Abstract
Gender gaps in educational expectations and postsecondary enrollment are well studied, but few scholars have investigated the extent to which students realize or fail to realize their expectations. Even fewer have examined how the likelihood of realizing one’s expectations may differ for men and women. Using 35 years of data, this study examines the role race, social class, and significant others’ influences have played in realizing educational expectations and how these relationships have differed for men and women. It also investigates how group gender differences in these characteristics have contributed to the gap between the proportion of men and proportion of women who have realized their educational expectations via college enrollment. Results show trends in realized expectations by gender over time. Group gender differences explain little of past gaps but returns on students’ characteristics differ by gender, which has been a key explanatory factor for differences between men and women in realizing their expectations. Implications of these findings relative to policy and program initiatives are discussed.
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Notes
We conducted all analysis in Stata. The “ice” command was used for multiple imputation, with the “mim” command prefix used to account for multiple imputed datasets in our analyses. The “svy” command was used to account for the weights and complex survey design.
We recognize that the presentation of the formulas in this way, which is done so to remain consistent with Fairlie, may be unfamiliar to some readers. However, Eqs. 1 and 2 are really just multiplicative expansions of an alternate formula, which, for Eq. 1 would basically be: \( \overline{Y}^{F} - \overline{Y}^{M} = (X_{i}^{F} - X_{i}^{M} )\hat{\beta }^{F} + (\hat{\beta }^{F} - \hat{\beta }^{M} )X_{i}^{M} \).
The specific Stata command we use to conduct the decomposition is “Fairlie” (Jann 2006).
Although there is virtually no gender gap in the ELS data in aggregate, gaps still exist by subgroups. For example, there is a 6.8 % gender gap for Asian students. Interestingly, across SES quintiles, the largest gender gap is among the three highest quintiles (Seifert et al. 2010).
In general, the findings from HS&B appear slightly different than the other datasets, and often do not fit apparent trends. As stated, we assume this is primarily due to different items being used on the survey instrument, and therefore different constructs actually being measured. Therefore, we advise caution in interpreting all of the HS&B results given such discrepancies.
$10,000 in 1972 = $2324 in 2004; $10,000 in 1980 = $4,580 in 2004; $10,000 in 1992 = $7,799 in 2004. To convert odds ratios to represent constant 2004 dollars we took the natural log of the odds ratio in order to get the logit coefficient, then multiplied this logit by the fraction of 2004 dollars represented, then converted this modified logit coefficient back to an odds ratio.
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This material is based upon work supported by the Association for Institutional Research, the National Center for Education Statistics, the National Science Foundation, and the National Postsecondary Education Cooperative under Association for Institutional Research Grant Number RG 09-167.
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Wells, R.S., Seifert, T.A. & Saunders, D.B. Gender and Realized Educational Expectations: The Roles of Social Origins and Significant Others. Res High Educ 54, 599–626 (2013). https://doi.org/10.1007/s11162-013-9308-5
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DOI: https://doi.org/10.1007/s11162-013-9308-5