data from macroeconomic consequences of the demographic transition ronald lee uc berkeley july 9,...
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Data from
Macroeconomic Consequences of the Demographic Transition
Ronald LeeUC BerkeleyJuly 9, 2008
Talk prepared for Rand Summer InstituteResearch 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 permanentchange: 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 An-Chi Tung
A Typical Asian Economic Lifecycle: National Transfer Accounts estimates for Taiwan, 1998
0
100
200
300
400
500
600
0 20 40 60 80
Age
Per
Cap
ita
Co
nsu
mp
tio
n a
nd
L
abo
r In
com
e
Consumption
Labor Income
Includes both private expends and in-kind public transfers (health, education, long term care)
Includes self employment, wages,unpaid family labor, & fringe benefits.Averages 0’s and both male and female.
Data from An-Chi Tung
A Typical Asian Economic Lifecycle: National Transfer Accounts estimates for Taiwan, 1998
0
100
200
300
400
500
600
0 20 40 60 80
Age
Per
Cap
ita
Co
nsu
mp
tio
n a
nd
L
abo
r In
com
e
Consumption
Labor Income
Flat cons age profile in adult years reflects extended family sharing.
Quite different than most industrial nations.
Data from An-Chi Tung
A Typical Asian Economic Lifecycle: National Transfer Accounts estimates for Taiwan, 1998
0
100
200
300
400
500
600
0 20 40 60 80
Age
Per
Cap
ita
Co
nsu
mp
tio
n a
nd
L
abo
r In
com
e
Consumption
Labor Income
Large deficits at young and old ages.
Data from An-Chi Tung
A Typical Asian Economic Lifecycle: National Transfer Accounts estimates for Taiwan, 1998
0
100
200
300
400
500
600
0 20 40 60 80
Age
Per
Cap
ita C
on
su
mp
tio
n a
nd
Lab
or
Inco
me Consumption
Labor Income
Reallocations from surplus to deficitages required.
Data from An-Chi Tung
A Typical Asian Economic Lifecycle: National Transfer Accounts estimates for Taiwan, 1998
0
100
200
300
400
500
600
0 20 40 60 80
Age
Per
Cap
ita C
on
su
mp
tio
n a
nd
Lab
or
Inco
me Consumption
Labor Income
Other income comes from assets, foreign loans, and remittances from abroad—its not all labor income.
Data from An-Chi Tung
A Typical Asian Economic Lifecycle: National Transfer Accounts estimates for Taiwan, 1998
0
100
200
300
400
500
600
0 20 40 60 80
Age
Per
Cap
ita
Co
nsu
mp
tio
n a
nd
L
abo
r In
com
e
Consumption
Labor Income
Asset income is particImpt for old age
Data from
Components of US Consumption, 2003
Public Other
Private Other
Private DurablesPrivate Health
Private Edu
Public Edu
Public Health
0
20000
40000
0 10 20 30 40 50 60 70 80 90
Age
Do
llar
s (U
S,
2000
)
Later I will measure HK investmentAs sum of pub and priv spendingon hlth and educ as shown here.
Unlike Taiwan and other Third World, in US cons rises strongly with age. True in other industrial too.
Data from
• 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.
Data from
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.
Data from
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.
0
0
Effective WorkersSupport Ratio
Effective ConsumerslP x y x
P x c x
Data from
Support Ratio for China, 1950-2100, Based on UN population projections and average LDC age profiles from NTA
0.5
0.6
0.7
0.8
0.9
1
1950 1970 1990 2010 2030 2050 2070 2090
Year
Eff
ecti
ve
Pro
du
cers
Per
Co
nsu
mer
2007
Population aging
First Dividend
Data from
Support Ratios for Five Less Developed Countries, 1950-2100, Based on UN population projections and average LDC age profiles from NTA
India
Brazil Niger
ChinaS. Korea
0.5
0.6
0.7
0.8
0.9
1
1950 1970 1990 2010 2030 2050 2070 2090
Year
Eff
ecti
ve
Pro
du
cers
Per
Co
nsu
mer
20072008
Data from
Support Ratios for Five Less Developed Countries, 1950-2100, Based on UN population projections and average LDC age profiles from NTA
India
Brazil Niger
ChinaS. Korea
0.5
0.6
0.7
0.8
0.9
1
1950 1970 1990 2010 2030 2050 2070 2090
Year
Eff
ecti
ve
Pro
du
cers
Per
Co
nsu
mer
2007
Niger S. Korea China India Brazil2050/08 1.20 0.78 0.86 1.09 0.96
Rate %/yr 0.43 -0.59 -0.35 0.22 -0.09
Data from
Spain
Italy
US
Japan
Germany
Spain, Low Fert.
Italy, Low Fert.
0.5
0.6
0.7
0.8
1950 1970 1990 2010 2030 2050 2070 2090
Year
Eff
ecti
ve P
rod
uce
rs P
er C
on
sum
erSupport Ratios for Five More Developed Countries, 1950-2100, based on UN long term population projections and the NTA age profile for the US.
Data from
Spain
Italy
US
Japan
Germany
Spain, Low Fert.
Italy, Low Fert.
0.5
0.6
0.7
0.8
1950 1970 1990 2010 2030 2050 2070 2090
Year
Eff
ecti
ve P
rod
uce
rs P
er C
on
sum
erSupport Ratios for Five More Developed Countries, 1950-2100, based on UN long term population projections and the NTA age profile for the US.
US Spain Italy Japan Germany2050/08 0.91 0.72 0.75 0.75 0.81
Rate %/yr -0.2 -0.8 -0.7 -0.7 -0.5
Data from
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
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
US Spain Italy Japan Germany Niger S. Korea China India Brazil
Country
Pro
po
rtio
nat
e C
han
ge
in S
up
po
rt R
atio
fro
m 2
007
to 2
050
Data from
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
Data from
IV. Further on Interage Flows of Income
• Comparison of Japan and Indonesia
Data from Maliki (Indonesia) and H. Ogawa (Japan)
Per capita consumption and labor income by age for Indonesia and Japan
-
200,000
400,000
600,000
800,000
1,000,000
0 20 40 60 80 100
Age
Per c
apita
co
nsum
ptio
n or
labo
r in
com
e
-
100,000
200,000
300,000
400,000
500,000
0 20 40 60 80 100
Age
Per
cap
ita c
onsu
mpt
ion
or la
bor
inco
me
in Y
en
Indonesia, 2002
Japan, 2004
• Differences in consumption– Education in Japan– Rising consumption
in old age in Japan
Data from Maliki (Indonesia) and H. Ogawa (Japan)
Aggregate Life Cycle Deficit for Indonesia (2005) in Rupiah
(30,000)
(20,000)
(10,000)
-
10,000
20,000
30,000
40,000
0 20 40 60 80 100
Age
Ag
gre
gat
ed C
on
sum
pti
on
-
Lab
or
Inco
me
Aggregate Life Cycle Deficit for Japan (2004) in Yen
(5,000)
(4,000)
(3,000)
(2,000)
(1,000)
-
1,000
2,000
3,000
4,000
5,000
0 20 40 60 80 100
Age
Ag
gre
ga
ted
Co
ns
um
pti
on
- L
ab
or
Inc
om
e
Data from Maliki (Indonesia) and H. Ogawa (Japan)
Aggregate Life Cycle Deficit for Indonesia (2005) in Rupiah
(30,000)
(20,000)
(10,000)
-
10,000
20,000
30,000
40,000
0 20 40 60 80 100
Age
Ag
gre
gat
ed C
on
sum
pti
on
-
Lab
or
Inco
me
Aggregate Life Cycle Deficit for Japan (2004) in Yen
(5,000)
(4,000)
(3,000)
(2,000)
(1,000)
-
1,000
2,000
3,000
4,000
5,000
0 20 40 60 80 100
Age
Ag
gre
ga
ted
Co
ns
um
pti
on
- L
ab
or
Inc
om
e
• Green arrows show transfers from surplus of prime working years.
• Red arrows show asset income consumed by elderly out of earlier savings.
Data from Maliki (Indonesia) and H. Ogawa (Japan)
-
200,000
400,000
600,000
800,000
1,000,000
0 20 40 60 80 100
Age
Per c
apita
co
nsum
ptio
n or
labo
r in
com
e
-
100,000
200,000
300,000
400,000
500,000
0 20 40 60 80 100
Age
Per c
apita
con
sum
ptio
n or
labo
r inc
ome
in Y
en
Indonesia, 2002
Japan, 2004
• 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.
Ac=30 Ayl=39
Ac=45 Ayl=45
100
0100
0
c
xPop x c xA
Pop x c x
Population weighted average age
Data from NTA Country Teams
Average Consumption-Earning Gap by Average Age of Population
Austria, 2000
Chile, 1997
Costa Rica, 2004
France, 2001
India, 1999Indonesia, 1999
Japan, 2004
Philippines, 1999
US, 2003
Uruguay, 1994Sweden, 2003
Thailand, 2004
Slovenia, 2004
Taiwan, 2003S. Korea, 2000
-12
-10
-8
-6
-4
-2
0
2
4
20 25 30 35 40 45Ave Age of Population
Av
Ag
e G
ap
13 yrs
Data from NTA Country Teams
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 and Consumption with Population Held Constant (stationary, e0=75)
S. Korea, 2000Taiwan, 2003
Slovenia, 2004
Thailand, 2004
Sweden, 2003
Uruguay, 1994
US, 2003
Philippines, 1999Japan, 2004
Indonesia, 1999
India, 1999
France, 2001Costa Rica, 2004
Chile, 1997
Austria, 2000
38
39
40
41
42
43
44
45
46
38 39 40 41 42 43 44 45
Average Age of Consumption
Ave
rage
Age
of L
abor
Inco
me
Ac = Ayl
Data from NTA Country Teams
Average Age of Labor Income and Consumption with Population Age Distr. Constant (stationary,
e0=75)
S. Korea, 2000Taiwan, 2003
Slovenia, 2004
Thailand, 2004
Sweden, 2003
Uruguay, 1994
US, 2003
Philippines, 1999Japan, 2004
Indonesia, 1999
India, 1999
France, 2001Costa Rica, 2004
Chile, 1997
Austria, 2000
38
39
40
41
42
43
44
45
46
38 39 40 41 42 43 44 45
Average Age of Consumption
Ave
rage
Age
of L
abor
Inco
me
Ac = Ayl
Low age of cons due to heavy spending on education.
High cons (health care) and work when old.
Generous old age support.
Very young Ayl
due to early start, early retirement.
Low age of cons due to heavy spending on education.
Low age of cons due to heavy spending on education.
Low age of cons due to heavy spending on education.
Low age of cons due to heavy spending on education.
Low age of cons due to heavy spending on education.
Data from NTA Country Teams
Average Age of Labor Income and Consumption with Population Held Constant (stationary, e0=75)
S. Korea, 2000Taiwan, 2003
Slovenia, 2004
Thailand, 2004
Sweden, 2003
Uruguay, 1994
US, 2003
Philippines, 1999Japan, 2004
Indonesia, 1999
India, 1999
France, 2001Costa Rica, 2004
Chile, 1997
Austria, 2000
38
39
40
41
42
43
44
45
46
38 39 40 41 42 43 44 45
Average Age of Consumption
Ave
rage
Age
of L
abor
Inco
me
Ac = Ayl
Difference in average ages is the distance above (-) or below (+) the diagonal.
Data from NTA Country Teams
Average Age of Labor Income and Consumption with Population Held Constant (stationary, e0=75)
S. Korea, 2000Taiwan, 2003
Slovenia, 2004
Thailand, 2004
Sweden, 2003
Uruguay, 1994
US, 2003
Philippines, 1999Japan, 2004
Indonesia, 1999
India, 1999
France, 2001Costa Rica, 2004
Chile, 1997
Austria, 2000
38
39
40
41
42
43
44
45
46
38 39 40 41 42 43 44 45
Average Age of Consumption
Ave
rage
Age
of L
abor
Inco
me
Ac = Ayl
Indonesia -3.3 yrs
Austria +2.2 yrs
Data from
• 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.
Data from
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
Data from
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):
0
x rxlW x e l x y x c x dx
Data from
The average wealth per capita in the population may be pos or neg
• Now find the average level of per capita in the whole population, call it W
• W = pop(x)*W(x)/totpop
0
0
rx
rx
e l x W x dxW
e l x dx
Data from
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
Data from
• 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.
Data from
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
Data from
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.
Diagram from Andy Mason
Old-age Reallocation System, Selected Countries.
0
50
75
100
75
75
50
50
25
25
0
0
25
100
100
Asset-based (%)
Public transfers (%)
Family Transfers (%)
US
Thailand
Costa Rica
JapanTaiw an
Korea
Familial transfers equally important in Thailand, Korea, and Taiwan (36-
40%). Net familial
transfers near zero in US, CR, and J. Large
public transfers in CR and J
Net public transfers to elderly are zero in
Thailand; about 25% in Taiwan and Korea.
Diagram from Andy Mason
Old-age Reallocation System, Selected Countries.
0
50
75
100
75
75
50
50
25
25
0
0
25
100
100
Asset-based (%)
Public transfers (%)
Family Transfers (%)
US
Thailand
Costa Rica
JapanTaiw an
Korea
Public transfers: Thailand none,
Japan and Costa Rica around 70%
US, Korea, Taiwan, middling
Diagram from Andy Mason
Old-age Reallocation System, Selected Countries.
0
50
75
100
75
75
50
50
25
25
0
0
25
100
100
Asset-based (%)
Public transfers (%)
Family Transfers (%)
US
Thailand
Costa Rica
JapanTaiw an
Korea
Reliance on assets : Japan, Taiwan, C.R.
are low; Thailand high; US middling
Data from
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.
Data from
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.
Data from
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.
Data from
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.
From Mason, Lee and Lee (2008)
0
0.05
0.1
0.15
0.2
0.25
1940 1960 1980 2000 2020 2040 2060
Net
Savin
g R
ate
High IG Transfers
Low IG Transfers
Simulated Saving Rate, ASEAN (S.E. Asian countries), 1950-2050
From Mason, Lee and Lee (2008)
Simulated Assets/Labor Income, ASEAN
0
2
4
6
8
1940 1960 1980 2000 2020 2040 2060
Ass
ets/
Labo
r In
com
e .
Low IG Transfers
High IG Transfers
Ratio of assets to labor income rises greatly in any case, but 3 or 4 times as much with low IG transfers.
From Mason, Lee and Lee (2008)
Simulated Consumption, ASEAN
60
80
100
120
140
160
1940 1960 1980 2000 2020 2040 2060
Co
nsu
mp
tio
n In
dex
(19
50=
100)
.
Low IG Transfers
High IG Transfers
With low IG transfers, saving is higher from 1990 to 2020, reducing consumption.
Thereafter, it is higher.
Data from
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.
Data from
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.
Data from NTA country teams
Figure 1. Per Child HK Spending (Public and Private) vs. Fertility
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
ln(TFR)
ln(H
K p
er C
hild
/Av
Lab
Inc
30-
49)
Data from NTA country teams
Figure 1. Per Child HK Spending (Public and Private) vs. Fertility
AustBrz
Chl
CR
Fin
FrHng
India
Indonesia
Jpn
Mex
Phil
Slv
Kor
SwdTwn
Thai
Urg
US
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
ln(TFR)
ln(H
K p
er C
hild
/Av
Lab
Inc
30-
49)
Data from NTA country teams
Figure 1. Per Child HK Spending (Public and Private) vs. Fertility
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
ln(TFR)
ln(H
K p
er C
hild
/Av
Lab
Inc
30-
49)
y = -1.05*x + 1.92R2 = 0.62
Data from
Now calculate total HK spending on all children
• Multiply TFR times HK per child, and plot its log against log(TFR).
Data from NTA country teams
Figure 5. Total Expenditures Per Woman for All Children's HK vs. Fertility
for 18 NTA countries (log scale)
0.00
0.50
1.00
1.50
2.00
2.50
0.00 0.20 0.40 0.60 0.80 1.00 1.20 1.40
ln(TFR)
ln(T
FR
X P
er C
hil
d H
K S
pen
din
g/
Av
Lab
In
c 3
0-49
)
Roughly a horizontal cloud, perhaps slightly negative.
6.8 years of labor income are invested in total HK on average.
1/12 of lifetime labor income for a couple.
Data from
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.
Data from
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.
Data from
The Standard Model: Rising Income Leads to Choice of Lower Fertility and Higher HK Investment per Child
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8
Number of children
Hu
man
Cap
ital
In
vest
men
t p
er
chil
d
Yn=1
Yn=6
Yn=4
Data from
The Standard Model: Rising Income Leads to Choice of Lower Fertility and Higher HK Investment per Child
0
1
2
3
4
5
6
7
1 2 3 4 5 6 7 8
Number of children
Hu
man
Cap
ital
In
vest
men
t p
er
chil
d
Yn=1
Yn=6
Yn=4
B
A
C
With same data, plot ln(HK/w) instead of HK, against ln(TFR) instead of n.
The budget lines collapse onto a single straight line.
Data from
Ln(n)
Ln(pqq/w)
A
C
B
D
ln(d)
Figure: The transformed budget constraint showing different quantity-quality choices.
Slope (elasticity) = -1
d is HK expenditure expressed in years of work at rate w
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.
Data from
• So our scatter plot shows a common transformed budget constraint with different fertility-HK choices.
• Differing incomes is one possible cause.
• Many others.
Data from
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))
112
W tHK t
F t
.33W t HK t
Baseline Specifications
Data from
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.
Data from
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)
From Lee and Mason (2008)
Figure 6. Macro Indicators: Baseline Results
60.0
70.0
80.0
90.0
100.0
110.0
120.0
130.0
0 1 2 3 4 5 6
Period
Va
lue
(p
erc
en
t o
f y
ea
r 0
)
Support ratio
C/ EA
Boom (demoraphic
dividend)
Fertility bust, but consumption remains high
Fertility recovers: modest effect on C/EA
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
60.0
70.0
80.0
90.0
100.0
110.0
120.0
130.0
0 1 2 3 4 5 6
Period
Va
lue
(p
erc
en
t o
f y
ea
r 0
)
Support ratio
C/ EA
During first dividend phase, consumption does not rise as much as support ratio.
The difference is invested in HK.
That is why ih later periods, consumption is proportionately higher than the support ratio.
Data from
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.