A Principled Approach to Assessing Missing-Wage Induced Selection Bias

Duo Qin, Sophie van Huellen, Raghda Elshafie, Yimeng Liu and Thanos Moraitis
Additional contact information
Raghda Elshafie: The Center for Victims of Torture, Egypt
Yimeng Liu: School of Economics and Resource Management, Beijing Normal University, China
Thanos Moraitis: Department of Economics, SOAS University of London, UK

No 216, Working Papers from Department of Economics, SOAS University of London, UK

Abstract: Multiple imputation (MI) techniques are applied to simulate missing wage rates of non-working wives under the missing-at-random (MAR) condition. The assumed selection effect of the labour force participation decision is framed as deviations of the imputed wage rates from MAR. By varying the deviations, we assess the severity of subsequent selection bias in standard human capital models through sensitivity analyses (SA). Our experiments show that the bias remains largely insignificant. While similar findings are possibly attainable through the Heckman procedure, SA under the MI approach provides a more structured and principled approach to assessing selection bias.

Keywords: wage; labour supply; selection; missing at random; multiple imputation (search for similar items in EconPapers)
JEL-codes: C21 C52 J20 J24 (search for similar items in EconPapers)
Pages: 26
Date: 2019-01
New Economics Papers: this item is included in nep-ecm and nep-lma
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:soa:wpaper:216

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