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

Giraleas, Dimitris and Emrouznejad, Ali and Thanassoulis, Emmanuel (2011): Productivity change using growth accounting and frontier-based approaches – Evidence from a Monte Carlo analysis.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Data envelopment analysis, Productivity and competitiveness, Simulation, Stochastic Frontier Analysis, Growth accounting
• Subjects: O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O47 - Empirical Studies of Economic Growth ; Aggregate Productivity ; Cross-Country Output Convergence; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; D - Microeconomics > D2 - Production and Organizations > D24 - Production ; Cost ; Capital ; Capital, Total Factor, and Multifactor Productivity ; Capacity

References

Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21-37.

Balk, B. (2008). Measuring Productivity Change Without Neoclassical Assumptions: A Conceptual Analysis. In ERIM Report Series Reference No. ERS-2008-077-MKT.

Berndt, E. R., & Fuss, M. A. (1986). Productivity measurement with adjustments for variations in capacity utilization and other forms of temporary equilibrium. Journal of Econometrics, 33, 7-29.

Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity. Econometrica, 50, 1393-1414.

Del Gatto, M., Di Liberto, A., & Petraglia, C. (2008). Measuring Productivity. In: Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

Diewert, W. E. (1992). The Measurement of Productivity. Bulletin of Economic Research, 44, 163-198.

EU KLEMS. (2008). EU KLEMS Database, see Marcel Timmer, Mary O'Mahony & Bart van Ark, The EU KLEMS Growth and Productivity Accounts: An Overview, University of Groningen & University of Birmingham. In.

Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries. American Economic Review, 84, 66-83.

Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (2008). The Measurement of Productive Efficiency and Productivity Growth. In: Oxford University Press.

Jondrow, J., Knox Lovell, C. A., Materov, I. S., & Schmidt, P. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19, 233-238.

Koszerek, D., Havik, K., Mc Morrow, K., Röger, W., & Schönborn, F. . (2007). An overview of the EU KLEMS growth and productivity accounts. In Economic paper no. 290: European Commission.

Kumbhakar, S. C., & Lovell, C. A. K. (2000). Stochastic Frontier Analysis: Cambridge University Press.

Meeusen, W., & van Den Broeck, J. (1977). Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error. International Economic Review, 18, 435-444.

O'Mahony, M., & Timmer, M., P. (2009). Output, Input and Productivity Measures at the Industry Level: The EU KLEMS Database. Economic Journal, 119, 374-403.

OECD. (2001). Measuring Productivity: Measurement of Aggregate and Industry-Level Productivity Growth. In: OECD.

ONS. (2007). The ONS productivity Handbook: A Statistical Overview and Guide In D. Camus (Ed.): Palgrave Macmillan.

Pastor, J. T., & Lovell, C. A. K. (2005). A global Malmquist productivity index. Economics Letters, 88, 266-271.

Portela, M. C. A. S., & Thanassoulis, E. (2010). Malmquist-type indices in the presence of negative data: An application to bank branches. Journal of Banking & Finance, 34, 1472-1483.

Samuelson, P. A. (1947). Foundations of Economic Analysis: Harvard University Press.

Sharma, S. C., Sylwester, K., & Margono, H. (2007). Decomposition of total factor productivity growth in U.S. states. The Quarterly Review of Economics and Finance, 47, 215-241.

Solow, R. M. (1957). Technical Change and the Aggregate Production Function. The Review of Economics and Statistics, 39, 312-320.

Thanassoulis, E. (2001). Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software: Springer.

Tinbergen, J. (1942). Theorie der Langfristigen Wirtschaftsentwicklung, (On the Theory of Long-Term Economic Growth). Weltwirtschaftliches Archiv, 55, 511-549.

Tulkens, H., & Vanden Eeckaut, P. (1995). Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects. European Journal of Operational Research, 80, 474-499.