Going Beyond Per Capita Rankings

State tax rankings are a perennial hot topic in debates over the size of government. Such rankings are most commonly based on taxes per capita; however, rankings based on per capita taxes—or even the broader category of per capita own-source revenue—are inadequate as basis for gauging the relative size of state and local government among the states, for reasons noted in the first part of this series. Even the all-inclusive categories of total revenues and total expenditures per capita are flawed as a basis for ranking the relative size of government among states.* The problem is not with the total revenue or total expenditure side of the equation, but with the per capita side.

As noted in part one, states with high per capita personal income tend to have high per capita taxes and own-source revenue. It turns out that states with high per capita income also tend to have high per capita total revenues and expenditures.† While not quite as strong as the relationship between income per capita and taxes and own-source revenue per capita, personal income per capita still explains over half the variation in revenue and expenditures per capita among states, excluding the outlier state of Alaska. The chart below shows the relationship between personal income per capita and combined state and local direct general expenditures per capita based on FY 2013 data, with each dot representing a state; the red dot denotes Minnesota.

py-exp

There is a strong relationship between per capita personal income and per capita expenditures. A separate scatter plot of the relationship between per capita income and per capita revenue shows a similar pattern. As was the case with taxes and own-source revenue, Minnesota’s FY 2013 total expenditures per capita ($9,031) and total revenue per capita ($9,442) are approximately what we would expect, given Minnesota’s per capita personal income ($47,410).

The powerful relationship between personal income per capita and total revenues and expenditures per capita is attributable to two factors:

  1. High per capita income states tend to have higher labor costs and a higher cost-of-living than low per capita income states. For example, average annual pay in general and for state and local government employees specifically is higher in high per capita income states than in low per capita income states.† The cost of both goods and services is also significantly higher in high per capita income states, based on the Bureau of Labor Statistics’ Regional Price Parities index (a measure of state and regional price differences).† Thus, a high per capita income state must spend more per capita and generate more revenue per capita than a low per capita income state, even if they are providing the same level of public services per capita.
  2. The demand for high quality public services—for example, good schools, superior roads and infrastructure, etc.—tends to increase as per capita income increases. The demand for higher quality public services among high per capita income states results in higher per capita spending and the need for higher per capita revenues. This is something of a chicken-or-the-egg situation, insofar as higher levels of public investments in education and other public amenities may have contributed to the higher level of per capita income in the first place; for example, in a 2007 interview, Minnesota State Economist Tom Stinson attributed Minnesota’s surge in per capita income relative to other states since 1960 to investments in K-12 and higher education.

These factors help to explain why high per capita income states tend to have higher per capita state and local government revenue and spending than low per capita income states. They also help to explain why simplistic comparisons of per capita revenue and spending levels among states that do not control for differences in per capita income are an inadequate gauge of the relative size of government between states.

To provide more meaningful comparisons of revenue and expenditure levels between states, the U.S. Census Bureau and the Minnesota Department of Revenue include state rankings based on total state and local government revenues as a percent of personal income (or per $1,000 of personal income) as a supplement to per capita rankings. The Census Bureau also provides rankings based on spending as a percentage of personal income. Rankings based on revenue and spending as a percentage of personal income help to control for the impact that the level of personal income has on the cost of and demand for public services and the resulting need for public revenues and expenditures—and thereby provide for a more meaningful comparison of the relative size of government among states.

Based on total revenues and expenditures as a percentage of personal income, Minnesota is not a particularly large or small government state. State and local government general revenues in FY 2013 were 20.1 percent of statewide personal income—the 22nd highest among the states. (Based on estimates presented in a July 2016 North Star article, the effects of tax increases enacted in 2013 should increase Minnesota’s revenue rank to approximately 19th in FY 2014; final FY 2014 rankings should be available within the next few months.) Meanwhile, FY 2013 direct general expenditures in Minnesota were 19.2 percent of personal income—the 27th highest.

As noted in the first part of this series, handwringing about a state’s high tax, revenue, or expenditure rank is not warranted, as there is no convincing evidence that reducing government revenue and shrinking public investments contribute to superior economic performance or a better quality of life. Nor is a low ranking necessarily anything to celebrate. To the extent that we do focus on such comparisons, the most meaningful rankings based on revenues and expenditures as a percent of personal income indicate that Minnesota is not a big government state.

 

*Unless otherwise noted, the rankings and other analysis in this article will be based on data for all fifty states and the District of Columbia.

These relationships are statistically significant at the 0.01 level.