Transcript Fundamental Principles of Public Finance

Revenue Forecasts,
Revenue Estimates, and
Tax Expenditure Budgets
Troy University
PA6650- Governmental Budgeting
Chapter 13
Three Revenue Prediction Tasks
• Revenue Forecast (or baseline)
• Revenue Estimates (or fiscal notes or
scores)
• Tax Expenditures
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
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
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
Revenue Forecast (baseline)
• UNIVARIATE PROJECTIONS AND
EXTRAPOLATIONS
– Simple plotting
– Moving average
– Other more sophisticated techniques
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
Revenue Forecast (baseline)
• MULTIPLE REGRESSION
– Y is the dependent variable
– Y = aX1 + bX2 + cX3 +….
– Sales tax, personal income tax, inflation rate,
unemployment rate, etc
– Least squares to find a regression line
Revenue Forecast (baseline)
• ECONOMETRIC MODELS
– Simultaneous system of interdependent
equations
• e.g., State income tax and sales tax deductions
• Multiple equations
• Sophisticated
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
Choosing the Method
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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
Forecasts for the Long Term
• Why?
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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
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
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
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