Identification and Estimation of Production Function with Unobserved Heterogeneity

Hiroyuki Kasahara, Paul Schrimpf and CMichio Suzuki

No 38, TUPD Discussion Papers from Graduate School of Economics and Management, Tohoku University

Abstract: This paper examines the nonparametric identifiability of production functions,considering firm heterogeneity beyond Hicks-neutral technology terms. We propose a finite mixture model to account for unobserved heterogeneity in production technology and productivity growth processes. Our analysis demonstrates that the production function for each latent type can be nonparametrically identified using four periods of panel data, relying on assumptions similar to those employed in existing literature on production function and panel data identification. By analyzing Japanese plant level panel data, we uncover significant disparities in estimated input elasticities and productivity growth processes among latent types within narrowly defined industries. We further show that neglecting unobserved heterogeneity in input elasticities may lead to substantial and systematic bias in the estimation of productivity growth.

Pages: 46 pages
Date: 2023-05
New Economics Papers: this item is included in nep-eff
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10097/00137216

Related works:
Working Paper: Identification and Estimation of Production Function with Unobserved Heterogeneity (2023)
Working Paper: Identification and Estimation of Production Function with Unobserved Heterogeneity (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:toh:tupdaa:38

Access Statistics for this paper

More papers in TUPD Discussion Papers from Graduate School of Economics and Management, Tohoku University Contact information at EDIRC.
Bibliographic data for series maintained by Tohoku University Library ().