Competition and Regulation in the Postal and Delivery Sector
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Competition and Regulation in the Postal and Delivery Sector

  • Advances in Regulatory Economics series

Edited by Michael A. Crew and Paul R. Kleindorfer

orldwide, postal and delivery economics has attracted considerable interest. Numerous questions have arisen, including the role of regulation, funding the Universal Service Obligation, postal reform in Europe, Asia and North America, the future of national postal operators, demand and pricing strategies, and the principles that should govern the introduction of competition. Collected here are responses to these questions in the form of 24 essays written by researchers, practitioners, and senior managers from throughout the world.
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Chapter 21: Are There Economies of Scale in Mail Processing? Getting the Answers from a Large-but-Dirty Sample

Lawrence Fenster, Diane Monaco and Edward S. Pearsall

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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...

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