Skip to main content

Advertisement

SpringerLink
Log in

Arab immigrants in the United States: how and why do returns to education vary by country of origin?

  • Original Paper
  • Published:
Journal of Population Economics Aims and scope Submit manuscript

Abstract

Using U.S. census data, the authors analyze the earnings of Arab males who completed their schooling before migrating to the United States. There is little return to precollege education, but education beyond 12 years is rewarded highly. Although Arabs share a common ethnicity, they are not a homogeneous group. Returns to education vary significantly by source-country, e.g., high for immigrants from Kuwait, low for Yemeni immigrants. Returns are related to economic development in the source-country and to pupil/teacher ratios. These findings have implications for immigration policy and point to the hazards of generalizing on the basis of ethnicity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. When immigrants from Central/South America are included, the educational advantage of immigrants from the Middle East climbs to 2.4 years relative to immigrants in general.

  2. Because our data predate the events of September 11, 2001, they are unaffected by changes in the wage structure triggered by these events. Davila and Mora (2005) and Kaushal et al. (2007) show that relative wages of Arab males working in the United States deteriorated immediately after the terrorist attacks, which they interpret as the short-run effects of discrimination, which have apparently dissipated over time.

  3. As Antecol (2000) observes, another advantage of using a common labor market is that it gets around the problem of cross-country differences in institutional factors. A possible disadvantage is that differential selection of immigrants by country could confound the effect of differences in the returns to education.

  4. Studies that find a payoff to lower class size and other measures of school quality include Altonji and Dunn (1996), Krueger and Whitmore (2001), Bedi and Edwards (2002), and Konstantopolous and Constant (2005). For an opposing view, see Hanushek (1986), Betts (1995, 1996), and Grogger (1996).

  5. For example, in the Arab countries of the present study, the primary-school pupil/teacher ratio averaged 32.1 in 1970 and 28.1 in 1980. (The average pupil/teacher ratios were weighted by the number of immigrants from the respective Arab countries.) In the United States, the corresponding pupil/teacher ratio those 2 years were 24.4 and 20.5, respectively, down from 30.2 in 1955 and 28.4 in 1960 (U.S. Department of Commerce 1960, 1987).

  6. Another advantage of this study is that the sample consists of males 25–64 years of age. Wilson (2002) finds that the effect of school quality, while significant for older workers, is imperceptible for workers under the age of 24; and the studies that find no significant effect of school quality tend to restrict their analysis to young workers, as observed by Card and Krueger (1996). Our sample is not hindered by lags in observing the effects of school quality.

  7. Based on 1980 U.S. census data, the estimated returns to education were 4.2%, 4.1%, 4.0%, and 3.1% for immigrants from Egypt, Lebanon, Morocco, and Iraq, respectively, compared to an average of 4.0% for all 67 countries. In 1990, the estimated returns were 4.8%, 4.9%, 4.1%, and 4.4%, respectively, compared to an overall average of 4.9%. Although returns were higher in 1990, Jaeger (2003) points out that the census question on schooling changed between 1980 and 1990, so that results are not directly comparable.

  8. We limit the analysis to male immigrants, as do most studies, for several reasons. First, Antecol (2000) and Blau et al. (2008) find that cultural factors strongly influence labor force activity of female immigrants in the United States, especially for women whose source-country labor force participation is low, as it is in Arab countries. Second, selectivity is more likely to be an issue for female immigrants. For example, Baker and Benjamin (1997) find that many recent female immigrants take dead-end jobs to finance acquisition of human capital by their husband, which distorts estimates of returns to education for females. Finally, from a practical perspective, the number of countries for which there are a sufficient number of female immigrants to satisfy sample restrictions is much lower for female immigrants, especially when it comes to estimation of the spline specification. Simply put, there are too few observations for female immigrants for us to attempt to explain cross-country differences in returns to education.

  9. Bratsberg and Ragan (2002) show that returns to source-country schooling are higher for immigrants who continue their schooling in the United States and that this difference in returns to source-country schooling does not reflect differences in ability. Instead, they argue that U.S. schooling certifies or upgrades source-country education. As they point out, including in the sample immigrants who obtain U.S. schooling would bias estimates of returns to source-country schooling.

  10. Because educational attainment is reported in the U.S. census in intervals and, for higher levels, in the form of the highest degree obtained, we follow the approach of Bratsberg and Terrell (2002) and convert educational attainment to years of schooling using the following rule: “years of schooling equals zero if educational attainment is less than first grade; 2.5 if first through fourth; 6.5 if fifth through eighth, educational attainment if ninth, tenth, eleventh, or twelfth; 12 if GED earned; 13 if some college, but no degree; 14 if associate degree; 16 if bachelor’s degree; 18 if master’s degree; 19 if professional degree; and 20 if doctorate degree” (p. 194).

  11. For example, Deschenes (2006) finds a convex relationship between years of education and ln(Wage), especially in recent years; and Angrist et al. (2006) show that the returns to education increase by quantile in 1990 and 2000.

  12. Alternatively, we allowed for higher returns for schooling beyond 11 years, the specification of Bratsberg and Ragan (2002). But results of both the J test and the Cox test indicated that a spline at 11 years must be rejected in favor of a spline at 12 years. We also compared a spline at 16 years with a spline at 12 years. Neither test allowed us to reject one model in favor of the other, but in both 1990 and 2000 goodness of fit was better with a spline at 12 years.

  13. As a referee observed, the Roy model suggests that we also control for income inequality, but such data are available for only five Arab countries.

  14. Data on per capita GDP are not available for Lebanon, but data exist on per capita GNP. For this country, we follow the procedure of Bratsberg and Terrell (2002): we impute GDP by adjusting GNP data for Lebanon by the ratio of GDP/GNP from the Arab countries for which both series are available. GNP data came from the U.S. Arms Control and Disarmament Agency (1979, 1989).

  15. In the model of Mehta (2000), increased school quality can lead to higher wages for those at both ends of the wage distribution but to no gains (or a loss) for workers in the middle. Although this model does not allow the effect of school quality to vary with years of schooling, it raises the prospect that returns to school quality may not be monotone.

  16. Alternatively, we could assess the effect of economic development and school quality with the two-step approach of Bratsberg and Terrell (2002). The major drawback to such an approach is the unacceptably small number of degrees of freedom. Even after pooling data for 1990 and 2000, we have observations on returns to education for only 25 country-year observations, compared to 6,600–7,100 observations when we estimate Eq. 3. Second, with the two-step approach, a single dependent variable is used to capture rate of return. But as the results of Table 3 show, the rate of return differs for low and high levels of education.

  17. The calculations reported in this paragraph are obtained by differentiating ln(Wage) with respect to PTR and plugging in values of schooling. For example, if \(S={18,\mbox{ d}\ln \left( {\mbox{Wage}} \right)} \mathord{\left/ {\vphantom {{18,\mbox{ d}\ln \left( {\mbox{Wage}} \right)} {\mbox{dPTR}}}} \right. \kern-\nulldelimiterspace} {\mbox{dPTR}}=-0.0168+0.0014(18)-0.0026\left( {18-12} \right)=-0.0072\).

  18. Their range of estimates, each statistically significant, was 0.42 to 0.71 depending on specification.

  19. In her study of Canadian men, Bedard (2003) allows the effect of a lower pupil/teacher ratio to vary with level of schooling. She finds (p. 399) “no statistically significant relationship between class size and mean wages, except at very low levels of schooling,” but her results suggest that, for men in the upper tail of the wage distribution, lowering class size does raise wages.

References

  • Abdel-Gadir AA (2001) The basics of the relationship between education and labor market: estimating the returns to investment in human capital. Arab Planning Institute, Kuwait (in Arabic)

    Google Scholar 

  • Abdel-Gadir AA (2002) Building human capital for economic development in the arab countries. Working paper no. 76, The Egyptian Center for Economic Studies, Cairo, Egypt

  • Altonji JG, Dunn TA (1996) Using siblings to estimate the effect of school quality on wages. Rev Econ Stat 78(4):665–71

    Article  Google Scholar 

  • Angrist JD, Chernozhukov V, Fernandez-Val I (2006) Quantile regression under misspecification, with an application to the U.S. wage structure. Econometrica 74(2):539–63

    Article  Google Scholar 

  • Antecol H (2000) An examination of cross-country differences in the gender gap in labor force participation rates. Labour Econ 7:409–426

    Article  Google Scholar 

  • Antecol H, Cobb-Clark DA, Trejo SJ (2003) Immigration policy and the skills of immigrants to Australia, Canada, and the United States. J Hum Resour 38(1):192–218

    Article  Google Scholar 

  • Arabsheibani GR, Manfor L (2001) Non- linearities in returns to education in Libya. Ed Econ 9(2):139–44

    Article  Google Scholar 

  • Assaad R (1997) The effects of public sector hiring and compensation policies on the Egyptian labor market. World Bank Econ Rev 11(1):85–118

    Google Scholar 

  • Baker M, Benjamin D (1997) The role of the family in immigrants’ labor-market activity: an evaluation of alternative explanations. Am Econ Rev 87(4):707–727

    Google Scholar 

  • Barro RJ, Lee JW (2000) International data on educational attainment: updates and implications. Center for International Development. Working paper no. 42, Harvard University Press, Cambridge

  • Bedard K (2003) School quality and the distribution of male earnings in Canada. Econ Ed Rev 22(4):395–407

    Article  Google Scholar 

  • Bedi AS, Edwards HY (2002) The impact of school quality on earnings and educational returns—evidence from a low-income country. J Dev Econ 68(1):157–85

    Article  Google Scholar 

  • Betts J (1995) Does school quality matter? Evidence from the national longitudinal survey of youth. Rev Econ Stat 77(3):231–50

    Article  Google Scholar 

  • Betts J (1996) Is there a link between school inputs and learning? Fresh scrutiny in an old literature. In: Burtless G (ed) Does money matter? Brookings Institution Press, Washington, DC

    Google Scholar 

  • Betts J, Lofstrom M (2000) The educational attainment of immigrants: trends and implications. In: Borjas G (ed) Issues in the economics of immigration. University of Chicago Press, Chicago, pp 51–115

    Google Scholar 

  • Blau FD, Kahn LM, Papps KL (2008) Gender, source country characteristics and labor market assimilation among immigrants: 1980–2000. Institute for the Study of Labor (IZA), Discussion Paper No. 3725

  • Borjas GJ (1982) The earnings of male Hispanic immigrants in the United States. Ind Labor Relat Rev 35(3):343–353

    Article  Google Scholar 

  • Borjas GJ (1987) Self-selection and the earnings of immigrants. Am Econ Rev 77(4):531–53

    Google Scholar 

  • Bratsberg B, Ragan Jr JF (2002) The impact of host-country schooling on earnings: a study of male immigrants in the united states. J Hum Resour 37(1):63–105

    Article  Google Scholar 

  • Bratsberg B, Terrell D (2002) School quality and returns to education of U.S. immigrants. Econ Inq 40(2):177–198

    Article  Google Scholar 

  • Burtless G (1996) Introduction and summary. In: Burtless G (ed) Does money matter? Brookings Institution Press, Washington, DC

    Google Scholar 

  • Camarota SA (2002) Immigrants from the Middle East: a profile of the foreign-born population from Pakistan to Morocco. Center for Immigration Studies, Backgrounders and Reports. The direct link is http://www.cis.org/articles/2002/back902.html

  • Card D, Krueger AB (1992) Does school quality matter? Returns to education and the characteristics of public schools in the United States. J Polit Econ 100(1):1–40

    Article  Google Scholar 

  • Card D, Krueger AB (1996) Labor market effects of school quality: theory and evidence. In: Burtless G (ed) Does money matter? Brookings Institution Press, Washington, DC

    Google Scholar 

  • Carlson L, Swartz C (1988) The earnings of women and ethnic minorities. Ind Labor Relat Rev 41(4):530–546

    Article  Google Scholar 

  • Chishti S, Khalaf B (2000) Earnings, education, experience and gender: Kuwait experience. In: Shahin W, Dibeh G (eds) Earnings, inequality, unemployment, and poverty in the Middle East and North Africa. Greenwood Press, London

    Google Scholar 

  • Chiswick BR (1978) The effect of Americanization on the earnings of foreign-born men. J Polit Econ 86(5):897–921

    Article  Google Scholar 

  • Cobb-Clark DA (1993) Immigrant selectivity and wages: the evidence for women. Am Econ Rev 83(4):986–993

    Google Scholar 

  • Constant AF, Gataullina L, Zimmermann KF (2009) Ethnosizing immigrants. J Econ Behav Organ 69:274–287

    Google Scholar 

  • Davila A, Mora MT (2005) Changes in the earnings of Arab men in the US between 2002 and 2002. J Popul Econ 18:587–601

    Article  Google Scholar 

  • Deschenes O (2006) Unobserved ability, comparative advantage, and the rising return to education in the United States, 1979–2002. Working paper, University of California, Santa Barbara

  • Fergany N (1998) Human capital and economic performance in Egypt. Unpublished Paper. Mishkat Center, Cairo

  • Grogger J (1996) Does school quality explain the recent black/white wage trend? J Labor Econ 14(2):231–53

    Article  Google Scholar 

  • Gwartney J, Long J (1978) The relative earnings of blacks and other minorities. Ind Labor Relat Rev 31(3):336–346

    Article  Google Scholar 

  • Hanushek EA (1986) The economics of schooling: production and efficiency in public schools. J Econ Lit 24(3):1141–1177

    Google Scholar 

  • Heckman J, Layne-Farrar A, Todd P (1996) Human capital pricing equations with an application to estimating the effect of school quality on earnings. Rev Econ Stat 78(4):562–610

    Article  Google Scholar 

  • Hoxby C (1996) How teachers’ unions affect education production. Q J Econ 111(3):671–718

    Article  Google Scholar 

  • Jaeger DA (2003) Estimating the returns to education using the newest current population survey education questions. Econ Lett 78(3):385–394

    Article  Google Scholar 

  • Jasso G, Rosenzweig MR (1986) What’s in a name? Country-of-origin influences on earnings of immigrants in the United States. Res Human Cap Dev: Migration, Hum Cap Dev 4:75–106

    Google Scholar 

  • Kaushal N, Kaestner R, Reimers C (2007) Labor market effects of September 11th on the Arab and Muslim residents of the United States. J Hum Resour 42(2):275–308

    Google Scholar 

  • Konstantopolous S, Constant A (2005) The gender gap reloaded: is school quality linked to labor market performance? IZA Discussion Paper 1830

  • Krueger AB, Whitmore DM (2001) The effect of attending a small class in the early grades on college-test taking and middle school test results. Econ J 111(1):1–28

    Article  Google Scholar 

  • Lane J, Hakim G, Miranda J (1999) Labor market analysis and public policy: the case of Morocco. World Bank Econ Rev 13(3):561–78

    Google Scholar 

  • Mehta SR (2000) Quality of education, productivity changes, and income distribution. J Labor Econ 18(2):252–81

    Article  Google Scholar 

  • Psacharopoulos G, Patrinos HA (2002) Returns to investment in education: a further update. World Bank Policy Research Working Paper No. 2881. The World Bank

  • Schoeni RF (1997) New evidence on the economic progress of foreign-born men in the 1970s and 1980s. J Hum Resour 32(4):683–740

    Article  Google Scholar 

  • UNDP (2006) Human development report 2006. Oxford, United Nations Development Program

  • UNESCO. UNESCO statistical yearbook. Various issues, United Nations Educational Scientific and Cultural Organization, Paris

  • U.S. Arms Control and Disarmament Agency (1979, 1989) World military expenditures and arms transfers. Government Printing Office, Washington, DC

    Google Scholar 

  • U.S. Census Bureau (2003) The Arab population: census 2000 brief

  • U.S. Department of Commerce (1960, 1987) Statistical abstract of the United States. Government Printing Office, Washington, DC

    Google Scholar 

  • U.S. Immigration and Naturalization Service (2003) Statistical yearbook of the immigration and naturalization service, 2003. U.S. Government Printing Office, Washington, DC

    Google Scholar 

  • Wilson K (2002) The effect of school quality on income. Econ Ed Rev 21(6):579–588

    Article  Google Scholar 

  • World Bank (2003) World development indicators 2003 CD-ROM. The cd is available at http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20899413~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html

  • World Bank (2006) World development report 2007: development and the next generation. World Bank and Oxford University Press

  • Zimmermann KF (2007a) The economics of migrant ethnicity. J Popul Econ 20(3):487–494

    Article  Google Scholar 

  • Zimmermann KF (2007b) Migrant ethnic identity: concept and policy implications. Ekonomia 10(1):1–17

    Google Scholar 

  • Zimmermann L, Zimmermann KF, Constant A (2007) Ethnic self-identification of first-generation immigrants. Int Mig Rev 41(3):769–781

    Article  Google Scholar 

Download references

Acknowledgements

The authors, while accepting responsibility for all shortcomings, acknowledge the helpful comments and suggestions of the editor, three referees, Ali Abdel-Gadir, Bernt Bratsberg, Wei Chi, Jeff DeSimone, James Heckman, Dong Li, Carol Tremblay, and Michael Suleiman, and the research assistance of Hana Janoudova. Part of the research was completed while Ragan was a visiting scholar at the Ragnar Frisch Centre for Economic Research, Oslo, Norway; he thanks the centre for its support.

Author information

Authors and Affiliations

  1. Institute of National Planning, Salah Salem St., Nasr City, Cairo, Egypt

    Ashraf El-Araby Aly

  2. Department of Economics, Kansas State University, Manhattan, KS, 66506, USA

    James F. Ragan Jr.

Authors
  1. Ashraf El-Araby Aly
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James F. Ragan Jr.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James F. Ragan Jr..

Additional information

Responsible editor: Klaus F. Zimmermann

Rights and permissions

Reprints and permissions

About this article

Cite this article

El-Araby Aly, A., Ragan, J.F. Arab immigrants in the United States: how and why do returns to education vary by country of origin?. J Popul Econ 23, 519–538 (2010). https://doi.org/10.1007/s00148-009-0245-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-009-0245-8

Keywords

JEL Classification

Search

Navigation