Job Market Paper

Do Past Privacy Choices Affect Consumers' Current Privacy Choices? 

Abstract. Understanding consumer privacy choices is important because firms trade off the competing goals of data access and privacy protection. This paper demonstrates that past privacy choices affect consumers’ current privacy choices. Such state-dependent choices suggest that privacy choices can have externalities within a platform in which one app's data requests can affect the ability of other apps to collect data. Specifically, I use a consumer-level panel to investigate data consent decisions by consumers on Alipay, a major digital platform that connects users and third-party apps. Taking advantage of a natural experiment that encourages users to accept data requests, I find that the probability of rejecting the next request declines 14.5%. This effect decays over time, is larger for users who did not previously look over the privacy terms, and is larger when the next request is in a weak preference service context, categorized by a large language model (LLM). The effect does not differ by whether the specific data requested in consecutive data consent decisions is the same nor by the overall rate of data consent given by the consumer. Overall, I interpret these results to suggest that the externalities arising from state-dependent data consent choices are temporary. Nevertheless, they can positively or negatively impact the ability of apps to collect data, and therefore platforms have incentives to encourage apps to provide consumer-friendly data request designs. 


with A. Goldfarb

Abstract. There has been increasing attention to privacy in the media and in regulatory discussions. This is a consequence of the increased usefulness of digital data. The literature has emphasized the benefits and costs of digital data flows to consumers and firms. The benefits arise in the form of data-driven innovation, higher-quality products and services that match consumer needs, and increased profits. The costs relate to the intrinsic and instrumental values of privacy. Under standard economic assumptions, this framing of a cost-benefit trade-off might suggest little role for regulation beyond ensuring consumers are appropriately informed in a robust competitive environment. The empirical literature thus far has focused on this direct cost-benefit assessment, examining how privacy regulations have affected various market outcomes. However, an increasing body of theory work emphasizes externalities related to data flows. These externalities, both positive and negative, suggest benefits to the targeted regulation of digital privacy.

 [31st Conference on Neural Information Processing Systems (NIPS 2017)]

with Q. Liang and E. Modiano

Abstract. Constrained Markov Decision Process (CMDP) is a natural framework for reinforcement learning tasks with safety constraints, where agents learn a policy that maximizes the long-term reward while satisfying the constraints on the long-term cost. A canonical approach for solving CMDPs is the primal-dual method which updates parameters in primal and dual spaces in turn. Existing methods for CMDPs only use on-policy data for dual updates, which results in sample inefficiency and slow convergence. In this paper, we propose a policy search method for CMDPs called Accelerated Primal-Dual Optimization (APDO), which incorporates an off-policy trained dual variable in the dual update procedure while updating the policy in primal space with on-policy likelihood ratio gradient. Experimental results on a simulated robot locomotion task show that APDO achieves better sample efficiency and faster convergence than state-of-the-art approaches for CMDPs.


From Awareness to Action: Experimental Evidence on Privacy Protection from a Mega Platform

with S. Ouyang

Abstract. Despite the growing literature on consumer privacy awareness and concern, we lack field evidence on the effectiveness of informational interventions in enhancing consumer privacy awareness and actions, and whether this leads to better engagement with the platform. This study employs an experimental A/B survey method on Alipay to examine how providing users with information about privacy protection tools affects their privacy-related behaviors and overall satisfaction with the platform. Preliminary results show that users in the treatment group, who receive the information shock, are more likely to visit the privacy center and use the central data-consent management settings, are more likely to read privacy terms before data consent, and interestingly, are more likely to increase their activities on the platform. Ongoing analysis explores the impact of this information on user trust, engagement intensity, as well as heterogeneous effects based on demographics, preferences, and knowledge expressed in the privacy survey.

Gender Homophily in Expert Endorsements: Evidence from Mental Health Care

with D. Goetz.

Abstract. Expert endorsements play a crucial role in guiding patients' choices of healthcare providers, but these endorsements may be subject to gender biases. This study investigates gender-based patterns in expert endorsements within the mental health domain, where provider quality signals are highly valuable. Analyzing data on over 250,000 mental health care professionals offering talk therapy, we find that male therapists receive endorsements from other men at nearly twice the rate of observably similar female therapists. This homophily exists after propensity score matching on the therapists’ price levels, graduation years, primary regions, and titles. We find the endorsement matters for demand. We then calibrate a structural model of patient choice to understand the counterfactual scenario in the absence of gender homophily. Our findings suggest that increasing cross-gender endorsements could lead to greater therapy utilization, particularly among populations that currently underutilize these services. The study highlights the importance of addressing gender biases in expert endorsements, especially in the current mental health context where male practitioners are underrepresented.

with K. Rajagopalan and T. Zaman