Happiness adaptation to income and to status in an individual panel☆
Introduction
In a seminal paper, Easterlin (1974) showed that an indicator of well-being for the post-war period in the US remained flat in spite of the considerable rise in income. In the cross-section for any particular year, however, income and happiness exhibit the expected positive association. One explanation that has been proposed for this “paradox” is the hypothesis that people only care about their relative position or “status”. A second explanation is that people adapt to their income over time. In this case the cross-sectional evidence can be explained by relative position effects to which individuals do not adapt.2 The narrow purpose of the present paper is to provide the first test of adaptation to status relative to adaptation to income. As explained above, these theories are both part of an explanation to the Easterlin paradox (if there is adaptation to income but not to status).
Our paper employs the approach developed in the small happiness literature that has emerged in economics following Easterlin's paper.3 Using individual-level panel data on happiness from households living in Germany between 1984 and 2000, we provide evidence on three behavioral hypotheses, namely adaptation, status effects and loss aversion. In particular, we compare the extent of adaptation to income with the extent of adaptation to status. Our main objective is to provide evidence on the relative sizes of the (short and long-run) effects of being on higher income compared to enjoying higher status. We also compare the effects across sub-samples of people with different ideological inclination, of different gender and with different employment status. Finally, we compare how losses versus gains affect happiness and provide one way to quantify them (in terms of current income).
One finding of our paper is that there is significant adaptation to income. We can reject the hypothesis that people do not adapt to income in the four years following an income shock. The size of adaptation is sufficiently large that no significant income effects on happiness remain after the fourth year. The adaptation effects we investigate are consistent with the model of Pollak (1970), Wathieu (2004), Rayo and Becker (2005), inter alia. A classic paper in psychology, Brickman et al. (1978), showed that individuals who had won between $50,000 and $1,000,000 at the lottery the previous year reported comparable life satisfaction levels as those that did not.4 Frederick and Loewenstein (1999) and Diener and Biswas-Diener (2002) present reviews of the evidence available, gathered largely in the psychology field. In the economics literature, Clark (1999) uses two waves of the British Household Panel to look at the relationship between workers’ job satisfaction and their current and past labour income. Gardner and Oswald (2007) use data on a panel of individuals who receive windfalls (by winning a lottery or receiving an inheritance) and Di Tella et al. (2003) estimate the effect of income lags in a panel of 12 OECD countries.5 The papers that are closest to our analysis are van Praag and Ferrer-i-Carbonell, 2004, van Praag and Ferrer-i-Carbonell, 2008 who study adaptation to income in the German panel using an alternative approach. For a literature review, see Clark et al. (2008b) and Di Tella and MacCulloch (2006). Layard (2005) discusses several policy implications. Our explanation is also related to the important work of van Praag and Kapteyn (1973) showing that income aspirations rise in proportion to income (sometimes called “preference drift”). Indeed, van de Stadt et al. (1985) find that the hypothesis of one-for-one changes in income aspirations and income cannot be rejected (see also van Praag and Ferrer-i-Carbonell, 2004 and Stutzer, 2004). Easterlin (2003) argues that family aspirations do not change as marital status and family size change but that material aspirations increase commensurately with household wealth.
Our paper also identifies significant status effects in a within-person analysis. We use the Treiman Standard International Occupation Prestige Score, a measure of the status attached to each job depending on the skills it requires, which has the advantage of having been designed by researchers in another context (see, for example, the description in Hoffmann, 2003). Controlling for changes in income, individuals declare themselves to be happier when they obtain a job that is deemed more prestigious. A one standard deviation increase in status is associated with a similar rise in happiness as an increase of 52 percent of one standard deviation in income during the first year. The evidence cannot reject the hypothesis that there is no adaptation to changes in status in the four years following a status shock. Using long-run (five year) averages, a one standard deviation increase in status is associated with a similar rise in happiness as an increase of 285 percent of a standard deviation in income. The short and long-run happiness effects of different kinds of labor and life events like unemployment, layoffs, marriage and divorce have been studied using happiness data from the German Socio-economic Panel by Lucas et al. (2004) and Clark et al., 2008a, Clark et al., 2008b. The effect of disabilities on long-run happiness is the focus of Wu (2001) and Oswald and Powdthavee (2008). Life satisfaction and financial satisfaction data have both been used to study the short and long-run effects of different kinds of events by van Praag and Ferrer-i-Carbonell (2008). Frey and Stutzer (2006) argue that people may mis-predict the extent to which they adapt to different kinds of goods and activities. Riis et al. (2005) provide some evidence in the context of renal patients receiving dialysis treatment. Helliwell (2003) and Blanchflower (2009) discuss international evidence.
Our estimates of status effects complement the findings in the growing literature testing if people care about their income relative to that of others, as in the models of interdependent preferences (where utility varies inversely with the average income of others) by Duesenberry (1949), Parducci (1968), Hamermesh (1975), Pollak (1976), Frank (1985) and Cole et al. (1992), inter alia. Empirical evidence on the effect of relative position using well-being data is presented in Clark and Oswald (1996), Blanchflower and Oswald (2004) and Brown et al. (2008).6 Senik (2004) studies the information content of reference group income. An interesting study by Luttmer (2005) involves a panel of almost 9000 individuals in the United States. He matches individual data on happiness and income with a measure of neighbor's income, given by the average earnings in the locality in which individuals live (which contain 150,000 inhabitants, on average). He then observes that similar decreases in happiness are produced when individual income falls as when the neighbor's income increases and concludes that there are sizeable relative income effects. Suggestive supporting evidence is provided in the form of larger estimated effects amongst individuals who socialize more in the neighborhood. In a similar spirit, Ferrer-i-Carbonell (2005) finds strong comparison income effects (particularly upwards). A related paper by Clark (2003) provides evidence showing that the happiness drop associated with falling unemployed is smaller the higher is the unemployment rate in this person's reference group.
We also present different patterns of habituation across sub-groups. In particular, we estimate the degrees of adaptation to income and to status for those individuals who declare themselves to be on the left of the political spectrum and compare them to those estimated for individuals on the right-end of the spectrum. This is interesting for two reasons. First, left and right-wing voters are important in determining economic policies. Second, it is hard to argue that the differential habituation patterns are due to left and right-wing individuals being affected by different stochastic processes for income and status. Indeed, under the assumption that income and status behave similarly for left and right-wingers, the differences in the estimates we present must be picking up true differences in preferences across these two sub-groups. Similarly, we present different estimates for other sub-groups (e.g., men compared to women) though the assumption of similar stochastic processes for income and status across them may be less compelling. As another strategy to deal with this potential concern, we show in Monte Carlo simulations how it is statistically unlikely to obtain our pattern of differential happiness adaptation across income and status due solely to their differential stochastic processes when the happiness data come from a model where there is equal adaptation.
Finally, our paper considers briefly loss aversion. Given that a standard utility function is concave in income, such tests are considerably harder than testing for adaptation and status, so our results remain exploratory.7 To identify a pure behavioral effect such as loss aversion, the challenge is to focus on sufficiently small changes to distinguish the asymmetric effect on happiness occurring solely from positive and negative short-run changes in income from the (non-behavioral) asymmetries that occur due to the utility function being concave in income. Still, we obtain some intriguing results. Our estimates indicate that a person on mean income of 60,971 DM (in 1995 values) reports similar happiness to someone on 64,031 DM, but who happens to be there as a result of a drop in their income of 2721 DM (the average drop in our sample). One way to gauge the size of the effect is to note that one standard deviation in income losses is only 21 percent of a standard deviation in income levels, and both give rise to similar changes in happiness.
More broadly, the questions discussed in this paper are particular examples of a problem that is common in economics and psychology, namely how to compare behavioral effects. ‘Economic psychology’ has made considerable progress without a unifying model or approach. Instead, progress has been made by individual researchers proposing alternative hypotheses that often imply considerable deviations from classical assumptions. A number of tests have then been performed establishing the statistical significance of these behavioral traits. But a shortcoming of this approach is that it is hard to get a sense of the relative importance of the effects. For example, although it is intuitively appealing that there are asymmetries implying some degree of loss aversion (see Kahneman and Tversky, 1979) previous research does not provide convincing answers concerning their relative economic importance. More precisely, we do not know how to value a study that ignores the possibility of loss aversion. If such effects are statistically significant but small in size, attention to loss aversion may be an unnecessary distraction.
The rest of the paper is organized as follows. Section 2 discusses the empirical strategy used to quantify behavioral effects. Section 3 presents the data while Section 4 presents the results. Section 5 discusses some evidence on loss aversion. The final section concludes.
Section snippets
Empirical strategy
Our purpose is to identify whether income and status have long-lasting (historical) impacts on happiness or whether these dissipate over time. To do so, we run a series of regression specifications that are based on the following general form:where lags and leads on both income, yit, and status, Sit, are
Data
We collect data from the German Socio-economic Panel (GSOEP), a longitudinal data set begun in 1984 that randomly samples households living in the western states of the Federal Republic of Germany. In 1990 the eastern states were added to provide a representative sample of the (reunited) Germany, although in this paper we concentrate only in the West German sample. Given the role of lags in our empirical strategy we consider only individuals for which we have at least 5 years of data. The GSOEP
Main results 1: full sample
Table 1 tests for the presence of adaptation to income compared to status. We start in column (1) by presenting a benchmark estimate with just log of current income, individual and year fixed effects, as well as a set of personal characteristics. It reports a positive and significant effect of current income on happiness. In terms of size, note that the summary statistics reported in Table A.1 show that happiness has a total standard deviation equal to 1.74 (the between-equals 1.36 and the
Further results: (asymmetric) change effects
Another way to illustrate the presence of adaptation is to estimate regressions where changes in income are included. This also helps us to approach loss aversion as an extension of the adaptation tests that are at the core of our study. People may care about changes in income for purely classical reasons as they may be better predictors of future income than current income levels. A more psychological version of the hypothesis that changes matter is focused on an asymmetry: some changes matter
Conclusions
An important question for economists is the extent to which people adapt to changed circumstances. In order to study aspects of this question we estimate a happiness equation with a distributed lag structure for income and status on individual panel data on 7812 people living in Germany between 1984 and 2000.
We find strong adaptation to changes in income but not to changes in status in the full sample. The adaptation effects to income are large in size. Once the long-run effects are estimated
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For generous comments and ideas, we thank Andrew Oswald, as well as Daniel Kahneman (who suggested the loss aversion tests), John Helliwell, Bo Honore, Bill Simpson, Sebastian Galiani, Angus Deaton, Julio Rotemberg, Matthew Weinzierl and seminar participants at Harvard Business School, the Brookings/Warwick Conference on Happiness in June 5–6 2003, Conference on Behavioral Economics organized by Federal Reserve Bank of Boston also in June 2003, the 2006 well being conferences at the CIAR and Notre Dame. The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research.
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