Estimating Promotional Effects with Retailer-Level Scanner Data*

Abstract Demand models produce biased results when applied to data aggregated across stores with heterogeneous promotional activity. We show how to modify extant aggregate demand frameworks to avoid this problem. First a consumer-level model is developed, which is then integrated over the heterogeneous stores to arrive at aggregate demand. Our approach is highly practical since it requires only standard scanner data of the type produced by the major vendors. Using data for super-premium ice cream, we apply the proposed methodology to the random coefficients logit demand framework

A nonparametric analysis of the black/white wage gap

This article estimates a nonparametric model of earnings to examine the black/white wage gap. This article extends the Oaxaca decomposition and adjusted wage ratios to a nonlinear form, to allow the use of this nonparametric approach. It finds that the results obtained nonparametrically are very similar to those obtained with variants of the (misspecified) Mincer model, suggesting that the parametric models may be appropriate in situations where the nonparametric estimator could not be applied.

Do Court Decisions Drive the Federal Trade Commission’s Enforcement Policy on Merger Settlements?

Federal Trade Commission, Merger enforcement, Merger litigation, Merger policy, Merger settlements,

Using semi-parametric methods in an analysis of earnings mobility

This paper describes a dynamic random effects econometric model from which inferences on earnings mobility may be made. It answers questions such as, given some initial level of observed earnings, what is the probability that an agent with certain characteristics will remain below a specified level of earnings (for example the poverty level) for a specified number of time periods? Existing research assumes that the distributions of the unobserved permanent and transitory shocks in the model are known up to finitely many parameters. However, predictions of earnings mobility are highly sensitive to assumptions about these distributions. The present paper estimates the distributions of the random effects non-parametrically. The results are used to predict the probabilities of remaining in a low state of earnings. The results from the non-parametric distributions are contrasted to those obtained under a normality assumption. Using the non-parametrically estimated distributions gives estimated probabilities that are smaller than those obtained under the normality assumption. Through a Monte Carlo experiment and by examining unconditional predicted earnings distributions, it is demonstrated that the non-parametric method is likely to be considerably more accurate, and that assuming normality may give quite misleading results. Copyright Journal compilation Royal Economic Society 2008. No claim to original US government works