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Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules

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  • David Greenstreet

Abstract

This paper develops a sequential learning estimator of production functions and productivity dynamics for unbalanced establishment panels. Extending an idea from the literature on dynamic industry models, establishments are uncertain about their own idiosyncratic productivities and update productivity beliefs using information revealed by their production experience. The estimator relies on the structure of this iterative learning process and thereby avoids placing any restriction on establishment strategic behavior. Consequently, the estimator is suitable for comparative studies of the behavioral sources of technological change across all types of industry. Estimation of productivity dynamics and of behavioral decision rules are separated into recursive stages. Using sequential learning estimates of productivity beliefs from the first stage, decision rules for exit, investment, and innovation effort can be estimated in a second stage. A test application with four Chilean industries confirms that the estimator produces plausible estimates with small standard errors. Decision rule estimates show that productivity beliefs affect investment and exit hazards in the expected direction.

Suggested Citation

  • David Greenstreet, 2007. "Exploiting Sequential Learning to Estimate Establishment-Level Productivity Dynamics and Decision Rules," Economics Series Working Papers 345, University of Oxford, Department of Economics.
  • Handle: RePEc:oxf:wpaper:345
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    File URL: http://www.economics.ox.ac.uk/materials/working_papers/paper345.pdf
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    Cited by:

    1. Markus Eberhardt & Christian Helmers & Hubert Strauss, 2013. "Do Spillovers Matter When Estimating Private Returns to R&D?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 436-448, May.
    2. Ezra Oberfield & Devesh Raval, 2012. "Micro data and macro technology," Working Paper Series WP-2012-11, Federal Reserve Bank of Chicago.
    3. Ezra Oberfield, 2013. "Productivity and Misallocation During a Crisis: Evidence from the Chilean Crisis of 1982," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 16(1), pages 100-119, January.
    4. Añón Higón, Dolores & Manjón Antolín, Miguel, 2012. "Multinationality, foreignness and institutional distance in the relation between R&D and productivity," Research Policy, Elsevier, vol. 41(3), pages 592-601.

    More about this item

    Keywords

    Microeconomic Productivity Dynamics; Unbalanced Panels; Sequential Learning; Exit Hazard; Investment Rates; Chilean Manufacturing;

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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