A Logit Model with Missing Information Illustrated by Testing for Hidden Unemployment in Transition Economies

In an important paper, Dempster, Laird and Rubin (1977) showed how the expectation maximization (EM) algorithm could be used to obtain maximum likelihood estimates of parameters in a multinomial probability model with missing information. This article extends Dempster, Laird and Rubin's work on the EM algorithm to the estimation of a multinomial logit model with missing information on category membership. We call this new model the latent multinomial logit (LMNL) model. A constrained version of the LMNL model is used to examine the issue of hidden unemployment in transition economies following the approach of Earle and Sakova (2000). We found an additional 0.5% hidden unemployment among workers describing themselves as self-employed in the transition economies of Central and Eastern Europe