Demography

, Volume 48, Issue 3, pp 931–956 | Cite as

A Longitudinal Analysis of the Relationship Between Fertility Timing and Schooling

Article

Abstract

This article quantifies the contribution of pre-treatment dynamic selection to the relationship between fertility timing and postsecondary attainment, after controlling for a rich set of predetermined characteristics. Eventual mothers and nonmothers are matched using their predicted birth hazard rate, which shares the desirable properties of a propensity score but in a multivalued treatment setting. I find that eventual mothers and matched nonmothers enter college at the same rate, but their educational paths diverge well before the former become pregnant. This pre-pregnancy divergence creates substantial differences in ultimate educational attainment that cannot possibly be due to the childbirth itself. Controls for predetermined characteristics and fixed effects do not address this form of dynamic selection bias. A dynamic model of the simultaneous childbirth-education sequencing decision is necessary to address it.

Keywords

Fertility timing Educational attainment Matching 

Notes

Acknowledgments

Financial support for this work was provided by the National Institute for Child Health and Human Development (Interdisciplinary Training Grant No. T32-HD007275). I am especially grateful to Ron Lee for extensive guidance on this paper. I also thank Mia Bird, David Card, Ken Chay, Avi Ebenstein, Jane Herr, David S. Lee, Robert D. Mare, Rachel Polimeni, Marit Rehavi, Lawrence Wu, and participants at the UC Berkeley Demography Department Brown Bag and the 2006 annual meeting of the Population Association of America for numerous useful suggestions. All errors are, of course, my own.

Supplementary material

13524_2011_50_MOESM1_ESM.doc (626 kb)
ESM 1(DOC 626 kb)

References

  1. Abadie, A., & Imbens, G. (2008). On the failure of the bootstrap for matching estimators. Econometrica, 76, 1537–1557.CrossRefGoogle Scholar
  2. Abrahamse, A. F., Morrison, P. A., & Waite, L. J. (1988). Beyond stereotypes: Who becomes a single teenage mother? Santa Monica, CA: The Rand Corporation.Google Scholar
  3. Angrist, J., & Evans, W. (1998). Children and their parents’ labor supply: Evidence from exogenous variation in family size. American Economic Review, 88, 450–477.Google Scholar
  4. Angrist, J., & Evans, W. (1999). Schooling and labor-market consequences of the 1970 state abortion reforms. In S. Polachek and J. Robst (Eds.), Research in Labor Economics (Vol. 18, pp. 75–114).Google Scholar
  5. Ashenfelter, O., & Card, D. (1985). Using the longitudinal structure of earnings to estimate the effect of training programs. Review of Economics and Statistics, 67, 648–660.CrossRefGoogle Scholar
  6. Ashcraft, A., & Lang, K. (2006). The consequences of teenage childbearing (NBER Working Paper No. 12485). Cambridge, MA: National Bureau for Economic Research.Google Scholar
  7. Bronars, S., & Grogger, J. (1994). The economic consequences of unwed motherhood: using twin births as a natural experiment. American Economic Review, 84, 1141–1156.Google Scholar
  8. Busso, M., DiNardo, J., & McCrary, J. (2009). New evidence on the finite sample properties of propensity score matching and reweighting estimators. Unpublished manuscript, Ford School of Public Policy, University of Michigan, Ann Arbor.Google Scholar
  9. Caliendo, M., & Kopeinig, S. (2005). Some practical guidance for the implementation of propensity score matching (IZA Discussion Paper No. 1588). Bonn, Germany: Institute for the Study of Labor.Google Scholar
  10. Card, D., & Sullivan, D. (1988). Measuring the effect of subsidized training programs on movements in and out of employment. Econometrica, 56, 497–530.CrossRefGoogle Scholar
  11. Chevalier, A., & Viitanen, T. (2003). The long-run labour market consequences of teenage motherhood in Britain. Journal of Population Economics, 16, 323–343.CrossRefGoogle Scholar
  12. Cristia, J. (2008). The effect of a first child on female labor supply: Evidence from women seeking fertility services. Journal of Human Resources, 43, 487–510.Google Scholar
  13. Dolton, P., Smith, J., & Azevedo, J. (2008). The impact of the UK new deal for lone parents on benefit receipt. Unpublished manuscript, Department of Economics, University of Michigan, Ann Arbor.Google Scholar
  14. Fletcher, J. M., & Wolfe, B. L. (2008). Education and labor market consequences of teenage childbearing: Evidence using the timing of pregnancy outcomes and community fixed effects (NBER Working Paper No. 13847). Cambridge, MA: National Bureau of Economic Research.Google Scholar
  15. Fredriksson, P., & Johansson, P. (2008). Dynamic treatment assignment: the consequences for evaluations using observational data. Journal of Business and Economic Statistics, 26, 435–445.CrossRefGoogle Scholar
  16. Frölich, M. (2004). Finite-sample properties of propensity-score matching and weighting estimators. Review of Economics and Statistics, 86, 77–90.CrossRefGoogle Scholar
  17. Geronimus, A., & Korenman, S. (1992). The socioeconomic consequences of teen childbearing reconsidered. Quarterly Journal of Economics, 107, 1187–1214.CrossRefGoogle Scholar
  18. Heckman, J., & Hotz, J. (1989). Choosing among alternative nonexperimental methods for estimating the impact of social programs: The case of manpower training. Journal of the American Statistical Association, 84, 862–874.CrossRefGoogle Scholar
  19. Heckman, J., & Smith, J. (1999). The pre-programme earnings dip and the determinants of participation in a social programme. Implications for simple programme evaluation strategies. The Economic Journal, 109, 313–348.CrossRefGoogle Scholar
  20. Hofferth, S. (1987). The social and economic consequences of teenage childbearing. In C. Hayes & S. Hofferth (Eds.), Risking the future: Adolescent sexuality, pregnancy, and childbearing (Vol. II, pp. 123–144). Washington, DC: National Academy Press.Google Scholar
  21. Hotz, J., McElroy, S., & Sanders, S. (2005). Teenage childbearing and its life cycle consequences: exploiting a natural experiment. Journal of Human Resources, 40, 683–715.Google Scholar
  22. Imbens, G. (2000). The role of the propensity score in estimating dose-response functions. Biometrika, 87, 706–710.CrossRefGoogle Scholar
  23. Jacobson, L., LaLonde, R., & Sullivan, D. (1993). Earnings losses of displaced workers. American Economic Review, 84, 685–709.Google Scholar
  24. Katz, L., & Autor, D. (1999). Changes in the wage structure and earnings inequality. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3A, pp. 1463–1555). New York: Elsevier.Google Scholar
  25. Kleplinger, D., Lundberg, S., & Plotnick, R. (1999). How does adolescent fertility affect the human capital and wages of young women? Journal of Human Resources, 34, 421–448.CrossRefGoogle Scholar
  26. Lechner, M. (1999). Earnings and employment effects of continuous off-the-job training in East Germany after unification. Journal of Business and Economic Statistics, 17, 74–90.CrossRefGoogle Scholar
  27. Lechner, M. (2001). Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. In M. Lechner & F. Pfeiffer (Eds.), Econometric evaluations of active labour market policies in Europe (pp. 43–58). Heidelberg, Germany: Physica-Verlag.CrossRefGoogle Scholar
  28. Lechner, M. (2002). Program heterogeneity and propensity score matching: An application to the evaluation of active labor market policies. Review of Economics and Statistics, 84, 205–220.CrossRefGoogle Scholar
  29. Lee, D. (2010). The early socioeconomic effects of teenage childbearing: A propensity score matching approach. Demographic Research, 23, 697–736. doi:10.4054/DemRes.2010.23.25 CrossRefGoogle Scholar
  30. Levine, D., & Painter, G. (2003). The schooling costs of teenage out-of-wedlock childbearing: Analysis with a within-school propensity-score-matching estimator. Review of Economics and Statistics, 85, 884–900.CrossRefGoogle Scholar
  31. Moffitt, R. (2005). Remarks on the analysis of causal relationships in population research. Demography, 42, 91–108.CrossRefGoogle Scholar
  32. Olsen, R., & Farkas, G. (1989). Endogenous covariates in duration models and the effect of adolescent childbirth on schooling. Journal of Human Resources, 24, 39–53.CrossRefGoogle Scholar
  33. Ribar, D. (1994). Teenage fertility and high school completion. Review of Economics and Statistics, 76, 413–424.CrossRefGoogle Scholar
  34. Ribar, D. (1999). The socioeconomic consequences of young women’s childbearing: Reconciling disparate evidence. Journal of Population Economics, 12, 547–565.CrossRefGoogle Scholar
  35. Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–50.CrossRefGoogle Scholar
  36. Rosenbaum, P., & Rubin, D. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516–524.CrossRefGoogle Scholar
  37. Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
  38. Sanders, S., Smith, J., & Zhang, Y. (2007). Teenage childbearing and maternal schooling outcomes: Evidence from matching. Unpublished manuscript, Department of Economics, University of Maryland, College Park.Google Scholar
  39. Sianesi, B. (2004). An evaluation of the Swedish system of active labor market programs in the 1990s. Review of Economics and Statistics, 86, 133–155.CrossRefGoogle Scholar
  40. Upchurch, D. M., Lillard, L. A., & Panis, C. W. A. (2002). Nonmarital childbearing: Influences of education, marriage, and fertility. Demography, 39, 311–329.CrossRefGoogle Scholar

Copyright information

© Population Association of America 2011

Authors and Affiliations

  1. 1.Gerald R. Ford School of Public PolicyUniversity of MichiganAnn ArborUSA

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