Download The Composite Index of Propensity to Risk – CIPR

E CON OMI C RE SE ARCH & CORPORAT E D E VE LOPME N T
Working
Paper
April 12, 2011
} MA C RO ECONO M ICS
147
} F INA N C IAL M AR KETS } EC O NOM IC POL IC Y
Alexandrina Scorbureanu, Dr. Arne Holzhausen
The Composite Index of Propensity to
Risk – CIPR
} S ECTO RS
Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
Working Paper
No. 147
The Composite Index of Propensity to
Risk – CIPR
Executive summary.......................................................................................................3
1. Introduction................................................................................................................3
2. Measures of risk aversion and factors influencing
the “risk attitude”......................................................................................................4
2.1. Measures of risk aversion..............................................................................4
2.2. Factors influencing the risk attitude .......................................................6
3. From “individual” to “aggregate” risk attitudes:
The composite index of risk propensity (CIPR)...........................................8
3.1 The risk drivers....................................................................................................8
3.2 Comparative analysis for Germany, Italy and France.................... 10
3.3 Comparative analysis for the Western European
Countries (WEU), Japan and the United States ................................ 12
3.4 Focus on the cases of Japan and the United States......................... 16
4. Propensity to risk and the structure of financial assets....................... 18
4.1 Dynamic picture of the financial assets structure during
the last fifteen years....................................................................................... 19
4.2 Links between the propensity to risk and the structure of
financial assets ................................................................................................ 19
5. Conclusions.............................................................................................................. 24
6. References................................................................................................................. 25
7. Appendices ............................................................................................................... 28
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AUTHORS:
ALEXANDRINA SCORBUREANU
Tel.: +49.89.38 00-1 36 34
[email protected]
DR. ARNE HOLZHAUSEN
Tel.: +49.89.38 00-1 79 47
[email protected]
Working Paper / No. 147 /April 12, 2011
EXECUTIVE SUMMARY
This report looks at the class of empirical results brought forward by experimental and
empirical economics, in an attempt to understand the propensity to risk of individuals at
aggregate, national levels and to analyze what changes it brings to people’s common
behavior when dramatic, unexpected events occur. Theoretical measures of risk aversion
are briefly presented to introduce the results obtained from empirical and experimental
studies in the last half of century. Then, a composite index of propensity to risk is
proposed (CIPR), by making use of the empirical results that, at the aggregate level of a
society, correspond to a number of risk drivers, correlated to the attitude of individuals
towards uncertainty. The results obtained for a set of developed countries (Western
Europe, Japan and the United States) reveal that while macro, worldwide dramatic events
generate unidirectional consequences for all nations – with the aversion to risk shifting
on increasing paths – the micro, country-specific events generate a variety of responses,
specific to each region. With the understanding of countries’ risk propensity to hand, we
derive conclusions on the aggregate portfolio investment decisions during the past
fifteen years.
The results indicate that the risk aversion index has a good explanatory power for the
investment decisions related to financial assets such as bank deposits and securities,
whereas investments in insurance products are not directly and exclusively predictable
from risk attitudes. This is mainly due to the complexity of insurance products,
combining investment opportunities and pure insurance products. Two additional
motivations are evident in correspondence to the investments in insurance: i) the long
time lags specific to these options can outbalance the portfolio investment decisions in
favor of bank deposits and securities, characterized by short-term perspectives, and ii)
different countries usually enact different insurance-related policies, such as pension
plans, regulations etc. In the long-term perspective, the results show that some countries
tend to be more risk-averse than others. In Japan, for instance, despite of its risk-averse
historical profile, the most recent evidence indicates how investment decisions have
turned onto a risk-seeking path recently – understandable given the ultra-low yield
environment in this country. The entrepreneurial spirit of Japanese investors is capable
of breaking the stereotypes we used to imagine about this culture. In Europe, the case of
France has a demographic explanation: given its relatively high birth rates, French
households seem to have a tendency of accepting uncertainty more readily than people
from other countries where the ageing process is in full swing, causing an increase in the
levels of risk aversion. Interestingly, Portugal, Spain and above all Greece were all on
decreasing risk aversion trends, showing indulgence for risky investments during the
“boom years,” whereas Ireland has developed an increasing sensibility to risk early on.
The United States is found to be representative for the risk-seeking behavior, although
the financial crisis has definitely prompted a dramatic change of perspective in the
opposite direction. Undoubtedly, the risk attitude is not stable over time: it changes with
the economic turmoil. Whenever unexpected events occur, the shield of protection
against risky investments is raised.
1. INTRODUCTION
“The greatest risk is the risk of riskless living.” Stephen Covey
The financial collapse of recent years doubtless has many causes. Certain is that it
changed substantially many of the common economic beliefs about markets, and
especially affected the attitude of individuals towards uncertainty and risks. If nothing
else, the belief in mathematical models built on the rationality of homo oeconomicus has
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been shattered. The confidence in risk-predicting models is both difficult to regain and
necessary. Since we cannot exclude risks from our lives, we must at least learn how to
live with them. Unfortunately there is a huge amount of uncertainty that simply cannot
be predicted1 in a plausible manner.
Behavioral theorists have long considered risk perceptions of individuals to derive
conclusions on their rational behavior. Blaise Pascal has already invoked, by the famous
wager contained in his Pensées published in 1670, the idea of “expected value”. He
claimed that, when faced with a number of alternative actions, each of which could give
rise to more than one possible outcome with different probabilities, the rational
procedure to compute utility equivalents was to identify all possible outcomes,
determine their values (positive or negative) and associate the probabilities that will
result from each course of action. More recently, the necessity to study how individuals
perceive uncertain outcomes and risks - as a subjective judgment of the characteristics
and severity of a risk – became imminent with the rapid development of nuclear
technologies and the promise for clean and safe energy back in 1960s. Behavioral
theorists, from both psychology and economics sciences, have developed models to
analyze what kinds of risks were accepted by the society.
This report surveys a number of results obtained by economists and psychologists, in an
attempt to explain the attitude of individuals vis-à-vis the risks at the aggregate level
reproducing the features of a society. Theory suggests and common sense confirms that
when dramatic, unexpected events occur, the general climate becomes more risk-averse.
A composite index at the country level is proposed, starting from factors correlated to the
risk attitude and reproducing the characteristics of a society, with the specific objective
to analyze differences among countries’ risk propensity profiles.
2. MEASURES OF RISK AVERSION AND FACTORS INFLUENCING
THE “RISK ATTITUDE”
2.1. Measures of risk aversion
It is a widely accepted assumption in microeconomics that rational individuals act as
though they were maximizing expected utility. The form that the utility function takes
when its components change (e.g. consumption basket, portfolio composition) allows us
to derive properties describing the individual attitude towards risky or uncertain
outcomes. The risk aversion reflects the uncertainty that “matters”, that is the amount of
uncertainty affecting our utilities. The degree of aversion to risk determines the behavior
of individuals in front of uncertain scenarios or outcomes. Some individuals may be
more reluctant than others when deciding whether to accept a bargain with an
uncertain payoff rather than another bargain with a certain, but possibly lower expected
payoff. Different values of expected utility have different meanings for each individual
and can lead to various decisions. In the expected utility theory, risk aversion is related to
the curvature of the utility function reproducing the reaction of individuals to different
uncertainty levels (probabilities); the curvature of the representative utility function is
typically measured by the risk aversion coefficient.
1
In the sense that the significance of the predictions is very low or the estimated outcomes vary with the
methodology used.
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The spectrum of risk attitudes is related to the form of utility functions reflecting the
behavior of individuals when choosing between risky, uncertain outcomes and certain
equivalents. For example, consider two possible monetary outcomes or lotteries, z1 and
z2 that may occur with chances p and (1-p) respectively. The average, expected outcome
at a given time is E(z)=pz1+(1-p)z2. This outcome generates some utility equivalent
U[E(z)]. Depending on the shape of utility functions, the utility equivalent is perceived
differently by different categories of individuals. For instance, risk averse individuals
have concave utility functions meaning that they are less likely to accept risky, uncertain
outcomes as their perceived utility value increases. As from figure 1 below, their
perceived value of utility arising from the payoff E(z) is Ua[E(z)]. On the contrary, riskseeking individuals exhibit convex utility functions meaning that they are more likely to
accept risky outcomes as the perceived utility value increases. For the same payoff E(z),
risk-seeking individuals will get a utility equivalent of Us[E(z)]. Risk-neutral individuals
have linear utility functions, meaning that they are tolerant, indifferent to risk as their
perceived utility value increases. In terms of perceived utility by the risk-neutral
individuals, the payoff E(z) will bring a utility of U0[E(z)].
In figure 1 note that, by comparing points A, B and C, given the concavity-convexity
property mentioned above, for the same expected outcome E(z), the risk-averse
individuals will perceive a higher utility value than the risk-neutral and risk-seeking
individuals (point A lies above point B, which lies above point C). In terms of utility
equivalents, this implies Ua[E(z)] > U0[E(z)] > Us[E(z)]. The difference in utility
equivalents relies on a “risk premium” argument, explained as follows: risk-averse
individuals are likely to receive a positive amount of money in order to accept playing a
game with an uncertain outcome. Vice-versa, risk-seeking individuals pay the “risk
premium” giving them the opportunity to bet on uncertain outcomes. Risk-neutral
individuals are not likely to pay nor to receive any premium associated with the bet,
given their indifference in face of risky outcomes.
Figure 1: Utility functions and the individual’s risk attitude
Risk averse
Ua[E(z)]
A
Utility scale
Risk neutral
U0[E(z)]
B
Risk seeking
C
Us[E(z)]
z1
0
E(z)
=pz1+(1-p)z2
z2
Outcome z
(monetary units)
The most popular measures of risk aversion commonly tested by empirical studies, are
the Arrow-Pratt measures of absolute and relative aversion to risk respectively (1964-65).
These measures rely on the properties of utility functions2 illustrated above. However,
2
The inverse ratio of utility functions derivatives denotes the “speed” at which the degree of aversion to risk
increases, everything else constant. In absolute terms, the degree of aversion to risk is measured using three
additional assumptions, corresponding to three behavioral scenarios. Constant Absolute Risk Aversion functions
(CARA) measure the degree of aversion to risk when the curvature of the utility functions increases with a constant
speed. Similarly, Increasing Absolute Risk Aversion (IARA) and Decreasing Absolute Risk Aversion (DARA)
functions measure the degree of aversion to risk when the curvature of the utility functions increases with an
increasing or decreasing speed respectively. A second class of risk aversion measures is provided by the Relative
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despite the beautiful mathematical setting around the risk aversion subject and the
interesting properties derived from utility functions, they cannot be observed directly
and therefore the degree of aversion to risk cannot be derived straightforwardly.
Chiappori and Salanié (2003) conclude that ”although risk aversion plays a crucial role in
the story, it is not directly observable.”
2.2. Factors influencing the risk attitude
As a response to the pessimistic predictions of microeconomics, empirical and
experimental studies conducted by economists and psychologists during the last years,
provided some interesting results related to the actual behavior of individuals in front of
risky scenarios. Several studies identified a number of factors and circumstances that
potentially influence individuals’ attitude to risk. One of these factors is the wealth
(Burguignon 2002; Laffont and Matoussi 1995; Ackerberg and Botticini 2000 and 2002,
Fukunaga and Huffman 2009). With the increase in income, individuals tend to become
more willing to accept risks. In general, low-income earners are more risk averse than
richer individuals.
The income effect shadows an “educational attainment effect” since individuals that
have studied longer have higher chances to find well-paid jobs on the labor market. Riley
and Chow (1992) confirmed that risk aversion decreases with wealth, adding that there is
a down sloping effect with education and age. Baron (2010) showed that people with
preferences for luxury goods (leisure activities, luxury vacation, yachts, etc) exhibit less
concave functions of utility for money and therefore they are more likely to take risks.
Zuckerman (1994) found that there are significant differences in the risk attitude
according to nationality, birth order, race and marital status. In addition, Barsky et al.
(1997) found differences in RRA (relative risk aversion measure) bounds according to
religion, and drinking and smoking behaviors. Also Lin (2009) confirms that drinking and
smoking behavior lead to increasing values for the absolute risk aversion. Interesting
effects were found as related to the professional profiles of individuals. In this respect,
King (1974) shows that people working in the primary industry sectors tend to be more
risk-averse than individuals being employed by tertiary sectors of activity.
These results are nowadays widely accepted and have been tested on different samples
of data to confirm the correlation between personality-related factors and the
individual’s attitude toward risk. Following the discussion from the two paragraphs
above, a selective list of factors and the class of risk they correspond to are presented in
table 1.
Risk Aversion (RRA) functions: Constant Relative Risk Aversion (CRRA), Increasing Relative Risk Aversion (IRRA)
and Decreasing Relative Risk Aversion (DRRA) respectively.
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Table 1. Individuals: socio-economic factors correlated to the risk attitude
Expected effects on the risk attitude
#
1
Factor
Income level dynamics
Type
Risk-
Risk-
Risk-
averse
neutral
seeking
Increasing
X
Constant
Decreasing
X
X
Reference: Burguignon (2002) and (2003), Laffont and Matoussi (1995), Ackerberg and Botticini (2000) and (2002),
Fukunaga and Huffman (2009).
2
Expenditure on leisure and
Increasing
cultural activities
Constant
X
X
Decreasing
X
Unemployed
X
Employee
X
Reference: Uhlig (2007), Baron (2010).
3
Professional status
Employer (entrepreneur)
X
Self employed
X
Reference: King (1974), Musetescu et.al. (2007).
4
Age
< 20 years old
X
21-39 years old
X
40-59 years old
X
> 60 years old
X
Lower secondary
X
Reference: Halek and Eisenhauer (2001).
5
Schooling level
Upper secondary
X
Tertiary school
X
Reference: Riley and Chow (1992), King (1974).
6
Occupational profile
Working in Agriculture
X
Working in Manufacturing
X
Working in Services
Working in Public Sector
X
X
Reference: Musetescu et.al. (2007), Friedman (1957), Skinner (1988).
7
Civil status
Single
X
Living in couple/ married
Divorced/ widowed
X
X
Reference: Zuckerman (1994), Halek and Eisenhauer (2001).
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3. FROM “INDIVIDUAL” TO “AGGREGATE” RISK ATTITUDES: THE
COMPOSITE INDEX OF RISK PROPENSITY (CIPR)
3.1 The risk drivers
Composite measures of risk can be calculated as to reproduce the characteristics of
aggregate utility functions, under the assumption that individuals “translate” their
behavior at the group level as to reproduce the features of a society. This assumption is
equivalent to the symmetric characteristics of a society that replicates individuals’
behavior. We have to admit that this is a strong assumption, but the debate concerning
its reliability is by far less popular than the discussion around the re-definition of the
GDP as a measure of wealth. Our purpose here is not related to an investigation of
individual psychology in front of risks but is rather related to the evaluation of an
aggregate level of propensity to risk.
In this sense, the composite index of propensity to risk at the country level is calculated
starting from empirical results at the individual level, formerly presented in table 1.
Factors predicting levels of risk aversion for individuals are identified on the
macroeconomic scale with those variables describing the socio-economic
characteristics of the population (professional and occupational profile, age profile,
schooling, civil status) and with variables that describe the economic environment (GDP
growth, share of expenditure allocated to leisure and cultural activities). In table 2, these
factors and their allocation to different classes of risk aversion are presented under the
“society’s” umbrella. For instance, the change in GDP growth rates from one year to
another has an impact on the perception of individuals about the economic
performance of their own country (the environment where they live), and, consequently,
it has an impact on their perception about risks. When the GDP of a year performs above
its own average level on a given time lag, this is equivalent to the situation in which an
individual earns more money at some date, outperforming his average earning level. His
additional gains will “modify” his attitude towards uncertainty and the individual will be
willing to take more risks than he did in the past, when he was poorer.
Relatively low levels of growth (or decreasing growth rates) should correspond to an
overall decrease in the level of propensity to invest in risky assets. People are more
reluctant vis-à-vis the investments with uncertain outcomes when they are poorer.
Following the same logic, a decrease in the budget shares allocated to leisure and
entertainment activities denotes a lower propensity to take risks. In addition to wealth,
different country risk profiles also arise from different population structures in different
regions of the world. A country characterized by i) relatively (too) old or (too) young
population structure, ii) relevant unemployment rates, iii) high rates of lower secondary
educational attainment, iv) high share of population mostly employed in primary and
secondary sectors, will be more likely to reproduce the “behavior” of a risk-averse
representative individual. These are all factors contributing to the country’s general
propensity to risk; in this study they are defined as “risk drivers”.
Using the above motivation as a starting point, an appropriate mapping procedure is
used to rank different values of a risk driver on the “risk propensity” scale. The scale of
risk propensity is chosen as to reproduce the characteristics of utility functions that
exhibit the property of Constant Absolute Risk Aversion (CARA). The scale of risk aversion
ranges from -1, corresponding to the risk-seeking behavior, to 1, corresponding to the riskaverse behavior. The average indicators of propensities to risk are computed in
correspondence to each risk driver. Finally, our purpose is to define the Composite Index
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of Propensity to Risk (CIPR) at the country-level as an simple average of all the drivers3 for
different levels of propensity to risk specific to each country. The contribution that each
driver has on the definition of the risk propensity index for each country reflects the
country’s relative “position” on the risk-propensity scale with respect to that particular
driver.
Table 2. Society: socio-economic factors (drivers) correlated to the general propensity to risk
Expected effects on the risk
#
Factor
Type
propensity
(per country and per year)
1
Diff. between real GDP growth rate (GDP-rgr) and the
Positive
average 1995-2009 GDP-rgr
Zero
Negative
2
3
Risk-
Risk-
Risk-
averse
Neutral
Seeker
(1)
(0)
(-1)
X
X
X
Diff. between the expenditure on leisure and cultural
Positive
X
activities (% of the total expenditure) and the 1995-
Zero
2009 average leisure expenditure
Negative
X
Population professional profile: distribution by
Unemployed
X
industries (number of individuals, 1000)
Employee
X
X
Employer
X
(entrepreneur)
Self employed
4
5
X
Population age structure
< 20 years old
X
(number of individuals)
21-39 years old
X
40-59 years old
X
> 60 years old
X
Population distribution by level of schooling
Lower secondary
X
(% of the total population aged 16 to 65)
Upper secondary
X
X
Tertiary school
6
Population distribution by sectors of employment
Working in
(number of individuals, 1000)
agriculture
X
X
Working in Industry
X
Working in Services
Working in Public
X
X
Sector
7
Population distribution by marital status
Single
(number of individuals)
Living in couple/
X
X
married
Divorced/ widowed
3
X
Each risk driver contributes by the same weight to the definition of the composite index.
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The Composite Index of Propensity to Risk (CIPR) measures the propensity to take risks of
the representative consumer from each country, combining the effects of all seven risk
drivers presented in the table 2 above (graphics 1 to 7 from the appendix show the
dynamics of these factors for the last fifteen years for each country). The index takes
values on a (-1.1) scale, with the following meanings: the negative values indicate riskseeking behavior; the index values close to zero indicate risk-neutrality, whereas positive
index values indicate risk aversion. The index evolution during the past fifteen years is
monitored for sixteen European countries and compared to the index values observed in
Japan and the United States. We provide a detailed insight into the index values
registered in Germany, Italy and France and a general overview of the other European
countries. Finally, attention is shifted towards the cases of Japan and the United States.
3.2 Comparative analysis for Germany, Italy and France
Considering the differences in the population structure and the economic backgrounds
in each of the three countries, we investigate the resulting diversity of profiles describing
the country risk propensity. Figure 1 below shows the evolution of the three CIPR indexes
over the past 15 years, while identifying trends for the future. Inspection of figure 1
reveals that the ups and downs of the index often follow the turmoil of events occurring
worldwide. Some of these events are worth mentioning here.
Figure 1. Composite Index of Propensity to Risk (CIPR) for Germany, Italy, and
France (1995-2009). Source: own calculations.
0,70
y = 0,023x + 0,0571
R2 = 0,2139
y = -0,0019x + 0,1675
R2 = 0,0014
0,70
y = 0,0388x - 0,2428
R2 = 0,5908
0,60
0,60
0,50
0,50
Risk
0,40
0,40
averse
0,30
0,30
0,20
0,20
0,10
0,10
0,00
1995
96
97
98
99
00
01
02
03
04
05
06
07
08
0,00
2009
-0,10
-0,10
Risk
-0,20
-0,20
seeking
-0,30
France
Germany
Italy
Linear (Italy)
-0,30
Linear (Germany)
Linear (France)
Firstly, the fall in the level of aversion to risk in France and Germany, equivalent to high
levels of propensity to risk, that are predicted by the index for the years 1996 to 1999 fits
well with the scenario of the “telecom bubble” at that time. In fact, before the “big bubble
burst” in the year 2000, the telecommunications industry had became a gigantic poker
game, with everybody buying telecom shares. Secondly, note that in Italy the risk-seeking
behavior has been particularly accentuated starting with the end of 2001. During this
year, the number of Italian bondholders in Argentina had risen at unexpected levels. As
the Argentine government defaulted in 2002, it generated a brusque increase in the
general risk aversion that dominated the Italian scene for the following two years. Ever
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since, Italy has irreversibly changed its risk attitude profile, switching from risk-seeking
to risk-averse.
Another event had a relevant contribution to the general increase in the level of risk
aversion in Germany and France (Italy reacted later to it): the terror attacks on the World
Trade Center’s Twin Towers in New York on September 11th 2001. The already risk-averse
Germany maintained high levels of diffidence until mid-2006, when France had already
turned back to be a risk-seeker. Finally, low levels of aversion to risk in 2006 and 2007
corresponded to the effects of the “housing bubble” phenomenon in the United States.
The year 2008 saw the collapse of financial markets that prompted an increase in the
levels of risk aversion in all three countries. In a long-term perspective, Italy and
Germany are more likely to increase their cautiousness (increasing trends of risk
aversion with slopes of 0.04 and 0.02 respectively), whereas France is slightly more likely
to take risks. This increasing trend can be related to the French population structure
during recent years, when this country has registered among the highest birth rates in
Europe. In fact, during the past few years, an increasing number of children per
household contributed to generating a more “relaxed” risk attitude. Sustained
expenditure shares on leisure activities also contributed lowering down the risk defense
shield in French households.
Figure 2 below shows a detailed view of the factors driving the risk propensity, to
emphasize differences between the three country risk aversion profiles, by each of the
seven components mapped on the (-1.1) scale of risk-aversion: employment by sectors of
activity, expenditure share on leisure and recreational activities, real GDP growth rates,
population age profiles and professional status, marital status and educational
attainment levels.
Figure 2. Comparative analysis of risk propensity profiles: Germany, Italy and France
(scale: 1=risk-averse
0=risk-neutral -1=risk-seeking). Source: own calculations.
Expenditure share on leisure
1
0,8
0,6
0,4
Employment by sector
0,2
GDP grow th rate
0
-0,2
-0,4
-0,6
-0,8
-1
Education prof ile
Population by age
Marital status
Italy average
Professional profile
France average
Germany average
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Risk propensity profiles reveal at a glance the existence of both similar features and net
differences among the three economies, depending on each driver. For example, the
expenditure share on leisure and recreational activities in Germany remained on a
decreasing path from 2000 until 2006 (see graphic 2 from the appendix). On average,
Germans spending less money on leisure each year, as compared to their average
spending during the last fifteen years, became more skeptical towards decisions
concerning risk taking when compared to their neighbors from France and Italy that saw
their leisure expenditures remain largely unchanged.
From the point of view of age profiles, the three risk propensity profiles almost overlap.
Differences in the population distribution among professional profiles indicate a higher
propensity to risk of those countries in which the number of entrepreneurs and liberal
professions is higher, that is in Germany and France. As far as marital status is
concerned, despite the low birth rates, Italy has the highest number of couples, known to
be less risk-averse than singles and widows. On the other side, the share of the
population having attained tertiary school levels in this country, including University
laureates, Masters and PhDs, is lower than in France and Germany. This fact serves to
increase the overall aversion to risk in Italy while the other countries remain on the riskaversion side, but with lower index values.
3.3 Comparative analysis for the Western European countries (WEU),
Japan and the United States
In this section, the comparative analysis is extended to thirteen more Western European
countries on one side and Japan and the United States on the other. The ranking of
countries by the average index values over the past fifteen years is presented in the
following table, in ascending order, according to the risk-aversion scale. Negative index
values indicate a risk-seeking behavior, values next to zero indicate risk neutrality and
positive values indicate risk aversion. The higher the index value (closer to one), the
higher the risk aversion, and in consequence, the lower the propensity to invest in risky
assets. Values next to minus one indicate high levels of risk propensity. When comparing
these results, we recommend adopting interpretation in terms of “risk aversion ordering”
rather than in value terms4.
4
For instance, instead of affirming that the United Kingdom is twice as risk averse as Italy it makes more sense to affirm that
the UK has in general a higher risk aversion level than Italy.
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Table 3. CIPR index: ranking of countries according to their
average propensity to risk in the last fifteen years. Source: own
calculations.
Rank*)
Countries
Average Index
Variance
(1995-2009)
1
United States
-0,037
0,024
2
Italy
0,068
0,051
3
Spain
0,072
0,051
4
Greece
0,092
0,046
5
Japan
0,107
0,043
6
Sweden
0,109
0,033
7
Austria
0,110
0,023
8
United Kingdom
0,121
0,030
9
France
0,152
0,052
10
Ireland
0,152
0,044
11
Netherlands
0,160
0,067
12
Belgium
0,163
0,056
13
Switzerland
0,167
0,023
14
Norway
0,174
0,011
15
Denmark
0,176
0,045
16
Portugal
0,183
0,026
17
Finland
0,191
0,042
18
Germany
0,241
0,050
*) ordered increasingly by risk aversion levels: negative values indicate risk seeking
behaviour; values next to zero indicate risk neutrality; positive values indicate risk
aversion.
The ranking of the CIPR averages from table 3 above leads to some interesting
conclusions. First of all, note that all WEU countries exhibit positive index values,
meaning that, in general, they can be associated with risk-averse behavior profiles.
Germany has, on average, the lowest propensity to risk (in other words, it is the most
risk-averse with an average CIPR equal to 0.241) followed by Finland, Portugal and
Denmark. By contrast, Mediterranean countries, such as Italy, Spain and Greece, are
among the top five least risk averse countries, being on average close to risk neutrality
(index values of 0.068, 0.072 and 0.092 respectively). It is not that surprising to find out
the United States is at the top of the list, following on average a risk-seeking behavior
during the past fifteen years (average index value equal to minus 0.037). Interestingly,
Japan is the fifth less risk averse country (index value of 0.107), just after Greece and
followed by Sweden and Austria, separated by very small differences. In a way, this seems
to be a rather surprising result, given that the Japanese culture is usually seen as a
symbol of cautiousness – therefore expected to be characterized by low degrees of risk
propensity. On the other hand, there exist some important arguments that the Japanese
are about to leave their “comfort zone”. Despite the high profile of their world-class
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manufacturers, the modern Japanese economy mostly relies on tertiary sectors
(accounting for 68 percent of the GDP in 2007) whereas the industrial and agricultural
contributions to the GDP are decreasing every year. Higher employment rates in the
services sector tips the risk propensity balance towards higher, positive values. Moreover,
as reported by the Financial Times Journal in March 2010, Japanese companies are now
convinced that future growth will be found outside their own country. Therefore, they are
committing themselves to new investments in China as they continue to retrench and
restructure at home. Investments abroad imply a higher degree of acceptance of market
risks on behalf of the Japanese investors – a confirmation of their increased willingness
to undertake risks. Increasing GDP growth rates5 have also contributed to “relax” the
Japanese’s perception of risks as well as the “permissive” regulation of foreign direct
investments promoted by the Japanese government since 2004.
The three panels of figure 3 below depict the index evolution in a number of selected
countries during the past fifteen years. Each panel presents the index evolution on a fiveyear timescale. As panel 1 shows, between 1995 and the end of 1997, Portugal, Spain,
Greece and Ireland behaved differently in terms of risk attitudes (with the first three
countries being risk-averse and Ireland a risk-seeker); during the years 1998 and 1999 all
these countries found a common denominator and almost converged towards a
common value of propensity to risk (in Portugal, Spain and Greece the index of risk
aversion constantly decreased and almost stabilized between the values 0.10 and 0.30
whereas Ireland constantly increased its aversion to risk up to the same values).
Between 2000 and 2004 (see panel 2) only Greece continued to decrease its degree of
aversion to risk, becoming more of a risk-taker, while the other countries continued on
increasing paths of aversion to risk; the most rapid increase was registered by Ireland
with an average increase of 0.11 points in the index value per year. The increase in the
level of risk aversion during this half of the decade may be interpreted as the reaction of
markets to the 9/11 terror attacks on the World Trade Centre in 2001. However, whereas
the strong reaction of Irish people to 9/11 might be understandable, the relatively high
level of risk aversion during the following years seems to be at odds with the building of
the great real estate bubble in the following years.
On the other hand, during 2005-2009, Portugal started to “drop its guard” against risks,
even if at a moderate pace, whereas the other countries continued to increase their risk
aversion levels, but starting from very low values (see panel 3). This period is
characterized by some relevant economic events such as the “housing bubble” (20052007) and ending up with the financial market crisis (end of 2008). These events have
had important repercussions on the risk attitudes of people living in Europe and not only
there. Risk aversion increased substantially in all countries, especially after December
2008. Greece and Portugal hesitated a bit longer before adjusting their risk aversion
levels on the uprising path.
5
OECD estimates for 2010 and 2011 are 10.2% and 9.3% respectively.
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Working Paper / No. 147 /April 12, 2011
Figure 3. Propensity to risk (CIPR) in selected Western European Union countries
(1995-2009)
Source: own calculations.
Panel 1): years 1995-1999.
0.70
0.60
0.70
y = -0.0562x + 0.474
R2 = 0.4823
y = -0.0597x + 0.4384
R2 = 0.3541
y = -0.0872x + 0.5605
R2 = 0.7589
y = 0.0826x - 0.1906
R2 = 0.7515
Risk
0.50
0.60
0.50
averse
0.40
0.40
0.30
0.30
0.20
0.20
0.10
0.10
0.00
1995
1996
1997
0.00
1999
1998
-0.10
-0.10
Risk
-0.20
-0.20
seeking
-0.30
-0.30
Portugal
Spain
Greece
Ireland
Linear (Ireland)
Linear (Greece)
Linear (Spain)
Linear (Portugal)
Panel 2): years 2000-2004.
0.70
0.60
0.70
y = 0.0813x - 0.1729
R2 = 0.7287
y = 0.0261x - 0.1739
R2 = 0.1044
y = -0.0872x + 0.5605
R2 = 0.7589
y = 0.1111x - 0.1773
R2 = 0.5556
0.50
0.60
0.50
0.40
0.40
Risk
averse
0.30
0.30
0.20
0.20
0.10
0.10
0.00
2000
2001
2002
0.00
2004
2003
-0.10
-0.10
Risk
-0.20
-0.20
seeking
-0.30
-0.30
Portugal
Spain
Greece
Ireland
Linear (Ireland)
Linear (Greece)
Linear (Spain)
Linear (Portugal)
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Working Paper / No. 147 /April 12, 2011
Panel 3): years 2005-2009.
0.70
0.60
0.70
y = 0.1426x - 0.3749
R2 = 0.8865
y = -0.0293x + 0.2619
R2 = 0.1272
y = -0.0872x + 0.5605
R2 = 0.7589
y = 0.1369x - 0.1676
R2 = 0.8912
0.50
0.60
0.50
Risk
0.40
0.40
averse
0.30
0.30
0.20
0.20
0.10
0.10
0.00
2005
2006
2007
0.00
2009
2008
-0.10
Risk
-0.10
-0.20
seeking
-0.20
-0.30
-0.30
Portugal
Spain
Greece
Ireland
Linear (Ireland)
Linear (Greece)
Linear (Spain)
Linear (Portugal)
A more detailed overview on the risk propensity profiles and differences among
countries arising from their socio-economic profiles – the risk drivers – is provided
through the panels of graphic 8 included in the appendix. The last panel of this figure
presents the evolution of CIPR for the remaining WEU countries (countries that are not
presented in the text) during the past fifteen years. Two cases are interesting to note
here: Switzerland and the United Kingdom. During the past fifteen years, the Swiss profile
entered the risk-seeking zone only once: during the years 1998-1999. Ever since it has
maintained a moderately constant risk aversion profile. As far as the UK is concerned, it
was the sole economy to maintain a risk-seeking strategy during 2002 besides the
Mediterranean countries – Portugal, Spain, Italy and Greece – refraining from reactions
to the market turmoil that characterized the years 2001-2002.
3.4 Focus on the cases of Japan and the United States
This section provides greater detail on the risk profiles of the United States and Japan,
being usually associated with diverse cultural backgrounds. When taking businessrelated decisions, Americans are usually expected to act as risk-lovers whereas the
Japanese people are traditionally associated with more conventional, risk-averse
stereotypes. Figure 4 shows the evolution of the propensity to risk levels during the past
fifteen years in these two countries, as obtained from the combination of main risk
drivers. The overall picture shows a profile of Japan coming from a highly risk-averse
region (with risk aversion coefficients above 0.40) and evolving towards more riskseeking profiles. In fact, it remained on a decreasing trend of risk aversion (equivalent to
the increasing risk propensity) with a linear slope of -0.030. On the contrary, Americans,
coming from the risk-seeking region had evolved towards risk-averse profiles, especially
between 2000 and 2005, while regaining confidence and desire to take risky decisions
during 2005-2007. The end of 2008 redirected both countries towards increasing trends of
aversion to risk.
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Working Paper / No. 147 /April 12, 2011
Figure 4. CIPR index for USA and Japan (1995-2009)
Source: own calculations.
0,70
y = 0,0122x - 0,135
R2 = 0,1253
y = -0,0302x + 0,3479
R2 = 0,4221
0,70
0,60
0,60
0,50
0,50
Risk
0,40
0,40
averse
0,30
0,30
0,20
0,20
0,10
0,10
0,00
1995
96
97
98
99
00
01
-0,10
02
03
04
05
06
07
0,00
2009
08
-0,10
Risk
seeking
-0,20
-0,20
-0,30
-0,30
Japan
USA
Linear (Japan)
Linear (USA)
It is interestingly to observe here the “following-up” effect that the two economies show
in terms of risk propensity, due to the active bilateral relationships enacted between
these markets. The United States economy seems to react more quickly to external
shocks, adjusting its levels of aversion to risk in response to the events that occur on the
global markets, whereas Japan follows on the same path, but at a slower pace. In a longterm perspective, the United States exhibits a slightly increasing risk aversion trend
towards less negative index values (confirmed by the positive linear slope of 0.01),
although remaining much of the time near the zones of risk-seeking and risk-neutral
profiles. By contrast, Japan shows a marked, decreasing trend towards less risk-averse
profiles, twice entering the risk-seeking region (at the end of 2000 and during December
2004).
Figure 5 below provides further details on the factors driving the risk propensity, to
emphasize the differences between the two countries, by each of the seven index
components. Mean CIPR values for the three countries in the past fifteen years put the
United States at the top of the risk-seekers list while Japan pursued, in general, more riskaverse strategies. Looking at the leisure expenditure profiles, Japanese people seem to be
less risk-averse than Americans since they spend more on tourism and cultural activities
each year. However, constant and decreasing rates in the GDP growth level (as compared
to the past fifteen years average benchmark) place them in a more risk-averse region
with respect to the Americans.
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Working Paper / No. 147 /April 12, 2011
Figure 5. Comparative analysis of risk propensity profiles for USA and Japan
(scale: 1=risk-averse
0=risk-neutral -1=risk-seeking) Source: own calculations.
Expenditure share on leisure
1
0,8
0,6
Em ploym ent by s ector
0,4
GDP growth rate
0,2
0
-0,2
-0,4
-0,6
-0,8
-1
Education profile
Population by age
Marital s tatus
Profess ional profile
USA average
Japan average
The educational profile seems to bring advantage once more to the Americans, with a
higher percentage of tertiary graduates, given that these individuals are more likely to
take riskier decisions than the average Japanese usually do. From the point of view of the
population distributions by marital status and age, the two economies are pretty much
alike.
4. PROPENSITY TO RISK AND THE STRUCTURE OF FINANCIAL
ASSETS
With the knowledge about the national risk propensity profiles to hand, we further
investigate the investment decisions at the portfolio aggregate level. Considering three
categories of financial assets - securities, bank accounts and insurance policies - we
expect to observe a re-distribution of investments in favor of the risky asset group (e.g.
securities) when the level of aversion to risk is relatively low. On the other hand, we
expect to observe a shift in the investments towards “safer” assets with sure, certain
outcomes (e.g. insurance and bank accounts) when risk aversion levels are relatively
high. Stated differently, the marginal increase in the portfolio share allocated to
securities should be reasonably explained by a marginal decrease in the level of aversion
to risk (or equivalently, to an increase in the risk propensity). On the other side, the level
of aversion to risk usually increases when dramatic, unexpected events occur. Therefore,
looking back at the events on September 11th 2001 or even at the recent financial crisis in
2008, we should observe contraction in the investments in securities in the following
years, 2002 and 2009 respectively.
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4.1 Dynamic picture of the financial assets structure during the last fifteen
years
The evolution of portfolio shares allocated by each country to investments in securities,
insurance policies and bank deposits is represented graphically in graphics 9, 10 and 11
respectively, that are included in the appendix. Note that the United States and Italy are
the countries with the highest portfolio shares invested in securities. Belgium and
Finland are also among the top five securities holders, whereas Japan and Norway have
the lowest levels of security investments, followed by the United Kingdom and
Netherlands. Looking at the securities investments between 1997-1998, it can be
observed that all the countries increased their shares in securities, with the highest peak
registered in Greece, that almost doubled its share from 38% to 62% of the total portfolio
value. It can also be observed how, after 2001, there was a decrease in the investments in
securities in all the countries that switched to an increasing path later on, in 2004.
As far as investments in pension policies are concerned, Netherlands has always been at
the top of the list, maintaining a portfolio share invested in pensions at above 50% during
the past fifteen years. The United Kingdom and Denmark are also among the leaders,
while Greece holds the lowest level of investments in this sector (at an almost constant
rate around 2-3% of the total), way below Italy and Spain following in this ranking. The
United States and Japan have maintained a steady portfolio during the past decade, with
investments in securities ranging between 25-30%. These differences reflect, of course,
the substantial differences between retirement systems enacted by governments from
each of these countries.
Relative changes in bank deposits have been much more dynamic than insurance and
securities markets during the past fifteen years, especially in Europe. In the year 1999
there was a tendency to decrease portfolio shares held in bank deposits, especially in
Mediterranean countries (Greece reached its historical low of 31% at that time). As
people started to be concerned about financial markets in 2001, an increasing wave of
money went to banks, as they represented a safer alternative. Recently, the collapse of the
stock markets has contributed to a similar effect in the portfolio re-distribution in all the
countries (Greece increased its share held in bank deposits from 51% to 72% in just two
years). Japan and the United States maintained a fairly unchanged bank deposits
investment profile over the past fifteen years, with average levels around 55% and 12%
respectively.
4.2 Links between the propensity to risk and the structure of financial
assets
The commonly accepted assumption among financial analysts is that, as people become
more averse to risk, they are less likely to invest their money in financial assets having an
uncertain outcome such as securities. At the country level, it means that economies in
which households invest more in securities rather than in other types of financial assets
are more likely to take risks and vice-versa. Stated differently, the risk propensity index
should, at least, be: i) negatively correlated with the amount of financial assets held in
securities (as the index values increase, the securities portfolio share decreases), and ii)
positively correlated with the amount of financial assets held in bank deposits (as risk
aversion increases, it is more likely that households shift their investments towards
safer, low-risk assets). As far as insurance policies are concerned, one would expect to
obtain positive correlation values as well, but this thesis is not confirmed by the
quantitative evidence. On closer examination, the index values and the corresponding
portfolio shares for each class of financial assets follow the rule of strong and negative
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correlations in the case of investments in securities and bank deposits (see the table 4
below). Apparently, this rule is not followed by investments in insurance policies, for at
least some of the reasons explained in the following.
Let us analyze first the relation between the risk-aversion index and the securities
portfolio shares. In this case, the risk aversion index (CIPR) has a good explanatory power
since, as expected, it is negatively correlated with the share invested in securities in
almost all of the analyzed countries. The negative correlation explains how increasing
levels of risk aversion generate a fall of investments in risky assets such as securities.
Nevertheless, a few outliers help strengthen the rule concerning the investments in
securities. One of these is the case of the United States, in correspondence to a riskseeking behavior profile. Note how, in this case, there is a positive, although weak,
correlation between risk aversion index values and portfolio shares invested in
securities. In fact, when there is an increase in the risk aversion index, from negative
values to less negative values (approaching zero), the positive correlation confirms the
idea that Americans might continue to invest in risky assets while being less risk-seekers
(but still keeping their risk-seeking behavior!), which may be reasonable enough.
Portugal, Austria and Belgium are the remaining exceptions from the general rule of
negative correlation. In these three cases, further investigation is needed in order to
understand why these countries would continue to prefer the risky assets, while being on
a positive and increasing path of aversion to risk.
Table 4. Correlations between CIPR and the financial assets structure
Rank*)
Countries
Correlation
Correlation
Correlation
(CIPR, Securities)
(CIPR, Bank deposits)
(CIPR, Insurance)
**)
1
United States
0,064
-0,139
0,220
2
Italy
-0,340
-0,152
0,826
3
Spain
-0,319
0,524
-0,597
4
Greece
-0,193
0,130
-0,391
5
Japan
-0,374
0,412
0,083
6
Sweden
-0,279
0,574
-0,334
7
Austria
0,128
-0,253
0,216
8
United Kingdom
-0,358
0,313
0,109
9
France
-0,596
0,302
-0,049
10
Ireland
-0,582
0,710
0,142
11
Netherlands
-0,777
0,564
0,845
12
Belgium
0,131
0,054
-0,313
13
Switzerland
-0,497
0,359
0,629
14
Norway
-0,342
0,209
0,134
15
Denmark
-0,684
0,565
0,407
16
Portugal
0,441
0,302
-0,298
17
Finland
-0,302
0,183
-0,038
18
Germany
-0,577
0,114
0,587
*) ordered increasingly by risk aversion levels.
**) numbers in italics: outliers from the generic rule, as explained in the text.
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The risk propensity index has a good predicting power in the case of bank deposits as
well. It is worth noting here that an increase in the level of aversion to risk at the country
level is associated with an increase of portfolio shares held in bank deposits. In fact,
increasing turmoil levels on financial markets determined an increase in the general
level of aversion to risk, with individuals becoming more reluctant toward the idea of
investing their money in securities. In this context, bank deposits offered a safer
alternative, with predictable but lower profit margins. In fact, in all the analyzed
countries – with the exception of the United States, Italy, and Austria – the representative
household increasingly preferred to invest in bank deposits whenever risk aversion levels
were seen to increase. Once more, the United States is an outlier to the rule; as before,
this exception finds its explanation in the risk-seeking profile of the representative
household living in this country. The cases of Italy and Austria require further analysis.
Looking at the investments in insurance policies it can be noted that they present no
clear evidence in relation to the level of risk aversion. This is partly due to the fact that, in
many countries, insurance itself also includes investment products, being subject to
market fluctuations. A second argument explaining different correlation signs obtained
for different countries might be related to some “portfolio lagged effect of insurance
markets”. According to this argument, different countries may have different “speeds” of
adjustment of their insurance-related investment decisions in response to the market
fluctuations. This may occur as a consequence of the long-term nature of insurance
products (which cannot easily be “sold”), as compared to the shorter perspective
provided by bank deposits and securities.
A more intuitive representation of country profiles related to their risk propensity
profiles and their “behavioral” consistency when it comes to investment decisions is
proposed in the three panels of figure 6 below, showing the positioning of countries
according to their risk propensity levels (average CIPR value over the past fifteen years)
and the correlation between CIPR and portfolio investment shares in the last fifteen
years. In particular, as explained in the previous paragraph, panel a) confirms the
assumption that the majority of risk-averse countries confirm their behavior through
negative correlations with the portfolio share invested in securities: as the risk aversion
level increases they are less likely to invest in risky assets. In particular, Netherlands is a
risk-averse country with a significantly negative correlation between its risk propensity
and the share invested in securities (the correlation is negative and its absolute value is
close to one). The United States exhibits risk-seeking behavior and there is a positive
correlation between its propensity to risk and the share invested in securities. This
behavior represents an exception to the rule, although the risk-seeking nature makes it
less uncommon than in the cases of Portugal, Belgium and Austria, which require further
investigation. Note from panel b) that Ireland is the country with the strongest
correlation between risk aversion and investments in bank deposits. Spain, Sweden,
Netherland and Denmark follow up on the list, with significant and positive correlation
values as well.
Although the evidence is not conclusive enough in the case of investments in insurance
products, we cannot refrain from commenting upon the most interesting results.
Netherlands and Italy seem to show the most significant and positive correlations
between risk aversion and investments in insurance policies (panel c), although their
risk aversion profiles are quite different (with Italy being much less risk-averse than
Netherlands). Switzerland and Germany follow up on the list, maintaining the
differences between risk aversion profiles. It is remarkable the role played by the United
States in this scenario: as the level of aversion to risk increases, Americans would also
increase their investments in insurance products. The countries with the highest
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Working Paper / No. 147 /April 12, 2011
propensity to increase their investments in insurance products when the general
aversion to risk increases are Netherlands, Italy, Switzerland, Germany and Denmark.
Finding Italy on this list of countries reveals another interesting “behavioral” property.
The general propensity to risk in this country is relatively high compared with the others.
There also exists a relatively high propensity to invest in “certain” outcomes when the
risk aversion level increases. These two attributes considered jointly can be interpreted
as a measure of “sensitive reactivity” of Italian markets towards changes in the global
financial scenario.
On the other side the “outliers” from this rule – those countries having a negative
correlation between their aversion to risk and the portfolio share invested in insurance
policies – are: Greece, Spain, Sweden, and once more, Belgium and Portugal. In France
and Finland the two variables show no significant correlation.
Figure 6. Relationship between the propensity to risk and the correlation
with portfolio shares
Panel a): Securities
0,60
DEGREE OF RISK AVERSION
Portugal
0,40
0,20
Austria
United States
0,00
0,00
-0,05
0,05
0,10
0,15
0,20
0,25
0,30
Greece
-0,20
Sweden
Spain
CORRELATION VALUES
-0,10
Belgium
UK
-0,40
Italy
Finland
Norway
Japan
Ireland
Switzerland
-0,60
France
-0,80
Germany
Denmark
Netherlands
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Panel b): Bank deposits
0,80
Ireland
Sweden
0,60
Denmark
Spain
Netherlands
Japan
0,40
Switzerland
Portugal
UK
France
Norway
0,20
Greece
Finland
Germany
Belgium
-0,10
0,00
0,00
-0,05
United States
CORRELATION VALUES
-0,20
0,05
0,10
0,15
0,20
0,25
0,30
Italy
Austria
-0,40
-0,60
-0,80
DEGREE OF RISK AVERSION
Panel c): Insurance products
0,90
Italy
Netherlands
0,70
Switzerland
Germany
0,50
Denmark
United States0,30
Ireland
Austria
0,10
-0,05
0,00
-0,10
CORRELATION VALUES
-0,10
Japan
0,05
0,10
Norway
UK
0,15
France
0,20
0,25
0,30
Finland
Sweden
-0,30
Greece
Belgium
Portugal
-0,50
Spain
-0,70
-0,90
DEGREE OF RISK AVERSION
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5. CONCLUSIONS
The CIPR index has a good explanatory power for investment decisions related to
financial assets. The quantitative evidence for insurance products reflects the difference
between the insurance investment decisions and the investment in other financial
assets. While the general aversion to risk increases, people will seek to reduce the
portfolio shares associated to high uncertainty levels (securities), investing more in
assets with sure outcomes, such as bank deposits.
From a long-term perspective, some countries tend to be more risk-averse than others.
Some may have different reaction speeds to the economic turmoil than others. In Japan,
for instance, households seem to be risk averse, but investment decisions have been on a
risk-seeking path recently. The entrepreneurial spirit of Japanese investors is capable of
breaking the stereotypes we were used to imagine about this culture. In Europe, the case
of France has a demographic explanation: given its high birth rates, French households
have a tendency to accept uncertainty more readily than people from other countries
such as Germany and Italy, where the ageing process is in full swing. In fact, both of these
countries show an increasing trend towards risk aversion. Portugal, Spain and above all
Greece were all on decreasing risk aversion trends, showing indulgence for risky
investments during the boom years, whereas Ireland has clearly developed an increasing
sensibility to risk. The United States is representative for the risk-seeking behavior,
although the financial crisis has definitely prompted a dramatic change of perspective in
the opposite direction. Undoubtedly, the risk attitude is not stable over time: it changes
with the economic turmoil. Whenever unexpected events occur, the shield of protection
against risky investments is raised. It follows that the shorter the observed periods of
time, the more reliable trends and forecasts are obtained, mostly when deeper analysis is
conducted around the “special” economic events.
Finally, it is not straightforward to infer that investments in insurance products will
increase in response to an erosion of risk propensity following dramatic events with
impact on the economy. The picture for the insurance industry is not as clear-cut as that
for bank deposits and securities. It differs from country to country. This is mainly due to
the fact that insurance is often represented by complex products, combining investment
opportunities and insurance features. For some, long-term contracts usually considered
in the insurance industry are simply too long when compared to the short-term
perspective depicted with highly profitable, attractive bonds. Instead of paying today for
the unpredictable day after tomorrow, individuals prefer to manage investments
quantifiable in the immediate future. The other side of the coin is that the additional
“flexibility” of insurance products means additional costs for the company; for that, an
intelligent compromise equilibrium solution must be found.
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Hartog J. (2000): “On a Simple Measure of Individual Risk Aversion,” Tinbergen Institute
Discussion Paper, pp.8.
Halek M., and J. G. Eisenhauer, (2001): ”Demography of risk aversion,” The Journal of Risk
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207-211.
Sung, J., and S. Hanna, (1996): “Factors related to risk tolerance”, Financial Counselling
and Planning, 7, 11-20.
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Blackburn D. W., and A. D. Ukhovy (2008): “Individual vs. Aggregate Preferences: The Case
of a Small Fish in a Big Pond,” Proceedings of the Risk Theory Society, 2007.
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King A. G. (1974): “Occupational Choice, Risk Aversion, and Wealth,” Industrial and Labor
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Laffont, J.-J., and M.S. Matoussi. (1995): “Moral Hazard, Financial Constraints and
Sharecropping in El Oulja.” Review of Economic Studies 62: 381-399.
Ackerberg, D.A., and M. Botticini. (2000): “The Choice of Agrarian Contracts in Early
Renaissance Tuscany: Risk Sharing, Moral Hazard, or Capital Market Imperfections?”
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Ackerberg, D.A., and M. Botticini. (2002): “Endogenous Matching and the Empirical
Determinants of Contract Choice.” Journal of Political Economy 110: 564-591.
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Proxy for Risk Aversion.’” AgEcon Search forthcoming.
Fukunaga, K., and W.E. Huffman, (2008): “The Role of Risk and Transaction Costs in
Contract Design: Evidence from Farmland Lease Contracts in US Agriculture.” American
Journal of Agricultural Economics 91: 237-249.
Guiso, L. and Jappelli, T. (1998): ”Background uncertainty and the demand for insurance
against insurable risk”, The Geneva Papers on Risk and Insurance Theory, vol. 23: 7-27.
Chetty R. (2006): “A New Method of Estimating Risk Aversion", American Economic Review
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Bansal R. and A. Yaron (2004): “Risk for the Long Run: A Potential Resolution of Asset
Pricing Puzzles," Journal of Finance, vol. 59, no. 4: 1481-1509.
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University of Pennsylvania working paper.
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Data sources:
International Monetary Fund (2010), GDP growth rates
Eurostat databases
tatistics Norway
Hellenic Statistical Authority
Central Statistics Office of Ireland
Istituto Nacional de Estadistica (Spain)
United States Census International Database
U.S. Bureau of Labor Statistics
The International Labor Organization (ILO)
Japan Statistics Bureau
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Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
7. APPENDICES
7.1. Graphics showing the evolution of the main drivers of risk propensity during the last fifteen years in
Germany, Italy and France
2009
ITA LY
FRA NCE
GE RMA NY
70
-3,0
-4,0
-5
,9
9
-5,0
-6,0
Italy
France
Germany
99
00
01
02
03
0,6
0,4
0,2
0,0
04
,0
5
9,
3
9,
0
40
9,
5
0
05
06
,1
5
-0
-0
-0,2
07
08
2009
,2
5
-0
,2
9
-2,0
98
15
41
1,
-0
,4
9
-1
-1
,2
9
-0
-0
,4
9
,0
9
,0
9
-0
-1,0
97
0,
1
2,
1
91
1,
71
0,
71
0,
,2
9
0,0
0,
51
61
0,
1,0
-0
real GDP growth rates
2,0
96
0,8
35
4,0
3,0
1995
2009
0,
08
55
07
0,
06
45
05
GERMANY
0,
04
FRANCE
25
03
30
9,
6,0
0,
02
40
0
6,5
,0
5
01
50
7,0
-0
00
9,
9,
5
7,5
,2
5
99
2009
8,0
-0
98
2008
8,5
,2
5
97
2007
9,0
-0
96
2006
Graphic 2b. Changes in the share of expenditure allocated
to leisure activities as compared to the average 1995-2009
share (=base). Source: own calculations.
changess in the share of total expenditure allocated to leisure activities %
1995
5,0
2005
9,
9,5
ITALY
Graphic 1b. Changes in the real GDP growth rates as
compared to the average 1995-2009 growth levels
(=base).
Source: own calculations
2004
,2
5
-6,0
2003
-0
-5,0
10,0
2002
,15
-4
,7
-4,0
2001
-0
-3,0
2000
,0
5
-2,0
1999
-0
,2
-0
0
0,0
-1,0
1998
,0
5
0,
1
8
1,
1
real GDP growth rates
1,0
1997
-0
2
2
1,
2
2,0
2
1,
1,
8
9
3,0
1996
9,
2,
7
3,
2
3,
4
4,0
1995
10,5
,1
0
2008
90
2007
9,
2006
,00
2005
10
2004
10
20 03
80
2002
9,
2 001
0
2000
9,
5
1999
30
1998
30
1997
9,
1996
5,0
share of total expenditure allocated to leisure and culturalactivities (%)
1995
Graphic 2a. Shares of expenditure allocated to leisure and
cultural activities (1995-2009). Source: Eurostat.
9,
Graphic 1a. Real GDP growth rates (1995-2009)
Source: International Monetary Fund
-0,4
-0,6
-0,8
Italy
France
Germany
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Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
Graphic 3. Population distribution by main industry (1995-2009)
Source: Eurostat and own calculations.
Population distribution by industry: MANUFACTURING
1.400
14.000
1.200
12.000
1.000
10.000
800
8.000
thousands
thousands
Population distribution by industry: AGRICULTURE
600
400
6.000
4.000
200
2.000
0
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
2006
2007
2008
0
2009
1995
1996
1997
1998
1999
GERMANY
2000
2001
ITALY
Population distribution by industry: SERVICES
2002
2003
FRANCE
2004
2005
2006
2007
2008
2009
2006
2007
2008
2009
GERMANY
Population distribution by industry: PUBLIC SECTOR
18.000
14.000
16.000
12.000
14.000
10.000
thousands
thousands
12.000
10.000
8.000
8.000
6.000
6.000
4.000
4.000
2.000
2.000
0
0
1995
1996
1997
1998
1999
2000
ITALY
2001
2002
FRANCE
2003
2004
2005
GERMANY
2006
2007
2008
2009
1995
1996
1997
1998
1999
2000
ITALY
2001
2002
FRANCE
2003
2004
2005
GERMANY
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Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
Graphic 4. Population distribution by employment status (1995-2009)
Source: Eurostat.
Population distribution by employment status: EMPLOYEES
Population distribution by employment status: UNEMPLOYED
40.000
7.000
35.000
6.000
30.000
in thousands
in thousands
5.000
4.000
3.000
2.000
25.000
20.000
15.000
10.000
1.000
5.000
0
0
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
2006
2007
2008
2009
1995
2010
1996
1997
1998
1999
2000
2001
ITALY
GERMANY
Population distribution by employment status: ENTREPRENEUR
2002
2003
FRANCE
2004
2005
2006
2007
2008
2009
2010
2008
2009
2010
GERMANY
Population distribution by employment status: SELF EMPLOYED
4.500
3.000
4.000
2.500
3.500
3.000
in thousands
in thousands
2.000
1.500
2.500
2.000
1.500
1.000
1.000
500
500
0
0
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
GERMANY
2006
2007
2008
2009
2010
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
2006
2007
GERMANY
Note: In 2003 Italy changed the data collection methodology for employment data. This change does not affect the risk propensity index since
entrepreneurs and self-employed are assigned the same degree of aversion to risk, being typically considered as risk-lovers (valued -1 on the risk
aversion scale).
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Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
Graphic 5. Population age structure (1995-2009)
Source: Eurostat and own calculations.
Population age profile: 20 to 39 years old
20
Millions
Millions
Population age profile: below 20 years old
18
30
25
16
14
20
12
10
15
8
10
6
4
5
2
0
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
2006
2007
2008
2009
0
2010
1995
1996
1997
1998
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
2006
2007
2008
2009
2010
2006
2007
2008
2009
2010
GERMANY
Population age profile: over 60 years old
Population age profile: 40 to 59 years old
30
Millions
Millions
1999
GERMANY
25
16
14
12
20
10
15
8
6
10
4
5
2
0
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
GERMANY
2006
2007
2008
2009
2010
0
1995
1996
1997
1998
1999
2000
2001
ITALY
2002
2003
FRANCE
2004
2005
GERMANY
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Economic Research & Corporate Development
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Graphic 6. Population distribution by level of schooling (1995-2009)
Source: Eurostat.
Population distribution by level of schooling: UPPER SECONDARY
Population distribution by level of schooling: LOWER SECONDARY
70
70
60
60
50
percentage from total
percentage from total
50
40
30
40
30
20
20
10
10
0
0
1995
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
1996
1997
1998
1999
2000
ITALY
ITALY
FRANCE
2001
2002
2003
2004
2005
2006
2007
2008
2009
FRANCE
GERMANY
GERMANY
Population distribution by level of schooling: TERTIARY
30
percentage from total
25
20
15
10
5
0
1995
1996
1997
1998
1999
2000
ITALY
2001
2002
FRANCE
2003
2004
2005
2006
2007
2008
2009
GERMANY
32
2009
Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
Graphic 7. Population distribution by civil status (1995-2009)
Source: Eurostat.
Population distribution by social status: COUPLES
40
Millions
Millions
Population distribution by social status: SINGLES
35
45
40
35
30
30
25
25
20
20
15
15
10
10
5
5
0
0
1995
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
1996
1997
1998
1999
2000
ITALY
ITALY
FRANCE
2001
2002
2003
2004
2005
2006
2007
2008
FRANCE
GERMANY
GERMANY
Millions
Population distribution by social status: WIDOWS
14
12
10
8
6
4
2
0
1995
1996
1997
1998
1999
2000
ITALY
2001
2002
FRANCE
2003
2004
2005
2006
2007
2008
GERMANY
33
2008
Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
7.2. Graphics showing the comparative analysis of the main drivers of risk propensity in Western
European Union Countries (average levels calculated for the last fifteen years) and the financial
assets structure
Graphic 8. Comparative analysis of risk propensity profiles by groups of European countries
(scale: 1=risk-averse
0=risk-neutral -1=risk-seeking) Source: own calculations.
Expenditure share on leisure
Expenditure share on leisure
1,00
1,00
0,80
0,80
0,60
0,60
0,40
Employment by sector
GDP growth rate
0,20
Employment by sector
0,20
-0,20
0,00
-0,40
-0,20
-0,60
-0,80
-0,40
-1,00
-0,60
Education profile
Population by age
Marital status
France average
Education profile
Professional profile
Germany average
Switzerland average
Population by age
Marital status
Austria average
Portugal average
Expenditure share on leisure
Professional profile
Spain average
1,00
0,80
0,80
GDP growth rate
0,20
0,40
Employment by sector
0,00
-0,20
-0,20
-0,40
-0,40
-0,60
-0,60
-0,80
-0,80
-1,00
-1,00
Population by age
Netherlands average
Professional profile
Belgium average
Ireland average
GDP growth rate
0,20
0,00
Marital status
Greece average
0,60
0,40
Education profile
Danemark average
Italy average
Expenditure share on leisure
1,00
0,60
Employment by sector
GDP growth rate
0,40
0,00
Education profile
Population by age
Marital status
Norway average
Sweden average
Professional profile
United Kingdom average
Finland average
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Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
CIPR index in WEU countries (1995-2009)
0,70
0,70
Risk
0,60
0,60
averse
0,50
0,50
0,40
0,40
0,30
0,30
0,20
0,20
0,10
0,10
0,00
1995
96
97
98
99
00
01
02
03
04
05
06
07
0,00
2009
08
-0,10
-0,10
Risk
-0,20
-0,20
seeking
-0,30
-0,30
Denmark
Sweden
Austria
Switzerland
Finland
United Kingdom
Norway
Belgium
Netherlands
portfolio share held in securities (%)
Graphic 9. Financial assets structure evolution between 1995 and 2009: SECURITIES
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
1995
96
97
ITALY
Sw itzerland
Sw eden
USA
98
99
00
GERMANY
Portugal
Finland
Japan
01
02
03
FRANCE
United Kingdom
Greece
Denmark
04
05
Netherlands
Norw ay
Belgium
06
07
08
0
2009
Ireland
Spain
Austria
35
Economic Research & Corporate Development
Working Paper / No. 147 /April 12, 2011
portfolio share held in insurance (%)
Graphic 10. Financial assets structure evolution between 1995 and 2009: INSURANCE
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
1995
96
97
98
ITALY
Sw itzerland
Sw eden
USA
99
00
01
GERMANY
Portugal
Finland
Japan
02
03
04
FRANCE
United Kingdom
Greece
Denmark
05
06
07
Netherlands
Norw ay
Belgium
08
0
2009
Ireland
Spain
Austria
portfolio share held in bank deposits (%)
Graphic 11. Financial assets structure evolution between 1995 and 2009: BANK DEPOSITS
80
80
70
70
60
60
50
50
40
40
30
30
20
20
10
10
0
1995
96
97
ITALY
Sw itzerland
Sw eden
USA
98
99
00
GERMANY
Portugal
Finland
Japan
01
02
03
FRANCE
United Kingdom
Greece
Denmark
04
05
Netherlands
Norw ay
Belgium
06
07
08
0
2009
Ireland
Spain
Austria
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Economic Research & Corporate Development
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These assessments are, as always, subject to the disclaimer provided below.
ABOUT ALLIANZ
Allianz SE is member of Transparency International Germany and supports the Principles of the United Nations
Global Compact and the OECD Guidelines for Multinationals through its Code of Conduct.
Allianz SE is one of the leaders of the insurance sector in the Dow Jones Sustainability Index, listed in FTSE4GOOD
and in the Carbon Disclosure Leadership Index (Carbon Disclosure Project, CDP6).
CAUTIONARY NOTE REGARDING FORWARD-LOOKING STATEMENTS
The statements contained herein may include statements of future expectations and other forward-looking
statements that are based on management’s current views and assumptions and involve known and unknown
risks and uncertainties that could cause actual results, performance or events to differ materially from those
expressed or implied in such statements. In addition to statements which are forward-looking by reason of
context, the words "may", "will", "should", "expects", "plans", "intends", "anticipates", "believes", "estimates",
"predicts", "potential", or "continue" and similar expressions identify forward-looking statements. Actual results,
performance or events may differ materially from those in such statements due to, without limitation, (i) general
economic conditions, including in particular economic conditions in the Allianz Group’s core business and core
markets, (ii) performance of financial markets, including emerging markets, and including market volatility,
liquidity and credit events (iii) the frequency and severity of insured loss events, including from natural
catastrophes and including the development of loss expenses, (iv) mortality and morbidity levels and trends, (v)
persistency levels, (vi) the extent of credit defaults, (vii) interest rate levels, (viii) currency exchange rates
including the Euro/U.S. Dollar exchange rate, (ix) changing levels of competition, (x) changes in laws and
regulations, including monetary convergence and the European Monetary Union, (xi) changes in the policies of
central banks and/or foreign governments, (xii) the impact of acquisitions, including related integration issues,
(xiii) reorganization measures, and (xiv) general competitive factors, in each case on a local, regional, national
and/or global basis.
Many of these factors may be more likely to occur, or more pronounced, as a result of terrorist activities and their
consequences. The company assumes no obligation to update any forward-looking statement.
NO DUTY TO UPDATE
The company assumes no obligation to update any information contained herein.
37