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Production with Storable and Durable Inputs: Nonparametric Analysis of Intertemporal Efficiency

Listed author(s):
  • Laurens Cherchye
  • Bram De Rock

We propose a nonparametric methodology for intertemporal production analysis that accounts for durable as well as storable inputs. Durable inputs contribute to the production outputs in multiple consecutive periods. Storable inputs are non-durable and can be stored in inventories for use in future periods. We explicitly model the possibility that firms use several vintages of the durable inputs, i.e. they invest in new durables and scrap older durables over time. Furthermore, we allow for production delays of durable inputs. We characterize production behavior that is dynamically cost efficient, which allows us to evaluate the efficiency of observed production decisions. For cost inefficient behavior, we propose a measure to quantify the degree of inefficiency. An attractive feature of this measure is that it can be decomposed in period-specfic cost inefficiencies. We demonstrate the usefulness of our methodology through an application to Swiss railway companies.

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Paper provided by ULB -- Universite Libre de Bruxelles in its series Working Papers ECARES with number ECARES 2016-33.

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Length: 33 p.
Date of creation: Sep 2016
Publication status: Published by:
Handle: RePEc:eca:wpaper:2013/235997
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  1. Charles Blackorby & R. Russell, 1999. "Aggregation of Efficiency Indices," Journal of Productivity Analysis, Springer, vol. 12(1), pages 5-20, August.
  2. Laurens Cherchye & Bram De Rock & Bart Dierynck & Filip Roodhooft & Jeroen Sabbe, 2013. "Opening the “Black Box” of Efficiency Measurement: Input Allocation in Multioutput Settings," Operations Research, INFORMS, vol. 61(5), pages 1148-1165, October.
  3. Maniadakis, Nikolaos & Thanassoulis, Emmanuel, 2004. "A cost Malmquist productivity index," European Journal of Operational Research, Elsevier, vol. 154(2), pages 396-409, April.
  4. Sebastian Nick & Heike Wetzel, 2016. "The hidden cost of investment: the impact of adjustment costs on firm performance measurement and regulation," Journal of Regulatory Economics, Springer, vol. 49(1), pages 33-55, February.
  5. Crawford, Ian & Polisson, Matthew, 2014. "Testing for intertemporal nonseparability," Journal of Mathematical Economics, Elsevier, vol. 52(C), pages 46-49.
  6. Mehdi Farsi & Massimo Filippini & William Greene, 2005. "Efficiency Measurement in Network Industries: Application to the Swiss Railway Companies," Journal of Regulatory Economics, Springer, vol. 28(1), pages 69-90, 07.
  7. Haney, Aoife Brophy & Pollitt, Michael G., 2013. "International benchmarking of electricity transmission by regulators: A contrast between theory and practice?," Energy Policy, Elsevier, vol. 62(C), pages 267-281.
  8. Fare, Rolf & Zelenyuk, Valentin, 2003. "On aggregate Farrell efficiencies," European Journal of Operational Research, Elsevier, vol. 146(3), pages 615-620, May.
  9. Nemoto, Jiro & Goto, Mika, 1999. "Dynamic data envelopment analysis: modeling intertemporal behavior of a firm in the presence of productive inefficiencies," Economics Letters, Elsevier, vol. 64(1), pages 51-56, July.
  10. Pollitt, Michael, 2005. "The role of efficiency estimates in regulatory price reviews: Ofgem's approach to benchmarking electricity networks," Utilities Policy, Elsevier, vol. 13(4), pages 279-288, December.
  11. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
  12. Fare, R. & Grabowski, R. & Grosskopf, S. & Kraft, S., 1997. "Efficiency of a fixed but allocatable input: A non-parametric approach," Economics Letters, Elsevier, vol. 56(2), pages 187-193, October.
  13. Haney, A.B. & Pollitt, M.G., 2012. "International benchmarking of Electricity Transmission by Regulators: Theory and Practice," Cambridge Working Papers in Economics 1254, Faculty of Economics, University of Cambridge.
  14. Steven T. Hackman & Robert C. Leachman, 1989. "A General Framework for Modeling Production," Management Science, INFORMS, vol. 35(4), pages 478-495, April.
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