Abstract
This research explores the historical roots of the division of labor in pre-industrial societies. Exploiting a variety of identification strategies and a novel ethnic level dataset combining geocoded ethnographic, linguistic and genetic data, it shows that higher levels of intra-ethnic diversity were conducive to economic specialization in the pre-industrial era. The findings are robust to a host of geographical, institutional, cultural and historical confounders, and suggest that variation in intra-ethnic diversity is a key predictor of the division of labor in pre-industrial times.
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Notes
Furthermore, a high degree of specialization of labor, tasks, and other functions within different specific groups of people has been linked to societal advancement and prosperity (Durkheim 1893). E.g., Trigger (1983) argues that the archeological evidence from the Gerzean period in Egypt (ca. 3500BCE) supports the view that the appearance of occupational specialization, such as the existence of craft specialists producing ornaments of gold, silver, cast copper, and lapis lazuli, was accompanied by the rise of complex social and economic institutions.
We use the term pre-industrial to highlight the fact that the ethnicities in our analysis are reflecting historical conditions in earlier stages of development before ethnicities industrialized.
See Ashraf and Galor (2018) for a recent overview of this literature.
While most of the previous literature has focused on the Ricardian comparative advantages generated within the population due to differences in abilities (Ricardo 1817; Ashraf and Galor 2013b), it is clear that the same effects can be generated by diversity of preferences (Yang and Sachs 2008). Thus, diversity in either the supply or demand side may underlie the division of labor.
I.e., the emergence within a society of individuals exclusively engaged in specific occupations, e.g., a baker, a butcher, or a metalworker. Importantly, the lack of economic specialization does not imply the lack of knowledge about an activity. E.g., members of the Aché tribe of Paraguay, while having the knowledge to produce arrows, bows, huts, among other goods, were not specialized.
Unlike the previous literature that has employed cross-country measures of linguistic fractionalization or polarization, which reflect the variation in the number of languages, these linguistic diversity measures reflect diversity within a language.
Our theory does not need to take a stand on which specific trait underlies the effect of intra-ethnic diversity on the division of labor, since any intergenerationally transmitted trait, such as preferences or skills, that leads to larger complementarities, should have qualitatively similar effects (Yang and Borland 1991; Yang and Sachs 2008). Indeed, e.g., in Yang and Borland (1991) the model can be interpreted so that diversity in skills or in preferences or both generate economic specialization. Moreover, this suggests that the mechanism of transmission of these traits should not affect the effect of intra-ethnic diversity on economic specialization. Specifically, the effects of intra-ethnic diversity should not depend on whether traits are culturally or genetically transmitted across generations.
While this paper focuses on the effect of intra-ethnic diversity and its interaction with environmental diversity, the analysis also sheds light on the role of geographical factors on the emergence of the division of labor, as well as their relative importance compared to intra-ethnic diversity. In particular, it explores the effect of geographical determinants of market size on the emergence of the division of labor.
As established in Sect. 3.2, SFE generated exogenous variation in the proxies of intra-ethnic diversity employed in this research.
Indeed, Depetris-Chauvin and Özak (2015, 2016) explore the long-run consequences of the pre-industrial division of labor in more detail. They provide evidence that there exists a strong positive association between an ethnicity’s pre-industrial level of economic specialization and its contemporary level of economic development.
Appendix A establishes similar results for the case when specialization is affected by intra-ethnic diversity in specific traits instead of a weighted average of intra-ethnic diversity across various traits. It shows that the estimated effect provides a lower bound for the total effect of intra-ethnic diversity among all traits affected by a SFE. In particular, it shows the robustness of this result to the potential negative effect of specific intergenerationally transmitted traits on economic specialization.
These effects have been found in both human and non-human species (Baker and Jenkins 1987). Moreover, the decrease in diversity due to migration and serial founder effects has been found in later migratory processes within continents (Wang et al. 2007; Friedlaender et al. 2008; Lao et al. 2008; Myres et al. 2011; Pinhasi et al. 2012).
Appendix A provides the proofs, the relation between the various parameters, and all the intermediate steps to obtain the results presented in this section.
The main reason behind the construction of the SCCS was to overcome Galton’s independence problem, i.e., the difficulties of drawing inferences from cross-cultural data due to spatial auto-correlation and historical dependence. The sample of ethnicities in the SCCS were chosen so as to minimize this problem (Murdock and White 1969).
Appendix Figures B.1–B.6 map the different subsamples individually to improve visibility.
The analysis assigns a higher value to specialization in order to differentiate the effect of specialization from technological development. Reassuringly, using a value of 2 for specialization does not alter the main results.
Moreover, given the theoretical association between division of labor and trade within and among economies, these novel measures are associated with intra-ethnic trade related measures available in the SCCS. In particular, the new measures are positively associated with trade among communities of the same ethnic group, the existence and type of money (media of exchange) and credit, the type of credit source, and the existence of writing and records (Appendix Tables C.1–C.4), suggesting that the new measures indeed capture the phenomenon under study. A major concern with the SCCS data is that it is only available for a small subset of ethnicities, especially once the availability of intra-ethnic diversity measures is taken into account.
The literature on diversity has measured this population attribute using various characteristics like religion, language, ethnicity, or genetics. Diversity within a population is usually defined as the probability that two random individuals in a population do not share the same characteristic. For example, religious, linguistic or ethnic diversity/fractionalization estimate the probability that two random individuals in a population do not share the same religion, speak the same language or have the same ethnic background. Similarly, genetic diversity or expected heterozygosity measure the expected genetic similarity between any two individuals in a population. It is important to note that all these measures capture diversity and do not measure any innate superiority of a certain type of characteristic over another. For example, a population in which there exists only one religion, language, ethnicity, or blood type, will be less diverse than one in which there are many, but the measures of diversity do not and cannot be used to identify if one specific religion, language, ethnicity or blood type is better than others.
The genetic diversity on the full set of 645 loci is almost perfectly correlated with the measure used in the paper for the 267 original ethnicities in Pemberton et al. (2013). Their correlation is 0.99 (\(p<0.01\)).
This approach contrasts with the usual approach employed in the literature which exploits variations in the number of languages or ethnic groups within a region. Thus, our analysis captures within ethnic group diversity as opposed to inter-ethnic diversity.
The main analysis focuses on genetic diversity as a proxy of intra-ethnic diversity in order to economize space and ease the presentation. Moreover, it should provide, under the identification assumptions discussed in Sect. 2.1, the lower bound on the causal effect of intra-ethnic diversity. Robustness to the proxy of intra-ethnic diversity are included in various parts of the main text and appendices.
Given space constraints, the results in the body of the paper focus on economic specialization measured by the number of activities that are specialized, i.e., \(s^1\). Section E.1 in the appendix establishes that all results presented in the main body of the paper are robust to the measure of economic specialization employed.
In order to ease the interpretation of the results and compare them across the different specifications presented in this paper, all tables report standardized coefficients. The standard coefficients report the number of standard deviation changes in the dependent variable for a one-standard deviation change in the independent variable.
It is worth noting that total area is determined by ethnic homeland borders, which can be arguably endogenous to both diversity and economic specialization.
The Caloric Suitability Index (CSI) measures for each cell of 10 km \(\times\) 10 km in the world, the average number of calories that could be potentially produced given the climatic conditions in that cell and the crops available in the pre-1500CE period.
Appendix F.5 shows the robustness of the analysis to using geodesic or great circle distances. Nonetheless, as established there, migratory distances constructed using HMISea are more fundamental. Specifically, they have larger explanatory power and when accounting for HMISea, the other measures become statistically insignificant.
Similar results are obtained in the full sample of 267 ethnicities for which genetic data alone is available. The analysis omits islands for which the HMISea does not provide travel speed estimates. Still, the results are robust to imputation based on geodesic distances or by using the HMIOcean measure, which includes more advanced navigation technologies available before the invention of the steam engine.
While the point estimates are different, we cannot reject the null hypothesis that the OLS and IV estimates are equal to each other under standard levels of confidence. In particular, comparing the OLS estimates in column 1 of Table 2 to the equivalent (i.e., same specification) IV estimates in column 2 in Table 5 generates a Chi-square statistic of 2.03 with a p value of 0.1545. Similarly, if we compare the OLS estimate for our most demanding specification (column 9 in Table 2) to the equivalent IV (column 10 in Table 5), we get a Chi-square statistic of 0.93 with a p value of 0.3340.
Appendix Tables H.5–H.8 fully replicate Table 5 for each of the linguistic proxies of intra-ethnic diversity.
Two-step econometric procedures yield consistent estimates of second stage parameters, although the second-step standard error estimates may be incorrect, if they do not account for the additional uncertainty due to the two-step procedure (Murphy and Topel 2002). In order to address this issue, the analysis employs a bootstrapping procedure to correctly estimate standard errors.
Given that the analysis exploits a unique source of variation, the predicted intra-ethnic diversity measures generated based on genetic or linguistic diversity are perfectly correlated. Indeed, they reflect a change in the scale of the measure of intra-ethnic diversity. Thus, the analysis is performed based on the predicted intra-ethnic (genetic) diversity, given the stronger predictive power of the SFE for this measure. The results are similar if instead the other proxies are used.
In particular, a random sample of ethnicities with both diversity and migratory distance data is drawn with replacement out of the original sample. Then Eq. (2) is re-estimated, accounting for the same set of controls as in the second-stage. Using these new estimates intra-ethnic diversity is predicted again and Eq. (4) is re-estimated. This procedure is repeated 1001 times and the distribution of the bootstrapped coefficients is used to compute the standard errors. A similar procedure was proposed in Ashraf and Galor (2013b).
Appendix Table F.1 shows the robustness of these results to accounting for continental fixed effects in all columns. It establishes that the coefficients are larger in all specifications when accounting for continental fixed effects.
Appendix Table G.2 shows the point estimates of the reduced form economic specialization-distance to East Africa for all the specifications in Table 7. The point estimates for pre-industrial distance to East Africa are remarkably stable and strongly statistically significant. Indeed, the stability of the point estimates suggests that selection on unobservables is unlikely to drive the results, thus providing supportive evidence for the plausible exogeneity of the instrument.
The estimated coefficients are again reported as standardized betas, which simplifies the comparison of the main effects across tables. Of course, this makes the interpretation of the interactions difficult, but given that both main effects and interactions are positive, the qualitative nature of the effects is directly observable from the table.
Acharya et al. (2016) prove that their sequential g-estimation method eliminates post-treatment bias. To estimate the average controlled direct effect Acharya et al. (2016) suggest the following two-step procedure: First, estimate the same regression used to estimate the average natural effect. Then, demidiate the outcome by subtracting the estimated effect of the bad control in this regression from the outcome variable. Finally, estimate the average controlled direct effect by using the new demidiated outcome variable on the basic set of controls without the mediator.
The average controlled direct effect can be larger than the average total effect if either the association between treatment (diversity) and mediator (development outcome) or between mediator (development outcome) and outcome (specialization) has the opposite sign of the association between treatment (diversity) and outcome (specialization).
This instrumental variable strategy follows in the spirit of Arellano and Bond (1991) and Blundell and Bond (1998), who also generate “atheoretical” instruments using moment conditions in a dynamic panel data setting. While the identification in Arellano and Bond (1991) and Blundell and Bond (1998) comes from temporal variations, Lewbel (2012) bases the identification on the heteroskedastic structure of residuals obtained in an auxiliary regression of the endogenous variable on the set of exogenous covariates included in the model. See Lewbel (2012) and Depetris-Chauvin and Özak (2016) for details.
The analysis estimates the minimal travel paths based on HMISea from the centroid of each ethnic homeland to the closest Neolithic frontier. The location of Neolithic frontiers is taken from various sources (Diamond 1997; Smith 1997; Benz 2001; Denham et al. 2003; Pinhasi et al. 2005; Smith 2006; Dillehay et al. 2007; Lu et al. 2009; Manning et al. 2011; Linseele 2013).
Alternatively, accounting for the degree of subsistence dependence on agriculture, as measured in the Ethnographic Atlas (v5), does not alter the results either.
The technological frontiers are London and Paris in Europe, Fez and Cairo in Africa, Constantinople and Peking in Asia, and Tenochtitlan and Cuzco in the Americas.
Figure 6 depicts the conditional correlation between pre-industrial division of labor and contemporary development across ethnicities after accounting for regional, ethnic and geographical characteristics. Depetris-Chauvin and Özak (2018) explore the long-run consequences of the pre-industrial division of labor in more detail. Specifically, they show that pre-industrial division of labor predicts contemporary levels of division of labor and economic development across ethnicities.
References
Acemoglu, D., Johnson, S., & Robinson, J. (2005). Institutions as a fundamental cause of long-run growth. Handbook of Economic Growth, 1, 385–472.
Acharya, A., Blackwell, M., & Sen, M. (2016). Explaining causal findings without bias: Detecting and assessing direct effects. American Political Science Review, 110(3), 512–529.
Ager, P., & Brueckner, M. (2018). Immigrants’ genes: Genetic diversity and economic development in the US. Economic Inquiry, 56(2), 1149–1164.
Ahlerup, P., & Olsson, O. (2012). The roots of ethnic diversity. Journal of Economic Growth, 17(2), 71–102.
Alesina, A., Devleeschauwer, A., Easterly, W., Kurlat, S., & Wacziarg, R. T. (2003). Fractionalization. Journal of Economic Growth, 8(2), 155–194.
Alesina, A., Giuliano, P., & Nunn, N. (2013). On the origins of gender roles: Women and the plough. The Quarterly Journal of Economics, 128(2), 469–530.
Alesina, A., Harnoss, J., & Rapoport, H. (2016). Birthplace diversity and economic prosperity. Journal of Economic Growth, 21(2), 101–138.
Andersen, T. B., Dalgaard, C.-J., & Selaya, P. (2016). Climate and the emergence of global income differences. The Review of Economic Studies, 83(4), 1334–1363.
Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the abecedarian, perry preschool, and early training projects. Journal of the American statistical Association, 103(484), 1481–1495.
Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton: Princeton University Press.
Arbatli, C. E., Ashraf, Q. H., Galor, O. & Klemp, M. (2018). Diversity and conflict. Working papers 2018-6, Brown University, Department of Economics.
Arbatlı, C. E., Ashraf, Q. H., Galor, O., & Klemp, M. (2020). Diversity and conflict. Econometrica, 88(2), 727–797.
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.
Ashraf, Q., & Galor, O. (2011). Dynamics and stagnation in the Malthusian epoch. The American Economic Review, 101(5), 2003–2041.
Ashraf, Q., & Galor, O. (2013a). Genetic diversity and the origins of cultural fragmentation. The American Economic Review, 103(3), 528–533.
Ashraf, Q., & Galor, O. (2013b). The out of Africa hypothesis, human genetic diversity, and comparative economic development. The American Economic Review, 103(1), 1–46.
Ashraf, Q., Galor, O., & Özak, Ö. (2010). Isolation and development. Jourmal of the European Economic Association, 8(2–3), 401–412.
Ashraf, Q. H., & Galor, O. (2018). The macrogenoeconomics of comparative development. Journal of Economic Literature, 56(3), 1119–55.
Atkinson, Q. D. (2011). Phonemic diversity supports a serial founder effect model of language expansion from Africa. Science, 332(6027), 346–349.
Baker, A. J., & Jenkins, P. F. (1987). Founder effect and cultural evolution of songs in an isolated population of chaffinches, Fringilla coelebs, in the Chatham Islands. Animal Behaviour, 35(6), 1793–1803.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.
Benjamini, Y., Krieger, A. M., & Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate. Biometrika, 93(3), 491–507.
Benz, B. F. (2001). Archaeological evidence of teosinte domestication from Guilá Naquitz, Oaxaca. Proceedings of the National Academy of Sciences, 98(4), 2104–2106.
Betti, L., Balloux, F., Amos, W., Hanihara, T., & Manica, A. (2009). Distance from Africa, not climate, explains within-population phenotypic diversity in humans. Proceedings of the Royal Society of London B: Biological Sciences, 276(1658), 809–814.
Birchenall, J. A. (2014). Disease and diversity in Africa’s long-term economic development. Technical report, University of California Santa Barbara.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.
Boix, C. (2015). Political order and inequality: Their foundations and their consequences for human welfare. Cambridge studies in comparative politics. Cambridge: Cambridge University Press.
Borland, J., & Yang, X. (1992). Specialization and a new approach to economic organization and growth. The American Economic Review, 82(2), 386–391.
Brumfiel, E. M., & Earle, T. K. (1987). Specialization, exchange, and complex societies. Cambridge: Cambridge University Press.
Chan, E. K., Timmermann, A., Baldi, B. F., Moore, A. E., Lyons, R. J., Lee, S.-S., et al. (2019). Human origins in a southern African palaeo-wetland and first migrations. Nature, 575(7781), 185–189.
Childe, V. G. & Wailes, B. (1996). Craft specialization and social evolution: In memory of V. Gordon Childe (Vol. 93). University of Pennsylvania Museum of Archaeology and Anthroplogy, Philadelphia.
Claessen, H. J. M., & Skalník, P. (1978). The early state. The Hague: Mouton.
Conley, T. (1999). Gmm estimation with cross sectional dependence. Journal of Econometrics, 92(1), 1–45.
Cook, C. J. (2015). The natural selection of infectious disease resistance and its effect on contemporary health. Review of Economics and Statistics, 97(4), 742–757.
Cook, C. J., & Fletcher, J. M. (2018). High-school genetic diversity and later-life student outcomes: Micro-level evidence from the wisconsin longitudinal study. Journal of Economic Growth, 23(3), 307–339.
Creanza, N., Ruhlen, M., Pemberton, T. J., Rosenberg, N. A., Feldman, M. W., & Ramachandran, S. (2015). A comparison of worldwide phonemic and genetic variation in human populations. Proceedings of the National Academy of Sciences, 112(5), 1265–1272.
Dean, J. S., Euler, R. C., Gumerman, G. J., Plog, F., Hevly, R. H., & Karlstrom, T. N. (1985). Human behavior, demography, and paleoenvironment on the Colorado Plateaus. American Antiquity, 50, 537–554.
Denham, T. P., Haberle, S. G., Lentfer, C., Fullagar, R., Field, J., Therin, M., et al. (2003). Origins of agriculture at Kuk Swamp in the highlands of New Guinea. Science, 301(5630), 189–193.
Depetris-Chauvin, E. (2014). State history and contemporary conflict: Evidence from sub-Saharan Africa. Technical report, Brown University.
Depetris-Chauvin, E., & Özak, Ö. (2015). Population diversity, division of labor and the emergence of trade and state. Technical report 1506. Department of Economics, Southern Methodist University.
Depetris-Chauvin, E., & Özak, Ö. (2016). The origins and long-run consequences of the division of labor. Technical report 1610. Department of Economics, Southern Methodist University.
Depetris-Chauvin, E. & Özak, Ö. (2018). The Long-run consequences of the division of labor. Technical report. Department of Economics, Southern Methodist University.
Desmet, K., Ortuño-Ortín, I., & Wacziarg, R. (2012). The political economy of linguistic cleavages. Journal of Development Economics, 97(2), 322–338.
Desmet, K., Ortuño-Ortín, I., & Wacziarg, R. (2015). Culture, ethnicity and diversity. National Bureau of Economic Research: technical report.
Diamond, J. M. (1997). Guns, germs, and steel: The fates of human societies (1st ed.). New York: W.W. Norton & Co.
Dillehay, T. D., Rossen, J., Andres, T. C., & Williams, D. E. (2007). Preceramic adoption of peanut, squash, and cotton in northern Peru. Science, 316(5833), 1890–1893.
Dryer, M.S.& Haspelmath, M. E. (2013). The world atlas of of language structures online. Leipzig: Max Planck Institute for Evolutionary Anthropology.
Durkheim, É. (1893). De la division du travail social (2nd ed.). Paris: F. Alcan.
Easterly, W., & Levine, R. (1997). Africa’s growth tragedy: Policies and ethnic divisions. The Quarterly Journal of Economics, 112, 1203–1250.
Fenske, J. (2014). Ecology, trade, and states in pre-colonial Africa. Journal of the European Economic Association, 12(3), 612–640.
Freeman, D., & Pankhurst, A. (2003). Peripheral people: The excluded minorities of Ethiopia. Lawrenceville, NJ: Red Sea Press.
Friedlaender, J. S., Friedlaender, F. R., Reed, F. A., Kidd, K. K., Kidd, J. R., Chambers, G. K., et al. (2008). The genetic structure of Pacific Islanders. PLoS Genet, 4(1), e19.
Galor, O. & Klemp, M. (2017). Roots of autocracy. Working paper 23301, National Bureau of Economic Research.
Galor, O. & Özak, Ö. (2015). Land productivity and economic development: Caloric suitability vs. agricultural suitability. SMU working paper series .
Galor, O., & Özak, Ö. (2016). The agricultural origins of time preference. American Economic Review, 106(10), 3064–3103.
Galor, O., Özak, Ö. & Sarid, A. ( 2016). Geographical origins and economic consequences of language structures. SMU working paper series .
Gennaioli, N., & Rainer, I. (2007). The modern impact of precolonial centralization in Africa. Journal of Economic Growth, 12(3), 185–234.
Giuliano, P., & Nunn, N. (2013). The transmission of democracy: From the village to the nation-state. The American Economic Review, 103(3), 86–92.
Giuliano, P. & Nunn, N. (2017). Understanding cultural persistence and change. NBER Working Papers 23617, National Bureau of Economic Research, Inc.
Glaeser, E. L., La Porta, R., Lopez-de Silanes, F., & Shleifer, A. (2004). Do institutions cause growth? Journal of Economic Growth, 9(3), 271–303.
Hallpike, C. R. (2008). The Konso of Ethiopia: A study of the values of a Cushitic people. Central Milton Keynes: AuthorHouse.
Hamilton, M. J., & Buchanan, B. (2009). The accumulation of stochastic copying errors causes drift in culturally transmitted technologies: Quantifying clovis evolutionary dynamics. Journal of Anthropological Archaeology, 28(1), 55–69.
Henrich, J., & Boyd, R. (2008). Division of labor, economic specialization, and the evolution of social stratification. Current Anthropology, 49(4), 715–724.
Hill, K., & Hurtado, A. M. (1996). Aché life history: The ecology and demography of a foraging people. New York: Aldine de Gruyter.
Hinton, P. (1979). The Karen, millennialism, and the politics of accommodation to lowland states. In C. F. Keyes (Ed.), Ethnic adaptation and identity: The Karen on the Thai Frontier with Burma (pp. 81–98). Institute for the Study of Human Issues.
Houthakker, H. S. (1956). Economics and biology: Specialization and speciation. Kyklos, 9(2), 181–189.
Kimura, M. (1983). The neutral theory of molecular evolution. Cambridge: Cambridge University Press.
Kiszewski, A., Mellinger, A., Spielman, A., Malaney, P., Sachs, S. E., & Sachs, J. (2004). A global index representing the stability of malaria transmission. The American Journal of Tropical Medicine and Hygiene, 70(5), 486–498.
Kiyotaki, N., & Wright, R. (1989). On money as a medium of exchange. The Journal of Political Economy, 97, 927–954.
Lange, T., Vansteelandt, S., & Bekaert, M. (2012). A simple unified approach for estimating natural direct and indirect effects. American Journal of Epidemiology, 176(3), 190–195.
Lao, O., Lu, T. T., Nothnagel, M., Junge, O., Freitag-Wolf, S., Caliebe, A., et al. (2008). Correlation between genetic and geographic structure in Europe. Current Biology, 18(16), 1241–1248.
Lewbel, A. (2012). Using heteroscedasticity to identify and estimate mismeasured and endogenous regressor models. Journal of Business & Economic Statistics, 30(1), 67–80.
Linseele, V. (2013). Early stock keeping in northeastern Africa. near eastern influences and local developments. In N. Shirai (Ed.), Neolithisation of Northeastern Africa. Ex Oriente.
López, S., Van Dorp, L., & Hellenthal, G. (2015). Human dispersal out of Africa: A lasting debate. Evolutionary Bioinformatics, 11, EBO-S33489.
Lu, H., Zhang, J., Liu, K.-B., Wu, N., Li, Y., Zhou, K., et al. (2009). Earliest domestication of common millet (Panicum miliaceum) in East Asia extended to 10,000 years ago. Proceedings of the National Academy of Sciences, 106(18), 7367–7372.
Lycett, S. J. (2008). Acheulean variation and selection: Does handaxe symmetry fit neutral expectations? Journal of Archaeological Science, 35(9), 2640–2648.
Lycett, S. J., & von Cramon-Taubadel, N. (2008). Acheulean variability and hominin dispersals: A model-bound approach. Journal of Archaeological Science, 35(3), 553–562.
Manica, A., Amos, W., Balloux, F., & Hanihara, T. (2007). The effect of ancient population bottlenecks on human phenotypic variation. Nature, 448(7151), 346–348.
Manning, K., Pelling, R., Higham, T., Schwenniger, J.-L., & Fuller, D. Q. (2011). 4500-year old domesticated pearl millet (Pennisetum glaucum) from the Tilemsi Valley, Mali: New insights into an alternative cereal domestication pathway. Journal of Archaeological Science, 38(2), 312–322.
Marx, K. (1867). Das kapital: Kritik der politischen oekonomie. Hamburg: O. Meissner.
Michalopoulos, S. (2012). The origins of ethnolinguistic diversity. The American Economic Review, 102(4), 1508.
Michalopoulos, S., & Papaioannou, E. (2013). Pre-colonial ethnic institutions and contemporary African development. Econometrica, 81(1), 113–152.
Moore, J. L., Manne, L., Brooks, T., Burgess, N. D., Davies, R., Rahbek, C., et al. (2002). The distribution of cultural and biological diversity in Africa. Proceedings of the Royal Society of London B: Biological Sciences, 269(1501), 1645–1653.
Murdock, G. P. (1967). Ethnographic atlas: A summary. Ethnology, 6, 109–236.
Murdock, G. P., & White, D. R. (1969). Standard cross-cultural sample. Ethnology, 8, 329–369.
Murphy, K. M., & Topel, R. H. (2002). Estimation and inference in two-step econometric models. Journal of Business & Economic Statistics, 20(1), 88–97.
Myres, N. M., Rootsi, S., Lin, A. A., Järve, M., King, R. J., Kutuev, I., et al. (2011). A major Y-chromosome haplogroup R1b Holocene era founder effect in Central and Western Europe. European Journal of Human Genetics, 19(1), 95–101.
Nettle, D. (1998). Explaining global patterns of language diversity. Journal of Anthropological Archaeology, 17(4), 354–374.
Nolan, P., & Lenski, G. E. (2011). Human societies: An introduction to macrosociology (11th ed.). Boulder: Paradigm Publishers.
Nunn, N. (2008). The long-term effects of Africa’s slave trades. The Quarterly Journal of Economics, 123(1), 139–176.
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V., Underwood, E. C., et al. (2001). Terrestrial ecoregions of the world: A new map of life on earth a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience, 51(11), 933–938.
Özak, Ö. (2010). The voyage of homo-economicus: Some economic measures of distance. Technical report. Department of Economics, Southern Methodist University.
Özak, Ö. (2018). Distance to the pre-industrial technological frontier and economic development. Journal of Economic Growth, 23(2), 175–221.
Pearl, J. (2000). Causality: Models, reasoning, and inference. Cambridge: Cambridge University Press.
Pearl, J. (2001). Direct and indirect effects. In Proceedings of the seventeenth conference on uncertainty in artificial intelligence (pp. 411–420).
Pemberton, T. J., DeGiorgio, M., & Rosenberg, N. A. (2013). Population structure in a comprehensive genomic data set on human microsatellite variation. G3: Genes, Genomes, Genetics, 3(5), 891–907.
Pinhasi, R., Fort, J., & Ammerman, A. J. (2005). Tracing the origin and spread of agriculture in Europe. PLoS Biology, 3(12), e410.
Pinhasi, R., Thomas, M. G., Hofreiter, M., Currat, M., & Burger, J. (2012). The genetic history of Europeans. Trends in Genetics, 28(10), 496–505.
Polanyi, K. (2001). The great transformation: The political and economic origins of our time, 2nd beacon paperback (ed ed.). Boston, MA: Beacon Press.
Ramachandran, S., Deshpande, O., Roseman, C. C., Rosenberg, N. A., Feldman, M. W., & Cavalli-Sforza, L. L. (2005). Support from the relationship of genetic and geographic distance in human populations for a serial founder effect originating in Africa. Proceedings of the National Academy of Sciences of the United States of America, 102(44), 15942–15947.
Ricardo, D. (1817). On the principles of political economy and taxation. London: J. Murray.
Rogers, D. S., Feldman, M. W., & Ehrlich, P. R. (2009). Inferring population histories using cultural data. Proceedings of the Royal Society of London B: Biological Sciences, 276(1674), 3835–3843.
Romer, P. M. (1987). Growth based on increasing returns due to specialization. The American Economic Review, 77(2), 56–62.
Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations, Whitestone, Dublin.
Smith, B. D. (1997). The initial domestication of Cucurbita pepo in the Americas 10,000 years ago. Science, 276(5314), 932–934.
Smith, B. D. (2006). Eastern north America as an independent center of plant domestication. Proceedings of the National Academy of Sciences, 103(33), 12223–12228.
Spolaore, E., & Wacziarg, R. (2013). How deep are the roots of economic development? Journal of Economic Literature, 51(2), 325–69.
Stigler, G. J. (1951). The division of labor is limited by the extent of the market. The Journal of Political Economy, 59, 185–193.
Sun, G.-Z. (2012). The division of labour in economics: A history, Vol. 142 of Routledge studies in the history of economics. London: Routledge.
Trigger, B. G. (1983). The rise of egyptian civilization. In B. G. Trigger, B. J. Kemp, D. O’Connor, & A. B. Lloyd (Eds.), Ancient Egypt: A social history (pp. 1–70). Cambridge: Cambridge University Press.
Wang, S., Lewis, C. M, Jr., Jakobsson, M., Ramachandran, S., Ray, N., Bedoya, G., et al. (2007). Genetic variation and population structure in native Americans. PLoS Genet, 3(11), e185.
Yang, X., & Borland, J. (1991). A microeconomic mechanism for economic growth. Journal of Political Economy, 99, 460–482.
Yang, X., & Sachs, J. D. (2008). Economic development and the division of labor. New York: Wiley.
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We wish to thank the editor and two anonymous referees, as well as Javier Birchenall, Klaus Desmet, Oded Galor, Pete Klenow, Stelios Michalopoulos, Dan Millimet, Andrei Shleifer and David Weil, as well as conference participants at Towards Sustained Economic Growth: Geography, Institutions, and Human, Barcelona GSE, 2018; Annual Meetings of the American Economic Association, 2017; NBER Summer Institute—Program on Macroeconomics and Income Distribution, National Bureau of Economic Research, 2017; 4th Economic History and Cliometric Lab, PUC Chile, 2016; Montreal Applied Economics Conference, CIREQ, 2017; Zeuthen Workshop, Copenhagen, 2016; Ethnicity and Diversity: Concepts and Measures, Causes and Consequences, Juan March Institute, 2016; Annual Meeting of the Latin American and Caribbean Economic Association (LACEA), 2015; and seminar participants at Brown University, Clark University, Southern Methodist University, Texas A&M University, University of California Santa Barbara, University of Connecticut, Banco de la República de Colombia, Universidad de los Andes, Universidad del Rosario, and Universidad Nacional de Colombia, for useful comments and discussions. Additionally, we thank Anthon Eff for sharing the EA and SCCS datasets, and James Fenske for sharing his mapping of ethnic groups to their historical homelands. Previous versions of the paper circulated under the title “The Origins of the Division of Labor in Pre-modern Times”
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Depetris-Chauvin, E., Özak, Ö. The origins of the division of labor in pre-industrial times. J Econ Growth 25, 297–340 (2020). https://doi.org/10.1007/s10887-020-09179-2
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DOI: https://doi.org/10.1007/s10887-020-09179-2
Keywords
- Comparative development
- Division of labor
- Economic specialization
- Intra-ethnic diversity
- Cultural diversity
- Population diversity
- Genetic diversity
- Linguistic diversity