Hiroyuki Kasahara, Paul Schrimpf, Michio Suzuki
arXiv 20 May 2023 · Econometrics · 6 citations (OpenAlex)
arXiv:2305.12067 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Doraszelski, U. and Jaumandreu, J (2018) Measuring the bias of technological change | 1.000 | 6 | 3 | 100% |
| 2 | Kasahara, H. and Shimotsu, K (2009) Nonparametric identification of finite mixture models of dynamic discrete choices self | 0.950 | 7 | 4 | 86% |
| 3 | Hu, Y. and Shum, M (2012) Nonparametric identification of dynamic models with unobserved state variables | 0.941 | 6 | 3 | 83% |
| 4 | Ackerberg, D. A., Caves, K., and Frazer, G (2015) Identification properties of recent production function estimators | 0.874 | 5 | 2 | 100% |
| 5 | Zhang, H (2019) Non-neutral technology, firm heterogeneity, and labor demand | 0.874 | 5 | 2 | 100% |
| 6 | Higgins, A. and Jochmans, K (2021) Identification of mixtures of dynamic discrete choices | 0.843 | 4 | 3 | 75% |
| 7 | Raval, D (2023) Testing the Production Approach to Markup Estimation | 0.811 | 4 | 2 | 100% |
| 8 | Demirer, M (2020) Production function estimation with factor-augmenting technology: An application to markups | 0.737 | 3 | 2 | 100% |
| 9 | Hao, Y. and Kasahara, H (2022) Testing the number of components in finite mixture normal regression model with panel data self | 0.644 | 2 | 2 | 100% |
| 10 | Bond, S. and Sderbom, M (2005) Adjustment costs and the identification of cobb douglas production functions | 0.644 | 2 | 2 | 100% |
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