Hostname: page-component-84b7d79bbc-tsvsl Total loading time: 0 Render date: 2024-07-26T00:46:07.298Z Has data issue: false hasContentIssue false
  • English
  • Français

Investigation of Factors Influencing the Technical Efficiency of Agricultural Producers Participating in Farm Credit Programs: The Case of Greece

Published online by Cambridge University Press:  28 April 2015

Anthony N. Rezitis ,
Kostas Tsiboukas  and
Stauros Tsoukalas
Anthony N. Rezitis
Affiliation:
Department of Farm Organization and Management, School of Natural Resources and Enterprise Management, University of Ioannina, Agrinio, Greece
Kostas Tsiboukas
Affiliation:
Department of Agricultural Economics, Agricultural University of Athens, Athens, Greece
Stauros Tsoukalas
Affiliation:
Department of Agricultural Economics, Agricultural University of Athens, Athens, Greece
Get access Rights & Permissions [Opens in a new window]

Abstract

This study investigates a number of factors influencing technical efficiency of Greek farms participating in the 1994 European Union (EU) farm credit program. Technical efficiency measures are obtained within the framework of a parametric stochastic frontier. Factors showing a positive effect on technical efficiency are value of liabilities, number of hours of mechanical operation, large land size, and rental land, whereas those showing a negative effect are value of EU product subsidies, value of off-farm family income, and hired labor. The value of investments incurred by farms because of their participation in the 1994 farm credit program does not show any significant effect on technical efficiency. The predicted levels of technical efficiency indicate that the average technical efficiency of farms 3 years after participating in the 1994 farm credit program is lower than the average technical efficiency of the same farms the year before participating in the program. Thus, the program has failed to increase the efficiency of farms.

Keywords

farm credit programstochastic frontiertechnical efficiencyQ10Q12Q16Q19
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 35 , Issue 3 , December 2003 , pp. 529 - 541
Copyright
Copyright © Southern Agricultural Economics Association 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, D.W. “Agricultural Credit in Latin America: A Critical Appraisal of External Funding Policy.” American Journal of Agricultural Economics 53(August 1971):163–168.CrossRefGoogle Scholar
Aigner, D., Lovell, C.A.K., and Schmidt, P.. “Formulation and Estimation of Stochastic Frontier Production Function Models.” Journal of Econometrics 6(July 1977):21–37.CrossRefGoogle Scholar
Battese, G.E., and Coelli, T.J.. “Prediction of Firm-Level Technical Efficiencies with a Generalized Frontier Production Function and Panel Data.” Journal of Econometrics 38(July 1988):387–399.CrossRefGoogle Scholar
Battese, G.E., and Coelli, T.J.. “A Stochastic Frontier Production Function Incorporating a Model for Technical Inefficiency Effects.” Working Paper in Econometrics and Applied Statistics No 69. Armidale, Australia: Department of Econometrics, University of New England, 1993.Google Scholar
Battese, G.E., and Coelli, T.J.. “A Model for Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data.” Empirical Economics 20(1995):325–332.CrossRefGoogle Scholar
Battese, G.E., and Corra, G.S.. “Estimation of a Production Frontier Model: With Application to the Pastoral Zone of Eastern Australia.” Australian Journal of Agricultural Economics 21 (December 1997):169–179.CrossRefGoogle Scholar
Bauer, P.W. “Recent Developments in the Econometric Estimation of Frontiers.” Journal of Econometrics 46(October-November 1990):39–56.CrossRefGoogle Scholar
Brummer, B., and Loy, J.-P.. “The Technical Efficiency Impact of Farm Credit Programmes: A Case Study of Northern Germany.” Journal of Agricultural Economics 51 (September 2000):405–418.CrossRefGoogle Scholar
Coelli, T.J. “A Monte Carlo Analysis of the Stochastic Frontier Production Function.” Journal of Productivity Analysis 6(September 1995):247–268.CrossRefGoogle Scholar
Coelli, T.J.. “A Guide to FRONTIER Version 4.1: A Computer Program for Stochastic Frontier Production and Cost Function Estimation.” Working Paper 96/07. Armidale, Australia: Centre for Efficiency and Productivity Analysis, University of New England, 1996.Google Scholar
Coelli, T.J., and Battese, G.E.. “Identification of Factors which Influence the Technical Efficiency of Indian Farmers.” Australian Journal of Agricultural Economics 40(August 1996):19–44.CrossRefGoogle Scholar
Dawson, P.J. “Farm Specific Technical Efficiency in the England and Wales Dairy Sector.” European Review of Agricultural Economics 14(December 1987):383–394.CrossRefGoogle Scholar
Greene, W.H. “The Econometric Approach to Efficiency Analysis.” The Measurement of Productive Efficiency. Fried, H.O., Lovell, C.A.K., and Schmidt, S.S., eds. Oxford, UK: Oxford University Press, 1993.Google Scholar
Greene, W.H. “Frontier Production Functions.” Handbook of Applied Economics, Volume 2, Microeconomics. Pesaran, M.H. and Schmidt, P., eds. Oxford, UK: Blackwell Scientific Publications, 1997.Google Scholar
Hallam, D., and Machado, F.. “Efficiency Analysis with Panel Data: A Study of Portuguese Dairy Farms.” European Review of Agricultural Economics 23(March 1996):79–93.CrossRefGoogle Scholar
Jondrow, J., Lovell, C.A.K., Materov, I.S., and Schmidt, P.. “On the Estimation of Technical Inefficiency in the Stochastic Production Function Model.” Journal of Econometrics 19(May 1982):233–238.CrossRefGoogle Scholar
Kodde, D.A., and Palm, F.C.. “Wald Criteria for Jointly Testing Equality and Inequality Restrictions.” Econometrica 54(September 1986):1243–1248.CrossRefGoogle Scholar
Kumbhakar, S.C, Ghosh, S., and McGuckin, J.T.. “A Generalized Production Frontier Approach for Estimating the Determinants of Inefficiency in US Dairy Farms.” Journal of Business Economics and Statistics 9(July 1991):279–286.Google Scholar
Meeusen, W, and van den Broeck, J.. “Efficiency Estimation of Cobb-Douglas Production Function With Composed Error.” International Economic Review 18(June 1977):435–444.CrossRefGoogle Scholar
Reifschneider, D., and Stevenson, R.. “Systematic Departures From the Frontier: A Framework for the Analysis of Firm Inefficiency.” International Economic Review 32(August 1991):715–723.CrossRefGoogle Scholar
Rezitis, A.N., Tsiboukas, K., and Tsoukalas, S.. “Measuring Technical Efficiency in the Greek Agricultural Sector.” Applied Economics 34(20 July 2002):1345–1357.CrossRefGoogle Scholar
Shultz, T.WTransforming Traditional Agriculture. New Haven, CT: Yale University Press, 1964.Google Scholar
Steitieh, A.M. “Input Productivity and Productivity Change of Crop Enterprise in Southern Brazil.” Ph.D. dissertation. The Ohio State University, Columbus, 1971.Google Scholar
Striewe, J., Loy, J.-P., and Koester, U.. “Analyse und Beurteilung der Einzelbertieblichen Investitionsforderung in Schleswing-Holstein.” Agrarwirtschaft 45(December 1996):423–434.Google Scholar
Taylor, T.G., Drummond, H.E., and Gomes, A.T.. “Agricultural Credit Programs and Production Efficiency: An Analysis of Traditional Farming in Southen Minas Gerais, Brazil.” American Journal of Agricultural Economics 68(February 1986):110–119.CrossRefGoogle Scholar