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Age structure effects on theeconomy
Thomas LindhResearch Director
and Professor in Economics
Why is age structure one of thedeterminants of the
macroeconomy?1. Biological and economic reasons imply
that human behavior and resourcesvary over the life cycle
2. Age structure change thereforecorrelate with macroeconomic change.
3. Useful because such changes can beindependently predicted for manyyears.
Age distribution of population
SavingsHousing
Stocks Consumption of goodsDemand for services
Children LaborEnterprise
Public expend.Tax base
Interest rates
Imports Exports Investment Wealth Industries Regional development
GDP growth Budget balance
Age structure effects on the economy
Wages Inflation Exchange rates Relative prices
Current account
Age profiles ISource: Malmberg & Lindh, Swedish estimates
• National savings– 50-64 pos– 0-14,75+ neg
• Government budget– 15-29,50-64 pos– 0-14, 65+ neg
• GDP growth– 15-64 pos– 0-14, 65+ neg
G r o w t h
-1 .5
-1
-0 .5
0
0 .5
1
1 .5
0 – 1 4 1 5 -2 9 3 0 -4 9 5 0 -6 4 6 5 -7 4 7 5 +
B u d g e t d e f ic it
-2
-1 .5
-1
-0 .5
0
0 .5
1
1 .5
2
0 – 1 4 1 5 -2 9 3 0 -4 9 5 0 -6 4 6 5 -7 4 7 5 +
N a t io n a l s a v in g r a t e
-2
-1
0
1
2
3
0 – 1 4 1 5 -2 9 3 0 -4 9 5 0 -6 4 6 5 -7 4 7 5 +
Age profiles IISource: Malmberg & Lindh, Swedish estimates
• Investment– 30-74 pos– 0-14, 75+ neg
• Current account– 50-64, 75+ pos– 65-74 neg
• Inflation– 65-74 pos– 50-64,75+ neg
In f la t io n
-3
-2
-1
0
1
2
3
4
5
0 – 1 4 1 5 -2 9 3 0 -4 9 5 0 -6 4 6 5 -7 4 7 5 +
C u r r e n t a c c o u n t
- 2
- 1 .5
- 1
- 0 .5
0
0 .5
1
1 .5
2
0 – 1 4 1 5 - 2 9 3 0 - 4 9 5 0 - 6 4 6 5 - 7 4 7 5 +
I n v e s t m e n t
- 4
- 3
- 2
- 1
0
1
2
3
0 – 1 4 1 5 - 2 9 3 0 - 4 9 5 0 - 6 4 6 5 - 7 4 7 5 +
OECD evidence
• 2007, Lindh & Malmberg “Age structure effectson investment, saving and trade”. Chapter 7 in:Population aging, intergenerational transfersand the macroeconomy, Clark et al. (eds).Edward Elgar Publishing, 163-191.
• 1999, Lindh & Malmberg “Age structure effectsand growth in the OECD, 1950-90” Journal ofPopulation Economics, 12(3), 431-449.
• 1998, Lindh & Malmberg “Age structure andinflation - A Wicksellian interpretation of theOECD data” Journal of Economic Behaviorand Organization, July, 36(1), 19-37.
NIER forecast2007-2009 (Jan -08)
NIER forecast 2006-2009
(Oct -07)
NIER forecasts 2007-2010 (June 08)
0%
1%
2%
3%
4%
5%
1995 2000 2005 2010 2015 2020
GDP growth forecasts Sweden
Global panel modelforecast using data up to1998
Actual GDP growthSwedish timeseries forecastfrom 2003
100
1000
10000
100000
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000
maddison*pwt backcast +95% -95%
Backcast of global GDP per capita modelSweden 1820-1950
Forthcoming, De laCroix et al. Journal ofMacroeconomics
Model in 2007 Lindh & Malmberg, Demographically based globalincome forecasts up to the year 2050. International Journal ofForecasting 23(4), 553-567. http://dx.doi.org/10.1016/j.ijforecast.2007.07.005
What about Germany?
• Report forthcoming financed byBertelsmann Stiftung
• Age structure effects on the Germaneconomy, with an internationalcomparison. Lindh & Malmberg, Institutefor Futures Studies
• Results still preliminary…• …but basically similar
German age structureEast West
1 000 000 800 000 600 000 400 000 200 000 0 200 000 400 000 600 000 800 000 1 000 000
Unter 5
5 - 10
10 - 15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50
50 - 55
55 - 60
60 - 65
65 - 70
70 - 75
75 - 80
80 - 85
85 - 90
90 und älter oder 90-95
95+
males females
3 000 000 2 000 000 1 000 000 0 1 000 000 2 000 000 3 000 000
Unter 5
5 - 10
10 - 15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50
50 - 55
55 - 60
60 - 65
65 - 70
70 - 75
75 - 80
80 - 85
85 - 90
90 und älter oder 90-95
95+
males females
800 000 600 000 400 000 200 000 0 200 000 400 000 600 000 800 000
Unter 5
5 - 10
10 - 15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50
50 - 55
55 - 60
60 - 65
65 - 70
70 - 75
75 - 80
80 - 85
85 - 90
90 und älter oder 90-95
95+
males females
4 000 000 3 000 000 2 000 000 1 000 000 0 1 000 000 2 000 000 3 000 000 4 000 000
Unter 5
5 - 10
10 - 15
15 - 20
20 - 25
25 - 30
30 - 35
35 - 40
40 - 45
45 - 50
50 - 55
55 - 60
60 - 65
65 - 70
70 - 75
75 - 80
80 - 85
85 - 90
90 und älter oder 90-95
95+
males females
1950
2000
Unification and age shares
S014 S1529
1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 20100.144
0.160
0.176
0.192
0.208
0.224
0.240
0.256
S3049 S5064 S65W
1950 1954 1958 1962 1966 1970 1974 1978 1982 1986 1990 1994 1998 2002 2006 20100.075
0.100
0.125
0.150
0.175
0.200
0.225
0.250
0.275
0.300
Children
Youngadults
Retirees
Middleaged
Matureadults
GDP/cap growth forecasts out-of-sample for Germany
1987:01
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005-0.12
-0.08
-0.04
0.00
0.04
0.08
0.12
1999:01
1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005-0.12
-0.08
-0.04
0.00
0.04
0.08
0.12
1960-2004estimatedagepattern
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
15-29 30-49 50-64 65+
Long term forecast of GermanGDP/capita up to 2050
1950 1958 1966 1974 1982 1990 1998 2006 2014 2022 2030 2038 2046-0.12
-0.08
-0.04
0.00
0.04
0.08
0.12
Age share variation
S3049 S5064 S65W
2000 2004 2008 2012 2016 2020 2024 2028 2032 2036 2040 2044 20480.16
0.18
0.20
0.22
0.24
0.26
0.28
0.30
0.32
Middle agedincreasing
Demographic part of growth in East and Westaccording to demographic projections
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
0.05
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2042
2044
2046
2048
2050
West G growth East G growth
Disregard the level of the forecasts, the interceptcannot be separately estimated
Inflation, Consumer Price Index
-1
-0.5
0
0.5
1
1.5
2
age 0_14 age 15_29 age 30_49 age 50_64 age 65_74 age 75+
Estimate Pooled
GDP per capita growth
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
age 0_14 age 15_29 age 30_49 age 50_64 age 65_74 age 75+
Country effects Pooled
EU15, China, USA,Japan and India
Age profile estimatesfrom panel data
WDI and WPP
Current Account Balance
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
age 0_14 age 15_29 age 30_49 age 50_64 age 65_74 age 75+
Coutry effects Pooled
Gross domestic savings
-4
-3.5
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
age 0_14 age 15_29 age 30_49 age 50_64 age 65_74 age 75+
Country effects Pooled
Gross capital formation
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
age 0_14 age 15_29 age 30_49 age 50_64 age 65_74 age 75+
Coutry effects Pooled
EU15, China, USA,Japan and India
Age profile estimatesfrom panel data
WDI and WPP
Favourite quote
”Understanding how to adjust economic policywith respect to future demographic changewill be a crucial question for policy makers inthe aging industrial countries”
(Alvin Hansen AER 1939
presidential address at AEA).
Is Doomsday here?
• Forecasts are contingent on assumptions– Correlations remaining the same
• Change over time although slowly in this case
– Demographic projections• Although fairly accurate 10-20 years uncertainty then
increases fast
– Nobody reacting to the forecast• But surely people are reacting and starting to adapt• Only remember the Hansen quote: to react adequately we
have to understand the mechanisms better• There are many margins to adapt on that work at widely
different horizons
How to adapt and when?
• Work longer• Start to work earlier• Work harder• Educate and work
better• Immigration• Save capital• Population growth
• Maybe 5-15 years• Immediate (short edu)• Immediate (but hard)• 10-40 years (cost)
• 2-30 years (integration)• Global long term horizon?• 25-60 years (massive
investment costs)
Adaptations interact, investment and benefits at different times
Global context
• Timing differs considerably within EU• Global competition may thwart some
adaptations and favour others• Aging tends to make us internationally
more dependent both on migration andcapital markets