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Delayed First Birth and New Mothers’ Labor Market Outcomes: Evidence from Biological Fertility Shocks

European Journal of Population volume 30, pages 35–63 (2014)Cite this article

Abstract

We investigate the impact of delaying the first birth on Italian mothers’ labor market outcomes around childbirth. The effect of postponing motherhood is identified using biological fertility shocks; namely, the occurrence of miscarriages and stillbirths. Focusing on mothers’ behavior around the first birth, our study is able to isolate the effect of motherhood postponement from that of total fertility. Our estimates suggest that delaying the first birth by 1 year raises the likelihood of participating in the labor market by 1.2 % points and weekly working time by about half an hour, while we do not find any evidence that late motherhood prevents worsening of new mothers’ job conditions (the so-called “mommy track”). Our findings are robust to a number of sensitivity checks, among which are controls for partners’ characteristics and a proxy for maternal health status.

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Notes

  1. The same policies also had an effect on the spacing of births, producing a shift to shorter birth intervals and affecting, therefore, the timing of post-first parities, as shown in Andersson (2004).

  2. So, the effect goes in the opposite direction with respect to that observed for the maternal benefits analyzed for Sweden.

  3. As we will see later, wages are not available in our dataset, and for this reason we will not be able to estimate the causal effect of delaying motherhood on wages.

  4. She estimates the reduced form effect of fertility timing, including the effect on total fertility, and not the “pure” effect of postponing motherhood.

  5. Indeed, some previous studies—using IVs strategies, and mainly twinning or sibling sex composition as instruments—have found a negative effect of the number of children on mothers’ labor market outcomes such as labor force participation, working hours, wages, and earnings (see Angrist and Evans 1998; Jacobsen et al. 1999; Cruces and Galiani 2007). However, the findings of papers using fertility or infertility shocks to identify the causal effects of the number of children are “mixed.” Rondinelli and Zizza (2010), for instance, focus on Italy and use the fact that women cite biological/physiological factors as a reason for the mismatch between their actual and their desired number of children as instruments in an instrumental variables strategy to identify the causal effect of the number of children on women’s labor market attachment. The authors find no effect of the number of children on labor force participation, weekly working hours, years of contribution, working time, type of contract, job quality, and potential experience.

  6. The number of births and the information about the mothers are found in the Population Registers, through the P4 Model introduced from ISTAT that releases these data. Both are available at www.demo-istat.it. The survey structure and main results are described in ISTAT (2006).

  7. The BBS only provides information on having experienced miscarriage and stillbirth in the past, while it does not gather information on the age at which a woman experienced it. For this reason, we cannot use fertility shocks to instrument postponement of parities higher than the first (e.g., for a woman with two children, we do not know if the miscarriage or stillbirth took place before or after the first parity).

  8. The question makes no distinction on the day/month at which the fetal loss took place, so it can equally refer to both miscarriages and stillbirths.

  9. Hence, this last selection criterion is important to insure that our identification strategy works properly.

  10. This has also been found for other countries, such as the US (Shapiro and Mott 1994).

  11. This correction can be implemented in Stata as described in Hole (2006).

  12. A normality assumption is used in the first step to estimate the interval regression. Stewart (1983) investigates the consequences of asymmetry in the error distribution, namely the case of log-normal and χ 2 distributions, and in both cases reports evidence of zero bias when using ML estimation. Only the estimates of the error’s variance appear to be biased owing to asymmetry. Bettin and Lucchetti (2012) analyze the impact of leptokurtosis and report a very small bias using ML methods. Thus, ML estimates seem to be fairly robust to deviations from the normality assumption.

  13. This is not the case in “traditional” 2SLS, unless higher order terms in \( \hat{a}_{i} \) are included in the second stage, which would require additional instruments.

  14. For which the association with miscarriage especially at low levels of consumption of these substances is, however, unclear.

  15. When the cause of miscarriages and stillbirths is chromosomal abnormalities, and not women’s health conditions, future fertility is not reduced, and especially in the case of very early miscarriages, the fertility postponement is unlikely to be substantial as a couple may replan a new pregnancy quite soon (ovulation typically restarts by 4–6 weeks since the loss of the fetus). By way of contrast, late miscarriages and especially stillbirths may imply for the couple a longer postponing of the planning of a new pregnancy and, therefore, a longer delaying of first birth. Unfortunately, the survey we use did not collect separate information for miscarriages and stillbirths, but only provides an aggregate variable. This implies that the effect which we estimate in the first stage (the fertility timing equation) will be the average effect of miscarriages and stillbirths on age at first birth.

  16. Wilde et al. (2010) review the main criticism toward using fetal loss as an instrument, although they clearly acknowledge that it is probably the “most clever and compelling instrument” (p. 13) used in the literature. Its main weaknesses according to the authors are due to potential under-reporting of miscarriage and stillbirth, and differences in predetermined characteristics between women who experienced it and women who did not. However, in our case, this criticism does not seem to apply (see also Table 3). Another potential criticism is that women who experienced a fertility shock may have worse health, affecting their labor market outcomes. We will provide evidence on this issue in Sects. 5.2–5.3. Last but not the least, the authors state that miscarriage and stillbirth are likely to cause only a limited increase in women’s age at first birth (a maximum of 2 years in their data). However, this is likely to be true for many instrumental variables’ estimates using quasi-experiments provided in the literature; for instance, those using institutional changes such as increasing compulsory schooling laws, length of maternal leave, etc.

  17. Marginal effects are not reported in the Table.

  18. See Angrist (2001) for the use of linear models in the presence of dichotomous dependent variables.

  19. Since the sample is made of two pooled cross sections and all women in the sample are mothers, mother’s age at the interview is perfectly collinear with mother’s age at first birth and child’s age, and is not included in the regressions.

  20. We used for LFP a linear probability model, which is robust to non-normality. However, the estimated effect is not very sensitive to changing the functional form. Using a probit model, for instance, the marginal effect of age at first birth is 0.011.

  21. A linear model and OLS were used for working hours following Miller (2011).

  22. ISCED stands for International Standard Classification of Education. ISCED 1: early childhood education; ISCED 2: primary schooling; ISCED 3: lower secondary schooling; ISCED 4: upper secondary schooling; ISCED 5: undergraduate degree; and ISCED 6: postgraduate degree.

  23. The characteristics included are those of the current husband or partner. These results should be interpreted with caution as some partners’ characteristics may be endogenous.

  24. These additional results are available from the authors upon request.

  25. In order to assess the existence of pure income effects on female labor outcomes, which may also be correlated with women’s delayed first birth.

  26. It is necessary to extend the age range with respect to the analysis in the BSS data in order to assess the long-term effects. However, we still drop very early and very late childbearing.

  27. We do not report fixed effects estimates since age at first birth is time invariant.

  28. Omitting self-reported health has little effect on the coefficient of age at first birth. These additional estimates are available upon request.

  29. Hourly wage has been computed by dividing gross monthly wage by weekly working hours multiplied by four.

  30. Presumably because health status is likely to affect employment status and has no additional effect conditional on employment.

  31. Thus, there is a multiple sample selection in this case, the first on working as an employee after childbearing and the second on being employed for the same employer. As we do not have good exclusion restrictions to address these forms of sample selection, we present in the text only the estimates conditional on working for the same employer. On the grounds that women who are more subject to job downgrading are more likely not to participate in the labor force after childbearing or to change employer, the estimates we present in the text may represent lower bound estimates for the risk of a job downgrade.

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Acknowledgments

We gratefully acknowledge Cinzia Castagnaro and Claudia Iaccarino from ISTAT for providing us with some useful data to integrate the publicly available version of the Italian Birth Sample Survey’s first wave. Two anonymous referees and participants in presentations given at the 7th European Workshop on Labour Markets and Demographic Change (St. Gallen), the University of Milan, and the University of Verona are gratefully acknowledged for their comments and suggestions. The usual disclaimers apply.

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  1. DEMM, Università degli Studi di Milano, via Conservatorio 7, 20122, Milan, Italy

    Massimiliano Bratti

  2. IZA, Bonn, Germany

    Massimiliano Bratti

  3. DSE, Università degli Studi di Verona, via dell’Artigliere 19, 37129, Verona, Italy

    Laura Cavalli

  4. Università Bocconi, Milan, Italy

    Laura Cavalli

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  1. Massimiliano Bratti
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Bratti, M., Cavalli, L. Delayed First Birth and New Mothers’ Labor Market Outcomes: Evidence from Biological Fertility Shocks. Eur J Population 30, 35–63 (2014). https://doi.org/10.1007/s10680-013-9301-x

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