The goal of this project is to investigate trends in unemployment data and how COVID-19 has affected various communities.With this investigation I am to answer the following questions: What does unemployment look like after COVID-19? Do stimulus checks have any impact on unemployment rates? How does unemployment vary by education? How does unemployment vary by region? How quickly has unemployment recovered from the shock of the March 2020 quarantine? Are their anomalies in the geographic trends? What can explain these anomalies?

Bureau of Labor Statistics’ official definition of “Unemployed”

1. Did not have a job at the time of the survey

2. Made at least one specific active effort to find a job during the prior 4 weeks

3. Were available to work

4. All those who were not working but were waiting to be called back from former employers after being laid off

Unemployment Rate = Unemployed / Labor Force

For this section of the project, I used six data sets from the Bureau of Labor Statistics. The first data set shows the seasonally adjusted unemployment rate in the United States measured by Unemployed / Labor Force. The next four data sets divide the seasonally adjusted unemployment rate by highest completed education. The sixth data set groups unemployment by US county.

These plots highlight the affect of covid-19 on US unemployment. I focus on total unemployment for the first few plots and then break down unemployment by education level. I highlighted the stimulus check rollouts to investigate whether that had an affect on unemployment rates.

#Unemployment choropleths The subsequent plots are choropleths that show August 2020 vs. August 2021 unemployment to show which geographic locations where hit hardest and how they recovered from covid-19 shutdowns. The goal of these plots were to draw hypotheses about the origins of the present day labor shortage. I chose August 2020 intentionally because the labor market was in the midst of the recovery from the March 2020 recession. I did not choose March 2020 as my marker because some industries would have not been responsive to the warnings of the pandemic. 5 months after the start of quarantine has given the labor market sufficient time to settle (in the sense that people understand the initial effects of COVID on the retail economy and have grown accustomed to working from home). I juxtaposed this with august 2021 in order to control for seasonal trends. I found Maine to be a specifically interesting case, thus I created two additional subsections of data that focused solely on Maine counties during these two time periods.