Will Wilkinson asks about inequality and the recession. I don’t know of anything ongoing data or analysis wise. Macro-economics blogger Will Ambrosini steps up and looks at 2008 growth -vs- 2007 income. That’s not strictly inequality, so I want to post a quick graph.
This is the 1 year increase in unemployment on the y-axis – not the unemployment, but the increase in unemployment to try and control for “natural rates” – against the 2006 Gini-coefficient, a measure of inequality, on the x-axis, by U.S. state and DC. It is positive, and almost statistically significant (p-value of 10.8%, t-stat 1.63). The rightmost outlier is District of Columbia.
Now of course the gini coefficient could just be a function of human capital – college educated workers have less unemployment. It is also probably high where there are a lot of employees in finance and/or where the real estate market was booming.
I’m not sure what’s the best way to set-up regressions like this, so I regressed the Gini-coefficient along with the percent of college graduates in the state, to mop-up human capital issues, along with the percent of total workers in the finance industry and percent of total workers in the real estate industry as good proxies for unemployed directly related to the boom – real estate turned out to be a cleaner estimate than construction (data from Census).
This is a down and dirty estimate of course. I may try this again at the county level later when I have more time. What I want to note, however, is that the gini coefficient is the only variable of those listed that comes close to clearing a statistical hurdle. We want to get that p-value ideally under 5%, though 10% is used sometimes. (Taking gini coefficient out doesn’t help the others’ p-values.) Note also that the percent of workers in finance has a negative sign. What are ways to make this regression better?
And what is it about inequality that could cause extra unemployment in a downturn, holding other things equal?