A Parametric Test for the Distinction between Unemployed and Out of the Labor Force Statuses

Abstract

Whether or not to empirically consider two (employed versus

not employed) or three (employed, unemployed, and out-of-laborforce)

classifications in labor supply studies is a controversial

issue. We develop a generalized censored probit likelihood

function that nests both possibilities. A novelty of this likelihood

function is that it allows researchers to test which representation

of the labor market is appropriate as well as to estimate the

degree to which classification errors may cloud inferences. Our

empirical results demonstrate that classifying the three groups is

useful to identify individuals labor force and employment

decisions separately. However, failure to incorporate classification

ambiguities may result in unemployed rates that are understated

and out-of-labor-force rates that are overstated.