TY - JOUR
AU - Valencia Caicedo,, Felipe
AB - Abstract Using newly collected subnational data, this article establishes the within‐country persistence of economic activity in the New World over the last half millennium, a period including the trauma of European colonisation, the drastic reduction of native populations and the imposition of potentially growth inhibiting institutions. High pre‐colonial density areas tend to be denser today due to locational fundamentals and agglomeration effects: colonialists established settlements near existing native populations for reasons of labour, trade, knowledge and defence. These areas, identified with pre‐colonial prosperity, also tend to have higher incomes today suggesting that at the subnational level, fortune persists. Tenochtitlán was home to one of the largest concentrations of indigenous peoples in the New World when it was conquered by the Spaniards five centuries ago, and constituted an urban agglomeration rivalling those of Europe. In the words of Hernán Cortés (1522): This great city of Tenochtitlan is as big as Seville or Cordoba… It has many plazas where commerce abounds, one of which is twice as large as the city of Salamanca … and where there are usually more than 60,000 souls buying and selling every type of merchandise from every land … There are as many as 40 towers, all of which are so high that in the case of the largest there are fifty steps leading up to the main part of it and the most important of these towers is higher than that of the cathedral of Seville. The quality of their construction, both in masonry and woodwork, is unsurpassed anywhere (Cortés, La Gran Tenochtitlán, Segunda Carta de Relación (1522). Authors’ translation). Mexico City, erected on the ruins of Tenochtitlan, remains one of the largest and most prosperous cities in Latin America. This article uses new subnational data from 18 countries in the Western Hemisphere to examine the degree to which such persistence is generally the case: Do rich (high pre‐colonial population density) areas before the arrival of Columbus tend to be populous and rich today? Most similar to the present work, Davis and Weinstein (2002) find persistence in Japanese population concentrations over very long historical spells, and despite massive wartime devastation.1 Other recent works suggest persistence in economic activity over thousands (Comin et al., 2010) or tens of thousands of years (Ashraf and Galor, 2013). Such persistence is consistent first, with the importance of locational fundamentals such as safe harbours, climates suitable to agriculture, rivers or concentrations of natural resources that, even if not used for exactly the same purposes, nonetheless retain value over time (Fujita and Mori, 1996; Ellison and Glaeser, 1999; Gallup et al., 1999; Easterly and Levine, 2003; Rappaport and Sachs, 2003). It may also suggest the importance of agglomeration effects, perhaps arising from increasing returns to scale ( Krugman, 1991,1993) or Marshallian externalities arising from human capital, infrastructure and technological externalities (Glaeser et al., 1992; Krugman, 1992; Rauch, 1993; Comin et al., 2010; Bleakley and Lin, 2012; Severnini, 2012) which may lead to path dependence and persistence across time even after the initial attraction of a site has faded in importance.2 The case of the trauma of the colonisation of the New World is especially interesting since, while Hiroshima in Davis and Weinstein’s (2002) work proved resistant to losing a quarter of its population, the American Indian population was almost obliterated, falling by up to 90%. Further, the conquest implied the wholesale imposition of distinct culture (preferences), political organisation and technologies. The power of agglomeration effects to preserve the spatial distribution of prosperity in the face of such shocks is far from obvious. In particular, working against persistence in the context of colonised areas, Acemoglu et al. (2002) argue for what they term a ‘reversal of fortune’: areas colonised that had large populations of exploitable indigenous populations developed extractive institutions that were, particularly during the second Industrial Revolution, growth impeding. Following Malthus in associating high pre‐colonial population density with more productive and prosperous areas in pre‐industrial periods (Becker et al., 1999; Galor and Weil, 1999; Lucas, 2004), they find a negative correlation between pre‐colonial population density and present day incomes. This dramatic finding against persistence has become something of a stylised fact in the literature and has been influential in moving institutions to centre stage in the growth debate. In the present context, it suggests that institutional forces can more than fully offset agglomeration and locational forces. This article revisits the persistence question at the subnational (state, department, region) level for the Western Hemisphere. We focus on the Americas because of the availability of anthropological and archaeological estimates of indigenous population densities before Columbus at a geographically disaggregated level, the near universal colonisation by one or more European powers and hence its approximation to a natural experiment and the diversity of subsequent growth experiences. We match the pre‐colonial population estimates to new data on present population density and per capita income generated from household surveys and poverty maps. We then incorporate a comprehensive set of geographic controls, including new measures of agricultural suitability and river density, which we show to have explanatory power as locational fundamentals determining pre‐colonial settlement patterns. Data at this finer level of geographical aggregation allow us to take a more granular look at the role of locational, agglomeration and institutional forces behind the distribution of economic activity. In particular, using subnational data with country fixed effects mitigates identification problems caused by unobserved country or region‐specific factors arising from particular cultural or historical inheritances, and national policies, albeit, now asking the question at a level of aggregation where the relative influence of forces for and against persistence may differ. Our empirical results suggest that, within countries, the forces for persistence dominate. Population density today is strongly and robustly correlated with pre‐colonial population density as is, to lesser extent, current per capita income. Both statistical and historical evidence suggests that both locational fundamentals and agglomeration externalities plausibly explain why such persistence should occur despite the violent interaction of cultures of entirely distinct cultural, economic, institutional and technological characteristics. These findings raise some concerns that there are very significant adjustment or sunk costs that may work against ‘big pushes’ seeking to change the location of economic activity radically (Krugman, 1991; Rauch, 1993). Also, the puzzle arises of why the income findings should diverge so sharply from those found at the aggregate level, and we examine various explanations for the observed difference. 1. Data The use of subnational data to explore differential performance along various dimensions is now well established. As noted above, Davis and Weinstein (2002) use regional‐level data for Japan to document the remarkable persistence of population densities, highlighting the importance of both locational fundamentals and increasing returns to scale. Mitchener and McLean (2003) exploit modes of production and geographical isolation leading to differential de facto institutions as explaining differential growth rates across US states. Banerjee and Iyer (2005) exploit the variation in colonial property rights institutions across India to explain relative performance in agricultural investments, productivity and human development outcomes. Michalopoulos and Papaioannou (2013) find regional economic performance in Africa to be determined more by pre‐colonial ethnic grouping than by subsequent national institutions. Within Colombia, present regional development outcomes are shown to be affected by colonial institutions such as slavery, Encomienda and early state capacity (García‐Jimeno, 2005); and slavery (Bonet and Roca, 2006; Acemoglu et al., 2012). Naritomi et al. (2012) analyse how variations in colonial de facto institutions in Brazil led to different public good provision outcomes in modern times. Acemoglu and Dell (2010) examine differences in productivity across Latin American subregions and postulate that the large differences in institutions and enforcement of property rights, entry barriers and freeness and fairness of elections for varying levels of government are important. Dell (2010) uses district level data from Peru and Bolivia to demonstrate the long‐term impact of the Mita on development through the channels of land tenure and long‐term public goods provision.3 In a kindred paper using subnational data across the hemisphere, Bruhn and Gallego (2012) argue that differences in the types of regional colonial activities, whether engendering extractive or inclusive institutions, lead to lower or higher incomes respectively. Most recently, Gennaioli et al. (2013) use subnational data from 110 countries to argue for the overriding importance of human capital in accounting for regional differences in development. 1.1. Population We compile subnational data on pre‐colonial population densities, contemporary population densities and household incomes for the 18 countries in the hemisphere listed in Table 1, which summarises the data by country. Table 1 Summary Statistics – Population Density and Income . Observations . Pre‐colonial population density . Current population density . Income . . . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Argentina 24 0.44 1.45 0.01 2.55 626.06 4.80 1.20 14,727.03 10,576.16 0.46 5,834.35 24,328.34 Bolivia 9 1.18 0.96 0.20 3.74 9.53 0.84 0.82 26.17 3,494.36 0.25 2,239.15 5,219.44 Brazil 27 2.55 0.97 0.20 8.58 53.39 1.40 1.41 346.75 7,590.93 0.46 3,343.24 18,287.33 Canada 13 1.22 1.06 0.02 3.00 6.34 1.19 0.01 24.40 34,540.71 0.17 27,479.80 48,436.04 Chile 13 2.65 0.87 0.01 4.66 53.05 1.99 1.05 393.50 12,852.48 0.24 9,545.53 19,533.39 Colombia 30 4.96 0.82 0.49 13.04 424.40 2.36 0.48 4,310.09 4,554.56 0.27 2,546.91 6,917.57 Ecuador 22 5.76 0.78 0.01 12.06 56.10 0.92 2.01 182.80 5,764.57 0.30 3,738.26 10,463.96 El Salvador 14 24.19 0.24 15.80 39.25 326.73 1.30 95.58 1,768.80 4,669.67 0.29 3,378.47 8,094.27 Guatemala 8 22.95 0.35 5.64 29.08 248.97 1.57 10.23 1,195.48 3,699.73 0.56 2,132.71 8,526.96 Honduras 18 8.09 0.55 1.00 17.64 134.67 1.22 15.81 614.83 3,171.35 0.30 1,512.21 5,170.91 Mexico 32 31.90 2.38 0.40 392.34 227.55 3.36 5.61 4,352.62 12,119.95 0.29 6,780.40 20,709.32 Nicaragua 17 29.82 0.89 1.00 60.00 103.28 1.20 8.58 473.80 1,896.24 0.22 1,250.37 2,658.39 Panama 9 13.40 0.67 0.06 24.78 38.66 0.88 2.42 116.80 9,046.41 0.31 4,880.31 13,950.97 Paraguay 18 1.27 0.56 0.20 3.29 58.62 2.28 0.10 579.36 4,162.39 0.18 2,923.94 5,516.21 Peru 24 17.36 1.30 0.78 100.15 31.80 0.18 1.08 222.23 5,623.75 0.35 2,846.11 10,980.10 US 48 0.39 1.34 0.02 2.17 169.50 0.99 5.16 1,041.54 44,193.13 0.14 34,533.35 62,765.91 Uruguay 19 0.11 2.05 0.00 0.85 33.44 1.80 2.25 263.51 8,195.26 0.21 6,024.20 13,965.81 Venezuela 19 1.78 0.42 0.35 2.78 96.70 0.48 0.40 415.52 9,788.84 0.13 7,843.90 13,191.90 . Observations . Pre‐colonial population density . Current population density . Income . . . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Argentina 24 0.44 1.45 0.01 2.55 626.06 4.80 1.20 14,727.03 10,576.16 0.46 5,834.35 24,328.34 Bolivia 9 1.18 0.96 0.20 3.74 9.53 0.84 0.82 26.17 3,494.36 0.25 2,239.15 5,219.44 Brazil 27 2.55 0.97 0.20 8.58 53.39 1.40 1.41 346.75 7,590.93 0.46 3,343.24 18,287.33 Canada 13 1.22 1.06 0.02 3.00 6.34 1.19 0.01 24.40 34,540.71 0.17 27,479.80 48,436.04 Chile 13 2.65 0.87 0.01 4.66 53.05 1.99 1.05 393.50 12,852.48 0.24 9,545.53 19,533.39 Colombia 30 4.96 0.82 0.49 13.04 424.40 2.36 0.48 4,310.09 4,554.56 0.27 2,546.91 6,917.57 Ecuador 22 5.76 0.78 0.01 12.06 56.10 0.92 2.01 182.80 5,764.57 0.30 3,738.26 10,463.96 El Salvador 14 24.19 0.24 15.80 39.25 326.73 1.30 95.58 1,768.80 4,669.67 0.29 3,378.47 8,094.27 Guatemala 8 22.95 0.35 5.64 29.08 248.97 1.57 10.23 1,195.48 3,699.73 0.56 2,132.71 8,526.96 Honduras 18 8.09 0.55 1.00 17.64 134.67 1.22 15.81 614.83 3,171.35 0.30 1,512.21 5,170.91 Mexico 32 31.90 2.38 0.40 392.34 227.55 3.36 5.61 4,352.62 12,119.95 0.29 6,780.40 20,709.32 Nicaragua 17 29.82 0.89 1.00 60.00 103.28 1.20 8.58 473.80 1,896.24 0.22 1,250.37 2,658.39 Panama 9 13.40 0.67 0.06 24.78 38.66 0.88 2.42 116.80 9,046.41 0.31 4,880.31 13,950.97 Paraguay 18 1.27 0.56 0.20 3.29 58.62 2.28 0.10 579.36 4,162.39 0.18 2,923.94 5,516.21 Peru 24 17.36 1.30 0.78 100.15 31.80 0.18 1.08 222.23 5,623.75 0.35 2,846.11 10,980.10 US 48 0.39 1.34 0.02 2.17 169.50 0.99 5.16 1,041.54 44,193.13 0.14 34,533.35 62,765.91 Uruguay 19 0.11 2.05 0.00 0.85 33.44 1.80 2.25 263.51 8,195.26 0.21 6,024.20 13,965.81 Venezuela 19 1.78 0.42 0.35 2.78 96.70 0.48 0.40 415.52 9,788.84 0.13 7,843.90 13,191.90 Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, current population density is the total population in 2000 divided by the area of the state or province in square kilometres and Income is in per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Open in new tab Table 1 Summary Statistics – Population Density and Income . Observations . Pre‐colonial population density . Current population density . Income . . . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Argentina 24 0.44 1.45 0.01 2.55 626.06 4.80 1.20 14,727.03 10,576.16 0.46 5,834.35 24,328.34 Bolivia 9 1.18 0.96 0.20 3.74 9.53 0.84 0.82 26.17 3,494.36 0.25 2,239.15 5,219.44 Brazil 27 2.55 0.97 0.20 8.58 53.39 1.40 1.41 346.75 7,590.93 0.46 3,343.24 18,287.33 Canada 13 1.22 1.06 0.02 3.00 6.34 1.19 0.01 24.40 34,540.71 0.17 27,479.80 48,436.04 Chile 13 2.65 0.87 0.01 4.66 53.05 1.99 1.05 393.50 12,852.48 0.24 9,545.53 19,533.39 Colombia 30 4.96 0.82 0.49 13.04 424.40 2.36 0.48 4,310.09 4,554.56 0.27 2,546.91 6,917.57 Ecuador 22 5.76 0.78 0.01 12.06 56.10 0.92 2.01 182.80 5,764.57 0.30 3,738.26 10,463.96 El Salvador 14 24.19 0.24 15.80 39.25 326.73 1.30 95.58 1,768.80 4,669.67 0.29 3,378.47 8,094.27 Guatemala 8 22.95 0.35 5.64 29.08 248.97 1.57 10.23 1,195.48 3,699.73 0.56 2,132.71 8,526.96 Honduras 18 8.09 0.55 1.00 17.64 134.67 1.22 15.81 614.83 3,171.35 0.30 1,512.21 5,170.91 Mexico 32 31.90 2.38 0.40 392.34 227.55 3.36 5.61 4,352.62 12,119.95 0.29 6,780.40 20,709.32 Nicaragua 17 29.82 0.89 1.00 60.00 103.28 1.20 8.58 473.80 1,896.24 0.22 1,250.37 2,658.39 Panama 9 13.40 0.67 0.06 24.78 38.66 0.88 2.42 116.80 9,046.41 0.31 4,880.31 13,950.97 Paraguay 18 1.27 0.56 0.20 3.29 58.62 2.28 0.10 579.36 4,162.39 0.18 2,923.94 5,516.21 Peru 24 17.36 1.30 0.78 100.15 31.80 0.18 1.08 222.23 5,623.75 0.35 2,846.11 10,980.10 US 48 0.39 1.34 0.02 2.17 169.50 0.99 5.16 1,041.54 44,193.13 0.14 34,533.35 62,765.91 Uruguay 19 0.11 2.05 0.00 0.85 33.44 1.80 2.25 263.51 8,195.26 0.21 6,024.20 13,965.81 Venezuela 19 1.78 0.42 0.35 2.78 96.70 0.48 0.40 415.52 9,788.84 0.13 7,843.90 13,191.90 . Observations . Pre‐colonial population density . Current population density . Income . . . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Mean . Coef. var . Min . Max . Argentina 24 0.44 1.45 0.01 2.55 626.06 4.80 1.20 14,727.03 10,576.16 0.46 5,834.35 24,328.34 Bolivia 9 1.18 0.96 0.20 3.74 9.53 0.84 0.82 26.17 3,494.36 0.25 2,239.15 5,219.44 Brazil 27 2.55 0.97 0.20 8.58 53.39 1.40 1.41 346.75 7,590.93 0.46 3,343.24 18,287.33 Canada 13 1.22 1.06 0.02 3.00 6.34 1.19 0.01 24.40 34,540.71 0.17 27,479.80 48,436.04 Chile 13 2.65 0.87 0.01 4.66 53.05 1.99 1.05 393.50 12,852.48 0.24 9,545.53 19,533.39 Colombia 30 4.96 0.82 0.49 13.04 424.40 2.36 0.48 4,310.09 4,554.56 0.27 2,546.91 6,917.57 Ecuador 22 5.76 0.78 0.01 12.06 56.10 0.92 2.01 182.80 5,764.57 0.30 3,738.26 10,463.96 El Salvador 14 24.19 0.24 15.80 39.25 326.73 1.30 95.58 1,768.80 4,669.67 0.29 3,378.47 8,094.27 Guatemala 8 22.95 0.35 5.64 29.08 248.97 1.57 10.23 1,195.48 3,699.73 0.56 2,132.71 8,526.96 Honduras 18 8.09 0.55 1.00 17.64 134.67 1.22 15.81 614.83 3,171.35 0.30 1,512.21 5,170.91 Mexico 32 31.90 2.38 0.40 392.34 227.55 3.36 5.61 4,352.62 12,119.95 0.29 6,780.40 20,709.32 Nicaragua 17 29.82 0.89 1.00 60.00 103.28 1.20 8.58 473.80 1,896.24 0.22 1,250.37 2,658.39 Panama 9 13.40 0.67 0.06 24.78 38.66 0.88 2.42 116.80 9,046.41 0.31 4,880.31 13,950.97 Paraguay 18 1.27 0.56 0.20 3.29 58.62 2.28 0.10 579.36 4,162.39 0.18 2,923.94 5,516.21 Peru 24 17.36 1.30 0.78 100.15 31.80 0.18 1.08 222.23 5,623.75 0.35 2,846.11 10,980.10 US 48 0.39 1.34 0.02 2.17 169.50 0.99 5.16 1,041.54 44,193.13 0.14 34,533.35 62,765.91 Uruguay 19 0.11 2.05 0.00 0.85 33.44 1.80 2.25 263.51 8,195.26 0.21 6,024.20 13,965.81 Venezuela 19 1.78 0.42 0.35 2.78 96.70 0.48 0.40 415.52 9,788.84 0.13 7,843.90 13,191.90 Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, current population density is the total population in 2000 divided by the area of the state or province in square kilometres and Income is in per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Open in new tab 1.1.1. Pre‐colonial population density This measures the estimated number of indigenous people per square kilometre just before colonisation. These data draw on a long tradition of academic research dating from the turn of the last century, much of it fuel for the debate over whether the colonial powers encountered a ‘pristine wilderness’ or, alternatively, a world densely inhabited by indigenous peoples subsequently devastated by disease and conquest (Denevan, 1992b). At the low end, estimates by Rosenblat (1976) are now considered grossly under‐reported (Thornton, 1987), while the estimates of 100 million people in the Western Hemisphere by Dobyns (1966) are considered untenably large. Denevan offered consensus estimates in The Native Population in the Americas in 1492 (Denevan, 1992,a; published in 1976 and revised in 1992) that are now the most comprehensive and refined to date; employed most recently by Bruhn and Gallego (2012). The 1992 edition presents updated figures, especially for regions such as the Amazon jungle that have received recent attention and reconsideration. We employ Denevan’s estimates and expand the sample further using analogous data on Canada from Ubelaker (1988) and Nicaragua from Newson (1982).4 Though the project of estimating populations half a millennium past is necessarily speculative, the estimates synthesise the most recent available geographical, anthropological and archaeological findings. In particular, they draw on documentary evidence such as reports by Europeans, actual counts from church and tax records, as well as contemporary and recollected native estimates and counts. Depending on the country, projections across similar geographic areas, regional depopulation ratios, age‐sex pyramids and counts from sub‐samples of the population (such as warriors, adult males, tribute payers) are used, as well as backward projections from the time of contact with Europeans. These are corroborated by evidence including archaeological findings, skeletal counts, social structure, food production, carrying capacity and environmental modification. Importantly, neither modern GDP, climate models nor current population measures are used in the construction of these estimates. As an example, for Central Mexico, Denevan’s compendium incorporates findings from the Teotihuacán Valley project of William Sanders. Data for this project come from documentary sources from the sixteenth century including Spanish secular and ecclesiastic tax lists of the Mexican Symbiotic Region. Estimates for 1519 population are based on existing statements about specific communities, the size of Indian armies and the number of religious conversions. The work also critically reviews the historiography for earlier estimates for the sixteenth century. Sanders complements this information from records and tributary censuses from the First Audiencia of 1528, along with conversion and depopulation ratios. The non‐tributary population is also assessed through a series of visits to nearby towns, and clerical and military estimates from the first conquerors. The estimates have been collated with the Aztec empire tribute list, the 1568 official count and calculations of early agricultural productivity. These detailed contemporaneous accounts are complemented with archaeological findings in Mexico, Hispaniola, Nicaragua and other regions of Central, South and North America. The high degree of disaggregation of the indigenous estimates allows for the mapping to modern political territorial Mexican boundaries. Figure 1 maps the pre‐colonial population densities for the hemisphere. While some related studies have focused on cities as the unit of observation, such data are not available at our frequency for the pre‐colonial period and we work with regional densities. However, as Davis and Weinstein (2002) note, for numerous reasons in particular related to defining a city over time, estimated regional population densities are arguably preferable. To check for data consistency with other sources, we aggregate our regional numbers to the national level. In particular, we compare our estimates to the McEvedy et al. (1978) population density estimates for 1500 and Bairoch et al. (1988) urbanisation index for that same year, used among others in Acemoglu et al. (2002) and find a correlation between our numbers and theirs of 0.65 and 0.62 respectively. Part of the difference emerges from the number of repeated observations for Central America in their national‐level sources for which we have more precise and varying estimates. Further, as has been noted by, among others Acemoglu et al. (2002), the McEvedy and Jones (1978) estimates are controversial and are mostly based on Rosenblat (1976) which are likely to understate the pre‐Columbian population (Guedes et al., 2013). Hence, although the various pre‐colonial population count numbers are positively correlated at the national level, Denevan’s estimates, in addition to offering subnational detail, are the most reliable. Fig. 1. Open in new tabDownload slide Pre‐colonial Population Density Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Fig. 1. Open in new tabDownload slide Pre‐colonial Population Density Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. 1.1.2. Present subnational population density This measures present population per square kilometre in each subnational unit and is drawn from a highly disaggregated spatial data set on population, income and poverty constructed on the basis of national census data by the World Bank (2008) for the World Development Report on Reshaping Economic Geography. Population is aggregated from the census by the present subnational unit and the density is then calculated.5 1.2. Income 1.2.1. Subnational income per capita Income in 2005 PPP US dollars is drawn from the same spatial data set.6 Household income data are preferable to national accounts data as a measure of regional prosperity. In the case of natural resource‐rich regions, income may or may not accrue to the locality where it is generated and hence may provide a distorted measure of level of development. As an example, the revenues from oil pumped in Tabasco and Campeche, Mexico, are shared throughout the country, although they are sometimes (but not always) attributed entirely to the source state in the national accounts (Aroca et al., 2005). This is a broader issue that emerges wherever resource enclaves are important. For instance, from a national accounts point of view, the richest subnational units in Argentina, Colombia, Chile and Peru respectively are Tierra del Fuego (oil), Casanare (oil), Antofagasta (copper) and Moquegua (copper), all of which, with the exception of the last, are average or below average in our household survey measured income. Further, the geographical inhospitability of these locales ensured and continues to ensure relatively little human habitation: Antofagasta is in the driest desert in the world and Tierra del Fuego is the closest point in the hemisphere to Antarctica. This combination can give rise to a negative, although relatively uninteresting, correlation of pre‐colonial population density with present income. That said, such correlations still emerge even in our income data due to the selection of the population in these areas: the very small population related to extraction of natural resources has relatively high levels of human capital and remuneration and, hence, we may still find that areas which the indigenous avoided are now relatively well‐off in per capita terms. 1.2.2. Income distribution The Gini of personal incomes in each region as calculated by Bruhn and Gallego (2012). 1.3. Locational Fundamentals To establish the importance of locational fundamentals, we match the population and income subnational data to a broad set of geographical controls. Accounts of 18th century explorers, and anthropological studies confirm the importance to Indian settlements of both arable land and waterways for food and transport, characteristics also attractive to subsequent European settlers and potentially current inhabitants.7 We incorporate two new measures to capture agricultural suitability and river density. 1.3.1. Suitability for agriculture Since agriculture was critical to early settlement, we employ a new measure of agricultural suitability as developed by Ramankutty et al. (2002) and first employed by Michalopoulos (2012) and subsequently by Ashraf and Galor (2013, 2011a). This measure uses a combination of three different data sets that integrate satellite‐based measures of cultivable land, climatic parameters that may restrict the use of this soil (mean‐monthly climate conditions including temperature, precipitation and potential sunshine hours) and the IGBP‐DIS global soil data sets that containing soil properties such as soil carbon density, nitrogen content, pH and water‐holding capacity. Combining these through a model of land suitability, Ramankutty et al. (2002) generate an index of the probability that a particular grid cell will be cultivated. We employ a spatial average of this measure over subnational units. 1.3.2. Waterways and coasts For measures of the ubiquity of settlement‐suitable waterways, we employ the recently developed HydroSHEDS data that provide globally consistent hydrographic information at high resolution as collected during a Space Shuttle flight for NASA’s Shuttle Radar Topography Mission (SRTM). HydroSHEDS generates a mapping of river systems from which we develop a measure of the density of rivers suitable for transport.8 Tables 1 and 2 summarise the data. Table 2 Summary Statistics: Geographical Controls . Mean . P50 . SD . Min . Max . Agriculture 0.56 0.58 0.28 0.00 1.00 Altitude 0.66 0.19 0.92 0.00 4.33 Distance to coast 0.87 0.91 0.12 0.45 1.00 Malaria 1.09 0.20 1.53 0.00 5.85 Rainfall 1.28 1.10 0.95 0.00 8.13 Rivers 3.28 3.29 1.23 0.00 6.92 Ruggedness 12.68 9.93 10.33 0.00 47.43 Temperature 19.97 20.40 5.83 2.38 29.00 . Mean . P50 . SD . Min . Max . Agriculture 0.56 0.58 0.28 0.00 1.00 Altitude 0.66 0.19 0.92 0.00 4.33 Distance to coast 0.87 0.91 0.12 0.45 1.00 Malaria 1.09 0.20 1.53 0.00 5.85 Rainfall 1.28 1.10 0.95 0.00 8.13 Rivers 3.28 3.29 1.23 0.00 6.92 Ruggedness 12.68 9.93 10.33 0.00 47.43 Temperature 19.97 20.40 5.83 2.38 29.00 Notes. Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Open in new tab Table 2 Summary Statistics: Geographical Controls . Mean . P50 . SD . Min . Max . Agriculture 0.56 0.58 0.28 0.00 1.00 Altitude 0.66 0.19 0.92 0.00 4.33 Distance to coast 0.87 0.91 0.12 0.45 1.00 Malaria 1.09 0.20 1.53 0.00 5.85 Rainfall 1.28 1.10 0.95 0.00 8.13 Rivers 3.28 3.29 1.23 0.00 6.92 Ruggedness 12.68 9.93 10.33 0.00 47.43 Temperature 19.97 20.40 5.83 2.38 29.00 . Mean . P50 . SD . Min . Max . Agriculture 0.56 0.58 0.28 0.00 1.00 Altitude 0.66 0.19 0.92 0.00 4.33 Distance to coast 0.87 0.91 0.12 0.45 1.00 Malaria 1.09 0.20 1.53 0.00 5.85 Rainfall 1.28 1.10 0.95 0.00 8.13 Rivers 3.28 3.29 1.23 0.00 6.92 Ruggedness 12.68 9.93 10.33 0.00 47.43 Temperature 19.97 20.40 5.83 2.38 29.00 Notes. Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Open in new tab 2. Empirical Results 2.1. Locational Fundamentals and Pre‐colonial Densities Figure 1 and Table 1 present a map and summary statistics of pre‐colonial population densities. What is immediately clear is the great heterogeneity of pre‐colonial population densities both within and between countries, as well as the substantial overlap of distributions across countries. The Latin American countries span densities averaging from around 0.4 person per square kilometre for Argentina to 1.7 for Venezuela, 2.5 for Brazil to 17 for Peru and 32 for Mexico. Further, Table 1 confirms a large range of variances of initial density within country. Mexico and Peru are not only dense on average, but have much larger variances than, for instance, the US. However, overall, the US and Canada fit comfortably in the Latin American distribution. With a mean population density of 0.39, the US is above Uruguay and is roughly the same as Argentina. Canada, at 1.22 is above Argentina, Bolivia, and Uruguay and is just below Paraguay and not so far from Venezuela. Looking at both mean and variance, the US and Argentina are effectively identical: (0.39, 1.34) versus (0.44, 1.45). A clear divide between hemispheres on the basis of population density is thus not clear. As a first check on the relevance of our locational fundamentals proxies, Table 3 reports the results of running DPrecol,ij=α+γLFij+μi+ϵij,(1) where DPrecol,ij, is pre‐colonial density. LF is a the vector of subnational locational fundamentals and μi is a country‐specific fixed effect. Table 3 presents estimates both with and without fixed effects (FE). We report the latter despite the fact that the territorial boundaries and corresponding national governments, institutions and other characteristics clearly were not established at the time. First, in subsequent regressions we care very much about abstracting from country‐wide effects and hence the analogous specification is desirable for reference. Second, the generation of the pre‐colonial populations was done with the present national boundaries defining the unit of analysis and by different authors and hence there may be subtle differences at that level. That said, the case for fixed effects is somewhat less compelling here. Table 3 Pre‐colonial Population Density and Locational Fundamentals (Pooled) . OLS . Between . OLS FE . Agriculture 0.9 0.9** 1.9*** (1.36) (0.38) (0.46) Rivers −0.10 −0.10 0.3 (0.16) (0.14) (0.21) Distance to coast 2.0 2.0 4.8*** (1.90) (1.35) (1.51) Temperature 0.2** 0.2*** 0.03 (0.06) (0.02) (0.05) Altitude 0.9*** 0.9*** 0.3 (0.26) (0.13) (0.27) Rainfall 0.1 0.1 −0.002 (0.17) (0.12) (0.10) Ruggedness 0.06* 0.06*** 0.005 (0.03) (0.01) (0.01) Malaria 0.1 0.1 −0.2* (0.09) (0.09) (0.11) Constant −11.1*** −11.1*** −11.3*** (1.32) (1.45) (1.99) N 330 330 330 R2 0.423 0.423 0.232 . OLS . Between . OLS FE . Agriculture 0.9 0.9** 1.9*** (1.36) (0.38) (0.46) Rivers −0.10 −0.10 0.3 (0.16) (0.14) (0.21) Distance to coast 2.0 2.0 4.8*** (1.90) (1.35) (1.51) Temperature 0.2** 0.2*** 0.03 (0.06) (0.02) (0.05) Altitude 0.9*** 0.9*** 0.3 (0.26) (0.13) (0.27) Rainfall 0.1 0.1 −0.002 (0.17) (0.12) (0.10) Ruggedness 0.06* 0.06*** 0.005 (0.03) (0.01) (0.01) Malaria 0.1 0.1 −0.2* (0.09) (0.09) (0.11) Constant −11.1*** −11.1*** −11.3*** (1.32) (1.45) (1.99) N 330 330 330 R2 0.423 0.423 0.232 Notes. Regression of subnational log pre‐colonial population density on locational fundamentals. Estimation by OLS with country fixed effects. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Agriculture is an index of probability of cultivation, given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level are in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table 3 Pre‐colonial Population Density and Locational Fundamentals (Pooled) . OLS . Between . OLS FE . Agriculture 0.9 0.9** 1.9*** (1.36) (0.38) (0.46) Rivers −0.10 −0.10 0.3 (0.16) (0.14) (0.21) Distance to coast 2.0 2.0 4.8*** (1.90) (1.35) (1.51) Temperature 0.2** 0.2*** 0.03 (0.06) (0.02) (0.05) Altitude 0.9*** 0.9*** 0.3 (0.26) (0.13) (0.27) Rainfall 0.1 0.1 −0.002 (0.17) (0.12) (0.10) Ruggedness 0.06* 0.06*** 0.005 (0.03) (0.01) (0.01) Malaria 0.1 0.1 −0.2* (0.09) (0.09) (0.11) Constant −11.1*** −11.1*** −11.3*** (1.32) (1.45) (1.99) N 330 330 330 R2 0.423 0.423 0.232 . OLS . Between . OLS FE . Agriculture 0.9 0.9** 1.9*** (1.36) (0.38) (0.46) Rivers −0.10 −0.10 0.3 (0.16) (0.14) (0.21) Distance to coast 2.0 2.0 4.8*** (1.90) (1.35) (1.51) Temperature 0.2** 0.2*** 0.03 (0.06) (0.02) (0.05) Altitude 0.9*** 0.9*** 0.3 (0.26) (0.13) (0.27) Rainfall 0.1 0.1 −0.002 (0.17) (0.12) (0.10) Ruggedness 0.06* 0.06*** 0.005 (0.03) (0.01) (0.01) Malaria 0.1 0.1 −0.2* (0.09) (0.09) (0.11) Constant −11.1*** −11.1*** −11.3*** (1.32) (1.45) (1.99) N 330 330 330 R2 0.423 0.423 0.232 Notes. Regression of subnational log pre‐colonial population density on locational fundamentals. Estimation by OLS with country fixed effects. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Agriculture is an index of probability of cultivation, given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level are in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab For robustness purposes, in Appendix A we also report the corresponding results using the MS (or M–S) estimator (Maronna and Yohai, 2000). Our data combine countries with very different levels and variance of initial population densities as well as often very dramatic spreads within countries. Mexico, for instance, has many states with modest densities but then Morelos and Mexico City are five to ten times as large. The MS estimator is designed to handle potential good and bad leverage points (explanatory values with extreme values) as well as conventional vertical outliers in a structured way that has been shown superior to other alternatives like quantile or robust regression. Locational fundamentals appear to explain over 23% of the within variation. Both between and within countries, agricultural suitability enters significantly and positively. In the between regression, altitude, temperature and ruggedness appear significantly and positively. Within countries, consistent with Rappaport and Sachs (2003), distance to the coast and Malaria appear most significant although the MS estimates again find ruggedness and rainfall (negatively) correlated. The relatively high densities found in often arid relatively unsuitable areas along the coasts (see Figure 1) may reflect the marine, rather than agricultural basis of the local economy. The three significant variables account for 19 points of the 23% explained. In sum, our locational fundamentals proxies suggest that indigenous populations were concerned with agriculture or fishing, being warm, avoiding malaria, staying high, perhaps to avoid other diseases or predators, and not being too wet. 2.2. Persistence: Overview and Specification We next explore the correlation of pre‐colonial population densities with present population densities and with present per capita income. Again, the summary statistics for all three variables are found in Table 1. 2.3. Current Population 2.3.1. Evidence for persistence in population Figure 2 presents the entire sample pooled and shows a very strong and significant unconditional correlation between pre‐colonial and present population densities some 500 years apart. Each point represents a state or subnational unit and there are a total of 365 observations. As a means of examining individual country experience and comparing our results to those of Davis and Weinstein (2002), Table 4 reports the raw and rank order correlation of present population density with pre‐colonial population density. The results confirm that the positive relationship found for both the US and in fact the entire hemisphere, is significant and powerful. The majority, 16 of 18 countries respectively, show a positive correlation, 13 significant. Canada is the only country to show a significant negative coefficient, largely driven by the Arctic Northwest Territories, Yukon and Nunavut which have relatively lower population densities today. Fourteen of the 18 countries show strongly significant correlations and/or rank correlations. Twelve show correlations that exceed 0.5, and Chile, El Salvador, Guatemala, Mexico, Nicaragua, Peru and Venezuela all exceed 0.75 in correlation and a similar number in rank correlation. The conflict in the two measures for Argentina is due to the fact that Buenos Aires went from low density to among the highest densities in our sample which lowers the raw correlation but leaves a relatively high and positive ranking in place. Overall, for the majority of countries showing persistence, the magnitudes are broadly similar to the 0.76 and 0.83 respectively found by Davis and Weinstein (2002) for the shorter 400 year period spanning 1600 to 1998 ad in Japan. Fig. 2. Open in new tabDownload slide Persistence in Subnational Population Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, current population density is the total population in 2000 divided by the area of the state or province in square kilometres. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Fig. 2. Open in new tabDownload slide Persistence in Subnational Population Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, current population density is the total population in 2000 divided by the area of the state or province in square kilometres. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Table 4 Persistence in Population Density (Country by Country) . N . Raw correlation . Rank correlation . Immigration . Argentina 24 0.29 0.63*** 0.95 Brazil 27 0.58*** 0.62*** 0.91 Bolivia 9 0.70** 0.67** 0.28 Chile 13 0.87*** 0.79*** 0.63 Canada 13 −0.69*** −0.66** 0.97 Colombia 30 0.72*** 0.69*** 0.63 Ecuador 22 0.59*** 0.49** 0.39 ElSalvador 14 0.84*** 0.66*** 0.50 Guatemala 8 0.81** 0.83** 0.27 Honduras 18 0.71*** 0.51** 0.48 Mexico 32 0.78*** 0.68*** 0.37 Nicaragua 25 0.89*** 0.82*** 0.60 Panama 17 0.072 −0.033 0.64 Paraguay 9 0.39 0.33 0.54 Peru 18 0.84*** 0.74*** 0.36 US 24 0.39*** 0.37** 0.97 Uruguay 23 −0.33 −0.32 0.96 Venezuela 48 0.84*** 0.82*** 0.69 . N . Raw correlation . Rank correlation . Immigration . Argentina 24 0.29 0.63*** 0.95 Brazil 27 0.58*** 0.62*** 0.91 Bolivia 9 0.70** 0.67** 0.28 Chile 13 0.87*** 0.79*** 0.63 Canada 13 −0.69*** −0.66** 0.97 Colombia 30 0.72*** 0.69*** 0.63 Ecuador 22 0.59*** 0.49** 0.39 ElSalvador 14 0.84*** 0.66*** 0.50 Guatemala 8 0.81** 0.83** 0.27 Honduras 18 0.71*** 0.51** 0.48 Mexico 32 0.78*** 0.68*** 0.37 Nicaragua 25 0.89*** 0.82*** 0.60 Panama 17 0.072 −0.033 0.64 Paraguay 9 0.39 0.33 0.54 Peru 18 0.84*** 0.74*** 0.36 US 24 0.39*** 0.37** 0.97 Uruguay 23 −0.33 −0.32 0.96 Venezuela 48 0.84*** 0.82*** 0.69 Notes. Correlation coefficient and Spearman rank correlation coefficient. Current population density is the log of the total population in 2000 divided by the area of the state or province in square kilometres, from national censuses and Bruhn and Gallego (2012). Pre‐colonial population density is the log of the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Immigration from Chanda et al. (2014). More detailed data sources and descriptions in the text. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table 4 Persistence in Population Density (Country by Country) . N . Raw correlation . Rank correlation . Immigration . Argentina 24 0.29 0.63*** 0.95 Brazil 27 0.58*** 0.62*** 0.91 Bolivia 9 0.70** 0.67** 0.28 Chile 13 0.87*** 0.79*** 0.63 Canada 13 −0.69*** −0.66** 0.97 Colombia 30 0.72*** 0.69*** 0.63 Ecuador 22 0.59*** 0.49** 0.39 ElSalvador 14 0.84*** 0.66*** 0.50 Guatemala 8 0.81** 0.83** 0.27 Honduras 18 0.71*** 0.51** 0.48 Mexico 32 0.78*** 0.68*** 0.37 Nicaragua 25 0.89*** 0.82*** 0.60 Panama 17 0.072 −0.033 0.64 Paraguay 9 0.39 0.33 0.54 Peru 18 0.84*** 0.74*** 0.36 US 24 0.39*** 0.37** 0.97 Uruguay 23 −0.33 −0.32 0.96 Venezuela 48 0.84*** 0.82*** 0.69 . N . Raw correlation . Rank correlation . Immigration . Argentina 24 0.29 0.63*** 0.95 Brazil 27 0.58*** 0.62*** 0.91 Bolivia 9 0.70** 0.67** 0.28 Chile 13 0.87*** 0.79*** 0.63 Canada 13 −0.69*** −0.66** 0.97 Colombia 30 0.72*** 0.69*** 0.63 Ecuador 22 0.59*** 0.49** 0.39 ElSalvador 14 0.84*** 0.66*** 0.50 Guatemala 8 0.81** 0.83** 0.27 Honduras 18 0.71*** 0.51** 0.48 Mexico 32 0.78*** 0.68*** 0.37 Nicaragua 25 0.89*** 0.82*** 0.60 Panama 17 0.072 −0.033 0.64 Paraguay 9 0.39 0.33 0.54 Peru 18 0.84*** 0.74*** 0.36 US 24 0.39*** 0.37** 0.97 Uruguay 23 −0.33 −0.32 0.96 Venezuela 48 0.84*** 0.82*** 0.69 Notes. Correlation coefficient and Spearman rank correlation coefficient. Current population density is the log of the total population in 2000 divided by the area of the state or province in square kilometres, from national censuses and Bruhn and Gallego (2012). Pre‐colonial population density is the log of the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Immigration from Chanda et al. (2014). More detailed data sources and descriptions in the text. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab The US is among the lowest of those showing a positive and significant correlation at 0.37 and, again, Canada is the only negative and significant entrant. In general, the Latin American countries show much higher degrees of population persistence than the US or Canada with Panama, Paraguay and Uruguay showing insignificant and small correlations. This partially appears to reflect differences in immigration where the US and Canada were very open, and Latin America, with a few exceptions and like Japan, relatively closed. The fourth column presents a measure of the importance of migration at the country level calculated from Chanda et al. (2014). The greatest recipients of immigrants were Argentina, Canada, Uruguay and the US, four of six countries with low or negative raw correlations and in fact the correlation of immigration and persistence is −0.65. Both Uruguay and Argentina had levels of immigration comparable to those of the US and Canada and hence similar frontier expansion effects that might weaken the initial ordering as in the US and Canadian plains or the Argentine pampas. Massive immigration that multiplies overall population by double digits can lead to the exploration of new areas and establishment of new population concentrations that might happen more slowly under normal population growth.9 For pooling the data in both the current population and income variables, we estimate: D2005,ij;Y2005,ij=α+βDprecol,ij+γLFij+μi+ϵij,(2) where D2005,ij, population density of subunit i of country j and Y2005,ij, present per capita income, are sequentially the dependent variables. Table 5 presents the results for current population. Here, fixed effects are potentially of greater importance than in the last Section, because of the desire to control for country‐level historical effects or policies that would affect the between dimension, and we focus primarily on those estimates. That said, all specifications are positive and significant. Population density shows strong persistence across time. Including the geographical fundamentals does not dramatically reduce either the significance or magnitude of the population density effect. Including the quadratic terms in the fundamentals (not shown) similarly leaves population significant and positive. As before, good agricultural conditions, not being too far from a coast, and an absence of malaria appear important determinants of modern population density. Colonial densities 500 years ago explain 15% of the variance. Including the locational fundamentals raises it to 46%.10 In sum, despite a reasonably large set of locational controls, the pre‐colonial densities themselves appear to be robustly significant. Fundamentals continue to be important as conquistadors and immigrants arrive to populate the continents. However, with the caveat that we are likely to be missing or mis‐measuring some locational fundamentals, we cannot rule out an important amount of persistence existing for reasons related to the existence of the populations themselves. Table 5 Persistence in Population Density (Pooled) . OLS . Between . Within FE . Within FE . Pre‐colonial density 0.3*** 0.3** 0.4*** 0.3** (0.10) (0.12) (0.14) (0.11) Agriculture 1.8*** (0.50) Rivers −0.2 (0.18) Distance to coast 4.8*** (1.37) Temperature 0.01 (0.02) Altitude 0.1 (0.15) Rainfall 0.08 (0.08) Ruggedness 0.0001 (0.01) Malaria −0.3*** (0.09) Constant 0.10 0.1 0.6 −4.5* (0.36) (0.55) (0.59) (2.18) N 365 365 365 330 R2 0.136 0.282 0.147 0.464 . OLS . Between . Within FE . Within FE . Pre‐colonial density 0.3*** 0.3** 0.4*** 0.3** (0.10) (0.12) (0.14) (0.11) Agriculture 1.8*** (0.50) Rivers −0.2 (0.18) Distance to coast 4.8*** (1.37) Temperature 0.01 (0.02) Altitude 0.1 (0.15) Rainfall 0.08 (0.08) Ruggedness 0.0001 (0.01) Malaria −0.3*** (0.09) Constant 0.10 0.1 0.6 −4.5* (0.36) (0.55) (0.59) (2.18) N 365 365 365 330 R2 0.136 0.282 0.147 0.464 Notes. Regression of log current population density against log pre‐colonial population density. Estimation by OLS with country fixed effects. Current population density is the total population in 2000 divided by the area of the state or province in square kilometres, from national censuses and Bruhn and Gallego (2012). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the Terrain Ruggedness Index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table 5 Persistence in Population Density (Pooled) . OLS . Between . Within FE . Within FE . Pre‐colonial density 0.3*** 0.3** 0.4*** 0.3** (0.10) (0.12) (0.14) (0.11) Agriculture 1.8*** (0.50) Rivers −0.2 (0.18) Distance to coast 4.8*** (1.37) Temperature 0.01 (0.02) Altitude 0.1 (0.15) Rainfall 0.08 (0.08) Ruggedness 0.0001 (0.01) Malaria −0.3*** (0.09) Constant 0.10 0.1 0.6 −4.5* (0.36) (0.55) (0.59) (2.18) N 365 365 365 330 R2 0.136 0.282 0.147 0.464 . OLS . Between . Within FE . Within FE . Pre‐colonial density 0.3*** 0.3** 0.4*** 0.3** (0.10) (0.12) (0.14) (0.11) Agriculture 1.8*** (0.50) Rivers −0.2 (0.18) Distance to coast 4.8*** (1.37) Temperature 0.01 (0.02) Altitude 0.1 (0.15) Rainfall 0.08 (0.08) Ruggedness 0.0001 (0.01) Malaria −0.3*** (0.09) Constant 0.10 0.1 0.6 −4.5* (0.36) (0.55) (0.59) (2.18) N 365 365 365 330 R2 0.136 0.282 0.147 0.464 Notes. Regression of log current population density against log pre‐colonial population density. Estimation by OLS with country fixed effects. Current population density is the total population in 2000 divided by the area of the state or province in square kilometres, from national censuses and Bruhn and Gallego (2012). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the Terrain Ruggedness Index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab 2.3.2. Population persistence mechanisms Beyond the geographical factors discussed above, plausible mechanisms through which populations themselves could drive persistence can be found in the value of the populations themselves, knowledge and information and complementary technologies brought by the colonists. Workers, citizens and souls In Latin America, native populations were indeed a source of tribute and labour and hence it is not surprising that Spanish cities would be built near existing population centres, whatever factors drove their initial settlement. In other regions under Spanish colonisation, the native populations were valued for other‐worldly and strategic reasons. The missions set up along the Alta California (now US) coast–San Diego, Los Angeles, Santa Barbara, San Jose and San Francisco – were established beside major native population centres (as in the Southwest) to recruit souls to Christianity, but also to create colonial subjects to occupy territories perceived under threat of English and Russian encroachment (Taylor, 2001). In these cases, it was the population agglomeration itself, rather than the locational fundamentals per se, that were the attraction as exploitation was not the primary motivation. Clearly, however important the people, to the degree that the initial settlements were driven by geography, the present spatial distribution of activity fundamentally reflects that geography. However, history suggests that location decisions are not always so straightforward as, for example escaping malaria. Tenochtitlan was allegedly determined by the God Huitzilopotchli through a dream to be established where an eagle was found eating a snake while perched atop a cactus. This turned out to be a small, swampy, island whose chief attraction appears to be that it was uncoveted by the neighbouring tribes and was defensible. Parkes (1969) notes that the Mexica (Aztecs) were the last tribe of seven to enter the valley and wandered as outcasts, selling their services as mercenaries to the dominant tribes, and eating reptiles and pond scum to survive. They had the worst pickings of a not entirely favourable locale. The valley of Mexico, and in particular Tenochtitlan, had unreliable weather, with a short growing season and frequent drought. Famine was not uncommon. The lake was subject to storms and a major flood in 1499 caused the loss of much of Tenochtitlan (Thomas, 1993). Simpson (1962, p.164) notes that ‘With the silting up of the lakes and consequent flooding, the city was frequently inundated with its own filth and became a pest hole. Epidemics were a scourge for centuries and were not brought under control until the opening of the Tequixquiac drainage tunnel in 1900’. Geography itself does not appear sufficient to drive the permanence of the largest and richest city in Latin America.11 There appears to be some path dependence arising from the agglomeration itself. Knowledge and commerce In the US, pre‐colonial native populations were relatively small, topping out at around two people per square kilometre, and they were generally, with the exception of South Carolina (Breen, 1984), not exploited for tribute or labour by French, Anglo and Dutch colonisers. This suggests that while the argument that the Spanish and Portuguese located near indigenous populations for purposes of tribute or forced labour through the Encomienda or Mita is compelling, it is not the only mechanism through which pre‐colonial agglomerations were perpetuated. To begin with, throughout the New World explorers depended on native cartography and knowledge to map the relevant geographical and demographic sites (De Vorsey, 1978). New settlement was likely not to be random but influenced by the previous ‘known world’. In addition, colonisers needed the knowledge and skills accumulated by the native populations. Cortés employed the stone masons and architects of the pyramids, canals and aqueducts of vanquished Tenochtitlan to remodel Moctezuma’s palace into his own and to raise the most important city in the New World from the ruins of the Aztec capital. The large population of craftsmen and artisans was of world calibre (Parkes, 1969). The conquistadors more fundamentally needed those with a knowledge of plant life, agronomy and hunting to feed their new towns. Hence, just by virtue of already supporting a civilisation in all its dimensions, Tenochtitlan was attractive beyond the brute labour force it offered and in spite of its actually lacklustre locational fundamentals. In non‐Spanish North America, the competing colonial powers also established many cities including Albany (Dutch), Augusta (British), New York (Dutch), Philadelphia (British), Pittsburgh (French and British), St.Louis (French) on or next to native population settlements. Partly, the colonists, like the native populations, valued the areas of rich alluvial lands along the major river systems that served as the primary mode of transportation and communication, or the strategic locations. Bleakley and Lin (2012) argue that portage sites around rapids or falls gave rise to agglomerations in commerce and manufacturing that persist today, suggesting path dependence and increasing returns to scale. However, the native populations were critical attractions in themselves as well, again, largely for informational, commercial and strategic reasons. As Taylor (2001, p. 49) notes, ‘On their contested frontiers, each empire desperately needed Indians as trading partners, guides, religious converts, and military allies. Indian relations were central to the development of every colonial region’. From Canada to Louisiana, trade and defence led the French to establish their trading posts as nodes of trade and negotiation for securing alliances and food. In the North, French and Dutch fur traders exploited existing networks of native tribes as suppliers of pelts. Quebec, for example was located in an area where the local natives were skilled hunters and the nearby and numerous Huron nation served as provisioners and trade middlemen. Similarly, on Vancouver Island and throughout the Pacific Northwest, the British traded extensively with natives in sea otter pelts. Pre‐colonial Indian population concentrations offered benefits to colonisers along many dimensions and those of trade in goods and information are classic positive externalities associated with agglomerations. Complementary technologies As a final effect working in the opposite direction, for some indigenous agglomerations, contact with European culture and technology may have perpetuated their dominance after an initial period of trauma, particularly given the proximity to the Industrial Revolution. Comin et al. (2010) for instance, document an association between technology in 1500 ad and present income, roughly our period. Ashraf and Galor (2011a) argue that at the moment of transition between technological regimes, more cultural diffusion facilitates innovation and the adoption of new technologies. As one suggestive example, Steckel and Prince (2001) argue that one reason that the US plains Native Americans were the tallest people in the world in the mid‐nineteenth century was the buffalo and game made more accessible with the introduction of horses, metal tools and guns by Europeans (Coatsworth, 2008). Our documented patterns of persistence may, therefore, be partly driven by the degree to which the conquest transferred the old world technological endowment. The population centres of the earlier Maya, Anasazi and Toltec civilisations have vanished. Perhaps partly because of their contact with the Spanish, the Aztec population centre persists.12 Taken together, locational fundamentals, agglomeration externalities and technological transfer may plausibly contribute to an explanation of why pre‐colonial densities, even after the decimation of the local population, mapped to early colonial densities which, in turn, have persisted to this day. The question does remain, in the light of the (Krugman, 1991; Rauch, 1993) discussion, about whether even if the Spaniards valued the Aztec survivors for themselves, the costs were really so prohibitive as to prevent them from being moved to a more desirable geographical location? The answer is probably yes. Tenochtitlan at its peak had somewhere between 200,000 and 350,000 inhabitants which after their drastic reduction still implies moving somewhere around 20,000 individuals, their homes, workshops, plots of land, infrastructure etc. Further, to take them somewhere new would imply the loss of all the accumulated knowledge of how to best survive in the area: how to farm it, how to defend it. We could argue that that the relatively few conquistadors made the task especially onerous. However, as we will see below, the few cases where we do find substantial reallocations of population are in response to major shifts in fundamentals and are often accompanied by substantial immigration. 2.4. Current Income 2.4.1. Evidence for persistence of income The previous subsection confirms for the Americas, Davis and Weinstein’s (2002) finding that population density is persistent over very long periods of time. A large literature argues from Malthus that high population densities in pre‐industrial periods signal higher productivity and prosperity (Becker et al., 1999; Galor and Weil, 1999; Acemoglu et al., 2002; Lucas, 2004). The relationship between present population and present income may be expected to be less tight than historically was the case for at least two reasons: as Ashraf and Galor (2011b) and Galor (2011) note, the traditional Malthusian relationship between population and wealth weakens with technological progress, and the natural resource endowment effects discussed earlier. Further, in the process of demographic transition, population density may be more correlated with high population growth and poverty. Hence, though there is a positive and significant relationship between the current population and income, it is not obvious that there should be. Again, Acemoglu et al. (2002) precisely argue at the country level, that high prosperity areas in pre‐colonial times, measured by population density, became low prosperity areas today as measured by GDP per capita. The causality, again is that higher indigenous population densities led to extractive institutions that subsequently depress growth. Figure 3 shows that our data when aggregated support this reversal (Appendix B compares our population data to the McEvedy and Jones data.) Fig. 3. Open in new tabDownload slide Income Per Capita 2005 against Pre‐colonial Population Density (Aggregate) Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, Income is per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Fig. 3. Open in new tabDownload slide Income Per Capita 2005 against Pre‐colonial Population Density (Aggregate) Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, Income is per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. However, Figures 4 and 5 suggest that, at the subnational level, the relationship is more complex with some countries showing very strong evidence of persistence. The high degree of heterogeneity among the country cases suggests that understanding the drivers of the observed patterns is more convincingly done through careful examination of each as is done below. That said, the overall summary of the available data (Table 6) – the FE regressions with and without geographical controls – pre‐colonial density suggests a positive and significant effect for our sample overall. Perhaps consistent with a modern industrial economy with better infrastructure, neither agricultural suitability nor distance to the coast appear important although high altitude, rainfall and malaria all work against present day income.13 Fig. 4. Open in new tabDownload slide Income Per Capita 2005 against Pre‐colonial Population Density (Subnational US) Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, Income is per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Fig. 4. Open in new tabDownload slide Income Per Capita 2005 against Pre‐colonial Population Density (Subnational US) Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, Income is per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Fig. 5. Open in new tabDownload slide Income Per Capita 2005 against Pre‐colonial Population Density (Subnational) Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, Income is per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Fig. 5. Open in new tabDownload slide Income Per Capita 2005 against Pre‐colonial Population Density (Subnational) Notes. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, Income is per capita (PPP 2005 US dollars) in 2000. Data from national censuses, Denevan (1992, b) and Bruhn and Gallego (2012). More detailed data sources and descriptions in the text. Table 6 Persistence in Income (Pooled) . OLS . Between . Within FE . Within FE . Pre‐colonial density −0.4 −2.8 0.1** 0.08*** (0.37) (1.70) (0.04) (0.02) Agriculture −0.1 (0.08) Rivers −0.02 (0.03) Distance to coast − 0.1 (0.43) Temperature −0.007 (0.01) Altitude −0.1*** (0.03) Rainfall −0.07** (0.02) Ruggedness −0.001 (0.00) Malaria −0.05*** (0.02) Constant 9.1*** 9.1*** 9.0*** 9.6*** (0.06) (0.24) (0.00) (0.41) N 365 365 365 330 R2 0.010 0.093 0.004 0.135 . OLS . Between . Within FE . Within FE . Pre‐colonial density −0.4 −2.8 0.1** 0.08*** (0.37) (1.70) (0.04) (0.02) Agriculture −0.1 (0.08) Rivers −0.02 (0.03) Distance to coast − 0.1 (0.43) Temperature −0.007 (0.01) Altitude −0.1*** (0.03) Rainfall −0.07** (0.02) Ruggedness −0.001 (0.00) Malaria −0.05*** (0.02) Constant 9.1*** 9.1*** 9.0*** 9.6*** (0.06) (0.24) (0.00) (0.41) N 365 365 365 330 R2 0.010 0.093 0.004 0.135 Notes. Regression of the Log of Income per capita in 2000 (PPP 2005 US dollars) against Pre‐colonial population density. Estimation by OLS with country fixed effects. Income per capita (in logs) is taken from national censuses. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level are in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table 6 Persistence in Income (Pooled) . OLS . Between . Within FE . Within FE . Pre‐colonial density −0.4 −2.8 0.1** 0.08*** (0.37) (1.70) (0.04) (0.02) Agriculture −0.1 (0.08) Rivers −0.02 (0.03) Distance to coast − 0.1 (0.43) Temperature −0.007 (0.01) Altitude −0.1*** (0.03) Rainfall −0.07** (0.02) Ruggedness −0.001 (0.00) Malaria −0.05*** (0.02) Constant 9.1*** 9.1*** 9.0*** 9.6*** (0.06) (0.24) (0.00) (0.41) N 365 365 365 330 R2 0.010 0.093 0.004 0.135 . OLS . Between . Within FE . Within FE . Pre‐colonial density −0.4 −2.8 0.1** 0.08*** (0.37) (1.70) (0.04) (0.02) Agriculture −0.1 (0.08) Rivers −0.02 (0.03) Distance to coast − 0.1 (0.43) Temperature −0.007 (0.01) Altitude −0.1*** (0.03) Rainfall −0.07** (0.02) Ruggedness −0.001 (0.00) Malaria −0.05*** (0.02) Constant 9.1*** 9.1*** 9.0*** 9.6*** (0.06) (0.24) (0.00) (0.41) N 365 365 365 330 R2 0.010 0.093 0.004 0.135 Notes. Regression of the Log of Income per capita in 2000 (PPP 2005 US dollars) against Pre‐colonial population density. Estimation by OLS with country fixed effects. Income per capita (in logs) is taken from national censuses. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level are in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab 2.4.2. Reconciling the subnational with national findings The summary regressions raise the question of why, at the subnational level, we find evidence for persistence, while at the national level Acemoglu et al. (2002) find reversals. We discuss three answers although clearly there could be others. First, it may be that national institutions drive the overall development trajectory and when fixed effects strip these out, only the local agglomeration effects, locational fundamentals and perhaps less important subnational institutional effects remain. However, it is not axiomatic that subnational institutions were not as important as national and that we should not see the same exclusionary institutions and consequences replicated at the local level. Colombia, for instance, is famously fragmented into highly independent subregions often segmented by harsh and mountainous terrain.14 As Safford and Palacios (1998, p. 55) note, ‘Provincial government remained effectively independent of the Audiencia [the local Spanish seat of control], and Santa Fe de Bogotá lacked formal authority of what is now western Colombia’. National conditions and institutions were arguably relatively less important than those local compared to other countries and, as noted earlier, a variety of local institutional structures co‐existed (García‐Jimeno, 2005; Bonet and Roca, 2006) and affected local development outcomes.15 But even countries considered more centrally consolidated show a high degree of fragmentation with a somewhat tentative reach of central institutions. To quote a nineteenth century observer from southern Chile: Not so many years ago the inhabitants of the central region spoke of those in the South as distant and unknown, without the beneficial influence of civilization or the protection of the Government. Those in the upper altitudes considered those of the valleys as residents of another hemisphere, to which one could not travel except at great risk and financial sacrifice. Anyone who claimed that he had gone to Santiago or come from the South was treated as a dreamer or a liar; if he could prove with documents and irrefutable witnesses that he had made the journey, he was celebrated as if he had gone to China (Pedro Ruiz Aldea, La Tarantula (Concepción) 9 August 1862, cited in Monteón 1982, p. 3)) The seeming absence of a blanketing national ‘civilisation’ or [institutions of] government protection allows local institutions to emerge, and potentially dominate the national. Hence, if, in fact, differing concentrations of indigenous peoples appreciably affect institutional structures, it is not clear that the local effects should not be as or more powerful than the national.16 Second, it may be that moving to the subnational level increases the importance of locational fundamentals or agglomeration effects as suggested by Davis and Weinstein (2002) relative to institutional effects. Indeed, studies that have tried to measure agglomeration economies carefully (Ciccone and Hall, 1996; Ellison and Glaeser, 1997; Duranton, 2005; Greenstone et al., 2010) have all done so using highly disaggregated subnational data. Summerhill (2010) finds that in São Paulo Brazil, a ‘potentially coercive’ colonial institution, the aldeamento that regulated indigenous populations is positively correlated with income per capita at the end of the twentieth century. He argues that there were both extractive and settler (effectively agglomeration) effects, and the net effect was positive. We offer similar findings. Appendix C uses the share of slaves in the population as a proxy for extractive institutions and suggests that regions with a higher incidence of slavery have both lower incomes and less, although still strongly positive and significant, persistence: we may have found stronger persistence in the absence of extractive institutions. Hence, it is possible that at the subnational level, the net effect yields persistence, while at the national level where local agglomeration and geographical effects are diluted, national institutions dominate.17 Finally, it is possible that the aggregate negative relationship between pre‐colonial density and present income is due to factors other than a correlation with extractive institutions. For example, Easterly and Levine (2012) show a negative correlation between indigenous density and the proportion of Europeans in colonial society which they, like Glaeser et al. (2004), argue affects income today through channels including human capital, institutions, technology or culture. Relatedly, Chanda et al. (2014) argue that the reversal arises from migrants from technologically advanced areas moving to low density places and thereby propelling them from backward to advanced. The resolution of the national/subnational discrepancy clearly depends on the proposed mechanism underlying the aggregate relationship. In this particular case, we would need to understand why the driver of the negative density‐European/advanced area relationship is not replicated at the subnational level although, again, it may simply be that local agglomeration and geographical forces dominate. Distinguishing among these hypotheses is difficult. Perhaps weighing in against the link of pre‐colonial densities with extractive/exploitative institutions is that, as in Bruhn and Gallego (2012), we find no correlation between pre‐colonial indigenous densities and present income inequality (See Appendix D). That is, if extractive/exploitative institutions were instituted more in high density areas with long‐lived impacts on the level of income, we might expect them to influence the distribution of that income as well. This appears not to be the case.18 In the next Section, we take a more careful historical look at individual low, medium and high density country cases to get a clearer view of how the different forces interacted to yield the patterns we see at the subnational level, and the two low density countries which offer our only negative evidence for reversals. 2.4.3. Low Density and Persistent: The US Persistence holds strongly in the US. California, Massachusetts and Rhode Island again, show the highest pre‐colonial density and above average incomes. Among the mid‐level pre‐colonial density states, New Jersey, Connecticut, Delaware, are also among the richest and Washington and Oregon are solidly above average. This mass of points on the two coasts drives the upward sloping relationship while a diffuse mass of largely southern and mountain states anchors the low pre‐colonial density‐low current income nexus. As noted earlier, higher incomes plausibly find their roots both in the initial native agglomerations and locational fundamentals that attracted both native populations and Europeans. Both effects continued to be important across the centuries. For example, New York, Boston and Chicago have all played to their locational particularities, especially in transport, but they have also built on their strengths in accumulated human capital and information (Glaeser, 2005). There is also an argument for poor institutions driving the poorer regions in line with Acemoglu et al. (2001). The adverse disease environment and climate of much of the South discouraged settlement and, in the end, colonisation required the importing of African slaves. The state most likely to capture the colonisation‐driven inversion dynamic might have been Mississippi since it incorporated the third largest native civilisation in North America, was abused by the Spaniards, and is now the poorest state. However, the reversal of the state’s fortune from a rich cotton centre in the nineteenth century is likely more due to the institutional, demographic and education legacy of African slavery than the long vanished native population (see again Appendix C). As Taylor (2001) notes, the Spanish conquistador Hernando de Soto arriving in the fertile Mississippi river valley in 1540–2 was impressed by the size of native populations, the expansive maize fields, the power of the chiefs to command large numbers of well trained warriors, even the pyramids, one of which was the third largest in North America after those of central Mexico (The pyramid at Cahokia near present day St. Louis). De Soto died on the banks of the Mississippi, frustrated at finding no gold, and the Spaniards withdrew to Mexico City but not before widespread pillaging and infection drastically reduced the native population. When the French returned a century later, only the Natchez people near present day Natchez, Mississippi remained in strength and organisation. French encroachments on Natchez territories in 1729 led to massacres by the French and their Choctaw allies and dispersion and sale into slavery in the French West Indies of the surviving population. With the passage of another century, Natchez and Mississippi would emerge very prosperous at the height of the cotton boom. Our results are consistent with Mitchener and McLean (2003) using population data from 1700 that combine immigrant and black population and confirm persistence from 1700 to 1880 and then a very slight reversal moving to the present. The latter twentieth century result, but not the earlier, loses all significance when they control for settler origin dummies suggesting that, as in Chanda et al. (2014) the composition of immigration is contaminating the institutional experiment. Further, the correlation of actual measured productivity across the period 1880 – the present shows strong persistence. For a low range of pre‐colonial densities, the US suggests the persistence of economic activity. 2.4.4. Low Density Reversals: Argentina and Chile Argentina and Chile, provide the only two examples of statistically significant ‘reversals’ (Figure 5). Hence, understanding the cause of their negative relationship is of particular interest. For Argentina, the evidence supports an idiosyncratic geographical fundamentals story rather than an institutional one. The richest areas in Figure 5–the Province of Buenos Aires, La Pampa, Cordoba, Santa Fe and Entre Rios surround Buenos Aires City–tend, in fact, to be in areas of low pre‐colonial population density. The other richer departments, Santa Cruz and Chubut, are relatively undiversified mineral producers in relatively unattractive climates and hence show the ‘resource inversion’ discussed earlier. At the other extreme, Corrientes and Misiones are relatively underdeveloped humid semi‐tropical areas that were traditionally isolated and show the highest pre‐colonial density and, hence, potentially extractive institutions. But it must be kept in mind that these densities map in both absolute and relative magnitude to those of Massachusetts and California within an overall distribution that, again, is remarkably similar to that of the US. Hence, a theory of institution‐driven inversion would need to explain why the endogenously emerging institutions would be so different from the US. In addition, Buenos Aires may well not have been such a paragon of inclusionary institutions that would account for its unusual growth. It was a major port of slave disembarkation in the New World and, in the last years of Spanish domination, it was 30% black (Andrews, 1980).19 It seems more likely that the present distribution of income arises largely from Buenos Aires’s status as the principal Atlantic port of the Spanish empire. This was not always the case. Despite the evolution of the surrounding pampas economy, prior to the mid 18th century, Buenos Aires was a backwater, surviving on smuggling contraband silver and slaves. This was largely due to the repression of natural locational advantage. By Spanish law, the production of silver and other products of the interior towns were directed over the Andes to Lima on the Pacific, where they were loaded on convoys passing through the Isthmus of Panama and then to Spain. The more logical route – through the Atlantic port of Buenos Aires, and then directly to Spain – was forbidden. However, largely for geostrategic reasons arising from the emergence of the North American colonies as a potential Atlantic power, the policy was reversed in 1776 when Spain established Buenos Aires as the capital of the new Viceroyalty of Rio de la Plata. Trade was now mandated through Buenos Aires and forbidden through Lima, leading to an abrupt reorientation of the country’s economy away from the traditional interior towns, and towards the emerging coastal economy (Scobie, 1964). Hence, by royal fiat, locational fundamentals went from being repressed to dominant. From here, the agglomeration‐related externalities arising from becoming the dominant Atlantic port can explain the pattern we see. Finally, the finding of Chanda et al. (2014) that immigration upends even the original reversal of fortune relationship applies with special relevance to Argentina in which, in 1900, 30% of the population were recent immigrants and most concentrated in Buenos Aires, compounding the shift in the economic centre of gravity there. Chile also shows a significant negative relationship between pre‐colonial densities and present income but one which, again, does not appear driven by the institutional story for three reasons. First, several observations at the highest end of the country’s relatively low density (4.7 per square kilometre) – Bio Bio, Maule, O’Higgins, Los Lagos and Araucania – are among the poorest. However, these form a contiguous region, with the area below the Bio Bio River that includes them dominated by the Mapuche Indians and conquered only very late in the nineteenth century and hence never experienced Spanish rule. More likely, the technological complementarities discussed earlier were at play – the institutional case is not as compelling, perhaps, as one stressing the costs of being out of the global technological loop. In fact, the eventual conquest had to wait for the Chileans to import recent advances in weaponry to which the Mapuches did not have access. The subsequent oppression exacerbated these lags. The capital, Santiago, offers a counter example of a regression discontinuity flavour: it has the same density and is contiguous to this region but it was conquered several hundred years earlier and is much more prosperous. Second, the country is one of extremes with extractive industries in some of the driest and coldest areas of the planet which were not attractive to native populations. This implies a relatively uninteresting correlation of relatively low pre‐colonial densities, and moderately high incomes (for a very few people) today. Finally, as in Argentina, the fact that the overall density is roughly equivalent to that of Canada raises the question of why such effects could be so much stronger in Chile. 2.4.5. Medium density and persistent: Colombia and Bolivia Colombia, a middle‐density country, is an important case for understanding the relative import of the different forces for and against persistence and, in particular, that of extractive institutions (García‐Jimeno, 2005; Acemoglu et al., 2012). First, though it is not among the countries with the highest pre‐colonial density, it is a classic example of Spanish conquest with the usual attendant institutions. Hence, while we might argue that something about Anglo or French colonists led to different coloniser‐native dynamics, this would not be the case in Colombia. Second, it had relatively little immigration so the Chanda et al. (2014) concern is mitigated. Third, as noted above, it is a perhaps the case which offers the greatest independence of subnational observations from national conditions and institutions. As noted earlier, the country is highly geographically fragmented and its regions have shown a fierce autonomy, long resisting centrally imposed rule. Yet, despite the relative strength of local institutions, Colombia still shows one of the cleanest examples of persistence in the sample (Figure 5) a fact confirmed also by Meisel (2014) using repeated censuses and records of tributary Indians. Not only the capital, but other areas of high pre‐colonial density–Valle de Cauca, Santander and Antioquia – have among the highest present day incomes. Hence, again, local agglomeration and locational forces appear to be dominant. One particular reversal within the country is illustrative of the relative strength of locational fundamentals in particular. Although understated in the figures, Cauca department and its principal city Popayán fell from one of the two most important regions in Colombia – a major provider of early Colombian presidents and possessor of one of the country’s two mints – to one of the poorer regions. The Spaniards favoured it for the availability of indigenous labour to extract its mineral wealth, and its subsequent use of imported African slaves defined its culture in fundamental ways. However, the city that it lost market share to, Cali, in Colombia’s now second richest department, Valle de Cauca, had an indigenous population density 30% larger and only 10% fewer slaves per capita than Cauca. In fact, it had the largest number of slaves of any department in Colombia.20 The period critical to the reversal appears to be 1878–1915 with the construction of the Pacific Railroad connecting Cali with Buenaventura, Colombia’s largest Pacific port and through the Panama Canal (finished in 1914) to the rest of the world, while Popayán remained relatively isolated (Safford and Palacios, 1998). It is likely that the location of the railroad, while importantly dictated by Cali’s proximity to the Cauca River, is partly due to political economy considerations. However, a story related to initial populations or slavery does not appear clearly. It seems more likely that a permanent shock to locational fundamentals altered the relative attractiveness of the two regions.21 Of interest is that Cali’s new locational advantage did not also diminish Antioquia and the Bogota/Cundinamarca agglomeration as industrial centres. In the colonial period, both had a locational advantage in terms of climate and soil suitable for agriculture, proximity to mineral reserves and disease inhibiting altitude. Yet, none of these are important to explaining the overwhelming dominance of both areas in the manufacturing and service sectors, while the need to cross, especially in Bogota’s case, several mountain ranges to access world markets is a major drag on competitiveness. This is suggestive that, as in the US case, agglomeration effects, in particular, the availability of talent and knowledge – are critical to the continued dominance of these areas.22 In sum, then, in a country where local institutions were relatively important, agglomeration and locational fundamentals appear to dominate. Bolivia also provides a case of a moderate level of indigenous density with significant persistence although it has fewer observations and important leverage points that drive the results. Like Colombia, it is also fragmented with Santa Cruz and la Paz as distinct poles of economic activity, much the same as Medellin, Bogota and Cali. The critical influence of the relatively high density Colchabamba and La Paz remain, although Santa Cruz, somewhat like Monterrey in Mexico, is an emerging industrial pole that weakens the relationships of persistence. 2.4.6. High density and persistent: El Salvador, Nicaragua, Perú and Mexico El Salvador, Nicaragua, Peru and Mexico have among the highest densities in our sample. While we might expect the strongest agglomeration as well as negative institutional effects in these countries, overall they show support for persistence. In Figure 5, Both El Salvador and Nicaragua show clear and statistically significant positive slopes. Mexico and Peru are the emblematic examples of the colonisation of the New World. Though less clearly significant than the other two, both offer support for the importance of the forces of persistence, albeit contaminated by changes in locational fundamentals. For Peru, Figure 5 suggests that Lima, La Libertad, Ica and Piura all correspond to very high pre‐colonial density areas that remain among the better off regions today. However, Lambayeque province weakens the statistical relationship by showing the highest density observation but below average current income. Lambayeque’s decline appears largely driven by compounding natural disasters–negative locational fundamental shocks. In pre‐colonial times, the region was a major centre of the Chimor and then Inca cultures. The Spanish colonisers subsequently built a livestock industry on appropriated native land and irrigation systems, as in Tenochtitlan, taking advantage of the infrastructure and knowledge of the previous civilisation. From 1650 to 1719, a dynamic sugar‐based hacienda economy emerged and generated numerous fortunes. However, after 1720 the economy collapsed into a century long period of stagnation. While this was partly due to competition from other Peruvian (including local native) and Caribbean producers, as Ramirez (1986) argues in her detailed study of the region, a plague of cane‐eating rats in 1701 followed by two devastating floods in 1720 and 1728 constituted idiosyncratic but very long‐lived shocks which, compounded by a shortage of finance (Cushner, 1980), caused widespread foreclosures and the bankruptcy of the traditional producing class. Only in the late colonial period did the regional economy recover somewhat to a now average‐level income as the new owners shifted from sugar to livestock and tobacco.23 Mexico appears to combine two distinct sets of growth dynamics that interact to obscure any clear relationship. The first is the persistence effect. The Mexican Federal District (city) is the highest density region in our sample and it is one of the richest regions in all of Latin America. Morelos, the second densest region in our sample, has above average income. Both suggest persistence in the most native intensive regions of the hemisphere. Tlaxcala, the third most dense area in Mexico ranks among the lower levels of prosperity. However, it seems unlikely that we can attribute it to especially extractive institutions since, in exchange for being the principal allies of the Spaniards and sheltering them in a particularly dire moment in the conquest of Tenochtitlan, the Tlaxcalans were granted ‘perpetual exemption from tribute of any sort,’ a share of the spoils of conquest and control of two bordering provinces, an agreement that was substantially respected for the duration of Spanish rule (Marks, 1994, p. 188).24 Among the very highest pre‐colonial densities in our sample, agglomeration effects again appear dominant. However, there is a second dynamic. The present high income of the low pre‐colonial density states of Baja California Sur, Nuevo León, Baja California Norte, Chihuahua, Sonora and Coahuila provide a strong countervailing ‘reversal’ that offsets the persistence effects. The proximity of these states to the increasingly dynamic US border makes it difficult to disentangle the influence of various types from the North (proximity to markets, knowledge spillovers), where it was in large part an appendage of the US economy. At the time of the establishment of the border at the Rio Grande, it was linked by population flows and contraband; during the civil war, it was a significant Southern export outlet; and, by the turn of the century, it had received substantial US investments in railroads and mining that gave the impetus to the development of capitalism in the North (Mora‐Torres, 2001). For instance, US firms operated mines in the North for export to US foundries (e.g. Consolidated Kansas City Smelting in Chihuahua). The three large foundries that formed the basis for the future dynamism of the principal industrial city in northern Mexico, Monterrey, Nuevo Leon (with spillovers to much of the north of Mexico) were primarily oriented towards the US market and the largest was established by the Guggenheim interests with US capital (Morado, 2003).25 As Marichal (1997) notes, the emerging industry in these areas gave impetus to a set of de facto and eventually de jure institutions and pro‐industry regulations which may well have only been able to emerge in an environment where the regulatory structure had not been driven by extractive considerations. That said, the fact that a positive correlation emerges when we abstract from the border states causes us to think that the proximity to the US was the primary driver of the prosperity of the low density North. 3. Conclusion This article documents that, within countries, economic activity in the Western Hemisphere has tended to persist over the last half millennium and in spite of the major trauma that was colonisation. Despite the drastic reduction in the previous populations, the imposition of new cultural and political forms and technologies, areas that were rich and populated before the arrival of Colombus remain highly populated and tend to be rich today. In the same way that Davis and Weinstein (2002) demonstrated the resiliency of Hiroshima’s population to mass devastation and a 25% fall in population, we show comparable correlations of population densities across time in the Americas despite, again, mass devastation and the up to 90% reduction in their population. Our findings inform the discussion surrounding the difficulty of changing the distribution of economic activity (Krugman, 1991; Rauch, 1993). Despite the graphical and statistical evidence for most countries that persistence in population is extremely strong, there are notable outliers. Buenos Aires, Argentina rose from a backwater to the densest population by far. Several Northern Mexican states, such as Baja California or Nuevo Leon emerged from obscurity to become industrial centres. Each case, however, was accompanied by a major ‘big push’: the release of a perverse restriction on locational advantage in Argentina, and the emergence of a major economic power next door to Mexico. In both Argentina and the US, the dispersion around the central tendency is quite high suggesting that massive migration can shake up the population substantially if not actually overturning it. Though a less uniform finding, and in spite of theoretical reasons to think it might not be the case, there is also overall a tendency for income to persist over time as well and there is no evidence for a reversal of fortune as Acemoglu et al. (2002) find at the country level. This is clearly the case for low pre‐colonial density countries like the US, but also for classic Latin conquest cases like Colombia, and, for the extreme high density cases like El Salvador, Mexico, Nicaragua and Peru. Across all our case studies, the large changes in relative positions, such as in Popayán (Colombia), Lambayeque (Peru), Buenos Aires, (Argentina) or the North of Mexico appear largely driven by shifts in locational fundamentals. Our case studies suggest reasons for both fundamentals and pre‐colonial densities to be important drivers of this persistence. Not only would colonisers also value the rivers, coasts, fertile land, natural resources and climate that initially attracted the native populations but they would need the native populations themselves as sources of human capital (architects, agronomists and craftsmen), trading partners, sources of information, strategic bulwarks against enemy encroachment and souls to save. Hence, scale economies and Marshallian externalities related to population were probably as relevant to determining where colonists located their settlements as locational fundamentals. In turn, the contact with new technologies may have, after the initial trauma, strengthened these agglomerations. Many of the regions of the highest pre‐colonial density remain among the most prosperous regions today: the persistent prosperity of California and New England, in the US, or Antioquia, or Bogota in Colombia, despite massive structural transformations away from natural resource‐based production towards more sophisticated manufacturing and services does suggest that the forces arising from concentrations of knowledge, trade or labour are critical. At the subnational level, geographical and agglomeration factors appear to cause fortune to persist. Appendix A: M–S estimates The M–S estimator is a combination of M and S estimates. In the case where some variables are categorical (0–1) and some are continuous and random which may contain leverage points, as is the case here, M estimates are not robust and S estimates are computationally intensive. The MS estimator combines both and though less well‐known than, for instance, quantile or robust regression for managing potential outliers, it has several advantages. First, it is more robust to bad leverage points. Second, it is likely to provide more efficient estimates of the standard errors than the bootstrapped quantile estimates since it adjusts for outliers. Finally, it attains the maximum breakdown point, being robust to up to half of the observations being contaminated. In practice, a sizeable share of our observations in all pooled specifications are identified as outliers and hence a high breakdown point is desirable. The standard ‘robust’ estimator in STATA is a class of M estimator, however it is not robust, in particular, to masked outliers. That is, when calculating whether a observation has a standardised distance above a critical value, it uses the variance calculated using outliers. An upward biased estimate of the standard deviation may therefore allow a true outlier to remain in the sample. The MS estimator obviates this problem. Table A1 shows our results to persist using this estimator. Table A1 MS Estimation . (1) . (2) . (3) . (4) . . Pop1500 . Pop2005 . Income . Income slavery . Pre‐colonial density 0.1*** 5.2*** (0.03) (0.63) Log pre‐colonial density 0.4*** (0.06) Agriculture − 0.6 1.7*** −0.2** −0.03 (0.40) (0.32) (0.07) (0.08) Rivers −0.1 −0.2 −0.03* −0.08*** (0.37) (0.21) (0.02) (0.02) Distance to coast 2.8 4.3*** −0.4** − 0.03 (2.58) (1.09) (0.14) (0.23) Temperature −0.05 0.01 −0.003 0.005* (0.06) (0.02) (0.00) (0.00) Altitude −0.07 0.01 −0.1** 0.09* (0.21) (0.08) (0.07) (0.04) Rainfall −0.2*** 0.05 −0.08*** 0.08** (0.08) (0.82) (0.01) (0.04) Ruggedness 0.02** −0.005 0.008** −0.003 (0.01) (0.01) (0.00) (0.00) Malaria −0.1 −0.2** 0.005 −0.05*** (0.09) (0.10) (0.01) (0.01) Brazil −1.9*** (0.09) Colombia −2.5*** (0.06) South 0.02 (0.08) Slavery −0.005** (0.00) Slavery × population −0.1*** (0.03) Constant −7.9*** −3.6** 9.4*** 11.0*** (2.50) (1.76) (0.16) (0.22) N 330 330 330 78 . (1) . (2) . (3) . (4) . . Pop1500 . Pop2005 . Income . Income slavery . Pre‐colonial density 0.1*** 5.2*** (0.03) (0.63) Log pre‐colonial density 0.4*** (0.06) Agriculture − 0.6 1.7*** −0.2** −0.03 (0.40) (0.32) (0.07) (0.08) Rivers −0.1 −0.2 −0.03* −0.08*** (0.37) (0.21) (0.02) (0.02) Distance to coast 2.8 4.3*** −0.4** − 0.03 (2.58) (1.09) (0.14) (0.23) Temperature −0.05 0.01 −0.003 0.005* (0.06) (0.02) (0.00) (0.00) Altitude −0.07 0.01 −0.1** 0.09* (0.21) (0.08) (0.07) (0.04) Rainfall −0.2*** 0.05 −0.08*** 0.08** (0.08) (0.82) (0.01) (0.04) Ruggedness 0.02** −0.005 0.008** −0.003 (0.01) (0.01) (0.00) (0.00) Malaria −0.1 −0.2** 0.005 −0.05*** (0.09) (0.10) (0.01) (0.01) Brazil −1.9*** (0.09) Colombia −2.5*** (0.06) South 0.02 (0.08) Slavery −0.005** (0.00) Slavery × population −0.1*** (0.03) Constant −7.9*** −3.6** 9.4*** 11.0*** (2.50) (1.76) (0.16) (0.22) N 330 330 330 78 Notes. Dependent variable is the log income per capita in 2000 (PPP 2005 US dollars). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Estimation by robust MS regression with country fixed effects. Income per capita (in logs) is taken from national censuses. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b), and Bruhn and Gallego (2012). Dummies for Brazil, Colombia and the US South (according to the US Census). Slavery is measured as a fraction of the population and is taken from Bergad (2007) and Nunn (2008). Interaction of slavery with pre‐colonial population density. Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE are in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table A1 MS Estimation . (1) . (2) . (3) . (4) . . Pop1500 . Pop2005 . Income . Income slavery . Pre‐colonial density 0.1*** 5.2*** (0.03) (0.63) Log pre‐colonial density 0.4*** (0.06) Agriculture − 0.6 1.7*** −0.2** −0.03 (0.40) (0.32) (0.07) (0.08) Rivers −0.1 −0.2 −0.03* −0.08*** (0.37) (0.21) (0.02) (0.02) Distance to coast 2.8 4.3*** −0.4** − 0.03 (2.58) (1.09) (0.14) (0.23) Temperature −0.05 0.01 −0.003 0.005* (0.06) (0.02) (0.00) (0.00) Altitude −0.07 0.01 −0.1** 0.09* (0.21) (0.08) (0.07) (0.04) Rainfall −0.2*** 0.05 −0.08*** 0.08** (0.08) (0.82) (0.01) (0.04) Ruggedness 0.02** −0.005 0.008** −0.003 (0.01) (0.01) (0.00) (0.00) Malaria −0.1 −0.2** 0.005 −0.05*** (0.09) (0.10) (0.01) (0.01) Brazil −1.9*** (0.09) Colombia −2.5*** (0.06) South 0.02 (0.08) Slavery −0.005** (0.00) Slavery × population −0.1*** (0.03) Constant −7.9*** −3.6** 9.4*** 11.0*** (2.50) (1.76) (0.16) (0.22) N 330 330 330 78 . (1) . (2) . (3) . (4) . . Pop1500 . Pop2005 . Income . Income slavery . Pre‐colonial density 0.1*** 5.2*** (0.03) (0.63) Log pre‐colonial density 0.4*** (0.06) Agriculture − 0.6 1.7*** −0.2** −0.03 (0.40) (0.32) (0.07) (0.08) Rivers −0.1 −0.2 −0.03* −0.08*** (0.37) (0.21) (0.02) (0.02) Distance to coast 2.8 4.3*** −0.4** − 0.03 (2.58) (1.09) (0.14) (0.23) Temperature −0.05 0.01 −0.003 0.005* (0.06) (0.02) (0.00) (0.00) Altitude −0.07 0.01 −0.1** 0.09* (0.21) (0.08) (0.07) (0.04) Rainfall −0.2*** 0.05 −0.08*** 0.08** (0.08) (0.82) (0.01) (0.04) Ruggedness 0.02** −0.005 0.008** −0.003 (0.01) (0.01) (0.00) (0.00) Malaria −0.1 −0.2** 0.005 −0.05*** (0.09) (0.10) (0.01) (0.01) Brazil −1.9*** (0.09) Colombia −2.5*** (0.06) South 0.02 (0.08) Slavery −0.005** (0.00) Slavery × population −0.1*** (0.03) Constant −7.9*** −3.6** 9.4*** 11.0*** (2.50) (1.76) (0.16) (0.22) N 330 330 330 78 Notes. Dependent variable is the log income per capita in 2000 (PPP 2005 US dollars). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Estimation by robust MS regression with country fixed effects. Income per capita (in logs) is taken from national censuses. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b), and Bruhn and Gallego (2012). Dummies for Brazil, Colombia and the US South (according to the US Census). Slavery is measured as a fraction of the population and is taken from Bergad (2007) and Nunn (2008). Interaction of slavery with pre‐colonial population density. Agriculture is an index of probability of cultivation given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE are in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Appendix B: McEvedy and Jones Versus Our Population Data Figure B1 shows that our aggregated measure of pre‐colonial density and the national measure of McEvedy et al. (1978) used by Acemoglu et al. (2002) are highly correlated and that the reason that the correlation is not higher is the repetition of values for Central America in the latter. There is clearly a difference in scale between the two measures and ours are probably preferable. As Acemoglu et al. (2002) note, the McEvedy and Jones data have been highly controversial and most recently Guedes et al. (2013) argue that they likely understate the true pre‐Columbian population by large magnitudes. Peru, for example they argue may be understated by a factor of 2–7; Mexico 2–5 times. McEvedy and Jones (1978) postulate a total value for Central America of 800,000 where other estimates of Guatemala alone run as high as 2 million. Our data reflect these more recent anthropological data and are more reliable. Fig. B1. Open in new tabDownload slide Aggregate Subnational Population Density Data Versus McEvedy and Jones Note. Graph plots the weighted average of the subnational pre‐colonial population data against McEvedy and Jones (1978). Fig. B1. Open in new tabDownload slide Aggregate Subnational Population Density Data Versus McEvedy and Jones Note. Graph plots the weighted average of the subnational pre‐colonial population data against McEvedy and Jones (1978). Appendix C: The Institutional Channel Slavery in Brazil, Colombia, and the US Though the article documents the relative importance of locational fundamentals, agglomeration externalities and perhaps technological transfer in determining present income, we also find evidence for the negative impact of extractive institutions, even if they did not dominate the others.26 As a proxy for extractive institutions we are able to collect data on the incidence of slavery at the subnational level for Brazil and Colombia and the US where censuses are available. As a direct measure of extractive institutions, we exploit the data on slavery, measured as the percentage of enslaved and ‘free coloured people’, in the three countries for which they are national historic censuses. For Brazil, we used the 1872 Census, for Colombia the 1851 Census and for the US, we used the 1860 Census as well as the data compiled in Nunn (2008).27 To capture the broader influence of slavery, both in the year of the census and in previous years, we include both slaves and the general black population which would include now‐freed slaves. While data comparability and classification issues are non‐trivial, the average share of the population enslaved in the mid nineteenth century was 28% in the American South, 13% in Brazil and 2.9% in Colombia. We use the more expansive measure that includes free Blacks which raises Brazil to first position, although the results do not change qualitatively when we use the more narrow measure. Log(Y2005,ij)=α+βDprecol,ij+δSLAVERYij+δintSLAVERYij×Dprecolij+γLFij+μi+ϵijC.1 where δint captures the interaction of pre‐colonial density and slavery and μi are now three fixed effects for Brazil, Colombia and the US South with the US North as the omitted category. Columns 1–5 in Table C1 progressively introduce the elements of C.1. Column 1 includes pre‐colonial density along with dummies for Brazil, Colombia and the American South.28 In the full sample, pre‐colonial density is significant and positive, lending support from a smaller sample to the case for persistence. Column 2 repeats the same regression with the smaller sample dictated by the more restrictive slavery variable with a loss in significance of the persistence term. Columns 3 add the slavery term and it enters negatively and significantly. Column 4 adds slavery interacted with initial population density. It enters negatively and of similar sign. Further, the coefficient on pre‐colonial density roughly doubles with the inclusion of the interaction of slavery and density in the levels specification and increases by 30% in the log specification suggesting that extractive institutions did have a negative agglomeration effect as postulated by Acemoglu et al. (2002). Table C1 Current Income and Slavery (Brazil, Colombia and US) . (1) . (2) . (3) . (4) . (5) . . OLS . OLS . OLS . OLS . OLS . Pre‐colonial density 2.9** 1.9 2.6** 5.5*** 4.9*** (1.16) (1.33) (1.27) (1.45) (1.65) Brazil −1.9*** −2.0*** −1.6*** −1.6*** −1.4*** (0.09) (0.11) (0.21) (0.20) (0.21) Colombia −2.5*** −2.4*** −2.4*** −2.6*** −2.4*** (0.07) (0.09) (0.08) (0.08) (0.19) South −0.09** −0.1*** 0.2 0.09 0.07 (0.04) (0.04) (0.13) (0.13) (0.11) Slavery −0.009** −0.006 − 0.005 (0.00) (0.00) (0.00) Slavery × population −0.1** −0.1*** (0.05) (0.05) Agriculture −0.2 (0.17) Rivers −0.02 (0.05) Distance to coast 0.05 (0.41) Temperature −0.0008 (0.01) Altitude 0.06 (0.08) Rainfall −0.03 (0.03) Ruggedness −0.005 (0.01) Malaria −0.06 (0.04) Constant 10.7*** 10.7*** 10.7*** 10.7*** 10.9*** (0.02) (0.03) (0.03) (0.02) (0.47) N 105 78 78 78 78 R2 0.937 0.940 0.947 0.953 0.954 . (1) . (2) . (3) . (4) . (5) . . OLS . OLS . OLS . OLS . OLS . Pre‐colonial density 2.9** 1.9 2.6** 5.5*** 4.9*** (1.16) (1.33) (1.27) (1.45) (1.65) Brazil −1.9*** −2.0*** −1.6*** −1.6*** −1.4*** (0.09) (0.11) (0.21) (0.20) (0.21) Colombia −2.5*** −2.4*** −2.4*** −2.6*** −2.4*** (0.07) (0.09) (0.08) (0.08) (0.19) South −0.09** −0.1*** 0.2 0.09 0.07 (0.04) (0.04) (0.13) (0.13) (0.11) Slavery −0.009** −0.006 − 0.005 (0.00) (0.00) (0.00) Slavery × population −0.1** −0.1*** (0.05) (0.05) Agriculture −0.2 (0.17) Rivers −0.02 (0.05) Distance to coast 0.05 (0.41) Temperature −0.0008 (0.01) Altitude 0.06 (0.08) Rainfall −0.03 (0.03) Ruggedness −0.005 (0.01) Malaria −0.06 (0.04) Constant 10.7*** 10.7*** 10.7*** 10.7*** 10.9*** (0.02) (0.03) (0.03) (0.02) (0.47) N 105 78 78 78 78 R2 0.937 0.940 0.947 0.953 0.954 Notes. Dependent variable is the log income per capita in 2000 (PPP 2005 US dollars). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Estimation by OLS with country fixed effects. Income per capita (in logs) is taken from national censuses. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Dummies for Brazil, Colombia and the US South (according to the US Census). Slavery is measured as a fraction of the population and is taken from Bergad (2007) and Nunn (2008). Interaction of slavery with pre‐colonial population density. Agriculture is an index of probability of cultivation, given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table C1 Current Income and Slavery (Brazil, Colombia and US) . (1) . (2) . (3) . (4) . (5) . . OLS . OLS . OLS . OLS . OLS . Pre‐colonial density 2.9** 1.9 2.6** 5.5*** 4.9*** (1.16) (1.33) (1.27) (1.45) (1.65) Brazil −1.9*** −2.0*** −1.6*** −1.6*** −1.4*** (0.09) (0.11) (0.21) (0.20) (0.21) Colombia −2.5*** −2.4*** −2.4*** −2.6*** −2.4*** (0.07) (0.09) (0.08) (0.08) (0.19) South −0.09** −0.1*** 0.2 0.09 0.07 (0.04) (0.04) (0.13) (0.13) (0.11) Slavery −0.009** −0.006 − 0.005 (0.00) (0.00) (0.00) Slavery × population −0.1** −0.1*** (0.05) (0.05) Agriculture −0.2 (0.17) Rivers −0.02 (0.05) Distance to coast 0.05 (0.41) Temperature −0.0008 (0.01) Altitude 0.06 (0.08) Rainfall −0.03 (0.03) Ruggedness −0.005 (0.01) Malaria −0.06 (0.04) Constant 10.7*** 10.7*** 10.7*** 10.7*** 10.9*** (0.02) (0.03) (0.03) (0.02) (0.47) N 105 78 78 78 78 R2 0.937 0.940 0.947 0.953 0.954 . (1) . (2) . (3) . (4) . (5) . . OLS . OLS . OLS . OLS . OLS . Pre‐colonial density 2.9** 1.9 2.6** 5.5*** 4.9*** (1.16) (1.33) (1.27) (1.45) (1.65) Brazil −1.9*** −2.0*** −1.6*** −1.6*** −1.4*** (0.09) (0.11) (0.21) (0.20) (0.21) Colombia −2.5*** −2.4*** −2.4*** −2.6*** −2.4*** (0.07) (0.09) (0.08) (0.08) (0.19) South −0.09** −0.1*** 0.2 0.09 0.07 (0.04) (0.04) (0.13) (0.13) (0.11) Slavery −0.009** −0.006 − 0.005 (0.00) (0.00) (0.00) Slavery × population −0.1** −0.1*** (0.05) (0.05) Agriculture −0.2 (0.17) Rivers −0.02 (0.05) Distance to coast 0.05 (0.41) Temperature −0.0008 (0.01) Altitude 0.06 (0.08) Rainfall −0.03 (0.03) Ruggedness −0.005 (0.01) Malaria −0.06 (0.04) Constant 10.7*** 10.7*** 10.7*** 10.7*** 10.9*** (0.02) (0.03) (0.03) (0.02) (0.47) N 105 78 78 78 78 R2 0.937 0.940 0.947 0.953 0.954 Notes. Dependent variable is the log income per capita in 2000 (PPP 2005 US dollars). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Estimation by OLS with country fixed effects. Income per capita (in logs) is taken from national censuses. Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus, from Denevan (1992, b) and Bruhn and Gallego (2012). Dummies for Brazil, Colombia and the US South (according to the US Census). Slavery is measured as a fraction of the population and is taken from Bergad (2007) and Nunn (2008). Interaction of slavery with pre‐colonial population density. Agriculture is an index of probability of cultivation, given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Adding locational fundamentals (column 5) changes the coefficient little and the MS estimator confirms the free standing and interactive terms significant. Though the sample is small, nonetheless, the results offer support for extractive institutions at least reducing, if not overturning the persistence induced by agglomeration externalities and fundamentals. In sum, despite legitimate concerns about the exogeneity of slavery, using a direct proxy for exclusionary institutions is suggestive of the existence of an Acemoglu et al. (2002) effect: slavery works against persistence. However, at the subnational level, the net effect of factors associated with of indigenous population densities – extractive institutions, agglomeration externalities or locational fundamentals – tends to leave a positive correlation with prosperity today. Appendix D: The Link Between Pre‐colonial Density and Present Income Inequality The regional data on income distribution of household incomes offer a proxy for extractive institutions that may shed light on whether in fact, indigenous densities mapped into extractive institutions and hence a skewed distribution of income. Table D1 presents the regression of current Gini on initial density and various other covariates the literature proposes to be important. Gini=α+βDprecol,ij+δD2005,ij+λlnY2005,ij+γLFij+μi+ϵijD.1 Table D1 Income Distribution (Pooled) . OLS . Between . Within FE . Within FE . Within FE . Log pre‐colonial density 0.006 0.003 0.002 −0.002 −0.003 (0.00) (0.01) (0.00) (0.00) (0.00) Log present density 0.006*** 0.003 (0.00) (0.00) Income −0.03** −0.02** (0.01) (0.01) Agriculture 0.02 (0.01) Rivers −0.006 (0.01) Distance to coast −0.02 (0.06) Temperature 0.002* (0.00) Altitude 0.01** (0.00) Rainfall 0.003 (0.01) Ruggedness 0.0002 (0.00) Malaria −0.004 (0.00) Constant 0.5*** 0.5*** 0.5*** 0.7*** 0.7*** (0.03) (0.05) (0.01) (0.12) (0.10) N 260 260 260 260 256 R2 0.023 −0.091 0.002 0.044 0.061 . OLS . Between . Within FE . Within FE . Within FE . Log pre‐colonial density 0.006 0.003 0.002 −0.002 −0.003 (0.00) (0.01) (0.00) (0.00) (0.00) Log present density 0.006*** 0.003 (0.00) (0.00) Income −0.03** −0.02** (0.01) (0.01) Agriculture 0.02 (0.01) Rivers −0.006 (0.01) Distance to coast −0.02 (0.06) Temperature 0.002* (0.00) Altitude 0.01** (0.00) Rainfall 0.003 (0.01) Ruggedness 0.0002 (0.00) Malaria −0.004 (0.00) Constant 0.5*** 0.5*** 0.5*** 0.7*** 0.7*** (0.03) (0.05) (0.01) (0.12) (0.10) N 260 260 260 260 256 R2 0.023 −0.091 0.002 0.044 0.061 Notes. Dependent variable is the Gini coefficient in 2000 (PPP 2005 US dollars) as estimated by Bruhn and Gallego (2012). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Income per capita (in logs) is taken from national censuses. Present population density is the number of people per square kilometre in 2000. Agriculture is an index of probability of cultivation, given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Table D1 Income Distribution (Pooled) . OLS . Between . Within FE . Within FE . Within FE . Log pre‐colonial density 0.006 0.003 0.002 −0.002 −0.003 (0.00) (0.01) (0.00) (0.00) (0.00) Log present density 0.006*** 0.003 (0.00) (0.00) Income −0.03** −0.02** (0.01) (0.01) Agriculture 0.02 (0.01) Rivers −0.006 (0.01) Distance to coast −0.02 (0.06) Temperature 0.002* (0.00) Altitude 0.01** (0.00) Rainfall 0.003 (0.01) Ruggedness 0.0002 (0.00) Malaria −0.004 (0.00) Constant 0.5*** 0.5*** 0.5*** 0.7*** 0.7*** (0.03) (0.05) (0.01) (0.12) (0.10) N 260 260 260 260 256 R2 0.023 −0.091 0.002 0.044 0.061 . OLS . Between . Within FE . Within FE . Within FE . Log pre‐colonial density 0.006 0.003 0.002 −0.002 −0.003 (0.00) (0.01) (0.00) (0.00) (0.00) Log present density 0.006*** 0.003 (0.00) (0.00) Income −0.03** −0.02** (0.01) (0.01) Agriculture 0.02 (0.01) Rivers −0.006 (0.01) Distance to coast −0.02 (0.06) Temperature 0.002* (0.00) Altitude 0.01** (0.00) Rainfall 0.003 (0.01) Ruggedness 0.0002 (0.00) Malaria −0.004 (0.00) Constant 0.5*** 0.5*** 0.5*** 0.7*** 0.7*** (0.03) (0.05) (0.01) (0.12) (0.10) N 260 260 260 260 256 R2 0.023 −0.091 0.002 0.044 0.061 Notes. Dependent variable is the Gini coefficient in 2000 (PPP 2005 US dollars) as estimated by Bruhn and Gallego (2012). Pre‐colonial population density is the number of indigenous people per square kilometre before the arrival of Columbus. Income per capita (in logs) is taken from national censuses. Present population density is the number of people per square kilometre in 2000. Agriculture is an index of probability of cultivation, given cultivable land, climate and soil composition, from Ramankutty et al. (2002). Rivers captures the density of rivers as a share of land area derived from HydroSHEDS (USGS, 2011). Temperature is a yearly average in °C; altitude measures the elevation of the capital city of the state in kilometres; and rainfall captures total yearly rainfall in metres, all are from Bruhn and Gallego (2012). Malaria is the malaria ecology index as calculated by Kiszewski et al. (2004). Ruggedness is the terrain ruggedness index from Nunn and Puga (2012). Inverse distance from the nearest coast is ratio of 1 over 1 plus the region’s average distance to the nearest coastline in thousands of kilometres from Gennaioli et al. (2013). More detailed data sources and descriptions in the text. Robust SE clustered at the country level in parentheses. *p < 0.1, **p < 0.05, ***p < 0.01. Open in new tab Column 3 suggests that there is no significant relationship between the log of pre‐colonial density and inequality. Column 4 includes the respective proxies for current population density and income which Glaeser et al. (2009) show are correlated with current urban inequality. The coefficients for our regional data in the log specification (column 4) are surprisingly close to theirs for cities: the coefficient on log of current population is 0.006 compared to 0.008 and log income −0.03 compared to their −0.06. Pre‐colonial density, however, remains insignificant in the log specification. Introducing locational fundamentals in both columns 5 confirms the lack of import. This remains the case using pre‐colonial and present density in levels. In any specification, the overall explanatory power of the free standing pre‐colonial density is so truly negligible as to raise some doubts about whether modern day inequality is arising through a channel related to pre‐colonial population densities, especially outside of those we have documented of present day population density and income. This, again, may be different at the national level but at the subnational level it appears not to be the case. Footnotes 1 " In a tragically similar case to that of Hiroshima and Nagasaki, Miguel and Roland (2011) find that heavily bombed areas of Vietnam also recovered almost fully relative to non‐bombed areas. 2 " For a discussion of the importance of these effects for the ongoing evolution of economic geography among developing countries see World Bank (2008). 3 " Regional differences in institutional arrangements have also been documented in the case of slavery in the US and Brazil (Degler, 1970), and share cropping and women’s rights in Colombia (Bushnell, 1993; Safford and Palacios, 1998). 4 " The details on the construction of the pre‐colonial density measures and their mapping to modern subnational units can be found in Bruhn and Gallego (2012). 5 " Censuses: US, El Salvador 2000: Brazil, Panama; 2001: Bolivia, Ecuador; 2002: Chile, Guatemala, Paraguay; 2005: Mexico, Nicaragua, Peru, 2006: Uruguay. All other countries: figures correspond to survey data estimates at the regional level or small‐area estimates based on survey and census data. 6 " Household‐level data sets are combined with limited or non‐representative coverage with census data to generate income maps for much of the hemisphere (Elbers et al., 2003). This approach addresses the problem that in some cases such as Mexico household income surveys are not representative at the ‘state’ level. We thank Gabriel Demombynes for providing the data. See original study for methodological details. We expect that while somewhat more complete, our data are similar to the census‐based data used by Acemoglu and Dell (2010). For Argentina, Colombia and Venezuela, the spatial database reports the unsatisfied basic needs index rather than income. We project subnational GDP (production) series on this index to scale it to household income. GDP source: Argentina (CEPAL, Consejo Federal de Inversiones, Colombia (DANE)), Venezuela (Instituto Nacional de Estadística). We expand the sample to include Canada and the US using the (2005) censuses. The resulting estimates of mean per capita income have been rescaled so that the population‐weighted average matches 2007 GDP per capita at 2005 US dollars (PPP adjusted). 7 " Denevan (1992b) discusses the extensive evidence on the importance of agriculture throughout the hemisphere in pre‐colonial times. De Vorsey Jr (1986, p. 13) cites the eighteenth century explorer William Bartram as noting that ‘An Indian town is generally so situated, as to be convenient for procuring game, secure from sudden invasion, a large district of excellent arable land adjoining, or in its vicinity, if possible on an isthmus betwixt two waters, or where the doubling of a river forms a peninsula; such a situation generally comprises a sufficient body of excellent land for planting corn, potatoes, squash, pumpkins, citrus, melons, etc’. 8 " HydroSHEDS stands for Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales. The HydroSHEDS project was developed by the World Wildlife Fund and US Geological Survey among other organisations. Densities were calculated using zonal statistics in ARC‐GIS map. Though HydroSHEDS depicts the flow of cells into a given river system, beyond a certain size we do not take into account the flow of the river per se for two reasons. First, settlements are not likely to be proportional to the size of a river, again, beyond a certain threshold. Second, due to the geographical projection, the cells do not map precisely one to one to actual flows. 9 " From the point of view of establishing the particular channels postulated by the reversal of fortune literature, it may be argued that capital cities have a sui generis dynamic and should be excluded. From a general point of view of understanding agglomeration effects and persistence, this is less clear – whatever the impetus that established these cities, the existing megalopolises in Latin America are not supported in the main by government activities at present. Precisely the emergence of such ‘urban giants’ has been studied by Ades and Glaeser (1995), while Krugman and Elizondo (1996) have focused on Mexico City. In the end, even dropping these overall strengthens the persistence results. We thank Daron Acegmoglu for bringing this point to our attention. 10 " Further, inversely weighting the observations by intensity of migration (not shown), as expected, dramatically strengthens the effect of initial density. Consistent with the finding above, interacting immigration with initial population density confirms a strong negative impact on persistence and weighting by the (1‐immigration) increases both the point estimate and significance (not shown). We thank David Weil for this suggestion. 11 " See also Michaels et al. (2012) for examples of the persistence of non‐fundamentals based urban location in France versus England. 12 " See Maloney and Valencia Caicedo (2014) for a discussion of technological transfer in the Americas. 13 " The results are sensitive to the transformation of pre‐population employed. An insignificant result is found in a log transformation because it dramatically reduces the influence of very high population density areas and hence potentially important high leverage points such as Mexico City or Cusco and increases the influence of the Galapagos or Tierra del Fuego which had and have few people. From a point of view of testing whether large densities of indigenous populations affected institutions and hence growth, the levels specification gives it the best chance and the MS estimates(see Appendix A) suggest that, in fact, the more extreme values are ‘good’ leverage points and not outliers. In no specification do we find a remotely statistically significant reversal. Further, for the sample used in the slavery regressions below, both functional forms generate significant and positive coefficients. See original working paper for a complete set of specifications(Maloney and Valencia Caciedo, 2013). 14 " The titles of the two principal English‐language histories emphasise precisely the lack of national integration: Colombia, Fragmented Country, Divided Society by Bushnell(1993) and The Making of Modern Colombia: A Nation in Spite of Itself by Safford and Palacios (1998). 15 " Several regions employed both native and African slaves and evolved extractive institutions to manage them, others far less. Similarly, the Mita, Resguardo and Encomienda are found to varying degrees in different departments. Independence saw several (repressed) attempts at regional succession, and the construction of a strong national state was effectively resisted. As an example, Bushnell (1993) recounts that the 1863 Constitution created nine US states of Colombia, but with far more restricted central power than was the case in the North American analogue. For instance, states issued their own stamps; the national government had responsibility only for ‘inter‐oceanic’ transport routes (i.e. pertaining to the Panama railroad) thereby weakening any integrative national infrastructure project; and the upper house of the national Congress was called the Senate of Plenipotentiaries ‘as if its members were the emissaries of sovereign nations’ (Bushnell, 1993, p. 122). 16 " Michalopoulos and Papaioannou (2013), for instance, find this to be the case in Africa where pre‐colonial tribal patterns are more correlated with present income than subsequent national institutions. 17 " Our thanks to Noam Yuchtman for suggesting this interpretation. 18 " We are grateful to a referee who prompted us to look at the distribution data. 19 " As a final point, Ades and Glaeser (1995, p. 221) argue that industry did not play a prominent role in the rise of Buenos Aires so that a case for it being more suited to the second wave of the Industrial Revolution seems unlikely. Even by 1914, only 15% of the labour force was in manufacturing and the government displayed ‘hostility toward manufacturing and innovation’. 20 " According to the 1843 Census of Colombia, 7.1% of the population were slaves in Cauca and 6.4% in Valle; in 1851 4.7% and 4.3% respectively. Initial indigenous density was 7.1 and 9.2 respectively. 21 " A similar story is the rise and fall of Mompóx, Colombia. This affluent port in the Magdalena River saw its demise when the river shifted course, allowing the development of Magangué. Since then, this UNESCO World Heritage Site has virtually remained stuck in time. 22 " The Bogota/Cundinamarca agglomeration dominates the country in most modern services and manufactures. The capital city, Bogota, has revealed comparative advantage (participation of sector in value added relative to country average greater than 1) in non‐food manufacturing (12% of value added), commerce (14%), financial services (10%), real estate services (10%), services to firms (7%), air transport services (1%), few of which are tied to locational fundamentals. In these areas and industry in general, it is the largest single producer in the country. In particular, it accounts for 50% of all financial services. Emphatically, it has neither comparative advantage or much production in the agriculture (0%) or minerals (0%) sectors which first attracted the colonists. As the capital city, it also shows a comparative advantage in public administration but this is not dominant or unusually large (9% of value added relative to 7% on average for the country as a whole). The enveloping department of Cundinamarca, maintains a comparative advantage in agricultural production and also in both agricultural and non‐agricultural manufacturing – it is the third and fourth largest national producer respectively. The growth of Antioquia historically was driven by mining and then by coffee. It maintains a comparative advantage in both, but each accounts for roughly 1% of departmental value added. It has a comparative advantage in manufacturing (18%) and commerce (11.5%) and is the second largest producer of manufactures, commerce and financial services after the Bogota/Cundinamarca agglomeration. 23 " Lambayeque did differ in its continued heavy reliance on Indian labour as competitor sugar‐growing areas shifted more towards African slaves, although it is not clear whether this should have generated more or less toxic extractive institutions. 24 " In fact it may have been the opportunities for adventurism in partnership with the Spaniards in other areas of the New World that diverted energies from the home region. Tlaxcalans aided the Spaniards in dominating conquered tribes moving North. The oldest church in the US, found in Santa Fe, New Mexico was constructed by Tlaxcalan artisans. 25 " As Marichal (1997) notes, these foundries emerged largely as a result of the McKinley tariffs of 1890, which taxed foreign imports at roughly 50%. This threatened both Mexican exports of ore to the US, as well as the smelters on the US side that processed them. The response was to move the smelters over the border to the railway centre of Monterrey. The result of the accumulated US capital investment was ‘that the northern economy became an extension of the US economy and that the North turned into the new centre of Mexican capitalism’ (Marichal, 1997, p. 9). 26 " The negative impact of slavery cannot be taken as a foregone conclusion since disentangling the endowment and institutional effect is difficult. Acemoglu et al. (2012) find that in Colombian municipalities where slave labour was demanded, poverty is higher and school enrolment, vaccination coverage and public good provision is lower, than where it was not. On the other hand, in São Paulo, Brazil, Summerhill (2010) finds no relationship between slavery and present incomes while Rocha et al. (2012) find slavery is positively correlated with human capital. 27 " We thank Jaime Bonet and Adolfo Meisel Roca for providing their colonial data for Colombia and for pointing us towards Tovar‐Pinzón et al.’s (1994) compendium of colonial statistics. 28 " The South is comprised of Alabama, Arkansas, Delaware, District of Columbia, Florida, Georgia, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, South Carolina, Tennessee, Texas, Virginia, West Virginia. References Acemoglu , D. and Dell , M. ( 2010 ). ‘ Productivity differences between and within countries ’, American Economic Journal: Macroeconomics , vol. 2 ( 1 ), pp. 169 – 88 . Google Scholar Crossref Search ADS WorldCat Acemoglu , D. , García‐Jimeno , C. and Robinson , J.A. ( 2012 ). ‘ Finding Eldorado: slavery and long‐run development in Colombia ’, NBER Working Paper No. 18177, National Bureau of Economic Research. Acemoglu , D. , Johnson , S. and Robinson , J.A. ( 2001 ). ‘ The colonial origins of comparative development: an empirical investigation ’, American Economic Review , vol. 91 ( 5 ), pp. 1369 – 401 . Google Scholar Crossref Search ADS WorldCat Acemoglu , D. , Johnson , S. and Robinson , J.A. ( 2002 ). ‘ Reversal of fortune: geography and institutions in the making of the modern world income distribution ’, Quarterly Journal of Economics , vol. 117 ( 4 ), pp. 1231 – 94 . Google Scholar Crossref Search ADS WorldCat Ades , A.F. and Glaeser , E.L. ( 1995 ). ‘ Trade and circuses: explaining urban giants ’, Quarterly Journal of Economics , vol. 110 ( 1 ), pp. 195 – 227 . Google Scholar Crossref Search ADS WorldCat Andrews , G.R. ( 1980 ). The Afro‐Argentines of Buenos Aires 1800–1900 , Madison, WI : University of Winsconsin Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Aroca , P. , Bosch , M. and Maloney , W.F. ( 2005 ). ‘ Spatial dimensions of trade liberalization and economic convergence: Mexico 1985–2002 ’, World Bank Economic Review , vol. 19 ( 3 ), pp. 345 – 78 . Google Scholar Crossref Search ADS WorldCat Ashraf , Q. and Galor , O. ( 2011a ). ‘ Cultural assimilation, cultural diffusion and the origin of the wealth of nations ’, NBER Working Paper No. 17640, National Bureau of Economic Research. Ashraf , Q. and Galor , O. ( 2011b ). ‘ Dynamics and stagnation in the malthusian epoch: theory and evidence ’, American Economic Review , vol. 101 ( 5 ), pp. 2003 – 41 . Google Scholar Crossref Search ADS WorldCat Ashraf , Q. and Galor , O. ( 2013 ). ‘ The “out of Africa” hypothesis, human genetic diversity, and comparative economic development ’, American Economic Review , vol. 103 ( 1 ), pp. 1 – 46 . Google Scholar Crossref Search ADS PubMed WorldCat Bairoch , P. , Batou , J. and Chevre , P. ( 1988 ). The Population of European Cities: Data Bank and Summary of Results: 800–1850 , Geneve : Librairie Droz . Banerjee , A. and Iyer , L. ( 2005 ). ‘ History, institutions, and economic performance: the legacy of colonial land tenure systems in India ’, American Economic Review , vol. 95 ( 4 ), pp. 1190 – 213 . Google Scholar Crossref Search ADS WorldCat Becker , G.S. , Glaeser , E.L. and Murphy , K.M. ( 1999 ). ‘ Population and economic growth ’, American Economic Review , vol. 89 ( 2 ), pp. 145 – 9 . Google Scholar Crossref Search ADS WorldCat Bergad , L. ( 2007 ). ‘ The Comparative Histories of Slavery in Brazil, Cuba, and the United States ’, Cambridge : Cambridge University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bleakley , H. and Lin , J. ( 2012 ). ‘ Portage and path dependence ’, Quarterly Journal of Economics , vol. 127 ( 2 ), p. 587 . Google Scholar Crossref Search ADS PubMed WorldCat Bonet , J. and Roca , A.M. ( 2006 ). ‘ El legado colonial como determinante del ingreso per cápita departamental en Colombia ’, No. 75, Banco de la República – Economía Regional. Breen , T.H. ( 1984 ). ‘ Creative adaptations: peoples and cultures ’, Colonial British America , pp. 195 – 232 . OpenURL Placeholder Text WorldCat Bruhn , M. and Gallego , F.A. ( 2012 ). ‘ Good, bad, and ugly colonial activities: do they matter for economic development? ’, Review of Economics and Statistics , vol. 94 ( 2 ), pp. 433 – 61 . Google Scholar Crossref Search ADS WorldCat Bushnell , D. ( 1993 ). The Making of Modern Colombia: A Nation in Spite of Itself , Berkeley, CA : University of California Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Chanda , A. , Cook , C.J. and Putterman , L. ( 2014 ). ‘ Persistence of fortune: accounting for population movements, there was no post‐Columbian reversal ’, American Economic Journal: Macroeconomics , vol. 6 ( 3 ), pp. 1 – 28 . Google Scholar Crossref Search ADS WorldCat Ciccone , A. and Hall , R.E. ( 1996 ). ‘ Productivity and the density of economic activity ’, American Economic Review , vol. 86 ( 1 ), pp. 54 – 70 . OpenURL Placeholder Text WorldCat Coatsworth , J.H. ( 2008 ). ‘ Inequality, institutions and economic growth in Latin America ’, Journal of Latin American Studies , vol. 40 ( 3 ), pp. 545 – 69 . Google Scholar Crossref Search ADS WorldCat Comin , D. , Easterly , W. and Gong , E. ( 2010 ). ‘ Was the wealth of nations determined in 1000 BC? ’, American Economic Journal: Macroeconomics , vol. 2 ( 3 ), pp. 65 – 97 . Google Scholar Crossref Search ADS WorldCat Cushner , N.P. ( 1980 ). Lords of the Land: Sugar, Wine, and Jesuit Estates of Coastal Peru, 1600–1767 , Albany, NY : SUNY Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Davis , D.R. and Weinstein , D. ( 2002 ). ‘ Bones, bombs, and break points: the geography of economic activity ’, American Economic Review , vol. 92 ( 5 ), pp. 1269 – 89 . Google Scholar Crossref Search ADS WorldCat De Vorsey , L. ( 1978 ). ‘ Amerindian contributions to the mapping of North America: a preliminary view ’, Imago Mundi , vol. 30 , pp. 71 – 8 . Google Scholar Crossref Search ADS WorldCat De Vorsey Jr , L. ( 1986 ). ‘ The colonial Georgia backcountry ’, in (E.J. Cashin, ed.), Colonial Augusta: “Key of the Indian Country” , pp. 3 – 26 , Macon : Mercer University Press . OpenURL Placeholder Text WorldCat Degler , C.N. ( 1970 ). Out of Our Past: The Forces that Shaped Modern America , New York : Harper & Row . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Dell , M. ( 2010 ). ‘ The persistent effects of Peru’s mining mita ’, Econometrica , vol. 78 ( 6 ), pp. 1863 – 903 . Google Scholar Crossref Search ADS WorldCat Denevan , W. ( 1992 a). The Native Population of the Americas in 1492 , Madison, WI : University of Wisconsin Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Denevan , W. ( 1992b ). ‘ The pristine myth: the landscape of the Americas in 1492 ’, Annals of the Association of American Geographers , vol. 82 ( 3 ), pp. 369 – 85 . Google Scholar Crossref Search ADS WorldCat Dobyns , H.F. ( 1966 ). ‘ An appraisal of techniques with a new hmispheric estimate ’, Current Anthropology , vol. 7 ( 4 ), pp. 395 – 416 . Google Scholar Crossref Search ADS WorldCat Duranton , G. ( 2005 ). ‘ Testing for localization using micro‐geographic data ’, Review of Economic Studies , vol. 72 ( 4 ), pp. 1077 – 106 . Google Scholar Crossref Search ADS WorldCat Easterly , W. and Levine , R. ( 2003 ). ‘ Tropics, germs, and crops: how endowments influence economic development ’, Journal of Monetary Economics , vol. 50 ( 1 ), pp. 3 – 39 . Google Scholar Crossref Search ADS WorldCat Easterly , W. and Levine , R. ( 2012 ). ‘ The European origins of economic development ’, NBER Working Paper No. 18162, National Bureau of Economic Research. Elbers , C. , Lanjouw , J.O. and Lanjouw , P.F. ( 2003 ). ‘ Micro–level estimation of poverty and inequality ’, Econometrica , vol. 71 ( 1 ), pp. 355 – 64 . Google Scholar Crossref Search ADS WorldCat Ellison , G. and Glaeser , E.L. ( 1997 ). ‘ Geographic concentration in US manufacturing industries: a dartboard approach ’, Journal of Political Economy , vol. 105 ( 5 ), pp. 889 – 927 . Google Scholar Crossref Search ADS WorldCat Ellison , G. and Glaeser , E.L. ( 1999 ). ‘ The geographic concentration of industry: does natural advantage explain agglomeration? ’, American Economic Review , vol. 89 ( 2 ), pp. 311 – 6 . Google Scholar Crossref Search ADS WorldCat Fujita , M. and Mori , T. ( 1996 ). ‘ The role of ports in the making of major cities: self‐agglomeration and hub‐effect ’, Journal of Development Economics , vol. 49 ( 1 ), pp. 93 – 120 . Google Scholar Crossref Search ADS WorldCat Gallup , J.L. , Sachs , J.D. and Mellinger , A.D. ( 1999 ). ‘ Geography and economic development ’, International Regional Science Review , vol. 22 ( 2 ), pp. 179 – 232 . Google Scholar Crossref Search ADS WorldCat Galor , O. ( 2011 ). Unified Growth Theory , Princeton, NJ : Princeton University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Galor , O. and Weil , D.N. ( 1999 ). ‘ From Malthusian stagnation to modern growth ’, American Economic Review , vol. 89 ( 2 ), pp. 150 – 4 . Google Scholar Crossref Search ADS WorldCat García‐Jimeno , C. ( 2005 ). ‘ Colonial institutions and long‐run economic performance in Colombia: is there evidence of persistence? ’, Working Paper, Documento CEDE . OpenURL Placeholder Text WorldCat Gennaioli , N. , La Porta , R., Lopez‐de Silanes , F. and Shleifer , A. ( 2013 ). ‘ Human capital and regional development ’, Quarterly Journal of Economics , vol. 128 ( 1 ), pp. 164 . Google Scholar Crossref Search ADS WorldCat Glaeser , E.L. ( 2005 ). ‘ Urban colossus: why is New York Americas largest city? ’, Economic Policy Review , vol. 11 ( 2 ), pp. 7 . OpenURL Placeholder Text WorldCat Glaeser , E.L. , Kallal , H.D., Scheinkman , J.A. and Shleifer , A. ( 1992 ). ‘ Growth in cities ’, Journal of Political Economy , vol. 100 ( 6 ), pp. 1126 – 52 . Google Scholar Crossref Search ADS WorldCat Glaeser , E.L. , La Porta , R., Lopez‐de Silanes , F. and Shleifer , A. ( 2004 ). ‘ Do institutions cause growth? ’, Journal of Economic Growth , vol. 9 ( 3 ), pp. 271 – 303 . Google Scholar Crossref Search ADS WorldCat Glaeser , E.L. , Resseger , M. and Tobio , K. ( 2009 ). ‘ Inequality in cities ’, Journal of Regional Science , vol. 49 ( 4 ), pp. 617 – 46 . Google Scholar Crossref Search ADS WorldCat Greenstone , M. , Hornbeck , R. and Moretti , E. ( 2010 ). ‘ Identifying agglomeration spillovers: evidence from winners and losers of large plant openings ’, Journal of Political Economy , vol. 118 ( 3 ), pp. 536 – 98 . Google Scholar Crossref Search ADS WorldCat Guedes , J.D. , Bestor , T.C., Carrasco , D., Flad , R., Fosse , E., Herzfeld , M., Lamberg‐Karlovsky , C.C., Lewis , C.M., Liebmann , M., Meadow , R., Patterson , N., Price , M., Reiches , M., Richardson , S., Shattuck‐Heidorn , H., Ur , J., Urton , G. and Warinner , C. ( 2013 ). ‘ Is poverty in our genes? ’, Current Anthropology , vol. 54 ( 1 ), pp. 71 – 9 . Google Scholar Crossref Search ADS WorldCat Kiszewski , A. , Mellinger , A., Spielman , A., Malaney , P., Sachs , S.E. and Sachs , J. ( 2004 ).‘ A global index representing the stability of malaria transmission ’, The American Journal of Tropical Medicine and Hygiene , vol. 70 ( 5 ), pp. 486 – 98 . Google Scholar Crossref Search ADS PubMed WorldCat Krugman , P. ( 1991 ). ‘ History versus expectations ’, Quarterly Journal of Economics , vol. 106 ( 2 ), pp. 651 – 67 . Google Scholar Crossref Search ADS WorldCat Krugman , P. ( 1992 ). Geography and Trade, vol. 1 of MIT Press Books , Cambridge : The MIT Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Krugman , P.R. ( 1993 ). ‘ On the relationship between trade theory and location theory ’, Review of International Economics , vol. 1 ( 2 ), pp. 110 – 22 . Google Scholar Crossref Search ADS WorldCat Krugman , P. and Elizondo , R.L. ( 1996 ). ‘ Trade policy and the third world metropolis ’, Journal of Development Economics , vol. 49 ( 1 ), pp. 137 – 50 . Google Scholar Crossref Search ADS WorldCat Lucas , R.E. ( 2004 ). ‘ The industrial revolution: past and future ’, Annual Report (May), pp. 5 – 20 . OpenURL Placeholder Text WorldCat Maloney , W.F. and Valencia Caciedo , F. ( 2013 ). ‘ The persistence of (subnational) fortune ’, available at http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2397299 (last accessed: 7 June 2015). Maloney , W.F. and Valencia Caicedo , F. ( 2014 ). Engineers, Entrepreneurs and Development in the Americas , Washington, DC : World Bank . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Marichal , C. ( 1997 ). ‘Avances recientes en la historia de las grandes empresas y su importancia para la historia económica de México’, in ( C. Marichal and M. Cerutti, eds.), Historia de las grandes empresas en México, 1850–1930 , pp. 77 – 84 , Mexico City : Universidad Autónoma de Nuevo León – Fondo de Cultura Económica . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Marks , R.L. ( 1994 ). Cortés: The Great Adventurer and the Fate of Aztec Mexico , New York : Alfred A. Knopf . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Maronna , R. and Yohai , V. ( 2000 ). ‘ Robust regression with both continuous and categorical predictors ’, Journal of Statistical Planning and Inference , vol. 89 ( 1–2 ), pp. 197 – 214 . Google Scholar Crossref Search ADS WorldCat McEvedy , C. and Jones , R. ( 1978 ). Atlas of World Population History , Harmondsworth : Penguin Books . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Meisel , A. ( 2014 ). ‘ No reversal of fortune in the long run: geography and spatial persistence of prosperity in Colombia, 1500–2005 ’, No. 841, Banco de la Republica de Colombia. Michaels , G. , Rauch , F. and Redding , S. ( 2012 ). ‘ Urbanization and structural transformation ’, Quarterly Journal of Economics , vol. 127 ( 2 ), pp. 535 – 86 . Google Scholar Crossref Search ADS WorldCat Michalopoulos , S. ( 2012 ). ‘ The origins of ethnolinguistic diversity ’, American Economic Review , vol. 102 ( 4 ), pp. 1508 – 39 . Google Scholar Crossref Search ADS PubMed WorldCat Michalopoulos , S. and Papaioannou , E. ( 2013 ). ‘ Pre‐colonial ethnic institutions and contemporary African development ’, Econometrica , vol. 81 ( 1 ), pp. 113 – 52 . Google Scholar Crossref Search ADS PubMed WorldCat Miguel , E. and Roland , G. ( 2011 ). ‘ The long run impact of bombing Vietnam ’, Journal of Development Economics , vol. 96 ( 1 ), pp. 1 – 15 . Google Scholar Crossref Search ADS WorldCat Mitchener , K.J. and McLean , I.W. ( 2003 ). ‘ The productivity of US states since 1880 ’, Journal of Economic Growth , vol. 8 ( 1 ), pp. 73 – 114 . Google Scholar Crossref Search ADS WorldCat Monteón , M. ( 1982 ). Chile in the Nitrate Era: The Evolution of Economic Dependence, 1880–1930 , Madison, WI : University of Wisconsin Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Mora‐Torres , J. ( 2001 ). The Making of the Mexican Border , Austin, TX : University of Texas Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Morado , C. ( 2003 ). ‘ Empresas mineras y metalurgicas en Monterrey, México. 1890–1908. parte ii. tres plantas metalúrgicas ’, No. 19, Ingenierias. Naritomi , J. , Soares , R.R. and Assuncao , J.J. ( 2012 ). ‘ Institutional development and colonial heritage within Brazil ’, Journal of Economic History , vol. 72 ( 1 ), pp. 393 – 422 . Google Scholar Crossref Search ADS WorldCat Newson , L. ( 1982 ). ‘ The depopulation of Nicaragua in the sixteenth century ’, Journal of Latin American Studies , vol. 14 ( 2 ), pp. 235 – 86 . Google Scholar Crossref Search ADS WorldCat Nunn , N. ( 2008 ). ‘Slavery, inequality, and economic development in the Americas: an examination of the Engerman‐Sokoloff hypothesis’, in ( E. Helpman, ed.), Institutions and Economic Performance , pp. 148 – 80 , Cambridge : Harvard University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Nunn , N. and Puga , D. ( 2012 ). ‘ Ruggedness: the blessing of bad geography in Africa ’, Review of Economics and Statistics , vol. 94 ( 1 ), pp. 20 – 36 . Google Scholar Crossref Search ADS WorldCat Parkes , H.B. ( 1969 ). A History of Mexico , New York : Houghton Mifflin . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Ramankutty , N. , Foley , J., Norman , J. and McSweeney , K. ( 2002 ). ‘ The global distribution of cultivable lands: current patterns and sensitivity to possible climate change ’, Global Ecology and Biogeography , vol. 11 ( 5 ), pp. 377 – 92 . Google Scholar Crossref Search ADS WorldCat Ramirez , S.E. ( 1986 ). Patriarchs: Land Tenure and the Economics of Power in Colonial Perú . Albuquerque, NM : University of New Mexico Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rappaport , J. and Sachs , J.D. ( 2003 ). ‘ The United States as a coastal nation ’, Journal of Economic Growth , vol. 8 ( 1 ), pp. 5 – 46 . Google Scholar Crossref Search ADS WorldCat Rauch , J.E. ( 1993 ). ‘ Does history matter only when it matters little? The case of city‐industry location ’, Quarterly Journal of Economics , vol. 108 ( 3 ), pp. 843 – 67 . Google Scholar Crossref Search ADS WorldCat Rocha , R. , Ferraz , C. and Soares , R. ( 2012 ). ‘ Settlement colonies across plantation fields: evidence on the relationship between human capital and long term development ’, mimeograph, PUC‐Rio. Rosenblat , A. ( 1976 ). ‘ The population of Hispaniola at the time of Columbus ’, in ( W.M. Denevan, ed.), The Native Population of the Americas in 1492 , pp. 43 – 66 , Madison : University of Wisconsin Press . OpenURL Placeholder Text WorldCat Safford , F. and Palacios , M. ( 1998 ). Colombia: Fragmented Land, Divided Society , New York : Oxford University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Scobie , J.R. ( 1964 ). Argentina: A City and a Nation , New York : Oxford University Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Severnini , E.R. ( 2012 ). ‘ The power of hydroelectric dams: Agglomeration spillovers ’, mimeograph , Berkeley University. OpenURL Placeholder Text WorldCat Simpson , L. ( 1962 ). Many Mexicos , Oakland, CA : University of California Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Steckel , R.H. and Prince , J.M. ( 2001 ). ‘ Tallest in the world: native Americans of the Great Plains in the nineteenth century ’, American Economic Review , vol. 91 ( 1 ), pp. 287 – 94 . Google Scholar Crossref Search ADS WorldCat Summerhill , W. ( 2010 ). ‘ Colonial institutions, slavery, inequality, and development: evidence from São Paulo, Brazil ’, Slavery, Inequality, and Development: Evidence from São Paulo, Brazil (April 14, 2010), MPRA Paper No. 22162. Taylor , A. ( 2001 ). American Colonies: The Settling of North America the Penguin History of the United States, Volume 1, Penguin History of the United States , New York : Penguin Books . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Thomas , H. ( 1993 ). Conquest: Montezuma, Cortés, and the Fall of Old México , New York : Touchstone . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Thornton , R. ( 1987 ). American Indian Holocaust and Survival: A Population History Since 1492, vol. 186, Norman, OK : University of Oklahoma Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Tovar‐Pinzón , H. , Tovar‐Mora , J.A. and Tovar‐Mora , C.E. ( 1994 ). Convocatoria al Poder del Número: Censos y Estadísticas de la Nueva Granada, 1750–1830 , Bogota : Archivo General de la Nación . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Ubelaker , D. ( 1988 ). ‘ North American Indian population size, AD 1500 to 1985 ’, American Journal of Physical Anthropology , vol. 77 ( 3 ), pp. 289 – 94 . Google Scholar Crossref Search ADS WorldCat USGS ( 2011 ). Lehner, B., Verdin, K., Jarvis, A. HydroSHEDS Technical Documentation , Washington, DC : World Wildlife Fund US . Available at http://hydrosheds.cr.usgs.gov (last accessed: 7 June 2015). Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC World Bank ( 2008 ). World Development Report 2009: Reshaping Economic Geography , Washington, DC : World Bank . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Author notes " We thank the editor Kjell Salvanes and three anonymous referees, Daron Acemoglu, Laura Chioda, Antonio Ciccone, Ernesto Dal Bo, Carl‐Johan Dalgaard, Leo Feler, Claudio Ferraz, Oded Galor, Steve Haber, Lakshmi Iyer, Aart Kraay, Guy Michaels, Omer Moav, James Robinson, Luis Servén, Andrei Shleifer, Hans‐Joachim Voth, David Weil, Romain Wacziarg, Noam Yuchtman and participants at the 2012 LACEA‐PEG conference, 2013 Warwick Summer School in Economic Growth, and 2013 Economic History Association Meeting, and the CESifo Summer Institute for helpful discussions. We are especially grateful to Miriam Bruhn and Francisco Gallego, and to Navin Ramankutty for their kind sharing of data and Henry Jewell for help in processing the HydroSTAT data. Special thanks to Mauricio Sarrias for inspired research support. This article was partly supported by the regional studies budget of the World Bank Office of the Chief Economist for Latin America and the Knowledge for Change Programme (KCP). © 2015 The World Bank. The Economic Journal © 2015 Royal Economic Society
TI - The Persistence of (Subnational) Fortune
JO - The Economic Journal
DO - 10.1111/ecoj.12276
DA - 2016-12-01
UR - https://www.deepdyve.com/lp/oxford-university-press/the-persistence-of-subnational-fortune-eHqivuyd8I
SP - 2363
VL - 126
IS - 598
DP - DeepDyve
ER -