Summary

The first two papers of my PhD thesis focus on non-competition agreements, while the final chapter examines the liability of digital social media platforms for user-generated content. All three papers develop a microeconomic model grounded in standard labor economics theory to investigate how regulatory measures regarding noncompetes and digital platform liability could enhance social welfare.

The chapter “Non-competition Agreements and Dedicated Human Capital” explores how the optimal stringency of a noncompete varies with the employee’s position inside a firm’s hierarchy. I find that in equilibrium, employees at both the top and bottom positions are subject to a noncompete while those in middle positions are free from the covenant. My analysis also contributes to the heated debate on whether noncompetes should be banned, particularly for bottom positions. I demonstrate that prohibiting noncompete agreements for bottom position of the firm’s hierarchy could bolster social welfare, provided that the expected quality of labor is moderate.

In “Guarding Expertise and Assets: Non-competition Agreements and Their Implications” I examine the contracting problem between a firm and potential employees, highlighting a key distinction from the previous chapter: the firm’s lesser maturity. Therefore, the choice of which agent to hire based on ability and the degree of access granted to the firm’s critical assets is crucial. I demonstrate that firms, conditional on hiring lower ability agents, primarily compensate them through access, coupled with the minimum wage and the most stringent noncompete. For higher ability agents, the maximum degree of access 1 is provided, while the wage and stringency of the noncompete depends on the damage the employee causes with competing. For low damages, the firm offers a lax noncompete with lower wages. Conversely, high potential damage necessitates higher wages and a stricter noncompete. The study’s findings are consistent with observed patterns in CEO contracts.

The final chapter, titled “How to Enforce Platforms’ Liability?”, delves into the liability of social media platforms for user-generated content, a subject that has attracted significant attention alongside the introduction of new regulations like the Digital Services Act. This chapter develops a theoretical model to explore how such regulatory frameworks influence the economic incentives of social media platforms dealing with users of varying popularity. The chapter argues that oversimplified regulations may worsen the problem of cherry-picking, where platforms penalize only low type users and not high type ones for violations. The paper proposes that regulators enhance their technology to monitor more content and adjust fines based on the audience size of users, observable ex post. Intriguingly, under this approach, the optimal fine might be reduced when more users commit violations.