Edited by Michael A. Crew and Paul R. Kleindorfer
Chapter 21: Are There Economies of Scale in Mail Processing? Getting the Answers from a Large-but-Dirty Sample
* Lawrence Fenster, Diane Monaco, Edward S. Pearsall and Spyros Xenakis 1. INTRODUCTION In this chapter we present econometric evidence that United States Postal Service (USPS) mail processing plants are mostly operated at levels where the returns to density and scale are decreasing. The evidence is derived from production functions ﬁt as stochastic switching regressions to large panel samples of pieces, work hours, capital usage, delivery points, delivery units and other plant-level information mostly drawn from USPS’s Management Operating Data System (MODS). Decreasing returns were found for production functions deﬁned for every aggregate of pieces handled by shape, for pieces fed in most single automated, mechanical and manual processes, and when piece-handlings were divided into inbound and outbound sub-streams. Samples drawn from MODS are problematic because they exhibit anomalies at frequencies suggesting that they are a dirty mix of good observations and occasional reporting mistakes. Our econometrics employs a Maximum Likelihood (ML) estimator for ﬁtting a two-regime stochastic switching regression model as ﬁrst proposed by Quandt (1972). The motivation for this approach is that the good observations are generated by the production function, while the bad observations are the result of data collection failures consistent with a diﬀerent regime. Our estimates indicate that mail processing is primarily an industrial process rather than a network support activity. Returns to scale typically exceed returns to density. This result is anomalous for an activity like transportation that directly supports a distribution network, but it is readily explained for an industrial process as...
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