ISSN 1881-6436

Discussion Paper SeriesNo. 15-01
Incorporating Sustainability Concerns in the Better Life Index:
Application of Corrected Concave Least Squares Method
Hideyuki Mizobuchi
April 2015

Faculty of Economics,
Ryukoku University
67 Tsukamoto-cho, Fukakusa, Fushimi-ku,
Kyoto, Japan
612-85771

Incorporating Sustainability Concerns in
the Better Life Index: Application of
Corrected Concave Least Squares Method

April 2015
Abstract
The OECD recently released a comprehensive set of 11 well-being indicators, the so-called
Better Life Index (BLI), for 36 countries. The BLI covers a wide range of socio-economic
aspects of life, which are essential to well-being. This well-being dataset allows us to compare
countries’ overall well-being. However, in spite of the BLI’s wider coverage of variables, it
fails to consider sustainability concerns. If two countries are characterized by the same socioeconomic life circumstances, they are evaluated equally in terms of current well-being.
However, once we incorporate sustainability concerns into the international comparison, thecountry that can sustain good-life circumstances for future generations is appreciated better.
This study provides a practical proposal for comparing overall well-being by incorporating
sustainability concerns. Using the World Bank’s adjusted net savings data as a sustainability
indicator, we add an extra dimension to the BLI. Then, we apply a composite indicator andaggregate these 12 indicators for each country into a single number. Moreover, we improve thecurrent method for constructing composite indicators by adopting corrected concave nonparametric least squares (C2NLS). It is a typical problem in a non-parametric approach basedon linear programming for countries’ scores of composite indicators to become equal and theirperformance cannot be distinguished. This becomes even more severe if the number of sample
countries is small or the number of aggregated indicators is large, which is the case of thepresent study dealing with 12 indicators for 36 countries. The use of C2NLS overcomes thisproblem and allows us to order all countries in the sample completely. The empirical results
show that the introduction of a sustainability index for comparisons does not change countries’
overall rankings significantly. However, it certainly changes the ranking of some countries inboth directions.
Keywords: Sustainability, Composite Indicators, Better Life Index, Data Envelopment Analysis,
Benefit of the Doubt Approach21.

Introduction

Per capita GDP has long been used as a proxy measure of well-being. However, it is
now widely recognized that income data provide only a partial perspective on the arrayof factors that affect people’s lives. Given the problems with using GDP per capita as ameasure of well-being, many researchers have been searching for alternative measures.
In particular, the importance of incorporating a wider range of socio-economic
conditions rather than income alone is now widely recognized. Drawing upon therecommendations for research on economic measurement problems by Stiglitz et al.
(2009), the OECD identified 11 dimensions as being essential to well-being. The
dimensions cover material living conditions, such as income and wealth, as well as
quality of life (QOL), such as community, environment, and work–life balance. These
dimensions are explored and analysed in detail by the OECD (OECD, 2011). The
OECD released 11 types of well-being indicators, known as the OECD Better Life
Index (BLI), which covers the 34 OECD member countries and two non-member
countries. 1 However, evaluation of overall well-being by summarizing the 11
individual indicators is left to users of the statistics.2
The 11 well-being indicators allow us to compare countries by the comprehensive wellbeing of their populations. However, there is an important component missing fromthese indicators, namely, sustainability. While the 11 well-being indicators capture thewell-being of the current population, it is also a critical issue whether current well-being
can be sustained in the future.
There is much in common between, on the one hand, the literature and debates on
measures of well-being and, on the other hand, those of sustainability.3 Levels of wellbeing are essentially what sustainability advocates would like to sustain. Thus, it isnecessary to measure well-being before discussing its sustainability. On the other hand,
without sustainability concerns, a country that guarantees the current generation better
life circumstances by depleting natural resources at the cost of future generations isevaluated similarly to another country that sustains current well-being in the future,
as long as the well-being of the current generation is the same in both countries. This,
however, is entirely unconvincing. While the OECD concedes it is necessary to
introduce sustainability concerns into the BLI, this has been left as a future issue. The
present study attempts to provide a practical proposal on how to measure well-being by
incorporating sustainability concerns. First, we add an extra indicator of sustainability
concerns to the 11 well-being indicators of the BLI. Second, we aggregate these 12
indicators by the composite indicators.
As Dasgupta (2001) and Arrow et al. (2004) advocate, the productive base of economies,
which consists of produced and natural capital and intangible assets, determines thewell-being of people. Thus, a smaller productive base predicts lower well-being of
future generations. The World Bank’s adjusted net savings (World Bank, 2011), which
are considered a good measure of sustainability, capture the change in the productive
base. Thus, we define the sustainability indicator by the adjusted net savings.

1

There were 34 countries covered in 2011. A revised dataset released in 2012 includes 36 countries,
incorporating Brazil and Russia.
2Your Better Life Index (http://www.oecdbetterlifeindex.org/) was designed as an interactive tool thatallows users to assign the importance of each of the 11 topics and track the performance of countries.
3
However, the two strands of research, such as measures of well-being and sustainability, tend to havebeen separated.

3

Composite indicators are used in order to measure multidimensional concepts, which
are characterized by multiple individual indicators. Since individual indicators may
trend in different directions to each other, the set of multiple indicators itself is notenough to provide an overall picture of multidimensional concepts across countries.
Among a number of techniques to construct the composite indicator, the ‘benefit of the
doubt’ (BOD) approach, which has received increasing attention from researchers,
avoids subjectivity in the determination of weights (Mahlberg and Obersteiner, 2001;
Cherchye et al., 2004, 2007; Despotis, 2005; OECD, 2008). Under BOD, the weights
are country-specific and endogenously determined such that they maximize the valueof each country’s resulting composite indicator. Thus, larger weights are given to theindividual indicators (topics of well-being) on which each country performs well. The
core idea is that a good relative score of a country on an individual indicator shows thatit considers the individual indicator as relatively important. Therefore, for international
comparisons based on BOD, a country cannot attribute the lower score of its composite
indicator to a harmful or unfair weighting scheme.
BOD is rooted in data envelopment analysis (DEA), which is designed to computeefficiency indices. DEA is an established technique to measure the relative efficiency
of decision-making units based on inputs and outputs of units in a sample. It measuresthe efficiency of each unit by its distance from the production frontier, which isrepresented by the best-practice units. Formally, BOD is tantamount to the inputoriented DEA model, with all individual indicators as outputs and a ‘dummy input’
equal to one for all countries.4
A well-known problem associated with DEA (thus, BOD) is that it often fails todifferentiate the performance of all decision-making units completely, with the resultthat some units are ranked equally. This arises from the DEA procedure of constructing
the production frontier based on a linear-programming technique. Kuosmanen andJohnson (2010) introduce an alternative method, namely, corrected concave nonparametric least squares (C2NLS) for computing the efficiency measure. 5 C2NLS
constructs the production frontier based on quadratic linear programming. This new
method offers certain advantages to the existing DEA. Kuosmanen and Johnson (2010)
show that the estimates based on C2NLS are consistent, and asymptotically unbiased,
and yield smaller mean-squared error than the corresponding DEA efficiency
estimators. In addition to these advantages, C2NLS has better discriminatory power than
DEA, which allows for the complete ordering of the efficiency scores of all the units ina sample. As suggested by Kuosmanen and Johnson (2010), the C2NLS method can beused for estimating shadow prices, setting performance targets, and identifying
benchmarks in a similar fashion to the standard DEA. To the best of our knowledge,
this is the first study that applies C2NLS to construct composite indicators.
Mizobuchi (2014) applies the BOD method to construct a composite indicator which
aggregates the 11 well-being indicators of the BLI. However, the problem of equal
rankings among many countries associated with the BOD is established but left
unresolved. The more indicators the composite indicator aggregates, the more countries
are likely to be ranked indifferently. The procedure proposed by the present study would
resolve this problem. Other than GDP per capita, the United Nations’ Human
4

Lovell et al. (1995) interpret the dummy input as a helmsman that pursues several policy objectives.
Constructing the production frontier based on C 2NLS is part of the entire process of estimating theefficiency measure known as stochastic non-parametric envelopment of data (StoNED). See Kuosmanen(2008) and Kuosmanen and Johnson (2010).
54

Development Index (HDI) is the most popular measure of well-being. In addition, it is
a composite indicator which aggregates fewer aspects than ours, such as income,education, and health. In the last 2 decades, a series of papers has introduced
sustainability concerns into composite indicators (Desai, 1995; Neumayer, 2001;
Costantini and Monni, 2005; Ray, 2014). Adjusted net savings or ecological footprints
are used as sustainability indicators. While these indicators are, like ours, motivated byintegrating sustainability concerns into measures of well-being or human development,
their procedures of constructing composite indicators involve a simple geometric mean
with ad hoc constant weight over countries, which has been adopted for the HDI. On
the other hand, the C2NLS method allows for a more general and flexible weighting
scheme which assigns different and favourable weights to each country, like BOD.
The rest of this paper unfolds as follows. Section 2 discusses two approaches toconstruct a composite indicator. Section 3 explains the data of well-being indicatorsand sustainability. Section 4 computes composite indicators under different cases andcompares them across countries. Section 5 concludes.
2. Methodology
The present study aggregates each of 36 countries’ 11 well-being indicators and a single
sustainability indicator into composite indicators. This is to compare countries’
performance in terms of well-being, along with accounting for sustainability concerns.
We adopt two approaches, namely, the BOD and C2NLS methods, to constructcomposite indicators. Since they are sufficiently versatile to be applicable to a variety
of problems and situations, we explain these methods below in a more general setting
independent of the number of countries and underlying individual indicators.
We assume there are 𝐾 countries and that the well-being of people in a country 𝑘 ischaracterized by a set of 𝑀 individual indicators, 𝒚𝑘 = (𝑦1,𝑘 , … , 𝑦𝑀,𝑘 )′, with 𝑦𝑚,𝑘representing the value of the 𝑚-th individual indicator of country 𝑘. Suppose that there
are some sustainability indicators among 𝑀 indicators, constituting 𝒚𝑘 . 6 BOD
aggregates these individual indicators using their weighted average. We denote a set ofweights for country 𝑘 by 𝝁𝑘 = (𝜇1,𝑘 , … , 𝜇𝑀,𝑘 )′, whose component 𝜇𝑚𝑘 represents
the weight of the 𝑚-th individual indicator. The composite indicator based on BOD for
country 𝑐, 𝐶𝐼𝐵𝑂𝐷,𝑐 , is formulated as follows:
𝐶𝐼𝐵𝑂𝐷,𝑐 =𝑀𝑀
𝑚𝑎𝑥 {Σ𝑚=1
𝜇𝑚,𝑐 𝑦𝑚,𝑐 : Σ𝑚=1
𝜇𝑚,𝑐 𝑦𝑚,𝑘 ≤ 1 for 𝑘

𝜇1,𝑐 ,…,𝜇𝑀,𝑐= 1, … , 𝐾; 𝜇𝑚,𝑐 ≥ 0 for 𝑚 = 1, … , 𝑀}

(1)

For the international comparison, the abovementioned procedure is repeated for every
country in our sample. The weight (𝜇1,𝑐 , … , 𝜇𝑀,𝑐 ) is determined endogenously tomaximize the value of the composite indicator for country 𝑐. Thus, a larger weight is
assigned to an individual indicator on which the country performs well. In thisprocedure, a good performance of country 𝑐 on an individual indicator is considered
to indicate that the country prioritizes this indicator. Therefore, countries cannot excuse
their poor performance by an unfair weighting scheme, because any weight other thanthat used for their evaluation would not improve their position. The first constraint in
6

Sustainability indicators and well-being indicators are treated alike. Thus, we do not differentiate themin our notation.

5(1) is that every country in a sample has a resulting composite indicator smaller than
one when applying the most favourable weights for the evaluated country 𝑐. Thus, theresulting composite indicator for country 𝑐 will be below one.
As Mahlberg and Obersteiner (2001) graphically illustrate, an alternative interpretationof 𝐶𝐼𝐵𝑂𝐷,𝑐 is possible. Given individual indicators 𝒚 as outputs and a dummy input
equal to one for all countries, 𝐶𝐼𝐵𝑂𝐷,𝑐 is considered as evaluating the performance ofcountry 𝑐 in terms of its productive efficiency.7 Strictly speaking, 𝐶𝐼𝐵𝑂𝐷,𝑐 equals the
distance between country c’s well-being indicator 𝒚𝑐 and the production frontier
constructed over countries’ input and output sample data by DEA. The production
frontier represents the optimal practices to produce well-being. Countries whose wellbeing indicators 𝒚 are on the frontier are considered the most efficient and are ranked
the highest under BOD. The farther from the frontier and the closer to the origin theindividual indicators of a country are, the lower its performance is evaluated.
One of the problems associated with BOD is that multiple countries are located on the
production frontier and they are evaluated the highest. Thus, we fail to distinguish theirperformance. Such weak discriminatory power of the composite indicator based on
BOD would be more evident in a case in which the dataset is small, which applies tothe present study dealing with 36 countries. Weak discriminatory power is a wellknown problem of DEA. C2NLS has a decisive advantage over DEA and BOD by
improving discriminatory power significantly.
C2NLS is implemented in two stages. First, the production frontier is estimated bysolving concave non-parametric least squares (CNLS). In the situation of a dummy
input that is equal to one, the production frontier is formulated as follows:𝑀Σ𝑚=1
𝜇𝑚,𝑖 𝑦𝑚,𝑖 + 𝜀𝑖 = 1 and𝑀𝑀Σ𝑚=1 𝜇𝑚,𝑖 𝑦𝑚,𝑖 ≥ Σ𝑚=1
𝜇𝑚,𝑗 𝑦𝑚,𝑖
𝐾
2|
min
Σ
𝜀
𝑖=1
𝑖
|
𝜀1 ,…,𝜀𝐾 ,for all 𝑖, 𝑗 = 1, … , 𝐾;
𝝁1 ,…,𝝁𝐾
𝜇𝑚,𝑘 ≥ 0 for all 𝑚 = 1, … , 𝑀 and all 𝑘 = 1, … , 𝐾 }
{

(2)

∗
∗
∗
∗
Let 𝝁1∗ = (𝜇1,1, … , 𝜇1,𝑀), … , 𝝁∗𝐾 = (𝜇𝐾,1, … , 𝜇𝐾,𝑀
) be a solution to optimization
problem (2). The composite indicator 𝐶𝐼𝐵𝑂𝐷,𝑐 is the efficiency measure for country 𝑐based on DEA.8 The corresponding efficiency measure based on CNLS, 𝐶𝐼𝐶𝑁𝐿𝑆,𝑐 , is𝑀∗
derived so that 𝐶𝐼𝐶𝑁𝐿𝑆,𝑐 = Σ𝑚=1
𝜇𝑚,𝑐𝑦𝑚,𝑐 for all 𝑐 = 1, … , 𝐾. Second, the efficiency
measures are adjusted so that the maximum value becomes one. Then, the composite
indicator based on C2NLS for country 𝑐 is defined as follows:

𝐶𝐼𝐶2𝑁𝐿𝑆,𝑐 = 𝐶𝐼𝐶𝑁𝐿𝑆,𝑐 − max 𝐶𝐼𝐶𝑁𝐿𝑆,𝑖𝑖∈[1,…,𝐾](3)

We explain the characteristics of 𝐶𝐼𝐶2𝑁𝐿𝑆 in comparison with 𝐶𝐼𝐵𝑂𝐷 . 𝐶𝐼𝐶2𝑁𝐿𝑆 shareflexible weighting with 𝐶𝐼𝐵𝑂𝐷 in the sense that every country is allowed to adopt afavourable weight.

7

The dummy input can be considered as a helmsman in each country, which is intended to provide
people with a better life. This is reflected by the values of individual well-being indicators This
interpretation is rooted in Lovell et al. (1995).
8
Formally, 𝐶𝐼𝐵𝑂𝐷,𝑐 is known as the output-oriented Farrell efficiency.

6

Optimisation problem (1) is formulated alternatively by the following equation (4),9which helps us to relate 𝐶𝐼𝐶2𝑁𝐿𝑆 to 𝐶𝐼𝐵𝑂𝐷 .

min

𝜀1 ,…,𝜀𝐾 ,𝝁1 ,…,𝝁𝐾

𝐾
Σ𝑖=1
𝜀𝑖2

{

𝜀𝑖 ≥ 0𝑀Σ𝑚=1 𝜇𝑚,𝑖 𝑦𝑚,𝑖 + 𝜀𝑖 = 1 and𝑀𝑀Σ𝑚=1 𝜇𝑚,𝑖 𝑦𝑚,𝑖 ≥ Σ𝑚=1
𝜇𝑚,𝑗 𝑦𝑚,𝑖

|
|

(4)

for all 𝑖, 𝑗 = 1, … , 𝐾;
𝜇𝑚,𝑘 ≥ 0 for all 𝑚 = 1, … , 𝑀 and all 𝑘 = 1, … , 𝐾 }Equation (4) is simply a sign-constrained variant of the CNLS problem of equation (2).
We can interpret these equations as follows: both BOD and CNLS maximize the valueof each country’s composite indicator by adopting its most favourable weight. While
BOD faces a constraint that the resulting composite indicator is below one, CNLS isfree from such a constraint. Thus, there are countries whose composite indicators
become larger than one under the CNLS approach. In the case in which multiple
countries are ranked the highest with the value of one for their composite indicators
under the BOD, the application of the CNLS approach allows us to differentiate theperformances of these countries.
3.

Data

3.1. OECD Better Life Index
Amid growing concerns about identifying an alternative approach to measuring wellbeing, in 2011, the OECD launched the Better Life Initiative and released a set of 11
well-being indicators covering the 34 OECD member countries, comprising advanced
and emerging economies. The data were updated in 2012 and more dimensions wereadded to calculate indicators. Moreover, the country coverage was expanded beyond
the OECD to include Brazil and Russia. We use the most recent data covering
individual indicators, which were released in 2014. The data are cross-sectional for asingle year around 2011, as explained later in this section 3.1.
The 11 individual well-being indicators evaluate topics that the OECD considers to be
essential to people’s well-being. Each individual indicator corresponding to each topic
is based on between one and four underlying secondary indicators, which are expressedin different units, such as dollars, years, or numbers of people. To compare andaggregate values expressed in different units, the values are normalized. This
normalization is performed according to a standard formula which converts the originalvalues of the individual indicators into numbers between 0 and 10, as follows:
value to convert − minimum value
× 10(5)
maximum value − minimum value
Within each topic, the secondary indicators are averaged with equal weight. For
example, while the topic of the environment is constructed using two secondary
indicators, water quality and air pollution, first, their scores are normalized in a range
water quality score+air pollution score
between 0 and 10. Then, they are aggregated as follows:.
29Endogenously determined weights 𝝁 in equation (1) are also a solution to equation (4).

7The 11 individual indicators and their corresponding 24 secondary indicators are shown
below.












1) Income(Household income; Household financial wealth)2) Jobs(Employment rate; Personal earnings; Job security; Long-term
unemployment rate)3) Housing(Rooms per person; Housing expenditure; Dwellings with basic
facilities)
4) Work–life balance(Employees working very long hours; Time devoted to leisure andpersonal care)5) Health(Life expectancy; Self-reported health)6) Education(Educational attainment; Years in education; Students’ skills)7) Community(Social network)8) Civic engagement(Consultation on rule-making; Voter turnout)
9) Environment(Water quality; Air pollution)10) Safety(Homicide rate; Assault rate)11) Life satisfaction(Life satisfaction)

Among the 11 individual well-being indicators, the first 3 are categorized undermaterial living conditions and the remaining 8 are categorized as QOL. According to
the dataset released by the OECD Better Life Initiative, the data years of the underlying
detailed indicators range from 2008 to 2013. Averaging them with each topic equallyweighted suggests a year close to 2011. Thus, we consider that the 11 indicators of eachcountry measure the socioeconomic situation of people around 2011.
Table 1 summarizes the statistics of the 11 well-being indicators; the complete data isprovided in the Appendix in Table A.1. As the OECD (2011, 2013) finds, these tables
show that while life is good in many dimensions in some countries, such as Australia,
Canada, Denmark, New Zealand, Norway, and Sweden, it is significantly less so inother countries, such as Chile, Mexico, Portugal, Russia, and Turkey. While the lattergroup of countries is characterized by lower per capita income, except for Portugal, theformer group does not necessarily comprise the richest countries.
Hereafter, we group countries based on per capita GDP to consider the link between
well-being and economic development, which is well reflected in per capita GDP. There
are three groups, as follows: four high-income countries with per capita GDP more than
USD 45,000; 13 middle-income countries with per capita GDP between USD 30,000

8and 40,000; and 19 low-income countries with per capita GDP less than USD 30,000.10
Table 1 suggests that people’s well-being improves in many aspects as income grows.
However, this is not always true, especially in some of the topics categorized under
QOL, such as community, education, civic engagement, and work–life balance. In theserespects, the average person in middle-income countries enjoys a better life than the
average person in high-income countries. It is also noteworthy that the life satisfaction
indicator, which has the largest standard deviation, differs significantly across countries.
3.2. The World Bank’s Adjusted Net Savings Dataset
Adjusted net savings, also known as genuine savings, are designated a sustainability
indicator provided by the World Bank. Its theoretical grounding is the notion thatsustainability requires the maintenance of a constant stock of the ‘productive base’. Thisis extended wealth, which is not limited to natural resources but also includes physical,
produced, and intangible capital, such as human capital and the rule of law. Adjusted
net savings are considered as the change in this total wealth over a given time period.
As Dasgupta (2001) and Arrow et al. (2004) advocate, the productive base is the sourceof well-being of future generations. Thus, negative adjusted net savings indicate future
generations fail to be given an opportunity set which is at least as large as that available
to current generations.
The World Bank computes adjusted net savings as follows:

Adjusted net savings = net national savings + education expenditure – natural
resource depletion – carbon dioxide damage.

Net national savings is gross fixed capital formation minus the consumption of fixed
capital, which indicates the amount of added produced capital. Education expenditureindicates the amount of added human capital, which makes up the larger share ofintangible capital. Natural resource depletion is the sum of net forest depletion, energy
depletion, and mineral depletion. Natural resource depletion with carbon dioxide
damage captures the loss of natural capital. As the productive base consists of produced,
natural, and intangible capital, adjusted net savings consists of changes in produced,
natural, and intangible capital.
Instead of using the variable of adjusted net savings released by the World Bank, we
re-compute the adjusted net savings, this time without including education expenditure,
as follows:

Adjusted net savings = net national savings – natural resource depletion –carbon dioxide damage.

There are two reasons we exclude education expenditure from the construction of thesustainability indicator in the present study. First, education expenditure is not a goodmeasure of the changes in intangible capital.11 Education expenditure captures changes
in human capital but lacks significant parts of other intangible capital, such as the ruleof law and social capital. Second, the inclusion of education expenditure leads to double
counting. An increase in government expenditure on education usually improves
10

The 4 high-income countries are Luxembourg, Norway, Switzerland, and the US; the 13 low-income
countries are Brazil, Chile, Czech Republic, Estonia, Greece, Hungary, Mexico, Poland, Portugal, Russia,
Slovak Republic, Slovenia, and Turkey; and the other 19 countries are middle-income countries.
11
Moreover, the depreciation of human capital is dismissed. Thus, strictly speaking, education
expenditure is a dubious measure, even for changes in human capital.

9people’s life conditions in terms of education. This might arise from smaller class sizes
or more motivated teachers. The returns from educational investment are consideredmore immediate than changes in produced and natural capital. Since the education wellbeing indicator of the BLI already captures the impact of education expenditure, we
exclude it from the sustainability indicator to avoid double counting. Finally, we
normalize the value of adjusted net savings into the range between 0 and 10 based onequation (5) and this defines the sustainability indicator.
Table 2 summarizes the statistics of adjusted net savings and the ingredients thereof,
along with the sustainability indicator. Net national savings, which indicate the net
investment of produced capital, are much larger than the depletion of natural resources
and carbon dioxide damage. The gap seems to be expanding as economies grow. Whilehigh-income countries seem to have larger natural resource depletion, once we exclude
Norway as an exception, their average level of natural resource depletion is smaller thanthat of middle-income countries. Thus, the results show that as economies grow, natural
resource depletion declines in general.
4. Results
We compute composite indicators based on BOD, 𝐶𝐼𝐵𝑂𝐷 and C2NLS, 𝐶𝐼𝐶2𝑁𝐿𝑆 in twocases: first, when 11 well-being indicators are aggregated, and second, when 12
indicators are aggregated (11 well-being indicators and 1 sustainability indicator). In
this section, we empirically show how the change in the methodology and the inclusionof a sustainability indicator changes the score and ranking of the composite indicators.
Table 3 presents the full empirical results, containing the score and ranking ofcomposite indicators along with existing HDI and GDP per capita. To ensurecomparability with composite indicators, we rescale the HDI score so that its maximum
value is 10, which is the same as the BLI. We compare the distribution of 𝐶𝐼𝐵𝑂𝐷 ,𝐶𝐼𝐶2𝑁𝐿𝑆 , HDI, and GDP per capita among countries. According to Table 4, the mean,
the variation characterized by the standard deviation, and the range of the distribution
characterized by the difference between the maximum and minimum scores are roughly
similar and comparable to each other for 𝐶𝐼𝐵𝑂𝐷 , 𝐶𝐼𝐶2𝑁𝐿𝑆 , and HDI. While 𝐶𝐼𝐶2𝑁𝐿𝑆
and HDI each have a similar mean, 𝐶𝐼𝐵𝑂𝐷 has a lower mean than these two indicators.
𝐶𝐼𝐵𝑂𝐷 and 𝐶𝐼𝐶2𝑁𝐿𝑆 are distributed in a relatively wider range than HDI. No matter
which composite indicators we adopt, their scores are shown to grow as per capita
income grows. However, the difference in the score of composite indicators 𝐶𝐼𝐵𝑂𝐷
and 𝐶𝐼𝐶2𝑁𝐿𝑆 between high-income and middle-income countries is much smaller thanthe difference in GDP per capita.
Table 3 shows that the lower discriminatory power of BOD becomes more evident inthis study. More than 20 countries among 36 countries are assigned the highest valueof one in both cases of aggregating 11 and 12 indicators. These are countries that havehigher scores of HDI and GDP per capita among the sample. It is obvious that BOD
fails to differentiate the performance of these countries and show its overall picture.
Moving from BOD to C2NLS, the comparison dramatically improves and we cancompletely distinguish countries’ performances. Figure 1 and Figure 2 compare twocomposite indicators, 𝐶𝐼𝐶2𝑁𝐿𝑆 and 𝐶𝐼𝐵𝑂𝐷 , along with the measure based on CNLS,𝐶𝐼𝐶𝑁𝐿𝑆 . Since the ratio between 𝐶𝐼𝐶2𝑁𝐿𝑆 and 𝐶𝐼𝐶𝑁𝐿𝑆 is constant for all countries,
comparing 𝐶𝐼𝐶2𝑁𝐿𝑆 and 𝐶𝐼𝐵𝑂𝐷 illustrates how 𝐶𝐼𝐶2𝑁𝐿𝑆 improves 𝐶𝐼𝐵𝑂𝐷 in terms

10

of discrimination power. It is shown that 𝐶𝐼𝐶2𝑁𝐿𝑆 differentiates the performance of thecountries that are ranked equally under 𝐶𝐼𝐵𝑂𝐷 by holding the ranking of othercountries almost constant. Thus, while the international comparison of well-being basedon 𝐶𝐼𝐶2𝑁𝐿𝑆 is similar to that based on 𝐶𝐼𝐵𝑂𝐷 , 𝐶𝐼𝐶2𝑁𝐿𝑆 enables us to undertake a moredetailed comparison than 𝐶𝐼𝐵𝑂𝐷 .
Next, we consider how the inclusion of the sustainability indicator changes thecomposite well-being indicators by comparing the two cases. Since the present study
deals with sustainability as just 1 among 12 well-being topics, the impact of the
inclusion of the sustainability indicator is rather modest and it does not change the score
and ranking of composite indicators dramatically. Table 3 shows that integrating thesustainability indicator raises the values of the composite indicators on average. Table
5 lists five countries whose values or rankings of their composite indicators rise themost significantly in the sample: Estonia, Israel, Korea, Russia, and Sweden. Table 5
reports the extent of the increase of both the values and the rankings of their composite
indicators after sustainability concerns are included. All five countries except Sweden
are relatively low income countries and have large adjusted net savings compared totheir lower socioeconomic indicators. Thus, the inclusion of the sustainability indicator
causes their ranking to rise. Korea significantly raises its ranking in both composite
indicators, which reflects that the value of the sustainability indicator is much higher
than the values of the other well-being indicators. In addition, Table 5 lists fivecountries whose values or rankings of their composite indicator decline the mostsignificantly in the sample: Australia, Finland, Germany, Greece, and Japan. All are
relatively high-income countries except Greece and have smaller adjusted net savings
compared to their higher socioeconomic indicators. While only Australia and Finlandshow lower composite indicators after inclusion of the sustainability indicator, the otherthree countries lose their ranking under 𝐶𝐼𝐶2𝑁𝐿𝑆 .
Table 6 shows the correlation of composite indicators with existing HDI and GDP per capita.
It shows that all composite indicators and HDI, which share a similar pattern of distribution,
are highly correlated with each other. The quest for an alternative welfare measure stems
from an acknowledgement of the limitations of GDP per capita as a welfare measure. What isthe extent of the difference of the ranking of countries under composite indicators from that
under GDP per capita? All composite indicators are largely positively correlated with GDP
per capita. Correlations are even larger in the Spearman rank correlation coefficient, whichis larger than 0.7. Table 1 suggests that GDP per capita and the scores of individual well-being
indicators are directly proportional in many dimensions. Larger values of individual
indicators raise the composite indicators of which they are components. Therefore, the positive
correlation between 𝑪𝑰𝑩𝑶𝑫 , 𝑪𝑰𝑪𝟐𝑵𝑳𝑺, and GDP per capita is straightforward in the case ofaggregating 11 indicators. Similarly,

Table 2 suggests that as GDP per capita grows, the sustainability indicator increases on
average like the remaining 11 well-being indicators. Thus, the positive correlation
between 𝐶𝐼𝐵𝑂𝐷 , 𝐶𝐼𝐶2𝑁𝐿𝑆 , and GDP per capita is also straightforward in the case ofaggregating the 12 indicators.
5. Conclusion
Well-being is a multidimensional concept. The OECD recently specified 11 topics thatare essential to people’s well-being and released 11 corresponding well-being
indicators. However, the OECD leaves the aggregation of the data to the user and asustainability indicator is not included among the 11 indicators. Thus, the present study
introduces an additional sustainability indicator from the World Bank’s adjusted net

11

savings and aggregates the 11 well-being indicators and the sustainability indicatorusing composite indicators. We adopt two composite indicators based on the BOD and
C2NLS approaches. Unlike HDI, both approaches aggregate individual indicators byinvestigating country-specific weights that favour each country.
The composite indicator based on BOD is now a standard tool for evaluating
multifaceted concepts, such as well-being. However, since more than half of countries
are ranked the highest under the application of BOD in the present study, BOD fails to
distinguish their performances. The composite indicator based on C2NLS we first
introduced here gives a similar cross-country ranking to that based on BOD. Moreover,it even allows us to differentiate completely the performance of countries that are
ranked equally under BOD. Thus, C2NLS enables a complete cross-country comparisonof overall well-being, improving on the BOD approach.
We quantify the impact of the inclusion of the sustainability indicator into other wellbeing indicators by using composite indicators based on C2NLS. The inclusion of thesustainability indicator has a rather modest effect and does not significantly change thescore and ranking of composite indicators for many countries. However, the composite
indicators of some countries are affected significantly by integrating the sustainability
indicator. Each of these countries has a large gap between its sustainability indicatorand other well-being indicators. While the composite indicators of countries whose
sustainability indicator is much larger than their other well-being indicators increase
their ranking, such as Korea, the composite indicators of countries whose sustainability
indicator is much smaller than their other well-being indicators lose their ranking, suchas Finland. Our results verify the usefulness of applying C2NLS for integrating
sustainability concerns into a composite well-being indicator.
The greater discriminatory power of the composite indicator based on C2NLS comparedwith BOD does not mean that C2NLS is a better construction method of composite
indicators. Our proposal to introduce C2NLS is justified merely from a practicalstandpoint. Future research should investigate the theoretical framework for evaluating
composite indicators.12

12

Index number theory proposes desirable axioms that plausible price indices need to satisfy (Balk,
2008). Axiomatic justification might be applicable to studies on composite indicators.

12

References
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13

Stiglitz, J.E., Sen, A. and Fitoussi, J.-P., (2009). Report by the Commission on the Measurement of
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World Bank, (2011). The Changing Wealth of Nations, Washington DC: World Bank Publications.

14

Table 1: Descriptive Statistics, OECD Well-being Indicators
housingMeanoverallhigh income
middle income
low income
MedianStd. Dev.
Max
Min

income

5.6
7.0
6.3
4.1
6.0
1.5
7.8
2.2

3.5
7.0
4.3
1.3
3.6
2.2
10.0
0.1

jobs

6.7
8.8
7.3
5.2
7.1
1.8
9.6
2.2

community

education

7.6
8.4
8.6
5.9
8.2
2.3
10.0
0.0

6.5
6.6
7.2
5.5
7.2
1.8
9.2
1.2

environment

civic
engagement

healthlifesatisfaction

7.2
8.3
7.9
5.9
7.7
1.8
9.8
2.9

5.2
5.5
5.8
4.3
5.3
1.7
9.5
2.1

7.0
8.5
8.0
5.2
7.8
2.0
9.4
0.6

energymineralsafety

6.2
8.8
7.4
3.7
7.3
3.0
10.0
0.0

work-life
balance
8.3
8.7
8.9
7.2
8.8
1.9
9.9
0.4

6.7
7.3
7.0
6.0
7.2
1.9
9.8
0.0

Table 2: Descriptive Statistics, World Bank Adjusted Net Savings, Constant 2011 PPP Dollars
Sustainability
index
Mean
overallhigh income
middle income
low income
MedianStd. Dev.
Max
Min

4.7
7.8
4.8
3.5
4.3
2.2
10.0
0.0

adjusted net savings

1632.7 ( 100% )5182.8 ( 100% )1760.3 ( 100% )353.9 ( 100% )1265.22481.47750.3-3704.6

net national savings

natural resources depletion

2333.5 ( 143% )7096.3 ( 137% )2182.2 ( 124% )1089.3 ( 309% )1599.33104.3
14439.5-3583.6

614.5 ( 38% )1810.6 ( 35% )
345.4 ( 20% )639.7 ( 182% )
101.3
1286.36644.10.0

15carbon dioxide damage

434.9
1770.8
202.3
363.9
46.9
1184.96608.8
0.0

164.0
30.4
142.0
237.1
12.1
483.2
2285.70.0

net forest
14.6
2.4
1.8
37.0
0.0
34.9
140.8
0.0

87.3 (
109.9 (
75.7 (
97.3 (
75.2
44.0
207.2
24.0

5%

)

2%

)

4%

)

28%

)

Table 3: Composite Indicators with HDI and GDP Per Capita

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 Statesover 12 indexes
BOD
C2NLS
10.000 ( 1 ) 9.684 ( 4 )10.000 ( 1 ) 9.295 ( 18 )10.000 ( 1 ) 9.207 ( 22 )8.297 ( 34 ) 8.171 ( 34 )10.000 ( 1 ) 9.660 ( 5 )7.104 ( 36 ) 6.912 ( 36 )9.842 ( 22 ) 9.231 ( 20 )10.000 ( 1 ) 10.000 ( 1 )9.370 ( 28 ) 8.983 ( 29 )10.000 ( 1 ) 9.586 ( 9 )9.412 ( 27 ) 9.084 ( 26 )10.000 ( 1 ) 9.435 ( 12 )9.339 ( 29 ) 8.966 ( 30 )9.482 ( 25 ) 9.113 ( 25 )10.000 ( 1 ) 9.314 ( 16 )10.000 ( 1 ) 9.601 ( 7 )9.615 ( 23 ) 9.158 ( 24 )9.211 ( 30 ) 8.836 ( 31 )10.000 ( 1 ) 9.302 ( 17 )10.000 ( 1 ) 9.589 ( 8 )10.000 ( 1 ) 9.217 ( 21 )9.113 ( 31 ) 8.994 ( 28 )10.000 ( 1 ) 9.449 ( 11 )10.000 ( 1 ) 9.369 ( 14 )10.000 ( 1 ) 9.937 ( 2 )10.000 ( 1 ) 9.411 ( 13 )8.776 ( 32 ) 8.588 ( 32 )8.302 ( 33 ) 8.289 ( 33 )9.582 ( 24 ) 9.199 ( 23 )9.420 ( 26 ) 9.016 ( 27 )10.000 ( 1 ) 9.321 ( 15 )10.000 ( 1 ) 9.506 ( 10 )10.000 ( 1 ) 9.721 ( 3 )8.087 ( 35 ) 7.758 ( 35 )10.000 ( 1 ) 9.643 ( 6 )10.000 ( 1 ) 9.246 ( 19 )over 11 indexes
BOD
C2NLS
10.000 ( 1 ) 9.729 ( 3 )10.000 ( 1 ) 9.224 ( 18 )10.000 ( 1 ) 9.159 ( 22 )8.297 ( 33 ) 8.132 ( 34 )10.000 ( 1 ) 9.637 ( 5 )7.104 ( 36 ) 6.887 ( 36 )9.842 ( 22 ) 9.202 ( 19 )10.000 ( 1 ) 10.000 ( 1 )9.245 ( 29 ) 8.932 ( 29 )10.000 ( 1 ) 9.592 ( 6 )9.412 ( 26 ) 9.035 ( 25 )10.000 ( 1 ) 9.427 ( 9 )9.339 ( 28 ) 8.933 ( 28 )9.469 ( 25 ) 9.034 ( 26 )10.000 ( 1 ) 9.244 ( 17 )10.000 ( 1 ) 9.569 ( 8 )9.574 ( 24 ) 9.075 ( 24 )9.211 ( 30 ) 8.805 ( 31 )10.000 ( 1 ) 9.282 ( 15 )9.860 ( 21 ) 9.386 ( 12 )10.000 ( 1 ) 9.166 ( 21 )9.113 ( 31 ) 8.930 ( 30 )10.000 ( 1 ) 9.424 ( 10 )10.000 ( 1 ) 9.315 ( 14 )10.000 ( 1 ) 9.876 ( 2 )10.000 ( 1 ) 9.372 ( 13 )8.776 ( 32 ) 8.544 ( 32 )8.249 ( 34 ) 8.164 ( 33 )9.582 ( 23 ) 9.151 ( 23 )9.378 ( 27 ) 8.953 ( 27 )10.000 ( 1 ) 9.273 ( 16 )10.000 ( 1 ) 9.408 ( 11 )10.000 ( 1 ) 9.674 ( 4 )8.012 ( 35 ) 7.711 ( 35 )10.000 ( 1 ) 9.583 ( 7 )10.000 ( 1 ) 9.196 ( 20 )a) Unit: Constant 2011 PPP US dollar
Note: Parentheses indicate country ranking

16

HDI
9.862 ( 2 )9.341 ( 20 )9.352 ( 19 )7.864 ( 36 )9.564 ( 8 )8.661 ( 32 )9.150 ( 25 )9.554 ( 10 )8.884 ( 27 )9.341 ( 20 )9.373 ( 17 )9.649 ( 6 )9.075 ( 26 )8.682 ( 31 )9.458 ( 13 )9.564 ( 8 )9.405 ( 16 )9.267 ( 23 )9.426 ( 14 )9.416 ( 15 )9.362 ( 18 )7.991 ( 34 )9.713 ( 3 )9.607 ( 7 )10.000 ( 1 )8.820 ( 28 )8.704 ( 30 )8.236 ( 33 )8.789 ( 29 )9.288 ( 22 )9.224 ( 24 )9.522 ( 11 )9.713 ( 3 )7.991 ( 34 )9.469 ( 12 )9.681 ( 5 )GDP a)
41671
42888
39840
14301
41333
20154
27047
41831
23310
38618
36264
40980
27046
22413
38216
42946
30159
34626
34316
31327
88848
15887
43148
31712
61896
21748
25828
22570
25128
28156
31732
41763
51302
17998
34800
49855

( 10 )(

7 )( 13 )( 36 )( 11 )( 33 )( 25 )(

8 )( 29 )( 14 )( 16 )( 12 )( 26 )( 31 )( 15 )(

6 )( 23 )( 18 )( 19 )( 22 )(

1 )( 35 )(

5 )( 21 )(

2 )( 32 )( 27 )( 30 )( 28 )( 24 )( 20 )
(

9 )

(

3 )

( 34 )( 17 )(

4 )Table 4: Descriptive Statistics of Composite Indicators
over 12 indexes
Meanoverallhigh income
middle income
low income
MedianStd. Dev.
Max
Min
a)

over 11 indexes

BOD

C2NLS

BOD

C2NLS

9.582010.0000
9.9073
8.978010.0000
0.688210.0000
7.10419.16089.53029.42308.66389.27040.603510.0000
6.91229.568410.0000
9.89788.954310.0000
0.695510.0000
7.10419.11189.4781
9.37728.61129.21290.607210.0000
6.8872

HDI

GDP a)

9.19449.68929.47938.62589.3571
0.533910.0000
7.8640

35046
62975
37798
22430
34471
14046
88848
14301

Unit: Constant 2011 PPP US dollar

Table 5: Difference in Value and Ranking of Composite Indicators
BOD
C2NLS
value
rankingvalueranking
Countries certainly increasing composite indicators
Estonia
0.12541
0.05040Israel
0.04081
0.08240Korea
0.1402200.2033
4
Russian Federation 0.0532
1
0.12460
Sweden0.000000.0981
1Countries cerainly decreasing composite indicators
Australia
0.00000-0.0454-1
Finland0.00000-0.0066-3
Germany
0.000000.0079-3
Greece0.0000-1
0.0327-2
Japan
0.000000.0200-2
Note: Parentheses indicate country ranking

Sustainability indicator

Average of 11 indicators

4.45
5.09
7.04
4.16
7.54

( 11 )

3.94
4.47
5.94
0.00
2.81

( 23 )

(

9 )

(

3 )

( 10 )(

5 )( 20 )(

7 )( 36 )( 30 )

5.3
5.9
5.7
4.3
8.0

( 28 )

(

2 )

8.1
7.6
7.3
4.2
6.3

(

1 )

(

8 )

( 24 )( 25 )( 33 )( 14 )( 34 )( 21 )

Table 6: Correlation among Composite Indicators
over 12 indexes
over 11 indexes
BOD
C2NLS
BOD
C2NLS
Correlation coefficient
Aggregation over 12 indexes
BOD
1.00000.95180.99880.9496
C2NSL
0.95181.00000.9501
0.9978Aggregation over 11 indexes
BOD
0.99880.95011.00000.9498C2NSL
0.9496
0.99780.94981.0000HDI
0.76390.73530.76700.7423GDP
0.56350.52200.5711
0.5265

HDI

GDP

0.76390.73530.56350.52200.76700.74231.00000.69630.5711
0.52650.69631.0000Spearman rank correlation coefficient
Aggregation over 12 indexes
BOD
1.0000C2NSL
0.8868Aggregation over 11 indexes
BOD
0.9823
C2NSL
0.8842
HDI
0.7910GDP
0.76720.88681.00000.98230.86300.8842
0.99250.79100.81690.76720.71090.86300.99250.81690.71091.00000.87310.78440.78440.87311.00000.82470.72100.78440.82471.00000.8572

0.78440.72100.85721.000017

Table 7: Average Weight for Composite Indicators
housing
Aggregation over 12 indexes
BOD
0.0937
C2NSL
0.0296Aggregation over 11 indexes
BOD
0.0587
C2NSL
0.0226

incomejobs

community

education

environment

civic
engagement

healthlifesatisfaction

safetywork-life
balance

0.11860.04560.02270.03070.06340.1266

0.09550.03990.05370.03060.04270.05860.12040.03890.04970.25930.24800.23460.17440.1802

0.11300.04600.07040.04470.08120.08420.1011
0.04170.0806
0.04860.02040.05370.10620.03910.04970.22350.28910.23390.19040.246618

sustainability

0.09820.0216

ChileTurkeyBrazilRussian Federation
Portugal
Italy
GreeceEstonia
MexicoSlovenia
FranceHungary
IsraelSlovak Republic
Belgium
Luxembourg
Czech Republic
United States
AustriaJapan
IcelandSpain
New Zealand
PolandGermany
Netherlands
SwedenFinland
Korea
IrelandUnited Kingdom
CanadaAustralia
Switzerland
NorwayDenmark
Chile
TurkeyBrazilRussian Federation
Portugal
Italy
GreeceEstonia
MexicoSlovenia
FranceHungary
IsraelSlovak Republic
Belgium
Luxembourg
Czech Republic
United States
AustriaJapan
IcelandSpain
New Zealand
PolandGermany
Netherlands
SwedenFinland
Korea
IrelandUnited Kingdom
CanadaAustralia
Switzerland
NorwayDenmark(index)
1.2
1.0

0.8

0.6

0.4

BOD

0.2

CNLS

C2NLS

0.0

Figure 1: Comparison of Composite Indicators Based on BOD and C2NLS (12 indices)(index)
1.2

1.0

0.8

0.6

0.4

BOD

0.2

CNLS

C2NLS

0.0

Figure 2: Comparison of Composite Indicators Based on BOD and C2NLS (11 indices)19

Table A.1: Well-being Indicators and Sustainability Indicatorhousing
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

7.6
5.8
7.3
4.2
7.8
3.7
4.6
6.2
4.4
6.3
6.4
6.3
3.8
3.8
5.9
7.3
4.2
5.1
4.9
5.9
6.2
3.7
6.9
6.6
7.7
3.5
6.6
3.3
4.1
5.8
6.8
6.3
6.3
2.2
6.0
7.8

(

income4 )( 21 )(

5 )( 27 )(

1 )( 32 )( 25 )( 16 )( 26 )( 12 )( 11 )( 12 )( 30 )( 30 )( 19 )(

5 )( 27 )( 23 )( 24 )( 19 )( 16 )( 32 )
(

7 )

(

9 )

(

3 )

( 34 )(

9 )( 35 )( 29 )( 21 )(

8 )( 12 )( 12 )( 36 )( 18 )
(

1 )

4.9
5.0
5.9
0.1
5.7
1.1
1.8
4.0
0.9
3.5
5.0
5.3
1.9
1.3
3.6
3.3
3.8
4.4
5.6
2.3
6.5
0.7
5.3
2.1
4.0
1.3
2.5
1.2
1.4
2.2
2.9
5.0
7.3
0.6
4.8
10.0

jobs
( 12 )
(

9 )

(

4 )

( 36 )(

5 )( 32 )( 27 )( 15 )( 33 )( 19 )
(

9 )

(

7 )

( 26 )( 29 )( 18 )( 20 )( 17 )( 14 )(

6 )( 23 )(

3 )( 34 )(

7 )( 25 )( 15 )( 29 )( 22 )( 31 )( 28 )( 24 )( 21 )
(

9 )

(

2 )

( 35 )( 13 )
(

1 )

8.5
8.6
7.3
5.7
8.0
5.8
6.3
8.0
5.6
7.5
6.5
8.3
2.2
4.8
8.7
5.9
6.7
5.6
7.9
7.6
8.3
6.0
8.7
7.5
9.2
5.2
4.5
6.8
4.5
6.5
2.6
7.8
9.6
4.3
7.6
7.9

community
(

6 )

(

5 )

( 18 )( 27 )(

9 )( 26 )( 23 )(

9 )( 28 )( 16 )( 21 )(

7 )( 36 )( 31 )(

3 )( 25 )( 20 )( 28 )( 11 )( 14 )(

7 )( 24 )(

3 )( 16 )(

2 )( 30 )( 32 )( 19 )( 32 )( 21 )( 35 )( 13 )(

1 )( 34 )( 14 )( 11 )

8.9
9.7
8.1
7.8
9.2
6.1
6.7
10.0
7.4
9.0
8.2
8.9
0.0
6.9
10.0
9.9
7.7
8.4
7.9
3.1
7.3
2.0
8.6
10.0
8.9
7.7
6.2
5.8
7.3
8.8
8.7
8.2
9.5
3.8
9.2
8.0

education( 10 )(

5 )( 19 )( 22 )(

7 )( 31 )( 29 )(

1 )( 25 )(

9 )( 17 )( 10 )( 36 )( 28 )
(

1 )

(

4 )

( 23 )( 16 )( 21 )( 34 )( 26 )( 35 )( 15 )(

1 )( 10 )( 23 )( 30 )( 32 )( 26 )( 13 )( 14 )( 17 )(

6 )( 33 )(

7 )( 20 )

7.8
6.7
7.5
1.9
7.7
4.4
7.8
7.8
8.1
9.2
5.9
8.0
6.1
6.7
7.3
7.1
5.4
5.1
7.9
8.0
4.5
1.2
7.8
7.3
7.3
8.5
4.4
6.1
6.2
7.6
5.4
8.0
7.4
2.5
6.4
7.1

(

environment
8 )( 21 )( 14 )( 35 )( 12 )( 32 )
(

8 )

(

8 )

(

3 )

(

1 )

( 27 )(

4 )( 25 )( 21 )( 16 )( 19 )( 28 )( 30 )
(

7 )

(

4 )

( 31 )( 36 )(

8 )( 16 )( 16 )(

2 )( 32 )( 25 )( 24 )( 13 )( 28 )(

4 )( 15 )( 34 )( 23 )( 19 )

9.1
7.3
7.2
6.0
8.6
3.3
7.5
9.0
8.4
9.0
8.4
8.8
4.6
7.3
8.8
8.3
5.4
6.8
6.9
5.3
8.0
4.5
6.9
9.0
8.9
4.8
7.8
4.3
8.1
7.1
6.0
9.8
8.3
2.9
9.0
7.9

Note: Parentheses indicate country ranking

20

(

2 )

( 20 )( 22 )( 27 )( 10 )( 35 )( 19 )(

3 )( 11 )(

3 )( 11 )(

8 )( 32 )( 20 )(

8 )( 13 )( 29 )( 26 )( 24 )( 30 )( 16 )( 33 )( 24 )
(

3 )

(

7 )

( 31 )( 18 )( 34 )( 15 )( 23 )( 27 )(

1 )( 13 )( 36 )(

3 )( 17 )

civic
engagement
( 1 )
9.5
( 15 )
5.6( 13 )
5.8( 24 )
4.4( 13 )
5.8( 25 )
4.3( 30 )
3.7
( 5 )
7.1
( 34 )
2.3( 12 )
5.9( 25 )
4.3( 27 )
3.9( 27 )
3.9( 22 )
4.8( 16 )
5.3( 11 )
6.0( 34 )
2.3( 23 )
4.5( 27 )
3.9
( 3 )
7.5
( 7 )
6.8
( 16 )
5.3( 20 )
5.0
( 4 )
7.2
( 8 )
6.5
( 16 )
5.3( 33 )
3.3( 36 )
2.1( 31 )
3.6
( 9 )
6.2
( 20 )
5.0
( 2 )
8.7
( 32 )
3.4
( 9 )
6.2
( 6 )
6.9
( 16 )
5.3

health
9.4
7.7
7.8
4.9
9.3
5.8
5.8
7.4
4.5
7.5
7.9
7.2
8.2
4.3
8.8
8.7
9.0
7.8
5.0
5.0
8.0
5.0
8.3
9.4
8.1
5.2
5.9
0.6
5.4
6.6
8.7
8.8
9.3
5.2
8.4
8.5

life satisfaction(

1 )( 19 )( 17 )( 33 )(

3 )( 25 )( 25 )( 21 )( 34 )( 20 )( 16 )( 22 )( 13 )( 35 )
(

6 )

(

8 )

(

5 )

( 17 )( 30 )( 30 )( 15 )( 30 )( 12 )(

1 )( 14 )( 28 )( 24 )( 36 )( 27 )( 23 )
(

8 )

(

6 )

(

3 )

( 28 )( 11 )( 10 )

8.7
9.1
7.8
8.1
9.4
6.1
6.5
9.4
2.1
8.9
6.4
7.3
0.0
0.6
9.1
6.7
7.8
4.2
4.1
4.2
7.9
8.9
8.8
8.4
9.7
3.4
1.4
2.7
4.0
4.1
4.7
8.9
10.0
0.5
7.2
7.5

( 11 )(

5 )( 15 )( 13 )(

3 )( 23 )( 21 )(

3 )( 32 )(

7 )( 22 )( 18 )( 36 )( 34 )(

5 )( 20 )( 15 )( 25 )( 27 )( 25 )( 14 )(

7 )( 10 )( 12 )(

2 )( 30 )( 33 )( 31 )( 29 )( 27 )( 24 )
(

7 )

(

1 )

( 35 )( 19 )( 17 )

safety
9.6
9.1
7.5
2.1
9.7
6.7
9.3
8.8
7.3
9.3
8.3
8.9
8.8
8.8
9.2
9.4
7.4
8.4
9.9
9.5
8.3
0.4
8.3
9.3
8.8
9.8
8.0
6.5
9.1
8.8
8.7
8.2
8.7
7.8
9.8
8.9

(

5 )

( 12 )( 30 )( 35 )(

4 )( 33 )(

8 )( 16 )( 32 )(

8 )( 24 )( 14 )( 16 )( 16 )( 11 )(

7 )( 31 )( 23 )
(

1 )

(

6 )

( 24 )( 36 )( 24 )(

8 )( 16 )(

2 )( 28 )( 34 )( 12 )( 16 )( 21 )( 27 )( 21 )( 29 )(

2 )( 14 )

work-life
sustainability
balance( 29 )( 23 )
5.3
3.94( 26 )
( 8 )
5.9
6.14
( 3 )
( 13 )
8.8
4.92( 21 )( 31 )
6.7
2.54( 25 )( 17 )
6.2
4.37( 32 )( 34 )
5.1
1.34( 16 )( 27 )
7.2
2.97
( 1 )
( 12 )
9.8
5.91( 14 )( 11 )
7.4
4.45( 14 )( 20 )
7.4
4.47( 12 )( 14 )
7.6
4.34
( 7 )
( 7 )
7.9
5.94
( 16 )( 36 )
7.2
0.00( 11 )( 15 )
7.8
3.66( 27 )( 35 )
5.7
1.95
( 7 )
( 18 )
7.9
4.57( 33 )
( 9 )
4.9
5.09
( 13 )( 32 )
7.5
2.72( 31 )( 30 )
5.2
2.81( 34 )
( 3 )
4.2
7.04
( 7 )
( 4 )
7.9
7.22
( 35 )( 24 )
2.6
3.01
( 3 )
( 6 )
8.8
6.59
( 23 )( 26 )
6.3
3.81
( 5 )
8.7
10.00 ( 1 )( 28 )( 21 )
5.6
3.49( 20 )( 33 )
6.8
1.83
( 7 )
( 10 )
7.9
4.16( 16 )( 25 )
7.2
3.00( 22 )( 19 )
6.6
3.87
( 2 )
( 28 )
9.4
3.15
( 6 )
( 5 )
8.1
7.54
( 19 )
( 2 )
7.1
8.07
( 36 )( 16 )
0.0
3.25( 23 )( 29 )
6.3
3.53( 29 )( 22 )
5.3
4.41