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Allocative Efficiency Measurement Revisited: Do We Really Need Input Prices?

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  • Oleg Badunenko
  • Michael Fritsch
  • Andreas Stephan

Abstract

The traditional approach to measuring allocative efficiency is based on input prices, which are rarely known at the firm level. This paper proposes a new approach to measure allocative efficiency which is based on the output-oriented distance to the frontier in a profit - technical efficiency space - and which does not require information on input prices. To validate the new approach, we perform a Monte-Carlo experiment which provides evidence that the estimates of the new and the traditional approach are highly correlated. Finally, as an illustration, we apply the new approach to a sample of about 900 enterprises from the chemical industry in Germany.

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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 591.

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Length: 24 p.
Date of creation: 2006
Date of revision:
Handle: RePEc:diw:diwwpp:dp591

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Keywords: Allocative efficiency; data envelopment analysis; frontier analysis; technical efficiency; Monte-Carlo study; chemical industry;

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Citations

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Cited by:
  1. Oleg Badunenko & Michael Fritsch & Andreas Stephan, 2006. "Allocative Efficiency Measurement Revisited: Do We Really Need Input Prices?," Discussion Papers of DIW Berlin 591, DIW Berlin, German Institute for Economic Research.
  2. Ali, Syed Zahid & Anwar, Sajid & Valadkhani, Abbas, 2012. "Macroeconomic consequences of increased productivity in less developed economies," Economic Modelling, Elsevier, vol. 29(3), pages 621-631.
  3. Jedrzej Bialkowski & Katrin Gottschalk & Tomasz Piotr Wisniewski, 2007. "Political orientation of government and stock market returns," Applied Financial Economics Letters, Taylor and Francis Journals, Taylor and Francis Journals, vol. 3(4), pages 269-273.
  4. Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.

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