Are local governments in the United States responsive to the political preferences of their constituents? Although residents can sort into localities with policies that align with their preferences, unrepresentative participation in public meetings -- where said policies are enacted -- calls this government-constituent alignment into question. Furthermore, the decentralization of local government has made comparisons of policy-making across places historically difficult due to a lack of standardized data. Using a novel text-as-data approach on a recently developed database of local government meetings, we find that attention in local political deliberations is moderately responsive to local partisan preferences. Evidence suggests that this relationship is strongest in larger cities. We conclude that despite the myriad barriers to participation in local democracy, the substance of policy-making in American local government generally reflects the politics of its residents.
On January 31, 2019, the U.S. Department of Housing and Urban Development (HUD), the U.S. Attorney's Office for the Southern District of New York (SDNY within DOJ), the New York City Housing Authority (NYCHA), and New York City (the City) signed an agreement to help NYCHA significantly improve housing conditions for its residents. Housing conditions targeted for improvement ranged from lead paint to heat to pest infestations. In turn, unsafe housing conditions can harm public housing residents' health, increasing their risk for conditions such as childhood and adult asthma.
Measuring improvement in pest conditions is difficult because there was no “pest census” at the time of the agreement—that is, no complete account of the presence or absence of pests in all units, buildings, and developments. Meanwhile, the administrative agreement requires that NYCHA reduces its pest population by certain magnitudes (e.g., 40-50% depending on the pest type), which makes it important to obtain unbiased measures of pest prevalence. Our collaboration focused on methodologies that could be used to monitor: (1) the baseline levels of pest infestations at the beginning of the legal oversight and (2) whether there are improvements over time. We explored the pros and cons of four strategies for estimating this prevalence: (1) using tenant-submitted work orders as measures of underlying issues, (2) using results from randomly-scheduled inspections, (3) using results from randomly-scheduled inspections but reweighting these results to account for unequal probabilities of being selected for an inspection and agreeing to have one's unit inspected, and (4) using predictive modeling to see whether we can predict inspection results using many predictors (e.g., work order history; building characteristics). We are preparing a manuscript that discusses broader lessons for the role of data science in monitoring compliance with legal oversight and provides recommendations for academics and policymakers