Population Forecasting for Fiscal Planning:
Issues and Innovations
This abstract consists of a concise list of conclusions from the
analysis in this paper.
A. Assessing Recent Official US Vital Rate Forecasts
- In retrospect, it appears that over the last fifty years, the Census
and Social Security forecasters attached too much importance to the most
recently observed levels of fertility and mortality.
- Recent Census Bureau projections of US fertility, based on race/ethnic
disaggregation, appear to be too high. Recent Social Security fertility
projections appear reasonable, although the range may be too narrow in
light of international experience.
- Recent projections of life expectancy gains by both Census and Social
Security appear to be substantially too low, in light of past US
experience and international levels and trends in low mortality
B. Uncertainty in Population Forecasts
- The standard method for dealing with uncertainty in demographic (and
many other) forecasts is the use of high, medium and low scenarios. This
approach is deeply flawed, because it is based on very strong and
implausible assumptions about the correlation of forecast errors over
time, and between fertility and mortality. The random scenario method is
an improvement, but it retains some of the same flaws.
- Stochastic population forecasts based on time series models of vital
rates (Lee-Tuljapurkar) appear to offer some important advantages,
although long forecast horizons in demography far exceed the intended
use of these models, and it is necessary to impose external constraints
on the models in some cases to obtain plausible forecast behavior. One
should not rely on mechanical time series forecasts in any case; they
should be assessed in relation to external information.
- A parsimonious time series model for mortality appears to perform
well within sample in applications in various countries, and suggests
future life expectancy gains in the US at roughly twice the rate
projected by Census and Social Security.
C. Results of Population Forecasts
- Middle forecasts by Census, Social Security, and Lee-Tuljapurkar (LT)
agree closely on the timing and extent of increase in old age dependency
ratios as the baby boom ages (although LT are somewhat higher due to
lower mortality), but Census shows some amelioration after 2040, due to
higher fertility. After 2040, LT forecasts continue to increase,
doubling by 2070 to .45, while Social Security forecasts increase
to .41. The Social Security range is three times as wide as that of
Census, reflecting inherent flaws in the scenario method.
- Middle forecasts of the Total Dependency Ratio by Census and LT agree
fairly closely , but are somewhat higher than Social Security (LT is .88
in 2070; Social Security is .83). The Social Security range is extremely
narrow, reflecting inherent flaws in the scenario method, but the Census
range is far too narrow as well.
- In Social Security forecasts, the correlation between errors in
forecasting Youth Dependency Ratios and Old Age Dependency Ratios is
close to –1.0. In Census forecasts, it is moderately positive. These
correlations result from the bundling of assumptions in scenarios. LT
forecasts show a correlation of -.6 to -.4, indicating partially
offsetting variations in the proportions of children and elderly, as one
D. Stochastic fiscal projections:
- We analyze the performance of Social Security projections of cost
rates since 1950, for forecast horizons of up to 35 years. Performance
was generally very good, with no systematic bias, small average errors,
and root mean squared errors smaller than the published high-low ranges.
Projections done from the mid-70s to the mid ‘80s have under-projected
costs by 12%, however.
- Middle LT forecasts suggest that government expenditures on the
elderly will increase by over 150% in relation to GDP by 2070, while
expenditures on children and age neutral expenditures will remain flat.
Taxes rise from 24% now to 38% of GDP in the median forecast to 2070 (if
debt/GDP is constrained), while the 95% probability range for taxes in
2070 goes from 25% to 53% of GDP.
- Increased costs of OASDI account for nearly 30% of the increase in
expenditures on the elderly, but a larger share, 57%, is due to health
costs in the median forecast. Fixing Social Security will not take care
of the long term budget problem.
- Investing 90% of the Social Security reserve fund in equities yielding
7% (real) would fix the system according to a deterministic simulation,
but in a stochastic forecast there is still a two thirds chance of
exhaustion, with a median exhaustion date of 2044, and a negative median
(but strongly positive mean) Fund balance in 2050.
- Raising the payroll tax rate by 2% immediately should nearly put the
system in long term actuarial balance according to Social Security
projections, but still leaves a 75% chance of fund exhaustion before
2070 in LT stochastic forecasts.
- Raising the normal retirement age to 71 by 2023 raises the median
long term actuarial balance above 0 in LT stochastic forecasts, but
still leaves a 43% chance of fund exhaustion before 2070.
Demography and Economics
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