The Division of Understanding: Specialization and Democratic Accountability

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 government policies 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 specialists can therefore raise welfare relative to the market allocation. The model speaks to debates on liberal arts education and the effects of AI.