How do dynamic scoring rules change projected revenue from tariffs compared with conventional budget scoring?
Executive summary
Dynamic scoring alters tariff revenue projections by folding macroeconomic feedback—growth effects, trade-volume responses, retaliation and higher interest costs—into estimates that conventional (static) scoring treats as fixed; for proposed U.S. tariffs that typically means lower revenue than conventional scores, often materially so but rarely overturning the sign of the conventional result [1] [2]. Empirical and model-based work suggests the typical reduction in projected tariff collections falls in the single-digit to low‑double‑digit percentage range (commonly cited around 10–20 percent) but results are highly model‑ and assumption‑dependent [3] [4].
1. What the two methods actually measure and why it matters
Conventional budget scoring measures the direct budget effects of a policy while holding aggregate economic growth and macro variables essentially fixed; dynamic scoring first produces a conventional estimate and then uses macroeconomic models to project how the policy changes GDP, investment, interest rates and labor supply, and translates those feedbacks into modified revenue and outlay paths [1] [2]. This matters for tariffs because tariffs simultaneously raise import prices (raising customs receipts per unit) and shrink import volumes, change domestic production and consumption patterns, invite retaliation, and can alter aggregate demand—channels that only dynamic analysis attempts to capture [3] [5].
2. How dynamic scoring typically shifts projected tariff revenue
Multiple analysts find that dynamic adjustments generally reduce projected tariff revenue relative to conventional scoring because higher tariffs depress trade volumes and can slow growth, shrinking the tax base; the Committee for a Responsible Federal Budget (CRFB) estimates dynamic effects might cut conventional tariff revenue estimates by about one‑tenth overall, and in some scenarios by nearer one‑fifth for a modest 10 percent tariff [3]. Academic and agency reviews show the same qualitative pattern: macro feedbacks often offset some or all of the static revenue gains from tariffs, producing smaller dynamic revenue totals than static ones, though the magnitude varies by model and assumptions about retaliation and interest‑rate effects [4] [6].
3. The complicating role of retaliation and interest‑rate “crowding out”
Dynamic scores must confront two offsetting mechanisms: retaliation by trading partners that reduces export demand and thus GDP and taxable income, and increased federal borrowing—if tariffs are used to finance other spending—that can raise interest rates and crowd out private investment, also reducing growth and increasing debt service costs [3] [6]. CBO/JCT dynamic work on tax legislation shows these second‑round effects can be large enough to offset or even reverse short‑run gains; analysts of tariff packages warn that inclusion of retaliation and higher borrowing can materially widen the gap between static and dynamic estimates [2] [6].
4. What the institutional record and model literature say about scale and uncertainty
Congressional practice now requires dynamic estimates for “major” legislation, but agencies and academic centers emphasize high uncertainty and model sensitivity: some JCT/CBO exercises find only modest dynamic feedback in the budget window, while other modeling regimes produce larger swings—especially once long‑run supply effects or broad trade wars are allowed [7] [4]. The CRFB and Yale Budget Lab work present concrete numeric contrasts—conventional tallies of trillions in tariff revenue over a decade can shrink appreciably when dynamic trade and macro adjustments are included, and different teams produce ranges rather than single‑point offsets [3] [5].
5. Bottom line: dynamic scoring makes tariff revenue smaller and more uncertain, not magically larger
In short, applying dynamic scoring to tariff proposals typically reduces estimated revenue versus conventional scoring—commonly by modest percentages (order 10–20 percent in many published exercises) but with possibilities for larger swings depending on retaliation assumptions, model structure and fiscal offsetting policies; the result is both smaller projected receipts and much wider confidence intervals than static estimates imply [3] [4] [2]. Because many behavioral responses are already treated by conventional models to some degree, dynamic scoring refines rather than overturns basic fiscal accounting, but it exposes the sensitivity of tariff revenue claims to macroeconomic feedback and political reactions [1] [8].