I’m in a Labor Economics class this quarter, and the assignment/discussion portion of the class is centered around one question: Why is unemployment so high? The part of the answer that I’m most interested in right now is (unsurprisingly) the geographic portion. What explains the huge variation across counties that we see here? (Darker red is a higher unemployment rate).
A big part of it can be explained by the fact that different places have different mixes of industries: resource extraction is doing great right now, so places with a lot of resource extraction are also doing great. So, I accounted for (1) the share of employment in different industries, (2) the growth of different industries from 2001-2007–maybe places with a lot of growth in construction have suffered more than places with a lot of growth in health care, and (3) ratio of those with a college degree to the labor force as a whole. Then, I taught myself a smidge of Python and made a new map, of which I am quite proud! Here, dark red is high positive “unexplained unemployment” while dark green is large negative “unexplained unemployment”. Dark green means that, after accounting for the local industry mix, that place has a lower unemployment rate than we would expect, while dark red means higher. Here she is: (Note: Alaska, Virginia, and part of northern Colorado are excluded for data reasons).
This map seems to tell a different story. Most of the south looks substantially better after accounting for industry mix, as does coastal California: Los Angeles, the nation’s largest county (and more populous than 42 states–and actually, it may have passed Michigan in the last six months), is doing pretty well, after accounting for its industrial patterns!
By the way, mining is the most reliably low-unemployment industry going. Manufacturing, local government, federal-civilian, federal-military, retail and forestry/fishing are the most reliably high-unemployment. Places that had a lot of growth in military, finance, and hospitality are also looking worse for it, while places with growth in manufacturing and retail did well–suggesting that some manufacturing jobs have been leaving their historical homes for places without much current manufacturing, and the places that the jobs moved to are doing just fine with it. Call it internal off-shoring?
There are some other interesting patterns. Look at the Nebraska-Kansas border for instance. In the first map, there doesn’t seem to be much difference, but in the second, Nebraska looks systematically better amongst border counties. The Louisiana-Texas border is similarly stark. It’s possible that state-level policies are helping Nebraska and Louisiana cope with the recession better. It then might be interesting to look at the county unemployment rates after accounting for state-wide conditions. That gives us this map:
The best way to read this map is probably to focus on one state at a time. For many states, there seems to be an urban/rural distinction: California, Maine, Massachusetts, New York, Ohio, Illinois, Washington, Oregon, Georgia, Tennessee, etc. But that’s only a tendency–Texas and New Mexico don’t seem to follow the pattern. Much of the Mexican border has low leftover unemployment–except for a few counties with deep red. There are also large swathes of inexplicably high unemployment left: along the lower Mississippi river, the Western Slope in Colorado, and parts of Alabama, Georgia, and South Carolina–not to mention the clear separation between Nevada counties.
There’s too much still going on for me to know just what to make of it. But I feel comfortable saying that there is a role for geography in understanding this–if there weren’t, the last map would have the colors scattered at random. But that doesn’t seem to be the case–or, I’m missing some other factors that will explain the rest of the geographic variation. Either way, I’m looking forward to doing more in this class.