David N.F. Bell Division of Economics Stirling Management School, University of Stirling and IZA

Transcription

1 The trade-off between unemployment and inflation David N.F. Bell Division of Economics Stirling Management School, University of Stirling and IZA and David G. Blanchflower Bruce V. Rauner Professor of Economics, Department of Economics, Dartmouth College, Division of Economics, Stirling Management School, University of Stirling IZA, CESifo and NBER September 2011 We thank Dick Easterlin and Andrew Oswald for helpful comments.

2 Abstract Previous literature has found that both unemployment and inflation lower happiness. The macroeconomist Arthur Okun characterised the negative effects of unemployment and inflation by the misery index - the sum of the unemployment and inflation rates. This paper extends the literature by looking at more countries over a longer time period. We find, conventionally, that both higher unemployment and higher inflation lower happiness. We also discover that unemployment depresses well-being more than inflation. We characterise this wellbeing tradeoff between unemployment and inflation using what we describe as the misery ratio. Our estimates with European data imply that a one percentage point increase in the unemployment rate lowers well being by two and a half times as much as a one percentage point increase in the inflation rate. Keywords Inflation, Misery-index, Trade-off, Unemployment, Well-being

3 Unemployment and inflation are major targets of macroeconomic policy, presumably because policymakers believe that a higher level of either variable has an adverse effect on welfare. The well-known macroeconomist, Arthur Okun, developed a measure known as the misery index the sum of the unemployment rate and the inflation rate which was intended to capture how increasing values of these indicators reduced national welfare. But the rapidly developing study of happiness (or well-being) means that a more direct approach can be taken to investigating how unemployment and inflation affect individual welfare. We use this approach to estimate the relative effects of unemployment and inflation on well-being. Our dataset comprises more than a million Europeans over the period 1975 to We extend previous literature in this area by including the recent recession and covering a wider group of countries. Our estimates imply that, across European countries, a one percentage point increase in the unemployment rate lowers well being by two and a half times as much as a one percentage point increase in the inflation rate. Section 1 provides background to the micro-economic research conducted on happiness. Section 2 examines previous research on the macroeconomics of happiness. Section 3 reports econometric evidence using macroeconomic data from an unbalanced country panel. Section 4 uses microeconomic data on life satisfaction. Section 5 provides conclusions and estimates of the size of the marginal rate of substitution between unemployment and inflation the slope of the indifference curve. Is unemployment more costly than inflation? The answer is unequivocally 'yes'. 1. Happiness and well-being It is now in vogue to try to understand the pursuit of happiness. The topic has attracted the attention of medical statisticians, psychologists, economists, and other investigators including Bell and Blanchflower (2007), Blanchflower and Oswald (2011), Easterlin (2003), Frey and Stutzer (2002), Gilbert (2006), Lucas et al (2004), Layard (2005), Oswald and Wu (2010); Powdthavee. (2010), Smith et al (2005), Ubel et al (2005). In general economists have focused on modelling two fairly simple questions, one on life satisfaction and one on happiness. These are typically asked as follows. Q1. Happiness (e.g. from the US General Social Survey) "Taken all together, how would you say things are these days would you say that you are very happy, pretty happy or not too happy?" Q2. Life satisfaction from the Eurobarometer Surveys "On the whole, are you very satisfied, fairly satisfied, not very satisfied, or not at all satisfied with the life you lead?" One definition of happiness is the degree to which an individual judges the overall quality of his or her life as favourable. Psychologists view it as natural that a concept such as happiness should be studied in part by asking people how they feel. As a validation of the answers to recorded happiness levels, it turns out that answers to happiness and life satisfaction questions are correlated with: 1

4 1. Objective characteristics such as unemployment. 2. Assessments of the person s happiness by friends and family members. 3. Assessments of the person s happiness by his or her spouse. 4. Heart rate and blood-pressure measures of response to stress. 5. The risk of coronary heart disease. 6. Duration of authentic or so-called Duchenne smiles. A Duchenne smile occurs when both the zygomatic major and obicularus orus facial muscles fire, and human beings identify these as genuine smiles (Ekman, Friesen and O Sullivan (1988); Ekman, Davidson and Friesen (1990)). 7. Skin-resistance measures of response to stress. 8. Electro-encephalogram measures of prefrontal brain activity (Davidson and Fox, 1982). The standard statistical approach to assessing responses to happiness questions is to use ordinary least squares (OLS) or ordered logit using micro-data, to estimate an equation with the happiness response as the dependent variable. Higher values of the dependent variable are associated with higher levels of happiness. Generally, it makes little difference if you use an OLS or an ordered logit, although the size of the coefficients will differ. It is apparent that there is a great deal of stability in happiness and life satisfaction equations, no matter what country is looked at, what dataset or time period used, whether the question relates to life satisfaction or happiness, or how the responses are coded (whether in three, four, five or even as many as ten categories). The main findings from happiness and life satisfaction equations estimated on individual level micro data are as follows (Blanchflower and Oswald, 2004). Happiness across countries is higher among: Women Married people The highly educated The healthy Those with high income The young and the old happiness is U-shaped in age (Blanchflower and Oswald, 2008a, 2009) Happiness is low among: Newly divorced and separated people Adults in their mid to late 40s The unemployed and the disabled Immigrants and minorities Those in poor health Commuters (Kahneman et al, 2004) 2

5 Wellbeing is correlated with life events such as being unemployed or being married. The data shows that richer people are happier and healthier. Gardner and Oswald (2007) have found that Britons who receive lottery wins of between 1,000 and 120,000 go on to exhibit better psychological health. But individuals in the USA were found to be less happy if their incomes are far above those of the poorest people (Blanchflower and Oswald, 2004). People, however, do appear to compare themselves more with well-off families, so that perhaps they get happier the closer their income comes to that of rich people around them. Relative income certainly appears to matter. Luttmer (2005), for the USA, finds that higher earnings of neighbours are associated with lower levels of self-reported happiness, controlling for an individual's own income. Alesina et al (2004), find, using a sample of individuals across the USA ( ) and Europe ( ) that individuals have a lower tendency to report themselves as happy when inequality is high, even controlling for individual income. The effect is stronger in Europe than in the USA. 2. Macroeconomics and happiness We now focus on national variations in well-being, before examining the extent to which these might be linked to national economic performance. Mean (10-step) life satisfaction scores for European countries based are presented in Table 1 for 2005, 2009 and Respondents were asked All things considered, how satisfied would you say you are with your life these days? Please use a scale from 1 to 10 where [1] means very dissatisfied' and [10] means very satisfied?. A few facts stand out. First, the Danes, the Swedes and the Finns are especially satisfied while the East Europeans, especially the Bulgarians, Hungarians and the Romanians, are the least satisfied. Second it is apparent from these data that there has been a decline in satisfaction levels since In every country except Estonia the 2010 number is lower than the 2005 number so the lowering of happiness coincides with the recession. These findings beg the question as to whether such comparisons are meaningful given language and cultural differences One way to overcome this in a simple way is to compare countries where the same language is spoken - Australia, Canada, New Zealand, UK, USA (as in Blanchflower and Oswald, 2005, 2006a). In those papers it was argued that Australia's high ranking on the HDI measure was a paradox given its much lower ranking on happiness and job satisfaction scores. Wolfers and Leigh (2006) disagreed. Another approach is to look for objective measures that might corroborate these findings. A recent paper by Banks, Marmot, Oldfield and Smith (2006) argued that Americans are less healthy than Europeans; differences in blood pressure form part of the author's evidence. Blanchflower and Oswald (2008) found that happier nations report systematically lower levels of hypertension. Happiness and blood pressure are negatively correlated across countries (r = -0.6). This seems to represent a first step toward the validation of cross-country estimates. Denmark has the lowest reported levels of high blood pressure in our data. Denmark also has the highest happiness levels. Portugal has the highest reported blood pressure levels and the lowest levels of life satisfaction and happiness. It appears there is a case to take more seriously the subjective 'happiness' measurements made across countries and seems meaningful to do cross-country comparisons. For developed countries there is little evidence that happiness or life satisfaction have increased over time. Two facts stand out from studies of life satisfaction and happiness in the UK and the USA over time. First, there is much how little has changed the distributions in the early 1970s 3

6 are virtually identical to those observed in Second, only a very small proportion of respondents report that they were 'not at all satisfied' with their lives, or in the case of the USA, that they were 'not at all happy'. Most people report that they are happy. So why doesn t happiness increase when a wealthy country gets richer? We are not certain, but possible explanations include: a) Social comparisons (you compare your 3 BMWs to others who also have 3 BMWs) b) Habituation: people adapt to their current lifestyle c) Mistaken choices: people make incorrect predictions about the effect of important decisions on their happiness (e.g. long commutes and working hours). However, happiness is positively correlated with higher GDP per capita (Wolfers and Leigh (2006)). When a nation is poor it appears that extra riches raise happiness. However, income growth in richer countries is not correlated with growth in happiness: this is the Easterlin hypothesis (Easterlin, 1974). If trend growth in GDP does not affect well-being, how is it affected by economic cycles? Charts 1 and 2 show recent data on unemployment and inflation in a variety of European countries. During 2010, when Western economies were still trying to recover from the Great Recession, both unemployment (Chart 1) and inflation (Chart 2) were negatively correlated at the national level with life satisfaction scores in Table 1. It is notable that the fit of the regression line were much stronger with unemployment (R 2 =0.153) than with inflation (R 2 =.024). DiTella, McCullough and Oswald (2001) show that people are happier when both inflation and unemployment are low. They find that unemployment depresses well-being more than does inflation. Wolfers (2003), has shown that greater macro volatility undermines wellbeing. Wolfers found that eliminating unemployment volatility would raise wellbeing by an amount roughly equal to that from lowering the average level of unemployment by a quarter of a percent. Interestingly, the effects of inflation volatility on well-being are markedly smaller. 3. Statistical analysis of Well-being, Living standards and Ability to Pay Bills In this section we make use of data at the individual level from the Eurobarometer surveys for a number of EU member countries along with data from Norway, Croatia and Turkey for the period (where available). We use data on broad measures of wellbeing including life satisfaction as well as relating to the respondent s family life, their living standards and their ability to pay the bills. We also examine their attitudes to inflation and unemployment. Table 2 makes use of data from a survey conducted between August and September 2010 Eurobarometer #74.1. The 10-step life satisfaction data used in column 1 are those reported in Table 1 based on this survey and the equation contains a standard set of explanatory variables (see Blanchflower and Oswald, 2011). Column 1 of the table confirms that in this dataset as in others, happiness is U-shaped in age, higher for married people and the most educated and especially low for the unemployed. Residents of the Nordic countries are happiest while East Europeans are the 4

7 least happy. In subsequent columns we show other facets of an individual s life including their family life, their standard of living; whether they can make ends meet and whether they can keep up with their commitments. The patterns in the data are broadly similar to those in the life satisfaction equation. Most notable is that the unemployed coefficient is always negative, large and highly significant, suggesting that unemployment lowers many aspects of an individual s life experience, making it hard for them to make ends meet and keep up with their commitments. Unemployment makes life tough. Column 1 of Table 3 reports the results of estimating a 4-step life satisfaction equation using micro data from Eurobarometer survey #73.4 for May The question asked is on the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with the life you lead? We use ordinary least squares with the dependent variable set to one if 'not at all satisfied' through to four if the respondent reported being 'very satisfied with the life you lead'. The equations has the same set of explanatory variables for age, gender, schooling, marital status and labour force status as in Table 2 and the results are similar. The same respondents were also asked what they considered the two most important issues facing (OUR COUNTRY) at the moment - crime; economic situation; rising prices\inflation; taxation; unemployment; terrorism; defence\foreign affairs; housing; immigration; healthcare system; the educational system; pensions; the environment; energy; other? The most cited in order were unemployment (51%); the economic situation (43%) and rising prices/inflation (19%). The distributions by country were as shown below. In every case, the proportion reporting that unemployment was one of the two most important problems was markedly higher than those reporting that inflation was one of the two most important problems. Country-by-country estimates of the importance of unemployment and inflation are shown in Table 3: In columns 2 and 3 of Table 3 we estimate the probability the individual reports either unemployment (column 2) or inflation (column 3) as one of the two most important problems in the country, holding constant characteristics, including age, schooling, gender, marital and labour force status. Young people were especially concerned about unemployment, but less about inflation. As might be expected, the unemployed themselves are worried about unemployment and to a lesser degree about inflation. The Turks, Irish, Italians, Macedonians, Latvians and Lithuanians are especially concerned about unemployment. The Hungarians were particularly concerned about inflation. 4. Measuring Misery The misery index was developed by Arthur Okun, an adviser to President Johnson. It is simply the unemployment rate added to the inflation rate. It is assumed that both a higher rate of unemployment and higher inflation create both economic and social costs for a country. A combination of rising inflation and more people out of work implies deterioration in economic performance and a rise in the misery index. 1 1 In the US in February 2011 the number is which is the highest since 1984 and is well below the post WW2 high of 20.8 in 1980 see 5

8 Rather than adding the two rates we are interested in the relative weight of unemployment and inflation in the misery index. If the unemployment rate is 4% and the inflation rate is 4% the misery index is 8. But the misery ratio is 1. We interpret the misery ratio as the rate at which at which individuals (or societies) trade off inflation and unemployment, while keeping their happiness constant. Alternatively, it measures the increase in the unemployment rate that is equivalent (in well-being terms) to a given increase in the inflation rate. Note that we arbitrarily treat the unemployment rate as the numerator and the inflation rate as the denominator in our misery ratio. Thus, higher rates of unemployment imply a higher misery ratio for a given inflation rate. Now consider the value of the ratio at the macro level. As can be seen below, for the US, the misery ratio was 4.2 in February Using averages for the variables since WW2, it is apparent that the misery ratio in the US has risen steadily since the 1970s. 2 USA Unemployment Inflation Misery Index (U+I) Misery Ratio (U/I) February Using equivalent data for Western European countries we also see evidence that the misery ratio has risen since the 1970s. Misery Ratio Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain UK Having defined the misery ratio at the macro level, we now examine a closely related micro concept. We estimate the relative weight of unemployment and inflation in individual well-being equations. We assemble the data for this exercise as follows: a 4-step life satisfaction question, Q2 2 We average across the years as this helps to solve the deflation (negative inflation) problem. 6

9 above, has been asked in some, but not all, Eurobarometer Surveys conducted for the EU since 1973 for member countries. As new countries such as Greece, Spain and Portugal joined they were added to the surveys so there are fewer years of data available for them. In 2004 the A10 countries joined. Bulgaria and Romania were also added to the surveys in 2004 as were the two EU Candidate Countries of Croatia and Turkey. We only have suitable macro-economic data from the OECD on six of these countries (Czech Republic; Estonia; Hungary; Poland, Slovakia and Slovenia) so the other twelve countries are excluded from our analysis. Data are available on Norway for when it was an EU Candidate Country and a member of the OECD. Overall, we make use of micro-data on over 920,000 individuals from twenty three countries - Austria; Belgium; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Hungary; Ireland; Italy; Luxembourg; the Netherlands; Norway; Poland; Portugal; Slovakia; Slovenia; Spain; Sweden, Turkey and the UK. We then map in annual data on unemployment, inflation for each country. In Table 4 we estimate OLS life satisfaction equations, with the same 4-step life satisfaction question as dependent variable we used in Table 4 but now also include OECD estimates of the inflation rate (HICP 3 ) and the unemployment rate as additional control variables. The distributions of the two variables are as follows. Unemployment rate (%) Inflation rate (%) Mean Standard deviation Minimum Maximum These data imply a mean misery ratio at the macro level of Comparable schooling data are not available in the Eurobarometer surveys for 1995 so that year is included only in column 1 and excluded after that. In all cases the standard errors are clustered at the level of the country*year to overcome the problem of the common component in the residuals, known widely as the Moulton (1986, 1991) problem. This adjustment is necessary when a regression at the level of the individual includes an explanatory variable at the level of the country and year. As expected, both the unemployment rate and the inflation rate have negative coefficients suggesting that an increase in either lowers happiness; below we use these data to estimate the misery index. Column 2 removes the education variables and now data for 1995 are added and this is the sample used in subsequent columns. Column 3 restricts the sample to the major western countries hence dropping the Czech Republic, Estonia, Hungary, Poland, Slovakia and Slovenia. Column 4 reduces the number of countries still further, dropping Austria, Finland, Norway and Sweden to be comparable with the sample used by Di Tella et al (2001) - Belgium; France; Denmark; Greece; Germany; Great Britain; Ireland; Italy; Luxembourg; Netherlands; Portugal and Spain. In all four columns the results are broadly similar. What do our estimates suggest about the relative size of the effects from the unemployment rate and the inflation rate? The effects of unemployment and inflation, in row 1 of Table 5 - which is 3 Harmonised Index of Consumer Prices 7

10 taken from column 1 of Table 4 - have coefficients of and.0065 respectively. These represent the effect upon wellbeing of a one percentage point change in each of the two independent variables. Following Di Tella et al (2001) henceforth DMO the implicit utilityconstant trade-off between inflation and unemployment can now be calculated. As in conventional economic theory, their methodology leads to a measure of the marginal rate of substitution between inflation and unemployment the slope of the indifference curve. This is yet another interpretation of the misery ratio. There are two consequences of unemployment society as a whole becomes more fearful of unemployment (Blanchflower (1991) and some people actually lose their jobs; there are aggregate and personal effects of unemployment. DMO argue that a way has to be found to measure the two unpleasant consequences of a rise in unemployment. DMO develop a way to take account of the extra cost of joblessness, namely, to work out the sum of the aggregate and personal effects of unemployment. They do so first by calculating the direct effect of an increase in the unemployment rate on society, as we have done above. Our estimate is DMO argue that it is apparent from the microeconomic life satisfaction and happiness data that the person who actually becomes unemployed experiences a much larger cost. We concur. The loss to the individual from being unemployed can be calculated from the coefficient on being unemployed in a life-satisfaction micro regression, like the one reported in row 1 of Table 9, estimated with OLS to keep the units consistent we get The entire well-being cost of a 1 percentage-point increase in the unemployment rate is therefore given by the sum of two components. Combining the two, we have =.0147 as society s overall wellbeing cost of a one percentage point rise in the unemployment rate. The implication is that the wellbeing cost of a 1 percentage point increase in the unemployment rate equals the loss brought about by an extra 2.27 percentage points of inflation. How do we get this? The reason is that (0.0147/0.0065) = 2.27, where is the marginal unemployment effect on well-being, and is the marginal inflation effect on well-being from row 1 of Table 8. Hence 2.27 is the marginal rate of substitution between inflation and unemployment. It is also our estimate of the misery ratio based on individual data and correcting for individual characteristics. This is markedly higher than the 1.66 obtained by DMO. 4 One alternative way to calculate the marginal rate of substitution between inflation and unemployment is by omitting the schooling variables from the regression and using a bigger sample which includes 1995, as in column 2. If we recalculate using the OLS coefficients in row 2 of Table 9 the unemployment/inflation trade-off becomes 2.47 (( )/.0061). Restricting the sample to DMO s smaller group of countries we get 2.57 but only 1.87 when the sample is restricted to the years 1975 to 1991, quite close to the 1.67 obtained by DMO. 4 Note that Di Tella et al (2001) use rolling three year averages and adjust for these omitted variable bias by running first stage micro life satisfaction equations in each country and year cell and then using the averaged residuals at the second stage of the regression. Using the micro data and adjusting the standard errors by clustering, the rhs variables by country and year accomplishes essentially the same adjustment. DMO do not make clear why they use three year rolling averages and we can see no compelling reasons to do so here; in any case this is unlikely to matter. 8

11 It is also feasible to obtain estimates for sub-groups. We find that females and males have similar trade-offs (2.68 and 2.26 respectively). The least educated, the married, the widowed and the old are more concerned about unemployment they put the highest weight on unemployment. Conversely, the young and the most educated put the greatest weight on inflation. This runs counter to the idea that older people care more about inflation as they are more likely to have experienced it during their adult lives Conclusion European macroeconomic policy for the last twenty years has focussed on controlling the inflation rate as its primary objective. Indeed, the Bank of England and the European Central Bank have been inflation targetters, although the Bank of England is allowed to take account of government policy on growth. 6 This was intended to bring much needed stability. For the period it appeared this stability had been attained. But since the start of the Great Recession, unemployment has increased rapidly. In the UK, despite inflation being well above target, the MPC s behaviour suggests that it is concerned about growth and unemployment at a time when the UK government is tightening fiscal policy. Our estimates of the misery ratio suggest that this is the correct approach if the objective is to maximise national well-being. In the Great Recession, unemployment has been a bigger problem than inflation. The main results of this paper can then be summarized as follows: 1. The northern European countries, especially the Danes, have generally higher happiness and life satisfaction scores than residents of Southern Europe, especially Portugal, Italy, Greece and Spain. Residents of former Eastern bloc countries have particularly low happiness scores. 2. Unemployment lowers happiness of the unemployed but also the happiness of everyone else. 3. Unemployment lowers an individual s standards of living and makes it harder to make ends meet, but it also diminishes the quality of their family life. 4. We estimate the unemployment/inflation trade-off as approximately two and a half. We find that the least educated and, somewhat surprisingly, the old put the highest weight on unemployment. Conversely, the young and the most educated put the greatest weight on inflation. 5. Unemployment is more costly than inflation in terms of its impact on wellbeing. The misery ratio is closer to 2.5 than to 1. 5 In contrast Lombardelli and Saleheen (2003) show that older people in the UK have higher expectations for inflation because they have experienced periods of higher inflation over their adult lives. They found that people in the age group had experienced the highest level of inflation, an average inflation rate of 7.3% over their adult lives. They found that lifetime inflation experience has a significant effect on people s inflation expectations. 6 Under the Bank of England Act of 2009, the objectives of the Bank of England are: (a) to maintain price stability, and (b) subject to that, to support the economic policy of Her Majesty s Government, including its objectives for growth and employment. 9

12 References Alesina, A, R. Di Tella and R.J. MacCullough (2004), 'Inequality and happiness: are Europeans and Americans different?', Journal of Public Economics, 88, pp Banks, J., M. Marmot, Z. Oldfield and J.P. Smith (2006), 'Disease and disadvantage in the United States and England', Journal of the American Medical Association, 295, pp Bell, D.N.F., and D.G. Blanchflower (2007), The Scots may be brave but they are neither healthy nor happy, Scottish Journal of Political Economy, 54, Blanchflower D.G. and A.J. Oswald (2004), 'Wellbeing over time in Britain and the USA', Journal of Public Economics, 88, pp Blanchflower, D.G. and A.J. Oswald (2005), 'Happiness and the Human Development Index: The paradox of Australia,' The Australian Economic Review, Volume 38(3), September 2005, pp (12). Blanchflower, D.G. and A.J. Oswald (2006), 'On Leigh-Wolfers and wellbeing in Australia', The Australian Economic Review, volume 39(2), pp Blanchflower, D.G. and A.J. Oswald (2008a), 'Is wellbeing U-shaped over the life cycle?', Social Science and Medicine, 66(8), April, pp Blanchflower, D.G. and A.J. Oswald (2008b), 'Hypertension and happiness across nations', Journal of Health Economics, 27(2), pp Blanchflower, D.G. and A.J. Oswald (2009), 'The U-Shape without controls: a response to Glenn', Social Science and Medicine, vol. 69, pp Blanchflower, D.G. and A.J. Oswald (2011), International happiness: a new view on the measure of performance, Academy of Management Perspectives, February, Vol. 25 Issue 1, pp Di Tella R., MacCulloch R.J. and A.J. Oswald (2001), 'Preferences over inflation and unemployment: evidence from surveys of happiness', American Economic Review, 91, pp Easterlin, R. (1974), 'Does economic growth improve the human lot? Some empirical evidence', in David, P.A., Reder, M.W. (Eds.), Nations and households in economic growth, essays in honor of Moses Abramowitz, Academic Press, New York. Easterlin R.A. (2003), 'Explaining happiness', Proceedings of the National Academy of Sciences, 100, pp Ekman, P., R. Davidson and W. Friesen (1990) 'The Duchenne smile: emotional expression and brain physiology II', Journal of Personality and Social Psychology, 58(2), pp

13 Ekman, P., W. Friesen and M. O Sullivan (1988), 'Smiles when lying', Journal of Personality and Social Psychology. 54(3), pp Frey B.S. and Stutzer A. (2002), 'What can economists learn from happiness research?', Journal of Economic Literature, 40(2), June, pp Gardner, J. and A.J. Oswald (2007), 'Money and mental wellbeing: a longitudinal study of medium sized lottery wins', Journal of Health Economics, 26, pp Gilbert D. (2006), Stumbling on happiness, Alfred A Knopf: New York. Kahneman D., Krueger A.B., Schkade D., Schwarz N., Stone A.A. (2004), 'Toward national wellbeing accounts', American Economic Review Papers and Proceedings, May, 94, pp Lombardelli, C. and J. Saleheen (2003) Public expectations of UK inflation, Quarterly Bulletin, Bank of England, Autumn, pp Lucas R.E, Clark A.E., Georgellis Y., Diener E. (2004), 'Unemployment alters the set point for life satisfaction', Psychological Science, 15 (1), pp Luttmer, E. (2005), 'Neighbors as negatives; relative earnings and well-being', Quarterly Journal of Economics, August, 120(3), pp Moulton, B.R. (1986), 'Random group effects and the precision of regression estimates', Journal of Econometrics, 32, pp Moulton, B.R. (1991), 'A illustration of a pitfall in estimating the effects of aggregate variables on micro units', Review of Economics and Statistics, 72, pp Oswald, A. J., and S. Wu (2010), Objective confirmation of subjective measures of human wellbeing: Evidence from the USA, Science, 327, Powdthavee, N. (2010), The happiness equation, Duxford, UK: Icon Books Ubel P.A., Loewenstein G, Jepson C. (2005), 'Disability and sunshine: can hedonic predictions be improved by drawing attention to focusing illusions or emotional adaptation?', Journal of Experimental Psychology: Appl, 11, pp Wolfers, J. (2003), 'Is business cycle volatility costly? Evidence from surveys of subjective wellbeing', International Finance, 6:1, pp Wolfers, J. and A. Leigh (2006), 'Happiness and the Human Development Index: Australia is not a paradox', Australian Economic Review, 39(2), June, pp

14 Table step life satisfaction by country Country Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK Source: Eurobarometers #63.1; #72.1; #74.1 Question. All things considered, how satisfied would you say you are with your life these days? Please use a scale from 1 to 10 where [1] means very dissatisfied' and [10] means very satisfied? 12

15 Table 2. Life satisfaction, family life, health and living standards (2010) Family life Standard of living Make ends meet Keep commitments Unemployed (31.12) (10.66) (34.80) (30.13) (30.56) Home worker (8.09) (3.13) (9.04) (9.56) (9.52) Student.7727 (10.75).5749 (7.72).7677 (10.79).3790 (9.01).3111 (9.68) Retired (8.48) (3.46) (12.75) (9.27) (7.00) Age < (14.05).9433 (14.84).8504 (13.96).1684 (4.71).0469 (1.71) Age (8.68).4742 (10.60).2427 (5.66) (1.54) (2.30) Age (3.57).1636 (3.92).0345 (0.86) (4.24) (3.33) Age (4.41).0712 (1.58).2149 (4.99).2258 (8.95).1556 (8.08) Age >= (9.27).2775 (4.95).5204 (9.71).4275 (13.58).3370 (14.05) ALS (8.89).2550 (6.84).3986 (11.16).2601 (12.42).2080 (13.02) ALS >= (19.21).4516 (10.92).9033 (22.82).6206 (26.72).4307 (24.33) Still studying.2186 (1.39).0231 (0.14).1828 (1.17) (0.06) (2.21) Male (4.86) (4.08) (4.05).0704 (4.86).0299 (2.71) Married.4809 (12.83) (31.47).4573 (12.35).1939 (8.91).1092 (6.59) Living together.2426 (5.04).6047 (12.13).1264 (2.65).0781 (2.79).0072 (0.34) Divorced/separated (5.20) (8.61) (7.83) (10.48) (12.19) Widowed.0003 (0.01) (3.15) (1.74) (2.30) (2.70) Austria.0474 (0.55) (6.53).2410 (2.82).6086 (12.06).2777 (7.24) Belgium.0268 (0.31) (3.32).3210 (3.74).0681 (1.35).0985 (2.56) Bulgaria (29.62) (14.70) (27.96) (22.31) (23.07) Cyprus (0.87) (1.50).0337 (0.32) (6.99) (13.73) Czech Republic (9.05) (7.87) (8.16) (5.80) (6.73) Denmark.8633 (9.87).4153 (4.62) (12.05).7940 (15.61).5011 (12.96) Estonia (10.53) (5.83) (7.22) (4.00) (3.18) Finland.5079 (5.79) (0.74).3816 (4.41).4671 (9.15).3586 (9.24) France (5.44) (2.75) (3.07) (1.61).0848 (2.22) Germany (4.12) (7.63) (0.93).3359 (7.34).1437 (4.13) Greece (13.28) (8.07) (8.58) (19.57) (23.63) 13

16 Hungary (21.58) (11.82) (20.80) (16.39) (10.63) Ireland.2480 (2.87).2996 (3.31).3531 (4.06).1772 (3.52).0389 (1.02) Italy (6.47) (10.91) (1.27).0971 (1.93).1318 (3.45) Latvia (19.20) (11.72) (17.56) (13.30) (11.86) Lithuania (15.23) (8.37) (11.29) (7.83) (1.71) Luxembourg.1568 (1.46) (0.60).4463 (4.21).6546 (10.39).4408 (9.20) Malta (3.34) (2.13) (3.22) (1.46) (7.01) Netherlands.4834 (5.53) (3.34).6248 (7.25).4150 (8.15).4389 (11.34) Poland (7.70) (5.01) (8.90) (5.01).1350 (3.49) Portugal (17.31) (10.71) (12.99).0340 (0.67) (8.95) Romania (23.77) (13.88) (19.05) (16.87) (11.97) Slovakia (9.64) (9.12) (6.57) (1.88) (1.93) Slovenia (4.97) (4.98) (6.16).0081 (0.16).0968 (2.51) Sweden.5430 (6.19).0877 (0.97).7734 (8.94).7500 (14.70).4667 (12.03) UK.1403 (1.72).1941 (2.31).5990 (7.45).4956 (10.41).2980 (8.24) Constant N Adj R-squared Source: Eurobarometer #74.1, August-September Notes: excluded categories: single: Austria; ALS <16 and age Column 1 also includes Turkish Cyprus. Column 1 is OLS and columns 2 and 3 estimated as dprobits. T-statistics in parentheses. Estimated by OLS. Question 1. All things considered, how satisfied would you say you are with your life these days? Please use a scale from 1 to 10 where [1] means very dissatisfied' and [10] means very satisfied? Question 2. Could you please tell me on a scale of 1 to 10 how satisfied you are with each of the following items, where 1 means you are very dissatisfied and 10 means you are very satisfied? 1) your family life 2) your present standard of living? Question 3. A household may have different sources of income and more than one household member may contribute to it. Thinking of your household s total monthly income, is your household able to make ends meet with great difficulty; with difficulty; with some difficulty; fairly easily; easily; very easily? 14

17 Question 4. Looking at this card, which of the following best describes how your household is keeping up with all its bills and credit commitments at present? 1) I am\ we are having real financial problems and have fallen behind with many bills and credit commitments 2) I am\ we are falling behind with some bills and credit commitments 3) I am\ we are keeping up but it is a constant struggle 4) I am\ we are keeping up but struggle to do so from time to time 5) I am\ we are keeping up without any difficulties? 15

18 Table 3. Life satisfaction and views on inflation and unemployment Life Inflation Unemployment Age < (14.52) (2.40).0290 (2.46) Age (9.46).0299 (2.88).0031 (0.38) Age (4.74).0160 (1.67) (1.29) Age (3.27) (1.91) (7.97) Age >= (8.80) (3.79) (13.42) Male (4.51) (4.56) (0.41) ALS (9.12) (4.11) (0.50) ALS (20.47) (11.68) (5.94) Still studying.2885 (12.13) (7.96).0071 (0.54) No FT education (3.74) (0.45).0200 (0.77) Married.1512 (11.54).0200 (2.16) (2.85) Living together.0754 (4.48).0140 (1.18) (0.24) Divorced/separated (5.18).0497 (3.56) (1.37) Widowed (2.36).0030 (0.22) (4.21) Home worker (5.37) (0.55).1220 (11.50) Unemployed (27.88) (9.74).5395 (52.53) Retired (6.05) (3.03) (0.39) Belgium.0281 (0.89) (2.28).0117 (0.59) Bulgaria (28.55) (2.76).1085 (5.12) Croatia (7.92).0083 (0.39).0218 (1.11) Cyprus (1.33) (0.46).0101 (0.43) Czech Republic (7.45) (4.37) (2.31) Denmark.5085 (15.78) (15.40).0458 (2.15) Estonia (10.82) (7.63).1041 (4.93) Finland.1579 (4.95) (13.35).0894 (4.20) France (3.71) (3.96).0165 (0.83) Germany (3.02) (4.31).0096 (0.54) Greece (26.66) (0.66).0700 (3.43) Hungary (20.79).0515 (2.39).0503 (2.49) Iceland.3500 (9.11) (2.45).0374 (1.52) Ireland.1685 (5.32) (8.82).1599 (7.44) 16

19 Italy (11.65) (4.63).1068 (5.16) Latvia (16.05) (11.38).1091 (5.17) Lithuania (20.50).0266 (1.24).1078 (5.11) Luxembourg.2234 (5.77) (7.37) (0.59) Macedonia (16.56) (11.51).2131 (9.66) Malta (3.30).0225 (0.85) (3.71) Netherlands.2452 (7.72) (11.71) (1.76) Poland (6.74) (2.42).0443 (2.18) Portugal (22.93) (1.91).0935 (4.48) Romania (32.76) (3.64).0357 (1.79) Slovakia (8.03) (5.61).0028 (0.15) Slovenia (2.21) (3.88) (0.48) Spain (3.62) (12.37).1312 (6.14) Sweden.3027 (9.55) (18.58).0906 (4.24) Turkey (10.90) (12.87).2841 (12.34) UK.2719 (9.14) (10.43).0093 (0.50) Constant N 30,580 30,215 30,215 Adjusted R-squared Source: Eurobarometer #73.4, May Notes: excluded categories: single: Austria; ALS <16 and age Column 1 also includes Turkish Cyprus. Column 1 is OLS and columns 2 and 3 estimated as dprobits. T-statistics in parentheses. Question 1. On the whole, are you very satisfied, fairly satisfied, not very satisfied or not at all satisfied with the life you lead - not at all satisfied; not very satisfied; fairly satisfied or very satisfied? Question 2. What do you think are the two most important issues facing (OUR COUNTRY) at the moment - crime; economic situation; rising prices\inflation; taxation; unemployment; terrorism; defence\ foreign affairs; housing; immigration; healthcare system; the educational system; pensions; the environment; energy; other? Dependent variable in column 1 is set to 1 if unemployment mentioned, zero otherwise and 1 if rising prices/inflation mentioned, zero otherwise. 17

20 Table 4. Life satisfaction, unemployment and inflation in Europe, (1) (2) (3) (4) Unemployment rate (7.16) (7.25) (6.47) (6.12) Inflation rate (3.60) (3.37) (3.19) (3.06) Unemployed (35.26) (37.42) (34.90) (33.34) Home worker (5.03) (11.29) (11.34) (10.28) Student.0715 (5.26).1011 (17.55).0970 (16.58).1058 (17.51) Retired (7.21) (10.66) (8.99) (7.50) Age < (19.05).1450 (20.49).1343 (19.00).1275 (17.40) Age (14.33).0823 (19.56).0731 (18.04).0700 (16.38) Age (6.34).0315 (9.89).0285 (8.88).0275 (8.00) Age (10.44).0359 (8.74).0355 (8.26).0339 (7.32) Age >= (15.04).1015 (14.35).0969 (13.18).0979 (12.38) ALS < (1.11) ALS (2.60) ALS >= (4.36) Still studying.1921 (3.48) Male (11.30) (11.50) (11.40) (9.98) Married.1275 (25.87).1185 (23.50).1184 (22.23).1083 (19.88) Living together.0547 (10.37).0509 (9.73).0488 (8.65).0346 (5.82) Divorced/separated (24.66) (25.65) (24.98) (26.62) Widowed (9.78) (13.02) (12.56) (14.29) Austria (0.87) (0.50) (0.40) Belgium.0419 (2.12).0769 (3.89).0780 (3.88).0833 (4.13) Czech Republic (11.68) (10.58) Denmark.4365 (21.95).4833 (24.27).4847 (23.49).4912 (23.62) Estonia (10.92) (9.61) Finland.1112 (5.60).1544 (7.72).1549 (7.67) France (11.49) (10.07) (9.65) (9.44) Germany (7.67) (6.52) (6.15) (5.75) Greece (15.84) (15.67) (15.52) (15.82) Hungary (29.26) (29.14) Ireland.1481 (8.04).1655 (9.08).1674 (8.97).1703 (9.09) 18

21 Italy (11.21) (10.86) (10.70) (10.75) Luxembourg.1439 (5.66).1749 (6.89).1781 (6.57).1862 (6.78) Netherlands.2371 (12.07).2769 (13.96).2794 (13.36).2858 (13.59) Norway.2025 (5.55).2507 (7.37).2541 (7.26) Poland (16.53) (14.83) Portugal (15.58) (16.01) (15.51) (15.55) Slovakia (12.21) (11.77) Slovenia (1.81).0128 (0.66) Sweden.2420 (12.01).2869 (13.69).2881 (13.42) Turkey (4.56) (5.47) UK.1033 (5.98).1118 (6.41).1133 (6.23).1192 (6.52) Constant N 905, , , ,263 R-squared Source: Eurobarometers, Column 1 excludes 1995, as that year data are not available for schooling. Excluded categories Spain; age 45-54; employed and single. Standard errors are clustered at the level of country and year. Column 3 sample is Austria ( ), Belgium ( ), Denmark ( ), Finland ( ), France ( ), Germany ( ), Greece ( ), Ireland ( ), Italy ( ), Luxembourg ( ), Netherlands ( ), Norway ( ), Portugal ( ), Spain ( ), Sweden ( ) and the UK ( ). Column 4 are the Di Tella et al countries of Belgium; Denmark, France; Germany; Greece; Ireland; Italy; Luxembourg; Netherlands; Portugal; Spain and the UK. The East European countries have data from All equations also include a full set of year dummies. 19

22 Table 5. Estimates of the misery ratio Unemployment Inflation Unemployment Elasticities rate rate coefficient All with schooling (column 1) All (column 2) All Western Europe All Di Tella et al All Di Tella et al < Age< Age Age Age Age Age Male Female ALS< ALS ALS >= ALS studying Married Single Divorced/separated Living as married Widowed Notes: all coefficients are statistically different from zero at the 5% level. Each row is obtained from a separate regression with age and its square, gender, 5 marital status dummies, 35 year dummies and 22 country dummies for Austria; Belgium; Czech Republic; Denmark; Finland; France; Germany; Greece; Hungary; Ireland; Italy; Luxembourg; Netherlands; Norway; Poland; Portugal; Slovakia; Spain; Sweden with the UK the excluded category. For calculation of unemployment/inflation trade-off see text. Di Tella countries are Belgium; France; Denmark; Greece; Germany; Great Britain; Ireland; Italy; Luxembourg; Netherlands; Portugal and Spain from All equations also include a full set of year dummies. 20

23 Life satisfaction Chart 1. Life Satisfaction and Unemployment, 2010 y = x R² = Unemployment rate (%) 21

24 Life satisfaction Chart 2. Life Satisfaction and Inflation, 2010 y = x R² = Inflation rate- HICP (%) 22