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Marriott Marquis, Catalina
Econometrics of Decisions and Demand
Friday, Jan. 3, 2020 2:30 PM - 4:30 PM (PDT)
- Chair: Joerg Stoye, Cornell University
Revealed Stochastic Choice with Attributes
AbstractMany theoretical models of stochastic choice are characterized by availability variation. Instead, most stochastic choice datasets have information on attribute values that vary across decision problems. This paper uses attribute variation to characterize a framework that encompasses existing interpretations of stochastic choice including context dependence, nested choice behavior, and consideration sets. The model has utility indices that depend on attribute values, and is characterized by a monotonicity condition relating probabilities and utility indices. Linear utility indices can be estimated for the model using existing methods without taking a stand on a particular reason why choice is stochastic.
Does Random Consideration Explain Behavior When Choice is Hard? Evidence from a Large-Scale Experiment
AbstractWe study population behavior when choice is hard because considering alternatives is costly. To simplify their choice problem, individuals may pay attention to only a subset of available alternatives. We design and implement a novel online experiment that exogenously varies choice sets and consideration costs for a large sample of individuals. We provide a theoretical and statistical framework that allows us to test random consideration at the population level. Within this framework, we compare competing models of random consideration. We find that the standard random utility model fails to explain the population behavior. However, our results suggest that a model of random consideration with logit attention and heterogeneous preferences provides a good explanation for the population behavior. Finally, we find that the random consideration rule that subjects use is different for different consideration costs while preferences are not. We observe that the higher the consideration cost the further behavior is from the full-consideration benchmark, which supports the hypothesis that hard choices have a substantial negative impact on welfare via limited consideration.
Prices, Profits, and Production: Identification and Counterfactuals
AbstractThis paper studies nonparametric identification and counterfactual bounds for heterogeneous firms that can be ranked in terms of productivity. We require observation of profits or other optimizing-values such as costs or revenues, and either prices or attributes that determine prices. We extend classical duality results for price-taking firms to a setup with rich heterogeneity, and with limited variation in prices. We characterize the identified set for production sets, and provide conditions that ensure point identification. We present a general computationally-feasible framework for sharp counterfactual bounds, such as bounds on quantities at a counterfactual price. We show that existing convergence results for quantile estimators may be directly converted to convergence results for production sets, which facilitates statistical inference.
A Panel Data Estimator for the Distribution and Quantiles of Marginal Effects in Nonlinear Structural Models with an Application to the Demand for Junk Food
AbstractIn this paper we propose a frame work to estimate the distribution of marginal effects in a general class of structural models that allow for arbitrary smooth nonlinearities, high dimensional heterogeneity, and unrestricted correlation between the persistent components of this heterogeneity and all covariates. The main idea is to form a derivative dependent variable using two periods of the panel, and use differences in outcome variables of nearby subpopulations to obtain the distribution of marginal effects. We establish constructive nonparametric identification for the population of "stayers" (Chamberlain (1982)), and show generic non-identification for the "movers". We propose natural semiparametric sample counterparts estimators, and establish that they achieve the optimal (minmax) rate. Moreover, we analyze their behavior through a Monte-Carlo study, and showcase the importance of allowing for nonlinearities and correlated heterogeneity through an application to demand for junk food. In this application, we establish profound differences in marginal income effects between poor and wealthy households, which may partially explain health issues faced by the less privileged population.
Nonparametric Counterfactuals in Random Utility Models
AbstractWe bound features of counterfactual choices in the nonparametric random utility model of demand, i.e. if observable choices are repeated cross-sections and one allows for unrestricted, unobserved heterogeneity. In this setting, tight bounds are developed on counterfactual discrete choice probabilities and on the expectation and c.d.f. of (functionals of) counterfactual stochastic demand.
- C1 - Econometric and Statistical Methods and Methodology: General
- C5 - Econometric Modeling