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  • 谭其骧：首都变迁的原因

    一、中原期与东移近海期

    总述上述七大首都（长安、洛阳、邺、开封、杭州、南京、北京）的兴替过程，可以看到，中国的建都史大致可分为前后两期。从殷周直到北宋这二千四百年是为前期，其时一统政权和统治北半个中国的大地区性政权的首都殷（邺）、长安、洛阳、开封，都在中原地区（北纬35°左右1度许，东经108°—114°）；江南的南京只做过统治南半个中国的地区性政权的都城，而位于华北平原北端的北京，则根本还够不上做较大政权的都城。所以这前期又可以叫做中原期。自十二世纪初叶赵宋南渡以后至今八百多年是为后期，一统政权和大地区性政权的首都都离开了中原：或向南移到了江南，杭州做了一百五十年的南宋都城，南京做了五十年的明朝初期首都，又做了此后二百二十年的陪都，直到近代还做过太平天国和民国的首都；或向北移到了北京，先还只是北半个中国金朝的首都，随后又发展成为元、明、清三代的大一统王朝的首都，直到近代还做过民国的首都，今天仍然是我们中华人民共和国的首都。杭州、南京、北京都在前期四大首都之东，距海不远，所以这后期又可以叫做东移近海期。

    为什么前期的大政权要选择中原内地的长安、洛阳、邺、开封为首都，后期的大政权要选择东部近海的杭州、南京、北京为首都？又为什么前期和后期在各个时代要选择不同的城市为首都？这需要我们对历史上择都的条件和首都在历史上所发生的作用作一番分析。

    二、七大古都的历史地位

    历代统治者主要是根据经济、军事、地理位置这三方面的条件来考虑，决定建立他们的统治中心——首都的。经济条件要求都城附近是一片富饶的地区，足以在较大程度上解决统治集团的物质需要，无需或只需少量仰给于远处。军事条件要求都城所在地区既便于制内，即镇压国境以内的叛乱，又利于御外，即抗拒境外敌人的入侵。地理位置要求都城大致位于王朝全境的中心地区，距离全国各地都不太远，道里略均，便于都城与各地区之间的联系，包括政令的传达、物资的运输和人员的来往。设若地理位置并不居中，但具有便利而通畅的交通路线通向四方，特别是重要的经济中心和军事要地，则不居中也就等于居中。所以地理位置这个条件也可以说成是交通运输条件。当然历史上任何时候都并不存在完全符合理想、三方面条件都十分优越的首都，所以每一个王朝的宅都，只能是根据当时的主要矛盾，选择比较而言最有利的地点。首都的选定一般都反映了该时期总的形势，反过来，首都的位置也对此后历史的发展产生一定的影响。

    明白了这个道理，那就不难理解历代首都的迁移，是历史发展的必然结果。

    先谈一谈从中原内地移向东部近海这个历史上前后期的大变动问题。这很简单。自殷周至隋唐，黄河中下游两岸是全国经济最发达的地区，又接近于王朝版图的地理中心，一个政权若能牢固掌握这一片地区，就尤足以控制全国，这就是这一段长达2400年之久的时期的首都离不开中原地区的原因。由于首都在中原，所以当时开凿的运河也都指向中原。五代北宋200年间，经济重心虽已南移江淮，但中原还是可以通过水运通向四方，所以首都仍然能够留在这个水运系统的枢纽地——开封。北宋覆亡以后，出现了南北分裂的局面，于是中原水运又因停止使用而归于淤废，从此以后，无论从经济、军事、交通哪一方面说，中原都处于不利的地位，这就是800年来首都再也不可能迁回到中原之故。

    再让我们逐一阐述一下七大首都何以先后被选为首都。

    中原四大首都中长安的条件最优，所以它作为首都的时间最长，以此为首都的周、秦、西汉、隋、唐也是历史上最兴旺的王朝。长安的条件优在哪里呢？汉高祖即位时都雒阳，听了娄敬、张良的话才西都关中，这两人的话很说明问题。

    娄敬说：“秦地被山带河，四塞以为固，卒然有急，百万之众可具也。因秦之故，资甚美膏腴之地，此所谓天府者也。陛下入关而都之，山东虽乱，秦之故地可全而有也。夫与人斗，不搤其亢，拊其背，未能全其胜也。今陛下入关而都，案秦之故地，此亦搤天下之亢而拊其背也。”

    张良说：“关中左崤函，右陇蜀，沃野千里，南有巴蜀之饶，北有胡苑之利，阻三面而守，独以一面东制诸侯。诸侯安定，河渭漕挽天下，西给京师；诸侯有变，顺流而下，足以委输，此所谓金城千里，天府之国也。”

    秦地，指崤山、函谷关以西战国秦国故地。关中，有广狭二义，广义等于秦地，狭义专指关中盆地，即八百里秦川。秦地对山东六国故地而言地居上游，关中盆地四面有山河（东崤、函、黄河，西陇山，南秦岭，北渭北山地）之固，所以建都关中，凭山河之固则退可以守，据上游之胜则进可以攻，对叛乱势力能“搤其亢”而“拊其背”，在军事上地位十分优越，是之谓“金城”。关中盆地“沃野千里”，是一片“甚美膏腴之地”，又可以取给于南方的巴蜀和北方的胡苑（胡人的牧区）以补不足。若山东诸侯有变，关中的物资足以供应顺流而下的王师，在经济上也有所恃而无恐，是之谓“天府”。关中在当时是这样一个金城天府之国，所以汉高祖便作出了在它的中心地带丰镐、秦咸阳的附近建立作为王朝首都的长安城的决定。

    历史证明这一决定是完全正确的。娄敬、张良抓住了当时初建的汉王朝内部最突出的问题，即中央与山东诸侯之间、统一与分裂势力之间的矛盾问题，他们之所以主张建都关中，主要着眼于都关中足以东制诸侯。此后自高祖至文、景，果然先后顺利地镇压住了多次异姓、同姓诸侯的叛乱，巩固了统一。他们还没有能够预计到日后形势的发展。武帝以后，汉与匈奴之间的矛盾代替了王朝中央与诸侯之间的矛盾，成为当时的主要矛盾，汉朝经过武、昭、宣三代的经营，终于取得了匈奴降服、置西域数十国于都护统辖之下的伟大胜利，这和建都长安便于经营西北这一因素也是分不开的。所以建都长安，确是既有利于制内，又有利于御外。

    隋唐时形势略与西汉相似，关中仍然以沃野著称，对内需要能制服山东和东南潜在的割据势力，对外需要能抵御西北方的强大边疆民族政权突厥与吐蕃的入侵，因而也和西汉一样定都于长安。

    但是，长安作为首都也有不利的一面。它的地理位置比较偏西，距离当时人口最稠密、经济最发达的黄河下游两岸远了一些，距离中唐以后财赋所出的江淮地区那就更远。关中尽管富饶，毕竟“土地狭”，不足以满足京师和西北边防所需大量饷给。西汉时问题虽已很显著，还不很严重，因为关中的不足主要仰给于山东，山东距关中还不算太远。到了隋唐，特别是中唐以后，两河藩镇割据，京师所需百物绝大部分都取之于数千里外的江淮地区，节级转运，劳费惊人，民间至传言“斗钱运斗米”，这一矛盾就越来越尖锐。勉强维持到唐末，终于通过朱全忠强迫昭宗迁都，结束了长安作为首都的历史。五代以后，黄河流域益形衰落，江南的经济地位和河朔的军事地位逐步上升，中原王朝内部便不再是东西对峙的问题，变成了南北争胜之局；主要的外患也不再来自西北，改为来自东北的契丹、女真和蒙古，从而长安又丧失了它在军事上的制内御外作用，所以首都一经撤离，就再也不可能搬回来了。

    洛阳在军事、经济两方面条件都比长安差。伊洛之间虽然也有一片平原，可是远不及关中平原的肥沃广袤；四周也有关河之固——东据成皋，西阻崤、渑，背倚大河，面向伊、洛，但诚如张良所说：“虽有此固，其中小，不过数百里，田地薄，四面受敌，此非用武之国也。”东汉都雒阳，所幸光武完成统一后王朝内部并不存在割据势力，故都洛百数十年得平安无事。但至末年董卓擅行废立，关东州郡起兵讨卓，以当时董卓之强，也就不得不离开这个“四面受敌”之地，西迁长安。

    东汉一代无论对内对外，武功都远不及西汉。特别是对西北边境，大有鞭长莫及之势。西域三绝三通，合计设有都护、长史的时间不过二十余年。安帝后历次羌乱，兵连师老，费用至数百亿，并、凉为之虚耗，三辅亦遭残破。当然，东汉国力之不竞是由多种原因造成的，但首都建在远离边境的雒阳，以致对经营边境有所忽略，不能不是原因之一。

    洛阳的优点主要在于它位居古代的“天下之中”。远在西周初年，周公所以要在这里营建成周雒邑，作为镇抚“东土”的大本营，就是因为它“在于土中”，“诸侯四方纳贡职，道里均矣”。西周为犬戎所破，平王东迁，即于此宅都。后来项羽烧了咸阳，汉高祖初即帝位时也曾都此数月，等到赤眉烧了长安，光武即定都于此。洛阳虽然比不上长安那样是“金城天府之国”中的首都，但它有了这一条为长安所不及，它的不大的四塞之固又为邺与开封所无，所以它在前期中原四大首都中的地位仅次于长安。曹丕舍弃了乃父曹操经营了十多年的邺都而迁都董卓劫迁献帝以来荒芜了30年的洛阳，北魏孝文帝自平城南迁，一度想都邺，而终于定都永嘉乱后荒废达180年之久的洛阳，足见曹丕和拓跋宏都认为都洛胜于都邺，他们考虑问题的着眼点显然是地理位置。邺地处河北，在中原范围内稍东稍北，曹魏为了对付西南的蜀汉和东南的孙吴，拓跋魏企图并吞南朝，混一诸夏，都洛当然比都邺合适。

    隋唐建都长安，隋炀帝、唐高宗都要另建洛阳为东都，经常来往于两都间。炀帝以居洛为常，洛阳是实际上的首都。高宗晚年亦多居洛，其后武周代唐，改东都为神都，正式定为首都。可见隋唐时代洛阳还有比长安更优越的一面，否则杨广、李治、武曌不会作出那样的决定。这不仅是因为它的地理位置在全国范围内比长安来得适中，更重要的在于它是当时的水运枢纽，东南取道通济渠、邗沟、江南运河，可通向富饶的江淮地区，东北取道永济渠可通向河北大平原，直抵王朝东北部的军事重镇涿郡即幽州（今北京），特别是江淮漕运自通济渠东来可以径抵洛阳城中输入含嘉仓，比之于都长安时需从洛阳或洛口再或水或陆，多走上千里路程才能到达目的地，省事省费实不可胜计。隋唐时代皇帝之所以屡次要东幸或移都洛阳，实际就是为了要解决皇室、百官和卫士等的给养问题。武则天死后中宗虽西还长安，不久玄宗开元初年起又屡次因关中岁歉而东幸洛阳。玄宗是颇厌惮往来的劳累的，但又不得不如此。直到开元二十二年裴耀卿改进了漕运办法，每岁可运二百数十万石至长安；二十五年牛仙客献计在关中用岁稔增价和籴之法，史称“自是关中蓄积羡溢，车驾不复幸东都矣”。长安的首都地位才得稳定下来，不至于为洛阳所夺。

    邺处于古代“山东”（一般指黄河流域东部大河南北、太行山东西）地区的中心，背靠山西高原，东南北三面是古代经济最发达的黄淮海大平原，所以它在军事上是无险可守的（曹操在邺城西北隅因城为基，筑铜雀等三台，这是人造的防御工事，当然比不上天然的山河之固），不及长安，也不及洛阳；在地理位置上不如洛阳那么适中。但以经济条件而言，则在长安、洛阳之上，凡是控制山东地区而不能奄有整个黄河流域的政权，一般都要宅都于此。商人七次迁都，自都殷（邺的前身）后凡273年竟不复迁。曹操情愿离开他经营多年的兖州和许，定都于邺；后来虽然统一了黄河流域，仍都此不迁，直到儿子曹丕手里才迁都洛阳。十六国时后赵、前燕，北魏分裂后的东魏、北齐都据有山东之地，也都定都于此。北魏明元帝神瑞二年因比岁霜旱，平城附近民多饥死，朝议欲迁都邺，以崔浩谏不宜动摇根本，乃分简尤贫者，使就食山东，而罢迁都之议。其后孝文帝南迁经邺，崔光清即建议定都于此，理由是：“邺城平原千里，漕运四通，有西门、史起旧迹，可以饶富。”孝文则认为“石虎倾于前，慕容灭于后，国富主奢，暴成速败”，不从。其实孝文这几句道貌岸然的话未必是他的真意，他之所以执意要都洛而不都邺，目的端在都洛便于南伐。但这几句话却充分反映了那个时期邺都经济条件的优越。

    自中唐以后国家财赋愈益依赖江淮漕运，所以五代北宋时，居水运枢纽的开封遂代替安阳（邺）、长安、洛阳，成为择都的首选。

    后期金、元、明、清之所以要选中北京定都，那是由于这几个政权都需要兼顾塞外与中原，而大运河漕运又足以解决都燕的供给。明初之所以都南京，那是由于元末明太祖以此为根据地经营四方完成一统的已成之势，并且正好就近控制东南财赋之地之故。至于南宋有半壁江山，不都南京而都杭州，上文已提到，除了由于自五代以来杭州在东南城市中最为繁盛这一因素外，主要是宋高宗绝意恢复中原的心理在起作用。

    《谭其骧历史地理十讲》（葛剑雄 孟刚选编）

  • 谭同学：民族走廊中的隙地开发与人群互动——以平川瑶为中心的讨论

    一、引言

    无论从地理形态还是社会文化上看，中国都是融多样性为一体的大国。依地理形态而言，施坚雅认为可分出长江上游、长江中游、长江下游、东南沿海、岭南、云贵、华北与西北等巨型区域。①冀朝鼎则综合地理、水利、政治、经济等因素，从“基本经济区”②理解中国历史。二者虽然不乏区别，但在方法上都有“从地方动力去理解国家历史”③的特点。区域“本身也是一个社会历史过程”，其“界临地区往往自成一个区域”。④而且，区域界限并不绝对，往往因为政治、经济和社会互动，具有变动的可能性。⑤

    区域之间有“界”，以绵延的山脉最为常见。“作为整体的山地，一般处于一些较大区域的边缘，构成区域的自然边界……高大广袤的山地对于区域边界的划分有着特别重要的意义，它对文化传播的阻隔作用远远大于长江大河”。⑥这些地域不仅地理上处于区域边缘，且因交通不便，常是国家统治薄弱的边缘。其中的人群还常有刻意“自我边缘化”，强化“蛮”的倾向，⑦以求不承担或少承担赋役。⑧在此意义上，从国家治理角度看区域间的边界地带，也有“空隙”的性质。对此，许倬云有较系统的论述：王朝国家体系“其最终的网络，将是细密而坚实的结构。然而在发展过程中，纲目之间，必有体系所不及的空隙。这些空隙事实上是内在的边陲。在道路体系中，这些不及的空间有斜径小道，超越大路支线，连紧各处的空隙。在经济体系中，这是正规交换行为之外的交易。在社会体系中，这是摈于社会结构之外的游离社群。在政治体系中，这是政治权力所不及的‘化外’，在思想体系中，这是正统之外的‘异端’”。⑨

    在借鉴许倬云论述的基础上，鲁西奇主张称此类区域间的空隙地带为“隙地”，并视其为“内地的边缘”。⑩进而，他将“隙地”的特征总结为：国家权力相对缺失；国家政治控制方式多元化；可耕地资源相对匮乏，经济形态多样化；人口来源复杂多样，很多属于“边缘人群”；社会关系网络多凭借武力，或以利相聚，或以义相结，或以血缘、地缘相类，具有强烈的“边缘性”；文化多元，异于正统意识形态的原始巫术、异端信仰与民间秘密宗教流行。11赵世瑜则认为，这种非均质化“地理缝隙”的一个重要标志是，在“编户齐民”之外，需要“代理人”治理。12此外，吴重庆还指出，隙地作为一种分析视角，也有助于理解近代革命根据地建设，以及当代农村人口“空心化”反向流动等现象。13

    从隙地看中国，无论在历史上还是在现实性上，都不失其价值。不过，作为区域间界限的隙地虽有其边缘性，却不绝对封闭。相反，在某些条件下，它们可以成为人们跨区域流动的“走廊”。历史上许多民族都有跨区域，甚至跨越多个区域迁徙的经历。为此，费孝通曾用“民族走廊”的概念，来指不同民族长期沿一定的自然环境（如河谷或山脉）迁徙，交往、交流、交融而又保持社会文化多样化的格局。14他还提议深入研究南岭、藏彝、西北三大民族走廊，以更好地理解中华民族“在历史上是怎样运动的”。15从宏观上看，民族走廊在宏观上或多或少有隙地的特征。若再往细处看，其内部往往在地理形态、生态条件、生计方式和社会文化等方面也具有多样性。因此，在民族走廊多样化的区块之间，会有一系列小尺度的隙地。

    其实，中国很多区域都有过多种民族迁徙、互动的历史。缘何民族走廊中的少数民族社会文化多样性会格外突出，或者说民族走廊究竟是如何形成的？指出其多样性本身，虽然对经验提炼有重要洞见，但更重要的是理清形成这种结果的过程和机制。从这个角度看，其人群自我边缘化以（部分）回避赋役的因素固然不可忽视，却难以解释为何他们在赋役无实质差别，甚至深受儒家“礼”仪浸淫的情况下，依然坚守少数民族认同。因此，宏观上具有大尺度隙地特征，内部又包含大量小尺度隙地的民族走廊，在形成、运转的机制层面，仍有值得进一步细究的地方。对这一问题的探索，在理解民族认同、民族关系的历史，以及民族走廊发展的现实思考上，均有价值。以下笔者将以对南岭民族走廊西端南侧桂林市恭城瑶族自治县平川河峡谷“平川瑶”的调查为基础，16结合相关文献，尝试探讨该问题。

    恭城县北部栗木镇、观音乡与桂林市灌阳县（水陆交替可达湘江），东北部与湖南省永州市江永县（古称永明），南部与桂林市平乐县、贺州市富川县接壤。平川河发端于观音乡与江永县交界的高山，向东南沿海拔800米—1300米左右高山所夹峡谷平川源（河谷海拔250米—350米），流经水滨、狮塘、蕉山、洋石、杨梅，在观音村的岩口寨出峡谷，再约2公里进入栗木镇地界，在该镇上宅村北侧汇入栗木河。栗木河往南约15公里，即东西向连接恭城、江永两县的恭城河，恭城河往南在平乐县汇入桂江。平川河无法通航甚至放排，从上游水滨村牛眼塘寨经山路到最近的集市栗木圩约35公里（1970年始有机耕路，1988年方通车）。河谷少量耕地可种单季水稻，接近河谷的坡地可种玉米、红薯、土豆，山地除了原生杂木，可种杉树、桐树、油茶树。

    二、隙地开发正当性终源于国家正统

    20世纪70年代，平川源曾发掘出一个陶罐，内有五十余枚古钱币，“开元通宝”居多，另有部分“宋元通宝”“大定通宝”。所有古钱都是发行量较大、流通实用型的，且都不晚于宋、金。蕉山村存有一个五足双耳石香炉，刻着龙凤、舞狮、麒麟、宝相花、龙犬等纹样（被考古人员断为唐代风格石雕）。17由此可知，明代之前平川源应已有一定数量的居民。

    明初，恭城县东部与湖南永明县交界地带发生叛乱，波及桂东北、湘西南，朝廷从桂西河池调兵镇剿。光绪《恭城县志》记道：

    明洪武初，势江源贼目梁朝天，湖南贼首雷虎子、马公三等纠党，由八角岩谋叛，攻破县城，杀戮官吏，时全州、永明二官俱被害。有莫祥才者，山东人也，统带庆远府之河池州宜山县、南丹州等处黄、韦、陈、周、石、唐、欧、赖、莫、贲、谭、覃、徐、祝、陆、廖、雷、马、梁、蒙、容、李、罗等二十三姓之药弩手三百、民壮五百，将贼剿平，克复城池，即以功授莫祥才白面寨巡检司，其弩手、民壮均给照，赐地方、租税，俾子孙永享焉。18

    县志未提及瑶兵。但是，1984年恭城县西岭乡新合村出土了一块题为《猺目万历二年石碑古记》的碑刻（以下简称《猺目碑记》），详细提到了瑶兵。19其碑文道：

    申告恳赏给照，七姓良猺赵中金、邓金通、赵进珠、邓启音、郑元安、盘金童。七姓猺目乃系广（东）德庆州肇庆府铁莲山风（封）川县，入广西恭城县到平源。雷伍（虎）子反，所有招主黄□□、黄明、李富山闻之广东有好良猺，即行招德（得）大朝兵马，之因洪武下山，景太（泰）元年闰三月初三日进平源，剿杀强首雷通天、李通地，贼首退散。给赏良猺，把手（守）山隘，开垦山场，安居乐土。恳给立至守把隘口，又到嘉靖□十七年七月十一日，被东乡贼脚阴家洞，抢得万名（民）不安。本县提调猺名邓贵明、郑海成、赵进旺，□（统）带猺丁拿得生工七名李，□□同解。本县赏给白银五十两，给猺目回源，守真山源隘口地方。后至万历十五年三月十八日，贼首越过苏被口並沙江，立剿（扰）万名（民）不安。本县提调猺名郑进旺、郑德元、赵殊禄，捅（统）带猺丁拿得生工名十，解报本县，即时打死。赏给白艮（银）七十两，给猺目回家，用心固守地方，至万历二十年。守把隘口地方，奉公守法，照越过地方，屡蒙恩赏。但良猺把守隘口地方，山场四至界内土名：赵中金把手（守）到平源，郑元安把守瓮塘源……五猺隘口山场与猺目，永远耕种、管业，开垦先立升科报税，不於（予）另招别猺影（侵）占猺源地界。    当夫上巡马脚不遗被猺，远任前公擅冷（令）后代子孙永远当差科派，那时有无凭只（证）德（得）报恩开垦，攻（功）劳实与朝。报□（万）历祠前，赴本县父台前，伏乞申详上司道府各处衙门计政存案，恳给印照付，猺目各收为据：子孙永远世代沾恩。详给施土司恩泽，历靖申告本县照验，准给申告准凭。    景泰元年闰三月初一进倒不（平）源

    洪武下山、万历二年八月十八日恳给印照20

    此碑错讹甚多。其中，“广东”缺“东”字，“银”错为“艮”，“侵”错为“影”，“平”错为“不”，因字形相近，疑为笔误；“风”（封）、“太”（泰）、“手”（守）、“名”（民）、“剿”（扰）、“於”（予）、“德”（得），字形差别较大，疑为汉语方言恭城话谐音别字；“只”（证）、“伍”（虎），疑为过山瑶勉语口音别字。碑文口吻、立场皆为“良猺”，新合村至今为过山瑶聚居村庄。综合看，撰写碑文者可能是文化程度不高的过山瑶。过山瑶中当至少有部分源于封川县（今封开县）铁莲山或附近山区，否则难以说出细致地名。口述者未必识字，只会发音“封川”，后来撰碑文、刻字者之文化程度恐不够知晓数百公里外的准确县名，而以为是“风川”。

    碑文无确切立碑时间信息，但内容表述为明万历二十年（1592年）之后一段时间，地方官不再强调甚至不再承认以前官方曾准许“良猺”世代享有土地及赋役优惠，以至后来“良猺”再次伸张自己的“权利”。其中疑点颇多。

    其一，若从广东封川县招瑶兵，水路距离约为河池两倍，陆路翻山越岭亦不比河池近，动静不可谓不大。且不说恭城“招主”难以获知封川“良猺”信息，至少志书不至于单记河池兵（详至弩兵23姓），而不记瑶兵（连《猺目碑记》所记赵、邓、郑、盘等常见“良猺”姓氏，都无一被提及）。明万历二十五年（1597年）恭城即首修县志，光绪版县志已是第四版21（前三版已散佚），记有其他几次剿“反”“贼”。前三版如有瑶兵记录，光绪版不应独删此记。

    其二，若“良猺”是明洪武年间，哪怕是洪武最后一年（1398年）下山，却到景泰元年（1450年）才“进平源，剿杀强首雷通天、李通地”（雷、李之名也像是俚语外号），中间隔了五十多年，耗时未免太长。

    其三，在恭城话中，“进平源”意为进入平川源，但碑文“入广西恭城县到平源”，“把手（守）到平源”，“进倒不（平）源”中所提“到／倒平源”（源自西南官话方言恭城话口语，无从判断“到”或“倒”哪种写法准确），却只表示临近平川源峡谷口的平地。

    不管真是官方通过查阅档案确认很久之前曾授予“良猺”“恩泽”，还是讨价还价之后妥协，结果是认可其占有5个“猺隘口山场”（含平川源隘口），“永远耕种、管业”，不允许另外再招其他“猺”来占用。而“良猺”也接受了“开垦先立升科报税”，只是不用“当差”。

    《恭城县志》记载，“雷虎子”事发明初，针对的是官府，故用词为“反”“叛”。《猺目碑记》所述时间却是明嘉靖、万历年间，“贼脚”“贼首”亦未针对官府，而是“抢”“民”，甚至只是“越过”被“良猺”认定属于自己“永远耕种、管业”的地界。“良猺”乃至官府视其为“贼”，但实属新流入当地的人群。当其土地开发范围跨过“猺源”隘口，进入河谷乃至峡谷口外平地时，与“良猺”发生了冲突。“良猺”作为胜利者，将这些冲突附会于五十年甚至更长时间之前镇剿“雷虎子”的历史，运用为国立功的叙事，证明其占有土地和免征差役的正当性。

    无独有偶，平川源的瑶民述及迁徙史，也说是明初“来恭城打雷虎子”（源流地则五花八门）。曾任水滨大队副大队长、水滨村村委会副主任的蒋礼发存有一本破损、散乱的手抄本《上五排历史》22（“排”是明嘉靖九年［1530年］至清宣统元年［1909年］官府在部分瑶山设置的村级管理单位，小村则数村为一排）。其中一篇《平川上五排嘉靖九年照碑记》（以下简称《嘉靖碑记》，碑已毁，但村中有几位老人表示民国时期见过）记道：

    计嘉靖九年（1530年）正月十五日给蒋政聪、周贵清、周福珠、俸仁聪等，各告称：祖公在于平川源上下二涧居住，洪武廿五年（1392年）被永明县雷午（虎）子越来作恶，洪武廿六年告军征剿，蒙上司行榜，仰本县责令本里故民欧（阳）用诚、周福谦招抚周庆陆、俸富三下山向化圣朝。23

    这里所说“上下二涧”，涉及明嘉靖九年实施的排瑶制。它以平川源及峡谷口10个大寨为中心，设10个排。下涧指的是下五排，包括老洼（今观音）、洋石、杨梅、井头、白藤底（今大坑底）诸寨。上涧指的是上五排，包括蕉山、狮塘、水滨、古骨圩（含矮寨）、大畔源诸寨（清乾隆二十七年［1762年］，第一排大畔源寨划归湖南永明县后，将较晚成村的狮尾、黄茅岭［今莲花］、石坪寨设为第一排）。其中，“雷虎子”写为“雷午子”，亦为过山瑶勉语口音所留痕迹（今水滨村只有牛眼塘寨1位老人还会说过山瑶勉语），所记“雷虎子”被征剿时间（明洪武二十六年［1393年］），与光绪《恭城县志》所记“洪武初”相比，有显著出入。此说附会色彩十分鲜明。

    不过，《嘉靖碑记》所载另一事多有印照。碑文记道：

    具记永乐三年（1405年）造册附籍，纳粮四石九斗三升，住种杀功解报，守护地方，至今一百七十余年，并无为非生祸。因被嘉靖六年（1527年）成江附籍良猺周良通等，（将）田地与獞人常金朝、常金龙、龙汝鉴占种。嘉靖七年三月十七日又被周镛、欧阳爵、卢姗等放傲，将本源盗卖王铭等，聚兵杀占、攻破山寨，杀死男妇一千余命，赶散良猺（往）湖广永明地方避住。（周贵）清等将情具告，蒙道行提周镛等，责令协同委官并县哨入源晓谕。军门杀伐利害，抚退王铭。回巢（源）照旧招佃，周贵清等复业本源住种。24

    平川源峡谷口外栗木镇上宅村的《周氏大宗族谱》对此事记道：

    嘉靖七年戊子（1528年），平川源被（恭城北乡栗木）大合（村）招主欧阳爵、本族地主周镛，受银三百两，（将）平（川）源田地尽数卖（恭城东乡）东寨贼（王）铭类，占夺平（川）源，杀死大小男妇一千余命。田地主（周）福谦、周祚、周郁、周郡通族等用呈具告回民瑶兵，备调发监三十四俍兵，四方普洗本乡三寨；胡北洗平三寨，胡伯抽巢，乡境得宁。25

    两则记载略有差异：其一，《嘉靖碑记》提到明嘉靖六年（1527年）就已有过“附籍良猺”将田地租给“獞人”耕种，次年才发生“良猺”土地“尽数”被“盗卖”和被驱赶、杀戮；其二，周氏族谱所记，大合村“招主欧阳爵”和“本族地主周镛”卖土地，属公卖而非“盗卖”。

    类似的事接二连三发生，说明当时有土地的一方，不管是汉人“招主”还是“附籍良猺”地主，将原本租给“良猺”的土地，收回佃权，改租或卖给新来的“猺人”或“獞人”，已非鲜例。新来的“獞人”未经过“良猺”村寨集体同意，从地主个人手中租、买土地之后，即自行耕种（被认作“占种”）。新来的“猺”“贼”则除了自行耕种，还要向原租种的“良猺”再收一道租，以至引发流血冲突。官方提审卖主，军队介入，但最后只是“抚退”而非剿灭“贼”。这更说明，问题实质是争夺土地经营权。周氏族谱既称王铭为“贼”，并记其占平川源、杀人之事，却不提“盗卖”，或为祖先讳。

    在当时的土地开发过程中，“良猺”可能确实贡献不小，且是以组织化的群体形式存在，以至于与土地所有者达成了默契，有集体性的优先耕种权。《嘉靖碑记》提及明永乐三年（1405年）纳粮的标准，或为暗示“良猺”耕种这些土地，原本赋税、租金比较低，因此夺佃、加租都不可接受。该碑记在后文中还提到，事件平息后上、下五排只需各“纳粮税”“六担”，由周、欧阳两姓代收，26此亦证明“良猺”为“附籍”。

    三、土地承载弹性空间及其自我维系

    经明嘉靖年间变故后，平川源“良猺”获得了官方认可的平川源土地经营权，以及相当一部分土地所有权（这可算是官方对欧阳、周氏等山主的惩罚，以此补偿受损的平川源“良猺”）。但是，平川源人口损失不少，而已开垦出来的土地得有适当数量的劳动力耕种，才有经济收益。于是，已有一定山主地位的平川源“良猺”，向官府申请并获得准许，可以村寨集体为单位，主动招徕其他缺少土地，甚至还处于流动状态的“猺”，从深山下到河谷或临近河谷的坡地进行耕种。对此，《嘉靖碑记》载道：

    （明嘉靖）九年（1530年）正月二十五日立赏蒋庆才、庆广招板瑶赵广富。正月二十七招二十五家。李朝聪招板猺赵老担，何涧清招板猺赵广聪，李庆惠招板猺盘大三……嘉靖九年，蒋政威（招）廿五家，田户开在赵广聪名下，蒋世姗招廿五户，开在赵保仔名下。27

    板瑶属于过山瑶的一个支系（但与此前流入平川源“附籍”的过山瑶，显然不属于同一群体），据说因“以头盖夹板而名”，源自广东北部。28但是，仅上五排一年之内就能招到板瑶上百家，甚至在正月3天就招徕到三十余家。由此推测，原本就在平川源及其周边深山游耕、游猎的板瑶，数量必定不少。否则，恐难短时间内有这么多人能够召之即来。依费孝通于1935年所做调查，桂东北大瑶山区的瑶民有控制人口的习惯，一般一对夫妇抚育2个孩子29（部分家庭或有老人，估算平均每家5口左右）。以此为参照粗略推算，该年上五排招徕板瑶即可能达到五百人以上。若下五排情形亦相似，则整个平川源招徕板瑶约一千人。这个数字大致接近此前平川源在冲突中损失的“一千余命”。若这种招徕行动，并不能将周边深山中带有一定流动性的人口悉数全引下山，则说明原本在深山中靠游耕、游猎生存的人口可能远超过千人。平川源及其周边山地能承载的人口有相当的弹性空间，由此可见一斑。

    平川瑶招主得在自己名下给招徕的板瑶开“田户”，意味着这些板瑶主要不是在深山中耕种林间旱地，而是在河谷种田，或在接近河谷的坡地进行开垦。虽然板瑶与平川瑶在语言、服饰、生活习惯上不同，但仅从土地耕作的角度来说，并不必然构成矛盾。然而，一种在水滨村口口相传的说法表明，这部分板瑶中的大多数，后来被平川瑶以武力赶出了平川源。

    水滨村不少村民曾为笔者讲述这段口传历史。其概要为：上五排招徕的大部分板瑶不习惯耕地农作，在清朝初期可能已放弃佃耕，而集中在平川河上游支流冷水源山谷中刀耕火种（冷水源乃从海拔300米左右的平川河谷急剧抬升到1200米左右的陡峭高山溪流，水温明显比平川河低得多，故得此名，属大村水滨寨地界）；冷水源有百来户板瑶，很强势，甚至敢葬人到岗子上寨（属水滨寨大家族周姓的土地）；约在清乾隆年间，水滨寨周姓联合其他寨瑶民，与冷水源板瑶打了一架，死伤不少（不同的人口述数字不同，少则十几个，多则一百多个），冷水源板瑶败走，不知其踪。

    板瑶在桂东北大瑶山区颇为有名，原因之一是入山较晚，没有或极少拥有土地。费孝通于1935年调查发现，板瑶因无地或少地而地位极低，故对耕地格外渴望。30由此反观平川瑶关于板瑶离开平川源的说法，似多有可疑之处。毋宁说，情形更可能是，平川源人口慢慢增加之后，平川瑶开始收回佃权，相当一部分板瑶不得已退到山上，而且是周边地带耕作条件相对较差的冷水源。在暴力驱赶之下，这部分板瑶最后失去了在平川源的土地经营权。但是，少量未聚在冷水源的板瑶，则可能既有通过入赘、过继等方式融入平川瑶村寨者，亦有继续耕种于周边深山者。

    平川源山脉连绵不断，耕地只占极少数，绝大部分土地是开发程度很低的山地，甚至未开发的原始森林。大部分板瑶离开，自然还有新的人群流入。

    清康雍两朝全面推行人丁不单收税的政策，康雍乾之际社会总体稳定，以及红薯、玉米、土豆等旱作物扩散，31致使人口快速膨胀。康熙早期全国人口“可能已经大大超过1亿5千万”，主要“平原和低山区已经人满为患”，32至乾隆晚期又“不止翻了一番”，达到3亿多，33大量人口不得不转向深山区。

    清乾隆年间，不仅有新的以刀耕火种为主的过山瑶，还有来自宝庆府（大致为今湖南邵阳）擅长犁耕锄掘农业的农民，不断涌入平川源及其周边山地。除全国人口，尤其平原人口膨胀的大背景之外，还与宝庆府在乾隆年间特别频繁地发生灾害，灾民难有就地喘息、恢复生产的机会有关。以下略摘几处道光版《宝庆府志》记录为证。

    乾隆“十一年（1746年），武冈、新化大水”；“十二年四月，城步大水……是岁城步大火”；“十三年，城步大疫、新宁水灾……六月新化水灾”；“十四年三月，新宁、武冈水灾……庐舍湮溺甚重”。34以及，乾隆三十年（1765年）“新宁大荒，城步大水大饿……斗米银六钱”；“三十二年秋，新化大水”；“三十三年秋，新化水灾……邵阳大旱，斗米银四钱”；“三十五年，新化旱，城步麦无收”；“三十八年，新化虫伤稼”；“四十年，新化大水”；“四十三年，宝庆大旱大饥，邵阳斗米银八钱、饿殍相望，城步大旱，饥民多聚集肆掠”；四十四年，“城步大饥，斗米银六钱，新化旱”；“四十五年，新宁、武冈、邵阳、新化大水”；“四十六年春，城步大水”；“四十七年春，雷震城步……夏四月，新宁地震”。35

    宝庆人流入平川源，主要靠开荒山耕种桐籽树、油茶树为生。这从水滨村周姓族谱中保留的《立批山场契约》（以下简称《乾隆契约》）可见一斑。该契约写道：

    立批山场人广西恭城坪川源水边村、大田头、旱地四脚（房）人等……鸣锣公议，今将承祖山场座落土名大冷水、小冷水一所……四抵分明。情愿凭中说合，将来批与新化宝庆客人谢代宗、桥柏、坤宗、李咸有叔侄兄弟，耕种开挖，六成生理。当日三面言定，批山价银六十四千。二家言定开山，就日交足，并无短少分厘。每年议定，地钱照户收租，每户租钱二百八十文，风（丰）年不加，次（歉）年不少，其（期）限钱十月十五送至上门。自批之后，青山地山载种桐树、茶树，一概任从客人耕管，主家不得异言幡（翻）悔，任从客人招流（留）耕种人等，主家族内再无异言，如有个民差俞（干预）不与客人相干。若有众姓叔侄人等，不许另生枝节。新化客人谢代宗、李咸有二人不许招流（留）吃酒、打架、赌博，长人不许首流（收留），并无耕种，不许宝山乱横。又有主家茶（查）出，送官禀报，自耳（理）其罪。今恐无凭，立写批字，付与客人收执为据是实。

    请中人：俸奇通、何昌万、蒋子民。请代笔人：蒋子亮

    乾隆五十九年（1794年）十月十五日立批，永远耕种。

    值得注意，《乾隆契约》表明：其一，来自湖南新化县的宝庆人租佃山地，仍得经过水滨村周姓4个“脚”（房支）集体同意；其二，宝庆人是每年按户集资的，但对水滨周姓人而言则属于宗族公款；其三，宝庆人还可另行招留新来的人耕种。

    宝庆人原本即熟悉犁耕、锄掘，其山地耕种技术远远高于此前的过山瑶，甚至也高于平川源本地瑶民。其经营山地的模式是“用‘打锣唱歌’的形式，大面积开垦山地，第一年以种粮为主，次年则植入杉树、桐树、油茶和毛竹，并套种粮食作物，第三年则长树长竹、培植成林”。36据水滨村不少老人估算，宝庆人的套种技术比起当地瑶民种桐籽树、油茶树之后就等着收桐籽、油茶籽的方式，在开荒头十来年经济效益起码高四五倍。1952年土改时，水滨村215户，划出地主、富农共12户，其中8户是宝庆人。37此时，宝庆人居于高山，却相对富裕，证明其土地开发技术的确比较先进。宝庆人也不像此前两拨名称不详的过山瑶，以及板瑶那样，主要生计方式是游耕，而是一旦有山场可开荒，便能就地长期生存下来。

    按《乾隆契约》，宝庆人可再招徕新人进山开垦。加之其开垦效率和收益比较高，进入平川源的宝庆人也日益增多。而本地瑶民当中，也有人抵制不住利益诱惑，不经过村寨集体公议，即将山场私自租给宝庆人开垦。久而久之，又引发了新的冲突。

    现存于平川源狮塘村的一块无题碑刻，记录了一份于清嘉庆二十二年（1817年）订立的契约（以下简称《嘉庆契约》）。其文如下：

    立写天理仁义合同人周姓，李、孟、蒋、卢姓等。今因却被无齿（耻）之徒盗批双水六底业山，并行批飘以（与）湖广楚南新化宝庆之歹（徒），再于加（嘉）庆十一年（1806年）盗批。不料周姓四围（房支）众等查实不服，捉挐批主。成（呈）赴县主不印（应），具（状）往府台宪主详徐，宋（宪）主不重粮田。众等往省投告，详县、宋（宪）主不周。众等归家鸣锣集议，合口同心，情愿将冷水源大罡头一概付众，言（延）请下排四姓村老、二甲商议：水源将来下应粮田，大罡头将来二村牧牛，其出众之物，不能私已受用；水源、六底、大罡方以为上下官务之费，钱文艮（银）两每村占一半。二村合议：虎羊同群，鸡鹊同巢，情愿甘心，甘心情愿，将冷水源抄群出众（全部充公），勒石题名，平半耕管，以清藤面分水为界，二村同心抚做；其后二村不得幡（翻）悔，下村狮公塘不得退速（缩）、为悮（违误），上村周姓不得异言。如有此情，任从证立之主合同执照。上有天神共照，中有二村排甲在场，一干人等立合同，二纸一样、各执一张，存照子孙永远，证立之后，恐有无名之辈，不许入境□（采）伐，不得假湧赫□。

    《嘉庆契约》所述，即本地瑶民私租水滨寨周姓所属冷水源山场给宝庆人，周姓宗族知晓后报官，但从县、府再到省，官司打了11年未果，最后水滨寨以出让冷水源一半山场为代价，请狮塘村四姓瑶民相助，合力赶走通过私人“盗批”租得土地的宝庆人。

    《嘉庆契约》未提及如何对待经过瑶民村寨集体商议租得土地的宝庆人。依笔者对水滨村的调查推测，当时宝庆人并未全部离开，他们中的少数通过入赘、过继等方式融入了平川瑶村寨，其他的则继续耕种于周边深山。不过，此后可能少有新的宝庆人流入，新流入者主要是灌阳人（邻县灌阳的瑶人和汉人，但其瑶人所持语言与平川瑶语不同）。据曾长期担任水滨大队支书的周明统回忆，1958年观音人民公社成立时，平川源动员了1100多人下山，到河谷地带兴建村寨，或加入人口较少的瑶寨居住。其中，宝庆人480多人，其他主要是灌阳人和少量过山瑶。（访谈时间：2020年7月）

    这个1100多人的数字，加上《嘉靖碑记》所提及招徕板瑶约一千人的信息，说明平川源周边山地应至少有养活一千余人的弹性空间。当河谷人口过少时，容易从深山中招徕流动人群，到河谷耕作。当河谷人口接近饱和，尤其是深山中流动人群数量超过土地承载的弹性空间时，则容易出现土地经营权纷争。

    当然，平川瑶内部同样也存在土地竞争。一旦形成纠纷，能内部协调的则内部解决，不能的则诉诸官司。但是，由于国家难以日常化地深入平川源展开治理，讼争往往十分漫长。例如，杨梅村与邻村洋石曾为一块有水源的山场（名为牛角湾），自清嘉庆年间开始即多有纠纷、讼争，直到民国29年（1940年）方由广西高等法院第七分院判决。38平川瑶为掌控土地所有权和经营权，日常更多依赖的还是自身社会团结的力量。

    四、多元社会结合与礼之践诸于野

    从现有可考信息看，明初至永乐三年（1405年），平川源外的大家族（自称“本地人”）与平川源内的“良猺”多为山主、佃户关系。“良猺”经“造册”登记，“附籍”于“本地人”，由其代向官府转缴赋税（这说明，“本地人”更早就已登记为“民”）。后者属于官府治理“良猺”的代理人。依习惯，山地为“良猺”村寨集体租赁经营（未提及水田），地主不能未经“良猺”村寨集体商议，就售卖或转租给新来的人群。其赋税也是以村寨为单位额定缴纳，寨内人口、土地数量变动，对官府和“本地人”而言并不重要。

    平川源“良猺”社会结合首靠姓氏、家族，人口较多的成单姓村寨，甚至一姓分成两三个村寨，人口较少的则多姓结为一寨。不过，姓氏、家族未必完全一致，如古骨圩寨蒋姓与白荆铺寨蒋姓并非同一家族，据传前者先到平川源，被称为“大蒋”，后者被称为“小蒋”。

    百余年后，明嘉靖六年（1527年）“良猺”与新来人群发生流血冲突，官府保护了前者的土地使用权，让其获得了一部分土地所有权。此后，对于租赁的山地，虽然“良猺”依然得给“本地人”山主缴纳租金，但获得了招徕其他人耕种，即转租土地的权利。官府虽然还无力对其“编户齐民”，但已不满于依靠平川源外“本地人”代为治理，于是自嘉靖九年（1530年）开始实施“排瑶制”。平川源被分为10个排，每排设“猺目”，“猺目”作为“户长”直接向官府纳粮缴税，用“猺人法”39治理村寨。排，是由外置入的行政框架，但其管辖范围和头目设置，照顾到了民间以姓氏、家族为社会单位的习惯，久而久之也成了平川源重要的社会单元。迄今为止，在平川源居民的口语中，还经常会用排、上五排和下五排，来指代不同范围的地界和人群。

    在地理分隔明显的条件下，单姓村寨变大后，亲缘网络也随之扩大，内部通婚成为一种需要。例如，据清道光年间狮塘村李姓所修族谱记载：原居高山寨，康熙四十六年（1707年）首次修族谱（已散佚）；本有8个房支，人口增多后曾经族老商议，将第一、二、三房改为姓孟，以便“异姓婚配”；后传至第15代，第二、五、六房绝后，第三、七房人口也少，但第七房在第7代有一户“接”（过继）了永明县一个名叫“卢万洪”的人为子，其后代承李、卢两姓，狮塘始有卢姓（后又搬到老寨，与盘姓结为一寨）；清中期，李姓第四房一户“接”了长房一人为子，继而人丁兴旺，与部分孟姓一道开辟了名为“老虎塘”的新寨子。40

    除了分宗、过继之外，入赘也是平川瑶调整社会结合的重要方式。据传，观音村老洼、洼里两寨村民即外来陈姓人入赘老洼寨盘姓瑶家，留下的后代。其族谱记道：“嘉靖年间”，陈仁意、仁忠兄弟“流落”到老洼打铁，仁忠的独子被该寨某瑶民“招”为女婿。老洼寨李姓、王姓，也自认是外来人员入赘瑶家而留下的后代。41石坪寨是清末从平川河对面的狮尾寨何姓分出来的，但至笔者入村做调查时，俸姓人口已近该寨一半。究其缘由，也是从蕉山村招了一位俸姓女婿上门，繁衍而成。古骨圩寨“大蒋”，据族谱记载，在明万历年间招了狮塘村某杨姓村民为上门女婿，其后代承蒋、杨二姓（1949年，蒋、杨两姓还合建了宗祠）。莲花寨俸姓村民自述原姓周，明初自湖南道州来到该地，改姓俸，清嘉庆年间宗族人口增至2个房支，为“通婚之便”，第二房恢复周姓（二姓族谱同修，字辈排行亦共用）。

    此类案例说明，自清康熙、乾隆年间开始，平川源已有某种程度的“同姓不婚”和宗族的“礼”仪，至嘉庆、道光年间，这种“礼”仪已成为日常现象。不过，通过部分人改姓、分宗的变通方法，实际上同姓内部仍可通婚。入赘者所生子嗣，虽世代住在女方村中，却可以承继两姓宗祧，甚至完全随父姓。儒家所尚“礼”仪，在特殊地理和经济社会条件下，明显发生了质的改变。

    尽管如此，以“礼”为内核的宗族礼仪、祠堂，以及用谱系明晰亲缘关系的做法，毕竟成了平川源瑶民社会结合的常规方式。甚至于，他们还尝试运用此类“礼”仪，与平川源外“本地人”建立起更宏大的联盟。清道光年间，水滨寨周姓编纂族谱，可谓典型案例。

    宋代，恭城出了一位名人周渭。他曾任监察侍御史，给恭城的“民”减税役，并倡举办学。周渭去世后，宋真宗“敕封为惠烈御史周王”42，恭城有不少村建祠崇祀（今县城附近仍有两座周王庙）。清乾隆年间，恭城县内不少周姓编纂族谱，认为周渭的太祖曾居湖北襄阳，并在唐太宗治下（627年—649年）任金紫光禄大夫，生有18个儿子，字辈为“弘”，后代分布于湘西南、粤北和桂东北（同时期，与恭城县较近的湖南宝庆新宁县、道州宁远县也有类似家谱，记为“十八弘”）。其中，栗木镇上宅村周氏族谱修于乾隆二十年（1755年），西岭乡西岭村周氏族谱修于乾隆二十八年（1763年）。周渭祖籍，宋史并无记载，宋、元乃至明代民间亦无家谱记载。在其去世千余年后，却有了清晰的亲属谱系图和跨越数省的迁徙路线图。毋宁说，在清康乾嘉之际，湘桂边区人群修纂族谱，常有某种形式的附会、联盟。

    清道光壬午年（1822年），平川源水滨寨周姓也修纂了族谱。其谱记道，他们与周渭乃同一宗支，皆为周弘颂的后代，而且金紫光禄大夫实际上有24个儿子，谓之“二十四弘”。水滨寨有村民提出，可能更早就修过族谱，道光版族谱只是照抄之前的记录。考虑到彼时村中识字者并不多，且一代代将《嘉靖碑记》之类的文字保存完好，却未见对此前的家谱有只字记录，此说并不可靠。其宗祠则建得更晚，祠堂门口的石碑上刻有“大清光绪六年（1880年）庚辰岁孟冬穀立  奉旨恩受国子监太学生周显煕立”。族谱追述千年亲属脉络难免失真，却能表明早则在清康乾之际，晚则在嘉道之际，儒家之“礼”已被平川源内一些大姓用来编制群体社会关系网络。子弟被恭城送到国子监就读（另有观音村陈姓族谱提及，在晚清出过“名登仕版”的“千总”“巡检”“例贡”），侧面反映了当地文教水平不低。

    清光绪十五年（1889年），《恭城县志》修纂记录道：原来恭城瑶民“间有纳税，亦百中之一，不当差……今则东、北两乡诸猺咸编户受约束、委（威）顺服从，尽皆纳税，多有读书明理、援例报捐者”43。考虑到嘉庆年间恭城曾修纂过县志（已散佚），这段光绪年间的县志记载说明，平川源瑶民在嘉庆至光绪年间（偏近光绪年间的可能性更大），已完成“编户齐民”（深山中少量过山瑶和宝庆人、灌阳人除外）。宣统元年（1909年），他们与栗木河上游的“本地人”一并被纳入恭城县第四区，在赋役上已无明确区别。

    不过，与儒家“礼”仪一样，梅山教、佛教、巫觋信仰在当地社会文化生活中，也扮演着重要角色。

    笔者在平川源实地调查过程中，常听说上、下五排曾经共有“三十六庵、七十二庙”（一说“三十六庵、四十八庙”）。除了单家独户祭拜外，不少庙为上、下五排共同祭祀（如白马将军庙），有的是几个村庄联合祭祀，有的是一村寨或一家族祭祀。直到民国时期，稍大点的寺、庙、庵都有数量不等的水田（通常1—3石），作为庙产，并有相应的组织——“会”，以及“会首”负责管理。

    许愿、还愿（二者中间还可以“暖愿”），是平川源瑶民常见的信仰行为。其中，较大的如“盘王愿”庙会五年一届，于农历十月十五、十六日举行；“婆王愿”庙会三年一届，农历十月十五、十六日举行（上五排可作为“客人”参观），抬婆王像出游各村；“李王愿”为轮祭，狮塘麒麟庙会为农历八月十五日，蕉山近水庙会为农历七月十四日，水滨天祠庙会为农历十月十五日。“暖愿”时间根据还愿时间定，一般在农历六月农闲时日。虽然平川源瑶民对外都认可“平川瑶”，祭盘王，但在内部，上五排瑶民自称“平顶瑶”或“狗头瑶”，不祭婆王，而下五排瑶民则自称“盘瑶”，不祭李王。

    梅山教信仰则更是贯穿于平川瑶的家祭、祠堂公共祭祀、人生礼仪、岁时节日庆典等各个环节。梅山教源于湖南中西部新化县、安化县一带的梅山，宋代开梅山道后，“梅山蛮”往北（武陵山区）、往西（湘西、黔东）、往南（湘西南、桂东北）迁徙，将其宗教带往各地并各具区域特色。44就平川源而言，上五排称“梅山教”，下五排对内称“梅山教”，对外称“淮南教”。水滨村有师公（民间宗教人士）认为，二者核心仪轨和供奉神灵都相同，称呼有别可能是因为下五排与外界汉人打交道稍多些，有攀附道教的色彩。但也有师公认为二者有实质区别，在还愿仪式中，上五排只吹笙挞鼓，而下五排还会打锣敲钹，并且戴着“鬼头”面具跳“鬼舞”（有巫的色彩）。

    平川源梅山教供奉1200多位神灵。传统上村民常将其与自家祖先像一起绘于布帛卷轴上，在重要祭祀场合当神箓悬挂。1984年，水滨村莲花寨某村民清理旧宅，发现俸姓、盘姓神箓各一卷（前者主绘于清乾隆九年［1744年］，增绘于乾隆四十五年［1780年］，后者绘于乾隆六十年［1795年］），合计长108.98米，成为重要文物（现常被称为“梅山图”）。

    此外，在民间信仰中，不少土地被认为具有神圣性，禁止开发。例如，清同治年间水滨村莲花寨、矮寨所在的两个排，公议立碑禁止村民在开天庙、白马庙之间凿山烧石灰，认为会破坏“神山龙脉”。其碑文如下：

    立碑禁神山后龙。两排六□（姓）众等始祖，历来原立开天、□（白）马二庙，左右后龙神山无敢犯。不料客岁崣山何兴秀不守王章，竟敢在左边擅动神山，打石烧灰……是以众等不服，即伸猺目、地老、大彰公论。而（何兴）秀等之情畏圣，以后不敢再行。两排众等勒碑封禁……如有不法之徒胆敢左右违乱后龙神山、打石烧灰，协同禀官究治，不徇私情私放。毋违封禁，切切矣。45

    平川源自清代中晚期开始编家谱、建祠堂甚至尚科考，认可“礼”的正统性，却未如诸多平原区域一样，46将其他民间信仰变成精神生活的“配角”。相反，当地不仅民间信仰种类繁多，而且瑶民还认为信盘王、梅山教和白马将军，有身份象征意义。究其缘由，水滨村一些老人的看法值得参考。蒋礼发表示，“如果盘王、梅山教都不信，怎么还能说是上、下五排的瑶人？”曾长期任大队、村支书的周明统则说：“现在是新中国、新社会，哪个边边角角都有党的光辉，样样都变好了，不讲这些（标准）了。原来要是不讲（信）盘王、不讲（信）梅山教，你怎么有资格在上、下五排做主人，怎么（占）有山、（占）有田？”言下之意，传统时期国家难以日常化管理平川源具体事务，按当地习惯，只有平川瑶人才能占有土地，而盘王、梅山教信仰则是其身份标志。

    五、民族认同更迭及其在隙地的层累

    明初，莫祥才带庆远府河池宜山、南丹之兵到恭城剿“雷虎子”。因其时宜山多聚“獞”“獠”和“狑”，南丹多聚“性颇轻悍”的“狼”和“㺜”（“㺜”的“语言与獞同而声音稍柔”，“服饰略同獞”）47，莫祥才之兵常被称为“狼兵”。这些“狼兵”被安置在恭城东南山隘口白面寨，以防“猺”（当地现有几个村，村民自称其后裔，属壮族）。此类做法，应与明前期、中叶桂东北招“獞”防“猺”、以“狼”制“獞”的政策有关。48在官方和文人记录中，此类冲突被简便地称作“猺乱”。49但若不细究土地、赋役、里甲制度以及“军”“民”“猺”“獞”“狼兵”等人群互动，就难以全面理解这些动乱。50

    言及莫祥才本人，光绪《恭城县志》称其为“山东人”。后世白面寨周边莫姓编纂族谱，更详记其出生地为山东青州府淄博临淄九德峰村，由此推断祖上应为汉人。但是，考虑到最早的《恭城县志》编于明万历二十五年（1597年），距离明初已有二百多年，莫姓族谱编纂更晚。因此，此类记录亦非没有可疑之处。

    据科大卫考证，在明代早期、中叶的广西，尤其是河池所在的桂西，土著被招募和编成军队称为“狼兵”，配备的指挥官一般也是土著首领。51莫祥才在河池统带300名弩兵，其职位应不会太高，甚至在恭城立功后，所授的“白面寨巡检司”也是一个基层武职。作为基层官员带兵，难以绕开日常语言沟通。从社会文化层面看，如莫祥才乃数千公里外的山东淄博人，到遍地是“獞”“獠”“狑”“狼”和“㺜”的广西河池担任基层军官，如何有效“统带”？若真如此，志书既然记他在恭城立功后的武职，按常理也应记他在河池的军职，实际却只字未提。此外，志书还记道，其所带弩兵有23个姓氏。其中，除莫、贲、覃、祝、陆、蒙等后世壮族常见姓氏外，其余皆为常见汉姓。在这样的区域，一支小规模弩兵姓氏如此之多，且汉姓占大部分，亦令人存疑。

    种种迹象表明，莫祥才可能属于河池的基层土官，在当时的族类观念中，属于“獞”“獠”“狑”“狼”或“㺜”中的某类。在二百多年后恭城县修纂志书时，因其后代已登记为“民”，并接受了儒家“礼”仪，自称为汉人（甚至他称也可能已是汉人），而附会祖先源自颇有“礼”仪象征意义的齐鲁大地，隐去了其在河池的官职。此外，志书还将当时弩兵后代自认，甚至他认的各种汉姓，附加到了关于明初的历史追述中。

    由此看，历史上的民族身份表述，不太可能是本质主义的。《猺目碑记》所载叙事，亦如此。它应属过山瑶附会征剿“雷虎子”的历史，以证明自己为“良猺”，且有占“猺山”隘口及其周边土地，以及减税、免役的正当性。立碑者及其所代表的人群，显然已十分清晰地认识到，哪怕这些隘口及周边山地极为偏僻，国家仍毫无疑义是至上的“正统”。其“到／倒平源”的表述表明，至少混杂了部分源自广东封川县的过山瑶，在紧靠平川源峡谷口的平地上建村寨。

    光绪《恭城县志》另有记载：“永乐二年（1404年），拨军屯田、设寨堡，守东、西、北（乡）”，是谓“耕兵”。52平川源峡谷口为北乡的主要“猺源”隘口，应有耕兵设寨。耕兵作为“军”户，不是本地“民”壮，在招“獞”防“猺”的政策背景下，亦不可能是“猺”，只可能是“獞”。

    《猺目碑记》中所涉过山瑶也居此地，时间若是“洪武下山”打“雷虎子”，较之于“獞人”耕兵稍早，若是“景泰元年”则稍晚。相近时间到平川源峡谷口外平地的过山瑶与“獞人”耕兵是否合寨混居，已不得而知，但起码应居住在临近村寨。在紧靠平川源峡谷口平地上，现有周家塘、老氹、岩口等3个自然村寨（老氹为岩口所分出），语言既不同于栗木平地“本地人”所说的“本地话”，也不同于平川源瑶语。这或可说明历史上过山瑶、“獞人”耕兵、平川瑶人与“本地人”，在此有过复杂交融。虽然此三寨人口，在清嘉庆至光绪年间“编户”时已被记为“平川猺”，但日常实践中的民族认同势必呈更复杂的“图层”叠加之状。直至当代，他们也只自称／他称为瑶族，至于是瑶族什么支系已说不清（但肯定不是平川瑶），更不是由“獞”改名而来的壮族。

    明初“雷虎子”起事在恭城河上游山区“势江源”，其后进犯恭城县城，水路、陆路均只需经过恭城中南部，而平川源在恭城最北端的群山中。再参考光绪《恭城县志》记载莫祥才带兵剿“雷虎子”的经过，平川源居民大概率既未参与“谋叛”，亦未参与“平叛”。即使是在该事件之后，官府授权部分“良猺”进入平川源居住，亦不至于驱赶或杀戮原居民。但此后原居民未再有单独的记录和表述，应是融入了“良猺”。其文化和民族身份已无从考据，但无疑成了被“良猺”文化和民族身份覆盖的“图层”。

    水滨村村民告知笔者，平川瑶语与临近的湖南江永县西北部瑶语能大致相通（但需要认真听，加上揣摩意思），而且都信奉梅山教，而与江永县西南部通过恭城河和恭城东部相连地带的瑶语完全不同（且后者不信梅山教）。由此看，其祖上自永明县西北部移入平川源的可能性比较大。他们与平川源峡谷口外、部分源自广东封川县的过山瑶，不属同一支系。但不管是明代之前平川源遗民的原因，还是明早期湖南永明县瑶民移入之后又有少量其他过山瑶融入，直到嘉靖年间，平川源瑶语中有少量特殊词汇为过山瑶勉语口音。以至于与西岭乡新合村《猺目碑记》将“雷虎子”记为“雷伍子”发音一样，平川源上五排《嘉靖碑记》将之记为“雷午子”（在其他语境下，平川瑶语将“虎”字发音为“hao35”，将“午”字发音为“pu41”，皆迥异于“伍”［nge13］）。此外，狮塘村杨姓于清道光年间所修族谱明确承认，祖上本为汉人，元末于长沙被陈友谅乱军所杀，家人不断迁逃，明洪武二年入平川源，入源后第三代一男丁过继给盘姓瑶家为子，后代承盘、杨二姓，才成瑶民。这说明，从明初到明中期，平川源“良猺”内部有其他人群（包括部分过山瑶、汉人）混融的痕迹，但时间长了，自称与他称都变为“平川猺”。

    当时“良猺”所说的“贼”也不同于“雷虎子”那样“反”“叛”国家的人群，而是土地开发越过“良猺”认定界限的“猺”。后者势必流入该区域较晚，在深山中游耕（通常加上游猎、采集），尚未侵犯“良猺”的土地界限时，双方并无矛盾。待其人口规模或游耕范围扩大，进入“良猺”认定拥有权属的地界时，才发生矛盾。广义上说，此类人群也可被称作“过山瑶”（但与此后招徕的板瑶，应属过山瑶不同支系）。进山较晚的过山瑶被较早定居下来的自称“良猺”的过山瑶，以“贼”的名义赶走。过了若干年，县官要求“良猺”当差，“良猺”依官方渠道“申”“报”“乞”“告”，最终达成纳税但不当差的协议。其申告理由，乃附会参与征剿“雷虎子”。如此一来，两类瑶民之间争夺土地，胜利方即表述成了为国立功，实则是“通过追溯祖先的历史来决定谁有没有入住权、是不是村落的成员”53。但是，虽然“良猺”获得官方确认占有土地的权利，且表面上不用服差役，却不得再如以往那样，开垦新土地后不“升科报税”。较之于以往的优免权，新“升科”这部分其实可算一种变相的“役”。54

    如同定居于“猺源”隘口的过山瑶一样，平川源的“良猺”也能认识到，占有土地若要变成合法“权利”，就得国家认可，国家才是产权的终极定义者。明永乐二年（1404年），平川源峡谷口外由“军”户设寨堡，有耕兵守值后，次年平川源内“良猺”就“造册附籍，纳粮”，恐非巧合。只不过，“附籍”意味着官府并不日常化地深入“猺山”治理“良猺”，而是靠峡谷口外平地“本地人”大家族间接治理。由此，平川源“良猺”虽仿照峡谷口外扼守隘口的过山瑶，声称因剿“雷午子”才获得平川源的居住权，但仍不忘强调，此乃“本里故民”周、欧阳等大姓“招抚”的结果，而后者之所以“招抚”，又源于“本县（官府）责令”。其“礼法话语建构”与资源、人员流动统合，实为边地与国家整合的方式。55

    由于不断有新的人群流入“猺山”寻求生存机会，加之“招主”依仗开发山地谋利，新流入人群与原已稳定居住下来的瑶民，易发生矛盾。明嘉靖九年（1530年），平川源“良猺”与峡谷口外“本地人”大家族新招徕的“獞”“猺”发生冲突，之后招徕“板猺”耕种。在约两百年后的清乾隆年间，“良猺”又与“板猺”冲突，再招徕宝庆人耕种。约在百年后的嘉庆年间，“良猺”与宝庆人也发生了冲突。但是，事实上第一、二拨具体支系名称不详的过山瑶，以及后来的“板猺”、宝庆人，只是因未经过“良猺”村寨公议而靠私人“盗批”租得土地的那部分（尽管是大部分）离开平川源河谷地带和靠近河谷的坡地而已。那些经过“良猺”村寨公议而租得土地的人，尽管是少数，却并未全部离开，而是有少量通过入赘、过继的方式融入“良猺”村寨，其他的则长期游移于周边深山，且多有混融。

    虽然不断有其他民族人群更迭认同，融入平川源，但其认同一层层叠加、“层累”56的方向却是有“山主”地位的“良猺”，而不是其他。观音村盘姓族祖上为科考（依规定，未编户的“猺”不得参加），于清咸丰初年改姓陈，对外自称汉人，但传了7代后，在民国年间又恢复姓盘。57杨梅村一家族祖上据传为湖北武昌汉人，明初入平川源，因“此时平源多属盘姓，不得已乃改盘姓”，民国十二年（1923年）立碑改姓杨，但承认是瑶人。58

    六、结论

    中国地大而形态复杂，生态和人类生计方式、社会文化也因此多样。这些因素构成了大小不等的区域，大区域间常有山川、河流等地理“缝隙”。它们既是区域间的界限，在某些条件下也是人们跨区域流动的通道。多民族流经此类地理“缝隙”，构成了民族走廊。民族走廊在宏观上有隙地特征，微观层面则内含各种小尺度的隙地。

    隙地中有大量未开发的土地，典型的如山地及山间小盆地、峡谷，承载人口有一定的弹性空间，这是构成民族走廊的关键。在常规年景，隙地相对封闭，较少外人涉足。周边区域人口膨胀或出现饥荒、战争时，流入隙地的人群规模和速度便会激增。这些人群不管是何种民族，上山首先是为活命，逃避的是具体的战争、饥荒，而非抽象的“逃避国家”59无政府主义。从宏观上看，他们“其实是国家生活在一个更大的经济体系之中，在结构上仍然是国家体系之内，是王朝国家整体性的经济与社会体系的组成部分”60。尽管他们在隙地开发中的确有少纳税甚至免赋役的诉求，但国家才是其财产权的基石。没有国家维系底线秩序，土地开发成果则随时可能为他人侵占。为此，民族走廊中的隙地开发有冲突时，人们哪怕附会，也倾向于援引国家正统权威或“象征体系”61，为自己占有土地、控制土地经营权和享受赋役优免，寻找正当性。

    然而，国家权力发挥作用总会受制于具体的时空条件，因之可以分为两种，一是专制权力，二是基础权力。62前者是后者的基础，却难以用作日常治理；后者细致入微，可用作日常治理，但成本也因此高得多。在民族走廊的隙地开发中，不同人群围绕土地占有、经营，既有合作，又有竞争。土地开发取得效益，需要一定规模的劳动力。在特定的生产技术条件下，土地承载的弹性空间变得狭小时，一拨又一拨新流入隙地的人群，难免加剧土地占有、经营权的竞争。在基础权力有限的情况下，国家深入民族走廊中的隙地开展日常化治理，并非易事。因此，援引国家权威，虽然可声明占有土地及其经营权的正当性，却不能依靠国家深入隙地日常化地厘定土地权利边界。土地权利的日常化维系，还得靠不同人群自身社会团结的力量。

    在这种状态下，民族走廊中隙地人群的动态社会结合，就变得相当关键。一些人群依靠宗教、语言、生活习俗亲近而整合有力，防止新流入隙地的人群占有自己的土地或土地经营权。除了运用过继、入赘、联宗等亲属和“拟制”亲属“联合”63关系网络，村寨地缘共同体亦有举足轻重的地位。以至于，针对外来流动人群哪怕只是获得土地经营权，村寨公议也往往是一个先决条件。国家设定的“附籍”治理关系，尤其是通过民族精英间接治理的组织——排，亦逐步演变成地方实践中的社会结合方式。随着国家在民族走廊隙地中的角色具体化，以及隙地中的主体人群尝试进一步组织化，扩展社会关系网，接近国家权威，儒家“礼”仪也就开始逐步融入其动态社会结合过程。编族谱、建宗祠以明晰亲缘，崇祭祖先，加固亲属或拟制亲属组织，乃至建立跨越村寨、超出隙地范围的区域性联盟。

    然而，儒家“礼”仪在隙地动态社会结合的实践中，也有不得不因地制宜变形的地方。例如，人们可以通过分宗改姓，用形式上的“同姓不婚”，来应对附近村寨无法满足姻亲关系网络需要时，不得不本宗之内开亲。至于过继、入赘等行为，也可形式上满足宗族“礼”仪，但实质上有重要差别。甚至，即使隙地人群深受儒家“礼”仪浸淫，乃至接受国家“编户”，其所承赋役与外界平地上一般的“民”没有实质差别之后，仍倾向于坚守自身原有认同。在社会文化象征上，意识模型相对于无意识模型，更易“操纵”象征效力，64在人群区分和互动中，则是一种“为派系和社会变迁而辩护”65的动态机制。具体到中国社会文化认同，正统之“礼”的社会文化构想或可称“意识形态模型”，“边缘人群”自用或自我期待的构想是与之颇有差别的“自制模型”，而对周边其他人群的构想则可称“观察者模型”。66而依民族走廊隙地中不同人群互动及其认同层累的经验看，三种意识模型可能并非谁“同化”谁的关系。隙地人群既模仿乃至附会正统之“礼”，接触、混融周边人群文化，且认为它们本就是自身文化的有机组成部分。“礼”的文教渗透和实践因地制宜，与其他文化配合得当。这种“我中有你、你中有我”67的格局，将他者一定程度上化为自我，同时又在他者镜像中呈现与他者深度混融的自我，构成意识模型的动态相互镜像化。

    隙地人群在混融多层其他群体文化的基础上，日用正统之“礼”，却仍坚守局部地域主导人群的民间信仰。究其缘由，固然可能与民间信仰转型有一定的滞后性有关，但更重要的在于控制土地。在国家基础权力无法日常化深入民族走廊中隙地的情况下，只有维系隙地中微观层面主体人群的民族身份，才有资格控制土地所有权或经营权，并在土地承载弹性空间变得狭小时，排斥其他新流入隙地的人群。由于某些风俗习惯、民间信仰具有标识民族身份的作用，隙地中的主体人群以及那些尝试通过各种方式融入该群体的人，即使深受儒家“礼”仪影响，也仍倾向于延用而不是中断这些风俗习惯、民间信仰。以至民族走廊中的隙地人群一方面“渐慕华风”68，另一方面又倾向于长期坚守少数民族认同。不了解这一点，界定“华夏边缘”69就难免平面化。

    在历史的长河中，不同人群在民族走廊的隙地中交往、交流、交融。其民族认同也因此一层又一层累积，最终积淀成一种社会记忆。民族认同层累离不开族源叙事，叙事中会有覆盖、改写、附会，甚至无中生有，但积淀成相对稳定的社会记忆之后，便再也无法简单还原。若不细致考究，则难以看清其层累的痕迹。族源叙事虽然未必真实，但层累起来的认同本身却是真实的，在相当长历史时期内有相当强的稳定性。至于其认同层累的方向，究竟导向哪一种民族，则与民族走廊隙地中特定的生态、生计和人群互动过程有关。在这个意义上，尽管民族走廊中不同人群会叙述各种迁徙史（什么民族到了什么地方），但这只是民族认同层累的一个方面，另一方面同样重要的是，到了什么地方慢慢就成了什么民族。对于后一种机制，目前的研究似乎还算不上充分。

    从这个角度看，民族认同研究不宜套用本质主义叙事，只讲述实体般的多民族迁徙史，并且常想方设法溯及远古。如此叙事，讲得再好，即便不是错误的，至少是只讲了历史的一方面。而关于民族认同在地生成机制的叙事，似还有必要花大力气深入研究。从民族走廊及其隙地中长时段、多民族的互动过程看，很显然，多样的人群层累成何种民族认同，与其所经历的地理空间、生态环境、社会互动和文化交流，以及各种制度限定下的政治经济过程，有着密切的联系。这正是民族走廊的形成，及其所孕育的中华民族“多元一体”70和而不同的机制。由此看，从隙地认识民族走廊，从民族走廊认识中国的构成机制，还大有潜力可挖。

    本文转自《开放时代》2025年第1期

  • 朱振：逝者能够拥有权利吗？

    霍菲尔德虽然进行了影响深远的权利的逻辑分析，但他确实没有讨论权利的主体问题，而且该问题也从未构成他那个时代的重要问题。因此，美国法律学者斯莫伦斯基(Kirsten Rabe Smolensky)指出：“霍菲尔德考虑的是两个也许还活着的人之间的法律关系。他并不讨论身后的权利，或未来世代、树木、动物以及法律学者、法官或立法者可能会赋予权利的所有其他事物。虽然霍菲尔德明确指出权利必须属于人而不是物，但他并没有讨论权利人的必要和充分特征。”不但法律理论如此，目前的法律实践一般也不承认死者享有权利，但这并不影响法律对死者权益的保护力度。相关措施包括：死者生前的意愿能够受到法律的承认和保护，这不仅存在于继承法领域(遗嘱继承),而且也延伸到对身后生育权的间接承认；死者可以作为受益人而存在，比如在诽谤死者名誉的案件中，其近亲属以自己的名义提起侵权之诉，并间接保护死者名誉，这就是人格权领域的间接保护说；一般而言，人们也都负有尊重死者的义务，有时这种义务还比较强大，需要以刑罚的手段禁止对这种义务的违反，比如德国、瑞士、我国台湾地区“刑法”中均规定了诽谤死者罪。

    但学界一般都不承认这些情形为死者享有权利的证据，即使人们负有义务，这种义务也不直接对应权利，并不能由此推导出死者享有某种权利。主要理由在于：第一，作为民法之基石的权利能力理论不可能支持死者权利说；第二，死者无法自主地作出选择和决定，不可能享有权利并承担义务；第三，从权利救济上说，死者无法行使诉权，死者权利的保护有着法律技术上的障碍。本文的任务就是挑战上述看法，回应主要的反对理由，并解决相关理论难题。本文的论证表明：权利能力不构成主体享有权利的前提条件；权利理论不是死者享有权利的障碍，反而提供了一种可能性，关键在于我们如何理解权利；诉权在逻辑上不构成权利享有的前提，法律可通过技术手段解决权利救济难题。本文意图不仅从概念上，而且从道德重要性上，辩护死者在上述的某些(尽管不是所有)情形下最好被赋予权利，即死者能以自己的名义拥有权利，成为权利主体(即使不能成为法律主体),而不只是其他主体之权利的间接保护对象或单纯的受益人。

    一、现有民法保护模式的理论与实践

    从《中华人民共和国民法通则》(以下简称《民法通则》)《中华人民共和国民法总则》(以下简称《民法总则》)再到《中华人民共和国民法典》(以下简称《民法典》),关于权利能力的规定始终都是清晰的，即自然人的权利能力始于出生，终于死亡。从这一规定来看，死者似乎并没有所谓的权利可言。但是司法实践有不同的认识和表述，尤其在关于侵犯死者名誉权案中。关于死者权利(尤其死者人格权或人格利益)的保护模式，以名誉权为例，从20世纪80年代到现在，民法的规定经历了“名誉权—名誉—精神损害赔偿—名誉”等不同的表述阶段，可以说已经非常复杂了。到现在为止，我们可以把民法关于死者权益的保护概括为直接保护与间接保护相结合的模式。

    从解释论上说，民法最多承认死者可以具有法律上所保护的人格权益，而不享有权利。在民法理论上，反对承认死者为权利主体的最为重要的理由来自民事权利能力理论，葛云松是这一反对意见的主要代表，他基于既有民事权利能力理论而反对死者拥有权利。葛云松提出了许多反对理由，其中较具理论意义的有两点：第一，民事权利能力包括享受权利和承担义务两个方面的能力，对于后者而言，死者完全不具备，这似乎成了死者权利的一个障碍；第二，权利是法律所保护的利益，而死者无利益可言，死者权利的提法是社会学角度而非法学角度的，于是葛云松质疑有何社会学上的论证能够说明死者自身有利益。他把这些反对理由总结为：“保护死者自身的权利或者利益的提法与民事权利能力理论和其他基本民事制度有着不可调和的逻辑矛盾。”

    另外，我国著作权法规定，作者的署名权、修改权、保护作品完整权的保护期限不受限制。自然人的作品，其发表权的保护期是作者终生及死后50年。权利能力理论必须为这一明显的例外提供说明，于是为了理由的融贯性，葛云松甚至反对从这一规定中解读出死者也享有永久性的人身权。他认为权利本身应该得到法律的保护，但以赋予永久人身权作为保护的方式并非良好解决之道。接着，他提出了一个看似融贯的解释方式：“完全可以规定死者丧失著作人身权但是赋予行政机关对于侵害死者生前的著作人身利益的行为加以行政处罚的权力(刑法上也可以有规定),或者将著作人身权的性质视为同时为财产权并和著作财产权一起发生继承，等著作权保护期经过后，由国家以刑法或者行政法手段保护。”

    这是一种比较别扭的解释模式，也显示了权利能力理论在解释上的局限。而且，权利能力理论也是否定死者权利的一个常见的理据，值得我们认真对待。从逻辑上说，先界定权利能力的实质规定性，然后以此为根据再回过头来否定死者权利存在的可能性，确实有循环论证的嫌疑。破解循环论证的关键是从理论源头上探索权利能力理论存在的真实目的和意义，而不是死守一个僵化的概念，以此来反对任何理论和实践的改变。首先，以权利能力理论作为反对的基础甚至是前提，说明反对者在潜意识中认为，权利能力是享有权利的前提，而且应坚守其中的权利义务一致性理论。实际上，这两个方面都是成问题的，权利能力不一定是享有权利的前提，而且承担义务的能力并不是享有权利的前提。其次，与葛云松的主张相反，存在坚实的社会学和哲学上的理据来辩护死者自身有利益。这些方面既涉及我们对一些基本概念的分析，也涉及我们对人的生命存在形式之多样性的理解。下文分别讨论这两个问题。

    二、权利能力与权利享有的逻辑分离

    我们在直觉中总有一个观念，权利似乎奠基于权利能力。这个问题也要进行具体辨析，其中的“权利”和“权利能力”都有复杂的含义。权利能力有实在法的含义，也有自然法的含义。这就需要我们探讨两个重要且相关的问题：权利能力理论主要是针对什么的?它必然和权利有关吗?解答这两个问题，需要我们深度探究权利能力的概念史和思想史。

    权利能力这个概念来自德国民法典，这一术语本身就是对德文单词的翻译。迪特尔·梅迪库斯认为，一般来说，权利能力是“成为权利和义务载体的能力”,这是从消极方面来理解权利能力。这意味着，权利能力并不以行为能力为前提，有权利能力的自然人可能完全没有行为能力或欠缺行为能力。行为能力也不以权利能力为前提，比如有的无权利能力的法人或其他组织也可以通过他人来作出行为。权利能力在民诉法上对应的概念是当事人能力，即合法地成为民事诉讼的原告或被告的能力。有权利能力就有当事人能力，但是当事人能力并不预设权利能力，有些无权利能力的法人或其他组织依然可以具有当事人能力。这就表明在实在法上，权利能力与行为能力、当事人能力并没有概念上的必然关联，它并不与主体的特定性质(即能否实际地主张权利或履行义务)相联系，其主要目的是确立主体的法地位或资格。而且这一地位或资格就每一个个体而言是有规范意义的，即规定这一制度的本来意图就是确立个体的平等地位，即每一个自然人都拥有平等的权利能力或法能力。因此，权利能力概念的规范内涵与平等的价值观紧密相连，而且这一点具有源远流长的思想史渊源。

    德沃金认为，任何充分的法理论都将诉诸平等及其道德意涵(比如正义、公平和正当程序),菲尼斯对此表示赞同。但他对德沃金的核心主张提出了一个异议，即谁对谁是平等的，以及谁对谁应当作为一个平等者而受到对待。这是关于平等范围的问题，即什么范围内的“人”应该是平等的。对此，他诉诸历史的考察，这一考察对于我们理解民法上的人格或权利能力至关重要。罗马法最早触及这个问题，《法学阶梯》就指出：“正义是给予每个人其权利的稳定的和持久的意愿。”关键在于这里的“每个人”指的是什么。在《法学阶梯》中，“所有的人都是人”;而奴隶制“违反了自然法/自然权利”,“因为根据自然法/自然权利，从一开始，所有的人生而自由”。(11)显然，在自然法或自然权利的意义上，所有人的平等是正义所要求的，而奴隶制是由现实的权力因素所导致的。而且菲尼斯还认为，《世界人权宣言》第1条的表述就采取了罗马法学家的措辞：“人人生而自由，在尊严和权利上一律平等。”所以，在自然法的意义上，所有的生命体(生物人或其他主体，比如动物)都有平等的法律资格。

    对“人”本身作生物人/法律人(享有权利能力的实体)的区分一直延续到德国民法典及以后。德国民法用Person和Mensch来表述“人”,Mensch指与动物相区分的生物人，与自然人(natürliche Person)同义。Person这个词更为常用，标志在于享有权利能力，既包括自然人，也包括法人。生物人以出生为标志即享有权利能力，这主要是启蒙时代“人人生而平等”的政治诉求在法律制度上的表达，这是权利能力概念所负载的伦理价值。实际上，这一意涵经由德国《基本法》第1条第1款合并第2条第1款的规定(即《基本法》的人性尊严条款)得到了强化。关于这一点，梅迪库斯指出：“人的尊严包含着人只能是权利主体而不能是权利客体的内涵。如果人是客体的话，那么他只是奴隶。自由地发展人格的权利也只能为具有权利能力的人所享有。”梅迪库斯接着提出了一个问题：“承认每一个自然人都享有权利能力，是否渊源于同样也凌驾于《基本法》之上的某种自然法(Naturrecht) ”他接着指出，这是一个法哲学问题。他似乎持一种肯定的观点，但同时又指出，权利能力产生于自然法也不能推导出权利能力始于出生之前，也不能说德国民法典第1条是违反自然法的，因为自然法也很难说明未出生的胎儿如何成为权利义务的载体。

    实在法上权利能力构造的主要功能是确定平等的法主体资格。既然权利能力基于平等的价值并负载伦理意涵，那么权利能力之享有不取决于实在法。作为那个时代的自然法观念的创造物，权利能力具有一定的先验性。实在法的规定不构成我们思考权利能力的限制，对此朱庆育有一段论述：“如果权利能力为实证法所赋予，即意味着，实证法可将其剥夺与限制。然而，任何文明的立法，皆不得否认自然人的主体地位，不得剥夺或限制自然人的权利能力。这意味着，自然人的权利能力乃是人性尊严的内在要求，并不依赖于实证法赋予，毋宁说，实证法不过是将自然人本就具有的权利能力加以实证化，权利能力先于实证法而存在。”(19)这实际上也表明，权利能力具有双重意涵。它既有自然意涵，也是一个法律规定，即一项法律设计。权利能力必然需要在法律上有一个明确的规定，权利能力始于出生，终于死亡，几乎是各国民法的通例。

    权利能力的制度构造主要是为了解决(所有自然人的)平等问题并回应法律人格构造物不断扩展的要求，以使得法律主体可以扩展到法人、非法人组织、非人动物甚至是人工智能产品。从技术上讲，“权利能力”是一个制度性概念，本身并未穷尽我们对权利能力的理解。因此从理论上说，人出生之前的存在形态和死亡之后的存在形态本身不应成为它们是否具有权利能力的障碍。作为一个可选项，我们可以赋予它们有限的权利能力，以构造法律上的权利。就像在德国民法上，“权利能力”也有例外，比如胎儿的权利能力，这就是德国法上的“权利能力的前置”,尽管这是一种不完全的权利能力。我国也有学者主张权利能力和权利的分离说，即自然人死亡后仍可享有某些民事权利，这种分离说在承认现有权利能力不变的情况下而直接赋予死者以权利。无论是哪种形式，都表明权利能力并不构成赋予死者以自己的名义享有权利的障碍。

    总之，在逻辑上保持实在法上“权利能力/权利”构造的一致性其实是没有必要的，我们可以通过扩展具有权利能力之主体的范围，或者通过权利能力与权利在概念上的分离，来实现赋予死者法律权利的目标。无论是哪种方式，都只是破除了赋予死者以权利的障碍，而没有论证这种权利为什么能够存在，这就需要来自权利理论本身的论证。

    三、从权利能力到权利：利益论的辩护思路

    在权利的概念分析上主要有两种理论，一是意志论，二是利益论。这两种理论既是关于权利之性质的概念分析，同时又指向了辩护权利的基本理据。权利的利益论和意志论反映了更为基础的道德分歧，比如意志论强调了自觉和自主性的重要性，利益论中的利益则被用来辩护某种主张成为权利的基础。剑桥大学的法哲学家克莱默(Matthew H. Kramer)对权利的利益论和意志论的基本观点作了如下总结：“对于利益论来说，一项权利的本质就在于对权利人某些方面之福祉的规范性保护。相反，对于意志论来说，一项权利的本质就在于权利人在规范性上做出重要选择的诸多机会，而这些选择涉及其他人的行为。”据此，利益构成了权利存在的一个必要条件，尽管不是充分条件。这表明利益是权利的概念性组成部分，而且尤为重要的是，利益是外在于我们对权利人本身的理解或界定的，即利益论诉诸一个外在于权利人自身(尽管和权利人相关)的因素来界定权利的本质。意志论反对利益是构成权利之存在的必要条件，遑论充分条件。权利人的能力和许可才是必要的或充分的条件，因为这两个因素都和权利人本身的某种性质相关。而在利益论者看来，这两个因素既非必要也非充分，因为他们对权利性质的理解已经不再受权利人自身之性质的限制。

    既然意志论把对权利性质的理解限定于权利人自身的某种独特性质(比如理性或选择的能力)上，那么正如克莱默所指出的，一个必要的结果就是，动物、婴儿、昏迷的人、年老糊涂的人、死者都不再拥有任何法律权利。因为，在意志论者看来，“这些生物没有能力以基本程度的精确性和可靠性来形成或表达其意愿，而对于充分地行使执行/放弃的法律权利来说，这种精确性和可靠性是必要的。他们无法把握执行或免除一项义务意味着什么，同样，他们也不能以最起码令人满意的方式沟通关于这一事项的任何决定，即使他们曾经能够充分地做出那些决定。简言之，他们并不拥有任何法律权利，因为他们不能成为权利人”。权利的意志论在逻辑上不会承认动物、胎儿或精神上无行为能力的人享有权利，因为这些生物都无法自主地作出选择。这就在概念层面上排除了这些生物以法律权利的形式而受到保护的可能性，但这并不意味着法律不进行保护，在法律上受到保护和以权利的形式受到保护是两个不同的论题。所以意志论者也会承认，这些存在者的利益应该受到法律的保护，只是反对以法律权利之名的保护。

    于是克莱默提出一种版本的权利利益论，以抗衡以哈特为代表的权利意志论。克莱默把其版本的利益论概括为两个命题：“第一，实际享有的一项权利保护了X的一种或多种利益，这是X实际享有该项权利的必要但非充分条件；第二，X有能力或被授权要求行使或放弃行使一项权利，这一单纯的事实是X享有该项权利的既非充分、也非必要条件。”这就取消了意志在辩护权利中的重要性，也就为支持死者权利的主张消除了障碍。下文主要概述克莱默的利益论以及对死者权利的辩护，这对我们从权利理论的角度论证死者权利的正当性很有意义，因为意志论在概念上无法支持死者能以自身的名义而享有权利。

    显然，通过切割权利人的某种特定性质与权利概念论之间的必然关联，权利利益论就为在逻辑上赋予动物或死者以法律权利开辟了空间。也就是说，在概念上，权利利益论不会成为赋予死者或动物以法律权利的障碍。但利益不是一个主张成为一项法律权利的充分条件，因为利益这个概念非常宽泛。在一般意义上，我们甚至会认为，植物、古老的建筑、文物等也具有利益。有权利即有利益，但有利益不一定就存在权利。于是问题的关键就在于，要辩护死者、动物以及其他不能表达的生物值得被赋予法律上的权利，除了利益，还需要一个额外的因素。于是克莱默借鉴了拉兹的界定，去探究存在者本身所具有的道德重要性；或者用他的话说，就是“存在者的道德地位”。

    这就对利益本身又作了某种意义上的区分，有的利益只是单纯的存在，而本身不具有道德的重要性。只有具有道德重要性的利益，才可能被作为法律上的权利保护。因此在克莱默看来，利益的存在本身并不能充分地告诉我们哪些类型的存在者能够拥有权利。除了利益，我们还需要进行道德反思。对此，克莱默指出：“虽然利益论与意志论的不同之处在于，它不排除任何存在者作为潜在权利人的地位，但它并不强迫其拥护者荒谬地推断每一个存在者实际上都是一个潜在的法律权利持有人。为了避免任何这样的推论，利益论的理论家们不得不进行一些类型的道德反思……”这样的道德反思对于权利利益论来说至关重要，因为它实质性补充了利益论略显空洞的概念分析。

    在进行实质性论证之前，我们先讨论一个方法论的问题。在探寻这一道德地位的过程中，克莱默采取了一种在不同的存在者之间进行类比的方式，他曾这样详细表述这一操作方法：“为了确定这种道德地位，我们必须首先挑出一类存在者，其可以毫无争议地描述为潜在的权利人。正如上文已指出的，精神上健全的成年人就形成了这样一个阶层。任何一个最起码合理的权利理论(在任何现代西方社会)都不能否认每一个这样的成年人都是法律权利和法律资格的一位潜在拥有者。于是，我们已经确定了一系列存在者，其可以作为一个无问题的参照点。为了探究任何其他类型(与我们现在的论题相关)之存在者的道德地位，我们必须探究这些存在者和精神上健全的成年人之间的异同之处。当然，我们同时必须要探究任何这些相似和不同之处的道德重要性。”正是从这一点出发，克莱默认为，区分有生命的和无生命的东西具有根本的道德重要性，而且我们一般会赋予正活着的、曾活着的或将活着的存在者以特殊的道德重要性。虽然我们一般也会尊重无生命的自然物(比如草坪)或人造物(比如建筑或艺术品),但我们只是把它们作为对象而不是作为主体来尊重或关心它们。它们并不具有潜在权利人的地位，其中的理由就是道德的，而不是概念的；因为在道德重要性的诸多方面，这些存在者和典范性的权利人之间的相似性是非常微弱的。法律可以保护它们并使其受益，但是它们也根本无法意识到这些利益。因此，法律义务(比如“勿踏草坪”)并不是向无意识的有机体所履行的，而只是关于它们的。

    于是，问题的关键就在于论证，动物、死者或胚胎等与典范性的权利人(比如心智健全的成年人)之间在道德重要性上的相似性是明确而紧密的，以至于值得以权利的方式来保护他/她/它们。像精神病人或婴幼儿等，他们与心智健全的成年人之间的相似性是非常明显的。而死者等存在者则差异很大，正如克莱默所认为的，死者既不是有生命的，也不是有意识的，而完全停止了作为曾经之存在者的存在状态。在这种情况下，我们怎样能够在死者和心智健全的成年人之间建立相似性并把权利赋予死者?对此，克莱默认为，对于利益论的理论家来说，“关键的一步就是，把每一位死者生命结束后的一个时期纳入他或她之存在的整个过程之中。通过强调生命结束之后的那一段时期的各种因素——例如，死者对其他人和各种事件发展的持续影响，在熟悉他或认识他的人们的脑海中留下的对其回忆，以及他积累并随后遗赠或并未遗赠的一系列个人财产——我们可以突出强调死者仍然存在的各种方式。当然，死者并不是作为一个典型的完整的物质性存在者而继续存在，而是在多种面向上继续存在于其同代人和继承者的生活之中。因此，在一个特定时期内，死者在道德上可以被同化为他生前曾成为的那个人。即使人们认为死者的利益应得到少量的法律保护，他们也应该接受这样一个做法，即并非偶然地保护死者利益的法律义务，都由此赋予了死者以法律权利”。这个论证思路其实和延伸生命的看法有类似之处，但不是像生育后代之权利的那种延伸生命；也不像传记生命那样，只是强调人生在世的生命历程所具有的意义。这是另一种意义上的延伸生命和传记生命的结合，即死亡之后的那个时期似乎构成了其生命的自然延续，而且生者对身后价值和意义的期待也构成了其传记生命的重要组成部分。

    显然死者即使可以成为权利人，也是在一定时间限度内的，而不可能永远是权利人。对于这一时间期限，有两个明显的特征：一是这种时间期限在法律上没有一个统一的标准，即它是因人而异的，比如李白、莎士比亚等肯定比普通人的影响要大；二是这一时间期限具有比较强的文化依赖性，在具有不同文化背景的国家，这一时间期限也是不一样的。对于前者而言，每个人在生前的影响力是不一样的，因此其出现在其他人生活中的持久性也是不一样的；对于后者而言，这一时间期限取决于对待死者的文化态度。后者尤其具有理论意义，我们可以称之为保护期限的文化依赖性。对此，克莱默有一段集中且有深度的论述：“在一个尊崇祖先的社会里，他们身后在人们生活中的突出地位，比起来在一个基本上忽视祖先的社会里，将会明显地更加持久。因此，与后一社会的祖先相比，前一社会的祖先较适宜被更加长久地归类为潜在的权利人。这种差异的产生，并不是因为对久已逝去的祖先的崇敬态度直接赋予其良好的道德品质，而是因为这种态度使祖先突出地成为人们生活中的被感知到的存在；这反过来又赋予了祖先一种道德地位，该地位某种程度上类似于他们终其一生所拥有的那种道德地位。他们年复一年地继续成为主体，法律保护正是为了主体才设立并保有的；而不是成为客体，与客体相关的保护措施是仅仅为了满足生者才被设立的。”自古以来我们就生活在一个尊崇祖先的社会里，“曾子曰：‘慎终追远，民德归厚矣’”。但是上述所讨论的期限也不是无限制的，尽管在我们所生活的社会，祖先可能更适宜长久地作为潜在的权利人。

    其实，克莱默重在解决方法论的问题，即破除死者等特殊主体能够成为权利之主体的理论和认识障碍，但他并未详述死者为什么能够具有利益。诸如拉兹、范伯格等学者都主张利益论，他们的侧重点各不相同。这里借鉴另一位权利利益论者范伯格对这一问题的看法，来详细阐述死者利益的重要性。范伯格认为，不能够拥有利益的存在者也就不能够拥有权利，这确实是一种典型的权利利益论。因此，问题的关键就在于死者是否还拥有利益。在范伯格看来，死者在活着的时候所拥有的某些利益是能够在其死亡之后继续存在的，而且大多数活着的人对保有这种利益有着真实的兴趣，因此赋予死者以权利就不只是一种理论的虚构。在人死亡之后，完全涉己的利益一般不会再存在，比如自尊。涉人的或与公共性有关的利益就有可能在身后继续存在，范伯格称这些欲求为“以自我为中心的”,具体包括在他人面前提出自己的主张或展示自己、成为他人喜爱或尊重的对象等。他尤其提到了名誉在其中的重要性：“保持一个好的声誉的愿望，如某个社会或政治事业取得胜利的愿望，或一个人所爱的人兴旺发达的愿望，从某种意义上可以说，是在其拥有者死后还继续存在的那些利益的基础，并且可以被死亡后的事件所促进或损害。”

    从这些论述中，我们似乎可以总结出一个标准，以判断什么样的利益可以超越死亡而长久存在。根据范伯格的论述，如果一项利益在其拥有者身后还能够由死亡后的事件所促进或损害，那么这项利益就具有长久的价值。这一标准其实还是比较宽泛的，它也能够包含生前已作出决定而需要死亡后的事件加以促进的情形，比如以遗嘱的形式在身后设立基金会或捐赠财产。如果这件事的目的是比较单一的，就是设立基金会，那么在身后促成这件事，还不能说是一项严格的死者权利。如果设立基金会或捐赠财产是为了身后的名誉，那么死后的事件就存在增进或减损死者利益的情形。因此，我们可以进一步限缩范伯格的标准，严格的死者利益只涉及其死后发生的事件能够独立增进或减损其利益，而不包括生前所做决定在身后是否得以实现的情形。

    其中一个典型的例子就是名誉权，范伯格一再拿名誉权作为范例来分析。而且范伯格同样也指出了死者权利的时间限制性，以及其他社会价值对死者利益的限制。他指出：“虽然一个死者的情感确实不可能受到伤害，但是我们不能因此说，他的如下主张——即比起来所应得的评价，他不会被想成是更糟糕的——在其死亡后不能继续存在。我应当认为，几乎每个活着的人都希望在他死后，至少在其同时代人的生命时期中，拥有这一被保护的利益。我们几乎不能指望法律能保护恺撒免受历史书的诽谤。这可能会妨碍历史研究，并限制社会上有价值的表达形式。甚至在其所有者死亡之后继续存在的那些利益也不会是不朽的。”这一段论述表达了两层意思：一是，一个人生前的利益确实在其身后长久存在，并具有独立的价值，这一利益对每个活着的人来说都是重要的；二是，死者利益不是不朽的，而是有时间限制的，不但对历史人物的评价是表达自由的一部分，而且这一利益本身也会受到一些社会价值的限制。

    同时，对这种身后所存在的利益的独立性，我们也需要有一个准确和全面的理解。第一，这种利益的独立性不能割裂该利益与死者生前利益的紧密关联，或者说，这种死后的利益脱离开其生前的感受也是不能独立存在的。它构成了生者整个人生历程的内在组成部分，正是为了保护人活着时的利益才保护其死后的利益。对此，德国法上的“死者自己人格权继续作用说”体现得最为明显，论述也最为深刻。从表面上看，这里存在一个悖论，也就是范伯格所提出的那个问题：一个人怎么会被他不知道的事情伤害呢?因为死者是永久性地无意识的，他不可能对其身后发生的事有什么认知，因此似乎也不会与身后发生的事有什么利害关系。范伯格对此的解答是，即使活着的人也有很多利益被侵犯了，而他自己并不知晓；不知晓，并不影响利益本身被侵害。第二，这种独立性只是价值本身的独立性，而不是独立于其他人。正是在涉他性的、公共性的社会关系中，一个人才会产生身后的有价值的独立利益。这种利益是相互的，每一个人都可能享有。也许正是为了保护每一个人的相互的利益，才有必要在法律上保护一种独立的死者的权利。

    上文所分析的权利利益论对于辩护死者权利来说是必要的。克莱默和范伯格不仅以权利利益论来辩护死者权利，他们也在某种程度上论证了死者为什么可以具有利益，以及这种利益的复杂性，即与死者生前利益以及其他密切相关人的利益的关系、利益的时间性、利益的文化依赖性等。这些论证都是非常重要的，但是他们都没有深入论证这种利益的更为深刻的哲学基础，即对人的生命复杂性的理解。死者权利所保护的利益并不完全是一种在时间上被分割开来的利益，除特殊情形下的公共利益，这种权利主要还是为了保护死者生前的个人期待，这种期待在死后的继续存在构成了其生命完整性的内在组成部分。和胚胎等存在形态还不同的是，人在死亡之后主要表现为一种精神性存在，而胚胎毕竟还是一种物质存在形态，具有发展成为完全的人的可能性。人的社会性存在和人的法律性存在之间确实有一定的断裂，从社会性上讲，人的存在及其意义是一个连续体，人的存在是多重生命的联合。而法律/权利能力的构造只截取了其中的一段，而没有注意到多重生命这一事实。下文的论证既是对利益在生命层面的扩展与深化，也是在回应葛云松的批评，因为我们可以找到一种社会学理论来辩护死者享有权利。

    四、人的生命的多重意涵与死者名誉权保护

    我们现有的民法理论对人的生命的理解是比较单一的，是一种薄的理解，即仅仅把人的生命理解为自然生命。正是在这个意义上，民法理论把人的自然生命的开始/死亡和权利/义务的存在直接关联起来，同时又和权利的享有直接关联起来。实际上，权利能力和权利不存在直接、必然的关联。现有的民法理论难以融贯地解释或解决这个问题，不得不在坚持权利能力理论的前提下，对主体生前和死后是否拥有权利的难题作了技术上的处理。比如，胎儿的权利或著作人格权就不是民法上的典型权利，而是边缘化的权利，类似于拟制的权利。而民法对人的生命应持有一种厚的理解，即对生命本身的一种多元而丰富的理解。

    我们对生命的理解不仅是对自然生命(活着)的理解，还包括活着的人是否对其生命的其他理解内容持续地享有权利，即使在其身后这一权利也没有终止。正如上文所说，肯定死者的权利在某种意义上就承认了对延伸生命和传记生命的重视。这主要是说，生命的意义和价值也许并未随着死亡而彻底丧失，它会自然延续到死亡之后的某个时间段，而这个时间段的生命状态依然构成了人在活着的时候对生命的感知和期待。比如中国古人讲三不朽：立德、立功、立言。中国儒家伦理强调生前就要追求死后的不朽，就是这种意义上的延伸生命和传记生命相结合的具体体现。人一生的经历类似于在写自己的传记，这样一部传记在其身后也有独立的意义。人的名誉与人格紧密相连，是传记生命里最核心的内容之一。财产部分在其死后转化成了继承权，已经变成别人的财产了。死亡的性质决定了在身后能够具有人身专属性的东西就是类似“立德、立功、立言”这样的事，在现代社会这些方面主要以人格权的形式体现出来。能够体现人的传记生命的，只有和人格利益相关的诸方面，它们具有独立的价值，是对人之生命整全性理解的重要组成部分。

    这表明，名誉等人格利益是外在的，具有一定的独立性，其意义和价值能够超越死亡本身，从而具有更为久远的意义。远在古希腊时期，亚里士多德就认为：“善恶都可被认为会发生在一个死者身上……比如说，荣誉和耻辱，以及他子女或其后代的好运和厄运。”而康德对“死后好名声”的独立性进行了更为深刻的阐述，他认为好名声是一种先天的外在的归属物，尽管只是观念中的。他尤其在方法上指出，承认死者可能受到伤害，这并不是要得出一些关于未来生活之预感或与已故灵魂之不可见关系的结论。这一讨论并未超过纯粹的道德与权利关系，在人们的生活中亦可发现。在这些关系中，人们都是理智的存在者，抽离掉了物理的形态；但是人们并未只成为精神，仍可感受到来自其他人的伤害。于是康德得出了这样一个结论：“百年之后编造我坏话的人，现在就已经在伤害我；因为纯粹的法权关系完全是理智的，在它里面，一切物理条件(时间)都被抽除了，而毁誉者(诽谤者)同样应当受惩罚，就像他在我有生之年做过这事似的。”康德把“好名声”或“名誉”理解为一个先天的概念，它被抽离掉了时空等物理形态，而变成一个纯粹的理智概念。在这种纯粹法权(权利)关系中，身后的毁誉行为就和生前的行为一样。通过这种纯粹哲学的建构，康德就辩护了一个死后的好名声的独特价值以及和人生前之生活的内在关联。

    即使我们不完全认可康德讨论这个问题的方式，也会尊重并赞同康德努力的方向，即论证名誉的价值可以追溯到生者的生活。而且康德并不是通过文学化的情感描述，而是通过一种深刻的哲学论证来达到这一点的。后来的哲学家也许更多地通过一种相对经验化的方式来论证这一点，比如我们在范伯格的著述中也可以看到这一论证的影子。亚里士多德的认识也深刻影响了后来的理论家对死者独立利益的认识，如范伯格就认可亚里士多德的看法，并在现代的意义上作了发挥。他从一个假设的例子开始论述：“假设我死后，一个仇人巧妙地伪造文件，非常有说服力地‘证明’我是一个花花公子、通奸者和剽窃者，并将这一‘信息’传达给公众，包括我的遗孀、孩子和以前的同事、朋友。我已经受到了这种诽谤的伤害，还能有任何怀疑吗?在这个例子中，我在死亡时所拥有的同伴们对我持续高度尊敬的‘以自我为中心’的利益并没有因为我的死本身而受挫，而是因为死之后发生的事情而受挫。……这些事都不会使我难堪或苦恼，因为死人是不会有感情的；但所有这些事都会迫使我无法实现我曾寄予厚望的目标，并伤害到我的利益。”

    范伯格的这一段论述表达了两个主要看法：一是，死亡本身会改变名誉侵权发生的条件，认知在生前不会成为一个必要条件，而在死后会成为一个必要条件。也就是说，侵害死者利益的事情一定得是公开的，从而为人所知的。二是，死者能够拥有可能被侵犯的独立利益，而且这种独立利益与在世的亲友紧密相关，因为他们曾是其寄予厚望的对象，但这种厚望因为侵权行为而落空了。于是，斯莫伦斯基对范伯格的观点进行了如下发挥：“最低限度地说，在死亡后继续存在的利益和与死者一起逝去的利益之间的区别取决于是否存在有关特定利益的记录。记录可以存在于一个仍活着的朋友或家庭成员的脑海中，也可以是书面记录。但是，如果一项利益在死后不能为人所知，那么法律就不能保护它。”并非死后能够继续存在的所有利益都能以权利的形式而受到保护，因为这样的利益实在太广泛了，不是每一项利益都值得以法律权利的形式保护。可能成为由法律权利来保护的死者利益的，最起码是死者生前所期望的利益，而且一般来说也是能够与活着的亲友发生关联的利益，因为后者通常也是其期望的对象。

    本文转自《河南大学学报（社会科学版）》2025年第1期。

  • 马卫东：大一统源于西周封建说

    大一统思想是中国传统文化的重要内容之一，在两三千年的历史岁月中，对于促进中国国家统一、中华民族形成及中华文化繁荣，曾起到过巨大的作用。然而，大一统思想最早形成于什么时代，源于什么样的历史实际，学界却长期存在着不同的看法。多数学者认为，《公羊传》所提出的“大一统”，是战国时代才开始出现的学说，战国以前既无一统的政治格局，也无一统的社会观念。近年来，有的学者提出，中国早在西周时期已是统一王朝，“现在我们不能再以为，只有到了战国时期才开始有统一的意志”，但似乎并没有在史学界引起普遍反响。因此，有必要继续对大一统思想的渊源作进一步深入的探讨。

    本文认为，《公羊传》大一统思想的基本内涵是“重一统”。其具体内容，包括以“尊王”为核心的政治一统；以“内华夏”为宗旨的民族一统；以“崇礼”为中心的文化一统。历史表明，《公羊传》的大一统理论是对西周、春秋以来大一统思想的理论总结。周代的大一统思想，是西周封建和分封制度的产物，它源于西周分封诸侯的历史实际及西周封建所造成的三大认同观念：天子至上的政治认同、华夷之辨的民族认同、尊尚礼乐的文化认同。中国大一统的政治局面和思想观念由西周封建所开创，是西周王朝对中国历史的重大贡献之一。

    一、《公羊》大一统说的内涵及其思想渊源

    “大一统”的概念，最早是由战国时代的《公羊传》提出来的，系对《春秋》“王正月”的解释之辞。《春秋·隐公元年》：“元年，春，王正月。”《公羊传》释曰：“元年者何？君之始年也。春者何？岁之始也。王者孰谓？谓文王也。曷为先言王而后言正月？王正月也。何言乎王正月？大一统也。”

    “大一统”的“大”字，以往多解释为大小的大。其实，这不符合《公羊传》的本义。这里的“大”字应作“重”字讲。按《公羊传》文例，凡言“大”者，多是以什么为重大的意思。如《公羊传·隐公三年》：“君子大居正。”《庄公十八年》：“大其为中国追也。”《襄公十九年》：“大其不伐丧也。”以“大”为“重”，这在先秦两汉文献中不乏其例。《荀子·非十二子》：“大俭约。”王念孙曰：“大亦尚也，谓尊尚俭约也。”《史记·太史公自序》：“大祥而众忌讳。”即重祥瑞而多忌讳。

    “大一统”的“统”字，《公羊传·隐公元年》何休曰：“统者，始也，总系之辞。”许慎《说文解字》释“统”曰：“统，纪也。”又曰：“纪，别丝也。”段玉裁：“别丝者，一丝必有其首，别之是为纪；众丝皆得其首，是为统。”

    刘家和先生在汉人解诂的基础上，深入分析了《公羊传》“一统”的涵义，认为《公羊传》的“一统”，“不是化多（多不复存在）为一，而是合多（多仍旧在）为一。……但此‘一’又非简单地合多为一，而是要从‘头’、从始或从根就合多为一。”

    “大一统”的“一统”，学界往往解释为“统一”，实属误解。关于“一统”与“统一”的区别，台湾学者李新霖先生曾有精辟的论述：“所谓一统者，以天下为家，世界大同为目标；以仁行仁之王道思想，即一统之表现。……所谓统一，乃约束力之象征，齐天下人人于一，以力假仁之霸道世界，即为统一之结果。”

    综合古今诠释，对《公羊传》“大一统”的内涵，我们可以作如下的理解：“大一统”就是“重一统”，具体而言是“重一始”或“重一首”，即通过重视制度建设、张扬礼仪道德，以主体的、原始的、根本的“一”，来统合“多”而为一体（合多为一）；“大统一”则是通过征伐兼并和强力政权消除政治上的“多”，实现国家统治的“一”（化多为一）。可见，从严格的意义上讲，“大一统”和“大统一”并不是两个等同的概念。

    《公羊传》根据《春秋》“王正月”，开宗明义地提出了大一统概念。在阐释历史事件时，又论述了大一统理论的具体内容。从《公羊传》的论述看，《公羊传》大一统理论主要包含三方面内容：以“尊王”为核心的政治一统；以“内华夏”为宗旨的民族一统；以“崇礼”为中心的文化一统。

    强调尊王，维护天子的独尊地位，是《公羊传》大一统理论的核心。《公羊传》首先通过对诸侯独断专行的批评，表达了尊王之义。如《春秋·桓公元年》：“郑伯以璧假许田。”《公羊传》释曰：“其言以璧假之何？易之也。易之，则其言假之何？为恭也。曷为为恭？有天子存，则诸侯不得专地也。”《春秋·僖公元年》：“齐师、宋师、曹师次于聂北，救邢。”《公羊传》释曰：“曷为先言次而后言救？君也。君则其称师何？不与诸侯专封也。”《春秋·宣公十一年》：“冬十月，楚人杀陈夏徵舒。”《公羊传》释曰：“此楚子也，其称人何？贬。曷为贬？不与外讨也。……诸侯之义，不得专讨。”在《公羊传》看来，诸侯的“专地”、“专封”、“专讨”都是违背“一统”的行为，所以《春秋》特加贬损，以维护周天子的权威。在《公羊传》中，关于尊王的论述很多，如“王者无外”（《公羊传·隐公元年》、《公羊传·成公十二年》），“不敢胜天子”（《公羊传·庄公六年》），“王者无敌”（《公羊传·成公元年》）等等，无不是主张“尊王”的慷慨之辞。在周代，天子是最高权力的代表，也是政治一统的标志。《公羊传》的尊王思想，实际上就是主张建立以天子为最高政治首脑，上下相维、尊卑有序的政治秩序，通过维护周天子的独尊地位来实现国家的政治一统。

    以华夏族为主体民族、尊崇华夏文明的“内华夏”思想，是《公羊传》大一统理论的另一重要内容。《公羊传·成公十五年》：“《春秋》，内其国而外诸夏，内诸夏而外夷狄。王者欲一乎天下，曷为以外内之辞言之？言自近者始也。”何休：“明当先正京师，乃正诸夏。诸夏正，乃正夷狄，以渐治之。叶公问政于孔子，孔子曰‘近者说，远者来’。”可见，如何处理华夷关系是大一统理论的应有之义。在华夷关系上，《公羊传》一方面确认华夷之辨，屡言“不与夷狄之执中国”（《公羊传·隐公七年》、《公羊传·僖公二十一年》），“不与夷狄之获中国”（《公羊传·庄公十年》），“不与夷狄之主中国”（《公羊传·昭公二十三年》、《公羊传·哀公十三年》），等等，反对落后的夷狄民族侵犯华夏国家。另一方面，又认为华夷之间的界限并非不可逾越，无论是华夏还是夷狄，只要接受了先进的周礼文化，就可成为华夏的成员，即唐代韩愈在《原道》一文中所概括的“诸侯用夷礼则夷之，进于中国则中国之”。因此，《公羊传》的“内华夏，外夷狄”思想，实际上就是主张建立以华夏族为主体民族，华夷共存、内外有别的民族统一体，并逐渐用先进的华夏文明融合夷狄民族，从而实现国家的民族一统。

    尊尚周礼文化的崇礼思想，也是《公羊传》大一统理论的重要内容之一。《公羊传》认为，天子与诸侯有严格的等级秩序和礼制规范。如《公羊传·隐公五年》：“天子八佾，诸公六，诸侯四。……天子三公称公，王者之后称公，其余大国称侯，小国称伯子男。”《公羊传》强调诸侯要严格遵守周礼，不得逾越，以维护天子的独尊地位。《公羊传》还通过天子、天王、王后、世子、王人、天子之大夫等名例表明尊王之义。如《公羊传·成公八年》：“其称天子何？元年春王正月，正也。”《公羊传·桓公八年》：“女在其国称女，此其称王后何？王者无外，其辞成矣。”《公羊传·僖公五年》：“曷为殊会王世子？世子贵也。”《公羊传·僖公八年》：“王人者何？微者也。曷为序乎诸侯之上？先王命也。”《公羊传》张扬周礼的目的，旨在“欲天下之一乎周也”（《公羊传·文公十三年》），即通过诸侯国和周边民族对周礼的认同，实现国家的文化一统，进而促成并维护国家的政治一统和民族一统。

    由上可知，《公羊传》大一统理论的最大特色就是“合多为一”。具体言之，在政权组织上，首先确认周王室为最高的政权机关，同时承认诸侯国地方政权的合法地位，由王室统合各诸侯国而实现国家的政治一统；在民族结构上，首先确认华夏族的主体民族地位，同时承认夷狄非主体民族，由华夏统合夷狄而实现国家的民族一统；在文化认同上，首先尊尚周礼文化为先进文化，同时涵容各具特色的地域文化，由周礼文化统合各地域文化而实现国家的文化一统。

    《公羊传》由阐释《春秋》而提出大一统学说，其理论直接源于《春秋》。《春秋》是孔子据《鲁春秋》编作的一部史书。在《春秋》一书中，孔子通过对春秋历史的笔削裁剪，表达了自己的政治观点，即所谓的《春秋》大义。其中，“大一统”便是《春秋》的首要之义。《孟子·滕文公下》：“《春秋》，天子之事也。”《史记·太史公自序》：“夫《春秋》，上明三王之道，下辨人事之纪，别嫌疑，明是非，定犹豫，善善恶恶，贤贤贱不肖，存亡国，继绝世，补敝起废，王道之大者也。”又《太史公自序》：“周道衰废……孔子知言之不用，道之不行也，是非二百四十二年之中，以为天下仪表，贬天子，退诸侯，讨大夫，以达王事而已矣。”《孟子》和《史记》所说的“天子之事”、“王道之大”、“以达王事”，即指《春秋》集中表达了孔子的大一统思想。

    除《春秋》一书外，孔子的大一统思想，在《论语》、《礼记》等文献中亦多有反映。如：《论语·季氏》：“天下有道，则礼乐征伐自天子出；天下无道，则礼乐征伐自诸侯出。”《礼记·坊记》：“子曰：‘天无二日，土无二王，家无二主，尊无二上。’”《礼记·曾子问》：“孔子曰：‘天无二日，土无二王，尝禘郊社，尊无二上。’”《论语·颜渊》：“四海之内，皆兄弟也。”《论语·子路》：“叶公问政，子曰：‘近者悦，远者来。’”《论语·子罕》：“子欲居住九夷，或曰：‘陋，如之何？’子曰：‘君子居之，何陋之有？’”以上的诸多论述，都是孔子大一统思想的体现。孔子的大一统思想，是《公羊传》大一统理论的直接来源。

    孔子所生活的春秋时代，天子日益衰微，诸侯势力坐大，“礼乐征伐自天子出”的政治格局趋于瓦解，社会陷入了诸侯争霸、战乱频仍的混乱局面。有鉴于此，孔子大声疾呼，推崇“一统”，渴望国家重新实现安定和统一。孔子的大一统思想也有其思想渊源。《论语·为政》：“殷因于夏礼，所损益可知也；周因于殷礼，所损益可知也；其或继周者，虽百世可知也。”《论语·八佾》：“周监于二代，郁郁乎文哉!吾从周。”《论语·阳货》：“如有用我者，吾其为东周乎!”可见，孔子的“大一统”思想，实质上是主张恢复上有天子、下有诸侯的西周式的、一统的社会秩序。《史记·太史公自序》载孔子曰：“我欲载之空言，不如见于行事之深切著明也。”这说明，孔子的大一统思想，应当有其更早的历史渊源。

    二、《公羊》“尊王”思想源于西周天子至上的政治认同

    从文献记载看，《春秋》和《公羊传》所阐述的大一统思想，早在西周、春秋时代已是一种重要的社会观念。“每一个时代的理论思维，从而我们时代的理论思维，都是一种历史的产物”，大一统思想亦不例外。历史表明，周代的大一统思想是西周封建和分封制度的产物，反映了周代社会的政治关系和意识形态。

    首先，西周封建和分封制度，加强了周天子的权力，使周天子确立了“诸侯之君”的地位。而周天子“诸侯之君”地位的确立，导致了西周一统政治格局与天子至上政治认同观念的形成。《公羊传》以“尊王”为核心的政治一统思想，源于西周一统政治形成的历史实际及周代对王权至上的认同观念。

    夏商时期，王权已经存在。在商代甲骨文和有关文献中，商王屡称“余一人”、“予一人”，表明商代的王权已经形成。然而，商代与西周的王权不可同日而语。在商王统治期间，邦畿之外方国林立。商王对外用兵，征服了一些方国，将其纳入王朝的“外服”。《尚书·酒诰》：“越在外服，侯、甸、男、卫、邦伯。”被征服的方国同商王朝有一定程度的隶属关系。然而，商代的“服国”不是出于商王朝的分封，其服国所辖的土地和人民并非商王赐予，而是其固有的土著居民；服国的首领原是方国的首长，同商王没有血缘关系；服国内仍保持着本族人的聚居状态；服国与商王朝的隶属关系在制度上也缺少明确的规定和保证。因此，商王在“外服”行使的政治权力是有限的。商王和服国首领之间，“犹后世诸侯之于盟主，未有君臣之分也”。在商王和服国首领君臣关系尚未确立的条件下，商王朝无法形成“礼乐征伐自天子出”的政治格局。

    西周的封建和分封制度的实行，“造成了比夏、商二代更为统一的国家，更为集中的王权”。分封制度下西周王权的加强，主要体现在天子与诸侯间君臣关系的确立以及相关的制度规定上。

    西周分封的基本内容，是“受民”、“受疆土”。“受民”、“受疆土”活动本身，便是对君主制的一种确认，即下一级贵族承认其所受的土地和民人，是出于上一级君主的封赐。分封的直接后果之一，是导致了天子与诸侯、诸侯与卿大夫之间君臣关系的确立。《左传·昭公七年》：“王臣公，公臣大夫，大夫臣士。”《仪礼·丧服传》郑玄：“天子、诸侯及卿大夫，有地者皆曰君。”《礼记·曲礼下》：“诸侯见天子曰臣某侯某。”周初经过分封，周天子由夏、商时的“诸侯之长”变成了名副其实的“诸侯之君”。

    天子与诸侯间的君臣关系，集中表现在西周天子的权利和诸侯所承担的义务上。对天子的权利和诸侯的义务，周王室有许多制度规定：

    策命与受命。周天子在分封诸侯时，要举行策命仪式，诸侯接受了策命，就等于接受了天子的统治。如周初封鲁，要求鲁公“帅其宗氏，辑其分族，将其类丑，以法则周公”；封卫，要求康叔“启以商政，疆以周索”；封晋，要求唐叔“启以夏政，疆以戎索”（《左传·定公四年》）。足证受命的诸侯要奉行天子的政令。诸侯国新君嗣位，也要经过天子的策命。《诗·大雅·韩奕》载韩侯嗣位，“王亲命之，缵戎祖考，无废朕命，夙夜匪解，虔共尔位”。周代的策命礼仪，实际是对分封制下天子和诸侯君臣关系的一种确认。

    制爵与受爵。在分封制下，周天子为诸侯规定了不同等级的爵命。《左传·襄公十五年》：“王及公、侯、伯、子、男、甸、采、卫、大夫各居其列。”《国语·周语中》：“昔我先王之有天下也，规方千里以为甸服。……其余以均分公、侯、伯、子、男，使各有宁宇。”《国语·楚语上》：“天子之贵也，唯其以公侯为官正也，而以伯子男为师旅。”爵命是诸侯的法定身份。诸侯阶层依据爵命分配权力、财富并对天子承担规定的义务。

    巡守与述职。在分封制下，天子有巡守的权利，诸侯有“述职”的义务。《孟子·告子下》：“天子适诸侯曰巡狩。”其具体内容便是“春省耕而补不足，秋省敛而助不给。入其疆，土地辟，田野治，养老尊贤，俊杰在位，则有庆，庆以地。入其疆，土地荒芜，遗老失贤，掊克在位，则有让。一不朝，则贬其爵；再不朝，则削其地；三不朝，则六师移之”（《孟子·告子下》）。可见，天子是通过巡守这一政治活动，来行使在政治上对诸侯的统治权力的。《孟子·告子下》：“诸侯朝于天子曰述职。”其具体内容，便是定期朝见天子，接受天子的政令。《国语·周语上》：“诸侯春秋受职于王。”《左传·僖公十二年》：“若节春秋来承王命。”《国语·鲁语上》：“先王制诸侯，使五年四王、一相朝。终则讲于会，以正班爵之义，帅长幼之序，训上下之则，制财用之节，其间无由荒怠。”述职是诸侯对天子履行义务的主要形式。

    征赋与纳贡。在经济上，天子有向诸侯征赋的权利，诸侯有向天子纳贡的义务。《国语·吴语》：“春秋贡献，不解于王府。”贡赋的多少，原则上根据诸侯的爵位高低来确定。《左传·昭公十三年》：“昔天子班贡，轻重以列。列尊贡重，周之制也。”不纳贡赋，要受到天子的惩罚。如春秋时齐桓公伐楚，理由之一是楚国“包茅不入，王祭不共”（《左传·僖公四年》）。

    调兵与从征。在军事上，天子有权从诸侯国征调军队，诸侯有从征助讨的义务。如在周初征讨东夷的战争中，鲁侯伯禽曾奉命“遣三族伐东国”。成王东征时，“王令吴伯曰：以乃师左比毛父。王令吕伯曰：以乃师右比毛父”。诸侯从征助讨，是义不容辞的义务。此外，诸侯征讨“四夷”或有罪之国有功，则应“献捷”、“献功”于周天子。《左传·庄公三十一年》：“凡诸侯有四夷之功，则献于王。”《左传·文公四年》：“诸侯敌王所忾而献其功。”诸侯向天子“献捷”、“献功”，实质上是对天子最高军事权力的一种确认。

    除了从制度上对最高王权进行确认外，西周统治者还从理论上对王权的至上性进行了阐述。西周统治者认为，周王的权力来源于上天。《诗·大雅·大明》：“有命自天，命此文王。”《诗·大雅·下武》：“三后在天，王配于京。”《诗·大雅·假乐》：“假乐君子，……受禄于天。”周王被视为上帝的儿子，代表上帝统治人间。《尚书·召诰》：“皇天上帝，改厥元子。”因此，周初统治者创造了“天子”一词，作为王的尊称。

    据统计，周法高《金文诂林》一书收集的青铜器，有65件有“天子”的称号。在《尚书》、《诗经》等先秦文献中，“天子”的称呼也屡见不鲜。如《诗·大雅·江汉》：“虎拜稽首，天子万年。……作召公考，天子万寿。明明天子，令闻不已。”刘家和先生深入分析了“天子”称号的历史意义：

    天只有一个，天下只有一个，天命也只有一个。……所以天之元子或天子在同一时间内应该也只能有一个，他就是代表唯一的天而统治唯一的天下的唯一的人。

    周代统治者通过王权神授理论，论证了王权的至上性。此外，还把“天命”和“德”联系起来，论证了王权至上的正当性。《尚书·召诰》：“王其德之用，祈天永命。”《尚书·大诰》：“天棐忱辞，其考我民。”《尚书·泰誓》：“天视自我民视，天听自我民听。”《尚书·康诰》：“天畏棐忱，民情大可见。”也就是说，上帝的旨意是通过“民情”表现出来的，周天子因为深得民心才获得了天命。周代统治者通过这种道德化的天命观，使王权获得了“天意”与“民心”的双重依据，有效地强化了周天子的绝对权威。

    西周天子与诸侯之间君臣关系的确立和王权的加强，使周天子在分封诸侯时，能够将周王室统一的社会制度推行到各个诸侯国。统一的社会制度在各个诸侯国的施行，表现在政治制度方面，主要是诸侯国都要实行分封制度、宗法制度、世卿世禄制等；在经济制度方面，诸侯国都要实行井田制度等；在军事制度方面，各诸侯国要实行国人当兵、野人不当兵及“三时务农一时讲武”的制度等。周天子与诸侯之间君臣关系的确立、统一的社会制度在各个诸侯国的施行，标志着西周政治一统格局已经形成。

    在分封制度下，各诸侯国一方面实行王室规定的统一的社会制度，另一方面又享有相当大的地方自治权。政治上，诸侯国有设置采邑地方政权和任命官吏的权力；经济上，诸侯国除向周王室交纳一定的贡赋外，其他经济收入一律归诸侯国所有；军事上，诸侯国有组建军队、任命将帅、调遣与指挥军队的权力。因此，西周分封制政体，不同于后世郡县制基础上的中央集权制政体。在中央集权制政体下，郡守、县令的任命权掌握在皇帝之手，郡县的财政归国家所有，郡县更无组建、调遣军队的权力。可见，西周分封制政体和后世的中央集权制政体，虽然本质上都是“一元”政治，但中央集权制政体的“一”之下，不存在着“多”，即不存在实行地方自治的郡县地方政权（周边少数民族地区的藩属政权除外）。而西周分封制政体的“一”之下，则存在着“多”，即存在着实行地方自治的诸侯国和采邑地方政权。

    为了实现分封制下的“一元”统治，西周王朝规定了本大末小的原则，使王室在各级政权机关中居于绝对的支配地位。据文献记载，天子的王畿有千里之广，诸侯国中的大国只有百里之地，而次国和小国尚不足百里。天子握有十四师的兵力，而诸侯大国不过三师、二师，小国仅一师。强大的经济和军事力量，保证了周王室在西周的政治格局中，成为了主体的、原始的、根本的“一”，能够统合其他的“多”（诸侯国）而为一体，建立起本大末小、强干弱枝的一统政治，即“礼乐征伐自天子出”的政治局面。

    随着分封制度的实行，王权至上观念也在畿内地区和各诸侯国境内得到极力宣扬，并且首先在上层社会形成了对王权至上的普遍认同。在周代文献中，对王权至上的认同和颂扬，记载颇多。如《尚书·洪范》：“惟辟作福，惟辟作威，惟辟玉食。臣无有作福、作威、玉食。”《诗·小雅·北山》：“溥天之下，莫非王土；率土之滨，莫非王臣。”《诗·大雅·下武》：“媚兹一人，应侯顺德。”《诗·大雅·文王有声》：“自西自东，自南自北，无思不服。”《诗·大雅·假乐》：“百辟卿士，媚于天子。”《大克鼎》：“天子其万年无疆，保乂周邦，畯尹四方”等等，都是周人尊王、王权至上观念的反映。

    孔子和《公羊传》以“尊王”为核心的政治一统思想，与西周以来天子至上的王权认同观念是一脉相承的，而这种天子至上的政治认同观念，又源于西周一统政治形成和确立的历史实际。周代的一统政治和一统观念，归根结底，都是西周封建诸侯与分封制度的产物。近代国学大师王国维在论述周初的分封诸侯时，曾有如下的论断：“新建之国皆其功臣昆弟甥舅，本周之臣子，而鲁卫晋齐四国又以王室至亲，为东方大藩，夏殷以来古国方之蔑矣。由是天子之尊非复诸侯之长，而为诸侯之君。……此周初大一统之规模，实与其大居正之制度，相待而成者也。”王国维先生以“大一统”源于周初封建，可谓是不易之论。

    三、《公羊》“内华夏”思想源于西周华夷之辨的民族认同

    西周封建诸侯和分封制度的实行，促成了华夏族的形成与华夏族主体民族地位的确立，而所谓的“华夷之辨”，则是反映了这一历史实际的民族认同。《公羊传》以“内华夏”为宗旨的民族一统思想，源于西周封建所造成的华夏族形成的历史实际以及周代社会“华夷之辨”的民族认同观念。

    关于华夏族，以往有些论著认为，它是随着夏代国家的形成而形成的。实际上并非如此。夏朝虽已产生了凌驾于社会之上的权力机构，但国家仍建立在氏族联合的基础之上。《史记·夏本纪》所载的夏后氏、有扈氏、有男氏、斟寻氏、彤城氏、褒氏、费氏、杞氏、缯氏、辛氏、冥氏、斟戈氏等，都是组成国家的不同氏族。即便商王朝的外服方国，也还是一些“自然形成的共同体”，其居民都是固有的土著居民。处于早期国家阶段的夏、商，组成国家的各氏族、方国都保持着相对单一的族属和血缘，它们与居于统治地位的夏族、商族之间存在着严格的血缘壁垒，彼此的生活方式、语言习惯、礼仪风俗有很大的差别。在这种国家形态下，难以形成一个具有民族自觉意识、共同文化和共同地域的更高形态的民族。

    华夏族作为中华民族统一体的主体民族，形成于西周大规模的封建之后，是周代封建和分封制度的产物。

    周人在克商以前，以周为首的反商联盟有了较大的发展。《逸周书·程典解》：“文王合六州之侯，奉勤于商。”周人把这个联盟称作“区夏”或“有夏”。《尚书·康诰》：“惟乃丕显考文王，……用肇造我区夏。”《尚书·君奭》：“惟文王尚克修和我有夏。”《尚书·立政》：“帝钦罚之，乃伻我有夏式商受命，奄甸万姓。”据沈长云先生研究，“‘夏者，大也’，《尔雅·释诂》及经、传疏并如此训。《方言》说得更清楚：‘自关而西，秦晋之间，凡物之壮大者而爱伟之，谓之夏。’……（周人）使用‘夏’这个人皆爱伟之的称谓来张大自己的部落联盟，来壮大反商势力的声威”。可见，周人是用“夏”来称呼以周邦为首的反商联盟。在周王朝大规模分封之前，这个在“夏”的名义下组成的军事联盟，尚未具有民族的含义。

    华夏族是在周初封建之后的历史进程中逐渐形成的。周初封邦建国时，所面临的最基本形势便是地广人稀。据朱凤瀚先生估算，周人当时的人口约十五万人。除了相当一部分留在王畿，剩下分到数十个国中，各国受封人口之少可想而知。周初分封的这种特殊的政治环境，造就了受封诸国“强烈的‘自群’意识”。周王室适应这一需要，于分封和分封之后的历史进程中，在周王室和各诸侯国的名称上冠以“夏”这个“人皆爱伟之的称谓”，即“诸夏”或“诸华”。所谓“诸夏”或“诸华”，是各诸侯国以整体的名义，一体向境内及周边其他各族所宣示的自称。后来，各诸侯国原有的各族居民，逐渐地接受了周人的礼乐文化，周王室和各诸侯国及其境内的居民，初步具有了“共同的语言、共同的经济基础、共同的地域、共同的文化意识”的民族要素。

    “诸夏”或“诸华”的共同标准语言——“雅言”。《论语·述而》：“子所雅言，《诗》、《书》、执礼，皆雅言也。”雅言即夏言，本是宗周地区的方言语音。随着分封的推行，雅言逐渐成为各诸侯国在举行礼仪活动等场合使用的标准语言。

    “诸夏”或“诸华”各国实行周王室规定的统一的政治、经济和军事制度。井田制度的普遍推行，表明各诸侯国已经具有了“共同的经济基础”。

    “诸夏”或“诸华”逐渐形成了原有各族居民的共同地域。周初封建打破了受封地区的血缘聚居局面，使不同族属的居民在同一地区实现了混居。《大盂鼎》云：“赐汝邦司四伯，人鬲自驭至于庶人六百又五十又九夫；赐夷司王臣十又三伯，人鬲千又五十夫。”鲁、卫、晋受封时，带去了“殷民六族”、“殷民七族”和“怀姓九宗”。这些不同族属的居民经过长时间的杂居、融合，到了西周后期，“在周封各诸侯国中已经基本看不到原有居民的身影，鲁国没有了‘商奄之民’，卫国没有了殷人……他们已共同融合为鲁人、卫人，标志着周封各诸侯国民族融合的完成”。这种情形，使得中原地区连成一片，逐渐演变成原有各族居民共同的地域。

    “诸夏”或“诸华”形成了共同的文化意识。随着分封，“诸夏”或“诸华”的居民逐渐接受了宗周的礼乐文化。《左传·定公十年》孔颖达疏：“中国有礼仪之大，故称夏。”《战国策·赵策二》：“中国者，聪明睿知之所居也，万物财用之所聚也，贤圣之所教也，仁义之所施也，诗书礼乐之所用也，异敏技艺之所试也，远方之所观赴也，蛮夷之所义行也。”“诸夏”或“诸华”居民对周礼文化的普遍认同，标志着“诸夏”或“诸华”共同文化意识的形成。

    总之，西周封建之后，受封诸侯国的各族居民经过融合，逐渐形成了一个有着“共同的语言、共同的经济基础、共同的地域、共同的文化意识”的民族——华夏族。

    华夏民族的形成，西周王朝的强大及其对境内和周边民族统治的加强，使华夏族的主体民族地位得以确立。而西周王朝的非主体民族，则是居于王朝境内和周边地区的“蛮夷戎狄”。华夏族的主体民族地位的确立，使华夏族在西周的民族格局中，成为了主体的、原始的、根本的“一”，能够统合其他的“多”（戎狄蛮夷）而为一体，共同组成了西周统一王朝的民族统一体。

    华夏族作为西周王朝主体民族的地位，在周王朝周边民族与周王朝的朝贡关系上有集中的反映。《逸周书·王会》记载了周成王召集的成周之会，参加这次盛会的有众多的东西南北的周边民族，各族都向周王献纳了方物。《王会》篇编撰于春秋末，周初是否有如此之多的民族参加了成周之会，史料上缺乏更多确切的说明。但西周时期许多周边民族与周王朝保持着朝贡关系，应当属实。《国语·鲁语下》：“昔武王克商，通道于九夷、百蛮，使各以其方贿来贡，使无忘职业。于是肃慎氏贡楛矢石砮，其长尺有咫。”《国语·周语上》：“今自大毕、伯士之终也，犬戎氏以其职来王。”《兮甲盘》：“王命甲政司成周四方积，至于南淮夷。淮夷旧我帛晦人，毋敢不出其帛、其积、其进人、其贾。”以上文献记载表明，臣服于周的民族与周王朝建立了朝贡关系。周朝还设官掌管戎狄蛮夷朝贡之事。《周礼·怀方氏》：“掌来远方之民，致方贡，致远物，而送逆之，达之以节。”《周礼·象胥》：“掌蛮、夷、闽、貉、戎、狄之国使，掌传王之言而谕说焉，以和亲之。”周边民族与周王朝的朝贡关系的建立，实质上是非主体民族对华夏主体民族统治地位在政治上的一种确认。

    华夏族形成之后，与周王朝境内和周边非主体民族的关系日益密切而广泛，民族融合的进程因此而大大地加速。《国语·郑语》记史伯所述西周末年的形势说：“当成周者，南有荆蛮、申、吕、应、邓、陈、蔡、随、唐；北有卫、燕、狄、鲜虞、潞、洛、泉、徐、蒲；西有虞、虢、晋、隗、霍、杨、魏、芮；东有齐、鲁、曹、宋、滕、薛、邹、莒；是非王之支子母弟甥舅也，则皆蛮、荆、戎、狄之人也。”可见，剩下的戎狄蛮夷已可得而数。春秋时期，大部分戎狄蛮夷在强国开疆拓土的过程中被征服而融合。西方的戎族，多被秦国所灭。北方狄族，多被晋国所灭。东方的夷族，多被齐、鲁所并。南方的群蛮，先后被楚国所灭。到了春秋末年，中原地区的戎狄蛮夷，已基本上融入华夏民族之中。

    随着华夏族的形成、华夏族主体民族地位的确立和华夏族的不断壮大，在西周、春秋时期，形成了“华夷之辨”的民族认同观念。周代文献中的“中国”、“华夏”、“四夷”、“五服”、“九服”等概念，都不同程度地反映了这种观念。

    “中国”一词，最早出现于成王时期的青铜器《何尊》铭文：“余其宅兹中国。”本义指京师洛邑地区。后来随着周人统治地域的扩大，“中国”一词的意义也逐渐改变，成为华夏诸国的代称。如《左传·庄公三十一年》：“凡诸侯有四夷之功，则献于王，王以警于夷，中国则否。”《左传·僖公二十五年》：“德以柔中国，刑以威四夷。”以中国指称华夏，正是华夏中心意识的一种反映。

    “华夏”一词，乃周人本其“尚文（彩）”之风尚，在沿用已久的“夏”字之前冠“华”而成的。《尚书·武成》：“华夏蛮貊。”孔安国传：“冕服采章曰华。”《左传·定公十年》：“裔不谋夏，夷不乱华。”孔颖达疏：“中国有礼仪之大，故称夏；有服章之美，谓之华。华夏一也。”华夏的称谓，体现了华夏族在文化上的优越感。

    五服与九服之说屡见于周代文献。《尚书·禹贡》：“五百里甸服。……五百里侯服。……五百里绥服。……五百里要服。……五百里荒服。”《国语·周语上》：“先王之制，邦内甸服，邦外侯服，侯卫宾服，蛮夷要服，戎狄荒服。甸服者祭，侯服者祀，宾服者享，要服者贡，荒服者王。”《周礼·职方氏》：“乃辨九服之邦国。方千里曰王畿，其外方五百里曰侯服，又其外方五百里曰甸服，又其外方五百里曰男服，又其外方五百里曰采服，又其外方五百里曰卫服，又其外方五百里曰蛮服，又其外方五百里曰夷服，又其外方五百里曰镇服，又其外方五百里曰藩服。”《荀子·正论》：“故诸夏之国同服同仪，蛮、夷、戎、狄之国同服不同制。封内甸服，封外侯服，侯卫宾服，蛮夷要服，戎狄荒服。甸服者祭，侯服者祀，宾服者享，要服者贡，荒服者终王。”五服、九服之说都把周王朝统辖的天下划分为三个层次：畿内、诸夏和夷狄，其意义与《春秋》的“内其国而外诸夏、内诸夏而外夷狄”基本一致，是华夷之辨原则在地域观念上的体现。

    在周人的观念中，华夷之辨主要表现在华夷之间在语言、习俗与经济生活等方面的区别。《论语·宪问》：“微管仲，吾其被发左衽矣。”孔子所说的“被发左衽”，即是夷狄的风俗。《礼记·王制》：“中国、夷、蛮、戎、狄，皆有安居、和味、宜服、利用、备器。五方之民，言语不通，嗜欲不同。”《礼记·檀弓》：“有直情而径行者，戎狄之道也，礼道则不然。”可见，周人主要以礼仪风俗作为区分华夷的标准。

    应当说明的是，华夷之辨的民族认同是双向的。《左传·襄公十四年》：“我诸戎饮食衣服，不与华同，贽币不通，言语不达。”《战国策·赵策二》：“远方之所观赴也，蛮夷之所义行也。”《史记·楚世家》载西周晚年楚国国君熊渠宣称：“我蛮夷也，不与中国之号谥。”至春秋中叶，楚武王仍云“我，蛮夷也”（《史记·楚世家》）。《史记·仲尼弟子列传》载子贡出使越国，越王亲往郊迎，曰：“此蛮夷之国，大夫何以俨然辱而临之？”《史记·秦本纪》载秦穆公曰：“中国以礼乐诗书法度为政，然尚时乱，今戎夷无此，何以为治？”这些例证都说明，西周、春秋时期中原地区之外的其他国家和民族，对华夷之别同样也是认同的。

    在周人的民族观念中，与华夷之辨相辅相成的，是华夷一统思想。《左传·昭公二十三年》：“古者，天子守在四夷。”《会笺》：“守在四夷，亦言其和柔四夷以为诸夏之卫也。”《左传·昭公九年》：“我自夏以后稷，魏、骀、芮、岐、毕，吾西土也；及武王克商，蒲姑、商、奄，吾东土也；巴、濮、楚、邓，吾南土也；肃慎、燕亳，吾北土也。”可见在周人的观念中，王朝的疆域包括周边各族在内。前文所引周代文献中的五服、九服之说，也无不把戎狄蛮夷包括在周王朝统辖的范围之内，诚如陈连开先生所言：“对于《禹贡》、《职方》中‘五服’、‘九服’的名称、内容，古今学者多有诠释，各家说法不尽相同，但都表达了以天子为首，以王畿为中心，包括华夷的统一思想。”

    《春秋》与《公羊传》的“内华夏、外夷狄”思想，与西周以华夏族为主体民族，华夷共存、内外有别的民族一统思想是一脉相承的。这种以“内华夏”为宗旨的民族一统思想，源于周初封建所造成的华夏族形成的历史实际以及周代社会对华夷之辨的认同观念。

    四、《公羊》“崇礼”思想源于西周尊尚礼乐的文化认同

    制礼作乐，是西周王朝统治集团为巩固政权而采取的一项重要措施。西周礼乐制度建设的成就，导致了尊尚礼乐的文化认同观念的形成。《公羊传》以“崇礼”为中心的文化一统思想，源于西周制礼作乐的历史实际以及周代社会尊尚礼乐的文化认同观念。

    关于周公制礼作乐，先秦文献中有明确的记载。《左传·文公十八年》：“先君周公制周礼曰：则以观德，德以处事，事以度功，功以食民。”《左传·哀公十一年》：“且子季孙若欲行而法，则周公之典在。”除《左传》外，《尚书·洛诰》还记载了成王对周公说：“四方迪乱，未定于宗礼，亦未克敉公功。”对制礼作乐的意义表示高度的重视。

    事实上，周公的制礼作乐，还处于周礼的草创阶段。经过后来数代君臣的补充和完善，西周中期以后周礼才渐趋完备。《诗经》中多次出现“以洽百礼”的诗句，反映了当时礼制的繁芜。据刘雨先生研究，西周金文材料所载的礼制，“周礼多数是在穆王前后方始完备”。詹子庆先生也认为，“从金文材料反映出，西周中期以后，各种礼仪制度化，如世官制、宗法分封制、昭穆制、册命制、舆服制等都有了定式”。因此，西周礼乐的系统化、完备化和程式化，是在西周中、后期才得以完成的。

    西周制礼作乐，对夏、殷之礼有继承，也有革新。《论语·八佾》：“周监于二代，郁郁乎文哉，吾从周。”《论语·为政》又说：“殷因于夏礼，所损益可知也；周因于殷礼，所损益可知也。”周礼与殷礼的不同之处，是殷礼亲亲，周礼尊尊。《史记·梁孝王世家》褚少孙补：“殷道亲亲，周道尊尊，其义一也。”“亲亲”与“尊尊”是殷周社会的两条重要政治原则。“亲亲”指血缘关系。“尊尊”指阶级关系。从“殷道亲亲”到“周道尊尊”的变化过程，“也就是阶级关系逐步支配并改造了血缘关系的过程”。因此，周礼最显著的特征体现为日益严密的等级制度，即《礼记·中庸》所说的：“亲亲之杀，尊贤之等，礼所生也。”

    西周制礼作乐，还赋予了周礼“德”的内容。周代的各种典礼都蕴含一定的道德意义，即所谓的“礼义”。《礼记·经解》：“故朝觐之礼，所以明君臣之义也；聘问之礼，所以使诸侯相尊敬也；丧祭之礼，所以明臣子之恩也。乡饮酒之礼，所以明长幼之序也；昏姻之礼，所以明男女之别也。”因此，周礼兼具政治统治和道德教化的功能，对维护和巩固西周政权发挥了重要作用。王国维先生说：“古之所谓国家者，非徒政治之枢机，亦道德之枢机也。……是故天子诸侯卿大夫士者，民之表也。制度典礼者，道德之器也。周人为政之精髓，实存于此。”

    西周封建诸侯和分封制度的实行，使周礼首先得到了受封诸侯国的认同。在分封制度下，各级政权之间的等级隶属关系集中反映在周王室制定的礼乐制度上。《左传·庄公十八年》：“名位不同，礼亦异数。”《左传·襄公二十六年》：“自上以下，隆杀以两，礼也。”周代的等级制度，在各种礼制中都有体现。如《国语·楚语下》：“天子举以大牢，祀以会；诸侯举以特牛，祀以太牢；卿举以少牢，祀以特牛；大夫举以特牲，祀以少牢；士食鱼炙，祀以特牲；庶人食菜，祀以鱼。”是为祭祀的等差；《礼记·礼器》：“天子七庙，诸侯五，大夫三，士一。”是为宗庙的等差；《周礼·小胥》：“正乐县之位，王宫县，诸侯轩县，卿大夫判县，士特县。”是为乐舞的等差；《周礼·大宗伯》：“以玉作六瑞，以等邦国：王执镇圭，公执桓圭，侯执信圭，伯执躬圭，子执谷璧，男执蒲璧。”是为命圭的等差；《周礼·典命》：“掌诸侯之五仪……上公九命为伯，其国家、宫室、车旗、衣服、礼仪皆以九为节。侯伯七命，其国家、宫室、车旗、衣服、礼仪皆以七为节。子男五命，其国家、宫室、车旗、衣服、礼仪皆以五为节。”是为不同等级的诸侯在宫室、车旗、衣服、礼仪等方面的等差。当然，《周礼》、《礼记》所提供的史料，有的要作具体分析，但绝大部分史料的来源是有根据的，可作为了解周礼的等级制度的参考资料。西周时期，受封诸侯国遵行周礼，既是诸侯国对其与周王室之间等级隶属的一种确认，也是受封诸侯国对周礼文化的一种认同。

    西周受封诸侯前往边陲建立邦国，带去了祝宗卜史等官吏、周之典籍以及各种天子赏赐的礼器等，也就把先进的周礼文化传播到了那个地区。西周诸侯受封建国后，又确立了以礼治国的方针，大力地推广周礼文化。周代文化以各诸侯国为中心，向四周辐射，使周礼逐渐得到了各国土著居民和周边民族的认同。如：

    鲁国原为东夷族的聚居区，东夷风俗盛行。鲁公伯禽受封之后，征服了徐戎、淮夷各族，“淮夷蛮貊，及彼南夷，莫不率从”（《诗·鲁颂·宫》）。同时“变其俗，革其礼，丧三年然后除之”（《史记·鲁周公世家》），对东夷风俗进行了改革，推行三年之丧等周礼。后来，被征服的东夷各族逐渐认同周礼文化，加速了东夷地区华夏化的进程。春秋时期，鲁国是“犹秉周礼”的礼仪之邦，后来成了儒家的发源地。

    齐国是在薄姑氏旧地上分封的国家，也处于东夷族的包围之中。太公至国，“修政，因其俗，简其礼”（《史记·齐太公世家》），因地制宜地推行周礼。春秋时期，齐桓公在建立霸业的过程中，“招携以礼，怀远以德”（《左传·僖公七年》），以周礼怀柔周边小国，周礼文化得到进一步传播。春秋后期齐相晏婴，原为“莱之夷维人也”（《史记·管晏列传》），却提出“礼之可以为国也久矣，与天地并”（《左传·昭公二十六年》）的主张，继承了齐人以礼治国的传统。经过几代人的努力，齐国成了“冠带衣履天下”（《汉书·地理志》）的文明大国。

    燕国原为商的势力范围，有山戎、孤竹、秽貊等族散居其地。燕国受封后，“修召公之法”（《史记·燕召公世家》），积极推广周礼文化，使周文化与当地的土著文化相互交融。1975年发现的昌平白浮墓，年代约在西周中期，墓主人为臣属于燕国的异族首领之一。“墓主的着装、佩戴的兵器遵循着本民族的习惯，而使用的青铜礼器和埋葬习俗已纳入西周燕国的轨道。”这反映出周礼文化与燕地土著文化融合的情形。春秋战国时期，周礼文化进一步传播到东北地区。《后汉书·东夷列传》：“东夷率皆土著，喜饮酒歌舞，或冠弁衣锦，器用俎豆。所谓中国失礼，求之四夷者也。”当地的民族文化，已融入了周礼文化的因素。

    晋国所封的唐地，“戎狄之民实环之”（《国语·晋语二》）。唐叔虞受封时，周成王令他“启以夏政，疆以戎索”（《左传·定公四年》）。春秋时期，随着晋国的对外扩张，周礼文化也向外辐射，对周边民族产生了深刻影响。晋卿狐偃原为狄族出身，但从其思想来看，他已经完全华夏化了。他倡导以礼教民，在城濮之战前，向晋文公陈述“民未知义”、“民未知信”、“民未知礼”（《左传·僖公二十七年》），强调周礼的基本精神。《左传·襄公十四年》载，戎子驹支面对范宣子的指责，义正词严地用历史事实驳斥晋国执政，最后赋《诗·小雅·青蝇》而退，大有中原饱学之士的风度。春秋后期，晋国周边的戎狄蛮夷基本融入了华夏族，这种民族融合是在“礼”的认同基础上才得以实现的。

    其他如楚、秦、吴、越等国，虽一度被视为蛮夷之邦，但后来逐渐接受了中原文化，也陆续加入了华夏的行列。这些国家都有独特的地域文化，不过始终都受到了周礼文化的影响。如楚大夫申叔时教太子诗、书、礼、乐及春秋、世、令、语、故志、训典等（《国语·楚语上》），与中原各国贵族教育的内容基本一致。吴国的公子季札受聘至鲁，“请观于周乐”，听乐工每奏一曲，都能逐一评论（《左传·襄公二十九年》），显示了很高的周文化修养。类似深谙周礼的人物，在秦、越亦不乏其例。这表明，周礼文化已传播到了楚、秦、吴、越等国，并逐渐得到了上述诸国的认同。

    西周时期尊尚礼乐的文化认同，使周礼文化在西周的文化格局中，成为了主体的、原始的、根本的“一”，能够统合其他的“多”（地域文化）而为一体，形成西周时期的文化一统格局。而文化一统又是促成政治一统的黏合剂，也是促进民族融合的催化剂。《春秋》与《公羊传》以崇礼为中心的文化一统思想，与周代尊尚礼乐的文化认同是一脉相承的。这种以崇礼为中心的文化一统思想，源于西周制礼作乐的历史实际以及周代社会尊尚礼乐的文化认同观念。

    东周以降，西周“礼乐征伐自天子出”的一统局面已被“礼乐征伐自诸侯出”所取代。但是，在思想上对“一统”的认同，仍在很大程度上支配着东周时期人们对历史走向和国家前途的认识，是人们重建统一王朝的精神动力。春秋大国争霸，仍以“尊王攘夷”为旗帜，藉天子的名义维护自己势力范围内的一统秩序。战国时期，“上无天子，下无方伯，力功争强，胜者为右”，重建统一王朝已成为历史发展的大势所趋。当时统治者梦寐以求和思想家大声疾呼的，无不是实现天下的统一。

    由于历史形势发生了变化，战国时期的大一统观念有了新的内容。《史记·李斯列传》：“今诸侯服秦，譬若郡县。夫以秦之强，大王之贤，由灶上骚除，足以灭诸侯，成帝业，为天下一统，此万世之一时也。”李斯所说的“天下一统”，实际上是“大统一”，即以武力兼并为手段，建立以郡县制为基础的中央集权式的统一国家。秦灭六国，建立了空前统一的大秦帝国。从此，中国古代的大一统思想进入了一个新的阶段。

    在中国历史上，自西周王朝以后，曾经历了春秋战国、魏晋南北朝、宋辽金西夏等几个分裂的时期，但始终没有像欧洲那样，形成多个独立的民族国家，而是在经过分裂、对峙和融合后，又出现了秦汉、隋唐、元明清等崭新的统一王朝，使中国社会一步一步地跨上更高的台阶。“一统”始终是中国历史发展的常态，而造就中国一统常态的重要原因之一，正是根植于中国传统文化中的大一统思想和精神。因此，弄清大一统思想的渊源及其历史发展，对我们深入理解在中国延绵两三千年之久、并对中华民族的历史产生过巨大影响的大一统思想，是十分必要的。

    本文原载《文史哲》2013年第4期

  • 陈伟：书于竹木：简牍文化及其载述的国家信史

    简牍及其周边

    简牍是指用于书写的竹、木片和写在竹、木片上的文献。从许慎《说文解字》开始，历代学者提出多种解释，大致认为简用竹制作，形状细长，也称牒、札；牍用木制作，比较宽厚，也称方、板。岳麓书院藏秦简中的令条规定：上呈皇帝的文书“对”（答问）、“请”（请示）、“奏”（报告），采用牍的时候，一牍不超过五行字（“用牍者，一牍毋过五行”）。三行、四行、五行牍的具体宽度，分别约等于3.45、3.83、4.34厘米。又说，“牍厚毋下十分寸一（约0.23厘米），二行牒厚毋下十五分寸一（约0.15厘米）”。综合起来看，容纳文字是在三行以上还是在两行以下，是牍与牒（也就是简）的主要区别。牍可以书写三至五行，比较宽厚；牒或曰简只能书写一或二行，比较窄而薄。这是对呈报皇帝文书的特别要求，但对了解一般简牍的状况也有参考意义。

    近年的发现显示，两行书写的简多用木制，但也有竹制；单行书写的简多为竹制，但也有木制。牍多用木制，但湖北、湖南也出土了竹制的牍。因而，简单地说“竹简”“木牍”，其实不够准确。

    单行和双行书写的简，往往用绳线连系成册以承载长篇文献。《史记·留侯世家》说黄石公“出一编书”，《汉书·诸葛丰传》说“编书其罪”，就涉及这一情形。这也是后世书籍观念中的编（也写作“篇”）、册（也写作“策”）的源头。牍的书写面比较大，可以单独承载不太长的文献，早先认为不存在编连的问题。不过，近期一再发现内容相关但形态各异的文书、簿籍编连成册。现在看来，只是典籍类文献才由形制相同的简书写编卷，而形态各异的文书簿籍造册归档时，并非如此规整。

    简牍上的文字，绝大多数是用毛笔蘸墨写成，偶尔也有红色字迹，即所谓“丹书”。古书中有所谓“漆书”，指的应是墨书。笔、墨、砚、刀，是简牍时代的文房四宝。写错的字，可用刀刮去再写。《史记·孔子世家》说：“至于为《春秋》，笔则笔，削则削，子夏之徒不能赞一辞。”当时处理文案的官员，因而被称作“刀笔吏”。《汉书·萧何曹参传》就说“萧何、曹参皆起秦刀笔吏”。

    《尚书·多士》记“惟殷先人有册有典”。甲骨文已有“册”字。由于“册”的字形类似简册，有学者推测商代已使用简牍。《诗经·小雅·出车》咏叹远征的军人“岂不怀归，畏此简书”。《左传》襄公二十五年记齐大臣崔杼作乱时，“南史氏闻大史尽死，执简以往”；襄公二十七年宋大夫向戌将赏赐文书拿给子罕看，子罕不以为然，“削而投之”。这些是西周、春秋时使用竹简的可靠记载。

    我国现代意义上的简牍发现，始于20世纪初，其后层出不穷，出土地点从西北地区扩展到大多数省份，迄今已发现200多批，总数超过30万枚、300万字。这些简牍的年代主要是战国中期至秦汉魏晋，最早一例是在公元前433年或稍晚入葬的随州曾侯乙墓竹简。春秋以前的简牍由于年代久远不易保存，加之埋藏条件的原因，目前尚未能得见。

    目前，已多次发现西汉纸张的遗存，居延、敦煌、放马滩等地所见的纸还带有文字或地图，显然是用于书写。不过，在东晋末年之前，简牍仍然是主要书写载体。《初学记》卷21“纸七”录《桓玄伪事》称：“古无纸故用简，非主于敬也。今诸用简者，皆以黄纸代之。”这是纸张取代简牍成为官方书写载体的标志。

    简牍的取材、制作、书写，都比较方便。《论衡·量知》就说：“截竹为筒，破以为牒，加笔墨之迹乃成文字。大者为经，小者为传记。断木为椠，析之为板，力加刮削，乃成奏牍。”《汉书·路温舒传》记载路温舒小时候放羊，自己制作木简，练习书写。可见简牍的便易性降低了识字、教育的门槛。商代、西周，学在官府，知识圈狭小，文献的种类、篇幅也有限，简牍的优势不容易发挥。春秋以降，私学勃兴，著述蜂起。战国时各国相继变法，建立以郡县制、官僚制为基础的新兴国家，文书、律令的行用骤然增长，简牍真正有了用武之地。在这个意义上可以不夸张地说，在我国春秋、战国、秦汉时期的政治发展和文化繁荣中，简牍扮演了重要角色。

    由于竹木带有天然纹路，并便于刻齿、挖槽，还可封泥、钤印，因而简牍还可衍生为具有保密、防伪功能的券、符、传、检、署等物件，在公私事务中发挥特别作用。

    （1）检、署

    署是在往来文书、信函上写明收件方以及传递方式的木片，同时也对文件内容起到屏蔽作用，类似于今天的信封。署与文件捆紧后，在捆扎处可敷设胶泥，再盖上印章，不开封不能看到里面的内容。

    检是封缄文书、物品的物件。《急就篇》卷三：“简札检署椠牍家。”颜师古注：“检之言禁也，削木施于物上，所以禁闭之，使不得辄开露也。”检有多种式样，但都带有封泥、钤印的凹槽。用检的文书，比只用署的文书保密效果更好。岳麓秦简“卒令丙三”说：“书当以邮行，为检令高可以旁见印章，坚约之，书检上应署，令并负以疾走。不从令，赀一甲。”这提示我们，检用于以邮行的文书，而不用于其他方式传递的文书。

    （2）券、符、传

    券是财务往来的凭据。一式两份或三份（“三辨券”），用同一木板或枝条剖分而成。券上通常有刻齿，用不同形态的齿表示不同数值，与所记载的数字对应，加强券的可靠性。

    符是从事一些特定事务的凭证。通常一式两份，通过“合符”来验证。西北汉简中发现较多出入符。居延汉简65.9长14.6厘米，刻齿在书写面的左侧，释文为：“始元七年闰月甲辰居延与金关为出入六寸符券齿百从第一至千左居官右移金关符合以行事……”表明这款符用于出入金关，一次制作1000套，各套的左符留在官署，右符放在金关。通关者领取左符到金关验符通行。居延汉简65.10刻齿在书写面的左侧，右半残缺，存留的一行文字与65.9相同。最近有学者测试，二者紧密契合，可能是一套符中的左符和右符。

    传是旅行证件。对因公出行者来说，传同时还是接受交通、食宿安排的凭据。云梦睡虎地秦简《法律答问》记：“今咸阳发伪传，弗智（知），即复封传它县，它县亦传其县次，到关而得。”显示传跟公函一样，封缄后由使用者携带，需要时拆开查验。

    从文物到文献

    简牍的出土位置，主要有墓葬、水井、工作或生活遗址。出土简牍的墓葬分布广泛，湖北地区发现最多。云梦睡虎地11号秦墓，1000多枚竹简集中放在棺内。而在大多数墓中，简牍是放置在棺外，比如椁室中。古井中堆积简牍，主要见于湖南。古人工作或生活遗址出土简牍，主要是在西北地区。

    简牍的揭取和保护通常由专业人员负责，在细心提取简牍的同时，还详细记录各个个体之间的相互关系，为后期的缀合、编连提供参照。在完成清洗、脱色后，需要及时拍摄图像，尽可能充分地获取各种信息。

    简牍文献的整理，是尽可能完整、系统地获取简牍中的文献信息，实现简牍从文物到文献的转换。主要工作环节可用以下几个例子说明。

    认字，是把简牍上书写的古代文字辨认出来。利用文字学、古文字学研究成果，简牍上的大多数字，学者可以认读。但也有一些难字需要推敲考订。郭店简中有一个字出现三次，整理者释为“蚄”，很难讲通。其实这个字是《说文》“杀”的古文，在简文中读“杀（shài）”，衰减的意思。《唐虞之道》7号简“孝之杀爱天下之民”，《语丛三》40号简“爱亲则其杀爱人”，是说把对亲人的爱推广给其他人，属于儒家仁爱的观念。《语丛一》103号简“礼不同、不奉（丰）、不杀”，与《礼记·礼器》所记孔子语相同，是这一释读的直接证据。

    断读，相当于标点，是通过阅读中的停顿，反映文章中的意群和脉络，从而正确地领会文意。断读分原则性断读和喜好性断读两种。喜好性断读，是指出于个人习惯，断句或长或短，不求划一。原则性断读，是说当断必断、当连必连，否则就会导致文句不通或使文意产生歧义。

    张家山汉简《二年律令》65～66号简整理本释文：“群盗及亡从群盗，……矫相以为吏，自以为吏以盗，皆磔。”注释说：“矫相，疑指矫扮他人。”简文中，“相以为吏”与“自以为吏”相对，是形容“盗”的两种情形。矫，指假托、诈称，同时修饰这两种情形。因而中间的逗号应改为顿号，读作“矫相以为吏、自以为吏以盗”，是说相互诈称官吏或者自我诈称官吏而进行盗窃。岳麓秦简《学为伪书》案卷中那位叫学的少年犯供述说：他父亲服劳役受欺侮，经常训斥他。“归居室，心不乐，即独挢（矫）自以为五大夫冯毋择子”，伪造书信进行诈骗。这就属于类似表述。

    编连与缀合，是简牍类文献整理的特殊作业。简牍出土时，原有的编绳大多朽断无存，简牍个体还往往开裂破碎。编连与缀合就是在这些情形下，重建业已丢失的、书写在不同简牍个体及其残片上的文本之间的联系和顺序。编连是对不同简牍个体之间顺序的安排。缀合则是针对同一支简牍而言，在简牍断裂之后，重新把残片拼合起来，以恢复原先的完整形态。在这里，简牍物质形态上的拼复与编次，与文本形态上的连接与整合相互依存，融为一体。

    郭店简《语丛一》31号简与97号简，分别书写“礼因人之情而为之”和“即（节）文者也”。整理本把二者分别看待。《礼记·坊记》说：“礼者，因人之情而为之节文，以为民坊者也。”《管子·心术上》说：“礼者因人之情，缘义之理，而为之节文者也。”《礼记·檀弓下》：“辟踊，哀之至也，有算，为之节文也。”相形之下，31号简显然应当与97号简连读，表述儒家对礼的起源的观念（礼基于人的情感并用仪节来调适）。在我提出这一看法的时候，“文”字还没有得到正确释读。而当学者随即释出“文”字后，这两枚简前后相次就更加确定了。

    缀合，是克服简牍破碎化，提升残片文献价值的关键步骤。我们在研撰《里耶秦简牍校释》过程中，把缀合的推进作为工作目标之一。下文引述亭“赀三甲”的木牍，由四个残片拼合后，方可知其大概。

    云梦睡虎地77号墓出土的西汉简牍《质日》，有的年份损坏严重。我们课题组同事用“寸简寸心”相激励，孜孜以求，一点一点地推进。经反复推敲，用8个残片缀合成一枚下半支简（“己酉 戊申道丈田来治籍 丁未将作司空”），并排定到《十一年质日》的2号位，就是集体攻关的一个实例。

    简牍文献记载的国家信史

    早前，因为简牍出土数量不足，并且大多支离破碎，其学术价值一般只说是证史、补史，处于辅助、补充的位置。现在由于资料的快速积累，尤其是有像睡虎地秦汉简这样数量多、保存也比较好的大宗材料，通过适当整理和互勘合校，简牍文献已经在行政与政区制度、律令与司法制度、经济制度、文书制度、算术与医药、风俗习惯等领域的创新性研究中成为主要的资料依据。

    简牍资料在秦郡县制方面提供了较多新知识，这里举三点说明。

    首先，新发现郡名“洞庭”“苍梧”。《史记·秦本纪》记载：“秦王政立二十六年，初并天下为三十六郡，号为始皇帝。”从南朝宋的裴骃开始，学者对三十六郡所指便聚讼不已。1947年，谭其骧先生发表《秦郡新考》，成为权威性意见。然而，秦简牍中有一些全新的发现。秦始皇二十七年的一件文书说：“今洞庭兵输内史，及巴、南郡、苍梧输甲兵……”（里耶秦简16-5）洞庭、苍梧与人们熟悉的巴郡、南郡并列，显然也是秦郡名。秦始皇三十四年的一件文书（里耶秦简8-758）说“苍梧为郡九岁”，表明在秦王政二十五年统一前夕，就已设立苍梧郡。在传世文献中，秦洞庭、苍梧二郡，毫无踪影。

    里耶秦简对洞庭郡及其属县有较多记录，因而可以推定秦洞庭郡其实相当于传世文献中的黔中郡。《汉书·武帝纪》记武帝元鼎六年“遂定越地”，设南海、苍梧等九郡。有学者认为，秦苍梧郡是西汉苍梧郡的前身，位于南岭以南。根据张家山汉简《奏谳书》所录秦案卷等简牍的证据,秦苍梧郡其实相当于传世文献中的长沙郡。

    其次，昭示中央直达基层的管理体制。在郡县制下，国家之于地方，“如身之使臂，臂之使指”，出土简牍使我们领略到这种体制实际运行的精致与效率。

    里耶秦简8-228记载丞相书的传递，从朝廷所在的内史开始，在传达至各县的同时，还传给南郡，南郡又传给洞庭，从而使这份文书迅速传播到郡县。里耶秦简9-2283是洞庭太守避免征发徭役的指令，从大概是郡治所在的新武陵分四条路线（“别四道”）传达给各县。迁陵县收到文书后，一面向上一站酉阳县回报，一面安排县内各官署传达：“迁陵丞欧敢告尉：告乡、司空、仓主听书从事。尉别书都乡、司空，司空传仓，都乡别启陵、贰春。皆勿留脱。”“别书”指另行抄录传递，在当时应是文书传播中的有效方式。

    最后，展现不同郡县间的行政、经济联系。秦代不同郡县之间可能有相当密切的联系。前面引述里耶秦简属于苍梧郡的指令，因为与洞庭各县有关，传达到洞庭郡迁陵县各乡。里耶秦简8-657则是由于琅邪尉的治所迁到即墨，琅邪郡通报各地。

    里耶秦简中常常出现的“校券”，是不同郡县间钱物往来的凭据。13-300记载迁陵县十四匹传马经过雉县（属南阳郡）时，借用了食料。雉县出具“稗校券”，要求迁陵接受“移计”，“署计年、名”反馈给雉县。这意味着，迁陵不需要交付钱物，而是借助“计”的形式确认债务，再通过中央财政平账。里耶秦简所记一段相关内容颇有故事性。亭来自僰道（属犍为郡），在迁陵担任“冗佐”（一种低层吏员）期间犯事，“赀三甲”，计4032钱。亭自称家里有能力赔偿。迁陵县出具校券，请僰道县索取。结果亭的妻子胥亡说：“贫，弗能入。”要求让亭在迁陵作劳役抵偿。于是迁陵要求僰道退还校券。

    这类事例显示，秦郡县制之下，除了中央与地方的纵向关系之外，地方郡县之间还存在密切的横向联系。这降低了各地政府的运行成本，增强了国家的凝聚力，也给民众带来一些便利。

    文书在秦汉国家治理中，发挥着重要作用。

    睡虎地秦简《秦律十八种》是秦统一之前的律典。其中在多种场合强调“以书”，显示当时已形成文书行政的规范。如《田律》要求“辄以书言”春雨和庄稼抽穗的情况；《金布律》要求官府输送财物时，“以书告其出计之年”；又要求在废旧公物需要及时处置的场合，“以书”呈报；《内史杂律》规定需要请示时，“必以书，毋口请”。

    里耶秦简是秦统一之后洞庭郡迁陵县的档案。较多文书写明“听书从事”，或者提出“书到时”如何运作的具体要求。

    民间的重要事务，如结婚、遗嘱、牛马奴隶等交易，也需要由官府用文书确认。岳麓秦简《识劫婉》案卷中，女主人翁婉，原本是一位叫沛的富豪的妾。沛的妻子在世时，婉已为沛生下两个孩子。沛的妻子去世后，沛免除婉妾的身份，成为庶人，又生了两个孩子。婉自述说，沛把她免为庶人后，娶她为妻，并让她参加宗族、乡里的活动。然而乡署的官员表示：沛免婉为庶人时，在户籍上登记“免妾”。但后来娶婉为妻，并没有报告，婉在户籍上的身份还是“免妾”。

    律令是秦汉帝国建立、运行的重要制度支撑。以睡虎地秦律发端，近五十年来，秦至西汉早期的律令简册层出不穷，蔚为大观。

    对于秦汉律的整体认识，学界颇有歧异，或比较笼统地称之为“律典”，或以为只有一条一条制定的单行律令，而不存在国家颁布的统一法典。

    较早出土的睡虎地秦律、张家山汉简《二年律令》，均已呈现出篇章分明的结构。云梦睡虎地汉律、荆州胡家草场汉律和益阳兔子山汉律目录大致相同，进一步展示出集篇为卷、两卷并存的格局。兔子山律目分为“狱律”“旁律”两部分，其中“狱律”包含告、盗、贼、囚、亡等十七篇，“旁律”包含田、户、仓、金布、市贩等二十七篇。当时的律分“罪名之制”和“事律”两类，大抵“罪名之制”是对犯罪行为的处罚规定，类似于刑事法律；“事律”是对违反制度行为的处罚规定，类似于行政法规。西汉早期律典中，“旁律”诸篇均属事律；“狱律”虽然以“罪名之制”诸篇为主，但却夹杂几篇“事律”（效、关市、厩律等）。这种安排很不好理解，或许与萧何制定“律九章”的历史有关。

    虽然律篇、律条的增删修订不断发生，但在一定时期内，全国存在一个统一的律典。这可以从几个方面来看。

    第一，在睡虎地秦律、里耶秦简和睡虎地汉简中，一再出现“雠律令”的记载。可见律令一有变动，就立即在全国组织校勘，保持同步。

    第二，秦汉时实行奏谳制度，重要案件向上级报告，疑难之狱请上级裁断。向上呈报时必须“具傅所以当者律令”（《岳麓书院藏秦简〔伍〕》66），把判决依据的律令一一附录在判决之后。可见全国上下遵循同一律令，中央立法机构掌握最终解释权。

    第三，张家山汉简《功令》规定各县道狱史在升任郡治狱卒史前，需要集中到中央司法部门（廷尉）参加“律令有罪名者”等内容的考试。考试作答、评分必定要有标准答案，这也显示统一律典的存在。

    第四，某些律篇、律条的变更，会带来律典的全面修订。例如张家山336号墓出土的《汉律十六章》，较多律篇与《二年律令》相同，但律条多有增删和补充，不再出现《收律》，相关律条皆删去“收”和“收孥相坐”的刑罚。这是文帝元年“除收帑诸相坐律令”的结果。胡家草场汉律是汉文帝十三年刑制改革后的律典，与此前的张家山《汉律十六章》和睡虎地汉律相比，刑罚制度判然有别。这证明律典中各篇各条存在密切关联，构成一个有机整体。

    刘邦军至咸阳，萧何“独先入收秦丞相、御史律令图书藏之”，并“作律九章”，奠定汉承秦制的基础。《史记·曹相国世家》记曹参去世后，民众歌颂说：“萧何为法，顜若画一。曹参代之，守而勿失。”司马贞《索隐》解释“顜”字说：“训直，又训明，言法明直若画一也。”《汉书·曹参传》写作“讲”，颜师古注：“讲，和也。画一，言整齐也。”“画一”之歌反映了当时人对律令整齐划一的真实感受。

    秦汉时期法的主要形态有律、令两种。令的资料目前公布的还不多，姑且不论。律就其具备的基本特征而言，称之为“律典”或者“早期律典”是适宜的。

    本文节编自《光明日报》（ 2025年01月04日 10版）

  • 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？