Long-Term Economic Growth and Budget Projections

CRS Report for Congress
Long-Term Economic Growth and
Budget Projections
Updated March 1, 2006
Brian W. Cashell
Specialist in Quantitative Economics
Government and Finance Division

Congressional Research Service ˜ The Library of Congress

Long-Term Economic Growth and Budget Projections
Summary
The federal budget affects, and is affected by, the performance of the economy.
Because of the effects of the economy on the budget, policy decisions regarding the
overall levels of spending and taxes require some idea of how the economy is likely
to perform. For this reason, both the Congressional Budget Office (CBO) and the
Office of Management and Budget (OMB) prepare, twice a year, a set of economic
projections on which to base consideration of budget policy.
Economic growth affects both spending and taxes. Faster economic growth
means higher tax receipts, because of the interaction of higher incomes with
progressive personal income tax rates. Faster economic growth reduces outlays
because the debt, and interest payments on that debt, are smaller than otherwise
would be the case. In the short run, faster economic growth also means lower rates
of unemployment, and thus reduced income support payments.
The long-run growth rate of the economy is primarily determined by the growth
rate of the labor force, and the rate of growth of productivity. Labor force growth can
be estimated from data regarding the age distribution of the population and is fairly
straightforward. Projecting productivity growth is more difficult.
Economists have an incomplete understanding of the factors that contribute to
productivity growth. Past variations in productivity growth have yet to be fully
explained. Many economic models take productivity growth as a given, something
that is determined outside of the models themselves. Often, projections of
productivity are simply extrapolations of recent trends, which may be adjusted to
reflect the relative optimism or pessimism of the forecaster.
This paper examines the accuracy of past forecasts of economic growth by
OMB, CBO, and the Blue Chip Economic Indicators, beginning with 1985. For the
most part, the three forecasts did about equally well. CBO and the Blue Chip
forecasts tended to be slightly pessimistic, on average, but there was no evidence of
a statistically significant bias in any of the forecasts. Neither was there any tendency
for errors to increase the further into the future the forecasts go.
Slower expected economic growth reduces projected revenues and increases
projected outlays. The effect on revenues is substantially larger than the effect on
outlays, but over five years, just 0.1% slower growth increases the cumulative
projected deficits by $38 billion. This report will be updated annually.

Contents
Projecting Long-Run Growth Rates................................1
Measures of Forecast Error......................................2
Budget Implications............................................4
Appendix ........................................................5
List of Tables
Table 1. Summary Measures of Error in Forecasts
of Economic Growth, 1985-2004.................................3
Table 2. Estimated Effect of 0.1% Slower
Economic Growth on the Budget.................................4
Table A1. Forecast and Actual Economic Growth........................5

Long-Term Economic Growth
and Budget Projections
The federal budget affects, and is affected by, the performance of the economy.
Because of the effects of the economy on the budget, policy decisions regarding the
overall levels of spending and taxes require some idea of how the economy is likely
to perform. For this reason, both the Congressional Budget Office (CBO) and the
Office of Management and Budget (OMB) prepare, twice a year, a set of economic
projections on which to base consideration of budget policy.
Economic growth affects both spending and taxes. Slower economic growth
means lower tax receipts, because of both lower incomes and progressive personal
income tax rates. Slower economic growth increases outlays because the debt, and
interest payments on that debt, are larger than otherwise would be the case. In the
short-run, slower economic growth also means higher rates of unemployment, and
thus increased income support payments.
Because the conduct of fiscal policy depends on the outlook for the economy,
these forecasts are important. This report examines the accuracy of both CBO and
OMB’s projections of real economic growth over successive five-year periods,
beginning in 1985. Specifically, the projections are those that are released at the
beginning of each calendar year to coincide with the release of the President’s
proposed budget. Those forecasts are compared to a private forecast, the Blue Chip
Economic Indicators. The Blue Chip forecast is an average of about 50 private
economic forecasts. Because it is a “consensus” forecast, it is a good standard
against which to assess the accuracy of the CBO and OMB forecasts. The Blue Chip
forecasts here were released in March of each of the years examined.
The projections examined here are of year-over-year growth in real economic
output. For the years 1985 through 1991, the projections refer to real gross national
product (GNP). For 1992 and thereafter, they refer to real gross domestic product
(GDP). Measures of both GNP and GDP have been subject to multiple revisions
over the years, which presents the problem of which vintage of data should be used
to assess the accuracy of the forecasts. The actual data used in this analysis are the
most recent available as published by the Bureau of Economic Analysis of the
Department of Commerce.
Projecting Long-Run Growth Rates
As a rule, when OMB and CBO make long-term projections of economic
growth, they do not attempt to predict the ups and downs associated with the business
cycle. If the economy is at full employment at the time of the forecast, then the
projection is based on an estimate of the likely long-term trend rate of economic
growth. This is the rate of growth believed to be consistent with stable inflation and

full employment. If the economy is less than fully employed, then the projection
typically shows the economy gradually achieving full employment and then
continuing at the estimated long-run trend rate of growth.
The long-run growth rate of the economy is primarily determined by the growth
rate of the labor force, and the rate of growth of productivity. Labor force growth can
be estimated from data regarding the age distribution of the population and is fairly
straightforward. Projecting productivity growth is more difficult.
Economists have an incomplete understanding of the factors that contribute to
productivity growth. Past variations in productivity growth have yet to be fully
explained. Many economic models take productivity growth as a given, something
that is determined outside of the models themselves. Often, projections of
productivity are simply extrapolations of recent trends, which may be adjusted to
reflect the relative optimism or pessimism of the forecaster.
Some events that may have both short- and long-term economic consequences
simply can not be predicted, such as the oil shocks of the 1970s, the Asian financial
crisis, terrorism, and the war in Iraq. Thus, while some forecasting error may be
attributable to a less than perfect understanding of the economy, some error will
always be due to the events that can never be fully anticipated.
Measures of Forecast Error
In order to compare the accuracy of the three forecasts over an extended period
of time, three “summary measures of error” are calculated. These measures attempt
to characterize the relative accuracy of the forecasts in an individual statistic. The
three measures are mean error, mean absolute error, and root mean squared error.
Individual errors are calculated by subtracting the actual data from the forecast. A
positive error indicates excessive optimism, and a negative error indicates excessive
pessimism.
Mean error is the simple arithmetic average of all the individual errors. Because
positive and negative errors tend to offset one another, a large mean error could be
indicative of a tendency towards overly optimistic or pessimistic forecasts. Mean
absolute error is the average of the absolute values of each of the individual errors.
Taking the absolute values of the individual errors before averaging them prevents
positive and negative errors from offsetting one another, and indicates how far off a
forecast tended to be, whether it was too high or too low. Root mean squared error
is calculated by taking the square root of the average of the squares of the individual
errors. Squaring the errors prevents positive and negative errors from offsetting one
another. It also places a larger weight on larger errors. This measure helps to
indicate those forecasters who, when wrong, tended to miss by a lot.
Table 1 shows these summary measures of error for each of the three
forecasters. The statistics are broken down by year. Separate error measures are
shown for 1-, 2-, 3-, 4-, and 5-year out forecasts. The “year 1” forecast is for the
calendar year following the year in which the forecast was published. In other words,
for the forecast released in early 1985, “year 1” refers to the forecast for 1986, “year

2” refers to the forecast for 1987, and so on. Year 1 forecasts include those released

as recently as 2004. All forecasts for which actual data are available are included in
the summary measures of error.
Table 1. Summary Measures of Error in Forecasts
of Economic Growth, 1985-2004
Year 1Year 2Year 3Year 4Year 5
Mean error
OMB -0.07 -0.01 -0.05 -0.11 -0.12
CBO -0.22 -0.31 -0.38 -0.43 -0.44
Blue Chip-0.32-0.46-0.35-0.28-0.25
Mean absolute error
OMB 1.20 1.06 1.04 1.13 1.19
CBO 1.21 1.03 1.04 1.16 1.24
Blue Chip1.151.120.991.031.09
Root mean squared error
OMB 1.48 1.37 1.45 1.44 1.50
CBO 1.51 1.28 1.38 1.39 1.52
Blue Chip1.411.391.401.311.41
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The figures indicate that, for the most part, the three forecasts did about equally
well. CBO and the Blue Chip forecasts tended to be slightly pessimistic, on average,
but there was no evidence of a statistically significant bias in any of the forecasts.
Neither was there any tendency for errors to increase the farther into the future the
forecasts go.
Mean absolute errors ranged between 1.03 and 1.24 percentage points.¹ Thus,
when not taking into account whether the forecasts were too high or too low, they
were consistently off by over a percentage point. Interestingly, the mean absolute
errors in the first year were about the same as in the fifth year, for all three
forecasters. The root mean squared errors were larger, but were similar for each of

¹ Mean absolute errors are the averages of the absolute values of the errors. That is, mean
absolute error indicates how far off a forecast tended to be, regardless of whether it was too
optimistic or too pessimistic.

the three forecasters.² Based on these statistics, it would be hard to argue that one of
the forecasters did significantly better than either of the others.
Budget Implications
These errors can translate into substantial effects on projections of the budget.
CBO has published estimates of the effects on projected budget totals of changing the
underlying economic projections. These estimates may also give a rough indication
of how much the actual budget might change if the economy does better, or worse,
than the economic forecast on which the projected budget is based.
Table 2 shows CBO’s most recent estimates of the sensitivity of budget
projections to changes in the underlying economic assumptions. The table shows
how much projected outlays, revenues, and the unified budget surplus would change
if real economic growth were to be 0.1% slower than the current baseline projection.
This assumes a reduction in the trend rate of economic growth and not a cyclical
decline. In other words, it is attributable to slower productivity growth.
Table 2. Estimated Effect of 0.1% Slower
Economic Growth on the Budget
(all figures in billions of dollars)
Total change in
budget surplus
Change inChange in(or reduction in the
Fiscal yearRevenuesOutlaysdeficit)
2006-1a-1
2007-4a-4
2008-6a-7
2009-101-11
2010-132-15
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^Jan^.^2006.
^{a. B}^e^tw^een^-^{$500 m}^illion^an^{d $500 m}^illion^.
Slower projected economic growth reduces projected revenues and increases
projected outlays. The effect on revenues is substantially larger than the effect on
outlays, but over five years, just 0.1% slower growth increases the cumulative
projected budget deficits by $38 billion.

² Root mean squared error emphasizes larger errors. Thus it can be used to identify a
forecast which, when it missed, tended to miss by a lot.

Appendix
The table that follows presents past five-year economic growth projections of
CBO, OMB, and the Blue Chip Economic Indicators, as well as the actual historical
growth rates.
Table A1. Forecast and Actual Economic Growth
(percent change)
1984 forecast of real GNP growth
1985 1986 1987 1988 1989
OMB 4.1 4.0 4.0 4.0 3.9
CBO 4.1 3.5 3.5 3.4 3.3
Blue Chip3.31.73.23.93.5
Actual 3.8 3.2 3.3 4.2 3.5
1985 forecast of real GNP growth
19861987198819891990
OMB 4.0 4.0 4.0 3.9 3.6
CBO 3.2 3.3 3.4 3.4 3.4
Blue Chip2.63.13.73.63.3
Actual 3.2 3.3 4.2 3.5 2.0
1986 forecast of real GNP growth
19871988198919901991
OMB 4.0 4.0 3.9 3.6 3.5
CBO 3.1 3.3 3.5 3.5 3.2
Blue Chip3.13.42.93.13.1
Actual 3.3 4.2 3.5 2.0 -0.3
1987 forecast of real GNP growth
19881989199019911992
OMB 3.5 3.6 3.6 3.5 3.4
CBO 3.0 3.0 3.1 2.7 2.5
Blue Chip3.32.42.52.73.0
Actual 4.2 3.5 2.0 -0.3 3.3
1988 forecast of real GNP growth
19891990199119921993
OMB 3.1 3.5 3.4 3.3 3.2
CBO 2.6 2.6 2.6 2.7 2.7
Blue Chip2.22.02.93.12.8
Actual 3.5 2.0 -0.3 3.3 2.7

1989 forecast of real GNP growth
19901991199219931994
OMB 3.2 3.3 3.2 3.2 3.2
CBO 2.1 2.2 2.2 2.3 2.3
Blue Chip1.72.43.12.92.7
Actual 2.0 -0.3 3.3 2.7 4.0
1990 forecast of real GNP growth
19911992199319941995
OMB 3.2 3.2 3.1 3.1 3.0
CBO 2.4 2.5 2.5 2.5 2.4
Blue Chip2.42.82.72.42.6
Actual -0.3 3.3 2.7 4.0 2.5
1991 forecast of real GNP growth
19921993199419951996
OMB 2.5 3.5 3.3 3.1 3.0
CBO 3.3 2.9 2.8 2.7 2.7
Blue Chip3.12.82.62.22.5
Actual 3.3 2.7 4.0 2.5 3.7
1992 forecast of real GDP growth
19931994199519961997
OMB 3.0 3.0 3.0 2.9 2.8
CBO 3.6 2.7 2.5 2.6 2.6
Blue Chip3.13.02.32.42.2
Actual 2.7 4.0 2.5 3.7 4.5
1993 forecast of real GDP growth
19941995199619971998
OMB 3.0 2.9 2.7 2.4 2.0
CBO 3.0 2.9 2.7 2.4 2.0
Blue Chip3.12.82.62.32.5
Actual 4.0 2.5 3.7 4.5 4.2
1994 forecast of real GDP growth
19951996199719981999
OMB 2.8 2.7 2.6 2.6 2.5
CBO 2.7 2.7 2.7 2.6 2.5
Blue Chip2.82.62.52.42.8
Actual 2.5 3.7 4.5 4.2 4.5
1995 forecast of real GDP growth
1996 1997 1998 1999 2000
OMB 2.5 2.5 2.5 2.5 2.5
CBO 1.8 2.4 2.3 2.3 2.3
Blue Chip2.22.02.32.92.8
Actual 3.7 4.5 4.2 4.5 3.7

1996 forecast of real GDP growth
19971998199920002001
OMB 2.3 2.3 2.3 2.3 2.3
CBO 2.0 2.1 2.2 2.2 2.2
Blue Chip2.11.92.02.42.3
Actual 4.5 4.2 4.5 3.7 0.8
1997 forecast of real GDP growth
19981999200020012002
OMB 2.0 2.2 2.3 2.3 2.3
CBO 2.0 2.2 2.1 2.1 2.1
Blue Chip2.12.12.52.42.3
Actual 4.2 4.5 3.7 0.8 1.6
1998 forecast of real GDP growth
19992000200120022003
OMB 2.0 2.0 2.2 2.4 2.4
CBO 2.0 1.9 2.0 2.1 2.3
Blue Chip2.22.22.22.42.5
Actual 4.5 3.7 0.8 1.6 2.7
1999 forecast of real GDP growth
20002001200220032004
OMB 2.0 2.0 2.2 2.4 2.4
CBO 1.7 2.2 2.4 2.4 2.4
Blue Chip2.22.32.52.52.6
Actual 3.7 0.8 1.6 2.7 4.2
2000 forecast of real GDP growth
2001 2002 2003 2004 2005
OMB 2.7 2.5 2.5 2.8 3.0
CBO 3.1 2.8 2.6 2.6 2.7
Blue Chip3.12.82.83.33.3
Actual 0.8 1.6 2.7 4.2 3.5
2001 forecast of real GDP growth
2002 2003 2004 2005 2006
OMB 3.3 3.2 3.2 3.1 3.1
CBO 3.4 3.3 3.0 3.0 3.0
Blue Chip3.43.53.43.43.4
Actual1.62.74.23.5 —
2002 forecast of real GDP growth
2003 2004 2005 2006 2007
OMB 3.8 3.7 3.6 3.2 3.1
CBO 4.1 3.7 3.2 3.2 3.2
Blue Chip3.63.43.33.23.1
Actual2.74.23.5 — —

2003 forecast of real GDP growth
2004 2005 2006 2007 2008
OMB 3.6 3.5 3.3 3.2 3.1
CBO 3.6 3.4 3.3 3.2 3.1
Blue Chip3.43.33.13.13.1
Actual4.23.5 — — —
2004 forecast of real GDP growth
2005 2006 2007 2008 2009
OMB 3.6 3.4 3.3 3.2 3.1
CBO 4.2 3.2 2.7 2.8 2.8
Blue Chip3.83.43.23.13.1
Actual3.5 — — — —
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