17 December 2013
Global Demographics and Pensions Research
http://www.credit-suisse.com/researchandanalytics
A comparative demographic
analysis of EU28
Global Demographics and Pensions Research
Research Analysts
Amlan Roy
+44 20 7888 1501
amlan.roy@credit-suisse.com
Sonali Punhani
+44 20 7883 4297
sonali.punhani@credit-suisse.com
Angela Hsieh
44 20 7883 9639
angela.hsieh@credit-suisse.com
 Our comparative demographic analysis of EU28 is detailed for 12 countries
(Denmark, Finland, France, Germany, Greece, Ireland, Italy, Netherlands,
Portugal, Spain, Sweden and the UK) with an outline for the remaining 16
countries. EU28’s consumers and workers are very different and this has
economic, social and political implications. We present these differences
highlighting that sustainability as a political union for the EU28 requires
acknowledging, appreciating and responding to these differences.
 EU28 is very heterogeneous. Germany is 192 times Malta’s population size.
Within our 12 selected countries (a) for labour force growth: Germany has the
lowest (-0.3% p.a.) in contrast to Ireland (1.2% p.a.) over 2010-2015 (b) for
old age dependency ratio (the number of 65+ aged persons per 100 working
age persons): Germany has the highest ratio (32) whereas Ireland has the
lowest ratio (17) in 2010.
 Demographics leads to high and rising age-related government
expenditures on public pensions, health care and long-term care which adds
to the existing debt burden of some countries. EU27’s age-related
expenditures of 20.5% in 2015 are projected to be 22.8% of GDP in 2035. We
believe EU28 needs to renegotiate the pensions, health and long-term care
promises as fiscal sustainability worsens for younger generations.
 Demographics also affects EU28 GDP growth through working age
population growth, labour productivity growth and labour utilization growth.
While France and Germany both posted 1.3% GDP growth over the 20002012 period, the shares of contributing factors are very different; the factor
contributions are also very different for the same country over different
periods 1980-90 vs. 2000-2012 (Exhibit 26).
 The ECB and other central banks need to factor in the fact that conventional
monetary policy is much less effective in an aging and demographically
changing world. This has implications for the monetary transmission
mechanism, objectives as well as communications and guidance.
 Household structures and median ages have changed in a historically
unprecedented manner, affecting consumer expenditure patterns, savings,
debt, wealth accumulation and aggregate capital flows. At an aggregate level,
they affect asset prices which lead to changed asset allocations and regional
as well as sectoral shifts.
 We highlight urbanization differences suggesting that policy makers pay
attention to geography and population densities as they have implications for
congestion, pollution, real estate, quality of health and life. City planning and
environmental issues come to the fore in expanding metropolises.
DISCLOSURE APPENDIX AT THE BACK OF THIS REPORT CONTAINS IMPORTANT DISCLOSURES AND
ANALYST CERTIFICATIONS.
CREDIT SUISSE SECURITIES RESEARCH & ANALYTICS
BEYOND INFORMATION®
Client-Driven Solutions, Insights, and Access
17 December 2013
This report updates our previous two reports on the EU’s underlying demographics and
the demographic underpinnings of European fiscal sustainability1. We focus on the core
demographic indicators as well as analyzing the differences in the areas of health,
pensions, education, growth, savings, trade and size of markets. Our detailed
analysis is restricted to 12 countries: Denmark, Finland, France, Germany, Greece, Ireland,
Italy, Netherlands, Portugal, Spain, Sweden and the UK.
EU28 is very heterogeneous and one of the most glaring difference lies in population size.
In this report, Exhibit 1 illustrates at a glance the population sizes across countries as
follows: those shaded in orange (>40 mn.), in blue (10-40 mn.), in purple (5-10 mn.) and in
green (< 5 mn.). Germany’s population size (82.7 million) is nearly 192 times larger than
that of Malta (0.4 million).
Exhibit 1: EU28 countries by population size, 2013
Color shaded: Countries with population greater than 40mn are shaded in orange; population size between 10-40 mn:
blue; population size between 5-10 mn: pink; less than 5mn population size: green
Source: UN, Credit Suisse
Exhibit 1 also illustrates the geographical differences across these countries, indicating
how geography is strategic to some countries in terms of location, trade and migration.
Exhibit 36 and Exhibit 37 in the Appendix show how different all the countries are in terms
of their population’s age distributions. Exhibit 38 displays the timeline of the progression of
the European Union from its six-member version to its current 28-member status.
The report is arranged as follows. In section 1 we focus on the core demographic and
economic cross-country comparisons. Section 2 shows that consumption trends vary
across the countries and they in turn affect savings, capital flows and trade. This is
discussed in section 2. In Section 3, we present and discuss labour force, productivity and
education trends while section 4 focuses on health and pensions. Section 5 describes the
urbanization patterns across the countries along with its implications on sustainability and
infrastructure. Section 6 concludes.
1
Credit Suisse Demographics Research, "Spotlighting the European Union's Demographics" (Dec 2011) and "European
Demographics & Fiscal Sustainability" (Jan 2013).
A comparative demographic analysis of EU28
2
17 December 2013
1. Core demographic and economic indicators
We present (in Exhibit 2) the scale of differences across 12 selected countries in terms of
the aggregate value of output GDP (current prices) as well as GDP per capita and also
rank them. Across the selected countries, the GDP per capita differences are nearly of
order 3:1 (highest:lowest) and nearly 20:1 (highest:lowest) for GDP. Note that the country
rankings change when they are ranked in terms of GDP relative to when they are ranked
on GDP per capita. Germany with the highest GDP in 2013 is ranked 6 th in terms of GDP
per capita due to its large population size. Denmark on the other hand is ranked 8 th in
terms of GDP but it has the highest GDP per capita owing to its smaller population. We
present the comparative economic and demographic characteristics for the remaining 16
of the EU28 countries in Exhibit 39 in the Appendix where Luxembourg stands out as the
highest GDP per capita country, with a level roughly twice that of Denmark. Thus
population size matters in terms of economic well-being as captured by GDP per capita.
Exhibit 2: Economic and demographic characteristics of selected 12 European countries
GDP, 2013
Current prices
(USD billion)
Denmark
324
Finland
GDP per capita, 2013
Population, 2013
Population density, 2010
Rank
Current prices
(USD)
Rank
Millions
Rank
Population per
square km
8
57,999
1
5.6
10
129
5
260
9
47,625
5
5.4
11
16
12
France
2,739
2
42,991
7
64.3
2
115
6
Germany
3,593
1
43,952
6
82.7
1
233
3
Greece
243
10
21,617
11
11.1
7
84
9
Ireland
221
11
47,882
3
4.6
12
64
10
Italy
Rank
2,068
4
33,909
9
61.0
4
201
4
Netherlands
801
6
47,651
4
16.8
6
407
1
Portugal
219
12
20,663
12
10.6
8
115
6
1,356
5
29,409
10
46.9
5
91
8
552
7
57,297
2
9.6
9
21
11
2,490
3
39,049
8
63.1
3
254
2
Spain
Sweden
UK
Source: IMF, UN, Credit Suisse
Exhibit 2 illustrates the stark differences across both population size and population
density. The highest to lowest ratio in terms of population (Germany vs. Ireland) is 18:1.
This has implications for voting in the union as well as sustainability. In terms of population
density, Netherlands’ population density is 25 times that of Finland. Of the EU28 countries,
Malta has the highest population density of 1,344 people per square km.
Our demographic perspective focuses mainly on people as “consumers and workers”. We
next present how different the population growth rates (Exhibit 3) and labour force growth
rates (Exhibit 4) are across the selected countries. Population growth rates contribute to
increasing numbers of consumers and labour force growth rates contribute to increasing
numbers of workers. The differences at a broad level influence aggregate consumption
expenditures and GDP.
Population growth has remained low for most of our sample countries except Ireland, the
only country with a population growth rate greater than 1% p.a. Germany’s natural
population change (births less deaths) has been declining over the last three decades and
it is the only country with negative population growth rate in our sample (Exhibit 3) over
2010-2015. As Exhibit 39 shows, the full set of EU28 countries have other countries that
are projected to have a declining population over 2010-2015: Bulgaria, Croatia, Estonia,
Hungary, Latvia, Lithuania and Romania.
A comparative demographic analysis of EU28
3
17 December 2013
Exhibit 3: Population growth rate, 1980-1985 & 20102015
Exhibit 4: Labour force growth rate, 1990-1995 &
2010-2015
Rate per annum (%)
Rate per annum (%)
1.1
1.2%
2.0%
1990-1995
1.0%
1980-1985
2010-2015
1.5%
0.8%
0.7
0.5
0.6%
0.4%
0.2
0.2%
0.3
0.3
0.4
1.2
1.0%
0.6
0.6
0.4
0.5%
0.2
0.2
0.3
0.3
0.3
0.4
0.4
0.5
0.0%
0
0
-0.1
-0.5%
0.0%
-0.2%
2010-2015
-0.1
-0.3
-1.0%
Source: UN, Credit Suisse
Source: ILO, Credit Suisse
Slow population growth has typically been followed by slow labour force growth as shown
in Exhibit 4. Germany and Finland are projected to have negative labour force growth over
2010-2015 in contrast to Ireland which is projected to have the highest population growth
and is also projected to have the highest labour force growth.
In Exhibit 5, we present the total fertility rate for the selected 12 countries over 1980-1985
and 2010-2015. Over 1980-1985, Ireland was the only country with its total fertility rate
higher than the replacement level of 2.1 children per woman. However, in the current
period 2010-2015, the total fertility rate for all 12 countries is below the replacement rate.
In terms of fertility rate changes, over the last three decades, Portugal and Ireland have
experienced a significant fertility rate decline while countries such as Denmark and
Sweden have seen an increase in fertility rates.
Exhibit 5: Total fertility & life expectancy at birth
Exhibit 6: Old age dependency ratio, 1980 & 2010
1980-1985 & 2010-2015
Ratio of population aged 65 years and over per 100 people aged 15-64 years
Total Fertility Rate
(Children per woman)
Denmark
Finland
Life Expectancy at Birth
(Years)
1980-1985
2010-2015
1980-1985
2010-2015
1.4
1.9
74.4
79.3
1.7
1.9
74.3
80.5
France
1.9
2.0
74.7
81.7
Germany
1.5
1.4
73.7
80.7
Greece
2.0
1.5
74.5
80.7
Ireland
2.8
2.0
73.1
80.6
Italy
1.5
1.5
74.8
82.3
Netherlands
1.5
1.8
76.1
80.9
Portugal
2.0
1.3
72.3
79.8
Spain
1.9
1.5
75.9
82.0
Sweden
1.6
1.9
76.3
81.7
UK
1.8
1.9
74.1
80.4
Source: UN, Credit Suisse
A comparative demographic analysis of EU28
35
33
31
29
27
25
23
21
19
17
15
1980
25
2010
25
25
31
26
26
27
28
32
29
23
17
Source: UN, Credit Suisse
4
17 December 2013
Life expectancy at birth has improved significantly over 1980-85 to 2010-2015 as shown in
Exhibit 5. Portugal, Italy and Ireland show the largest increases in projected life
expectancy (7.5 years) of the 12 sample countries whereas Denmark and Netherlands
show moderate increases of 4.8 years. Currently (2010-2015), life expectancy at birth is
the highest for Italy at 82.3 years, followed by Spain and Sweden. Later in the report we
present and discuss another life expectancy indicator (life expectancy at age 65) as this
affects health and long-term care expenditures in the later stages of life.
Exhibit 6 presents the high old-age dependency ratios (defined as the ratio of population
aged 65+ per 100 population aged 15-64) which are caused by low fertility rates and
increased life expectancies. Germany, again has the highest old age dependency ratio
reflecting the heaviest burden of its ageing population. In our previous report 2 , we
highlight the characteristics and differences across the five oldest countries in the world.
Of the five oldest countries, four are from Europe – Germany, Italy, Greece and Sweden.
Ireland has the lowest old age dependency ratio of the 12 selected countries, while Cyprus
has the lowest ratio of the EU28 countries.
Exhibit 7 decomposes overall population change into two components: natural population
change (births minus deaths) and net migration. The patterns of migration differ across the
selected countries. Italy, Sweden, Portugal and Spain have high levels of immigration
dominating their overall population growth. In Germany, net immigration was large enough
to offset the negative natural population change from 1985 to 2005, which led to a positive
overall population growth rate during this time period. However, since 2005, the negative
natural population change has been greater than the positive net immigration change.
Therefore, the overall population of Germany has started to decline and with it the number
of domestic German consumers has declined too.
Exhibit 7: Population change components: natural population change & net migration, 1980-2020
Thousands
4,000
Germany
Greece
600
Italy
2,000
500
3,000
1,500
2,000
1,000
1,000
500
0
0
400
300
200
-1,000
1980-1985
100
0
1990-1995
2000-2005
2010-2015
-100 1980-1985
1990-1995
2000-2005
Spain
4,000
200
2010-2015
Sweden
350
300
3,000
250
150
2,000
200
50
1,000
150
0
0
100
100
50
-50
-1001980-1985
2000-2005
2010-2015
400
Portugal
250
1990-1995
-200
-500
1980-1985
-1,0001980-1985
1990-1995
2000-2005
1990-1995
2000-2005
2010-2015
2010-2015
Natural Population Change
-150
Net Migration
0
1980-1985
1990-1995
2000-2005
2010-2015
Source: UN, Credit Suisse
Within the EU28, Austria and Czech Republic also have a very high levels of net migration
dominating their overall population change. In contrast, the following EU28 countries had
negative net levels of migration i.e., outward migration or emigration dominated – Bulgaria,
Romania, Poland, Lithuania and Croatia.
2
A comparative demographic analysis of EU28
Credit Suisse Demographics Research, "Macro Fiscal Sustainability to Micro Economic Conditions of the Old in the Oldest Five
Countries" (2011)
5
17 December 2013
2. Consumption, savings and capital flows
We stress that population size is very simply the number of domestic consumers within a
country. Our selected countries have different economic structures (we consider the share
of household consumer expenditures to GDP) as shown in Exhibit 8. The share of
household final consumption expenditure in overall GDP ranges from 45% in Netherlands
to 74% in Greece. Since 1980, the share of household final consumption expenditures in
GDP has decreased for Ireland by 17% but has increased significantly for Greece by 9%.
Exhibit 8: Household final consumption
expenditure, 1980 & 2012
Exhibit 9: Consumption by type of goods, 2012
Share of GDP (%)
Share of total consumption (%)
100%
74
Greece
Portugal
66
UK
66
80%
61
Italy
France
58
Germany
58
57
1980
30.5
32.4
28.4
13.3
11.6
57.0
57.4
58.5
57.7
38.1
35.0
32.3
31.3
4.8
7.5
9.1
11.0
13.3
0%
48
Sweden
26.2
14.4
48
Ireland
58.3
20%
49
Denmark
56.0
62.2
57.6
60.4
30.2
24.0
12.2
15.7
40%
2012
Finland
56.1
60%
59
Spain
59.4
29.9
7.9
45
Netherlands
40
50
60
Source: World Bank, Credit Suisse
70
80
Durable goods
Non-durable goods
Services
Source: OECD, Credit Suisse (*2011 data)
Exhibit 9 illustrates the breakdown of total consumption expenditure in terms of durables,
non-durables and services in 2012. The share of durables in total consumption
expenditures was the lowest for Greece and the highest for the UK. Spain spent the
highest share of its consumer expenditures on services while France spent the lowest.
Detailed analysis of consumption differences across these countries requires an
understanding of their household structure. In Exhibit 10, we show the distribution of
households by household size and how it has changed over time. The general trend
across these countries is an increase in the share of one and two person households and
a decline in the share of four and five persons or more households. Household size
matters as the expenditure patterns of larger sized households differ from the expenditure
patterns of smaller sized households.
The share of one person households is the highest in Sweden and Denmark (47% and
40% of total households) and the lowest in Portugal (21% of total households) of the 12
selected countries. Since 1980, Sweden and Spain have experienced the highest increase
in the share of one person households. Ireland has the highest share of 5 people or more
households and it has also experienced a dramatic decline in the share of 5+ households
by 21% since 1980.
A comparative demographic analysis of EU28
6
17 December 2013
Exhibit 10: Households distribution (by size): 2012 & change since 1980
Share of total households (%)
1 person
2012
2 people
Change from
1980 to 2012
(2012- 1980)
2012
3 people
Change from
1980 to 2012
4 people
Change from
1980 to 2012
2012
Denmark
40
11
33
2
11
-5
Finland
39
14
36
10
11
France
34
11
33
5
15
Germany
39
10
35
7
Netherlands
37
14
33
Sweden
47
15
Greece
23
Ireland
2012
5 people or more
Change from
1980 to 2012
Change from
1980 to 2012
2012
11
-5
5
-3
-9
9
-10
5
-5
-5
12
-6
6
-6
13
-6
10
-6
4
-5
4
12
-5
13
-9
6
-4
26
-5
11
-4
11
-4
5
-2
9
31
6
20
-0
17
-7
9
-8
24
8
32
12
17
2
15
-0
12
-21
Italy
30
12
30
7
19
-3
16
-5
5
-10
Portugal
21
9
34
9
22
0
17
-6
6
-13
Spain
25
15
29
7
19
-0
19
-3
8
-19
UK
34
6
33
6
16
-1
13
-1
5
-9
Source: Euromonitor, Credit Suisse
Changing family structure has implications for household consumption expenditures which
then translate to aggregate consumption expenditures too. The consumption basket of a
one-person household is very different compared to that of a four-person household. Age
structure matters too. As shown in Exhibit 11, middle aged people consume very
differently compared to the old, for example in France and Greece the 60+ spend more on
health and food/ beverages and less on clothing, housing and recreation compared to the
30-39 year olds. In countries such as Denmark, Italy and Portugal, the 30-39 year olds
tend to spend higher on health compared to the 60+. One possible reason could be that
the young professional people in advanced countries are recognizing the need for better
health and lifestyles as they live and work longer than previous corresponding cohorts did.
Exhibit 11: Share of consumption by age of household head and by type of goods, 2012
Share of consumption by type of goods (%)
Denmark
Finland
France
Germany
Greece
Ireland
30-39
60+
30-39
60+
30-39
60+
30-39
60+
30-39
60+
30-39
Clothing/ Footwear
4.3
4.4
6.3
3.9
5.1
3.8
5.9
4.4
4.0
3.4
4.4
60+
4.3
Housing
31.3
33.8
25.4
33.6
28.1
25.6
25.0
27.4
28.0
24.2
26.9
17.1
Health/ Medical
3.1
2.9
3.2
7.5
3.4
5.2
3.4
7.5
6.1
7.8
4.3
6.6
Recreation/ Education
17.0
17.7
21.1
16.1
17.7
15.2
17.5
16.9
21.0
18.6
22.8
23.3
Food/ Beverages/ Tobacco
15.9
15.5
16.7
18.7
15.8
20.0
15.8
15.6
19.5
23.5
15.0
19.9
Other
28.4
25.7
27.3
20.1
29.8
30.2
32.3
28.2
21.4
22.5
26.5
29.0
30-39
60+
30-39
60+
30-39
60+
30-39
60+
30-39
60+
30-39
Clothing/ Footwear
7.9
8.0
6.5
4.5
6.4
6.6
5.5
5.2
6.4
3.7
7.2
5.0
Housing
23.8
25.5
23.1
31.1
15.6
16.9
19.7
25.1
25.9
31.8
24.6
26.4
Italy
Netherlands
Portugal
Spain
Sweden
UK
60+
Health/ Medical
3.3
2.6
2.1
4.4
6.7
5.7
3.0
4.1
1.7
5.4
1.4
2.5
Recreation/ Education
19.9
20.1
16.5
14.8
20.7
21.0
29.1
24.5
19.3
15.5
23.9
21.8
Food/ Beverages/ Tobacco
18.4
18.1
14.0
16.5
20.5
20.9
16.8
19.1
14.6
17.1
12.7
16.2
Other
26.7
25.8
37.8
28.6
30.0
28.9
25.9
22.0
32.1
26.5
30.2
28.1
Source: Euromonitor, Credit Suisse
A comparative demographic analysis of EU28
7
17 December 2013
The contra of household consumption expenditures is household savings as household
savings is the part of disposable income that is not consumed. Household savings rates vary
significantly across these countries, ranging from -7.5% of gross disposable income in
Greece to 16.1% in Germany (Exhibit 12). These household saving differences (via
aggregate savings) have implications for the overall debt and fiscal sustainability across
these countries. Varying debt levels and fiscal sustainability make it difficult for a common
set of rules to work in an uniform manner across these very diverse economies.
Exhibit 12: Gross savings rate of households &
NPISH**, 2000 & 2013
Exhibit 13: Stock, private bond and public bond
market capitalization, 1990 & 2011
As a percentage of gross disposable income
Share of GDP
20
2000
2013
13.5
14.4
15.4
Stock market
capitalization
16.1
Private bond market
capitalization
Public bond market
capitalization
1990
2011
1990
2011
1990
2011
Denmark
32
64
100
181
52
39
Finland
21
51
35
21
4
12
5
France
30
65
49
56
23
63
0
Germany*
19
37
40
24
20
50
Greece
11
19
5
34
37
53
Ireland
41
16
2
113
52
29
Italy
15
17
26
38
77
91
Netherlands
52
78
22
72
40
48
Portugal
14
32
6
69
34
49
Spain
25
76
12
54
27
45
Sweden
48
104
51
55
27
26
UK
87
127
13
12
25
59
15
12.2
9.7 10.2
10
5.6
10.5
6.2
-5
-10
-7.5
Source: AMECO, Credit Suisse (*Ireland data is for 2002)
12.4
Source: World Bank, Credit Suisse (*Germany data is for 1992)
**NPISH: Non-profit institutions serving households.
The way these savings are channelized depends on the development of financial markets in
these economies. One measure of financial market development is the size of the stock and
bond markets (public and private) as shown in Exhibit 13. The UK in 2011 had the highest
stock market capitalization as a share of GDP, while Ireland had the least. Sweden has seen
the largest increase in its stock market capitalization since 1990. Private bond market
capitalization ranged from 12% in the UK to 181% in Denmark in 2011 while public bond
market capitalization ranged from 12% in Finland to 91% in Italy.
Our previous research3 highlighted strong statistical links between private savings, current
account and budget deficits according to the equation below:
Sp = I + CA + (G – T)
where Sp = Private Saving, G = Government Expenditures, T = Taxes, CA = Current
Account Balance, and I = Investments. A country’s private saving can thus be absorbed into
one of the following: a) Budget deficit; b) Purchase of wealth from foreigners; and c)
Investment in domestic capital.
Exhibit 14 presents the savings, investments and current account balance in Germany and
Greece over 1980-2012. Germany has gross national savings higher than investment and a
positive current account balance whereas Greece has gross national savings lower than
investment and a negative current account balance. Saving-investment patterns along with the
budget deficits are related to the current account balance and trade dynamics and it is
important to note the differences across the countries. Savings are also affected by the term
structure of interest rates (the yield curve) which is influenced at shorter maturities by monetary
policy. In a demographically changing world, monetary policy effectiveness is reduced.4
Credit Suisse Demographics Research, "Demographics, Capital Flows and Exchange Rates" (2007)
3
4
See end of report references to J. Bullard et al (2012) and P Imam (2013).
A comparative demographic analysis of EU28
8
17 December 2013
Exhibit 14: Savings, investments & current account
balance, Germany & Greece, 1980- 2012
Exhibit 15: Share of exports, imports and trade
openness, 2012
Share of GDP (%)
Share of GDP (%); Trade openness- Sum of exports and imports
Germany
8
30
Ireland
6
108
Netherlands
25
87
79
166
4
2
Denmark
55
Germany
52
15
Sweden
49
10
Finland
40
40
Portugal
39
39
UK
32
34
Spain
32
31
Italy
30
29
59
Greece
27
32
59
France
27
30
20
0
-2
-4
1980
1985
1990
1995
2000
2005
2010
Greece
0
35
-5
25
-10
15
-15
-20
5
1980
1985
1990
1995
2000
Current account balance (LHS)
Gross national savings (RHS)
2005
2010
0
Total investment (RHS)
50
105
46
98
43
91
80
78
66
63
Trade openness: 57
50
Exports
Source: IMF, Credit Suisse
192
84
100
150
200
Imports
Source: World Bank, Credit Suisse
The current account balance is related to the trade balance which is affected by the trade
openness of a country (defined as the sum of the share of exports and imports in GDP).
Exhibit 15 shows Ireland has the highest trade openness (192% of GDP) and France has
the lowest (57% of GDP). Exhibit 16 shows the top export destinations of countries in 2012.
Exhibit 16: Exports by destination, 2012
Denmark
Finland
France
Germany
Greece
Ireland
Share of exports (%)
EU27
57.1
EU27
51.5
EU27
59
EU27
56.2
EU27
43
EU27
59.1
Norway
6.6
Russia
9.9
USA
6.1
USA
7.9
Turkey
10.8
USA
19.8
USA
5.5
USA
6
China
3.5
China
6.1
USA
3.8
Switzerland
5.5
China
2.5
China
4.5
Switzerland
3.2
Switzerland
4.5
FYR Macedonia
3
Japan
2.3
Hong Kong
1.8
Norway
3.1
Russia
2.1
Russia
3.5
Libya
2.9
China
1.7
Italy
Netherlands
Portugal
Spain
Sweden
UK
Share of exports (%)
EU27
55.5
EU27
72.7
EU27
69.8
EU27
61.1
EU27
55.4
EU27
48.5
USA
6.1
USA
4.6
Angola
6.6
USA
4.1
Norway
10.1
USA
13.3
Switzerland
5.5
China
1.8
USA
4.1
Morocco
2.4
USA
6
Switzerland
3.4
China
2.7
Russia
1.6
China
1.7
Turkey
2.1
China
3.2
China
3.3
Turkey
2.5
Switzerland
1.3
Brazil
1.5
Switzerland
2.1
Russia
1.9
Hong Kong
1.9
Source: WTO, Credit Suisse
A comparative demographic analysis of EU28
9
17 December 2013
The highest share of exports of these countries goes to other European countries. US and
China also figure in the list of the top five export destinations of most of the countries.
As Exhibit 17 shows the largest share of imports of the selected countries also come from
within EU27. China, US and Russia also figure in the list of the top five countries from
where imports of most of the selected countries originated in 2012.
Exhibit 17: Imports by origin, 2012
Denmark
Finland
France
Germany
Greece
Ireland
Share of imports (%)
EU27
70.3
EU27
50.3
EU27
58.6
EU27
56
EU27
45
EU27
China
7
Russia
17.6
China
8
China
8.6
Russia
12.4
USA
13
Norway
5.3
China
7.7
USA
6.4
USA
5.7
Saudi Arabia
5.5
China
5.6
USA
2.8
Norway
3.6
Switzerland
2.3
Russia
4.7
China
4.7
Switzerland
Russia
1.2
USA
3.1
Russia
2.3
Switzerland
4.2
South Korea
3.9
Norway
Italy
Netherlands
Portugal
Spain
Sweden
58.9
2
1.9
UK
Share of imports (%)
EU27
53.4
EU27
51.6
EU27
64.2
EU27
49.1
EU27
67.6
EU27
47.4
China
7.4
China
8.2
China
4.7
China
7
Norway
9.1
USA
8.9
Russia
4.2
USA
6.8
Angola
3.2
USA
3.9
Russia
5.3
China
8.2
USA
3.2
Russia
5.2
Algeria
2.6
Russia
3.2
China
4.1
Norway
4.8
Switzerland
2.8
Norway
3.1
Brazil
2.5
Nigeria
2.8
USA
3.2
Switzerland
4
Source: WTO, Credit Suisse
3. Labour force: activity rates, employment, education
and productivity
In order to better understand the drivers of economic growth, it is important to understand
the productivity and the skills base of the country. The contribution to economic growth
comes not only by focusing on numbers of workers but also on their relative labour
productivity and hours worked. Economic activity rates refer to the percentage of people of
the working age group that are actively employed. Exhibit 18 presents the gender activity
gap (male less female activity rates) across countries, highlighting that governments and
societies need to encourage and embrace higher female labour force participation as
women are a highly educated and skilled part of the labour force.
Exhibit 18: Gap between male and female economic
activity rates, 1990 & 2013
Exhibit 19: Economic activity rate by age- Greece,
1990 & 2013
Male – female economic activity rates (%)
%
40
1990
35
2013
30
25
20
20
16
15
10
8
9
9
11
12
13
5
0
Source: ILO, Credit Suisse
A comparative demographic analysis of EU28
13
13
16
22
100
90
80
70
60
50
40
30
20
10
0
1990
2013
EAR increase from 1990-2013:
35-39 yr olds: 10%
55-59 yr olds : 10%
Source: ILO, Credit Suisse
10
17 December 2013
This requires changes in labour market practices and institutions but mostly in attitudes.
Exhibit 19, Exhibit 20 and Exhibit 21 display the change in economic activity rates between
1990 and 2013, over the work life-cycle for a typical worker in Greece, Italy and Spain.
While the economic activity rates have increased, they have not extended in the latter part
of the work life cycle at ages beyond 50 and they need to in order to keep pace with
increased life expectancies. It is important to stress that females live longer than males
and EU28 needs to modernize labour markets to allow women’s education and skills to be
better utilized than that which is being done currently.
Exhibit 20: Economic activity rate by age - Italy,
1990 & 2013
Exhibit 21: Economic activity rate by age - Spain,
1990 & 2013
%
%
90
1990
100
90
80
70
60
50
40
30
20
10
0
2013
80
70
60
50
EAR increase from 1990-2013:
35-39 yr olds: 4%
55-59 yr olds : 13%
40
30
20
10
0
Source: ILO, Credit Suisse
1990
2013
EAR increase from 1990-2013:
35-39 yr olds: 16%
55-59 yr olds : 16%
Source: ILO, Credit Suisse
Rising youth unemployment and income inequality 5 has become a major issue in the
advanced world since the onset of the 2008 credit crisis. In 2012, Greece and Spain had
the highest youth unemployment rate (of total labour force aged 15-24 years) amongst the
EU28 countries, more than double their overall unemployment rates (Exhibit 22).
Exhibit 22: Total vs. youth unemployment, 2012
%
60
55
Total unemployment (% of
total labour force)
50
53
Youth unemployment (% of
labour force aged 15-24)
40
38
35
%
30
30
20
10
25
24
24
19
8
10
8
5
8
21
16
15
14
24
11
9
5
8
8
0
Source: ILO, Credit Suisse
5
A comparative demographic analysis of EU28
Credit Suisse Demographics Research, "Youth unemployment and income inequality", part of Credit Suisse 2014 Global Outlook
(2013)
11
17 December 2013
Over 2000-2012, Italy, Portugal and Spain saw the most significant rise in their youth
unemployment rate. Austria, Germany and Netherlands are the only three countries out of
the EU28 with youth unemployment rate below 10%. Income inequality, which is
measured by the Gini coefficient, also shows a wide disparity across the 28 countries.
Latvia has the highest Gini ratio of 35.9 (i.e. lower income inequality) compared to
Slovenia’s lowest Gini ratio of 23.7 in 2012.
Exhibit 23 highlights the productivity differences within our sample while looking at value
added per worker in agriculture, industry and services. The productivity differences are
quite stark even across the larger more advanced European countries. They are very
different across the 16 other countries. Part of the productivity differences are attributable
to education as well as the employment structures across different countries. Exhibit 24
presents the differences in tertiary education levels across the countries within our sample
– the share of population aged 15-64 years with tertiary education in 2012 ranged from
13.8% in Italy to 34.7% in the UK.
Exhibit 23: Gross value added per worker, 2011
Exhibit 24: Share of population aged 15-64 years
with tertiary education, 2012
Thousands of current USD
%
Agriculture
Industry
Services
60.5
118.3
103.8
40
62.9
111.3
87.9
35
60.6
81.9
102.8
30
46.3
88.3
79.0
16.0
65.0
72.9
25
56.0
178.1
94.7
20
45.2
74.3
92.5
15
57.7
145.5
92.2
10
9.3
36.5
50.9
44.6
92.8
71.4
91.6
138.3
94.1
40.5
89.8
71.2
Denmark
Finland
France
Germany
Greece
Ireland
Italy
Netherlands
Portugal
Spain
Sweden
UK
Source: UN, ILO, Credit Suisse
34.7
34.7
32.8
27.9
23.0
28.6
28.6
29.6
30.1
24.8
16.8
13.8
Source: Eurostat, Credit Suisse
In terms of Human Development Index (a quality of life index) for 2012, Netherlands is
ranked the highest (rank 4), Portugal the lowest within the selected 12 countries (rank 43)
and Bulgaria the lowest within EU28 (rank 57) as shown in Exhibit 25.
Exhibit 25: Human Development Index, Global Gender Gap Index, and
Corruption Perception Index for the 12 selected countries
Human Development Index 2012
Rank
Global Gender Gap Index 2013 Corruption Perception Index 2012
Country
Value
Rank
Country
Score
Rank
Country
Score
4
Netherlands
0.921
2
Finland
0.84
1
Denmark
90
5
Germany
0.92
4
Sweden
0.81
1
Finland
90
7
Ireland
0.916
6
Ireland
0.78
4
Sweden
88
7
Sweden
0.916
8
Denmark
0.78
9
Netherlands
84
15
Denmark
0.901
13
Netherlands
0.76
13
Germany
79
20
France
0.893
14
Germany
0.76
17
UK
74
21
Finland
0.892
18
UK
0.74
22
France
71
23
Spain
0.885
30
Spain
0.73
25
Ireland
69
25
Italy
0.881
45
France
0.71
30
Spain
65
26
UK
0.875
51
Portugal
0.71
33
Portugal
63
29
Greece
0.86
71
Italy
0.69
72
Italy
42
43
Portugal
0.816
81
Greece
0.68
94
Greece
36
Source: UNDP, World Economic Forum, Transparency International, Credit Suisse
A comparative demographic analysis of EU28
12
17 December 2013
Gender equality is measured by the Global Gender Gap Index created by the World
Economic Forum. We note that in 2013 Finland outperforms the other EU28 countries
whereas Greece and Hungary underperform. In terms of the Corruption Perception Index,
Finland again is on top of the list with the highest ranking along with Denmark (rank=1 i.e.,
the lowest corruption perception) while Greece has the highest corruption perception
(rank= 94) in 2012.
While education and productivity differences are important to note, what really matters in
the aggregate is how that translates to GDP and GDP growth. Exhibit 26 depicts a growth
accounting decomposition of the demographic drivers 6: working age population growth,
labour productivity growth and labour utilization growth for France, Germany, Italy, Greece,
Spain and the UK.
Not only are the cross-country growth patterns important to compare over time periods
1980-1990 vs. 2000-2012, but are also at least as important as the differing contributions
of these three drivers to the GDP growth trends across these countries. Understanding the
demographic dynamics of these drivers is essential to understanding and appreciating the
growth dynamics. Labour productivity growth has been a dominant contributor to GDP
growth for France, Germany, Italy and the UK, while working age population growth was
important in explaining GDP growth in Greece in the 1980s and in Spain over 2000-2012.
Exhibit 26: GDP growth decomposed: demographic drivers, 1980-2012
Rate per annum (%)
Germany
France
4
4
Real GDP: 2.2
2
2.7
1
0
-1
0.9
1.3
2
1.1
1
0.5
-0.4
0
-1.3
-1
1980-1990
Greece
5
1.6
1.0
0.8
0.0
3
1.0
-0.4
-0.1
-0.3
-0.5
-1
-1.0
2000-2012
1.7
-0.3
0.5
0.8
0.0
-0.2
0.3
0.2
-0.1
1980-1990
2000-2012
2000-2012
3.0
Spain
3.1
2.5
0.4
UK
2.2
1.7
2.0
3.4
0.9
-1.2
1.9
1.5
1.0
1.0
1.1
-0.3
0.5
0.0
2.1
1.4
0.4
-0.3
0.5
-0.2
1980-1990
2000-2012
-0.5
-2
1980-1990
1.5
-0.5
1
0
2.0
1.0
-1.4
2
1.2
2.3
1.2
4
1.0
0.5
0.6
1980-1990
2000-2012
2.0
1.5
1.3
0.4
2.7
-2
-2
2.5
2.5
3
3
Italy
3.0
1.9
1980-1990
2000-2012
Source: UN, GGDC, Credit Suisse
6
A comparative demographic analysis of EU28
Credit Suisse Demographics Research, "A demographic perspective of economic growth" (2009)
13
17 December 2013
4. Health and pensions
Exhibit 27 presents the forecasts for age-related government expenditure, including public
pensions, health care and long-term care, as a percentage of GDP across the 12 selected
countries. The total age-related expenditure is projected to increase in all the countries,
with Netherlands showing the most significant increase from 17.6% in 2010 to 26.3% in
2060. France, on the other hand, is projected to spend the greatest amount on age-related
items throughout the period (2010-2060). By 2060, the French government is projected to
spend 28.7% of its GDP on age-related expenditures whereas the UK is projected to
spend the least 20.2% of GDP. While most countries are projected to devote a greater
share of government spending on pensions, health care and long-term care, Italy and
Denmark present a contrast to the dominant trend with a fall in their pensions expenditure
over 2010-2060. The increasing promises on age-related expenditure and fiscal
sustainability concerns have prompted us to study the impact of demographic variables on
a country’s sovereign rating7 detailed in a previous research report.
Exhibit 27: Forecasted age-related expenditure components, 2010-2060
Age-related Expenditure Components (as % of GDP)
Pensions
Health Care
Long-term Care
2010
2035
2060
2010
2035
2060
2010
2035
10.1
10.5
9.5
7.4
8.2
8.4
4.5
6.4
8
Finland
12
15.5
15.2
6
6.9
7
2.5
4.4
5.1
France
14.6
15.2
15.1
8
9.1
9.4
2.2
3.2
4.2
Germany
10.8
12.4
13.4
8
9.1
9.4
1.4
2.2
3.1
Greece
13.6
14.6
14.6
6.5
6.9
7.4
1.4
1.8
2.6
Ireland
7.5
9.4
11.7
7.3
7.9
8.3
1.1
1.6
2.6
Italy
15.3
15
14.4
6.6
7
7.2
1.9
2.3
2.8
Netherlands
6.8
10
10.4
7
8
8
3.8
6.1
7.9
Portugal
12.5
13.1
12.7
7.2
7.5
8.3
0.3
0.4
0.6
Spain
10.1
11.3
13.7
6.5
7.2
7.8
0.8
0.9
1.5
Sweden
9.6
10.2
10.2
7.5
8
8.1
3.9
5.2
6.4
UK
7.7
8
9.2
7.2
7.9
8.3
2
2.5
2.7
Denmark
2060
Source: European Commission, Credit Suisse
Exhibit 28 presents gross pension replacement rates for the average male earner in 2012.
The replacement rate is the ratio of gross pension entitlement divided by pre-retirement
income. There exist big differences across our selected countries, with gross pension
replacement rate ranging from 33% in the UK to 91% in Netherlands. Countries with a
higher than 50% replacement rate promise on public pensions will impose a greater fiscal
burden on younger generations, making the current sustainability pressures worse due to
weaker growth, higher unemployment and rising inequality. 8
Unlike replacement rates that only look at the benefit level at the point of retirement, gross
pension wealth which takes into account life expectancy, retirement age and indexation of
pensions, provides a more comprehensive measure of the stock of future flows of pension
benefits. We present pension wealth statistics for our selected countries in Exhibit 29.
Countries with higher pension replacement rates tend to have higher pension wealth such
as Netherlands and Denmark. UK, with the lowest pension replacement rate also has the
lowest pension wealth amongst the 12 countries. A NETSPAR paper on pension wealth9
7
Credit Suisse Demographics Research, "Demographics, Debt & Sovereign ratings" (2013)
8
See "Rising Youth Unemployment: a Threat to Growth and Stability" and "Youth Unemployment and Income Inequality", previous
reports by Credit Suisse's Demographics Research team.
9
R. Alessie, V. Angelini & P. Santen, "Pension wealth and household saving in Europe - Evidence from Sharelife", NETSPAR
discussion paper (2011)
A comparative demographic analysis of EU28
14
17 December 2013
estimates the displacement effect of pension wealth on household savings. We believe
that gross pension wealth multiples of more than 10 impose inequitable and unsustainable
burdens on younger generations. In a previous research report titled “Why Increasing
Longevity Matters to Us All? 10” we highlighted that that these pensions challenges will
need to be comprehensively tackled at various levels by: individuals and families,
governments, insurance companies, pension funds, asset managers and corporates.
Exhibit 28: Gross pension replacement rates for the
average male earner, 2012
Exhibit 29: Gross pension wealth for the average
earner, 2012
Gross pension entitlement divided by gross pre-retirement incomes
Gross pension wealth is the total value of lifetime flow of pension incomes,
measured as a multiple of gross annual individual earnings
Netherlands
Denmark
Spain
Italy
France
EU27
Sweden
Finland
Portugal
OECD34
Greece
Germany
Ireland
UK
25
91
Men
79
74
71
20
59
58
56
55
55
54
54
Women
15
10
5
42
0
37
33
0
20
40
60
80
100
Source: OECD, Credit Suisse
Source: OECD, Credit Suisse
We believe that in addition to studying life expectancy at birth, it is also very important to
consider conditional life expectancy i.e. life expectancy remaining at a given age, typically
65 or 80 years, as that matters more for examining the ageing burden and managing
longevity risk. In Exhibit 30 we show life expectancy at age 65, which measures the length
of remaining life at age 65 across the 12 countries.
Exhibit 30: Life expectancy at age 65, 2012
Exhibit 31: Healthy life years at age 65, 2011
Years
Years
22
21.7
21.5
20.8
21
20.5
20
19.5
19.6
19.6
19.6
19.6
19.9
19.8
19
19
18.5
18
17.5
Source: Eurostat, Credit Suisse (* Data is for 2011)
19.9
19.9
20.9
Germany
Portugal
Italy
Finland
EU 27
Greece
Spain
France
Netherlands
Ireland
UK
Denmark
Sweden
Males
Females
6.7
7.9
8.1
8.4
8.6
9.1
9.7
9.7
10.4
10.9
11.1
12.4
13.9
7.3
6.4
7
8.6
8.6
7.8
9.2
9.9
9.9
11.7
11.9
13
15.2
Source: Eurostat, Credit Suisse
The indicator Healthy Life Years (HLY) at age 65 measures the number of years that a person
at age 65 is still expected to live in a healthy condition.
10
A comparative demographic analysis of EU28
Credit Suisse Demographics Research, "How Increasing Longevity Affects Us All?: Market, Economic & Social Implications
(March 2012)
15
17 December 2013
France, Italy and Spain have the highest life expectancy at age 65 whereas Denmark has
the lowest. In Exhibit 31 we present healthy life expectancy at age 65, i.e., how many years
a person aged 65 is still expected to live in a healthy condition. Interestingly, longer life
expectancy at 65 does not necessarily translate into longer healthy life at 65 in the case of
France and Italy. Denmark, on the other hand, with relatively short life expectancy at 65, has
a longer healthy life at 65 for both males and females than that of France and Italy.
Of the 12 selected countries, Netherlands had the highest health expenditure per capita in
2011 (5,123 international dollar) while Portugal had the lowest (2,624 international dollar)
as shown in Exhibit 32. Netherlands’ health expenditure per capita, despite being the
highest amongst the 12 countries, is still 60% lower than that of the USA. In Exhibit 33, we
decompose the health expenditure into private spending and public spending.
Netherlands’ total health expenditure accounted for 12% of its GDP, and a significant
share of the spending came from public funding in 2011 (10.2% of GDP). This contrasts
with Greece where public and private funded health spending account for 6.6% and 4.2%
of GDP (more equal share) respectively in 2011.
Exhibit 32: Health expenditure per capita, 2011
Exhibit 33: Health expenditure, 2011
PPP, Constant 2005 international $
Share of GDP
5,500
Finland
UK
Sweden
Ireland
Spain
Italy
Portugal
Greece
Germany
Denmark
France
Netherlands
5,000
4,500
4,000
3,500
3,000
2,500
2,000
2.2
1.6
1.8
2.8
2.5
2.2
3.7
4.2
2.7
1.7
2.7
1.7
0
Source: WHO, Credit Suisse
2
6.6
7.7
7.6
6.6
7.0
7.3
Total: 8.9
9.3
9.4
9.4
9.4
9.5
10.4
6.6
6.6
8.4
9.5
8.9
10.2
4
6
8
Health expenditure as % of GDP
Private Public
10.8
11.1
11.2
11.6
12
10
12
Source: WHO, Credit Suisse
The Survey of Health, Aging and Retirement in Europe (SHARE) is a cross- national panel
database of micro data on the health, socio-economic status and social networks of
people from 19 European countries aged 50 or over. A paper comparing the health among
16 SHARE countries11 found that Germany shows almost the same high levels of adverse
health outcomes as East European countries. Differences in institutional factors have a
major impact on health disparities and must be addressed to increase healthy and active
ageing in Europe.
11
A comparative demographic analysis of EU28
M. Eriksen, S. Vestergaard & K. Andersen-Ranberg "Health among Europeans- a cross sectional comparisons of 16 SHARE
countries" (2013)
16
17 December 2013
5. Urbanization
In Exhibit 34, we compare the urbanization rates across the selected countries in 1980
and 2015. Of the 12 countries, the projected urbanization rate for 2015 ranges from 62%
in Greece to 88% in France. Over 1980-2015, Portugal and Netherlands have seen a
significant increase in their urbanization rates.
Exhibit 34: Urban population, 1980 & 2015
Exhibit 35: Distribution of urban population, 2015
Share of total population (%)
Share of urban population (%)
100
100%
90
1980
2015
84
86
87
80
78
80
85
74
62
63
90%
80%
70%
19.6
12.8
17.9
24.3
9.5
9.3
6.5
35.3
49.5
20.4
60%
69
70
88
7.1
18.2
14.5
11.7
7.5
50%
63
12.9
40%
60
30%
50
77.7
75.0
64.6
55.3
20%
57.1
55.6
37.6
10%
40
0%
France
Source: UN, Credit Suisse
Germany Greece
Italy
<0.5 mn
0.5-1mn
1-5 mn
Spain
5-10 mn
Sweden
> 10mn
UK
Source: UN, Credit Suisse
In Exhibit 35, we emphasize that it is not enough to only focus on the sheer size of the
urban population but that it is equally important to consider the distribution of urban
population. Germany and Sweden have a more sustainable urbanization model, with
about 75% of its urban population residing in smaller city sizes which have less than 0.5
million population. France, on the other hand, has nearly 20% of its urban population
clustering in mega cities with more than 10 million residents. This speaks to the SRI
concerns of investors as well as policy makers.
While population densities of countries vary between 16 people per square km in Finland
to 407 in Netherlands, the densities in major cities are often in the multiples of thousands.
While these create economies of scale, there are congestion and pollution costs
associated with larger metropolises. Many older European cities are having to deal with
expansion due to population increases while being unable to change large parts of the old
traditional infrastructure .
6. Conclusions
In this report, we highlight EU28’s varying and changing demographics, many of which
have been overlooked by countries. The underlying demographics affects the EU28 at
multiple levels—political, economic and social. Our demographic focus is not only on the
“number of people” but on the “changing nature of people as consumers and workers”.
The changing behavior of consumers and workers affect balance sheets of households,
corporates as well as countries.
How demographically heterogeneous is EU28? We outline some differences by
highlighting the extremes across the countries: population ranges from 0.43 million in
Malta to 82.7 million in Germany in 2013, total fertility rate ranges from 1.3 in Portugal to 2
in Ireland and France over 2010-2015 and old age dependency ratio ranges from 16 in
Cyprus to 32 in Germany in 2010. We present scores and rankings that capture quality of
life, gender equality and corruption index, which are also related to demographics.
A comparative demographic analysis of EU28
17
17 December 2013
Apart from looking at the standard demographic variables to make our point, we believe
our main contribution is to link demographics (consumer and worker characteristics) to
macroeconomic aggregates like GDP growth, unemployment, government budgets, trade
deficits and capital flows. Consumer demand for goods and services and worker supply of
goods and services are both vital to understand GDP. People are not only living longer but
much more importantly they are living differently in a global economy which is
technologically changing and evolving in terms of its inter-connectedness.
Rising youth unemployment and income inequality have been gaining attention as
important economic and social issues in Europe. Income inequality also shows wide
disparity across the 28 countries with Gini ratios ranging from 23.7 in Slovenia to 35.9 in
Latvia (i.e., lowest income inequality) in 2012.
Education, health, urbanization and employment (or unemployment) as well as distribution
of income and wealth (inequality) underlie both the social and economic dynamics of
households, societies and countries.
Any framework for a reconstituted EU needs to better understand and incorporate
the underlying demographic heterogeneity to avoid the mistakes of the past where
political agreements ignored the economic and demographic differences across
member states.
A comparative demographic analysis of EU28
18
17 December 2013
References
Credit Suisse Demographics Research, "Youth unemployment and income inequality",
part of Credit Suisse 2014 Global Outlook (2013)
Credit Suisse Demographics Research, “Demographics, Debt & Sovereign ratings” (2013)
Credit Suisse Demographics Research, "Rising youth unemployment: A threat to growth
and stability" (2013)
Credit Suisse Demographics Research, "European Demographics & Fiscal Sustainability"
(2013)
Credit Suisse Demographics Research, "How Increasing Longevity Affects Us All?:
Market, Economic & Social Implications (2012)
Credit Suisse Demographics
Demographics" (2011)
Research,
"Spotlighting
the
European
Union's
Credit Suisse Demographics Research, "Macro Fiscal Sustainability to Micro Economic
Conditions of the Old in the Oldest Five Countries" (2011)
Credit Suisse Demographics Research, "A demographic perspective of economic growth"
(2009)
Credit Suisse Demographics Research, "Demographics, Capital Flows and Exchange
Rates" (2007)
J. Bullard et. al., “Demographics, Redistribution & Optimal Inflation”, Federal Reserve
Bank of St. Louis Review (2012)
M. Eriksen, S. Vestergaard & K. Andersen-Ranberg "Health among Europeans - a cross
sectional comparisons of 16 SHARE countries" (2013)
P. Imam, “Shock from Graying: Is the Demographics Shift Weakening Monetary Policy
Effectiveness”, IMF Working Paper (2013)
R. Alessie, V. Angelini & P. Santen, "Pension wealth and household saving in EuropeEvidence from Sharelife", NETSPAR discussion paper (2011)
A comparative demographic analysis of EU28
19
17 December 2013
Appendix:
Exhibit 36: Age and gender distribution of population of selected 12 countries, 2015
In thousands
Denmark
France
Finland
80+
80+
80+
70-74
70-74
70-74
60-64
60-64
60-64
50-54
50-54
50-54
40-44
40-44
40-44
30-34
30-34
30-34
20-24
20-24
20-24
10-14
10-14
10-14
0-4
0-4
300
200
100
0
100
200
300
0-4
300
200
100
0
100
200
300
3,000
80+
80+
70-74
70-74
70-74
60-64
60-64
60-64
50-54
50-54
50-54
40-44
40-44
40-44
30-34
30-34
30-34
20-24
20-24
20-24
10-14
10-14
10-14
0-4
0-4
0
2,000
4,000
Share of age 80+: 5.8%
500
0
Share of age 80+: 4.4%
500
Male
400
1,000
70-74
70-74
70-74
60-64
60-64
60-64
50-54
50-54
50-54
40-44
40-44
40-44
30-34
30-34
30-34
20-24
20-24
20-24
10-14
10-14
10-14
0-4
0-4
200
400
600
200
100
0
100
200
300
3,000
80+
70-74
70-74
70-74
60-64
60-64
60-64
50-54
50-54
50-54
40-44
40-44
40-44
30-34
30-34
30-34
20-24
20-24
20-24
10-14
10-14
10-14
0-4
0-4
200
400
Share of age 80+: 5.4%
600
400
3,000
1,000
0
1,000
2,000
3,000
2,000
3,000
UK
80+
0
200
Share of age 80+: 6.6%
Spain
Portugal
200
2,000
Share of age 80+: 2.9%
80+
400
0
0-4
300
Share of age 80+: 6%
600
3,000
Italy
80+
0
2,000
Share of age 80+: 5.2%
80+
200
200
Female
Ireland
Greece
400
1,000
0-4
1,000
80+
600
0
Sweden
Netherlands
Germany
2,000
1,000
Share of age 80+: 5.9%
80+
4,000
2,000
Share of age 80+: 5.1%
Share of age 80+: 4.3% of total population
0-4
2,000
1,000
Share of age 80+: 5.8%
0
1,000
Male
2,000
Female
3,000
3,000
2,000
1,000
0
1,000
Share of age 80+: 4.9%
Source: UN, Credit Suisse
A comparative demographic analysis of EU28
20
17 December 2013
Exhibit 37: Age and gender distribution of population of the remaining 16 of EU28 countries, 2015
In thousands
Croatia
Belgium
Austria
Bulgaria
80+
80+
80+
80+
70-74
70-74
70-74
70-74
60-64
60-64
60-64
60-64
50-54
50-54
50-54
50-54
40-44
40-44
40-44
40-44
30-34
30-34
30-34
30-34
20-24
20-24
20-24
20-24
10-14
10-14
10-14
10-14
0-4
0-4
0-4
400
200
0
200
600
400
400
200
0
200
400
600
0-4
200
300
200
100
0
100
200
300
100
0
100
200
400
Hungary
Czech Republic
Cyprus
Estonia
80+
80+
80+
80+
70-74
70-74
70-74
70-74
60-64
60-64
60-64
60-64
50-54
50-54
50-54
50-54
40-44
40-44
40-44
40-44
30-34
30-34
30-34
30-34
20-24
20-24
20-24
20-24
10-14
10-14
10-14
10-14
0-4
0-4
0-4
0-4
60
40
20
0
20
40
60
600
80
400
200
0
200
400
600
600
60
40
20
0
20
40
400
200
0
200
400
600
60
Malta
Lithuania
Latvia
Luxembourg
80+
80+
80+
70-74
70-74
70-74
60-64
60-64
60-64
50-54
50-54
50-54
40-44
40-44
40-44
30-34
30-34
30-34
20-24
20-24
20-24
10-14
10-14
10-14
0-4
0-4
0-4
100
50
0
50
150
100
100
50
0
50
100
150
80+
70-74
70-74
70-74
60-64
60-64
60-64
50-54
50-54
50-54
40-44
40-44
40-44
30-34
30-34
30-34
20-24
20-24
20-24
10-14
10-14
10-14
0-4
0-4
0-4
0
50-54
40-44
30-34
20-24
10-14
0-4
20
20
1,000
1,000
2,000
500
10
0
10
20
0
500
1,000
10
0
10
20
30
Slovenia
Slovakia
80+
1,000
60-64
30
80+
2,000
70-74
Romania
Poland
80+
80+
70-74
60-64
50-54
40-44
30-34
20-24
10-14
0-4
300
200
100
0
100
200
300
100
50
0
50
100
Source: UN, Credit Suisse
Exhibit 38: Timeline of the European Union
Original Members
Year
1951
Member
States
Belgium, France,
West Germany, Italy,
Luxembourg,
Netherlands
New Members Added
1973
+
Denmark,
Ireland, UK
1981
+
Greece
+
1986
1995
2004
Portugal,
Spain
Austria,
Finland,
Sweden
Cyprus, Czech
Republic, Estonia,
Hungary, Latvia,
Lithuania,
Malta, Poland,
Slovakia, Slovenia
+
+
2007
2013
+ Romania, +
Bulgaria
Croatia
Source: European Commission, Credit Suisse
A comparative demographic analysis of EU28
21
17 December 2013
Exhibit 39: Key demographic and economic highlights of the remaining 16 of EU 28 countries, current year
GDP
Current prices
(USD billion)
GDP per capita Population
Current prices
(USD)
Millions
Population
density
Population Total fertility Life expectancy
growth rate
rate
at birth
Old-age dependency
ratio
Population per
square km
Rate per
annum (%)
Children per
woman
Years
Population aged 65+ per
100 population 15-64
2013
2013
2013
2010
2010-2015
2010-2015
2010-2015
2010
Austria
418
49,256
8.50
100.2
0.4%
1.47
81.0
26
Belgium
507
45,537
11.10
358.4
0.4%
1.85
80.4
26
Bulgaria
54
7,411
7.22
66.6
-0.8%
1.53
73.5
27
Croatia
59
13,312
4.29
76.7
-0.4%
1.50
77.0
26
Cyprus
22
24,706
1.14
119.0
1.1%
1.46
79.8
16
Czech Republic
199
18,868
10.70
133.8
0.4%
1.55
77.6
22
Estonia
24
18,127
1.29
28.8
-0.3%
1.59
74.3
26
Hungary
131
13,172
9.95
107.6
-0.2%
1.41
74.5
24
Latvia
30
14,924
2.05
32.4
-0.6%
1.60
72.1
27
Lithuania
47
15,633
3.02
47.0
-0.5%
1.51
72.1
22
Luxembourg
61
110,573
0.53
196.4
1.3%
1.67
80.5
20
Malta
9
22,323
0.43
1,344.1
0.3%
1.36
79.7
21
Poland
514
13,334
38.22
118.2
0.0%
1.41
76.3
19
Romania
184
8,630
21.70
91.7
-0.3%
1.41
73.7
21
Slovakia
97
17,929
5.45
110.8
0.1%
1.39
75.3
17
Slovenia
47
22,719
2.07
101.4
0.2%
1.50
79.5
24
Source: UN, IMF, Credit Suisse
A comparative demographic analysis of EU28
22
GLOBAL FIXED INCOME AND ECONOMIC RESEARCH
Dr. Neal Soss
Global Head of Economics and Demographics Research
(212) 325 3335
neal.soss@credit-suisse.com
Eric Miller
Co-Head, Securities Research & Analytics
(212) 538 6480
eric.miller.3@credit-suisse.com
ECONOMICS AND DEMOGRAPHICS RESEARCH
GLOBAL / US ECONOMICS
Dr. Neal Soss
(212) 325 3335
neal.soss@credit-suisse.com
Jay Feldman
(212) 325 7634
jay.feldman@credit-suisse.com
Dana Saporta
(212) 538 3163
dana.saporta@credit-suisse.com
Isaac Lebwohl
(212) 538 1906
isaac.lebwohl@credit-suisse.com
Casey Reckman
(212) 325 5570
casey.reckman@credit-suisse.com
Argentina, Venezuela
Daniel Chodos
(212) 325 7708
daniel.chodos@credit-suisse.com
Latam Strategy
Di Fu
Juan Lorenzo Maldonado
(212) 538 4125
(212) 325 4245
juanlorenzo.maldonado@credit-suisse.com di.fu@credit-suisse.com
Colombia, Peru
Daniel Lavarda
55 11 3701 6352
daniel.lavarda@credit-suisse.co
Iana Ferrao
55 11 3701 6345
iana.ferrao@credit-suisse.com
Leonardo Fonseca
55 11 3701 6348
leonardo.fonseca@credit-suisse.com
LATIN AMERICA (LATAM) ECONOMICS
Alonso Cervera
Head of Latam Economics
52 55 5283 3845
alonso.cervera@credit-suisse.com
Mexico, Chile
BRAZIL ECONOMICS
Nilson Teixeira
Head of Brazil Economics
55 11 3701 6288
nilson.teixeira@credit-suisse.com
Paulo Coutinho
55 11 3701-6353
paulo.coutinho@credit-suisse.com
EURO AREA / UK ECONOMICS
Neville Hill
Head of European Economics
44 20 7888 1334
neville.hill@credit-suisse.com
Christel Aranda-Hassel
44 20 7888 1383
christel.aranda-hassel@credit-suisse.com
Giovanni Zanni
44 20 7888 6827
giovanni.zanni@credit-suisse.com
Axel Lang
44 20 7883 3738
axel.lang@credit-suisse.com
Steven Bryce
44 20 7883 7360
steven.bryce@credit-suisse.com
Mirco Bulega
44 20 7883 9315
mirco.bulega@credit-suisse.com
Violante di Canossa
44 20 7883 4192
violante.dicanossa@credit-suisse.com
EASTERN EUROPE, MIDDLE EAST AND AFRICA (EEMEA) ECONOMICS
Berna Bayazitoglu
Head of EEMEA Economics
44 20 7883 3431
berna.bayazitoglu@credit-suisse.com
Turkey
Sergei Voloboev
44 20 7888 3694
sergei.voloboev@credit-suisse.com
Russia, Ukraine, Kazakhstan
Carlos Teixeira
27 11 012 8054
carlos.teixeira@credit-suisse.com
South Africa
Alexey Pogorelov
7 495 967 8772
alexey.pogorelov@credit-suisse.com
Russia, Ukraine, Kazakhstan
Natig Mustafayev
44 20 7888 1065
natig.mustafayev@credit-suisse.com
EM and EEMEA cross-country analysis
Nimrod Mevorach
44 20 7888 1257
nimrod.mevorach@credit-suisse.com
EEMEA Strategy, Israel
JAPAN ECONOMICS
NON-JAPAN (NJA) ECONOMICS
Hiromichi Shirakawa
Head of Japan Economics
81 3 4550 7117
hiromichi.shrirakawa@credit-suisse.com
Dong Tao
Head of NJA Economics
852 2101 7469
dong.tao@credit-suisse.com
China
Robert Prior-Wandesforde
65 6212 3707
robert.priorwandesforde@credit-suisse.com
Regional, India, Indonesia, Australia
Christiaan Tuntono
852 2101 7409
christiaan.tuntono@credit-suisse.com
Hong Kong, Korea, Taiwan
Santitarn Sathirathai
65 6212 5675
santitarn.sathirathai@credit-suisse.com
Regional, Malaysia, Thailand
Michael Wan
65 6212 3418
michael.wan@credit-suisse.com
Singapore, Philippines
Weishen Deng
852 2101 7162
weishen.deng@credit-suisse.com
China
Takashi Shiono
81 3 4550 7189
takashi.shiono@credit-suisse.com
GLOBAL DEMOGRAPHICS & PENSIONS RESEARCH
Dr. Amlan Roy
Head of Global Demographics
44 20 7888 1501
amlan.roy@credit-suisse.com
Sonali Punhani
44 20 7883 4297
sonali.punhani@credit-suisse.com
Angela Hsieh
44 20 7883 9639
angela.hsieh@credit-suisse.com
Gergely Hudecz
33 1 7039 0103
gergely.hudecz@credit-suisse.com
Czech Republic, Hungary, Poland
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