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The Use of Bootstrapped Malmquist Indices to Reassess Productivity Change Findings: An Application to a Sample of Polish Farms

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  • Balcombe, Kelvin George
  • Davidova, Sophia
  • Latruffe, Laure

Abstract

The paper assesses the extent to which sampling variation affects findings about Malmquist productivity change derived using Data Envelopment Analysis (DEA), in the first stage calculating productivity indices and in the second stage investigating the farm-specific change in productivity. Confidence intervals for Malmquist indices are constructed using Simar and Wilson's (1999) bootstrapping procedure. The main contribution of the paper is to account in the second stage for the information provided by the bootstrap in the first stage. The DEA standard errors of the Malmquist indices given by bootstrapping are employed in an innovative heteroscedastic panel regression, using a maximum likelihood procedure. The application is to a sample of 250 Polish farms over the period 1996-2000. The confidence interval's results suggest that contrary to what was reported by previous studies, the second half of 1990s for Polish farms was characterised not so much by productivity regress but rather by stagnation. As for the determinants of farm productivity change, we find that the integration of the DEA standard errors in the second-stage regression is significant in explaining a proportion of the variance in the error term. Although our heteroscedastic regression results differ with those from the standard Ordinary Least Squares, in terms of significance (fewer parameters are significant in our heteroscedastic regression) and sign (of the parameter of the share of other income in total income), they are consistent with theory and previous research. Family farms concentrating on farming experienced larger productivity progress than farms with hired labour and income diversification.

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Bibliographic Info

Paper provided by International Association of Agricultural Economists in its series 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia with number 25793.

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Date of creation: 2006
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Handle: RePEc:ags:iaae06:25793

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Keywords: productivity; Malmquist; bootstrapping; second-stage regression; Poland; Productivity Analysis;

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  1. SIMAR, Léopold & WILSON, Paul, 1996. "Estimating and bootstrapping malmquist indices," CORE Discussion Papers 1996060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Simar, L. & Wilson, P.W., . "Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models," CORE Discussion Papers RP -1304, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Tim J. Coelli & D. S. Prasada Rao, 2005. "Total factor productivity growth in agriculture: a Malmquist index analysis of 93 countries, 1980-2000," Agricultural Economics, International Association of Agricultural Economists, vol. 32(s1), pages 115-134, 01.
  4. Chaplin, Hannah & Davidova, Sophia & Gorton, Matthew, 2002. "Non-Agricultural Diversification of Farm Households and Corporate Farms in Central Europe," Workshop on the Farm Household-Firm Unit: Its Importance in Agriculture and Implications for Statistics, April 12-13, 2002, Wye Campus,Imperial College 15728, International Agricultural Policy Reform and Adjustment Project (IAPRAP).
  5. Allen, Douglas W & Lueck, Dean, 1998. "The Nature of the Farm," Journal of Law and Economics, University of Chicago Press, vol. 41(2), pages 343-86, October.
  6. Chieko Umetsu & Thamana Lekprichakul & Ujjayant Chakravorty, 2003. "Efficiency and Technical Change in the Philippine Rice Sector: A Malmquist Total Factor Productivity Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 943-963.
  7. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
  8. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
  9. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
  10. Bernhard Brümmer, 2001. "Estimating confidence intervals for technical efficiency: the case of private farms in slovenia," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 28(3), pages 285-306, October.
  11. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  12. Bernhard Br�mmer & Thomas Glauben & Geert Thijssen, 2002. "Decomposition of Productivity Growth Using Distance Functions: The Case of Dairy Farms in Three European Countries," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(3), pages 628-644.
  13. Tortosa-Ausina, Emili & Grifell-Tatje, Emili & Armero, Carmen & Conesa, David, 2008. "Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks," European Journal of Operational Research, Elsevier, vol. 184(3), pages 1062-1084, February.
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Citations

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Cited by:
  1. Olson, Kent D. & Vu, Linh, 2009. "Productivity Growth, Technical Efficiency and Technical Change on Minnesota Farms," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49204, Agricultural and Applied Economics Association.
  2. Boon Lee, 2013. "Productivity Performance of Singapore’s Retail Sector: A Two-Stage Non-Parametric Approach," Economic Analysis and Policy (EAP), Queensland University of Technology (QUT), School of Economics and Finance, vol. 43(1), pages 67-78, March.
  3. Aleksandra PARTEKA & Joanna WOLSZCZAK-DERLACZ, 2011. "Dynamics of productivity in higher education. Cross-European evidence based on bootstrapped Malmquist indices," Departmental Working Papers 2011-10, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  4. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "Data envelopment analysis 1978–2010: A citation-based literature survey," Omega, Elsevier, vol. 41(1), pages 3-15.
  5. Boon Lee, 2011. "Productivity, Technical and Efficiency Change in Singapore's Services Sector, 2005 to 2008," School of Economics and Finance Discussion Papers and Working Papers Series 269, School of Economics and Finance, Queensland University of Technology.
  6. Boon L Lee, 2012. "Efficiency and Productivity of Singapore's Manufacturing Sector 2001-2010: An analysis using Simar and Wilson's (2007) bootstrapped truncated approach," School of Economics and Finance Discussion Papers and Working Papers Series 283, School of Economics and Finance, Queensland University of Technology.
  7. Arjomandi, Amir & Valadkhani, Abbas & Harvie, Charles, 2011. "Analysing Productivity Changes Using the Bootstrapped Malmquist Approach: The Case of the Iranian Banking Industry," MPRA Paper 50397, University Library of Munich, Germany.
  8. Assaf, A. George & Gillen, David & Barros, Carlos, 2012. "Performance assessment of UK airports: Evidence from a Bayesian dynamic frontier model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(3), pages 603-615.
  9. Rodrigo Taborda & Julieth Santamaría, 2011. "Productivity growth in electric energy retail in Colombia. A bootstrapped malmquist indices approach," DOCUMENTOS DE TRABAJO 009152, UNIVERSIDAD DEL ROSARIO.

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