REFUGEE INCOME INEQUALITY: EVIDENCE FROM NATIONAL DATA ANALYSIS IN THE UNITED STATES
{$ Etel}:
income determinants, refugees, economic integration, human capital, country of origin, gender gap, LASSO, OLS regressionAbstrak
This research scrutinizes the income determinants of resettled exiles in the United States using data from the 2022 Annual Survey of Refugees (ASR) with 1,152 participants. The anylize applies Ordinary Least Squares (OLS) retrogression and variable selection via the Least Absolute Shrinkage and Selection Operator (LASSO).The analysis reveal that the strongest predictor of income is the country-of- origin income level, where displaced persons from upper-middleincome countries earn 68.2% furtherthan those from low-income nations (p < 0.001). Education is a meaningful factor, with the highest returns seen among those holding a bachelor’s degree (69.9%).A gender gap persists, with women earning 12.5% less than men. The 51–60 age group shows a significant lower in income (-27.3%), and more recent arrivals earn 14–16% less, recommending a gradual way for economic adjustment .The research suggests that refugee income is influenced by human capital, pre-migration factors, and rules for joining together, calling for enhanced credential recognition and employment policies that respect gender.
Bibliografik havolalar
1. Al-Hamad, A., Yasin, Y. M., & Metersky, K. (2024). Predictors, barriers, and facilitators to refugee women’s employment and economic inclusion: A mixed methods systematic review. PLOS ONE, 19(7), e0305463. https://doi.org/10.1371/journal.pone.0305463
2. American Immigration Council. (2023). Starting anew: The economic impact of refugees in America. American Immigration Council.
https://www.americanimmigrationcouncil.org/report/economic-impact-refugees-america/.
3. Baumgartner, P., Palinkas, M., & Bilger, V. (2024). Employment outcomes of refugee women and men: Multiple gender gaps and the importance of high-skill jobs. Journal of Ethnic and Migration Studies, 50(20), 4987–5008. https://doi.org/10.1080/1369183X.2024.237994.
4. Becker, G. S. (1964). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press.
5. Borjas, G. J. (1985). Assimilation, changes in cohort quality, and the earnings of immigrants. Journal of Labor Economics, 3(4), 463–489. https://doi.org/10.1086/298065
6. Brell, C., Dustmann, C., & Preston, I. (2020). The labor market integration of refugee migrants in high-income countries. Journal of Economic Perspectives, 34(1), 94–121. https://doi.org/10.1257/jep.34.1.9.
7. Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963
8. Chiswick, B. R. (1978). The effect of Americanization on the earnings of foreign-born men.
9. Journal of Political Economy, 86(5), 897–921. https://doi.org/10.1086/260706
10. Fasani, F., Frattini, T., & Minale, L. (2022). (The struggle for) Refugee integration into the labour market: Evidence from Europe. Journal of Economic Geography, 22(2), 351–393. https://doi.org/10.1093/jeg/lbab011
11. Ghertner, R., Macartney, S., & Dost, M. (2024). The fiscal impact of refugees and asylees at the federal, state, and local levels from 2005 to 2019. Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health & Human Services. https://aspe.hhs.gov/reports/fiscal-impact-refugees-asylees
13. Gorshkov, A. (2024). Wage assimilation of immigrants: The role of the job ladder. Labour Economics, 90, 102594. https://doi.org/10.1016/j.labeco.2024.102594
14. Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson.
15. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
16. Kosyakova, Y., Salikutluk, Z., & Hartmann, J. (2023). Gender employment gap at arrival and its dynamics: The case of refugees in Germany. Social Science Research, 113, 102898. https://doi.org/10.1016/j.ssresearch.2023.102898
17. MacKinnon, J. G., & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29(3), 305– 325. https://doi.org/10.1016/0304- 4076(85)90158-7
18. Mincer, J. (1974). Schooling, experience, and earnings. National Bureau of Economic Research.
19. Naseh, M., Potocky, M., & Hall, B. (2024). Understanding economic integration in immigrant and refugee populations: A scoping review of concepts and metrics in the United States. Economies, 12(7), 167. https://doi.org/10.3390/economies12070167
20. Office of Human Services Policy. (2024). Refugees’ experience prior to resettlement predicts early post- resettlement outcomes. U.S. Department of Health & Human Services. https://aspe.hhs.gov/reports/pre- resettlement-economic-outcomes
21. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
22. Portes, A., & Zhou, M. (1993). The new second generation: Segmented assimilation and its variants. Annals of the American Academy of Political and Social Science, 530, 74–96. https://doi.org/10.1177/0002716293530001006
23. Ramsey, J. B. (1969). Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society: Series B, 31(2), 350–371. https://doi.org/10.1111/j.2517-6161.1969.tb00796.x
24. Seabold, S., & Perktold, J. (2010). Statsmodels: Econometric and statistical modeling with Python. Proceedings of the 9th Python in Science Conference. https://doi.org/10.25080/Majora92bf1922-011
25. Shaw, A., Carter, K., Abramowitz, S., & Rutten, M. (2024). Economic integration of refugees in the USA: A scoping review using the socio-ecological model (2007–2023). Journal of International Migration and Integration, 25, 2011–2038. https://doi.org/10.1007/s12134-02401192-
3
26. Soehl, T., & Van Haren, I. (2023). The effect of social capital on migrant labor market success: Evidence from refugee sponsorship in Canada. Ethnic and Racial Studies, 46, 3224– 3248. https://doi.org/10.1080/01419870.2022.2163329
27. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. https://doi.org/10.1111/j.25176161.1996.tb02080.x
28. UNHCR. (2024). Global Trends: Forced Displacement in 2024. United Nations High Commissioner for Refugees. https://www.unhcr.org/global-trends-report-2024
29. Urban Institute. (2024). 2022 Annual Survey of Refugees. Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/E209224V1
30. U.S. Department of Homeland Security (DHS). (2024). Yearbook of Immigration Statistics 2024: Refugee Arrivals. Office of Homeland Security Statistics. https://ohss.dhs.gov/topics/immigration/yearbook/2024/table13
