Analysing the contextual factors that promote and constrain policy learning in local government

Policy learning is an important part of the policy process that crosscuts multiple frameworks. However, policy learning is difficult to model and predict, and even less is known about the extent to which community characteristics affect policy learning. This article examines the contextual factors that promote and constrain policy learning in local government by identifying factors that promote learning. The central theory is that community characteristics, experience with a policy problem, and the type of subnational government will be important factors. We examine this in the context of primary data collected after Hurricane Harvey from open-ended interviews with 22 local government officials across Southeast Texas. Using a mixed methodology, we leverage the results of content analysis and Poisson regression. We find that cities with higher median incomes and larger populations are more likely to experience policy learning. Experience is not an important predictor of policy learning, suggesting possible maturation effects in the learning process. Given the long-term challenges posed by natural hazards and disasters, this puts smaller and less affluent communities at higher risk. If equitable risk and recovery is the goal of disaster policy, efforts must be made to either increase capacity or provide more resources to local governments with less capacity.