Reviewed by:

• America by the Numbers: Quantification, Democracy, and the Birth of National Statistics by Emmanuel Didier
• Dan Bouk (bio)

America by the Numbers: Quantification, Democracy, and the Birth of National Statistics By Emmanuel Didier. Translated by Priya Vari Sen. Cambridge, MA: MIT Press, 2020. Pp. 432.

The story of probability theory in the eighteenth and nineteenth centuries—its roots in insurance, law, and statecraft, as well as the subsequent epistemological resistance to it—is now well-established through classic works like Daston's Classical Probability in the Enlightenment (Princeton University Press, 1988), Hacking's Taming of Chance (Cambridge University Press, 1990), and Porter's The Rise of Statistical Thinking (Princeton University Press, 1986). With this book, Didier follows the continuing probabilistic revolution into the early twentieth century in a work that beautifully complements Sarah Igo's Averaged American (Harvard University Press, 2007). Igo established the ways that probability and statistics contributed in this same period to the building of selves in a mass society. Didier's parallel study casts attention on the making of political aggregates at the dawn of the welfare state. Didier attends to the political uses of statistical theory in the tradition of Alain Desrosieres' The Politics of Large Numbers (Harvard University Press, 1998). A new generation of scholars is now extending this history into the mid- and late twentieth century—scholars like Theodora Dryer, Arunabh Ghosh, Chris Phillips, and Alma Steingart. Didier's book is an essential piece in this literature.

This book was originally published in 2009 in French with a title that translates roughly to "What Does America Consist of?" That title captures the book's theoretical core. Didier focuses on the production of two aggregates: crop estimates and unemployment statistics. He argues that in the early twentieth century, America (as depicted in these aggregates) consisted of an assortment of judgments made through distributed, participatory statistical labor. But the introduction of a new technology—statistical sampling—instead turned America into a collection of "sample frames" and "representative" respondents (or non-respondents) whose data could be extracted by professional statisticians serving technocrats. [End Page 608]

Didier begins with the material practices used by Department of Agriculture statisticians in the 1910s and 20s. A statistician in each state enlisted volunteer farmers—"a sort of parliament of the entire agricultural territory"—chosen to represent their regions through reports on local conditions, which the statistician collected and edited, drawing on his own experiences, interviews, and observations from around the state and from past years' reports, shaping from this mass of "plasma" a smoothed map, a weighted chart of the state's estimated yields for each important crop (p. 135). A federal board then released regular estimates that set an informational standard used by farmers, speculators, politicians, and everyone else. Didier traces out this example in depth, using it to illustrate how an aggregate should be seen as a "consolidation" from diverse materials. Readers interested in where these practices came from should read Jamie Pietruska's Looking Forward (Chicago, 2017), published in the intervening decade since Didier's French original.

Having presented this consolidated aggregate, America, Didier traces what he calls its deliquescence. The Great Depression and state responses to it sidelined crop estimates. Alternative aggregates began forming within the crop reporting system. In 1921, Agricultural Department statisticians started experimenting with techniques for gathering objective data about specific farms, instead of relying on the regional estimates of farmer-reporters. In the 1930s, they partnered with Iowa State University, building a set of techniques for stratified random sampling (to pick the farms to look at) while arguing that only such samples could be truly "representative" of all farms.

All that remained was one monumental puzzle: how to "materially transform America into a vast urn containing small areas of land from which to draw samples" (p. 197). The solution began with 3,070 maps in envelopes filed in thirty-eight drawers in Iowa, a set of maps that encompassed the entire nation. Census Bureau funding in the mid-1940s allowed those maps to...

审核人：

• 美国的数字：量化、民主和国家统计的诞生，埃曼纽尔·迪迪埃 (Emmanuel Didier)
• 丹·布克（生物）

美国的数字：量化、民主和国家统计的诞生，作者：Emmanuel Didier。由马萨诸塞州剑桥市参议员 Priya Vari 翻译：麻省理工学院出版社，2020 年。Pp。432.

18 和 19 世纪概率论的故事——其根源于保险、法律和治国方略，以及随后对它的认识论抵制——现在通过经典著作如达斯顿的启蒙运动中的古典概率（普林斯顿大学出版社，1988 年）、Hacking 的《驯服机会》（剑桥大学出版社，1990 年）和 Porter 的《统计思​​维的兴起》（普林斯顿大学出版社，1986 年）。在这本书中，迪迪埃 (Didier) 将持续的概率革命延续到了 20 世纪初，其作品完美地补充了莎拉伊戈的平均美国人（哈佛大学出版社，2007 年）。Igo 确立了概率和统计在同一时期对大众社会中自我建设的贡献方式。迪迪埃的平行研究将注意力集中在福利国家诞生之初的政治聚合体的形成上。Didier 在 Alain Desrosieres 的《大数政治》（哈佛大学出版社，1998 年）的传统中关注了统计理论的政治用途。新一代学者正在将这段历史延伸到 20 世纪中后期——像 Theodora Dryer、Arunabh Ghosh、Chris Phillips 和 Alma Steingart 这样的学者。迪迪埃 (Didier) 的书是此类文献中必不可少的一部分。

这本书最初于 2009 年以法语出版，书名大致翻译为“美国由什么组成？” 这个标题抓住了本书的理论核心。Didier 专注于两个总量的产生：作物估计和失业统计。他认为，在 20 世纪初期，美国（如这些汇总所描述的）由通过分布式、参与式统计劳动做出的各种判断组成。但新技术——统计抽样——的引入反而将美国变成了“样本框架”和“代表性”受访者（或非受访者）的集合，其数据可由为技术官僚服务的专业统计学家提取。[第608页结束]

Didier 从 1910 年代和 20 年代农业部统计人员使用的材料实践开始。每个州的一名统计员招募了志愿农民——“整个农业领土的一种议会”——选择通过关于当地情况的报告来代表他们的地区，统计员根据自己的经验、采访和观察收集和编辑这些报告。在该州周围以及过去几年的报告中，从大量“等离子体”中形成了一张平滑的地图，该地图是该州每种重要作物的估计产量的加权图表（第 135 页）。一个联邦委员会随后发布了定期估计，设定了农民、投机者、政治家和其他所有人使用的信息标准。Didier 深入描绘了这个例子，用它来说明如何将聚集体视为不同材料的“整合”。对这些做法的来源感兴趣的读者应该阅读 Jamie Pietruska 的《展望未来》（芝加哥，2017 年），出版于 Didier 的法语原作之后的十年间。

在展示了这个统一的聚合体美国之后，迪迪埃追踪了他所谓的潮解。大萧条和国家对此的反应搁置了作物估计。替代总量开始在作物报告系统内形成。1921 年，农业部统计人员开始试验收集特定农场客观数据的技术，而不是依赖农民记者的区域估计。在 1930 年代，他们与爱荷华州立大学合作，建立了一套分层随机抽样技术（选择要查看的农场），同时认为只有这样的样本才能真正“代表”所有农场。

剩下的只是一个巨大的难题：如何“在物质上将美国变成一个巨大的骨灰盒，其中包含可以从中抽取样本的小块土地”（第 197 页）。解决方案始于爱荷华州 38 个抽屉中的 3,070 张装在信封中的地图，这是一套涵盖整个国家的地图。1940 年代中期人口普查局的资金允许这些地图...