Finally, despite making unambiguous statements about the causal role of genetic diversity in economic success in their original paper, 2 in their reply to our criticism (d’Alpoim Guedes et al. 2012 ), Ashraf and Galor ( 2012 :2) claim that “the measure of intra-population genetic diversity that we employ should be interpreted as a proxy (i.e., a correlated summary measure) for diversity amongst individuals in a myriad of observable and unobservable personal traits that may be physiological, behavioral, socially-constructed, or otherwise.” However, Ashraf and Galor have not shown any data indicating that genetic diversity is somehow linked to “diversity more broadly defined” (Ashraf and Galor 2012 :2) or what this might mean. They thus make a conceptual leap from a strict genetic definition of diversity (heterozygosity) to a common usage of diversity. Their statement fails to make sense with respect to alleles and their impact on behavior and biology.

While Ashraf and Galor acknowledge that serial founder events play a causal role in shaping global genetic diversity, they fail to integrate the larger concept into their analysis and instead continue to describe “migratory distance” as having “adverse effects” on genetic diversity. For one, “adverse” is inaccurate phrasing because a reduction or an increase in genetic differentiation need not be thought of as beneficial or adverse. But more importantly, it is not the distance that is thought to have the primary effect, but rather the founder effect itself. It is for this reason that the “predicted genetic homogeneity” estimates for subcontinental populations presented in Ashraf and Galor’s figure 4 have no demonstrated scientific basis.

Ashraf and Galor’s description of the human pattern of global genetic diversity is consistently inaccurate, leading to concerns that the authors do not understand the data they are attempting to characterize. For example, they repeatedly contend that “migratory distance” to various settlements across the globe affected genetic diversity. This is misleading. The pattern of human genetic diversity they are referring to was primarily affected by the sequential series of founder effects that occurred during the peopling of the world; geographic distance is largely a proxy for these founder effects (Ramachandran et al. 2005 ). This proxy is accurate for roughly predicting global trends of genetic diversity on a continental scale but does not predict regional genetic diversity within continents. Human populations, stratified by heterozygosity, can be grouped into just four classes: Africa, West Eurasia, East Eurasia, and a fourth class comprising the remaining populations, nearly all of which have low heterozygosity. This class includes Native American populations. We prefer to use sequence data rather than genotype data to measure heterozygosity, as this avoids ascertainment issues involving the choice of SNPs used. Table S36 of Meyer et al. ( 2012 ), which used high coverage sequence data from 11 humans, shows the pattern clearly. In other words, genetic diversity varies on a continental scale, with Africa the most diverse, the Americas the least, and Eurasia having intermediate values. No amount of regression analysis and bootstrapping can alter the fact that, in essence, Ashraf and Galor are working with only four data points: Africa, Europe, Asia, and the Americas. This would be the case even if the raw data of Ashraf and Galor were perfect and free of noise.

Factual Errors in Data

Ignoring for the moment the fact that Ashraf and Galor have made a serious error in their interpretation of genetic diversity and migratory distance, the remaining variables in their model, including prehistoric population densities and geographic control factors, are poorly chosen. They do not reference the broader literature in archaeology or anthropology and thus demonstrate a critical lack of knowledge. Moreover, their poor choice of data sources leads to serious inaccuracies in their dependent variable of population density as well as in their control variables.

Problems with population density estimates Ashraf and Galor (2013) use population density as a proxy for economic success in 1500 CE (as well as in 1 CE and 1000 CE, in their appendix). As it is the outcome variable in their historical models, accurate estimates of population density are crucial to demonstrating the validity of their theory. A closer examination of the data used by Ashraf and Galor reveals that these population density estimates are inaccurate. For the Americas, in particular, the population density estimates they use are strikingly lower than most values in the archaeological literature. Ashraf and Galor (2013) derive their estimates of population size from a poor and outdated source (McEvedy and Jones 1978). They then divide these population estimates by modern national boundaries. With regard to data from the Americas, McEvedy and Jones (1978) provide no citations for how they have derived their population estimates for 1 CE and 1000 CE. Despite well-known problems with estimating population density using both archaeological and historical data (Cook 1981),3 there is no discussion of potential error. For example, McEvedy and Jones (1978:292) argue that the total population in Mexico in 1500 CE was no more than 5 million. They do so based on data from Rosenblat (1945, 1967), a source that uses problematic postconquest records. In fact, scholars contemporary with McEvedy and Jones (1978) proposed estimates in the 5–6 million range for the area corresponding only to the Aztec empire (e.g., Sanders and Price 1968). The Aztecs controlled a territory that covered no more than one quarter of contemporary Mexico and that excluded all of northwest Mexico and the Yucatán. Even while, at the time McEvedy and Jones (1978) were writing, other estimates for Mexico’s population were set at around 18–30 million (Cook and Borah 1971), McEvedy and Jones (1978:272) discredit those estimates on the puzzling claim that they were not in line with those of other populations at “comparable levels of culture.” More recently, Denevan (1992b:291) has suggested that the population size within the current boundaries of Mexico reached over 21 million. For central Mexico alone, Lovell (1992b) and Denevan (1992a) summarize estimates that range from 25.2 million on the high side to 13.8 million on the low side.4 Population estimates in McEvedy and Jones rely heavily on sources that derive their data from the memoirs of the Conquistadors, none of whom were demographers. Using historical census data for population numbers in the Americas is fraught with problems. The Spanish did not carry out the first detailed inventory of labor and landholdings until half a century after the conquest of New Spain (1521), with the compilation of the 1579–1581 Relaciones Geográficas (Acuña 1984). By that time, at least nine epidemics (primarily smallpox, influenza, measles, mumps, and epidemic typhus) had torn through these densely populated territories. It is unknown how these epidemics affected native population sizes, but contemporary Spanish sources suggest it reduced the population significantly (de Sahagún 1950–1982 [c. 1545–1590], 1956). The Relaciones Geográficas, for example, describe numerous ghost towns, and there may have been mortality rates as high as 60%–90% for each individual epidemic (de Motolinía o Benavente 1971 [1540]; Prem 1992). The 1500 CE data in McEvedy and Jones (1978) do not account for these factors and, consequently, misrepresent the population estimates for pre-Columbian Mexico. For the rest of Central America, including Guatemala, Belize, El Salvador, Honduras, Nicaragua, Costa Rica and Panama, McEvedy and Jones (1978:294) estimate a total population size of only 800,000 in CE 1500. Population estimates for Guatemala alone in 1520 range from early, very conservative estimates of 300,000 (de Solano 1974) to more recent estimates of over 2 million (Lovell 1992a). Similar problems affect population size estimates for South and North America. McEvedy and Jones (1978:309) estimate that the total population of Peru was 2 million in CE 1500. However, a more exhaustive study by Cook (1981:113) estimates the population of Peru at CE 1520, a decade prior to the Spanish conquest, as being between 5.5 and 9.4 million. Some models give a figure as low as 4 million, while others have estimates as high as 14–15 million (Cook 1981:113), but no sources have estimates for pre-Columbian Peru as low as those provided by McEvedy and Jones. The lack of data on smaller archaeological sites dispersed throughout the continent further reduces these estimates. For North America (excluding Mexico), this is particularly problematic. To arrive at their population estimates for North America at ca. CE 1500, McEvedy and Jones (1978:286–289) use data from an outdated source (Mooney 1928). Here again, the lack of any citations for their population estimates ca. CE 1 or 1000 lead us to believe that these are indeed numbers from nowhere. In addition to providing an uncritical reading of historical sources, McEvedy and Jones (1978), and by extension Ashraf and Galor (2013), have ignored empirical and theoretical advances in North American archaeology over the last century. While they recognize that high population densities existed in certain communities across the vast territory of the modern United States, these are severely underestimated by calculating population density using the entire surface area of the modern nation-state. If they were to carry out a more accurate analysis, boundaries corresponding to the extent of ancient polities should have been drawn around sites. As a result of factual errors and uncritical analyses, it is unclear if any of the data used by Ashraf and Galor for population estimates in the Americas before 2000 CE have any connection to reality. We challenge Ashraf and Galor to provide primary evidence for these population estimates.

Problems with correction factors One of the striking features of the Ashraf and Galor paper is its extensive use of basic regression models (including models constructed with migratory distance as an instrumental variable for genetic diversity) and various corrections for confounding variables. Ashraf and Galor believe that the inclusion of these factors, detailed in appendix F of their paper, strengthens their claim that there is a causal relationship between genetic diversity and economic development. We argue, however, that the controls they use are inadequate empirically. The “Log Neolithic transition timing” is based on data from Putterman (2008), a source that does not take into account current data and debates in the field. For instance, Putterman (2008) gives a start date for the origin of agriculture in Mexico at 4,100 years ago, a datum that contradicts over a decade of archaeological research in the region. In fact, the earliest domesticated maize in Mexico has been radiocarbon dated to cal. 6250 BP (Piperno and Flannery 2001). Similarly, estimates of start dates for agriculture at 9,000 years ago no longer apply to southern China, where we now believe foragers only began to plant rice 2,000 years later (Fuller et al. 2009), and there is a complete disregard for recent work detailing early East African animal domestication (e.g., Marshall and Hildebrand 2002). Additional problems exist with how the authors have “corrected” for land suitability for agriculture. The authors use a measure described by Ramankutty et al. (2002), the “land suitability index,” which incorporates climate, moisture, and soil data. Using these criteria, Ramankutty et al. (2002:388) identify the Amazon as an area that has great potential for cropland given that these factors intersect in an ideal fashion in this area. As a result, correcting for land suitability using this index would not take into account the fact that a large portion of Brazil is covered by the Amazonian rainforest, an ecozone whose agricultural potential and preconquest population density have been much in dispute in recent years (Heckenberger et al. 2008). Tropical forests have historically only supported very small population densities as they present a number of important nutritional challenges (Bailey and Headland 1991; Bailey et al. 1989). Although tropical forests have a high biomass, studies in behavioral ecology indicate that they are specifically lacking in carbohydrates (namely tubers), and that species that do contain carbohydrates are widely dispersed, meaning that travel times to acquire them are long and costly (Bailey et al. 1989:60–61). Acquiring food in these environments is highly unpredictable owing to fluctuations and difficulty of access in terms of space and time (Bailey et al. 1989). As a consequence, the corrections Ashraf and Galor apply to account for geography are not always appropriate and sometimes lead to an overestimation of potential areas for population growth.