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The Effect of Unions on Productivity: U.S. Surface Mining of Coal

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Author Info

  • Patricia Byrnes

    (University of North Carolina, Chapel Hill, North Carolina 27514)

  • Rolf Färe

    (University of North Carolina, Chapel Hill, North Carolina 27514)

  • Shawna Grosskopf

    (University of North Carolina, Chapel Hill, North Carolina 27514)

  • C. A. Knox Lovell

    (University of North Carolina, Chapel Hill, North Carolina 27514)

Abstract

The purpose of this paper is to compare the abilities of two competing analytical techniques---mathematical programming and statistical regression---to shed light on the union/nonunion productivity differential in U.S. surface coal mining. The programming approach has the virtues of being nonparametric (and thus extremely flexible) and of being able to provide a decomposition of productivity differentials into three components---differences in technical efficiency, differences in scale efficiency, and differences in congestion. Identification of these three components provides an aid to management in its search for the sources of, and remedies for, productivity gaps. The econometric approach, on the other hand, is neither flexible nor does it provide such a decomposition; its chief virtue lies in the fact that, being stochastic, it allows for the presence of noise and measurement error that plagues most if not all empirical data. Thus, the two approaches have complementary virtues. The two techniques are used to investigate productivity in two samples of U.S. surface coal mines. The programming approach finds a large and significant positive union/nonunion productivity differential. This differential is due primarily to greater congestion occurring in the smaller nonunion mines in one sample, and to scale inefficiency in the smaller nonunion mines in the other sample. The econometric analysis finds nearly the same union/nonunion productivity differential, but sheds no light on the composition of, and thus the cure for, the differential.

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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 34 (1988)
Issue (Month): 9 (September)
Pages: 1037-1053

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Handle: RePEc:inm:ormnsc:v:34:y:1988:i:9:p:1037-1053

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Related research

Keywords: efficiency; mathematical programming; statistical estimation; unionization;

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Cited by:
  1. Kao, Chiang, 2010. "Congestion measurement and elimination under the framework of data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 123(2), pages 257-265, February.
  2. Zanakis, Stelios H. & Mandakovic, Tomislav & Gupta, Sushil K. & Sahay, Sundeep & Hong, Sungwan, 1995. "A review of program evaluation and fund allocation methods within the service and government sectors," Socio-Economic Planning Sciences, Elsevier, vol. 29(1), pages 59-79, March.
  3. Homburg, Carsten, 2001. "Using data envelopment analysis to benchmark activities," International Journal of Production Economics, Elsevier, vol. 73(1), pages 51-58, August.
  4. 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.
  5. Tsolas, Ioannis E., 2011. "Performance assessment of mining operations using nonparametric production analysis: A bootstrapping approach in DEA," Resources Policy, Elsevier, vol. 36(2), pages 159-167, June.
  6. Fang, Hong & Wu, Junjie & Zeng, Catherine, 2009. "Comparative study on efficiency performance of listed coal mining companies in China and the US," Energy Policy, Elsevier, vol. 37(12), pages 5140-5148, December.
  7. Dervaux, Benoît & Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1998. "Radial and nonradial static efficiency decompositions: a focus on congestion measurement," Transportation Research Part B: Methodological, Elsevier, vol. 32(5), pages 299-312, June.

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