Search

We investigate the testable implications of the theory of stable matchings. We provide a characterization of the data that are rationalizable as a stable matching when agents' preferences are unobserved. The characterization is a simple nonparametric test for stability, in the tradition of revealed preference tests. We also characterize the observed stable matchings when monetary transfers are allowed, and the stable matchings that are best for one side of the market (extremal stable matchings). We find that the theory of extremal stable matchings is observationally equivalent to requiring that there be a unique stable matching, or that the matching be consistent with unrestricted monetary transfers. We also present results on rationalizing a matching as the median stable matching.

Self-employed workers provide useful evidence about the nature of flexible labor supply and possible reference-dependence. Many previous studies analyzed taxi cabdrivers. Newer rideshare platforms provide greater work hour flexibility and use surge pricing, making future marginal wages salient. We estimate stopping probability and structural labor supply models, previously applied only in the fixed-shift taxicab setting, to rideshare work. Stopping probability does increase significantly upon reaching an apparent income target. However, the degree of estimated loss aversion is much lower than in fixed-shift taxi data and does not change with experience as in taxi analyses.

A key feature of online gaming, which serves as an important measure of consumer engagement with a game, is level progression, wherein players make play-or-quit decisions at each level of the game. Understanding users' level progression behavior is therefore fundamental to game designers. In this paper, we propose a dynamic model of consumer level-progression decisions to shed light on the underlying motivational drivers. We cast the individual play-or-quit decisions in a dynamic framework with forward-looking players and consumer learning about the evolution patterns of their operation efficiencies (defined as the average score earned per operation for passing a level). We develop a boundedly rational approach to model how individuals form predictions of their own operation efficiency and playing utility. This new approach allows researchers to flexibly capture players' over/unbiased/under estimation tendencies and risk averse/neutral/seeking preferences, two features that are particularly relevant when modeling the game-playing behavior. We develop an algorithm for estimating such dynamic model, and apply our model to level-progression data from individual players with one online game. We find that players in the sample tend to overestimate their operation efficiency, as their predicted values are significantly higher than the mean estimates inferred from their playing history with their completed levels. Furthermore, players are found risk seeking with moderate amount of uncertainty. We uncover two segments of players labeled as “Experiencers” vs. “Achievers”: while the former tend to derive a higher utility from the playing process, the latter are more goal oriented and derive a higher benefit from completing the entire game. Two counterfactual simulations demonstrate that the proposed model can help adjust the uncertainty level and configure a more effective level-progression point schedule to better engage players and improve the game developer's revenue.

Using an empirical framework based on the Mussa-Rosen model of monopoly quality choice, we calculate the degree of quality degradation in cable television markets and the impact of regulation on those choices. We find lower bounds of quality degradation ranging from 11 to 45% of offered service qualities. Furthermore, cable operators in markets with local regulatory oversight offer significantly higher quality, less degradation, and greater quality per dollar, despite higher prices.

We propose a novel methodology for nonparametric identification of first-price auction models with independent private values, which allows for one-dimensional auction-specific unobserved heterogeneity, based on recent results from the econometric literature on nonclassical measurement error in Hu and Schennach (2008). Our approach can accommodate a wide variety of applications in which some location of the conditional distribution of bids (e.g. min or max of the support, mean, etc.) is increasing in the unobserved heterogeneity. This includes settings in which the econometrician fails to observe the reserve price, the cost of bidding, the number of bidders, or some factor (“quality”) with a non-linear effect on bidder values.

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 poor consumers. This paper presents an interdisciplinary 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 poor and rapidly growing new rich populations, requiring marketers and researchers to understand how to market to the poor and the new 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 cross country regressions offering broad brush exploratory insight, to country-industry-specific research that exploits unique characteristics of a particular emerging market.