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The effects of teenage childbearing on long-term health in the US: a twin-fixed-effects approach

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Abstract

This paper explores the effect of teenage childbearing on long-term health outcomes and behaviors of mothers using the Midlife Development in the US dataset. Within-family estimations, using samples of siblings, and twin pairs, are employed to overcome the bias generated by unobserved family background and genetic traits. The results suggest no significant effects on health outcomes, and modest effects on health behaviors, including exercise and preventive care. However, accounting for life-cycle effects demonstrates that teenage childbearing has significant effects on both health outcomes and behaviors early in life, but very few significant effects later in life. Moreover, teenage childbearing has a particularly acute effect among minorities. Finally, this paper provides evidence that the effects operate through reduced income and labor force participation, and matching with a lower “quality” spouse.

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Notes

  1. In the US, the teen birth rate rose from 50 to 55 births per 1000 women between the ages of 15 and 19 in the late 1970s to around 60 in the early 1990s.

  2. Birth rate before the age of 20 is a standard measure of teenage childbearing used by government agencies such as the Centers for Disease Control and Prevention. While the negative effects might be more acute for adolescent mothers, nearly three in four teen births occur between the ages of 18 and 19 in 2013 (Hamilton et al. 2015).

  3. Inequalities in early life can be responsible for teenage pregancy, and investments in early stages of childhood might reduce disparities in later stages of life (Doyle et al. 2009).

  4. Some recent studies find that teen mothers are more likely to report poor physical and mental health (Patel and Sen 2012; Liao 2003; Hobcraft and Kiernan 2001), and are more likely to be current smokers (Hobcraft and Kiernan 2001). However, these studies do not attempt to establish a causal link.

  5. Defining teenage childbearing as childbearing before the age of 20 is standard in the literature (Geronimus and Korenman 1992; Ribar 1994, 1999; Klepinger et al. 1999; Webbink et al. 2008, 2011; Fletcher 2012). The implications of this definition are discussed in Sect. 3, and I demonstrate that the results are similar excluding childbearing of aged 19.

  6. For example, younger women are at a greater risk of pregnancy complications, such as anemia (Mirowsky and Ross 2002).

  7. While there might be more stigma associated with adolescent fertility, many adult teenage pregnancies are out of wedlock, at least at the time of conception, which is often viewed disfavorably, especially among those with religious or traditional views of marriage.

  8. Dropping out refers to not graduating high school or not attending and completing post-secondary education. While graduating college might not be essential in developing countries, a large fraction of the population in the United States attains an Associate or Bachelor’s degree, and studies document a large college-wage premium, especially among women (Katz and Murphy 1992; Card 1999; Dougherty 2005).

  9. While the medical literature suggests that adult health outcomes between fraternal and identical twin pairs are not significantly different (Christensen et al. 1995; Duffy 1993), I also use a sample of identical twins. Because the number of identical-twin pairs is small (27 pairs), I focus on the results for the samples of twins, which, consistent with the medical literature, are similar to the results for identical twins (available upon request).

  10. http://www.midus.wisc.edu.

  11. See Lundborg (2013) for an assessment of the representativeness of the MIDUS sample. Compared to the 1995 CPS data, the MIDUS sample contains more educated individuals than the general US population. Also, there are more whites in the siblings and twins samples (over 90 %), compared to the CPS sample (about 85 %).

  12. Chronic conditions and diseases are the leading causes of death and disability, and are among the most costly health problems in the US (http://www.cdc.gov).

  13. Because very few women reported fair and poor mental health (23 among twins sample), this paper does not use poor mental health as the main outcome. 20 % of the twins sample reported excellent mental health. Mental health disorders are the leading cause of disability in the US, are associated with increased prevalence of chronic diseases and lower participation in health-promoting behaviors, and are costly conditions (http://www.cdc.gov and http://www.ahrq.gov).

  14. The estimates are reported in an online appendix (Appendix A).

  15. Examples of vigorous physical activity in the survey are running or lifting heavy objects. An alternative measure of physical exercise, the number of times per month engaged in moderate physical activity (examples in the survey are bowling or using a vacuum cleaner) yield similar results.

  16. Note that sample of siblings (twins) where at least one is a teenage mother is used in the analysis, and only siblings (twins) in the identifying samples identify the teenage childbearing coefficient (see Table 3 for observation numbers).

  17. Teen mothers and non-teen mothers obviously have the same set of parents, thus, differences in parental education is due to a positive correlation between the number of “non-teen mothers” siblings and parental education.

  18. In order to create the indices, each outcome is rescaled to map higher values to better health or health behaviors. Then, the z-score of each outcome is calculated by subtracting the mean of mothers who did not have teenage childbearing and dividing by the corresponding standard deviation.

  19. PCA is a statistical technique of data reduction, which converts the correlated variables into an uncorrelated linear combinations of variables (principal components) that account for most of the variance. Following Kling et al. (2007), an equally weighted average of z-scores is also used to construct the indices; however, the results are consistent with using the PCA method (results are available upon request).

  20. Following Lundborg (2013), education ranges from 1 to 4 based on the following categories: less than high school, GED or high school diploma, some college (no BA degree), and college degree or more. Following the approach of Ashenfelter and Krueger (1994), which is used by Lundborg (2013), the reports of siblings (twins) for parental education are averaged before obtaining a categorical parental education variable to address measurement error. In addition, the report of the sibling (twin) is used if there is only one report.

  21. An alternative approach to explore life-cycle effects is to estimate the effect of teenage childbearing on health and health behaviors in the follow up survey (2004/2006) conditional on health and health behaviors in 1995/1996. The results (see online appendix–Appendix B) provide some evidence of convergence over the lifecycle; however, the results are imprecise due to potential attrition bias and small sample size.

  22. In particular, Fletcher (2012) splits the sample into two using 45 as an age cutoff, while Webbink et al. (2008) restrict the sample to females older than 40. Both studies do not find significant differences using the various subsamples. I also re-estimate the life-cycle effects by splitting the sample (rather than using interactions) and find similar results as reported in Table 4 (available upon request).

  23. Mullahy and Robert (2010) find that having kids ages 0–5 is associated with about 2 fewer minutes of exercise.

  24. These results are imprecise as there are only 25 (12) and 18 (9) non-white mothers (families) in the samples for siblings and twins, respectively.

  25. I did not set the threshold to age 18 because the number of teen mothers are significantly reduced (25 and 14 in the samples of siblings and twins).

  26. The caveat applies that failing to find significant differences between adolescent-teenage childbearing and adult-teenage childbearing does not necessarily imply the effects are the same.

  27. Conley et al. (2006) find that associations between birth weight and infant mortality for identical and fraternal twins vary by gestational age, which suggests that the role of genes or environment in birth weight-mortality associations vary across different situations. Therefore, within-twins estimations may not control completely for genetic endowments at conception. Moreover, Stenberg (2013) emphasizes the gene and environment interactions in interpreting the heritability estimates. It should be noted that the purpose of using twin data in this paper is to control for unobserved endowments.

  28. The two measures are different for 5 observations in the sample of twins.

  29. Education is measured as dummy variables indicating whether the respondent (at least) graduated from high school or attended college. Married and Medicaid indicate whether the respondent is currently married and covered by Medicaid. Child age is the age of the youngest child. Living with adult child/grandchild indicates whether the respondent lives with an adult child or grandchild at the time of the survey. Labor force participation indicates whether the respondent (spouse) is currently working for pay. Income is annual personal income of the respondent (spouse) in the past 12 months before taxes, excluding pensions, investments, or any other financial assistance/income, which ranges from 1 to 13 for different income levels (loss, 0/none, 1–1000, 1000–1999, 2000–2999,..., 100,000 or more).

  30. These results are consistent with recent studies exploring the effects of labor market outcomes on mental health and the effects of childbearing on labor market decisions. For example, Mendolia (2014) shows that spouse’s job loss decreases individual and family mental health, which could be explained by the decrease in household income, and Herrarte et al. (2012) show that having a newborn has a negative effect on women’s labor market decisions.

  31. Note that introducing the mechanism as a control variable also reduces the sample size due to missing values. Therefore, direct comparison of Appendix D with the results reported in Table 3 has limitations.

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Acknowledgments

The author would like to thank Melissa S. Kearney, Laura M. Argys, Jere Behrman, Kenneth L. Leonard, Vikesh Amin, Dana C. Andersen, and participants at the 2014 PAA conference for many valuable comments. This paper has been significantly improved from the insightful comments of two anonymous referees.

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Correspondence to Pınar Mine Güneş.

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Güneş, P.M. The effects of teenage childbearing on long-term health in the US: a twin-fixed-effects approach. Rev Econ Household 14, 891–920 (2016). https://doi.org/10.1007/s11150-016-9326-0

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