Several days ago I was discussing the link between education and unemployment with my economist friend Bill Lafayette, PhD. The seemingly endless Republican Primary had recently thrust higher education into the national spotlight. At issue was whether or not we should always encourage people to seek higher education. This is political season in the U.S.and issues like this become cloudy and distorted to the point they are unrecognizable. But the timing of the discussion was interesting. The Bureau of Labor Statistics (BLS) just released the most recent unemployment statistics showing that the national unemployment rate for those without a high school degree was 12.9% while the national unemployment rate for those with at least a Bachelor’s degree was 4.2%. The difference between these unemployment rates during the current U.S. recession has been consistently between 8 and 10%.
I’m not advocating for a four-year degree for everyone, but the data is clear and the facts are unavoidable – you are in a substantially better position professionally (and economically) if you have at minimum a Bachelor’s degree. Of course, this club has some obvious barriers to entry. The two most obvious are cost and aptitude. But another potential barrier is location – how far must one travel to attend an institute of higher learning? Bill told me about an initiative that former Ohio governor James Rhodes had championed several decades ago. Governor Rhodes wanted every Ohioan to live within 20 miles of a college or university. That gave me an idea. I wanted to see where this at-risk population lived in relation to the location of colleges and universities – hence this installment of Urban Decision Group’s Map of the Week series.
Colleges and universities were defined as anything having a NAICS code of 61131009. The data was extracted from a business database provided by InfoGroup. I don’t assume 100% accuracy with any third-party data sets, but the data we use from InfoGroup is actually pretty good stuff. They provided point data geocoded to the address of the institution. I then established 10-mile rings around each point. Normally, if I were establishing a trade area, I would never use a simple ring around a point. But we can get away with it in this case because of the shear volume of points create several areas of overlap. The 10-mile radius around each college and university represent areas that we are not concerned about. The areas we are interested in are everything outside of these rings; they represent population centers that are more than ten miles away from an institution of higher learning. So I laid out a 10 square mile grid across the U.S.only for those areas that were not within 10 miles of a college or university. This area represents territory where location could prove to be a barrier to higher education.
The next step was to define what the undereducated at-risk population actually is. The data was extracted from the American Community Survey (ACS) 2006-2010 data at the county level and ultimately aggregated into the 10 square mile grid cells. I decided to focus on the age group of 35-64. People in this age group are generally less mobile than young people. This group consists of households with children, mortgages, and many other things that prohibit a semi-transient lifestyle. Then I broke the data into three sets. The first set consists of those people without a high school diploma. The second set contains those with no college and just a high school diploma. The final set was simply the percent of the population that only had a high school diploma. The logic in choosing this data is that no single data set could define what the at-risk population was, but the combination of the three would provide a pretty good definition. Each of the data sets was normalized and a final normal score was calculated for each grid cell. Normal score values are guaranteed to fall between 0 and 1. A value trending towards 1 indicates more of the population is at-risk.
When viewed on a map, we can identify the location of the undereducated at-risk population. If members of this population group were to become unemployed, they are the most at-risk for prolonged periods of unemployment. You can make the argument that with the ubiquity of the Internet and the rise in online courses available through many colleges and universities, location no longer matters. This may be true for a small subset of the population but the at-risk population that we identified is less likely to have high-speed internet or even awareness that such opportunities may exist.
Like Urban Decision Group’s previous Maps of the Week, our intent is not only to inform but to inspire. Decision and policy makers can direct resources more efficiently if they have a clear picture illustrating where they should go. This week’s map is no exception. Again, I’m not advocating that everyone in this population group needs a four year degree. But at minimum everyone should have reasonable access to technical job training and vocational schools. Education not only benefits those that receive it, but improves the health of the entire economy. The proof is in the gap between unemployment rates for the educated and the undereducated.