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Women Left Behind? Poverty and Headship in Africa

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Two stylized facts about poverty in Africa motivate this article: female-headed households tend to be poorer, and poverty has been falling in the aggregate since the 1990s. These facts raise two questions. First, how have female-headed households fared? Second, what role have they played in Africa’s impressive recent aggregate growth and poverty reduction? Using data covering the entire region, we reexamine the current prevalence and characteristics of female-headed households and ask whether their prevalence has been rising, what factors have been associated with such changes since the mid-1990s, and whether poverty has fallen equiproportionately for male- and female-headed households. Lower female headship is associated with higher gross domestic product. However, other subtle transformations occurring across Africa—changes in marriage behavior, family formation, health, and education—are positively related to female headship, resulting in a growing share of female-headed households. This shift has been happening alongside declining aggregate poverty incidence. However, rather than being left behind, female-headed households have generally seen faster poverty reduction. As a whole, this group has contributed substantially to the reduction in poverty despite their smaller share in the population.

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  1. Begun with the seminal paper by Buvinic et al. (1978).

  2. Posel and Rogan (2012) explored the issue for South Africa for 1997–2006.

  3. Regressions using the DHS-based alternatives give similar results.

  4. At the time of writing, it was unclear what the equivalent poverty line would be when using the newly released 2011 PPP. However, different PPPs do not affect comparisons within country, the focus here.

  5. We adjust the poverty line to $2.80/day (equivalent to $1.25 when switching to the square root scale) using a pivot point of N = 5 (Ravallion 2015).

  6. Comparability was defined based on the consumption module and survey design: same survey months, similar survey design, diary/recall consistency, and national representativeness.

  7. Similar FHH prevalence rates are obtained using the consumption surveys.

  8. The wealth index is a composite measure of a household’s living standards generated using principal components analysis using data on the ownership of assets and amenities. It places households on a continuous scale of relative wealth and into wealth quintiles.

  9. Table S1.2 in Online Resource 1 provides the list of countries by macro region. The sample is restricted to the 26 countries with at least two surveys and uses the earliest and latest years (52 surveys total).

  10. Chad (1996–2004), Congo (2005–2011), and Lesotho (2004–2009) have fewer years between surveys than other countries (Table S1.2, Online Resource 1).

  11. When we run the model with country fixed effects, we observe two main findings. First, conditional on all controls, only years of education and age at first marriage retain a significant association with the dependent variable. We conclude that, as expected, the fixed-effects estimator has little power for identifying the effect of variables that vary more across than within countries over time. Second, the trend remains significant and unaccounted for by the regressors. Thus, although the fixed effects pick up omitted factors and explain within-country variance, this is insufficient to account for changes over time and is not the result of the dropped observations: the trend remains fully explained by the OLS run without the eight countries.

  12. Online Resource 1 provides data definitions and sources.

  13. Our findings are not inconsistent with evidence that higher education and age at first marriage are associated with lower divorce prevalence (Clark and Brauner-Otto 2015). These and other factors may simultaneously reduce divorce rates but put upward pressure on the formation of FHHs through other processes, such as fewer remarriages following a dissolution.

  14. Computed using the REGO command in STATA (Huettner and Sunder 2012). REGO decomposes the R 2 into the contribution of the regressors into Shapley (for individual regressors) or Owen values (for within-group regressors). We restrict the decomposition to Shapley values.

  15. Logging the changes and plotting the proportional changes gives a similar picture.

  16. This is a type of decomposition proposed by Ravallion and Huppi (1991).

  17. The corresponding decomposition is not presented because it provides little additional information given the small group sizes.

  18. The number of country spells is sometimes lower because of missing marital status for some countries.

  19. See Online Resource 1, Fig. S2.2.


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The authors are grateful to Elena Bardasi, Mayra Buvinic, Luc Christiaensen, Louise Grogan, Sylvie Lambert, Martin Ravallion, Adam Wagstaff, the journal’s three referees, and seminar participants at the World Bank, the University of Fribourg, Switzerland, and the 2015 ECINEQ Conference for useful comments. They also thank the World Bank Research Support Budget for its funding support. These are the views of the authors and need not reflect those of the World Bank and its affiliated organizations.

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Correspondence to Dominique van de Walle.

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Milazzo, A., van de Walle, D. Women Left Behind? Poverty and Headship in Africa. Demography 54, 1119–1145 (2017).

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