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Does biological relatedness affect child survival?

  • David Bishai

    (Johns Hopkins University)

  • Heena Brahmbhatt

    (Johns Hopkins University)

  • Ron Gray

    (Johns Hopkins University)

  • Godfrey Kigozi

    (Makerere University)

  • David Serwadda

    (Makerere University)

  • Nelson Sewankambo

    (Makerere University)

  • El Daw Suliman

    (Johns Hopkins University)

  • Fred Wabwire-Mangen

    (Makerere University)

  • Maria Wawer

    (Johns Hopkins University)

Objective: We studied child survival in Rakai, Uganda where many children are fostered out or orphaned. Methods: Biological relatedness is measured as the average of the Wright’s coefficients between each household member and the child. Instrumental variables for fostering include proportion of adult males in household, age and gender of household head. Control variables include SES, religion, polygyny, household size, child age, child birth size, and child HIV status. Results: Presence of both parents in the household increased the odds of survival by 28%. After controlling for the endogeneity of child placement decisions in a multivariate model we found that lower biological relatedness of a child was associated with statistically significant reductions in child survival. The effects of biological relatedness on child survival tend to be stronger for both HIV- and HIV+ children of HIV+ mothers. Conclusions: Reductions in the numbers of close relatives caring for children of HIV+ mothers reduce child survival.

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File URL: http://www.demographic-research.org/volumes/vol8/9/8-9.pdf
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Article provided by Max Planck Institute for Demographic Research, Rostock, Germany in its journal Demographic Research.

Volume (Year): 8 (2003)
Issue (Month): 9 (May)
Pages: 261-278

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Handle: RePEc:dem:demres:v:8:y:2003:i:9
Contact details of provider: Web page: http://www.demogr.mpg.de/

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  1. Ainsworth, Martha & Filmer, Deon, 2002. "Poverty, AIDS, and children's schooling - a targeting dilemma," Policy Research Working Paper Series 2885, The World Bank.
  2. Filmer, Deon & Pritchett, Lant, 1998. "Estimating wealth effects without expenditure data - or tears : with an application to educational enrollments in states of India," Policy Research Working Paper Series 1994, The World Bank.
  3. John Shea, 1997. "Instrument Relevance in Multivariate Linear Models: A Simple Measure," The Review of Economics and Statistics, MIT Press, vol. 79(2), pages 348-352, May.
  4. Anne Case & Christina Paxson & Joseph Ableidinger, 2002. "Orphans in Africa," NBER Working Papers 9213, National Bureau of Economic Research, Inc.
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