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A Cohort Perspective on Gender Gaps in College Attendance and Completion

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Abstract

In the last 30 years, women experienced dramatic increases in college attendance and completion. Women now make up the majority of college attenders and completers, and their numbers continue to grow. Recent research shows that these gender differences are driven largely by changes among women in rates of college attendance. What is causing these dramatic increases in college attendance among women? Studying three distinct cohorts representing the high school graduating classes of 1972, 1982, and 1992, this article studies two possible mechanisms leading to women’s changing patterns of college attendance: changing academic achievement, and changing pathways into and through college. Results show that changes in the effects of achievement on college attendance decisions are driving women’s increasing college attendance. The expansion of higher education—particularly the route through 2-year college to 4-year college—increased opportunities for enrollment and women disproportionately took advantage of these opportunities. High-achieving women, who in the past did not attend college, are now attending and using these non-traditional paths to increase their rates of college attendance.

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Notes

  1. This series of transitions represents the traditional pathways through college. Mobility between 4-year colleges and discontinuous enrollment are two commonly ignored transitions that affect college completion and disproportionally affect lower SES students (Goldrick-Rab 2006). Although beyond the scope of this paper, these transitions may provide an explanation for the gender differences in college completion among men and women already enrolled in college.

  2. Buchmann and DiPrite (2006), by contrast, show, for a nationally representative cohort of 12th graders in 1992, that men and women do not differ significantly in their probability of enrolling in 4-year college but differ in their probability of completing 4-year college once enrolled. Conditional on 4-year college attendance, women are significantly more likely to complete a bachelor’s degree compared to men. More than 75 % of the gender difference in college completion can be explained by differences in rates of completion once in college. In other words, in this cohort, men and women attend college at the same rates, but women are more successful once they enter college. This result, however, is an artifact of the particular cohort studied by Buchmann and DiPrete. As they note, differences in transitions into college were smaller for the cohorts preceding the cohort they studied and larger for the subsequent cohorts. In other words, rates of transition into college were changing both before and after this cohort.

  3. The HS&B sophomore cohort is not representative of the 12th graders in 1982. Rather, it is representative of the 10th graders from 1980 who were in 12th grade in 1982. Whereas NELS freshened their sample in the second follow-up to be representative of the 12th graders in 1992, HS&B was not refreshed at the time of the first follow-up (when respondents were in the 12th grade). The senior cohort of HS&B, by contrast, is representative of the 12th graders in 1980 and directly comparable with both NLS and NELS cohorts. However, transcript data were collected only for the sophomore cohort of HS&B, not for the senior cohort. Because this analysis makes use of the transcript studies in each survey to measure academic achievement, the senior cohort is not used.

  4. NCES began a fourth cohort study of 10th graders in 2002, the Education Longitudinal Study of 2002 (ELS). Like NLS, HS&B, and NELS, this study includes a representative sample of high school seniors in 2004. Ideally this survey would be used alongside the other three to create a fourth data point. At this time, data from ELS are only available through 2006, two years after the respondents should have graduated from high school. Because men and women differ in the timing of entry into post-secondary education as well as whether they attend (Freeman 2004), including this survey along with the other three will make gender differences (in favor of women) appear larger than they would be if I observed these students several years later.

  5. The fifth follow-up was administered only to a sample of the 22,652 respondents who participated in at least one of the previous five waves. The final N for the fifth follow-up was 12,841 respondents. In this analysis, this fifth survey was only used to fill in missing data from post-secondary education reports. This was only necessary in a small number of cases.

  6. Ingels and Dalton (2008) carried out an analysis of differences between freshened and unfreshened samples from NELS and the Education Longitudinal Study of 2002 (ELS), and an analysis of differences between the sophomore cohort seniors and the senior cohort seniors from HS&B (see Appendix A. 7.8) to get a sense for how different the representative sample of 12th graders may be from the unrepresentative sample. The first test showed only one significant difference between freshened and unfreshened samples; in ELS the unfreshened sample was slightly whiter than the freshened sample. The second test showed that the sophomore cohort included fewer white students and fewer low SES students compared to the senior cohort. Assuming that these cohorts should be demographically similar, for my purposes, these differences should balance each other out.

  7. To check the sensitivity of the results to the exclusion of those missing rank information/not participating in the transcript study I conducted two tests: (1) only including test scores as measures of achievement, and (2) imputing missing data on high school class rank for those who did not participate in the transcript studies or were missing rank information. Substantively the results of my analyses did not change in either case. Because a large percentage of observations were imputed on rank when the whole sample was included I prefer to use the sample that includes only those with complete class rank information.

  8. Missing data on independent variables are imputed using STATA’s mi routine. The most significant source of missing data was parents’ education. Models were run with imputation flags to indicate whether observations that were imputed were significantly different from observations that were not imputed. None of these flags were statistically significant at the p < .05 level.

  9. Accomplishing this task is obviously more difficult with the NLS and HS&B, which do not conduct follow-up surveys in 1980 and 1990, respectively. The strategy taken in this article was to identify the year of first college attendance. Because the vast majority of respondents across surveys start college within the first 2 years after high school completion, well within the 8 year period, only a handful of cases are affected by these limitations. It is still possible that rates of college attendance are over-reported for the NLS and HS&B cohorts. The result is that, if anything, cohort differences are potentially larger than those observed in this paper.

  10. This definition of college attendance is obviously measured with some error. An individual who attends college for one week and then drops out will be classified as an attender even though he essentially did not attend. This sort of error does not undermine this study unless it happens disproportionately among women and disproportionately among those in later cohorts. Given the consistency in gender gaps reported in this analysis and those reported in other work using different datasets, it is unlikely that this definition is affecting aggregate results.

  11. I would ideally also include a measure of friends’ educational plans or expectations. Unfortunately, the measures of friends’ educational plans or expectations vary rather dramatically across surveys. As a result, it is impossible to know whether differences across cohorts are the result of real changes or the result of differences in the survey questions. The questions are as follows. NLS: What will most of your close friends do next year? Enter military service; get vocational, trade training; become full time homemakers; enter job training; work full time; go to college. HS&B: Closest senior friends plan to go to college? False; True. NELS: How many of your friends plan to attend a 4-year college or university? None of them; a few of them; some of them; most of them; all of them.

  12. The NLS measure does not specify an age limit and has a more inclusive set of categories.

  13. I also tested three-way interactions with parental education, and educational and occupational expectations. The effect of these variables does not vary significantly across cohorts and genders, and does not explain cohort differences in the likelihood of attending college. For these reasons, these interactions are left out of the analysis.

  14. Almost all 2-year colleges have open admissions.

  15. As noted earlier, Buchmann and DiPrete (2006) consider the effect of 2-year colleges on gender gaps in college attendance and completion for the NELS cohort. They find that 2-year colleges had little effect on the gender gap in both college attendance and completion for this cohort. By contrast, this article considers the effect of 2-year colleges across cohorts.

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Acknowledgments

I would like to thank Sara Goldrick-Rab, Eric Grodsky, Renee Luthra, Robert Mare, Meredith Phillips, Adam Ziegfeld and two anonymous reviewers for their advice and comments on previous drafts.

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  1. Department of Sociology, Center for Research on Inequalities and the Life Course, Yale University, P.O. Box 208265, New Haven, CT, 06520, USA

    Jennifer Flashman

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Correspondence to Jennifer Flashman.

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Table 5 Original variable text of expectation measures
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Flashman, J. A Cohort Perspective on Gender Gaps in College Attendance and Completion. Res High Educ 54, 545–570 (2013). https://doi.org/10.1007/s11162-013-9285-8

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