Computing Confidence Intervals for the Postal Service’s Cost-Elasticity Estimates

Margaret M Cigno,Bzhilyanskaya Y Lyudmila,Soiliou D Namoro

doi:10.4236/ojs.2021.115036

Abstract

This paper provides methods for assessing the precision of cost elasticity estimates when the underlying regression function is assumed to be polynomial. Specifically, the paper adapts two well-known methods for computing confidential intervals for ratios: the delta-method and the Fieller method. We show that performing the estimation with mean-centered explanatory variables provides a straightforward way to estimate the elasticity and compute a confidence interval for it. A theoretical discussion of the proposed methods is provided, as well as an empirical example based on publicly available postal data. Possible areas of application include postal service providers worldwide, transportation and electricity.

Highlights

IntroductionPosts are confronted with declining volumes and revenues
Around the world, posts are confronted with declining volumes and revenues
This paper provides methods for assessing the precision of cost elasticity estimates when the underlying regression function is assumed to be polynomial

Summary

Introduction

Posts are confronted with declining volumes and revenues. In such an environment, it is vitally important that price signals are properly constructed using accurate cost data at the product level. Because posts are multi-product firms they exhibit complex cost behaviors. The presence of common costs and the thin line between fixed and variable costs make it challenging for posts to accurately determine cost elasticity by product. The United States Postal Service (Postal Service) uses quadratic and translog regression equations, among others, for estimating volume variabilities of cost-related variables, such as street time or vehicle capacity, with respect to diverse cost drivers, such as mail volume [1] [2] [3]. The term “volume variability” (or variability) is used by the Postal

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