arXiv subjects

Kaushil Patel

Publications and source records attributed to Kaushil Patel.

3 recordsLinked to original sources

arXiv2026

Left-Digit Bias: Theory and Empirical Evidence from a Tax Salience Experiment

Demand often drops sharply at whole-dollar prices. To explain this phenomenon, known as left-digit bias, we develop a random utility model in which consumers consider a good only if its price is below a heterogeneous attention threshold. Masses of consumers with integer attention thresholds generate demand discontinuities. When a tax is introduced, the model predicts a weaker demand response compared to rational choice for left-digit biased consumers. Using data from a tax experiment, we find that the model matches behavioral moments and partially identify the distribution of willingness to pay relative to thresholds. A weak demand response need not imply a low excess burden of taxation and understanding the specific mechanism is important for welfare analysis.

econ.GN↗

arXiv2025

Price-Based Attention and Welfare

To choose between two discrete goods, a consumer pays attention to only those with prices below a threshold. From these, she chooses her most preferred good. We assume consumers in a population have the same preference but may have different thresholds. Similar models of bounded rationality have been studied in the empirical marketing literature. We fully characterize the model, and using observational choice data alone, we identify the welfare implications of a price change. The behavioral content of our model overlaps with an important class of random utility models, but the welfare implications are meaningfully different. The distribution of equivalent variation under our model first-order stochastically dominates that under the random utility model.

econ.TH↗

arXiv2024

Identifying Heterogeneous Decision Rules From Choices When Menus Are Unobserved

Given only aggregate choice data and limited information about how menus are distributed across the population, we describe what can be inferred robustly about the distribution of preferences (or more general decision rules). We strengthen and generalize existing results on such identification and provide an alternative analytical approach to study the problem. We show further that our model and results are applicable, after suitable reinterpretation, to other contexts. One application is to the robust identification of the distribution of updating rules given only the population distribution of beliefs and limited information about heterogeneous information sources.

econ.TH↗