Transcript Slide 1

Thoughts on Uses of NTA
Ronald Lee
January 15, 2009
Berkeley, CA
NTA hands-on workshop
Plan
• I present some ideas
–
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–
Support ratios (fiscal and general)
Fiscal projections (can be stochastic)
Intergenerational accounts (history and projs)
Economic growth and macro impact
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Demand for wealth
Transfers vs assets
Fertility and human capital investment
Simulations/Optimal growth paths
– NTA for subgroups
– Are some policies too generous to elderly?
• General discussion of possibilities, and suggestions for a
single compelling index. Full GA=general bequest?
• Also, hardcopy handout of some notes.
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How Population Aging Will Affect
Government Budgets
The fiscal support ratio for year t is:
Population(x,t) weighted sum of taxes based on fixed
age schedule of taxes, e.g. for 2000
________________________________________
Population(x,t) weighted sum of benefits based on fixed
age schedule of benefits e.g. for 2000
It shows the fiscal effects of changing population
age distribution, e.g. as population ages, given
the current program and price structure.
Fiscal Support Ratio Projections, 2000-2100
Fiscal Support Ratio Projections, 2000-2100
No pressure of population aging on
state and local budgets – education
dominates their budgets.
Fiscal Support Ratio Projections, 2000-2100
Major pressure on Federal budget,
which covers public pensions
(Social Security) and health care
for the elderly (Medicare)
Given current program structure
• Balancing federal budget at end of century
requires
– Cutting benefits by one third or
– Raising taxes by 50%
• As health care costs rise, larger
adjustments will be necessary.
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Dependency and Support
• Concern about pop aging is mostly about old
age dependency.
• Sharpest concerns for age-sensitive public
sector programs
– pensions
– health care
– Long term care
• But should place these in broader context
– Full range of public programs
– Private consumption
• Use shape of estimated profile I just showed.
Support Ratios
• Effective labor is weighted sum of pop using
labor income age profile.
• Effective consumers is similar.
• Ratio of effective labor to effective consumers is
the “Support Ratio”.
• Other things equal, consumption per effective
consumer is proportional to the support ratio.
P  x  yl  x 
Effective Workers

0
Support Ratio 


Effective Consumers  P  x  c  x 

0
1
Support Ratio for China, 1950-2100, Based on UN population
projections and average LDC age profiles from NTA
Effective Producers Per Consumer
Population aging
0.9
0.8
0.7
First Dividend
0.6
0.5
1950
200
7
1970
1990
2010
2030
Year
2050
2070
2090
1
Support Ratios for Five Less Developed Countries, 1950-2100, Based
on UN population projections and average LDC age profiles from NTA
Effective Producers Per Consumer
S. Korea
China
India
0.9
0.8
0.7
Brazil
Niger
0.6
0.5
1950
200
2008
7
1970
1990
2010
2030
Year
2050
2070
2090
1
Support Ratios for Five Less Developed Countries, 1950-2100, Based
on UN population projections and average LDC age profiles from NTA
Effective Producers Per Consumer
S. Korea
China
India
0.9
0.8
0.7
Brazil
Niger
0.6
0.5
1950
2050/08
Rate %/yr
1970
Niger
1.20
0.43
1990
S. Korea
0.78
200
7 -0.59
2010
China
0.86
-0.35
2030
Year
2050
India
1.09
0.22
2070
Brazil
0.96
-0.09
2090
Support Ratios for Five More Developed Countries, 1950-2100, based on UN
long term population projections and the NTA age profile for the US.
Spain
Effective Producers Per Consumer
0.8
US
0.7
Germany
Italy
0.6
Italy,
Low Fert.
Spain,
Low Fert.
Japan
0.5
1950
1970
1990
2010
2030
Year
2050
2070
2090
Support Ratios for Five More Developed Countries, 1950-2100, based on UN
long term population projections and the NTA age profile for the US.
Spain
Effective Producers Per Consumer
0.8
US
0.7
Germany
Italy
0.6
Japan
0.5
1950
1970
2050/08
Rate %/yr
US
0.91
-0.2
1990
Spain
Italy
2030
2050
0.72
0.75
Year
-0.8
-0.7
2010
Japan Germany
2070
2090
0.75
0.81
-0.7
-0.5
Italy,
Low Fert.
Spain,
Low Fert.
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Fiscal Projections for USA
• Support ratios do not take into account
economic change:
– productivity growth,
– price changes for benefits,
– planned changes in programs
• Fiscal projections incorporate all of these,
in addition to population change.
Mechanics of projections
• Projected productivity growth shifts both the tax
profiles and the benefit profiles
– Productivity growth alters public pension cost profiles
in ways that depend on the system’s rules
– Costs of health care benefits are projected separately
to reflect faster growth
• Projected interest rates are used to update
program debt or trust fund values
• Demography, productivity, and interest rates can
all be modeled and projected stochastically,
leading to probabilistic budget projections.
Total govt
expenditures double
relative to GDP
26% rises to 52%
Expenditures on the
elderly triple relative to
GDP, from 8% to 26%
References for this lecture
• Ronald Lee and Ryan Edwards (2002)
"The Fiscal Effects of Population Aging in
the US: Assessing the Uncertainties,"
James M. Poterba, ed., Tax Policy and
Economy v.16 (NBER: MIT Press, 2002)
pp. 141-181.
Stochastic fiscal projections
• Obviously great uncertainty about these
projections.
• Useful to put probability intervals around
them.
• Approaches:
– Time series analysis of inputs: Lee,
Tuljapurkar, Edwards, Anderson, Miller
– Random scenario (Lutz and collaborators)
– Based on UN projections (Miller)
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Generational Accounts: Historical
and Projected Future
• How estimate historical accounts?
– Use historical estimates of population age
distributions.
– Use control totals which are generally much
simpler, e.g. for public education, because
govt programs were limited.
– Make assumptions about the shapes of age
profiles, e.g. for education or pensions it is not
hard.
• In US, have IPUMS back to 1850.
Ref for this section
• Bommier, Antoine, Ronald Lee, Timothy
Miller, and Stephane Zuber (2004) “Who
Wins and Who Loses? Public transfer
accounts for US generations born 18502090,” National Bureau of Economic
Research Working Paper No. 10969,
December 2004
(http://www.nber.org/papers/w10969).
Public Education Benefits Received by Age and
Time (2004 US $) per Native Born Individual
Age
0
1
2
…
109
110
Time (Calendar Year)
1850 1851 …
2004
…
2200
The changing age profiles of taxes and benefits in the US:
1900, 1930 and 2000
1850
0
% per
capita
gdp
80
1930
2000
Calculating NPV for generation
born in year t
• (x,s) = tax paid at age x in year s
• β(x,s) = benefit received at age x in year s
• l(x,t+x) = proportion of births in year t surviving to
age x in year t+x.
• NPV(t)= ∑e-rxl(x,t+x)[β(x,s+x) - (x,s+x)]
• r=.03 in baseline. Also try .02, .05. Also historical,
varying year to year.
• NPV can be calculated from any age; here mostly at
birth, i.e. age 0.
USA and France: A Comparison
15
10
5
0
NPVs for the US
-5
-10
Education
Public Pensions + Health Benefits
-15
Combined
18
50
18
60
18
70
18
80
18
90
19
00
19
10
19
20
19
30
19
40
19
50
19
60
19
70
19
80
19
90
20
00
20
10
20
20
20
30
20
40
20
50
20
60
20
70
20
80
20
90
-20
Year of Birth
15
10
5
0
NPVs for France
-5
Education
-10
Public Pensions + Health Benefits
Combined
-15
18
50
18
60
18
70
18
80
18
90
19
00
19
10
19
20
19
30
19
40
19
50
19
60
19
70
19
80
19
90
20
00
20
10
20
20
20
30
20
40
20
50
20
60
20
70
20
80
20
90
-20
Year of Birth
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Social Security benefits
plus Medicare
Ronald Lee and Tim Miller,
Chapter 7, New Americans
Ronald Lee and Tim Miller,
Chapter 7, New Americans
Ronald Lee and Tim Miller,
Chapter 7, New Americans
Ronald Lee and Tim Miller,
Chapter 7, New Americans
Ronald Lee and Tim Miller,
Chapter 7, New Americans
Ronald Lee and Tim Miller,
Chapter 7, New Americans
Total Fiscal Impact by Age at Arrival and
Educational Status of Immigrants
+$350,000
$0
-$250,000
Ronald Lee and Tim Miller,
Chapter 7, New Americans
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Economic growth and
macroeconomy
• Human capital and fertility change.
• Ramsey type dynamic optimization over
the demographic transition, with NTA type
transfers taken as given. Plan saving and
consumption around this. Use approach
like Auerbach-Kotlikoff Dynamic Fiscal
Policy. Miguel Romero is working on.
• NTA style growth approach
Most important points
• Population aging drives increasing demand for
wealth
• Demand for wealth can be satisfied through
transfer wealth, either public or private, or
through assets.
• Depending on which, population aging will
generate more assets (domestic or abroad), or
more transfer burden.
• But there are also advantages to transfers, so
above is just one element to consider in forming
old age support policies.
Aggregate Life Cycle Deficit for those 65+ as share of Aggregate
Consumption by Population Share 65+. For actual age profiles
compared to Av Age Profile of Four Poorest Countries
0.25
Agg LCD 65+/Agg Cons
0.2
0.15
0.1
0.05
0
0
0.05
0.1
0.15
Proportion of population 65+
0.2
0.25
From "ActVsFixLCD.RDL" in Fixed
vs Actual Oct25 Revised.RDL
Human capital and fertility change
Figure 13. Per Child HK Spending (Public and Private)
vs. Fertility
ln(HK per Child/Av Lab Inc 30-49)
2.00
y = -0.928x + 1.8163
R2 = 0.5984
SI
TWJP
1.50
SE
FR
ES
KR
HUAT
MX
BR
US
FI
DE
1.00
TH
CR
CL
Amount spent on HK is
around 5 or 6 years of
labor income, regardless of
fertility.
UY
PH
CN
0.50
ID
A strong negative relationship,
with a slope close to -1 (loglog).
IN
0.00
KE
-0.50
0.00
0.20
0.40
0.60
0.80
1.00
ln(TFR)
1.20
1.40
1.60
1.80
Production and Human capital
• Human capital (HK)
– Portion of wage, W(t),
workers invest in their
children is inversely related
to their fertility, F(t)
– Human capital of workers
one period later is
– HK(t+1) = h(F(t)) W(t)
• Wage (W)
– Wage is increasing in
human capital
– W(t) = g(HK(t))
Baseline Specifications
HK  t  1 
W t 
12F  t 
W  t    HK  t 
.33
Other sources of variation in
fertility/HK choice
• Pref for HK: Rate of return to HK; survival rates;
consumption value of HK.
• Price of HK due to medical technology,
transportation improvements, etc.
• Price of number: family allowances, fines for
second child, changing access to effective
contraceptives
• Cultural influences on varying share of income
allocated to total HK expenditures and on
number.
Model—basic structure
• Take fertility variations as given, trace out
consequences for HK, w, consumption.
• 3 generations: children, workers, retirees;
usual accounting identities.
• No saving or physical capital.
• HK drives wage growth; wage growth
drives HK growth. (Lee and Mason 2008)
Figure 6. Macro Indicators: Baseline Results
Value (percent of year 0)
Boom
130.0
(demoraphic
dividend)
120.0
110.0
100.0
Support ratio
C/ EA
90.0
Fertility bust, but
80.0 consumption
70.0 remains high
Fertility recovers:
modest effect on C/EA
60.0
0
1
2
3
4
5
6
Period
Bottom line: Low fertility leads to higher consumption.
Human capital investment has moderated
the impact of fertility swings on standards of living.
Figure 6. Macro Indicators: Baseline Results
Value (percent of year 0)
130.0
120.0
110.0
100.0
Support ratio
C/ EA
90.0
80.0
70.0
During first dividend phase, consumption
does not rise as much as support ratio.
60.0
0
2
4 in HK.5
The1 difference
is 3
invested
6
That is why ih Period
later periods, consumption is
proportionately higher than the support
ratio.
Human Capital and fertility
• Lee-Mason paper developing NTA relation between HK
investment per child and Total Fertility Rate in survey
year.
• Cannot establish causality; here just assume that
changing fertility drives changing HK.
• Simple little model makes HK of kid depend on HK of
parent generation.
• Labor income depends on own HK
• Elder generation supported by transfers.
• Simulate out the effect of low fertility on consumption:
does increased HK compensate for fewer workers per
elderly?
• Alexia has developed more sophisticated model building
on this.
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NTA style growth approach
Here take a different approach – no
optimization--emphasizes institutional
setting
• Assume
– share of old age consumption supported by
asset income stays constant over time.
– altruistic sharing maintains the shape of the
cross sectional consumption age profile.
– Demography is known in advance.
• Can solve recursively for unique growth
path and asset holdings.
Two scenarios: high level of transfers to
elderly (65%); or low level (35%)
• Other assumptions
– Productivity growth raises income age profile by 2%
per year.
– Open economy; rate of return on assets is 3%.
• Aggregate saving is calculated to maintain asset
share of old age consumption support.
• Results will be shown relative to a 2% growth
trajectory from prod gr.
Simulated Saving Rate, ASEAN
(S.E. Asian countries), 1950-2050
0.25
Net Saving Rate
0.2
Low IG Transfers
0.15
0.1
0.05
High IG Transfers
0
1940
1960
1980
2000
2020
2040
2060
Simulated Assets/Labor Income,
ASEAN
Assets/Labor Income .
8
6
Ratio of assets to labor
income rises greatly in any
case, but 3 or 4 times as
much with low IG transfers.
Low IG Transfers
4
High IG Transfers
2
0
1940
1960
1980
2000
2020
2040
2060
Consumption Index (1950=100) .
Simulated Consumption, ASEAN
160
Low IG Transfers
140
High IG Transfers
120
100
With low IG transfers, saving is
higher from 1990 to 2020,
reducing consumption.
80
60
1940
Thereafter, it is higher.
1960
1980
2000
2020
2040
2060
These sorts of results are qualitatively like
those from optimization approaches
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•
Timing of swings differs
Level of savings rates differs
Capital/labor income ratios differ
Big picture is the same:
1. The demographic transition leads to a major increase
in capital per worker.
2. The greater the role of transfers to the elderly, the
smaller is the increase in capital intensity.
3. Eventually consumption rises with lower transfers, but
initially it is lower.
4. Population aging leads to a decline in savings rates
but an increase in capital intensity.
Other ideas
• NTA for subgroups by race/ethnicity,
educational attainment, or other. Discuss.
• If elderly receive public transfers and then
make large net private transfers to others,
is this a problem? Or is this good?
A single compelling index
• One possibility is B, the total bequest to a birth.
• B is the sum of net public and gross private
downward transfers per child,
B = OC + HK + AIV + EOL
• This is a longitudinal net present value at birth
measure, which might be constructed from the
cross-sectional accounts under appropriate
assumptions. Compare to NPV of life time
earnings, e.g.
• Eventually perhaps it can be calculated
longitudinally as we did for the main public
transfers in the US and in France
B in the US for generation born in
2000 (rough, many assumps)
• Use cross-section for 2000.
• Assume transfers rise at 1.5% per year
(prod gr)
• Discount at 3%
• Adjust public transfers for future budget
balance (50-50 cut taxes, cut benefits)
• Assume public debt = public capital
Value of B for US newborns in 2000
(NPV at birth)
• NPV of Public Transfers (Pub Ed, Soc Sec and
Medicare only) assuming budget is balanced 5050 by cutting taxes and benefits:+47K
(assumes govt debt = value of pub capital)
– Note key importance of public education!
• NPV of Intervivos familial transfers received
including consumption: 220K
• Private end of life bequests:
27K
• Total:
294K
B in context: US 2000
• Relative to NPV of child’s life time
earnings = 34%
• Health and Education as a share of total
bequest = 33%
• Private as a share of B = 84%
Some questions for future work
• How has B changed over 20th Cent?
– Has it fallen with rise of transfers to elderly in
second half of century?
– Private transfers have surely fluctuated along
with fertility, lower for baby boom gens, higher
after.
• Compare similar calculations across
countries.
P is sum of gross upward transfers to adults,
mainly to elderly (double counting with B, so
caution needed)
• Components of P
– Familial transfers to elderly
– Public transfers including
END