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Estimating Production Functions with Heterogeneous Firms

Author

Listed:
  • Salvador Navarro

    (University of Wisconsin-Madison)

  • David Rivers

    (University of Wisconsin-Madison)

  • Amit Gandhi

    (University of Wisconsin-Madison)

Abstract

We present a new approach to the estimation of production functions that allows for richer patterns of firm heterogeneity than can be accommodated under the proxy variable methods of and Olley/Pakes and Levinsohn/Petrin. In particular, we show that the economics of the firms static input choice contains the necessary identifying information to control for the endogeneity problem in the production function. From an econometric point of view, our estimation proceeds in a single GMM step, and thus standard asymptotic standard errors are available. We consider the identification and estimation of models with heterogeneity in both input and output prices, as well as heterogeneity in factor specific productivity. Our empirical results show that we control for more of the endogeneity problem than the proxy variable approach, resulting in estimates of labor productivity nearly half as small.

Suggested Citation

  • Salvador Navarro & David Rivers & Amit Gandhi, 2008. "Estimating Production Functions with Heterogeneous Firms," 2008 Meeting Papers 935, Society for Economic Dynamics.
    • Handle: RePEc:red:sed008:935
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    References listed on IDEAS

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    1. Mortensen, Dale & Pissarides, Christopher, 2011. "Job Creation and Job Destruction in the Theory of Unemployment," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 1-19.
    2. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Papers 2005-W04, Economics Group, Nuffield College, University of Oxford.
    3. Nina Pavcnik, 2002. "Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(1), pages 245-276.
    4. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    5. Ackerberg, Daniel & Lanier Benkard, C. & Berry, Steven & Pakes, Ariel, 2007. "Econometric Tools for Analyzing Market Outcomes," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 63, Elsevier.
    6. Ackerberg, Daniel & Caves, Kevin & Frazer, Garth, 2006. "Structural identification of production functions," MPRA Paper 38349, University Library of Munich, Germany.
    7. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    8. Fox, Jeremy T. & Smeets, Valérie, 2007. "Do Input Quality and Structural Productivity Estimates Drive Measured Differences in Firm Productivity?," Working Papers 07-2, University of Aarhus, Aarhus School of Business, Department of Economics.
    9. Robert E. Lucas Jr., 1978. "On the Size Distribution of Business Firms," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 508-523, Autumn.
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    11. Stephen Bond & Måns Söderbom, 2005. "Adjustment Costs and the Identification of Cobb Douglas Production Functions," Economics Series Working Papers 2005-W04, University of Oxford, Department of Economics.
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    Cited by:

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    2. Abdoulaye Kané, 2022. "Measurement of total factor productivity: Evidence from French construction firms," EconomiX Working Papers 2022-9, University of Paris Nanterre, EconomiX.

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