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HIV Status and Labor Market Participation in South Africa

Listed author(s):
  • James Levinsohn

    (Yale University and NBER)

  • Zoë M. McLaren

    (University of Michigan)

  • Olive Shisana

    (Human Sciences Research Council, Pretoria, South Africa)

  • Khangelani Zuma

    (Human Sciences Research Council, Pretoria, South Africa)

We use econometric methods based on the propensity score to estimate the causal effect of HIV status on employment outcomes in South Africa. Relying on rich data from a national survey, which included HIV testing, we control for systematic differences between HIV-positive and HIV-negative individuals. We provide the first nationally representative estimates of the impact of HIV status on employment outcomes for southern Africa. Being HIV positive is associated with an increase of 6 to 7 percentage points in the likelihood of unemployment overall and 10 to 11 percentage points for those who are less educated. This disadvantage reinforces existing inequalities in South Africa. © 2013 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

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File URL: http://www.mitpressjournals.org/doi/pdf/10.1162/REST_a_00237
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Article provided by MIT Press in its journal Review of Economics and Statistics.

Volume (Year): 95 (2013)
Issue (Month): 1 (March)
Pages: 98-108

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Handle: RePEc:tpr:restat:v:95:y:2013:i:1:p:98-108
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  1. DiNardo, John & Fortin, Nicole M & Lemieux, Thomas, 1996. "Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach," Econometrica, Econometric Society, vol. 64(5), pages 1001-1044, September.
  2. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
  3. Bloom, David E. & Mahal, Ajay S., 1997. "Does the AIDS epidemic threaten economic growth?," Journal of Econometrics, Elsevier, vol. 77(1), pages 105-124, March.
  4. Sebnem Kalemli-Ozcan, 2012. "AIDS, “reversal” of the demographic transition and economic development: evidence from Africa," Journal of Population Economics, Springer;European Society for Population Economics, vol. 25(3), pages 871-897, July.
  5. 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.
  6. Harsha Thirumurthy & Joshua Graff Zivin & Markus Goldstein, 2008. "The Economic Impact of AIDS Treatment: Labor Supply in Western Kenya," Journal of Human Resources, University of Wisconsin Press, vol. 43(3), pages 511-552.
  7. 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.
  8. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, 07.
  9. 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).
  10. 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.
  11. Kenneth Y. Chay & Michael Greenstone, 2005. "Does Air Quality Matter? Evidence from the Housing Market," Journal of Political Economy, University of Chicago Press, vol. 113(2), pages 376-424, April.
  12. 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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