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Uncovering the impact of the HIV epidemic on fertility in Sub-Saharan Africa: the case of Malawi

Abstract

We evaluate the impact of the HIV/AIDS epidemic on the reproductive behaviour for all women in Malawi, HIV-negative and HIV-positive alike, allowing for heterogeneous response depending on age and prior number of births. HIV/AIDS increases the probability that a young woman gives birth to her first child, while it decreases the probability to give birth of older women and of women who have already given birth. The resulting change in the distribution of fertility across age groups is likely to be more demographically and economically important than changes in the total number of children a woman gives birth to.

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Notes

  1. See, for instance, Bloom and Mahal (1997), Bell et al. (2004), Corrigan et al. (2005), Young (2005, 2007), McDonald and Roberts (2006), Werker et al. (2006), Papageoriou and Stoytcheva (2008) and Santaeulalia-Llopis (2008).

  2. The reason young HIV-positive women are more fertile is probably because they were more sexually active than uninfected women in the first place, and thus more likely to become both HIV-positive and pregnant. Since they became infected recently, they are still quite healthy (Ntozi 2002; Fabiani et al. 2006).

  3. Randomised experiments have shown that circumcision reduces the risk of becoming infected with HIV.

  4. Juhn et al. (2008) find no effect among HIV negative women, but, in line with the literature referred to in the beginning of this section, HIV positive women have 20% lower fertility than HIV negative women.

  5. The countries studied by Fink and Linnemayr (2008) are Cameron, Cote d’Ivoire, Ghana, Kenya and Senegal.

  6. In the main estimations, we have almost 150,000 observations (in the smallest sample in the robustness analysis almost 15,000 observations) distributed over 25 years and 18 districts.

  7. Clustering the standard errors at the district level does not significantly change our results, probably because district dummies capture most of the district correlation. Estimations with standard errors clustered by district are available from the authors.

  8. Conditional on unobserved group effects, the error terms ε it are independent, and their joint probability is consequently equal to the product of the probability of each term.

  9. Maximization is done by adaptive Gaussian quadrature using the gllamm procedure in Stata.

  10. Available at http://www.measuredhs.com/.

  11. Women were sampled to be representative of 15–49-year-old women in the survey years, resulting in a younger age-distribution as we go back in time.

  12. The sample was further reduced because of missing information regarding ethnicity for six women and regarding relative household wealth for another six women.

  13. The few cases where a woman gave birth to more than one child in a year are thereby not treated differently than cases where a woman gave birth to one child.

  14. The data is provided by the US Census Bureau in the HIV surveillance database, http://www.census.gov.

  15. In a related study, Durevall and Lindskog (2009), we use district adult mortality in 1998 and HIV prevalence in the general population in 2004. Though these variables might be of better quality, they only allow for cross-sectional analysis. The results are, however, very similar.

  16. Young (2007) and Kalemli-Ozcan (2009) also used the Estimation and Projections Package (EPP) to create time series for HIV rates.

  17. The household wealth variable has been constructed using information on household assets. See Rutstein and Johnson (2004) for further information about the DHS wealth index.

  18. The coefficient on number of siblings, that is the number of children the mother of the woman has given birth to, is almost statistically significant at the 10% level in all specifications.

  19. There are several potential explanations for differences in results even though Young (2007) also use individual level data from the DHS project. For example, our measure of the communal effect is based on much smaller geographical areas, districts, whereas Young (2007) uses countries. Moreover, we use annual time dummies, while Young (2007) includes a linear time trend in some specifications.

  20. The TFR is usually calculated for women 15–49, which of course gives a larger number.

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Acknowledgements

We would like to thank Lennart Flood, Måns Söderbom, Rick Wicks and two anonymous referees for valuable comments. We also thank Sida/Sarec for financial support.

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Correspondence to Annika Lindskog.

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Responsible editor: Junsen Zhang

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Durevall, D., Lindskog, A. Uncovering the impact of the HIV epidemic on fertility in Sub-Saharan Africa: the case of Malawi. J Popul Econ 24, 629–655 (2011). https://doi.org/10.1007/s00148-009-0303-2

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Keywords

  • Africa
  • AIDS
  • Demographic transition

JEL Classification

  • I10
  • J13
  • O12