A Note on the Importance of Normalizations in Dynamic Latent Factor Models of Skill Formation

Emilia Del Bono, Josh Kinsler and Ronni Pavan

No 13714, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: In this paper we highlight an important property of the translog production function for the identification of treatment effects in a model of latent skill formation. We show that when using a translog specification of the skill technology, properly anchored treatment effect estimates are invariant to any location and scale normalizations of the underlying measures. By contrast, when researchers assume a CES production function and impose standard location and scale normalizations, the resulting treatment effect estimates are biased. Interestingly, the CES technology with standard normalizations yields biased treatment effect estimates even when age-invariant measures of the skills are available. We theoretically prove the normalization invariance of the translog production function and then produce several simulations illustrating the effects of location and scale normalizations for different technologies and types of skills measures.

Keywords: dynamic factor analysis; human capital; children; measurement; policy (search for similar items in EconPapers)
JEL-codes: C13 C18 I38 J13 J24 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2020-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)

Published - published as 'Identification of dynamic latent factor models of skill formation with translog production' in: Journal of Applied Econometrics, 2022, 37 (6), 1256 - 1265

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