Gender Imbalance in Higher Education: Insights for College Administrators and Researchers

Abstract

University administrators often strive for racial, socioeconomic, and geographic diversity in their student populations. Today, administrators face a new demographic challenge as women increasingly outnumber men in applications, enrollments, and graduation rates. This article discusses the causes and potential consequences of the growing gender imbalance and the legality of admissions policies that attempt to restore balance by giving preference to males. Using multiple analytic approaches, we test whether a public institution with increasing female enrollments responded by giving preferences in admissions to males. We conclude with insights for administrators and researchers.

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Fig. 1

Notes

  1. 1.

    For instance, the Dean of Admissions and Financial Aid at Kenyon College in Ohio wrote an op-ed piece in the Washington Post entitled "To All the Girls I've Rejected," where she reported that her university sometimes admitted less-qualified male applicants (Britz 2006). The Vice President of Enrollment at Dickinson College in Pennsylvania also reported to a New York Times journalist that the college gives preference to males in admissions (Lewin 2006). One of the more colorful comments was delivered by the Dean of the College of William and Mary who, in defense of the admissions preference for males, stated “We are, after all, the College of William and Mary, not Mary and Mary” (Broaddus 2009).

  2. 2.

    See also Bowen and Bok (1998) and Epenshade and Chung (2005) for early studies of racial or ethnic preferences in college admissions.

  3. 3.

    In addition to Texas, California, Florida, and New York have percent plans, all of which differ in their criteria for admission and requirements.

  4. 4.

    The Texas Higher Education Opportunity Project is a multi-year study directed by Marta Tienda (Princeton University) and her collaborators to study the college planning and enrollment behavior of students in Texas. More information on the study and access to most of the administrative data used here can be found at http://www.texastop10.princeton.edu.

  5. 5.

    See Table 6 in Appendix for descriptive statistics on all variables overall and by gender.

  6. 6.

    Following Long and Tienda (2008), we combine students’ verbal and math scores on the SAT and ACT exams. For students who only took the ACT, we convert their scores to the SAT scale using a conversation table provided by the College Board (Dorans 2002).

  7. 7.

    According to H.B. 588 (http://www.legis.state.tx.us/tlodocs/75R/billtext/html/HB00588F.htm), in addition to the student's academic record, Texas public universities can consider many other student attributes in the admissions process, such as family income and parental education; whether the applicant would be the first generation in the family to attend or graduate from college; and "any other consideration the institution considers necessary to accomplish the institution's stated mission."(Page 3). Correspondingly, each university collects and uses a different set of criteria to determine admissions. We are unable to observe and control for all determinants of admission; our regressions include all of the variables that were provided by our data source, with the exception of a few for which the majority of students were missing data.

  8. 8.

    The Texas Higher Education Coordinating Board’s information on applications, acceptances and enrollments is available here http://www.txhighereddata.org/index.cfm?objectId=27282A55-A77E−2A0D-87B58BE320C6B099.

  9. 9.

    As with Eq. (1), these models are estimated with cluster-robust standard errors. The small number of clusters in the analysis (in this analysis, the clusters are the 14 years of applicant data) often yields cluster-robust standard errors that are downwards biased (Bertrand et al. 2004; Donald and Lang 2007; Cameron et al. 2008). To determine the extent of the bias, we implement the wild cluster bootstrap-t procedure described in Cameron et al. (2008) using the year of admission as the cluster. There were no meaningful differences in statistical inference using the bootstap p-values and the cluster-robust p-values.

  10. 10.

    The triple difference is the difference in the two double-differences as follows: Post H.B. 588 difference—Pre H.B. 588 difference or (0.005–(−0.005), which approximately equals 0.009.

  11. 11.

    In 2009, the state legislature amended the percent plan in 2009 to allow UT-Austin to cap top ten admits at 75 % of the entering freshman class.

  12. 12.

    For instance, the College of Charleston, a public institution in South Carolina, where the female ratio is 2 to 1, launched a rugby team and new majors in business in an effort to attract males (Grasgreen 2010).

  13. 13.

    See http://www.ed.gov/college-affordability/college-ratings-and-paying-performance for a description of the Obama Administration’s proposal.

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Correspondence to Lisa Dickson.

Appendix

Appendix

See Table 6.

Table 6 Characteristics of sample

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Conger, D., Dickson, L. Gender Imbalance in Higher Education: Insights for College Administrators and Researchers. Res High Educ 58, 214–230 (2017). https://doi.org/10.1007/s11162-016-9421-3

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Keywords

  • Gender
  • Higher education
  • Affirmative action