seminars

The Turing Valley: How AI Capabilities Shape Labor Income

Do improvements in Artificial Intelligence (AI) benefit workers? We study how AI capabilities influence labor income in a competitive economy where production requires multidimensional knowledge, and firms organize production by matching humans and AI-powered machines in hierarchies designed to use knowledge efficiently. We show that advancements in AI in dimensions where machines underperform humans decrease total labor income, while advancements in dimensions where machines outperform humans increase it. Hence, if AI initially underperforms humans in all dimensions and improves gradually, total labor income initially declines before rising. We also characterize the AI that maximizes labor income. When humans are sufficiently weak in all knowledge dimensions, labor income is maximized when AI is as good as possible in all dimensions. Otherwise, labor income is maximized when AI simultaneously performs as poorly as possible in the dimensions where humans are relatively strong and as well as possible in the dimensions where humans are relatively weak. Our results suggest that choosing the direction of AI development can create significant divisions between the interests of labor and capital.

Labor as Capital: AI and the Ownership of Expertise

Workplace surveillance generates data that can train AI systems to replicate worker expertise. Using a large online survey experiment of U.S. full-time workers, we show that workers adjust their knowledge contributions when made aware of this dynamic: they rationally withhold expertise due to career concerns. We formalize this behavior in a model of knowledge supply under surveillance-enabled AI and use it to evaluate alternative policies. Individual data ownership— workers’ preferred policy—eliminates knowledge withholding but creates negative externalities: one worker’s data strengthens the firm’s bargaining position against others, potentially making all workers worse off. In contrast, collective data ownership achieves the first-best outcome, promoting knowledge sharing while allowing workers to benefit from AI-driven productivity gains. These findings highlight the importance of labor agreements in shaping AI adoption in labor markets.

Norms at Work: Masculinity, Well-being and Performance in Academia

Workplaces across many industries are characterized by what is stereotypically called “masculine” norms: i.e. highly competitive and aggressive norms, often portrayed as necessary to increase performance. Using rich survey and archival data from faculty and staff in business schools, we develop a novel way to measure these hyper-competitive norms and show that they are negatively correlated with employee well-being, both increasing turnover intentions and reducing workplace well-being. We then examine why these norms persist despite their negative consequences and find that the associated lower well-being is not offset by higher performance – neither in terms of research quantity nor impact. Finally, we show that no organizational subgroup thrives in hyper-competitive environments. While neither men nor women benefit from such norms, even “superstar” performers in the top performance deciles experience negative implications.

Real-Time Monitoring and Relational Contracts in Usage-Based Insurance

The rise of the Internet of Things (IoT) and big data technologies enables insurance companies to monitor policyholders’ behavior in real time, leading to innovative usage-based insurance (UBI). This paper studies the optimal UBI contract that employs both a traditional objective signal (e.g., official accident report) and a novel subjective signal (e.g., driver safety score) about the insured’s behavior in the presence of moral hazard. We show that under limited liability, the subjective signal may not be used even when enforceable if it is relatively imprecise. Moreover, the objective and subjective signals can serve as either complements or substitutes, depending on their precisions. While a more precise subjective signal always enhances insurance market efficiency, the welfare implication of the objective signal can be non-monotonic. In particular, when a more precise objective signal leads to a highly efficient traditional insurance contract, it may reduce the efficiency of the UBI contract or even make the subjective signal infeasible to use. Our paper thus explains the conditions under which UBI programs can emerge and highlights key factors for the success of UBI programs. In addition, we show that UBI market regulation can mitigate distortions in UBI contract design and investment in monitoring technologies.

Neglect

This paper studies how problems are, or are not, solved in collaborations. We develop a dynamic model of neglect, which we define as the failure to solve problems even when doing so would benefit all members of a collaboration. Neglect arises because solving a problem requires revealing it, which has the unintended consequence of making others less optimistic about the future of the relationship. In equilibrium, neglect arises when the party who learns of a problem derives more value from the collaboration than others. We characterize equilibrium dynamics, show when and why neglect arises, study how communication and exit convey information, and consider how collaborations can be structured to encourage the revelation of problems as they emerge.

Narrative Entanglement: The Case of Climate Policy

Political economy models often assume that voter beliefs are consistent with available information. Recent work emphasizes instead the role played by narratives, subjective causal models that may be incorrectly specified. In this paper, we study the role of political narratives in the context of climate policy. We develop a theory of narrative entanglement, where policy dimensions—initially distinct—become strategically intertwined through narratives created by politicians to sway support. Shocks in one dimension can thus influence unrelated policy areas. We test this theory in the context of EU climate policy before versus after Russia’s invasion of Ukraine, which affected the economic costs of climate policy but not its ability to address climate change. Using a large language model to analyze speeches in the EU Parliament, we find that narratives are strongly entangled: Members of the European Parliament that emphasize the need to address climate change also emphasize economic benefits, while those denying climate change stress economic costs. After the energy price shock associated with the invasion of Ukraine by Russia, narratives shift not only in the economic dimension but also in the climate dimension, with speeches becoming less likely to imply that climate policy is necessary to combat climate change. This pattern holds at the individual politician level, with politicians from right-wing parties showing a more pronounced narrative change than those from the left.

Mini-Workshop on Organizational Economics