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Identification and Estimation of Production Function with Unobserved Heterogeneity

Author

Listed:
  • Hiroyuki Kasahara
  • Paul Schrimpf
  • Michio Suzuki
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.

Suggested Citation

  • Hiroyuki Kasahara & Paul Schrimpf & Michio Suzuki, 2023. "Identification and Estimation of Production Function with Unobserved Heterogeneity," Papers 2305.12067, arXiv.org.
    • Handle: RePEc:arx:papers:2305.12067
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Tong Li & Yuya Sasaki, 2017. "Constructive Identification of Heterogeneous Elasticities in the Cobb-Douglas Production Function," Papers 1711.10031, arXiv.org.
    2. Hien Thu Pham & Nhan Buu Phan & Shino Takayama, 2020. "Productivity, Efficiency and Firm Size Distribution: Evidence from Vietnam," Discussion Papers Series 617, School of Economics, University of Queensland, Australia.
    3. Nhan Buu Phany & Shino Takayamaz, 2020. "Analyses of Corruption and Productivity with Empirical Study in Vietnam," Discussion Papers Series 628, School of Economics, University of Queensland, Australia.
    4. Grieco, Paul & Pinkse, Joris & Slade, Margaret, 2018. "Brewed in North America: Mergers, marginal costs, and efficiency," International Journal of Industrial Organization, Elsevier, vol. 59(C), pages 24-65.
    5. Ryo Okui & Takahide Yanagi, 2020. "Kernel estimation for panel data with heterogeneous dynamics," The Econometrics Journal, Royal Economic Society, vol. 23(1), pages 156-175.
    6. Ming Li, 2021. "Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model," Papers 2110.00982, arXiv.org, revised Nov 2024.
    7. Malein, Viktor (Малеин, Виктор) & Ponomarev, Yuriy (Пономарев, Юрий), 2019. "Analysis of Impact of New Technologies in Metallurgy on the Industry Production Function and Total Factor Productivity [Совокупная Факторная Производительность В Черной Металлургии: Влияние Новых Т," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 3, June.
    8. KASAHARA Hiroyuki & NISHIDA Mitsukuni & SUZUKI Michio, 2017. "Decomposition of Aggregate Productivity Growth with Unobserved Heterogeneity," Discussion papers 17083, Research Institute of Economy, Trade and Industry (RIETI).
    9. Emannuel Dhyne & Joep Konings & Joep Konings & Stijn Vanormelingen,, 2018. "IT and productivity: A firm level analysis," Working Paper Research 346, National Bank of Belgium.
    10. Li, Tong & Sasaki, Yuya, 2024. "Identification of heterogeneous elasticities in gross-output production functions," Journal of Econometrics, Elsevier, vol. 238(2).
    11. Konings, Jozef & Dhyne, Emmanuel & Van den bosch, Jeroen & ,, 2018. "The Return on Information Technology: Who Benefits Most?," CEPR Discussion Papers 13246, C.E.P.R. Discussion Papers.

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