Routinized Deference: Predictive Screening and Bureaucratic Capacity in Child Protective Services

Abstract Predictive algorithms are increasingly used to support frontline decision-making in public administration, but evidence remains limited on how they affect bureaucratic capacity. This study examines county-level adoption of predictive screening tools in U.S. Child Protective Services using NCANDS data (2010–2023) and a stacked difference-in-differences design. Leveraging staggered adoption, I find no change in the total number of investigations opened and a sizable decline (about 15%) in the share of investigations that are substantiated. These results are inconsistent with operational capacity gains and more consistent with misclassification that erodes analytical capacity. These findings are consistent with a mechanism of routinized deference: under workload and accountability pressures, algorithmic recommendations can become procedurally defensible defaults. Overall, this study argues that predictive screening is not capacity-enhancing by default, as routinized deference can undermine the professional judgment central to bureaucratic work.