Abstract

This paper provides a new identification result for a large class of consumer problems using a revealed preference approach. I show that the utility maximization hypothesis nonparametrically identifies production functions via restrictions from the first-order conditions. In addition, I derive a nonparametric characterization of the class of models that operationalizes the identification strategy. Finally, I use a novel and easy-to-apply inference method for the estimation of the production functions. This method can be used to statistically test the model, can deal with any type of latent variables (e.g., measurement error), and can be combined with standard exclusion restrictions. Using data on shopping expenditures from the Nielsen Homescan Dataset, I show that a doubling of shopping intensity decreases prices paid by about 15%. At the same time, I find that search costs more than double within the support of the data, hence largely diminishing net benefits of price search.

Abstract

Classically, testing whether decision makers belong to specific preference classes involves two main approaches. The first, known as the functional approach, assumes access to an entire demand function. The second, the revealed preference approach, constructs inequalities to test finite demand data. This paper bridges these methods by using the functional approach to test finite data through preference learnability results. We develop a computationally efficient algorithm that generates tests for choice data based on functional characterizations of preference families. We provide these restrictions for various applications, including homothetic and weakly separable preferences, where the latter's revealed preference characterization is provably NP-Hard. We also address choice under uncertainty, offering tests for betweenness preferences. Lastly, we perform a simulation exercise demonstrating that our tests are effective in finite samples and accurately reject demands not belonging to a specified class.

Abstract

Children welfare is at the center of many welfare reforms such as cash transfers to families and training programs to parents. A key goal for policy-makers is to evaluate the costs and benefits of such reforms. The main challenge lies in that the outcome of interest, children welfare, is unobservable. To address this issue, I consider a collective labor supply model with children where adult members have preferences over their own leisure, expenditures, and children welfare. I show that the model nonparametrically partially identifies the impacts of parental inputs on children welfare in panel data. I then propose a novel estimation strategy that accommodates measurement error and can be used to efficiently construct valid confidence sets. Using Dutch data on couples with children, I investigate the structure of the expected production technology and how it varies with household characteristics. I find that the production of children welfare is characterized by decreasing returns to scale and large heterogeneity across household types. In particular, I find that children from disadvantaged households, whose parents have low education levels and are not homeowners, are significantly worse off. My results highlight the importance for welfare reforms to include policies targeted at improving children home environment.

Abstract

We analyze choice behavior using Dynamic Random Utility Model (DRUM). Under DRUM, each consumer or decision-maker draws a utility function from a stochastic utility process in each period and maximizes it subject to a menu. DRUM allows for unrestricted time correlation and cross-section heterogeneity in preferences. We fully characterize DRUM when panel data on choices and menus are available. Our results cover consumer demand with a continuum of choices and finite discrete choice setups. DRUM is linked to a finite mixture of deterministic behaviors that can be represented as the Kronecker product of static rationalizable behaviors. We exploit a generalization of the Weyl-Minkowski theorem that uses this link and enables conversion of the characterizations of the static Random Utility Model (RUM) of McFadden-Richter (1990) to its dynamic form. DRUM is more flexible than Afriat’s (1967) framework and more informative than RUM. In an application, we find that static utility maximization fails to explain population behavior, but DRUM can explain it. 

Abstract

This paper derives a novel representation of the exponential discounting model that allows one to assess departures from the model via a measure of efficiency. The approach uses a revealed preference methodology that does not make any parametric assumption on the utility function and allows for unrestricted heterogeneity. The method is illustrated using longitudinal data from checkout scanners and gives insights into sources of departure from exponential discounting.

Abstract

Welfare effects of price changes are often estimated with cross-sections; however, these do not identify demand models with heterogeneous consumers. We develop a method that utilizes moments of uncompensated demand to construct local approximations to moments of compensated demand, robust to unobserved preference heterogeneity. Our method applies to any cross-section and delivers a nonparametric approximation for the entire distribution of consumer welfare. In an application using data on grocery purchases, we study the distributional impact of inflation by means of cost-of-living indices.

Abstract

This paper proposes a simple computational tool for the estimation of a large class of models using the Entropic Latent Variable Integration via Simulation (ELVIS) by exploiting the Adversarial Method of Moments (AMM). The model may have unobservable variables and be partially identified. We show that AMM improves the stability and computational tractability of ELVIS, thus increasing its practical appeal. Our application shows that AMM can greatly reduce the computational burden of ELVIS in estimation exercises. 

Abstract

This paper considers a collective model of exponential discounting within a random utility framework. This allows us to rationalize time inconsistent behavior by explicitly accounting for individual heterogeneity and changes in preferences. The collective model gives rise to separate and tractable Euler equations for each household member. Under the assumption that the distribution of preferences is stable, we obtain testable restrictions that can be used to make robust counterfactual statements.