AI adoption in bureaucracies
研究了2019-2024年美国联邦机构中人工智能职业暴露与劳动力模式的关系，发现高暴露机构出现常规岗位减少、专家岗位增加和工资压缩，为理解制度约束下的技术变革提供参考。
Abstract This paper examines relationships between AI occupational exposure and workforce patterns in U.S. federal agencies from 2019–2024. Using administrative employment data, we document systematic associations between agencies’ concentrations of AI-exposed occupations and employment dynamics. Agencies with higher AI exposure exhibit declining routine employment shares, expanding expert roles, and wage compression effects. We develop a theoretical framework incorporating institutional constraints distinguishing public organisations: employment protections, standardised compensation systems, and political oversight. The model features strategic interactions between budget-maximising directors and electoral-sensitive overseers, predicting workforce evolution under institutional constraints. Our identification exploits fixed occupational exposure scores, so observed changes in agency-level exposure reflect workforce composition shifts rather than measurement artefacts. Patterns suggest agencies with greater AI-susceptible occupations experience reallocation rather than displacement, providing insights for understanding technological change in institutionally constrained environments and informing governance frameworks balancing modernisation with democratic accountability.