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The Diverse Causal Effects of Diversity on Democracy

John Gerring Boston University

Department of Political Science 232 Bay State Road Boston MA 02215 <jgerring@bu.edu> (corresponding author)

Dominic Zarecki Boston University

Department of Political Science 232 Bay State Road Boston MA 02215

<dominicz@bu.edu>

Michael Hoffman Princeton University

Department of Politics 130 Corwin Hall

Princeton NJ 08544 <mthoffma@princeton.edu>

Comments welcome! Please do not cite without permission

Draft: February 1, 2012

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Abstract Diverse identities coexisting within the same society are often viewed as problematic for economic and political development. We argue that different types of social diversity have differential effects on regime-type – specifically, ethnolinguistic diversity increases prospects for democracy while religious diversity decreases prospects for democracy. Crossnational regressions in a variety of econometric formats, as well as subnational analyses of the United States, provide strong corroborating evidence for these hypotheses.

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Diverse identities coexisting within the same society are often viewed as problematic for economic and political development. Diversity is said to provide a focal point of conflict (Hegre, Sambanis 2006; Montalvo, Reynal-Querol 2005b; Reynal-Querol 2002; Sambanis 2001; Vanhanen 1999; Varshney 2007; Wilkinson 2009), poor governance (Alesina, Baqir, Easterly 1999; Englehart 2000; Lieberman 2009), low social capital (Alesina, LaFerrara 2005), and poor economic performance (Alesina, LaFerrara 2005; Easterly, Levine 1997; Montalvo, Reynal-Querol 2005a).

Skeptics argue that cleavages based on ascriptive identities are inconsequential or endogenous; in the latter case, the causal agent is to be found in whatever factors trigger the mobilization of specific identities at specific times (Fearon, Laitin 2000, 2003). Even so, few good things are attributed to diversity. This is especially true in the context of the developing world, where diversity is usually regarded as an obstacle to be overcome rather than a virtue to be praised.

For similar reasons, diversity is also generally viewed as problematic for the establishment and consolidation of democratic political institutions. Arguably, social diversity provides the basis for enduring conflicts and for clientelistic relationships, impeding the development of attachments to the state and nation. It may also pose coordination problems, contributing to a decline in the provision of public goods. None of this seems propitious for democracy (Anckar 1999; Lijphart 1977; Mill 1861/1958: 230; Rabushka, Shepsle 1971).

To be sure, social divisions are never impassable. In this vein, a good deal of work has been devoted to identifying institutional arrangements that might heal, or at least mitigate, the destructive potential of cleavages grounded in ethnic, linguistic, and religious identities (Lijphart 2004; Reilly 2001; Reynolds 2002; Varshney 2001). Yet, on balance, and ceteris paribus, the consensus seems to be that social diversity has either a negative or – at best – no relationship to democracy.1 We agree that social diversity is highly consequential to regime-type. But we argue that different types of social diversity have differential effects. Specifically, we argue that ethnolinguistic diversity has a positive impact on democracy while religious diversity has a negative impact on democracy.

The paper proceeds as follows. In the first section, we explore the theory, including a discussion of several illustrative regions of the world and possible causal mechanisms. In the second part, we interrogate the issue empirically, beginning with crossnational evidence and continuing with evidence drawn from a single polity – the United States – that exemplifies a good deal of within-case variation. The final section summarizes the conclusions and discusses the strengths and weaknesses of the argument.

Theory

Democracy means many things. Our theory focuses on the electoral (aka competitive, contestation, minimal, Schumpeterian) dimension of democracy; that is, the degree of electoral competition found in a polity. It does not embrace other dimensions – deliberative, egalitarian, liberal, majoritarian, consensus, participatory, and so forth (Coppedge, Gerring 2011).

Diversity also means many things. For present purposes, we are interested in differences that are not (or not solely) defined in economic terms. These social bases of difference may be categorized crudely as linguistic, religious, or ethnic (including race and caste).

None of these concepts is easy to define or to operationalize. What is a language (as 1 This is also the conclusion of one recent comprehensive survey of democracy (Coppedge 2012). A more detailed discussion of studies focused on diversity and democracy is included in a later section of the paper (see also Table A1).

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contrasted with a dialect)? What is a religion (as contrasted with an intra-religious sect)? What is an ethnic group (as contrasted with an intra-ethnic group)? These are of course matters of degree. We rely on coding conducted by other researchers, and thus are subject to criteria employed by various studies. However, several points deserve clarification.

Our concern is with differences across religious sects that coincide with points of tension at the present time such as the Sunni/Shia division within Islam and the Catholic/Protestant division within Christianity. These are considered different religions, for coding purposes. We are not concerned with differences across sects that do not lead to conflict at the present time such as various divisions within Protestantism or within Sunni Islam.

The concept of ethnicity is even more problematic. According to Fearon (2003: 201), an ethnic group has the following characteristics:

1. Membership in the group is reckoned primarily by descent by both members and non-members. 2. Members are conscious of group membership and view it as normatively and psychologically important to them. 3. Members share some distinguishing cultural features, such as common language, religion, and customs. 4. These cultural features are held to be valuable by a large majority of members of the group. 5. The group has a homeland, or at least ‘remembers’ one. 6. The group has a shared and collectively represented history as a group… 7. The group is potentially ‘stand alone’ in a conceptual sense – that is, it is not a caste or caste-like group (e.g., European nobility or commoners).

We regard this as an ideal-type definition in which not all seven characteristic must be present in order for a group to be classified as ethnic.

Naturally, there is considerable overlap across the categories of language, religion, and ethnicity. A group may be defined by any one, or all three, of these categories. Ethnicity and language are especially difficult to disentangle because language is a principal carrier of ethnic identity. Consequently, in our theoretical discussion and in the following empirical analysis we treat these two aspects of identity under the same rubric – as ethnolinguistic.

Ultimately, we are not interested in which specific group identities exist within a given society but rather in the degree of diversity found across a society. Mathematically, diversity may refer to (a) the total number of different group identities in a population, (b) the range separating the most divergent groups (according to some characteristic), (c) the share belonging to the largest group (a “homogeneity” index), (d) the degree to which cleavages align so as to create two equally sized groups (a “polarization” index), or (e) the overall distribution of groups (a “fractionalization” index).2 Of course, other possibilities exist but these seem to be the most common understandings in everyday speech and in the academic literature.

Our theory centers on the notion of fractionalization, as measured by the Herfindahl index,

where sij is the share of group i in society j. (Read: 1 minus the summation of each group’s proportion of the total in a given country, squared.) The resulting index, which varies from 0 to 1, approximates the probability that two randomly chosen individuals within a society are members of different groups. This is a highly intuitive interpretation of what it means to live in a diverse society.

With these clarifications in mind, our main arguments may be reformulated with greater precision. Ethnolinguistic fractionalization has a positive impact on electoral democracy while religious fractionalization has a negative impact.

In order to clarify these hypotheses, their ceteris paribus conditions, and potential

2 There are of course other options, as well as variations in how some of these concepts can be modeled (Okediji 2005).

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counterfactuals, it may be helpful to frame them in the context of a specific country. Let us take the case of Iraq, whose regime fate is highly uncertain as we write. Our argument is that Iraq’s considerable ethnolinguistic divisions – between Arabs, Kurds, Turkmen, and Assyrians – militate toward a democratic form of government, while its religious divisions (between Shia and Sunni Muslims) militate toward an authoritarian form of government. Of course, all of this presumes that Iraq is sustained as a nation-state; it could be that over the next decade Iraq will fragment into two or more sections. This eventuality lies beyond the scope of our theorizing.3

Now, we turn to a discussion of causal mechanisms. Why might different types of diversity have diverse causal impacts on regime-type? Ethnolinguistic Diversity

One does not have to search hard for anecdotal evidence favoring the positive role of ethnolinguistic diversity. In Europe, the oldest continuous democracy, the Swiss Federation, is also one of the most diverse. In the Americas, it may be relevant that the first democratizer, the United States, was also the most diverse country in the hemisphere. In East and Southeast Asia, many of the leading democratizers are extremely diverse, including Indonesia, Malaysia, Papua New Guinea, the Philippines, and Thailand. This goes as well for many of the smaller island nations of the Pacific including Fiji, Micronesia, Palau, the Solomon Islands, and Vanuatu. In South Asia, the leading democratizers – India, Nepal, and Sri Lanka – also lead the pack in ethnolinguistic diversity. In Africa, leading democratizers include Benin, Ghana, Kenya, Mali, Mauritius, Namibia, South Africa, and Zambia – all of which lie above the mean in ethnolinguistic fractionalization, even in this highly diverse continent. In the Middle East there is very little variation in democracy, most of the region remaining stolidly autocratic until 2011. However, it is worth noting that that the average rate of ethnolinguistic fractionalization across the region is extremely low (.19), according to our main index – as contrasted with .60 in sub-Saharan Africa. We regard this as potentially illuminating when it comes to explaining the varying course of regimes in the two neighboring regions.

The main factor at work here may follow the Madisonian logic of Federalist Paper #10 (Madison 1787 / 2011). The more groups there are in a society, and the greater the dispersion of interests and values, the harder it will be for an authoritarian ruler to coopt the opposition, and the easier it will be for opposition leaders to find potential bases of support. In such a situation, it will be difficult for a single faction to successfully monopolize power (Horowitz 1985: 37-8; Reilly 2001). Diversity thus necessitates cooperation, widely regarded as an element of democracy.

The shift from a single dominant ethnolinguistic group to two nearly equal groups seems especially consequential. Here, we can anticipate that there will be a natural home for opposition, one that will be fairly easy to mobilize, regardless of which group controls government at any point in time. Of course, political opposition may crystallize along many potential cleavages, and one would expect it to do so in the normal course of events. The point is that a viable opposition may be harder to organize if there are no preexisting social bases to draw upon. This is especially relevant in authoritarian or newly democratized countries, where many citizens may be un-mobilized or actively repressed, and in poor countries, where citizens are likely to be less educated, more dispersed (across rural areas), and where communication and transportation networks are likely to be weak. By providing an easily apprehensible shared identity, ethnicity helps to solve problems of trust and collective action that political oppositions everywhere struggle to overcome (Fearon, Laitin 1996; 3 Our hypotheses do suggest that if the fragments of the former Iraq gain sovereignty, and they turn out to be more ethnolinguistically homogeneous than the old (unified) nation-state, they will on that account be less likely to democratize. Likewise, if the fragments of a unified Iraq turn out to be more religiously homogeneous, they will on that account be more likely to democratize.

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Hardin 1995). To be sure, a bipolar politics grounded in ethnolinguistic identities may also be prone to

violent conflict (Montalvo, Reynal-Querol 2005b), a fact that might prevent the development and consolidation of democratic institutions. Thus, we theorize that the relationship between ethnolinguistic diversity and democracy is likely to be linear (as captured in a fractionalization index) rather than curvilinear (as captured in various polarization indices). More groups are merrier.

Moreover, the greater the diversity of a polity, the harder it will be for a small clique to claim legitimacy as the exclusive leadership of that country (Lewis 1965: 14; Peters 1992). Consider that a single clique may claim to represent the (relatively homogeneous) people of Bhutan, for they are plausibly one people. But it will be much harder for a clique to claim to represent the highly diverse peoples of Nepal. Arguably, legitimate authority in the modern era rests either on choice (i.e., democracy) or on a claim of identity between rulers and citizenry. Making the latter claim will be harder insofar as a citizenry is highly diverse. Although authoritarian systems may attempt to achieve descriptive representation in the councils of power it will be difficult to convince the populace that this translates into substantive representation in a system run by a single individual or a small coterie of individuals. Therefore, we reason that an authoritarian ruler or clique will be at pains to maintain the legitimacy of their rule over a highly diverse society.

Let us examine this issue from the position of political elites. We shall assume that those at the apex have strong incentives to maintain the integrity of the state, i.e., to preserve its sovereignty from internal and external threats. After all, any challenge to sovereignty is a challenge to their personal authority. One common threat is secession, and one can surmise that secession is a greater threat when a country has strong ethnolinguistic divisions, and when these divisions correspond to discrete territories (as they usually do). It follows that leaders of an ethnolinguistically fractionalized country must be especially wary of threats to secession. One method of combating secession is through suppression. Sometimes, this works. But it is less likely to work if there are a multitude of sizeable ethnolinguistic groups in a country. A state can actively suppress one or two groups’ demands, but it is difficult to keep the screws down on all groups within a society. Other methods must be employed, at least with respect to some members of the polity – the core constituency of the state (aka the selectorate). These methods are likely to contain democratic elements, even if they do not culminate in a full electoral democracy. The reason for this is that trust is likely to be low across diverse groups, and there are many groups whose loyalty must be secured. Where trust is low, agreements between the state and potentially fissiparous social groups will not achieve credible commitment; both sides will fear that the other might renege, and the fear will be especially intense on the part of the weaker out-group. A solution to this perennial problem is an institutionalized power-sharing role for diverse ethnolinguistic groups (Andeweg 2000; Lijphart 2004). Note that power-sharing means rule of law (the law laid down by the power-sharing agreement) and rule by consent of the governed, at least to some limited extent. The claim then is that this democratization scenario is more likely when ethnolinguistic fractionalization within a country is great, rather than when it is minimal. For in the latter instance there will be a lower threat of defection and fewer incentives for leaders to cede power.

Let us now examine the setting of ethnolinguistic diversity from the perspective of citizens. Over time, we expect that the existence of a multitude of groups will soften the demands of any single group. This may come about for pragmatic reasons, i.e., because a small group realizes that it will not be able to exert its will over a highly fragmented social arena. It may also come about for deliberative reasons, i.e., because members of each group come to recognize the legitimacy of other groups’ values, interests, and experiences. This may lead, over time, to a more encompassing conception of the public interest. It may also lead members of that society to prize democratic virtues such as tolerance, compromise, and rule of law.

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Even where conflicts break out these conflicts may be less damaging to the polity than they would in a less heterogeneous polity. One small group does not have the wherewithal to wreak havoc throughout a highly diverse society. Its appeals will seem fractious rather than compelling. So, unless that minority group possesses extraordinary advantages of wealth and military power, conflicts in a heterogeneous polity are likely to be containable. India’s history offers a case in point. Rent by violent conflict between Hindus and Muslims, along with a Maoist insurrection in the countryside, India’s democracy has held steady. At no point have these conflicts threatened to derail elections at a national level (though they have certainly upset the rule of law and electoral processes in certain states). This fulfills Madison’s premise that the violence of faction would be calmed not by suppressing conflict but rather by enlarging the scope of the polity so that multiple conflicts, and multiple lines of cleavage, are embraced (Hardgrave 1994: 72).

Finally, a number of recent works have emphasized the role of ethnic parties in the process of democratization (Birnir 2007a, 2007b; Chandra 2005). Arguably, a country with strong subnational ethnic identities is better placed to socialize out-groups into mainstream politics, establish a stable party system, and introduce a modicum of (clientelist) accountability in the fragile years after a democratic transition than a country without such identities, or with much weaker ethnic identities. If so, then ethnicity serves a consolidating role.

Religious Diversity

While there are reasons to imagine that ethnolinguistic diversity enhances the prospects for democracy, there are also reasons to imagine that religious diversity might harm prospects for democracy. Note that among the most religiously fractionalized countries in the world we find Cameroon, Central African Republic, Ivory Coast, Kenya, Lebanon, Nigeria, Suriname, Zaire, and Zimbabwe. In each of these cases, one might surmise that religious diversity has impeded democratic transition or consolidation.

It is important to clarify that our argument pertains to religious diversity per se, not to the role of specific religions. Writers often regard Protestantism as a force for democracy (Woodberry 2012) and Islam as a force against democracy (Fish 2002).4 These debates are irrelevant to the present discussion (though they constitute important background conditions in the empirical analysis below). Why, then, might religious diversity impair democracy? First, while ethnolinguistic out-groups can be enlisted in the struggle for democracy, it may be more difficult to enlist religious groups in this struggle. The goals of religious leaders are spiritual and ecclesiastic. Democratic norms – of tolerance, compromise, consensus, and popular rule – are not high on most religions’ list of virtues. Thus, while a religious group may come to support democracy (as the Catholic church slowly, over time, came to support democracy in Europe and Latin America) it seems unlikely that religious leaders will closely identify with this mission. Second, religions presuppose a liturgy and a set of accepted norms and practices. Religions establish what is right and what is wrong. Of course, ethnicities and languages are also carriers of norms and practices, but they are generally not as explicit or as stringent as they are in a religion. There is a difference between a custom and a moral law. Seul (1999: 553) observes that religions often provide “cosmologies, moral frameworks, institutions, rituals, traditions, and other identity-supporting content that answers to individuals' needs for psychological stability in the form of a predictable world, a sense of belonging, self-esteem, and even self-actualization.” Simply put, the psychological stakes of religion are potentially higher than those of ethnicity, and therefore less

4 But see Robertson, Stepan (2004), Sadowski (2006), Tessler (2002). Buruma (2010) and Stepan (2000) offer general treatments of the topic of religion and democracy.

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prone to compromise when the views of different religions are in conflict. Third, religions are about what everyone should do, while ethnicities (and associated languages) are about what members of a particular group should do. Transgressing an ethnic custom is possible only for someone who is a member of that group. Transgressing a religious law is something that one cannot escape, regardless of one’s descent group or professed religion. Of course, we are treating these categories as ideal-types. There are obviously lots of in-between types – e.g., religions, like modern Judaism within secular states like the United States, that often function more like ethnicities. However, the Judaism of “true believers”, i.e., so-called orthodox Jews, carries the same universalist force of other fundamentalist religions in the modern era. This is the sense in which religious diversity is dangerous for democracy. One religion’s commandments are often at variance with another’s. Indeed, inter-religious strife often erupts over legislation governing education, dress, comportment, diet, the use of inebriants, family law, the rights and status of women, religious worship, and the management of sites deemed to be of special spiritual significance. In these instances compromise is unlikely since virtually any solution serves as an insult to the core values of at least one group. More generally, if one happens to believe strongly in a particular god, one wishes other people to believe in that same god and to follow the true path. It is offensive if people flout practices that one takes to be holy and sacred. The same does not apply to matters of ethnicity or language – or at least, not to the same extent. Moreover, it is difficult to separate religion and state completely. Fox’s (2008) thorough examination of the relationship between religion and state worldwide finds that only one country (the US) meets his definition of separation of religion and state fully. In practice, most countries are likely to face constraints on their ability to separate religion and state. These constraints will force them to choose between one religion and another, and such a choice is bound to be both difficult and dangerous. Lacking the repressive apparatus to enforce compliance with such laws, democracies are likely to face particular risks in addressing these issues. Consequently, the issue of separation of religion and state makes democracy more difficult in countries with several competing religious groups.

Fourth, religions are not simply free-floating values, norms, and practices. They are also concrete – and often quite elaborate – organizational structures. Religions have priests, whereas there are no priests of ethnicity or of language (at least, none with powers of excommunication). This means that religion is both easier to mobilize and harder to demobilize. It is self-sustaining. Fifth, ethnicity and language are generally defensive in nature – members seek to maintain their membership, but not to gain new members. Because ethnicities are generally descent-based, one cannot join an ethnicity.5 (Language enters this discussion only insofar as it serves as a marker for ethnicity.) Ethnicity is a closed market. By contrast, religion is an open market. Anyone can convert. Of course, some religions are more proselytizing than others, and attitudes to new converts may change over time. Nonetheless, all religions have experienced a proselytizing moment, i.e., the moment of founding. And nearly all religions seek new members. As such, religions are threatening to each other’s existence in a way that rival ethnicities are not. The only way one ethnic group can destroy another is by killing or banishing all its members. Groups based on religion can of course pursue this approach; but they can also follow the (presumably easier) path of conversion – voluntary or coerced. Typically, the state is employed as the agent of this coercion; hence, the importance of state control for creating and/or maintaining religious dominance over a society. But even in situations of state neutrality religions vie for souls.

Sixth, this competition among religions has a zero-sum quality. While one can be a member of more than one ethnic group (by virtue of mixed parentage) and one can speak more than one 5 Of course, there are exceptions (Jung 2000).

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language (as most members of multi-linguistic societies do), it is more difficult to claim membership in more than one religion. Syncretism is common, but usually sub rosa and frequently subject to sanction. Consequently, followers of different religions compete not only over resources (as do ethnic groups), but over members themselves. This means that groups based on religion have clearer boundaries than groups based on ethnic or linguistic ties. If mobilized for political purposes, these boundaries may not be conducive to democratic rules, as discussed below.

Additionally, these structures themselves have political implications. Since very few religious authorities are democratically-elected, there is likely to be an incompatibility between sources of legitimacy for religious leaders and civil leaders. This incongruence could potentially damage the prospects for democracy in a society where religion serves as a powerful identity.

Seventh, religious fractionalization is likely to enhance, rather than mitigate, religiosity within a society. Note that religious monopolies throughout the world are often characterized by lax provision of religious services and a religious establishment that makes few demands upon its members, contributing to a slow secularization of society. By contrast, religious diversity is often thought to generate a competitive environment in which organized religions work hard to maintain and increase their flocks and to overcome free-riding by demanding strict adherence to the faith. As a result, overall religiosity may enhanced and its quality may become more fundamentalist (Finke, Stark 1998; Iannaccone 1991). Religious fractionalization, then, may increase the salience of religious divisions, making democracy more difficult to manage. It is also possible that religiously diverse countries will tend to experience fiercer competition over resources. Since religious countries tend to experience much more rapid and sustained levels of population growth than secular societies (Norris and Inglehart 2004), it is likely that resources in these countries will, over time, become more scarce. Such scarcity is likely to lead to conflict, particularly in the political arena. Such conflict could dampen the prospects for democracy in these countries. We are not saying that religious diversity leads to civil war. While there is some evidence that ethnic diversity fosters civil war, or at least a certain type of “ethnic” civil war (Hegre, Sambanis 2006; Reynal-Querol 2002; Sambanis 2001; Vanhanen 1999), there is no evidence that religious diversity has this effect.6 The reason for this, we surmise, is that members of different religions often coexist within the same territories, while ethnic groups tend to be geographically distinct. This means that the latter, but not the former, are fodder for territorially-based struggles. Note that despite the ongoing animosity between Hindus and Muslims in India, the latter have revolted only in Kashmir, where they constitute a distinct ethnicity. Thus, our argument does not presume civil war as a mechanism.

The point, rather, is that religious competition is more likely to be conceived as a winner-take-all game than ethnic competition. It is difficult to work out a uniform law of the land for a country with multiple religions (of true believers). Granted, it is difficult to work out a uniform law of the land for a country with multiple ethnicities and languages as well. However, in the latter instance one can envision talking one’s way to a mutually agreed upon solution; the mechanisms of democracy may suffice. By contrast, with religious differences one must harmonize edicts that are laid down by law and sanctified by centuries of established practice. This is not an easy task. Indeed, the quintessentially political act of compromise may be viewed as corrupt from within a fundamentalist context. Thus, there may be a tacit affinity between religious diversity and authoritarian rule that does not exist for ethnic or linguistic diversity.

It may seem as if the previous discussion conflates religious diversity with religiosity. This is partially true, as research on religiosity suggests that it is enhanced by religious competition within a 6 Svensson (2007) argues that conflicts that are religious in nature are less likely to be resolved by negotiated settlement because religious demands are less open to compromise, but this argument is not linked explicitly to civil war.

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society (see citations above). But the more important point is that when one religion is supreme within a polity (i.e., when diversity is limited), many of the conflicts between religion and democracy do not materialize, or they materialize in a more muted form. A state religion plays a role in establishing a political culture, and that culture serves to unify the nation. Since there is no significant religious minority, democratization (and the attendant empowerment of that minority) is not a threat to the dominant religion. By contrast, where several religions vie for primacy democratization would presumably entail suspending, or limiting, the authority of the previously supreme religious authority. Religious minorities would presumably make this their first political demand.

Note again the contrast to ethnicity. A majority ethnic group ensconced within an authoritarian regime might also be threatened by democratization, which would entail sharing political power and the spoils of power more widely. However, its numeric dominance would presumably translate into a dominant position within a democratic polity and this could be used to secure a dominant flow of resources. Since an ethnic group – almost by definition – is unconcerned with ethnic practices outside its own group, democratization does not entail a loss of privilege or prestige for ethnic leaders, and there is no sacrilege involved with respect to the dominant ethnic group’s principles and practices.

Crossnational Tests Recent empirical work on our chosen questions consists mostly of crossnational regression analyses, summarized in Table A1. The general finding is that ethnolinguistic diversity (fractionalization or polarization) is either negatively correlated with democracy, or statistically insignificant. As shown in this table, of the 20 studies reviewed (including, in many cases, several tests within one study), only three models (all in separate studies) find a significant and positive effect of ethnolinguistic fractionalization on democracy. Of the remaining models, several (11 models in 8 studies) report a negative effect, and the remainder (14 models in 12 studies) find no significant relationship. Of the eight studies that examine religious diversity, one study finds a positive relationship with democracy (Akdede 2010), one finds a negative relationship (Boix, Stokes 2003), and the rest find no relationship.

There are reasons to be skeptical of extant work on these subjects. First, most of the reviewed studies are focused on other theoretical questions (as signaled in the final column of Table A1). This means that measures of diversity serve as controls and are not subjected to sustained inquiry. Relatedly, the chosen specifications and estimators may not be appropriate for testing our hypotheses. Note that in specifying a model it is important to exclude covariates that may be endogenous to diversity. Because these are presumed to be structural-level factors, many of the covariates usual to democratization studies threaten to introduce problems of endogeneity. Third, many studies are restricted to time-series analyses, even though there is no measurable variation in these variables and even though the outcome is sluggish in most countries. Moreover, the time-series are usually short (Akdede 2010; Fish, Kroenig 2006). Fourth, cross-sectional tests sometimes focus only on a single year (Fish, Brooks 2004). Fifth, many tests comprise only a single indicator of ethnic or linguistic diversity, meaning that results are especially prone to measurement error. Sixth, few studies employ a variety of estimators. Finally, no study that we are aware of employs data drawn from within-country units, where problems of causal inference are less severe.

Thus, although many recent crossnational studies of democracy have included a covariate measuring some aspect of ethnic, linguistic, or religious diversity, few have explored these topics in

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much detail and none should be regarded as definitive.

Analysis

Our key independent variables capture two aspects of a country’s diversity. Ethnolinguistic fractionalization is constructed by Easterly and Levine (1997) with data drawn from the Atlas Narodov Mira (1964).7 Religious fractionalization is drawn from Alesina et al. (2003). Both employ the Herfindahl fractionalization index, as explained above. Country codings for these two variables are listed in Table A4. Because these variables are only weakly correlated (Pearson’s r = 0.35) we include them both in the benchmark model. (They are tested separately in Table A5.) Additional methods of measuring diversity are explored in Appendix B. Given the perils of highly aggregated measures of democracy (Coppedge, Gerring 2011) it seems wise to adopt a variety of outcomes rather than relying entirely upon one or two. Each is explained as it is introduced in the following set of tests. Note that all continuous indicators are transformed to a 0-100 scale, with 100 defining the democratic end of the spectrum, in order to facilitate comparisons.

Our sample includes all sovereign and semi-sovereign countries from 1960 to the present. Tests feature both cross-sectional and time-series variation. However, because variation in the theoretical factor of interest is only cross-sectional, we privilege the former in making causal inferences about the relationship between diversity and democracy. A variety of different specifications are provided, as described below. For each covariate, definitions and sources are included in Table A2 and descriptive statistics in Table A3. The hypothesized direction of causal effect is indicated in parentheses next to each variable in the following discussion.

Regression tests in Table 1 adopt the Polity2 variable from the Polity IV dataset (Marshall, Jaggers 2007) as a measure of democracy.8 These tests rely principally on an ordinary least squares (OLS) estimator with Newey-West standard errors and a one-period lag structure (to capture autocorrelation).9 All right-side variables are lagged one period behind the outcome.

Model 1 presents the benchmark model. Covariates include GDP per capita (+), urbanization (+), population (+), oil production per capita (-), landlock (-), distance from the equator (+), English legal origin (+), percent speaking a European language (+),percent Muslim (-), percent Protestant (+), percent Catholic (?), percent Buddhist (?), a diffusion variable (+), and annual dummies. A sample of 168 countries and 5,401 observations is contained in this analysis.

Model 2 is regarded as a maximal specification, adding several regional dummies – West Europe, Latin America, Middle East, and Africa.

Model 3 treats diversity as a long-term structural feature of the landscape. Here, we include only “structural” covariates that are not likely to be affected (or at least not strongly affected) by the ethnolinguistic or religious fractionalization of a society over the course of its historical development. This includes oil production per capita, landlock, island, distance from the equator, English legal origin, percent speaking a European language, percent Muslim, percent Protestant, percent Catholic, percent Buddhist, a diffusion variable, and regional dummies (listed above).

In the next series of models we test alternate methods of correcting for temporal autocorrelation, retaining the benchmark specification. Model 4 employs a Prais-Winsten regression with AR(1) autocorrelation and panel corrected standard errors (PCSE). Model 5 is identical except 7 A small number of missing observations for this variable are imputed (with linear-regression) based on information from other measures of ethnic and linguistic fractionalization, most of which are highly correlated (see Table B3). 8 Missing data for micro-states is imputed from other sources. 9 The Newey-West (1987) estimator is an extension of the Huber/White/sandwich robust estimator (White 1980), taking into account not only heteroskedasticity but also autocorrelation.

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that we substitute a lagged dependent variable for annual dummies. Model 6 replicates model 5 except that standard errors are clustered by country.

Model 7 returns to the benchmark specification and estimator, this time with a full sample, filling in missing data for all sovereign and semisovereign countries from 1960 to 2005 (during periods of their actual existence) with multiple imputation techniques (King et al. 2001). Sample size jumps to 8,400.

Model 8 aggregates data at 10-year intervals so that the panel is decadal rather than annual. Here, the sample drops to 870 observations.

Next, we test for non-linearities in the relationship between diversity and regime-type by introducing a set of dummies in place of the fractionalization indices. Model 9 re-codes ethnolinguistic diversity with three dummies, imposing breakpoints in the 0-1 scale at .2 and .8. Model 10 does the same for religious fractionalization, with breakpoints at .2 and .7. (.7 is chosen rather than .8 because there are fewer cases in the high range of the religious fractionalization index.) Both models exclude the lowest category, i.e., the category with the lowest level of ethnolinguistic or religious fractionalization.

Results displayed in Table 1 offer strong corroboration for our theory. Ethnic fractionalization is positively correlated with democracy, while religious fractionalization is negatively correlated, in all tests at standard thresholds of statistical significance. Models 8 and 9 show no evidence of non-linearity in these relationships, justifying our choice of a continuous index.

Table 2 explores alternate measures of democracy, maintaining the benchmark specification adopted in previous tests. Model 1 adopts Vanhanen’s (2000) Competitiveness measure of electoral contestation, calculated as 100 minus the percent votes won by the largest party in presidential or parliamentary elections (or both, averaged). Contestation is central to the electoral model of democracy. Indeed, a lack of competition is often taken as a sign of a lack of democracy (Key 1949).10

Models 2 and 3 test the Political rights and Civil liberty indicators from the Freedom House (2007) dataset. These variables are coded by integers from 1-7 and thus offers a more limited range of variation, as well as a somewhat shorter time-span (beginning in 1971). In common with other continuous measures, both are re-scaled to range from 0 to 100, with 100 representing the highest level of democracy. (Treating these outcomes as ordinal does not change the substantive findings reported here.)

The remainder of Table 2 features various non-continuous outcomes, mostly based on the underlying coding of the Polity2 variable. Models 4 and 5 approach the outcome as trichotomous, adopting cutoffs in the original Polity2 variable at -5 and +5. This is a plausible approach given that Polity2 is not an interval scale in any strict sense of the term. Model 4 employs an ordered logit estimator while Model 5 employs a random effects ordered probit estimator (following Epstein et al. 2006). Model 6 treats democracy as a binary outcome, imposing a cutoff in the Polity2 scale at 5, and analyzed with a logit estimator (following Beck, Katz, Tucker 1998).

Model 7 regards regular Turnover as a measure of democracy. This variable marks a change of top leaders of a country under regular circumstances, thus differentiating between irregular changes (e.g., by coup or revolution) and those that conform to constitutional law. Data is drawn from the Archigos dataset (Goemans et al. 2009). The simple fact of turnover in leadership is a feature that we expect to find in electoral democracies and which we generally expect to be more common in democracies than in autocracies. Indeed, some have argued that turnover in power is a definitional characteristic of democracy (Alvarez et al. 1996; Przeworski 2000). The resulting 10 Moreover, there is a strong empirical correlation in our crossnational dataset between this variable and Polity2 (Pearson’s r = .91), offering evidence of convergent validity across indicators.

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indicator is analyzed with a logit estimator. Model 8 explores democratic transitions, using an event-history model. Here, the question of

interest is not the likelihood of being either democratic or autocratic but rather the likelihood (or, more specifically, the rate) of transitioning from autocracy to democracy. Both Cox and Weibull models are plausible and both have been employed in the literature (Bernhard et al. 2001; Kapstein, Converse 2009). We prefer the Weibull model because it explicitly models changes in the likelihood of democratization over time – a likelihood that presumably increases substantially over the period of observation (Kapstein, Converse 2009). Estimates indicate hazard ratios, i.e., the estimated percentage change in the baseline hazard rate resulting from 1-unit change in the independent variable. A positive relationship indicates that a transition is hastened; a negative relationship indicates that a transition is postponed. Note that we do not include proximate causes of transition such as economic growth, instability, or trade in our model; nor do we include a measure of democratic consolidation (e.g., number of previous transitions). This is because all of these factors are quite likely endogenous to the factors of theoretical interest and would therefore serve as confounders. Annual dummies are replaced here by decade dummies, so as not to exhaust the available degrees of freedom.

Model 9 measures movements toward greater democracy along the 21-point Polity2 scale. That is, any movement up the scale is registered – as Polity2 at T minus Polity2 at T-1. By contrast, years where no change occurs or where a country slides in the autocratic direction are coded as zero. Because of the truncated nature of the relationship (resulting in a pile of zeros at the left end of the scale), this analysis is conducted with a zero-inflated Poisson estimator (Long 1997).

Models 10 and 11 mirror the previous two models. Model 10 analyzes autocratic transitions in an event-history format, while model 11 analyzes movements toward autocracy using a zero-inflated Poisson estimator.

Results shown in Table 2 are robust wherever outcomes are allowed to vary freely, i.e., in models 1-7. Where outcomes are constrained – i.e., where the outcome is unidirectional (towards democracy or towards autocracy) – results are less robust. Even so, coefficients for the key theoretical variables retain statistical significance (in the predicted direction) in most models – with religious fractionalization showing somewhat greater robustness than ethnic fractionalization.

A number of additional robustness tests are conducted in order to validate the pattern of findings introduced in Tables 1 and 2. To begin with, the benchmark analysis is checked for influential cases. DFBETA diagnostics reveal the impact of a given observation on each parameter, specifically the standardized difference in the parameter estimate due to deleting the observation. Here, we are concerned with the set of observations provided by each country, so we manually re-run the benchmark model without those countries judged to have high DFBETAs for Ethnic fractionalization (Bangladesh, Swaziland) or Religious fractionalization (Bahrain, Gambia, Nepal, Indonesia, Suriname) for any given observation. Changes in this model are negligible relative to those reported in Table 1, suggesting that results are not subject to influential cases.

Additional tests are presented in data tables in the appendices. Table A5, in Appendix A, introduces a series of specification tests based on the benchmark model (Model 1, Table 2). Each test removes a single variable from the benchmark, leaving all other elements intact. Results reveal changes in the coefficients for the key variables, but none change their sign or lose statistical significance.

Table A6 tests the robustness of the benchmark model when the sample is restricted by removing a single region of the world (seriatim) – Latin America, Asia, Africa, Middle East, Western Europe, and the OECD. Again, there is movement in the coefficients but no loss of significance.

A final set of robustness tests, contained in Appendix B, examines alternate measures of diversity. A thorough search reveals thirteen additional measures that are relatively authoritative,

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broad in coverage, and distinct. (Several additional measures were excluded by reason of poor coverage and/or high intercorrelation.) Details about the construction and sources for each indicator are provided in Table B1, with descriptive statistics in Table B2. (As previously, we treat this data as constant over the 1960-2003 period.) A correlation table including all fifteen measures is included in Table B3. A principal components factor analysis, shown in Table B4, reveals that most ethnic and linguistic diversity variables cohere closely to a single dimension, while religious diversity constitutes a second dimension.11 This is consistent with our theoretical discussion, in which these two aspects of diversity are differentiated.

Table B5 introduces each of these alternative measures into models 1-8 from Table 1. For purposes of comparison, benchmark results from Table 1 are reproduced in rows 1 (for Ethnolinguistic fractionalization) and 13 (for Religious fractionalization). Other rows in Table B5 display results for tests in which alternative indicators are substituted for these variables. Thus, in row 2, Vanhanen’s measure of Ethnolinguistic homogeneity is tested in place of our usual Ethnolinguistic fractionalization index (drawn from Easterly and Levine). All other elements of these models are identical to those in Table 1, so this is a direct replication with alternate operationalizations of key variables.12

Results for alternative measures of ethnolinguistic diversity are generally concordant with results for the Easterly/Levine variable. That is, indicators of heterogeneity are correlated with greater democracy and indicators of homogeneity with less democracy. Granted, standard thresholds of statistical significance for these alternate measures are not always reached. However, this is hardly surprising, given the multifarious ways in which indices of ethnolinguistic diversity can be constructed (see Table B1).

There are only two alternative measures of religious diversity against which to gauge the robustness of our core variable, constructed by Alesina et al. (2003). Both alternative measures are drawn from Reynal-Querol (2002); one measures religious fractionalization and the other religious polarization, as shown in rows 14-15 of Table B5. Somewhat surprisingly (since they are not very highly correlated with the Alesina et al. variable), they both show a strong and statistically significant relationship to democracy, in the predicted (negative) direction.

In summary, an extensive set of robustness tests altering the outcome measure of democracy, the model specification, and the estimator of our benchmark models shows a surprisingly resilient set of findings. Ethnolinguistic diversity is usually associated with greater democracy, and religious diversity with less democracy, in crossnational tests focused on the contemporary era. To be sure, the coefficients for our key variables are not constant. Indeed, they gyrate rather dramatically, attesting to the fact that covariates are not orthogonal (the treatment is by no means random with respect to potential confounders) and results are therefore model dependent. Consequently, it is appropriate to emphasize the direction of the coefficient and the t statistics rather than point estimates.

It should also be obvious that many factors other than diversity affect a country’s regime-type, and some of the action may be stochastic (un-explainable, at least in generalizable terms). Model-fit statistics are modest for all tests shown in Tables 1 and 2 except those that introduce a lagged dependent variable (a variable that plays no explanatory role).13 It is not surprising, therefore, to discover that the impact of diversity on regime-type is modest. Using the coefficients reported in

11 Linguistic diversity might be discerned as a third dimension, but it is relatively indistinct. 12 Ethnoling fract (Easterly/Levine) is included as a covariate in all tests of religious diversity, and Relig fract (Alesina) is included as a covariate in all tests of ethnic and/or linguistic diversity, so as to replicate the specification of models in Tables 1. 13 In Model 1, Table 1, R2=.53.

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model 1, Table 1, we find that a one-standard deviation increase in Ethnolinguistic fractionalization implies a 4.1 increase in the rescaled 100-point Polity2 index, while a one-standard deviation increase in Religious fractionalization implies a decrease of 8.4 in the Polity2 index. Note that because the outcome is expressed along a 100-point scale the coefficients can be interpreted as percent changes, i.e., +4.1% and -8.4%.

These are not trivial effects. Indeed, if one judges the significance of a predictor on the basis of standardized coefficients they are comparable to, or greater than, most other structural (distal) factors that are thought to influence regime-types. For example, of the sixteen covariates in our benchmark model (model 1, Table 1), only two – percent Muslim and per capita GDP (ln) – have standardized coefficients that are greater than that estimated for Religious fractionalization. Ethnolinguistic fractionalization lies in the middle of the pack. Yet, a one-standard deviation change in this variable has a greater estimated impact on regime-type than a one-standard deviation change in urbanization, population, oil production, landlock, distance from the equator, percent Protestant, and percent Buddhist, and is comparable to a one-standard deviation change in percent Catholic, percent speaking European language, island, and diffusion. To the extent that one regards any of these other factors as significant historical influences on the development of democracy, we might consider granting the same status to Ethnolinguistic fractionalization. Additional Threats to Inference

Not all threats to inference are easy to test empirically. In drawing inferences from the data presented in this section one must be cognizant of circumstances that might cause a polity to be more or less diverse. They include: (a) the changing borders of a polity, (b) migration in and out of a polity, (c) fertility and mortality rates, and (d) changing ethnic, linguistic, or religious identities.

It will be seen that violations to SUTVA (Rubin 2005) are inherent in the chosen research design. Units contaminate each other; the treatment of one unit may affect the treatment of another. That said, we do not anticipate that this contamination is very large. During the observed period of analysis (1960-) borders remained mostly constant, migration was fairly minimal in most nation-states, and fertility and mortality patterns had slow-moving effects.

Another potential threat to inference is posed by endogeneity between the outcome and the factors of theoretical interest in the crossnational analysis. This is most likely to manifest itself prior to the observation period, i.e., prior to a country’s international recognition as sovereign. In particular, one might be concerned that democratic nations would attract diverse immigrants by virtue of their greater tolerance for diversity. The United States provides a case in point. This is not a problem for our tests of religious diversity since whatever biases may exist would appear to run against the grain of the hypothesis. It is a problem for tests of ethnolinguistic diversity; consequently, our discussion is focused primarily on the latter.

Even so, we do not view it as a very serious problem because the choice of destination for most migrants is influenced primarily by geographic proximity and socioeconomic opportunities, and is probably not greatly affected by the receiving country’s regime-type. Likewise, there are few “settler” societies, i.e., nations composed primarily of recent emigrants from other lands who settle voluntarily in a region (Hartz 1964). (Reassuringly, when these countries – Australia, Israel, New Zealand, Singapore, South Africa, United States – are removed from the sample, the results of the benchmark model are virtually identical.) Likewise, it is important to bear in mind that involuntary movements of population via slavery, indentured servitude, or penal colonies account for diversity within many countries with large non-indigenous populations (e.g., Cape Verde, Mauritius, and Sao Tome in Africa, all of the Caribbean, as well as non-island countries in the Americas that border the Atlantic Ocean). “Regime” factors were not at work in these migrations.

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Now let us consider diversity as a possible factor in country formation. Over the past several centuries, it is plausible that social heterogeneity within a territory makes it less likely that a state will form and, once formed, survive. Another way of stating this is to say that if a state is unable to overcome heterogeneity – if it is unable, over time, to create a more homogeneous population – it is more likely to fragment. If France had been unsuccessful in generating the identity “French” (Weber 1979) it would be less likely to remain today as a sovereign unit. Likewise, if Yugoslavia had been more successful in generating a Yugoslav identity it would be more likely to remain today as a sovereign unit. If so, then one would expect greater homogeneity within national borders over time, both as a product of successful nationalizing initiatives and – where these are unsuccessful – of state fission (into smaller, more homogeneous units). National identity movements, insofar as they characterize the modern era, might lead to greater homogeneity across sovereign units over time. But they are orthogonal to our analysis, affecting all countries more or less equally.

Likewise, one might be concerned about the strong correlation between ethnolinguistic diversity and newness. Note that by measuring ethnic diversity in the late twentieth century we are measuring an outcome that is many centuries in the making. As a general rule, newer countries are more ethnolinguistically diverse, partly as a product of the arbitrary fashion in which they were formed (by colonizing powers) and partly because they have not experienced centuries of tutelage under the direction of a central state and with reasonably clear borders (or at least with the concept of a border). Kenya is more ethnolinguistically diverse than China, even though a fraction of China’s size. However, there is no apparent relationship between the age of a country (variously coded) and democracy. Consequently, when various measures of state formation and state history are introduced into the models shown in Tables 2 and 3 these specifications show virtually no change in the coefficient for Ethnoling fractionalization.

Let us entertain one additional conjecture. Suppose that, over time, “successful” polities become more heterogeneous – because they attract more immigrants and incorporate more territory – while unsuccessful polities become more homogeneous, or fragment into smaller, more homogeneous units. This is a problem if the factors driving success also drive democratization and if these factors are not controlled in our analyses. But it is not clear that powerful polities are more or less likely to become, and remain, democratic.

It is important to bear in mind that the positive relationship between ethnolinguistic diversity and democracy is contingent upon per capita GDP. Once this factor is removed from the model, the ethnolinguistic fractionalization variable shows a strongly negative relationship to democracy. Plausibly, this reflects the negative impact of ethnolinguistic heterogeneity on long-term growth performance (Easterly, Levine 1997), and the positive impact of long-term growth performance on democracy (Boix, Stokes 2003; Epstein et al. 2006). If so, then ethnolinguistic heterogeneity has no direct (unmediated) effect on democracy, though it may have some positive impact (through channels other than economic growth).

A more plausible reading of the evidence, in our opinion, is that high ethnolinguistic diversity is associated with low growth not because of any causal relationship between the two but rather because certain structural conditions – e.g., light or indirect colonial rule (Grier 1999), arbitrary post-colonial borders (Englebert 2000), late state formation (Bockstette et al. 2002), poor institutions (Acemoglu, Johnson, Robinson 2005), various underlying geographic factors (Diamond 1992; Sachs, Warner 1997) – affect both outcomes. If this story is correct, per capita GDP is an essential covariate, capturing the collective impact of these prior causes. Given a level of economic development (achieved for many reasons that are outside our theoretical concern), a country is more likely to democratize if it is more ethnolinguistically diverse. This is the argument we find most compelling.

Interpreting observational data is always fraught with difficulties, and perhaps nowhere

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moreso than when relationships are distal and there are no discrete interventions (allowing for clearly delineated pre-and post-tests). However, historical knowledge and causal intuition do not suggest anything to mitigate the plausibility of the causal effects between ethnolinguistic and religious fractionalization, on the one hand, and electoral democracy, on the other. Within-Country Analyses

In addition to crossnational data, we examine data drawn from different electoral districts within the United States. Here, one finds less variation in the quality of electoral democracy but also less background heterogeneity, easing problems of causal inference.

Two measures of electoral democracy are constructed for our analysis. As previously, Competitiveness is defined as 100 minus the share of the top vote-getter. Turnover is understood as a change of seat from one party to another (including independents), and is similar to the turnover variable drawn from the Archigos dataset (see Table 2). Together, these two measures are regarded as observable features of electoral democracy, our outcome of interest. Regrettably, we cannot measure a broader range of democracy measures, as we did for our crossnational analyses. However, given the high correlations across different measures of electoral democracy, we consider this evidence, drawn from a single country with a large number of electoral districts, to provide important corroborating evidence for the main findings (contained in Tables 1-2). A number of studies have focused on the relationship between district diversity and electoral competitiveness in American elections. Commonly, measures of district diversity incorporate demographic characteristics including ethnicity/race, urban/rural, education, and wealth (e.g., Koetzle 1998; Sullivan 1973). Alternatively, Ensley, Tofias, and de Marchi (2009) construct a measure of policy/ideological diversity. Not surprisingly, results depend on the factors included in the diversity measure, as well as its method of construction. In any case, the main shortcoming of these studies from our perspective is that the authors’ theoretical understanding of diversity is quite different – and much broader – than ours, such that the resulting studies are only peripherally relevant.14

Fortuitously, the United States boasts an extraordinary number of differently-composed electoral districts at national, state, and sub-state levels. The following analyses include presidential, Senate, House, gubernatorial, upper state house, and lower state house elections - a total of 74,287 elections. Electoral data used in coding our key variables is drawn from a variety of sources: (a) for presidential and senatorial elections, the Office of the Clerk Election Statistics15; (b) for House electoral data, Lublin (1997), (c) for state upper and lower house electoral data, Carsey et. al. (2007), and (d) for gubernatorial data, Parker (2010).

Ethnicity is understood according to US census categories: (a) black, (b) white, (c) Asian or Pacific islander, (d) native American or Alaskan native, (e) other single race, or – in the most recent census – (f) two or more races. This data is employed to construct an ethnic fractionalization index, using the Herfindahl formula. We are unable to construct an analogous measure of Religious fractionalization, as data on this subject is not available for most subnational electoral districts.

Regression analyses employ a variety of additional covariates judged to be exogenous

14 One study, by Joseph Aistrup (2004) includes disaggregated variables in an analysis of competitiveness across US counties. However, the author’s findings are marred by two problems. First, because information for ethnic/racial measures of diversity is not widely available for counties Aistrup infers this data from larger units. Second, in testing racial diversity he does not include controls for percent White or Black; consequently, the racial diversity variable may not be measuring diversity per se (see discussion below). 15 http://clerk.house.gov/member_info/electionInfo/index.html

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influences on competition and/or turnover. This includes population,16 the percentage share of different social groups (White, Black, Non-English, and Other), Urban (percent living in urban areas), Income per capita (natural logarithm), High school (percent above age 25 with a high school degree), and College (percent above age 25 completing college). Some of these covariates are treated as constants since they change little over the observed period; these are taken from the 2000 US Census records. Historical data (at decadal intervals) is available for House districts from Lublin (1997) and for the entire United States from the US Census17 and the Bureau of Economic Analysis.18 Redistricting poses a challenge for this analysis since it alters the composition of House and state house districts – rendering the Turnover variable meaningless and also creating problems of missing-ness in covariate data for districts for which there is little historical data (since contemporary data cannot be projected backward). This challenge is treated in two ways. First, a dummy variable is generated to mark the first election after redistricting. Tests reveal that this variable has little impact on the variables of theoretical interest, and is in any case proxied by year dummies in many of the subsequent analyses. Thus, this variable is omitted in results reported below. Second, we omit earlier observations for state house elections whenever there is a substantial (>2%) change in the number of districts within a state after redistricting (signifying a significant change in the composition of that state’s districts).

Appendix C includes variable definitions (Table C1), descriptive statistics (Table C2), and a table describing the distribution of data across key variables, regions, and time-periods (Table C3). Table 3 provides the main results of our regression tests. Models 1-10 focus on Competitiveness, as discussed. Model 1 tests the benchmark model with a full sample of election districts, all available covariates (as listed above), along with state and year dummies. Model 2 restricts the specification to covariates that have the strongest theoretical and empirical rationale – in our view, population, percent white, and state dummies. Subsequent models repeat this contrast between maximal and minimal specifications with samples drawn from Senate elections (3&4), gubernatorial elections (5&6), state upper house elections (7&8), and state lower house elections (9&10). State dummies are not included in Senate and gubernatorial election analyses (for obvious reasons). Otherwise, specifications are identical across all models. Models 11&12 adopt Turnover as the outcome of interest, explored with a logit estimator in maximal and minimal samples.

Results show that Ethnic fractionalization is associated with higher competition or greater turnover in all but two tests (models 3 and 6). These are both relatively small samples (N=376 and N=317), so we do not regard them as necessarily vitiating the gist of the findings represented in Table 3. To be sure, the overall model-fit of these regression tests is weak and the registered impact of the variable of theoretical interest modest, as it was in our crossnational tests. Across all districts, an increase in Ethnic fractionalization of one standard deviation suggests a rise in 1.9 in the index of political competition (a 100-point scale), based on results in Model 1. Even so, it is worth noting that only one variable – Population (ln) – rivals the consistency of Ethnic fractionalization in this series of tests. Insofar as one can predict electoral competition and turnover, the degree of ethnic diversity found in a district appears to offer a strong predictive guide. 16 Population data is drawn from decennial US Census reports (E.g. http://www.census.gov/main/www/cen2000.html) with values imputed in order to cover every year in the dataset. 17 Ethnicity data from www.census.gov/population/www/documentation/twps0029/tab08.html, education data from www.census.gov/hhes/socdemo/education/data/census/half-century/tables.html (Tables 5 and 6, both sexes), and other data from selected US Census documents. 18 For income data, see www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N, Table 2.1.

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Discussion Before concluding, it is worth reemphasizing that most of the evidence presented here falls far from the experimental ideal. The treatment is not randomized, units are not independent over the observed time-period, many potential confounders can be identified (one can only hope that they have been successfully measured and conditioned upon), and no general theory promises deliverance from potential specification problems. Under the circumstances, results must be regarded as suggestive rather than conclusive. This is true, of course, for most studies based on observational data, especially when countries form the principal unit of analysis. Nevertheless, where stronger research designs are impossible one must be satisfied with the evidence that is available (Gerring 2011). Relatedly, it should be clear that the purpose of this empirical exercise is not to construct precise estimates of a treatment effect but rather to indicate the general direction and plausibility of a potential causal relationship. Primary attention is therefore paid to robustness tests, designed to test various assumptions, rather than to point estimates.

With these caveats and clarifications, we conclude that ethnolinguistic diversity enhances prospects for electoral democracy while religious diversity diminishes prospects for electoral democracy. The crossnational evidence offers somewhat stronger support for the second hypothesis, which holds up under virtually every robustness test and is stronger in its impact. However, the latter is bolstered by subnational evidence drawn from thousands of electoral contests in the United States. Thus, we enlist evidence drawn from multiple levels of analysis in the service of a broad hypothesis that ought to apply both across countries and within countries.

We have also identified causal mechanisms to support our claims. Although we cannot specify precisely why – i.e., which concatenation of mechanisms connect each causal factor to the outcome – we have given plausible reasons to support the theory presented in the first section of this paper. We conclude that diversity does indeed matter for democracy, albeit in sharply contrasting ways.

Given the diversity of diversity – which can be defined according to any measurable characteristic found among a population – this is not a very surprising result. Indeed, it is hardly credible to imagine that all elements of “diversity” would operate in same fashion with respect to a broad, macro-political outcome like electoral democracy.

If our conclusions are correct, several practical implications follow. First, in considering the likely democratization trajectory of a country one must be careful to analyze its ethnolinguistic structure separate from its religious structure. Second, provided the country is able to hold together (an important proviso), ethnolinguistic cleavages may be regarded as an asset while religious cleavages may serve as a barrier to democratization. Finally, attempts to create and sustain democratic institutions might focus on mitigating theologically-based disagreements, lest these disagreements become politicized to the point at which compromise and consensus becomes impossible.

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25

Table 1: Continuous Polity2 Outcomes

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Sample Natural Natural Natural Natural Natural Natural Full Full Natural Natural Panel Annual Annual Annual Annual Annual Annual Annual 10-year Annual Annual

Estimator OLS, NW SE

OLS, NW SE

OLS, NW SE

PW, PCSE

PW, PCSE

OLS, Cluster SE

OLS, NW SE

OLS, Robust SE

OLS, NW SE

OLS, NW SE

Ethnoling fract 14.710*** 11.269*** 4.782** 9.648* 2.685*** 1.616*** 18.234*** 20.522*** 4.387** [2.244] [2.303] [2.086] [5.175] [0.809] [0.599] [1.696] [4.024] [2.020] Religious fract -36.652*** -30.200*** -18.173*** -32.153*** -4.934*** -2.627*** -30.259*** -33.057*** -36.389*** [2.975] [3.207] [2.785] [6.663] [1.272] [0.772] [2.150] [5.178] [3.008] Ethnoling fract 5.635*** (med) [1.237] Ethnoling fract 7.407*** (high) [2.089] Religious fract -8.639*** (med) [1.301] Religious fract -15.244*** (high) [1.939] Benchmark covariates X X X X X X X X X Structural covariates X Region dummies X X Year dummies X X X X X X X X Yt-1 X X Countries 168 168 179 168 168 168 199 195 168 168 Years 1960-2001 1960-2001 1960-2001 1960-2001 1960-2001 1960-2001 1960-2005 1960-2005 1960-2001 1960-2001 Observations 5401 5401 6781 5401 5373 5373 8400 869 5401 6690 R2 0.532 0.550 0.547 0.133 0.869 0.938 0.594 0.661 0.529 0.494

Outcome: Polity2, rescaled from 0 to 100. Estimator: OLS (ordinary least squares), NW (Newey-West), SE (standard errors), Robust (robust SEs), Cluster (SE’s clustered

by country), PW PCSE (Prais-Winsten regression with AR(1) autocorrelation and panel correct standard errors). Displayed: coefficients and standard errors for variables of theoretical interest. *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests). All right-side variables lagged one period.

Benchmark covariates = GDP per capita, urbanization, population (ln), oil production per capita, landlock, distance from the equator, English legal origin, percent speaking a European language, percent Muslim, percent Protestant, percent Catholic, percent Buddhist, diffusion.

Structural covariates = oil production (per capita), landlock, island, distance from the equator, English legal origin, European language speakers (%), Muslim (%), Protestant (%), Catholic (%), Buddhist (%), diffusion.

Region dummies = West Europe, Asia, Latin America, Middle East, Africa.

26

Table 2: Alternate Outcomes

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Outcome Competit- iveness

Civil Liberties

Political Rights Polity2 Polity2 Polity2 Turnover Polity2 Polity2 Polity2 Polity2

Coding 0-100 1-100 0-100 Trich- otomous

Trich- otomous

Dich- otomous

Dich- otomous

Democratic transition

Towards democracy

Autocratic transition

Towards autocracy

Estimator OLS, NW SE

OLS, NW SE

OLS, NW SE

Ordered logit

Ordered probit

Logit (MLE)

Logit (MLE)

Weibull Hazard,

Cluster SE

Zero Inflated Poisson

Weibull Hazard,

Cluster SE

Zero Inflated Poisson

Ethnolinguistic 12.796*** 14.608*** 16.362*** 0.743*** 0.486*** 0.928*** 0.570** 0.137 0.000 -0.290 -0.231*** fract [1.634] [1.901] [2.336] [0.143] [0.083] [0.188] [0.252] [0.240] [0.051] [0.178] [0.069] Religious -19.363*** -26.128*** -34.719*** -2.830*** -1.684*** -3.626*** -1.383*** -1.098*** -0.474*** 0.759*** -0.092 fract [2.148] [2.433] [3.071] [0.187] [0.108] [0.238] [0.265] [0.281] [0.073] [0.270] [0.102] Benchmark controls X X X X X X X X X X X Decade dummies X X Year dummies X X X X X X X X X Failures/Transitions 3925/179 2914/104 Countries 165 167 167 168 168 168 155 168 168 172 168 Years 1960-2000 1971-2003 1971-2003 1960-2002 1960-2002 1960-2002 1960-2001 1960-2002 1960-2002 1960-2002 1960-2002 Observations 5001 4268 4137 5401 5401 5401 4857 5682 5373 5682 5373 R2 (pseudo) 0.508 0.617 0.584 (0.3445) (0.3419) (0.4606) (0.1075) Log pseudolikelihood 13392 -5244 13676 -3123

All right-side variables lagged one period. Panels: annual. X = covariates included in model. Benchmark controls = GDP per capita, urbanization, population (ln), oil production per capita, landlock, distance from the equator, English legal origin, percent speaking a European language, percent Muslim, percent Protestant, percent Catholic, percent Buddhist, diffusion. Displayed: coefficients or hazard ratios (Models 8, 10) along with standard errors. *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests).

27

Table 3: District-level Analyses (US)

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) Outcome Competitiveness Turnover Estimator OLS with Newey-West standard errors and lag(1) autocorrelation Logit Sample All Senate Governor State upper house State lower house All Ethnic fract 10.263*** 9.821*** 19.053 22.974** 23.412* -9.401 8.191*** 8.995*** 9.872*** 9.328*** 0.782*** 0.678*** [0.919] [0.799] [17.520] [10.402] [13.782] [11.134] [2.109] [1.849] [1.055] [0.914] [0.174] [0.153] Pop (ln) 2.074*** 2.352*** 1.421*** 1.574*** 1.691*** 0.376 8.400* 10.239** 18.771*** 18.462*** 0.045* 0.050*** [0.137] [0.091] [0.451] [0.449] [0.591] [0.666] [4.818] [4.881] [2.354] [2.442] [0.024] [0.016] White 3.579** 20.328*** 18.680 36.750*** 6.082 -15.566 6.957** 21.172*** 3.608* 19.294*** 0.220 1.534*** [1.714] [0.651] [15.573] [11.707] [12.172] [10.195] [3.440] [1.583] [2.116] [0.717] [0.273] [0.140] Black -17.003*** -14.578 -25.614** -11.608*** -16.577*** -1.407*** [1.672] [15.460] [11.760] [3.317] [2.078] [0.265] Non-English -8.822*** -25.173 31.689* 0.566 -10.256*** -0.596*** [1.216] [19.772] [16.479] [2.770] [1.500] [0.229] Other -12.924*** 69.353* -58.558* -5.608 -14.514*** -1.321*** [1.932] [35.730] [30.928] [5.270] [2.712] [0.383] Urban 1.615*** -13.617** -16.677** 1.468* 2.766*** -0.043 [0.339] [5.792] [7.022] [0.835] [0.413] [0.051] Income/cap 1.224*** 30.755*** -2.006 4.484*** 2.244*** 0.038 [0.330] [6.529] [8.479] [1.501] [0.705] [0.055] High school 5.206*** 14.211 37.941** 11.121 1.244 0.041 [0.916] [16.240] [17.591] [8.674] [3.236] [0.177] College 2.522* -57.414*** -24.727 31.968*** -9.845** 0.296 [1.497] [17.850] [26.152] [7.104] [4.137] [0.268] State dummies X X X X X X X X Year dummies X X X X X X Constant X X X X X X X X X X X X

Years 1948- 2003

1948- 2003

1980- 2000

1980- 2000

1977- 2000

1977- 2000

1968- 2003

1968- 2003

1968- 2003

1968- 2003

1948- 2003

1948- 2003

Obs (elections) 74425 74425 376 376 317 317 16882 16882 52999 52999 69155 69160 R2 (pseudo) 0.177 0.155 0.230 0.088 0.158 0.014 0.180 0.167 0.187 0.160 (0.0619) (0.028)

X = covariates included in model. All = lower and upper state house, House, Senate, Governor, President. *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests).

28

Appendix A: Crossnational Analyses

29

Table A1: Extant Crossnational Studies

Study Outcomes Variables Research design Period Finding Focus

Aghion, Alesina, Trebbi 2004 PR (trichotomous) Ethnoling fract CS: Ordered probit 1990 - Yes Aghion, Alesina, Trebbi 2004 PR (trichotomous) Ethnic fract CS: Ordered probit 1990 - Yes Aghion, Alesina, Trebbi 2004 PR (trichotomous) Ling fract CS: Ordered probit 1990 - Yes Aghion, Alesina, Trebbi 2004 PR (trichotomous) Ethnoling polariz CS: Ordered probit 1990 - Yes Akdede 2010 PR+CL Ethnic fract Ordered probit 1992-2006 - Yes Akdede 2010 PR+CL Relig fract Ordered probit 1992-2006 + Yes Aleman and Yang 2011 ƅ3ROLW\��OLEHUDOL]LQJ� Ethnic fract TSCS: Hazard Model 1970-1999 + No Aleman and Yang 2011 ƅPolity (de-liberalizing) Ethnic fract TSCS: Hazard Model 1970-1999 0 No Alesina et al 2003 PR, Polity2 Ling fract TSCS: SUR 1970-1995 - No Alesina et al 2003 PR, Polity2 Ethnic fract TSCS: SUR 1970-1995 - No Alesina et al 2003 PR, Polity2 Relig fract TSCS: SUR 1970-1995 0 No Barro 1999 PR, CL Ethnoling fract TSCS: SUR w/ lagged Y at 5-year intervals 1972-1995 - No Bernhard et al 2001 Survival Ethnic fract TSCS: Hazard model 1919-1995 - No Bernhard et al 2001 Survival Relig fract TSCS: Hazard model 1919-1995 0 No Boix and Stokes 2003 DD Relig fract TSCS: Dynamic probit 1850-1990 - No Clague et al 2001 PR Ethnic fract TSCS: Two-sided tobit; cross-section 1960-1994 + No Colaresi, Thompson 2003 Polity2 Ethnoling fract TSCS 1960-1992 0 No Fish, Brooks 2004 PR, CL Ethnic fract CS: OLS 2000 0 Yes Fish, Brooks 2004 PR, CL Relig fract CS: OLS 2000 0 Yes Fish, Brooks 2004 PR, CL Ling fract CS: OLS 2000 0 Yes Fish, Brooks 2004 PR, CL Ethnic �raction CS: OLS 2000 0 Yes Fish, Kroenig 2006 PR, CL Ethnic fract TSCS: Prais-Winsten w/ lagged Y 1991-2004 0 Yes Fish, Kroenig 2006 PR, CL Ling fract TSCS: Prais-Winsten w/ lagged Y 1991-2004 0 Yes Fish, Kroenig 2006 PR, CL Relig fract TSCS: Prais-Winsten w/ lagged Y 1991-2004 0 Yes Gasiorowski 1995 Trichotomous Ethnoling fract TSCS 1950-1989 0 No Hadenius 1992 Hadenius index Ethnoling fract TSCS: OLS 1988 - No Nieswiadomy, Strazicich 2004 PR Ethnoling fract TSCS: Tobit, period averages 1972-2001 - No Papaioannou, Siourounis 2008 Democratization Ethnic fract TSCS: Dynamic probit, binary probit 1975-2000 0 No Papaioannou, Siourounis 2008 Democratization Relig fract TSCS: Dynamic probit, binary probit 1975-2000 0 No Ross 2001 Polity2 Ethnoling fract TSCS 1971-1997 0 No Sing 2010 Survival Ethnoling fract TSCS: RE dynamic probit 1946-2002 + No Smith 2004 Polity (dichotomous) Ethnoling fract TSCS 1960-1999 0 No Teorell 2010 ƅ35 Ethnic fract TSCS: OLS w/ lagged Y 1972-2006 0 No Teorell 2010 ƅ35 Relig fract TSCS: OLS w/ lagged Y 1972-2006 0 No Wright 2008 Democratization Ethnoling fract TSCS 1946-2002 0 No

Units of analysis = countries or country-years. Outcomes = outcome measures of democracy. Finding = negative (-), positive (+), or null (0). Focus = the main focus of the study. Y = dependent variable. CS = cross-section. TSCS = time-series cross-section. SUR = seemingly unrelated regression. RE = random effects. GMM = generalized method of moments. DD = democracy/dictatorship variable (Alvarez et al. 1996; Cheibub et al. 2010). PR = Political Rights variable (Freedom House). CL = Civil Liberty variable (Freedom House). Homog = homogeneity. Heterog = heterogeneity. Ling = linguistic. Relig = religious. Fract = fractionalization.

30

Table A2: Crossnational Variables: Definitions and Sources

Outcomes

Civil liberties Index measuring the legal and practical protections of human rights (Freedom House 2007). Original scale: 1-7 (7=least democratic). Rescaled to 0-100 (100=most democratic). Civil_liberty_FH_neg

Competitiveness 100 – votes won by the largest party in presidential or parliamentary elections (or both, averaged) as % of total votes cast (Vanhanen 1990: 17). Competition_Vanhanen

Political rights Political rights index (Freedom House 2007). Original scale: 1-7 (7=least democratic). Rescaled to 0-100 (100=most democratic). Pol_rights_FH_neg

Polity2 Polity2 variable (Polity IV), with some additional data imputed as described in the text. Original scale: -10 to +10. Rescaled to 0-100. Polity4_imp_F

Polity2, dichotomous (5) 0 if Polity2 <5, 1 if Polity2 >=5, based on imputed Polity2 variable. Polity4_imp_dich5 Polity2, dichotomous (0) 0 if Polity2 <0, 1 if Polity2 >=0, based on imputed Polity2 variable. Polity4_imp_dich0 Polity2, trichotomous 0 if Polity2 <-5, 1 if Polity2 >=-5 & <5, 2 if Polity2 >=5, based on imputed Polity2

variable. Polity4_imp_trich_F Turnover Top political leaders enter and exit in a prescribed manner, as recorded in the Archigos

dataset (Goemans et al. 2009). Regular_Archigos

Predictors

Africa Dummy (coding by authors). Africa Buddhism Percent Buddhist (CIA WorldFactbook on-line). Buddhism Catholic Percent Catholic (CIA WorldFactbook on-line). Catholic Diffusion A country’s diffusion score for a particular year is the average of Polity2 scores for all

other countries, weighted by the inverse of the distance to the country in question (coding by authors). Polity4_imp_geo

English legal origin English legal origin, i.e., former British colony (La Porta et al 1999). English_legal_origin Ethnoling fract Ethnolinguistic fractionalization: 1 – summation of the square of each racial/linguistic

group’s proportion (Easterly, Levine 1997). Ethnolinguistic_fract_imp European language Percent speaking a European language (CIA WorldFactbook on-line). European_language GDP per cap (ln) GDP per capita, natural logarithm (World Bank 2007). GDPpc_ln_07 Island Dummy (coding by authors). Island Landlock 1 if country is landlocked, 0 otherwise (Acemoglu, Johnson, Robinson). Landlock Latin America Dummy (coding by authors). LatinAmerica Latitude (ln) Latitude (ln). Latitude_ln Middle East Dummy (coding by authors). MiddleEast Muslim Percent Muslim (CIA WorldFactbook on-line). Muslim Oil production per cap Oil production (Humphreys 2005) per capita. Oil_production_MH_pc Population (ln) Population (World Bank 2007), natural logarithm. pop_ln Protestant (%) Percent Protestant (CIA WorldFactbook on-line). Protestant Trend Annual count variable (coding by authors). Trend Urbanization Urban population, percent of total (World Bank 2007). Urban_Pop_WDI_07 West Europe Dummy (coding by authors). WestEurope

31

Table A3: Descriptive Statistics

Variable Obs Mean SD Min Max

Africa 13787 0.20 0.40 0.00 1.00 Buddhism 9908 6.05 21.12 0.00 99.00 Catholic 10002 32.11 35.87 0.00 99.10 Civil liberties 5919 51.75 32.58 0.00 100.00 Competitiveness 6215 26.15 26.27 0.00 70.00 Diffusion 9548 0.08 0.39 -1.06 2.33 English legal origin 9815 0.33 0.47 0.00 1.00 Ethnoling fract 10491 0.34 0.28 -0.07 1.00 European language 10466 0.30 0.42 0.00 1.06 GDP per cap (ln) 6593 7.47 1.55 4.03 10.99 Island 12893 0.32 0.47 0.00 1.00 Landlock 10096 0.17 0.38 0.00 1.00 Latin America 10049 0.10 0.30 0.00 1.00 Latitude (ln) 9770 -1.58 0.93 -4.50 -0.22 Middle East 10002 0.09 0.29 0.00 1.00 Muslim 10923 21.30 35.27 0.00 100.00 Oil production per cap 7898 0.06 0.32 0.00 5.83 Political rights 5919 51.58 37.48 0.00 100.00 Polity2 8028 -0.16 7.43 -10.00 10.66 Polity2, dichotomous (5) 8166 0.38 0.48 0.00 1.00 Polity2, dichotomous (0) 8166 0.44 0.50 0.00 1.00 Polity2, trichotomous 8166 0.92 0.89 0.00 2.00 Population (ln) 10982 14.35 2.79 4.61 20.99 Protestant (%) 10923 16.13 27.07 0.00 100.00 Religious fract 9956 0.44 0.23 0.00 0.86 Turnover 6589 0.11 0.31 0.00 1.00 Urbanization 9480 47.75 25.23 2.00 100.00 West Europe 10096 0.11 0.31 0.00 1.00

32

Table A4: Coding of Key Diversity Variables

Country

Ethno -ling Fract

Relig Fract Country

Ethno -ling Fract

Relig Fract Country

Ethno -ling Fract

Relig Fract Country

Ethno -ling Fract

Relig Fract

Afghanistan .4484 .2717 Estonia .4299 .4985 Mali .8086 .1820 Suriname .7500 .7910 Albania .0017 .4719 Ethiopia .6771 .6249 Malta .1033 .1223 Swaziland .0000 .4444 Algeria .2937 .0091 Fiji .8000 .5682 Marshall Islands .0606 .5207 Sweden .0650 .2342 Andorra .6165 .2326 Finland .1050 .2531 Mauritania .2700 .0149 Switzerland .3076 .6083 Angola .7728 .6276 France .1454 .4029 Mauritius .7085 .6385 Syria .0948 .4310 Antigua/Barbuda .0000 .6840 Gabon .7967 .6674 Mexico .1741 .1796 Taiwan .2551 .6845 Argentina .1769 .2236 Gambia .7804 .9700 Micronesia .6617 .6469 Tajikstan .4686 .3386 Armenia .0619 .4576 Georgia .4098 .6543 Moldova .4705 .5603 Tanzania .8902 .6334 Australia .1128 .8211 Germany .0438 .6571 Monaco .6459 .3047 Thailand .3569 .0994 Austria .0332 .4146 East (GDR) -.0490 - Mongolia .0737 .0799 Togo .7285 .6596 Azerbaijan .1310 .4899 West (FRG) .0438 .6571 Morocco .3480 .0035 Tonga .0000 .6214 Bahamas .0000 .6815 Ghana .7061 .7987 Mozambique .7863 .6759 Trinidad .2313 .7936 Bahrain .3182 .5528 Greece .0778 .1530 Myanmar .3840 .1974 Tunisia .0703 .0104 Bangladesh .0000 .2090 Grenada .0000 .5898 Namibia .7283 .6626 Turkey .1636 .0049 Barbados .0703 .6934 Guatemala .4767 .3753 Nepal .4500 .1417 Turkmenistan .3274 .2327 Belarus .3726 .6116 Guinea .7598 .2649 Netherlands .0634 .3866 Uganda .8358 .6157 Belgium .3638 .2127 Guinea-Bissau .8500 .6128 New Zealand .1476 .8110 Ukraine .3875 .6157 Belize .4090 .5813 Guyana .2378 .7876 Nicaragua .0992 .4290 UAE .4393 .3310 Benin .6831 .5544 Haiti .0644 .4704 Niger .7329 .2013 UK .1062 .6944 Bermuda .0000 .7112 Honduras .0974 .2357 Nigeria .8567 .7421 United States .2090 .8241 Bhutan .4375 .3787 Hong Kong .2368 .4191 Norway .0699 .2048 Uruguay .0667 .3548 Bolivia .5994 .2085 Hungary .0651 .5244 Oman .2419 .4322 Uzbekistan .3655 .2133 Bosnia/Herz. .6087 .6851 Iceland .1000 .1913 Pakistan .6216 .3848 Vanuatu .5441 .7044 Botswana .3775 .5986 India .7422 .3260 Palau .2982 .7147 Venezuela .0525 .1350 Brazil .0558 .6054 Indonesia .6906 .2340 Panama .1908 .3338 Vietnam .1176 .5080 Brunei .5000 .4404 Iran .6848 .1152 PNG .8027 .5523 W. Samoa .0000 .7871 Bulgaria .1157 .5965 Iraq .3650 .4844 Paraguay .4111 .2123 Yemen .0167 .0023 Burundi .0133 .5158 Ireland .0904 .1550 Peru .4316 .1988 Yugoslavia .2778 .5530 Cambodia .1336 .0965 Israel .3271 .3469 Philippines .7238 .3056 Zaire .8723 .7021 Cameroon .852 .7338 Italy .0389 .3027 Poland .0390 .1712 Zambia .8294 .7359 Canada .3762 .6958 Jamaica .0125 .6160 Portugal .0025 .1438 Zimbabwe .5986 .7363 Cape Verde .3750 .0766 Japan .0099 .5406 Qatar .4690 .0950 CAR .7856 .7916 Jordan .0297 .0659 Romania .1220 .2373 Chad .6662 .6411 Kazakhstan .5948 .5898 Russia .3237 .4398 Chile .0506 .3841 Kenya .8270 .7765 Rwanda .0609 .5066 China .2333 .6643 Kiribati .5000 .5541 Saint Kitts .0000 .6614 Colombia .0558 .1478 Korea, South .0000 .6604 Saint Lucia .5833 .3220 Comoros 1.0000 .0137 Korea, North -.0723 .4891 Saint Vincent .0000 .7028 Congo .6693 .6642 Kuwait .2641 .6745 Sao Tome .0000 .1866 Costa Rica .0532 .2410 Kyrgyzstan .5490 .4470 Saudi Arabia .1448 .1270 Cote d’Ivoire .8565 .7551 Laos .2500 .5453 Senegal .7789 .1497 Croatia .0909 .4447 Latvia .5115 .5556 Serbia/Mont .2778 .5530 Cuba .2146 .5059 Lebanon .1140 .7886 Seychelles .0000 .2323 Cyprus .3000 .3962 Lesotho .2098 .7211 Sierra Leone .8130 .5395 Czech Republic .2655 .6591 Liberia .8031 .4883 Singapore .3215 .6561 Denmark .0275 .2333 Libya .1214 .0570 Slovakia .2100 .5655 Djibouti .7143 .0435 Liechtenstein .2522 .3343 Slovenia .1546 .2868 Dominica .5000 .4628 Lithuania .2577 .4141 Solomon Is .5714 .6708 Dominican Rep .0108 .3118 Luxembourg .2167 .0911 Somalia .3569 .0994 Ecuador .3254 .1417 Macau .2335 .5511 South Africa .8310 .8603 Egypt .0231 .1979 Macedonia .4430 .5899 Soviet Union .5986 - El Salvador .0514 .3559 Madagascar .0627 .5191 Spain .2745 .4514 Eq. Guinea .6250 .1195 Malawi .6224 .8192 Sri Lanka .3257 .4853 Eritrea .5815 .4253 Malaysia .6104 .6657 Sudan .5122 .4307

33

Table A5:

Specification Tests

Models Ethnoling fract

Religious Fract

Benchmark (Model 1, Table 1) 14.710*** -36.652*** [2.244] [2.975] minus Ethnolinguistic fract -32.963*** [2.890] minus Religious fract 8.832*** [2.223] minus per cap GDP (ln) 7.270*** -33.847*** [2.025] [2.620] minus urbanization 14.821*** -36.642*** [2.255] [2.978] minus population (ln) 15.093*** -35.018*** [2.279] [2.956] minus oil production per cap 14.812*** -39.758*** [2.202] [2.861] minus landlock 14.526*** -36.226*** [2.240] [2.914] minus island 12.047*** -35.114*** [2.272] [2.943] minus distance from equator (ln) 11.760*** -39.303*** [2.249] [2.938] minus English legal origin 18.916*** -33.138*** [2.213] [2.991] minus % European language 8.805*** -31.020*** [2.156] [2.801] minus % Muslim 11.366*** -13.231*** [2.257] [2.632] minus % Protestant 14.798*** -35.992*** [2.254] [3.004] minus % Catholic 13.842*** -30.849*** [2.235] [2.651] minus % Buddhist 16.597*** -33.179*** [2.224] [2.859] minus diffusion 13.298*** -38.535*** [2.226] [2.976]

Specification tests of benchmark model (Model 1, Table 1) in which individual covariates are excluded, seriatim, and results for key variables are recorded. Thus, in Model 1, Ethnolinguistic fract is removed from the benchmark model and results for Religious fractionalization are recorded. *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests).

34

Table A6: Sub-sample Tests

(1) (2) (3) (4) (5) (6) (7)

Sample Entire –Latin America –Asia –Africa –Middle

East –West Europe –OECD

Ethnolinguistic fract 14.710*** 18.176*** 17.139*** 8.956*** 9.414*** 14.428*** 17.014*** [2.244] [2.336] [2.346] [3.530] [2.408] [2.216] [2.232] Religious fract -36.652*** -36.322*** -44.948*** -37.885*** -34.522*** -29.151*** -27.182*** [2.975] [3.315] [3.260] [3.645] [3.173] [3.443] [3.754] Benchmark controls X X X X X X X Annual dummies X X X X X X X Constant X X X X X X X Observations 5401 4601 4758 3772 4846 4771 4292 Countries 168 148 149 118 151 153 139

Years 1960- 2001

1960- 2001

1960- 2001

1960- 2001

1960- 2001

1960- 2001

1960- 2001

R2 0.5317 0.576 0.576 0.4953 0.526 0.500 0.486 Sub-sample tests in which the benchmark model (model 1, Table 1), reproduced as model 1 above, is replicated without key regions – Latin America (model 2), Asia (model 3), Africa (model 4), Middle East (model 5), West Europe (model 6), and the OECD (model 7). Y = dependent variable with one-period forward lag. Benchmark controls = GDP per capita, urbanization, population, oil production per capita, landlock, distance from the equator, English legal origin, percent speaking a European language, percent Arab, percent Muslim, percent Protestant, percent Catholic, percent Buddhist, diffusion. Estimator = ordinary least squares with Newey-West standard errors. *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests).

35

Appendix B: Alternate Crossnational Measures of Diversity

36

Table B1: Variable Definitions Formulas

Fract (Herfindahl index)

where sij is the share of group I in society j (Alesina et al. 2003). (Read: 1 minus the summation of each religious group’s squared proportion of the total in a given country.)

Polariz (Q index) 2

1

0.510.5

Ni

ii

Q S S

�§ · � ¨ ¸© ¹

¦

where iS is the size of each group in proportion to the total population (Bossert, Pattanaik, & Xu 2003; Esteban & Ray 1994; Montalvo & Reynal-Querol 2005a, 2005b).

Homog (homogeneity index) Size of largest group as share of total population.

Indices

Cultural fract (Fearon) cdiv_Fearon2

Groups (>1% population) defined as racial/linguistic. Formula: Herfindahl index with each group’s proportion multiplied by a factor capturing the resemblance between groups’ languages. Base year: 1990s. Source: Fearon (2003).

Ethnoling fract (Easterly, Levine) Ethnolinguistic_fract_AVELF_imp

Groups defined as racial/linguistic (Atlas Narodov Mira). Formula: Herfindahl index. Base year: 1964 (Easterly, Levine 1997).

Ethnoling homog (Vanhanen) Ethnolinguistic_homog_Vanhanen

Groups defined as national/linguistic; includes only the most important divisions. Formula: homogeneity index. Base year: 1980s-90s (Vanhanen 1999).

Ethnoling fract (Fearon) elf_Fearon2

Groups (>1% population) defined as linguistic. Formula: Herfindahl index. Base year: 1990s (Fearon 2003).

Ethnic homog (Vanhanen) Ethnic_homogeneity_Vanhanen

Groups defined as ethnic, including only the most important divisions (with rare intergroup marriage as the proxy). Formula: homogeneity index. Base year: 1980s-90s (Vanhanen 1999).

Ethnic fract (Alesina) Ethnic_fractionaliz_Alesina

Groups defined as racial/linguistic. Formula: Herfindahl index. Base year: 1990s-2001 (Alesina et al. 2003).

Ethnic fract (Fearon) ef_Fearon2

Groups (>1% population) defined ethnic (those with commonly accepted characteristics). Formula: Herfindahl index. Base year: 1990s (Fearon 2003).

Ethnic fract (Reynal-Querol) ETHFRAC2_Reynal_Querol

Groups defined as ethnolinguistic, including only the most important divisions (source: World Christian Encyclopedia). Formula: Herfindahl index. Base year: 1982 (Reynal-Querol 2004).

Ethnic polar (Reynal-Querol) ETHPOL2_Reynal_Querol

Groups defined as ethnolinguistic, including only the most important divisions (source: World Christian Encyclopedia). Formula: Q index. Base year: 1982 (Reynal-Querol 2004).

Largest group (Fearon) plural_Fearon2

Groups (>1% population) defined as those with commonly accepted characteristics. Formula: homogeneity index. Base year: 1990s (Fearon 2003).

Ling fract (Alesina) Linguistic_fractionaliz_Alesina

Groups defined as linguistic, i.e., sharing mother tongue (source: Encyclopedia Britannica). Formula: Herfindahl index. Base year: 2001 (Alesina et al. 2003).

Ling heterog 1 (Gunnemark) Linguistic_heterogen1_Gunnemark

Groups defined as linguistic. Formula: % not speaking the official language. Base year: 1980s (Gunnemark 1991).

Relig fract (Alesina) Religious_fractionaliz_Alesina

Groups defined as religious (source: Encyclopedia Britannica). Formula: Herfindahl index. Base year: 2001 (Alesina et al. 2003).

Relig fract (Reynal-Querol) RELFRAC2_Reynal_Querol

Groups defined as religious. Formula: Herfindahl index. Base year: 1980s (Reynal-Querol 2002).

Relig polar (Reynal-Querol) RELPOL2_Reynal_Querol

Groups defined as religious. Formula: Q index. Base year: 1980s (Reynal-Querol 2002).

37

Table B2:

Descriptive Statistics

Variable Obs Mean SD Min Max Cultural fract (Fearon) 31907 0.32 0.20 0.00 0.73 Ethnic fract (Alesina) 39120 0.44 0.26 0.00 0.93 Ethnic fract (Fearon) 33167 0.47 0.26 0.00 1 Ethnic fract (Reynal-Querol) 28764 0.44 0.28 0.01 0.96 Ethnic homog (Vanhanen) 43047 68.41 25.56 15.00 100.00 Ethnic polar (Reynal-Querol) 28764 0.52 0.25 0.02 0.98 Ethnoling fract (Fearon) 26872 0.43 0.29 0.00 0.93 Ethnoling fract (Easterly/Levine) 44731 0.34 0.28 -0.07 1 Ethnoling homog (Vanhanen) 30840 68.66 24.52 18.00 100.00 Largest group (Fearon) 32957 0.65 0.24 0.12 1.00 Ling fract (Alesina) 41393 0.39 0.28 0.00 0.92 Ling heterog 1 (Gunnemark) 32080 0.36 0.41 0.00 1.00 Relig fract (Alesina) 43876 0.44 0.23 0.00 0.86 Relig fract (Reynal-Querol) 28764 0.28 0.24 0.00 0.78 Relig polar (Reynal-Querol) 28764 0.47 0.36 0.00 1

38

Table B3: Correlation Table

1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

1. Cultural fract (Fearon) 1.0000 2. Ethnoling fract (Easterly, Levine) .7708 1.0000 3. Ethnoling homog (Vanhanen) -.7523 -.7865 1.0000 4. Ethnoling fract (Fearon) .8617 .9282 -.7982 1.0000 5. Ethnic homog (Vanhanen) -.7371 -.7734 .9678 -.7753 1.0000 6. Ethnic fract (Alesina) .7489 .7424 -.8616 .7594 -.8671 1.0000 7. Ethnic fract (Fearon) .7831 .7603 -.8518 .7777 -.8719 .9382 1.0000 8. Ethnic fract (Reynal-Querol) .7225 .8289 -.8146 .8444 -.7935 .8015 .8118 1.0000 9. Ethnic polar (Reynal-Querol) .3890 .3028 -.3318 .3995 -.3444 .4901 .4712 .5267 1.0000 10. Largest group (Alesina) -.7776 -.7552 .8427 -.7585 .8676 -.9249 -.9830 -.7888 -.4276 1.0000 11. Ling fract (Alesina) .7326 .9260 -.7159 .8933 -.7112 .6989 .7105 .7734 .2658 -.6777 1.0000 12. Ling heterog 1 (Gunnemark) .6298 .9509 -.6989 .8103 -.6801 .6605 .6718 .7339 .1919 -.6720 .8647 1.0000 13. Relig fract (Alesina) .2217 .3500 -.3425 .3571 -.3177 .2593 .3024 .3364 .0996 -.3034 .3343 .3224 1.0000 14. Relig fract (Reynal-Querol) .5004 .5967 -.6275 .5519 -.6126 .5247 .5461 .5484 .2420 -.5630 .4997 .5559 .5859 1.0000 15. Relig polar (Reynal-Querol) .5119 .6336 -.6906 .5973 -.6740 .6017 .6138 .5870 .3152 -.6255 .5347 .5956 .5628 .9537

39

Table B4: Factor Analysis

Principal components factor analysis Factor Eigenvalue Difference Proportion Cumulative

1 10.24 8.83 0.68 0.68 2 1.41 0.36 0.09 0.78 3 1.05 0.34 0.07 0.85 4 0.71 0.22 0.05 0.89 5 0.49 0.14 0.03 0.93 6 0.35 0.09 0.02 0.95 7 0.26 0.11 0.02 0.97 8 0.15 0.03 0.01 0.98

Factor loadings (pattern matrix)

and unique variances 1 2 3 Unique Ethnoling fract (Easterly) 0.9194 -0.0266 0.3266 0.0474 Ethnoling homog (Vanhanen) -0.9179 0.0298 0.0465 0.1545 Ethnoling fract (Fearon) 0.9173 -0.0997 0.1984 0.1092 Largest group (Fearon) -0.9075 0.1522 0.1670 0.1254 Cultural fract (Fearon) 0.8411 -0.2116 0.0430 0.2460 Ethnic homog (Vanhanen) -0.9114 0.0581 0.0744 0.1605 Ethnic fract (Alesina) 0.9001 -0.2072 -0.1908 0.1105 Ethnic fract (Fearon) 0.9170 -0.1814 -0.1763 0.0905 Ethnic fract (R-Querol) 0.8967 -0.1308 -0.0425 0.1771 Ethnic polariz (R-Querol) 0.4507 -0.2671 -0.6479 0.3058 Ling fract (Alesina) 0.8574 -0.0720 0.3805 0.1148 Ling heterog 1 (Gunnemark) 0.8331 0.0302 0.4201 0.1286 Relig fract (Alesina) 0.4296 0.7139 -0.0607 0.3021 Relig fract (R-Querol) 0.7099 0.6021 -0.1445 0.1126 Relig polariz (R-Querol) 0.7597 0.5335 -0.1849 0.1040

40

Table B5: Regression Tests of Alternate Diversity Measures

(1) (2) (3) (4) (5) (6) (7) (8)

1. Ethnoling fract (Easterly) 14.710*** 11.269*** 4.782** 9.648* 2.685*** 1.616*** 18.234*** 20.459*** Ethnolinguistic_fract_imp [2.244] [2.303] [2.086] [5.175] [0.809] [0.599] [1.696] [3.354]

2. Ethnoling homog (Vanhanen) -0.152*** -0.088*** -0.012 -0.107 -0.029** -0.018*** -0.163*** -0.179*** Ethnolinguistic_homog_Vanhanen [0.027] [0.027] [0.026] [0.081] [0.012] [0.007] [0.020] [0.041]

3. Ethnoling fract (Fearon) 10.486*** 5.392** -1.770 5.998 2.495** 1.649*** 10.355*** 12.088*** elf_Fearon2 [2.369] [2.544] [2.409] [6.378] [0.979] [0.529] [1.831] [3.783]

4. Largest group (Fearon) -3.425 -1.464 -0.830 4.915 -1.395 -1.200* -7.894*** -10.982** plural_Fearon2 [2.804] [2.880] [2.695] [8.156] [1.177] [0.723] [2.288] [4.649]

5. Cultural fract (Fearon) 3.922 -1.192 -1.155 -1.132 1.636 1.300* 6.817*** 8.450* cdiv_Fearon2 [2.922] [2.970] [2.742] [7.866] [1.132] [0.674] [2.333] [4.702]

6. Ethnic homog (Vanhanen) -0.166*** -0.125*** -0.067*** -0.109* -0.030*** -0.018*** -0.176*** -0.197*** Ethnic_homogeneity_Vanhanen [0.022] [0.024] [0.022] [0.065] [0.010] [0.006] [0.016] [0.030]

7. Ethnic fract (Alesina) 4.495* 4.867* -3.497 -3.146 1.227 0.958 2.824 4.992 Ethnic_fractionaliz_Alesina [2.457] [2.490] [2.346] [6.661] [0.971] [0.678] [1.841] [3.744]

8. Ethnic fract (Fearon) 4.065 2.229 0.886 -3.304 1.362 1.120* 6.839*** 9.445** ef_Fearon2 [2.566] [2.580] [2.436] [7.096] [1.064] [0.653] [2.069] [4.195]

9. Ethnic fract (R-Querol) 10.634*** 6.279** -0.838 1.030 1.436 0.759 11.132*** 12.784*** ETHFRAC2_Reynal_Querol [2.562] [2.606] [2.550] [6.525] [0.991] [0.563] [2.022] [4.124]

10. Ethnic polar (R-Querol) 13.795*** 14.098*** 9.577*** 1.155 1.579* 0.649 12.411*** 13.510*** ETHPOL2_Reynal_Querol [2.588] [2.542] [2.394] [5.147] [0.836] [0.548] [1.865] [3.787]

11. Ling fract (Alesina) 7.817*** 5.435** 1.728 2.159 1.543* 0.983* 10.984*** 11.850*** Linguistic_fractionaliz_Alesina [2.299] [2.327] [2.130] [5.297] [0.791] [0.573] [1.648] [3.254]

12. Ling hetero 1 (Gunnemark) 9.742*** 8.332*** 4.561*** 6.086 2.003*** 1.209*** 12.188*** 13.064*** Linguistic_heterogen1_Gunnemark [1.770] [1.856] [1.694] [4.418] [0.671] [0.455] [1.244] [2.530]

13. Relig fract (Alesina) -36.652*** -30.200*** -18.173*** -32.153*** -4.934*** -2.627*** -30.259*** -31.915*** Religious_fractionaliz_Alesina [2.975] [3.207] [2.785] [6.663] [1.272] [0.772] [2.150] [4.263]

14. Relig fract (R-Querol) -28.163*** -25.551*** -27.241*** -34.268*** -4.010*** -2.007*** -15.877*** -25.224*** RELFRAC2_Reynal_Querol [3.631] [3.607] [3.126] [9.502] [1.510] [0.747] [1.762] [5.389]

15. Relig polar (R-Querol) -16.695*** -11.984*** -16.598*** -21.461*** -2.082** -1.033** -14.108*** 16.043*** RELPOL2_Reynal_Querol [2.407] [2.438] [1.969] [6.133] [0.950] [0.503] [1.739] [3.451]

Coefficients and standard errors for alternate measures of diversity tested in models 1-8 from Table 1. *** p<0.01, ** p<0.05, * p<0.1 (two-tailed tests). Row 1 reproduces results for our key variable, Ethnoling fract, from Table 1. In rows 2-12, alternative measures of ethnic and linguistic diversity are substituted for this variable in the same models. Row 13 reproduces results for our other key variable, Relig fract, from Table 1. In rows 14-15, alternative measures of religious diversity are substituted for this variable in the same models. All variables and sources described in Table B1.

41

Appendix C: United States Analyses

42

Table C1: Variable Definitions

Variable Definition (sources described in text)

Black Black population as percent of total. College Percent of population above 25 with bachelor's degree. Competition 100 - margin of victory between top two vote-getters. Ethnic fract 1 - summation of the square of each ethnic group's proportion. High school Percent of population above 25 with high school degree. Income per cap (ln) Personal income per capita, natural logarithm. Non-English Proportion who speak another language and speak English less than very well. Other Other race as percent of total. Population (ln) Population, natural logarithm. Turnover 1 if incumbent party lost, 0 otherwise. Urban Urban population as percent of total. White White population as percent of total.

Table C2: Descriptive Statistics

Variable Obs Mean SD Min Max

Black 74425 0.11 0.18 0.0003 0.98 College 74425 0.18 0.10 0.02 0.64 Competition 74425 53.92 35.92 0.00 100 Ethnic fract 74425 0.26 0.18 0.006 0.87 High school 74425 0.29 0.13 0.03 0.92 Income per cap (ln) 74425 9.89 0.32 7.24 11.49 Non-English 74425 0.11 0.13 0.001 0.86 Other 74425 0.04 0.07 -2.98E-08 0.75 Population 74425 11.08 1.08 8.97 19.49 Turnover 69164 0.14 0.35 0.00 1.00 Urban 74425 0.73 0.29 0.00 1.00 White 74425 0.80 0.21 0.008 1.00

43

Table C3: Data Description (US)

Ethnic heterogeneity Competition Turnover Coverage

min max Mean min max mean mean Years units elections Offices Lower state house 0.018 0.771 0.262 0 100 52.5 0.133 1968-2003 4161 52999 Upper state house 0.021 0.762 0.278 0 100 55.7 0.162 1968-2003 1805 16882 House of Rep 0.006 0.649 0.189 0 100 60.8 0.093 1972-1992 435 3699 Senate 0.061 0.635 0.323 0 100 78.5 0.184 1980-2000 100 376 Governor 0.061 0.635 0.313 35 100 83.4 0.341 1977-2000 50 317 President 0.188 0.415 0.270 77 100 90.8 0.571 1948-2000 1 14

Regions Northeast 0.006 0.771 0.229 0 100 59.1 0.133 16490 Midwest 0.007 0.726 0.190 0 100 57.6 0.137 21372 West 0.023 0.749 0.310 0 100 60.1 0.157 15594 South 0.014 0.721 0.329 0 100 41.3 0.132 20817 Total 0.006 0.771 0.263 0 100 53.9 0.139 74273

Eras 1948-69 0 100 65.2 -- 2241 1970-79 0 100 59.6 0.149 16802 1980-89 0 100 52.1 0.131 21363 1990-2003 0 100 51.5 0.139 33881

Total 0.006 0.771 0.263 0 100 53.9 0.139 41 6552 74287

Regions: summarizes data for all but presidential elections by region. Empty cells: data not relevant --: insufficient data *: population in millions