Minnesota’s GDP Growth Rate Exceeded the US Average,
Payroll Employment Growth
Payroll Employment in Minnesota Has Grown Faster than the US Average
Manufacturing Employment
Minnesota’s Unemployment Rate Has Been Well Below the US Average
From 2004 to 2007 Minnesota Underperformed the US Averages Personal income growth US 6.2% MN4.4% Per capita personal income growth US 16.6% MN13.5% GDP growth US 8.4% MN4.8% GDP per capita growth US 5.4% MN2.6%
Minnesota Payroll Employment Has Struggled Since Early 2006
Minnesota’s Unemployment Rate Now Is Similar to the US Average
US Manufacturing Employment Fell Faster Than MN,
Minnesota Ranked 30 th in Employment Growth,
Minnesota Ranked 24 th in Real Per Capita GDP Growth,
Real Per Capita GDP Growth Compared to Neighboring States
Real Per Capita GDP Growth Compared to Midwestern States
Real Per Capita GDP Compared to High Tech States
Portfolio Theory Suggests Using a Tax System that Minimizes Volatility for a Given Growth Rate Given the trend growth rate, variance and covariance of each major tax, an Efficiency Frontier Line (EFL) can be estimated – The EFL shows combinations of taxes that provide the lowest volatility for each growth rate – Points below the frontier are suboptimal. The EFL is determined using quadratic programming to minimize state tax revenue volatility, σ 2 T, given growth rates g T – Minimize – Subject to: and and where ω is the weight of each tax.
Actual FY Portfolio Efficient Tax Mix Portfolio Difference: (Efficient Less Actual) Trend Growth Rate7.70% 0.00% Volatility (Standard Deviation) 3.26%3.09%-0.17% Share of Total Tax Revenue General Sales 31.2%60.3%+29.2% Corporate Income 7.4%13.1%+5.6% Individual Income 48.1%9.2%-39.0% Other Revenues 13.3%17.4%+4.2% Total 100.0% Actual vs. Efficient MN One-Year Tax-Mix Given the Current Trend Growth Rate
Estimating the Volatility of a System of Taxes Markowitz’s modern portfolio theory used as a guide: – The expected growth rate in revenues is the weighted sum of the individual growth rates – Portfolio volatility is the square root of the weighted sum of the variances and covariances of the individual components