The Department of Homeland Security publishes a data set on colleges and universities for the 2017-2018 school year. The data set allows for the investigation of how much full-time college enrollment within a county increases the average wages within that same county. The intuition is that full-time college students enter the local labor market upon graduation and take up higher paying wages, thus increasing the average wage in the local labor market.
Constructing the master data set involves first calculating the percentage of total enrollment that is full-time enrollment, or full-time enrollment divided by total enrollment. This variable is constructed at the county level and is called “full_time_percent” in this analysis. The second task is to merge in county data on average wages and total employment.
I run a regression of average weekly wages on full_time_percent and control for monthly employment. A regression predicts how much a one percentage point increase in full-time enrollment increases average weekly wages in the local labor market. Controlling for monthly employment is important because the size of the local labor market can impact both average weekly wages and also percentage of full-time enrollment.
The results of the regression analysis is in Figure 1, below. The figure says that a one percentage point increase in full-time enrollment leads to a $136.66 increase in average wages. The coefficient is statistically significant which means that the finding is unlikely to be due to chance. (Note: The coefficient is statistically significant at the 10% level.)
|Figure 1. Impact of Full-Time College Enrollment on Average Wages in the Local Labor Market|
|Full Time Percent||136.6575||79.92021||Yes|
|Note: Significiant at the 10% level.|
This short analysis demonstrates that college enrollment can have an impact on the wages of the local labor market. The idea is that graduates enter the local labor market and take high paying jobs, which increases the average wage in the county. The finding is statistically significant.
Source: https://hifld-geoplatform.opendata.arcgis.com/datasets/colleges-and-universities , https://www.bls.gov/web/cewqtr.supp.toc.htm