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Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis

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  • Giraleas, Dimitris
  • Emrouznejad, Ali
  • Thanassoulis, Emmanuel

Abstract

This study presents some quantitative evidence from a number of simulation experiments on the accuracy of the productivity growth estimates derived from growth accounting (GA) and frontier-based methods (namely Data envelopment Analysis-, Corrected ordinary least squares-, and Stochastic Frontier Analysis-based Malmquist indices) under various conditions. These include the presence of technical inefficiency, measurement error, misspecification of the production function (for the GA and parametric approaches) and increased input and price volatility from one period to the next. The study finds that the frontier-based methods usually outperform GA, but the overall performance varies by experiment. Parametric approaches generally perform best when there is no functional form misspecification, but their accuracy greatly diminishes otherwise. The results also show that the deterministic approaches perform adequately even under conditions of (modest) measurement error and when measurement error becomes larger, the accuracy of all approaches (including stochastic approaches) deteriorates rapidly, to the point that their estimates could be considered unreliable for policy purposes.

Suggested Citation

  • Giraleas, Dimitris & Emrouznejad, Ali & Thanassoulis, Emmanuel, 2011. "Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis," MPRA Paper 37429, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:37429
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    References listed on IDEAS

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    Cited by:

    1. Kao, Chiang & Liu, Shiang-Tai, 2014. "Measuring performance improvement of Taiwanese commercial banks under uncertainty," European Journal of Operational Research, Elsevier, vol. 235(3), pages 755-764.
    2. Lin, Winston T. & Chen, Yueh H. & Shao, Benjamin B.M., 2015. "Assessing the business values of information technology and e-commerce independently and jointly," European Journal of Operational Research, Elsevier, vol. 245(3), pages 815-827.
    3. Kerstens, Kristiaan & Van de Woestyne, Ignace, 2014. "Comparing Malmquist and Hicks–Moorsteen productivity indices: Exploring the impact of unbalanced vs. balanced panel data," European Journal of Operational Research, Elsevier, vol. 233(3), pages 749-758.
    4. Alexander Yu. Apokin & Irina Ipatova, 2016. "How R&D Expenditures Influence Total Factor Productivity and Technical Efficiency?," HSE Working papers WP BRP 128/EC/2016, National Research University Higher School of Economics.
    5. Ferrari, Alessandra & Girardone, Claudia & Wilson, John O.S., 2016. "Integration, productivity and technological spillovers: Evidence for eurozone banking industriesAuthor-Name: Casu, Barbara," European Journal of Operational Research, Elsevier, vol. 255(3), pages 971-983.
    6. Hurlin, Christophe & Minea, Alexandru, 2013. "Is public capital really productive? A methodological reappraisal," European Journal of Operational Research, Elsevier, vol. 228(1), pages 122-130.
    7. Lin, Winston T. & Chuang, Chia-Hung, 2013. "Investigating and comparing the dynamic patterns of the business value of information technology over time," European Journal of Operational Research, Elsevier, vol. 228(1), pages 249-261.
    8. Christopher F. Parmeter & Valentin Zelenyuk, 2016. "A Bridge Too Far? The State of the Art in Combining the Virtues of Stochastic Frontier Analysis and Data Envelopement Analysis," Working Papers 2016-10, University of Miami, Department of Economics.

    More about this item

    Keywords

    Data envelopment analysis; Productivity and competitiveness; Simulation; Stochastic Frontier Analysis; Growth accounting;

    JEL classification:

    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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