Selecting between different productivity measurement approaches: An application using EU KLEMS data

Giraleas, Dimitris and Emrouznejad, Ali and Thanassoulis, Emmanuel (2012): Selecting between different productivity measurement approaches: An application using EU KLEMS data.

Abstract

Over the years, a number of different approaches were developed to measure productivity change, both in the micro and the macro setting. Since each approach comes with its own set of assumptions, it is not uncommon in practice that they produce different, and sometimes quite divergent, productivity change estimates. This paper introduces a framework that can be used to select between the most common productivity measurement approaches based on a number of characteristics specific to the application/dataset at hand; these were selected based on the results of previous simulation analysis that examined the accuracy of different productivity measurement approaches under different conditions. The characteristics in question include input volatility through time, the extent of technical inefficiency and noise present in the dataset and whether the parametric approaches are likely to suffer from functional form miss-specification and are examined using a number of well-established diagnostics and indicators. Once assessed, the most appropriate approach can be selected based on its relative accuracy under these conditions; accuracy can in turn be assessed using simulation analysis, either previously published or designed specifically to emulate the characteristics of the application/dataset at hand. As an example of how this selection framework can be implemented in practice, we assess the productivity performance of a number of EU countries using the EU KLEMS dataset.

• 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.

Banker, R. D., Chang, H., & Cooper, W. W. (2004). A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity. European Journal of Operational Research, 153, 624-640.

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.

Coelli, T. (2002). A comparison of alternative productivity growth measures: with application to electricity generation. In K. J. Fox (Ed.), Efficiency in the Public Sector: Springer.

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.

Eurostat, & OECD. (2007). Methodological Manual on Purchasing Power Parities. In: European Commission.

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.

Jacobs, R. (2001). Alternative Methods to Examine Hospital Efficiency: Data Envelopment Analysis and Stochastic Frontier Analysis. Health Care Management Science, 4, 103-115.

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.

Knox Lovell, C. A. (1996). Applying efficiency measurement techniques to the measurement of productivity change. Journal of Productivity Analysis, 7, 329-340.

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.

Odeck, J. (2007). Measuring technical efficiency and productivity growth: a comparison of SFA and DEA on Norwegian grain production data. Applied Economics, 39, 2617-2630.

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

OECD. (2009). Measuring Capital. In.

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.

Resti, A. (2000). Efficiency measurement for multi-product industries: A comparison of classic and recent techniques based on simulated data. European Journal of Operational Research, 121, 559-578.

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.