Goonj Mohan - Research

Measuring Assortative Matching: A Unified Axiomatic Framework (joint with Montserrat Vilalta-Bufí)

Measuring assortative matching (AM) in marriage markets remains unsettled. Aggregate measures mask heterogeneity across groups, and changing marginal distributions hinder comparisons over time. To address these challenges, we propose an axiomatic framework for type-specific AM measures. Our preferred measure, the Average Homogamy Share, averages male and female homogamy rates. We then derive aggregate measures and extend our axiomatic framework to aggregate indices. Using US data, we document rising AM among highly educated individuals and declining AM among low-educated groups, while racial AM remains high. Positive AM has increased among low-educated and White individuals, suggesting growing segregation along both dimensions.

The Data Economy and Polarization on Social Media

This paper studies polarization chosen by a social media platform (SMP) that maximizes the value of user-generated data. The platform invests in interactions on its network, facing two opposing forces: the relative-size effect, which favors higher polarization to expand data volume, and the diversification effect, which favors lower polarization to enhance data value. Their balance yields an optimal polarization, consistent with evidence that a platform raises polarization in one country but reduces it in another. The framework further shows that user bias for same-type interactions is internalized, amplifying incentives to polarize, and that taxation in highly polarized environments backfires, curtailing cross-type interactions severely and thus raising final polarization.

Regulating a Social Media Platform in the Data Economy

This paper studies the regulation of a social media platform (SMP), focusing on the tension between platform incentives and user data control rights. The SMP earns evenue from data based personalized advertising, while users are connected on a network exhibiting data externalities. A microfounded model reveals a fundamental friction: users prefer limited data sharing, whereas unrestricted data collection maximizes platform revenue. Consequently, the SMP offers its service to collect user data and users simultaneously accept or reject the offer. Externalities dilute quality of service offered and excessive data is shared in equilibrium. Regulatory measures such as the data minimization principle restricts data access to the SMP and increases user welfare. In contrast, a strict consent policy akin to the GDPR burdens users with complete data control and decreases user welfare.

Segregation via Social Media Platforms

This paper studies the incentive of a firm to segregate users onto its different social media platforms (SMPs). I propose a network model with data externalities, where each SMP earns revenue from user-data driven personalization. A firm offers a price vector to each user to join either platform. Each user decides which platform to join and whether to share data. Users benefit from personalization but incur harm due to privacy concerns. Two factors affect the decision of the firm. 1) The firm has an incentive to segregate users as that reduces the price offered to a user. 2) The firm has an incentive not to segregate users as that increases the amount of data generated. Balancing these two factors, the firm determines a threshold privacy level and it segregates users when their privacy concerns are above this threshold. Finally, the results provide a policy intervention as a solution. When privacy concern of users is high, implementing a strict privacy policy reduces the likelihood of segregation by the firm.

Works in Progress

Measuring Assortative Matching: A Unified Axiomatic Framework (joint with Montserrat Vilalta-Bufí)

The Data Economy and Polarization on Social Media

Regulating a Social Media Platform in the Data Economy

Segregation via Social Media Platforms

Voluntary Disclosure, Location and Price Choice in a Hotelling Model (joint with Nenad Kos)