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Population constraints on pooled surveys in demographic hazard modeling

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Author Info

  • Michael S. Rendall
  • Ryan Admiraal
  • Alessandra De Rose
  • Paola Di Giulio

    (Max Planck Institute for Demographic Research, Rostock, Germany)

  • Mark S. Handcock
  • Filomena Racioppi
Registered author(s):

    Abstract

    In non-experimental research, data on the same population process may be collected simultaneously by more than one instrument. For example, in the present application, two sample surveys and a population birth registration system all collect observations on first births by age and year, while the two surveys additionally collect information on women’s education. To make maximum use of the three data sources, the survey data are pooled and the population data introduced as constraints in a logistic regression equation. Reductions in standard errors about the age and birth-cohort parameters of the regression equation in the order of three-quarters are obtained by introducing the population data as constraints. A halving of the standard errors about the education parameters is achieved by pooling observations from the larger survey dataset with those from the smaller survey. The percentage reduction in the standard errors through imposing population constraints is independent of the total survey sample size.

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    File URL: http://www.demogr.mpg.de/papers/working/wp-2006-039.pdf
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    Bibliographic Info

    Paper provided by Max Planck Institute for Demographic Research, Rostock, Germany in its series MPIDR Working Papers with number WP-2006-039.

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    Length: 43 pages
    Date of creation: Nov 2006
    Date of revision:
    Handle: RePEc:dem:wpaper:wp-2006-039

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    Web page: http://www.demogr.mpg.de/

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    1. Imbens, Guido W & Lancaster, Tony, 1994. "Combining Micro and Macro Data in Microeconometric Models," Review of Economic Studies, Wiley Blackwell, vol. 61(4), pages 655-80, October.
    2. Judith K. Hellerstein & Guido W. Imbens, 1999. "Imposing Moment Restrictions From Auxiliary Data By Weighting," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 1-14, February.
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