Dworczak, Kominers, and Akbarpour (2021) study when certain market structures are optimal in the presence of heterogeneous preferences. A key assumption to apply the results is that the social planner knows the joint distribution of the value of the good and marginal value of money. We show that...

Many 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...

This paper studies aggregate complementarity without price or income variation. We show that for a class of utility functions, variation in non-price observables allows one to recover a measure of complementarity similar to Hicksian complementarity. In addition, the entire Slutsky matrix can be...

This paper presents a cardinal measure of choice consistency for perturbed utility models. The measure of choice consistency is built on additive errors to the model. The additive errors are meaningful since the perturbed utility model is cardinal and utility differences are meaningful. We...

This paper provides nonparametric identification results for a class of latent utility models with additively separable unobservable heterogeneity. These results apply to existing models of discrete choice, bundles, decisions under uncertainty, and matching. Under an independence assumption,...

We investigate deterministic stochastic choice in the standard consumer framework. We present a model of statistical consumer theory where the individual maximizes their utility with respect to a distribution of bundles that is constrained by a statistic of the distribution (e.g. mean...

This paper takes seriously that the quasilinear utility model is an approximation. We interpret approximation error as arising because individuals are satisficers. We investigate the consequences of individual satisficng for modelling aggregate demand, providing an approximate aggregation...