Being a Jennifer Caldwell Fund recipient allowed me to continue working with the University of Washington Center for Human Rights (UWCHR) researching immigration detention. From September 2020 to August 2022, I was part of a team within the UWCHR working on a quantitative social science project seeking to understand the impact of immigration detention capacity (bed spaces at nearby detention facilities) on local immigration enforcement practices such as the number of encounters, arrests, and removals. We are also interested in examining whether detention capacity impacts bond rates that people receive in immigration courts. In order to do this, we rely on ICE detention and enforcement data via Freedom of Information (FOIA) requests and bond hearing data via the Executive Office for Immigration Review (EOIR) database.
The first part of my job in this project was to address one of the challenges we ran into with the EOIR data—bond hearing locations are not directly linked to detention facilities. As we were only given information on the bond by the court at which the bond was set and not the facility where the person was detained at the time, I worked to link bond hearing locations with associated detention facilities as accurately as possible. Throughout the process, I worked closely with other members in the team and reached out to various experts (lawyers, non-profits, advocacy groups) in local areas to address questions and further challenges that arose from the mapping process. The completion of this part of the project helped the team move forward onto working on bond amounts and mapping bond amounts to facilities.
My next task with the project involved operationalizing detention capacity and getting the enforcement data ready so that we can use these variables in various regression models to answer our research questions. After I finished cleaning and wrangling various enforcement data (number of encounters, arrests, and removals), I worked closely with our project manager and another team member to construct different measures for detention capacity. This was a formidable task for the team as the various datasets we were working with were incomplete and aggregated at different geographical units. To get all the variables on the same unit level so that they can be used in regression, we went from a raw daily headcount of people detained at different facilities to average headcounts for different time units. Utilizing known facility capacity, we then constructed data on available bed space. These measures allowed us to move onto the next part of the project, which was to run our models.
My final task was to set up the statistical models needed to test our hypotheses using R, a popular data science programming language. As we have an enormous panel dataset with very complex structure, my team and I spent time consulting with various experts on statistical modeling at the UW. I assisted the team with running diagnostics testing on the data to identify trends, lags, and cycles that would determine the type of modeling needed. Finally, I set up some templates for different panel data modeling techniques and helped with interpretation of our initial results.
I felt very lucky to have been a part of this project at UWCHR as it was a tremendous learning experience. Not only was I able to do meaningful work with real life impacts, I had multiple opportunities to engage with experts and advocacy groups outside of the UW to learn about the work being done outside of academia. Additionally, I was able to hone my research skills including data cleaning, coding, and workflow management. It has been a privilege working with such a committed group of researchers in a supportive and inspiring place like UWCHR.