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The Dynamics of Family Formation and Women’s Work: What Facilitates and Hinders Female Employment in the Middle East and North Africa?

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Abstract

Despite increases in educational attainment, women’s employment rates remain very low in the Middle East and North Africa. Difficulties reconciling work and family formation have been identified as an important but under-researched factor in low female employment rates. This paper investigates the dynamic relationship between family formation and women’s employment. The paper studies Egypt, Jordan, and Tunisia, leveraging unique retrospective data on work, marriage, childbearing, and child rearing. The data allow us to estimate discrete time hazard models for the duration of different labor market statuses. This paper examines three sets of outcomes: (1) duration in employment, (2) duration in non-employment, and (3) duration in different labor market states and specific types of work. We explore the different roles of getting married, being married, expecting children, having children, or having young children as constraints to employment. Findings show that anticipating marriage and getting married are strongly associated with women’s employment outcomes. Non-employment is an absorbing state, particularly after marriage.

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Fig. 1

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Fig. 2

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Fig. 3

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Fig. 4

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

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Notes

  1. See Assaad and Krafft (2013) for more information on the ELMPS, Assaad (2014b) for more information on the JLMPS, and Assaad et al. (2016) for more information on the TLMPS. Data are nationally representative when weighted. All descriptive statistics are weighted. Results are generally similar for regressions when weighted, but some models (for Tunisia, with the smallest sample) do not converge, so we consistently present regression results that are unweighted.

  2. This was primarily an issue in Tunisia, where data were generally missing at random due to problems in tablet programming that did not require responses to all relevant questions (Assaad et al. 2016).

  3. We specifically draw on questions of: “Employment status”, “Economic sector”, “Contract”, and “Social security” to construct these outcomes.

  4. Although we make no assumptions about whether public sector wage work is formal, it almost always is.

  5. Formal work is defined as work with a contract or social insurance coverage.

  6. Since we are relying on retrospective data, there are problems in retrospectively reporting unemployment (Assaad et al. 2018d) that preclude estimation of unemployment as a separate state.

  7. Married refers to the celebration, co-habitation, and consummation of the marriage, which are co-incident. For Muslims, the marriage contract (katb kitab) may be signed at the same time but may also be signed earlier than what we refer to as marriage.

  8. We tested, alternatively, using presence of children instead of number but the results were not substantively different. We also tested combining some of the age groups, but the results indicated that there were substantive differences that merited the disaggregation.

  9. In the residential mobility section that contains this question, moves are defined as lasting at least six months. In Egypt, moves require moving at the shyakha level (third level of administrative geography), but moving from urban to urban or rural to rural within the same governorate (first level of administrative geography) does not count. In Jordan, moves are on the sub-district level (third level of administrative geography). In Tunisia, moves require moving at the governorate level (first level of administrative geography), but moving from urban to urban or rural to rural within the same governorate does not count.

  10. The yearly time-varying location of residence in Egypt and Tunisia can be constructed from detailed questions on geographical mobility and the calendar time of changes in the place of residence.

  11. Information about parental employment and occupation statuses is for when the individual was age 15. Father’s occupation coded as skilled agriculture also included non-employed, don’t know, and missing.

  12. The data, unfortunately, do not capture any time-varying aspects of values, so we do not include those explicitly in the models, but do note they are likely to be a key aspect of context.

  13. This would include correlation across women in the same PSU, across spells for the same woman, and within spells for the same woman. Note that women rarely have multiple spells; for example, there are 6223 spells of work across the countries for 5909 women.

  14. The Kaplan–Meier estimator does not allow for time-varying covariates. We therefore code the distinction between spells starting before marriage versus at or after marriage based on the status in the first year of the spell. The multivariate models, below, do allow for time-varying covariates.

  15. These results are quite consistent across school-leaver cohorts as well as by education; see Appendix Figs. 5, 6, 7 and 8.

  16. We additionally tested using individual fixed effects (conditional logit models in a discrete time setting) for the employment and non-employment models. Since the models only identify off of women who have variation in their outcomes, all of those with only one right-censored spell are dropped, which substantially reduces the sample size and generates a selected sample. Nonetheless, key results, for example leaving work in anticipation of marriage and being less likely to start work with young children, persisted.

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Acknowledgements

The authors acknowledge funding from the Economic Research Forum project “Comparative Labor and Human Development Research Using Labor Market Panel Surveys Data.” The authors appreciate the comments and suggestions of participants at the Population Association of America 2018 conference.

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Appendix

Appendix

See Figs. 5, 6, 7 and 8; Tables 7, 8 and 9

Fig. 5
figure 5

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Annual rates of transition (percentages) between labor market statuses by status, marital status, and whether one year before or at marriage and country, less educated. Notes: Less educated is preparatory or less in Tunisia, secondary or less in Egypt and Jordan.

Fig. 6
figure 6

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Annual rates of transition (percentages) between labor market statuses by status, marital status, and whether one year before or at marriage and country, more educated. Notes: More educated is secondary and above in Tunisia, university and above in Egypt and Jordan.

Fig. 7
figure 7

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Annual rates of transition (percentages) between labor market statuses by status, marital status, and whether one year before or at marriage and country, school-leaver cohorts 1989 and earlier.

Fig. 8
figure 8

Source: Authors’ calculations based on ELMPS 2012, JLMPS 2010, and TLMPS 2014

Annual rates of transition (percentages) between labor market statuses by status, marital status, and one year before or at marriage and country, school-leaver cohorts 1990 and later.

Table 7 Discrete time competing risk multinomial logit models of labor market spells by type of employment, Egypt
Table 8 Discrete time competing risk multinomial logit models of labor market spells by type of employment, Jordan
Table 9 Discrete time competing risk multinomial logit models of labor market spells by type of employment, Tunisia

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Selwaness, I., Krafft, C. The Dynamics of Family Formation and Women’s Work: What Facilitates and Hinders Female Employment in the Middle East and North Africa?. Popul Res Policy Rev 40, 533–587 (2021). https://doi.org/10.1007/s11113-020-09596-6

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