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Measurement error in earnings data: replication of Meijer, Rohwedder, and Wansbeek’s mixture model approach to combining survey and register data

Stephen Jenkins and Fernando Rios-Avila ()

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Meijer, Rohwedder, and Wansbeek (MRW, Journal of Business & Economic Statistics, 2012) developed methods for prediction of a single earnings figure per worker from mixture factor models fitted using earnings data from multiple linked data sources. MRW applied their method using parameter estimates of Kapteyn and Ypma's mixture factor model (KY, Journal of Labour Economics 2007) fitted to earnings data for Swedish workers aged 50+. First, we replicate MRW's empirical analysis using the Swedish model estimates. Second, we check the generality of their empirical finding with a new application. Using estimates of a KY model fit to a linked dataset on earnings for UK employees of all ages, we confirm that MRW's principal findings about the performance of their various predictors of true earnings also hold in this different setting.

Keywords: earnings prediction; measurement error; mixture factor model; labour earnings; Kapteyn-Ypma model (search for similar items in EconPapers)
JEL-codes: J01 R14 (search for similar items in EconPapers)
Pages: 10 pages
Date: 2021-06-01
New Economics Papers: this item is included in nep-ltv and nep-ore
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Published in Journal of Applied Econometrics, 1, June, 2021, 36(4), pp. 474 - 483. ISSN: 1099-1255

Downloads: (external link)
http://eprints.lse.ac.uk/108951/ Open access version. (application/pdf)

Related works:
Journal Article: Measurement error in earnings data: Replication of Meijer, Rohwedder, and Wansbeek's mixture model approach to combining survey and register data (2021) Downloads
Working Paper: Measurement Error in Earnings Data: Replication of Meijer, Rohwedder, and Wansbeek's Mixture Model Approach to Combining Survey and Register Data (2021) Downloads
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