The deadliest local police departments kill 6.91 times more frequently than the least deadly departments, net of risk, in the United States.

I use data linking counts of homicides by police to police department (PD) and jurisdiction characteristics to estimate benchmarked (i.e. risk-adjusted) police homicide rates in 2008-2017 among the 711 local PDs serving 50,000 or more residents, a sample with demographics resembling all mid-to-large Census places. The benchmarked rate estimates capture PD deadliness by comparing PDs to peers whose officers face similar risks while adjusting for access to trauma care centers to account for differential mortality from deadly force. Compared to existing estimates, differences in benchmarked estimates are more plausibly attributable to policing differences, speaking to whether the force currently used is necessary to maintain safety and public order. I find that the deadliest PDs kill at 6.91 times the benchmarked rate of the least deadly PDs. If the PDs with above-average deadliness instead killed at average rates for a PD facing similar risks, police homicides would decrease by 34.44%. Reducing deadliness to the lowest observed levels would decrease them by 70.04%. These estimates also indicate the percentage of excess police homicides-those unnecessary for maintaining safety-if the baseline agency is assumed to be optimally deadly. Moreover, PD deadliness has a strong, robust association with White/Black segregation and Western regions. Additionally, Black, Hispanic, foreign-born, lower income, and less educated people are disproportionately exposed to deadlier PDs due to the jurisdictions they reside in. Police violence is an important public health concern that is distributed unevenly across US places, contributing to social disparities that disproportionately harm already marginalized communities.

It Is Surprisingly Difficult to Measure Income Segregation.

Recent studies have shown that U.S. Census- and American Community Survey (ACS)-based estimates of income segregation are subject to upward finite sampling bias (Logan et al. 2018; Logan et al. 2020; Reardon et al. 2018). We identify two additional sources of bias that are larger and opposite in sign to finite sampling bias: measurement error-induced attenuation bias and temporal pooling bias. The combination of these three sources of bias make it unclear how income segregation has trended. We formalize the three types of bias, providing a method to correct them simultaneously using public data from the decennial census and ACS from 1990 to 2015-2019. We use these methods to produce bias-corrected estimates of income segregation in the United States from 1990 to 2019. We find that (1) segregation is on the order of 50% greater than previously believed; (2) the increase from 2000 to the 2005-2009 period was much greater than indicated by previous estimates; and (3) segregation has declined since 2005-2009. Correcting these biases requires good estimates of the reliability of self-reported income and of the year-to-year volatility in neighborhood mean incomes.