Research in Higher Education

, Volume 53, Issue 4, pp 383–405 | Cite as

The Earnings Benefits of Majoring in STEM Fields Among High Achieving Minority Students

Article

Abstract

The purpose of this study was to improve our understanding of the association between major field of study in college and early career earnings among a sample of academically accomplished minority students. Results demonstrate the economic benefits minority students experience from majoring in a Science, Technology, Engineering and Math field during college, and highlight the importance of gaining employment in a closely related field in order to secure those benefits. The results also illustrate the need to carefully account for self-selection when estimating the earnings premiums in relation to educational experiences during college. Implications for policy and research are discussed.

Keywords

Earnings College majors STEM Job congruence 

References

  1. Anderson, E., & Kim, D. (2006). Increasing the success of minority students in science and technology. Washington: American Council on Education.Google Scholar
  2. Andrews, D. W., & Buchinsky, M. (2001). Evaluation of a three-step method for choosing the number of bootstrap repetitions. Journal of Econometrics, 103, 345–386.CrossRefGoogle Scholar
  3. Barron’s Profiles of American Colleges. (1999). Profiles of American colleges (23rd ed.). Woodbury: Barron’s Educational Series, Inc.Google Scholar
  4. Becker, G. (1967). Human capital and the personal distribution of income: An analytical approach. Ann Arbor: University of Michigan Press.Google Scholar
  5. Berger, M. C. (1988). Predicted future earnings and choice of college major. Industrial and Labor Relations Review, 41(3), 418–429.CrossRefGoogle Scholar
  6. Björklund, A., & Kjellström, C. (2002). Estimating the return to investments in education: How useful is the standard Mincerian equation? Economics of Education Review, 21, 195–210.CrossRefGoogle Scholar
  7. Black, D., & Smith, J. (2004). How robust is the evidence on the effects on college quality? Evidence from matching. Journal of Econometrics, 121, 99–124.CrossRefGoogle Scholar
  8. Bureau of Labor Statistics. (2008). Databases & Tables. Washington: U.S. Department of Labor. Retrieved September 28, 2008, from http://www.bls.gov/webapps/legacy/cpswktab4.htm.
  9. Caliendo, M., & Kopeinig, S. (2005). Some practical guidance for the implementation of propensity score matching. Discussion paper No. 1588. Retrieved February 6, 2007, from http://ssrn.com/abstract=721907.
  10. Chen, X., & Weko, T. (2009). Students who study science, technology, engineering, and mathetatics (STEM) in postsecondary education. Washington: U.S. Department of Education, NCES, 2009-161. Available on-line at http://nces.ed.gov/pubs2009/2009161.pdf.
  11. Coble, C., & Allen, M. (2005). Keeping America competitive: Five strategies to improve mathematics and science education. Denver, CO: Education Commission of the States. Retrieved October 9, 2010, from http://www.ecs.org/clearinghouse/62/19/6219.pdf.
  12. Cohn, E., & Geske, T. G. (1990). The economics of education (3rd ed.). New York: Pergamon Press.Google Scholar
  13. College Board. (1999). 1999 college bound seniors: A profile of SAT program test takers. Summary reporting service. New York: The College Board. Retrieved December 17, 2007, from http://professionals.collegeboard.com/data-reports-research/sat/archived/1999.
  14. College Board. (2007). SAT data tables: Interpreting SAT and SAT subject test scores: SAT percentile ranks. New York: The College Board. Retrieved December 17, 2007, from http://professionals.collegeboard.com/data-reports-research/sat/data-tables.
  15. Dale, S. B., & Krueger, A. B. (2002). Estimating the payoff to attending a more selective college: An application of selection on observables and unobservables. Quarterly Journal of Economics, 117, 1491–1527.CrossRefGoogle Scholar
  16. Daymont, T. N., & Andrisani, P. J. (1984). Job preferences, college major, and the gender gap in earnings. Journal of Human Resources, 19(3), 408–428.CrossRefGoogle Scholar
  17. Dehejia, R. H., & Wahba, S. (2002). Propensity score matching methods for non-experimental causal studies. Review of Economics and Statistics, 84, 151–161.CrossRefGoogle Scholar
  18. Dowd, A. C., Malcom, L. E., & Bensimon, E. M. (2009). Benchmarking the success of Latina and Latino students in STEM to achieve national graduation goals. Los Angeles: University of Southern California. Retrieved October 9, 2010, from http://cue.usc.edu/news/NSF-Report.pdf.
  19. Frehill, L. M., Di Fabio, N. M., & Hill, S. T. (2008). Confronting the “New” American Dilemma. White Plains National Action Council for Minorities in Engineering. Retrieved October 9, 2010, from http://www.nacme.org/user/docs/NACME%2008%20ResearchReport.pdf.
  20. Gerald, D. E., & Hussar, W. J. (2002). Projections of education statistics to 2012. NCES Report No. 2002030. Washington: U.S. Department of Education, National Center for Education Statistics.Google Scholar
  21. Griliches, Z. (1977). Estimating the returns to schooling: Some econometric problems. Econometrica, 45, 1–21.CrossRefGoogle Scholar
  22. Grubb, W. N. (1992). The economic returns to baccalaureate degrees: New evidence from the class of 1972. The Review of Higher Education, 15, 213–231.Google Scholar
  23. Grubb, W. N. (1997). The returns to education in the sub-baccalaureate labor market, 1984–1990. Economics of Education Review, 16, 231–245.CrossRefGoogle Scholar
  24. Gwartney, J. D., & Long, J. E. (1978). The relative earnings of blacks and other minorities. Industrial and Labor Relations Review, 31, 336–346.CrossRefGoogle Scholar
  25. Ham, J. C., Li, X., & Reagan, P. B. (2006). Propensity score matching, a distance-based measure of migration, and the wage growth of young men. IEPR working paper 05.13. Los Angeles: University of Southern California, Institute of Economic Policy Research.Google Scholar
  26. Hanson, S. L. (2004). African American women in science: Experiences from high school through the post-secondary years and beyond. National Women’s Study Association Journal, 16, 96–115.Google Scholar
  27. Hecker, D. (1996). Earnings and major field of study of college graduates. Occupational Outlook Quarterly, 40(2), 10–21.Google Scholar
  28. Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.CrossRefGoogle Scholar
  29. Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa: Psychological Assessment Resources.Google Scholar
  30. Institute for Higher Education Policy (IHEP). (2006). Expanding access and opportunity: The impact of the Gates Millennium Program. Washington: IHEP. Available on-line at http://www.ihep.org/assets/files/publications/a-f/ExpandingAccessOpp.pdf.
  31. Institute for Higher Education Policy (IHEP). (2009). Diversifying the STEM pipeline: The Model Replication Institutions Program. Washington: IHEP. Available on-line at http://www.ihep.org/assets/files/publications/a-f/%28Report%29_Diversifying_the_STEM_Pipeline_Report.pdf.
  32. Kienzl, G. S., George-Jackson, C. E., & Trent, W. (2009). Underrepresented students entering STEM fields. San Diego: American Education Research Association.Google Scholar
  33. Knox, W. E., Lindsay, P., & Kolb, M. N. (1993). Does college make a difference? Long-term changes in activities and attitudes. Westport: Greenwood Press.Google Scholar
  34. Leslie, L. L., McClure, G. T., & Oaxaca, R. L. (1998). Women and minorities in science and engineering: A life sequence analysis. The Journal of Higher Education, 69, 239–276.CrossRefGoogle Scholar
  35. Leslie, L. L., & Oaxaca, R. L. (1998). Women and minorities in higher education. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 13, pp. 304–352), New York: Agathon Press.Google Scholar
  36. Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. http://ideas.repec.org/c/boc/bocode/s432001.html. Version 3.0.0.
  37. Levine, J., & Wycokoff, J. (1991). Predicting persistence and success in baccalaureate engineering. Education, 111, 461–468.Google Scholar
  38. Maple, S. A., & Stage, F. K. (1991). Influences on the choice of math/science major by gender and ethnicity. American Educational Research Journal, 28, 37–60.Google Scholar
  39. Melguizo, T. (2010). Are students of color more likely to graduate from college if they attend more selective institutions? Evidence from the first cohort of recipients and non-recipients of the Gates Millennium Scholarship (GMS) program. Education Evaluation and Policy Analysis, 32, 230–248.CrossRefGoogle Scholar
  40. Melguizo, T., Kienzl, G., & Alfonso, M. (Forthcoming). Comparing the educational attainment of community college transfer students and four-year rising juniors using propensity score matching methods. The Journal of Higher Education.Google Scholar
  41. Mincer, J. (1974). Schooling experience, and earnings. New York: Columbia University Press.Google Scholar
  42. Monks, J. (2000). The returns to individual and college characteristics: Evidence from the National Longitudinal Survey of Youth. Economics of Education Review, 19, 279–289.CrossRefGoogle Scholar
  43. Montmarquette, C., Cannings, K., & Mahseredjian, S. (2002). How do young people choose college majors? Economics of Education Review, 21, 543–556.CrossRefGoogle Scholar
  44. Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. Oxford: Oxford University Press.Google Scholar
  45. National Academy of Science (NAS). (2005). Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. Washington: National Academies Press.Google Scholar
  46. Oakes, J. (1990). Opportunities, achievement, and choice: Women and minority students in science and mathematics. Review of Research in Education, 16, 153–222.Google Scholar
  47. Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students revisited: A third decade of research (Vol. 2). San Francisco: Jossey-Bass.Google Scholar
  48. Passel, J. S., & Cohn, D. (2008). U.S. population projections: 20052050. Washington: Pew Hispanic Center. Retrieved October 11, 2010, from http://pewhispanic.org/files/reports/85.pdf.
  49. Paulsen, M. B. (1998). Recent research on the economics of attending college: Returns on investment and responsiveness to price. Research in Higher Education, 39, 471–498.CrossRefGoogle Scholar
  50. Paulsen, M. B. (2001). The economics of human capital and investment in higher education. In M. Paulsen & J. Smart (Eds.), The finance of higher education: Theory, research, policy, & practice (pp. 55–94). New York: Agathon Press.Google Scholar
  51. Reynolds, C. L., & Desjardins, S. L. (2009). The use of matching methods in higher education: Answering whether attendance at a 2-year institution results in differences in educational attainment. Higher Education: Handbook of Theory and Practice, XXIII, 47–104.CrossRefGoogle Scholar
  52. Roksa, J. (2005). Double disadvantage or blessing in disguise? Understanding the relationship between college major and employment. Sociology of Education, 78(3), 207–232.CrossRefGoogle Scholar
  53. Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  54. Rosenfeld, R. A., & Kalleberg, A. L. (1990). A cross-national comparison of the gender gap in income. American Journal of Sociology, 96, 69–106.CrossRefGoogle Scholar
  55. Rumberger, R. W., & Thomas, S. L. (1993). The economic returns to college major, quality and performance: A multilevel analysis of recent graduates. Economics of Education Review, 12, 1–19.CrossRefGoogle Scholar
  56. Schneider, B., Carnoy, M., Kilpatrick, J., Schmidt, W., & Shavelson, R. (2007). Estimating causal effects using experimental and observational designs: A think tank white paper. Washington: American Education Research Association.Google Scholar
  57. Scopp, T. S. (2003). The relationship between the 1990 Census and Census 2000 Industry and Occupation Standards. U.S. Census Bureau. Retrieved February 24, 2010, from http://www.census.gov/hhes/www/ioindex/pdfio/tech_0203.pdf.
  58. Sedlacek, W. E. (2004). Beyond the big test: Noncognitive assessment in higher education. San Francisco: Jossey-Bass.Google Scholar
  59. Sianesi, B. (2004). An evaluation of the active labour market programmes in Sweden. The Review of Economics and Statistics, 86, 133–155.CrossRefGoogle Scholar
  60. Smart, J. C., Feldman, K. A., & Ethington, C. A. (2000). Academic disciplines: Holland’s theory and the study of college students and faculty. Nashville: Vanderbilt University Press.Google Scholar
  61. Song, C., & Glick, J. E. (2004). College attendance and choice of college majors among Asian-American students. Social Science Quarterly (Blackwell Publishing Limited), 85, 1401–1421.Google Scholar
  62. St. John, E. P., & Chung, C. (2004). Student aid and major choice: A study of high-achieving students of color. In E. P. St. John (Ed.), Readings on equal education: Improving access and college success for diverse students (Vol. 20, pp. 217–247). New York: AMS Press.Google Scholar
  63. St. John, E. P., Hu, S., Simmons, A., Carter, D. F., & Weber, J. (2004). What difference does a major make? The influence of college major field on persistence by African American and White students. Research in Higher Education, 45, 209–232.CrossRefGoogle Scholar
  64. Taylor, D. (2007). Employment preferences and salary expectations of students in science and engineering. Bioscience, 57(2), 175–185.CrossRefGoogle Scholar
  65. Thomas, S. L. (2000). Deferred costs and economic returns to college major, quality, and performance. Research in Higher Education, 41, 281–313.CrossRefGoogle Scholar
  66. Thomas, S. L. (2003). Longer-term economic effects of college selectivity and control. Research in Higher Education, 44, 263–299.CrossRefGoogle Scholar
  67. Tienda, M., & Li, D. T. (1987). Minority concentration and earnings inequality: Blacks, Hispanics, and Asians compared. American Journal of Sociology, 93, 141–165.CrossRefGoogle Scholar
  68. Trent, W. T., Nicholson, D. O., & McKillip, M. M. (2006a). GMS effect on diversifying math, science, computer science and engineering. A report to the Bill and Melinda Gates Foundation. Retrieved August 26, 2009, from https://norc.gatesscholarsresearch.org/login.aspx?ReturnUrl=%2finternal.aspx.
  69. Trent, W. T., Nicholson, D. O., & McKillip, M. M. (2006b, November). The benefits and implications of strategic recruitment of students of color. Wiscape Forum: How states and institutions shape racial dynamics in higher education, Madison.Google Scholar
  70. Trent, W. T., St John, E. P., & Hune, S. (2005). The pipeline of Gates Millennium Scholars STEM fields. Retrieved August 26, 2009, from https://norc.gatesscholarsresearch.org/login.aspx?ReturnUrl=%2finternal.aspx.
  71. Trusty, J. (2002). Effects of high school course-taking and other variables on choice of science and mathematics college majors. Journal of Counseling & Development, 80, 464.CrossRefGoogle Scholar
  72. Turner, S. E., & Bowen, W. G. (1999). Choice of major: The changing (unchanging) gender gap. Industrial and Labor Relations Review, 52, 289–313.CrossRefGoogle Scholar
  73. van de Werfhorst, H. G. (2002). Field of study, acquired skills, and the wage benefits from a matching job. Acta Sociologica, 45, 287–303.CrossRefGoogle Scholar
  74. White House. (2010, September 16). President Obama to Announce Major Expansion of “Educate to Innovate” Campaign to Improve Science, Technology, Engineering and Math (STEM) Education. Retrieved from http://www.whitehouse.gov/the-press-office/2010/09/16/president-obama-announce-major-expansion-educate-innovate-campaign-impro.
  75. Willis, R. J., & Rosen, S. (1979). Education and self-selection. Journal of Political Economy, 87, S7–S35.CrossRefGoogle Scholar
  76. Wolniak, G. C. (2004). How major field of study in college affects job satisfaction: A study of the job satisfaction of college alumni and influences of undergraduate major, major-job field congruence, and income. Unpublished Doctoral Thesis, University of Iowa, Iowa City, IA.Google Scholar
  77. Wolniak, G. C., & Pascarella, E. T. (2005). The effects of college major and job field congruence on job satisfaction. Journal of Vocational Behavior, 67, 233–251.CrossRefGoogle Scholar
  78. Wolniak, G. C., Seifert, T. A., Reed, E. J., & Pascarella, E. T. (2008). College majors and social mobility. Research in Social Stratification and Mobility, 26, 123–139.CrossRefGoogle Scholar
  79. Zhang, L. (2008). Gender and racial gaps in earnings among recent college graduates. The Review of Higher Education, 32(1), 51–72.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Rossier School of EducationUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Education and Child DevelopmentNORC at the University of ChicagoChicagoUSA

Personalised recommendations