The central argument of Alessandro Stanziani’s article “European Statistics, Russian Numbers and Social Dynamics, 1861–1914”—that statistical data is socially constructed, and that the ways in which it is socially constructed should be an object of historical inquiry—helps to answer the question of why an archival researcher with literary inclinations should take note of the mass of statistical material produced throughout the nineteenth century and especially in its final decades. Statistical reports were regularly published in overtly literary publications such as Faddei Bulgarin’s Northern Bee (Северная пчела) from the 1820s onward. Vissarion Belinskii, one of the preeminent Russian literary critics of the 1830s–1840s, reviewed statistical works alongside literary fiction, poetry, and theater. In a review of a theory of statistics, he weighed in on the importance of the discipline and its place among other sciences:

While Belinskii reminds us that statistics has a place on the bookshelf of every civically-engaged reader, Stanziani’s article argues that the ways in which nineteenth-century readers appraised all that was “accidental and external” was not entirely detached from the “phenomenology of spirit.” In other words, numerical data had—and still has—a social component. Or, to respond to Belinskii, the ways in which droughts and cattle mortality are measured have a great deal to do with social processes.

The material Stanziani works with, as well as his conclusions, can be fruitfully interpreted through the lens of New Historicism. The linchpin of New Historicist interpretation is the anecdote, a textual excerpt where the reciprocal relationship between text and history plays out. The basic assumption behind New Historicist interpretation is that history shapes discourse, materially as well as ideologically, while discourse can shape history through its uses: as a particular kind of practice, as a behavioral model, as propaganda, as a linguistic environment, and so on. The anecdote (about an epistemological tug of war between cattle mortality and phenomenology of spirit, for instance) reveals the pervasiveness of a particular discursive pattern in a social group, or in a society at large. Discourse, in turn, determines historical possibilities.Footnote ² When a literary critic picks up a theory of statistics, reviews it alongside fiction and drama, and juxtaposes statistical “accidents” and the phenomenology of spirit, future researchers might conclude that statistics was worth reading and reviewing, as was fiction. They might also conclude that, like fiction, statistics was part of a discursive field that influenced socio-historical processes.Footnote ³ Stanziani emphasizes the anecdotal aspects of statistics, such as their authors’ interpretive stances or political agendas, over its prescriptive or predictive impulses, and argues that the former inform the latter. Most explicitly, he argues in his conclusion that the resulting statistical knowledge played a role in the Russian politics of the time (more on that below). The representations of individual positions on collecting, reading, and interpreting statistics, and especially the resulting opinions on economic policy, are shown here to influence contemporary politics. New Historicism therefore provides an appropriate theoretical paradigm for this response.

The primary archival materials supporting Stanziani’s investigation are the relatively little-studied local (zemstvo) statistics. Local statistics are shown to be an important object of study because they expose what Stanziani calls “the social construction of data” more effectively than the better-known statistical projects initiated by the central government. For example, zemstvo statisticians were impervious to the numerical methods promulgated by Moscow-based statisticians. Stanziani demonstrates how this resistance to mathematics revealed the political agendas of both sides: the liberationist slant of the Moscow scholars and the more insularly ameliorative agendas of many local reports. Zemstvo statistics also indicated the tensions between local survey respondents, who often belonged to different social or professional classes (for example, church officials of varying ranks who reported differently on the conditions of local peasants), as well as between the survey respondents and the peasants (the peasants are shown to be equally likely to lie to presumably invasive government statisticians and to the supposedly friendly local surveyors). Finally, local statistical reports show that the peasantry was the ultimate object of statistical investigations: their productivity, the value of their land, but also their political leanings and grievances.

One of the most memorable moments in Stanziani’s article is his discussion of the measurement of time in statistical reports. He argues that statisticians had two options for when to begin measuring the annual cycle of crop production: at the harvest or at sowing. The former approach implied that the annual business cycle began in July and ended in June. It emphasized savings and consumption rather than investment. The latter approach implied that the yearly cycle began in March-April and ended in the early spring; it emphasized credit and the problems of financing production. Stanziani writes that the former approach dominated statistical surveys between 1890 and 1925 (with the exception of the Chernigov school of statistics), while the latter approach became the norm after 1925. He argues: “Investigators who adopted the first approach were led to emphasize the lack of land and the excessive fiscal burden. This approach was widespread among most statisticians both before and after the revolution of 1917. The second approach led analysts to stress not so much the lack of land as lack of capital, including management.” Stanziani concludes: “Statisticians made use of ‘scientific’ notions of time not only to impose urban, academic values on peasants’ attitudes, but also to project their own social and political utopias. Reconstructing time was not confined to the past; it influenced future trends and, as such, projected statisticians’ images of the ‘new’ world: a perfect peasant economy, a socialist world, or to some extent, a ‘western’ market society.”Footnote ⁴

In other words, statisticians have agendas, and their agendas influence their work. One purpose behind collecting statistical surveys, then and now, is to cancel out, or at least to “average out,” the competing agendas of their authors. Although this article discusses surveying, its (perhaps intentional) focus is on the more individually driven aspects of statistics, such as mathematical modeling, management techniques, expeditions, and what can be called the “reception theory” of statistics (how and why non-statisticians read statistical reports). This angle allows the author to indicate certain startling moments in the discipline’s development in Russia. Stanziani repeatedly mentions the “Chernigov school” of statistics, a local statistical center in Ukraine that offered methodological and ideological alternatives to some of the approaches developed in Moscow. How did local statistical institutions such as the Chernigov school originate, survive, and successfully compete against metropolitan institutions at a time of increasingly strict institutional centralization in western Europe as well as in Russia? By the 1880s, France had all but eliminated non-Paris-based statistical institutions, as well as competing schools and methods of analysis. This article demonstrates the continuing importance of zemstvo statistics after 1861; furthermore, it reveals their methodological diversity. Stanziani shows how zemstvo statistics retained their relevance through the final decades of the nineteenth century and into the twentieth century thanks to a combination of unique socio-historical factors (lack of census, rigid social stratification, lax scientific institutionalization, and so on). Given that local institutions were presumably better placed than the Moscow statisticians to take advantage of local statistics, their heterogeneity, and their specific goals, one wonders whether the distinctiveness of Russian statistics during this period lies not only in the variety of individual approaches and opinions, but in their localization, as well as simply in the fact that local statistical institutions were able to exist. One wonders, in fact, whether the Chernigov school was the norm rather than the exception, and whether more local statistical institutions from this period await their rediscovery in the archives. More generally, if the history of Russian statistics in the final decades of the nineteenth century can be written based on zemstvo statistics (and not on Moscow statistics), what does this imply about the contemporary history of the Russian state? Unlike mathematics with its many applications, and even unlike political economy with its commercial uses, statistics at the time was a tool of the state, in name as well as in spirit. Why was the state unable to bring its statisticians, and its statistics, under control?

Another related question raised by Stanziani’s article is: how did the statisticians get trained, hired, and institutionally supported in spite of the apparent social gap between them and their superiors, and, especially, in spite of their occasionally radical political opinions? If statisticians used their work to sustain and promote their political agendas as demonstrated in this article, why did the Russian state support their careers while ignoring their political leanings? To better understand this inconsistency, it might be helpful to step back to the question of time, as well as to the definitional ambiguity of statistics as a discipline.

One question that Stanziani’s article implicitly raises, and leaves unanswered, is “what exactly is statistics?” Statistics is notoriously difficult to define, especially in a historical perspective. The predominantly descriptive statistics of the eighteenth and the first half of the nineteenth century was not the same thing as, but shared many objectives with, the numerical statistics of the late 1800s or of today. Consequently, many researchers essentially allow statistics (and, to a lesser extent, statisticians) to self-identify: an economically-focused project, ideally with the word “statistics” in the title, is read as a statistical work. Stanziani primarily works here with numerical statistics, but also with material more akin to the reports compiled in the early 1800s, such as the extensive descriptive and interpretive commentary on the peasantry provided by local church officials. In fact, a helpful heuristic for the statistics discussed in this article might be “works that attempt to gauge the conditions of the peasantry.” It conforms to the common contemporary definition of statistics as an enumeration of the state’s sources of power (cited by Belinskii) and positions the peasant as such a source within a predominantly peasant empire.

Stanziani underlines the political implications of statistical reports when he notes in conclusion that the period under discussion witnessed an “increasing enthusiasm for statistics as a tool for the scientific management of politics.” He points out that in Russia, the professional tensions surrounding the design and implementation of statistical reports were exacerbated by the fact that statisticians and their superiors typically belonged to very different social strata and therefore had conflicting political interests. As a result, statisticians sought out reportable phenomena that supported their reformist inclinations: for example, they “tended to accept the peasants’ underestimations of land and harvest and sometimes even excluded land purchased by peasants from their figures.” In contrast, supporters of government policies emphasized the increasing wellbeing of the peasantry. Stanziani argues that both biases exemplify “a major historical phenomenon: the emerging role of economic knowledge in the political arena.” Although Stanziani does not elaborate on his use of the term “political arena,” it appears to mean, primarily, class tensions: the conflicts between local officials and peasants, government officials and peasants, professional statisticians and their Moscow-based supervisors, and so on.

It is worth noting that the encroachment of competing political agendas on statistical surveys was hardly an “emerging” phenomenon in late-nineteenth-century Russia. The French surveys conducted in the first decade of the Napoleonic Empire reveal obvious personal, local, and class allegiances, and the respective economic interests, of their respondents. These were often in conflict with official policy.Footnote ⁵ Similarly, the late-eighteenth-century surveys conducted on the metropolitan periphery of the British Empire—Sir John Sinclair’s Statistical Account of Scotland (1791–1799) or the surveys of Ireland sporadically implemented throughout the eighteenth century—reveal nascent nationalistic tensions behind their ostensibly economic, ameliorative agendas.Footnote ⁶

With this in mind, what was unique about the Russian uses of statistics during this period, and what did they have in common with antecedent and contemporary European statistics? Stanziani points out that the typical “Weberian” antagonism between professional statisticians and supervising bureaucrats was exacerbated in Russia by the enduring, significant social gap between these two groups. The statisticians objected not only to their superiors’ scientific incompetence; they derided their illiberal political opinions. The social gap made the conflict political as well as professional. This, as well as the preeminence of the peasantry as an object of Russian statistical inquiry, makes us wonder whether this question should be recast as: what role did the Russian uses of statistics play in the construction of the Russian social hierarchy, and how did it compare to the roles played by western European statistical inquiries?

Statistics, in addition to being about peasants and sources of state power, is typically concerned with forecasting. It describes not only the situation on the ground, but also how it came to be and, more importantly, how it might evolve. Stanziani argues that the ways of measuring the yearly time budget designed by Russian statisticians essentially politicized time: they turned it into an arena of competing visions of “new worlds.” These future worlds (a perfect peasant economy, a socialist world, a ‘western’ market society) would be presumably populated by content, productive, affluent crop growers. Time therefore became a conduit for the productive development of a sociopolitical class. As noted earlier, the peasant was the unpredictable subject of the majority of statistical investigations discussed in this article. The debates among statisticians on the sources of rural poverty (lack of land versus lack of capital) and on the appropriate measurements of economic growth in the countryside implicitly tried to anticipate the peasants’ capacity for unrest. Statisticians were figuring out how to predict what the peasants might do, both in the short term—what the next year’s harvest might be, how much capital or land would be needed—and in the long term—the competing visions of more perfect peasant societies. In other words, the anticipatory impulse of statistics, when applied to the peasants, was an attempt to make their behavior as a class predictable.

Reading statistics and participating in their collection was a social marker prior to the discipline’s professionalization. It conferred the status of enlightened civic engagement upon its practitioners. The eighteenth-century surveys conducted by gentleman scholars in Scotland and Ireland essentially underlined the stability of their social class. In a similar vein, Russian noblemen of the late eighteenth and early nineteenth century (Mikhail Shcherbatov, Gavriil Derzhavin, Nikolai Karamzin, and others) read or compiled statistically informed treatises. If we interpret statistics as a political tool—for example, as a way to predict the potential behavior of a social class—then the gentleman scholars were engaged in a project not entirely dissimilar to the futuristic tendencies of the Russian statisticians described by Stanziani. They sought to project a sense of continuity of their own social class, perhaps in the face of external military or cultural domination; to demonstrate their reliability as state servants; to point up the exclusivity of local knowledge available to the literate landowners; and so on. The Russian statisticians who reinvented time were also concerned not only with the peasantry but, arguably, with projecting the capacities of their own nascent technocratic class, one that continued to expand in the early twentieth century and acquired significant political importance after the October revolution.

All of this is not to say that Russian statistics should be read as a chronicle of class struggle. Rather, the social “constructedness” of numerical data argued by Stanziani implies that statistics can be read as a cultural history of class interactions, their conflicts but also their endurance and transformation. One wonders whether the political tensions around Russian statistics demonstrated by Stanziani reveal not so much the endurance of Russian social hierarchy and the powerful political tensions it generated as its mutability during the period under discussion. After the abolition of serfdom, the meaning of each social class—the landowning gentry as well as the rest of the system—was gradually renegotiated. Statisticians were not alone in tracing out the story of these transformations. Making the peasants predictable (or, at least, comprehensible to an urban reader) was also one of the great Russian literary themes after the 1850s. So was the transformation of the social hierarchy: the demise of the landowning gentry, the growth of the urban professional class, and the rise of the physical and biological sciences. The anecdotes on the sociopolitical underpinnings of numerical statistics presented by Stanziani—and here I am using the word “anecdote” in its New Historicist sense, meaning an example of the pervasiveness of a particular kind of discourse—are therefore not dissimilar from the themes in Ivan Turgenev’s or Lev Tolstoy’s contemporary novels. On a basic level, the discourse was about an empire in a state of transition. The transitional state of the political system allowed for the hiring of radically leaning statisticians, the survival of competing schools of statistical methodology, and, arguably, the diversity of local statistics described in this article.