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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 2 Economic Research & Corporate Development 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 3 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 4 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 5 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 6 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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). 7 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 8 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 9 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 10 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 11 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 12 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 13 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 14 Economic Research & Corporate Development 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) 15 Economic Research & Corporate Development 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. 16 Economic Research & Corporate Development 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. 17 Economic Research & Corporate Development 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. 18 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 19 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 20 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 21 Economic Research & Corporate Development 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 22 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 23 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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. 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(2007): “Long-Run Risks and Financial Markets," Federal Reserve Bank of St. Louis Review, vol. 89, no. 4: 283-99. Uhlig H. (2007): “Leisure, growth and long run risks”, Chicago University Working Paper. Baron, J. (2010): “Risk attitude, investments and tastes for luxuries versus necessities,” University of Pennsylvania working paper. 26 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 27 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 28 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 29 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). 30 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 31 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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 34 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 36 Economic Research & Corporate Development Working Paper / No. 147 /April 12, 2011 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