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Does the Family and Medical Leave Act (FMLA) Increase Fertility Behavior?

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Abstract

The Family and Medical Leave Act (FMLA), implemented in August 1993, grants job-protected leave to any employee satisfying the eligibility criteria. One of the provisions of the FMLA is to allow women to stay at home for a maximum period of 12 weeks to give care to the newborn. The effect of this legislation on the fertility response of eligible women has received little attention by researchers. This study analyzes whether the FMLA has influenced birth outcomes in the U.S. Specifically, I evaluate the effect of the FMLA by comparing the changes in the birth hazard profiles of women who became eligible for FMLA benefits such as maternity leave, to the changes in the control group who were not eligible for such leave. Using a discrete-time hazard model, results from the difference-in-differences estimation indicate that eligible women increase the probability of having a first and second birth by about 1.5 and 0.6 % per annum, respectively. Compared to other women, eligible women are giving birth to the first child a year earlier and about 8.5 months earlier for the second child.

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Notes

  1. Although it is a federal policy, knowledge of the FMLA is still not as widespread since only 58.2% of employees at covered worksites have any knowledge of FMLA (Department of Labor 2007).

  2. The United Nations watchdog on labor issues, the International Labor Organization (ILO) prescribes a minimum of 14 weeks maternity leave along with some cash benefit (International Labor Organization 2000).

  3. The states are California, Connecticut, District of Columbia, Maine, Massachusetts, Minnesota, New Jersey, Oregon, Rhode Island, Tennessee, Washington, Wisconsin and Vermont. Information obtained from Waldfogel (1999).

  4. Averett and Whittington (2001) investigate the impact of maternity leave on influencing births during the period 1985 to 1992, which was a period before the FMLA was introduced.

  5. Less than 25% of the contiguous states of the U.S. had any state legislation establishing job-protected maternity leave.

  6. See U.S. Department of Labor website (http://www.dol.gov/whd/fmla/CONSADReport.pdf, accessed May 14, 2012) for a detailed explanation of what constitutes a covered employer.

  7. Under the FMLA, if two spouses are employed by the same employer, then the combined leave allowed is 12 weeks.

  8. Waldfogel (1999) noted that the NLSY data as being a potentially good source of information in investigating the FMLA’s impact on leave coverage. At the time of her research, the span of the data was too short to carry out any meaningful investigation.

  9. Ineligible women are those who do not satisfy all the eligibility criteria and who have worked some positive number of hours in each of the years during the survey period 1989–2010. Therefore, women who never worked in any year during this time period are excluded.

  10. I also cluster at the state level. Later, I show the results are robust to this specification.

  11. The authors estimated the effect of the “desired number of children” on the probability of being in a maternity leave job. They found that the coefficient on “desired number of children” was negative and insignificant. A statistically insignificant result indicates little/no evidence of selection bias caused by women sorting into firms offering maternity leave because they want to have children.

  12. Each wave of the NLSY79 asks respondents whether they are employed at a job that offers job-protected maternity leave. Job-protected maternity leave guarantees an individual the right to return to work after being granted leave from the same employer conditional upon the employee spending no more than the maximum allowable leave. As mentioned earlier, varying degrees of job-protected maternity leave existed in states before the FMLA was implemented.

  13. There is also a selection bias whereby women who desire children may choose to not work. In which case, the probability of giving a birth increases for these ineligible women. By including women who are not working, I am actually underestimating the impact of the policy. Also, women in larger firms with longer tenure and more work hours may be more committed to career and may be less likely to have children or more likely to delay. This would bias against finding fertility effects of FMLA on eligible women.

  14. Indeed, in addition to childcare, employer-side characteristics would be correlated with employment eligibility. As part of the empirical analysis, I consider firm size (as well as income levels) in the investigation of the impact of FMLA eligibility on birth outcomes. In the interest of space, these regression results were not reported.

  15. In 2007, there were 33,740 private schools in the US. See the U.S. Department of Education National Center for Education Statistics, Private School Universe Survey 2007–2008 available at http://nces.ed.gov/programs/digest/d09/tables/dt09_058.asp (accessed October 2, 2010). In 2002, the total number of firms as reported by the Census Bureau was 22,974,655. See the U.S. Census Bureau: State and Country Quick Facts available at http://quickfacts.census.gov/qfd/states/00000.html (accessed October 2, 2010).

  16. For example, in 2007, only 8 % of private sector workers could access paid family leave benefits. See U.S. Department of Labor, Bureau of Labor Statistics, National Compensation Survey: Employee Benefits in Private Industry in the United States at http://www.bls.gov/ncs/ebs/sp/ebsm0006.pdf (accessed September 21, 2010).

  17. I also ran the model using completed fertility as the dependent variable. The results were qualitatively similar.

  18. See Section 2 for a definition of a covered employer and how this relates to a woman being eligible for FMLA benefits. I also present models where the sample is disaggregated according to firm size. For firms with less than 50 employees, the coefficient of interest was not significantly different from zero. This result is not surprising because firms in this size category (with a few exceptions as noted in Section 2) by definition are not covered institutions, thus making the employees ineligible for FMLA benefits.

  19. Therefore, the more educated are better able to utilize the leave benefits of the FMLA because of better knowledge about policy. Recall, only 58.2% of employees at covered worksites had any knowledge of FMLA (Department of Labor 2007). Another reason why the FMLA has been ineffective in for other groups such as those in marginal employment can be explained by the fact that the majority of these individuals are not covered under the current FMLA eligibility criteria. That is, they are employed in institutions with less than 50 employees. See further details in the section on Sensitivity Analysis.

  20. In addition, I run regressions based on the size of the firm and find that the effect of the interaction term (FMLA*eligibility) is significant at the 10% level for large firms (those with at least 500 employees).

  21. The p-value for the Likelihood Ratio test on the difference between the placebo and the true results is zero in both panels. In addition, the AIC/BIC provide strong support for the true model.

  22. I also run the same regression, this time including a quadratic trend and interacting with eligibility status. The results (not shown) are also robust to this specification.

  23. Data source: http://www.census.gov/epcd/www/smallbus.html (accessed January 25, 2011).

  24. Inspection of the data reveals positive skewness in the outcomes used in this analysis.

  25. See the 2011 Statistical Abstract of U.S. Census Bureau at http://www.census.gov/compendia/statab/cats/births_deaths_marriages_divorces.html (Accessed July 19, 2011).

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Acknowledgments

I thank Naci Mocan, Patricia Anderson, Lucie Schmidt and the seminar participants of the 2010 Southern Economic Association Conference and the Louisiana State University Economics Department Brown-bag seminar series for helpful discussions and suggestions. Two anonymous referees provided helpful comments.

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Authors and Affiliations

  1. Belmont University, Nashville, TN, USA

    Colin Cannonier

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Appendix

Appendix

Fig. 3
figure 3

Kaplan-Meier Survival Function of the proportion of women remaining after a first birth by year and eligibility status

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Fig. 4
figure 4

Kaplan-Meier Survival Function of the proportion of women remaining after a second birth by year and eligibility status

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Table 7 Description of Variables
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Table 8 Predicted response time (years) before getting a birth (Eligible Women)
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Table 9 Estimates from Logit Regression: Sensitivity Analysis on the likelihood of a Birth (Marginal Effects)
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Table 10 Kaplan-Meier Survival Function Estimates of the proportion of women remaining after a birth outcome
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Table 11 Kaplan-Meier Survival Function Estimates of the number of women remaining after a birth outcome
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Cannonier, C. Does the Family and Medical Leave Act (FMLA) Increase Fertility Behavior?. J Labor Res 35, 105–132 (2014). https://doi.org/10.1007/s12122-014-9181-9

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