Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/244513 
Year of Publication: 
2015
Series/Report no.: 
Working Paper No. 9/2015
Publisher: 
Örebro University School of Business, Örebro
Abstract: 
This paper considers Bayesian inference procedures for regression models with ordinally observed explanatory variables. Taking advantage of a latent variable interpretation of the ordinally observed variable we develop an efficient Bayesian inference procedure that estimates the regression model of interest jointly with an auxiliary ordered probit model for the unobserved latent variable. The properties of the inference procedure and associated MCMC algorithm are assessed using simulated data. We illustrate our approach in an investigation of gender based wage discrimination in the Swedish labor market and find evidence of wage discrimination.
Subjects: 
Markov Chain Monte Carlo
latent variables
ordered probit
wage discrimination
JEL: 
C11
C25
C35
J31
Document Type: 
Working Paper

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