Research
A Revealed Preference Approach to Identification and Inference in Producer-Consumer Models
(Revise and Resubmit, JBES)
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.
Download Paper (March 2024)
Send me an email at charles.gauthier@kuleuven.be for the latest version
This paper supersedes "Price Search and Consumption Inequality: Robust, Credible, and Valid Inference" (First version: March 2021)
Finite Tests from Functional Characterizations (joint with Raghav Malhotra and Agustín Troccoli Moretti)
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.
Download Paper (July 2024)
Production Heterogeneity in Collective Labor Supply Models with Children
This paper provides a revealed preference characterization of a collective labor supply model with household production where adult members have preferences that depend on their own leisure, expenditures, and children welfare. We show that the nonparametric revealed preference restrictions allow to partially identify the impacts of parental inputs on children welfare. We propose a novel estimation strategy that exploits these restrictions and allows for production heterogeneity. In our application, we apply our methodology to Dutch data on couples with children. We find extensive heterogeneity in the production technology, decreasing returns to scale in the production of children welfare, and a positive impact of education on the effects of parental inputs on children welfare.
Download Paper (July 2024)
Dynamic and Stochastic Rational Behavior (joint with Victor Aguiar, Nail Kashaev and Martin Plávala)
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.
Download Paper (April 2023)
Robust Inference on Discount Factors
The exponential discounting model is a predominant tool for analyzing dynamic choice in applied work. Its attractiveness rests in that time preferences are summarized by a single parameter—the discount factor. This allows one to tractably analyze a decision maker's intertemporal choices, which is crucial in a vast range of applications. Accordingly, many studies have tried to recover its key time parameter. However, a common feature in this literature is the specification of the consumer's preferences. This constitutes a potentially important limitation as erroneously specifying preferences may lead to spurious estimates of the discount factor. As such, this paper provides set estimates of individual-specific discount factors by using the concavity of the utility function without relying on parametric assumptions. Furthermore, I develop a novel methodology that allows me to evaluate the sensitivity of discounts factors with respect to measurement error in variables. Contrary to the experimental literature, my methodology is applicable to choices over multidimensional goods. Given observations on prices and demands from a checkout scanner panel data set, I find that accounting for unobserved heterogeneity is important as observable characteristics fail to capture differences in discounting.
Download Paper (September 2021)
An Adversarial Approach to Estimation in Partially Identified Models
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.
Coming later (2024)
A Collective Random Utility Model of Exponential Discounting (joint with Victor Aguiar)
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.
Coming later (2024)