Abstract The level of ethnic diversity is believed to have significant consequences foreconomic and political development within countries. In this article, we provide a theo-retical and empirical analysis of the determinants of ethnolinguistic diversity in the world.We introduce a model of cultural and genetic drift where new groups endogenously emergeamong peripheral populations in response to an insufficient supply of collective goods. Inline with our model, we find that the duration of human settlements since prehistoric timeshas a strong positive association with current levels of ethnolinguistic diversity. Diversity isfurther negatively correlated with the length of modern state experience and with distancefrom the equator. Our results are thus consistent with both “evolutionary” and “constructivist”hypotheses of ethnolinguistic fractionalization.
Keywords Ethnic diversity · Ethnicity · Human origins
JEL Classification N40 · N50 · Z10
1 Introduction
It is widely agreed that ethnic diversity within countries can have far-reaching consequencesfor political processes and economic development. Accepting this observation naturally leadsto the question: Why are some countries more ethnically fractionalized than others? Forinstance, why is the probability that two randomly chosen individuals speak different lan-guages only 0.2% in South Korea whereas the same probability is roughly 92% in Uganda?1
1 The estimates are taken from Alesina et al. (2003).
P. AhlerupGothenburg Centre of Globalization and Development, University of Gothenburg, Gothenburg, Sweden
O. Olsson (B) · P. AhlerupDepartment of Economics, University of Gothenburg, Box 640, 405 30 Gothenburg, Sweden
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The broad aim of this paper is to offer theory and evidence to improve our understandingof the determinants of ethnic diversity across the world. We explore the explanatory powerof two main hypotheses regarding the formation of ethnic identities: the constructivist view,arguing that ethnic identifications are primarily a product of state formation processes duringmodernity, and the evolutionary view, contending that ethnic divisions have deep roots in his-tory and ecology and should be analyzed in an evolutionary framework. A key prediction fromour evolutionary model of ethnicity is that the antiquity of uninterrupted human settlementin a given area should be positively correlated with current levels of ethnic fractionalizationin that area. The intuition behind this hypothesis is that among prehistoric hunter-gathererpopulations, random genetic and cultural drift that accumulated over time repeatedly causednew groups to form in order to secure an efficient provision of collective goods. We arguethat the ethnic legacy from prehistory should still be detectable in the current distribution ofethnic groups.
In order to empirically identify this effect, we use recent research on the human genome todevelop a new variable that captures the proximate duration of settlement by modern humansfor all countries in the world. Our hypothesis of a positive effect of the duration of humansettlements on ethnolinguistic diversity receives strong empirical support, also when we con-trol for other proxies for human settlement duration and other relevant factors. In fact, thesettlement duration variable alone explains almost a third of the total cross-country variationin ethnolinguistic diversity. We also find that ethnic diversity decreases with distance fromthe equator, and that, in line with the constructivist view, various indicators of state historyare associated with decreased ethnic heterogeneity. The results have particular relevance forthe highly fractionalized African countries. Although factors such as the very long presenceof humans, the proximity to the equator, and the lack of historical state experience all serveto explain the current high level of ethnic diversity in Africa, our results also suggest thatfractionalization should decrease with time as states mature.
Our work is motivated by a large literature in social science on the political and economicimpacts of ethnic diversity. A comprehensive overview of much of this literature can be foundin Alesina and La Ferrara (2005). In economics, an early influential study is Easterly andLevine (1997), who show that the high degree of ethnic fractionalization in Africa couldexplain a large part of the continent’s dismal growth performance. There is however a wide-spread recognition that the negative association between ethnic fractionalization and growthis not uniform. Collier (2000) finds it only in nondemocratic countries and Easterly (2001)claims that the effect is weaker where institutional quality is higher. Alesina and La Ferrara(2005) confirm Easterly and Levine’s (1997) basic results and find that the negative effectis less pronounced in rich countries, and that after controlling for this effect the impact ofdemocracy is nonsignificant. Moreover, ethnically fractionalized countries tend to be morecorrupt and have longer bureaucratic delays, as well as weaker provision of public goodssuch as infrastructure, school attainment, and health (La Porta et al. 1999; Alesina et al.2003). The provision of public goods in ethnically diverse societies tends to be biased towardexcludable goods rather than non-excludable goods such as education and defense (Alesinaand Wacziarg 1998; Kimenyi 2006). Dimensions of ethnic diversity have also been discussedin conjunction with civil wars and political instability (Collier and Hoeffler 2004).
The increased scholarly interest in the effects of ethnic diversity stimulated the creationof two new works on the measurement of ethnic fractionalization by Alesina et al. (2003)and Fearon (2003). In our empirical section, we use the index for linguistic fractionalizationcreated by Alesina et al. (2003), but our results are robust to using Fearon (2003) indexbased on cultural diversity. Although measures of ethnic diversity sometimes also take intoaccount aspects like race and religion, we choose to focus on ethnolinguistic diversity since
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this aspect is conceptually closest to existing works in the literature (Easterly and Levine1997; Michalopoulos 2011).
In political science and sociology, a rich tradition has studied the sources of ethnicityas well as the impact of ethnicity on state formation and other historical developments. Aclassic work in this field—and highly related to our approach—is Horowitz (1985), wholike us analyze patterns of ethnic fissions and fusions though history and associated con-flicts. In the constructivist literature, ethnic identification is basically regarded as a sociallyconstructed phenomenon appearing during modernity (since circa 1800) for the purpose ofuniting disparate nations into states (Gellner 1983; Anderson 1983; Hobsbawm and Ranger1983). The vehicles for the achievement of nation-states were to be found in a combination ofefficient printing technology, universal literacy, and industrialization that broke up traditionalsocieties.
What we refer to as the evolutionary tradition is a class of theories with the commonfeature that they regard ethnic identification as a natural and evolutionarily successful behav-ior that has existed throughout history.2 In van den Berghe’s (1981, 1995) sociobiologicalmodel of ethnicity, ethnic groups are regarded as extended kinships that are successful as ameans of social organization because cooperation based on kin has evolved as an evolution-ary favorable strategy for solving collective action problems. Dawkins (1976) and Boyd andRicherson (2005) also see a close connection between the evolution of genes and of culturaltraits or behaviors. Just like genes, cultural markers (such as modes of social organization) aresubject to mutation, natural selection, and random drift. The evolutionary view has obviouslinks to current advances in genetic research, which is another building block of our analysis.The rapid progress in this area has drastically changed the scientific community’s view onhow the world was populated in prehistoric times, and also sheds light on how differentethnic groups are related genetically (Oppenheimer 2003). This path-breaking research onthe human genome has so far had little impact in social science.
The level of ethnic diversity is usually taken as a given in economics. A recent exceptionto this is Fletcher and Iyigun (2009) who study how the pattern of conflicts between 1400 and1900 CE have affected current levels of ethnic and religious fractionalization. Their analysisreveals that countries with historical conflicts between Muslims and Christians tend to bemore homogenous today whereas regions with conflicts between Protestants and Catholics,or with Jewish pogroms, are more ethnically heterogeneous. Leeson (2005) argues, basedon historical examples, that colonial policy was often purposely designed to increase ethnicfractionalization. In an interesting study of neighboring communities in Kenya and Tanzania,Miguel (2004) shows that governments in former colonies might have an important role toplay in fostering a national identity over tribal identification.
In a theoretical model of ethnic conflict, Caselli and Coleman (2006) propose that peopleon the losing side of a conflict might switch ethnicity endogenously if the costs of switchingare not too high. Our modeling framework is more closely related to Alesina and Spolaore(1997, 2003), where the decision to break up or form nations (rather than ethnic groups, asin our case) is modeled as a trade-off between economies of scale and preference heteroge-neity.3 More specifically, we provide a micro model of “ethnogenesis,” in which genetic andcultural drift over time increases cultural distances among people and causes the collectivegoods provision from the core to the periphery to deteriorate.
2 We include the so-called “primordial view” of ethnicity in this category of works, associated with, e.g., Shils(1957). See Smith (1986) for a nice overview of the arguments.3 There is also literature on social distance and social identity, e.g., Akerlof (1997) and Akerlof and Kranton(2000), that shares our basic notion that cultural proximity plays an important role in political and economicdecisions.
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Despite the widespread recognition of the importance of ethnicity in politics and economicdevelopment, we are aware of only one other systematic attempt to account for the interna-tional variation in ethnic diversity. Michalopoulos (2011) argues that geographical variationin a given area should reduce inter-regional migration and lead to more ethnic groups.4 Thisprediction concerning the role of geographical friction receives empirical support in a cross-country analysis and in a study of a large number of adjacent pairs of regions. Our approachdiffers in some important ways from that of Michalopoulos (2011), especially in that ourtheory of ethnic diversity is centered on cultural drift and on collective goods provision,rather than on human capital transmission. Furthermore, our empirical focus is on the timesince original settlement, which we find strong support for in the empirical analysis.5
Two other papers also explicitly take into account genetic and/or cultural evolution ina long-run perspective. Using data on genetic distances between populations from Cavalli-Sforza et al. (1994), Spolaore and Wacziarg (2009) show that income convergence appearsto be faster among genetically “close” countries and peoples due to a stronger diffusion oftechnology. Ashraf and Galor (2010) argue that there is a trade-off in how the level of geneticvariation in populations within countries affects economic performance. On the one hand,more genetic variation should make an intergenerational transmission of human capital moredifficult. On the other hand, more diversity should improve the creation of new technologicalideas. Using data on genetic diversity (heterozygosity) within countries, Ashraf and Galor(2010) demonstrate that there appears to have been an inverted U-shaped relationship betweendiversity and economic performance in 1500 and 2000 CE. Our approach differs from thesetwo in that our ultimate dependent variable is ethnic fractionalization rather than economicperformance.
The above brief literature overview suggests that the present paper makes at least threecontributions to the existing literature: First, it offers a collective goods-based model ofendogenous ethnic group formation with genetic and cultural drift as the engine of ethnicfractionalization. Second, it provides the first attempt at measuring the duration of unin-terrupted human settlement in a cross-country setting. Third, it provides a comprehensiveempirical assessment of the determinants of ethnic diversity across the world. The mainfindings in this regard are that the timing of initial settlement by modern humans still canexplain a large fraction of existing differences in ethnic fractionalization, but also that stateexperience during modernity is a key factor.
The rest of the paper is structured as follows. In Sect. 2, we argue that the diverse literatureon the constructivist and evolutionary explanations can be combined with recent ecologicaland genetic research into one joint framework for understanding ethnic diversity. In Sect. 3,we outline our model of ethnic fractionalization. In Sect. 4, we discuss the construction ofour measures for the duration of human settlements. Section 5 outlines the empirical strategyand presents the main empirical results. Section 6 concludes the paper.
2 Literature overview
In this section we discuss the main theories on the evolution of ethnic groups and ethnicdiversity. An ethnic group is a social entity with two basic features: (1) the group members
4 See also Ashraf and Galor (2007) for a model where geography has an important effect on the level ofcultural assimilation.5 Our empirical analysis shows that there is an important role for geographical diversity, as predicted by bothour model and the study by Michalopoulos (2011).
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have a shared belief in a common history or ancestry, often associated with a homeland,a founding migration, or a settlement of new territory, and (2) the group currently forms acultural community, manifested for instance in a common language, religion and/or sharedcustoms. There is usually also a sense of solidarity among members (Fearon 2003; Bates2006).
Ethnicity is distinct from concepts such as race and nation. Although both latter termsare generally poorly defined in the literature, race usually refers to physical distinguishingfeatures such as skin color, hair texture, or stature. The concepts of nation and nationalism,on the other hand, are also based on notions of a shared ancestry and a cultural community,yet most authors consider nationalism to be a concept primarily to be used in conjunctionwith discussions on (nation-)state formation during modernity (Gellner 1983).
2.1 The evolutionary view
At the core of the evolutionary view lies an emphasis on the history and origins of ethnicgroups. Smith (1986) contends that nations and ethnic identification have been in place atleast since the emergence of the first civilizations. Already in the late third millennium BC,there was a system of states in the Near East based on ethnic core populations, includingthe Egyptian and Sumerian civilizations. Now, if fully developed nation-states with distinctwritten languages, religions, customs, and traditions were in place in the Near East as earlyas 3000 BC, where did they originate from?
One potential explanation is provided by the sociobiological theory of ethnic origins,associated mainly with Shils (1957) and van den Berghe (1981, 1995). Firmly rooted inevolutionary biology, van den Berghe develops a model of ethnicity as “extended kinship.”The basis for the argument is that humans, like other mammals, are by nature nepotistic,favoring kin in the daily struggle for survival (Jones 2000). By the evolutionary logic, givena lifetime budget constraint of time and energy, an individual has a greater chance of pass-ing on her genes to future generations if she invests all her resources in her offspring andfamily, rather than if she spends her time and effort on unrelated people. This means thatnepotistic genotypes will generally have a greater reproductive success and tend to dominateall populations.
The nepotism argument applies also to members of the extended family since they alsocarry one’s genes, though not to the same extent as direct offspring. The evolutionary logicdictates that individuals develop a sense of loyalty with their close family, their extendedfamily, their clan, and so on. Since extended kinships eventually become very large and sinceit is usually hard to distinguish kin just by physical appearance, particular cultural markerssuch as dialects, customs, and traditions evolve in order to differentiate from “the others.”Such behavioral traits can be analyzed within the same evolutionary framework as genetics(Dawkins 1976; Boyd and Richerson 2005). Over generations, extended families evolve tobecome ethnic groups.
Nepotistic individuals who organized in extended family groups had an advantage in hav-ing an efficient mechanism for sustaining collective action. Family ties restricted free-ridingbehavior and provided an informal rule-based system in the absence of codified law or aruling elite. Family networks supplied selective disincentives against cheating on deliveringcollective goods. In line with this logic, we conjecture that a primary reason for the existenceof ethnic groups is their role in organizing collective action.
A direct implication of the evolutionary view is that we should expect that distinguishableextended kinships of the type described above have existed throughout most of human history.It is by now generally agreed that the history of “anatomically modern humans” (AMH) goes
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back roughly 200,000 years (McDougall et al. 2005). Genetic research on human originssuggests that all human beings in the world today originate from a founding population of afew thousand individuals in East Africa (Oppenheimer 2003).
As AMH migrated from their East African home to other parts of sub-Saharan Africa,they started an inevitable process of ethnic and genetic fractionalization. Since public goodscould not be effectively provided over long distances, groups necessarily organized in smallunits. A result of this process was genetic and cultural drift. Genetic drift is a general ten-dency for genetic diversity to be reduced among small and relatively isolated populations astime passes. If there were initially for instance five lineages in a founding group—labeledA, B, C, D, and E—there could after say ten generations be only the D-lineage, hence allsubsequent offspring had D as their ancestor. As we shall see, although genetic drift is arandom process, the rate at which it occurs has an estimated expected value.6 Cultural drift isin an analogous manner the tendency for multiple cultural traits to be reduced within an iso-lated population. Cultural drift implies that two groups that initially shared the same cultureshould, after a number of generations in isolation, display two quite distinct sets of culturalcharacteristics, and different enough for all parties to recognize them as two different ethnicgroups (Cavalli-Sforza et al. 1994).
Genetic and cultural drift does not only occur between groups, but is also present withingroups, and can emerge over time as a result of clustering at for instance the village level whenthere is little interbreeding between villages. Such drift eventually causes even non-migratingpeoples to form distinct ethnic groups. Related to drift is the concept of a “founder’s effect,”which arises when a small fraction of the whole population move on to establish a newcolony. This smaller group will naturally have a lower degree of genetic variation than thelarger remaining population. Events such as population bottlenecks, where for some reasonthe population size is dramatically reduced, can have the same effect.
In order to get an idea of what such a fractionalized prehistoric society might have lookedlike, it is illustrative to consider Papua New Guinea (PNG), where isolated groups have popu-lated the greater part of the country to this day. In PNG, an estimated 820 different languagesare currently spoken among its 5.6 million inhabitants (CIA 2007). Two factors appear tohave contributed to this enormous diversity. First, PNG’s geography is quite extreme withmountains and impenetrable rain forests where groups easily became isolated. Second, PNGis believed to have been populated for some 65,000 years and is therefore one of the countrieswith the longest continuous presence of AMH outside Africa.
Our view of ethnic fractionalization thus implies that there should be strong linkagesbetween the formation of genetic and ethnic groups. The most intuitive reason for this is ofcourse that both cultural and genetic characteristics are essentially transmitted from parents tochildren. Attempts at linking genetic and ethnic diversity into one framework have previouslybeen made outside economics. After the publication of Dawkins (1976), a field of “memet-ics” has emerged where the evolution of cultural traits are analyzed with the basic tools ofDarwinian genetics. It has been estimated that the development of mutually unintelligiblelanguages takes a mere 1,000 to 1,500 years if a population with a common language is splitinto two groups (Cavalli-Sforza and Cavalli-Sforza 1995). Empirically, it has been shown thatlinguistic groups to some degree follow genetic patterns, suggesting “parallelism betweengenetic and linguistic evolution” (Cavalli-Sforza et al. 1988, p. 6002). Research by Dunnet al. (2005) on indigenous peoples in South Asia further suggests that there are close links
6 A third factor behind genetic changes, distinct from migration and genetic drift, is natural selection. Asobserved by Dawkins (2006), it is not obvious why natural selection should give rise to differences in linguisticor religious structures. Dawkins’ conjecture is rather that most cultural traits have evolved through randomdrift.
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Original settlement
Ethnic diversity
Sedentary agriculture
Statehood
Western colonialism
Geography (topography, bio-geography, climate)
Random shocks (natural disasters, climate change, migrations)
time
Fig. 1 Evolutionary, constructivist, and geographical influences on ethnic diversity over time
between the genetic relatedness among groups and differences found in language structure.A recent paper by Atkinson (2011), based on the phonetic diversity of 504 extant languagesaround the world, shows that phonetic diversity is largest in Africa and then diminishes withdistance from that continent, suggesting a diffusion of languages through a process linked tothe peopling of the world.
Since cultural and genetic drift are present in all hunting-gathering societies, we thereforeargue that areas with a long settlement history during prehistoric times ought to be ethnicallymore diverse than areas with a shorter settlement history, all other factors held constant.
2.2 The constructivist view
Contrasting the reasoning behind the evolutionary view, the constructivist discourse points toa plethora of more recent factors with potential to affect the current levels of ethnic diversity.Figure 1 outlines some of these factors. The rise of Neolithic agriculture was a dramaticturning point in human history. Initiated in the Fertile Crescent in the Near East around10,500 before present (BP), it spread westward to Europe and eastward to the Indus Valley.In addition, independent transitions occurred in China (9,000 BP), in South America (4,300BP), and in a few other places (Smith 1998; Putterman 2008). From having been nomadichunter-gatherers, people became sedentary farmers relying on domesticated crops and ani-mals. Sedentism and farming revolutionized human lives in several aspects. Two of the mostimportant changes were a large increase in population growth and the introduction of a newclass of specialists including warriors, craftsmen, priests, and rulers (Diamond 1997; Olssonand Hibbs 2005).
On all continents, the rise of sedentary agriculture and a more stratified society was rel-atively soon followed by the emergence of states (supratribal authority), writing, and mon-umental structures resulting from grand collective efforts, such as the pyramids in Egypt,Sumer, and Mexico; what is usually referred to as “civilization.” Gellner (1983) argues thatsince the masses of farmers were relatively immobile and since literacy was only reservedfor a small elite, the type of cultural homogenization that took place in Europe from the turn
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of the nineteenth century was not possible in the early civilizations. On the other hand, Smith(1986) finds that the ancient Sumerians—scattered around cities in the densely populatedIraqi river plains—had a strong sense of a distinct ethnic identity with a common languageand religion, as had the Egyptians and many other peoples during the same time in the NearEast. In China, the state gradually incorporated surrounding ethnic groups into the dominantHan culture (Diamond 1997; Morris 2010).
More recent historical accounts of medieval and modern state formations in for instanceFrance, Germany, and Spain also suggest that statehood experience in general has had ahomogenizing influence on culture and ethnic identity (Fletcher and Iyigun 2009). A reason-able conjecture from these observations is that within states, extended kinships partly losetheir raison d’être, i.e., the role as the most efficient mode of organizing collective action andthe provision of public goods. State institutions like codified law, courts, taxation, and mili-tary protection substitute for the services provided by extended kinships, which is the reasonwhy many small ethnic groups disappear in such an environment. The implied hypothesis isthat the length of statehood experience, and the associated time since the effective transitionto agriculture and civilization, has a negative influence on ethnic diversity.
States have not only created institutions that have passively reduced heterogeneity, buthave also actively pursued policies designed to bring about more homogenous populations.This process gained momentum when the modern industrial European states at the turn ofthe eighteenth century acquired both the means and the motivation for nation-building. Themodern industrialized society’s increasing division of labor created, in combination with arapidly changing production, problems for which the creation of a dynamic and mobile work-force was a solution. The industrial society required strangers to easily communicate withand understand each other, and therefore demanded sufficient homogeneity in both languageand culture (Gellner 1983).
The ambition to obtain an ability to wage and win wars was another driving force behindthe deliberate attempts to create homogenous populations. Referring to the European experi-ence, Tilly (1992, p. 106) finds that “rulers frequently sought to homogenize their populationsin the course of installing direct rule” because “within a homogenous population, ordinarypeople were more likely to identify with their rulers, communication could run more effi-ciently, and an administrative innovation that worked in one segment was likely to workelsewhere as well. People who sensed a common origin, furthermore, were more likely tounite against external threats.”
In Europe, this process started well before the era of industrialization, when direct rulereplaced indirect rule by intermediaries. A high level of homogeneity in the population wasin fact both an ultimate result of the process and a factor making the process faster and moreeffective; it is easier to unite a population that is not too diverse to begin with. The processwhereby the European states encouraged national rather than ethnic or local loyalty beganin the eighteenth century, yet it was not until after the middle of the nineteenth century thatstates forcefully began to expand into non-military activities and populations increasinglycame to view the state as the natural primary provider of services previously provided at thelocal level.
Another major historical process with potentially dramatic repercussions was Europeancolonialism from the fifteenth century onward. This heterogeneous process, coherently ana-lyzed in Osterhammel (2005) and Olsson (2009), is often thought to have had quite disparateeffects depending on the time and duration of colonial dominance, the geography of the area,and the initial wealth of the population. In stark contrast to the homogenizing domestic pol-icies of the time, Europeans who ruled colonial states lacked strong incentives to ethnicallyhomogenize the colonial territories, since they were created only to benefit the colonial power.
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On the contrary, “divide-and-rule” was a commonly used principle for keeping colonies undercontrol, from the days of Cortes’ exploitation of ethnic conflicts during his conquest of theAztec empire, to the cynical differential treatment of Hutus and Tutsis by the Belgians intwentieth century Rwanda.
Yet in some areas, a process of homogenization was nevertheless started. Horowitz (1985)argues that the introduction of the larger colonial polities typically induced previously dividedindigenous groups to assimilate into larger ethnic units in order to achieve a stronger politicalinfluence. In some areas, colonization may even have led to less diversity. During the colo-nization of the Americas, large segments of the indigenous populations were killed by theintroduction of, for them, lethal diseases (Diamond 1997) and new population groups wereforcefully introduced in the form of slaves of African descent. Whether the overall result ofthe colonial era was increased or decreased ethnic diversity is therefore an empirical issue.
2.3 Geography and ecology
The process of evolution is tied to the geographical context, and several micro-level factorshave the potential to influence the degree of ethnic diversity. A stylized fact from ecology isthat species richness, or diversity, is a product of isolation and adaptation, and that it is greatercloser to the equator. Speciation, the process that generates species richness, requires time,wherefore species diversity is positively influenced by the so-called “time-for-speciation”(Stephens and Wiens 2002). Studying pre-colonial North America, Mace and Pagel (1995)find that language diversity follows the same latitudinal pattern as found for other mammalsand for birds. They also find that in this pre-colonial environment, linguistic diversity washigher in areas with more habitat diversity.
Differences in skin color alone do not create ethnic groups, but classification of people intogroups may be easier where there are notable differences in skin color, making the formationand identification of ethnic groups more rapid and detailed (Caselli and Coleman 2006). Thisimplies that diversity within a country can be related to latitude, as well as within-countrydifferences in latitude, humidity, and altitude, since paleoanthropology and medical sciencehave shown that variation in human skin color comes partly from differences in UV radiation,which in turn is determined by latitude, altitude, and humidity. In fact, natural variations inUV radiation, with latitude and altitude, and in precipitation can explain most of skin colorvariation (Chaplin 2004). The residual variation can to some degree be explained by recentmigrations, where populations have not yet had enough time to adjust (Diamond 2005),which implies that similarity of skin color is a weak predictor of close genetic connections(Jablonski 2004).
The impact of latitude on ethnic diversity is complex. Cashdan (2001) finds that the cor-relation between the two is largely due to climatic variability, habitat diversity, and pathogenloads. Where climate is variable and unpredictable, populations are forced to become gen-eralists and use wider ecological niches, and the presence of high pathogen loads can, whenlocal populations have adapted to them, be an isolating force by working both as barriers totheir own movement outside their territory and other populations’ movement into or conquestof the territory. Collard and Foley (2002) find that the number of “cultures” within a certainarea follows geographical patterns, i.e., falls with latitude, and rises with temperature andrainfall, and that this pattern holds both in “new continents” such as the Americas and “oldcontinents” such as Africa.
A problem with geographical factors in our empirical study is that they are likely to affectour dependent variable both directly in a “biological” sense and indirectly through their influ-ence on society in general, as indicated in Fig. 1. For instance, as emphasized by Diamond
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(1997) and Olsson and Hibbs (2005), populations living in areas with a biogeography favor-able for agriculture, e.g., riverine habitats with irrigation potential and many suitable plantsand animals for domestication, were the first to make the transition and develop dense seden-tary farming populations. A high population density entails less isolation, ceteris paribus, andtherefore less diversity. As mentioned above, the transition to agriculture was usually soonfollowed by the formation of states (Chanda and Putterman 2007). Hence, a high populationdensity should have decreased ethnic diversity both by decreasing isolation and by fosteringstatehood.
As any species spreads out from its origin, genetic diversity declines naturally due togenetic drift and founder’s effects, as discussed above. A popular hypothesis, developed inmore detail in Sect. 4.1, maintains that the first humans initially followed the coastlines asthey spread from Africa, with a beachcombing lifestyle, which means that areas closer to thecoast, and maybe waterways connected to it, were settled quite a long time before the inland(Oppenheimer 2003; Macaulay et al. 2005). This suggests that coastal areas on the one handcould harbor more diverse populations due to their longer histories as settlements. However,populations in these areas are less isolated. It is reasonable to assume that over the millennia,the latter effect should eventually come to dominate.
3 An evolutionary model of ethnic fractionalization
In this section, we present a model of a key source of ethnic diversity: The process of culturaldrift among prehistoric populations who roamed the earth for most of modern man’s history.The main building block is the assumption that ethnic groups are primarily a kinship-basedtype of social organization with the main purpose of facilitating collective actions and theprovision of certain collective goods. Members of an initial group might potentially breakaway and form their own community if their distance to the core of the previous group in termsof geography, kinship, or culture becomes too large. The main reasons to focus the model onhow the duration of settlements affects ethnic diversity, rather than to also incorporate otherbroad factors such as state experience, colonialism and geography, is that this concept is themost novel, and addresses issues that economists are not generally acquainted with.
3.1 Basics
Let us assume a culturally homogeneous tribe of prehistoric hunter-gatherers whose popula-tion of (constant) size P is uniformly spread along a one-dimensional island of total size s withJ ≥ 1 valleys. The island has just been colonized by the tribe and no “fissions” (split-ups intonew tribes or ethnic groups) have yet occurred. Individual members of the tribe (which mightbe thought of as households) receive positive utility from consumption but also from leisure.
All members of the tribe have descended from a “founding father” and they all thereforeconstitute an extended kinship. Distance along the one-dimensional territory that the tribehas settled reflects geographical as well as “kinship distance” so that people living closertogether are closer kin. The tribe has two main defining elements: First, it forms a culturalcommunity that differentiates it from other groups and second, it serves as a coordinatingdevice for certain types of collective action. For simplicity, let us think of culture as languageso that all members of the tribe initially speak the same language.7 There is no state-likecentral authority and individuals are basically equals, with a great degree of freedom.
7 Boyd and Richerson (2005, p. 3) define culture as “…information that people acquire from others by teach-ing, imitation, and other forms of social learning.”
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Certain forms of collective action and cooperation are necessary for individuals to survivein the long run. Even in hunter-gatherer communities, an accumulation of certain powers toa chiefly authority are common and tend to increase with the intensity of production. Sahlins(1972, p. 189) describes the potential services provided by chieftains in primitive societies:
… subsidizing religious ceremony, social pageantry, or war; underwriting craft produc-tion, trade, the construction of technical apparatus and of public and religious edifices;redistributing diverse local products; hospitality and succor of the community (in sev-erality or in general) during shortage.
In the absence of laws, even primitive communities usually have some generally acceptedmechanism for dispute settlement. Other examples of collective action among hunter-gath-erer societies that require some central coordination include hunting of large game (likemammoths), collective division of hunted game or spoils from war, and the recurring migra-tions that are a central feature of hunter-gatherer life. Cooperation within the tribe (a kind ofkinship nepotism) has evolved as an evolutionary favorable strategy throughout the historyof human development and is taken as given here. In order to fix ideas, let us think about thecollective good in the model below as services related to a mammoth hunt that involves allmembers of the tribe.
The utility of each individual i ∈ 1, 2, . . . , P is given by the utility function
Ui (ci , li ) = α ln ci + (1 − α) ln li , (1)
where ci is private consumption, li is leisure, and α ∈ (0, 1) is a parameter. The loglinearform ensures diminishing marginal utility in each of the two arguments.
Consumption is the difference between the value of individual production yi and the indi-vidual contribution to the chief or to a collective good, τi . Such a contribution could takethe form of food that the chiefly household receives, perhaps to be used for redistribution toweaker members or for the organization of a village feast. For mathematical convenience,consumption is defined as ci = yi
τi� c̄ > 1. The ratio of production to contributions must
exceed a subsistence level c̄, which must be greater than unity; Only regions where thissubsistence condition can be met will be populated.
Individual production yi is given by the production function
yi = gi eγ
i L1−γ
i , (2)
where gi is the effective quality of the collective good that individual i benefits from. ei isthe individual effort induced into production, Li is the land available to each individual, andγ ∈ (0, 1) is an elasticity parameter that yields constant returns to scale in effort and land.For simplicity, land is normalized to Li = 1 for all i . Each individual’s total endowment oftime is normalized to unity, implying that li = 1 − ei .8
3.2 Collective goods and cultural distance
The key factor in the model is gi , the effective level of the collective good experienced byindividual i . This level is
gi = g
mi (t) + di + fi≥ 0, (3)
8 We implicitly assume that land is not an excludable, rival, or constraining factor for hunter-gatherers duringthis era of very low population densities. While this is not an entirely realistic assumption, it allows us to focuson more central aspects.
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where g is the level of the collective good at its origin. di represents the geographical distancebetween individual i’s location and that where the collective goods are provided, fi is anadditional fixed cost of travelling outside i’s own valley, and mi (t) ≥ 1 is a cultural distancefunction (of time t).
For simplicity, let us think of the location of the collective good provision as being equiv-alent to the place where the chief lives. The larger the level of g, the greater the importanceof chiefs.9 Even though the model is more generally applicable, we discuss the collectivegood here as services related to the annual hunt of mammoth.10 Killing animals the size of amammoth requires careful planning, preparation, coordination, and resource pooling in theform of weaponry and means of transportation. The chief of the tribe coordinates this jointeffort that involves the whole tribe. The total (fixed) cost of supplying and coordinating thehunting party once a year is k, and each individual pays a tribute τi = k/P in the form ofmammoth meat. This assumption is made to convey economies of scale in the provision ofcollective goods. A tribe that does not supply k resources will not kill any mammoth.
All participating tribespeople have to travel to the chief’s village in order to take part inthe hunt. Depending on distance and terrain, this involves an individual cost (in terms of alower effective level of gi ) given by the term di + fi . The distance from some individual ito the chief di depends on the size of the area, s, and on where the chief lives. In line withAlesina and Spolaore (1997) and the Hotelling result, the chief always resides in the middleof the tribe’s territory. This is the socially optimal location given that the whole tribe has togather annually for the mammoth hunt.
Initially, and with only one tribe, the assumption of centrality implies that the locationof the chief is at s/2. Formally, the distance from any location zi ∈ [0, s] to the chief isdi = ∣
∣ s2 − zi
∣∣, necessitating that di ∈ [0, s/2]. Due to the uniform distribution of people
across this territory, the average tribesman will initially be located at a distance of s/4 fromthe center. That di is exactly specified is not meant to suggest that people are sedentary.Their “location” on the line should rather be thought of as the “average location” in theirmovements as nomadic hunter-gatherers.
The population of each valley j ∈ {1, J } form clans that live separated from each other.The valleys have the same land quality and a uniform population density of p̄ > 0. Valleyj has a geographical extension s j such that
∑Jj=1 s j = s, and the size of the clan popu-
lation within each valley is N j = s j p̄ < P . The whole tribe meets once a year in orderto hunt mammoth, but for the sake of simplicity, permanent migration to other valleys isassumed away.11 As an illustration, consider an individual i in valley 1 who is located atsome spot z < s/2, i.e. to the west (left) of the central valley c = 3, as shown in Fig. 2a. Thegeographical distance to the center is here di = s/2 − z and J = 5.
Apart from di , the annual mammoth hunt involves an individual fixed cost fi ≥ 0 if thechief is located outside individual i’s own valley. fi reflects general geographical frictions.
9 This might also be thought of as an indicator of the level of how socially advanced the society is. A verylow g would suggest a primitive society without strong needs for collective action.10 We recognize that the nature of the collective goods provided by the chief probably varies with the sizeof the community. Coordinated hunts of large game are probably most relevant for rather small communitieswith maybe a thousand individuals. For larger communities, a more realistic example of a collective goodsupplied by a chief might be the provision of a common meeting or market place for exchanging informationor essential goods. One might even consider a basket of collective goods with varying importance. Since wewant to keep the model simple and intuitive, we refrain from introducing such additional complications.11 A more relaxed assumption regarding migration would potentially have obscured our main point, andcomplicated the model in an unnecessary way.
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0 s/2z s1+s2 ss1 s1+s2+sc s-s5
Valley 1 Valley 2 Central valley 3=c Valley 4 Valley 5=J
gi (a)
(b)
i
Fig. 2 a Example of effective levels of the collective good for a tribe populating five valleys (J = 5). bSimulated time pattern for ethnic fissions in three valleys. Note: The example in Figure 2b simulates Eq. 7and assumes J = 3 and a unanimous decision among individuals within each clan to form a new tribe. Thelocation of the old chief remains unchanged when a fission has occurred. The valley sizes are s1 = 20, s2 = 40(center valley), and s3 = 40, and valley populations are N1 = 1, 000, N2 = 2, 000, N3 = 2, 000. Hence, thedecisive individuals are located at z1 = 20 and z3 = 60 and their distances to original and potential new chiefsare d1 = s/2 − s1 = 30, dnew
λ = 500 and α = 1/2. The implied solutions to setting (7) equal to zero are t∗1 = 38 and t∗2 = 146
For instance, valleys that are separated by high mountains or jungles have a high fi . If thecollective good is provided in i’s home valley, we set fi = 0. If not, then fi = f j > 0.
There is a further disadvantage to the peripheral tribespeople in the mammoth hunt sincethey also experience a cultural distance to the chief. Let us think of mi (t) as reflecting dialec-tal differences. This distance is an increasing function of time due to cultural drift, which inturn is a result of the fact that the clans practice assortative mating in the form of homogamy,i.e. they only breed with people in their own valley.12 Given the isolation of the valleys,the clan populations will be subject to drift that moves them socially apart, despite initiallysharing a common cultural community. During the hunt, the people from peripheral valleys
12 This simplifying assumption only affects the speed of the process. The qualitative conclusions of the modelstand as long as homogamy is the dominant form of mating in the clan.
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simply do not understand some of the information or directions given by the chief. As aresult, their hunting efficiency is lower and they kill fewer or smaller mammoth, while theystill have to contribute to the chief with the same amount of meat k/P as everyone else.
What are the dynamics of the cultural distance function mi (t)? As mentioned above, weview cultural and genetic drift as highly related processes. The basic dynamics of geneticdrift have been successfully characterized by genetic research. A general and commonly usedformula for genetic distance between population j and the mother population is
Fst = t
N j. (4)
where Fst ∈ (0, 1) is the genetic distance between the two populations and t is time (ingenerations) since the two populations became separated (Cavalli-Sforza et al. 1994).13 Fst
is thus a positive function of time. The expression shows that genetic drift decreases with thesize of the clan population N j .
Based on (4), we propose that the dynamic equation for cultural drift for any individual iat location zi is
mi (t) ={
1 + λFst = 1 + λtN j
if zi /∈ sc
1 otherwise.(5)
where λ > 0 is a parameter describing how genetic drift translates into cultural drift. Ingeneral, it will be the case that λ is larger than unity so that cultural drift is a faster processthan genetic drift.
Equation 5 gives that individual i will experience cultural drift in relation to the centralvalley if she lives outside the central valley, the latter being the equivalent to the subset sc
on the real line. The level of drift outside the central valley will be the same regardless ofthe number of other valleys. If i lives in the central valley, she will forever speak the samedialect as the chief and mi (t) = 1.
The structure of (5) implies that the effective level of collective goods will be a negative,convex, and discontinuous function of geographical distance from the center, as illustrated inFig. 2a. To the left and right of the central valley’s boundaries, people speak different dialectsand have a fixed cost of travelling to the central valley. gi therefore makes a discontinuousjump downward and the curve gets a less pronounced slope. The height of the jump increaseswith time. Cultural (linguistic) drift thus gradually causes a deterioration in the effectivesupply of collective goods for people in the periphery up to the point where they no longercan make sense of what their chief is telling them. In essence, this is what makes a fissionevent more and more likely.
3.3 The hunter-gatherer equilibrium
After having specified all these functional forms, we can reformulate the utility function as
Ui = α ln
(g
mi (t) + di + fieγ
i
)
− α ln
(k
P
)
+ (1 − α) ln (1 − ei ) .
We are now ready to study optimal individual behavior. The model has two stages. Theindividual’s first choice is whether to remain within the ethnic group where she currently
13 The exact formula is Fst = 1 − exp(
− t2ηN j
)
where η > 0 is the share of the population in reproductive
age. In (4), we implicitly assume that half the population is in reproductive age; η = 1/2. It can further beshown that at relatively small values of t , the exact expression above will be linear with respect to time, as in(4) (Cavalli-Sforza et al. 1994).
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belongs or to form a new group together with her nearest neighbors. In the second stage,the individual decides on optimal levels of effort, leisure, and production within the chosengroup. The model is solved through backward induction.
By taking the usual first-order conditions for maximum, one can solve for the optimallevel of effort, e∗, in the second stage: ∂Ui
∂ei= αγ
e∗i
− (1−α)1−e∗
i= 0. After some manipulations,
the equilibrium levels of effort and leisure are e∗i = αγ
1−α+αγand l∗i = 1−αγ
1−α+αγ. Note that
the optimal level of effort will be independent of the spatial frictions. The implied indirectlevel of utility is thus
Vi = ln
(
(αγ )αγ (1 − αγ )(1−α)
(1 − α + αγ )(1−α+αγ )
)
+ α ln
(g
mi (t) + di + fi
)
− α ln
(k
P
)
. (6)
The more complicated, and crucial, decision is made in the first stage. Taking the sec-ond-stage behavior into account, each individual considers whether she should remain in thetribe or leave it and form a new one. Let us refer to the break-up of one existing tribe intomore than one tribe as an ethnic fission. The possibility of this kind of decision means thatthe core group is unable to prevent kinsmen from breaking away, i.e. fission decisions canbe made by peripheral groups even though such fissions cause a greater per capita cost ofcollective goods for the kinsmen who stay in the old group. We argue that this regime is themost reasonable for primitive hunter-gatherer societies while it is not well applied to the lateragricultural or industrial eras.14
It is intuitively clear that an individual in this model will be more inclined to form a newethnic group the greater her distance to the chief and the greater the accumulated level of cul-tural drift in her valley. Obviously, the people in the central valley, who are close to the originof the collective good and to each other in terms of space and kinship, will never attempt toform a new group since they pay a smaller contribution per head in the situation with onetribe than with two or more tribes. Thus, it will be people in the geographical periphery whowill be the founders of new tribes.
As the collective good here refers to an annual mammoth hunt, people in the peripheralvalley have to weigh the benefits of a more efficient communication and coordination of thehunt when having a tribe of their own and a chief who speak their own dialect, against thegreater individual sacrifices in terms of a larger quantity of meat per person k/P that thiswill involve.
Formally, the decision hinges upon the relative indirect utilities from the two choicesfor the people in the most peripheral valleys. The general mathematical condition for anindividual at any location zi in valley j to be willing to form a new tribe is
V newi − Vi = α ln
(mi (t) + di + f j
1 + dnewi
)
︸ ︷︷ ︸
collective good supply-effect (+)
−α ln
(P
N j
)
︸ ︷︷ ︸
cost effect (-)
≥ 0, (7)
14 After reviewing an example of how village fissioning in the Amazonas typically happened when unitsreached the size of about 600 people, Sahlins (1972, p. 98) describes this tendency as: “…primitive society isfounded on an economic disconformity, a segmentary fragility that lends itself to and reverberates particularlocal causes of dispute, and in the absence of ‘mechanisms for holding a growing community together’ realizesand resolves the crisis by fission.”
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where V newi is the indirect utility for i after having joined the new tribe and Vi is the utility
from status quo.15
The expression in (7) can be broken up into two parts. The second part—the cost effect—isnegative and reflects the increase in tribute paid for collective goods if i joins the new tribe.The intuition is that there will then be a smaller population to share the fixed cost k. Thelarger is N j , the smaller is the additional cost. This negative effect of a fission is potentiallydominated by the positive first part—the collective good supply-effect—which reflects thegains from a shorter effective geographical and cultural distance to the chief of the new tribe.These net gains with a new group will grow with time due to internal cultural drift since mi (t)grows over time. The collective good supply-effect is also stronger the shorter is the distanceto the new chief dnew
i as compared to the old one di . In line with intuition, V newi − Vi further
increases with geographical frictions between the central valley and valley j, f j .The expression in (7) can form the basis for several different scenarios concerning the
collective decisions to break away and form new groups. In general, if we assume that someindividual i in valley j is the decisive individual, the clan in valley j will form a new tribewhen V new
i − Vi ≥ 0. Hence, by inserting (5) into (7) and setting V newi − Vi = 0, we can
solve for the critical time t∗ when the ethnic fission will occur:
t∗ = P(
1 + dnewi
) − N(
1 + di + f j)
λ(8)
= p̄
λ
[
s(
1 + dnewi
) − s j(
1 + di + f j)]
Equation 8 provides the central result on the model. t∗ can be thought of as showing thespeed of ethnic fractionalization where a small t∗ means a fast rate. To paraphrase the “time-for-speciation”-concept discussed in ecology, t∗ captures “time-for-fractionalization”. Therate of ethnic fractionalization will decrease with population density p̄. For a given size of theterritory, a higher p̄ means that cultural and genetic drift are slower and it will therefore takea longer time for a fission to happen. Similarly, a strong link between cultural and geneticdrift (a high λ) will result in a faster rate of fractionalization. All else equal, t∗ will furtherdecrease with s j , the relative size of valley j .16 Not surprisingly, we also find that ethnicfissions will happen at a fast rate (implying a low t∗) if the fixed cost f j of travelling to thecentral valley is large. In this sense, geographical or other spatial frictions between valleyswill cause a faster rate of ethnic fractionalization.
The expression above provides a very clear hypothesis regarding the impact of time onthe level of ethnic fractionalization. In Fig. 2b, we simulate Eq. 7 for a hypothetical island,Primus, settled in year 0, with a total population of 5,000 individuals and a total size ofs = 100. There are three valleys with valley sizes s1 = 20, s2 = 40 (center valley), ands3 = 40, and valley populations N1 = 1, 000, N2 = 2, 000, and N3 = 2, 000. For people inthe peripheral valleys 1 and 3, there is a fixed cost f j = 5 of travelling to the central valley.17
The decision rule that we apply is that all individuals in a peripheral valley must unanimouslyagree to form a new tribe for this to happen. The individuals at s1 and s − s3 will be those
15 In the expression above, it should be remembered that V newi is independent of the parameters in m since
m (0) = 1. Furthermore, fi = 0. g is identical in the two scenarios and therefore cancels out in the utilitycomparison.16 However, both s and s j are likely to affect di and dnew
i for the decisive individual, which makes the neteffect unclear.17 We further assume α = 1/2, λ = 500 and that the chief’s location in the central valley remain unchangedafter the first fission. See the notes to Fig. 2b for more information. The main analytical results in (7) and (8)are not sensitive to the parameter values chosen for the simulations.
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that are worst off in the case of a fission and are therefore the decisive individuals in thisexample.
Using these numbers, we obtain the result that valley 1 will form a new tribe after 38generations (or 950 years, if each generation corresponds to 25 years) whereas it will takevalley 3 as much as 146 generations (3,650 years) to reach the same decision. The intuitionwhy is clear; in the smaller valley 1, the worst off individual is further away from wherecollective goods are provided in the central valley than is the decisive individual in the largervalley 3. Hence, valley 1 forms a new tribe first, as shown in Fig. 2b.
Let us now think of another island, Secundus, that is identical to our first island, Primus,except that it is settled at a later date. If Secundus was populated 50 generations after Primus,then an external observer would find two tribes on Primus at t = 50 and only the original oneon Secundus. 100 generations later—i.e. at t = 150—there would be three tribes on Primusand two tribes on Secundus. This extremely simplified setting provides, in a condensed form,the basic intuition behind the conjecture that the duration of human settlements should bepositively related to levels of ethnic diversity.18
Does this example imply that “ethnic history” ends when all three valley populations havesplit up and formed new groups? In the very simplified setting of our model above, the answeris yes. However, a very intuitive extension would be to include more layers of geographicaldisaggregation than just islands and valleys. One could easily imagine that the fractionaliza-tion process continues in the same way within valleys if there exists some basis for furthergeographical disaggregation into smaller and smaller neighbourhoods. If the possible levelof collective goods g is very small, perhaps due to a very scattered geography which makessocial interaction across space extremely difficult, a process of fractionalization might con-tinue until every band has formed their own cultural unit with a unique language. Such ascenario appears to be consistent with the extreme ethnic diversity that we find among con-temporary hunting-gathering groups in the jungles of Papua New Guinea (Diamond 1997). Ifwe had also allowed population growth in our model, even small bands might eventually splitup when they become too large. Hence, we would not in general propose that there shouldexist a natural end period when all ethnic fissions cease.
In sum, the main predictions from the model are that the duration of human settlements,as well as both spatial and geographical frictions, should have a positive effect on ethnicdiversity, whereas population density should have a negative impact.
4 Data
4.1 Original human settlement
In our empirical analysis we use an indicator for the historical duration of human settle-ments, Origtime. We have sought to establish the date of the first uninterrupted settlement byanatomically modern humans (AMH) for a sample of 191 countries. Oppenheimer (2003)and Bradshaw Foundation (2007) are our main sources for this data, complemented withEncyclopedia Britannica (2007) for islands.19 Oppenheimer (2003) provides a synthesis of
18 The stylized framework here developed can be extended in several ways. Natural extensions would beto explicitly model population growth or various mechanisms for how states can influence levels of ethnicdiversity. Indeed, in a previous version of this article we modeled how states could make investments aimed atreducing cultural distances between groups in order to enhance communication and the diffusion of collectivegoods. Other extensions one might consider are to include more evolutionary forces, such as natural selection.19 Bradshaw Foundation (2007) builds largely on Oppenheimer (2003).
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genetic, archeological, climatological, and fossil evidence for constructing the likely paths ofhow AMH settled the world. It should be recognized from the start that the data has severalsources of potential measurement error. The most definitive evidence of human presence ina country, i.e., fossils of accurately dated human skeletons or artefacts, are only rarely avail-able for individual countries. What researchers need to rely on instead is deductive reasoningbased mainly on genetic evidence.
Genetic research on human origins has developed rapidly since the initiation of the HumanGenome Project in 1989. Every cell nucleus of the human body contains DNA that childreninherit from their parents. This genetic material hosts up to 100,000 genetic sites, or “loci,”which can be mapped by geneticists. Only very few of these loci provide any useful infor-mation on human origins since the rate of genetic recombination is often too high fromgeneration to generation. The most commonly used genetic marker is mitochondrial DNA(mtDNA), which is only inherited down the female line. This genetic marker is very rarelysubject to mutation, and the rate of mutation is random but with an estimated expected value.Thus, by observing two persons’ mtDNA, one can make a rough estimate of how far backthese persons had a common ancestor down the female line. By also taking into accounttheir current geographical residence, researchers are able to construct phylogeographic trees,mapping the likely paths of migration of AMH from their East African origins, as well asthe approximate dates of these migrations.
There is still not full consensus among researchers regarding the contours of the peo-pling of the world. Like most other researchers from Stringer and Andrews (1988) onward,Oppenheimer (2003) sides with the “Recent African Origin”-hypothesis proposing that allmodern human beings in the world today are the descendants of a small population thatmigrated from Africa and then over several millennia settled the whole world. The com-peting hypothesis—the “Multiregional”-hypothesis, suggesting that modern man originatedindependently in several regions from existing branches of the homo-family—is nowadaysbelieved to be false by most scholars (Tishkoff and Verelli 2003).
A more controversial assumption made by Oppenheimer (2003) and Bradshaw Foundation(2007) is that the first migrants out of Africa did not move through the Levant into the NearEast and Europe, but rather through a southern “beachcombing” route. This route first crossedthe Red Sea at the Gate of Grief between Eritrea and Yemen about 85,000 BP during an iceage with low sea levels. The descendants of this first group outside Africa then followedthe beaches of the Indian Ocean toward India, South East Asia, and Australia in a relativelyshort time. The previous standard hypothesis—still endorsed by many researchers—is thatAMH walked out of Africa through the Levant during an earlier warm interglacial period.Recent genetic evidence (Macaulay et al. 2005), as well as very early archeological findingsof AMH in Australia, appear to support a beachcombing route.20
Let us present the broad outlines of the peopling of the world as it is represented in Orig-time. The journey started 160,000 BP in the Rift Valley area of Ethiopia and Kenya. The restof continental sub-Saharan Africa was populated around 135,000 BP. From Eritrea, modernhumans crossed the Red Sea to Arabia, and had spread to most of South Asia including Chinaby 75,000 BP. By 74,000 BP, a gigantic volcanic eruption at Toba in Sumatra left the Indianand South East Asian peninsulas in desolation and presumably extinguished a large part ofall humans alive outside Africa. South East Asia was not repopulated until 65,000 BP andIndia not until 52,000 BP. Meanwhile, AMH presumably settled Australia already 65,000BP.
20 See Oppenheimer (2003) for an exhaustive discussion of this issue. A recent attempt to provide a timetablefor the peopling of the world based on the northern route is Liu et al. (2006).
Notes The main sample is the sample used in Column 4 in Table 3. Origtime is scaled to 100,000 years. Seethe Appendix and the notes to Tables 3 and 4 for variable descriptions
From South Asian and Near Eastern origins, Eastern and Southern Europe were set-tled around 45,000 BP, followed by North Africa and Central Asia. By 22,000 BP, modernhumans crossed the Bering Strait into North America. Only about 10,000 years later, bothof the American continents were settled. Following the retreat of the ice caps after the lastice age, northern continental Europe and Scandinavia were populated around 8,000 BP. Theislands in the Caribbean and in the Pacific were gradually reached in the last millennia. Themost recently settled country in our sample is the Seychelles, which remained uninhabiteduntil French colonists settled it in 1756. Table 6 in the Data Appendix contains the estimatedOrigtime for the 191 countries in our sample.
The early migration patterns outside Africa thus largely appears to have followed a longi-tudinal, rather than a latitudinal, direction. This is reflected in the strong negative correlationbetween Origtime and absolute latitude, −0.55, that is presented in Table 2.
An indicator with which Origtime share some underlying logic is Migdist, which approx-imates the geodesic migratory distance from the location of human origins in Ethiopia toall countries in the world. Migratory distance from Ethiopia has been shown by several sci-entific works to be negatively related to the degree of genetic diversity (heterozygosity) inthe populations (Ramachandran et al. 2005; Liu et al. 2006). Ashraf and Galor (2010) is thefirst effort in economics that employs this methodology for assessing the predicted geneticdiversity within countries. Countries far from Ethiopia were on average settled relativelylate, and a correlation between Migdist and Origtime of −0.53, suggests that Migdist is anexplanatory factor for the variation in Origtime.21
In order to reconstruct the distances of likely migration paths out of Africa, we followRamachandran et al. (2005) in assuming a number of “stepping stones,” or waypoint loca-tions, from which humans settled the world. For European countries, for instance, the pathof migration is assumed to be from central Ethiopia to Cairo, on to Istanbul, and then on tothe centroid of each country.22 A noteworthy feature of Migdist is, in other words, that it
21 In an earlier version of this article we also included an indicator of the approximate genetic distance betweensix broadly defined main populations in the world. Results obtained from using this variable were fully in linewith the results obtained when Origtime or Migdist are used.22 The other waypoints used, apart from Cairo and Istanbul, were Anadyr in Northeastern Russia, PrinceRupert in Northern Canada (both of these relevant for American countries), and Pnomh Penh in Cambodia(used for calculating distances to island countries in Southeast Asia and in the Pacific).
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assumes a northern exit route out of Africa through the Levant. The distances in kilometers,calculated using the Great Circle Formula, range from 779 km for Kenya to 5539 km forSwitzerland to 26,836 km for the most distant country Uruguay.23
4.2 Ethnolinguistic diversity
So far we have discussed ethnolinguistic diversity in general terms and avoided being specificon exactly how one should measure diversity. Reducing the multiplicity of ethnolinguisticdiversity to a one-dimensional measure necessarily means missing some of the politicalnuances, but since the focus in this analysis is not on the effects of diversity but on itssources, this issue is of minor importance. In the years following Easterly and Levine (1997)researchers generally used ethnolinguistic fractionalization (ELF), constructed using datacollected by Soviet ethnographers in the 1960s. “Fractionalization” refers to the probabilitythat two randomly selected individuals from a population come from different groups, andthe larger the number of groups above the threshold size chosen for inclusion, the higher thefractionalization. More recent indices of ethnic diversity include the fractionalization-indicescreated by Fearon (2003) and Alesina et al. (2003).
In the analysis below we use Alesina et al.’s measure based on linguistic diversity (ALF).We believe that this measure comes closest to capturing the kind of language-based culturaldrift that we envisaged in the model. Compared to for instance Alesina et al.’s (2003) indexof ethnic fractionalization, which exploits both the racial and linguistic dimensions, ALF isconceptually more closely related to the measures of ethnolinguistic fractionalization used byEasterly and Levine (1997) and Michalopoulos (2011).24 A full list of the variables includedin this section as well as sources and detailed descriptions are presented in the Data Appendix.
where Fractionali zationi is our measure of ethnolinguistic fractionalization in countryi, Origtimei is the duration of settlements, and Xi is a set of geographical and historicalcontrol variables including indicators of geographical diversity and continental dummies, aswell as state history and variables related to colonialism, etc. εi is an error term. We estimateour models with OLS. Our main hypothesis is that α1 > 0.
Ideally, we would have also liked to include a comprehensive analysis of potential deter-minants drawn from the constructivist literature, and the effect of state history in particular.A concern when including a measure of state history is that there could be a reverse causationbetween Fractionali zationi and our measure of state history in the sense that areas withhomogeneous populations might have been more likely to host successful state formations.Although we think the relationship between the two variables is very interesting, the prob-
23 If we assume an initial settlement of Ethiopia 160,000 years ago and a settlement of Uruguay 12,000 yearago as in Oppenheimer (2003), the implied speed of conquering the (mainland) world would be approximately200 m per year.24 ELF has a 0.76 correlation with Alesina et al.’s (2003) ethnic fractionalization and a 0.88 correlation withAlesina et al.’s linguistic fractionalization.
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Table 2 Pair-wise correlations in the main sample
Variables 1 2 3 4 5 6 7 8 9
1 Fractionalization 1.000
2 Origtime 0.656 1.000
3 Latitude −0.411 −0.548 1.000
4 Agritime −0.225 −0.324 0.332 1.000
5 Dist. to Coast or River 0.392 0.416 −0.147 −0.075 1.000
6 Average Temperature 0.311 0.529 −0.692 −0.301 −0.006 1.000
7 Area 0.130 0.126 −0.103 −0.145 0.624 0.024 1.000
Notes The main sample is the sample used in Column 4 in Table 3. See the Appendix and the notes to Tables 3and 4 for variable descriptions
lem of identification has compelled us to only include factors related to state history in a fewspecifications.
5.2 Results
In line with the predictions from our model, the correlations in Table 2 show that ethnic frac-tionalization is higher in countries with a longer duration of human settlement (Origtime).Though the correlations in Table 2 are illuminating, we move directly to results from multi-variate regressions. Column 1 of Table 3 shows that Origtime alone can explain 36% of theobserved variation in ethnolinguistic diversity. The size of the coefficient implies that 10,000years earlier human settlement is associated with a 3.4 percentage point higher probabilitythat two randomly selected individuals in a population come from different ethnolinguisticgroups. A scatter plot for the partial relationship between Fractionalization and Origtime,based on a specification with several geographical controls in Column 3, is shown in Fig. 3.
We add our control variables step-wise in Columns 2 to 4. Recall the discussion in Sect. 4where we outlined some of the potential determinants of the duration of human settlement,Origtime. Two determinants stood out; migratory distance and distance from the equator.The first waves of human settlements were largely directed by geographical factors such asclimate and vegetation, and areas closer to the equator were generally populated first, soLatitude should have a negative effect on ethnic diversity. In our benchmark specification inColumn 4, we confirm that the general ecological pattern of a higher species richness closer tothe equator is found also for human ethnolinguistic diversity. The effect of migratory distanceis accounted for in Table 4.
The introduction of sedentary agriculture had dramatic effects on population density andsocial stratification, and foreshadowed the rise of great civilizations. The effects that thesedevelopments had on the fragmentation of the population is captured by the inclusion of theactual timing of the Neolithic transition, Agritime, as an independent variable. With the fullset of continent dummies included, this variable is significant at the 10% level.25 The negativecoefficient is consistent with an interpretation that longer time with denser populations, with
25 The continent dummies are Africa, Asia, Europe, North America, and South America. Pacific is the excludedcategory.
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Table 3 Main results
Dependent variable is Fractionalization
(1) (2) (3) (4)
Origtime 0.344*** 0.343*** 0.326*** 0.266***
(0.034) (0.043) (0.056) (0.095)
Latitude −0.019 −0.039* −0.054**
(0.019) (0.023) (0.024)
Agritime −0.002 −0.016 −0.096*
(0.031) (0.032) (0.053)
Dist. to Coast or River 0.100* 0.111*
(0.053) (0.062)
Average Temperature −0.002 0.002
(0.003) (0.004)
Area −0.016 −0.012
(0.019) (0.020)
Fraction Coastal 0.189 0.172
(0.167) (0.174)
Continent dummies No No No Yes
Observations 176 152 139 139
R2 0.36 0.38 0.47 0.49
Notes Fractionalization is Linguistic Fractionalization from Alesina et al.’s (2003). Origtime is the durationof uninterrupted human settlement, scaled to 100,000 years. Latitude is the log of absolute latitude of countrycentroid. Agritime is the log of the number of years since the neolithic transition. Dist. to Coast or River isthe log of the mean distance to nearest coast or navigable river. Average Temperature is mean of all grid celltemperatures. Area is the log of country area. Fraction Coastal is the percentage of area within 100 km of thecoast or a navigable river. The continent dummies are Africa, North America, South America, Europe, andAsia. (Pacific is the excluded continent.) Estimated with OLS. Unreported constants included. Robust standarderrors in parentheses*** p < 0.01; ** p < 0.05; * p < 0.1
supra-tribal authority, and with greater opportunity for social stratification, may have actedto reduce ethnolinguistic diversity.
There are two effects associated with a larger part of a country’s area being close to a coastor a river, as discussed in Sect. 2.3. First, the beachcombing hypothesis generally implies anearlier date of settlement in these countries and therefore a positive effect on ethnic diversity.Second, our conjecture that populations in these areas should be less isolated suggests anegative effect on diversity. We predict that the latter effect is likely to have dominated.26
When we include a measure of the mean distance to a coast or a river, we find that countrieswith a longer mean distance do have a higher level of ethnic fractionalization. However, thepercentage of land within 100 km of the coast or sea-navigable river, Fraction Coastal , is notsignificant. Both a shorter Dist. to Coast or River and a higher Fraction Coastal are shown byAshraf and Galor (2011) to be associated with higher historical levels of population density.Since higher levels of population density could have had a direct homogenizing effect weinvestigate the confounding effect of historical levels of population density further in Table 5.
26 If Origtime was an exact indicator of the duration of human settlements, the second effect would indeeddominate, as the first effect would be fully captured by Origtime. We owe this point to an anonymous referee.
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ECU COL
BOL
NICMDGDOM
JAM
HND
GTM
VENCRIBRA
PER
PRY
UZB
DZA
TTO
PAN
TKM
URY
MEX
TJKKGZ
LBY
NZL
PHL
ARG
AFG
LVA
SAU
KAZ
PAK
JORAZENOR
EST
AUSSYR
LKA
IRQ
KHM
EGY
MAR
NPL
LAO
TUN
THA
LTU
ISR
BEL
ARM
MNG
BDI
CHLFIN
CAN
IRL
MYSMMR
IND
UGA
PNGUSA
PRT
SVK
LBN
NLD
KWT
RUS
SWEARE
POL
MKD
IDN
COG
CZE
BLR
GEO
ALB
CAF
DNKOMNBFA
CHE
ESP
ROK
ZAR
GBR
GAB
TCD
MDA
BGD
BIH
MLIBEN
VNMMWI
UKRTGO
ZMB
IRN
NER
BWA
YEM
BGR
ROMSVNAUT
GRC
CMR
JPN
NGA
TUR
KEN
HUN
GHA
HRVZWE
SDN
TZA
ITA
AGOCIVGIN
MRT
SLE
FRA
CHN
NAM
LBR
SEN
LSO
MOZ
DEU
GNB
SOM
GMB
ZAF
ETH
-.5
0.5
Fra
ctio
naliz
atio
n
-1 -.5 0 .5 1
Origtime
coef = .32553296, (robust) se = .05619087, t = 5.79
Fig. 3 Partial relationship between ethnolinguistic diversity (fractionalization) and duration of original humansettlements (Origtime) for 139 countries. Note The figure shows the added variable plot for Origtime in Col-umn 3, Table 3. Each observation is represented by its three-letter identification code, following the WorldBank classification
In our set of baseline controls we also include both area and mean temperature. The num-ber of ethnic groups should be larger in countries that span over greater territories (s in ourmodel). Internal cohesion and provision of collective goods suffer with increased internaldistances. The level of intragroup interaction will naturally be lower in groups that are spreadout over greater physical distances, facilitating the formation of new groups. The results showthat neither Area nor Average Temperature affect ethnolinguistic diversity.27
Our theoretical model shows how geographical factors influence the level of ethnic diver-sity. Since there is no direct empirical equivalent of geographical frictions, we proxy forit with within-country variation in altitude, Altitude Variation. The first column in Table 4
27 We have conducted a range of robustness checks on the main results in the benchmark model. The latesettlement and homogenous populations of the Nordic countries and a number of relatively small islands couldraise concerns that the results regarding Origtime may be unduly driven by a small number of observations.To check if this was the case, all countries settled within the last 10,000 years were omitted from the sampleand the benchmark model was reestimated. The sample was reduced to 122 countries, but Origtime remainedsignificant at 5% (Coeff. = 0.268, SE = 0.130). Furthermore, to ensure that the results were not driven by theinclusion of excessively influential or unusual observations we used DFITS and Cook’s Distance to identifypotential outliers. Omitting these leaves Origtime significant at 1 the percent level. Finally, OLS is designed toestimate the mean of the dependent variable, while Quantile Regression, or more correctly Median Regression,is designed to estimate the median. Robust Regressions is another method designed to ensure that the results arenot driven by outliers. We reestimated the benchmark model with these methods and could confirm Origtimeis robustly significant. We conduct several more tests in the working paper version of this paper (availableonline or on request). For instance, we test for, and can reject, spatial autocorrelation using an approach similarto that in Ashraf and Galor (2010). We also run several regressions with other indicators of ethnic diversity asdependent variables which does not change the main tendencies.
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Table 4 Alternative channels and additional controls
Dist. to Coast or River 0.111* 0.132** 0.121* 0.119* 0.113* 0.118*
(0.062) (0.058) (0.063) (0.062) (0.067) (0.069)
All baseline controls Yes Yes Yes Yes Yes Yes
Additional controls No Yesa No No Yesb Yesa,b,c
Continent dummies Yes Yes Yes Yes Yes Yes
Observations 139 139 139 136 133 135
R2 0.49 0.51 0.49 0.49 0.50 0.52
Notes Fractionalization is Linguistic Fractionalization from Alesina et al.’s (2003). Origtime is the durationof uninterrupted human settlement, scaled to 100,000 years. Altitude Variation is the absolute deviation frommean altitude. MigDist is migratory distance from Rift Valley, scaled to 1 million km. Land Quality Variation isthe standard deviation in mean land quality within countries. Population Density in 1 CE/1500 CE is estimatedlog population density in year 1 CE/year 1500 CE. Latitude is the log of absolute latitude of country centroid.Agritime is the log of the number of years since the neolithic transition. Dist. to Coast or River is the log of themean distance to nearest coast or navigable river. All Baseline Controls means that included are also AverageTemperature, Area, and Fraction Coastal. Average temperature is mean of all grid cell temperatures. Area isthe log of country area. Fraction Coastal is the percentage of area within 100 km of the coast or a navigableriver. The Additional Controls are: aAverage of grid cell altitudes, weighted by grid cell area; bthe log ofpercentage of arable land in 2000 CE; c the percentage of area in the temperate zone, the percentage of areain the subtropic zone, and a dummy for landlockedness. Estimated with OLS. Unreported constants included.Robust standard errors in parentheses*** p < 0.01; ** p < 0.05; * p < 0.1
repeats the benchmark specification to facilitate comparisons within Table 4. Consistent withour model, Altitude Variation has a clear positive effect on ethnolinguistic diversity whenincluded in Column 2 in Table 1, and the effect on the estimate for Origtime is not substantial.
Ashraf and Galor (2010) show that migratory distance (MigDist) is related to historicallevels of population density and current levels of income. If Origtime was only determinedby migratory distance and distance from the equator, one should expect Origtime to become
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insignificant when both these factors are held constant. The results in Columns 3 show thisnot to be the case. Our conclusion is that when it comes to ethnolinguistic fractionalization,the actual duration of uninterrupted human settlements affect diversity beyond the effectof both distance from the equator and migratory distance, even if the latter two are goodcandidates for explaining parts of the variation in this duration.
Michalopoulos (2011) shows that ethnolinguistic diversity is higher in areas with greatervariation in land quality and in elevation. We can confirm this finding when we add hisvariable Land Quality Variation in Column 4. That fact that little happens to the estimatefor Origtime shows that the mechanisms outlined in this paper and in Michalopoulos (2011)complement each other.28
Our baseline control variables include several of the factors that are shown to affect his-torical levels of population density in Ashraf and Galor (2011). The authors also include avariable that combines an indicator of land quality and the percentage of arable land. We haveaccess to arable land in 2000 CE and include this in Column 5. Combined, these variablesmay now be interpreted as affecting diversity via historical levels of population density. Wealso include Population density in year 1 CE and in year 1500 CE to directly account to thismechanism. The fact that both of the population density measures as well as the percentageof arable land are likely to be endogenous in this setting prevents us from making a full anal-ysis of the population density-link in this paper. What we can say is that Origtime remainssignificant both when we add determinants and actual assessments of historical populationdensities. This indicates that Origtime must have an effect on present levels of ethnolinguisticfractionalization that does not go via historical levels of population density.
As a final robustness test, in the last column of Table 4 we include more geography con-trols. Adding indicators for the fraction of the area that lie in the temperate zone, the fractionof the area that lie in the subtropical zone, and a dummy for being landlocked, does notthreaten the significance of the estimate for Origtime, and Altitude Variation remains signif-icantly estimated. MigDist remains nonsignificant, and the variation in land quality does notsurvive in this expanded specification.
The impact of colonialism was briefly touched upon in the overview section above. Amongthe 139 countries included in our benchmark model, the 80 countries that are coded as formerEuropean colonies in Olsson (2009) have an average ethnolinguistic fractionalization of 0.48while the average for the 59 others is 0.31. However, the former colonies outside sub-SaharanAfrica have an average of 0.29, which is slightly lower than the average of 0.38 for the coun-tries that were not colonized and are not European. The casual observation of an associationbetween colonial status and ethnolinguistic diversity is thus to a large extent driven by thedifference between European and sub-Saharan African countries. Regardless, any seriousinvestigation of the international variation in diversity must address the issue of colonialism.
As we see in Table 5, the binary indicator for being a former European colony (Former Col-ony) is not significant. There is no systematic difference in ethnolinguistic diversity betweenformer colonies and countries not colonized. In a sample of only former colonies, in Column2, the length of the colonial period (Duration) is positively associated with ethnic fraction-alization, perhaps indicating a legacy of colonial policies of divide-and-rule. Another aspectof colonialism is that it entailed global migration flows. A concern is that these more recentflows have distorted the original composition of populations to the extent that we might notbe able to identify an effect from prehistoric conditions. In results not shown, we confirmed
28 We note that our indicator Altitude Variation is very similar to one indicator used by Michalopoulos (2011),Variation in Elevation. Even if we have developed our indicators independently from each other, and basedon different theoretical models, we find that these two variables are very highly correlated. We take this as anindication to add a cautious note to our theoretical interpretation of the significance of Altitude Variation.
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Table 5 Constructivism and alternative indicators for ethnolinguistic diversity
(1) (2) (3) (4) (5)Sample Full Colonies Full Full Full
Dependent variable is
Fractionalization Cultural fractionalization Language groups
Notes Fractionalization is Linguistic Fractionalization from Alesina et al. (2003). Cultural Fractionalizationis ethnic fractionalization adjusted for the linguistic distance between groups. Language Groups is the (log) ofthe number of different language groups. Origtime is the duration of uninterrupted human settlement, scaledto 100,000 years. Former Colony is a dummy for (once) being colonized by Europeans. Colonial duration isthe number of years between year of colonization and year of independence. State Antiquity is territorial statecapacity between 1 CE and 1950 CE. All Baseline Controls: Latitude, Agritime, Dist. Coast or River, AverageTemperature, Area, Fraction Coastal. Latitude is the log of absolute latitude of country centroid. Agritime is thelog of the number of years since the neolithic transition. Dist. to Coast or River is the log of the mean distanceto nearest coast or navigable river. Average Temperature is mean of all grid cell temperatures. Area is the logof country area. Fraction Coastal is the percentage of area within 100 km of the coast or a navigable river.The continent dummies are Africa, North America, South America, Europe, and Asia. (Pacific is the excludedcontinent.) Estimated with OLS. Unreported constants included. Robust standard errors in parentheses*** p < 0.01; ** p < 0.05; * p < 0.1
that the previous findings were robust also when historical migration flows where taken intoaccount.29
To capture historical state capacity, i.e., the extent to which the state has effectively exer-cised control over its present territory, we include State Antiquity from Putterman (2007) inColumn 3. As hypothesized, a longer history of control of the present territory is associatedwith less ethnic diversity. A natural concern here is that areas with a more homogenous pop-ulation might have proven to provide more fertile grounds for the formation of sustainable
29 We limited the sample to countries where at least 75% of all the ancestors of the current inhabitants ofa country lived within the borders of that country in 1500 CE, with data from Putterman and Weil (2010).We are not aware of any general indicator of migration flows before 1500 CE, such as one that covers theIron Age Bantu expansion in sub-Saharan Africa. We reestimated the benchmark model on the remaining 95countries and estimate for Origtime changed, but not by much (Coeff. = 0.221, SE = 0.113). A placebo testof the validity of the mechanism we outline for Origtime was formed by estimating the main specificationon a sample where less than 75% of all the ancestors of the current inhabitants of a country lived within theborders of that country in 1500 CE. Origtime should not be significant in such a sample, and we found that itwas not (Coeff. = 0.169, SE = 0.324). We conjecture that this is additional indirect support for the validity ofour interpretation.
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states (Tilly 1992). We should therefore be careful about interpreting this relationship ascausal since there is potentially a reverse causality from ethnic diversity to state history.
A more strict interpretation of our theoretical model is that it determines the number ofgroups in a certain area. However, it is a well-known property of fractionalization measuressuch as ours, that they increase with the number of groups, and the determinants of ethno-linguistic fractionalization is more relevant for the wider literature. Also, indicators of thenumber of groups in a country is much more sensitive to the exact threshold size that one haschosen for which groups to include in the list all groups for each country.
In the last two columns in Table 5, we use alternative diversity indicators to assess whetherOrigtime affects also these related linguistically based diversity indicators. Origtime is signif-icant with the predicted sign both when we use Cultural Fractionalization , which essentiallyis and index of ethnic fractionalization adjusted for the linguistic distance between groups,and the (log) of the number of language groups in the country.
6 Concluding remarks
Ethnolinguistic diversity has caught the attention of many a social scientist struggling tounderstand problems such as low provision of public goods, low quality governance, persis-tent economic backwardness, and civil wars. The general approach in much of this researchhas been to treat diversity as an exogenous factor, and few have explicitly referred to theevolutionary or constructivist discourses on the origins of ethnic or linguistic groups.
In this article we have briefly portrayed this literature and synthesized it with findingsfrom ecology, anthropology, and genetics, showing how geographical and ecological factorsinfluence human ethnolinguistic diversity. We have constructed a measure for the historicalduration of human settlements in an area and a theoretical model explaining how such mea-sures should be related to diversity. The empirical analysis clearly indicates that diversityis higher in countries where humans settled earlier and where geographical conditions haveenabled and encouraged isolation.
Our results have important specific implications for how social scientists investigate theeffect of ethnolinguistic diversity on economic and political outcomes. For instance, an oftenemployed method for assessing the effect of diversity on economic and political performancehas been to include a measure of ethnic fractionalization as one of many potential regressors.Since a historically more potent state is associated with a lower degree of ethnic diversity,and since there is a positive correlation between indicators of this strength and many indica-tors of economic and political performance, the negative coefficient on ethnicity obtained inthese regressions could reflect an omitted variable bias—they may be mere statistical arte-facts created by the omission of long-term state strength from the regression. Indeed, whenBockstette et al. (2002) include ethnic diversity and State Antiquity jointly in their growthregressions, they obtain a coefficient for ethnic diversity that is not statistically significant.30
On a more general level, a reasonable projection of our results is that the world is goingto experience a continuing decrease in levels of ethnolinguistic diversity in the centuriesahead as relatively young and currently ethnically diverse states mature. We believe that theeconomic and political impact of this process is a promising area for future research and
30 In the empirical analysis of a companion paper, Ahlerup (2010) treats ethnic diversity as endogenous tolong-run development. Drawing on insights from the present paper, instrumental variables are used in orderto establish an effect of ethnic diversity on present income levels, on economic growth, and on public goodsprovision.
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that a serious understanding of human diversity will require a synthesis of evolutionary andconstructivist arguments, as proposed in this paper.
Acknowledgments We have received useful comments from Robert Bates, Samuel Bowles, Erwin Bulte,Carl-Johan Dalgaard, Jeff Frieden, Oded Galor, Andrea Mitrut, Karl-Ove Moene, Ken Shepsle, David Weil,Brian Wood, Ömer Özak, two anonymous referees, and seminar participants at Brown, Copenhagen,Gothenburg, Harvard, Lund, Stockholm, the European Economic Association meetings in Milan, the Long-Run Development Conference in Vancouver, and the Nordic Workshop in Development Economics. Parts ofthe paper were written during Ahlerup’s visit to University of California at Berkeley. A special thanks also toStelios Michalopoulos and Louis Putterman for generously sharing data with us. Ahlerup recognizes financialsupport from Osher Pro Suecia Foundation. Olsson gratefully acknowledges financial support from the SwedishInternational Development Cooperation Agency, the Swedish Research Council, and the Wallander-Hedeliusfoundation.
Data Appendix
Variable descriptions
Africa. Dummy for Africa. Source: Cepii.Agritime. Log number of years since the neolithic transition. Source: Putterman (2008).Altitude Variation. Absolute deviation from mean grid cell altitude in country. Source:
Based on the G-Econ Dataset (2006).Arable land. The fraction of a country’s total land area that was arable in 2000 CE. Included
in log form. Source: World Development Indicators.Area. The log of surface area in km2. Source: Cepii.Average Altitude. Average of grid cell altitudes. Source: Based on the G-Econ Dataset
(2006).Average Temperature. Average of grid cell temperatures. Source: Based on the G-Econ
Dataset (2006).Colonial Duration. Duration of colonization by Europeans. Source: Olsson (2009).Cultural Fractionalization. Ethnic fractionalization adjusted for the linguistic distance
between groups. Source: Fearon (2003).Dist. to Coast or River. Log of mean distance to nearest coastline or sea-navigable river.
Source: Center for International Development, Harvard University.Europe. Dummy for Europe. Source: Cepii.Former Colony. Dummy for being colonized by Europeans. Source : Olsson (2009).Fractionalization. Linguistic Fractionalization. Source: Alesina et al. (2003).Fraction Coastal. Percentage of land within 100 km of coastline or sea-navigable river.
Source: Gallup et al. (1999).Land Quality Variation. The standard deviation in mean land quality within countries, calcu-
lated from information on climatic and soil suitability for cultivation. Source: Michalopou-los (2011).
Landlocked. A dummy for being landlocked.Language Groups: The log of the number of different language groups. Source: Fearon
(2003).Latitude. Log absolute value of centroid latitude. Source: Center for International Develop-
ment, Harvard University.Migdist. Migratory distance from Ethiopia, scaled to 1 million km. Source: See description
in Sect. 4.
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North America. Dummy for North America. Source: Cepii.Origtime. Duration of human settlement. Source: See detailed description in Sect. 4.Popden 1 CE. The log of population density in year 1 CE. Source: Population size estimated
by Worldmapper (2006). Area from Cepii.Popden 1500 CE. The log of population density in year 1500 CE. Source: Population size
estimated by Worldmapper (2006). Area from Cepii.South America. Dummy for South America. Source: Cepii.State Antiquity. State power over territory year 1 to 1950 CE. Source: Putterman (2007).Subtropic zone area. The percentage of the country’s area that lie in the subtropic zone.
Source: Gallup et al. (1999).Temperate zone area. The percentage of the country’s area that lie in the temperate zone.
Source: Gallup et al. (1999).
Table 6 Origtime
Country Origtime Country Origtime Country Origtime
Afghanistan 40000 Cameroon 135000 France 45000
Albania 45000 Canada 22000 Gabon 135000
Algeria 40000 Cape Verde 500 Gambia 135000
Andorra 45000 Central Afr. Rep. 135000 Georgia 52000
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