Using results from Convex Analysis, we investigate a novel approach to identification and estimation of discrete‐choice models that we call the mass transport approach. We show that the conditional choice probabilities and the choice‐specific payoffs in these models are related in the sense of conjugate duality, and that the identification problem is a mass transport problem. Based on this, we propose a new two‐step estimator for these models; interestingly, the first step of our estimator involves solving a linear program that is identical to the classic assignment (two‐sided matching) game of Shapley and Shubik (1971). The application of convex‐analytic tools to dynamic discrete‐choice models and the connection with two‐sided matching models is new in the literature. Monte Carlo results demonstrate the good performance of this estimator, and we provide an empirical application based on Rust's (1987) bus engine replacement model.

Emerging markets are fast-growing developing countries that are creating not only a rapidly expanding segment of middle class and rich consumers but also have a sizable segment of Bpoor^consumers. This paper presents an inter-disciplinary perspective integrating insights from quantitative and behavioral marketing, social psychology, industrial organization, and development economics with the purpose of generating and answering research questions on emerging markets. We organize our discussion around three themes. First, there is substantial heterogeneity in the social, cultural, economic, and institutional environments as well as rapid change in these characteristics. Coupled together, the heterogeneity and dynamics increase the scope of variables and interrelationships that have traditionally been investigated. Second, emerging markets continue to have sizeable Bpoor^and rapidly growing Bnew rich^populations, requiring marketers and researchers to understand how to market to the poor and the Bnew rich.^Exploiting these features in research can help deepen our theoretical understanding of markets and marketing. Third, from a methodological perspective, differences in types of available secondary data and the lower cost of collecting primary data create opportunities to develop new approaches for addressing research questions. We also encourage scholars to move beyond crosscountry regressions offering broadbrush exploratory insight, to country-industry-specific research that exploits unique characteristics of a particular emerging market. This article emerged out of presentations and discussions among the authors in a session titled BEmerging Markets^at the 9th Invitational Choice Symposium hosted by Erasmus University in the Netherlands in 2013.

This paper considers identification and estimation of a general model for online price competition. We show that when the number of competing firms is unknown, the underlying parameters of the model can still be identified and estimated employing recently developed results on measurement error. With the estimates of model parameters, we are able to analyze the competitive effects of online competition when the number of firms changes. We illustrate our methodology using UK data for personal digital assistants and employ the estimates to simulate competitive effects. Our results reveal that heightened competition has differential effects on the prices paid by different consumer segments.

This note illustrates a new method for making causal inferences with ecological data. We show how to combine aggregate outcomes with individual demographics from separate data sources to make causal inferences about individual behavior. In addressing such problems, even under the selection on observables assumption often made in the treatment effects literature, it is not possible to identify causal effects of interest. However, recent results from the partial identification literature provide sharp bounds on these causal effects. We apply these bounds to data from Chilean mayoral elections that straddle a 2012 change in Chilean electoral law from compulsory to voluntary voting. Aggregate voting outcomes are combined with individual demographic information from separate data sources to determine the causal effect of the change in the law on voter turnout. The bounds analysis reveals that voluntary voting decreased expected voter turnout, and that other causal effects are overstated if the bounds analysis is ignored.

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