Determinants of Birthweight Outcomes: Quantile Regressions Based on Panel Data

Publikation: Working paperForskning

  • Stefan Holst Bache, Syddansk Universitet, Danmark
  • Christian Møller Dahl, Danmark
  • Johannes Tang Kristensen, Syddansk Universitet, Danmark
  • Institut for Økonomi
Low birthweight outcomes are associated with large social
and economic costs, and therefore the possible determinants of low birthweight
are of great interest. One such determinant which has received
considerable attention is maternal smoking. From an economic perspective
this is in part due to the possibility that smoking habits can be
influenced through policy conduct. It is widely believed that maternal
smoking reduces birthweight; however, the crucial difficulty in estimating
such effects is the unobserved heterogeneity among mothers. We
consider extensions of three panel data models to a quantile regression
framework in order to control for heterogeneity and to infer conclusions
about causality across the entire birthweight distribution. We obtain
estimation results for maternal smoking and other interesting determinants,
applying these to data obtained from Aarhus University Hospital,
Skejby (Denmark). We examine the use of both balanced and unbalanced
panels. In conclusion, our results show the importance of considering
conditional quantiles and controlling for unobserved heterogeneity
when estimating determinants of birthweight outcomes. An example of
this is the change in magnitude and significance of prenatal smoking.
Controlling for unobserved effects does not change the fact that smoking
reduces birthweight, but it shows that the effect is primarily a problem
in the left tail of the distribution on a slightly smaller scale.
OriginalsprogEngelsk
UdgivelsesstedAarhus
UdgiverInstitut for Økonomi, Aarhus Universitet
Antal sider22
StatusUdgivet - 2008

Se relationer på Aarhus Universitet Citationsformater

ID: 11262266