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The lack of consensus among economists regarding the impact of tax changes upon the economy is a major impediment to the use of dynamic scoring in the budgeting process, as noted in the preceding North Star article. Another problem with dynamic scoring models is that, while they emphasize the potential drag on the economy resulting from tax increases or the potential stimulus resulting from tax reductions, they generally overlook the potential positive effects resulting from public expenditures or the negative effects of spending reductions.

A recent example of this occurred when the Tax Foundation used their dynamic scoring model to assess the impact of Bernie Sanders’ tax plan, which includes tax increases on capital gains and dividends. According to the Tax Foundation model, the Sanders plan would lead to a dramatic drop in GDP. However, this model did not take into account the reduction in medical bills that people would have to pay and the corresponding increase in consumer purchasing that this would enable. When asked about this omission, a Tax Foundation economist told Forbes Magazine, “that’s not their department.”

Sanders Graph

North Star has no position on the merits or lack thereof of Senator Sanders’ tax and healthcare plan. However, it seems reasonable that if analysts measure the drag on the economy resulting from higher taxes, they should also take into account the benefits resulting from the reduction in medical payments coming out of the pockets of consumers. The Tax Foundation dynamic scoring model boils down to two things, according to Forbes: “Tax increases are bad.  Tax cuts are good.” And what about the offsetting benefits of public investments? “Not their department.”

The Tax Foundation’s model is not unique among dynamic scoring models, which generally do not take into account the public benefit and economic stimulus resulting from government spending. At one level, this omission is understandable. As noted in the preceding article, it is extremely difficult to quantify the impact of tax changes upon the economy, with little agreement among economists on how to do so; according to Matt Gardner of the Institute on Taxation & Economic Policy, it is even more difficult to quantify the economic impact of various forms of public expenditures upon the economy in general and tax revenue in particular. Consequently, adding the impact of changes in public expenditures to a dynamic scoring model will magnify the high degree of uncertainty already inherent in these models.

At the federal level, the lack of understanding of the complex interactions resulting from tax changes across multiple sectors of the economy, an even greater lack of understating of the impact of changes in public spending, and a lack of hard data quantifying these relationship makes dynamic scoring highly problematic. At the state level, all of these problems are compounded. At least 21 states have experimented with dynamic scoring since the early 1990s, although nearly all such efforts have been abandoned. According to Governing Magazine,

In the end though, the overwhelming conclusion of staff across the states was that policymakers found dynamic scoring disappointing. Not only did tax cuts not pay for themselves (and no credible model shows that they do), but even very aggressive estimates of dynamic effects were too small and too uncertain to be a source of easy money that could be used to fill a budget gap.

An interesting example of dynamic scoring occurred in Kansas. In 2012, that state enacted major income tax reductions. As reported by Governing, conventional static scoring anticipated an $824 million reduction in state revenue resulting from these tax cuts, while dynamic scoring anticipated a somewhat smaller revenue loss of $714 million. Kansas policymakers ultimately chose to stick with the static scoring estimate, which turned out to be a fortuitous decision. The more pessimistic static model actually understated the revenue loss; had policymakers relied on the more optimistic dynamic model, the resulting size of the budget hole that they would have had to address would have been $110 million greater than it was.

Occasionally there is discussion of dynamic scoring in the Gopher State. Minnesotans should be cautious. While dynamic scoring models may help to educate policymakers about the revenue impact of competing policy options under different scoring assumptions, ultimately dynamic scoring estimates are too uncertain and unreliable to serve as a basis for budget decision. When making decisions on how to balance the state budget, the safer and smarter alternative would be to rely on less flashy but more reliable static scoring.

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