ON THE ADJUSTMENT OF GROSS FLOW ESTIMATES FOR CLASSIFICATION ERROR WITH APPLICATION TO DATA FROM THE CANADIAN LABOR-FORCE SURVEY

Gross flows represent transition counts between a finite number of sta tes for individuals in a population from one point in time to the next . Such flows are important for researchers and policy analysts; for ex ample, gross labour flows for understanding labour market dynamics. Un fortunately, the observed flows are typically subject to classificatio n error. As a result, the problem of adjusting observed flows for clas sification error has received considerable attention. Currently, three methods for adjustment of classification error are available. All the se methods use the key assumption of independent classification errors (ICE) in conjunction with interview-reinterview data. We first give m odifications to two of the methods that ensure that margins of the adj usted flow table agree with the published ''stocks'' without requiring a final margin adjustment. We then propose epsilon-response contamina tion models and procedures for studying the robustness of ICE under di fferent scenarios of departures from ICE applicable for all the method s. Our empirical results, based on data from the Canadian Labour Force Survey, show that for many scenarios the ICE assumption is fairly rob ust. There are, however, situations where this is not the case. We thu s suggest that users apply the proposed procedures to check the robust ness of ICE under scenarios relevant for their own applications. Final ly, we provide valid chi-squared tests for modeling flow tables adjust ed for classification error under ICE so that the adjusted flows could be further smoothed under the model. These tests are also illustrated using data from the Canadian Labour Force Survey.