During winter break 2022 Katelyn Hemmer ’24 worked with Juan Diego Prieto, Oden Postdoctoral Fellow in Political Science, on a research project investigating state-specific welfare responses to COVID in the United States. Katelyn shared her experiences as an SRP with the Humanities Center.
“Our research went through many different iterations exploring the same question: were there any interesting state-by-state welfare responses to the COVID-19 pandemic that deviated from the pattern identified by existing literature? The journey we took to find answers to this question was full of obstacles and delays, and we ended the (admittedly very short) project with some answers about just one state, New York. Before discussing why New York showed a different pattern than other states, let me explain how we came to focus on this state.
In the original design of our research project, we were hoping to use the U.S. as a case study in comparison to Juan Diego’s research regarding other countries’ welfare responses to the pandemic. Originally, we were going to use Twitter as a measure of public opinion toward different welfare measures, but when it came time to begin the research project, Twitter was not accepting applications for academic use because of the massive layoffs at the company. At the end of November, we divided up the 50 states and began to document the social assistance measures enacted in response to the pandemic during 2020 and 2021. After a week of painstakingly picking through .gov websites and budget bills, we learned that the Oxford COVID policy tracker already documented social assistance and had the data that we needed. We created graphs to show how much state-specific social assistance was provided during the pandemic and found that the model for a “normal” state (in that it provided the average amount of social assistance) was Delaware. The states that were the least like Delaware’s model were New York, Nebraska, and North Dakota. We had seen that the graphs for Nebraska and North Dakota were confusing as they showed relatively little state-specific social assistance until the very end of 2021, which is when most states were taking away extra pandemic assistance. Juan Diego investigated the story of Nebraska and North Dakota and basically found that the Oxford data had been coded wrong and that the uptick in social assistance had been due to different researchers marking policies as different levels of assistance. We had to disregard the data for Nebraska and North Dakota as well as be more skeptical about the Oxford data for the other states.
New York showed that a significant amount of social assistance was provided during the COVID pandemic from March 2020 to August 2021. After August, the Oxford data showed that social assistance in New York sharply decreased. Though we still have some doubts about the accuracy of the Oxford data, there does seem to be a correlation between the timing of the expiration of the majority of the social assistance programs and the resignation of Governor Andrew Cuomo. Most of the COVID-19 policies in NY were put into place by executive order set to expire in roughly two months. As the pandemic continued, the executive orders would be renewed before they expired. When Kathy Hochul took Cuomo’s place as Governor following Cuomo’s sexual harassment scandal, she took the opportunity to allow many social assistance programs to expire. However, the Oxford data shows that all the COVID-related social assistance programs were removed but looking more closely at the government’s website shows this is not accurate. Some of the policies remain in place and have become part of a more permanent welfare system such as the Emergency Rental Assistance Program (ERAP).
Ultimately, we were able to find some evidence that supports the idea state government and politics has some effect on social assistance. Our preliminary research has given us many guiding questions for future research projects. We also can provide feedback to the Oxford COVID policy tracker so that they can streamline how they weigh different policies and make their data more accurate. Through this experience I got an idea of what formal policy research looks like and how to effectively manage my time while working remotely.