Supported by the Office on Violence Against Women, lethality prevention programs for intimate partner violence have been implemented in at least 39 states. Yet, little is known about their effectiveness. Exploiting the staggered implementation of the lethality assessment program and the domestic violence high risk team model by law enforcement agencies in the United States, this paper examines the effect of lethality prevention programs on violence against women. Using data from the Uniform Crime Reporting Program, judicial records, and a researcher-constructed data set on lethality prevention programs, I employ a staggered difference-in-differences design to compare outcomes for early adopters of these programs to outcomes for late adopters. I find that the lethality assessment program reduced rates of male-perpetrated female homicides and increased rates of domestic violence protection order filings, but I do not find evidence that the domestic violence high risk team model impacted these outcomes. 

This paper examines how firearm dispossession laws for domestic abusers affect the use of protection orders and intimate partner violence (IPV). We construct a novel county-level panel of protection order filings from annual judicial reports in Colorado, Minnesota, Nevada, Tennessee, Vermont, and the District of Columbia, and combine these data with county-level intimate-partner homicide outcomes. We use a difference-in-differences design to exploit the staggered rollout of firearm dispossession laws. A key source of treatment heterogeneity is the variation in judicial discretion and enforcement procedures. Some states mandate the surrender of firearms upon issuance of a protection order, while others grant judges discretion. States also vary in whether they specify clear surrender procedures. Preliminary estimates suggest that states that enacted a firearm dispossession law experienced a statistically significant decrease in male-perpetrated female homicides. 

Existing research shows that nuisance ordinances increase eviction filing rates and could have potentially disparate impacts on people of color and people with disabilities; however, little is known about where people move and the frequency of movement. Using restricted census and researcher-collected data, we employ a staggered difference-in-differences empirical strategy to examine the impact of nuisance ordinances on residential mobility and neighborhood quality in many of the largest U.S. cities. This approach estimates treatment effects for numerous cities that enact nuisance ordinances at different points in time and examines heterogeneous treatment effects. We hypothesize that nuisance ordinances will increase residential mobility amongst renters in low-income neighborhoods and will result in many households moving to lower-quality neighborhoods. We expect these effects to be more pronounced for black and Hispanic renters and renters with disabilities.

Prior research has found that temporary moratoriums and legal aid programs can successfully reduce evictions, but little is known about the effect of pre-filing notices (documents informing tenants that their landlord intends to evict them). Exploiting a new law in Maryland, this paper examines the effect of warning tenants of upcoming eviction lawsuits ten days in advance on eviction statistics. It also examines the spillover effects on rental assistance and legal aid. Using data from district courts and the Census Household Pulse Survey, I employ a difference-in-differences design to compare outcomes in Maryland to those in nearby states before and after the policy change. I find that introducing a pre-filing notice reduced the average monthly county-level eviction and eviction filing rates in Maryland. It also reduced the individual probability of applying for rental assistance in the first few months after treatment and had inconclusive effects on the use of legal aid.

Housing affordability is an area of interest for policymakers and the public. There are a variety of methods for measuring housing affordability, with the most common method being the housing cost-to-income ratio approach. This approach defines housing as affordable if housing costs do not exceed 30% of household income, but it fails to account for differences in the level of household income and household composition. The residual income approach, which defines housing as affordable if the difference between household income and housing costs is sufficient to pay for some socially acceptable basket of non-housing necessities, has recently gained attention from researchers due to its ability to address some of the shortcomings of the housing cost-to-income ratio approach. This report details the history, advantages, and disadvantages of the housing cost-to-income ratio and residual income approaches to measuring housing affordability. It then uses data from the 2023 American Community Survey, the 2023 American Housing Survey, and the Economic Policy Institute's 2024 Family Budget Calculator to conduct a comparative analysis of these approaches. The report finds that compared to the housing cost-to-income ratio approach, the residual income approach estimates higher rates of housing affordability challenges among non-elderly U.S. households, non-elderly low-income households, and non-elderly households with children. The two approaches also diverge when determining which states are the most unaffordable. However, they produce similar estimates for the percentage of households facing various indicators of housing insecurity (e.g., difficulty affording housing payments and threat of eviction). 

The Hate Crime Statistics Act of 1990 directs the Department of Justice to acquire and report data on hate crimes. This data is collect by local, state, federal and tribal law enforcement agencies and reported to the FBI's Uniform Crime Reporting (UCR) Program. The Bureau of Justice Statistics (BJS) also collects data on hate crimes through its annual National Crime Victimization Survey (NCVS). While the FBI uses a two-tier decision-making process to investiagte and verify hate crimes, the BJS historically classifies incidents as hate crimes if there is evidence the offender used abusive language, left hate symbols, or the police confirmed the incident was a hate crime. This report discusses the methological differences between the UCR and NCVS's approaches to identifying hate crimes and uses data from both programs to examine patterns in hate crimes from 2015 to 2024. The report finds that hate crimes reported to both UCR and NCVS increased from 2015 to 2024 and that most hate crimes were motivated by the victim's race, ethnicity or ancestry. While UCR found that most hate crimes in 2024 occurred at the victim's home, the NCVS found that most crimes occurred at school and that individuals 12-17 made up 29.6% of hate crime victimizations in 2024.