Off-Model Budget Estimates

This post is part of a broader series explaining the modeling assumptions underlying our estimates. See here for other posts on this topic.

Our main methodological approach for estimating the budgetary impacts of a policy change is microsimulation. These models, which involve projecting the micro-level attributes of the relevant population and then applying policy rules to simulated individuals, are ideal for estimating the impacts of a because they allow for a ground-up calculation of budgetary effects and the rules can be parameterized. Microsimulation models are best suited for topics such as individual tax or Social Security benefits – policy areas with formulaic tax/benefit rules and existing sources of relevant microdata.

However, not all proposals fit easily into this framework. Some policy areas lack the requisite data to build a microsimulation model, either because the proposal would introduce an entirely new program (for example, a financial transactions tax) or because there simply is no publicly available micro-level data (for example, corporate taxes). Other reform proposals, such as providing additional funding to the IRS or instituting a carbon tax, operate at a more aggregated level than is required for microsimulation.

In these cases, to produce budget estimates, we build “off-model” estimates: standalone models that are separate from our core suite of microsimulation models. These models are often based on aggregate data and involve topic-specific behavioral feedback assumptions. Below, we describe our methods to produce off-model estimates for different topic areas.

Financial Transactions Tax

To estimate the revenue effects of a financial transactions tax (FTT), we begin by estimating the historical relationship between financial transactions and corporate net income. We then project future financial transactions using this relationship and our projections of corporate net income, which are based on the Congression Budget Office’s (CBO) Long-Term Budget Outlook (LTBO). To estimate the behavioral response to the FTT, we apply an elasticity of transaction volume with respect to the tax rate of –0.3.¹ The change in tax rate for this elasticity application is calculated as the percent change from current-law Securities and Exchange Commission fees. FTT liability is not currently included in our distribution estimates.

Carbon Tax

To estimate the revenue effects of a carbon tax proposal, we begin with a projection of domestic carbon emissions from the U.S. Energy Information Administration’s (EIA) Energy Outlook. These projections are broken out by fuel type over a 25-year outlook. We then impute the sector-level composition of carbon emissions based on data from the Environmental Protection Agency (EPA). This breakdown allows us to estimate the effects of proposals which apply different tax rates to the emissions of different sectors. To project how carbon emissions would change in response to taxes, we draw on elasticities presented in the IMF-World Bank Climate Policy Assessment Tool (CPAT) working paper.² The elasticities presented are own price elasticities of demand by sector, good, and country. It is a compilation of over 400 studies and 2,000 elasticities. We do not currently include the carbon tax in our distributional estimates.

IRS Funding

To estimate the revenue effects of additional IRS funding, we begin with estimates of the return on investment (ROI) for audits, infrastructure (like computer systems), and enforcement from the economics literature.³ For each category, we assume that the peak ROI will not be reached until the IRS can fuly utilize the funds allocated to that category. Once the peak ROI is reached, the ROI is assumed to deteriorate over time as the investments decay and audits get more complicated. Our peak ROI values are 7.1 for audits, 3.7 for infrastructure, and 5.2 for enforcement. These calculations alone yield revenue estimates similar to CBO’s initial estimates for the IRS funding provision contained in the Inflation Reduction Act (IRA). However, in addition to its direct impacts on tax collections, IRS funding may also raise revenue indirectly by increasing the rate of voluntary compliance. We estimate this “deterrence” effect as having an ROI of 2.5 and the indirect effect of modernizing service and information technology functions as having an ROI of 2.