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HIV Status and Labor Market Participation in South Africa

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  • James A. Levinsohn
  • Zoë McLaren
  • Olive Shisana
  • Khangelani Zuma

Abstract

Because individuals with HIV are more likely to fall into poverty, and the poor may be at higher risk of contracting HIV, simple estimates of the effect of HIV status on economic outcomes will tend to be biased. In this paper, we use two econometric methods based on the propensity score to estimate the causal effect of HIV status on employment outcomes in South Africa. We rely on rich data on sexual behavior and knowledge of HIV from a large national household-based survey, which included HIV testing, to control for systematic differences between HIV-positive and HIV-negative individuals. This paper provides the first nationally representative estimates of the impact of HIV status on labor market outcomes for southern Africa. We find that being HIV-positive is associated with a 6 to 7 percentage point increase in the likelihood of being unemployed. South Africans with less than a high school education are 10 to 11 percentage points more likely to be unemployed if they are HIV-positive. Despite high unemployment rates, being HIV-positive confers a disadvantage and reinforces existing inequalities in South Africa.

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Bibliographic Info

Paper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 16901.

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Date of creation: Mar 2011
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Publication status: published as \HIV Status and Labor Market Participation in South Africa," with Zoe McLaren, Olive Shisana, and Khangelani Zuma, Forthcoming in Review of Economics and Statistics.
Handle: RePEc:nbr:nberwo:16901

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  1. Markus Frölich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
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  3. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, Institute for the Study of Labor (IZA).
  4. C Arndt & J D Lewis, 2000. "The Macro Implications of HIV/AIDS in South Africa: A Preliminary Assessment," South African Journal of Economics, Economic Society of South Africa, vol. 68(5), pages 380-392, December.
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  6. James J. Heckman & Salvador Navarro-Lozano, 2003. "Using Matching, Instrumental Variables and Control Functions to Estimate Economic Choice Models," NBER Working Papers 9497, National Bureau of Economic Research, Inc.
  7. David E. Bloom & Ajay S. Mahal, 1995. "Does the AIDS Epidemic Really Threaten Economic Growth?," NBER Working Papers 5148, National Bureau of Economic Research, Inc.
  8. Harsha Thirumurthy & Joshua Graff Zivin & Markus Goldstein, 2006. "The Economic Impact of AIDS Treatment: Labor Supply in Western Kenya," Working Papers 947, Economic Growth Center, Yale University.
  9. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," NBER Technical Working Papers 0251, National Bureau of Economic Research, Inc.
  10. Sebnem Kalemli-Ozcan, 2012. "AIDS, “reversal” of the demographic transition and economic development: evidence from Africa," Journal of Population Economics, Springer, vol. 25(3), pages 871-897, July.
  11. Clive Bell & Shantayanan Devarajan & Hans Gersbach, 2003. "The long-run economic costs of AIDS : theory and an application to South Africa," Policy Research Working Paper Series 3152, The World Bank.
  12. James Habyarimana & Bekezela Mbakile & Cristian Pop-Eleches, 2010. "The Impact of HIV/AIDS and ARV Treatment on Worker Absenteeism: Implications for African Firms," Journal of Human Resources, University of Wisconsin Press, vol. 45(4), pages 809-839.
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