Long-Run Consequences of Labor Coercion: Evidence from Russian ∗

Johannes C. Buggle† Steven Nafziger‡ This version: July 2015

Abstract

This paper examines the long-run consequences of serfdom in the countries of the former Rus- sian Empire. We combine novel data measuring the intensity of labor coercion at the district level in 1861 with several intermediate and present-day outcomes. Our results show that a greater legacy of serfdom is associated with lower economic well-being today. We apply an instrumental variable strategy that exploits the transfer of serfs from monastic lands in 1764 to establish a causal link between past serfdom and current levels of economic development. Besides establishing this long-run correlation, we also explore the mechanisms that generated such persistence. We find a positive correlation between the earlier experience of serfdom and pre-Soviet land and political inequality, with possible negative implications for human capital investment and agglomeration. While Soviet policies largely eliminated existing land rights and political institutions, tracking the evolution of city populations throughout the 20th century con- firms that pre-Revolutionary differences in local development largely persisted. In the absence of racial or cultural markers for the prior experience of serfdom, our results suggest that lower levels of urbanization, structural change, and associated incentives for human capital investment likely explain most of the long-run persistence of differences in economic development.

Keywords: Labor Coercion, Serfdom, Development, Russia, Persistence JEL-Codes: N33, N54, O10, O43

∗We are grateful to Yann Algan, Quamrul Ashraf, Elena Nikolova, Sergei Guriev, Katia Zhuravskaya, and seminar participants at Sciences Po and the WEast Workshop on Economic History and Development in Budapest for helpful comments. We also thank the EBRD for providing the geo-coded LiTS survey waves for 2006 and 2010. Parts of this research were undertaken while Buggle was visiting the Economics Department at UC Berkeley, whose hospitality is appreciated. All errors remain our own. †Sciences Po. [email protected] ‡Williams College. [email protected] 1 Introduction

Throughout human history, “coercive” labor relations have been a widespread phenom- ena, from in ancient Rome to forced cotton harvests in contemporary Uzbekistan.1 Economists have long argued that unfree labor goes along with severe economic inefficien- cies, but whether these economic impediments persist once the institution is abolished has only recently entered the discussion. In this paper, we study whether institutions of unfree labor can have economic consequences long after their demise, using one of the most prominent examples of coerced labor in recent history: Russian serfdom. We uncover a robust negative relationship between this institutional heritage and economic development today and go on to investigate the mechanisms underlying this persistence. Our results provide additional evidence on the economic importance of institutional legacies and add to the emerging empirical literature doc- umenting adverse long-run consequences of forced labor (e.g. Engerman and Sokoloff (1997), Dell (2010), Nunn (2008b), Acemoglu et al. (2012), Acharya et al. (2013)).2 Russian serfdom was a system of labor coercion that existed from the 16th century to 1861, and has been perceived as a crucial institution in the region’s economic history. According to Acemoglu and Robinson “Russia’s economic institutions were highly extractive, based on serfdom, keeping at least half of the population tied to the land” (Acemoglu and Robinson, 2012).3 Indeed, at a time when the was fundamentally changing the economies of , around 50% of peasants in European Russia were obliged to work for the landowning nobility or pay them a portion of their income in the form of quit-rent. Amid broader efforts at modernization following the loss of Crimean War, the Russian state initiated the legal of these serfs in 1861 and followed that declaration with a drawn out process of land reform that transferred property rights (generally assigned to the communal village) and associated payment obligations to the newly freed peasants. The changes that these formerly privately “owned” peasants went through may be usefully contrasted with the experience of the rest of the peasantry, who resided on state or Imperial family-owned lands prior to 1861, and who saw a reform process in the 1860s that changed relatively little of their landholdings or obligations. These peasants possessed more land and faced a more liberal (at least on average) policy and institutional environment prior to the 1860s, and their reform experience likely solidified these differences in the short and medium term. In this paper, we

1Around 21 million people in the world today are in forced labor, coerced either by private individuals or the state according to the International Labor Organization ILO 2012 Global Estimate of . 2Important differences exist between Russian serfdom and most institutions of forced labor that have been considered in the literature. First of all, most serfs tended to enjoy considerable autonomy in their day-to-day time allocation decisions unlike, for example, the majority of American slaves. Secondly, although there were important exceptions, Russian serfs generally differed little from their masters with respect to race, ethnicity, or religion. Serfdom was a distinct social category, but it was fundamentally based on ownership and control of labor. This means that “race” or “religion” as mechanisms of persistence, certainly important in the North and South American cases, can largely be excluded. This allows us to focus more cleanly on the persistent effects of an economic institution, albeit one that had important social and political implications for Tsarist Russia. 3Slavery had a long history in Kievan and Muscovite Russia. The laws and customs regarding debt servitude and other forms of personal obligation helped structure those that later formalized serfdom (Hellie (1982)).

2 leverage this heterogeneity within the pre-1861 peasantry to identify longer-run consequences of serfdom. Newly collected district (uezd) level data from a tax census conducted in the late 1850’s allow us to map the variation in the share of the population who were serfs across the European part of Imperial Russia. To test for differences in long-run economic outcomes across districts with high and low levels of historical serfdom, we match our measure of serfdom’s intensity with outcome data from today (especially from the Life in Transition Survey (LiTS)) and from intermediate periods. We document that households in districts where serfdom was widespread before 1861 are poorer today, conditional on a large set of local bio-geographic characteristics, household variables, proxies for early development, and provincial fixed effects. According to our OLS estimates, a standard deviation increase in the share of the population who were serfs is associated with up to a 10 - 14% lower average household consumption today. OLS estimates would be biased, however, if unobserved district characteristics influenced where serf labor was more common and, at the same time, affected economic outcomes in the long-run. To address these omitted variable concerns, we make use of plausibly exogenous variation in the extent of serfdom derived from the transfer of monastic land and monastic serfs to state control by Catherine the Great in the 18th century. Monastic serfs, which had been subject to largely the same constraints as privately owned serfs, were, as a result of this reform, integrated into the “state peasantry” by the early 19th century. We exploit this historical experiment by using the geographic distribution of monasteries before the onset of Catherine’s reforms in 1764 to instrument for the intensity of serfdom at the district level just prior to emancipation. The instrument is a strong predictor of serfdom’s intensity, and the IV results again show that the our measure of the historical institution is negatively related to current household expenditures, with estimated coefficients larger than in the OLS specifications.4 Critically, we then move on to investigate the robustness of our basic results and explore the potential channels behind this correlation. We confirm the negative relationship between serfdom’s intensity and long-run economic outcomes by employing night-time luminosity in 2008 at the (historical) district level as a proxy for the level of development. We also trace the evolution of city population from 1700 to 1989 and show that cities in areas with relatively more serfs had lower levels of urbanization and did not catch up over time. In addition, we find a negative relationship between serfdom and the number of factories and industrial output per worker just after emancipation. These findings imply possible agglomeration and local spillover effects that continued to manifest themselves over time, thus constituting one potential link between serfdom and modern outcomes. In terms of other possible channels of persistence, we first show that serfdom was positively correlated with land inequality and the share of land owned by the nobility in 1900. Given the possible association between land inequality (and the presence of a landed elite) and the provision of local public goods, especially in rural areas, this very likely slowed development prior to the initiation of collectivization and other Soviet-era policies. Indeed, consistent with a literature that links unequal landownership to reduced human

4We discuss several possible reasons for the larger coefficient under the IV specifications.

3 capital investment (possibly with inter-generational implications) in other contexts, serfdom is associated with with fewer schools per district in 1856, lower school enrollment in 1880, and lower educational attainment today. In terms of modern public goods, we do not find differences in access to basic services such water, electricity and heating, which are all fairly widely distributed. However, former serf areas have a significantly lower road and rail density when measured in the Soviet period, which is consistent with an agglomeration interpretation. Finally, we explore the linkages between the local legacy of serfdom and cultural preferences today. We find little evidence for a distinctive set of cultural attitudes in areas where serfdom was more prevalent. Whether serfdom generated a legacy for subsequent Russian economic development has long been a topic of scholarly interest. Alexander Gerschenkron (1966), among others, at- tributed the slow pace of development in late-Tsarist Russia to serfdom and particular features of the emancipation process that seemingly perpetuated many institutional restrictions in the countryside (also see Dennison (2011), Robinson (1972), and Lenin (1911)). However, empir- ical work on Russian serfdom and emancipation, both in general and in terms of documenting subsequent effects, is relatively limited. One notable exception is Markevich and Zhuravskaya (2015), who estimate that provinces with above average levels of serfdom (as a share of the total population) were growing relatively faster after the emancipation. In a related work, Nafziger (2013) shows that the emancipation process largely defined the subsequent structure of factor endowments and land prices in the countryside prior to the Revolution of 1917. However, these studies fail to fully address the possibility of omitted variable bias, which is the explicit goal of our IV strategy. At the same time, whether economic differences between high and low serf regions per- sisted beyond the Imperial period has not previously been researched. This may not be sur- prising given that the Soviet Union stands between then and now, and that regime constituted a complete revolution in economic, political, and social institutions. However, researchers have found long-run economic effects of historical institutional variation in other contexts, even in the face of subsequent and dramatic changes (see for example Nunn (2008a) and Michalopou- los and Papaioannou (2013)). Given the evident importance of serfdom for Russia’s economic development, both prior to 1861 and before 1917, it is particularly valuable to investigate the extent to which this institutional regime generated long-run effects. If one establishes a correla- tion between the past and the present, identifying the underlying mechanisms that allowed for the persistent impact of serfdom through the Soviet period is obviously imperative, particularly such an exercise informs our understanding of factors underlying the economic difficulties that countries in this region have faced since the end of communist rule. Therefore, our study also contributes to the literature on historical development and persistence (Nunn (2013) provides an excellent survey). While we emphasize the agglomeration implications of a local legacy of serfdom, our empirical investigation also provides novel evidence on how factor inequality, in particular land, can stifle economic development through underinvestment in human capital and public goods, at least in the short-run (tying into the studies by Galor et al. (2009), Galor and

4 Moav (2004), Galor and Moav (2006), and others). The paper is structured as follows. Section 2 describes the historical background, including the measurement and likely determinants of the geographic variation of serfdom. In Section 3 we estimate the impact of serfdom on long-run development. In that section, we also develop our causal empirical framework that relies on the location of monasteries prior to 1764. Section 4 investigates potential channels of persistence. Section 5 concludes.

2 The History, Measurement, and Determinants of Serfdom

In this section, we briefly describe the historical background of and the data we use to measure its intensity. In framing our causal argument, we also examine the determinants of the variation in serfdom, focusing on geography and the conversion of monastic serfs to state peasants in the late 18th century.

2.1 Historical Background 5

While feudal institutions declined in Western Europe after the Black Death, Russian serf- dom emerged as a set of formal institutional constraints and informal practices in the 16th and 17th centuries. In return for service to the Tsars during this period of state expansion, the elite received large land grants that came with the right to draw upon the labor of the resident popu- lation. However, with competition among the servitor class and the ease of fleeing to open land, it was difficult for the land-owning class to exploit their laboring peasantry. The high land-labor ratio motivated the land-owning nobility to act to reduce the mobility of the peasantry and to increase control over various aspects of their lives. These attempts came to be supported by the state through various decrees, culminating in the 1654 Ulozhenie that sharply constrained peasant mobility and formalized the legal rights of the serf-owning nobility. Over the 18th cen- tury, further measures affirmed the control of the nobility over their resident peasantry, with the 1775 “emancipation” of the nobility freeing the serf-owning class from any corresponding obligations for state service. By 1800, the legal and institutional context of Russian serfdom was firmly in place. Serfdom varied widely across owners and estates but can be described by certain common characteristics. First of all, serfs constituted a distinct social estate apart from the nobility, the clergy, and even other peasants, and they faced substantive restrictions on their personal, family, and community autonomy (Wirtschafter (1997)). This had implications for their rights under Russian civil, criminal, and property law, including restrictions on owning land without the ap- proval of their lords. Serf owners held substantial authority over the daily lives of their peasants and, subject to erratically enforced laws, could unilaterally intervene in marriage, employment,

5This account is drawn from various studies. Good summaries in English are Moon (1999) and Blum (1961)

5 educational, religious, judicial, and other matters.6 Second, serf-owners demanded seigniorial rents and obligations. Extraction either took place in the form of labor obligations, through cash or in-kind payments, or a combination of the two. In many cases, owners actively managed the labor decisions of their serfs, both on and off the estate, either in person or through managerial staff. Such estates generally possessed demesnes, with labor service on the owner’s land com- pensated by the granting of use-rights to the rest of the property. On other estates, management was relatively hands off, with the serfs granted substantial freedom to allocate their labor as they saw fit. This latter variant was more common in less agriculturally productive regions, where owners tended to transfer the use of all property to the serfs in return for some cash or in-kind payments. Despite substantial, estate by estate, variation, these broad parameters of serfdom suggest an institutional regime that was antithetical to economic development. The labor, property, and education decisions of serfs were often circumscribed. This likely resulted in limitations on investment, the misallocation of labor and other resources, disincentives for the adoption of better agricultural techniques, and a host of other constraints. And given the prevalence of serfdom, these microeconomic impediments may have slowed industrial development and kept incomes low, especially among the peasantry. Many contemporary observers acknowledged the disincentives for economic growth that the institution generated prior to 1861, but supporters of the status quo argued for continuing the institution less in economic terms than in order to maintain the Imperial regime, to defend Russian or Slavic traditions, and to support some form of elite tutelage over masses ill-equipped for freedom.7 Although a long tradition of Russian and non-Russian scholars have subsequently debated the economic impact of serfdom (and emancipation), there is remarkably little empirical evidence one way or the other. Dennison (2011) has recently argued that serfdom generated adverse distributional and growth effects, although her conclusions are based on evidence from a single large estate. Soviet works – perhaps best exemplified by Koval’chenko (1967) – marshaled considerable quantita- tive evidence to argue that serfdom (and, by extension, the Imperial economy) was in decline prior to 1861. However, the data that these scholars employed tended to be rather selective, and their Marxist ideological framework generally placed the argument before the evidence. The classic work by Domar and Machina (1984) utilized more comprehensive information on the price of land with and without resident peasants to argue that from the perspective of the estate owner, serfdom was profitable and, therefore, worth defending when emancipation was proposed.8 But profitability is not the same as efficiency, and there is little hard evidence on the corresponding growth implications of serfdom from a neoclassical perspective. An important exception is the work of Markevich and Zhuravskaya (2015), who evaluate the impact of serf- dom by looking at differential economic changes between areas with more or fewer serfs before

6Following several decrees in the early 19th century, the nobility’s autonomy on their estates included the possibility of emancipating their serfs on largely their own terms. This option was exercised relatively infrequently. 7See the discussions and citations in Emmons (1968), Field (1976), Khristoforov (2011), and Zaionchkovskii (1960). 8Their study echoes the conclusions of Fogel and Engerman (1974) on the profitability of American slavery.

6 and after 1861. They argue for strongly negative effects of serfdom (and subsequent conver- gence after 1861), although this conclusion is based on relatively limited provincial data, and they do not explicitly address the possibility of unobserved determinants of serfdom driving the results.9 Thus, despite limited causal evidence, most scholarship on Russian serfdom asserts that it undermined economic development while it existed. Relative to the period of serfdom, considerably more empirical attention has been paid to the short and medium-term consequences of emancipation in the half century before the Bolshevik Revolution. Soviet studies (e.g. Druzhinin (1978), Litvak (1972), and Zaionchkovskii (1960)) argued that emancipation and the accompanying land reforms actually worsened former serf land holdings and imposed considerable new burdens on the rural economy.10 As emphasized by this literature, while former serfs retained land as part of the reforms, they received these property rights collectively through the newly formalized commune, the land they received was often different (worse) in amount and quality from what they had before, and they were held jointly responsible for (possibly higher than before) mortgage-like payments in return. More recent research has generated some contradictory findings regarding the amount and price of the landholdings that former serfs received after the reform process. Works such as Domar (1989), Hoch (2004), and Kashchenko (2002) argue that the majority of former serfs were made better off - at least in terms of factor endowments and obligations - than Soviet and other previous studies found. Unfortunately, both the prior Soviet scholarship and revisionist studies relied on somewhat questionable empirical evidence that was not necessarily representative, was too aggregate to identify differences, or covered an intermediate stage of a very complicated and drawn-out reform process. In his highly influential interpretation of Russian economic backwardness, Gerschenkron (1966) went beyond pre/post 1861 factor endowment comparisons to emphasize the negative implications of the peasantry’s joint liability and insecure communal property rights for agri- cultural productivity and labor mobility after 1861. While centered on the experience of the former serfs, Gerschenkron and others writing in this vein have tended to focus on broader institutional impediments to Imperial Russian rural development after 1861.11 Although eman- cipation and subsequent land measures were perhaps most dramatic for the former serfs, by the 1880s, the different types of peasants were administratively unified and possessed similar in- stitutions based on communal self-governance, (generally collective) property rights, and joint liabilities for taxes and land payments. Of course, such nominal similarities may have hidden many persistent de facto differences in the institutions and economic conditions faced by the

9Markevich and Zhuravskaya (2015) rely on provincial aggregates for just two periods before 1861 to establish the baseline comparison. In general, this study, and related works such as Nafziger (2012b) face a key difficulty in evaluating the economic impact of Russian serfdom: the dearth of quality data prior to 1861. A partial exception is Deal (1981), who directly compares agricultural productivity among serf and non-serf peasants prior to 1861. 10Within the Marxist historical framework, the emancipation reforms thus hastened the economic decline and “proletariatization” of the countryside, which contributed to the demise of capitalism and the Communist Revo- lution. This interpretation was Lenin’s starting point in his works on late Imperial Russia. None of these Soviet works were based on causal identification. 11This includes the important book by Allen (2003).

7 different peasant groups. Indeed, as Nafziger (2013) shows using more detailed data than pre- vious studies, landholdings were smaller, land inequality was greater, and the associated land and tax obligations were higher in districts with relatively more former serfs, well into the 20th century.12 Gerschenkron (1966) emphasized the role of the Imperial state in eventually easing insti- tutional impediments on rural development through the famous Stolypin land reforms of the early 20th century (named for the Prime Minister Pëtr Stolypin). He argued that these reforms fostered better incentives in peasant agriculture by offering mechanisms for consolidating plots and exiting the commune. But these were just the first steps in a series of dramatic changes that would deeply impact rural Russian society over the rest of the century: the Bolshevik Rev- olution, collectivization, famine, World War II, and the slow collapse of the agricultural sector from the 1970s onward. None of these changes explicitly or differentially targeted former serfs, but they may have generated or reinforced important and persistent effects that built upon pre-existing geographic, institutional, and economic differences between the types of peasants. This possibility complicates our interpretation of the mechanisms by which serfdom might have generated persistent economic change after it ended – we return this issue below.

2.2 Measuring 19th–Century Serfdom

While serfdom was a defining feature of Russian society by the early 19th century, not all peasants resided on privately owned land or were subject to quasi-feudal exploitation by the gentry class. By the 1850s, less than 50% of peasants were actually in some kind of feudal rela- tionship with the nobility. Peasants residing on state or Romanov family-owned land (we refer to the latter as “court peasants”) were governed by specific state administrative bodies, pos- sessed more land and freedom to engage in contracts, and were generally only liable for direct (and lower) tax-like obligations (Deal (1981), Druzhinin (1946 and 1958), Nafziger (2013)). As we noted above, some of these factor endowment differences persisted in the decades af- ter 1861, and different groups of peasants may have faced persistently different institutional conditions, despite the, at least nominal, administrative and legal convergence following serf emancipation. The geographic distribution of serfdom has long been known, but scholars have generally focused on specific estates or small geographic areas (i.e. Dennison (2011)), or relied on coarse statistics from aggregate data. With regards to the latter, the most comprehensive accounts of the quantitative dimensions of serfdom are those by Hoch and Augustine (1979) and Kabuzan (2002), which rely on data generated by 10 tax censuses undertaken between 1719 and 1858.13 These studies report aggregate totals indicating that the share of serfs in the total Imperial popu- lation crested at just over 50% at the turn of the 18th century, falling to roughly 35% just before emancipation.

12In this sense, Nafziger (2013) provides evidence consistent with the older Soviet literature. 13These censuses were initiated by Peter the Great and collected data on the populations that were obligated for taxes of different types.

8 In the present work, we focus on the local presence of serfdom at the administrative level of the district (uezd), the largest sub-unit of a province.14 In defining our measure, we rely on administrative records and the 10th tax census of 1858. We took the number of serfs from the tax census, as reported in Troinitskii (1982 (1861)), to construct our main indicator of serf- dom’s intensity, Serfs, perc. of pop, which divides the total number of serfs by the total district population.15 Since we do not know the total number of peasants per district, we use the over- all population as a denominator.16 The resulting measure covers 495 historical districts (in 50 provinces) in European Russia without Poland and Finland.17

Figure 1 about here

As shown by the distribution function in Figure 1, while over 90% of districts contained some serfs just before emancipation, in only few did the share of serfs in the total population exceed 80%. Table 1 displays summary statistics for our main measure of the intensity of serfdom – in our study area, serfs were approximately 38% of a district’s population on average.

Table 1 about here

Serfdom in the late 1850s displayed sizable geographic heterogeneity. Figure 2 shows this variation across European Russia.18 The map shows that serfdom was largely concentrated in a band from Kiev to the upper Volga. However, even in the high-serfdom areas, and even within provinces, there was considerable variation in the share of the population that was subject to feudal obligations.

Figure 2 about here

2.3 Determinants of Serfdom

Regarding heterogeneity across districts, we first undertake an investigation into the deter- minants of the “incidence” of serfdom.19 A number of potential explanatory factors comes to mind. First, the geographic location of a district likely determined whether serfdom reached a

14To engage in this level of analysis, we digitized a 19th century district-level map of European Russia. 15Unfortunately, district-level population totals from the 10th tax census are unavailable. As a result, we draw on Bushen (1863), which provides the total populations for 1863. Given the possibility of emancipation-induced migration, this might seem to introduce some measurement error. However, the 1863 population figures were based on administrative records of the tax-paying population, which were unlikely to have been quickly adjusted (and which may have largely relied upon the 10th tax census). 16An ideal intensity measure would use the number of peasants as denominator. Ongoing research into the 10th tax census may make this possible. Another source of potential measurement error might arise from using a snapshot of serfdom in 1858, which neglects prior changes in serfdom’s intensity. 17In extensions not reported here, we take advantage of additional district-level data on the types of obligations that serfs faced and other characteristics of serfdom c. 1858. These data are drawn from various sources generated in the preparatory work for the emancipation reforms. While we find several intriguing results with these other measures, focus on our primary measure of the incidence of serfdom in the current paper. 18The picture is very similar if the denominator only includes the rural population. 19All of the variables mentioned in this section are summarized in Table 1. More details on the variables can be found in the Appendix.

9 locality or not. As Muscovy expanded away from Moscow in the 16th-18th centuries, state ser- vice was often rewarded with the allocation of land in newly incorporated areas, the residents on which eventually came to be serfs. Therefore, we calculate the direct (log) distance from each district centroid to Moscow using GIS software. Furthermore, we also take into account a district’s geographic location by controlling for the latitude and longitude of its centroid. Variation in land productivity might have led to differences in the demand for coerced agri- cultural labor or in the desirability of land in return for state service. An important proxy for agricultural productivity is the suitability of the soil for growing crops. As grains, and in partic- ular wheat, were dominant in the Empire’s most agriculturally productive areas, we use modern geo-spatial data to produce a time-invariant measure of the land’s suitability for growing wheat (in other specifications, we also control for the soil’s suitability for growing other grains such as oat, barley and rye). Other environmental conditions might have affected local agricultural productivity, the mobility of the population (hence, the incentives for maintaining serfdom), and other determinants of local incomes. Therefore, we also construct variables that measure the fraction of land covered with forest today, the ruggedness of the terrain, and the presence of a river. These environmental variables are obtained from the FAO-GAEZ database.20 Perhaps the most prominent hypothesis about the emergence of serfdom states that a high land-labor ratio made feudal labor coercion more likely (Domar (1970)).21 Scarcity provided the incentives to tie labor to the land by creating and perpetuating institutional constraints on mobility - i.e. serfdom. Although the employment of serfs on private estates was not exactly a choice variable (since the laws governing peasant labor mobility applied to all estates), and the spread of private estates was likely driven by geography and Muscovite expansion, this framework might have some microeconomic relevance in the Russian context if estate owners were more willing to free their peasants prior to 1861 in areas where labor was relatively more abundant. To account for this possibility, we control in many specifications for population density in 1600, taken from the History Database of the Global Environment (HYDE), version 3.1 (Klein Goldewijk et al., 2011).22

Table 2 about here

In Table 2, we explore the possible determinants of the distribution of serfdom. We estimate OLS regressions relying on either across-district (Column 1) or within-province variation (Col- umn 2). Longitude and distance to Moscow do show up negatively, consistent with the direction of Muscovite expansion mattering. In many specifications, the suitability for growing wheat is

20We acknowledge that the Soviet authorities did engage in agricultural and resource practices that may have impacted these and other environmental conditions over the 20th century. In all likelihood, any changes were relatively small, likely unrelated to the incidence of serfdom, and mostly occurred outside of European Russia. 21More recently, Acemoglu and Wolitzky (2011) studied both the formation and consequences of coercive labor relations in a principal-agent framework, arguing that labor scarcity affects both labor demand and the outside options of workers and thus has an ambiguous effect on coercion. 22This database provides a raster for the entire world of estimated population densities for different points in time with a spatial resolution of 5-minutes and has been previously used in economics by Fenske (2013).

10 the best predictor of serfdom’s intensity and displays a strong positive association. This is con- sistent with the spread of noble estates to relatively agriculturally productive areas, although when we add other grain suitability measures in column (3), wheat suitability loses its signif- icance. Although districts with higher population density in 1600 display, on average, smaller population shares of serfs (Column 4), this difference is statistically insignificant, suggesting that mechanisms other than Domar’s were at work in driving the expansion of serfdom. Indeed, we find that districts that were further away from a city in 1700 (as reported in the dataset of Bairoch et al. (1988)), a proxy for early development and economic activity, show a lower in- cidence of serfdom (Column 4). As with the suitability findings, this would suggest a process by which noble estates were set up in more advantageous areas. We also find that a district’s province explains a large part of serfdom’s intensity. Moving from the cross-district specifi- cation in Column 1 to the provincial fixed-effect model of Column 2 increases the R-squared from 0.4 to 0.7, although this significantly reduces the explanatory power of longitude, the area covered with forest, and the distance to Moscow. Thus, provincial fixed effects largely control for geography and the direction of Muscovite expansion. Wheat suitability retains significance, and almost all of the coefficients have the same signs as in Column 1. As evident in the explanatory power of even the fixed-effect model, other factors surely in- fluenced the extent of serfdom prior to emancipation. In Column 5, we pursue one possible channel by adding the number of monasteries before 1764 as an additional covariate. This is meant to capture the extent of the expropriation of monastic lands (with attached serfs) that occurred under Catherine the Great in the 18th century. Prior to this, monastic estates typically arose out of land donations from wealthy donors (including the state), suggesting that their distribution over space likely paralleled the allocation of private holdings. Following this ex- propriation, these peasants were eventually transferred into the state peasantry, thus generating a policy-driven source of variation in the share of serfs in the total population c. 1860. The coefficient that we estimate on this variable turns out to be negative and highly statistically sig- nificant. This effect remains when we control for the local share of members of the Orthodox church, measured by later census data in 1897. We return this important finding below when we exploit the presence of monasteries prior to 1764 as a plausibly exogenous source of variation in serfdom measured prior to 1861.

3 Serfdom and Long-Run Development

This section describes our baseline tests for the long-run impact of serfdom on modern development outcomes. We first define our outcome measures and detail our core estimation strategy. This leads us to propose an instrumental variables approach that is motivated by the findings in Table 2. We then provide our baseline results.

11 3.1 Defining Outcomes

Constructing outcomes for our long-run investigation is challenging, as income per capita is not available at a unit of analysis comparable to our historical data on serfdom. Moreover, our historical sample encompasses several current Eastern European countries, in addition to the Russian Federation.

Household Expenditure and Wealth As a way to circumvent these data limitations, we con- struct our main outcome variables from the 2006 and 2010 waves of the Life in Transition Survey. (LiTS). The LiTS is collected by the European Bank of Reconstruction and aims to assess household and individual well-being in transition countries. The survey collects infor- mation on household expenditure, ownership of assets, the availability of local public goods, the level of education, and individual attitudes and beliefs regarding a number of economic issues. The geo-locations of the Primary Sampling Units (PSU) of the two waves are available, so that respondents from several modern countries can be precisely located within the historical dis- tricts of our Imperial Russia data. Figure 3 shows the PSU locations overlaying the variation in historical serfdom. Summary statistics of the outcome variables that we consider are displayed in Table 1, with additional information provided in the Appendix.

Figure 3 about here

Our main indicator for economic development today is household expenditure per capita, which is only assessed in the 2006 wave. Household heads reported household spending over a 30-day recall period for food and consumption goods, such as clothing, transport, or recre- ation, and over a 12-month recall period for investments and durable goods such as education, healthcare, furniture, and others. To be comparable across countries, the expenditures are ex- pressed in US Dollars. They are also adjusted for the size of the household and are, therefore, a measure of economic well-being per capita.23 Since household expenditure is strictly positive and skewed, we use its logarithm as our dependent variable. In addition to this measure, we also use LiTS to construct outcome variables describing durable asset ownership and access to basic local public goods in the settlement of residence. Regarding the former, we focus on the principal component derived from a vector of indicator variables that take on the value 1 if a household owns a car, a mobile phone, a secondary residence, or a computer. An advantage of LiTS is the availability of data on other individual and household charac- teristics that potentially affect our outcomes of interest. In our specifications, we control for household composition in terms of household size, the share of household members younger than 18, the share of household members older than 60, and the share of male household mem- bers. We also utilize the religion of the respondent as an indicator of the household’s religion, an

23Although, expenditures in LiTS are measured using a recall method, the accuracy is remarkably good when compared to other household consumption data that records actual expenditures (Zaidi et al. (2009)).

12 indicator variable for whether the primary sampling unit is defined as rural or urban, a dummy for the LiTS survey wave, and other respondent characteristics. 24

Night-Time Luminosity An alternative measure of economic well-being at the local level is night-time luminosity, as measured by satellite pictures of the earth over a series of nights. Night-time lights have increasingly been used in the development literature to overcome the lack of sub-national income data, particularly when considering spatial variation. Luminos- ity correlates strongly with traditional measures of economic development at the country level and at smaller sub-national units of analysis ( Henderson et al. (2012) and Michalopoulos and Papaioannou (2013)).25 Therefore, we extend our main analysis by calculating a measure of log average night-time light luminosity (noted in the source as “Average Visible, Stable Lights, and Cloud Free Coverages”) within the borders of each of our historical districts in 2008. The variable is summarized in Table 1 and discussed further in the Appendix.

3.2 Estimation Strategy

To assess the effect of 19th century serfdom on modern socio-economic outcomes, we begin by estimating the following OLS regression:

‘ yi,d,p = α + β ∗ ser f domi,d,p + Xi,d,p ∗ ω + province_ f ep + ei,d,p (1) where i represents the modern unit of analysis (individual, household, survey cluster, or district), d refers to the historical district, and p generally indicates the historical province.26 ser f domi,d,p denotes our main measure of serfdom, the share of serfs out of the total population in a given (historical) district d, located in province p, and linked to modern unit of observation i. The coefficient of interest is β, which gives the reduced form relationship between the incidence of serfdom and modern socio-economic outcomes.

In the case of LiTS outcomes, the matrix Xi,d,p includes household-level and survey controls (household size, share aged 0-18, share aged 60+, share male, religion, and indicators for LiTS wave and rural/urban) and variables defined for historical districts that we link to the PSUs (ge- ographical location of the district, forest cover, ruggedness, the presence of a river, the distance to Moscow, crop suitability, population density in 1600, and distance to a city in 1700). We also include historical province-level fixed effects, denoted by province_ f ep. Including such fixed effects leaves only within-province variation and helps to rule out that the results are driven by provinces that were not reached by serfdom, such as the Baltics. This is a demanding specifi- cation, but we believe that the resulting point estimates are better identified than if we were to

24In the Life in Transition Survey the section on the household’s economic situation is answered by the house- hold head, while all other sections on attitudes, education, religion and labor are answered by a randomly selected household member. Whenever we use individual level responses provided by this randomly selected household member, we additionally control for his age, age sq. and gender. 25The data is freely available and can be downloaded at http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html 26In extensions below, we also employ outcomes defined at the historical district level.

13 rely on larger administrative divisions, such as modern countries.27 In the case of the household survey data from the LiTS, we cluster standard errors at the level of the PSU. With outcome data evaluated at the district level (i.e. luminosity), we cluster standard errors at the level of the historical province.

3.3 Identification

The outcomes that we consider may be influenced by historical or modern factors that we are unable to control for. If such unobservables are associated not only with outcomes today, but also with the extent of serfdom in the past, our estimated (negative) coefficients are possibly biased. For example, additional geographic conditions we cannot observe might have made serfdom more likely in a certain place, but these characteristics might potentially have direct effects on household expenditures today, either positive or negative. We aim to minimize such biases by conditioning on a large number of individual and geographic factors, and by control- ling for historical population density and distance to early cities as a proxy for early economic development. Furthermore, the use of province fixed effects soaks up considerable variation in the extent of serfdom, hopefully making what remains more plausibly exogenous. Nevertheless, the estimated coefficient could still be biased, and so we turn to a novel instrumental variable strategy based on a historical “experiment” from the 18th century.28 To address this, our IV strategy exploits plausibly exogenous variation in our measure of serfdom that resulted from the secularization of church estates under Catherine the Great. In 1764, Catherine issued an edict transferring monastic estates (including convent estates and properties held by the Orthodox hierarchy) and the resident peasant population to state con- trol.29 Prior to this date, peasants residing on monastic land were subject to many of the same constraints as privately owned serfs. Indeed, one professed reason for the reform was that the state was concerned about the especially exploitative conditions faced by the church peasants (Zakharova (1982)).30 The 1764 decree secularized church and monastic lands in Siberia and the central provinces of Russia, with later decrees through the early 19th centuries doing like- wise for Western and Southwestern provinces as Imperial authority was consolidated in those areas.31 Following these reforms, the majority of monastic institutions were closed or consoli-

27Our results are robust to using country-fixed effects (not reported here). In additional robustness regressions we also considered historical urbanization rates after emancipation as control variables, which are arguably en- dogenous to serfdom and strongly correlated, and find that serfdom still matters for explaining numerous outcome variables. 28If we are measuring the “treatment” of serfdom with some random error, the IV approach also helps us address the classical bias that might result. We discuss this below. 29Technically, former church peasants were referred to as “economic peasants” until reforms of the 1830s (under Minister of State Domains P. D. Kiselev) integrated them with the rest of the state peasantry. de Madariaga (1981) describes the motivations and historical context for this law. On occasion below, we refer to these varied church properties as monastic estates or land as a shorthand. 30Although possible, we know of no direct evidence that monasteries played any special role in promoting local human capital accumulation among church peasants prior to 1764. Following the reforms, monastic institutions possibly did possess wealth available to support local economic activity, but it unlikely that this was significantly more than other large property holders (i.e. the local nobility or other state institutions). 31As noted by Zinchenko (1985), the Western provinces exhibited extensive property holdings among Orthodox

14 dated, with those remaining receiving a relatively low level of support directly from the state. Catherine’s 1764 reform transferred approximately 2 million church peasants (over 10% of the peasant population at that time) to state oversight and eventual membership in the state peas- antry.32 If the original establishment of these church properties roughly paralleled the granting of populated land for state service (or was correlated with the unobservable determinants of the latter), then the geographic distribution of the expropriated estates may be interpreted as an exogenous source of variation in the presence of state peasants by the 1850s.33 The historical literature on Russian monasticism supports this interpretation: while some pre-1400 monastic settlements were initially established as remote hermitages, the greater number of monasteries, convents, and other church estates that emerged from the 15th to 18th centuries largely obtained their property through grants from the Tsar and other large private landowners (out of their own service land grants) as Muscovy grew into an Empire.34 Although there are surely unobserved and location-specific factors that lay behind the granting of certain properties to church insti- tutions, related concerns about the exclusion restriction should be mitigated by considering the extent of expropriated land aggregated over a district. As our measure of this expropriation, we use the total number of monasteries per district that stopped functioning in or before 1764, compiled from Zverinskii (2005 (1897)).35 For the instrument to be valid, it first needs to be strongly correlated with serfdom’s intensity. As we already showed in Column 3 of Table 2, the population share of serfs was indeed significantly lower in districts where the number of monasteries prior to 1764 was high. In our results tables, we also report the Kleibergen-Paap Wald F statistic of the first stage, which is always above 10 and ranges from 12 to 143. Table 3 investigates the first-stage relationship in more detail. Column 1 shows the coefficient of the baseline first stage relationship (-1.072). Columns 2 and 3 re-estimate the first-stage but adjusts the instrument by population in column 2 and area in column 3. In either regression the instrument is a negative and significant predictor of serf- dom – to ease interpretation, we focus on the number of monasteries below (while controlling for other characteristics of the districts). Column 4 tests for robustness of the first-stage rela- and non-Orthodox religious institutions into the 19th century, with secularization occurring only in response to ethnic-religious unrest (particularly the Polish Rebellion of 1830–31) and as part of the broader state peasant reforms in the 1840s. See de Madariaga (1981) and Zakharova (1982) for references to the specific laws that enacted the different confiscations of church lands between 1764 and the 1840s. Our results largely hold if we focus only on the provinces affected by the 1764 law. 32See Zakharova (1982), who refers to the changes wrought by the original act. These peasants were largely spread over the often far-flung properties of approximately 500 monasteries. 33Kamenskii (1997) writes of the peasants on expropriated church estates peasants that, “They were relieved of their labor obligations to the religious institutions, saw and increase in the size of their landed allotments and now found it easier to engage in trade and handcraft.”. The subsequent improvement in the lives of the former church peasants is a central theme of Zakharova (1982). 34For English-language examples of this large historiography, see Kloss (2013), Ostrowski (1986), Thyret (2010), and Weickhardt (2012). Romaniello (2000) remarks on the geographically parallel processes of monastic and state “colonization,” especially in the Volga Region. In more settled areas, a significant amount of monastic land was initially collateral posted by gentry borrowers who defaulted on their loans to monasteries. 35See Figure C1 in the Appendix for a spatial representation of the number of monasteries per district. Of course, we would prefer to have the amount and location of all variants of expropriated church lands and the corresponding number of peasants, but such data are currently unavailable.

15 tionship excluding provinces that were not subject to the 1764 decree. In addition, columns 5 and 6 show that districts with below median distance to Moscow display a larger reduction in serfdom (-1.069) than districts that are further away (-0.074), potentially reflecting a stronger enforcement of the decree around the capital. Finally, the first-stage coefficient is larger for districts with above-median suitability for growing wheat (-1.279 vs -0.0823), see columns 7 and 8, illustrating once again that serfdom was stronger in agriculturally suitable regions.

Table 3 about here

Second, a valid instrument should affect the outcomes of interest only through its ef- fect on serf intensity, that is it has to satisfy the exclusion restriction, or, more formally, corr(monasteries_pre1764d,p,c, ei,d,p,c = 0) conditional on the set of observable controls and province fixed effects. Although this is not strictly testable, we do find that the geographic dis- tribution of these monasteries was unrelated to other characteristics of serfdom, including the share of serfs on quit rent only (specifications not reported here). There is little evidence that the monastic institutions that remained after 1764 were especially wealthy, as state support was kept at a relatively low level.36 Along with the similarities in the expansion of monastic and private holdings, this gives some assurance that our expropriation variable is not related to other unobservable historical variables that might impact long-run outcomes.37 In additional robustness tests reported in Table 4, we examine whether the prevalence of pre-1764 monasteries was correlated with other indicators of early economic development. In particular, we use population of cities in different centuries reported in Bairoch et al. (1988) as an indicator of economic development. We do not find that monasteries were associated with larger city populations before 1764, only from 1800 onwards.38

Table 4 about here

With these conditions in mind, we estimate the following 2SLS system of equations:

ser f dom = α + β ∗ monasteries_pre1764 + X‘ ∗ ω + province_ f e + e (2)

y = α + β ∗ ser\ f dom + X‘ ∗ ω + province_ f e + e (3)

where in equation (2) we regress serfdom on the total number of existing monasteries in each district before 1764, monasteries_pre1764, and the complete set of controls X. In equation (3)

36While the Church did play a role in providing basic schooling over the 19th century, this was typically done at the level of the village priest, rather than by relatively more insular monastic bodies. 37Table C1 in the Appendix reports the estimated relationships between geographic controls and our measure of monasteries. Monasteries prior to 1764 were more prevalent closer to Moscow, in eastern parts of provinces, and in areas less suitable for wheat. All of these factors likely reflect the timing of settlement as Muscovite Russia expanded. 38The sample size varies as the dataset of Bairoch et al. (1988) covers more cities. Unfortunately, only few cities are reported prior to 1750, therefore the power of the regressions with population data for the years before 1750 is limited.

16 we regress various outcome variables y on serfdom, using the predicted values ser\ f dom from equation (2).

3.4 Results

Household Expenditure and Wealth The OLS estimates from equation (1) with the log of household expenditure as the dependent variable are reported in Panel A of Table 5. Overall, we find a large, negative, and statistically significant correlation between serfdom’s intensity and this measure of economic well-being. In Column 1, we control for household and survey controls, as well as province fixed effects.39 The estimated coefficient becomes larger in abso- lute value but is equally significant in Column 2 when we control for the set of environmental conditions that are potentially correlated with the geographic spread of serfdom. In Column 3 we add controls that proxy for early economic development, i.e. log population density in 1600 and log distance to the nearest city in 1700. The coefficient only slightly decreases in absolute terms. Finally, to help alleviate concerns about other factors possibly driving (cultural) persis- tence, in particular religious differences related to the presence of Orthodox monasteries (the primary target of Catherine’s measures), the model of Column 4 includes the district-level share of the population who were mainstream Orthodox in 1897. This reduces the size of estimated coefficient, but we can still reject the null at the 10 % significance level. These estimated effects are economically meaningful: a one standard deviation increase in serfdom (around 25 percent- age points) is associated with a lower level of average household expenditure, depending on the specification, of between about 10 and 14%.40

Table 5 about here

Overall, the OLS coefficient on historical serfdom’s link to modern household expenditures is relatively stable, except when past religion is included in column (4). Selection on unobserv- ables can be assessed more formally by investigating the movement of the coefficient of interest when controls are included, following the theoretical work by Altonji et al. (2005) and the ap- plication in Nunn and Wantchekon (2011), defining 1 as an acceptable threshold. Dividing the coefficient with the full set of controls (Column 4) by the difference between the coefficient with full controls and the coefficient with only baseline controls (Column 1), we obtain a ra- tio of about 2.1. This ratio implies that to produce a coefficient equal to zero, selection on unobservables must be 2 times larger than selection on observables. Looking at the reduced form, we find a strong, positive and significant correlation between the presence of monasteries and household expenditures through all specifications (Panel B, Table 5). The IV estimates, reported in Panel C, confirm the negative effect of serfdom on expenditure. The magnitudes of the estimated effect of serfdom obtained in the second-stage

39In general, when introducing modern country fixed effects instead of the fairly conservative provincial fixed effects, we find similar, albeit slightly larger results (not reported). 40Since the dependent variable is in log, the estimated coefficients are interpreted as a percentage change in the dependent variable given a one unit change in the independent variable (semi-elasticities).

17 are two to three times larger in absolute values than the estimated OLS coefficient. A one standard deviation increase in the incidence of historical serfdom lowers household expenditure in the IV specification by 27 to 33%. There are a number of possible explanations for the different magnitudes of the coefficients in the OLS and IV specifications. First, the larger estimates in the IV regressions may be a sign of omitted variables in the OLS specifications, which are correlated with serfdom and long-run development in opposing directions, thus biasing the OLS coefficient downwards. If this is the case, our IV estimates indicate the “true” causal relationship between serfdom and outcomes. Second, the smaller OLS coefficients may result from measurement error in the potentially en- dogenous variable (the population share of serfs) that the IV overcomes. Since we cannot know the precise mechanism of serfdom’s long-run impact, and our indicator is an admittedly crude measure of a heterogeneous institution, this is a distinct possibility. Moreover, the relatively large magnitudes of the IV coefficients may indicate that our instrument picks up other determi- nants of long-run economic development and, therefore, violates the exclusion restriction. This would be true if the areas where the state or private landowners donated land to monasteries were systematically different (i.e. better) than areas where only private estates (and their serfs) existed, or if monasteries themselves influenced the process of long-run development. The evi- dence we present above and our reading of the historical literature suggests that both scenarios are possible but unlikely. A final possibility is that the IV estimates reflect a local average treat- ment effect for a subsample of districts that were affected by the transfer and complied with it, while the OLS estimates averages over all areas. Rerunning the OLS estimation with full set of controls for only those provinces that were affected by the transfer increases indeed the magnitude of the OLS coefficient from -0.38 to -0.78, and considering only districts with below median distance to Moscow similarly increases the OLS coefficient to -0.75. Thus, it is plau- sible that at least some of the increase in the magnitude of the coefficient comes from the fact that the IV estimates represent a local average treatment affect for a subset of districts in which the decree was enforced and an actual transfer of serfs took place. Unfortunately, which of the four explanations prevails cannot be distinguished empirically.41 We see similar findings to those in Table 5 when using the principal component of durable assets owned by the household as measure of economic well-being today. These results are reported in Table 6. Both the OLS estimates in Panel A, as well as the IV coefficients in Panel C point towards a significantly lower level of asset ownership among households living in former serf areas. A one standard deviation increase in serfdom reduces contemporaneous asset ownership by about 0.1 of a standard deviation in the OLS estimations, and 0.3 of a standard deviation in the IV estimations.

Table 6 about here

41For a very similar discussion of larger IV magnitudes compared to OLS estimations see Dell (2012) on the long-term effects of the Mexican Revolution.

18 The Differential Effect of Land Suitability in Serf and Non-Serf Areas The OLS and IV regressions establish a negative association between serfdom and household-level economic outcomes today. Another strategy to estimate the long-run effects of serfdom in a more indirect fashion is to differentiate the effects of observable characteristics on long-run economic suc- cess in areas where peasants were more or less exploited under the institutional regime.42 We conduct such exercise in the case of land suitability for agricultural production. In the absence of agricultural exploitation one would expect suitable land to be conductive to economic de- velopment for many reasons, including forward linkages to industrial production.43 However, in areas where seigniorial extraction from peasants occupying the good land was practiced, as in the case of Russian serfdom, the positive effect of land quality on subsequent economic for- tunes likely goes away. Indeed, this is what Table 7 shows. Within provinces to which serfdom did not spread, in particular the Baltics, land suitability shows the expected positive and sig- nificant correlation with household expenditure and household wealth in columns (1) and (3). If one considers areas where serfdom was present, the previously positive impact of land turns negative and insignificant, see column (2) and (4).

Table 7 about here

Robustness: Night-Time Luminosity Next, we investigate the effects of historical serfdom on contemporary development using the log of night-time light intensity in 2008, measured within the historical districts, as our dependent variable. This type of measure is increasingly utilized when focuses on sub-national units in the absence of other aggregate economic mea- sures (e.g. Henderson et al. (2012)). Table 8 reports the results for the OLS, reduced form, and IV regressions. Overall, areas with higher shares of serfdom display lower levels of luminosity at night today, in both OLS and IV specifications, conditioning on province fixed effects and geographic controls (Column 1).

Table 8 about here

The estimated coefficients stay relatively stable when additional controls are subsequently included in Columns 2 and 3. The overall magnitudes of the effects are economically meaning- ful, but, as in our expenditure results, they vary substantially between a 20% reduction in the OLS and a 75% reduction in night light intensity in the IV specification for a one standard de- viation change in serfdom.44 As an alternative indicator for long run development, we can use log population density in 2000 as outcome variable, conditioning on log population density in 1600.45 As Column 4 shows, serfdom is associated with significantly lower population density

42We would like to thank Katia Zhuravskaya for this suggestion. 43Non-serf provinces are Kurliand, Lifland, and Estliand. 44A 75% reduction in luminosity might seem very large, but is not unreasonable compared to magnitudes in other papers studying long-run persistence using night-time lights. Michalopoulos and Papaioannou (2013) for example report that pre-colonial ethnic political centralization increases luminosity by between 30 and 70 % in Africa. 45See the Data Appendix for more detail on the sources of these data.

19 in the year 2000, and the magnitudes of the coefficients vary between a 20% (OLS) and 75% (IV) decrease for a one standard deviation change in the incidence of serfdom.

City Population in the Soviet Union When did the formerly serf areas fall behind? And was there any process of convergence during the Soviet period? We investigate these questions using a sample of cities for which we can follow their population at different periods of time over the 20th century.46 In particular, we rely on population data collected by Acemoglu et al. (2011) for a sample of 321 cities documented in the Soviet censuses of 1926, 1939, 1959, 1970 and 1989. After locating these cities in our historical districts using GIS, we regress log population in each year on our measure of serfdom. We find a negative association between city population and the incidence of historical serfdom in the surrounding district for every year – see Table 9. Increasing serfdom by one standard deviation reduces city population by on average between 20% and 50%. Intriguingly, when comparing across years, the magnitude of the effect becomes larger later in the Soviet period. One admittedly speculative reason for this increasing gap is that areas with a high level of urbanization at the beginning of the period increasingly benefited from supportive Soviet policies over time. The well-known urban bias of the Communist leadership and centralized control over investment and factor allocation decisions certainly may have generated such pressures.

Table 9 and 10 about here

The pattern of persistent differences in urbanization according to the experience of serfdom is also consistent with the results obtained from models using city growth as the dependent vari- able, controlling for the initial level of population. We present results from such specifications in Table 10. Here, we again find that historical serfdom has a negative, although not always significant, association with population growth. The implication is that, if anything, former serf areas were falling further behind over the Soviet period. One can also see the long-run lack of catch-up city growth in Figures 4 and 5, in which we plot average log city population from 1750 to 1989 separately for areas with above and below median serfdom (Figure 4) and by quartiles of serfdom (Figure 5).47 The gap in city population exists from 1750 on, narrows between 1850 and 1929, but then widens until the end of the Soviet Union in 1989.48 We return to this pattern in our discussion of possible mechanisms for the persistence of serfdom’s effects below.

Figure 4 and 5 about here

46City population as a measure for economic development has been used extensively by economic historians in the absence of reliable economic data. 47For this exercise, we combine data from Bairoch et al. (1988) for 1750, 1800, 1850 and Acemoglu et al. (2011) for the later years. Note that the resulting panel of cities is not balanced. 48The absence of a significant growth differential between 1970 and 1989 is consistent with a faltering of Soviet economic policies (that had reinforced urban patterns) during this period of decline.

20 4 Mechanisms

The previous section has documented a robust negative relationship between the intensity of Russian serfdom just before emancipation and economic outcomes today. Which mecha- nisms can explain these long-run effects of serfdom? In general, the literature has suggested a number of possibilities for why variation in historical institutions – especially coercive labor institutions – might generate long-run economic effects. These proposed mechanisms include asset and income inequality (e.g. Engerman and Sokoloff (1997), Nunn (2008a)), human cap- ital accumulation (e.g. Bertocchi and Dimico (2014)), and culture (e.g. Nunn (2012)). As in Acharya et al. (2013), these channels may have political implications (i.e. reinforcing the power of a local elite), with persistent consequences for local institutional development and economic policies. In this section, we explore how proxies for these channels, measured in the decades around 1900, are related to the incidence of serfdom at the district level just prior to emanci- pation. If the legacy of serfdom is associated with one or more such proxies, we interpret the results as constituting suggestive evidence that these mechanisms might have operated.49

4.1 Inequality in the Distribution of Land

To begin, we investigate whether the incidence of serfdom was associated with subsequent inequality in the distribution of factors of production, particularly land. As the nobility owned the overwhelming majority of privately-held land prior to emancipation, and subsequent reforms largely failed to undertake much redistribution, it would not be surprising to see higher levels of land inequality in regions with elevated levels of serfdom. Using proxies of inequality generated from land statistics collected in 1905 as outcomes, we estimate models similar to those in the previous sub-section. Our empirical results strongly support a link between serfdom and land inequality through the end of the Imperial period – see Table 11.50

Table 11 about here

Following specifications similar to our main results, we find that the incidence of serfdom is positively and significantly associated with the percentage of land owned by the nobles in 1905 (Column 1).51 More interesting is the result in Column 2, where we find no significant association between serfdom and the amount of land peasants received in the reform process

49Most of our “channel” variables are measured in the Imperial period, largely due to data availability and the change in administrative borders in the Soviet period. 50All of these variables are described and summarized in greater detail in Nafziger (2013). That study also provides full references to the underlying sources. 51Although not reported here, we also find that serfdom is negatively associated with the share of all land held as communal allotments in 1905 (although at only marginal significance). This may capture a key element of the institutional climate fostered by the emancipation reforms: the reinforcement of the peasant land commune as a central pillar of rural society. Although the decision over whether to adopt communal property rights was mostly dictated by the emancipation statutes themselves, the reforms did allow communities to adopt more individualized rights, and some took up this option in largely communal areas (Nafziger, 2013). However, by the 1880s if not earlier, the formalization of the communal structure of rural Russian society was supposed to apply in the same way for peasants of different types.

21 of the 1860s per household in 1905 (Column 2; measured in desiatina, where 1=2.7 acres). This suggests that differences in the peasant emancipation processes did not lead to variation in property ownership, at least once geographic factors are fully accounted for. Considering the distribution of land holdings, we find a positive relationship between the in- cidence of serfdom and a Gini index covering holdings of all types of landed property (Column 3; private + communal land received in the reforms) and a Gini index covering non-allotment land private holdings only (Column 4), the latter of which becomes insignificant in the IV specification. These estimated effects are large. A one standard deviation increase in serfdom corresponds to a greater share of land owned by the nobility (mean: 20.86; sd: 14.04) of be- tween 5 (IV) and 8 (OLS) percentage points. Similarly, an increase in serfdom by one standard deviation is associated with an increase in the Gini index of all land holdings (mean: 0.49; sd: 0.16) by 0.05 (IV) to 0.075 (OLS). Thus, in areas with more serfdom, land holdings remained relatively concentrated into the 20th century, particularly in the hands of the gentry class. While the association between serfdom and subsequent land inequality is compelling, how did this higher level of wealth inequality impede long-run economic growth, particularly after the Soviet policies largely dissolved preexisting property rights?52 One possibility has been put forward by standard political economy models (i.e. Galor et al. (2009)): higher inequality may have created (political) impediments for policies related to human capital accumulation and other public goods. This might occur via conflict over appropriate policies or through elite capture of local institutions, leading to a lower level of broad public good provision.53 Drawing on additional data, we can evaluate whether our proxy measures – defined prior to the Soviet Union – of such outcomes are associated with the incidence of serfdom prior to 1861.

4.2 Schooling and Human Capital Investment

Continuing our basic set-up, the models of Table 12 examine the relationship between serf- dom and educational outcomes before and after emancipation. To measure the historical ex- pansion of human capital institutions, we use the number of schools in a district in 1856 (per thousand inhabitants), the percentage of rural boys and girls enrolled in primary schools in 1880, and total rural enrollment rates in 1894.54 As reported in Table 12, serf areas had fewer schools per thousand inhabitants before emancipation (in 1856), although the coefficient loses statistical significance in the IV estimation (Column 1). We then find that serfdom was asso- ciated with lower enrollment rates of boys and girls in rural primary schools in 1880, and the

52A land inequality channel has been suggested for the case of slavery on plantation economies in the New World by Engerman and Sokoloff (1997), although Nunn (2008b) does not find empirical support for such an association when he examines the legacy of slavery within the U.S. 53See Nafziger (2011) for a discussion of public good provision in the Imperial Russian context that touches upon such inequality-related channels. Another prominent political economy mechanism that links land inequality and development is (the absence of) political competition, as suggested by Besley et al. (2010) for the U.S. South. While possible, this seems to be less directly relevant for the Russian case. 54See Nafziger (2012a) for more detail regarding these data.

22 IV estimates show a statistically significant difference (Columns 2 and 3).55 As the dependent variables are in logs, a one standard deviation increase in serfdom reduces enrollment rate of boys by around 24 %, and the enrollment rate of girls by around 33 %. We find similar results when considering log rural enrollment rates of boys and girls measured in 1894 (Column 4).

Table 12 about here

Similarly, Table 13 considers modern educational outcomes from the LiTS. Employing lin- ear specifications, we examine the educational attainment of surveyed individuals, including whether the respondent completed secondary school and whether they received some sort of post-secondary education (both dummy variables). Our results show that the incidence of his- torical serfdom is significantly associated with a lower probability of completing secondary education, both in the OLS and IV regressions, with and without various controls (Columns 1 and 2). Increasing our measure of serfdom by one standard deviation is associated with a reduc- tion in the likelihood that the respondent has completed secondary education by 4.5 % (OLS) to 15 % (IV). Similarly, respondents in serf areas are also considerably less likely to have some education above the secondary level (Columns 3 and 4). In Columns 5 and 6, we investigate whether respondents profess a greater demand for education from the government. While indi- viduals living in serf areas are less likely to mention education as the first government priority, this difference is not statistically significant. Taking the results of Tables 12 and 13 together, we find some evidence for a human capital mechanism behind the persistent development effects of serfdom. As we touch upon further below, this may represent a specific causal channel, or it could reflect the persistently lower level of structural change (and consequent demand for education) in formerly serf areas.56

Table 13 about here

4.3 Public Goods and Infrastructure Density

Following our basic empirical model yet again, we also investigate the availability of several public goods and services in LiTS villages in 2006 (Table 14), as well as the density of the road and railway networks during the Soviet period (Table 15). To proxy for the availability of pub- lic goods, we construct a principal component out of several indicator variables documenting the availability of a certain type of service among surveyed households. More precisely, we combine questions on access to piped water, central heating, a sewage system, and ground-line telephones. Overall, the results show little significant difference in our measure of pubic good access between areas with high or low serfdom: the OLS coefficients, reported in Panel A of Table 14, are actually positive, and the IV estimates in Panel C, while negative and significant in

55We use the log of enrollment rates, as this variable is strictly positive but highly skewed. 56A direct (or intergenerational) causal channel is perhaps unlikely given well-known Soviet efforts to improve the provision of basic schooling and higher education opportunities, even in rural areas.

23 Columns 1 and 2, are not robust to including historical controls for development levels (Column 3) or the Orthodox share of the population (Column 4).

Table 14 about here

Following Dell (2010), we also investigate the availability of infrastructure in a given dis- trict. Using GIS maps of the road and rail network for the territory of the former Soviet Union in 1996 (see the Data Appendix), we construct measures of infrastructure density defined as the total length of all roads or railways in a district, divided by its area in square kilometers. As reported in Columns 1 and 2 of Table 15, we find road density to be significantly and negatively related to historical serfdom in both the OLS and IV models. A one standard deviation greater intensity of serfdom translates into up to a standard deviation lower road density. In the case of railways, the OLS coefficient of serfdom reported in Columns 3 and 4 is negative, but insignif- icant. The effect becomes significant; however, when we instrument for serfdom, as Panel C shows.

Table 15 about here

The results suggest that basic public goods today are roughly equally distributed among districts that experienced a high or low presence of serfdom in the past.57 On the other hand, Soviet infrastructure investment seems to have been significantly lower in areas where more of the population was previously enserfed. Infrastructure is an important development outcome on it own, but, obviously, it is also an important input into economic growth, especially when it comes to industrial production and market development. Low road and rail densities in the Soviet period could therefore indicate that former serf areas were less industrialized, saw more limited access to markets, and potentially experienced lower rates of structural change from agriculture to industry.

4.4 Structural Change and Urbanization

Consistent with our earlier findings on city population and infrastructure density, serfdom may have impeded concurrent and subsequent economic diversification or agglomeration, with possible local and persistent consequences for productivity, industrial development, and in- comes. Table 16 reports estimates from models that utilize several dependent variables related to these channels. In Column 1, we consider the rate of urbanization (rather than city size) in 1913. As the OLS and IV results show, historical serfdom was strongly associated with lower rates of urbanization. The reduction in the urbanization rate of between 4.4 to 22 percentage points implied by a standard deviation increase in serfdom is large given a mean of 10.09 and a standard deviation of 12.15 of this variable. 57In extensions (not reported here), we explore each of the public goods combined in the principal component analysis, as well as a small number of others. Although there are some robust individual associations with histor- ical serfdom (e.g. lower phone access), the overall results are very mixed and do not point towards a definitive differential pattern of public good provision.

24 Table 16 about here

Columns 2 to 4 investigate industrial production using unique district-level data from just after emancipation. In Column 2, we employ the number of factories per capita in 1868 as the dependent variable. Serfdom is associated with fewer factories although the coefficient is small and only marginally significant in the IV specification. Using the number of factory workers per capita in 1868 in Column (3), our results do not indicate a significant difference, although the coefficient is negative. Finally, we divide factory production per district by the number of factory workers and use this metric as a rough indicator for productivity. As both the OLS and IV specifications in Column (4) indicate, productivity was significantly lower in areas with higher levels of serfdom. A one standard deviation increase in serfdom corresponded to 16 to 55% lower industrial productivity. These results are suggestive of the presence of some initial impediments in structural change shortly after emancipation. Unfortunately, similar district- level indicators of structural change are very limited for later dates. In exploring one exception, we find relatively ambiguous results using the share of males with a primary occupation in agriculture in 1897 (Column 5). According to the OLS regression, the legacy of serfdom reduced male agricultural employment in 1897. However, this difference goes away in the IV specification. This may be due to measurement error in this variable, since it reflects a wide variety of primary occupations.58 Thus, the results are suggestive although not definitive in depicting a long-run impact of prior serfdom on subsequent structural change.59

4.5 Cultural attitudes

Did economic exploitation over several centuries shape peoples beliefs and attitudes, per- haps fostering a “culture of serfdom,” with persistent implications for economic development? In this subsection, we explore whether serfdom generated long-run effects on cultural prefer- ences. Several recent studies have documented that institutions can impact cultural norms in the long-run (for an overview see Nunn (2012) and Alesina and Giuliano (2013)), which can persist over generations, and which may or may not be manifested as political preferences. Moreover, it is possible that various institutional restrictions, social and economic inequality, or persistent limitations on urban development under and after serfdom generated long-lasting norms and beliefs that undermined income growth in modern post-Soviet economies.

Table 17 about here

58This measure includes fishing, hunting, and both modern and traditional forms of agricultural work in the numerator. It excludes various types of industrial or service-sector secondary occupations, which were often seasonally important. 59Future work with finer occupational data in the 1897 census may provide clearer results. We also investigated data on the height (in mm) of military recruits in the 1870s as an additional proxy for economic development in the post-emancipation period. OLS estimation show that recruits were significantly smaller on average when they were born in areas with high serfdom. However, IV coefficients do not support that finding. While the IV coefficients are much larger, the coefficients are statistically insignificant. Results are available upon request.

25 To explore this possibility, we rely on survey responses regarding various beliefs, as elicited in the 2006 and 2010 rounds of LiTS. Our results are presented in Table 17.60 We first con- sider whether individuals think incomes should be made more equal (Column 1), and relatedly whether governments should reduce inequality (Column 2). In the OLS regression, respondents seem to be more likely to prefer less income inequality in Column (1), but the IV estimate is insignificant, and there is no differential interest in having government reduce inequality (Col- umn 2). We then draw on a number of questions that ask about the beliefs regarding the reasons for poverty. Individuals were asked whether they agree that poverty exists because of injustice in society (Column 3), because the poor are unlucky (Column 4), because they are lazy (Col- umn 5), and because poverty is inevitable (Column 6). Our results suggest that individuals in formerly high serf areas think, on average, that the poor are poor because they are unlucky. They are less likely to mention laziness as a reason for poverty in the OLS model (column 5), but surprisingly the sign of the coefficient flips in the IV specification (as it does with respect to the inevitability of poverty). While these results deserve further exploration, for now we simply note that there seems to be systematic differences in how residents of former serf areas see the of poverty today. This could be based in cultural differences, or it could reflect more fundamental structural or institutional factors that have accompanied the relatively poorer economic conditions in such areas. We find no evidence that individuals living in areas with historically greater intensity of serfdom were more likely to engage in political actions, such as demonstrations (Column 7), attending a strike (Column 8), or joining a political party (Column 9). This contrasts with the results in Dower et al. (2015), who find that emancipation generated considerable collective action in the form of peasant unrest in the early 1860s among the newly freed former serfs. However, given the legacy of political repression in the late Imperial period and under the Soviet Union, perhaps this is not surprising. Finally, preferences for a market economy (versus a planned economy) or for democracy (versus autocracy) are not statistically different between areas with a greater or lesser history of serfdom.61 While cultural channels have been emphasized in the literature on persistent effects of past labor coercion (i.e. Nunn and Wantchekon (2011)), we find only limited support for this in the Russian and former Soviet case. Again, this is perhaps understandable given the dramatic social changes and political controls enacted under the Soviet authorities. Moreover, and unlike societies with legacies of racially delineated slavery, the absence of significant racial or religious differences between former serfs and the rest of the population may have limited any such cultural distinctiveness in the subsequent decades.

60The Data Appendix gives details about the survey questions and the coding of each variable. 61We also explored measures of interpersonal trust and trust in governmental institutions as outcomes but did not find any significant differences. These results are available upon request.

26 5 Conclusion

In this paper, we have explored whether feudal labor obligations practiced for centuries in the generated persistent effects on economic development that lasted until today. Our empirical analysis builds on a unique collection of data that allows us to measure the extent of serfdom for almost 500 historical districts. Our results suggest that greater exposure to serfdom over 150 years ago is associated with lower levels of household expenditure, more limited ownership of durable assets, and reduced night-time luminosity today. These effects are robust to including a rich set of geo-biographic conditions, indicators of historical development, and provincial fixed effects. In attempting to establish causality, we rely on a unique quasi- experiment in the form of Catherine the Great’s expropriation of monastic and church lands in the late 18th century. We argue that once geographic controls and other historical conditions are taken into account, this transfer exogenously increased the share of non-serf peasants in a fashion that is plausibly unrelated to other underlying conditions in those districts that might influence long-run development. Our results are robust to the use of this IV, with a considerably larger effect of our measure of historical serfdom. Our empirical evidence echoes the classical works that have attributed adverse economic consequences to Russian serfdom, and adds to a recent literature that has found negative long- run effects of forced labor in other contexts (i.e. Acharya et al. (2013), Acemoglu et al. (2012), Bertocchi and Dimico (2014), Dell (2010), and Nunn (2008b)), despite the absence of religious or racial markers of past coercion in the Russian context. Moving beyond the simple corre- lation, we have endeavored to examine the importance of different channels that may explain this persistence. We find suggestive evidence that inequality in land ownership and associated obstacles to human capital accumulation produced uneven conditions prior to Russian industri- alization. These impediments that former serf areas suffered limited urbanization and the shift towards modern industrial activities. These effects persisted throughout the Soviet period, likely perpetuating differences in human capital and public good investments. Thus, our results imply that early industrialization and subsequent agglomeration effects can be one important channels of persistence that has not yet been considered exhaustively in the persistence literature. On the contrary, while culture could have been plausibly affected by labor coercion, our results show only limited differences in modern cultural beliefs, which is consistent with the absence of racial or ethnic-religious markers for earlier serfdom. The failure to develop adequate institutions to support market and political development has been a theme of recent research into ’s transition since the fall of the Soviet Union (e.g. Aslund (2013)). This literature has generally focused on the inefficiencies generated by remnants of Soviet-era institutions and the difficulties in developing modern replacements. Our study points to possible deeper historical roots for the institutional impediments that the Russian Federation and other former members of the Russian Empire currently face in their efforts at economic reform and modernization, a theme that has been proposed but remains relatively unexplored (e.g. Dower and Markevich (2014), Gaidar (2012); Lankina (2012), and

27 Roland (2012)). Many interesting questions remain open for further research. For example, one might ask how specific Imperial and Soviet policies, institutions, and economic conditions translated dif- ferent experiences of serfdom into heterogeneity in outcomes in the long run. A related area that demands further investigation is the role of geographic heterogeneity in the characteristics of serfdom (size and type of obligations, the nature of estate governance, etc.) and in the partic- ulars of the emancipation reforms, for generating variation in long-run outcomes. Serfdom was a set of institutional practices that varied widely across space, and some scholars (Markevich and Zhuravskaya, 2015) have found significant differences in how areas developed immediately after 1861, depending on their specific experiences of the prior regime. These and other issues constitute a rich set of research questions that we hope to take up in future work.

28 References

ACEMOGLU,D.,C.GARCÍA-JIMENO, AND J.A.ROBINSON (2012): “Finding Eldorado: Slavery and Long-Run Development in Colombia,” Journal of Comparative Economics, 40, 534–564.

ACEMOGLU, D., T. A. HASSAN, AND J.A.ROBINSON (2011): “Social Structure and Devel- opment: A Legacy of the Holocaust in Russia,” Quarterly Journal of Economics, 126.

ACEMOGLU,D. AND J.ROBINSON (2012): Why Nations Fail: The Origins of Power, Prosper- ity and Poverty, Profile.

ACEMOGLU,D. AND A.WOLITZKY (2011): “The Economics of Labor Coercion,” Economet- rica, 79, 555–600.

ACHARYA,A.,M.BLACKWELL, AND M.SEN (2013): “The Political Legacy of American Slavery,” Working paper.

ALESINA,A. AND P. GIULIANO (2013): “Culture and Institutions,” NBER WP 19750.

ALLEN, R. C. (2003): Farm to Factory: A Reinterpretation of the Soviet Industrial Revolution, Princeton University Press.

ALTONJI, J. G., T. E. ELDER, AND C.R.TABER (2005): “Selection on Observed and Un- observed Variables: Assessing the Effectiveness of Catholic Schools.” Journal of Political Economy, 113, 151.

ASLUND, A. (2013): How Capitalism Was Built: The Transformation of Central and Eastern Europe, Russia, the Caucasus, and Central Asia, Cambridge University Press, 2nd ed.

BAIROCH, P., J. BATOU, AND P. CHÈVRE (1988): Population des villes européennes de 800 à 1850: banque de données et analyse sommaire des résultats (la), Librairie Droz.

BERTOCCHI,G. AND A.DIMICO (2014): “Slavery, Education, and Inequality,” European Economic Review.

BESLEY, T., T. PERSSON, AND D.M.STURM (2010): “Political competition, policy and growth: theory and evidence from the US,” The Review of Economic Studies, 77, 1329–1352.

BLUM, J. (1961): Lord and Peasant in Russia from the Ninth to the Nineteenth Centuries, Princeton University Press.

BUSHEN, A., ed. (1863): Nalichnoe naselenie Imperii za 1858 god, vol. 2 of Statisticheskiia tablitsy Rossiiskoi Imperii.

DE MADARIAGA, I. (1981): Russia in the Age of Catherine the Great, Yale University Press.

29 DEAL, Z. (1981): Serf and State Peasant Agriculture: Khar’kov Province, 1842-1861, Arno Press.

DELL, M. (2010): “The Persistent Effects of Peru’s Mining Mita,” Econometrica, 78, 1863– 1903.

——— (2012): “Path dependence in development: Evidence from the Mexican Revolution,” Mimeo.

DENNISON, T. (2011): The institutional framework of Russian serfdom, Cambridge University Press.

DOMAR,E. AND M.MACHINA (1984): “On the Profitability of Russian Serfdom,” The Journal of Economic History, 44, 919–955.

DOMAR, E. D. (1970): “The Causes of Slavery or Serfdom: a Hypothesis,” The Journal of Economic History, 30, 18–32.

——— (1989): “Were Russian Serfs Overcharged for Their Land by the 1861 Emancipation? The History of One Historical Table,” Research in Economic History, Supplement 5b, 429– 439.

DOWER, P., E. FINKEL,S.GEHLBACH, AND S.NAFZIGER (2015): “Collective Action and Representation in Autocracies: Evidence from Russia’s Great Reforms,” Mimeo.

DOWER, P. AND A.MARKEVICH (2014): “A History of Resistance to Privatization in Russia,” Journal of Comparative Economics, 42, 855–873.

DRUZHININ, N. M. (1946 and 1958): Gosudarstvennye krest’iane i reforma P. D. Kiselev, Akademiia nauk SSSR.

——— (1978): Russkaia derevnia na perelome 1861-1880 gg., Nauka.

EMMONS, T. (1968): The Russian Landed Gentry and the Peasant Emancipation of 1861, Cambridge University Press.

ENGERMAN,S. AND K.SOKOLOFF (1997): “Factor Endowments, Institutions and Differential Paths of Growth among the New World Economies,” in How Latin America Fell Behind, ed. by S. Haber, Stanford University Press, Stanford.

FAL’BORK,G. AND V. CHARNOLUSKII, eds. (1900-1905): Nachal’noe narodnoe obrazovanie v Rossii., Imperatorskoe volÊ´znoeekonomicheskoe obshchestvo.

FENSKE, J. (2013): “Does land abundance explain African institutions?” The Economic Jour- nal, 123, 1363–1390.

30 FIELD, D. (1976): The End of Serfdom: Nobility and Bureaucracy in Russia, 1855-1861, Har- vard University Press.

FOGEL, R. W. AND S.L.ENGERMAN (1974): Time on the Cross: the Economics of American Negro Slavery, New : Norton.

GAIDAR, Y. (2012): Russia: A Long View, The MIT Press.

GALOR,O. AND O.MOAV (2004): “From Physical to Human Capital Accumulation: Inequal- ity and the Process of Development,” The Review of Economic Studies, 71, 1001–1026.

——— (2006): “Das Human-Kapital: A Theory of the Demise of the Class Structure,” The Review of Economic Studies, 73, 85–117.

GALOR,O.,O.MOAV, AND D.VOLLRATH (2009): “Inequality in Landownership, the Emer- gence of Human-Capital promoting Institutions, and the Great Divergence,” The Review of Economic Studies, 76, 143–179.

GERSCHENKRON, A. (1966): “Agrarian Policies and Industrialization: Russia 1861-1917,” in The Cambridge Economic History of Europe.

HELLIE, R. (1982): Slavery in Russia, 1450-1725, University of Chicago Press.

HENDERSON, J. V., A. STOREYGARD, AND D.N.WEIL (2012): “Measuring economic growth from outer space,” American Economic Review, 102, 994–1028.

HOCH, S. (2004): “Did Russia’s Emancipated Serfs Really Pay too Much for Too Little Land? Statistical Anomalies and Long-Tailed Distributions,” Slavic Review, 63, 247–274.

HOCH,S.L. AND W. AUGUSTINE (1979): “The Tax Censuses and the Decline of the Serf Population in Imperial RUssia, 1833-1858,” Slavic Review, 38, 403–425.

KABUZAN, V. M. (2002): Krepostnoe naselenie Rossii v XVIII v. – 50-x godakh XIX v., BLITS.

KAMENSKII, A. (1997): The Russian in the Eighteenth Century: Searching for a Place in the World, M. E. Sharpe.

KASHCHENKO, S. (2002): Otmena krepostnogo prava v stolichnoi gubernii, St. Petersburg State University.

KHRISTOFOROV, I. (2011): Sud’ba Reformy: Russkoe krest’ianstvo i pravitel’stvennoi politike do i posle otmeny krepostnogo prava (1830-1890-e gg.)., Sobranie.

KLEIN GOLDEWIJK,K.,A.BEUSEN,G.VAN DRECHT, AND M.DE VOS (2011): “The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years,” Global Ecology and Biogeography, 20, 73–86.

31 KLOSS, B. M. (2013): “Monasticism During the Formation of a Centralized State,” Russian Studies in History, 52, 5–37.

KOVAL’CHENKO, I. (1967): Russkoe krepostnoe krest’ianstvo v pervoi polovine XIX veka, Moscow University Press.

LANKINA, T. (2012): “Unbroken Links? From Imperial Human Capital to Post-Communist Modernisation,” Europe-Asia Studies, 64, 623–643.

LENIN, V. I. (1911): “The Peasant Reform and the Proletarian-Peasant Revolution,” Collected Works, 17.

LITVAK, B. (1972): Russkaia derevnia v reforme 1861 goda, Nauka.

MARKEVICH,A. AND E.ZHURAVSKAYA (2015): “Economic Effects of the Abolition of Serf- dom: Evidence from the Russian Empire,” Working paper.

MICHALOPOULOS,S. AND E.PAPAIOANNOU (2013): “Pre-Colonial Ethnic Institutions and Contemporary African Development,” Econometrica, 81, 113–152.

MOON, D. (1999): The Russian Peasantry 1600-1930: The World the Peasants Made, Long- man.

NAFZIGER, S. (2011): “Did Ivan’s Vote Matter? The Political Economy of Local Democracy in Tsarist Russia,” European Review of Economic History, 15, 393–441.

——— (2012a): “The Political Economy of Primary Education in Tsarist Russia,” Working paper.

——— (2012b): “Serfdom, Emancipation, and Off-Farm Labour Mobility in Tsarist Russia,” Economic History of Developing Regions, 27, 1–37.

——— (2013): “Russian Serfdom, Emancipation, and Land Inequality: New Evidence,” Work- ing paper.

NUNN, N. (2008a): “The Long-Term Effects of Africa’s Slave Trades,” The Quarterly Journal of Economics, 123, 139–176.

——— (2008b): “Slavery, Inequality, and Economic Development in the Americas,” Institu- tions and Economic Performance.

——— (2012): “Culture and the historical process,” Economic History of Developing Regions, 27, S108–S126.

——— (2013): “Historical Development,” Handbook of Economic Growth, 2, 347.

NUNN,N. AND L.WANTCHEKON (2011): “The Slave Trade and the Origins of Mistrust in Africa,” American Economic Review, 101, 3221–52.

32 OSTROWSKI, D. (1986): “Church Polemics and Monastic Land Acquisition in Sixteenth- Century Muscovy,” Slavonic and East European Review, 64, 355–379.

ROBINSON, G. (1972): Rural Russia under the Old Regime, Berkeley, CA: University of Cali- fornia Press, 3 ed.

ROLAND, G. (2012): “The Long Run Weight of Communism or the Weight of Long Run History?” in Economies in Transition: The Long-Run View, ed. by G. Roland, Palgrave Macmillan, 153–171.

ROMANIELLO, M. P. (2000): “Controlling the Frontier: Monasteries and Infrastructure in the Volga Region, 1552-1682,” Central Asian Survey, 19, 429–443.

THYRET, I. (2010): “Economic Reconstruction or Corporate Raiding? The Borisoglebskii Monastery in Torzhok and the Ascription of Monasteries in the 17th Century,” Kritika: Ex- plorations in Russian and Eurasion History, 11, 490–511.

TROINITSKII, N. A., ed. (1905): Pervaia vseobshchaia perepis’ naseleniia Rossiiskoi Imperii, 1897 g., vol. Multiple, Central Statistical Committee, Ministry of the Interior.

TROINITSKII, N. A. (1982 (1861)): The Serf Population in Russia According to the 10th Na- tional Census, Oriental Research Partners.

TSENTRAF’NYI STATISTICHECKIKH KOMITET,MINISTERSTVOVNUTRENNYKHDEL (1872): Statisticheskii Vremennik Rossiiskoi Imperii, vol. 6 of II.

——— (1884): Sel’skiia uchilishcha v Evropeiskoi Rossii i Privislianskikh guberniiakh, vol. 1 of Statisticheskii vremennik Rossiiskoi imperii (Series III).

——— (1906): Statistika zemliavladeniia 1905 g., vol. Multiple.

——— (1914): Statisticheskii ezhegodnik Rossii 1913 g.

WEICKHARDT, G. G. (2012): “Muscovite Law on Monasteries,” Russian History, 39, 13–41.

WIRTSCHAFTER, E. (1997): Social Identity in Imperial Russia, Northern Illinois University Press.

ZAIDI,S. ETAL. (2009): Satisfaction with life and service delivery in Eastern Europe and the former Soviet Union: some insights from the 2006 life in transition survey, 162, World Bank Publications.

ZAIONCHKOVSKII, P. A. (1960): Otmena krepostnogo prava v Rossii, Uchpedgiz, 2nd ed.

ZAKHAROVA, L. (1982): “K posledstviiam sekuliarizatsii tserkovnykh zemel’ v Rossii,” Vo- prosy istorii, 12, 165–167.

33 ZINCHENKO, A. L. (1985): “Reforma gosudarstvennoi derevni i sekuliarizatsiia v zapadnykh guberniiakh Rossiiskoi imperii,” Istoricheskie zapiski, 112, 98–125.

ZVERINSKII, V. V., ed. (2005 (1897)): Monastyri zakrytye do Tsarstvovaniia Imperatritsy Eka- terinay II, Dmitrii Bulanin.

34 A Figures

Figure 1: Distribution of Serfs as Share of Population, c. 1858. N = 495.

35 Figure 2: Spatial Distribution of Serfs as Share of Population c. 1858.

36 Figure 3: Location of LiTS Primary Sampling Units (PSU)

37 Figure 4: Avg City Population 1750-1989 and below/above Median Serfdom

Figure 5: Avg City Population 1750-1989 and Serfdom (Quartiles)

38 B Tables

Table 1: Summary Statistics

Mean Sd Obs Serfdom and Monasteries Serfs, perc. of pop 38.52 25.01 494 Monasteries_pre1764 1.61 3.12 494

Control Variables at the District-Level Longitude 37.10 8.58 494 Latitude 54.10 3.84 494 Forest Cover 36.05 23.57 494 Ruggedness 91.29 4.91 494 Wheat Suitability 6621.15 2184.24 494 River (0-1) 0.55 0.50 494 Distance to Moscow (ln) 6.28 0.66 494 Oat Suitability 6070.91 2303.28 494 Rye Suitability 6000.32 2259.95 494 Barley Suitability 6067.84 2335.83 494 (ln) Population Density 1600 1.13 0.95 494 (ln) Distance Nearest City 1750 0.19 0.67 494 Share Orthodox 1897 82.98 25.49 494

Additional Outcomes at the District-Level Intermediate Outcomes Schools before 1856 (p. thousand inhabitants) 0.05 0.13 489 log rural enrollment rate of boys in 1880 2.48 0.56 492 log rural enrollment rate of girls 1880 0.30 1.20 492 log rural enrollment rate of boys and girls in 1894 -1.99 0.47 494 log(teacher wage 1910) 5.77 0.20 492 Perc. Land owned by Nobles, 1905 20.86 14.04 494 Peasant Land per HH, 1905 14.87 15.55 494 Land Gini 1905 0.49 0.16 470 Land Gini 1905 (only private land) 0.77 0.14 479 Urbanization 1913 10.09 12.15 494 Factories p.c. 1868 0.00 0.00 486 Factory Worker p.c. 1868 0.01 0.06 486 log(factory production per worker) 6.09 0.93 436 Male agricultural employment 1897 71.58 14.81 494

39 Table 1: Summary Statistics (continued)

Mean Sd Obs Road Density Soviet Union 1.67 1.22 494 Rail Density Soviet Union 2.08 1.90 494

Contemporary Outcomes Ln Luminosity 2008 0.57 1.01 494

Contemporaneous Survey Outcomes (LiTS) Economic and Public Goods Log Equivalent Expenditure Per Capita 4.96 0.85 5628 Household Durable Assets (Principal Component) 0.02 1.34 13150 Public Goods (Principal Component) 0.02 1.55 5923

Education At least secondary education 0.71 0.45 13156 Above secondary education 0.56 0.50 13156 Mentioned education first gov priority 0.23 0.42 13154

Cultural Attitudes Equal incomes 4.73 2.97 6860 Reduce Inequality 4.04 1.00 12506 Poor: society unjust 0.42 0.49 13156 Poor: unlucky 0.09 0.29 5980 Poor: lazy 0.31 0.46 5980 Poor: inevitable 0.19 0.39 5980 Demonstrated 1.38 0.59 12156 Striked 1.30 0.51 12156 Joined Party 1.18 0.45 12156 Pref Market Economy 0.38 0.48 11837 Pref Democracy 0.49 0.50 11927 Summary statistics for the main variables. See the Data Appendix for variable definitions and sources.

40 Table 2: The Determinants of Serfdom

Serfs, perc. of pop (1) (2) (3) (4) (5)

Longitude -0.838*** -0.855 -0.821 -0.976 -1.156* (0.258) (0.675) (0.665) (0.668) (0.673) Latitude -0.992 1.425 1.314 1.067 1.170 (0.839) (1.293) (1.287) (1.342) (1.309) Forest Cover 0.361*** 0.100 0.084 0.097 0.062 (0.123) (0.085) (0.077) (0.082) (0.079) Ruggedness 0.708* 0.124 0.126 0.166 0.136 (0.361) (0.175) (0.175) (0.169) (0.185) Wheat Suitability 0.004*** 0.002** 0.003 0.002** 0.001 (0.001) (0.001) (0.002) (0.001) (0.001) River (0-1) 2.202 0.657 0.698 0.645 0.615 (2.105) (1.495) (1.493) (1.475) (1.357) Distance to Moscow (ln) -12.687*** -1.244 -1.167 -2.474 -4.222 (4.713) (7.219) (7.373) (7.958) (6.756) Oat Suitability 0.003 (0.003) Rye Suitability -0.002 (0.003) Barley Suitability -0.003 (0.004) (ln) Population Density 1600 -2.949 -2.420 (2.114) (2.196) (ln) Distance City 1750 -1.858 -2.433** (1.205) (1.168) Monasteries_Pre1764 -1.072*** (0.280) Share Orthodox 1897 0.151 (0.121) Constant 95.703 -65.164 -59.217 -35.391 -27.246 (73.231) (98.157) (97.725) (108.576) (104.369) R-squared 0.39 0.72 0.72 0.72 0.74 N 494 494 494 494 494 Province FE No Yes Yes Yes Yes Notes: OLS regressions. The unit of observation is the district. The dependent variable is serfs as a share of population. Heteroscedastic-robust standard errors in parentheses, clustered at the Province in Cols (2) - (5). * p < 0.10, ** p < 0.05, *** p < 0.01.

41 Table 3: First Stage and Compliers

Serfs, perc. of pop (1) (2) (3) (4) (5) (6) (7) (8) Number of Monasteries Monasteries per 10000 people Monasteries per area Only regions affected by Decree Dist. MoscowM Wheat SuitabilityM

Monasteries_pre1764 -1.072*** -1.036*** -1.069*** -0.733* -0.823*** -1.279*** (0.280) (0.330) (0.340) (0.427) (0.269) (0.368)

42 Monasteries_pre_1764 p. tth. -6.912** (2.952) Monasteries_pre_1764 p. area -10320.945*** (3659.925) Constant -27.246 -18.866 -25.527 -21.355 -73.040 39.788 101.725 -262.012* (104.369) (110.319) (105.313) (110.752) (170.066) (57.829) (126.949) (132.134) R-squared 0.74 0.71 0.73 0.72 0.47 0.81 0.80 0.73 N 494 483 494 410 250 244 246 248 Notes: OLS regressions. The unit of observation is the district. The dependent variable is serfs as a share of population. All regressions control for geographic controls that are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow, as well as Province fixed effects. Heteroscedastic-robust standard errors in parentheses, clustered at the Province. * p < 0.10, ** p < 0.05, *** p < 0.01. Table 4: Testing for Correlation of the Instrument with Early Development

Log City Population in 1600 1700 1750 1800 1850 (1) (2) (3) (4) (5)

Monasteries_pre1764 -0.001 -0.006 0.028 0.032** 0.031** (0.033) (0.023) (0.017) (0.013) (0.015) R-squared 0.25 0.42 0.27 0.14 0.10 N 23 28 103 171 174 Notes: OLS regressions. The unit of observation is a city. The dependent variable is log city population, taken from Bairoch et al. (1988). All regressions control for geographic controls, which include latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic- robust standard errors in parentheses, clustered at the district. * p < 0.10, ** p < 0.05, *** p < 0.01.

43 Table 5: Contemporary Household Expenditure

Log Equivalent Expenditure Per Capita (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serf, perc. of pop (x100) -0.555*** -0.598*** -0.528*** -0.377* (0.167) (0.182) (0.196) (0.195) R-squared 0.43 0.43 0.44 0.44 N 5628 5628 5628 5628

Panel B: Reduced Form Monasteries_pre1764 0.025*** 0.030*** 0.021** 0.020** (0.009) (0.010) (0.010) (0.009) R-squared 0.43 0.43 0.44 0.44 N 5628 5628 5628 5628

Panel C: Instrumental Variables Serf, perc. of pop (x100) -1.587*** -1.535*** -1.262** -1.249** (0.428) (0.396) (0.506) (0.515) R-squared 0.42 0.42 0.43 0.43 N 5628 5628 5628 5628 F-Stat (excl. instrument) 24.41 29.24 21.97 24.51 Base and Household Controls Yes Yes Yes Yes Geographic Controls No Yes Yes Yes (ln) Pop Density 1600 No No Yes Yes (ln) Distance Nearest City 1750 No No Yes Yes Share Orthodox 1897 No No No Yes FE Province Province Province Province Cluster PSU PSU PSU PSU Notes: The unit of observation is the individual. The dependent variable is Log Equivalent Expenditure Per Capita, taken from LiTS wave 2006. All regressions control for a set of base controls (religious denomination of the respondent, LiTS survey wave and an indicator whether the PSU is rural or urban) and household controls (household size, share of household members aged 0-18, share of household members aged 60+, share of male household members). Geographic controls are latitude and longitude of the PSU, area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the primary sampling unit. * p < 0.10, ** p < 0.05, *** p < 0.01.

44 Table 6: Contemporary Household Assets

Household Assets (Principal Component) (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serf, perc. of pop (x100) -0.314 -0.417** -0.478** -0.283 (0.199) (0.200) (0.217) (0.225) R-squared 0.38 0.39 0.39 0.39 N 13055 13055 13055 13055

Panel B: Reduced Form Monasteries_pre1764 0.019* 0.026** 0.025** 0.022* (0.010) (0.011) (0.012) (0.011) R-squared 0.39 0.39 0.39 0.39 N 13055 13055 13055 13055

Panel C: Instrumental Variables Serf, perc. of pop (x100) -1.220* -1.558** -1.689** -1.601* (0.645) (0.663) (0.810) (0.849) R-squared 0.38 0.38 0.39 0.39 N 13055 13055 13055 13055 F-Stat (excl. instrument) 143.26 110.46 66.27 61.35 Base and Household Controls Yes Yes Yes Yes Geographic Controls No Yes Yes Yes (ln) Pop Density 1600 No No Yes Yes (ln) Distance Nearest City 1750 No No Yes Yes Share Orthodox 1897 No No No Yes FE Province Province Province Province Cluster PSU PSU PSU PSU Notes: The unit of observation is the individual. The dependent variable is principal component of household asset ownership (car, second residence, mobile phone, computer), taken from LiTS waves 2006 and 2010. All regressions control for a set of base controls (religious denomination of the respondent, LiTS survey wave and an indicator whether the PSU is rural or urban) and household controls (household size, share of household members aged 0-18, share of household members aged 60+, share of male household members). Geographic controls are latitude and lon- gitude of the PSU, area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district cen- troid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the primary sampling unit. * p < 0.10, ** p < 0.05, *** p < 0.01.

45 Table 7: The Differential Effect of Land Suitability on Economic Outcomes in Serf and Non-Serf Areas

(1) (2) (3) (4) Log Equivalent Expenditure Per Capita Household Assets (Principal Component)

No Serfdom Serfdom No Serfdom Serfdom

Land Suitability for Growing Wheat 0.150*** -0.020 0.116* -0.011 (0.048) (0.039) (0.068) (0.029) R-squared 0.28 0.42 0.38 0.39 N 1708 4118 4012 9569 FE Province Province Province Province Cluster PSU PSU PSU PSU Notes: The unit of observation is the individual. The dependent variable are Log Equivalent Expenditure Per Capita, taken from LiTS wave 2006 and the principal component of household asset ownership (car, second residence, mobile phone, computer), taken from LiTS waves 2006 and 2010. All regressions control for a set of base controls (religious denomination of the respondent, LiTS survey wave and an indicator whether the PSU is rural or urban) and household controls (household size, share of household members aged 0-18, share of household members aged 60+, share of male household members), as well as geographic controls which are latitude and longitude of the PSU, area of the district covered by forest, ruggedness of the district, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Non-serf provinces are Kurliand, Lifland, and Estliand. Heteroscedastic-robust standard errors in parentheses, clustered at the primary sampling unit. * p < 0.10, ** p < 0.05, *** p < 0.01.

46 Table 8: Contemporary Night-time Luminosity and Population Density

Ln Luminosity 2008 (ln) Pop Density 2000 (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serfs, perc. of pop -0.008** -0.006** -0.006* -0.008*** (0.003) (0.003) (0.003) (0.003) R-squared 0.49 0.55 0.56 0.63 N 494 494 494 494

Panel B: Reduced Form Monasteries_pre1764 0.060*** 0.053*** 0.054*** 0.059*** (0.014) (0.011) (0.011) (0.011) R-squared 0.50 0.56 0.57 0.63 N 494 494 494 494

Panel C: Instrumental Variable Serfs, perc. of pop -0.058*** -0.050*** -0.051*** -0.055*** (0.017) (0.016) (0.016) (0.016) R-squared 0.06 0.22 0.22 0.31 N 494 494 494 494 F-Stat (excl. instrument) 13.82 14.56 14.63 14.63 Geographic Controls Yes Yes Yes Yes (ln) Pop Density 1600 No Yes Yes Yes (ln) Distance Nearest City 1750 No Yes Yes Yes Share Orthodox 1897 No No Yes Yes FE Province Province Province Province Cluster Province Province Province Province Notes: The unit of observation is the district. The dependent variable is (ln) luminosity in 2008 in Cols (1) - (3) and (ln) Population Density in 200 in Col (4). Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the Province. * p < 0.10, ** p < 0.05, *** p < 0.01.

47 Table 9: Persistence throughout Soviet Times: Log City Population 1929 - 1989

Log City Population in 1926 1939 1959 1970 1989 (1) (2) (3) (4) (5)

Panel A: Ordinary Least Squares Serfs, perc. of pop -0.007* -0.007* -0.009** -0.010** -0.011** (0.004) (0.004) (0.004) (0.004) (0.004)

Panel B: Reduced Form Monasteries_pre1764 0.023 0.027 0.035** 0.040** 0.044** (0.016) (0.017) (0.016) (0.016) (0.017)

Panel C: Instrumental Variable Serfs, perc. of pop -0.016 -0.019 -0.025* -0.028** -0.031** (0.013) (0.014) (0.014) (0.014) (0.015) F-Stat (excl. instrument) 13.83 13.83 13.83 13.83 13.83 Geographic Controls Yes Yes Yes Yes Yes (ln) Pop Density 1600 Yes Yes Yes Yes Yes Share Orthodox 1897 Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes N 321 321 321 321 321 Notes: The unit of observation is a city. The dependent variable is log city population. Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the district. * p < 0.10, ** p < 0.05, *** p < 0.01.

48 Table 10: Did Serf areas catch up? Growth in City Population 1929 - 1989

City Population Growth 1926-1939 1939-1959 1959-1970 1970-1989 1926-1989 (1) (2) (3) (4) (5)

Panel A: Ordinary Least Squares Serfs, perc. of pop -0.000 -0.002 -0.001* -0.000 -0.004 (0.002) (0.001) (0.001) (0.001) (0.003) logpop26 -0.096** -0.080 (0.037) (0.051) logpop39 -0.017 (0.018) logpop59 0.032*** (0.009) logpop70 0.056*** (0.009)

Panel B: Reduced Form Monasteries_pre1764 0.007 0.008** 0.004* 0.001 0.023*** (0.005) (0.004) (0.002) (0.002) (0.008)

Panel C: Instrumental Variable Serfs, perc. of pop -0.005 -0.006** -0.003** -0.001 -0.017*** (0.004) (0.003) (0.001) (0.001) (0.006) logpop26 -0.106*** -0.105** (0.036) (0.053) logpop39 -0.025 (0.019) logpop59 0.028*** (0.010) logpop70 0.055*** (0.009) F-Stat (excl. instrument) 12.49 12.69 12.30 12.11 12.49 Geographic Controls Yes Yes Yes Yes Yes (ln) Pop Density 1600 Yes Yes Yes Yes Yes Share Orthodox 1897 Yes Yes Yes Yes Yes Province FE Yes Yes Yes Yes Yes N 321 321 321 321 321 Notes: The unit of observation is a city. The dependent variable is the growth rate of city population. Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the district. * p < 0.10, ** p < 0.05, *** p < 0.01.

49 Table 11: Channels - Serfdom and Economic Inequality 40 Years after Emancipation (1905)

Perc. Land owned by Nobles Peasant Land per HH Land Gini (All Land) Land Gini (Private Land) (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serfs, perc. of pop 0.294*** -0.024 0.003*** 0.001** (0.052) (0.023) (0.000) (0.000) R-squared 0.78 0.66 0.70 0.69 N 494 494 470 479

Panel B: Reduced Form Monasteries_pre1764 -0.208** -0.100 -0.003 -0.001 50 (0.096) (0.080) (0.002) (0.001) R-squared 0.71 0.66 0.62 0.68 N 494 494 470 479

Panel C: Instrumental Variable Serfs, perc. of pop 0.194** 0.093 0.002** 0.001 (0.080) (0.067) (0.001) (0.001) R-squared 0.77 0.65 0.69 0.69 N 494 494 470 479 F-Stat (excl. instrument) 14.63 14.63 14.78 14.634 Notes: The unit of observation is a district. All regressions control for geographic controls, (ln) pop density in 1600, (ln) distance to city in 1700, the share of orthodox in 1897 and province fixed effects. Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the province. * p < 0.10, ** p < 0.05, *** p < 0.01. Table 12: Channels: Historical Education

Schools before 1856 (log) rural enrollment rate (log) rural enrollment rate (log) rural enrollment rate per thousand inhabitants of boys in 1880 of girls in 1880 boys and girls in 1894 (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serfs, perc. of pop -0.001*** -0.002 -0.004 -0.001 (0.000) (0.002) (0.003) (0.001) R-squared 0.53 0.53 0.70 0.69 N 489 492 492 494

Panel B: Reduced Form Monasteries_pre1764 0.001 0.012* 0.017* 0.010** (0.001) (0.006) (0.009) (0.004) R-squared 0.52 0.53 0.70 0.69 N 489 492 492 494

Panel C: Instrumental Variable Serfs, perc. of pop -0.001 -0.011** -0.016* -0.010** (0.001) (0.005) (0.008) (0.005) R-squared 0.53 0.48 0.68 0.64 N 489 492 492 494 F-Stat (excl. instrument) 14. 69 14.71 14.71 14.63 Notes: The unit of observation is a district. All regressions control for geographic controls, (ln) pop density in 1600, (ln) distance to city in 1700, the share of orthodox in 1897 and province fixed effects. Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the province. * p < 0.10, ** p < 0.05, *** p < 0.01.

51 Table 13: Channels: Contemporary Education

At least secondary Above secondary Mentioned education first education education government priority (1) (2) (3) (4) (5) (6)

Panel A: Ordinary Least Squares Serf, perc. of pop (x100) -0.180** -0.148** -0.153** -0.091 -0.090 -0.110 (0.070) (0.074) (0.075) (0.076) (0.063) (0.070) R-squared 0.05 0.05 0.16 0.17 0.04 0.04 N 13051 13051 13051 13051 13049 13049

Panel B: Reduced Form Monasteries_pre1764 0.011*** 0.010*** 0.009*** 0.007** 0.003 0.004 (0.003) (0.003) (0.003) (0.003) (0.002) (0.003) R-squared 0.05 0.05 0.16 0.17 0.04 0.04 N 13051 13051 13051 13051 13049 13049

Panel C: Instrumental Variables Serf, perc. of pop (x100) -0.626*** -0.741*** -0.556*** -0.498** -0.202 -0.265 (0.212) (0.260) (0.176) (0.226) (0.150) (0.197) R-squared 0.04 0.04 0.16 0.16 0.04 0.04 N 13051 13051 13051 13051 13049 13049 F-Stat (excl. instrument) 110.28 61.60 110.28 61.60 110.10 61.58 Base and Household Controls Yes Yes Yes Yes Yes Yes Geographic Controls Yes Yes Yes Yes Yes Yes (ln) Pop Density 1600 No Yes No Yes No Yes (ln) Distance Nearest City 1750 No Yes No Yes No Yes Share Orthodox 1897 No Yes No Yes No Yes FE Province Province Province Province Province Province Cluster PSU PSU PSU PSU PSU PSU Notes: The unit of observation is the individual. All regressions control for a set of base controls (religious denomination of the respondent, LiTS survey wave and an indicator whether the PSU is rural or urban) and household controls (household size, share of household members aged 0-18, share of household members aged 60+, share of male household members). Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the principal sampling unit. * p < 0.10, ** p < 0.05, *** p < 0.01.

52 Table 14: Channels - Contemporary Public Goods Provision

Access to Public Goods (Principal Component) (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serf, perc. of pop (x100) 0.077 0.228 0.560 0.491 (0.400) (0.444) (0.456) (0.475) R-squared 0.38 0.39 0.41 0.41 N 5923 5923 5923 5923

Panel B: Reduced Form Monasteries_pre1764 0.044** 0.052** 0.023 0.024 (0.021) (0.023) (0.024) (0.024) R-squared 0.39 0.39 0.41 0.41 N 5923 5923 5923 5923

Panel C: Instrumental Variables Serf, perc. of pop (x100) -2.937** -2.855** -1.565 -1.628 (1.392) (1.231) (1.564) (1.601) R-squared 0.35 0.36 0.40 0.40 N 5923 5923 5923 5923 F-Stat (excl. instrument) 18.92 21.38 15.1 18.05 Base and Household Controls Yes Yes Yes Yes Geographic Controls No Yes Yes Yes (ln) Pop Density 1600 No No Yes Yes (ln) Distance Nearest City 1750 No No Yes Yes Share Orthodox 1897 No No No Yes FE Province Province Province Province Cluster PSU PSU PSU PSU Notes: The unit of observation is the individual. The dependent variable is a principal component of indicator variables measuring the access of the household to water, heating, a sewage system and a landline telephone. All regressions control for a set of base controls (religious denomination of the re- spondent, LiTS survey wave and an indicator whether the PSU is rural or urban) and household controls (household size, share of household members aged 0-18, share of household members aged 60+, share of male household members). Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic- robust standard errors in parentheses, clustered at the primary sampling unit. * p < 0.10, ** p < 0.05, *** p < 0.01.

53 Table 15: Channels - Road and Railway Density in 1996 in Countries of the Former Soviet Union

Road Density Railroad Density (1) (2) (3) (4)

Panel A: Ordinary Least Squares Serfs, perc. of pop -0.013** -0.011** -0.000 0.000 (0.005) (0.005) (0.009) (0.007) R-squared 0.51 0.53 0.30 0.37 N 494 494 494 494

Panel B: Reduced Form Monasteries_pre1764 0.054*** 0.051*** 0.124*** 0.103*** (0.015) (0.014) (0.039) (0.028) R-squared 0.51 0.53 0.33 0.39 N 494 494 494 494

Panel C: Instrumental Variable Serfs, perc. of pop -0.052*** -0.047*** -0.134** -0.097*** (0.014) (0.013) (0.055) (0.037) R-squared 0.33 0.38 -0.62 -0.07 N 494 494 494 494 F-Stat (excl. instrument) 13.81 14.63 11.03 14.63 Geographic Controls Yes Yes Yes Yes (ln) Pop Density 1600 No Yes No Yes (ln) Distance Nearest City 1750 No Yes No Yes Share Orthodox 1897 No Yes No Yes FE Province Province Province Province Cluster Province Province Province Province Notes: The unit of observation is the district. The dependent variable is the length of roads or railways divided by the district area in square kilometers, multiplied by 100. Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the province. * p < 0.10, ** p < 0.05, *** p < 0.01.

54 Table 16: Channels - Structural Change and Urbanization at the Turn of the 19th Century

Urbanization Factories p.c. Factory Worker log (factory Male agricultural 1913 1868 p.c. 1868 production per worker) employment 1897 (1) (2) (3) (4) (5)

Panel A: Ordinary Least Squares Serfs, perc. of pop -0.176*** 0.000 -0.000 -0.007** -0.081** (0.056) (0.000) (0.000) (0.003) (0.040) R-squared 0.28 0.38 0.16 0.28 0.55 N 494 486 486 436 494

Monasteries_pre1764 0.964*** 0.000 0.000 0.035** -0.018 55 (0.299) (0.000) (0.000) (0.016) (0.159) R-squared 0.29 0.39 0.16 0.28 0.54 N 494 486 486 436 494

Panel C: Instrumental Variable Serfs, perc. of pop -0.899*** -0.000* -0.000 -0.032** 0.017 (0.241) (0.000) (0.000) (0.013) (0.136) R-squared -0.33 0.33 0.15 0.16 0.54 N 494 486 486 436 494 F-Stat (excl. instrument) 14.63 14.51 14.51 14.18 14.63 Notes: The unit of observation is a district. All regressions control for geographic controls, (ln) pop density in 1600, (ln) distance to city in 1700, the share of orthodox in 1897 and province fixed effects. Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. Heteroscedastic-robust standard errors in parentheses, clustered at the province. * p < 0.10, ** p < 0.05, *** p < 0.01. Table 17: Channels - Cultural Attitudes and Preferences

Equal incomes Govt should reduce inequality Poverty bc society unjust Poverty bc bad luck Poverty bc lazy Poverty inevitable Demonstrated Striked Joined Party Pref Market Economy Pref Democracy (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Panel A: Ordinary Least Squares Serfs, perc. of pop (x100) -1.970** -0.077 -0.081 0.090* -0.286*** 0.208** 0.140 0.163* 0.048 -0.086 0.018 (0.947) (0.204) (0.082) (0.047) (0.109) (0.099) (0.116) (0.097) (0.076) (0.102) (0.118) R-squared 0.09 0.07 0.10 0.06 0.08 0.06 0.06 0.08 0.05 0.07 0.05 N 6850 12506 13156 5980 5980 5980 12156 12156 12156 11837 11927

Panel B: Reduced Form

56 Monasteries_pre1764 0.073 0.020 0.002 -0.005* -0.010* 0.009** -0.003 -0.004 -0.004 -0.005 -0.003 (0.057) (0.014) (0.004) (0.002) (0.005) (0.004) (0.006) (0.003) (0.004) (0.005) (0.006) R-squared 0.09 0.07 0.10 0.06 0.08 0.06 0.06 0.08 0.05 0.07 0.05 N 6850 12506 13156 5980 5980 5980 12156 12156 12156 11837 11927

Panel C: Instrumental Variables Serfs, perc. of pop (x100) -4.918 -1.443 -0.174 0.333** 0.690* -0.589* 0.206 0.306 0.310 0.353 0.206 (3.783) (1.056) (0.312) (0.168) (0.419) (0.329) (0.427) (0.244) (0.258) (0.391) (0.449) R-squared 0.09 0.06 0.10 0.05 0.06 0.04 0.06 0.08 0.04 0.07 0.05 N 6850 12506 13156 5980 5980 5980 12156 12156 12156 11837 11927 F-Stat (excl. instrument) 68.98 63.03 61.60 19.25 19.25 19.25 68.98 68.98 68.98 60.23 58.71 Notes: The unit of observation is the individual. All regressions control for the age, age squared and gender of the respondent, as well as a set of base controls (religious denomination of the respondent, LiTS survey wave and an indicator whether the PSU is rural or urban) and household controls (household size, share of household members aged 0-18, share of household members aged 60+, share of male household members). Geographic controls are latitude and longitude of the district, the area of the district covered by forest, ruggedness of the district, suitability of the soil for growing wheat, presence of a river in the district, and (ln) distance of the district centroid to Moscow. All regressions include province fixed effects. Heteroscedastic-robust standard errors in parentheses, clustered at the primary sampling unit. * p < 0.10, ** p < 0.05, *** p < 0.01. C Data Appendix and Supplementary Tables and Figures

C.1 Data Description and Sources • Serfdom Data: The main explanatory variable, Serfs, perc. of pop, is constructed using the sum of total male and female serfs in 1858 per district (taken from Troinitskii (1982 (1861))), divided by district population in 1858 (taken from Bushen (1863)). The latter source also allows us to construct a measure of population density for 1858.

• Geographic Controls: Longitude and latitude information based on own calculations at the district’s centroid using ArcGIS. Ln Distance to Moscow gives log of the straight line distance in meters from the centroid of each historical district to Moscow (own calculation using ArcGIS). Suitability of the soil for low-input rain-fed wheat, oat, rye and barley, as well as rugged- ness and forest cover are taken from the FAO-GAEZ database. The presence of a river (0/1) is calculated using a digitized map of rivers pro- duced by the Alterra Centre for Geo-Information (accessed through http://climate- adapt.eea.europa.eu/geonetwork/srv/en/main.home). Ln Population Density in 1600 is taken from the HYDE 3.1 databse which provides population density estimates from 10000 BC to AD 2000 for ev- ery hundred years, and every ten years from 1700 on (downloadable from http://themasites.pbl.nl/tridion/en/themasites/hyde/download/index-2.html). The distance to nearest city is constructed by measuring the distance from the district centroid to the closest city as reported in the data by Bairoch et al. (1988) using ArcGIS. Ln Population Density in 2000 is based on the Gridded Population of the World (GPW), v3, data.

• Monasteries prior 1764: Total number of orthodox monasteries per district prior to 1764, obtained from Zverinskii (2005 (1897))).

• Log Equivalent Expenditure Per Capita comes from questions asking “Approximately how much does your household spend on each of these items per month?” a) Food, beverages and tobacco, b) Utilities (electricity, water, gas, heating, fixed line phone.), c) Transportation (public transportation, fuel for car) and “And approximately how much did your household spend on each of these items during the past 12 months?” a) Education (including tuition, books, kindergarten expenses), b) Health (including medicines and health insurance), c) Clothing and footwear, d) Durable goods (e.g. furniture, household appliances. TV,car, etc). Expressed in US Dollars and adjusted by household size. Source is Life in Transition Survey, wave 2006.

• Asset ownership: Question asks “Do you or anyone in your household own any of the following?” We construct a principle component out of ownership of a car, a second residence, a mobile phone, or a computer. Source is Life in Transition Survey, wave 2006 and 2010.

• Access to Public Goods: Question asks “Do you have access to [UTILITY] in this dwelling?” and UTILITY are different type of public goods, such as water, central heat- ing, telephone, public sewage system. Source is Life in Transition Survey, wave 2006 and 2010.

57 • Base and household controls: Household size, share of male, share of persons aged 0-18, share of persons aged 60+, religious denomination of the respondent, rural/urban sampling village, survey round. Source is Life in Transition Survey, wave 2006 and 2010.

• Education: Secondary equals 1 if the educational level of the respondent is either sec- ondary education, professional education, tertiary education or postgraduate education, 0 otherwise. Above secondary education equals 1 if the educational level of the respondent is either professional education, tertiary education or postgraduate education, 0 other- wise. We also use the question “In your opinion, which of these fields should the first priority for extra (government) investment?” to construct an indicator variable equal 1 if the respondent mentions education. • Attitudes: Equal incomes vs inequality: Question asks “Now I’d like you to tell me your views on various issues. How would you place your views on this scale? 1 means you agree com- pletely with the statement on the left; 10 means you agree completely with the statement on the right; and if your views fall somewhere in between, you can choose any number in between: Incomes should be made more equal (1) vs We need larger income differences as incentives for individual effort (10)”. Reduce Inequality: Question asks “To what extent do you agree with the following state- ments: The gap between the rich and the poor today in this country should be reduced” and respondents can strongly agree (1), agree (2), neither agree nor disagree (3), disagree (4) or strongly disagree (5). Poor: ... : Question asks “In your opinion, what is the main reason why there are some people in need in our country today? Because they have been unlucky - Because of lazi- ness and lack of willpower - Because of injustice in our society - It is an inevitable part of modern life” We create dummies that takes on the value 1 if the respondent mentioned one of the choices. Demonstrated/Striked/Joined Party: Question asks “How likely are you to... attend a lawful demonstration - participate in a strike - join a political party” and respondents can answer “have done (3) - might do (2) - would never do (1)” Pref Market Economy: Question asks “With which one of the following statements do you agree most? A market economy is preferable to any other form of economic system - Under some circumstances, a planned economy may be preferable to a market econ- omy - For people like me, it does not matter whether the economic system is organized as a market economy or as a planned economy” The variable takes on the value 1 if the respondent states that “A market economy is preferable to any other form of economic system ” and 0 otherwise. Pref Democracy: Question asks “With which one of the following statements do you agree most? Democracy is preferable to any other form of political system - Under some circumstances, an authoritarian government may be preferable to a democratic one - For people like me, it does not matter whether a government is democratic or authoritarian” The variable takes on the value 1 if the respondent states that “Democracy is preferable to any other form of political system ” and 0 otherwise.

• City Population 1929 - 1989: Data measuring the populations of Russian cities from 1929 to 1989 is taken from Acemoglu et al. (2011).

• Road and Railway Density in the Countries of the former Soviet Union: Road and Railway densities in 1996 are constructed using digitized maps provided by the Coal Quality and Resources of the Former Soviet Union project of the U.S. Geologi-

58 cal Survey (Brownfield et al. 2001), accessed via http://pubs.usgs.gov/of/2001/ofr-01- 104/fsucoal/html/data1.htm.

• Historical Outcomes: We drew on a variety of sources to construct other historical out- come measures to investigate the mechanisms behind the persistent impact of serfdom. Male literacy in 1897 is defined for rural residents in their 20s from data reported in Troinitskii (1905). That source also provides the share of the adult male population with agriculture as the primary occupation. Rural enrollment rates for 1880 and 1894 are de- fined with both numerators and denominators taken from Tsentraf’nyi statisticheckikh komitet, Ministerstvo vnutrennykh del (1884) and Fal’bork and Charnoluskii (1900- 1905), respectively – see Nafziger (2012a) for more information. Fal’bork and Charno- luskii (1900-1905) also provide the number of formally recognized primary schools by district in 1856. The urbanization rate in 1913 is derived from Tsentraf’nyi statis- ticheckikh komitet, Ministerstvo vnutrennykh del (1914). The land Gini (both types), the percentage of land owned by the nobility or in communal tenure, and the amount of land possessed per peasant household, all defined in 1905, are from Tsentraf’nyi statis- ticheckikh komitet, Ministerstvo vnutrennykh del (1906), with additional details provided in Nafziger (2013). Finally, information on factory production and employment in 1868 is compiled from Tsentraf’nyi statisticheckikh komitet, Ministerstvo vnutrennykh del (1872).

• Night-Time Luminosity: We use the log of average luminosity at night mea- sured as “Average Visible, Stable Lights, and Cloud Free Coverage” for the year 2008. The data is taken from the National Geophysical Data Center (http://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html).

59 C.2 Supplementary Tables

Table C1: Determinants of Monasteries

Number of Monasteries before 1764 (1) (2) (3) (4) (5)

Longitude -0.029* -0.124*** -0.118*** -0.105*** -0.107*** (0.016) (0.034) (0.032) (0.033) (0.032) Latitude 0.016 0.045 0.025 0.072 0.094 (0.053) (0.110) (0.109) (0.116) (0.117) Forest Cover 0.023*** -0.000 -0.005 0.000 -0.003 (0.007) (0.008) (0.008) (0.008) (0.007) Ruggedness 0.038** 0.037 0.035 0.040* 0.042* (0.016) (0.024) (0.024) (0.024) (0.024) Wheat Suitability -0.000 -0.000** 0.000 -0.000** -0.000** (0.000) (0.000) (0.000) (0.000) (0.000) River (0-1) -0.026 -0.035 -0.007 -0.040 -0.029 (0.147) (0.136) (0.133) (0.138) (0.137) Distance to Moscow (ln) -0.479*** -0.480** -0.431** -0.351* -0.304 (0.128) (0.202) (0.194) (0.204) (0.209) Oat Suitability 0.000* (0.000) Rye Suitability -0.000 (0.000) Barley Suitability -0.001** (0.000) (ln) Pop Density 1600 0.180 0.207 (0.143) (0.142) (ln) Distance Nearest City 1750 -0.246** -0.264** (0.121) (0.124) Share Orthodox 1897 0.018*** (0.007) Province FE No Yes Yes Yes Yes N 494 494 494 494 494 Notes: Negative binomial regressions. The unit of observation is the district. Heteroscedastic-robust standard errors in parentheses, clustered at the province. * p < 0.10, ** p < 0.05, *** p < 0.01.

60 C.3 Supplementary Figures

Figure C1: Spatial Distribution of Monasteries Prior to 1764

61