2 resultados para data treatment

em Scottish Institute for Research in Economics (SIRE) (SIRE), United Kingdom


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Many OECD countries are increasingly relying on migrants to address shortages of trained health professionals. One key concern is whether migrant health professionals provide equivalent health care. We compare the treatment provided by migrant and non-migrant health professionals using administrative data from the Scottish dental system. A difference-in-differences model is estimated to examine whether migrant dentists respond differently to case mix and individual circumstances as compared with their non-migrant counterparts, and assess the extent to which any differences diminish over time. After controlling for both observed and unobserved differences between individual dentists and the cohort of patients that they treat, we find that migrant dentists have marginally different practice styles, and the variation diminishes over time within two years of practice.

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This paper discusses how to identify individual-specific causal effects of an ordered discrete endogenous variable. The counterfactual heterogeneous causal information is recovered by identifying the partial differences of a structural relation. The proposed refutable nonparametric local restrictions exploit the fact that the pattern of endogeneity may vary across the level of the unobserved variable. The restrictions adopted in this paper impose a sense of order to an unordered binary endogeneous variable. This allows for a uni.ed structural approach to studying various treatment effects when self-selection on unobservables is present. The usefulness of the identi.cation results is illustrated using the data on the Vietnam-era veterans. The empirical findings reveal that when other observable characteristics are identical, military service had positive impacts for individuals with low (unobservable) earnings potential, while it had negative impacts for those with high earnings potential. This heterogeneity would not be detected by average effects which would underestimate the actual effects because different signs would be cancelled out. This partial identification result can be used to test homogeneity in response. When homogeneity is rejected, many parameters based on averages may deliver misleading information.