In this article, we describe lclogit, a Stata command for fitting a discrete-mixture or latent-class logit model via the expectation-maximization algorithm.
For further information: Herwig Immervoll (Herwig.IMMERVOLL@oecd.org, +33 1 45 24 92 14); Daniele Pacifico (Daniele.PACIFICO@oecd.org, +33 1 45 24 76 85) JT03397415 Complete document available on OLIS in its original format This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.
The aim of this paper is to analyse the role of unobserved preference heterogeneity in structural discrete choice models of labour supply. Within this framework, unobserved heterogeneity has been estimated either parametrically or semiparametrically through random coefficient models. Nevertheless, the estimation of such models by means of standard, gradient-based methods is often difficult, in particular if the number of random parameters is high. For this reason, the role of unobserved taste variability in empirical studies is often constrained since only a small set of coefficients is assumed to be random. However, this simplification may affect the estimated labour supply elasticities and the subsequent policy recommendations. In this paper, we propose a new estimation method based on an EM algorithm that allows us to fully consider the effect of unobserved heterogeneity nonparametrically. Results show that labour supply elasticities and other post-estimation results change significantly only when unobserved heterogeneity is considered in a more flexible and comprehensive manner. Moreover, we analyse the behavioural effects of the introduction of a working-tax credit scheme in the Italian tax-benefit system and show that the magnitude of labour supply reactions and the post-reform income distribution can differ significantly depending on the specification of unobserved heterogeneity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.