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Why is fertility on the rise in Egypt? The role of women’s employment opportunities

A Correction to this article was published on 15 August 2020

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Can declining employment opportunities for women reverse the fertility transition? This paper presents evidence that the demographic transition has not just stalled but in fact reversed in Egypt. After falling for decades, fertility rates increased. The paper examines the drivers of rising fertility rates, with a particular focus on the role of declining public sector employment opportunities for women. Estimates show the effect of public sector employment on the spacing and occurrence of births using discrete-time hazard models. The paper then uses the results to simulate total fertility rates. The models address the potential endogeneity of employment by incorporating woman-specific fixed effects, incorporating local employment opportunities rather than women’s own employment, and using local employment opportunities as an instrument. Results indicate that the decrease in public sector employment, which is particularly appealing to women, may have contributed to the rise in fertility but is unlikely to be its main cause.

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  • 15 August 2020

    The Responsible Editor indicated in the original article is incorrect, the correct Responsible Editor for this paper is Junsen Zhang.


  1. See Krafft (2016) for the derivation of this ambiguous result and a discussion of its applicability to Egypt.

  2. Retherford et al. (2010 show that estimates of TFR using ASFRs are similar but not identical to estimates of TFR using PPR. The different methods and assumptions are expected to generate slightly different estimates.

  3. See Assaad and Krafft (2013) for additional information on the ELMPS. Weights are used with this paper’s descriptive statistics. Regressions do not use sampling weights since sampling is unrelated to the dependent variable and in such a case unweighted methods are preferred (Deaton 1997; Winship and Radbill 1994).

  4. Comparisons of annual fertility rates for the early 2000s indicate relatively comparable data for that period across the ELMPS 2006 and ELMPS 2012.

  5. Data quality is always a concern for surveys, particularly when relying on retrospective data. Assaad et al. (2018b) exploit the panel nature of the ELMPS to validate the retrospective data. The key covariate (public sector employment) performs well in these checks. Consistency of reporting across panel and retrospective data for public sector jobs was in the range of 85–89% (Assaad et al. 2018b). Additionally, Assaad and Krafft (2013) validate the 2012 data against other labor force surveys and censuses.

  6. Single calendar years are also included in the IV models since they include annual estimates of employment.

  7. Throughout the paper, although descriptive statistics are presented for the mean observed values of the incorporated continuous variables, all the continuous variables (prenatal care, life expectancy, adult literacy, GDP per capita, and local public sector employment) are shifted to have a mean of zero in the multivariate analyses (the observed mean is subtracted from the observed values). This allows the baseline hazard across parities and births to be a more meaningful reference value.

  8. See Krafft (2016) for details on how crude birth rates (CBRs) have been evolving as well. CBRs are available annually and, starting in 2007, began to rise substantially and track quite closely with the TFRs, corroborating their trends.

  9. Ages 25–39 are used to capture employment during peak fertility years. Ages 20–24 are not included since many of those employed in the public sector are university graduates and would still be in school and then job hunting in this age range.

  10. This paper focuses on public sector employment rather than unemployment since unemployment is much more difficult to detect accurately in retrospective data than public sector employment (Assaad et al. 2018b). The effects of unemployment are also more challenging to causally identify.

  11. General secondary graduates also had a large increase, however, this small group (6% of women ages 15–64 in 2012) is primarily composed of students who are enrolled in higher education and thus neither married nor bearing children. When restricting the analysis of general secondary to non-students, their TFR was nearly constant, 3.9 in 2006 and 4.0 in 2012.

  12. Marriage is nearly universal in Egypt (Salem 2015).

  13. Figure 9, in Appendix 1, shows the results interacting age groups and single calendar years, which are quite noisy.

  14. Throughout, fifth and higher order parities are coded as a single category for estimation, as are intervals of 10 years and longer.

  15. Shifting religious values over time or across generations are another potential explanation. Unfortunately, religious affiliation is available for only a subset of women (married and 18–39 in 2012), precluding the calculation of a TFR. Models of childbearing estimated for the subset of women with religion data and adding interactions between years and religion are noisy but suggest that fertility has been rising for Muslim women to a greater extent than Christian women.

  16. The effects of various covariates, such as women’s own public sector employment, can be identified only from those women with variation in these characteristics, since fixed effects estimates are based on within-woman variation. Among the women who are observed working in the public sector at some point in the time period analyzed, 33% varied over time in their public sector status.

  17. For all of the simulations, individuals’ characteristics, aside from public sector employment, are as observed in the sample. In the 3SRI models, the residual is assumed to be zero.

  18. That spouse employment in the public sector is statistically insignificant and relatively small in magnitude compared to the odds ratios for women also suggests that the old-age security rationale for fertility is not driving the impact of public sector work. Having either the husband or the wife in the public sector would secure such a pension.


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I thank Ragui Assaad, Marc Bellemare, Paul Glewwe, Deborah Levison, Djavad Salehi-Isfahani, Maia Sieverding, seminar participants at the University of Minnesota, conference participants at the Population Association of America 2014 annual conference, and conference participants at the Economic Research Forum 2016 annual conference for helpful questions and comments. The comments of anonymous referees substantially improved the paper and are much appreciated.


The author gratefully acknowledges support from the Minnesota Population Center at the University of Minnesota funded through the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) of the NIH under award number R24HD041023.

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Appendix 1. Descriptive statistics and models

Table 6 Sample characteristics
Table 7 Age-specific fertility rates (births per 1000 women), 1980–2014
Table 8 Percentage of population employed in the public sector, 1991–2011, by sex, ages 25–39
Table 9 Employment rate (percentage) and percentage of the population employed in the public sector by education and age, women aged 15–64, 1998, 2006, and 2012
Table 10 Hazards for discrete-time survival analysis model for births, age groups and grouped calendar years model, full 1990–2011 sample. Dependent variable: hazard (in year) of a birth. Coefficients have been transformed into hazards. Standard errors in parentheses

Appendix 2. Models incorporating spouse characteristics

A series of additional analyses are conducted controlling for spouse characteristics. Spouse data are not available for all women, as the husband may not be present due to death, migration, separation, or divorce. Approximately 89% of women included in the sample have a spouse present in the household at the time of the survey. The age group of the spouse at each year, his education (categorically, as with women), and his time-varying employment in the public sector (based on his retrospective labor market history data) are incorporated as controls in this subset of regressions. The regressions with these additional controls can help test the possibility that there are substantial fertility preference differentials among individuals and households who work in the public sector. The results are presented in Table 11. Importantly, the impact of women’s public sector work persists with odds ratios across births that are similar to Table 2. The impact on moving from the second to the third birth and the third birth to the fourth birth remains statistically significant. The spouse being employed in the public sector is not statistically significant.Footnote 18

Table 11 Discrete-time survival analysis model (logit) for births including spouse characteristics. Dependent variable: hazard (in year) of a birth. Coefficients have been transformed into odds ratios. Standard errors in parentheses

Appendix 3. Models incorporating interactions between having a son, public sector, and parity

As well as decisions about fertility depending on how many children the family already has, whether or not the family has a son is an important part of fertility decisions (Sieverding and Hassan 2016; Yount et al. 2000). There may be more discretion for women to not have third, fourth, or fifth children due to the pull of public sector work if they have already had a son. Table 12 presents an exploration of the potential relationship between public sector work, child sex composition, and parity. Models are presented both without fixed effects (akin to specification 3 in Table 2) and with fixed effects (similar to specification 4 in Table 4). Interactions between public sector work and parity, as well as parity and having a son, along with the three-way interaction between public sector work, parity, and having a son are all presented.

First, as the literature suggests and was true in the other models, having a son reduces the odds of a subsequent birth. The effect is significant for all parities, and increasingly so, suggesting that once they have several children, if women have a son childbearing is more likely to slow or stop. For the main effects of public sector work interacted with parity (the main effects being in the case with no son yet), there are not significant effects and in the model without fixed effects, odds ratios even lose the pattern of decreasing in later parities when the woman is in the public sector. However, for those women who have a son, the impact of public sector work on later births is large, a significant odds ratio of 0.452 for going from the third to fourth birth in the model without fixed effects, and a similar but insignificant odds ratio (p = 0.158) in the fixed effects model. Essentially, the relationship between public sector work and fertility depends on having already had a few children, including a male child. Since 69% of women have three children and 87% of women with a third child have a son, the majority of women have the potential to have their fertility influenced by work.

Table 12 Discrete-time survival analysis models for births with interactions between having a son, public sector, and parity. Dependent variable: hazard (in year) of a birth. Coefficients have been transformed into odds ratios. Standard errors in parentheses

Appendix 4. Variation in local employment opportunities

Figure 10 provides examples of the estimated variation in local employment opportunities over time for eight combinations of governorate and urban/rural. There is a substantial amount of variation in the estimated local employment opportunities over time. Although there is some consistency in overall trends, there are also clearly differences by location. This variation may be caused by where government jobs are allocated across a variety of different ministries and programs, such as the Social Fund for Development, which targets poor areas (with mixed success), or the national Youth Employment Program (Abou-Ali et al. 2010; De Gobbi and Nesporova 2005).

Fig. 10
figure 10

Example urban/rural and governorate level trends in percentage of 25–39-year olds with public sector employment. Source: Author’s calculations based on ELMPS 1998, 2006, and 2012. Note: Age 25–39 sample is for the year in question, not necessarily the age in survey year

Appendix 5. First stage of 3SRI model

Table 13 Probit marginal effects (first stage) for employment in public sector in year. Dependent variable: probability (in year) of public sector employment

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Krafft, C. Why is fertility on the rise in Egypt? The role of women’s employment opportunities. J Popul Econ 33, 1173–1218 (2020).

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  • Fertility
  • Female labor supply
  • Employment and fertility
  • Egypt
  • Middle East and North Africa

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