Data and modelling strategies in estimating labour force gross flows affected by classification errors

Gross flows among labour force states are of great importance in understanding labour market dynamics. Observed flows are typically subject to classification errors, which may induce serious bias. In this paper, some of the most common strategies, used to collect longitudinal information about labour force condition are reviewed, joinlty with the modeling approaches developed to correct gross flows, when affected by classification errors. A general framework for estimating gross flows is outlined. Examples are given of different model specifications, applied to data collected with different strategies. Specifically, two cases are considered, i.e., gross flows from (i) the U.S. Survey of Income and Program Dynamics and (ii) the French Labour Force Survey, a yearly survey collecting retrosective monthly information.