Forecasting Local Revenue - Policy Analysis Chapter 4
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The document discusses MARTA's difficulties in accurately predicting annual revenue and proposes alternatives to address budget deficits. MARTA is the transit system for metropolitan Atlanta, including 38 rail stations, 609 buses, and 48 miles of track. It relies on sales taxes, fares, and other sources for revenue but has faced challenges forecasting revenue. To remedy budget shortfalls, the alternatives examined are enlarging incomes, cutting services, raising fares, and legislative actions like allowing fare increases or route reductions. Criteria for evaluating options are effectiveness, efficiency, equity, and compliance with legislation.
Forecasting Local Revenue - Policy Analysis Chapter 4
- 1. MARTA Presented by: John, Keke & Ken
- 2. 8.Tell the story 1.Define the problem 2.Assemble some evidence 3.Construct the alternatives 4.Select the criteria 5.Project the outcomes 6.Confront the tradeoffs 7.Decide! Policy Analysis Eight-Step Path
- 3. Background MARTA ---The Metropolitan Atlanta Rapid Transit Authority the 9th largest transit system in the nation. include Clayton County C-TRAN, Cobb Commuity Transit, the GRTA Xpress system, Gwinnett County Transit, and a number of local circulator systems 38 rail stations,609 buses and 15 small buses ,48 miles of tracks. Till to FY 2007
- 4. Define the Problem D ifficulties to make an accurate prediction of MARTA’s annual revenue
- 5. Model of Local Revenue Pie Chart Sales Taxes Property Taxes Licenses Fines Charges for Services Others
- 6. Revenue for MARTA Sales Taxes Others Fares
- 8. Assemble Some Evidence Sales Taxes Taxable Sales Employment Per Capita Income Inflation Population Retail Sales
- 9. Construct the Alternatives Single-equation regression models Multiple-equation regression models Causal Forecasting 03 Judgmental Forecasting 01 Causal land and deductive forecasts Observational and inductive forecasts Experiential forecasts Trend F orecasting 02 Time-series data analysis Computer software applications for forecasting Proportionate chandge or averaging techniques
- 10. Select the Criteria Accuracy of the result
- 12. Decision! Average time-series data to develop trends by such techniques as proportionate change Basic averaging-ratio method with exponential smoothing or move to nonlinear regression Extrapolative and causal techniques employ bivariate or multivariate least-squares regression equations
- 13. Question I f the prediction does not meet the actual annual revenue for MARTA, how can we do ?
- 14. Define a New Problem Budget deficits and even the level of carry-over reserves have been insufficient
- 15. Stakeholders Riders 1 2 Appointed MARTA Board 3 Public Secotor Unions 4 Bondholders 5 Bond-rating Agencies
- 16. Assemble Some Evidence Population Statistics Report Riders Geological Distribution Statistics Report Years of MARTA Budgets and Audit Reports Local Economic Development Research Residents Income Survey Other Public Transit Operational Experience Bond Market Trend Survey Legislative Documents Related to Operation of Public Enterprise … …
- 17. Construct the Alternative Enlarge Incomes Cut Services Raise Fares The Metropolitan Atlanta Rapid Transit Authority Act of 1965 allows MARTA to adjust the fares to cover at least 35% operating costs Cut back / Shorten some routes with few riders Postpone purchase of new vehicles Reduce frequency of old device’s update Provide more value-added services Cooperate with other counties or cities
- 18. Select the Criteria Effectiveness Efficiency Equity Legislative
- 19. Project the Outcomes Enlarge Incomes Cut Services Raise Fares Legislative Equity Efficiency Effectiveness
- 20. Confront the Tradeoff Pareto Efficiency Cardol-Hicks Standard
- 21. Decision! Enlarge Incomes Cut Services Raise Fares The Metropolitan Atlanta Rapid Transit Authority Act of 1965 allows MARTA to adjust the fares to cover at least 35% operating costs Cut back / Shorten some routes with few riders Postpone purchase of new vehicles Reduce frequency of old device’s update Provide more value-added services Cooperate with other counties or cities √
- 22. John, Keke and Ken