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Using panel data to partially identify HIV prevalence when HIV status is not missing at random

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  • Bruno Arpino

    (Department of Decision Sciences and Dondena Centre for Research on Social Dynamics, Bocconi University.)

  • Elisabetta De Cao

    (Dondena Centre for Research on Social Dynamics, Bocconi University.)

  • Franco Peracchi

    (Tor Vergata University and EIEF)

Abstract

Although population-based surveys are now considered the "gold standard" for estimating HIV prevalence, they are usually plagued by problems of nonignorable non- response. This paper uses the partial identification approach to assess the uncertainty caused by missing HIV status due to unit and item nonresponse. We show how to exploit the availability of panel data and the absorbing nature of HIV infection to narrow the worst-case bounds without imposing assumptions on the missing-data mechanism. Applied to longitudinal data from rural Malawi, our approach results in a substantial reduction of the width of the worst-case bounds. We also use plausible instrumental variable and monotone instrumental variable restrictions to further narrow the bounds.

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Bibliographic Info

Paper provided by Einaudi Institute for Economics and Finance (EIEF) in its series EIEF Working Papers Series with number 1113.

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Length: 39 pages
Date of creation: 2011
Date of revision: Aug 2011
Handle: RePEc:eie:wpaper:1113

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  1. Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables with an Application to the Returns to Schooling," NBER Technical Working Papers 0224, National Bureau of Economic Research, Inc.
  2. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
  3. Horowitz, J.L. & Manski, C.F., 1995. "Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and estimation Using Weights and Imputations," Working Papers 95-12, University of Iowa, Department of Economics.
  4. Kreider, Brent & Pepper, John V., 2007. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 432-441, June.
  5. Jean-Pierre Lachaud, 2005. "HIV prevalence and poverty in Africa : micro and macro-econometric evidence applied to Burkina Faso," Documents de travail 113, Groupe d'Economie du Développement de l'Université Montesquieu Bordeaux IV.
  6. Nicoletti, Cheti & Peracchi, Franco, 2004. "The effects of income imputation on micro analyses: evidence from the ECHP," ISER Working Paper Series 2004-19, Institute for Social and Economic Research.
  7. Cheti Nicoletti & Franco Peracchi & Francesca Foliano, 2009. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," CEIS Research Paper 145, Tor Vergata University, CEIS, revised 30 Sep 2009.
  8. Rebecca L. Thornton, 2008. "The Demand for, and Impact of, Learning HIV Status," American Economic Review, American Economic Association, vol. 98(5), pages 1829-63, December.
  9. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
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