Abstract The scientific debate on the relation between Gross Domestic Product (GDP) and self reported indices of life satisfaction is still open. In a well-known finding, Easterlin reported no significant relationship between happiness and aggregate income in time-series analysis. However, life satisfaction appears to be strictly monotonically increasing with income when one studies this relation at a point in time across nations. Here, we analyze the relation between per capita GDP and life satisfaction without imposing a functional form and eliminating potentially confounding country-specific factors. We show that this relation clearly increases in country with a per capita GDP below 15,000 USD (2005 in Purchasing Power Parity), then it flattens for richer countries. The probability of reporting the highest level of life satisfaction is more than 12% lower in the poor countries with a per capita GDP below 5,600 USD than in the counties with a per capita GDP of about 15,000 USD. In countries with an income above 17,000 USD the probability of reporting the highest level of life satisfaction changes within a range of 2% maximum. Interestingly enough, life satisfaction seems to peak at around 30,000 USD and then slightly but significantly decline among the richest countries. These results suggest an explanation of the Easterlin paradox: life satisfaction increases with GDP in poor country, but this relation is approximately flat in richer countries. We explain this relation with aspiration levels. We assume that a gap between aspiration and realized income is negatively perceived; and aspirations to higher income increase with income. These facts together have a negative effect on life satisfaction, opposite to the positive direct effect of the income. The net effect is ambiguous. We predict a higher negative effect in individuals with higher sensitivity to losses (measured by their neuroticism score) and provide econometric support of this explanation.

Citation: Proto E, Rustichini A (2013) A Reassessment of the Relationship between GDP and Life Satisfaction. PLoS ONE 8(11): e79358. https://doi.org/10.1371/journal.pone.0079358 Editor: Mariano Sigman, University of Buenos Aires, Argentina Received: July 2, 2013; Accepted: September 30, 2013; Published: November 27, 2013 Copyright: © 2013 Proto and Rustichini. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was funded by NSF (grant SES-0924896) (http://www.nsf.gov/) ESRC (grant RES-074-27-0018) (http://www.esrc.ac.uk/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: Aldo Rustichini is a PLOS ONE Editorial Board member. This does not alter the authors' adherence to all the PLOS ONE policies on sharing data and materials.

Introduction The debate on whether higher income in a country is associated with higher life satisfaction is considered of crucial importance for scientific and for policy reasons. For example, if one thinks that the answer to the question is fundamentally affirmative, then alternative measurements of the wealth of a nation are redundant, and traditional values gross domestic product measures suffice. Instead, if the answer is negative, then there is a fundamental need to re-evaluate what public policies take as criterion of performance. The debate is still open. In a well-known finding, [1] reported no significant relationship between happiness and aggregate income in time-series analysis. For example, Easterlin shows that the income per capita in the USA in the period 1974–2004 almost doubled, but the average level of happiness showed no appreciable trend upwards. This puzzling finding, appropriately called the Easterlin Paradox, has been confirmed in similar studies by psychologists ([2]) and political scientists ([3]), and has been confirmed for European countries ([4]) (although there is some disagreement on the conclusion when an analysis based on time-series is used, see in particular [5] and [6]). On the other hand, life satisfaction appears to be strictly monotonically increasing with income when one studies this relation at a point in time across nations ([3]; [7]; [6]). To reconcile the cross-sectional evidence with the Easterlin Paradox, some have suggested that the positive relation in happiness vanishes beyond some value of income ([8]; [3]; [3]; [9]). This last interpretation has been questioned by [7] and [6], who claim that there is a positive relation between GDP and life satisfaction in developed countries. From the opposite perspective, it is being questioned by [10], who provide some evidence of no long-run effect even for developing countries. Differently from the previous literature, we perform our analysis without imposing a particular functional form to the econometric model; thus our conclusions will be independent of any hypothesis on the function linking happiness and income that we estimate. We instead partition all individual observations into quantiles of per capita GDP by the country of residence (with the 1st quantile of the distribution containing the fraction of individuals living in the poorest country), then we estimate the relation of happiness by using the quantiles. We initially consider a partition using 15 quantiles, then we repeat the analysis for partitions of 30 and 50 quantiles as a robustness check. The second important methodological feature of our analysis is the introduction of a country-specific effect, to control for time-invariant country-specific unobservable variables, therefore eliminating a potential source of country-specific measurement errors and omitted-variable bias. The introduction of this control is of crucial importance for analysis based on survey data, because the questionnaires are generally different across countries, and there are pervasive effects due to culture and language. Many measurement errors in indices of life satisfaction are possible, for example, a well known error is the differential item functioning, defined as the inter-personal and inter-cultural variation in interpreting and using the response categories for the same question ([11]). [12] using vignettes to correct for Individual-Specific Scale Biases show that variations in response scales explain a large part of the cross European country differences found in raw data. If the differential item functioning generates a systematic measurement error in the life satisfaction reports, this could lead to either a positive or negative bias depending on the correlation between the measurement error and other variables in the regression. For example, if Western countries tend to over-report their life satisfaction, this could generate a positive bias in cross-country estimates of the impact of income on life satisfaction. Omitted-variable bias could be equally problematic. For example, if cultural elements determine a time invariant preference for public good supply in some country, or if income distribution – usually very persistent in time – is correlated with both life satisfaction and GDP, this would result in a bias in the relation between GDP and life satisfaction. Controlling for country specific effects eliminates all biases that could be generated by the time invariant unobservable variables mentioned in the examples. Furthermore, the panel structure of the WVS offers the possibility to include the year fixed-effect that, together with individual employment status and personal income, allows to control for the main effects of the short-run business cycles that it is well known to have an impact on life satisfaction ([9]; [10]). [6] and [13] also estimated the effect of life satisfaction over GDP, using the WVS and controlling for country effects, but they impose a logarithmic functional form. [14] allow for the possibility of a different functional structure between rich and poor countries, but do not introduce any control for country fixed-effect (hence for countries' unobserved heterogeneity). To further assess the importance of taking into account the unobserved heterogeneity, we perform a second analysis of the relationship between aggregate income and life satisfaction on more homogeneous territorial units. We restrict our sample to all countries within the European Union (EU) before the first enlargement (we will refer to this group of countries as the EU14) to eliminate potentially confounding factors at the country level; we perform our analysis using the European regions defined following the Nomenclature of Territorial Units for Statistics (NUTS2) used by the EU as a base of observation. Finally, we use the data on EU14 to investigate possible explanations of the non strictly monotonic pattern between GDP and life satisfaction. The paper is organized as follows: Section Results first presents a broad outline of the main results, it then proceeds with a detailed presentation of the analysis, starting with country based analysis, and then following with a region based analysis. In section Discussion, we discuss possible reasons for the patterns we discovered and provide conclusions. Data are presented in section Materials.

Discussion We have reexamined the relationship between life satisfaction and GDP without imposing a particular functional form and found robust evidence of a clearly increasing relationship among poor countries and a non monotonic relation for richer countries. This finding lends support to the idea that the conflict between cross-sectional evidence – showing a positive relationship between GDP and life satisfaction – and the times-series evidence – generally finding no relationship – can be reconciled if the positive effect of GDP disappears after some bliss point ([8]; [3]; [21]; [9]). Furthermore, our analysis shows evidence of a non monotonic relationship between GDP and life satisfaction toward the end of the spectrum among the richest countries, with Life satisfaction slightly decreasing after a bliss point. Our findings on the relationship between GDP and life satisfaction are not in contrast with the previous cross sectional analysis. The differences with this literature are easily explained by the method we use; we replicated the results in the cross-country based literature when similar methods are used. We found a strictly monotonic relation between GDP and life satisfaction if we do not introduce country-specific dummies (in column 2 of table 1). We also replicated the results of [13], who estimated the effect of life satisfaction over GDP by using the WVS and controlling for country effects with a logarithmic model (in column 5 table 1). Similarly, our findings are not in contrast with the previous times-series based analysis, mostly focused on developed countries, but it allows us to pool data to an extent which is larger than what is allowed by separate times-series analysis at country level. Such non-monotonicity of the relationship suggests the need for a new way of thinking about this relationship, which we think has independent interest, and provides a bridge between existing economic theory and richer, although more informal, theories of human behavior like Personality Theory. Therefore, we investigated the reasons for the non-monotonic relationship. It is well known that life satisfaction is increasing in personal income at a decreasing rate (e.g. [22]). [23] find that the marginal life satisfaction with respect to income declines at a rate faster than the one implied by a logarithm utility function. This finding is substantially supported by [24] who argue, using USA data, that the effect of income on the emotional dimension of well-being is strictly increasing until an annual income of 75,000 USD, but has no further positive influence for higher values. However, a considerable literature following the Easterlin paradox suggest that this link is complicated by the existence of other effects acting with an opposite sign. The first is that the aspirations adapt to the new situations, an idea originally proposed by [25] and recently reassessed by [26]. [27], [28], [29], [30] provide some empirical evidence on how aspirations increase in income. The second is the effect of the relative income on individual life satisfaction – the so-called “Keeping up with the Joneses” hypothesis – an idea that can be dated back to [31]. [32], [22], [33], [34], [35] among others present empirical validations of this hypothesis ([17] provide an extensive survey of the theoretical and empirical literature explaining the Easterlin Paradox). In the view we propose, higher GDP leads to higher aspirations (driven by the existence of more opportunities or by comparison with the Joneses), which drives effort and individual commitment, which in turn do, on average, produce higher income. This higher income would typically produce higher life satisfaction. If we did stop here we would predict higher income to be associated with higher life satisfaction, perhaps at a decreasing rate. However, higher income now sets up a race between aspiration and realization; when realization is lower than aspiration, the psychological cost paid is disappointment, which increases with this gap (see [2, Unpublished Data Section], for a formal characterization and a structural estimation of this model using the British Household Panel and the German Socioeconomic Panel Surveys). If we again only look at the relationship between income and life satisfaction, we might observe a non-monotonic relationship for higher incomes. Indeed, with a simple example we have shown that if the relationship between life satisfaction and GDP is the result of combined effects of aspiration, realized personal income, and disappointment, the net effect may be non-monotonic. Our tests give support to the idea of a positive effect due to personal income and a negative effect due to the negative distance between personal income and regional GDP. This view implies that since the negative effect on happiness is induced by disappointment, this effect should be stronger in individuals who are more sensitive to losses and pay a higher psychological cost for the disappointment, which is another prediction that we test. The measure that we used of this sensitivity is the neuroticism score. In the data, we found that the way in which the relationship between personal income and life satisfaction is affected by Neuroticism is consistent with this interpretation. Individual welfare is affected by the gap between realized and desired income. When the gap is negative, for lower level of income, extra income decreases in absolute terms this negative gap; therefore individuals with higher Neuroticism score, that are more sensitive to reduced negative outcomes, become more satisfied. Our analysis implies that GDP long term growth is certainly desirable among poorer countries, but is it a desirable feature among developed countries as well? Recent evidence provided by [36] shows the negative effect of high aspiration can also be rationally predicted by individuals that, nevertheless may still choose options that do not always maximize happiness, but which are compatible with high income aspirations. This implies that individuals may still prefer to live in richer countries, even if this would result in a decreased level of life satisfaction. In other words, the fact that individuals aspire to a higher income may not be considered, from an individual perspective, a negative feature of an economy even if this might result in a lower level of reported life satisfaction among the richest countries. Finally, it is perhaps worth noting that our correlations between indices of well being and indices of aggregated wealth does not necessary imply a causality relation running from GDP to life satisfaction. This relationship is indeed very complex, both the presence of omitted variables and the existence of reverse causality, as recent contributions ([37] and [38]) have emphasized, which cannot be excluded.

Materials We used World Values Survey (WVS) dataset (and the integrated European Value Survey) for the country based analysis, and the European Values Survey in the European region based analysis. The data are generally available for five waves: 1981–1984, 1989–93, 1994–99, 1999–04, 2005–08. We consider all available country-wave observations, excluding a few country-waves explicitly considered not representative in the WVS (the country waves excluded are Argentina, 1981–1984, 1989–93, 1999–04; Bangladesh, 1999–04; Chile, 1989–93, 1994–99; China, 1989–93, Dominican Republic, 1994–99; Egypt, Arab Rep. 1999–04; India 1989–93; Mexico, 1989–93; Nigeria, 1989–93; Pakistan 1999–04; South Africa 1989–93). The list of the country-waves and the number of observations per country-wave are presented in section S3. The dataset is repeated cross-section (i.e. individuals in the sample are different in each wave). In the WVS, the variable used to measure personal satisfaction is the answer to the question: “All things considered, how satisfied are you with your life as a whole these days?” coded on a scale from 1 (dissatisfied) to 10 (satisfied). From the WVS we also derive the personal income measure, generally coded in 10 steps (and for a few country in 11 steps). The income ladder is provided as a common variable in the WVS, and it is derived by income ladders specific to each countries. Education, measured by age of leaving education, is ordinally coded from 1 to 10, ranging from less than 12 years old of age until to more than 21 years old. The categories for employment status are: full time, part time, self-employed, retired, housewife, student, unemployed, other. Town size is coded from 1 to 8, ranging from less than 2000 until 500,000 and more. The country-level per capita GDP is from the World Bank World Development Indicators dataset, and they are in constant 2005 US international dollars, PPP adjusted. In Table 5 and 6 of section S2 of File S1, we present a description of the main variables. Data are partitioned in 15 quantiles according to the per capita GDP level (in the Section S1 of File S1 we repeat the analysis with 30 and 50 quantile partitions). The resulting GDP brackets of each quantile and the county-wave combinations in each bracket are presented in section S3 of File S1. The European regions are defined following the Nomenclature of Territorial Units for Statistics (NUTS2) used by the EU; we have data for 171 regions. The regional per capita GDP data are from the Eurostat dataset; the values in Euros are PPP adjusted. We then transform the regional GDP data into constant 2005 USD, by using the Consumer Price Index (CPI) from the World Bank-World Development Indicators dataset (in a few cases the WVS regional classification did not match exactly the EUROSTAT classification, so we needed to aggregate some of the WVS regions, details are available upon request). A list of the region-wave combinations in each quantile for the 5 quantile partition is available in section S4 of File S1. In the third analysis, aimed to investigate the reason of the non monotonic pattern unveiled in the country and region based analysis, we derived the personality traits from some personality questions present in the 1989–93 wave (this exercise is presented in the section S5 of File S1). For this reason, this analysis only used data from this wave. In section S2 of File S1 we provide a description of the main variables used in the three analysis performed in the paper.

Acknowledgments The authors thank several coauthors and colleagues for discussions on related research, especially Wiji Arulampalam, Sascha Becker, Gordon Brown, Dick Easterlin, Peter Hammond, Alessandro Iaria, Graham Loomes, Kyoo il Kim, Rocco Macchiavello, Anandi Mani, Fabien Postel-Vinay, Dani Rodrik, Jeremy Smith, Chris Woodruf, Fabian Waldinger.

Author Contributions Analyzed the data: EP AR. Wrote the paper: EP AR.