U.S Unemployment & Education

Constructed in Python using pandas, matplotlib, numby scipy We analyze unemployment trends by first downloading data from the U.S. Burea of Labor Statistics, and then from Kaggle's CSV files from 2010-2020 and from 1948 to present time. Our analysis uncovers that there's an Education gap: Professional degree holders consistently saw unemployment rates 2–3× lower than those with only a primary school education; A Race gap: Black unemployment remained roughly 1.8–2× higher than White unemployment throughout 2010–2019, regardless of economic conditions; A Sex gap: The men/women unemployment gap narrowed significantly post-2013, with women recovering faster after the 2008 recession, and COVID caveat: April 2020 data is excluded as an outlier due to the pandemic spike distorting long-term trends. I will be completely transparent unemployment_data_us.csv does not cross-tabulate race × sex × education simultaneously — the columns are independent. True 3-way breakdowns require BLS CPS microdata. And the Education attainment data (education_usa.csv) ends at 2010 and cannot be directly joined to the unemployment data without interpolation.