Dynamic scoring

Dynamic scoring is a forecasting technique for government revenues, expenditures, and budget deficits that incorporates predictions about the behavior of people and organizations based on changes in fiscal policy, usually tax rates. Dynamic scoring depends on models of the behavior of economic agents which predict how they would react once the tax rate or other policy change goes into effect. This means the uncertainty induced in predictions is greater to the degree that the proposed policy is unlike current policy. Unfortunately, any such model depends heavily on judgment, and there is no evidence that it is more effective or accurate.

For example, a dynamic scoring model may include econometric model of a transitional phase as the population adapts to the new policy, rather than the so-called static-scoring alternative of standard assumption about behavior of people being immediately and directly sensitive to prices. The outcome of the dynamic analysis is therefore heavily dependent on assumptions about future behaviors and rates of change. The dynamic analysis is potentially more accurate than the alternative, if the econometric model correctly captures how households and firms will react to a policy changes. This has been attacked as assumption-driven compared to static scoring which makes simpler assumptions about behavior change due to the introduction of a new policy.

United States national government, 2015–2018

Using dynamic scoring has been promoted by Republican legislators to argue that supply-side tax policy, for example the Bush tax cuts of 2001 and 2011 GOP Path to Prosperity proposal, return higher benefits in terms of GDP growth and revenue increases than are predicted from static scoring. Some economists argue that their dynamic scoring conclusions are overstated, pointing out that Congressional Budget Office (CBO) practices already include some dynamic scoring elements and that to include more may lead to politicization of the department.