The Ancient Origins of the Cross-CountryHeterogeneity in Risk Preferences∗

Anke Becker Thomas Dohmen Benjamin EnkeArmin Falk

August 7, 2014

AbstractUtilizing representative data on 80,000 individuals from 76 countries, this

paper studies the world-wide distribution of risk preferences. While a pop-ulation’s demographic composition as well as the health risks inherent inan environment predict country-level risk attitudes, a large fraction of thesubstantial between-country variation remains unexplained by traditional so-ciodemographic, economic, and geographic variables. In contrast, ancientmigration patterns, as proxied for by aggregate data on human genetic vari-ation, bear a strong association with the between-country heterogeneity inrisk preferences. Specifically, we establish that differences in risk attitudesbetween populations are strongly related to their genetic distance. This cor-relation is not only robust across a wide range of regression specifications, butalso holds when employing a measure of predicted genetic distance, which isexplicitly constructed to reflect information about the migratory movementsof our early ancestors. In addition, we provide evidence that the hetero-geneity in risk attitudes within a population is related to the diversity of therespective genetic pool. These findings suggest that the specific ways in whichmankind migrated out of Africa thousands of years ago has left a footprint inthe contemporary cross-country distribution of one of the key economic traits.

JEL classification: D01, D03Keywords : Persistence, risk preferences, genetic distance, genetic diversity

∗Financial support from the European Research Council through ERC Starting Grant# 209214 is gratefully acknowledged. We are grateful to Quamrul Ashraf, Klaus Desmet, NathanNunn, Uwe Sunde, Hans-Joachim Voth, and Romain Wacziarg for very helpful discussions. Valu-able comments were also received from seminar audiences at Bonn, Munich, the Annual Congressof the EEA 2013, the 2013 North-American ESA conference, and the 2014 NBER Economicsof Culture and Institutions Meeting. Becker, Dohmen, Enke, Falk: University of Bonn, De-partment of Economics, Adenauerallee 24-42, 53113 Bonn, Germany. anke.becker@uni-bonn.de,tdohmen@uni-bonn.de, benjamin.enke@uni-bonn.de, armin.falk@uni-bonn.de

1 Introduction

Preferences over risk are a key primitive in many important economic decisions. Ac-cordingly, in the quest for better understanding individual decision-making, empiri-cal economists have recently increased their efforts to measure the within-populationdistribution of risk preferences, and to evaluate the determinants and behavioralconsequences of certain profiles of risk attitudes. A recurring theme in much of thiswork is that attitudes towards risk are highly heterogeneous and predict risk-takingin many field contexts.1

However, despite potentially important implications for both individual decision-making and aggregate economic outcomes, we still lack solid information on howrisk preferences are distributed on a global scale. Is it true, for instance, that riskattitudes are heterogeneous within, yet “identically distributed” across countries?If not, i.e., if there is strong heterogeneity across populations, then in which senseis this heterogeneity systematic and what are its (historical) roots?

Taking these questions as point of departure, this paper uses a unique dataseton risk preferences from over 80’000 individuals in 76 countries to analyze the cross-country distribution of risk attitudes. As part of the Gallup World Poll 2012, wecollected two survey measures of risk preferences - one qualitative and one quantita-tive measure - which were previously experimentally validated and are known to bepredictive of financially incentivized risk taking behavior (Falk, Becker, Dohmen,Huffman and Sunde, 2014). In each country, we obtained these items from a rep-resentative population sample, allowing a detailed analysis of the socioeconomic,geographic, and climatic patterns underlying the distribution of this key economictrait. We show that there is substantial variation between countries and that thisheterogeneity is only weakly explained by variables which correlate with risk atti-tudes at the individual level. In particular, while within-country studies typicallyfind that higher income is strongly associated with lower levels of risk aversion,we do not find a significant relationship between a population’s average degree ofrisk aversion and national income. The average age and the proportion of femalesin a population, on the other hand, correlate with low levels of risk-taking in oursurvey instruments. In addition, we establish that the health risks inherent in anenvironment (as proxied for by life expectancy, deaths due to communicable dis-eases, and the rate of traffic deaths) confers a significant positive relationship withaverage levels of willingness to take risks. While these correlations are interesting,a large fraction of the cross-country variation in risk attitudes remains unexplained

1See e.g. Jaeger, Dohmen, Falk, Huffman, Sunde and Bonin (2010), Dohmen, Falk, Huffman,Sunde, Schupp and Wagner (2011), and Noussair, Trautmann and Van de Kuilen (2014).

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by these socioeconomic and geographic variables.The main contribution of this paper is to provide evidence that standard proxies

for the geographical patterns of our ancient ancestors’ migration paths constitutethe single best comprehensive set of predictors of the between-country variation inrisk attitudes. For this purpose, we employ aggregate data on human genetic vari-ation, which were introduced into the economics literature in the seminal contribu-tions of Spolaore and Wacziarg (2009) and Ashraf and Galor (2013b) and originatefrom the work of population geneticists. Importantly, these data are known to re-flect the particular ways in which early mankind settled our planet, thus facilitatingan interpretation of our findings in terms of the historical origins of the contem-porary distribution of risk preferences rather than as causal effect of genes on riskpreferences. Hence, by using genetic data as measure of ancient migratory patterns,we argue that the observed heterogeneity in risk preferences across contemporarypopulations today is partly driven by the mechanics of migratory movements inancient times.

Our empirical analysis of the relationship between risk preferences and ancientmigration patterns starts off by establishing a strong raw correlation between theabsolute difference in average risk attitudes between two countries and the FSTgenetic distance between the respective populations, as derived by Spolaore andWacziarg (2009). This correlation is not only statistically significant, but also largein magnitude. As a first robustness check, we investigate to what extent this cor-relation is likely to be driven by omitted variables. To this end, we demonstratethat the association is robust to an extensive set of covariates, including controlsfor differences in the countries’ demographic composition, their geographic position,prevailing climatic and agricultural conditions, institutions, and economic develop-ment. In all of the corresponding regressions, the point estimate is very stable,suggesting that unobserved heterogeneity is unlikely to drive our results, either (Al-tonji, Elder and Taber, 2005). In addition, our result holds when we exclude entirecontinents or observations with large genetic distances from the analysis. It is alsorobust to employing the Nei genetic distance as independent variable, which uses analternative theoretical procedure to compute distance estimates from the observedallelic frequencies around the world (Cavalli-Sforza, Menozzi and Piazza, 1994).

In an important extension of our baseline results, we show that qualitativelysimilar results obtain if we use a measure of predicted genetic distance to explain thecross-country variation in risk preferences. This measure was constructed by Ashrafand Galor (2013b) by directly exploiting information on the geographic patterns ofearly migratory movements and hence not only highlights the robustness of our

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main insight, but also provides additional evidence for the specific interpretation ofour results as reflecting the latent impact of early migration patterns.

Given the association between genetic distance and differences in mean risk atti-tudes, we also investigate the genetic correlates of the second moment of the within-country distribution of preferences by employing the predicted genetic diversity asdeveloped in Ashraf and Galor (2013b) as explanatory variable. Analogously to ourmain result, we provide evidence suggesting that the degree of the within-countryheterogeneity in individual risk attitudes is increasing in the (predicted) diversity ofthe respective genetic pool. This finding is akin to the results in Ashraf and Galor(2013a), who show that genetic diversity correlates with ethnolinguistic diversity atthe country level.

Thus, along two major dimensions, the distribution of genetic variation aroundthe globe explains a considerable fraction (roughly 10 %) of the cross-country vari-ation in risk preferences. However, throughout the paper, we emphasize that theserelationships should not be taken as evidence for the existence of some genes whichmake entire populations more or less risk-averse. In fact, the genetic data are con-structed using information on allelic frequencies in neutral genes, i.e., in those geneswhich only change randomly and are not subject to selection pressure. In addition,it is well-known that aggregate genetic data do not only pick up biologically, butalso socially transmitted traits (Cavalli-Sforza et al., 1994).

While we cannot rule out that our results (partly) reflect direct genetic effects,we provide an interpretation of the correlations between aggregate genetic variablesand population-level risk attitudes in terms of the major historical event whichshaped the contemporary distribution of aggregate genetic endowments. Accordingto the widely accepted “Out of Africa theory”, starting around 50’000 years ago,early mankind migrated out of East Africa and continued to explore and populateour planet through a series of discrete successive migratory steps, each of whichconsisted of a breakup of formerly united populations. These migration patterns hadtwo important implications for the distribution of genetic variation, which persistuntil today and directly facilitate our interpretation of the respective variables asreflecting ancient migration patterns.

First, whenever two populations split apart from each other in order to foundseparate settlements, their genetic distance increases over time due to random ge-netic drift. Thus, the genetic distance between two populations is a measure oftemporal distance and can in fact be used to compute the length of time since thetwo populations last shared common ancestors. Note that populations who havespent a long time apart from each other were likely also exposed to a large number

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of differential historical experiences. However, recent evidence suggests that envi-ronmental factors and idiosyncratic experiences affect preferences over risk (Callen,Isaqzadeh, Long and Sprenger, 2014). The argument made in this paper is thenthat large differences in historical experiences (as they plausibly arise during longperiods of separation) have accumulated over time and have given rise to differencesin risk attitudes across countries today. According to this logic, even in the absenceof any direct genetic effect on preferences, genetic distance should correlate with(absolute) differences in risk preferences because a larger genetic distance betweentwo countries indicates a larger number of idiosyncratic experiences for each of therespective populations.

Second, the fact that over the course of thousands of years sub-populations re-peatedly broke apart from the previous population also had a long-lasting impacton the genetic diversity within populations. Since those (sub-) populations break-ing apart from the previous population usually took with them only a fraction ofthe variation in the parental genetic pool, genetic diversity decreases along humanmigratory routes out of East Africa (so-called serial founder effect). Note that, bydefinition, the serial founder effect requires that the migration samples are small andnon-representative in genetic terms (since otherwise no reduction in genetic diver-sity would take place). However, if the sequence of founder populations consists ofsmall non-representative draws from finite distributions, then by pure randomnessthe heterogeneity of a population in terms of its individual traits will on average alsodecrease along migration paths. This implies that genetic diversity and preferenceheterogeneity should be correlated, again even in the absence of any genetic effecton preferences.

While we are unable to pinpoint the exact mechanisms through which ancientmigration patterns have caused the contemporary distribution of risk preferences,our results nevertheless highlight the importance of the precise pattern of temporallyvery distant population fissions in understanding today’s heterogeneity in one ofthe key economic traits. In particular, notice that the informational content of thegenetic variables we use was largely determined thousands of years ago. Yet thehypothesis we investigate is that these ancient variables correlate with importanteconomic attitudes today.

This paper forms part of a nascent recent literature on endogenous preferences,which seeks to establish that many economically relevant attitudes are affected bythe composition of one’s environment or past experiences.2 More specifically, ourfindings on the historical origins of the international variation in risk preferences are

2See Fehr and Hoff (2011) for a recent survey.

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consistent with other recent efforts to better understand the historical, biological,and cultural origins of beliefs and preferences. As in our approach, this literaturestrand has increasingly attempted to relate economic characteristics to genetic orlinguistic variables. For example, an earlier version of Guiso, Sapienza and Zingales(2009) shows that the genetic distance between two populations is negatively relatedto mutual trust, which in turn is positively associated with the extent of bilateraltrade flows. In an approach similar to ours, Desmet, Le Breton, Ortin and Weber(2009) demonstrate that, within Europe, genetic distance correlates with opinionsand attitudes as expressed in the World Values Survey. Tabellini (2008) and Chen(2013), on the other hand, show that specific structural features of languages cor-relate with trust and future-oriented behavior.

Galor and Özak (2014) show that agro-climatic conditions in pre-industrial timeshave an impact on future-orientation today. Grosjean (forthcoming) shows thatthe high prevalence of homicides in the US south is driven by the fact that incolonial times this area was settled by herders rather than farmers, who sustaineda reputation for violence to prevent theft. Using the same dataset as the one inthis paper, Becker, Dohmen, Enke, Falk, Huffman and Sunde (2014) document anddescribe the worldwide heterogeneity in risk, time, and social preferences.

Just like the genetic variables we use are very old and hence point to the contem-porary implications of temporally distant events such as migration patterns, a setof recent papers shows that important beliefs can also have deep historical originsand persist for a long time. Nunn and Wantchekon (2011) show that the degreeof interpersonal (mis-) trust in Africa is partly driven by ancestors’ experience ofthe slave trade hundreds of years earlier. Similarly, Alesina, Giuliano and Nunn(2013) demonstrate that cross-country differences in beliefs about the appropriaterole of women in society have their historical origins in traditional (plough) agricul-tural practices. Additional evidence on the long-run persistence of cultural beliefsand values is provided by Voigtländer and Voth (2012) who demonstrate the localcontinuity of cultural beliefs (anti-semitism in Germany) over 500 years.

Taken together, the above findings suggest an intimate relationship betweentemporally distant events, persistent cultural attitudes, and many economically rel-evant characteristics. However, presumably given the previous lack of data, thispaper is the first contribution to study the cross-country origins of precisely mea-sured risk preferences.3

3Vieider, Chmura and Martinsson (2012) and Rieger, Wang and Hens (2014) also collect dataon the prevalence of risk attitudes in multiple countries. Their samples cover a much smaller collec-tion of countries and contain university students only, rendering conclusions about population-leveldifferences in risk attitudes difficult.

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Recent evidence suggests that such differences in persistent cultural traits canhave real economic effects not just at an individual, but also at an aggregate level.4

For example, Dohmen, Enke, Falk, Huffman and Sunde (2014) show that the cross-country variation in time preferences is tightly linked to macroeconomic outcomessuch as national income, growth, savings rates, education, and institutions. In ad-dition, the seminal studies by Spolaore and Wacziarg (2009) and Ashraf and Galor(2013b) directly relate to our work in that they pioneer the use of genetic variablesin order to investigate the deep determinants of comparative development. Spo-laore and Wacziarg (2009, 2011) find a strong relationship between genetic distanceand income differences across countries. They argue that lower cultural distancebetween two countries - reflected by lower genetic distance - facilitates the diffusionof knowledge and technology and therefore development.5 Focusing on the within-country variation in genetic endowments, Ashraf and Galor (2013b) and Galor andKlemp (2014) document an inverted U-form relationship between genetic diversityand national income. They argue that this pattern reflects the trade-off betweenmore innovation and lower trust and cooperation that comes with higher culturaldiversity. Using the same approach, Arbatli, Ashraf and Galor (2013) show thatgenetic diversity is indeed related to the emergence of civil conflicts. Our papernicely dovetails with this set of contributions as it shows that the genetic vari-ables indeed capture variation in an economically important trait (either betweenor within countries), as is implicitly or explicitly assumed by these papers.

The remainder of this paper is structured as follows. In the next section, wepresent our dataset on risk preferences in 76 countries. In section 3, we discuss therelation between ancient human migration patterns and genetic variation. Section4 develops our main result on the connection between differences in average riskattitudes and genetic distance. In section 5, we examine the within-country hetero-geneity in risk preferences and the differential degrees of genetic diversity associatedwith it. Finally, section 6 offers a concluding discussion on the relationship betweenmigration patterns, culture, and preferences.

4For individual-level evidence see Fernández and Fogli (2006), Fernández (2007), and Fernán-dez and Fogli (2009). Also see Guiso, Sapienza and Zingales (2006) for an overview.

5Using a different approach, Gorodnichenko and Roland (2010) argue that genetic distance tothe United States can be used as an instrument for the cultural “individualism” dimension in orderto show a potential causal effect on GDP. Proto and Oswald (2014) investigate the correlationbetween genetic distance to Denmark and happiness. Giuliano, Spilimbergo and Tonon (2014)show that the negative correlation between genetic distance and bilateral trade vanishes oncetransportation costs are accounted for. In contrast, our results are robust to controlling fortransportation costs.

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2 Representative Cross-Country Data on Risk Pref-

erences

Studying the patterns underlying the world-wide distribution of risk preferencesrequires clean and meaningful data on risk attitudes from representative populationsamples in a broad set of countries. We first discuss how the corresponding datasetwas collected and then provide some summary statistics.

2.1 Survey Procedure

Our data on risk preferences around the globe are part of a unique dataset whichwas collected as part of the Gallup World Poll 2012. In almost all countries aroundthe world, the World Poll surveys representative population samples about socialand economic issues via face-to-face or telephone interviews. In 76 countries, as partof the regular questionnaire, we included a series of survey items which were explic-itly designed to measure each respondent’s degree of risk aversion. Our dataset ischaracterized by three noteworthy features: (i) Representative population samples,(ii) geographical representativeness in terms of the countries being covered, and(iii) behaviorally validated survey items.

First, contrary to small- or medium-scale experimental work, we obtained prefer-ences measures from representative population samples in each country, thus allow-ing generally valid inferences on between-country differences in preferences, whichmight or might not be the same as in cross-country comparisons of convenience (stu-dent) samples. The median sample size was 1’000 participants per country; in total,we collected preference measures for more than 80,000 participants worldwide.6

Our sample of 76 countries is extensive in the sense that it is not restricted towestern industrialized nations, but covers all continents and represents 87 % of theworld population. Specifically, our sample includes 15 countries from the Americas,27 from Europe and Central Asia, and 14 from South-East Asia and Pacific. Mostnotably, the data comprise 20 nations in Africa and the Middle East, 11 of which aresub-saharan African countries. This contrasts with other surveys such as the WorldValues Survey, which is considerably less representative in geographical terms.

The set of survey items consisted of two measures of the underlying risk pref-erence - one qualitative subjective self-assessment and one quantitative measure.These items were explicitly designed, tested, and selected for the purpose of thisstudy.

6Notable outliers are China (2,574 obs.), Haiti (504 obs.), India (2,540 obs.), Iran (2,507 obs.),Russia (1,500 obs.) and Suriname (504 obs.).

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The subjective self-assessment directly asks for an individual’s willingness totake risks: “Generally speaking, are you a person who is willing to take risks, orare you not willing to do so? Please indicate your answer on a scale from 0 to 10,where a 0 means “not willing to take risks at all” and a 10 means “very willing totake risks”. You can also use the values in between to indicate where you fall on thescale.”

The quantitative measure is derived from a series of five interdependent hy-pothetical binary lottery choices, a format commonly referred to as the “staircaseprocedure” (Cornsweet, 1962). In each of the five questions, participants had todecide between a 50-50 lottery to win 300 euros or nothing (which was the same ineach question) and varying safe payments. The questions were interdependent inthe sense that the choice of a lottery resulted in an increase of the safe amount beingoffered in the next question, and conversely. For instance, in the first question, thefixed payment was 160 euros. In case the respondent chose the lottery (the safepayment), the safe payment increased (decreased) to 240 (80) euros in the secondquestion (see Appendix F for an exposition of the entire sequence of survey items).In essence, by adjusting the fixed payment according to previous choices, the ques-tions “zoom in” onto the respondent’s certainly equivalent and make efficient use oflimited and costly survey time. In addition, this staircase-type procedure allows fora relatively fine assessment of an individual’s attitudes towards risk; the sequenceof questions has 25 = 32 possible outcomes which partition the real line from 0 eu-ros to 320 euros into evenly spaced intervals, so that in principle the respondent’scertainty equivalent can be pinned down to a range of 10 euros. Also note that theamounts of money were scaled across countries using median household income.

The subjective self-assessment and the outcome of the quantitative lottery stair-case were aggregated into a single index which describes an individual’s degree ofrisk aversion. Aggregation was done by first computing the z-scores of each surveyitem at the individual level and then averaging these two z-scores. The resultingindividual-level index is then standardized at the (individual-level) world sample(see Becker et al. (2014) for details). Thus, both types of questions receive equalweight in the index. However, none of the results in this paper depend on thisparticular aggregation procedure.

Despite the lack of financial incentives, there are strong reasons to believe thatour measures capture respondents’ true risk attitudes. Our survey items underwentan extensive selection and validation procedure. Specifically, as described in detailin Falk et al. (2014), among a large set of alternative survey items, these questionsperform best in predicting behavior in standard incentivized risk experiments. In

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combination, our survey questions explain 17 % of the variance of incentivizedexperimental choices, while the correlations between experimental choices and oursurvey items are roughly 0.35 for both items. Note that these correlations are largein magnitude, in particular given the benchmark of a typical test-retest correlationof 0.6 (Falk et al., 2014).

The explicit ex-ante validation of our survey items constitutes a significantmethodological advance over the often ad-hoc choice of “plausible” or “interesting”questions in international surveys. Several additional arguments provide reassuringevidence for the behavioral validity of these items. First, the qualitative subjectiveself-assessment has previously been used in a number of within-country studies andhas been shown to be predictive of both experimental behavior and risk-taking inthe field in representative population samples (Dohmen et al., 2011). Second, Viei-der, Lefebvre, Bouchouicha, Chmura, Hakimov, Krawczyk and Martinsson (2013)provide direct evidence that this question captures differences in incentivized exper-imental risk-taking across countries and is hence suitable for cross-cultural surveys.They show that, within student samples, responses to the subjective self-assessmentcorrelate with incentivized lottery choices at the country level, i.e., the average cer-tainty equivalent of a given lottery correlates with average self-assessments acrosscountries.

Additionally, the quantitative lottery staircase measure is not only akin to stan-dard experimental lottery-choices, but also lacks any context and is hence arguablyless prone to culture-dependent interpretations. It is therefore especially suitablefor a multinational study like ours.

Finally, before the questionnaire was fielded, it was pre-tested in a pilot studyin 21 countries in order to assess potential difficulties in understanding and culturaldifferences in the interpretation of questions.7 In each country, the sample wasselected to consist of equal shares of men and women and to represent differenteducational backgrounds. Respondents in the pilot study were explicitly asked toprovide feedback about the survey questions. In particular, they were asked to ratethe difficulty of questions, state any potential problems in understanding, and togive general comments.8

7The pilot study was run in Algeria, Armenia, Azerbaijan, Bangladesh, Belarus, Cambodia,Croatia, Hungary, Jordan, Kazakhstan, Kenya, Kyrgystan, Lebanon, Poland, Romania, Russia,Saudi Arabia, Tajikistan, Turkey, Turkmenistan, and Uzbekistan, with 10 to 15 respondents ineach country.

8For example, respondents were explicitly asked to explain a “50-percent chance” in their ownwords and to provide an interpretation of “safe payment”.

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2.2 Summary Statistics

From previous within-country studies, it is well known that attitudes towards riskvary considerably within populations. However, it is not a priori clear whether weshould also expect heterogeneity across countries. It could be the case, for instance,that (perhaps conditional on a few observables like the age or gender structure) riskattitudes are “identically distributed” across countries.

Figure 1 addresses this issue by depicting the distribution of the average de-gree of willingness to take risks around the world. Darker colors indicate a higherpropensity to take risks. As can be inferred, the data indicate a large heterogeneityacross countries (µ = 0.01, σ = 0.31), with Portugal being the most risk averse andSouth Africa the least risk averse country in the sample. Recall that the individual-level risk aversion index was standardized, i.e., the total standard deviation in theworld sample is one. Thus, the standard deviation in country-level means is abouta third of the standard deviation in the individual-level data. A formal variancedecomposition reveals that about 10 % of the total variance in risk preferences canbe attributed to between- (rather than within-) country variation.

Figure 1: Cross-country heterogeneity in risk attitudes. White is the world mean of zero. Darkerblue (red) indicates higher (lower) levels of risk aversion. All values are expressed in standarddeviations around the world mean.

What explains this variation in one of the key economic traits? Visual inspectionof the world map of risk preferences suggests the presence of geographic patterns.Table 1 provides summary statistics of the first and the second moment of thewithin-country distribution of risk attitudes by region. Note that all values should

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be interpreted in terms of fractions of the standard deviation in the individual-leveldata. The average degree of willingness to take risks is highest in Africa, while theAmericas, Europe, and Asia tend to be rather homogeneous in their (relatively low)willingness to take risks. Note that, for example, on average, populations in Sub-Saharan Africa are 0.43 standard deviations (expressed in terms of the individual-level data) more risk-tolerant than countries in Europe, Asia, and South America.To gauge the quantitative importance of these cross-regional differences, considerthe outcome of the staircase lottery procedure. According to this measure, theaverage certainty equivalent of Sub-Saharan countries of a 50-50 bet of receiving300 euros or nothing is about 45 euros higher than that of the typical European,Asian, or Southern American population.

Also note that the within-country heterogeneity in risk attitudes varies acrossregions. This cross-country variation, however, is not as strong as the variationpertaining to the first moment of the within-country distribution.

Section 4 will investigate the regional patterns underlying the distribution of riskpreferences more formally. As it turns out, however, the cross-country heterogeneitydescribed above does not only follow geographical patterns, but is well-explained byaggregate data on human genetic variation. Conducting and interpreting such ananalysis requires quantifying and understanding genetic variation and its historicalroots, an issue to which we turn now.

Table 1: Summary statistics for willingness to take risks by region

Region # Obs. Avg. will. SD will.to take risks to take risks

North America 2 0.148 0.885South America 13 -0.036 0.911Europe & Central Asia 27 -0.113 0.951South Asia 5 -0.049 0.940East Asia & Pacific 9 -0.100 0.898Middle East & North Africa 9 0.164 0.982Sub-Saharan Africa 11 0.344 0.984

Full Sample 76 0.012 0.944(σ = 0.305) (σ = 0.093)

Column 3 provides the mean of the within-country average willingness to take risksand column 4 the mean of the within-country standard deviation in risk attitudes.All values expressed in standard deviations around the individual-level world mean.

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3 Risk Preferences, Genetic Variation, and The Great

Human Expansion

This section briefly reviews a few stylized facts from the fields of population ge-netics and anthropology which clarify the origins of the contemporary distributionof genetic variation and allow for an interpretation of the relationship between theaggregate distribution of genetic endowments and preferences over risk.

One of the key roots of the contemporary distribution of human genetic varia-tion around the globe can be found in the exodus of ancient mankind from Africa.According to the most widely accepted theory of the origins and the dispersal ofearly humans, the single cradle of mankind is to be found in East or South Africaand can be dated back to roughly 100’000 years ago (see e.g. Henn, Cavalli-Sforzaand Feldman (2012) for an overview). Starting from East Africa, a small sample ofhunters and gatherers exited the African continent around 50’000-60’000 years agoand thereby started what is now also referred to as the “great human expansion”.This expansion continued throughout Europe, Asia, Oceania, and the Americas, sothat mankind eventually came to settle on all continents. A noteworthy feature ofthis very long-run process is that it occurred through a large number of discretemigration steps, each of which consisted of a sub-sample of the original popula-tion breaking apart and leaving the previous location to move on and found newsettlements elsewhere (so-called serial founder effect). This pattern of successivebreakups, in turn, had two important implications for the distribution of humangenetic endowments, which persist until today and directly facilitate our interpre-tation of the historical meaning of the genetic variables we use. We now discusseach of these implications in turn and clarify how they can potentially organize andexplain the contemporary cross-country distribution of risk preferences.

3.1 Genetic Distance Between Populations and Differences

in Average Risk Attitudes

First, the series of migratory steps implied a frequent breakup of formerly unitedpopulations. After splitting apart, these sub-populations often settled geographi-cally distant from each other and hence stopped interbreeding, thereby preventinga mixture of the respective genetic pools. However, since every genetic pool is sub-ject to random drift (“noise”), geographical separation implies that over time the

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genetic distance between sub-populations gradually became (on average) larger.9

Thus, in essence, the genealogical relatedness between two populations reflects thelength of time elapsed since these populations shared common ancestors. In fact,akin to a molecular clock, population geneticists have made use of this observationby constructing mathematical models to compute the timing of separation betweengroups. This makes clear that, at its very core, genetic distance constitutes notonly a measure of genealogical relatedness, but also of temporal distance betweentwo populations.

There are (at least) two channels through which the length of separation of twogroups might have an impact on differences in risk attitudes. Recent work showsthat risk attitudes are transmitted across generations (Dohmen, Falk, Huffmanand Sunde, 2012) and that part of this transmission is genetic in nature (Cesarini,Dawes, Johannesson, Lichtenstein and Wallace, 2009). Thus, our measure of geneticdistance in neutral genes might correlate with population-level differences in geneticendowments which do have a direct effect on risk attitudes.

However, while we cannot rule out that the correlation between summary statis-tics of genealogical relatedness and individual characteristics reflects the latentcausal impact of differences in genetic endowments, it is entirely possible that thiscorrelation is driven by non-genetic channels alone. Importantly, it is well-knownthat aggregate genetic data may pick up differences in individual traits which are en-tirely due to differential socialization practices or differential historical experiences(Cavalli-Sforza et al., 1994).

In this respect, note that the fact that two populations have spent a long timeapart from each other plausibly implies that they were subject to differential his-torical experiences. However, recent work by e.g. Callen et al. (2014) highlightsthat risk preferences are malleable by idiosyncratic experiences and, more gener-ally, by the composition of people’s social environment. Building on this insight,the main argument in this paper is that the differential historical experiences whichhave accumulated over thousands of years, have eventually given rise to differentialattitudes towards risk. To put it a bit more schematically, our argument is that

High High Differential Differentialgenetic ⇔ temporal ⇒ historical ⇒ riskdistance distance experiences attitudes

Thus, if this channel was present, large differences in historical experiences (as

9To see this, intuitively, simply note that the expectation of the absolute difference of tworandom variables increases in the variance of the latter.

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proxied for by large genetic distance) should predict (absolute) differences in riskpreferences between countries. Notably, our discussion implies that the co-evolutionof random genetic change and preference formation across populations should cor-relate even in the absence of any biological transmission channel.

Main Hypothesis. The genetic distance between two countries predicts the abso-lute difference in average risk attitudes between the respective populations.

Note that this is an inherently bilateral, rather than a directional, statement.The hypothesis is not that some specific gene correlates with levels of risk aver-sion. Rather, differences in aggregate genetic endowments should correlate withdifferences in risk attitudes.

The particular way in which human migratory steps proceeded has had a sig-nificant impact not only on the distribution of genetic endowments, but also onthe prevalence of different languages. In fact, population geneticists and linguistshave long noted the close correspondence between genetic and linguistic trees, inwhich a fission indicates the separation of two groups (Cavalli-Sforza et al., 1994).Thus, the magnitude of genetic and linguistic distances are both driven by thetiming of separation between groups. But given that linguistic distances betweentwo populations also proxy for ancient migration patterns, we will be able to usea corresponding linguistic distance variable to further underline the relationshipbetween the specific patterns of our ancestor’s migrations and the contemporaryinternational distribution of risk preferences.

3.2 Genetic Diversity and Heterogeneity in Risk Preferences

The serial founder pattern of ancient migration patterns has had a second importantimplication for the distribution of genetic variation, which pertains to the geneticdiversity within groups. Specifically, a well-established fact in the population genet-ics literature is that whenever a sub-population split apart from its parental colony,those humans breaking new ground took with them only a fraction of the geneticdiversity of the previous genetic pool. In consequence, through the sequence ofsuccessive fissions, the total diversity of the gene pool significantly decreases alonghuman migratory routes. We argue that this reduction in genetic diversity is corre-lated with a reduction in preference heterogeneity, i.e., correlates with the secondmoment of the within-country distribution of risk attitudes.

Again, at least two channels could drive such an effect. First, to the extentthat there are direct genetic effects on preferences, a reduction in the diversity

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of the genetic pool could drive a reduction in the heterogeneity in risk attitudes.However, even in the absence of direct genetic effects, the mechanics of the serialfounder effect (in particular the small size of the founder populations) imply asecond channel through which ancient migration patterns could have an impact onthe within-country heterogeneity in risk attitudes.

To see this, first note that by definition the serial founder effect requires thatsubsequent founder populations are genetically non-representative small samples ofthe original populations because otherwise no reduction in genetic diversity wouldoccur along human migration routes. Thus, even if risk attitudes are unrelatedto genetic endowments, the expected variance of traits in a small founder popula-tion is smaller than the variance of traits in the original population: Intuitively, insmall samples, some of the original heterogeneity disappears by pure coincidencethrough sampling from a finite distribution. Thus, even in the absence of directgenetic effects on particular traits, preference heterogeneity and genetic diversityshould correlate across populations. This second channel through which ancientmigration patterns could have an impact on the contemporary distribution of riskattitudes across country is admittedly somewhat more subtle in nature. Giventhat the between-country variation in the within-country standard deviation in riskattitudes is also rather small in magnitude (see Table 1), we view this second im-plication of human migration patterns as ancillary hypothesis.

Ancillary Hypothesis. The heterogeneity in risk preferences in a given countrycorrelates with the genetic diversity within the respective population.

3.3 Data Sources

Conceptually, both of our quantitative genetic measures constitute indices of ex-pected heterozygosity, which can be thought of as the probability that two randomlymatched individuals will be genetically different from each other in terms of a pre-defined spectrum of genes. Technically, indices of heterozygosity are derived usingdata on allelic frequencies, where an allele is a particular variant taken by a gene.Importantly, such genetic measures are based on neutral genetic markers only, i.e.,on genes which are not subject to selection pressure. Thus, differences in such genesmerely reflect random drift and do not arise from evolutionary fit. In this context,an important distinction is to be made between heterozygosity indices which arecalculated based on observed allelic frequencies and those which are predicted byexploiting today’s knowledge of the structure of our early ancestors’ major migra-tion movements.

15

Genetic distance between countriesIn the between-population case, intuitively, the relative frequency of alleles at agiven locus can be compared across populations and the deviation in frequenciescan then be averaged over loci. This is exactly the approach pursued in the work ofthe population geneticists Cavalli-Sforza et al. (1994). The main dataset assembledby these researchers consists of data on 128 different alleles for 42 world popula-tions. By aggregating differences in these allelic frequencies, the authors computethe FST genetic distance, which provides a comprehensive measure of genetic re-latedness between any pair of 42 world populations. In addition, using the samedataset, Cavalli-Sforza et al. (1994) compute the so-called Nei distance for all pop-ulation pairs. While this genetic distance measure has slightly different theoreticalproperties than FST , the two measures are highly correlated (ρ = 0.95).10

Since genetic distances are available only at the population rather than at thecountry level, one needs to aggregate the data in order to be able to conduct cross-country analyses. This task was accomplished by Spolaore and Wacziarg (2009)who matched the 42 populations in Cavalli-Sforza et al. (1994) to countries usingethnic composition data from Fearon (2003). Thus, this measure represents theexpected genetic distance between between two randomly selected individuals, onefrom each country.

Rather than physically measure the genetic composition of populations, one canalso derive predicted genetic distances. The construction of such predicted valueswas pioneered in Ashraf and Galor (2013b) and directly exploits the migration pat-terns of the ancestors of today’s populations. Notably, these data are independentof those on observed genetic distance and thus allow for an important out-of-samplerobustness check.

Technically, values of predicted heterozygosity (either between or within coun-tries) are derived using the following methodology. The Human Genome DiversityPanel (HGDP) selected a set of 53 populations which are commonly believed tobe native to their current location and have been relatively free from genetic ad-mixture. From these populations, researchers sampled data on a large set of (783)loci, thus providing the most comprehensive and reliable measure of the geneticcomposition of populations that is currently available. However, since the numberof these ethnic groups is fairly small, the sample for which both genetic data andour preference measures are available spans only 16 countries, rendering empiricalcross-country analyses difficult. To overcome this problem in the context of explain-

10Appendix A provides a formal derivation of the logic behind both distance measures.

16

ing GDP, Ashraf and Galor (2013b) make use of the observation of the populationgeneticists Ramachandran, Deshpande, Roseman, Rosenberg, Feldman and Cavalli-Sforza (2005) that (in the sample of 53 native populations) the observed geneticdistance between ethnic groups correlates with the migratory distance between therespective groups (ρ = 0.89). Based on this insight, Ashraf and Galor (2013b)derived values of predicted genetic distance for a much larger sample of groups.Specifically, they apply the coefficient of a regression of observed genetic distance onmigratory distance to the migratory distances between any two countries’ capitals.11

However, by construction, these predicted genetic distance estimates only pertainto the native populations of a given pair of countries. Thus, to the extent thatthe contemporary populations in a country pair differ from the native ones, thesegenetic distance estimates are at fault. In order to derive values of predicted geneticdistance pertaining to the contemporary populations, we combined the dataset ofAshraf and Galor (2013b) with the “World Migration Matrix” of Putterman andWeil (2010), which describes the share of the year 2000 population in every countrythat has descended from people in different source countries as of the year 1500.Thus, the contemporary predicted genetic distance between two countries equalsthe weighted genetic distance between the contemporary populations. Formally,suppose there are N countries, each of which has one native population. Let s1,ibe the share of the population in country 1 which is native to country i and denoteby di,j the predicted genetic distance between the native populations of countries iand j. Then, the (weighted) predicted genetic distance between countries 1 and 2

as of today is given by

Predicted Fst1,2 =N∑i=1

N∑j=1

(s1,i × s2,j × di,j)

The correlation of this predicted genetic distance measure with the observedFST distance is ρ = 0.54, providing reassuring evidence for the meaningfulness ofthis construction.

Genetic diversity within countriesJust as values of genetic distance can be computed in the between-country case,one can derive an index of expected heterozygosity within a given population. In-

11The derivation of migratory distances follows the methodology in Ramachandran et al. (2005).In particular, population movements are restricted to landmass as much as possible by requiringmigrations to occur along five obligatory waypoints, one for each continent. The migratory dis-tance between any two ethnic groups is then computed as the sum of the geodesic (great-circle)distances between the ethnic groups’ capitals along the shortest possible path which encompassesthe intermediate waypoints, see Ashraf and Galor (2013b).

17

tuitively, genetic diversity takes into account the extent to which different allelesare present at a given locus within a population. Our data on human genetic diver-sity within countries are taken directly from Ashraf and Galor (2013b). Thus, akinto the predicted genetic distance measure described above, these values consist ofpredicted genetic diversities, which are derived by making use of the patterns un-derlying the serial founder effect. Specifically, a second empirical fact establishedby Ramachandran et al. (2005) is that observed genetic diversity linearly decreasesas one moves away from East Africa along human migratory routes. Thus, similarlyto the construction of the predicted genetic distance variable, one can compute themigratory distance of any population in the world from East Africa and then applythe coefficient of a regression of observed diversity on migratory distance to it inorder to arrive at values of predicted diversity.12

Linguistic distance between countriesThe construction of linguistic distances follows the methodology proposed by Fearon(2003). The Ethnologue project classifies all languages of the world into languagefamilies, sub-families, sub-sub-families etc., which give rise to a language tree. Insuch a tree, the degree of relatedness between different languages can be quantifiedas the number of common nodes two languages share. If two languages belong todifferent language families, the number of common nodes is 0. In contrast, if twolanguages are identical, the number of common nodes is 15. Following the argumentby Fearon (2003), who argues that the marginal increase in the degree of linguisticrelatedness is decreasing in the number of common nodes, we transformed thesedata according to

Linguistic distance = 1−√

# Common nodes15

to produce distance estimates between languages in the interval [0, 1]. For eachcountry pair, we calculate the weighted linguistic distance according to the popu-lation shares speaking a particular language in the respective countries.13 Thus, aswith genetic distance data, the weighted linguistic distance represents the expectedlinguistic distance of two randomly drawn individuals, one from each country.

12Again, this measure is adjusted to account for post-Columbian migration flows. In addi-tion, this measure is adjusted for the genetic diversity arising within each country due to geneticdifferences between different ethnic groups. See Ashraf and Galor (2013b).

13For this purpose, we restricted the Ethnologue data to languages which make up at least 5%of the population in a given country.

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4 The Deep Determinants of Cross-Country Differ-

ences in Risk Preferences

This section develops our main result on the relationship between differences inthe average degree of risk aversion between countries and the genetic distance be-tween the respective populations. Since genetic distance is an inherently bilateralvariable, this analysis will necessitate the use of a dyadic regression framework inwhich the absolute difference in risk attitudes within any country pair appears asdependent variable. We start our analysis by discussing an important special caseof this general estimation methodology, which consists of simple level regressions ofa population’s average risk tolerance on a set of selected explanatory variables.

4.1 Unilateral Approach

As a first step towards understanding the heterogeneity in average risk attitudesdepicted in Figure 1, this section relates country-level risk preferences to a numberof demographic variables whose individual-level analogues are known to correlatewith risk preferences. In particular, within-population studies have establishedthat being male, younger, and wealthier are all positively associated with a higherwillingness to take risks (Dohmen et al., 2011). Accordingly, for a sample of 73countries, columns (1) through (3) of Table 2 present OLS regressions of the averagewillingness to take risks in a country on the population-level counterparts of therespective socioeconomic variables.14

The results show that, analogously to within-country studies, older populationsand those with a higher proportion of females indeed tend to be more risk-averse.This result obtains despite the rather small variation in gender proportions acrosscountries. Notably, the average age of a population explains 12 % of the between-country variation in risk preferences. Perhaps surprisingly, however, (log) nationalincome is uncorrelated with the average degree of risk aversion. This finding con-trasts not only with the positive association between risk tolerance and incomeusually found in individual-level data, but also with the cross-country findings re-ported in Vieider et al. (2012). These authors conduct incentivized experimentson student samples in 30 countries and show a negative correlation between na-tional income and the average willingness to take risks in their student sample. Ourfindings thus suggest that in studying between-country variation in economic pref-

14As described above, the 2012 Gallup World Poll collected preference data in 76 countries.However, due to a lack of data on genetic distance and / or genetic diversity, we excluded Bosniaand Herzegovina, Serbia, and Suriname from the sample.

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erences, caution should be exercised in drawing inferences from non-representativepopulation samples.

While the average willingness to take risks in a country is uncorrelated withnational income, a possible conjecture is that risk preferences are related to theriskiness of the health environment. For instance, it is possible that high-risk en-vironments lead people to “get used” to dealing with large risks. In this spirit,columns (4) through (7) of Table 2 regress average risk attitudes on three proxiesfor the health risks inherent in an environment. These estimations establish thatlarger health risks positively correlate with a higher willingness to take risks: Pop-ulations with a lower life expectancy at birth, a higher fraction of deaths due tocommunicable diseases, or a higher rate of traffic deaths are significantly less riskaverse.15 Our health proxies explain roughly 15 % of the cross-country variation inrisk preferences, which suggests that risk preferences interact with the riskiness ofthe environment.

Table 2: Determinants of average willingness to take risks

Dependent variable: Average willingess to take risks

(1) (2) (3) (4) (5) (6)Average age -0.021∗∗∗

(0.01)

Fraction female -4.02∗∗(1.64)

Log [GDP p/c PPP] -0.035(0.03)

Life expectancy at birth -0.015∗∗∗(0.00)

% of deaths due to 0.0055∗∗∗communicable diseases (0.00)

Traffic death rate 0.012∗∗∗(0.00)

Constant 0.84∗∗∗ 6.10∗∗ 0.33 1.08∗∗∗ -0.11∗∗ -0.18∗∗∗(0.27) (2.50) (0.27) (0.37) (0.04) (0.06)

Observations 73 73 73 73 73 71Adjusted R2 0.124 0.061 0.006 0.153 0.140 0.098

OLS estimates, robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

15These correlations are robust to using alternative proxies for the riskiness of the healthenvironment such as the prevalence of HIV or the number of homicides within a given country, seeAppendix B. Also note that the high correlations among our health risk proxies render multipleregression analyses involving these variables difficult.

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Table 3 introduces special cases of the genetic and linguistic data into these levelregressions. Note that South Africa is the least risk-averse country in our sample.Thus, a special case of our research hypothesis discussed above is that those coun-tries who are genetically and linguistically very far from South Africa (and havehence been separated from the South African population for a long time) shouldalso have very different preference profiles, i.e., be more risk-averse. Consistent withthis idea, as columns (1) and (2) establish, both of our proxies for ancient migrationpatterns (which in essence constitute a proxy for the migratory distance betweenSouth Africa and any other country in the sample) are strongly related to higherlevels of risk aversion. This effect is not only statistically significant, but also largein magnitude. For example, the standardized beta on the gentic distance coefficientequals almost 50 %, and each of the regressors alone explains about 20 % of thebetween-country variation in risk preferences. Note that the adjusted R2 measuresuggests that our migration proxies constitute the best single predictors of a popu-lation’s attitudes towards risk as compared to the sociodemographic or health riskvariables presented in Table 2. Figure 2 depicts the strong raw relationship betweenaverage risk attitudes and genetic distance to South Africa. As is well-known, thegenetic distance between African and non-African populations is relatively large;thus, all of our subsequent analyses will take special care in ensuring that our re-sults are not driven by the African continent alone, or by unobserved heterogeneitypertaining to entire continents in general.

Accordingly, columns (4) and (5) introduce a set of sociodemographic, geo-graphic and climatic control variables into the regression. The corresponding re-gressions show that genetic distance to South Africa remains a significant predictorof population-level risk attitudes. Interestingly, after conditioning on genetic dis-tance from South Africa, the coefficient on linguistic distance vanishes. We willreturn to this issue in the framework of our dyadic regressions to be developed inthe next section. Notably, however, columns (4) and (5) already indicate that ge-netic distance from South Africa is a much better predictor of risk attitudes thansimple geographic variables. Note, for instance, that the geodesic distance fromSouth Africa (i.e., the shortest path on the earth’s surface between South Africaand any other country) exhibits a strong raw correlation with average risk attitudes(see column(3)), yet genetic distance fully trumps geodesic distance in the mul-tivariate regressions. This finding already suggests that the relationship betweengenetic distance and risk preferences is not an artifact of the correlation betweengenetic and geographic variables. Instead, the particular migration routes of ourancestors - rather than simple shortest-distance calculations between contemporary

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Table 3: Average willingness to take risks and FST genetic distance to South Africa

Dependent variable: Average willingess to take risks

(1) (2) (3) (4) (5)

Fst genetic distance to South Africa -0.61∗∗∗ -0.45∗∗ -0.48∗∗(0.17) (0.18) (0.22)

Linguistic distance to South Africa -0.54∗∗ 0.016 0.37(0.23) (0.32) (0.62)

Geodesic distance to South Africa -0.51∗∗∗ -0.008 -0.21(0.14) (0.15) (0.40)

Average age -0.029∗∗ -0.036∗∗(0.01) (0.01)

Fraction female 0.095 1.02(1.74) (1.94)

Log [GDP p/c PPP] 0.14∗∗ 0.15∗∗(0.06) (0.06)

Life expectancy at birth -0.006 -0.00003(0.01) (0.01)

Constant 0.35∗∗∗ 0.45∗∗ 0.46∗∗∗ 0.77 -1.17(0.11) (0.20) (0.13) (2.49) (3.10)

Geographic and climatic controls No No No Yes YesContinent FE No No No No Yes

Standardized beta (%) -47.7 -35.1 -37.3Observations 73 73 73 73 73Adjusted R2 0.217 0.141 0.219 0.352 0.304

OLS estimates, robust standard errors in parentheses. Standardized beta refers to Fst genetic distance toSouth Africa. The control variables include latitude, longitude, average precipitation, and average tempera-ture. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

locations - need to be taken into account in order to understand the cross-countrydistribution of risk preferences.

While the unilateral regressions just discussed are important to understand thedeterminants of particular levels of risk aversion, they make inefficient use of thegenetic distance data. In particular, the corresponding regressions ignore the infor-mational content of genetic distances between countries other than South Africa.Thus, analogously to Spolaore and Wacziarg (2009), we now generalize our regres-sions into a dyadic approach, which makes explicit use of the genetic distance dataas bilateral variable.

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Sub-Saharan Africa ME & North Africa EU & Central Asia East AsiaSouth Asia South America North America

Figure 2: Average willingness to take risks and FST genetic distance to South Africa

4.2 Dyadic Approach: Baseline Results

The dyadic regression methodology pursued in the remainder of this section takeseach possible pair of countries as unit of observation. Accordingly, we match eachof the 73 countries with every other country into a total of 2,628 country pairs andrelate the genetic distance pertaining to any country pair to the (absolute) differencein average risk attitudes between the respective populations. Our baseline regressionequation is hence given by

|riski − riskj| = α + β × genetic distancei,j + εi,j

where riski and riskj represent the average willingness to take risks in countries iand j, respectively, while εi,j is a country pair specific disturbance term. Regardingthe latter, notice that our empirical approach implies that each country will ap-pear multiple times as part of the dependent variable. Thus, in order to allow forclustering of the error terms at the country-level, we employ the two-way clusteringstrategy of Cameron, Gelbach and Miller (2011), i.e., we cluster at the level of thefirst and of the second country of a given pair. This procedure allows for arbitrarycorrelations of the error terms within a group, i.e., within the group of countrypairs which share the same first country or which share the same second country,respectively.

Columns (1) and (2) of Table 4 provide the results of unconditional OLS re-gressions of absolute differences in risk attitudes on the genetic distance measures.Column (1) shows that the FST genetic distance discussed above is a strong predic-

23

tor of differences in average risk attitudes. In quantitative terms, the standardizedbeta is large and indicates that a one standard deviation increase in genetic dis-tance comes along with an increase of roughly one-third of a standard deviationin differences in risk attitudes. Interestingly, in this baseline regression, both thestandardized beta and the explained variance are well in the ballpark of the respec-tive values in Spolaore and Wacziarg’s (2009) analysis of the relationship betweendifferences in national income and genetic distance.

Column (2) of Table 4 introduces the Nei genetic distance. Interestingly, thecoefficient on this distance measure is very similar to the one on FST and highlystatistically significant. We will continue to use this measure to exemplify therobustness of our results below.

Column (3) relates the absolute difference in average risk attitudes to the lin-guistic distance between the respective populations. Again, this proxy for ancientmigration patterns exhibits a strong unconditional relationship with differences inrisk preferences. However, as columns (4) and (5) indicate, similarly to the levelregressions discussed in the previous section, the coefficient on linguistic distancevanishes once genetic distance is accounted for. Note that this pattern is consis-tent with our interpretation of the genetic and linguistic variables as proxying forthe timing of separation between ancient groups. It is a well-known fact in thepopulation genetics and linguistics literatures that genetic distance appears to bea higher-quality measure of the underlying patterns of separation. First, while lan-guages generally maintain a certain structure over long periods of time, in somecases they change or evolve very quickly. For instance, during the European settle-ment of the African continent, in many cases were the ancient languages replacedby those of the colonizers, but due to the relatively small number of colonizers thegenetic pools remained rather unchanged. Thus, in general, the slow-moving natureof aggregate genetic endowment makes genetic distance a more robust measure ofancient breakups of populations, also see Cavalli-Sforza (1997). In addition, anyquantitative measure of linguistic distance suffers from the fact that there is no nat-ural metric on languages. While language trees are a useful tool to circumvent thisproblem, they remain coarse in nature, potentially introducing severe measurementerror. For example, 1210 country pairs in our sample share the maximum linguisticdistance of one, while the genetic distance in this subset varies between 0.02 and0.99.16

16Also see the corresponding discussion in Mecham, Fearon and Laitin (2006).

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Table 4: Risk preferences and genetic distance

Dependent variable:Absolute difference in average risk attitudes(1) (2) (3) (4) (5)

Fst genetic distance 0.37∗∗∗ 0.37∗∗∗(0.10) (0.11)

Nei genetic distance 0.40∗∗∗ 0.40∗∗∗(0.10) (0.11)

Linguistic distance 0.20∗∗∗ -0.003 0.011(0.07) (0.08) (0.08)

Constant 0.22∗∗∗ 0.22∗∗∗ 0.16∗∗ 0.22∗∗∗ 0.21∗∗∗(0.04) (0.03) (0.07) (0.06) (0.06)

Standardized beta (%) 32.4 33.9 32.5 33.5Observations 2628 2628 2628 2628 2628Adjusted R2 0.105 0.114 0.021 0.104 0.114

OLS estimates, twoway-clustered standard errors in parentheses. Standardized be-tas refer to the genetic variables. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

4.3 Dyadic Approach: Controlling for Demographics, In-

come, Institutions, Geography, and Climate

The argument made in this paper is that the strong unconditional relationship be-tween differences in risk preferences and genetic distance reflects the latent causalimpact of ancient migration patterns (and the differential historical experiencesassociated with them) on risk attitudes. However, as in other studies employingaggregate data on human genetic variation, causality is by no means obvious. Inparticular, to the extent that such variables are correlated with genetic distance,differences in idiosyncratic country characteristics that drive differential risk atti-tudes could bias our coefficient of interest. To address this issue, we subject ourresult to an extensive and comprehensive set of control variables, which are com-monly used in the comparative development and economic geography literatures.Specifically, we provide evidence suggesting that our result is neither driven byconfounding demographic, economic, or institutional factors nor by geographic orclimatic variables. Again note that since our dependent variable consists of absolutedifferences, all of our control variables will also be bilateral in nature. In essence,in what follows, our augmented regression specification will be

|riski − riskj| = α + β × genedisti,j + γ × di,j + δ × |xi − xj|+ εi,j

25

where dij is a vector of inherently bilateral measures between countries i and j (suchas their geodesic distance or a colonial relationship dummy). The vectors xi andxj, on the other hand, denote variables which are idiosyncratic to a specific country(such as GDP, average age, or average temperature). Details on the definitions andsources of all control variables can be found in Appendix F.

4.3.1 Controlling for Demographics, Income, and Institutions

Demographics and Population Structure

Consistent with previous within-country evidence, section 4.1 established sizablecorrelations between risk preferences on the one and the average age and the fractionof females in a country on the other hand. Since genetic distance might correlatewith between-country differences in these (or other) demographic variables, we needto ensure that our coefficient of interest does not spuriously pick up the effect ofdemographic differences or other variables pertaining to the population structure.Accordingly, as a first step, column (2) of Table 5 adds to the baseline specification(restated for convenience in column (1)) the absolute differences in average age andthe proportion of females, but this does not affect the coefficient on genetic distance.

Data on genetic and linguistic distances between populations correlate with mea-sures of the religious composition of the respective populations. Thus, analogouslyto our linguistic distance measure, we constructed an index of religious distancebetween two countries. To this end, we followed the same methodology as in deriv-ing linguistic distances, i.e., we first computed the number of common nodes tworeligions share in the “religion tree” of Fearon (2006) and transformed this seriesaccording to

Distance between two religions = 1−√

# Common nodes5

We then computed weighted religious distance estimates between two countriesby weighing the distances between religions with the respective population shares,so that our final distance measure represents the expected religious distance betweentwo randomly drawn individuals, one from each country. This measure exhibits amodest correlation with genetic distance (ρ = 0.18). Column (3) of Table 5 furthercontrols for this distance measure. The results show that religious distance indeedpredicts differences in risk taking even conditional on genetic and linguistic distance.However, this effect operates independently of the genetic distance effect.

A potentially important determinant of how people perceive risks is the compo-sition of their social environment. For instance, a large ethnic fractionalization ora high population density might increase the occurrence of civic conflicts, poten-

26

tially altering the way people cope with risky choice situations. Thus, column (4)introduces as additional covariates the absolute differences in (i) ethnic as well asreligious fractionalization, (ii) the fraction of the population who are of Europeandescent, and (iii) a country’s (log) population density. While differences in religiousfractionalization are indeed correlated with differences in risk preferences, the coeffi-cient on the fraction of people of European descent is actually negative.17 However,neither in terms of the size of the point estimate nor in terms of its statistical sig-nificance does the inclusion of these covariates affect our result.

National Income and Institutions

A potential concern with our baseline specification is that it ignores differences incomparative development across countries. In particular, given that genetic distanceis known to correlate strongly with differences in national income (Spolaore andWacziarg, 2009), our coefficient of interest could spuriously pick up the effect ofliving standards on risk-taking behavior. In order to ensure that this does not driveour results, column (5) of Table 5 introduces the average log real GDP per capitaPPP from 2001 through 2010 as measure of national income. If anything, this causesthe coefficient on genetic distance to become slightly larger. The general insightemerging from this result is robust to including alternative measures of economicdevelopment. Appendix C.2 shows that including differences in inequality or theaverage number of years of education in a country pair yields very similar results.

It is conceivable that the degree of development of the institutional environmenthas an impact on how people perceive and cope with economic risks. Thus, we addi-tionally condition on differences in institutional quality. Our proxies for differencesin this dimension consist of two sets of variables, which proxy for (i) differencesin contemporary institutions and (ii) the historical origins of the vast differencesin institutional quality across countries. Specifically, we control for (absolute dif-ferences in) a variable gauging the extent of democracy and the World Bank legalrights index as proxies of contemporary institutional quality. Our measures of thehistorical origins of institutions consist of a set of dummies, which take on the valueone if both countries in a pair share the same legal origin, if they were in a colonialrelationship at some point, if they had a colonial relationship after 1945, or if theyhad a common colonizer post 1945.

Column (6) of Table 5 shows that introducing these institutional variables haslittle, if any, effect on our main result. This insight is robust to using alternative

17This negative coefficient is probably an artifact of this variable’s correlation with geneticdistance (ρ = 0.09). In an unconditional regression, the coefficient on the fraction of Europeandescent is almost zero and far from being significant (p = 0.98).

27

Table 5: Risk preferences, demographics, and economic structure

Dependent variable:Absolute difference in average risk attitudes

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

Fst genetic distance 0.37∗∗∗ 0.36∗∗∗ 0.37∗∗∗ 0.35∗∗∗ 0.38∗∗∗ 0.37∗∗∗(0.10) (0.11) (0.11) (0.10) (0.10) (0.10)

Linguistic distance -0.013 -0.072 -0.085 -0.086 -0.077(0.07) (0.07) (0.08) (0.08) (0.08)

∆ Average age 0.0024 0.0024 0.0055∗ 0.0092∗∗ 0.010∗∗∗(0.00) (0.00) (0.00) (0.00) (0.00)

∆ Proportion female 0.040 -0.021 -0.072 -0.16 -0.10(0.74) (0.74) (0.72) (0.70) (0.70)

Religious distance 0.11∗ 0.11∗ 0.11∗ 0.094(0.06) (0.06) (0.06) (0.06)

∆ Religious fractionalization 0.16∗∗ 0.16∗∗ 0.18∗∗(0.07) (0.07) (0.07)

∆ Ethnic fractionalization 0.019 0.030 0.023(0.06) (0.06) (0.06)

∆ % Of European descent -0.050∗∗∗ -0.048∗∗∗ -0.042∗∗(0.02) (0.02) (0.02)

∆ Log [Population density] 0.0089 0.011 0.011(0.02) (0.02) (0.02)

∆ Log [GDP p/c PPP] -0.035∗∗ -0.033∗∗(0.02) (0.02)

∆ Democracy index -0.0049∗∗(0.00)

∆ Legal rights index -0.010∗(0.01)

1 if common legal origin -0.019(0.02)

1 if ever colonial relationship -0.035(0.03)

1 if colonial relationship post 1945 -0.0045(0.04)

1 if common colonizer post 1945 0.027(0.03)

Constant 0.22∗∗∗ 0.17 0.22 0.23 0.32 0.31(0.04) (0.72) (0.72) (0.70) (0.68) (0.69)

Standardized beta (%) 32.4 32.2 32.4 30.8 33.7 32.3Observations 2628 2628 2628 2628 2628 2628Adjusted R2 0.105 0.105 0.112 0.128 0.139 0.145

OLS estimates, twoway-clustered standard errors in parentheses. The standardized beta refers to genetic distance.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

28

measures of institutional quality such as the social infrastructure index of Hall andJones (1999b) or an index gauging the constraints imposed on the executive (seeAppendix C.2).

4.3.2 Controlling for Geography and Climate

Recall that our “world map” of risk preferences in section 2.1 suggests the presenceof geographic patterns in the distribution of risk attitudes. In principle, it maywell be that specific contemporary geographic or climatic features cause specificpatterns of attitudes towards risk. However, human migration patterns (and hencegenetic distance measures) are substantially correlated with geographic and climaticvariables. For instance, by the serial founder effect, genetic distance co-moves withstandard geographic distance measures. Thus, to ensure that any effects stemmingfrom variations in geography or climate are not wrongly attributed to genetic dis-tance, we now condition on an exhaustive set of corresponding control variables.We first consider a number of different geographical distance measures, before weturn to differences in various micro-geographic and climatic characteristics betweencountries. Finally, we take special care in ensuring that our result is neither drivenby the correlation between risk preferences and the health environment, nor by thepresence of of regional fixed effects.

Distance Metrics

Column (1) of Table 6 restates our baseline regression from Table 4, while column (2)repeats the regression including all demographic, economic, and institutional co-variates from Table 5. Column (3) introduces four distance metrics as additionalcontrols into this regression. Our first geographical control variable consists of thegeodesic distance (measuring the shortest distance between any two points on earth)between the most populated cities of the countries in a given pair. Relatedly, weintroduce a dummy equal to one if two countries are contiguous. Finally, we alsocondition on the “distance” between two countries along the two major geographicalaxes, i.e., the difference in the distance to the equator and the longitudinal (east-west) distance. Again, the introduction of these variables has virtually no effecton the coefficient on genetic distance. Appendix C.2 further demonstrates the ro-bustness of this result against including the transportation cost between countries’capitals as further distance measure.

These results suggest that it is not geographic distance per sé which drives dif-ferential risk attitudes but rather the precise pattern of migration steps and thedistribution of temporal distances associated with these successive breakups.

29

Micro Geography and Climate

Given that geographic distance as such does not seem to drive our result, we nowcontrol for more specific information about differences in the micro-geographic andclimatic conditions between the countries in a pair. To this end, we make use ofa wealth of information on the agricultural productivity of land, different featuresof the terrain, and climatic factors. More specifically, we consider the size of acountry, the average suitability of land for agriculture (based on climatic conditionsand soil quality), the percentage of arable land, the log of the year of the timingof the neolithic transition (as proxy for the natural productivity of land), an indexof terrain roughness and the mean as well as the standard deviation of a country’selevation.

In addition, we condition on a comprehensive set of climatic covariates. Tothis end, we make use of the concept of (twelve) Köppen-Geiger climatic zones.Specifically, for each country pair, we construct twelve variables which reflect theabsolute difference in the fraction of the population which live in the respectiveclimate zone. This set of variables alone explains about 15 % of the variationin differences in risk attitudes. Additionally, we control for differences in averageprecipitation and average temperature.

The inclusion of these geographic and climatic controls has virtually no effect onthe genetic distance point estimate. Note that this is a remarkable result: Despitethe large and comprehensive set of geographic and climatic variables (and the cor-relations between genetic distance and certain geographic features), the coefficienton genetic distance is very stable. Again, this suggests that the specific temporalpatterns of population movements rather than geographic characteristics cause dif-ferences in risk preferences. In Appendix C.2, we show that our results are robust toincluding other micro-geographic features such as whether a country is landlockedor the percentage of land within 100 km of a waterway

Health Risks

Recall that the average willingness to take risks in a country is strongly positivelycorrelated with the health risks in the respective environment. This relationshipextends to our bilateral framework: For example, in an unconditional regression ofabsolute differences in risk attitudes on absolute differences in life expectancy, thecoefficient on the latter is highly statistically significant. Thus, to ensure that thecoefficient on genetic distance is not significantly biased upward due to the omissionof these variables, we now condition on differences in life expectancy, the fractionof deaths due to communicable diseases, and the fraction of people who are at risk

30

Table 6: Risk preferences, geography, and climate

Dependent variable:Absolute difference in average risk attitudes

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

Fst genetic distance 0.37∗∗∗ 0.37∗∗∗ 0.35∗∗∗ 0.37∗∗∗ 0.32∗∗∗ 0.30∗∗∗(0.10) (0.10) (0.08) (0.09) (0.09) (0.10)

Geodesic distance 0.022∗∗ 0.015∗ 0.0097 0.0087(0.01) (0.01) (0.01) (0.01)

1 for contiguity -0.050 -0.029 -0.027 -0.017(0.04) (0.03) (0.03) (0.03)

∆ Distance to equator -0.0036∗∗∗ -0.0035∗∗∗ -0.0025∗∗∗ -0.0045∗∗∗(0.00) (0.00) (0.00) (0.00)

∆ Longitude -0.0025∗∗∗ -0.0017∗∗ -0.0012∗∗ -0.00018(0.00) (0.00) (0.00) (0.00)

∆ Log [Area] 0.0014 0.0024 0.0029(0.00) (0.00) (0.00)

∆ Land suitability for agriculture -0.031 -0.024 -0.019(0.04) (0.04) (0.05)

∆ % Arable land -0.000044 0.00012 0.00023(0.00) (0.00) (0.00)

∆ Log [Neolithic transition] 0.016 -0.030 -0.067∗(0.04) (0.04) (0.04)

∆ Terrain roughness 0.069 0.049 0.076(0.10) (0.09) (0.08)

∆ Mean elevation 0.021 0.019 -0.013(0.03) (0.02) (0.03)

∆ SD Elevation -0.12∗∗∗ -0.11∗∗∗ -0.047(0.03) (0.02) (0.03)

∆ Ave precipitation 0.00025 0.00027 0.00067∗∗(0.00) (0.00) (0.00)

∆ Ave temperature 0.00021 -0.000061 0.0012(0.00) (0.00) (0.00)

∆ Life expectancy 0.0070∗∗∗ 0.0032(0.00) (0.00)

∆ Fraction of deaths due to communicable diseases 0.0014 0.0015(0.00) (0.00)

∆ % Population at risk of malaria -0.065 -0.11∗∗(0.05) (0.06)

Constant 0.22∗∗∗ 0.31 0.29 0.051 0.14 -0.25(0.04) (0.69) (0.66) (0.54) (0.53) (0.52)

Demographic and economic controls No Yes Yes Yes Yes Yes∆ % Population in 12 KG climate zones No No No Yes Yes YesContinent FE No No No No No Yes

Standardized beta (%) 32.4 32.3 31.0 32.5 28.3 26.0Observations 2628 2628 2628 2628 2628 2628Adjusted R2 0.105 0.145 0.203 0.266 0.286 0.344

OLS estimates, twoway-clustered standard errors in parentheses. See Table 5 for a listing of the demographic and economic control variables.The standardized beta refers to genetic distance. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

31

of contracting malaria.Column (6) of Table 6 shows the inclusion of these variables does not change the

spirit of our results. While life expectancy still confers a significant relationship withrisk attitudes, the point estimate on genetic distance remains well in the ballparkof our previous analyses and is highly significant statistically.

Regional Fixed Effects

As in all cross-country regressions, a potential concern is that our main variableof interest might simply pick up regional effects. For example, the largest geneticdistances occur between countries from different continents, in particular betweenAfrican and non-African countries. To account for this possibility, we constructedan extensive set of 28 continental dummies each equal to one if the two countrieswere from two given continents.18 For example, we have a regional dummy equalto one if both countries are from Sub-Saharan Africa, and another one equal to oneif one country is from Sub-Saharan Africa and the other one from North America.Note that this is a conservative specification as it takes out a considerable part ofthe variation in genetic distance, which by the patterns of our ancestors’ migrationpaths ought to be correlated with continental fixed effects.

The inclusion of these fixed effects reduces by only about 7 % the magnitude ofthe coefficient on genetic distance, suggesting the presence of very weak continentalfixed effects (conditional on the large set of covariates). However, the genetic dis-tance effect remains statistically significant at any conventional level. Its size is stilllarge with a standardized beta of 26 %. Section 4.4 provides additional evidenceon the robustness of our results to continental fixed effects by excluding continentsone-by-one from the analysis. In Appendix C.2, we present a further regression inwhich we insert a set of 73 country dummies each equal to one if a given countryis part of a country pair. Even under this very conservative specification, whichalso includes all covariates, the coefficient on genetic distance is highly significantstatistically and has a standardized beta of 17 %.

4.3.3 Selection on (Un-)Observables

In sum, conditioning on a large set of economic, institutional, geographic, climatic,and demographic variables, the relationship between genetic distance and differencesin risk preferences is highly significant and considerably large in magnitude. Fur-thermore, the corresponding point estimate is very robust: While the standardizedbeta is 26 % in the full regression model, it is 32 % in the unconditional regression.

18Appendix F provides an overview of the matching of countries to continents.

32

In fact, following the work of Altonji et al. (2005) and Bellows and Miguel(2009), this latter observation on the effect of observables can also be used to assessthe extent to which unobservable omitted variables likely bias our result. Theseauthors show that by comparing the coefficients in the unconditional and the fullregression model, the ratio β̂full

β̂unconditional−β̂fullyields an estimate of how much larger

the bias arising from unobservable factors would need to be relative to the biasresulting from observables, in oder to generate the observed coefficient if the truecoefficient was actually zero. If this ratio equals α, then omitted variables can onlyexplain the observed coefficient if the bias resulting from unobservables equals atleast α times the bias resulting from observed variables. In our case, comparingthe coefficients in columns (1) and (6) of Table 6, the ratio equals 4.3. Thus, ifthe genetic distance effect was to be explained away by selection on unobservables,then this bias would have to be at least 4.3 times as strong as selection on the verylarge and comprehensive set of covariates that we included in our regressions.

4.4 Dyadic Approach: Robustness

This section illustrates the robustness of the insights from the previous section bypresenting two additional sets of results. First, we take special care in ensuringthat the relationship between genetic distance and risk attitudes is not driven byrelatively large genetic distances. Second, we provide a further robustness check toaddress potential concerns that continent fixed effects might drive our result.

While the majority of the genetic distances in our sample are relatively small,we also observe a number of large genetic distances, implying that the sampledistribution in our dataset is right-skewed (see Appendix A.3 for a visualization).In order to ensure that our results are not driven by these large distances, we pursuetwo distinct strategies. First, using the same specification as in our full regressionmodel in column (6) of Table 6, we exclude the largest genetic distances step-by-step.Second, we reduce the magnitude of differences in genetic distances by estimatinga log-log relationship between differences in risk preferences and genetic distance.Columns (1) through (4) of Table 7 present the results of the regressions utilizingthe restricted samples, which demonstrate that excluding large distances does notaffect the statistical significance of our result. If anything, the standardized betabecomes even larger in these samples. Furthermore, column (9) shows that thespirit of our results is unaffected in a log-log specification.

Our control variables already accounted for the presence of differences in regionalfixed effects. To provide further evidence that no single region drives our result,columns (5) through (8) of Table 7 present the results from regressions which ex-

33

clude the four major regions of the world one-by-one. While the point estimate ongenetic distance is somewhat variable across the different specifications, the resultsnevertheless illustrate the robustness of our qualitative insight. In particular, in allspecifications, the results remain statistically significant despite the large drop inthe number of observations.

In Appendix C.1 we provide further robustness checks by employing differentdependent variables than the absolute difference in average risk attitudes. First, weshow that - conditional on all covariates from our full regression model in column (6)of Table 6 - our main result continues to hold when we use the absolute differencein the 25th, 50th or 75th percentile of risk attitudes as dependent variable. Second,recall that our index of risk aversion consists of a linear combination of two typesof survey times, namely a qualitative self-assessment and a series of quantitativebinary lottery choices. By employing both items separately, we show that the effectof genetic distance on risk preferences is not specific to a particular type of surveyquestion, which further emphasizes the robustness of our results.

4.5 Dyadic Approach: Alternative Genetic Distance Mea-

sures

Up to this point, all of our analyses were conducted using the concept of FST geneticdistance, which in turn is based on observed variation in allelic frequencies acrosspopulations. We now conduct three additional robustness checks, each of whichaddresses a particular feature of the genetic data.

First, we present a set of regressions which utilize additional information onthe measurement precision of the genetic distance data. Since the data on allelefrequencies are collected from different sample sizes across countries, the precisionof the measurement varies across country pairs. Thus, using bootstrap analysis,Cavalli-Sforza et al. (1994) compute standard errors of the FST genetic distance foreach pair. Following the methodology proposed by Spolaore and Wacziarg (2009),we utilize this information by linearly downweighing each observation by its stan-dard error (see Appendix D). The results from the corresponding weighted leastsquares regression, again featuring all covariates from our main analysis, are pre-sented in column (2) of Table 8. The resulting standardized beta coefficient is highlysignificant and very similar to the one from the unweighted regression, providingreassuring evidence that our results are not driven by measurement error.

34

Tab

le7:

Rob

ustness:

Exclude

largegeneticdistan

cesor

continents

Depen

dentvariable:

Restrictedsample:

Fst<

Exclude

continents

Log[A

bs.diffe

rence

Baseline

<0.85

<0.7

<0.5

Americas

EU

&Central

Asia

Africa&

ME

SEAsia&

Pacific

inrisk

attitudes]

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Fstgeneticdistan

ce0.30

∗∗∗

0.35

∗∗∗

0.46

∗∗∗

0.44

∗∗∗

0.33

∗∗∗

0.20

∗∗0.30

∗∗∗

0.44

∗∗∗

(0.10)

(0.09)

(0.11)

(0.10)

(0.09)

(0.09)

(0.07)

(0.10)

Log[Fst

geneticdistan

ce]

0.19

∗∗∗

(0.06)

Con

stan

t-0.25

-0.11

-0.18

-0.18

0.21

0.47

0.18

0.07

1-3.28∗

∗

(0.52)

(0.52)

(0.53)

(0.57)

(0.52)

(0.42)

(0.48)

(0.61)

(1.50)

Dem

ograph

ican

decon

omic

controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Geograp

hican

dclim

atic

controls

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Con

tinent

FE

Yes

Yes

Yes

Yes

No

No

No

No

Yes

Stan

dardized

beta

(%)

26.0

29.1

32.9

26.0

29.5

17.6

24.4

37.9

19.3

Observation

s26

2825

5523

2319

8517

1111

2813

7817

1126

28AdjustedR

20.34

40.34

50.31

00.23

10.33

00.27

40.14

90.342

0.26

2

OLS

estimates,twow

ay-clustered

stan

dard

errors

inpa

rentheses.

EU

=Europ

e,ME

=MiddleEast,SE

Asia=

SouthAsiaan

dSo

uth-EastAsia.

The

variab

lesin

logs

aredefin

edaslog[0.01+x],

wherexdeno

testheoriginal

variab

le.SeeTab

le5an

dTab

le6foralisting

ofthedemog

raph

ic,e

cono

mic,g

eograp

hic,

andclim

atic

controlv

ariables.The

stan

dardized

beta

refers

togeneticdistan

ce.

∗p<

0.10,

∗∗p<

0.05

,∗∗∗p<

0.01

35

Second, while all of our main analyses were conducted using the concept of FSTgenetic distance, section 4.2 already introduced the Nei genetic distance, which alsoexhibits a strong unconditional association with differences in risk attitudes. Thisconcept is based on the same observed variation in allelic frequencies, but uses adifferent methodology to arrive at genetic distance estimates between populations.We now use the Nei distance to assess the robustness of our conditional regressionresults. Column (3) of Table 8 shows that - again conditional on all covariates fromour full regression model - the Nei distance is a strong predictor of differences inrisk attitudes. This holds both for the unweighted and the weighted regression (seecolumn (4)). Again, the standardized betas are well within the range of those inthe regressions employing the concept of FST .

In sum, this set of results suggests that the particular ways in which populationgeneticists collect data on allelic frequencies and aggregate them into genetic dis-tance estimates does not drive our results. However, in an abstract sense, all of ouranalyses rely on a single set of data on observed genetic variation. Thus, we nowshow that we obtain qualitatively similar results when employing data on predictedgenetic distances between countries. In fact, using these data does not only consti-tute an important robustness check with an independent dataset, but is interestingfor the interpretation of our results in itself. Since the construction of predictedgenetic distance estimates directly exploits ancient migration patterns, this analysiswill also provide more direct evidence on our interpretation of the effect of geneticdistance on differences in risk attitudes.

Columns (5) and (6) of Table 8 report the results of unconditional and con-ditional regressions of differences in risk attitudes on the predicted FST distance.While the standardized beta is somewhat lower than when using observed geneticdistance, the effect is still relatively large and statistically significant. In fact,the lower point estimate might well reflect attenuation bias as resulting from theabstract (and potentially noisy) construction of the predicted genetic distance mea-sure. In our view, the results obtained with the predicted measure (which areessentially only a linear transformation of migratory distances) further emphasizethat it is indeed ancient migration patterns rather than genetic distance as suchwhich is at the core of our analysis.

36

Table 8: Alternative genetic distance measures

Dependent variable:Absolute difference in average risk attitudes

OLS WLS OLS WLS OLS OLS(1) (2) (3) (4) (5) (6)

Fst genetic distance 0.30∗∗∗ 0.38∗∗∗(0.10) (0.09)

Nei genetic distance 0.29∗∗∗ 0.35∗∗∗(0.09) (0.09)

Predicted Fst genetic distance 0.16∗ 0.26∗∗∗(0.09) (0.09)

Constant -0.25 -0.27 -0.18 -0.21 0.29∗∗∗ -0.21(0.52) (0.54) (0.54) (0.56) (0.03) (0.54)

Demographic and economic controls Yes Yes Yes Yes No YesGeographic and climatic controls Yes Yes Yes Yes No YesContinent FE Yes Yes Yes Yes No Yes

Standardized beta (%) 26.0 29.9 24.3 24.5 11.2 17.9Observations 2628 2628 2628 2628 2628 2628Adjusted R2 0.344 0.320 0.343 0.290 0.012 0.339

Twoway-clustered standard errors in parentheses. The weighted least-squares regressions linearly downweighobservations by the standard error of the genetic distance estimate, as obtained from bootstrap analysis byCavalli-Sforza et al. (1994), see Appendix D for details. See Tables 5 and 6 for a listing of the demographic,economic, geographic, and climatic controls. The standardized beta refers to genetic distance. ∗ p < 0.10, ∗∗

p < 0.05, ∗∗∗ p < 0.01

5 Preference Heterogeneity and Genetic Diversity

The serial founder effect hides an additional - more subtle - implication of the ideathat ancestral migration patterns can organize the contemporary distribution ofrisk preferences: If the successive breakups of population groups leads to a loss ingenetic diversity, then it is plausible that such breakups should also be associatedwith a reduction in preference heterogeneity. Thus, preference heterogeneity andgenetic diversity should correlate, a hypothesis which we now test.

To this end, we relate the standard deviation in risk attitudes to the (predicted)genetic diversity in that country. Formally, the regression equation is given by

σi = α + β × genetic diversityi + γ × xi + εi

where σi denotes the standard deviation in risk attitudes within a country, xi avector of covariates, and εi a disturbance term. Table 9 provides an overview of theresults of corresponding OLS regressions.

37

Column (1) indicates that genetic diversity exhibits a substantial raw correlationwith the standard deviation in risk attitudes. The estimated coefficient is not onlystatistically significant, but also quantitatively large with a standardized beta of25 %. Figure 3 provides a graphical illustration of this relationship.

Columns (2) through (7) add several control variables to the baseline specifi-cation. The details of the corresponding regressions, i.e. the coefficients on thecovariates, can be found in Appendix E.1. Turning to our control variables, incolumn (2) we first condition on the average willingness to take risks, which ispotentially correlated with its standard deviation.

Column (3) introduces a large vector of sociodemographic and economic controlvariables. Since attitudes towards risk are correlated with age and gender, theheterogeneity of a society along these two dimensions might also be correlated withthe heterogeneity in risk attitudes. To the extent that the age and gender structureis correlated with genetic diversity, this would introduce bias into our estimation.Thus, we add as additional controls an index of gender heterogeneity, the standarddeviation in age, and the levels of these two variables. In addition, we conditionon log GDP p/c (both linearly and squared), log population size, and the standarddeviation in log household income.

Analogously to the between-country case, genetic diversity is substantially cor-related with linguistic and religious heterogeneity. In order to ensure that ourresults are not driven by these more specific cultural variables, in column (4) weadditionally condition on linguistic and religious diversity.19

Columns (5) through (7) introduce three sets of geographic, climatic, and healthcovariates. First, in column (6) we condition on life expectancy and the fractionof people at risk of contracting malaria. Column (6) controls for absolute latitude,longitude, average precipitation, average temperature, the (log) Neolithic revolutiontiming, and terrain roughness. Finally, column (7) includes continental fixed effects.

The inclusion of the large set of control variables in columns (2) through (5)has a negligible, if any, effect on the genetic diversity coefficient. However, ascolumns (5) and (6) indicate, the inclusion of geographic covariates, in particularcontinental fixed effects, leads the coefficient on genetic diversity to drop in sizeand to become insignificant. Part of the reason for this is that genetic diversitycorrelates with these geographical variables and continent dummies. By the serial

19These diversity indices were derived using the same methodology as in the case of linguistic(religious) distances. Specifically, we match a country with itself and then compute the expectedlinguistic (religious) distance between any two individuals within a given country. As argued byFearon (2003), such measures are more informative than mere fractionalization indices since theytake into account the degree of similarity between languages / religions.

38

Table 9: Preference heterogeneity and genetic diversity

Dependent variable:SD in willingness to take risks

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

Predicted genetic diversity 0.11∗∗ 0.10∗ 0.12∗∗ 0.12∗∗ 0.14∗∗ 0.11 0.003(0.05) (0.05) (0.06) (0.06) (0.06) (0.08) (0.12)

Constant 0.87∗∗∗ 0.87∗∗∗ 88.2∗∗∗ 87.2∗∗∗ 88.8∗∗∗ 105.2∗∗∗ 101.6∗∗∗(0.04) (0.04) (19.97) (19.96) (18.74) (16.89) (21.56)

Level of risk aversion No Yes Yes Yes Yes Yes YesDemographic and economic controls No No Yes Yes Yes Yes YesOther diversity measures No No No Yes Yes Yes YesHealth risk proxies No No No No Yes Yes YesGeographic and climatic controls No No No No No Yes YesContinent FE No No No No No No Yes

Standardized beta (%) 24.9 23.0 26.4 27.2 30.3 25.3 0.7Observations 73 73 73 73 73 73 73Adjusted R2 0.049 0.043 0.241 0.223 0.253 0.308 0.264

OLS estimates, robust standard errors in parentheses. Control variables include the average willingness to take risks, themean and standard deviation of a country’s age distribution, the proportion of females as well as a gender heterogeneityindex, log GDP p/c and its square, linguistic and religious diversity, life expectancy, the fraction of people at risk of con-tracting malaria, distance to the equator, longitude, average temperature and precipitation, terrain roughness, the timingof the Neolithic revolution, and seven continental dummies. The standardized beta refers to genetic diversity. ∗ p < 0.10,∗∗ p < 0.05, ∗∗∗ p < 0.01

BWACMR

GHAKEN

MWI

NGA

RWAZAF

TZA

UGA

ZWE

DZA

EGY

IRN

IRQISR

JOR

MAR

SAU

ARE

AUT

HRV

CZE

EST

FIN

FRA

GEO

DEU GRC

HUN

ITA

KAZ

LTUMDA

NLD

POL

PRT

ROU

RUS

ESPSWECHETUR

UKR

GBR

AUS

KHM

CHN

IDNJPN

KOR

PHLTHA

VNM

AFG

BGD

INDPAK

LKA

ARG

BOL BRA

CHL

COL CRI

GTM

HTI

MEX

NICPER

VEN

CAN

USA

.7.8

.91

1.1

1.2

SD in

willi

ngne

ss to

take

risk

s

0 .2 .4 .6 .8 1Predicted genetic diversity

Sub-Saharan Africa ME & North Africa EU & Central AsiaEast Asia & Pacific South Asia South AmericaNorth America

Figure 3: Genetic diversity and preference heterogeneity

founder effect and the paths of human migration, genetic diversity is largest inAfrica and lowest in South America, with intermediate values prevailing in Europe,Asia, and North America. By including geographic controls and continent dummies,one takes out a substantial fraction of the variation not only in genetic diversity butalso in preference heterogeneity, which was rather low to begin with. Thus, we viewthis set of results as corroborating the idea that ancient migration patterns have

39

had a long-lasting influence on the contemporary distribution of risk preferences.

6 Conclusion

We document and analyze the prevalence and correlates of risk attitudes of rep-resentative population samples in countries spanning all continents. Perhaps sur-prisingly, attitudes towards risk are highly heterogeneous not just within, but alsoacross countries. The main contribution of this paper is to demonstrate that thiscross-country heterogeneity has its deep historical origins in ancestral migrationpatterns. To this end, we show data on genetic variation, which directly reflectthe patterns of migration of our ancestors, explain a considerable fraction of thecross-country variation in risk preferences along two major dimensions. Specifically,countries which are genetically close to each other share similar risk attitudes on av-erage, while genetically homogeneous societies tend to have less dispersed preferencedistributions.

Our paper forms part of a growing literature which seeks to understand the his-torical origins of the contemporary variation in preferences and beliefs. However,the correlations between genetic variation and preference variation could also shedlight on the potential relationship between preferences and culture. In fact, thereare good reasons to believe that there is a close correspondence between geneticvariation, cultural variation, and variation in risk preferences. Since genetic dis-tance increases in the time of separation between two populations and one wouldexpect that longer times spent apart from each other also mean more time to in-dependently develop an own culture, genetic and cultural distance are likely toco-evolve over time. Similarly, just like variation in within-country genetic diver-sity is the result of the serial founder effect (i.e., of non-representative sub-samplesforming new populations), it is intuitive that the serial founder effect also caused areduction in cultural diversity along human migration paths. Again, genetic diver-sity and cultural diversity should correlate. Thus, in essence, the contemporaneousdistribution of genetic variation serves as a proxy for cultural variation both withinand across countries.

However, our paper shows that these cultural proxies correlate with risk pref-erences. In this sense, our results not only provide evidence on the deep origins ofthe distribution of risk preferences, but also suggest that the contemporary cross-country variation in this trait is closely linked to cultural variation in a broadersense. In fact, risk preferences might be best understood as forming part of apopulation’s long-run cultural endowment.

40

References

Alesina, Alberto, Arnaud Devleeschauwer, William Easterly, SergioKurlat, and Romain Wacziarg, “Fractionalization,” Journal of EconomicGrowth, 2003, 8 (2), 155–194.

, Paola Giuliano, and Nathan Nunn, “On the origins of gender roles: Womenand the plough,” Quarterly Journal of Economics, 2013, 128 (2), 469–530.

Altonji, Joseph G, Todd E Elder, and Christopher R Taber, “Selectionon observed and unobserved variables: Assessing the effectiveness of Catholicschools,” Journal of Political Economy, 2005, 113 (1), 151–184.

Arbatli, Cemal Eren, Quamrul Ashraf, and Oded Galor, “The Nature ofCivil Conflict,” Working Paper, 2013.

Ashraf, Quamrul and Oded Galor, “Genetic Diversity and the Origins of Cul-tural Fragmentation,” American Economic Review: Papers & Proceedings, 2013,103 (3), 528–33.

and , “The Out of Africa Hypothesis, Human Genetic Diversity, and Com-parative Economic Development,” American Economic Review, 2013, 103 (1),1–46.

Barro, Robert J and Jong-Wha Lee, “A new data set of educational attainmentin the world, 1950–2010,” Journal of Development Economics, 2012.

Becker, Anke, Thomas Dohmen, Benjamin Enke, Armin Falk, DavidHuffman, and Uwe Sunde, “Human preferences: A global perspective,” Work-ing Paper, 2014.

Bellows, John and Edward Miguel, “War and local collective action in SierraLeone,” Journal of Public Economics, 2009, 93 (11), 1144–1157.

Callen, Michael, Mohammad Isaqzadeh, James D Long, and CharlesSprenger, “Violence and risk preference: Experimental evidence fromAfghanistan,” American Economic Review, 2014, 104 (1), 123–148.

Cameron, A Colin, Jonah B Gelbach, and Douglas L Miller, “Robustinference with multiway clustering,” Journal of Business & Economic Statistics,2011, 29 (2).

41

Cavalli-Sforza, L Luca, “Genes, peoples, and languages,” Proceedings of the Na-tional Academy of Sciences, 1997, 94 (15), 7719–7724.

Cavalli-Sforza, Luigi, Paolo Menozzi, and Alberto Piazza, The history andgeography of human genes, Princeton university press, 1994.

Cesarini, David, Christopher T Dawes, Magnus Johannesson, Paul Licht-enstein, and Björn Wallace, “Genetic variation in preferences for giving andrisk taking,” Quarterly Journal of Economics, 2009, 124 (2), 809–842.

Chen, M Keith, “The effect of language on economic behavior: Evidence from sav-ings rates, health behaviors, and retirement assets,” American Economic Review,2013, 103 (2), 690–731.

Cornsweet, Tom N., “The Staircase-Method in Psychophysics,” American Jour-nal of Psychology, 1962, 75 (3), 485–491.

Desmet, Klaus, Michel Le Breton, Ignacio Ortuno Ortin, and ShlomoWeber, “Stability of nations and genetic diversity,” Working paper, 2009.

Dohmen, Thomas, Armin Falk, David Huffman, and Uwe Sunde, “Theintergenerational transmission of risk and trust attitudes,” Review of EconomicStudies, 2012, 79 (2), 645–677.

, , , , Jürgen Schupp, and Gert G Wagner, “Individual risk attitudes:Measurement, determinants, and behavioral consequences,” Journal of the Euro-pean Economic Association, 2011, 9 (3), 522–550.

, Benjamin Enke, Armin Falk, David Huffman, and Uwe Sunde, “Pa-tience and the wealth of nations,” Working Paper, 2014.

Falk, Armin, Anke Becker, Thomas Dohmen, David Huffman, and UweSunde, “An experimentally validated preference survey module,” Working Paper,2014.

Fearon, James, “Is genetic distance a plausible measure of cultural distance?,”unpublished, Stanford University, 2006.

Fearon, James D, “Ethnic and cultural diversity by country,” Journal of EconomicGrowth, 2003, 8 (2), 195–222.

Fehr, Ernst and Karla Hoff, “Introduction: Tastes, Castes and Culture: theInfluence of Society on Preferences,” Economic Journal, 2011, 121 (556), F396–F412.

42

Fernández, Raquel, “Women, work, and culture,” Journal of the European Eco-nomic Association, 2007, 5 (2-3), 305–332.

and Alessandra Fogli, “Fertility: The role of culture and family experience,”Journal of the European Economic Association, 2006, 4 (2-3), 552–561.

and , “Culture: An Empirical Investigation of Beliefs, Work, and Fertility,”American Economic Journal: Macroeconomics, 2009, 1 (1), 146–177.

Gallup, John L, Jeffrey D Sachs, and Andrew D Mellinger, “Geographyand economic development,” International Regional Science Review, 1999, 22 (2),179–232.

, et al., “The economic burden of malaria.,” American Journal of TropicalMedicine and Hygiene, 2000, 64 (1-2 Suppl), 85–96.

Galor, Oded and Marc P Klemp, “The Out of Africa Hypothesis of ComparativeDevelopment Reflected by Nighttime Light Intensity,” Working Paper, 2014.

and Ömer Özak, “The Agricultural Origins of Time Preference,” WorkingPaper, 2014.

Giuliano, Paola, Antonio Spilimbergo, and Giovanni Tonon, “Genetic dis-tance, transportation costs, and trade,” Journal of Economic Geography, 2014,14 (1), 179–198.

Gorodnichenko, Yuriy and Gerard Roland, “Culture, institutions and thewealth of nations,” Working Paper, 2010.

Grosjean, Pauline, “A history of violence: The culture of honor as a determinantof homicide in the US South,” Journal of the European Economic Association,forthcoming.

Guiso, Luigi, Paola Sapienza, and Luigi Zingales, “Does Culture Affect Eco-nomic Outcomes?,” Journal of Economic Perspectives, 2006, 20 (2), 23–48.

, , and , “Cultural biases in economic exchange?,” Quarterly Journal ofEconomics, 2009, 124 (3), 1095–1131.

Hall, Robert E and Charles I Jones, “Why do some countries produce so muchmore output per worker than others?,” Quarterly Journal of Economics, 1999,114 (1), 83–116.

43

Hall, Robert Ernest and Charles Jones, “Why Do Some Countries ProduceSo Much More Output Per Worker Than Others?,” The Quarterly Journal ofEconomics, 1999, 114 (1), 83–116.

Henn, Brenna M, Luigi L Cavalli-Sforza, and Marcus W Feldman, “Thegreat human expansion,” Proceedings of the National Academy of Sciences, 2012,109 (44), 17758–17764.

Jaeger, David A, Thomas Dohmen, Armin Falk, David Huffman, UweSunde, and Holger Bonin, “Direct evidence on risk attitudes and migration,”The Review of Economics and Statistics, 2010, 92 (3), 684–689.

Mecham, R Quinn, James Fearon, and David Laitin, “Religious Classifi-cation and Data on Shares of Major World Religions,” unpublished, StanfordUniversity, 2006, 1 (5), 18.

Michalopoulosa, Stelios, “The origins of ethnolinguistic diversity,” AmericanEconomic Review, 2012, 102 (4), 1508–1539.

Nordhaus, William D, “Geography and macroeconomics: New data and newfindings,” Proceedings of the National Academy of Sciences of the United Statesof America, 2006, 103 (10), 3510–3517.

Noussair, Charles N, Stefan T Trautmann, and Gijs Van de Kuilen,“Higher order risk attitudes, demographics, and financial decisions,” The Reviewof Economic Studies, 2014, 81 (1), 325–355.

Nunn, Nathan and Leonard Wantchekon, “The Slave Trade and the Originsof Mistrust in Africa,” American Economic Review, 2011, 101 (7), 3221–52.

Porta, Rafael La, Florencio Lopez de Silanes, Andrei Shleifer, and RobertVishny, “The quality of government,” Journal of Law, Economics, and Organi-zation, 1999, 15 (1), 222–279.

Proto, Eeugenio and Andrew J Oswald, “National Happiness and GeneticDistance: A Cautious Exploration,” Working Paper, 2014.

Putterman, Louis and David N Weil, “Post-1500 population flows and thelong-run determinants of economic growth and inequality,” Quarterly Journal ofEconomics, 2010, 125 (4), 1627–1682.

44

Ramachandran, Sohini, Omkar Deshpande, Charles C Roseman, Noah ARosenberg, Marcus W Feldman, and L Luca Cavalli-Sforza, “Supportfrom the relationship of genetic and geographic distance in human populations fora serial founder effect originating in Africa,” Proceedings of the National Academyof Sciences of the United States of America, 2005, 102 (44), 15942–15947.

Rieger, Marc Oliver, Mei Wang, and Thorsten Hens, “Risk preferencesaround the world,” Management Science, 2014.

Spolaore, Enrico and RomainWacziarg, “The diffusion of development,” Quar-terly Journal of Economics, 2009, 124 (2), 469–529.

and , “Long-Term Barriers to the International Diffusion of Innovations,” in“NBER International Seminar on Macroeconomics 2011” University of ChicagoPress 2011, pp. 11–46.

Tabellini, Guido, “Institutions and culture,” Journal of the European EconomicAssociation, 2008, 6 (2-3), 255–294.

Vieider, Ferdinand M, Mathieu Lefebvre, Ranoua Bouchouicha,Thorsten Chmura, Rustamdjan Hakimov, Michal Krawczyk, and Pe-ter Martinsson, “Common components of risk and uncertainty attitudes acrosscontexts and domains: Evidence from 30 countries,” Working Paper, 2013.

, Thorsten Chmura, and Peter Martinsson, “Risk attitudes, development,and growth: Macroeconomic evidence from experiments in 30 countries,” Discus-sion Paper, Social Science Research Center Berlin, 2012.

Voigtländer, Nico and Hans-Joachim Voth, “Persecution Perpetuated: TheMedieval Origins of Anti-Semitic Violence in Nazi Germany,” Quarterly Journalof Economics, 2012, 127 (3), 1339–1392.

45

A Description and Summary Statistics of Main Vari-

ables

A.1 Construction of Genetic Diversity Data

Measures of heterozygosity are computed using data on allelic frequencies withinpopulations, where an allele is a specific variant taken by a gene. Suppose thereare K different genes within a population and that each gene k takes Lk differentvariants within a population. The expected heterozygosity in a population is thengiven by

H = 1− 1

K

K∑k=1

Lk∑l=1

p2k,l

which equals zero if and only if, at each locus, only one allele is present in thepopulation.

Since genetic diversity data are available only for a small sample of countries,we employ predicted values of genetic diversity as pioneered by Ashraf and Galor(2013b). Specifically, these authors make use of the observation that observedgenetic diversity is a decreasing function of migratory distance from East Africa(Addis Ababa). Thus, one can apply the coefficient from a regression of observedgenetic diversity on migratory distance to derive values of predicted genetic diversity(by multiplying the coefficient with expected migratory distance) for each ethnicgroup within a given country. This migratory distance is then adjusted in twoways. First, since today’s population shares not only reflect migration patterns fromancient times but also migration in the post-Columbian era, one needs to account forthe change in genetic diversity resulting from these more recent migrations. To thisend, the genetic diversity of the ancestral populations of the source countries canbe combined with data on post-Columbian population flows in order to constructa weighted average expected heterozygosity measure for today’s populations in anygiven country. However, merely computing the predicted genetic diversity withingroups would fail to account for the genetic diversity arising within each country dueto genetic differences between groups. Thus, in a second step, the predicted geneticdiversity within each country is adjusted using the FST genetic distance betweenthe groups within a given country in order to arrive at a final measure of expectedheterozygosity within a country. After these adjustments, the predicted geneticdiversity reflects the predicted probability that two randomly matched individualsfrom a given country have different alleles at a given spectrum. Ashraf and Galor

46

(2013b) labeled this measure ancestry-adjusted predicted genetic diversity.

A.2 Construction of Genetic Distance Data

Fst genetic distance Our illustration of FST follows Spolaore and Wacziarg(2009). Suppose there are two populations (a and b) of equal size and one genethat can take on two different variants (allele 1 and allele 2). Let pa (pb) be thefrequency of allele 1 in population a (b). As above, the heterozygosity of a givenpopulation is given by the probability that two randomly selected individuals havedifferent alleles at a given locus:

Ha = 1− (p2a + (1− pa)2)

and analogously for population b. The average allele frequencies across the twopopulations are given by

p̄ =pa + pb

2

and1− p̄ =

2− pa − pb2

respectively. Thus, heterozygosity in both populations combined is computed as

Ha,b = 1− (p̄2 + (1− p̄)2)

while the average heterozygosity in both populations is given by

Have =Ha +Hb

2

FST compares Have and Ha,b and is calculated as

FST = 1− Have

Ha,b

=(pa − pb)2

4p̄(1− p̄)

Thus, FST equals zero if and only if the allele distributions are identical acrosspopulations and one if and only if the populations do not share a single alleleat a given locus, facilitating the interpretation of FST as a measure of expectedheterozygosity. In addition, note that under the assumption that only randomgenetic drift affects FST , it can be shown that this measure is an increasing functionof the length of time during which the two populations were separated (Cavalli-Sforza et al., 1994, p. 26).

Taking the FST distance between two populations as given, the weighted FST

47

distance between two countries is given by

FWST =

I∑i=1

J∑j=1

(si × sj × di,j)

where si and sj represent the population shares of the I and J different sub-populations in the two countries and di,j denotes the genetic distance betweensub-populations i and j. This measure was taken from Spolaore and Wacziarg(2009) and normalized to be between 0 and 1.

Nei distance The Nei distance works with the concept of “kinship”, which has aclose correspondence to that of heterozygosity. In the one-gene-two-allele case fromabove, the Nei distance between two populations is defined as

Nei = −ln{papb + (1− pa)(1− pb)[(1−Ha)(1−Hb)]1/2

}= −ln

{papb + (1− pa)(1− pb)

[(p2a + (1− p2a))(p2b + (1− pb)2)]1/2

}which equals zero if and only if the allele distributions are identical across pop-ulations. The weighted Nei distance between two countries is then computed byapplying population shares as in the FST case. Again, we normalize the resultingdata from Spolaore and Wacziarg (2009) to be between 0 and 1.

48

A.3 Summary Statistics

Table 10: Summary statistics of main unilateral variables

Obs Mean SD Min Max

Average willingness to take risks 73 0.01 0.31 -0.79 0.99SD in willingness to take risks 73 0.94 0.09 0.75 1.17Predicted genetic diversity 73 0.70 0.21 0 1

Table 11: Summary statistics of main bilateral variables

Obs Mean SD Min Max

Abs. difference in avg. willingness to take risks 2628 0.34 0.28 0 1.78Fst genetic distance 2628 0.34 0.24 0 1Nei genetic distance 2628 0.30 0.23 0 1Predicted Fst genetic distance 2628 0.32 0.19 0 1Linguistic distance 2628 0.87 0.20 0 1

Table 12: Raw correlations among bilateral variables

(1) (2) (3) (4) (5)Abs. difference in avg. risk attitudes (1) 1Fst genetic distance (2) 0.324 1Nei genetic distance (3) 0.339 0.947 1Predicted Fst genetic distance (4) 0.112 0.540 0.529 1∆ Genetic diversity (5) -0.0346 0.369 0.352 0.795 1

49

0.5

11.

5D

ensi

ty

-1 -.5 0 .5 1Will take risks

kernel = epanechnikov, bandwidth = 0.0914

Average willingness to take risks

0.5

11.

52

Den

sity

0 .5 1 1.5 2Absolute difference in average risk attitudes

kernel = epanechnikov, bandwidth = 0.0501

Abs. difference in average willingness to take risks

01

23

4D

ensi

ty

.7 .8 .9 1 1.1 1.2SD in willingness to take risks

kernel = epanechnikov, bandwidth = 0.0357

SD willingness to take risks

0.5

11.

52

Den

sity

0 .2 .4 .6 .8 1Fst genetic distance

kernel = epanechnikov, bandwidth = 0.0454

Fst genetic distance

05

1015

20D

ensi

ty

.6 .65 .7 .75 .8Predicted genetic diversity (ancestry adjusted)

kernel = epanechnikov, bandwidth = 0.0081

Predicted genetic diversity

Figure 4: Kernel density estimates of main variables

50

B Health Risks and Risk Preferences

As established in section 4.1, the average willingness to take risks in a country pos-itively correlates with the riskiness of the health environment. We further illustratethis point by providing a number of raw correlations between health risk proxiesand country-level risk attitudes.

Variable CorrelationLife expectancy −0.41∗∗∗

Fraction of deaths due to communicable diseases 0.39∗∗∗

Log [HIV prevalence] 0.38∗∗∗Traffic deaths rate 0.33∗∗∗Log [# homicides] 0.23∗

Table 13: Raw correlations between health risk proxies and average willingness to take risks

C Additional Between-Country Regressions

C.1 Alternative Dependent Variable Specifications

This Appendix provides additional robustness checks on our between-country re-sults. Column (1) of Table 14 restates our full regression model, i.e., the resultsof column (5) in Table 6. Columns (2) through (4) employ the same regressionspecification, yet use the absolute difference in the 25th, 50th, and 75th percentileas dependent variable. Interestingly, the results show that the relationship betweengenetic distance and risk attitudes is not specific to the mean of the distribution ofrisk preferences, but extends to other parts of the distribution.

Recall that our main measure of a country’s average willingness to take risksconsists of a weighted average of two survey questions, a hypothetical staircase lot-tery task, and a qualitative question. Columns (5) through (10) demonstrate thatour basic result extends to using each of these survey questions separately. Specifi-cally, columns (5) and (6) employ the absolute difference in the average qualitativeself-assessment as dependent variable, while columns (7) through (8) use the aver-age outcome of the five-step lottery choice procedure. Finally, columns (9) and (10)present two regressions which utilize only the first choice in the sequential lotteryprocedure, i.e. the choice between 160 euros for sure and a 50-50 bet of receiving300 euros or nothing. While this measure clearly ignores a lot of information that iscontained in subsequent choices, it seems especially reliable in that it does not putany weight on increasingly fine decisions which participants might or might not be

51

capable of answering. The corresponding results show that even according to thisvery rough measure of risk attitudes does genetic distance explain about 10 % ofthe variation in preferences.

C.2 Additional Control Variables

Column (1) of Table 15 reproduces our full between-country regression includingall control variables (i.e., column (5) of Table 6). Column (2) introduces four addi-tional geographical control variables to demonstrate robustness. First, we include ameasure of transportation costs (taken directly from Spolaore and Wacziarg (2009))as additional proxy for geographical distance. Second, we construct a dummy equalto one if and only if one of the countries in a given pair is landlocked and the otherone is not. Third, we also condition on differences in the percentage of land whichis located within 100 kilometers of a waterway. Finally, as an additional proxy forthe disease environment, we include differences in the percentage of deaths thatare due to communicable diseases or nutrition. The coefficient on genetic distanceremains almost unchanged.

Column (3) presents an additional regression in which we utilize additional in-dicators of governance, institutional quality, and the level of development. Specifi-cally, we condition on differences in a social infrastructure index, an index gaugingconstraints on the executive, the absolute difference in the average years of school-ing, differences in life expectancy, and in the Gini index.. Despite the large dropin the number of observations, the genetic distance effect remains highly significantand in the quantitative neighborhood of the previous estimations.

Finally, column (4) presents an additional, very conservative, specification whichemploys country fixed effects rather than regional fixed effects. Specifically, weconstruct 73 dummies each equal to one if a given country is part of a countrypair and zero otherwise. While the resulting point estimate is somewhat lower thanin the previous regressions, it is still highly significant statistically and features astandardized beta of still 17 %.

52

Tab

le14:Alterna

tive

depe

ndentvariab

les

Dep

endentvariab

le:Absolutediffe

rencein...

Averag

ePercentiles

Self-assessment

Lotterychoices

(baseline)

25th

50th

75th

(average)

(average

outcom

e)(average

1stchoice)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Fstgeneticdistan

ce0.30

∗∗∗

0.37

∗∗∗

0.31

∗∗∗

0.30

∗∗∗

0.50

∗∗∗

0.45

∗3.02

∗∗∗

1.89

∗∗0.15

∗∗∗

0.09

1∗∗

(0.10)

(0.12)

(0.12)

(0.10)

(0.14)

(0.25)

(1.03)

(0.86)

(0.05)

(0.04)

Con

stan

t-0.25

-0.82

-0.41

0.06

20.76

∗∗∗

-0.059

2.69

∗∗∗

-0.61

0.09

4∗∗∗

-0.095

(0.52)

(0.66)

(0.59)

(0.60)

(0.08)

(1.17)

(0.34)

(3.65)

(0.02)

(0.16)

Dem

ograph

ican

decon

omic

controls

Yes

Yes

Yes

Yes

No

Yes

No

Yes

No

Yes

Geograp

hican

dclim

atic

controls

Yes

Yes

Yes

Yes

No

Yes

No

Yes

No

Yes

Con

tinent

FE

Yes

Yes

Yes

Yes

No

Yes

No

Yes

No

Yes

Stan

dardized

beta

(%)

26.0

29.6

25.1

21.6

17.9

15.9

25.6

16.0

31.2

18.9

Observation

s26

2826

2826

2826

2826

2826

2826

2826

2826

2826

28AdjustedR

20.34

40.26

40.32

20.40

50.03

20.20

00.06

50.364

0.09

70.41

6

OLS

estimates,t

oway-clustered

stan

dard

errors

inpa

rentheses.

The

depe

ndentvariab

lesconsistof

diffe

rent

measuresof

theab

solute

diffe

rencein

risk

attitudes:

(1)averag

es,(2)-(4)pe

rcentiles,

(5)-(6)averag

e(qua

litative)

self-assessments,(7)-(8)averag

eou

tcom

eof

thestaircaselotterysequ

ence,(9)-(10)

averag

edecision

infirst

lotterychoice

(safeam

ount

=16

0euros).SeeTab

le5an

dTab

le6foralisting

ofthedemog

raph

ic,econ

omic,geog

raph

ic,an

dclim

atic

control

variab

les.

∗p<

0.10

,∗∗p<

0.05

,∗∗∗p<

0.01

53

Table 15: Additional control variables

Dependent variable:Absolute difference in average risk attitudes

(1) (2) (3) (4)

Fst genetic distance 0.30∗∗∗ 0.31∗∗∗ 0.34∗∗∗ 0.32∗∗∗ 0.19∗∗(0.10) (0.10) (0.10) (0.12) (0.08)

Freight cost (surface transport) -0.00034∗ -0.00046∗∗ -0.0010(0.00) (0.00) (0.00)

∆ Landlocked -0.025 -0.014 -0.046∗(0.03) (0.03) (0.02)

∆ Mean distance to nearest waterway -0.037∗ -0.056∗∗ -0.19∗∗∗(0.02) (0.02) (0.06)

∆ Log [HIV prevalence] 0.0024 -0.015(0.01) (0.02)

∆ Traffic death rate 0.0016 0.00099(0.00) (0.00)

∆ Executive constraints 0.027(0.02)

∆ Gini coefficient 0.0035∗∗∗(0.00)

∆ Education -0.00044(0.01)

∆ Social infrastructure index -0.032(0.04)

Constant -0.25 -0.26 -0.30 -0.25 0.66∗∗(0.52) (0.52) (0.52) (0.80) (0.30)

Demographic and economic controls Yes Yes Yes Yes YesGeographic and climatic controls Yes Yes Yes Yes YesContinent FE Yes Yes Yes Yes NoCountry FE No No No No Yes

Standardized beta (%) 26.0 27.3 30.8 29.0 16.5Observations 2628 2628 2016 1275 2628Adjusted R2 0.344 0.347 0.399 0.434 0.640

OLS estimates, twoway-clustered standard errors in parentheses. See Table 5 and Table 6 for a listing of thedemographic, economic, geographic, and climatic control variables. A country FE equals one if and only if agiven country appears in a country pair.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

54

D Details for WLS Regressions

Following Spolaore and Wacziarg (2009), the regression weights in Table 8 werecomputed as follows:

Weight =Maximum standard error + 1− standard error of observation

Maximum standard error

Thus, the regression weights are between zero and one and linearly downweighobservation for which the genetic distance estimate is associated with a high stan-dard error.

E Details for Within-Country Analysis

E.1 Detailed Results for Table 9

Tables 16 and 17 provide the details for columns (1) through (7) of Table 9.

55

Table 16: Preference heterogeneity and genetic diversity I

Dependent variable:SD in willingness to take risks

(1) (2) (3) (4)

Predicted genetic diversity 0.11∗∗ 0.10∗ 0.12∗∗ 0.12∗∗(0.05) (0.05) (0.06) (0.06)

Average willingness to take risks 0.027 0.070 0.064(0.03) (0.04) (0.04)

SD in age 0.0016 0.0035(0.01) (0.01)

Average age 0.0075 0.0070(0.01) (0.01)

Fraction female -88.0∗∗∗ -86.9∗∗∗(19.99) (19.96)

Gender heterogeneity index 43.5∗∗∗ 43.0∗∗∗(9.86) (9.85)

Log [Ave GDP p/c PPP] 0.24∗∗ 0.22∗(0.12) (0.13)

Log [GDP p/c PPP] squared -0.017∗∗ -0.015∗(0.01) (0.01)

SD in log [HH income] 0.046 0.049(0.03) (0.03)

Log [Population] 0.0018 0.0016(0.01) (0.01)

Linguistic diversity 0.041(0.06)

Religious diversity -0.019(0.07)

Constant 0.87∗∗∗ 0.87∗∗∗ 88.2∗∗∗ 87.2∗∗∗(0.04) (0.04) (19.97) (19.96)

Standardized beta (%) 24.9 23.0 26.4 27.2Observations 73 73 73 73Adjusted R2 0.049 0.043 0.241 0.223

OLS estimates, robust standard errors in parentheses. ∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗

p < 0.01

56

Table 17: Preference heterogeneity and genetic diversity II

Dependent variable:SD in willingness to take risks

(1) (2) (3) (4)

Predicted genetic diversity 0.12∗∗ 0.14∗∗ 0.11 0.0031(0.06) (0.06) (0.08) (0.12)

Life Expectancy at birth 0.00098 0.0063∗∗ 0.0083∗∗(0.00) (0.00) (0.00)

% of population at risk of malaria -0.088∗∗ -0.063 -0.062(0.04) (0.06) (0.06)

Distance to equator 0.0011 0.0021(0.00) (0.00)

Longitude -0.00020 0.00027(0.00) (0.00)

Temperature -0.0012 0.0012(0.00) (0.00)

Terrain roughness 0.095 0.11(0.10) (0.10)

Precipitation -0.00033 -0.00030(0.00) (0.00)

Log [Timing neolithic revolution] -0.11∗∗ -0.10∗(0.05) (0.05)

Constant 87.2∗∗∗ 88.8∗∗∗ 105.2∗∗∗ 101.6∗∗∗(19.96) (18.74) (16.89) (21.56)

Level of risk aversion Yes Yes Yes YesDemographic and economic controls Yes Yes Yes YesOther diversity measures Yes Yes Yes YesContinent FE No No No YesStandardized beta (%) 27.2 30.3 25.3 0.7Observations 73 73 73 73Adjusted R2 0.223 0.253 0.308 0.264

OLS estimates, robust standard errors in parentheses. See Table 16 for the control variables.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01

57

F Definitions and Data Sources of Covariates

D.1 Definitions and Data Sources

List of countries by regionEast Asia and Pacific: Australia, Cambodia, China, Indonesia, Japan, Philippines,South Korea, Thailand, VietnamEurope and Central Asia: Austria, Croatia, Czech Republic, Estonia, Finland,France, Georgia, Germany, Greece, Hungary, Italy, Kazakhstan, Lithuania, Moldova,Netherlands, Portugal, Romania, Russia, Spain, Sweden, Switzerland, Turkey, Ukraine,United KingdomLatin America and Caribbean: Argentina, Bolivia, Brazil, Chile, Colombia, CostaRica, Guatemala, Haiti, Mexico, Nicaragua, Peru, VenezuelaMiddle East and North Africa: Algeria, Egypt, Iran, Iraq, Israel, Jordan, Morocco,Saudi Arabia, United Arab EmiratesNorth America: United States, CanadaSouth Asia: Afghanistan, Bangladesh, India, Pakistan, Sri LankaSub-Saharan Africa: Botswana, Cameroon, Ghana, Kenya, Malawi, Nigeria, Rwanda,South Africa, Tanzania, Uganda, Zimbabwe

Average age, proportion female, standard deviation age, gender hetero-geneity index, standard deviation of household income Computed fromGallup’s sociodemographic background data. Gender heterogeneity is computed as(2× proportion female − 1)2.

Ethnic and religious fractionalization Indices due to Alesina, Devleeschauwer,Easterly, Kurlat and Wacziarg (2003) capturing the probability that two randomlyselected individuals from the same country will be from different ethnic (religious)groups.

Percentage of European descent Constructed from the “World Migration Ma-trix” of Putterman and Weil (2010).

Population density Population as of 2010 from the Penn WorldTables.

National income Real GDP per capita purchasing-power parity 2010, taken fromthe PennWorldTables.

58

Democracy index The 1960-2000 mean of an index that quantifies the extentof institutionalized democracy, as reported in the Polity IV data set. The PolityIV democracy index for a given year is an 11-point categorical variable (from 0 to10) that is additively derived from Polity IV codings on the (i) competitiveness ofpolitical participation, (ii) openness of executive recruitment, (iii) competitivenessof executive recruitment, and (iv) constraints on the chief executive. Data takenfrom Ashraf and Galor (2013b).

Legal rights index World Bank legal rights index as of 2010. Measures thedegree to which collateral and bankruptcy laws protect the rights of borrowers andlenders and thus facilitate lending. The index ranges from 0 to 10, with higherscores indicating that these laws are better designed to expand access to credit.

Legal origins Taken from La Porta, Lopez-de Silanes, Shleifer and Vishny (1999).

Colonial relationship dummies Taken from the CEPII Geodist database athttp://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=6.

Geodesic distance, contiguity, longitude, latitude, area, 1 if landlockedTaken from CEPII GeoDist database. The longitudinal distance between two coun-tries is computed as

Longitudinal distance = min{|longitudei−longitudej|, 360−|longitudei|−|longitudej|}

Suitability for agriculture Index of the suitability of land for agriculture basedon ecological indicators of climate suitability for cultivation, such as growing degreedays and the ratio of actual to potential evapotranspiration, as well as ecologicalindicators of soil suitability for cultivation, such as soil carbon density and soil pH,taken from Michalopoulosa (2012).

Percentage of arable land Fraction of land within a country which is arable,taken from the World Bank Development Indicators.

Timing of the neolithic transition The number of thousand years elapsed,until the year 2000, since the majority of the population residing within a country’smodern national borders began practicing sedentary agriculture as the primarymode of subsistence. The measure is weighted within each country, where theweight represents the fraction of the year 2000 CE population (of the country for

59

which the measure is being computed) that can trace its ancestral origins to thegiven country in the year 1500 CE. Measure taken from Ashraf and Galor (2013b).

Terrain roughness Degree of terrain roughness of a country, taken from Ashrafand Galor (2013b). Data originally based on geospatial undulation data reportedby the G-ECON project (Nordhaus, 2006).

Mean and standard deviation of elevation Mean elevation in km above sea,taken from Ashraf and Galor (2013b). Data originally based on geospatial elevationdata reported by the G-ECON project (Nordhaus, 2006).

Percentage at risk of contracting malaria The percentage of population in re-gions of high malaria risk (as of 1994), multiplied by the proportion of national casesinvolving the fatal species of the malaria pathogen, P. falciparum. This variable wasoriginally constructed by Gallup, Sachs et al. (2000) and is part of Columbia Uni-versity’s Earth Institute data set on malaria.

Average precipitation Average monthly precipitation of a country in mm permonth, 1961-1990, taken from Ashraf and Galor (2013b). Data originally basedon geospatial average monthly precipitation data for this period reported by theG-ECON project (Nordhaus, 2006).

Average temperature Average monthly temperature of a country in degree Cel-sius, 1961-1990, taken from Ashraf and Galor (2013b). Data originally based ongeospatial average monthly temperature data for this period reported by the G-ECON project (Nordhaus, 2006).

Percentage of area in 12 Köppen-Geiger zones Percentage of area withina country which forms part of each of the 12 Köppen-Geiger climatic zones. Datataken from John Luke Gallup, http://www.pdx.edu/econ/jlgallup/country-geodata.

Freight cost of surface transport Cost of 1,000 kg of unspecified freight trans-ported over sea or land with no special handling, taken from Spolaore and Wacziarg(2009). Data originally retrieved from http://www4.importexportwizard.com/.

Percentage of land within 100 km of a waterway The percentage of acountry’s total land area that is located within 100 km of an ice-free coastlineor sea-navigable river. This variable was originally constructed Gallup, Sachs and

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Mellinger (1999) and is part of Harvard University’s CID Research Datasets onGeneral Measures of Geography.

Fraction of deaths by communicable diseases Fraction of total deaths whichare due to communicable diseases. Data taken from World Bank Indicators.

Executive constraints The 1960-2000 mean of an index (1-7) that quantifies theextent of institutionalized conatrsints on the executive, as reported in the Polity IVdata set. Data taken from Ashraf and Galor (2013b).

Life expectancy Life expectancy at birth, taken from the World Bank Indicators.

Gini coefficient Taken from the World Bank Indicators.

Average years of schooling The mean over the 1960-2000 time period, of the5-yearly figure, reported by Barro and Lee (2012), on average years of schoolingamongst the population aged 25 and over.

Social infrastructure Institutional quality index originally developed by Halland Jones (1999a). Data taken from Ashraf and Galor (2013b).

Linguistic and religious diversity Computed akin to linguistic and religiousdistance indices by matching a country with itself.

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Appendix F: Staircase Lottery Task

The question format was as follows:

What would you prefer: a 50 percent chance of winning 300 Euro whenat the same time there is 50 percent chance of winning nothing, or wouldyou rather have the amount of x Euro as a sure payment?

The precise sequence of questions was given by the following “tree” logic:

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Figure 5: Tree for the staircase lottery task (numbers = sure payment)

160

80

40

2010B

30AB

6050B

70A

AB

120

10090B

110AB

140130B

150A

A

A

B

240

200

180170B

190AB

220210B

230A

AB

280

260250B

270AB

300290B

310A

A

A

A

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