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Does it matter where you came from? Ancestry composition and economic performance of US counties, 1850–2010

Abstract

What impact on local development do immigrants and their descendants have in the short and long term? The answer depends on the attributes they bring with them, what they pass on to their children, and how they interact with other groups. We develop the first measures of the country-of-ancestry composition and of GDP per worker for US counties from 1850 to 2010. We show that changes in ancestry composition are associated with changes in local economic development. We use the long panel and several instrumental variables strategies in an effort to assess different ancestry groups’ effect on county GDP per worker. Groups from countries with higher economic development, with cultural traits that favor cooperation, and with a long history of a centralized state have a greater positive impact on county GDP per worker. Ancestry diversity is positively related to county GDP per worker, while diversity in origin-country economic development or culture is negatively related.

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Notes

  1. A substantial body of research has shown the persistence of cultural traits between the first and second generation of immigrants. See the reviews by Fernández (2010) and Bisin and Verdier (2011) of the empirical and theoretical literature on cultural transmission. Among recent theoretical contributions see Doepke and Zilibotti (2017). Beyond the second generation, the persistence varies across cultural attitudes and countries of origin, but even for faster moving traits, differences remain by the fourth generation (Giavazzi et al. 2019). See also Abramitzky et al. (2020) on differences in the pace of name-based assimilation by country of origin.

  2. Note that we do not rely on self-reported ethnicity—available only since 1980—which also reflects the evolving nature of ethnic identity as a social construct.

  3. The use as an instrument of the past spatial distribution of immigrants, often adjusted for the national growth rate, has been a common strategy in the immigration literature. See Card (2001) and more recently Peri (2012), among others. See also Bartik (1991) in the local development literature. For details on our instrumenting strategy see Sect. 5.3.2.

  4. The literature here is vast and cannot be adequately surveyed here. See the reviews by Acemoglu et al. (2005), Nunn (2009), Spolaore and Wacziarg (2013) and Alesina and Giuliano (2015).

  5. On the importance of culture for economic outcomes see Guiso et al. (2006), Guiso et al. (2008) Tabellini (2010), Fernández (2010), Alesina and Giuliano (2015) and Bisin and Verdier (2011).

  6. The literature on the effect of immigration is vast; see Borjas (2014) for a review, as well as the work of Borjas (1994), Card (2001), Ottaviano and Peri (2012) and Peri (2012). See also Hatton and Williamson (1998), who provide evidence from the Age of Mass Migration and Abramitzky and Boustan (2017), who put more recent work on immigration into its historical context. Finally, Tabellini (2020) studies the political and economic effects on natives’ employment and wages of variations inimmigration flows to US cities in the period between 1910 and 1930 induced by WWI and the Immigration Acts of the 1920s.

  7. See Spolaore and Wacziarg (2009) on barriers to diffusion and Alesina et al. (1999), Miguel and Gugerty (2005) and Easterly and Levine (1997) on ethnicity and local public spending.

  8. Ashraf and Galor (2013) find that the relationship between genetic diversity and country-level economic development is nonlinear, first increasing, then decreasing, resulting in an interior optimum level of diversity. In Putterman and Weil (2010), the standard deviation of state history generated by the post-1500 population flows is positively related to the income of countries today. Ager and Brückner (2013) show that fractionalization of first-generation immigrants across counties in the United States from 1870 to 1920 is positively related to economic growth, while polarization is negatively related. Alesina et al. (2016a), using recent immigration data for 195 countries, present evidence of a positive relationship between birthplace diversity of immigrants and output, TFP per capita, and innovation. Ottaviano and Peri (2006) find that increased first-generation immigrant diversity is good for wages across US cities between 1970 and 1990. Docquier et al. (2018), using a state level panel for 1960–2010, provide support for the existence of positive skill complementarities associated with the birthplace diversity of immigrants, although the gains depend on the cultural and economic distance of the immigrants.

  9. For a description of the role of counties, see the National Association of Counties http://www.naco.org/sites/default/files/documents/Counties-Matter.pdf, accessed 1 August 2017.

  10. Even the places where we do not match exactly are illuminating. The dots in the upper left are variations of “Southern Europe, Not Specified” or “Baltic States, Not Specified.” While these birthplaces have generally been valid responses, because we built up our ancestry measure from the actual birthplace of a migrant or her parents, we are far more likely to classify someone to a particular country, so put a smaller share in these generic ancestries.

  11. We show in the Online Appendix B  that this approach is exactly what one ought to do under the assumption of perfect competition in output and factor markets and a constant returns to scale Cobb Douglas production function. This result holds even if the output market is monopolistically competitive, provided the markup is common across the United States.

  12. Since individual effects for very small ancestry groups cannot be precisely estimated, we include only the ancestries that make up at least 0.5% of the population in 2010, which accounts for 93% of the population. In the estimation, we use people of English origin as the reference point and omit their fraction from the regression. The test, therefore, is whether the coefficients for the other ancestries are jointly zero.

  13. In the Online Appendix, see Fig. A-5, we report a similar figure obtained by combining ancestries from individual countries in larger groupings (for instance: Scandinavia, instead of individual Scandinavian countries). The results are similar but not as sharp, suggesting that there are important distinctions even between similar countries.

  14. We only show results for origin variables that cover over 99% of the population in every county. Summary statistics for these variables appear in the Online Appendix Table A-2.

  15. Tabellini (2010) focuses on answers from the WVS that measure: (1) generalized trust; (2) the respect of others as a desirable characteristic children should have; (3) obedience as a desirable children’s characteristic; (4) feeling of control of one’s own fortune. The basic idea is that trust, respect, and control are cultural traits that enhance productive social interaction, while obedience is not a useful trait in a society that values independence.

  16. We use census divisions instead of states, since states vary tremendously in size and census divisions are much more similar in terms of geographic and population size. States such as Rhode Island also have very few county groups, and so including a fixed effect for them removes almost all variation.

  17. The primary driving force behind this correlation is the historical legacy of settlement, starting with the English. While the English are a large portion of the population in much of the United States, they are disproportionately present in rural areas in the poor South and Appalachian states, which received little immigration after their first settlement. Later immigrants, such as the Italians or Irish, while poor when they arrived, went to cities and prosperous areas, especially in the Northeast. Finally, the Great Migration of African Americans shifted them from the poor rural South to growing urban areas.

  18. In the the Online Appendix we show that Nickell (1981) bias due to T being relatively short (around 14) does not affect these results. Note, moreover, that t indexes decades.

  19. The test is for second-order serial correlation in the first difference of the errors, which provides information on first-order serial correlation of the errors in level.

  20. The coefficient of first lag is highly significant and sizable (.44), while the one for the second lag is smaller and significant at the 10% level. While the second order lag is only sometimes significant across the different specifications, excluding it often causes the Arellano and Bond (1991) test of serial correlation to fail to reject the hypothesis of no serial correlation of \(\epsilon _{ct}\), and so we standardize on including two lags. The long-run multiplier, in a single equation context, is \(\beta /(1-\rho _{1}-\rho _{2})\), where \(\beta\) is the coefficient of each ancestry-weighted endowment variable, and \(\rho _{1}\) and \(\rho _{2}\) are the coefficients on the lags of county GDP.

  21. Where available, we assign the values of Ghana, a West African country that was at the heart of the slave trade, to African Americans, and typically use overall US values for Native Americans. The results are nearly identical if we also allow those with African ancestries from the West Indies to have their own independent effect.

  22. We lag the variables one decade to avoid the obvious identification problem of reflection: if neighboring countys affect each other simultaneously, then it requires an identification assumption to separate a county effect from a neighbor effect. A lag implicitly assumes that it takes a decade for a shock in one county to affect its neighbors, which seems the most sensible assumption. Note that fixed effects are far more flexible for spatial correlations than the standard functional form assumptions of spatial lags. The only concern is whether shocks may propagate spatially, which does not seem to be the case.

  23. We have used the Roodman (2009) Stata routine xtabond2 for single equation GMM estimation and the Abrigo and Love (2015) routine for VAR GMM estimation.

  24. We obtained very similar results using Trust instead of Principal Component of Culture, but we prefer the specification with Principal Component, as it is based on multiple complementary cultural traits that denote the ability to interact with others. Thrift did not play a significant role when included.

  25. The definition of \(s^{jk}\) is based on the difference of some country-of-origin measure z between group j and group k as \(s_{t}^{jk}=1-|z^{j}-z^{k}|/r\), where \(r=\max _{j\in \{1\ldots A\}}z^{j}-\min _{j\in \{1\ldots A\}}z^{j}\) is the range of values that z can take. As two groups become more similar along the z dimension, their similarity approaches 1.

  26. One is simply racial fractionalization: \(frac_{c,t}^{R}=1-\sum _{a\in \{AA,NA\}}(\pi _{ct}^{a})^{2}.\) The other is weighted fractionalization which is the difference of the racial ancestries from all other ancestries: \(frac_{c,t}^{wR}=1-\sum _{r\in \{AA,NA\}}\left( \sum _{k=1}^{A}\pi _{ct}^{r}\pi _{ct}^{k}s_{t}^{r,k}+\sum _{j=1}^{A}\pi _{ct}^{j}\pi _{ct}^{r}s_{t}^{j,r}\right) .\) Note that both \(frac_{c,t}^{R}\) and \(frac_{c,t}^{wR}\) are just the elements of regular fractionalization \(frac_{c,t}\) and weighted fractionalization \(frac_{c,t}^{wR}\) that have an African American or Native American ancestry.

  27. We have explored allowing for a quadratic term in fractionalization and weighted fractionalization. In our preferred dynamic specification, the quadratic term is not significant, and we have not found an internal optimum in any specification and so do not report these results.

  28. Polarization measures how far a county is from being composed of only two equally sized groups. Ager and Brückner (2013) found that polarization was negatively related to economic growth across counties in the US from 1870 to 1920, while fractionalization was positively related to growth. Their measures of polarization and fractionalization are calculated by dividing the population into first-generation immigrants from different countries, African Americans, and all second- or higher- generation whites together. Our calculations treat ancestry groups as distinct even past the first generation.

  29. On the importance of human capital for regional development see Gennaioli et al. (2014).

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Acknowledgements

The authors of this paper are a descendant of the Great Puritan Migration, a first-generation Bulgarian, and a first-generation Italian. We would like to thank Ethan Struby, Lauren Hoehn Velasco, and Ana Lariau Bolentini for their excellent research help, and Andrew Copland and Hayley Huffman for their editorial assistance. This work benefited greatly from the comments of participants in the Boston College Macroeconomics Lunch, the Harvard University Economic History Seminar, seminars at the University of Delaware, the College of the Holy Cross, the EIEF, and EAP OECD, and the Brown University “Deep Rooted Factors in Comparative Development” Conference, the 2015 Barcelona Summer GSE forum, the 50th Anniversary Essex University Economics Conference, and the NBER 2015 Political Economy Summer Institute. In addition, we gratefully acknowledge useful conversations with and suggestions from Alberto Alesina, Oded Galor, Nathan Nunn, Luigi Pascalli, Enrico Spolaore, Francesco Trebbi, and David Weil. We also thank the editor and referees for very useful comments, suggestions, and guidance.

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Fulford, S.L., Petkov, I. & Schiantarelli, F. Does it matter where you came from? Ancestry composition and economic performance of US counties, 1850–2010. J Econ Growth 25, 341–380 (2020). https://doi.org/10.1007/s10887-020-09180-9

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Keywords

  • Immigration
  • Ethnicity
  • Ancestry
  • Economic development
  • Culture
  • Institutions
  • Human capital

JEL Classification

  • J15
  • N31
  • N32
  • O10
  • Z10