Skip to main content

Advertisement

Log in

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

Research in Higher Education Aims and scope Submit manuscript

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.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Notes

  1. For a thorough review of the literature please see Kienzl et al. (2009).

  2. Weights for scholarship recipients were based on race, gender, and program status, while weights for non-recipients were defined according to race and Pell-Grant status. All regression model estimates were based on weighted data, while PSM procedures do not allow for the application of additional data weights given the PSM weighting algorithms built into the analytic routine.

  3. Ideally, we wanted to use humanities as the base category. However, given the small number of Asian/Pacific Islanders majoring in these fields, the sample sizes were too small. To increase the size of the base category we therefore decided to use both Humanities and Education majors, both of which are commonly identified as the lowest earning fields.

  4. For a more detailed description of the steps necessary to implement PSM please see Melguizo (2010), Melguizo et al. (forthcoming) and Reynolds and Desjardins (2009). The estimates of the probit models, the graphs that illustrate the distribution of the propensity score by major field of study, and the balancing tests are available in a web appendix.

  5. We chose this matching technique because it weights observations according to the closeness of the match through simple regression estimated for each treated unit, as recommended by Reynolds and Desjardins (2009) due to the precision of the estimators. The only limitation they cite is that it is computationally intensive but given computing power this is not a major concern for us.

  6. Figures available from authors upon request.

  7. We use a trimming level of 5 instead of using the common support function of the psmatch2. Reynolds and Desjardins (2009) explain how trimming is more appropriate than just using the common support function of the psmatch2 (Leuven and Sianesi 2003) which drops treatment observations whose pscore is higher than the maximum or less than the minimum pscore of the controls.

  8. The estimation of the standard errors are not straightforward because the estimated variance of the treatment effect also includes variance due to the estimation of the propensity score, the imputation of the common support, and also the order in which individuals were matched (Caliendo and Kopeinig 2005). A common technique used in the literature to estimate standard errors is bootstrapping, which enables the inclusion of the first steps of the estimation (Andrews and Buchinsky 2001; Black and Smith 2004; Sianesi 2004).

  9. The results of cross-tabulations are available from the authors upon request.

References

  • Anderson, E., & Kim, D. (2006). Increasing the success of minority students in science and technology. Washington: American Council on Education.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Barron’s Profiles of American Colleges. (1999). Profiles of American colleges (23rd ed.). Woodbury: Barron’s Educational Series, Inc.

  • Becker, G. (1967). Human capital and the personal distribution of income: An analytical approach. Ann Arbor: University of Michigan Press.

    Google Scholar 

  • Berger, M. C. (1988). Predicted future earnings and choice of college major. Industrial and Labor Relations Review, 41(3), 418–429.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

  • 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.

  • Cohn, E., & Geske, T. G. (1990). The economics of education (3rd ed.). New York: Pergamon Press.

    Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Dehejia, R. H., & Wahba, S. (2002). Propensity score matching methods for non-experimental causal studies. Review of Economics and Statistics, 84, 151–161.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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 

  • Griliches, Z. (1977). Estimating the returns to schooling: Some econometric problems. Econometrica, 45, 1–21.

    Article  Google Scholar 

  • 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 

  • Grubb, W. N. (1997). The returns to education in the sub-baccalaureate labor market, 1984–1990. Economics of Education Review, 16, 231–245.

    Article  Google Scholar 

  • Gwartney, J. D., & Long, J. E. (1978). The relative earnings of blacks and other minorities. Industrial and Labor Relations Review, 31, 336–346.

    Article  Google Scholar 

  • 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.

  • 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 

  • Hecker, D. (1996). Earnings and major field of study of college graduates. Occupational Outlook Quarterly, 40(2), 10–21.

    Google Scholar 

  • Heckman, J. J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–161.

    Article  Google Scholar 

  • Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments (3rd ed.). Odessa: Psychological Assessment Resources.

    Google Scholar 

  • 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.

  • 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.

  • Kienzl, G. S., George-Jackson, C. E., & Trent, W. (2009). Underrepresented students entering STEM fields. San Diego: American Education Research Association.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

  • Levine, J., & Wycokoff, J. (1991). Predicting persistence and success in baccalaureate engineering. Education, 111, 461–468.

    Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • Mincer, J. (1974). Schooling experience, and earnings. New York: Columbia University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Montmarquette, C., Cannings, K., & Mahseredjian, S. (2002). How do young people choose college majors? Economics of Education Review, 21, 543–556.

    Article  Google Scholar 

  • Murnane, R. J., & Willett, J. B. (2011). Methods matter: Improving causal inference in educational and social science research. Oxford: Oxford University Press.

    Google Scholar 

  • National Academy of Science (NAS). (2005). Rising Above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future. Washington: National Academies Press.

  • 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 

  • Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students revisited: A third decade of research (Vol. 2). San Francisco: Jossey-Bass.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Roksa, J. (2005). Double disadvantage or blessing in disguise? Understanding the relationship between college major and employment. Sociology of Education, 78(3), 207–232.

    Article  Google Scholar 

  • Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  • Rosenfeld, R. A., & Kalleberg, A. L. (1990). A cross-national comparison of the gender gap in income. American Journal of Sociology, 96, 69–106.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

  • Sedlacek, W. E. (2004). Beyond the big test: Noncognitive assessment in higher education. San Francisco: Jossey-Bass.

    Google Scholar 

  • Sianesi, B. (2004). An evaluation of the active labour market programmes in Sweden. The Review of Economics and Statistics, 86, 133–155.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • Taylor, D. (2007). Employment preferences and salary expectations of students in science and engineering. Bioscience, 57(2), 175–185.

    Article  Google Scholar 

  • Thomas, S. L. (2000). Deferred costs and economic returns to college major, quality, and performance. Research in Higher Education, 41, 281–313.

    Article  Google Scholar 

  • Thomas, S. L. (2003). Longer-term economic effects of college selectivity and control. Research in Higher Education, 44, 263–299.

    Article  Google Scholar 

  • Tienda, M., & Li, D. T. (1987). Minority concentration and earnings inequality: Blacks, Hispanics, and Asians compared. American Journal of Sociology, 93, 141–165.

    Article  Google Scholar 

  • 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.

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Turner, S. E., & Bowen, W. G. (1999). Choice of major: The changing (unchanging) gender gap. Industrial and Labor Relations Review, 52, 289–313.

    Article  Google Scholar 

  • van de Werfhorst, H. G. (2002). Field of study, acquired skills, and the wage benefits from a matching job. Acta Sociologica, 45, 287–303.

    Article  Google Scholar 

  • 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.

  • Willis, R. J., & Rosen, S. (1979). Education and self-selection. Journal of Political Economy, 87, S7–S35.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Zhang, L. (2008). Gender and racial gaps in earnings among recent college graduates. The Review of Higher Education, 32(1), 51–72.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tatiana Melguizo.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Melguizo, T., Wolniak, G.C. The Earnings Benefits of Majoring in STEM Fields Among High Achieving Minority Students. Res High Educ 53, 383–405 (2012). https://doi.org/10.1007/s11162-011-9238-z

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-011-9238-z

Keywords

Navigation