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Using Firm Optimization to Evaluate and Estimate Returns to Scale

Author

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  • Gorodnichenko, Yuriy

    (University of California, Berkeley)

Abstract
At the firm level, revenue and costs are well measured but prices and quantities are not. This paper shows that because of these data limitations estimates of returns to scale at the firm level are for the revenue function, not production function. Given this observation, the paper argues that, under weak assumptions, micro-level estimates of returns to scale are often inconsistent with profit maximization or imply implausibly large profits. The puzzle arises because popular estimators ignore heterogeneity and endogeneity in factor/product prices, assume perfect elasticity of factor supply curves or neglect the restrictions imposed by profit maximization (cost minimization) so that estimators are inconsistent or poorly identified. The paper argues that simple structural estimators can address these problems. Specifically, the paper proposes a full-information estimator that models the cost and the revenue functions simultaneously and accounts for unobserved heterogeneity in productivity and factor prices symmetrically. The strength of the proposed estimator is illustrated by Monte Carlo simulations and an empirical application. Finally, the paper discusses a number of implications of estimating revenue functions rather than production functions and demonstrates that the profit share in revenue is a robust non-parametric economic diagnostic for estimates of returns to scale.

Suggested Citation

  • Gorodnichenko, Yuriy, 2008. "Using Firm Optimization to Evaluate and Estimate Returns to Scale," IZA Discussion Papers 3368, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp3368
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    2. Alvaro Garcia-Marin & Nico Voigtländer, 2019. "Exporting and Plant-Level Efficiency Gains: It's in the Measure," Journal of Political Economy, University of Chicago Press, vol. 127(4), pages 1777-1825.
    3. Gorodnichenko, Yuriy & Svejnar, Jan & Terrell, Katherine, 2015. "Does Foreign Entry Spur Innovation?," Department of Economics, Working Paper Series qt1766k8hs, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    4. Yuriy Gorodnichenko & Monika Schnitzer, 2013. "Financial Constraints And Innovation: Why Poor Countries Don'T Catch Up," Journal of the European Economic Association, European Economic Association, vol. 11(5), pages 1115-1152, October.
    5. Eifert, Benn & Gelb, Alan & Ramachandran, Vijaya, 2008. "The Cost of Doing Business in Africa: Evidence from Enterprise Survey Data," World Development, Elsevier, vol. 36(9), pages 1531-1546, September.
    6. Kazuhiko Yokota & Kyosuke Kurita & Shujiro Urata, 2016. "In Search of the Learning-by-Exporting Effect: Role of Economies of Scale and Technology," China Economic Policy Review (CEPR), World Scientific Publishing Co. Pte. Ltd., vol. 5(01), pages 1-27, June.
    7. Benoit Dostie, 2011. "Wages, Productivity and Aging," De Economist, Springer, vol. 159(2), pages 139-158, June.
    8. Gorodnichenko, Yuriy & Grygorenko, Yegor, 2008. "Are oligarchs productive? Theory and evidence," Journal of Comparative Economics, Elsevier, vol. 36(1), pages 17-42, March.
    9. Gorodnichenko, Yuriy & Svejnar, Jan & Terrell, Katherine, 2020. "Do foreign investment and trade spur innovation?," European Economic Review, Elsevier, vol. 121(C).
    10. Daniel Berkowitz & Hong Ma & Shuichiro Nishioka, 2017. "Recasting the Iron Rice Bowl: The Reform of China's State-Owned Enterprises," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 735-747, July.
    11. Enghin Atalay, 2014. "Materials Prices And Productivity," Journal of the European Economic Association, European Economic Association, vol. 12(3), pages 575-611, June.
    12. Daniel Berkowitz, 2016. "Capital-Labor Substitution, Institutions and Labor Shares," Working Paper 5981, Department of Economics, University of Pittsburgh.
    13. Daniel Berkowitz, 2018. "Market Distortions and Labor Share Distributions: Evidence from Chinese Manufacturing Firms," Working Paper 6466, Department of Economics, University of Pittsburgh.

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    More about this item

    Keywords

    production function; identification; returns to scale; covariance structures;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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