Economic Assimilation and Skill Acquisition: Evidence From the Occupational Sorting of Childhood Immigrants

Abstract

We study the economic assimilation of childhood immigrants to the United States. The linguistic distance between English and the predominant language in one’s country of birth interacted with age at arrival is shown to be closely connected to occupational sorting in adulthood. By applying big-data techniques to occupations’ detailed skill requirements, we provide evidence that childhood immigrants from English-distant countries who arrived after the primary school years reveal comparative advantages in tasks distinct from those for which (close to) Anglophone immigrants are better suited. Meanwhile, those who arrive at younger ages specialize in a bundle of skills very similar to that supplied by observationally equivalent workers. These patterns emerge even after we net out the effects of formal education. Such findings are compatible with the existence of different degrees of complementarity between relative English-learning potential at arrival and the acquisition of multiple capabilities demanded in the U.S. labor market (math/logic, socioemotional, physical, and communication skills). Consistent with the investment-complementarity argument, we show that linguistic distance and age at arrival also play a significant role on the choice of college major within this population.

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Notes

  1. 1.

    We acknowledge that assimilation and integration are more general processes by which immigrants become fully fledged members of their host societies (see Akerlof and Kranton 2010; Fukuyama 2006; Portes and Rumbaut 2006). Here we focus only on the human capital and worker productivity elements of immigrants’ economic assimilation.

  2. 2.

    This measure was originally developed by the Max Planck Institute of Evolutionary Anthropology and has been previously employed by Clarke and Isphording (2016) to assess investments in health by immigrants; Isphording and Otten (2014) to assess language acquisition by immigrants; Isphording and Otten (2013) to examine international trade patterns; and Adsera and Pytlikova (2012) to discuss international migration patterns.

  3. 3.

    Throughout the article, we consider that an individual’s mother tongue is the predominant language spoken in her country of birth, as listed at https://www.ethnologue.com/.

  4. 4.

    We see these as important avenues for future research. Rangel and Shi (2016) found that age at immigration and country of origin influence credit accumulation in different high school disciplines for respondents of the National Longitudinal Study of Adolescent to Adult Health (Add Health). Stiefel et al. (2010) uncovered that among high schoolers in New York City, late-arrival immigrants perform better than natives in math but not in English. Bohlmark (2008) reported similar findings in Sweden.

  5. 5.

    See, for example, Grogger and Trejo (2002) and also studies reviewed in Borjas (1999).

  6. 6.

    See also Angrist and Lavy (1997), Berman et al. (2003), Chiswick and Miller (2002, 2007, 2010), Bleakley and Chin (2004, 2010), Dustmann and Fabri (2003), Dustmann and van Soest (2001, 2002), and Guven and Islam (2015), among others.

  7. 7.

    See Long (1990) and Newport (1990) on the relation between age and second-language learning in particular.

  8. 8.

    Another important consideration is the increase in immigration flows from countries in Asia and Africa where the main language is not English. See Elo et al. (2015).

  9. 9.

    We restrict ourselves to occupations that are listed in all calendar years covered in our pooled data. In that way, we do not address the impact of the emergence of new occupations over our outcomes of interest.

  10. 10.

    Approximately 100 occupations per year were gradually transitioned from extrapolated DOT data. By version 13.0 of the O*NET database, occupational data collected between 2001 to 2007 from more than 128,000 workers in 95,000 establishments are included.

  11. 11.

    The first factor accounts for 96 % to 100 % (O*NET) of variation in all the variables included in each index.

  12. 12.

    See details at http://www.eva.mpg.de.

  13. 13.

    As described by Isphording and Otten (2014), only one consonant has to be substituted between the English word yu and German word du. Meanwhile, to transfer maunt3n—the transcription of mountain—into bErk (the German transcription), one has to remove or substitute each consonant and vowel.

  14. 14.

    We use the reports downloaded from the Educational Testing Service (ETS) web page with tables that list average scores per country of test-taker’s nativity https://www.ets.org/s/toefl/pdf/94227_unlweb.pdf).

  15. 15.

    Data for this are made available in the Integrated Public Use Microdata Series (IPUMS, Ruggles et al. 2015).

  16. 16.

    In the economics literature, premarket skills refer to investments in human capital that predate the entry into the labor force.

  17. 17.

    In nonreported estimates, we also find that the pattern of skill accumulation is not an interactive function of parental origin and age of immigration among those coming from countries that are close to Anglophone.

  18. 18.

    This new working sample has descriptive statistics presented in Table S8 (Online Resource 1).

  19. 19.

    Recent studies have argued for this imperfect substitutability in the United States (Lewis 2013; Ottaviano and Peri 2012; Peri and Sparber 2009), Germany (D’Amuri et al. 2010), Spain (Amuedo-Dorantes and de la Rica 2011), and the UK (Manacorda et al. 2012).

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Acknowledgments

We are grateful to Ingo Isphording and Sebastian Otten for sharing their data of the Levenshtein linguistic-distance measure. Sections of the analysis were performed while Rangel was a visiting scholar at Princeton University’s Program in Latin American Studies. He is thankful for their hospitality. We would also like to thank participants of the All-California Labor Economics Conference, Southern Economic Association meetings, Population Association of America meetings, and workshops at Duke University for helpful comments. Thanks also to Bernardo Blum, Charles Clotfelter, Elizabeth Frankenberg, and anonymous referees for detailed and insightful suggestions. All remaining errors are our own. The views expressed herein are our own and do not reflect the official policy or position of the Department of Defense or the U.S. Government.

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Correspondence to Marcos A. Rangel.

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Appendix

Table 5 Occupation-based measures of immigrant skills
Table 7 Descriptive statistics from the pooled 1990/2000 U.S. Censuses and 2009–2013 ACS data: Means, with standard deviations in parentheses
Table 8 DOT and O*NET skill measurements from pooled 1990–2013 U.S. census/ACS data: Means, with standard deviations in parentheses

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Bacolod, M., Rangel, M.A. Economic Assimilation and Skill Acquisition: Evidence From the Occupational Sorting of Childhood Immigrants. Demography 54, 571–602 (2017). https://doi.org/10.1007/s13524-017-0558-2

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Keywords

  • Immigration
  • Assimilation
  • Skills
  • English
  • Specialization