IDEAS home Printed from https://ideas.repec.org/p/ver/wpaper/27-2005.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://dse.univr.it/RePEc/ver/Wpaper/WP27.pdf
    File Function: First version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Bogetoft, Peter, 1995. "Incentives and productivity measurements," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 67-77, April.
    4. 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.
    5. Larson, Donald F. & Borrell, Brent, 2001. "Sugar policy and reform," Policy Research Working Paper Series 2602, The World Bank.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Peter Bogetoft, 2000. "DEA and Activity Planning under Asymmetric Information," Journal of Productivity Analysis, Springer, vol. 13(1), pages 7-48, January.
    12. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    2. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    3. Tim J. Coelli, 1995. "Recent Developments In Frontier Modelling And Efficiency Measurement," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 39(3), pages 219-245, December.
    4. Martín Rossi, 2015. "The Econometrics Approach to the Measurement of Efficiency: A Survey," Working Papers 117, Universidad de San Andres, Departamento de Economia, revised Feb 2015.
    5. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    6. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    7. Adugna Lemi & Ian Wright, 2020. "Exports, foreign ownership, and firm-level efficiency in Ethiopia and Kenya: an application of the stochastic frontier model," Empirical Economics, Springer, vol. 58(2), pages 669-698, February.
    8. Markose Chekol Zewdie & Michele Moretti & Daregot Berihun Tenessa & Zemen Ayalew Ayele & Jan Nyssen & Enyew Adgo Tsegaye & Amare Sewnet Minale & Steven Van Passel, 2021. "Agricultural Technical Efficiency of Smallholder Farmers in Ethiopia: A Stochastic Frontier Approach," Land, MDPI, vol. 10(3), pages 1-17, March.
    9. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    10. Federico Belotti & Silvio Daidone & Giuseppe Ilardi & Vincenzo Atella, 2013. "Stochastic frontier analysis using Stata," Stata Journal, StataCorp LP, vol. 13(4), pages 718-758, December.
    11. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    12. Federico Belotti & Giuseppe Ilardi & Andrea Piano Mortari, 2019. "Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency," CEIS Research Paper 459, Tor Vergata University, CEIS, revised 30 May 2019.
    13. Dairo Estrada & Poldy Osorio, 2004. "Effects of Financial Capital on Colombian Banking Efficiency," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(47), pages 162-201, December.
    14. Uwe Jensen & Hermann Gartner & Susanne Rässler, 2010. "Estimating German overqualification with stochastic earnings frontiers," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 94(1), pages 33-51, March.
    15. Danuse Nerudova & Marian Dobranschi, 2019. "Alternative method to measure the VAT gap in the EU: Stochastic tax frontier model approach," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-38, January.
    16. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    17. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    18. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    19. Manlagnit, Maria Chelo V., 2015. "Basel regulations and banks’ efficiency: The case of the Philippines," Journal of Asian Economics, Elsevier, vol. 39(C), pages 72-85.
    20. Alecos Papadopoulos, 2021. "Stochastic frontier models using the Generalized Exponential distribution," Journal of Productivity Analysis, Springer, vol. 55(1), pages 15-29, February.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ver:wpaper:27/2005. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael Reiter (email available below). General contact details of provider: https://edirc.repec.org/data/isverit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.