How Do Academic Achievement and Gender Affect the Earnings of STEM Majors? A Propensity Score Matching Approach

Abstract

The United States government recently enacted a number of policies designed to increase the number of American born students graduating with degrees in science, technology, engineering and mathematics (STEM), especially among women and racial and ethnic minorities. This study examines how the earnings benefits of choosing a STEM major vary both by gender and across the distribution of academic achievement. I account for the selection into college major using propensity score matching. Measures of individual educational preferences based on Holland’s theory of career and educational choice provide a unique way to control for college major selection. Findings indicate that the earnings benefit to STEM major choice ranges from 5 to 28 % depending both on academic achievement and on gender and that high-achieving students benefit more from STEM major choice. Further, high achieving men benefit more from STEM majors than high-achieving women. Earnings differences in major choice may play an important role in explaining the underrepresentation of women in STEM major fields, especially among high achieving students.

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Notes

  1. 1.

    Hill et al. (2010) reports that in 2006, the proportion of women earning bachelor’s degrees in these subjects is ~20 % each.

  2. 2.

    See, for example: Ashenfelter and Mooney (1968), Weisbrod and Karpoff (1968), Hansen et al. (1970), Paglin and Rufolo (1990), Blackburn (2004), Grogger and Eide (1995), and Tobias (2003).

  3. 3.

    Other studies have used this sample as well. These studies include Graham and Gisi (2000), Stone and Friedman (2002), Gallo and Hubschman (2003), and Neumann et al. (2009). The data used for this paper are owned by ACT Inc. and were requested from and provided by ACT Inc. after a confidentiality agreement was in place.

  4. 4.

    Further evidence of this is provided in Arcidiacono et al. (2010).

  5. 5.

    There is a vast literature that examines the economic effects of college major choice. Since this analysis is interested in earnings differences across STEM majors, I refer the reader to Hecker (1996), Rumberger and Thomas (1993), Thomas (2000, 2003) and Pascarella (2005).

  6. 6.

    The following are brief examples of what activities each personality type may enjoy. The realistic (R) type would include individuals who prefer working with tools, instruments or mechanical equipment. The investigative (I) personality type includes individuals who prefer to work with theory and information and perform tasks that are analytical, intellectual or scientific. The artistic (A) personality type would prefer to express oneself through activities such as painting, designing, singing, dancing, and writing. The social (S) personality type prefers helping others through activities such as teaching or counseling. The enterprising (E) prefers directing others through activities such as sales, supervision or management. Finally, the conventional (C) personality type prefers tasks that include maintaining accurate, orderly files and keeping records or accounts. A more detailed description can be found in Holland (1997) and Smart et al. (2000).

  7. 7.

    RIASEC scores are measured when the student takes the ACT (so in 11th/12th grade). Although it is possible that access to certain advanced placement courses may affect vocational interests, studies have shown that RIASEC scores are stable between 8th, 10th and 12th grade (Low et al. 2005; Tracey et al. 2005).

  8. 8.

    For an extensive discussion of Holland's theory, and how it relates to education and educational decisions, refer to Smart et al. (2000).

  9. 9.

    Caliendo and Kopeinig (2008) discusses ways to estimate the propensity score and reports that for binary treatments, there is little difference between a probit specification and a logit specification. I use a probit specification in what follows.

  10. 10.

    The program used to estimate the propensity score is the STATA program, “PSCORE.” The test of the balancing property used in this program is described in Becker and Ichino (2002).

  11. 11.

    This method has been identified as being a good way to estimate treatment effects across subgroups (Heckman et al. 1997; Caliendo and Kopeinig 2008).

  12. 12.

    Heckman et al. (1997), the authors look at the effect of four subgroups. For each subgroup, a different specification of the propensity score was estimated.

  13. 13.

    Because there are so many subgroups, the results of the propensity score estimation and t-tests of the means are omitted; these results will be provided upon request. In addition, Sianesi (2004) suggests another check of the balancing property is to compare the pseudo R-square values from the propensity score specification before matching and on the matched sample; the pseudo R-square in the matched sample should be considerably lower than those in the original sample. The results of this test, available upon request, indicate that the pseudo R-square values dropped considerably after matching, and they all fall below a value of 0.03.

  14. 14.

    A more detailed explanation of this method is presented in Rosenbaum (2002) and Caliendo and Kopeinig (2008).

  15. 15.

    Heckman et al. (1997) reports that the choice of matching algorithm can affect the results in small samples; however, as the sample size increases, all of the PSM estimators will eventually yield the same results (Caliendo and Kopeinig 2008). Further, because the sample size is large, sacrificing consistency for a reduction in bias does not affect the resulting statistical inferences.

  16. 16.

    The econometrician must also decide the caliper size. Smith and Todd (2005) reports that there is no way to know the appropriate caliper size a priori. Following Rosenbaum and Rubin (1985), I choose a caliper size to be one quarter of the standard deviation of the propensity score for each subgroup. The caliper sizes range from 3 to 5 %.

  17. 17.

    This requirement is another reason why researchers tend to focus on the ATT; while the ATE requires conditional independence between treatment and the outcomes both of treated group and the untreated groups, estimates of the ATT require conditional independence only between control outcomes and treatment status (Caliendo and Kopeinig 2008).

  18. 18.

    In addition, there is evidence that an individual's RIASEC scores do not change over time (Tracey et al. 2005).

  19. 19.

    Using RIASEC scores as a measure of vocational or educational preferences has limitations. First, there may be measurement error when collecting information on respondents’ preferences for the various activities from which the RIASEC scores are generated. Second, it is possible that characteristics relevant to career or education choices are not well-measured by the RIASEC scales. A detailed discussion of these limitations is provided in Armstrong et al. (2008). Despite these potential limitations, Holland’s theory remains the prevailing theory of occupational preferences in the vocational psychology literature.

  20. 20.

    Due to length considerations, I do not provide the results of the propensity score specifications. These results are available from the author upon request. Although specifications differ both across gender and across quartiles of achievement, a number of consistent patterns emerge. First, men are more likely to choose a STEM major, regardless of the level of achievement. Second, ACT scores have a significant, positive effect on the probability of participation. Finally, a number of the RIASEC scores have significant effects on the probability of treatment. Specifically, the results indicate a positive relation between STEM major choice and the science and technology scale, and a negative relation between STEM major choice and both the art and business organization scales. In addition, a visual inspection of the distributions of propensity scores for the treated and control observations is used to determine whether the common support assumption is satisfied. To further ensure that the common support assumption is satisfied, I use a minima and maxima comparison and eliminate observations that fall outside the range of both the treatment group and the control group; for most of the subgroups, all of the observations fall within the common support and no trimming is necessarily. For the subgroups in which observations do lie outside the common support, no more than two treated observations are eliminated for each group.

  21. 21.

    It should be noted that this decrease in the ATT for initial earnings between the third and fourth quartile may be an artifact of dividing the distribution of test scores into quartiles. However, dividing the sample into quintiles and estimating the ATTs yields similar results. The ATTs for the initial earnings of women starting are all significant at the 0.001 level and are as follows: 20.2 % for the first quintile, 25.7 % for the second quintile, 25.8 % for the third quintile, 19.5% for the 4th quintile and 21.7 % in the top quintile.

  22. 22.

    Due to length considerations, I include the comparison of ATTs for current earnings only. The results for the initial earnings follow the same pattern, and are available from the author upon request.

References

  1. ACT. (2009). ACT interest inventory technical manual (Vol. 319, pp. 337–1429). Iowa City, IA: ACT, Inc.

    Google Scholar 

  2. Albrecht, J., Van den Berg, G. J., & Vroman, S. (2005). The knowledge lift: The Swedish adult education program that aimed to eliminate low worker skill levels. IZA Discussion Paper No. 1503 (February). http://ssrn.com/abstract=673516. Accessed 13 Feb 2013.

  3. Albrecht, J., & Vroman, S. (2002). A matching model with endogenous skill requirements. International Economic Review, 43(1), 283–305. doi:10.1111/1468-2354.t01-1-00012.

    Article  Google Scholar 

  4. Allen, J., & van der Velden, R. (2001). Educational mismatches versus skill mismatches: Effects on wages, job satisfaction, and on-the-job search. Oxford Economic Papers, 3, 434–452.

    Article  Google Scholar 

  5. Almlund, M., Duckworth, A. L., Heckman, J., & Kautz, T. (2011). Chapter 1. Personality psychology and economics. In S. M. E. A. Hanushek & Woessmann L. (Eds.) Handbook of the economics of education (pp. 1–181). Elsevier. http://www.sciencedirect.com/science/article/pii/B9780444534446000018.

  6. Ambady, N., Shih, M., Kim, A., & Pittinsky, T. L. (2001). Stereotype susceptibility in children: Effects of identity activation on quantitative performance. Psychological Science, 12(5), 385–390.

    Article  Google Scholar 

  7. Arcidiacono, P. (2004). Ability sorting and the returns to college major. Journal of Econometrics, 121(1–2), 343–375. doi:10.1016/j.jeconom.2003.10.010.

    Article  Google Scholar 

  8. Arcidiacono, P., Hotz V. J., & Kang, S. (2010). Modeling college major choices using elicited measures of expectations and counterfactuals. Working Paper. National Bureau of Economic Research. http://www.nber.org/papers/w15729. Accessed 6 July 2012.

  9. Armstrong, P. I., Day, S. X., McVay, J. P., & Rounds, J. (2008). Holland’s RIASEC model as an integrative framework for individual differences. Journal of Counseling Psychology, 55(1), 1–18. doi:10.1037/0022-0167.55.1.1.

    Article  Google Scholar 

  10. Ashenfelter, O., & Mooney, J. D. (1968). Graduate education, ability, and earnings. The Review of Economics and Statistics, 50(1), 78–86.

    Article  Google Scholar 

  11. Associated Press. (2010). Obama unveils funds to train teachers. Boston Globe, January 7. http://www.boston.com/news/nation/washington/articles/2010/01/07/obama_unveils_funds_to_train_teachers/. Accessed 6 July 2012.

  12. Becker, S. O., & Ichino, A. (2002). Estimation of average treatment effects based on propensity scores. Stata Journal, 2(4), 358–377.

    Google Scholar 

  13. Beede, D. N., Julian, T. A., Langdon, D., McKittrick, G., Khan, B., & Doms, M. E. (2011). Women in STEM: A gender gap to innovation. SSRN Electronic Journal. doi:10.2139/ssrn.1964782. http://www.ssrn.com/abstract=1964782.

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

    Article  Google Scholar 

  15. Blackburn, M. L. (2004). The role of test scores in explaining race and gender differences in wages. Economics of Education Review, 23(6), 555–576. doi:10.1016/j.econedurev.2004.04.005.

    Article  Google Scholar 

  16. Brainard, S., & Carlin, L. (1998). A six-year longitudinal study of undergraduate women in engineering and science. Journal of Engineering Education, 87(4), 369–375.

    Article  Google Scholar 

  17. Bryson, A., Dorsett, R., Purdon, S., & Great Britain Department for Work and Pensions. (2002). The use of propensity score matching in the evaluation of active labour market policies. London: Department of Work and Pensions.

  18. Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72. doi:10.1111/j.1467-6419.2007.00527.x.

    Article  Google Scholar 

  19. Cawley, J., Heckman, J., & Vytlacil, E. (2001). Three observations on wages and measured cognitive ability. Labour Economics, 8(4), 419–442. doi:10.1016/S0927-5371(01)00039-2.

    Article  Google Scholar 

  20. Ceci, S. J., Williams, W. M., & Barnett, S. M. (2009). Women’s underrepresentation in science: Sociocultural and biological considerations. Psychological Bulletin, 135(2), 218–261. doi:10.1037/a0014412.

    Article  Google Scholar 

  21. Chen, X., & Weko, T. (2009). Students who study science, technology, engineering and mathematics (STEM) in postsecondary education. US: U.S Department of Education.

    Google Scholar 

  22. Cohoon, J. M., & Aspray, W. (2006). A critical review of the research on women’s participation in postsecondary computing education. In J. Mcgrath Cohoon & W. Aspray (Eds.), Women and information technology research on underrepresentation (pp. 137–182). Cambridge: MIT Press.

    Google Scholar 

  23. Committee on Prospering in the Global Economy of the 21st Century (U.S.), & Committee on Science and Engineering. (2007). Rising above the gathering storm: Energizing and employing america for a brighter economic future. Washington, DC: National Academies Press.

  24. Crisp, G., Nora, A., & Taggart, A. (2009). Student characteristics, pre-college, college, and environmental factors as predictors of majoring in and earning a STEM degree: An analysis of students attending a hispanic serving institution. American Educational Research Journal, 46(4), 924–942. doi:10.3102/0002831209349460.

    Article  Google Scholar 

  25. Dehejia, R. H., & Wahba, S. (1999). Causal effects in nonexperimental studies: Reevaluating the evaluation of training programs. Journal of the American Statistical Association, 94(448), 1053–1062.

    Article  Google Scholar 

  26. Dehejia, R. H., & Wahba, S. (2002). Propensity score-matching methods for nonexperimental causal studies. The Review of Economics and Statistics, 84(1), 151–161.

    Article  Google Scholar 

  27. Eccles, J. S. (2007). Where are all the women? Gender differences in participation in physical science and engineering. In S. J. Ceci & W. M. Williams (Eds.) Why aren’t more women in science: Top researchers debate the evidence (pp. 199–210). Washington: American Psychological Association. http://content.apa.org/books/11546-016.

  28. Eide, E., & Waehrer, G. (1998). The role of the option value of college attendance in college major choice. Economics of Education Review, 17(1), 73–82. doi:10.1016/S0272-7757(97)00004-6.

    Article  Google Scholar 

  29. Foschi, M. (1996). Double standards in the evaluation of men and women. Social Psychology Quarterly, 59(3), 237–254.

    Article  Google Scholar 

  30. Gallo, P. J., & Hubschman, B. (2003). The relationships between alumni participation and motivation on financial giving. Chicago, IL: American Educational Research Association.

    Google Scholar 

  31. Gangl, M. (2004). RBOUNDS: Stata Module to Perform Rosenbaum Sensitivity Analysis for Average Treatment Effects on the Treated. http://ideas.repec.org/c/boc/bocode/s438301.html. Accessed 6 July 2012.

  32. Graham, S. W., & Gisi, S. L. (2000). The effects of instructional climate and student affairs services on college outcomes and satisfaction. Journal of College Student Development, 41(3), 279–291.

    Google Scholar 

  33. Griffith, A. L. (2010). Persistence of women and minorities in stem field majors: Is it the school that matters? Economics of Education Review, 29(6), 911–922. doi:10.1016/j.econedurev.2010.06.010.

    Article  Google Scholar 

  34. Grogger, J., & Eide, E. (1995). Changes in college skills and the rise in the college wage premium. Journal of Human Resources, 30(2), 280–310.

    Article  Google Scholar 

  35. Gupta, S., Tracey, Terence J. G., & Gore, P. A. (2008). Structural examination of RIASEC scales in high school students: Variation across ethnicity and method. Journal of Vocational Behavior, 72(1), 1–13. doi:10.1016/j.jvb.2007.10.013.

    Article  Google Scholar 

  36. Hansen, W. Lee, Weisbrod, B. A., & Scanlon, W. J. (1970). Schooling and earnings of low achievers. The American Economic Review, 60(3), 409–418.

    Google Scholar 

  37. Hecker, D. E. (1996). Earnings and major field of study of college graduates. Occupational Outlook Quarterly.

  38. Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training programme. The Review of Economic Studies, 64(4), 605–654.

    Article  Google Scholar 

  39. Hill, C., Corbett, C., & St. Rose, A. (2010). Why so few? Women in science, technology, engineering, and mathematics. Washington, DC 20036: American Association of University Women. www.aauw.org.

  40. Holland, J. L. (1997). Making vocational choices: A theory of vocational personalities and work environments. Odessa, FL: Psychological Assessment Resources.

    Google Scholar 

  41. Honda, M. (2011). STEM Education Innovation Act of 2011, H.R. 3373. http://www.opencongress.org/bill/112-h3373/text. Accessed 6 July 2012.

  42. Imbens, G. W. (2003). Sensitivity to exogeneity assumptions in program evaluation. The American Economic Review, 93(2), 126–132.

    Article  Google Scholar 

  43. Ingram, B. F., & Neumann, G. R. (2006). The returns to skill. Labour Economics, 13(1), 35–59. doi:10.1016/j.labeco.2004.04.005.

    Article  Google Scholar 

  44. King, M., Ruggles, S., Alexander, J. T., Flood S., Genadek, K., Schroeder, M. B., Trampe, B., & Vick, R. (2010). Integrated Public Use Microdata Series, Current Population Survey: Version 3.0. [Machine-readable Database]. Minneapolis: University of Minnesota.

  45. Kokkelenberg, E. C., & Sinha, E. (2010). Who succeeds in STEM studies? An analysis of Binghamton University undergraduate students. Economics of Education Review, 29(6), 935–946. doi:10.1016/j.econedurev.2010.06.016.

    Article  Google Scholar 

  46. Lechner, M. (2002). Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(1), 59–82. doi:10.1111/1467-985X.0asp2.

    Article  Google Scholar 

  47. 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(3), 239–276.

    Article  Google Scholar 

  48. Levine, J., & Wycokoff, J. (1991). Predicting persistence and success in Baccalaureate engineering. Education, 111(4), 461–468.

    Google Scholar 

  49. Linn, M. C., & Petersen, A. C. (1985). Emergence and characterization of sex differences in spatial ability: A meta-analysis. Child Development, 56(6), 1479–1498.

    Article  Google Scholar 

  50. Lovaglia, M. J., Lucas, J. W., Houser, J. A., Thye, S. R., & Markovsky, B. (1998). Status processes and mental ability test scores. American Journal of Sociology, 104(1), 195–228.

    Article  Google Scholar 

  51. Low, K. S., Douglas, M. Y., Roberts, B. W., & Rounds, J. (2005). The stability of vocational interests from early adolescence to middle adulthood: A quantitative review of longitudinal studies. Psychological Bulletin, 131(5), 713–737. doi:10.1037/0033-2909.131.5.713.

    Article  Google Scholar 

  52. Macfarlane, A., & Luzzadder-Beach, S. (1998). Achieving equity between women and men in the geosciences. Geological Society of America Bulletin, 110(12), 1590–1614. doi:10.1130/0016-7606.

    Article  Google Scholar 

  53. Maple, S. A., & Stage, F. K. (1991). Influences on the choice of math/science major by gender and ethnicity. American Educational Research Journal, 28(1), 37–60.

    Article  Google Scholar 

  54. Margolis, J., Fisher, A., & Miller, F. (1999). Caring about connections: Gender and computing. IEEE Technology and Society Magazine, 18(4), 13–20. doi:10.1109/44.808844.

    Article  Google Scholar 

  55. Melguizo, T., & Wolniak G. C. (2011). The earnings benefits of majoring in STEM fields among high achieving minority students. Research in Higher Education (September 1). doi:10.1007/s11162-011-9238-z. http://www.springerlink.com/index/10.1007/s11162-011-9238-z.

  56. Mincer, J. (1974). Schooling, experience, and earnings. New York: National Bureau of Economic Research; distributed by Columbia University Press.

    Google Scholar 

  57. Montmarquette, C., Cannings, K., & Mahseredjian, S. (2002). How do young people choose college majors? Economics of Education Review, 21(6), 543–556. doi:10.1016/S0272-7757(01)00054-1.

    Article  Google Scholar 

  58. National Science Foundation, Division of Science Resources Statistics. (2009). Women, minorities, and persons with disabilities in science and engineering: 2009. Arlington, VA: National Science Foundation, Division of Science Resources Statistics. http://www.nsf.gov/statistics/wmpd/. Accessed 11 July 2012.

  59. Neumann, G., Olitsky, N., & Robbins, S. (2009). Job congruence, academic achievement, and earnings. Labour Economics, 16(5), 503–509.

    Article  Google Scholar 

  60. Nguyen, H.-H. D., & Ryan, A. M. (2008). Does stereotype threat affect test performance of minorities and women? A meta-analysis of experimental evidence. Journal of Applied Psychology, 93(6), 1314–1334. doi:10.1037/a0012702.

    Article  Google Scholar 

  61. Oleski, D., & Subich, L. M. (1996). Congruence and career change in employed adults. Journal of Vocational Behavior, 49(3), 221–229. doi:10.1006/jvbe.1996.0041.

    Article  Google Scholar 

  62. Paglin, M., & Rufolo, A. M. (1990). Heterogeneous human capital, occupational choice, and male-female earnings differences. Journal of Labor Economics, 8(1), 123–144.

    Article  Google Scholar 

  63. Pascarella, E. T., & Terenzini. P.T. (2005). How college affects students: A third decade of research. The Jossey-Bass Higher and Adult Education Series. Jossey-Bass. http://books.google.com/books?id=Wn8kAQAAMAAJ. Accessed 6 July 2012.

  64. Polachek, S. W. (1978). Sex differences in college major. Industrial and Labor Relations Review, 31(4), 498–508.

    Article  Google Scholar 

  65. Rask, K. (2010). Attrition in STEM fields at a Liberal Arts College: The importance of grades and pre-collegiate preferences. Economics of Education Review, 29(6), 892–900. doi:10.1016/j.econedurev.2010.06.013.

    Article  Google Scholar 

  66. Rasmus, L., & Mortensen D. T. (2010). Labor market friction, firm heterogeneity and aggregate employment and productivity. Madison: University of Wisconsin.

  67. Rosenbaum, P. R. (2002). Observational studies. New York: Springer.

    Google Scholar 

  68. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.

    Article  Google Scholar 

  69. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39(1), 33. doi:10.2307/2683903.

    Google Scholar 

  70. Rosser, S. V. (2004). The science glass ceiling: Academic women scientists and the struggle to succeed. Routledge. http://books.google.com/books?id=-4nlynXNjFQC. Accessed 25 April 2012.

  71. 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), 1–19. doi:10.1016/0272-7757(93)90040-N.

    Article  Google Scholar 

  72. Semeijn, J., Boone, C., van der Velden, R., & van Witteloostuijn, A. (2005). Graduates’ personality characteristics and labor market entry an empirical study among Dutch economics graduates. Economics of Education Review, 24(1), 67–83. doi:10.1016/j.econedurev.2004.03.006.

    Article  Google Scholar 

  73. Seymour, E., & Hewitt, N. (1997). Talking about leaving: Why undergraduates leave the sciences. Boulder, CO: Westview Press.

    Google Scholar 

  74. Sianesi, B. (2004). An evaluation of the swedish system of active labor market programs in the 1990s. The Review of Economics and Statistics, 86(1), 133–155.

    Article  Google Scholar 

  75. Smart, J. C., Feldman, K. A., & Ethington, C. A. (2000). Academic disciplines: Holland’s theory and the study of college students and faculty. Vanderbilt Issues in Higher Education: Vanderbilt University Press.

    Google Scholar 

  76. Smith, J. A., & Todd, P. E. (2005). Does matching overcome LaLonde’s critique of nonexperimental estimators? Journal of Econometrics, 125(1–2), 305–353. doi:10.1016/j.jeconom.2004.04.011.

    Article  Google Scholar 

  77. Stone, J., & Friedman, S. (2002). A case study in the integration of assessment and general education: Lessons learned from a complex process. Assessment and Evaluation in Higher Education, 27(2), 199–211.

    Article  Google Scholar 

  78. Swaney, K., & Prediger, D. (1985). The relationship between interest-occupation congruence and job satisfaction. Journal of Vocational Behavior, 26(1), 13–24. doi:10.1016/0001-8791(85)90022-3.

    Article  Google Scholar 

  79. Thomas, S. L. (2000). Deferred costs and economic returns to college major, quality, and performance. Research in Higher Education, 41(3), 281–313.

    Article  Google Scholar 

  80. Thomas, S. L. (2003). Longer-term economic effects of college selectivity and control. Research in Higher Education, 44(3), 263–299.

    Article  Google Scholar 

  81. Tobias, J. L. (2003). Are returns to schooling concentrated among the most able? A semiparametric analysis of the ability-earnings relationships. Oxford Bulletin of Economics and Statistics, 65(1), 1–29. doi:10.1111/1468-0084.00038.

    Article  Google Scholar 

  82. Topel, R. (2012). Job mobility, search, and earnings growth: A reinterpretation of human capital earnings functions. In Research in Labor Economics, 35:401–435. Bingley: Emerald Group Publishing. http://www.emeraldinsight.com/10.1108/S0147-9121(2012)0000035038. Accessed 4 Feb 2013.

  83. Tracey, Terence J. G., Allen, J., & Robbins, S. B. (2012). Moderation of the relation between person–environment congruence and academic success: Environmental constraint, personal flexibility and method. Journal of Vocational Behavior, 80(1), 38–49. doi:10.1016/j.jvb.2011.03.005.

    Article  Google Scholar 

  84. Tracey, Terence J. G., & Robbins, S. B. (2006). The Interest: Major congruence and college success relation—a longitudinal study. Journal of Vocational Behavior, 69(1), 64–89. doi:10.1016/j.jvb.2005.11.003.

    Article  Google Scholar 

  85. Tracey, Terence J. G., Robbins, S. B., & Hofsess, C. D. (2005). Stability and change in interests: a longitudinal study of adolescents from grades 8 through 12. Journal of Vocational Behavior, 66(1), 1–25. doi:10.1016/j.jvb.2003.11.002.

    Article  Google Scholar 

  86. Trusty, J. (2002). Effects of high school course-taking and other variables on choice of science and mathematics college majors. Journal of Counseling and Development, 80(4), 464–474. doi:10.1002/j.1556-6678.2002.tb00213.x.

    Article  Google Scholar 

  87. Tsang, M. C., & Levin, H. M. (1985). The economics of overeducation. Economics of Education Review, 4(2), 93–104.

    Article  Google Scholar 

  88. Viscusi, W. Kip. (1979). Job hazards and worker quit rates: An analysis of adaptive worker behavior. International Economic Review, 20(1), 29–58.

    Article  Google Scholar 

  89. Voyer, D., Voyer, S., & Bryden, M. P. (1995). Magnitude of sex differences in spatial abilities: A meta-analysis and consideration of critical variables. Psychological Bulletin, 117(2), 250–270.

    Article  Google Scholar 

  90. Weisbrod, B. A., & Karpoff, P. (1968). Monetary returns to college education, student ability, and college quality. The Review of Economics and Statistics, 50(4), 491–497.

    Article  Google Scholar 

  91. White House. (2009). President Obama Launches “Educate to Innovate” Campaign for Excellence in Science, Technology, Engineering and Math (Stem) Education. The White House. http://www.whitehouse.gov/the-press-office/president-obama-launches-educate-innovate-campaign-excellence-science-technology-en. Accessed 16 Apr 2012.

  92. White House. (2010). President Obama to Announce Major Expansion of “Educate to Innovate” Campaign to Improve Science, Technology, Engineering and Math (STEM) Education. The White House. http://www.whitehouse.gov/the-press-office/2010/09/16/president-obama-announce-major-expansion-educate-innovate-campaign-impro. Accessed 16 Apr 2012.

  93. Whitten, B. L., Foster, Suzanne R., Duncombe, Margaret L., Allen, Patricia E., Heron, P., McCullough, L., et al. (2003). What works? Increasing the participation of women in undergraduate physics. Journal of Women and Minorities in Science and Engineering, 9(3–4), 239–258.

    Google Scholar 

  94. Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data (1st ed.). Cambridge, MA: The MIT Press.

    Google Scholar 

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Acknowledgments

I would like to thank ACT Inc. for making their data available to me. In addition, I would like to thank the editor, two anonymous referees, Steve Robbins, Jeff Allen, Paul Westrick and Mark Kurt for their support and comments.

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Correspondence to Neal H. Olitsky.

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Olitsky, N.H. How Do Academic Achievement and Gender Affect the Earnings of STEM Majors? A Propensity Score Matching Approach. Res High Educ 55, 245–271 (2014). https://doi.org/10.1007/s11162-013-9310-y

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Keywords

  • STEM major choice
  • Treatment effects
  • Propensity score matching
  • Earnings

JEL Classification

  • I2
  • J3