The Justice Lab at UW-Madison focuses on ending racial, economic, and health disparities across the rural-urban interface.

Project on Arkansas Imprisonment, Reentry, and Health Disparities

While we know that the formerly incarcerated from urban areas are disproportionately disadvantaged and that these individuals returned to disadvantaged urban communities, we lack clarity on who is returning to rural communities and how these communities are impacted. The Project on Arkansas Reentry, Imprisonment, and Health Disparities are unique data designed to answer questions about who is returning to rural communities and their context of reception. Papers from this study include: “The influence of incarceration and Re-entry on the availability of health care organizations in Arkansas” and “Mass imprisonment across the rural-urban interface,” published in 2017 at The Annals of the American Academy of Political and Social Science. Subsequent articles will continue to investigate who is returning and what shape their rural communities are in upon return.

The project on Arkansas Re-entry and Health Disparities (ARRHD) is an effort to examine the socio-economic changes and health outcomes associated with prison re-entry at the county level in the state of Arkansas. In particular, the project examines the incidence of new HIV cases in Arkansas counties as a function of both socio-demographic characteristics and re-entry rates.

Additionally, the ARRHD project aims to assess the influence of prisoner re-entry on socio-economic characteristics of micro-places (e.g. block groups) in the state of Arkansas. The data is a unique combination of secondary data provided in two data sets that can be merged into a multilevel format. The first data set consists of individual inmate re-entry records from the Arkansas Department of Corrections between 1990 and 2008. Address data are included in the ADC data, allowing inmates to be geocoded to communities after release (we assume most inmates return to their community of prior residence).

The second dataset consists of county-level data related to new incidents of HIV annually between 1990 and 2011, the density of community organizations collected from annual county and zip code business pattern census data between 1990 and 2010, and structural census covariates associated with socio-economic and demographic characteristics of residents collected from the 1990, 2000, and 2010 decennial census. County-level segregation data will be created using block-group census data on race and ethnicity from decennial censuses. The index of dissimilarity will be included for all pairs of Whites, Blacks, and Hispanics. The P* Isolation index will be includes for each of these three groups as well.

In 1997, the Standard Industrial Classification (SIC) code system used to classify types of businesses was replaced with the North American Industry Classification System (NAICS). The NAICS system was developed under supervision by the Office of Management and Budget (OMB), and was a collaborative effort between the US, Canada, and Mexico. While SIC and NAICS codes are not strictly comparable, and the NAICS system has evolved over time, it is possible to establish near comparability in broad classifications of industry (e.g. farming/forestry, mining, construction, retail finance, etc.). All SIC and NAICS codes have been recoded to the 2007 NAICS system; the latest system developed during the study period. The key outcomes variables include the spatial concentration of ADOC reentries across counties (n= 120,000 from 1990-2011), characteristics of individuals (n= 70,000 from 1990-2011) ADOC reentries, and the county level incidence of HIV/AIDS cases from 1990-2011. Papers from this study will further address who returns and the characteristics of those rural communities to which they return. Other inquiries will address the following questions like: 1) Are racially and economically disadvantaged rural communities more likely to have prisoners reenter? 2) How do prior trends of reentry predict subsequent HIV/AIDS incidents? 3) How does the availability of healthcare organizations mitigate incidents of HIV/AIDS independent of context?