Abstract

How persistent are cultural traits? Using data on anti-Semitism in Germany, we find local continuity over 600 years. Jews were often blamed when the Black Death killed at least a third of Europe’s population during 1348–50. We use plague-era pogroms as an indicator for medieval anti-Semitism. They reliably predict violence against Jews in the 1920s, votes for the Nazi Party, deportations after 1933, attacks on synagogues, and letters to Der Stürmer . We also identify areas where persistence was lower: cities with high levels of trade or immigration. Finally, we show that our results are not driven by political extremism or by different attitudes toward violence. JEL Codes: N33, N34, N93, N94, D74.

I. I ntroduction

A growing theoretical literature argues that cultural norms are powerful determinants of individual behavior and that they can persist over long periods ( Bisin and Verdier 2001 ; Doepke and Zilibotti 2008 ; Tabellini 2008 ; Acemoglu and Jackson 2011 ). From fertility and trust to corruption, there is also convincing empirical evidence that events and institutional arrangements in the distant past influence norms and preferences today, and that parental investment contributes to long-term persistence of attitudes ( Guiso, Sapienza, and Zingales 2008 ; Jha 2008 ; Fernández and Fogli 2009 ; Algan and Cahuc 2010 ; Nunn and Wantchekon 2011 ). That being said, culture often evolves quickly. Attitudes toward homosexuals, working women, and premarital sex have changed radically since the 1960s ( Fernández-Villaverde, Greenwood, and Guner 2010 ). A key challenge in cultural economics is to explain when norms and beliefs persist and when they are malleable. A fuller appreciation of what influences transmission will ultimately contribute to a deeper understanding of the origins of cultural differences themselves.

This article analyzes the historical roots of anti-Semitism in interwar Germany. Germany’s persecution of Jews is one of the defining events of the twentieth century. The extent to which it reflects a deep-seated history of anti-Semitism is controversial ( Goldhagen 1996 ; Eley 2000 ). We explore the long-term persistence of interethnic hatred by using a new data set of almost 400 towns where Jewish communities are documented for both the medieval period and interwar Germany. 1 When the Black Death arrived in Europe in 1348–50, Jews were often blamed for poisoning the wells. Many towns and cities (but not all) murdered their Jewish populations. Nearly 600 years later, defeat in World War I was followed by a countrywide rise in anti-Semitism. As in 1350, the threshold for violence against Jews declined. This led to waves of persecution, even before the Nazi Party seized power in 1933—but only in some locations.

We find persistence of anti-Semitic attitudes and behavior for more than half a millennium. Localities that burned their Jews in 1348–50 showed markedly higher levels of anti-Semitism in the interwar period: attacks on Jews were 6 times more likely in the 1920s in towns and cities with Black Death pogroms; the Nazi Party’s share of the vote in 1928—when it had a strong anti-Jewish focus—was 1.5 times higher; 2 readers’ letters to a virulently anti-Semitic Nazi newspaper ( Der Stürmer ) were more frequent; attacks on synagogues during the “Night of Broken Glass” ( Reichskristallnacht ) in 1938 were more common; and a higher proportion of Jews was deported under the Nazis. There is also evidence that we do measure anti-Semitism and not merely a tendency toward violence or political radicalism. Finally, we examine Jewish settlement patterns in the medieval period. Although economic and institutional factors mattered, these variables explain little of the geography of violence after 1919.

How can the same form of extreme behavior be found in the same localities 600 years later? Our second main contribution is to examine the conditions under which anti-Semitism persisted. For a number of conditioning variables, the long-term transmission of hatred weakens; for example, cities with a strong tradition of long-distance trade (members of the Hanseatic League in northern Germany) show significantly lower persistence over the long term than other communities. The same is true of southern German cities that were more open to trade. Urban centers that grew rapidly after 1750 exhibit a markedly weaker connection between medieval and modern-day anti-Semitism. In contrast, neither a tradition of being governed by a bishop nor relative geographical isolation have a direct effect on the persistence of anti-Semitism.

Our findings suggest that local persistence partly reflects a lack of mobility. Most of the towns in our study were small, with a median population of no more than 9,000 inhabitants in 1933 and with at most a few thousand in the Middle Ages. Immigration and marriages across towns were relatively rare. These characteristics would have facilitated the persistence of beliefs at the local level. 3 With industrialization after 1820 came migration, and where immigration was massive the extent of persistence declined. Symbolic practices and festivals may have helped perpetuate hostile beliefs. Passion plays, for instance, often portrayed Jews as engaged in deicide ( Glassman 1975 ). Anti-Semitic sculptures decorated churches and private houses, and book printing widely distributed the same demeaning images. 4 In some towns, festivals commemorated pogroms; in the Bavarian town of Deggendorf, for example, the attack on the town's Jewish community in 1337 was celebrated every year until 1968 ( Schoeps 1998 ). Several tracts of Martin Luther are also strongly anti-Semitic ( Oberman 1984 ). The long-term persistence of hatred is hardly unique to Germany. England, France, and Spain also expelled their Jews during the Middle Ages. Nonetheless, anti-Semitism lingered. Until recently, Spanish children played a game called “Killing Jews” around Easter—in a country where Jews have been almost entirely absent since 1492 ( Perednik 2003 ). 5 England between 1290 and 1656 also showed similar hostility despite an absence of Jews. 6

This article contributes to the literature on the long-run effects of local culture. Alesina and La Ferrara (2005) find that cultural and religious fragmentation is robustly associated with such outcome variables as civil wars, corruption, and public good provision. 7 The historical roots of present-day conditions have also attracted attention. Fernández and Fogli (2009) show that the fertility of immigrants’ children continues to be influenced by fertility rates in their parents’ country of origin. Algan and Cahuc (2010) demonstrate that inherited trust predicts economic performance. Guiso, Sapienza, and Zingales (2008) argue that free medieval cities in Italy have higher levels of interpersonal trust today. There is also evidence that nationalities allowed to lend under Ottoman rule have higher bank penetration in the present ( Grosjean 2011 ), that having been ruled by the Habsburg empire is associated with lower corruption in today’s successor states ( Becker et al. 2011 ), that the historic use of the plow in agriculture affects contemporaneous gender roles ( Alesina, Giuliano and Nunn 2011 ), that the effect of changing religious norms on literacy may be irreversible ( Botticini and Eckstein 2007 ), and that the slave trade in Africa led to permanently lower levels of trust ( Nunn and Wantchekon 2011 ). 8Jha (2008) finds that Indian trading ports with a history of peaceful cooperation between Hindus and Muslims saw less violent conflict during the period 1850–1950 and in 2002. This is consistent with our finding that persistence of anti-Semitism is weaker in cities that are more trade-oriented. Our work is also related to research on “deep” parameters, such as technological starting conditions, genetic origin, and population composition ( Spolaore and Wacziarg 2009 ; Comin, Easterly, and Gong 2010 ; Putterman and Weil 2010 ).

The Holocaust and its antecedents have been a topic of intense research interest. Whereas some argue that it can never be rationally explained ( Levi 1987 ), others have pointed to underlying economic and political causes ( Arendt 1994 ; Glaeser 2005 ; Cohn 2007 ). In contrast, Goldhagen (1996) argues that a deep-rooted history of anti-Semitism was ultimately responsible for a wave of hatred. He observes that “the most telling evidence supporting the argument that antisemitism has fundamentally nothing to do with the actions of Jews, and … nothing to do with an antisemite’s knowledge of the real nature of Jews, is the widespread historical and contemporary appearance of antisemitism, even in its most virulent forms, where there are no Jews, and among people who have never met Jews.” Goldhagen’s claims are controversial. 9

In addition to arguments for the transmission of a cultural trait over centuries, even in the absence of Jews themselves ( Goldhagen 1996 ; Perednik 2003 ), there are functionalist interpretations. These are based on economic and social factors, such as the particular benefits from murdering money lenders ( Cohn 2007 ). With regard to more recent episodes of anti-Semitism, some authors have emphasized the role of modernization. Increasing social mobility and civic rights are said to have heightened the fears of non-Jews about their own social status ( Almog 1990 ; Arendt 1994 ; Lindemann 2000 ). Where governments imposed Jewish emancipation, anti-Semitism flourished; but weak states saw no such reaction ( Birnbaum and Kochan 1992 ). Political economy models of hatred focus on the incentives for “entrepreneurs” to foster misperceptions as a rallying cry for groups ( Glaeser 2005 ). Another alternative is the scapegoat theory, which argues that Jews are blamed for misfortune in times of crisis ( Ettinger 1980 ; Katz 1980 ; Fein 1987 ).

All of these approaches have difficulties explaining the waxing and waning of anti-Semitism over time as well as the differences in levels across countries ( Brustein and King 2004 ). Our procedure is different. We use two widely separated events that lowered the countrywide threshold for violence against Jews—the Black Death and defeat in World War I—and identify the locations where hatred of Jews led to extreme acts. With respect to the earlier literature, our first contribution is to provide direct empirical evidence that twentieth-century anti-Semitism at the local level has deep historical roots. 10 We do so for a wide range of outcome variables that are relevant in the context of historical debates on the origins of the Holocaust and its connection with German culture. We also show that the same attitudes can persist over the very long run: some six centuries in this case. We examine closely whether the attitude in question—anti-Semitism—is driven by time-invariant, location-specific factors. There is no evidence to support such a conclusion. In particular, economic, geographical, and institutional variables that are correlated with medieval pogroms in the cross-section are largely irrelevant for twentieth-century anti-Semitism. Controlling for these variables does not affect the link between modern persecution patterns and those in the Middle Ages. Although it is not conclusive, this evidence suggests that anti-Semitism persisted even without direct economic benefits and in areas where Jews were largely absent for centuries. 11 If so, then this strengthens the case for theoretical models in the style of Bisin and Verdier (2001) , where children acquire preferences through adaptation and imitation and parents attempt to socialize their offspring to their own preference trait (even if the trait is not useful or is even detrimental). Our second contribution is that we offer one of the first systematic examinations of when cultural traits do and do not persist. 12 That persistence is lower in more “open” cities (Hanseatic cities and southern German trading cities) lends qualified support to models in which investment by parents is partly shaped by their utilitarian motives ( Doepke and Zilibotti 2008 ; Tabellini 2008 ).

The article proceeds as follows. Section II describes our data and the historical background of anti-Semitism in the Middle Ages. Section III presents our main empirical results. In Section IV we analyze Jewish settlement patterns, the correlates of medieval violence, and the determinants of persistence. The interpretation of our findings is discussed in Section V , and Section VI concludes.

II. D ata and H istorical C ontext

We use data on anti-Semitism during two eras—the medieval period and the years 1920–45. Our measure of medieval violence against Jews are the pogroms that occurred during the Black Death (1348–50). A wave of Jew burning swept through much of Western Europe on the plague’s arrival ( Cohn 2007 ). Germany is an especially useful setting for our purposes; elsewhere, Jews had often been expelled altogether before the Black Death. 13 There is considerable variation in the extent of pogroms at both the local and regional level in Germany. We can therefore compare medieval outbreaks of anti-Semitic violence with similar acts committed in the same location more than half a millennium later, between 1920 and 1945.

II.A. Pogroms and Jewish Settlements in the Middle Ages

Jews first settled in Germany during the Roman period. 14 The documentary record begins around 1000, when there are confirmed settlements in major cities like Worms, Speyer, Cologne, and Mainz ( Haverkamp 2002 ). By the fourteenth century, there were almost 400 confirmed localities with Jewish communities. 15

Pogroms against Jews began not long after the earliest confirmed settlements were established. The crusades in 1096, 1146, and 1309 witnessed mass killings of Jews in towns along the Rhine. 16 In addition, there is a long history of sporadic, localized, and deadly attacks. The so-called Rintfleisch pogroms in Bavaria and Franconia in the late thirteenth century destroyed many communities ( Toch 2003 ). In the same category are the Guter Werner attacks (1287) in the mid-Rhine area and the Armleder pogroms (1336) in Franconia and Saxony ( Toch 2010 ). Many of the pogroms before the plague began when Jews were accused of ritual murder, well poisoning, or desecration of the host.

By far the most widespread and violent pogroms occurred at the time of the Black Death. One of the deadliest epidemics in history, the plague spread from the Crimea to southern Italy, France, Switzerland, and into Central Europe. The disease killed between a third and half of Europe’s population between 1348 and 1350 ( McNeill 1975 ). Faced with a mass epidemic of unprecedented proportions, Christians were quick to blame Jews for poisoning wells. Once confessions were extracted under torture, the allegations spread from town to town.

The Jews of Zurich were relatively fortunate—they were merely expelled. Despite intervention by the pope and notwithstanding declarations by the medical faculties in Montpellier and Paris that the allegations of well poisoning were false, many towns murdered their Jewish populations. In Basle, approximately 600 Jews were gathered in a wooden house, constructed for the purpose, on an island in the River Rhine. There they were burned ( Gottfried 1985 ). In some areas, peasants and unruly mobs set on the Jews who had been expelled or tried to flee ( Gottfried 1985 ).

The chronicles of towns that burned their Jews rarely provide a detailed explanation. In Nuremberg, the bishop’s pro-notary, Michael de Leone, recorded his feelings in 1349 in two poems. He concluded that “the Jews deserved to be swallowed up in the flames” ( Cohn 2007 ). We know that the city authorities and local princes often tried to shield “their” Jews, but few were successful. In Basle, for example, the authorities initially did not intend to persecute the Jews. Yet “the citizens marched to the city-hall and compelled the council to take an oath that they would burn the Jews.” 17 The city council of Strasbourg similarly intended to save its Jewish inhabitants, but a mob led by the butchers’ and tanners’ guilds deposed the council ( Schilter 1979 [1698]); the successors then arrested the Jews, who were burned on St. Valentine’s Day ( Foa 2000 ). 18 Variation at the local level evidently cannot be fully explained by economic, social, or political motives. Instead, we argue that the attacks reflect to what extent large parts of the populace could be induced to agitate strongly for killing Jews.

A similar dearth of sources restricts the analysis of locations where no assaults are recorded. Several city councils received letters warning them about Jews poisoning wells but faced no mob when they decided against persecution. In some cases, it is not certain that any Jews inhabited the town at the time of the Black Death. In many cases, however, we can be certain that Jews lived in towns that did not carry out attacks. In Halberstadt in central Germany, for instance, transactions with Jewish money lenders are recorded right before and during the Black Death; there is no record of any violence. The most likely conclusion is that in locations where Jews lived but no pogrom occurred, anti-Semitic sentiment was weaker or absent. Hence there was less pressure put on the authorities (by artisans and peasants) to expel or burn the local Jewish community.

We use the Germania Judaica (GJ) as the main source for the medieval period ( Avneri 1968 ). Initiated as a research project by the German Society for the Advancement of Jewish Studies in 1903, GJ was conceived as a comprehensive description of Jewish settlement history in the German empire from the origins to the Congress of Vienna in 1814. Its three completed volumes begin with the earliest known Jewish settlements in Germany and end in 1519. We principally use data from volume 2, which covers the period 1238 to 1350. We supplement GJ with information from the recent work by Alicke (2008) . Doubtful cases of Jewish settlements or occurring of pogroms in 1349 are not included in the data set. This leaves 325 towns with a confirmed Jewish settlement and unambiguous information on pogroms in 1349.

The scholars producing GJ drew on a number of original documents and secondary works. An important source of information are the so-called Memorbücher . These collections, compiled in the Middle Ages, contain remembrances of dead community members and prayers; from the thirteenth century onward, they developed into a recognizable literary form. Some of them contain more detailed information, such as lists of victims during particular outbursts of violence (e.g., during the 1348–50 pogroms or during the First Crusade in 1096). Many of the plague pogroms are recorded in the Martyrologium of the Nürnberg Memorbuch ( Salfeld 1898 ). Several other communities, such as Deutz, also compiled their own versions. As our indicator for violence against Jews in the Middle Ages, we code for whether there was a pogrom. A typical entry in GJ reads as follows: “Heiligenstadt— … fortified by 1278, later capital of the principality of Eichsfeld, today Kreisstadt in Thuringia. At the time of the Black Death, the Jews of Heiligenstadt were systematically killed. Survivors were recorded in Erfurt in 1365 and in Frankfurt in 1389. Heiligenstadt only admitted Jews again in 1469.”

Most towns with Jewish populations were sites of mass killings in 1348–50. Of 325 observations, 235 (72%) recorded attacks. The map ( Figure I ) shows the frequency of pogroms during 1348–50 in Germany (in terms of its 1938 borders). Areas where every town saw violent attacks on Jews are shown as black; the shade intensity of other areas decreases with decreasing pogrom frequency. Areas without data are those parts of Germany for which there are no records of medieval Jewish settlements. Even though the map contains some unshaded areas, the information derived from GJ covers all the major parts of Germany. The Rhineland, Franconia, and Hesse stand out as regions with numerous attacks.

Figure I View largeDownload slide Pogroms in 1348–50 Notes: Map of Weimar Republic: Pogrom frequency is defined at the county (Kreis) level as the number of cities with pogroms in 1349 divided by the number of cities with a Jewish community. The lowest category [0–.001] indicates Jewish settlement with no pogroms. We define pogrom frequency = 1.1 if a county has more than one city with a Jewish community and pogroms in each of these cities. Detailed map: Data from Haverkamp (2002) . Locations with a confirmed Jewish settlement in the fourteenth century but no pogrom are indicated by a circle; a square indicates a pogrom during the time of the Black Death.

Figure I View largeDownload slide Pogroms in 1348–50 Notes: Map of Weimar Republic: Pogrom frequency is defined at the county (Kreis) level as the number of cities with pogroms in 1349 divided by the number of cities with a Jewish community. The lowest category [0–.001] indicates Jewish settlement with no pogroms. We define pogrom frequency = 1.1 if a county has more than one city with a Jewish community and pogroms in each of these cities. Detailed map: Data from Haverkamp (2002) . Locations with a confirmed Jewish settlement in the fourteenth century but no pogrom are indicated by a circle; a square indicates a pogrom during the time of the Black Death.

However, the frequency of attacks varied substantially even at the local level. The detailed map shows parts of southwest Germany. Cities and towns with a confirmed Jewish settlement are marked with a circle; those that suffered a pogrom during the Black Death are marked with a square. Some contiguous towns with Jewish settlements experienced very different histories of medieval anti-Semitic violence. For example, the Jews of Göppingen were attacked, whereas those in Kirchheim escaped unharmed. The same contrast is visible for Reutlingen and Tübingen as well as Rottenburg and Horb—towns no more than 16 miles (25 km) apart. We exploit this level of variation at the local level in the quantitative analysis section.

It is beyond the scope of this article to detail the history of Jews in Germany between the Middle Ages and the nineteenth century. We know that after the dramatic attacks in 1348–50, resettlement was slow and never attained the same density of Jewish communities as in the early fourteenth century. In many places, the “Jewish presence was extremely short-lived and transient, sometimes spanning just a single year or little more” ( Toch 2003 ). Major towns typically expelled the few remaining Jews in the fourteenth or fifteenth century. Although some resettled in surrounding villages, many migrated to Eastern Europe; as a result, Jewish communities had largely disappeared from Germany by 1550. They did not return in larger numbers until the seventeenth century, when mercantilist rulers welcomed Jewish commercial and financial expertise ( Burnett 1996 ).

The number of Jews in Germany grew from the eighteenth century onward, but they were subject to many discriminatory rules (curtailing the right to marry, limiting the total numbers allowed in a city, etc.). After emancipation in the early nineteenth century, the number of Jews living in Germany increased more rapidly. By the turn of the century, Germany was once more home to numerous Jewish communities. It is crucial to recognize that the proportion of Jews at the city level in Weimar Germany is unrelated to Black Death pogroms (see Table III later). That is, whether a city saw pogroms did not affect resettlement half a millennium later; without continuous Jewish settlement, there was no institutionalized local remembrance of plague pogroms. 19

Table I Mean Std. Dev. Min. Max. Obs. Population in 1933 46,118 115,863 207 756,605 325 %Jewish in 1933 1.44 1.45 0.020 15.7 325 %Protestant in 1925 48.4 34.0 0.97 97.6 325 Synagogue in 1933 0.87 0.34 0 1 319 Indicators for twentieth-century anti-Semitism POG 1920s 0.063 0.242 0 1 320 NSDAP 1928 0.036 0.049 0.00083 0.313 325 DVFP 1924 0.080 0.097 0 0.588 325 DEPORT 197.1 839.5 0 10,049 301 STÜRMER 3.77 10.7 0 110 325 SYNATTACK 0.903 0.297 0 1 278 Black Death pogrom ( POG 1349 ) 0.723 0.448 0 1 325 Mean Std. Dev. Min. Max. Obs. Population in 1933 46,118 115,863 207 756,605 325 %Jewish in 1933 1.44 1.45 0.020 15.7 325 %Protestant in 1925 48.4 34.0 0.97 97.6 325 Synagogue in 1933 0.87 0.34 0 1 319 Indicators for twentieth-century anti-Semitism POG 1920s 0.063 0.242 0 1 320 NSDAP 1928 0.036 0.049 0.00083 0.313 325 DVFP 1924 0.080 0.097 0 0.588 325 DEPORT 197.1 839.5 0 10,049 301 STÜRMER 3.77 10.7 0 110 325 SYNATTACK 0.903 0.297 0 1 278 Black Death pogrom ( POG 1349 ) 0.723 0.448 0 1 325 View Large

Table I Mean Std. Dev. Min. Max. Obs. Population in 1933 46,118 115,863 207 756,605 325 %Jewish in 1933 1.44 1.45 0.020 15.7 325 %Protestant in 1925 48.4 34.0 0.97 97.6 325 Synagogue in 1933 0.87 0.34 0 1 319 Indicators for twentieth-century anti-Semitism POG 1920s 0.063 0.242 0 1 320 NSDAP 1928 0.036 0.049 0.00083 0.313 325 DVFP 1924 0.080 0.097 0 0.588 325 DEPORT 197.1 839.5 0 10,049 301 STÜRMER 3.77 10.7 0 110 325 SYNATTACK 0.903 0.297 0 1 278 Black Death pogrom ( POG 1349 ) 0.723 0.448 0 1 325 Mean Std. Dev. Min. Max. Obs. Population in 1933 46,118 115,863 207 756,605 325 %Jewish in 1933 1.44 1.45 0.020 15.7 325 %Protestant in 1925 48.4 34.0 0.97 97.6 325 Synagogue in 1933 0.87 0.34 0 1 319 Indicators for twentieth-century anti-Semitism POG 1920s 0.063 0.242 0 1 320 NSDAP 1928 0.036 0.049 0.00083 0.313 325 DVFP 1924 0.080 0.097 0 0.588 325 DEPORT 197.1 839.5 0 10,049 301 STÜRMER 3.77 10.7 0 110 325 SYNATTACK 0.903 0.297 0 1 278 Black Death pogrom ( POG 1349 ) 0.723 0.448 0 1 325 View Large

Jewish emancipation occurred only during and after the French occupation in the early nineteenth century. The possibility of equal rights for Jews after the defeat of France led to a wave of unrest, the so-called Hep-Hep riots in 1819 ( Katz 2004 ). These began in Würzburg and quickly spread to a large number of German, Polish, and Danish cities. The size of the Jewish community grew in the nineteenth century, and anti-Semitism increased after reunification in 1871. An anti-Semitic petition in 1880 called for limiting Jewish immigration and influence, and anti-Semitic parties were winning significant shares of the total vote by the 1890s ( Wawrzinek 1927 ). By 1914, however, the anti-Semitic parties had dwindled to near insignificance ( Levy 1974 ).

II.B. Anti-Semitism in Germany after World War I

Anti-Semitism in Germany grew during and after World War I. During the war, right-wing organizations spread rumors that Jews were not serving at the front but were engaged in war profiteering. The German Army High Command ordered a count of all Jews in uniform—allegedly to counter such rumors—but never published the results. After the collapse of 1918, Jews were blamed for Germany’s defeat in World War I. This led to another increase in the level of anti-Semitic agitation. Jews who had served in high office included Walther Rathenau, who coordinated the supply of raw materials for the war. Matthias Erzberger, another prominent politician and a Jew, opposed the war openly from 1917 onward; he signed the humiliating armistice with the Entente in 1918. As chairman of the armistice commission and later as finance minister, he implemented many of the provisions of the Versailles Treaty. These led to a large tax hike to pay for reparations.

In addition, Jews provided some of the leadership for the German revolution of 1918 and for attempts to establish socialist regimes thereafter. In Munich, Kurt Eisner proclaimed a Soviet Republic; Gustav Landauer and Eugen Levine also held positions of great influence. Rosa Luxemburg attempted to organize a revolution along Bolshevist lines. 20 This ultra-left bid for power was eventually thwarted by demobilized army units. Radical right-wing groups quickly seized on the involvement of leading Jewish politicians in the revolution, the armistice, and the Treaty of Versailles. The false claim that Germany’s army had been “stabbed in the back”—and not actually defeated in battle—pointed to domestic unrest as the key factor that lost the war.

Anti-Semitism was already widespread before the Nazi Party’s rise to power in 1933. Student associations often excluded Jews. Jewish cemeteries were frequently desecrated; synagogues were besmirched with graffiti. Politicians made anti-Semitic speeches ( Walter 1999 ). Jews were not welcome in many hotels and restaurants, and entire towns declared themselves to be open for Christian guests only ( Borut 2000 ).

According to the census of 1925, there were more than 560,000 Jews living in Germany. The vast majority (66%) resided in the most populous cities; the rest were evenly divided between smaller cities and more than 1,000 towns and villages with fewer than 10,000 inhabitants. For the regional patterns of twentieth-century violence, our main source is Alicke (2008) . From the wealth of information in his encyclopedia of Jewish communities in German-speaking areas, we focus on evidence about anti-Semitic violence in the 1920s and 1930s. Our main sample includes 325 cities with unambiguous information on Black Death pogroms and Jewish communities in both medieval and interwar Germany. We also use an extended data set that contains all locations mentioned in Alicke (2008) —that is, 1,428 cities with Jewish communities in interwar Germany.

Pogroms before 1933 were rare but not unknown. We find 38 communities that witnessed major attacks on Jews before the Nazi rise to power. To qualify as such, there had to have been physical violence. 21 During Weimar Germany’s period of economic decline and social unrest after 1918, numerous right-wing parties with anti-Semitic programs sprang up. Hitler’s National Socialist German Workers Party (NSDAP, a.k.a. the Nazi Party) was only one of many, but it was among the most radical. The German National People’s Party (DNVP) continued the anti-Semitic rhetoric of the Imperial era ( Hertzman 1963 ). Closest to the NSDAP was the German People’s Freedom Party (DVFP), which split from the DNVP in 1922 because of the latter’s lack of radical anti-Semitism. We use Hänisch’s (1988) election data in addition to commonly used control variables. 22

During its early years, the Nazi Party emphasized its extremist worldview and anti-Semitic beliefs while attempting to seize power by violent means. After the so-called Beer Hall Putsch, the party was banned for several years. The DVFP absorbed much of the Nazi vote in the May 1924 election ( Striesow 1981 ). 23 We find a correlation of .59 between the voting results of the DVFP in 1924 and the Nazi Party in 1928, which is significant at the 1% level. Readmitted to the polls in 1928, the Nazi Party won 3.6% of the eligible votes in our main sample. 24 In some localities, as many as 34% of voters supported the party’s program; in others, not a single vote was cast in favor of the NSDAP. 25

The Nazi Party’s public profile later changed when it tried to garner middle-class support. Toward this end, during 1928–33, it tried to appear “respectable”; leaders pledged to use only legal means to win power. 26 This change in tactics is generally dated after 1928 ( Stachura 1978 ; Childers 1983 ). 27 During the Great Depression, the Nazi Party increasingly exploited economic and social issues. Anti-Semitism never disappeared from the party’s manifestos and propaganda, but it was toned down. Surveying trends in research during the past two decades, Heilbronner (2004) concludes:

Until the 1960s most studies of the Nazi Party and National Socialism argued that anti-Semitism was an essential factor in explaining Nazi success before 1933. But in recent decades, numerous studies have shown that anti-Semitism was probably somewhat underrepresented in Nazi Party activity and propaganda in the period before 1933, particularly in the last years before Hitler became Chancellor.

After the turning point in 1928, the NSDAP’s campaigns were directed at disaffected protest voters who may or may not have shared its more radical ideas. With the party’s gains in electoral appeal, the distribution of votes by district increasingly approximates a normal distribution, so locations with radical views are less easily identified as the party’s mass support swamps the factors that drove its early results. 28 Beginning in 1930, the NSDAP’s vote share increased everywhere; hence, relative differences between the average and the most fervently pro-Nazi district become harder to identify. For these reasons, we regard election results until 1928 as more accurate indicators of a local population’s ideological orientation (however, we also analyze the post-1928 election results). Figure II displays the geographical distribution of votes for the Nazi Party in 1928. Bavaria, the upper Rhine region, and Schleswig-Holstein are areas of high support. There is ample variation at the regional level, with areas of extremely low vote shares immediately adjacent to those with high proportions of votes for the Nazi Party.

Figure II View largeDownload slide Percentage of Votes for the NSDAP in the German National Election of 1928

Figure II View largeDownload slide Percentage of Votes for the NSDAP in the German National Election of 1928

We also collect data on Reichskristallnacht . Although much of the violence was centrally directed, it required local cooperation. In a number of towns and cities, there were no attacks. We collect information from Alicke (2008) on whether synagogues were damaged or destroyed in 1938. The local record is not always clear on why this happened. In a handful of cases, local mayors refused to participate or stopped SA troopers from burning down the synagogue. Historical narratives ( Alicke 2008 ) often emphasize “technical” constraints, including fire hazard and ownership issues. We take no position here on whether these were merely a pretext. However, we see no good reason why there should have been fewer practical difficulties in German municipalities that once had participated in medieval pogroms.

Next, we use data on deportations of German Jews to assess the strength of anti-Semitic sentiment in each town. The German federal archive (Bundesarchiv) has used available records to compile detailed, municipal-level data from available records on deportations—including the name, date of birth, date of deportation, destination, and (where known) ultimate fate of each individual. 29 Mass deportations to the east began in 1941. As early as 1938, however, Polish Jews living in Germany were rounded up, transported to the German–Polish border, and forced to cross. Before that date, and during the pogroms of the Reichskristallnacht , Jews from some towns were deported to camps in the Reich.

In our empirical analysis, we examine how many deportations took place while conditioning on the number of Jews living in a town. In our view, any remaining differences reflect local sentiment. This is because many rules for the treatment of Jews were far from clear-cut. The Reichssicherheitshauptamt (RSHA) of the Schutzstaffel (SS) under Adolf Eichmann was in charge of overall coordination, but there was substantial room for local factors to affect deportations. Raul Hilberg’s classic treatment of the destruction of European Jewry argues that “each city has its own deportation history” ( Hilberg 1961 , 320). Meyer (2004) also emphasizes local variation and notes that—in areas where the Gestapo and representatives of the Reichsorganisation der Juden worked well together and developed mutual trust—local Jews fared better (including some cases of rescues). General histories note the variability and chaos associated with deportations, especially early on. Not even age was consistently applied as a selection criterion. 30

The Nazi newspaper Der Stürmer provides our final indicator for anti-Semitism. Published with a front-page banner declaring “the Jews are our misfortune,” it was by far the most anti-Jewish of all the Nazi papers. Der Stürmer typically mixed tales of Jewish ritual murders with dark conspiracy theories. It also contained a section with letters to the editor (chosen by the paper for their interest and attitude of the letter writer). These letters typically involve a mixture of denunciation and rhetorical questions about how despicable it is to mingle with Jews. For example, a Hamburg schoolgirl wrote to the newspaper in 1935 ( Hahn 1978 ) as follows: 31

Dear Stürmer! I attend a well-known higher secondary school in Hamburg. Regrettably, we still have many Jewish fellow students. Equally regrettably, many German girls are still close friends with these Jewish girls. On special occasions, when we wear [BDM] 32 uniforms in school, these girls walk arm-in-arm with their Jewish friends. You can imagine what an impression this gives! When confronting the girls in question, they say “stop instigating hatred all the time! Jews are human beings, too, and ‘Eva’ is a ‘modest’, ‘decent’, ‘nice’ girl!” … I consider these friendships very dangerous, since the Jews and their corrupting ideas destroy the souls of the girls slowly but surely. Girls at 14 are too innocent to realize the true intentions of their Jewish “girlfriends.” I am myself barely 15 years old.

We use four years of letters to the editor of Stürmer , from 1935 to 1938, and code the location of the letter writer. We total the number of letters in three categories: those published as article equivalents (an obvious sign of approbation by the editors), those denouncing named individuals still talking to or doing business with Jews, and those asking questions about Jews (e.g., the number of Jews remaining in a city). The vast majority of all cities with information on Jewish settlement in the interwar years did not send a single letter to the Stürmer . At the other end of the distribution, we find cities like Nuremberg (where the Stürmer was edited and NSDAP party congresses were held) with 73 letters, Munich (where the party was founded and the Beer Hall Putsch took place) with 77, Cologne with 110, and Berlin with 354 letters.

II.C. Data Overview

We construct our data set as follows. We first collect information on all the municipalities with twentieth-century data on Jewish population and anti-Semitic outcome variables, relying on the work of Alicke (2008) . Next, we check for direct evidence of Jewish settlement in the fourteenth century. This procedure yields information on 325 cities (our main sample). For the twentieth century, we have data on 1,427 towns and cities within the 1938 borders of Germany but with many towns and cities having no confirmed Jewish settlements (extended sample).

Table I gives an overview of the key variables in the main sample. Jews are typically a small part of the population (1.4%). 33 In the average city, about half of the population was Protestant and most of the remainder Catholic. In 87% of locations, there was a synagogue or a dedicated place for religious worship by Jews in 1933. About 6% of cities witnessed pogroms during the 1920s. The average city gave 3.6% of votes to the NSDAP in 1928 and 8% to the Völkisch-Nationalist DVFP in 1924. For both elections, there is substantial variation by municipality. The average town reported 197 deportees; the range was from 0 to 10,049. The number of anti-Semitic letters to the Stürmer during 1935–38 ranges from 0 to 110 (which we scale by town/city population in the empirical analysis). In about 90% of cities with synagogues or prayer rooms, these were damaged or destroyed during the Reichskristallnacht.

In Table II , we explore basic correlation patterns in our data. We find that all our indicators of twentieth-century anti-Semitism are significantly and positively correlated with medieval pogroms. In addition, the six variables for modern anti-Semitism are mostly positively correlated with each other ( Online Appendix Section II.A reports the same information as Tables I and II but for the extended sample).

Table II (1) (2) (3) (4) (5) (6) (7) (1) POG 1349 1 (2) POG 1920s 0.170*** 1 (3) DVFP 1924 0.105* 0.539*** 1 (4) NSDAP 1928 0.128** 0.444*** 0.831*** 1 (5) % DEPORT 0.230*** 0.056 −0.065 −0.010 1 (6) STÜRMER/pop 0.109** 0.0266 0.158*** 0.225*** 0.014 1 (7) SYNATTACK 0.127** 0.001 −0.020 −0.020 −0.066 −0.039 1 (1) (2) (3) (4) (5) (6) (7) (1) POG 1349 1 (2) POG 1920s 0.170*** 1 (3) DVFP 1924 0.105* 0.539*** 1 (4) NSDAP 1928 0.128** 0.444*** 0.831*** 1 (5) % DEPORT 0.230*** 0.056 −0.065 −0.010 1 (6) STÜRMER/pop 0.109** 0.0266 0.158*** 0.225*** 0.014 1 (7) SYNATTACK 0.127** 0.001 −0.020 −0.020 −0.066 −0.039 1 View Large

Table II (1) (2) (3) (4) (5) (6) (7) (1) POG 1349 1 (2) POG 1920s 0.170*** 1 (3) DVFP 1924 0.105* 0.539*** 1 (4) NSDAP 1928 0.128** 0.444*** 0.831*** 1 (5) % DEPORT 0.230*** 0.056 −0.065 −0.010 1 (6) STÜRMER/pop 0.109** 0.0266 0.158*** 0.225*** 0.014 1 (7) SYNATTACK 0.127** 0.001 −0.020 −0.020 −0.066 −0.039 1 (1) (2) (3) (4) (5) (6) (7) (1) POG 1349 1 (2) POG 1920s 0.170*** 1 (3) DVFP 1924 0.105* 0.539*** 1 (4) NSDAP 1928 0.128** 0.444*** 0.831*** 1 (5) % DEPORT 0.230*** 0.056 −0.065 −0.010 1 (6) STÜRMER/pop 0.109** 0.0266 0.158*** 0.225*** 0.014 1 (7) SYNATTACK 0.127** 0.001 −0.020 −0.020 −0.066 −0.039 1 View Large

Next, we examine the comparability of localities with and without Black Death pogroms. Table III shows various outcome variables; it reports their means (conditional on Black Death pogroms) in Panel A and the corresponding regression results in Panel B. 34 Columns (1) and (2) compare long-run economic development as proxied by city growth over two periods: 1300–1933 and 1750–1933. 35 Neither period shows statistically significant differences between towns and cities with and without pogroms. The same is true for the percentage of Protestants in 1925 (column (3)). It is noteworthy that the percentage of Jews in the population in 1933 is not significantly different either (column (4)). This suggests that Jews did not systematically avoid settling in locations where medieval pogroms had occurred. Finally, columns (5)–(8) examine economic outcome variables in 1933. These include, respectively: the percentage of blue-collar workers, because these individuals voted predominantly for the Communist Party, which may affect Nazi votes ( Childers 1983 ; Hamilton 1982 ); the unemployment rate; the percentage of manufacturing employment, which captures differences in industrialization; and the percentage of retail and trade employment, because many Jews worked in this sector. None of these variables differs significantly between the two samples, so there is little reason to question the comparability of the localities with and without pogroms in 1349. In Online Appendix Section II.A we show that this holds also for our extended sample, as well as for election turnout in the 1920s and 1930s (which is often used as a key indicator of social capital; see Guiso, Sapienza, and Zingales 2008 ).

Table III (1) (2) (3) (4) (5) (6) (7) (8) City pop growth %Protestant %Jewish %Blue collar %Unemployed %Manufacturing %Retail & trade 1300–1933 1750–1933 1925 1933 1933 1933 1933 1933 Panel A: Means by Pogrom in 1349 POG 1349 = 1 2.38 2.06 46.8 1.44 41.1 17.0 35.2 22.0 (1.20) (0.97) (33.3) (1.48) (10.8) (7.8) (12.6) (10.3) POG 1349 = 0 2.28 1.92 52.6 1.44 40.0 15.0 31.8 19.0 (1.63) (0.96) (35.8) (1.38) (11.8) (8.2) (13.9) (11.2) Panel B: Regressions on POG 1349 POG 1349 0.120 0.234 −6.887 0.169 −0.953 0.0443 1.000 0.123 (0.534) (0.251) (4.520) (0.165) (1.131) (0.758) (1.367) (0.958) Observations 46 112 325 325 325 325 325 325 Adjusted R2 0.075 −0.004 0.036 0.094 0.401 0.469 0.369 0.554 (1) (2) (3) (4) (5) (6) (7) (8) City pop growth %Protestant %Jewish %Blue collar %Unemployed %Manufacturing %Retail & trade 1300–1933 1750–1933 1925 1933 1933 1933 1933 1933 Panel A: Means by Pogrom in 1349 POG 1349 = 1 2.38 2.06 46.8 1.44 41.1 17.0 35.2 22.0 (1.20) (0.97) (33.3) (1.48) (10.8) (7.8) (12.6) (10.3) POG 1349 = 0 2.28 1.92 52.6 1.44 40.0 15.0 31.8 19.0 (1.63) (0.96) (35.8) (1.38) (11.8) (8.2) (13.9) (11.2) Panel B: Regressions on POG 1349 POG 1349 0.120 0.234 −6.887 0.169 −0.953 0.0443 1.000 0.123 (0.534) (0.251) (4.520) (0.165) (1.131) (0.758) (1.367) (0.958) Observations 46 112 325 325 325 325 325 325 Adjusted R2 0.075 −0.004 0.036 0.094 0.401 0.469 0.369 0.554 View Large

Table III (1) (2) (3) (4) (5) (6) (7) (8) City pop growth %Protestant %Jewish %Blue collar %Unemployed %Manufacturing %Retail & trade 1300–1933 1750–1933 1925 1933 1933 1933 1933 1933 Panel A: Means by Pogrom in 1349 POG 1349 = 1 2.38 2.06 46.8 1.44 41.1 17.0 35.2 22.0 (1.20) (0.97) (33.3) (1.48) (10.8) (7.8) (12.6) (10.3) POG 1349 = 0 2.28 1.92 52.6 1.44 40.0 15.0 31.8 19.0 (1.63) (0.96) (35.8) (1.38) (11.8) (8.2) (13.9) (11.2) Panel B: Regressions on POG 1349 POG 1349 0.120 0.234 −6.887 0.169 −0.953 0.0443 1.000 0.123 (0.534) (0.251) (4.520) (0.165) (1.131) (0.758) (1.367) (0.958) Observations 46 112 325 325 325 325 325 325 Adjusted R2 0.075 −0.004 0.036 0.094 0.401 0.469 0.369 0.554 (1) (2) (3) (4) (5) (6) (7) (8) City pop growth %Protestant %Jewish %Blue collar %Unemployed %Manufacturing %Retail & trade 1300–1933 1750–1933 1925 1933 1933 1933 1933 1933 Panel A: Means by Pogrom in 1349 POG 1349 = 1 2.38 2.06 46.8 1.44 41.1 17.0 35.2 22.0 (1.20) (0.97) (33.3) (1.48) (10.8) (7.8) (12.6) (10.3) POG 1349 = 0 2.28 1.92 52.6 1.44 40.0 15.0 31.8 19.0 (1.63) (0.96) (35.8) (1.38) (11.8) (8.2) (13.9) (11.2) Panel B: Regressions on POG 1349 POG 1349 0.120 0.234 −6.887 0.169 −0.953 0.0443 1.000 0.123 (0.534) (0.251) (4.520) (0.165) (1.131) (0.758) (1.367) (0.958) Observations 46 112 325 325 325 325 325 325 Adjusted R2 0.075 −0.004 0.036 0.094 0.401 0.469 0.369 0.554 View Large

III. B aseline R esults

In this section we present our main results. As described in Section II , the Black Death was a common shock that lowered the overall threshold for violence against Jews. In some cities, citizens responded with pogroms, but Jews were unharmed in other cities. We therefore argue that pogroms during the Black Death in 1348–50 at least partly reflect medieval anti-Semitism. Similarly, the general upsurge in anti-Semitic sentiment in Germany after World War I made the expression of anti-Semitic attitudes and violent acts against Jews more likely. We demonstrate that across a range of indicators, towns and cities with a medieval history of violence against Jews also engaged in more persecution in the 1920s and 1930s.

III.A. Comparison of Two Cities

To fix ideas, let us compare two cities: Würzburg, with a population of 101,000 in 1933, and Aachen, with a population of 162,000. Würzburg had a Jewish community since 1100 ( Alicke 2008 ) and Aachen since 1242 ( Avneri 1968 ). The former was the site of a pogrom during the Black Death; the latter was not.

Würzburg’s Jews suffered persecution early. A pogrom in 1147 destroyed the community. During the Rintfleisch pogroms in 1298, some 800 Jews died. There were also pogroms in the 1920s, and the Stürmer published 23 letters from readers in this city (a frequency 10 times higher than average). In Würzburg the Nazi Party garnered 6.3% of the vote in May 1928, when the mean district recorded 3.6%. We know that 943 Jews were deported after 1933 (out of a community of 2,145, which is equivalent to 44%). 36

Aachen provides a stark contrast with Würzburg. Jews were first recorded in 1242, paying taxes. The town had a Judengasse (street for Jews) in 1330. For Aachen, the GJ explicitly states that there is no record of anti-Semitic violence, either before or during the Black Death—even though, in 1349, the citizens of Brussels wrote to the Aachen authorities urging them “to take care that the Jews don’t poison the wells” ( Avneri 1968 ). Aachen also saw no pogroms in the 1920s. The Stürmer published only 10 letters from Aachen (or less than half the number from Würzburg, despite a population that was 60% larger). Only 1% of voters in Aachen backed the NSDAP in 1928. Of the 1,345 Jews living there, 502 (37%) are known to have been deported. We now investigate how general these differences are.

III.B. Empirical Strategy and Overview of Results

i represents the various proxies for anti-Semitism in the Weimar Republic and Nazi Germany at the city level i , POG i 1349 is an indicator variable for Black Death pogroms, and X i is a vector of control variables. Our main control variables are city population, the percentage of the population that is Jewish, and the percentage that is Protestant. i and do not limit ourselves to normal ones. Where the outcome variable’s distribution is highly skewed, we use Poisson maximum likelihood (ML) estimation. To demonstrate the strength of our results (and control for nonlinearities), we also use propensity score matching estimation on the same correlates. We use three empirical strategies: standard regression techniques, propensity score matching, and matching by geographical location. Regressions take the following general form: Here ASrepresents the various proxies for anti-Semitism in the Weimar Republic and Nazi Germany at the city level i , POGis an indicator variable for Black Death pogroms, and Xis a vector of control variables. Our main control variables are city population, the percentage of the population that is Jewish, and the percentage that is Protestant. 37 Depending on the indicator, we allow for different distributions of the error term εand do not limit ourselves to normal ones. Where the outcome variable’s distribution is highly skewed, we use Poisson maximum likelihood (ML) estimation. To demonstrate the strength of our results (and control for nonlinearities), we also use propensity score matching estimation on the same correlates.

In addition, we match towns by geographic location, based on longitude and latitude. As argued in the rich literature in labor economics (see Card and Krueger 1997 ), comparing places close to each other can help overcome the problems associated with omitted variables. Hence, we directly compare towns that are no more than a few miles apart and for which one saw a pogrom in 1349 while the other(s) did not. 38

Before turning to the regression results, we examine differences in various twentieth-century outcome variables between cities that did and did not experience Black Death pogroms. As Table IV shows, pogroms in the 1920s were substantially more frequent in towns with a history of medieval anti-Semitism. Similarly, vote shares for the Nazi party (NSDAP) in 1928 and for the anti-Semitic DVFP in 1924 (when the Nazi Party was banned) were more than a percentage point higher—which is substantial, given that the average vote shares were (respectively) 3.6% and 8%. Our three proxies for anti-Semitism in the 1930s also show marked differences for towns with Black Death pogroms: the proportion of Jewish population deported is more than 10% higher, 39 letters to the editor of Der Stürmer were about 30% more frequent, and the probability that local synagogues were damaged or destroyed during the Reichskristallnacht of 1938 is more than 10% higher. In the next section, we show that these differences are significant both statistically and in terms of quantitative importance.

Table IV Pogrom in 1349 All towns Obs. No Yes Pogrom in 1920s (% of towns) 1.1 8.2 6.3 320 NSDAP May 1928 (% of valid votes) 2.7 4.0 3.6 325 DVFP May 1924 (% of valid votes) 7.2 8.4 8.0 325 Deportations (per 100 Jews in 1933) 24.2 35.6 34.0 278 Stürmer letters (per 10,000 inhabitants) 0.59 0.86 0.82 325 Synagogue attack (% of towns) 79.1 93.8 90.3 278 Pogrom in 1349 All towns Obs. No Yes Pogrom in 1920s (% of towns) 1.1 8.2 6.3 320 NSDAP May 1928 (% of valid votes) 2.7 4.0 3.6 325 DVFP May 1924 (% of valid votes) 7.2 8.4 8.0 325 Deportations (per 100 Jews in 1933) 24.2 35.6 34.0 278 Stürmer letters (per 10,000 inhabitants) 0.59 0.86 0.82 325 Synagogue attack (% of towns) 79.1 93.8 90.3 278 View Large

Table IV Pogrom in 1349 All towns Obs. No Yes Pogrom in 1920s (% of towns) 1.1 8.2 6.3 320 NSDAP May 1928 (% of valid votes) 2.7 4.0 3.6 325 DVFP May 1924 (% of valid votes) 7.2 8.4 8.0 325 Deportations (per 100 Jews in 1933) 24.2 35.6 34.0 278 Stürmer letters (per 10,000 inhabitants) 0.59 0.86 0.82 325 Synagogue attack (% of towns) 79.1 93.8 90.3 278 Pogrom in 1349 All towns Obs. No Yes Pogrom in 1920s (% of towns) 1.1 8.2 6.3 320 NSDAP May 1928 (% of valid votes) 2.7 4.0 3.6 325 DVFP May 1924 (% of valid votes) 7.2 8.4 8.0 325 Deportations (per 100 Jews in 1933) 24.2 35.6 34.0 278 Stürmer letters (per 10,000 inhabitants) 0.59 0.86 0.82 325 Synagogue attack (% of towns) 79.1 93.8 90.3 278 View Large

III.C. 1920s Pogroms

Pogroms in the 1920s were infrequent and highly localized affairs. Although they were embedded in a broader context of anti-Semitic agitation and acts, such as attacks on shops, we only count recorded acts of physical violence. Cities with Black Death pogroms had, on average, significantly more pogroms in the 1920s than cities without pogroms in 1349. As shown in Panel A of Table V our main sample comprises 320 cities with observations on pogroms in both 1349 and the 1920s. In 232 localities, the Black Death coincided with pogroms. The 1920s saw 20 pogroms in Weimar Germany. The frequency of attack was 8.2% in the 232 cities with pogroms in 1349 versus 1.1% in the remaining 88 cities. A town having experienced a medieval pogrom thus raises the probability of witnessing another pogrom in the 1920s by a factor of approximately 6.

Table V Pogrom in 1349 Total No Yes Panel A: Pogrom in 1920s No 87 213 300 98.9% 91.8% 93.8% Yes 1 19 20 1.1% 8.2% 6.3% Total 88 232 320 Panel B: Synagogue attacks No 14 13 27 20.9% 6.2% 9.7% Yes 53 198 251 79.1% 93.8% 90.3% Total 67 211 269 Pogrom in 1349 Total No Yes Panel A: Pogrom in 1920s No 87 213 300 98.9% 91.8% 93.8% Yes 1 19 20 1.1% 8.2% 6.3% Total 88 232 320 Panel B: Synagogue attacks No 14 13 27 20.9% 6.2% 9.7% Yes 53 198 251 79.1% 93.8% 90.3% Total 67 211 269 View Large

Table V Pogrom in 1349 Total No Yes Panel A: Pogrom in 1920s No 87 213 300 98.9% 91.8% 93.8% Yes 1 19 20 1.1% 8.2% 6.3% Total 88 232 320 Panel B: Synagogue attacks No 14 13 27 20.9% 6.2% 9.7% Yes 53 198 251 79.1% 93.8% 90.3% Total 67 211 269 Pogrom in 1349 Total No Yes Panel A: Pogrom in 1920s No 87 213 300 98.9% 91.8% 93.8% Yes 1 19 20 1.1% 8.2% 6.3% Total 88 232 320 Panel B: Synagogue attacks No 14 13 27 20.9% 6.2% 9.7% Yes 53 198 251 79.1% 93.8% 90.3% Total 67 211 269 View Large

Table VI , column (1) reports the ordinary least squares (OLS) regression of pogroms in the 1920s on Black Death pogroms. There is a positive and significant association even after controlling for population size, the percentage of the population that is Jewish, and the percentage that is Protestant. The effect is quantitatively important, as Black Death pogroms are associated with a probability of 1920s pogroms that is more than 6 percentage points higher. This result is confirmed by propensity matching while using the same covariates (Panel B of Table VI ). 40

Table VI Dep. variable: (1) (2) (3) (4) (5) (6) 1920s pogroms NSDAP 1928 DVFP 1924 Deportations Stürmer letters Synagogue attacks OLS OLS OLS ML ML OLS Panel A: Baseline regressions POG 1349 0.0607*** 0.0142** 0.0147 0.142** 0.369** 0.124** (0.0226) (0.00567) (0.0110) (0.0706) (0.144) (0.0522) ln(Pop) 0.0390** −0.00254 −0.00123 0.241*** 0.848*** 0.0498*** (0.0152) (0.00219) (0.00418) (0.0841) (0.0419) (0.0117) %Jewish 0.0135 0.00174 0.00701 0.0743** 0.218*** 0.0262** (0.0114) (0.00190) (0.00442) (0.0348) (0.0383) (0.0132) %Protestant 0.00034 0.00029*** 0.00083*** −0.0039*** −0.0053** 0.00036 (0.00042) (0.000088) (.00018) (0.0012) (0.0023) (0.00060) ln(# Jews 1933) 0.815*** (0.0822) Observations 320 325 325 278 325 278 Adjusted R2 0.054 0.043 0.080 0.098 Panel B: Matching estimation a POG 1349 0.0744*** 0.0133*** 0.0203** 161.7*** 2.386*** 0.103* (0.0182) (0.00486) (0.0102) (41.33) (0.570) (0.0553) Observations 320 325 325 278 325 278 Panel C: Geographic matching b POG 1349 0.0819*** 0.0116** 0.0238*** 195.8*** 2.864*** 0.152** (0.0162) (0.00456) (0.00746) (33.55) (0.579) (0.0677) Median distance 20.4 20.0 20.0 21.9 22.2 23.7 Mean distance 23.4 23.1 23.1 28.3 32.6 27.6 Observations 320 325 325 278 325 278 Dep. variable: (1) (2) (3) (4) (5) (6) 1920s pogroms NSDAP 1928 DVFP 1924 Deportations Stürmer letters Synagogue attacks OLS OLS OLS ML ML OLS Panel A: Baseline regressions POG 1349 0.0607*** 0.0142** 0.0147 0.142** 0.369** 0.124** (0.0226) (0.00567) (0.0110) (0.0706) (0.144) (0.0522) ln(Pop) 0.0390** −0.00254 −0.00123 0.241*** 0.848*** 0.0498*** (0.0152) (0.00219) (0.00418) (0.0841) (0.0419) (0.0117) %Jewish 0.0135 0.00174 0.00701 0.0743** 0.218*** 0.0262** (0.0114) (0.00190) (0.00442) (0.0348) (0.0383) (0.0132) %Protestant 0.00034 0.00029*** 0.00083*** −0.0039*** −0.0053** 0.00036 (0.00042) (0.000088) (.00018) (0.0012) (0.0023) (0.00060) ln(# Jews 1933) 0.815*** (0.0822) Observations 320 325 325 278 325 278 Adjusted R2 0.054 0.043 0.080 0.098 Panel B: Matching estimation a POG 1349 0.0744*** 0.0133*** 0.0203** 161.7*** 2.386*** 0.103* (0.0182) (0.00486) (0.0102) (41.33) (0.570) (0.0553) Observations 320 325 325 278 325 278 Panel C: Geographic matching b POG 1349 0.0819*** 0.0116** 0.0238*** 195.8*** 2.864*** 0.152** (0.0162) (0.00456) (0.00746) (33.55) (0.579) (0.0677) Median distance 20.4 20.0 20.0 21.9 22.2 23.7 Mean distance 23.4 23.1 23.1 28.3 32.6 27.6 Observations 320 325 325 278 325 278 View Large

Table VI Dep. variable: (1) (2) (3) (4) (5) (6) 1920s pogroms NSDAP 1928 DVFP 1924 Deportations Stürmer letters Synagogue attacks OLS OLS OLS ML ML OLS Panel A: Baseline regressions POG 1349 0.0607*** 0.0142** 0.0147 0.142** 0.369** 0.124** (0.0226) (0.00567) (0.0110) (0.0706) (0.144) (0.0522) ln(Pop) 0.0390** −0.00254 −0.00123 0.241*** 0.848*** 0.0498*** (0.0152) (0.00219) (0.00418) (0.0841) (0.0419) (0.0117) %Jewish 0.0135 0.00174 0.00701 0.0743** 0.218*** 0.0262** (0.0114) (0.00190) (0.00442) (0.0348) (0.0383) (0.0132) %Protestant 0.00034 0.00029*** 0.00083*** −0.0039*** −0.0053** 0.00036 (0.00042) (0.000088) (.00018) (0.0012) (0.0023) (0.00060) ln(# Jews 1933) 0.815*** (0.0822) Observations 320 325 325 278 325 278 Adjusted R2 0.054 0.043 0.080 0.098 Panel B: Matching estimation a POG 1349 0.0744*** 0.0133*** 0.0203** 161.7*** 2.386*** 0.103* (0.0182) (0.00486) (0.0102) (41.33) (0.570) (0.0553) Observations 320 325 325 278 325 278 Panel C: Geographic matching b POG 1349 0.0819*** 0.0116** 0.0238*** 195.8*** 2.864*** 0.152** (0.0162) (0.00456) (0.00746) (33.55) (0.579) (0.0677) Median distance 20.4 20.0 20.0 21.9 22.2 23.7 Mean distance 23.4 23.1 23.1 28.3 32.6 27.6 Observations 320 325 325 278 325 278 Dep. variable: (1) (2) (3) (4) (5) (6) 1920s pogroms NSDAP 1928 DVFP 1924 Deportations Stürmer letters Synagogue attacks OLS OLS OLS ML ML OLS Panel A: Baseline regressions POG 1349 0.0607*** 0.0142** 0.0147 0.142** 0.369** 0.124** (0.0226) (0.00567) (0.0110) (0.0706) (0.144) (0.0522) ln(Pop) 0.0390** −0.00254 −0.00123 0.241*** 0.848*** 0.0498*** (0.0152) (0.00219) (0.00418) (0.0841) (0.0419) (0.0117) %Jewish 0.0135 0.00174 0.00701 0.0743** 0.218*** 0.0262** (0.0114) (0.00190) (0.00442) (0.0348) (0.0383) (0.0132) %Protestant 0.00034 0.00029*** 0.00083*** −0.0039*** −0.0053** 0.00036 (0.00042) (0.000088) (.00018) (0.0012) (0.0023) (0.00060) ln(# Jews 1933) 0.815*** (0.0822) Observations 320 325 325 278 325 278 Adjusted R2 0.054 0.043 0.080 0.098 Panel B: Matching estimation a POG 1349 0.0744*** 0.0133*** 0.0203** 161.7*** 2.386*** 0.103* (0.0182) (0.00486) (0.0102) (41.33) (0.570) (0.0553) Observations 320 325 325 278 325 278 Panel C: Geographic matching b POG 1349 0.0819*** 0.0116** 0.0238*** 195.8*** 2.864*** 0.152** (0.0162) (0.00456) (0.00746) (33.55) (0.579) (0.0677) Median distance 20.4 20.0 20.0 21.9 22.2 23.7 Mean distance 23.4 23.1 23.1 28.3 32.6 27.6 Observations 320 325 325 278 325 278 View Large

Finally, we report results for geographical matching (Panel C). The probability of a pogrom in the 1920s is 8.2 percentage points higher in cities with medieval pogroms than in nearby cities without attacks on Jews during the Black Death. The mean and median distances between matched cities are low—about 20 miles. 41 The effects identified by geographical matching are significant and of a similar magnitude as our previous estimates. This strongly suggests that our findings are not driven by unobserved heterogeneity at the local level.

A history of medieval violence against Jews is associated with large and statistically significant shifts in the probability of another pogrom, but the correlation is not perfect. Not all towns that burned their Jews in 1348–50 saw attacks in the 1920s; in fact, the majority did not. Many factors can reduce the extent to which anti-Semitic attitudes survive in one location. At the end of the Section IV.C , we examine some of the city characteristics that are associated with lower persistence.

III.D. Voting Results

We now turn to parliamentary elections during the Weimar Republic. The May 1928 election is arguably the most reliable indicator for anti-Semitism because the NSDAP emphasized the anti-Semitic and radical side of its program before the party’s turning point in the late 1920s. Thereafter, it aspired to greater respectability in the eyes of middle-class voters and toned down its anti-Semitic rhetoric. In column (2) of Table VI we analyze this result further. In 1928, the NSDAP vote share was 1.4 percentage points higher in electoral districts with a past of anti-Semitic violence (after we control for population size and the percentage of Jews and Protestants). This means that the NSDAP added more than a third to its typical vote share in cities that had pogroms in 1349. The control variables show that Protestants voted for the NSDAP in greater numbers than the average population, confirming the findings in Falter (1991) . 42 According to the point estimates in column (2), an increase of one standard deviation (33%) in the population share of Protestants raises the NSDAP vote share by about 1 percentage point—an effect slightly smaller than that of medieval pogroms. Finally, the percentage of Jews in the population is positively (but not significantly) correlated with NSDAP votes in 1928. The same result holds if we use propensity matching by control variables (Panel B).

To illustrate the strength of these findings, consider the two towns of Königheim and Wertheim. They are 6.4 miles apart and in 1933 had populations of 1,549 and 3,971, respectively. Both had a Jewish settlement before the Black Death. Königheim did not witness a pogrom during the plague, but Wertheim did. The NSDAP received 1.6% of valid votes in Königheim in 1928; in Wertheim, it received 8.1%. The analysis in Panel C of Table VI (see column (2)) generalizes this type of comparison by matching each town in our main data set to its two nearest neighbors with a different history of medieval anti-Semitic violence. The results confirm the magnitude and statistical significance of our previous estimates.

Column (3) in Table VI repeats the same regressions for the DVFP in May 1924, which attracted much of the vote for the temporarily banned NSDAP. On average, Black Death pogroms are associated with a DVFP vote share that is 1.5–2.2 percentage points higher. Although the OLS regression result is marginally below statistical significance, both propensity score matching and matching by geography suggest large and significant differences. To put matters in context, in the sample overall, the DVFP polled 8% in 1924; thus, the matching results imply that DVFP votes are about a quarter higher in cities where Black Death pogroms occurred. This is the same order of magnitude as for Nazi Party votes in 1928.

III.E. Deportations, Stürmer Letters, and Attacks on Synagogues

In this section we analyze deportations of Jews between 1933 and 1944. Although they resulted from a centrally directed policy, deportations in any one town and village partly reflected the level of hostility shown by local authorities as well as support of (or denunciations by) neighbors and acquaintances.

Column (4) in Table VI shows regression results for deportations during the Nazi regime. As the dependent variable, we use data on the number of Jews transported at the city level for the period 1933–45. Poisson ML regression is our favored estimation technique because the distribution of deportations is heavily right-skewed. According to Wooldridge (2002) , linear models may not be appropriate for “corner-solution” specifications, where a significant mass of the nonnegative observations is close to zero. 43 We add the size of the Jewish population in 1933 to our regular set of controls. 44 On average, 197 Jews were deported from cities in the main sample. Thus, the coefficient of .14 from the ML estimation implies that cities with Black Death pogroms deported about 30 more Jewish inhabitants on average than cities without medieval pogroms. Panels B and C present matching estimations by other covariates and geography, respectively. This estimation technique (which does not rely on a particular probability distribution) yields quantitatively stronger results, with more than 100 additional deportees in cities with Black Death pogroms. 45

A simple way to illustrate our results is to graph deportations from towns and cities with and without 1349 pogroms; see Figure III . We plot the kernel density of the percentage of Jewish population that was deported after 1933. The distribution for cities with Black Death pogroms is shifted sharply to the right, indicating that their Jewish inhabitants were deported more often.

Figure III View largeDownload slide Deportations of Jews Conditional on Black Death Pogroms This figure plots the kernel density of the number of deported Jews between 1933 and 1945 divided by Jewish population in 1933 at the city level (weighted by city population in 1933). The data used corresponds to our main sample (including only towns with a documented medieval-era Jewish settlement).

Figure III View largeDownload slide Deportations of Jews Conditional on Black Death Pogroms This figure plots the kernel density of the number of deported Jews between 1933 and 1945 divided by Jewish population in 1933 at the city level (weighted by city population in 1933). The data used corresponds to our main sample (including only towns with a documented medieval-era Jewish settlement).

Next, we turn to letters to the editor of the Nazi newspaper Der Stürmer . In towns with Black Death pogroms, there was one letter sent for every 11,570 inhabitants; in towns without a pogrom, the frequency falls to one per 16,860. Column (5) in Table VI shows that the correlation between 1349 pogroms and the number of Stürmer letters is significant in our sample. Because the dependent variable is right-skewed, we again use Poisson ML. The estimated impact is sizable. With an average of 3.8 letters per city, the ML coefficient implies an additional 1.5 letters for cities with Black Death pogroms. The matching estimations confirm this result by indicating more than two additional letters.

Finally, we examine data from the Reichskristallnacht (on November 9, 1938), limiting the analysis to localities that were home to synagogues or prayer rooms. Towns with a history of pogroms had a markedly greater tendency to register attacks. As shown in Panel B of Table V , synagogues were damaged or destroyed in 93.8% of German cities with pogroms but in only 79.1% of cities without pogroms.

Column (6) in Table VI reports the results from a linear probability model that regresses Black Death pogroms on an indicator variable for whether a city’s synagogue was damaged or destroyed during the Reichskristallnacht . The coefficient is positive, large, and significant. More populous cities had a higher probability of attack; the coefficient for Protestants is positive but not significant. The estimated coefficients show that cities with Black Death pogroms were about 12% more likely to damage or destroy synagogues during Reichskristallnacht . Both propensity score and geographic matching confirm the significance and magnitude of this result, and they imply a 10%–15% higher attack probability.

In the Online Appendix , we test the robustness of these results. In Section II.B we control for a variety of socioeconomic variables and show that the OLS results are robust to province and prefecture fixed effects. In Section II.C we explore the robustness of our results to alternative specifications for each twentieth-century outcome variable ( Tables A.7–A.13 ). In Section II.D, we control for spatial correlation and show that the vast majority of our results hold for various sample splits: we document persistence of anti-Semitism within the subsamples of eastern versus western cities, large cities versus small towns, and Protestant versus Catholic areas.

III.F. Principal Components Analysis and the Extended Sample

Do our measures of anti-Semitism in interwar Germany capture a broader, underlying pattern of attitudes, or are they isolated phenomena that occasionally coincide with medieval violence? To answer this question, we obtain the first principal component from all six twentieth-century outcome variables: pogroms in the 1920s, the share of DVFP votes 1924, the share of NSDAP votes 1928, Stürmer letters, deportations per 100 Jews in 1933, and an indicator variable for whether a synagogue was destroyed (or damaged). We scale all variables (except for the vote shares) by city population in 1933. 46 To exploit as much variation as possible, we calculate the principal component for the extended sample, which includes all cities with Jewish communities in Weimar Germany. 47 All variables have positive factor loadings, suggesting that our indicators capture an underlying anti-Semitic attitude. The first principal component explains 27% of the sample variance.

Next, we employ the principal component as a dependent variable. To interpret the results, we standardize all variables except for the POG 1349 indicator. Thus, the coefficient for POG 1349 tells us by how many standard deviations the principal component increases in cities that had medieval pogroms. The results are presented in Table VII . Whether we use our standard set of control variables (column (1)) or an extended one (column (2)), matching estimation (column (3)), or simple geographical matching (column (4)), we obtain a strong and significant result for medieval pogroms. According to the estimates, this effect is large. Black Death pogroms increase the dependent variable by 0.25–0.32 standard deviations.

Table VII (1) (2) (3) (4) (5) (6) (7) (8) OLS OLS ME a GeoMatch b OLS OLS ME a GeoMatch b Main Sample Extended Sample POG 1349 0.290** 0.254 * 0.264** 0.318*** 0.333*** 0.303** 0.274** 0.315*** (0.132) (0.135) (0.127) (0.0819) (0.127) (0.130) (0.126) (0.0808) JewCom 1349 0.0158 −0.0378 mv mv (0.105) (0.109) ln(Pop 1933) −0.0875 0.0532 mv −0.191*** −0.0339 mv (0.0646) (0.0644) (0.0421) (0.0345) %Jewish 1933 0.0215 −0.200 * mv 0.154*** 0.112*** mv (0.0971) (0.105) (0.0439) (0.0374) %Protestant 1925 0.284*** 0.297*** mv 0.287*** 0.282*** mv (0.0757) (0.0755) (0.0411) (0.0396) %Blue collar 1933 −0.367** −0.109 (0.149) (0.0874) %Industry employ. 0.0832 −0.0622 (0.156) (0.0853) %Self-employed in retail & trade 0.169** 0.248*** (0.0725) (0.0613) Observations 311 311 311 311 1035 1035 1035 1184 Adjusted R 2 0.052 0.099 0.124 0.206 (1) (2) (3) (4) (5) (6) (7) (8) OLS OLS ME a GeoMatch b OLS OLS ME a GeoMatch b Main Sample Extended Sample POG 1349 0.290** 0.254 * 0.264** 0.318*** 0.333*** 0.303** 0.274** 0.315*** (0.132) (0.135) (0.127) (0.0819) (0.127) (0.130) (0.126) (0.0808) JewCom 1349 0.0158 −0.0378 mv mv (0.105) (0.109) ln(Pop 1933) −0.0875 0.0532 mv −0.191*** −0.0339 mv (0.0646) (0.0644) (0.0421) (0.0345) %Jewish 1933 0.0215 −0.200 * mv 0.154*** 0.112*** mv (0.0971) (0.105) (0.0439) (0.0374) %Protestant 1925 0.284*** 0.297*** mv 0.287*** 0.282*** mv (0.0757) (0.0755) (0.0411) (0.0396) %Blue collar 1933 −0.367** −0.109 (0.149) (0.0874) %Industry employ. 0.0832 −0.0622 (0.156) (0.0853) %Self-employed in retail & trade 0.169** 0.248*** (0.0725) (0.0613) Observations 311 311 311 311 1035 1035 1035 1184 Adjusted R 2 0.052 0.099 0.124 0.206 View Large

Table VII (1) (2) (3) (4) (5) (6) (7) (8) OLS OLS ME a GeoMatch b OLS OLS ME a GeoMatch b Main Sample Extended Sample POG 1349 0.290** 0.254 * 0.264** 0.318*** 0.333*** 0.303** 0.274** 0.315*** (0.132) (0.135) (0.127) (0.0819) (0.127) (0.130) (0.126) (0.0808) JewCom 1349 0.0158 −0.0378 mv mv (0.105) (0.109) ln(Pop 1933) −0.0875 0.0532 mv −0.191*** −0.0339 mv (0.0646) (0.0644) (0.0421) (0.0345) %Jewish 1933 0.0215 −0.200 * mv 0.154*** 0.112*** mv (0.0971) (0.105) (0.0439) (0.0374) %Protestant 1925 0.284*** 0.297*** mv 0.287*** 0.282*** mv (0.0757) (0.0755) (0.0411) (0.0396) %Blue collar 1933 −0.367** −0.109 (0.149) (0.0874) %Industry employ. 0.0832 −0.0622 (0.156) (0.0853) %Self-employed in retail & trade 0.169** 0.248*** (0.0725) (0.0613) Observations 311 311 311 311 1035 1035 1035 1184 Adjusted R 2 0.052 0.099 0.124 0.206 (1) (2) (3) (4) (5) (6) (7) (8) OLS OLS ME a GeoMatch b OLS OLS ME a GeoMatch b Main Sample Extended Sample POG 1349 0.290** 0.254 * 0.264** 0.318*** 0.333*** 0.303** 0.274** 0.315*** (0.132) (0.135) (0.127) (0.0819) (0.127) (0.130) (0.126) (0.0808) JewCom 1349 0.0158 −0.0378 mv mv (0.105) (0.109) ln(Pop 1933) −0.0875 0.0532 mv −0.191*** −0.0339 mv (0.0646) (0.0644) (0.0421) (0.0345) %Jewish 1933 0.0215 −0.200 * mv 0.154*** 0.112*** mv (0.0971) (0.105) (0.0439) (0.0374) %Protestant 1925 0.284*** 0.297*** mv 0.287*** 0.282*** mv (0.0757) (0.0755) (0.0411) (0.0396) %Blue collar 1933 −0.367** −0.109 (0.149) (0.0874) %Industry employ. 0.0832 −0.0622 (0.156) (0.0853) %Self-employed in retail & trade 0.169** 0.248*** (0.0725) (0.0613) Observations 311 311 311 311 1035 1035 1035 1184 Adjusted R 2 0.052 0.099 0.124 0.206 View Large

So far, we have analyzed twentieth-century anti-Semitism only for cities with confirmed Jewish settlements in the fourteenth century. Using the main sample allows for a clear interpretation of the POG 1349 coefficient, but it discards more than three quarters of the observations available in the extended sample. We now use all cities and towns for which we have information on twentieth-century outcome variables. To interpret the POG 1349 coefficient in this context, we must control for the existence of medieval Jewish communities; this is captured by the dummy variable JewCom 1349 . Columns (5)–(8) in Table VII report this analysis. Results for Black Death pogroms are strongly similar to those for the main sample and are highly significant throughout.

IV. O rigins of P ersecution and C orrelates of P ersistence

Our results suggest a high degree of persistence in terms of anti-Semitic acts and sentiment at the local level. In this section, we explore two questions: What factors explain Black Death pogroms, and when does cultural transmission of anti-Semitism fail?

IV.A. Correlates of Medieval Jewish Settlement and Black Death Pogroms

We collect and analyze data on medieval city characteristics to explain where Jews settled first and also why pogroms occurred in some places but not others. Some economic and political variables are correlated with medieval settlement and (more weakly) with Black Death pogroms, but they are generally uncorrelated with twentieth-century anti-Semitism.

For the extended sample, we have information on where Jews had settled by 1350. In addition, the first mention of Jewish settlement is recorded in GJ. Because we have no direct indicators of the size of Jewish communities, we allow these two outcome variables to proxy for the attractiveness of individual cities to Jews. We also employ explanatory variables that proxy for a city’s economic and political openness: whether a city was self-governing as a Free Imperial city, had been incorporated by 1349, or had obtained market rights before the Black Death. Similarly, a city’s location on a navigable river and its membership in the Hanseatic League are indicative of trade openness. In addition, we include two indicator variables that are political in nature, one each for cities run by a bishop and Stauffer cities. 48 Finally, we also control for geographical isolation (proxied by ruggedness of terrain) and the age of a city.

Columns (1)–(4) in Table VIII analyze the correlates of medieval Jewish settlement. Both openness indicators and political variables have some explanatory power. Jewish settlement was more frequent in Hanseatic cities, but Jewish communities were not significantly older in 1349. The same is true for cities that were incorporated in 1349. Free Imperial cities (membership in this group partly overlapped with the Hanseatic League) owed allegiance to the emperor, not to regional princes. They were directly represented in the Imperial Diet, and many of them were self-governed by bourgeois elites. Free Imperial cities—as well as cities with market rights and those governed by bishops—had more and older Jewish communities, which suggests that they were the most attractive to Jews. Cities on navigable rivers had older Jewish communities, whereas the opposite was true for more isolated towns. Cities founded by the Stauffer emperors had Jewish communities more often than other cities but were no more likely to have old, established Jewish settlements. Finally, cities with a longer municipal history had more and older Jewish communities. Overall, the pattern of Jewish settlement is in line with expectations—the larger the potential for trade, the earlier Jews settled.

Table VIII Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) Jewish Comm. existed in 1349 ln(Age of Jewish comm. in 1349) Pogrom in 1349 First principal component a POG 1349 0.276** 0.324** (0.136) (0.142) Hanse 0.161** 0.0896 −0.0730 −0.441*** (0.0659) (0.198) (0.0887) (0.138) Incorporated 1349 0.108*** 0.0915 0.0367 −0.0195 (0.0373) (0.0926) (0.0544) (0.141) Free Imperial 0.331*** 0.463*** 0.131** −0.0530 (0.0475) (0.144) (0.0598) (0.194) Market town 1349 0.161*** 0.270** 0.104 * 0.0557 (0.0437) (0.109) (0.0533) (0.142) Navigable River 0.0316 0.240** 0.0838 −0.107 (0.0391) (0.0923) (0.0514) (0.136) Bishop 0.282*** 0.744*** 0.0504 0.188 (0.0770) (0.236) (0.0756) (0.269) Staufer 0.363*** 0.123 0.207*** −0.235 (0.0802) (0.185) (0.0770) (0.224) Isolated town 0.0301 −0.313*** −0.0518 −0.00398 (0.0485) (0.0908) (0.0531) (0.164) ln(Age of city in 1349) 0.123*** 0.212*** 0.156*** −0.0955 (0.0192) (0.0683) (0.0396) (0.110) Observations 765 700 298 269 325 323 311 309 Adjusted R2 0.087 0.073 0.128 0.147 0.028 0.066 0.050 0.045 Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) Jewish Comm. existed in 1349 ln(Age of Jewish comm. in 1349) Pogrom in 1349 First principal component a POG 1349 0.276** 0.324** (0.136) (0.142) Hanse 0.161** 0.0896 −0.0730 −0.441*** (0.0659) (0.198) (0.0887) (0.138) Incorporated 1349 0.108*** 0.0915 0.0367 −0.0195 (0.0373) (0.0926) (0.0544) (0.141) Free Imperial 0.331*** 0.463*** 0.131** −0.0530 (0.0475) (0.144) (0.0598) (0.194) Market town 1349 0.161*** 0.270** 0.104 * 0.0557 (0.0437) (0.109) (0.0533) (0.142) Navigable River 0.0316 0.240** 0.0838 −0.107 (0.0391) (0.0923) (0.0514) (0.136) Bishop 0.282*** 0.744*** 0.0504 0.188 (0.0770) (0.236) (0.0756) (0.269) Staufer 0.363*** 0.123 0.207*** −0.235 (0.0802) (0.185) (0.0770) (0.224) Isolated town 0.0301 −0.313*** −0.0518 −0.00398 (0.0485) (0.0908) (0.0531) (0.164) ln(Age of city in 1349) 0.123*** 0.212*** 0.156*** −0.0955 (0.0192) (0.0683) (0.0396) (0.110) Observations 765 700 298 269 325 323 311 309 Adjusted R2 0.087 0.073 0.128 0.147 0.028 0.066 0.050 0.045 View Large

Table VIII Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) Jewish Comm. existed in 1349 ln(Age of Jewish comm. in 1349) Pogrom in 1349 First principal component a POG 1349 0.276** 0.324** (0.136) (0.142) Hanse 0.161** 0.0896 −0.0730 −0.441*** (0.0659) (0.198) (0.0887) (0.138) Incorporated 1349 0.108*** 0.0915 0.0367 −0.0195 (0.0373) (0.0926) (0.0544) (0.141) Free Imperial 0.331*** 0.463*** 0.131** −0.0530 (0.0475) (0.144) (0.0598) (0.194) Market town 1349 0.161*** 0.270** 0.104 * 0.0557 (0.0437) (0.109) (0.0533) (0.142) Navigable River 0.0316 0.240** 0.0838 −0.107 (0.0391) (0.0923) (0.0514) (0.136) Bishop 0.282*** 0.744*** 0.0504 0.188 (0.0770) (0.236) (0.0756) (0.269) Staufer 0.363*** 0.123 0.207*** −0.235 (0.0802) (0.185) (0.0770) (0.224) Isolated town 0.0301 −0.313*** −0.0518 −0.00398 (0.0485) (0.0908) (0.0531) (0.164) ln(Age of city in 1349) 0.123*** 0.212*** 0.156*** −0.0955 (0.0192) (0.0683) (0.0396) (0.110) Observations 765 700 298 269 325 323 311 309 Adjusted R2 0.087 0.073 0.128 0.147 0.028 0.066 0.050 0.045 Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) Jewish Comm. existed in 1349 ln(Age of Jewish comm. in 1349) Pogrom in 1349 First principal component a POG 1349 0.276** 0.324** (0.136) (0.142) Hanse 0.161** 0.0896 −0.0730 −0.441*** (0.0659) (0.198) (0.0887) (0.138) Incorporated 1349 0.108*** 0.0915 0.0367 −0.0195 (0.0373) (0.0926) (0.0544) (0.141) Free Imperial 0.331*** 0.463*** 0.131** −0.0530 (0.0475) (0.144) (0.0598) (0.194) Market town 1349 0.161*** 0.270** 0.104 * 0.0557 (0.0437) (0.109) (0.0533) (0.142) Navigable River 0.0316 0.240** 0.0838 −0.107 (0.0391) (0.0923) (0.0514) (0.136) Bishop 0.282*** 0.744*** 0.0504 0.188 (0.0770) (0.236) (0.0756) (0.269) Staufer 0.363*** 0.123 0.207*** −0.235 (0.0802) (0.185) (0.0770) (0.224) Isolated town 0.0301 −0.313*** −0.0518 −0.00398 (0.0485) (0.0908) (0.0531) (0.164) ln(Age of city in 1349) 0.123*** 0.212*** 0.156*** −0.0955 (0.0192) (0.0683) (0.0396) (0.110) Observations 765 700 298 269 325 323 311 309 Adjusted R2 0.087 0.073 0.128 0.147 0.028 0.066 0.050 0.045 View Large

Some of our political and economic variables are related to the pattern of Black Death attacks. We find significantly higher probabilities of pogroms in Free Imperial cities, cities with market charter, those founded by the Stauffer, and older cities. This suggests that more commercial centers—where Jews might have played a more prominent role in economic life—witnessed greater pogrom frequencies ( Cohn-Sherbok 2002 ). The overall explanatory power of all the variables in columns (5) and (6) is lower than in our regression on Jewish settlement patterns.

Do medieval economic or political correlates of Jewish settlement and pogroms directly predict twentieth-century anti-Semitism? If so, then medieval pogroms might simply be proxying for geographical, economic, or political factors that have remained unchanged. We address this question by adding the medieval explanatory variables plus the 1349 pogrom indicator to regressions where the principal component of anti-Semitism is the dependent variable (columns (7) and (8) in Table VIII ). With the exception of Hanseatic cities (see Section IV.B for an interpretation), none of the medieval correlates is significantly associated with twentieth-century anti-Semitism. However, the coefficient for medieval pogroms remains positive and highly significant. Online Appendix Section II.E provides additional results for medieval Jewish settlement and pogroms. In particular, Table A.19 uses medieval correlates to predict pogrom probabilities in 1349 and then includes this prediction in regressions with twentieth-century outcome variables. Although POG 1349 remains highly significant, predicted pogrom probability is insignificant in all specifications.

IV.B. Persistence before and after the Black Death

Anti-Semitic attacks in Germany were not limited to the fourteenth and twentieth centuries; there were scattered pogroms as early as the eleventh century, and violence also erupted when Jews returned in larger numbers in the nineteenth century. If our argument about the persistence of anti-Semitic sentiment at the local level is correct, then we should find that pogroms and other expressions of Jew-hatred occurred in the same locations before and after 1349.

For each location in our main sample, we analyze the number of reported attacks before 1347, the presence of Judensau sculptures, and participation in the 1819 Hep-Hep riots as a function of Black Death pogroms. 49Judensau sculptures, which portrayed Jews in demeaning poses, were part of churches as well as public and private buildings. The Hep-Hep riots were frequently violent protests against possible emancipation of the Jews.

As Table IX shows, there is a highly consistent and significant pattern of differences. The number of pre-plague attacks is about 1.5 times higher in towns and cities that also attacked their Jews in 1349. 50 There are no Judensau sculptures in localities without a pogrom in 1349, and only 1.1% of these towns witnessed Hep-Hep riots. In contrast, we find Judensau sculptures and early nineteenth-century attacks in (respectively) 5% and 6% of all cities and towns with Black Death pogroms. Overall, the evidence is consistent with the persistence of anti-Semitic attitudes and behavior at the local level over the entire period from the eleventh to the twentieth century.

Table IX (1) (2) (3) # POG pre-1347 Judensau Hep-Hep POG 1349 = 1 0.481 0.055 0.060 POG 1349 = 0 0.322 0 0.011 Difference 0.159 * 0.055** 0.048 * p -value 0.09 0.02 0.06 Observations 325 325 325 (1) (2) (3) # POG pre-1347 Judensau Hep-Hep POG 1349 = 1 0.481 0.055 0.060 POG 1349 = 0 0.322 0 0.011 Difference 0.159 * 0.055** 0.048 * p -value 0.09 0.02 0.06 Observations 325 325 325 View Large

Table IX (1) (2) (3) # POG pre-1347 Judensau Hep-Hep POG 1349 = 1 0.481 0.055 0.060 POG 1349 = 0 0.322 0 0.011 Difference 0.159 * 0.055** 0.048 * p -value 0.09 0.02 0.06 Observations 325 325 325 (1) (2) (3) # POG pre-1347 Judensau Hep-Hep POG 1349 = 1 0.481 0.055 0.060 POG 1349 = 0 0.322 0 0.011 Difference 0.159 * 0.055** 0.048 * p -value 0.09 0.02 0.06 Observations 325 325 325 View Large

IV.C. When Did Cultural Transmission Fail?

How do we make sense of anti-Semitism persisting for more than half a millennium? To understand why persistence exists in the first place, we examine conditioning variables that may weaken it. We focus on three types: political variables, economic indicators, and geographical characteristics. In Table X , we test for whether the transmission of anti-Semitic attitudes looks visibly different in each subgroup. For the Hanseatic cities (column (1)), the interaction term with POG 1349 is negative and significant. The combined effect implies that the extent of transmission from the medieval period is essentially zero. Observe also that once we include an interaction term, membership in the Hanseatic League by itself does not systematically predict less Jew-hatred in the 1920s and 1930s. 51 What disappears is the predictability of twentieth-century hatred based on fourteenth-century pogroms. 52

Table X (1) (2) (3) (4) (5) (6) (7) Hanseatic Open city City growth Industrial Bishop Geographic isolation POG 1349 0.311** 0.375 * 0.257 0.777** 0.293** 0.384** 0.309 * (0.141) (0.198) (0.225) (0.312) (0.134) (0.165) (0.187) Hanseatic −0.133 (0.175) Hanseatic × POG 1349 −0.444** (0.208) Open 0.158 (0.128) Open × POG 1349 −0.298** (0.148) PopGrowth −0.131 (0.166) PopGrowth × POG 1349 −0.432** (0.168) %Industrial −0.00351 (0.00730) %Industrial × POG 1349 −0.0143 * (0.00859) Bishop 0.292 (0.371) Bishop × POG 1349 −0.185 (0.451) Isolated 1, 2 0.176 −0.0037 (0.228) (0.190) Isolated 1, 2 × POG 1349 −0.268 −0.0438 (0.260) (0.237) Observations 311 214 110 311 311 311 311 Adjusted R2 0.060 0.063 0.081 0.068 0.047 0.048 0.046 (1) (2) (3) (4) (5) (6) (7) Hanseatic Open city City growth Industrial Bishop Geographic isolation POG 1349 0.311** 0.375 * 0.257 0.777** 0.293** 0.384** 0.309 * (0.141) (0.198) (0.225) (0.312) (0.134) (0.165) (0.187) Hanseatic −0.133 (0.175) Hanseatic × POG 1349 −0.444** (0.208) Open 0.158 (0.128) Open × POG 1349 −0.298** (0.148) PopGrowth −0.131 (0.166) PopGrowth × POG 1349 −0.432** (0.168) %Industrial −0.00351 (0.00730) %Industrial × POG 1349 −0.0143 * (0.00859) Bishop 0.292 (0.371) Bishop × POG 1349 −0.185 (0.451) Isolated 1, 2 0.176 −0.0037 (0.228) (0.190) Isolated 1, 2 × POG 1349 −0.268 −0.0438 (0.260) (0.237) Observations 311 214 110 311 311 311 311 Adjusted R2 0.060 0.063 0.081 0.068 0.047 0.048 0.046 View Large

Table X (1) (2) (3) (4) (5) (6) (7) Hanseatic Open city City growth Industrial Bishop Geographic isolation POG 1349 0.311** 0.375 * 0.257 0.777** 0.293** 0.384** 0.309 * (0.141) (0.198) (0.225) (0.312) (0.134) (0.165) (0.187) Hanseatic −0.133 (0.175) Hanseatic × POG 1349 −0.444** (0.208) Open 0.158 (0.128) Open × POG 1349 −0.298** (0.148) PopGrowth −0.131 (0.166) PopGrowth × POG 1349 −0.432** (0.168) %Industrial −0.00351 (0.00730) %Industrial × POG 1349 −0.0143 * (0.00859) Bishop 0.292 (0.371) Bishop × POG 1349 −0.185 (0.451) Isolated 1, 2 0.176 −0.0037 (0.228) (0.190) Isolated 1, 2 × POG 1349 −0.268 −0.0438 (0.260) (0.237) Observations 311 214 110 311 311 311 311 Adjusted R2 0.060 0.063 0.081 0.068 0.047 0.048 0.046 (1) (2) (3) (4) (5) (6) (7) Hanseatic Open city City growth Industrial Bishop Geographic isolation POG 1349 0.311** 0.375 * 0.257 0.777** 0.293** 0.384** 0.309 * (0.141) (0.198) (0.225) (0.312) (0.134) (0.165) (0.187) Hanseatic −0.133 (0.175) Hanseatic × POG 1349 −0.444** (0.208) Open 0.158 (0.128) Open × POG 1349 −0.298** (0.148) PopGrowth −0.131 (0.166) PopGrowth × POG 1349 −0.432** (0.168) %Industrial −0.00351 (0.00730) %Industrial × POG 1349 −0.0143 * (0.00859) Bishop 0.292 (0.371) Bishop × POG 1349 −0.185 (0.451) Isolated 1, 2 0.176 −0.0037 (0.228) (0.190) Isolated 1, 2 × POG 1349 −0.268 −0.0438 (0.260) (0.237) Observations 311 214 110 311 311 311 311 Adjusted R2 0.060 0.063 0.081 0.068 0.047 0.048 0.046 View Large

Although lower persistence of anti-Semitism in Hanseatic cities is suggestive, the result requires further investigation. Is it openness to trade that undermines racial hatred? Because there are no Hanseatic cities in southern Germany, we construct a measure designed to capture similar conditions for cities south of Cologne (the southernmost member of the Hanseatic League). In line with the first set of medieval correlates in Table VIII , we derive the “open” index as the sum of four indicators: incorporated by 1349; Free Imperial city; market charter by 1349; and located on a navigable river. The interaction effect of this index with medieval pogroms is negative and significant at the 5% level. Here, just as in the case of Hanseatic cities, we find lower persistence. Yet there is an important difference: whereas Hanseatic cities are (on average) significantly less anti-Semitic than the rest of the sample, “open” cities in the South are similar to their nonopen counterparts. 53 Thus, there is reason to doubt that openness itself increases tolerance; the effect is clear for Hanseatic cities but not southern ones. Instead, openness may have been associated with economic success and higher migration rates, which in turn undermined persistence. To examine this possibility in more detail, we look at fast-growing cities. 54 We therefore include an interaction term with the (standardized) population growth between 1750 and 1933. 55 As column (3) of Table X shows, cities that grew faster saw substantially and significantly less persistence of anti-Semitic attitudes.

Industrialization is mildly associated with less persistence. In column (4), we include an interaction term with the percentage of 1933 employment in industry and manufacturing. The coefficient is negative and significant at the 10% level. 56 Next we look at cities ruled by local bishops, which were governed by the equivalent of a religious prince. Such cities were typically less important as commercial centers than other Free Imperial cities. We find that their levels of anti-Semitism are somewhat lower on average, but there is essentially no difference in transmission from the rest of the sample.

Finally, we construct two measures of geographical isolation. First, the ruggedness of the terrain around each location in our sample is derived using the Nunn and Puga (2010) algorithm. We use a dummy that indicates whether ruggedness is above the median, and we set this measure of geographic isolation to 0 for towns and cities on navigable rivers. 57 Second, we construct a dummy equal to 1 if the nearest larger city is more than 31 miles (50 km) away (and set to 0 otherwise). 58 Columns (6) and (7) report the results. For both indicators, more isolated cities are mildly more anti-Semitic in the twentieth century, but persistence is, counterintuitively, lower but not significantly so from a statistical perspective. 59

In combination, the results for industry structure and for city growth suggest that the industrial transformation of cities after 1750 undermined the long-term transmission of Jew-hatred. The fastest-growing cities did not expand because of their own population’s fertility but instead as a result of migration ( Hochstadt 1999 ). Essen, Berlin, Düsseldorf, Hamburg, Frankfurt, and Cologne all fall in this category. This dynamic gives a clear interpretation to our results: where a large inflow of outsiders weakened the transmission of attitudes from one generation to the next, anti-Semitism in the twentieth century cannot be predicted by fourteenth-century attitudes. This suggests that the overall pattern of persistence documented in this article may reflect the effects of relatively low levels of mobility. Long-term transmission is also absent for members of the Hanseatic League and for southern German cities that were more open to trade. Just how much more tolerant trade-oriented cities were is slightly ambiguous: Hanseatic cities were generally more tolerant than the rest, but this level effect is weaker for southern open cities.

Our results make sense in the context of generally low migration rates prior to 1820. There is not much reliable data in the aggregate, but existing observations suggest that the relevant migratory flows—new inhabitants coming to live permanently in relatively small towns—were small. Prussian statistics indicate that the migration rate per generation was approximately 2% before 1820, and the number of new inhabitants of towns in Swabia and Tyrol was in the same range. 60 Even though migration everywhere increased rapidly after 1820, most inhabitants of a typical town in our sample must have been direct descendants of those who lived there in 1350. 61

V. I nterpretation of R esults

In this section, we test for whether our results could simply be driven by political extremism, by general right-wing attitudes, or by a different attitude toward violence.

V.A. Other Election Results

In columns (1) and (2) of Table XI , we examine the correlation between medieval pogroms and NSDAP election results after 1928. 62 We find that the effect becomes weaker in 1930 and vanishes in 1933. The various specifications in the Online Appendix (see Table A.16 ) confirm these results. In 1930, the magnitude of the effect is unchanged with respect to 1928, even though the NSDAP won about five times more votes in 1930. This suggests that the number of Nazi voters with historically rooted anti-Semitic motives did not grow during the rise of the NSDAP. Given the declining importance of anti-Semitic agitation for the NSDAP after 1928, it is easy to rationalize the nonsignificant correlation with medieval pogroms. Moreover, with increasing party shares of the popular vote, it becomes more difficult to identify extreme local attitudes.

Table XI Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) NSDAP NSDAP DNVP KPD KPD Principal component a (county-level regressions) 1930 1933 1924 1924 1928 POG 1349 0.0137 −0.0113 −0.0267** 0.00915 0.0101 0.263** 0.252*** 0.252*** (0.0101) (0.0125) (0.0131) (0.00873) (0.00724) (0.126) (0.110) (0.110) ln(Pop) −0.00816** −0.0111*** −0.00505 0.0138*** 0.0125*** −0.131 0.00111 −0.00260 (0.00320) (0.00359) (0.00419) (0.00305) (0.00249) (0.0702) (0.0699) (0.0701) %Jewish 0.00240 0.0100*** −0.00337 −0.0077*** −0.00335 0.0118 0.0277 0.0315 (0.00320) (0.0038) (0.00403) (0.0023) (0.00204) (0.0794) (0.0719) (0.0721) %Protestant 0.00128*** 0.0023*** 0.0020*** 0.000035 0.00017 * 0.209*** 0.305*** 0.304*** (0.00015) (0.0002) (0.0002) (0.00012) (0.0001) (0.0715) (0.0662) (0.0675) Violent crime p.c. 1908–12 0.448*** 0.431*** (0.0961) (0.109) Simple theft p.c. 1908–12 0.0187 (0.0657) Observations 325 325 325 325 325 263 263 263 Adjusted R2 0.219 0.426 0.372 0.102 0.103 0.041 0.215 0.212 Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) NSDAP NSDAP DNVP KPD KPD Principal component a (county-level regressions) 1930 1933 1924 1924 1928 POG 1349 0.0137 −0.0113 −0.0267** 0.00915 0.0101 0.263** 0.252*** 0.252*** (0.0101) (0.0125) (0.0131) (0.00873) (0.00724) (0.126) (0.110) (0.110) ln(Pop) −0.00816** −0.0111*** −0.00505 0.0138*** 0.0125*** −0.131 0.00111 −0.00260 (0.00320) (0.00359) (0.00419) (0.00305) (0.00249) (0.0702) (0.0699) (0.0701) %Jewish 0.00240 0.0100*** −0.00337 −0.0077*** −0.00335 0.0118 0.0277 0.0315 (0.00320) (0.0038) (0.00403) (0.0023) (0.00204) (0.0794) (0.0719) (0.0721) %Protestant 0.00128*** 0.0023*** 0.0020*** 0.000035 0.00017 * 0.209*** 0.305*** 0.304*** (0.00015) (0.0002) (0.0002) (0.00012) (0.0001) (0.0715) (0.0662) (0.0675) Violent crime p.c. 1908–12 0.448*** 0.431*** (0.0961) (0.109) Simple theft p.c. 1908–12 0.0187 (0.0657) Observations 325 325 325 325 325 263 263 263 Adjusted R2 0.219 0.426 0.372 0.102 0.103 0.041 0.215 0.212 View Large

Table XI Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) NSDAP NSDAP DNVP KPD KPD Principal component a (county-level regressions) 1930 1933 1924 1924 1928 POG 1349 0.0137 −0.0113 −0.0267** 0.00915 0.0101 0.263** 0.252*** 0.252*** (0.0101) (0.0125) (0.0131) (0.00873) (0.00724) (0.126) (0.110) (0.110) ln(Pop) −0.00816** −0.0111*** −0.00505 0.0138*** 0.0125*** −0.131 0.00111 −0.00260 (0.00320) (0.00359) (0.00419) (0.00305) (0.00249) (0.0702) (0.0699) (0.0701) %Jewish 0.00240 0.0100*** −0.00337 −0.0077*** −0.00335 0.0118 0.0277 0.0315 (0.00320) (0.0038) (0.00403) (0.0023) (0.00204) (0.0794) (0.0719) (0.0721) %Protestant 0.00128*** 0.0023*** 0.0020*** 0.000035 0.00017 * 0.209*** 0.305*** 0.304*** (0.00015) (0.0002) (0.0002) (0.00012) (0.0001) (0.0715) (0.0662) (0.0675) Violent crime p.c. 1908–12 0.448*** 0.431*** (0.0961) (0.109) Simple theft p.c. 1908–12 0.0187 (0.0657) Observations 325 325 325 325 325 263 263 263 Adjusted R2 0.219 0.426 0.372 0.102 0.103 0.041 0.215 0.212 Dep. variable (1) (2) (3) (4) (5) (6) (7) (8) NSDAP NSDAP DNVP KPD KPD Principal component a (county-level regressions) 1930 1933 1924 1924 1928 POG 1349 0.0137 −0.0113 −0.0267** 0.00915 0.0101 0.263** 0.252*** 0.252*** (0.0101) (0.0125) (0.0131) (0.00873) (0.00724) (0.126) (0.110) (0.110) ln(Pop) −0.00816** −0.0111*** −0.00505 0.0138*** 0.0125*** −0.131 0.00111 −0.00260 (0.00320) (0.00359) (0.00419) (0.00305) (0.00249) (0.0702) (0.0699) (0.0701) %Jewish 0.00240 0.0100*** −0.00337 −0.0077*** −0.00335 0.0118 0.0277 0.0315 (0.00320) (0.0038) (0.00403) (0.0023) (0.00204) (0.0794) (0.0719) (0.0721) %Protestant 0.00128*** 0.0023*** 0.0020*** 0.000035 0.00017 * 0.209*** 0.305*** 0.304*** (0.00015) (0.0002) (0.0002) (0.00012) (0.0001) (0.0715) (0.0662) (0.0675) Violent crime p.c. 1908–12 0.448*** 0.431*** (0.0961) (0.109) Simple theft p.c. 1908–12 0.0187 (0.0657) Observations 325 325 325 325 325 263 263 263 Adjusted R2 0.219 0.426 0.372 0.102 0.103 0.041 0.215 0.212 View Large

It is possi