When money does not buy happiness: The case of “frustrated achievers”

The Journal of Socio-Economics 38 (2009) 159–167

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When money does not buy happiness: The case of “frustrated achievers” Leonardo Becchetti ∗ , Fiammetta Rossetti Università di Roma Tor Vergata. Facoltà di Economia, Dipartimento di Economia e Istituzioni, Via Columbia 2, 00133 Rome, Italy

a r t i c l e

i n f o

Article history: Received 20 August 2007 Received in revised form 23 July 2008 Accepted 30 August 2008 JEL classification: A12 A13 Keywords: Life satisfaction Relative income Frustrated achievement

a b s t r a c t An increase in real per capita income is generally expected to be associated with nonnegative variations in life satisfaction. The alternative (association with negative changes) is generally defined as “frustrated achievement” [Graham, C., Pettinato, S., 2002. Happiness and Hardship: Opportunity and Insecurity in New Market Economies. The Brookings Institution Press, Washington, D.C.]. We investigate the determinants of “frustrated achievement” in the German socioeconomic panel on more than 30,000 individuals collected between 1992 and 2004. We observe a parallel reduction in self-declared life satisfaction corresponding to almost one-third of yearly increases in (equalised) real household income. Our econometric findings show that the lack of a full-time job, health deterioration, relative income effects, marital status shocks and poorer social life are the main factors associated with this phenomenon. © 2008 Elsevier Inc. All rights reserved.

1. Introduction The debate on the relationship between income and happiness tends to be polarized around two opposite stances. On the one side, it is argued that “money buys happiness”, since almost all empirical studies find a positive, though often nonlinear, relationship between life satisfaction and personal income (see, among others, Easterlin, 2001; Frey and Stutzer, 2000; Di Tella et al., 2003). The evidence for this contention seems to be reinforced (i) by empirical results on panel data which control for fixed effects (usually interpreted as time invariant inherited individual traits) and analyse the relationship between changes in income and changes in happiness (Winkelmann and Winkelmann, 1998; Ravallion and Lokshin, 2001; Ferrer-i-Carbonell and Frijters, 2004; Senik, 2004; Ferrer-i-Carbonell, 2005; Clark et al., 2005) and (ii) by studies which seek to solve the causality problem by identifying exogenous changes in income (such as lottery wins1 or changes in real income in Russia and East Germany since transition and reunification, respectively) and find that they exert a positive effect on life satisfaction (Gardner and Oswald, 2006; Frijters et al., 2004a,b, 2008).

∗ Tel.: +39 06 72595706x5719; fax: +39 06 2020500. E-mail address: [email protected] (L. Becchetti). 1 It might be objected that not even lottery wins are completely exogenous because the propensity to buy lottery tickets is positively correlated with optimistic personality traits which are also likely to be correlated with a inherited propensity to be happy. This is well expressed by the famous joke in which a Rabbi repeatedly prays to God to make him a lottery winner. After repeated appeals, God replies that he would be willing to help the Rabbi if only he would buy a ticket. 1053-5357/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.socec.2008.08.009

On the other side, the alternative position (money does not “buy”, or has very weak effects on, happiness) emphasizes that the marginal effects of income on happiness are decreasing, that the dynamic relationship between the two variables is much weaker than the cross-sectional one, and that the gap in per capita income between rich and poor countries is not matched by an equivalent gap in terms of life satisfaction (Myers, 1992; Diener and Lucas, 1999; Argyle, 2001; Nettle, 2005; Layard, 2005). A related strand of criticism pays special attention to the non-economic consequences of economic events and interprets these findings by arguing that the impact on happiness of personal enrichment is dampened by several side effects, such as positional competition (Duesemberry, 1949; Frank, 2005; Layard, 2005), hedonic adaptation,2 and the crowding-out of relational life (Becchetti and Santoro, 2007). Rather than adopting one of these two positions, we argue that it might be a constructive and insightful alternative to look at this issue from a different angle. By using panel data in which individual declarations on life satisfaction in different domains (income, job, leisure, etc.) are repeated in time, it is possible to divide responses into four groups according to the possible combinations between the sign of changes in real per capita income and changes in selfdeclared life satisfaction. In this way we can identify the exact proportion of those for whom “income buys happiness” (i.e. positive changes in per capita income are accompanied by positive

2 For a discussion of hedonic adaptation see, among others, Rayo and Becker (2004), Clark (1999), Lucas et al. (2003, 2004), Di Tella et al. (2005), Gilbert et al. (1998), Riis et al. (2005), Oswald and Powdthavee (2005), Stutzer (2004), Ubel et al. (2005), Wilson and Gilbert (2003), and Wu (2001).

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changes in self-declared life satisfaction) and, conversely, the socalled group of “frustrated achievers” for whom income does not buy happiness (i.e. the improvement in monetary well-being is indeed accompanied by a reduction in life satisfaction). Focusing on this latter group of people and analysing the determinants of frustrated achievement may shed light on the problematic side of the income/happiness relationship. The contribution of our paper to the above-mentioned literature, which focuses on the “dark side” of the income–happiness relationship, is that it is the first to analyse the issue by means of an econometric estimate with panel data and taking the role of relational goods explicitly into account. Panel estimates have a significant advantage in the presence of a cardinal indicator based on subjective valuations such as self-declared happiness. It is in fact more difficult to assess whether a “quite happy” declaration by one individual corresponds to a “quite happy” declaration by another than it is to rely on the fact that the same individual has increased his/her life satisfaction if s/he declares himself “quite happy” one year and “very happy” the next. Moreover, as is well known, econometric analysis can be used to evaluate the effect of a given factor on the probability of being a frustrated achiever, net of the contribution of all the other controls. In this regard, the paper not only looks at statistical significance but also provides the magnitudes of the association between shocks such as a deterioration in relational life, separation, divorce, and unemployment, on the one hand, and the probability of becoming a frustrated achiever on the other. A second innovation of the paper is its focus on the impact of social and relational life. Our findings document that these variables are significant. Hence their exclusion in standard happiness regressions may create serious problems of omitted variable bias. We believe that results of our paper may be of direct interest to policymakers, whose policies generally focus on growth and implicitly assume that if this goal is achieved and translated into an increase in real income for all citizens, the life satisfaction of the latter will be positively affected. Hence, insofar as life satisfaction has a significant impact on the decision to confirm the government in office, achievement of the growth target should significantly contribute to political re-election. However, if we discover that, for a significant number of individuals (frustrated achievers), improved economic conditions are indeed associated with a reduction of happiness, it follows that growth cannot be the sole factor in political success and must be associated with policies aimed at affecting those factors deemed responsible for frustrated achievement. The paper performs the evaluation just outlined. It is divided into five sections including the introduction and conclusions. Section 2 discusses some theoretical and empirical results on the income/happiness nexus in the literature. Section 3 presents descriptive evidence on episodes of frustrated achievement, whilst Section 4 discusses the econometric findings. Section 5 concludes. 2. The income/happiness nexus There is a very large body of empirical literature on the income/happiness nexus (see, among others, Blanchflower and Oswald, 2004; Di Tella et al., 2001, 2003; Graham and Pettinato, 2005; Luttmer, 2005; Winkelmann and Winkelmann, 1998; Ferreri-Carbonell, 2005).3

3 The large majority of the findings originate from six main sources: (i) the World Value Survey; (ii) the World Poll of the Gallup organization; (iii) the Eurobarometer; (iv) the European Household Survey Panel; (v) the British Household Survey Panel; and (vi) the German Socioeconomic Panel. Another short panel, used by Ravallion and Lokshin (2002), is the Russia Longitudinal Monitoring Survey, which, however, had a very limited time length (3 years) at the time of that study.

The starting point of this literature can be identified in the socalled Easterlin paradox concerning the relationship between the rising real per capita GDP and the stationary share of self-declared very happy people in the postwar USA (for a similar conclusion see also Schor, 1991; Frank, 1990; Scitowsky, 1976).4 Besides this descriptive evidence – which in fact is not particularly informative because it omits consideration of the impact of other concurring factors on self-declared happiness – four main regularities seem to emerge from cross-sectional or panel evidence: (i) personal income is significantly and positively related to self-declared happiness but with declining marginal effects, (ii) the income gap between developing and developed countries does not seem to be reflected in a similar happiness gap (Diener et al., 1993; Inglehart, 1990); (iii) relative income matters (see, among others, Ferrer-i-Carbonell, 2005); (iv) the crowding-out of the non-material components of happiness may generate a secondary negative effect which partially offsets the direct positive happiness/income nexus (Becchetti et al., 2008). Intertemporal evidence on fact (i) is widespread and has been confirmed by almost all empirical papers, although the significance of the effect is dampened in the presence of relative income regressors (fact iii). The evidence for fact (ii) exhibits some descriptive paradoxes (Mexico has average happiness and life satisfaction levels above those of most developed countries, even though the average satisfaction of most industrialized countries is above that of less developed ones)5 and seems to show that within-country relative income matters more than income comparisons across countries. Such findings, however, should be treated with great caution because intercountry comparisons are highly likely to be affected by cultural biases, income in domestic currency needs to be adjusted for PPPs, and personal income is generally measured by individual placement into country deciles of the domestic income distribution (see the World Value Survey). Which of the theories can help us explain the above-mentioned regularities? Fact one accords with the standard assumption on the utility function, which is generally assumed to be concave in income, but less likely to be subject to a satiation effect than when it is evaluated with respect to other arguments, given that money has the unique characteristic of being a means of exchange and a reserve of value.6 The other facts which may mitigate the direct positive relationship between income and happiness are generally explained by several competing theories (hedonic adaptation, positional competition, comfort/stimulation trade-off, and crowding-out of relational life). According to the hedonic adaptation hypothesis, the catchingup by expectations on realizations down-shifts the instantaneous utility of income whenever a higher income threshold is reached.

4 Evidence in favour of the paradox is also reported by Blanchflower and Oswald (2004) for the UK, by Frey and Stutzer (2002) on a large sample of countries using data from the World Database of Happiness and the U.S. Bureau of Census, and by Veenhoven (1993) for Japan over the period 1958–1987. However, the Easterlin paradox is not a regularity invariably confirmed across countries and time. When Castriota (2006) repeated the Easterlin exercise on Eurobarometer data for some European countries in the last decade, he found that the paradox applied to Germany but not to Italy, where there was a quite strong positive relationship between happiness and per capita income. 5 The Mexican result is even more puzzling if we relate it to the “feet votes” of the significant number of Mexicans who seek to migrate into the US. 6 Consider, however, that self-declared happiness is only a proxy for what is usually defined as the utility function because it represents the perception (at the time of the interview) of the degree of satisfaction that an individual has felt in a given time interval. According to Kahneman et al. (2004), this perception may be crucially influenced by most recent experiences. Since such measurement biases occur in all other empirical studies in this literature, we assume that they are random and level out in large samples of respondents.

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As a consequence, the observed intertemporal association between happiness and income becomes much flatter than that postulated by the theory (Frey and Stutzer, 2002) and is consistent with the empirical evidence. According to Scitowsky (1976), affluence does not always generate higher life satisfaction because of a trade-off between comfort and stimulation. Higher comfort dampens the stimulation for costly investment in the goods which may make individuals happier in the long run. Finally, since status can be considered a zero sum game, the dominance of the relative income effect7 and of “positional competition” may paradoxically eliminate any significant impact of a positive change in personal income if all the other members of the relevant reference group also register the same change (a phenomenon which is generally called the “treadmill effect”) (Duesemberry, 1949; Frank, 2005; Layard, 2005). The more extreme perspective of the set point theory (Costa et al., 1987; Cummins et al., 2003) argues that shocks to individual happiness have only short-run effects since personal well-being depends solely on the individual personality in the long run. In this case, after any economic shock generated by income changes, the level of individual happiness reverts, after a small perturbation, to its long-run equilibrium. We focus on the determinants of frustrated achievement in order to shed light on this “dark side” of the income/happiness nexus, seeking to assess the quantitative importance and the determinants of the paradox. 3. Descriptive findings on frustrated achievers Given the complexity and variety of the above-mentioned findings and explanations in the happiness/income literature, we opt for an approach which differs from the one generally used by empirical analyses. We do not search for a unique income/happiness law for all individuals, which is highly likely to be an average of all the different theoretical and empirical facts described above and which, de facto, would prevent us from exploring any of those single mechanisms in depth. We instead start from acknowledgement that higher income may be associated with higher life satisfaction for some people and lower life satisfaction for others. We are therefore interested in knowing how large the second group of people is, and in conducting more in-depth investigation of the “pathologies” of the income/happiness relationship in that group, in order to evaluate which concurring? factors may have caused the inversion of the expected nexus. To achieve this goal, we identify a subgroup of individuals whom we call “frustrated achievers”.8 According to our definition, a “frustrated achiever” is an individual who reports, in a given year with respect to the previous one, a negative variation in his/her self-declared life satisfaction accompanied by a positive variation in his/her real household income. As a consequence we create the following dummy variable

 FA

= 1|t−(t−1) LSAT < 0 and t−(t−1) RY > 0 = 0

otherwise

7 The argument of the relevance of the relative income hypothesis has been put forward by Duesemberry (1949) and, more recently, by Frank (2005), Layard (2005) and Ferrer-i-Carbonell (2005). 8 The definition is not ours and can be attributed to Graham and Pettinato (2002). Throughout the rest of the paper we will refer for simplicity to frustrated or nonfrustrated achievers although, given the panel structure of our data, we should more properly refer to individual-year episodes of frustrated or non-frustrated achievement.

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where LSAT is the declared level of life satisfaction, RY is real household income for the same individual and t−(t−1) indicates a variable change from period t to period t − 1.9 A taxonomy with the main classifications based on the 1-year changes in real household income and self-declared happiness is set out in Table 1. Our empirical work is based on data from the German SocioEconomic Panel (GSOEP) which updates year by year information on a representative sample of German individuals since 1984. The GSOEP data cover a wide range of topics, such as education, occupational and family biographies, employment, participation and professional mobility, earnings, health, personal satisfaction, household composition, living situation, personality traits, worries, and many others. During the survey period new individuals were added to the original sample, and the survey was constantly adapted and developed in response to ongoing social developments. The unbalanced panel created from the original dataset goes from 1984 to 2004 and provides information on 32,880 individuals for a total of 168,626 observations. Preliminary descriptive evidence from our database shows that “frustrated achievers” are a non-negligible share (16.00%) of the overall sample and account for almost one-third (32.91%) of total (individual year) achievers and slightly less than half (46.23%) of all frustrated individuals (individuals experiencing a 1-year happiness reduction episode). Fig. 1 shows that the shares of frustrated achievers over total achievers and over the total sample do not exhibit any time trend, although the range of values across the sample period varies significantly from the peak of 1985 to the trough of 1998 (from around 40 to 28%). A first issue to be resolved concerns average levels and changes in real household income for frustrated and non-frustrated achievers. Frustrated achievement may in fact arise simply because changes in real household income are smaller than they are for non-frustrated achievers. If we focus on subgroup means, we find that average real household income is slightly lower for frustrated achievers (3370 against 3451 Dmarks). Furthermore, frustrated achievers have a median monthly income change of 320 Dmarks (slightly less than a 10% change on the overall sample average income). For one fourth of them the change is more than 697 Dmarks. The difference with respect to non-frustrated achievers is not marked, since their median income change is 331 Dmarks and the top 25% of non-frustrated achievers experience a positive income change of more than 716 Dmarks. Hence, the abovementioned concern is not confirmed by the data.10 With regard to the distribution of income changes across frustrated achievers, the average change in happiness for frustrated achievers with the top 25% of negative income changes is surprisingly similar to that of frustrated achievers with the bottom 25% of negative income changes (−1.76 against −1.77). This happiness change is striking if we consider the 0–10 digit scale of the happiness indicators and the average happiness level in the sample (7.09). A question which arises when trying to identify factors associated with frustrated achievement is the correlation between the latter and reduced life satisfaction in specific domains. The GSOEP

9 The exact formulation of the question in the GSOEP is as follows “In conclusion, we would like to ask you about your satisfaction with your life in general. How satisfied are you with your life, all things considered?” Please answer according to the following scale:“0” means completely dissatisfied,“10” means completely satisfied. 10 Analysis of the shape of the income distributions of frustrated and nonfrustrated achievers confirms this point. The results are omitted for reasons of space but are available from the authors upon request.

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Table 1 Taxonomy of the main subgroups based on yearly changes in real household income and life satisfaction. Higher life satisfaction Higher real household income Stationary real household income Lower real household income

Stationary life satisfaction

Lower life satisfaction

Satisfied achievers

Frustrated achievers

Satisfied losers

Frustrated losers

Fig. 1. Percentage of frustrated achievers on the sample of the achievers, considering real personal income and real household income.

enables us to answer this question because it records information on this issue. Table 2 compares satisfaction in specific domains for frustrated achievers vis-à-vis the other three main subgroups (non-frustrated achievers, frustrated losers, and non-frustrated losers). We find that the former have, with respect to non-frustrated achievers, a significantly higher share of negative yearly changes in satisfaction with health (47.31 against 11.24%), dwelling (40.42 against 11.09%), leisure time (41.33 against 11.59%), household income (41.84 against 9.30%), and work (26.02 against 5.91%).

As well known, it is not advisable to regress overall life satisfaction on variables measuring life satisfactions in specific domains. Doing so would create serious problems of endogeneity, since it is highly likely that individual psychological traits may affect the dependent variable and the other (domain specific) life satisfaction regressors in the same direction. However, the descriptive information in Table 2 is very useful as a first approximation with which to understand the areas (dwelling, income, leisure, and health) to which we must look for variables measuring objective factors

Table 2 Percentages of reductions and increases in specific satisfaction domains for the four (achievers/losers) subgroups. Frustrated achievers

Satisfaction with life today Satisfaction with dwelling Satisfaction with health Satisfaction with housework Satisfaction with work Satisfaction with leisure time Satisfaction with household income

Frustrated losers

% of reductions in satisfaction domains

% of increases in satisfaction domains

% of reductions in satisfaction domains

% of increases in satisfaction domains

100 40.42 47.31 22.74 26.02 41.33 41.84

0 28.48 24.55 63.79 59.34 34.58 31.54

100 41.19 47.70 24.40 25.31 41.05 50.25

0 27.02 24.46 60.93 60.76 33.68 24.94

% of reductions in satisfaction domains

% of increases in satisfaction domains

% of reductions in satisfaction domains

% of increases in satisfaction domains

0 11.09 11.24 5.97 5.91 11.59 9.30

100 76.44 77.13 88.57 88.07 78.85 80.64

0 10.82 10.93 6.14 5.84 10.68 11.73

100 76.54 77.79 87.99 88.32 79.73 77.93

Satisfied achievers

Satisfaction with life today Satisfaction with dwelling Satisfaction with health Satisfaction with housework Satisfaction with work Satisfaction with leisure time Satisfaction with household income

Satisfied losers

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Table 3 Summary statistics for the variables considered in the econometric estimates. Obs Demographics Age Male

Number of waves

Mean

S.D.

Min

Max

295,898 295,904

21 waves 21 waves

44.28411 0.4850255

17.1553 0.4997766

16 0

99 1

Education Education in years Marital status and shocks Married Separated Single Divorced Widowed Divorced with children Marriage Separation

693,410

21 waves

11.46329

2.612631

7

18

270,758 270,758 270,758 270,758 270,758 270,758 225,900 225,900

21 waves 21 waves 21 waves 21 waves 21 waves 21 waves 21 waves 21 waves

0.6258689 0.0236854 0.2301022 0.0552781 0.0650655 0.0159737 0.0195573 0.0140947

0.4838986 0.1520672 0.4208988 0.2285228 0.2466418 0.1253737 0.1384735 0.1178819

0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1

Relational goods RTI Attend social gatherings Attend cultural events Participate in sports Perform voluntary work Attend church or religious events Delta RTI

183,530 79,432 161,780 148,205 148,357 128,425 78,986

15 waves 8 waves 13 waves 12 waves 12 waves 10 waves –

1.905942 3.179185 1.715249 2.069923 1.446255 1.788686 −0.0466161

0.6337488 0.8496427 0.7706925 1.256503 0.9062111 0.993874 0.5495759

1 1 1 1 1 1 −3

4 4 4 4 4 4 3

Employment status Full-time employment Regular part time employment

157,763 157,763

14 waves 14 waves

0.4714033 0.0693635

0.4991831 0.2540721

0 0

1 1

Income Real household income Equalised real household income Reference household income

396,118 321,030 869,498

21 waves 21 waves 21 waves

3022.769 1344.773 3044.25

1861.714 799.2077 361.0008

0 0 593.1865

80273.44 40136.72 4510.57

Health Number of annual doctor visits

273,048

21 waves

10.454

17.79104

0

396

which should prove correlated with the above-mentioned relevant specific satisfaction domains. In this regard, the best candidates in our database are: (i) (for leisure) time spent on social activities and marital status variables; (ii) (for health) hours spent on visits to the doctor; (iii) (for work) employment status variables; (iv) (for income) measures of relative income and wealth. First, for a broader evaluation of the nexus between FA and investment in relational life we build a “Relational Time Index” (RTI) which captures consumption of relational goods.11 The original GSOEP dataset comprises five indicators of relational activities for each individual: (i) “attend social gatherings”; (ii) “attend cultural events”; (iii) “partecipate in sports”; (iv) “perform voluntary work”; and (v) “attend church or religious events”. Each of these variables can take values from 1 to 4, depending on the amount of time is devoted to each particular relational activity. More specifically, 1 stands for “Never”, 2 for “Less Frequently”, 3 for “Every Month” and 4 for “Every Week”. We built a “Relational Time Index” (henceforth RTI) by taking the mean values for these five variables at the individual level. We decided to build the RTI in this way for two main reasons. First, we want a synthetic indicator on the relational time of individuals which goes beyond the information given by a single variable.

11 Relational goods constitute a particular kind of local public goods which need to be simultaneously co-produced and co-consumed (Gui, 2000; Uhlaner, 1989). They are local because non-rivalry and non-excludability are limited to producers/consumers of the specific relational good. They constitute a particular kind of public good because they should be better defined as anti-rival (rather than non-rivalrous) given that one person’s satisfaction is not just reduced, but indeed increased, by the fact that the other is also participating and obtaining pleasure.

Second, the synthetic indicator helps us partly to solve the problem of missing data. In fact, none of the above-mentioned five variables is in the dataset for all of the 21 waves. We therefore calculate the RTI index on the basis of non-missing individual variables for each individual year so as to reduce the missing variable problem. For the sake of completeness, and as a robustness check, we present the results of the selected specifications when using either the aggregate RTI index or its individual components as separate regressors. Another variable typically used in the empirical literature is relative income. Several papers argue that positional status and the ratio between one’s own income and that of the reference group significantly affects life satisfaction (see, among others, Duesemberry, 1949; Frank, 2005; Layard, 2005). From the empirical point of view, the problem is obviously the definition of the reference group’s income. Our benchmark is Ferrer-i-Carbonell (2005), who works on the same database and calculates relative income as the average income of individuals of the same age, education and regional subgroup. In a similar way, we divide observations into classes according to gender, age and education. In particular, we consider 3-year classes of age and education. Since age in our sample ranges from 19 to 99 years we had 27 classes, whilst classes of education (ranging from 7 to 18 years in our sample) are 4 in number. Consequently, combining the age, education and gender criteria led to the definition of 2 × 27 × 9 classes. We therefore build the reference household income of each individual by taking the mean of the real household income group to which s/he belongs. Table 3 reports the descriptive features of the variables that we select as potential explanatory factors in frustrated achievement. Forty-seven percent of the individual-year observations correspond to full-time employed workers, around 7% to regular part-time

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Table 4 Share of frustrated achievers on total achievers conditional to given values of potential explanatory variables. % of frustrated among achievers Demographics Male Female Education in years ≤10 Education in years ≥12

32.30 33.48 34.68 31.59

Marital status and shocks Married Separated Single Divorced Widowed Marriage Separation

32.32 36.67 33.15 32.05 33.51 29.32 36.93

Relational goods RTI = 1 RTI = 2 RTI = 3 RTI = 4 D RTI = −3 (2) D RTI = −2 (20) D RTI = −1 (309) D RTI = 0 (2011) D RTI = 1 (245) D RTI = 2 (17) D RTI = 3 (2)

34.88 30.87 30.45 32.52 50.00 42.55 36.35 33.30 27.78 27.42 40.00

Employment status Full-time employment Regular part time employment

32.36 34.06

Income variables Real household income ≤25th percentile Real household income ≥75th percentile Reference household income ≤25th percentile Reference household income ≥75th percentile Equalised real household income ≤25th percentile Equalised real household income ≥75th percentile

34.59 31.39 33.12 32.68 35.39 30.99

Health Number of annual doctor visits ≤25th percentile Number of annual doctor visits ≥75th percentile

31.97 34.40

Total sample average

32.91

D RTI percent of observed cases in parenthesis.

workers, 63% to married individuals, and 23% to singles. The average number of doctor visits in the sample is 10. Table 4 compares the average levels of frustrated achievers on total achievers in correspondence to the relevant values of our explanatory variables. A first important result shows that frustrated achievement increases when social life deteriorates: the sharpest reduction in the RTI variable (decrease of three points in the scale) is associated with an extremely high level of FA (50%, that is, 13% more than the sample average). A similar phenomenon occurs with a fall of two points in the scale (42.55%, or 10 points more than the sample average). Consistently with this evidence, the share of frustrated achievers progressively declines as we move towards positive changes, the only exception being the last interval, for which, however, we have very few observations (two cases for three rung changes on both sizes). Inspection of the share of FA in each of the individual indicators making up the RTI index (Table 5) shows that the widest variation concerns attendance at social gatherings (FAs represent more than 40% among those with the lowest intensity, and 34.2% among those with the highest), whilst the lowest variation is observed for participation at church or religious events.

Besides changes in the composite RTI indicator and its components, our descriptive evidence documents that civil status and shocks on relational life significantly affect the probability of frustrated achievement. The strongest effect is produced by separation (the percentage of individuals undergoing separation who prove to be FA is 4% more than sample average). The hypothesis that negative shocks are partially absorbed after some time is confirmed by the fact that we do not observe the same deviation for divorced respondents. The importance of the shock on relational life is again confirmed by the finding that the share of FA is more than three points lower than the sample average in case of marriage (29.32%). Relational goods are not the only factor significantly affecting the share of frustrated achievement. Among the other variables, the strongest deviation from the sample mean is produced by equalised real household income below the 25th percentile, which raises the share of FAs by around two and a half points. This implies that frustrated achievement may be partly determined by placement in the lower part of the income distribution. Positive income changes which do not significantly alter this relative income position are accompanied by frustration. All other variables produce results as expected, albeit with smaller differences.

4. Econometric findings The goal of the econometric estimation is to verify whether the indications of the descriptive analysis translated into significant effects, net of the concurrent impact of control variables. To this end, we perform a random effect probit panel estimate12 in which the dependent variable is dichotomous and takes the value of one when we register an episode of frustrated achievement and zero otherwise. We restrict the sample to only the individual-year episodes of achievement because our main interest is to determine why, in the presence of an increase in real per capita income, individuals may have negative, and not positive, happiness reactions.13 We estimate the model using two different income measures for the calculation of frustrated achievement (real household income and equalised real household income). To calculate equalised real household income, we use the OECD approximation, in which real household income is divided by a factor A, where A = 1 + 0.5 (Nadults − 1) + 0.3Nchildren . For any of the two cases we propose three different specifications. The first does not contain any variable of social activity and enables us to work on a sample of more than 47,000 observations. In the second, we introduce the aggregate RTI index of social activity, which reduces the sample to around 37,000 observations. In the third, we introduce the single social activity regressors instead of the aggregate RTI index, with a further reduction of sample size to around 15,000 units.

12 It is not possible to use fixed effects because it would eliminate from the sample all individuals registering no episode of frustrated achievement throughout the entire sample period, thereby creating a severe selection bias problem. 13 In essence, we assume that the condition of FA and non-FA is determined by shocks occurring in the observed individual’s life together with income changes. Some shocks shift the respondents from non-FA to FA status. The econometric specification was such that any shock was accounted for and used as control in our analysis. The significance and magnitude of our coefficients tell us whether a given factor had a significant effect on this shift and the extent to which it affected the probability of being FA. Since shocks affect the probability of changing status we cannot say how they affect individuals in a given status. This would imply that we consider their status as fixed and invariant with respect to the shock itself, whilst what we are observing is exactly permanence or transition from one status (FA) to another (non-FA) or viceversa.

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Table 5 Percentage of frustrated achievers on total achievers according to intensity of different social activities. Possible levels of the components of RTI Frustrated achievers when using real household income (%)

Components of RTI Attend social gatherings Attend cultural events Participate in sports Perform voluntary work Attend church or religious events

Frustrated achievers when using real equalised household income (%)

1

2

3

4

1

2

3

4

40.19 34.27 34.07 33.29 32.04

35.84 31.17 32.03 32.61 30.91

33.42 31.04 32.72 31.56 31.15

34.20 30.42 31.14 30.53 31.63

40.66 34.40 34.15 33.41 32.20

35.50 31.33 32.13 32.48 31.02

33.56 30.81 32.87 31.58 31.40

34.22 30.73 31.24 30.66 31.79

Overall, relational goods seem to be a quite important factor in explaining frustrated achievement, both on the side of marital status shocks and on that of investment in social activities. All these findings imply that if individuals suffer from a marital shock, a health problem or a deterioration of relational life, a concurrent increase in income may not be enough to generate an increase in happiness, and therefore causes “frustrated achievement”. In order to evaluate the economic significance of the impact of the most relevant variables in the estimate, we calculated the effect of a change in a given regressor on the dependent variable. Based on the estimated coefficients of the specification presented in column 1 of Table 5, we find that the most relevant regressors are not just statistically, but also economically, significant. Among family shocks, a marriage reduces the probability of being a frustrated achiever by 5%, whilst separation increases it by 3%. A full-time job reduces such probability by around 2%. Ten additional visits to the doctor per year increase the probability by 1%. With regard to income, we observe that an increase of 1000 DM from the mean value of the reference income raises the probability of being a frustrated achiever by 2%. Furthermore, inspection of the magnitude of the effects from the estimate in Table 5 column 2, which includes the RTI index, shows that, under the assumption of linearity, a onepoint change in the RTI index from its mean (1.90) reduces it by 6%, and when the index is disaggregated (Table 5, column 3), we find that the same marginal change in attendance at social gatherings

We first comment on those results which appear remarkably stable to changes in specification and sample size. The number of annual doctor visits is positive and extremely significant in all regressions, confirming that health deterioration may be one of the factors that generate frustrated achievement (Table 6). Another variable which is always significant (with negative sign) is fulltime employment status, implying that a second crucial element associated with episodes of frustrated achievement is lack of job stability. The third factor which is always significant is relative income. This finding confirms that a relative increase in the reference group’s income with respect to that of the respondent is positively associated with episodes of frustrated achievement. This result may explain the apparent paradox whereby the share of frustrated achievers with reduction in income satisfaction is higher than that of non-frustrated achievers (41.84 against 9.30 in Table 2). Marital status shocks are also generally significant in almost all estimates. The exception is the negative marital status shock (separation) when we introduced the aggregate RTI index as regressor in the real household income estimate, and the positive marital status shock (marriage) when we introduced individual variables measuring investment in social activity. In regard to variables measuring social activity, the aggregate RTI index is strongly significant and with the expected sign, whilst attendance at social gatherings and performance of voluntary work are the two variables which are relevant when individual indicators are considered. Table 6 The determinants of frustrated achievement. a. Frustrated achievers on the basis of the “real household income”

Age Male Number of annual doctor visits Education in years Divorced with children Marriage Separation

b. Frustrated achievers on the basis of the “equalised real household income”

(1)

(2)

(3)

(1*)

(2*)

(3*)

−.0041321 (−4.72) −.0015361 (−0.07) .0054825 (9.83) −.0002251 (−0.04) −.0953841 (−1.10) −.2471887 (−3.83) .1428234 (1.87)

−.0041759 (−4.20) .007093 (0.29) .0050214 (7.75) .0003885 (0.06) −.136367 (−1.42) −.2368047 (−3.35) .1035295 (1.28)

−.0041917 (−2.59) .0639939 (1.66) .0057518 (5.45) −.0056082 (−0.53) .042965 (0.30) −.0459732 (−0.35) .52274 (2.78)

−.0047513 (−5.40) .0038744 (0.18) .0052411 (9.55) −.000919 (−0.16) −.1237057 (−1.42) −.2224749 (−3.45) .2353443 (3.53)

−.0049814 (−4.98) .0059818 (0.24) .0049019 (7.70) .0003591 (0.06) −.1859762 (−1.91) −.2340857 (−3.30) .2316646 (3.23)

−.0050296 (−3.11) .0804999 (2.09) .0054811 (5.30) −.0064308 (−0.60) .0548401 (0.39) −.0359636 (−0.27) .6154978 (4.15)

−.0908031 ((4.47)

RTI Attend social gatherings Attend cultural events Participate in sports Perform voluntary work Attend church or religious events

−.0982724 (−4.82) (.0716415 ((3.19) (.0394674 ((1.40) .00606 (0.36) (.0488238 ((2.26) .0332712 (1.72)

Full-time employment Regular part time employment Real household income Reference household income Equalised real household income Constant term

(.0720821 ((3.07) .0173423 (0.45) 2.98E(06 (0.64) (.0001301 ((3.51)

(.0832482 ((3.14) (.0024677 ((0.06) .0000105 (1.98) (.000124 ((2.98)

.1822566 (1.60)

.0457681 (0.35)

Observations R2 /log likelihood

47,114 −29871.411

36,899 −23407.079

(.0995408 ((2.36) (.0868826 ((1.22) .0000154 (1.47) (.0001726 ((2.64)

(.0719164 ((3.20) (.0460986 ((1.64) .0116915 (0.70) (.0483584 ((2.23) .0319085 (1.65) (.0747719 ((3.15) .0137635 (0.36)

(.0871278 ((3.24) (.0118181 ((0.27)

(.1156947 ((2.73) (.0850565 ((1.19)

.2889426 (1.39)

−.0001311 ((3.56) 5.35E(06 (0.50) (.2818962 ((2.44)

(.0001283 ((3.10) .0000248 (1.98) .4028823 (3.04)

(.0001825 ((2.81) .0000244 (1.06) .3913612 (1.86)

15,222 −9533.5988

46,923 −29769.425

36,710 −23283.941

15,256 −9542.663

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(one point from its mean of 3.17) reduces it by 7%. Note also that introduction of the disaggregated RTI variable raises the magnitude of the separation effect to around 9%. 5. Conclusions Why does income not bring happiness in some cases, and how frequently does this happen? Our empirical analysis sought to answer these simple questions by investigating the determinants of frustrated achievement on a large sample of German respondents over the 1984–2003 sample period. Our results demonstrate that, in the presence of health deterioration, lack of full employment, negative relative income effects, adverse marital status shocks, and changes in social life indicators, a concurrent increase in income may not be enough to generate an increase in happiness, and therefore causes “frustrated achievement”. We believe that our findings have important consequences in terms of policy because they show that success in pursuing the sole goal of economic growth, even when its benefits are equally distributed among the population, does not necessarily increase individual well-being. This is an important message for policymakers in rich countries if we assume that individual well-being has a significant impact on political consensus, and that the factors affecting it may be influenced by policies. If our results are taken seriously, policymakers must therefore expect that, even with an increase in real per capita income, they may face a reduction in consensus among up to one-third of the population. Such a loss can be reduced by means of initiatives which improve working status and participation in social life, and which affect the conditions that reduce the probability of family crises. Ours findings also have important consequences in terms of welfare indicators. If one-third of the consensus obtained with economic growth may be eroded by these collateral factors, it is evident that it is not possible to measure a population’s satisfaction solely with GDP growth. This indicator needs to be integrated with others which concern health, competition for status, quality of social life and of family relationships, and job satisfaction. Furthermore, not all growth-oriented policy measures have the same positive effects. Those which are more effective in terms of life satisfaction and should be privileged are the ones which create non-negative externalities in health conditions, family status, social life and job satisfaction and stability. References Argyle, M., 2001. The Psychology of Happiness, Rev. ed. Routledge, East Sussex, Great Britain. Becchetti, L., Santoro, M., 2007. The wealth-unhappiness paradox: a relational goods/Baumol disease explanation. In: Bruni, L., La Porta, L. (Eds.), Handbook on the Economics of Happiness. Elgar, pp. 239–262. Becchetti L., Londono Bedoya D., Trovato G., 2008. Income, Relational Goods and Happiness, Applied Economics, forth. Blanchflower, D.G., Oswald, A.J., 2004. Well-being over time in Britain and the USA. Journal of Public Economics 88, 1359–1386. Castriota, 2006, Does the Easterlin paradox works in other countries, mimeo. Clark, A.E., 1999. Are wages habit-forming? Evidence from micro data. Journal of Economic Behavior and Organization 39, 179–200. Clark, A.E., Etilé, F., Postel-Vinay, F., Senik, C., Van der Straeten, K., 2005. Heterogeneity in reported well-being: evidence from twelve European countries. Economic Journal 115, C118–C132. Costa, P.T., McCrae, R.R., Zonderman, A.B., 1987. Environmental and dispositional influences on well-being. Longitudinal follow-up of an American national sample. British Journal of Psychology 78, 299–306. Cummins, R., Eckersley, R., Pallant, J., van Vugt, J., Misajon, R., 2003. Developing a national index of subjective wellbeing: The Australian Unity Wellbeing Index. Social Indicators Research 64, 159–190. Diener, E., Lucas, R.E., 1999. Personality and subjective well-being. In: Kahneman, D., Diener, E., Schwarz, N. (Eds.), Foundations of Hedonic Psychology: Scientific Per-spectives on Enjoyment and Suffering, Chapter 11. Russell Sage Foundation, New York.

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