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Does Schooling Affect Women’s Desired Fertility? Evidence From Malawi, Uganda, and Ethiopia

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Demography

Abstract

Demographic scholarship suggests that schooling plays an important role in transforming fertility preferences in the early stages of fertility decline. However, there is limited evidence on the relationship between schooling and fertility preferences that addresses the endogeneity of schooling. I use the implementation of Universal Primary Education (UPE) policies in Malawi, Uganda, and Ethiopia in the mid-1990s to conduct a fuzzy regression discontinuity analysis of the effect of schooling on women’s desired fertility. Findings indicate that increased schooling reduced women’s ideal family size and very high desired fertility across all three countries. Additional analyses of potential pathways through which schooling could have affected desired fertility suggest some pathways—such as increasing partner’s education—were common across contexts, whereas other pathways were country-specific. This analysis contributes to demographic understandings of the factors influencing individual-level fertility behaviors and thus aggregate-level fertility decline in sub-Saharan Africa.

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Notes

  1. Primary schooling had a significant negative effect on pregnancy and marriage in Kenya (Duflo et al. 2012; Dupas 2011; Ferre 2009) and Nigeria (Osili and Long 2008), and a negative effect on total fertility in Malawi (Zanin et al. 2015). Secondary schooling had a negative effect on pregnancy and marriage in Malawi (Baird et al. 2010) and Kenya (Ozier 2010), and a negative effect on sexual debut in Uganda (Alsan and Cutler 2013). The literature has focused primarily on adolescent fertility outcomes rather than total fertility, likely because of the long time span needed to observe total fertility. Nonetheless, it is well documented that delays to fertility almost universally result in lower total fertility (Bongaarts 2002).

  2. The primary school gross enrollment rate is defined as total enrollment in primary school divided by the primary age school population. This figure can exceed 100 % if children over primary school age are still in primary school.

  3. I was unable to empirically investigate whether school affected fertility preferences of men because the association between exposure to UPE and years of schooling (the first stage) is not statistically significant for males in Malawi and Uganda. This is likely due to the greater benefit to girls than to boys from the removal of fees (World Bank 2009).

  4. Dates in the Ethiopian data are converted to the Gregorian calendar to ensure comparability with other countries.

  5. Major ethnolinguistic groups were those accounting for at least 10 % of the sample. I focused on ethnolinguistic background rather than ethnic group because of the large number of ethnic groups in these countries. Ethiopia and Uganda had approximately 80 and 40 ethnic groups listed in the DHS, respectively; thus, controlling for individual ethnic groups was problematic because of the small number of respondents in each group. Individual ethnic groups can be classified into broader ethnolinguistic groups sharing common linguistic and sociohistorical roots. Ethnolinguistic background captured ethnic diversity at a higher level of aggregation more suitable to this analysis.

  6. None of the women in the sample gave nonnumeric responses to ideal family size. This was consistent with Bachan and Frye’s (2013) finding that nonnumeric responses to ideal family size have decreased over time in Africa.

  7. I reran analyses using continuous variables that measured the frequency of watching television, reading a newspaper, and listening to radio. Results were substantively unchanged.

  8. In my final model, I did not include the control for time (birth cohort) in the first stage because this caused the birth cohort variable and exposure to UPE variable to be imprecise in the first stage in two of the three countries. This was not surprising given that the exposure to UPE variable was constructed using birth cohort (the simple correlation between exposure to UPE and birth cohort was .83 in Malawi, .82 in Uganda, and .84 in Ethiopia). Nonetheless, in the alternative model specification that controlled for time (birth cohort) in the first stage, exposure to UPE and birth cohort were jointly highly significant in the first stage in all three countries (p < .001), and the second-stage estimates were substantively unchanged (Table 5 in the appendix).

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Acknowledgments

Background support for this study was provided by the grant Team 1000+ Saving Brains: Economic Impact of Poverty-Related Risk Factors for Cognitive Development and Human Capital “0072-03” provided to the Grantee, The Trustees of the University of Pennsylvania by Grand Challenges Canada. I am grateful to Jere Behrman, Lawrence Wu, Delia Baldassarri, Jennifer Jennings, Amber Peterman, Florencia Torche, Paula England, Jennifer Hill, and three anonymous reviewers for helpful comments on earlier versions of this article.

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  1. Department of Sociology, New York University, 295 Lafayette Avenue, 4th Floor, New York, NY, 10012, USA

    Julia Andrea Behrman

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Correspondence to Julia Andrea Behrman.

Appendix

Appendix

Table 5 Second-stage (ivreg) results of the effect of schooling on women's ideal number of children and desire for six or more children including controls for birth cohort in the first stage
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Table 6 Naïve OLS estimation of the association between years of schooling and women’s ideal number of children and desire for six or more children
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Table 7 Summary statistics of control variables
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Behrman, J.A. Does Schooling Affect Women’s Desired Fertility? Evidence From Malawi, Uganda, and Ethiopia. Demography 52, 787–809 (2015). https://doi.org/10.1007/s13524-015-0392-3

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