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.
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Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number
2008-20.
Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data I10 - Health, Education, and Welfare - - Health - - - General
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