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Does host-country education mitigate immigrant inefficiency? Evidence from earnings of Australian university graduates

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Abstract

Imperfect transferability of skills remains a dominant argument in explaining lower earnings of immigrants. Acquisition of host-country education plays a critical role in overcoming this disadvantage. Using a stochastic frontier approach to compare earnings of native and foreign-born graduates from Australian universities, the authors evaluate the importance of host-country education in reducing earnings inefficiency of immigrants. Although immigrants are found to be initially more inefficient than natives, they assimilate toward the earnings frontier over time. Substantial variation in inefficiency and assimilation patterns exists across immigrants with differing residency status and ethnicity. Non-English background increases inefficiency for immigrants, but more so for permanent residents. Consistent with the tightening of selection criteria in Australia, recent immigrant cohorts are found to be more efficient.

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Notes

  1. Inefficiency may well be the lack of knowledge of the host-country’s labor market (informational deficiencies) or reflect sociocultural aspects. In that vein, assimilation could capture all these aspects of integration toward the earnings frontier. While we cannot specifically discern between the two, our results indicate a strong rationale for interpreting inefficiency in immigrant earnings as primarily the result of labor market informational deficiencies, and their assimilation is mostly attributable to the removal of these disadvantages.

  2. The degree to which immigrants assimilate is restricted if immigrants cannot reach their earnings potential due to discriminatory behavior by employers. We assume such demand-side factors to be constant across individuals. If that is not true then our estimates provide upper bounds for technical inefficiency.

  3. While we are unable to control for unobserved heterogeneity in the absence of panel data, we use a rich dataset with two indicators of ability (attending a G8 university and honors). Moreover, we use cohort dummies to control for otherwise unobserved immigrant quality which has been identified as one of the most important sources of heterogeneity when comparing immigrants to natives and evaluating their assimilation experience in cross-sectional studies (e.g., Borjas 1994). Finally, the survey respondents are interviewed within four months of completing their degree. Although workers with different abilities are likely to invest differently in their human capital production during their life, by observing workers and analyzing their earnings very close to their degree attainment we are able to measure assimilation as a process over years spent in Australia, but prior to their accumulation of human capital here.

  4. While it is not possible to test for this, we believe our immigrant sample is similar to that of Reagan and Olsen (2000). The approximate age of arrival for our sample of permanent residents is 14 which is close to theirs (11 years). Besides, emigration data from the Australian Bureau of Statistics show that departure of permanent residents from Australia amounted to < 1% of all departures in 2015 (http://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3401.0.30.001Jan%20to%20Mar%202015?OpenDocument)

  5. Other papers in the literature that follow this approach are Bottasso and Sembenelli (2004), Abedullah and Qaim (2015), and Yaisawarng et al. (2014).

  6. We also estimate a model where the technical inefficiency component is assumed to follow a half-normal distribution. The results are quantitatively and qualitatively similar and are available from the authors upon request. An alternative specification, assuming the technical inefficiency component follows a truncated normal distribution, was also considered, which would have allowed us to parameterize the mean and the variance of the technical inefficiency component separately [as in Wang (2002)]. Unfortunately, this model does not converge. A plausible explanation for the failed convergence is a misspecification in the truncated normal model (Kumbhakar et al. 2015).

  7. Models C and E fail to converge with the cohort indicator variables included as parameters for both the variance of inefficiency and the variance of the idiosyncratic error term. This is likely due to the composition of the arrival year for non-resident immigrants—only 3% of non-residents arrived before year 2000. Hence, in our results we include the cohort variables in the list of variables controlling for heteroscedasticity in the inefficiency term, but not for the idiosyncratic error term. As a robustness check, Models A, B, and D were also run with the cohort variables excluded from the parameterization of the variance of the idiosyncratic error term, which yielded no qualitative differences in the results.

  8. Effectively, this trims annual salaries below $500 and above $200,000. The average hourly wage ranges from $0.44 to $114, which allows for a bigger margin on both ends than, for example, Loh (1996), who treats wages outside $1–100 as outliers. Additionally, trimming age at the 5% tails amounts to removing those below age 21 and above 50. This is important to eliminate sources of selection as a four-year undergraduate degree completion should not occur before age 21. Similarly, completion of an undergraduate degree above age 50 could potentially lead to a selected sample, as these students are likely to be doing a refresher degree or have atypical careers.

  9. The G8 popularly known as Group of Eight is considered to be Australia’s eight leading research universities.

  10. Coefficient estimates from the model that only allows heteroscedasticity in the inefficiency component are quantitatively and qualitatively similar. These are available from the authors upon request.

  11. While discriminatory employer policies cannot be ruled out, it is unlikely to explain different assimilation patterns among similar ethnic groups. Understanding the role of discrimination, however, is not the objective of this study and provides an important avenue for future work.

  12. The marginal effect of non-resident immigrant status on mean inefficiency is 0.186 (the marginal effect of the immigrant variable). The marginal effect of permanent resident immigrant status on mean inefficiency is 0.069 [the marginal effect of the immigrant variable plus the marginal effect of the permanent resident indicator variable (− 0.117)].

  13. The marginal effect of time in Australia for non-resident immigrants on mean inefficiency is − 0.004 (the marginal effect of the immigrant*‘years since migration’ variable). The marginal effect of time in Australia for permanent resident immigrants on mean inefficiency is − 0.002 (the marginal effect of the immigrant*‘years since migration’ variable plus the marginal effect of the immigrant*‘permanent resident’*‘years since migration’ variable (0.002)).

  14. The rates of assimilation could also reflect country-specific differences in general, or varying quality of migrants from different countries in particular.

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Authors and Affiliations

  1. School of Economics and Finance, Queensland University of Technology, Brisbane, QLD, 4000, Australia

    Dipanwita Sarkar

  2. Department of Economics and Finance, University of Dayton, 300 College Park, Dayton, OH, 45469, USA

    Trevor C. Collier

Authors
  1. Dipanwita Sarkar
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  2. Trevor C. Collier
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Correspondence to Trevor C. Collier.

Additional information

The authors thank Michael Kidd, Courtney Collins, and participants at the 2012 Southern Economic Association Meetings for helpful comments. All remaining errors are ours. We gratefully acknowledge funding provided by the Early Career Academic Recruitment and Development Grant at Queensland University of Technology.

Appendix

Appendix

See Tables 6 and 7.

Table 6 Summary statistics by immigrant cohort for permanent residents
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Table 7 Summary statistics by immigrant cohort for non-residents
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Sarkar, D., Collier, T.C. Does host-country education mitigate immigrant inefficiency? Evidence from earnings of Australian university graduates. Empir Econ 56, 81–106 (2019). https://doi.org/10.1007/s00181-017-1363-x

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