Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Paper
  • Published:
Journal of Population Economics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

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.

References

  • Angeles G, Guilkey DK, Mroz TA (2005) The determinants of fertility in Rural Peru: program effects in the early years of the national family planning program. J Popul Econ 18(2):367–389

    Article  Google Scholar 

  • Barmby T, Cigno A (1990) A sequential probability model of fertility patterns. J Popul Econ 3(1):31–51

    Article  Google Scholar 

  • Becker G, Lewis HG (1973) On the interaction between the quantity and quality of children. J Polit Econ 81(2):S279–S288

    Article  Google Scholar 

  • Bell C, Devarajan S, Gersbach H (2004) Thinking about the long-run economic costs of AIDS. In: Haacker M (ed) The macroeconomics of HIV/AIDS. International Monetary Fund, Washington

    Google Scholar 

  • Bloom D, Mahal A (1997) Does the AIDS epidemic threaten economic growth. J Econom 77(1):105–124

    Article  Google Scholar 

  • Chimbwete C, Watkins SC, Msiyaphazi Zulu E (2005) The evolution of population policies in Kenya and Malawi. Popul Res Policy Rev 24(1):85–106

    Article  Google Scholar 

  • Corrigan P, Glomm G, Mendez F (2005) AIDS crisis and growth. J Dev Econ 77(1):107–124

    Article  Google Scholar 

  • Doctor HV, Weinreb AA (2003) Estimation of AIDS adult mortality by verbal autopsy in Rural Malawi. AIDS 17(17):2509–2513

    Article  Google Scholar 

  • Durevall D, Lindskog A (2009) How does communal HIV/AIDS affect fertility?: evidence from Malawi. Scandinavian Working Papers No 369, University of Gothenburg

  • Fabiani M, Nattabi B, Ayella E, Ogwang M, Declich S (2006) Differences in fertility by HIV serostatus and adjusted HIV prevalence data from an antenatal clinic in Northern Uganda. Trop Med Int Health 11(2):182–187

    Article  Google Scholar 

  • Fink G, Linnemayr S (2008) HIV, education, and fertility: long-term evidence from Sub-Saharan Africa. Harvard School of Public Health, Cambridge, Mimeo

    Google Scholar 

  • Fortson J (2009) HIV/AIDS and fertility. Am Econ J Appl Econ 1(3):170–194

    Article  Google Scholar 

  • GoM (2007) Malawi HIV and AIDS monitoring and evaluation report 2007. Government of Malawi, Lilongwe

    Google Scholar 

  • Gray RH, Wawer MJ, Serwadda D, Sewankambo N, Li C, Wabwire-Mangen F, Paxton L, Kiwanuka N, Kigozi G, Konde-Lule J, Quinn TC, Gaydos CA, McNairn D (1998) Population-based study of fertility in women with HIV-1 infection in Uganda. Lancet 351(9096):98–103

    Article  Google Scholar 

  • Grieser M, Gittelsohn J, Shankar AV, Koppenhaver T (2001) Reproductive decision making and the HIV/AIDS epidemic in Zimbabwe. J South Afr Stud 27(2):225–243

    Article  Google Scholar 

  • Juhn C, Kalemli-Ozcan S, Turan B (2008) HIV and fertility in Africa: first evidence form population based surveys. NBER Working Paper No 14248

  • Kalemli-Ozcan S (2003) A stochastic model of mortality, fertility and human capital investment. J Dev Econ 70(1):103–118

    Article  Google Scholar 

  • Kalemli-Ozcan S (2009) AIDS, reversal of the demographic transition and economic development: evidence from Africa. NBER Working Paper No 12181

  • Lewis JC, Ronsmans C, Ezeh A, Gregson S (2004) The population impact of HIV on fertility in Sub-Saharan Africa. AIDS 18(suppl 2):35–43

    Article  Google Scholar 

  • Lorentzen P, McMillan J, Wacziarg R (2008) Death and development. J Econ Growth 13(2):81–124

    Article  Google Scholar 

  • Magadi MA, Agwanda A (2007) The link between HIV/AIDS and recent fertility patterns in Kenya. Measure Evaluation Working Paper No 07–92, Caroline Population Centre, University of North Carolina

  • McDonald S, Roberts J (2006) AIDS and economic growth: a human capital approach. J Dev Econ 8(1):228–250

    Article  Google Scholar 

  • Measure DHS (2008) Statcompiler database. http://www.statcompiler.com

  • Morah E (2007) Are people aware of their HIV-positive status responsible for driving the epidemic in Sub-Saharan Africa: the case of Malawi. Dev Policy Rev 25(2):215–242

    Article  Google Scholar 

  • Neyman J, Scott E (1948) Consistent estimates based on partially consistent observations. Econometrica 16(1):1–32

    Article  Google Scholar 

  • Noël-Miller CM (2003) Concerns regarding the HIV/AIDS epidemic and individual childbearing: evidence from Rural Malawi. Demogr Res 1(10):319–348

    Article  Google Scholar 

  • NSO (National Statistical Office of Malawi) (1993) Malawi population and housing census 1987, vol II. NSO, Zomba

    Google Scholar 

  • NSO (2002) Census analytical report. NSO, Zomba

    Google Scholar 

  • NSO and ORC Macro (2001) Malawi demographic and health survey 2000—final report. NSO, Zomba

    Google Scholar 

  • NSO and ORC Macro (2005) Malawi demographic and health survey 2004—final report. NSO, Calverton

    Google Scholar 

  • NSO and UNICEF (2008) Multiple indicator survey 2006: Malawi. NSO, Zomba

    Google Scholar 

  • Ntozi J (2002) Impact of HIVAIDS on fertility in Sub-Saharan Africa. Paper prepared for the fourth meeting of the follow-up committee on the implementation of the DND and the ICPD-PA Yaounde, Economic Commission for Africa in collaboration with UNFPA

  • Oladapo OT, Daniel OJ, Odusoga OL, Ayoola-Sotubo O (2005) Fertility desires and intentions of HIV-positive patients at a Suburban specialist center. J Natl Med Assoc 97(12):1672–1681

    Google Scholar 

  • Papageoriou C, Stoytcheva P (2008) What do we know about the impact of AIDS on cross-country income so far? Working Paper No 2005-01 Department of Economics, Louisiana State University

  • Rutenberg N, Biddlecom AE, Kaona FAD (2000) Reproductive decision-making in the context of HIV and AIDS: a qualitative study in Ndola, Zambia. Int Fam Plann Perspect 26(3):124–130

    Article  Google Scholar 

  • Rutstein SO, Johnson K (2004) The DHS wealth index. DHS Comparative Report No 6 ORC Macro. NSO, Calverton

    Google Scholar 

  • Santaeulalia-Llopis R (2008) Aggregate effects of AIDS on development. Washington University in Saint Louis, Saint Louis, Mimeo

    Google Scholar 

  • Soares RR (2005) Mortality reductions, educational attainment, and fertility choice. Am Econ Rev 95(3):580–601

    Article  Google Scholar 

  • Soares RR (2006) The effect of longevity on schooling and fertility: evidence from the Brazilian demographic and health survey. J Popul Econ 19(1):71–97

    Article  Google Scholar 

  • Terceira N, Gregson S, Zaba B, Mason P (2003) The contribution of HIV to fertility decline in rural Zimbabwe, 1985–2000. Popul Stud 57(2):149–164

    Article  Google Scholar 

  • Ueyama M, Yamauchi F (2009) Marriage behavior response to prime-age adult mortality—evidence from Malawi. Demography 46(1):43–63

    Article  Google Scholar 

  • Werker E, Ahuja A, Wendell B (2006) Male circumcision and AIDS: the macroeconomic impact of a health crisis. Harvard Business School Working Paper No 07-025

  • Westoff CF, Cross AR (2006) The stall of the fertility transition in Kenya. DHS Analytical Studies No 9. ORC Macro, Calverton

    Google Scholar 

  • Yeatman SE (2009) The impact of HIV status and perceived status on fertility desires in Rural Malawi. AIDS Behav 13(suppl 1):12–19

    Article  Google Scholar 

  • Young A (2005) The gift of dying: the tragedy of AIDS and the welfare of future African generations. Q J Econ 120(2):423–466

    Article  Google Scholar 

  • Young A (2007) In sorrow to bring forth children: fertility amidst the plague of HIV. J Econ Growth 12(4):283–327

    Article  Google Scholar 

  • Zaba B, Gregson S (1998) Measuring the impact of HIV on fertility in Africa. AIDS 12(suppl 1):41–50

    Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Department of Economics, University of Gothenburg, P.O. Box 640, 405 30, Gothenburg, Sweden

    Dick Durevall & Annika Lindskog

Authors
  1. Dick Durevall
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Annika Lindskog
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Annika Lindskog.

Additional information

Responsible editor: Junsen Zhang

Rights and permissions

Reprints and permissions

About this article

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-009-0303-2

Keywords

JEL Classification

Search

Navigation