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

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Author Info

  • Bruno Arpino
  • Elisabetta De Cao
  • Franco Peracchi

Abstract

Good estimates of HIV prevalence are important for policy makers in order to plan control programs and interventions. Although population-based surveys are now considered the "gold standard" to monitor the HIV epidemic, they are usually plagued by problems of nonignorable nonresponse. This paper uses the partial identification approach to assess the uncertainty caused by missing HIV status. 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, the Malawi Diffusion and Ideational Change Project (MDICP), our approach results in a reduction of the width of the worst-case bounds by about 18.2 percentage points in 2004, 13.2 percentage points in 2006, and 2.4 percentage points in 2008. We also use plausible instrumental variable and monotone instrumental variable restrictions to further narrow the bounds.

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File URL: ftp://ftp.dondena.unibocconi.it/WorkingPapers/Dondena_WP048.pdf
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Bibliographic Info

Paper provided by "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi in its series Working Papers with number 048.

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Length: 37 pages
Date of creation: Aug 2011
Date of revision:
Handle: RePEc:don:donwpa:048

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Related research

Keywords: partial identification; nonignorable nonresponse; panel data; HIV prevalence; Malawi Diffusion and Ideational change Project data;

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References

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  1. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.
  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. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
  4. repec:ese:iserwp:2004-19 is not listed on IDEAS
  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. Kreider, Brent & Pepper, John V., 2003. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Staff General Research Papers 10229, Iowa State University, Department of Economics.
  7. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
  8. Charles F. Manski, 1989. "Anatomy of the Selection Problem," Journal of Human Resources, University of Wisconsin Press, vol. 24(3), pages 343-360.
  9. 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.
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