Nonparametric Production Technologies with Multiple Component Processes

We develop a nonparametric methodology for assessing the efficiency of decision-making units operating in a production technology with several component processes. The latter is modeled by the new multiple hybrid returns-to-scale (MHRS) technology, formally derived from an explicitly stated set of production axioms. In contrast with the existing models of data envelopment analysis (DEA), the MHRS technology allows the incorporation of component-specific and shared inputs and outputs that represent several proportional (scalable) component production processes as well as nonproportional inputs and outputs. Our approach does not require information about the allocation of shared inputs and outputs to component processes or any assumptions about this allocation. We demonstrate the usefulness of the suggested approach in an application in the context of secondary education and also in a Monte Carlo study based on a simulated data generating process.

[1]  R. Färe,et al.  Profit, Directional Distance Functions, and Nerlovian Efficiency , 1998 .

[2]  Finn R. Førsund,et al.  Calculating scale elasticity in DEA models , 2004, J. Oper. Res. Soc..

[3]  W. Cook,et al.  Multicomponent Efficiency Measurement and Shared Inputs in Data Envelopment Analysis: An Application to Sales and Service Performance in Bank Branches , 2000 .

[4]  Laurens Cherchye,et al.  Opening the "Black Box" of Efficiency Measurement: Input Allocation in Multioutput Settings , 2013, Oper. Res..

[5]  W. Cook,et al.  Sales performance measurement in bank branches , 2001 .

[6]  V. V. Podinovski,et al.  Bridging the gap between the constant and variable returns-to-scale models: selective proportionality in data envelopment analysis , 2004, J. Oper. Res. Soc..

[7]  V. V. Podinovski,et al.  Production trade-offs and weight restrictions in data envelopment analysis , 2004, J. Oper. Res. Soc..

[8]  Wendi Arant-Kaspar,et al.  Opening the Black Box , 2016, Coll. Res. Libr..

[9]  Byeong-Ho Gong,et al.  Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data , 1992 .

[10]  V. V. Podinovski,et al.  Selective convexity in DEA models , 2005, Eur. J. Oper. Res..

[11]  Victor V. Podinovski,et al.  Differential Characteristics of Efficient Frontiers in Data Envelopment Analysis , 2010, Oper. Res..

[12]  Rolf Färe,et al.  A “calculus” for data envelopment analysis , 2008 .

[13]  Joe Zhu,et al.  Partial Input to Output Impacts in DEA: Production Considerations and Resource Sharing among Business Subunits , 2013 .

[14]  Liang Liang,et al.  Cone ratio models with shared resources and nontransparent allocation parameters in network DEA , 2015 .

[15]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[16]  V. V. Podinovski,et al.  Side effects of absolute weight bounds in DEA models , 1999, Eur. J. Oper. Res..

[17]  Valentin Zelenyuk,et al.  A scale elasticity measure for directional distance function and its dual: Theory and DEA estimation , 2013, Eur. J. Oper. Res..

[18]  Timo Kuosmanen Weak Disposability in Nonparametric Production Analysis with Undesirable Outputs , 2005 .

[19]  Shawna Grosskopf,et al.  Some Remarks on Productivity and its Decompositions , 2003 .

[20]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[21]  Paul Rouse,et al.  Data Envelopment Analysis with Nonhomogeneous DMUs , 2013, Oper. Res..

[22]  Wade D. Cook,et al.  Multicomponent efficiency measurement and core business identification in multiplant firms: A DEA model , 2004, Eur. J. Oper. Res..

[23]  John Ruggiero,et al.  Efficiency estimation and error decomposition in the stochastic frontier model: A Monte Carlo analysis , 1999, Eur. J. Oper. Res..

[24]  Robert G. Chambers,et al.  Marginal Values and Returns to Scale for Nonparametric Production Frontiers , 2016, Oper. Res..

[25]  Emmanuel Thanassoulis,et al.  Weights restrictions and value judgements in Data Envelopment Analysis: Evolution, development and future directions , 1997, Ann. Oper. Res..

[26]  Ole Bent Olesen,et al.  Efficiency analysis with ratio measures , 2015, Eur. J. Oper. Res..

[27]  Barnabé Walheer,et al.  Multi-Output Profit Efficiency and Directional Distance Functions , 2014 .

[28]  John Ruggiero,et al.  On the measurement of technical efficiency in the public sector , 1996 .

[29]  Joe Zhu,et al.  Incorporating Multiprocess Performance Standards into the DEA Framework , 2006, Oper. Res..

[30]  R. Färe,et al.  Productivity Developments in Swedish Hospitals: A Malmquist Output Index Approach , 1994 .

[31]  Petros Hadjicostas,et al.  One-sided elasticities and technical efficiency in multi-output production: A theoretical framework , 2006, Eur. J. Oper. Res..

[32]  Emmanuel Thanassoulis,et al.  Data Envelopment Analysis:the mathematical programming approach to efficiency analysis , 2008 .

[33]  R. Färe,et al.  On directional scale elasticities , 2015 .

[34]  Kaoru Tone,et al.  Data Envelopment Analysis , 1996 .

[35]  Victor V. Podinovski,et al.  Combining the assumptions of variable and constant returns to scale in the efficiency evaluation of secondary schools , 2014, Eur. J. Oper. Res..

[36]  J. Beasley Determining Teaching and Research Efficiencies , 1995 .

[37]  Rajiv D. Banker,et al.  Estimation of returns to scale using data envelopment analysis , 1992 .

[38]  Joe Zhu,et al.  Multiple Variable Proportionality in Data Envelopment Analysis , 2011, Oper. Res..

[39]  Laurens Cherchye,et al.  Multi-output efficiency with good and bad outputs , 2015, Eur. J. Oper. Res..

[40]  Subhash C. Ray Data Envelopment Analysis: Contents , 2004 .

[41]  Ole Bent Olesen,et al.  Efficiency measures and computational approaches for data envelopment analysis models with ratio inputs and outputs , 2017, Eur. J. Oper. Res..