Macroeconomic Consequences
of the Demographic Transition
Ronald Lee
UC Berkeley
July 9, 2008
Talk prepared for Rand Summer Institute
Research supported by NIA R37 AG025247
Thanks to Andy mason and NTA country teams
Data from
Plan
•
•
•
•
Demographic transition
Dependency ratios and support ratios
Savings rates and capital intensification
Human capital
Data from
I. The Demographic Transition
• A classic illustration: The transition in
India, 1890-2100.
• Mixture of historical estimates, UN
projections, and simulation based on fitted
variations with time.
Data from
Data from
Pre fertility
decline; child
dependency ratio
rises
During fertility decline, child
dependency ratio declines
Population
aging: old age
dep ratio rises
Data from
The total dep ratio rises,
falls, then rises again,
ending up where it started.
The changes in the total
dependency ratio are transitory.
Data from
But there is a big permanent
change:
At start, many children and
few elderly.
At end, few children and
Many elderly.
Data from
Comments on simulation
• Assumed TFR stabilized at 2.1; but often has
declined below replacement.
• Assumed e0 stopped rising at 80, but many
countries already above this.
• Some countries experienced important baby
booms and busts which distort this classic
shape.
• Many countries now have declining populations
and declining working age pops.
Data from
II. The economic life cycle:
• Age profiles of consumption and labor
income
• Use estimates from the National Transfer
Accounts project, or NTA.
• Consumption patterns are quite similar for
Third World countries in Asia and Latin
America.
• Consumption in Industrial populations looks
different.
Data from
Per Capita Consumption and
Labor Income
A Typical Asian Economic Lifecycle:
National Transfer Accounts estimates for Taiwan, 1998
Includes self
employment,
wages,unpaid
family labor, &
fringe benefits.
Averages 0’s
and both male
and female.
600
500
400
Labor Income
Consumption
300
200
100
0
0
20
40
60
Age
Data from An-Chi Tung
Includes both
private expends
and in-kind public
transfers (health,
education, long
term care)
80
Per Capita Consumption and
Labor Income
A Typical Asian Economic Lifecycle:
National Transfer Accounts estimates for Taiwan, 1998
600
Labor Income
500
Flat cons age profile in adult
years reflects extended
family sharing.
Quite different than most
industrial nations.
400
Consumption
300
200
100
0
0
20
40
60
Age
Data from An-Chi Tung
80
Per Capita Consumption and
Labor Income
A Typical Asian Economic Lifecycle:
National Transfer Accounts estimates for Taiwan, 1998
600
Labor Income
500
400
Consumption
300
200
100
0
0
20
Large deficits
40 and
at young
old ages.Age
Data from An-Chi Tung
60
80
A Typical Asian Economic Lifecycle:
National Transfer Accounts estimates for Taiwan, 1998
Per Capita Consumption and Labor
Income
600
500
Reallocations from
surplus to deficit
400 ages required.
Consumption
300
200
Labor Income
100
0
0
20
40
60
Age
Data from An-Chi Tung
80
A Typical Asian Economic Lifecycle:
National Transfer Accounts estimates for Taiwan, 1998
Other income comes from assets, foreign loans, and
remittances from abroad—its not all labor income.
Per Capita Consumption and Labor
Income
600
500
400
Consumption
300
200
Labor Income
100
0
0
20
40
60
Age
Data from An-Chi Tung
80
Per Capita Consumption and
Labor Income
A Typical Asian Economic Lifecycle:
National Transfer Accounts estimates for Taiwan, 1998
600
Labor Income
Asset income is partic
Impt for old age
500
400
Consumption
300
200
100
0
0
20
40
60
Age
Data from An-Chi Tung
80
Components of US Consumption, 2003
Unlike Taiwan and other Third World,
in US cons rises strongly with age.
True in other industrial too.
Dollars (US, 2000)
40000
Public Health
Private Edu
Public Edu
Private Health
Private Durables
20000
Later I will measure HK investment
Private
Other
As sum of pub
and priv
spending
on hlth and educ as shown here.
Public Other
0
0
10
20
30
40
Age
Data from
50
60
70
80
90
• Levels of age profiles change fast with
economic development.
• Shapes of age profiles change slowly,
• Are broadly similar across countries at
very different levels of development.
III. 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.
Proportionate Changes in the Support Ratio from 2007 to
2050 for Selected MDC and LDC
Proportionate Changes in the Support Ratio from 2007 to 2050 for Selected MDC and LDC
Proportionate Change in Support Ratio from 2007 to 2050
0.3
0.2
0.1
0
US
Spain
Italy
Japan
Germany
Niger
-0.1
-0.2
-0.3
-0.4
Country
S. Korea
China
India
Brazil
Not written in stone. Many policy
possibilities:
• Change the age profile of labor income
–
–
–
–
Later retirement
Earlier entry into labor force
Higher female labor force participation
Reform seniority system
• Change the age profile of consumption
– In many industrial nations, the elderly consume much
more than younger adults.
– Makes population aging more costly
– Role of public transfer policy: pensions, health care,
long term care
• Change the demographic trends: immig, fert
IV. Further on Interage Flows of
Income
• Comparison of Japan and Indonesia
Per capita
consumption or labor
income
Per capita consumption and labor income by
age for Indonesia and Japan
1,000,000
• Differences in
consumption
Indonesia,
2002
800,000
600,000
400,000
200,000
0
20
40
60
80
100
Per capita consumption
or labor income in Yen
Age
– Education in Japan
– Rising consumption
in old age in Japan
500,000
Japan, 2004
400,000
300,000
200,000
100,000
0
20
40
60
80
100
Age
Data from Maliki (Indonesia) and
H. Ogawa (Japan)
Aggregate Life Cycle Deficit for Indonesia (2005) in Rupiah
Aggregated Consumption Labor Income
40,000
30,000
20,000
10,000
-
0
20
40
60
80
100
(10,000)
(20,000)
(30,000)
Age
Aggregate Life Cycle Deficit for Japan (2004) in Yen
Aggregated Consumption - Labor
Income
5,000
4,000
3,000
2,000
1,000
(1,000)
0
20
40
60
80
100
(2,000)
(3,000)
(4,000)
(5,000)
Age
Data from Maliki (Indonesia) and
H. Ogawa (Japan)
Aggregate Life Cycle Deficit for Indonesia (2005) in Rupiah
Aggregated Consumption Labor Income
40,000
30,000
20,000
10,000
-
0
20
40
60
80
100
(10,000)
(20,000)
(30,000)
Age
Aggregate Life Cycle Deficit for Japan (2004) in Yen
Aggregated Consumption - Labor
Income
5,000
4,000
3,000
2,000
1,000
(1,000)
0
20
40
60
80
100
• Green arrows show
transfers from surplus
of prime working
years.
• Red arrows show
asset income
consumed by elderly
out of earlier savings.
(2,000)
(3,000)
(4,000)
(5,000)
Age
Data from Maliki (Indonesia) and
H. Ogawa (Japan)



100
Population weighted
average age
Ac
Per capita
consumption or labor
income
Ac=30 Ayl=39
Indonesia,
2002
1,000,000
800,000
600,000
400,000
200,000
0
20
40
60
80
100
Per capita consumption
or labor income in Yen
Age
Ac=45 Ayl=45
500,000
Japan, 2004
400,000
xPop  x c  x 
0
100
0
Pop  x c  x 
• In Indonesia, average unit
of income is earned at 39
and consumed at 30
• Travels 9 years down the
age scale.
• In Japan, it is earned and
consumed at nearly the
same age.
300,000
200,000
100,000
0
20
40
60
80
100
Age
Data from Maliki (Indonesia) and
H. Ogawa (Japan)
Average Consumption-Earning Gap by Average Age of
Population
4
Austria, 2000
2
Slovenia, 2004
0
Japan, 2004
Uruguay, 1994
US, 2003
Av Age Gap
-2
-4
France, 2001
Sweden, 2003
13 yrs
S. Korea, 2000
Costa Rica, 2004
Thailand, 2004Taiwan, 2003
-6
-8
Chile, 1997
Indonesia, 1999
India, 1999
-10
Philippines, 1999
-12
20
25
30
35
Ave Age of Population
Data from NTA Country Teams
40
45
How much of the difference in age
gaps is due to the shapes of the
age profiles?
Data from NTA Country Teams
Average Age of Labor Income
Average Age of Labor Income and Consumption
with Population Held Constant (stationary, e0=75)
46
India, 1999
45
Japan, 2004
US, 2003
Philippines, 1999
Chile,Sweden,
1997 2003
Indonesia, 1999
44
43
Thailand, 2004
42
France, 2001
Taiwan, 2003
Costa Rica, 2004 Uruguay, 1994
S. Korea, 2000
41
Slovenia, 2004
40
Austria, 2000
39
Ac = Ayl
38
38
39
40
41
42
Average Age of Consumption
Data from NTA Country Teams
43
44
45
Average Age of Labor Income
Average Age of Labor Income and Consumption
with Population Age Distr. Constant (stationary,
e0=75)
High cons (health
care) and work
when old.
46
India, 1999
Low age of cons
due to heavy
spending on
education.
45
44
43
Japan, 2004
Philippines, 1999
Chile,Sweden,
1997 2003
Indonesia, 1999
Thailand, 2004
42
France, 2001
Taiwan, 2003
US, 2003
S. Korea, 2000
Costa Rica, 2004 Uruguay, 1994
41
Slovenia, 2004
40
Austria, 2000
39
Generous old
age support.
Ac = Ayl
38
38
39
40
Very young Ayl
due
41 to early
42 start, 43
early retirement.
Average Age of Consumption
Data from NTA Country Teams
44
45
Average Age of Labor Income
Average Age of Labor Income and Consumption
with Population Held Constant (stationary, e0=75)
Difference in average
ages is the distance
above (-) or below (+) the
diagonal.
46
45
India, 1999
Japan, 2004
US, 2003
Philippines, 1999
Chile,Sweden,
1997 2003
Indonesia, 1999
44
43
Thailand, 2004
42
France, 2001
Taiwan, 2003
Costa Rica, 2004 Uruguay, 1994
S. Korea, 2000
41
Slovenia, 2004
40
Austria, 2000
39
Ac = Ayl
38
38
39
40
41
42
Average Age of Consumption
Data from NTA Country Teams
43
44
45
Average Age of Labor Income
Average Age of Labor Income and Consumption
with Population Held Constant (stationary, e0=75)
46
India, 1999
45
Japan, 2004
US, 2003
Philippines, 1999
Chile,Sweden,
1997 2003
Indonesia, 1999
44
Indonesia -3.3 yrs
43
Thailand, 2004
42
France, 2001
Taiwan, 2003
Costa Rica, 2004 Uruguay, 1994
S. Korea, 2000
41
Austria +2.2 yrs
Slovenia, 2004
40
Austria, 2000
39
Ac = Ayl
38
38
39
40
41
42
Average Age of Consumption
Data from NTA Country Teams
43
44
45
• total range in age gap was 13 years
• range due to differences in profiles is 5.5
years.
• So both population age distribution and
shapes of age profiles help determine gap.
V. Wealth and the age gap: the
golden rule case
• Demographic and economic steady state
• Saving and capital such as to maximize
per capita consumption.
• r=n+g
Now suppose babies had to go into
debt to feed themselves….
• At the start of life, c(x)>yl(x); dependency.
• Suppose we keep a notional account of debt and
credit over the life cycle, discounted to age 0.
• Credit gained (or lost) at age x is:
e-rx l(x) [yl(x)-c(x)]
where r is interest rate, l(x) is survival from 0 to age
x.
• Cumulated up to age x, we get W(x):
W  x    e l  x   yl  x   c  x  dx
x
0
 rx
• Now find the average level of per capita in the
whole population, call it W
• W = pop(x)*W(x)/totpop

W

0
e l  x W  x  dx
 rx


0
e l  x dx
 rx
The average wealth per capita in the population
may be pos or neg
The Willis result
W = c(Ac – Ay) , where c = per capita cons
– If Ac>Ay then indivs need to hold onto some
output for later consumption, so wealth, W, is
on average positive in the population.
– If Ac<Ay then indivs consume before they
produce, and must go into debt on average,
so W is negative.
• Alternatively: W/c = Ac – Ayl
• So wealth relative to consumption is
roughly proportional to Ac - Ayl
• Given comparative analysis of Ac-Ayl,
suggests that demand for wealth rises
over the demographic transition.
• Why?
– Older people hold more wealth; in old
population, there are more of them.
– Longer life means workers need to
accumulate more wealth for longer old age.
– Lower fertility means adults consume more
and need to save more to maintain in old age.
VI. The role of intergenerational
transfers
• We just considered the wealth needed to
achieve consumption targets.
• Wealth can be held in two forms:
– Transfer wealth (expected future transfers
received minus expected future transfers
made)
– Assets or Capital
NTA data on shares of old age
support from different sources
• Asset income (land, equities, interest, etc.)
• Family transfers (not including bequests at
death)
• Public transfers (Pay As You Go pensions,
health care, and long term care)
• Triangle graph shows shares, not levels,
so must add to 100%.
• Bequests not included; just old age cons.
Old-age Reallocation System, Selected Countries.
Familial transfers equally
important in Thailand, Korea,
and Taiwan (36-40%).
Net public transfers to
elderly are zero in Thailand;
about 25% in Taiwan and
Korea.
100
0
25
Net familial transfers
near zero in US, CR,
and J. Large public
transfers in CR and J
75
Public
transfers (%)
Thailand
US
50
Asset-based
(%)
50
Korea
Taiw an
Costa Rica
Japan
75
25
100
0
100
75
50
Fam ily Transfers (%)
Diagram from Andy Mason
25
0
Old-age Reallocation System, Selected Countries.
Public transfers:
Thailand none,
Japan and Costa
Rica around 70%
US, Korea, Taiwan,
middling
100
0
25
75
Public
transfers (%)
Thailand
US
50
Asset-based
(%)
50
Korea
Taiw an
Costa Rica
Japan
75
25
100
0
100
75
50
Fam ily Transfers (%)
Diagram from Andy Mason
25
0
Old-age Reallocation System, Selected Countries.
100
Reliance on assets :
Japan, Taiwan, C.R.
are low; Thailand
high; US middling
0
25
75
Public
transfers (%)
Thailand
US
50
Asset-based
(%)
50
Korea
Taiw an
Costa Rica
Japan
75
25
100
0
100
75
50
Fam ily Transfers (%)
Diagram from Andy Mason
25
0
VII. Demographic Transition and
Capital Accumulation
• Changing dependency gets most attention
for ec dev and pop aging.
• Changes in capital accumulation may be
more important.
Calculating the demand for wealth and
capital over the demographic transition
• Based on different theoretical models,
approaches.
• Model with Social Planner maximizing
discounted social welfare function with full
foresight.
• Model with individuals saving and
consuming over their life cycles to
maximize their life time utility.
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
From Mason, Lee and Lee (2008)
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
From Mason, Lee and Lee (2008)
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
From Mason, Lee and Lee (2008)
2040
2060
These sorts of results are qualitatively like
those from optimization approaches
•
•
•
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.
VIII. Human capital and the
demographic transition
• Measure public and private expenditures
on health and education at each age.
– Sum these for health ages 0-18
– Sum for education ages 0-26
– Gives synthetic cohort HK investment per
child
• Construct ratio of HK to average yl(x),
ages 30-49.
• Plot log of HK/w against log of TFR.
Figure 1. Per Child HK Spending (Public and Private)
vs. Fertility
ln(HK per Child/Av Lab Inc 30-49)
2.00
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
0.00
0.20
0.40
0.60
0.80
ln(TFR)
Data from NTA country teams
1.00
1.20
1.40
Figure 1. Per Child HK Spending (Public and Private)
vs. Fertility
ln(HK per Child/Av Lab Inc 30-49)
2.00
Twn
1.80
1.60
1.40
Swd
Jpn
Slv
Hng
Aust
Fr
Kor
Brz
US
Mex
Fin
1.20
Thai
1.00
Chl
CR
Urg
0.80
Phil
0.60
Indonesia
0.40
0.20
0.00
0.00
India
0.20
0.40
0.60
0.80
ln(TFR)
Data from NTA country teams
1.00
1.20
1.40
Figure 1. Per Child HK Spending (Public and Private)
vs. Fertility
ln(HK per Child/Av Lab Inc 30-49)
2.00
1.80
1.60
1.40
1.20
1.00
0.80
0.60
y = -1.05*x + 1.92
R2 = 0.62
0.40
0.20
0.00
0.00
0.20
0.40
0.60
0.80
ln(TFR)
Data from NTA country teams
1.00
1.20
1.40
Now calculate total HK spending on
all children
• Multiply TFR times HK per child, and plot
its log against log(TFR).
ln(TFR X Per Child HK Spending/
Av Lab Inc 30-49)
2.50
Figure 5. Total Expenditures Per Woman for All Children's HK vs.
Fertility
for 18 NTA countries (log scale)
2.00
1.50
1.00
Roughly
a horizontal
cloud,
6.8
years of
labor income
are invested
negative.
inperhaps
total HKslightly
on average.
0.50
0.00
0.00
1/12 of lifetime labor income for a
couple.
0.20
0.40
0.60
0.80
ln(TFR)
Data from NTA country teams
1.00
1.20
1.40
Association is non-causal
• We don’t know whether fertility decline causes
rising HK investments per child.
• Desire to make bigger HK investments causes
fertility decline.
• Some other factor like rising income causes both
fertility and HK changes.
• Here is one theory about a causal path from
income growth to other changes. In some
models the HK growth causes income growth.
The standard Quantity-Quality
model
• Assume that the share of total labor
income spent on HK is fixed, consistent
with scatter plot.
• Draw budget constraints for differing levels
of income.
• Quantity and quality interact
multiplicatively in the budget constraint,
both with positive income elasticities for
constant price.
The Standard Model: Rising Income Leads to Choice of Lower
Fertility and Higher HK Investment per Child
Human Capital Investment per
child
7
Yn=6
6
5
4
Yn=4
3
2
1
Yn=1
0
1
2
3
4
5
Number of children
6
7
8
The Standard Model: Rising Income Leads to Choice of Lower
Fertility and Higher HK Investment per Child
Human Capital Investment per
child
7
With same data, plot ln(HK/w) instead
of HK, against ln(TFR) instead of n.
Yn=6
6
B
5
The budget lines collapse onto a
single straight line.
4
Yn=4
3
2
C
1
Yn=1
A
0
1
2
3
4
5
Number of children
6
7
8
Figure: The transformed budget constraint showing
different quantity-quality choices.
d is HK
expenditure ln(d)
expressed in
years of work at
rate w
Ln(pqq/w)
B
D
Quite similar to empirical scatter
Intercept of scatter indicates years of
work expended on HK is 6.8.
Share of lifetime labor income is 1/12.
Ln(n)
Slope (elasticity) = -1
C
A
• So our scatter plot shows a common
transformed budget constraint with
different fertility-HK choices.
• Differing incomes is one possible cause.
• Many others.
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 
12 F  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.
From Lee and Mason (2008)
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.
From Lee and Mason (2008)
Conclusions for changes over the
transition
• Support ratios change over demographic transition;
ending where started, roughly.
– Importance in long view may be exaggerated.
– In shorter view, we are in painful payback phase.
• Bigger effect is on capital intensity
– These raise productivity per worker
– Raise wealth and asset income
• Increased human capital results from low fertility—so
closely related to aging: same cause for both.
– Raises productivity.
• However, increased demand for wealth can be met either
by increased asset holdings or through increased transfer
wealth.
• Major role for policy and institutions at every point;
nothing inevitable.