1. Projecting NTAs Tim Miller tmiller@demog.berkeley.edu Third NTA Workshop Honolulu, Hawaii January 20, 2006 Thanks to the United Nations CELADE for support in developing these models.

2. Plans for next year (next 3 years?) Deepen accounting: Details on consumption (capital/current), reallocations (capital/credit/land), and transfers (education, health, other). Develop Wealth Accounts. Project accounts: 2007 to 2100? Widen accounts: Education and Gender Extend accounts: Back to 1776?

3. Three Models 1.Random Country Model (RCM) Population Forecast 2.Forecasting Educational Distribution 3.Forecasting Public Sector Transfers x

4. Random Country Model Forecast: The 4 Steps of the Method 1.Select path of TFR, e0, and NMR by repeated random draws from a set of similar countries. 2.Forecast population based on standard cohort- component method. 3.Repeat steps 1 and 2; typically 1,000 or 10,000 times. 4.Calculate predictive distributions for variables of interest (population size, OADR, e0, etc).

5. Population Forecasts Advantages of Probabilistic Forecast –Does not use expert opinion. –Quantifies our uncertainty about the future. –More “realistic” projections (since allow for variability). Advantages of Scenario Forecast –Uses expert opinion. –What-if scenarios. –Truly unique forecasts without historical precedent.

6. Random Country Model (RCM) Forecast Tells us what the future would look like based on the collective past experience of 192 UN member countries from 1950 to 2005. UN Probabilistic Forecasts assume: countries with similar demographic parameters are being exposed to the same set of unknown social forces which will shape their future demographic trajectories. Alternative Scenario Forecasts: Why this country is unique among UN member states?

7. UN Scenario Forecasts Are Pessimistic about Future Longevity

8. UN Scenario Forecasts Too Certain About Future Migration

9. Random Country Model Forecast for Chile

10. Economic Support Ratio, Chile

11. Random Country Model Forecast for the United States

12. Economic Support Ratio, U.S.

13. Forecasting Educational Distributions The history and future of educational distributions are interesting in their own right as measures of social progress. In addition, these serve as important components of economic growth models and perhaps NTA accounts.

14. Educational Model Uses data from a single census to provide historical record as well as basis for forecasting the future. Easily replicated! Can measure the changing educational distribution of populations throughout the world over a considerable period of time.

15. 3 Box Model Primary Population By Age and Sex Secondary Population By Age and Sex Tertiary Population By Age and Sex Alpha Beta Births Deaths

16. Educational Distribution From Chilean Census 2002

17. Progression to Secondary Level

18. Progression to Tertiary Level

19. Rapid Change in Educational Distribution of Elderly

20. Rapid Change in Educational Distribution of Work Force

21. Forecasting Public Sector Transfers Basic Accounting Method for Aggregate Expenditures: B(t) = sum { b(x,e,t) * p(x,e,t) } where b(x,e,t) = per-capita expenditures by age x, education or other characteristic e, and time t. and p(x,e,t) = population by age, education, and time. ?

22. Simplifying Assumptions Only Age and Time Matter: B(t) = sum { b(x,e,t) * p(x,e,t) }

23. Simplifying Assumptions No change in policy nor behavior. Simple period effects on budget factors: B(t) = sum { b(x,2005)* exp(r*(t-2005)) * p(x,e,t) }

24. Expenditures and Taxes by Age, Chile

25. Expenditures Projected to Grow More Rapidly Than Taxes Due to Population Aging

26. Taxes increase by 30% over the next 45 years due to population aging. The Projected Fiscal Tax Ratio in Chile: 2004 to 2050

27. In Chile, transition to a new joint public-private system offsets projected tax increase. Pension projection due to population aging and policy changes.

28. Projecting Public Transfers Requires expert country-specific knowledge. Like those of NTA teams! As we develop NTAs, we will learn more about public transfers and public/private composition of spending (education, health, etc). How this varies among countries and over time.

29. Future Work Probabilistic Population Forecasts like the Random Country Model for NTA countries. History and forecasts of educational distributions for NTA countries. Implications of “educational dividend.” Revision of a generalized public transfer model: –Assumption of fixed age shape is often wrong. Model should allow for cohort-effects. –Major use of the model: exploring effect of policy changes. –As we develop NTAs, we will learn more about how budget factors differ between countries and over time.