Hacking Equity 1 revisited
by Eddie Lopez
Revisiting the Tech and Narrative Lab’s Hacking Equity: COVID Data, Uncovering Trends, and Can You Hack It? Discoveries
With the Tech and Narrative Lab’s Hacking Equity 2: Justice Reinvestment wrapping up, I thought it might be a good time to revisit its predecessor. Specifically, I wanted to share a little about what my group, Can You Hack It?, did for last year’s event, some of the trends we discovered, and just a little bit about the experience overall.
Hacking Equity is a tech and public policy hackathon hosted by our very own Tech and Narrative Lab. It originated last October, and has since been an annual event. Currently, all of its instances have been in conjunction with RAND and the Atlanta University Center Consortium.
In each event to date, the hackathon has centered around some of the biggest policy issues of the time, and tasked teams with using available data to paint a clearer picture of the situation. For this year, that was justice reinvestment – centering around how the U.S. might reallocate resources relating to crime, punishment, and rehabilitation. However, as for the inaugural event, we wanted to look at how vulnerable populations fared during the pandemic.
While not the winners of the contest, I wanted to share what my group discovered in the original Hacking Equity. We ultimately focused on two groups: those within the prison system, and those within the Payment Protection Program. For the purposes of this blog post, I am going to focus on the prison population, as that is the analysis I led.
The Prison Population
The prison population was a really interesting group for us, as it was a population many of our group hadn’t really considered during the pandemic. However, the risk that this group faced was very real.
While already a vulnerable population, prisoners were further made vulnerable during the pandemic due to how confined spaces allow COVID to spread. Consequently, we wanted to see how prisoners and the prison staff were affected during the COVID-19 pandemic. We hypothesized that the prison system, COVID rate and death rate would be relatively higher than the overall population, due to the confining nature of the prison system.
Upon actually diving into the data, the first thing we noticed was just how opaque the data was. We utilized the New York Times provided data as it was the best data we could find. However, even their data was limited in the numbers they could provide, and proved hard to use for statistical analysis. Often times, our group had to make assumptions on population metrics (i.e., whether the max prison population in 2020 or the current prison population was a better metric) in order to yield results. The assumptions and process are laid out in greater detail in our documented R.file – loaded and available at the TNL GitHub.
As for results, we had one major takeaway, and then one interesting trend to note. For the takeaway, we found that by state, the prison population infection rate seemed to be higher relative to the normal population for the data we used. This was something we visualized in the following graphic:
As for the interesting trend, we found that the COVID-19 death rate amongst inmates seemed to line up extremely well with the distribution of the non-white American population. This is shown below:
The reason why we marked this as “interesting trend” versus “key takeaway,” was that the visual was something we didn’t feel confident enough in to make the jump from correlation to causation. Other visuals also followed this general pattern, with the United State’s total COVID death rate being an example of such:
I wanted to add in this section just to express how cool I thought this experience was. It is always super neat to be able to explore data and be able to see what it is uncovering… but to then explore that with some fellow PRGS students and some people from the AUC consortium? Even neater.
Thank you so much for reading, and wishing the best for those who were able to compete in Hacking Equity 2 this year!!! Hopefully it was a blast.
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