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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.
Byrnes et al. (Thu,) studied this question.