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Production function estimation using subjective expectations data

Author

Listed:
  • Agnes Norris Keiller
  • Aureo de Paula
  • John Van Reenen
Abstract
Standard methods for estimating production functions in the Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on a firm's expectations of its future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to dynamic panel methods, our proposed estimator can be implemented on very short panels (including a single cross-section), and Monte Carlo simulations show it outperforms alternative estimators when firms' material input choices are subject to optimization error. Implementing a range of production function estimators on UK data, we find our proposed estimator yields results that are either similar to or more credible than commonly-used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that TFP implied by our proposed estimator is more strongly associated with future jobs growth than existing methods, suggesting that failing to adequately account for input endogeneity may underestimate the degree of dynamic reallocation in the economy.

Suggested Citation

  • Agnes Norris Keiller & Aureo de Paula & John Van Reenen, 2024. "Production function estimation using subjective expectations data," Papers 2407.07988, arXiv.org.
    • Handle: RePEc:arx:papers:2407.07988
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    References listed on IDEAS

    as
    1. Nicholas Bloom & Takafumi Kawakubo & Charlotte Chunming Meng & Paul Mizen & Rebecca Riley & Tatsuro Senga & John Van Reenen, 2021. "Do Well Managed Firms Make Better Forecasts?," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2021-18, Economic Statistics Centre of Excellence (ESCoE).
    2. de Roux, Nicolás & Eslava, Marcela & Franco, Santiago & Verhoogen, Eric, 2020. "Estimating Production Functions in Differentiated-Product Industries with Quantity Information and External Instruments," IZA Discussion Papers 14006, Institute of Labor Economics (IZA).
    3. Luigi Pistaferri, 2001. "Superior Information, Income Shocks, And The Permanent Income Hypothesis," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 465-476, August.
    4. James Levinsohn & Amil Petrin, 2003. "Estimating Production Functions Using Inputs to Control for Unobservables," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 70(2), pages 317-341.
    5. Hall, Robert E, 1988. "The Relation between Price and Marginal Cost in U.S. Industry," Journal of Political Economy, University of Chicago Press, vol. 96(5), pages 921-947, October.
    6. Catherine Buffington & Lucia Foster & Ron Jarmin & Scott Ohlmacher, 2016. "The Management and Organizational Practices Survey (MOPS): An Overview," Working Papers 16-28, Center for Economic Studies, U.S. Census Bureau.
    7. Catherine Buffington & Kenny Herrell & Scott Ohlmacher, 2016. "The Management and Organizational Practices Survey (MOPS): Cognitive Testing," Working Papers 16-53, Center for Economic Studies, U.S. Census Bureau.
    8. Bloom, Nick & Bunn, Philip & Mizen, Paul & Smietanka, Pawel & Thwaites, Greg & Young, Garry, 2017. "Tracking the views of British businesses: evidence form the Decision Maker Panel," Bank of England Quarterly Bulletin, Bank of England, vol. 57(2), pages 110-120.
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    More about this item

    JEL classification:

    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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