京的成效相当好。兑换率的波动幅度是从八○○到一一○○文铜钱对一两银，但大多数时间都维持在八五○或九五○上下。[41]谈到钱铺间的连保，可注意的是类似的要求初期并未应用到旧式的银行（称做银号）上面，直到1860年数家半官方的银行宣告破产以后，银号才需要连保。由此看出，尽管银本位经济已经继续了几个世纪，政府对银的控制总是落后一步。

    利用城市商人的一个主要方式，是托付给他们一笔公家资金作为投资之用。这种制度称做“发商生息”，在前几个朝代就有了。受到这种资金的商人绝大多数都是当铺与盐商。政府收取的利息是月息一分至二分。一般来说，这笔利息是指定作为特殊用途的。[42]雍正皇帝特别爱好这个制度，用所得的利息来资助八旗与绿营军。清廷的内务府也非常依赖发商的利息为其财源。乾隆皇帝时仍继续这个制度，后来他改变主意，1759年时宣告发商生息于政体有损，下令加以限制。1769年，他下令将已经发给长芦盐商的资金改称做“赏借项款”。[43]使用这个新名词的理由是政府所订的利率较法定准许的月息三分利率低得多。但是，旧的名词与制度仍被清廷、省府、州县政府以及半官方或非官方的组织继续使用下去。可注意的是信托资金对商人并不一定有好处。1783年长沙府的当铺为某种原因婉拒从省府接受更多的资金，托辞说他们手头已有足够的信托资金了。[44]

    另一种利用城市商人的方式是“自动捐献”，称做“捐输”或“报效”，这是城市商人资助政府的军备、公共建设、水患、饥荒的救济，皇帝出巡与皇帝生日等的开销。根据两淮地区盐政管理官方记录的数字显示，在1738年至1804年之间，这个地区的盐商在四十多个场合总共捐献了三千七百五十万两银子。[45]根据盐政的报告，盐商们都是“情愿”甚至“踊跃”认捐，恭请皇帝“赏收”。在另一方面，盐商们又不时请求以分期付款的方式来捐献。有几次，皇帝对商人的忠诚报效与急公好义表示嘉奖，而只赏收一半的捐款。商人在这种情形下所得到的直接回报不过是所谓“议叙”与名义好听而已。然而在其他场合，皇帝为显示对商人的仁慈宽大，准许他们免费取得额外的“余盐”，或是允许他们延期偿付滞纳的盐税与信托基金的利息。皇恩的殊荣，甚至免除盐商对政府的负债，1780年减免了一百二十万两银子，1782年与1784年大约是三百八十六万六千两。[46]另一批重要的自动捐款，是由广东的盐商与洋行（行商）所认捐的。从1773年至1832年间的捐款总数大约是四百万两银子，数目虽不是大得惊人，也是一笔巨款。[47]

    如果能比较清代各皇帝所采行的经济政策，特别是有关商业贸易的细节，甚至比较一个皇帝在不同时期的经济政策，将是一件极有趣的事。遗憾的是这样的比较已远超出本文的范围。然而我们可以强调的是康熙、雍正、乾隆三帝都绝不是蒙昧无知不肯用心的专制君主。康熙皇帝有一次在1717年曾夸称他对盐政方面深刻的了解。[48]雍正皇帝无疑地非常通晓一般的财经事务，但1728年有一次也承认他并不特别了解有关茶政上各种渎职情事，以及有关茶与马的贸易，因此不能给负责的官员特定的指示。[49]乾隆皇帝在1748年曾有很合理的意见，认为一般来说还是把市场方面的事交给人民，准许他们自由流通货物较好。政府的干涉，虽然出于好意，常常由于处理不当而产生扰民的障碍。[50]清代皇帝一般都可以称得上对商人宽大而同情的。但在另一方面，他们对商人有时也出诸操纵甚至有喜怒无常的态度。

    三、城市商人的反叛取向

    相对于政府统制，重要的一点是检讨商人是否曾抗议或反叛这种统制，和采用什么方式。有关这方面讨论，我们可以19世纪学者汪士铎（1802—1889年）所做的观察作为起点。他认为，商人与城市的文人一样，似乎是最不倾向反叛的，或者我们可以说，他们表现非常低度的反叛取向。汪士铎在1853—1856年间，因太平天国之乱曾躲藏在长江下游的南京与绩溪之间，这段时期所保存的日记中有如下一段：

    天下最愚，最不听教诲、不讲理者乡人。自守其所谓“理”而不改。教以正，则哗然动怒；导以为非为乱，则挺然称首。其间妇人又愚于男子。山民又愚于通涂之民。惟商贾则巧猾而不为乱，山民之读书者不及也。在外经商之人，又文弱于当地之商贾。知四民之中，最易作乱者农。工次之。武生次之。山中之士次之。商贾之士次之。城士之士，则硜硜然可以决其不为乱[51]。

    这种议论显然是概括而充满偏见的，但我们或可了解这不完全是处于一个大动乱暴力时代所发的愤激之言。无论如何汪士铎是个相当独立敢言的学者，他不受传统儒家思想的束缚，而且是热心于提倡改革、恢复秩序的人。至少他在这段话里提出一个启发性的见解，就是在传统的四种功能团体中，城市商人与城市文人的反叛取向最低。

    进一步说，根据汪士铎的推论做一初步检查，显示其中确有一些历史的真实性。[52]中国历史上曾记载无数次农民叛变，但几乎看不到任何城市商人领导的叛变。从唐宋时代以降，我们看到走私盐商与海盗商人的记载，然而他们行动的范围似限于山林、沼泽、海岛与外海上，有时在他们势力范围内，他们也会打劫城镇，因而可算是城市商人的敌人。在明清时代，关于矿工、伐木者与城市匠人的暴动与罢工事件，也有所闻。

    当然，一个社会中叛乱取向的问题，或广泛地说暴力取向的问题，其研讨不一定只限于功能团体。举例来说，这个问题可以就个人或团体从年龄、性别、地位、财富、角色、功能、教育、风俗、传统或是其他的角度来探讨。甚至汪士铎所作的粗略的推论也提到其中几方面。然而，对这个问题更深一步的方法论，却已远超出本文的范围，而且坦白地说，也不是作者能力所及的。为说明城市商人在清初时代抗议与叛变的性质与程度，我们可以看看下面四个例子，他们所谓的“商人与手工业者反抗清朝封建统治的斗争”。[53]这四个例子记述的事实均是“罢市”，就是商人与生意人拒绝做生意以示抗议。

    （一）1660年在山西潞安的罢市

    这次罢市的背景是源于明代生产御用丝织品称做“皇紬”的制度。在山西潞安做这一行生意的“机户”，必须以固定的官价供应这项货品，而官价显然是经常不足以抵付生产所需的开销。明末清初时代，皇紬年度配额是三千匹（一匹为六丈八尺）。1652年诏令将配额减去一千五百二十匹零四丈八尺，每匹的价格则从十两银子增至十三两。1658年，配额又由一千四百七十九匹零二丈减去一千一百七十九匹零二丈，因此实际上所需要的仅是三百匹。但是到1660年，机户发动一次罢市，据说将其织机焚毁，手里捧着账簿记载着他们的损失，准备向北京城进发，直接向皇帝请愿。

    据潞安一位朝廷官员王鼐的奏折，这些机户在明末时原有三千张以上的织机，但大多数都已破产，因为他们得依照政府命令按行服务，所谓“抱牌当行”，结果是他们生产的丝得不到适当的偿付而大受损失。从1644年到1660年，所留存的织机仅两三百张。据奏折所言，皇帝的削减配额，延长限期，先行付款，以及“合理实价”，使得机户争着愿为皇室服务。但是本省官吏的取用以及外省采购使者的要求勒索，却使他们遭受损失。理论上，机户们可以从他们出售的丝得到官价付款，但是经过层层勒索，特别是付差官差役的催紬费、验紬费及纳紬费，实际所余无几。

    我们在王鼐的奏折中可看到很生动的描写：“臣乡山西，织造潞紬，上供官府之用，下资小民之生。……为工颇细，获利最微。……今年（1660年）四月，臣乡人来言，各机户焚烧紬机，辞行碎牌，痛苦奔逃，携其赔累簿籍，欲赴京陈告，以艰于路费，中道而阻。天有簿籍，必有取用衙门，有衙门必有取用数目。小民含苦未伸，臣闻不胜骇异。”他接着建议严禁本省不得滥行取用，隔省不许擅差私造。从方志记载中，我们不清楚他的建议采行至何种程度，因为只说到山西巡抚下令立碑严禁。推想大概是，差役与差官不许继续强索，而机户也不许再度罢市。[54]

    （二）1660年安徽芜湖的罢市

    这次罢市是抗议芜湖内地税关过度的附加税与其他各种名目的勒索。根据阴历十月十三日御史李见龙弹劾户部郎中兼湖钞关监督郑秉衡的奏折，在郑秉衡的指使下，若干名不法的官吏征收额外的火耗与特别捐款用来充实其官邸的维持费用。郑秉衡还发明了“皇税”一词，对民船上装载的日用必需品甚至如薪柴与米都征以税。结果是，全部地区的商民发动罢市三天，以1660年阴历七月十四日为始。本地生员韦譞佩等向总督与巡抚请愿，结果总督命令知县接受商民所具甘结，同意地方人民发动罢市是因为征收薪柴与米的征税。据奏折上说，御史闻知这事是得自于从芜湖到北京来诉苦的商人，因而有关这事的消息传遍京城。[55]

    这项弹劾似乎并未发生多大效力，因为罢市的事件已经发生了一年，而且显然已不了了之。对我们来说，有关这次罢市最感兴趣的一点，是其行动的有秩序以及商人与士人间的合作。

    （三）1682年浙江杭州的罢市

    这次罢市是抗议土棍（地方流氓）与旗丁（八旗兵丁）的高利贷，他们对那些无力偿债的人捉去儿女以为抵偿，有时甚至牵连到负债人的亲戚与邻居。杭州北门的商民发动罢市抗议，这事传到一位同情人民的道台王梁那里。第二天，当王梁去与其他官吏会合调查这件事的途中，八旗兵王和尚等一共几百个人，拦住他的仪仗，辱骂他并打破他轿子的顶盖。这次不寻常的暴动，迫使总督与满洲将军连合上奏向朝廷陈明情况，结果皇帝下诏严厉处罚王和尚及其同谋者。这时候，总督则下令店铺恢复营业。这个例子中特殊的一点，是它说明了在一个征服王朝下政治与经济生活的复杂性。[56]

    （四）1698年福建浦城的罢市

    下面这段故事主要是根据直隶任邱人、出于书香门第的庞垲的墓志铭而来的。在戊寅年，即1698年（彭泽益误认为1758年），庞垲受命为福建建宁府知府。他到任后不久，传来报告说建宁府所辖的浦城县令，因为政令过于严苛，迫使人民反叛。城中愤怒的百姓趁着黑夜，攻击县府的“册局”，放火烧毁文件与记录，并杀死了一个当值的胥吏。县令害怕逃走，当地人民接着发动一次总罢市。庞垲得知这事，立刻赶到浦城，要求当地的教官与典史召集乡绅、生员与人民在明伦堂集合。在这些人面前，庞垲宣布县令的错误与罪状，并加以谴责，使士绅与人民气平下来。然后，他再提醒他们无法纪行为的不当。他让县府的财务与库房重新核对与收集未被焚的文件。他命令各行生意人恢复营业，城内秩序始告恢复。

    在这时，总督郭世隆不满省中百姓攻击县府（称为围城），发动罢市的事件日益增加，想借此用高压手段压制罢市，以为警戒。由于县令与地方士绅间的强烈不睦，总督欲借不法结党、阴谋叛变的罪名惩罚所有的士绅。庞垲反对这个做法，他强调县令残酷作风的不当。最后，只有一名变乱者被处死刑，另二人流放。浦城百姓为感谢庞垲的大力相助，建立一个书院来纪念他。他死于1735年。[57]

    显然地在福建省其他城市尚有类似的抗议与罢市的事件。当时的总督郭世隆（1643—1716年）出身山西的绿营。[58]上述故事中的县令是鲍鋐，沈阳人，以前曾任笔帖式（满文bitheshi，即书记官，七品、八品或九品），多半是个旗人。[59]从这件事也可以看出旗人与一般汉人的敌对。

    本文选编自《东汉的豪族》

  • 薛其坤：探究微观量子世界

    本文系讲演稿整理而得

    欧姆定律是接近200年前，由德国物理学家欧姆提出的一个非常经典的电学规律，它说的是通过一个导体，导体的电阻与加在导体两端的电压差成正比，与流过这个导体的电流成反比。大家都非常熟悉。换一句话来说，流过这个导体的电流正比于加在这个导体两端的电压，反比于这个材料的电阻。这个材料的电阻越大，它越绝缘；在额定的电压下，它的电流就越小。

    欧姆定律讲的是沿着电流流动方向关于电压、电阻、电流基本关系的科学规律。我们很好奇，自然就想问“在垂直于电流流动的方向上，是不是也会有类似欧姆定律关于电流、电压、电阻关系的东西呢？”答案：“是！”

    这就是欧姆定律提出50多年以后，在1879年由美国物理学家埃德温霍尔发现的霍尔效应。霍尔效应实验是一个非常精妙的实验，他把这个导线变成了这样一个平板，当时用的材料是金。在垂直于这个金的平板方向上，再加一个磁场，当然沿着电流流动的方向仍然有欧姆定律的存在。但是由于这个磁场下，流动的电子受到洛伦兹力的作用，它会在垂直于电流的方向也发生偏转。

    在这样一个磁场下，电流除了欧姆定律方向的电流在流动以外，电子还在横向发生偏转，形成电荷的积累，形成电压。这个电压就叫霍尔电压，这个现象就是霍尔效应。加一个磁场就可以产生霍尔效应，那么我们自然想问，是不是不需要磁场也能实现这样一个非常伟大的霍尔效应呢？答案也是“是”！

    他发现霍尔效应一年以后，就做了这样一个试验，把材料金换成铁，靠铁本身的磁性产生的磁场，也发现了类似的霍尔效应。因为科学机理完全不一样，命名为反常霍尔效应。

    不管怎么样，霍尔效应、反常霍尔效应是非常经典的电磁现象之一。为什么呢？它用一个非常简单的科学实验、科学装置就把电和磁这两个非常不一样的现象在一个装置上完成了。

    当然了，霍尔效应非常有用。今天我给大家列举了一些大家非常熟悉的例子。比如测量电流的电流钳，我们读取信用卡的磁卡阅读器，汽车的速度计，这都是霍尔效应的应用。它已经遍布在我们生活的每一个方面，是一个极其伟大的科学发现，同时对我们社会技术进步带来了极大的便利。

    这不是这个故事的结束。100年以后，德国物理学家冯·克利青把研究的材料从金属变成半导体硅，结果他就发现了量子霍尔效应，或者说霍尔效应的量子版本。他用了一个具体材料，就是我们熟知的每一个计算机、每一个芯片都有的场效应晶体管。这个场效应晶体管中有硅和二氧化硅的分界面，在这个界面上有二维电子气。就是在这样一个体系中，在半导体材料中，他发现了量子霍尔效应。

    在强磁场下，冯·克利青先生发现了霍尔电阻，右边这个公式，=h/ne2，h是以普朗克科学家命名的一个常数，是一个自然界的物理学常数。n是自然数——1、2、3、4、5。e就是一个电子带的电量，这是一个非常伟大的发现。为什么呢？我一说就明白，因为测到的霍尔电阻和研究的材料没有任何的关系。硅，可能任何材料都会有这个，它只和物理学常数，和自然界的一些基本性能相关，和具体材料没有任何关系。因此它就打开了我们认识微观世界、认识自然界的大门。

    同时，量子霍尔效应给我们材料中运动的电子建造了一个高速公路，就像左边大家看到的动画一样，电子的高速公路上，它的欧姆电阻，平行于电流方向的电阻变成0，像超导一样。因此，用量子霍尔效应这样的材料做一个器件的话，它的能耗会非常低。

    大家今天看到的是两条道的情况，是n=2。如果n=3，这个高速公路的一边就有3条道；如果n=4，电子的高速公路就变成4条道，所以这样一种理解就把自然数n，1、2、3、4、5、6、7、8和微观世界的电子高速公路密切结合起来。大家可以看到，我们对自然界的理解，对量子世界的理解又大大前进了一步。

    冯·克利青在1980年发现量子霍尔效应以后，由于这个巨大的科学发现，五年以后他被授予诺贝尔物理学奖。

    硅有量子霍尔效应，是不是其他半导体材料也会有量子霍尔效应呢？有三位物理学家在第二年，1982年就把研究的材料从硅变成了可以发光的砷化镓，结果，他们发现了分数化的，不是一二三四了，三分之一、五分之一，分数化的量子霍尔效应，1998年这三位物理学家获得诺贝尔物理学奖。

    在我们这个世纪，大家都知道石墨烯，有两位物理学家利用石墨烯这个量子材料继续做一百年前的霍尔效应实验，结果发现了半整数的量子霍尔效应。随着量子霍尔效应的不断发现，我们对自然界，对材料，对量子材料，对未来材料的理解在电子层次上、在量子层次上逐渐加深，所以推动了科学，特别是物理学的巨大进步。

    量子霍尔效应有很多应用，今天我讲一个大家比较熟悉的应用，那就是重量的测量。我们每天都希望测测体重，重量的测量无处不存在。1889年国际度量衡大会定义了公斤千克的标准，是9:1的铂铱合金做成的圆柱体，以后的一百多年，全世界都用这个做为标准称重量。

    但是在118年以后的2007年，我们发现这个标准变化了：减轻了50微克。一个标准减少50微克是一个巨大的变化，全世界的标准就不再标准了，而且随着时间的推移也会进一步变化。因此我们需要更精确，可以用得更久的重量标准。

    在2018年的时候，国际度量衡大会重新定义了公斤的标准，那就是基于刚才我提到的量子霍尔效应，和另一个诺奖工作、约瑟夫森效应提出了一个全新的称，叫量子称或者叫基布尔称，它对重量的测量精度可以达到10的负8次方克，而且是由物理学的自然界常数所定义的，1万年、10万年、1亿年也不会发生变化。这是我举的一个大家能理解的例子。

    刚才我提到了三个不同版本的量子霍尔效应。它们需要一个磁场，就像霍尔效应一样，而且一般情况下需要的磁场都特别强，一般是10个特斯拉，10万个高斯，这是非常强大的磁场，我们庞大地球产生的磁场只有0.5高斯，我们要用的磁场是地球磁场强度的20万倍。能不能把它去掉磁场也能观察到量子霍尔效应呢？我带领的团队与合作者一起，在2013年的时候完成了这个实验，在世界上首次发现了不需要任何磁场、只需要材料本身的磁性而导致的量子霍尔效应，或者叫量子反常霍尔效应。

    这样一个发现是不是也是材料驱动的呢？是的。我在这里给大家复习一下我们所熟悉的材料。在我们一般人的概念中，我们自然界的材料只有3类，导电的金属，不导电的绝缘体，还有一个是半导体，介于两者之间。

    第一代半导体有硅、锗，第二代半导体有砷化镓、锑化汞，第三代、第四代还有氮化镓、碳化硅、金刚石等等。在研究材料和材料的相变基础上，包括量子霍尔效应上，有两个物理学家，一个是大家可能比较熟悉的华人物理学家张首晟，和宾夕法尼亚大学的Charles Kane，在这基础上他们提出了一个全新的材料：拓扑绝缘体，也就是大家在屏幕的最右边所能看到的。

    什么是拓扑绝缘体？我给大家简单解释一下。这个图大家可能比较熟悉，最左边是一个陶瓷的碗，是绝缘的、不导电的。再朝右是一个金做成的碗，是导电的，叫导体。拓扑绝缘体就是一个陶瓷碗镀了一层导电的膜。如果把这个镀了膜的碗进一步进行磁性掺杂，使它有磁性的话，它就会变成一个只有边上镀金的碗。这个边上镀金碗就叫磁性拓扑绝缘体材料。

    按照张首晟等的理论，它就可以让我们能观察到量子反常霍尔效应。但是，这个材料是一个三不像的矛盾体：它有磁性，它要拓扑，它还要绝缘，我们还要把它做成薄膜，这就要求一个运动员篮球打得像姚明那么好，跑步像博尔特那么快，跳水要全红蝉那么伶俐，这样的材料非常难以制备。为什么呢？因为大部分磁性材料都是导电的，铁、钴、镍都是导电的；另外，磁性和拓扑在物理上是很难共存的；还有一点，在两维薄膜的情况下，很难实现铁磁性，使这个才有真正的磁性。因此真正观测到量子反常霍尔效应，在实验室看到它，这是一个极其具有挑战性的实验。

    我带领的团队和另外三个团队紧密合作，我们动员了20多位研究生，奋斗了4年，尝试了一千多个样品，最后在2012年10月份，全部完成了量子反常霍尔效应发现，完成了实验。我们证明了确实在边上镀金的碗（磁性拓扑绝缘体）中，存在量子反常霍尔效应这样一个新的规律。

    今天，我特别把当时发现量子反常霍尔效应的样品带到了现场。大家可以看到，看到很多电级，电级之间有方块，每个方块上就是首先观察到的量子反常霍尔效应的样品。

    这里我再给大家讲一下制备这个材料，对原子磁场的控制，对科学发现非常重要。这是其中一个例子，我们学生制备的，采集的一些照片。中间大家会看到，拓扑绝缘体碲化铋薄膜的扫描隧道显微镜照片，上头每一个亮点代表一个原子，更重要的是，在这个范围内你找不到一个缺陷。说明我们材料的纯度非常高，我们在其他材料中也能做到这个水平。

    这是另一个拓扑绝缘体材料：硒化铋。大家可以看到，这么大的范围内，你只看到你想要的原子，没有任何缺陷，而且薄膜是原子级的平整，这为我们最后发现量子反常霍尔效应奠定了非常好的基础。

    最近，我们继续在朝这个方向努力，我们正在攻克的一个问题就是高温超导机理这个重大科学问题。我再次放了博士后制备的研究高温超导机理异质结样品的电镜照片，大家从上可以看到有5个样品，不同的颜色代表这个异质结的结合部。大家可以看到，每个亮点几乎是接近一个原子，我们制备的异质结，两个材料的结合部几乎达到了原子尺度的完美，只有这样，我们才能在这样一个非常难以攻克的高温超导机理上有所作为，我们会沿着这个方向继续努力下去。

  • 贺雪峰：公私边界与国家权力

    一、

    2009年暑假到鄂东南宗族性地区调研，发现当地村民组特别重要，因为村民组基本上都是一个房头，十几户到几十户，一个姓，自家人，又是村民组，过去的生产队，村民组组长往往也是由本房头最有威信的中年人担任。房头是私，因为大家都是自己人，一家人。这个私是相对于农民家庭这个“小私”的“大私”。村民小组则是公，是国家划定的基本管理单位，是村委会下设小组，且村民小组长一般要由村委会任命（可以由村民推荐或推选），如果说国家是公的话，国家的公是“大公”，村委会是国家在农村最基层的行政建制，可以算是国家这个“大公”在农村最基层的代理人，则村民组就只是“小公”，是最小的“公”了。也正是在村民组一级，“大私”与“小公”重合，形成了国家与社会有效对接，村民组长对内利用自己人的身份来低成本解决矛盾，达成集体行动，提供超出农户的公共品，对外则代表房头利益，维护房头利益。村民组或房头内的事情都可以自治，国家就可以进行低成本的简约治理。国家不介入村民组或房头内的事务，房头就有自治的空间与动力，房头内就需要且会产生唱黑脸的人，说直话的人，也就具有相当的主体性。

    2021年暑假再到鄂东南宗族性地区调研，发现之前的公私边界早已打破，国家这个“大公”一直延伸到农户，之前公私同构的村民组和房头快速弱化，不再具有集体行动能力。国家权力延伸到农户的原因很简单，就是过去需要农户出钱出力建设的村庄公共品，由国家下乡资源来进行建设了，不再需要农民出钱出力了，之前村民组或房头内的公共事务需求没有了，房头退化为一种文化现象和价值倾向，从治理层面返回到社会与文化的层面，也就是从“公”的领域退出而仅保留“私”的领域。

    一旦国家权力进入到农户家门口，国家就直接与农户打交道。国家要为农民做好事，上项目，项目落地就要占用农户土地，农户就可能索要超出应得利益的好处。因为不损害其他农户的利益，钉子户索要超额好处就没有心理上、道德上的障碍，也没有舆论上的问题，因为国家好处不得白不得。国家就不得不与钉子户讨价还价，外来工程队就不得不与钉子户死缠死打。一户钉子户获利，其他农户迅即成为钉子户，村庄没有人有理由出来“唱黑脸”、“说直话”，以阻止钉子户效应。这样一来，国家发现好事不好做。

    小结一下，过去在国家与农户之间实际上是存在着公私同构的村组（宗族或房头）的，现在国家借资源下乡，将权力直接延伸到农户家门口，不再有公私同构的村组这个缓冲带，之前“大私”范围内部解决的大量细小琐碎事务外溢出来，变成国家事务，由此造成新的治理困境。甚至调研乡镇，有一农户家中老人去世，村干部没有上门帮助，农户就到村部大闹，说村干部为什么不去帮他家处理丧事。而实际上过去办理丧事都是靠房头而不需要村干部帮助的。

    二、

    鄂东南地区是湖北宗族化程度最高的地区，相对鄂东南来讲，湖北省绝大多数农村宗族早已解体，是我们所说原子化农村，也就是说，作为大私的宗族房头早在建国初期就已经消灭或消失，村组建制都不再是依托宗族房头这样的大私，而是在地缘基础上，通过村社集体来建设地缘共同体。虽然缺少“大私”，人民公社时期“三级所有、队为基础”，生产队是农民共同生产与分配单位，自治程度相当高。分田到户以后，实行村民自治，农民要承担“三提五统”，要分摊共同生产费，国家很少介入到村庄内部事务。村庄自治就必须要将农民组织起来，筹资筹劳，出钱出力，就要依靠积极分子，团结大多数，孤立钉子户，以达成公共工程和公益事业建设上的集体行动。将农民组织起来的最重要办法是召开会议，讲清道理，形成共识，实行农民的自我教育、自我管理和自我服务。当然也要通过民主选举、民主决策、民主管理、民主监督。

    简言之，在没有宗族房头的农村，通过村民自治来形成地缘基础上的村社共同体，村社共同体成为国家与农民之间的缓冲地带或联结纽带。

    取消农业税后，国家不仅不再向农民收取税费，而且开始大量向农村转移资源，之前主要依靠村民出钱出力建设的村庄公共品，现在都由国家来建设，国家直接将服务延伸到农户家门口，农民再组织起来建设村庄公共品就没有必要。也是因此，之前通过自治来达到的自我教育、自我管理和自我服务就显得多余，村民自治就逐步被村级治理行政化所代替，村干部主要工作就是要完成上级布置的任务，而不是深入群众、动员群众、组织群众，村级组织也就逐步丧失了解决村庄小事的能力，村庄任何一件事情都可以直接上升到国家的权力层面，国家就不得不进一步介入到农户之间甚至农户内部的琐碎事务之中。

    三、

    国家为农民服务当然是很重要的，但是不应该包办代替，而必须要在国家与农户个体之间建立起一个缓冲性的结构，这个结构无论是小公大私同构的村民组（房头），还是组织起来的村社集体。不将农民组织起来，由国家直接面对一家一户农户，农户之间各种细小琐碎事务将极大地降低治理效率，结果就是好事不好办和好事办不好。

  • 韩琦：安第斯文明的起源：卡拉尔一苏佩

    传统观点认为，南美安第斯文明的母文化是查文·德万塔尔文化（公元前1200—前200年）。但随着考古发掘取得新进展，卡拉尔—苏佩文明取代了前者地位，被认为是安第斯地区的第一个文明，其存在于公元前3000年至前1800年之间，清晰展现着秘鲁中北部地区第一个复杂社会的样貌。

    20世纪中期以来，不断有考古学家对卡拉尔—苏佩遗址进行考察和研究，但直到1994年秘鲁圣马尔克斯大学的考古学家露丝·沙迪团队对苏佩河谷进行调查，并随之进行系统考古发掘，学界才对它形成新的认知。随着考古挖掘的深入和新成果的出版发表，卡拉尔—苏佩文明的古老性和重要性最终得到证实。2009年，卡拉尔—苏佩圣城被联合国教科文组织列入《世界遗产名录》。

    卡拉尔—苏佩位于秘鲁海岸的中北部地区、利马以北约182公里。秘鲁中北部地区的面积为81497平方公里，包括圣塔、内佩纳、塞钦、库莱布拉斯、瓦尔梅、福塔雷萨等十几个沿海河谷。与其他世界文明中心相比，秘鲁海岸似乎不太可能成为文明发祥地，因为东部安第斯山脉和西部太平洋形成的反气旋作用导致这里极度干旱。然而，该地区有50多条从山脉到大海的河流穿过，利用这种水源发展的灌溉对卡拉尔—苏佩文明的出现发挥了决定性作用。

    在众多河谷中，苏佩河谷在文明起源时期脱颖而出，仅在这一小盆地就发现了20多个可以被归属于同一时期的城市定居点，它们几乎都有公共建筑、圆形广场、住宅等，都有用土坯、石头、树干和植物纤维建成的阶梯式金字塔，其中还有雕像、马黛茶杯器、石器、棉纺织品、烧焦的食品及其他用品。从建筑规模看，卡拉尔城最大，城市布局分布有序，纪念性建筑种类繁多。其距离大海23公里，处于苏佩河谷中段的初始部分，被认为是该地区居民点的首都，被称为“圣城”。

    早在公元前3000年之前，就有一些家族群体在苏佩河谷定居，他们建立集中居住区，疏干湿地，开辟农田，修建灌溉渠道。公元前3000年至公元前2600年，首都地区的城市定居点不断壮大，定居者们在空地上修建广场用于公共活动，并有了第一个金字塔。大约在公元前2600年至公元前2300年，人们对卡拉尔圣城进行整体设计，修建了金字塔和下沉式圆形广场。在公元前2300年至公元前2100年间，大型金字塔、广场等公共建筑的规模和体积都有所扩大。到公元前2100年至公元前1800年，由于劳动力的减少，定居者们用较小的石块改建公共建筑，最后掩埋了一些重要的建筑部分，卡拉尔圣城被废弃。

    总体来看，卡拉尔—苏佩文明的主要特征表现在以下几个方面：

    以农业生产、渔业生产和贸易交换为主要经济形式。苏佩河谷的居民发展出技术比较先进的集约化农业。他们使用简单的工具（如木棍和鹿角）来掘土，修建灌溉水渠以将河水引入农田。考古证据表明，他们已经懂得通过对各种植物品种的实验，来改善粮食和经济作物的种类、提高产量。他们种植的作物主要有：土豆、红薯、南瓜、豆类、花生、辣椒、玉米、葫芦、鳄梨、番石榴、马黛茶、烟草等，其中棉花是交易的主要产品。沿海居民则捕鱼并采集各种海洋生物，主要包括凤尾鱼、沙丁鱼、贻贝和蛤蜊等。农业和渔业形成一种长期的经济互补关系。

    居民们通过以物易物的方式交换产品。沿海居民提供海产品，如离太平洋仅有500米的居民点阿斯佩罗被认为是卡拉尔的渔镇，那里的居民开发了包括使用钩子、麻线、船等在内的捕鱼技术，特别是发明了棉纤维渔网。渔民负责将海产品分发到河谷中的定居点，而河谷居民会给渔民提供所需的渔网和衣物、用作钓线的棉纤维、用作漂浮物的葫芦、制造船桨的木材以及水果蔬菜等，高地居民会提供农产品（粮食）和畜产品（羊驼）。这样，该区域形成一个类似专业化生产的贸易网络，而卡拉尔圣城无疑是这一网络的中心。很显然，这个网络还延伸到更远的地方，因为在卡拉尔—苏佩地区发现了来自高原的洛克木棒、秃鹰羽毛，亚马逊丛林的陆生蜗牛、灵长类动物皮、各种鸟类羽毛以及厄瓜多尔赤道海岸的多刺牡蛎。

    灌溉技术的使用、渔网的发明以及活跃的贸易交换提高了生产力，促进了地区经济发展和生产剩余积累，从而使苏佩社会能够以地方政府的形式加强其政治一体化进程，这种政府形式的有效性可以从国家承担的大型纪念性建筑群建设中得到体现。

    先进的城市规划和建筑。卡拉尔圣城拥有复杂的城市布局。该城占地66公顷，包括一个核心区和一个外围区。核心区包括32座公共建筑和一些住宅建筑群，外围区有一些住宅建筑群。核心区又分为两大部分，北部为上城，南部为下城。北部的公共建筑分为A、B、C三组，每组都有两个金字塔、广场、官员住房。其中B组的金字塔最大，长160米，宽150米，高18米，坐北朝南，背靠河谷，面向下沉式圆形广场，是卡拉尔城的主建筑。下城建筑有下沉式露天剧场、露天剧场神庙、长桌神庙、圆形祭坛神庙，以及平民住宅区等。

    金字塔结构的墙壁上抹有泥土，被涂成白色或浅黄色，偶尔涂成红色。每座金字塔都有一个通向顶部的中央阶梯，其上有几个房间。在主房间都有一个圣火祭坛，祭坛中央有一个火炉，火炉下方配有导风的地下管道。圣火祭坛具有仪式功能，被用于火化各种祭品。

    卡拉尔位于地震活跃区，其建造者使用“希克拉斯”技术，即将石块装在芦苇纤维编织的网格袋中，尺寸和重量各不相同，但非常均匀，有一定的松散度，用它们来支撑挡土墙，填充金字塔。这样，当发生强烈地震时，“希克拉斯”会以有限的方式微动，发挥着柔性地基作用，由此实现建筑物的结构稳定。规模宏大的城市和坚固的建筑表明卡拉尔人已经具有先进的组织能力和工程技术。

    社会分层和阶级分化已经出现。卡拉尔—苏佩文明显示出复杂的社会结构，已经出现明显的社会分层。如从事体力劳动的生产者，包括渔民、农民、工匠；精英阶层，包括商人、定居点的领导者和祭司。精英们不再直接为自己的生计进行生产，而是致力于专门的活动，如加强远距离贸易；进行天文观测来测量时间和制定历法；在公共活动的建筑施工中试验和应用算术与几何知识；举行仪式和献祭活动。

    考古发现揭示出精英阶层和普通民众存在较为明显的区分。城市中心各区的公共建筑和住宅建筑在位置、大小和所用材料上都有区别；服装穿着方式和个人佩戴的饰品上，如男性权威人士的项链和大耳环，女性的项链和头巾，也体现了社会区别。一些装饰品、项链是用从遥远的地方（如厄瓜多尔海岸）所获材料制作的，专供少数社会上层人物使用。

    中央集权的国家雏形已经显露。苏佩河谷的人口分布在苏佩河两岸被称作“帕查卡”的城市定居点中，这些定居点的规模和建筑体量各不相同。每个“帕查卡”都由几个“艾柳”组成，这些“艾柳”是通过亲缘关系结合在一起的族群，拥有相同的祖先，通过祖先来确定身份，并由族长领导。族长中有一个主要首领——库拉卡，负责指挥全体居民。这种政府制度在苏佩河谷20多个城市定居点中运行，由于卡拉尔居于核心地位，它发挥领导和组织其他城市定居点的作用，形成一个广泛而有序的互惠、交流网络。

    卡拉尔是一座和平与和谐之城，考古发掘中没有发现战争的痕迹，没有防御城墙，没有武器，没有残缺不全的尸体，这与通过战争产生国家的理论解释有所不同。美国考古学家乔纳森?哈斯认为，卡拉尔人进行了人类建立政府的实验，他们将个人自由交给一个中央集权机构，由中央集权机构决定创建一个作为仪式中心的城市，并要求大家为共同或更大的利益努力工作。人们之所以选择成立“中央政府”，是因为意识到合作将使个人和整个社区受益。考古学家露丝?沙迪认为，对神的崇拜是凝聚力和社会平衡背后的驱动力。人们之所以接受中央集权政府的存在，是因为他们相信统治者可以在人与生者的社会和神与死者的社会之间进行调解，政府的管理对于保证生活是必要的。卡拉尔社会展现出一定的复杂性，种种迹象表明，卡拉尔不仅仅是一个简单的农业社会，而且是一个具有一定组织能力和复杂结构的社会实体，已经具备早期国家的基本要素。

    宗教作为意识形态与政治权力相结合。卡拉尔的金字塔、广场和祭坛等雄伟建筑不仅是宗教仪式的场所，也是社会和政治活动的中心。金字塔象征着与天界的联系，广场则是集体仪式和庆典的场所。卡拉尔人信奉多神教，崇拜多种神灵。这些神灵与自然现象、农业、天气和其他重要生活领域有关。祭祀活动在卡拉尔占据重要地位。人们通过圣火祭坛进行各种形式的献祭，包括毛发、珠子、石英碎片、骨器、木器、纺织品、鱼类、贝类等，这些被认为是向神灵表达敬意和请求庇护的方式。统治者和祭司被视为祖先和神灵的代表或中介，他们通过控制宗教仪式、祭祀活动和宗教建筑来巩固自己的权威。卡拉尔的宗教活动是在音乐伴奏中进行的，在这里出土了一套由秃鹰和鹈鹕翼骨制成并绘有鸟类和猴子图案的横笛（共32支），一套由骆马骨和鹿骨制成的号角（共38支），一套由芦苇和棉线制作的排笛。在没有军事力量的情况下，宗教成为卡拉尔统治者凝聚和控制社会的力量，它使卡拉尔—苏佩河谷的居民团结起来。

    科技知识在文明发展中发挥重要作用。卡拉尔人开发了先进的农业灌溉系统，修建水渠和水库，这对于他们在干旱环境中维持农业生产至关重要。在设计和建造大型纪念性建筑以及修建灌溉水渠时，显然运用了算术和几何知识。有证据表明，卡拉尔人已经具备天文学知识，并将其应用到与经济、宗教活动有关的历法制定中。在卡拉尔上城C组的公共广场中央竖立着一块巨石，是当时用来观测天文的。他们已经发明一种记录信息的工具系统，如在上城C组的画廊金字塔中，考古学家发现一件纺织品遗物，被认为是“基普”，即用作记录工具的一套打结绳线。同时，在上城B组小金字塔的三个石块上还发现了基普的图画。这说明卡拉尔人已经在使用基普，比印加人早数千年之久。考古学家还发现，一些药用植物多次出现在墓葬中，表明卡拉尔人已经了解一些植物的药用价值。在纺织技术方面，他们利用棉花纤维编织连衣裙，采用穿插和缠绕的方法，还制作了渔网、鞋类、包类、绳索等。圣火祭坛下方建造的地下通风系统，能够引导风力保持火焰燃烧，并将烟雾排到室外。需要指出的是，虽然早在公元前4000年厄瓜多尔的瓦尔迪维亚等地就已经开始生产陶器，但卡拉尔人并没有使用或自己生产陶器。他们用葫芦作为器皿，用木头雕刻勺子，用石头雕制盘子。因此，卡拉尔文明属于“前陶瓷”文明，这一点已被考古学家们认定。

    由于强烈地震和灾难性气候变化，卡拉尔—苏佩文明在公元前1800年左右被遗弃。虽然如此，它在农业、城市建筑、社会政治组织、宗教文化等方面对后来安第斯文明的发展产生深远影响。可以说，卡拉尔—苏佩文明是安第斯文明的摇篮。

    本文转自《光明日报》2024年11月25日

  • JEFFREY DING《Technology and the Rise of Great Powers：HOW DIFFUSION SHAPES ECONOMIC COMPETITION》

    CONTENTS
    1    Introduction
    2    GPT Diffusion Theory  
    3    The First Industrial Revolution and Britain’s Rise
    4    The Second Industrial Revolution and America’s Ascent
    5    Japan’s Challenge in the Third Industrial Revolution 
    6    A Statistical Analysis of Software Engineering Skill Infrastructure and Computerization
    7    US-China Competition in AI and the Fourth Industrial Revolution 
    8    Conclusion

    1 Introduction

    IN JULY 2018, the BRICS nations (Brazil, Russia, India, China, and South Africa) convened in Johannesburg around a specific, noteworthy theme: “Collaboration for Inclusive Growth and Shared Prosperity in the Fourth Industrial Revolution.” The theme was noteworthy in part because of its specificity. Previous iterations of the BRICS summit, which gathers five nations that account for about 40 percent of the world’s population and 25 percent of the world’s GDP,1 had tackled fuzzy slogans such as “Stronger Partnership for a Brighter Future” and “Broad Vision, Shared Prosperity.” What stood out not only about that year’s theme but also in comments by BRICS leaders at the summit was an unambiguous conviction that the world was undergoing a momentous season of technological change—one warranting the title “Fourth Industrial Revolution.”2

    Throughout the gathering, leaders of these five major emerging economies declared that the ongoing technological transition represented a rare opportunity for accelerating economic growth. When Chinese president Xi Jinping addressed the four other leaders of major emerging economies, he laid out the historical stakes of that belief:

    From the mechanization of the first industrial revolution in the 18th century, to the electrification of the second industrial revolution in the 19th century, to the informatization of the third industrial revolution in the 20th century, rounds of disruptive technological innovation have … fundamentally changed the development trajectory of human history.3

    Citing recent breakthroughs in cutting-edge technologies like artificial intelligence (AI), Xi proclaimed, “Today, we are experiencing a larger and deeper round of technological revolution and industrial transformation.”4

    While the BRICS summit did not explicitly address how the Fourth Industrial Revolution could reshape the international economic order, the implications of Xi’s remarks loomed in the backdrop. In the following months, Chinese analysts and scholars expanded upon them, especially the connection he drew between technological disruption and global leadership transitions.5 One commentary on Xi’s speech, published on the website of the authoritative Chinese Communist Party publication Study Times, detailed the geopolitical consequences of past technological revolutions: “Britain seized the opportunity of the first industrial revolution and established a world-leading productivity advantage.… After the second industrial revolution, the United States seized the dominance of advanced productivity from Britain.”6 In his analysis of Xi’s address, Professor Jin Canrong of Renmin University, an influential Chinese international relations scholar, argued that China has a better chance than the United States of winning the competition over the Fourth Industrial Revolution.7

    This broad sketch of power transition by way of technological revolution also resonates with US policymakers and leading thinkers. In his first press conference after taking office, President Joe Biden underscored the need to “own the future” as it relates to competition in emerging technologies, pledging that China’s goal to become “the most powerful country in the world” was “not going to happen on [his] watch.”8 In 2018, the US Congress stood up the National Security Commission on Artificial Intelligence (NSCAI), an influential body that convened leading government officials, technology experts, and social scientists to study the national security implications of AI. Comparing AI’s possible impact to past technologies like electricity, the NSCAI’s 756-page final report warned that the United States would soon lose its technological leadership to China if it did not adequately prepare for the “AI revolution.”9

    Caught up in the latest technical advances coming out of Silicon Valley or Beijing’s Zhongguancun, these sweeping narratives disregard the process by which emerging technologies can influence a power transition. How do technological revolutions affect the rise and fall of great powers? Is there a discernible pattern that characterizes how previous industrial revolutions shaped the global balance of power? If such a pattern exists, how would it inform our understanding of the Fourth Industrial Revolution and US-China technological competition?

    Conventional Wisdom on Technology-Driven Power Transitions

    International relations scholars have long observed the link between disruptive technological breakthroughs and the rise and fall of great powers.10 At a general level, as Yale historian Paul Kennedy has established, this process involves “differentials in growth rates and technological change, leading to shifts in the global economic balances, which in turn gradually impinge upon the political and military balances.”11 Yet, as is the case with present-day speculation about the effects of new technologies on the US-China power balance, largely missing from the international relations literature is an explanation of how technological change creates the conditions for a great power to leapfrog its rival. Scholars have carefully scrutinized how shifts in economic balances affect global military power and political leadership, but there is a need for further investigation into the very first step of Kennedy’s causal chain: the link between technological change and differentials in long-term growth rates among great powers.12

    Among studies that do examine the mechanics of how technological change shapes economic power transitions, the standard explanation stresses dominance over critical technological innovations in new, fast-growing industries (“leading sectors”). Britain became the world’s most productive economy, according to this logic, because it was home to new advances that transformed its burgeoning textile industry, such as James Hargreaves’s spinning jenny. In the same vein, Germany’s mastery of major breakthroughs in the chemical industry is seen as pivotal to its subsequent challenge to British economic leadership. Informed by historical analysis, the leading-sector (LS) perspective posits that, during major technological shifts, the global balance of economic power tips toward “the states which were the first to introduce the most important innovations.”13

    Why do the benefits of leading sectors accrue to certain countries? Explanations vary, but most stress the goodness-of-fit between a nation’s domestic institutions and the demands of disruptive technologies. At a general level, some scholars argue that rising powers quickly adapt to new leading sectors because they are unburdened by the vested interests that have built up in more established powers.14 Others point to more specific factors, including the degree of government centralization or sectoral governance arrangements.15 Common to all these perspectives is a focus on the institutions that allow one country to first introduce major breakthroughs in an emerging industry. In the case of Britain’s rise, for example, many influential histories highlight institutions that supported “heroic” inventors.16 Likewise, accounts of Germany’s success with leading sectors focus on its investments in scientific education and industrial research laboratories.17

    The broad outlines of LS theory exert substantial influence in academic and policymaking circles. Field-defining texts, including works by Robert Gilpin and Paul Kennedy, use the LS model to map out the rise and fall of great powers.18 In a review of international relations scholarship, Daniel Drezner summarizes their conclusions: “Historically, a great power has acquired hegemon status through a near-monopoly on innovation in leading sectors.”19

    The LS template also informs contemporary discussion of China’s challenge to US technological leadership. In another speech about how China could leverage this new round of industrial revolution to become a “science and technology superpower,” President Xi called for China to develop into “the world’s primary center for science and high ground for innovation.”20 As US policymakers confront China’s growing strength in emerging technologies like AI, they also frame the competition in terms of which country will be able to generate radical advances in new leading sectors.21

    Who did it first? Which country innovated it first? Presented with technical breakthroughs that inspire astonishment, it is only natural to gravitate toward the moment of initial discovery. When today’s leaders evoke past industrial revolutions, as Xi did in his speech to the BRICS nations, they tap into historical accounts of technological progress that also center the moment of innovation.22 The economist and historian Nathan Rosenberg diagnoses the problem with these innovation-centric perspectives: “Much less attention … if any at all, has been accorded to the rate at which new technologies have been adopted and embedded in the productive process. Indeed the diffusion process has often been assumed out of existence.”23 Yet, without the humble undertaking of diffusion, even the most extraordinary advances will not matter.

    Taking diffusion seriously leads to a different explanation for how technological revolutions affect the rise and fall of great powers. A diffusion-centric framework probes what comes after the hype. Less concerned with which state first introduced major innovations, it instead asks why some states were more successful at adapting and embracing new technologies at scale. As outlined in the next section, this alternative pathway points toward a different set of institutional factors that underpin leadership in times of technological leadership, in particular institutions that widen the base of engineering skills and knowledge linked to foundational technologies.

    GPT Diffusion Theory

    In September 2020, the Guardian published an opinion piece arguing that humans should not fear new breakthroughs in AI. Noting that “Stephen Hawking has warned that AI could ‘spell the end of the human race,’ ” the article’s “author” contends that “I am here to convince you not to worry. Artificial intelligence will not destroy humans. Believe me.”24 If one came away from this piece with the feeling that the author had a rose-tinted view of the future of AI, it would be a perfectly reasonable judgment. After all, the author was GPT-3, an AI model that can understand and produce humanlike text.

    Released earlier that year by OpenAI, a San Francisco–based AI lab, GPT-3 surprised everyone—including its designers—with its versatility. In addition to generating poetry and essays like the Guardian op-ed from scratch, early users demonstrated GPT-3’s impressive capabilities in writing code, translating languages, and building chatbots.25 Six months after its launch, one compilation listed sixty-six unique use cases of GPT-3, which ranged from automatically updating spreadsheets to generating website landing pages.26 Two years later, OpenAI’s acclaimed ChatGPT model, built on an improved version of GPT-3, would set the internet aflame with its wide-ranging capabilities.27

    While the name “GPT-3” derives from a class of language models known as “generative pre-trained transformers,” the abbreviation, coincidentally, also speaks to the broader significance of recent breakthroughs in AI: the possible arrival of the next general-purpose technology (GPT). Foundational breakthroughs in the ability of computers to perform tasks that usually require human intelligence have the potential to transform countless industries. Hence, scholars and policymakers often compare advances in AI to electricity, the prototypical GPT.28 As Kevin Kelly, the former editor of WIRED, once put it, “Everything that we formerly electrified we will now cognitize … business plans of the next 10,000 startups are easy to forecast: Take X and add AI.”29

    In this book, I argue that patterns in how GPTs diffuse throughout the economy illuminate a novel explanation for how and when technological changes affect power transitions. The emergence of GPTs—fundamental advances that can transform many application sectors—provides an opening for major shifts in economic leadership. Characterized by their scope for continuous improvement, pervasive applicability across the economy, and synergies with other technological advances, GPTs carry an immense potential for boosting productivity.30 Carefully tracking how the various applications of GPTs are adopted across various industries, a process I refer to as “GPT diffusion,” is essential to understanding how technological revolutions disrupt economic power balances.

    Based on the experience of past GPTs, this potential productivity boost comes with one notable caveat: the full impact of a GPT manifests only after a gradual process of diffusion into pervasive use.31 GPTs demand structural changes across a range of technology systems, which involve complementary innovations, organizational adaptations, and workforce adjustments.32 For example, electrification’s boost to productivity materialized about five decades after the introduction of the first electric dynamo, occurring only after factories had restructured their layouts and there had been interrelated breakthroughs in steam turbines.33 Fittingly, after the release of GPT-3, OpenAI CEO Sam Altman alluded to this extended trajectory: “The GPT-3 hype is way too much … it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.”34

    Informed by historical patterns of GPT diffusion, my explanation for technology-driven power transitions diverges significantly from the standard LS account. Specifically, these two causal mechanisms differ along three key dimensions, which relate to the technological revolution’s impact timeframe, phase of relative advantage, and breadth of growth. First, while the GPT mechanism involves a protracted gestation period between a GPT’s emergence and resulting productivity boosts, the LS mechanism assumes that there is only a brief window during which countries can capture profits in leading sectors. “The greatest marginal stimulation to growth may therefore come early in the sector’s development at the time when the sector itself is expanding rapidly,” William Thompson reasons.35 By contrast, the most pronounced effects on growth arrive late in a GPT’s development.

    Second, the GPT and LS mechanisms also assign disparate weights to innovation and diffusion. Technological change involves a phase when the technology is first incubated as a viable commercial application (“innovation”) and a phase when the innovation permeates across a population of potential users (“diffusion”). The LS mechanism is primarily concerned about which country dominates innovation in leading sectors, capturing the accompanying monopoly profits.36 Under the GPT mechanism, successful adaptation to technological revolutions is less about being the first to introduce major innovations and more about effectively adopting GPTs across a wide range of economic sectors.

    Third, regarding the breadth of technological transformation and economic growth, the LS mechanism focuses on the contributions of a limited number of leading sectors and new industries to economic growth in a particular period.37 In contrast, GPT-fueled productivity growth is spread across a broad range of industries.38 Dispersed productivity increases from many industries and sectors come from the extension and generalization of localized advances in GPTs.39 Thus, the LS mechanism expects the breadth of growth in a particular period to be concentrated in leading sectors, whereas the GPT mechanism expects technological complementarities to be dispersed across many sectors.

    A clearer understanding of the contours of technological change in times of economic power transition informs which institutional variables matter most. If the LS trajectory holds, then the most important institutional endowments and responses are those that support a monopoly on innovation in leading sectors. In the context of skill formation, institutional competencies in science and basic research gain priority. For instance, the conventional explanation of Germany’s industrial rise in the late nineteenth century attributes its technological leadership to investments in industrial research labs and highly skilled chemists. These supported Germany’s dominance of the chemical industry, a key LS of the period.40

    The impact pathway of GPTs brings another set of institutional complementarities to the fore. GPT diffusion theory highlights the importance of “GPT skill infrastructure”: education and training systems that widen the pool of engineering skills and knowledge linked to a GPT. When widespread adoption of GPTs is the priority, it is ordinary engineers, not heroic inventors, who matter most. Widening the base of engineering skills associated with a GPT cultivates a more interconnected technological system, spurring cross-fertilization between institutions optimized for applied technology and those oriented toward foundational research.41

    Returning to the example of late-nineteenth-century advances in chemicals, GPT diffusion spotlights institutional adjustments that differ from those of the LS mechanism. In a decades-long process, innovations in chemical engineering practices gradually enabled the chemicalization of procedures common to many industries beyond synthetic dyes, which was controlled by Germany. Despite trailing Germany in the capacity to produce elite chemists and frontier chemical research, the United States was more effective at adapting to chemicalization because it first institutionalized the discipline of chemical engineering.42

    Of course, since GPT diffusion depends on factors aside from human capital, GPT skill infrastructure represents one of many institutional forces at work. Standards-setting organizations, financing bodies, and the competitiveness of markets can all influence the flow of information between the GPT domain and application sectors.43 Since institutions of skill formation produce impacts that spill over into and complement other institutional arrangements, they comprise the focus of my analysis.44

    Assessing GPT Diffusion across Industrial Revolutions

    To test this argument, I employ a mixed-methods approach that pairs qualitative historical analysis with quantitative methods. Historical case studies permit me to thoroughly trace the interactions between technologies and institutions among great powers in previous industrial revolutions. I then explore the generalizability of GPT diffusion theory beyond the chosen set of great powers. Using data on nineteen countries from 1995 to 2020, I analyze the theorized connection between GPT skill infrastructure in software engineering and computerization rates.

    To investigate the causal processes that connect technological changes to economic power transitions, I set the LS mechanism against the GPT diffusion mechanism across three historical case studies: Britain’s rise to preeminence in the First Industrial Revolution (IR-1); America’s and Germany’s overtaking of Britain in the Second Industrial Revolution (IR-2); and Japan’s challenge to America’s technological dominance in the Third Industrial Revolution (IR-3), or what is sometimes called the “information revolution.” This case setup allows for a fair and decisive assessment of the explanatory relevance of GPT diffusion theory in comparison to LS theory. Because the IR-1 and IR-2 function as typical cases where the cause and outcome are clearly present, they are ideal for developing and testing mechanism-based theories.45 The IR-3, a deviant case in that a technological revolution is not followed by an economic power transition, provides a different but still useful way to compare the two mechanisms.

    The IR-1 (1780–1840) is a paradigmatic case of technology-driven power transition. It is well established that the IR-1’s technological advances propelled Great Britain to unrivaled economic supremacy. As for the specific causal pathway, international relations scholarship tends to attribute Britain’s rise to its monopoly over innovation in cotton textiles and other leading sectors. According to these accounts, Britain’s technological leadership in the IR-1 sprang from its institutional capacity to nurture genius inventors in these sectors. Since the publication of these field-defining works, economic and technology historians have uncovered that the impacts on British industrialization of the two most prominent areas of technological change, cotton textiles and iron, followed different trajectories. Often relying on formal econometric methods to understand the impact of key technologies, these historical accounts question the prevailing narrative of the IR-1.

    The IR-2 (1870–1914) supplies another opportunity to pit GPT diffusion theory against the LS account. International relations scholars interpret the IR-2 as a case in which Britain’s rivals challenged its economic leadership because they first introduced significant technological advances in leading sectors. Particular emphasis is placed on Germany’s ability to corner market shares in chemicals, which is linked to its strengths in scientific education and industrial research institutions. More granular data on cross-national differences in engineering education suggest that the U.S. technological advantage rested on the country’s wide base of mechanical engineers. Combined with detailed tracing of the pace and extent of technology adoption during this period, this chapter’s evidence suggests modifications to conventional understandings of the IR-2.

    In the IR-3 (1960–2000), fundamental breakthroughs in information and communication technologies presented another opening for a shift in economic leadership. During this period, prominent thinkers warned that Japan’s lead in industries experiencing rapid technological change, including semiconductors and consumer electronics, would threaten U.S. economic leadership. Influential scholars and policymakers advocated for the United States to adopt Japan’s keiretsu system of industrial organization and its aggressive industrial policy approach. Ultimately, Japan’s productivity growth stalled in the 1990s. Given the absence of an economic power transition, the primary function of the IR-3 case therefore is to provide disconfirming evidence of the two explanations. If the components of the LS mechanism were present, then the fact that an economic power transition did not occur would damage the credibility of the LS mechanism. The same condition applies to GPT diffusion theory.

    In each of the cases, I follow the same standardized procedures. First, I test three pairs of competing propositions about the key technological trajectories, derived from the different expectations of the LS and GPT mechanisms related to the impact timeframe, phase of relative advantage, and breadth of growth. Then, depending on whether the LS or GPT trajectory better accords with the historical evidence, I analyze the goodness-of-fit between the institutional competencies of leading industrial powers and the prevailing trajectory. For instance, if an industrial revolution is better characterized by the GPT trajectory, then the corresponding case analysis should show that differences in GPT skill infrastructure determine which powers rise and fall. Although I primarily distinguish GPT diffusion theory from the LS model, I also examine alternative factors unique to the particular case, as well as two other prominent explanations of how advanced economies differentially benefit from technological changes (the varieties of capitalism and threat-based approaches).

    The historical case analysis supports the explanatory power of the GPT mechanism over the LS mechanism. In all three periods, technological changes affected the rise and fall of great powers in a gradual, decades-long impact pathway that advantaged those that effectively diffused GPTs across a broad range of sectors. Education and training systems that cultivated broad pools of engineering skills proved crucial to GPT diffusion.

    Evaluating these two competing explanations requires a clear understanding of the cause and outcome that bracket both the GPT and LS mechanisms. The hypothesized cause is a “technological revolution,” or a period characterized by particularly disruptive technological advances.46 Since the shape of technological change is uneven, not all improvements in useful knowledge are relevant for power transitions.47 However, some extraordinary clusters of technological breakthroughs, often deemed industrial revolutions by historians, do have ramifications for the rise and fall of great powers.48 I am primarily interested in the pathway by which these technological revolutions influence the global distribution of power.

    The outcome variable of interest is an economic power transition, in which one great power sustains productivity growth at higher levels than its rivals. The balance of power can shift in many ways; here I focus on relative economic growth rates because they are catalysts for intensifying hegemonic rivalries.49 Productivity growth, in particular, determines economic growth over the long run. Unique in its fungibility with other forms of power, sustained economic growth is central to a state’s ability to exert political and military influence. As demonstrated by the outcomes of interstate conflicts between great powers, economic and productive capacity is the foundation of military power.50

    Lastly, the quantitative analysis supplements the historical case studies by scrutinizing the generalizability of GPT diffusion theory outside of great powers. A key observable implication of my argument is that the rate at which a GPT spreads throughout the economy owes much to that country’s institutional capacity to widen the pool of pertinent engineering skills and knowledge. Using a novel method to estimate the breadth of software engineering education at a cross-national level, I analyze the theorized connection between GPT skill infrastructure and computerization rates across nineteen advanced and emerging economies from 1995 to 2020. I supplement my time-series cross-sectional models with a duration analysis and cross-sectional regressions. Robust to many alternative specifications, my results show that, at least for computing technology, advanced economies that have higher levels of GPT skill infrastructure preside over higher rates of GPT diffusion.

    Key Contributions

    The book makes several contributions to scholarship on power transitions and the effects of technological change on international politics. First, it puts forward a novel explanation for how and when significant technological breakthroughs generate a power transition in the international system. GPT diffusion theory revises the dominant theory based on leading sectors, which holds significant sway over academic and policymaking circles. By deepening our understanding of how technological revolutions influence shifts in economic leadership, this book also contributes to long-standing debates about the causes of power transitions.51

    Second, the findings of this book bear directly on present-day technological competition between the United States and China. Emphasizing where fundamental breakthroughs are first seeded, the LS template strongly informs not only assessments of the US-China competition for technological leadership but also the ways in which leading policymakers in both countries formulate technology strategies. It is no coincidence that the three cases in this study match the three technological revolutions referenced by Chinese president Xi in his speech on the IR-4 to the BRICS summit.

    As chapter 7 explores in detail, GPT diffusion theory suggests that Xi, along with other leading policymakers and thinkers in both the United States and China, has learned the wrong lessons from previous industrial revolutions. If the IR-4 reshapes the economic power balance, the impact will materialize through a protracted period during which a GPT, such as AI, acquires a variety of uses in a wide range of productive processes. GPT skill infrastructure, not the flashy efforts to secure the high ground in innovation, will decide which nation owns the future in the IR-4.

    Beyond power transitions, Technology and the Rise of Great Powers serves as a template for studying the politics of emerging technologies. An enduring dilemma is that scholars either assign too much weight to technological change or underestimate the effects of new technologies.52 Approaches that emphasize the social shaping of technology neglect that not all technologies are created equal, whereas technologically deterministic approaches discount the influence of political factors on technological development. By first distinguishing GPTs, together with their pattern of diffusion, from other technologies and technological trajectories, and then showing how social and political factors shape the pace and direction of GPT diffusion, my approach demonstrates a middle way forward.

    Roadmap for the Book

    The book proceeds as follows. Chapter 2 fleshes out the key differences between GPT diffusion theory and the LS-based account, as well as the case analysis procedures and selection strategy that allow me to systematically evaluate these two causal mechanisms. The bulk of the evidence follows in three case studies that trace how technological progress affected economic power transitions in the First, Second, and Third Industrial Revolutions.

    The first two case studies, the IR-1 and IR-2, show that a gap in the adoption of GPTs, as opposed to monopoly profits from dominating LS innovations, was the crucial driver of an economic power transition. In both cases, the country that outpaced its industrial rivals made institutional adjustments to cultivate engineering skills related to the key GPT of the period. The IR-1 case, discussed in chapter 3, reveals that Britain was the most successful in fostering a wide pool of machinists who enabled the widespread diffusion of advances in iron machinery. In considering the IR-2 case, chapter 4 highlights how the United States surpassed Britain as the preeminent economic power by fostering a wide base of mechanical engineering talent to spread interchangeable manufacturing methods.

    The IR-3 case, presented in chapter 5, demonstrates that technological revolutions do not necessarily always produce an economic power transition. The fact that Japan did not overtake the United States as the economic leader would provide disconfirming evidence of both the LS and GPT mechanisms, if the components of these mechanisms were present. In the case of the LS mechanism, Japan did dominate innovations in the IR-3’s leading sectors, including consumer electronics and semiconductor components. In contrast, the IR-3 does not discredit the GPT mechanism because Japan did not lead the United States in the diffusion of information and communications technology across a wide variety of economic sectors.

    Chapter 6 uses large-n quantitative analysis to explore how GPT diffusion applies beyond great powers. Chapter 7 applies the GPT diffusion framework to the implications of modern technological breakthroughs for the US-China power balance. Focusing on AI technology as the next GPT that could transform the international balance of power, I explore the extent to which my findings generalize to the contemporary US-China case. I conclude in chapter 8 by underscoring the broader ramifications of the book.

    2 GPT Diffusion Theory

    HOW AND WHEN do technological changes affect the rise and fall of great powers? Specifically, how do significant technological breakthroughs result in differential rates of economic growth among great powers? International relations scholars have long observed that rounds of technological revolution lead to upheaval in global economic leadership, bringing about a power transition in the international system. However, few studies explore how this process occurs.

    Those that do tend to fixate on the most dramatic aspects of technological change—the eureka moments and first implementations of radical inventions. Consequently, the standard account of technology-driven power transitions stresses a country’s ability to dominate innovation in leading sectors. By exploiting brief windows in which to monopolize profits in new industries, the country that dominates innovation in these sectors rises to become the world’s most productive economy. Explanations vary regarding why the benefits of leading sectors tend to accrue in certain nations. Some scholars argue that national systems of political economy that accommodate rising challengers can more readily accept and support new industries. Leading economies, by contrast, are victims of their past success, burdened by powerful vested interests that resist adaptation to disruptive technologies.1 Other studies point to more specific institutional factors that account for why some countries monopolize leading sectors, such as the degree of government centralization or industrial governance structures.2

    An alternative explanation, based on the diffusion of general-purpose technologies (GPTs), draws attention to the less spectacular process by which fundamental innovations gradually diffuse throughout many industries. The rate and scope of diffusion is particularly relevant for GPTs, which are distinguished by their scope for continual improvement, broad applicability across many sectors, and synergies with other technological advances. Recognized by economists and historians as “engines of growth,” GPTs hold immense potential for boosting productivity.3 Realizing this promise, however, necessitates major structural changes across the technology systems linked to the GPT, including complementary innovations, organizational changes, and an upgrading of technical skills. Thus, GPTs lead to a productivity boost only after a “gradual and protracted process of diffusion into widespread use.”4 This is why more than five decades passed before key innovations in electricity, the quintessential GPT, significantly transformed manufacturing productivity.5

    The process of GPT diffusion illuminates a pathway from technological change to power transition that diverges from the LS account (figure 2.1). Under the GPT mechanism, some great powers sustain economic growth at higher levels than their rivals do because, during a gradual process spanning decades, they more intensively adopt GPTs across a broad range of industries. This is analogous to a marathon run on a wide road. The LS mechanism, in contrast, specifies that one great power rises to economic leadership because it dominates innovations in a limited set of leading sectors and captures the accompanying monopoly profits. This is more like a sprint through a narrow running lane.

    Why are some countries more successful at GPT diffusion? Building from scholarship arguing that a nation’s success in adapting to emerging technologies is determined by the fit between its institutions and the demands of evolving technologies, I argue that the GPT diffusion pathway informs the institutional adaptations crucial to success in technological revolutions.6 Unlike institutions oriented toward cornering profits in leading sectors, those optimized for GPT diffusion help standardize and spread novel best practices between the GPT sector and application sectors. Education and training systems that widen the base of engineering skills and knowledge linked to new GPTs, or what I call “GPT skill infrastructure,” are essential to all of these institutions.

    FIGURE 2.1. Causal Diagrams for LS and GPT Mechanisms

    The differences between these two theories of technological change and power transition are made clear when one country excels in the institutional competencies for LS product cycles but does not dominate GPT diffusion. Take, for example, chemical innovations and Germany’s economic rise in the late nineteenth century. Germany dominated major innovations in chemicals and captured nearly 90 percent of all global exports of synthetic dyestuffs.7 In line with the LS mechanism, this success was backed by Germany’s investments in building R&D labs and training doctoral students in chemistry, as well as a system of industrial organization that facilitated the rise of three chemical giants.8 Yet it was the United States that held an advantage in adopting basic chemical processes across many industries. As expected by GPT diffusion theory, the United States held institutional advantages in widening the base of engineering skills and knowledge necessary for chemicalization on a wide scale.9 This is when the ordinary tweakers and the implementers come to the fore, and the star scientists and inventors recede to the background.10

    The rest of this chapter fleshes out my theoretical framework. It first clarifies the outcome I seek to explain: an economic power transition in which one great power becomes the economic leader by sustaining productivity growth at higher levels than its rivals. The starting point of my argument is that the diffusion of GPTs is central to the relationship between technological change and productivity leadership. This chapter explicates this argument by justifying the emphasis on both GPTs and diffusion, highlighting the differences between the GPT and LS mechanisms. It then extends the analysis to the institutional competencies that synergize with GPT trajectories. From the rich set of technology-institution interactions identified by evolutionary economists and comparative institutionalists, I justify my focus on institutions that enable countries to widen the skill base required to spread GPTs across industries. After differentiating my argument from alternative explanations, the chapter closes with a description of the book’s research methodology.11

    The Outcome: Long-Term Economic Growth Differentials and Power Transitions

    Power transitions are to the international system as earthquakes are to the geological landscape. Shifts in the relative power of leading nations send shock waves throughout the international system. What often follows is conflict, the most devastating form of which is a war waged by coalitions of great powers for hegemony over the globe.12 Beyond heightened risks of conflict, the aftershocks of power transitions reverberate in the architecture of the international order as victorious powers remake international institutions in their own images.13

    While the power transition literature largely tackles the consequences of power transitions, I treat the rise and fall of great powers as the outcome to be explained. This follows David Baldwin’s instruction for international relations scholars “to devote more attention to treating power as a dependent variable and less to treating it as an independent variable.”14 Specifically, I explore the causes of “economic power transitions,” in which one great power sustains economic growth rates at higher levels than its rivals.15

    It might not be obvious, at first glance, why I focus on economic power. After all, power is a multidimensional, contested concept that comes in many other forms. The salience of certain power resources depends on the context in which a country draws upon them to exert influence.16 For my purposes, differentials in economic growth are the most relevant considerations for intensifying hegemonic rivalry. An extensive literature has demonstrated that changes in relative economic growth often precede hegemonic wars.17

    Moreover, changes in global political and military leadership often follow shifts in economic leadership. As the most fungible mode of power, economic strength undergirds a nation’s influence in global politics and its military capabilities.18 The outcomes of interstate conflicts bear out that economic and productive capacity is the foundation of military power.19 Paul Kennedy concludes that

    all of the major shifts in the world’s military-power balances have followed alterations in the productive balances … the rising and falling of the various empires and states in the international system has been confirmed by the outcomes of the major Great Power wars, where victory has always gone to the side with the greatest material resources.20

    How does one identify if or when an economic power transition occurs? Phrased differently, how many years does a great power need to lead its rivals in economic growth rates? How large does that gap have to reach? Throughout this book, I judge whether an economic power transition has occurred based on one great power attaining a lead in overall economic productivity over its rivals by sustaining higher levels of productivity growth rates.21 Productivity growth ensures that efficient and sustainable processes are fueling growth in total economic output. Additionally, productivity is the most important determinant of economic growth in the long run, which is the appropriate time horizon for understanding power transitions. “Productivity isn’t everything, but in the long run it is almost everything,” states Nobel Prize–winning economist Paul Krugman.22

    Alternative conceptualizations of economic power cannot capture how effectively a country translates technological advance into national economic growth. Theories of geo-economics, for instance, highlight a state’s balance of trade in certain technologically advanced industries.23 Other studies emphasize a state’s share of world-leading firms.24 National rates of innovation, while more inclusive, measure the generation of novel technologies but not diffusion across commercial applications, thereby neglecting the ultimate impact of technological change.25 Compared to these indicators, which account for only a small portion of the value-added activities in the economy, productivity provides a more comprehensive measure of economic leadership.26

    This focus on productivity is supported by recent work on power measurement, which questions measures of power resources based on economic size. Without accounting for economic efficiency, solely relying on measures of gross economic and industrial output provides a distorted view of the balance of power, particularly where one side is populous but poor.27 If national power was measured by GDP alone, China was the world’s most powerful country during the first industrial revolution. However, China’s economy was far from the productivity frontier. In fact, as the introduction chapter spotlighted, the view that China fell behind the West because it could not capitalize on productivity-boosting technological breakthroughs is firmly entrenched in the minds of leading Chinese policymakers and thinkers.

    Lastly, it is important to clarify that I limit my analysis of productivity differentials to great powers.28 In some measures of productivity, other countries may rank highly or even outrank the countries I study in my cases. In the current period, Switzerland and other countries have higher GDP per capita than the United States; before World War I, Australia was the world leader in productivity, as measured by GDP per labor-hour.29 However, smaller powers like pre–World War I Australia and present-day Switzerland are excluded from my study of economic power transitions, as they lack the baseline economic and population size to be great powers.30

    There is no exact line that distinguishes great powers from other countries.31 Kennedy’s seminal text The Rise and Fall of the Great Powers, for instance, has been challenged for not providing a precise definition of great power.32 Fortunately, across all the case studies in this book, there is substantial agreement on the great powers of the period. According to one measure of the distribution of power resources, which spans 1816 to 2012 and incorporates both economic size and efficiency, all the countries I study rank among the top six at the beginning of the case.33

    The Diffusion of GPTs

    Scholars often gravitate to technological change as the source of a power transition in which the mantle of industrial preeminence changes hands. However, there is less clarity over the process by which technical breakthroughs translate into this power shift among countries at the technological frontier. I argue that the diffusion of GPTs is the key to this mechanism. In this section, I first outline why my theory privileges GPTs over other types of technology. I then set forth why diffusion should be prioritized over other phases of technological change, especially innovation. Finally, I position GPT diffusion theory against the leading sector (LS) model, which is the standard explanation in the international relations literature.

    Why GPTs?

    Not all technologies are created equal. When assessed on their potential to transform the productivity of nations, some technical advances, such as the electric dynamo, rank higher than others, such as an improved sleeping bag. My theory gives pride of place to GPTs, such as electricity and the steam engine, which have historically generated waves of economy-wide productivity growth.34 Assessed on their own merits alone, even the most transformative technological changes do not tip the scale far enough to significantly affect aggregate economic productivity.35 GPTs are different because their impact on productivity comes from accumulated improvements across a wide range of complementary sectors; that is, they cannot be judged on their own merits alone.

    Recognized by economists and economic historians as “engines of growth,” GPTs are defined by three characteristics.36 First, they offer great potential for continual improvement. While all technologies offer some scope for improvement, a GPT “has implicit in it a major research program for improvements, adaptations, and modifications.”37 Second, GPTs acquire pervasiveness. As a GPT evolves, it finds a “wide variety of uses” and a “wide range of uses.”38 The former refers to the diversity of a GPT’s use cases, while the latter alludes to the breadth of industries and individuals using a GPT.39 Third, GPTs have strong technological complementarities. In other words, the benefits from innovations in GPTs come from how other linked technologies are changed in response and cannot be modeled from a mere reduction in the costs of inputs to the existing production function. For example, the overall energy efficiency gains from merely replacing a steam engine with an electric motor were minimal; the major benefits from factory electrification came from electric “unit drive,” which enabled machines to be driven individually by electric motors, and a radical redesign of plants.40

    Taken together, these characteristics suggest that the full impact of a GPT materializes via an “extended trajectory” that differs from those associated with other technologies. Economic historian Paul David explains:

    We can recognize the emergence of an extended trajectory of incremental technical improvements, the gradual and protracted process of diffusion into widespread use, and the confluence with other streams of technological innovation, all of which are interdependent features of the dynamic process through which a general purpose engine acquires a broad domain of specific applications.41

    For example, the first dynamo for industrial application was introduced in the 1870s, but the major boost of electricity to overall manufacturing productivity did not occur until the 1920s. Like other GPT trajectories, electrification required a protracted process of workforce skill adjustments, organizational adaptations, such as changes in factory layout, and complementary innovations like the steam turbine, which enabled central power generation in the form of utilities.42 To track the full impact of these engines of growth, one must travel the long roads of their diffusion.

    Why Diffusion?

    All technological trajectories can be divided into a phase when the technology is incubated and then first introduced as a viable commercial application (“innovation”) and a phase when the innovation spreads through a population of potential users, both nationally and internationally (“diffusion”).43 Recognizing this commonly accepted distinction, other studies of the scientific and technological capabilities of nations primarily focus on innovation.44 I depart from other works by giving priority to diffusion, since that is the phase of technological change most significant for GPTs.45

    Undeniably, the activities and conditions that produce innovation can also spur diffusion.46 A firm’s ability to conduct breakthrough R&D does not just create new knowledge but also boosts its capacity to assimilate innovations from external sources (“absorptive capacity”).47 Faced with an ever-shifting technological frontier, building competency at producing new innovations gives a firm the requisite prior knowledge for identifying and commercializing external innovations. Other studies extend these insights beyond firms to regional and national systems.48 In order to absorb and diffuse technological advances first incubated elsewhere, they argue, nations must invest in a certain level of innovative activities.

    This connection between innovation capacity and absorptive capacity could question the GPT mechanism’s attention to diffusion. Possibly, a country’s lead in GPT innovation could also translate directly into a relative advantage in GPT diffusion.49 Scholarship on the agglomeration benefits of innovation hot spots, such as Silicon Valley, support this case to some extent. Empirical analyses of patent citations indicate that knowledge spillovers from GPTs tend to cluster within a geographic region.50 In the case of electricity, Robert Fox and Anna Guagnini underscore that it was easier for countries with firms at the forefront of electrical innovation to embrace electric power at scale. The interconnections between the “learning by doing” gained on the job in these leading firms and academic labs separated nations in the “fast lane” and “slow lane” of electrification.51

    Being the first to pioneer new technologies could benefit a state’s capacity to absorb and diffuse GPTs, but it is not determinative. A country’s absorptive capacity also depends on many other factors, including institutions for technology transfer, human capital endowments, openness to trade, and information and communication infrastructure.52 Sometimes the “advantages of backwardness” allow laggard states to adopt new technologies faster than the states that pioneer such advances.53 In theory and practice, a country’s ability to generate fundamental, new-to-the-world innovations can widely diverge from its ability to diffuse such advances.

    This potential divergence is especially relevant for advanced economies, which encompass the great powers that are the subject of this research. Although innovation-centered explanations do well at sorting the advantages of technological breakthroughs to countries at the technological frontier compared to those trying to catch up, they are less effective at differentiating among advanced economies. As supported by a wealth of econometric research, divergences in the long-term economic growth of countries at the technology frontier are shaped more by imitation than innovation.54 These advanced countries have firms that can quickly copy or license innovations; first mover advantages from innovations are thus limited even in industries, like pharmaceuticals, that enforce intellectual property rights most strictly.55 Nevertheless, advanced countries that are evenly matched in their capacity for radical innovation can undertake vastly different growth trajectories in the wake of technological revolutions. Differences in diffusion pathways are central to explaining this puzzle.

    This diffusion-centered approach is especially well suited for GPTs. Since GPTs entail gradual evolution into widespread use, there is a longer window for competitors to adopt GPTs more intensively than the leading innovation center. In other technologies, first-mover benefits from pioneering initial breakthroughs are more significant. For instance, leadership in the innovation of electric power technologies was fiercely contested among the industrial powers. The United States, Germany, Great Britain, and France all built their first central power stations within a span of three years (1882–1884), their first electric trams within a span of nine years (1887–1896), and their first three-phase AC power systems within a span of eight years (1891–1899).56 However, the United States clearly led in the diffusion of these systems: by 1912, its electricity production per capita had more than doubled that of Germany, its closest competitor.57 Thus, while most countries at the technological frontier will be able to compete in the production and innovation of GPTs, the hardest hurdles in the GPT trajectory are in the diffusion phase.

    GPT Diffusion and LS Product Cycles

    GPT diffusion challenges the LS-based account of how technological change drives power transitions. The standard explanation in the international relations literature emphasizes a country’s dominance in leading sectors, defined as new industries that experience rapid growth on the back of new technologies.58 Cotton textiles, steel, chemicals, and the automobile industry form a “classic sequence” of “great leading sectors,” developed initially by economist Walt Rostow and later adapted by political scientists.59 Under the LS mechanism, a country’s ability to maintain a monopoly on innovation in these emerging industries determines the rise and fall of lead economies.60

    This model of technological change and power transition builds on the international product life cycle, a concept pioneered by Raymond Vernon. Constructed to explain patterns of international trade, the cycle begins with a product innovation and subsequent sales growth in the domestic market. Once the domestic market is saturated, the new product is exported to foreign markets. Over time, production shifts to these markets, as the original innovating country loses its comparative advantage.61

    LS-based studies frequently invoke the product cycle model.62 Analyzing the effects of leading sectors on the structure of the international system, Gilpin states, “Every state, rightly, or wrongly, wants to be as close as possible to the innovative end of ‘the product cycle.’ ”63 One scholar described Gilpin’s US Power and the Multinational Corporation, one of the first texts that outlines the LS mechanism, as “[having] drawn on the concept of the product cycle, expanded it into the concept of the growth and decline of entire national economies, and analyzed the relations between this economic cycle, national power, and international politics.”64

    The product cycle’s assumptions illuminate the differences between the GPT and LS mechanisms along three key dimensions. In the first stage of the product cycle, a firm generates the initial product innovation and profits from sales in the domestic market before saturation. Extending this model to national economies, the LS mechanism emphasizes the clustering of LS innovations and attendant monopoly profits in a single nation.65 “The extent of national success that we have in mind is of the fairly extreme sort,” write George Modelski and William Thompson. “One national economy literally dominates the leading sector during its phase of high growth and is the primary beneficiary of the immediate profits.”66 The GPT trajectory, in contrast, places more value on where technologies are diffused than where an innovation is first pioneered.67 I refer to this dimension as the “phase of relative advantage.”

    In the next stage, the product innovation spreads to global markets and the technology gradually diffuses to foreign competitors. Monopoly rents associated with a product innovation dissipate as production becomes routinized and transfers to other countries.68 Mirroring this logic, Modelski and Thompson write, “[Leading sectors] bestow the benefits of monopoly profits on the pioneer until diffusion and imitation transform industries that were once considered radically innovative into fairly routine and widespread components of the world economy.”69 Thompson also states that “the sector’s impact on growth tends to be disproportionate in its early stages of development.”70

    The GPT trajectory assumes a different impact timeframe. The more wide-ranging the potential applications of a technology, the longer the lag between its initial emergence and its ultimate economic impact. This explains why the envisioned transformative impact of GPTs does not appear straightaway in the productivity statistics.71 Time for complementary innovations, organizational restructuring, and institutional adjustments such as human capital formation is needed before the full impact of a GPT can be known. It is precisely the period when diffusion transforms radical innovations into routine components of the economy—the stage when the causal effects of leading sectors are expected to dissipate—that generates the productivity gap between nations.

    The product cycle also reveals differences between the LS and GPT mechanisms regarding the “breadth of growth.” Like the product cycle’s focus on an innovation’s life cycle within a singular industry, the LS mechanism emphasizes the contributions of a limited number of new industries to economic growth in a particular period. GPT-fueled productivity growth, on the other hand, is dispersed across a broad range of industries.72 Table 2.1 specifies how LS product cycles differ from GPT diffusion along the three dimensions outlined here. As the following section will show, the differences in these two technological trajectories shape the institutional factors that are most important for national success in adapting to periods of technological revolution.

    Table 2.1 Two Mechanisms of Technological Change and Power Transitions

    MechanismsImpact TimeframePhase of Relative AdvantageBreadth of GrowthInstitutional Complements
    LS product cyclesLopsided in early stagesMonopoly on innovationConcentratedDeepen skill base in LS innovations
    GPT diffusionLopsided in later stagesEdge in diffusionDispersedWiden skill base in spreading GPTs

    While I have highlighted the differences between GPT diffusion and LS product cycles, it is important to recognize that there are similarities between the two pathways.73 Some scholars, for example, associate leading sectors with broad spillovers across economic sectors.74 In addition, lists of leading sectors and lists of GPTs sometimes overlap, as evidenced by the fact that electricity is a consensus inclusion on both lists. Moreover, both explanations begin with the same premise: to fully uncover the dynamics of technology-driven power transitions, it is essential to specify which new technologies are the key drivers of economic growth in a particular time window.75

    At the same time, these resemblances should not be overstated. Many classic leading sectors do not have general-purpose applications. For instance, cotton textiles and automobiles both feature on Rostow’s series of leading sectors, and they are studied as leading sectors because each has “been the largest industry for several major industrial nations in the West at one time or another.”76 Although these were certainly both fast-growing large industries, the underlying technological advances do not fulfill the characteristics of GPTs. In addition, many of the GPTs I examine do not qualify as leading sectors. The machine tool industry in the mid-nineteenth century, for instance, was not a new industry, and it was never even close to being the largest industry in any of the major economies. Most importantly, though the GPT and LS mechanisms sometimes point to similar technological changes, they present very different understandings of how revolutionary technologies bring about an economic power transition. As the next section reveals, these differences also map onto varied institutional adaptations.

    GPT Skill Infrastructure

    New technologies agitate existing institutional patterns.77 They appeal for government support, generate new collective interests in the form of technical societies, and induce organizations to train people in relevant fields. If institutional environments are slow or fail to adapt, the development of emerging technologies is hindered. As Gilpin articulates, a nation’s technological fitness is rooted in the “extent of the congruence” between its institutions and the demands of evolving technologies.78 This approach is rooted in a rich tradition of work on the coevolution of technology and institutions.79

    Understanding the demands of GPTs helps filter which institutional factors are most salient for how technological revolutions bring about economic power transitions. Which institutional factors dictate disparities in GPT adoption among great powers? Specifically, I emphasize the role of education and training systems that broaden the base of engineering skills linked to a particular GPT. This set of institutions, which I call “GPT skill infrastructure,” is most crucial for facilitating the widespread adoption of a GPT.

    To be sure, GPT diffusion is dependent on institutional adjustments beyond GPT skill infrastructure. Intellectual property regimes, industrial relations, financial institutions, and other institutional factors could affect GPT diffusion. Probing inter-industry differences in technology adoption, some studies find that less concentrated industry structures are positively linked to GPT adoption.80 I limit my analysis to institutions of skill formation because their effects permeate other institutional arrangements.81 GPT skill infrastructure provides a useful indicator for other institutions that standardize and spread the novel best practices associated with GPTs.82

    It should also be noted that the institutional approach is one of three main categories of explanation for cross-country differences in economic performance over the long term.83 Other studies document the importance of geography and culture to persistent cross-country income differences.84 I prioritize institutional explanations for two reasons. First, natural experiments from certain historical settings, in which institutional divergence occurs but geographical and cultural factors are held constant, suggest that institutional differences are particularly influential sources of long-term economic growth differentials.85 Second, since LS-based accounts of power transitions also prioritize institutional adaptations to technological change, my approach provides a more level test of GPT diffusion against the standard explanation.86

    One final note about limits to my argument’s scope. I do not investigate the deeper origins of why some countries are more effective than others at developing GPT skill infrastructure. Possibly, the intensity of political competition and the inclusiveness of political institutions influence the development of skill formation institutions.87 Other fruitful lines of inquiry stress the importance of government capacity to make intertemporal bargains and adopt long time horizons in making technology investments.88 It is worth noting that a necessary first step to productively exploring these underlying causes is to establish which types of technological trajectories and institutional adaptations are at work. For instance, LS product cycles may be more closely linked to mercantilist or state capitalist approaches that favor narrow interest groups, whereas, political systems that incorporate a broader group of stakeholders may better accommodate GPT diffusion pathways.

    Institutions Fit for GPT Diffusion

    If GPTs drive economic power transitions, which institutions fit best with their demands? Institutional adaptations for GPT diffusion must solve two problems. First, since the economic benefits of GPTs materialize through improvements across a broad range of industries, capturing these benefits requires extensive coordination between the GPT sector and numerous application sectors. Given the sheer scope of potential applications, it is infeasible for firms in the GPT sector to commercialize the technology on their own, as the necessary complementary assets are embedded with different firms and industries.89 In the AI domain, as one example of a potential GPT, firms that develop general machine learning algorithms will not have access to all the industry-specific data needed to fine-tune those algorithms to particular application scenarios. Thus, coordination between the GPT sector and other organizations that provide complementary capital and skills, such as academia and competitor firms, is crucial. In contrast, for technologies that are not general-purpose, this type of coordination is less conducive and could even be detrimental to a nation’s competitive advantage, as the innovating firm could leak its technical secrets.90

    Second, GPTs pose demanding conditions for human capital adjustments. In describing the connection between skill formation and technological fitness, scholars often delineate between general skills and industry-specific skills. According to this perspective, skill formation institutions that optimize for the former are more conducive to technological domains characterized by radical innovation, while institutions that optimize for the latter are more favorable for domains marked by incremental innovation.91 GPT diffusion entails both types of skill formation. The skills must be specific to a rapidly changing GPT domain but also broad enough to enable a GPT’s advance across many industries.92 Strong linkages between R&D-intensive organizations at the technological frontier and application areas far from the frontier also play a key role in GPT diffusion. This draws attention to the interactions between researchers who produce innovations and technicians who help absorb them into specific contexts.93

    Education and training systems that foster relevant engineering skills for a GPT, or what I call GPT skill infrastructure, address both constraints. Engineering talent fulfills the need for skills that are rooted in a GPT yet sufficiently flexible to implement GPT advances in a wide range of sectors. Broadening the base of engineering knowledge also helps standardize best practices with GPTs, thereby coordinating information flows between the GPT sector and application sectors. Standardization fosters GPT diffusion by committing application sectors to specific technological trajectories and encouraging complementary innovations.94 This unlocks the horizontal spillovers associated with GPTs.95

    Indeed, distinct engineering specializations have emerged in the wake of a new GPT. New disciplines, such as chemical engineering and electrical engineering, have proved essential in widening knowledge bases in the wake of a new GPT.96 Computer science, another engineering-oriented field, was central to US leadership in the information revolution.97 These professions developed alongside new technical societies—ranging from the American Society of Mechanical Engineers to the Internet Engineering Task Force—that formulated and disseminated guidelines and benchmarks for GPT development.98

    Clearly, the features of GPT skill infrastructure have changed over time. Whereas informal associations systematized the skills crucial for mechanization in the eighteenth century, formal higher education has become increasingly integral to computerization in the twenty-first century.99 Some evidence suggests that computers and other technologies are skill-biased, in the sense that they favor workers with more years of schooling.100 These trends complicate but do not undercut the concept of GPT skill infrastructure. Regardless of the extent of formal training, all configurations of GPT skill infrastructure perform the same function: to widen the pool of engineering skills and knowledge associated with a GPT. This can take place in universities as well as in informal associations, provided these settings train engineers and facilitate the flow of engineering knowledge between knowledge-creation centers and application sectors.101

    Which Institutions Matter?

    The institutional competencies for exploiting LS product cycles are different. Historical analysis informed by this frame highlights heroic inventors like James Watt and pioneering research labs at large companies.102 Studying which countries benefited most from emerging technologies over the past two centuries, Herbert Kitschelt prioritizes the match between the properties of new technologies and sectoral governance structures. Under his framework, for example, tightly coupled technological systems with high causal complexity, such as nuclear power systems and aerospace platforms, are more likely to flourish in countries that allow for extensive state support.103 In other studies, the key institutional factors behind success in LS product cycles are education systems that subsidize scientific training and R&D facilities in new industries.104

    These approaches equate technological leadership with a state’s success in capturing market shares and monopoly profits in new industries.105 In short, they use LS product cycles as the filter for which institutional variables matter. Existing scholarship lacks an institutional explanation for why some great powers are more successful at GPT diffusion.

    Competing interpretations of technological leadership in chemicals during the late nineteenth century crystallize these differences. Based on the LS template, the standard account accredits Germany’s dominance in the chemical industry—as represented by its control over 90 percent of global production of synthetic dyes—to its investments in scientific research and highly skilled chemists.106 Germany’s dynamism in this leading sector is taken to explain its overall industrial dominance.107

    GPT diffusion spotlights a different relationship between technological change and institutional adaptation. The focus turns toward institutions that complemented the extension of chemical processes to a wide range of industries beyond synthetic dye, such as food production, metals, and textiles. Under the GPT mechanism, the United States, not Germany, achieved leadership in chemicals because it first institutionalized chemical engineering as a discipline. Despite its disadvantages in synthetic dye production and chemical research, the United States was more effective in broadening the base of chemical engineering talent and coordinating information flows between fundamental breakthroughs and industrial applications.108

    It is important to note that some parts of the GPT and LS mechanisms can coexist without conflict. A state’s capacity to pioneer new technologies can correlate with its capacity to absorb and diffuse GPTs. Countries that are home to cutting-edge R&D infrastructure may also be fertile ground for education systems that widen the pool of GPT-linked engineering skills. However, these aspects of the LS mechanism are not necessary for the GPT mechanism to operate. In accordance with GPT diffusion theory, a state can capitalize on GPTs to become the most powerful economy without monopolizing LS innovation.

    Moreover, other dimensions of these two mechanisms directly conflict. When it comes to impact timeframe and breadth of growth, the GPT and LS mechanisms advance opposing expectations. Institutions suited for GPT diffusion can diverge from those optimized for creating new-to-the-world innovations. Research on human capital and long-term growth separates the effects of engineering capacity, which is commonly tied to adoptive activities, and other forms of human capital that are more often connected to inventive activities.109 This divergence can also be seen in debates over the effects of competition on technological activity. On the one hand, Joseph Schumpeter and others have argued that monopoly structures incentivize more R&D activity because the monopolists can appropriate all the gains from technological innovation.110 On the other hand, empirical work demonstrates that more competitive market structures increase the rate of technological adoption across firms.111 Thus, while there is some overlap between these two mechanisms, they can still be set against each other in a way that improves our understanding of technological revolutions and power transitions.

    This theoretical framework differs from related work on the political economy of technological change.112 Scholars attribute the international competitiveness of nations to broader institutional contexts, including democracy, national innovation systems, and property rights enforcement.113 Since this book is limited to the study of shifts in productivity leadership at the technological frontier, many of these factors, such as those related to basic infrastructure and property rights, will not explain differences among technologically advanced nations.

    In addition, most of the institutional theories put forth to explain the productivity of nations are technology-agnostic, in that they treat all forms of technological change equally. To borrow language from a former chairman of the US Council of Economic Advisers, they do not differentiate between an innovation in potato chips and an innovation in microchips.114 In contrast, I am specific about GPTs as the sources of shifts in competitiveness at the technological frontier.

    Other theories identify key technologies but leave institutional factors at a high level of abstraction. Some scholars, for instance, posit that the lead economy’s monopoly on leading-sector innovation eventually erodes because of “ubiquitous institutional rigidities.”115 Unencumbered by the vested interests that resist disruptive technologies, rising challengers inevitably overtake established powers. Because these explanations are underspecified, they cannot account for cases where rich economies expand their lead or where poorer countries do not catch up.116

    When interpreting great power competition at the technological frontier, adjudicating between the GPT and LS mechanisms represents a choice between two different visions. The latter prioritizes being the first country to introduce novel technologies, whereas the former places more value on disseminating and transforming innovations after their inception. In sum, industrial competition among great powers is not a sprint to determine which one can create the most brilliant Silicon Valley; it is a marathon won by the country that can cultivate the closest connections between its Silicon Valleys and its Iowa Citys.

    Alternative Explanations

    Although I primarily set GPT diffusion theory against the LS model, I also consider two other prominent explanations that make specific claims about how technological breakthroughs differentially advantage leading economies. Crucially, these two lines of thinking could account for differences in GPT diffusion, nullifying the import of GPT skill infrastructure.

    Threat-Based Arguments

    According to one school of thought, international security threats motivate states to invest in science and technology.117 When confronted with more threatening geopolitical landscapes, states are more incentivized to break down status quo interests and build institutions conducive to technological innovation.118 Militaries hold outsized influence in these accounts. For example, Vernon Ruttan argues that military investment, mobilized against war or the threat of war, fueled commercial advances in six technologies designated as GPTs.119 Studies of the success of the United States and Japan with emerging technologies also stress interconnections between military and civilian technological development.120 I group these related arguments under the category of threat-based theories.

    Related explanations link technological progress with the balance of external threats and domestic roadblocks. Mark Taylor’s “creative insecurity” theory describes how external economic and military pressures permit governments to break from status quo interest groups and promote technological innovation. He argues that the difference between a nation’s external threats and its internal rivalries determines its propensity for innovation: the greater the difference, the greater the national innovation rate.121 Similarly, “systemic vulnerability” theory emphasizes the influence of external security and domestic pressures on the will of leaders to invest in institutions conducive to innovation, as well as the effect of “veto players” on their ability to do so.122

    Certainly, external threats could impel states to invest more in GPTs, and military investment can help bring forth new GPTs; however, there are several issues with adapting threat-based theories to explain differences in GPT diffusion across great powers. First, threat-based arguments tend to focus on the initial incubation of GPTs, as opposed to the gradual spread of GPTs throughout a national economy. During the latter phase, a great deal of evidence suggests that civilian and military needs can greatly conflict.123 Besides, some GPTs, such as electricity in the United States, developed without substantial military investment. Since other civilian institutions could fill in as strong sources of demand for GPTs, military procurement may not be necessary for spurring GPT diffusion. Institutional adjustments to GPTs therefore can be motivated by factors other than threats. Ultimately, to further probe these points of clash, the impact of security threats and military investment must be traced within the historical cases.

    Varieties of Capitalism

    The “varieties of capitalism” (VoC) explanation highlights differences among developed democracies in labor markets, industrial organization, and interfirm relations and separates them into coordinated market economies (CMEs) and liberal market economies (LMEs). VoC scholars argue that CMEs are more suited for incremental innovations because their thick intercorporate networks and protected labor markets favor gradual adoption of new technological advances. LMEs, in contrast, are more adept at radical innovation because their fluid labor markets and corporate organization make it easier for firms to reorganize themselves around disruptive technologies. Most relevant to GPT diffusion theory, VoC scholars argue that LMEs incentivize workers to acquire general skills, which are more accommodative to radical innovation, whereas CMEs support industry-specific training, which is more favorable for incremental innovation.124

    It is possible that differences between market-based capitalism and strategically coordinated capitalism account for GPT diffusion gaps between nations. Based on the expectations of the VoC approach, LMEs should be more likely to generate innovations with the potential to become GPTs, and workers in LMEs should possess more general skills that could spread GPTs across firms.125 Examining the pattern of innovation during the information revolution, scholars find that the United States, an LME, concentrated on industries experiencing radical innovation, such as semiconductors and telecommunications, while Germany, a CME, specialized in domains characterized by incremental innovation, such as mechanical engineering and transport.126

    Despite bringing vital attention to the diversity of skill formation institutions, VoC theory’s dichotomy between general and industry-specific skills does not dovetail with the skills demanded by specific GPTs.127 Cutting across this sometimes arbitrary distinction, the engineering skills highlighted in GPT diffusion theory are specific to a fast-evolving GPT field and general enough to transfer ideas from the GPT sector across various sectors. Software engineering skills, for instance, are portable across multiple industries, but their reach is not as ubiquitous as critical thinking skills or mathematics knowledge. To address similar gaps in skill classifications, many political economists have appealed for “a more fine-grained analysis of cross-national differences in the particular mix of jobs and qualifications that characterize different political economies.”128 In line with this move, GPT skill infrastructure stands in for institutions that supply the particular mix of jobs and qualifications for enabling GPT diffusion. The empirical analysis provides an opportunity to examine whether this approach should be preferred to the VoC explanation for understanding technology-driven power transitions.

    Research Methodology

    My evaluation of the GPT and LS mechanisms primarily relies on historical case studies, which allow for detailed exploration of the causal processes that connect technological change to economic power transitions. Employing congruence-analysis techniques, I select cases and assess the historical evidence in a way that ensures a fair and rigorous test of the relative explanatory power of the two mechanisms.129 This sets up a “three-cornered fight” among GPT diffusion theory, the rival LS-based explanation, and the set of empirical information.130

    The universe of cases most useful for assessing the GPT and LS mechanisms are technological revolutions (cause) that produced an economic power transition (outcome) in the industrial period. Following guidance on testing competing mechanisms that prioritize typical cases where the cause and outcome are clearly present, I investigate the First Industrial Revolution (IR-1) and the Second Industrial Revolution (IR-2).131 Both cases featured clusters of disruptive technological advances, highlighted by some studies as “technological revolutions” or “technology waves.”132 They also saw economic power transitions, when one great power sustained growth rates at substantially higher levels than its rivals.133 I also study Japan’s challenge to American economic leadership—which ultimately failed—in the Third Industrial Revolution (IR-3). This deviant case can disconfirm mechanisms and help explain why they break down.134

    These cases are highly crucial and relevant for testing the GPT mechanism against the LS mechanism. All three cases favor the latter in terms of background conditions and existing theoretical explanations. Scholarship has attributed shifts in economic power during this period to the rise of new leading sectors.135 Thus, if the empirical results support the GPT mechanism, then my findings would suggest a need for major modifications to our understanding of how technological revolutions affect the rise and fall of great powers. The qualitative analysis appendix provides further details on case selection, including the universe of cases, the justification for these cases as “most likely cases” for the LS mechanism, and relevant scope conditions.136

    This overall approach adapts the methodology of process-tracing, often conducted at the individual or micro level, to macro-level mechanisms that involve structural factors and evolutionary interactions.137 Existing scholarship on diffusion mechanisms, which the GPT mechanism builds from, emphasizes the influence of macro-level processes. In these accounts the diffusion trajectory depends not just on the overall distribution of individual-level receptivity but also on structural and institutional features, such as the degree of interconnectedness in a population.138 This approach aligns with a view of mechanistic thinking that allows for mechanisms to be set at different levels of abstraction.139 As Tulia Falleti and Julia Lynch point out, “Micro-level mechanisms are no more fundamental than macro-level ones.”140

    To judge the explanatory strength of the LS and GPT mechanisms, I employ within-case congruence tests and process-tracing principles to evaluate the predictions of the two theoretical approaches against the empirical record.141 In each historical case, I first trace how leading sectors and GPTs developed in the major economies, paying particular attention to adoption timeframes, the technological phase of relative advantage, and the breadth of growth—three dimensions that differentiate GPT diffusion from LS product cycles.142

    For example, my assessment of the two mechanisms along the impact time-frame dimension follows consistent procedures in each case.143 To evaluate when certain technologies were most influential, I establish when they initially emerged (based on dates of key breakthroughs), when their associated industries were growing fastest, and when they diffused across a wide range of application sectors. When data are available, I estimate a GPT’s initial arrival date by also factoring in the point at which it reached a 1 percent adoption rate in the median sector.144 Industry growth rates, diffusion curves, and output trends all help measure the timeline along which technological breakthroughs substantially influenced the overall economy. The growth trajectory of each candidate GPT and LS is then set against a detailed timeline of when a major shift in productivity leadership occurs.

    I then turn to the institutional factors that could explain why some countries were more successful in adapting to a technological revolution, with a focus on the institutions best suited to the demands of GPTs and leading sectors.145 If the GPT mechanism is operative, the state that attains economic leadership should have an advantage in institutions that broaden the base of engineering human capital and spread best practices linked to GPTs. Additional evidence of the GPT diffusion theory’s explanatory power would be that other countries had advantages in institutions that complement LS product cycles, such as scientific research infrastructure and sectoral governance structures.

    These evaluation procedures are effective because I have organized the competing mechanisms “so that they are composed of the same number of diametrically opposite parts with observable implications that rule each other out.”146 This allows for evidence in favor of one explanation to be doubly decisive in that it also undermines the competing theory.147 In sum, each case study is structured around investigating a set of four standardized questions that correspond to the three dimensions of the LS and GPT mechanisms as well as the institutional complements to technological trajectories (table 2.2).148

    table 2.2 Testable Propositions of the LS and GPT Mechanisms

    DimensionsKey QuestionsLS PropositionsGPT Propositions
    Impact timeframeWhen do revolutionary technologies make their greatest marginal impact on the economic balance of power?New industries make their greatest impact on growth differentials in early stages.GPTs do not make a significant impact on growth differentials until multiple decades after emergence.
    Key phase of relative advantageDo monopoly profits from innovation or benefits from more successful diffusion drive growth differentials?A state’s monopoly on innovation in leading sectors propels it to economic leadership.A state’s success in widespread adoption of GPTs propels it to economic leadership.
    Breadth of growthWhat is the breadth of technology-driven growth?Technological advances concentrated in a few leading sectors drive growth.Technological advances dispersed across a broad range of GPT-linked industries drive growth.
    Institutional complementsWhich types of institutions are most advantageous for national success in technological revolutions?Key institutional adaptations help a state capture market shares and monopoly profits in new industries.Key institutional adaptations widen the base of engineering skills and knowledge for GPT diffusion.

    In each case study, I consider alternative theories of technology-driven power transitions. Countless studies have examined the rise and fall of great powers. My aim is not to sort through all possible causes of one nation’s rise or another’s decline. Rather, I am probing the causal processes behind an established connection between technological advances in each industrial revolution and an economic power transition. The VoC framework and threat-based theories outline alternative explanations for how significant technological advances translated into growth differentials among great powers. Across all the cases, I assess whether they provide a better explanation for the historical case evidence than the GPT and LS mechanisms.

    I also address case-specific confounding factors. For example, some scholars argue that abundant inputs of wood and metals induced the United States to embrace more machine-intensive technology in the IR-2, reasoning that Britain’s slower adoption of interchangeable parts manufacturing was an efficient choice given its natural resource constraints.149 For each case, I determine whether these types of circumstantial factors could nullify the validity of the GPT and LS mechanisms.

    In tracing these mechanisms, I benefit from a wealth of empirical evidence on past industrial revolutions, which have been the subject of many interdisciplinary inquiries. Since the cases I study are well-traversed terrain, my research is primarily based on secondary sources.150 I rely on histories of technology and general economic histories to trace how and when technological breakthroughs affected economic power balances. Notably, my analysis takes advantage of the application of formal statistical and econometric methods to assess the impact of significant technological advances, part of the “cliometric revolution” in economic history.151 Some of these works have challenged the dominant narrative of previous industrial revolutions. For instance, Nick von Tunzelmann found that the steam engine made minimal contributions to British productivity growth before 1830, raising the issue that earlier accounts of British industrialization “tended to conflate the economic significance of the steam engine with its early diffusion.”152

    I supplement these historical perspectives with primary sources. These include statistical series on industrial production, census statistics, discussions of engineers in contemporary trade journals, and firsthand accounts from commissions and study teams of cross-national differences in technology systems. In the absence of standardized measures of engineering education, archival evidence helps fill in details about GPT skill infrastructure for each of the cases. In the IR-1 case, I benefit from materials from the National Archives (United Kingdom), the British Newspaper Archive, and the University of Nottingham Libraries, Manuscripts, and Special Collections. My IR-2 case analysis relies on collections based at the Bodleian Library (United Kingdom), the Library of Congress (United States), and the University of Leipzig and on British diplomatic and consular reports.153 In the IR-3 case analysis, the Edward A. Feigenbaum Papers collection, held at Stanford University, helps inform US-Japan comparisons in computer science education.

    My research also benefits greatly from new data on historical technological development. I take advantage of improved datasets, such as the Maddison Project Database.154 New ones, such as the Cross-Country Historical Adoption of Technology dataset, were also beneficial.155 Sometimes hype about exciting new technologies influences the perceptions of commentators and historians about the pace and extent of technology adoption. More granular data can help substantiate or cut through these narratives. Like the reassessments of the impact of previous technologies, these data were released after the publication of the field-defining works on technology and power transitions in international relations. Making extensive use of these sources therefore provides leverage to revise conventional understandings.

    When assessing these two mechanisms, one of the main challenges is to identify the key technological changes to trace. I take a broad view of technology that encompasses not just technical designs but also organizational and managerial innovations.156 Concretely, I follow Harvey Brooks, a pioneer of the science and technology policy field, in defining technology as “knowledge of how to fulfill certain human purposes in a specifiable and reproducible way.”157 The LS and GPT mechanisms both call attention to the outsized import of particular technical breakthroughs and their interactions with social systems, but they differ on which ones are more important. Therefore, a deep and wide understanding of advances in hardware and organizational practices in each historical period is required to properly sort them by their potential to spark LS or GPT trajectories.

    This task is complicated by substantial disagreements over which technologies are leading sectors and GPTs. Proposed lists of GPTs often conflict, raising questions about the criteria used for GPT selection.158 Reacting to the length of such lists, other scholars fear that “the [GPT] concept may be getting out of hand.”159 According to one review of eleven studies that identified past GPTs, twenty-six different innovations appeared on at least one list but only three appeared on all eleven.160

    The LS concept is even more susceptible to these criticisms because the characteristics that define leading sectors are inconsistent across existing studies. Though most scholars agree that leading sectors are new industries that grow faster than the rest of the economy, there is marked disagreement on other criteria. Some scholars select leading sectors based on the criterion that they have been the largest industry in several major industrial nations for a period of time.161 Others emphasize that leading sectors attract significant investments in R&D.162 To illustrate this variability, I reviewed five key texts that analyze the effect of leading sectors on economic power transitions. Limiting the lists of proposed leading sectors to those that emerged during the three case study periods, I find that fifteen leading sectors appeared on at least one list and only two appeared on all five.163

    My process for selecting leading sectors and GPTs to trace helps alleviate concerns that I cherry-pick the technologies that best fit my preferred explanation. In each historical case, most studies that explicitly identify leading sectors or GPTs agree on a few obvious GPTs and leading sectors. To ensure that I do not omit any GPTs, I consider all technologies singled out by at least two of five key texts that identify GPTs across multiple historical periods.164 I apply the same approach to LS selection, using the aforementioned list I compiled.

    Following classification schemes that differentiate GPTs from “near-GPTs” and “multipurpose technologies,” I resolve many of the conflicts over what counts as a GPT or leading sector by referring to a set of defining criteria.165 For instance, while some accounts include the railroad and the automobile as GPTs, I do not analyze them as candidate GPTs because they lack a variety of uses.166 I support my choices with empirical methods for LS and GPT identification. To confirm certain leading sectors, I examine the rate of growth across various industry sectors. I also leverage recent studies that identify GPTs with patent-based indicators.167 Taken together, these procedures limit the risk of omitting certain technologies while guarding against GPT and LS concept creep.168 The qualitative analysis appendix outlines how I address other issues related to LS and GPT identification, including concerns about omitting important single-purpose technologies and scenarios when certain technological breakthroughs are linked to both LS and GPT trajectories.

    These considerations underscore that taking stock of the key technological drivers is only the first step in the case analysis. To judge whether these breakthroughs actually brought about the impacts that are often claimed for them, it is important to carefully trace how these technologies evolved in close relation with societal systems.

    As a complement to the historical case studies, this book’s research design includes a large-n quantitative analysis of the relationship between the breadth of software engineering skill formation institutions and computerization rates. This tests a key observable implication of GPT diffusion theory, using time-series cross-sectional data on nineteen advanced and emerging economies across three decades. I leave the more detailed description of the statistical methodology to chapter 6.

    Summary

    The technological fitness of nations is determined by how they adapt to the demands of new technical advances. I have developed a theory to explain how revolutionary technological breakthroughs affect the rise and fall of great powers. My approach is akin to that of an investigator tasked with figuring out why one ship sailed across the ocean faster than all the others. As though differentiating the winning ship’s route from possible sea-lanes in terms of trade wind conditions and course changes, I first contrast the GPT and LS trajectories with regard to the timing, phase, and breadth of technological change. Once the superior route has been mapped, attention turns to the attributes of the winning ship, such as its navigation equipment and sailors’ skills, that enabled it to take advantage of this fast lane across the ocean. In similar fashion, having set out the GPT trajectory as the superior route from technological revolution to economic leadership, my argument then highlights GPT skill infrastructure as the key institutional attribute that dictates which great power capitalizes best on this route.

    3 The First Industrial Revolution and Britain’s Rise

    FEW HISTORICAL EVENTS have shaken the world like the First Industrial Revolution (IR-1, 1780–1840). Extraordinary upheaval marked the contours and consequences of the IR-1. For the first time in history, productivity growth accelerated dramatically, allowing large numbers of people to experience sustained improvements in their living standards. Small towns transformed into large cities, new ideologies gathered momentum, and emerging economic and social classes reshaped the fabric of society. These changes reverberated in the international sphere, where the ramifications of the IR-1 included the transition to industrialized mass warfare, the decline of the absolutist state, and the birth of the modern international system.

    Among these transformations, two phenomena stand out. The first is the remarkable technological progress that inaugurated the IR-1 period. Everything was changing in part because so many things were changing—water frames, steam engines, and puddling processes not least among them. The second is Britain’s rise to unrivaled economic leadership, during which it sustained productivity growth at higher levels than its rivals, France and the Netherlands. The following sections adjudicate the debates over the exact timeline of Britain’s industrialization, but there is no doubt that Britain, propelled by the IR-1, became the world’s most advanced economic power by the mid-nineteenth century.

    No study of technological change and power transitions is complete without an account of the IR-1. For both the LS and GPT mechanisms, the IR-1 functions as a typical case that is held up as paradigmatic of technology-driven power transitions. The standard account in international relations scholarship attributes Britain’s industrial ascent to its dominance of innovation in the IR-1’s leading sectors, including cotton textiles, iron metallurgy, and steam power.1 Present-day scholarship and policy discussions often draw upon stylized views of the IR-1, analogizing present developments in information technology and biotechnology to the effects of steam power and cotton textiles in the industrial revolution.2

    A deeper inquiry into the IR-1 and Britain’s economic rise challenges many of these conventional views. First, it reveals that general-purpose transformations linked to advances in iron metallurgy diffused widely enough to significantly affect economy-wide productivity only after 1815—a timeline that aligns with the period when Britain significantly outpaced its rivals in industrialization. Other prominent advances, including the steam engine, made only limited contributions to Britain’s rise to industrial prominence in this period owing to a prolonged period of gestation before widespread adoption. Second, the IR-1 case also demonstrates that it was Britain’s advantage in extending mechanization throughout the economy, not monopoly profits from innovations in cotton textiles, that proved crucial to its industrial ascendancy. Third, the historical data illustrate that the dispersion of mechanical innovations across many sectors fueled British productivity growth. Across these three dimensions, the IR-1 case matches the GPT trajectory better than the LS trajectory.

    Since no country monopolized innovations in metalworking processes and Britain’s competitors could also absorb innovations from abroad, why did Britain gain the most from this GPT trajectory? In all countries, as technical advances surged forward, institutional adjustments raced to cultivate the skills required to keep pace. Importantly, France and the Netherlands were competitive with Britain—and even surpassed it in some respects—in scientific research infrastructure and education systems for training expert engineers. These institutional settings in France and the Netherlands, however, produced knowledge and skills that were rather divorced from practical applications.

    Britain’s comparative advantage rested on another type of skill infrastructure. It depended less on heroic innovators like James Watt, the famed creator of the modern steam engine, and more on competent engineers who could build and maintain new technological systems, as well as make incremental adaptations to implement these systems in many different settings.3 As expected by GPT diffusion theory, Britain benefited from education systems that expanded the base of mechanically skilled engineers and disseminated knowledge of applied mechanics. Britain’s competitors could not match its system for cultivating a common technical language in applied mechanics that encouraged the knowledge exchanges between engineers and entrepreneurs needed for advancing mechanization from one industry to the next.

    To trace these mechanisms, I gathered and sorted through a wealth of evidence on the IR-1. Historical accounts served as the foundational materials, including general economic histories of the IR-1, histories of influential technologies and industries like the steam engine and the iron industry, country-specific histories, and comparative histories of Britain, France, and the Netherlands. I also benefited from contemporary assessments of the IR-1’s institutional features provided by trade journals, proceedings of mechanics’ institutes, recruitment advertisements published in local newspapers, and essays by leading engineers. This evidence stems from archival materials at the British Newspaper Archive, the National Archives (United Kingdom), and the University of Nottingham Libraries, Manuscripts, and Special Collections. Triangulating a variety of sources, I endeavored to back up my claims with statistical evidence in the form of industrial output estimates, patenting rates, and detailed biographical information on British engineers.

    The assessment of the GPT and LS mechanisms against historical evidence from the IR-1 proceeds as follows. To begin, the chapter reviews Britain’s rise to industrial preeminence, which is the outcome of the case. Next, it sorts the key technological breakthroughs of the period by their potential to drive two types of trajectories—LS product cycles and GPT diffusion. I then assess whether Britain’s rise in this period is better explained by the GPT or LS mechanism, tracing the development of candidate leading sectors and GPTs in terms of impact timeframe, phase of relative advantage, and breadth of growth. If the GPT trajectory holds for this period, there should be evidence that Britain was better equipped than its competitors in GPT skill infrastructure. Another section evaluates whether the historical data support this expectation. Before concluding the chapter, I address alternative factors and explanations.

    A Power Transition: Britain’s Rise

    When did Britain ascend to industrial hegemony? The broad outlines of the story are well known. Between the mid-eighteenth century and the mid-nineteenth century, the industrial revolution propelled Great Britain to global preeminence. Although Britain did not boast the world’s largest economy—China held that title during this period—it did capitalize on the technologies of the industrial revolution to become “the world’s most advanced productive power.”4 France and the Netherlands, its economic rivals, did not keep pace with Britain’s productivity growth.

    While both the LS and GPT models agree that Britain established itself as the preeminent industrial power in this period, a clearer sense of when this shift occurred is essential for testing the explanatory power of the LS and GPT mechanisms during this period. One common view of Britain’s industrialization, brought to prominence by Rostow, depicts an accelerated takeoff into sustained growth. Rostow’s timeline dates this takeoff to the last two decades of the eighteenth century.5 In alignment with this periodization, some scholars writing in the LS tradition claim that Britain achieved its industrial ascent by the late eighteenth century.6

    A different perspective, better supported by the evidence that follows, favors a delayed timeline for Britain’s ascent to industrial preeminence. Under this view, Britain did not sustain economic and productivity advances at levels substantially higher than its rivals until the 1820s and after. To clarify the chronology of Britain’s industrial ascent, the following sections survey three proxies for productivity leadership: GDP per capita, industrialization, and total factor productivity.

    GDP PER-CAPITA INDICATORS

    Trend lines in GDP per capita, a standard proxy for productivity, confirm the broad outlines of Britain’s rise. Evidence from the Maddison Project Database (MPD) points to the decades after 1800, not before, as the key transition period (figure 3.1).7 These trends come from the 2020 version of the MPD, which updates Angus Maddison’s data and incorporates new annual estimates of GDP per capita in the IR-1 period for France, the Netherlands, and the United Kingdom.8 In 1760, the Netherlands boasted the world’s highest per-capita income, approximately 35 percent higher than Britain’s.9 The Dutch held this lead for the rest of the eighteenth century through to 1808, when Britain first overtook the Netherlands in GDP per capita. By 1840, Britain’s GDP per capita was about 75 percent higher than that of France and about 10 percent ahead of that of the Netherlands.10

    figure 3.1 Economic Power Transition in the IR-1. Source: Maddison Project Database, version 2020 (Bolt and van Zanden 2020).

    It should be noted that GDP per-capita information for the early years of the IR-1 is sometimes missing or only partially available. For years prior to 1807, the MPD bases Dutch GDP per-capita estimates on data for just the Holland region, so the Dutch economy’s decline during this time could be an artifact of changes in data sources.11 At the same time, to ensure that the MPD data can be used to provide accurate information on historical patterns of economic growth and decline, researchers have made adjustments to partial data series and consulted experts to assess their representativeness.12 Furthermore, the Holland-based data in the early 1800s already indicated a decline in Dutch GDP per capita. Although data scarcity makes it difficult to mark out exactly when Britain’s GDP per capita surpassed that of the Netherlands, the MPD remains the best source for cross-country comparisons of national income in this period.

    INDUSTRIALIZATION INDICATORS

    Industrialization indicators depict a mixed picture of when Britain sustained leadership in economic efficiency. By one influential set of metrics compiled by economic historian Paul Bairoch, Britain’s per-capita industrialization levels had grown to 50 percent higher than those of France in 1800, from a position of near-equality with France and Belgium in 1750. For scholars who map the trajectories of great powers, these estimates have assumed a prominent role in shaping the timeline of British industrial ascendance.13 For instance, Paul Kennedy employs Bairoch’s estimates to argue that the industrial revolution transformed Britain into a different kind of world power.14

    Further examination of Bairoch’s estimates qualifies their support for an accelerated timeline of Britain’s industrial ascendance. First, by limiting his definition of “industrial output” to manufacturing industry products, Bairoch excludes the contribution of notable sectors such as construction and mining, a distinction even he admits is “rather arbitrary.”15 Second, the gap between Britain and France in per-capita industrialization levels in 1800 still falls within the margin of error for Bairoch’s estimates.16

    Moreover, a delayed timeframe is supported by industrialization measures that encompass more than the manufacturing industries. In 1700, the Netherlands had a substantially higher proportion of its population employed in industry (33 percent) compared to the United Kingdom (22 percent). In 1820, the proportion of people employed in UK industry had risen to 33 percent—higher than the Dutch corresponding rate of 28 percent.17 One expert on the pre-industrial revolution in Europe notes that the Netherlands was at least as industrialized as England, if not more so, throughout the eighteenth century.18 Lastly, aggregate industrial production trends map out a post-1815 surge in British industrialization, providing further evidence that Britain did not solidify its productivity advantage until decades into the nineteenth century.19

    PRODUCTIVITY INDICATORS

    Total factor productivity (TFP) indicators, which capture the efficiency by which production factors are converted into useful outputs, further back the delayed ascent story. As was the case with trends in aggregate industrial output, TFP growth in Britain did not take off until after 1820.20 In truth, British TFP growth was very modest throughout the eighteenth century, averaging less than 1 percent per year.21

    While the paucity of reliable data on total factor productivity in France hinders cross-national comparisons in this period, some evidence suggests that Britain did not surpass the Netherlands in TFP until after 1800.22 Historian Robert Allen estimated TFP in agriculture by calculating the ratio between actual output per worker and the output per worker predicted by a country’s available agricultural population and land. On this metric for the year 1800, the Netherlands ranked higher than Britain and all other European nations.23 The Dutch also attained the highest absolute TFP in Europe for almost all of the eighteenth century.24

    Which periodization of Britain’s industrial ascent better reflects the empirical evidence? On balance, measures of per-capita GDP, industrialization levels, and total factor productivity support a deferred timeline for Britain’s industrial rise. This clarification of when an economic power transition occurred during the IR-1 provides a stable target to test the competing LS and GPT mechanisms.

    Key Technological Changes in the IR-1

    Before evaluating the LS and GPT mechanisms in greater depth, the technological elements of the IR-1 must be further specified. Hargreaves’s spinning jenny (1764), Arkwright’s water frame (1769), Watt’s steam engine (1769), Cort’s puddling process (1784), and many other significant technical advances emerged during the First Industrial Revolution. The most likely sources of GPT and LS trajectories can be identified with guidance from existing work that calls attention to key technologies and accepted criteria for these two categories. Narrowing the assessment of these two mechanisms to a limited set of technologies makes for a more viable exercise.

    Candidate Leading Sectors: Cotton Textiles, Iron, and Steam Power

    A strong degree of consensus on the leading sectors that powered Britain’s rise in the IR-1 makes it relatively easy to identify three candidate sectors: cotton textiles, iron, and steam power.25 Among these, historians widely recognize the cotton textile industry as the original leading sector of the First Industrial Revolution.26 New inventions propelled the industry’s rapid growth, as its share of total value added to British industry rose from 2.6 percent in 1770 to 17 percent in 1801.27 In characterizing the significance of the cotton industry, Schumpeter went as far as to claim, “English industrial history can, in the epoch 1787–1842 … be almost resolved into the history of a single industry.”28

    If the cotton textile industry places first in the canon of the IR-1’s leading sectors, then the iron industry follows close behind. In their account of Britain’s rise, Modelski and Thompson single out these two major industries, employing pig iron production and cotton consumption, as indicators for Britain’s leading sector growth rates.29 According to the traditional view of the IR-1, the iron and cotton industries were the only two that experienced “highly successful, rapidly diffused technical change” before the 1820s.30

    I also evaluate the steam power industry as a third possible leading sector. A wide range of LS-based scholarship identifies steam power as one of the technological foundations of Britain’s leadership in the nineteenth century.31 Most of this literature labels only the steam engine itself as the leading sector, but since leading sectors are new industries, the steam engine–producing industry is the more precise understanding of the leading sector related to major advances in steam engine technology. Compared to the iron and cotton textile industries, it is much more uncertain whether the steam engine–producing industry, which experienced relatively slow growth in output and productivity, meets the analytical criteria for a leading sector during the IR-1 case.32 Still, I include the steam engine–producing industry as a potential leading sector, leaving it to the case analysis to further study its growth trajectory.

    Candidate GPTs: Iron, Steam Engine, and the Factory System

    Since the possible sources of GPT trajectories in the IR-1 are less established, I drew on previous studies that mapped out technological paradigms in this period to select three candidate GPTs: the steam engine, mechanization based on advances in iron machinery, and the factory system.33 As a possible source of GPT-style effects, the steam engine is a clear choice. Alongside electricity and (ICT) technology, it has been described as one of the “Big Three” GPTs, appearing in nearly all catalogs of GPTs.34 Here the emphasis is on the capacity of steam engines to transform a wide variety of industrial processes across many sectors, as opposed to the potential growth of the steam engine–producing industry.

    Of the two paradigmatic industries of the IR-1, cotton and iron, the latter was a more plausible driver of GPT-style effects for Britain. As the demand for iron-made machinery grew, iron foundries development of new generations of machine tools, such as cylinder-boring machines, contributed to the creation of a mechanical engineering industry.35 This spurred the mechanization of production processes in a wide range of industries, including agriculture, food processing, printing, and textiles.36 Although both cotton textiles and iron were fast-growing industries, developments in iron better resembled a “motive branch” driving pervasive effects across the economy.37

    In addition, the late eighteenth century saw the emergence of centralized factories, which significantly increased the scale of goods production. The factory system offered the potential to change the techniques of production across many industries. One widely cited classification scheme for GPTs picks out the factory system as an “organizational GPT” in the late eighteenth to early nineteenth century period.38 Other scholars describe this organizational innovation as “one of the most fundamental changes of ‘metabolism’ in the Industrial Revolution.”39

    I also considered but ultimately decided against including developments in railroads as a candidate GPT. Among five core texts that classify GPTs across many historical periods, at least two highlighted the significance of the railroad to the IR-1.40 In my view, the railroad represented a disruptive advance, but it did not acquire the variety of uses to qualify as a GPT. Railways carried many types of freight and made new business models possible, but their function was limited to transport.41

    Sources of LS and GPT Trajectories

    Table 3.1 recaps the potential technological sources for both the GPT and LS mechanisms. It is important to clarify three points about the sorting process. First, it is notable but not surprising that the candidate leading sectors and GPTs draw from similar technological wellsprings. Both mechanisms agree that some inventions, like Cort’s puddling process for making wrought iron, mattered much more than others in terms of their impact on the economic balance of power.

    Table 3.1 Key Sources of Technological Trajectories in the IR-1

    Candidate Leading SectorsCandidate GPTs
    Cotton textile industryFactory system
    Iron industryMechanization
    Steam engine–producing industrySteam engine

    Where the mechanisms separate is in how this process transpired. Cort’s puddling process and other ironmaking innovations, under the GPT model, are expected to follow an impact pathway characterized by three features: an extended lag before they affect productivity growth, the spread of mechanization across the economy, and widespread complementary innovations in many machine-using industries. Under the LS model, the same technological sources are expected to affect economic growth in a way that is lopsided in the early stages of development, fueled by monopolizing iron exports, and limited to technological innovations in the iron industry.

    Second, it is still useful to classify three distinct candidate GPTs in the IR-1, despite the fact that developments in factory systems, mechanization, and steam engines were mutually reinforcing in many respects. Steam engines depended on the transition from hand-tool processes to machinery-based production systems; at the same time, the impact of steam engines on coal mining was to boost the iron industry, spurring mechanization. Yet a number of historians distinguish the expansion of mechanization in the British industrial revolution from transformations linked to the steam engine, arguing that the latter’s economic impact materialized much later than the former.42 Thus, while these candidate GPT trajectories are interconnected, it is still possible to locate various GPTs at different stages of their life cycle.

    Third, not all of these technological changes necessarily had a decisive impact on Britain’s capacity to sustain higher productivity levels than its rivals during the period of interest. They are labeled as candidates for a reason. As this chapter will show, the steam engine did not achieve widespread diffusion until after Britain had already established economic leadership. When subjected to more rigorous empirical analysis, developments in some technologies may not track well with the proposed LS and GPT trajectories for this period.

    GPT vs. LS Trajectories in the IR-1

    Spelling out possible sources of technological trajectories in the IR-1 provides a bounded terrain for testing the validity of the GPT and LS mechanisms. By leveraging differences between the two mechanisms with respect to impact timeframe, phase of relative advantage, and breadth of growth, I derive three sets of opposing predictions for how technological changes translated into an economic power transition in this period. I then assess whether, and to what extent, the developments in the IR-1 supported these predictions.

    OBSERVABLE IMPLICATIONS RELATED TO IMPACT TIMEFRAME

    When did the revolutionary technologies of the IR-1 disrupt the economic balance of power? If the impact timeframe of the LS mechanism holds, then radical technical advances in the cotton textile, iron, and/or steam engine–producing industries should have substantially stimulated British economic growth shortly after the emergence of major technological advances in the 1760s and 1770s.43 Accordingly, scholars theorize that leading sectors propelled Great Britain to industrial superiority in the late eighteenth century.44 In line with this conception of a rapid timeline, Modelski and Thompson expect that the growth of two lead industries, cotton and iron, peaked in the 1780s.45

    On the other hand, if the GPT mechanism was operational, the impact of major technological breakthroughs on Britain’s industrial ascent should have arrived on a more gradual timeline. Key advances tied to mechanization, steam power, and the factory system emerged in the 1770s and 1780s. Given that GPTs require a long period of delay before they diffuse and achieve widespread adoption, the candidate GPTs of the IR-1 should not have had substantial economy-wide repercussions until the early decades of the nineteenth century and after. I use the year 1815 as a rough cut-point to separate the accelerated impact timeframe of leading sectors from that of GPTs in this period.

    OBSERVABLE IMPLICATIONS RELATED TO THE PHASE OF RELATIVE ADVANTAGE

    The LS mechanism places high value on the innovation phase of technological change. Where major breakthroughs arise is key. Accordingly, Britain’s capacity to pioneer major technological advances should explain economic growth differentials in the IR-1. Concretely, the LS mechanism expects that Britain’s rise was fueled by its dominance of innovation in the cotton textile, iron, and steam engine–producing industries, as well as the resultant monopoly rents from exports in these sectors.

    The GPT mechanism emphasizes a less-celebrated phase of technological change. Where innovations diffuse is key. Differentials in the rate and intensiveness of GPT adoption generate the gap between an ascending industrial leader and other competitors. The GPT mechanism suggests that Britain’s rise to industrial preeminence can be traced to its superior ability to diffuse generic technological changes across the economy.

    OBSERVABLE IMPLICATIONS RELATED TO BREADTH OF GROWTH

    The last set of observable implications relate to the breadth of growth during the IR-1. As illustrated in the descriptions here of the candidate leading sectors, many accounts attribute Britain’s industrial ascent to a narrow set of critical advances.46 In one of the first texts dedicated to the investigation of technology and international relations, William Ogburn declared, “The coming of the steam engine … is the variable which explains the increase of Britain as a power in the nineteenth century.”47 According to GPT diffusion theory, Britain’s rise to industrial preeminence came from the advance of GPTs through many linked sectors.

    Taken together, these three sets of diverging predictions guide my assessment of the GPT mechanism against the LS mechanism. I make expectations specific to the IR-1 case by using the relevant information on particular technologies and the timeline of British industrialization. Table 3.2 lays out the specific, testable predictions that provide the framework of evaluation in the following sections.

    Table 3.2 Testable Predictions for the IR-1 Case Analysis

    Prediction 1: LS (impact timeframe)The cotton textile, iron, and/or* steam engine–producing industries made a significant impact on Britain’s rise to industrial preeminence before 1815.
    Prediction 1: GPTMechanization, the steam engine, and / or the factory system made a significant impact on Britain’s rise to industrial preeminence only after 1815.
    Prediction 2: LS (relative advantage)Innovations in cotton textile, iron, and/or the steam engine–producing industries were concentrated in Britain.British advantages in the production and exports of textiles, iron, and/or steam engines were crucial to its industrial superiority.
    Prediction 2: GPTInnovations in iron, the steam engine, and/or the factory system were not concentrated in Britain.
    British advantages in the diffusion of mechanization, steam engines, and/or the factory system were crucial to its industrial superiority.
    Prediction 3: LS (breadth of growth)Productivity growth in Britain was limited to the cotton textile, iron, and/or steam engine – producing industries.
    Prediction 3: GPTProductivity growth in Britain was spread across a broad range of industries linked to mechanization, the steam engine, and/or the factory system.
    * The operator “and/or” links all candidate leading sectors and GPTs because it could be the case that only some of these technologies drove the trajectories of the period.

    Impact Timeframe: Delayed Surge vs. Fast Rise of British Industrialization

    The painstaking reconstruction of temporal chronology is at the heart of tracing mechanisms. Tremendous technological changes occurred during this period, but when exactly did they make their mark on Britain’s industrial superiority? The period when significant technological innovations emerge often does not match up with the time when their impacts are felt. Unfortunately, when drawing lessons from the IR-1 on the effect of technology on international politics, scholars have conflated the overall significance of certain technologies with near-immediate impact.48 Establishing a clear timeline of when technological changes catalyzed a shift in productivity leadership during the IR-1 is therefore an important first step in comparing the LS and GPT mechanisms.

    DIVERGING TIMELINES: COTTON VS. IRON

    Time-series data on the output growth of twenty-six industries that accounted for around 60 percent of Britain’s industrial production help differentiate the growth schedules of the cotton textiles and iron industries. According to these data, the major upswing in British industrialization took place after 1815, when the aggregate growth trend increased from 2 percent to a peak of 3.8 percent by 1825. In line with the expectations of the LS model, the cotton textile industry grew exceptionally fast following major technological innovations in the 1760s, but from the 1780s there was a deceleration in the output growth of cotton textiles. Based on the relatively early peak of the cotton industry’s expansion, David Greasley and Les Oxley conclude that “it appears unlikely that cotton played the major role in the post-1815 upswing in British industrialization.”49

    Following a completely different trajectory, growth in iron goods was more in line with the GPT model. Starting in the 1780s, the growth rate of the British iron industry accelerated to a peak of about 5.3 percent in the 1840s.50 “Compared to cotton textiles, change in iron was gradual, incremental, and spread out over a longer period of time,” Espen Moe writes.51 With a limited role for cotton, the gradual expansion of the iron industry led Britain’s post-1815 industrial surge, as its trend output tracked much more closely with that of aggregate industry. In sum, the cotton industry followed the growth path of a leading sector, whereas developments in the iron industry reflected the impact timeline of a GPT.

    The timing of Britain’s mechanization, linked to the expanded uses of iron in machine-making, also aligned with the GPT trajectory. The first metalworking tools for precision engineering, including Wilkinson’s boring mill of 1774 and Maudslay’s all-iron lathe, appeared in the late eighteenth century, but they would remain in a “comparatively rudimentary state” until about 1815.52 According to accounts of qualified engineers and the 1841 Select Committee on Exportation of Machinery, over the course of the next two decades improvements and standardization in such machine tools ushered in a “revolution” in machine-making.53 The gradual evolution of the mechanical engineering industry provides additional support for a delayed impact timeframe for mechanization. According to British patent data from 1780 to 1849, the share of mechanical engineering patents among the total number of patents increased from an average of 18 percent in the decade starting in 1780 to a peak of 34 percent in the one starting in 1830.54

    DELAYED, OUT-OF-PERIOD EFFECTS: STEAM ENGINE AND FACTORY SYSTEM

    Compared to mechanization, the steam engine had not diffused widely enough through Britain’s economy by the mid-nineteenth century to make a substantial impact on overall industrial productivity. Detailed investigations of steam engine adoption have forced a reassessment of commonly held assumptions about the rapid impact of steam power on British productivity growth.55 According to one growth accounting analysis, which compares the impact of steam against water power as a close substitute source of power, steam power’s contribution to British productivity growth was modest until at least the 1830s and most influential in the second half of the nineteenth century.56 This revised impact timeframe conflicts with international relations scholarship, which advances faster trajectories for steam’s impact as a leading sector.57

    Steam engine adoption was slow. In 1800, thirty years after Watt patented his steam engine, there were only about thirty-two engines operating in Manchester, which was a booming center of industrialization.58 Even into the 1870s, many important sectors in the British economy, such as agriculture and services, were virtually unaffected by steam, as most of steam power’s applications were concentrated in mining and in cotton textiles.59 During the period when LS accounts expect its peak growth, the steam engine could not claim generality of use.

    The process by which the steam engine gained a broad variety and range of uses entailed complementary innovations that followed many years after the introduction of Watt’s steam engine. It took sixty years for steam to become the prime driver of maritime transport; that was made possible only after cumulative enhancements to the power of steam engines and the replacement of paddle wheels by screw propellers, which increased the speed of steam-powered ships.60 Watt’s original low-pressure design engines consumed large amounts of coal, which hindered widespread adoption. After 1840, aided by inventions such as the Lancashire boiler and discoveries in thermodynamics, it became economically viable to deploy steam engines that could handle higher pressures and temperatures.61 In sum, steam power may be the quintessential example of the long delay between the introduction of a GPT and significant economy-wide effects.

    It is worth assessing whether interconnections between developments in steam and those in iron and cotton give grounds for an earlier impact trajectory.62 Yet both forward and backward linkages were limited in the early stages of the steam engine’s evolution. Regarding the latter, the steam engine–producing industry did not substantially stimulate the iron industry’s expansion. In the late 1790s, at a peak in sales, Boulton and Watt steam engines consumed less than 0.25 percent of Britain’s annual iron output.63 Forward linkages to textiles, the most likely sector to benefit from access to cheaper steam power, were also delayed. Steam-powered inventions in textiles did not overtake water-powered textile processes until after 1830.64 Of course, over the long run steam power was a critical technological breakthrough that changed the energy budget of the British economy.65 However, for investigating the mechanisms that facilitated Britain’s rise to become the clear productivity leader in First Industrial Revolution, the steam engine played a modest role and most of its effects were out-of-period.

    A similar timeline characterizes the progression of the factory system, another candidate GPT considered for this period. The factory system diffused slowly and took hold in only a limited number of trades during the time when Britain was establishing its industrial preeminence. The British textile industry, as the earliest adopter of this organizational innovation, had established nearly five thousand steam- or water-powered factories by the 1850s.66 Other industries, however, were much slower to adopt the factory system. In the first decades of the nineteenth century, small workshops and domestic production still dominated the metal trades as well as other hardware and engineering trades.67

    Moreover, factories were still relatively small even into the mid-nineteenth century, and some industries adopted a mixed factory system in which many processes were outsourced to household workers.68 It was not until steam power overtook water power in the 1830s and 1840s as a source of power in factories that the subsequent redesigns of factory layouts led to large gains in productivity.69

    What does this clarified chronology of technological impacts in the IR-1 mean for the explanatory power of the GPT and LS mechanisms? Of the three candidate leading sectors, only cotton textiles, which expanded rapidly and peaked in terms of output growth in the 1780s and 1790s, followed the impact timeframe of a leading sector. As figure 3.2 shows, by 1814 British cotton exports had already surpassed 75 percent of the value they would attain in 1840. Yet Britain sustained productivity growth rates at higher levels than its rivals only in the first decades of the nineteenth century. Thus, the period when cotton should have made its greatest impact on Britain’s industrial ascent does not accord with the timeline of Britain’s industrialization surge.

    The hurried timeline of the LS mechanism contrasts with the more delayed impact of other technological advances. As predicted by the GPT mechanism, all three candidate GPTs—mechanization, the steam engine, and the factory system—had little impact on Britain’s industrial rise until after 1815. In fact, the diffusion timelines for the steam engine and factory system were so elongated that their impact on Britain’s rise to industrial preeminence was limited in this period. In 1830, steam engine adoption, as measured by total horsepower installed, was only one-quarter of the 1840 level, whereas by 1830 the level of iron production had reached about 50 percent of its corresponding value in 1840 (see figure 3.2). This is consistent with the steady expansion of mechanization across industry in the early decades of the 1800s as the GPT trajectory most attuned with the timing of Britain’s rise to economic leadership.

    figure 3.2 Technological Impact Timeframes in the IR-1. Note: British cotton exports, iron production, and steam engine adoption over time. Source: Robson 1957, 331–35; Mitchell 1988, 280–81; Crafts 2004, 342.

    Phase of Relative Advantage: Diffusion of Iron vs. Monopoly Profits from Cotton

    Thus far, the empirical evidence has presented a bird’s-eye view of the overall timeline of technological change and industrialization in the IR-1, but there are two other dimensions on which the GPT and LS trajectories diverge. According to the expectations of the LS mechanism, the phase of technological development central to Britain’s relative economic rise was its dominance of key innovations in cotton textiles, iron, or the steam engine. The GPT mechanism predicts, in contrast, that Britain’s advantage in the diffusion of mechanization, the steam engine, or the factory system was the key driver.

    The rest of this section tests two sets of predictions derived from the diverging assumptions of the two mechanisms. First, regarding the geographic clustering of major technological breakthroughs in the IR-1, I assess whether innovations in candidate leading sectors and GPTs were concentrated in Britain. Next, regarding the comparative consequences of these technologies, I evaluate whether Britain’s industrial superiority drew more from its advantages in the production and exports of the IR-1’s leading sectors or from its advantage in the diffusion of the IR-1’s GPTs.

    INNOVATION CLUSTERING IN THE IR-1’S BREAKTHROUGHS

    Did Britain dominate innovation in the leading sectors of the IR-1? At first glance, there is no question that radical advances in candidate leading sectors clustered in Britain. This list includes Watt’s steam engine, Arkwright’s water frame, Cort’s puddling process, and many more. Per one analysis of 160 major innovations introduced during the nineteenth and twentieth centuries, Britain was home to 44 percent of major innovations from 1811 to 1849—a rate that was double that of the closest competitor (the United States at 22 percent).70

    Further investigation into British superiority in technological innovation paints a more mixed picture. According to another list of technical advances by country of origin, Britain accounted for only 29 percent of major innovations in the years from 1826 to 1850, a period that corresponds to when it cemented its productivity leadership.71 Moreover, the European continent introduced many significant innovations, including the Jacquard loom, mechanical flax spinning, chlorine bleaching, the Leblanc soda–making process, and the Robert continuous papermaking machine.72 France, in particular, generated many of the major industrial discoveries, such as in chemicals, clocks, glass, papermaking, and textiles.73

    Thus, some scholars argue that Britain’s comparative edge was in more incremental improvements. Reflecting on technological creativity in the IR-1, economic historian Joel Mokyr argues, “Britain seems to have no particular advantage in generating macroinventions … the key to British technological success was that it had a comparative advantage in microinventions.”74 A proverb from the time captured this distinction: “For a thing to be perfect it must be invented in France and worked out in England.”75 This suggests that digging deeper into the different phases of technological development can help uncover the roots of Britain’s industrial leadership.

    MONOPOLY PROFITS VS. DIFFUSION DEFICIT

    First, as was the case with the period when they made their impact, developments in cotton and iron followed very different paths with respect to the phase of technological change that determined economic differentials. Britain’s cotton textile industry, the most likely source of monopoly profits, grew faster than other industries before 1800, and it sold most of its goods abroad. Technological innovations such as the spinning jenny and the water frame triggered exponential increases in the efficiency of cotton production, and Britain’s cotton output increased by 2,200 percent from 1770 to 1815.76 From 1750 to 1801, cotton’s share of Britain’s major exports increased from 1 percent to 39.6 percent.77

    Certainly, the growth of British cotton exports was remarkable, but what was the impact of associated monopoly rents on overall growth differentials? Supported by improved quantitative estimates of the cotton industry’s impact on the British economy, historians generally accept that the cotton industry was much more significant for enhancing Britain’s trade balance than for boosting its economic productivity.78 According to one estimate, between 1800 and 1860, the cotton industry accounted for 43 percent of the threefold increase in the value of exports but only 8 percent of the threefold increase in national income.79

    Overall, exports constituted a small proportion of British economic activity during the IR-1. From 1770 to 1841, British exports as a percentage of overall industrial demand increased only from 13 to 16 percent.80 Now, these figures probably underrate trade as a critical engine of growth for Britain in the IR-1, as they ignore gains from the reinvestment of profits from overseas trade.81 But the impact of reinvestment has been challenged, and it is not apparent why reinvestments from exports were more important than reinvestments from the profits generated by domestic production.82

    The iron industry’s impact on Britain’s economic rise did not run through monopoly profits from innovation. From 1794 to 1796, British ironmakers contributed 11 percent of Britain’s manufacturing exports. This proportion actually declined to just 2 percent by the 1814–1816 period and stayed around that rate into the 1830s.83 It is also questionable whether Britain held a relative advantage in iron exports during the late eighteenth century, which is when LS accounts expect monopoly profits to drive British industrialization.84 In fact, British industries continued to rely on imports of high-grade iron from Sweden and Russia well into the nineteenth century.85

    An alternative pathway, captured by the GPT trajectory, posits that Britain’s advantage came from the diffusion of iron machinery advances across a wide range of sectors. To trace this trajectory, it is necessary to pay more attention to what a prominent historian of the IR-1 calls one of the astonishing things about the phenomenon: the gap between “innovation as ‘best practice’ technique and the diffusion of innovation to become ‘representative’ technique.”86

    Britain was more successful than its industrial rivals in the diffusion of mechanization. Contemporary observers from the European continent often remarked upon Britain’s ability to bridge this gap between best practice and representative practice.87 Writing in 1786 in their Voyages aux Montagnes, French observers F. and A. de la Rochefoucauld-Liancourt, commenting on Britain’s relative advantage in the widespread adaptation of the use of iron, noted

    the great advantage [their skill in working iron] gives them as regards the motion, lastingness and accuracy of machinery. All driving wheels and in fact almost all things are made of cast iron, of such a fine and hard quality that when rubbed up it polishes just like steel. There is no doubt but that the working of iron is one of the most essential of trades and the one in which we are most deficient.88

    But France’s deficiency in iron machinery was not a product of its lack of access to key innovations. In fact, France was the world’s center of science from the late eighteenth century until the 1830s.89 Rather, as the following quote illustrates, Britain’s industrial rivals fell behind in “diffused average technology” and the “effective spread of technical change more widely.” Economic historian Peter Mathias writes:

    It is remarkable how quickly formal knowledge of “dramatic” instances of new technology, in particular steam engines, was diffused, and how quickly individual examples of “best-practice” technology in “show piece” innovations were exported. The blockage lay in the effective spread of technical change more widely—diffused average technology rather than single instances of best-practice technology in “dramatic” well-publicized machines.90

    Advances in iron metallurgy played a crucial role in a GPT trajectory that spread from a sector that improved the efficiency of producing capital goods. The GPT trajectory unfolds as the technology becomes more general-purpose through interactions between the upstream capital goods sector and the user industries that enlarge the range of applications. Rosenberg’s depiction of this type of system highlights the nineteenth-century American machine tool industry as the innovative capital goods sector.91 In this case, Britain’s metal-processing works were the crucial wellspring. Specifically, technical advances in iron fed into metalworking industries from which broadly similar production processes diffused over a large number of industries.92 Maxine Berg, a professor of history at the University of Warwick, pinpoints these industries as the “prime mechanism for technological diffusion.”93

    Scholars also identify Watt’s improved steam engine as a potential source of both LS- and GPT-based effects. Here I focus on testing the LS prediction about the steam engine–producing industry because the previous section showed that the steam engine and the factory system, as candidate GPTs, diffused too slowly to make a meaningful impact on the economic power transition in the IR-1.

    It is tough to make a case that the growth of the steam engine–producing industry generated a substantial source of monopoly profits for Britain. Equipped with an exclusive patent, James Watt and Matthew Boulton set up a firm in 1775 to sell steam engines.94 In the period from 1775 to 1825, however, the firm sold only 110 steam engines to overseas customers.95 By 1825, France and the United States were manufacturing the Watt engine and high-pressure engines at more competitive prices, and overseas demand declined sharply.96 Thus, the international sales history of this firm severely weakens the significance of the monopoly profits associated with the innovation of the steam engine.97

    In sum, the evidence from this section supports two conclusions. British advantages in the production and export of iron, steam engines, and cotton textiles (the best representative of the LS trajectory) had muted effects on its overall industrialization and productivity advances. Second, the contributions of technological breakthroughs in iron metallurgy and steam power to Britain’s industrial rise track better with the GPT mechanism, based on relative advantages in widespread technological diffusion as opposed to monopoly profits from innovation.

    Breadth of Growth: Complementarities of Iron vs. Spillovers from Cotton Textiles

    The breadth of growth in the IR-1 is the last dimension on which the LS and GPT trajectories disagree. Was Britain’s industrial rise driven by technological changes confined to a narrow range of leading sectors, or was it based on extensive, complementary innovations that enabled the spread of GPTs? Making use of data on sectoral sources of productivity growth, trade flows, and patents, I evaluate these competing propositions about the breadth of technological change in the industrial revolution.

    WIDESPREAD PRODUCTIVITY GROWTH

    In differentiating between the narrow view and the broad view of technical change during the IR-1, a natural starting point is to estimate the contribution of various industries to British productivity growth. Deirdre McCloskey’s calculations of sectoral contributions to productivity growth support the broad view. Though cotton accounted for a remarkable 15 percent of Britain’s total productivity growth, nonmodernized sectors still drove the lion’s share (56 percent) of productivity gains.98

    Manufacturing trade data provide another testing ground. If other manufacturing industries outside of textiles and iron were technologically stagnant during the first fifty years of the nineteenth century, then British competitiveness in these industries should decline relative to textiles and iron. The narrow view implies that Britain should have imported other manufactures. Peter Temin’s analysis of British trade data, however, finds the opposite. Throughout the first half of the nineteenth century, British manufacturing exports matched the increase in cotton exports throughout the first half of the nineteenth century.99 In a wide range of manufactures, such as arms and ammunition, carriages, glass, and machinery and metals, Britain held a clear comparative advantage. This pattern points to some general pattern of change that spanned industries. “The spirit that motivated cotton manufactures extended also to activities as varied as hardware and haberdashery, arms, and apparel,” Temin concludes.100

    The patent record also depicts a landscape of extensive technological change.101 From 1780 to 1840, about 80 percent of all patented inventions came from outside the textiles and metals industries.102 Per Christine MacLeod’s data on British patents covering the 1750–1799 period, most capital goods patents originated from sectors outside of textile machinery and power sources.103 As summed up by historian Kristine Bruland, the historical evidence supports “the empirical fact that this was an economy with extensive technological change, change that was not confined to leading sectors or highly visible areas of activity.”104

    GPTS AND COMPLEMENTARY INNOVATIONS

    At this point, indicators of the multisectoral spread of innovation in the IR-1 should not be sufficient to convince a skeptical reader of the GPT mechanism’s validity. Broad-based growth could be a product of macroeconomic factors, such as sound fiscal and monetary policy or labor market reforms, rather than a GPT trajectory.105 Proving that the dispersion of technological change in Britain’s economy reflected a GPT at work requires evidence that connects this broad front to mechanization.106

    Input-output analysis, which sheds light on the linkages between industries, suggests that improvements in the working of iron had broader economic significance. To better understand the interrelationships among industries during the industrial revolution, Sara Horrell, Jane Humphries, and Martin Weale constructed an input-output table for the British economy in 1841. Across the seventeen industries included in the analysis, the two industries most closely associated with mechanization—metal manufacture and metal goods—scored the highest on combined backward and forward linkages.107 These two domains were “lynchpins of linkage effects.”108

    Patent indicators confirm these results. When patents are grouped according to standard industry taxonomies, the resulting distribution shows that the textile industry contributed to 15 percent of the patents issued between 1711 and 1850, making it the most inventive industry in aggregate terms.109 However, when patents are sorted by general techniques as opposed to industry sectors, the same data reveal the underlying drive force of mechanical technology: it is linked to almost 50 percent of all British patents during this period.110

    Along all three dimensions of technological trajectories in the IR-1, the process-tracing evidence bolsters the validity of the GPT mechanism. First, slower-moving developments in mechanization lined up with a delayed timeline of Britain’s industrialization. Other candidate leading sectors and GPTs either peaked too early (cotton) or got started too late (steam engine, factory system). Second, Britain gained its industrial dominance from a relative advantage in widespread adoption of iron metalworking and linked machinery. Third, the benefits from this GPT advantage circulated throughout the economy, rather than remaining concentrated in the iron industry.

    The standard explanation of how the IR-1 gave rise to a power transition, as captured by the LS mechanism, analyzes technological change at the level of industries that grow faster than others. The historical evidence reveals the limitations of these industry taxonomies. Instead, advantages in the diffusion of production machinery—a general pattern of change that extended across a wide range of economic activities—propelled Britain to industrial dominance.111

    Institutional Complementarities: GPT Skill Infrastructure in the IR-1

    Having mapped Britain’s industrial rise to a GPT trajectory linked to mechanization, there is still a need to explain why Britain was best positioned to exploit this trajectory. If other countries at the technological frontier can also cultivate mechanical innovations at home and absorb them from abroad, why were Britain’s competitors unable to benefit from the diffusion of metalworking processes to the same extent? This section supports an explanation based on Britain’s institutional competencies in widening the pool of engineering skills and knowledge linked to mechanization.

    Which types of institutions for skill provision were most conducive to national success in the IR-1? One common refrain is that Britain’s leadership was rooted in the genius of individual innovators like James Watt, and such genius did not transfer as quickly across borders during the IR-1.112 Though recent scholarship has weakened this view, many influential histories center on the “heroic” inventors of the industrial revolution.113 Consistent with the LS template, this view focuses on the institutions that helped drive heroic invention in Britain, such as the development of a patent system.114

    The pathway by which mechanization propelled Britain’s industrial ascent, established as a GPT trajectory in the previous section, emphasizes another set of institutions for skill formation. In line with GPT diffusion theory, Britain owed its relative success in the IR-1 to mechanics, instrument-makers, and engineers who could build machines according to blueprints and improve upon them depending on the application context. Under this view, the institutions that trained the “tweakers” and “implementers,” rather than those that cultivated genius inventors, take center stage.115

    Widening the Base of Mechanical “Tweakers” and “Implementers”

    At first, rapid advances in precise metalworking exposed a skills shortage in applied mechanics. Beginning in the 1770s, a cascade of recruitment advertisements in local newspapers sought out an “engine-maker” or a “machine-maker.”116 Reflecting on this skills mismatch, the president of Britain’s Institute of Civil Engineers stated that the use of cast iron in machine parts “called for more workmen than the millwright class could supply.”117

    A number of institutional adjustments helped Britain meet this demand for mechanically skilled tweakers and implementers. Initially, Britain benefited from a flexible apprenticeship system that empowered workers in related domains to get trained in applied mechanics.118 Thus, to develop the workforce to build and maintain the machinery of the IR-1, Britain could draw from a wide pool of blacksmiths, millwrights, gunsmiths and locksmiths, instrument-makers, mechanics, and toolmakers.119

    In addition, institutes dedicated to broadening the base of mechanical expertise helped diffuse ironmaking and machine-making skills. Starting in the 1790s, private and informal initiatives created a flurry of trade associations that supported a new class of mechanical and civil engineers and helped connect them with scientific societies and entrepreneurs.120 Critical centers included the Andersonian Institution in Glasgow, the Manchester College of Arts and Sciences, the School of Arts in Edinburgh, the Mechanical Institution in London, the Society for the Diffusion of Useful Knowledge, and hundreds of mechanics’ institutes.121 These institutes helped to absorb knowledge from foreign publications on science and engineering, recruit and upskill new mechanical engineers from a variety of trades, and spread mechanical engineering knowledge more widely.122

    It is important to note that these institutional features differed from those more suited to the “heroic inventor” model. In Britain’s cotton textile industry, the classic leading sector of the IR-1, the key institutional complements deviated greatly from the education and training systems that widened the base of mechanical expertise in the IR-1. Collating information from biographies of British engineers, online databases, and detailed economic histories, Ralf Meisenzahl and Joel Mokyr constructed a database of 759 British individuals who made incremental improvements to existing inventions during the industrial revolution.123 Notably, based on their analysis of interactions between tweakers and their institutional surroundings, they found that the textile industry was an outlier in terms of protectiveness over intellectual property rights and reluctance to share information about new techniques. Less than one-tenth of tweakers in textiles published their knowledge to a broader audience or joined professional societies, in stark contrast to the two-thirds of tweakers in mechanically inclined fields who did so.124 Over 80 percent of the tweakers who were active primarily in textiles took out at least one patent, compared to just 60 percent for tweakers overall.125

    These trends in applied mechanics underscore the significance for British mechanization of “collective invention,” a process that involved firms sharing information freely with one another and engineers publishing technical procedures in journals to spur the rapid diffusion of best-practice techniques. According to one analysis of how various districts adapted to the early phases of industrialization, areas that practiced collective invention often cultivated “a much higher degree of technological dynamism than locations which relied extensively on the patent system.”126

    Britain’s Comparative Advantage over France and the Netherlands in GPT Skill Infrastructure

    Britain’s competitors also grasped the significance of Britain’s wide pool of mechanical skills. Whereas codified knowledge crisscrossed western Europe and North America via patent specifications, global exchanges among scientific societies, and extensive visits by foreign observers to British workshops and industrial plants, the European continent struggled greatly to absorb tacit knowledge, especially the know-how embodied in the practical engineering skills of British mechanical tweakers and implementers.127 France and the Netherlands fiercely poached British engineers, as the transfer of tacit knowledge in the fields of large-scale ironworking and machine construction almost always necessitated the migration of skilled workers from Britain.128 “It was exactly in the skills associated with the strategic new industries of iron and engineering that [Britain’s] lead over other countries was most marked,” argues Mathias.129

    Why did this repository of engineering skills develop more fruitfully in Britain than in its industrial rivals? A growing body of evidence suggests that Britain’s institutions better adapted its distribution of skills to mechanization. Britain’s institutional advantage was rooted in the system of knowledge diffusion that connected engineers with entrepreneurs, cities with the countryside, and one social class with another. Institutes that trained mechanics took part in a broader “mushrooming of associations” that spread technical knowledge in early-nineteenth-century Britain.130 By the mid-nineteenth century, there were 1,020 such associations in Britain, with a total membership of approximately 200,000; clearly, these networks are essential to any explanation that links human capital to Britain’s industrial ascent.131 Compared to their peers on the continent, British mechanics had superior access to scientific and technical publications.132 As a result, the British system of the early nineteenth century had no match in its abundance of people with “technical literacy.”133

    The French system, by way of comparison, lacked similar linkages and collaborations between highly educated engineers and local entrepreneurs.134 Though France produced elite engineers at schools like the École Polytechnique, it trained too few practitioners to widen the base of mechanical skills.135 For example, Napoleon’s early-nineteenth-century reform of France’s higher education system encouraged the training of experts for narrow political and military ends, thereby limiting the ability of trainees to build connections with industry.136 These reforms and other industrial policies directed French engineers toward projects associated with luxury industries and specialized military purposes, which “tended to become locked away from the rest of the economy in special enclaves of high cost.”137 To illustrate, through the mid-1830s, only one-third of École Polytechnique graduates entered the private sector.138 France’s system for disseminating mechanical knowledge and skills was vastly inferior to that of the British.

    The Netherlands also failed to develop a base of mechanical skills that linked scientific research to practical ends. In some mechanical sciences, the Dutch generated plenty of potentially useful innovations, even pioneering key breakthroughs that eventually improved the steam engine.139 Yet the Dutch struggled to translate these scientific achievements into practical engineering knowledge because they trailed the British in forming institutional settings that made widespread knowledge of applied mechanics possible. Records of Dutch educational systems, the dearth of societies that held lectures and demonstrations for mechanical learning, and the materials available at libraries in technical colleges all “reflected a profound lack of interest in applied mechanics.”140 In his study of Dutch technological leadership, Karel Davids argues that, during the first three-quarters of the eighteenth century, “collaboration between science and industry in the Netherlands failed to merge in the very period that relations between the two became rapidly closer in Britain.”141

    Britain’s advantage in GPT diffusion was not rooted in its higher education system, which lagged far behind the French education system during the IR-1 period.142 France had already established more than twenty universities before the French Revolution. The French system of higher technical education, from the late eighteenth century through the 1830s, had no rival. The Grande Écoles system, including the elite École Polytechnique (established in 1794), trained expert scientists and engineers to take on top-level positions as industrial managers and high-level political personnel.143 Up until 1826, England had set up only two universities, Oxford and Cambridge. These institutions made limited contributions to training the workforce necessary for industrialization. One study with a sample of 498 British applied scientists and engineers born between 1700 and 1850 found that only 50 were educated at Oxford or Cambridge; 329 were not university-educated.144

    At this point, curiosity naturally leads us to ask why Britain accumulated an advantage in GPT skill infrastructure. Due to practical constraints of time and space, I acknowledge but do not delve into the deeper causes for the notable responsiveness of Britain’s institutions to the skill demands of mechanization. In surveying valuable lines of inquiry on this subject, chapter 2 points to government capacity to adopt long time horizons and reach intertemporal bargains. In the IR-1 case, two specific factors are also worthy of consideration. Attributing Britain’s later success to pre-industrial training practices, some studies suggest that Britain’s apprenticeship system allowed for agile and flexible adaptation to fluctuations in the demand for skills, especially in mechanical trades.145 Looking even further back, other scholars probe the geographical origins of Britain’s mechanical skills, underscoring the lasting effects of Britain’s adoption of watermills in the early Middle Ages.146

    Alternative Explanations of Britain’s Rise

    The history of the IR-1 is certainly not a neglected topic, and the literature features enthusiastic debates over a wide range of possible causes for Britain’s rise. Prominent explanations tie Britain’s early industrialization to population growth,147 demand and consumption standards,148 access to raw materials from the colonies,149 slavery,150 and trade.151 The obvious concern is that various contextual factors may confound the analysis of the LS and GPT mechanisms.

    I am not rewriting the history of the IR-1. I am drawing from one particularly influential and widely held view of the IR-1—that technological advances drove Britain’s industrial ascent—and investigating how technological change and institutional adaptations produced this outcome. The most relevant alternative factors, therefore, are those that provide a different interpretation of how technologies and institutions coevolved to result in Britain’s industrial hegemony. Although I primarily focus on the LS mechanism as the most formidable alternative explanation to the GPT diffusion theory, other explanations also warrant further investigation.

    Threat-Based Explanations

    Threat-based theories assert that external threats are necessary to incentivize states to innovate and diffuse new technologies. Did Britain owe its technological leadership to war and its military’s impetus to modernize? During the IR-1 period, Britain was embroiled in the Revolutionary and Napoleonic Wars (1793–1815), a near-continuous stretch of conflicts involving France and other European states. If threat-based explanations stand up in the IR-1 case, then the historical record should show that these wars made an essential and positive contribution to Britain’s adoption of iron machinery and mechanization.

    Some evidence supports this argument. By 1805, the British government’s needs for iron in the war effort accounted for 17 percent of the total British iron output in 1805.152 This wartime stimulus to iron production facilitated improvements in iron railways, iron ships, and steam engines.153 In particular, military investments in gunmaking produced important spin-offs in textiles and machine tools, most famously encapsulated by Watt’s dependence on John Wilkinson’s cannon boring techniques to make the condenser cylinders for his steam engine.154

    On the flip side, war’s disruptive costs are likely to have offset any stimulus to Britain’s mechanization. Aside from Wilkinson’s cannon boring device and some incremental improvements, wartime pressures did not produce any major technological breakthroughs for the civilian economy.155 Military needs absorbed productive laborers from Britain’s civilian economy, resulting in labor shortages.156 War also limited both the domestic demand for iron, by halting investment in construction, agriculture, and other industries, and the foreign demand for British iron, by cutting off foreign trade. Historian Charles Hyde notes, “In the absence of fighting, overall demand for iron might have been higher than it was.”157

    Furthermore, any temporary benefits that accrued to Britain’s iron industry in wartime were wiped out in the transition to peacetime. In one influential text, historian Thomas Ashton reviewed how each of the wars of the eighteenth century, including the Revolutionary and Napoleonic Wars, affected Britain’s iron industry.158 He observed a similar pattern in each case. At first, the outbreak of hostilities boosts demand for iron in the form of armament, and trade disruptions protect domestic producers against foreign competitors. This initial boom is followed by a severe crash, however, when the iron industry adjusts to the conflict’s aftermath. A trade depression follows. Converting foundries to make plowshares instead of cannons incurs heavy losses, made even more painful by the fact that war conditions promoted “feverish” developments that were unsustainable in the long run.159

    On a more fundamental level, threat-based theories have limited leverage in explaining Britain’s relative rise because its economic competitors were also embroiled in conflicts—in many cases, against Britain. The Dutch fought Britain in the fourth Anglo-Dutch War (1780–1784) as well as in the Napoleonic Wars.160 Of course, during this time, France was Britain’s main military opponent. Thus, since the Netherlands and France also faced a threatening external environment, the net effect of the war on economic growth differentials should have been minimal.161 If anything, since France fought on many more fronts than Britain during this period, proponents of threat-based explanations would expect France to have experienced more effective and widespread diffusion of iron machinery throughout its economy. The case analysis clearly discredits that expected outcome.

    VoC Explanations

    Can Britain’s particular brand of capitalism account for its technological rise? The varieties of capitalism (VoC) approach posits that liberal market economies (LMEs) are particularly suited to radical innovation. Consistent with this framework, international political economy scholars emphasize that Britain’s free market economy supported gains in rapidly changing technological domains like consumer goods, light machine tools, and textiles.162 During the IR-1 period, Britain began to develop the institutional features that would cement it as a LME, including decentralized collective bargaining and high levels of corporatization.163 Most pertinent to GPT diffusion theory, VoC scholars expect LMEs like Britain to excel at cultivating general skills, which help transfer GPT-related knowledge and techniques across firms.

    Taking measure of Britain’s human capital development in general skills in this period is therefore central to evaluating whether its technological leadership can be explained by its form of capitalism. Overall, estimates of literacy rates and school attendance demonstrate that the general level of human capital in Britain was notably low for an industrial leader.164 British literacy rates for males were relatively stagnant between 1750 and 1850, and average literacy rates in Britain were much lower than rates in the Netherlands and barely higher than those in France around the turn of the nineteenth century.165 In fact, general levels of educational attainment in Britain, as measured by average years of schooling, declined from around 1.4 years in 1740 to 1.25 years in 1820.166 Contrary to VoC theory’s expectations, Britain did not hold an advantage in general skills during this period.

    The VoC explanation’s applicability to the IR-1 period is further limited by issues with designating Britain as the only LME in this period. Like Britain, the Netherlands functioned as a relatively open economy and exhibited tendencies toward economic liberalism, but it was not able to adapt and diffuse significant technological changes.167 Though France is now considered a coordinated market economy, in the early nineteenth century it took on some of the characteristics of LMEs by implementing capital market reforms and trade liberalization.168 The VoC approach therefore struggles to resolve why these two LMEs diverged so greatly in their adaptation to mechanization.

    Case-Specific Factors

    Among other factors specific to the IR-1 setting, one alternative explanation emphasizes Britain’s fortunate geographic circumstances. More specifically, classic works have argued that proximity to plentiful coalfields was essential to British industrialization.169 These natural resource endowments enabled the expansion of coal-intensive industries, such as the iron industry. In this line of thinking, the fact that coal was cheaper in Britain than elsewhere in Europe explains why Britain was the first to sustain productivity leadership.170

    The relationship between coal and industrialization does not necessarily undermine the GPT mechanism. For one, in principle, Britain’s competitors could also have effectively leveraged coal resources. The southern provinces of the Netherlands were located close to Belgian coalfields.171 Over the course of the eighteenth century, Dutch industry had mostly shifted to coal, and away from peat stocks, as a key source of energy.172 Even if Britain’s industrial rivals had to pay more by importing coal, the expected productivity gains associated with adopting new technologies should have outweighed these costs. Moreover, GPT skill infrastructure could have mediated the relationship between coal and mechanization, as Britain’s edge in metalworking skills spurred the adoption of new coal-using technologies, which strengthened the connection between proximity to coal and economic growth.173

    Summary

    In many ways, the industrial revolution marked an exceptional transformation. It is to any number of historical trends what the birth of Jesus is to the Gregorian calendar—an inflection point that separates “before” and “after.” For my purposes, however, the industrial revolution is a typical case showing how technological revolutions influence the rise and fall of great powers. Evidence from great powers’ different adaptations to technological changes in this period therefore helps test GPT diffusion theory against the LS mechanism.

    In sum, GPT diffusion theory best explains why Britain led Europe’s industrial transformation in this period. Britain effectively capitalized on general-purpose improvements in mechanization owing to its institutional advantages that were conducive to widening the pool of mechanical skills and knowledge. According to GPT diffusion theory, countries like this disproportionately benefit from technological revolutions because they adapt more successfully to the GPT trajectories that transform productivity. In line with these expectations, Britain was more successful than its industrial rivals in sustaining long-term economic growth, which became the foundation of its unrivaled power in the early and mid-nineteenth century.

    On the flip side, this chapter’s case analysis undercuts the LS-based explanation. The timeframe for when leading sectors were expected to stimulate Britain’s productivity growth did not align with when Britain’s industrialization took off. Britain’s economic ascent owed more to the widespread adoption of iron metalworking and linked production machinery than to monopoly profits from cotton textiles. The key institutional complements were not those that produced heroic inventions—Britain’s rivals held their own in these areas—but rather those that fostered widespread knowledge of applied mechanics.

    Do these findings hold in other periods of technological and geopolitical upheaval? The IR-1 was one of the most extraordinary phases in history, but it was not the only era to attain the title of an “industrial revolution.” To further explore these dynamics, it is only appropriate to turn to the period some have labeled the Second Industrial Revolution.

    4 The Second Industrial Revolution and America’s Ascent

    IN THE LATE nineteenth and early twentieth centuries, the technological and geopolitical landscape transformed in ways familiar to observers of today’s environment. “AI is the new electricity” goes a common refrain that compares current advances in machine intelligence to electrical innovations 150 years ago. Those fundamental breakthroughs, alongside others in steel, chemicals, and machine tools, sparked the Second Industrial Revolution (IR-2), which unfolded from 1870 to 1914.1 Studies of how present-day technological advances could change the balance of power draw on geopolitical competition for technological leadership in the IR-2 as a key reference point.2

    Often overshadowed by its predecessor, the IR-2 is equally important for investigating causal patterns that connect technological revolutions and economic power transitions. The presence of both cause and outcome ensures a fruitful test of the GPT and LS mechanisms. The beginning of the period featured remarkable technological innovations, including the universal milling machine, the electric dynamo, the synthesis of indigo dye, and the internal combustion engine. According to some scholars, one would be hard-pressed to find another period with a higher density of important scientific advances.3 By the end of the period, Britain’s decline and the rise of Germany and the United States had yielded a new balance of economic power, which one historian describes as a “shift from monarchy to oligarchy, from a one-nation to a multi-nation industrial system.”4 Arguably, British industrial decline in the IR-2 was the ultimate cause of World War I.5

    International relations scholars hold up the IR-2 as a classic case of a power transition caused by LS product cycles.6 According to this view, Britain’s rivals cornered market shares in the new, fast-growing industries arising from major technological innovations in electricity, chemicals, and steel.7 Specifically, scholars argue that Germany surpassed Britain in the IR-2 because it was “the first to introduce the most important innovations” in these key sectors.8 Analysis of emerging technologies and today’s rising powers follows a similar template when it compares China’s scientific and technological capabilities to Germany’s ability to develop major innovations in chemicals.9 Thus, as a most likely case for the LS mechanism, which is favored by background conditions and existing theoretical explanations, the IR-2 acts as a good test for the GPT mechanism.

    Historical evidence from this period challenges this conventional narrative. No country monopolized innovation in leading sectors such as chemicals, electricity, steel, and motor vehicles. Productivity growth in the United States, which overtook Britain in productivity leadership during the IR-2, was not dominated by a few R&D-based sectors. Moreover, major breakthroughs in electricity and chemicals, prominent factors in LS accounts, required a protracted process of diffusion across many sectors before their impact was felt. This made them unlikely key drivers of the economic rise of the United States before 1914.

    Instead, the IR-2 case evidence supports GPT diffusion theory. Spurred by inventions in machine tools, the industrial production of interchangeable parts, known as the “American system of manufacturing,” embodied the key GPT trajectory.10 The United States did not lead the world in producing the most advanced machinery; rather, it had an advantage over Britain in adapting machine tools across almost all branches of industry. Though the American system’s diffusion also required a long gestation period, the timing matches America’s industrial rise. Incubated by the growing specialization of machine tools in the mid-nineteenth century, the application of interchangeable parts across a broad range of manufacturing industries was the key driving force of America’s relative economic success in the IR-2.11

    Since a nation’s efficacy in adapting to technological revolutions is determined by how well its institutions complement the demands of emerging technologies, the GPT model of the IR-2 highlights institutional factors that differ from those featured in standard accounts. LS-based theories tend to highlight Germany’s institutional competencies in scientific education and industrial R&D.12 In contrast, the case analysis points toward the American ability to develop a broad base of mechanical engineering skills and standardize best practices in mechanical engineering. Practice-oriented technical education at American land-grant colleges and technical institutes enabled the United States to take better advantage of interchangeable manufacturing methods than its rivals.

    This chapter’s evidence comes from a variety of sources. In tracing the contours of technological trajectories and the economic power transition in this period, I relied on histories of technology, categorization schemes from the long-cycle literature, general accounts of economic historians, and revised versions of historical productivity measures. I investigated the fit between institutions and technology in leading economies using annual reports of the US Commissioner of Education, British diplomatic and consular reports, cross-national data on technological diffusion, German engineering periodicals, and firsthand accounts from inspection teams commissioned to study related issues.13 My analysis benefited from archival materials based at the Bodleian Library’s Marconi Archives (United Kingdom), the Library of Congress (United States), and the University of Leipzig and from records of the British Foreign Office.

    This chapter proceeds as follows. I begin by chronicling the economic power transition that took place during the IR-2 to clarify that the United States, not Germany, ascended to industrial preeminence. I then identify the key technological breakthroughs, which I sort according to their ties to GPT and LS trajectories. Along the dimensions of impact timeframe, phase of relative advantage, and breadth of growth, this chapter demonstrates that the GPT trajectory aligns better with how the IR-2 enabled the economic rise of the United States. Next, I evaluate whether differences in GPT skill infrastructure can account for the American edge over Britain and Germany in interchangeable manufacturing methods. Toward the chapter’s end, I also tackle alternative explanations.14

    A Power Transition: America’s Ascent

    To begin, tracing when an economic power transition takes place is critical. In 1860, Britain was still at the apogee of its industrial power.15 Most historians agree that British industrial preeminence eroded in the late nineteenth century. By 1913, both the United States and Germany had emerged as formidable rivals to Britain with respect to the industrial and productive foundations of national power. According to Paul Kennedy’s influential account, before World War I Britain was “in third place,” and “in terms of industrial muscle, both the United States and imperial Germany had moved ahead.”16 Aided with more data than was available for the IR-1 case, I map the timeline of this economic power transition with various measures of industrial output and efficiency.

    In the IR-2 case, clarifying who surpassed Britain in economic efficiency takes on added gravity. Whereas in the IR-1 Britain separated itself from the rest, both the United States and Germany challenged British industrial power in the IR-2. But studies of this case often neglect the rise of the United States. Preoccupied with debates over whether Germany’s overtaking of Britain sparked World War I, the power transition literature has directed most of its attention to the Anglo-German competition for economic leadership.17 Some LS-based accounts explain only Germany’s rise in this period without investigating America’s ascent.18

    As the rest of this section will show, Germany and the United States both surpassed Britain on some measures of economic power, but the United States emerged as the clear productivity leader. Therefore, any explanation of the rise and fall of technological leadership in this period must be centered on the US experience. The following sections trace the contours of the IR-2’s economic power transition with a range of indicators for productivity leadership, including GDP per capita, industrialization, labor productivity, and total factor productivity.

    GDP PER-CAPITA INDICATORS

    Changes in total GDP over the course of the IR-2 provide a useful departure point for understanding changes in the balance of productive power. At the beginning of the period in 1871, Germany’s economy was around three-quarters the size of the British economy; by the end of the period, in 1913, Germany’s economy was approximately 14 percent larger than Britain’s. The growth trajectory of the American economy was even starker. Over the same time period, overall economic output in the United States increased from 1.2 times to around 3.4 times that of Britain’s total GDP.19 This trend is further confirmed by the growth rates of overall GDP for the three countries. In the period between 1870 and 1913, the US GDP grew roughly 5.3 times over, compared to 3.3 for Germany and 2.2 for the United Kingdom.20

    While gross economic size puts countries in contention for economic leadership, the most crucial outcome is sustained economic efficiency. Compared to total output, trend lines in real GDP per capita mark out a broadly similar picture of the IR-2 period, but they also differ in two significant respects (figure 4.1). First, whereas the United States was already the largest economy by total output in 1870, the United Kingdom maintained a slight lead in real GDP per capita over the United States in the 1870s. The United Kingdom’s average GDP per capita over the decade was about 15 percent higher than the US equivalent.21 US GDP per capita was roughly on par with Britain’s throughout the 1880s and 1890s, but the United States established a substantial lead starting around 1900.22

    FIGURE 4.1. Economic Power Transition during the IR-2. Source: Maddison Project Database, version 2020 (Bolt and van Zanden 2020).

    Second, in contrast to trend lines in aggregate economic output, Germany did not surpass Britain in GDP per capita before World War I. Germany certainly closed the gap, as its GDP per capita increased from around 50 percent of British GDP per capita in 1870 to around 70 percent in the years before World War I. However, Germany never even came close to overtaking the United Kingdom in GDP per capita during this period.23 This is an important distinction that justifies the focus on US technological success in the IR-2, since surpassing at the technological frontier is a different challenge than merely catching up to the technological frontier.

    INDUSTRIALIZATION INDICATORS

    Industrialization indicators back up the findings from the GDP per-capita data. The United States emerged as the preeminent industrial power, boasting an aggregate industrial output in 1913 that equaled 36 percent of the global total—a figure that exceeded the combined share of both Great Britain and Germany.24 More importantly, the United States became the leading country in terms of industrial efficiency, with a per-capita industrialization level about 10 percent higher than Britain’s in 1913.25

    Once again, the emphasis on productivity over aggregate output reveals that the economic gap between Germany and Britain narrowed but did not disappear. In aggregate terms, Germany’s share of the world’s industrial production rose to 16 percent in 1913. This eclipsed Britain’s share, which declined from 32 percent of the world’s industrial production in 1870 to just 15 percent in 1913.26 However, Germany did not overtake Britain in industrial efficiency. In 1913, its per-capita industrialization level was about 75 percent of Britain’s.27 The magnitude of this gap was approximately the same as the gap between German per-capita GDP and British per-capita GDP.

    PRODUCTIVITY INDICATORS

    Lastly, I consider various productivity statistics. Stephen Broadberry’s work on the “productivity race” contains the most comprehensive and rigorous assessments of productivity levels in Britain, Germany, and the United States in this period.28 Comparative statistics on labor productivity line up with findings from other indicators (figure 4.2). The United States surpassed Britain in aggregate labor productivity during the 1890s or 1900s, whereas Germany’s aggregate labor productivity increased relative to but did not fully overtake Britain’s over the IR-2 period.29

    FIGURE 4.2. Comparative Labor Productivity Levels in the IR-2. Source: Broadberry 2006, 110.

    Another set of productivity indicators, Maddison’s well-known and oft-cited historical data on comparative GDP per hour worked, supports Broadberry’s comparative measures of labor productivity levels.30 According to Maddison’s estimates of the average rate of productivity growth from 1870 to 1913, the American and German economies were both growing more productive relative to the British economy. The growth rate of America’s GDP per hour worked was 1.9 percent compared to 1.8 percent for the German rate and 1.2 percent for the UK rate.31

    It should be noted that the United Kingdom may have retained a total factor productivity lead in this period. Based on 1909 figures, the last measurements available before World War I, the US aggregate TFP was a little over 90 percent of Britain’s. By 1919, US aggregate TFP was nearly 10 percent larger than Britain’s.32 The United States could have surpassed Britain in overall TFP before World War I, but the data do not clearly demonstrate this outcome. Still, the TFP data track well with the general trends found in other measures of economic efficiency, including a marked increase in US TFP in the 1890s and 1900s as well as a steady narrowing of the gap between UK and German TFP throughout the period. Issues related to the availability, reliability, and comparability of capital stock estimates during this period, however, caution against concluding too much from the TFP trends alone.33

    Albeit with some caveats, the general thrust of evidence confirms that the United States overtook Britain in productivity leadership around the turn of the twentieth century. In productive efficiency, Germany significantly narrowed the gap but did not surpass Britain. A clarified picture of the outcome also helps guide the assessment of the LS and GPT mechanisms. In contrast to work that focuses on Anglo-German rivalry in this period, I prioritize explaining why the United States became the preeminent economic power. Moreover, if GPT diffusion theory holds for this period, it should also explain why the United States was more successful than Germany in overtaking Britain in productivity during this period.

    Key Technological Changes in the IR-2

    Which technological changes could have sparked the economic power transition before World War I? The IR-2 was an age of dizzying technological breakthroughs, including but not limited to the electric dynamo (1871), the first internal combustion engine (1876), the Thomas process for steel manufacturing (1877), and the synthesis of indigo dye (1880).34 Tracking down how every single technical advance could have affected the growth differentials among Britain, Germany, and the United States is an unmanageable task. I narrow the scope of analysis to the most likely sources of LS and GPT trajectories based on previous scholarship that calls attention to the significance of certain technological developments in the IR-2. Once confirmed to meet the established criteria for leading sectors and GPTs, these technological drivers serve as the fields of reference for assessing the validity of the GPT and LS mechanisms in this case.

    Candidate Leading Sectors

    I focus on the chemicals, electrical equipment, motor vehicles, and steel industries as the leading sectors of the IR-2. These choices are informed by scholars who study the implications of technological change during this period from a LS perspective. The first three sectors feature in the standard rendering of the IR-2 by prominent historical accounts, which centers major discoveries in chemistry and electricity as well as the invention of the internal combustion engine.35 Among those who study the effect of technological revolutions on the balance of power, there is near-consensus that the chemicals and electrical industries were technologically advanced, fast-growing industries during this time.36 Some scholars also identify the automobile industry as a key industry in this period.37 Others reason, however, that automobiles did not emerge as a leading sector until a later period.38

    The automobile, chemicals, and electrical industries all experienced prodigious growth during the IR-2, meeting the primary qualification for leading sectors. According to statistics from the US census, the percentage increase in value added by manufacture in each of the chemicals, electrical, and automobile industries was much higher than the average across all industries from 1899 through 1909. In fact, the automobile and electrical equipment industries boasted the two highest rates of percentage growth in value added over this period among sectors with a market size over $100 million.39

    I also consider developments in steel as a possible source of leading-sector product cycles. It is hard to ignore the explosive growth of the steel industry in both Germany, where it multiplied over 100-fold from 1870 to 1913, and the United States, where it multiplied around 450 times over the same period.40 In addition, many scholars list steel as one of the leading sectors that affected the economic power balance in the IR-2.41 Rostow identifies steel as part of “the classic sequence” of “great leading sectors.”42 In sum, I consider four candidate leading sectors in this period: the automobile, chemicals, electrical equipment, and steel industries.

    Candidate GPTs

    I analyze chemicalization, electrification, the internal combustion engine, and interchangeable manufacture as potential drivers of GPT-style transformations in the IR-2. Of these four, electricity is the prototypical GPT. It is “unanimously seen in the literature as a historical example of a GPT.”43 Electricity is one of three technologies, alongside the steam engine and information and communications (ICT) technology, that feature in nearly every article that seeks to identify GPTs throughout history.44 Electrical technologies possessed an enormous scope for improvement, fed into a variety of products and processes, and synergized with many other streams of technological development. Empirical efforts to identify GPTs with patent data provide further evidence of electricity as a GPT in this period.45

    Like advances in electricity, clusters of innovations in chemicals and the internal combustion engine not only spurred the rapid growth of new industries but also served as a potential source of GPT trajectories. Historians of technology pick out chemicalization, alongside electrification, as one of two central processes that transformed production routines in the early twentieth century.46 Historical patent data confirm that chemical inventions could influence a wide variety of products and processes.47

    In line with GPT classification schemes by other scholars, I also evaluate the internal combustion engine as a candidate GPT, with the potential to replace the steam engine as a prime mover of many industrial processes.48 After its introduction, many believed that the internal combustion engine would transform a range of manufacturing processes with smaller, divisible power units.49

    Lastly, I examine the advance of interchangeable manufacture, spurred by innovations in machine tools, as a candidate GPT in this period. Though the machine tool industry was neither new nor especially fast-growing, it did play a central role in extending the mechanization of machine-making first incubated in the IR-1. The diffusion of interchangeable manufacture, or the “American system,” owed much to advances in turret lathes, milling machines, and other machine tools that improved the precision of cutting and shaping metals. Rosenberg’s seminal study of “technological convergence” between the American machine tool industry and metal-using sectors highlighted how innovations in metalworking machines transformed production processes across a wide range of industries.50 Following Rosenberg’s interpretation, historians recognize the nexus of machine tools and mechanization as one of the key technological trajectories during this period.51

    Sources of LS and GPT Trajectories

    I aimed to include as many candidate technological drivers as possible, provided that the technological developments credibly met the criteria of a leading sector or GPT.52 All candidate leading sectors and GPTs I study in this period were flagged in multiple articles or books that explicitly identified leading sectors or GPTs in the IR-2 period, which helped provide an initial filter for selection. This allows for a good test of the GPT diffusion mechanism against the LS product cycles mechanism.53 This sorting process is an important initial step for evaluating the two mechanisms, though a deeper excavation of the historical evidence is required to determine whether the candidates actually made the cut.

    table 4.1 Key Sources of Technological Trajectories in the IR-2

    Candidate Leading SectorsCandidate GPTs
    Steel industryInterchangeable manufacture
    Electrical equipment industryElectrification
    Chemicals industryChemicalization
    Automobile industryInternal combustion engine

    There is substantial overlap between the candidate GPTs and leading sectors in the IR-2, as reflected in table 4.1, but two key distinctions are worth emphasizing. First, one difference between the candidate GPTs and leading sectors is the inclusion of machine tools in the former category. The international relations scholarship on leading sectors overlooks the impact of machine tools in this period, possibly because the industry’s total output did not rank among the largest industries, and also because innovation in machine tools was relatively incremental.54 One survey of technical development in machine tools from 1850 to 1914 described the landscape as “essentially a series of minor adaptations and improvements.”55 Relatedly, the steel industry, commonly regarded as an LS, is not considered a candidate GPT. Under the GPT mechanism, innovations in steel are bound up in a GPT trajectory driven by advances in machine tools.

    Second, even though some technological drivers, such as electricity, are considered both candidate leading sectors and candidate GPTs, there are different interpretations of how developments in these technological domains translated into an economic power transition. In the case of new electrical discoveries, control over market share and exports in the electrical equipment industry represents the LS trajectory, whereas the gradual spread of electrification across many industries stands in for the GPT trajectory. Two trajectories diverge in a yellow wood, and the case study evidence will show which one electricity traveled.56

    GPT vs. LS Trajectories in the IR-2

    Equipped with a better grasp of the possible technological drivers in the IR-2, I follow the same procedures used in the previous chapter to assess the validity of the GPT and LS mechanisms.

    OBSERVABLE IMPLICATIONS RELATED TO THE IMPACT TIMEFRAME

    GPT diffusion and LS product cycles present two competing interpretations of the IR-2’s impact timeframe. The LS mechanism expects growth associated with radical technological breakthroughs to be explosive in the initial stages. Under this view, off the back of major breakthroughs such as the first practical electric dynamo (1871), the modern internal combustion engine (1876), and the successful synthesis of indigo dye (1880), new leading sectors took off in the 1870s and 1880s.57 Then, according to the expected timeline of the LS mechanism, these new industries stimulated substantial growth in the early stages of their development, bringing about a pre–World War I upheaval in the industrial balance of power.58

    The GPT trajectory gives a different timeline for when productivity benefits from major technological breakthroughs were realized on an economy-wide scale. Before stimulating economy-wide growth, the candidate GPTs that emerged in the 1880s—tied to advances in electricity, chemicals, and the internal combustion engine—required many decades of complementary innovations in application sectors and human capital upgrading. These candidate GPTs should have contributed only modestly to the industrial rise of the United States before World War I, with impacts, if any, materializing toward the very end of the period.

    Critically, one candidate GPT should have produced substantial economic effects during this period. Unlike other GPT trajectories, interchangeable manufacture had been incubated by earlier advances in machine tools, such as the turret lathe (1845) and the universal milling machine (1861).59 Thus, by the late nineteenth century, interchangeable manufacturing methods should have diffused widely enough to make a significant impact on US industrial productivity.

    OBSERVABLE IMPLICATIONS RELATED TO THE PHASE OF RELATIVE ADVANTAGE

    When spelling out how the IR-2 produced an economic power transition, the two mechanisms also stress different phases of technological change. According to the LS mechanism, Britain’s industrial prominence waned because it lost its dominance of innovation in the IR-2’s new industries. The United States and Germany benefited from monopoly profits accrued from being lead innovators in electrical equipment, chemical production, automobiles, and steel. In particular, Germany’s industrial rise in this period draws a disproportionate share of attention. Many LS accounts attribute Germany’s rise to its dominance of innovations in the chemical industry, “the first science-based industry.”60 Others emphasize that the American global lead in the share of fundamental innovations after 1850 paved the way for the United States to dominate new industries and become the leading economy in the IR-2.61

    The GPT mechanism has different expectations regarding the key determinant of productivity differentials. Where innovations are adopted more effectively has greater significance than where they are first introduced. According to this perspective, Britain lost its industrial preeminence because the United States was more effective at intensively adopting the IR-2’s GPTs.

    OBSERVABLE IMPLICATIONS RELATED TO BREADTH OF GROWTH

    Finally, regarding the breadth of growth, the third dimension on which the two mechanisms diverge, the LS trajectory expects that a narrow set of modernized industries drove productivity differentials, whereas the GPT trajectory holds that a broad range of industries contributed to productivity differentials. The US growth pattern serves as the best testing ground for these diverging predictions, since the United States overtook Britain as the economic leader in this period.

    Table 4.2  Testable Predictions for the IR-2 Case Analysis

    Prediction 1: LS (impact timeframe)The steel, electrical equipment, chemicals, and/or* automobile industries made a significant impact on the rise of the United States to productivity leadership before 1914.
    Prediction 1: GPTElectrification, chemicalization, and/or the internal combustion engine made a significant impact on the rise of the United States to productivity leadership only after 1914.The extension of interchangeable manufacture made a significant impact on the rise of the United States to productivity leadership before 1914.
    Prediction 2: LS (phase of relative advantage)Innovations in the steel, electrical equipment, chemicals, and/or automobile industries were concentrated in the United States.German and American advantages in the production and export of electrical equipment, chemical products, automobiles, and/or steel were crucial to their industrial superiority.
    Prediction 2: GPTInnovations in machine tools, electricity, chemicals, and/or the internal combustion engine were not concentrated in the United States.
    American advantages in the diffusion of interchangeable manufacture were crucial to its productivity leadership.
    Hypothesis 3: LS (breadth of growth)Productivity growth in the United States was limited to the steel, electrical, chemicals, and/or automotive industries.
    Hypothesis 3: GPTProductivity growth in the United States was spread across a broad range of industries linked to interchangeable manufacture.
    *The operator “and/or” links all the candidate leading sectors and GPTs because it could be the case that only some of these technologies drove the trajectories of the period.

    The two explanations hold different views about how technological disruptions produced an economic power transition, related to the impact timeframe of new advances, the phase of technological change that yields relative advantages, and the breadth of technology-fueled growth. Based on the differences between the LS and GPT mechanism across these dimensions, I derive three sets of diverging predictions for how technological changes contributed to relative shifts in economic productivity during this period. Table 4.2 collects these predictions, which structure the case analysis in the following sections.

    Impact Timeframe: Gradual Gains vs. Immediate Effects from New Breakthroughs

    The opening move in assessing the LS and GPT mechanisms is determining when the IR-2’s eye-catching technological advances actually made their mark on leading economies. Tracking the development timelines for all the candidate leading sectors and GPTs of the IR-2 produces two clear takeaways. First, innovations related to electricity, chemicals, and the internal combustion engine did not make a significant impact on US productivity leadership until after 1914. Second, advances in machine tools and steel—the remaining candidate GPT and leading sector, respectively—contributed substantially to US economic growth before World War I; thus, their impact timeframes fit better with when the United States overtook Britain as the preeminent economic power.

    DELAYED TIMELINES: CHEMICALS, ELECTRICITY, AND THE INTERNAL COMBUSTION ENGINE

    Developments in chemicals, electricity, and internal combustion provide evidence against the LS interpretation. If the LS mechanism was operational in the IR-2, advances in chemicals should have made a significant impact on US productivity leadership before World War I.62 Yet, in 1914, the United States was home to only seven dye-making firms.63 Major US chemicals firms did not establish industrial research laboratories like those of their German counterparts until the first decade of the twentieth century.64 Terry Reynolds, author of a history of the American Institute of Chemical Engineers, concludes, “Widespread use of chemists in American industrial research laboratories was largely a post–World War I phenomenon.”65 Thus, it is very unlikely that chemical innovations made a meaningful difference to growth differentials between the United States and Britain before 1914.

    At first glance, the growth of the German chemical industry aligns with the LS model’s expectations. Germany was the first to incorporate scientific research into chemical production, resulting in the synthesis of many artificial dyes before 1880.66 Overtaking Britain in leadership of the chemical industry, Germany produced 140,000 tons of dyestuffs in 1913, more than 85 percent of the world total.67

    While Germany’s rapid growth trajectory in synthetic dyes was impressive, the greater economic impacts of chemical advances materialized after 1914 through a different pathway: “chemicalization,” or the spread of chemical processes across ceramics, food-processing, glass, metallurgy, petroleum refining, and many other industries.68 Prior to key chemical engineering advances in the 1920s, industrial chemists devoted limited attention to unifying principles across the manufacture of different products. The rapid expansion of chemical-based industries in the twentieth century owed more to these later improvements in chemical engineering than earlier progress in synthetic dyes.69 Ultimately, these delayed spillovers from chemicalization were substantial, as evidenced by higher growth rates in the German chemical industry during the interwar period than in the two decades before World War I.70

    Electrification’s impact timeframe with respect to US productivity growth mirrored that of chemicalization. Scholarly consensus attributes the US productivity upsurge after 1914 to the delayed impact of the electrification of manufacturing.71 From 1880 to 1930, power production and distribution systems gradually evolved from shaft and belt drive systems driven by a central steam engine or water wheel to electric unit drive, in which electric motors powered individual machines. Unit drive became the predominant method in the 1920s only after vigorous debates in technical associations over its relative merits, the emergence of large utilities that improved access to cheap electricity, and complementary innovations, like machine tools, that were compatible with electric motors.72

    Quantitative indicators also verify the long interval between key electrical advances and electrification’s productivity boost. Economic geographer Sergio Petralia has investigated the causal relationship between adoption of electrical and electronic (E&E) technologies, operationalized as E&E patenting activity in individual American counties and the per-capita growth of those counties over time. One of his main findings is that the effects of E&E technology adoption on growth are not significant prior to 1914.73 This timeline is confirmed by a range of other metrics, including the energy efficiency of the American economy, electric motors’ share of horsepower in manufacturing, and estimates of electricity’s total contribution to economic growth.74

    The diffusion of internal combustion engines across application sectors was also slow. Despite its initial promise, the internal combustion engine never accounted for more than 5 percent of the generation of total horsepower in US manufacturing from 1869 to 1939.75 In 1900, there were only eight thousand cars in the entire United States, and the U. motor vehicle industry did not overtake its French competitor as the world’s largest until 1904.76 Furthermore, the turning point for the mass production of automobiles, Ford’s installation of a moving assembly line for making Model Ts, did not occur until 1913.77

    KEY TIMINGS: MACHINE TOOLS AND STEEL

    When assigning credit to certain technologies for major upheavals in global affairs, awe of the new often overwhelms recognition of the old. Based on the previous analysis, it is unlikely that new breakthroughs in electricity, chemicals, and internal combustion fueled the economic power transition that transpired in this period. Instead, careful tracing reveals the persevering impact of earlier developments in machine tools.78 During the IR-2, technical advances in machine tools were incremental, continuous improvements that helped disseminate transformative breakthroughs from the mid-nineteenth century, such as the turret lathe and the universal milling machine.79

    Profiles of key application sectors and quantitative indicators validate the GPT mechanism’s expected impact timeframe for machine tools. Marking 1880 as the date when “the proliferation of new machine tools in American industry had begun to reach torrential proportions,” Rosenberg outlines how three application sectors—sewing machines, bicycles, and automobiles—successively adopted improved metal-cutting techniques from 1880 to 1910.80 As the American system took hold, the number of potential machine tool users multiplied 15-fold, from just 95,000 workers in 1850 to almost 1.5 million in 1910.81 Patenting data identify the last third of the nineteenth century as the period when extensive technological convergence characterized the machine tool industry and application sectors.82

    FIGURE 4.3. Technological Impact Timeframes in the IR-2. Note: US chemical production, horsepower from electric central stations, and machine intensity over time. Source: Murmann 2003; US Census Bureau 1975.

    Figure 4.3 depicts the diverging impact timeframes of interchangeable manufacturing methods, electrification, and chemicalization. Machine intensity substantially increased from 1890 to 1910, as measured by horsepower installed per persons employed in manufacturing. By contrast, the United States did not experience significant increases in electrical and chemical production until after 1910.

    Of all the candidate leading sectors, the steel industry best fits the expectations of the LS mechanism regarding when industries transformed by radical innovations stimulated growth in the rising powers. Just as the 1780s were a period when the technological conditions for cotton production were transformed, the mid-nineteenth century featured major breakthroughs in the steel industry that allowed for the mass production of steel, such as the Siemens-Martin open-hearth furnace (1867) and Bessemer converter (1856).83 Over the course of the IR-2 period, the United States and Germany quickly exploited these breakthroughs in steelmaking to massively boost steel production.

    The overtaking of Britain by both Germany and the United States in total steel production by the early 1890s matches the timeline of Britain’s overall economic decline.84 Paul Kennedy cites Germany’s booming steel output as a key factor driving its industrial rise; by 1914, German steel output was larger than that of Britain, France, and Russia combined.85 Likewise, US steel output grew from one-fifth of British production in 1871 to almost five times more than British steel output in 1912.86 Given these impressive figures, the next section investigates the American and German advantages in steel production in further detail.

    Phase of Relative Advantage: The American System’s Diffusion

    The second dimension on which the GPT and LS trajectories differ relates to the phase of technological change that accounted for the relative success of the United States in the IR-2. Cross-country historical evidence on the IR-2’s technological drivers illustrates that the United States had true comparative advantages over other advanced economies that were rooted in its absorption and diffusion capabilities.

    INNOVATION CLUSTERING IN STEEL, ELECTRICITY, CHEMICALS, AND/OR MOTOR VEHICLES?

    In electricity, industrial powers fiercely contested innovation leadership as the United States, Germany, Great Britain, and France all built their first central power stations, electric trams, and alternating current power systems within a span of nine years.87 However, the United States clearly led in diffusing these systems: US electricity production per capita more than doubled that of Germany, the next closest competitor, in 1912. Along this metric of electrification, Britain’s level was just 20 percent of the US figure.88

    To be clear, Britain fell behind in adopting electrification, even though it introduced some of the most significant electrical innovations.89 In 1884, for example, British inventor Charles Parsons demonstrated the first steam turbine for practical use, an essential step for commercializing electric power, but this technology was more rapidly and widely adopted in other countries.90 The British Institution of Electrical Engineers aptly captured this phenomenon in an 1892 resolution: “Notwithstanding that our countrymen have been among the first in inventive genius in electrical science, its development in the United Kingdom is in a backward condition, as compared with other countries, in respect of practical application to the industrial and social requirements of the nation.”91

    In chemicals, the achievements of both the US and German chemical industries suggest that no single country monopolized innovation in this sector. Germany’s synthetic dye industry excelled not because it generated the initial breakthroughs in aniline-violet dye processes—in fact, those were first pioneered in Britain—but because it had perfected these processes for profitable exploitation.92 Similar dynamics characterized the US chemical industry.93

    In most cases, the United States was not the first to introduce major innovations in leading sectors. Many countries introduced major innovations in chemicals, electricity, motor vehicles, and steel during this period (table 4.3).94 Across the four candidate leading sectors, American firms pioneered less than 30 percent of the innovations. Contrary to the propositions of the LS mechanism, innovations in steel, electricity, chemicals, and motor vehicles were spread across the leading economies.

    table 4.3 Geographic Distribution of Major Innovations in Leading Sectors, 1850–1914

    ChemicalsElectricityMotor VehiclesSteel
    France2111
    Germany3330
    Great Britain1311
    United States2310
    Various other countries0102
    Sole US share25%27%17%0%
    Source: Van Duijn 1983, 176–79 (compilation of 160 innovations introduced during the nineteenth and twentieth centuries).

    Moreover, the limited role of electrical and chemical exports in spurring American growth casts further doubt on the significance of monopoly profits from being the first to introduce new advances.95 The British share of global chemical exports almost doubled the US share in 1913.96 Overall, the United States derived only 8 percent of its national income from foreign trade in 1913, whereas the corresponding proportion for Britain was 26 percent.97 Even though the United States was the quickest to electrify its economy, Germany captured around half of the world’s exports in electrical products.98

    If monopoly profits from innovation clustering in any leading sector propelled the industrial rise of the United States and Germany, it would be the steel industry. Both nations made remarkable gains in total steel output over this period, and scholars commonly employ crude steel production as a key indicator of British decline and the shifting balance of industrial power in the decades before World War I.99 Thus, having established the delayed impact of the electrical, chemical, and automobile industries in this period, the steel industry takes on an especially large burden for the LS mechanism’s explanatory power in this period.

    Yet Britain capitalized on many major innovations in steelmaking, including the Talbot furnace, which became essential to producing open-hearth steel.100 Moreover, trade patterns reveal that Britain still held a comparative advantage in the export of steel between 1899 and 1913.101 How to square this with Germany’s dominance in total steel output?

    The prevailing wisdom takes total steel output figures to stand for superior American and German technological know-how and productivity.102 In truth, new steelmaking processes created two separate steel industries. Britain shifted toward producing open-hearth steel, which was higher in quality and price. According to the British Iron Trade Association, Britain produced about four times more open-hearth steel than Germany in 1890.103 On the other hand, Germany produced cheap Thomas steel and exported a large amount at dumping prices. In fact, some of Germany’s steel exports went to Britain, where they were processed into higher-quality steel and re-exported.104 In sum, this evidence questions what one scholar deems “the myth of the technological superiority and outstanding productivity of the German steel industry before and after the First World War.”105

    AMERICAN MACHINE TOOLS—GPT DIFFUSION ADVANTAGE

    Though new industries like electricity and chemicals hog much of the spotlight, developments in machine tools underpin the most important channel between differential rates of technology adoption and the IR-2’s economic power transition. After noting the importance of the electrical and chemical industries during the period, British historian Eric Hobsbawm elevates the importance of machine tools: “Yet nowhere did foreign countries—and again chiefly the USA—leap ahead more decisively than in this field.”106

    In line with the expectations of GPT diffusion theory, comparative estimates confirm a substantial US lead in mechanization in the early twentieth century. In 1907, machine intensity in the United States was more than two times higher than rates in Britain and Germany.107 In 1930, the earliest year for which data on installed machine tools per employee are available, Germany lagged behind the United States in installed machine tools per employee across manufacturing industries by 10 percent, with a significantly wider gap in the tools most crucial for mass production.108

    This disparity in mechanization was not rooted in the exclusive access of the United States to special innovations in machine tools. In terms of quality, British machine tools were superior to their American counterparts throughout the IR-2 period.109 German firms also had advantages in certain fields like sophisticated power technology.110 Rather, the distinguishing feature of the US machine tool industry was excellence in diffusing innovations across industries.111 Reports by British and German study trips to the United States provide some of the most detailed, reliable accounts of transatlantic differences in manufacturing methods. German observers traveled to the United States to learn from their American competitors and eventually imitate American interchangeable manufacturing methods.112 British inspection teams reported that the US competitive edge came from the “adaptation of special apparatus to a single operation in almost all branches of industry”113 and “the eagerness with which they call in the aid of machinery in almost every department of industry.”114

    Fittingly, one of the most colorful denunciations of American innovation capacity simultaneously underscored its strong diffusion capacity. In an 1883 address to the American Association for the Advancement of Science, Henry Rowland, the association’s vice president, denigrated the state of American science for its skew toward the commercialization of new advances. Rowland expressed his disgust with media representations that upheld the “obscure American who steals the ideas of some great mind of the past, and enriches himself by the application of the same to domestic uses” over “the great originator of the idea, who might have worked out hundreds of such applications, had his mind possessed the necessary element of vulgarity.”115 Yet, it was America’s diffusion capacity—in all its obscurity and vulgarity—that sustained its growth to economic preeminence.

    Breadth of Growth: The Wide Reach of Interchangeable Manufacture

    What were the sources of American productivity growth in the IR-2? The pattern of American economic growth is most pertinent to investigate because the United States overtook Britain in productivity leadership during the IR-2. Regarding the breadth of economic growth, the LS trajectory expects that American productivity growth was concentrated in a narrow set of modernized industries, whereas the GPT trajectory holds that American productivity growth was dispersed across a broad range of industries. Sector-level estimates of total factor productivity (TFP) growth provide useful evidence to assess these diverging propositions.

    WIDESPREAD PRODUCTIVITY GROWTH

    The historical data support GPT diffusion theory’s expectation of pervasive US productivity growth. John Kendrick’s detailed study of US productivity growth in this period depicts a relatively balanced distribution. Among the industries studied, nearly 60 percent averaged between 1 and 3 percent increases in output per labor-hour from 1899 to 1909.116 Broad swathes of the US economy, outside of the leading sectors, experienced technological change. For instance, the service sector, which included segments of the construction, transport, wholesale, and retail trade industries, played a key role in the US capacity to narrow the gap with Britain in productivity performance.117

    R&D-centric sectors were not the primary engines of US growth. In a recent update to Kendrick’s estimates, a group of researchers estimated how much of US productivity growth was driven by “great inventions sectors,” a designation that roughly corresponds to this chapter’s candidate leading sectors.118 They found that these sectors accounted for only 29 percent of U.S. TFP growth from 1899–1909.119 Despite employing 40 percent of all research scientists in 1920, the chemical industry was responsible for only 7 percent of US TFP growth throughout the following decade.120

    MACHINE TOOLS AND BROADLY DISTRIBUTED PRODUCTIVITY GROWTH

    Broad-based productivity growth in the US economy does not necessarily mean that a GPT was at work. Macroeconomic factors or the accumulation of various, unconnected sources of TFP growth could produce this outcome. Therefore, if the GPT trajectory captures the breadth of growth in the IR-2, then the historical evidence should connect broadly distributed productivity growth in the United States to developments in machine tools.

    The extension of the American system boosted productivity in a wide range of sectors. Applications of this system of special tools reshaped the processes of making firearms, furniture, sewing machines, bicycles, automobiles, cigarettes, clocks, boots and shoes, scientific instruments, typewriters, agricultural implements, locomotives, and naval ordnance.121 Its influence covered “almost every branch of industry where articles have to be repeated.”122 Per a 1930 inventory of American machine tools, the earliest complete survey, nearly 1.4 million metalworking machines were used across twenty industrial sectors.123 In his seminal study of American productivity growth during this period, Kendrick identifies progress in “certain types of new products developed by the machinery and other producer industries [that] have broad applications across industry lines” as a key source of the “broad, pervasive forces that promote efficiency throughout the economy.”124

    The breadth of productivity spillovers from machine tools was not boundless. Machine-using industries constituted a minority of the manufacturing industries, which themselves accounted for less than one-quarter of national income.125 However, users of new machine tools extended beyond just manufacturing industries. Technologically intensive services, such as railroads and steam transportation, also benefited significantly from improved metalworking techniques.126 In agriculture, specialized machine tools helped advance the introduction of farm machinery such as the reaper, which revolutionized agricultural productivity.127

    In describing how machine tools served as a transmission center in the US economy, Rosenberg describes the industry as a pool of skills and technical knowledge that replenishes the economy’s machine-using sectors—that is, an innovation that addresses one industry’s problem gets added to the pool and becomes available, with a few modifications, for all technologically related industries.128 As sales records from leading machine tool firms show, many application sectors purchased the same type of machine. In 1867, Brown and Sharpe Manufacturing Company sold the universal milling machine, just five years after its invention, not only to machinery firms that made tools for a diverse range of industries but also to twenty-seven other firms that produced everything from ammunition to jewelry.129 In this way, the machine tool industry functioned, in Rosenberg’s words, as “a center for the acquisition and diffusion of new skills and techniques in a machinofacture type of economy.”130

    Indeed, advances in machine tools had economy-wide effects. The social savings method estimates how much a new technology contributed to economic growth, compared to a counterfactual situation in which the technology had not been invented.131 Referencing this method to differentiate between the impacts of new technologies in this period, economic historian Joel Mokyr puts forward the American system of manufacturing as the most important:

    From a purely economic point of view, it could be argued that the most important invention was not another chemical dye, a better engine, or even electricity.… There is one innovation, however, for which “social savings” calculations from the vantage point of the twentieth century are certain to yield large gains. The so-called American System of manufacturing assembled complex products from mass-produced individual components. Modern manufacturing would be unthinkable without interchangeable parts.132

    Institutional Complementarities: GPT Skill Infrastructure in the IR-2

    With confirmation that the pattern of technological change in the IR-2 is better characterized by the GPT trajectory, the natural next step is to probe variation among leading economies in adapting to this trajectory. Why was the United States more successful than Britain and Germany in adapting to the demands of interchangeable manufacture? According to GPT diffusion theory, the historical evidence should reveal that the US edge was based on education and training systems that broadened and systematized mechanical engineering skills. These institutional adaptations would have resolved two key bottlenecks in the spread of interchangeable manufacture: a shortage of mechanical engineering talent and ineffective coordination between machine tool producers and users.

    Widening the Base of Mechanical Engineers

    Which institutions for skill formation were most central to the ability of the United States to take advantage of new advances in machine tools? Established accounts of economic rivalry among great powers in the IR-2 focus on skills linked to major innovations in new, science-based industries. Emphasizing Germany’s advantage in training scientific researchers, these studies attribute Germany’s technological success in this period to its investments in R&D facilities and advanced scientific and technical education.133 Such conclusions echo early-twentieth-century British accounts of Germany’s growing commercial prowess, which lauded German higher education for awarding doctorates in engineering and its qualitative superiority in scientific research.134

    American leadership in the adoption of interchangeable manufacturing methods was beholden to a different set of institutions for skill formation. Progress in this domain did not depend on new scientific frontiers and industrial research laboratories.135 In fact, the United States trailed both Britain and Germany in scientific achievements and talent.136 Widespread mechanization in the United States rested instead on a broad base of mechanical engineering skills.

    Alongside the development of more automatic and precise machine tools throughout the nineteenth century, this new trajectory of mechanization demanded more of machinists and mechanical engineers. Before 1870, US firms relied on informal apprenticeships at small workshops for training people who would design and use machine tools.137 At the same time, engineering education at independent technical schools and traditional colleges and universities did not prioritize mechanical engineers but were mostly oriented toward civil engineering.138 Yet craft-era methods and skills were no longer sufficient to handle advances that enhanced the sophistication of machine tools.139 Thus, in the mid-eighteenth century, the US potential for mechanization was significantly constrained by the need for more formal technical instruction in mechanical engineering.

    Over the next few decades, advances on three main fronts met this need for a wider pool of mechanical engineering expertise: land-grant schools, technical institutes, and standardization efforts. In 1862, the US Congress passed the first Morrill Land-Grant Act, which financed the creation of land-grant colleges dedicated to the agricultural and mechanical arts. Although some of these schools offered low-quality instruction and initially restricted their mission to agricultural concerns, the land-grant funds also supported many important engineering schools, such as the Massachusetts Institute of Technology (MIT) and Cornell University.140 The number of US engineering schools multiplied from six in 1862, when the Morrill Act was passed, to 126 in 1917.141 These schools were especially significant in widening the base of professional mechanical engineers. In 1900, out of all students pursuing mechanical engineering at US higher education institutions, 88 percent were enrolled in land-grant colleges.142

    The establishment of technical institutes also served demands for mechanical engineering training. Pure technical schools like the Worcester Polytechnic Institute, founded in 1868, and the Stevens Institute of Technology, founded in 1870, developed mechanical engineering curricula that would become templates for engineering programs at universities and colleges.143 Embedded with local and regional businesses, technical institutes developed laboratory exercises that familiarized students with real-world techniques and equipment. In this respect, these institutes and land-grant colleges “shared a common belief in the need to deliver a practice-oriented technical education.”144

    Another significant development in the spread of mechanical engineering knowledge was the emergence of professional engineering societies that created industrial standards. The most prominent of these were the American Society of Mechanical Engineers (ASME), founded in 1880, the American Section of the International Association for Testing Materials, set up in 1898, and the Franklin Institute, which became America’s leading technical society around the start of the IR-2.145 As these associations coordinated to share best practices in mechanical engineering, they improved knowledge flows between the machine tool industry and application sectors.146 Standardization in various machine processes and components, such as screw threads, helped spread mechanization across disparate markets and communities.147

    It should be emphasized that these efforts were effective in producing the skills and knowledge necessary for advancing mechanization because they broadened the field of mechanical engineering. Mechanical engineering instruction at land-grant schools and technical institutes and through professional associations allowed for more students to become “average engineers,” as opposed to “the perpetuation of a self-recognized elite.”148 Recent research finds that this diffused engineering capacity produced enduring benefits for American industrialization. By collecting granular data on engineering density for the United States at the county level, William Maloney and Felipe Caicedo capture the engineering talent spread across various US counties in 1880 and parse the effect of engineering capacity on industrial outcomes decades later. They find that there is a statistically significant, positive relationship between the level of engineering density in 1880 and the level of industrialization decades later.149

    The Comparative Advantage of the United States over Britain and Germany in GPT Skill Infrastructure

    Both Britain and Germany fell short of the US standard in GPT skill infrastructure. For Britain, the key gap was in the supply of mechanical engineering talent. British educational institutions and professional bodies fiercely guarded the apprenticeship tradition for training mechanical engineers.150 For instance, the University of Oxford did not establish an engineering professorship until 1908.151 Meanwhile, American engineers systematically experimented with machine redesigns, benefiting from their training at universities and technical institutes.

    These diverging approaches resulted in stark differences in skill formation. In 1901, probably around 2,600 students were enrolled in full-time higher technical education in the United Kingdom.152 Limiting this population to those in their third or fourth year of full-time study—an important condition because many UK programs, unlike German and American institutions, did not progress beyond two years of study—leaves only about 400 students.153 By comparison, in mechanical engineering programs alone, the United States in 1900 had 4,459 students enrolled in higher technical education.154 Controlling for population differences, the United States substantially outpaced Britain in engineering density, as measured by the number of university-educated engineers per 100,000 male laborers.155

    Germany developed a more practical and accessible form of higher technical education than Britain. From 1870 to 1900, enrollments in the German technische Hochschulen increased nearly fourfold, from 13,674 to 32,834 students.156 Alongside the technische Mittelschulen (technical intermediate schools comparable to American industrial trade schools and lower-level engineering colleges), the technische Hochschulen cultivated a broad base of mechanical engineers.157 Germany’s system of technical education attracted admirers from around the world. Some went there to study in the schools, and others went to study how the school system worked, with the aim of borrowing elements of the German model.158

    Germany’s problems were with weak linkages between mechanical engineering education and industrial applications. Key German standards bodies and technical colleges prioritized scientific and theoretical education at the expense of practical skills—a trend “most pronounced in mechanical engineering.”159 According to an expert on German standard-setting in this period, “no national standards movement was inaugurated in [the machine industry] until after the outbreak of [World War I].”160 German experts on engineering education, intent on reforming technical instruction to get engineers more experience with factor organization and project management in the field, recommended, for example, that practical training courses be offered in partnerships with engineering associations.161 Articles in the Zeitschrift des Vereines Deutscher Ingenieure (Journal of the Association of German Engineers) lamented that the technische Hochschulen and technical universities were not equipping students with practical skills to operate in and manage factories and workshops.162 These issues slowed Germany’s incorporation of interchangeable parts and advanced machine tools.

    A report by Professor Alois Riedler of the Technical University of Berlin, who was commissioned by the Prussian Ministry of Education to tour American engineering schools in the 1890s, illustrates the differences in engineering education between the United States and Germany. According to Riedler, extensive practical training and experience with shop and laboratory applications were distinctive features of an American engineering education. To substantiate differences in practical instruction between engineering departments in the two countries, Riedler analyzed the time allocated to theoretical and practical instruction across four-year courses of study.163 Compared to their German peers, American students spent far more time on exercises in mechanical technical laboratories and other types of practical training (figure 4.4). In the Technische Universität Berlin (Technical University Berlin), practical exercises in the laboratory and shop accounted for less than 6 percent of total instruction time over a four-year course of study.164 In contrast, engineering students at Cornell University spent more than one-third of their course engaged in laboratory study and shopwork. As a consequence of reports by Riedler and others, German institutions began establishing laboratories for mechanical engineering around 1900.165

    FIGURE 4.4. Comparison of Curricula at German and American Engineering Schools (1893). Source: US Bureau of Education (BOE) 1895, 684–86. Note: In this BOE report, the German schools are labeled Technological University in Austria, Technological University in Prussia, and Technological University in South Germany. A reasonable assumption, informed by background research, is that these refer to Technical University Wien, Technical University Berlin, and Technical University of Munich, respectively. Though TU Wien is in Austria, it is used to illustrate trends in German engineering education because many German schools saw it as an influential model.

    It should be made clear that, in the United States, institutional adaptations to new opportunities presented by interchangeable manufacture were not rooted in cultivating highly skilled scientific talent. The best and brightest American scientists furthered their education at European universities.166 Even proponents of American engineering education concluded that “strictly scientific and intellectual education in American technological schools” did not even match “the average of a secondary industrial school” in Germany.167 According to one study conducted by the National Association of German-American Technologists, an organization that regularly circulated ideas between the two countries, German technical institutes held an edge over their US peers in research on different technologies in mechanical engineering.168

    The deeper roots of US institutions’ greater effectiveness at adapting to the skill formation needs of interchangeable manufacture cannot be fully explored here. The legacy of the Morrill Act certainly looms large, as do the contributions of a diverse set of institutional adaptations unrelated to that groundbreaking federal policy, including independent centers like the Franklin Institute, technical high schools, professional associations, and specialized engineering programs initiated at preexisting universities.169 Other potential sources for the US advantage in GPT skill infrastructure include its openness to foreign technicians and the unique challenges and culture of the American frontier.170

    LS-Based Theories and Chemical Engineering

    Analyzing the education and training systems for chemical advances provides a secondary test to determine which institutions are most apt to bring national success in technological revolutions.171 LS accounts typically point to Germany’s innovation capacity as the key determinant of its competitiveness in chemicals, especially in the key segment of synthetic dye production.172 To extend this lead in synthetic dyes, Germany profited from leading industrial research labs and scientific education institutions, which employed the world’s top academic chemists and produced about two-thirds of the world’s chemical research.173

    By comparison, the US capacity to innovate in chemicals was weak. From 1901 to 1930, only one American researcher received a Nobel Prize in Chemistry, while German and British researchers captured almost three-fourths of the Nobel Prizes in Chemistry in that span.174 In 1899, German publications accounted for half of all citations in American chemical journals, essentially double the share credited to American publications.175 At the same time, American scholarship barely registered in Europe-based chemistry journals, where the best research was published. According to one analysis of references in Annual Reports on the Progress of Chemistry, an authoritative British review journal, American publications accounted for only 7 percent of the citations in 1904.176

    As was the case with machine tools, effective adaptation to new chemical technologies in the United States rested on a different set of institutional competencies. Despite trailing Germany in chemical breakthroughs and top chemists, the United States pioneered a chemical engineering discipline that facilitated the gradual chemicalization of many industries. A crucial step in this process was the emergence of unit operations, which broke down chemical processes into a sequence of basic operations (for example, condensing, crystallizing, and electrolyzing) that were useful to many industries, including ceramics, food processing, glass, metallurgy, and petroleum refining.177 American institutions of higher education, most notably MIT, quickly adopted the unit operations model and helped cultivate a common language and professional community of chemical engineering.178 As Rosenberg and Steinmueller conclude, “American leadership in introducing a new engineering discipline into the university curriculum, even at a time when the country was far from the frontier of scientific research, was nowhere more conspicuous than in the discipline of chemical engineering early in the 20th century.”179

    In contrast, Germany was slow to develop the infrastructure for supporting chemical engineers. Up through the interwar period, the chemical engineering profession “failed to coalesce in Germany.”180 Chemical engineering did not become a distinct academic subject area in Germany until after the Second World War.181 Because German universities did not equip chemists with engineering skills, the burden of training chemists was shifted to firms.182 Additionally, the German chemical industry maintained a strict division of labor between chemists and mechanical engineers. The lack of skill systematization resulted in more secrecy, less interfirm communication, and a failure to exploit externalities from common chemical processes.183

    The United States reaped the spoils of technological convergence in chemicalization not just because it trained large numbers of chemical engineers but also because it strengthened university-industry linkages and standardized techniques in chemical engineering.184 Without the connective tissue that promotes information flows between the chemical sector and application sectors, a large base of chemical engineers was insufficient. Britain, for instance, was relatively successful at training chemical engineers during the interwar period; however, the weak links between British educational institutions and industrial actors limited the dissemination of technical knowledge, and the concept of unit operations did not take hold in Britain to the degree that it did in America.185 Additionally, professional engineering associations in the United States, including the American Institute of Chemical Engineers, advanced standardization in the chemical industry—an initiative not imitated in Britain until a decade later.186 Unlike their American peers, it was not until after World War II that British chemical engineers saw themselves as “members of a professional group that shared a broad commonality cutting across the boundary lines of a large number of industries.”187

    Since substantial, economy-wide benefits from these chemical breakthroughs did not materialize until after the end of the IR-2 period, it is important to not overstate these points. Nonetheless, tracing which country best exploited chemical innovations through the interwar period can supplement the analysis of institutional complementarities for machine tools.188 Evidence from the coevolution of chemical technologies and skill formation institutions further illustrates how institutional adaptations suited to GPT trajectories differed from those suited to LS trajectories.

    Alternative Factors

    Like its predecessor, the IR-2 has been the subject of countless studies. Scholars have thoroughly investigated the decline of Britain and the rise of the United States and Germany, offering explanations ranging from immigration patterns and cultural and generational factors to natural resource endowments and labor relations.189 My aim is not to sort through all possible causes of British decline. Rather, I am tracing the mechanisms behind an established connection between the IR-2’s technological breakthroughs and an economic power transition. Thus, the contextual factors most likely to confound the GPT diffusion explanation are those that provide an alternative explanation of how significant technological changes translated into the United States supplanting Britain in economic leadership. Aside from the LS mechanism, which has been examined in detail, the influence of international security threats and varieties of capitalism deserve further examination.

    Threat-Based Explanations

    How did external threats influence technological leadership in the IR-2? Scholars have argued that US military investment, mobilized against the threat of a major war, was crucial to the development of many GPTs.190 US national armories’ subsidization of the production of small arms with interchangeable parts in the early nineteenth century was crucial, some studies argue, to the diffusion of the American system to other industries in the second half of the century.191

    Though firearms production provided an important experimental ground for mechanized production, military support was not necessary to the development of the American system of manufacturing. Questioning the necessity of government funding and subsidies for the spread of the American system, one study credits the development of interchangeable manufacture to four civilian industries: clock manufacturing, ax manufacturing, typewriter manufacturing, and watch manufacturing.192 In particular, the clock industry played a crucial role in diffusing mechanized production practices. More attuned to the dynamics of the civilian economy than the small arms manufacturers, clockmakers demonstrated that interchangeable manufacture could drastically increase sales and cut costs.193 In his definitive study of the history of American interchangeable parts manufacture, David Hounshell concludes that “the sewing machine and other industries of the second half of the 19th century that borrowed small arms production techniques owed more to the clock industry than to firearms.”194

    Military investment and government contracting did not provide long-term sources of demand for interchangeable manufacturing methods.195 Over the course of the IR-2, the small arms industry’s contribution to American manufacturing declined, totaling less than 0.3 percent of value added in American industry from 1850 to 1940.196 Thus, arguments centered on military investment neglect that the spread of the American system, not its initial incubation, is the focal point for understanding how the IR-2 catalyzed an economic power transition.197

    Another threat-based argument posits that countries that face more external threats than internal rivalries will achieve more technological success.198 In the IR-2 case, however, the United States was relatively isolated from external conflicts, while the United Kingdom and Germany faced many more threats (including each other).199 Moreover, the United States was threatened more by internal rivalries than by external enemies at the beginning of the IR-2, as it had just experienced a civil war.200 This argument therefore provides limited leverage in the IR-2 case.

    VoC Explanations

    What about the connection between America’s particular type of capitalism and its economic rise? Rooted in the varieties of capitalism (VoC) tradition, one alternative explanation posits that the United States was especially suited to embrace the radical innovations of the IR-2 because it exhibited the characteristics of a liberal market economy (LME). GPT diffusion theory and VoC-based explanations clash most directly on points about skill formation. The latter’s expectation that LMEs like the United States should excel at cultivating general skills could account for US leadership in GPT diffusion during this period.201

    The empirical evidence casts doubt on this explanation. In the early twentieth century, the leading nations had fairly similar levels of enrollment rates in elementary and post-elementary education. In 1910, enrollment rates for children ages five to nineteen in the United States was about 12 percent lower than Britain’s rate and only 3 percent higher than Germany’s.202 Years of education per worker increased by essentially the same proportion in both Britain (by a factor of 2.2) and the United States (by a factor of 2.3) between 1870 and 1929.203 In terms of higher education expenditures per capita, the two countries were essentially tied.204 Differences in the formation of general skills cannot account for the technological leadership of the United States in this period.205

    Moreover, the degree to which the United States fully embraced the characteristics of LMEs is disputed. In the view of studies that position the United States as a model for managerial capitalism in this period, US industrial governance structures enabled the rise of giant managerialist firms.206 This approach primarily sees America’s rise to industrial preeminence through the most visible actors in the American system of political economy: oligopolies in the automobile, steel, and electrical industries. There was significant diversity, however, in firm structure. Though many giant corporations did grow to take advantage of economies of scale and capital requirements in some mass-produced goods (such as automobiles), networks of medium-sized firms still dominated important segments of these new industries, such as the production of electric motors. One-third of the fifty largest manufacturing plants in the United States made custom and specialty goods.207 From 1899 to 1909, sectors that relied on batch and custom production, including machine tools, accounted for one-third of value added in manufacturing.208 No specific brand of capitalism fulfilled the demands of production across all domains.209

    Case-Specific Factors

    Another traditional explanation highlights American natural resource abundance as a key factor in transatlantic differences in mechanization. Compared to its European competitors, the benefits to the United States derived from its endowment of natural resources, such as plentiful supplies of timber, biased its manufacturing processes toward standardized production.210 “The American turn in the direction of mass production was natural,” claims one influential study of diverging approaches to mechanization in this period.211

    The extent to which natural resource endowments determined transatlantic differences in technological trajectories is disputed. Undeterred by natural resource differences, German engineers and industrialists frequently used American machine tools and imitated US production technology.212 In fact, around the early twentieth century, the level of machine intensity in German industries was catching up to the rate in American industries.213 The US-Germany gap in mechanization was more about Germany’s struggles in proficiently using advanced tools, not the choice of methods shaped by natural resource endowments. Crucially, skill formation and embedded knowledge about working with new machinery influenced the efficient utilization of American capital-intensive techniques.214

    Summary

    The standard version of the Second Industrial Revolution’s geopolitical aftershocks highlights Germany’s challenge to British power. Germany’s relative economic rise, according to this account, derived from its advantage in industrial research and scientific infrastructure, which enabled it to capture the gains from new industries such as electricity and chemicals. However, a range of indicators emphasize that it was the United States, not Germany, that surpassed Britain in productivity leadership during this period. The US industrial ascent in the IR-2 illustrates that dominating innovation in leading sectors is not the crucial mechanism in explaining the rise and fall of great powers. Britain’s decline was not a failure of innovation but of diffusion. As the renowned economist Sir William Arthur Lewis once mused, “Britain would have done well enough if she merely imitated German and American innovations.”215

    Indeed, the IR-2 case further supports the conclusion that capacity to widely diffuse GPTs is the key driver of long-term growth differentials. The US success in broadening its talent base in mechanical engineering proved critical for its relative advantage in adapting machine tools across a broad range of industries. Like all GPT trajectories, this process was a protracted one, but it aligns better with when the United States surpassed Britain in productive leadership than the more dramatic breakthroughs in chemicals, electricity, and automobiles. To further investigate how the LS mechanism breaks down and why the GPT mechanism holds up, we turn to the high-tech competition in the twentieth century between the United States and Japan—or what some label the Third Industrial Revolution.

    5 Japan’s Challenge in the Third Industrial Revolution

    IN THE TWO previous cases, an industrial revolution preceded a shift in global leadership. Britain established its economic dominance in the early nineteenth century, and the United States took the mantle in the late nineteenth century. During the last third of the twentieth century (1960–2000), the technological environment underwent a transformation akin to the First and Second Industrial Revolutions. A cluster of information technologies, connected to fundamental breakthroughs in computers and semiconductors, disrupted the foundations of many industries. The terms “Third Industrial Revolution” (IR-3) and “Information Age” came to refer to an epochal shift from industrial systems to information-based and computerized systems.1 Amid this upheaval, many thought Japan would follow in the footsteps of Britain and the United States to become the “Number One” technological power.2

    Of the countries racing to take advantage of the IR-3, Japan’s remarkable advances in electronics and information technology garnered a disproportionate share of the spotlight. “The more advanced economies, with Japan taking the lead in one industry after another, [were] restructuring their economies around the computer and other high tech industries of the third industrial revolution,” Gilpin wrote.3 In the late 1980s and early 1990s, a torrent of works bemoaned the loss of US technological leadership to Japan.4 In a best-selling book on US-Japan relations, Clyde Prestowitz, a former US trade negotiator, declared, “Japan has … become the undisputed world economic champion.”5

    Japan’s dominance in the IR-3’s leading sectors was perceived as a threat to international security and to US overall leadership of the international system.6 Former secretary of state Henry Kissinger and other prominent thinkers warned that Japan would convert its economic strength into threatening military power.7 Per a 1990 New York Times poll, 58 percent of Americans believed that Japan’s economic power was more of a threat to American security than the Soviet Union’s military power.8

    Historical precedents loomed over these worries. US policymakers feared that falling behind Japan in key technologies would, like relative declines experienced by previous leading powers, culminate in an economic power transition. Paul Kennedy and other historically minded thinkers likened the US position in the 1980s to Britain’s backwardness a century earlier: two industrial hegemons on the brink of losing their supremacy.9 Often alluding to the LS mechanism, these comparisons highlighted Japan’s lead in specific industries that were experiencing significant technological disruption, such as consumer electronics and semiconductors. As David Mowery and Nathan Rosenberg wrote in 1991, “Rapidly growing German domination of dyestuffs helped to propel that country into the position of the strongest continental industrial power. The parallels to the Japanese strategy in electronics in recent decades are striking.”10

    Many voices called for the United States to mimic Japan’s keiretsu system of industrial organization and proactive industrial policy, which they viewed as crucial to the rising power’s success with leading sectors.11 Kennedy’s The Rise and Fall of the Great Powers attributed Japan’s surge in global market shares of high-tech industries to R&D investments and the organizing role of the Ministry of International Trade and Industry (MITI).12 These claims about the basis of Japan’s leadership in the information revolution relied on LS product cycles as the filter for the most important institutional factors.

    The feared economic power transition, however, never occurred. To be sure, Japanese firms did take dominant positions in key segments of high-growth industries like semiconductors and consumer electronics. Additionally, the Japanese economy did grow at a remarkable pace, averaging an annual 2.4 percent increase in total factor productivity (TFP) between 1983 and 1991. However, Japan’s TFP growth stalled at an average of 0.2 percent per year in the 1990s—a period known as its “lost decade.” By 2002, the per capita GDP gap between Japan and the United States was larger than it had been in 1980.13 Becoming the world’s leading producer in high-tech industries did not catalyze Japan’s overtaking of the United States as the leading economy.

    The IR-3 case is particularly damaging for LS-based explanations. Japan took advantage of the IR-3’s opportunities by cornering the market in new, technologically progressive industries, fulfilling the conditions posited by the LS mechanism for Japan to become the foremost economic power. Yet, as the case study evidence will reveal, an economic power transition did not occur, even though all these conditions were present. The Japanese challenge to American technological leadership in the last third of the twentieth century therefore primarily functions as a deviant, or falsifying, case for the LS mechanism.14

    By contrast, the IR-3 case does not undermine the GPT mechanism. Since Japan did not lead the United States in the diffusion of general-purpose information technologies, the conditions for an economic power transition under the GPT mechanism were absent in the IR-3. Since there could be many reasons why an economic power transition does not occur, the absence of a mechanism in a negative case provides limited leverage for explaining how technology-driven economic power transitions occur. Still, the IR-3 case evidence will show that LS theory expects an outcome that does not occur—a US-Japan economic power transition—in part because it fails to account for the relative success of the United States in GPT diffusion. This advantage stemmed from its superior ability to cultivate the computer engineering talent necessary to advance computerization. In that regard, this deviant case can help form better mechanism-based explanations.15

    Surprisingly, few scholars have revisited claims that Japan’s leadership in leading sectors meant that it was on its way to economic preeminence.16 Decades of hindsight bring not just perspective but also more sources to pore over. Revised estimates and the greater availability of data help paint a more granular picture of how the US-Japan productivity gap evolved in this period. To narrow down the crucial technological trajectories, I pieced together histories of semiconductors and other key technologies, comparative histories of technological development in the United States and Japan, and general economic histories of the IR-3. In addition, I leveraged bibliometric techniques to estimate the number of universities in both countries that could supply a baseline quality of software engineering education. Surveys on computer utilization by Japanese agencies, presentations on computer science education by Japanese and American analysts at international meetings, documents from the Edward A. Feigenbaum Papers collection, and back issues of Nikkei Computer (日経コンピュータ) at the Stanford University East Asia Library all helped flesh out the state of GPT skill infrastructure in the IR-3.

    The evaluation of the GPT and LS mechanisms against historical evidence from the IR-3 proceeds as follows. The chapter first makes clear that a US-Japan economic power transition did not take place. Subsequently, it reviews and organizes the technological breakthroughs of the IR-3 into candidate leading sectors and GPTs. It then examines whether all the components of the GPT or LS mechanism were present. Since the outcome did not occur in this case, it is important to trace where the mechanisms break down. All the aspects of the LS mechanism were present in the IR-3, but the GPT mechanism was not operational because Japan fell behind the United States in diffusing information technologies across a broad range of sectors. Based on this evidence, the next section explains why institutional explanations rooted in LS trajectories are unconvincing. Before turning to alternative factors and explanations, the chapter analyzes whether GPT skill infrastructure was a factor in sustained US technological leadership.

    A Power Transition Unfulfilled: Japan’s Rise Stagnates

    In a 1983 article for Parade, the Pulitzer Prize–winning journalist David Halberstam described Japan’s industrial ascent as America’s “most difficult challenge for the rest of the century” and “a more intense competition than the previous political-military competition with the Soviet Union.”17 By the end of the century, however, the possibility of Japan displacing the United States as the technological hegemon was barely considered, let alone feared.18 The economic power transition that accompanied the IR-1 and IR-2 did not materialize in this case. Indeed, most indicators presented a clear trend: Japan’s economy catches up in the 1980s, stagnates in the 1990s, and ultimately fails to overtake the US economy in productivity leadership.

    GDP PER-CAPITA INDICATORS

    During the three decades after 1960, Japan’s economy experienced remarkable growth, reaching a GDP per capita in 1990 that was 81 percent of the US mark that year. In the following ten years, known as Japan’s “lost decade,” Japan’s growth in GDP per capita stalled. By 2007, Japan’s GDP per capita had dropped back down to 73 percent of that of the United States (figure 5.1).

    INDUSTRIALIZATION INDICATORS

    Comparative industrialization statistics tell a similar story. In terms of global manufacturing output, Japan gained on the United States through the 1970s and 1980s and nearly matched the United States, at 20 percent of global manufacturing output, in the early 1990s. Japan’s share of global manufacturing output subsequently declined to around 10 percent in 2010, while the US share increased in the 1990s and held at 20 percent until 2010.19 In manufacturing industries, Japan’s labor productivity growth from 1995 to 2004 averaged only 3.3 percent, whereas the United States averaged 6.1 percent in the same metric.20

    FIGURE 5.1. Japan’s Catch-up to the United States in GDP per Capita Stalls in the 1990s. Note: Real GDP per capita in 2011$ prices. Source: Maddison Project Database, version 2020 (Bolt and van Zanden 2020).

    PRODUCTIVITY INDICATORS

    Productivity statistics also reveal a general trend of convergence without overtaking. From a total factor productivity of just half that of the United States in 1955, Japan’s TFP grew steadily. By 1991, the productivity gap between the United States and Japan was only 5 percent. As was the case with the GDP per capita and industrialization figures, Japan’s productivity growth then slowed, and the gap between the United States and Japan widened during the 1990s (figure 5.2). Throughout this decade, Japan averaged just 0.2 percent annual TFP growth.21 By 2009, Japan’s TFP dropped to only 83 percent of the US figure. The US-Japan labor productivity gap followed a similar course.22

    FIGURE 5.2. Japan’s Catch-up to the United States in Productivity Stalls in the 1990s. Source: Jorgenson, Nomura, and Samuels 2018, 18.

    Key Technological Changes in the IR-3

    Parsing through the different trajectories of technological change is a necessary first step to determine whether the LS and GPT mechanisms were operative in this period. This task is complicated by the tremendous technological changes that emerged in the IR-3, such as the first microprocessor (1971), the production of recombinant DNA (1972), the VHS format for video recording (1976), and the first personal computer (1981). Guided by past scholars’ efforts to map the key nodes of the information revolution as well as analytic measures for leading sectors and GPTs, this section takes stock of key technological drivers that affected the US-Japan economic power balance.

    Candidate Leading Sectors

    Amid a shifting technological landscape, the most likely sources of LS trajectories were information and communications technologies (ICTs). Certainly, scholars have highlighted technological developments in a wide range of industries as possible leading sectors, including lasers and robotics.23 Nonetheless, Japan’s success in the computer, consumer electronics, and semiconductor industries was most relevant for its prospects of overtaking the United States as the foremost economic power. In each of these three leading sectors, Japan dominated the production of key components.24

    All three relatively new industries achieved extraordinary growth off the back of technological breakthroughs, fulfilling the established criteria for leading sectors. In the US economy during the 1980s, computer and data-processing services ranked as the fastest-growing industries in terms of jobs added.25 After Japan’s MITI identified semiconductors and computers as strategic industries in 1971, both industries experienced extremely high growth rates in the next two decades.26 The US electronics industry also experienced a remarkable surge during the late twentieth century, growing thirty times faster than other manufacturing industries by some estimates.27 These trends in computers and electronics held across advanced industrialized countries.28

    Candidate GPTs

    Given that the IR-3 is often known as the “information revolution,” clusters of ICT innovations naturally serve as the most likely sources of GPT trajectories. Efforts to map this era’s GPTs specifically highlight computers,29 semiconductors,30 and the internet.31 Each of these technological domains exhibited great scope for improvement and complementarities with other technologies.

    Because advances in computers, semiconductors, and the internet were all closely connected, I group technological developments in these three domains under “computerization,” the process in which computers take over tasks such as the storage and management of information. This is consistent with other studies that identify the general category of ICT as the GPT operative in this period.32 The growing prevalence of software-intensive systems enabled computers to become more general-purpose. Computerization also benefited from advances in both semiconductors, which reduced costs for investments in IT equipment, and the internet, which connected computers in efficient networks.33

    Sources of LS and GPT Trajectories

    In sum, the IR-3’s candidate leading sectors and GPTs all revolved around ICTs (table 5.1). Other technical advances in lasers, new sources of energy, and biotechnology also drew attention as possible sources of LS and GPT trajectories. I do not trace developments in these technologies because their potential was largely unrealized, at least within the context of US-Japan economic competition during the IR-3.

    Though candidate leading sectors and GPTs converge on ICTs, they diverge on the key trajectories. The GPT perspective emphasizes the process by which firms transfer tasks and activities to computers. In contrast, LS accounts spotlight the growth of key industry verticals. For example, the consumer electronics industry fits the mold of previous candidate leading sectors like automobiles and cotton textiles, which were large and fast-growing but limited in their linkages to other industries. Taking stock of candidate leading sectors and GPTs merely functions as a preliminary filter. The rest of the chapter will further flesh out the differences between the LS- and GPT-based explanations of how these technologies affected the US-Japan economic rivalry.

    table 5.1 Key Sources of Technological Trajectories in the IR-3

    Candidate Leading SectorsCandidate GPTs
    Computer industryComputerization
    Consumer electronics industry
    Semiconductor industry

    GPT vs. LS Mechanisms: The (Non) Spread of ICTs across Japan’s Economy

    In both the IR-1 and IR-2 cases, a technological revolution sparked a shift in global economic leadership. The case analyses aimed to determine whether the historical evidence fit better with observable implications derived from the GPT or LS mechanism. Revolutionary technological breakthroughs also occurred in the IR-3, but an economic power transition never occurred. Thus, if the case study reveals that Japan dominated innovation in the computer, consumer electronics, and semiconductor industries, then this would provide disconfirming evidence against the LS mechanism. Likewise, if the historical evidence shows that Japan led the way in computerization during this period, this would undermine GPT diffusion theory.

    LS Mechanism Present: Japan Dominates the Production of Key ICTs

    A bevy of evidence establishes that the LS mechanism was operative during the IR-3. From the mid-twentieth century through the 1980s, Japan captured a growing global market in new industries tied to major technological discoveries in semiconductors, consumer electronics, and computers. In dynamic random-access memory (DRAM) chips, one of the highest-volume verticals in the semiconductor industry, Japanese firms controlled 76 percent of the global market share.34 By one US federal interagency working group estimate, from 1980 to 1987 the United States lost the lead to Japan in more than 75 percent of critical semiconductor technologies.35

    Japanese industry also gained competitive advantages in consumer electronics. From 1984 to 1990, US firms lost global market share in thirty-five of thirty-seven electronics categories as Japanese firms took over the production of many electronic products.36 Japan occupied dominant shares of global production of color televisions and DVDs.37 It was also the first economy to commercialize high-definition television (HDTV) systems, a highly touted part of the consumer electronics market.38

    A similar trend held in computers, especially in computer hardware components like flat panel displays.39 The US trade balance in computers with Japan turned from a surplus in 1980 into a $6 billion deficit by 1988.40According to the Yearbook of World Electronics Data, in 1990 Japan’s share of global computer production eclipsed the share held by the United States, which had previously led the world.41

    Comparisons of LS growth rates also indicate that Japan was poised to overtake the United States as the economic leader. In an International Organization article published in 1990, Thompson posited that average annual growth rates in leading sectors across major economies heralded shifts in economic leadership. Over the nineteenth century, Britain’s growth rate in leading sectors peaked in the 1830s before flattening between 1860 and 1890, a period when the United States and Germany outstripped Britain in LS growth rates.42 Crucially, Thompson’s data showed that Japan outpaced the United States in growth rates within leading sectors from 1960 to 1990.43 Linking these historical trends, Thompson identified Japan as America’s main competitor for “systemic leadership.”44

    Comprehensive assessments of Japan’s relative industrial strength support this account of LS growth rates. US government, academic, and industry entities issued a plethora of reports warning of Japan’s growing global market share and exports in key technologies. One review of six such reports, all published between 1987 and 1991, found a growing consensus that US capabilities in many of these technologies were declining relative to Japan’s.45 A 1990 US Department of Commerce report on trends in twelve emerging technologies, including supercomputers, advanced semiconductor devices, and digital imaging technology, projected that the United States would lag behind Japan in most of these technologies before 2000.46 The 1989 MIT Commission on Industrial Productivity’s Made in America: Regaining the Productive Edge serves as a particularly useful barometer of Japan’s position in leading sectors.47 Made in America argued that the United States was losing out to Japan in eight manufacturing sectors, including consumer electronics, semiconductors, and computers. As business historian Richard Langlois summarizes, “By the mid-1980s, by most accounts, America had ‘lost’ consumer electronics and was in imminent danger of losing semiconductors and computers.”48

    Some argued that Japan’s advantage in these leading sectors was rooted in certain institutional arrangements. Observers regularly pointed to Japan’s keiretsu system, which was structured around large, integrated business groups, as the key institutional factor in its success in high-tech industries. The MIT Commission’s Made in America report, for instance, questioned whether the US system of industrial organization could match up against “much stronger and better organized Japanese competition.”49 This aligned with a common narrative in the mid-1980s that “American firms should become more like Japanese firms.”50

    Others pointed to Japan’s industrial policy, coordinated by MITI, as the key institutional competency that explained Japan’s success in leading sectors. Academics and policymakers pushed for the United States to imitate Japan’s industrial policy approach, which they perceived as effective because of MITI’s ability to strategically coordinate R&D investments in key technologies.51 For instance, scholars regarded the “Fifth Generation Project,” a national initiative launched by MITI in 1982, as a stepping-stone to Japan’s building of the world’s most advanced computers.52 The American aversion to industrial policy and a decentralized economic policymaking apparatus, by comparison, was alleged to be detrimental to innovation in the IR-3’s new technologies.

    By the turn of the millennium, such arguments were no longer being put forward. Despite capturing key LS industries and rapidly catching up to the United States in the 1980s, Japan did not ultimately overtake the United States as the lead economy. Contrary to the expected outcome of the LS mechanism, Japan’s control of critical sectors in semiconductors and consumer electronics did not translate into strong, sustained economic growth. This outcome challenges the LS mechanism’s validity in the IR-3 case.

    An Absent GPT Mechanism: The United States Leads Japan in ICT Diffusion

    Does evidence from the IR-3 also discredit the GPT mechanism? If the components of the GPT mechanism, like the LS mechanism, were present during this period, this would weaken the explanatory power of GPT diffusion theory. However, in contrast to its success in key leading sectors, Japan lagged in adopting computerized technologies. Thus, GPT diffusion theory would not expect Japan to have overtaken the United States in the IR-3.

    To account for sustained US economic leadership in the IR-3, this section traces the developments of the IR-3 in the United States and Japan across the three dimensions that differentiate GPT from LS trajectories. First, relative to the LS mechanism, the impact timeframes of the IR-3’s technological breakthroughs are more elongated. Developments in ICTs did not spread to a wide range of economic applications until the 1990s. Second, though Japan excelled in the production of computers and electronics, it fell behind in the general pace of computerization across the economy. Lastly, Japan’s advantages were concentrated in a narrow range of ICT-producing industries, whereas the United States benefited from broad-based productivity growth.

    IMPACT TIMEFRAME

    The advance of computerization, like past GPT trajectories, demanded a prolonged period of organizational adaptation and complementary innovations. It is reasonable to date the computerization GPT’s emergence as the year 1971—the year when Intel introduced the microprocessor, which greatly expanded the functionalities of computers.53 It was also the year when the share of information technology equipment and software reached 1 percent in the net capital stocks of the median sector in the US economy.54 Before then, during the 1960s, mainframe computers powered by integrated circuits serviced only a limited range of commercial purposes, such as producing bank statements and managing airline reservations. With the internet’s rise in the 1990s, new information and communication networks further spread computerization to different business models, such as e-commerce.55 Alongside this stream of complementary technical advances, firms needed time to build up their computer capital stock and reorganize their business processes to match the needs of information technology.56

    Computers traveled a gradual and slow road to widespread use. By the late 1980s, many observers bemoaned the computer revolution’s failure to induce a surge of productivity growth. In 1987, the renowned economist Robert Solow distilled this “productivity paradox” in a famous quip: “We see the computers everywhere but in the productivity statistics.”57 A decade later, however, the growing adoption of information technology triggered a remarkable surge in US productivity growth.58 It took some time, but the American economy did eventually see the computers in the productivity statistics.

    The landscape of US-Japan technology competition looks very different when accounting for the elongated lag from computerization’s arrival to its widespread diffusion. Japan’s control over key segments of ICT production in the 1970s and 1980s did not correspond to an advantage in GPT diffusion. A more patient outlook illuminates that the delayed impact of computerization aligns with when the United States extended its productivity lead over Japan. After 1995, while Japan’s economic rise stalled, labor and total factor productivity grew rapidly for a decade in the United States. The difference was that the United States benefited greatly from an ICT-driven productivity acceleration.59

    FIGURE 5.3. The US-Japan Computerization Gap Widens in the 1990s. Source: Milner and Solstad 2021; Comin 2010, 381.

    PHASE OF RELATIVE ADVANTAGE

    If the GPT mechanism was operative for Japan’s rise in the IR-3, Japan should have led the United States in computerization. Though Japan continued to contest US leadership in the production of certain computer architectures and devices, it failed to keep up with the United States in the adoption of computers across industries. As figure 5.3 reveals, the gap between the United States and Japan in computerization widened in the 1990s. In fact, South Korea, which lagged behind Japan in generating new innovations in computer systems, surpassed Japan in its computer usage rate during the 1990s. Taken together, these indicators suggest that Japan’s problem was with GPT diffusion, not innovation.

    The pathway by which ICTs drove the US-Japan productivity gap is particularly revealing. In sectors that produced ICTs, Japan’s TFP acceleration was similar to the US trajectory; however, in sectors that intensively used IT, Japan’s TFP growth lagged far behind that of its rival.60 In particular, US ICT-using service industries adapted better to computerization. In terms of labor productivity growth in these industries, the United States experienced the strongest improvement out of all OECD countries from the first half of the 1990s to the second half of the decade.61 The contribution of ICT-using services to Japan’s labor productivity growth, by contrast, declined from the first half to the second half of the decade.62

    Japan eventually adopted the GPT trajectory associated with ICT. Like all advanced economies at the frontier, Japan could draw from the same technology pool as America. Using a growth accounting framework that accounts for cyclical factors, Takuji Fueki and Takuji Kawamoto trace Japan’s post-2000 resurgence in TFP growth to the extension of the ICT revolution to a broader range of IT-using sectors.63 By then, however, Japan was at least five years behind the United States in taking advantage of computerization.

    BREADTH OF GROWTH

    Alongside the dispersion of ICTs throughout the US economy, the sources of productivity growth also spread out. In the United States, spillovers from ICT advances, especially in services, contributed to economy-wide TFP growth. Industry-level patterns of TFP growth reveal that US productivity growth became noticeably more broad-based after 1995, a trend that accelerated further after 2000.64

    In contrast, Japan’s advantages in the IR-3 were concentrated in a narrow range of sectors. After 1995, Japanese productivity growth remained localized, only transitioning to a more broad-based pattern after 2000.65 Michael Porter’s exhaustive account of national competitiveness across leading economies describes Japan as a “study in contrasts,” with some of the most internationally competitive industries found side by side with some of the most uncompetitive.66 The hype over Japan’s success in leading sectors induced some analysts to generalize from a few exceptional industrial sectors while overlooking developments in struggling sectors.67

    LS Mechanisms and “Wintelism”

    Some international political economy scholars divide trends in US competitiveness into a phase of relative decline in the leading sectors of the 1980s (consumer electronics, computer hardware, and parts of the semiconductor industry) and a period of resurgence in the new leading sector of the 1990s (software electronics).68 This explanation for why Japan’s LS advantage did not convert into an economic power transition could conceivably restore credibility to the LS mechanism. However, even this generous interpretation fails to capture the dynamics of the IR-3.69

    Consider one prominent line of LS-based thinking that emphasizes US advantages in adapting to “Wintelism,” a type of industrial structure best suited for new advances in software electronics. Wintelism, a portmanteau of “Windows” and “Intel,” refers to the transformation of the computer industry from a vertically integrated oligopoly into a horizontally segmented structure dominated by components providers with controlling architectural standards, such as Intel and Microsoft.70 Compared to Japan, the US institutional environment was more supportive of horizontal, specialized value-chains in software electronics. It is put forward that Japan’s inability to adapt to the Wintelist industrial paradigm explains why it was unable to overtake the United States as the leading economy.71

    The Wintelism argument falls prey to general issues with the LS mechanism.72 Like LS accounts of Japan’s advantage in semiconductors and consumer electronics in the 1980s, the Wintelism argument still places too much value on ICT-producing industries. Capturing profit shares in the global software electronics industry is not the same as translating new advances in software electronics into economy-wide growth.73 The pathway from new technologies to overall productivity growth involves much more than just the success of companies like Microsoft and Intel.

    Indeed, as a GPT diffuses more widely, large monopolists may hinder coordination between the GPT and application sectors. Both Microsoft and Intel, for instance, often restricted sharing information about their technology roadmaps, thereby hindering complementary innovations and adoption of microelectronics in applications sectors such as automobiles. In fact, regulatory and technological forces were necessary to limit the influence of dominant firms and encourage the development of complementary technologies, which were crucial to widening the GPT trajectory of computerization.74 It is conceivable that the United States could have achieved a greater rate of computerization if its computer industry had not been dominated by two firms that held key architectural standards.

    Overall, hindsight is not kind to LS-based accounts of Japan’s institutional advantages in the IR-3. While matching institutional competencies to particularly significant technological trajectories is a sound approach, the LS trajectory fails to capture how technological changes opened opportunities for an economic power transition. Japan’s industrial structure and sectoral targeting policies reaped economic gains that were temporary and limited to specific industries.75 To understand why the United States gained lasting and broad-based advantages from computerization, a different set of institutional complementarities must be explored.

    Institutional Complementarities: GPT Skill Infrastructure in the IR-3

    In line with GPT diffusion theory, institutional adaptations that widened the base of computer engineering skills and knowledge proved crucial to the enduring technological leadership of the United States in the IR-3.76 Computerization required not just innovators who created new software architectures but also programmers who undertook more routine software engineering tasks. It was the US ability to tap into a more expansive pool of the latter that fueled a more intensive pace of computerization than in Japan.

    Widening the Base of Computer Engineers

    GPTs generate an imbalance between the possibility of sweeping changes across many domains and the constraints of existing skills. Historically, engineering disciplines have developed procedures for adapting new GPT-linked knowledge across localized applications and expanded access to such knowledge to a broader set of individuals. Similarly, the key element of the American GPT skill infrastructure in the IR-3 was the development of a computer science discipline—the latest in a line of engineering disciplines that have emerged in the wake of GPTs.77

    US education effectively adapted to changes in the computerization trajectory. The recognition of computer science as an independent discipline, as evidenced by the early and rapid growth of computer science departments in the United States, helped systematize the knowledge necessary for the spread of computerization.78 Led by top universities and the Association of Computing Machinery (ACM), US institutions piloted new training programs in computer science. In 1968, the ACM published an influential and forward-looking curriculum that helped colleges organize their computing education.79 These adaptations converted the practical experience from developers of new computer and software breakthroughs into general, accessible knowledge.80

    The development of this GPT skill infrastructure met significant hurdles. In the 1970s, the US computer science discipline struggled to meet demands for software engineering education in the midst of conflicts over the right balance between theory-oriented learning and hands-on programming work.81 Reacting to this situation, the ACM’s 1978 curriculum revision directed computer science toward application-based work, stating that programming exercises “provide a philosophy of discipline which pervades all of the course work.”82 Industry pressure and the US Department of Defense’s Software Engineering Institute, a partnership established with Carnegie Mellon University in 1984, expanded the number of software engineering specializations at universities.83 Teaching capacity was another inhibiting factor. Computer science departments had to limit course enrollments because they could not fill the faculty positions to meet exploding student demand, resulting in a decline in computer science enrollments in the mid-1980s.84

    Though the process was not seamless, overall trends reflect the US success in widening the pool of engineering skills and knowledge for advancing computerization. From 1966 to 1996, the number of undergraduate computer science degrees awarded annually in the United States grew from 89 to about 24,500.85 In 1986, at its peak during this period, computer science accounted for 12.5 percent of all science and engineering degrees awarded in the United States.86 According to benchmarking efforts by the Working Group for Software Engineering Education and Training, US institutions accounted for about one-third of the world’s bachelor’s degree programs in software engineering in 2003.87 Throughout this period, the United States also benefited from a system open to tapping foreign software engineering talent.88

    The US Comparative Advantage over Japan in GPT Skill Infrastructure

    Did GPT skill infrastructure factor decisively in Japan’s inability to keep pace with the United States in the diffusion of ICTs? Varying data reporting conventions, especially the fact that Japanese universities subsumed computer science under broader engineering fields, make it difficult to precisely quantify the US-Japan gap in software engineering skills.89 One narrative that gained momentum in the late 1980s accredited Japan’s success in high-tech manufacturing industries to its quantitative advantage in engineers. A National Science Foundation (NSF) report and two State of the Union addresses by President Ronald Reagan endorsed this belief.90 For example, the NSF’s 1997 special report on Japan’s scientific and technological capabilities declared, “By 1994, with roughly one-half the population, Japan produced more engineering and computer science degrees at the undergraduate level than the United States.”91 Such statements blended computer science degrees with all categories of engineering education.

    Computer science–specific data reveal the US edge in ICT human resources. According to data from Japan’s Information Technology Promotion Agency, Japan awarded about 16,300 computer science and mathematics bachelor’s degrees in 2009, while the United States awarded 63,300 of these types of degrees that same year.92 One survey by the Japanese Information Technology Services Industry Association found that only 3.6 percent of college graduates who entered Japan’s information service industry in April 1990 received their degree from a computer science department.93 By counting immigrants entering computer-related professions and total university graduates in ICT software and hardware fields, one study estimated annual inflows into the US and Japanese ICT labor pools. In 1995, these inflows into the US ICT labor pool outpaced those in Japan by 68 percent. By 2001, this gap between the two countries’ annual inflows of ICT talent had reached almost 300 percent.94 Therefore, in the years when the US advantage in ICT diffusion was most pertinent, the skill gap between the United States and Japan in computer and software engineering talent grew even wider.95

    Moreover, a computer science degree in Japan did not provide the same training as one in America. First, Japanese universities were slow to adapt to emerging trends in computer science. In both 1997 and 2007, the Information Processing Society of Japan modeled its computing curriculum revisions on American efforts that had been made six years earlier.96 The University of Tokyo, Japan’s leading university, did not establish a separate department of computer science until 1991, which was twenty-six years later than Stanford.97 Overly centralized governance of universities also inhibited the development of computer science as an independent discipline in Japan.98 As Jeffrey Hart and Sangbae Kim concluded in 2002, “The organizational and disciplinary flexibility of US universities in computer science has not been matched in any of the competing economies.”99

    Software engineering presented a particular challenge for Japan. In 1988, the Japanese-language industry journal Nikkei Computer surveyed six thousand Japanese firms that used office computers. Situated outside the computer industry, these firms were involved in a broad range of fields, including materials manufacturing, finance, services, government, and education. More than 80 percent of the responding companies disclosed shortages of software programmers and designers.100 On average, outside firms provided one-quarter of their information technology personnel, and their reliance on outsourcing was magnified for programmers, system designers, and application managers.101 A nonprofit foundation for ICT development in Japan reported similar barriers to computer utilization in 1991. The survey found that companies relied heavily on computer personnel, especially software engineers, dispatched temporarily from other organizations.102 Small and medium-sized software departments, which could not afford to invest in on-the-job training, were especially disadvantaged by the lack of formal software engineering education in Japan.103

    Bibliometric techniques can help substantiate the gap between the United States and Japan in skill infrastructure for software engineering. I analyzed around seven thousand publications from 1995 in the Web of Science Core Collection’s “Computer Science, Software Engineering” category.104 To gauge the breadth of institutional training in software engineering, I counted the number of Japanese and American universities that employed at least one researcher represented in this dataset. According to my estimates, the United States boasted 1.59 universities per million people that met this baseline quality of software engineering education, while Japan only had 1.17 universities per million people. This amounts to a gap of around 40 percent.

    Lastly, weak industry-university linkages in computer science hampered Japan’s development of GPT skill infrastructure. Imposing centralized control over universities, the Japanese Ministry of Education, Science, and Culture (MESC) inhibited cooperation between new departments of information science and the corporate labs where much of the computing talent was concentrated.105 Japanese researchers regularly complained about the size of MESC grants, as well as the ministry’s restrictions on their ability to seek alternative funding sources. Japan’s overall budget level for university facilities in 1992 remained the same as it was in 1975. Additional government funds went instead to independent centers of excellence, diverting resources away from efforts to broaden the pool of training institutions in software engineering.106

    Alternative Factors

    How do alternative explanations perform in this case? A range of factors beyond GPT skill infrastructure could have influenced diverging rates of ICT diffusion in the United States and Japan. I focus on the role of external threats and varieties of capitalism because they present alternative mechanisms for how states adapted differently to the technological revolution that occurred in the IR-3.

    Threat-Based Explanations

    Threat-based theories struggle to account for differences in the US and Japanese technological performance in this period. A “cult of vulnerability” permeated Japan’s leaders over this period as they coped with tensions in East Asia and the oil crises of the 1970s.107 Likewise, the growth of the US “national security state,” fueled by the dangers of the Cold War, functioned as “the secret to American innovation.”108 Under his creative insecurity framework, Taylor holds up both Japan and the United States as exemplars of the IR-3 period, reasoning that they both partly owed their technological success to the galvanizing effects of a threatening international environment.109 General threat-based explanations therefore cannot explain differences in technological outcomes between the United States and Japan, namely, why the United States was more successful in ICTs than Japan.

    A related argument points to the significance of US military procurement for computerization. As was the case with its influence on the American system of manufacturing in the IR-2, the US military provided the demand for initial investments in computers and semiconductors. In the 1940s and 1950s the US military was a key patron behind computing breakthroughs.110 Assured by large military procurements, innovative firms undertook risky, fundamental research that produced spillovers to many other industries. For instance, the first all-purpose electronic digital computer, the University of Pennsylvania’s electronic numerical integrator and calculator (ENIAC), was developed during World War II. The ENIAC was supported by funding from the Army Ballistics Research Laboratory, and the first program run on the computer was a simulation of the ignition of the hydrogen bomb.111

    In place of the military, could other entities have served as a large demand source for ICTs? Commercial entities like Bell Labs and IBM also developed fundamental breakthroughs in ICTs. According to Timothy Bresnahan and Manuel Trajtenberg, it was “only a coincidence” that US government demand played such a pivotal role in semiconductor development.112 Others argue that while commercial advances in semiconductors and computers would likely still have occurred absent the impetus of military funding, they would have emerged after substantial delay.113

    Resolving this debate depends on one’s view of the key stage in computerization. Those who emphasize the importance of military procurement often hold up the importance of first-mover advantages in the American computer industry.114 However, decades after the military helped develop the first computers and transistors, Japan had cornered the market in many related industries. The significance of military procurement is diminished when a GPT’s dissemination, as opposed to its emergence, is taken as the starting point. By 1960, the start of the IR-3 period, ICT development in the United States was already much less reliant on military support. In 1955, the demand for Bell’s transistors from two large telephone networks alone was nearly ten times more than from all military projects.115 In fact, as the commercial sector increasingly drove momentum in ICTs, military involvement arguably hindered continued advances in the commercial sector, as there was tension between the different technical cultures.116

    On balance, the most significant aspect of the military’s involvement in the advance of computerization in the United States was its role in building up GPT skill infrastructure. The US military played a key role in cultivating the computer science discipline in its early years. Beginning in the 1960s, defense agencies supported academic research in computer science, such as the aforementioned Software Engineering Institute, which created centers of excellence and broadened the base of computer science education.117 From 1977 through the mid-1980s, defense funding supported more than half of academic computer science R&D.118 At the same time, military investment in computer science did not come without downsides. Defense funding was concentrated in elite research universities at the cutting edge of the field, such as Carnegie Mellon and Stanford, whereas nondefense government funding supported computer science education across a wider range of US universities.119 On the effects of military computer science funding, Stanford professor Terry Winograd wrote, “It has resulted in a highly unequal situation in which a few schools have received almost all the resources. Although this may have led to more effective research in the short run, it has also been a factor contributing to the significant long-term shortage of trained computer researchers.”120

    VoC Explanations

    The varieties-of-capitalism (VoC) approach provides another possible explanation for why the US economy benefited more than Japan’s from the innovations of the IR-3.121 According to the VoC framework, firms in coordinated market economies (CMEs) provide industry-specific training that is more conducive to incremental innovation, whereas worker training in more general skills in liberal market economies (LMEs) proves more favorable for radical innovations. VoC scholars point to some evidence from the international pattern of innovation during the IR-3 that supports these expectations. Based on patent data from 1983–1984 and 1993–1994, Peter Hall and David Soskice find that Germany, a CME, specialized in technology classes characterized by incremental innovation, whereas the United States, an LME, specialized in domains characterized by radical innovation.122 Therefore, the VoC perspective expects that Japan, a CME like Germany, was unable to keep up with the United States in the IR-3 because high-tech sectors such as computer software and biotechnology demanded radical innovations.123

    This VoC-derived explanation provides an incomplete account of the IR-3 case. First, comprehensive empirical investigations into the innovative performance of CMEs and LMEs, especially the success of Japan as a radical innovator, undermine the explanatory power of VoC theory for the IR-3 period. Hall and Soskice’s initial analysis was based on four years of data on patent counts from only two countries, the United States and Germany. Taylor’s more extensive analysis, which covered thirty-six years (1963–1999) of patent counts and forward citations for all LME and CME countries, found that the predictions of VoC theory are not supported by the empirical data.124 In fact, contrary to the expectations of the VoC explanation, Japan was a leader in radical innovation, ranking second only to the United States in patent counts weighted by forward citations, which are a strong proxy for the radicalness of innovations.125

    Second, VoC theory does not make distinctions between different types of general skills, which varied in their significance to national success in the IR-3. Regarding general training in terms of foundational schooling, Japan was making substantial improvements in average years of schooling, enrollment ratio, and access to higher education.126 GPT diffusion theory specifies the key general skills as those that best suited the advance of computerization. Consistent with these expectations, the case study evidence points to the US-Japan gap in software engineering, a set of general skills that permeated sectoral boundaries, as the crucial factor in US success with widespread computerization.

    Case-Specific Factors

    Other factors unique to the IR-3 case deserve further consideration. Among these alternative explanations, one popular theory was that Japan’s kanji system (Chinese-based characters) contributed to its slow adoption of computers.127 Marshall Unger, a specialist in the Japanese writing system, highlighted difficulties with representing kanji in computerized formats, which resulted in higher costs for storing data and for word-processing functions.128 American computers had to handle only ninety-five printable characters, whereas Japanese personal computers needed to store six thousand Japanese characters.129 Not only did language differences increase the cost of Japanese computers, but they also prevented Japanese adopters from using off-the-shelf computers from the United States, as these did not support Japanese language functions.

    While particularities of the Japanese language may have initially hindered Japan’s computerization, it is important not to overstate the impact of this language barrier. In a review of this theory, another expert on computational linguistics argued that Unger overemphasized the additional overhead and speed costs associated with Japanese writing systems.130 Moreover, users and companies adapted over time. By the end of the 1980s, advances in processor technology allowed computers to support the greater word-processing demands of Japanese language systems.131 Therefore, during the critical years when the US-Japan computerization gap widened, the impact of the kanji system was less pronounced.

    Summary

    Through much of the late twentieth century, it was only a matter of time until Japan achieved economic preeminence—at least in the eyes of many analysts and scholars. Invoking the assumptions of the LS mechanism, they expected that this economic power transition would be brought about by Japan’s advantages in new sectors such as consumer electronics, semiconductor components, and computer hardware. Today, after Japan’s decade-long slowdown in productivity growth, there is virtually no discussion of it overtaking the United States as the leading economic power.

    Looking back, one might be tempted to conclude that history has vindicated past critics who labeled the claims of Japan’s imminent ascension to technological hegemony as “impressionistic,”132 as well as the retrospective analyses that called out such projections for being “premature.”133

    This chapter’s conclusions suggest a more nuanced interpretation. It is not that the prognoses of LS-based accounts were overeager or overly subjective. The real issue is that they were based on faulty assumptions about the pathway by which technological advances make economic power transitions possible. Indeed, the IR-3 case provides strong negative evidence against the LS mechanism, revealing that the expected outcome of an economic power transition failed to materialize in part because of the US advantage in GPT diffusion. The relative success of the United States in diffusing the trajectory of computerization across many ICT-using sectors, in line with GPT diffusion theory, was due to institutional adaptations to widen the skill base in computer engineering. In sum, the US advantage in GPT diffusion accounts for why the economic power transition expected by the LS account failed to transpire.

    6 A Statistical Analysis of Software Engineering Skill Infrastructure and Computerization

    I HAVE ARGUED that the shape of technological change is an overlooked dimension of the rise and fall of great powers. Most researchers point to various institutions to explain why some countries experience more scientific and technological progress than others. A central insight of this book is that the institutional factors most relevant for technological leadership depend on whether key technological trajectories map onto GPT diffusion or LS product cycles. GPT diffusion theory posits that great powers with better GPT skill infrastructure, defined as the ability to broaden the engineering skills and knowledge linked to a GPT, will more effectively adapt to technological revolutions.

    This chapter evaluates a key observable implication of GPT diffusion theory. The expectation is that where there is a wider pool of institutions that can train engineering talent related to a GPT, there will be more intensive rates of GPT diffusion. Using data on computerization and a novel approach to estimate the number of universities that provide quality software engineering education in a country, this chapter first tests the theorized connection between GPT skill infrastructure and GPT adoption on time-series cross-sectional data for 19 advanced and emerging economies from 1995 to 2020. I supplement this panel analysis with two additional tests: a duration model of the speed by which 76 countries achieved a certain computerization threshold, as well as a cross-sectional regression of data on 127 countries averaged over the 1995–2020 period. While the historical case studies verified this relationship among great powers, large-n quantitative analysis allows us to explore how GPT diffusion applies beyond the chosen case studies.

    To preview the findings, the evidence in this chapter backs GPT diffusion theory. Countries better positioned to widen their base of software engineering skills preside over higher rates of computer adoption. This relationship holds even when accounting for other factors that could affect computerization and different specifications of the independent and dependent variables. This chapter proceeds by operationalizing computerization rates and skill infrastructure in software engineering and then statistically testing the relationship between the two variables.

    Operationalizing the Independent Variable: GPT Skill Infrastructure in Software Engineering

    My key independent variable is skill infrastructure connected to computerization. The computer, a prototypical GPT, represents a natural choice for this type of inquiry, as engineering education data for many past GPTs is nonexistent for many countries.1 Plus, enough time has passed for us to see the effects of computerization. The statistical analysis focuses on the effects of skill formation institutions in software engineering, the computer science discipline tasked with training generalists in computing technology.2 Concretely, this chapter’s measure of GPT skill infrastructure captures the breadth of a country’s pool of software engineering skills and knowledge.

    Efforts to measure the GPT skill infrastructure in software engineering face three challenges. First, standardized measures of human capital in computer science across countries are not available. The UNESCO Institute for Statistics (UIS) collects internationally comparable data on technicians and researchers in various fields, but this dataset does not include information specific to computer science and has limited temporal coverage.3 Second, variation across countries in the format of computer science education undercuts some potential benchmarks, such as undergraduate enrollments in computer science programs. In some countries, computer science education is subsumed under a broad engineering category, not recognized as a separate degree course.4 Lastly, comparisons of computer science education struggle to account for the quality of such training. International rankings of universities for computer science garner media coverage, but they rely on subjective survey responses about reputation and largely concentrate on elite programs.5

    To the extent possible, my measure of GPT skill infrastructure addresses these obstacles. The goal is to operationalize engineering-oriented computer science education in a way that can be standardized across countries and accounts for differences in the format and quality of computer science education. My novel approach estimates the number of universities in each country that can be reasonably expected to provide a baseline quality of software engineering education. To establish this baseline in each country, I count the number of universities that employ at least one researcher who has published in a venue indexed by the Web of Science (WoS) Core Collection’s category on “Computer Science, Software Engineering.” In this category, the WoS citation database extends back to 1954 and allows for reliable cross-country comparisons based on institutional affiliations for published papers and conference proceedings.6 This approach is also insulated from distinctions related to whether certain degrees count as “computer science” programs or as “general engineering” courses. A particular university’s naming scheme has no bearing; as long as an institution retains at least one researcher who has published in the software engineering field, it counts in the GPT skill infrastructure measure.

    To gather data on the number of universities that contribute to software engineering skill formation around the world, I analyze 467,198 papers from the WoS Core Collection’s “Computer Science, Software Engineering” category published between the years 1995 and 2020. I use the Bibliometrix open-source software to derive institutional and country affiliations from this corpus.7 Specifically, I collect the university and country affiliations for the corresponding authors of all 467,198 publications. For each country, I count the number of distinct university affiliations. Hypothetically, if country X’s researchers were all concentrated at a single center of excellence, it could boast more researchers represented in the corpus than country Y but still score lower on my metric. For making comparisons across countries, the number of distinct university affiliations is a better indicator of a country’s access to a broad pool of training institutions for software engineering, which is central to GPT skill infrastructure.

    FIGURE 6.1. Software Engineering Skill Infrastructure by Country (2007). Source: Author’s calculations based on Web of Science Core Collection database.

    I estimate a country’s GPT skill infrastructure in a particular year by averaging its score on this metric in that year along with its scores in the two previous years. This step provides checks against the risk that developments specific to a particular year—a conference cancellation, for example—may muddle the measure. To illustrate country distributions on this metric, figure 6.1 depicts the number of universities that meet my baseline for software engineering skill formation in G20 countries for one of the middle years in the dataset.

    As with all bibliometric research, given the bias toward English-language papers in publication datasets, this method could undercount publications from non-English-speaking countries.8 Fortunately, this linguistic bias found in social science papers is less pronounced in engineering and mathematics papers, which comprise my dataset.9 Another factor that mitigates this bias is the very low standard for quality engineering education. Even if an institution’s researchers publish a substantial portion of their writings in a non-English language, as long as just one publication landed in the WoS “Computer Science, Software Engineering” category, that institution would still count in my definition of GPT skill infrastructure.

    I considered other measures of software engineering skills, but none were suitable for this type of analysis. Data on the number of full-time equivalent telecommunication employees, collected by the ITU, shed some light on the distribution of ICT skills in various economies. Concretely, this indicator captures the total number of people employed by telecommunication operators for the provision of fixed-telephone, mobile-cellular, and internet and data services.10 The rationale is that the number of employees in this critical ICT services industry represents the broader pool of software engineering talent in a country. However, since this measure is biased toward specialists who develop and install computer technologies and other ICTs, it overlooks many engineers who intensively use ICTs in their work, even if they are not involved in developing software and computing tools.11

    The time coverage of other measures was limited. For instance, the International Telecommunication Union (ITU) database on ICT skills like programming or coding in digital environments only goes back to 2019.12 In its Global Competitiveness Index, the World Economic Forum surveys business executives on the digital skills of their country’s population, but this data series starts in 2017.13

    Operationalizing the Dependent Variable: Computerization Rates

    Divergences among nations in scientific and technological capabilities have attracted a wide range of scholarship. While my focus is on the overall adoption rate of GPTs within economies, many scholars and government bodies have made significant contributions to quantifying national rates of innovation, often based on patenting activity, publications, and R&D investments.14 Cross-national data on the diffusion of specific technologies, by comparison, has been sparse.15 The Cross-country Historical Adoption of Technology (CHAT) dataset, which documents the intensity with which countries around the world use fifteen historically significant technologies, has helped address this deficiency.16 Other studies of cross-national technology adoption gaps quantify the diffusion of the internet and government uses of information technology.17

    My primary measure of computerization is the proportion of households with a computer. These data are sourced from the International Telecommunication Union’s World Telecommunication/ICT Indicators (WTI) database.18 In this dataset, access to a computer includes use of both desktop and portable computers but excludes devices with some computing ability, such as TV sets and mobile phones.19 By estimating the number of households in a country with access to a computer, this measure elucidates cross-country differences in the intensity of computerization. Though observations for some countries start in 1984, there is limited coverage before 1995, which serves as the initial year for the data collection effort detailed in this chapter.

    After the ITU was tasked with supplying indicators for access to ICTs around the world—a crucial target of the United Nations’ Millennium Development Goals (MDG) adopted in 2000—the agency started to track the number of personal computers by country.20 The ITU produces computer usage figures through two methods. First, when available, survey data from national and supranational statistical offices (such as Eurostat) are used. Though the MDG initiative has encouraged national statistical offices to help the ITU in monitoring ICT access, data coverage is still incomplete. If data on the number of households with a computer are unavailable for a country in one year, the ITU makes an estimate based on computer sales and import figures, adjusted to incorporate the average life of a computer as well as other related indicators, such as the number of computer users. For example, the computer usage indicator for Latvia comes from Eurostat in 2013, an ITU estimate in 2014, and the Central Statistical Bureau of Latvia in 2015.

    Despite its limitations, I prefer the ITU’s computerization indicator over alternative measures. Francesco Caselli and Wilbur John Coleman examine the determinants of computer adoption across a large sample of countries between 1970 and 1990. To estimate the intensive margin of diffusion, they use the value of a country’s computing equipment imports per worker as a proxy for its computer investment per worker.21 However, imports do not account for a country’s computer investments sourced from a domestic computer industry; this is an issue that becomes more salient in the later years of the dataset.22

    A more optimal indicator would estimate computer access and usage among businesses, since such economic activity is more likely to produce productivity improvements than household use. I examined a few alternatives. The CHAT dataset employs the number of personal computers per capita, which is one of three measures highlighted by the authors as conveying information on GPTs.23 However, this indicator still does not capture the degree of computerization in productive processes, as opposed to personal use, and has limited temporal coverage compared to the ITU’s household computerization measure.24 The OECD collects some data on ICT access and usage by businesses, but this effort did not start until 2005 and covers only OECD countries.25

    Fortunately, it stands to reason that a country’s household computer adoption can serve as a proxy for its computerization rates in business activities. In the appendix, I provide further support for this claim. Comparing the available data on household and business computerization for twenty-six countries between 2005 and 2014, I find a strong correlation between these two variables (correlation coefficient = 0.8).26

    Summary of Main Model Specifications

    To review, this chapter tests whether GPTs diffuse more intensively and quickly in countries that have institutional advantages in widening the pool of relevant engineering skills and knowledge. The first hypothesis reads as follows:

    H1: Countries with higher levels of GPT skill infrastructure in software engineering will sustain more intensive computerization rates.

    With country-years as the unit of analysis, I estimate time-series cross-sectional (TSCS) models of nineteen countries over twenty-six years. Quantitative analysis permits an expansion of scope beyond the great powers covered in the case studies. As outlined in the theory chapter, differences in GPT skill infrastructure are most relevant for economies that possess the absorptive capacity to assimilate new breakthroughs from the global technological frontier.27 Since less-developed economies are often still striving to build the baseline physical infrastructure and knowledge context to access the technological frontier, variation in GPT skill infrastructure among these countries is less salient. Thus, I limit the sample to nineteen G20 countries (the excluded member is the European Union), which represent most of the world’s major industrialized and emerging economies.

    Before constructing TSCS regressions, I first probe the relationship between GPT skill infrastructure in software engineering and computerization rates. Prior to the inclusion of any control variables, I plot the independent and dependent variable in aggregate to gauge whether the hypothesized effect of GPT skill infrastructure is plausible. The resulting bivariate plot suggests that there could be a strong, positive relationship between these two variables (figure 6.2).28

    The basic trend in figure 6.2 provides evidence for the contention that countries better equipped with computer science skill infrastructure experience higher rates of computerization. Although these preliminary results point to a strong relationship between these two variables, further tests are needed to rule out unseen confounders that could create spurious correlations and influence the strength of this relationship. TSCS regression analysis facilitates a deeper investigation of the relationship between GPT skill infrastructure and computerization.

    FIGURE 6.2. Software Engineering Skill Infrastructure and Computerization. Source: Author’s calculations, available at Harvard Dataverse: https://doi.org/10.7910/DVN/DV6FYS.

    To control for factors that could distort the relationship between computer-related skill infrastructure and computerization, I incorporate a number of control variables in the baseline model. Rich countries may be able to spend more on computer science education; additionally, they also more easily bear the expenses of adopting new technologies, as exemplified by large disparities between developed and developing countries in information technology investment levels.29 The inclusion of GDP per capita in the model accounts for economic development as a possible confounder. I use expenditure-side real GDP at current purchasing power parities (PPPs), which is best suited to comparing relative living standards across countries. A country’s total population, another control variable, addresses the possibility that larger countries may be more likely to benefit from network effects and economies of scale, which have been positively linked to technology adoption.30 I also include the polity score for regime type. Research suggests that democratic governments provide more favorable environments for technology diffusion, and studies have confirmed this connection in the specific context of internet technologies.31

    Finally, the baseline model includes two control variables that represent alternative theories of how technological changes differentially advantage advanced economies. First, I include military spending as a proportion of GDP in the regressions. The case studies have interrogated the influential view that military procurement is an essential stimulus for GPT adoption.32 By examining the relationship between military spending and computerization across a large sample of countries, the statistical analysis provides another test of this argument. Moreover, the varieties of capitalism (VoC) scholarship suggests that liberal market economies (LMEs) are especially suited to form the general skills that could aid GPT adoption across sectors. Therefore, the baseline model also controls for whether a country is designated as an LME according to the VoC typology.33

    In terms of model specification, I employ panel-corrected standard errors with a correction for autocorrelation, a typical method for analyzing TSCS data.34 Given the presence of both autocorrelation and heteroskedasticity, I estimate linear models on panel data structures using a two-step Prais-Winsten feasible generalized least squares procedure.35

    Time-Series Cross-Sectional Results

    Table 6.1 gives the results of the three initial models, which provide further support for the theoretical expectations.36 Model 1 incorporates controls that relate to economic size and level of development. Model 2 adds a control variable for regime type. Lastly, model 3 includes a variable that represents a prominent alternative theory for how technological breakthroughs can differentially advantage certain economies. This also functions as the baseline model. In all three models, the coefficient on the GPT skill infrastructure measure is positive and highly statistically significant (p < .05).

    Table 6.1 Results of Time-Series Cross-Sectional Models

    Dependent Variable
    Computerization
    (1)(2)(3)
    GPT skill infrastructure3.760**4.064***4.227**
    (1.643)(1.676)(1.666)
    GDP per capita29.754***29.319***29.435***
    (3.760)(3.737)(3.789)
    Total population6.969***7.046***6.781***
    (1.625)(1.654)(1.549)
    Polity score−0.456−0.472*
    (0.295)(0.277)
    Military spending−0.940
    (2.413)
    Liberal market economy−2.194
    (3.961)
    Constant−374.599***−368.051***−361.885***
    (60.452)(61.173)(58.374)
    Observations383370370
    Note: Standard errors in parentheses.
    *p < .10; **p < .05; ***p < .01

    The effect of GPT skill infrastructure on GPT adoption is also substantively significant. Given the coefficient of the GPT skill infrastructure measure in the baseline model,37 a 1 percent increase in the universities per 100,000 people that provide software engineering education results in an increase of the computerization rate by 0.042 percentage points.38 Though the substantive effect seems small at first glance, its magnitude becomes clear when contextualized by differences in GPT skill infrastructure across the sample. For example, in China over this time period, the average number of universities per 100,000 people that met my baseline for GPT skill infrastructure was 0.040. The corresponding figure for the United States was 0.248. According to the coefficient estimate for GPT skill infrastructure, this difference of 520 percent corresponds to a difference of nearly 22 percentage point units in the computerization rate.

    It should be noted that only two control variables, economic development and population, came in as statistically significant in the baseline model. As expected, wealthier countries and more populous countries presided over more intensive adoption of computing technologies. The null result for regime type is worth highlighting, as the effects of democracy on technology adoption are disputed.39 Finally, contrary to the expectations of competing explanations to GPT diffusion theory, the effects of military spending and VoC are insignificant. This is consistent with the findings from the historical case studies.

    Quantitative appendix table 1 displays the results after incorporation of three additional controls. First, trade linkages expose countries to advanced techniques and new ideas, opening the door to technology diffusion. A high level of trade openness has been associated with more intensive adoption of information technologies.40 Relatedly, there is evidence that a country’s openness to international trade has a positive and sizable effect on various measures of innovation, including high-technology exports, scientific publications, and patents.41 Second, higher urban density has been linked to faster diffusion of technologies such as the television and the internet.42 Model 8 incorporates a trade openness variable and an urbanization variable.

    Third, patterns at the regional level could shape how computerization spreads around the world. Scholars have identified such regional effects on the diffusion of ideas, policies, and technologies.43 In model 9, I assess spatial dynamics with dummy variables for the following regions: East Asia and Pacific; Europe and Central Asia; Latin America and Caribbean; the Middle East and North Africa; North America; South Asia; and sub-Saharan Africa.44 The positive effect of GPT skill infrastructure on computerization stays strong and highly statistically significant across these two models.

    To ensure that the results were not determined by my choice of independent variable, I constructed an alternative specification of GPT skill infrastructure. Reanalyzing data on 467,198 software engineering publications, I counted the number of distinct authors for each country, as a proxy for the breadth of researchers who could foster human capital in software engineering. Though I still maintain that the primary specification best captures software engineering skill infrastructure, this alternative construction guards against possible issues with institution-based indicators, such as problems with institutional disambiguation and nonstandardized author affiliations.45 With this alternative specification, the estimated effect of GPT skill infrastructure on computerization remains positive and significant for the baseline model as well as for models with additional controls.46

    Duration Analysis

    When it comes to whether great powers can harness the potential of GPTs for productivity growth, the speed of adoption—not just the intensity of adoption—is pertinent. In the historical case studies, technological leaders adapted more quickly to industrial revolutions because of their investments in widening the base of engineering knowledge and skills associated with GPTs. This leads to the second hypothesis.

    H2: Countries with higher levels of GPT skill infrastructure in software engineering will experience faster levels of computer adoption.

    In testing this hypothesis, the dependent variable shifts to the amount of time it takes for a country to reach a particular computerization rate. A critical step is to establish both the specific computerization rate that constitutes successful “adoption” of computers as well as when the process of diffusion begins. Regarding the former, I count the “first adoption” of computerization as when the proportion of households with a computer in a country reaches 25 percent. This approach is in line with Everett Rogers’s seminal work on the S-shaped curve for successful diffusion of an innovation, which typically takes off once the innovation reaches a 10 to 25 percent adoption rate.47 For the duration analysis, since many of the countries enter the dataset with levels of computer adoption higher than 10 percent, the 25 percent level threshold is more suitable.48

    I take 1995 as the starting point for the diffusion of computers as a GPT. Though an earlier date may be more historically precise, the 1995 date is more appropriate for modeling purposes, as the data on computerization rates for countries before this time are sparse. In a few cases, a country clearly achieved the 25 percent computerization threshold before 1995.49 As a practical measure to estimate the duration models, I assume that the time it took for these countries to adopt computers was one year. Right-censoring occurs with the last year of data, 2020, as many countries still had not reached the 25 percent computerization rate.

    Using these data, I employ a Cox proportional hazards model to estimate the time it takes for countries to reach a 25 percent computerization rate based on the start date of 1995. Often used by political scientists to study conflict duration or the survival of peace agreements, duration models are also commonly used to investigate the diffusion of new technologies and to determine why some firms take longer to adopt a certain technology than others.50 Freed from the demands of TSCS analysis for yearly data, the duration analysis expands the county coverage, incorporating all upper-middle-income economies or high-income economies, based on the World Bank’s income group classifications.51 The resulting sample, which excludes countries that never attained the 25 percent computerization threshold, includes seventy-six countries.

    Table 6.2 reports the estimated coefficients from the duration analysis. Positive coefficients match with a greater likelihood of reaching the computerization threshold. I use the same explanatory variable and controls as the baseline model from the TSCS analysis. These variables all enter the model with their measures in 1995. Model 4a takes the 25 percent computerization rate as the adoption threshold, while model 4b adjusts it to 20 percent to ensure that this designation is not driving results.

    Table 6.2  Time to Computerization by Country

    Dependent Variable
    25% Threshold(4a)20% Threshold(4b)
    GPT skill infrastructure0.673***0.517***
    (0.137)(0.119)
    GDP per capita1.186***1.110***
    (0.335)(0.288)
    Total population0.1270.062
    (0.085)(0.074)
    Polity score0.0220.024
    (0.025)(0.023)
    Military spending0.017−0.042
    (0.218)(0.198)
    Liberal market economy0.7600.785
    (0.503)(0.493)
    N (number of events)76 (74)83 (83)
    Likelihood ratio test (df = 6)112.9***111.2***
    Note: Standard errors in parentheses.
    *p < .10; **p < .05; ***p < .01

    As the models demonstrate, the effect of GPT skill infrastructure on the speed by which countries achieve computerization is positive and highly statistically significant, providing support for hypothesis 2. Based on model 4a’s hazard ratio for the independent variable (1.96) for a given year, a tenfold increase in software engineering university density doubles the chances of a country reaching the computerization threshold.52 These results hold up after introducing additional control variables (quantitative appendix table 3).53

    A Cross-Sectional Approach: Averages across the 1995–2020 Period

    As an additional check, I collapse the panel dataset into cross-sectional averages of GPT skill infrastructure and computerization over the 1995–2020 period in a large sample of countries. In certain aspects, cross-sectional evidence could be more appropriate for comprehending the impact of features, like skill formation institutions, that are difficult to capture in yearly intervals because they change gradually.54 This approach allows for more countries to be included, as the yearly data necessary for TSCS analysis were unavailable for many countries. Limiting the sample based on the same scope conditions as the duration analysis leaves 127 countries.

    I also include the same set of controls used in the previous analyses of the panel data: GDP per capita, total population, regime type, military spending, and liberal market economies. I employ ordinary least squares (OLS) regression to estimate the model. Since both a scale-location plot and a Breausch-Pagan test demonstrate that heteroskedasticity is not present in the data, it is appropriate to use an OLS regression estimator with normal standard errors.

    The results of the regression analysis provide further support for the theoretical expectations. To recap, the independent variable is the estimated average skill infrastructure for software engineering between 1995 and 2020, and the dependent variable is the average computerization rate during the same period. Since analyzing bibliographic information on yearly software engineering publications for 127 countries is a demanding exercise, I estimated the average number of universities that nurture software engineering skills based on publications for the two middle years (2007, 2008) in the dataset, instead of deriving this average based on publication data across the entire time range.55 Table 6.3 displays the results, with the incremental inclusion of control variables, also averaged over the period 1995–2020, across three models.56 The coefficient on the GPT skill infrastructure measure remains positive and highly statistically significant across all three models (p < .01).

    Table 6.3 GPT Skill Infrastructure Predicts More Computerization

    Dependent Variable
    Computerization
    (5)(6)(7)
    GPT skill infrastructure3.211***3.737***3.761***
    (0.528)(0.609)(0.649)
    GDP per capita16.617***15.536***14.977***
    (1.564)(1.723)(1.812)
    Total population−1.647***−0.739−0.831*
    (0.440)(0.485)(0.500)
    Polity score−0.066−0.070
    (0.147)(0.180)
    Military spending0.577
    (1.604)
    Liberal market economy5.017
    (4.269)
    Constant−83.099***−86.165***−79.773***
    (20.577)(22.316)(23.636)
    Observations127112110
    R20.8120.8330.834
    Note: Standard errors in parentheses.
    *p < .10; **p < .05; ***p < .01

    I perform several additional tests to confirm the robustness of the results. I first include the same additional controls used in the preceding TSCS analysis. Quantitative appendix table 4 shows that the main findings are supported. One limitation of models that rely on cross-sectional averages is endogeneity arising from reverse causality. In other words, if greater diffusion of computers throughout the economy spurs more investment in institutions that broaden the pool of software engineers, then this could confound the baseline model’s estimates. To account for this possibility, in model 16 in quantitative appendix table 5, I operationalize GPT skill infrastructure using the estimate for the year 1995, the start of the period, instead of its average level over the 1995–2020 period.57 Thus, this model captures the impact of GPT skill infrastructure in 1995 on how computerization progressed over the remaining sample years.58 The effect remains positive and statistically significant.

    While this chapter’s primary aim is to investigate empirical patterns expected by GPT diffusion theory, the quantitative analysis can also probe whether computerization is positively influenced by institutions that complement LS product cycles. To that end, I add a control variable that stands in for the institutional competencies prioritized by the LS model.59 Measures of computer exports and ICT patents serve as two ways to capture a country’s ability to generate novel innovations in the computer industry.60 In the resulting analysis, the LS-linked variables do not register as statistically significant.61

    These results should be interpreted with care. In many cases, measures of institutional capacity to build a strong, innovative computer sector may be highly correlated with measures of GPT skill infrastructure. Because the statistical approach struggles to differentiate between the causal processes that connect these two features and computerization, the historical case studies take on the main burden of comparing the GPT diffusion and LS mechanisms against each other. Still, the inclusion of variables linked with the LS mechanism does suggest that, in the context of computerization, there is ineffectual evidence that the presence of a strong leading sector spills over into other sectors and generates multiplier effects—a key observable implication of LS accounts.62 Additionally, these models drive home the importance of differentiating between institutions linked to innovative activities (in the sense of introducing new products and processes) and engineering-oriented institutions, like GPT skill infrastructure, which are more connected to adoptive activities.63

    Summary

    Using a variety of statistical methods, this chapter tested the expectation that countries better equipped to widen the base of engineering talent in a GPT will be more successful at diffusing that GPT throughout their economies. The combination of TSCS models, duration analysis, and cross-sectional regressions lends credence to the strength of the relationship between GPT skill infrastructure and computerization. The results hold across a range of additional tests and robustness checks.

    There are two major limitations to this chapter’s approach. First, the statistical analysis should be interpreted mainly as an independent evaluation of GPT diffusion theory, not as an additional comparison between GPT diffusion theory and the causal pathway linked to LS product cycles. In the large-scale statistical analysis, indicators linked with the LS mechanism can also be associated with higher computerization rates, making it difficult to weigh the two explanations against each other. The rich historical detail in the case studies therefore provides the prime ground for tracing causal mechanisms.

    Second, this chapter evaluates only one aspect of GPT skill infrastructure. A more comprehensive assessment would include not just the capacity to widen the pool of software engineering talent, which was the independent variable in this analysis, but also the strength of information flows between the GPT sector and application sectors. For instance, in the IR-2 case, both the United States and Germany trained large numbers of mechanical engineers, but American technological institutes placed more emphasis on practical training and shop experience, which strengthened connections between the US mechanical engineering education system and industrial application sectors. Building on data collection efforts that are starting to measure these types of linkages, such as the proportion of publications in a technological domain that involve industry-academia collaborations, future research should conduct a more complete assessment of GPT skill infrastructure.64

    Notwithstanding these limitations, the quantitative analysis backs a key observable implication of GPT diffusion theory: advanced economies’ level of GPT skill infrastructure is strongly linked to GPT adoption rates. Not only does this provide some initial support for the generalizability of this book’s central argument beyond just great powers, but it also gives further credibility to GPT diffusion theory’s relevance to US-China competition today.

    7 US-China Competition in AI and the Fourth Industrial Revolution

    THE FIRST MACHINE to defeat a human Go champion; powerful language models that can understand and generate humanlike text; a computer program that can predict protein structures and enable faster drug discovery—these are just a few of the newest discoveries in AI that have led some to declare the arrival of a Fourth Industrial Revolution (IR-4).1 As for the latest geopolitical trends, China’s rise has been the dominant story of this century as national security establishments grapple with the return of great power competition. Located squarely at the intersection of these two currents, the US-China technological rivalry has become an inescapable topic of debate among those interested in the future of power—and the future itself.

    Who will lead the way in the Fourth Industrial Revolution? To answer this question, leading thinkers and policymakers in the United States and China are both drawing lessons from past technology-driven power transitions to grapple with the present landscape. Unfortunately, as this chapter argues, they have learned the wrong lessons. Specifically, the leading-sector perspective holds undue influence over thinking about the relationship between technological change and the possibility of a US-China power transition. Yet careful tracing of historical cases and statistical analysis have revealed that the GPT mechanism provides a better model for how industrial revolutions generate the potential for a power transition. When applied to the IR-4 and the evolving US-China power relationship, GPT diffusion theory produces different insights into the effects of the technological breakthroughs of today on the US-China power balance, as well as on the optimal strategies for the United States and China to pursue.

    This chapter sketches out the potential impacts of today’s emerging technologies on the US-China power balance. I first describe the current productivity gap between the United States and China, with particular attention to concerns that the size of this gap invalidates analogies to previous rising powers. Next, I review the array of technologies that have drawn consideration as the next GPT or next LS. Acknowledging the speculative nature of technological forecasting, I narrow my focus to developments in AI because of its potential to revitalize growth in ICT industries and transform the development trajectories of other enabling technologies.

    The essence of this chapter is a comparison of the implications of the LS and GPT mechanisms for how advances in AI will affect a possible US-China economic power transition. In contrast to prevailing opinion, which hews closely to the LS template, GPT diffusion theory suggests that the effects of AI on China’s rise will materialize through the widespread adoption of AI across many sectors in a decades-long process. The institutional factors most pertinent to whether the United States or China will more successfully benefit from AI advances are related to widening the base of AI-related engineering skills and knowledge. I also spell out how the implications of GPT diffusion theory for the US-China power balance differ from those derived from alternative explanations.

    The objective here is not to debate whether China will or will not catch up to the United States, or whether technological capabilities are more significant than all other considerations that could affect China’s long-term economic growth. Rather, this chapter probes a more limited set of questions: If emerging technologies were to significantly influence the US-China economic power balance, how would this occur? Which country is better positioned to take advantage of the Fourth Industrial Revolution? What would be the key institutional adaptations to track?2

    A Power Transition in Progress?

    Over the past four decades, there has been no greater shift in the global economic balance than China’s rise. China is either already the world’s largest economy, if measured by purchasing power parity (PPP) exchange rates, or is projected to soon overtake the United States, based on nominal exchange rates.3 China’s impressive economic growth has led many to proclaim that the era of US hegemony is over.4

    Economic size puts China in contention with the United States as a great power competitor, but China’s economic efficiency will determine whether a power transition occurs. Countries like Switzerland outpace the United States on some measures of economic efficiency, but they lack the economic size to contend. Other rising powers, such as India, boast large economies but lag far behind in economic efficiency. Mike Beckley concludes: “If the United States faces a peer competitor in the twenty-first century … it will surely be China.”5 For this conditional to be true, China’s productivity growth is critical.6 This is not the first time in history that China has boasted the world’s largest economy; after all, it held that distinction even as Britain was taking over economic leadership on the back of the First Industrial Revolution.

    Where does China currently stand in comparison to the productivity frontier? Based on 2018 figures, China’s real GDP per capita (at 2010 PPPs) is about 30 percent that of the United States.7 From 2000 to 2017, China’s total factor productivity (TFP) never surpassed 43 percent of US TFP (figure 7.1).8 In 2015, labor productivity in China remained at only 30 percent of that in the United States, though this figure had doubled over the past two decades.9

    These numbers suggest that China sits much further from the productivity frontier than past rising powers. If the US-China power relationship is fundamentally different from those in previous eras, the relevance of conclusions from previous cases could be limited.10 For instance, in the early years of the IR-1, Britain was only slightly behind the Netherlands, the productivity leader at the time. The United Kingdom’s GDP per capita was 80 percent of Dutch GDP per capita in 1800.11 Similarly, at the beginning of the IR-2, the United States trailed Britain in productivity by a small margin. In 1870, labor productivity and TFP in the United States were 90 percent and 95 percent, respectively, of labor productivity and TFP in Britain.12 During the 1870s, average GDP per capita in the United Kingdom was about 15 percent higher than average GDP per capita in the States.13

    FIGURE 7.1. US-China Productivity Gap (2000–2017). Source: Penn World Table version 9.1; Feenstra, Inklaar, and Timmer 2015.

    Still, that China could surpass the United States in productivity leadership is not outside the realm of possibility.14 The IR-3 case is a better comparison point for the current productivity gap between the United States and China. In 1960, the start of the IR-3 case, Japanese GDP per capita was 35 percent of US GDP.15 At the time, Japan’s TFP was 63 percent of US TFP, and Japan’s labor productivity was only 23 percent of US labor productivity, a lower proportion than the China-US ratio at present.16 Despite the initial chasm, the TFP gap between the United States and Japan narrowed to only 5 percent in 1991.17

    Indeed, productivity growth is crucial for China to sustain its economic rise in the long term. For the 1978–2007 period, Xiaodong Zhu decomposed the sources of China’s economic growth into labor deepening, human capital, capital deepening, and total factor productivity growth. He found that growth in TFP accounted for 78 percent of China’s growth in GDP per capita.18 The burden on TFP improvements will only increase, given the diminishing impact of other drivers of China’s growth miracle, such as urbanization and demographic dividends.19

    Whether China can sustain productivity growth is an open question. Plagued by inefficient infrastructure outlays, China’s aggregate TFP growth declined from 2.8 percent in the decade before the global financial crisis to 0.7 percent in the decade after (2009–2018).20 If calculated using alternative estimates of GDP growth, China’s TFP growth was actually negative from 2010 to 2017, averaging −0.5 percent.21 This productivity slowdown is not unique to China. Even before the 2008 global financial crisis, there was a slowdown in TFP growth among advanced economies due to waning effects from the information and communications technologies (ICTs) boom.22 China’s labor productivity growth also decelerated from 8.1 percent in 2000–2007 to 4.2 percent in 2011–2019, but the later period’s slowed growth rate was still six times greater than the US labor productivity growth rate in the same period.23

    Adaptation to technological advances will be central to China’s prospects of maintaining high rates of productivity growth. China’s leaders worry about getting stuck in the “middle-income trap,” a situation in which an economy is unable to advance to high-income status after it exhausts export-driven, low-cost manufacturing advantages. Many studies have stressed the linkage between China’s capacity to develop and absorb emerging technologies and its prospects for escaping the middle-income trap.24 The Chinese government also increasingly pushes the development and adoption of information technology and other cutting-edge technologies as a way to increase TFP.25 Thus, tracking China’s future productivity growth necessitates a better understanding of specific technological trajectories in the current period.

    Key Technological Changes in the IR-4

    ICTs, the key technological drivers of the IR-3, are still at the heart of the IR-4. From visionaries and daydreamers to economists and technology forecasters, there is a wide-ranging consensus that AI will breathe new life into the spread of digitization. The World Economic Forum calls AI the “engine that drives the Fourth Industrial Revolution.”26 Kai-Fu Lee, the former head of Google China, boldly asserts, “The AI revolution will be on the scale of the Industrial Revolution but probably larger and definitely faster.”27 To further explore AI’s role in the IR-4, I examine this technological domain as a source of both GPT and LS trajectories.

    Candidate Leading Sectors

    LS accounts forecast that in future waves of technological change ICTs will continue to drive economic transformation. According to one analysis of US-China technological rivalry in the twenty-first century, ICTs are “widely regarded as the current leading sector.”28 I reviewed five key texts that predicted future leading sectors, all written by scholars who study historical cycles of technological change and global leadership transitions.29 These forecasts also highlight other candidate leading sectors, including lasers and new sources of energy, but they converge on ICTs as the leading sector of the next wave of technological disruption.

    Informed by the LS model, AI’s effects on global technological competition are often framed through its potential to open up new opportunities for latecomers to catch up and leapfrog advanced countries in key segments like AI chips. China’s national AI development plan outlines its ambition to become the world’s leading center of innovation in AI by 2030.30 Scholars analyze China’s capacity to develop global intellectual monopolies in certain AI applications and enhance independent innovation in AI so as to guard against other countries leveraging weaponized interdependence.31 Descriptions of China’s AI strategy as aimed toward seizing “the commanding heights” of next-generation technologies reflect the belief that competition in AI will be over global market shares in strategic sectors.32

    Candidate GPTs

    Among the possible GPTs that could significantly impact a US-China economic power transition, AI stands out. Like the literature on leading sectors, the GPT literature also converges on ICTs as a continued driver of technological revolution. Kenneth Carlaw, Richard Lipsey, and Ryan Webb, three pioneers of GPT-based analysis, identify programmable computing networks as the basic GPT that is driving the modern ICT revolution.33 Crucially, AI could open up a new trajectory for this ICT revolution. Recent breakthroughs in deep learning have improved the ability of machines to learn from data in fundamental ways that can apply across hundreds of domains, including medicine, transportation, and other candidate GPTs like biotechnology and robotics. This is why AI is often called the “new electricity”—a comparison to the prototypical GPT. Economists regard it as the “next GPT”34 and “the most important general-purpose technology of our era.”35

    Several studies have found evidence for a GPT trajectory in AI. One study, using a novel dataset of preprint papers, finds that articles on deep learning conform with a GPT trajectory.36 Using patent data from 2005 to 2010 to construct a three-dimensional indicator for the GPT-ness of a technology, Petralia ranks technological classes based on their GPT potential.37 His analysis finds that image analysis, a field that is closely tied to recent advances in deep learning and AI, ranks among the top technological classes in terms of GPT-ness.38 Another effort employs online job posting data to differentiate among the GPT-ness of various technological domains, finding that machine learning technologies are more likely to be GPTs than other technologies such as blockchain, nanotechnology, and 3D printing.39

    To be sure, forecasts of future GPTs call attention to other technological trends as well. Other studies have verified the GPT potential of biotechnology.40 Robotics, another candidate GPT for the IR-4, could underpin “the next production system” that will boost economy-wide productivity, succeeding the previous one driven by information technology.41 While my primary reasons for limiting my analysis to AI are based on space constraints as well as on its prominence in the surrounding literature, it is also important to note that developments in both biotechnology and robotics are becoming increasingly dependent on advances in deep learning and big data.42

    The Limits of Technological Foresight

    Unlike exercises to pinpoint key technologies of previous industrial revolutions, which benefited from hindsight, identifying technological drivers in the IR-4 is a more speculative exercise. It is difficult to find true promise amid the hype. The task is made harder by the fact that even experts and technological forecasting bodies regularly miss the next big thing. In 1945, a team led by Dr. Theodore von Kármán, an eminent aerospace engineer, published Toward New Horizons, a thirty-two-volume text about the future of aviation. The study failed to foresee major new horizons such as the first human in space, intercontinental ballistic missiles (ICBMs), and solid-state electronics—all of which emerged within fifteen years.43 In the early 1990s, the US Army conducted a technology forecast assessment to identify the technologies most likely to transform ground warfare in the next century. When the forecast was evaluated in 2008 by the Army’s senior scientists and engineers, it graded out at a “C.” Among its most significant misses was the development of the internet.44

    I am no Cassandra. It is very possible that if I were writing this book in 2000, this chapter would focus on the promise of nanotechnology, not AI. At that time, President Bill Clinton had just unveiled the National Nanotechnology Initiative. In a 2003 speech, Philip J. Bond, the undersecretary for technology at the Department of Commerce at the time, declared:

    Nano’s potential rises to near Biblical proportions. It is not inconceivable that these technologies could eventually achieve the truly miraculous: enabling the blind to see, the lame to walk, and the deaf to hear; curing AIDS, cancer, diabetes and other afflictions; ending hunger; and even supplementing the power of our minds, enabling us to think great thoughts, create new knowledge, and gain new insights.45

    Decades later, there is a collective exhaustion around the hype surrounding nanotechnology, a phenomenon one scientist calls “nanofatigue.”46

    One lesson that stands out from mapping the technological landscape in past industrial revolutions is that the most significant GPTs of an era often have humble origins. In the IR-2, new innovations in electricity and chemicals garnered the most attention, but America’s economic rise owed more to advances in machine tools that were first introduced decades earlier. In the same way, “old” GPTs like electricity could still shock the world.47 Today there is still a lot of potential for expanded industrial electrification, which could have a major impact on productivity.48 Similarly, high-capacity battery technologies could transform productivity on a broad scale.49 Interestingly, patent data also demonstrate the continued importance of electrical technologies. Among the top ten GPT candidates as ranked by Petralia’s indicator of “GPT-ness,” there were as many technological classes in the electrical and electronic category as there were in the computers and communications category.50

    For my purposes, it is reassuring that developments in AI also draw on a long history. In the United States, the legitimization of AI as an important field of research dates back to the 1960s.51 Thus, though the rest of the chapter takes AI as the most important GPT for the near future, it does so with a humble mindset, acknowledging that looking forward to the future often starts with digging deeper into the past.

    The GPT vs. LS Mechanisms in the IR-4

    There has been no shortage of speculation about whether the United States or China is better fit for the new AI revolution. Each week it seems there is a new development in the “AI arms race” between the two nations.52 Many believe that China is an AI superpower on the verge of overtaking the United States in the key driver of the IR-4.53 As the following sections will show, these discussions tend to follow the LS template in their assumptions about the trajectory of AI development and key institutional adjustments.

    Conversely, GPT diffusion theory provides an alternative model for how AI could affect the US-China power balance, with implications for the optimal institutional adaptations to the AI revolution. I conclude that, if the lessons of past industrial revolutions hold, the key driver of a possible US-China economic power transition will be the relative success of these nations in diffusing AI throughout their economies over many decades. This technological pathway demands institutional adaptations to widen the base of AI engineering skills and knowledge. While recognizing that international competition over AI is still in the early innings, this chapter outlines a preliminary framework for assessing which country’s roster is better equipped for success.

    Impact Timeframe: The Decisive Years in the US-China AI Competition

    If guided by the LS mechanism, one would expect the impacts of AI on US-China power competition to be very significant in the early stages of the technology’s trajectory. Indeed, many prominent voices have articulated this perspective. Consider, for example, a report titled “Is China Beating the US to AI Supremacy?,” authored by Professor Graham Allison, the director of Harvard Kennedy School’s Belfer Center for Science and International Affairs and Eric Schmidt, former CEO of Google and cochair of the National Security Commission on Artificial Intelligence (NSCAI). For Allison and Schmidt, the decisive years in US-China AI competition are just around the corner. Assuming that AI advances will be rapidly adopted across many economic domains, their aim is to “sound an alarm over China’s rapid progress and the current prospect of it overtaking the United States in applying AI in the decade ahead.”54 Shaped by a similar framework, other influential texts also predict that China’s productivity boost from AI will come to fruition in the 2020s.55

    If GPT diffusion theory serves as the basis for analysis, these influential texts severely underestimate the time needed for economic payoffs from AI. Historical patterns of GPT advance have borne out that, even in early adopter countries, it takes at least three or four decades for these fundamental technologies to produce a significant productivity boost.56

    Using this pattern as guidance, we can roughly project AI’s impact timeframe, after establishing an initial emergence date for this GPT. In 2012, the AlexNet submission to ImageNet, a competition that evaluates algorithms on large-scale image classification, is widely recognized as spurring this current, deep learning–based paradigm of AI development.57 If using the metric of when a GPT achieves a 1 percent adoption rate in the median sector, the AI era probably began in the late 2010s.58 As of 2018, according to the most recent census survey on the extent of AI adoption in the US economy, only 2.75 percent of firms in the median sector reported using AI technologies.59 Thus, regardless of which arrival date is used, if AI, like previous GPTs, requires a prolonged period of gestation, substantial productivity payoffs should not materialize until the 2040s and 2050s.60

    Of course, other factors could affect AI’s expected impact timeframe, including the possibility that the general process of technological adoption is accelerating. Some evidence indicates that the waiting time for a significant productivity boost from a new GPT has decreased over time.61 Lee argues that the AI revolution will be faster than previous GPT trajectories owing to the increasingly frictionless distribution of digital algorithms and more mature venture-capital industry.62 Nevertheless, preliminary evidence suggests that AI will face similar implementation lags as previous GPTs, including bottlenecks in access to computing resources, human capital training, and business process transformations.63

    Phase of Relative Advantage: Innovation-centrism and China’s AI Capabilities

    Debates about China’s scientific and technological power reduce complex dynamics to one magic word—“innovation.”64 Whether China can generate novel technologies is often the crux of debates over China’s growing scientific and technological capabilities and a potential US-China power transition.65 For David Rapkin and William Thompson, the prospect of China overtaking the United States as the leading power is dependent on “China’s capacity to innovate”—specifically as it relates to revolutionary technological changes that allow challengers to leapfrog the leader in economic competition.66 “If … China’s innovativeness continues to lag a considerable distance behind that of the US, then China overtaking the US might wait until the twenty-second century,” they posit.67 China’s innovation imperative, as Andrew Kennedy and Darren Lim describe it in language common to LS analysis, is motivated by “monopoly rents generated by new discoveries.”68

    Innovation-centric views of China’s AI capabilities paint an overly optimistic picture of China’s challenge to US technological leadership. Allison and Schmidt’s Belfer Center paper, for instance, emphasizes China’s growing strength in AI-related R&D investments, leading AI start-ups, and valuable internet companies.69 Likewise, the NSCAI’s final report suggests that China is poised to overtake the United States in the capacity to generate new-to-the-world advances in AI, citing shares of top-cited, breakthrough papers in AI and investments in start-ups.70 These evaluations match up with viewpoints that are bullish on China’s overall technological capabilities, which also point to its impressive performance along indicators of innovation capacity, such as R&D expenditures, scientific publications, and patents.71

    Some other comparisons of US and Chinese AI capabilities arrive at the opposite conclusion but still rely on the LS template. For instance, two Oxford scholars, Carl Frey and Michael Osborne, have likened claims that China is on the verge of overtaking the United States in AI to overestimates of Japan’s technological leadership in computers in the 1980s. In their view, just like Japan, China will fail to overtake the United States as the world’s technological leader because of its inability to produce radical innovations in AI. In fact, they claim, the prospects are even bleaker this time around: “China, if anything, looks less likely to overtake the United States in artificial intelligence than Japan looked to dominate in computers in the 1980s.”72

    If analysis of US-China competition in AI was centered on GPT diffusion theory, it would focus more on China’s capacity to widely adopt AI advances. In this scenario, it is neither surprising nor particularly alarming that China, like other great power contenders such as Japan in the IR-3, Germany in the IR-2, and France in the IR-1, contributes to fundamental innovations. No one country will corner all breakthroughs in a GPT like AI. The key point of differentiation will be the ability to adapt and spread AI innovations across a wide array of sectors.

    A diffusion-centric perspective suggests that China is far from being an AI superpower. Trends in ICT adoption reveal a large gap between the United States and China. China ranks eighty-third in the world on the International Telecommunication Union’s ICT development index, a composite measure of a country’s level of networked infrastructure, access to ICTs, and adoption of ICTs.73 By comparison, the United States sits among the world’s leaders at fifteenth. Though China has demonstrated a strong diffusion capacity in consumer-facing ICT applications, such as mobile payments and food delivery, Chinese businesses have been slow to embrace digital transformation.74

    In fact, it is often Chinese scholars and think tanks that acknowledge these deficiencies. According to an Alibaba Research Institute report, China significantly trails the United States in penetration rates of many digital technologies across industrial applications, including digital factories, industrial robots, smart sensors, key industrial software, and cloud computing.75 China also significantly trails the United States in an influential index for adoption of cloud computing, which is essential to implementing AI applications.76 In 2018, US firms averaged a cloud adoption rate of over 85 percent, more than double the comparable rate for Chinese firms.77

    To be fair, China has achieved some success in adopting robots, a key application sector of AI. China leads the world in total installations of industrial robots. Aided by favorable industry composition and demographic conditions, China added 154,000 industrial robots in 2018, which was more than were installed by the United States and Japan combined.78 Based on 2021 data from the International Federation of Robotics, China outpaces the United States in robot density as measured by the number of industrial robots per 10,000 manufacturing employees.79

    However, China’s reputed success in robot adoption warrants further scrutiny. The IFR’s figures for employees in China’s manufacturing sector significantly underestimate China’s actual manufacturing workforce. If these figures are revised to be more in line with those from the International Labor Organization (ILO), China’s robot density would fall to less than 100 robots per 10,000 manufacturing employees, which would be around one-third of the US figure.80 On top of that, talent bottlenecks hamper robot diffusion in China, since skilled technicians are required to reprogram robots for specific applications.81 An unused or ineffective robot counts toward robot density statistics but not toward productivity growth.

    Breadth of Growth: Picking Winners vs. Horizontal Approaches to AI Development

    Divergent perspectives on the breadth of growth in technological revolutions also separate LS-based and GPT-based views of the US-China case. If technological competition in the IR-4 is limited to which country gets a bigger share of the market in new leading industries like AI, then direct sectoral interventions in the mold of China’s AI strategy could be successful. However, if the breadth of growth in the IR-4 follows the GPT trajectory of the three previous industrial revolutions, another approach will be more effective.

    China’s AI strategy has hewed closely to the LS model. This approach builds off a series of directives that prioritize indigenous innovation in select frontier technologies, an emphasis that first appeared in the 2006 “National Medium- and Long-Term Plan for the Development of Science and Technology” and extends through the controversial “Made in China 2025” plan.82 Since the mid-2000s, the number of sectoral industrial policies issued by the State Council, China’s cabinet-level body, has significantly increased.83 Appropriately, the State Council’s 2017 AI development plan outlined China’s ambitions to become the world’s primary innovation center for AI technology.84

    On the breadth of growth dimension, tension between GPT diffusion theory and China’s application of the LS template is rooted in differing expectations for how the economic boost from revolutionary technologies will unfold. Take, for example, China’s 2010 “Strategic Emerging Industries” (SEI) initiative, which targets seven technological sectors based on opportunities for China to leapfrog ahead in new industries.85 Oriented around assumptions that a limited number of technologically progressive industries will drive China’s future growth, the SEI defines success based on the resultant size of these industries, as measured by their value added as a share of GDP.86

    In contrast, GPT diffusion theory expects that, in the country that best capitalizes on the IR-4, productivity growth will be more dispersed. In this view, the AI industry never needs to be one of the largest, provided that AI techniques trigger complementary innovations across a broad range of industries. Relatedly, some Chinese thinkers have pushed back against industrial policies that favor narrow technology sectors. A research center under China’s own State Council, in a joint analysis with the World Bank, concluded in 2012: “A better innovation policy in China will begin with a redefinition of government’s role in the national innovation system, shifting away from targeted attempts at developing specific new technologies and moving toward institutional development and an enabling environment that supports economy-wide innovation efforts within a competitive market system.”87 The economy-wide transformation enabled by AI, if it lives up to its potential as a GPT, demands a more broad-based response.

    When it comes to technology policy, there is always a push and pull between two ends of a spectrum. Vertical industrial policy, or “picking winners,” targets certain technologies, often leading to top-down intervention to ensure that the nation’s firms are competitive in specific industries. Horizontal industrial policy promotes across-the-board technological development and avoids labeling certain technologies as more significant than others. This book argues that both camps have it partly right, at least when it comes to ensuring long-term economic growth in times of technological revolution. Picking technological winners is needed in that some technologies do matter more than others; however, the “winners” are GPTs, which require horizontal industrial policies to diffuse across many application sectors. Institutions for skill formation in AI engineering, the subject of the next section, split the difference between these two approaches.

    Institutional Complementarities: GPT Skill Infrastructure in the IR-4

    In 2014, Baidu, one of China’s leading tech giants, hired Andrew Ng away from Google, poaching the cofounder of Google’s deep learning team. Three years later, Baidu lured Qi Lu away from Microsoft, where he had served as the architect of the company’s AI strategy. Their departures were headline news and spurred broader discussions about China’s growing AI talent.88

    When Alibaba, another one of China’s tech giants, celebrated its listing on the Hong Kong stock exchange in November 2019, it showcased a different form of AI talent. In one picture of the gong-ringing celebration, Yuan Wenkai, who works for an Alibaba-owned logistics warehouse, stood third from the right. A former tally clerk who graduated from a run-of-the-mill Guangdong vocational school, Yuan holds substantial expertise in automation management. His success with boosting the sorting capacity of a logistics warehouse by twenty thousand orders per hour—responding to elevated demand during the shopping frenzy of Single’s Day (November 11)—merited an invite to the ceremony.89

    Even as AI systems exceed human-level performance at tasks ranging from playing Go to translating news articles, human talent will remain crucial for designing and implementing such systems.90 According to one global survey of more than three thousand business executives, landing the “right AI talent” ranked as the top barrier to AI adoption for companies at the frontier of incorporating AI into their products, services, and internal processes.91 But what makes up the “right AI talent”? In its distilled form, GPT diffusion theory suggests that China’s chance of leading the AI revolution rests more on the Yuan Wenkais of the world than the Andrew Ngs. The most important institutional adjustments to the IR-4 are those that widen the pool of AI engineering skills and knowledge.

    Indeed, alongside the maturation of the AI field, recent reports have emphasized skills linked to implementing theoretical algorithms in practice and in ways suited for large-scale deployment. In early 2022, the China Academy of Information and Communications Technology (CAICT), an influential research institute under the Ministry of Industry and Information Technology, published two reports that identified AI’s “engineering-ization” (工程化) as a significant trend that involves addressing challenges in transforming AI-based projects from prototypes to large-scale production.92 Relatedly, per Burning Glass job postings from 2010 to 2019, the overall increase in demand for “AI-adjacent” positions in the United States far exceeded that for “core AI” positions.93 Covering skills needed to implement AI throughout many sectors and legacy systems, this pool of AI-adjacent jobs includes positions for systems engineers and software development engineers.

    GPT Skill Infrastructure for AI: A US-China Comparison

    At present, the United States is better positioned than China to develop the skill infrastructure suitable for AI. First, the United States has more favorable conditions for expanding the number of AI engineers. According to three separate projects that mapped out the global AI talent landscape, many more AI engineers work in the United States than in any other country.94 In 2017, Tencent Research Institute and BOSS Zhipin (a Chinese online job search platform) found that the number of AI “practitioners” (从业者) in the United States far surpassed the corresponding Chinese figure. Figure 7.2 captures this gap across four key AI subdomains: natural language processing (by three times), chips and processors (by fourteen times), machine learning applications (by two times), and computer vision (by three times).95 Overall, the total number of AI practitioners in the United States was two times greater than the corresponding figure for China.96 Furthermore, data from two separate reports by LinkedIn and SCMP Research confirm that the United States leads the world in AI engineers.97

    In addition to statistics on the AI workforce, the quantity and quality of AI education is another consideration for which country is better positioned to develop GPT skill infrastructure for AI. Again, the United States leads China by a significant margin in terms of universities with faculty who are proficient in AI. In 2017, the United States was home to nearly half of the world’s 367 universities that provide AI education, operationalized by universities that have at least one faculty member who has published at least one paper in a top AI conference.98 In comparison, China had only 20 universities that met this standard. After replicating this methodology for the years 2020–2021, the US advantage is still pronounced, with 159 universities to China’s 29 universities.99

    FIGURE 7.2. A US-China Comparison of AI Practitioners in Key Subdomains. Source: Tencent Research Institute and BOSS Zhipin 2017.

    These findings contradict prominent views on the present global distribution of AI engineering talent. In his best-selling book AI Superpowers: China, Silicon Valley, and the New World Order, Kai-Fu Lee argues that the current AI landscape is shifting from an age of discovery, when the country with the highest-quality AI experts wins out, to an age of implementation, when the country with the largest number of sound AI engineers is advantaged.100 In an age of implementation, Lee concludes, “China will soon match or even overtake the United States in developing and deploying artificial intelligence.”101 Pitted against the statistics from the previous passages, Lee’s evidence for China’s lead in AI implementers is meager. His attention is concentrated on anecdotes about the voracious appetite for learning about AI by Chinese entrepreneurs in Beijing.102 While this analysis benefits from Lee’s experience as CEO of Sinovation Ventures, a venture capital fund that invests in many Chinese AI start-ups, it could also be colored by his personal stake in hyping China’s AI capabilities.

    Drawing on Lee’s book, Allison and Schmidt also assert that China is cultivating a broader pool of AI talent than the United States today. Specifically, they point out that China graduates three times as many computer science students as the United States on an annual basis.103 Yet the study on which this figure is based finds that computer science graduates in the United States have much higher levels of computer science skills than their Chinese peers. In fact, the average fourth-year computer science undergraduate in the United States scores higher than seniors from the top programs in China.104 Therefore, estimates of China’s pool of AI engineering talent will be misleading if they do not establish some baseline level of education quality. This is another reason to favor the indicators that support an enduring US advantage in AI engineers.105

    Second, as previous industrial revolutions have demonstrated, strong linkages between entrepreneurs and scientists that systematize the engineering knowledge related to a GPT are essential to GPT skill infrastructure. In the AI domain, an initial evaluation suggests that this connective tissue is especially strong in the United States. Based on 2015–2019 data, it led the world with the highest number of academic-corporate hybrid AI publications—defined as those coauthored by at least one researcher from both industry and academia—more than doubling the number of such publications from China.106 Xinhua News Agency, China’s most influential media organization, has pinpointed the lack of technical exchanges between academia and industry as one of five main shortcomings in China’s AI talent ecosystem.107

    These preliminary indicators align with assessments of the overall state of industry-academia exchanges in China. Barriers to stronger industry-academia linkages include low mobility between institutions, aimless government-sponsored research collaborations, and misguided evaluation incentives for academic researchers.108 One indicator of this shortcoming is the share of R&D outsourced by Chinese firms to domestic research institutes: this figure declined from 2.4 percent in 2010 to 1.9 percent in 2020. Over the same time period, the share of Chinese firms’ R&D expenditures performed by domestic higher education institutions also decreased from 1.2 percent to 0.4 percent.109

    Moreover, the US approach to AI standard-setting could prove more optimal for coordinating information flows between labs working on fundamental AI advances and specific application sectors. Market-mediated, decentralized standardization systems are particularly suited for advancing technological domains characterized by significant uncertainty about future trajectories, which clearly applies to AI.110 In such fields, governments confront a “blind giant’s quandary” when attempting to influence technological development through standards-setting.111 The period when government involvement can exert the most influence over the trajectory of an emerging technology coincides with the time when the government possesses the least technical knowledge about the technology. Government intervention therefore could lock in inferior AI standards compared with market-driven standardization efforts.

    In that light, China’s state-led approach to technical standards development could hinder the sustainable penetration of AI throughout its economy. For example, the Chinese central government plays a dominant role in China’s AI Industry Alliance, which has pushed to wrest leadership of standards setting in some AI applications away from industry-led standardization efforts.112 Excessive government intervention has been a long-standing weakness of China’s standardization system, producing standards not attuned to market demands and bureaucratic rivalries that undermine the convergence of standards.113 Wang Ping, a leading authority on this topic, has argued that China needs to reform its standardization system to allow private standards development organizations more space to operate, like the Institute of Electrical and Electronics Engineers in the United States and the European Committee for Electrotechnical Standardization.114

    In sum, the United States is better positioned than China to not only broaden its pool of AI engineering skills but also benefit from academia-industry linkages in AI engineering. In previous industrial revolutions, these types of institutional adaptations proved crucial to technological leadership. Still, much uncertainty remains in forecasts about GPT skill infrastructure for AI, especially with regard to determining the right measures for the right AI talent. Recent studies of market demand for AI- and ICT-related jobs suggest that employers are softening their demands for a four-year degree in computer science as a requirement for such positions.115 Certificate programs in data science and machine learning that operate under the bachelor-degree level could play an important role in expanding the pool of AI engineering talent.116 Taking into account these caveats, this section’s evaluation of GPT skill infrastructure at the very least calls into question sweeping claims that China is best placed to capitalize on the IR-4.

    Reframing National AI Strategies

    The preceding conclusions offer a marked contrast with how American and Chinese policymakers devise national AI strategies. Policy proposals for pursuing US leadership in AI consistently call for more AI R&D as the highest priority. For example, the report “Meeting the China Challenge: A New American Strategy for Technology Competition,” published in 2020 by a working group of twenty-eight China specialists and experts, provided sixteen policy recommendations for how the United States should ensure its leadership in AI and three other key technological domains. The very first recommendation was for the United States to significantly expand investment in basic research, raising total R&D funding to at least 3 percent of GDP.117 The Trump administration’s “American AI Initiative,” launched to maintain US leadership in AI “in a time of global power competition,” also listed AI R&D spending as its very first policy recommendation.118

    The Chinese government also prioritizes investments in R&D, sometimes at the expense of other routes to productivity growth oriented around technology adoption and education.119 China’s five-year plan (2021–2025) aims to raise basic research spending by over 10 percent in 2021, targeting AI and six other key technological domains.120 China consistently sets and meets ambitious targets for R&D spending, but that same commitment has not extended to education funding. While China’s R&D spending as a percentage of GDP in 2018 was higher than that of Brazil, Malaysia, Mexico, or South Africa (other middle-income countries that industrialized on a similar timeline), China’s public expenditure on education as a percentage of GDP was lower than the figure in those countries.121 According to a group of experts on China’s science and technology policy, one possible explanation for this disparity between attention to R&D versus education is the longer time required for efforts in the latter to yield tangible progress in technological development.122

    As both the United States and China transition from initiating a new GPT trajectory to diffusing one across varied application sectors, investing in the broader AI-adjacent skill base will become more crucial than cornering the best and the brightest AI experts. Policies directed at widening the AI talent base, such as enhancing the role of community colleges in developing the AI workforce, deserve more attention.123 Applied technology centers, dedicated field services, and other technology diffusion institutions can incentivize and aid adoption of AI techniques by small and medium-sized enterprises.124 Reorienting engineering education toward maintaining and overseeing AI systems, not solely inventing new ones, also fits this frame.125

    A strategy oriented around GPT diffusion does not necessarily exclude support for the exciting research progress in a country’s leading labs and universities. R&D spending undoubtedly will not just help cultivate novel AI breakthroughs but also contribute to widening the GPT skill infrastructure in AI. All too often, however, boosting R&D spending seems to be the boilerplate recommendation for any strategic technology.126 GPTs like AI are not like other technologies, and they demand a different toolkit of strategies.

    Alternative Factors

    In exploring how the IR-4 could bring about an economic power transition, it is important to compare the implications derived from the GPT diffusion mechanism to those that follow from explanations stressing other factors. Consistent with the previous chapters, I first consider threat-based explanations and the varieties of capitalism (VoC) approach. I then address how US-China competition over emerging technologies could be shaped by differences in regime type, a factor that is particularly salient in this case.

    Threat-Based Explanations

    One potentially dangerous implication of threat-based explanations is that war, or manufacturing the threat of war, is necessary for economic leadership in the IR-4. Crediting the US military’s key role in spurring advances in GPTs during the twentieth century, Ruttan doubts that the United States could initiate the development of GPTs “in the absence of at least a threat of major war.”127 Extending Ruttan’s line of thinking to the US strategic context in 2014, Linda Weiss expressed concerns that the end of the Cold War, along with the lack of an existential threat, removed the impetus for continued scientific and technological innovation. Specifically, she questioned “why China has not yet metamorphosed into a rival that spurs innovation like the Soviet Union and Japan.”128 Weiss only needed a little more patience. A few years later, the narrative of a US-China “Tech Cold War” gained momentum as both sides of the bilateral relationship trumped up threats to push national scientific and technological priorities.129

    GPT diffusion theory strongly refutes the notion that manufacturing external threats is necessary for the United States or China to prevail in the IR-4. An external menace did not drive the rise of the United States in the IR-2. Across all cases, military actors were involved in but not indispensable to spurring the development of GPTs, as many civilian entities also fulfilled the purported role of military investment in providing a large initial demand for incubating GPTs. Furthermore, threat-based interpretations extend only to the moment when one country stimulates the first breakthroughs in a GPT. Even if stoking fears can galvanize support for grand moonshot projects, these do not determine which country is able to benefit most from the widespread adoption of advances in GPTs like AI. That hinges on the more low-key toil of broadening the engineering skill base and advancing interoperability standards in GPTs—not fearmongering.

    VoC Explanations

    Applying the VoC framework to US-China competition in AI gives more ambiguous results. The VoC approach states that liberal market economies (LME)—prototypically represented by the United States—are more conducive to radical innovation than coordinated market economies (CME).130 It is unclear, however, whether China fits into the VoC framework as a CME or LME. While some label China a CME, others characterize it as an LME.131 This disputed status speaks to the substantial hybridity of China’s economy.132 China has been treated as a “white space on the map” of VoC scholarship, which was originally developed to classify different forms of advanced capitalist economies.133 This makes it difficult to derive strong conclusions from VoC scholarship about China’s ability to adapt to the IR-4’s radical innovations.

    The same holds if we focus on the skill formation aspect of the VoC framework. China’s education system emphasizes training for general skills over vocational skills.134 In this respect, it is similar to LMEs like the United States, which means VoC theory provides limited leverage for explaining how the IR-4 could differentially advantage the United States or China. On this topic, GPT diffusion theory points to differences in AI engineering education as more significant than distinctions based on the CME-LME models.

    Case-Specific Factors: Regime Type

    What is the effect of regime type on technological leadership in the IR-4? The distinction between authoritarian China and the democratic United States takes center stage in arguments about the future of great power rivalry.135 Regime type could also influence the specific aspect of great power competition that GPT diffusion tackles—whether one great power is able to sustain productivity growth at greater rates than its rivals by taking advantage of emerging technologies. Some evidence suggests that, owing to investments in inclusive economic institutions, democracies produce more favorable conditions for growth than autocracies.136 Additionally, empirical work shows that democracies outgrow autocracies in the long run because they are more open to absorbing and diffusing new techniques.137 Other studies find, more specifically, that internet technologies diffuse faster in democracies, possibly because nondemocracies are threatened by the internet’s potential to empower antigovernment movements.138

    On the other hand, the impact of democracy on technological progress and economic growth is disputed. Drawing on data from fifty countries over the 1970–2010 period, Taylor finds that regime type does not have a strong relationship with national innovation rates, as measured by patenting rates.139 One review of the econometric evidence on democracy and growth concludes that “the net effect of democracy on growth performance cross-nationally over the last five decades is negative or null.”140 Moreover, China’s rapid economic growth and adoption of internet technologies stands out as an exception to general claims about a democratic advantage when it comes to leveraging new technologies as sources of productivity growth. Contrary to initial expectations, incentives to control online spaces have made some autocratic regimes like China more inclined to spread internet technologies.141 Other scholars point out that the stability of China’s authoritarian regime has encouraged substantial contributions to R&D and technical education, the type of investments in sustained productivity growth typically associated with democracies.142

    It is not within the scope of this chapter to settle these debates.143 Still, the juxtaposition of the GPT and LS mechanisms does speak to how regime type could shape US-China competition in the IR-4. Though the conventional wisdom links democracy to freewheeling thought and capacity for innovation, the most important effects of regime type in US-China technological competition during the IR-4, under the GPT diffusion framework, may materialize through changes in GPT skill infrastructure. Democracies tend to be more politically decentralized than autocracies, and decentralized states could be more responsive to the new demands for engineering skills and knowledge in a particular GPT. This accords with evidence that new technologies consistently diffuse more quickly in decentralized states.144

    Summary

    How far can we take GPT diffusion theory’s implications for the US-China case? I have presented support for the GPT mechanism across a range of historical case studies, each of which covers at least four decades and two countries. At the same time, it is necessary to acknowledge limitations in translating lessons from past industrial revolutions and power transitions to the present.

    To begin, it is important to clarify that my findings directly address the mechanism by which technological breakthroughs enable China to surpass the United States in economic productivity.145 The scenario in which China overtakes the United States as the most powerful economy is different from one in which the US-China power gap narrows but does not fully disappear. Scholars have rightly noted that the latter scenario—“posing problems without catching up,” in the words of Thomas Christensen—still significantly bears on issues such as Taiwan’s sovereignty.146 Even so, the possibility of China fully closing the power gap with the United States is especially crucial to study. When rising and established powers are close to parity, according to power transition theory, the risk of hegemonic war is the greatest.147 China’s ability to sustain economic growth also affects its willingness and ability to exert influence in the international arena.

    Next, GPT diffusion theory speaks to only one pathway to productivity leadership. The historical case studies have demonstrated that institutional responses to disruptive technological breakthroughs play a key part in economic power transitions. However, China’s prospects for long-term economic growth could also hinge on demographic and geographic drivers.148

    A number of factors could affect whether lessons from the past three industrial revolutions extend to the implications of present-day technological advances for a US-China power transition. The most plausible transferability issues can be grouped into those that relate to the nature of great power competition and those that relate to the nature of technological change.

    First, as put forward by Stephen Brooks and William Wohlforth, China’s rise in the twenty-first century could face structural barriers that did not exist in previous eras.149 Relying in part on data from 2005–2006, they argue that the present gap between the United States and China in military capabilities, as captured in long-term investments in military R&D, is much larger than past gaps between rising powers and established powers.150 Arguably, the US-China gap in military expenditures has narrowed to the extent that comparisons to historical distributions of military capabilities are more viable. In 2021, China accounted for 14 percent of global military expenditures. This updated figure, albeit still much lower than the US share (38 percent), reflects China’s military modernization efforts over the past two decades during a time of declining US military spending.151 This ratio is more comparable to distributions of military capabilities in the historical periods analyzed in the case studies.152

    Another structural barrier China faces is that the growing complexity of developing and deploying advanced military systems now makes it more difficult for rising powers to convert economic capacity into military capacity than it was in the past.153 There are a few reasons why it is still relevant to study China’s ability to convert the technological breakthroughs of the IR-4 into sustained productivity growth. To start, rising states could still benefit from the steady diffusion of some complex military technologies connected to advances in the commercial domain, such as armed uninhabited vehicles.154 In addition, military effectiveness does not solely derive from extremely complex systems like the F-22 stealth fighter. Converting production capacity to military strength could be more relevant for China’s investments in asymmetric capabilities and those suited for specific regional conflicts, such as ground-based air defense systems and the rapid replacement of naval forces.155 Lastly, there remains a strong connection between economic development and countries’ capabilities to “produce, maintain, and coordinate complex military systems.”156

    As for the second set of transferability issues, the technological landscape itself is changing. Accelerated globalization of scientific and technological activities may reduce the likelihood of adoption gaps between advanced economies when it comes to emerging technologies.157 Despite these considerations, there are also compelling reasons to think that the nature of technological change in this current period only magnifies the importance of GPT diffusion theory. Cross-country studies indicate that while new technologies are spreading between countries faster than ever, they are spreading to all firms within a country at increasingly slower rates. Networks of multinational firms at the global technology frontiers have reduced cross-national lags in the initial adoption of new technologies, but the cross-national lags in the “intensive adoption” of new technologies, as measured by the time between the technologies’ initial adoption to intensive penetration throughout a country, has only grown.158 These trends give more weight to the GPT mechanism.

    Finally, even if the rise and fall of great technologies and powers is fundamentally different in the twenty-first century, previous industrial revolutions still exert substantial influence in the minds of academics and policymakers.159 To justify and sustain their agendas, influential figures in both the United States and China still draw on these historical episodes. At the very least, this chapter submits different lessons to be learned from these guiding precedents.

    When some of the leading thinkers of our era declare that the AI revolution will be more significant than the industrial revolution, it is difficult to not get caught up in their excitement. Somehow, every generation winds up believing that their lives coincide with a uniquely important period in history. But our present moment might not be so unprecedented. Unpacking how AI could influence a possible US-China power transition in the twenty-first century requires first learning the lessons of GPT diffusion from past industrial revolutions.

    8 Conclusion

    STUDIES OF HOW TECHNOLOGY interacts with the international landscape often fixate on the most dramatic aspect of technological change—the eureka moment. Consistent with this frame, the standard explanation for the technological causes of economic power transitions emphasizes a rising power’s ability to dominate profits in leading sectors by generating the first implementation of radical inventions. This book draws attention, in contrast, to the often unassuming process by which an innovation spreads throughout an economy. The rate and scope of diffusion is particularly relevant for GPTs—fundamental advances like electricity or AI that have the potential to drive pervasive transformation across many economic sectors.

    Based on the process of GPT diffusion, this book puts forward an alternative theory of how and when significant technological breakthroughs generate differential rates of economic growth among great powers. When evaluating how technological revolutions affect economic power transitions, GPTs stand out as historical engines of growth that can provide major boosts to national productivity. Though each is different, GPTs tend to follow a common pattern: after multiple decades of complementary innovations and institutional adaptations, they gradually diffuse across a broad range of industries. Everything, everywhere, but not all at once.

    This impact pathway markedly diverges from existing theories based on leading sectors. Akin to a sprint on a narrow lane, great power competition over leading sectors is framed as a race to dominate initial breakthroughs in the early growth periods of new industries. In contrast, GPT diffusion theory proposes that by more effectively adopting GPTs across many application sectors, some great powers can sustain higher levels of productivity growth than their competitors. Like a marathon on a wide road, great power competition over GPTs is a test of endurance.

    Disruptive technological advances can bring about economic power transitions because some countries are more successful at GPT diffusion than others. A nation’s effectiveness at adapting to emerging technologies is determined by the fit between its institutions and the demands of those technologies. Thus, if economic power transitions are driven by the GPT trajectory, as opposed to LS product cycles, the institutional adaptations that matter most are those that facilitate information exchanges between the GPT sector and application sectors, in particular the ability of nations to widen the engineering skill base linked to a new GPT.

    Three historical case studies, designed and conducted in a way to assess the explanatory power of the GPT mechanism against the LS mechanism, provide support for GPT diffusion theory. The case studies cover periods characterized by both remarkable technological change—the “three great industrial revolutions” in the eyes of some scholars—and major fluctuations in the global balance of economic power.1 Overall, the case study evidence underscores the significance of GPT diffusion as the key pathway by which the technological changes associated with each industrial revolution translated into differential rates of economic growth among the great powers.

    In the case of Britain’s rise to economic preeminence during the First Industrial Revolution, expanded uses of iron in machine-making spurred mechanization, the key GPT trajectory. The gradual progression of mechanization aligned with the period when Britain’s productivity growth outpaced that of France and the Netherlands. Britain’s proficiency in adopting iron machinery across a wide range of economic activities, rather than export advantages from dominating innovation in leading sectors such as cotton textiles, proved more central to its industrial ascent. Though its industrial rivals boasted superior systems of higher technical education for training expert scientists and top-flight engineers, Britain benefited from mechanics’ institutes, educational centers like the Manchester College of Arts and Sciences, and other associations that expanded access to technical literacy and applied mechanics knowledge.

    The Second Industrial Revolution case also favors the GPT mechanism’s explanation of why certain great powers better adapt to periods of remarkable technological change. The LS mechanism focuses on Germany’s discoveries in new science-based industries, such as chemicals, as the driving force behind its catching up to Britain before World War I. However, the United States, emerging as the preeminent economic power during this period, was more successful than Germany in exploiting the technological opportunities of the Second Industrial Revolution. Enabled by innovations in machine tools, the extension of interchangeable manufacturing techniques across many American industries functioned as the key GPT trajectory that fueled the rise of the United States. Scientific infrastructure or industrial R&D capabilities, areas in which the United States lagged behind its industrial rivals, cannot account for its advantage in adopting special-purpose machinery across nearly all branches of industry. Rather, the United States gained from institutional adaptations to widen the base of mechanical engineering talent, including through the expansion of technical higher education schools and the professionalization of mechanical engineering.

    Evidence from the US-Japan rivalry amid the information revolution exposes more gaps in the LS account. During the late twentieth century Japan captured global market shares in new fast-growing sectors such as consumer electronics and semiconductor components, prompting many to predict that it would overtake the United States as the leading economic power. Yet such an economic power transition, an inevitability based on the expectations of the LS mechanism, never occurred. Instead, the United States sustained higher rates of economic growth than Japan owing, in part, to greater spread of computerization across many economic sectors. Japan’s productivity growth kept up with the US rate in sectors that produced information technology but lagged far behind in sectors that intensively used information technology. Once again, differences in institutional adaptations to widen the GPT skill base turned out to be significant. While Japanese universities were very slow to adapt their training to the demand for more software engineers, US institutions effectively broadened the pool of such skills by cultivating a separate discipline of computer science.

    As a supplement to the case studies, I conducted a large-n statistical analysis to test whether countries with superior GPT skill infrastructure preside over higher rates of GPT diffusion. Leveraging time-series cross-sectional data on software engineering education and computerization rates in nineteen countries (the G20 economies) across twenty-five years, the quantitative analysis confirmed this crucial expectation derived from GPT diffusion theory. I found less support for other factors often assumed to have a positive effect on an economy’s broader technological transformation, including institutional factors linked to securing LS product cycles. This empirical test validates a core component of GPT diffusion theory across a sample of the world’s major emerging and developed economies.

    Main Contributions

    First, at its core, Technology and the Rise of Great Powers introduces and defends GPT diffusion theory as a novel explanation for how and when technological change can lead to a power transition. Historical case studies and statistical analysis substantiate the explanatory power of GPT diffusion theory over the standard explanation of technology-driven power transitions based on leading sectors, which exerts enduring influence in policy and academic circles.2 In doing so, the book answers the call by scholars such as Michael Beckley and Matthew Kroenig for the international relations field to devote more attention to the causes of power transitions, not just their consequences.3

    By expounding on the significance of GPT skill infrastructure, the book points toward next steps to better understanding the politics behind some of the most significant technological advances in human history. To investigate why some countries are more successful in cultivating GPT skill infrastructure, promising avenues of research could tap into existing work that concentrates on centralization, inclusiveness of political institutions, government capacity to adopt long time horizons, and industrial organization.4 In these efforts, being careful to differentiate between various pathways by which technological changes make their mark, as this book does with the GPT and LS mechanisms, will be especially important when underlying political factors that satisfy the demands of one type of technological trajectory run counter to the demands of another.

    Future work should also probe other institutional factors beyond GPT skill infrastructure that contribute to cross-national differences in GPT adoption. This opens up a universe of institutions that are often ignored in innovation-centered accounts of technological leadership, including gender gaps in engineering education,5 transnational ethnic networks that facilitate technology transfer,6 and “technology diffusion institutions,” such as standard-setting organizations and applied technology centers.7

    In positioning the LS mechanism as the main foil to the GPT mechanism, my intention is to use this clash between theories to productively advance our understanding of the rise and fall of great technologies and powers. Contestation should not be misread as disparagement. In one sense, GPT diffusion theory builds on previous scholarship about leading sectors, which first identified the need to flesh out more specific linkages between certain technological advances and more highly aggregated economic changes in the context of power transitions.8 Testing, revising, and improving upon established theories is essential to gradual yet impactful scientific progress—not so unlike the incremental, protracted advance of a GPT.

    Second, the book’s central argument also suggests revisions to assessments of power in international politics. Recognizing that scientific and technological capabilities are becoming increasingly central to a nation’s overall power, researchers tend to equate technological leadership with a country’s ability to initiate “key ‘leading sectors’ that are most likely to dominate the world economy into the twenty-first century.”9 For instance, an influential RAND report, “Measuring National Power in the Postindustrial Age,” proposes a template for measuring national power based on a country’s capacity to dominate innovation cycles in “leading sectors.”10 In this effort, the authors draw directly from LS-based scholarship: “The conceptual underpinnings of this template are inspired by the work of Schumpeter, Rostow, Gilpin, Kennedy, and Modelski and Thompson.”11 This study has gained considerable traction in academic and policymaking circles, inspired further workshops on national power, and has been called “the definitive US study on CNP [Comprehensive National Power].”12

    Contrary to these approaches, this book submits that evaluations of scientific and technological power should take diffusion seriously. Assessments that solely rely on indicators of innovation capacity in leading sectors will be misleading, especially if a state lags behind in its ability to spread and embed innovations across productive processes. A more balanced judgement of a state’s potential for technological leadership requires looking beyond multinational corporations, innovation clusters like Silicon Valley, and eye-popping R&D numbers to the humble undertaking of diffusion. It shines the spotlight on a different cast of characters: medium-sized firms in small towns, engineers who tweak and implement new methods, and channels that connect the technological frontier with the rest of the economy.

    In an article published in the Review of International Political Economy journal, I illustrated the value of this diffusion-oriented approach in gauging China’s scientific and technological capabilities.13 Preoccupied with China’s growing strength in developing new-to-the-world advances, existing scholarship warns that China is poised to overtake the United States in technological leadership. This is mistaken. There is still a large gap between the United States and China when it comes to the countries’ readiness to effectively spread and utilize cutting-edge technologies, as measured by penetration rates of digital technologies such as cloud computing, smart sensors, and key industrial software. When the focus shifts away from impressive and flashy R&D achievements and highly cited publications, China’s “diffusion deficit” comes to light. Indeed, a diffusion-centric assessment indicates that China is much less likely to become a scientific and technological superpower than innovation-centric assessments predict.

    Relatedly, the GPT diffusion framework can be fruitfully applied to debates about the effects of emerging technologies on military power. Major theories of military innovation focus on relatively narrow technological developments, such as aircraft carriers, but the most consequential military implications of technological change might come from more fundamental advances like GPTs. In an article that employs evidence from electricity’s impact on military effectiveness to analyze how AI could affect the future of warfare, Allan Dafoe and I challenge studies that predict AI will rapidly spread to militaries around the world and narrow gaps in capabilities.14

    Third, as chapter 7 spells out in detail, GPT diffusion theory provides an alternative model for how revolutionary technologies, in particular AI, could affect the US-China power balance. This, in turn, implies different optimal policies for securing technological advantage. Drawing on the LS template, influential thinkers and policymakers in both the United States and China place undue emphasis on three points: the rapid timeframe of economic payoffs from AI and other emerging technologies; where the initial, fundamental innovations in such technologies cluster; and growth driven by a narrow range of economic sectors.

    GPT diffusion theory suggests diverging conclusions on all three dimensions. The key technological trajectory is the relative success of the United States and China in adopting AI advances across many industries in a gradual process that will play out over multiple decades. It will be infeasible for one side to cut the other off from foundational innovations in GPTs. The most important institutional factors, therefore, are not R&D infrastructure or training grounds for elite AI scientists but rather those factors that widen the skill base in AI and enmesh AI engineers in cross-cutting networks with entrepreneurs and scientists.15

    Yet, the United States is fixated on dominating innovation cycles in leading sectors. When it comes to their grand AI strategy, US policymakers are engrossed in ensuring that leading-edge innovations do not leak to China, whether by restricting the immigration of Chinese graduate students in advanced technical fields or by imposing export controls on high-end chips for training large models like GPT-3 and ChatGPT.16 A strategy informed by GPT diffusion theory would, instead, prioritize improving and sustaining the rate at which AI becomes embedded in a wide range of productive processes. For instance, in their analysis of almost 900,000 associate’s degree programs, Center for Security and Emerging Technology researchers Diana Gehlhaus and Luke Koslosky identified investment in community and technical colleges as a way to unlock “latent potential” in the US AI talent pipeline.17 This recommendation accords with an OECD working paper on the beneficial effects of a wider ICT skills pool on digital adoption rates across twenty-five European countries. The study finds that “the marginal benefit of training for adoption is found to be twice as large for low-skilled than for high-skilled workers, suggesting that measures that encourage the training of low-skilled workers are likely to entail a double dividend for productivity and inclusiveness.”18

    At the broadest level, this book demonstrates a method to unpack the causal effects of technological change on international politics. International relations scholars persistently appeal for the discipline to better anticipate the consequences of scientific and technological change, yet these demands remain unmet. By one measure, between 1990 and 2007, only 0.7 percent of the twenty-one thousand articles published in major international relations journals explicitly dealt with the topic of science and technology.19 One bottleneck to researching this topic, which Harold Sprout articulated back in 1963, is that most theories either grossly underestimate the implications of technological advances or assume that technological advance is the “master variable” of international politics.20

    This book shows that the middle ground can be a place for fruitful inquiry. Technology does not determine the rise and fall of great powers, but some technological trends, like the diffusion of GPTs, do seem to possess momentum of their own. Social and political factors, as represented by GPT skill infrastructure, shape the pace and direction of these technological trajectories. This approach is particularly useful for understanding the effects of technological change across larger scales of time and space.21

  • 康拉德·黑塞：论宪法规范力

    一、实际上的宪法和法律上的宪法

    1862年4月16日，费迪南·拉萨尔（Ferdinand Lassalle）在追求进步和自由的柏林地区协会发表了关于立宪主义的演讲。[1]他的基本论点为，宪法问题本就不是法律问题，而是权力问题。因为一国宪法是该国实际存在的权力关系的体现：体现为军队的军事力量，体现为大地主影响力的社会力量，体现为大规模工业和资本的经济力量。此外，虽然精神力量与上述力量不属于同一类别，但也体现在一般意识和普通教育中。以上因素的相互作用，是决定社会上一切法律和法律制度的推动力量，使法律和法律制度不能与其本质存在根本的不同，因此这些因素都是一国“实际上的宪法”（wirkliche Verfassung）。用拉萨尔的话来说，那些通常被称为宪法的内容，即“法律上的宪法”（rechtliche Verfassung），只是一纸空文而已。只有与实际上的宪法相一致时，法律上的宪法才能发挥激励和规范的作用。否则，二者就会发生无法避免的冲突。从长远来看，法律上的宪法，仅是一纸空文，必然会屈服于国家实际存在的权力关系。

    不管是政治家还是律师都这样教育我们：宪法问题本就不是法律问题，而是权力问题。在拉萨尔发表以上观点40年后，格奥尔格·耶利内克（Georg Jellinek）发表了如下观点：“宪法的发展为我们提供了一个巨大的教训，但是其巨大的意义仍然没有得到足够的重视，即法律条文无法实际控制国家权力的分配。真正的政治力量是按照独立于所有法律形式的规律运行的。”[2]在当下，这种思想显然没有过时，其只是被简化了，以显性或隐性的方式仍然存在，因为被拉萨尔看作权力的决定性因素之一的一般意识和普通教育已经完全退居幕后。这种想法似乎更加吸引人，它是如此显而易见；它似乎相当清醒地站在事实的基础上，把所有的幻想抛到一边；它似乎已被历史经验所证实，宪法的历史似乎确实告诉我们，在日常生活的政治斗争中，就像在国家生活的决定性问题上，政治现实的力量总是大于法律规范的力量，规范性总是不得不让位于现实性。我们只需要回顾一下拉萨尔在前述演讲中论及的普鲁士预算冲突，令耶利内克发出前述颓丧之言的议会政治地位的变化，或者《魏玛宪法》自始就无可辩驳的失败。

    就后果来看，现实条件具有决定性作用意味着：法律上的宪法发挥作用的前提是现实与规范的完全一致，但这只是一种假设的特殊情形。（原则上，静态的、理性的规范和运动的、非理性的现实之间存在着一种无法消除的内在张力。）因此，既然根据上述观点，宪法的冲突状态是持续存在的：即在其本质性构成部分上，也即非纯技术性构成部分上，法律上的宪法受制于实际上的宪法。那么，关于实际上的宪法具有决定性作用的观点，无非是对法律上的宪法的否定。人们可以用鲁道夫·索姆（Rudolf Sohm）的著名短语[3]的变体说：宪法法与宪法本质相矛盾。[4]

    对宪法的这种否定也就包含了对宪法学作为一门法律科学的价值的否定。宪法学与所有的法律科学一样，是一门规范科学，这使它区别于作为纯粹现实科学的政治社会学和政治学。如果宪法规范等同于对不断变化的事实关系的反映，那么宪法学就会成为一门没有法的法学。最终，它除了反复陈述和评论现实政治所创造的事实之外，将没有其他任务。在这种情况下，宪法学研究不是为使命性的公正的国家秩序服务，而是为了对既有权力关系进行法律正当化辩护。倘若真如上述所言，对于一个学科而言是不光彩的。如果接受此种对于宪法的否定态度，如果将实际上的宪法视为唯一的决定性因素，那么宪法学就失去了作为规范科学的特征，从而变成了一门纯粹的事实科学，也就不再区别于社会学或政治学。

    如果宪法的确仅仅是对事实权力的表达，那么这种对宪法的否定以及对宪法学作为一门法律科学的价值的否定是合理的。如果宪法自身有约束国家生活的力量，即使是有限的，那么前面的否定就失去了基础，由此便引出了“宪法规范力”的问题。除了事实的情况与特定的政治和社会现实因素的决定性力量之外，是否也存在宪法的决定性力量呢？它的基础是什么？它的范围有多大？宪法学人基于职业本位认为决定国家生活进程的主要是法律，这难道不是一种虚构吗？毕竟国家生活进程实际上是由完全不同的力量决定的。这些问题在宪法领域尤为突出，因为与法律体系的其他领域相比，宪法缺乏执行其规范的外部保障。对于这一问题的解答，关乎法律上的宪法这一观念的存废，也关乎作为一门规范科学的宪法学的存废。

    二、宪法规范力的可能与界限

    寻找上述问题的答案的尝试，必须以法律上的宪法与政治和社会现实的相互限制为出发点；[5]还必须进一步考虑法律上的宪法在这一出发点下发挥作用的限度和可能性；最后，也必须追问发挥这种作用的先决条件。

    1.只有把法律上的宪法与现实条件结合在一起，并且在两者不可分割的联系以及相互限制中，才能认识到法律规范在具体现实中的意义。任何孤立的观点，即只注意到一方的观点，并不能得出答案。对于只注意到法律规范的人来说，规范只能“适用”或“不适用”，没有其他可能；对于只注意到政治和社会现实的人来说，要么是忽略了这个问题，要么是倾向于忽视法律规范化的意义。

    虽然法律上的宪法与政治和社会现实的相互限制这一出发点是不言自明的，但仍需要对其予以特别强调。因为保罗·拉班德（Paul Laband）和格奥尔格·耶利内克学派的“法律实证主义”，以及卡尔·施米特（Carl Schmitt）宪法理论的“社会学实证主义”，[6]在很大程度上都是以这种规范与现实的二分法为特征的，[7]而且这种思维的影响甚至到现在都没有被克服。在宪法中，现实与规范、实然与应然的分离被认为是不可逾越的。正如论者曾多次指出的，[8]此种分离或明或暗地证实了事实关系是唯一决定性力量的命题。[9]甚至每次向某一方向的重点转移，都几乎不可避免地走向榨干现实的规范或榨干规范的现实的两种极端。因此，有必要在放弃规范性与排挤事实性之间寻找一条道路。只有当我们避免在原则性的非此即彼的意义上回答所提出的问题时，才能找到这条道路。

    宪法规范并非独立于现实而存在。宪法规范的本质寓于效力之中，也即宪法所规范的状态必然要实现于现实之中。这一有效性要求不能脱离实现它的历史条件，这些历史条件处于多种相互依存的关系中，创造了不能忽视的特殊规律。这里的历史条件包括自然的、技术的、经济的和社会的条件。只有考虑到这些条件，宪法规范力的要求才能得到实现。同样，这些条件也包括在一个民族中已经成为现实精神的内容，即具体的社会观念和价值观念，这两者对于法律命题的效力、理解和权威性具有决定性的影响。

    但是，宪法规范的效力要求并不等同于其实现条件，相反，它作为一个单独的要素与这些条件同列。因此，宪法不仅是一种实然的表达，也是一种应然的表达。宪法不仅仅是对影响其效力的实际条件——政治和社会现实——的反映，而且凭借其效力要求，宪法规范尝试规范和塑造政治和社会现实。宪法受到这些现实条件的限制，同时又反过来限制着它们。因而，宪法规范不能被追溯到某一原则，既不能追溯到纯粹的规范性，也不能追溯到纯粹的政治的、社会的或经济的条件。现实的限定性和宪法的规范性只能加以区分，但不能相互分离，也不能相互等同。

    2.因此，实际上的宪法和法律上的宪法处于交互关系之中。[10]二者相互联系，但并不相互依存；相反，法律上的宪法具有独立的意义，即使只是相对而言。在生成国家现实性的力量场中，宪法的效力要求也是一种必要要素。宪法的效力要求在多大程度上得以实现，宪法就具备多大程度的规范力。这就会导致进一步的问题，即在此背景下，宪法规范力实现的可能性和局限性的问题。

    如前所述，对宪法规范力实现的可能性和局限性的分析，仅能源于对法律上的宪法的实效性的深刻把握。这绝非前人未见之论。这一点对于立宪主义国家理论而言是不言而喻的，将宪法从国家现实的整体中分离出来的观念对其而言也是匪夷所思的。这一点在威廉·洪堡（Wilhelm von Humboldt）的政治著作中有着最明确的表述。

    洪堡在他早期的一篇著作中曾写道：“任何国家的宪法，如果仅由理性按照既定的计划先行制定，都不会取得成功；只有在更强大的偶然性与对立的理性的斗争中产生的宪法，才会生机勃勃。”换言之，这样的宪法能够与具体的历史条件相联系，并将其实现条件与以理性为标准的法律规范相结合。洪堡继续写道，“……只有立足于当下所有的独特条件，才能有所建树。理性所欲贯彻的蓝图，尚需从理性所欲加工的对象方面得到规范与修正。如此，宪法方得历久弥新、利国利民。反之，宪法即便得到了施行，其也必将永远徒劳无功。理性或许有塑造现有物质的能力，但绝无产生新物质的力量。这种力量只存在于事物的本质之中，真正起作用的正是这种力量，真正明智的理性只是激发这种力量的作用，并设法引导它们。在这一点上，理性是谦虚的。宪法不能像树苗嫁接到树上那样嫁接到人的身上。在时间和自然没有预先发挥作用的地方就有类似的行为，就好像用线把花绑在树上一样，正午的第一缕阳光就会灼伤它们。”[11]

    1813年12月，洪堡在关于德国宪法的备忘录中进一步阐发了相关观点：“宪法是这样一种东西：其存在于生活之中，人们可以感受到它的存在，但却无法完全理解其起源，因而很难对其加以效仿。每一部宪法，即使仅仅被看作是一种理论结构，也必须在时间、环境、民族性格中找到其生命力的物质萌芽，而这种萌芽只需要据此继续发展；想要纯粹根据理性和经验的原则来建立宪法的想法是非常荒谬的。”[12]

    洪堡首先明确了宪法规范力的局限性：宪法——这里指“法律上的宪法”——如果不想“永远贫瘠”，就不能不顾历史环境及其力量，只是抽象地、理论地构建国家，宪法无法生成那些不是当下独特条件所固有的东西。在缺乏上述先决条件的地方，宪法就不能赋予“形式和变化”得以继续发展；在不能发挥事物本质力量的地方，宪法也不能引导这种力量；在宪法无视其时代的精神、社会、政治或经济规律的地方，宪法就缺乏其生命力的不可或缺的萌芽，并且无法确保其逆于这些规律而设置的状态能够出现。

    但与此同时，这也决定了宪法的生命力和作用力的性质和界限。当宪法有能力面向未来塑造当前独特的条件下所固有的情况时，宪法规范就能产生效力。正如洪堡所表述的那样，当宪法由必要性原则决定时，宪法就能获得力量和威望。[13]换言之，宪法的生命力和作用力的基础寓于如下方面：宪法必须与时代的自发力量和活力倾向相结合；宪法必须确保这些力量得以施展并合理地安排其相互间的关系秩序；宪法必须是由对象（即事实）决定的具体生活条件的总体秩序。然而，宪法规范力并不仅仅基于对既定事物的灵活适应。[14]法律上的宪法可能会成为独自发挥作用的力量，但其前提是这种力量是当下独特条件所固有的东西。法律上的宪法本身不能完成任何事情，永远只能设置一个任务。然而，当人们承担起这项任务时，当人们愿意让宪法所规范的秩序来决定自己的行为时，当人们面对一时功利考量心生疑窦和抗拒并坚决贯彻宪法秩序时，也就是说，当在人们的普遍意识中，特别是在那些对于宪法运行身负重任者的意识中，活跃着的不仅仅是权力意志，而且还有尊崇宪法的意志时，宪法就会成为一种有效的力量。

    尊崇宪法的意志源于三个方面：基于对不可动摇的、客观的和规范的秩序的必要性和内在价值的洞察，确信是这种秩序使国家生活摆脱无节制和无形式的恣意状态；基于这样一种信念，即宪法所构成的秩序不仅是一种事实秩序，而且是已被合法化，并将永远被重新合法化的秩序；还基于这样一种认识，即这种秩序不像逻辑规律那样独立于人的意志而存在，而是只能通过人的意志行为加以实施和维持。[15]这种意志之所以能够起作用，是因为国家生活，就像人的全部生活一样，不仅受制于看似不可避免的力量，人们也总被要求对其加以积极地塑造，设定其应担负的任务并完成这些任务。如果我们对国家生活中始终存在的使命性的这一面视而不见，那么我们的思想就会变得贫乏且危险。我们将不可避免地忽视现实的整体性和特殊性，这不仅是一个不可避免的现实问题，而且是一个使命性的秩序问题，即规范问题。

    3.宪法规范力的本质和作用在于激发和引导事物本质中的力量，而且宪法本身也是一种自身有效的力量。如前所述，这是其局限性的根源，然而，这也是使宪法展现出最佳规范力的先决条件，这就涉及宪法的内容和宪法的实践。笔者将极尽简要地概述其中一些最重要的条件：

    （1）宪法的内容越是植根于其所处的环境，其规范力的实现就越有保障。

    因此，宪法规范力最基本的前提——这一点从上述内容中可以明显看出——是其不仅考虑社会、政治或经济方面的规律性，而且最重要的是反映其所处时代的精神情况，这能使其作为一种适当且公正的秩序得到普遍意识的肯定和支持。

    然而，同样地，宪法必须也能够适应这些条件的变化。除了纯粹的组织技术条款之外，宪法必须尽可能地局限于一些基本原则。鉴于如今社会和政治现实的变化越来越快，这些原则的具体实现形态也应该不断地得到新的、但同时也是根据这些基本原则的发展。[16]另一方面，任何一时的或特殊的利益在宪法中的——借用一种人们喜闻乐见的表述——“有宪法效力的固定化”，都不可避免地使宪法必须经常进行修改，进而贬损其规范力。

    最后，在不断变化的政治和社会现实中，宪法绝不能仅仅凭借着某一片面的模式保持其生命力和规范力。宪法如欲保持其基本原则的规范力，就必须在慎重考虑的基础上吸收对立模式的要素。没有不具有约束性的基本权利，没有不具有权力集中可能性的三权分立，没有不具有一定程度的单一性要素的联邦制。如果宪法试图完全纯粹地实现这些原则，那么，至迟到下文出现的国家紧急状态将会表明，宪法规范力的边界已被逾越，并将被现实所取代，宪法试图实现的原则将被彻底废弃。

    （2）宪法规范力的最佳发展不仅仅是宪法内容的问题，也是宪法实践的问题。这里的决定性因素是所有参与宪法生活的人的态度，也就是前文所述的“尊崇宪法的意志”，这一点的重要性体现在各个方面。

    所有一时的权宜之计，即使能够实现，也无法与虽然可能有所不利，但仍然坚持尊崇宪法所带来的不可估量的收益相提并论。正如瓦尔特·布克哈特（Walter Burckhardt）所说：“一个人所承认的宪法意志，必须诚实地得到坚持，即使必须为此放弃某些利益，甚至是某些正当的利益。倘若有人为了遵循宪法的要求而主动牺牲自身利益，那么他就强化了对于宪法的尊崇，同时也捍卫了一种对于国家而言——尤其是对于民主国家而言——不可或缺的善”，而凡是回避这种牺牲的人，“都是在用一种远远超过所有好处且一旦耗费就再也无法挽回的资本，换取些微蝇头小利。”[17]

    最后，宪法解释对于维护和巩固宪法规范力具有决定性意义。宪法解释必须有助于宪法规范力的最佳化实现。显然，逻辑归纳或概念建构的方式无法满足这一要求。如果法律，尤其是宪法的规范力受制于具体的社会环境，那么解释工作就不能忽视这些现实条件。宪法解释必须充分考虑这些条件，并将其与宪法原则的规范性内容联系起来。最理想的解释，是那种立足于实际情况的具体条件且能够将规范性安排的意义发挥至最优的解释。

    这意味着对宪法的解释可以甚至必须随着实际情况的变化而变化。但与此同时，通过解释促成宪法变迁，其界限在于宪法解释所受的规范性安排的意义约束。宪法命题的目的及其明确的规范意志决不能因事实的变迁而被牺牲。当规范性安排的意义在变化了的现实之中不再能够实现时，那就只剩修改宪法这一唯一的可能性。否则，就意味着取消规范与现实之间不可避免的紧张关系，从而取消法律本身。然而，在限制范围之内，续造性解释始终是可能并且必要的。这种灵活性正是宪法规范力的基本条件，因而也是宪法稳定性的基本条件。如果宪法缺乏这种灵活性，其与现有法律状态迟早会不可避免地彻底决裂。

    三、宪法规范力的影响因素

    1.总结如下：宪法受到历史与现实的制约。宪法不能脱离所处时代的具体社会环境，只有考虑到这些环境因素，宪法规范力的要求才能实现。然而，宪法不仅仅是对现实的反映，凭借其规范性因素，宪法也规范和塑造政治和社会现实。宪法规范力的可能性和局限性都源于“实然”与“应然”的交互性关系。

    宪法能够赋予与之相关的现实以“形式和变化”。宪法能够激发“存在于事物本质中的力量”并发挥其作用。此外，宪法本身也能够成为一种积极的力量，在政治和社会现实中发挥作用并在一定程度上决定现实。宪法不受侵犯的观念越是深入人心，尤其是深入那些对于宪法运行身负重任者的意识中，这种力量就越能够在面对反抗时得到坚决贯彻。因此，宪法规范力的强弱首先受到尊崇规范的意志和尊崇宪法的意志的影响。

    然而，在那些为宪法所规范者尚未内蕴于当下的独特条件之处，宪法规范力就会触及自身的界限。这些界限并不是固定不变的。因为，与自然的、社会的、经济的和其他的规律一样，尊崇宪法的意志也同样属于这一独特条件的要素。如果宪法基于特殊的力量而极富活力，宪法规范力或许能将其界限推至极远，但绝对无法完全消除这些界限。世界上没有任何力量，甚至宪法也不能改变特定的自然条件。但这仅仅意味着要紧的地方在于宪法的塑造性任务务必保持在这些界限之内。如果宪法符合这些有效性条件，即使是有能力突破或改变宪法规范的有权势者也必须遵守宪法，即使在困难时期宪法也不会失去其规范力，那么宪法就是一种活跃的力量，进而有能力保护国家生活不受无节制、不定型的恣意的暴虐。因此，检验宪法规范力是否得到维护并不是在安定祥和时，而是在紧急状态下。就此而言，这也正是卡尔·施米特著名论点的相对真理所在：紧急状态是决定宪法规范力的关键。但决定性的问题不在于紧急状态是否证明了事实性优于规范性这个次要意义，而在于规范性优于事实性的地位是否得到了维护。

    2.由于迄今为止几乎没有人以这种形式讨论过这个问题，所有这一切只能意味着一个初步的、也必然是粗略的定位。然而，这种定位已经能够回答开头提出的问题。宪法绝不是拉萨尔所说的那张废纸；宪法也不像耶利内克教导我们的那样“无法实际控制国家权力的分配”，也不像自命不凡的自然主义者和社会学主义者仍想让我们相信的那样。宪法并非独立于其所处时代的具体历史环境，但其本身也不附属于历史环境。在现实条件与其规范内容发生冲突的情况下，宪法的规范内容不一定是较弱的一方。相反，在一些可实现的条件下，即使是在冲突的情况下，宪法也能保持其规范力。只有在这些条件无法满足的情况下，宪法问题才会变成权力问题，法律上的宪法才会屈服于实际上的宪法。但这一事实并不能成为彻底否定宪法的理由：宪法法与宪法本质并不矛盾。

    与此同时，即使是如今，宪法学也无须退位。如果说法律上的宪法相对于实际上的宪法有其自身的意义，那么宪法学并没有丧失其作为一门法律科学的合法性，其并非狭义的社会学或政治学意义上的现实科学。当然，也不像实证主义所认为的那样，其仅仅是一门关于规范的科学。相反，由于其研究对象更加依赖于政治和社会现实，而且宪法规范的实施缺乏外部保障，因此其应兼容并包这两个方面，而且应当比其他法学学科更为如此。宪法的法律规范性与现实相关性的紧密联系迫使宪法学绝不能忽视规范性的条件，假如其不愿与研究对象失之交臂的话。如果要让宪法学的论述在现实面前站得住脚，当然就不能仅局限于用历史的、社会的、经济的或其他的方法对“严格法律”的思维进行外部补充。[18]相反，宪法学必须从根本上洞察所有决定国家生活进程的原则和力量之间的必然关系。因此，宪法学特别依赖于与之相邻的现实学科，如历史学、社会学和经济学。

    然而，从以上论述中也可以看出，宪法学必须保持对其局限性的谦逊认知。因为宪法规范力只是生成国家现实的力量之一。而且，这是一种有限的力量，其效果取决于上述先决条件。此间的任务极为艰巨，因为宪法规范力的保障不是一劳永逸的，而是一种使命性的东西。只有在特定的条件之下，宪法规范力才能以最佳的方式得以实现。这种最佳的实现是宪法学研究活动方向的根本指针。较之不遗余力地找寻宪法问题本质上是权力问题的论据，力保宪法问题不成为权力问题才是宪法学真正有益的建树。

    这意味着宪法学必须洞悉宪法规范力能够获得最佳效力的条件，必须发展宪法教义学，并从这一角度解释宪法条文。这意味着，宪法学的主要任务是强调、唤醒和维护尊崇宪法的意志，而尊崇宪法的意志是宪法规范力最可靠的保障；[19]这意味着，在必要时宪法学有义务挺身诤言——在国家生死攸关的问题上寄托于幻想，危莫大焉。

    四、《德国基本法》的规范力

    最后，我将通过审视（德国）当前的宪制状况来证明，我们应当意识到本文提出的问题。

    有人可能认为，当今时代显然已经用清晰可见的方式驳斥了对法律上的宪法的质疑。事实上，似乎有许多迹象表明，与过去相比，如今法律上的宪法对于国家生活有着更为重大的意义。国内政治似乎在很大程度上被“法律化”了。在联邦与州的关系中，在国家机关的关系及其职能中，宪法论证和辩论发挥着主导作用。即使是为政治生活提供动力和方向的政党，也会受到法律秩序的约束，尽管其在本质上显然不易被法律规范。政治权力无权修改《德国基本法》中的基本原则，这意味着宪法原则高于人民主权原则。法律上的宪法压倒一切的重要性体现在宪法法院目前仍然未知的、几乎无限的管辖权上，宪法法院有权依法对有争议的案件乃至国家生活的基本问题，作出最后的裁决。此外，法律上的宪法渗透到了法律生活的各个领域，甚至渗透到了原本与宪法严格分离的民法领域，并且，在联邦最高法院的作用下，宪法被赋予了主导地位。

    以上事实应予重视。但这也不能掩盖：我们仍旧面临，或许在很大程度上面临，宪法规范力的问题。如前所述，宪法规范力取决于宪法的实践和宪法的内容是否满足某些先决条件，而如今，这些先决条件只在有限的范围内存在。

    前文所提及的“尊崇宪法的意志”对于宪法实践具有决定性作用。这一决定性作用不仅要在皇皇大处得到体现，更要在细微之处得到贯彻。批判性的观察者不难看出，当今时代，人们往往并不情愿为了宪法的规定而牺牲自身的利益。相反，人们乐于为了一些蝇头小利而出卖强化宪法之尊崇可以获得的收益。目前，《德国基本法》显然只是在有限的程度上扎根于（尊崇宪法的）普遍意识并得到其支持，这会使上述倾向变得更加危险。[20]

    《德国基本法》的一系列规定，因其内容的缘故，使得宪法规范力同样面临着深刻的质疑。德意志联邦共和国宪制制度中存在的宪法与现实之间的紧张关系，经常会引起人们的关注。[21]最著名，尽管可能不是最重要的例子，是《德国基本法》第38条第1款。该款规定：“当选的联邦议员是全体人民的代表，不受任何指令的约束，只遵从自己的良知。”[22]在现代工业社会中，尤其是在现代人的生活态度发生深刻变化的情况下，自由原则逐渐成为一个严重的问题。[23]

    在这种情况下，宪法原则的可能性和有效性在完全相反的潮流和趋势的现实面前是否仍然存在的问题，就摆到了我们面前。这些问题尚未涉及非常情况。与《魏玛宪法》不同的是，《德国基本法》迄今还没有在蓬勃的经济增长和相对稳定的政治条件下经受过严峻的考验。如上所述，对宪法规范力最大的考验是政治、经济或社会生活中出现的紧急情况，这些紧急情况无法通过正常的宪法责任和权力来补救。《德国基本法》没有准备好接受这种对其规范力的检验。[24]

    众所周知，《德国基本法》根据《魏玛宪法》第48条的经验，取消了紧急状态的规定。在紧急状态下，《德国基本法》只包含了一些孤立的、有限的职责，其甚至不足以应对稍微严重的紧急情况。[25]紧急状态权的问题没有在1949年作出最终的决定，因为根据《占领法规》，[26]该问题属于占领国的保留事项之一。根据《波恩条约》[27]第5条第2款，只有当德国当局获得适当的法律授权，从而有能力应对严重扰乱公共安全和秩序的行为时，这项保留才会失效。

    德国当局尚未获得授权，占领国的干预保留仍然存在。然而，只有在联邦共和国受到外部威胁或攻击时，这种干预保留才有意义。《波恩条约》第5条未提及对公共安全秩序或宪制生活造成严重威胁的其他情况，例如经济紧急状态。此外，占领国是否会在必要时行使干预保留也是个问题。因此，有一个事实是不可回避的，即除了上述例外情况，联邦德国没有关于紧急状态权的宪法规定。

    紧急状态权的存在是使用这一权力的动力，当然也存在危险。但危险并不能证明我们愿意冒更大的风险来承担没有紧急状态权可能带来的问题。倘若认为没有考虑到的危险便不会发生，这将是一种危险的错觉。如果这种危险确实发生了，那么就不存在规范性的规定，消除危险只能靠事实的力量。人们可能会试图通过一项过于积极的紧急立法来证明所采取的措施是合理的。但是，这种过于积极的紧急立法的内容“必然无戒律”。因此，其不包含任何规范性的规定，也就不可能产生任何规范力。因此，在《德国基本法》中放弃对紧急状态权的规定，是宪法对事实力量的屈服。没有对紧急状态权的规定，便无法检验宪法规范力是否得到维护。唯一悬而未决的是，国家宪制生活是否以及如何重新回到规范的状态。

    没有人会希望本文所提及的宪法规范力与政治和社会现实之间的紧张关系演变成两者之间的严重冲突。这种冲突的结果无法预测，因为即使是在冲突的情况下，宪法保持其规范力的条件在当下也仅能部分实现。我们国家未来的问题究竟是权力问题还是法律问题，这将取决于宪法规范力及其基本前提——尊崇宪法的意志，能否得到恪守和强化。

    康拉德·黑塞（Konrad Hesse, 1919—2005）。刘亚巍、曾韬 译，本文德语版原载K. Hesse, Die Normative Kraft der Verfassung（1959），J. C. B.Mohr（Paul Siebeck），Tübingen，为黑塞1958年在弗莱堡大学法律系的就职演讲稿。

  • 我们童年的游戏是从哪里来的

    丢手绢、打沙包、跳房子、翻花绳、跳皮筋……

    一、我们小时候的游戏，都是哪来的？

    老鹰捉小鸡/丢手绢/“东西南北”是哪国发明的？

    丢手绢加拿大小孩也在玩，只不过手里不一定有手绢，名字叫“Duck, Duck, Goose”。

    老鹰捉小鸡，英欧洲小孩叫“狐狸与鹅”。

    “东西南北”折纸游戏竟然变成大马特色了。

    尼德兰画家彼得·勃鲁盖尔在1560年所作的这幅画里，画了80多种儿童游戏，其中不乏我们非常熟悉的滚铁圈，骑“马”、跳山羊、捉迷藏、抽陀螺、老鹰捉小鸡，甚至抛羊拐。

    此时的中国是明嘉靖三十九年，日本是永禄三年。

    在智利，丢手绢叫“Corre, Corre la Guaraca（快跑快跑小傻瓜）”，玩法跟我们大同小异；“123不许动”在希腊叫“我是一座雕像”，区别在于他们可能带了点cosplay的成分；“石头剪子布”在苏门答腊群岛叫“蚂蚁大象人”。抛羊拐在韩国抛的是石头，在东南亚抛的是小沙包，但游戏规则近乎一致。

    不但如此，有文物反映，在公元前三百多年的古希腊和罗马，羊拐游戏就已经十分普遍，出土于庞贝古城的画作上甚至有两个女神玩羊拐的场景。

    古希腊雕塑中，少女在玩羊拐 （约公元前330—300年）
    出土于庞贝古城的画作

    类似古老的游戏还有翻花绳。

    关于它最早的记录是在1768年，不同版本遍布世界各地，除了欧美之外，还包括非洲、 澳大利亚、太平洋岛屿甚至北极。在英语俚语中，人们用“Cat’s cradle”来特指这个游戏；在俄罗斯，它被称为“弦游戏”；在以色列，这款游戏被叫作“编织”。

    1765年的日本浮世绘，两名女士在玩翻绳游戏

    “东南西北”很可能也是舶来品本土化的产物。

    虽然主流观点认为折纸游戏的起源是中国，随后传播至日本，但“东南西北”这种形式的折纸布局现存最早记载是12世纪西班牙的占星文献，有人认为其起源大概率是西方宗教。

    我们玩“东南西北”大多数时候是捉弄人，但这种被叫作“Paper fortune teller（算命纸先生）”的折纸玩具被英国儿童用来占卜。其玩法同我们类似，在内部各个面上写上各种事件和指令，由玩家报出方位及开合次数，最后对应的句子即为其未来之“遭遇”。

    西方儿童用来占卜的“东南西北”
    16世纪约翰·汉密尔顿大主教的占卜星盘（折痕同“东南西北”的折法一致）

    关于跳房子最早的记载是17世纪，在1677年出版的一本名叫《Poor Robin’s Almanack》的书中，这个游戏被称为“苏格兰跳蛙”，其中有苏格兰人在找平的砖地或木板上划分扁或圆形的区域用来跳跃。此外，也有人认为跳房子的历史可以追溯到公元前1200年的印度或古罗马时代。

    印度人管它叫“Stapu”，拉美地区叫它“rayuela”，在土耳其语里是“Seksek”，保加利亚称其为“asдама”……总之就是全世界都在玩。

    英格兰莫克姆的一种传统跳房子游戏

    二、“民间传统游戏”全世界都在玩

    有研究表明，类似“丢手绢”的游戏广泛流行于世界各地，如英国、德国、瑞典、美国、印度甚至加纳和智利等国，游戏形式几乎一致——在游戏过程中，大家通常会唱某一首特定儿歌，像我们的《丢手绢》，法国的《 邮差没有来》。

    在美国，“丢手绢”叫“鸭子，鸭子，鹅”，玩法也是一群人围成一个圈，而一个人喊着“duck”转圈，直到在某人身后喊出“goose”，追逐者换人。这种游戏在美国的不同地区有变体，比如“Drip, drip, drop（滴滴滴）”“Mush pot”。

    在印象中，小时候玩的游戏里，玩法同“丢手绢”很像的，还有“白毛女”——小孩们拉着手围成一圈唱歌，圈中蹲一个人蒙着眼，在歌谣停止时指出一个人代替他。

    日本也有类似的游戏“笼中鸟”，但与我们玩时大声喊出“白毛女就是你”并随机指一个人不同，日本的玩法多了一些神秘学意味——歌声停止时，站在当“鬼”的人的正背面的的，就要代替“笼中鸟”当“替死鬼”。比起这个游戏本身，我们更为熟悉的是游戏时唱这首童谣《かごめかごめ（笼中鸟）》，它的变奏曾以各种形式出现在《犬夜叉》等动画里。

    正如《丢手绢》这首歌是作曲家关鹤岩在1948年为了延安保育员的孩子们游戏所作，游戏的出现早于儿歌。“白毛女”游戏时唱的歌谣，明显出现于1951年《白毛女》电影上映之后。从文献上看，《かごめかごめ（笼中鸟）》这首童谣是江户中期以后出现的，且也有极大可能是为了配合游戏形式所作。

    虽然歌曲的创作背景各有不同，但纵观游戏形式本身与它所流行的地区，很容易看出其中贸易往来与殖民主义的影子。

    此外，关于儿童游戏的发源和传播虽然少有学者考证，但也不是完全没有。

    有学者专门研究过“老鹰捉小鸡”。这个小游戏的足迹遍及除南极洲以外的六大洲，大多数国家都将其作为本国的传统民间游戏来待。而在中国，不同的民族也都认为其是自己民族的传统游戏，同本民族的文化有着千丝万缕的联系。

    在日本，这个游戏被叫作“比比丘女”，源于1300年前的平安时代中期，后来演化为“捉鬼子”。在韩国济州岛，它被叫作“大雁”，被认为是韩国传统文化的一部分。在越南，它被认为是起源于童谣舞曲的“龙蛇”游戏；在俄罗斯，它被叫作“鸢”，在本土传播了几个世纪；在土耳其，它被叫作“狼爸爸”，同土耳其人半狼半人阿塞纳的传说有关；在英国，它叫“狐狸与鹅”，与游戏相关的歌谣有三百多年的历史；在马达加斯加，它叫“拉萨林德拉”，早在法国人入侵之前就存在……

    1818至1830年间，歌川国芳绘制的《新板儿童游戏浮世绘》。比较有意思的是，从世界范围来看，疑似只有中国和日本的玩法包含了“被抓住就要改换阵营”这项规则。

    据学者考证，如果一定要给“老鹰捉小鸡”的传播路径找一个历史脉络的话，它的来源很有可能是古印度“尸毗王割肉养鹰救鸽”的传说。但无论真相是否如此，“老鹰捉小鸡”在世界范畴内的广泛存在是因传播居多还是独立演化占主流，其成形的核心一定是一些我们所熟知的底层逻辑——勇敢、善良、守护，为了他人挺身而出的信念，自我牺牲的觉悟。从这个角度来讲，无论哪种说法都说得通。

    学者彼得·弗兰说：“大陆与大陆之间在相互影响，中亚大草原上发生的事情可以在北非感同身受，巴格达发生的事件可以在斯堪的纳维亚找到回响，美洲的新发现会影响中国产品的价格，进而使印度北部的马匹市场需求剧增。”

    “儿童游戏的变迁与传播历程印证了古今文化的共通性”，像一根来路不明的引线，串联起整个的人类文明。

    从这个角度来说，可能真的是“你的童年我的童年大家都一样”，这个是世界是一个巨大的闭环。

    三、儿童游戏，文明史中的善意角落

    如此之多的儿童游戏近乎全球统一，是巧合吗？

    不排除有巧合的成分，确实有些相似文化产物可以在完全不同的社会条件下被独立孕育。

    但对于“游戏”这一贯穿智人进化全过程的行为，更大的可能性，依然是“传播”。

    数字时代之前，我们小时候习以为常的东西，经常有匪夷所思的历史源流。其中最知名的案例，应该是“七颗星星”的故事。

    关于这个故事，有一种说法是——像每个中国人小时候都听过七颗星星变成七仙女的传说，希腊神话中有七姐妹星的故事，澳大利亚原住民也有类似的故事。至此社会学家发现，几乎包括少数原始族裔在内的全世界大多地方都有类似的传说，然而我们如今只能观测到模糊星云中的六颗星。

    至此，“比较神话学”发现，这也许是人类第一个故事，它成型于人类走出非洲之前，是所有人类曾为一母同胞的证据。

    至于这些我们小时候习以为常的游戏，早在我们“文化传播”这个概念形成之前，就已经在世界范围内传播了。

    儿童游戏简单的框架和逻辑中，所蕴含的是全人类共通的朴素哲学和文化基因。

    从“体育”这一伴随人类发展的早期教育概念展开，“老鹰捉小鸡”的本质是家庭与责任感，守护与抵抗；“白毛女”或“笼中鸟”中隐含着对社会性压迫和囚禁的反抗；羊拐和翻绳体现着对简单物质的最极致利用……

    而无论是哪一种儿童游戏，其最本质功能之一，是对人与人关系的维系。

    游戏，是人在成长过程中最早的社会化行为。遵从游戏规则，便是一种社会实践。

    儿童游戏的附属品，是伙伴，是团队，是从周边衍生而来的关系。所谓“有人跟我玩”，是一个人从童年时期开始建立的，最初的社会支持与安全感。

    而既然儿童游戏建立在“人与人关系”的基础上，那它们无论传播多远，跨越多漫长的时间，遍布多少形形色色的人种、国家和民族，好像都是一件很正常的事情。

    毕竟，这个世界由人构成，是所有人与人关系的集合。

    而这些童年游戏的存在，更是人类曾在大众所忽视的地方彼此友善过的证据。

    如果不是有人在孩童阶段牵起那双同自己不一样的手，这些游戏又是如何在充斥着战争、侵略、迫害，贸易与文化倾轧的人类文明史中悄然传播的呢？

  • 冯裕强：集体化时期工分稀释化视域下乡村公共产品供给研究——以广西容县华六大队为例

    改革开放以来，不少学者对人民公社制度进行了批判，认为它是低效率的、平均主义的制度。例如，有学者认为由于集体经济产权不完整，影响社员的生产积极性，最终导致劳动质量降低。①但是，也有不少学者对此进行反思，认为导致平均主义的原因主要是国家进行工业建设，加上当时的国际环境等因素，不得不从农村抽取剩余产品，而且人民公社在20世纪60年代初“去工业化”后，大量劳力只能进行单一的农业生产，产出极为有限。即便如此，农村还进行了大量的农田水利建设，这对后来的发展起到重要的铺垫作用，也应算作当时的劳动效率。②争议难分高下。笔者不揣浅陋，试图从工分稀释化视角对乡村公共产品供给进行考究，以期对人民公社有更全面的了解，同时也为当下乡村振兴提供经验启示。

    “工分稀释化”，虽有学者提到相近的概念或现象③，但至今尚未有学者对其进行明确定义。笔者以为，工分被稀释主要包括两方面：一是工分的直接稀释，即把非农业生产的工分拿回农业之内进行分配，从而导致工分被稀释，分值下降；其次是物资的间接稀释，即国家、集体从生产队抽走大量物资，从而使队内可分配给社员部分总额减少，最终造成工分贬值。

    乡村公共产品是巩固农业基础地位、保障农村社会稳定、促进农村经济可持续发展的重要基础。国内学术界对农村公共产品的界定④大体一致，主要是指乡村中由集体或政府提供，为广大村民的生产、生活服务，具有一定非竞争性和非排他性的社会产品，具体包括农村基础设施、农田水利主干网络、基础教育、公共卫生、社会保障等。

    一、农田水利基础设施

    华六生产大队（以下简称“华六大队”）位于广西壮族自治区容县南部，隶属于石寨公社，距离县城20多公里，是汉族聚居地，面积约为19.33平方公里，共有十个生产队。⑤容县面积为2257平方公里，其中陆地占97.51％，水域占2.49％，⑥境内岭谷相间，丘陵广布，俗称“八山一水一田”。由于地处山区，为了更好发展农业，华六大队在集体化时期修建了大量农田水利设施。据统计，1963年—1966年间，华六大队修建了大陂、三蛤、枪刀山和长冲等水库，⑦大部分生产队都有受益的山塘或水库。

    为了修建这些水库，必然要耗费大量劳动力。华六大队除了平时抽调社员进行水利建设外，还组建了20人—30人的专业队从事农田水利基本建设。曾任记分员的陀某说：“专业队就是专门开田、开荒、种山，每个生产队抽出几个人。比如我们大队有几十个人，天天都在专业队干活，生产队一样要（给他们）记工分。”（TXL，四队记分员，2017年3月16日）⑧专业队的职责很多，包括水利建设、开荒、大队企业、护理林场等，劳动收入归大队所有。曾任林业员的庞某回忆说：“山上的林木就由专业队队员种植，以前（1958年“大炼钢铁”）烧得太光了，没有林木了。每个队要2—3个，都是年轻的男女民兵。”（PWQ，华六大队林业员、党支部书记，2017年3月20日）曾参加过专业队的肖某也说：“县有县的专业（队），乡有乡的专业（队）。像最大的石剑水库、小垌水库，还有乡的红田水库，每个队抽几个人去。那些水库都是那些人去做的，统一调动。”（XYH，六队专业队队员，2017年4月15日）

    生产队一年中要进行大量的农田水利基本建设，那么这些非农业生产用工究竟在总用工中占多大比例？以1975年为例，根据各级单位的统计数据，八队、华六大队、石寨公社（统计7个生产队）和容县（统计233个生产队）的农业生产用工占总劳动日的比重分别是82.63%、83.70%、85.95%和82.70%。⑨一般而言，统计的生产队越多，就越接近整个县的平均水平。总体上看，公社以下各级单位的生产用工占总劳动日的比值都在容县的生产用工占总劳动日的比值——82.7%上下浮动，也就是说整个县大约要用17%的劳动日去从事非农业生产。这并非特例，在山西省东北里生产队，1977年的非农业生产用工占比达7.7%，这还不包括高达18.98%的农田基建工。⑩可见，在集体化时期，大量非农业生产用工存在于全国各地。除了基建用工，还有各级专业队队员、生产队干部、集体抽调的社员都要回生产队记工分，这些人员的劳动对当年生产队收入的增加并未起直接作用，因此，在外面挣的大量工分拿回生产队进行分配，必然会稀释生产队的工分值。

    那么生产队的实际工分值在集体化时期有什么变化呢？本文从容县档案馆保存的历年分配统计表中整理出表111。

    通过表1可以看到各级单位从1963年到1981年社员分配收入和工分值的变化情况。在生产队一级，由于资料的缺失，我们只能比较完整地看到20世纪70年代的数据变化。总体上，八队从1971年至1981年分配给社员的金额、工分值和人均分配收入都呈波浪式上升，在1979年达到最高值； 华六大队与石寨公社在相同的项目上虽然也呈波浪式上升，但是振幅相对小得多，除了个别年份回落，大部分年份是逐年增长的。分配给社员的金额与工分值、人均分配收入总体上呈正相关。分配给社员的金额越大，工分值和人均分配收入越高，就意味着人民公社在增产的同时社员也实现了增收，集体经济运行良好。三个不同区域都在1979年达到最高值，人均分配收入分别达到98.9元、77.95元和84.44元。需要注意的是，这里的分配金额并不是真金白银，而是生产队把一年所有的劳动产品和收入都折算成货币，扣除所有费用和税金之后的纯收入，生产队实际拥有的现金并没有这么多。

    工分值的高低取决于两方面，一是生产队分配给社员的金额，二是工分的总量。分配给社员的金额是用总收入减去各项支出后得到的数据。而生产队的总收入是农业、林业、畜牧业、副业、渔业和其他收入相加的总和。虽然国家规定生产队应该以发展农业生产为主，12但是非农业生产对生产队的收入也有重要影响。那么生产队的农业生产和非农业生产收入占比各有多少呢？我们以1974年为例。

    本文发现，1974年，农业生产收入占总收入的比重，从生产队到公社再到容县是逐渐降低的，但容县比玉林地区的平均值低了近13个百分点，也就是说容县的非农业生产收入占总收入的比重比玉林地区高出约13个百分点（见表2）。通过对各年份数据进行比对，13%是容县非农业生产收入占比超过玉林地区的正常比值。那么是什么原因导致容县的非农业生产收入占比如此之高呢？要解决这一问题，必须了解林业、畜牧业、副业、渔业和其他收入的占比具体是多少，进而明了容县与玉林地区拉开差距的原因。

    经对比，在畜牧业、渔业和其他收入占总收入的比重方面，容县和玉林地区相差不大，差异产生的主要原因在于林业和副业的收入占总收入的比重，容县比玉林地区分别高出6.33个和4.82个百分点（见表3）。林业收入主要来源于山林，容县地处丘陵，全县有480438人，水田面积为29万亩，人均水田面积仅为0.6亩，山地总面积为225万亩，人均山地面积为4.67亩。13“全县179个大队，山区大队98个，占全县大队55%……一九七一年生产木材28234立方米，占全县木材生产31874立方米的90%。”14华六大队就是这98个山区大队其中之一。据1960年普查，华六大队总面积为24038亩（约16平方公里），其中林地面积为17189亩，151974年，华六大队有1778人和1813亩耕地（1683亩水田），16人均有9.67亩山林、0.95亩水田。如此丰富的森林资源，林业产品具体又有什么呢？1974年的统计年报表显示，石寨公社造林719.3亩，其中用材林（松木和杉木）483.4亩，油茶196亩，玉桂14亩；收获的林副产品有：油茶籽515.9担、油桐籽73.45担、松脂9215.95担；收获的水果为：沙田柚41.5担、龙眼65担和荔枝88.6担；另外还有茶叶96.87担、桑蚕茧129.16担等。这些产品收入是属于林业收入还是副业收入？此问题涉及容县林业与副业的收入来源问题。在八队分类账本中，林业收入主要来源于售卖原木，副业收入内容则更多，包括松脂、纸浆、茶叶、砖瓦、石灰等。这与1975年容县林业局统计分类相符。1975年容县产量较大的林副产品有：油桐籽812担、松脂238131担、木柴183541担、木炭2199担、土纸（纸浆）4220担、沙田柚142830担；木材产品35489立方米（原木30911立方米）17。因此，容县的林业收入主要来源于各类木材，副业收入则主要来源于松脂、木柴和沙田柚等，而就收入占比来说，松脂的收入无疑是最大的。早在1963年，容县就申请建立容县松脂基地，通过调配物资和劳动力有计划地造林和割松脂。181972年，十队割松脂收入达4720.05元，除去人工和材料，净收入3794.2元。19正是有了松脂和其他各类林木和林副产品，才使得容县的非农业生产收入占总收入的比例远高于其他县。

    明晰公社的各项收入后，可以发现，表2中分配给社员的部分占总收入的比重，八队与其他各级单位之间差别较大，除了八队的超过60%，其他各级单位都在55%以下。这意味着整个地区人民公社平均分配到社员的部分占比并不高。导致这样的原因与生产队的管理水平有着密切关系。八队与其他单位相比，税率（主要是农业税——公粮）和集体提留基本保持一致，相差不大；其缴纳的公粮基本保持不变，高产年份会稍提高，减产年份会稍减；集体提留主要包括公积金、公益金、储备粮基金、生产费基金和统筹金，这些不管如何都是要拿出来发展生产和上交集体的。关键是在费用支出占总收入比重方面，八队比玉林地区全部生产队的平均水平低5.66个百分点。根据八队的账本和收益分配统计表的金额，本文计算出八队在1977年和1979年分配给社员的部分占总收入的比重分别为66.9%和64.8%，20分配给社员部分占比很高，说明八队在支出控制和经济管理方面做得比较好。

    费用支出主要包括生产费用、管理费用和其他费用，支出越多，能分配给社员的收入就越少，工分值就越低，所以费用支出直接影响工分值的大小。那么，其他生产队的费用支出高的原因到底是什么？在玉林市档案局笔者发现一份1976年的档案——《关于人民公社收益分配的情况问题和意见》，其内容可以较好地说明这一问题。

    该份档案主要是对1975年玉林地区人民公社收益分配中的分配收入出现的一些问题进行总结并提出整改意见。1975年全地区粮食大增产，但是分配给社员的部分占比并不高，主要原因是费用开支大，全自治区费用支出占总收入的27%，但是玉林地区费用支出占总收入的比重高达33%。费用开支大的原因有八点。第一，有的地方发展生产不坚持“自力更生”原则，远途高价购买或调换化肥，费用开支大，生产成本高；第二，有的地方农田基建补助花样多，标准高，集体负担重；第三，有的地方扩建学校，增加民办教师，从而增大了集体费用的开支；第四，有的地方变相增加脱产人员，加重了集体负担；第五，有的地方社员上调家禽、生猪派购任务，要生产队补钱、补粮，增加集体负担；第六，有的地方的乱支乱补、大吃大喝、请客送礼、挥霍学杂费等不正之风还没有彻底刹住；第七，有的地方搞账外分配，或者高价（市场价）买入猪肉，然后按照牌价（较低价格）分配给社员；第八，有的地方存在贪污、挪用、超支欠款等问题。21从这些原因中，可以看到生产队在经营管理中存在的各种问题，虽然说这些现象并不必然存在于每个生产队，但是如果不严格控制支出，必然会严重影响社员的收入。

    在表1中，本文还注意到工分值的变化。八队的工分值从1965年的0.35元逐渐上升到1981年的0.53元，1979年和1981年都突破了0.5元。由于影响工分值的因素非常多，生产队能够保持增长已属不易。1963年，华六大队的工分值为0.19元，此后逐步增长到1981年的0.55元。与华六大队相比，石寨公社的工分值增势更为平缓，在20世纪70年代总体保持在0.4元左右。这三级单位的工分值虽然涨幅不大，总劳动日却大量增加。通过计算可知，八队在1979年的劳动日是1965年2.1倍；华六大队和石寨公社1979年的劳动日都是1965年的1.76倍。工分主要是靠劳动力挣的，劳动力越多意味着工分越多。1979年，八队的劳动力为70人，是1965年45人的1.5倍；华六大队1979年的劳动力为915人，是1965年719人的1.27倍；石寨公社1979年的劳动力为12643人，是1965年9118人的1.39倍。22可见劳动力的增加速度远没有工分的增长速度快，工分的快速增长必然导致工分被稀释。同时应注意到，人口增长特别是劳动力增长自然使劳动工分增加，但是过多的劳动投入，在单位土地上带来的产出，并不会均一地带来同等幅度的增产。以八队和石寨公社为例，经笔者计算，八队1979年亩产1126.93斤，是1965年亩产886.87斤的1.27倍，而同期八队工分总量增长了1.1倍；1979年石寨公社亩产为1146.15斤，是1965年亩产917.31斤的1.25倍，工分总量却增长了0.76倍。23即便扣除部分工分用于非农业生产，工分的增长速度仍高于每亩的增产速度。这便是黄宗智所讲的“过密化”或“内卷化”现象：“在人多地少和土地的自然生产力有限的现实下，单位土地面积上越来越多的人力投入只可能导致其边际报酬的递减。”24

    为避免农业生产上的过度内卷，充分利用劳动力巩固和发展集体经济，1959年年初中共中央农村工作部对当年全国农村人民公社的劳动力进行了分配规划，提出将51.4%的农村劳动力用于农业生产，剩余的48.6%用于国家工业交通、林牧渔副业、社办工业、交通运输、基本建设、生活服务等方面；在农业中，从事粮食生产的约为8000万个劳动力，占农村总劳动力的38.1%，从事其他作物生产的约有2793万个劳动力，占农村总劳动力的13.3%。25也就是说，从事农业生产的劳动力仅占总劳动力的一半，而真正种植粮食的劳动力不到4成。26所以十队的一位妇女说：“强的劳动力又抽出去了呀，就剩下二、三级的婆娘在家了，有的上山搞副业，没有多少劳动力的。” （XJA，十队社员，2017年3月25日）五队老队长补充道：“（种田）天天都是那帮人的。上调的人做不了，他不做这个就去做那个，做田就是做田的，我搞副业就是搞副业，分了工的。”（WGM，五队队长，2017年3月24日）

    笔者在各生产队的账册中，发现不少专业队和副业人员的回队账单。例如，十队“1971年5月10日，收许有昌交款回队12—3月48元”（修建广西金红铁路，简称“6927工程”），27八队“1972年1月26日，收其文11—12月回队款23.6元” （专业队修船坝），28“1977年3月 14日收世天泥水工入队8元”。29当时规定专业队队员和从事副业的人员必须按一定比例将收入交回生产队，生产队按同等劳动力记工分，这样才能参与生产队的分配，同时生产队还要按时给外出的专业队队员寄口粮。例如，1969年广西从玉林抽调民工18000人，参加金红铁路修建工程，其中容县被抽调3000人。工程文件规定，“民工的生活待遇，每人每月30元，其中40%交回生产队，参加生产队分配，60%由民工个人支配。民工的口粮供应，除从生产队带足本人的口粮外，按工种定量标准，不足部分由国家供应”。30由于路途遥远，口粮是无法送去的，生产队只能通过转账的方式给民工购买口粮，如八队“1970年9月20日，支成才转6927（工程）粮200斤，每百斤9.3元，金额18.6元”。31可见，除去各级专业队队员、副业人员、民办教师等精干劳动力，真正进行农业生产的劳动力是很有限的。在非常有限的劳动力从事农业生产的情况下，其产出自然不会太高。

    1974年，广西壮族自治区革命委员会水利电力局提出要大力组建专业队。“不但骨干水利工程要坚持常年施工，而且社社队队都要组织农田基建专业队，大搞常年施工。一个队、一个社、一个县如果抽出百分之十的劳动力，一年坚持施工十个月，就等于抽调百分之五十的劳动力每年突击两个月要完成的工作量。”32在容县，仅从1974年至1975年2月25日，全县动工大小水利工程727处，完工243处；完成造田、造地10896亩（其中造田5337亩，造地5559亩），另开茶山地9059亩；完成改土面积11.63万亩，共用去452.8万工日。33那么在集体化时期，容县在农田水利基本建设上大概用了多少工呢？

    图1显示，新中国成立后，容县在集体化时期的农田水利基本建设完成的劳动工日的变化。由于这是官方统计资料，所以其中的数据只统计较大的工程，如华六大队除了大陂水库，其他四个小水库均未统计在内，34即还有很多大队、生产队自主修建的小型水库、山塘、沟渠等都没有统计在内。即便如此，以上数据也在总体上体现了集体化时期劳动投入的规律。新中国成立初期，由于国力较弱，集体经济制度还未建全，人们只能对小型水利设施进行修缮，投入的材料和劳动都很少，只有23.5万工日。1953年至1959年间，容县从农业互助组过渡到人民公社，完成的工作量明显增加，完成劳动日35也随之剧增，特别是1958年前后，也就是在“大跃进”时期，劳动投入达到一个小高峰，共投入565万工日。在1970年到1978年间，无论是在工作量上，还是在完成劳动日上，都呈现梯度式剧增之势，特别是在1976年，达到历史的高峰，耗费了1007万工日36。1980年以后，农田水利基本建设基本处于停滞状态。另外，在所有完成劳动工日中，水利用工占了绝大部分，主要是用于兴修大小型水利工程。1980年，由于集体经济的解体，大量农田水利基本建设失去了生产队的人力和物力支持。

    综上可知，一方面，在集体化时期，容县乃至整个广西都抽调了大量劳力进行农田水利基本建设。采取的方法是，专业队常年施工与群众性突击相结合。专业队不仅有建设专项水利工程的，还有从事造田、造地等农田水利基本建设的，另外在级别上还分为大队级、公社级和县级的专业队。这样无论是在农忙时，还是在农闲时，大量劳力都被抽调出去进行各类农田水利基本建设。另一方面，这些农田水利基本建设属于共同生产条件的改进投入，对山区生产队的农业生产尤为重要。虽然短期内对农业生产的影响并不明显，但在灾荒之年，它在一定程度上可以避免或者降低灾害带来的减产程度，甚至可以保证部分农田旱涝保收。

    二、生活性公共产品

    人民公社除了为当地提供大量农田水利基础设施外，还为广大社员提供了各类生活性公共产品，包括文化教育、医疗保健和社会救济等。这些公共产品并不是完全由国家来提供，绝大部分是由当地人民群众自力更生、自筹解决的。这些公共产品的积累并不会在短期内提高农业生产率，只有经过较长时段后，才能显现它们的作用和影响力，所以卢晖临主张要“打开视野看效率”，特别是延后的效率。37而要实现这些积累，社员不得不从相对干瘪的腰包中再掏出一部分劳动产品，这样就会导致分配给社员的产品总量减少，体现在工分上就是工分贬值，进而影响社员的生产积极性。

    （一）以民办教师为主的基础教育

    1969年之前，华六大队有两所小学，共4名公办教师，当地整体文化水平较低。据1964年第二次全国人口普查，华六大队有1392人，具有初小（小学一年级至四年级）以上文化水平的只有664人，占总人口的47.7%；石寨公社有18130人，具有初小以上文化水平的有9425人，占总人口的52%，其中只读完初小，13岁—40岁的青壮年有2686人；读完高小（小学五年级至六年级）的有3348人；初中文化水平有606人；高中文化水平有90人；拥有大学文凭的只有9人。38为提高广大人民群众的科学文化水平，1969年广西要求各地将农村公办小学下放给大队、生产队办，农村公办中学下放给当地社、镇革命委员会直接领导和管理。经县、社统一调整后，仍缺教师的大队，根据实际需要，选拔民办教师充实教师队伍。选拔的要求是：家庭出身好，并有一定教学能力，如果是复退军人和知识青年，则优先录用。对于这些民办教师的工资待遇，补助多少由贫下中农讨论决定。39

    1970年，华六大队共有4所小学，1所小学附初中，公办教师7人，民办教师9人。40在集体化时期，公办教师的薪酬全部由国家支付，而民办教师的薪酬由生产队承担（统筹）。华六大队的年终统计表显示，1973年，十队上交了981斤统筹粮和161元统筹金，其中统筹金是为4名大队干部、4名民办教师以及1名兽医上交的。41然而，同年，华六大队共有13名民办教师，一般生产队原则上选派1名教师，十队由于和九队合开一所分校选派了2名。据当时的大队干部介绍，并不是所有的民办教师都可以统筹，只有教得比较好的才有资格统筹。至于没有得到统筹的教师则回各自的生产队记工分，大队再发给少量的补贴。42

    随着教育事业的发展，到1978年，容县有6161名教师，其中民办教师3626名，占教师总数的59%；43华六大队共有7名公办教师，16名民办教师，44民办教师占比约为69.6%。由于小学教师大部分是民办教师，业务水平低，课堂教学中出现差错屡见不鲜，再加上“半天学习、半天劳动”，“以劳代学”的教学安排，学生学习规律被打乱，知识基础较差，甚至出现大量留级现象。为提高教学质量，容县积极采取多项措施，通过举办轮训班，办函授学校、进修学校，开展巡回辅导等提高教师的业务水平。45

    教学质量不高，除了教师能力不足以外，民办教师的工资待遇没有得到很好的保障，使其不能安心教学也是重要原因。“我县民办教师（不包括自筹教师）的生活待遇有两种，一是国家补助加大队统筹，二是国家补助加生产队记工分，不足部分由学校学费或勤工俭学收入补足。不管是采用哪种办法的，都有较长期拖欠民办教师工资的问题。”46拖欠情况包括：一是教师工资统筹不上来。例如1978年，华六大队5个民办教师，总共被拖欠工资551元，人均被拖欠110.2元。二是教师工资未发齐。部分大队给民办教师一年只发十个月的工资，而且每月工资未达到初中教师30元、小学教师24元的标准。三是教师粮食收不齐。部分大队规定，民办教师的粮食要本人到各生产队收，然而，实际上有的粮食收不全，有的收到次等谷。47可见，当时民办教师待遇存在长期拖欠和粮食以次充好等问题，极大地影响了教师的正常生活。

    根据收益分配统计表的数据，华六大队在1980年已经从1979年的13个生产队分为18个生产队，不少生产队内部开始酝酿分田分地了。民办教师的工资和粮食主要由生产队提供，生产队的解体必然会引起民办教师群体的动荡。1981年，容县教育局在汇报普及教育工作时指出：“我县最难解决的问题有：我县民办教师比例大，群众负担较重，近年来，由于生产队体制的改变，民办教师的粮、款很难统筹解决，严重地影响着民办教师生活和工作的安定。”48十队的许某正是由于分队，导致报酬没有兑现，退出了教师队伍。“（19）80年分田到户，这里（十队）分成三个小队，我们没有统筹得上，我就不做了。”（XJA，十队民办教师，2017年3月25日）由于民办教师和其他上调人员的物资、工分很难从生产队进行统筹，教师队伍面临严峻挑战。从表1中1979年至1981年的数据变化便能推断出各级单位大量缩减支出。虽然华六大队和石寨公社的劳动日与分配给社员部分的金额都减少了，但是分配给社员部分的金额占总收入的比重增加了（华六大队增加了7.54个百分点、石寨公社增加了4.93个百分点）。

    为解决民办教师的教学质量和后勤保障问题，1981年，容县教育局和财政局开始整顿民办教师队伍，辞退了思想品质、业务水平和健康状况不能胜任教学工作的教师，业务能力强、业绩突出的民办骨干教师则被吸收为公办教师。49华六大队的民办教师除了部分因为工资低没有坚持下来，大部分后来都转为编制内教师，成为真正的骨干力量。

    容县在1950年至1981年间，小学生人数从13236人增加到78134人，教职工从641人扩大到3749人（含民办教师1910人）；中学生人数从898人增加到30435人，教职工从98人增加到2317人（含民办教师1039人）。50小学生数量增长了近4.9倍，教职工人数增长4.8倍，适龄儿童的入学率高达93.4%。1981年，容县中小学民办教师人数仍占教师总数的48.6%。据统计，当年全国有民办教师近396.7万人51，占教师总数的47%。支撑这支庞大的民办教师队伍的是全国600多万个生产队52。保守估算，一位民办教师的月工资约为24元，国家和生产队各承担12元，生产队每年还要另外提供600斤口粮（100斤口粮折价为9.5元，600斤口粮折价为57元），因此每位民办教师需要生产队每年为其支付201元，全国396.7万名民办教师每年需要生产队支付79736.7万元，10年便接近80亿元，平均每个生产队10年共支付1333元支持基础教育。事实上还有相当一部分民办教师没有得到统筹，需要回生产队记工分参加分配。53当然，这些支出是值得的。据1982年第三次全国人口普查统计，华六大队有1720人，小学以上文化水平的人数为1398人，占总人口的81.28%，比1964年高出33个百分点；整个公社有24492人，小学以上文化水平的有20275人，占比为72.95%，比1964年高出20个百分点。54可以说，民办教师在广大农村地区，极大提高了社员的科学文化水平，这些学生在改革开放后，逐渐成长为社会主义建设的主力军。

    （二）以赤脚医生为主的公共卫生

    除了教育事业，农村的医疗卫生事业也主要由生产队负担。在毛泽东要求“把医疗卫生工作的重点放到农村去”的“六二六”指示的推动下，全国各地都把这项工作当作一项重要的政治任务，迅速组织医疗队，开展农村合作医疗。

    为积极响应中央号召，使广大群众看得上病，看得起病，吃得起药，1966年5月1日，容县人民委员会卫生科根据中央文件，制定《关于实行合作医疗的卫生所的有关意见》，这份文件成为容县后来开展合作医疗的重要纲领。“凡实行合作医疗的区，则在全区范围内看病不收诊费（门诊、出诊）、注射费、处置费；凡有条件的卫生所要开设中、西药柜，以利方便病者，减轻社员合作医疗负担，解决医生部分工资和卫生所办公费，还可以解决部分贫下中农的医药困难的减免；医生到生产队巡回要背下乡中西成药下去，以利方便病者，但要实行保本保值，收入归卫生所；诊费、药价要坚决贯彻执行国家规定的标准收费；为了减少病者负担，每个医生（接生员）都要学会针灸、使用针灸和使用中草药医疗疾病。”55从这些意见中可以知道，容县主张医生要通过各种手段，尽可能地减轻人民群众的负担，收入归集体，强调中西医结合，特别要充分利用中药和针灸为社员治病。

    要健全合作医疗制度，除了上述规定外，还要解决好医生的生活问题。1967年1月，容县发布《关于人民公社成立卫生所，医生、接生员实行合作医疗制度的通知》，规定每个公社成立卫生所，每个卫生所安排医生1—3名（逐步配备中、西医生1—2人），接生员1—2人。医生和接生员领取的粮食和工资全部由公社统筹解决，医生的月工资为15元至30元，接生员的月工资为15元至20元，另外，他们每月领取大米30斤；统筹粮由生产队统一送当地粮所，粮所则每月按量供应大米。56此文件对医生与接生员的待遇进行了相关规定，但是“粮食、工资全部由公社统筹解决”只是把问题抛给了公社，工资到底怎么解决并没有明确规定。统筹粮经粮所再转到医生手中虽然更有保障和方便管理，在现实中却很难实行，尤其像华六大队这样的山区大队，离县城路途遥远且崎岖不平，医生每月领粮既费时又费力，所以后来大部分大队都是让医生到生产队挑粮而不是到粮所领取。为进一步减轻人民群众负担，容县对药品、医疗器械的采购和零售价格作出规定：“今后凡已实行合作医疗大队的卫生室到所在供销社（县医药公司各门市部）采购中、西药品，医疗器械不论金额多少，一律按批发价作价供应。各卫生所一律按当地供销社零售价销售。”57这些规定从成本、服务等方面要求尽量以最低价格为广大人民群众提供服务。

    由于合作医疗制度是个新事物，具体怎么做只能不断探索，寻找适合本地的制度。容县采取树立典型、相互学习的办法让合作医疗尽快办起来。1970年石头公社的合作医疗办得较好，成为各公社学习的对象。该公社的卫生队伍包括：大队医生、采制药人员、接生员和生产队卫生员。大队级人员的报酬向生产队统筹，实行工分加补贴的办法，医生一般每月补助5—10元，其他工作人员每月补助3—4元，卫生员则回生产队记工分。对于合作医疗资金的筹集，由生产队统一计算按参加人数支付。每人交1元，其中个人交0.5元，生产队交0.4元，大队和公社各交0.05元。在收费制度方面，大队卫生所一般收挂号费0.05元，出诊费0.1元，注射费0.1元，接生费每个小儿0.5元，这些费用由病人负担，药费全部由合作医疗开支。如果是重病号到公社以上医院治病或住院，合作医疗支付60%，剩余的40%由病人负担。合作医疗主张自力更生、全民办医，贯彻“三土（土医、土法、土药）”“四自（自种、自采、自制、自用）”方针。石头公社各级单位均设有草药室，以草药为主（用量要求达到70%—80%），中药为辅，适量西药备急，其中草药的来源为：抽专人采集和群众献药相结合，三级有专人采药、制药，采药、制药人员报酬由大队负责。58石头公社根据本社的经济状况，各级单位分摊社员的部分医疗资金，同时大力采用中草药治疗疾病。因为容县山多，药材丰富，可就地取材，加上生产队种植草药，大大节省了药费开支。

    “合作医疗是收每个人的钱，那时没有收钱（看病），试过两年吃药不要钱，之后就不行了，反正像大队的企业那样。”（TFQ，三队赤脚医生，2017年3月17日）华六大队的佟某当时是一名赤脚医生，1947年出生，1965年在容县学医，1968年9月在大队开始行医。对于赤脚医生的报酬，他说：“最初几年就是吃工资，（19）68—（19）72年，24元每月，8毛钱一天。工资是从各生产队统筹，整个大队有副业人员、大队干部、医生，全部按照整个村有多少收入，再分配每个月多少钱，各个生产队抽多少上来，统一分配的。1972年以后就是吃工分，做医生就相当于搞副业一样，每天记12分。算起来就是三四毛1天，有些生产队只有两三毛，那时很穷的。以前我们容县村医大部分都是吃工分。一个月是50斤稻谷，一年600斤。”（TFQ，三队赤脚医生，2017年1月6日）但是，对于1972年以后一直是吃工分的说法，在曾任大队干部的陈某那里得到不一样的答案。“医生就是从利润那里支付工资，粮食就从村里统筹，老师的工资和粮食也是从村里统筹。”（CPY，华六大队会计辅导员，2017年7月6日）陈某1974年12月至1978年冬在大队任会计辅导员。59由于合作医疗制度在不断完善，华六大队根据上级相关政策，既实行过工分加补贴，也实行过工资制。

    1972年，广西制定了《农村合作医疗制度试行草案》，规定“凡是参加合作医疗者，按规定交纳合作医疗基金或以药代金。基金由个人和集体（公益金）负担，负担比例由社队根据情况自行确定。由生产队统一计算按参加人数支付”；要合理解决赤脚医生的报酬和口粮，其报酬要略高于同等劳动力的水平。60“合理解决”意味着赤脚医生的报酬既可以是工资的形式也可以是工分的形式，只要合情合理，并能够调动医护人员的积极性就行，所以1973年，容县各公社的赤脚医生报酬存在各式各样的形式，例如“实行工资制，开支从收入中解决……合作医疗变大队企业，收入归大队，赤脚医生实行工分加补贴，全部向生产队统筹”。61到1974年，石寨公社有23个医生，报酬都是以工资的形式发放，每月工资最低20元，最高30元。62到1977年，《广西壮族自治区农村合作医疗章程》规定赤脚医生的报酬为：“实行‘工分加补贴’的办法，每年由大队根据赤脚医生的政治思想、工作表现、技术水平、劳动态度等情况评定，一般应略高于同等劳动力的收入水平”。63那么“工分加补贴”具体是如何实行呢？这在1978年《关于加强合作医疗基金筹集和稳定赤脚医生报酬的请示报告》中有介绍：“每个赤脚医生每月在队记260分或300分，每天补助贰角生活费，每月补助六元，有的补九元，按该医生所在队分值计算工分所得部分，平均每月加生活补贴不达24—30元的，再从合作医疗收入中补足。”641979年，容县179个大队中，实行合作医疗的有155个，共有627名赤脚医生。在本大队报销的比例，大部分在30%—50%之间，上送报销比例在20%—40%之间，其中华六大队的合作医疗报销额度是30%。65

    在广大赤脚医生的努力下，1982年，容县60岁—90岁的人口从1964的26517人增长到42699人，占当年总人口的比重由7.07%提高到7.69%。661982年全国人口已超10亿人，60岁以上人口比重达到7.62%，比1964年的6.13%高出近1.5个百分点，67这在一定程度上说明我国医疗水平和卫生保健系统更加完善，而这离不开无数赤脚医生和基层医护工作者的默默奉献。1980年全国农村赤脚医生总人数达146.3万人，其中女赤脚医生48.9万人，农村生产队卫生员235.7万人，农村接生员63.5万人。68而这些不脱产医护人员的工资、口粮主要靠生产队解决。仅就工资方面，医生的月工资为24元，一年为288元，146.3万人一年工资共为42134.4万元，10年便是42亿元。事实上生产队所付出的要远远高于这一数字。国家只支付了少量的管理费和药费，以非常低的成本构建了完善的农村医疗卫生系统，保障了社员的身心健康，提高了出勤率，促进了集体经济的健康发展。

    （三）保障困难群众的基本生活

    在大部分人的回忆中，似乎并没有什么困难户，因为大家都很穷。然而，贫富只是相对的。在各生产队的账本中，笔者发现不少困难户领取国家救济金的凭证。例如，笔者在八队的账本中看到1972年3月7日，“大队拨来仕华救济金10元，交丽梅领”，69后面还有刘丽梅的印章。大队保存的阶级档案显示，陆仕华生于1932年9月，1972年已40岁，一家6口人，育有两儿两女，均不满10岁。70从这些情况来看，陆仕华一家的生活非常艰难。

    生产队用来救助军烈属、五保户和困难户的资金、粮食，一般是用公益金。公益金“要根据每一年度的需要和可能，由社员大会认真讨论决定，不能超过可分配的总收入的百分之二至三……生产大队对于生活没有依靠的老、弱、孤、寡、残疾的社员，遭到不幸事故、生活发生困难的社员，经过社员大会讨论和同意，实行供给或者给以补助”。71

    除了上述困难户外，还有一类困难户往往被人们所忽视，那就是“超支户”，顾名思义，即一年的收入不足以抵扣一年开支的农户。社员一年的劳动收入是通过工分来兑现的，生产队通过工分把各种生产、生活资料分配给社员。如果他们一年的工分收入不足以抵扣其一年的开支，那么这一年不仅没有盈余，反而欠生产队的钱粮。本文以八队的陆仕忠一户为例展开说明。

    1976年，陆仕忠一户共有7人，夫妻二人加五个子女，大女儿1962年生，14岁，属于半劳力；第二个是儿子，1964年生，12岁，其他均为10岁以下儿童。72从表4的支出中，可以看到，陆仕忠一家支出金额最高的是口粮，全年消耗口粮3548.1斤，平均每人消耗506.87斤，需支付335.17元，占总支出的91%；当年挣得工分8599.3分，每个工分值为0.38元，全年总收入为326.77元，不足以抵扣总支出（367.93元），超支了41.07元。

    八队在1976年共有6户超支，11户有盈余，4户平收，总户数为21户，超支户约占29%。这个比例在华六大队应该说是非常低的。1976年，华六大队超支户高达186户，占比55.3%，欠款共计11287元，73不管是占比还是欠款数额都在集体化时期达到最高值。由于欠款数额不断累积，到人民公社后期，生产队处于入不敷出的艰难境地。

    为何会产生如此多的超支户？这是一个不得不探讨的问题。

    超支户的存在，表面上看是农户挣的工分不够多，不足以抵扣从生产队获得的生活物资，本质上是因为生产队的物资不足导致工分含金量不高，以至于农户的工分不够支付其生活开销。如果物资充足，每一个工分所含的物资就更多，大部分农户的工分是足以支付其生活开销的。而物资短缺又与农业的产出密切相关。那么农业产出为何不高呢？当笔者把这一问题抛给村里的老人时，往往得到的答案是：没有肥料和农药。

    农谚说“有收无收在于水，收多收少在于肥”。“那个时候由于生产条件落后，种子也很落后，肥料在市面上也很少有卖。一般都没有肥料来卖，到后期才有这个碳铵和这个氨水。（19）80、（19）79年以前都是没有肥料供应的，基本上是山上的草皮泥，也就是这些人上山铲这个草皮泥来烧，烧了以后再撒到水田里面去，过去都是这样耕种的，也没有什么杂交种子，都是落后的种子，一般是（收获）200—300斤每亩，现在（每亩）都有1000—1200斤。”（CPY，华六大队会计辅导员，2017年1月6日）“过去主要是没有肥料，没有这个良种，现在则有良种、有农药，所以生产好，过去喝粥也难有喝。”（HZN，六队队长，2017年1月6日）

    在八队1977年的分类账中，“农业支出”记录了一整年的所有支出项目。经笔者统计，八队当年共购买了复合肥2斤，尿素415.1斤；碳铵10950斤，包括一级碳铵和次级碳铵（肥力较低，价格较便宜）；农药品种有“乐果”“毒杀芬”“六六粉”“敌百虫”等；早稻浸谷种2270斤，晚稻谷种2884斤，共5154斤。74八队当年有130.5亩耕地，其中水田117亩，旱地13.5亩（4.2亩自留地），75除去自留地，集体实际拥有耕地126.3亩，两季共252.6亩，平均每亩施1.64斤尿素和43.35斤碳铵，每亩水田要22斤谷种，农药以“六六粉”为主。投入这些生产要素后，当年八队共收获109523斤稻谷，亩产468斤。76

    此外，农业产出低还受到生态环境的制约。正如黄宗智对新自由主义经济学理论批判的那样，农业不同于工业，不是投入的生产要素越多，单位产出就越多，甚至总量和产出几乎可以无限制扩大。把农业等同于工业，本身就是对农业的误解。农业说到底是人在土地上种植植物的有机问题，而不是一个机器生产的无机问题。因为农业生产受地力和生态环境的限制，土地不可能无限产出。77很可能一场洪涝或者干旱就能把农民辛苦劳作一年的成果化为乌有。

    从表5可知，容县在1969年—1982年的14年间，影响早稻的各类自然灾害频繁发生，发生率从高到低依次排列是：病虫害、龙舟水、倒春寒和夏涝。需要注意的是，表5并未统计对晚稻影响较大的寒露风。当这些灾害组合性地发生时，会给农业造成致命打击。例如1976年，由于倒春寒的发生，当地烂秧严重，既损失了大量稻种，又推迟了播种季节。不巧的是，当年不仅出现龙舟水，病虫害也大发生，由于预防及时和经营管理得较好，早稻损失不大。但是，由于早稻种植推迟，导致晚稻插播也推迟，这样就使晚稻在扬花灌浆期遭遇寒露风。“抽穗扬花期遇到寒露风天气，直接影响抽穗开花的速度，使空秕粒增多，降低千粒重，造成减产。”78当年水稻产量八队比1975年减收5874斤，人均分配口粮减少20斤；华六大队减产110075斤，人均分配口粮减少54.9斤；石寨公社减产1178295斤，人均分配口粮减少56斤。79这最终导致华六大队的超支户数量由1975年144户增加到186户，占比为55.3%。同年，容县减产3423万斤，人均分配口粮减少70.6斤，超支户由35005户增加到36779户，增加了1774户。80这些数据说明，农业生产深受生态环境的制约，尤其是自然灾害对农作物的影响。然而，经济学家们往往有意或无意地忽视了这一重要因素。生产队有超支户、平收户和盈余户，其中最容易由不欠生产队转变成欠生产队的农户是平收户。自然灾害对平收户的影响，就像“一个处身于水深没颈的人，即使是一阵轻波细浪，也可能把它淹没”。81可以说，生产经营中的任何一个环节出现异常，都有可能使平收户变为超支户。这也是为什么在集体化时期，人民公社要进行大量的农田水利基本建设。有了完善的农田水利设施，可较好地降低自然灾害对农作物的损害程度，使得农民在面对寒露风时，并不是无能为力。由于容县历年出现寒露风概率最多的时间是从每年10月11日至11月10日82，所以较好的办法是种植早熟和中熟的稻种，这样就可以让水稻在抽穗扬花期避开寒露风，但这需要优良的稻种。此外，根据广大人民群众长期的耕作经验：“有水不怕寒露风”，在寒露风到来之前往田里灌水，就可以保存地温和增加稻田小环境的温度，从而减轻寒露风对水稻的危害。83而要有大量水源，就需要水库贮存水，以及通过相应的沟渠和设施把水引入田中。

    当然，生态环境并不是造成农户超支的主要原因，它只能在一定程度上限制生产队农业产出的总量。造成农户超支的主要原因是国家与集体从生产队中提取了过多物资。国家之所以提取大量物资，是为了满足工业化的需要。陈云在1950年6月说：“中国是个农业国，工业化的投资不能不从农业上打主意。搞工业要投资，必须拿出一批资金来，不从农业打主意，这批资金转不过来。”84刘少奇也认为：“发展中国经济，使中国工业化，是需要巨大的资金的……但是从哪里并且怎样来筹集这些资金呢？……只有由中国人民自己节约……而要人民节省出大量的资金，就不能不影响人民生活水平提高的速度，就是说，在最近一二十年内人民生活水平提高的速度不能不受到一些限制。这并不是为了别的，只是为了创造劳动人民将来更好的生活”。851955年7月31日，毛泽东强调：“为了完成国家工业化和农业技术改造所需要的大量资金，其中有一个相当大的部分是要从农业方面积累起来的。这除了直接的农业税以外，就是发展为农民所需要的大量生活资料的轻工业的生产，拿这些东西去同农民的商品粮食和轻工业原粮相交换，既满足了农民和国家两方面的物资需要，又为国家积累了资金。”86可见，在集体化时期，人民生活水平的提高和加快工业化进程是矛盾的。国家从长远考虑，只能牺牲人民生活水平的快速提高。

    1960年，《中共中央关于农村人民公社分配工作的指示》指出：中央原来规定的总扣留占40%左右，分配给社员的部分占60%左右。如果当地收入水平较高，如每人分配在100元以上的，扣留可以多于40%；如果收入水平较低，如每人分配在50元以下的，扣留可以少于40%。87也就是说，正常情况下，人民公社要向国家和集体贡献大约四成左右的劳动成果。虽然人民公社制度在不断调整，但这一核心规定一直贯穿于集体化时期。1974年玉林地区分配给社员的部分占总收入的比重只有53.94%，该地区当年分配给社员的部分占比最高的是平南县，为55.77%，最低的是陆川县，为48.54%。88当“上下左右向生产队伸手，四面八方挖生产队墙角”89时，社员辛苦劳作一年，分配总量甚至不足一半，超支户怎能不多？

    除了生产水平低、生态环境制约和国家、集体抽取过多物资，还有一个重要原因直接影响超支户的数量，即人民公社的分配制度。分配制度是生产关系的一部分，采用什么样的分配制度取决于生产发展的水平。1962年通过的《农村人民公社工作条例修正草案》指出，粮食分配应根据本队的情况和大多数社员的意见，分别采取各种不同的办法，可以采取基本口粮和按劳动工分分配粮食相结合的办法，也可以采取按劳动工分分配加照顾的办法等。不管采取何种办法，都应该做到既要调动大多数社员的劳动积极性，又要确保困难户能够吃到一般标准的口粮。90虽然国家要求生产队要遵循按劳分配、多劳多得的原则，避免分配上的平均主义，但是在实际分配中，基本口粮占比往往较大，很难进行真正意义上的按劳分配。

    “在目前口粮不高的情况下，必须首先保证各等人口留粮放在安全线上，过分强调多劳多吃，是不符合粮食分配原则，是不正视当前粮食状况，是没有全面了解社员的要求，其后果，必然引起今后粮食安排的被动，亦不能达到发挥全体社员的劳动积极性。”91所以，华六大队在集体化时期粮食分配的70%按人口定量分配，30%按劳动工分分配。在农业生产水平较低的情况下，生产队首先要保证每一位社员都有口饭吃，也就是学者们所说的生存伦理92，当社员的基本生活都难以保障时，生产队就会面临解体的风险。如果国家政策允许生产队切实贯彻按劳分配，多劳多得，不劳动者不得食的社会主义分配原则，社员的生产积极性可能会大大增加，超支户的数量也可能会减少，但是也可能会导致部分农户的生活非常困难，甚至饿死人。这样的结果不仅国家政策不允许，熟人社会中的道德规范也是不允许的。虽然按三七开的比例分配物资具有一定的平均主义倾向，但它在保证大部分人的基本生活和激励劳动力积极出工参加生产活动上较好地进行了平衡。

    三、小结

    第一，人民公社为广大乡村提供了丰富的公共产品，内容涉及社员生产、生活的方方面面。与当下的政策不同，集体化时期的公共产品均由生产队或生产大队自我供给，生产、运输、管理、消费等各个环节都在本地进行，并没有获得足够的财政和物资支持。然而，这恰恰表明集体经济具有社会经济的属性，即经济活动和参与经济活动中的人及其所在的社会网络是紧密地结合在一起的，它们是相互嵌入的关系，集体经济的效益最终是让所有社员都能够受益，而不是像资本主义经济那样，脱离地方社会和文化，以攫取地方社会资源为目的进行经济活动，虽然经济效益非常可观，但是将所有的问题和矛盾都遗留给当地，以竭泽而渔的方式破坏当地的可持续发展。潘毅认为，社会经济的要旨，就是以人为本，立足社区而不是让资本剥削社区，互助合作，民主参与，人类与土地和谐共生。生产不是为了消费，而是为了解决民生，追求共同富裕，是一种多元化的社会所有制。在本质上，社会经济不是服务于资本累积，而是将社会重新嵌入社会关系中的一种新形态的经济模式。93

    正如毛泽东在《中国农村的社会主义高潮》编者按中所言：“人民群众有无限的创造力。他们可以组织起来，向一切可以发挥自己力量的地方和部门进军，向生产的深度和广度进军，替自己创造日益增多的福利事业。”94作为社会经济重要组成部分的集体经济，在生产力发展水平较低的条件下，在农村修建了大量水利设施，尽可能地提高了土地生产效率，同时增强了生产队抗灾、救灾能力。此外，人民公社还广泛组织群众发展基础教育事业和医疗卫生事业。这些福利事业不仅价格低廉，而且在广度和深度上都动员了社员进行自我教育、自我成长和自我保健，满足了社员自身发展的需要。事实上，农民在集体化过程当中所受到的洗礼要远远高于笔者所看到的，包括管理水平、纪律教育和科技创新等，所有的这些都在塑造着“新型农民”，为改革开放后国家的飞速发展，提供了优质劳动力。所以，笔者以为，要实现乡村的再次振兴，必须把广大人民群众重新组织起来，使经济回归社会，尤其是作为社会经济的集体经济，这是一条可供选择的路径。

    第二，通过研究发现，人民公社时期的农业生产效率客观上的确存在效率低下的问题，例如人们的收入水平较低，生活条件改善缓慢等。但是，在这些事实背后蕴含着错综复杂的原因及逻辑，当本文剥离这些原因后再度审视集体经济制度时，发现导致社员收入不高的原因是工分被稀释了。农户总收入计算公式能很好地对此进行说明。

    由于农户的劳动力在一年或者数年内，基本保持不变或者变化不大，所以，农户总工分事实上是在相对平稳的区间内浮动。因此，影响农户总收入的因素主要是生产队的工分值。而导致生产队工分值变化的因素主要有两个，即生产队的纯收入（总收入－生产成本）与生产队总工分数。当纯收入保持不变时，生产队的总工分越多，即分母越大，工分值越小；当总工分数保持不变时，纯收入越少，工分值也会随之变小。所以，工分稀释化主要包括两个方面，一方面是工分的直接稀释，即把非农业生产的工分拿回农业之内进行分配，从而导致工分被稀释，分值下降；另一方面是实物和现金等物质上的间接稀释，即从生产队中抽走、消耗大量物资，减少生产队的纯收入，进而降低了生产队的工分值。如果把各级单位强加在生产队身上的各种“包袱”给抛弃掉，工分值和社员所得将会大大提高。

    第三，在学界，对人民公社批判最多的就是平均主义和“大锅饭”，其中“大锅饭”几乎成了人民公社的代名词，污名化非常严重。笔者以为把造成平均主义的原因归结为人民公社制度本身是值得商榷的。因为“人民公社低效率的原因是综合的，既有公社自身的原因，也有公社自身之外的原因，但公社自身之外的原因是主要原因，而不是相反”。95当国家和集体从生产队拿走过多的剩余产品时，可供分配的产品自然不足，人均占有量也就无法提高，如此才导致所谓的平均主义。经研究，本文发现，在生产力、人力和物资都非常有限的条件下，人民公社的农业生产仍能保持较平稳的增长，实属不易。同时，人民公社为支援国家工业化建设，提供了大量公购粮和农副产品；为满足人们的生产生活需要，在农村地区提供了丰富的基建、教育、医疗和社会保障等社会公共品。可以说，它的效果是多元的。因此，对人民公社的评价，不能仅局限于某一方面或某一时段，而应放大到整个国家层面和历史的脉络中进行考究，才能得出较为客观的结论。

    本文转自《开放时代》2024年第6期

  • 熊谋林：“实证法学”的概念术语回顾与回归 ——基于文献的实证法学研究整合路径

    一、引言

    近十年来，法学界关于实证研究范式和思想的学术讨论异常活跃，各大期刊均围绕相关概念以专题形式发表论文。就在实证法学家们关于如何定义、命名传统概念争论不休时，出现了大量基于裁判文书网和各大平台所发布的大数据而创造出的新兴概念。与此同时，诸多研究用新的范式、范式革命等新词，实现实证研究在近十年脱胎换骨的创新或改变。然而，关于新旧范式的结论已遭到学者的质疑，曾赟就指出“认为实证法学是近来才兴起的法学新范式的观点是值得商榷的”。①当然，近十年的这一场牵涉术语和概念的争议，既有语言翻译和学科习惯问题，②也有可能来源于学者的“代际之争”，③还有可能是“概念泛化”“名副其实”“空谈”的玄学。④但最重要的原因，或许是法学家们对计算机技术过于崇拜，或对新技术、新产品表现出学术恐惧，从而造成对实证分析的基本对象和进路理解过于激进。不少实证法学家们都陷入足够大的样本或全样本的技术陷阱，以为只有这样才能构造近似客观真相的大数据。然而，夏一巍的研究却反映出，大样本对于实证研究没有必要，只需500个随机抽样样本就可达到与几十万样本近似的分析结果。⑤

    事实上，作为方法的实证研究与其他研究方法一起，在“文革”结束后的法制建设过程中即被广泛讨论。那个特殊时代所讨论的法学研究方法，丝毫不比今天逊色，甚至堪称更加出彩。学者们从法学内部拓展到外部，寻求与自然科学和社会科学相结合来繁荣和发展法学研究。这一时期主要围绕计算机和定量分析而展开，并由此直接产生了“数量法学”⑥和“电脑法学”⑦。这两概念从开始就超越研究方法的范畴，被作为一个学科构想而提出，并进一步推动与实证研究相关的讨论。

    遗憾的是，无论是研究方法还是学科概念，当前所讨论的实证研究概念几乎没有注意到二十世纪八十年代的讨论。从某种意义上说，忽视二十世纪八十年代的这场宏伟、壮丽、深邃的讨论，也是穿新鞋走老路的关键。今天所呈现出的学术盛况，是否真的能达到真知灼见的程度，可能需要打上大问号。法学领域的实证研究概念之争，恰恰可以归结于没有进行文献回顾的主观论述。然而，没有文献回顾的概念之争，在理论和学科意义上不仅对实证研究没有任何好处，反而会制约实证研究的发展，其所建立的概念范畴本身更是沙滩上的大厦。一方面，欠缺原始或初期概念的细致研究，仅凭一种想象的概念和领地之争，显然无法综合评估各种概念的来龙去脉。另一方面，实证法学家们提出的以自我为中心的概念，甚至出现与自己先前的学术立场相左，或者与文献不符的失真论断。以左卫民为代表的法律实证研究和以陈柏峰为代表的法律经验研究，⑧主要或直接将苏力的“社科法学”定位在质性研究、个案研究、田野研究上，并由此引发法律经验研究是否是实证研究，以及社科法学和自科法学之争。然而，苏力在提出社科法学时的表述和表达上，不仅肯定社科法学是实证研究，更高度肯定基于数学、统计上的定量研究。⑨

    总体来看，这一场关于如何命名法学领域的实证研究的大讨论，虽然法学核心期刊的论文产出绝对可观，但方家大论基本以自己的学科、背景和理解构筑概念。可怕的是，实证法学家们所营造出的概念争议，事实上也成为实证研究阵营分化的起点。⑩其结果是，在实证研究尚未成形或成为可接受的研究方法之前，出现了力量分散的学术阵营分化。是故，程金华呼吁保持“开放、多元、互补、合作”的学术共同体，(11)尤陈俊也有关于“彼此尊重，砥砺前行……相互学习、借鉴和融合”的评论。(12)

    面对这些概念或术语相互分离、山头并立的局面，已有学者试图从不同角度解决实证研究领域的术语问题。这主要表现为分离和整合两条路径。分离路径，试图缩小“实证研究”的范围，从而将不属于实证研究的范式排除在外。整合路径，试图用新术语的内涵和外延去解决先前术语的问题。然而，无论哪种路径，各种概念论者仍以自己的学术立场或倡导为中心，展现出相互攻击、自我否定、互相蚕食的学术生态现象。

    就分离路径来说，主要表现为曾经趋同于实证研究的学者，逐渐用自己的新兴概念，将自己从传统实证研究中分离出来。例如，左卫民笔下的法律实证研究长期被定义以“数据”为核心的定量研究，甚至直接用“前统计法学”“计量法学”“定量法学”来概括。(13)近年来，左卫民不仅认为计算法学“可以视为法律实证研究的衍生或者2.0版”，(14)而且创立以大数据为中心的“自科法学”，从而与传统实证研究的小数据和社科法学基于个案的“‘实’而不‘证’”相分离。(15)曾赟在论述数据法学应该是独立于实证法学、计算法学的新学科时，也提出“不宜将定性研究归于实证法学研究”。(16)张永健和程金华在探讨法律实证研究的内涵时，本意是用“法律实证研究”的两种形态来整合“实证法学”和“实证社科法学”，将定量和定性的研究都放在实证研究体系之下，但他们围绕“是否应用社会科学的范式”所创造的两种概念，事实上又成为加剧社科法学和法律实证研究差异的重要诱因。(17)与此同时，侯猛注意到法律实证研究的名称问题，鼓励用“实证研究”“经验研究”“定性研究”或“定量研究”来区分各自的差异。(18)陈柏峰为建立田野调查的质性方法坐标，创设基于质性的“法律经验研究”，刻意区别于以定量为基础的法律实证研究。(19)

    就整合路径来说，各种新兴概念都在试图统筹和统一其他既有概念。左卫民基于法律大数据时代的特性，认为人工智能法学、计量法学、计算法学的概念周延性“值得推敲”，“自科法学”更加妥当。(20)马长山在认识到“近年来各地设置了名目繁多的新兴学科，如互联网法学、信息法学、人工智能法学、数据法学、计算法学、认知法学、未来法学等”后，提出应当将这些新名称统一为“数字法学”。(21)马长山的观点得到姜伟的支持。(22)胡铭在谈到数字法学的相关概念时，认为其包括网络法学、数据法学、计算法学、人工智能法学“等基本板块”。(23)苏宇却试图把数据法学、网络法学、互联网法学、网络信息法学、数字法学、计算法学、人工智能法学等新概念用“信息技术—法学”融合在一起。(24)刘艳红在谈人工智能法学领域的名称不一、内涵不清、学科归属不明的问题时，将网络与信息法学、数字法学、大数据法学、计算法学等统一在人工智能法学之下，并在“法学一级学科之下设置全新的二级法学科”。(25)肖金明、方琨在高度赞扬计算法学时，认为这是“对人工智能法学、数据法学到数字法学的理论概括”。(26)

    本文不希望从概念术语发展体系上提出新概念，而是告诉读者这些概念的前世今生，并基于整合路径重申什么术语才是统一法学领域的实证研究、实证法学学科并促进其发展的最好术语。

    二、中国实证研究的当代起源：钱学森的系统工程及其影响

    (一)系统工程下的法治系统工程学和系统法学

    从知网文献来看，源于二十世纪八十年代的法学研究方法的讨论，主要是关于数学研究方法和系统科学的讨论，这其中不乏包含实证研究的宝贵结论和分析。

    1979年，钱学森先生在其系统工程的总体框架下，号召建立包括法治系统工程在内的14个系统工程。(27)受钱学森的影响，吴世宦发表了《建立我国法治系统工程学浅议》一文，围绕数学表达式、数据表格或网络图形、语言方式模型，呼吁建立评价法治状况好坏的法治模型评估和法治系统工程学。吴世宦认为，法治系统工程，需要用模型和最优化解决。他提出一个可科学表达法治状态和法治状况的数学模型，因为这有利于研究思考问题、集体讨论协调、应用计算机、定性定量分析、建立通常方法。他认为法治系统工程主要是对法治问题作出治乱预测、系统分析、方案评比、政策评价，并给出符合法律制度方案的最优决策。(28)

    针对吴世宦的论文，钱学森指出“系统工程如同土木工程一样，是直接改造客观世界的，是技术工作，不是什么‘学’；围绕有关法治的模型建构问题似乎是个社会学的问题”。(29)钱学森的评价和建议对吴世宦有所触动，他们相互折中，随后合作发文，号召使用电脑和系统工程的方法，建立社会主义法治系统工程。(30)他们强调使用电子计算机和系统工程的方法，应用电脑办理案件、检索和检查典型案例、建立犯罪治理工程和法律咨询中心，对法律进行纵向和横向系统性分类。(31)

    紧接着，钱学森将他和吴世宦的文章总结为6条，包括：建立法制信息库，把资料、法律、法规、规定、案例等存入库里；将信息库用于法制工作中，检索资料、情报、档案，以提高律师工作效率；运用普遍正在搞的人工智能、知识工程和专家系统技术；利用计算机建立系统识别技术，识别办案线索，理出真实案情；利用计算机检索法律，识别出法律漏洞，建立完善周密的法制系统；建立法制和法治系统和体系，但需要做具体工作。(32)

    自此以后，广大研究者不仅深入讨论法治系统工程，(33)而且从方法论阐述系统科学或系统论对法学研究的意义。(34)夏勇和熊继宁等尤其在谈论系统科学方法引入法学领域时，分别肯定了以中国法学现状、实践第一、以经验材料为基础的“三论”科学思维。(35)韩修山在讨论信息论、系统论、控制论对法学研究的影响时就提出，“科学研究的内容由对事物及其运动规律的定性分析转入定量分析，日趋数学化、精确化”，并论述法学不能脱离“三论”。(36)自此以后，法学研究方法的讨论如火如荼，但基本没有偏离钱学森和吴世宦所设计的框架。

    吴世宦的专著《论法治系统工程学》虽然并没有给出法治系统工程学概念，只定义了如系统、工程、系统工程等相关的内容，但阐明了系统工程事实上就是应用定量研究，“从应用的角度来说，系统工程实际上就是定量化系统思想方法的实际应用”。(37)由此可见，“法治系统工程学”有着明显的实证意蕴。

    (二)作为独立学科的数量法学与电脑法学

    1985年4月26-28日，中国政法大学法治系统科学研究会与中山大学法治系统工程研究会联合发起的全国首次法制系统科学讨论会举行。这次会议是“把以系统论、控制论、信息论为代表的现代科学成果引进法学研究和法制建设领域的初步尝试”。(38)钱学森受邀参加此次会议。钱学森基于数学方程、数学模型、电子计算机模拟建立法学系统工程的理论，提出需要“把法学这门学问现代化”的宏伟设想。在具体路径上，他明确指出“要用电子计算机，就是要定量”。他给这门现代化的学问命名为“数量法学”，具体依据是数量经济学和中国社会科学院已经成立的数量经济研究所。钱学森认为会议只是开端，请司法部部长邹瑜“下决心建立个研究单位”，因为“需要一支强大的队伍”。(39)

    从知网文献来看，作为方法的法学领域的实证研究，最初以钱学森命名的“数量法学”而提出。这个概念从提出时就具有法学学科下的二级学科概念。宋健明确把社会科学的定量研究方法纳入其中。(40)与此同时，吴世宦对以计算机为核心的法治系统工程学也有不同理解。他提出“电脑法学”概念，认为其“以研究电脑与法律的相互关系为对象，是运用系统科学思想研究电脑在法学领域的应用保护和发展的原理和方法，探索法治最优化途径的科学”。(41)后来，吴世宦等提出了利用数学模型建构法治系统和系统工程，包含法律规范、行为和心理控制、经济法、青少年犯罪治理、量刑、森林经营系统、整治“不正之风”系统工程的模型和模拟。(42)

    (三)计算机主导下的数学方法和定量分析

    二十世纪八十年代所讨论的系统科学思想，以计算机、数学和定量分析为基础，为整个法学界和法学家展现了全新的视角。较早参与计算机法律话题讨论的，应该是龚瑞祥和李克强。他们在1983年发表的《法律工作的计算机化》一文中详细介绍了西德、苏联、美国运用计算机处理法律工作和资料的方方面面，充分肯定未来的世界里计算机将参与到每个工作环节，强调计算机的定量分析重要性。“现代社会和科学的发展，还要求进行定量分析，要求有系统的观念，用复杂的系统来如实地反映复杂的系统。”计算机引入法学研究后，才可以对各种复杂因素展开定量研究和系统分析，因为“法律现象作为一种社会现象十分复杂，数据庞大，随机因素很多”。(43)他们将这种变革，称为“法律科学方法论的革命”和“社会科学化”“法律工作计算机化”的新纪元。(44)应当承认，龚瑞祥和李克强的这篇论文受到钱学森和吴世宦的影响，文中不仅多次提到控制论、信息论、系统论、法学控制论、法治系统工程学等内容，而且高度肯定计算机参与整合、运算、处理资料和情报等法律工作。

    值得注意的是，这一时期各种法学研究方法的讨论呈现出两个特点。一是基本每篇关于方法论的文章，都要提到数学和计算机运算的影响。(45)二是一些文章专门探讨数学方法在法学研究中的影响和运用。(46)计算机技术所承载的定量分析或定量研究，几乎是所有方法论文章反复论述的内容。

    沈志坤总结了二十世纪八十年代法学研究的十大新趋势，其中有三点与实证研究显著关联。第一，多学科的综合研究，将数学和预测等新自然科学技术嵌入到“纯法学”中，法学与经济学、社会学等人文社会学科结合。第二，开始注重定量研究。第三，研究手段的更新，主要表现在信息化收集、处理和法学研究从个体走向集体研究。(47)孙国华也总结出二十世纪八十年代法学研究的四个新趋势，每个都事实上有实证研究的味道。一是社会学化，即把法律现象作为社会现象对待，运用社会学方法来研究；二是数学化、科学化，即把数学方法、现代一般科学方法引入法学研究，采用包括计算机在内的储存和处理资料的手段实现数量和定量分析；三是多方面性和综合化，即对法律现象进行多方面综合研究；四是大科学化，从个人朝集体合作研究发展，吸收经济学家、社会学家、心理学家、数学家、统计学家和其他专家。(48)

    钱学森的系统工程和吴世宦的法治系统工程(学)，启迪着改革开放后的一批批法学家。正如熊继宁在缅怀钱学森的讲座中总结的那样，“现在看来，钱老的学术思想，仍具有相当的超前性，我们至今仍在为实现他当时的设想而努力”。(49)也正因为“超前性”，钱学森提出的数量法学和吴世宦的法治系统工程学、电脑法学，在那个刚刚恢复学术生机的年代并没有成形为学科。(50)但这场借助自然科学和数学的方法论探讨，对跨学科、跨专业的交叉研究影响深远，尤其是以数学或定量研究和分析为核心的方法更是广泛衍生到具体的法学科目，甚至提升到法学教育和法制建设的历程中。(51)

    (四)实证法学与相关衍生概念

    尽管早期的方法论讨论并未用“实证法学”名称，但实证思想经过系统论或系统科学的讨论后，各种文章逐渐加入“实证”或“实证研究”两个词。(52)熊继宁在谈到法学理论的危机时，指出“原始社会有没有法律，并不是靠纯粹思辨所能解决的，它必须借助于实证研究的成果才能说明。但是到目前为止，几乎没有人进行实证的研究”。(53)季卫东和齐海滨对声势浩荡的系统论方法首先提出质疑，转而将实证和实证研究全面纳入法学研究方法，并首次给实证研究做了定义。他们讨论的内容异常丰富，其文章以“实证”为高频词，分别提到7次“实证研究”、10次“实证主义”和6次“实证主义法学”，应该算是法学领域实证研究的里程碑式开端。(54)

    经过长达多年的讨论，或许受季卫东和齐海滨论述的影响，葛洪义在他的文章《实证法学和价值法学的协调与我国法学研究》中正式命名了“实证法学”。他明确提出了作为与规范或价值法学相对应的“实证法学”概念，并定义或强调“实证法学侧重于用科学分析和逻辑推理的方法研究现实中的法律、法律规范和法律制度”。(55)

    由此以观，当代中国法学研究朝科学化道路的迈进史，事实上就是一部实证研究的发展史。作为方法的实证研究，其开端必然与数学、计算机、系统工程、定量分析四个科学命题不可分割。以实际、实践、实证为核心的法学科学化思维并非偶然，既有来自外部学科的影响，也有法学家从法社会学或从法理学角度的内部呼唤。作为学科概念的实证法学，除了数量法学和电脑法学外，还涌现出以实证研究和定量分析为核心的诸多概念，如科技法学、计量法学、系统法学、综合法学、信息法学、司法统计学、法律计量学、计量法律学。(56)虽然这些概念本来同出一脉，但名称却比较混乱。徐永康充分注意到这个现象，并明确指出这是由于观察角度不同，在引进外国的概念翻译过程中因使用习惯和学科用语而出现差异。(57)这一评价颇为中肯，用在过去几十年都一点不为过。

    三、传统概念体系下的相关术语

    (一)实证法学

    实证法学，也有用作“法学实证”。关于实证法学，如前述，葛洪义早在1987年就提出这个概念。(58)但是，这个概念在更早时候作为“实证主义法学”的简称。(59)可能也正因为如此，季卫东和齐海滨才在文章中广泛讨论法律实证主义、实证主义法学和实证研究。(60)直到今天，仍有成果用“实证法学”讨论其在法理学方面的方法价值，(61)尤其是大量使用“分析实证法学”。(62)文献中能够查阅到的用“实证法学”作为标题的文章，主要是某主题的具体研究或在注释法层面运用。(63)值得注意的是，澳门大学法学院最近成立了以刘建宏为主导的“实证法学研究中心”，并在澳门政府的支持下成立“实证法学中心实验室”，旨在将人工智慧和大数据处理技术融入法学研究方法论中。(64)

    熊秉元和叶斌在探讨法律经济学、法律和经济时，认为“实证法学是由实证、而非规范的角度，构建法学理论，采取的方法论是‘先了解社会，再了解法律’”。(65)但他们没有具体讨论“实证法学”概念。张永健和程金华在论证法律实证研究的概念和外延时，认为法律实证研究包含实证社会科学和实证法学，两者差异表现为英文和中文学术的差异。他们笔下的实证法学是“仅对法律进行实证分析”“仅对法律现象做实证分析”“一种是不应用社会科学范式，但运用资料对法进行实然分析”“只研究法律相关的事实问题”“与法学以外的问题或者知识并没有直接的关联”。(66)笔者在早期讲座中，从研究层面使用和解读了“实证法学”概念，“一切致力探索事实真相、证明或解读法律运作机制等研究，都是实证法学研究，具体包括访谈、问卷调查、案例分析、大数据研究等”。(67)最近，由丁文睿翻译的论文再次使用了“实证法学”概念，但文中也同时使用“实证法律研究”。(68)

    《法学研究》在2013年第6期刊发左卫民和黄辉讨论“法学实证研究”的两篇文章，但均未涉及术语概念，而是直接围绕实证研究展开讨论。(69)尽管如此，左卫民认为“实证研究则是在社科法学的基础上，强调基于实证数据来真实、准确、全面地把握某种法律现象，并在此基础上或进行深度阐释，或提出法律改革建议”，并要从“‘前统计法学’提升到‘计量法学’”，作根本性提升。(70)徐文鸣在论述“法学实证研究”的概念时，借助文献指出“法学实证研究是一种归纳推理的方法，从广义上看包括任何系统地收集、整理和分析信息(数据)的研究”，并在区分定性和定量基础上，提出定量分析是狭义的法学实证研究，“强调遵守统计学的基本原则和程序，收集、处理和分析大样本数据”。(71)

    (二)实证主义法学

    实证主义法学，早期在法理学内部作为方法讨论，故也常用成“法学实证主义”或“法律实证主义”。究其本质来说，实证主义法学仍然是实证研究。如前述，季卫东和齐海滨围绕实证主义法学、法律实证主义和马克思主义的实践法学来讨论实证研究。(72)刘同苏在系统论述法理学上的学派概念时，用“法律实证主义”来区分自然法学派，他将法理学从哲学独立成为自成学派的学科归功于法律实证主义，尤其是将奥斯汀称为法律实证主义的第一代大师，将其功绩定位为描述了“法理学的对象是实在法”，将凯尔逊的贡献总结为表达了“只有实在法，才是纯粹法学的对象”。(73)刘同苏所运用的“法律实证主义”虽然限定于法理学贡献，但其论述法律实证主义的实证方法时又用“实证就是现实的验证，就是客观试验”。(74)

    新近几年有学者将实证主义法学作为实证法学家所理解的“实证研究”来讨论。何柏生从数学角度论证实证主义法学时，就认为“实证主义法学是一种描述性的法学理论，重视逻辑分析方法和量化分析方法，摒弃法的价值，将法学的研究对象限定于实在法领域”。他认为法学要想科学化就必须数学化，因为“法学问题的不断定量化才是法学不断走向科学化的关键”。(75)虽然何柏生的论述反映出定性和定量研究的双举，但他所定义的实证主义法学更多是定量研究。

    事实上，法理学界所用的法学实证主义、实证主义法学、法律实证主义，应是对英文positive law或者legal positivism的翻译有些问题，更准确的含义可能是存在法、实在法、成文法，或法律存在/实在/成文主义。其本来的含义，大概是法律是“制定(laid down and set firmly)”或“存在(exists)”的法。(76)然而，实证主义法学从实证角度理解法，反而在概念上阴差阳错地走向了以实证为核心的实证法学道路。其最大的贡献，是讲明了法律的起源和法律的内容。例如，博登海默在评价奥斯汀的positive law时指出，“奥斯汀希望将普通法排除在成文法之外，因为普通法并不能归结为是君主的命令”。(77)也正因为这样，奥斯汀所提出的法律是由一个君主(或他的代理人)所发出的命令，才招致legal positivism是关于“独裁”的批评。(78)

    虽然从白建军的论述来看，实证分析和实证主义哲学完全是两回事开始，后续研究在论述相关概念时也都标榜实证研究与实证主义法学有区别，(79)然而，诚如季卫东和齐海滨所描述的一样，实证主义法学只不过是实证研究的早期形态，只是表达不同而已。尽管翻译词汇在汉语中已经形成习惯，并且一时半会改不了，但这却歪打正着地肯定了法学研究的实证精神，实证主义法学从一个纯法理学问题上升到各种法律部门的实证研究。因此，实证主义法学的本质依然是以实证为核心的法律或法学研究流派，故本文将其放在传统概念中讨论。

    (三)法律实证

    法律实证，也有用作“实证法律”，在早期作为“法律实证主义”的法理学派出现。作为耳熟能详的实证研究的方法术语，多以研究或分析作后缀，但2000年后才差不多得以大量使用。

    从知网检索情况来看，白建军在区分“实证”和“实证主义”关系后，较早地提出法律实证分析概念，将其作为一种分析和研究方法呈现。(80)在后来的专著中，白建军将“法律实证分析”提高到“法律实证研究”。(81)他注意到法律实证分析与“实证主义法学”或“实证主义哲学”的联系，“都强调感觉、经验、客观观察在认识活动中的重要性”，(82)但他的贡献是明确将“法律实证分析”区别于法理学上的“实证主义法学”。他认为，实证主义是对世界理论认识的哲学思想，是从事科学研究活动的成果；实证分析是研究方法、认识工具，是获得理论认识所凭借的工具；实证分析不同于实证主义哲学，法律实证分析也不等于实证主义法学；法律实证分析只是法学研究的一种具体方法，不是一种独立的法哲学或法理学理论。据此，他认为，“所谓法律实证分析，是指按照一定程序规范对一切可进行标准化处理的法律信息进行经验研究、量化分析的研究方法。也可以说，法律实证分析就是其他学科中实证分析方法向法律研究的移植，借助实证分析方法改造法学传统研究模式的一种方式”。(83)值得注意的是，白建军笔下的法律实证分析包含定量和定性的研究，绝不能误认为他的法律实证只包含定量研究。他关于法律实证分析的三个要素，两个分别指向了“经验”和“量化”。

    左卫民认为，法律实证研究，“本质上是一种以数据分析为中心的经验性法学研究。详言之，就是以法律实践的经验现象作为关注点，通过收集、整理分析和运用数据，特别是尝试应用统计学的方法进行相关研究的范式”。(84)他笔下的法律实证研究定位在“以数据分析为中心的”定量研究中，并明确肯定“可以认为是一种‘定量法学’”。(85)在进一步解读后，他认为“法律实证研究是一种法学研究范式，其研究对象和研究方法与具有‘血缘关系’的经验研究存在较大差异”。(86)

    张永健、程金华从简单和复杂的二维层面，论述法律实证研究涵盖“法律+X”的实证社会科学和只对法律作实证分析的实证法学。他们将法律实证研究定义为“研究和‘法’有关的各种事实”“只要应用资料的(定性或者定量)方法去分析法律”。(87)他们将法理解为广义的“法”，包含立法者制定的法律，行政机关制定的规章，法院的判决，社会规范，以及与法有关的人，并认为法律实证研究包括定性研究和定量研究两种范式。(88)总体来看，他们试图用“应用资料”来整合社科法学和定量实证研究的道路值得肯定，但使用的各种概念内涵不明，甚至因交叉使用而疑问处不少。

    陈柏峰将法律实证研究限定为“对法律问题的定量实证分析”。他明确指出，“法律实证研究以法律规范为参照，通过逻辑演绎来说明变量之间的规律关系，通过中立观察所获取的数据来验证理论假设，用数据统计方法分析法律现象中的数量关系”。不仅如此，他还将法律实证研究限定在“大样本”中，“法律实证研究强调针对研究对象收集较大范围内的样本和数据，根据大样本数据的分析得出结论，阐述因果系”。(89)

    (四)社科法学

    学界公认苏力是“社科法学”术语的提出者。苏力所表述的社科法学，仍是实证研究的一种称谓而已，而且更多是参考法律经济学、数学、统计学论证定量研究。其文章《也许正在发生——中国当代法学发展的一个概览》出现“社科法学”13次，“实证研究”出现6次，“经济学”出现4次，“数学”和“统计学”各出现2次以上。例如，苏力指出，越来越多的学校和课程讲授分析论证的方法，数学公式普遍进入教室，更多学者注重当代社会科学的实证研究传统。他把这种现象总结为社科法学派，最大的共同特点是，从法律话语与社会实践联系起来考察其实践效果，侧重于用实证研究去发现因果关系，发现法律实践的制度条件。(90)

    然而，这篇文章虽然反复论述社科法学，但苏力并未给“社科法学”下定义。可能也正是因为如此，社科法学才长期被误解为是只注重或侧重于田野调查的质性研究。从苏力描述的内容来看，他将社科法学和实证研究的框架勾勒了出来，尤其是肯定运用统计学对社科法学的重要性。他在肯定实证研究的贡献时，呼吁社科法学学者关注现实、注重实证研究，以此作出理论贡献。他基于“更多的专业化的实证研究成果的出现以及它们的方便获得”，对社科法学持乐观态度。他在谈到以统计学和定量实证研究的学术市场转变时，提出“我们的法律正处在一个向将由比喻意义上的统计学家和经济学家主宰的过渡期”，这更有利于社科法学的变化。(91)

    在《法律与社会科学》创刊序里，苏力谈到中国法学界的转变时，再次提出必须以定量实证为中心大力度地促进社会科学的发展，“就是要实证……但更要注意现代社会科学的研究方法，包括统计分析和博弈论”。(92)这些内容再次高度反映出，苏力笔下的“社科法学”应当是定性和定量相结合的实证研究，而且更加注重定量研究。在苏力看来，只有“知识的转变和社会科学的兴起也才可能参与真正的世界性的学术竞争”，这是中国文明重新崛起的需要和必然。为此，苏力表达了《法律与社会科学》的宗旨和目的：“努力推动法学的经验研究和实证研究，推动法学与其他诸多社会科学的交叉学科研究。”(93)

    自苏力提出社科法学后，有学者尝试解读社科法学。例如，王夏昊解读为“社科法学是指以其他社会科学的方法研究法律的学科的总称”。(94)更具体的内容，大概在2014年前后，才由苏力或侯猛单独或一起总结完成。在《法律与社会科学》2014年8月出版的年刊上，徐涤宇在“什么是社科法学”的框架下，提出“首先要讨论的问题是社科法学到底是什么”。侯猛据此正式解读出与王夏昊相似的社科法学的概念，但他的贡献是放在英语世界来理解这个含义。侯猛认为，英文概念“Law and Social Science”或“Social Science of Law”比中文概念“一种跨学科的研究或者说跨学科的知识，即法学和其他学科的知识”更加清晰。侯猛进一步强调社科法学的基调是跨学科，并因为对法学的交叉科学、跨学科法律研究、法律和社会科学、跨法学等总感觉不行，以及鉴于更简洁和上口的表达、更容易和法教义学对话等原因，“直接称为社科法学”。(95)

    苏力在2014年9月发表的文章中，将社科法学全面解读为，“是针对一切与法律有关的现象和问题的研究，既包括法律制度研究、立法和立法效果研究，也包括法教义学关注的法律适用和解释，主张运用一切有解释力且简明的经验研究方法”。(96)与侯猛从知识层面来解读不同，苏力这次从“研究”和“经验研究”层面来解读，而且还包含他早期区分的“诠释法学”或“法教义学”。虽然苏力的概念中仍然有“一切有解释力且简明的研究方法”，文中也明确表态“社科法学强调并注重经验和实证研究”，(97)但这篇文章中没有再用“统计学”或“统计分析”等定量研究的相关词汇。在界定是否为社会科学研究上，苏力提出“社科法学的研究不应当仅仅以学者的学科出身来界定，而应当以其研究法律问题的思路和方法来界定”。(98)但学科出身和思路、方法到底是什么，经验研究和实证研究到底又是什么，苏力没有回答，这或许暗示苏力也已开始改变早期的社科法学含义，并支持法律经验研究与法律实证研究分离。

    就在苏力发文的同一期学刊中，侯猛再次提出“社科法学的英文名称是Social Science of Law。中文直译‘法律的社会科学’，只是简称社科法学而已”。在文章里，他虽然澄清社科法学不能被“误认为是法学的分支学科”，(99)但在评论“不再是法社会学”时，事实上又在强调“法社会学转向社科法学”。(100)或许，从某种意义上来讲，社科法学可能正是法社会学的代名词。这样的论断从季卫东将社科法学和法社会学交替使用，也可看出其端倪。(101)6年以后，侯猛发文再次更新了社科法学的定义，将其描述为“法社科研究，全称是法律的社会科学研究(social sciences of law)，又简称社科法学，是指运用社会科学的知识和方法来研究法律问题”。(102)与之前社科法学处在云雾里不同，侯猛这时的解释应该才是最清晰和直接的。在文章中，侯猛提出了与苏力早先所强调的社科法学的进路有相似之处，但社科研究与实证研究的关系上却有所不同。一方面，他肯定社科法学和实证研究的通用含义，因为社会科学方法也包括定性和定量。另一方面，他又注意到运用自然科学进行法律实证研究不能与法社科研究等同，同时并非所有法社科研究都可称为实证研究，尤其是冯象和苏力的法律与文学作品不再是实证研究。(103)

    总体来看，社科法学的定位、内涵、外延在不断变化中，但各种讨论都充分肯定社科法学与实证研究的相同或相似性。侯猛早期事实上一直在强调实证，不仅在文章标题中同时使用社科法学和实证，而且在内容上也肯定实证研究。同时，他在论述社科法学的优势时指出，“实证研究，也是社科法学相较于诠释法学的比较优势”，尤以“建设实证的社科法学传统”更明。侯猛虽然认为实证研究并不等于定量研究，更不能轻视定性研究，但也指出“实证研究的一个基本趋势是定量化”。(104)这些都反映了他对实证研究和定量研究的高度肯定。只不过，自从法律实证研究与法律经验研究分野以后，侯猛才呼吁分别使用定性研究或定量研究，实证研究或经验研究，同时将社科法学区别于法律实证研究。(105)

    (五)计量法学

    计量法学，顾名思义是因计量方法而产生，注重对数量关系变化的法律现象进行研究。后来，这个概念被作为交叉学科上的概念而提出。就中国大陆而言，虽然何勤华较早提出“计量法律学”概念，但“计量法学”这个术语是由屈茂辉领衔的团队创办的“数理—计量法学”研究中心、论坛一步一步发展壮大。经过10多年的“计量法学”用语以后，大概在2022年的第八届数理—计量法学论坛上被更名为“数量法学”。笔者特在2023年年会上请教为何要改名为“数量法学”。屈茂辉及其团队的解释是，“我们最初所称的‘计量法学’乃特别强调对具有数量关系的法律现象进行研究，乃借鉴计量经济学而来，但容易误解为是专门研究《中华人民共和国计量法》的法学分支。为了简便同时也为避免误解，就改为‘数量法学’”。这个名称的转变，恰好与钱学森提出的“数量法学”不谋而合。这也再次说明，本文将当代法学领域的实证研究的源头定位在二十世纪八十年代具有合理性。纵观“计量法学”或“数量法学”的轨迹，二者一直放在法学实证研究中，专门探讨定量实证研究的方法和学科概念。这可以从2022年年会会议综述的表达看出，(106)只不过不像前几届在主标题中加入法学实证研究。(107)

    大概在2008年，屈茂辉获得资助，主持湖南省软科学“法学中的数理计量方法及其运用研究”项目，开始研究计量方法在法学中的运用。为此，屈茂辉在和学生合作的论文中，阐明“计量方法在法学研究中的运用，是指以一定的法学理论和统计资料为基础，综合运用数学、统计学与计算机技术，以建立数学模型为主要手段，研究具有数量关系的法律现象”。(108)在谈到法学研究中为什么需要计量方法时，他们认为这是法学研究对象的全面把握要求，是法律规则制定、适用、评价的科学化要求，是中国法学研究的国际化要求。难能可贵的是，他们在谈到法学计量方法与实证分析的关系时，明确提出计量方法“是实证分析研究范式下的较为普遍的方法或者一般方法，两者是种属关系”。(109)更为重要的是，他们将定性与定量分析作为增加法学科学性的共同路径，随着实证分析的地位提高，法学研究从描述性的定性分析层面走向定性和定量分析相结合的新层面。(110)然而，在论证计量方法初见端倪时，他们将源头定位在白建军的实证研究和李晓明的数学量刑、社科法学中的实证研究、刘复瑞的数量法学上。这也就说明，他们没有注意到计量方法就是钱学森和吴世宦笔下的系统工程、法治系统工程、数量法学所表达的定量研究方法。

    2010年，屈茂辉和张杰首次阐述“计量法学”概念。与先前只谈方法不同，这次是从学科和方法两方面阐述。他们所描述的计量法学，无论是从方法还是学科上都没有离开“法学实证”这一关键词。在研究方法层面上，计量法学和传统法学不一样，“主要运用定量的研究方法并结合传统法学的研究方法进行法学研究”。在学科层面，计量法学“是一门研究具有数量变化关系的法现象的法学学科，它有其独立的研究对象和特殊的研究价值”或“是通过以一定的法学理论和统计资料为基础，综合运用数学、统计学与计算机技术，以建立数学模型为主要手段，来研究具有数量关系的法律现象的学科”。(111)

    2012年，在计量方法方面，屈茂辉更加明确地在定量层面强调实证研究方法。所谓计量法学方法，是“实证研究中通过对研究对象的观察、实验和调查会产生大量数据，必须对这些数据进行统计分析，探寻各个影响变量之间复杂的因果联系”。(112)与此同时，他明确提出这种计量法学方法就是必须使用的定量方法，并阐述了计量法学、计量研究、实证研究对民法学研究的重要性。(113)同年，屈茂辉再次在学科概念下，围绕“大样本”全面阐述计量法学。“计量法学是指通过收集大样本数据，对具有数量变化关系的法律现象进行运用定量研究的交叉学科。它是一门独立的学科，其研究对象是具有数量变化关系的法律现象，研究方法是实证方法和计量方法。”屈茂辉阐明，计量法学的英文渊源是Lee Loevinger于1949年发表的Jurimetrics：the Next Step Forward。(114)在学科价值方面，他认为计量法学是对当前法学研究方法的创新，使中国法学向精细化方向发展，是实现中国法学的国际化途径之一。在实践价值方面，他总结了计量法学的三个贡献：计量法学自身确定的客观标准可作为社会控制和监督的工具，运用系统、实证的观测对其他权力的运用方式来实现；计量法学通过得到控制和监督结果进而反思效用，用定量方法来对政府政策绩效进行评估；计量法学改变了传统方式的法学体系，注重引入统计、计量和社会效果的预测评估方法，更加强调法学的定量、实证和技术性。据此，屈茂辉认为，计量法学是颠覆了传统法学的研究方法。(115)

    2014年，屈茂辉与匡凯发文讨论计量法学的学科发展史。从实证研究的影响出发，基于定量研究的缺乏，论述计量法学在二十世纪八十年代以前、八九十年代、九十年代以后的三个阶段的作用。他们基于实证研究，特别是定量方法还处于推广阶段，提出了中国未来的法学定量研究的三个着力点：在立法预测和立法后评价方面发挥突出作用；引入判决预测和数据论证两个司法运用领域并产生积极效果；定量研究应在建立数据库上加大投入力度。(116)与之前的文章讨论相比，这篇文章虽然在讨论计量法学，但无论是标题，还是整篇文章，都在表达定量实证研究。从屈茂辉近几年的文章标题用语可以明确发现，他虽然是计量法学的提出者，但事实上一直是法学实证研究的坚定守护者。(117)

    四、近十年出现的新兴术语概念

    (一)计算法学

    目前很难精确定位谁是“计算法学”的提出者，张妮和蒲亦非(以下简称“张蒲”，合著《计算法学导论》)应该是这一概念的首倡者。自从这个概念提出以后，广大学者反复在方法和学科层面讨论。然而，张蒲二人所提出的计算法学本身就是实证研究的代名词，更进一步讲就是基于数量法学、计量法学、量化法学而衍生出来的定量研究。

    关于计算法学的概念，张蒲指出“计算法学是以具有数量变化关系的法律现象作为研究的出发点，采用统计学、现代数学、计算智能等技术方法对相关数据进行研究，旨在通过实证研究评估司法的实际效果、反思法律规范立法的合理性，探究法律规范与经济社会的内在关系”。(118)很明显，这里的计算法学，事实上是定量分析、实证研究，或数量法学、计量法学的定量研究。关于定量研究，张蒲在论述计算法学与传统研究差异时明确指出，“计算法学则主要运用定量的研究方法进行法学研究”。(119)关于数量法学的源头，二人仍定位在刘瑞复的文章。关于计量法学，二人提到屈茂辉在2009年发表的《论计量方法在法学研究中的运用》以及洛文杰的《计量法学：展望新纪元》两篇文章。在比较数量法学和计量法学的不同称谓后，张蒲认为“计算有从现有数量推断、预测出未知的意思”，“计算法学与计算机和计算智能联系在一起，以建立计算机网络、大型数据库、强大计算功能为背景”。他们的计算法学只是强调了计算机的作用，将计算机技术应用于法律现象的模拟研究，用计算机建立立法、司法模型，甚至用计算机进行多主体模拟效果。(120)

    张蒲在《计算法学导论》第一章从数量变化、应用法学、量化分析、分析手段的数学和实证、数量关系等五个层面全面阐述了计算法学的含义。(121)然而，这本书余下几章的内容，差不多就是张妮先前量刑失衡和精神损害赔偿的定量实证研究学位或期刊论文。(122)因此，就张蒲而言，计算法学本质上就是定量研究和实证研究，计算法学只是术语差异而已。这从张妮在序言中提到的对白建军和屈茂辉的感激，可以明确找到证据。(123)

    2019年，张妮在和徐静村合作的论文中，更新了她先前的定义，把人工智能嵌入进来。更新的定义在肯定研究层面的含义时，更多从人工智能和大数据挖掘方面强调计算分析的能力。他们认为，计算法学与计量法学、法信息学、计算法律学等概念相关，并将计算法学定义为“是随着人工智能在法学中深入应用而产生的一门交叉学科，使用建模、模拟等计算方法来分析法律关系，让法律信息从传统分析转为实时应答的信息化、智能化体系，旨在发现法律系统的运行规律”。(124)与此同时，他们把计算法学作为学科概念提出，“计算法学是法学与计算机科学、现代统计学的交叉学科，基于现代人工智能技术和大数据挖掘技术，属于法学的研究分支”。(125)在张妮与蒲亦非的其他论文中，计算法学作为“新兴学科交叉分支”被明确复述。(126)

    计算法学在近年朝研究方法和学科概念两个方向发展，但都没有离开实证研究。第一个方向是，将计算法学作为一种研究方法，强调挖掘和处理大数据或海量判决书方面的能力。(127)但无论是否和如何用大数据和人工智能修饰计算机应用技术，基本都在论证实证研究或定量研究的能力和价值。例如，肖金明、方琨就将计算法学定位在法律实证研究中，“立于计算法学作为大数据时代学科演化之果的准确定位，基于法律实证研究的法学范式变革的明确定向”。(128)申卫星、刘云在梳理计算法学与数量法学的概念上，基于从法律计量学、法律信息学走向计算法学的基本思路，提出计算法学是“利用计算工具探索法律问题的实证分析，是指变传统的规范法学研究为以事实和数据为基础的实证研究，特别是在大数据时代，利用大数据挖掘技术对传统法律问题进行实证分析将成为探究法律问题的新方向”。(129)申卫星关于计算法学本身就是实证研究或实证分析的观点或论述，得到广泛支持。(130)

    第二个方向是，将计算法学作为学科定义，并植入数学计算、计量法学、司法统计、数量分析、实证研究等元素。由季卫东领衔创立的中国计算机学会计算法学分会，将计算法学定义为“计算法学包括对于借助计算机科学和技术为手段开展的任何法学研究，其中包括利用司法统计资料进行判决分析和预测的计量法律学”。值得注意，这里的计量法律学，也就是何勤华所使用的概念。在进一步阐述计算能力时，他们还明确使用了“法律实证研究”。在描述其为法治中国搭建一个真正跨业界、国界、生态圈协作的开放性大平台业务方向时，第一个例子便是“基于中国大数据优势的预测式侦查和警务以及电子证据，同时开展关于判决预测和法律文书自动生成的实证研究”。(131)与此同时，季卫东在讨论计算法学的疆域时，也提出“计算法学的基本架构应该具备四个不可或缺、相辅相成的维度，即计量法律、自动推理、数据算法、网络代码”，并运用司法统计、大数据法学、数量分析方法等表现出实证法学的定量含义。他不仅引用张妮和蒲亦非的教材说明计算法学还处于初级阶段，还在讨论计量法律时提到数量分析方法和计量法律学。(132)因此，季卫东所描述的计算法学仍基于实证法学和实证研究而展开，这恰好又回到了他之前的理念和起点上。唯一的问题是，计算法学能否、何时朝他笔下三个维度展开，以及当前是否就具备了他质疑系统法学方法论时所提出的现实基础和条件。

    在最近的研究中，刘建宏和余频也探讨了计算法学的相关问题。虽然他们得出的结论与先前的讨论相似，但他们的立场恰恰是回归“实证法学”。一方面，他们认为“计算法学代表了经验研究的2.0阶段，以数据为主导”，只是在“数据处理量级和数据处理效能上都有显著进展”而已。另一方面，他们虽然高度肯定“计算法学在方法论上强调数据主导和计算工具的应用”，但仍然指出“不能脱离传统法学研究的本体”。(133)从研究中心和实验室的名字叫“实证法学研究中心”和“实证法学中心实验室”可以看出，回归“实证法学”似乎是实证法学家们的初衷和用意。

    (二)法律经验研究

    “法律经验研究”概念首先由陈柏峰于2016年提出并在过去几年里反复被使用。(134)法律经验研究也被描述为“法律的经验研究”，(135)或“经验地研究法律”。(136)虽然这三个术语之间内部有差异，但都以“经验”为核心，都从强调经验研究和实证研究说起。例如，陈柏峰早期在论证法律实证研究与经验的关系时，将对西方理论背后的经验缺乏足够的认识和警醒、过于相信个人生活或调研个案的直接经验、对间接经验缺乏反思，直接总结为法律实证研究的经验偏差，并阐明经验是法律实证研究的一部分。(137)

    从陈柏峰近乎完美的跨界学术历程来看，他事实上是从规范法学或法律社会学开始，(138)将法律社会学的经验研究嵌入社会学的田野调查。(139)他本人也是从注重田野和经验的实证研究者，(140)再到基于田野的社会学或社科法学的坚定支持者，(141)最后才提出自己独立的法律经验研究体系。(142)纵观陈柏峰的学术出版物，无论他是否明确以实证贯注他的研究，无论他用了什么概念和术语，基本上可以将其描述为基于乡村田野调查、经验观察的法学、社会学或法社会学实证研究者。一方面，陈柏峰出版物履历可以给出答案，他的研究基本上是围绕村镇、农村、乡村、基层、“混混”、农民而展开经验或田野调查。另一方面，陈柏峰自己也坦诚，“自2005年进入乡村研究领域以来，笔者坚持走经验研究的路线，坚持田野的灵感、野性的思维、直白的文风，关注了乡村司法、农地制度、农民自杀、村庄性质等多方面的问题”。(143)他也曾坦诚“基于实证分析结论，本文提出了保护贫弱农户地权的政策建议”。(144)只不过，他的法社会学、社会学、社科法学的实证研究道路并非沿着定量方法发展，而是朝定性方向走。关于这个结论，陈柏峰的名著《乡村江湖：两湖平原“混混”研究》封底的内容简介“对当前乡村社会性质变迁作定性理解”便是最好的证据。(145)

    在2016年以前，陈柏峰虽然在各种研究中倾注对田野研究和经验研究的热情，但并没有独立地提出特有称谓。为了展示陈柏峰的法律经验研究的创新性，《法商研究》直接开设“法学新视野”专题讨论其“法律经验研究”。陈柏峰认为“法律经验研究的任务，是对法律现象作出质性判断，分析法律现象或要素之间的关联和作用机制”，“在法律经验研究中，田野工作至关重要，它是问题意识的来源，也是机制分析的场域”。(146)后来的笔谈中，他清晰阐明法律经验研究就是在苏力的社科法学上发展出的成熟方法论，田野调查是获取经验的最主要渠道。(147)然而，如前述，苏力提出社科法学时质性和经验研究论述不多，反而是实证和定量元素更多。

    在后来的研究中，陈柏峰继续将法律经验研究总结为注重田野调查的质性研究，以此区别于他所定义的注重定量研究的法律实证研究。他指出，“将对法律问题的定量实证分析称为法律实证研究，将对法律问题田野调查基础上的质性研究称为法律经验研究，后者特别强调对研究对象的质性把握，强调研究者的经验质感”。(148)然而，在如此看重二者区别的同时，他又用英文“Empirical Legal Research”将法律实证研究和法律经验研究放在一个概念之下。(149)陈柏峰没有深入论证为何法律实证研究可以和定量研究画等号，但程金华的评述或许给予了他启发，“因为定义不同，学者们对于法律实证研究同‘社科法学’和‘法社会学’的关系认定也不一样。美国学者通常把‘Empirical Legal Studies’等同为定量研究……中国也有学者把法律实证研究等同于定量研究的，比如参见白建军……”(150)然而，程金华关于白建军和苏力的总结，也仅是个人理解，而非基于文献的考察。事实上，二者在提出之初都包含定量研究和定性研究，甚至是以定量为主。(151)

    与陈柏峰将法律经验研究解读为质性田野研究不同，侯猛解读的“法律的经验研究”概念，事实上是放在与社会科学研究方法、实证法学研究、法律的社会科学研究、社科法学、实证研究同一水平。关于法律经验的研究与社会科学研究方法的关系，他指出法律的经验研究用宏观社会、微观社会、微观个体三种基本社会科学视角进行观察。在表达法律的经验研究的规模时，他用实证法学研究的英文概念(empirical legal research)。在关于法律的经验研究与社科法学的概念时，他指出法律的经验研究“主要运用社会科学的知识和方法，因此又称为法律的社会科学研究，在国内通常被称为社科法学”。(152)因此，侯猛笔下的“法律的经验研究”是定性和定量的结合，只是“定性方法的运用争议较小，但定量方法的运用就存有不同争议”。(153)贺欣在论证“经验地研究法律”时，也指出法律经验研究的根本特点是，“运用社会科学的方法，从法律的外部来研究法律”。(154)但贺欣笔下的“经验地研究法律”和“社会科学的方法”也是定量和定性的结合，只不过“定量的研究更像科学”，“定性的研究更像艺术”。(155)

    陈柏峰的田野研究和经验调查，绝对助力其成为最了解中国乡村法治的法学家。这无疑与其博士生导师、华中科技大学社会学教授贺雪峰注重经典阅读和田野调查的“两经训练”有关。在贺雪峰看来，“社会学的长处是注重经验，注重用事实说话”，(156)故“中国社会科学研究应该坚持田野的灵感、野性的思维、直白的文风”。(157)从这个角度来看，陈柏峰用苏力的社科法学解读其定性、田野调查、经验研究，也只是围绕自己的社会学博士学位和学术经历解读定性的法律经验研究。也正因为如此，陈柏峰所提出的法律经验研究，在侯猛和贺欣看来，只是社科法学下位概念的定性研究。但如前述，苏力的社科法学概念本身，也只是实证研究的另一种提法，只不过近年来重新冠名而已。各种迹象表明，陈柏峰关于法律实证研究分化的理解，虽名义是定性和定量研究的分化，(158)但更准确的表达应是陈柏峰将过去和现在的理解分化，或者是法学与社会学的分化。

    (三)人工智能法学

    尽管人工智能或大数据已经在法学界被反复提及多年，但“人工智能法学”很长一段时间并没有作为一个正式概念被提出。申卫星将2017年称为“人工智能元年”。(159)虽然多数学者在论述人工智能法学并未直接提及实证法学或法学实证研究，但大量作品事实上又在用实证研究及相关概念反复论述。

    从知网检索情况来看，程龙于2018年正式以“人工智能法学”这一概念署名发文。他认为“为实现具有主体性、整体性、体系性和可对话性的强法律人工智能研究即人工智能法学，需要以研究主体跨界参与、人才培养方式转变、研究方法革新和国际间交流合作等方式达致”。(160)就当前来看，虽多从学科和教学体系方面阐述，但仍有不少学者从研究方法的层面来谈人工智能法学。就作为方法的人工智能法学而言，实证法学家们论述了大数据背景下的实证或定量研究的重要性。例如，左卫民在论证法律界对人工智能的疏离时，尤其强调法学界对定量法学研究不多、善于运用统计方法的研究不多，强调人工智能算法和模型的重要性。(161)与此同时，论述计算法学和数量、数据、数字法学的学者，又反复强调人工智能的意义。例如，季卫东在讨论计算法学时，将人工智能和计算法学结合在一起。(162)即使刘艳红在讨论人工智能法学时并没有提到实证研究，但她呼吁建立传统社会科学和自然科学的新文科时，因注重“法学的实践性”而没有远离实证范畴。(163)郑妮在谈到人工智能法学的概念误区时，也提到“人工智能法学也更具备立足现实、关注当下的基本品格，秉持实证主义法学、实践性法学的思想观念”。(164)就学科体系来看，刘艳红认为，人工智能法学不是“人工智能+部门法学或(计算)数据信息+法学”，而是由“人工智能+法学”交叉融合而成的独立新型学科，所以她建议“应在法学一级学科之下设立全新的二级学科人工智能法学”。(165)

    虽然人工智能法学在学科和教育范畴很难直接与实证法学直接画上等号，但若从钱学森笔下的数学方法、计算机建模、人工智能、数量法学来看，以及吴世宦的电脑法学等来看，人工智能法学仍然可以划归于实证研究行列。当代学者关于人工智能法学的表达和理解，无一例外地运用了与实证法学相关的其他概念。例如，苏宇在对江溯关于法律人工智能的专访中提问：“江老师您好，请问您是怎样接触到‘信息技术+法学’，或者说是数据法学/网络信息法学的呢？是怎样的一种机缘呢？”(166)江溯本人算是开展实证研究的学者，他还承接白建军担任北京大学实证法务研究所主任。(167)更好的例子，应该是岳彩申、侯东德主编的《人工智能法学研究》，几乎每期都刊登实证研究论文。此外，《现代法学》在计算法学专题中刊登人工智能的算法文章。(168)

    (四)数据法学

    较早提出“数据法学”这个概念的应当是何海波。他在迈向数据法学的专题絮语中，从方法层面阐明数据法学就是指“以数据获取和分析为重心的法律实证研究”。(169)不难看出，数据法学本身就是实证研究的一个分支，只是因为数据提供了资料、思路、方法。在迈向数据法学的第二期专题絮语中，何海波明确“为进一步推动以数据为基础的实证研究，我们再次组织这个专题”。总体上来看，何海波没有强调数据法学的特有方法和学科概念，而是充分尊重实证研究的传统定位。这从专题絮语末尾可以充分看出：“《清华法学》历来重视法律实证研究，发表过多篇实证研究文章。”(170)

    与何海波相比，曾赟大力提倡数据法学作为一种独立的法学学科，并认为这是继法教义学、实证法学、计算法学后的第四种法学知识新形态。曾赟将数据法学定义为，“以法律数据为研究对象，运用数据科学方法创造法律数据产品和发现法学知识的独立的法律科学”。(171)基于法律大数据和全样本的研究方法特征，他认为这是数据法学不属于实证法学和计算法学的关键特征。他围绕法律大数据方法归纳出数据法学的三个特征，法律大数据是物质特征，机器学习算法是技术特征，算力支持是动力特征。他在没有比较和论证情况下，就直接判定法律大数据方法与实证法学、数据法教义学、计算法学的研究方法有明显不同。他用SIR模型举例来说明其理由，但没说明其逻辑基础、算法来源、阻断方法的内容。事实上，这个例子本身反映出他笔下的数据法学，恰恰是实证法学或计算法学的一部分。他一方面用“毒品基本传播数RO则可采用法律大数据方法计算得出”说明计算模拟和模型研究方法是计算法学和法律大数据研究的计算方法，另一方面又说明“RO可以通过抽样调查得出，而抽样调查的方法就是实证法学研究方法”。(172)

    曾赟关于数据法学的学科和定位不明且自我矛盾，这也就决定了数据法学不可能是独立学科。按照他对数据法学的定位逻辑，数据法学方法是基于算法的理性演绎和基于法律数据的归纳推理。但理性演绎和归纳推理恰恰是大多数实证研究的品格，只不过是“算法”复杂程度和样本量多少的差异。曾赟本人长期肯定和偏爱实证研究，更主张实证研究限于定量研究。他直接命名为“实证研究”的多篇成果说明，数据法学本身就是实证研究。(173)

    (五)数字法学

    “数字法学”这个中文术语到底谁先提出，可能很难精准定位。从知网和图书检索来看，大致可以归功于马长山或胡铭。马长山注重学科含义，胡铭注重研究方法。但无论如何表达，是否明确肯定或使用“实证”，数字法学在实证研究层面与“实证”都是相同或相似的。例如，广州大学法学院创办的《数字法学》创刊词指出，本集刊“集中展示优秀的数字法学理论最新研究成果，以规范研究、实证研究、多学科交叉研究的方法”。(174)

    马长山从学科层面提出概念时，认为“数字法学是新法科的重要学科，它是以数字社会的法律现象以及其规律性为研究内容的科学，是对数字社会的生产生活关系、行为规律和社会秩序的学理阐释和理论表达”，并认为这是“数字时代法律变革的必然要求和未来趋势，是数字时代的一场法学理论‘革命’”。(175)然而，他论证的数字法学三种演进路径，事实上都与实证研究不可分割。在表达新文科方法论路径时，他指出应突破传统文科的理论工具和研究手段，特别要运用算法，将文科的定性方法与定量方法相统一。在讨论认识论路径时，强调用算法把数字法学视为由归纳演绎向数据分析，由知识理性向计算理性，由人类认知向机器认知的范式转型。在讨论本体论路径时，他将计算法学等作为方法论的参照，阐明本体论中仍然是定量分析。他高度肯定计算法学是现代法学的当代转型，强调数字法学只不过比计算法学的范围和属性更为庞大复杂。(176)

    胡铭在讨论数字法学时，没有给出概念定义，只是围绕“数字+法学”或“法学+数字”讨论基本定位和范畴。但是，他的多方面论述，似乎高度肯定了数字法学仍然是定量实证研究的一种方式和路径而已。在强调数字技术作为法治工具时，他提出改造升级现有定量法律实证研究，有助于更契合社会科学的研究范式。在讨论多元化贡献时，他强调保留法律实证研究等偏社会科学研究范式的方法论，引入大数据、机器学习等方法，数字法学的贡献就是丰富既有以统计学算法为主的工具箱。(177)在最新的研究中，胡铭更是交叉使用“数字法学”和“实证法学”，只不过新增实验方法而已。例如，文章摘要里明确描述“数字法学研究有必要引入实验方法。相较传统的实证法学研究方法，实验方法在挖掘数据规律、确定变量之间因果关系等方面具有可复制性、可验证性等优势”。(178)总体来看，就胡铭所解答的数字法学与实证法学而言，最大的区别也仅在于传统与非传统。

    姜伟和龙卫球编写的《数字法学原理》，采用“本体论”概念，认为数字法学是“将基于数字技术应用而产生的法律现象本身作为研究对象，侧重对于具体法律问题和法律制度的分析”。(179)此外，这本书介绍的数字法学研究方法，包括规范分析、社会学、比较法、计算法学四种。(180)在后续的讨论中，姜伟也将数字法学上升为独立的学科层面，把数字法学看成是法学的分支。虽然似乎看不出姜伟的数字法学概念与实证有多大关系，但他在论述数字法学的学科特点时，又强调数字法学是“综合和交叉学科”“计算性的实证法学”“实践性的理论学科”。(181)因此，姜伟和龙卫球笔下的数字法学，无论是直接归结于社会学的社科法学，还是直接归结于计算性的实证法学，实质都是基于实证研究的实证法学。

    (六)其他概念

    一是认知法学。张妮、蒲亦非以量化为核心，在2021年发文首次提出“认知法学”概念，并认为“从计量法学、计算法学发展到认知法学是法学研究的必然趋势”。(182)然而，张妮本人是从《量刑的模糊评价研究》的实证研究结论开始，(183)发展为法学实证分析、法学量化分析、法学定量研究的博士论文《精神损害的定量研究——以医疗损害赔偿裁判为例》。(184)之后，张妮又从“实证研究”概念开始，(185)发展出“量化法学”，再经计量法学发展出“计算法学”和“认知法学”概念。(186)例如，张妮本人在论述量化法学或计算法学的概念时，依然表达了定量研究是在实证法学基础上发展出来的，“法学定量研究是实证法学研究与现代计算机科学发展的必然趋势”。因此，虽然张妮是各种新概念的提出者，但她的多篇课题成果始终围绕司法案例，利用数学和统计学方法展开法学定量研究、实证法学研究。这也是张妮本人多年来长期混用各种概念和术语的重要原因。

    二是实践法学。这个概念最早由左卫民提出，但他倒没有为“实践法学”定义。不过，按照他的论述，“未来中国刑事诉讼法学的研究应该在坚持规范研究、价值分析的基础上，适度迈向实践法学，不仅要注重借鉴社会学传统的访谈、参与式观察等定性实证研究方法，更要注重借鉴最近几十年来在社会学、经济学、统计学等领域兴起和发展的数理分析等定量方法”，(187)他笔下的实践法学仍是实证研究的另一种表达而已。

    三是自科法学。这个概念同样由左卫民提出。他认为在法律大数据时代，传统的实证研究方法对有限的数据进行基于人力计算的整理、分类与分析，可能不再受到重视、没有生存空间。因此，他认为“法学与其他学科的交融似乎开始由社会科学扩张到自然科学。这种交融的产物似乎可与社科法学相对应地称之为‘自科法学’，即运用自然科学的思维方法以及技术，特别是统计学、数据科学等来研究法律问题与现象的法学研究范式”。(188)他认为社科法学是为了使法学研究经验化，自科法学是为了法学研究的科学化。鉴于此，他认为首先需要思维理念的变革，不再简单借助统计工具解决传统法学研究的问题。其次，自科法学的关键功能是强调法学研究的证伪思维，借助数学、统计学、计算机科学等学科方法排除个人主观影响，利用数据来判断某一法律问题究竟只是局部、偶然现象还是制度流弊。(189)左卫民坦诚无意介入学科之争，传统、单一概念无法概括新的研究范式，但他又认为实证研究在大数据的助力下应该迈向自科法学。这个论断意义深远，不仅暗示着尚未普及就已经作为传统的实证研究可能终结，也可能是将其提倡的定量实证研究提升到新高度。但如前文所述，自科法学的论断事实上与二十世纪八十年代钱学森的论断如出一辙。从左卫民将法学研究范式的讨论定位在苏力的《也许正在发生》可以看出，(190)他同样没有意识到，二十世纪八十年代已有关于法学向社科和自科发展的阐述。

    事实上，如前文所提，我国法学界关于“实证研究”的概念，还有如“法律计量学”“法律信息学”“计算法律学”“信息法学”“未来法学”等，限于篇幅无法一一梳理。但可以肯定，无论是否明确用实证研究，也不管是否坚守实证研究，更不管以哪种定义模式，最近十年的争议本身是围绕大数据分析而产生的新兴概念。从这个角度来说，周翔的评论可能最为中肯，“大数据技术对于实证研究而言有一种接力的价值，两者的共性大于差异。大数据技术主要应定位于加强实证研究的某些环节，但不改变实证研究基本的方法论框架”。(191)但问题是，“大数据”真的就那么重要吗？如笔者几年前早已论断那样，如果没有裁判文书网所承载或开源的判决资料，难道法学家就不研究法律的实际运行状态了吗？

    五、回归“实证法学”的倡议和路径

    (一)多元语境下的实证法学本质

    前文已反复说明，不管这些传统或新兴术语如何解释或描述，法学家们都不约而同地直接或间接阐释实证研究。因此，与其用多种复杂多变的概念创造新意，还不如在法学领域里坚守实证研究这个传统领地。原因很简单，实证研究是前述各种术语的本质，作为研究方法的实证法学当然也是法学家开展实证研究的本质。即使作为学科概念，实证法学也是法学领域、法学教育和法学家身份的本质。与此相比，其他术语只不过是法学家们在多元语境中的替代性术语罢了。对于法学和法学家来说，无论是学科还是方法概念，实证应该是那些善于求真的法学家们讨论问题的起点和终点。理解这个本质，有如下几方面值得注意：

    第一，主流实证法学家在提倡概念或术语多元化的过程中，可能需要根植于实证研究这个基本概念。左卫民应该是思考实证研究较为深入的学者，不可否认也是法律实证研究的守护者。在过去十年里，他在不同文章中围绕定量研究创造和提出了多种概念术语，均在阐明法律或法学实证研究的各种内容。他基于美国定性和定量实证研究范式的迅猛发展，开始用实践法学呼吁用数理和计量研究方式研究中国的客观实践。后来，他的这些观察陆续发展为作为专门探讨实证研究的法律实证研究、实证研究、实证法律研究、法学实证研究、定量法学、计量法学，阐明实证研究的定量、数量、数理特征。直到最近，他用计算法学、自科法学等高级版本，为实证研究做更新换代。然而，左卫民的解读不仅没有使实证研究的概念术语定型和更加稳固，反而因定量研究和数据分析而缩小了实证研究的圈子。

    第二，就传统术语的发展和演变过程来说，实证法学家们应当从源头上始终抓住实证研究这个本质。当代中国的实证研究起源已长期被误解，应当重新将实证研究归功于钱学森在二十世纪八十年代所激励的范式讨论和转型。与此同时，社科法学从提出开始，就没有否定实证研究，也没有否定定量研究，而是包含定量和定性两种范式的实证研究。然而，该中文称谓实在有欠科学性，并且经过近十年翻新，社科法学已从注重定量和定性的实证研究，被解读为只注重定性、经验、田野调查的质性研究。无论是主张定量为核心的实证研究学者，还是社科法学的传承者，言过其实地将质性研究作为社科法学的主阵地，都应当反思实证研究与社科法学的初始关系。与此相比，屈茂辉所在的湖南大学长期是计量或数量法学的主要阵地，以定量的实证研究为特色，有别于个案分析的实证研究，但从不否定其实证法学定位。

    第三，主张新兴概念的实证研究者，应当在科技和技术背景下坚守实证研究与实证法学的原动力和阵地。关于这一点，计量法学(数量法学)论者注意到人工智能对实证研究的影响，但仍然倾力和坚守实证法学研究这个本体，值得赞赏。(192)于晓虹、王翔指出，“计算法学是计量法学进入大数据时代的产物。从学科构成看，计算法学属于实证法学的范畴”。(193)再如，何海波在论述数据法学时，仍然将其作为实证研究的表达方式。(195)还如，胡铭在论述数字法学时，仍然强调定量法律实证研究方法的充分运用。(195)因此，就新兴概念来说，无论是精于实证的法学家，还是本就不擅长或不熟悉实证的法学家，都应当注意，只有做出好的实证研究，才能展现出其学科和学术的吸引力。如果不能如此，新兴概念要么只有概念意义，要么只能是非学术层面的商业或技术概念。此时，擅长实证研究的法学家又如何坚守其学术阵地，历史教训已经相当深刻!虽然二十世纪八十年代广泛讨论研究方法，但除了高赞和畅想概念之美以外，基本没有参与讨论者在其概念体系下坚守发展。否则，中国今天的定量研究早已领先世界，而不会出现仍唯美国马首是瞻的怪圈。

    第四，回到当代中国实证研究的真正起点，不刻意用中断年代或阻断学术传承的方式，创造法学研究的新范式或新概念。中国实证法学家应当做的是，在阅读文献的立场上，在尊重前人术语的基础上，展现学术传承的高风亮节。如果每一个概念都能追本溯源，学术研究早就从华而不实的思想讨论，迈到丰富的具体研究中了。无论概念家们在论证时是否引用，或者是否知道钱学森与吴世宦的贡献，但其围绕数学、统计、计算机分析等各种论述均没有超越法治系统工程和系统科学方法的范畴。数据法学、数字法学、计算法学、人工智能法学与早期的计量法学、计量法律学、数量法学本质上并无差异。只不过，今天所谓的大数据时代为新术语创造了新素材，但这不能成为创造新概念或术语的绝对理由。值得指出的是，这些新兴概念本身是否严谨、概念是否周延，仍有疑问。

    第五，实证法学家们使用外国概念或术语论证传统与新兴概念时，既应充分尊重翻译和用语习惯，也应注意各种外文词汇的原始语境其实就是实证研究。信息化时代即使不懂英语，也可随意通过翻译软件查阅中文含义，然后大论连篇。抑或，随意翻阅几篇中文大论，参加几次会议拾取后生牙慧，参考同行的简短评语，有心的术语家也可凭高超的灵感创造新概念。然而，仅凭这些学术捷径，显然不足以创造出具有理论和现实品格的真研究，反而会因术语翻译陷入无休止或自说自话的争论中。对于传统概念来说，关于empirical legal studies或empirical legal research在中文就有法律实证研究、实证法律研究、实证法学、法学实证研究、实证法学研究、法律经验研究等翻译。英文术语不仅被用在传统概念中，而且还直接或间接用于或论证几乎所有新兴概念中，如计算法学、法律经验研究、数据法学、数字法学、人工智能法学等。对于新兴概念来说，洛文杰的jurimetrics在各种文章中被翻译或成论证成计算法学、数据法学、人工智能法学、法律计量学、计量法律学。然而，应当注意到，洛文杰的jurimetrics是与法理学jurisprudence相对应的概念，“jurimetrics强调路径上的实践(practical)，jurisprudence强调哲学上的思辨(speculations)”。(196)只要稍微阅读原文，便可发现他笔下的jurimetrics更是在肯定“调查”重要性，作为实证研究的词汇和词义而提出。例如，这篇文章empirical使用了3次，statistics或statistical使用了6次，甚至还用了定性qualitatively和定量quantitatively。

    (二)实证法学可以实现整合的路径

    解决了法学领域实证研究的本质问题，技术上就可以探寻整合实证法学的路径。经过前文对各种术语的定义和产生背景的追溯，笔者认为，相比于其他概念而言，只有实证法学可以实现法学领域实证研究的真正统一。在约定俗成和用语习惯的背景下，法学家们都在用“实证”或“实证研究”这两个高频词。既然如此，实证法学在统领各种概念语系后，先作为方法的实证研究，进而上升为作为学科层面的实证法学，也就顺理成章了。一方面，法学家的身份证和法学的学科定位，使得“法学”自然是核心的内容。另一方面，实证作为各种概念体系下的“中心词”，就如身份证所对应的人一样，使实证法学家的个体身份特征才有独特标示。从这两个层面来看，实证法学作为研究方法和学科层面的上位概念，不仅可以统领作为某一种或某一面的下位概念，也是整合学术研究队伍的最佳途径。与此相比，其他概念或术语不可能具有这种得天独厚的优势，也不可能实现学术整合。具体理由如下：

    第一，从语法和法学学科的表达习惯来看，“实证法学”比“法学实证”更好。尽管与其他概念相比，法学实证已经是很不错的表达了，但仍没有达到实证法学的效果。一方面，法学实证中的“法学”是作为形容词“法学的”修饰词，“实证”是作为实词的名词。这样一来，法学实证的重心就没有放在“法学”的学科概念上，反而是将“实证”放在中心词地位强调法学的实证。与此相反，实证法学作“实证的法学”解读，就不仅将“法学”作为核心上位和中心词理解，而且“实证的”也强调了“实证”是作为法学下位概念的修饰词而已。另一方面，实证法学这个称谓也符合其他部门法层面的二级学科用语习惯。既然实证法学要作为二级学科概念，就应当参考其他术语。除了法学理论或法律史学外，与刑法学、民法学、行政法学等其他二级学科一样，实证法学倒比较适合这个用语习惯。从术语渊源来讲，实证法学作为“实证的法学”表达，也和英文的表达结构差不多。

    第二，从术语概念的产生时间来看，葛洪义的实证法学应当是继钱学森的数量法学后，形成较早体系的概念。差不多与实证法学是同一时期的概念，也就是何勤华笔下的计量法律学或计量法学，这也是从数量法学和法治系统工程学所演化而来。虽然数量法学用得更早，也更正统，但这个术语过于强调数学或量化研究。从学科发展来说，如果用小概念而放弃更大概念，因小失大无异于舍本逐末。如前述，无论是季卫东、齐海滨笔下的实证研究，还是葛洪义明确提出的实证法学，两者都基于区分规范(价值)研究的法学方法展开。虽然实证法学概念在初期并未展现出今天的内容，甚至还在计量和实证主义法学之间寻找容身处。但应看到，这一概念扎根“客观规律”或“现实世界”，(197)以及“实际存在的法律制度和它的实际运行状况”。(198)与此相比，法律实证研究或社科法学是更晚概念，其他新兴概念更是最近十年才出现。

    第三，从关于实证的学科和方法含义来看，实证法学比法律实证或实证法律更具有含义。一方面，法律实证或实证法律(研究)是从方法层面把实证作为分析法律的研究手段，这很难上升到学科概念。原因很简单，只有纯规范的法律问题才能展现出与法律条文紧密相关，大量法社会问题和法运行、法经济、法心理、法律史问题并没有直接展现与法律条文相关，而是展现出法学背后的大量其他问题。例如，社科法学或法社会学所研究的问题，本身并不一定与法律条文相关，但他们所研究的每一个小问题又时刻关系到法律的生命力。另一方面，实证法律或法律实证(研究)总是围绕法律而转，而没有从学科上升至与法律相关的学问，这就大大降低了实证法学本身的学术价值。如果能上升到法学的学科和学问层面，实证法学就能够建立与理论法学或部门法学相对应的学术倾向，更与新兴概念号召成立二级学科的使用规律相似。

    第四，从内涵来看，实证法学不仅可树立以法学为中心的学科体系，而且中立地将各种研究方法纳入其中。就学科而言，实证法学比社科法学、计算法学、人工智能法学等各种概念更能突显法学的中心地位，也可避免数量法学或计量法学有模仿经济学的称谓痕迹。这样一来，不会出现社科法学用社会科学这一上位概念掩盖社会学、法社会学的寓意，也不会出现像数量法学、计算法学一样过于强调统计学和计算机专业或技术的特性。事实上，左卫民2013年提出的以定量和数据分析为中心才是实证研究的评价或定义，同时认为社科法学是个案式研究而无法关注普遍性。(199)这才导致法律实证研究和社科法学的方法彻底分离，并最终挤出法律经验研究这个概念，并在近年来讨论得越来越深入。然而，正如张永健和程金华试图调和的一样，法律实证研究包括定性和定量研究。(200)社科法学和法律实证研究事实上都在强调实证，彼此并无否定实证研究特性的意思。多数新兴概念无论是从方法还是学科概念来看，均不自觉地朝算法、大数据分析、计算机等理工科方向跑，这自然也有其固有缺陷。

    第五，实证法学的概念，以极其简便和简单的概念术语回归“法学”，有利于形成以法学家和法科学生为中心的方法和学科概念。就当下讨论实证研究的学者们而言，应时刻以法学家的教育背景和承受法学教育的法科学生为中心。脱离法学家和法科学生的法学教育，或者割裂法学研究者能力的任何高技术表达，都注定只能远离法学。即使听起来多么酷炫和合理，最终只能因过高的起点，让法学家的参与度越来越低，终因方法恐惧而被挡在起点上。例如，那些擅长用人工智能法学和计算法学来包装其学科和方法论者，要么是用高科技公司的数据挖掘和建模技术构建商业路径来吸眼球，要么是为计算机学科作嫁衣。就商业动机来说，高科技概念背后的大数据采集本身就源于法律灰色产业。无论是否愿意承认，大量以司法判决为核心的数据爬虫公司，已用或正用计算机手段违法或违规地进行信息或数据采集。此时，新兴概念所赖以生存的技术路线，已经脱离和远离了学术、学科的法学本来面貌，成为大数据公司挖掘判决文书的最好借口。就为其他学科作嫁衣来说，最好的证据是季卫东所领衔的计算法学，以“中国计算机学会计算行业分会”的形式搭建在中国计算机学会下面。但应当注意，中国计算机学会的官方口号是“为计算领域的专业人士服务”，(201)但法学家在计算(机)领域的专业度又是什么呢？除了法学概念或规则的阐释和帮助以外，技术问题还只能由计算机专家解决，这不过是貌合神离的两张皮。

    第六，实证法学家永远是各种术语概念下的学术核心力量，将各种概念回归实证法学本身也算是名正言顺。当前各种概念所催生的学术组织或学术活动，参加者要么以擅长定性研究的学者为主，要么以擅长定量研究的学者为主，要么以实证法学的综合性或下位概念的支持者为主。再以中国计算机学会计算法学分会的管理层为例，(202)会长季卫东是主张计量法律学、法社会学、社科法学、实证分析、实证研究的学者；副会长左卫民是众所周知的法律实证研究学者；副会长申卫星又是阐释计算法学为“利用计算工具探索法律问题的实证分析……以事实和数据为基础的实证研究”的学者；(203)秘书长林喜芬也是擅长实证研究的知名学者，在中外法学期刊都大量发文。中国计算机学会计算法学分会只是若干场学术活动的缩影，人工智能法学也大概如此。可以毫不隐讳地说，离开擅长实证研究的学者，任何传统和新兴概念注定不可能发展起来。例如，阙梓冰总结的10篇计算法学成果，基本主要来自实证研究的学者或论证计算法学作为实证研究方法的学者。(204)当然，实证法学家参加各种新兴概念所组织的活动，一方面可以理解为拓宽路径的有力形式，另一方面也可理解为新兴概念在挖墙脚。如果是前者，实证法学家需要的恰恰是回到学术起点，先将实证法学发展壮大起来，而不是与自己所擅长或经营的学术阵地渐行渐远。如果是后者，就不必说了。

    (三)“实证法学”术语重申和概念重解

    要整合既有法学领域关于实证的研究或学科提案，需要进一步完善“实证法学”概念。当然，具体定义可能需探索，但整合学术队伍和研究实力在任何情况下是正当的。如前述，笔者在几年前讲座中，从研究层面使用了实证法学概念(以下简称“旧定义”)。(205)然而，在近几年“实证法学导论”授课中，笔者越感自己提出的旧定义不太完整，各种问题还不少。因此，为展示实证法学的宏观路径，笔者2020年就已在授课中重新定义实证法学。本文中，笔者将“实证法学”重新定义为，“利用各种资料对法学相关问题展开实证研究的学问”(以下简称“新定义”)。笔者希望，这可以为实现整合路径做些铺垫。

    从概念内容来看，新定义注重各种资料、法学相关问题、实证研究、学问等四个方面的内容。“各种资料”是实证研究的素材，这在旧定义的解读中已经说明，不仅包含数据，还包含文字、语言、符号、声音、手语、代码等。新定义不问资料以何种形式呈现，只要它是一种素材，不强调数据或经验，只要可以成为和称为研究资料，都是资料。如过去在讲座所描述的，“实证资料的核心是信息转换”，“任何资料的本质是一种信息交流，而有效交流的核心是建立一套恰当的信息转换机制，使不同形式的资料之间有效互通”，“无论资料形式是什么，都是建立在信息交换机制下的产物”。(206)新定义将研究对象定位为“法学相关问题”，换句话说，只要与法学相关的问题，都是实证研究的对象。如此看来，新定义不是仅将法律作为实证研究的对象，而是将与法学相关问题作为研究对象。新定义突出实证研究的本质特征，这不仅可以突出“实证”的方法本质和特征，而且也可由其方法产品筑成作为学问基础的“研究”。总之，新定义与旧定义相比，自认为更为合理，但限于篇幅无法在本文详细展开，如下略述其在方法或学科层面实现整合目标的优势：

    首先，新定义从“资料”层面来看，不仅避免了传统概念的实证维度争议，也避免了新兴概念所倡导的是否为大数据分析的争议。资料决定方法，有什么样的资料，就有什么样的方法。要整合实证法学当前出现的概念争议怪圈，首先要将资料这个基本问题解决掉。就传统概念来说，是否属于实证的概念争议，本质上集中在定量和定性对应的资料之争。张永健和程金华充分注意到这个问题，指出定性和定量研究资料只有形态、获取方式、学术努力方向差异。(207)与此同时，就新兴概念来说，近年来的争议实质就在于计算机技术所代表的大数据、大样本，和传统研究方法的小数据、小样本分析，这本身也是个资料面的问题。唯一的区别是，人财物成本差异和技术路径的实现方式不一致。但只要稍有统计学知识，即可知抽样对总体的代表性原理，夏一巍也充分证明了这一点。(208)因此，“各种资料”只强调资料是哪一种，资料的多少、大小、形式、内容、属性差异完全不是问题。

    其次，新定义从研究对象来看，强调“法学相关问题”，而不仅仅是法律或法律相关问题。就实证法学本身的概念而言，较先前的“法律”对象来说，新定义的概念在广度和深度上更加有利。笔者的旧定义将实证研究定位在“法律运作”，这明显太窄。与此相似，各种传统概念都将“法律”作为概念本体，但“法律”这个研究对象显然不够宽大。(209)就新兴概念来说，不管是否明确表示其研究对象，几乎也是以“法律”为核心，如法律经验研究就定位于“法律问题”。(210)如果能跳出像规范法学那样以“法律”为中心，回归实证法学家所关注的包含但不限于法律的法学问题，没有人会怀疑身在法学院的法学家研究法学问题的能力。

    再次，用“实证研究”概括所有研究方法，可以消除因方法差异而引发的概念和群体分化，实现方法和理念整合。就实证研究本身来说，本来应只有具体方法不同，而不存在是否是实证研究的差异。然而，当前关于是否属于实证研究的概念争议，很大程度上也是方法不同所导致的是否是实证研究的差异。例如，左卫民在长期定义法律实证研究或其相关概念时，都用大范围、大样本、全样本、大数据、超大样本定义实证研究，将其理解为运用统计学或计算机技术的定量数据分析。近年来提倡的法律经验研究以经验和田野的质性研究为核心，并因此区别于基于定量分析的实证研究。(211)与此同时，新兴概念的数据法学和计算法学本身就承认是实证研究，人工智能法学、数字法学等本来也仅是计算机参与程度不同的实证研究。

    最后，新定义将核心放在“学问”上，将从方法和成果层面的实证研究拔高到基于学问体系而成的学科概念。尽管从目前来看，基于方法和成果层面的实证研究还需努力，所有讨论实证法学的学科概念都为时尚早。原因很简单，实证法学不论是从方法和成果层面展示其独特性和可接受性，还是要在新文科理念下形成自己的学科，都必须先由众多实证法学家所组成的“研究队伍”产出够分量的实证研究学问。只有当学问的体系足够丰富和多样，才有可能发展为学科概念上的实证法学。从目前来看，实证法学家们还停留在自己“道”的讨论上，远没有形成“术”的整合，这种局面不可能有助于形成学问体系。因此，只有把握“学问”本身的学术意义，平息概念之争，才有可能实现学问共同体。

    六、结语

    整体上来看，过去十多年各种概念体系下所组织的有关实证研究的年会交流频繁，但基本属于同一个小众群体在不同场合的学术奔波，内行也都明白这些年会都在勉为其难地苦苦支撑。因此，从整个法学研究的科学化道路，以及发展实证研究的研究队伍上看，改变当前学术分化的局面是刻不容缓的。为此，本文在梳理各种相关概念的来龙去脉后，本着壮大实证研究队伍的基本立场，共商中国实证法学发展道路。本文尊重既有文献的讨论，尊重前辈先贤的知识贡献，注重梳理各种概念的前后联系。写作过程中虽保持客观真实和事实描述，但难免因篇幅删减出现描述或表达不到位的问题。文献回顾最难，这不仅因为要评前人，而且可能总结不准。故虽费劲完成，但也可能费力不讨好，甚至还会因此得罪前辈先贤。但笔者相信，中国实证法学的发展必然首先需要整合和统一理念，总会有人为了学术共同理想而挺身而出，只不过是谁和什么时候而已!若能如此理解，笔者最大的心愿是，读者和同仁能回归实证法学和实证研究本身，开展学术研究和共建学术队伍。

    笔者重申，一个人的研究叫爱好，一群人的研究叫队伍。因此，所有致力实证研究的法学家们，应该思考的是实证法学及其队伍的未来发展和学术影响力问题。当务之际是共商和共谋学问大计，搁置人为构置或理解所引发的窝里斗，寻找发展中国实证法学的同一片蓝天。在此，笔者坦诚地呼吁，停止有关法学领域的概念或术语争论，停止一切分裂或分离实证研究队伍的做法，将实证法学作为统一的术语和概念研究中国法制、法治特色，为世界贡献中国法学的智慧。理由极其简单，只有如下四点：

    首先，深入了解当代实证法学真正起源于二十世纪八十年代后，今天及未来很长一段时间关于实证法学的概念和方法讨论，不可能超越由钱学森和吴世宦所引领的数学、计算机、人工智能、法治系统工程的方方面面。除了文字表达差异或技术途径的具体化以外，各方面内容都不会逃离系统论、信息论、控制论三个维度。

    其次，本文已充分展示，各种术语及其概念体系下都是以实证研究为本质，任何关于概念或方法的争议只是文献断代式误解或曲解。只有充分了解和梳理现有文献的情况，才能有理有据地提出新概念。否则，任何唐突地创造概念，或以偏好或擅长为概念基础，只会让实证法学研究永远都停留在概念阶段。

    再次，只有同心协力和万众一心，才能真正实现实证法学研究队伍的发展和壮大。过去十多年，各种学术活动都是新标题、老面孔，真正的学术新人实际很少。虽然名为实证研究的数量有爆发式增长，但主要是概念和思想的量产，真正的实证研究还一如既往地艰难挣扎在起步和发表阶段。实证研究长期被称为小众，原因就在于，真正的实证研究在中文世界产出量少，实证研究的学问体系还未真正建立。

    最后，用最简单、中立、宽广的概念，比用高大上的概念更能吸引新兴学者参与到研究队伍中。实证法学最大的危机，不是仍是或将来还是小众，而在于新概念的技术和理念复杂性，造成没有人愿意和能够加入这个群体。法学的文科属性决定了其与自然科学的课程和体系的差异，如过于强调数学、数量、数理、数据、数字，或计算机、计算、算法、人工智能，实证法学永远难以有质的发展。

    笔者相信，实证法学家愿意回归实证研究的本来面目，愿意将开展以法学问题为核心的实证研究作为己任。故，实证法学家们真正需要做的是，从建立或继续建立自己的学问研究体系入手，围绕某一个问题、领域、学科持续深入跟进。在更多人的理解和努力下，将每个问题、领域、学科做深做大，形成群体或集体性的学问整合体系。只有如此，才能发展出真正意义上的实证法学。当然，这至少还需要一代或两代学者的勤奋耕耘，最后才有可能实现学科层面的实证法学宏伟目标。

    在“首届智慧法治高峰论坛暨第九届数量法学论坛”上，作者报告了与本文相关的问题，但限于主题只提到文献依据而没有展开系统回顾。虽王禄生教授高度肯定报告内容，但屈茂辉教授和魏建教授对相关概念是否属于实证法学有不同意见。本文的完成，应该特别感谢三位同仁的启发式讨论，故作者认为有必要从最基础的概念术语梳理着手。

    本文转自《湖湘法学评论》(长沙)2024年第1期

  • 黄波粼 钟子善：上海农村集体托幼实践的考察（1958—1962）

    从思想史的脉络来看，关于托儿所与幼儿园的构想无疑具有相当长之历史。从柏拉图的《理想国》①，到启蒙运动时期众多的空想社会主义者②，至恩格斯③，后至康有为的《大同书》④、青年毛泽东⑤等都提出过“幼儿公育”的想法。从实践层面而言，清末以降，许多政治、社会力量都认识到了公共育儿的必要性，这使得公育思想在本土化建构中与实体建设并举⑥，托幼事业逐渐成为各种现代性设计不可或缺的一项重要内容。新中国成立后，公共托幼的必要性和重要性更加突出，因为恩格斯曾提出随着生产资料转归社会所有，“孩子的抚养和教育成为公共的事业”⑦，列宁也曾强调托儿所及幼儿园是共产主义“幼芽的标本”⑧。新中国托幼实践的大规模开展，大体发生在1958年至1962年，学界在这方面成果较丰，大多从妇女史的视角来回应马克思主义理论下的“妇女解放”问题。⑨有学者认为，大力推广集体托幼有着培育“共产主义新人”的意义。⑩有学者通过分析20世纪50年代末的农村集体托幼进一步指出，兴办托幼并非只为培育“共产主义新人”，它更是一个塑造“共产主义新农民”的过程。11尽管现有成果已经对新中国集体托幼有了比较深刻的认识，但仍有一些问题需要继续追问。例如，回到具体的历史进程和情境之中，国家诠释的“共产主义”具有哪些意涵？它们又是如何被农民接受的？本文拟以地方档案为主要史料，考察1958年—1962年的上海农村12集体托幼实践，着力展现其围绕各个阶段中心工作而曲折发展的轨迹，通过回溯若干具体措施深入农村的过程，呈现一段塑造共产主义新农民的历史。

    一、 塑造农民对于共产主义精神的政治认同（1958年9月—12月）

    延安时期，毛泽东给延安第一保育院题词：“儿童万岁”13，又强调一定要为教育后代而努力。新中国成立初期，“社会主义老大哥”苏联的集体托幼模式引发国人极大兴趣，14集体托幼在当时看来是一种与共产主义生活相适应的教养模式，被视为“共产主义萌芽”，直接关乎共产主义新一代的培养，“为将来的共产主义社会准备了‘人’的条件”，因而是“一万年都要做的工作”。15

    1958年北戴河会议后，毛泽东就指出人民公社办托儿所的重要性：“是搞钢铁，搞棉花、小麦重要？还是孩子重要？这是涉及下一代的问题。托儿所一定要比家里好些，才能看到人民公社的优越性”。16当年9月，国务院下发的《关于教育工作的指示》明确提出：全国应在3年—5年内，完成“使学龄前儿童大多数都能入托儿所和幼儿园”的任务。17上海农村托幼组织的大幅增长也是从1958年9月开始的。与此同时，上海农村确立将“家务劳动社会化”作为向共产主义过渡的重要内容。其中，“儿童教养集体化”是“家务劳动社会化”的首要目标18，即开办托儿所和幼儿园，集中教养7岁以下的社员子女19。除房屋外，摇篮、凳子、床铺、被子等都是开办园所必须具备的，全托还需食具、毛巾、水瓶、浴室、脚盆等。仅凭公社或生产大队积累下来的少量物资与资金很难满足办所办园所必要的物质条件。如何在“少花钱”乃至“不花钱”的情况下快速搭好托幼机构的架子，将儿童“迎”进来，成为摆在基层干部面前的首要难题。考虑到添置新的设备、物资需要花一大笔钱，且在短期内还不易购买到，因此不仅要坚持“因陋就简，勤俭节约，自给自足”的原则，还要发动群众通过借、调、征用等方式凑起必需物品。宝山县红旗人民公社第五生产大队全托幼儿园的建立就是一个典型例子。首先，大队向农民宣传“儿童集体化”的伟大意义，解释什么是“我为人人、人人为我”20的共产主义精神，并深入浅出地说明全托可以解放劳动力投入生产，能够更好地教养儿童，以及为何须自力更生，即如何遵循“勤俭办园”的原则。接着，动员有空房的或房子较大的社员，紧缩一部分住房，或移居到其他社员家中。最后，腾出一幢房子共八间，四间做宿舍，三间做教室，一间做炊事房，天井做小活动场，场地上扎竹篱做运动场地和花园。此外，要求入园幼儿的家长自带床及其他日常品，公共用具则是发动妈妈们有什么带什么，发扬集体互助精神。对实在困难、拿不出钱的家庭，就与其他孩子的家长协商合用，不够的部分，由干部发动其他社员适当添一些。最终，办园所需物品都是妈妈们自己送来的，共计24张床（包括床板）、50条被头，每个小孩1只矮凳、2只碗、1只匙。连没有小孩的金大妹也借出了长凳、马桶等。21典型事例的示范，鼓舞各地以共济互助精神大力兴办集体托幼。安乐生产队幼儿园的物资筹备过程亦是如此。该园于1958年11月7日建立，是由大队直接领导，社员在“不花钱”的原则下办起来的。22上海农村之所以在1958年秋季出现集体托幼的高潮，很大程度上是因为广泛推行此法。

    无论是房屋还是日常用具，筹备托幼，“物”的基本设施自然是题中之义，而将“人”和“物”两者结合起来考虑也是对建立集体托幼机构的基本要求。“人”的要素首先是要解决部分干部和多数家长“思想不通”的问题。对干部群体而言，并非所有的基层干部都支持开办园所，其原因在于他们认为托幼事业对农业生产不但没什么帮助反而会拖生产的后腿，因而“不划算”。另外，在“热心”办园所的干部眼中，集体托幼对于家长无疑是一件天大的好事，但事实上，多数家长，尤其妇女并不这样认为。尽管她们对于基层传达的集体托幼与自身解放的关系已是耳熟能详，却因“人在田里，心在家里”的切身体验，生出不少顾虑：孩子交给别人看不放心，怕别人照管不好得了病，怕保育员偏心眼等。23

    为了“打通思想”，大队干部通常会先在干部会议上通过算细账——“孩子在田里农作物损失，劳动力不能发挥等赔账”——让干部群体在兴办园所的必要性上达成共识。后要求干部召开妈妈或社员会议，设身处地地以妇女的实际家庭经济利益算家庭帐。如一家六口人，夫妻二人，一个老人带三个孩子，若三个孩子都送托儿所或幼儿园的话，老人就能去挣工分了。按一个老人一天最少挣5分计算，一个月可得150分。此外，针对妈妈们心理上的不安，以及小孩放在家里容易发生危险和事故等，进一步打消妈妈的顾虑。24不过，做通家长的思想工作并非易事，在农村工作“全面开花”的形势下需要耗费一定的时间与精力。

    “打通思想”之余，还须确定由谁来照管园所的幼儿。由于以农业生产为中心任务，当时的保教人员往往由大队干部指定女性半劳动力或辅助劳动力来担任，由此形成“青壮年上前方，老弱做后勤”的人员配备模式。上海县马桥大队、奉贤县南桥大队及松江县张朴生产队53个托儿所和13个幼儿园的保育员绝大多数是老妈妈，年龄最大的72岁，最小的46岁，平均年龄在50岁左右。不少身体残疾，无法参加劳动生产的妇女也当起了保育员。有个托儿所的盲人阿姨王修金，一个人同时带三个孩子。25还有部分园所则是“小囡带小囡”。宝山县红旗人民公社第五生产大队的全托幼儿园由5个教养员负责，年龄最大的21岁，最小的才13岁。26一旦孩子在户外活动，有些保教人员在体力上难免力有不逮。在“物”与“人”的准备环节，两者通常是同步进行的。由于“边组织、边教育、边行动”27，很多园所在短短几天就办了起来。值得注意的是，早在1956年教育部、卫生部、内务部三个部门就曾联合发文，“收3周岁以下的儿童者为托儿所，收3至6周岁的儿童者为幼儿园”28。因此，尽管因陋就简是一贯方针，但这一时期上海农村在开办园所的过程中还是严格遵循了将幼儿园与托儿所分开等相关要求。托儿所以生产队（即一个自然村）为单位开办，便于妈妈接送，幼儿园则以两三个生产队为单位联合开办，或以生产大队为单位开办。

    由此可见，人民公社化初期，兴办集体托幼除了具有基本的公共育儿功能，还有塑造农民对共产主义精神的政治认同的任务。农民群体的政治认同之所以重要，在于他们是党和国家确定的阶级柱石之一。需要明确的是，这里的农民实际指的是村民、农村基层干部、园所保育员及幼儿。尽管这些群体有各自的角色，但实质上仍是农民。1958年12月，中共中央高度肯定湖北省委《关于做好当前人民生活的几项工作的规定》，这份文件指出，办好园所“适用于农村，原则上也适用于城市”29，明确了托幼工作先面向农村的取向，更加凸显了这一时期塑造农民对共产主义精神政治认同的重要性。

    和多数群众运动一样，上海农村在推行集体托幼时因为急于求成造成了不少问题。特别是为了“赶、学、比、超”与应付上级检查，很多公社不管孩子是否有老人带，家长有无需要、有无意愿，大讲“托儿化”“包下来”，以“强迫命令”的形式组织儿童进园进所过集体生活。30从1958年9月30日实现公社化，到10月下旬，不到一个月的时间里上海农村成立了1400多个幼儿园，4300多个托儿所，480个托儿组，收托儿童10万多人。31当年年底，江苏省苏州专区所辖六县与南通专区崇明县先后划入上海，这时，上海农村地区辖有11个县。32由于所辖区域的扩大以及农村福利工作的持续推进，托幼机构数量及入园入所的幼儿人数就更多了。据上海市妇联统计，这一时期共办幼儿园、托儿所29603个，收托孩子 582762名，收托孩子占上海农村学龄前儿童总数的80%。33

    人民公社化初期，上海农村在推行集体托幼时虽在“物”“人”等问题上遇到诸多困难，但在动员及组织农民的过程中完成了塑造具有共产主义精神的“新农民”的第一步。尽管，因推广时急于求成而问题渐显，但上海农村的集体托幼并没有停止，反而在“全民托幼”的大潮中真正步入“实践期”，进入一个崭新阶段。这是因为它始终配合农业生产发展34和各个阶段的政策调整而进行，中心工作起起伏伏，农村集体托幼实践便随之波浪式地向前发展。35

    二、 共产主义议题凸显与农民集体托幼需求（1959年1月—8月）

    国家要求在农村大力推行集体托幼，并不意味着所有农民都会自觉参与。一些家长担心孩子在托儿所可能会“吃不饱”，或被大孩子“欺辱”，36上海市委妇女工作委员会发现“儿童实到数往往少于报名数，有时仅及一半”37。多数社员对生产大队负担全部托费不满意，有人发牢骚说：“领这两个囡，工分弄光”38，认为有孩子入托特别是有多个孩子入托的家庭占尽便宜39。之所以出现此类声音，是因为虽然新中国成立前后的土地改革重组了农村权力关系，使农民感受到了国家权威，但尚未改变其劳作和生活秩序，仍然如传统乡土社会时一样生活，没有产生公共育儿的需求。40随着20世纪50年代后期党和国家在农村的中心工作——人民公社化运动的到来，这种秩序才被彻底改变。自此，参加集体劳动与做好分配成为农民的基本任务和利益所在。为了保障个人在人民公社中的利益并维持其运作发展，他们较为普遍地“自动”产生了集体托幼的需求。

    站在历史的比较视野来看这一问题，会更加清晰。不独中国共产党，20世纪二三十年代进行乡村幼稚园试验的陶行知和国民政府也曾在推行托幼的同时着力推进集体合作，却都没能将二者结构性地联系起来。

    1926年，在关于创设乡村幼稚园问题的文章中，陶行知将平民化视作建立乡村幼稚园的关键之一。如其所言，农民贫且忙，幼稚园应济“农村需要”。41因此，他试图把建设乡村幼稚园与改善农民生计结合起来：通过就地取材、物尽其用解决房屋、用具等设备，以农民生产和生活时间为准安排幼儿活动，并将幼儿的康健放在第一位，以此实现幼稚园“下乡”。42陶行知建设乡村幼稚园的初衷在于通过解决农村幼儿集中教养问题纾解农民的“穷愚”困境，带有造福农民的公益性和福利性。但由于时局动荡与经费短缺，以及农村生产方式43等诸多因素，陶行知的乡村幼稚园试验没有也不可能促生农民内在的托幼需求。在此情况下，他力图通过建设乡村幼稚园为国育儿的愿望也只能止于零星试验。

    20世纪30年代国民政府主导的托幼事业，其政策体系包括宗旨、课程、经费、管理及师资等，无疑为托幼的发展提供了政策保障，这套政策体系的建构是“自上而下”与“自下而上”相结合，既有中央政府为主导的取向，也有专家学术研究成果的推进，如陶行知等的幼稚园试验对中央政策的制定助益良多。44因而，在相当程度上，国民政府认可了陶行知等人乡村幼稚园建设的理论及实践，只不过注重为推行托幼提供政策保障的做法仅在表面上加深了乡村幼稚园建设的“国家化”程度，并没有改变民间“自治”的格局。45诚如时人所批评的，在乡村，大部分的合作都被豪绅所把持，外界无法跳过他们去直接组织农民。46国民政府虽曾试图将政权下沉至乡村，但因轻视乡村又缺乏动员能力，以失败告终，47即国家权力难以“下乡”。概言之，虽然国民政府加大了政策层面的介入力度，但仍属民间“自治”的乡村幼稚园注定很难与农民的集体托幼需求结构性地联系起来。九一八事变后，国民政府的托幼事业虽有发展，但极不平衡。1932年，即便在托幼机构较为发达的上海，其幼稚园的幼儿总数也只有1045人，48而上海县、青浦县、南汇县、松江县、金山县、川沙县保姆所幼儿数合计仅190人，49乡村托幼严重落后。此外，当局更将扩大托幼规模与“培养教化国民”联系起来，50在未能创造出农民内在的集体托幼需求的情况下，此等流于空泛、无所着落的说教51预示着乡村托幼只能停留在专家的实践层面。

    反观20世纪50年代后期，中国共产党在农村推行集体托幼时，在国家强力主导下，首先将分散的小农全部组织到共产主义的人民公社之内。实践中，开始时大办集体托幼的热潮和随后的整顿总体上还算稳步、健康地发展。521959年3月以后迅猛发展，全国形成集体托幼高潮，这使“共产主义”成为农民不得不面对的严肃议题。对于一些无子女农民的不满，干部教育他们要基于集体利益，且要有长远眼光，认识到“办托儿所、幼儿园是人民公社的优越性，现在没有囡，将来有囡，现在没有囡，下代有囡”53。一些保育员认为自己工分低，还被家长看不起，情愿去田里生产，干部就开展“我为人人、人人为我”的辩论，讲明带孩子比生产更重要，使她们明了保育事业的意义，树立托幼工作的光荣感。54此种情形非上海独有，全国各地屡见不鲜。如河北省徐水县就“小孩子要不要由公社来抚养”展开辩论，没有小孩的社员认为自己“吃亏了”，不同意开办，有小孩的社员当即反驳：“你现在养活俺小孩，将来你老了还不是由俺小孩养活你吗？”结果，那些怕吃亏的人最终被“辩倒了”。55所谓“辩论”，事实上就是毛泽东批评的“动不动‘辩你一家伙’”56，实际上已容不得落后分子。上海市嘉定县要求办幼托的大字报就占了50%，奉贤县的家长一定要把未满3岁的幼儿送入全托，为解决孩子穿衣服的后顾之忧，还把布票也交给了幼儿园。57无论抱怨还是从众，对于农民来说，传统的劳作和生活秩序发生了彻底改变。既然进了共产主义的人民公社，就要参加集体劳动并做好分配。这意味着在家带孩子就少了参加集体生产的劳动力，影响全家拿工分及年底收入，于是，农民在传统乡土社会所没有的集体托幼需求被激发出来。

    概言之，与陶行知和国民政府推行的乡村幼稚园相比，中国共产党主导的人民公社化运动在凸显共产主义议题的同时，结构性地激发出农民集体托幼的需求。此种需求虽然源于国家这一外部性动力，却因为农民难以选择而成为其内在需要。毛泽东认为，小孩进托儿所，教育、食宿等都由社会负担，不是破灭家庭，而是废除家长制。58这也回应了费孝通对传统乡土社会孩子由家庭抚育的思考。费孝通曾明言，若“以家庭和保育院来比较的话，大体上家庭里所生长出来的孩子比较健全些”59。他还认为，乡土社会的农民只有在偶然或临时的非常态中才需要伙伴和团体60，除非中国社会乡土性的基层发生了变化61。如果说20世纪50年代中期的农业合作化运动所带来的正是这样一种根本性变化的话，那么50年代末的人民公社化运动所造成的农村集体托幼则是这种根本性变化的更高表现形式。

    关于人民公社化之后农民迫切需要集体托幼的原因，上海市幼儿保健教育委员会的调查报告极具代表性：

    对于有孩子入托特别是有多个孩子入托的家庭而言，孩子越多，用在孩子身上的补贴就越多。日托尚且如此，贴粮贴钱的全托对孩子的补贴则更多，生产大队除补贴25斤左右粮食外，每年用于一个全托儿童的费用更是多达30—40元。62

    类似的情况还发生在七一公社联明生产大队。由于是棉、粮、菜夹种地区，该大队较为富裕，一个劳动力全年平均收入为272.28元，63如果以此为基数，补贴一个全托儿童的30元—40元相当于一个劳动力一年收入的11%—14.9%。也就是说，一个家庭如果有一个孩子进全托，其所耗补贴相当于一个劳动力一个多月的收入。事实上，有些家庭不止一个孩子进全托。加之，托儿所、幼儿园的日常开支由大队公益金担负，在家长负担一定工分后，大队全年托幼经费的支出占公益金的65.6%，远高于困难户补助、工伤等其他福利性开支。64由于集体托幼实行包下来的政策，特别是“吃饭不要钱”65，在上海农村，甚至出现这样的情形：

    不少夫妻一方或两人从事非农业生产的职工家庭以及有亲戚在农村的家庭也将孩子送入托儿所幼儿园中，甚至有少数人代人领养，自己却“拿了领养费”。对于这些未参加集体生产劳动的家庭而言，无疑享受了与社员一样的福利。66

    农民内在的集体托幼需求正是在这样的情境下被激发了出来。换言之，人民公社化初期的集体托幼成为农村干部群众接轨共产主义的重要工具。

    国家不仅激发了农民集体托幼的需求，还通过兴办更完善的托幼组织充分保障这种需求的实现。1959年3月，全国妇联提出“在具有基本条件的地方”必须“积极办好全托”之后，上海市妇联要求配合人民公社化运动和农业生产发展需要，切实将办好全托纳入全面规划。67不仅将原有的临时性、季节性的托儿所全部转为日托，还兴办了大量新的日托与全托。大量园所的兴办确实解决了不少农村妇女生产牵累与孩子照管的难题，使女性安心投入生产。至此，上海农村的集体托幼真正成为一项“运动”。4月，仅嘉定、奉贤、松江三个县，共办起5539个托儿所，11个县共办15635个托儿所，入托幼儿达到233999名。68

    然而，1958年年底至1959年年初麻疹的流行，打乱了工作步调。由于卫生知识、传染病预防及隔离条件缺乏，致麻疹传染面扩大，造成不少幼儿死亡。69家长们非常惊慌，纷纷把孩子抱回家，致使托儿所、幼儿园缩减。不久，上海召开五级干部会议贯彻第二次郑州会议精神，“精简生活服务人员到7%左右”以补充、加强农业生产战线。在此形势下，奉贤县一生产队队长认为劳动力如此紧张，却让十几个人带孩子“不合算”，急欲把幼儿园“砍掉”，将省下来的保教人员充实到大田上去，70部分托幼机构形成了“无人照管孩子”的局面。这些言行不免偏颇，但由此也可以看出，生活还是要让位于生产。这种影响在1959年夏季全面体现出来，上海农村托儿所和幼儿园数量出现下降。如金山县新农公社，原有109个幼儿园，收托 1000 多名孩子，在五级干部会议后仅留下13个幼儿园，收50多名孩子，下降90%。71

    三、 在革命口号中促使农民奔向共产主义（1959年9月—1962年）

    1959年庐山会议后，中央层面开始“反右倾”斗争，这为处于低迷的农村托幼事业带来了转机。在“反右倾”的总形势下，农村福利工作的“倒退”往往被视为“右倾”的表现。1959年9月，上海开始着手恢复、提高农村福利方面的工作，再次确立“一手抓生产，一手抓生活”的工作方针。为了强调政治挂帅，各级党委书记都亲自抓托幼工作。72在干部群众中间，针对错误观点或行为进行了思想教育。73最为常见的教育方式是“调查”。例如，川沙县对一些公社进行调查后，发现不少家长“有需要而未送托”，在深入了解原因的基础上，不仅教育他们应关心儿童的安全，还设法帮助解决实际问题。74据说这个办法很灵验，通过调查及时发现并解决各种问题，使很多家长、保教员及基层干部都期盼将日托转为全托。75

    “反右倾、鼓干劲”开始后，上海市委在基层公社紧锣密鼓地宣传八届八中全会精神，要求各项工作“鼓足干劲”76，提出了不少脱离实际的园所建设任务和办园目标，农村再次掀起“全面跃进”的高潮，加强对托幼的领导成为基层干部的重要工作。如南汇县惠明公社明六生产队周水连听了会议精神传达后，回去立即办了4个托儿所和4个幼儿园。77

    在“大跃进”的氛围下，为了不断开办新幼儿园以完成高指标，1959年12月，上海市教育局制定《农村幼儿园民办公助办法（草案）》（以下简称《办法》），要求继续在做好师资培养，提供教材，充实公社幼教干部，发动“公带民、老带新”等工作的基础上，对一些经济困难的幼儿园给予一定的补助，并对办得好的幼儿园给予一定的奖励，以促进农村幼儿园的繁荣和发展。如补助新建幼儿园3元—5元，补助困难幼儿园每班每季度不超过6元，每季度被评为先进的幼儿园奖励不超过10元。此外，还规定补助及奖励应作充实教育设备之用，如教养员用的参考资料、图书、、教具，或简单的卫生医药箱、毛巾、脸盆、肥皂等。78该《办法》的出台是自1958年以来，上海市财政第一次大规模地对农村人民公社开办的园所给予资金补助。在当时财政极为有限的情况下，这项补助极为可贵，充分表露了政府对托幼工作的期待，在一定程度上也有助于解决实际的资金困难。随着《办法》的逐步落实，上海的农村托幼事业迎来了1960年的大发展。

    1960年4月，为了掀起集体托幼的新高潮，上海市委周密部署，各部门高度重视并做了大量工作。9日，时任上海市委第一书记的柯庆施提出上海必须“逐步分批实现公社化”79；中旬，上海市卫生局与教育局向上海农村派驻“幼托事业工作队”80，宣传托幼的意义，培训保育人员，组织示范教学，制定规章制度等81。该工作队抓住薄弱环节，帮助兴办托幼，同时也起到了“督促”公社及大队干部的作用。如奉贤县肖塘公社原来只有7所幼儿园，在工作队的帮助下办起了82所幼儿园，托儿所也从97所增加到172所。82当月，上海成立市幼儿保健教育委员会，旨在加强对托幼工作的统一领导。83不久，该委员会向上海市委提交了《关于幼儿园、托儿所发展情况和今后打算的报告》，在肯定前期依靠“穷办法、土办法”兴办托幼事业做法的基础上提出，今后必须坚持“边发展、边整顿、边巩固、边提高”的原则，才能促进托幼事业“多、快、好、省”地发展。此外，它还对农村托幼作出系统性规划：“县、公社、生产队各级的幼托工作组织，层层要有专人负责，以加强对这项工作的领导”，要求农村入园入托儿童国庆前达到85%以上。为了完成指标以“出色的成绩迎接国庆”，须掀起三个高潮：结合“六一”评比表扬先进儿童工作者和先进儿童工作集体，造声势、树标兵；7月，对幼儿园、托儿所，组织一次夏令卫生工作大检查，以提高卫生保健水平；9月，再组织一次托幼工作的全面性对口检查。84这些部署不可谓不细致周到。

    为了完成上述指标，上海农村各县首先指定专人负责托幼工作的领导。金山、青浦、松江等县设立生活福利委员会85，嘉定、上海、川沙、浦东、奉贤等县成立了生活福利办公室。通过“六一”评选工作，南汇、崇明两县又在生活福利办公室下设托幼小组或托幼办公室领导托幼工作，宝山县则专门建立了托幼委员会。这些职能部门或小组的建立，使得托幼工作经常被提到议事日程并做统一布署。基层公社也专门配备了负责托幼工作的干部。如泥城公社的党委书记就对托幼工作做到“五抓”（一抓干部群众的思想教育；二抓规划，做到心中有数；三抓统一安排，安排生产的同时安排托幼工；四抓具体问题，如粮食、房屋、设备、工分等；五抓专线领导，层层有人领导，副书记挂帅）。86其次，掀起检查评比、树立标兵的浪潮。公社频繁组织各类检查评比活动，在检查评比后，将检查的情况与各生产队发展托幼的进度表，分发至各生产队，激起“落后”生产队的赶超想法。如金山县紧紧抓住兴塔公社红旗幼儿园这一标兵，开现场会交流经验，全县掀起了“学兴塔、赶兴塔、超兴塔”的高潮。87此外，在浦东、上海、川沙、宝山、松江、崇明、嘉定等县的25个公社和7个镇的妇联联合倡议“积极发展幼儿园，做到凡是无人照管的儿童全部入园入所”之下，大办托幼的友谊竞赛在这些区域不断涌现。88

    在上下贯通的组织领导下，至1960年6月，上海农村掀起了一股大办托儿所、幼儿园的热潮。据《文汇报》称，仅嘉定一个县，一个多月内入园入托幼儿就增加了18000多名，当月，全县入托入园幼儿占学前儿童人数的70%以上。其中，势头较好的如城西、封浜等公社这一比例更是达到90%。89又据上海市妇联农村工作部1960年4月统计，农村入托入园人数上升到 40 万。90另据统计，1960年6月中旬，上海农村共有托儿所20201所，收托幼儿251338人，共有幼儿园8354所，收托幼儿26216人，托儿所的收托比例提高到80%，幼儿园的收托比例提高到72.6%。91在以指标为先的“跃进”氛围中，这些数据可能有浮夸成分，但也从侧面呈现出农民在革命口号下奔向共产主义的历史情境。

    “大跃进”期间，高指标、浮夸风并非农村集体托幼所独有，但这项工作似乎又有其独特价值，当时的革命口号精炼地体现了这种价值—— “一夜托儿化”，“实行寄宿制，消灭三大差别”92。从某种意义上讲，彼时的农村集体托幼在这种夸张的“革命”氛围中，已然浮现毛泽东所期待的“六亿神州尽舜尧”93的美好图景。

    实际上，“大跃进”期间，上海农村的园所大多是匆匆上马，存在诸多问题，比如保育员的卫生知识、业务能力、托儿所设备、环境卫生、管理水平等跟不上，加之其他条件限制，前面描绘的集体托幼成效恐怕与真实情况存在不小落差。当时就有人质疑1958年的集体托幼，比如，“囡多占便宜，我们负担领囡费，做来做去担几个共囡，啥叫按劳取酬”94，引起了有子女入托家庭和无子女入托家庭之间的矛盾95；有的因托儿所“路远不便”，认为没必要再办96；有的在孩子入托后不久就要接回家，态度还十分强硬97。出现这些现象的原因或许是托幼工作不够扎实而使农民心生怨言，但也从侧面说明上海农村此时仍未实现以集体托幼形式塑造共产主义新农民的目标。

    尽管“大跃进”时期的集体托幼成果存在一定的虚报浮夸，98但上海农村的集体托幼实践还是取得了一定成效。以上海农村某生产大队为例，自1958年人民公社建立的三年里，该大队由原来1个农忙托儿所发展为3个常年托儿所。幼儿在托儿所里生活得很健康，也未发生过重大事故，家长说“有了托儿所孩子高兴，妈妈也可安心做生活”99。通过兴办集体托幼，上海农村妇女不仅摆脱了孩子的拖累，全心搞生产，还有时间学习文化并脱盲。其中，有的人当上了保育员、教养员和妇女干部；100有的人以往毫无卫生知识，现在能当保育员；有的人过去一字不识，如今能当教养员；有的人以往从不关心政治，现在当起了妇女干部。这不仅大大解放了妇女，还提高了人民公社托幼事业的工作质量和业务水平。101仅1958年下半年，上海市妇联与卫生、教育等部门联合训练公社托幼工作干部就达 250 余人，102这些人能够在人民公社中胜任各自的工作，客观上展现了农村集体托幼实践的效果。

    1961年，中央提出“调整、巩固、充实、提高”的方针之后，人民公社迎来了大调整，生产工作成为农村的中心任务，托幼工程自然退居“次要”。此后，农村集体托幼进入常态化阶段。随着工作重心的转移，1962年8月，上海市幼儿保健教育委员会撤销103，农村托幼工作恢复到由上海市妇联农村工作部主管，该部将办园办所的决定权下放给社员群众，托幼机构“办不办，怎么办，办什么样子的”成为群众自己决定的一项事务。104不难发现，上海农村托幼实践进行到此时，已经因为中心工作的变化而承载起新的政治话语。“大跃进”时期培育“共产主义接班人”的宏大历史进程也进入尾声。

    四、融入托幼日常的具体措施：培育“共产主义接班人”

    1959年六一儿童节，《人民日报》发表社论，号召将托儿所和幼儿园办成“培养共产主义事业接班人的基地”105。上海在推动农村集体托幼实践落地生根的过程中，既要有切合受教育对象特点的动员技术，又要根据“因陋就简”的现实条件做出周密安排，在此基础上，再为具体的国家任务服务。上海实施若干措施，将共产主义从方方面面点滴渗入农民的日常生活，对“接班人”的共产主义塑造愈发深入。

    （一）注重卫生保健

    在对农民进行集体托幼的有效动员之后，还需培训具有一定业务能力的保育员，这是农村集体托幼能够顺利开展的必备条件。1958年年底以前，由于多种原因，园所的保育员难堪重任，尤其缺乏卫生保健知识。对于实质上仍是农民，业余充当保育员的群体，需要经常性地开展培训工作。1959年春，上海农村开始建构由文教、妇联及卫生部门三者相互协作、配合的培训体系，在编写保健知识丛书的基础上，由各级医务系统如县医院、护士学校、妇幼保健所及公社医院主导，分层分批对保育员进行脱产或不脱产、定期或不定期、长期或短期的卫生保健知识培训，106如培训保育员必须学会和做好除“七害”107、讲卫生，晨间检查，预防传染病，孩子有病会隔离和报告，培养孩子的卫生习惯，孩子的饮食和卫生，安排孩子的生活，各种消毒工作，保护孩子的安全，教养孩子等十件事，使其逐步达到初级保育护士水平。108

    事实上，要做好幼儿疾病预防及应急处理，仅凭培训保育员这一项措施是远远不够的。因此，在“预防为主、防治结合”的要求下，公社还依托地段与区域的专业医务力量，搭建幼儿疾病预防与治疗平台，通过预防接种，建立上下贯通、层层负责的卫生保健网，并以此作为示范向全县推广。如川沙县蔡路公社幼儿园与卫生院取得联系，通过卫生院妇幼科医生或保健员经常来园做卫生保健的业务指导，建立定期检查制度，并按时进行预防接种。109自1959年卫生保健网建立后，对保教人员及专业医务人员执行卫生保健措施起到了监督、引导与帮助的作用。同时，该卫生保健网在预防幼儿常见传染病上也产生了良好效果110，一旦出现病孩也能及时治疗111，幼儿因病致死的情况很少发生。1960年，园所的麻疹发病率较1958年同期成倍下降，很多园所甚至一年来都没有发生过麻疹。112

    此外，上海市妇联农村工作部于1959年3月制订了《关于农村托儿所、幼儿园工作暂行条例（草稿）》，其中，不少详尽的规定为幼儿的卫生保健提供了制度保障。比如，在园所选址方面，必须选择兼顾幼儿安全和方便家长接送的地方，应注意平坦宽敞、清洁卫生、空气流通、阳光充足，水塘、河沟、畜圈、马路及医院等旁边不宜设园所；在食具方面，幼儿的开水壶、碗筷须自备一套，并设自来水冲洗脸、手，避免传染疾病；在预防保健方面，定期为幼儿预防接种和健康检查，有病须立即隔离；在生活制度方面，吃饭、睡眠、游戏、洗脸要有秩序。每日晨间检查，食具、玩具每周须消毒1至2次，饭前便后要洗手。113据上海市妇联农村工作部检查，有1/4的园所落实较好114，这些规定促使幼儿养成了清洁卫生的习惯。115一些模范幼儿园如宝山县吴淞公社卫星幼儿园的日常保健常态化，每日写生活记录，随时掌握幼儿的吃睡、大小便情况，还设有隔离室。116以上举措可谓具体而微。

    应该说，通过卫生保健的宣传、人员培训、体系建构及制度落实等各个环节，农民对于以“新农民”之姿投入托幼卫生保健事业的认知还是有所提高，学习保健知识及养成卫生习惯的积极性也随之被激发出来。农民与集体、国家之间的联结被强化的同时，对共产主义的认同也会显著加深。

    （二） 优先供应饮食

    在构建卫生保健体系的同时，还需优先供应幼儿的饮食。“身体是革命的本钱”，幼儿茁壮成长、体魄强健是推行农村集体托幼的必然要求。正是出于这种考虑，基层干部对幼儿的膳食给予了极大关照，在饮食供应方面，托幼组织在同期的农村福利组织中处于被优先供给的地位。

    上海市妇联农村工作部要求幼儿饮食必须由专人负责，单独做适合幼儿年龄的饭菜，并按50∶1的比例配备炊事员。117幼儿的一日三餐要与成人饮食有所区别，且专门烧各式小菜，以换口味。就连幼儿平日喝的开水，洗脸洗脚用的热水，也都由食堂供应，以减少疾病的发生。118并且，根据幼儿年龄定粮，实行“专人管理、计划用粮”。如奉贤县三官公社胡村生产队的全托幼儿园每人每月定粮，大的孩子（虚岁7岁—8岁）每天12两左右，小的孩子（4岁—6岁）9两左右。119“吃得饱”这一要求基本能得到保障，甚至还略有积余。

    在“吃得饱”的基础上，还讲究营养均衡、荤素搭配，即“吃得好”。一些生产队特意为全托幼儿园配置了一定面积的自留地，供保教人员耕种以提高幼儿的伙食水平。如南汇县大团公社沙庙生产队的全托幼儿园自种高粱、黄豆、玉米、芋艿、卷心菜、红萝卜等，做到了生产队不再贴粮食，蔬菜也可基本自给。120这些粮蔬专供幼儿园使用，不用上交集体。为了保证蛋白质的供应，人民公社还通过各种手段尽量予以满足。不少时候，由生产大队专为幼儿购买含有蛋白质成分较高、易于消化吸收的鱼、蛋、肉类等食物。121园所自养的家禽家畜也可稍作补充，改善幼儿伙食。如宝山县新生生产队幼儿园养了鸡、鸭、猪、羊和兔子，每逢节日都可以吃到荤菜122，有些幼儿园甚至隔一天就能吃到荤菜123。此外，不少公社的供销部专为幼儿提供一定量的糕点、点心、糖果、饼干及线粉124，在夏季，还为幼儿供给特定的食物或饮料以防暑降温125。至于经费，一般由大队公益金担负。126公益金不足时主要靠农民自己解决，保教人员、食堂等工作人员也主要来源于农民，这使得农村集体托幼看似颇有群众“自主办园”的味道。但很显然，这场实践的主导力量、决定性因素还是党和政府。

    由于公社对托幼饮食及营养方面的重视，园所的幼儿一般比散养在家的幼儿待遇更好，有生产队队长称“小囡粮食够吃，荤菜、红枣、饼干样样优先供应，比家里养得壮多了”127。在缺粮少食的困难时期，这些共产主义“幼苗”无疑享受着优先待遇。一个显著的事实就是，送进全托的孩子除本人口粮外，生产队给每个孩子每月平均补贴2斤左右的粮食，散养幼儿则没有。也难怪一些没有子女入托的社员自认为“吃亏”。128应该指出的是，对托幼“优先供应饮食”是符合实际的合理安排。广大农民在回报很少的情况下兢兢业业投入集体托幼实践，其历史作用不应被低估。即便有不足，主要也源于难以逾越的历史条件限制，不应过于苛责。

    （三）强调全面教育

    集体托幼实践的基本诉求在于“教养结合”以培育共产主义接班人。所谓教养结合，即仅关注幼儿的卫生保健、饮食营养是远远不够的，还必须加入“教”的因素。上海市教育局明确要求从幼儿园的性质和“两大任务”129出发，强调幼儿园既是福利机构又是教育机构，特别批评了对“教育机构”性质认识不足的问题。130为此，在幼教人员的配备上，上海市妇联农村工作部要求个人成分、身体状况、工作态度等基本条件满足外，还特别强调“最好是具有一定的文化水平的青壮年”担任。131总的来看，这一时期农村托幼在培养德育和智育方面的“接班人”这一问题上做出了许多尝试性探索。

    为了培育“德才兼备”的共产主义接班人，园所十分注重幼儿的德育。在德育方面，主要包括集体主义教育、人民公社的认同教育及热爱国家与领袖方面的教育。中国福利会幼儿园不仅编写出版书籍，还利用游戏及表演让幼儿明白集体主义的好处与意义，如“集体劳动生产出来的农作物，比个人劳动生产出来的农作物既多又好”，同时将集体主义意识落实在幼儿日常行为中，要求各班互助浇水施肥，“共享”劳动成果，提醒孩子“长出来的菜是大家的，不分你的我的”。132此外，教养员时常带领幼儿观察公社出现的各种新兴事业和群众福利事业，如工厂、食堂、敬老院、俱乐部等，使孩子们懂得“有了共产党和毛主席，成立了人民公社，人们的生活将越过越好”133，教幼儿学唱《打夯歌》《人民公社真正好 》《国旗歌 》《爱毛主席 》等儿歌134，要求中班孩子学会写“毛主席”三个字135。

    在智育方面，要求逐步培养幼儿的感官、语言、思维、动手等诸多能力。1960年前后，上海市教育局出版了大量幼儿教材与教辅资料，要求幼儿学习拼音、认识汉字及学会计算，并针对幼儿年龄，提出了不同层次的要求。如小班学会1—5的数的概念，中班学会10以内的加减法，大班学会20以内不进位、不退位的加减法，学会口编应用题等。136其中，南汇县惠南幼儿园在计算教学方面被列为先进，该幼儿园在教学思想上实现了智育与德育两方面的紧密结合，联系政治形势，生活实际以及生产实际进行计算教学。例如，给幼儿讲“人民公社好”时，把平时使用的教具和增添的新教具结合起来反映农村全面发展，表现农民生活水平的提高，并采取多样化、针对性的教学形式，用蜡光纸剪成钢铁元帅、棉花姑娘、麦公公之类等进行计算教学，用实物和玩具、卡片进行计算，用计算木架数数、计算等。137凡此种种或许可以说明，上海农村集体托幼实践在贯彻落实各项国家任务方面发挥了积极作用。

    无论是幼儿，还是从事托幼事业的工作人员在承接以上三项具体措施的过程中，感受到的不再是宏大高远的共产主义意象，而是融入日常的共产主义。由此，农村集体托幼实践对农民的共产主义塑造润物无声地走向具体深入。

    五、结语

    从全国范围看，人民公社化时期的农村集体托幼实践成绩斐然，帮助大量农民，尤其妇女，有更多精力投入农业生产。但这场集体托幼实践并非只为实现家务劳动社会化以解放妇女，更有着力塑造共产主义新农民之意图。通过考察1958年至1962年的上海集体托幼实践可以发现，由于这场实践始终跟随党和国家各个时期的中心工作，而且实行了注重卫生保健等具体措施，其对农民的共产主义塑造体现为一系列的革命性实践。

    人民公社化初期，无论是推行“儿童集体化”还是倡导“我为人人、人人为我”，都将农村集体托幼与塑造农民对共产主义精神的政治认同结合起来。人民公社化运动在凸显共产主义这一严肃议题的同时，结构性地激发出农民集体托幼的内在需求，集体托幼实践由此真正成为一项“运动”，集体托幼成为农民接轨共产主义的重要工具。“大跃进”及其后的常态化集体托幼促使农民在革命口号中奔向共产主义。而培训保育员，构建卫生保健网，优先供应幼儿饮食，以及强调全面教育，则是推行农村集体托幼的具体措施，共产主义由此融入农民的日常。这些革命性实践为农民诠释出集体主义、人民公社、爱国、爱领袖等诸多意涵，促使他们形成对共产主义的政治认同。农村集体托幼实践塑造共产主义新农民的丰富历史图景也由此展开。

    最后，似有必要对农村集体托幼实践塑造出的共产主义新农民的内涵作进一步讨论。前已述及，新农民不仅包括有孩子的农民、无孩子的农民，也包括农村基层干部、园所的保教人员，还包括园所的幼儿。对这些看似不同群体的农民而言，集体托幼都有一个从最初的诸多顾虑发展成为其内在需求的过程，且这种需求在“大跃进”的情境中达到最高潮。站在国家的角度来看，无论有孩子的农民还是无孩子的农民，都是为了更好地担负起他们作为”国家的农民”的责任，农村的基层干部与园所的保教人员为的是更好地完成“国家的托幼工作”，园所的幼儿则是为了更好地成为“国家的接班人”。因此，虽然陶行知和国民政府的乡村幼稚园实践与人民公社化初期中国共产党主导的农村集体托幼实践都担负着“为国”的重任，但只有后者才能有效完成这一任务。

    本文转自《开放时代》2024年第6期

  • 黄玉顺：杨叔姬：辩证美恶的春秋女哲

    杨叔姬（生平不详），杨氏，晋国大夫羊舌职（？—前570年）之妻，羊舌肸xī（叔向）之母，史称“羊舌叔姬”。孔颖达说：“羊舌，氏也，爵为大夫，号曰‘羊舌大夫’。”[②] 杨叔姬之“姬”并非姓氏，因为其丈夫羊舌职为姬姓，同姓不婚，则杨叔姬不可能姓姬；“姬”是古代女子通用之美称，犹如“子”是古代男子通用之美称。至于杨叔姬的“杨”，究竟是其父族姓氏，还是其夫族姓氏，暂无定论。或以为羊舌氏即“杨氏”，因为叔向食邑在杨（今山西省洪洞县东南）。[③] 如《左传》“晋杀祁盈及杨食我”杜预注：“杨，叔向邑”[④]；又“分羊舌氏之田以为三县”孔颖达疏：“伯石（叔向之子）为杨石，明杨氏是羊舌之田也”[⑤]。但孔颖达却又说：“《谱》云：‘……羊舌，其所食邑也。’”[⑥] 因此，叔向的食邑究竟是“杨”，还是“羊舌”，待考。

    但杨叔姬的儿子叔向，即杨叔姬与羊舌职的次子羊舌肸，却是春秋时期大名鼎鼎的人物，姬姓，羊舌氏，名肸，字叔向，又称“叔肸”“杨肸”，晋国大夫，乃是当时著名的政治家，与郑国的子产、齐国的晏婴齐名。

    杨叔姬的事迹，见于《左传》《国语》及刘向《列女传》等。

    一

    杨叔姬是一位极具智慧的女性，这主要表现在她对丈夫羊舌职加以规劝和对儿子羊舌肸加以训诫的言论之中。

    （一）文献的记载

    刘向《烈女传》记载的杨叔姬对丈夫的规劝：

    羊舌子好正，不容于晋，去而之三室之邑。三室之邑人相与攘羊而遗（wèi）之，羊舌子不受。叔姬曰：“夫子居晋，不容；去之三室之邑，又不容于三室之邑，是于夫子不容也，不如受之。”羊舌子受之，曰：“为肸xī与鲋亨（pēng）之。”叔姬曰：“不可。南方有鸟，名曰乾吉，食（sì）其子不择肉，子常不遂。今肸与鲋，童子也，随大夫而化者，不可食以不义之肉。不若埋之，以明不与（yù）。”于是乃盛以瓮，埋垆阴。后二年，攘羊之事发，都吏至，羊舌子曰：“吾受之不敢食也。”发而视之，则其骨存焉。都吏曰：“君子哉！羊舌子不与（yù）攘羊之事矣。”君子谓叔姬为能防害远疑。[⑦]

    这里“羊舌子”即指杨叔姬的丈夫羊舌职，故下文杨叔姬称其为“夫子”。“好正”意谓正直。三室之邑，地名，不详。据《左传》载：“天子建国，诸侯立家，卿置侧室，大夫有贰宗，士有隶子弟，庶人、工商，各有分亲，皆有等衰。”郑玄注：“侧室，众子也。”孔颖达疏：“正室是適dí子（嫡子），故知侧室是众子，言其在適子之旁侧也”；“其侧室一官，必用同族，是卿荫所及，唯知宗事”。[⑧]《左传》“赵有侧室曰穿”郑玄注：“侧室，支子”；孔颖达疏：“正室是適子，知侧室是支子，言在適子之侧也”；“（赵）盾为正室，故谓（赵）穿为侧室”。[⑨] 此说可资参考。

    攘，偷窃。遗，馈赠。“肸”指羊舌肸（叔向）；“鲋”指羊舌鲋（叔鱼），羊舌职和杨叔姬的儿子，即叔向的同母弟。亨，同“烹”。上古“烹”“享”“亨”不分，作“亯”，许慎《说文》解释：“亯，献也”；“象进孰（熟）物形。《孝经》：‘祭则鬼亯之。’”徐铉注音：“许两切，普庚切，许庚切。”[⑩] 乾吉，鸟名，出处不详。食，喂养。遂，成长。“大夫”指羊舌职。“化”，变化。“随大夫而化”，意谓儿子会受父亲的影响而变化心性。“不与”，没有参与。垆，通“庐”；垆阴，屋后。都吏，都邑的官吏。

    （二）“不可食以不义之肉”的哲学意义

    刘向赞誉杨叔姬“能防害远疑”，纯粹是从“明哲保身”的功利角度而论；其实不仅如此，杨叔姬强调“不可食以不义之肉”，乃是一个涉及“义利之辨”的问题。这是一个重要的中国思想传统，例如，《左传》开篇即载：“大（tài）叔（共叔段）又收贰以为己邑，至于廩延。……公（郑庄公）曰：‘不义，不昵，厚将崩。’”郑庄公还指出：“多行不义必自毙。”[11] 这是说共叔段的贪利忘义，必将不得善终。

    至于杨叔姬所谈及的怎样教养儿子的问题，卫国大夫石碏què也曾指出：“臣闻：爱子，教之以义方，弗纳于邪。”[12] 这里的“义”“义方”，孔颖达解释为：“义者，宜也。教之义方，使得其宜。”[13] 诚然，“义”经常可以释为“宜”。例如《中庸》也这样讲：“义者，宜也。”[14] 不过，“义”也常释为“正”。“义”兼“正”与“宜”二义，后来成为儒家正义论的两条基本的正义原则，即正当性原则和适宜性原则。[15] 石碏这里所谈的“义”，乃是与“邪”相对而言的，显然意谓“正”，诚如孟子所说：“义，人之正路也。”[16] 这与杨叔姬所要表达的意思是一致的，她所警诫的“不义”，是指接受“攘羊”，正是说的不正当、非正义。

    这种“义利之辨”的思想传统，后来孔孟儒学特别加以发挥，朱熹称之为“儒者第一义”[17]。如孔子说：“君子喻于义，小人喻于利”[18]；“见利思义”[19]；“不义而富且贵，于我如浮云”[20]。《孟子》开篇就讲：“何必曰利？亦有仁义而已矣。”[21] 孟子的问题意识是：“其所取之者，义乎，不义乎？”[22] 他主张“非其义也，非其道也，一介不以与人，一介不以取诸人”[23]；“不义之禄而不食也”，“不义之室而不居也”[24]；否则，“君臣、父子、兄弟终去仁义，怀利以相接，然而不亡者，未之有也”[25]。对于儒家这种“义利之辨”的思想来说，杨叔姬乃是其先驱之一。

    二

    刘向对杨叔姬的赞誉，主要是突出她洞察人性、推知人生、预见命运的智慧，从而“颂曰：叔向之母，察于情性，推人之生，以穷其命”[26]；但实际上，杨叔姬的言论所蕴含的思想意义远不止此。

    （一）文献的记载

    据《国语》载：

    叔鱼生，其母视之，曰：“是虎目而豕喙huì，鸢yuān肩而牛腹，谿xī 壑可盈，是不可厌也，必以贿死。”遂不视。[27]

    叔鱼，羊舌鲋，叔向的同母弟弟，晋国大夫。“其母”即杨叔姬。虎目，指涉贪欲，出自《周易》“虎视耽耽，其欲逐逐”[28]。鸢肩，像鸱鸟两肩上耸，形容其丑陋。牛腹，指其胃口很大，与下文“谿壑可盈”相呼应。“谿壑可盈，是不可厌”，犹今所谓“欲壑难填”。韦昭注：“（叔鱼）后为赞理，受雍子女而抑邢侯，邢侯杀之”；“食我既长，党于祁盈，盈获罪，晋杀盈及食我，遂灭祁氏、羊舌氏，在鲁昭二十八年”。厌，满足。贿，受贿，这里具体指“雍子入其女于叔鱼”（详下）。

    此事另见于刘向《烈女传》：

    叔姬之始生叔鱼也，而视之曰：“是虎目而豕啄，鸢肩而牛腹，溪壑可盈，是不可厌也，必以赂死。”遂不见。及叔鱼长zhǎng，为国赞理。邢侯与雍子争田，雍子入其女于叔鱼以求直，邢侯杀叔鱼与雍子于朝。……遂族邢侯氏，而尸叔鱼与雍子于市。叔鱼卒以贪死，叔姬可谓智矣。[29]

    赞理，理官（掌管诉讼）的助理。“入其女以求直”，将女儿嫁给叔鱼，以求胜诉。这里“族”谓灭族，动词。尸，暴尸示众。

    这里刘向评价杨叔姬“智”，是指她能预见羊舌氏将来会遭到毁灭的命运。有意思的是，这种预见的原初依据，却是她的儿子相貌之丑恶。这在今天看起来颇为荒诞，似乎丑人必是恶人、必有恶报。不过，这并不是杨叔姬思想的特色；这件事情的意义并不在此，而在其涉及美丑善恶的关系问题。且看《左传》的一段记载：

    初，叔向之母妒叔虎之母美而不使，其子皆谏其母。其母曰：“深山大泽，实生龙蛇。彼美，余惧其生龙蛇以祸女（rǔ）。女（rǔ）敝族也。国多大宠，不仁人间（jiàn）之，不亦难乎？余何爱焉！”使往视寝，生叔虎，美而有勇力，栾怀子嬖（bì）之，故羊舌氏之族及于难。[30]

    叔虎，叔向的异母弟弟。叔虎之母是叔向的父亲羊舌职之妾。“不使”，不让她侍奉羊舌职。杜预注：“不使见叔向父。”敝族，衰败的家族。大宠，有权势的宠臣。杜预注：“六卿专权。”间，在君主和羊舌氏之间离间。爱，不舍，此处指嫉妒。嬖，宠爱。栾怀子，栾盈，姬姓，栾氏，名盈，栾桓子之子，晋国下军佐。在栾盈（栾氏）和范宣子（范氏）的斗争中，叔虎因依附栾盈而被杀，其兄叔向亦受牵连而被囚，最终导致羊舌氏被灭族。

    （二）“不仁人间之不亦难乎”的哲学意义

    杨叔姬的这句话不可轻轻放过：“不仁人间之，不亦难乎？”这里，杨叔姬特别强调了“仁”；众所周知，“仁”是后世儒家的核心观念。同时，杨叔姬还强调“义”，谓之“德义”（详下），如上文谈到的“不可食以不义之肉”；我们知道，“义”也是后世儒家的一个核心观念，即儒家的社会正义原则。[31] 这就涉及“义”与“仁”的关系问题。其实，在中国思想传统中，“仁”与“义”不是并列的观念，即不是后世所理解的并立的“德目”，而是一种观念奠基关系，即“仁→义”。[32] 孟子指出：“仁，人之安宅也；义，人之正路也”；这是“居仁由义”的理路。[33] 孟子还说：“仁，人心也；义，人路也。”[34] 这就是说，正义原则是由仁爱精神奠基的。显然，杨叔姬的思想已经蕴含着这种观念奠基关系。

    （三）“彼美其生龙蛇”的哲学意义

    杨叔姬所说的“深山大泽，实生龙蛇”，比喻“彼美，其生龙蛇以祸汝”。《左传》的时代，龙并不一定是后世的正面形象。[35] 如《左传》载：“郑大水，龙斗于时门之外洧wěi渊，国人请为萗cè焉。子产弗许，曰：‘我斗，龙不我觌dí也；龙斗，我独何觌焉？禳ráng之，则彼其室也。吾无求于龙，龙亦无求于我。’乃止也。”[36] 又如：“董父，实甚好龙，能求其耆欲以饮食之，龙多归之，乃扰畜龙，以服事帝舜。”孔颖达疏：“扰，顺也。顺龙之所欲而畜养之。”[37] 这里杨叔姬所说的“龙”，颇类似西方人所说的“dragon”，乃是凶恶的形象。

    杨叔姬将“龙”与“蛇”相提并论，也是这种意味。“蛇”古字为“它”，《说文》解释：“它，虫也。从虫而长，象冤曲垂尾形。上古艸居患它，故相问：‘无它乎？’”[38] 这就犹如今天见面的问候：别来无恙？段玉裁注：“相问‘无它’，犹后人之‘不恙’‘无恙’也。”[39] 最古的例证，《周易》古经三处谈到“有它”，均指作为敌对势力的外族：《比卦》“有孚盈缶，终来有它”[40]；《大过卦》“有它，吝”[41]；《中孚卦》“有它，不燕”[42]。[43] 显然，“它”即“蛇”是一种凶险的“他者”（the other）的象征。[44]

    上面这段记载中的“彼美……其生龙蛇以祸汝”和“美而有勇力……故羊舌氏之族及于难”，初步透露了杨叔姬的“甚美必有甚恶”思想（详下）。

    三

    杨叔姬最具有哲学意义的思想，就是“甚美必有甚恶”的命题，揭示了美丑善恶之间的辩证关系。同时，她所提出的“有奇福者必有奇祸”，也是颇具哲学意义的命题。

    （一）文献的记载

    命题“甚美必有甚恶”，出自《左传》的记载：

    初，叔向欲娶于申公巫臣氏，其母欲娶其党。叔向曰：“吾母多而庶鲜，吾惩舅氏矣。”其母曰：“子灵之妻杀三夫，一君、一子，而亡一国、两卿矣，可无惩乎？吾闻之：‘甚美必有甚恶。’是郑穆少妃姚子之子，子貉之妹。子貉早死无后，而天锺美于是，将必以是大有败也。昔有仍氏生女，黰zhěn黑而甚美，光可以鉴，名曰玄妻。乐正后夔取之，生伯封，实有豕心，贪惏lán无厌，忿颣lèi无期，谓之封豕。有穷后羿灭之，夔是以不祀。且三代之亡、共子之废，皆是物也，女（rǔ）何以为哉？夫有尤物，足以移人。苟非德义，则必有祸。”叔向惧，不敢取。平公强使取之，生伯石。伯石始生，子容之母走谒诸姑曰：“长zhǎng叔姒生男。”姑视之。及堂，闻其声而还，曰：“是豺狼之声也。狼子野心。非是，莫丧羊舌氏矣。”遂弗视。[45]

    申公巫臣，芈mǐ姓，屈氏，名巫臣（一名巫），字子灵，曾任申县之尹，故称“申公”。其妻夏姬，姬姓，郑穆公之女，春秋时期四大美女之一，原为陈国司马夏御叔之妻，故史称“夏姬”；先后七次嫁人，最后与巫臣私奔晋国。“叔向欲娶于申公巫臣氏”，叔向想娶巫臣和夏姬的女儿为妻。党，亲族。“母多而庶鲜”，杨叔姬的亲族女子陪嫁过来的很多，但她们能生儿子的却很少。杜预注：“言父多妾媵，而庶子鲜少，嫌母氏性不旷。”“惩舅氏”，以杨叔姬的亲族女子为戒。

    子灵之妻，即夏姬。“三夫”指夏姬的三任丈夫，杜预注：陈御叔、楚襄老、巫臣（此时巫臣已死）。“一君、一子”，杜预注：陈灵公（与夏姬私通）、夏徵舒（夏姬之子）。“一国、两卿”，杜预注：陈国；孔宁、仪行父（均与夏姬私通）。“可无惩乎”，能不引以为戒吗？“郑穆少妃姚子之子，子貉之妹”，夏姬是郑穆公的妃子姚子之女，郑灵公子貉之妹。“天锺美于是”，上天将美丽集中在夏姬身上。

    有仍氏，古国名。黰，通“鬒zhěn”，稠密的头发。乐正后夔，帝舜的乐正，杜预注：“夔，舜典乐之君长。”贪惏，贪婪。忿颣，忿怒狼戾。孔颖达疏：“其人贪耆财利饮食，无知厌足，忿怒狼戾，无有期度，时人谓之大猪。”有穷，夏代国名。共子，晋国太子申生。杜预注：“夏以末喜，殷以妲己，周以褒姒，三代所由亡也。共子，晋申生，以骊姬废。”“是物”，这个东西，指美色。尤物，特异的东西，指特别美丽的女子。

    伯石，又称“杨石”，即杨食我（？－前514年），杨氏，即羊舌氏，名食我，字伯石，叔向之子；其母是叔向之妻、夏姬之女。子容之母，叔向之嫂。“走谒诸姑”，跑去见她的公婆（即杨叔姬）。长叔，指叔向。姒，指叔向之妻、夏姬之女。杜预注：“兄弟之妻相谓姒。”

    杨叔姬所说的“非是，莫丧羊舌氏”，意谓除此人（伯石）以外，没人能够毁掉羊舌氏家族；言下之意，此人将毁掉羊舌氏。如《国语》载：

    杨食我生，叔向之母闻之，往，及堂，闻其号也，乃还，曰：“其声，豺狼之声，终灭羊舌氏之宗者，必是子也。”[46]

    这段事迹，另见于刘向《烈女传》，文字颇有出入：

    叔向欲娶于申公巫臣氏，夏姬之女，美而有色，叔姬不欲娶其族。叔向曰：“吾母之族，贵而无庶。吾惩舅氏矣。”叔姬曰：“子灵之妻杀三夫、一君、一子而亡一国、两卿矣。尔不惩此，而反惩吾族，何也？且吾闻之，有奇福者必有奇祸，有甚美者必有甚恶。今是郑穆少妃姚子之子，子貉之妹也。子貉早死，无后，而天钟美于是，将必以是大有败也。昔有仍氏生女，发黑而甚美，光可监人，名曰玄妻。乐正夔娶之，生伯封，宕有豕心，贪婪毋期，忿戾无厌，谓之封豕。有穷后羿灭之，夔是用不祀。且三代之亡及恭太子之废，皆是物也。汝何以为哉！夫有美物，足以移人。苟非德义，则必有祸也。”叔向惧而不敢娶。平公强使娶之，生杨食我，食我号曰伯硕。伯硕生时，侍者谒之叔姬曰：“长姒产男。”叔姬往视之，及堂，闻其号也而还，曰：“豺狼之声也。狼子野心。今将灭羊舌氏者，必是子也。”遂不肯见。及长，与祁胜为乱，晋人杀食我。羊舌氏由是遂灭。君子谓叔姬为能推类。[47]

    这里的“叔姬不欲娶其族”指夏姬之族，不同于《左传》“其母欲娶其党”指杨叔姬之族。宕，放纵。祁胜，晋国大夫祁盈的家臣。

    （二）“甚美必有甚恶”的哲学意义

    杨叔姬所说的“恶”，兼有两层含义，即形象上的“丑”和道德上的“恶”。这是古汉语“恶”字的常见用法，例如《左传》“美疢chèn不如恶石”[48]；“姬纳诸御，嬖，生佐，恶而婉；太子痤cuó，美而很（狠）”[49]；“己恶而掠美为昏”[50]；“丑类恶物，顽嚚不友”杜预注：“丑，亦恶也。”[51] 与此相应，“美”也兼指形象上的美丽和道德上的美善。[52] 这与英文一样，“beauty”兼具美丽、美德之义，“ugliness”兼具丑陋、丑恶、邪恶之义。

    命题“甚美必有甚恶”，杨叔姬虽然说是“吾闻之”，似乎那是一句既有的名言，而不是她的首创；但是，在早于杨叔姬的传世文献中，我们却找不到这样的表述。当然，在杨叔姬之前或其同时，也有两个比较类似的表达，均见于《左传》：（1）“齐庆封来聘，其车美。孟孙谓叔孙曰：‘庆季之车，不亦美乎！’叔孙曰：‘豹闻之：“服美不称，必以恶终。”美车何为？’”[53] 这是说其人之德与其车之美不相称，并非杨叔姬所说的“恶是从美转化而来”之意。（2）“侨又闻之：内官不及同姓，其生不殖。美先尽矣，则相生疾，君子是以恶之。”[54] 以上两例，“服美不称，必以恶终”“美尽疾生”的表述，不仅不同于杨叔姬的表述，而且都只谈及具体的“车”“疾”，而没有杨叔姬的表述那种普遍性的全称命题的涵盖力。这就是说，至少从既有的传世文献来看，命题“甚美必有甚恶”乃是杨叔姬的首创。

    当然，必须承认，杨叔姬的这番议论，与关于妺（mò）喜（末喜）、妲己、褒姒的“红颜祸水”传统观念是不无干系的。然而，我们必须承认：杨叔姬的表述“甚美必有甚恶”乃是全称判断，其字面含义所呈现出来的乃是一种普遍命题，即揭示了美与丑、善与恶之间的普遍的辩证关系。

    不仅如此，还应当注意的是，杨叔姬并没有将“甚美必有甚恶”绝对化，她说：“夫有尤物，足以移人。苟非德义，则必有祸。”这就是说，“甚美必有甚恶”并非绝对的，而是有条件的，那就是“非德义”，即缺乏道德上的正义性，才会由美转恶；反之，如果具有“德义”，则可以说“甚美未必甚恶”。

    关于美丑善恶之间的辩证关系，人们通常熟知的是老子的思想：“天下皆知美之为美，斯恶已；皆知善之为善，斯不善已”[55]；“信言不美，美言不信”[56]。但是，老子生活的时代，至今仍然存疑。若根据孔子问礼于老子的历史记载，即老子生活在春秋晚期，则晚于杨叔姬。据《史记》载：“（孔子）适周问礼，盖见老子云。辞去，而老子送之曰：‘吾闻富贵者送人以财，仁人者送人以言。吾不能富贵，窃仁人之号，送子以言，曰：“聪明深察而近于死者，好议人者也。博辩广大危其身者，发人之恶者也。为人子者毋以有己，为人臣者毋以有己。”’”[57] 又载：“孔子适周，将问礼于老子。老子曰：‘子所言者，其人与骨皆已朽矣，独其言在耳。且君子得其时则驾，不得其时则蓬累而行。吾闻之，良贾深藏若虚，君子盛德容貌若愚。去子之骄气与多欲，态色与淫志，是皆无益于子之身。吾所以告子，若是而已。’孔子去，谓弟子曰：‘鸟，吾知其能飞；鱼，吾知其能游；兽，吾知其能走。走者可以为罔，游者可以为纶，飞者可以为矰。至于龙，吾不能知其乘风云而上天。吾今日见老子，其犹龙邪！’”[58] 据此可见，杨叔姬揭示美丑善恶之间的辩证关系，确实是在早于老子的时代。

    （三）“有奇福者必有奇祸”的哲学意义

    此外还值得注意的是，刘向《列女传》还记载了杨叔姬的另外一个命题“有奇福者必有奇祸”。这是揭示祸福相倚的辩证原理。众所周知，老子也有这样的命题：“祸兮福之所倚，福兮祸之所伏”[59]；“以智治国，国之贼；不以智治国，国之福”[60]。但是，杨叔姬揭示祸福相倚的道理，仍然早于老子。不仅如此，在杨叔姬之前的文献中，也找不到她这样的表述；换言之，命题“有奇福者必有奇祸”同样是杨叔姬的首创。

    综括全文，杨叔姬是春秋时期的一位杰出的女哲学家。她早于老子揭示了美丑善恶之间的辩证关系，提出了“甚美必有甚恶”的哲学命题。并且，她所提出的“甚美必有甚恶”命题不是绝对的，而是有条件的，即“非德义”。显然，她所强调的“德义”原则，包括“不可食以不义之肉”原则，乃是儒家“义利之辨”的思想先驱之一。同时，她还先于老子揭示了祸福相倚的道理，提出了“有奇福者必有奇祸”的哲学命题。此外，她还触及了后来儒家“仁→义”之间的奠基关系的正义论原理，这一点同样难能可贵。

    本文原载《吉林师范大学学报》（人文社会科学版）2024年第6期

  • 韩昇：武则天时代的官僚阶层与科举

    本文载于《学术月刊》2024年第11期

    武则天登基以来，内部大狱频兴，朝政空转；外部烽火四起，挫折连连。国势日蹙，完全无法同唐太宗“贞观之治”同日而语，和唐高宗在位时期相比也颇为不如。从人事的角度观察，没有治国统兵的人才是一个重要的原因。要真实反映武则天的用人状况，必须进行全面考察，不可以偏概全。这里从两条线、三个层面切入，观其全貌。

    所谓的两条线，第一条线是理应掌管国政的朝官，第二条线是武则天真正委以重任的近幸宠臣。第二条线还可以细分为武家子弟、宠幸嬖臣；以及酷吏等两个层面。结合第一条线，构成了任用官吏的三个层面。

    一、朝官

    第一条线。朝廷最高执政者为一般所称的宰相，亦即唐朝的“同中书门下三品”，或称“同中书门下平章事”，武则天改“中书”和“门下”为“凤阁”“鸾台”，故中书门下称作“凤阁鸾台”。从嗣圣元年（684）武则天废中宗、垂帘听政以来，直到她被政变推翻的神龙元年（705）的二十一年间，任用了五十三位“凤阁鸾台三品（平章事）”头衔的宰相：

    刘祎之，武承嗣，魏玄同，苏良嗣，韦思谦，韦待价，张光辅，王本立，范履冰，邢文伟，周允元，岑长倩，裴居道，傅游艺，格辅元，乐思晦，崔神基，狄仁杰，杨执柔，崔元琮，李昭德，姚璹，李元素，韦巨源，陆元方，苏味道，王孝杰，杨再思，杜景俭，王方庆，李道广，娄师德，武三思，武攸宁，姚元崇，李峤，魏元忠，吉顼，王及善，豆卢钦望，张锡，韦安石，李怀远，顾琮，李廻秀，唐休璟，韦承庆，朱敬则，韦嗣立，宗楚客，崔玄𬀩，张柬之，苏瓌。

    鸾台（门下省）和凤阁（中书省）的首长亦是宰相。这两个机构掌管皇帝诏敕和军国政令，在皇城内办公，最能接近大内里面的武则天，宛如皇帝左右的鸾凤。

    鸾台纳言：王德真，苏良嗣，韦思谦，裴居道，魏玄同，武承嗣，武攸宁，史务滋，欧阳通，姚璹，娄师德，狄仁杰，李峤，韦安石。

    凤阁内史：裴居道，岑长倩，张光辅，邢文伟，宗秦客，豆卢钦望，王及善，武三思，狄仁杰，李峤，杨再思。

    鸾台凤阁的首长人数亦多，经常变更，受酷吏政治迫害者约三分之一上下。

    宰相是朝廷最高首长，中流砥柱，安危所系，本应最为稳定。唐朝建立以后，高祖任用的裴寂，太宗任用房玄龄、杜如晦等宰相，任职时间都很长，对于安定社稷、保持政策的连续性起到重要的作用。然而，到了武则天时代，这种局面骤然巨变，宰相更迭极为频繁，没有任何一个朝廷部门堪与相比。而且，这个席不暇暖的群体，即使把政争中遭到贬黜的情况排除在外，也至少有四分之一以上受到酷吏的迫害乃至屠戮。宰相被呼来唤去，弃之如同敝屣，则所有官吏的处境可想而知，武则天时代政情的高度不稳和内斗的极端残酷，实态毕露。

    为什么宰相群体更替最频繁呢？因为武则天对他们把控最严也最直接。武则天身处大内，既无政绩也无功绩，无以服人；同时自然没有共创事业的部属，堪以寄任。而且，她作为高宗内眷的妇女身份，不方便经常和朝廷大臣聚乐宴饮，了解外请，增进感情。所以，她只能紧紧控制权力中枢的宰相群体，通过他们掌控全局。宰相作为政令下达、沟通内外最重要的渠道，必须盯紧看牢。由于朝廷乃至社会民情、官吏所思所想，只能通过文件和密报等间接途径获取，又要应对篡唐立周的改朝换代剧变，随时有被颠覆的危险，这些都会极大加剧生性多疑的武则天的猜忌。所以，她采取频繁更替宰相乃至施以毒手的苛酷手段，势所必然。这里是她控制全局的关键，亦是命门所在。

    安危系于此地，宰相的治国能力并不重要，竭尽忠诚才是关键。所以，武则天时代的宰相群体有两个特点：第一，实务政绩型官员很少，大多出自政务官员。第二，武氏子弟实际掌权。武氏子弟是武则天政权最稳定的人事，无论他们是否身处宰相位置，宰相都要听命于他们。推而广之，武则天宠幸的汉子，宰相也要接受其统辖。例如征伐契丹时，武三思为主帅，宰相姚璹为副；征伐突厥时，薛怀义为主帅，宰相李昭德为副等等，乃至造天枢、进颂词之类事务，也是武氏子弟统领宰相实施。

    与此形成鲜明对照的是中央最高行政部门的尚书省，其首长在武则天时代最为稳定，二十一年间仅有六位，分别是左仆射：苏良嗣，武承嗣，王及善；右仆射：韦待价，岑长倩，豆卢钦望。除了岑长倩一人被迫害致死外，其余五人基本平安。左右仆射为尚书省主官，武则天时代曾经改称“左相”“右相”，实际上有名无实，和宰相沾不上边。武承嗣作为武氏子弟，不管担任什么职务都大权在握。尚书省长官之所以相对稳定且平安，根本原因就是没有实权。武则天通过宰相直接指挥六部、九卿，作为六部上级主管部门的尚书省形同摆设，主官在位唯唯诺诺，乏善可陈，故各人本传事迹记载寥寥，滥竽亦可充数。韦待价以军功起家，武则天用他担任天官（吏部）尚书、文昌右相，“素无藻鉴之才，自武职而起，居选部，既铨综无叙，甚为当时所嗤”。韦待价自知非治国之才，“既累登非据，颇不自安，频上表辞职，则天每降优制不许之”。武则天为什么坚持把不懂行政的人放在行政主管的位置上呢？其实就是为了将其虚化为承旨画押的华丽道具，便于她直接掌控朝廷。

    尚书省上层的人事，如表1所示（分为武则天垂帘听政与称帝两个时段）：

    表1

    尚书省长官被弱化乃至虚化，但尚书省的职能不可完全废弃，因而出现上权下移的情况，亦即尚书省的左丞（正四品上）和右丞（正四品下）实际处理都省事务。尚书省主官左、右相（从二品）位高权虚，人少稳定，但左、右丞官员颇多，频有更迭，表明他们才是真正主事者。以下级官员主持事务，是独裁者常用的集权手段。由于官位卑下，受到超常重用时感恩戴德，听话卖力。而且，还因为官位卑下，制度上无权参预重大国务，所以让他们参预何种事务，以及参预到什么程度等等，君主皆可随心所欲，权力收放自如。武则天以此手段控制尚书省。

    外朝机构主要是六部，其人事任用情况如表2：

    表2

    人事变更的频度，依次为吏部、兵部、刑部、礼部、户部、工部。用这个指标观察武则天时代各个官署的情况，可以发现它们在朝廷权力结构上的重要性同人事变更频度成正比，越是重要，掌控越严，人事更迭越发频繁。由此归纳出武则天朝的权力秩序及其结构如图1。

    图1

    这明显是一个以军政为中心的朝廷：一切以皇帝集权独裁为最高目标，由吏部担纲彻底更换官吏队伍，兵部作为权力支柱，刑部作为整肃工具，礼部制造改朝换代的理论与合法性。皇权笼罩于全社会，生产、技术、民生等皆处于从属地位。武则天彻底改变了唐太宗建立的社会发展国策与朝廷架构。

    朝廷中最受重视的吏部和兵部，副职的变动异常的频繁，还多次出现其他部难以见到的官员再任的情况。这明显是武则天直接插手，安插委任亲信；同时表明在至关重要的权力部门，武则天倚重副职，越级操控部务，使之完全听命于皇帝。

    朝官这条线高级官员的选任情况，根据《旧唐书》和《新唐书》的不完全记载，列示如表3：

    表3

    两《唐书》官员传记固然不能覆盖官员全体，然而，达到一定的量亦足以反映用人原则和基本面貌。根据上表所示，至少可以确认以下两点：

    第一，官员大都出自官宦之家。

    唐朝建立后，功臣和高官后裔，特别是军功子弟在仕宦上获得优待。唐高宗仪凤年间，魏元忠上封事指出：

    当今朝廷用人，类取将门子弟，亦有死事之家而蒙抽擢者。

    北魏孝文帝迁都洛阳推行新官制，按照官职高低分为甲乙丙丁“四姓”等级，确立了优先录用官宦子弟的制度规定，北齐、北周、隋朝和唐朝都沿袭这一原则，武则天亦是如此，故官宦出身者出仕比例甚高，功臣子弟更受重用。太宗、高宗朝名将薛仁贵，儿子薛讷，“则天以讷将门，使摄左武威卫将军、安东道经略”。

    从唐朝建立到武则天全面掌权，经过了大约半个世纪，许多功臣业已凋零，功勋门第逐渐变味为官宦之家。官宦出身既是政治可靠的凭证，在入仕升迁上受到重视，也是官场的护身符，在仕途挫折罹难时，能够起到从轻处罚或者事过境迁后东山再起的佑庇作用。开国初期的重视功勋，逐渐蜕变为建政后常规铨选时讲究家世亲缘，武则天对此颇为坚持，有所发挥。岑文本是唐太宗任用的宰辅重臣，其侄子岑长倩因此得到重用，高宗时出任宰相，支持武则天夺权，故长年身居权力中枢，直到武则天欲立武承嗣为皇位继承人之际，因为主张维持亲子继承而得罪武则天，下狱处死。此后在朝廷举荐人才的时候，凤阁侍郎韦嗣立推荐岑长倩族子岑羲入朝任职，并说明其为朝廷罪犯亲属，武则天不但批准了岑羲的任用，而且还为受牵连的高管亲属的任用开了绿灯，“由是缘坐近亲，相次入省”。对落难或者受牵连的官宦子弟网开一面予以任用，显然不是个例，而成为规则，维护着优待官宦子弟入仕的一贯方针。

    第二，注重名门家世，尤其是亲缘关系。武氏子弟不循正常途径入仕，应置于第二条人事线论述。武则天的母亲自称出自天下名门之弘农杨氏，实为隋朝皇族之杨氏。隋室杨氏因为是武则天外家的缘故，一直受到重用。武周“时武承嗣、攸宁相次知政事”，武则天对地官尚书杨执柔说：“‘我令当宗及外家，常一人为宰相。’由是执柔同中书门下三品。”武氏和杨氏联合坐庄朝政，成为一条规则。

    李唐与隋杨乃姻亲，政治上虽为敌手，亲情却深。李渊的母亲和隋文帝独孤皇后为亲姊妹，隋朝灭亡后，李渊对隋杨皇族给予照顾，亲自做媒将隋朝纳言杨达女儿嫁给武士彟，让这位河东木材商人粘上皇亲国戚的边，成为武则天日后飞黄腾达不可或缺的门槛。武则天显然领悟到王朝政治的奥秘，深知金字塔权力结构的顶端是少数门阀士族垄断权力，运用朝廷强力部门作为工具，实现对整个官僚体系的控制。在她的理解中，管理社会的核心不是遵守规则，而是追求权力的无限扩大，笼罩一切。权力需要人来掌握，掌权的人越少则权力集中，越有利于皇权。因此，等级森严的寡头政治成为她的营造蓝图。武氏家族（包括赐予“武”姓的皇子）居于金字塔尖，被选中的士族与近宠佞幸组成朝廷上层。有所不同的是被选中的士族相对稳定，而近宠佞幸与酷吏则频频变动，道理在于这些人作为工具固然必不可少，但落到具体的走狗却需要经常更换。近宠佞幸与酷吏属于第二条人事线研究的对象，留待后述。被选中的少数门阀士族颇受重用，飞黄腾达，纷纷跻身于权力上层，与武氏家族共同构成核心统治集团。例如杨氏家族在“则天时，又以外戚崇宠。一家之内，驸马三人，王妃五人，赠皇后一人，三品以上官二十余人，遂为盛族”；韦氏家族之“巨源与安石及则天时文昌右相待价，并是五服之亲，自余近属至大官者数十人”。

    唐朝是贵族建立的王朝，高祖李渊以此为荣，建政初期曾经对宰臣裴寂说道：

    我李氏昔在陇西，富有龟玉，降及祖祢，姻娅帝王，及举义兵，四海云集，才涉数月，升为天子。至如前代皇王，多起微贱，劬劳行阵，下不聊生。公复世胄名家，历职清要，岂若萧何、曹参起自刀笔吏也。惟我与公，千载之后，无愧前修矣。

    得意之情溢于言表。倚重士族和功勋家族成为唐朝人事的重要原则，唐太宗修《氏族志》和武则天重用士族皆为此原则的一脉相传，除了武氏因武则天而破格崛起之外，老牌士族左右高层政治的局面一仍其旧，未有改观。武则天重用士族，寄任之深甚至扭曲制度。唐朝制度规定，近亲不得同时担任高官要职，以防止某一家族权力过大。对此项规定，武则天采取变通的办法规避，李峤担任宰相，两年后其舅张锡也升任宰相，武则天让李峤转任成均祭酒，“舅甥相继在相位，时人荣之”。士族对于权位的诉求也直言不讳。垂拱年间的宰臣韦思谦把两个儿子韦嗣立和韦承庆径直托付给武则天，说：“臣有两男忠孝，堪事陛下。”武则天欣然接受，对韦嗣立明言：“今授卿凤阁舍人，令卿兄弟自相替代。”果如其言，先是韦嗣立接替韦承庆担任凤阁舍人，然后由韦承庆轮替韦嗣立出任天官侍郎，不久又接下韦嗣立的宰辅要职，等到韦承庆去世，又让韦嗣立接任黄门侍郎，前后四度轮替，宛如左右手传接一般。

    优待功臣后人，讲究官宦家世，倚重名门士族这三条铨选的基本原则，武则天无不坚持贯彻，比起唐太宗时代逐步开放用人的家世条件，有所倒退。陈寅恪未对武则天用人的实际情况进行整体考察，断言武则天破格用人，培养出新兴阶级攘夺替代西魏、北周、杨隋及唐初将相旧家之政权尊位，“故武周之代李唐，不仅为政治之变迁，实亦社会之革命。”此番议论完全得不到事实的支持。武则天称帝充其量只是僭主篡政，酷吏政治绝非社会革命，新兴阶级亦非权力所能制造，只能是社会生产形态所决定的客观存在。

    一朝有一朝的组织原则。武则天朝对于太宗朝组织原则的最大改变，是把对唐朝的忠诚演变为对她个人的效忠。她遴选并重用的官宦士族都遵循这条最高原则。

    从小生活在权贵圈子里成长的功臣高官子弟，对于政治人事嗅觉最为敏感，察言观色得风向之先，其中想飞黄腾达的人跟风最紧。丘和、丘行恭父子建唐时立有大功，皆获陪葬皇陵的殊荣。丘行恭之子丘神勣jì属于最早投靠武则天的功臣子弟，充当鹰犬，出手害死章怀太子，与酷吏周兴、来俊臣齐名；岑文本侄子岑长倩等一批功臣子弟因为支持武则天取代李唐而得到重用，俱见前述。李大亮的族孙李廻秀，在武则天晚年当上宰相，“颇托附权幸，倾心以事张易之、昌宗兄弟，由是深为谠正之士所讥”。

    在逢迎武则天近幸方面，王朝体制内的士族亦不遑多让。崔义玄精通儒经，以学干禄，为唐高宗立武则天为皇后出谋卖力，主持审判长孙无忌。因为这份功劳，两个儿子崔神基和崔神庆都得到武则天的重用。武则天晚年，朝中大臣拼死控告武则天的男宠张昌宗犯罪，崔神庆受命审理此案，竟然为其开脱。张昌宗、张易之兄弟为武则天晚年之最爱，士族官员趋势附炎，卑躬攀附，父子三人皆为宰相的韦氏，韦承庆讨好张氏兄弟；几度进谏武则天的宰相李峤，其实和张氏兄弟交情甚深，以至于武则天倒台后，他们都为此遭到贬黜。宰相杨再思历仕三朝，主持政务十余年，地道的官油子。他善于体察上意，皇上喜欢的，他吹捧得天花乱坠，皇上讨厌的，他诋毁得丑陋无比。有人私下问他身居高位何苦如此呢？他道出为官数十载的心得：正直的官员招灾惹祸，唯有望风顺旨才能保全性命。原来赞美颂圣的合唱队充斥着虚情矫饰的歌手，声嘶力竭的领唱者往往最洞悉内里幽暗。杨再思年轻就通过科举，腹有经纶，黠于应对。张昌宗遭诉，群情汹汹。武则天询问杨再思意见，杨再思说张昌宗炼仙丹给皇上服用，皇上身强体健便是国家万幸，所以张昌宗功劳莫大。避开犯罪事实，只谈皇上重于社稷，利君则利国，情郎瞬间成为英雄，迎合了武则天万难割舍的感情。张易之兄弟大宴朝官，饮酒互捧，张昌宗容貌粉嫩而得武则天欢心，一众官员赞美张昌宗貌似莲花，杨再思挺身纠正道：“人言六郎面似莲花；再思以为莲花似六郎，非六郎似莲花也。”这等话术浸染弥漫成为武周王朝的官风。

    各路出身的王朝官员汇聚在一起，国家正事做不了，真话说不得，有失品格的种种表演，未必都是他们猥琐卑劣，而是那个时代的政治生态所致。当然，他们的所作所为反过来也强化了那种环境，互为因果，最终无人幸免。于是，官场晋升的秘径变成通途，“时朝廷谀佞者多获进用，故幸恩者，事无大小，但近谄谀，皆获进见”。

    拍马溜须而不做事，即使身居高位也不敢有所作为。在朝不为恶，偶尔说些合乎道理的建言，这在正常的社会属于常识底线，但在武周却足以振聋发聩，勇气有加，难能可贵。武周时代朝官的水平，后人颇有评论：

    豆卢钦望、张光辅、史务滋、崔元综、周允元等，或有片言，非无小善，登于大用，可谓具臣。

    苏味道、李峤等，俱为辅相，各处穹崇。观其章疏之能，非无奥赡；验以弼谐之道，罔有贞纯。

    崔融、卢藏用、徐彦伯等，文学之功，不让苏、李，止有守常之道，而无应变之机。

    崔（融）与卢（藏用）、徐（彦伯），皆攻翰墨。文虽堪尚，义无可则。备位守常，斯言罔忒。

    这些评价并非贬低之辞。武则天晚年请狄仁杰举荐宰辅高官，狄仁杰当面询问武则天是否觉得当朝主官乃“文吏”之流，不堪大任？武则天深以为然。一朝皆凡庸，是谁之过？然而，到此地步，不想崩溃只能举贤任能，转机因此萌生，历史总要做出选择。

    二、近幸宠臣

    武周政权的朝官，在大清洗的肃杀氛围中，实际上已经沦为摆设，把朝廷门面装潢得煞有介事，敷衍日常事务，跑腿当差。真正掌握权力的是第二条线，亦即武则天委以重任的近幸宠臣。同第一条朝官线的重要区别，在于他们基本不经过吏部铨选正途入仕。这条线上的人物可以分为两个层面，首先是中心层面，有处于权力中枢、出将入相的武氏子弟，以及武则天信赖有加的男宠团队。其次是前台层面，有刮起血雨腥风、致令人人自危的酷吏集团。这两拨人的权力都来源于武则天。

    首先来看中心层面的武氏子弟。武则天时代构成政治权力的亲属基础者，有下面这些人。在亲属关系上，他们分别是武则天的侄子和侄孙两代人；在政治秩序上，他们分别被封为亲王和郡王。

    亲王

    梁王  武三思  武则天长兄武元庆之子。

    魏王  武承嗣  武则天次兄武元爽之子。

    陈王  武承业  武承嗣弟，追封。

    定王  武攸暨  武则天伯父武士让之孙，始封千乘王，尚太平公主后进封。

    亲王四人：武三思和武承嗣为武则天异母侄子，武承业为追封，三人皆为侄子辈；武攸暨因为尚太平公主而进封亲王，为侄孙辈，乃特例。

    郡王

    武崇训  武三思子，尚安乐公主，封高阳王。

    武崇烈  武崇训弟，封新安王。

    武延基  武承嗣子，始封南阳王，后袭父封，坐私议张昌宗，被杀。

    武延义  武延基弟，袭父封，继魏王。

    武延秀  武延义弟，封淮阳王。

    武延晖  武承业子，袭父封，嗣陈王。

    武延祚  武延晖弟，封咸安王。

    武攸宜  武则天堂兄武惟良之子，封建安王。

    武攸绪  武攸宜弟，封安平王。

    武攸宁  武则天堂兄武怀运之子，武攸暨之兄，封建昌王。

    武攸归  武攸宁弟，封九江王。

    武攸止  武攸归弟，封恒安王。

    武攸望  武攸止弟，封会稽王。

    武懿宗  武则天堂兄武志元之子，封河内王。

    武嗣宗  武懿宗弟，封临川王。

    武尚宾  武则天堂兄武仁范之子，封河间王。

    武重规  武尚宾弟，封高平王。

    武载德  武重规弟，封颍川王。

    作为武氏子弟集团的附庸，可以加上宗秦客、宗楚客、宗晋卿和纪处讷四人。前三人为武则天外甥，纪处讷则是武三思的连襟。

    武氏子弟集团最醒目的特色，是完全未见科举出身者。且不论唐朝高度重视文化，自开国以来就建立起文化程度甚高的官吏队伍，从社会发展而言，以武力开国的王朝到了和平年代，其军功集团的后代也在时代潮流的推动下转而向学，子弟通过科举途径入仕晋升，继续仰仗家世门荫者日渐稀少，受人轻视。武士彟作为唐朝开国功臣，其家族子弟这等学历，透露出武氏家族对于文化的态度，落伍于时代。这批武氏权贵中，最有文化，以至于史家给予记载的是武三思，“略涉文史”，仅此而已。他留下诗歌创作的记录是赞颂张昌宗才高貌美，乃神仙王子晋转世。

    武氏姻亲子弟，宗秦客、宗楚客、宗晋卿和纪处讷四人同样未见学业与科举记载。如果把视野扩大到整个第二条人事线，亦即将武则天男宠团队也一并考察，情况如下：

    薛怀义原名韦小宝，街头摆摊出身，以魁梧雄壮获得宠幸。武则天为了掩盖这段少年劣迹，令其出家为僧，编入女婿薛氏的士族谱中，主持朝廷宗教事业，找人编撰《大云经》，陈说符命，发现武则天是弥勒下凡。

    张易之、张昌宗兄弟，出自贞观名臣张行成家族。张行成少时追随大师刘炫，勤学不倦，应科举及第，历仕太宗、高宗两朝，为一代名臣。张易之兄弟是张行成的族孙，不可思议的是文化家族的子弟竟然不循科举正道，张易之是依靠门荫入仕的，因为白皙美貌，擅长音声。其弟张昌宗首先被太平公主发掘出来，用得称心，转而推荐给武则天，同样表现不俗，大得欢心。张昌宗推荐兄长张易之说：“臣兄易之器用过臣，兼工合炼。”原来这对兄弟兼具炼丹才能。由此可知，他们自小研修道家房中阴阳之术，耽于学业，故难应科举，只好走门荫之路。张易之、张昌宗兄弟几乎专宠，武氏权贵争相为他们牵马前导，招摇过市，加上以权贪赃，惹来妒忌非议，沸沸扬扬。武则天为了遮掩丑闻，让他们主持朝廷文化事业，集中天下美少年和宰辅大臣们，济济一堂，组建文化机构“控鹤监”，更美其名称“奉宸府”，编撰《三教珠英》等大型文集，煌煌千余卷。

    薛怀义的宗教事业，张易之兄弟的文化事业，再往前追溯到北门学士的巨著编撰，有人称之为武则天大力推动的文化盛世。

    做出如此不凡成就的张氏兄弟，虽然没有科举出身，亦非胸无点墨，史书记载张氏兄弟勉强能写成文章，至于和武则天酬唱应对的诗文，自有宋之问、阎朝隐等文学工匠代笔。

    武氏子弟与男宠团队，以及他们同武则天的关系如何呢？在政治风头上，男宠团队风光无限。早先得宠的薛怀义，乃至后来的新欢张易之兄弟，进出内外，武承嗣、武三思一帮武氏子弟争先恐后为其牵马执辔，献诗赞颂，卑辞厚礼，媚态可掬。作为武周皇族却要竭力逢迎男宠，武氏子弟心有不甘，武承嗣的儿子武延基与妻子永泰郡主，以及懿德太子等人私下聚集议论，谈到张易之兄弟任意出入宫中，无不愤恨难耐，摩拳擦掌。这些议论竟然不翼而飞传入武则天耳朵，武则天大怒，逼令武延基自尽。私下非议竟然要付出生命的代价，武则天心中的情感天平清晰可见。然而，这只是表面现象，在政治利益的天平上，武氏子弟才是根本，是武周政权的根基和血脉，武周政权总归要传给姓武的，以至于武则天的亲生子女都要改姓武，试图将他们塞进武氏血脉。武氏子弟充当男宠的马前卒，武则天当然知道，且乐见所为，如果企图反抗则铁腕镇压，绝不留情。这是什么道理呢？并不是男宠金贵，而是武则天将他们当作自己的化身和试金石，测试属下是否绝对驯服而已。服从男宠就是服从武则天，男宠不为人齿，却能够做到诚心悦服，证明对于武则天的驯服臻于精纯，绝对到肝脑涂地，万死不辞。

    武延基是未经风浪的权贵子弟，自命不凡，这恰是心生二志的萌芽，咎由自取。其父辈武承嗣和武三思则迥然不同。武承嗣写不了诗文，却将马牵得十分安稳，让薛怀义和张易之兄弟享尽荣耀。武三思粗通文墨，双眼如炬看出张昌宗乃神仙转世，亲自写诗，还组织编排大型音乐舞蹈表现仙人下凡的绚丽场面，让崔融动情绝唱：“昔遇浮丘伯，今同丁令威。中郎才貌是，藏史姓名非”，把自己感动得涕泗俱下。为什么父子两代差距如此巨大呢？道理就在于武则天同兄弟的关系。武承嗣的父亲武元爽、武三思的父亲武元庆，以及其他诸武的父辈如武惟良、武怀运等等，武则天幼年饱受他们的欺负，尤其是武则天的母亲对他们恨之入骨绝不宽恕，让武则天掌权后给予摧残泄恨，武元庆、武元爽遭黜，配流岭外而死；武惟良、武怀运被诬陷下毒害死外甥女韩国夫人，被处死。武则天的兄弟，自己不死，就只能等待处死。武承嗣和武三思早年都曾随父亲配流边荒，武则天决意篡唐建周以后，出于政治需要才把他们召回京城。骤落暴起，亲尝政治炎凉与绝情，武承嗣和武三思对于姑妈早已胆战心惊，变得十分乖巧，虽然身居高官，却十分清楚权力来自何方，对此顶礼膜拜。这种出格的表现有违自然，看似尽忠，实为恐惧。捆绑到篡唐立周的战船上，构成吴越同舟的共同命运，捍卫武则天就是保卫自身的政治特权，背后的驱动力不是绝对忠诚的感情，而是荣辱与共的利益。

    利益为本，必定得陇望蜀。武承嗣欲望和野心膨胀起来，想独占权力，便策动武则天尽诛李唐子孙，同时组织宵小请愿，试图成为太子，吞下武周的果实。武则天未遂其愿，致令武承嗣怏怏而死。作为政治精算师的武则天，是信任有父仇的侄儿，还是相信亲生的儿子呢？武承嗣越界了，利令智昏，自取灭亡。从他儿子武延基非议张易之兄弟一事，武则天难道看不出来武承嗣不为人知的家庭内部只讲利益不尽忠诚的真情吗？武承嗣和武延基父子之死，显现出武则天的底线：皇位传给姓武的亲生儿子，武氏子弟掌控朝廷，成为武周政权的核心。所以，武则天花费更多的心血培育武氏第三代，几乎都封为郡王，出将入相，以保武周江山长远稳固。武则天的政治算盘在内心早已权衡清楚，决不是晚年在大臣的谏言下幡然醒悟，立子继承。大臣们的谏言因为契合武则天的心意而被采纳，同时也给了跃跃欲试的武氏子弟一个无法扭转的交代。通过和朝中大臣讨论继承人问题，武则天也摸清了大臣们的政治态度。她这个决定是明智的，武氏第三代在内政外交上的庸劣表现，根本不可能作为皇帝撑起大局，与其被推翻，不如回归政治合法性。之所以成为糊不上墙的烂泥，武氏几代人皆无学业与科举，已经有了答案。

    其次来看前台层面的酷吏集团。在唐朝，武则天时代首次出现酷吏，完全改变了政治规则和社会风气，影响深远。唐朝的出现不仅是一次成功的改朝换代，而且是一场重要的政治革新。五胡十六国南北朝分裂时代，恃力使诈成为政治常态，社会上层失德，下层失信，导致国家数百年难以真正统一。唐太宗总结历史教训，致力于重建法律与制度，取信于民。唐朝建立到武周时代将近七十年，垂拱而治，依靠的就是官民互信，制度公平。到唐太宗晚年，“天下刑几措，是时州县有良吏，无酷吏”。武则天僭主当政，威望不足，忧惧群臣不服，便重用一批酷吏大规模整肃异己，构陷告密，开启酷吏政治时代。酷吏政治与武则天执政相始终，甚至长于武周政权的存在时间。武则天倒台之后，酷吏政治随之而去。但是，它没有消亡，而是潜伏在帝制体内，不时兴风作浪。

    酷吏作为僭主独裁的主要工具，威慑并实际控制整个官僚阶层，因此，他们无疑处于政治权利结构的顶层。另一方面，酷吏的所作所为，乃秉承上意，因此，他们常常被轻视为君权行使的道具，而非具有独立意志和利益的集团。事实并不尽然，当工具坐大的时候，便逐渐膨胀起欲望，从狐假虎威，假公济私，直至奴大欺主。武则天对薛怀义隐忍再三，唐朝多少皇帝死于宦官之手，说明任何政治集团一旦成形便有了主张和利益诉求。所以，酷吏集团不可仅仅当作皇权的影子简单处理。当告密和清洗全面铺开之后，海量的案件并非君主所能掌控，检举何人，镇压什么，都与发起的酷吏的感情、学识、见地和利益息息相关。他们的指向性变成强有力的鞭子和精神指挥棒，逼迫并规定着官僚队伍的思想观念、施政行为和价值取向，进而深深地影响文化程度不高的芸芸众生，形成弥漫世间的社会风气。最终出现的结果往往和君主最初的政治蓝图不尽吻合，甚至相去甚远，原因就在于君主和酷吏文化水平和利益见识的落差。君主用工具剪裁世界，酷吏则以其品行见识塑造世界。大千世界从来不是单方面所能制造的，而是各方面合力的产物。

    酷吏的身世塑造其品行和情感，文化见识规定其眼光和行为。这两者又极大地左右着官僚队伍乃至整个社会的文明水平。吏治庸劣从来都是社会堕落的驱动力。

    武则天时代，告密成风，酷吏成群。然而，能够得到武则天重视，挑选出来兴风作浪，成为酷吏代表的主要有以下这些人：

    来俊臣，乡间地痞；左台御史中丞。

    周兴，少习法律；秋官侍郎，尚书左丞。

    傅游艺，吏员；同凤阁鸾台平章事。

    丘神勣，官宦子弟；左金吾卫大将军。

    索元礼，胡人；游击将军。

    侯思止，家奴无赖，文盲；朝散大夫，左台侍御史。

    万国俊，乡间地痞；朝散大夫，肃正台侍御史。

    来子珣，无学，告密入仕；左台监察御史。

    王弘义，告密入仕；左台侍御史。

    郭霸，吏员，革命举；左台监察御史。

    吉顼，进士；天官侍郎，同凤阁鸾台平章事。

    让一个时代陷入血腥恐怖的酷吏，只有吉顼一人是进士出身。少时读过书的仅见周兴，曾经学习法律，为日后翻弄法条打下基础，属于刀笔吏。上述11人中，9人出自乡间地痞无赖，甚至侯思止还是个文盲，却官至左台侍御史，主持监察炼狱。这批人的行迹与文化程度，显然无法通过朝廷正规的仕进考察，所以都由武则天直接提拔重用。武则天用的人，再荒唐也不容议论。侯思止言行举行粗野愚蛮，成为官场笑柄。武则天知道后，怒斥嘲笑者：“我已用之，卿何笑也？”当听说了侯思止那些惊动四座的话语，自己也忍不住喷笑。侯思止丑态百出，在武则天看来却是愚忠可靠，故其官位坐得十分牢靠。11人中，有文化学业者2人，占18%。另一方面，升任宰相的也是2人，同样占18%。文化低同官职高形成鲜明的对照。

    武则天时代用人的两条线、三个层面，第一条朝官线基本遵循入仕正常规则考察录用。在武则天酷吏大清洗的恐怖气氛下，动辄犯咎下狱，故上上下下明哲保身，敷衍了事。他们整体文化水平最高，权力却最小，得过且过，形同摆设。第二条线的中心层面，有武氏子弟和武则天男宠团队，文化程度颇低，职位最高，握有大权，构成武周政权的政治人事基础；前台层面的酷吏集团，基本由地痞无赖出身者组成，通过诬告或者兼进谀词而获重用，飞速蹿升，权势熏天。和中心层面相比，前台层面的酷吏集团是必须的存在，至于具体的个人则需要经常更换，败亡亦在瞬间。他们得意之时极尽残忍，破灭之际人剐其肉，遗臭万年。他们刮起互害之风，自己无一幸免，“既为祸始，必以凶终”。

    从人事结构来看，武则天时代是武氏子弟、男宠团队和酷吏集团联合管控朝官，进而掌控全社会；同时也是无知对文化的压制，权力对于法律制度的践踏。

    三、士族政治与科举

    武则天基本遵循唐朝官员入仕与晋升的铨选原则，另一方面则在权力的上层重用武氏子弟、男宠团队和酷吏，掌控百官的黜骘乃至生杀大权，主宰政局。她重用之人学历低，非贵族名门出身，格外引人注目，以至于有研究者把武则天作为唐朝政局的分水岭，认为武则天大量提拔庶族寒门，改变了门阀士族对于政治的垄断。陈寅恪进一步把视野扩大到北周，认为当年宇文泰组建关陇地区胡汉各族实力人物组成的“关陇集团”，垄断政治直到武则天方才打破。武则天大批提拔科举出身的人入仕，形成“新兴阶级”，如此则武则天不仅在唐朝，乃至在中国古代历史上都是改变历史进程的领袖。以一人之力改变三代王朝的历史方向，这样的功业恐怕空前绝后。

    陈寅恪对于南北朝隋唐史研究的贡献，在于提出了宏大的问题，启发历史学家去思考和论证。学说的成立，首先要通过证伪的检验，其次才是不同视角的分析论辩，在思想碰撞中发展。

    陈寅恪对武则天历史定位的基点是魏晋南北朝以来的士族政治。首先需要厘清的概念是这个历史阶段的士族与士族政治。士族指的是社会的统治阶层。士族与皇帝为主导的政治、军事势力结合，相互依靠，掌控并长期把持中央王朝到地方的政治权力。曹魏建立“九品官人之法”，表面高举“唯才是举”大旗，很快转为重视家世，到了西晋则日益强调家世礼法，从铨选制度上极大强化了官僚士族的特权地位，世袭垄断政治权力，形成固化的士族政治形态。魏晋南北朝的士族，大多起自东汉崩溃以后一再出现的大动乱，在兵荒马乱中聚集亲族乡党据险自保，组成自立武装，割据乡村，概称为“坞壁”。几百年的战乱和外族入侵，使得坞壁得以长期维持，遂演变为世家大族，将地方社会碎片化，以至于重新建立的各个王朝都必须得到他们的支持才能控制地方。世家大族大小不等，大者跨郡连州，千家万户；小者数百家一族，武断乡曲。他们通过联姻构成亲族网络，跻身于王朝官僚之中，凭借在乡势力支持政权，利用国家权力垄断地方。婚和宦是支撑士族长久不衰的两大法宝。士族内部有高下等级之分，这种区分不仅凭借在乡实力和官位高低，还根据文化和声誉，虽然不像确定官品那样清晰严格，但也有必备的条件：连续几代人中出现公卿宰辅一级的高官，属于政治条件；颇有文化学养，遵循礼法家教，属于文化条件。政治和文化两方面条件都具备的世家大族，受到社会普遍的承认与重视，例如北朝隋唐的崔、卢、李、郑、王等山东士族，韦、裴、柳、薛、杨、杜等关中士族，被视为最高的门第。其下还有各个州郡级别的士族等级，构成从朝廷到地方的世家大族等级结构。王朝在此基础上，结合在当朝官职的高低，编撰氏族谱，作为铨选的家庭条件和分配政治权利的依据。北魏孝文帝开其端，划分甲乙丙丁等第；后续王朝全都跟进。唐太宗修《氏族志》，唐高宗和武则天重修《姓氏录》，表明对于士族等级秩序的高度重视。据此可知，说武则天力图打破士族政治，不知从何说起。兼具实力、官品、文化三者优势的士族，得到各大政治势力的积极拉拢，成为其政权的支柱。他们在朝身居高位，在地雄踞一方，并且根据各自的身份地位形成比较固定的通婚圈，备受瞩目，演变成社会上重视的“门阀”。这种政治生态称为“士族门阀政治”。在确定士族身份等第的时候，文化条件颇为重要，品行与学术决定家族的声誉和社会影响。官职高却没有文化被视为权势豪门，地方上有实力缺少文化的家族被称作豪强，总之同具有文化色彩的“士”难以沾边。所以，士族研究从这个角度区分兼具文化学养者为士族，仅凭官职或者强宗势力者为世家大族。当然，这一区分并不是那么严格。作为统治阶层，常见笼统使用士族一词。

    在世家大族或者士族等级秩序的框架之内，其下层被称作“庶族”“寒门”等。以往的研究对于士庶之分并不清晰，如果以五品以上官职划线，那么庶族就是下层官吏直至小地主之家，缺乏权势的中小地主自然被归为“寒门”。他们也被称作“庶族地主”等。然而，无论士族、庶族，他们都属于统治阶层。即使武则天时代出现大量提拔庶族寒门的现象，既不构成“新兴阶级”，也完全称不上“社会革命”，充其量只是统治阶层内部的成分调整。何况武则天任用的官员，如前面列示的三个层面，酷吏多为无业的地痞游民，连“寒门”都构不上；武氏与男宠固然文化水平低，但其家族在唐朝已经上升为功臣权贵，甚至是皇族，无法再用“庶族”指称他们；而朝官的选任与唐朝开国以来的状况没有大的变化。综合三个层面所展示的真实状况，无法支持陈寅恪所谓武则天缔造庶族寒门“新兴阶级”的假说。陈寅恪并未提供实证分析的根据，不知所本，故只能对其结论提出商榷。

    其次，陈寅恪提出的“新兴阶级”，最重要的特点是科举进士出身，工于为文。亦即武则天之前，唐朝铨选重视门第家世，用的是“西魏、北周、杨隋及唐初将相旧家”，而武则天破格录用科举出身者，形成与所谓“关陇集团”对立的“新兴阶级”。

    这里涉及两个问题：1.支撑起北周、隋、唐政权的旧家，亦即所谓的“关陇集团”的存续状况。宇文泰以后来所封的八柱国、十二大将军等二十余家创业家族为核心建立西魏、北周政权，所言甚是。但是，这一创业功勋集团从北周宇文护专政时起就遭受猜忌和镇压；北周武帝辉煌的功业昙花一现，人亡政毁；杨坚政变建隋，抑制并清洗宇文泰组建的关陇集团主要家族，隋炀帝则重用江南士族。李渊建唐，依靠的是河东士族与大姓，唐太宗则强调用人上的五湖四海。这一历史进程呈现了走出关陇的清晰脚印。政权长治久安的人事基础在于用人区域和社会阶层的广泛性，统治者只要不失心智，自然深谙个中道理。

    2.政治史所讲的地域政治集团，是指集中任用某地人的政策与原则。西魏、北周的统治地域仅仅局限于关陇地区，只能任用关陇人事，别无他选，并不是拥有广阔的统治区域而有意识地专门任用关陇人事。所以，所谓“关陇本位政策”或者“关陇集团”，说了等于没说。更何况严酷的政治现实，生死攸关，且政治目标与利益各不相同，从来都是一朝天子一朝臣，甚至是一朝天子数朝臣，哪有一朝大臣数朝皇帝，更加不可思议的竟然是一朝大臣三代王朝，从理论到现实都不成立。从宇文护到隋文帝，执政者仅有关陇地区的从政经历，故人事基础局限在这里。即便如此，他们也在扩大用人的面，压制宇文泰的创业“旧家”。明白无误的变化出现在隋炀帝时代。隋朝成为全国性政权之后，用人的区域日渐扩大。隋炀帝曾经指挥统一江南的战争，皇后又出自南朝皇族萧氏，故他重视南方，拔擢江南士人，委以重任，甚至主导朝政，极大改变了关陇官僚居多的成色。唐朝自创业时起，就以太原组建的班底构成核心人事圈，笼络山东士族。武士彟就在此时进入政治核心圈，崛起于政坛；武则天也是因为功臣之女才选入宫中，日后执掌权柄。武氏是李唐政权下的既得利益者，和其他创业家族命运与共，构成唐朝人事的基本盘。武则天出于一己之私，打压李唐政权的忠实支持者，但她主要用没有社会根基的酷吏集团作为打手整肃官僚，并没有从根本上改变官僚队伍的成分和用人路线。其道理显而易见，武则天要的是至尊皇权，而不是摧毁自己赖以生存的政权根基；她要在最高权贵阶层中长期占有武氏一席之地，并不为酷吏之流痞子政客谋求利益，改变权贵阶层的结构；她处心积虑推进李、武联姻，就是为了补武氏合法性短板从而获得长远安定；她深知根深蒂固的士族阶层的重要性，所以对韦氏、杨氏、崔氏等老牌士族笼络重用，甚至让他们父子兄弟同时身居要职，宽容他们对于男宠团队乃至武氏子弟的轻蔑；她扮演官僚、“旧家”敌对者的角色以煽动下层，却没有改变李唐依靠官宦士族的组织路线。所以，武则天表面上看似泼辣凌厉，其实内心极其精明，她走在极端政策的边缘，却在最关键之处未越雷池一步。从本质上看，她是士族政治的坚定维护者，而非掘墓人。

    北周隋唐的相关性在于三代王朝的建立者同出一源，此偶然现象的关键在于北周、杨隋皆短祚，事起仓促，只要不是被其他政治势力所征服，剩下的便是同一平台脱颖而出的新秀。新的创业者都是受到当政者压迫而心生异志的雄才，而非同一事业的前仆后继者。理念、目标和利益各不相同，如何构成同一体质的政治集团呢？所以，所谓的“关陇集团”把持北周、隋、唐三朝政治的议论，属于想象的建构。

    不存在垄断三朝政治的所谓“关陇集团”，却出现一个确定不移的现象，那就是官员铨选与晋升中，科举出身者日益增多，反映出对于文化的要求越来越高，成为大势所趋。为什么会出现这样的变化呢？这一趋势究竟是个人意志的产物，还是国家社会发展的必然？

    自从东汉末年董卓被杀时起，朝廷就失去了对全国的统制，内战爆发，一步步沦为彻底的分裂割据，直至唐朝建立为止，中国在战乱和分裂中度过了将近四个半世纪的漫长岁月，其间虽然有过西晋和隋朝短暂的统一，却都以失败告终。分裂战乱的时代，真理由思辨的洞彻发现沦落为暴力的胜负角逐所决定，乱世的最高道理就是胜利。所以，这个时期过眼云烟般的繁多政权无不把实力和功绩作为用人的根本标准。曹操一再发布《求贤令》，公开倡导重用反道德能取胜的人，开启其端。魏文帝时代创立“九品官人之法”，任命中央到地方各级中正官来评选人才。这个制度存在根本性的内在冲突，亦即选用士族来贯彻“唯才是举”，不啻缘木求鱼，随着时间推移越来越走向反面。士族出身的中正用“家世”条件评定人才品级，结果“九品中正制”成为加强并固化士族门阀的强有力机器。另一方面，频仍的战争涌现出许多勇武的将领，在军事化的国家机器中占据主流。“九品官人之法”制造门阀士族，军国体制制造军功阶层，源源不断，在王朝政权内混杂合流，形成门阀和军功两大特色，在权斗中共存。权斗源于军功阶层对于文化和士人的蔑视，痛下杀手，必欲将其奴仆化。北魏崔浩事件等等，层出不穷的文化大狱莫不因此而生。共存则是对现实的屈服。攀援上权力宝座的各色人物，无不企图固化既得利益，军功和权势皆不可长久，丛林互噬注定没有胜者，欲求长在，最有效的途径就是转变为门阀，故不可一世的军功阶层不得不向现实低头，与士族合流，借其金字招牌悬挂在列戟的大门之上。北魏孝文帝确定胡汉姓族等第，令其通婚联姻，这样的事例屡见不鲜，道理如出一辙。皇帝亲自做媒，不是开婚姻介绍所的业余爱好，而是营造铁打江山的专业操作。

    军功士族门阀政治，是东汉灭亡以来中国长期不能统一、政权无法稳固和不断发生社会动乱的根源之一。因此，想要建设稳固富强国家的统治者都必须改变这种局面，任何根本性的社会变革，一定依靠法律、制度乃至文化价值观的改造重塑。人治最不可靠，凌驾于法律制度之上的人治，既可行善，亦可为恶，方向上飘忽不定，则易于颠覆。隋文帝建国之后，断然拆除士族门阀政治的制度台柱，“九品及中正至开皇中方罢”。自曹魏创立以来沿用数百年的“九品官人之法”终于被废除。此举有利于扩大政权的社会基础，故为后面的王朝所遵循。

    军功士族门阀政治是战乱时代军国体制的产物，打破门阀政治不仅是制度的变革，更是治国理念的转变。古人说马上得天下，王朝是靠武力打出来的。可是，政权建立之后，国家能否继续采用军国体制管理呢？唐太宗对此有深刻的认识，他还是秦王、天策上将的时候就积极延揽四方文学之士，皆为一时之选，其佼佼者号称“十八学士”。戎马倥偬之际，唐太宗仍然和文士一起深入研讨如何治理国家，从根本上认识到治国不能采用军事命令式的行政强制，更不能听任权力恶性膨胀，凌驾于一切之上，必须讲道理，重规则，建立完善的法律与制度，提升社会文化道德水平，才能实现国家繁荣强大、长治久安的目标。政治路线须要人去落实，什么样的人做什么样的事，所以官吏铨选至关重要。隋文帝废除“九品官人之法”，代之以科举考试，隋炀帝进一步确立进士科的主流地位。唐朝建立之后，强化了科举在铨选中的比重，特别是唐太宗以军事统帅的威望率领创业的军功部属转变崇尚武力的思想观念，积极倡导文治，大力办学，拓展科举入仕的途径，坚持不懈，推动社会形成尊重文化、推重科举的风气。唐朝科举设秀才、明经、进士、明法、明书、明算六科，秀才科后来停止，明法、明书、明算三科为专门科目，故常设科目为明经和进士。盛唐以后进士科越来越显赫，压过明经科，求仕进者趋之若鹜。唐五代时人王定保撰述唐代科举状况，说道：“进士科始于隋大业中，盛于贞观、永徽之际。搢绅虽位极人臣，不由进士者，终不为美，以至岁贡常不减八九百人。”显而易见，唐朝甫建即大力推进科举制，发展迅速，到唐太宗贞观年代已经被世人视为仕进正道，很受尊崇，以至于权贵子弟通过门荫或者军功起家者都以不能由科举入仕而深感不足，一生抱憾。唐太宗以广收天下英才为己任，当年见到新进士缀行而出的场面，欣然说道：“天下英雄入吾彀中矣！”偃武修文打下唐朝将近三百年的根基。文化的盛大，不是用读书人的多少所能表现，最重要的是社会各个阶层都崇尚文化，以此为荣，蔚然成风，这就是王定保盛赞贞观时代的立论所在。读书人虽多却甘当鹰犬，重用近乎文盲的酷吏镇压士人，哪怕编撰出颂扬的诗文，王定保并不以为是文化盛世。把科举作为入仕正道是唐朝既定国策，坚持推动，不待武则天方才肇始。这个重要转变是军功立国后走向长治久安的必由之路，具有客观的必要性与必然性，从这个意义上看，唐太宗并无首创之功，却是及早的觉悟者，避免了有害无益的弯路和折腾。

    科举制同九品官人之法相比，颇具公平性。后者注重家世出身，造成士族高门几乎垄断官场的僵化局面。科举则允许士子报名投考，凭借个人成绩录用，打开了社会下层之人上升的通道。侯君集、孙伏伽等都是自寒素举进士入仕、初唐位居朝廷大臣的著名例子。唐高祖武德五年（622），居住在邺城的陇西人李义琛、李义琰兄弟，及其堂弟李上德三人同年考上进士，成为佳话，载入史册。投名报考的科举制一旦取代九品官人之法，下层士子入仕的比例必然越来越高，毋庸置疑。然而这是一个发展的进程，积累数十年的科考，到武则天时代寒门出身增多，只是结果的呈现。社会的进步必定是人身及身份性限制的日益解除。科举制代表的正是这个方向。

    需要特别指出，论述科举制时往往把录用人数的增多作为制度推进的直接有力证据，实则南辕北辙。科举制不是一般的入学考试，而是入仕当官的铨选，所以不可能大量录取，其名额由每年需要补充的官吏员数来决定。唐太宗励行小朝廷，精兵简政，其组建的朝廷人数有各种记载，这里选取较多的一种，《新唐书·百官一》记载：“初，太宗省内外官，定制为七百三十员。”乱世的社会平均年龄颇低，故初唐朝廷需要世代更新的人数很少，决定了科举录取人数必定较少。依照唐朝“壮室而仕，耳顺而退”的三十年仕宦期，到唐高宗年代，科举录用人数呈现梯度增长，并随时代推移构成阶梯式上升，都是自然而然之势，绝非个人一己托天之功。

    依据官吏世代更新的需要决定科举登科人数的原则，唐高宗显庆二年（657），主持人事的黄门侍郎、知吏部选事刘祥道上疏，核算当时内外文武官，九品以上者13465人，取大数放宽至14000人，每年补充500人都要多出不少，而当年补充了1400人，超过需要2倍多。亦即唐高宗时代，科举登科加上依靠门荫等入仕者早已供大于求，不能再扩大科举登科人数了。由此可知，唐太宗贞观时代科举录取人数较少，反映当时严格执行职官的编制。高宗时代已经在10∶1的求仕压力下增加了很多登科名额，人浮于事。到了武则天时代，为了夺权乃至篡唐建周，武则天“务收人心”，做了重要改变，一是取消考试糊名，致使贿赂公行。例如来俊臣握生杀大权之时，大肆收受请托，每次铨选都要违法安排数百人入仕，至于王公权贵的插手科考，难以尽述。二是不按成绩，任意扩大录取人数，天授二年（691），十道举人，大批拔擢官吏；长寿元年（692）一月，武则天接见各地举人，“无问贤愚，悉加擢用，高者试凤阁舍人、给事中，次试员外郎、侍御史、补阙、拾遗、校书郎”，数以百计。而且还开启了试官制度，大批未能通过考试和品行考察的人，先行任职，把政务和民生当作儿戏。新录用的官吏如此之多，致使各个部门冗官泛滥，社会流传歌谣称曰：“补阙连车载，拾遗平斗量；欋推侍御史，碗脱校书郎。”

    官多至滥，并不会给下层寒士带来普遍的机会。武则天任用士族主持铨选，李峤录用权势之家的亲戚二千余人，以员外郎身份到各部门掌管事务，同在编的主官发生激烈争执，甚至互相殴击。此类官场乱象，是士族权门内部利益之争，和平民有什么关系呢？在古代史上，冗员滥官从来是权力泛滥的表现，带来的是更大的不公平。从武则天时代到中宗、睿宗朝急剧膨胀的“墨敕官”“斜封官”，请托贿赂充斥官场，便是其结果，而庶族寒门更无升迁的希望，如何形成与士族“旧家”对抗的“新兴阶级”呢？冗员滥官是对法制的破坏，绝不是科举制的进步，更不是所谓的“社会革命”。只有一律平等的公平制度，才是下层士子的上升通道。

    武则天时代的官僚阶层，呈现出非制度性越级提拔的武氏子弟、男宠团队、酷吏集团真正掌握权力，控制朝官的状态。朝官则基本遵循铨选途径入仕，没有改变唐初以来士族与官宦子弟为主的基本面貌。魏晋南北朝隋唐时代最重要的社会变革是科举制取代九品官人之法，拆除士族门阀政治的制度性支柱。这场变革肇始于隋朝，成为主流而蔚然大观于唐太宗时代，武则天时代未见制度上的进步，冗员滥官却对制度造成重大伤害，反而阻碍了寒门士子的正常上升。