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Disease risk and fertility: evidence from the HIV/AIDS pandemic

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Abstract

A fundamental question about human behavior is whether fertility responds to disease risk. The standard economic theory of household fertility decision-making generates ambiguous predictions, and the response has large implications for human welfare. We examine the fertility response to the HIV/AIDS pandemic using national household survey data from 14 sub-Saharan African countries. Instrumental variable (IV) estimates using distance to the origin of the pandemic suggest that HIV/AIDS has increased the total fertility rate (TFR) and the number of surviving children. These results rekindle the debate about the fertility response to disease risk, particularly the HIV/AIDS pandemic, and highlight the question of whether the HIV/AIDS pandemic has reduced GDP per capita.

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Notes

  1. Oster (2012a) and Chin (2013) conduct placebo tests helping to support the validity of this instrumental variable in health behavior regressions, and we present similar placebo results.

  2. Oster (2012a) uses distance to the origin as an instrument for HIV prevalence in risky sexual behavior regressions. Chin (2013) uses distance to the origin as an instrument for HIV prevalence in intimate partner violence regressions.

  3. These 14 countries are also the countries used in Oster (2012a). Chin (2013) uses a smaller number of countries because intimate partner violence modules are not offered in all DHS countries.

  4. There are at least two reasons why these studies yield a variety of findings. First, they employ different identification strategies. Second, they use data from different countries. It is difficult to adjudicate between the various identification strategies, as they are all established, reliable empirical approaches. Our study is among those (including Boucekkine et al. (2009), Fink and Linnemayr (2009), Fortson (2009), Juhn et al. (2012), Kalemli-Ozcan (2012), Karlsson and Pichler (2015)) using data from a large set of sub-Saharan African countries.

  5. HIV prevalence age profiles for women in high-HIV countries typically peak around the age 30, as do pregnancy age profiles, further suggesting that HIV/AIDS may have a larger effect on the survival of later births.

  6. Karlsson and Pichler (2015) provide an in-depth discussion of a large set of possible mechanisms linking HIV to fertility. In general, this set includes the mechanisms identified in other studies displayed in Table 1.

  7. Kalemli-Ozcan and Turan (2011) provide a detailed follow-up analysis of the data in Young (2005) and do not provide any theoretical predictions or discussion of mechanisms.

  8. The cross-sectional analyses of country- and region-level data in Kalemli-Ozcan (2012) suggest that HIV has increased fertility, whereas the time series analysis in Kalemli-Ozcan (2012) suggests positive and negative effects.

  9. These countries (and survey rounds) are as follows: Burkina Faso 2003, Cameroon 2004, Ethiopia 2005, Ghana 2003, Guinea 2005, Kenya 2003, Lesotho 2004, Malawi 2004, Mali 2006, Senegal 2005, Sierra Leone 2005, Swaziland 2006, Zambia 2007, and Zimbabwe 2005. Following Oster (2012a), we omit Liberia and the Democratic Republic of the Congo because of ongoing large-scale civil conflict in these countries during the period over which we observe fertility behavior.

  10. The controls in \( {X}_c^{\hbox{'}} \) are the same as those in the previous literature using distance to the origin of the pandemic as an instrumental variable (Oster 2012a, Chin 2013). One exception is that unlike Oster (2012a), we do not control for the number of children ever born because fertility is our outcome of interest.

  11. We use the natural log of HIV prevalence for two reasons. First, we follow Oster (2012a) in our use of log of HIV prevalence, who shows that ln(HIV) is linear in distance from the origin of the HIV/AIDS pandemic and presents simulation results that is well-explained by a simple model in which the probability of interaction between two individuals decreases with distance (see Appendix A.1 in Oster (2012a)). Second, a leading alternative functional form (i.e., HIV and HIV squared) does not suit this setting as many DHS clusters in our sample have HIV prevalence equal to zero, leading to a high degree of collinearity between HIV and HIV squared.

  12. The estimation uses ivreg2 in Stata 14.2 with standard errors robust to the presence of arbitrary heteroskedasticity.

  13. As in Oster (2012a), we address a major barrier to using ln(HIV) by assigning HIV prevalence of 0.1% to clusters where zero individuals tested HIV-positive in the cluster.

  14. These estimates are very similar to those in Oster (2012a).

  15. The samples (i.e., countries and survey rounds) for the falsification test are as follows: Burkina Faso 1992, Cameroon 1991, Ethiopia 2000, Ghana 1993, Guinea 1999, Malawi 2006, Mali 1995, Senegal 1992–93, Zimbabwe 1999.

  16. When we include indicator variables for survey year, our first stage becomes weaker. We present the Anderson-Rubin confidence intervals to address this concern. In neither the TFR regression nor the surviving number of children regression does the Anderson-Rubin confidence interval (CI) include zero.

  17. As an additional robustness check, we re-estimate the main results omitting one country at a time. Throughout, we find a positive and statistically significant effect of HIV prevalence on TFR. This suggests that it is not a particular country that is driving our results.

  18. Our first-stage results also reveal a positive association between cluster-level educational attainment and HIV prevalence. There are at least two explanations for the fact that educational attainment and HIV prevalence are correlated at the cluster level. One reason is that higher education areas likely are areas with more economic activity, and previous research has found that economic activity may cause HIV prevalence (Oster 2012b). Another reason is that higher education areas may be areas with high population density, and population density increases HIV transmission.

  19. In the 1990s, fertility decline in several sub-Saharan African countries slowed down. For example, between the late 1960s and the mid-1990s, TFR in Kenya declined from 8.1 to 5.4 (United Nations 2017). One way to incorporate our estimate into the account of the slowdown in fertility decline is to calculate counterfactual TFR if HIV prevalence were not as high. Our TFR estimate for Kenya in 2003 is 4.3. Our point estimate for the effect of HIV prevalence on TFR is 1.97. This implies that if Kenya’s HIV prevalence were 50% lower, this would reduce TFR by approximately 1.37, yielding a TFR of 3.56. This is very close to the average for all less developed countries around that time, which was 3.3 (United Nations 2017). Even lower HIV prevalence would imply even lower TFR, suggesting that the rise of the HIV/AIDS pandemic is not the only major factor affecting Kenya’s path along the fertility transition.

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Acknowledgements

We thank Erdal Tekin and three anonymous reviewers for excellent comments. Ran Duan and Mark Jarrett provided timely research assistance. The findings, interpretations, and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the aforementioned individuals or agencies. All errors are our own.

Funding

This study was not funded by a research grant or other funding source.

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Authors and Affiliations

  1. Department of Economics, Baylor University, Waco, TX, USA

    Yoo-Mi Chin

  2. White House Social and Behavioral Sciences Team and Department of Economics, Reed College, Portland, OR, USA

    Nicholas Wilson

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Correspondence to Nicholas Wilson.

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Chin, YM., Wilson, N. Disease risk and fertility: evidence from the HIV/AIDS pandemic. J Popul Econ 31, 429–451 (2018). https://doi.org/10.1007/s00148-017-0669-5

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