Genetic distance, cultural differences, and the formation of regional trade agreements


Genetic distance between countries’ populations has been shown to proxy cross-country differences in cultures and preferences. In an unbalanced panel of 133 countries from 1970 to 2012, the study finds that higher genetic distance between two countries decreases their probability of having a trade agreement, even when controlling for geographic distance and other controls. The impact of cultural differences proxied by genetic distance is persistent over time and economically significant: While increasing the geographic distance between two countries by 1% decreases the probability of a regional trade agreement by 0.11% points, increasing their genetic distance by 1% decreases the probability by 0.06% points.

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  1. 1.

    Knack and Keefer (1997) find that countries which are ethnically more homogeneous have higher levels of trust.

  2. 2.

    Zou et al. (2009) show that individuals’ behavior depends on what they perceive to be the consensus or “common sense” view within their culture; for similar arguments see also Roth et al. (1991). Henrich (2000) and Henrich et al. (2001) show that behavior in the ultimatum game depends on the culture of the experiment subjects.

  3. 3.

    Buchan et al. (2002) find that Japanese experiment subjects have a lower level of trust than their American counterparts. Gächter et al. (2010) and Herrmann et al. (2008) find significant differences in the willingness to punish non-cooperative players in experiments in different cultural backgrounds. These are not isolated findings: Cross-cultural differences in behavior in trust games are corroborated in a meta-analysis by Johnson and Mislin (2011).

  4. 4.

    Brander (1986) is probably the first one to characterize trade negotiations as an attempt to escape the prisoner’s dilemma of unilateral strategic trade policy.

  5. 5.

    Roth et al. (1991) find that while subjects in different countries exhibit similar behavior in experimental markets, individual bargaining behavior varies considerably across countries. Gelfand et al. (2015) find that strategies which lead to successful negotiations in the United States are detrimental in Egypt. For a literature survey on cultural differences and negotiations, see Gelfand et al. (2012).

  6. 6.

    For example, cross-cultural differences such as norms around kinship correlate with human genetic diversity, see Jones (2003). For a general introduction to the relationship between human genetic and cultural diversity, see Stone and Lurquin (2007).

  7. 7.

    Ahlerup and Olsson (2012) provide an overview of this literature; see also Ashraf and Galor (2013).

  8. 8.

    FDI data are often missing for many country pairs, restricting Leblang’s (2010) analysis to 28 FDI-receiving countries. Our sample comprises more countries and over 40 years.

  9. 9.

    All the cited papers use probit models in their analysis. Besides probit models, a plethora of methods have been used to analyze the determinants of RTAs: Egger and Larch (2008) use spatial econometric probit models and Márquez-Ramos et al. (2011) use ordered probit models to explain the drivers of different levels of trade integration between countries. Kohl and Brouwer (2014) use a clustering algorithm to identify “natural” trade integration blocs and estimate the impact of determinants of these blocs using a probit model.

  10. 10.

    We use Mario Larch’s Regional Trade Agreements Database from Egger and Larch (2008) in its updated version rta_20170310.dta which can be accessed at

  11. 11.

    The data are available at Spolaore and Wacziarg (2009) use the original genetic distance data from Cavalli-Sforza et al. (1994) which covers only 42 populations.

  12. 12.

    For details on the calculation of these measures, see Spolaore and Wacziarg (2016a). They also show that genetic distance is correlated with a cultural difference measure based on question-specific distances from the World Valued Survey (WVS) for 98 questions. Contrary to genetic distance which is available for 180 countries, this measure is only available for 74 countries. To maintain a large sample, we do not include it in our regressions.

  13. 13.

    During the stalled negotiations for a potential trade agreement between the European Union and the United States, a commonly repeated argument was that differences in legal philosophies in consumer protection law (precautionary principle in the EU versus risk assessment and cost–benefit principles in the US) made an agreement difficult to reach, see Bergkamp and Kogan (2013).

  14. 14.

    Data are available at Legal systems are categorized as either civil law, common law, Muslim law, customary, or a mixture of these categories. We treat mixed legal systems as a separate category.

  15. 15.

    Data are from Kreutz (2010) and contain information about armed conflicts between 1946 and 2005. \((War Duration)_{ij}=(War End Date)_{ij}-(War StartDate)_{ij}\) is the number of days of war between country i and j after 1945. We focus on wars after World War II as it marks the beginning of the current international order and because we focus on RTA formation between 1970 and 2012.

  16. 16.

    Note that country-year fixed effects automatically control for year fixed effects, i.e., across-the-board differences in RTA formation across years which affect all countries in a similar way.

  17. 17.

    Baier et al. (2014) approximate these multilateral resistance terms by GDP-weighted averages of bilateral distances with trade partners. These terms also control for a country’s remoteness, i.e., for its average trade costs across all its trade partners, similar to the approximation proposed by Baier and Bergstrand (2009) in a trade gravity context. Our fixed effects control for these terms, circumventing the need to construct proxy indices.

  18. 18.

    For earlier years, our regressors and country-year fixed effects perfectly separate the dependent variable, so maximum likelihood estimates of logit or probit models do not exist and using a linear probability model does not make sense. For a discussion of perfect separation, see, e.g., Mansournia et al. (2018).

  19. 19.

    This is well-known in the gravity literature, see, e.g., Head and Mayer (2014), p. 140: In a bilateral gravity equation of symmetric bilateral trade flows regressed on symmetric trade cost measures, estimated importer and exporter dummies are identical. This also applies in our setting. Including origin and destination-specific dummies or country-specific dummies delivers numerically identical coefficients.

  20. 20.

    Baier and Bergstrand (2004) discuss correlation of errors across countries within an RTA (e.g., across EU member countries) but do not consider the more general case of correlation of a given country’s trade policy across all its potential partner countries we consider. The correlation within an RTA of Baier and Bergstrand (2004) is modelled on the value of the dependent variable, introducing endogeneity bias in the calculation of the standard errors. Our approach avoids this.

  21. 21.

    The variance–covariance estimator by Cameron et al. (2011) assumes \(E\left({\varepsilon }_{ijgh}{\varepsilon }_{lm{g}^{^{\prime}}{h}^{^{\prime}}}|{x}_{ijgh},{x}_{lm{g}^{^{\prime}}{h}^{^{\prime}}}\right)=0\mathrm{ unless }g={g}^{^{\prime}}\mathrm{or }h={h}^{^{\prime}}\) where ij and lm refer to two country pairs (i.e., observations in the data) where we now indicate explicitly the two groups (i.e., clusters), in our application the first and the second country in a country pair, by g and h. If \(g={g}^{^{\prime}}\) or \(h={h}^{^{\prime}}\), i.e., within an origin or destination country, \({\varepsilon }_{ij}={\varepsilon }_{ji} \forall i, j,\) then \(E\left({\varepsilon }_{ijgh}{\varepsilon }_{lm{g}^{^{\prime}}{h}^{^{\prime}}}|{x}_{ijgh},{x}_{ji{g}^{^{\prime}}{h}^{^{\prime}}}\right)=E\left({\varepsilon }_{ijgh}{\varepsilon }_{ijgh}|{x}_{ijgh},{x}_{ijgh}\right)\), and hence the estimator allows for arbitrary correlation between \({\varepsilon }_{ijgh}\) and \({\varepsilon }_{ji{g}^{^{\prime}}{h}^{^{\prime}}}\), including perfect correlation.

  22. 22.

    The dependent variable is in levels and the regressor is in logarithms, i.e., if genetic distance increases by 1%, the probability for an RTA increases by \({\beta }_{1}/100\) units, i.e., \(\frac{{\beta }_{1}}{100}\times 100={\beta }_{1}=-0.090\)% points, see Wooldridge (2002), p. 656.

  23. 23.

    In unreported regressions, we estimated the columns of Table 1 on the larger samples which are possible when not including all regressors. The effect of genetic distance remains very similar.

  24. 24.

    The persistent negative effect of distance on bilateral trade flows has been referred to as the distance puzzle. It has spurred a large literature which tries to explain this fact, e.g., Lin and Sim (2012), Yotov (2012), and Larch et al. (2016). None of these papers investigates the impact of genetic distance over time on bilateral trade flows.

  25. 25.

    For an overview of EU decision making and its history concerning trade policy issues, see chapter 12 in Baldwin and Wyplosz (2015).

  26. 26.

    In our sample, the correlation between genetic distance and geographic distance in levels across all years is 0.410, and 0.514 in logarithms.

  27. 27.

    Using a different specification, Melitz and Toubal (2019) do find that genetic distance matters even for trade flows between European countries.

  28. 28.

    The countries included are Austria, Belgium, the Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Russia, Spain, Sweden, Switzerland, and the United Kingdom.


  1. Ahlerup, P., & Olsson, O. (2012). The roots of ethnic diversity. Journal of Economic Growth, 17(2), 71–102.

    Article  Google Scholar 

  2. Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., & Wacziarg, R. (2003). Fractionalization. Journal of Economic Growth, 8(2), 155–194.

    Article  Google Scholar 

  3. Ashraf, Q., & Galor, O. (2013). Genetic diversity and the origins of cultural fragmentation. American Economic Review, 103(3), 528–533.

    Article  Google Scholar 

  4. Baier, S. L., & Bergstrand, J. H. (2004). Economic determinants of free trade agreements. Journal of International Economics, 64(1), 29–63.

    Article  Google Scholar 

  5. Baier, S. L., & Bergstrand, J. H. (2009). Bonus vetus OLS: A simple method for approximating international trade-cost effects using the gravity equation. Journal of International Economics, 77(1), 77–85.

    Article  Google Scholar 

  6. Baier, S. L., Bergstrand, J. H., & Mariutto, R. (2014). Economic determinants of free trade agreements revisited: Distinguishing sources of interdependence. Review of International Economics, 22(1), 31–58.

    Article  Google Scholar 

  7. Baldwin, R., & Wyplosz, C. (2015). The economics of European integration (5th ed.). McGraw-Hill Education.

  8. Bergkamp, L., & Kogan, L. (2013). Trade, the precautionary principle, and post-modern regulatory process. European Journal of Risk Regulation, 4(November), 493–507.

    Article  Google Scholar 

  9. Bergstrand, J. H., Egger, P., & Larch, M. (2016). Economic determinants of the timing of preferential trade agreement formations and enlargements. Economic Inquiry, 54(1), 315–341.

    Article  Google Scholar 

  10. Bove, V., & Gokmen, G. (2018). Genetic distance, trade, and the diffusion of development. Journal of Applied Econometrics, (November 2017), 3–9.

  11. Brander, J. A. (1986). Rationales for strategic trade and industrial policy. In P. R. Krugman (Ed.), Strategic trade policy and the new international economics (pp. 23–46). MIT Press.

  12. Buchan, N. R., Croson, R. T. A., & Dawes, R. M. (2002). Swift neighbors and persistent strangers: A cross-cultural investigation of trust and reciprocity in social exchange. American Journal of Sociology, 108(1), 168–206.

    Article  Google Scholar 

  13. Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2011). Robust inference with multiway clustering. Journal of Business & Economic Statistics, 29(2), 238–249.

    Article  Google Scholar 

  14. Cavalli-Sforza, L. L., Menozzi, P., & Piazza, A. (1994). The history and geography of human genes. Princeton University Press.

  15. Chaudhry, A., & Ikram, R. (2015). Does genetic proximity to high growth countries affect a country’s own growth? Economic Modelling, 51, 444–453.

    Article  Google Scholar 

  16. Chen, M. X., & Joshi, S. (2010). Third-country effects on the formation of free trade agreements. Journal of International Economics, 82(2), 238–248.

    Article  Google Scholar 

  17. Davies, R. B., & Guillin, A. (2014). How far away is an intangible? Services FDI and distance. World Economy, 37(12), 1731–1750.

    Article  Google Scholar 

  18. Desmet, K., Le Breton, M., Ortuño-Ortín, I., & Weber, S. (2011). The stability and breakup of nations: A quantitative analysis. Journal of Economic Growth, 16(3), 183–213.

    Article  Google Scholar 

  19. Disdier, A.-C., & Head, K. (2008). The puzzling persistence of the distance effect on bilateral trade. The Review of Economics and Statistics, 90(1), 37–48.

    Article  Google Scholar 

  20. Egger, P., & Larch, M. (2008). Interdependent preferential trade agreement memberships: An empirical analysis. Journal of International Economics, 76(2), 384–399.

    Article  Google Scholar 

  21. Egger, P., Larch, M., Staub, K. E., & Winkelmann, R. (2011). The trade effects of endogenous preferential trade agreements. American Economic Journal: Economic Policy, 3(3), 113–143.

    Google Scholar 

  22. Egger, P., & Tarlea, F. (2015). Multi-way clustering estimation of standard errors in gravity models. Economics Letters, 134(C), 144–147.

    Article  Google Scholar 

  23. Felbermayr, G. J., & Toubal, F. (2010). Cultural proximity and trade. European Economic Review, 54(2), 279–293.

    Article  Google Scholar 

  24. Gächter, S., Herrmann, B., & Thöni, C. (2010). Culture and cooperation. Philosophical Transactions of the Royal Society B, 365(1553), 2651–2661.

    Article  Google Scholar 

  25. Gelfand, M. J., Severance, L., Fulmer, C. A., & Al-Dabbagh, M. (2012). Explaining and predicting cultural differences in negotiation. In G. Bolton & R. Croson (Eds.), The Oxford handbook of economic and conflict resolution (pp. 332–356). Oxford University Press.

  26. Gelfand, M. J., Severance, L., Lee, T., Bruss, C. B., Lun, J., Abdel-Latif, A.-H., et al. (2015). Culture and getting to yes: The linguistic signature of creative agreements in the United States and Egypt. Journal of Organizational Behavior, 36, 967–989.

    Article  Google Scholar 

  27. Giuliano, P., Spilimbergo, A., & Tonon, G. (2014). Genetic distance, transportation costs, and trade. Journal of Economic Geography, 14(1), 224–225.

    Article  Google Scholar 

  28. Gowa, J., & Mansfield, E. D. (1993). Power politics and international trade. American Political Science Review, 87(2), 408–420.

    Article  Google Scholar 

  29. Guimarães, P., & Portugal, P. (2010). A simple feasible procedure to fit models with high-dimensional fixed effects. The Stata Journal, 10(4), 628–649.

    Article  Google Scholar 

  30. Guiso, L., Sapienza, P., & Zingales, L. (2009). Cultural biases in economic exchange? Quarterly Journal of Economics, 124(3), 1095–1131.

    Article  Google Scholar 

  31. Head, K., Mayer, T., & Ries, J. (2010). The erosion of colonial trade linkages after independence. Journal of International Economics, 81(1), 1–14.

    Article  Google Scholar 

  32. Henrich, J. (2000). Does culture matter in economic bahaviour? Ultimatum game bargaining among the Machiguenga of the Peruvian Amazon. American Economic Review, 90(4), 973–979.

    Article  Google Scholar 

  33. Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., & McElreath, R. (2001). In search of homo economicus: Behavioral experiments in 15 small-scale societies. American Economic Review Papers and Proceedings, 91(2), 73–78.

    Article  Google Scholar 

  34. Herrmann, B., Thöni, C., & Gächter, S. (2008). Antisocial punishment across societies. Science, 319(5868), 1362–1367.

    Article  Google Scholar 

  35. Johnson, N. D., & Mislin, A. A. (2011). Trust games: A meta-analysis. Journal of Economic Psychology, 32(5), 865–889.

    Article  Google Scholar 

  36. Jones, D. (2003). Kinship and deep history: Exploring connections between culture areas, genes, and languages. American Anthropologist, 105(3), 501–514.

    Article  Google Scholar 

  37. Knack, S., & Keefer, P. (1997). Does social capital have an economic payoff? A cross-country investigation. Quarterly Journal of Economics, 112(4), 1251–1288.

    Article  Google Scholar 

  38. Kohl, T., & Brouwer, A. E. (2014). The development of trade blocs in an era of globalization. Environment and Planning A, 46(7), 1535–1553.

    Article  Google Scholar 

  39. Kreutz, J. (2010). How and when armed conflicts end: Introducing the UCDP conflict termination dataset. Journal of Peace Research, 47(2), 243–250.

    Article  Google Scholar 

  40. Larch, M., Norbäck, P. J., Sirries, S., & Urban, D. M. (2016). Heterogeneous firms, globalisation and the distance puzzle. World Economy, 39(9), 1307–1338.

    Article  Google Scholar 

  41. Leblang, D. (2010). Familiarity breeds investment: Diaspora networks and international investment. American Political Science Review, 104(3), 584–600.

    Article  Google Scholar 

  42. Lin, F., & Sim, N. C. S. (2012). Death of distance and the distance puzzle. Economics Letters, 116(2), 225–228.

    Article  Google Scholar 

  43. Magee, C. S. (2003). Endogenous preferential trade agreements: An empirical analysis. Contributions to Economic Analysis & Policy, 2(1), article 15.

  44. Mansournia, M. A., Geroldinger, A., Greenland, S., & Heinze, G. (2018). Separation in logistic regression: Causes, consequences, and control. American Journal of Epidemiology, 187(4), 864–870.

    Article  Google Scholar 

  45. Márquez-Ramos, L., Martínez-Zarzoso, I., & Suárez-Burguet, C. (2011). Determinants of deep integration: Examining socio-political factors. Open Economies Review, 22(3), 479–500.

    Article  Google Scholar 

  46. Marshall, M. G., Gurr, T. R., & Jaggers, K. (2016). POLITYTM IV PROJECT. Political regime characteristics and transitions, 1800–2016. Dataset Users’ Manual. Version 2016 <p4v2016 and p4v2016d>. Center for Systemic Peace Paper.

  47. Martin, P., Mayer, T., & Thoenig, M. (2012). The geography of conflicts and regional trade agreements. American Economic Journal: Macroeconomics, 4(4), 1–35.

    Google Scholar 

  48. Mayer, T., & Zignago, S. (2011). Notes on CEPII’s distances measures: The geodist database (CEPII working paper 2011 – 25).

  49. Melitz, J., & Toubal, F. (2014). Native language, spoken language, translation and trade. Journal of International Economics, 93(2), 351–363.

    Article  Google Scholar 

  50. Melitz, J., & Toubal, F. (2019). Somatic distance, trust and trade. Review of International Economics, 27(3), 786–802.

    Article  Google Scholar 

  51. Pemberton, T. J., DeGiorgio, M., & Rosenberg, N. A. (2013). Population structure in a comprehensive genomic data set on human microsatellite variation. G3: Genes, Genomes, Genetics, 3(5), 891–907.

    Article  Google Scholar 

  52. Roth, A. E., Prasnikar, V., Okuno-Fujiwara, M., & Zamir, S. (1991). Bargaining and market behavior in Jerusalem, Ljubljana, Pittsburgh, and Tokyo: An experimental study. American Economic Review, 81(5), 1068–1095.

    Google Scholar 

  53. Spolaore, E., & Wacziarg, R. (2009). The diffusion of development. Quarterly Journal of Economics, 124(2), 469–592.

    Article  Google Scholar 

  54. Spolaore, E., & Wacziarg, R. (2016a). Ancestry, language and culture. In The Palgrave handbook of economics and language (pp. 174–211).

  55. Spolaore, E., & Wacziarg, R. (2016b). War and relatedness. Review of Economics and Statistics, 98(5), 925–939.

    Article  Google Scholar 

  56. Spolaore, E., & Wacziarg, R. (2018). Ancestry and development: New evidence. Journal of Applied Econometrics, 33(5), 748–762.

    Article  Google Scholar 

  57. Stone, L., & Lurquin, P. F. (2007). Genes, culture, and human evolution. Blackwell Publishing.

  58. Wooldridge, J. M. (2002). Introductory econometrics. A modern approach (2nd ed.). South-Western.

  59. Yotov, Y. V. (2012). A simple solution to the distance puzzle in international trade. Economics Letters, 117(3), 794–798.

    Article  Google Scholar 

  60. Zou, X., Tam, K. P., Morris, M. W., Lee, S. L., Lau, I. Y. M., & Chiu, C. Y. (2009). Culture as common sense: Perceived consensus versus personal beliefs as mechanisms of cultural influence. Journal of Personality and Social Psychology, 97(4), 579–597.

    Article  Google Scholar 

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Correspondence to Wenxi Lu.

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Heid gratefully acknowledges financial support from the Australian Research Council (DP190103524) and Lu from the National Natural Science Foundation of China (91846301). We thank Ralph-Christopher Bayer and Laura Márquez-Ramos for useful comments. All remaining errors are our own.



See Tables 4 and 5.

Table 4 List of countries
Table 5 Descriptive statistics of the different samples

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Heid, B., Lu, W. Genetic distance, cultural differences, and the formation of regional trade agreements. Rev World Econ (2021).

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  • Trade agreements
  • Trade policy
  • Trade negotiations
  • Genetic distance
  • Cultural differences

JEL Classification

  • F13
  • F14
  • F15
  • Z10