Abstract
Existing research shows that on average first-generation immigrants earn less than native-born workers in the United States, especially during their first decade in the country, but eventually overtake the native-born; second-generation immigrants (2g) tend to earn more than subsequent generations (3+g). However, the labor market outcomes of the “1.5 generation” (1.5g), the foreign born who migrate as children, have not been thoroughly analyzed. This paper hypothesizes that the 1.5g could have an earnings advantage relative to subsequent generations due to the higher ability of their parents; however, the accumulation of destination-specific human capital (i.e., acculturation) also declines with age at migration (AAM). Using a Mincerian earnings regression as applied to CPS data (1994–2016), this analysis tests whether there is a within-group “critical” AAM threshold after which the earnings advantage of the 1.5g becomes an earnings disadvantage. Results show that this threshold is approximately ages 5–9, varying by ethnicity and gender.
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Notes
As per Chiswick (2008), “tied movers” are “those who move because of the migration decisions of others”. Their earnings disadvantages appear greater initially and diminish with duration.
Positive selection in migration refers to the notion that ceteris paribus, immigrants “have more innate ability or motivation relevant to the labor market than native-born persons”(Chiswick 1978). However, the 1.5g may not always be positively selected (Borjas 1985, 2015; Chiquiar and Gordon 2005). In this case depending on the level of selection, it may take more than one generation for their children to catch up to the native-born. Earnings trajectories are not uniform for all immigrants (Abramitzky et al. 2014; Lubotsky 2007).
Aydemir and Sweetman (2006) find that 1.5g males have 14% lower earnings than native-born males, though the earnings differential for females is insignificant. This suggests both that the earnings penalty may reflect strong effects for later arrivals and that results are likely to differ by gender.
Recent research has suggested that acculturation is a two-directional cultural exchange and learning process. While the proliferation of food, music, and culture popular among many immigrant groups suggests this is undoubtedly true, this study focuses on the unidirectional acculturation process of immigrants in the U.S.
Using age at arrival interacted with origin in a non-English speaking country as an instrument for language proficiency, the authors studied wages for individuals who arrived 1960–1974. They found that improving English ability increases log wages by 0.33, but 90% of this effect is through educational attainment.
Using test scores for Canadian children, Worswick (2009) found that though mathematics and reading scores of the 2g with parents whose native language was neither English nor French and native-born did not differ, the 2g are at a substantial disadvantage on vocabulary tests early on (around age 4). The 2g children do catch up and this disadvantage disappears by age 14.
In previous works, Rumbaut classified the 1.5g as those who immigrated before age 12 and this threshold is frequently used by economists wishing to distinguish childhood immigrants from the first generation. He further classifies those that arrived between the ages of 13–17, with or without their parents, as the 1.25 generation and those who arrived before age 5 and completed their education in the U.S. as the 1.75 generation.
These classifications are very close to those used by Schaafsma and Sweetman (2001) when studying the impact of AAM on the immigrant earnings profile using Canadian data. The youngest AAM group is slightly larger to account for measurement error in the AAM variable, which may arise due to wide intervals in year of migration, as reported in the CPS (see web appendix for more details).
Earnings of the 1g are greater than subsequent generations after a certain duration in the destination, estimated to be 28 years (vs. the 3+g) in an earlier analysis.
Child migrants in the US are more likely to start large firms and may have greater risk tolerance than the native-born (Kerr and Kerr 2016).
Borjas (1992, 1994) models the extent to which educational patterns and skills by ethnicity and origin of first generation immigrants can determine future human capital investments for the second generation, concluding that economic assimilation is limited. That is, labor market and educational outcomes show some persistence across generations and more recent cohorts are showing little convergence in outcomes. However, while these studies and follow-up work such as Borjas (2015) focus on differences in educational attainment and earnings mobility across generations, they look specifically at the first generation who immigrated to the U.S. as adults (over the ages of 16 or 18), omitting those who immigrated as children.
It is not possible to distinguish those with immigrant grandparents from those with native-born grandparents in the CPS. Thus, all those without at least one immigrant parent are grouped together as 3+g for the reference group.
Conversely, if immigrants are negatively selected, we might expect to see successive generations having higher earnings than previous generations, but still less ability than the average population (i.e., not an eventual catch up).
Although the 1.5g and 2g with both foreign-born parents may initially have the same selection transmitted from their parents, Jasso and Rosenzweig (2010) have shown that the decision of immigrant parents to sponsor their children is positively selected. That is, more highly skilled (including better educated) children are more likely to be sponsored. Thus, it may be the case that even within a family the 1.5g are more positively selected and/or receive more parental attention and resources than their 2g siblings.
Similarly, Borjas and Bratsberg (1996) find that return migration tends to be highest for the wealthiest countries closest to the U.S., but varies by ethnicity.
Aslund et al. (2009) also emphasize the causal effects of age at migration on cultural and labor force outcomes using Swedish data. They show that children who arrive at earlier ages are more socially integrated and that the effect of AAM on acculturation is stronger than on economic outcomes (i.e., labor force participation and wages), highlighting parental influences and early experiences.
Friedberg (1992) also finds a relative age at arrival threshold of around 10, at which earnings of immigrants are on par with natives, and after which earnings are lower.
Given large intervals for year of immigration in the CPS, measuring age at migration accurately is difficult. In order to reduce measurement error, I used the 2000 decennial PUMS 5% sample and the 2009–2014 ACS sample and constructed a country-year panel with historical immigration counts. The year of immigration for people reporting that interval is then calculated as the weighted average of the percent immigrating in that year/the total immigrating over the intervalled period, multiplied by each year. This sort of index was constructed for each country for each interval. Please see web appendix for more details and numerical example.
Due to the difficulty of appropriately measuring work experience with the CPS, the coefficient on work experience may be biased, particularly for females, as it does not account for time out of the labor force. If an individual has taken time out of the labor force, the coefficient on experience will be biased downward.
Alternative specifications will be discussed in Section 5.
A drawback to the specifications in eq. (1)-(3) is that it is not possible to strictly isolate the effects of AAM and duration (years since migration=YSM) due to the nature of their relationship: Age (here proxied by experience and education)=AAM+YSM. The younger immigrants arrive the longer they’ve been in the country when they are observed in the sample. Those that arrived at late ages with a long duration would be out of the labor force for this and thus excluded from this analysis (Friedberg 1992). Mechanically, the coefficient on AAM includes an effect of YSM. This is also true in reverse, relevant for the many studies quantifying the effect of duration in the destination on earnings. For adult immigrants with potentially secondary and tertiary education completed and labor force experience obtained outside of the U.S., each additional year in the U.S. may have a significant effect on earnings. Thus YSM is standardly included in those analyses. However, for child migrants who will have the vast majority of their significant labor force experience, and much of their education, completed in the U.S., AAM is the more relevant variable. Therefore, I cannot include AAM, YSM, and age (or experience). Alternative specifications and robustness checks are discussed in Section 5.
This data is constructed from the Integrated Public Use Microdata Series (IPUMS). Though ideally a longer time sample would be used, a variable for birthplace was only added in 1994.
One note of caution when using the CPS to assess age at arrival or duration is that often years of immigration are coded in intervals. For the 1950s-1970s, some of these intervals are a little larger and may result in slightly less accuracy in estimating a precise year of immigration. See the web appendix for the strategy used to address this.
See Web Appendix for details.
See Web Appendix Table WA1 for more variables.
Though immigrants are more urbanized than other generations, this degree of urbanization does not vary by AAM. Including a control for urban residence is important to account for higher wages in urban areas.
There are no data on the legal status of migrants in the sample. It is known that undocumented migrants are undercounted in the CPS, and Passel and Taylor (2010) (“Unauthorized Immigrants and their U.S.-Born Children”) estimate that unauthorized immigrants comprise slightly more than 4% of the adult population. However, the report also states that 79% of children of unauthorized migrants were born in the U.S. Therefore, the number of 1.5g that are unauthorized immigrants is reduced. While some of these migrants are included in the sample, it is likely that the inclusion of those omitted would lead to a slight downward bias in the estimates. Given the research, there is no indication that this share varies by AAM. Given recent trends, we could speculate that teenagers coming alone would be more likely to be undocumented, but a downward bias in their results would not change any of the conclusions in the paper.
Those who arrived in their teen years (AAM 14–18) constitute 40–50% of all 1.5g immigrants in each group (e.g., 2.2% out of the total 4.8% for the full sample), thus contributing significantly to the 1.5g (AAM<19). The remaining two AAM groups constitute 25–30% of the sample each (See Web Appendix Table WA2).
This estimate is calculated by taking β1j +β2j2 from eq. 2 for each AAM(j) 1–18 and then taking the average of the change in the point estimates.
For pooled regression results, see Appendix Tables 6 and 7, respectively. Given the correlations (ρ = 0.19, ρ = 0.31) between Hispanic and the older AAM categories (AAM 8–13, AAM 14–18), the coefficients on these groups are upward biased (less negative) for the Full Sample (col. 1), Males (col. 4), and Females (col. 5) where non-Hispanics are pooled with Hispanics.
Faulkner (2011) suggests that economic mobility can differ markedly by gender even within an ethnic group.
Given β1 = 0.039, β2 = −0.005, β3 = 0.001 solving β1+β2j+β3j2=0 for j ~ 9.67 → an AAM threshold of 9, after which earnings of the 1.5g are lower than the 3+g (i.e., by 10 they are negative). Full regressions with β1−β3 in Web Appendix Table WA4.
Borjas (1995) states, “...there is very little movement of Hispanics out of Hispanic neighborhoods even in the third generation. The average Hispanic immigrant lives in a neighborhood that is 35 percent Hispanics; the average second-generation Hispanic lives in one that is 33-percent Hispanic; and the typical third generation Hispanic lives in one that is 29-percent Hispanics.”
Full regression results can be found in Web Appendix Table WA5.
Another seemingly possible explanation might be differences in labor force participation of 1.5g women vs. 2g women as compared with 1.5g men vs. 2g men. However, this does not appear to be the case, as there is virtually no difference in female labor force participation for non-Hispanics for the 1.5g vs. the 3+g (See Web Appendix Table WA6).
Even in surveys such as the NLSY, the extent of residential segregation is underestimated as third-generation workers are classified as “nonethnics” (Borjas 1995).
Ceteris paribus, concentrations greater than 25% have a negative effect on earnings compared to concentrations less than 5%.
For regression results, see Web Appendix Table WA8. Inclusion of region of origin in the original specification would not allow for comparisons between the 1.5g for whom it can be identified and 3+g for whom it cannot. By including controls for Hispanic and Asian, I am able to control for the two most prominent immigrant-sending regions (Latin America and Asia) - in effect a region of origin-specific effect.
If a respondent answers Asian only or Hawaiian Pacific Islander only to the race question, he/she is designated as Asian. If the respondent does not report Hispanic ethnicity or Asian race, he or she is considered to be in the “other” non-Hispanic group. These groupings are used to be consistent with changes in the race question over time.
Mexicans are identified as those who report being born in Mexico for the 1.5g, or those with either parent born in Mexico (for the 2g), or those with native-born parents who report Mexican ancestry (for the 3+g).
An alternative specification discussed in Schaafsma and Sweetman (2001) seeks to identify and estimate the effects of AAM on earnings. In this estimation, Schaafsma and Sweetman (2001) first estimate a human capital earnings function of native-born Canadians and construct a measure of predicted log earnings. They then regress the difference between immigrant log earnings and native-born log earnings on the same function for immigrants to Canada with the addition of AAM and cohort effects. My analysis using this technique generated similar thresholds.
Though it is possible that AAM could be influenced by weeks worked and education for an older child migrant in period t, by observing the individual in period t+1 (post-schooling completion), I am comparing earnings conditional on schooling. Similarly, weeks worked at time t in the origin country should not influence earnings in time t+1 over and above their effect on labor force participation and educational attainment (already controlled for).
Given the time period of the sample, those born in 1980 and 1990 must be combined to avoid a truncation problem (i.e., only a handful of observations for early AAM born in the early 1990s).
Given the large time period under study, a valid concern is that the results may be different at the beginning and end of the time period. In order to address this, I conducted the main portion of the analysis separately for 1994–2004 and 2005–2016. Although the pooled sample more closely reflects the second period not only due to the larger sample size, but also larger share of 1.5g individuals (5.5% for the later period vs. 3.8% for the earlier period), the results are consistent for both samples, though some results are insignificant for the earlier period given the smaller sample size.
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Acknowledgements
The author would like to thank two anonymous referees, Barry R. Chiswick, Carmel Chiswick, Tara Sinclair, Scott Wentland, Ben Bridgman, and seminar audiences at George Washington University, the Bureau of Economic Analysis, and the Federal Reserve Bank of Minneapolis.
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This analysis was conducted at The George Washington University. The views expressed in this paper are solely those of the author and not necessarily those of the US Bureau of Economic Analysis or the US Department of Commerce.
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Appendix A
Appendix A
Earnings of 1.5g by Age at Migration. (a) By Ethnicity and (b) Gender. Notes: The earnings differential at each age is calculated using the AAM, and AAM sq coefficients calculated in Table WA3 for each group
Earnings of 1.5g vs. 3+g by Age at Migration: Disaggregation. (a) Asians vs. Other Non-Hispanics and (b) Mexicans vs. Other Hispanics. Notes: The earnings differential at each age is calculated using the AAM, and AAM sq coefficients calculated in Table WA3 for each group
Alternative AAM Specifications: 1.5g vs. 3+g. (a) Linear and (b) Dichotomous
Earnings of 1.5g vs. 3+g by AAM (Cohorts). (a) 60's Cohort and (b) 70's Cohort and (c) 80's Cohort. Source for above figures: 1994–2016 CPS, March Supplement. Notes: Shaded areas represent 95% Confidence Intervals
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Gindelsky, M. Testing the acculturation of the 1.5 generation in the United States: Is there a “critical” age of migration?. Rev Econ Household 17, 31–65 (2019). https://doi.org/10.1007/s11150-017-9400-2
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DOI: https://doi.org/10.1007/s11150-017-9400-2