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Deepening the measurement of technical inefficiency in private farming in Georgia: locally parametric regression

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  • Temel, Tugrul

    (Vrije Universiteit Amsterdam, Faculteit der Economische Wetenschappen en Econometrie (Free University Amsterdam, Faculty of Economics Sciences, Business Administration and Economitrics)

  • Lucas, Andre

Abstract

This study deepens the measurement of technical inefficiency in private maize farming in Georgia, applying locally parametric (LP) reg ression method, whicb builds on the stochastic frontier production function approach. Detailed survey data for 221 mixed farms for 1997 are used in the estimations. Findings suggest: (i) maize production can be further increased by breaking up large farms into smaller parcels; and (ii) increased schooling and farm experience of new private farm operators would further reduce the inefficiency. Furthermore, global and local estimations of the inefficiency suggest different policy directions as to the future of maize farming. The global estimations, revealing non-discriminant overestimation of the inefficiency, prejudice broad-based farm reforms. On the contrary, the local estimations, pointing out relatively large farms as the key source of the inefficiency, favor the design of specific policies for the effective operation of lage farms.

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Bibliographic Info

Paper provided by VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics in its series Serie Research Memoranda with number 0007.

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Date of creation: 2003
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Handle: RePEc:dgr:vuarem:2003-7

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Keywords: technical inefficiency; regression methods; stochastic frontier production function; maize farming in Georgia;

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  1. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
  2. Baltagi, Badi H. & Hidalgo, Javier & Li, Qi, 1996. "A nonparametric test for poolability using panel data," Journal of Econometrics, Elsevier, vol. 75(2), pages 345-367, December.
  3. Adams, Robert M & Berger, Allen N & Sickles, Robin C, 1999. "Semiparametric Approaches to Stochastic Panel Frontiers with Applications in the Banking Industry," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 349-58, July.
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