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Tecnhnology estimation for quality pricing in supply-chain relationships

Author

Listed:
  • Angelo Zago

    (Dipartimento di Scienze economiche (Università di Verona))

Abstract

The paper designs an optimal payment system for a group of producers implementing it empirically. It shows how to implement the first best through higher prices for better quality commodities, deriving the optimal pricing schedule. It also takes into account producers' heterogeneity by modelling inefficiency and illustrating how technical efficiency interacts with producers' ability to produce output for a given level of inputs and hence affects revenues. The technology and the technical efficiency of producers are then estimated with a stochastic production function model. The estimation results are then used to simulate the pricing scheme.

Suggested Citation

  • Angelo Zago, 2005. "Tecnhnology estimation for quality pricing in supply-chain relationships," Working Papers 27/2005, University of Verona, Department of Economics.
    • Handle: RePEc:ver:wpaper:27/2005
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    File URL: http://dse.univr.it/RePEc/ver/Wpaper/WP27.pdf
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    References listed on IDEAS

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    1. Andrew P. Barkley & Lori L. Porter, 1996. "The Determinants of Wheat Variety Selection in Kansas, 1974 to 1993," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(1), pages 202-211.
    2. Bogetoft, Peter, 1995. "Incentives and productivity measurements," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 67-77, April.
    3. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    4. Larson, Donald F. & Borrell, Brent, 2001. "Sugar policy and reform," Policy Research Working Paper Series 2602, The World Bank.
    5. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    6. Jean-Marc Bourgeon & Robert G. Chambers, 1999. "Producer Organizations, Bargaining, and Asymmetric Information," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(3), pages 602-609.
    7. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    8. Lopez, Rigoberto A. & Spreen, Thomas H., 1984. "The Impact of Alternative Payment Arrangements on the Performance of Florida Sugarcane Cooperatives," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 16(2), pages 99-108, December.
    9. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    10. Richard J. Sexton, 1986. "The Formation of Cooperatives: A Game-Theoretic Approach with Implications for Cooperative Finance, Decision Making, and Stability," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(2), pages 214-225.
    11. Olson, Jerome A. & Schmidt, Peter & Waldman, Donald M., 1980. "A Monte Carlo study of estimators of stochastic frontier production functions," Journal of Econometrics, Elsevier, vol. 13(1), pages 67-82, May.
    12. Jean-Marc Bourgeon & Robert G. Chambers, 2008. "Implementable Ramsey-Boiteux Pricing in Agricultural and Environmental Policy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(2), pages 499-508.
    13. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    14. Steven Buccola & Yoko Iizuka, 1997. "Hedonic Cost Models and the Pricing of Milk Components," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 452-462.
    15. 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.
    16. James Vercammen & Murray Fulton & Charles Hyde, 1996. "Nonlinear Pricing Schemes for Agricultural Cooperatives," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(3), pages 572-584.
    17. Peter Bogetoft, 2000. "DEA and Activity Planning under Asymmetric Information," Journal of Productivity Analysis, Springer, vol. 13(1), pages 7-48, January.
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    More about this item

    Keywords

    Quality; optimal contract; nonlinear pricing; stochastic frontier analysis.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness

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