José Ignacio Cuesta

I am a Ph.D. Candidate in the Department of Economics at the University of Chicago. I work on Public Economics and Industrial Organization, with a focus on Consumer Finance and Health markets. I will be available for interviews at the 2019 AEA/ASSA Annual Meeting.

Curriculum Vitae [pdf]

jicuesta@uchicago.edu

REFERENCES

Ali Hortaçsu | hortacsu@uchicago.edu

Michael Greenstone | mgreenst@uchicago.edu

Neale Mahoney | neale.mahoney@chicagobooth.edu

Pietro Tebaldi | ptebaldi@uchicago.edu


JOB MARKET PAPER

Price Regulation in Credit Markets: A Trade-off between Consumer Protection and Credit Access

with Alberto Sepúlveda

December 2018 | [pdf]

Interest rate caps are widespread in consumer credit markets, yet there is limited evidence on its effects on market outcomes and welfare. Conceptually, the effects of interest rate caps are ambiguous and depend on a trade-off between consumer protection from banks' market power and reductions in credit access. We exploit a policy in Chile that lowered interest rate caps by 20 percentage points to understand its impacts. Using comprehensive individual-level administrative data, we document that the policy decreased transacted interest rates by 9%, but also reduced the number of loans by 19%. To estimate the welfare effects of this policy, we develop and estimate a model of loan applications, pricing, and repayment of loans. Consumer surplus decreases by an equivalent of 3.5% of average income, with larger losses for risky borrowers. Survey evidence suggests these welfare effects may be driven by decreased consumption smoothing and increased financial distress. Interest rate caps provide greater consumer protection in more concentrated markets, but welfare effects are negative even under a monopoly. Risk-based regulation reduces the adverse effects of interest rate caps, but does not eliminate them.


WORKING PAPERS

Quality Regulation and Competition: Evidence from Pharmaceutical Markets

with Juan Pablo Atal and Morten Sæthre

September 2018 | [pdf]

We study the effects of quality regulation on market outcomes by exploiting the staggered phase-in of bioequivalence requirements for generic drugs in Chile. We estimate that the number of drugs in the market decreased by 25%, average paid prices increased by 10%, and total sales decreased by 20%. These adverse effects were concentrated among small markets. Our results suggest that the intended effects of quality regulation on price competition through increased (perceived) quality of generics---and therefore reduced vertical differentiation---were overturned by adverse competitive effects arising from the costs of complying with the regulation.


Distorted Quality Signals in School Markets

with Felipe González and Cristián Larroulet

September 2018 | [pdf]

Information plays a key role in markets with consumer choice. In education, data on schools is often gathered through standardized testing. However, the use of these tests has been controversial because of distortions in the metric itself. We study the Chilean educational market and document that low-performing students are underrepresented in test days, generating distortions in school quality information. These distorted quality signals affect parents’ school choice and induce misallocation of public programs. These results provide novel evidence for the costs that distortions in quality signals generated by standardized tests in accountability systems impose on educational markets.


WORK IN PROGRESS

Vertical Integration between Hospitals and Insurers

with Carlos Noton and Benjamín Vatter

November 2018 | [slides]

We study the welfare implications of vertical integration between insurers and health care providers. We develop a model of health insurance, provision, and demand that shows that vertically integrated firms have incentives to increase some negotiated hospital prices as they benefit from steering demand from competing hospitals and insurers towards their related partners. We estimate the model using rich administrative data on plan choices and hospital claims from the Chilean market, where vertically integrated systems account for almost half of all hospital claims. Using our structural estimates, we compute the minimum efficiency gains in vertically integrated hospitals that make banning vertical integration welfare detrimental.


Distance to Physicians and Value of Choice in Individual Health Insurance

with Pietro Tebaldi

June 2018 | [slides]

Health insurance plans differ no only vertically, but also horizontally, which is driven by provider networks' differences. The latter provides a rationale for oligopolistic competition in health insurance. However, if buyers place low value on horizontal attributes, then a design that assigns uninsured to the same carrier---chosen through a competitive procurement process (Diamond, 1992)---might enhance welfare, by limiting adverse selection and imperfect competition. In this paper, we combine individual-level data on plan choices in the California ACA marketplace with the universe of hospital, clinics, and physicians' networks covered by each plan. We estimate willingness to pay for provider networks and find substantial heterogeneity: young households without children place a high value on premiums relative to networks, whereas we find the opposite for older households and households with children. We also show that if a large network PPO plan was the only option in the market, and this plan was pricing to extract a 15% mark-up (85% medical-loss-ratio), the median consumer would be better off than under the observed market structure.


PUBLICATIONS

Identification of Average Marginal Effects Under Misspecification when Covariates are Normal

with Jonathan Davis, Andrew Gianou and Alejandro Hoyos

Forthcoming, Econometric Reviews | [pdf] [publisher]

A previously known result in the econometrics literature is that when covariates of an underlying data generating process are jointly normally distributed, estimates from a nonlinear model that is misspecified as linear can be interpreted as average marginal effects. This has been shown for models with exogenous covariates and separability between covariates and errors. In this paper, we extend this identification result to a variety of more general cases, in particular for combinations of separable and non-separable models under both exogeneity and endogeneity. So long as the underlying model belongs to one of these large classes of data generating processes, our results show that nothing else must be known about the true DGP---beyond normality of observable data, a testable assumption---in order for linear estimators to be interpretable as average marginal effects. We use simulation to explore the performance of these estimators using a misspecified linear model and show they perform well when the data are normal but can perform poorly when this is not the case.