Journal of Happiness Studies

, Volume 20, Issue 1, pp 203–228 | Cite as

Rising Income Inequality During the Great Recession Had No Impact on Subjective Wellbeing in Europe, 2003–2012

  • M. D. R. EvansEmail author
  • Jonathan Kelley
  • S. M. C. Kelley
  • C. G. E. Kelley
Research Paper


The Great Recession increased income inequality by an average of 6%. We assesses the impact of that on subjective wellbeing (happiness, life satisfaction). Data: European Quality of Life survey, 25 representative national samples at three time points, over 70,000 respondents. Analysis: variance-components multi-level models controlling for GDP per capita (an essential point) and individual-level predictors. Findings: income inequality has no statistically significant impact before, during, or after the Great Recession. Instead (contrary to much previous research) a straightforward individualistic utilitarian–materialist understanding is supported: money does increase wellbeing but inequality itself—the gap between rich and poor—is irrelevant.


Subjective wellbeing Life satisfaction Happiness Utility Great Recession Inequality Europe Income GDP per capita Multi-level models Well-being Quality of life Socioeconomic development Income inequality Poverty 

1 Introduction

Inequality in income and wealth has played a prominent role in political conflicts for millennia, especially prominent during Communism’s twentieth century heyday and more recently in our era at the beginning of the twenty-first century. It is widely suspected of being the culprit in many social ills (Babones 2008; Beckfield 2004; Landman and Larizza 2009; Piketty 2014; Wilkinson and Pickett 2009). Strikingly, in the pursuit of socioeconomic development, many governments, institutions, and distinguished scholars alike treat equality as an ideal, unquestioningly assuming that inequality is bad, either in itself or because it has bad consequences (OECD 2011, 2015; Stiglitz et al. 2010, and countless others). Moreover, in the United States and across Europe, the issue of income inequality has become politicized and bound up in diverse issues of race, immigration, gender equality, socialism and the role of state in society.

Touching on so many sensitive and conflicted areas, income inequality has become a fraught topic appropriated by and entangled in political and sociological disagreement. This political context makes it particularly important to seek clear empirical evidence on how much inequality actually impacts important social issues.

In this contentious atmosphere, the “Great Recession” inflamed the issue of income distribution, sparking widespread protests against the perceived power and abuses of the wealthiest elite—including the once influential “Occupy Wall Street” movement which seized national attention in 2011 in the US. Amid this renewed attention to inequality, arguments that incomes at the very top of society are flaring back up faster than incomes in the lower quintiles abound (e.g. Saez 2012), raising the issue of whether, post-recession, the impacts of inequality may be even greater. In this context, it is key to understand the true relationship between recession, inequality, and social outcomes.

This article refines and extends earlier studies about the influence of national income inequality on the quality of life, especially happiness/subjective wellbeing. The early studies in this thriving florescence of research on the issue yielded very mixed empirical results on whether (and how strongly, and in which direction) national-level income inequality affects subjective wellbeing (Alesina et al. 2004; Berg and Veenhoven 2010; Graham and Felton 2006; O’Connell 2004; Oishi et al. 2011; Oshio and Kobayashi 2011; Rozer and Kraaykamp 2013 and the literature reviewed there; Schneider 2012; Verme 2011; Zagorski et al. 2014). Until recently, the consensus in the field probably held that inequality reduces wellbeing. For a thorough review of this literature, see (Veenhoven 2015).

Now the situation has clarified. The overwhelming evidence in more recent analyses using more appropriate measurement and methods is eliciting a new consensus that national income inequality does not hurt individuals’ subjective wellbeing in advanced societies in ordinary times (Esping-Andersen and Nedoluzhko 2017; Kelley and Evans 2017a, b; Kenworthy 2017; Nielsen 2017; Rozer and Kraaykamp 2013; Zagorski et al. 2014).

However, the possibility remains that sudden jolts of inequality fracture long term arrangements, meanings, and interpretations, so that disruptions of the long-term inequality equilibrium can distress people and reduce their wellbeing. This is the Equilibrium Rupture hypothesis (Esping-Andersen and Nedoluzhko 2017).

The Great Recession was an equilibrium rupture in Esping-Andersen and Nedoluzhko’s (2017) sense, so it is an appropriate case for a test of the Equilibrium Rupture Hypothesis. This paper’s purpose is to assess whether the Great Recession had the consequences for happiness anticipated in the Equilibrium Rupture Hypothesis. In fact, our analysis and sensitivity tests will demonstrate that inequality did not reduce happiness in this extraordinary circumstance. Thus, the Great Recession, contrary to the Equilibrium Rupture Hypothesis, did not create a context in which inequality reduces happiness. Looking more broadly, the Great Recession is also unlike another extraordinary circumstance, the fall of Communism in Europe, which both reduced happiness for some (older, on the political left) and increased it for others (younger, on the right) (Kelley and Evans 2017b).

Our multilevel analysis of the Equilibrium Rupture Hypothesis examines the impact of national income inequality on individuals’ subjective wellbeing in Europe before, during, and after the Great Recession using excellent data from the European Quality of Life Surveys. We adjust for the level of national socioeconomic development (GDP per capita) and a variety of individual controls. We also assess the possibility that the impact could be different for the most disadvantaged members of society.

2 Prior Research and Hypotheses

2.1 Simple Materialism, Rational Choice, Utilitarianism (Supported Hypothesis)

The view best supported by our evidence—dubbed “simple materialism”—posits that prosperity at both the national level and the individual level enhances subjective wellbeing, but that, net of that, inequality does not actually impact subjective wellbeing (Bentham 1780; Coleman 1990; Smith 1776; Zagorski et al. 2014). This is really a family of closely related theories dating back at least to utilitarianism and the foundations of classical economics. For present purposes, the key aspect is individualism: people’s own interests matter to them, but the interests, the wealth, or the poverty of others do not, at least not in advanced nations.1

This materialist/rational-choice/utilitarian view is supported in existing research by well-established and conceptually clear links between material wellbeing and subjective wellbeing. This appears on both an individual and societal level: nations with higher GDPs can afford better public services and infrastructures which improve the quality of life. Individuals with higher incomes purchase more goods and services that satisfy them and that they enjoy. [In early research, there was some controversy about this, but it turns out to have been largely a problem of poor measurement and inappropriate methods. See the comprehensive review in Veenhoven (2015)].

Although simple to state, these theories are not easy to test. Correlations between aggregate-level characteristics—in this case inequality—and individual outcomes are particularly prone to omitted-variables bias (also known as confounding-variables bias). Hence, to disentangle the true relationships between aggregate-level and individual-level variables, it is key to take into account the influence of potential confounding variables, in particular by making sure to include measures of socioeconomic development (e.g. GDP per capita) and of the individuals’ own income in models of the effect of inequality on subjective wellbeing.

2.2 Rejected Alternatives: Political-Influence and Relative Deprivation

2.2.1 Political-Influence/Dominant Elite

Theories addressing the issue of the impact of inequality on subjective wellbeing need to link the macro-level contextual factor, inequality, to the individual-level outcome, subjective wellbeing (Van Praag 2010), a process which has been contentious and fraught with methodological and practical issues. Scholars have posited several pathways by which income inequality might make people less satisfied with their lives. For example, policy-capture theory (political-influence, dominant elite) posits that in unequal societies, people at the top of the income distribution use their wealth to access political power and use it to slant or redirect government policies in ways that benefit them (and people like them) more, and the disadvantaged less (Schwabish et al. 2006). In this view, access to valued goods and services in a society tends to be so structured that national income inequality signals poor services for the middle and lower classes, even net of socioeconomic development (Wilkinson and Pickett 2009). Policy capture theory predicts a strong cross-national result: all else equal, in the cross-section, the more unequal the society, the lower individuals’ subjective wellbeing, and, over time, inequality will have a uniform negative impact on individuals’ subjective wellbeing.

2.2.2 Relative Deprivation

Another alternative locates the harms of inequality in social psychology. The reigning assumption in policy circles seems to be a variant of relative deprivation theory holding that the great majority of the population will always feel that inequality is unjust or evil and will hence feel oppressed by it (Sen 1973; Stiglitz 2012), thereby reducing their quality of life in all domains (Seidl et al. 2005). Under this model, the impact of inequality on subjective wellbeing would rise with increasing public attention to and discontent with inequality, as occurred in the wake of the Great Recession, as people became more aware of their relative position.

The hypotheses are summarized in Table 1.
Table 1

Predictions from alternative theories about the effect of societal income inequality on individuals’ well-being (quality of life, happiness, utility)


Panel 1: preferred theory: simple materialism, rational choice, utilitarianism: Individual’s own income, and the prosperity of the society in which they live, is all that mattersa predictions

Panel 2: rejected alternative theory: dominant elite: Elite’s political power and influence aids them and hurts the poorb predictions

Panel 3: rejected alternative theories: relative deprivation, jealousy; zero-sum distribution of fixed amount of incomec predictions

Well-being in the present

H1: inequality has no effect in advanced societies

Inequality reduces well-being for those outside the elite

Inequality impairs well-being for those below the elite, especially those far below

Changes over time, before, during and after the great recession

H2: changes in inequality have no effect in advanced societies

Damage from inequality remains the same (recession did not change the distribution of power)

Greater damage if inequality increases, less if it decreases

Predictions inconsistent with our subsequent findings shown in italics

aBentham (1780), Coleman (1990), Smith (1776) and Zagorski et al. (2014)

bWilkinson and Pickett (2009) and Schwabish et al. (2006)

cSen (1973), Stiglitz (2012) and Seidl et al. (2005)

3 Data, Measurement, and Methods

3.1 Data

The individual cases are from representative samples of 25 European countries which participated in the European Quality of Life Surveys of 2003, 2008,2 and 2012. These surveys are conducted by Eurofound, a tripartite European Union Agency. Only countries which participated in all three surveys are included. The EQLS conducts personal interviews with a minimum of 1000 respondents per country and aims to draw from sampling frames that include at least 95% of the citizens of each participating country (EuroFound 20122013). Where such a sampling frame is not available, the EQLS generates a listing of potential cases by a random route procedure. Full details are available on the EuroFound website.

3.1.1 Replication and Revision

The original data are freely available on the Eurofound website. A processed dataset in a form convenient for reanalysis, together with complete Stata programming for all analyses and measurement decisions will be freely available on our website at the time of publication.

3.2 Measurement: Focal Variables

3.2.1 Subjective Wellbeing

Nomenclature: following long-standing custom in the literature, we use “subjective wellbeing”, “happiness”, “life satisfaction”, “subjective utility” and kindred words interchangeably. Distinctions could be made between them, but this is not usual, nor are any particular distinctions made consistently in the several literatures that use these concepts (Rozer and Kraaykamp 2013).

Happiness/life satisfaction/subjective wellbeing measures have a long-established record of validity via links to physiological measures, behaviors, peer perceptions, experiences, and predictive validity (Calvo et al. 2012; Diener 1984; Diener et al. 2013; Diener and Suh 1997; Frey and Stutzer 2002; Headey and Wearing 1992: 148–159; Headey et al. 2013; Radcliff 2001: 950). Careful research shows that subjective wellbeing measures translate successfully into many languages (Veenhoven 1993), making them suitable for comparative, cross-national analysis. Moreover, happiness and life satisfaction are highly correlated around the world, as shown by an analysis of surveys from 127 countries (Rojas and Veenhoven 2013).

Thus, subjective wellbeing measures have many attractive properties. Nonetheless, we should remember that it has been shown that unpredictable, unrelated events of everyday life can affect mood, thereby adding random measurement error to measures of subjective wellbeing (Schwarz and Strack 1999). Despite this finding, research demonstrates that single-item measures of subjective wellbeing have good reliability and that multiple-item scales have even better reliability (Diener and Suh 1997; Headey and Wearing 1992).

Following the classic Headey et al. (1993) analysis demonstrating the attractive psychometric properties of a two-item subjective wellbeing index built by combining the answers to the happiness and life satisfaction questions, we use these two indicators of subjective wellbeing in the European Quality of Life Survey data: [Q31 Life satisfaction]

All things considered, how satisfied would you say you are with your life these days? Please tell me on a scale of 1 to 10, where 1 means very dissatisfied and 10 means very satisfied.

and: [Q42 Happy]

Taking all things together on a scale of 1 to 10, how happy would you say you are? Here 1 means you are very unhappy and 10 means you are very happy.

Ideally, these questions would offer a scale of 0–10, because that introduces less random measurement error, but, in practice, these perform quite well. We use a robust equal-interval scoring. This approach is justified because many subjective wellbeing/quality of life measures, even crude ones, behave like equal-interval measures: they behave like crude measures of underlying continuous variables tapped at approximately equal intervals (Headey et al. 1993; Ng 1997). Moreover, prior research shows that treating these and similar wellbeing questions as cardinal rather than ordinal leads to quantitatively very similar results but with better precision in the cardinal approach (Frey and Stutzer 2002). Moreover, sensitivity analysis shows that coefficient estimates are not sensitive to the scoring of subjective wellbeing (Evans and Kelley 2004). In these circumstances, the Occam’s Razor criterion of preferring the simpler of two effective methods means that treatment as a continuous variable with the equal-interval scoring is to be preferred, so we use it here. Descriptive statistics for each measure independently and for the combined scale calculated for each country separately are in “Appendix 1”.

Answers to the two subjective wellbeing items are highly correlated (r = 0.66). They also have very similar correlations with a range of criterion variables (Table 2 below, columns 1 and 2), as they should on classical measurement principles if they measure a single concept (Treiman 2009). Although “happy” has more short-term emotive connotations than “satisfied”, they are best combined into a single scale to reduce random measurement error.
Table 2

Measurement: inter-item correlation (Panel A); correlations with criterion variables (Panels B and C) N = 71,099–94,626 depending on item missing data


Life satisfaction


Panel A: correlations

Life satisfaction






Panel B: correlations with criterion variables







Education (years)



Family income (log)



Panel C: correlations with national characteristics

Inequality (Gini)



GDP per capita (log)



27 European nations with population over 1 million, 2003, 2008 and 2012

European Quality of Life Surveys 2003, 2008 and 2012

3.2.2 Inequality: Gini

To measure inequality, we use Gini coefficients for each nation at the time of the survey. Use of the Gini is conventional (e.g. OECD 2006, 2011; World Bank 2013, 2014). The Gini (or Gini Index or Gini coefficient) is a measure of dispersion (Gini 1921), typically used to summarize the degree of scatter in an income distribution, usually in a nation. Empirically, it is extremely highly correlated (about 0.99) with the share of income that comes to the top quintile of income recipients (Nielsen and Aldersen 1995). Most cross-national studies of societal income inequality use the Gini and so we will use it here despite its known weaknesses, such as its sensitivity to household living arrangements (Firebaugh 1999). Its key advantage over alternative measures of dispersion such as the variance lies in its familiarity, and in comparability across countries. The Gini coefficients used here, provided by the European Quality of Life Survey, are very close to those from the World Bank and other standard sources.

Scandinavian nations, together with Austria, Slovenia and Hungary, are the most egalitarian European nations, as is well known. Germany, France and former Czechoslovakia are a little more egalitarian than the European average. Poland, Baltic countries and the UK are a little less egalitarian than the average. Turkey is the least egalitarian.

Controlling for other changes, the Gini coefficient rises around 10% on average from our 2003 baseline to the 2012 survey, presumably reflecting the effects of the Great Recession.

3.3 The Great Recession/Changes Over Time

Dating the Great Recession is not easy, because different countries had different experiences and, for example, it could be argued that Poland never experienced it at all. Using the traditional definition of recession (two consecutive quarterly declines in GDP), for the EU as a whole (28 member countries), the first wave of the Great Recession began at the start of the second quarter of 2008 and lasted through the second quarter of 2009; the second wave began at the start of the fourth quarter of 2011 and lasted through the second quarter of 2012; and the third (and final) wave began in the fourth quarter of 2012 and lasted through the first quarter of 2013 (International Monetary Fund 2016).

There are many fine points to be explored here, but for our purposes the key points are that the 2003 survey clearly predates the Great Recession, the 2008 survey was mostly conducted during the early, slightly panicked days of the start of the first wave, and the 2012 survey was conducted while most of Europe was in the third (less severe) wave. Subjectively, the worst part of the Great Recession, arguably reducing happiness worldwide, was in September and October 2008, around the time when the U.S. government agreed to an unprecedented purchase of toxic assets in the form of mortgage backed securities (Dodds et al. 2011).

3.4 Economic Development/GDP Per Capita

To obtain a correct estimate of the effect of national-level income inequality on subjective wellbeing, it is necessary to control for socioeconomic development, because socioeconomic development is a major factor reducing inequality and it also may directly affect wellbeing through reductions in corruption, provision of infrastructure, enhancement of the rule of law, government effectiveness, impartial regulation of business, political stability, and democracy (Inglehart and Welzel 2005; Rothstein and Holmberg 2011; Veenhoven 1984; Welzel and Inglehart 2010). Because the associations among different aspects of socioeconomic development are very high (mostly over 0.80) and nearly constant over time, their effects cannot plausibly be separated, but are well summarised by per capita GDP (Inglehart and Welzel 2005; Kaufmann et al. 2008; Breznau et al. 2011) and the strong link of per capita GDP to subjective wellbeing has long been known (Veenhoven 1984).

Omitting from a statistical model a causal variable that is highly correlated with an included causal variable grossly inflates the apparent effect of the included causal variable. The problem is even worse if the omitted variable is a direct cause of the included causal variable as well as the dependent variable (Pearl 2009). Many past studies of inequality’s effect are guilty of such an omission. The problem is that their findings may be seriously distorted, possibly even reversed, by this confounding-variables bias (Pearl 2009). This is particularly true when the data have a multilevel structure (Kim and Frees 2007).

As well as these general strictures about omitted variables bias, there is also specific evidence that the omission of per capita GDP can seriously distort the effect of inequality. Meticulous research at the country level (N = 119 nations) reveals that the bivariate correlation of societal inequality with societal mean subjective wellbeing is very different from the partial correlation controlling for per capita GDP (Berg and Veenhoven 2010). While this concerns a broader range of nations than we will investigate here, it casts serious doubt on findings about the apparent effect of the Gini that come from models where the effect of socioeconomic development is not controlled. Similarly, a review of the literature shows that apparent negative effects of inequality on subjective wellbeing usually reflect actual deprivation (in our case low per capita GDP) rather than inequality per se (Veenhoven 2008). The per capita GDP of all the countries in the world is readily available (IMF 2012, 2016).

3.5 Other Country-Level Variables

Of course, countries differ in an infinity of ways, but our focus in this paper is on the impact of inequality on happiness, so to get an unbiased measure of inequality’s effect we need only to control potential confounding variables that are correlated with inequality, but not in the model. This is a key reason for including GDP per capita: in general, as GDP rises, inequality falls (albeit with lots of random variation), so it is crucial to include GDP as a control.

To obtain correct estimates of the effects of higher-level variables it is important not to include too many in the model. Best practice suggests that we want at least 30 countries per higher-level variable (Hox 1995), so we are already pushing the envelope and it would be very unwise to include more. In particular, loading the model with finer distinctions among European countries would run the risk of producing incorrect estimates for all the higher-level variables.

3.6 Individual Level Control Variables

3.6.1 Income

Whether individual and/or family prosperity and spending, either absolute or relative to others, does or does not increase happiness is a very important question and the focus of a large and lively research tradition (Aknin et al. 2011; Deaton 2008; Easterlin 2005; Hagerty and Veenhoven 2003; Inglehart and Klingemann 2000; Jagodinski 2010; Lane 2000; Sacks et al. 2010; Stevenson and Wolfers 2008; Veenhoven and Hagerty 2006; Koralewicz and Zagorski 2009; Zagorski 2011). But micro-level income is not the focus of this paper, it is merely included as a control variable: its inclusion ensures that the estimated effect of societal income inequality is not biased by its omission. Instead, this paper focuses rather on the impact of the national-level context, on the impact that societal income inequality has on individuals’ subjective wellbeing.

Thus, individual-level family income, like GDP, is just a control variable in our models. Although there are exceptions, prior research, like ours, generally finds that more affluent people are happier (e.g. Argyle 1999; Bygren 2004; Clark and Senik 2010; Daly and Rose 2007; Frey and Stutzer 2002; Koralewicz and Zagorski 2009). As a result, including income lifts the percent of variance explained and may slightly reduce the standard error of other estimates.

Importantly, the detailed measure of income in the European Quality of Life surveys is far superior to the measure in the World Value Study/European Value Study data that has been used in many other analyses, including those that are key in establishing that income inequality did not impair subjective wellbeing in the advanced societies prior to the Great Recession (Esping-Andersen and Nedoluzhko 2017; Kelley and Evans 2017a, b; Kenworthy 2017). This means that individual income in this study will be a more effective control variable. That, in turn, will place the measurement of the effect of national-level inequality on subjective wellbeing on a firmer foundation.

In addition to its role as a control variable, income is also used to define a subsample—the cases in the bottom half of their country’s income distribution. We use this subsample to check whether inequality’s effect on subjective wellbeing is the same for disadvantaged people as for the population as a whole.

3.6.2 Other Individual-Level Control Variables

Age has a significant effect on subjective wellbeing in most analyses (e.g. Headey and Wearing 1992; Yang 2008; Koralewicz and Zagorski 2009), so we control it here. It is measured here in single years. Gender it has long been known that women are a little happier than men (Forest 1996; Shmotkin 1990; Yang 2008). In our analyses, Female is a dichotomous variable scored 1 for women and 0 for men. Religion Religious affiliation (which, if any, religion one identifies with) generally does not affect subjective wellbeing so we will not include it here, but the fellowship dimension of religion has long been demonstrated to enhance happiness in the US and many other nations (Headey and Wearing 1992; Inglehart and Baker 2000; Lim and Putnam 2010; Rozer and Kraaykamp 2013; Star and Maier 2008), with the optimal scoring being the natural log of the number of worship service attendances per year (Evans and Kelley 2004); so we include it with that scoring in our models. Marital status has repeatedly been shown to have an important effect on happiness (e.g. Schnittker 2008). “Married” is a dichotomous variable scored 1 for currently married people and 0 for others. “Formerly married” is a dichotomous variable scored 1 for formerly married people and 0 for others. Education reduces distress (Ross and van Willigen 1997) and increases wellbeing (Zagorski et al. 2010). It is scored as years of schooling. It is used in our main model as a control variable, but we also use it to examine a different angle on disadvantage. One of the models in Table 3 is estimated just for those individuals with no more than a secondary-school education (with the goal of checking that inequality’s effects on subjective wellbeing are the same for individuals who are on the lower rungs of the educational ladder).

4 Methods

For the bivariate descriptions of the relationships between happiness and inequality and between happiness and socioeconomic development, we present graphs of fitted means with confidence bands to show the snugness of the fit.

For the multivariate analyses, we follow the approach that is becoming standard in the area: multi-level models estimated by GLS with random intercepts and fixed effects (e.g. Kelley and Evans, 2017a, b; Kenworthy 2017; Rozer and Kraaykamp 2013). These are also known as variance components models. Following best practice for multi-level models, we do not group-mean-center them (Kelley et al. 2017). Because our models allow curvature in several individual-level variables, the coefficients themselves are not particularly digestible, but the results can be effectively grasped in graphs of the predicted values. We therefore show in text the predicted values of happiness as the Gini varies across a range that is represented in each group of societies, with the other predictor variables controlled by whole population standardization (Kelley and Evans 1995: 165–166).

Because there is little measurement error in the independent variables (save for individual income and perhaps church attendance) and measurement error in the dependent variable does not bias the metric coefficients and therefore the predicted values (Bollen 1989; Treiman 2009), the results should be close to unbiased. Confidence bands around the predicted values provide useful information about the precision of the estimates.

One might wonder if the effects of inequality differ by the country’s level of socioeconomic development. Europe, the focus of our study is not a good setting in which to explore that issue, because Europe is too rich and inequality and development are too highly correlated to make estimation robust with this few countries. In any case, prior research strongly suggests that inequality has neutral to positive effects on happiness across the whole span of socioeconomic development including countries much poorer than any in Europe (Kelley and Evans 2017a, b; Kenworthy 2017).

Our models assume that the relationship, if any, between Gini and individual-level happiness is linear. To assess the plausibility of this assumption, we assessed curvilinearity of this relationship in a simple bivariate model and also in a model controlling for GDP (“Appendix 2”). Neither showed significant curvilinearity, so, following Occam’s Razor, we prefer the linear models and use them here.

There are many angles of approach to the issue of changes over time here. We first provide a summary analysis of the effect of inequality which constrains the effect to be equal across our three time periods (so the magnitude of inequality differs over time, but the magnitude of its effect does not). The effect of inequality is not statistically significant in this model, so we explore the possibility that there might be an inequality effect in some of the periods, but not others. To this end, we relax the constraint by estimating our model separately for each of the 3 time periods.

An alternative, intuitively appealing angle on the impact of change is the “difference-in-difference” approach. Unfortunately, difference-in-difference models have a bad practical reputation. A much-cited paper notes multiple, serious drawbacks to them (Bertrand et al. 2002): many time periods are needed for stable estimation; many cases are needed for stable estimation; aggregate data do not adequately guard against confounding/omitted variables problems; and OLS versions are not trustworthy because of correlated errors over time. Moreover, very clear and strict criteria must be met for difference-in-difference models to be justified and these criteria are not met in the case of inequality and happiness in the advanced societies (Kelley and Evans 2017b), so the use of difference-in-difference models is not appropriate.

5 Descriptive Results

At the country level, with no controls, national income inequality has a clear negative relationship with mean subjective wellbeing: the higher the inequality, the lower the subjective wellbeing of the populace (Fig. 1a). By contrast, again at the country level with no controls, GDP per capita has a clear positive relationship with mean subjective wellbeing: the higher the GDP, the higher the subjective wellbeing of the populace. The GDP effect is somewhat larger.
Fig. 1

a Inequality and well-being. b Economic development and well-being

To discover whether these bivariate relationships are real or merely proxy effects of other variables, we turn to the multi-level analysis.

6 Analytic Results

The key question is to what degree, if at all, societal inequality influences the subjective wellbeing of the denizens of these countries. To answer that question, we turn to the multi-level regression analysis described in the Methods section above.

Considering inequality alone—entering only the Gini in the analysis—it appears to have a small but statistically significant effect on happiness, although the R-squared is extremely small (Table 3, column 1).
Table 3

Multi-level analysis of well-being (average of life satisfaction and happiness items). 27 European nations with population over one million, most surveyed three times (2003, 2008, and 2012); 81 surveys in all.

Source: European Quality of Life Surveys 2003, 2008 and 2012. All variables are standardized to a mean of 0 and standard deviation of 1 in the pooled individual-level sample. They are therefore in the same metric as correlations


Model 1 whole population


Model 2 whole population


Model 3 whole population


Model 3 for those in the bottom half of their nation’s income distribution


Model 3 for those with at most a high school education


National level

 Inequality (Gini)






 GDP per capita






Individual level











 Age squared





 Church going (ln)















 Formerly marrieda





 Income (ln)





























ns regression coefficient not significantly different from zero at p < 0.05 two-tailed

p < 0.05, ** p < 0.01, *** p < 0.001

aSingle never married are the reference (comparison) group

But this is misleading. Model 2 augments Model 1 by adding in per capita GDP as another contextual predictor (Table 3, column 2). In this simple model, the Gini effect is reduced to near 0 and is not statistically significant. In contrast, the per capita GDP effect is large, positive, and significant. The R-squared is substantially increased, too. This is not surprising, given that the correlations of the component items measuring wellbeing with per capita GDP were about twice as large (in absolute value) as their correlations with the Gini.

The finding that, net of GDP, inequality as measured by the Gini does not affect subjective wellbeing is robust to the inclusion of individual-level controls in Model 3 (Table 3, column 3).

6.1 Sensitivity Tests

6.1.1 Inequality and the Poor

But what if inequality damages only the bottom half of society? Could the advantages gained by the top half be masking the disadvantages suffered by the less fortunate half? It turns out that this is not the case. Looking at the final model discussed above (including Gini coefficient, GDP per capita and individual level controls), but restricted this time to respondents in the lower half of their nation’s income distributions, we find a pattern that matches the population-wide pattern (Table 4, column 4). GDP matters to subjective wellbeing, but inequality does not, even to the bottom half of society, who presumably suffer more of the ill effects of unequal distribution of resources.
Table 4

Description: before, during, and after the Great Recession. 25 European nations with population over 1 million, each surveyed in 2003, 2008 and 2012/2013.

Source: European Quality of Life Surveys 2003, 2008 and 2012. Average for 25 nations in each time period (except column 4 which is an average for individuals). Only nations surveyed at all three time points are included (see Table 6 for details). Adjustments in columns 2 and 5 are from models like those of Table 5

Time period

Observed inequality (mean Gini coefficient)


Inequality adjusted for changes in earnings (adjusted Gini)


Mean income (euros per month)


Observed well-being (mean points out of 100)


Estimated well-being, adjusted for changes in GDP and income (points out of 100)


Before recession, 2003






Beginning of recession, 2008






Later, 2012 (some nations still in recession, some not)






Change, 2008 versus before recession (2008 as % of 2003)

6% higher observed inequality

9% higher adjusted inequality

12% higher income

2% higher observed wellbeing

4% lower adjusted wellbeing

Focusing even more sharply on the lower rungs of society—we still find no significant effect of inequality for people in the bottom 25% of the income distribution (Z = −0.074, ns) or even for the bottom 10% (Z = −0.69, ns). Similarly, we saw above that inequality has no effect for the population as a whole, nor for those with at most a secondary school education. Moreover, when we narrow the focus even further to people with 8 years of education or less inequality still fails to have a significant effect on subjective wellbeing (Z = 0.02, ns).

Thus, we find no special relationship between inequality and subjective wellbeing among the very poor, the poor, the near poor, and those just getting by. This further supports our materialist hypothesis that absolute affluence matters to subjective wellbeing, but that the distribution within society does not.

6.1.2 Inequality and the Educationally Disadvantaged

As a second sensitivity test, consider the impact of income inequality on the subjective wellbeing of the educationally disadvantaged—those with, at most, a secondary school education. For this group, too, the results show that GDP matters to subjective wellbeing, but inequality does not (Table 3, column 5).

Thus, across this time span as a whole, inequality does not harm the subjective wellbeing of people in general (Model 3, the main model). Nor, in particular, does it make disadvantaged people less satisfied with their lives. We turn next to the question of whether this changed across the course of the Great Recession.

7 The Great Recession

7.1 Description of the Great Recession

Before the Great Recession, back in 2003, observed inequality (the mean Gini coefficient for the 25 nations having data for the 3 time points) stood at 0.288, very close to the adjusted Gini (adjusted for changes in earnings across the full period studied), 0.281 (Table 4, Columns 1 and 2). As of 2008, as the Great Recession took hold, the average observed Gini climbed to 0.305 (again close to the adjusted Gini of 0.310). In 2012, when some nations were still in recession while others were climbing out of it, the observed Gini had held nearly steady (0.303) and the adjusted Gini had climbed slightly (0.310). Thus, there was a noticeable rise in inequality as the recession struck: observed inequality rose by 6% and adjusted inequality by 9% from 2003 to 2008 and remained at about the same level in 2012.

Despite the Great Recession, mean incomes grew over the period, from 883 Euros per month in 2003 to 988 in 2008—a 12% gain—and to 1089 in 2012 (Table 4, column 3). So, if nothing else changed, wellbeing should have increased too, since income is a major source of wellbeing (an effect of 0.240 in Table 3 above).

Over the period, observed subjective wellbeing did indeed slightly increase, from 66 to 68, or about 2% (Table 4, column 4). First, wellbeing increased slightly—by just over 1 point in 100—from 2003 to the onset of the Great Recession around 2008 (t = 7.28, p < 0.001; Table 4, column 4). Then it increased again by around half a point from 2008 to 2012 (t = 4.00, p < 0.001).

On the simple model we are considering so far (Table 3, column 3), inequality seems to have nothing to do with all this—it has no statistically significant effect, so increases in inequality do not matter to wellbeing one way or the other.

But increases in income and GDP do matter quite a lot (Table 3, column 3). Adjusting for those benign effects, everything else about the Great Recession may well be hurtful, reducing wellbeing from about 70 points to 66, or about 5% (Table 4, column 5). This decline could have many sources—increasing existential insecurity associated with terrorism, unrelenting media emphasis on the seriousness of the recession, Europe’s changing geopolitical situation, technological change, globalization, even climate change (Kelley 2016), among many other possibilities. We cannot say which.3

However, the model of Table 4 underlying all these estimates is only a crude approximation. It assumes (rightly, as we will see) that inequality has the same effect before, during, and after the Great Recession. It also assumes (sometimes wrongly) that income, GDP per capita, and other variables also have the same effect before, during, and after the Great Recession, and that (also wrongly) nothing else changes.

A better estimate allows for changes over time. We turn to these changes now, some of which turn out to be large.

7.2 Analysis: Separate Estimates for Before, During, and After the Great Recession

Let us consider the influence of the various social forces in our model before the recession, during its onset, and as it wanes (Table 5).
Table 5

Before, during, and after the Great Recession: Multi-level analysis of well-being (average of life satisfaction and happiness items). 27 European nations with population over 1 million, each surveyed in 2003, 2008 and 2012.

Source: European Quality of Life Surveys 2003, 2008 and 2012; only nations surveyed in all three periods are included. All variables are standardized to a mean of 0 and standard deviation of 1 in the pooled individual-level sample. They are therefore in the same metric as correlations


Before recession 2003


Beginning of recession 2008


Later 2012


National level

 Inequality (Gini)




 GDP per capita




Individual level









 Age squared




 Church going (ln)












 Formerly marrieda




 Income (ln)




















ns regression coefficient not significantly different from zero at p < 0.05 two-tailed

p < 0.05, ** p < 0.01, *** p < 0.001

aSingle never married are the reference (comparison) group

Things did change. Most notably, the initially very large influence of GDP per capita decreases sharply, from 0.350 before the recession to 0.227 later on (Table 5, row 2). That is still a strong effect, but only about 2/3 its former strength. By contrast, the initially substantial effect of individual income rises slightly over the course of the recession, from 0.212 to 0.244.

The overall pattern suggests that the Great Recession—or other changes happening at the same time—made subjective wellbeing somewhat more individualistic and less dependent on national characteristics.

The key result for our purposes is that inequality failed to have a statistically significant effect at any of the three time points—no impact in the boom years before the Great Recession, no impact in the panicky days as the Great Recession set in, and no impact as the Great Recession waned (Table 5, row 1). Thus, the appreciable growth in inequality over the course of the Great Recession, an increase of some 6%, had no effect on Europeans’ subjective wellbeing.

7.2.1 Changes in Wellbeing in the Period of the Great Recession

The Great Recession increased income inequality but, as we have seen, that increase had no effect on wellbeing one way or the other.

Income and GDP also increased over the period—whether the Great Recession make the gains less than they would otherwise have been is another issue, one beyond the scope of this analysis. But any gain in income was clearly a benefit, more so in later years. And any gain in GDP is also a benefit, more so in earlier years. Other changes not measured here are various, numerous, and hard to assess (terrorism, politics, technology, globalization, climate change, etc.).

A further complication is that the way people evaluated the world also changed—compare the “rules of the game” for 2003 (the regression coefficients in Table 5, column 1) with those for 2008 in the beginning of the recession (the regression coefficients in column 2) or 2012 toward the end (the regression coefficients in column 3). The regression coefficients show us how the levels on our predictor variables are “translated” into amounts of wellbeing. One can assess the hypothetical impact of changing the rules of the game, for example, by applying the regression coefficients from 2003 by a whole population standardization to the data from 2008 and calculating the counterfactual predicted value—what the mean happiness would have been in 2008 if the Gini, GDP, income education and all the other predictor variables retained the distribution of values they actually had in 2008, but (counterfactually) had the effects that they did in 2003. This in essence takes the regression equation for 2003 as a full description of wellbeing in the world in 2003. We then apply the 2003 regression equation to the 2008 population to predict what wellbeing would be for each person in 2008 if they had followed the 2003 patterns. This gives a comparison of 2003 and 2008 for exactly the same people and exactly the same levels of national inequality and GDP. The only difference is that the links between all these things and wellbeing follow the 2003 pattern, not the 2008 pattern. Thus, the difference shows how wellbeing in 2003 and 2008 intrinsically differ, quite apart from changes in inequality and GDP at the national level, and changes in education, occupation, income, and such at the individual level.

Then we can compare the actual 2008 predicted value to the counterfactual 2008 value to see the magnitude of the impact of the changing rules of the game. For these analyses we use the models of Table 5 (with the addition of an unemployment measure, since unemployment went up noticeably over the period). For example, what we call the 2003 ‘rules of the game’ are the regression coefficients from the equation of Table 5 column 1. We evaluate them by whole population standardization alternatively for the 2003 population, the 2008 population, and the 2012 world. This gives an estimate of hypothetical well-being for those three times, if the predictors had the same effects at the other times as they did in 2003, as reflected in the 2003 regression coefficients. The 2012 ‘rules of the game’ are based analogously on the 2012 regression results. This method follows Kelley and Evans (1995: 165–166) and avoids the difficulties described by Jones and Kelley (1984). Further details are available from the authors on request. This matters:
  • On the pre-recession 2003 ‘rules of the game’ wellbeing was about 65 points for the world at that time. Applying those rules to the world as it became early in the Great Recession in 2008 would have found things better, around to 68 wellbeing points. By 2012 the world had changed further and would have generated wellbeing around 70 on the old rules. So, by the standards of 2003, the Great Recession period would have led to an increase in wellbeing of around 5 points. The gain was mainly from growing incomes and growing GDP—and that growth would presumably have been even faster in the absence of a recession.

  • On the 2012 post-recession ‘rules of the game’, life is evaluated a little differently. The past looks worse than people saw it at the time, 62 points (rather than 65). But the present still looks better than the past, rising to 67 points—not as high as it seems on the 2003 rules of assessment but still 5 points better than before.

Thus, the evaluation of wellbeing over the period of the Great Recession depends on the (changing) standards people apply to the world at different times. But all alike—2003 rules, 2008 rules, and 2012 rules—concur in evaluating life as about 5 points better at the end of the period that it was at the beginning.

8 Discussion

8.1 Summary

Prior research reveals that income inequality did not reduce subjective wellbeing in advanced societies prior to the Great Recession (Esping-Andersen and Nedoluzhko 2017; Kelley and Evans 2013; Kelley and Evans, 2017a, b; Kenworthy 2017; Nielsen 2017; Rozer and Kraaykamp 2013; Zagorski et al. 2014). These results are robust, because they have been demonstrated both in the WVS/EVS surveys and the European Quality of Life Surveys.

But the Great Recession’s profound economic and social dislocation led to a resurgence in inequality, arguably an Equilibrium Rupture and many scholars have argued that it generated or amplified the negative effects of inequality on many aspects of social life. To find out, this paper has used powerful and robust multi-level (variance-components) models to analyze the European Quality of Life surveys across a time span from the prosperous days of 2003, to the depths of the Great Recession (2008), to the moderate recovery time of 2012. Our aim was to discover whether rising inequality harmed Europeans’ quality of life, specifically their subjective wellbeing.

The simple bivariate correlations of societal inequality with subjective wellbeing are negative. But this is misleading because of the confounding effect of a key omitted variable, national socioeconomic development (GDP per capita): unequal societies are, on average, much poorer (r = −0.46) and so they are disadvantaged because of that. Our variance-components multi-level models controlling for national per capita GDP demonstrate that national levels of inequality, as measured by the Gini coefficient, have no statistically significant effects on subjective wellbeing in Europe in 2003–2012: before, during, and after the Great Recession, inequality has no impact on subjective wellbeing in Europe. Importantly, this paper shows that, even in a sharp economic downturn accompanied by rising inequality—an example of an Equilibrium Rupture (Esping-Andersen and Nedoluzhko 2017)—in Europe, a nation’s income inequality does not affect its citizenry’s happiness.

The possibility has been raised that, because of humankind’s evolutionary history of living in relatively small groups, humans are not adept at perceiving the macro situation in large-scale societies (Nielsen 2017; Tamas and Dunbar 2013). However, other research indicates a high correlation between perceptions of inequality reported in survey data and Gini values (Evans and Kelley 2017). Thus, it seems likely that sheer ignorance is not the explanation.

These results contribute to a growing body of evidence that inequality (net of national prosperity) does not reduce happiness and life satisfaction. Similar results have been found for health (e.g. Beckfield 2004; Bobak et al. 2000; Eckersley 2015). Our results extend that evidentiary base to a period that is an example of an Equilibrium Rupture.

There is a significant limitation to the generalizability of our results in that the database does not include any countries outside Europe. Thus, extremely poor countries are not included. Moreover, we cannot safely generalize from these results to the newly developed rich nations of Asia, for example. In short, this paper is a starting point rather than a terminus: assessment of the relationship between inequality and quality of life during periods of Equilibrium Rupture in the rest of the world should be a priority for future research.

8.2 Reflection on the Literature

This paper replicates recent analyses finding that societal-level income inequality does not reduce subjective wellbeing in advanced societies. The replication is important because Kelley and Evans’ (2013, 2017a) multi-level models which come to a similar finding in a methodologically sound way rely on a much weaker control for individual-level family income (which is only measured as relative income in the World Value Study/European Value Study pooled data file which they use) as well as applying to the pre-recession period. Other high quality existing research also substantiates the claim that, prior to the Great Recession, national-level income inequality (net of national prosperity) did not lower the quality of life (Macunovich 2011; Rozer and Kraaykamp 2013; Zagorski et al 2014). Our research extends that evidence base, first by replicating the pre-Great-Recession result, and then by demonstrating that even though inequality rose during the Great Recession, it continued to have no impact on subjective wellbeing. Moreover, even among the disadvantaged—the poor and those with little schooling—inequality has no impact on subjective wellbeing.

Thus, we have found solid support for the claim that national-level income inequality, and changes in it, do not reduce individuals’ overall subjective wellbeing in advanced societies, all else equal.

From a slightly different angle, it is noteworthy that our findings do not support the political-influence hypothesis (Table 1, panel 2). The political-influence hypothesis posits that inequality is detrimental because it allows the elite’s political power and influence to divert public resources to their private good, thereby benefitting them and hurting the poor (e.g. Wilkinson and Pickett 2009; Schwabish et al. 2006). The finding that inequality has no impact on the subjective wellbeing of the populace in general and especially the finding that it has no impact on the subjective wellbeing of the poor and even the very poor are results that are strongly contrary to the political-influence hypothesis: there is no link between inequality and wellbeing, so if, as posited, the elite have been trying to rig the system, they have failed.

Nor do our findings support the relative deprivation/jealousy/zero-sum distribution of fixed amount of income family of hypotheses (Table 1, panel 3) which posit that societal inequality impairs quality of life across the board (e.g. Sen 1973; Stiglitz 2012; Seidl et al. 2005). The finding that income inequality is irrelevant to subjective wellbeing clearly undermines the claim that income inequality induces feelings of relative deprivation—unhappiness and anger at being somehow cheated of one’s just share.

Instead our findings support the simple materialism/, rational choice/utilitarianism family of hypotheses (Table 1, panel 1) which posit that an individual’s own income, and the prosperity of the society in which they live, are the only dimensions of income that matter to wellbeing (Bentham 1780; Coleman 1990; Smith 1776; Zagorski et al. 2014). Exploring the specific mechanisms whereby national affluence and individual prosperity enhance wellbeing should be a priority for future research.

This paper and its immediate predecessors provide over 100 primary models and sensitivity tests that are variants on those models—adding additional control variables, experimenting with different estimation techniques, exploring a variety of functional forms, and examining different time periods. Accordingly, it seems reasonable to take “In advanced countries, the national-level Gini coefficient does not affect subjective wellbeing in plausibly specified multi-level models that include a reasonable array of controls, especially GDP per capita” as the working hypothesis in this domain.

It is, however, noteworthy that despite its ready availability and familiarity, the Gini is far from a perfect measure of inequality. For example, it is notoriously vulnerable to fertility, aging, and family structure (e.g. Firebaugh 1999; Nielsen and Aldersen 1995). Perhaps more importantly, the Gini tells us about how much redistribution—in standardized terms—would be required to achieve income equality, but it does not tell us how high the incomes of the elite are. It remains possible that the Gini effects are as we and recent research have found them, but that the actual magnitude of elite incomes will induce the relative deprivation and political influence effect posited in some prior theories. Thus, exploring whether the null effects of inequality on wellbeing found for the Gini stand up when using other measures of inequality should be a priority for future research.

Our results suggest that what matters is not inequality but prosperity: The observed relationship many prior scholars found between inequality and wellbeing is in fact rather a manifestation of the fact that the richer nations of Europe are also generally more equal. Across time periods we find a substantial relationship between GDP per capita and subjective wellbeing. It is possible that this substantial per capita GDP effects we observe largely reflects the legal, political, and social enhancements that modernization brings (rather than the economic ones), but we still find that family income has a substantial impact on individuals’ wellbeing which rules out a post-materialist interpretation. Although family income is only a control variable in our analysis, these substantial effects illustrate the continued relationship between income and happiness outcomes, even in advanced societies.

8.3 Policy Implications

There appears to be an emerging consensus in the policy community that subjective wellbeing ought to be the key criterion of policy success (e.g. Stiglitz et al. 2010; Thin 2012). That lends an extra lash of urgency to the imperative of improving our models and methods.

The best evidence that we have to date is that redistribution beyond the minimum for advanced societies does not enhance subjective wellbeing/quality of life. This makes it at least arguable that the substantial resources and vast bureaucracy devoted to inequality reduction might be better spent on policies that produce a higher yield in happiness. In particular, the strong impact of individual income on wellbeing suggests that poverty reduction should have a very high priority. Moreover, as we have noted above, the strong effect of GDP per capita very probably substantially reflects the enhanced infrastructure, quality of government, and social services that normally form part of the socioeconomic development process (Kaufmann et al. 2009). If, as this suggests, socioeconomic development is an important source of subjective wellbeing and income inequality is largely irrelevant, then we should be focusing policy on promoting socioeconomic development and should relegate concern with inequality to the trash bin of history


  1. 1.

    In developing nations, there is a complication in that inequality may signal new opportunities opening, thereby raising hope for the future, hence making people happier.

  2. 2.

    This round was originally scheduled for 2007 and is sometimes referred to as the 2007 survey, but most of the surveys were conducted in 2008 (Eurofound 2016), so we will label it the "2008 wave".

  3. 3.

    The small number of countries does not allow us to assess these other contextual effects. The combined effect of all of them together shows up in our model's intercept.


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Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.University of NevadaRenoUSA
  2. 2.International Survey CenterRenoUSA
  3. 3.University of California, BerkeleyBerkeleyUSA
  4. 4.American Institutes for ResearchWashingtonUSA

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