Comprehensive Analysis of MARA Model and Future Economic Trends

This content provides an overview of the MARA Model, focusing on identifying revenue challenges, exploring long-term expenditures, creating data sets for analysis, and proposing potential solutions. It also discusses the impact of technology on the economy, demographic trends, energy economy changes, and more. The 2040 MARA Model offers insights into future scenarios, while the Outlook compares different time frames and levels of detail in forecasting economic trends. Additionally, it outlines fiscal principles for lawmakers when constructing long-term budgets.

• MARA Model
• Economic Trends
• Future Insights
• Revenue Challenges
• Fiscal Principles

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1. MARA Model INVENTION WITH INTENTION TIMING

2. HB 330 MARA Charge (a) identifying structural revenue challenges with economic, demographic, and geographical variability considerations; (b) exploring revenue sufficiency and probable long-term expenditures by state and local government... (c) creating data sets and models for future analysis by the legislature; and (d) proposing potential solutions and possible legislation for consideration by the 2023 legislature

3. Progress to Date Speakers on various topics How technology will impact the future economy Population and demographic trends supported by data Potential changes to the energy economy supported by data Change in education, health, land use, property tax, and other topics Cost of living trends including housing and childcare Data on various topics were presented Model planning has been underway, but not brought to the committee till now

4. 2040 MARA MODEL IS Tool to provide insights to the future IS NOT Not a precise calculation of revenues or costs in the future Pinch points Not tell the legislature what it should do Values in context Example how big of a problem shortfalls in gas tax be in comparison to shortfall in capacity at the state prison Not a budget

5. Outlook versus MARA Model OUTLOOK 2040 MARA MODEL 3-5 years out 18 years out Relatively detailed Less detail but covers all state, local & schools Focused on state general fund Considers more global trends Focused on present law, but also introduces expenditure pressures Considers items beyond present law in order to capture demands anticipated with the new economy and other trends Primarily internal LFD work, with relatively little outside input Looped through various committees and stakeholders for input

6. Outlook 2025/2027 is more detailed The recent Pew memo offers state lawmakers six fiscal principles to follow when constructing a long- term budget: Analyzing major revenue sources and spending categories. Looking ahead at least three years. Estimating baseline revenue and spending, using present law and current services approaches to anticipate the amount of funding needed to maintain consistent service levels for programs in future years. Accounting for the effects of potential policy changes. Distinguishing between one-time and ongoing revenue and expenditures to estimate structural balance. Identifying the key factors driving the state s structural position.

7. M - Modular include modules of significant revenue and cost drivers of state and local policy and financial areas I - Inclusive open communication with legislative committees, local governments, stakeholders, and experts 2040 MARA Model D - Data-driven forecasting data from State Accounting Budget & Human Resource System (SABHRS) for state and local, IHS Markit, Regional Economic Models Inc. (REMI) population, K-12 education, Department of Revenue, others M I D I L I - Insightful - Uses and considers advances in technology, modern consumer preferences, population L - Long-term outlook looking forward from present to 2040

8. Education Property tax Public Safety 2040 MARA Model Tax and revenue Public Works MODULAR Infra- structure Health

9. INCLUSIVE Vetting all aspects Stakeholders Legislative Committees Agencies Budget Office Local Governments, including schools Experts in complex modeling and experts in analysis Pew Public

10. DATA DRIVEN primary data sets IHS Markit economic forecasting data that includes labor, population, inflation, and many others Accounting data - state SABHRS, local reports to DOA and school reports to OPI Department of Revenue and Justice tax, including detailed property tax data REMI population forecasts by county IHS Markit, NW Power Planning, Princeton Net Zero America for scenarios of the energy economy System Actuarial reports for pension assumptions Census definitions for state, local, and school accounting data Other federal and state data sets

11. INSIGHTFUL The input from all the speakers over the past year Input from all stakeholders, legislators, agencies, experts Develop trended costs and revenues As time allows model more details and scenarios

12. Scenarios impacting global variables Energy economy Population growth Pension Funding IHS Markit NW Power and Conservation Council Power Plan Princeton Net Zero America alternate energy options IHS Markit Alternate IHS Markit REMI Current law Shorter amortization at higher rate, from system actuarial analysis

13. Population by County Global Variables that are used to model the future Market value by class by county Personal Income by county Green are dynamic and changes with various scenarios, these are examples, there will be additional variables Global Variables Gray, automation, is not dynamic and it is uncertain if we will find a way to incorporate this element Per capita personal income by county Pension costs by system Automation Labor/Tech

14. Model Process Modeled Output Scenarios IHS Markit Accounting data Other Population Economy Reference Energy Population Pensions Income tax $ Property tax $ Expenditures $ Global Variables Data

15. LONG-TERM to 2040 Reference Case Revenue and Expenditure Example Reference Case 4 3.5 3 2.5 The future is unknown 2 Reference case and multiple scenarios 1.5 1 0.5 0 2025 2030 2035 2040 Revenue Expenditure

16. More about the model and how it will work NICK VANBROWN LEAD ANALYST - DATA

17. Forecast Modules Modeling Attach to Financial Data

18. Modules

19. Education Property tax Public Safety 2040 MARA Model Tax and revenue Public Works Infrastructure Health Health

20. Medicaid HEALTH EXAMPLE

21. Example: Health Number of Participants in that year Medicaid Cost in a given Year Cost Per Participant in that Year

22. Number of Participants in that year Example: Health Percentage of Population Medicaid Population Groups % Adult Adult % Child Child % Disabled Disabled Over 65 % Over 65 Adult Expansion % Adult Expansion

23. Number of Participants in that year Example: Health Forecasted Population Percentage of Population % Adult Adult % Child Child % Disabled Disabled % Over 65 Over 65 % Adult Expansion Adult Expansion

24. Example: Health Number of Participants in that year

25. Example: Health Number of Participants in that year Medicaid Cost in a given Year Cost Per Participant in that Year

26. Cost Per Participant in that Year Example: Health Medicaid Cost $ Per Adult $ Per Child $ Per Disabled $ Per Over 65 $ Per Adult Expansion

27. Example: Health Number of Participants in that year Medicaid Cost in a given Year Cost Per Participant in that Year

28. Input and Feedback Committees Agencies Education OBPP Property tax Public Safety Stakeholders 2040 MARA Model Contractors Tax and revenue Public Works Infrastructure Health

29. Modules Everything Else Education Public Safety State Data Property tax Personal Services 2040 MARA Model Tax and revenue Public Works Operating Infrastructure Health Equipment & Assets

30. State Data Personal Services Operating Equipment & Assets

31. Forecast Modules Modeling Attach to Financial Data

32. Business Intelligence Software Link Data Sets

33. Business Intelligence Software Link Data Sets Time

34. Business Intelligence Software Link Data Sets Time Location

35. Business Intelligence Software Accounting data from SABHRS Nightly Link Data Sets Time Location Regular Updates IHS Markit economic forecasting data Monthly Department of Revenue Property Tax Data Annually REMI population forecasts by county Biannually

36. Business Intelligence Software Link Data Sets Time Location Regular Updates Visualize Modules Roll Up

37. Business Intelligence Software Link Data Sets Time Location Regular Updates Visualize Modules Roll Up Define Variables Modular Global

38. EXAMPLE Population GLOBAL VARIABLE

41. Demographic Age Change

43. ON Scenarios OFF

44. Forecast Modules Modeling Attach to Financial Data

45. Financial Data Sets 1. State Accounting Data (SABHRS) 2. School Accounting Data (Trustee Reports) 3. Local Government Accounting Data (Financial Reports)

46. STATE ACCOUNTING DATA

47. SABHRS Breakout Example SABHRS Data Medicaid Other Modules All Else

48. SABHRS Breakout Example SABHRS Data Medicaid Other Modules All Else History

49. Pulling it Together Forecast Modules Modeling Financial Forecast History

50. Reference Case Scenarios Discussion In The End

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