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The effect of fertility on female labor supply in a labor market with extensive informality

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Abstract

This paper presents new evidence on the causal link between fertility and female labor supply by focusing on how informal employment interacts with maternal labor supply. We employ an IV strategy based on an unused data source for twin births in Turkey|a large middle-income country with extensive labor informality. We find that, following the first birth, female labor supply declines significantly and mothers who drop out of labor force are mostly the informally employed ones. This is contrary to the perception that informal jobs might be easier to sustain during motherhood as they are more flexible. Following further increases in family size, formally employed mothers start dropping out of labor force and their hours of work also decline. Higher fertility also leads to lower wages and lower job search intensity among mothers. We document substantial differences between maternal versus paternal labor supply in response to changes in family size. Unlike mothers, fathers increase their labor supply, which mostly comes from elevated informal employment|possibly due to a decline in their reservation wages. As a result, wages decline, hours of work increase, and job search activity shifts from formal to informal search methods for fathers. These results suggest that higher fertility might be associated with increased vulnerabilities and high labor income risks in countries with pervasive labor informality. Our estimates are robust to using alternative IV specifications based on gender composition of siblings.

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Fig. 1

Notes

  1. Sevinc (2011) also uses a twin-birth IV framework to estimate the causal impact of fertility on female labor supply in Turkey. However, he uses the Turkish Demographic Health Surveys, which is a rich data source for demographic variables, but not representative for labor market outcomes. The main advantage of our approach is that it allows for identifying the twin births within the THLFS, which is the data set used to calculate the official labor market statistics in Turkey; so, our econometric analysis yields nationally representative results.

  2. For other country- or region-specific studies, see Moschion (2013) for Australia, Cruces and Galiani (2007) for Latin America, de Jong et al. (2017) for sub-Saharan Africa, and Majbouri (2019) for Iran.

  3. See Aaronson et al. (2021) for long-term cross-country evidence and a comprehensive recent review of the related literature.

  4. The existing evidence for advanced countries is mixed. Earlier studies tend to find large favorable effects (Blau and Robins 1988; Ribar 1992; Anderson and Levine 1999), while later papers more frequently document smaller or null impact (Fitzpatrick 2010, 2012; Havnes and Mogstad 2011; Cascio and Schanzenbach 2013; Bick 2016) perhaps due to declining elasticities over time as different policies have been introduced and existing policies have become more widespread. There is relatively more established consensus on the finding that childcare policies are likely more effective for low-income single mothers (Kimmel 2005; Tekin 2007; Cascio 2009). See Olivetti and Petrongolo (2017) and Morrissey (2017) for recent reviews of this literature.

  5. See the OECD statistics database: https://stats.oecd.org.

  6. See https://data.tuik.gov.tr/Bulten/Index?p=Labour-Force-Statistics-2021-45645 for the most recent statistics.

  7. See Tunali (2022) for a very detailed guide about the THLFS micro-level data sets.

  8. Data on the month-of-birth and year-of-birth of children in our sample are obtained from TurkStat as a separate module.

  9. For older mothers, there is a risk of under-counting the number of children as older children might have left the household due to various reasons. We address this issue in one of our specifications by following Angrist and Evans (1998) and focusing on mothers with at most 32 years old|so that the observed number of children closely follows the total number of children. This data limitation restricts our ability to analyze the longer-term impact of fertility on maternal labor supply.

  10. We drop households with triplets and quadruplets from the analysis.

  11. There are approximately 25,000 observations who report zero or missing wages. We keep them in our sample when we run employment regressions, which is the core of our analysis. However, for the earnings regressions, we restrict our attention to salaried workers and drop the observations with zero or missing wages.

  12. Note that, in Eq. 1, y represents labor market outcomes in a general way. The type and functional form of the outcome variables may change (i.e., log, continuous, discrete, etc.) across different regressions.

  13. The monotonicity assumption and SUTVA should also be strongly valid in multiple-birth IV settings.

  14. Note that Angrist et al. (2010) use this combined instrument to improve the precision and efficiency of their IV estimates after finding null effects in each of their samples.

  15. Note that a twin birth is a strong fertility shock and it generates very strong first-stage correlations, which is evident from the large F-statistics reported in Table 3. In general, twin-birth instruments do not suffer from the weak instruments problem. There are some recent developments in the econometrics literature questioning the robustness of the canonical t-ratio-based inference used to calculate the F statistics for first-stage regressions in 2SLS models. Specifically, building on Stock and Yogo (2005), Lee et al. (2022) develop a practical method|which they call the tF procedure|to adjust the confidence intervals by developing continuous critical value functions that generate finite expected lengths for bounded confidence sets|see also Olea and Pflueger (2013) for an alternative approach. In particular, they show that, on average, standard errors in first-stage relationships tend to be understated by almost 20 percent in usual t-ratio-based inference. We re-calculate our F statistics using the various adjustment factors provided by Lee et al. (2022). Although the standard errors reported in Table 3 decline by 12–23 percent after implementing those adjustments, they remain statistically significant at 1 percent.

  16. See Bratti (2015) for an excellent summary of the relevant literature discussing the importance of the timing of the first birth.

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We thank Arthur van Soest, two anonymous referees, Abdurrahman Aydemir, Naci Mocan, participants of the 5th Istanbul Human Capital Conference at Bahcesehir University, and seminar participants at TED University for valuable comments and suggestions. The usual disclaimer holds.

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Tumen, S., Turan, B. The effect of fertility on female labor supply in a labor market with extensive informality. Empir Econ 65, 1855–1894 (2023). https://doi.org/10.1007/s00181-023-02399-6

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