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Does Paid Family Leave Cause Mothers to Have More Children? Evidence from California

Abstract

Literature on the labor market and health effects of paid family leave largely overlooks the impacts on fertility, particularly in the United States. Increased childbearing following the introduction of a modest paid family leave policy in the U.S. could explain the contrasting short–term gains and long–term losses in women’s labor market outcomes found in recent work. We exploit the nation’s first paid family leave program, implemented in California in 2004. Using the universe of U.S. births and a difference-in-differences strategy, we find that access to leave increases fertility by 2.8 percent, driven by higher order births to mothers in their 30s, as well as Hispanic mothers and those with a high school degree. Our results are robust to corrective methods of inference, including synthetic controls. Our findings may inform the discussion of a national paid family leave policy.

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Notes

  1. For studies on paid leave and women’s careers in the U.S., see (Byker 2016; Timpe 2019; Bailey et al. 2019; Rossin-Slater et al. 2013; Bana et al. 2018; Campbell et al. 2018; Das and Polachek 2015; Baum and Ruhm 2016; Stanczyk 2019) For studies of effects of leave across OECD countries, see Olivetti and Petrongolo (2017) and Rossin-Slater (2018), among others.

  2. New Jersey implemented paid leave in 2009, Rhode Island in 2015, and New York in 2018 (though this policy incrementally increased each year until 2021). Both Washington and Washington D.C. implemented policies in 2020; Massachusetts did in 2021. Federal government employees have access to paid leave as of October 2020. The DoD also has a paid leave policy for its employees.

  3. The average duration of paid parental leave in OECD countries (excluding the U.S.) is 57 weeks (Blau and Kahn 2013). In contrast, CA-PFL offers six weeks.

  4. Examples of existing literature that find significant positive effects on fertility in other developed countries include: Olivetti and Petrongolo (2017), Shim (2014), Lalive and Zweimüller (2009), Hoem (1993), Raute (2019), and Dahl et al. (2016). Others find no significant effects: Tudor (2020), Dahl et al. (2016).

  5. Prior research has shown that individuals respond to financial incentives for seemingly non-economic decisions, such as marriage, divorce, and childbirth (Alm et al. 1999; Alm and Whittington 1999; Persson and Hoem 2014).

  6. Further, paid leave’s potential as a pro-natal policy tool is particularly important as the U.S. faces record low fertility. The U.S. birth rate has dropped below the replacement rate, continuing a decade-long decline and coinciding with the lowest number of children born in the country in any year since 1968 (Hamilton et al. 2019). A birth rate persistently below the replacement rate often indicates long-term challenges of replacing the workforce and supporting an aging society. Many European countries expand family leave and childcare provisions as a means of increasing fertility and stabilizing the population (Shim 2014).

  7. This is a large effect but falls in the range of estimates of fertility effects of some European studies, e.g. Shim (2014), Lalive and Zweimüller (2009), and Raute (2019).

  8. We utilize the heteroskedasticity adjustment from Ferman and Pinto (2019) for one treated unit but many control units to adjust for differences in treated and control group size. In addition to testing the sensitivity of our results to alternate post definitions and alternate control groups, we further use synthetic control methods as originally proposed by Abadie and Gardeazabal (2003) and furthered in Abadie et al. (2010, 2015).

  9. Using Vital Statistics birth records, the first finds more previously childless women gave birth, while fewer women had later parity births in response to unpaid leave. The second analyzes mothers surveyed in the National Longitudinal Survey of Youth and finds that the probability of childbirth increases with maternity leave but that this effect is larger for higher parity births.

  10. See https://www.oecd.org/els/soc/PF2_1_Parental_leave_systems.pdf

  11. Cannonier (2014) finds that FMLA increased fertility, but only among high-SES, white women. This is likely because they were more able to self-finance, or have a partner who could finance, their leave.

  12. Employees must have worked at their place of business for at least a year and for at least 1,250 hours over said year to be eligible. Additionally, only public agencies and private firms employing at least 50 workers within 75 miles are covered.

  13. Mothers who have a Cesarean section could qualify for eight weeks of post-partum leave under CA-SDI.

  14. This amount increased to 70 percent in 2018, but that is outside our sample window.

  15. This policy is not a good fit for regression discontinuity because of the length of time between announcement and implementation, allowing parents to manipulate birth timing, and children born as early as August 2003 technically being eligible for the program, eliminating a clear point of discontinuity.

  16. California public-sector employees may be covered if the agency or unit that employs them opts into the program.

  17. The California Family Rights Act, an extension of the FMLA passed in 1993, requires employers of 50 or more employees to provide job-protected leave.

  18. The geographic identifier of state is not included in the publicly available data after 2004.

  19. Population counts for the denominator come from the SEER data detailed below.

  20. The average age of first-time mothers in the U.S. was 25 years in 2006 (CDC NCHS). Female fertility significantly decreases after age 37 (The American College of Obstetricians and Gynecologists Committee).

  21. We primarily consider 10 years of data (1999-2008) from 50 states and Washington D.C.

  22. Results are similar if we drop New Jersey altogether, or exclude New Jersey and extend the analysis through 2011 (see Fig. 7.)

  23. Education is not reported consistently throughout our sample. Therefore, we interpret our estimates with caution. See Thunell (2017) for estimates by education and income.

  24. For example, share white is the number of white females ages 20 to 39 divided by the total number of females ages 20 to 39, and share age 30-34 is the number of females ages 30 to 34 divided by the number of females ages 20 to 39.

  25. The general fertility rate is generally defined as births per 1,000 aged 15-44 females but we use births to 1,000 aged 20-39 females for reasons explained above.

  26. In the Natality data, we are unable to explicitly see either employment or take up of leave.

  27. Appendix Fig. 7 shows the event study using data from July 1999 through June 2011, through the Great Recession. Because New Jersey implemented paid family leave in 2009, it is omitted for all years in this figure. Without New Jersey, there is a slight upward pre-trend prior to policy implementation. However, the DD estimate is similar to our main findings (2.539 additional births per 1,000 females 20-39 using data through 2011 versus 2.511 additional births per 1,000 females 20-39). Both estimates are statistically significant at the 1 percent level using robust standard errors clustered at the state level, but less significant using the Ferman and Pinto p-values detailed below (0.143 for the estimate through 2011 and 0.052 for our main estimate). Of particular interest in Fig. 7, the effect of CA-PFL on fertility rates dips slightly in 2008 and 2009, likely as a result of the Great Recession, but increases in 2010, and all estimates remain statistically different from zero throughout the post period.

  28. Because all the control units are included in the omitted period, the cyclicality of births appears in the event study coefficients estimating fertility in California relative to the omitted group (all the control states and California in the omitted period). In the month level event studies, the omitted period is July 2003, 12 months before the policy was implemented.

  29. It is primarily the lack of race controls that account for the pre-trend in Fig. 9. A. Figures with just race or just age controls are available upon request.

  30. Throughout the paper, we will use the FP p-values to measure and determine statistical significance. The tables show both the robust standard errors in parentheses and the FP p-values in brackets.

  31. Results by parity for all women 20-39 are shown in Appendix Fig. 10. Results by parity for women in their 20s are shown in Appendix Fig. 11. Results by individual parity (second, third, fourth, etc.) available upon request. Our findings are driven entirely by second and third births.

  32. Event studies shown in Fig. 12.

  33. They estimate Black mothers had the largest gain in leave-taking, 10.6 percentage points, compared to a 7.1 percentage point increase among White mothers and a statistically significant 6.2 percentage point increase among Hispanic mothers.

  34. Lichtman-Sadot (2014) finds evidence that women shifted births from the first half of 2004 to the second half.

  35. Our event studies do suggest that women from all other states are collectively a reasonable control group.

  36. We do not use SCM as our main method of analysis because the results are subjective to researcher decisions regarding inclusion of lags of both the outcome and control variables, and inference is more difficult.

  37. Vermont is excluded because the hedonic matrix is assymetrical.

  38. In results not shown, we find 0.22 fewer first births per 1,000 females 30-39 using SCM, but this estimate is not statistically different from zero. For both OLS and SCM, the increase in overall fertility is driven by an increase in higher order births rather than first births.

  39. The ACS is considered preliminary and not necessarily representative prior to 2003, but it is the best data source, to our knowledge, to answer this question.

  40. Lichtman-Sadot (2014) shows that women shifted their births from the first half of 2004 to the second half of 2004 to take advantage of the new leave policy. Oloomi (2016) shows that women have their first birth 2 years earlier following CA-PFL.

  41. If we extend our analysis through 2010, the estimates for the additional years are slightly smaller but remain positive and statistically different from zero.

  42. It is significantly less Hispanic and more Black

  43. Lalive and Zweimüller (2009) and Raute (2019) find fertility increases of similar magnitude.

  44. The number of children a woman entering reproductive years is expected to bear based on current fertility rates.

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Acknowledgements

We would like to thank Sandy Black, Rich Murphy, Steve Trejo, Marika Cabral, Francine Blau, Nicholas Sanders, John Cawley, Sharon Sassler, David Slusky, Alex Hollingsworth, Wendy Stock and seminar participants at the University of Texas at Austin, Cornell University, and DIW Berlin as well as participants in the 2019 Population Association of American Annual Conference, the 2019 American Society of Health Economists Annual Conference, and the 2019 Southern Economic Association Annual Conference for helpful comments and suggestions.

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All authors contributed to the study conception, design, material preparation, data cleaning, analysis, and drafting of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Pamela Meyerhofer.

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The data used in this paper require restricted access from the National Center for Health Statistics (NCHS) as state-level identifiers are not publicly available after 2004. As we cannot identify or collect additional information on specific individuals, IRB approval is not required for this project. We complied with all requirements of our data use agreement, specifically no cell or table reports any statistics for a group size smaller than 10 to maintain privacy. Consequently, we also cannot make the data publicly available.

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Appendix

Appendix

Fig. 7
figure 7

Event Study July 1999 - June 2011. Note: This plots (3) for the general fertility rate (births per 1,000 women 20-39) in California compared to women in all other states (except NJ) using 1999-2011 CDC NVSS Natality Data. Includes controls for proportion of the state-year 20-39 female population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year 20-39 female population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group), the unemployment rate, per capita income, and log of the population as well as state fixed effects and birth month-year fixed effects. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Fig. 8
figure 8

Month-Level Event Study for All Birth Rate. Note: This plots (3) for the general fertility rate (births per 1,000 women 20-39) in California compared to women in all other states using 1999-2008 CDC NVSS Natality Data using birthmonth level data. Includes controls for proportion of the state-year 20-39 female population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year 20-39 female population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group), the unemployment rate, per capita income, and log of the population as well as state fixed effects and birth month-year fixed effects. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Fig. 9
figure 9

Non-Residualized Event Studies. Note: This plots (3) for the general fertility rate (births per 1,000 women 20-39) in California compared to women in all other states using 1999-2008 CDC NVSS Natality Data. Figure 9.A includes only state and month-year fixed effects. Figure 9.B adds controls for proportion of the state-year 20-39 female population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year 20-39 female population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group) as well as state fixed effects and birth month-year fixed effects. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Fig. 10
figure 10

Event Studies By Parity, Mothers 20-39. Note: This plots (3) for the general fertility rate by parity (first (second+) births to women age 20-39 per 1,000 women 20-39) in California compared to women in all other states using 1999-2008 CDC NVSS Natality Data. Includes birth month-year and state fixed effects, and state-year controls for proportion of the state-year 20-39 female population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year 20-39 female population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group), the unemployment rate, per capita income, and log of the population. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Fig. 11
figure 11

Event Studies By Parity, Mothers 20-29. Note: This plots (3) for the age-specific general fertility rate by parity (first (second+) births to women age 20-29 per 1,000 women 20-29) by parity in California compared to women in all other states using 1999-2008 CDC NVSS Natality Data. Includes birth month-year and state fixed effects, and state-year controls for proportion of the state-year 20-39 female population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year 20-39 female population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group), the unemployment rate, per capita income, and log of the population. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Fig. 12
figure 12

Event Studies by Educational Attainment. Note: This plots (3) for the education-specific general fertility rate (births to women age 20-39 with < HS (HS, Some College, or BA+) degree per 1,000 women 20-39) in California compared to women in all other states using 1999-2008 CDC NVSS Natality Data. Includes birth month-year and state fixed effects, and state-year controls for proportion of the state-year 20-39 female population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year 20-39 female population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group), the unemployment rate, per capita income, and log of the population. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Fig. 13
figure 13

Differential Migration to California. Note: Data from 2001 - 2008 American Community Survey. This plots (3) for moving between states in the last year (migrate1 = 3 or 4) and moving between states in the last year conditional on having a birth in the last year (yngch = 0) in California compared to those (men and women) in all other states. Includes month-year and state fixed effects, and state-year controls for proportion of the state-year population age 20-24, 30-34, and 35-39 (age 25-29 is omitted), proportion of the state-year population by race/ethnicity (NH-black, NH-Asian, Hispanic, and other. NH-white is the omitted group), the unemployment rate, per capita income, and log of the population. The dotted-vertical line marks passage (September, 2002), while the solid vertical line denotes the start of benefits (July 1, 2004)

Table 8 Baum and Ruhm (2016) Controls

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Golightly, E., Meyerhofer, P. Does Paid Family Leave Cause Mothers to Have More Children? Evidence from California. J Labor Res 43, 203–238 (2022). https://doi.org/10.1007/s12122-022-09329-y

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Keywords

  • Maternity leave
  • Paternity leave
  • Paid family leave
  • Fertility