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Fertility and economic instability: the role of unemployment and job displacement


In this paper, we study the separate effects of unemployment and job displacement on fertility in a sample of white collar women in Austria. Using an instrumental variable approach, we show that unemployment incidence as such has no negative effect on fertility decisions, but the very fact of being displaced from a career-oriented job has. Fertility rates for women affected by a firm closure are significantly below those of a control group, even after 6 years, and this is so irrespective of the incidence or the duration of the associated unemployment spell.

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Fig. 2


  1. See Adsera (2005) for a study of the effects of aggregate unemployment rates on fertility rates across a number of European countries. Perry (2003) shows that the US college-educated females’ fertility behaves pro-cyclically whereas Dehejia and Lleras-Muney (2004) report several examples of pro-cyclical fertility, mainly in more developed countries. Kravdal (2002) for Norway and Meron et al. (2002) for France show a negative effect of individual unemployment experience on fertility, while Gutierrez-Domenenech (2008) studies its impact on fertility timing and marriage behaviour in Spain. See also Sobotka et al. (2011) for a recent review on the effect of recessions on fertility.

  2. See Kohler and Kohler (2002), Ranjan (1999), or Kreyenfeld (2010) for studies trying to associate the fertility decline in (Eastern) Europe with general economic uncertainty.

  3. Eligibility for parental leave benefits follows similar rules, with the exception that mothers younger than 25 are eligible after having worked 26 weeks in the previous year. In Austria, women who have children are entitled to 16 weeks of maternity protection, which is usually divided into 8 weeks before and 8 weeks after birth and up to 2 years of parental leave. The maternity protection period is paid at 100 % of the previous salary, the remaining period is paid at a flat rate of 408 Euros per month.

  4. In our companion paper (Del Bono et al. 2012), we study fertility effects of plant closures in more detail and find that negative fertility effects prevail for workers in white-collar jobs in the third tertile of the wage distribution and those with longer pre-displacement tenure and higher wage growth.

  5. Using the same data employed in this study, Del Bono et al. (2012) show evidence that the labour market and fertility histories of women employed in the firm 1 year before closure do not differ significantly from those of women in the displaced group.

  6. Unemployment is defined as being in receipt of unemployment benefits and not simply as a period of non-employment.

  7. Our checks show that using propensity scores weights constructed using individual characteristics helps to reduce further the (small) pre-displacement differences, seen in Figs. 1 and 2, and balance the treated and control sample. By contrast, propensity score weights constructed using firm characteristics only have no effect on the differences between treated and control groups, either before or after displacement. In the interest of space, we do not report here these results, but refer to our companion paper (Del Bono et al. 2012) for a full discussion on these issues.

  8. We conducted a similar analysis considering the probability of having another child in the next 3 or 6 years as the dependent variable. The results of this analysis are very similar to those presented here on the number of children because the main effects are found for women with no previous children.

  9. Standard errors are clustered at the individual level. Clustering at the firm level does not change the results in a meaningful way because of the small number of individuals employed within the same firm, particularly in the displaced group.

  10. The average number of children is 0.19 and 0.43 after 3 and 6 years since the reference date, respectively. See Table 1.

  11. The full results of this specification are available on request from the authors. Additional control variables are reported in the footnote of the table. Information about the partner or the marital status of the women was not available in the data. There is no information on the level of educational qualification of the individual either, but this is captured by age of entry in the labour market and apprenticeship status.

  12. We have data for 9 years, six regions and four industries (manufacturing, sales, transport and services).

  13. Note that the same time (year and quarter), industry and region dummies are in the main fertility regression as well.

  14. Results for the number of births in the next 3 years are very similar and are available upon request.


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We are grateful to workshop participants in Essen and Milano. This project received funding from the Austrian Science Fund (NRN Labor Economics and the Welfare State). Emilia Del Bono acknowledges the support provided by the ESRC Centre on Micro-Social Change at ISER (grant RES-518-28-001). All errors and opinions are the authors’ sole responsibility.

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Correspondence to Rudolf Winter-Ebmer.

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Bono, E.D., Weber, A. & Winter-Ebmer, R. Fertility and economic instability: the role of unemployment and job displacement. J Popul Econ 28, 463–478 (2015).

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  • Fertility
  • Unemployment
  • Firm closures
  • Human capital

JEL Classification

  • J13
  • J64
  • J65
  • J24