Rebates, inventories, and intertemporal price discrimination

We demonstrate that universally redeemed rebates can increase manufacturer profits by reducing the incentives of downstream retailers to hoard inventories when optimal wholesale prices vary predictably over time. By bypassing retailers and making direct contracts with buyers, the manufacturer can increase the variations in effective prices paid by consumers without concomitantly creating larger incentives for retailers to hold inventories. During profitable, high-demand periods, manufacturer revenues are ordinarily constrained by'competition'from retailer inventories, thus limiting profits. However, by selectively offering rebates to consumers while maintaining high wholesale prices, low-demand periods can be accommodated without inducing retailer hoarding.     

Hierarchical logistic regression models for imputation of unresolved enumeration status in undercount estimation

"In this article we describe a logistic regression modeling approach for nonresponse in the [U.S.] Post-Enumeration Survey (PES) that has desirable theoretical properties and that has performed well in practice.... In the 1990 PES, interviews were not obtained from approximately 1.2% of households in the sample, and approximately 2.1% of the individuals in interviewed households were considered unresolved after follow-up....The missing binary enumeration statuses for these unresolved cases were replaced with probabilities estimated under a statistical model that incorporated covariate information observed for these cases. This article describes an approach to modeling missing binary outcomes when there are a large number of covariates."     

A multiple imputation method for incomplete correlated ordinal data using multivariate probit models

The multiple imputation technique has proven to be a useful tool in missing data analysis. We propose a Markov chain Monte Carlo method to conduct multiple imputation for incomplete correlated ordinal data using the multivariate probit model. We conduct a thorough simulation study to compare the performance of our proposed method with two available imputation methods – multivariate normal-based and chain equation methods for various missing data scenarios. For illustration, we present an application using the data from the smoking cessation treatment study for low-income community corrections smokers.     

Reliant behavior in the United States and Japan

Japanese economic success is often attributed to culturally reinforced psychological conditioning that promotes interpersonal reliance, cooperation, and a group interest orientation. This article provides direct experimental evidence on differences in behavior among future business people in the United States and Japan. Utilizing a simple, two person extensive form game of perfect information introduced by Selten (1975), we provide evidence that, contrary to some views, the Japanese can be less reliant on the behavior of others and are more likely to take actions at variance with group welfare in some settings. Thus, popular explanations of Japanese economic achievements may require further exploration.     

THE POTENTIAL FOR BIAS IN PRINCIPAL CAUSAL EFFECT ESTIMATION WHEN TREATMENT RECEIVED DEPENDS ON A KEY COVARIATE

Motivated by a potential-outcomes perspective, the idea of principal stratification has been widely recognized for its relevance in settings susceptible to posttreatment selection bias such as randomized clinical trials where treatment received can differ from treatment assigned. In one such setting, we address subtleties involved in inference for causal effects when using a key covariate to predict membership in latent principal strata. We show that when treatment received can differ from treatment assigned in both study arms, incorporating a stratum-predictive covariate can make estimates of the "complier average causal effect" (CACE) derive from observations in the two treatment arms with different covariate distributions. Adopting a Bayesian perspective and using Markov chain Monte Carlo for computation, we develop posterior checks that characterize the extent to which incorporating the pretreatment covariate endangers estimation of the CACE. We apply the method to analyze a clinical trial comparing two treatments for jaw fractures in which the study protocol allowed surgeons to overrule both possible randomized treatment assignments based on their clinical judgment and the data contained a key covariate (injury severity) predictive of treatment received.