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The Impact of College Education on Fertility: Evidence for Heterogeneous Effects

Abstract

As college-going among women has increased, more women are going to college from backgrounds that previously would have precluded their attendance and completion. This affords us the opportunity and motivation to look at the effects of college on fertility across a range of social backgrounds and levels of early achievement. Despite a substantial literature on the effects of education on women’s fertility, researchers have not assessed variation in effects by selection into college. With data on U.S. women from the National Longitudinal Survey of Youth 1979, we examine effects of timely college attendance and completion on women’s fertility by the propensity to attend and complete college using multilevel Poisson and discrete-time event-history models. Disaggregating the effects of college by propensity score strata, we find that the fertility-decreasing college effect is concentrated among women from comparatively disadvantaged social backgrounds and low levels of early achievement. The effects of college on fertility attenuate as we observe women from backgrounds that are more predictive of college attendance and completion.

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Notes

  1. 1.

    In addition to the lower likelihood of labor force participation, Budig and England (2001) found that employed mothers suffer an average per-child wage penalty of approximately 5%, possibly resulting from employer discrimination against mothers (Correll et al. 2007). Amuedo-Dorantes and Kimmel (2005) found, however, that educated women who delay fertility do not experience a motherhood wage penalty.

  2. 2.

    Musick et al. (2009) argued that the effect of college on fertility is largely the result of unintended births and thus questioned whether opportunity costs explain fertility differences by education. Still, unintended fertility differences may be associated with opportunity costs if disadvantaged women lack a strong sense of efficacy and positive future outlook because of fewer economic and traditional family prospects and are thus less likely to take precautions to avoid pregnancy (Edin and Kefalas 2005; Mirowsky and Ross 2007).

  3. 3.

    We use Poisson rather than negative binomial models because we did not find evidence of overdispersion (i.e., the variance of the outcome is not greater than the mean of the outcome).

  4. 4.

    We explored several representations of age and decided that a squared term adequately represented the observed curvature.

  5. 5.

    Normality of ε is assumed for inference but not for summary slope estimation.

  6. 6.

    Given the additional complexity of combining multiple imputed data sets with our heterogeneous treatment effects analyses, we use single imputations. We note, however, that single imputations typically suffer from the problem of over-fitting and do not fully represent the uncertainty in the procedure.

  7. 7.

    Roughly two-fifths of timely college attendees began at a community college compared with two-thirds of those who attended college after age 19. Less than one-fifth of timely college completers began at a community college.

  8. 8.

    In order to achieve balanced propensity score strata, the covariates are slightly different in our two model specifications.

  9. 9.

    Because we use regression models to generate propensity scores, different specifications result in different classifications of individuals to strata. We tried several specifications before we decided on our model reported here. Our results were robust to these alternative specifications.

  10. 10.

    We did not initially, however, have a sufficient number (roughly 20 cases) of non–college goers in the final stratum for the multilevel model. We therefore collapsed the final two strata and adjusted for the estimated propensity score in Level 1 analyses for the college attendance and completion models. We also did not initially have a sufficient number of college completers in the first stratum, and we therefore also collapsed the first two strata and adjusted for the estimated propensity score for the college completion model.

  11. 11.

    Hispanic was not balanced in Stratum 1, as shown in Table 4, for the college attendance model and was thus added as a covariate in our Level 1, Stratum 1 model.

  12. 12.

    Comparing Table 4 with an analogous table for college completion (results available upon request), we observe that women with a college degree and their matched controls are more advantaged than women with some college and their matched controls.

  13. 13.

    To facilitate implementation of our method, we use the Stata module –hte– (Jann et al. 2010; available for public use).

  14. 14.

    In results not shown, we also explored heterogeneous effects of college attendance by age 20, age 21, and age 22 on number of children by age 41. Substantive conclusions are comparable with analyses based on college attendance by age 19.

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Acknowledgement

Financial support for this research was provided by the National Institutes of Health, Grant 1 R21 NR010856-01; a UCLA Faculty Research Grant; and by the California Center for Population Research at UCLA, which receives core support from the National Institute of Child Health and Human Development, Grant R24 5R24HD041022. Versions of this paper were presented at the Department of Sociology and the Institute for Policy Research at Northwestern University, Center for Poverty and Inequality at Stanford University, and the Population Association of America 2009 Annual Meeting. We thank Paula England, Ben Jann, Yana Kucheva, Robert Mare, Kelly Musick, Fabian Pfeffer, Hiromi Ono, Judith Seltzer, Yu Xie, and several anonymous reviewers from Demography for helpful suggestions. The ideas expressed herein are those of the authors.

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Correspondence to Jennie E. Brand.

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Brand, J.E., Davis, D. The Impact of College Education on Fertility: Evidence for Heterogeneous Effects. Demography 48, 863–887 (2011) doi:10.1007/s13524-011-0034-3

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Keywords

  • College education
  • Fertility
  • Causality
  • Heterogeneity
  • NLSY