Inequality and Happiness: The Role of Perceptions of the Distributive Process (Master Thesis)

Inequality and Happiness:

The Role of Perceptions of the Distributive Process

Master Thesis MSc Sociology

August 2014

Candidate number: 370276

ABSTRACT

The stock of research has produced contradicting findings on the much-debated

happiness-impact of inequality. The strength and direction of this impact has

been thought to depend on several external factors: relative concerns, personal

future expectations, and perceptions of the distributive process. Previous findings

provide support for these presumed links, although some links are still awaiting

empirical assessment. For instance, it is not clear yet whether inequality amplifies

the positive happiness-impact of having optimistic beliefs about one’s future.

Furthermore, the level of inequality and people’s perceptions on the distributive

process, have been thought to shape people’s incentives to compete for high

social positions. However, it remains questionable whether rising inequalities are

interpreted in the same way by people regardless of the (perceived) social-

distributive contexts. Rather than people being intrinsically inclined to compete

(given a certain level of inequality), a great deal may depend on the cultural-

ideological context, fostered by the (perceived) distributive context. Certain

context could undo any effect of the level of inequality upon people’s competitive

incentives. Eventually, the resulting intensity of competitive pressures is thought

to affect happiness. Cross-national survey data were analysed using regression

analysis. Two datasets were used, because no single dataset contains all required

information: one worldwide (18 countries) and one European (33 countries).

Overall, happiness turned out not to be unaffected by inequality, regardless of the

kind of popular perceptions on the distributive process in societies. Instead, the

effect of inequality was rather dependent upon the perceived corruption-level (for

Europe), being negative for corrupt countries. The patterns were also identical for

people with and without strong relative concerns. Finally, the happiness-benefits

Master Thesis MSc Sociology - Candidate No. 370276 1

of being optimistic about one’s own future were not larger in more unequal

societies. However, regarding this, the results were sensitive to the specific

inequality-measure used.

ACKNOWLEDGEMENTS

I would like to thank Robert de Vries and Tim Huijts, for

supervising me in this masterthesis-project. I benefited

largely from their insights and advices. Furthermore, I

would like to thank the people close to me for indirectly

inspiring me, or for suggesting interesting literature, in

our discussions about inequality.


CONTENTS

1. Introduction

04

1.1 Literature Review

06

2. Theory

08

2.1 Theoretical Overview

08

2.2 Theoretical Framework

12


3. Data and Methods

14

3.1 Dependent Variable

15

3.2 Explanatory Variables: Micro-Level

16

3.3 Explanatory Variables: Macro-Level

17

3.4 Control Variables

18

3.5 Methods 19

4. Results 20

4.1 Popular Meritocratic Perceptions and Relative

Concerns 20

4.2 Optimism/ Pessimism 21

5. Conclusion and Discussion

30

APPENDIX 37

A Variable-Details

37

B Data-Sources

47

C Model-Comparison

48

D Additional Results

49


REFERENCES 52

1. INTRODUCTION

Research has indicated that happiness is ranked the most

important eventual life-goal by people (Inglehart, 1985:

110). Moreover, all sorts of states and events have been

associated with happiness, such as marriage (Myers, 1999;

Lee & Ono, 2012) and employment (Whelan & McGinnity, 2000;

Stavrova et al., 2011; Russell et al., 2013). In addition, a

whole body of literature has focused on the effect of

income-inequality on happiness. This theme has particularly

risen from the 1970s, from which large-scale comparative

survey-data became available. A potential other source of

the increased attention since then is the sudden reversal of

a long-standing income-equalizing trend throughout the world

(Esping-Andersen, 2009: 56). This could have had

consequences for people’s happiness.

Still, much is unclear about the direction of the effect and

how the interplay of several factors may attenuate

potentially harmful effects of inequality. Previous studies

have arrived at contradicting results. Some studies have


found a negative impact of inequality on happiness: in

multiple world-regions in the 1970s by Veenhoven (1984) and

in the 2000’s by Fischer (2009) and in the same years for

South-America (Graham & Felton, 2005) and for Japan (Oshio &

Kobayashi, 2010), for the US throughout the last four

decades (Blanchflower & Oswald, 2003; Oishi, Kesebir &

Diener, 2011) and for the European non-rich and the American

rich by Alesina et al. (2004). Conversely, other studies have

documented a positive effect: for Canada during the 1970s by

Tomes (1986), for Britain in the 1990s (Clark, 2003) and

across world-regions for the last two decades (Berg &

Veenhoven, 2010; Bjørnskov et al, 2013). A null-effect was

reported by Senik (2002) for Russia in the 1990s.

This work aims to clarify this confusion about the impact of

inequality, with possibly fruitful implications for policy.

For this aim, two different theoretical approaches were

integrated. Particularly some puzzling differences between

Eastern-European and Western-European findings could be

clarified. Specifically, Caporale et al (2009) showed that

the effect of inequality (a higher gap between the

respondent’s income and the average income of his

demographic reference-group) was positive for the happiness

of Eastern-Europeans and negative in West-Europe (Senik,

2008; Caporale et al., 2009). According to the authors, this

pattern reflects regional differences in perceived social

mobility possibilities and people’s relative concerns.

Specifically, relative concerns are people’s pre-occupations

with their location in the economic hierarchy (Duesenberry,


1949; Hopkins, 2008). In short, relative concerns could

foster different interpretations of the same inequalities,

depending on which (perceived) societal-distributive context

people find themselves in. Indeed, this perceived context

differs systematically along the East-West line, with

Eastern Europeans being much more susceptible in calling

their society meritocratic (Marshall et al., 1999; Roex,

2013).

The perspective here combines the relative concerns-approach with

another approach focusing on people’s perceptions of social

mobility and the distributive process. Here it is expected

that inequality has a different impact on people in

perceived meritocracies than in societies perceived as less

meritocratic (‘perceived non-meritocracies’). Central are

the assumptions that both inequality and popular

meritocratic perceptions influence the intensity of status-

competition in a society, and that this competition is

thought to reduce happiness among people. Furthermore,

people with stronger relative concerns are thought to be

more vulnerable for these forces, because such concerns

capture a competitive element. In short, this study aims to

answer the following main question:

How do meritocratic perceptions on the income-distributive process, either as

popular society-wide perceptions or as personal perceptions, influence the effect

that inequality has on people’s happiness?

Specifically, a society-wide endorsed ‘meritocratic promise’

of economic opportunities for all who deploy their


abilities, may impose a certain cultural-normative pressure

on people to compete for high income-positions (Neves, 2000;

Swierstra & Tonkens, 2008 Fourcade, 2013; Roex, 2013).

However, empirical studies have only addressed the role of

people’s personal perceptions on the distributive process,

rather than popularly held perceptions in societies (Alesina

et al., 2004; Fischer, 2009; Jiang, Lu & Sato, 2011;

Bjørnskov et al., 2013). This study will contribute to the

literature by examining both the role of popular and personal

meritocratic perceptions. The inequality-level and both

popular and personal meritocratic perceptions have been

thought to influence people’s incentives, opportunity-sets

and strategies (Alesina et al., 2004; Jiang, Lu & Sato,

2011; Bjørnskov et al., 2013).

Furthermore, it has been thought that those living in more

meritocratic societies, or who perceive their society as

more meritocratic, are more optimistic about their own

financial future mobility (Caporale et al., 2009; Jiang, Lu

& Sato, 2011; Bjørnskov et al., 2013). Conversely, Alesina

et al. (2004) conjectured that meritocratic perceptions

could foster anxiety in people about downward mobility.

Common in these works is the expectation that a rising

income-inequality amplifies these effects. In fact,

optimists have higher top-incomes to endeavour and

pessimists have a deeper bottom to fear. No previous study

has examined both rivalling hypotheses about the effects of

personal meritocratic perceptions, nor has the possible

interaction between the effects of such perceptions and


inequality been examined. This study provided a first

indicative look at this issue.

1.1 Literature Review

The literature on the link between inequality and happiness

consists of predominantly quantitative comparative work. The

ambiguity of reported effects of inequality across these

previous studies, could point to external factors that (a)

vary between contexts/ studies and (b) influence the effect

that inequality has on happiness. For instance, the extent

to which the poorest or the richest benefit from a change in

the income-distribution, was shown to matter for people’s

happiness. Specifically: when the poorest 20% suffered, the

average happiness in a society declined, whereas happiness

increased when the richest 20% experienced an income-loss

(Blanchflower & Oswald, 2004). Furthermore, studies

suggested that the effect of inequality depended upon

people’s evaluations of the underlying distributive process

(Fischer, 2009; Jiang, Lu & Sato, 2011; Bjørnskov et al.,

2013). Specifically, inequalities resulting from

discriminative processes reduced the average happiness in a

society. Conversely, inequality that resulted from a more

meritocratic distributive process, positively influenced

people’s happiness. Additionally, the effect of inequality

may depend on people’s relative concerns (Caporale et al.,

2009). In fact, many studies have suggested that such

concerns are widespread among people, and that these affect


their happiness (Frey & Stutzer, 2002; Blanchflower &

Oswald, 2004; Ferrer-i-Carbonell, 2005; Luttmer, 2005; Ball

& Chernova, 2008; Clark et al., 2008; Senik, 2008; Layard et

al., 2009). For instance, Easterlin (1974) showed that in

advanced economies, people’s social status or relative

income influenced their happiness. Moreover, a low social

status was shown to induce frustration (Henry, 2009), and

the position of one’s income in the income-distribution (the

relative income) had stronger happiness-implications than the

absolute income (Ball & Chernova, 2008), or equally strong

(Layard et al., 2009). Likewise, Caporale et al. (2009)

found that the happiness-gains of earning a high absolute

income were smaller when the average incomes of similar

people was high as well (although only for Western-

Europeans).

However, disagreement persists regarding whether people

compare themselves to these similar others (Senik, 2002;

Clark, 2003; Ferrer-i-Carbonell, 2005; Caporale et al.,

2009), or rather to the top (Blanchflower & Oswald, 2004;

Hopkins & Kornienko, 2009). Hopkins & Kornienko (2009: 554)

assumed that although people with relative concerns compete

with similar others in everyday life, they eventually aspire

to reach the top. This view will be taken here as well.

Similarly, relative concerns can emphasize an individual’s

rank or rather his deviation from the mean in the income-

distribution (Hopkins, 2008). This has different

consequences for how inequality influences happiness. For

instance, assume that the incomes of the top rise, whereas


the incomes of all other income-groups remain stable.

Members of the other income-groups would become more unhappy

if their social comparisons are mean-oriented and directed

at the top-incomes, rather than rank-oriented. Following

Hopkins (2008) and Hopkins & Kornienko (2009), I assumed

rank-oriented relative concerns, because people are probably

more aware of their approximate rank than of the mean

income.

Finally, as mentioned in the introduction, some scholars

expected that inequality would amplify the happiness-

benefits of being optimistic about one’s own socio-economic

future. This has not been examined yet. Furthermore, people

with meritocratic perceptions were expected to be more

optimistic about their future than other people (Jiang, Lu &

Sato, 2011; Bjørnskov et al., 2013). Suggestive is the

finding that people who perceive their society as

meritocratic, tend to be more happy with inequality

(Fischer, 2009; Bjørnskov et al., 2013). Moreover, Jiang, Lu

& Sato (2011) showed that optimists were indeed more happy.

However, the literature has not yet examined whether people

with meritocratic perceptions are more often such optimists

compared to other people. Furthermore, research has not yet

tested the equally plausible counter-hypothesis expressed by

Alesina et al. (2004) that meritocratic perceptions would

foster pessimism about one’s future mobility.


2. THEORY

The consequences of inequality on happiness have received

attention from multiple disciplines, which has generated a

plurality of theoretical approaches. Some main approaches

are discussed below. Here, I used the ideas from Hopkins

(2008) and Hopkins & Kornienko (2009; 2010), Elster (2009),

Swierstra & Tonkens (2008) and Fourcade (2013) in

particular. Given the evident variability of the effect of

inequality on happiness between studies, any theoretical

account should include a contextual factor upon which this

effect is dependent. Here, popular meritocratic perceptions

were newly considered as a contextual factor.

2.1 Theoretical Overview

It has been argued that having relative concerns is a

natural human tendency, resulting from biological-

psychological tendencies or from rational action

(Duesenberry, 1949; Frank, 1985; Hopkins, 2008). By

contrast, other arguments have presented relative concerns

as a latent human attribute that is reinforced by some

popular beliefs and norms about success and competition

(Merton, 1939; Caporale et al., 2009, p. 48; Verhaeghe,

2011). Either way, in the relative concerns-approach, the effect

of inequality on happiness differs between individuals,


depending on their relative position (Duesenberry, 1949;

Ball & Chernova, 2004; Caporale et al., 2009; Bjørnskov et

al., 2013). Specifically, if more inequality implies a lower

position for an individual, his happiness will decline, and

vice versa. However, an alternative variant of this approach

(Hopkins, 2008; Hopkins & Kornienko, 2009; 2010) expects one

identical effect of inequality for all people, regardless of

their income-position. This variant, which I will call the

status-competition model, depicts people with relative concerns

as strategic actors in a status-competition in which

eventually all people lose (in terms of happiness). Firstly,

the approach elaborates on a competition for a high visible

position (to be distinguished from genuinely high income-

positions) (Hopkins, 2008; Hopkins & Kornienko, 2009; 2010).

Essentially, in order to assess their own relative position,

people must also assess that of others. Because they lack

direct information on this, people focus at visible clues

such as consumption-patterns. In turn, people actively

display their position by their consumption-patterns

(Hopkins & Kornienko, 2009). One sort of consumption has

been thought to function primarily to signal social

position, coined ‘conspicuous consumption’ by Veblen (1899:

50-70). Specifically, less inequality is thought to catch

people in a strategic dilemma that prevents them from a more

adequate satisfaction of their desires. Indeed, competitors

mutually reinforce each other’s excessive involvement. Each

competitor has a choice regarding how to divide his budget

between ‘conspicuous consumption’ and ‘normal consumption’.


In making this choice, he anticipates on the behaviour of

others (Hopkins, 2008; Hopkins & Kornienko, 2009; 2010). An

individual increases his visible position by consuming

relatively more status-goods than most similar others,

creating a discrepancy between his visible position and his

income-position. However, this discrepancy is only a

temporary one, because similar others and those situated

above him will wipe out his previous accomplishments and

restore the initial rank-order by their own conspicuous

consumption. Subsequently, our individual needs to consume

additional status goods in other to retain this discrepancy.

However, his capacity to do so will face a limit at some

point, imposed by his budget constraint. The lower his

genuine income-position, the more quickly this limit will

appear. Consequently, eventually the visible and genuine

relative position of all individuals will be equal.

Regarding happiness, people will have ‘lost’ some happiness by

strategically spending an excessive amount on status-goods;

money they could have spent on more intrinsically preferred

goods. Moreover, people have not achieved a higher visible

position with this conspicuous consumption. Thus, this

status-competition involves a happiness-loss for all, and

more so when the intensity of this competition is higher.

This intensity will be higher in more equal societies. Note

that this goes contrary to Wilkinson & Pickett’s (2010)

expectation that it is rather more inequality that fosters

competitive pressures. Conversely, the status-competition

model expects that more equality provides stronger


incentives for individuals to intensely compete for a high

visible status. Specifically, imitating the consumption

patterns of the richer ranks is easier for competitors in

more equal societies, because of the smaller income-

differences between them and the richer rank. Thus,

increasing one’s visible position has become relatively

cheaper. Conversely, in more unequal societies, it is harder

for people to increase their visible status. Therefore,

individuals will more quickly refrain from participating in

this status-competition. In the end, for both societies,

people’s visible position will eventually be equal to their

genuine income-position. But the overall happiness-loss is

larger in more equal societies, because people are longer

caught into this competition.

However, the status-competition model does not state that

more equality does always make people worse off. The model

elaborates on another status-competition that would generate

the opposite relationship between inequality and happiness:

the competition for high income-positions. Instead of

choosing how to divide their monetary budget between normal

and conspicuous consumption, people here choose how to

divide their time between leisure and effort (Hopkins &

Kornienko, 2010). If incomes are less equally distributed,

the additional income-gain of investing an additional amount

of effort for a higher income-position, is higher.

Consequently, the competition for income-positions will be

more intense in more unequal societies. This will catch

people into a similar strategic dilemma, in which each


contestant is held to exert more effort than would have been

necessary without competitive pressures (Hopkins, 2008;

Hopkins & Kornienko, 2010). In such cases, inequality will

harm the happiness of all. In sum, the effect of income-

inequality on happiness is ambiguous in the status-

competition model. The eventual direction of the effect of

equality on happiness depends on which of the described

forces are more salient in a society (Hopkins, 2008).

Another approach may provide insights on the relative

salience of each of these forces. This approach emphasizes

the role of people’s perceptions on the distributive process

generating the income-differences. The literature has

particularly focused on people’s perceptions on the

importance of effort and ability for individual economic

opportunities, and on the perceived role of discrimination

(Alesina et al., 2004; Fischer, 2009; Jiang, Lu & Sato,

2011; Bjørnskov et al., 2013). This approach will be called

the perceived meritocracy thesis. Occupying a high relative position

may have a strong moral superior connotation when

meritocratic perceptions are high, since such a position is

thought to reflect several virtues of an individual, such as

industriousness. Conversely, a low relative position may be

associated with laziness and is therefore vulnerable for

stigma (Neves, 2000; Swierstra & Tonkens, 2008; Fourcade,

2013). Specifically, popular meritocratic perceptions can be

considered as fostering a society-wide social norm to

endeavour and achieve high positions. This norm disfavours

being poor or unemployed, when this is considered to reflect


laziness. An individual’s relative position may determine

the amount of received immaterial social benefits or costs

(disapproval). In all, meritocratic perceptions increase

both the financial incentives to compete (given the high

perceived success-probability: Boudon, 1981), as well the as

social incentives to compete.

The social norms-literature provides insights to clarify

these social incentives. Here I adopt a notion of social

norms advocated by Bicchieri (2006) and Elster (2009),

rather than of Parsons (1937) and Merton (1968), in which

people can be strongly concerned about the responses of

others without having internalized the norms themselves.

Indeed, social norms are why popular meritocratic perceptions

may be more important determinants of people’s strategies

than their personal perceptions. For example, a low-income

individual who questions the ‘meritocratic fairytale’ may be

aware of a high popular belief in this throughout his

society. This awareness can reduce his happiness, because he

can be considered as lazy and can expect social sanctions.

Research has indicated that people have a strong distaste

for being disapproved by others (Christensen et al., 2004;

Spitzer et al., 2007), which is considered the most

fundamental social sanction (Elster, 2009). Thus, social

pressure may also induce people with low meritocratic

perceptions to compete for high status-positions in

popularly perceived meritocracies. Consequently, people

collectively exert more effort than would be necessary

without such a competition, and some happiness is lost. My


assumptions go contrary to Bjørnskov et al.’s (2013)

suggestion that the ‘sceptical’ individuals would opt for

unemployment. However, by opting for unemployment, they risk

facing disapproval from others. The accompanying disutility

from this may outweigh the benefits from refraining from a

presumably senseless effort. Illustratively, studies have

shown a negative happiness-impact of joblessness (Whelan &

McGinnity, 2000; McKee-Ryan et al., 2005; Stavrova et al.,

2011; Russell et al., 2013), which was stronger in countries

with a stronger social norm to work (Stavrova et al., 2011).

Alternatively, other accounts focus on people’s personal

perceptions rather than on popular perceptions. Such

accounts often state that individuals with meritocratic

perceptions may be optimistically incentivized to compete

for high positions (Jiang, Lu & Sato, 2011; Bjørnskov et

al., 2013). In fact, Senik (2002) and Caporale et al. (2009)

conjectured that Eastern-Europeans tend to interpret rising

inequalities between their own incomes and that of similar

others, as a sign of rising opportunities to move upwards.

By contrast, Alesina et al. (2004) conjectured that

perceptions of a high social mobility may make people

anxious about the possibility of moving downward, and more

so when the income-differences are larger. A meritocracy

involves positions that are not pre-fixed for a lifetime and

contingent upon the employee’s performance relative to other

potential incumbents. Therefore, meritocratic perceptions

may foster status-insecurity in people.

2.2 Theoretical Framework


Here I will combine the relative-concerns approach and the perceived

meritocracy-thesis. As discussed above, the status-competition

model expects people’s pressure to compete for a high status

to depend upon the level of inequality in societies. Even

more, such competitive pressures could be fed (or

discouraged) by certain popular discourses in societies: the

popularity of meritocratic perceptions. In fact, such

perceptions can heighten the pressure to compete, regardless

of the level of income-inequality in a society. Thus,

societies with highly popular meritocratic perceptions will

witness an intense status-competition, regardless of the

level of inequality.

Regarding the competition for high visible positions in such

‘perceived meritocracies’, the rising financial costs of

increasing one’s visible position when inequality is rising,

may be exceeded by the social benefits of doing so. Thus,

popular meritocratic perceptions could partly wipe out the

‘discouraging’ effect of inequality on people to compete for

a high visible status. Indeed, these social benefits may

induce people to keep consuming status-goods beyond their

budget constraint. Today, buying on credit is a widespread

phenomenon. Conversely, regarding ‘perceived non-

meritocracies’, the intensity of this competition depends

more strongly on the inequality-level. This is because

additional (social) incentives to compete are virtually absent

in such societies. In conclusion, the positive association between

inequality and happiness would be weaker in perceived meritocracies than in

perceived non-meritocracies, or even be absent.


Similarly, the competition for high income-positions, while

initially weakened by a larger equality, could be re-

intensified by higher popular meritocratic perceptions.

Whereas the financial benefits of increasing one’s income-

position may be relatively low, the social benefits are

high. Because inequality partly determines the financial

benefits of rising-up to a higher income-position,

inequality influences the intensity of competition (and thus

people’s happiness) among perceived meritocracies.

Conversely, in perceived non-meritocracies, the inequality-

level does not influence the intensity of this competition

(and thus people’s happiness). This is because ‘investing’

much effort in a competition for high income-positions is

popularly considered as ‘naïve’ in such societies: social

positions are dominantly thought to be unrelated to

individuals’ effort. Consequently, both the financial and

social incentives of exerting much effort to achieve a high

income-position, are low in such societies, regardless of

their inequality-levels. In all, the negative impact of inequality on

happiness will be stronger for perceived meritocracies relative to perceived non-

meritocracies.

Net of all, inequality would be positively (or less

negatively) associated with happiness in perceived non-

meritocracies. Combined with the lower popularity of

meritocratic perceptions in Eastern-Europe (Marshall et al.,

1999; Roex, 2013), an East-West difference may be expected

in the inequality-happiness relationship. Furthermore, the

predicted patterns may be more pronounced among people with


stronger relative concerns, who are by definition more

responsive to competitive pressures.

Thus:

H1: inequality has a negative impact on happiness in perceived

meritocracies, whereas its impact in perceived non-meritocracies is likely

reversed, or weaker-negative.

This study will also consider individuals’ personal

perceptions of the distributive process. When people

perceive their society as meritocratic, their perceptions

can either emphasize the opportunities to move upwards or

the risks of moving downwards. In turn, people’s optimism or

pessimism will have larger happiness-impacts in more unequal

societies, since higher inequalities provide a higher top to

look forward to for optimists and a deeper bottom to fear

for pessimists. In sum, meritocratic perceptions may

generate different effects of inequality on happiness. If

meritocratic perceptions foster optimism in people, any

negative effects of inequality will be softened and any

positive effects will be strengthened. The inverse applies

if meritocratic perceptions foster pessimism.

Thus:

H2a: People with meritocratic perceptions are more often optimistic about

their future mobility than other people, and optimism increases people’s

happiness. The level of inequality enlarges this happiness-gain of optimism.

Alternatively:


H2a: People with meritocratic perceptions are more often pessimistic about

their future mobility than other people, and pessimism reduces happiness.

The level of inequality enlarges this happiness-penalty of pessimism.

3. DATA AND METHODS

For hypothesis 1, data were used from the most recent World

Values Study (WVS) (2010-2012). This dataset covers

countries from many continents and an item about relative

concerns. Furthermore, the strong international coordination

of the data collection process has produced a high cross-

national comparability of the data. Moreover, the WVS has

been used by a number of previous studies on happiness and

inequality (e.g. Ball & Chernova, 2008; Fischer, 2009;

Bjørnskov et al., 2013), which eases the reflection upon the

present results from previous findings. The dataset contains

51 countries and 72,739 adult-respondents, randomly sampled

from their national populations through stratified

probability-sampling. Face-to-face interviews had been used

to collect the data. More information about the data can be

found at www.worldvaluessurvey.org. Unfortunately,

information on personal meritocratic perceptions (and on the

popularity of meritocratic perceptions through aggregation)

was only available for 18 countries. This information was

extracted from the International Social Survey Programme’s

Social Inequality Module 2009, which did not cover all WVS-

countries (Table A3). In these countries, 27,345 respondents

were interviewed. For the descriptive and predictive


analyses, only those respondents were retained who had no

missing values at any of the explanatory, dependent and

control variables. As a result, 4,530 respondents were

omitted. Income and people’s self-reported class-position

entailed a relatively large number of missing values (5%).

Still, I kept these variables in the analyses because these

were substantial confounders.

For the second hypotheses, the third European Quality of

Life Survey (EQLS) (Eurofound, 2011-2012) was used. This

dataset contains information on respondents’ optimism/

pessimism about their future financial situation. Again, the

data are highly comparable between countries thanks to a

strong central coordination of the data-collection. The

sampling procedure is very similar to that of the WVS. More

information about the data is provided in the reports of

Vila et al. (2013) and Eurofound & GfK EU3C (2013). The

EQLS-dataset contains 34 countries from all different

European regions and cover all 28 EU-member-states.

Eventually, one country was dropped from the analyses,

because of missing values at virtually all macro-level

variables (Kosovo). Again, respondents with missing values

were omitted, resulting in a workable sample of 29,745

adult-respondents. In order to prevent a more dramatic loss

of respondents, I decided to control for people’s income-

quartile (in their country) instead of their absolute

income. Furthermore, I omitted EGP as a control-variable, as

I would lose as much as 56% of my workable sample by

including this variable. Finally, for the analyses assessing


the link between meritocratic perceptions and optimism/

pessimism, the sample had shrunk to only 23 countries and

22,512 respondents. Fortunately, the proportional

representation of optimists, pessimists and neutrals was

virtually identical as in the larger sample.

The variables are discussed below. Appendix A displays the

exact wording of the selected survey-items for the variables

of interest, and their operationalization. In addition,

descriptive statistics are displayed in Table A2. Appendix

B outlines how to access the used datasets.

3.1 Dependent Variable

Happiness consists of an affective and cognitive component

(Blanchflower & Oswald, 2004; Berg & Veenhoven, 2010;

Russell et al., 2013). The affective component configures

people’s overall, ‘typical’ mood, whereas the cognitive

aspect reflects the extent to which people think their life

fits their desired life (Berg & Veenhoven, 2010). Self-

reported happiness and life-satisfaction are the two most

often used quantitative measures for happiness. A recent

discussion focuses on the claim that these two measures

actually measure different things. Whereas some authors find

a high correlation between the two (Alesina et al., 2004),

others do not (Bjørnskov et al., 2013). Still, Blanchflower

& Oswald (2004) generated very similar outcomes with each

measure. Here I found that the two correlate fairly high but


probably not as strong as one would expect if they measure

the same (rho = .53, p < .001). Because self-reported

happiness more directly asks people about their happiness, I

preferred this item. Specifically, respondents had rated

their happiness at an ordinal point-scale running from not

at all happy to completely happy.

Previous findings grant some validity to the self-reported

happiness measure: it is highly correlated with physical

responses that have often been thought to reflect happiness

(Seidlitz et al., 1997; Alesina et al., 2004; Blanchflower &

Oswald, 2004; Ball & Chernova, 2008). However, there can be

cross-cultural variation in how respondents interpret and

respond to happiness-questions at a survey (Bjørnskov et

al., 2013). Fortunately, as Berg & Veenhoven (2010) argue,

the fact that people in different countries and among

different studies have been shown to react very similarly in

their self-reported happiness to a host of events (for

example: Di Tella et al., 1997; Frey & Stutzer, 2002;

Blanchflower & Oswald, 2004; Berg & Veenhoven, 2010; Russell

et al., 2013), provides confidence in the belief that the

influence of cultural differences is not a serious problem.

Clearly, most respondents were happy, both in the WVS- and

EQLS sample (see Table A2). In the WVS, as much as 86%

reported to be (very) happy, whereas the remaining 14%

reported to be not very happy or not at all happy. In the

EQLS, slightly more than half of the sample (54%) scored an

8 on a scale from 1 (very unhappy) to 10 (very happy). Only

less than 10% (8%) scored below 5. Moreover, as much as 72%


scored between a 7 and 10. In both datasets, happiness was

non-normally distributed.

3.2 Explanatory Variables: Micro-Level

Relative concerns were captured by an item asking respondents

how important it is for them ‘to be rich and possess many

high-priced goods’ (World Values Survey Association, 2012:

6). This captures both concerns with both visible status and

income-position. Respondents could indicate the strength of

their concerns at a 6-pointscale. The sample was subdivided

into a group with strong relative concerns and those without

strong concerns about their position. For the full sample,

relative concerns were entered as a continuous variable.

Notably, approximately two-third of the sample reported only

weak or no relative concerns.

Furthermore, respondents were asked to express their

expectations about their financial situation for the coming

year. People could indicate that their situation would

likely improve (optimism), remain stable (neutral), or

deteriorate (pessimism) (Appendix A). Almost one-third of the

sample was pessimistic about their future financial

mobility, whereas 16% was optimistic, and half of the sample

neutral (52%).

People with high meritocratic perceptions were expected to be

either more optimistic or more pessimistic than others.

Unfortunately, no single dataset captured information about


both the degree of optimism/ pessimism and people’s

meritocratic perceptions. Therefore, I had to resort to the

popularity of meritocratic perceptions in the respondent’s

society as a predictor of his degree of optimism/ pessimism.

Because meritocratic perceptions were entered in a logistic

regression here, countries were allocated to one of several

consecutive dummy-categories, ordered from a very low

popularity to a very high popularity of such perceptions

(Appendix A and Table A2-A3). This popularity was assessed

using the same measure as for the first set of hypotheses

(discussed below).

3.3 Explanatory Variables: Macro-Level

The degree of income-inequality has been measured in various

(but highly inter-correlated) ways in the literature

(Wilkinson & Pickett, 2010). One of the most adopted

measures is the Gini-coefficient, which takes the whole

income distribution into account. The Gini-coefficient

ranges between 0 (complete equality) and 1 (complete

inequality) (Berg & Veenhoven, 2010; Wilkinson & Pickett,

2010) and is sometimes expressed in a percentage (‘Gini-

index’). The Gini-indexes used here were derived from Solt’s

(2009a) carefully constructed database: the Standardized

World Income Inequality Database (SWIID). His calculations

are based upon multiple informational sources and have

successfully integrated cross-nationally different Gini-

reports into consistent estimates for all countries (Solt,


2009b). The estimates mostly cover the year 2011, except for

a few countries for which the most recent available (post-

2008) year was chosen (Table A3). Unfortunately, the Gini-

statistic has often been based upon low-quality information

for developing countries (Berg & Veenhoven, 2010).

Therefore, I conducted a robustness-check with countries’

income-quintile ratio over 2011, which is less sensitive to

such problems (Veenhoven & Berg, 2010). These statistics are

provided by the United Nations over 2011 (United Nations

Development Programme, 2011: 135-138).

The Gini-index ranged from 24% (Slovenia) to 43% (Macedonia)

among the EQLS-countries and to 50% (Philippines) among the

WVS-countries. The mean was 34 for the WVS-group and 30 for

the EQLS. The distribution of Ginis was somewhat skewed

towards low Ginis (below 30%). Overall, inequality appears

to be slightly lower among the European country-group than

among WVS-countries. Country-levels are displayed in Table

A3.

For meritocratic perceptions, I replicated Marshall et al.’s

(1999) scale from the International Social Survey Project

Social Inequality Survey (ISSP) 1992 (see Table A1). These

items cover respondents’ accounts of how people become poor

or rich, how wages/ salaries are determined, and on equal

opportunities. Respondents could select answer-categories

that point to individual skills or effort or to structural

disadvantages. Alternatively, Bjørnskov et al. (2013) used a

3-item scale from another WVS-wave (1995-1996), assessing

respondents’ accounts of how people become poor and rich.


The present WVS-wave only contains one of these items, which

nevertheless correlated high with the old measure on the

country-level (rho = .77, p <.001). Still, I preferred the

ISSP-measure for its much higher reliability and richness.

This 13-item scale (also used in Roex, 2013) reached an

alpha of .68, whereas the WVS-measure’s alpha was only .56

at best. Moreover, the ISSP-measure is theoretically richer

since it also includes respondents’ perceptions of ‘anti-

meritocratic’ distributive mechanisms.

Assigning a mean-score of meritocratic perceptions to

countries, aggregated from individual scores on the

abovementioned ISSP-scale, would have been a suitable

measure. However, country-scores did barely vary here, which

is problematic for assessing any influences of meritocratic

perceptions (King et al., 1994). Instead, more variance was

achieved by calculating the 75th percentile score (3.92)

across the whole ISSP-sample and then assessing the

proportion of people with this score in each individual

country. Between countries, the size of this group of people

differed considerably: from only 5% (China) to 72% (New

Zealand). The overall median size for the WVS-countries was

26%. For the logistic regression predicting people’s

optimism/ pessimism from the popularity of meritocratic

perceptions, countries were ordered from ‘1’ (lowest

popularity) to ‘6’ (highest popularity). The specific

allocation and country-levels are displayed in Appendix A

and Table A3.


3.4 Control Variables

The selection of control variables was based on the

following criteria: variables must (1) be significant

predictors of one of the explanatory variables and of the

dependent variable (2) not have a dramatic number of missing

values (3) cause no multi-collinearity (VIF, Tollerance,

correlations). The micro-level control-variables for the

WVS-analyses were: income, educational attainment, health,

marital status, age, class, religion and sex. On the macro-

level, eventually no control variables were added. The

possible candidates lost significance in multi-level models,

and some (post-communism and GDP) correlated strongly (r

> .60, p < .01) with meritocratic perceptions. Leaving them

out did not affect the results.

The micro-level control-variables for the EQLS-analyses

were: income, educational attainment, health, marital

status, age, sex, and past financial mobility. The macro-

level control-variables were the corruption perception

index-score (CPI) and post-communism. For the WVS-analyses,

CPI was not included for substantive reasons, the included

measure of meritocratic perceptions also captures perceived

corruption. Because of multi-collinearity between CPI and

post-communism in the EQLS-data, post-communism was included

in a model estimated only for ‘low-corruption’ countries.

Because of a potential confounding role for meritocratic

perceptions in the EQLS-analyses, models were estimated both

with and without meritocratic perceptions. Specifically,


models were estimated twice because of many missing

countries at meritocratic perceptions.

In analyzing the link between meritocratic perceptions and

optimism/ pessimism, I controlled for people’s income,

educational attainment and past financial mobility. At the

macro-level, I controlled for post-communism and the level

of unemployment. A cross-tabulation revealed a near-perfect

correlation between meritocratic perceptions and post-

communism. Fortunately, this issue was only present among

countries with a high unemployment rate. Therefore, I

included the post-communism dummy only for low-unemployment

countries. Appendix A provides details about all control-

variables.

3.5 Methods

Multi-level linear regression analysis was conducted on

happiness for testing hypotheses 1 and 2a-b. This

corresponds with the multi-level nature of the theory and

recognizes the fact that people are embedded in countries,

which could generate more similar observations for people

within the same country (Hox, 2002). All continuous

variables were centred for the multi-level analyses, in

order to obtain the coefficients and variances for the point

where each predictor has its mean value (Hox, 2002).

Furthermore, I used single-level logistic regression

analysis for predicting people’s optimism/ pessimism from


meritocratic perceptions, for a more complete test of

hypotheses 2a-b. In all analyses, no weights were used,

because this does not solve the persistent problem of

selective representation of several groups in the sample.

Specifically, respondents that belong to subgroups known for

a high non-response, are likely untypical for their

subgroup. Thus, assigning a larger weight to those

individuals would not generate a more representative picture

(Peterson, 2005). For similar reasons, no mean-imputation

was used for the missing values. In fact, it is very likely

that the answers of the responders differ from what the non-

responders would have answered.

4. RESULTS

Table 1 displays the results of the multi-level linear

regression of happiness concerning Hypothesis 1. The

findings for happiness regarding Hypotheses 2a-b are

provided in Table 2. These analyses on happiness are

separate analyses because I had to rely on two different

datasets. Finally, Table 3 displays the findings of the

logistic (single-level) regression of optimism/ pessimism.

For all analyses, the alpha-level for the significance-tests

was set at p < .05. In the discussion of the results,

coefficients of the complete theoretical model will be used.

This model turned out to be a solid fit to the observed

patterns, relative to the more basic models (for a model

comparison, see Appendix C). Finally, because causality


cannot be fully proved by regression analysis (Agresti &

Finlay, 2009), but merely suggested, words as 'effect' must

here be taken as referring to only provisional suggestions

of true effects.

4.1 Popular Meritocratic Perceptions and Relative Concerns

Overall, the results do not support Hypothesis 1. The

coefficient for the level of inequality was not significant.

Moreover, the coefficient was not different between

countries (b = .001, p = .057). This pattern remained when

countries were divided into strongly unequal (Gini above the

median level) and less unequal (Gini equal or below the

median). Neither did the popularity of meritocratic

perceptions interact with inequality in their effects on

happiness. However, for one subsample (high-corruption

countries) of the EQLS-countries, the coefficient of

inequality was significant and negative (Model 1, Table A8).

Consistent with one of the underlying assumptions, the

coefficient for relative concerns was negative and

statistically significant. However, the coefficient did not

significantly vary across countries. Moreover, the

(non-)effect of inequality was virtually identical across

people with and without relative concerns. Furthermore, the

interaction-term with meritocratic perceptions remained

insignificant and small across these subsamples (Models 2

and 3, Table 1). Finally, shifting towards an income-


quintile measure of inequality did not alter the results

(Model 1, Table A7).

4.2 Optimism/ Pessimism

On average, optimists rated their happiness .41 points

higher than pessimists do (Model 1, Table 2). Moreover, this

effect of being optimistic rather than pessimistic, differed

significantly between countries (b = .093, p = .006), but

that of being optimistic rather than neutral did not (b

= .031, p = .058). Generally, optimists were happier than

neutrals (changing the reference-category towards neutrals

yielded: b = .128 with p = .008), and neutrals in turn were

happier than pessimists. Moreover, this latter happiness-gap

varied across countries (b = .048, p = .008). Nevertheless,

none of the interaction-terms was significant. Limiting the

sample to low-corruption countries yielded similar results,

with two exceptions. Firstly, the coefficient of being

optimistic rather than pessimistic did not differ

significantly between these countries (b = .057, p = .096).

Secondly, this ‘optimism-coefficient’ was somewhat smaller

among these countries (Model 2, Table 2). Notably, the

results were not robust for shifting the measure of

inequality towards the income-quintile ratio: both the main-

effect of inequality as well as its interaction-terms with

optimism, became significant (see Models 2-3, Table A7).

Moreover, in the analyses including the continuous measure

for popular meritocratic perceptions (with 23 countries),


Gini gained statistical significance (b = -. 059, p = .003)

(Model 2, Table A8). In the discussion-section, I elaborate

further on these additional findings.

Consistent with Hypothesis 2a, (popular) meritocratic

perceptions generally increased individuals’ likelihood of

being optimistic (rather than neutral). However, the effect

was not linear: lower-tread countries did not always have

larger negative log odds for optimism (Table 3). Contrary to

Hypothesis 2b, it were countries belonging to the lower

treads that showed larger positive log-odds for pessimism.

The relationship between being pessimistic (rather than

neutral) and meritocratic perceptions was more consistently

linear than for the optimism-figures, although the

relationship became slightly less linear after controlling

for post-communism and the unemployment-level (Models 3 and

4, Table 3).


Table 1: Multilevel Regression Results on Happiness Predicted from Inequality and Relative Concerns

Total Sample People with People without relative concerns relative concerns

M1 M2 M3

b SE b SE b SE

Intercept +0.550*** (0.039) +0.580**

* (0.057) +0.527***

(0.044)

Individual-Level characteristics Relative Concerns - 0.007* (0.003)

Income Quintile

- Lowest - 0.188*** (0.026) -

0.184*** (0.047) - 0.179***

(0.032)

- Quintile 2 - 0.104*** (0.025) -

0.160*** (0.043) - 0.073* (0.031)

- Quintile 3 - 0.025 (0.024) - 0.059 (0.042) - 0.004 (0.030)

- Quintile 4 - 0.014 (0.025) - 0.013 (0.042) - 0.013 (0.031)

- Highest (ref.) - - - - - -

Educational attainment


- Low +0.029* (0.014) - 0.004 (0.025) +0.046** (0.016)

- Medium +0.022* (0.010) - 0.006 (0.019) +0.036** (0.012)

- High (ref.) - - - - - - Health

- Poor - 0.846*** (.019)

- Fair - 0.532*** (0.012)

- Good - 0.304*** (0.011)

- Very good (ref.) - -

Marital Status - Single -

0.170*** (0.012)

- Divorced - 0.233*** (0.015)

- Widowed - 0.204*** (0.017)

- Married (ref.) - -

Age - 16-24 +0.006 (0.019)

- 25-34 - 0.058*** (0.015)

- 35-49 - 0.103*** (0.013)

- 50-64 - 0.091*** (0.013)

- 65+ (ref.) - -


Class - Bottom/ Working

- 0.039*** (0.010) - 0.001 (0.018) -

0.055***(0.011)

- Lower-Middle(ref.) - - - - - -

- Upper(-Middle)

+0.070*** (0.011) +0.088**

* (0.020) +0.061***

(0.014)

Religiosity - No Denomination

- 0.039*** (0.010)

- Denomination(ref.) - -

Sex

- Male - .052*** (0.008)

- Female (ref.) - -

Country-Level Variables Income-inequality +0.004 (0.004) +0.003 (0.004) +0.003 (0.00

4) Meritocratic Perceptions +0.003 (0.002) +0.003 (0.002) +0.004 (0.00

2) Income-Inequality × Meritocratic

- 0.001 (0.001) - 0.001 (0.001) - 0.001 (0.001)

Perceptions Individual-Level Variance 96.7% 97.1% 96.5%


Country-Level Variance 03.3% 02.9% 03.5%

Nindividuals 22,815 07,346 15,469Ncountries 00,018 00,018 00,018Source: World Values Survey (2010-2012). Unstandardized coefficients. * p < .05, ** p < .01, *** p < .001. In Models 2 and 3, the same micro-control variables were used as for Model 1 . These had very similar coefficients and p-values except for income, education and class.


Table 2: Multilevel Regression Results on Happiness Predicted from Inequality and Optimism/ Pessimism

All Countries Low-Corruption Countries

M1 M2

b SE b SE

Intercept +0.780*** (0.115) +1.586*** (0.103) Individual-Level Variables Optimism/ Pessimism - Optimistic +0.408*** (0.065) +0.255** (0.077) - Neutral +0.280*** (0.048) +0.212* (0.079) - Pessimistic (ref.) - - - - Income Quartile

- Lowest - 0.441*** (0.031)

- Quartile 2 - 0.222*** (0.030)

- Quartile 3 - 0.118*** (0.028)

- Highest (ref.) - - Educational attainment- Low - 0.097* (0.041)

- Medium - 0.091*** (0.026)

- High (ref.) - - Health


- Very Bad - 2.709*** (0.069)

- Bad - 1.933*** (0.044)

- Fair - 1.093*** (0.031)

- Good - 0.580*** (0.028)

- Very good (ref.) - - Marital Status

- Single - 0.534*** (0.032)

- Divorced - 0.694*** (0.034)

- Widowed - 0.697*** (0.035)

- Married (ref.) - - Age - 18-24 - 0.130* (0.051)

- 25-34 - 0.364*** (0.039)

- 35-49 - 0.473*** (0.032)

- 50-64 - 0.300*** (0.030)

- 65+ (ref.) - -Past Financial Mobility - Better +0.328*** (0.038) - Same +0.321*** (0.025) - Worse (ref.) - - Sex


- Male - 0.155*** (0.021)

- Female (ref.) - -

Country-Level Variables Income-inequality +- 0.007 (0.008) +- 0.003 (0.006) Post-Communism +- 0.229 (0.125) +- 0.565 (0.267) Treads Cross-Level Interaction Income-Inequality ×Optimistic

+0.003 (0.006) +0.001 (0.005)

Income-Inequality ×Neutral +0.002 (0.004) +0.001 (0.005) Income-Inequality ×Pessimistic (ref.)

- - 0

Individual-Level Variance 92.6% 96.3%

Country-Level Variance 07.4% 03.7%Nindividuals 29,745 15,534Ncountries 00,033 00,016Source: European Quality of Life Survey (2011-2012). Unstandardized coefficients. * p < .05, ** p < .01, *** p < .001. In Model 2, the same micro-control variables were used as for Model 1. These had very similar coefficients and p-values.


Table 3: Logistic Regression Results on Optimism/ Pessimism

All Countries Low-/ Medium-Unemployment All Countries Low-/ Medium-Unemployment Countries Countries OPTIMISM PESSIMISM

M1 (basic) M2 (post-comm) M3 (basic) M4 (post-comm)

b SE b SE b SE b SE

Intercept +- 2.445*** (0.290) +-

2.389*** (0.323) +1.021*** (.314) +0.428 (0.336)

Country-Level MeritocraticPerceptions - Lowest - 0.333** (0.115) - 0.188 (0.171) +1.669*** (0.095) 1.209*** (0.129) - Tread 2 +0.038 (0.083) +0.065 (0.087) +0.624*** (0.089) +0.439*** (0.092)

- Tread 3 - 0.509*** (0.070) -

0.636*** (0.083) +0.842*** (0.075) +0.549*** (0.082)

- Tread 4 - 0.614*** (0.070) -

0.633*** (0.074) +0.725*** (0.075) +0.327*** (0.080)

- Tread 5 - 0.046 (0.062) - 0.007 (0.065) +0.638*** (0.073) +0.728*** (0.075) - Highest (ref.) - - - - - - - -

Post-Communism - 0.033 (0.072) +0.616*** (0.054)


Individual Level + Past Financial Mobility

+

- Better +0.703*** (0.059) - 2.042*** (0.071)

- Same - 1.116*** (0.049) -

2.351*** (0.038)

- Worse (ref.) - - - - Educational Attainment

- Low - 0.816*** (0.085) +0.321*** (0.065)

- Medium - 0.171*** (0.049) +0.063 (0.047)

- High (ref.) - - - - Income Quartile

- Lowest +0.607*** (0.060) +0.386*** (0.053)

- Quartile 2 - 0.250*** (0.059) +0.237*** (0.052)

- Quartile 3 - 0.075 (0.058) +0.109* (0.052)- Highest (ref.) - - - -

Nindividuals 22,512 18,295 22,512 18,295Ncountries 23 17 00,023 00,017Source: European Quality of Life Survey (2011-2012). Unstandardized coefficients. * p < .05, ** p < .01, *** p < .001. In Models 2 and 4, the same micro-control variables were used as for Models 1 and 3. These had very similar coefficients and p-values.


Overall, the largest and most robust differences appeared between (1) the lowest-ranked countries

and the rest, and between (2) the highest-ranked countries and the rest.


5. CONCLUSION AND DISCUSSION

This study considered two ways in which meritocratic

perceptions and inequality interact in their impacts on

happiness. The expectations were tested with data from

widely-established survey projects and with carefully

constructed reliable and valid measures. However, this study

leaves some points for improvement for future work. In this

section I elaborate more deeply on the results in order to

arrive at conclusions.

Regarding the expected influence of popular meritocratic

perceptions upon the inequality-happiness relationship, the

data offered no support. Inequality did not have a stronger

negative effect upon happiness in perceived meritocracies

than in perceived non-meritocracies. Indeed, people’s

happiness was generally not affected by the level of

inequality, and this pattern did not differ between

countries. This absence of statistically significant effects

was not attributable to the low power of the analyses

(country-level N = 18): an analysis of 33 EQLS-countries

revealed the same null-effect of inequality. A closer look

suggests that the inequality-effect differs between country-

groups (Eastern- versus West-Europe) rather than between

individual countries. Indeed, estimating the models

separately for high-corruption and low-corruption countries

(which distinction mainly overlaps the East-West line),

yielded different inequality-effects: inequality negatively

influenced happiness in the first country-group (Model 1,


Table A8) whereas it did not in the latter (Model 2, Table

2). Indeed, including an interaction-term for ‘low

corruption’ with Gini for the full sample, revealed that the

inequality-effect is somewhat milder among low-corruption

countries (b = .032, marginally significant at p = .056).

This supports the idea of differential happiness-

implications of inequality for Eastern- and West-Europeans.

Importantly, however, the direction of the interaction is in

the opposite direction as expected.

Status-competition was proposed to be a central underlying

mechanism for generating the expected patterns. From this

the assumption followed that the expected effects would be

stronger for people with relative concerns. However, the

patterns appeared to be similar between groups (Table 1).

Furthermore, rather than having a large negative impact upon

happiness, such competitive concerns did only weakly reduce

people’s happiness. In fact, happiness was only 0.035 points

(0.007*5) lower for those with the strongest relative

concerns compared with those with the weakest, while

happiness-scores ranged from 1 to 4 in the sample. More

importantly: their negative consequences did not differ

between societies. However, it should be reminded that

respondents who self-report to be free from such concerns,

actually may have such concerns and are subject to the same

mechanisms. Specifically, their survey-responses could have

been formed by a norm to present oneself as more progressive

(or post-material) than ‘most others’, a tendency suggested

by earlier findings (Neugarten et al., 1965).


Altogether, the findings challenge the central elements of

the status-competition model and perceived meritocracy-

thesis. Possibly, the intensity of the status-competition

does not depend on the inequality-level or on the popularity

of meritocratic perceptions. Specifically, it could be that

popular meritocratic perceptions do not induce any

additional pressure on people to compete. Similarly,

different inequality-levels could fail to produce the

predicted incentives for people to compete. Probably, people

are unaware of the actual shape of the income distribution

and inequality, as shown in an American study (Ariely,

2012). Then, the actual inequality-level of a country does

not influence people’s happiness if their perceptions only

weakly match with objective figures. Rather, it is the

society people think they live that influences their feelings

and interactions (Thomas & Thomas, 1928: 571-573).

Unfortunately, recent cross-national data on people’s

inequality-perceptions were unavailable. Alternatively,

people may have an intrinsic desire for status-goods or to

work hard. In that case, intense status-competitions do not

involve the theorized happiness-deadweight-loss. Because of

the potentially important role of individuals’ perceptions and

desires, future work should dig into the perceived inequality-

level and perceived specific income-gaps, as well as the

perceived costs and benefits of competing for status and

people’s preferences.

Regarding the hypotheses about people’s optimism/ pessimism,

I had to rely on European data for testing them.


Consequently, the resulting findings cannot be generalized

to non-European contexts. Specifically, it was assessed

whether people with personal meritocratic perceptions were

more often optimistic (expecting an improvement in their

financial situation) or pessimistic (expecting a

deterioration) rather than neutral (expecting stability).

Because I had to rely on popular meritocratic perceptions,

this is only an indicative test. Self-evidently, personal

perceptions are unlikely to be fully predictable by

popularly held perceptions. Nevertheless, the data partly

supported Hypothesis 2a. Overall, meritocratic perceptions

fostered optimism in people, but not in a smooth linear

fashion. Illustratively, the likelihood of being an optimist

(rather than neutral) was 1.40 times larger in countries

where meritocratic perceptions were most popular (tread 6),

compared to the likelihood for people in the lowest-ranked

countries (tread 1) (Model 1, Table 3). This is a

substantial difference. However, people in ‘tread 2’

countries did not significantly differ in their tendency to

be optimists from people in the highest-ranked countries.

Conversely, the results did not support Hypothesis 2b. In

fact, high meritocratic perceptions diminished people’s

tendency to be pessimistic. Compared to people in ‘tread 6’

countries, the likelihood of being pessimistic (rather than

neutral) was as much as 5 times larger for people in ‘tread

1’ countries (Model 3, Table 3). This relationship was not

fully linear, although more consistent than that for

optimism. The inconsistency of these relationships could be


due to a greater role for people’s personal perceptions

(compared to popular perceptions) in determining their

optimism and pessimism.

Regarding happiness, the prediction was supported that

optimists were on average most happy, whereas pessimists

were the least happy. However, on an observed range of

happiness-scores from 1 to 10, the effect of being

optimistic (b = .041, Model 1, Table 2) or neutral instead

of pessimistic (b = .28) were rather small. Optimists gained

only .041 additional happiness-points compared to

pessimists, while overall, the observed happiness-scores

ranged from 1 to 10, with approximately 95% located between

4 and 10. Moreover, although the happiness-gains of being

optimistic or neutral (instead of pessimistic) differs

cross-nationally, these differences are only small (b = .09,

p = .006 and b = .05, p = .008 respectively). Thus, the

happiness-gap between optimists and pessimists appears to be

small in virtually each country. Moreover, contrary to what

was expected, the size of this happiness-gap in a given

country was not related to the national inequality-level.

Rather, other country-level factors may be at play.

Interestingly, optimism yielded a stronger happiness-gain

for people in countries with, on average, an unhappier

population (b = -.108, p = .010). Furthermore, the size of

the happiness-gap between optimists and pessimists did not

differ across low-corruption countries. Possibly, the size

of this happiness-gap is related to countries’ perceived

corruption-level.


Thus, the ideas behind the expectation of larger happiness-

gaps between optimists and pessimists in more unequal

societies, have become questionable. Specifically,

inequality was here thought to heighten the top-incomes that

optimists expect to acquire. However, the optimism-item only

assessed whether people expected an improvement in their

financial situation and did not ask the expected magnitude

of this improvement. Indeed, it could be that the upward

mobility expected by most optimists is limited, while people

perceive that inequality does only lift the top-incomes.

Similarly, if people think that it is predominantly the

bottom that ‘suffers’ from inequality, pessimists who only

perceive to ‘fall down’ to a position above this bottom, are

likely unaffected by a higher inequality. Alternatively,

happiness is probably not affected by the financial rewards

or punishments people expect to receive when they will move

up- or downwards. Rather, the expected upward or downward

mobility in itself may suffice to affect an individual’s

happiness. This because people may care more about their

relative status (relative income) than about the

accompanying financial benefits (absolute income). Indeed,

earlier research has supported this (Ball & Chernova, 2008).

Thus, if inequality enlarges these financial rewards or

punishments of social mobility, this does possibly not

affect happiness.

The models of Table 2 were re-estimated while controlling

for popular meritocratic perceptions (Model 2, Table A8).

Notably, this revealed a negative effect of inequality on


happiness. However, this was not the case for low-corruption

and high-corruption countries separately (b = -.045, p

= .058 and b = -.047, p = .273). This could be due to a

smaller macro-level N in the separate analyses and multi-

collinearity between the macro-level control-variables.

Indeed: Cpi is largely captured by the item-scale for

meritocratic perceptions. Thus, the suddenly appearing

inequality-effect for the whole sample, when controlling for

meritocratic perceptions, could be explained by another

finding of this study: the differing inequality-effect

between low- and high-corruption countries. Alternatively,

meritocratic perceptions could have suppressed an actual

effect of inequality in the original models. However, this

was not the case here: inequality did not influence the

popularity of meritocratic perceptions at the country-level

(b = -.657, p = .321). Another potential cause of this

suddenly appearing inequality-effect is the interplay of

several unknown macro-factors that are related with the

variables of interest. However, including meritocratic

perceptions as a predictor in the model did not yield a

significant effect for inequality in the WVS-analyses.

Because of this lack of robustness, one should not lend much

confidence to these results. In case any effect is present,

this will only apply for Europe.

A negative effect for inequality did also appear in the

EQLS-sample when using the income-quintile ratio (Table A7).

Furthermore, a higher inequality now accentuated the

happiness-gains of being optimistic instead of pessimistic.


Still, contrary to the central assumption behind Hypothesis

1, the effect of inequality on happiness did not differ

cross-nationally (b = .016, p = .482). Controlling for

popular meritocratic perceptions yielded similar results.

Clearly, the different results from using the Gini or the

income-quintile, need some clarification. Consulting the

stock of previous studies, it does not appear that Gini

structurally fails to generate significant results. On the

contrary, many effects were found (e.g. Alesina et al.,

2004, Berg & Veenhoven, 2010; Jiang, Lu & Sato, 2011; Oishi,

Kesebir & Diener, 2011; Bjørnskov et al, 2013).

Alternatively, it could be that countries varied more

regarding their income-quintile ratios than regarding their

Ginis, making it more likely for the first measure to reveal

effects (Agresti & Finlay, 2009). But instead, here Gini had

the largest spread (range and standard deviation). In fact,

having both a theoretical range from 1 to 100, the income-

quintile ratio had only an observed range of 6.7, while this

was 18.7 for Gini.

One explanation of these puzzling differential results could

be that the Gini considers the whole income-distribution,

whereas the income-quintile ratio focuses at the extremes.

Possibly, people’s happiness is more responsive to these

extremes, because their relative concerns may be more

focused towards the top and bottom. However, societies with

large gaps between the extremes also tend to have high Ginis

(r = .84, p < .001). Still, the distance between the top-

incomes and the mean-income will be more consistently


largest among countries with high quintile-ratios than among

high-Gini countries. Consequently, top-oriented people’s

happiness would be more responsive to the income-quintile

ratio.

Because of the careful estimation of the Ginis used here,

the results generated through this measure deserve credit.

Indeed, the problem of unreliable Gini-estimates for

developing countries, does not apply to the EQLS-countries.

Moreover, the measure is less arbitrary and more informative

than the quintile-ratio, since it considers all income-

differences rather than only one certain part. But as the

quintile-measure is also based upon good-quality data (UN-

rapport) and reveals effects despite a relatively small

range, future work towards which measure generates more

valid results, is welcome.

Since happiness was severely non-normally distributed in

both datasets, future research could assess whether the

results of this study are robust against using a multi-level

logistic regression method. Taking the log of happiness did

not ‘normalize’ this variable, according to normality-tests.

Linear regression analysis was here preferred because

relatively much information is retained in such analysis. By

contrast, in logistic regression analysis, observations on

continuous variables have to be allocated to wider dummy-

categories, which causes information-loss. Furthermore, with

a large individual-level N, linear regression has been shown

to yield results that are fairly robust against using a non-

normally distributed dependent variable (Agresti & Finlay,


2009: 284). Certainly, the confidence in the results would

increase if logistic regression reveals similar findings

(regarding statistical significance and the relative

strength of effects).

Thus, future research could improve upon this study in

several ways. Firstly, by conducting a large-scale

quantitative study, I only assessed relationships between

phenomena. Future qualitative work could supplement the

findings here by revealing the underlying mechanisms

(Goldthorpe, 2007). Especially the micro-level should be

more explicated: individuals’ perceptions and desires.

Furthermore, quantitative work should use cross-temporal

data, ideally panel-data in which the repeated measurement

had followed closely after the change in inequality. In

fact, happiness may be more sensitive for changes in the

inequality-level. Whereas people at Time 2 do structurally

compare their situation with that of their past-selves at

T1, people between countries do possibly not. Moreover,

cross-temporal data could also provide more (although still

provisional) insights about the causality of relationships,

by revealing the sequence of phenomena. Finally, datasets

should be combined by in order to obtain a larger number of

countries. Berg & Veenhoven (2010)’s work is a good example.

Here, the number of countries was rather small, particularly

for the WVS-analyses. Apart from statistical power, re-

estimating the patterns among a larger number of countries

would give more confidence in the results, because of a

stronger leverage. Here, the leverage was low, given that


one needs at least at least rk countries in order to estimate a

model with k macro-level variables having r categories

(Agresti & Finlay, 2009: 442).

Overall, the theoretical framework was not supported. This

exclusion of theoretically plausible patterns is an

improvement of current knowledge. Specifically, cross-

national differences in the popularity of meritocratic

perceptions cannot explain the previously found differential

effects of inequality on happiness. Instead, the happiness-

consequences of inequality depended upon the perceived

corruption-level of countries. Specifically, inequality was

only harmful in countries with a high perceived corruption-

level (Model 1 Table A8). This new finding would, when

replicated in more future studies, contribute to a better

understanding of the inequality-happiness link. Indeed, I

found no robust evidence of differences in the nature of the

inequality-happiness relationship between individual

countries, although it appeared to differ between country-

groups. Furthermore, some robustness-checks offered fertile

ground for further research: the expected patterns turned up

when using the income-quintile ratio. Furthermore,

controlling for meritocratic perceptions revealed an effect

for Gini. Here, I offered a few speculations on the reason

why, and it remains plausible that these effects were only

due to statistical artifacts. Future research is invited to

explore the role of meritocratic perceptions with a larger

number of countries or with different measures. Also

important is to consider the impacts of capital-inequality


besides only of income-inequality. Capital-inequalities are

even larger (Piketty, 2014: 40 and 336) and possibly more

important for people’s happiness than income-inequalities.

Finally, from happiness-concerns, some could conclude that

governments do not need to curb the rising inequalities. But

given the instability of the inequality-effect, and given

the many adverse effects of inequality on other happiness-

determinants (Wilkinson & Pickett, 2010), a continued

increase in the inequality-level could still become harmful

for (the overall high levels of) happiness in the long-term.


APPENDIX

APPENDIX A: Variable-details

HAPPINESS

Question wording self-reported happiness (World Values

Survey Association, 2012: 2):

“Taking all things together, would you say you are:1 Very happy2 Rather happy3 Not very happy4 Not at all happy”

As a scale-variable, this variable was reverse-coded.

Question wording self-reported happiness (Eurofound, 2011:

20):

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

INCOME-INEQUALITY

The Gini-indexes in the SWIID (Solt, 2009a) are based on

people’s equivalized disposable, after-tax (post-transfer)

household-income. More information on these data can be

found in Solt (2009b).


Regarding the income-quintile measure, the UN did not report

values for Taiwan, Cyprus, Luxembourg, Malta and Iceland.

For these countries (except Taiwan), I calculated the

income-quintile ratio from information provided by the EQLS-

data. I used two income-measures in order to calculate

income-quintiles: people’s self-reported monthly net

household-income (Eurofound, 2011: 28) and this income in

equivalized PPP-euro’s (Y11_Income_percapita in Eurofound,

2011-2012). From this, then the income-quintile ratio was

determined by taking the mean from the income-quintile

ratios according to the two respective income-measures.

Unfortunately, due to a fairly large number of missing

values at the income-items, these calculations may be less

reliable and probably more biased than the UN’s statistics.

Country-scores for both inequality-measures are displayed in

Table A3.

RELATIVE CONCERNS

The item for relative concerns (World Values Survey

Association, 2012: 6):

“Now I will briefly describe some people. Using this card, would you pleaseindicate for each description whether that person is very much like you, like you,somewhat like you, not like you, or not at all like you? – It is important to thisperson to be rich; to have a lot of money and expensive things”.“1 Very much like me2 Like me3 Somewhat like me4 A little like me5 Not like me6 Not at all like me”


This variable was reverse-coded such that higher scores

represent stronger relative concerns. For the separate

analyses for people with and without relative concerns, this

variable was dichotomized:

(Strong) relative concerns: people who answered “somewhat like

me”, “like me” or “very much like me”.

Weak or no relative concerns: people who answered “a little like

me”, “not like me” or “not at all like me”.

MERITOCRATIC PERCEPTIONS (WVS)

Table A1: Replication of Marshall et al.’s (1999) Item-scale

ISSP-2009 Items in the Scale of This Study (and Roex, 2013) (ISSP, 2009) and corresponding ISSP-1992 Items in Marshall et al., 1999 (Appendix 1, p. 365-366)

Please tick one box for each of these to show how important you think it is for getting ahead in life (‘1’ essential – ‘5’ not important at all)


a) how important is coming from a wealthy family? (question 24 p. 366)

b) how important is having a good education yourself? * (question 6, p. 365)

c) how important is having ambition? * (question 12, p. 365)

d) how important is hard work? * (question 12, p. 365)

e) how important is knowing the right people? (question 19, p. 366)

f) how important is having political connections? (question 19, p. 366)

g) how important is giving bribes? (question 21, p. 366)

h) how important is a person’s race? (question 27, p. 366)

i) how important is a person’s religion? (question 27, p. 366)

j) how important is being born a man or a woman? (question 26, p. 366)

To what extent do you agree or disagree with the following statements? (‘1’ strongly agree – ‘5’ strongly disagree)

a) To get all the way to the top in <country> today, you have to be corrupt. (question 21, p. 366)

b) In <country>, only the rich can afford the costs of attending university. (question 17, p. 365)

c) In <country> people have the same chances to enter university, regardless of their gender, ethnicity or social background. * (question 17, p. 365)

*reverse-coded in order to make higher scores represent stronger meritocratic perceptions


MERITOCRATIC PERCEPTIONS (EQLS)

For the logistic regression, countries were categorized

according to how popularly meritocratic perceptions were

endorsed by their resident-respondents. These categories

were then entered as dummies in the regression. The

categories were: countries

1) with 14.8% or less ‘high meritocratic perceivers’

2) where this group is larger than 14.8%, but maximally

17.8%

3) larger than 17.8%, but maximally 21.6%



6) larger than 47%

OPTIMISM/ PESSIMISM

The item for optimism/ pessimism (Eurofound, 2011: 30):

“When it comes to the financial situation of your household, what are yourexpectations for the 12 months to come, will the next 12 months be better, worseor the same?” “1 Better2 The same3 Worse”

These answers were each entered into the analyses as

dummies.

CONTROL-VARIABLES

Post-communism for countries was assessed through a consult of

the previous literature that had distinguished countries


regarding their communist past (Marshall et al., 1999;

Armingeon & Careja, 2004; Salavecz et al., 2010). Here, the

following EQLS-countries were coded ‘post-communistic’:

Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia,

Lithuania, Macedonia, Montenegro, Poland, Romania, Serbia,

Slovakia and Slovenia.

Country’s corruption perception index (Cpi) over 2011 (Transparency

International, 2011) was used to distinguish ‘high-

corruption’ countries from ‘low-corruption’ countries. Low-

corruption countries are those with scores below or at the

median-level: Austria, Belgium, Cyprus, Germany, Denmark,

Estonia, Spain, Finland, France, Ireland, Luxembourg,

Netherlands, Portugal, Sweden, UK and Iceland. Between the

two groups of countries, the spread of Gini-scores was

fairly equal. Originally, the Cpi constitutes a scale from 0

(high perceived corruption) to 10 (low perceived

corruption).

Country’s unemployment rate in 2011 was taken from the World

Bank (2014a) and gives the proportion of jobless people in

the labour force that is willing and capable to work

(International Labour Organisation, 1982). Because of

missing information about Taiwan in the World Bank data, I

used Taiwanese government statistics (Directorate-General of

Budget, Accounting and Statistics Taiwan, 2013) for Taiwan’s

figures. For the regression predicting people’s optimism/

pessimism, countries were divided into a ‘low/ medium’

unemployment and a ‘high’ unemployment group (see Table A3).


The latter group captured countries with the 26% highest

unemployment rates: Croatia, Estonia, Latvia, Spain,

Portugal and Slovakia. Dichotomizing was chosen above using

multiple treads of unemployment-levels, because within some

of these finer categories, the popularity of meritocratic

perceptions (treads) barely varied and many treads did not

occur in several categories.

Country’s Gross Domestic Product (GDP) in 2011 was taken from the

World Bank (World Bank, 2014b). GDP is calculated by summing

all gross-value produced by a country’s populace, as well as

revenues from product-taxes, and subtracting all subsidies

from this. This summed product is then divided by the number

of residents in the country. Eventually, this variable was

not included in the final analyses because it was not

significant in the multi-level models.

In the WVS-data, income was assessed by an item in which

respondents were asked to position their household on an

‘income scale on which 1 indicates the lowest income group

and 10 the highest income group’ in their country’ (World

Values Survey Association, 2012: 18). To keep the model more

parsimonious, the income-deciles were transformed into

quintiles. The WVS contained no other information on

people’s incomes other than this self-ranking question. In

addition, the EQLS offered information on people’s income-

quartile. This was computed by the EQLS-staff based upon

people’s self-reported equivalised net-household income in


purchasing power parity (PPP) Euros (Y11_Income_percapita in

Eurofound, 2011-2012). As a predictor, this latter variable

appeared a stronger confounder than people’s income

quartile, but contained more missings.

For age, dummy categories were computed, because of non-

normality of the continuous age-distribution (with a skew

towards younger ages and a very small tail at the right).

The same categorization was used as was ready-made offered

in the EQLS, which originated from a similar item as in the

WVS that asked people about their current age:

a) 18-24

b) 25-34

c) 35-49

d) 50-64

e) 65+

For sex, a dummy was made that assigned a 1 to male

respondents and a 0 to women. This dummy was extracted from

a variable ‘sex’, that was assessed by the interviewers’

observation (World Values Survey Association, 2012;

Eurofound, 2011).

WVS Health was assessed by asking respondents to self-evaluate

their current health. They were offered four answer

categories in the WVS and five in the EQLS, resulting in

ordinal measures of self-reported health ranging from very

good to poor or very bad. For each category, a dummy was

made.


The education-dummies were extracted from an item containing

internationally comparable educational levels as its answer-

categories (World Values Survey Association, 2012: 19;

Eurofound, 2011: 23), as recoded from nation-specific items

by the staff of the survey-projects. Respondents were asked

about their highest attained educational level. The answer-

categories were very fine-tuned. Here, a more parsimonious

categorization was chosen, because it captured the

confounding influences of educational attainment more

strongly:

Education: WVS EQLS

Low primary school (in)complete primary

school

incomplete secondary-

vocational school

Medium secondary-vocational school complete

secondary education

incomplete (secondary)

academic school

High academic degree tertiary

education degree

For marital status, dummies were extracted from an item asking

people to their current marital status. The following

dummies were made: ‘married/ cohabitation’, ‘divorced/

separated), (single) and (widowed) ’. This is a widely used

categorization in previous work.

Class was assessed by people’s self-reported class (World

Values Survey Association, 2012: 18). Because of multi-


collinearity when making a dummy for each category (which

was not related with income or education), a more

parsimonious categorization was made: (1) bottom or working

class, (2) middle class, (3) upper-middle and upper class.

Alternatively, the dataset also contains variables that

would have enabled the construction of a measure of

objective class (EGP-style), but this information is only

available for 1 country (Turkey).

People were also asked about their religion (World Values

Survey Association, 2012: 10). The datafile contained a host

of self-categorisations, which were coded into more

sparsimonous categories consistent with that in earlier WVS-

based studies (Bloom & Arikan, 2012). However, because many

categories were missing in multiple countries, I eventually

used a dummy distinguishing atheist from religious

respondents.

People’s past financial mobility was assessed by extracting dummies

from an item asking people about ‘the financial situation of

their household compared to 12 months ago’ (Eurofound, 2011:

30). Respondents could report an improvement, stability, or

deterioration of their situation. For each possibility, a

dummy was constructed.


Table A2: Descriptive Statistics of Dependent, Independent and Control Variables

Variable Percentage Mean (SD) Range

Country-Level Variables

Meritocratic Perceptions

WVS

Meritocratic Perceptions

EQLS

- Tread 1- Tread 2- Tread 3- Tread 4- Tread 5- Tread 6

GINI-index WVS

GINI-index EQLS

CPI (Low-Corruption)

Post-Communism

Individual-Level Variables

Happiness WVSHappiness EQLSOptimism

- Pessimists- Neutrals- Optimists

Relative Concerns

n/a

8.78.721.721.726.113.1

n/a

n/a

48.5

42.4

n/a

n/a

31.652.016.4

n/a

11.6

29.3 (17.17)

n/an/an/an/an/an/a

34.3 (8.03)

30.1 (4.48)

n/a

n/a

3.1 (0.67)

7.3 (1.95)

n/an/an/a

02.9 (1.36)

n/a

5 to 72

0/10/10/10/10/10/1

23.8 to 50.2

23.8 to 42.6

0/1

0/1

1 to 4

1 to 10

0/10/10/1

1 to 6

0/1


Age WVS

- 18-24- 25-34- 35-49- 50-64- 65+

Age EQLS

- 18-24- 25-34- 35-49- 50-64- 65+

Sex (male) WVSSex (male) EQLSHealth WVS

- Poor- Fair- Good- Very good

Health EQLS

- Very bad- Bad- Fair- Good- Very good

Income WVS

- lowest quintile- second quintile- third quintile- fourth quintile- highest quintile

Income EQLS

- lowest quartile- second quartile- third quartile- highest quartile

Educational Attainment WVS

- Low- Medium

17.727.026.517.3

7.915.026.026.224.9

47.3

43.3

6.927.244.621.3

2.68.829.038.221.4

14.628.037.416.93.1

24.325.025.325.4

21.154.724.1

n/an/an/an/a

n/an/an/an/an/a

n/a

n/a

n/an/an/an/a

n/an/an/an/an/a

n/an/an/an/an/a

n/an/an/an/a

n/an/an/a

0/10/10/10/1

0/10/10/10/10/1

0/1

0/1

0/10/10/10/1

0/10/10/10/10/1

0/10/10/10/10/1

0/10/10/10/1

0/10/10/1


- HighEducational Attainment EQLS

- Low- Medium- Highest

Marital Status WVS

- Single- Married/ Cohabitation- Divorced/ Separated- Widowed

Marital Status EQLS

- Single- Married/ Cohabitation- Divorced/ Separated- Widowed

Past Financial Mobility

- Downwards- Stable- Upwards

Subjective Class

- Bottom/ Working-Class- Middle- Upper-Middle/ Upper

Denomination (Atheist)

12.763.224.2

19.465.87.87.0

16.260.610.612.6

35.952.911.2

37.240.722.1

33.8

n/an/an/a

n/an/an/an/a

n/an/an/an/a

n/an/an/a

n/an/an/a

n/a

0/10/10/1

0/10/10/10/1

0/10/10/10/1

0/10/10/1

0/10/10/1

0/1

WVS: Individual-Level N = 22,815; Macro-Level N = 18. EQLS: Individual-Level N = 29,745,Macro-Level N = 33 (for Meritocratic Perceptions N= 22,512 and 23 respectively).

Table A3: Country-Scores

Total Sample (All Countries)

Countries GINI

Meritocratic Perceptions (Continuous)

Meritocratic Perceptions (Tread)

Income Quintile-Ratio

WVS-Sample

Australia 33.27* 51 7.0

Chili 47.08 30 3.6


China 47.38** 5 8.4

Taiwan 30.07 25

Japan 29.39* 44 3.4

New Zealand 31.85 72 6.8

Phillipines 50.20** 24 9.0

United States 37.20 46 8.5

WVS and EQLS

Cyprus 29.09 27 5 5.9***

Estonia 31.97 27 5 6.3

Germany 28.95 25 4 4.3

Poland 29.26 19 3 5.6

Russia 42.93 13 8.2

Slovenia 23.81 16 2 4.8

Spain 33.41 32 5 6.0

Sweden 25.87 48 6 4.0

Turkey 40.08 17 2 8.0

Ukraine 25.65* 07 3.9

EQLS-Sample

Austria 27.44 24 4 4.4

Belgium 25.13 21 3 4.9

Bulgaria 34.89 22 4 10.2

Croatia 30.76 18 3 5.2 Czech Republic 25.16 23 4 3.5

Denmark 26.12 58 6 4.3

Greece 33.15 6.2


Finland 25.54 50 6 3.8

France 30.53 32 5 5.6

Hungary 27.87 10 1 4.8

Italy 32.94 21 3 6.5

Ireland 28.36 5.7

Latvia 35.63 18 3 6.3

Lithuania 34.93 6.7

Luxembourg 26.84 4.1***

Malta 27.52 5.2***

Macedonia 42.55* 9.3

Montenegro 29.34* 4.6

Netherlands 26.47 5.1

Portugal 33.22 24 4 7.9

Romania 31.92 4.9

Serbia 28.73* 4.1

Slovakia 26.27 14 1 4.0

UK 35.51 47 5 7.2

Iceland 24.82 47 5 4.9***

*2010; **2009; ***calculated from EQLS income-data

APPENDIX B: Data-Sources


WVS Wave 6 and 3

http://www.worldvaluessurvey.org/WVSDocumentationWV6.jsp

http://www.worldvaluessurvey.org/WVSDocumentationWV3.jsp

Third EQLS

UK Data Archive, login required. After log-in, click on

‘Discover’, and search for ‘EQLS 2011’

ISSP Social Inequality Module 2009

GESIS Data Archive, log in required. Go to:

https://dbk.gesis.org/dbksearch/sdesc2.asp?no=5400&db=E&tab=3

Solt’s (2009a; 2009b) SWIID

Go to:

http://thedata.harvard.edu/dvn/dv/fsolt/faces/study/StudyPage.xhtml?

studyId=36908&tab=files

Download ‘SWIIDv4_0.zip’.

APPENDIX C: Model Comparison

Table A4: Model Comparison of Happiness on Income-Inequality, Relative Concerns,Meritocratic Perceptions

Total Sample (All Subgroups)

Including: M1 M2 M3 M4 M5 M6*


Intercept X X X X X X Micro-Level Controls X X X X X

Relative Concerns X X X X

Income-Inequality X X X

Meritocratic Perceptions X X

Income-Inequality × Meritocratic Perceptions

X

-2 Loglikelihood (df) 44,986.5 40,755.7

(20)40,775.8 (21)

40,784.5 (22)

40,791,3 (23)

40,805.6 (24)

*Model 6 appears as Model 1 in Table 1.

Table A5: Model Comparison of Happiness on Income-Inequality and Optimism/ Pessimism


Including: M1 M2 M3 M4 M5*

Intercept X X X X X Micro-Level Controls X X X X

Macro-Level Controls X X X

Optimism-Dummies X X

Income-Inequality ×Optimism-Dummies

X

-2 Loglikelihood (df) 121,318.4 116,231.1 (19)

116,231.2 (20)

116,210.4 (22)

116,236.6 (24)

*Model 5 appears as Model 1 in Table 2 in the text.

Table A6: Model Comparison of Optimism/ Pessimism on Meritocratic Perceptions



Including: M1 M2 M3*

Intercept X X X

Micro-Level Control-Variables X X

Meritocratic-Perceptions-Dummies X

-2 Loglikelihood (df) 9,853.0 626.6 (14) 2,297.9 (24)*Model 3 appears as Model 1 and Model 3 in Table 3 in the text.


APPENDIX D: Additional Results

Table A7: Robustness-Analysis Results on Happiness Influenced from Income-Quintile Ratio (Inequality)

Replication M1 Table 1 text Replication M1 Table 2 text Replication M2 Table 2 text

M1 (WVS) M2 (EQLS) M3 (EQLS)

b SE b SE b SE

Intercept +0.552*** (0.039) +

+1.318***(0.089)

+1.527***

(0.094)

Individual-Level characteristics Optimism/ Pessimism

- Optimistic +0.418***

(0.060) +0.241** (0.08

0)

- Neutral +0.286***

(0.045) +0.239** (0.07

6) - Pessimistic (ref.) - - - - Relative Concerns - 0.007* (0.003)

Country-Level Variables Income-inequality (Income-Quintile Ratio) +0.009 (0.015) -

0.202***(0.042)

+- 0.151*

(0.063)

Meritocratic Perceptions +0.003 (0.002) +. Income-Inequality × - 0.001 (0.001)


Meritocratic Perceptions Income-Inequality × Optimistic

00.092* (0.038) - 0.021 (0.06

7) Income-Inequality × Neutral

00.053 (0.029) 00.079 (0.06

3) Income-Inequality × Pessimistic (ref.)

- - - -

Individual-Level Variance 96.7% 95.9% 97.4%Country-Level Variance 03.3% 04.1% 02.6%Nindividuals 21,701 29,745 15,534Ncountries 00,017 00,033 00,016Source: World Values Survey (2010-2012) and European Quality of Life Survey (2011-2012). Note: The same controls were used as in the corresponding original models. Unstandardized coefficients. * p < .05, ** p < .01, *** p < .001.


Table A8: Additional Models EQLS

Replication M2 Table 2 text Replication M1 Table 2 text

M1 (High-Corruption Countries) M2 (Control for Meritocratic Perceptions)

b SE b SE

Intercept +0.895** (0.184)++1.252***

(0.090)

Individual-Level characteristics Optimism/ Pessimism

- Optimistic +0.513 (0.071) +0.378*** (0.079)

- Neutral +0.295*** (0.046) +0.273**

* (0.058)

- Pessimistic (ref.) - - - - Country-Level Variables

Income-inequality (GINI) - 0.045* (0.017) - 0.057** (0.018)

Meritocratic Perceptions + +0.021*** (0.003)

Income-Inequality × Optimistic

+0.023 (0.013) 00.034 (0.018)


Income-Inequality × Neutral

+0.016 (0.009) 00.023 (0.013)

Income-Inequality × Pessimistic (ref.)

- - - -

Individual-Level Variance 96.8% 95.9%Country-Level Variance 03.2% 04.1%Nindividuals 14,211 22,512Ncountries 00,017 00,023Source: European Quality of Life Survey (2011-2012). Note: The same controls were used as in the corresponding original models. Unstandardized coefficients. * p < .05, ** p < .01, *** p < .001.


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Inequality and Happiness: The Role of Perceptions of the Distributive Process (Master Thesis)

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