The accuracy of predicted wages of the non-employed and implications for policy simulations from structural labour supply models

Robert Breunig and Joseph Mercante
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Joseph Mercante: Treasury, Government of Australia

No 2009-03, Treasury Working Papers from The Treasury, Australian Government

Abstract: The main focus of this paper is on the accuracy of predicted wages for the nonemployed. We first examine whether the three groups of non-employed–the unemployed, the marginally attached, and the not in the labour force–should be modelled separately or together. We conclude that these are three distinct states and that they should not be pooled in modelling wages. We predict wages separately for the three non-employed groups using a range of two-state and four-state sample selection models.We predict wages separately for the three non-employed groups using a range of two-state and four-state sample selection models. Using a panel data set from Australia, we test the accuracy of predicted wages for the non-employedby focusing on those individuals who subsequently enter employment. We find that conditional predictions, which incorporate the estimated sample selection correction, perform poorly for all groups, especially for the marginally attached and the not in the labour force. Unconditional predictions from the sample selection models perform better but never out-perform a simple linear regression. These results may have important implications for policy simulations from structural labour supply models.

Keywords: labour supply; selection bias; multinomial logit; wage predictions (search for similar items in EconPapers)
JEL-codes: C52 J22 J30 J64 (search for similar items in EconPapers)
Pages: 49 pages
Date: 2009-03, Revised 2009-03
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Journal Article: The Accuracy of Predicted Wages of the Non‐Employed and Implications for Policy Simulations from Structural Labour Supply Models (2010)
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