Research Topics

This paper describes the application of a semiparametric approach, known as a varying coefficients model (Hastie and Tibshirani 1993), to implement a Oaxaca-Blinder type of decomposition in the presence of self-selection into treatment groups for a continuum of comparison groups. The flexibility of this methodology may allow for detecting heterogeneity of the role of endowment and coefficient effects when analyzing endogenous dose treatments. The methodology is then used to revisit the impact of obesity on wages (Cawley 2004), using body mass index (BMI) as the continuous group variable. The results suggest that body weight does have a negative impact on wages for white women, but the impact decreases for higher BMI levels. For white men, the impact is also negative and significant, but positive for low levels of BMI, which explains why they are not significant in the linear instrumental variables approach.

 Methods for Analyzing the Determinants of Poverty and Inequality

Recentered influence functions (RIFs) are statistical tools popularized by Firpo, Fortin, and Lemieux (2009) for analyzing unconditional partial effects on quantiles in a regression analysis framework (unconditional quantile regressions). The flexibility and simplicity of these tools has opened the possibility of extending the analysis to other distributional statistics using linear regressions or decomposition approaches. In this paper, I introduce three Stata commands to facilitate the use of RIFs in the analysis of outcome distributions: rifvar() is an egen extension used to create RIFs for a large set of distributional statistics;  rifhdreg facilitates the estimation of RIF regressions, enabling the use of high-dimensional fixed effects; and oaxaca_rif to implement  Oaxaca-Blinder type decomposition analysis (RIF decompositions).