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Choice of Inputs and Outputs for Production Analysis

In: Handbook of Production Economics

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  • Subhash C. Ray

    (University of Connecticut)

Abstract

Production creates value by transforming inputs into outputs. Classification of variables as inputs or outputs depends on the scope of decision-making by the firm. Inputs enter the boundary of the firm from outside without any prior processing by the firm and once transformed into outputs exit its jurisdiction without any further processing. This chapter highlights the defining characteristics of inputs and outputs both for single stage and multistage production. The necessary conditions that must be met for a valid aggregation of several inputs into total expenditure are discussed both for nonparametric and parametric models of production. Several statistical tests of hypotheses related to aggregation of several inputs or exclusion of individual inputs in nonparametric models are discussed. The technology set of feasible input-output bundles invariably depends on many environmental or contextual variables that are outside the control of the producer. In parametric Stochastic Frontier Analysis, they can be directly included as determinants of the mean or variance the technical efficiency factor causing shifts in the production frontier. In nonparametric Data Envelopment Analysis, influence of such factors is measured through a second stage regression of efficiency scores on the contextual variables. The alternative approaches of a second stage least squares regression and a truncated regression are briefly discussed. The chapter ends with examples of input-output choice in several popular areas of application like manufacturing, banking, and health care.

Suggested Citation

  • Subhash C. Ray, 2022. "Choice of Inputs and Outputs for Production Analysis," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 26, pages 1083-1116, Springer.
  • Handle: RePEc:spr:sprchp:978-981-10-3455-8_20
    DOI: 10.1007/978-981-10-3455-8_20
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    References listed on IDEAS

    as
    1. 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.
    2. Subhash C. Ray, 2022. "Conceptualization and Measurement of Productivity Growth and Technical Change: A Nonparametric Approach," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 20, pages 821-870, Springer.
    3. Inmaculada Sirvent & José L. Ruiz & Fernando Borrás & Jesús T. Pastor, 2005. "A Monte Carlo Evaluation Of Several Tests For The Selection Of Variables In Dea Models," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 4(03), pages 325-343.
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    5. Berger, Allen N. & Mester, Loretta J., 1997. "Inside the black box: What explains differences in the efficiencies of financial institutions?," Journal of Banking & Finance, Elsevier, vol. 21(7), pages 895-947, July.
    6. Ray, Subhash C. & Das, Abhiman, 2010. "Distribution of cost and profit efficiency: Evidence from Indian banking," European Journal of Operational Research, Elsevier, vol. 201(1), pages 297-307, February.
    7. 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.
    8. Ray,Subhash C., 2012. "Data Envelopment Analysis," Cambridge Books, Cambridge University Press, number 9781107405264, September.
    9. 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.
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    11. SIMAR , Léopold, 1995. "Aspects of Statistical Analysis in DEA-Type Frontier Models," LIDAM Discussion Papers CORE 1995061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    12. 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.
    13. Simar, L., 1991. "Estimating efficiencies from frontier models with panel data: a comparison of parametric, non-parametric and semi-parametric methods with boot strapping," LIDAM Discussion Papers CORE 1991026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Rolf Fare & Shawna Grosskopf & Valentin Zelenyuk, 2004. "Aggregation bias and its bounds in measuring technical efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 11(10), pages 657-660.
    15. Léopold Simar & Paul W. Wilson, 1998. "Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models," Management Science, INFORMS, vol. 44(1), pages 49-61, January.
    16. N Adler & B Golany, 2002. "Including principal component weights to improve discrimination in data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(9), pages 985-991, September.
    17. Rajiv Banker & Hsihui Chang & Ram Natarajan, 2007. "Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data," Journal of Productivity Analysis, Springer, vol. 27(2), pages 115-121, April.
    18. Stephen M. Miller, 2022. "Empirical Analysis of Production Economics: Applications to Banking," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 29, pages 1165-1191, Springer.
    19. Rajiv D. Banker & Ram Natarajan, 2008. "Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis," Operations Research, INFORMS, vol. 56(1), pages 48-58, February.
    20. Banker, Rajiv & Natarajan, Ram & Zhang, Daqun, 2019. "Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using Data Envelopment Analysis: Second stage OLS versus bootstrap approaches," European Journal of Operational Research, Elsevier, vol. 278(2), pages 368-384.
    21. Sickles,Robin C. & Zelenyuk,Valentin, 2019. "Measurement of Productivity and Efficiency," Cambridge Books, Cambridge University Press, number 9781107036161, September.
    22. Sumon Kumar Bhaumik, 2022. "Technical Efficiency and Its Determinants in the Manufacturing Sector: What We Know and What We Should Know," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 36, pages 1411-1432, Springer.
    23. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    24. Hung-Jen Wang, 2002. "Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model," Journal of Productivity Analysis, Springer, vol. 18(3), pages 241-253, November.
    25. Varian, Hal R, 1984. "The Nonparametric Approach to Production Analysis," Econometrica, Econometric Society, vol. 52(3), pages 579-597, May.
    26. Sealey, Calvin W, Jr & Lindley, James T, 1977. "Inputs, Outputs, and a Theory of Production and Cost at Depository Financial Institutions," Journal of Finance, American Finance Association, vol. 32(4), pages 1251-1266, September.
    27. Rajiv D. Banker & Richard C. Morey, 1986. "The Use of Categorical Variables in Data Envelopment Analysis," Management Science, INFORMS, vol. 32(12), pages 1613-1627, December.
    28. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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    More about this item

    Keywords

    Multistage production; Input aggregation; Contextual variables; Second stage regression;
    All these keywords.

    JEL classification:

    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory

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