The Heritage Foundation

Executive Summary #1544 on Taxes

May 3, 2002

May 3, 2002 | Executive Summary on Taxes

Executive Summary: The Correct Way to Measure the Revenue Impact of Changes in Tax Rates

The debate between static and dynamic scoring may seem an esoteric inside-the-Beltway squabble, but the choice of how to estimate revenues has important implications. In the short term, better revenue estimating methods would make it easier to implement tax rate reductions. In the long term, shifting to a simple and fair tax code would be expedited if revenue estimators were allowed to consider the beneficial impact of tax reform on economic performance.

When lawmakers consider tax policy changes, Congress's Joint Committee on Taxation (JCT) and the Treasury Department's Office of Tax Analysis (OTA) are responsible for estimating the likely impact on future tax collections; but these estimates assume that tax policy changes--regardless of their magnitude--have no impact on the economy's performance. As a result, these "official" estimates commonly overstate both the amount of tax revenue that will be generated by tax increases and the amount of revenue the government will "lose" due to tax rate reductions. This static methodology has been widely criticized because it provides policymakers with inaccurate numbers and creates a bias against lower tax rates.

Dynamic analysis--sometimes referred to as reality-based scoring--is based on the commonsense assumption that taxes do affect the economy. Dynamic scoring recognizes, for instance, that higher tax rates discourage work, saving, and investment. Because of these negative "feedback effects," tax rate increases will generate less revenue than predicted by static estimates. Conversely, because lower tax rates increase economic growth and result in more jobs, higher wages, and bigger profits, dynamic scoring will show that certain tax cuts will be at least partially self-financing. This more accurate methodology should be used instead of static scoring.

Because dynamic scoring would make tax rate reductions more attractive, opponents of tax cuts want to maintain the current system of static scoring. An objective examination of the historical evidence, however, demonstrates that dynamic scoring gives policymakers more accurate information.
Dynamic scoring does not predetermine outcomes; it simply ensures that lawmakers will have the most comprehensive data when making decisions. When taking steps to modernize and correct the revenue-estimating process, policymakers should consider the following points:

  • Learn from history. Static scoring routinely overestimates how much revenue will be generated by tax increases. The 1990 luxury tax, the income tax rate increases of 1990 and 1993, and the 1986 capital gains tax rate increase are all examples in which revenues fell far short of static predictions. Conversely, the 1981 Reagan tax cuts, the 1978 capital gains tax reduction, the Kennedy tax cuts of the 1960s, the 1986 Tax Reform Act, and the 1997 capital gains tax cut all demonstrate how pro-growth tax changes generate revenue feedback.
  • Don't make the perfect the enemy of the good. It is impossible to predict all the effects of any single change in government policy. The fact that dynamic scoring cannot pinpoint all the multiyear effects of a change in tax policy, however, is not an argument for maintaining a static process that guarantees an answer that is wrong and farther from the truth.
  • Not all tax cuts are created equal. The higher the tax rate, the bigger the supply-side response when the rate is reduced. Likewise, since capital is more mobile than labor, reducing tax rates on capital will have a greater impact than similar tax reductions on labor income. And some tax cuts, such as credits and rebates, will have little or no revenue feedback effects since incentives to engage in productive behavior remain unchanged.
  • Open the process to public scrutiny. Even though they are the ones who pay the bills, taxpayers today are not allowed to examine the static models and methodology used by the JCT and OTA. Even if the revenue-estimating process is not improved, policymakers should insist on full disclosure. If policymakers adopt dynamic scoring, an open process will keep the system honest by inhibiting those who are tempted to overstate or understate the dynamic impact of tax policy changes.
  • The goal of tax policy is to maximize economic growth, not tax revenues. For years, budget deficits and surpluses have played a big role in the political debate. As a result, some tax policy proposals, such as reductions in the capital gains tax, are judged primarily by their effect on tax collections. Yet there is no evidence that fiscal balance has any impact on the economy. Putting revenue maximization ahead of sound tax policy is therefore a misguided approach and should be discarded.
  • Include estimates of private and governmental compliance costs. According to the Tax Foundation, the current tax system imposes $194 billion in compliance costs on the productive sector of the economy. In addition to these costs to the private sector for lawyers, lobbyists, accountants, tax preparers, and lost man-hours, approximately $13 billion in direct government expenditures is associated with taxation. Yet in calculating projected gains and losses, revenue estimators confess that "staff does not estimate the administrative costs incurred by either the IRS or taxpayers that may result from proposed legislation."

To make America's economy more competitive and to boost the economy's performance, tax policy will have to change. In the short term, immediate tax rate reductions are needed to boost growth; in the long term, the entire tax code should be replaced by a simple, flat tax. But these pro-growth changes will be harder to achieve if revenue estimators continue to use outdated and inaccurate static models. Dynamic revenue estimates would provide policymakers with more accurate information. Dynamic forecasting is based on a proper understanding of how the economy works, and history has shown this approach to be far more realistic and accurate than static estimates.

Daniel J. Mitchell, Ph.D., is McKenna Senior Fellow in Political Economy in the Thomas A. Roe Institute for Economic Policy Studies at The Heritage Foundation.

About the Author

Daniel J. Mitchell, Ph.D. McKenna Senior Fellow in Political Economy