The case of the downsizing decision

New CEO Charles Rampart's decision to make deep across-the-board cuts at Universal Products Company, Ltd. presents division manager Andrew Jordan with a thorny problem. Plagued by slow growth, a declining stock price, and an increasingly skeptical investment community, UPC needs to control costs and control them fast. But Jordan's division is the most profitable in the company, and the 11% cut proposed by Rampart could destroy already shaky morale and seriously threaten the division's ability to compete. "There comes a time in every manager's career when he has to fight a bad decision made by his boss," argues Sam Godwyn, Jordan's vice president for marketing and sales. "To cut across the board is to take a blunt axe to the company when a surgeon's scalpel is called for." He suggests it is better to line up support for an alternative plan that links cuts to a long-term strategy and that differentiates between successful and unsuccessful divisions. "It would be a terrible mistake for us to focus only on the narrow needs of the division when the future of the whole company is at stake," counters Mary Wyatt, Jordan's vice president for finance. Yes, the downsizing will hurt the division in the short term, but the real issue is getting behind the new CEO. Supporting the downsizing decision is a necessary investment in this future credibility and effectiveness--whatever the short-term costs. Four commentators debate Jordan's dilemma and how he should resolve it.     

Intra-LATA toll elasticities

Regulatory commission hearings on intra-LATA toll competition have focused, for the purpose of revenue rebalancing, on the market elasticity of demand for intra-LATA toll service. This paper presents estimates of the intra-LATA toll elasticity based on a detailed model of residential customers' demand for short-haul service. The results are compared with those from other studies. The consensus of the studies indicates a residential elasticity of around -0.4.     

Recent Progress on Endogeneity in Choice Modeling

We describe recent progress in several areas related to endogeneity, including: choice set formation and attention to attributes; interactions among decision-makers; respondents' strategic behavior in answering stated preference choices; models of multiple discrete/continuous choice; distributions of willingness-to-pay; and methods for handling traditionally endogenous explanatory variables.     

VEHICLE CHOICE BEHAVIOR AND THE DECLINING MARKET SHARE OF U.S. AUTOMAKERS*

We develop a consumer‐level model of vehicle choice to shed light on the erosion of the U.S. automobile manufacturers' market share during the past decade. We examine the influence of vehicle attributes, brand loyalty, product line characteristics, and dealerships. We find that nearly all of the loss in market share for U.S. manufacturers can be explained by changes in basic vehicle attributes, namely: price, size, power, operating cost, transmission type, reliability, and body type. U.S. manufacturers have improved their vehicles' attributes but not as much as Japanese and European manufacturers have improved the attributes of their vehicles.     

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.     

Mixed MNL models for discrete response

This paper considers mixed, or random coefficients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly efficient. The adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately defined artificial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a flexible and computationally practical approach to discrete response analysis. Copyright © 2000 John Wiley & Sons, Ltd.     

Self-selecting tariffs under pure preferences among tariffs

The behavioral assumptions for welfare analysis of self-selecting tariffs are generalized to be consistent with those maintained in empirical models of tariff choice. When customers have pure preferences among tariffs, it is shown that the optimal self-selecting tariffs provide strictly greater welfare than mandatory marginal cost prices, contain marginal prices that do not equal marginal cost, and can Pareto dominate an existing tariff. As an illustration of the theoretical results, optimal self-selecting tariffs are calculated empirically for a local telephone company.     

On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths

An exciting development in modeling has been the ability to estimate reliable individual-level parameters for choice models. Individual partworths derived from these parameters have been very useful in segmentation, identifying extreme individuals, and in creating appropriate choice simulators. In marketing, hierarchical Bayes models have taken the lead in combining information about the aggregate distribution of tastes with the individual's choices to arrive at a conditional estimate of the individual's parameters. In economics, the same behavioral model has been derived from a classical rather than a Bayesian perspective. That is, instead of Gibbs sampling, the method of maximum simulated likelihood provides estimates of both the aggregate and the individual parameters. This paper explores the similarities and differences between classical and Bayesian methods and shows that they result in virtually equivalent conditional estimates of partworths for customers. Thus, the choice between Bayesian and classical estimation becomes one of implementation convenience and philosophical orientation, rather than pragmatic usefulness.