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  3. 2023
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  5. Demand Estimation with Latent Choice Sets
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Demand Estimation with Latent Choice Sets

Paper Session

Saturday, Jan. 7, 2023 10:15 AM - 12:15 PM (CST)

Hilton Riverside, Magazine
Hosted By: American Economic Association
  • Chair: Adam Kapor, Princeton University

A Method to Estimate Discrete Choice Models that is Robust to Consumer Search

Jason Abaluck
,
Yale University
Giovanni Compiani
,
University of Chicago
Fan Zhang
,
University of California-Berkeley

Abstract

We state conditions under which choice data suffices to identify preferences when consumers
may not be fully informed about attributes of goods. Our approach can be used to test for full
information, to forecast how consumers will respond to information, and to conduct welfare analysis
when consumers are imperfectly informed. In a lab experiment, we successfully forecast the response
to new information when consumers engage in costly search. In data from Expedia, our method
identifies which attribute was not immediately visible to consumers in search results, and we then
use the model to compute the value of additional information.

Identification and Estimation in Many-to-One Two-Sided Matching without Transfers

YingHua He
,
Rice University
Shruti Sinha
,
Toulouse School of Economics
Xiaoting Sun
,
Simon Fraser University

Abstract

In a setting of many-to-one two-sided matching with non-transferable utilities, e.g., college admissions, we study conditions under which preferences of both sides are identified with data on one single market. Regardless of whether the market is centralized or decentralized, assuming that the observed matching is stable, we show nonparametric identification of preferences of both sides under certain exclusion restrictions. To take our results to the data, we use Monte Carlo simulations to evaluate different estimators, including the ones that are directly constructed from the identification. We find that a parametric Bayesian approach with a Gibbs sampler works well in realistically sized problems. Finally, we illustrate our methodology in decentralized admissions to public and private schools in Chile and conduct a counterfactual analysis of an affirmative action policy.

Demand Analysis under Latent Choice Constraints: An Application to the US Dialysis

Nikhil Agarwal
,
Massachusetts Institute of Technology
Paulo Somaini
,
Stanford University

Abstract

Consumer choices are constrained in many markets due to either supply-side rationing or information frictions. Examples include matching markets for schools and colleges; entry-level labor markets; limited brand awareness and inattention in consumer markets; and selective admissions to healthcare services. Separating these choice constraints from latent consumer demand is essential for analyzing policy counterfactuals and welfare. We use a general random utility model for consumer preferences that allows for endogenous characteristics and a reduced-form choice-set formation rule that can be derived from models of the examples described above. The choice-sets can be arbitrarily correlated with preferences. We study non-parametric identification of this model, propose an estimator, and apply these methods to study admissions in the market for kidney dialysis in California. Our results establish identification of the model using two sets of instruments, one that only affects consumer preferences and the other that only affects choice sets. Moreover, these instruments are necessary for identification – our model is not identified without further restrictions if either set of instruments does not vary. These results also suggest tests of choice-set constraints, which we apply to the dialysis market. We find that dialysis facilities are less likely to admit new patients when they have higher than normal caseload and that patients are more likely to travel further when nearby facilities have high caseloads. Finally, we estimate consumers' preferences and facilities' rationing rules using a Gibbs' sampler.

Picking Your Patients: Selective Admissions in the Nursing Home Industry

Ashvin Gandhi
,
University of California-Los Angeles

Abstract

Do healthcare providers pick their patients? This paper uses patient-level administrative data on skilled nursing facilities in California to estimate a structural model of selective admission practices in the nursing home industry. I exploit within-facility covariation between occupancy and admitted patient characteristics to distinguish admission patterns attributable to selective admission practices from those attributable to heterogeneous patient preferences. In spite of anti-discrimination laws, I find strong evidence of selective admission practices that disproportionately harm Medicaid-eligible patients with lengthy anticipated stays. Counterfactual simulations show that enforcing a prohibition on selective admissions would increase access for these residents at the cost of crowding out short-stay non-Medicaid patients from their preferred facilities. I simulate two additional policies intended to mitigate selective admissions: raising the Medicaid reimbursement rate and expanding capacity. I find the latter to be less costly and more effective than the former.

Discussant(s)
Elisabeth Honka
,
University of California-Los Angeles
Nikhil Agarwal
,
Massachusetts Institute of Technology
Adam Kapor
,
Princeton University
Ashley Swanson
,
Columbia University
JEL Classifications
  • L0 - General
  • C1 - Econometric and Statistical Methods and Methodology: General