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

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

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

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

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

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

  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. 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. 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. 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.

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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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  1. Department of Economics, University of Massachusetts Dartmouth, 287 Old Westport Rd., North Dartmouth, MA, 02747, USA

    Neal H. Olitsky

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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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  • DOI: https://doi.org/10.1007/s11162-013-9310-y

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