Diversity, Institutions, and Economic Development: Post-WWII Displacement in Poland

Volha Charnysh∗

Abstract

How does cultural diversity affect social organization? Do institutional differences between diverse and homogeneous communities have implications for economic development? This paper argues that heterogeneity not only impedes informal mechanisms of cooperation, but also increases demand for formal institutions. Greater reliance on formal law and public authority, in turn, facilitates economic development by enabling arm’s length transactions and encouraging entrepreneurship. I test this argument using an original dataset on the size and composition of migrant groups settled in 1,217 municipalities transferred from Germany to Poland in 1945. I find that homogeneous migrant groups were more successful in reestablishing private-order institutions that relied on informal enforcement mechanisms, such as volunteer fire brigades, while diverse migrant communities depended on the state for the provision of public goods and enforcement of cooperative behavior. Economically similar during state socialism, communities settled by diverse migrants in the 1940s registered higher incomes and greater entrepreneurship following the transition to a market economy. Their residents also express greater confidence in formal institutions, such as courts and the police.

∗Ph.D. Candidate, Department of Government, Harvard University, [email protected]. Fieldwork for this project was funded by the Social Science Research Council’s Mellon International Dissertation Research Fellowship (IDRF) and the Krupp Foundation’s Center for European Studies Graduate Dissertation Research Fellowship.

1 1 Introduction

The impact of diversity on social organization and economic outcomes is a topic of great discussion. Some believe that diversity undermines the formation of informal private-order institutions – such as communities, business networks, guilds, and collective reprisal systems – that reduce transaction costs and facilitate economic exchange (Greif, 1989, 2006; North, 1981). This is why, the argument goes, diversity lowers public goods provision and weakens overall economic performance. What has been largely overlooked in this literature, however, is that while diversity weakens informal norms and networks, it also increases the incentives to adopt more formal and complex institutional solutions and to rely on state enforcement. Long-run economic implications of diversity may thus be conditional on the effectiveness of formal legal institutions. Indeed, to the extent that diversity increases demand for formal rules, it may facilitate economic development by enabling arm’s length transactions and removing barriers to entry that often accompany more informal coordination and enforcement practices. I test this argument using an original micro-level dataset on the size and diversity of migrant population settled in 1,217 communities transferred from Germany to Poland in the aftermath of World War II (WWII). In 1945, Poland ceded 46% of its prewar territory east of the Curzon line to the Soviet Union and gained an equivalent 26% of its prewar territory from Germany. The shift in borders trig- gered resettlement of nearly six million people, or one-ﬁfth of Poland’s pre-war population, from the USSR, Central Poland, and Western and Southern Europe into the communities abandoned by ethnic Germans. Arbitrary resettlement procedures adopted by the Polish authorities produced varying de- grees of cultural heterogeneity at the local level. Some formerly German municipalities were settled by Polish migrants from the same region, while others were populated by migrant groups of different origins. Polish communities resettled earlier in the process were able to stay together upon migration while the communities resettled at later stages were dispersed across different areas. The resulting variation in the composition of migrant communities allows us to examine the importance of shared norms and networks for institutional development and long-run economic outcomes. This paper shows that homogeneous migrant groups were more likely to establish informal private-

2 order institutions, such as volunteer fire brigades, while diverse migrant groups faced greater coordination challenges and eventually came to rely on formal legal institutions and state organizations. I further show that although greater reliance on formal institutions in diverse communities produced no economic benefits during state socialism, it began to pay off after Poland transitioned to a market economy in 1989. In the 1990s and 2000s, communities settled by diverse migrant groups had higher per capita incomes and greater entrepreneurship levels than communities settled by migrants from the same region. Moreover, firm owners and managers operating in historically diverse communities were less likely to report encountering obstacles such as corruption, inadequate functioning of the judiciary, theft, and organized crime in the 2005 Business Environment and Enterprise Performance Survey (EBRD-World Bank). More recent survey data also indicates that historically diverse communities perceive formal institutions and related organizations more favorably: In the 2010 Life in Transition (LiTs) survey, trust in the government, the courts, and the police was higher in the localities populated by diverse migrant groups in the 1940s (EBRD). These results suggest that different levels of cultural diversity, at a critical historical juncture in Poland’s history, not only led to different patterns of social organization at the micro-level, but also resulted in divergent economic outcomes. Paradoxically, forced migration and an increase in cultural diversity produced a wealthier and more entrepreneurial society more than half a century later. The paper builds on the body of research that emphasizes the importance of formal institutions and third-party enforcement for economic development (Greif, 1993, 1994; Woolcock, 1998; Cook et al., 2004; Ogilvie and Carus, 2014). My findings confirm that markets function best when supported by formal rules that apply uniformly to all economic agents (Ogilvie and Carus, 2014). I advance on this work, however, by emphasizing variation in the reliance on formal institutions across different communities subject to the same laws and regulations (Hendley, 1999). The findings suggest that we need to pay attention not only to the effectiveness of formal institutions, but also to their use by economic actors, which may depend on the availability of informal institutional alternatives (Gans- Morse, 2016). Indeed, even when the formal institutional environment is conducive to economic exchange, informal enforcement and coordination mechanisms may continue to predominate. The observation that diverse communities are more economically successful than homogeneous

3 communities challenges the predominant view of diversity as harmful to economic development and suggests that in the long run, a society can minimize the costs and maximize the benefits of diversity by adopting appropriate institutional mechanisms. My findings also challenge the prevailing view of social capital – defined as norms and networks that facilitate collective action – as contributing to economic growth (Knack and Keefer, 1997; Woolcock, 1998) and weakening with cultural heterogeneity (Alesina and Ferrara, 2002, 2000; Costa and Kahn, 2003; Putnam, 2007). I find that in the Polish case, diversity undermined some indicators of social capital (volunteer fire brigades), reinforced others (institutional trust), and failed to affect yet others (sport clubs, generalized trust). Overall, however, my analysis suggests that informal norms and networks may be a poor substitute to formal institutions in developed market economies such as Poland. The remainder of the paper is organized as follows. The next section presents the argument on the effects of cultural diversity on informal and formal institutions and economic outcomes in more detail. Then I provide a brief background on population transfers in Poland and apply my theory in the Polish context. Description of data, measurement strategies, and regression analyses follow. I conclude by discussing the contributions of this research and the generalizability of my findings.

2 Theoretical Argument

I start with the insight that community-level characteristics, such as cultural homogeneity or diversity, shape how cooperation is enforced at the local level (Greif and Tabellini, 2015). Homogeneity - – in ethnicity, language, religion, or social status — confers a comparative advantage to informal enforcement mechanisms that rely on shared norms and networks (Bandiera et al., 2005; Habyarimana and Weinstein, 2009; Miguel and Gugerty, 2005) and reduces demand for more formal enforcement procedures. Culturally diverse societies, on the other hand, lack dense intergroup ties and shared norms and thus have more to gain from adopting formal legal institutions1 that are generally provided and enforced by the state. In the language of transaction cost economics, cultural differences and weak intergroup ties increase exchange hazards and thus the need for more complex and formal enforcement

1The paper uses Gans-Morse’s (2016) deﬁnition of formal legal institutions as “both the formal rules (e.g., laws and government decrees) that shape economic relations and also the state organizations charged with enforcing these rules.”

4 and coordination mechanisms (Williamson, 1985). The demand for formal institutions has been typically explained by the expansion of the size and scope of markets and by the greater effectiveness of formal institutions in handling arms’ length transactions (North, 1990; Milgrom et al., 1990). And yet, there is ample evidence that economic actors may not use formal institutions, even when such institutions are highly functional. Moreover, the availability of informal enforcement mechanisms can push more formal procedures into the background or undermine their efficiency (Macaulay, 1963; Dyer and Singh, 1998; Uzzi, 1997; Ellickson, 1991; Granovetter, 1985; Gans-Morse, 2016; Milhaupt and West, 2000). The availability of informal enforcement mechanisms affects reliance on formal rules in a number of ways. First, when available, informal rules are often the preferred method for regulating transactions because they do not depend on sophisticated legal knowledge or third-party enforcement and spare economic actors time and effort on renegotiating contracts (Ellickson, 1991; Uzzi, 1997; Dyer and Singh, 1998).2 Second, resorting to formal channels may carry social sanctions in communities where most economic actors rely on informal rules. For example, in postwar Italy formal laws were frequently violated and those who followed the law could be punished (Della Porta and Vannucci, 1999). Third, the predominance of informal enforcement mechanisms may lower the expectations about the effectiveness of formal rules by demonstrating that formal institutions cannot fully control economic activities.3 Wallace and Latcheva (2006) show in the post-Communist context that greater participation in the informal economy is associated with lower trust in formal institutions. Most important, when a society originates in an informal institutional equilibrium, a coordinated shift to an equilibrium in which formal institutions rule is difficult because the benefits of relying on one or the other set of rules depend on the number of people who follow these rules (Greif and Kingston, 2011, p.31), as discussed in more detail below. These two forms of enforcement — through informal rules and through formal legal institutions -– are not mutually exclusive and have historically coexisted in most societies. However, the predominance of one enforcement arrangement over the other, especially when informal norms have evolved

2This is especially relevant for informal strategies that do not violate formal law. 3This is especially relevant for informal strategies that violate formal law.

5 in opposition to formal legal institutions, is likely to have important economic consequences. When formal institutions and organizations are weak or predatory, informal enforcement mechanisms can act in place of the formal law and facilitate economic exchange (McMillan and Woodruff, 2000). Cultural homogeneity is beneficial in these settings. For example, La Ferrara (2003) shows that in the absence of legal enforcement in Ghana, membership in a tight-knit community enlarges the set of available cooperative strategies because social sanctions and reciprocity norms apply not only to the individual, but also to other group members. Similarly, Greif (1993) shows that trade along ethnic lines allowed medieval traders to better monitor their agents by exchanging information about their behavior at the time when the legal system did not yet exist. Under state socialism, many prof- itable economic activities were officially prohibited, but could be supported by informal enforcement mechanisms. Over time, informal norms and relationships gave rise to a vibrant shadow economy, improving the welfare of the economic actors engaged in it and increasing the diversity of goods available for purchase (McMillan and Woodruff, 2000). When formal institutions for the protections of private property and contract enforcement are in place, however, the benefits conferred by informal enforcement mechanisms diminish and the persistence of informal arrangements, created to make up for the previously inadequate legal system, is likely to limit competition and encourage corrupt practices. For example, Estrin and Mickiewicz (2010) show that the scale of the shadow economy lowers the likelihood of entrepreneurial entry by an individual. Indeed, negative economic outcomes in transition economies in Eastern Europe and the former Soviet Union are often blamed on the enduring private-order arrangements that create entry barriers to outsiders (McMillan and Woodruff, 2000). Similar logic applies to public goods provision: Informal norms and networks are key for the delivery of public goods in states that lack the power to tax and mobilize resources, but provide “second-best solution” in states with well-functioning legal institutions and effective state organizations because they can generate inequalities (Durlauf and Fafchamps, 2004). Thus, whether or not heterogeneiety undermines economic and social outcomes depends on the reach and effectiveness of formal institutions. Overall, informal mechanisms are far less efficient in developed market economies. One of the key advantages of formal institutions (and public authority more broadly) is their generalized, impersonal

6 character (Ogilvie and Carus, 2014).4 Formal law, at least in principle, applies to all economic agents impartially, regardless of identity or group membership, and thus enables arms’ length transactions. Informal norms and networks, on the other hand, favor members over non-members. Greater reliance on generalized formal institutions thus widens the scope of exchange beyond the boundaries of tight- knit homogeneous groups and increases competition. Reliance on informal enforcement mechanisms, on the other hand, can undermine economic efficiency by creating barriers to outsiders. This is why the gradual expansion of formal law and public authority, rather than the strengthening of informal norms and networks, contributed to the growth of trade in Europe (Ogilvie and Carus, 2014; North, 1990; Greif, 1993). Can communities switch from the reliance on informal institutions to the reliance on formal rules when those rules are more economically efficient? Such transitions have occurred in many developed economies, but they take time (Gans-Morse, 2016). Once a society begins in an equilibrium where informal mechanisms predominate, a coordinated shift to an equilibrium where formal institutions prevail is difficult because both formal and informal rules are by their very nature self-reinforcing (Greif and Kingston, 2011, p. 31). For example, research in economics has shown that the more people rely on reciprocal transactions, the harder it is to exchange goods on the market and thus the greater the incentives to continue transacting informally (Kranton, 1996; Estrin and Mickiewicz, 2010). Similarly, legal scholars have concluded that reliance on the formal law is warranted only when a sufficiently large population follows it (Hendley, 1999). Thus, in the absence of exogenous shocks, a society where informal mechanisms take precedence over formal procedures is unlikely to shift to a new equilibrium in the short term. In sum, to the extent that cultural diversity weakens informal enforcement mechanisms and increases the demand for formal legal institutions, it can have important medium- and long-run economic implications. When the formal institutions are weak or predatory, diversity will carry significant economic disadvantages. In the presence of an effective formal legal system, diversity can be economically beneficial. In short, economic implications of diversity are conditional on the broader

4Historically, this was not always the case. Formal institutions are not by definition generalized and there are numerous examples of formal laws that benefit specific groups. However, community-level enforcement mechanisms (and social capital more generally) are always particularized as they rely on network closure (Ogilvie and Carus, 2014, p. 435).

7 institutional environment. The following section provides background on post-WWII population transfers and applies the argument on the institutional and economic implications of diversity to the Polish context.

3 Historical Background and Hypotheses

3.1 Polish Society on the Eve of WWII

During the 19th century, when industrialization fundamentally transformed traditional agrarian economies in Western Europe, the territory of modern-day Poland was governed by Russian, Prussian, and Hab- sburg empires.5 Because of Poland’s peripheral location, its industry remained underdeveloped and the market economy had barely penetrated the countryside. When the country finally became independent in 1918, 75% of the population still lived in small rural communities.6 Local life revolved around the parish, and the priest was deeply involved in economic and social affairs, in addition to offering spiritual guidance (Bartkowski, 2003, p. 90). Formal institutions and state authority did not penetrate deeply into the countryside. Even after the dissolution of imperial power, in rural areas the state was identified with coercion, and its agents – the police officer, the clerk, or the bailiff – feared and resented (Bartkowski, 2003, p. 53). Instead, cooperation was sustained informally, without the involvement of third parties. Gossip and ostracism were sufficient for deterring most anti-social behaviors. Local “aristocracies” were called upon when re- solving economic disputes (Bartkowski, 2003, p. 73). Informal institutions of economic cooperation and mutual aid ensured that even the poorest members had a stake in the system. Widows and or- phans were cared for by communal charity organizations (mounts of piety, or banki pobózne;˙ zakładki and magazyny wiejskie). Communal labor (tłoka, powaba) was used to build roads or clear fields from timber. Land, forest, and water were communally owned, and peasants were organized into small agricultural production co-operatives (spółdzielnie produkcyjne) to manage the risks of farming (Bartkowski, 2003, p. 75). Each community had a volunteer fire brigade (Ochotnicza Straz˙ Pozarna,˙

5The three empires had very different formal institutions and divergent attitudes toward Polish nationalism, and the partition legacies are visible to this day in electoral outcomes and economic indicators. 6While Poland had a sizable Jewish, Ukrainian, and German minorities, segregation was high. The Jewish minority lived predominantly in urban areas; the Ukrainian and German minorities were concentrated in homogeneous villages.

8 OSP), which not only took care of ﬁres and natural disasters, but also served as a local militia and socialized young men into becoming loyal community members.

3.2 Displacement in the aftermath of WWII

WWII shook up traditional rural communities across Poland. At least 1.5 million people were sent to Germany as slave laborers (USHMM, 2010). Hundreds of thousands who lived in the territories annexed to the Reich, Danzig-West Prussia and the Warthegau, were forcibly relocated to a different occupation zone (the General Government) to free up space for ethnic Germans. The Jewish population was first rounded up into ghettos and then nearly completely eliminated. The final – and perhaps the most dramatic – disruption occurred when the Polish borders were shifted westward. In 1945, Poland lost 46% of its prewar territory east of the Curzon line (Kresy) to the Soviet Union and gained an equivalent 26% of prewar territory east of the Oder-Neisse line from Germany (see Figure 1). The border changes were followed by population transfers, uprooting more than fourteen million ethnic Poles and Germans. Nearly eight million Germans fled or were expelled from the lands east of the Oder-Neisse, and the area was repopulated by over five million ethnic Poles who came from the territories annexed by the Soviet Union, central Poland, and a number of southern and western European states. Only a small Polish minority that had lived in the area before WWII was allowed to stay. 7 By 1948, immigrants accounted for 81% of the population in the former German territories (called the “Recovered Territories”).8 The Polish authorities sought to complete the population transfers as swiftly as possible to ensure the permanence of the new borders and to secure ownership of the German assets that were being stolen and vandalized by the Red Army. However, relocating millions of people in a state devastated by war, and at a time of intense internal political struggle, was a logistical nightmare and “more than the new administration could handle” (Kenney, 1997, p.158). As a result, resettlement proceeded at a breakneck pace and in a haphazard manner. While the direction of the railway lines from east to

7According to the 1939 Nazi German census, from a total of 8,855,000 people lived in the area, of which 700,000 spoke Polish. In 1948 census by the Polish authorities, the so-called autochthonous population amounted to 936,744 people. 8For comparison, 73.7% of the population living in the area that remained in Poland after WWII, had lived there before the onset of WWII, according to the 1950 census.

9 Figure 1: Territorial Changes in Poland in 1945. German Population was Expelled and Poles from Centeral Poland, USSR, and Western Europe were Settled in the Dark Green Area.

west shaped the broad distribution of regional groups within the former German territories, the actual shares of each group and the resulting diversity of migrant populations at the local level depended on the arbitrary decisions by ofﬁcials who lacked basic knowledge of local socio-economic conditions. While the authorities prioritized transferring entire communities and families, opportunities for communal settlement dwindled due to housing shortages. As a rule, only smaller groups of families were able to settle in the same location, but not entire communities (Dworzak and Goc, 2011; Kersten, 2001). Assignment decisions were made indiscriminately, and migrants were frequently sent from one destination to another when it turned out that the ofﬁcials had underestimated housing capacity in a given area. Zaborowski (1970) describes the process at one of the transfer stations created by the Pol-

10 ish Repatriation Office (PaństwowyUrz ˛adRepatriacyjny, PUR) as follows: “A PUR employee would write the name of a destination in chalk on the side of the railway car, and the cars would be uncoupled and shunted down the tracks.” PUR Director Wladyslaw Wolski lamented that migrants were often offloaded midway to their destinations, in the middle of an open field, because the conductors lacked planned itineraries (Ciesielski, 2000, p. 23). Haphazard assignment by short-staffed administrators produced considerable variation in the local- level distribution of migrant groups. For example, most of the residents of the Galician village of Budki Nieznanowskie were settled together in the village of Gierszowice, Opole province. For these migrants, only the material environment had changed following migration: they now lived in brick rather than wooden houses and farmed larger plots of land with more sophisticated machinery. The inhabitants of the nearby Galician village of Busk, by contrast, were dispersed across 19 villages and 8 counties of Opole province. They were settled next to migrants from other parts of Poland, who spoke different dialects and followed different religious rituals (Dworzak and Goc, 2011). Although all new residents of the formerly German territories were Polish on paper, migrants from different pre-WWII provinces saw each other as culturally distinct due to the legacy of imperial par- titions and low mobility prior to 1945. Reports of conflicts and misunderstandings between different migrant groups were ubiquitous in settlers’ memoirs and in the officials’ accounts alike. Thum (2011, p. 13) writes: “Settlers from central Poland turned up their noses at those ’from beyond the Bug’ (zza Buga). They called them Zabuzhanie, which could be translated as ’hillbillies’, implying that eastern Poles had been living in the back of beyond.” Indeed, a Polish sociologist who surveyed several heterogeneous villages in 1958, over a decade after the transfers, found that daily interactions were still more frequent within rather than between groups (Chmielewska, 1965). The following subsection applies the theory on how diversity affects social organization and economic outcomes to the experiences of diverse and homogeneous migrant communities in the formerly German territories and develops observable implications for economic development before and after the transition to a market economy.

11 3.3 Diverse and Homogeneous Migrant Communities Upon Resettlement

WWII weakened both formal and informal institutions across Poland, but the situation in the territory transferred from Germany in 1945 was particularly dire. Migration ruptured social networks; numerous communal institutions were dissolved and their members dispersed across different regions; elites and authority figures were often separated from their communities upon resettlement. Public-order institutions were also lacking. The German administration departed in 1945, and the emerging Polish institutions were “weak, facade-like structures unable to control the situation” until 1950 (Grabowski, 2002, p. 148). In this environment, homogeneous migrant groups had an important advantage over communities settled by migrants from different regions: Shared norms and, in many cases, shared networks. Upon migration, such groups were able to quickly replicate the familiar patterns of associational behavior and reestablish order. One migrant describes his arrival from rural Galicja to village Pyrzany in the former German territories in 1944 as follows: “Houses in the very center of the village were occupied by the pastor, the organist, and others who deserved it. The poor settled in houses on the outskirts” (Zbigniew Czarnuch quoted in Halicka, 2013). Although the village already had a village elder (soltys), a man from central Poland who had arrived earlier, the more numerous Galicja group elected their own representative. In order to protect the village from bandits and fires, migrants formed a volunteer fire brigade that doubled as a militia. They then founded a preschool and reopened the preexisting German bakery and grocery stores — all within a year of migration (Halicka, 2013). In this homogeneous migrant community, informal norms and networks facilitated the creation of private-order organizations, such as the volunteer fire brigades, and permitted the area to resume ordinary economic activity soon after the migrants arrived. Informal mechanisms compensated for the nonexistent and later weak organs of the state, ensuring that the new owners’ rights to the formerly German property were protected and that pre-existing economic infrastructure was put to good use. The localities settled by migrants from different regions faced greater organizational challenges. A pastor in the Wroclaw diocese described his diverse parish as follows: “A mix of people with diverse habits, customs, and traditions, who had been uprooted from their various milieus [. . . ], an

12 agglomeration of marked regional antagonisms and of distinct, self-regulating groups of people” (Ur- ban, 1965). In the absence of shared norms and networks, these communities were reluctant to invest into creating private-order organizations for the provision of public goods or norm enforcement. For many migrants, it was easier to opt out of collective activities altogether to pursue one’s self-interest, sometimes at the expense of others. In diverse communities, not only the formerly German property, but also societal privileges were “up for grabs.” Previous status or connections had little inﬂuence; the lucky few who had arrived early were able to claim the best houses and assume leadership roles (Grabowski, 2002). Securing ownership of these new assets from contenders — both inside and outside of the community — was much more challenging, however. Crimes against private property were rampant and informal enforcement mechanisms ineffective in a culturally diverse post-migration setting. Polish sociologist Rybicki (1967) viewed such motley migrant assortments as “communities only in a formal sense” because they lacked solidarity and collective action infrastructure.

3.4 Divergent Cooperation Mechanisms

Population transfers were followed by the imposition of the Communist regime and the expansion of formal institutions and state authority. One of the fastest growing new organizations was the Commu- nist party, which at the grass-roots level functioned as a provider of public goods, political organizer, and an organ of social control. I hypothesize that diverse and homogeneous migrant communities reacted to the expansion of the state administration and formal institutions differently from one another. In homogeneous communities, Communist institutions were more likely to compete with informal social and economic structures; as a result, their influence and penetration were limited. In diverse migrant communities, resistance to the new order -– and the dense organizational network that came with it – was weaker. At the highest levels of cultural fragmentation, the state filled an important need for service provision and norm enforcement. It offered public goods (policing, protection from fires and natural disasters, elderly care, infrastructure maintenance) that heterogeneous communities struggled to produce on their own in the absence of shared norms and networks. The communist state satisfied this need, even though it encroached on private property and stifled political competition.

13 The shift in approaches to the provision of elderly care illustrates this argument. Jasiewicz (1972) describes how the traditional custom of families taking care of their elderly failed in heterogeneous migrant communities because “the village opinion had little constraining power and the rural authorities lacked the capacity to act effectively.” He ﬁnds that the elderly in diverse communities increasingly turned to the state, which guaranteed care and a pension in exchange for their farms. Thus, I expect diverse migrant communities to have higher demand for formal institutions than homogeneous communities, where effective informal alternatives were available.

H1: Diverse migrant communities were more likely to rely on formal institutions than homogeneous migrant communities, where informal norms and networks facilitated public goods provision and norm enforcement.

3.5 Economic Implications of Different Enforcement Mechanisms

I argue that the resulting differences in the reliance on formal and informal rules across homogeneous and diverse migrant communities produced divergent economic outcomes and that the comparative advantages of formal and informal cooperation mechanisms depended on the broader institutional environment. Between 1950 and 1989, formal institutions and the economic system in Poland were not conducive to private entrepreneurship and to the accumulation of wealth. In 1950, centralized planning was introduced, industry was nationalized, and the new Communist government embarked on the collectivization of agriculture. The collectivization was especially successful in the formerly German territories where large German estates were tranformed into State Agricultural Farms (Pa´nstwowe Gospodarstwo Rolne), even though private farming remained dominant. The focus on the heavy industry undermined the growth of the service sector, and many small workshops and businesses were nationalized or forced into bankruptcy by taxation. The restrictions on private entrepreneurship were partially reversed in 1957, following worker protests in Poznan´ and the appointment of Władysław Gomułka as the First Secretary. The next three decades can be characterized by the gradual scaling back of the ’socialist’ model of economy and the introduction of market elements. Small-scale private businesses began to appear across Poland.

14 By 1980, 602,000 legal private businesses were registered, and many more operated in the grey and black markets (Kochanowski, 2010, p. 185). Even so, the legal environment left much to be desired. As Kornai (1990, p.136) argues,“half-hearted reform caused ...difﬁculties due to the absence of legal institutions for the consistent protection of private property and for the enforcement of private contracts.” In this setting, the dependence on formal law and state enforcement carried few economic advantages. Informal norms and networks, on the other hand, facilitated access to scarce goods and increased opportunities for economic exchange in the shadow economy. According to historical accounts, most ofﬁcially registered private businesses had to rely on informal connections to obtain tools and supplies (Kochanowski, 2010, p. 185). Informal connections were even more important in the growing shadow economy.

H2: Reliance on formal institutions in diverse communities carried no economic advantages during state socialism, while informal coordination and enforcement mechanisms in homogeneous communities facilitated entrepreneurship and produced higher incomes.

Once Poland transitioned to a market economy, however, the benefits of formal and informal enforcement mechanisms should have been reversed. In 1989, Poland adopted laws that guaranteed property rights and contract enforcement. The establishment of the legal basis for private entrepreneurship led to the rapid growth of the private sector. Between 1989 and 1994, the share of private-sector employment increased five-fold, from 12% to 61% in 1994 (Dickinson, 2000). No- tably, most of the growth has occurred through the establishment of small (under 49 people) and medium-sized (50 to 249 employees) firms rather than through the privatization of large state-owned enterprises, and thus was to a large extent driven by economic behavior at the micro level. In this institutional setting, I expect the persistence of informal economic relations that consoli- dated during state socialism to have become a liability and the reliance on formal law to begin paying off. Informal networks benefit a smaller number of people and can limit the entry of new firms. By contrast, reliance on formal institutions removes barriers between outsiders and insiders, increasing competition and allocating resources to highest productivity users (Ogilvie and Carus, 2014). There- fore, I expect historically diverse migrant communities to have a better business environment overall

15 and to register more private businesses and report higher incomes than historically homogeneous migrant communities.

H3a: Diverse migrant communities have higher entrepreneurship rates and higher incomes than homogeneous migrant communities in a market economy.

H3b: Diverse migrant communities have a more favorable business environment than homogeneous migrant communities in a market economy.

The following section describes the historical and contemporary data used to test these proposi- tions.

4 Data and Measurement

4.1 Historical Data on Population Transfers

The data on the origin of migrants and on the distribution of the indigenous population comes from a survey of 1,217 municipalities (gminy) in the former German territories in 1948, when the population transfers were largely completed. The census recorded the sizes of four distinct population groups: repatriates from the USSR, settlers from Central Poland, reemigrants from Western Europe, and the autochthonous population (see Table A1 in the Appendix for more information). While this catego- rization does not encompass all cleavages, Polish sociologists generally agree that they represent the main categories among the post-WWII residents of the Recovered Territories (Chmielewska, 1965). Even though cultural differences existed not only between but also within these groups,9 at the micro- level migrants in each category typically came from the same pre-war province. Thus, I use the shares of these four groups to estimate the degree of cultural diversity in a given municipality. Figure 2 plots the spatial distribution of the four main categories of the population in the former German territories at the level of historical municipalities. We see that the indigenous population

9For example, reemigrants from Western Europe were a diverse group originating in France, Germany, Yugoslavia, and other states. However, in a given municipality only one of these subgroups would be present. Similarly, indigenous Warmiaks spoke a different dialect than indigenous Silesians; the population of rural Galicia had little in common with that of Lithuania. And yet inter-group distinctions were negligible when compared to intra-group distinctions in each given municipality in the former German territories. Warmiaks and Silesians lived on the opposite sides of Poland; repatriates from Galicia wound up hundreds of kilometers away from repatriates from Lithuania because migration proceeded from east to west.

16 was highly concentrated in two small regions and that most of the territory was populated almost exclusively by migrants.

Figure 2: Population Shares of (1) Repatriates from USSR (Top Left); (2) Settlers from Central Poland (Top Right); (3) Reemigrants from Western Europe (Bottom Left); (4) Autochthonous Population (Bottom Right) at the Level of the 1948 Municipalities in the Former German Territories.

The 1948 census also included data on age and gender of the population. Age was recorded as the number of men and women (1) under 18 years of age; (2) 18 to 59 years of age, and (3) age 60 and older.

17 4.2 Measuring Uprootedness and Diversity

I decompose diversity into (1) the share of migrants and (2) the heterogeneity of migrant population, following the approach adopted by Alesina et al. (2013). The ﬁrst component (Divresettled) measures migrants as a proportion of the total population at the municipal level. The second component, Divmig, measures the diversity of migrant groups in each municipality. It is based on the Herﬁndahl index and estimates the probability that two migrants randomly drawn from the population came from different

10 regions. If s j is the share of migrants from region j, then

j Divmig = ∑ j=1[s j ∗ (1 − s j)]

Diversity of the population at the local level is thus a sum of diversity within the migrant popula-

11 tion (Divmig) and diversity resulting from the inﬂow of migrants (Divresettled). Figure 3 shows how migrant diversity varies across historical municipalities in the former German territories.

4.3 Historical Control Variables

As noted above, the ofﬁcials tasked with resettlement were unfamiliar with socio-economic characteristics of the German territories and assigned migrants to particular locations in an arbitrary manner. However, the assignment was not random. To account for possible economic differences between areas settled by diverse and homogeneous migrant groups, I collected commune-level data from the 1939 census conducted by Nazi Germany on the occupational distribution of the German population (the number of people employed in agriculture, industry, and other occupations) and the size of farms (in hectares or ha) in each locality. Even though most of the population whose occupational structure was recorded by the German census was no longer present, controlling for the share of the population employed in industry in 1939 (Share in Industry) permits accounting for the pre-war economic potential of the areas settled by Polish migrants after 1945. Furthermore, the level of landholding inequality in prewar Germany shaped the redistribution of land by the Polish authorities in the late 1940s. Farms

10This index implies that a community composed of many small groups is more diverse than a community with two equally sized groups. Another popular measure is polarization index, which reaches maximum when two equally sized groups face each other. In the Polish data at the municipal level, the correlation coefﬁcient between polarization and diversity is 0.94, so the choice of index does not affect the results. 11 An alternative speciﬁcation is interacting size (s j) and diversity (Divmig), but the share of migrant population is highly correlated with the interaction term (cor=0.69, p<0.01).

18 Figure 3: Diversity of Migrant Population (Fractionalization Index) at the Municipality Level in 1948.

19 under 12 ha were transferred to individual migrant families; larger farms were assigned to multiple families to share; and units above 50 ha were turned into State Agricultural Farms (Państwowe Gospodarstwa Rolne, PGR). To account for these differences, I control for Landholding Inequality (Herfindahl Index) computed from the proportion of farms in each size category. To account for the possible influence of the German economy, I control for the Logarithm of Distance to Germany (within post-1945 borders) from the centroid of each municipality (in km). I also include fixed effects for pre-WWII German states (Regierungsbezirke), which varied in infrastructure and industrial potential and had much more durable borders than those of Polish provinces.12

4.4 Outcome Variables

Strength of Informal Enforcement Mechanisms

The role played by informal enforcement mechanisms in a given society is difficult to quantify. An additional difficulty arises from the fact that more than seventy years have passed since the population transfers were completed. To get around this problem, I draw on the insight that differences in the strength of informal norms and networks can be gleaned from the type and density of local organizations (Greif and Tabellini, 2015). In the Polish context, a good proxy for the differences in the strength of informal rules is the presence of volunteer fire brigades (OSPs). OSPs are a revered form of association that relies on reciprocity and social sanctions rather than on formal law and ex- ternal enforcement procedures to provide a number of local public goods. Volunteer fire brigades have historically fulfilled many social functions in addition to fire protection, including socializing young men into becoming loyal community members, organizing and performing at local festivals, and competing with neighboring villages in sporting events (Bartkowski, 2003). To measure the prevalence of Fire Brigades, which until the 1990s operated informally, I use the registration data collected following the introduction of the 1989 Law on Associations. The law en- couraged all OSPs to register in order to receive equipment, funds, and training from the government. This contemporary indicator is a reliable proxy for historic variation in the prevalence of OSPs be-

12The states are Liegnitz, Oppeln, and Breslau in Silesia (existed as one province from 1815 to 1919 and from 1938 to 1941); Königsberg, Allenstein, Gumbinnen, and Marien-werder (1773–1829 and 1878–1945); Frankfurt and Potsdam in province Brandenburg (1815 -1946); and Stettin and Köslin in Pomerania (1815 - 1945).

20 cause more than 90% of the currently registered OSPs existed prior to 1989 (Klon-Jawor, 2013). I was also able to conﬁrm that most OSPs emerged in the late 1940s by calling the local administration and the OSP units themselves and establishing the exact founding dates for many units (see Appendix, Figure A2). To ensure that I am testing the importance of informal norms and networks rather associational activity more broadly, I use data on Sports Clubs as a placebo test. Like volunteer brigades, sports clubs have existed both before and after 1989 and attracted predominantly young men. Yet unlike volunteer ﬁre brigades, sports clubs do not depend on reciprocity and social sanctions; therefore, they should be equally prevalent in diverse and homogeneous migrant communities. Pre-1989 Economic Outcomes

I argue that reliance on informal norms and networks in homogeneous communities facilitated resistance to the state and participation in the shadow economy, while reliance on formal institutions in diverse communities carried no economic advantages during state socialism (H2). The size of the shadow economy is not directly observable, and the existing approaches to measuring it cannot be replicated at the municipal level.13 Instead, I measure the extent of private economic activity as well as the level of wealth, to get at the economic advantages of formal and informal rules during state socialism indirectly. I use the municipal-level indicators (1) In Private Handicraft and (2) Shops14 per 1,000 residents as proxies for economic activity. Because success in the private sector was impossible without informal connections (Kochanowski, 2010), a greater number of private entrepreneurs or more retail outlets can signify greater reach of the informal sector. I also use the number of (1) TV-sets and (2) Phones per 1,000 people in 1980 as an indicator of successful participation in legal and semi-legal forms of exchange, as TV-sets and phones were expensive and hard to obtain.15 Post-1989 Economic Outcomes

I argue that diverse migrant communities had higher entrepreneurship rates and higher incomes

13For example, by using excess currency demand (Schneider and Enste, 2000). 14Only some of these retail outlets were privately owned; however, this is the best measure of economic activity available at the municipal level for this time period. 15All of these variables were collected at the municipal level from Statystyka Gmin (1983) and Rocznik Statystyczny Miast (1985).

21 than homogeneous migrant communities after 1989 (H3a). To test this hypothesis, I use municipal- level data on registered Private Enterprises (per 1,000 residents). I also use information on per capita Personal Income Tax collected within each municipality during the same period. The earliest measures are available from 1995.16 Because the basic tax rate does not vary across municipalities, income tax correlates with the actual incomes reported by the residents, unless tax compliance varies across homogeneous and diverse communities. Both differential tax compliance and differences in incomes would be consistent with my theory, but the data does not allow for distinguishing between these two possibilities. Business Environment

I further argue that reliance on formal law in diverse migrant communities contributed to a more favorable business environment in the post-1989 period (H3b). I examine the differences in business environment as reported by firm owners and managers themselves in the 2005 BEEPS (EBRD-World Bank). Unlike surveys conducted in 1999, 2002, and 2013, this survey contains geographic identi- fiers, which makes it possible to examine the importance of historical context. The survey draws on private and state firms with a broad range of economic activities. BEEPS respondents were presented with a list of potential obstacles to the operation and growth of businesses and asked to estimate how “problematic” each of these obstacles were. In line with the theory, the predominance of informal over formal rules should generate obstacles such as inadequate “Functioning of the judiciary”, high “Cor- ruption”, as well as the presence of “Street crime, theft and disorder” and “Organized crime/mafia”. I used these options to create dummy variables, coded 1 if respondents identified a given practice as a “major” or “moderate” obstacle and coded 0 if respondents saw it as “No obstacle” or “Minor obstacle”.

4.5 The Unit of Analysis

My primary units of analysis are municipalities, self-contained social units with legislative and gov- erning bodies, and thus appropriate for studying the effects of diversity on social and economic out-

16The tax data as well as the entrepreneurship variables were collected from the Local Data Bank of the Polish Main Statistical Ofﬁce (Głowny Urz ˛adStatystyczny).

22 comes. However, because municipal boundaries have changed considerably since 1939,17 I aggre- gated historical communal (1939) and municipal (1948) data to the level of contemporary, larger municipalities. To match units from different time periods to each other, I digitized and georefer- enced the map of municipalities printed by the Central Ofﬁce for National Measurements (Główny Urz ˛adPomiarów Kraju) for internal use in 1950. I then superimposed this map onto a shapeﬁle of the contemporary Polish municipalities and assigned each historical unit to the contemporary unit that covered most of its territory. I allowed a 1-km error in the placement of historical unit boundaries because historical maps tend to be less accurate than maps created with modern tools. If contemporary municipality borders split historical municipalities, I weighted the historical data by the proportion of the overlapping area.18 As a result, 1,217 historical municipalities mapped onto 630 contemporary units.19 Statistics for all variables used in the regression analyses at the level of contemporary municipalities are presented in the Appendix (Table A3).

5 Results

5.1 Reliance on Informal Enforcement Mechanisms

In line with H1, we should expect a homogeneous village to be more likely to establish a volunteer fire brigade, which depends on informal norms and networks, than a heterogeneous village, which should be more likely to rely on the state for fire protection. Models 1 and 2 in Table 1 examine the relationship between the prevalence of volunteer Fire Brigades (per 10,000 people) and the diversity of migrant groups at the municipal level. The coefficient on Migrant Diversity is negative and significant: Diverse migrant communities

17In the 1948 census, towns and rural municipalities, comprising a group of closely situated villages, were listed separately. However, in 1954, municipalities were reorganized into even smaller units (gromady), and in 1973 gromady were abolished and municipalities (gminy) were reintroduced with different borders than the pre-1954 units. Between 1973 and 2013, municipality boundaries underwent further changes. In particular, some small towns were joined to the neighboring rural municipalities and recategorized into “urban-rural” municipalities. Towns were classiﬁed as “urban” municipalities and groups of villages as “rural” municipalities. 18This method assumes homogeneous distribution of population across territory, which is an oversimplication but results in relatively low distortion at the micro-level. 19The number of municipalities used in the analysis is lower because of data missingness in some regions and varies slightly over time due to administrative changes.

23 Table 1: Migrant Diversity and Prevalence of Organizations that Rely on Informal Enforcement Mech- anisms. Volunteer Fire Brigades were Measured in 1997 in Model (1) and in 2010 in Model (2). Sport Clubs were Measured in 2010 in Model (3). OLS Regression with Fixed Effects.

Dependent variable (per 10,000 people): Fire Brigades (’97) Fire Brigades (’10) Sport Clubs (’10) (1) (2) (3) Migrant Diversity −7.270∗∗∗ −6.113∗∗∗ 30.175 (1.850) (1.444) (25.099)

Share Migrants −2.946∗∗ −2.372∗∗ 16.869 (1.420) (1.133) (19.698)

Share Urban −5.382∗∗∗ −4.867∗∗∗ 0.553 (0.825) (0.635) (11.031)

Share in Industry −5.950∗∗ −6.581∗∗∗ −55.015∗ (2.413) (1.896) (32.948)

Landholding Inequality 6.517∗∗ 6.028∗∗∗ −7.724 (2.653) (2.064) (35.876)

log(Population) −1.202∗∗∗ −1.005∗∗∗ −17.647∗∗∗ (0.365) (0.288) (5.000)

log(Distance to Germany) 0.474 −0.056 8.105 (0.383) (0.296) (5.136)

Constant 21.557∗∗∗ 21.261∗∗∗ 164.457∗ (6.635) (5.273) (91.651)

Observations 567 593 593 R2 0.351 0.389 0.082 Adjusted R2 0.330 0.370 0.053 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

24 have fewer volunteer fire brigades than homogeneous migrant communities. Moving from lowest to highest levels of diversity in the sample is associated with a decrease in the number of volunteer fire brigades by 4.5 per 10,000 (mean=6, sd=5.5). Diverse migrant communities are less likely to have established private-order organizations that rely on informal rules and provide a local public good. As a placebo test, I examine the prevalence of sports clubs. Results in Model 3 suggest that Migrant Diversity is not correlated with the prevalence of Sports Clubs (per 10,000 people); the model also explains almost no variation in this placebo outcome variable. Thus, the main difference between homogeneous and diverse communities lies in their use of informal cooperation mechanisms toward the provision of a public good rather than in their overall levels of association. These results mirror the findings in the literature on the greater capability of homogeneous communities for bottom-up provision of public goods (e.g., Habyarimana and Weinstein, 2009; Miguel and Gugerty, 2005), but challenge the claims about the overall higher levels of social capital in homogeneous settings (e.g. Alesina and Ferrara, 2002; Costa and Kahn, 2003; Putnam, 2007).

5.2 Economic Outcomes Before 1989

I also expect that during state socialism the availability of informal norms and networks in homogeneous communities would facilitate entrepreneurship, while the reliance on formal law in diverse communities would carry no economic advantages (H2). Regressions in Table 2 explore whether this hypothesis holds in the 1980s, when private entrepreneurship was legal but formal institutions remained inadequate. The coefﬁcient on Migrant Diversity is not signiﬁcant in the models with the number of people In Private Handicrafts (Model 1) or the number of TV-sets (Model 3) per 1,000 people as a dependent variable. However, diversity of migrant groups seems to matter for the prevalence of Phones and Shops per 1,000 people (Models 2 and 4). Moving from lowest to highest levels of migrant diversity is associated with a decrease in the number of shops by 1.5 units (mean=7, sd=2.29) and with a decrease in the number of phones by 4 units (mean=32, sd=20.29). While the differences in economic activity and prosperity at the subnational level are rather small, probably due to the economic restrictions

25 during the period of state socialism,20 they indicate that homogeneous communities were slightly better off economically.

Table 2: Migrant Diversity and Economic Outcomes at the Municipality Level in 1982. Dependent Variables are (1) People In Private Handicrafts per 1000 people; (2) Shops (Public and Private) per 1000 people; (3) the Logarithm of TV-sets per 1000 People. (4) the Logarithm of Phones per 1000 People. OLS Regression with Fixed Effects.

Dependent variables (per 1000 people): In Private Handicrafts Shops log(TVs) log(Phones) (1) (2) (3) (4) Migrant Diversity 2.573 −1.744∗∗∗ 0.033 −0.382∗∗ (2.295) (0.595) (0.061) (0.152)

Share Migrants −1.555 0.140 0.064 0.512∗∗∗ (1.800) (0.463) (0.051) (0.119)

Share Urban 9.196∗∗∗ 1.841∗∗∗ 0.198∗∗∗ 1.012∗∗∗ (1.000) (0.257) (0.026) (0.066)

Share in Industry 8.432∗∗∗ −0.774 0.309∗∗∗ 0.487∗∗ (3.034) (0.781) (0.081) (0.200)

Landholing Inequality −9.811∗∗∗ −0.178 −0.089 −0.363∗ (3.305) (0.850) (0.090) (0.218)

log(Population) −0.195 −0.958∗∗∗ 0.011 0.069∗∗ (0.468) (0.120) (0.012) (0.031)

log(dist. to Germany) 1.533∗∗∗ 0.058 0.003 0.015 (0.464) (0.119) (0.012) (0.031)

Constant 2.373 14.666∗∗∗ 5.086∗∗∗ 2.213∗∗∗ (8.344) (2.134) (0.219) (0.550)

Observations 579 571 553 576 R2 0.362 0.221 0.303 0.552 Adjusted R2 0.341 0.196 0.280 0.537 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

20In particular, before 1989 the central authorities determined how many retail and services outlets could operate in urban and rural settlements of various sizes by distributing licenses for operating a particular business. For example, a rural settlement was allowed to have just one shoe-making, tailoring, blacksmith, construction, and repair business (Kaminska,´ 2006). Furthermore, the installation of a telephone depended on state investment in telephone cables, in addition to an individual’s resources.

26 In sum, regression analysis provides some evidence for H2: in the 1980s, homogeneous communities had more shops and phones per capita than diverse communities. However, homogeneity was not associated with the greater prevalence of TV sets or higher employment in the private sector. Un- fortunately, the lack of data does not allow to test whether homogeneous communities were relatively more engaged in the shadow economy than diverse communities.

5.3 Economic Outcomes After 1989

I further argue that after Poland transitiond to a market economy and strengthened commercial laws, reliance on formal institutions in diverse communities contributed to higher entrepreneurship rates and incomes (H3a). Models 1-3 in Table 3 explore the relationship between Migrant Diversity in 1948 and per capita Income Tax in 1995, 1998, and 2000. The coefficient on Migrant Diversity is positive and significant in all three models. The magnitude of the coefficient increases from 1995 to 2000, suggesting that historically more diverse communities are doing better over time. The results are substantively meaningful. In 1995, moving from lowest to highest levels of diversity is associated with an increase in income tax per capita by 17 Zl. (mean=101 Zl., sd=22 Zl.). Models 4-6 in Table 3 show that diversity is also positively associated with the number of Private Enterprises (per 1000 people). The coefficients on Migrant Diversity are positive and significant as well as substantively meaningful. Estimates in Model 4, which uses data on the enterprises registered in 1995, suggest that moving from lowest to highest levels of diversity is associated with an increase in the number of private enterprises by 5 units (mean=37, sd=21). The coefficients on Migrant Diversity slightly decrease over time, suggesting that municipalities may be slowly converging in entrepreneurship rates. At the same time, diversity is not associated with the greater prevalence of state interprises in the 1990s or in the later periods (not presented).

Overall, the results support H3a: diverse migrant communities performed better in a market economy. Together with the evidence that prior to 1989 diverse and homogeneous communities had com- parable levels of entrepreneurship and wealth and that if anything, homogeneous communities performed slightly better, ﬁndings in Table 3 suggest that historical levels of diversity began to pay off only following Poland’s transition to a market economy and the introduction of formal institutions for

27 Table 3: Diversity and post-1989 Economic Outcomes. Models (1)-(3) Focus on Per Capita Income Tax (in Zloty) in 1995, 1998, and 2000. Models (4)-(6) Focus on the Number of Private Enterprises per 1,000 People in 1995, 1998, 2000. OLS Regression with Fixed Effects.

Dependent variable: log(Income Tax) log(Private Enterprises) 1995 1998 2000 1995 1998 2000 (1) (2) (3) (4) (5) (6) Migrant Diversity 0.287∗∗∗ 0.306∗∗∗ 0.320∗∗∗ 0.300∗∗ 0.218∗ 0.229∗∗ (0.077) (0.085) (0.089) (0.142) (0.112) (0.105)

Share Migrants −0.162∗∗∗ −0.055 0.095 −0.037 0.111 0.157∗ (0.061) (0.067) (0.070) (0.112) (0.088) (0.082)

Share Urban −0.055 0.137∗∗∗ 0.378∗∗∗ 0.613∗∗∗ 0.533∗∗∗ 0.543∗∗∗ (0.034) (0.037) (0.039) (0.062) (0.049) (0.046)

Share in Industry 0.031 0.293∗∗∗ 0.649∗∗∗ 0.607∗∗∗ 0.594∗∗∗ 0.460∗∗∗ (0.102) (0.111) (0.117) (0.187) (0.147) (0.137)

Landholding Inequality 0.029 −0.116 −0.312∗∗ −0.433∗∗ −0.449∗∗∗ −0.439∗∗∗ (0.111) (0.121) (0.127) (0.203) (0.160) (0.150) log(Population) 0.030∗ 0.132∗∗∗ 0.075∗∗∗ 0.036 0.003 −0.007 (0.015) (0.017) (0.018) (0.028) (0.022) (0.021) log(Distance to Germany) −0.005 0.002 −0.017 0.017 −0.023 −0.012 (0.016) (0.017) (0.018) (0.029) (0.023) (0.021)

Constant 4.507∗∗∗ 4.400∗∗∗ 4.275∗∗∗ 3.045∗∗∗ 3.948∗∗∗ 4.146∗∗∗ (0.283) (0.310) (0.326) (0.519) (0.408) (0.382)

Observations 593 593 593 593 593 593 R2 0.251 0.376 0.520 0.452 0.501 0.506 Adjusted R2 0.227 0.357 0.505 0.435 0.485 0.490 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

28 enforcing contracts and protecting private property.

5.4 Business Environment

In line with H3b, diverse migrant communities should have a more favorable business environment than homogeneous migrant communities. To test this hypothesis, I examine entrepreneurs’ responses to the 2005 BEEPS survey (EBRD-World Bank). The dependent variables measure the presence (coded 1) or absence (coded 0) of a particular obstacle to business, as reported by respondents. The main explanatory variable is Migrant Diversity in 1948. Group-level controls are type (Rural Munic- ipality, City under 50,000 and City with 50,000-250,000 people), Share of Migrants, and Distance to Germany. Firm-level controls (firm is the lowest unit of analysis in the survey) are an indicator of whether the firm operates in the service or other sectors (Service Sector), firm size (factor variable with three levels (1) < 50 Employees, (2) 50-249 Employees, and (3) > 250 Employees), and an indicator of whether the firm is public or private (Private Sector). To account for the grouped nature of the data21 and to examine the importance of migrant diversity, a group-level variable, I use multilevel logit models with varying intercepts. Regression results in Table 4 suggest that entrepreneurs operating in more diverse localities are significantly less likely to identify functioning of the judiciary, corruption, street crime and theft, as well as organized crime, as significant obstacles to doing business. The results are substantively meaningful (see Figure 4 for average marginal probabilities). For example, the probability that a respondent identifies functioning of the judiciary as an obstacle falls from 0.69 in homogeneous units to 0.31 in units at the highest levels of Migrant Diversity. The probability of identifying organized crime as an obstacle falls even more steeply, from 0.48 in most homogeneous units to 0.06 in most diverse units. At the same time, historical levels of migrant diversity are not significantly associated with a presence of other obstacles, such as customs and trade regulations, business licensing and permits, access to financing, or uncertainty about regulatory policies (see Table A5 in the Appendix).

21Most geographical units include responses from multiple entrepreneurs, as shown in Table A4 in the Appendix.

29 Table 4: Migrant Diversity and Business Environment. Dependent Variables are Coded 1 if Respon- dents Identiﬁed (1) Functioning of the Judiciary; (2) Corruption; (3) Street Crime, Theft and Disor- der; or (4) Organized Crime/Maﬁa as “Moderate Obstacle” or “Major Obstacle” to Doing Business. BEEPS 2005. Multilevel Logit Regression.

Dependent variable: Judiciary Corruption Crime & Theft Organized Crime (1) (2) (3) (4) Migrant Diversity −3.640∗ −3.630∗ −4.480∗ −6.768∗ (2.146) (1.975) (2.672) (3.649)

Share Migrants −0.400 −0.028 −0.224 −0.713 (0.862) (0.846) (1.077) (1.485)

Size: >250 Employees −0.123 0.713 0.415 −0.082 (0.766) (0.861) (0.817) (0.982)

Size: <50 Employees 0.142 0.393 −0.492 −0.062 (0.376) (0.399) (0.412) (0.518)

Service Sector −0.335 −0.302 0.393 0.242 (0.332) (0.341) (0.370) (0.465)

City: 50-250,000 0.203 1.054∗ −0.101 −0.306 (0.604) (0.539) (0.749) (1.006)

City: < 50,000 −0.010 −0.133 −0.874 −1.669 (0.488) (0.381) (0.662) (1.015)

Private Sector 1.320∗ 2.333∗∗ 1.159 0.016 (0.757) (1.127) (0.786) (0.850)

log(Distance to Germany) −0.401 −0.464∗ 0.144 0.077 (0.282) (0.237) (0.429) (0.501)

Constant 2.628 0.757 0.062 1.829 (2.377) (2.404) (3.007) (3.824)

Observations 212 207 208 205 Log Likelihood −138.888 −125.911 −119.113 −84.207 Akaike Inf. Crit. 299.776 273.822 260.226 190.413 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

30 1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25 Predicted Probability of Judiciary as Obstacle of Judiciary Probability Predicted Predicted Probability of Corruption as Obstacle of Corruption Probability Predicted

0.00 0.00

0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6 Migrant Diversity in 1948 Migrant Diversity in 1948

1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25 Predicted Probability of Crime and Theft and as Obstacle of Crime Probability Predicted Predicted Probability of Organized Crime as Obstacle Crime of Organized Probability Predicted

0.00 0.00

0.2 0.3 0.4 0.5 0.6 0.2 0.3 0.4 0.5 0.6 Migrant Diversity in 1948 Migrant Diversity in 1948

Figure 4: Average Marginal Probability of Identifying Speciﬁc Obstacles to Doing Business, from Top Left to Bottom Right: (1) Functioning of the Judiciary; (2) Corruption; (3) Street Crime and Theft; (4) Organized Crime in Communities at Various Levels of Diversity. Probabilies are Calculated Based on the Multilevel Logit Regression Models in Table 4. 2005 BEEPs data.

31 6 Alternative Explanations and Additional Evidence

6.1 Human Capital

Could differences in human capital or complementary skillsets explain the economic success of diverse resettled communities in Poland following the transition to a market economy? The lack of detailed historical data on the occupations of migrants in the 1940s does not allow us to directly explore this possibility. However, the data on municipal-level educational outcomes, a proxy for human capital, was collected by the national censuses in 1978, 1988, and 2002. If the differences in education among diverse and homogeneous migrant communities existed in 1948, they should still be visible in the 1978 census and may either fade away or continue growing in subsequent periods. Regressions of the share of the population with secondary education or above on migrant diversity are presented in the Appendix (Table A6). There are no statistically significant differences in education in 1978 or 1988, but the coefficient on migrant diversity is positive and significant at the 95% level for education in the 2002 census. This suggests that higher incomes in historically diverse communities may be driving greater investment in human capital, but not the reverse.

6.2 State Policies

Another possibility is that the socialist state treated historically homogeneous and historically diverse migrant communities differently. For example, it may have invested more resources in diverse areas in order to facilitate integration. There is no qualitative evidence to support this claim: overall the state strove to eliminate differences among migrants from different regions and create a unified nation (Ther, 1996). Furthermore, the regime’s economic policies were centralized, such that policies were not tailored to local needs. As an additional test, I regress the number of schools and libraries, which were provided by the state, as well as the size of the public sector and the number of people employed in collectivized agriculture, on migrant diversity (see Appendix, Table A7). The coefficient on Migrant Diversity is not significant: the results confirm that state policies did not vary across heterogeneous and homogeneous resettled communities.

32 6.3 Institutional Trust

This paper contends that the main difference between diverse and homogeneous communities is the degree of economic actors’ reliance on formal or informal institutions at the local level. As shown in Section 5.1, homogeneous communities have more volunteer fire brigades, which rely on informal norms and networks to deliver a public good. I provide no direct evidence that the demand for formal institutions was higher in diverse communities, however. Because the demand for the law is difficult to quantify,22 I examine confidence in formal institutions and organizations tasked with enforcing them, to provide additional evidence for the institutional mechanisms linking diversity to economic outcomes. Such data is available from the 2010 EBRD Life in Transition (LiTs) survey (N=1616).23 While trust in courts, the police, and the government is not the same as reliance on formal institutions, the probability that people will use formal over informal institutional mechanisms is typically correlated with their confidence in these institutions (Wallace and Latcheva, 2006). LiTs respondents were drawn using two-stage sampling. First, 74 Primary Sampling Units (PSUs) were selected randomly, with probability proportional to size. Second, within each PSU, households were selected using the random walk procedure. I analyzed responses from 21 PSUs located in the formerly German territories (listed in Table A8 in the Appendix), in combination with the historical data on Migrant Diversity and the Share of Migrants in 1948. I also include Distance to Germany and Urban/Rural status at the PSU level. To account for the grouped nature of the data24 and to examine the importance of Migrant Diversity, a PSU-level variable, I use multilevel logit models with varying intercepts. I also include a number of respondent-level controls: age, gender, education level, self-assessment of material wellbeing, church attendance, and employment status. To construct the dependent variable (Trust) I coded responses “Some trust” or “Complete trust” as 1, and responses “Complete distrust”, “Some distrust”, “Neither trust nor distrust” as 0.

22Some studies have used the number of court cases, but this may instead speak to a well-functioning market (Williamson, 1979; Pistor, 1996). 23Ideally, we would measure institutional trust as well as the features of the business environment immediately after the 1989 transition, as it could be that better economic outcomes in diverse communities drive attitudes toward public-order institutions, and not the reverse. Unfortunately, measures of institutional trust in earlier surveys are available only at the province level. 24Respondents within a PSU are likely to be more similar to one another than respondents from another PSU, which violates the assumption of independent and identically distributed observations.

33 Table 5: Migrant Diversity and Trust in Formal Institutions. Dependent Variables are Trust (1) In Government; (2) In Police; (3) In Courts; and (4) Generalized Trust. Migrant Diversity and Share of Migrants are Measured in 1948. Individual-level Variables are Coded from the Survey Questionnaire. 2010 EBRD Life in Transition Survey. Multilevel Logit Regression.

Dependent Variable: Trust In Government In Police In Courts Generalized (1) (2) (3) (4) Migrant Diversity 4.017∗∗ 3.757∗ 4.962∗∗ 1.257 (1.877) (2.108) (2.329) (2.306)

Share Migrants 4.003∗∗ 3.601∗∗ 3.836∗∗ 0.172 (1.560) (1.756) (1.933) (1.901)

Male −0.159 −0.281 0.012 0.064 (0.254) (0.231) (0.249) (0.237)

Education Level 0.101 0.062 0.229∗∗ 0.081 (0.096) (0.089) (0.095) (0.093)

Income 0.247∗∗∗ 0.117 0.149 0.228∗∗∗ (0.089) (0.080) (0.091) (0.081)

Church Attendance −0.274 −0.169 0.327 0.079 (0.288) (0.274) (0.291) (0.288)

Employed −0.283 −0.150 −0.107 0.325 (0.296) (0.266) (0.293) (0.282)

log(Age) 0.412 0.828∗∗ 0.287 −0.092 (0.363) (0.335) (0.359) (0.335)

Type: Urban −1.273∗∗ −0.607 −1.034 −0.053 (0.590) (0.637) (0.717) (0.695)

log(Distance to Germany) 0.298 0.439 0.196 −0.566 (0.330) (0.364) (0.411) (0.396)

Constant −10.312∗∗∗ −10.795∗∗∗ −9.948∗∗ −0.157 (3.456) (3.684) (4.089) (3.916)

Observations 444 444 444 444 Log Likelihood −213.809 −255.895 −223.296 −239.050 Akaike Inf. Crit. 451.618 535.790 470.592 502.099 Bayesian Inf. Crit. 500.768 584.940 519.742 551.249 Note: 34 ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01 1.00 1.00

0.75 0.75

0.50 0.50

0.25 0.25 Predicted Probability of Trust Courts in Probability Predicted Predicted Probability of Trust the Police in Probability Predicted

0.00 0.00

0.0 0.2 0.4 0.6 0.0 0.2 0.4 0.6 Migrant Diversity in 1948 Migrant Diversity in 1948

1.00

0.75

0.50

0.25 Predicted Probability of Trust Government in Probability Predicted

0.00

0.0 0.2 0.4 0.6 Migrant Diversity in 1948

Figure 5: Average Marginal Probability of Trust in Formal Institutions and Related Organizations across various levels of migrant diversity. Results based on the Multilevel Logit Regression Models in Table 5. 2010 EBRD LiTs data.

35 Regressions in Table 5 (Models 1-3) show that respondents in diverse migrant communities are more likely to report trust in the government, the police, and courts than respondents in homogeneous migrant communities, even when individual-level controls that have been shown to inﬂuence institutional trust in the literature are included. The results are substantively meaningful, as can be seen from the average marginal probability plots (Figure 5). Moving from lowest to highest levels of migrant diversity is associated with an increase in predicted probability of expressing trust in the government from 0.09 to 0.38; in courts from 0.08 to 0.46; and in the police from 0.18 to 0.53. At the same time, residents of homogeneous and diverse communities have similar levels of generalized trust (Model 4). Thus, homogeneous and diverse communities differ in their perception of formal institutions and related ogranizations rather than in the overall levels of trust. While the results in Table 5 cannot be interpreted causally, as trust in formal institutions measured as late as 2010 may also be the product of higher incomes and entrepreneurship rates in the 1990s and 2000s, they provide suggestive evidence for my argument that residents of diverse communities are more likely to rely on formal institutions than residents of homogeneous migrant communities.

7 Discussion and Conclusion

The case of population transfers in the aftermath of WWII provides us with an important opportunity to examine the implications of cultural diversity for long-run social and economic development, an issue ever more relevant today, as Europe is struggling to cope with an influx of refugees from war- torn regions and as the number of forced migrats is reaching record-breaking levels across the world. Using an original historical dataset on the composition of migrant populations at the municipal level in the territory transferred from Germany to Poland in 1945, I show that cultural diversity does not exert a persistently negative effect on socio-economic development. I find that municipalities settled by diverse migrant groups in the 1940s had higher per capita incomes as well as greater entrepreneurship rates following Poland’s transition to a market economy than communities settled by more homogeneous migrant groups. I also find that residents of diverse communities report more institutional trust (in courts, police, and the government) and that diverse communities offer a more favorable environment to doing business. Intriguingly, economic differences between the historically

36 diverse and homogeneous communities were negligible prior to 1989 – although data limitations do not allow us to fully examine the frequency and nature of economic transactions during state socialism – and became visible only following Poland’s transition to a market economy. The paper makes several theoretical and empirical contributions. First, it brings together the inter- disciplinary scholarship on diversity, institutions, and social capital by theorizing how coordination and enforcement mechanisms evolve in diverse and homogeneous communities over time. I show that diversity may affect economic outcomes indirectly, by influencing the reliance on informal and formal institutions, and that some of its beneficial effects may become visible only decades later, following a major institutional transformation. This conclusion challenges the predominant view of diversity in political science and economics as unambiguously harmful to economic development and suggests that in the long run, a society can minimize the costs and maximize the benefits of diversity by adopting robust formal institutions. The findings also provide a more nuanced view on the relevance of social capital for economic growth by showing that informal norms and networks may be a poor substitute to formal institutions in developed market economies. In doing so, the paper suggests important scope conditions for the arguments that emphasize the disadvantages of ethnic, social, or linguistic heterogeneity -– namely, weak formal institutions and low levels of economic development. Indeed, the disruption of informal norms and networks and the formation of linkages beyond community boundaries may be a crucial stage in the strengthening of formal institutions and nation-building processes (Migdal, 1988). Additionally, the paper advances the research on the benefits of formal legal institutions by ar- guing that at the subnational level, reliance on formal law and state enforcement may vary with the availability of informal institutional alternatives, independently of their relative effectiveness (Gans- Morse, 2016). Because institutions are path dependent, private actors may continue to use informal ties following a change in the formal institutional environment, even when the economic disadvantages of such strategies become evident. Can the lessons from the Polish case apply elsewhere? A possible limitation to the generalizability of my findings is the fact that in the post-WWII period most migrants shared Polish nationality and the Roman Catholic faith, even as they spoke various dialects and came from regions with distinct

37 institutional legacies. In this Poland differs from the ethnically and religiously diverse African states, where the impact of diversity on economic development has been evaluated as especially negative (Miguel, 2004; Easterly and Levine, 1997). Migrants settled in the former German territories may have also faced comparatively smaller cooperation problems than culturally diverse communities in many developing countries because they lived in a state that sought to assimilate its diverse citizens into one nation and limited political and economic competition. And yet the mechanisms argued to account for the success of homogeneous groups in the literature – shared norms, dense networks, and similar preferences (e.g. Habyarimana and Weinstein, 2009; Miguel and Gugerty, 2005; Alesina et al., 1999) – were also present in the Polish case. Indeed, the paper conﬁrms the conclusion in much of extant research that homogeneous migrant communities are better at bottom-up public goods provision than diverse migrant communities. Where the paper diverges from the existing scholarship is in that it demonstrates that weak informal coordination and enforcement mechanisms at the community level do not necessarily lead to poorer economic outcomes in the long term. I show that over time diverse communities can make up for the weakness of informal norms and networks by relying on formal institutions and state enforcement. Thus, I argue that the effects of diversity are conditional on the broader institutional environment and that the predominance of formal over informal rules in diverse communities can even produce economic beneﬁts in the long run. In doing so, I clarify the conditions under which cooperation and social order can be secured in a diverse society and contribute to the growing literature that emphasizes the role political and economic institutions play in mediating the relationship between diversity and economic outcomes (Gao, 2016; Miguel, 2004; Weldon, 2006). Why did so many other studies fail to uncover a similarly positive relationship between diversity and development? One reason may be their disproportionate focus on the provision of public goods by local communities (Algan et al., 2011; Khwaja, 2009; Miguel and Gugerty, 2005), even though in most parts of the world public goods are provided by the state (Banerjee et al., 2008; Singh and vom Hau, 2016). In fact, bottom-up provision of public goods may be a second-best solution, producing better developmental outcomes only when the state cannot organize the provision of public goods by taxing individuals (Fafchamps, 2006).

38 Another important reason for the largely pessimistic view of cultural diversity may be the dearth of analyses that take “a longer term, historically informed perspective” in order to trace how cooperation evolves over time in different cultural settings (Wimmer, 2016, p. 1409). The limitations of ahistori- cal analyses that have dominated the ﬁeld are increasingly recognized by scholars who emphasize that homogeneity is to a large extent the product of institutional and economic development (Darden and Mylonas, 2016; Wimmer, 2016; Singh and vom Hau, 2016). Indeed, it is no coincidence that the most ethnically homogeneous states are presently located in Europe, where top-down attempts to obliter- ate ethnic, religious and linguistic differences – from mass schooling to genocide – were especially successful in the 19th and 20th centuries. One way to better understand the endogenous relationship between the cultural composition of a society and its institutional and economic development is then to adopt a historical approach (Wimmer, 2016; Singh and vom Hau, 2016). I take an important step in this direction by studying how Polish communities reached new cooperative equilibria following an exogenous shock to their cultural makeup over several decades and by comparing the impact of diversity on economic outcomes before and after Poland’s transition to a market economy. Exploring the complex relationship between diversity, institutions, and economic development through a prism of history could be a fruitful direction for future research.

References Alesina, A., R. Baqir, and W. Easterly (1999). Public goods and ethnic divisions. The Quarterly Journal of Economics 114(4), 1243–1284.

Alesina, A. and E. L. Ferrara (2000). Participation in heterogeneous communities. Quarterly Journal of Economics 115(3), 847–904.

Alesina, A. and E. L. Ferrara (2002). Who trusts others? Journal of Public Economics 85(2), 207– 234.

Alesina, A., J. Harnoss, and H. Rapoport (2013). Birthplace diversity and economic prosperity. IZA Discussion Paper No. 7568.

Algan, Y., C. Hémet, and D. Laitin (2011). Diversity and public goods: A natural experiment with exogenous residential allocation. Technical report, Forschungsinstitut zur Zukunft der Arbeit (IZA), Berlin, Germany. IZA Discussion Paper No. 6053.

Bandiera, O., I. Barankay, and I. Rasul (2005). Cooperation in collective action. Economics of

39 Transition 13(473-78), 3.

Banerjee, A., L. Iyer, and R. Somanathan (2008). Handbook of Development Economics, Volume 4, Chapter Public Action for Public Goods. Chapter 49, pp. 3117–3154. Elsevier.

Bartkowski, J. (2003). Tradycja i Polityka. Wplyw tradycji kulturowych polskich na wspolczesne zachowania spoleczne i polityczne. Warszawa: Zak Academic Publishing House.

Chmielewska, B. (1965). Społeczne Przeobrazenia˙ srodowisk´ Wiejskich na Ziemiach Zachodnich, na Przykładzie Pi˛eciuWsi w Województwie Zielonogórskim. Poznan:´ Instytut Zachodni.

Ciesielski, S. (2000). Przesiedlenie Ludnosci´ Polskiej z Kresów Wschodnich do Polski 1944-1947. Wybór Dokumentów. Warszawa: Wydawnictwo Neriton.

Cook, K. S., E. R. Rice, and A. Gerbasi (2004). The emergence of trust networks under uncertainty: The case of transitional economies—insights from social psychological research. In S. Rose- Ackerman, B. Rothstein, and J. Kornai (Eds.), Creating Social Trust in Post-Socialist Transition, pp. 193–212. Palgrave Macmillan.

Costa, D. L. and M. E. Kahn (2003). Understanding the decline in social capital, 1952 – 1998. Kyklos 56(1), 17–46.

Darden, K. and H. Mylonas (2016). Threats to territorial integrity, national mass schooling, and linguistic commonality. Comparative Political Studies 49(11), 1446–1479.

Della Porta, D. and A. Vannucci (1999). Corrupt exchanges: Actors, resources, and mechanisms of political corruption. New York, NY: Aldine de Gruyter.

Dickinson, P. G. (2000). Transforming economies of Eastern Europe: An evaluation of the role and contribution of the small-scale private sector (with speciﬁc reference to Poland). European Business Review 12(2), 84 – 92.

Durlauf, S. N. and M. Fafchamps (2004). Social capital. In P. Aghion and S. Durlauf (Eds.), Handbook of Economic Growth, Volume 1, Chapter 26, pp. 1639–1699. Elsevier.

Dworzak, E. and M. Goc (2011). Pochodzenie terytorialne ludnosci´ napływowej i geograﬁa powo- jennych osiedlen´ na wsi Opolskiej. Opolski Rocznik Muzealny 18(2).

Dyer, J. H. and H. Singh (1998). The relational view: Cooperative strategy and sources of interorga- nizational competitive advantage. Academy of Management Review 23(4), 660–679.

Easterly, W. and R. Levine (1997). Africa’s growth tragedy: Policies and ethnic divisions. The Quarterly Journal of Economics 112(4), 1203–1250.

EBRD (2010). Life in transition. A survey of people’s experiences and attitudes.

40 EBRD-World Bank (2005). Business Environment and Enterprise Performance Survey.

Ellickson, R. C. (1991). Order without Law: How Neighbors Settle Disputes. Cambridge, MA: Harvard University Press.

Estrin, S. and T. Mickiewicz (2010). Shadow economy and entrepreneurial entry. IZA Discussion Paper No. 5053.

Fafchamps, M. (2006). Development and social capital. The Journal of Development Studies 42(7), 1180–1198.

Gans-Morse, J. (2016). Demand for law and the security of property rights: The case of post-Soviet Russia. American Political Science Review, Forthcoming.

Gao, E. (2016). Tribal diversity, fragmented groups, and public goods provision in Jordan. Compar- ative Political Studies 49(10), 1372–1403.

Grabowski, T. (2002). Breaking through to individualism: Poland’s western frontier, 1945-1995. Master’s thesis, Berkeley University.

Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. Amer- ican Journal of Sociology 91, 481–510.

Greif, A. (1989). Reputation and coalitions in Medieval trade: Evidence on the Maghribi traders. Journal of Economic History 49(4), 857–882.

Greif, A. (1993). Contract enforceability and economic institutions in early trade: the Maghribi traders’ coalition. American Economic Review 83(3), 525–548.

Greif, A. (1994). Cultural beliefs and the organization of society: A historical and theoretical reﬂec- tion on collectivist and individualist societies. Journal of Political Economy 102(5), 912–950.

Greif, A. (2006). Institutions and the Path to the Modern Economy: Lessons from Medieval Trade. Cambridge, UK: Cambridge University Press.

Greif, A. and C. Kingston (2011). Institutions: Rules or equilibria? In N. Schoﬁeld and G. Caballero (Eds.), Political Economy of Institutions, Democracy and Voting. Springer Science & Business Media.

Greif, A. and G. Tabellini (2015). The clan and the corporation: Sustaining cooperation in China and Europe. IDEAS Working Paper Series from RePEc.

Habyarimana, James, M. H. D. P. and J. M. Weinstein (2009). Coethnicity: Diversity and the Dilem- mas of Collective Action. New York, NY: Russell Sage Foundation.

Halicka, B. (2013). Polens Wilder Westen. Erzwungene Migration und die Kulturelle Aneignung des

41 Oderraums 1945-1948. Zürich: Ferdinand Schöningh.

Hendley, K. (1999). Rewriting the rules of the game in Russia: The neglected issue of demand for law. East European Constitutional Review 8(3), 89–95.

Jasiewicz, Z. (1972). Rodzina wiejska na Ziemiach Zachodnich i Północnych jako przedmiot badan´ etnograﬁcznych. LUD 56, 34 – 55.

Kaminska,´ W. (2006). Pozarolnicza Indywidualna Działalnos´cGospodarcza´ w Polsce w Latach 1988- 2003. Warszawa: AAN.

Kenney, P. (1997). Rebuilding Poland: Workers and Communists, 1945-1950. Ithaca, NY: Cornell University Press.

Kersten, K. (2001). Forced migration and the transformation of Polish society in the postwar period. In P. Ther and A. Siljak (Eds.), Redrawing Nations: Ethnic Cleansing in East-Central Europe, 1944-1948. Rowman Littleﬁeld.

Khwaja, A. I. (2009). Can good projects succeed in bad communities? Journal of Public Eco- nomics 93, 899–916.

Klon-Jawor (2013). Ochotnicze Straze˙ Pozarne˙ w Polsce. Research Report. Stwaryszenie Klon-Jawor.

Knack, S. and P. Keefer (1997). Does social capital have an economic payoff? A cross-country investigation. Quarterly Journal of Economics 112(4), 1251–88.

Kochanowski, J. (2010). Tylnymi Drzwiami: "Czarny Rynek" w Polsce 1944-1989. Warsaw: Neriton.

Kornai, J. (1990). The afﬁnity between ownership forms and coordination mechanisms: The common experience of reform in socialist countries. Journal of Economic Perspectives 4(3), 131–47.

Kranton, R. E. (1996). Reciprocal exchange: A self-sustaining system. American Economic Re- view 86(4), 830–851.

La Ferrara, E. (2003). Kin groups and reciprocity: A model of credit transactions in Ghana. American Economic Review 93(5), 1730–1751.

Macaulay, S. (1963). Non-contractual relations in business: A preliminary study. American Socio- logical Review 28(1), 55–67.

McMillan, J. and C. M. Woodruff (2000). Private order under dysfunctional public order. Michigan Law Review 98, 2421–2458.

Migdal, J. S. (1988). Strong Societies and Weak States: State-Society Relations and State Capabilities in the Third World. Princeton, NJ: Princeton University Press.

42 Miguel, E. (2004). Tribe or nation? Nation building and public goods in Kenya versus Tanzania. World Politics 56(3), 327–362.

Miguel, E. and M. K. Gugerty (2005). Ethnic diversity, social sanctions, and public goods in Kenya. Journal of Public Economics 89, 2325–2368.

Milgrom, P. R., D. C. North, and B. W. Weingast (1990). The role of institutions in the revival of trade: The law merchant, private judges, and the champagne fairs. Economics and Politics 2(1), 1–23.

Milhaupt, C. J. and M. D. West (2000). The dark side of private ordering: An institutional and empirical analysis of organized crime. The University of Chicago Law Review 67(1), 41–98.

North, D. (1981). Structure and Change in Economic History. New York, NY: Norton.

North, D. (1990). Institutions, Institutional Change and Economic Performance. Cambridge, MA: Cambridge University Press.

Ogilvie, S. and A. Carus (2014). Institutions and economic growth in historical perspective. In P. Aghion and S. N. Durlauf (Eds.), Handbook of Economic Growth, Chapter 8, pp. 403–513. Elsevier.

Pistor, K. (1996). Supply and demand for contract enforcement in Russia: Courts, arbitration, and private enforcement. Review of Central and East European Law 1, 55–87.

Putnam, R. D. (2007). E pluribus unum: Diversity and community in the twenty-ﬁrst century. The 2006 Johan Skytte Prize Lecture. Scandinavian Political Studies 30(2), 137–74.

Rybicki, P. (1967). Ziemie Zachodnie ze˙ stanowiska teorii socjologicznej. In W. Markiewicz and P. Rybicki: (Eds.), Przemiany Społeczne na Ziemiach Zachodnich, pp. 338–384. Poznan:´ Instytut Zachodni.

Schneider, F. and D. Enste (2000). Shadow economies: Size, causes, and consequences. The Journal of Economic Literature 38(1), 77–114.

Singh, P. and M. vom Hau (2016). Ethnicity in time: Politics, history, and the relationship between ethnic diversity and public goods provision. Comparative Political Studies, 1–38.

Ther, P. (1996). The integration of expellees in Germany and Poland after World War II: A historical reassessment. Slavic Review 55(4), 779–805.

Thum, G. (2011). How Breslau Became Wroclaw during the Century of Expulsions. Princeton, NJ: Princeton University Press.

Urban, W. (1965). Archidiecezja Wroclawska w latach, 1945-1965. Nasza Prezlosc: Studia z Dziejow Kosciola i Kultury Katolilckiej w Polsce 22, 11–68.

43 USHMM (2010). Poles: Victims of Nazi era. United States Holocaust Memorial Museum, https://web.archive.org/web/20100327054444/http://www.ushmm.org/education/resource/poles/poles.php.

Uzzi, B. (1997). Social structure and competition in inter-ﬁrm networks: The paradox of embeddedness. Administrative Science Quarterly 42, 35–67.

Wallace, C. and R. Latcheva (2006). Economic transformation outside the law: Corruption, trust in public institutions and the informal economy in transition countries of Central and Eastern Europe. Europe-Asia Studies 58(1), 81–102.

Weldon, S. A. (2006). The institutional context of tolerance for ethnic minorities: A comparative, multilevel analysis of Western Europe. American Journal of Political Science 50(2), 331–349.

Williamson, O. (1979). Transaction-cost economics: The governance of contractual relations. Journal of Law Economics 22(2), 233–261.

Williamson, O. (1985). The Economic Institutions of Capitalism. New York, NY: Free Press.

Wimmer, A. (2016). Is diversity detrimental? Ethnic fractionalization, public goods provision, and the historical legacies of stateness. Comparative Political Studies 49(11), 1407–1445.

Woolcock, M. (1998). Social capital and economic development: Toward a theoretical synthesis and policy framework. Theory and Society 20(20), 151–208.

Zaborowski, H. (1970). Czym jest dla ciebie miasto Wroclaw? In B. Jalowiecki (Ed.), Zwiazani z miastem. . . , pp. 175–183. Wroclaw: Ossolineum.

44 Appendix

Table A1: Description of the Four Main Population Groups in the Resettled Territories.

Indigenous/autochthonous population (autochthoni): At the end of the war, about 1.2 million people living in German territories declared Polish nationality. Of these people, about one million passed verification procedure. According to a survey conducted in 1949, of the 2,769 autochthons, 28% spoke poor Polish, 34% - middling Polish, and only 37% were fluent. The autochthonous population was concentrated pri- marily in Opole and Warminsko-Mazurskie provinces. While this group included geographically specific ethnic groups such as Mazurians in northern Poland and Silesians in southwest Poland, in any given county or municipality autochthonous population was homogeneous.

Repatriates from the USSR (repatrianci): About 1.9 million repatriates arrived from the territories ac- quired by the Soviet Union when the borders have shifted. These migrants originated in territories that are today part of Lithuania, Belarus, and Ukraine, and a small proportion of this group was exiled to Siberia when the Soviet Union invaded these territories in 1939 and then allowed to repatriate to Poland after 1945. The population from western Ukraine, the ﬁrst to relocate as they ﬂed the attacks of the Ukrainian Insurgent Army (UPA), made up 51.88% of all repatriates. Repatriates came from predominantly rural areas; some 60.81% lived in rural areas prior to relocation. This migrant group was representative of the general population in Kresy: 33.38% of them were peasants; 16.64% were workers; 11.63% craftsmen; and just 23% were white-collar employees.These migrants had strong ties to the Catholic Church and patiotic values (Halicka 2013).

Settlers from Central Poland (osadnicy): Another 2.2 million migrants came from central Poland. These migrants left homes voluntarily and are thus not entirely representative of the general population. They came predominantly from rural areas, but also included the inhabitants of small towns in central Poland and of large cities destroyed by war, including Warsaw, Poznan, Bialystok, and Grudziadz. Under circular No. 22 issued in March 1946, farmers from regions damaged during the war received priority for resettlement. These migrants also had little agency in choosing their destinations in western Poland.

Reemigrants (reemigranci): An additional, smaller group of settlers arrived as voluntary re-emigrants from other European states (Germany, France, Belgium, Romania, Yugoslavia). They were mostly working class families, who had immigrated in the late 19th or early 20th century into the industrial centers of Europe. Those who decided to return to Poland believed they would ﬁnd better career opportunities under the Communist regime. The Polish government campaigned particularly intense to attract skilled workers in the mining industry, because after the Germans had left there was a serious shortage of miners in Lower and Upper Silesia. Most of these re-emigrants, however, were manual laborers. Similarly to other groups, these settlers had no agency in deciding where to settle.

45 Table A2: Distribution of Volunteer Fire Brigades by Year of Founding, Opole province. 100 80 60 40 Number of OSPs Number by Period 20 0

1900 1920 1940 1960 1980 2000

year

46 Table A3: Summary of the Main Variables Used in the Analyses.

Statistic N Mean St. Dev. Min Max Share migrants (1948) 631 0.86 0.27 0.0 1.0 Migrant diversity (1948) 631 0.42 0.14 0.02 0.66 Share of the largest group (1948) 621 0.67 0.15 0.37 1.0 Share from USSR (1948) 631 0.28 0.20 0.0 0.88 Share Autochthonous (1948) 631 0.14 0.28 0.0 1.0 Share from Central Poland (1948) 631 0.54 0.25 0.0 1.0 Share from other countries (1948) 631 0.05 0.07 0.0 0.64 Share urban (1948) 631 0.21 0.34 0.0 1.0 Share men (1948) 631 0.48 0.03 0.22 0.63 Share in industry (1939) 593 0.26 0.12 0.04 0.64 Land fractionalization (1939) 593 0.67 0.43 0.15 0.77 In Private Handicrafts per 1000 (1982) 615 10.95 7.33 0.00 61.59 Shops per 1000 (1982) 606 6.96 2.29 3.09 41.67 TVs per 1000 (1982) 589 210.90 36.48 68.00 364.00 Phones per 1000 (1982) 612 31.86 20.29 4.50 139.10 Libraries per 1000 (1982) 615 0.41 0.21 0.25 1.74 Schools per 1000 (1982) 615 0.54 0.28 0.00 1.59 Employed in Public Sector per 1000 (1982) 615 280.10 127.49 15.48 1310.00 Employed in Collectivized Agriculture per 1000 (1982) 615 79.54 61.80 0.00 318.00 Employed in Nationalized Industry per 1000 (1982) 615 78.20 97.71 0.00 1081.00 Share with secondary education (1978) 631 0.16 0.08 0.00 0.58 Share with secondary education (1988) 631 0.15 0.06 0.07 0.49 Share with secondary education (2002) 632 0.25 0.08 0.12 0.59 Volunteer Fire Brigades per 10,000 (1997) 632 6.53 5.5 0.00 38.36 Sports Clubs per 10,000 (2010) 632 18.69 64.06 0.00 1076.00 Income tax per capita, Zl. (1995) 632 101.40 21.70 8.52 154.80 Income tax per capita, Zl. (1998) 632 169.50 64.91 13.60 656.70 Private enterprises per 1000 (1995) 632 36.64 21.31 5.82 223.50 Private enterprises per 1000 (1998) 632 54.66 27.25 13.10 312.70

47 Table A4: 2005 BEEPS Sampling Localities in the Formerly German Territories. Unit Code: citowvil Population Size Share Migrants Migrant Diversity Distance to Germany Szczawno Zdroj 6 Under 50K 0.99 0.66 28.92 Gdansk city 7 250K-1MLN 0.89 0.39 292.00 Boguszow Gorce 10 Under 50K 0.99 0.62 93.32 Koszalin 17 50K-250K 0.99 0.38 132.17 Olsztyn city 20 50K-250K 0.92 0.49 403.57 Opole city 21 50K-250K 0.53 0.45 213.42 Szczecin 27 250K-1MLN 1.00 0.50 15.78 Swidnica city 28 50K-250K 0.99 0.41 108.97 Wroclaw city 29 250K-1MLN 0.99 0.40 137.54 Zielona Gora 30 50K-250K 0.99 0.36 53.29 Gliwice city 31 250K-1MLN 0.48 0.57 273.55 Zabrze 33 50K-250K 0.32 0.60 281.99 Pruszcz Gdanski 40 Under 50K 0.75 0.50 291.02 Bytom 45 50K-250K 0.43 0.55 286.46 Krosnice 52 Under 50K 0.98 0.50 163.90 Kolobrzeg 55 Under 50K 1.00 0.17 93.38 Ostroda 62 Under 50K 0.90 0.46 369.43 Szczytno 63 Under 50K 0.84 0.51 436.39 Gluszyca 69 Under 50K 0.99 0.57 107.03 Dobrzen Wielki 73 Under 50K 0.07 0.64 206.08 Glogowek 78 Under 50K 0.21 0.34 219.71 Dabrowa 79 Under 50K 0.20 0.40 201.14 Prudnik 82 Under 50K 0.93 0.53 197.56 Zdzieszowice 83 Under 50K 0.07 0.49 232.38 Dobrzen Wielki 84 Under 50K 0.07 0.64 206.08 Strzelce Opolskie 88 Under 50K 0.23 0.60 240.55 Renska Wies 89 Under 50K 0.04 0.29 235.24 Pyskowice 92 Under 50K 0.21 0.49 267.54 Ziebice 94 Under 50K 0.99 0.50 154.32 Nowa Sol 97 Under 50K 0.99 0.58 64.29 Czerwiensk 101 50K-250K 1.00 0.50 42.25 Olesnica 119 Under 50K 1.00 0.50 163.63 Dlugoleka 124 Under 50K 1.00 0.36 152.11 Strzelin 125 Under 50K 1.00 0.66 150.70 Olawa 126 Under 50K 0.99 0.52 161.16 Strzelin 127 Under 50K 1.00 0.66 150.70 Bielawa 128 Under 50K 1.00 0.57 122.93 Dzierzoniow 129 Under 50K 0.99 0.54 123.54 Olawa 130 Under 50K 1.00 0.51 162.64 Wisznia Mala 131 Under 50K 0.99 0.35 138.86 Dlugoleka 132 Under 50K 1.00 0.36 152.11 Klodzko 133 Under 50K 0.99 0.53 135.75 Jaworzyna Slaska 134 Under 50K 1.00 0.60 103.73 Prusice 135 Under 50K 1.00 0.43 131.65 Twardogora 136 Under 50K 0.99 0.48 169.54 Twardogora 137 Under 50K 0.99 0.48 169.54 Wisznia Mala 138 Under 50K 0.99 0.35 138.86 Zabkowice Slaskie 139 Under 50K 0.99 0.53 140.89 Zlotoryja 143 Under 50K 0.99 0.35 63.28 Zawonia 144 Under 50K 0.99 0.45 152.06 Boleslawiec 146 Under 50K 1.00 0.60 36.33 Zawonia 147 Under 50K 0.99 0.45 152.06 Krosnice 148 Under 50K 0.98 0.50 163.90 Oborniki Slaskie 149 Under 50K 0.99 0.48 129.20 Milicz 150 Under 50K 0.95 0.52 159.11 Legnica 151 50K-250K 1.00 0.54 79.02 Zielona Gora 165 Under 50K 1.00 0.54 55.96 Swiebodzin 166 Under 50K 0.98 0.44 57.28 Kuznia Raciborska 168 Under 50K 0.02 0.49 259.25 Walbrzych 180 50K-250K 0.99 0.61 97.64 Sopot 186 50K-250K 0.89 0.25 287.49 Trzebnica 206 Under 50K 0.99 0.57 144.13

48 Table A5: Migrant Diversity and Business Environment. Dependent Variables are Coded 1 if Respon- dents Identiﬁed (1) Customs and Trade Regulations; (2) Tax Administration; (3) Business Licensing and Permits; (4) Access to Financing; or (5) Uncertainty about Regulatory Policies as a “Moderate Obstacle” or a “Major Obstacle” to Doing Business. BEEPS 2005. Multilevel Logit Regression.

Dependent variable: Regulations Tax Administration Licensing Financing Uncertainty (1) (2) (3) (4) (5) Migrant Diversity −2.709 −1.931 0.946 2.003 2.407 (2.202) (2.244) (2.318) (2.053) (2.012)

Share Migrants −0.211 −0.543 −2.204∗∗ −0.163 −0.025 (0.901) (0.960) (0.864) (0.862) (0.869)

Size: > 250 Employees −0.0005 1.482 −0.854 1.430∗ 0.320 (0.909) (0.928) (1.162) (0.840) (0.794)

Size: < 50 Employees 0.269 0.302 0.755 0.506 −0.156 (0.423) (0.383) (0.466) (0.382) (0.389)

Service Sector 0.260 −0.557∗ 0.269 −0.830∗∗ −0.198 (0.363) (0.332) (0.373) (0.338) (0.330)

City: 50-250,000 0.671 0.261 −0.507 −1.251∗∗ −0.001 (0.569) (0.529) (0.604) (0.553) (0.507)

City: <50,000 0.578 1.326∗∗∗ −0.387 0.051 0.657∗ (0.418) (0.411) (0.447) (0.395) (0.382)

Private Sector 0.752 2.169∗∗ −0.136 1.729∗∗ 1.234∗ (0.856) (0.967) (0.879) (0.840) (0.670) log(Distance to Germany) 0.040 0.137 −0.432∗ −0.278 −0.106 (0.259) (0.238) (0.252) (0.245) (0.233)

Constant −1.140 −1.693 2.091 −0.476 −1.141 (2.428) (2.570) (2.461) (2.332) (2.255)

Observations 187 203 201 202 204 Log Likelihood −105.756 −123.347 −103.601 −120.352 −125.134 Akaike Inf. Crit. 233.512 268.695 229.202 262.704 272.267 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

49 Table A6: Migrant Diversity and Human Capital. The Dependent Variable in all Models is the Share of the Population with Secondary Education and Above according to the 1978, 1988, and 2002 Census at the Municipality Level. OLS Regression with Fixed Effects.

Dependent variable: Proportion With Secondary Education & Above 1978 1988 2002 (1) (2) (3) Migrant Diversity 0.019 0.020 0.055∗∗∗ (0.018) (0.012) (0.018)

Share Migrants 0.059∗∗∗ 0.041∗∗∗ 0.057∗∗∗ (0.014) (0.010) (0.014)

Share Urban 0.141∗∗∗ 0.122∗∗∗ 0.152∗∗∗ (0.008) (0.005) (0.008)

Share in Industry 0.004 0.043∗∗∗ 0.064∗∗∗ (0.023) (0.016) (0.023)

Landholding Inequality −0.154∗∗∗ −0.091∗∗∗ −0.111∗∗∗ (0.026) (0.018) (0.025)

log(Population) 0.026∗∗∗ 0.012∗∗∗ 0.012∗∗∗ (0.004) (0.002) (0.004)

log(Distance to Germany) 0.003 0.002 −0.0003 (0.003) (0.003) (0.004)

Constant 0.034 0.090∗∗ 0.171∗∗∗ (0.064) (0.045) (0.065)

Observations 540 593 593 R2 0.693 0.726 0.682 Adjusted R2 0.682 0.718 0.672 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

50 Table A7: Migrant Diversity and State Policy at the Municipality Level in 1982. Dependent Vari- ables are (1) Libraries; (2) Schools; (3) Employed in Public Sector; (4) Employed in Collectivized Agriculture (5) Employed in Nationalized Industry (per 1000 people). OLS Regression with Fixed Effects. Dependent variables: Libraries Schools Public Sector Agriculture Industry (1) (2) (3) (4) (5) Migrant Diversity −0.033 −0.106 65.765 6.130 34.310 (0.065) (0.085) (41.842) (15.828) (32.256)

Share Migrants −0.025 0.054 118.657∗∗∗ 9.710 50.328∗∗ (0.051) (0.067) (32.810) (12.411) (25.294)

Share Urban −0.279∗∗∗ −0.337∗∗∗ 158.510∗∗∗ −86.369∗∗∗ 58.250∗∗∗ (0.028) (0.037) (18.235) (6.898) (14.058)

Share in Industry −0.238∗∗∗ −0.418∗∗∗ 73.343 −179.139∗∗∗ 308.001∗∗∗ (0.086) (0.113) (55.314) (20.924) (42.642)

Landholding Inequality 0.279∗∗∗ −0.012 −258.642∗∗∗ 53.382∗∗ −113.311∗∗ (0.094) (0.123) (60.262) (22.796) (46.456) log(Population) −0.025∗ −0.042∗∗ 15.096∗ 0.781 4.645 (0.013) (0.017) (8.525) (3.225) (6.572) log(Distance to Germany) −0.017 −0.023 17.776∗∗ −5.538∗ 12.880∗∗ (0.013) (0.017) (8.455) (3.198) (6.518)

Constant 0.745∗∗∗ 1.311∗∗∗ −27.164 143.758∗∗ −175.448 (0.237) (0.310) (152.138) (57.550) (117.284)

Observations 579 579 579 579 579 R2 0.385 0.389 0.345 0.604 0.328 Adjusted R2 0.365 0.369 0.323 0.591 0.306 Note: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01

51 Table A8: PSU-level Variables Used in the Analysis of the 2010 EBRD Survey. Share of Migrants Migrant Diversity Distance to Germany PSU name Type (1948) (1948) (km) Legnica Urban 1.00 0.54 79.02 Lubin Urban 0.99 0.57 81.57 Siechnice Rural 0.99 0.28 149.01 Stara Kamienica Rural 0.99 0.55 45.38 Wroclaw Urban 0.99 0.40 137.54 Miedzyrzecz Rural 0.96 0.50 62.56 Szczaniec Rural 0.98 0.52 66.54 Namyslow Rural 0.99 0.52 187.30 Opole Urban 0.53 0.45 213.42 Debrzno Rural 0.96 0.14 186.46 Gdansk Urban 0.89 0.39 292.00 Wicko Rural 0.98 0.31 237.69 Elblag Urban 0.98 0.30 341.29 Pieniezno Rural 1.00 0.45 388.87 Rozogi Rural 0.71 0.03 458.71 Pyrzyce Rural 0.98 0.35 32.76 Stargard Szczecinski Urban 1.00 0.41 39.41 Stargard Szczecinski Rural 1.00 0.33 40.60 Szczecin Urban 1.00 0.50 15.78 Gliwice Urban 0.48 0.57 273.55 Zabrze Urban 0.32 0.60 281.99