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Nonparametric production technologies with weakly disposable inputs. (English) Zbl 1403.90424

Summary: In models of production theory and efficiency analysis, the inputs and outputs are assumed to satisfy some form of disposability. In this paper, we consider the assumption of weak input disposability. It states that any activity remains feasible if its inputs are simultaneously scaled up in the same proportion. As suggested in the literature, the Shephard technology incorporating weak input disposability could be used to evaluate the effect of input congestion. We show that the Shephard technology is not convex and therefore introduces bias in evaluation of congestion. To address this, we develop an alternative convex technology whose use in the evaluation of congestion removes the noted bias. We undertake a further axiomatic investigation and obtain a range of production technologies, all of which exhibit weak input disposability but are based on different, progressively relaxed, convexity assumptions. Apart from the evaluation of input congestion, such technologies should also be useful in applications in which some inputs are closely related or are overlapping, and therefore satisfy only the weak input disposability assumption incorporated in the new models.

MSC:

90B50 Management decision making, including multiple objectives
90C08 Special problems of linear programming (transportation, multi-index, data envelopment analysis, etc.)
91B38 Production theory, theory of the firm
90B30 Production models

References:

[1] Agrell, P. J.; Bogetoft, P.; Brock, M.; Tind, J., Efficiency evaluation with convex pairs, Advanced Modeling and Optimization, 7, 2, 211-237 (2005) · Zbl 1161.90521
[2] Banker, R. D.; Charnes, A.; Cooper, W. W., Some models for estimating technical and scale efficiencies in data envelopment analysis, Management Science, 30, 9, 1078-1092 (1984) · Zbl 0552.90055
[3] Bogetoft, P., DEA models on relaxed convexity assumptions, Management Science, 42, 3, 457-465 (1996) · Zbl 0884.90001
[4] Bogetoft, P.; Tama, J. M.; Tind, J., Convex input and output projections of nonconvex production possibility sets, Management Science, 46, 6, 858-869 (2000) · Zbl 1231.90228
[5] Briec, W.; Kerstens, K.; Vanden Eeckaut, P., Non-convex technologies and cost functions: Definitions, duality and nonparametric tests of convexity, Journal of Economics, 81, 2, 155-192 (2004) · Zbl 1106.91342
[6] Byrnes, P.; Färe, R.; Grosskopf, S.; Lovell, C. A.K., The effect of unions on productivity: US surface mining of coal, Management Science, 34, 9, 1037-1053 (1988)
[7] Chambers, R. G.; Chung, Y.; Färe, R., Profit, directional distance functions, and nerlovian efficiency, Journal of Optimization Theory and Applications, 98, 2, 351-364 (1998) · Zbl 0909.90040
[8] Charnes, A.; Cooper, W. W.; Rhodes, E., Measuring the efficiency of decision making units, European Journal of Operational Research, 2, 6, 429-444 (1978) · Zbl 0416.90080
[9] Deprins, D.; Simar, L.; Tulkens, H., Measuring labor-efficiency in post offices, (Marchand, M.; Pestieau, P.; Tulkens, H., The performance of public enterprises: Concepts and measurements (1984), Amsterdam: North-Holland), 243-267
[10] Dervaux, B.; Kerstens, K.; Vanden Eeckaut, P., Radial and nonradial static efficiency decompositions: A focus on congestion measurement, Transportation Research. Part B: Methodological, 32, 5, 299-312 (1998)
[11] Färe, R.; Grosskopf, S., Measuring congestion in production, Zeitschrift für Nationalökonomie/Journal of Economics, 43, 3, 257-271 (1983) · Zbl 0519.90019
[12] Färe, R.; Grosskopf, S., Research note. Decomposing technical efficiency with care, Management Science, 46, 1, 167-168 (2000)
[13] Färe, R.; Grosskopf, S., Non-parametric productivity analysis with undesirable outputs: Comment, American Journal of Agricultural Economics, 85, 4, 1070-1074 (2003)
[14] Färe, R.; Grosskopf, S.; Lovell, C. A.K., The measurement of efficiency of production (1985), Kluwer Academic Publishers: Kluwer Academic Publishers Boston
[15] Färe, R.; Grosskopf, S.; Lovell, C. A.K., Production frontiers (1994), Cambridge University Press: Cambridge University Press Cambridge
[16] Färe, R.; Karagiannis, G., The denominator rule for share-weighting aggregation, European Journal of Operational Research, 260, 3, 1175-1180 (2017) · Zbl 1403.90410
[17] Grosskopf, S., The role of the reference technology in measuring productive efficiency, The Economic Journal, 96, 382, 499-513 (1986)
[18] Kuosmanen, T., DEA with efficiency classification preserving conditional convexity, European Journal of Operational Research, 132, 2, 326-342 (2001) · Zbl 0979.90074
[19] Kuosmanen, T., Weak disposability in nonparametric productivity analysis with undesirable outputs, American Journal of Agricultural Economics, 87, 4, 1077-1082 (2005)
[20] Kuosmanen, T.; Podinovski, V. V., Weak disposability in nonparametric production analysis: Reply to Färe and Grosskopf, American Journal of Agricultural Economics, 91, 2, 539-545 (2009)
[21] Mehdiloozad, M.; Sahoo, B. K.; Roshdi, I., A generalized multiplicative directional distance function for efficiency measurement in DEA, European Journal of Operational Research, 232, 3, 679-688 (2014) · Zbl 1305.90232
[22] Mehdiloozad, M.; Zhu, J.; Sahoo, B. K., Identification of congestion in data envelopment analysis under the occurrence of multiple projections: A reliable method capable of dealing with negative data, European Journal of Operational Research (2017)
[23] Olesen, O. B.; Petersen, N. C.; Podinovski, V. V., Efficiency analysis with ratio measures, European Journal of Operational Research, 245, 2, 446-462 (2015) · Zbl 1346.90440
[24] Olesen, O. B.; Petersen, N. C.; Podinovski, V. V., Efficiency measures and computational approaches for data envelopment analysis models with ratio inputs and outputs, European Journal of Operational Research, 261, 2, 640-655 (2017) · Zbl 1403.90429
[25] Petersen, N. C., Data envelopment analysis on a relaxed set of assumptions, Management Science, 36, 3, 305-314 (1990) · Zbl 0699.90009
[26] Podinovski, V. V., Bridging the gap between the constant and variable returns-to-scale models: Selective proportionality in data envelopment analysis, Journal of the Operational Research Society, 55, 3, 265-276 (2004) · Zbl 1097.91026
[27] Podinovski, V. V., Selective convexity in DEA models, European Journal of Operational Research, 161, 2, 552-563 (2005) · Zbl 1067.90084
[28] Podinovski, V. V.; Chambers, R. G.; Atici, K. B.; Deineko, I. D., Marginal values and returns to scale for nonparametric production frontiers, Operations Research, 64, 1, 236-250 (2016) · Zbl 1336.90052
[29] Podinovski, V. V.; Kuosmanen, T., Modelling weak disposability in data envelopment analysis under relaxed convexity assumptions, European Journal of Operational Research, 211, 3, 577-585 (2011) · Zbl 1237.90158
[30] Ray, S. C., Data envelopment analysis. Theory and techniques for economics and operations research (2004), Cambridge University Press: Cambridge University Press Cambridge · Zbl 1103.91052
[31] Rockafellar, R. T., Convex analysis (1970), NJ: Princeton University Press: NJ: Princeton University Press Princeton · Zbl 0229.90020
[32] Shephard, R. W., Semi-homogeneous production functions and scaling of production, (Eichhorn, W.; Henn, R.; Opitz, O.; Shephard, R., Production theory (1974), Springer-Verlag: Springer-Verlag New York), 253-285 · Zbl 0297.90031
[33] Tone, K.; Sahoo, B. K., Degree of scale economies and congestion: A unified DEA approach, European Journal of Operational Research, 158, 3, 755-772 (2004) · Zbl 1056.90094
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