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Unveiling Plant‐Product Productivity via First‐Order Conditions: Robust Replication of Orr (2022)

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  • Joonkyo Hong
  • Davide Luparello

Abstract

We assess the replicability of Orr's (2022) method for estimating within‐plant productivity across product lines, which combines demand estimation with cost minimization. The original study uses input price shocks in other output markets as instrumental variables, with exclusion restrictions based on downstream purchase shares. Reconstructing the original dataset of Indian machinery producers from 2000–2007, we reproduce the main productivity patterns and demonstrate their robustness to variations in the exclusion threshold. The main results remain robust in extended samples (2010–2019, 2000–2019) when calibrating demand parameters to Orr's (2022) estimates for 2000–2007, as estimation on these extended periods yields inadmissible demand systems.

Suggested Citation

  • Joonkyo Hong & Davide Luparello, 2026. "Unveiling Plant‐Product Productivity via First‐Order Conditions: Robust Replication of Orr (2022)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 41(2), pages 216-222, March.
    • Handle: RePEc:wly:japmet:v:41:y:2026:i:2:p:216-222
    DOI: 10.1002/jae.70033
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    References listed on IDEAS

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    1. Brodeur, Abel & Mikola, Derek & Cook, Nikolai & Brailey, Thomas & Briggs, Ryan & de Gendre, Alexandra & Dupraz, Yannick & Fiala, Lenka & Gabani, Jacopo & Gauriot, Romain & Haddad, Joanne & Federice, A, 2024. "Mass Reproducibility and Replicability: A New Hope," I4R Discussion Paper Series 107, The Institute for Replication (I4R).
    2. Anton Korinek, 2023. "Generative AI for Economic Research: Use Cases and Implications for Economists," Journal of Economic Literature, American Economic Association, vol. 61(4), pages 1281-1317, December.
    3. Scott Orr, 2022. "Within-Firm Productivity Dispersion: Estimates and Implications," Journal of Political Economy, University of Chicago Press, vol. 130(11), pages 2771-2828.
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