IDEAS home Printed from https://ideas.repec.org/a/tpr/restat/v103y2021i4p666-679.html
   My bibliography  Save this article

Estimating the Production Function under Input Market Frictions

Author

Listed:
  • Ajay Shenoy

    (University of California, Santa Cruz)

Abstract

Behind many production function estimators lies a crucial assumption that the firm's choice of intermediate inputs depends only on observed choices of other inputs and on unobserved productivity. This assumption fails when market frictions distort the firm's input choices. I derive a test for the assumption, which is rejected in several industries. Using weak identification asymptotics, I show that when the assumption fails, a simplified dynamic panel estimator can be used instead of choice-based methods because it requires choices to be distorted. I propose criteria for choosing between estimators, which in simulations yield lower error than either estimator alone.

Suggested Citation

  • Ajay Shenoy, 2021. "Estimating the Production Function under Input Market Frictions," The Review of Economics and Statistics, MIT Press, vol. 103(4), pages 666-679, October.
    • Handle: RePEc:tpr:restat:v:103:y:2021:i:4:p:666-679
    DOI: 10.1162/rest_a_00927
    as

    Download full text from publisher

    File URL: https://doi.org/10.1162/rest_a_00927
    Download Restriction: Access to PDF is restricted to subscribers.
    File URL: https://libkey.io/10.1162/rest_a_00927?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ming Li, 2021. "A Time-Varying Endogenous Random Coefficient Model with an Application to Production Functions," Papers 2110.00982, arXiv.org.
    2. Michael Rubens, 2023. "Management, productivity, and technology choices: evidence from U.S. mining schools," RAND Journal of Economics, RAND Corporation, vol. 54(1), pages 165-186, March.
    3. Alvaro Aguirre & Matias Tapia & Lucciano Villacorta, 2021. "Production, Investment and Wealth Dynamics under Financial Frictions: An Empirical Investigation of the Selffinancing Channel," Working Papers Central Bank of Chile 904, Central Bank of Chile.
    4. Anderton, Robert & Botelho, Vasco & Reimers, Paul, 2023. "Digitalisation and productivity: gamechanger or sideshow?," Working Paper Series 2794, European Central Bank.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tpr:restat:v:103:y:2021:i:4:p:666-679. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kelly McDougall (email available below). General contact details of provider: https://direct.mit.edu/journals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.