How Deep Are the Roots of Economic Development? PDF

Journal of Economic Literature 2013, 51(2), 325–369 http://dx.doi.org/10.1257/jel.51.2.325 How Deep Are the Roots of Economic Development? † Enrico Spolaore and Romain...

Journal http://dx.doi.org/10.1257/jel.51.2.325 How Deep Are the Roots of Economic Development? Enrico The empirical literature on economic study of proximate determinants factors, rooted in long-term the measurement and estimation income by explicitly taking into populations. The evidence suggests that have been transmitted across surveys this new literature and through which intergenerationally development, biologically (via behavioral or symbolic transmission). transmitted traits have affected productivity, or have operated enhancing innovations across “The further backward you look, the further forward you can see” (attributed to Winston Churchill).1 1. Introduction W hy is income per capita higher in some societies and much lower in others? Answers to this perennial question * Spolaore: Tufts University, National nomic Research, and CESIfo. Wacziarg: California at Los Angeles, National Research, and CEPR. We thank Leonardo Bursztyn, Janet Currie, Oded Galor, David Weil, and several anonymous referees for useful input. † Go to http://dx.doi.org/10.1257/jel.51.2.325 article page and view author disclosure statement(s). 326 Journal of Economic back the debate one more degree, a key question remains as to why the proximate determinants of the wealth of nations vary across countries. A burgeoning literature seeks to better understand the deep causes of development, rooted in geography and history. As the empirical literature has moved from studying the proximate determi- nants of growth and development to ana- lyzing ever deeper, more fundamental factors, important questions have arisen: How much time persistence is there in development outcomes? How far back in time should we go in order to understand contemporary economic development? Through what specific mechanisms do long-term geographic and historical fac- tors affect outcomes today? If economic development has deep historical roots, what is the scope for policy to affect the wealth of nations? This article discusses the current state of knowledge on these issues, focusing on recent empirical work shedding light on the complex interactions among geography, history, and compara- tive development. Throughout, we illus- trate the major milestones of the recent literature in a unified empirical frame- work for understanding variation in eco- nomic development. Our starting point is the long-standing debate on geography and development. There is no doubt that geographic factors, such as latitude and climate, are highly cor- related with development, but the inter- pretation of this correlation remains hotly debated. While some of the effects of geog- raphy may operate directly on current pro- ductivity, there is mounting evidence that much of the correlation operates through indirect mechanisms, i.e., through the his- torical effects of initial geographic tions on the spatial distribution of human characteristics, such as institutions, human capital, social capital, and cultural traits, Spolaore and Wacziarg: 2. Geography and Development 2.1 Long-Term Effects of Geography The hypothesis that geographic factors affect productivity and economic develop- ment has a long pedigree, going back to Machiavelli (1531), Montesquieu (1748), and Marshall (1890). A vast empirical literature has documented high correlations between current levels of income per capita and a series of geographic and biological variables, such as climate and temperature (Myrdal 1968; Kamarck 1976; Masters and McMillan 2001; Sachs 2001), the disease environment (Bloom and Sachs 1998; Sachs, Mellinger, and Gallup 2001; Sachs and Malaney 2002), natural resources (Sachs and Warner 2001), and transportation conditions (Rappaport and Sachs 2003). In order to illustrate the main empiri- cal findings of the contributions discussed herein, we punctuate this paper with our own empirical results based on a unified data set, regression methodology and sample. This analysis is not meant to be an exhaustive recapitulation of existing results, but simply to illustrate some important milestones in the recent literature. We use, alternately, log per capita income in 2005 (from the Penn World Tables version 6.3) as a measure of contemporary economic performance, and population density in 1500 (from McEvedy and Jones 1978) as a measure of economic performance in 1500, and regress these on a variety of proposed determinants of develop- ment, starting here with geographic factors.3 3 As is well known, in the preindustrial, era population density is the appropriate measure of a society’s economic performance since any technologi- cal improvement leads to increases than to increases in per capita income. and empirical analysis of the relationship lation size, population density, and Malthusian times, see Kremer (1993). sions of this topic, see Galor (2005) bution by Ashraf and Galor (2011a). 328 Journal Geography (Dependent Whole Sample: World (1) Absolute latitude 0.044 (6.645)*** Percent land area in –0.049 the tropics (0.154) Landlocked dummy –0.742 (4.375)*** Island dummy 0.643 (2.496)** Geographic conditions (Olsson–Hibbs)b Biological conditions (Olsson–Hibbs)c Constant 7.703 (25.377)*** (25.360)*** Observations 155 Adjusted R2 0.440 Notes: a The Olsson and Hibbs sample excludes United States) and countries whose b First principal component of number mals (all variables from Olsson and c First principal component of absolute of landmass in millions of sq km (all Robust t statistics in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent and Sokoloff 1997 and 2002; Acemoglu, Johnson, and Robinson 2001, 2002; Easterly and Levine 2003). Diamond (1997) famously argues that the roots of comparative development lie in a series of environmental advantages enjoyed by the inhabitants of Eurasia at the transition from a hunter–gatherer economy to agricul- tural and pastoral production, starting roughly Spolaore and Wacziarg: and an earlier acceleration of technological innovation, with long-term consequences for comparative development. According to Diamond, the proximate determinants of European economic and political success (“guns, germs, and steel”) were therefore the outcomes of deeper geographic advan- tages that operated in prehistoric times. The descendants of some Eurasian populations (Europeans), building on their Neolithic advantage, were able to use their technologi- cal lead (guns and steel) and their immunity to old-world diseases (germs) to dominate other regions in modern times—including regions that did not enjoy the original geo- graphic advantages of Eurasia. In order to test Diamond’s hypotheses, Olsson and Hibbs (2005) provide an empiri- cal analysis of the relation between initial biogeographic endowments and contempo- rary levels of development.4 They use several geographic and biological variables: the size of continents, their major directional axis (extent of East–West orientation), climatic factors, and initial biological conditions (the number of animals and plants suitable to domestication and cultivation at each location 12,000 years ago). We revisit their empirical results in columns 2 through 5 of table 1. In order to reduce the effect of post- 1500 population movements, the Olsson– Hibbs sample excludes the neo-European countries (Australia, Canada, New Zealand, and the United States), as well as countries whose current income is based primarily on extractive wealth. Column 2 replicates the estimates of column 1 using this restricted sample—the joint explanatory power of geo- graphic variables rises to 55 percent, since the new sample excludes regions that are rich today as a result of the guns, germs, and steel of colonizing Europeans rather than purely geographic factors. 4 See also Hibbs and Olsson (2004). 330 Journal 2.2 The Legacy of the Neolithic Transition The long-term effects of geographic and biogeographic endowments also play a cen- tral role in the analysis of Ashraf and Galor (2011a). While their main goal is to test a central tenet of Malthusian theory (that per capita income gains from technologi- cal improvements in the preindustrial era were largely dissipated through population growth), their approach leads them to pro- vide further evidence relating to Diamond’s hypotheses and the legacy of geography. Ashraf and Galor demonstrate that the spread of agriculture (the Neolithic transi- tion) was driven by geographic conditions (climate, continental size and orientation) and biogeographic conditions (the availabil- ity of domesticable plant and big mammal species). They empirically document how geographic factors influenced the timing of the agricultural transition. They also show that biogeographic variables, consistent with Olsson and Hibbs (2005), are strongly correlated with population density in 1500, but argue that the only way these variables matter for economic performance in pre- industrial times is through their effect on the timing of the adoption of agriculture. This paves the way to using biogeographic as instruments for the timing of the Neolithic transition in a specification explaining popu- lation density in 1500. Table 2 illustrates these findings in our unified empirical setup. In column 1, we regressed the number of years since the Neolithic transition (obtained from Chanda and Putterman 2007) on a set of geographic variables—i.e., this is the first stage regres- sion.7 These geographic conditions account 7 For comparability we use the above, except instead of the Olsson–Hibbs ces of geographic and biological conditions, include the number of annual or perennial the number of domesticable big mammals, so as to main- tain consistency with Ashraf and Galor (2011a). Spolaore and Wacziarg: Dependent Variable: Estimator: Absolute latitude Percent land area in the tropics Landlocked dummy Island dummy Number of annual or perennial wild grasses Number of domesticable big mammals Years since agricultural transition Constant Observations 2 Adjusted R Notes: Robust t statistics in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent deviation change in years of agriculture is associated with 63 percent of a standard deviation change in log population density in 1500 (OLS). The corresponding standard- ized beta coefficient using IV is 88 percent. All of the other regressors feature much smaller standardized effects. In addition to providing strong support in favor of the Malthusian view that tech- nological improvements impact popula- tion density but not per capita income in 332 Journal of 2.3 Reversal of Fortune and the Role of Institutions Diamond’s book, as well as the empirical work by Olsson and Hibbs and Ashraf and Galor, suggests an important role for geog- raphy and biogeography in the onset and diffusion of economic development over the past millennia. However, these analy- ses leave open the question of whether the effects of geography operate only through their historical legacy, or also affect contem- poraneous income and productivity directly. Nunn (2009) makes a closely related point when discussing Nunn and Puga (2007), an attempt to estimate the magnitude of direct and indirect (historical) effects of a specific geographic characteristic: terrain rugged- ness, measured by the average absolute slope of a region’s surface area. Nunn and Puga (2007) argue that ruggedness has a negative direct effect on agriculture, construction, and trade, but a positive historical effect within Africa because it allowed protection from slave traders. They find that the histori- cal (indirect) positive effect is twice as large as the negative (direct) contemporary effect. A broader issue with Diamond’s geo- graphic explanation is that it denies a role for specific differences between populations, especially within Eurasia itself. For example, Appleby (2010) writes: “How deep are the roots of capitalism? [... ] Jared Diamond wrote a best-selling study that emphasized the geographic and biological advantages the West enjoyed. Two central problems vex this interpretation: The advantages of the West were enjoyed by all of Europe, but only England experienced the breakthrough that others had to imitate to become capi- talistic. Diamond’s emphasis on physical fac- tors also implies that they can account for the specific historical events that brought on Western modernity without reference to the individuals, ideas, and institutions that played so central a part in this historic Spolaore and Wacziarg: per capita income in 2005 is negative. In the regression that corresponds to their baseline (column 3), looking only at former European colonies, the effect is large in magnitude and highly significant statistically: the standard- ized beta on 1500 density is 48 percent and the t-statistic is 7. Similar results hold for the whole World minus Europe (column 1), and also when restricting attention only to coun- tries not currently populated by more than 50 percent of their indigenous population (columns 5 and 7).11 These important find- ings suggest that the observed correlation between geographic variables and income per capita are unlikely to stem from direct effects of geography on productivity. In con- trast, they point to indirect effects of geogra- phy operating through long-term changes in nongeographic variables. Acemoglu, Johnson, and Robinson (2002) argue that the reversal reflects changes in the institutions resulting from European colonialism: Europeans were more likely to introduce institutions encouraging invest- ment in regions with low population density and low urbanization, while they introduced extractive, investment-depressing institu- tions in richer regions. This interpretation is consistent with Acemoglu, Johnson, and Robinson (2001), where the focus is on an indirect biogeographic channel: European settlers introduced good (productivity- enhancing) institutions in regions where they faced favorable biogeographic conditions (low mortality rates), and bad institutions in regions where they faced unfavorable biogeographic conditions (high mortality rates).12 11 To define whether a country’s composed of more than 50 percent of descendents of its 1500 population we rely on the World Migration Matrix of Putterman and Weil (2010), which greater detail in section 3. 12 For a critical reassessment in Acemoglu, Johnson, and Robinson (2001), see Albouy 334 Journal (Dependent Whole Europe Sample: World Only (1) (2) Log of 0.027 0.117 population (0.389) (1.276) density, year 1500 Beta coeffi- 3.26% 22.76% cient on 1500 density Observations 171 35 2 R 0.001 0.052 Log of –0.246 a population (3.304)*** density, year 1500 Beta coeffi- –27.77% cient on 1500 density Observations 136 2 R 0.077 Notes: All regressions include a constant *** Significant at the 1 percent ** Significant at the 5 percent * Significant at the a Empty sample. b No European countries in human capital, and political power,” which, in turn, were embodied in persistent societal traits and institutions. Societies that were endowed with climate and soil conditions well-suited for growing sugar, coffee, rice, tobacco, and other crops with high m arket Spolaore and Wacziarg: of wealth and power emerged in societies with small-scale crops (grain and livestock), with beneficial consequences for long-term economic performance. An alternative to the institutional explana- tion for the reversal of fortune is rooted in the composition of world populations. For while Europeans may have left good institu- tions in former colonies that are rich today, they also brought themselves there. This point is stressed by Glaeser et al. (2004), who write: “[Acemoglu, Johnson, and Robinson’s] results do not establish a role for institutions. Specifically, the Europeans who settled in the New World may have brought with them not so much their institutions, but themselves, that is, their human capital. This theoretical ambiguity is consistent with the empirical evidence as well” (274). The top panel of table 3 shows that when Europe is included in the sample, any evi- dence for reversals of fortune disappears: the coefficient on 1500 population density is essentially zero for the broadest sample that includes the whole world (column 1). For countries that were not former European colonies, there is strong evidence of persis- tence, with a positive significant coefficient on 1500 density. The evidence of persistence is even stronger when looking at countries that are populated mostly by their indigenous populations (the evidence is yet stronger when defining “indigenous” countries more strictly, for instance requiring that more than 90 percent of the population be descended from those who inhabited the country in 1500).13 In other words, the reversal of fortune is a feature of samples that exclude Europe and is driven largely by countries inhabited by populations that moved there 2we obtain in 13 The R commensurate in magnitude to those obtained from com- parable specifications in Acemoglu, (2002). As expected, as the sample European colonies, the explanatory ment for current development falls, and correspondingly 336 Journal play a central role in explaining comparative economic success.14 In sum, the evidence on reversal of for- tune documented by Acemoglu, Johnson, and Robinson (2002) is consistent with an indirect rather than direct effect of geogra- phy on development, but is open to alterna- tive interpretations about the mechanisms of transmission. A key issue is whether the differential settlement of Europeans across colonies after 1500 affect current former colonies exclusively through tions, as argued by Acemoglu, Johnson, and Robinson (2001, 2002, 2005), or through other relevant factors and traits Europeans, such as human capital (Glaeser et al. 2004) or culture (Landes 1998). Disentangling the effects of specific soci- etal characteristics, such as different aspects of institutions, values, norms, beliefs, other human traits, etc., is intrinsically difficult, because these variables are conceptually elusive to measure, deeply interlinked, and endogenous with respect to economic devel- opment. In spite of these intrinsic difficulties, a growing body of historical and empirical research, focusing on natural experiments, has attempted to provide insights on the complex relationships between geography and human history and their implications for comparative development (for example, see the contributions in Diamond and Robinson 2010). As we discuss in the next two sections, recent contributions stress the importance of persistent characteristics transmitted inter- generationally over the long run. This litera- ture is consistent with anthropological work, 14 The effects of ethnic/cultural nomic outcomes within a common national documented by Brügger, Lalive, and Zweimüller (2009), who compare different unemployment patterns across the language barrier in Switzerland, and living in Latin-speaking border communities percent longer to leave unemployment than their neigh- bors in German-speaking communities. Spolaore and Wacziarg: Building on previous work by Bockstette, Chanda, and Putterman (2002) and Chanda and Putterman (2007), they consider two indicators of early development: early state history and the number of years since the adoption of agriculture. They then construct two sets of historical variables, one set repre- senting the history of the location, the other set weighted using the migration matrix, representing the same variables as they pertain not to the location but the contem- poraneous population inhabiting this loca- tion. Inevitably, measuring these concepts is fraught with methodological issues. For instance, when it comes to state antiquity, experience with centralization that occurred in the distant past is discounted exponen- tially, while no discounting is applied to the measure of the years of agriculture. While these measurement choices will surely lead to future refinements, it is the comparison between the estimates obtained when look- ing at the history of locations rather than pop- ulations that leads to interesting inferences. According to this approach, the United States has had a relatively short exposure to state centralization in terms of location, but once ancestry-adjusted it features a longer familiarity with state centralization, since the current inhabitants of the United States are mostly descended from Eurasian populations that have had a long history of centralized state institutions.15 Clearly, in this work the New World plays a big role in identifying the difference in the 15 Germany and Italy, two countries from which many ancestors of current Americans originate, over their histories between fractured For instance, Italy was a unified Empire, but a collection of city-states partly under foreign control, prior The index of state antiquity for such ods that occurred in the distant past brown.edu/fac/louis_putterman/Antiquity%20data%20 page.htm for details on the computation to lengthy periods of unification parts of their territories by domestic 338 Journal Historical Correlates of income 2005 Years of agriculture Ancestry-adjusted years of agriculture State history Ancestry-adjusted state history Note: Observations: 139 while the corresponding figure is almost 22 percent for state history. To summarize, a long history of central- ized states as well as an early adoption of agriculture are positively associated with per capita income today, after ancestry adjust- ment.16 Putterman and Weil also find that the variance of early development history across ancestor populations predicts within- country income inequality better than simple measures of ethnic and linguistic heteroge- neity. For example, in Latin America, coun- tries that are made up of a lot of Europeans along with a lot of Native Americans tend to display higher income inequality than coun- tries that are made up mostly of European descendants. Finally, to further elucidate why correcting for ancestry matters, they also 16 Interestingly, Paik (2010) Europe, an earlier onset of agriculture related with subsequent economic performance after the Industrial Revolution, contrary to of Putterman and Weil. Paik argues that the mechanism is cultural: a late adoption of agriculture individualist values that were conducive cess in the Industrial era. Spolaore and Wacziarg: The (Dependent Main regressor: Years of agriculture Ancestry-adjusted years of agriculture State history Ancestry-adjusted state history Absolute latitude 0.042 Percent land area in the –0.188 tropics (0.592) Landlocked dummy –0.753 Island dummy 0.681 Constant 7.699 Beta coefficients on the 3.75% bold variable Observations 150 2 R 0.475 Notes: Robust t statistics in parentheses. *** Significant at the 1 percent ** Significant at the 5 percent * Significant at the European ancestry confers a strong advan- tage in development, using new data on European settlement during colonization and its historical determinants. They find that the share of the European population in colonial times has a large and significant impact on income per capita today, even when eliminating Neo-European countries 340 Journal (Dependent Main regressor: Share of descendants of Europeans, 1.058 per Putterman and Weil Ancestry-adjusted years of agriculture, in thousands Ancestry-adjusted state history genetic distance to the Fst United States, weighted Constant 8.064 Observations 150 2 R 0.526 Notes: All regressions include controls in the tropics; landlocked dummy; *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent Table 6 captures the essence of these results. Still controlling for our four base- line geographic variables, we introduce the share of Europeans (computed from the Putterman and Weil ancestry matrix) in a regression explaining log per capita income in 2005. The effect is large and statistically significant (column 1), and remains signifi- cant when confining attention to a sample of countries with fewer than 30 percent of Europeans. Introducing the Putterman and Weil ancestry-adjusted historical variables (columns 3 and 4), we find that years of agri- culture and state history remain significant after controlling for the share of Europeans, suggesting that historical factors have an Spolaore and Wacziarg: How that Europeans carried with them besides general education, scientific and techno- logical knowledge, access to international markets, and human capital creating institu- tions. They also brought ideologies, values, social norms, and so on. It is difficult for us to evaluate which of these were crucial either alone or in combination” (27). This exempli- fies the difficult issue of disentangling, with the imperfect data that must be used to study comparative development, the effects of dif- ferent human characteristics. The bottom line, however, is that human traits are impor- tant to account for comparative development patterns, quite apart from the effects of geo- graphic and institutional factors. 3.3 The Persistence of Technological Advantages The deep historical roots of development are at the center of Comin, Easterly, and Gong (2010). They consider the adoption rates of various basic technologies in 1000 BC, 1 AD, and 1500 AD in a cross-section of countries defined by their current bound- aries. They find that technology adoption in 1500, but also as far back as 1000 BC, is a significant predictor of income per capita and technology adoption today. The effects of past technology continue to hold when including continental dummies and other geographic controls. At the level of technol- ogies, then, when examining a worldwide sample of countries (including European countries), there is no evidence of a rever- sal of fortune. Interestingly, Comin, Easterly, and Gong (2010) also find that the effects of past tech- nological adoption on current technologi- cal sophistication are much stronger when considering the past history of technology adoption of the ancestors of current popu- lations, rather than technology adoption in current locations, using the migration matrix provided in Putterman and Weil (2010). Hence, Comin, Easterly, and Gong’s results 342 Journal information about each population’s ances- tral composition.17 Genetic distance is a summary measure of differences in allele frequencies between populations across a range of neutral genes (chromosomal loci). The measure we used, FST genetic distance, captures the length of time since two popu- lations became separated from each other. When two populations split apart, random genetic mutations result in genetic differen- tiation over time. The longer the separation time, the greater the genetic distance com- puted from a set of neutral genes. Therefore, genetic distance captures the time since two populations have shared common ancestors (the time since they were parts of the same population), and can be viewed as a sum- mary measure of relatedness between popu- lations. An intuitive analogue is the concept of relatedness between individuals: two sib- lings are more closely related than two cous- ins because they share more recent common ancestors—their parents rather than their grandparents. Figure 1 (from Cavalli-Sforza, Menozzi, and Piazza 1994, page 78) is a phylogenetic tree illustrating how different human popu- lations have split apart over time. Such phy- logenetic trees, constructed from genetic distance data, are the population analogs of family trees for individuals. In this tree, the greatest genetic distance observed is between Mbuti Pygmies and Papua New Guineans, where the FS T distance is 0.4573, 17 To accommodate the fact that some countries are composed of different genetic groups (e.g., the United States), we computed a measure of “weighted genetic distance,” representing the expected between two randomly chosen individuals, country, using the genetic distances respective ancestor populations. That sider the inhabitants of countries genetic groups as a new homogeneous “population” in the biological sense, but treat each formed by distinct populations, to differences in ancestor-transmitted countries. This is the measure used discussed below. Spolaore and Wacziarg: How 0.2 0.15 Figure 1. Source: Cavalli-Sforza, Menozzi, and 344 Journal than from a stranger. Populations that share more recent common ancestors have had less time to diverge in a wide range of traits and characteristics—many of them cultural rather than biological—that are transmitted from a generation to the next with variation. Similarity in such traits facilitates communi- cation and learning, and hence the diffusion and adaptation of complex technological and institutional innovations. Under this barriers interpretation, dif- ferences in traits across populations hinder the flow of technologies, goods and people, and in turn these barriers hurt development. For instance, historically rooted differences may generate mistrust, miscommunica- tion, and even racial or ethnic bias and dis- crimination, hindering interactions between populations that could result in a quicker diffusion of productivity-enhancing innova- tions from the technological frontier to the rest of the world. The barriers framework in Spolaore and Wacziarg (2009) predicts that, ultimately, genetic distance should have no residual effect on income differences (unless another major innovation occurs), as more and more societies, farther from the frontier, come to imitate the frontier technology. This is consistent with the diffusion of economic development as emerging from the forma- tion of a human web, gradually joined by dif- ferent cultures and societies in function of their relative distance from the technological frontier (McNeill and McNeill 2003). We test the idea that genealogical relat- edness facilitates the diffusion of develop- ment in our unified empirical framework. Table 7, columns 1 and 2 introduce genetic distance to the United States in our basic income level regression, controlling baseline geographic variables.19 Genetic 19 Since several countries in our technological frontier (the United several distinct genetic groups, we sure of genetic distance, capturing Spolaore and Wacziarg: Genetic Distance and (Dependent Main regressor: genetic distance to the United Fst 1500 match Fstgenetic distance to the United States, weighted, current match Absolute latitude Percent land area in the tropics Landlocked dummy Island dummy Share of descendants of Europeans, per Putterman and Weil Constant Beta coefficients on the bold variable Observations R2 Notes: Robust t statistics in parentheses. *** Significant at the 1 percent level. ** Significant at the 5 percent level. * Significant at the 10 percent difference between the Ecuador–U.S. genetic distance and the Brazil–U.S. genetic distance. The specifications we use are as follows: First, we estimate the effect of simple weighted genetic distance, denoted between country i and country j, on lute difference in log per capita income between the two countries, controlling for a 346 Journal Income Difference (Dependent variable: Specification includes: Simple GD Estimator: OLS (1) Fstgenetic distance, weighted (0.687)** Fstgen. dist. relative to the United States, weighted Absolute difference in the shares of people of European descent Absolute difference in latitudes 0.562 Absolute difference in longitudes –0.117 (0.230) Geodesic distance –0.017 (0.030) =1 for contiguity –0.536 (0.057)** =1 if either country is an island 0.123 (0.097) =1 if either country is landlocked 0.047 (0.089) Difference in percent land area 0.156 in KG tropical climates =1 if pair shares at least –0.000 one sea or ocean (0.076) Freight rate (surface transport) –0.506 (0.748) Constant 1.211 (0.161)** Standardized Beta, absolute 19.47 GD (percent) Standardized Beta, relative GD (percent) Standardized Beta, difference of Economic Literature 2013, 51(2), 325–369 † Spolaore and Romain Wacziarg* growth and development has moved from the to the analysis of ever deeper, more fundamental history. A growing body of new empirical work focuses on of the effects of historical variables on contemporary account the ancestral composition of current that economic development is affected by traits generations over the very long run. This article provides a framework to discuss different channels transmitted characteristics may impact economic genetic or epigenetic transmission) and culturally (via An important issue is whether historically development through their direct impact on indirectly as barriers to the diffusion of productivity- populations. (JEL J11, O33, O47, Z13) have evolved over time. Decades ago, the emphasis was on the accumulation of fac- tors of production and exogenous techno- logical progress. Later, the focus switched to policies and incentives endogenously affecting factor accumulation and innova- tion. More recently, the attention has moved to the institutional framework underly- ing these policies and incentives. Pushing Bureau of Eco- University of Bureau of Economic 1 This is the usual form of the quote attributed to Winston Churchill—for instance, by Queen Elizabeth II in her 1999 Christmas Message. According to Langworth to visit the (2008, 577), Churchill’s words were “the longer you can look back, the farther you can look forward.” 325 Literature, Vol. LI (June 2013) affecting income and productivity over the long run.2 We review the literature on the legacy of geographic conditions in section 2. A major theme emerging from the recent literature is that key human characteristics affecting development are transmitted from one generation to the next within popula- tions over the long run, explaining why deep historical factors still affect outcomes today. A growing body of new empirical work has focused on the measurement and estimation of long-term effects of historical variables on contemporary income by explicitly tak- ing into account the ancestral composition of current populations (Spolaore and Wacziarg 2009; Putterman and Weil 2010; Comin, Easterly, and Gong 2010; Ashraf and Galor 2013). We survey contributions to this new literature in section 3. In section 4, we provide a general taxon- omy to discuss different channels through which inherited human characteristics may impact economic development. Our discus- sion builds on an extensive evolutionary lit- erature on the complex interactions among genetic, epigenetic, and cultural transmis- sion mechanisms, and on the coevolution of biological and cultural traits (Cavalli-Sforza and Feldman 1981; Boyd and Richerson 1985; Richerson and Boyd 2005; Jablonka and Lamb 2005), as well as on a growing literature on cultural transmission and eco- nomic outcomes (e.g., Bisin and Verdier 2000, 2001; Tabellini 2008, 2009; Alesina, Giuliano, and Nunn 2013). An important issue is whether historically transmitted characteristics affect economic development through their direct impact on productivity, or operate indirectly as barriers to the diffu- sion of technological and institutional inno- vations across populations. condi- 2 For recent discussions of these issues from differ- ent perspectives, see Galor (2005, 2011) and Acemoglu, Johnson, and Robinson (2005). How Deep Are the Roots of Economic Development? 327 Table 1, column 1 shows that a small set of geographic variables (absolute latitude, the percentage of a country’s land area located in tropical climates, a landlocked coun- try dummy, an island country dummy) can jointly account for 44 percent of contempo- rary variation in log per capita income, with quantitatively the largest effect coming from absolute latitude (excluding latitude causes the R 2 to fall to 0.29). This result captures the flavor of the above-cited literature docu- menting a strong correlation between geog- raphy and income per capita. While the correlation between geography and development is well established, the debate has centered around causal mecha- nisms. A number of prominent economists, including Myrdal (1968), Kamarck (1976), and Sachs and coauthors, argue that geo- graphic factors have a direct, contemporane- ous effect on productivity and development. In particular, Sachs (2001) claims that eco- nomic underdevelopment in tropical coun- tries can be partly explained by the current negative effects of their location, which include two main ecological handicaps: low agricultural productivity and a high burden of diseases. Tropical soils are depleted by heavy rainfall, and crops are attacked by pests and parasites that thrive in hot climates without winter frosts (Masters and McMillan 2001). Warm climates also favor the transmission of tropical diseases borne by insects and bacte- ria, with major effects on health and human capital. In sum, according to this line of research, geography has direct current effects on productivity and income per capita. Other scholars, in contrast, claim that Malthusian geography affects development indirectly through historical channels, such as the effects of prehistoric geographic and biologi- in population rather For a theoretical cal conditions on the onset and spread of agri- between popu- culture and domestication (Diamond 1997; long-term growth in Olsson and Hibbs 2005), and the effects For in-depth discus- and the recent contri- of crops and germs on the settlement of European colonizers after 1500 (Engerman of Economic Literature, Vol. LI (June 2013) Table 1 and Contemporary Development variable: log per capita income, 2005; estimator: OLS) Olsson–Hibbs Olsson–Hibbs Olsson–Hibbs Olsson–Hibbs Old World samplea samplea samplea samplea only (2) (3) (4) (5) (6) 0.052 (7.524)*** 0.209 –0.410 –0.650 –0.421 –0.448 (0.660) (1.595) (2.252)** (1.641) (1.646) –0.518 –0.499 –0.572 –0.505 –0.226 (2.687)*** (2.487)** (2.622)** (2.523)** (1.160) 0.306 0.920 0.560 0.952 1.306 (1.033) (3.479)*** (1.996)** (3.425)*** (4.504)*** 0.706 0.768 0.780 (6.931)*** (4.739)*** (5.167)*** 0.585 –0.074 0.086 (4.759)*** (0.483) (0.581) 7.354 8.745 8.958 8.741 8.438 (61.561)*** (58.200)*** (61.352)*** (60.049)*** 102 102 102 102 83 0.546 0.521 0.449 0.516 0.641 the neo-European countries (Australia, Canada, New Zealand, and the current income is based primarily on extractive wealth (Olsson and Hibbs 2005). of annual or perennial wild grasses and number of domesticable big mam- Hibbs 2005) latitude; climate suitability to agriculture; rate of East–West orientation; size variables from Olsson and Hibbs 2005). level. in 10,000 BC (the Neolithic Revolution). These advantages included the larger size of Eurasia, its initial biological conditions (the diversity of animals and plants avail- able for domestication in prehistoric times), and its East–West orientation, which facili- tated the spread of agricultural innovations. Building on these geographic advantages, Eurasia experienced a population explosion How Deep Are the Roots of Economic Development? 329 Columns 3–5 add the two main Olsson– Hibbs geographic variables, first separately and then jointly: a summary measure of bio- logical conditions and a summary measure of geographic conditions.5 Both geographic and biological conditions variables are highly significant when entered separately. When entered jointly, the geographic conditions variable remains highly significant and the overall explanatory power of the regressors remains large (52 percent). These empiri- cal results provide strong evidence in favor of Diamond’s hypotheses, while suggesting that the geographic component of the story is empirically more relevant than the bio- logical component. Column 6 goes further in the attempt to control for the effect of post- 1500 population movements, by restrict- ing the sample to the Old World (defined as all countries minus the Americas and Oceania). The effect of geography now rises to 64 percent—again highly consistent with Diamond’s idea that biogeographic condi- tions matter mostly in the Old World.6 5 These are the first principal components of the above-listed factors. Since latitude is a component of the geographic conditions index, we exclude our measure of latitude as a separate regressor in the regressions that include geographic conditions. 6 Olsson and Hibbs also find that geographic variables continue to be positively and significantly correlated with income per capita when they control for measures of the political and institutional environment. They show that such political and institutional measures are positively correlated with geographic and biogeographic conditions, consistent with the idea that institutions could mediate the link between geography and development. As they notice (934), controlling for political–institutional variables raises well-known issues of endogeneity and reverse causality (for instance, richer countries can have the resources and abil- ity to build better institutions). They write: “Researchers have struggled with the joint endogeneity issue, proposing various instrumental variables to obtain consistent esti- mates of the proximate effects of politics and institutions on economic performance, along with the related question of how much influence, if any, natural endowments exert on economic development independent of institutional development. None of these attempts is entirely persuasive in our view.” We return to these important issues below. of Economic Literature, Vol. LI (June 2013) for 70 percent of the variation in the date of adoption of agriculture, and most enter with a highly significant coefficient. Column 2 shows the reduced form—again, geographic factors account for 44 percent of the varia- tion in population density in 1500, consistent with the results of table 1 for the contempo- rary period.8 Ashraf and Galor (2011a) argue that, while geographic factors may have continued to affect economic development after the introduction of agriculture, the availability of prehistoric domesticable wild plant and animal species did not influence population density in the past two millennia other than through the timing of the Neolithic transi- tion. Therefore, they use these variables, obtained from the Olsson and Hibbs (2005) data set, as instruments to estimate the effect of the timing of the Neolithic transition on population density. The results of column 3 (OLS) and column 4 (IV) of table 2 illustrate their findings: years since the agricultural transition has a strong, statistically significant positive effect on population density in 1500. Interestingly, the IV effect is quantitatively larger than the OLS estimate.9 The magni- tude of the effect is large, as a one standard factors 8 Interestingly, the effect of latitude is negative. Ashraf and Galor (2011a) indeed observe that: “in contrast to the positive relationship between absolute latitude and con- temporary income per capita, population density in pre- industrial times was on average higher at latitudinal bands closer to the equator.” Thus, the effects of geographic factors have varied over different periods of technologi- cal development, in line with the idea that the effects of geography on development are indirect. 9 Ashraf and Galor (2011a) argue that, in regressions of this type: “reverse causality is not a source of concern, (...) [but] the OLS estimates of the effect of the time elapsed since the transition to agriculture may suffer from omit- ted variable bias (...)” (2016). The sign of the expected OLS bias therefore depends on the pattern of correlations same set of variables as between the omitted factors, the dependent variables summary indi- and the included regressors. Finding an IV effect that is we directly larger than the OLS effect is also broadly consistent with wild grasses and IV partly addressing measurement error in years since the agricultural transition, although care must be exercised with this inference in the multivariate context. How Deep Are the Roots of Economic Development? 331 Table 2 Geography and Development in 1500 AD Years since agricultural Population Population Population transition density in 1500 density in 1500 density in 1500 OLS OLS OLS IV (1) (2) (3) (4) –0.074 –0.022 0.027 0.020 (3.637)*** (1.411) (2.373)** (1.872)* –1.052 0.997 1.464 1.636 (2.356)** (2.291)** (3.312)*** (3.789)*** –0.585 0.384 0.532 0.702 (2.306)** (1.332) (1.616) (2.158)** –1.085 0.072 0.391 0.508 (3.699)*** (0.188) (0.993) (1.254) 0.017 0.030 (0.642) (1.105) 0.554 0.258 (8.349)*** (3.129)*** 0.426 0.584 (6.694)*** (6.887)*** 4.657 –0.164 –2.159 –2.814 (9.069)*** (0.379) (4.421)*** (5.463)*** 100 100 98 98 0.707 0.439 0.393 — level. reindustrial societies, the results in Ashraf p and Galor (2011a), as summarized in table 2, add an important qualifier to the Olsson and Hibbs (2005) results. They show, not only that an earlier onset of the Neolithic transition contributed to the level of tech- nological sophistication in the preindustrial world, but also that the effect of Diamond’s biogeographic factors may well operate through the legacy of an early exposure to agriculture. Economic Literature, Vol. LI (June 2013) evelopment” (11). We return to these d important questions below. Acemoglu, Johnson, and Robinson (2002) address the issue of whether geography may have had a direct effect on development by documenting a “reversal of fortune” among former European colonies. This reversal of fortune suggests that the effect of geogra- phy was indirect. The simplest geography story states that some geographic features are conducive to development, but this story is inconsistent with the reversal of fortune since the same geographic features that made a society rich in 1500 should presum- ably make it rich today.10 More sophisticated geography-centered arguments rely on the idea that geographic features conducive to development vary depending on the time period. A reversal of fortune would be con- sistent with nonpersistent direct effects of geography on productivity: features of geog- raphy that had positive effects on productiv- ity in the past could have become a handicap in more recent times. However, such shifts would then have to be explained by specific changes in nongeographic factors (e.g., a technological revolution). To proxy for levels of economic produc- tivity and prosperity in a Malthusian world, Acemoglu, Johnson, and Robinson (2002) use data on urbanization patterns and popu- lation density. Contemporary income per capita is regressed on these measures of economic performance in 1500 to assess whether a reversal of fortune has occurred. The bottom panel of table 3 mirrors their main results: in various samples that all exclude European countries, the relationship between population density in 1500 and log 10 Acemoglu, Johnson, and Robinson (2002) state that: “The simplest version of the geography hypothesis empha- sizes the time-invariant effects of geographic variables, such as climate and disease, on work effort and productiv- ity, and therefore predicts that nations and areas that were relatively rich in 1500 should also be relatively prosperous today” (1233). How Deep Are the Roots of Economic Development? 333 This line of research is part of a body of historical and empirical work emphasizing institutional differences across societies, including seminal contributions by North and Thomas (1973), North (1981, 1990), and Jones (1988), and more recently Engerman and Sokoloff (1997, 2002), Sokoloff and Engerman (2000), and Acemoglu, Johnson, and Robinson (2001, 2002, 2005). In particu- lar, Engerman and Sokoloff (1997) provide a path-breaking investigation of the interplay between geographic and historical factors in explaining differential growth performance in the Americas (United States and Canada versus Latin America). They point out that Latin American societies also began with vast supplies of land and natural resources per capita, and “were among the most pros- perous and coveted of the colonies in the seventeenth and eighteenth century. Indeed, so promising were these other regions, that Europeans of the time generally regarded the thirteen British colonies of the North American mainland and Canada as of rela- tively marginal economic interest—an opin- ion evidently shared by Native Americans who had concentrated disproportionally in the areas the Spanish eventually developed. Yet, despite their similar, if not less favorable, factor endowment, the U.S. and Canada ulti- mately proved to be far more successful than the other colonies in realizing sustained eco- nomic growth over time. This stark contrast in performance suggests that factor endow- ment alone cannot explain the diversity of outcomes” (Engerman and Sokoloff 1997, 260). Their central hypothesis was that dif- ferences in factor endowments across New World colonies played a key role in explain- ing different growth patterns after 1800, but population today is that those effects were indirect. Different factor endowments created “substantial dif- ferences in the degree of inequality in wealth, we discuss in much of the empirical strategy (2012). See Acemoglu, Johnson, and Robinson (2012) for a reply. of Economic Literature, Vol. LI (June 2013) Table 3 Reversal of Fortune variable: log per capita income, 2005; estimator: OLS) Not Former Former Former Former European European European European Non colony, Non Colony, Colony Colony Indigenous Indigenous Indigenous Indigenous (3) (4) (5) (6) (7) (8) With European Countries 0.170 0.193 b b b b (2.045)** (2.385)** 22.34% 20.00% 73 138 0.050 0.040 Without European Countries –0.393 –0.030 –0.232 –0.117 –0.371 –0.232 (7.093)*** (0.184) (2.045)** (1.112) (4.027)*** (2.740)** –47.88% –3.08% –32.81% –11.72% –51.69% –26.19% 98 38 33 103 28 70 0.229 0.001 0.108 0.014 0.267 0.069 term (estimates not reported). Robust t statistics in parentheses. level. level. 10 percent level. sample, regression results identical to those in the bottom panel. value and economies of scale ended up with unequal slave economies in the hands of a small elite, implementing policies and insti- tutions that perpetuated such inequality, lowering incentives for investment and inno- vation. In contrast, a more equal distribution How Deep Are the Roots of Economic Development? 335 after the discovery of the New World, and now constitute large portions of these coun- tries’ populations—either European coloniz- ers (e.g., in North America and Oceania) or African slaves (e.g., in the Caribbean). These regularities suggest that the broader features of a population, rather than institu- tions only, might account for the pattern of persistence and change in the relative eco- nomic performance of countries through history. Of course, the quality of institutions might be one of the features of a population (perhaps not the only feature) that makes it more or less susceptible to economic success, but the basic lesson from table 3 is that one cannot abstract from the ancestral structure of populations when trying to understand comparative development. This central idea is the subject of sections 3 and 4, so we will say little more for now. Recent work casts additional doubt on the view that national institutions are para- mount. In a paper on African development, Michalopoulos and Papaioannou (2010) find that national institutions have little effect when one looks at the economic performance of homogeneous ethnic groups divided by national borders. They examine the effects on comparative development of national contemporary institutions structures and ethnicity-specific precolonial societal traits, using a methodological approach that com- bines anthropological data on the spatial dis- tribution of ethnicities before colonization, historical information on ethnic cultural and institutional traits, and contemporary light density image data from satellites as a proxy of regional development. Overall, their find- ings suggest that long-term features of popu- lations, rather than institutions in isolation, the regressions of table 3 are the R2 falls. In general, R 2 s are quite low because we are Johnson, and Robinson regressing two different measures of development on each expands beyond former other (per capita income and population density in 1500), power of past develop- and both variables (particularly historical population den- sity) are measured with significant amounts of error. of Economic Literature, Vol. LI (June 2013) such as by Guglielmino et al. (1995), show- ing in the case of Africa that cultural traits are transmitted intergenerationally and bear only a weak correlation with environmental characteristics: “Most traits examined, in particular those affecting family structure and kinship, showed great conservation over generations. They are most probably trans- mitted by family members.” income in 3. Development and the Long-Term institu- History of Populations 3.1 Adjusting for Ancestry brought by Historical population movements play a central role in the debate regarding the mechanism linking geography and economic development, as well as the interpretation of reversals of fortune. Recent research has focused on the measurement and estimation of the long-term effects of historical factors on contemporary income by explicitly tak- ing into account the ancestral composition of current populations. We review some of these contributions in this section. An important contribution within this line of research is Putterman and Weil (2010). They examine explicitly whether it is the historical legacy of geographic locations or the historical legacy of the populations currently inhabiting these locations that matters more for contemporary outcomes. To do so, they assemble a matrix showing the share of the contemporary population of each country descended from people in different source countries in the year 1500. The definition of ancestry is bound to have some degree of arbitrariness, since it refers to ancestral populations at a specific point differences on eco- in time. However, choosing 1500 is sensible setting are also since this date occurs prior to the massive population movements that followed the find that job seekers discovery of the New World, and data on take about 18 population movements prior to that date are largely unavailable. How Deep Are the Roots of Economic Development? 337 between historical factors and their ancestry- adjusted counterparts, because outside the New World, everyone’s ancestry is largely from their own location. Putterman and Weil explore how their two historical variables— each either ancestry adjusted or not—affect the level of income per capita and within- country income inequality in the world today. Their key finding is that it is not as much the past history of locations that matters as it is the history of the ancestor populations. Tables 4 and 5 illustrate their approach in our unified empirical framework. Table 4 starts with simple correlations. The corre- lations between state history and years of agriculture, on the one hand, and per capita income in 2005, on the other hand, are of the expected positive signs, but are much larger when ancestry-adjusting—almost doubling in magnitude. These results are confirmed in the regressions of table 5. In these regres- sions, we start from the specification that controls for the baseline set of four geo- graphic variables, and add the Putterman and Weil variables one by one, either ances- try-adjusted or not. The variables represent- ing the history of the locations enter with an insignificant coefficient (columns 1 and 3), while the ancestry-adjusted variables enter with positive, statistically significant coef- ficients (columns 2 and 4). A one standard deviation change in ancestry-adjusted years of agriculture can account for 17 percent of a standard deviation of log per capita income, coefficients as the Republic of Venice and Prussia), however, Italy and have fluctuated Germany do not display state antiquity indices that are and unified states. that different from other European countries. The United country under the Roman States overall has a state antiquity index roughly commen- and local polities, surate with that of European countries, despite the addi- to its unification in 1861. tion of populations, for instance descended from Native cases discounts peri- Americans or African slaves, that may have had limited (see http://www.econ. exposure to centralized states. While the measurement of state antiquity can be questioned on several grounds, of the index). Due there is little doubt that ancestry adjustment implies that or control of substantial the United States had a longer experience with centralized regional states (such states than the history of Native Americans would suggest. of Economic Literature, Vol. LI (June 2013) Table 4 Development, with and without Ancestry Adjustment Ancestry Ancestry Log per capita Years of adjusted years State adjusted agriculture of agriculture history state history 0.228 1.000 0.457 0.817 1.000 0.257 0.618 0.457 1.000 0.481 0.424 0.613 0.783 1.000 show that a variable capturing the extent of European ancestry accounts for 41 percent of the variation in per capita income, a topic to which we turn in the next subsection. Putterman and Weil’s results strongly suggest that the ultimate drivers of devel- opment cannot be fully disembodied from characteristics of human populations. When migrating to the New World, popula- tions brought with them traits that carried the seeds of their economic performance. This stands in contrast to views emphasizing the direct effects of geography or the direct effects of institutions, for both of these characteristics could, in principle, operate irrespective of the population to which they apply. A population’s long familiarity with certain types of institutions, human capital, norms of behavior or more broadly culture seems important to account for comparative documents that within development. is negatively cor- 3.2 The Role of Europeans the worldwide results Easterly and Levine (2012) confirm and is associated with to economic suc- expand upon Putterman and Weil’s find- ing, showing that a large population of How Deep Are the Roots of Economic Development? 339 Table 5 History of Populations and Economic Development variable: log per capita income, 2005; estimator: OLS) Years of Ancestry-adjusted Ancestry-adjusted agriculture years of agriculture State history state history (1) (2) (3) (4) 0.019 (0.535) 0.099 (2.347)** 0.074 (0.245) 1.217 (3.306)*** 0.040 0.047 0.046 (6.120)*** (6.168)*** (7.483)*** (7.313)*** –0.148 0.061 0.269 (0.502) (0.200) (0.914) –0.671 –0.697 –0.555 (4.354)*** (3.847)*** (4.122)*** (3.201)*** 0.562 0.531 0.503 (2.550)** (2.555)** (2.216)** (2.338)** 7.270 7.458 6.773 (22.429)*** (21.455)*** (22.338)*** (19.539)*** 17.23% 1.50% 21.59% 148 136 135 0.523 0.558 0.588 level. level. 10 percent level. and restricting the sample to countries where the European share is less than 15 percent—that is, in non-settler colonies, with crops and germs associated with bad institutions. The effect remains high and significant when controlling for the quality of institutions, while it weakens when con- trolling for measures of education. of Economic Literature, Vol. LI (June 2013) Table 6 Europeans and Development variable: log per capita income, 2005; estimator: OLS) Sample with Control for Control Share of less than 30% years of Control for for genetic Europeans of Europeans agriculture state history distance (1) (2) (3) (4) (5) 2.892 1.079 1.108 0.863 (4.743)*** (3.506)*** (4.782)*** (5.519)*** (3.601)*** 0.105 (2.696)*** 1.089 (3.108)*** –4.576 (2.341)** 7.853 7.676 7.195 8.637 (24.338)*** (17.030)*** (21.984)*** (21.594)*** (20.941)*** 92 147 134 149 0.340 0.580 0.656 0.545 for the following geographic variables: absolute latitude; percent land area island dummy. Robust t statistics in parentheses. level. effect on contemporary development over and beyond the effect of European ancestry. In other words, while the traits c haracterizing European populations are correlated with development, the historical legacy of state centralization and early agricultural adoption matters independently. Easterly and Levine (2012) interpret these findings as consistent with the human-cap- ital argument by Glaeser et al. (2004) that Europeans brought their human capital, and the Galor and Weil (2000) and Galor, Moav, and Vollrath (2009) emphasis on the role of human capital in long-run development. However, Easterly and Levine (2012) also write: “Of course, there are many other things Deep Are the Roots of Economic Development? 341 provide a message analogous to Putterman and Weil’s: earlier historical development matters, and the mechanism is not through locations, but through ancestors—that is, intergenerational transmission. The basic lesson from Putterman and Weil (2010), Easterly and Levine (2012), and Comin, Easterly, and Gong (2010) is that historical factors—experience with settled agriculture and with former political institutions, and past exposure to frontier technologies—predict current income per capita and income distribution within coun- tries, and that these factors become more important when considering the history of populations rather than locations. These contributions point to a key role for persis- tent traits transmitted across generations within populations in explaining develop- ment outcomes over the very long run. 3.4 Genetic Distance and Development Genealogical links among populations over time and space are at the center of Spolaore and Wacziarg (2009), where we emphasized intergenerationally transmitted human traits as important determinants of development. The main goal of this paper was to explore the pattern of diffusion of economic development since the onset of the Industrial Revolution in Northwestern Europe in the late eighteenth century and early nineteenth century. The idea is to identify barriers to the adoption of these new modes of production, with a specific focus on human barriers (while controlling for geographic barriers). The bottom line is, again, that human traits matter, but the paper emphasizes barrier effects stemming from differences in characteristics, rather than the direct effect of human character- istics on economic performance. We compiled a data set, based on work by Cavalli-Sforza, Menozzi, and Piazza (1994), providing measures of genetic dis- tance between pairs of countries, using of Economic Literature, Vol. LI (June 2013) and the smallest is between the Danish and the English, where the genetic distance is 0.0021.18 To properly interpret the effect of genetic distance on differences in economic out- comes, two important clarifications are in order. First, since genetic distance is based on neutral change, it is not meant to cap- ture differences in specific genetic traits that can directly matter for survival and fit- ness. Hence, we emphasize that empiri- cal work using genetic distance provides no evidence for an effect of specific genes on income or productivity. Evidence of an “effect of genetic distance” is not evidence of a “genetic effect.” Rather, it can serve as evidence for the importance of intergenera- tionally transmitted traits, including traits that are transmitted culturally from one gen- eration to the next. Second, the mechanism need not be a direct effect of those traits (whether cultur- ally or genetically transmitted) on income and productivity. Rather, divergences in human traits, habits, norms, etc. have cre- ated barriers to communication and imita- tion across societies. While it is possible that intergenerationally transmitted traits have direct effects on productivity and economic performance (for example, if some parents transmit a stronger work ethic to their chil- dren), another possibility is that human traits also act to hinder development through a barrier effect: more closely related societies are more likely to learn from each other and adopt each other’s innovations. It is easier for someone to learn from a sibling than from a cousin, and easier to learn from a cousin genetic distance one from each associated with their is, we do not con- 18 Among the more disaggregated data for Europe, also composed of different used in Spolaore and Wacziarg (2009), the smallest genetic distance (equal to 0.0009) is between the Dutch and the of those countries as Danish, and the largest (equal to 0.0667) is between the accurately capture the Lapps and the Sardinians. The mean genetic distance traits within and across across European populations is 0.013. Genetic distances in the empirical work are roughly ten times smaller on average across popula- tions of Europe than in the world data set. Deep Are the Roots of Economic Development? 343 San (Bushmen) Mbuti Pygmy Bantu Nilotic W. African Ethiopian S.E. Indian Lapp Berber, N. African Sardinian Indian S.W. Asian Iranian Greek Basque Italian Danish English Samoyed Mongol Tibetan Korean Japanese Ainu N. Turkic Eskimo Chukchi S. Amerind C. Amerind N. Amerind N.W. American S. Chinese Mon Khmer Thai Indonesian Philippine Malaysian Polynesian Micronesian Melanesian New Guinean Australian FST Genetic Distance 0.1 0.05 0 Genetic Distance among Forty-two Populations Piazza (1994). of Economic Literature, Vol. LI (June 2013) distance as of 1500, reflecting the distance between indigenous populations, is nega- tively and significantly related to log income per capita in 2005. The effect rises in mag- nitude when considering genetic distance to the United States using the current genetic composition of countries. In other words, ancestry-adjusted genetic distance once more is a better predictor of current income than a variable based on indigenous charac- teristics, consistent with the results in table 5. Column 3 of table 7 introduces genetic distance alongside the share of Europeans, showing that genetic distance to the United States bears a significant partial correla- tion with current income that is not entirely attributable to the presence of Europeans. While these simple regressions are infor- mative, a better test of the hypothesis that genetic distance captures human barriers to the diffusion of development relies on a bilateral approach, whereby absolute log income differences are regressed on bilat- eral genetic distance, analogous to a gravity approach in international trade. This was the main approach in Spolaore and Wacziarg (2009), and is reflected in tables 8 and 9. The bilateral approach offers a test of the barriers story: if genetic distance acts as a barrier, it should not be the simple distance between countries that matters, but their genetic distance relative to the world technological frontier. In other words if genetic distance acts as a barrier, it should not be the genetic distance between, say, Ecuador and Brazil that should better explain their income dif- ference, but their relative genetic distance to the United States, defined as the a bsolute for the distance between two individuals, randomly selected from each of the two countries in a pair. Formally, the weighted FST genetic distance between countries 1 and 2 is defined as: I J 12  = ∑  ∑ ( s1i  × s2 j  × dij   ) sample, especially the FST  W States) are composed of i=1 j=1 used a weighted mea- where ski is the share of group i in country k, dij is the FST the expected genetic genetic distance between groups i and j. How Deep Are the Roots of Economic Development? 345 Table 7 Economic Development, Cross-Sectional Regressions variable: log per capita income, 2005) Indigenous Ancestry-adjusted Control for the genetic distance genetic distance share of Europeans (1) (2) (3) States, –4.038 (3.846)*** –6.440 –4.576 (3.392)*** (2.341)** 0.034 0.030 0.015 (5.068)*** (4.216)*** (1.838)* –0.182 –0.041 –0.384 (0.582) (0.135) (1.189) –0.637 –0.537 –0.521 (3.686)*** (2.971)*** (3.051)*** 0.584 0.607 0.557 (2.389)** (2.392)** (2.262)** 0.863 (3.601)*** 8.451 8.618 8.637 (23.577)*** (21.563)*** (20.941)*** –23.85% –27.11% –20.30% 155 154 149 0.499 0.496 0.545 level. vector Xijof additional bilateral variables of a geographic nature: | log Yi− log Yj|= β0 + β1   FST  W (1) ij  FST  W ij  , + β  ′2   Xij + εij . the abso- Second, we estimate the same specifica- tion, but using as a regressor relative genetic of Economic Literature, Vol. LI (June 2013) Table 8 Regressions with Genetic Distance absolute value of difference in log per capita income, 2005) Control for Relative GD Horserace Europeans Relative GD 2SLS with OLS OLS OLS 1500 GD (2) (3) (4) (5) 2.735 0.607 (0.683) 5.971 5.465 5.104 9.406 (1.085)** (1.174)** (1.038)** (1.887)** 0.620 (0.124)** 0.217 0.268 –0.369 0.112 (0.277)** (0.242) (0.250) (0.200)* (0.294) –0.016 0.024 –0.308 0.245 (0.214) (0.205) (0.198) (0.240) –0.018 –0.025 0.025 –0.049 (0.029) (0.028) (0.027) (0.031) –0.475 –0.469 –0.351 –0.395 (0.059)** (0.060)** (0.064)** (0.066)** 0.143 0.147 0.181 0.180 (0.093) (0.094) (0.095)* (0.093)* 0.040 0.034 0.076 0.011 (0.085) (0.087) (0.085) (0.085) 0.124 0.113 0.182 0.050 (0.095)* (0.096) (0.093) (0.092)** (0.100) –0.027 –0.027 –0.008 –0.050 (0.067) (0.068) (0.066) (0.079) –0.127 –0.162 –0.550 0.078 (0.835) (0.835) (0.783) (0.674) 1.083 1.078 0.984 0.941 (0.169)** (0.171)** (0.170)** (0.169)** 4.32 28.57 26.16 24.43 45.01

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