Decomposition analysis for a binary choice model
IZA, 2000 - 25 pages
This paper introduces a new and simple decomposition method for a binary choice model that is equivalent to the Blinder-Oaxaca decomposition analysis for wage differentials. The decomposition method is first developed for a single probit model and later generalized to a simultaneous equations model. Using Taylor expansion, we approximate the differences in the probabilities of choosing option 1 over option 0 between two groups in order to find the effects of the differences in "eachʺ individual characteristic and the differences in "eachʺ coefficient. We implement this decomposition analysis studying the racial gap in female labor market participation rates. The racial gap of participation rates among women can be almost exclusively explained by the differences in the coefficients.
What people are saying - Write a review
We haven't found any reviews in the usual places.
age variables analysis for wage binary choice equation binary choice model Brunello Catherwood Library Characteristics Diff children under age choosing option Coefficients Constant consistent estimators contributes to reducing decompose Decomposition 1 Diff decomposition analysis differences in coefficients differences in individual differences in participation differences in probabilities differences in standard differences in unconditional East Germany effects of differences equation is estimated estimate the labor estimated independently Ethnic German explained by differences extend the decomposition find the effects gap of participation Germany income labor economics labor market participation latent variable log-wage equation Non-Labor Inc Non-working women number of children observed individual characteristics PARTICIPATION CHOICE probability of choosing R. T. Riphahn race women racial differences racial gap rates of choosing reducing the racial represent the effects selection bias simultaneous equations model single probit model standard normal CDF step approximation stochastic component Taylor expansion total differences 0.066 unconditional expectations vector wage differentials