Abstract

This paper provides a new identification result for a large class of models in which consumers participate in production. I show that consumer preferences are necessary and sufficient to identify production functions through cross-equation restrictions implied by first-order conditions. In addition, I derive a nonparametric revealed preference characterization of the class of models that exhausts its empirical implications. Finally, I use a novel and easy-to-apply inference method that is valid under partial identification. 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 and shopping intensity from the NielsenIQ 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 are significant, hence largely diminishing benefits of price search. 

Abstract

We propose a nonparametric method for estimating the distribution of consumer welfare from cross-sectional data with no restrictions on individual preferences. First demonstrating that moments of demand identify the curvature of the expenditure function, we use these moments to approximate money-metric welfare measures. Our approach captures both nonhomotheticity and heterogeneity in preferences in the behavioral responses to price changes. We apply our method to US household scanner data to evaluate the impacts of the price shock between December 2020 and 2021 on the cost-of-living index. We document substantial heterogeneity in welfare losses within and across demographic groups. Moreover, we show that a naive measure of consumer welfare would underestimate the welfare loss by approximately 4% on average. By decomposing the behavioral responses into the components arising from nonhomotheticity and heterogeneity in preferences, we find that both factors are essential for accurate welfare measurement, with heterogeneity contributing more substantially. 

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

This paper develops a framework to study the statistical power of revealed-preference tests. With randomly sampled budgets and mild smoothness of demand, statistical learning implies that any model consistent with the data must approximate true choice behaviour. We interpret this result as follows: passing a revealed-preference test is informative only to the extent that the data are sufficiently rich to rule out economically meaningful departures from the maintained model. We make this precise by linking sample size and confidence to the magnitude of detectable departures, and by characterising how power rises with additional observations. Extending our approach beyond revealed-preference inequalities to smooth functional restrictions yields practical tests, even when exact revealed-preference tests are computationally infeasible. We also provide confidence intervals for smooth functionals of demand, including welfare effects. Simulations show that standard sample sizes can generate widely different power across models, contextualizing why some conditions "rarely reject'' in practice.

Abstract

Children's 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's 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's welfare. I show that the model nonparametrically partially identifies the impacts of parental inputs on children's 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's 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 of welfare reforms including policies aimed at improving children’s 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

The timing of parental and educational interventions can have lasting impacts on child development, with early adolescence representing a critical window where investments may amplify earlier gains or compensate for prior adversity. This paper proposes a dynamic model where the household makes investment decisions regarding time and money inputs into the production of child human capital and derives utility from leisure, household expenditure, and the child human capital. We use this structural model to study dynamic complementarities between early childhood and early adolescence interventions, while also addressing measurement error issues in child human capital. We propose a novel estimation framework that allows for a tractable estimation of models with latent variables and partial identification. We apply this framework to study household decision-making and child human capital accumulation using data from a cohort of children in Ghana that were randomly assigned to a preschool education program in early childhood and subsequently re-randomized into a parenting skills program during early adolescence. 

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.