🤖 AI Summary
This study addresses how specialization driven by learning economies confines most workers to narrow domains, leaving only a small cadre of integrators with cross-domain knowledge—a dynamic that undermines democratic accountability and public governance. When policies span multiple domains, the electorate’s collective cognitive limitations bias electoral outcomes toward integrators’ interests, reducing the efficiency with which public resources are converted into services. Integrating insights from political economy and the theory of the division of labor, the paper develops a theoretical model and mechanism analysis to reveal how markets fail to internalize the political returns to systemic knowledge. The findings demonstrate that moderately broadening specialists’ knowledge breadth can alleviate systemic knowledge deficits, thereby enhancing governance effectiveness and social welfare, offering fresh perspectives for liberal education and public discourse in the AI era.
📝 Abstract
This paper studies how the organization of production shapes democratic accountability. I propose a model in which learning economies make specialization productively efficient: most workers perform one-domain tasks, while a small set of integrators with cross-domain knowledge keep the system coherent. When policy consequences run across domains, integrators understand them better than specialists. Electoral competition then tilts targeted services toward integrators' interests, while low aggregate system knowledge weakens governance and reduces the fraction of public resources converted into citizen-valued services. Labor markets leave these civic margins unpriced, failing to internalize the political returns to system knowledge. Broadening routine specialists can therefore raise welfare relative to the market allocation. The model speaks to debates on liberal art education and the effects of AI.