1. Revenue Forecasts, Revenue Estimates, and Tax Expenditure Budgets Troy University PA6650- Governmental Budgeting Chapter 13

2. Three Revenue Prediction Tasks Revenue Forecast (or baseline) Revenue Estimates (or fiscal notes or scores) Tax Expenditures

3. Revenue Forecast (baseline) Forecast of what revenue will be collected in the budget period under the current las Office of Tax Analysis in the Department of the Treasury (for OMB) Congressional Budget Office (for Congress) Forecasts can be very objective or very subjective depending on technique

4. Revenue Forecast (baseline) Forecaster should understand the tax, how it is administered, and collection procedures Should be plotted on a graph against time Openness is a virtue Approach depends on the task to be served Individual revenue sources should be forecast separately Revenues need to be monitored and checked against the forecast

5. Revenue Forecast (baseline) Different Approaches –Extrapolation or projections Simple, low cost, moving average –Deterministic modeling –Multiple Regression dependent/independent multiple variables –Econometric Models Set of interdependent equations –Microdata models Small sample from taxpayer data files

6. Revenue Forecast (baseline) UNIVARIATE PROJECTIONS AND EXTRAPOLATIONS –Simple plotting –Moving average –Other more sophisticated techniques

7. Revenue Forecast (baseline) DETERMINISTIC MODELING –Pre-established formula (rule of thumb) e.g., link between GDP and personal income tax or VAT to GDP One variable may help predict the other

8. Revenue Forecast (baseline) MULTIPLE REGRESSION –Y is the dependent variable –Y = aX 1 + bX 2 + cX 3 +…. –Sales tax, personal income tax, inflation rate, unemployment rate, etc –Least squares to find a regression line

9. Revenue Forecast (baseline) ECONOMETRIC MODELS –Simultaneous system of interdependent equations e.g., State income tax and sales tax deductions Multiple equations Sophisticated

10. Revenue Forecast (baseline) MICRODATA MODELS –Taken from a sample of taxpayers –10-year baseline of tax records –Must be careful of tax and policy changes

11. Choosing the Method Resources available Materiality of the forecast Availability of historic revenue data Availability and probable quality of causal data Time period of the forecast Explainability of the forecast Format A look back, the future environment, the approach, the forecast

12. Forecasts for the Long Term Why? –To guide a city on a major infrastructure program –To show a credit rating agency long term health –To let planners know implications of a specific project –To inform the public when on the brink of a financial disaster –To educate the legislature on pending legislation Medium term forecasts are 3-5 outyears Wrong forecasts sometimes happen

13. Revenue Estimating Aka “scoring” (US) and “tax costing” (UK) Difference between receipts under current law and receipts under a proposed change in the law (what will the fiscal impact be?) Static component –Taxpayers that will not behave differently Dynamic component –Macro- and microeconomic analysis of behavioral change when tax law changes

14. Tax Expenditure Budgets Revenue losses attributable to provisions of the federal tax laws which allow a special exclusion, exemption, or deduction from gross income or which provide a special credit, a preferential rate of tax, or a deferral of tax liability Compares a normal state with no exclusions to how much you are losing from exclusions Benchmark and deviations from the norm Page 534 Table 13-2

15. Conclusion Revenue prediction has 3 divisions –Forecasting collections in future years –Estimating the impact of proposed changes in tax laws –Calculating revenues currently sacrificed by existing tax law Mixture of art and science