ISSN 1881-6436

Discussion Paper SeriesNo. 14-01
Title: Measuring Socio-economic Factors and Sensitivity of Happiness
Name: Hideyuki Mizobuchi
June 2014

Faculty of Economics,
Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku,
Kyoto, Japan
612-8577

1

Measuring Socio-economic Factors andSensitivity of Happiness
Hideyuki Mizobuchi†
25 April 2014
Abstract
There is significant variation in average subjective well-being across countries. What makes
people in some countries happier or more miserable than others? We know that a wide rangeof socio-economic circumstances affects individuals’ subjective well-being. Many studies
focus on select aspects of the lives of individuals in each country and can explain only a partof the difference in subjective well-being across countries. Thus, they have not been able tofully explain cross-country variation in average well-being. Covering a comprehensive set ofsocio-economic variables, we provide a fuller account of external factors that generate
differences in average well-being across countries. However, these factors still cannot
completely explain the cross-country variation. While an individual’s subjective well-being isaffected by socio-economic status, every individual does not necessarily draw the same levelof subjective well-being from a given condition of life. There are evidently differences in thesensitivity of individuals’ happiness to socio-economic conditions and this partly explainscross-country variation in average subjective well-being. In this study, we decompose thedifference in average subjective well-being across countries into a comprehensive set ofsocio-economic factors along with cross-country difference in sensitivity of happiness. We
adopt Data Envelopment Analysis (DEA) to estimate a happiness function and specify thesensitivity score for each country in a sample. We draw on a comprehensive set of well-being
indicators released by the Better Life Initiative of the Organisation for Economic Cooperation
and Development, along with measures of income inequality. These indicators, which assess
the population’s average life circumstances in multiple socioeconomic factors, comprise the
10 representative factors of well-being. We find that the health factor and sensitivity term
play the largest role in generating variation in subjective well-being. Even within countries,
the average level of subjective well-being varies between different population groups.
Drawing on a set of indicators that assess the life circumstances of different groups withineach country, our decomposition formulation allows for a full explanation of the differencesin average life satisfaction between the groups. Women’s higher subjective well-being is
attributed mainly to a work–life balance factor, while its impact is largely offset by a jobs
factor. In addition, women’s greater sensitivity of happiness helps to raise their subjective
well-being relative to men. On the other hand, high income earners have higher subjective
well-being than low income earners in most countries. Life circumstances of high income
earners are better in most socio-economic aspects. However, high income earners have lower
sensitivity of happiness than low income earners. High income earners are happier than low
income earners but are not as happy as would be expected from their better life circumstances.
†

Faculty of Economics, Ryukoku University, 67 Fukakusa Tsukamoto-cho, Fushimi-ku, Kyoto 6128577, Japan; mizobuchi@econ.ryukoku.ac.jp

2

Key Words: Subjective well-being, Happiness function, Data envelopment analysis, Better lifeindex
JEL Classification: I31, D24, D63, O15

3

1.

Introduction

Economists have asked the question of what makes some countries rich and others
poor for a long time. Gross domestic product (GDP) per capita has long been used asa proxy measure for how well off people are, or in other words, peoples’ well-being.
Thus, much effort has been devoted to explaining cross-country differences in GDP
per capita or per worker.1 However, there are clearly factors which affect people’s
well-being other than just income.2 Happiness research shows that self-reported life
satisfaction is a more reasonable measure of individual well-being than personal
income. 3 This study focuses on a country’s average subjective well-being (orhappiness) and explores why it differs across countries in order to answer the questionof what makes some countries rich and others poor.4
There are a large number of determinants of happiness. Numerous studies address
what factors influence people’s subjective well-being and to what extent. Thesestudies are concerned not only with the influence of economic factors, such as income
and jobs, on people’s subjective well-being. The studies also show that non-material
aspects of people’s lives, such as social relations with family and friends, areimportant for determining their subjective well-being. Frey (2008) surveys the effectof a variety of factors on subjective well-being.
However, there are two shortcomings with the early studies on identifying thedeterminants of subjective well-being and quantifying their influences. First, theyoften focus on select aspects of people’s lives and estimate their impact on happinessby holding other factors constant. By missing many determinants of subjective wellbeing, they fail to explain completely cross-country variations.5 Second, they often
assume a happiness function that is too simplistic in structure, such as a linear
functional form or a limited number of cross-terms among explanatory variables. An
assumption of a simple functional form a priori restricts the interaction amongexplanatory variables. Thus, the studies fail to measure accurately the influences ofeach determinant on subjective well-being.
This study has two key features to overcome these shortcomings. First, we deal with acomprehensive set of socio-economic determinants which capture people’s life
circumstances. Second, we adopt a Data Envelopment Analysis (DEA) technique toestimate a happiness function. We begin by explaining why we choose this estimation
approach. Among the reasons why earlier studies do not consider a comprehensive setof indicators in the happiness function is that these studies rely on a regression
1

See Caselli (2005).
An alternative approach is to aggregate income and other factors, such as life expectancy, leisure, andinequality, to enrich a measure of overall well-being. See Fleurbaey and Gaulier (2009) and Jones andKlenow (2010).
3
There are multiple measures of happiness. This study adopts the 0–10 point scale of the Cantril
Ladder of life satisfaction.
4
The terms ‘happiness’, ‘subjective well-being’, and ‘life satisfaction’ are used interchangeably, as iscommon in the literature.
5
Helliwell and Wang (2012, 2013) also investigate the socio-economic the cross-country variation ofsubjective well-being and explain it from different life circumstances using individual survey data.
Helliwell and Wang (2013) explain three quarters of the international differences in subjective wellbeing by a much smaller number of variables (six) to characterize people’s life circumstances. By
comparing people’s lives using more than double this number of variables, we can attribute a higher
proportion of the differences in subjective well-being to the differences in life conditions.
24

approach. Highly correlated explanatory variables do not allow us to derive
statistically meaningful results because of multi-collinearity. On the other hand, DEA
is a deterministic approach based on linear programming to construct a nonparametric piece-wise frontier over the data. Therefore, first of all, since it is adeterministic approach, it is immune from problems of multi-collinearity even whenhighly correlated explanatory variables exist. Second, since it does not a priori assume
any functional form, it allows us to capture a variety of types of interaction between
underlying variables which contribute to people’s well-being.
DEA was originally proposed by Charnes et al. (1978) for constructing production
frontiers and measuring the productive efficiency of firms. Just as every firm does notproduce the same amount of output from a given input, every individual does not
enjoy the same amount of subjective well-being from given socio-economic situations.
In addition, just as efficient firms produce more from a given input than less efficientfirms, sensitive people have higher subjective well-being from a given socioeconomic condition than less sensitive people. Thus, the application of DEA to ourfield of study allows us to derive sensitivity of happiness for each country in addition
to specification of the happiness function.
Application of DEA to national average subjective well-being specifies sensitivity ofpeople in each country. This suggests that countries’ socio-economic conditions of
their people’s lives influence the national average subjective well-being, but do not
fully determine average subjective well-being because of differences in sensitivity. As
Frey and Stutzer (2002), Frey (2008), and Oishi (2010) advocate, more personal
factors, such as personality, demography, and culture, also influence people’s
subjective well-being. Thus, people in some countries appear intrinsically happierthan others, even when faced with the same socio-economic situations. Countries’
sensitivity terms reflects such factors uncovered by socio-economic variables.
Recently, the DEA technique has been applied widely to the measurement of qualityof life and overall well-being. However, it has been restricted mostly to aggregating
separate well-being indicators or variables into a single measure; this is surveyed by
Cherchye et al. (2007). For happiness research, Guardiola and Picazo-Tadeo (2013)
utilize DEA to address the problem of how to relate people’s satisfaction withdifferent domains of their lives to overall life satisfaction. While the researchers focus
on overall life satisfaction, which equals our subjective well-being, they address a
different question.
Although we understand that there are many underlying socio-economic determinantsof subjective well-being, the manner of selecting variables or indicators which
influence people’s well-being is difficult practically. Drawing upon the 2009
recommendations of France’s commission on economic and social measurement(Stiglitz et al., 2009), which went beyond GDP to measure wealth and progress, theOrganization for Economic Cooperation and Development (OECD) identified 11
well-being dimensions as being essential to people’s well-being and released 24
underlying indicators (OECD, 2011). The dimensions cover material living conditions,such as income and wealth, as well as quality of life, such as community, environment,
and work–life balance. Drawing on these OECD data, we estimate a happiness
function based on the DEA technique and decompose the cross-country variation in
subjective well-being into a set of factors covering every aspect of people’s lives.
The OECD has made available a set of indicators differentiated by population groups,
such as males and females, and high and low income earners in each country. Average

5subjective well-being of females is higher than that of males while that of highincome earners is higher than that of low income earners. Setting one populationgroup as the reference group, our decomposition formula allows us to fully
decompose the difference in average subjective well-being between different
population groups into socio-economic factors and sensitivity of happiness. We
investigate the reason why females and high income earners are happier than the otherpopulation groups.
The paper is presented as follows. Section 2 explains the procedure of estimation anddecomposition. Section 3 explains data of the OECD well-being indicators. Section 4
provides the empirical results. Section 5 concludes.
2. Method: Model and Estimation Strategy
National average subjective well-being differs largely across countries. Our ultimate
goal is to explain where this difference in subjective well-being comes from. It is wellknown that people’s subjective well-being depends on their life circumstances in awide range of socio-economic aspects.6 Thus, we relate the average level of subjective
well-being of people in a country to the socio-economic status of the country. 7 In
Equation (1) below, 𝑆𝑊𝐵 𝑐 indicates national average subjective well-being ofcountry 𝑐 . Socio-economic vector 𝒙𝑐 = (𝑥1𝑐 , … , 𝑥𝑁𝑐 ) consists of 𝑁 socio-economic
variables and characterizes life circumstances of people in country 𝑐 in terms of 𝑁
socio-economic aspects.8 The relationship between 𝑆𝑊𝐵 𝑐 and 𝒙𝑐 is summarized by ahappiness function 𝐻 as follows.
𝑆𝑊𝐵 𝑐 = 𝜃 𝑐 𝐻(𝒙𝑐 )(1)

By assuming a cross-national happiness function 𝐻 that is common to all the countries,
we can attribute the differences in subjective well-being across countries by thedifferences in 𝒙. However, individuals who share the same life circumstances do notnecessarily have the same level of subjective well-being. This is because socioeconomic vector 𝒙 does not cover more personal aspects of people’s subjective wellbeing, such as personality, demography, and culture. 9 Thus, some individuals are
intrinsically happier than others, even though their life conditions are the same.

6

See Frey (2008).
For simplicity, the average level of subjective well-being of people in a country is called ‘national
average subjective well-being’ or ‘subjective well-being of a country’ in this paper
8
For example, the 𝑛-th component 𝑥𝑛𝑐 corresponds to the 𝑛-th socio-economic condition of country 𝑐,such as income, jobs, and environment. However, each socio-economic condition is often characterized
by a vector consisting of multiple indicators; for example, the environmental quality of country 𝑐 might
be captured by qualities of air and water as well as green spaces. Thus, it is appropriate to adopt asocio-economic vector consisting of 𝑁 sub-vectors, such as 𝒙𝑐 = (𝒙1𝑐 , … , 𝒙𝑐𝑁 ). While our empiricalapplication adopts this framework, it complicates our description of the model and estimation strategy.
Thus, for simplicity, we consider the case in which each socio-economic condition is characterized by
one indicator, such as 𝒙𝑐 = (𝑥1𝑐 , … , 𝑥𝑁𝑐 ).
9
De Neve et al. (2012) investigate the role of personality types in explaining people’s subjective wellbeing. Oishi (2010) discusses the role of cultural differences in explaining cross-country differences inpeople’s subjective well-being.
7

6

Rather than identifying such uncovered factors in further detail and measuring theirrespective contributions, we introduce an overarching concept of sensitivity ofhappiness 𝜃 to capture these influences on national average subjective well-being.10 It
is constructed so that 0 ≤ 𝜃 ≤ 1.11 The maximum value of 1 indicates that a country
is sensitive to its socio-economic conditions. A smaller value of 𝜃 indicates a lesssensitive or more insensitive country. For example, given any socio-economic vector
𝒙, a larger share of individuals who are intrinsically unhappy in a country raises its
national average subjective well-being beyond the level expected from its socioeconomic status 𝐻(𝒙), leading to a smaller 𝜃.
Before we discuss how to estimate the happiness function 𝐻 and the sensitivity term 𝜃,
we explain how to decompose the cross-country differences in subjective well-being
into multiple factors. Multilateral comparison requires a reference country. We
investigate the likely reasons why the subjective well-being of each country is higher
or lower than a hypothetical reference country. Suppose 𝐾 countries constitute oursample. We construct a hypothetical reference country characterized by the sensitivity
term and the socio-economic vectors that are averaged over 𝐾 countries so that 𝜃̅ =𝐾
𝐾
(1⁄𝐾 )Σ𝑘=1
̅ = (1⁄𝐾 )Σ𝑘=1
𝜃 𝑘 and 𝒙
𝒙𝑘 = (𝑥̅1𝑘 , … , 𝑥̅𝑁𝑘 ) . The subjective well-being of
̅). This is the level of happinesspeople in the reference country is expressed by 𝜃̅𝐻 (𝒙which we expect people to experience when they face the average life circumstancesacross countries in every socio-economic aspect and when they are characterized byaverage sensitivity of happiness. We can attribute the difference between 𝑆𝑊𝐵 𝑐 and̅) to the difference in the sensitivity term and 𝑁 socio-economic factors between
𝐻(𝒙country 𝑐 and the reference country by the following Equations (2) and (3).
𝑆𝑊𝐵 𝑐
𝜃 𝑐 𝐻(𝒙𝑐 )
=
̅𝐻(𝒙
̅𝐻(𝒙
̅)
̅)
𝜃
𝜃
=

𝜃𝑐
⏟
𝜃̅

×

𝐻(𝒙𝑐 )𝑐𝑐
𝐻(𝑥̅⏟ 1 , 𝑥2 , … , 𝑥𝑁 )𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦

1𝑠𝑡 𝑓𝑎𝑐𝑡𝑜𝑟

𝐻(𝑥̅1 , … , 𝑥̅𝑛−1 , 𝑥𝑛𝑐 , 𝑥𝑛𝑐 , … , 𝑥𝑁𝑐 )×⋯××⋯
𝐻(𝑥̅1 , … , 𝑥̅𝑛−1 , 𝑥̅𝑛 , 𝑥𝑛𝑐 , … , 𝑥𝑁𝑐 )⏟(2)𝑛−𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟

𝐻(𝑥̅1 , … , 𝑥̅𝑁−1 , 𝑥𝑁𝑐 )
×
̅)
𝐻(𝒙
⏟
𝑁−𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟

10

We find that introducing the concept of sensitivity of happiness is a practical solution to controlling
for the uncovered factors because these factors involve subjects which are difficult to measure.
11
Sensitivity corresponds to efficiency, which is a measure of productive performance and requires the
fewest inputs to produce the most outputs. Just as a firm producing more output from the same input isconsidered to be more efficient, in this study, a country with greater subjective well-being from thesame socio-economic vector is considered to be more sensitive. 𝜃𝑐 is within the range between 0 and 1
because 𝜃𝑐 is the counterpart to the Farell measure, which is normalized within the same range.

7

𝑆𝑊𝐵 𝑐𝜃 𝑐 𝐻(𝒙𝑐 )
=
̅𝐻(𝒙
̅𝐻(𝒙
̅)
̅)
𝜃
𝜃
=

𝜃𝑐
⏟
𝜃̅

𝐻(𝑥1𝑐 , 𝑥̅2 , … , 𝑥̅𝑁 )
×
×⋯
̅)
⏟ 𝐻(𝒙

𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦
1𝑠𝑡 𝑓𝑎𝑐𝑡𝑜𝑟𝑐𝑐
𝐻(𝑥1 , … , 𝑥𝑛−1 , 𝑥𝑛𝑐 , 𝑥̅𝑛+1 , … , 𝑥̅𝑁 )𝐻(𝒙𝑐 )××⋯×𝑐
𝑐
𝐻(𝑥1𝑐 , … , 𝑥𝑛−1
, 𝑥̅ 𝑛 , 𝑥̅𝑛+1 , … , 𝑥̅𝑁 )𝐻(𝑥1𝑐 , … , 𝑥𝑁−1
, 𝑥̅𝑁 )⏟⏟
𝑁−𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟
𝑛−𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟(3)

Both equations construct the contribution of each socio-economic factor in the samemanner. This is captured by the increase in subjective well-being associated with thedifference of a corresponding socio-economic variable between country 𝑐 and thereference country, holding all other socio-economic variables fixed. While Equation̅) by changing from each socio-economic(2) starts from 𝐻(𝒙) and approaches 𝐻(𝒙𝑐variable of country 𝑐, 𝑥𝑛 to the cross-country average 𝑥̅𝑛 in the order from 1st socioeconomic factor to 𝑁-th socio-economic factor one by one, Equation (3) starts from̅) and approaches 𝐻(𝒙) by changing from the cross-country average of each𝐻(𝒙socio-economic variable 𝑥̅𝑛 to that of country 𝑐.
The contribution of the sensitivity term is determined uniquely in both Equations (2)and (3). However, the formulation for each socio-economic factor varies according tothese two equations. Equations (2) and (3) display equally reasonable decomposition
̅). Thus, we adoptof the difference of subjective well-being between 𝑆𝑊𝐵 𝑐 and 𝐻(𝒙the geometric mean of Equations (2) and (3) as our preferred decomposition formula
following the convention originated by Fisher (1922) and Malmquist (1953). It isrepresented as follows.
𝑆𝑊𝐵 𝑐
=
̅𝐻(𝒙
̅)
𝜃

𝜃𝑐
⏟
𝜃̅

1/2

𝐻(𝒙𝑐 )
𝐻(𝑥1𝑐 , 𝑥̅2 , … , 𝑥̅ 𝑁 )
×(
⋅
)
𝑐
𝑐
̅)
𝐻(𝒙
⏟𝐻(𝑥̅1 , 𝑥2 , … , 𝑥𝑁 )×

𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦

1𝑠𝑡 𝑓𝑎𝑐𝑡𝑜𝑟
1/2
𝑐)
𝑐
𝑐
𝑐 𝑐
𝐻(𝑥̅1 , … , 𝑥̅ 𝑛−1 , 𝑥𝑛 , 𝑥𝑛 , … , 𝑥𝑁 𝐻(𝑥1 , … , 𝑥𝑛−1
, 𝑥𝑛𝑐 , 𝑥̅ 𝑛+1 , … , 𝑥̅𝑁 )
⋯×(
) ×
𝑐
𝑐 ⋅
𝑐
𝑐
⏟𝐻(𝑥̅1 , … , 𝑥̅ 𝑛−1 , 𝑥̅𝑛 , 𝑥𝑛 , … , 𝑥𝑁 ) 𝐻(𝑥1 , … , 𝑥𝑛−1 , 𝑥̅𝑛 , 𝑥̅ 𝑛+1 , … , 𝑥̅𝑁 )𝑛−𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟
1/2𝑐)𝐻(𝑥̅1 , … , 𝑥̅ 𝑁−1 , 𝑥𝑁
𝐻(𝒙𝑐 )
⋯×(
⋅
)
𝑐
̅)
𝐻(𝒙
𝐻(𝑥1𝑐 , … , 𝑥𝑁−1
, 𝑥̅𝑁 )⏟
𝑁−𝑡ℎ 𝑓𝑎𝑐𝑡𝑜𝑟(4)

Equation (4), a decomposition formula, defines the contribution of each socioeconomic factor by the geometric mean of corresponding ones in Equations (2) and(3).
Taking the natural logarithm on both sides of Equation (4), we can additively
decompose the differences in subjective well-being into a percentage scale between
country 𝑐 and the reference country. We can go one step further by multiplying bothsides of the natural logarithm of (4) by the subjective well-being of the reference
̅). This allows us to additively decompose the differences of subjective
country 𝜃̅ 𝐻(𝒙8well-being between country 𝑐 and the reference country in the original 10 point scale
into the sensitivity term and 𝑁 socio-economic factors.12
Now, we turn our attention to the problem of estimating 𝐻(𝒙) and 𝜃 𝑐 . We adopt the
DEA approach introduced by Charnes et al. (1978). DEA was originally invented as atool for measuring productive efficiency of firms. It uses linear programming toconstruct a non-parametric piece-wise production frontier over observed input–output
data and to measure the efficiency of each unit relative to the frontier. The production
frontier specifies the maximum amount of output attainable from given inputs. The
difference between a firm’s actual amount of output and its maximum amountindicated by the production frontier captures the firm’s efficiency.
Considering socio-economic vector 𝒙 as input and subjective well-being 𝑆𝑊𝐵 as
output attainable from 𝒙, DEA estimates a happiness function in the same way it
estimates a production frontier. The estimated happiness function indicates the
maximum level of subjective well-being expected from a given socio-economic
vector. This is the level that sensitive countries have given their socio-economic
situations. Under this setting, the concept of productive efficiency corresponds tosensitivity of happiness. The application of DEA computes the sensitivity ofhappiness for a country characterized by 𝑆𝑊𝐵 and 𝒙 as follows.13
𝑆𝑊𝐵
𝐾
𝐾{𝜑: Σ𝑘=1= 𝑚𝑎𝑥
𝑥𝑛𝑘 𝜆𝑘 ≤ 𝑥𝑛 for 𝑛 = 1, … , 𝑁; Σ𝑘=1
𝑆𝑊𝐵 𝑘 𝜆𝑘
1𝐾
𝐻(𝒙) 𝜆 ,…,𝜆≤ 𝜑𝑆𝑊𝐵; 𝜆𝑘 ≥ 0 for 𝑘 = 1, … , 𝐾}

(5)

Equation (5) constructs a piece-wise linear happiness function that tightly envelopes
observed data 𝑆𝑊𝐵 𝑘 and 𝒙𝑘 for 𝑘 = 1, … , 𝐾 under assumptions of convexity andconstant returns to scale. Thus, just as all the firms on the production frontier are
12

For example, in a simple case of 𝑁 = 2, the decomposition formula (4) is as follows.

𝑆𝑊𝐵𝑐
=
̅ 𝐻(𝒙
̅)
𝜃

𝐻(𝑥 𝑐 ,𝑥 𝑐 )

𝜃𝑐
̅
⏟
𝜃

𝐻(𝑥 𝑐 ,𝑥̅ )

1/2

1 2
× ( 1 2𝑐 ⋅)⏟𝐻(𝑥̅1 ,𝑥2) 𝐻(𝑥̅1,𝑥̅2 )𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦

𝐻(𝑥̅ ,𝑥 𝑐 )𝐻(𝑥 𝑐 ,𝑥 𝑐 )

1/2

1 2
× ( 1 2) ⋅)⏟𝐻(𝑥̅1 ,𝑥̅2 𝐻(𝑥1𝑐 ,𝑥̅2)

1𝑠𝑡 𝑓𝑎𝑐𝑡𝑜𝑟.

2𝑛𝑑 𝑓𝑎𝑐𝑡𝑜𝑟

In this case, the percentage differences in subjective well-being between country c and the reference
country are decomposed as follows.
(

̅ 𝐻(𝒙
̅)
𝑆𝑊𝐵𝑐 −𝜃
𝑆𝑊𝐵𝑐
)
×
100
≈
ln
(
) × 100 =
̅
̅ 𝐻(𝒙
)
̅
̅)
𝜃 𝐻(𝒙
𝜃

𝜃𝑐

1

𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (%)
1

𝐻(𝑥̅1 ,𝑥2𝑐 )𝐻(𝑥 𝑐 ,𝑥 𝑐 )𝐻(𝑥 𝑐 ,𝑥̅ )

1 2
1 2
ln
⏟ 𝜃̅ × 100 + (⏟2) ln (𝐻(𝑥̅1 ,𝑥2𝑐) ⋅ 𝐻(𝑥̅1 ,𝑥̅2 )) × 100 +1𝑠𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 (%)

𝐻(𝑥1𝑐 ,𝑥2𝑐 )( ) ln (⋅) × 100 .
𝐻(𝑥̅1 ,𝑥̅2 ) 𝐻(𝑥1𝑐 ,𝑥̅2 )⏟2
2𝑛𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 (%)

Furthermore, the differences in subjective well-being between country c and the reference country are
additively decomposed as follows.
𝑆𝑊𝐵𝑐𝜃𝑐̅ ̅) +̅) ≈ ln ̅ ) × 𝜃̅𝐻(𝒙̅) = ln
𝑆𝑊𝐵𝑐 − 𝜃̅ 𝐻(𝒙⏟ 𝜃̅ × 𝜃𝐻(𝒙⏟ 𝜃𝐻(𝒙̅𝐻(𝑥1𝑐 ,𝑥2𝑐 )𝑑𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 (0−10 𝑠𝑐𝑎𝑙𝑒)𝐻(𝑥1𝑐 ,𝑥̅2 )1𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 (0−10 𝑠𝑐𝑎𝑙𝑒)𝑐𝑐𝑐̅ ̅) + ( ) ln (𝐻(𝑥̅1 ,𝑥2) ⋅ 𝐻(𝑥1𝑐,𝑥2 )) × 𝜃̅𝐻(𝒙̅).
( ) ln (𝑐 ) ⋅ 𝐻(𝑥̅ ,𝑥̅ ) ) × 𝜃 𝐻(𝒙
2
𝐻(𝑥̅
,𝑥
𝐻(𝑥̅1 ,𝑥̅2 ) 𝐻(𝑥1 ,𝑥̅2 )
⏟
⏟2
1 2
1 2
1

1𝑠𝑡 𝑓𝑎𝑐𝑡𝑜𝑟 (0−10 𝑠𝑐𝑎𝑙𝑒)

2𝑛𝑑 𝑓𝑎𝑐𝑡𝑜𝑟 (0−10 𝑠𝑐𝑎𝑙𝑒)

13

Just as efficiency of a firm is captured by the ratio between its actual output and its maximum output,
sensitivity of happiness for a country is captured by the ratio between its actual subjective well-being
and maximum subjective well-being 𝑆𝑊𝐵/𝐻(𝒙).

9considered to be efficient, all countries on the constructed happiness function are
considered to be sensitive to socio-economic vector 𝒙. Their subjective well-being is
calculated by 𝐻(𝒙) from its socio-economic vector 𝒙. On the other hand, just as
technically inefficient firms operate below the production frontier, the average
subjective well-being of less sensitive countries 𝑆𝑊𝐵 is below 𝐻(𝒙) . The ratio
𝑆𝑊𝐵/𝐻(𝒙) , which ranges between 0 and 1, shows how far each country
characterized by 𝑆𝑊𝐵 and 𝒙 is located from the happiness function, which defines thesensitivity of subjective well-being. The sensitivity term 𝜃 𝑐 of country c is directly
calculated by 𝜃 𝑐 = 𝐻(𝒙𝑐 )/𝑆𝑊𝐵 𝑐 from Equation (5). Note that we can compute̅)/𝐻(𝒙̂)
𝑆𝑊𝐵/𝐻(𝒙) for any combination of 𝑆𝑊𝐵 and 𝒙. Thus, we can compute 𝐻(𝒙̅ and 𝒙̂ . Given any 𝑆𝑊𝐵 , Equation (5) derives 𝐻(𝒙̅)/𝑆𝑊𝐵 andfor any arbitrary 𝒙
̂)/𝑆𝑊𝐵 , which leads to (𝐻(𝒙̅)⁄𝑆𝑊𝐵 )⁄(𝐻(𝒙̂)/𝑆𝑊𝐵) = 𝐻(𝒙̅)/𝐻(𝒙̂) . The𝐻(𝒙Appendix intuitively explains how to estimate a happiness function based on DEA
and implement the decomposition of the difference in subjective well-being in a
simple case of two countries with one socio-economic variable.
3.

Data: OECD Better Life Index

Income per capita has long been used for comparing people’s well-being acrosscountries. However, it is now widely recognized that income provides only a partial
perspective on people’s lives. The search for alternative measures of well-being hasreceived growing attention. The Commission on the Measurement of Economic
Performance and Social Progress, appointed by former French president Nicholas
Sarkozy in 2008 (Stiglitz et al., 2009), discusses previous studies and unresolved
issues on measuring well-being.
Drawing upon the recommendations of the commission (Stiglitz et al., 2009), theOECD’s Better Life Initiative in 2011 released multiple indicators evaluating people’s
lives in each member country based on 11 socio-economic dimensions identified as
being essential to well-being.14 We call these indicators the OECD Better Life Index(BLI). The data was updated in 2012 and 2013 to include the latest data withadditional indicators. The present study adopts the latest version of data released in2013, which covers 36 countries, including Brazil and Russia.15
The BLI consists of two types of indicators: 11 well-being indicators and 24 headline
indicators. The 24 headline indicators evaluate the socio-economic status of people ineach country and include air quality, water quality, and students’ test scores. They areclassified under 11 more general socio-economic dimensions, such as environment
and education. Thus, each dimension of well-being might be characterized by multiple
headline indicators. Headline indicators are selected under a number of quality criteria,such as conceptual and policy relevance and comparability of concepts and survey
questions. Each well-being indicator is calculated from its underlying headline
indicators and is scaled over the range 0–1.16 On the other hand, a variety of data
The OECD launched the Better Life Initiative on its 50th anniversary, held under the theme ‘Better
Policies for Better Lives’. It aims to better understand what drives the well-being of people and whatcountries need to do to achieve greater progress for all (OECD, 2011).
15
There were 34 countries covered in 2011. The revised dataset released in 2012 includes 36 countries,
incorporating Brazil and Russia.
16
See OECD (2011) for a detailed description of the selection of headline indicators and theconstruction of well-being indicators.
14

10

constitutes 24 headline indicators and they are measured in different scales, such as
years, people, or dollars.
One of the 24 headline indicators is national average of reported life satisfaction,
based on the Cantril Ladder question by the Gallup World Poll on a 0–10 point scaleand categorized under a dimension of ‘subjective well-being’. The remaining 23
headline indicators are categorized under 10 dimensions of well-being. The reliabilityof reported life satisfaction as a measure of subjective well-being is widely accepted
and the fact that it is affected by a variety of factors associated with people’s lives iswidely reported (Frey and Stutzer, 2002; Frey, 2008). With regard to national
averages of reported life satisfaction as national averages of subjective well-being,
and the other 23 headline indicators as socio-economic variables, we explain the
differences in subjective well-being across countries by the differences in socioeconomic conditions. Recently, there has been an accumulation of empirical evidence
that individuals dislike income equality. 17 Thus, we add the Gini index as anadditional component of the socio-economic vector and categorize it under the income
dimension.
Section 2 considers each component of the socio-economic vector 𝒙 = (𝑥1 , … , 𝑥𝑁 )capturing a distinct aspect of the socio-economic status of people’s lives. In thissection, we adopt a more realistic framework in which multiple indicators characterizea distinct socio-economic status, as explained in Footnote 8. Thus, the socio-economic
vector we deal with in this section consists of 10 sub-vectors, such that 𝒙 =(𝒙1 , … , 𝒙10 ) , and each sub-vector corresponds to a distinct aspect of the socioeconomic status. The socio-economic vector comprises 23 headline indicators and theGini index, which are classified under 10 socio-economic dimensions, following theclassification of the BLI.18 Thus, while we adopt BLI headline indicators as well as
the Gini index, we explain the difference in subjective well-being by the difference inthe 10 essential socio-economic factors of well-being, rather than the detailed 23
underling indicators.
[Place Table 1 appropriately here]
Table 1 summarizes the 23 headline indicators of the BLI along with the Gini index.
In addition, Table 1 provides correspondence between them and the 10 dimensions ofwell-being. Among the 10 dimensions, the first three are categorized as material
living conditions and the remaining seven as quality of life. Dimensions of thematerial living standard show larger variation across countries and larger correlation
with subjective well-being than those of quality of life. However, larger correlation isobserved only for indicators with higher variation. It is not true for dimensions of thequality of life. Although headline indicators in the community and health dimensions
vary a little across countries, significant correlation relative to subjective well-being is
documented. On the other hand, headline indicators in the safety dimension differ
significantly among countries and are only slightly correlated with subjective wellbeing.
In addition to a national average of indicators, the OECD Better Life Initiative
releases headline indicators for different population groups within each country. Life
conditions for males and females are similar in many dimensions. Life conditions for
17
18

See Diener et al. (1995), Veenhoven and Ehrhardt (1995), and Ferrer-i-Carbonell and Ramos (2013).
We classify the Gini index under an income dimension.

11

females have a slightly higher subjective well-being than that for males on average byaround 0.1 in a 0–10 point scale. On the other hand, high income earners enjoy better
lives than low income earners in all dimensions except for the environment dimension,
which is characterized by water quality. 19 High income earners report significantly
higher subjective well-being than low income earners by around 1 in a 0–10 point
scale. Applying Equation (4), we can attribute the differences in subjective well-being
between different population groups in each country to the differences in life
circumstances surrounding these groups and sensitivity of happiness. For example, if
we are concerned with why females in Ireland have around a 50% higher subjective
well-being than males in the same country, we can set Ireland’s females as country 𝑐
and Ireland’s males as the reference country. We can then decompose the ratio of
female subjective well-being to male subjective well-being to the sensitivity term and10 socio-economic factors from Equation (4). However, we need to be aware of thefact that the coverage of indicators for different population groups is severely limited.
This limited coverage of indicators might produce bias in our results. We investigate
how serious the bias is by applying the decomposition formula to different selectionsof indicators.
All the headline indicators of the BLI cannot be used without any transformation in
our computation. Each component in the socio-economic vector needs to impact
positively on subjective well-being in the framework of DEA. Thus, we adopt a linear
monotone transformation for some headline indicators.20
4. Results and Discussion
Variation in subjective well-being across countries
Applying Equation (4) to the 24 indicators for total population, we can fully
decompose the differences in subjective well-being between each country in a sample
and the reference country into 10 socio-economic factors and a sensitivity term.
[Place Figure 1 appropriately here]
Figure 1 presents the full empirical results of the decomposition of the differences insubjective well-being between each country and the reference country. 21 It shows to
19

The BLI measure of water quality is the ratio of people who responded they are satisfied with water
quality in the Gallup World Poll. Since this indicates individual perceptions of water quality, a smaller
value of water quality for high income earners does not necessarily mean worse conditions of the waterin their environment. Most likely, high income earners set a higher standard for the water quality than
low income earners.
20
Linear monotone decreasing transformation is applied to seven indicators. The transformed
indicators 𝑥̃1.1 , 𝑥̃1.2 , 𝑥̃2.3 , 𝑥̃3.2 , 𝑥̃3.3 , 𝑥̃6.1 , 𝑥̃9.1 , 𝑥̃9.2 , and 𝑥̃10.1 are used as components of socio-economic
vector 𝒙. They are defined as follows.
a) 𝑥̃1.1 = 100 − 𝑥1.1 ; b) 𝑥̃1.2 = 100 − 𝑥1.2 ; c) 𝑥̃2.3 = 1 − 𝑥2.3 ; d) 𝑥̃3.2 = 100 − 𝑥3.2 ; e) 𝑥̃3.3 = 100 −World highest score of Sudan (=156.2)−𝑥6.1
𝑥3.3 ; f) 𝑥̃6.1 =; g) 𝑥̃9.1 = 100 − 𝑥9.1 ; h)(=156.2)−World
World highest score of Sudan

lowest score of Gabon (=6.7)

𝑥̃9.2 = 100 − 𝑥9.2 ; and i) 𝑥̃10.1 = 100 − 𝑥10.1 . While Stevenson and Wolfers (2008; 2013) use thenatural logarithm of individual income for explaining subjective well-being, we use monetary measuresof income and wealth without any transformation. Our non-parametric estimation of the happiness
function based on DEA allows us to capture the decreasing marginal utility of income and wealth on
subjective well-being.
21
We note that countries’ subjective well-being is lower than the reference country by 6.47% on̅).
average, reflecting the relatively high level of subjective well-being of the reference country 𝐻(𝒙12what extent each country’s well-being is larger than the reference country and where
the difference comes from by quantifying the contribution of each factor in the 0–10
point scale.22 Each country has a different reason for exceeding or falling below thelevel of the reference country. While the highest subjective well-being, that of
Switzerland, is attributed mainly to income and health factors, the second highest
subjective well-being, that of Norway, is attributed mainly to housing and work–lifebalance factors. The civic engagement factor plays the largest role for enhancing
subjective well-being in Mexico.
Positive impacts of some factors are often cancelled out by negative impacts of otherfactors. While Korea, Japan, and Russia are countries with low subjective well-being,
life conditions of their people are better in some aspects of socio-economic status.
However, significant effects of some other factors on subjective well-being, such as
health, environment, and civic engagement factors, cancel out these positive
influences.
A significant sensitivity term evidently explains differences in subjective well-being
in many countries. Insensitivity of happiness is found mostly in countries with low
subjective well-being. For example, the largest sensitivity terms are found in Greeceand Hungary, which are characterized by the lowest and the third lowest values ofsubjective well-being. This means that their subjective well-being is lower than
expected from their socio-economic status. While their life circumstances are poor,
the circumstances are not as bad as their subjective well-being indicates. Australia is
an exceptional country that has high subjective well-being and high socio-economic
status but shows lower sensitivity of happiness. It means that while their life
circumstance is good, their subjective well-being is not as high as expected.
[Place Table 2 appropriately here]
The decomposition results of each country in Figure 1 enable us to detect the main
drivers of the differences in subjective well-being among countries. The results
answer the question of why the people of some countries are happier or moremiserable than those of others. Table 2 summarizes country results. We note thatcountries’ subjective well-being is lower than the reference country by 0.393 on
average, reflecting the relatively high level of subjective well-being of the reference
country 𝐻(𝑥̅ ) . The average difference in subjective well-being and the averagecontribution of each factor depend on the selection of the reference country. Thus, it ismore appropriate to compare the standard deviations of each contributing factor rather
than their means in order to understand the relative contribution of factors ingenerating the distribution of subjective well-being.
Eight socio-economic factors certainly contribute to the variation in average
subjective well-being across countries. In particular, the health factor considerably
influences the differences in subjective well-being across countries with the largest
standard deviation of 0.374; the second largest socio-economic factor is civic
engagement at 0.272. On the other hand, safety and community factors have little
impact on the variation in subjective well-being across countries. Clearly, thesensitivity term also plays an important role in generating the differences in subjective

22

Figure A2 decomposes the differences in subjective well-being relative to the reference country on apercentage scale. Countries are ordered according to the value of their subjective well-being.

13

well-being with standard deviation of 0.362, which is comparable to the largest healthfactor.
In addition to investigating the separate role of each factor, we consider the overall
impact of material living standards as well as the quality of life. In summing up thestandard deviation of factors in each domain, material living standards add up to 0.395
and quality of life adds up to 1.067. Even allowing for the sensitivity term, the factors
categorized under quality of life explain more than half of the total variation in
subjective well-being across countries.
Differences in subjective well-being between different population groups within eachcountry
Although coverage of indicators is limited, indicators for different population groups
are available. By applying Equation (4) to them, we can investigate the reason why
subjective well-being of a group is higher than that of other groups in each country.
While females’ subjective well-being is on average higher than that of males, highincome earners’ subjective well-being is on average higher than low income earners’
subjective well-being. We explain the reasons for such gaps between different
population groups below.
[Place Figure 2 appropriately here]
By setting males in each country as the reference country, Equation (4) allows us todecompose the ratio of females’ subjective well-being to males’ subjective well-beingin each country.23 Figure 2 presents the full empirical results of the decomposition of
the differences in subjective well-being between females and males for each country.
It shows to what extent females’ well-being is larger than that of males and where thisdifference comes from by quantifying the contribution of each factor in the 0–10 point
scale.24 The reasons why females are happier than males are similar across countries.
While work–life balance and safety factors raise females’ subjective well-being, jobs
and health factors lower it in most countries. Finland and the US are exceptional inthat the health factor boosts females’ subjective well-being.
Clearly, the sensitivity term plays a significant role in expanding the differences insubjective well-being in many countries. Significant sensitivity terms are foundespecially among countries with larger gender gaps in subjective well-being.
Differences in subjective well-being between males and females are attributed largely
to differences in sensitivity of happiness in such countries. Poland is an extreme
example. Although females are surrounded by worse life circumstances in every
aspect in Poland, females are much happier than males by 5.129%. It is simply
attributed to the fact that females’ happiness are much more sensitive than males’.
[Place Table 3 appropriately here]

23

In the previous analysis on the distribution of national average subjective well-being, the samereference country is used for every country. However, in the decomposition of the differences insubjective well-being between females and males, a different reference is used for each country. In
addition, we note that the happiness function is constructed from 72 data points, which comprise males
and females in 36 countries.
24
Figure A3 decomposes the differences in subjective well-being of females relative to males on apercentage scale. Countries are ordered according to the differences in subjective well-being between
females and males.

14

The decomposition results of each country in Figure 2 enable us to detect the main
drivers of the differences in subjective well-being between females and males. Thisanswers the question of why females are happier than males on average. Table 3
summarizes country results. We note that females’ subjective well-being is lower thanmales’ by 0.085 on average. It is more appropriate to compare the mean of eachcontributing factor rather than the standard deviation in order to understand therelative contribution of factors in generating the differences of subjective well-being
between females and males.
Among eight socio-economic factors, half boost females’ subjective well-being andthe remaining half lower it. In particular, work–life balance and safety factors
considerably raise females’ subjective well-being and display higher averages of
0.098 and 0.037, respectively. On the other hand, the jobs factor significantly lowers
females’ subjective well-being with an average of –0.098. Clearly, the sensitivity term
also plays a key role in raising females’ subjective well-being with an average of
0.077, which is comparable to the largest work–life balance factor.
[Place Figure 3 appropriately here]
In a similar manner, setting low income earners in each country as the reference
country, Equation (4) allows us to decompose the ratio of high income earners’
subjective well-being to low income earners’ subjective well-being in each country.25
Figure 3 presents the full empirical results of the decomposition of the differences insubjective well-being between high and low income earners for each country. It shows
to what extent high income earners’ well-being is larger than low income earners’ andwhere this difference comes from by quantifying the contribution of each factor in the
0–10 point scale. 26 Almost all socio-economic factors boost high income earners’
subjective well-being, except for the environment factor. Especially, high income
earners’ subjective well-being in Russia is sharply lowered by the environment factor.
On the other hand, the sensitivity term is found to be negative in many countries.
However, it becomes largely negative in some countries such as Portugal, Greece andBelgium.
[Place Table 4 appropriately here]
The decomposition results of each country in Figure 3 enable us to detect the main
drivers of the differences in subjective well-being between high and low income
earners. The results answer the question of why high income earners are happier than
low income earners on average. Table 4 summarizes country results. We note thathigh income earners’ subjective well-being is higher than low income earners’ by 0.84
on average. We compare the mean of each contributing factor rather than the standard
deviation in order to understand the relative contribution of factors in generating thedifferences of subjective well-being between high and low income earners.

25

In the previous analysis on the distribution of national average subjective well-being, the samereference country is used for every country. However, in the decomposition of the differences insubjective well-being between high and low income earners, a different reference is used for eachcountry. In addition, we note that the happiness function is constructed from 72 data points, which
comprise high and low income earners in 36 countries.
26
Figure A4 decomposes the differences in subjective well-being of high income earners relative to
low income earners on a percentage scale. Countries are ordered according to the differences insubjective well-being between high and low income earners.

15

Most socio-economic factors raise high income earners’ subjective well-being, except
for the environment factor. The environment factor lowers subjective well-being withan average of – 0.07. Since the reported satisfaction of water quality only constitutes
the environment factor for high and low income earners, a negative environment
factor does not mean that high income earners live in worse environmental condition
but rather that high income earners are more dissatisfied with the same water quality
than low income earners. However, the sensitivity term also plays a key role forlowering high income earners’ subjective well-being with an average of –0.112. This
negative sensitivity term means that high income earners are supposed to be happierbased on their life circumstances. However, their subjective well-being is not as highas expected given their socio-economic status. This leads to their lower sensitivity ofhappiness compared to low income earners.
Discussion: Selection of countries and variables
We perform the following four kinds of sensitivity analysis to check to what extent
our results are driven by the coverage of countries and the selection of variables. Allanalyses are conducted for national average data only. By applying Equation (4) indifferent cases, we decompose the differences of subjective well-being between eachcountry and the reference country.
Case A: Greece is excluded
Case B: Inequality is excluded
Case C: Variables for explaining differences between females and males
Case D: Variables for explaining differences between high and low income
earners

The first two analyses investigate the validity of the results on the decomposition of
the variation in average subjective well-being across countries. The last two analyses
investigate the validity of the results of the decomposition of the differences of
average subjective well-being between female and male groups, and between highincome and low income groups.
[Place Table 5 appropriately here]
Table 5 reports average and standard deviation of contributing factors in the 0–10
point scale. Figure 1 shows that Greece’s sensitivity term of happiness is considerablylarge at –20.78% relative to the reference country’s subjective well-being. This isnearly double Hungary’s value of –13.69%, which is the second largest in absolutevalue. Thus, our first analysis (Case A) implements Equation (4) and excludes Greece.
Comparing the standard deviation of contributing factors between Case A and thereference case, we see that the role of the sensitivity term is underestimated when
Greece is excluded. However, this hardly affects the evaluations of the othercontributing factors. Even though Greece can be considered as an outlier, the relative
importance of contributing factors is unaffected, other than the sensitivity term.
The Gini index is the only indicator measuring the distribution of socio-economic
status within countries, unlike other variables, which measure countries’ averagesocio-economic status. The second analysis (Case B) implements Equation (4) andexcludes the Gini index to see the extent to which dealing only with countries’
average values of socio-economic status would bias our decomposition results. By
comparing the standard deviation of contributing factors between Case B and thereference case, we see that the exclusion of the Gini index hardly changes
measurement of contributing factors including the income factor. It might partly

16justify the manner that we only focus on national average of socio-economic status
omitting its distribution in all contributing factors other than income factor.
As explained in the previous section, the coverage of indicators for different
population groups is severely limited compared to that for total population. Thus, thethird and fourth analyses implement Equation (4) using the data of countries’ average
subjective well-being for total population, but adopt the same set of indicators as
those for male and female groups (Case C) and high and low income earners (Case D).
Bias in estimation is rather small, except for the following factors. Education andcivic engagement are overestimated, while environment and health are underestimatedin both cases. In addition, in Case D, the income factor, which captures only
disposable income, is certainly overestimated. Thus, it is likely that the largest role,that of the work–life balance factor for driving the differences in subjective wellbeing between females and males and that of the income factor for driving thedifferences in subjective well-being between high and low income earners, is rather
discounted if we use the full set of indicators. However, since the overall influence ofcontributing factors are well differentiated, the limited set of indicators would not
make much difference on the relative contributions of socio-economic factors andsensitivity term.
5. Conclusion
People’s well-being is a multi-dimensional concept associated with a variety of socioeconomic conditions affecting their lives. The OECD recently specified 11 well-being
dimensions as being essential to people’s well-being and released 24 underlying
indicators. Utilizing these underlying indicators with the Gini index, we explored therelationship between subjective well-being and socio-economic status related topeople’s overall well-being. Previous studies have estimated a happiness function byregression analysis using a select number of variables. However, in cases in which acomprehensive set of indicators such as BLI appear, multi-collinearity observed
among explanatory variables makes it difficult to derive statistically significant results.
Another problem is that they have often imposed restricted inter-relation amongexplanatory variables. On the other hand, our study adopted DEA for estimating thehappiness function, which is a deterministic method requiring no specification offunctional form for the happiness function. The application of DEA to happinessresearch allowed us to incorporate highly correlated indicators into explanatory
variables of the happiness function and to deal with any interaction between factorsthat contribute to people’s subjective well-being. In addition, using DEA, we werealso able to specify sensitivity of happiness for each country, which reflects all factors
uncovered by the socio-economic variables.
Based on the estimated happiness function, we decomposed the variation of countries’
average subjective well-being into 10 socio-economic factors and a sensitivity term.
The health factor and sensitivity term made the largest contributions. The overall
contribution of factors classified under quality of life was much larger than the overall
contribution of factors classified under material living standard and explain more thanhalf of the total variation in subjective well-being across countries. In addition, we
decomposed the differences of subjective well-being between different populationgroups in each country. On average, females’ subjective well-being was slightly
higher than males’. However, this reflected the positive influence of work–lifebalance and safety factors and the negative influence of the jobs factor. Females

17

showed greater sensitivity of happiness. On the other hand, subjective well-being ofhigh income earners was considerably higher than that of low income earners. The life
circumstances of high income earners were better in many aspects of socio-economic
status, which raised their subjective well-being. However, sensitivity of happiness inhigh income earners was smaller than in low income earners. While females were
happier than expected given their socio-economic situation, high income earners were
happier than low income earners but not as happy as would be expected from their
better socio-economic situations.
This study found that the sensitivity term plays a significant role in generating
differences in average subjective well-being across countries as well as differences inaverage subjective well-being between different population groups; it is an
overarching concept which reflects all factors, not only socio-economic aspects. A
shortcoming of our study is that it cannot explain the reason why sensitivity ofhappiness differs across countries. By identifying and quantifying uncovered factors
behind the differences in sensitivity of happiness, we could enrich the happiness
function with more explanatory variables. This revealed the determinants ofsensitivity of happiness and enabled us to provide more detailed decomposition of thedifferences of subjective well-being. We leave this for future research.

18

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20

Appendix: Estimation Procedure of the Happiness Function and Implementationof Decomposition
We graphically illustrate how to implement the decomposition of the differences insubjective well-being based on Equations (4) and (5) using a simple case of a single
socio-economic variable 𝑥 and two observations: countries 𝐴 and 𝐵. Points 𝐴 and 𝐵 in
Figure 4 correspond to the observation of each country. 𝑥 𝐴 and 𝑥 𝐵 indicate a socioeconomic variable for each country, respectively; 𝑆𝑊𝐵 𝐴 and 𝑆𝑊𝐵 𝐵 indicate
subjective well-being for each country, respectively.
[Place Figure 4 appropriately here]
Since the slope of 0𝐵 is higher than that of 0𝐴, country 𝐵 is considered as being moresensitive to its socio-economic variable than country 𝐴. Thus, Equation (5) constructs
a happiness function 𝐻(𝑥) by the ray from the origin through point 𝐵. 𝐻(𝑥) depicts
the hypothetical value of subjective well-being that sensitive country 𝐵 would draw
from any given socio-economic variable 𝑥. Thus, the ratio of the actual subjective
well-being of each country to 𝐻(𝑥) indicates each country’s sensitivity of happiness 𝜃.
The sensitivity of country 𝐵 is evaluated at 1, such that 𝜃 𝐵 = 𝐻(𝑥 𝐵 )/𝑆𝑊𝐵 𝐵 = 1. On
the other hand, 𝐻(𝑥 𝐴 ) is smaller than 𝑆𝑊𝐵 𝐴 and, thus, point 𝐴 is below the happiness
function. Therefore, country 𝐴 ’s sensitivity is below 1, such that 𝜃 𝐴 =𝑆𝑊𝐵 𝐴 ⁄𝐻(𝑥 𝐴 ) < 1.
Point 𝐶 indicates the hypothetical reference country for implementing Equation (4).
Both the socio-economic variable and the sensitivity term for country 𝐶 are theaverages of the values of the two countries, such that 𝑥̅ = (𝑥 𝐴 + 𝑥 𝐵 )/2 and 𝜃̅ =(𝜃 𝐴 + 𝜃 𝐵 )/2. Thus, subjective well-being of country 𝐶 is calculated by 𝑆𝑊𝐵 𝐶 =𝜃̅𝐻 (𝑥̅ ).
We apply Equation (4) to the data of three countries to investigate the reasons why
country 𝐴 is less happy than the reference county 𝐶 and country 𝐵 is happier thanreference country 𝐶 in the ratio form as follows.
𝑆𝑊𝐵𝐴

=
𝑆𝑊𝐵𝐶

𝜃𝐴
𝜃⏟𝐶
𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(𝑟𝑎𝑡𝑖𝑜)

𝑆𝑊𝐵𝐵

=
𝑆𝑊𝐵𝐶

𝜃𝐵
𝜃⏟𝐶
𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(𝑟𝑎𝑡𝑖𝑜)

𝐻(𝑥 𝐴 )

×

,

𝐶)
𝐻(𝑥
⏟

(A1)

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(𝑟𝑎𝑡𝑖𝑜)
𝐻(𝑥 𝐵 )

×

.

𝐶)
𝐻(𝑥
⏟

(A2)

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(𝑟𝑎𝑡𝑖𝑜)

Taking the natural logarithm of Equations (A1) and (A2) at both sides, we canadditively decompose the differences in subjective well-being on a percentage scale
into sensitivity term and socio-economic factor as follows.
(

𝑆𝑊𝐵𝐴 −𝑆𝑊𝐵𝐶
𝑆𝑊𝐵𝐶

𝑆𝑊𝐵𝐴

𝜃𝐴

) × 100 ≈ ln (𝑆𝑊𝐵𝐶 ) × 100 = ln
⏟ (𝜃𝐶 ) × 100 +𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(%)
𝐻(𝑥 𝐴 )ln (𝐻(𝑥 𝐶 )) × 100
⏟

(A3)

,

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(%)(

𝑆𝑊𝐵𝐵 −𝑆𝑊𝐵𝐶
𝑆𝑊𝐵𝐶

𝑆𝑊𝐵𝐵

𝜃𝐵

) × 100 ≈ ln (𝑆𝑊𝐵𝐶 ) × 100 = ln
⏟ (𝜃𝐶 ) × 100 +𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(%)

21

(A4)

𝐻(𝑥 𝐵 )ln ( 𝐶 ) ) × 100
⏟ 𝐻(𝑥

.

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(%)

Moreover, by multiplying Equations (A3) and (A4) by 𝑆𝑊𝐵 𝐶 , we can additively
decompose the differences in subjective well-being in the original 0–10 point scale
into differences in sensitivity term and socio-economic factor as follows:
𝑆𝑊𝐵𝐴

𝜃𝐴

𝐶
𝑆𝑊𝐵 𝐴 − 𝑆𝑊𝐵 𝐶 ≈ ln (𝑆𝑊𝐵𝐶 ) × 𝑆𝑊𝐵 𝐶 = ln
⏟ ( 𝜃𝐶 ) × 𝑆𝑊𝐵 +𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(0−10 𝑠𝑐𝑎𝑙𝑒)𝐻(𝑥 𝐴 )ln (𝐻(𝑥 𝐶 )) × 𝑆𝑊𝐵 𝐶
⏟

(A5)

,

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(0−10 𝑠𝑐𝑎𝑙𝑒)
𝑆𝑊𝐵𝐵
𝜃𝐵
𝐶
𝐶

𝐶
𝑆𝑊𝐵 𝐵 − 𝑆𝑊𝐵 ≈ ln (𝑆𝑊𝐵𝐶 ) × 𝑆𝑊𝐵 = ln
⏟ ( 𝜃𝐶 ) × 𝑆𝑊𝐵 +𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(0−10 𝑠𝑐𝑎𝑙𝑒)𝐻(𝑥 𝐵 )ln (𝐻(𝑥 𝐶 )) × 𝑆𝑊𝐵 𝐶
⏟

.

(A6)

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(0−10 𝑠𝑐𝑎𝑙𝑒)As explained in Section 4, we can also apply Equation (4) to explain the differences insubjective well-being between different population groups. Suppose that country 𝐴 is
the male group and country 𝐵 is the female group in the same country. If we set
country 𝐵 as the reference rather than country 𝐶, Equation (4) allows us to decompose
the differences in subjective well-being between the male and female groups into
multiple factors as follows.
𝑆𝑊𝐵𝐴

=
𝑆𝑊𝐵𝐵
(

𝜃𝐴
𝜃⏟𝐵
𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(𝑟𝑎𝑡𝑖𝑜)

𝑆𝑊𝐵𝐴 −𝑆𝑊𝐵𝐵
𝑆𝑊𝐵𝐵

𝐻(𝑥 𝐴 )

×

,

𝐵)
𝐻(𝑥
⏟

(A7)

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(𝑟𝑎𝑡𝑖𝑜)
𝑆𝑊𝐵𝐴

𝜃𝐴

) × 100 ≈ ln (𝑆𝑊𝐵𝐵 ) × 100 = ln
⏟ (𝜃𝐵 ) × 100 +𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(%)
𝐻(𝑥 𝐴 )ln (𝐻(𝑥 𝐵 )) × 100
⏟

(A8)

,

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(%)
𝑆𝑊𝐵𝐴

𝜃𝐴

𝐵
𝑆𝑊𝐵 𝐴 − 𝑆𝑊𝐵 𝐵 ≈ ln (𝑆𝑊𝐵𝐵 ) × 𝑆𝑊𝐵 𝐵 = ln
⏟ (𝜃𝐵 ) × 𝑆𝑊𝐵 +𝑠𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦(0−10 𝑠𝑐𝑎𝑙𝑒)𝐻(𝑥 𝐴 )ln (𝐻(𝑥 𝐵 )) × 𝑆𝑊𝐵
⏟

𝐵

(A9)

.

𝑠𝑜𝑐𝑖𝑜−𝑒𝑐𝑜𝑛𝑜𝑚𝑖𝑐 𝑓𝑎𝑐𝑡𝑜𝑟(0−10 𝑠𝑐𝑎𝑙𝑒)

Equations (A7), (A8) and (A9) help us to investigate the reasons why females’
subjective well-being is higher than males’ subjective well-being. Similarly, we canalso explain the reasons why subjective well-being of high income earners is higher
than that of low income earners.

22

Table 1: Summary Statistics of Variables and their Correlation with Subjective Well-being
Variables
𝑆𝑊𝐵

Unit

Mean
Total

Male

FemaleSubjective well-being
Cantril ladder of life satisfaction

High

Low

Std. Dev.
Total

Coeff. Vari.a Correlation b
Total
Total

[0, 10]
6.6
6.5
6.6
7.1
6.2
0.9
0.13
𝒙1
Housing
𝑥1.1
Dwellings without basic facilities
percent
2.3
3.2
1.39
𝑥1.2
Housing expenditure
percent
20.8
3.0
0.14
𝑥1.3
Rooms per person
persons
1.6
0.4
0.27
𝒙2
Income𝑥 2.1
Household net adjusted disposable income
current PPP US$
22383
43877
8778
6943
0.31
𝑥 2.2
Household net financial wealthcurrent PPP US$
36710
27426
0.75
𝑥 2.3
[0,1]
0.3
0.1
0.22
Gini coefficient for disposable income c
𝒙3
Jobs
𝑥 3.1
Employment rate
percent
66.1
72.5
59.8
81.6
45.4
7.2
0.11
𝑥 3.2
Job security
percent
10.6
10.6
10.5
4.8
0.45
𝑥 3.3
Long-term unemployment rate
percent
3.1
3.2
3.1
1.8
5.5
2.6
0.82
𝑥 3.
Personal earnings
current US$
33402
36192
29527
43137
20140
12371
0.37
𝒙
Community
𝑥 .1
Quality of support network
percent
89.6
89.0
90.2
92.9
84.5
5.7
0.06
𝒙5
Education
𝑥 5.1
Educational attainment
percent
74.0
74.8
73.3
17.0
0.23
𝑥 5.2
Student skills
standarized score
493.3
488.7
497.9
544.4
446.2
30.3
0.06
𝑥 5.3
Years in education
years
17.4
17.1
17.8
1.2
0.07
𝒙6
Environment
𝑥 6.1
Air pollution
20.8
9.3
0.45
micrograms per m2
𝑥 6.2 Water quality
percent
83.0
84.1
82.3
83.7
84.8
10.9
0.13
𝒙7
Civic engagement
𝑥 7.1 Consultation on rule-making
standarized score
7.1
2.7
0.38
𝑥 7.2 Voter turnout
percent
71.9
72.3
71.6
78.3
67.8
11.9
0.17
𝒙
Health𝑥 .1
Life expectancy
years
79.6
76.7
82.4
3.1
0.04
𝑥 .2 Self-reported healthpercent
67.7
70.5
65.4
78.4
59.6
14.5
0.21
𝒙9
Safety𝑥 9.1 Assault rate
percent
4.1
4.5
3.7
2.3
0.57
𝑥 9.2 Homicide ratecases per 100000
3.0
4.9
1.2
5.1
1.72
𝒙10
Work-Life Balance𝑥10.1 Employees working very long hours
percent
9.9
13.3
5.7
9.9
0.99
𝑥10.2 Time devoted to leisure and personal care
hours
14.6
14.8
14.4
0.8
0.06
a) The coefficient of variation is standard deviation divided by average. It ranges from 0 to 1.
b) The correlation between each indicator and subjective well-being; both refer to national average of total populations.
c) The Gini coefficient of disposable income is a part of the OECD Income Distribution database. All other variables are underlying data for the OECD Better Life Index.

23

1.00
-0.47
0.10
0.60
0.56
0.42
0.34
0.71
0.04
-0.58
0.58
0.54
0.19
0.15
0.12
-0.19
0.65
0.24
0.36
0.43
0.63
0.01
0.01
-0.16
0.23

Table 2: Decomposition of Differences in Subjective Well-Being Relative to Reference Country (0–10 point scale)
Subjective
well-being

Sensitivity

Housing

IncomeMean-0.393-0.010-0.021-0.026
Std. Dev.
0.866
0.362
0.103
0.165
Max
0.733
0.226
0.220
0.517
Min
-2.489-1.295-0.356-0.360
Zero contributionsa
057a) Number of counties in which each factor has no influence on subjective well-being.

Jobs

Community-0.0370.126
0.115-0.411
3

0.000
0.002
0.000
-0.01534

Education-0.0250.124
0.071-0.72319

Environment

Civic
engagement-0.0230.102
0.046-0.53611-0.1300.272
0.450-0.9840

Health
-0.1020.374
0.285-1.477
2

Table 3: Decomposition of Differences in Subjective Well-being between Females and Males (0–10 point scale)
Subjective
well-being

Sensitivity

Jobs

Community

Mean
0.085
0.077
-0.0980.015
Std. Dev.
0.202
0.181
0.225
0.037
Max
0.482
0.776
0.331
0.156
Min
-0.308-0.122-1.0490.000
Zero contributionsa
12524a) Number of counties in which each factor has no influence on subjective well-being.

Education

Environment

Civic
engagement-0.0030.012
0.024-0.04723-0.0010.073
0.310-0.239
23

0.019
0.061
0.235
-0.18216

Health-0.0580.137
0.116-0.606
7

Safety
0.037
0.125
0.562
-0.00628

Table 4: Decomposition of Differences in Subjective Well-being between High and Low Income Earners (0–10 point scale)
Subjective
well-being

Sensitivity

Income

Jobs

Mean
0.840
-0.1120.353
0.165
Std. Dev.
0.499
0.476
0.273
0.255
Max
1.889
0.798
1.287
1.261
Min
-0.203-1.4070.041
0.000
Zero contributionsa
501a) Number of counties in which each factor has no influence on subjective well-being.

Community

Education

0.069
0.222
1.301
-0.078
13

0.086
0.117
0.424
0.000
10

24

Environment

Civic
engagement-0.0700.269
0.131-1.526
4

0.077
0.119
0.456
0.000
16

Health
0.272
0.235
0.807
0.000
3

Work-Life
Balance
0.098
0.179
0.666
-0.026
16

Safety
0.002
0.021
0.065
-0.06328

Work-Life
Balance-0.0200.172
0.166-0.95419

Table 5: Comparison of Contribution Factors under Difference Cases
Sensitivity
Housing
IncomeJobs
Community
Education
Environment
Civic engagement
HealthSafetyWork-Life Balance

A: Greece excluded
Std. Dev.
0.29470.10050.16680.12550.00360.12760.1026
0.27870.38370.02110.1804

B: Inequality excluded
Std. Dev.
0.36200.10330.16490.12740.00250.12420.1021
0.27230.37370.02060.1715

C: Male and female
Average-0.0138

D: High and low income
Average-0.0156-0.0189-0.0397-0.0082-0.0011-0.0531-0.0430-0.1116-0.0286-0.0022-0.0140-0.0437-0.0412-0.1308
0.0021-0.0266

25

Reference
Average
Std. Dev.
-0.0097
0.3619-0.02140.1033-0.0258
0.1652-0.0374
0.1262-0.0005
0.0025-0.0249
0.1242-0.0234
0.1021-0.1298
0.2721-0.10190.37380.00190.0206-0.02010.1715

Switzerland
NorwaySwedenIcelandNetherlands
Denmark
Finland
CanadaAustria
MexicoNew ZealandAustralia
IsraelUnited States
Luxembourg
Ireland
Belgium
United Kingdom
Germany
BrazilFranceChile
Spain
Czech Republic
Slovenia
Korea
Japan
Slovak Republic
PolandItaly
Russian Federation
Estonia
TurkeyGreecePortugal
Hungary
-3

-2.5

-2

Sensitivity
Jobs
Environment
Safety

-1.5

-1

-0.5

0

Housing
Community
Civic engagement
Work_Life_Balance

0.5

1

1.5
2
(0-to-10 scale)

IncomeEducation
HealthHappiness

Figure 1: Explaining Differences in Subjective Well-Being Relative to Reference Country (010 point scale)26

IrelandFinland
Korea
Japan
United States
PolandEstonia
Iceland
CanadaAustria
Belgium
Spain
TurkeyPortugal
Switzerland
NorwaySwedenNetherlands
New ZealandUnited Kingdom
BrazilGreeceRussian Federation
MexicoLuxembourg
Germany
Denmark
Australia
FranceCzech Republic
Slovak Republic
IsraelHungary
Chile
Slovenia
Italy
-1.5

-1

Sensitivity
Community
Civic engagement
Work_Life_Balance

-0.5

0

Housing
Education
HealthHappiness

0.5

1

1.5
(0-to-10 scale)

Jobs
Environment
SafetyFigure 2: Explaining Differences in Subjective Well-being between Females and Males (0–10
point scale)27

Portugal
Korea
GreeceSpain
Hungary
MexicoBelgium
Chile
Slovenia
Italy
Luxembourg
United States
FranceGermany
TurkeyIsraelSlovak Republic
Finland
AustriaJapan
Netherlands
United Kingdom
BrazilPolandEstonia
Switzerland
Denmark
Russian Federation
NorwayIceland
Australia
Czech Republic
CanadaNew ZealandSwedenIreland
-2

-1.5

-1

-0.5

0

0.5

1

1.5

2
2.5
(0-to-10 scale)Sensitivity

IncomeJobs

Community

Education

Environment

Civic engagement

HealthHappiness

Figure 3: Explaining Differences in Subjective Well-being between High and Low Income
Earners (0–10 point scale)

28

𝑆𝑊𝐵

𝑆𝑊𝐵 = 𝐻(𝑥)

𝑆𝑊 𝐵𝐵

c

𝐵

𝐻 𝑥̅
𝐻 𝑥̅ /𝜃̅
𝐻 𝑥𝐴

𝑆𝑊𝐵𝐴
0

v

c

𝑥𝐴

𝐶

𝐴

𝑥̅

Figure 4: Estimating Sensitivity Term and Happiness Function

29

𝑥𝐵

𝑥

Table A1: Correlation among Variables for Total Population
1

2

3

4

5

6

7

8

9

10

11

12

0.54360.1916 0.1526 0.1238

13

14

15

16

17

18

19

20

21

22

23

24

Subjective well-being
0 Life satisfaction-0.466 0.1015 0.5971

Housing
1 Dwellings without basic facilities
2 Housing expenditure
3 Rooms per person

1.0000 -0.2084 -0.5282 -0.6853 -0.3838 0.5737 -0.4633 0.5116 0.0167 -0.6444 -0.6633 -0.2682 -0.4050 -0.3724 0.4908 -0.5460 -0.3857 0.0572 -0.5400 -0.4625 0.3639 0.3536 0.6374 -0.6686-0.2084 1.0000 0.1173 0.1419 0.1018 -0.3641 0.0078 -0.3199 0.1177 0.0767 0.2072 0.1187 0.1290 0.1562 -0.1070 0.3051 0.2080 -0.0736 0.3245 0.3649 -0.2007 -0.3089 -0.0733 0.0577-0.5282 0.1173 1.0000 0.7562 0.5708 -0.3309 0.5734 -0.2040 -0.1803 0.7620 0.6231 0.2223 0.5631 0.3561 -0.4337 0.6581 0.3257 0.2908 0.6019 0.5449 -0.3739 -0.3411 -0.3037 0.4644

Income4 Household net adjusted disposable income
5 Household net financial wealth
6 Gini coefficient for disposable income-0.6853 0.1419 0.7562 1.0000 0.7428 -0.4744 0.4938 -0.3153 -0.2403 0.9249 0.5796 0.3217 0.4941 0.1929 -0.4054 0.6061 0.2730 0.2518 0.6557 0.5022 -0.4280 -0.4512 -0.3351 0.4484-0.3838 0.1018 0.5708 0.7428 1.0000 -0.1573 0.3859 -0.1919 -0.2881 0.7284 0.3701 0.2641 0.3552 0.0159 -0.1412 0.3660 0.0907 0.0928 0.5067 0.3449 -0.2270 -0.2763 -0.0836 0.1830
0.5737 -0.3641 -0.3309 -0.4744 -0.1573 1.0000 -0.3187 0.3926 -0.0182 -0.5053 -0.5505 -0.4893 -0.7218 -0.5617 0.4611 -0.5540 -0.3242 0.0240 -0.5119 -0.0969 0.5225 0.7138 0.4605 -0.4975

Jobs
7 Employment rate
8 Job security
9 Long-term unemployment rate
10 Personal earnings-0.4633 0.0078 0.5734 0.4938 0.3859 -0.3187 1.0000 -0.1405 -0.5425 0.4943 0.6387 0.4028 0.4224 0.4215 -0.3838 0.7047 0.2101 0.1353 0.3805 0.2453 -0.2922 -0.1407 -0.3186 0.3712
0.5116 -0.3199 -0.2040 -0.3153 -0.1919 0.3926 -0.1405 1.0000 -0.4146 -0.2545 -0.5975 -0.3993 -0.1530 -0.1952 0.3448 -0.2134 0.1194 0.2519 -0.2611 -0.1471 0.2760 0.4226 0.7193 -0.5434
0.0167 0.1177 -0.1803 -0.2403 -0.2881 -0.0182 -0.5425 -0.4146 1.0000 -0.2069 -0.0380 -0.1478 -0.1150 0.0447 -0.0975 -0.2805 -0.2003 -0.3257 -0.1033 -0.0785 -0.0191 -0.1458 -0.2712 0.1987-0.6444 0.0767 0.7620 0.9249 0.7284 -0.5053 0.4943 -0.2545 -0.2069 1.0000 0.6144 0.3217 0.5655 0.2784 -0.3336 0.6119 0.3109 0.2970 0.7001 0.4896 -0.4779 -0.5299 -0.3076 0.4906

Community
11 Quality of support network-0.6633 0.2072 0.6231

0.5796 0.3701 -0.5505

0.6387 -0.5975 -0.0380 0.6144

1.00000.4733 0.4553 0.4536 -0.5839 0.6431

0.2114 0.0285

0.4750 0.4363 -0.4790 -0.4520 -0.6229 0.6376

Education
12 Educational attainment
13 Student skills
14 Years in education-0.2682 0.1187 0.2223-0.4050 0.1290 0.5631-0.3724 0.1562 0.3561

0.3217 0.2641 -0.4893
0.4941 0.3552 -0.7218
0.1929 0.0159 -0.5617

0.4028 -0.3993 -0.1478 0.3217
0.4224 -0.1530 -0.1150 0.5655
0.4215 -0.1952 0.0447 0.2784

0.47330.45530.45361.0000 0.5616 0.3121 -0.3179 0.2506
0.5616 1.0000 0.6329 -0.3596 0.4875
0.3121 0.6329 1.0000 -0.2445 0.4466

0.1626 -0.2170
0.4498 -0.0913
0.3325 0.0176

0.1351 -0.0637 -0.5926 -0.4331 -0.4156 0.2956
0.5500 0.0016 -0.7138 -0.7019 -0.2422 0.3804
0.4142 0.0118 -0.4644 -0.4798 -0.3414 0.4401

Environment
15 Air pollution
16 Water quality

0.4908 -0.1070 -0.4337 -0.4054 -0.1412 0.4611 -0.3838 0.3448 -0.0975 -0.3336 -0.5839 -0.3179 -0.3596 -0.2445 1.0000 -0.3106 -0.1461 0.0687 -0.1230 -0.2479 0.3679 0.1554 0.4977 -0.4776-0.5460 0.3051 0.6581 0.6061 0.3660 -0.5540 0.7047 -0.2134 -0.2805 0.6119 0.6431 0.2506 0.4875 0.4466 -0.3106 1.0000 0.5161 0.1343 0.6282 0.3933 -0.2852 -0.3677 -0.2569 0.3625

Civic engagement
17 Consultation on rule-making
18 Voter turnout-0.3857 0.2080 0.3257
0.0572 -0.0736 0.2908

0.2730 0.0907 -0.3242
0.2518 0.0928 0.0240

0.2101 0.1194 -0.2003 0.3109
0.1353 0.2519 -0.3257 0.2970

0.2114 0.1626 0.4498 0.3325 -0.1461 0.5161 1.0000 -0.2056
0.0285 -0.2170 -0.0913 0.0176 0.0687 0.1343 -0.2056 1.0000

0.2657 0.2546 -0.4149 -0.1803
0.2395 0.2016 0.1158 -0.0531

Health19 Life expectancy
20 Self-reported health-0.5400 0.3245 0.6019-0.4625 0.3649 0.5449

0.6557 0.5067 -0.5119
0.5022 0.3449 -0.0969

0.3805 -0.2611 -0.1033 0.7001
0.2453 -0.1471 -0.0785 0.4896

0.4750 0.1351 0.5500 0.4142 -0.1230 0.6282
0.4363 -0.0637 0.0016 0.0118 -0.2479 0.3933

1.0000 0.3775 -0.3024 -0.6603 -0.1356 0.3685
0.3775 1.0000 -0.0339 -0.1214 -0.2273 0.1367

Safety21 Assault rate
22 Homicide rate

0.3639 -0.2007 -0.3739 -0.4280 -0.2270 0.5225 -0.2922 0.2760 -0.0191 -0.4779 -0.4790 -0.5926 -0.7138 -0.4644
0.3536 -0.3089 -0.3411 -0.4512 -0.2763 0.7138 -0.1407 0.4226 -0.1458 -0.5299 -0.4520 -0.4331 -0.7019 -0.4798

Work-Life Balance
23 Employees working very long hours
24 Time devoted to leisure and personal care

0.6374 -0.0733 -0.3037 -0.3351 -0.0836 0.4605 -0.3186 0.7193 -0.2712 -0.3076 -0.6229 -0.4156 -0.2422 -0.3414 0.4977 -0.2569-0.6686 0.0577 0.4644 0.4484 0.1830 -0.4975 0.3712 -0.5434 0.1987 0.4906 0.6376 0.2956 0.3804 0.4401 -0.4776 0.3625

0.5613 0.4158 -0.181

0.7075 0.0369 -0.578 0.5783

30-0.193 0.6478

0.2356 0.3638

0.2657 0.2395
0.2546 0.2016

0.433 0.6315

0.3679 -0.2852 -0.4149 0.1158 -0.3024 -0.0339
0.1554 -0.3677 -0.1803 -0.0531 -0.6603 -0.1214

0.0145 0.0062

1.0000 0.6644
0.6644 1.0000-0.163 0.2264

0.0075 0.0491
0.1704 0.0511

0.2094 -0.2757
0.2616 -0.3432

0.0075 0.1704 -0.1356 -0.2273 0.2094 0.2616 1.0000 -0.7813
0.0491 0.0511 0.3685 0.1367 -0.2757 -0.3432 -0.7813 1.0000

Table A2: Decomposition of Differences in Subjective Well-Being Relative to Reference Country (%)
Difference relative to thereference countrya

Australia
Austria
BelgiumBrazilCanadaChile
Czech Republic
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
Iceland
IrelandIsrael
Italy
Japan
Korea
Luxembourg
MexicoNetherlands
New Zealand
NorwayPolandPortugal
Russian Federation
Slovak Republic
Slovenia
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
United States
Mean
Std. Dev.

0-10 scale
0.206
0.381
-0.066-0.2540.381-0.447-0.6470.467-1.6320.381-0.350-0.254-1.997-2.519
0.551
0.026
0.117
-1.175-0.959-0.959
0.026
0.294
0.467
0.206
0.635
-1.066-2.123-1.399-1.066-0.853-0.6470.551
0.717-1.751-0.1590.026

Percentage
3.226
5.966
-1.030-3.9725.966-7.002-10.1277.308-25.5425.966-5.475-3.972-31.258-39.426
8.633
0.409
1.827
-18.396-15.006-15.006
0.409
4.605
7.308
3.226
9.940
-16.687-33.238-21.906-16.687-13.353-10.127
8.633
11.230-27.412-2.4900.409-0.4140.870-6.47413.619( 11 )( 7)( 17 )( 19 )( 7)( 22 )( 23 )( 5)( 32 )( 7)( 21 )( 19 )( 34 )( 36 )( 3)( 14 )( 13 )( 30 )( 26 )( 26 )( 14 )( 10 )( 5)( 11 )( 2)( 28 )( 35 )( 31 )( 28 )( 25 )( 23 )( 3)( 1)( 33 )( 18 )( 14 )

Contributions of percentage difference relative to the reference country

Sensitivity-1.560( 28 )
3.137
( 1)
1.498
( 23 )
3.137
( 1)
3.137
( 1)
3.137
( 1)
-0.919( 27 )
3.137( 1)-0.766( 26 )
3.137( 1)-0.236( 25 )
2.172( 22 )-20.783( 36 )-13.691( 35 )
3.137
( 1)
3.137
( 1)
3.137
( 1)
-9.570( 33 )
3.137
( 1)
3.137
( 1)
3.137
( 1)
3.137
( 1)
3.137
( 1)
3.137
( 1)
3.137
( 1)
-4.046( 31 )-6.998( 32 )
3.137( 1)-2.519( 29 )-10.015( 34 )-3.953( 30 )
3.137
( 1)
3.137
( 1)
3.137
( 1)
3.137
( 1)
0.140
( 24 )

Housing
0.392( 13 )
0.224( 16 )
0.000( 17 )-0.511( 26 )
5.541( 1)-0.243( 25 )-1.701( 30 )
0.280( 14 )-1.720( 31 )-0.039( 24 )-0.006( 22 )
0.000( 19 )-2.982( 35 )-1.412( 28 )
0.922
( 9)
0.598
( 12 )-0.763( 27 )-0.006( 23 )
1.646
( 7)
1.724
( 6)
2.040
( 4)
0.248
( 15 )
0.820( 10 )-2.727( 34 )
3.460( 2)-2.600( 33 )
0.000( 18 )-2.237( 32 )-1.661( 29 )
0.000( 19 )
0.000( 19 )
1.044
( 8)
2.119
( 3)
-5.529( 36 )
1.777
( 5)
0.746
( 11 )

Income-0.372( 17 )
0.577
( 5)
0.061
( 11 )-5.485( 33 )-12.049( 35 )
0.115( 8)-1.512( 24 )
0.068( 9)-3.582( 28 )
0.169
( 7)
0.366
( 6)
-0.549( 20 )-2.026( 25 )-4.230( 30 )-0.162( 16 )-1.352( 22 )
4.243( 3)-0.393( 19 )
5.334( 2)-0.393( 18 )-6.799( 34 )-1.390( 23 )
0.721( 4)-0.126( 15 )-3.773( 29 )-2.819( 27 )
0.061( 11 )
0.000( 14 )-4.888( 32 )
0.031( 13 )
0.061( 10 )-2.681( 26 )
7.786( 1)-1.196( 21 )-13.386( 36 )-4.708( 31 )

Jobs
1.342
0.666
-1.3960.761
1.766-0.595-0.7091.028-1.2380.453-0.4130.359-3.198-1.242
1.785-4.446-0.110-2.6100.000-2.8900.859-1.037
0.694
0.775
1.779
-0.096-1.1580.000-1.724-0.594-6.4571.308
0.000-3.7361.629
0.733-0.1495.645-0.0151.958-1.5083.948-0.4921.885( 5)( 13 )( 29 )( 10 )( 3)( 23 )( 24 )( 7)( 27 )( 14 )( 21 )( 15 )( 33 )( 28 )( 1)( 35 )( 20 )( 31 )( 18 )( 32 )( 8)( 25 )( 12 )( 9)( 2)( 19 )( 26 )( 17 )( 30 )( 22 )( 36 )( 6)( 16 )( 34 )( 4)( 11 )

Community
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
-0.033( 35 )
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
-0.072( 36 )
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)
0.000
( 1)

Education
0.000
( 9)
0.000
( 9)
-0.044( 28 )-0.490( 32 )
0.007
( 6)
0.000
( 9)
0.000
( 9)
0.000
( 9)
0.428
( 2)
0.000
( 9)
-0.192( 30 )
0.000
( 9)
0.000
( 9)
0.000
( 9)
-0.048( 29 )
0.000
( 9)
0.022
( 5)
-0.606( 33 )
0.070
( 4)
0.090
( 3)
-1.388( 35 )-0.890( 34 )
0.000
( 8)
0.000
( 9)
0.000
( 9)
0.000
( 9)
-11.366( 36 )
1.121
( 1)
0.000
( 9)
0.000
( 9)
-0.231( 31 )
0.000
( 9)
0.000
( 7)
-0.016( 27 )
0.000( 26 )
0.000( 9)

Environment
Civic engagement
0.669
( 3)
0.682
( 6)
0.269
( 9)
0.354
( 10 )
0.000( 27 )-2.563( 23 )
0.000( 18 )-1.023( 20 )
0.154( 11 )
0.065( 16 )-2.945( 35 )-4.318( 27 )
0.000( 18 )-1.551( 22 )
0.350
( 7)
0.431
( 9)
0.000
( 18 ) -13.714( 35 )
0.270
( 8)
1.167
( 3)
-0.013( 28 )-4.684( 31 )
0.685( 2)-4.671( 30 )-2.165( 33 )-0.747( 19 )
0.000( 18 ) -15.277( 36 )
0.403( 5)-1.308( 21 )
0.120( 15 )
0.606( 8)-2.396( 34 )-7.140( 33 )-1.109( 32 )-2.728( 24 )
0.142( 14 )
0.206( 13 )
0.000( 18 )
2.355
( 2)
0.000
( 17 )
0.015( 17 )
0.000( 18 )
6.875( 1)-0.276( 29 )-0.637( 18 )
0.223( 10 )
0.203( 14 )
0.724
( 1)
0.678
( 7)
0.000
( 18 )-4.066( 26 )
0.000( 18 )-3.771( 25 )-8.423( 36 )-5.133( 32 )
0.000( 18 )-4.447( 28 )
0.072( 16 )
0.900( 5)-0.439( 30 )
0.130( 15 )
0.147( 13 )
1.010
( 4)
0.589
( 4)
-8.395( 34 )-0.636( 31 )-4.494( 29 )
0.147( 12 )
0.329( 11 )
0.389
( 6)
0.216
( 12 )

Health
2.260
( 8)
0.625
( 18 )
1.413( 12 )
0.216( 20 )
5.291( 1)-2.153( 27 )-3.736( 30 )
0.972( 15 )-4.949( 32 )
0.496( 19 )-0.297( 23 )-1.968( 26 )
0.676( 17 )-3.574( 29 )
3.140
( 6)
1.415
( 11 )
4.463( 3)-1.375( 24 )-23.217( 36 )-18.386( 35 )
1.108( 14 )
0.000( 21 )
2.061
( 9)
1.539
( 10 )
1.324( 13 )-3.059( 28 )-10.006( 33 )-12.906( 34 )-1.448( 25 )-3.748( 31 )
0.761( 16 )
3.168
( 5)
4.548
( 2)
0.000
( 21 )
3.571
( 4)
2.893
( 7)

SafetyWork-Life Balance
0.000
( 8)
-0.188( 32 )
0.050
( 7)
0.065
( 14 )
0.000
( 8)
0.000
( 16 )-1.124( 36 )
0.547
( 9)
0.000
( 8)
2.054
( 3)
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
1.043
( 7)
0.000
( 8)
0.000
( 31 )
0.000
( 8)
0.313
( 11 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.764
( 2)
0.000
( 16 )
0.000
( 8)
0.330
( 10 )
0.371
( 4)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
1.026( 1)-3.351( 35 )
0.587( 3)-1.159( 33 )
0.000
( 8)
1.437
( 6)
-0.789( 35 )-1.548( 34 )
0.000
( 8)
0.789
( 8)
0.074
( 6)
0.128
( 13 )
0.000
( 8)
2.611
( 1)
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
2.536
( 2)
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
0.000
( 16 )
0.000
( 8)
1.498
( 4)
0.000
( 8)
1.447
( 5)
0.000
( 8 ) -14.941( 36 )
0.122
( 5)
0.184
( 12 )
0.000
( 8)
0.000
( 15 )-0.0030.013-0.3761.920-0.3621.601-1.358
6.084

0.030
0.338

-2.0684.231-0.1722.743a) While the difference in the 0–10 scale is subjective well-being of each country minus subjective well-being of the reference country, the difference in percentage is the ratio of subjective well-being of each country
to subjective well-being of the reference country minus one.

31

Table A3: Decomposition of Differences in Subjective Well-being between Females and Males (%)
Difference between female andmale aAustralia
Austria
BelgiumBrazilCanadaChile
Czech Republic
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
Iceland
IrelandIsrael
Italy
Japan
Korea
Luxembourg
MexicoNetherlands
New Zealand
NorwayPolandPortugal
Russian Federation
Slovak Republic
Slovenia
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
United States
Mean
Std. Dev.

0-10 scale
0.000
0.197
0.197
0.099
0.197
-0.307-0.101
0.000
0.292
0.389
-0.1010.000
0.099-0.2040.197
0.482-0.203-0.308
0.387
0.387
0.000
0.000
0.099
0.099
0.099
0.292
0.196
0.000
-0.101-0.308
0.197
0.099
0.099
0.196
0.099
0.294

Percent
0.000
2.703
2.899
1.504
2.703
-4.581-1.575
0.000
5.609
5.407
-1.5040.000
1.980-4.2562.632
7.197-2.817-5.129
6.669
6.669
0.000
0.000
1.342
1.379
1.307
5.129
4.001
0.000
-1.681-4.960
3.175
1.325
1.290
3.774
1.460
4.256

0.085
0.202

1.331
3.273

( 23 )( 12 )( 11 )( 16 )( 12 )( 34 )( 30 )( 23 )( 4)( 5)( 29 )( 23 )( 15 )( 33 )( 14 )( 1)( 32 )( 36 )( 2)( 2)( 23 )( 23 )( 19 )( 18 )( 21 )( 6)( 8)( 23 )( 31 )( 35 )( 10 )( 20 )( 22 )( 9)( 17 )( 7)

Contributions of percentage difference between female and maleSensitivity-1.138( 33 )
1.622( 11 )
5.026
( 4)
0.000
( 19 )
1.875( 10 )
0.000( 19 )
0.447( 16 )
0.000( 19 )
0.630( 15 )
2.530
( 6)
0.792
( 13 )
0.670( 14 )
2.535( 5)-2.246( 36 )
0.000( 19 )
2.306
( 8)
0.000
( 19 )-0.880( 32 )
0.000( 19 )
0.000( 19 )
0.329( 17 )
0.000( 19 )
0.102( 18 )
0.000( 19 )
0.000( 19 )
13.620( 1)
11.564( 2)
0.000( 19 )-1.681( 34 )-1.964( 35 )
5.297
( 3)
1.094
( 12 )
0.000( 19 )
2.514( 7)-0.705( 31 )
2.202
( 9)

Jobs
-0.635-0.168-1.613-8.683-0.352-5.826-0.073-0.2880.000
0.326-0.117-0.019-1.0310.000-0.0804.934-5.223-1.929-2.978-0.836-0.213-14.376-0.203-0.872-0.6420.000
0.000-1.9230.000
0.078-1.349-0.090-0.757-8.402-0.206-0.515

1.293
3.230

-1.502
3.306( 21 )( 14 )( 28 )( 35 )( 19 )( 33 )( 10 )( 18 )( 4)( 2)( 13 )( 9)( 26 )( 8)( 11 )( 1)( 32 )( 30 )( 31 )( 24 )( 17 )( 36 )( 15 )( 25 )( 22 )( 4)( 4)( 29 )( 4)( 3)( 27 )( 12 )( 23 )( 34 )( 16 )( 20 )

Community
0.000( 14 )
0.411
( 4)
0.000
( 14 )
0.000( 14 )
0.000( 14 )
2.322
( 2)
0.000
( 14 )
0.000( 14 )
0.000( 14 )
0.349
( 6)
0.000
( 14 )
0.000( 14 )
0.000( 14 )
0.000( 14 )
0.000( 14 )
0.000( 14 )
0.093( 10 )
0.000( 14 )
0.000( 14 )
2.485
( 1)
0.000
( 14 )
0.000( 14 )
0.057( 12 )
0.354
( 5)
0.000
( 14 )
0.000( 13 )
0.000( 14 )
0.287
( 8)
0.000
( 14 )
0.000( 14 )
0.000( 14 )
0.087( 11 )
1.237
( 3)
0.314
( 7)
0.000
( 14 )
0.256( 9)

Education
0.005( 18 )
0.160( 13 )
0.000( 33 )
2.127
( 2)
0.190
( 10 )
0.000( 20 )
0.000( 20 )
0.172( 12 )
0.000( 20 )
0.000( 20 )
0.041( 15 )
0.000( 34 )
0.000( 20 )
0.000( 20 )
1.056
( 6)
0.000
( 20 )
0.148( 14 )
0.346
( 8)
1.065
( 5)
0.000
( 20 )
0.675( 7)-0.225( 35 )
0.000( 19 )
0.185( 11 )
1.298
( 4)
0.000
( 20 )
4.805
( 1)
1.885
( 3)
0.000
( 20 )
0.000( 20 )
0.012( 17 )
0.012( 16 )
0.000( 20 )-3.508( 36 )
0.287
( 9)
0.000
( 20 )

Environment
Civic engagement-0.127( 33 )
0.000
( 5)
0.000
( 4)
0.000
( 5)
-0.332( 34 )
0.000
( 5)
0.000
( 4)
0.000
( 5)
-0.088( 32 )
0.000
( 5)
0.000
( 4)
0.000
( 5)
0.000
( 4)
0.333
( 3)
-0.022( 30 )
0.000
( 5)
0.000
( 4)
5.965
( 1)
0.000
( 4)
-0.077( 31 )
0.000( 4)-0.052( 30 )
0.000( 4)-0.428( 33 )
0.476
( 1)
0.000
( 5)
0.000
( 4)
-2.010( 35 )
0.000
( 4)
0.000
( 5)
-0.043( 31 )
0.000( 5)-0.002( 28 )
0.266( 4)-0.719( 35 )
0.000
( 5)
0.000
( 4)
0.000
( 5)
0.000
( 4)
0.000
( 5)
0.000
( 4)
0.000
( 5)
0.000
( 4)
-0.129( 32 )
0.140
( 2)
0.000
( 5)
-0.001( 27 )-0.005( 28 )
0.000
( 4)
0.000
( 5)
0.000
( 4)
-4.198( 36 )
0.000
( 4)
0.000
( 5)
0.000
( 4)
1.836
( 2)
0.000
( 4)
0.000
( 5)
0.000
( 4)
0.000
( 5)
-0.764( 36 )-0.021( 29 )
0.000
( 4)
0.000
( 5)
0.000
( 4)
-1.467( 34 )
0.042
( 3)
0.000
( 5)
0.000
( 4)
0.000
( 5)
-0.017( 29 )
0.000( 5)

Health0.196( 8)-0.230( 21 )-0.182( 19 )-0.764( 24 )
1.190( 3)-2.991( 31 )-2.282( 30 )-0.289( 22 )-0.986( 27 )
1.614( 1)-2.167( 29 )-0.223( 20 )
0.000( 10 )
0.000( 10 )-0.091( 18 )
0.000( 10 )-0.060( 17 )-1.948( 28 )-0.935( 26 )-4.723( 34 )-0.791( 25 )
0.200
( 7)
0.491
( 5)
0.000
( 10 )-0.323( 23 )-4.292( 33 )-12.369( 36 )-5.491( 35 )
0.000( 10 )-3.074( 32 )
0.000( 10 )
0.307
( 6)
0.496
( 4)
0.000
( 10 )
0.100
( 9)
1.538
( 2)

SafetyWork-Life Balance
0.000
( 8)
1.699
( 11 )
0.000
( 8)
0.908
( 14 )
0.000
( 8)
0.000
( 34 )
7.210
( 2)
1.614
( 12 )
0.007( 7)-0.118( 35 )
0.000
( 8)
1.915
( 7)
0.000
( 8)
0.000
( 20 )
0.203
( 5)
0.225
( 17 )
0.000
( 8)
0.000
( 20 )
0.171
( 6)
0.494
( 16 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
1.746
( 9)
0.000
( 8)
0.000
( 20 )
0.000
( 8)
1.961
( 6)
0.000
( 8)
0.000
( 20 )
0.000
( 8)
9.517
( 3)
0.000
( 8)
9.744
( 2)
0.000
( 8)
0.000
( 20 )
7.699
( 1)
6.831
( 4)
0.721
( 4)
0.035
( 18 )
0.000
( 8)
1.719
( 10 )
0.000
( 8)
0.975
( 13 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
0.000
( 20 )
3.873( 3)-0.467( 36 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
0.000
( 20 )
0.000
( 8)
0.000
( 20 )-0.085( 36 )
0.000( 19 )
0.000
( 8)
1.781
( 8)
0.000
( 8)
12.814
( 1)
0.000
( 8)
1.985
( 5)
0.000
( 8)
0.792
( 15 )

0.229
0.583

0.298
1.135

-0.0400.202-1.058
2.542

0.550
1.820

0.000
1.343

1.560
3.107

a) While the difference in the 0–10 scale is subjective well-being of females minus subjective well-being of males for each country, the difference in percentage is the ratio of subjective well-being of females tosubjective well-being of males minus one for each country.

32

Table A4: Decomposition of Differences in Subjective Well-being between High and Low Income Earners (%)
Difference between high-income
and low-income people aAustralia
Austria
BelgiumBrazilCanadaChile
Czech Republic
Denmark
Estonia
FinlandFranceGermany
GreeceHungary
Iceland
IrelandIsrael
Italy
Japan
Korea
Luxembourg
MexicoNetherlands
New Zealand
NorwayPolandPortugal
Russian Federation
Slovak Republic
Slovenia
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
United States
Mean
Std. Dev.

0-10 scale
0.294
0.846
1.260
0.753
0.197
1.258
0.293
0.576
0.657
0.935
1.175
1.095
1.356
1.278
0.294
-0.203
1.014
1.242
0.835
1.590
1.184
1.270
0.757
-0.101
0.294
0.661
1.889
0.569
1.004
1.252
1.329
-0.203
0.577
1.073
0.754
1.178

Percent
4.139
12.26020.66111.9552.632
20.972
4.445
8.004
12.62913.35319.91317.95932.27731.1783.922-2.77816.09323.44014.90434.57518.492
19.237
10.970-1.361
3.922
11.39446.082
10.53617.93421.96323.733-2.5987.796
21.90511.77819.319

0.840
0.499

15.10110.715( 30 )( 21 )( 10 )( 22 )( 33 )( 9)( 29 )( 27 )( 20 )( 19 )( 11 )( 15 )( 3)( 4)( 31 )( 36 )( 17 )( 6)( 18 )( 2)( 14 )( 13 )( 25 )( 34 )( 31 )( 24 )( 1)( 26 )( 16 )( 7)( 5)( 35 )( 28 )( 8)( 23 )( 12 )

Contribution of percentage difference between high-income and low-income people

Sensitivity-5.698( 28 )-4.676( 27 )
10.282( 3)-9.825( 31 )-3.949( 26 )-1.672( 24 )-14.297( 33 )
0.162( 12 )-27.054( 36 )-1.645( 23 )
8.569
( 4)
1.247
( 11 )
12.655( 2)-0.525( 19 )-0.594( 20 )-8.660( 30 )-0.638( 21 )
6.790
( 5)
0.000
( 14 )
0.000( 14 )
5.198
( 6)
0.000
( 14 )
4.211( 8)-10.366( 32 )-2.779( 25 )-15.032( 35 )
19.465( 1)
0.000( 14 )-14.377( 34 )
2.103( 10 )
4.311( 7)-6.412( 29 )
0.000( 14 )
0.066( 13 )-1.094( 22 )
4.201( 9)

Income5.740( 14 )
0.818( 35 )
5.107( 17 )
11.346( 6)
4.720( 19 )
12.901( 3)
4.682( 20 )
2.815( 24 )
10.522( 8)
2.033( 29 )
5.571( 15 )
1.429( 33 )
11.596( 5)
6.877( 12 )
2.538( 26 )
5.374( 16 )
10.091( 9)
10.577( 7)
2.513( 27 )
1.131( 34 )
1.851( 30 )
11.909( 4)
2.703( 25 )
6.975( 11 )
0.547( 36 )
7.450( 10 )
2.121( 28 )
23.841( 1)
4.769( 18 )
2.865( 23 )
6.598( 13 )
1.689( 32 )
1.813( 31 )
16.204( 2)
2.917( 22 )
3.618( 21 )

Jobs
0.510
0.260
1.528
0.042
1.647
0.157
5.409
0.362
14.213
0.521
0.939
1.028
3.863
9.228
0.981
4.444
1.271
2.519
0.288
0.311
2.141
0.091
0.347
0.645
0.151
8.334
2.739
0.000
22.524
1.466
9.306
0.584
0.532
0.881
1.209
5.208

-1.390
8.546

6.007
5.040

2.936
4.678

( 26 )( 31 )( 14 )( 35 )( 13 )( 32 )( 6)( 27 )( 2)( 25 )( 20 )( 18 )( 9)( 4)( 19 )( 8)( 16 )( 11 )( 30 )( 29 )( 12 )( 34 )( 28 )( 22 )( 33 )( 5)( 10 )( 36 )( 1)( 15 )( 3)( 23 )( 24 )( 21 )( 17 )( 7)

Community
1.059
( 8)
0.050
( 19 )
0.000( 21 )
0.000( 21 )-0.130( 35 )
0.005( 20 )-1.178( 36 )
0.152( 16 )
0.389( 13 )
3.626
( 3)
0.000
( 21 )
0.000( 33 )
0.753
( 9)
0.000
( 21 )-0.051( 34 )
0.000( 21 )
3.451
( 4)
0.000
( 21 )
0.289( 14 )
28.279( 1)
0.491( 11 )
0.000( 32 )
1.070
( 7)
0.000
( 21 )
0.149( 17 )
0.000( 21 )
3.168
( 5)
0.000
( 21 )
0.050( 18 )
0.392( 12 )
0.000( 21 )
0.652( 10 )
0.000( 21 )
4.434
( 2)
0.286
( 15 )
1.433( 6)

Education
0.000( 28 )
6.139
( 2)
0.865
( 16 )
6.450
( 1)
0.156
( 25 )
3.886
( 5)
0.000
( 28 )
0.774( 17 )
0.000( 28 )
0.000( 28 )
1.335( 13 )
2.128
( 8)
0.000
( 27 )
0.000( 28 )
0.362( 21 )
0.664( 18 )
4.677
( 4)
0.891
( 15 )
0.000( 28 )
0.138( 26 )
5.205
( 3)
3.226
( 6)
0.000
( 28 )
0.379( 20 )
1.183( 14 )
0.000( 28 )
0.274( 23 )
0.000( 28 )
1.374( 12 )
2.003
( 9)
0.239
( 24 )
0.585( 19 )
0.277( 22 )
2.731
( 7)
1.502
( 11 )
1.549( 10 )

Environment
Civic engagement
1.839
( 2)
0.000
( 21 )
0.000( 14 )
0.024( 20 )-2.928( 32 )
0.000( 21 )
1.028
( 6)
0.000
( 21 )-0.535( 24 )
0.630( 14 )-3.317( 33 )
0.000( 21 )
0.000( 17 )
2.776
( 8)
1.514
( 4)
0.000
( 21 )
0.320( 12 )
1.905( 10 )
0.439
( 9)
1.362
( 11 )-0.429( 23 )
0.000( 21 )-0.326( 19 )
3.379
( 6)
1.813
( 3)
0.812
( 13 )-0.358( 20 )
11.128( 1)-0.424( 22 )
0.102( 19 )-4.600( 35 )
0.000( 36 )-3.319( 34 )
0.561( 15 )-1.064( 26 )
0.000( 21 )
0.024( 13 )
0.000( 21 )-1.367( 28 )
0.000( 21 )
0.481
( 8)
0.000
( 21 )-2.773( 31 )
0.231( 18 )-1.572( 29 )
2.009
( 9)
1.006
( 7)
0.000
( 21 )
0.414( 10 )
0.993( 12 )-0.811( 25 )
6.290
( 2)
0.000
( 14 )
4.306( 4)-28.255( 36 )
0.000( 21 )
0.327( 11 )
0.000( 21 )
1.904
( 1)
0.544
( 16 )
1.421
( 5)
0.466
( 17 )-1.364( 27 )
0.000( 21 )
0.000( 14 )
3.513( 5)-2.536( 30 )
0.000( 21 )-0.018( 18 )
5.136( 3)-0.413( 21 )
2.925( 7)

Health0.687( 31 )
9.646
( 6)
5.808
( 13 )
2.913( 21 )
0.092( 33 )
9.012
( 8)
7.053
( 9)
2.225
( 22 )
12.334( 3)
7.017( 10 )
3.929( 16 )
9.074
( 7)
0.786
( 30 )
4.827( 15 )
1.008( 28 )
0.000( 34 )
0.000( 34 )
3.726( 17 )
11.789( 4)
6.083( 12 )
3.125( 20 )
6.554( 11 )
2.201( 23 )
0.000( 34 )
3.264( 19 )
5.165( 14 )
14.008( 2)
14.949( 1)
3.266( 18 )
10.685( 5)
1.392( 27 )
1.668( 25 )
1.662( 26 )
0.126( 32 )
1.839( 24 )
0.798( 29 )

1.356
4.768

1.361
1.823

-1.219
4.891

4.686
4.296

1.364
2.353

a) While the difference in the 0–10 scale is subjective well-being of high income earners minus subjective well-being of low income earners for each country, the difference in percentage is the ratio of subjective wellbeing of high income earners to subjective well-being of low income earners minus one for each country.

33