Literature Summary

The White House FY 2019 Infrastructure Plan

The White House FY 2019 Infrastructure Plan
  • President Trump recently released his updated infrastructure plan along with the Fiscal Year 2019 Budget. The plan proposes to increase federal infrastructure investment by $200 billion to provide incentives for a total new investment of $1.5 trillion in infrastructure.

  • However, based on previous experience reviewed herein, most of the grant programs contained in the infrastructure plan fail to provide strong incentives for states to invest additional money in public infrastructure. Indeed, an additional dollar of federal aid could lead state and local governments to increase infrastructure total spending by less than that dollar since state and local governments can often qualify for the new grant money within their existing infrastructure programs. We estimate that infrastructure investment across all levels of government would increase between $20 billion to $230 billion, including the $200 billion federal investment.

  • We estimate that the plan will have little to no impact on GDP.

Setting Behavioral Responses in PWBM’s Dynamic Simulations

Setting Behavioral Responses in PWBM’s Dynamic Simulations

  • A literature survey is provided for the key behavioral parameters in tax analysis: labor supply elasticity; saving elasticity and openness to international capital flows.
    • Tax changes affect after-tax wages. The labor supply elasticity parameter controls the simulator’s labor supply response to changes in after-tax wages.
    • Tax changes also affect net-of-tax interest, dividend, and capital gains. The saving elasticity parameter controls how much national saving increases in response to changes in after-tax asset returns.
    • The openness to international capital flows parameter controls the share of new issues of U.S. financial assets that foreign savers purchase. A larger share means greater insulation of domestic investment from variation in domestic saving.
  • However, enough uncertainty exists regarding these key parameters, and so the PWBM model allows the user to try different values.