Researchers have paid increasing attention to issues of access and retention among first-generation college students but have focused less on their post-college outcomes. We extend this literature by investigating if there is a generational wage gap, that is, a gap between first- and continuing-generation students’ wages. We also ask how the generational wage gap varies across institutions, majors, and achievement levels, and what accounts for it. Using data from the Baccalaureate and Beyond Longitudinal Study, we show that 10 years after completing college there is a substantial generational wage gap. However, for women, the generational wage gap fades when controlling for individual characteristics such as race and motherhood status. For men, the generational wage gap does not disappear when controlling for individual characteristics, but does disappear when controlling for labor market characteristics. In addition, we find that the generational wage gap is more a product of how students are distributed into industries, jobs, and work locations than how they are distributed into educational institutions, majors, and achievement levels.
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Limiting the sample to adults who graduated before age 40 removes 5% of the sample. We find no differences in employment rates for first- and continuing-generation men (95%), and only slightly higher employment rates for first-generation college women compared to continuing-generation women (84 vs. 79%). Two-stage selection models, predicting the probability of employment in the first stage and salary in the second stage, did not identify potential issues related to selection into employment.
We use the mi impute chained command in Stata 14 (StataCorp 2015). We imputed values for occupation (17), job sector (32), school size (45), hours worked (112), postgraduate major (156), race (157), gender (157), age (184), having dependents (271), parental education (410), major (418), institutional selectivity (493), job placement rate (598), GPA (653), and location (662).
To account for possibly non-linear effects of age, we also included a squared term for age.
For more information, see http://www.ers.usda.gov/data-products/urban-influence-codes.aspx.
Following the consensus in the literature, we apply survey weights and replicate weights to construct descriptive statistics (Kish & Frankel, 1974).
Following Winship and Radbill (1994), we do not apply weights when running the regression analyses.
β coefficients indicate the change in the difference in the logs of expected counts for a one unit change in the predictor variable; after exponentiating, coefficients can be interpreted as an incidence rate ratio (IRR).
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We would like to thank Lisa Keister, Angie O’Rand, Toby Parcel, Moris Triventi, and Greg Wolniak for their helpful comments on the manuscript.
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Manzoni, A., Streib, J. The Equalizing Power of a College Degree for First-Generation College Students: Disparities Across Institutions, Majors, and Achievement Levels. Res High Educ 60, 577–605 (2019). https://doi.org/10.1007/s11162-018-9523-1
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