Sex Offender Registries: Fear without Function?

I use three separate data sets and designs to determine whether sex offender registries are effective. First, I use state-level panel data to determine whether sex offender registries and public access to them decrease the rate of rape and other sexual abuse. Second, I use a data set that contains information on the subsequent arrests of sex offenders released from prison in 1994 in 15 states to determine whether registries reduce the recidivism rate of offenders required to register compared with the recidivism of those who are not. Finally, I combine data on locations of crimes in Washington, D.C., with data on locations of registered sex offenders to determine whether knowing the locations of sex offenders in a region helps predict the locations of sexual abuse. The results from all three data sets do not support the hypothesis that sex offender registries are effective tools for increasing public safety.

Sex Offender Law and the Geography of Victimization

Sex offender laws that target recidivism (e.g., community notification and residency restriction regimes) are premised—at least in part—on the idea that sex offender proximity and victimization risk are positively correlated. We examine this relationship by combining past and current address information of registered sex offenders (RSOs) with crime data from Baltimore County, Maryland, to study how crime rates vary across neighborhoods with different concentrations of resident RSOs. Contrary to the assumptions of policymakers and the public, we find that, all else equal, reported sex offense victimization risk is generally (although not uniformly) lower in neighborhoods where more RSOs live. To further probe the relationship between where RSOs live and where sex crime occurs, we consider whether public knowledge of the identity and proximity of RSOs may make offending in those areas more difficult for (or less attractive to) all potential sex offenders. We exploit the fact that Maryland’s registry became searchable via the Internet during our sample period to investigate how laws that publicly identify RSOs may change the relationship between the residential concentration of RSOs and neighborhood victimization risk. Surprisingly, for some categories of sex crime, notification appears to increase the relative risk of victimization in neighborhoods with greater concentrations of RSOs

The Minimum Wage, EITC, and Criminal Recidivism

For recently released prisoners, the minimum wage and the availability of state Earned Income Tax Credits (EITCs) can influence both their ability to find employment and their potential legal wages relative to illegal sources of income, in turn affecting the probability they return to prison. Using administrative prison release records from nearly six million o enders released between 2000 and 2014, we use a difference-in- differences strategy to identify the effect of over two hundred state and federal minimum wage increases, as well as 21 state EITC programs, on recidivism. We find that the average minimum wage increase of 8% reduces the probability that men and women return to prison within 1 year by 2%. This implies that on average the wage effect, drawing at least some ex-o enders into the legal labor market, dominates any reduced employment in this population due to the minimum wage. These reductions in reconvictions are observed for the potentially revenue generating crime categories of property and drug crimes; prison reentry for violent crimes are unchanged, supporting our framing that minimum wages affect crime that serves as a source of income. The availability of state EITCs also reduces recidivism, but only for women. Given that state EITCs are predominantly available to custodial parents of minor children, this asymmetry is not surprising. Framed within a simple model where earnings from criminal endeavors serve as a reservation wage for ex-o enders, our results suggest that the wages of crime are on average higher than comparable opportunities for low-skilled labor in the legal labor market

Employer Neighborhoods and Racial Discrimination

Using a large field experiment, we show that racial composition of employer neighborhoods predicts employment discrimination patterns in a direction suggesting in-group bias. Our data also show racial disparities in the geographic distribution of job postings. Simulations illustrate how these patterns combine to shape disparities. When jobs are located far from Black neighborhoods, Black applicants are doubly disadvantaged: discrimination patterns disfavor them, and they have fewer nearby opportunities. Finally, building on prior work on Ban-the-Box laws, we show that employers in less Black neighborhoods appear much likelier to stereotype Black applicants as potentially criminal when they lack criminal record information