Testimony before Committee on Ways and Means
United States House of Representatives
September 21, 2011
My name is William Beach. I am the Director of the Center for Data Analysis at The Heritage Foundation. The views I express in this testimony are my own, and should not be construed as representing any official position of The Heritage Foundation.
It is difficult to find any serious economist who would argue that the federal government’s tax and spending policies make no difference to U.S. economic performance. In this age of massive and growing federal debt, it is even more difficult to find a politically engaged citizen who fails to see the connection between federal fiscal policy and economic performance.
Indeed, all across the political spectrum and throughout the leading schools of economic thought, a broad consensus exists that what governments do with tax dollars and how they raise those revenues matters in the larger, dynamic, economic world.
Thus, it is crucial that economic models that organize complex theory and data be available and used by policymakers to chart the most beneficial course for the country given the policy options available to us. Some observers, however, warn policymakers away from the use of economic models, even to analyze the likely outcomes of policy change. Certainly economic models used to make decisions can be faulty, wrongly constructed, and manipulated by individuals with their own political or financial agenda; but the usefulness of this policy tool far outweighs these known disadvantages. Indeed, the absence of dynamic economic analysis in major policy debates should be enough to stop such a debate until it is informed by such analysis.
Why? Let me offer three reasons.
First, only the most trivial policy decisions likely escape the requirement that policymakers weigh the alternatives of various courses of action. The central mission of a policymaker is to design a path to a certain policy goal without wasting the public’s resources. Almost by definition, this design exercise reduces itself to a series of mental experiments supported by data and projections about the costs and benefits of competing paths to the same goal.
It should be readily apparent that urging an evaluation of competing paths hardly needs to be argued; and, for most decisions by Members of Congress, such a careful evaluation is made. For most: but not for all. The practice of including projections of how policy change and economic activity interact is not employed routinely in evaluating competing policy paths. The tools for doing so exist in relative abundance; we can find them in the offices of the Joint Committee on Taxation, at the Congressional Budget Office, at the Energy Information Agency, and at Treasury.
However, their use has not been instituted formally into the policy evaluation process. While critics of these models raise concerns about model accuracy and the time it takes to create economic estimates, the most likely reason for their disuse stems from how few tax and regulatory increases would be enacted if Members were required to publicly debate the economic as well as the fiscal costs of policy changes.
Second, evaluating the economic effects from policy change absolutely matters to policy decisions. The ability of governments to extract resources from the private sector, either through taxes, spending, or regulations, alters the path of private output. That change need not be negative, since the provision of public goods usually affects the growth path positively. Most change, however, moves the economy below its potential, largely because not all private resources used by the public sector go for the creation of goods and services that complement private output. The point is that the path is changed.
Changes to economic activity directly affect the base for revenues and the costs of public debt, among other effects. Given such direct influences on fundamental parts of government finance, alternative paths to the same policy goal will produce different “feedbacks” from the private economy. Each feedback may affect the efficiency of attaining a policy end. All of this means that decision making must include the evaluation of how the economy is affected by policy change.
Third and finally, model builders bear a large responsibility to make substantial improvements to the accuracy, suitability, and cycle time of their policy models. Not all of the stories about the dangers of dynamic models are baseless. Models that rely solely on historical data can produce false signals about how policy change will likely affect future economic behavior. Likewise, models that assume that the economy settles down to a stable growth path even after accounting for behavioral changes can deceive policymakers into thinking that policy change will likewise always produce the same, stable policy results.
Model builders, however, are acutely aware of these design and theoretical issues. Today’s economic policy models carefully sort through the fundamental requirement that behavioral changes be prominent drivers to economic estimates. Likewise, today’s complex and nuanced economic models nevertheless perform with the speed that policymakers require. It is unacceptable to deliver estimates of how policy changes will likely affect future economic activity after the policy change has been adopted. Given the suite of models currently available, this “cycle time” concern of policymakers can be laid firmly to rest. 
To be frank, there is also the view that dynamic scoring and analysis is a part of the legislative process advanced by advocates of tax reductions and limited government. This misimpression has done much to keep this useful tool out of policymakers’ hands. Let me illustrate.
Heritage used a model of the U.S. economy in 2007 to estimate the economic effects of a relatively modest tax bill by Congressman Charles Rangel when he chaired the Ways and Means Committee. Chairman Rangel’s reduction of the corporate income tax rate from a top rate of 35 percent to 33 percent drew Heritage’s praise, and our model indicated that this rate reduction alone would support the creation of as many as an additional 220,000 jobs. Other provisions of Chairman Rangel’s plan, however, neutralized this good effect.
It may surprise some on this Committee to learn that Heritage’s Center for Data Analysis has published the only dynamic analysis and score of the justly famous tax reform proposal by Senators Ron Wyden (D–OR) and Dan Coats (R–IN), which previously was co-sponsored by Senator Judd Gregg (R–NH). Our analysis showed policymakers that this bipartisan reform effort could mean that:
- The federal deficit would be an average of $61 billion (nominal) lower per year;
- The nation’s debt-to-GDP ratio would be 3.9 percentage points lower by the end of 2020, indicating a significant reduction in publicly held debt;
- An average family of four would have about $4,095 more disposable income every year;
- Foreign investment in the U.S. would be an average of $292 billion (nominal) higher each year, and U.S. multinational corporations would repatriate and invest an average of $19 billion (nominal) more in the U.S. per year;
- 2.3 million more jobs would be created on average each year;
- The aggregate net wealth (assets minus liabilities) of U.S. households would be $643 billion higher by the end of 2020; and
- Real GDP would be an average of $298 billion higher per year.
No one knows, of course, what policymakers will do, even when they possess the very best analytical tools. This we do know, however: The standard, conventional, or static tax models that are used today by the official revenue estimators in Congress’s Joint Committee on Taxation (JCT) and the Congressional Budget Office are highly inaccurate because they do not include the economic effects of tax policy changes. It is this record of inaccuracy and, thus, bad policy advice that has fueled the interest in dynamic analysis and scoring.
In the real world, we know that businesses and consumers will respond to both tax cuts and tax hikes, and they do so in fairly predictable ways. Tax cuts often spur investment, which spurs hiring and increases payroll taxes—and they lead to a positive feedback effect for government treasuries. Yet it is exactly this kind of feedback effect that static analyses miss.
It happened in the early 1960s, when President Kennedy’s plan to reduce the top marginal tax rate from 91 percent to 70 percent took effect. Total tax revenues actually climbed 4 percent, despite predictions that the cuts would plunge the country deeply into debt. It happened again when President Reagan reduced the top rate from 70 to 50 percent in 1981. Economists employing the static models now in use at key government agencies predicted federal revenues would fall by $330 billion over 5 years. Instead, they fell by $79 billion, and the economy boomed.
Even more interesting is the recent revenue growth from capital gains. The JCT forecast revenue declines following the 2003 tax rate reduction. That is exactly what many in official Washington expected, too. However, revenues from capital gains taxes exploded.
In these cases, taxpayers got higher post-tax incomes, expanded economic opportunities, and better financial security. The government got a fast-growing economy, more people working, more taxable earnings per worker and, thus, more revenue than “static” estimates had predicted.
Advocates of dynamic scoring must be careful not to oversell its capabilities or benefits. There are legitimate disagreements about which economic model best captures the economic effects of tax policy changes. There is also little reason to believe that tax cuts, even the best ones, will pay for themselves right away through super-nova revenue reflows from a stronger economy. Finally, the technical difficulties of economic modeling mean that this technique should be reserved for only the most important tax issues.
Even so, we get better, more transparent government by encouraging the introduction of more economics into the evaluation of tax policy choices and the occasional use of dynamic scoring models to advise policymakers on the really big tax bills. Better government and better tax policy is, I believe, a winning combination of benefits that assures the widespread adoption of dynamic analysis and scoring.