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Demography

, Volume 55, Issue 1, pp 165–188 | Cite as

Racial and Ethnic Variation in the Relationship Between Student Loan Debt and the Transition to First Birth

Article

Abstract

The present study employs discrete-time hazard regression models to investigate the relationship between student loan debt and the probability of transitioning to either marital or nonmarital first childbirth using the 1997 National Longitudinal Survey of Youth (NLSY97). Accounting for nonrandom selection into student loans using propensity scores, our study reveals that the effect of student loan debt on the transition to motherhood differs among white, black, and Hispanic women. Hispanic women holding student loans experience significant declines in the probability of transitioning to both marital and nonmarital motherhood, whereas black women with student loans are significantly more likely to transition to any first childbirth. Indebted white women experience only a decrease in the probability of a marital first birth. The results from this study suggest that student loans will likely play a key role in shaping future demographic patterns and behaviors.

Keywords

Debt Fertility Young adulthood Gender Race/ethnicity 

Notes

Acknowledgments

A previous version of this paper was presented at the 2016 annual meeting of the Population Association of America. This study was supported in part by the National Science Foundation Research Fellowship Program under Grant No. 2016-1449440. Any opinions, findings, and conclusions or recommendations expressed in this study are those of the authors and do not necessarily reflect the view of the National Science Foundation.

Supplementary material

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References

  1. Addo, F. R. (2014). Debt, cohabitation, and marriage in young adulthood. Demography, 51, 1677–1701.CrossRefGoogle Scholar
  2. Addo, F. R. (2016). Financial integration and relationship transitions of young adult cohabiters. Journal of Family and Economic Issues, 38, 84–99.CrossRefGoogle Scholar
  3. Addo, F. R., Houle, J., & Dwyer, R. (2016). Young, black, and (still) in the red: Parental wealth, race, and student loan debt. Race and Social Problems, 8, 64–76.CrossRefGoogle Scholar
  4. Allison, P. D. (2010). Survival analysis using SAS: A practical guide (2nd ed.). Cary, NC: SAS Institute.Google Scholar
  5. American Association of University Women (AAUW). (2016). The simple truth about the gender pay gap. Washington, DC: AAUW. Retrieved from http://www.aauw.org/research/the-simple-truth-about-the-gender-pay-gap/ Google Scholar
  6. Aronson, P. (2008). Breaking barriers or locked out? Class-based perceptions and experiences of postsecondary education. In J. T. Mortimer (Ed.), Social class and transition to adulthood: New directions for child and adolescent development (Jossey-Bass Education Series No. 119, pp. 41–54). San Francisco, CA: Wiley Subscription Services.Google Scholar
  7. Aud, S., Fox, M., & KewalRamani, A. (2010). Status and trends in the education of racial and ethnic groups (NECES Report No. 2010-015). Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved from http://eric.ed.gov/ERICWebPortal/recordDetail?accno=ED510909 Google Scholar
  8. Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399–424.CrossRefGoogle Scholar
  9. Austin, P. C. (2013). The use of propensity score methods with survival or time-to-event outcomes: Reporting measures of effect similar to those used in randomized experiments. Statistics in Medicine, 33, 1242–1258.CrossRefGoogle Scholar
  10. Avery, C., & Turner, S. (2012). Student loans: Do college students borrow too much—Or not enough. Journal of Economic Perspectives, 26(1), 165–192.CrossRefGoogle Scholar
  11. Barry, M. N., & Dannenberg, M. (2016). Out of pocket: The high cost of inadequate high schools and high school student achievement on college affordability (Policy brief). Washington, DC: Education Reform Now. Retrieved from https://www.insidehighered.com/sites/default/server_files/files/EdReformNow%20O-O-P%20Embargoed%20Final.pdf Google Scholar
  12. Baum, S., & Johnson, M. (2015). Financing public higher education: The evolution of funding (Report). Washington, DC: Urban Institute.Google Scholar
  13. Baum, S., Ma, J., & Payea, K. (2013). Education pays 2013: The benefits of higher education on society (Trends in Higher Education Series report). New York, NY: College Board.Google Scholar
  14. Baum, S., Ma, J., Pender, M., & Bell, D. (2015). Trends in student aid 2015 (Trends in Higher Education Series report). New York, NY: College Board.Google Scholar
  15. Becker, G. S. (1981). A treatise on the family. Cambridge, MA: Harvard University Press.Google Scholar
  16. Bogue, D. J. (2010). Contraception, attitude-practice, and fertility differentials among US Hispanic, African-American, and white women. Journal of Population Research, 27, 275–292.CrossRefGoogle Scholar
  17. Bozick, R., & Estacion, A. (2014). Do student loans delay marriage? Debt repayment and family formation in young adulthood. Demographic Research, 30(article 69), 1865–1891. https://doi.org/10.4054/DemRes.2014.30.69
  18. Cherlin, A. J. (2004). The deinstitutionalization of American marriage. Journal of Marriage and Family, 66, 848–861.CrossRefGoogle Scholar
  19. Child Trends. (2015). Births to unmarried women: Indicators on children and youth (Data Bank report). Bowling Green, OH: Child Trends. Retrieved from http://www.childtrends.org/wp-content/uploads/2015/03/75_Births_to_Unmarried_Women.pdf
  20. Complete College America. (2015). Four-year myth: Make college more affordable. Restore the promise of graduating on time. Indianapolis, IN: Complete College America. Retrieved from http://completecollege.org/wp-content/uploads/2014/11/4-Year-Myth.pdf Google Scholar
  21. Corbett, C., & Hill, C. (2012). Graduating to a pay gap: The earnings of women and men one year after college graduation. Washington, DC: American Association of University Women. Retrieved from http://www.aauw.org/files/2013/02/graduating-to-a-pay-gap-the-earnings-of-women-and-men-one-year-after-college-graduation.pdf Google Scholar
  22. Dowd, A. C. (2008). Dynamic interactions and intersubjectivity: Challenges to causal modeling in studies of student loan debt. Review of Educational Research, 78, 232–259.CrossRefGoogle Scholar
  23. Dynarski, S. M. (2016). The dividing line between haves and have-nots in home ownership: Education, not student debt (Evidence Speaks Reports Vol. 1, No. 17). Washington, DC: Brookings Institute. Retrieved from https://www.brookings.edu/wp-content/uploads/2016/07/home-ownership-FINAL2b.pdf
  24. Emmons, W. R., & Noeth, J. B. (2015). Why didn’t higher education protect Hispanic and black wealth? (In the Balance No. 12). St. Louis, MO: Federal Reserve Bank of St. Louis. Retrieved from https://www.stlouisfed.org/publications/in-the-balance/issue12-2015/why-didnt-higher-education-protect-hispanic-and-black-wealth
  25. Federal Student Aid. (n.d.). Interest rates and fees. Retrieved from https://studentaid.ed.gov/sa/types/loans/interest-rates
  26. FinAid. (n.d.). Historical interest rates. Retrieved from http://www.finaid.org/loans/historicalrates.phtml
  27. Fry, R. (2014). The changing profile of student borrowers: Biggest increase in borrowing has been among more affluent students (Social & Demographic Trends report). Washington DC: Pew Research Center. Retrieved from http://www.pewsocialtrends.org/2014/10/07/the-changing-profile-of-student-borrowers/
  28. Furstenberg, F. F. (2014). Fifty years of family change: From consensus to complexity. Annals of the American Academy of Political and Social Science, 654, 12–30.CrossRefGoogle Scholar
  29. Gibson-Davis, C., & Rackin, H. (2014). Marriage or carriage? Trends in union context and birth type by education. Journal of Marriage and Family, 76, 506–519.CrossRefGoogle Scholar
  30. Gicheva, D. (2016). Student loans or marriage? A look at the highly educated. Economics of Education Review, 53, 207–216.CrossRefGoogle Scholar
  31. Gladeaux, L., & Perna, L. (2003). Borrowers who drop out: A neglected aspect of the student loan trend (National Center Report No. 05-2). San Jose, CA: National Center for Public Policy and Higher Education.Google Scholar
  32. Goldrick-Rab, S. (2006). Following their every move: An investigation of social-class differences in college pathways. Sociology of Education, 79, 67–79.CrossRefGoogle Scholar
  33. Goldscheider, F. K., & Waite, L. J. (1986). Sex differences in the entry into marriage. Journal of Marriage and the Family, 92, 91–109.Google Scholar
  34. Grinstein-Weiss, M., Parentie, D. C., Taylor, S. H., Guo, S., & Raghavan, R. (2016). Racial disparities in education debt burden among low- and moderate-income households. Children and Youth Services Review, 65, 166–174.CrossRefGoogle Scholar
  35. Guzzo, K. B., Nash, S. P., Manning, W. D., Longmore, M. A., & Giordano, P. C. (2014). Unpacking the “black box” of race-ethnic variation in fertility. Race and Social Problems, 7, 135–149.CrossRefGoogle Scholar
  36. Hayford, S., & Guzzo, K. B. (2016). Fifty years of unintended births: Education gradients in unintended fertility in the US, 1960–2013. Population and Development Review, 42, 313–341.CrossRefGoogle Scholar
  37. Hayford, S., Guzzo, K. B., & Smock, P. J. (2014). The decoupling of marriage and parenthood? Trends in the timing of marital first births, 1945–2002. Journal of Marriage and Family, 76, 520–538.CrossRefGoogle Scholar
  38. Hayford, S. R., & Morgan, S. P. (2008). The quality of retrospective data on cohabitation. Demography, 45, 129–141.CrossRefGoogle Scholar
  39. Hofferth, S. L., & Goldscheider, F. (2010). Family structure and the transition to early parenthood. Demography, 47, 415–437.CrossRefGoogle Scholar
  40. Huber, P. J. (1967). The behavior of maximum likelihood estimation under nonstandard conditions. In L. M. Le Cam & J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability. Vol. 1: Statistics (pp. 221–233). Berkeley: University of California Press.Google Scholar
  41. Huelsman, A. (2015). The debt divide: The racial and class bias behind the “new normal” of student borrowing. New York, NY: Demos.Google Scholar
  42. Huinink, J., & Kohli, M. (2014). A life-course approach to fertility. Demographic Research, 30(article 45), 1293–1326.  https://doi.org/10.4054/DemRes.2014.30.45
  43. Jackson, B., & Reynolds, J. (2013). The price of opportunity: Race, student debt, and college achievement. Sociological Inquiry, 83, 335–368.CrossRefGoogle Scholar
  44. Jones, J., & Schmitt, J. (2014). A college degree is no guarantee (CEPR Report). Washington, DC: Center for Economic and Policy Research. Retrieved from http://cepr.net/publications/reports/a-college-degree-is-no-guarantee Google Scholar
  45. Kamenetz, A. (2006). Generation debt: Why now is a terrible time to be young. New York, NY: Riverhead Books.Google Scholar
  46. Keels, M. (2014). Choosing single motherhood. Contexts, 13(2), 70–72.CrossRefGoogle Scholar
  47. Kroeger, T., Cooke, T., & Gould, E. (2016). The class of 2016: The labor market is still far from ideal for young graduates (Report). Washington, DC: Economic Policy Institute. Retrieved from http://www.epi.org/files/pdf/103124.pdf Google Scholar
  48. Lamidi, E. (2016). A quarter century change in nonmarital births: Differnces by educational attainment (Family Profiles Report No. FP-16-05). Bowling Green, OH: National Center for Family & Marriage Research.Google Scholar
  49. Lichter, D. T., Johnson, K. M., Turner, R. N., & Churilla, A. (2012). Hispanic assimilation and fertility in new destinations. International Migration Review, 46, 767–791.CrossRefGoogle Scholar
  50. Lundberg, S., Pollak, R., & Stearns, J. E. (2016). Family inequality: Diverging patterns in marriage, cohabitation, and childbearing. Journal of Economic Perspectives, 30(2), 79–102.CrossRefGoogle Scholar
  51. Manning, W. D. (2013). Trends in cohabitation: Over twenty years of change, 1987–2010 (Family Profiles Report No. FP-13-12). Bowling Green, OH: National Center for Family & Marriage Research.Google Scholar
  52. Mathews, T. J., & Hamilton, B. E. (2016). Mean age of mothers is on the rise: United States, 2000–2014 (NCHS Data Brief No. 232). Hyattsville, MD: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.Google Scholar
  53. Mezza, A., Daniel, R., Shane, S., & Sommer, K. (2016). On the effect of student loans on access to homeownership (Finance and Economics Discussion Series 2016-010). Washington, DC: Board of Governors of the Federal Reserve System.Google Scholar
  54. Min, S., & Taylor, M. (2016, August). Estimating the effect of student loan debt on timing of marriage among race/ethnic groups. Presented at the annual meeting of the American Sociological Association, Seattle, WA.Google Scholar
  55. Minicozzi, A. (2005). The short term effect of educational debt on job decisions. Economics of Education Review, 24, 417–430.CrossRefGoogle Scholar
  56. Mulder, C. H. (2006a). Population and housing: A two-sided relationship. Demographic Research, 15(article 13), 401–412.  https://doi.org/10.4054/DemRes.2006.15.13 CrossRefGoogle Scholar
  57. Mulder, C. H. (2006b). Home-ownership and family formation. Journal of Housing and the Built Environment, 21, 281–289.CrossRefGoogle Scholar
  58. Nau, M., Dwyer, R. E., & Hodson, R. (2015). Can’t afford a baby? Debt and young Americans. Research in Social Stratification and Mobility, 42, 114–122.CrossRefGoogle Scholar
  59. Oppenheimer, V. K. (1988). A theory of marriage timing. American Journal of Sociology, 94, 563–591.CrossRefGoogle Scholar
  60. Patten, E. (2016). Racial, gender wage gaps persist in U.S. despite some progress (Fact Tank report). Washington, DC: Pew Research Center. Retrieved from http://www.pewresearch.org/fact-tank/2016/07/01/racial-gender-wage-gaps-persist-in-u-s-despite-some-progress/
  61. Payne, K. K. (2012). Timing of first marital birth (Family Profiles Report No. FP-12-11). Bowling Green, OH: National Center for Family & Marriage Research. Retrieved from https://www.bgsu.edu/content/dam/BGSU/college-of-arts-and-sciences/NCFMR/documents/FP/FP-12-11.pdf Google Scholar
  62. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  63. Rothstein, J., & Rouse, C. E. (2011). Constrained after college: Student loans and early-career occupational choices. Journal of Public Economics, 95, 149–163.CrossRefGoogle Scholar
  64. Schwartz, C., & Mare, R. (2005). Trends in educational assortative mating from 1940 to 2003. Demography, 42, 621–646.CrossRefGoogle Scholar
  65. Scott-Clayton, J., & Li, J. (2016). Black-white disparity in student loan debt more than triples after graduation (Evidence Speaks Reports Vol. 2, No. 3). Washington, DC: Brookings Institute. Retrieved from https://www.brookings.edu/research/black-white-disparity-in-student-loan-debt-more-than-triples-after-graduation/ Google Scholar
  66. Smock, P. J., & Greenland, F. R. (2010). Diversity in pathways to parenthood: Patterns, implications, and emerging research directions. Journal of Marriage and Family, 72, 576–593.CrossRefGoogle Scholar
  67. South, S. J., & Crowder, K. (2010). Neighborhood poverty and nonmarital fertility: Spatial and temporal dimensions. Journal of Marriage and Family, 72, 89–104.CrossRefGoogle Scholar
  68. Sweeney, M., & Raley, R. (2014). Race, ethnicity, and the changing context of childbearing in the United States. Annual Review of Sociology, 40, 539–558.CrossRefGoogle Scholar
  69. Titus, M. (2007). Detecting selection bias, using propensity score matching, and estimating treatment effects: An application to private returns using a masters degree. Research in Higher Education, 48, 487–521.CrossRefGoogle Scholar
  70. Wei, C. C., & Horn, L. (2013). Federal student loan debt burden of noncompleters (Statistics in Brief No. 2013-155). Washington, DC: Institute of Education Sciences, National Center for Education Statistics, U.S. Department of Education.Google Scholar
  71. White, H. A. (1980). Heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48, 817–838.CrossRefGoogle Scholar
  72. Wooldridge, J. M. (2007). Inverse probability weighted estimation for general missing data problems. Journal of Econometrics, 141, 1281–1301.CrossRefGoogle Scholar
  73. Wright, G., & Serrato, S. (2015). Default rate declines, yet 611,000 defaulted on federal student loans. Despite decreases, for-profit colleges account for the greatest share of defaults [Press release]. Oakland, CA: The Institute for College Access and Success. Retrieved from http://ticas.org/sites/default/files/pub_files/cdr_2015_nr.pdf
  74. Wu, L. L., & Martin, S. P. (2009). Effects of exposure on prevalence and cumulative relative risk: Direct and indirect effects in a recursive hazard model. Sociological Methodology, 39, 185–232.CrossRefGoogle Scholar
  75. Yang, Y., & Morgan, S. P. (2003). How big are educational and racial fertility differentials in the U.S.? Social Biology, 50, 167–187.Google Scholar

Copyright information

© Population Association of America 2018

Authors and Affiliations

  1. 1.Department of SociologyFlorida State UniversityTallahasseeUSA
  2. 2.Center for Demography and HealthFlorida State UniversityTallahasseeUSA
  3. 3.The Pepper Institute on Aging and Public PolicyFlorida State UniversityTallahasseeUSA

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