ISSN 1881-6436

Discussion Paper SeriesNo. 13-01*

Measuring World Better Life Frontier
Hideyuki Mizobuchi
April 2013

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

Measuring World Better Life Frontier
Hideyuki Mizobuchi†‡
14 April, 2013
Abstract
The OECD Better Life initiative recently released a comprehensive set of 11 indicators ofwell-being covering a group of countries. Each individual indicator corresponds to a key topic
that is essential to well-being. However, the problem of aggregating them is left to users of
this dataset. Using these as individual indicators, we propose a composite indicator of overall
well-being, which is intended to measure the performance of each country in terms ofproviding well-being to its people. The ‘benefit of the doubt’ approach (BOD), a well-known
aggregation tool based on a weighed sum, assigns the most favourable weights for each entity
under investigation. BOD may also be considered to evaluate the performance of each entity
in terms of its efficiency. Regarding individual indicators as outputs, it constructs thebenchmark production frontier from observed individual indicators. A composite indicator
based on BOD equals the distance between each entity’s individual indicator and the
production frontier, indicating its efficiency. It is widely considered that the well-being of acountry’s people stems from its productive base, which is characterized by capital assets andsocial infrastructures. Thus, the productive base can be considered the input used to produce
well-being, which is reflected by individual indicators. Therefore, when we apply BOD toaggregate individual well-being indicators across countries, we implicitly assume that all
countries have the same productive base, as BOD addresses only the output and neglects the
input. This inaccurate assumption leads to a distorted performance measure. Data
Envelopment Analysis (DEA), in which BOD has its roots, is a tool to measure the efficiencyof each entity by allowing for differences in inputs as well as outputs across entities. DEA
also measures efficiency by using the distance to the production frontier; however, unlike
BOD, DEA constructs the production frontier more accurately by utilizing the information of
inputs as well as outputs, leading to a better performance measure. We apply DEA toaggregate 11 individual well-being indicators into a composite indicator using the WorldBank’s estimates of each country’s productive base.
Key Words: Composite Indicators, Better Life Index, Data Envelopment Analysis, Benefit of
the Doubt approach†

Faculty of Economics, Ryukoku University, 67 Fukakusa Tsukamoto-cho, Fushimi-ku, Kyoto 6128577, Japan; mizobuchi@econ.ryukoku.ac.jp‡
This research was funded by the Ministry of the Environment, Government of Japan. The results andconclusions of this paper do not necessarily represent the views of the funding agency. We are gratefulto Jiro Nemoto, Shunsuke Managi and Shigemi Kamo for their helpful comments and suggestions. We
also wish to thank seminar participants in the annual meetings of the Society of Environmental
Economics and Policy Studies, Tohoku University, September 2012 and the Japanese Economic
Association, Kyushu Sangyo University, October, 2012 as well as Okayama University. All remaining
errors are the author’s responsibility.

11.

Introduction

GDP per capita has long been used as a proxy measure of well-being. However, it is
now widely recognized that GDP data provide a partial perspective on the array offactors that affect people’s lives. The consensus on the deficiency of per capita
income as a measure of well-being has led to a search for alternative measures ofwell-being. Research over the last two decades has considerably improved ourunderstanding of them. The past research and unresolved issues are summarized in therecommendation of the Commission on the Measurement of Economic Performanceand Social Progress, appointed by President Nicholas Sarkozy of France (Stiglitz et al.
(2009)).
The OECD has actively led the research seeking an alternative measure of well-being.
On the occasion of the OECD’s 50th Anniversary, held under the theme ‘Better
Policies for Better Lives’, the organization launched the OECD Better Life Initiative.
Drawing upon the recommendations of the commission (Stiglitz et al. (2009)), theOECD 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,
such as community, environment and work–life balance. These dimensions are
explored and analysed in detail in OECD (2011). The web-based tool Your Better Life
Index, a key instrument of the OECD Better Life Initiative, profiles the 34 OECDmember countries and two non-member countries across the 11 topics of well-being.1
However, evaluating the overall well-being by summarizing the 11 individual
indicators is left to users of the statistics.2
Since each individual well-being indicator may trend in different directions, the set of
11 individual indicators itself is not enough to compare overall well-being of people
across countries. A composite indicator is formed by compiling the underlying
individual indicators into a single number. Composite indicators can measure
multidimensional concepts, which are characterized by multiple individual indicators.
Thus, they are used to analyse a wide range of concepts such as competitiveness,single market integration, sustainability and well-being. A composite indicator ofwell-being based on the 11 individual indicators also enables a comparison of theoverall well-being of people across countries.
There exist many composite indicators and, more specifically, composite indicators ofwell-being. Despite their increasing use, identifying the ideal composite indicators
remains controversial. Much criticism associated with indicator construction stems
from the fact that the weights assigned to individual indicators are often fixed, andthat individual indicators of all countries are aggregated under these fixed weights(Cherchye et al. (2007)). Weight plays a crucial role in the construction of acomposite indicator; it determines the trade-offs between underlying individual
indicators. Moreover, by assigning zero to the other individual indicators, it selects theset of individual indicators actually incorporated. Thus, weights used to construct thecomposite indicator of well-being reflect a particular value judgement on how ‘good’a life is. Schokkaert (2007) reports much interpersonal variation in people’s opinions
1

The number of countries covered was 34 in 2011. The 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.

2

on the ‘good life’. Therefore, no matter how elaborately weights are chosen, people
across countries, and even within a society, are very likely to disagree on them. In thecontext of international comparison of well-being, certain countries (i.e. theirauthorities or experts) are likely to consider the predetermined fixed weights as unfair
because undue importance is attached to individual indicators on which they
underperform.3
Among a number of construction techniques of the composite indicator, the ‘benefitof the doubt’ approach (BOD), which has received increasing attention fromresearchers, avoids subjectivity in the determination of weights (Mahlberg andObersteiner (2001), Cherchye et al. (2004), Cherchye et al. (2007), Despotis (2005)
and OECD (2008)). Under BOD, the weights are country-specific and endogenously
determined such that they maximize the value of each country’s resulting composite
indicator. Thus, larger weights are given to the individual indicators (topics of wellbeing) on which each country performs well. The core idea is that a good relativescore of a country on an individual indicator shows that it considers the individual
indicator as relatively important. Therefore, a country cannot attribute the lower scoreof its composite indicator to a harmful or unfair weighting scheme under theinternational comparison based on BOD.
BOD is rooted in Data Envelopment Analysis (DEA), which is designed to computeefficiency indexes. 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. BOD is formally tantamount to the inputoriented DEA model, with all individual indicators as outputs and a ‘dummy input’
equal to one for all the countries. 4 As Dasgupta (2001) and Arrow et al. (2004)
advocate, it is widely believed that the productive base of economies, which consistsof a variety of capital assets and a social infrastructure, determines the well-being ofpeople. Thus, countries’ productive bases are reasonably considered as inputs toachieve greater well-being indicators. There is a wide difference in the productive
bases among countries. Thus, BOD’s assumption that inputs are invariant across units
is inappropriate when computing composite indicators of well-being.
The specification of inputs and outputs enables us to apply the original DEA toevaluating countries’ performances in terms of efficiency. Since DEA specifies thebest practice countries more appropriately than does BOD, the frontier represented bythem correctly describes the benchmark technology of providing well-being. We callthis the ‘World Better Life Frontier’. This provides an unbiased estimate of efficiency,
and thus an accurate measure of countries’ performances. The efficiency score on
DEA is also the weighted sum of 11 individual indicators, and it defines the DEA
composite indicator of well-being. In the present paper, we apply BOD and DEA tocompute the composite indicator aggregating 11 individual well-being indicators. A
series of studies by the World Bank (Kunte et al. (1998) and World Bank (2006,2011)) estimates the comprehensive wealth of many countries, which corresponds to3

In addition to the problem of paternalism, Cherchye et al. (2007) also indicates that if weights are
fixed, the eventual country ranking depends on the measurement unit and the particular normalization
option adopted for individual indicators, such as rescaled scores, distances to goalposts, or z-scores.
4
Lovell et al. (1995) interprets the dummy input as a helmsman that pursues several policy objectives.

3

their productive bases. Our application of DEA relies on the most recent of thesestudies (World Bank (2011)).
Mahlberg and Obersteiner (2001) was the first study to apply BOD to the constructionof a composite indicator of well-being. They revisited the construction procedure of
the UN’s Human Development Indicator (HDI). The existing HDI is a weighted sumof three individual indicators—income, longevity and educational attainment—using
the fixed weight. Mahlberg and Obersteiner (2001) propose an alternative HDI basedon BOD by endogenising the weight assigned to the three individual indicators.
However, as they admit, the application of BOD does not allow them to distinguishbetween resources, which differentiate countries’ conditions of providing humandevelopment or well-being.
Zaim et al. (2001) is an exception that considers the differences among countries’
inputs used for providing well-being to their people. The production process they
considered transforms two inputs (produced capital and labour) into one private good(real GDP) and four social goods (infant survival rate, life expectancy at birth,
primary school enrolment rate and secondary school enrolment rate). They propose analternative measure of well-being called ‘achievement index’, which captures eachcountry’s success in providing social goods, given fixed inputs and private goods.
Thus, countries that provide social goods from lesser inputs are more highly evaluated,which is the same conclusion that the measure we propose is likely to derive.
However, there are two differences. First, the set of inputs and outputs differs. The
inputs and outputs in the present paper have a wider coverage than in Zaim et al.
(2001); they are carefully chosen to follow the current studies in the literature, as
mentioned above.5 Second, their achievement index is the ratio of DEA efficiency
scores between the evaluated country and the reference country, Australia, while we
simply use countries’ efficiency scores. The evaluated country’s efficiency score is
calculated by the radial distance between social goods of that country and the frontier.
Their calculation is based on the assumption that inputs and private goods are fixed at
the same level as in the reference country.6 Thus, their index and the country ranking
based on it largely depend on the choice of reference country.7
There are different approaches to comparing overall well-being across countries,
which are also well founded in economic theory. Fleurbaey and Gaulier (2009) and
Jones and Klenow (2010) aggregate several dimensions of well-being by expressing
them in monetary units. 8 The calculation of the former is based on an income
equivalent, while that of the latter is based on a consumption equivalent. Bothapproaches impute the individual preferential attitude to several dimensions of well5

Despotis (2005a, 2005b) also address the process of converting countries’ resources to well-being or
human development. However, they are concerned with countries’ capabilities of converting economic
prosperity into better lives for their people. Thus, while income is considered as input, life expectancy
and adult literacy rate are considered as outputs.
6
Thus, Zaim et al. (2001) evaluates countries’ performance based solely on each country’s productionof social goods, while ignoring that of private goods. Because private goods such as income, wealthand dwellings also affect people’s well-being, their study fails to capture overall well-being.
7
The definition of the ratio of efficiency scores prevents us from interpreting the achievement index asa weighted average of individual indicators of well-being. On the other hand, the index we propose issimply an efficiency score, which is the weighted average of individual indicators.
8
Fleurbaey and Gaulier (2009) deals with leisure, unemployment, health and inequality in addition to
income. Jones and Klenow (2010) deals with life expectancy, leisure and inequality in addition to
consumption.

4

being, explicitly using the expected utility function. While they consider several
dimensions of well-being as goods being consumed and propose composite indicators(summary statistics in their words) based on consumption theory, we consider them as
outputs and propose composite indicators based on production theory.
The paper unfolds as follows. Section 2 discusses two approaches to construct acomposite indicator. Section 3 explains data of well-being indicators andcomprehensive wealth. Section 4 computes composite indicators under different casesand compares them across countries. Section 5 concludes.
2. Methodology
The present paper aggregates 34 countries’ 11 individual well-being indicators into
composite indicators and compares countries’ performance in terms of well-being. We
adopt two approaches to construct composite indicators. Since they are sufficiently
versatile to be applicable to a variety of problems and situations, we explain thembelow in a more general setting independent of the number of countries andunderlying individual indicators.
We consider that there are countries and that the well-being of people in a country
is characterized by a set of individual indicators,…,, withrepresenting the value of the th individual indicator of country . BOD aggregatesthese individual indicators using their weighted average. Denoting
as the weightof the th individual indicator of country , the composite indicator based on BOD
for country ,, , is formulated as follows:
,

,…,

Σ
0 for

:Σ

1 for

1, … , ;(1)

1, … ,For an international comparison, the above procedure is repeated for each country in,…,is endogenously determined to maximize theour sample. The weightvalue of the composite indicator for country . Thus, a larger weight is assigned to anindividual indicator on which the country performs well. In this procedure, the good
performance of country on an individual indicator is considered to indicate that thecountry prioritizes it. Therefore, countries cannot argue that their poor performance isdue to an unfair weighting scheme, because any weight other than that used for theirevaluation would not improve their position.
There are two constraints in (1). The first is that every country in a sample has aresulting composite indicator smaller than one when applying the most favourable
weights for the evaluated country . This guarantees that the resulting composite
indicator for country will be below one. The second constraint limits the weights tobe non-negative. This means that the composite indicator is a non-decreasing functionof individual indicators. Thus, each individual indicator is necessarily formed so thatthe larger score reflects an improvement in a specific aspect.
As Mahlberg and Obersteiner (2001) graphically illustrate, an alternativeinterpretation ofas outputs, is possible. Considering individual indicators
and a dummy input equal to one for all the countries,
isconsideredas,evaluating the performance of country in terms of its productive efficiency. The
dummy input can be considered as a helmsman in each country, which intended to5

provide the people a better life; this is reflected by the values of individual well-being
indicators.9
Countries’ input and output sample data enable us to construct the production set, which is the set of achievable individual well-being indicators, through theminimal extrapolation principle as follows:∈:
1, … ,

Σfor

1, … ,

;

0 for

(2)

Thus,
is the smallest subset ofcovering individual indicators of all thecountries and satisfying free disposability and convexity. The boundary ofis theproduction frontier and represents the optimal practices to produce well-being, whichis the benchmark to evaluate countries’ performances. Countries with individual
indicators on the frontier are considered the best performers and are ranked highest
under BOD. The farther from the frontier and closer to the origin the individual
indicators of a country are, the lower its performance is evaluated. Thus, the distance
between individual indicators of a country and the frontier measures its performance.
We can relate the composite indicator to the production frontier as follows:
,

:

/ ∈

(3)

The right hand side of (3) is the smallest proportional contraction of
to the bestpractice frontier, indicating the distance fromto the best practice frontier. For
countries with individual indicators on the frontier, no contraction is necessary, andthus equals one. For countries with individual indicators located farther below thefrontier, individual indicators must be well expanded to reach the frontier, and thus
is less than one.

′

′

0

0

Figure 1: Frontier constructed by BOD

Figure 2: Frontier constructed by DEA

Figure 1 illustrates what
measures in the case of two individual indicators—
and —and four countries— , , and . Each dot represents the underlying
individual well-being indicator of each country. Here the line connecting , andconstitutes the best practice frontier. , and on the frontier are classified as the9This interpretation is rooted in Lovell et al. (1995).

6

best performing, while , which lies below the frontier, is classified as the worst
performing. To expand to reach the frontier, its individual indicator needs to be
divided by a number less than one. Therefore, the composite indicator for country is
also less than one. This leads to
1 and
,
,
,
,
0 ⁄0
1.
Here we highlight the assumption of invariant input associated with BOD. This
assumption dismisses the fact that each country faces different conditions forproviding well-being to its people. As Dasgupta (2001) and Arrow et al. (2004)
advocate, it is widely believed that the productive base of economies, which consistsof a variety of assets and a social infrastructure, determines people’s well-being.
When considering individual well-being indicators as outputs and the productive baseas corresponding inputs, is a composite indicator based on BOD still a reasonable
performance measure in terms of welfare?
Figure 2 depicts how the inappropriate assumption of invariant input by BOD distorts
the calculation of the composite indicators. Suppose a situation where country is
endowed with a productive base two times greater than the other three countries, andthat the productive bases of the other three countries are equal to each other. As
mentioned before, BOD forms the best practice frontier connecting , and , andclassifies the three countries on the frontier as the best performing. However, theproduction frontier in output space coincides with a set of outputs that is attainablefrom the given input. Hence, once we assume constant returns to scale, individual
well-being indicators of country which are attainable from the same level ofproductive base as the other countries, are indicated by . Thus, when weappropriately incorporate differences in the conditions that affect countries’ abilities toprovide well-being to their people, the best practice frontier is formed by connecting, and as drawn in Figure 2. Thus, while the three countries on the frontier areclassified as being the best performing, only is classified as being the worst
performing. This leads to
1 and
,
,
,
,
0 ⁄0
1. It suggests that neglecting the differences among the productive bases
distorts estimation of the production frontier, leading to a biased performance measureof countries.
Countries endowed with a larger productive base have an advantage in providing
well-being to their people compared with those endowed with a smaller one. Since
BOD neglects differences in the size of the productive bases, it overestimatescountries having a larger productive base. Reasonable performance measures in termsof well-being should evaluate countries’ capabilities of converting the productive baseto well-being indicators. Therefore, countries’ performances must be evaluated bycomparing the combination of inputs and outputs across countries.
BOD’s estimation procedure for composite indicators or productive efficiency isrooted in DEA (Charnes et al. (1978)). The original DEA is the established tool tomeasure the efficiency of decision-making units based on the comparison of
combinations of inputs and outputs across units in a sample. An efficiency scoreobtained under DEA is the weighted average of outputs of the evaluated country, forwhich it defines the composite indicator. By applying DEA to the aggregate ofindividual well-being indicators along with the productive bases, we can moreaccurately estimate the production frontier. This leads to a more appropriate
performance measure for each country. DEA itself is an established tool; however, we
7

describe it in further detail below to contrast it with BOD in terms of calculating thecomposite indicator.
We consider the general case that each country is bestowed with a productive basecharacterized by a set of types of wealth,…,, withthe value of10
th wealth in country . Countries’ individual well-being indicators are considered
as being produced from this wealth vector. As with BOD, DEA constructs thethrough the minimal extrapolation principle as follows:
production set
, ∈
:
Σ
for
1, … , ;(4)Σfor1, … , ;0 for
1, … ,,
deals with input and output pairs. Whileis required to
Unlikeinclude individual indicators of all the countries,
is required to include pairs, of all the countries. Inclusion of the productive base changes the shape of the
production set, leading to a different frontier.
: / ∈
,
(5)
As formulated in (5),, radially measures the distance from individual wellbeing indicators of country , , to the benchmark production frontier. An alternative
formulation of, is given below to compare, and, from theviewpoint of an endogenous weighting scheme.
Σ
:Σ
Σ
for
,
,…,

,

,…,

1, … , ; Σ
0 for
1, … ,

1;0 for

1, … ,;(6)

Both DEA and BOD select country-specific weights that maximize the composite
indicator for each country under evaluation. However, we find a difference inconstraint between DEA and BOD in (1) and (6). The constraints in (1) and (6) statethat when we apply favourable weights for the evaluated country , the resulting
composite indicators of other countries are under the upper bounds. While (1) sets one
as the upper bound for eachas the upper bound across countries, (6) sets ∑country , which varies across countries. Weights assigned to the wealth vector
are optimally chosen for the evaluated country under the assumption,…,that ∑
1 . Thus, if the evaluated country has a relatively small
productive base, the upper bound of the constraint for many countries becomes greaterthan one so that ∑1, relaxing the constraint of the optimization problem
and making the composite indicator of the evaluated country greater than one. It
indicates that between two countries with the same well-being indicators, the one withthe smaller productive base will be more highly evaluated. Generally, countries withsmaller productive bases raise their ranking when changing from BOD to DEA. These
are the properties that we believe a reasonable composite indicator should satisfy.

10

Social infrastructures of country , such as law and community ties, also constitute its productive
base. They are considered social capital, which is a component of the wealth vector .

8

3.

Data

3.1. OECD Better Life Index
In the midst of growing concerns about identifying an alternative approach tomeasuring well-being, in 2011, the OECD launched the Better Life Initiative andreleased a set of 11 well-being indicators covering the 34 OECD member countries,
comprising advanced and emerging economies. The data have been updated in 2012
to include the latest figures and add more dimensions to calculate indicators.
Moreover, the country coverage has been expanded beyond the OECD to include
Brazil and Russia. We use the most recent data covering individual indicators drawn
from Your Better Life Index. Unfortunately, since the data of the productive base,which are necessary for computing the composite indicator in the present paper, aremissing for Estonia and Slovenia, we focus on the remaining 34 countries.
The 11 individual well-being indicators evaluate topics that the OECD considers
essential to people’s well-being. Each individual indicator corresponding to each topic
is based on one to three secondary indicators. These underlying secondary indicators
are expressed in different units such as dollars, years or number of people. To
compare and aggregate values expressed in different units, the values are normalised.
The normalisation is done according to a standard formula which converts the originalvalues of the individual indicators into numbers between 0 and 10 as follows:
10. Within each topic, the secondary indicators areaveraged with equal weight. For example, while the topic of environment isconstructed using two secondary indicators, water quality and air pollution, theirscores are first normalized in a range between 0 and 10. They are then aggregated aswater quality score air pollution score
follows:. Eleven individual indicators and theircorresponding 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) Safety9(Homicide rate; Assault rate)11) Life Satisfaction(Life Satisfaction)

Among the 11 individual indicators, the first three topics are categorized undermaterial living conditions and the remaining eight are categorized as quality of life.
According to the file released by the OECD Better Life Initiative, the data years of theunderlying detailed indicators range from 2005 to 2011. Averaging them with each
topic equally weighted suggests a year close to 2009. Thus, we consider that the 11
indicators of each country measure the socioeconomic situation of people around2009.
Table 1 summarises the statistics of the 11 indicators. A complete listing of the data isfound in Table A.1 of the Appendix. As OECD (2011) suggests, these tables showthat while life is good in many dimensions in countries such as Australia, Canada,
Sweden, New Zealand, Norway and Denmark, it is significantly less so in countries
such as Turkey, Mexico, Chile, Estonia, Portugal and Hungary. While the lattercountries are characterized by their lower per capita income, the former are notnecessarily the richest countries, that is, those with the highest per capita income.
From here onwards, 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. The grouping is as follows: seven high-income countries with per capita GDP
greater than USD 40,000, 18 middle-income countries with per capita GDP between
USD 20,000 and 40,000 and five low-income countries with per capita GDP below
USD 20,000. Table 1 suggests that people’s well-being improves as income grows inevery aspect apart from work–life balance. In this respect, on average, people inmiddle-income countries enjoy a better life than do those in high-income countries.
3.2. In the World Bank: Comprehensive Wealth AccountAcknowledging the deficiency of GDP as a measure for tracking sustainable
development, the World Bank has been developing comprehensive wealth accounts
along with statistics addressing genuine saving. Their wealth accounts are reported ina series of publications (World Bank (1997, 2006, 2011)). The concept ofcomprehensive wealth in its wealth account is broad, and it includes produced capital,
natural capital, human capital, social capital and institutional capital. It corresponds tothe productive base as a source of well-being of both the present and future
generations. Among their publications, we use the data in the most recent publication(World Bank (2011)), which covers more than 120 countries. We use the estimates ofcomprehensive wealth and its subcomponents by country for 2005. 11 We use thefollowing primary subcomponents.
Comprehensive wealth = produced capital + natural capital + intangible capital
Produced capital, comprising machinery, structures and equipment.

11

Since individual well-being indicators are considered to be data from 2009, there is a difference indata periods between individual well-being indicators and the productive base. Because of data
limitations, we omit this problem. One justification is that the magnitude of the comprehensive wealthof each country is significantly large because of the accumulation of past investment; thus, it is rather
stable over several years. We leave extending the data of well-being indicators or the productive basein order to match their data period to future research.

10




Natural capital, comprising agricultural land, protected areas, forests andsubsoil assets.12
Intangible capital, comprising human capital, social capital and institutional
capital. The rule of law and government, which contribute to an efficienteconomy, are also included in institutional capital.

Comprehensive wealth is estimated as the present value of future consumption.
Produced capital is derived from historical investment data using a perpetualinventory method. The stock value of natural capital is based on country data ofphysical stocks and estimates of natural resource rents based on prices and costs data.
Intangible capital is measured as the difference between comprehensive wealth,produced capital and natural capital. Thus, since all the measurement errors associatedwith comprehensive wealth, produced capital and natural capital are drawn into themeasure of intangible capital, intangible capital data must be cautiously interpreted.
All estimates are as per the US dollar in 2005. Table 2 summarizes the statistics ofcomprehensive wealth and its subcomponents. A complete listing of the data is foundin Table A.2 of the Appendix. World Bank (2011) finds that intangible capital
represents the largest share of the wealth of all the countries, and that its share
increases as per capita income grows. In addition, the important role of natural capitalis highlighted. In the early stage of development, countries are relatively dependent on
natural capital such as agricultural land, subsoil assets and forests. Countries use theseto build produced capital and intangible capital in the course of economic
development.
Table 2 reveals a similar pattern between wealth and development even among oursample, which have relatively higher incomes compared with other countries includedin World Bank (2011). Intangible capital has the largest share in all the countrygroups. Natural capital has the smallest share among the three types of capital;
however, natural capital of low-income countries is 11% on average, much higher
than that of the other groups.13
4. Result
We compute composite indicators based on BOD formulated by (1)and.
DEA formulated by (7)measures countries’ performances by
incorporating the individual indicators of well-being as well as the productive bases
for countries in a sample. Although the productive base of each country consists of its
produced, natural and intangible capital, we computeunder three compositionsof the productive base. The comparison of the composite indicator calculated under
the three different conditions reveals the impact of including each asset into the
productive base for composite indicator construction. Denoting produced, natural and
12

The 11 subcomponents of natural capital are as follows: 1) Crop, 2) Pasture land, 3) Timber, 4) Nontimber forest, 5) Protected areas, 6) Oil, 7) Natural gas, 8) Hard coal, 9) Soft coal, 10) Coal and 11)
Minerals.
13
The fact that the share of intangible capital is higher in middle- than high-income countries seems tocontradict the finding by World Bank (2011), which indicates that its share increases as an economy
grows. Since middle- and high-income countries in our sample are considered as high-income countries
in World Bank (2011), the differences in per capita income and the level of economic development
between these two groups are neglected there.

11intangible capital by , and, respectively, we consider the following cases of
the input vector , which corresponds to the productive base:Case 1:Case 2:
Case 3:,

Case 3 is ideal for using the comprehensive set of produced, natural and intangible
capital. In cases 1 and 2, as the second best, the productive bases are approximated bypart of the productive bases, such as produced capital or tangible capital. In case 3, we
differentiate intangible capital from produced and natural capital, rather than summingup all three forms of capital into a single measure of the productive base.14 This isbecause intangible capital differs considerably in character from produced and natural
capital.
Table 3 presents the full empirical result, containing the scores of composite
indicators and corresponding rankings along with existing HDI and GDP per capita,
which is denoted by income in the table. To ensure comparability with composite
indicators, we rescale HDI score so that its maximum value is one. It is shown thatcomposite indicators and HDI fail to completely discriminate among all the countries
in our sample, and that some countries are ranked equally. While HDI indicates ties
among at most three countries lying between the lowest score zero and highest scoreone, a larger number of countries are equally ranked highest in BOD and DEA.
However, comparing the number of countries equally ranked (19 out of 34 for BOD,
six for DEA case 1, eight for DEA case 2, 10 for DEA case 3), we note the better
discriminating power ofthan. Incorporating the information about the
productive base as well as individual well-being indicators allows us to distinguishcountries’ performances in greater detail.
We compareand, adopted in the present paper, with an existing wellbeing measure, HDI. 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 similar for
and HDI. In
contrast,
is distributed in a wider range and has a higher mean thanandHDI, which improves discriminating power by reducing the number of equally ranked
countries, which has already been mentioned.
Table 5 shows the correlation of composite indicators with HDI score and GDP per
capita. It shows thatand HDI, which share a similar pattern of distribution, arehighly 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.
We now consider the relationship between composite indicators and GDP per capita.
Whileis positively correlated with GDP per capita,
is negatively
correlated with it. Table 1 suggests that GDP per capita and the score of individual
well-being indicators are directly proportional. Larger values of individual indicators
raise the composite indicator, of which they are components. Therefore, thepositive correlation between
and GDP per capita is straightforward. However,interpreting the negative correlation between
and GDP per capita is not as
intuitive. Strikingly, all the countries in the low-income group, except for Hungary,
14

Note that cases 1 and 2 adopt DEA with single input while case 3 adopts DEA with two inputs.

12are ranked highest undercorrelation.

. Table 6 illustrates the derivation of the negative

Before we continue the detailed analysis of, we compare the computed scores
under the three different cases. In Table 5, the largest correlation coefficientofis approximately 0.95, which is found between cases 2 and 3 ofin terms ofscore values as well as ranking. The most difficult aspect of implementing
ispreparing the productive bases of countries, especially the intangible capital. The
similarity between cases 2 and 3 suggests that integrating the natural capital into the
productive base is the most critical step in calculating. Thus, even whenestimates of intangible capital are unavailable, by using produced and natural capital,
we approximate the result that should be obtained under the most appropriate
definition, which states that the productive base consists of produced, natural andintangible capital.
The source of this negative correlation between
and GDP per capita can beinferred from Table 6. 15 The productive base strongly correlates negatively withand positively with GDP per capita, leading to negative correlation between
and GDP per capita. High-income countries can afford to invest a variety ofassets that are accumulated into their large productive base. However, these countries
are likely to fail in providing the level of well-being appropriate for their large
productive base. This leads to larger correlations between the productive base andand between the productive base and GDP per capita in absolute terms than theand GDP per capita.
correlation between
The large negative correlation between
and the productive base does notnecessarily imply that their country rankings are in reverse order. The US and Canada
raise theirranking compared with their ranking based on the productive base.
In particular, the US is ranked highest, with countries having the lowest GDP per
capita and the smallest productive base. Although the large productive bases of the
US and Canada are advantageous to these countries, their people enjoy a better life
than is explained by their large productive base, leading to a higher evaluation of theirperformance. Similarly, Belgium and the United Kingdom, whose productive bases
among the highare comparably large, are also ranked relatively high underincome countries, indicating the significant well-being their people enjoy, which isbalanced with their large productive bases. On the other hand, countries that lose theircountry ranking are not always endowed with a large productive base. The productive
bases of Greece, New Zealand and Spain are approximately USD 40,000, below theaverage of USD 45,000. However, their country rankings based on
are evenlower than those based on the productive base. This indicates that the well-being of
their people is even lower than that expected from their relatively modest productive
base.
and
choose the country-specific weights that maximise the resulting
composite indicator score of each country under evaluation. Therefore, higher weights
are assigned to the individual indicators on which each country performs well.
Therefore, it is possible to conclude that the composite indicators reflect the policy
priority of each country on country-specific weights. Table 7 shows the average of the15

From now onwards,
refers to the composite indicator based on DEA under case 3, which
ideally assumes the productive base comprising produced, natural and intangible capital.

13

weight for each individual indicator in equations (1) and (7). A clear distinction inand, indicating that neglecting therelative weights is found between
productive base distorts the inference of policy priorities of countries. Three
individual indicators characterising material living conditions—housing, income, andjobs—have relative priority in. Especially, the components of health and safety
have the largest weights. On the other hand, the remaining individual indicators
characterising quality of life have relative priority in. Especially, the largest
weight is attached to a component of income. Civic engagement retains a constant rolein every composite indicator. This might reflect that institutional characteristics in the
productive base which affect civic engagement are not captured by capital assets
considered in this paper.
5. Conclusion
Well-being is a multidimensional concept. The OECD recently specified 11 topics
that are essential to people’s well-being. However, the fact that the OECD leaves theiraggregation to the user motivates our research. We adopt two approaches, BOD and
DEA, to construct a composite indicator from 11 individual indicators of well-being.
Unlike HDI, both approaches aggregate individual indicators by investigating
country-specific weights that maximise the composite indicator of each country.
The composite indicator based on BOD is distributed similarly to and is highly
correlated with the existing HDI. It is also positively correlated with GDP per capita.
On the other hand, the composite indicator based on DEA is negatively correlatedwith HDI as well as GDP per capita. Especially, the group of countries with the least
GDP per capita is now ranked highest under DEA. Although these countries havevery low well-being indicator scores, the volume of their productive base is further
limited. Thus, once we consider that they find it difficult to provide well-being to theirpeople, even though scores of their individual indicators of well-being are relatively
low among a sample of countries, their performances are highly appreciated. Many
countries with a larger productive base fail to provide an adequate level of well-being
and are devaluated under DEA, leading to a significantly negative correlation between
the DEA composite indicator and the productive base. Nevertheless, there are
exceptional countries. Although the US and the United Kingdom have high GDP per
capita and larger productive bases, they successfully ensure a sufficiently high level ofwell-being for their people. Thus, they are highly ranked even under DEA. On theother hand, while the productive bases of Greece, New Zealand and Spain are
relatively modest in size, they are ranked very low under DEA. This reflects asignificantly low level of well-being that is unjustifiable considering their productive
base.
While our analysis is preoccupied with comparing countries’ current performances,
how their performance changes over periods is also important. There is significant
DEA literature that measures the change in efficiency and divides it into several
components. Once individual well-being indicators become available for multiple
periods, they can easily be used to consider the change in countries’ abilities to ensure
their people’s well-being based on their productive base. We leave this exercise to
future research.

14

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16

Table 1: Descriptive Statistics, OECD Well-being Indicators
housing

income

5.6
6.8
5.8
3.7
6.0
1.6
7.8
1.3

4.0
6.3
4.1
1.0
4.0
2.3
10.0
0.2

Mean
overallhigh income
middle income
low income
MedianStd. Dev.
Max
Min

jobs

6.4
8.3
6.3
4.5
6.5
1.6
9.0
2.6

community

education

7.6
8.7
7.9
5.5
8.0
2.0
10.0
0.0

6.3
6.9
6.8
4.1
7.0
2.0
9.4
0.9

environment civic
healthengagement
7.5
8.7
7.8
5.3
7.9
1.9
10.0
3.6

5.3
6.2
5.3
4.4
5.5
1.6
9.4
1.8

Table 2: Descriptive Statistics, World Bank Comprehensive Wealth Accounts

productive
base
Mean
overallhigh income
middle income
low income
MedianStd. Dev.
Max
Min

457361 ( 100% )708331 ( 100% )464774 ( 100% )115625 ( 100% )52875125191991752973167

produced
capital84443 ( 18% )135950 ( 19% )83794 ( 18% )19888 ( 17% )
89836
49614
21342511330

naturalcapitalintangible
capital15693 ( 3% )27890 ( 4% )10584 ( 2% )13147 ( 11% )
8752
20283
1101632095

17357226 ( 78% )544491 ( 77% )370396 ( 80% )82590 ( 71% )405853202336753128
24137

6.9
8.5
7.2
4.3
7.6
2.0
9.5
0.1

life
safetysatisfaction
6.1
8.3
6.0
3.5
7.0
2.8
10.0
0.0

7.9
8.8
8.5
5.5
8.5
2.1
9.9
0.8

work-life
balance
6.7
6.9
7.1
5.4
6.9
1.8
9.7
1.6

Table 3: Composite Indicator with HDI and Per Capita Income
HDI
incomeDEA
productive
base
case 1case 2case 3Australia
1.0000 ( 1 ) 0.4964 ( 27 ) 0.4793 ( 29 ) 0.6836 ( 26 ) 0.9841 ( 2 ) 47566 ( 3 ) 518805 ( 18 )Austria
0.9894 ( 20 ) 0.5405 ( 22 ) 0.6062 ( 24 ) 0.7232 ( 23 ) 0.9341 ( 18 ) 41063 ( 7 ) 570655 ( 12 )Belgium
1.0000 ( 1 ) 0.7001 ( 12 ) 0.8339 ( 14 ) 0.8680 ( 15 ) 0.9384 ( 16 ) 38580 ( 11 ) 562365 ( 14 )Brazil
0.7877 ( 31 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.7524 ( 33 ) 10521 ( 34 ) 79143 ( 33 )Canada1.0000 ( 1 ) 0.7485 ( 10 ) 0.6751 ( 21 ) 0.8099 ( 16 ) 0.9596 ( 7 ) 40023 ( 9 ) 538697 ( 17 )Chile
0.7053 ( 33 ) 0.9069 ( 7 ) 0.8496 ( 13 ) 1.0000 ( 1 ) 0.8480 ( 30 ) 13689 ( 31 ) 101900 ( 32 )Czech Republic
0.9450 ( 22 ) 0.6563 ( 13 ) 0.8911 ( 10 ) 1.0000 ( 1 ) 0.9171 ( 22 ) 25553 ( 25 ) 180821 ( 26 )Denmark
1.0000 ( 1 ) 0.3850 ( 33 ) 0.4447 ( 32 ) 0.4716 ( 33 ) 0.9469 ( 13 ) 37377 ( 12 ) 742955 ( 4 )Finland1.0000 ( 1 ) 0.4831 ( 28 ) 0.5490 ( 27 ) 0.5692 ( 30 ) 0.9320 ( 19 ) 34765 ( 17 ) 570255 ( 13 )France0.9236 ( 28 ) 0.6320 ( 16 ) 0.7273 ( 16 ) 0.7308 ( 21 ) 0.9352 ( 17 ) 34385 ( 18 ) 586448 ( 11 )Germany
1.0000 ( 1 ) 0.5893 ( 20 ) 0.7270 ( 17 ) 0.7510 ( 18 ) 0.9564 ( 8 ) 36226 ( 14 ) 547200 ( 16 )Greece0.9418 ( 23 ) 0.5169 ( 25 ) 0.6188 ( 23 ) 0.6631 ( 27 ) 0.9171 ( 22 ) 30201 ( 22 ) 392814 ( 22 )Hungary
0.9260 ( 27 ) 0.6396 ( 15 ) 0.8848 ( 11 ) 0.8888 ( 13 ) 0.8618 ( 27 ) 18001 ( 29 ) 173008 ( 27 )Iceland1.0000 ( 1 ) 0.3934 ( 32 ) 0.4784 ( 30 ) 0.4784 ( 32 ) 0.9532 ( 11 ) 40096 ( 8 ) 902961 ( 2 )Ireland1.0000 ( 1 ) 0.4016 ( 30 ) 0.5467 ( 28 ) 0.5481 ( 31 ) 0.9617 ( 5 ) 35878 ( 15 ) 599116 ( 9 )Israel
0.9391 ( 25 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.9394 ( 15 ) 28452 ( 23 ) 327471 ( 23 )Italy
0.9148 ( 29 ) 0.6410 ( 14 ) 0.7234 ( 18 ) 0.7925 ( 17 ) 0.9245 ( 21 ) 30895 ( 20 ) 498277 ( 19 )Japan
1.0000 ( 1 ) 0.4599 ( 29 ) 0.5650 ( 25 ) 0.7289 ( 22 ) 0.9511 ( 12 ) 35011 ( 16 ) 548751 ( 15 )Korea
0.9455 ( 21 ) 0.5826 ( 21 ) 0.8064 ( 15 ) 0.9124 ( 12 ) 0.9447 ( 14 ) 26675 ( 24 ) 248180 ( 25 )Luxembourg
1.0000 ( 1 ) 0.3937 ( 31 ) 0.4612 ( 31 ) 0.6014 ( 29 ) 0.9171 ( 22 ) 93388 ( 1 ) 917529 ( 1 )Mexico0.7043 ( 34 ) 0.8364 ( 8 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.8098 ( 31 ) 12887 ( 32 ) 131384 ( 30 )Netherlands
1.0000 ( 1 ) 0.5926 ( 19 ) 0.6558 ( 22 ) 0.7414 ( 19 ) 0.9617 ( 5 ) 44583 ( 5 ) 593546 ( 10 )New Zealand
1.0000 ( 1 ) 0.5194 ( 24 ) 0.4279 ( 33 ) 0.6071 ( 28 ) 0.9628 ( 3 ) 30797 ( 21 ) 414114 ( 20 )Norway1.0000 ( 1 ) 0.2653 ( 34 ) 0.2355 ( 34 ) 0.3393 ( 34 ) 1.0000 ( 1 ) 56499 ( 2 ) 861798 ( 3 )Poland
0.9895 ( 19 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.8576 ( 28 ) 18366 ( 28 ) 135941 ( 29 )Portugal
0.8528 ( 30 ) 0.6016 ( 18 ) 0.8563 ( 12 ) 0.8685 ( 14 ) 0.8555 ( 29 ) 22339 ( 26 ) 305831 ( 24 )Russian Federation 0.9368 ( 26 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.7938 ( 32 ) 15704 ( 30 ) 73167 ( 34 )Slovak Republic
0.9406 ( 24 ) 0.7383 ( 11 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.8810 ( 26 ) 21414 ( 27 ) 142372 ( 28 )Spain
1.0000 ( 1 ) 0.5369 ( 23 ) 0.7206 ( 19 ) 0.7228 ( 24 ) 0.9288 ( 20 ) 30908 ( 19 ) 408384 ( 21 )Sweden1.0000 ( 1 ) 0.6259 ( 17 ) 0.7008 ( 20 ) 0.7008 ( 25 ) 0.9543 ( 10 ) 39295 ( 10 ) 627951 ( 8 )Switzerland
1.0000 ( 1 ) 0.5033 ( 26 ) 0.5625 ( 26 ) 0.7386 ( 20 ) 0.9554 ( 9 ) 44375 ( 6 ) 736795 ( 5 )Turkey0.7617 ( 32 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.7333 ( 34 ) 10886 ( 33 ) 114830 ( 31 )United Kingdom
1.0000 ( 1 ) 0.7763 ( 9 ) 0.9207 ( 9 ) 0.9207 ( 11 ) 0.9139 ( 25 ) 37001 ( 13 ) 662625 ( 7 )United States
1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 1.0000 ( 1 ) 0.9628 ( 3 ) 45614 ( 4 ) 734195 ( 6 )BOD18

Table 4: Descriptive Statistics, Composite Indicator with HDI and Per Capita Income
DEA
BOD
HDI
incomecase 1case 2case 3Mean
0.94720.65190.73380.79000.9131
33206
Median1.00000.61370.72520.77170.934634575
Std. Dev.
0.08570.21080.21250.18520.064115595
Max
1.00001.00001.00001.00001.000093388
Min0.70430.26530.23550.33930.733310521

Table 5: Correlation among Composite Indicators
DEA
BOD
case 1case 2
Correlation coefficient
BOD
1.0000-0.5289-0.5273
DEA: case 1-0.5289
1.00000.8977
DEA: case 2-0.5273
0.89771.0000
DEA: case 3-0.54240.88940.95200.78590.6314

HDI

income-0.54240.88940.95201.00000.7859-0.6658-0.6799-0.63480.6314-0.5989-0.6623-0.6144case 3-0.6658-0.5989-0.6799-0.6623-0.6348-0.61441.00000.66040.66041.0000Spearman rank correlation coefficient
BOD
1.0000-0.5868
DEA: case 1-0.5868
1.0000
DEA: case 2-0.64930.9235
DEA: case 3-0.6367
0.9243-0.64930.92351.00000.9470-0.6367
0.92430.94701.00000.7689-0.5443-0.6507-0.5918

0.8136-0.5832-0.6736-0.6422-0.6507-0.6736-0.5918-0.64221.00000.77900.77901.0000HDI
incomeHDI
income0.76890.8136-0.5443-0.5832

Table 6: Correlation among Composite Indicators and Underlying Statistics
DEA
BOD
HDI
case 1case 2case 3productive base
0.6689-0.6641-0.7298-0.7604
0.7489produced capital
0.6449-0.7737-0.8194-0.78800.7246natural capital
0.1783-0.2850-0.5277-0.4849
0.2869intangible capital
0.6568-0.6086-0.6549-0.7049
0.725919

income0.85390.91090.24530.8153

Table 7: Average Weight for Composite Indicators
housing
incomejobs
community
BOD
DEA: case 1
DEA: case 2
DEA: case 3

0.01000.00090.01840.00440.00760.12180.08810.12130.00110.00010.00390.00050.00760.00040.00080.0001

education
0.00610.00240.00200.0011environment civic
healthengagement
0.01090.00650.02080.00440.00760.01150.01130.01090.00260.00820.00740.017720

life
safetysatisfaction
0.01390.02090.00660.00440.00570.00240.00520.0063work-life
balance0.01040.00030.00450.0022

Table A.1: OECD Well-being Indicators
Australia
Austria
BelgiumBrazilCanadaChile
Czech Republic
Denmark
FinlandFranceGermany
GreeceHungary
Iceland
IrelandIsrael
Italy
Japan
Korea
Luxembourg
MexicoNetherlands
New Zealand
NorwayPolandPortugal
Russian Federation
Slovak Republic
Spain
SwedenSwitzerland
TurkeyUnited Kingdom
United Stateshousing

incomejobs

community

education

environment

7
6
7.3
3.9
7.8
3.1
4.7
6
6.3
6.4
6.1
3.8
3
6
7.8
4.1
5.3
4.7
5.7
6.1
4.2
7
6.1
7.5
3.4
6
7.1
4
7.1
6.6
6
1.3
6.2
7.8

4.5
5.4
6.5
0.2
6.1
0.7
2
4
3.7
5.4
5.2
3.1
1.4
4.6
3.6
4.2
5.3
6
2.5
8.1
0.9
5.9
2.8
3.9
1.3
3
1.6
1.3
3.6
4.9
7.9
0.9
6
10

7.8
8
6.8
4.8
7.8
4.3
6
7.7
6.6
6.4
7.3
5.4
4.3
8.3
6.2
6.3
5.8
7.1
5.1
8.5
4.5
8.9
7.5
8.8
5.3
5.3
5.7
4
4.1
7.2
9
2.6
7.9
7.5

9.6
8.8
8.5
7.4
8
5.7
7.7
9.4
8.6
8
9
5.5
7
10
9.9
6.6
7.7
7.8
4.1
8.4
4.6
8.6
9.1
8.5
7.4
5.7
6.7
7.8
8.8
8
8.6
0
9.2
8

7.6
6.3
7.5
1.9
7.7
3.8
7.5
7.4
9.4
5.8
7.7
5.7
6.9
7.2
6.6
4.9
5
8.8
7.8
4.7
0.9
7.2
7.8
7.3
7.9
4.5
6.1
6.6
4.8
8.1
7.5
1.4
5.9
7

9
7.8
7.5
7.5
8.7
3.6
8
9.3
9.2
7.8
9.3
3.9
7.4
9.6
8.8
4.1
6.8
7.3
6.3
9.2
5
7.8
8.8
9.2
5.3
7.9
4.4
8.4
6.6
10
8.7
3.8
9.7
7.9

21

civic
engagement
9.4
6.2
5.8
4.7
5.6
4.2
4.2
6.7
6.4
4.5
4.4
4.7
4.8
5.4
5.6
1.8
5
4.8
5.9
6.6
4.7
5.5
7.6
6.3
5.2
4
1.9
3.1
5.6
8.2
3.3
5.5
6.3
7.7

health
9.1
7.4
7.9
5
9
6
6.3
7
7.1
7.6
7
7.9
3.9
8.4
8.7
8.8
7.6
5
4.8
7.9
5.2
8.1
9.2
8.5
4.9
5.4
0.1
4.9
8.3
8.5
9.5
4.9
7.9
8.4

life
satisfaction
8.6
8.8
7.3
6.6
8.7
5.9
4.9
10
8.4
7.1
6
1.5
0
6.8
7
8.5
3.9
3.9
7
7.4
6.9
9
7.9
9.2
2.5
1
1.2
3.4
5.5
8.3
9
1.2
6.7
7.6

safety
9.3
9
6.9
0.8
9.6
5
9
8.5
9
7.8
8.6
8.5
8.5
9.2
9.1
6.9
8
9.9
9
7.9
0.8
7.9
9.2
8.9
9.5
7.4
6.7
8.8
8.3
7.8
8.4
7.3
9.4
8.8

work-life
balance
5.6
6.2
8.9
6.3
6.5
6.2
6.1
9.7
7.6
7.7
8.1
7.9
7.3
5.7
7
5.1
7.5
3
5
7.5
1.6
8.7
6.4
8.8
6
7.7
7.8
6.8
8.8
8.2
7.6
2.6
6.5
5.7

Table A.2: World Bank Comprehensive Wealth Accountproductive
producednaturalbase
capitalcapital
(
18
)
(
9
)
111671
39978
Australia
518805Austria
570655 ( 12 ) 112799 ( 7 )
9065
(
14
)
(
12
)
Belgium
56236598822
4933( 33 )( 34 )
Brazil
79143
11330
14979
( 18 )
Canada538697 ( 17 )
89811
36925
( 31 )
Chile
101900 ( 32 )
19268
18869
(
26
)
(
26
)
Czech Republic
18082144254
4596
Denmark
742955 ( 4 ) 130827 ( 6 )19616( 14 )
Finland570255 ( 13 )
96566
19220
( 15 )
France586448 ( 11 )
93619
8610
(
16
)
(
13
)
Germany
54720098285
5715( 22 )
Greece392814 ( 22 )
74237
7979
(
27
)
(
27
)
Hungary
173008
35162
5975
(
2
)
(
4
)
Iceland90296113747012363
Ireland599116 ( 9 ) 112374 ( 8 )
11190
(
23
)
(
25
)
Israel327471
47232
4842( 17 )
Italy
498277 ( 19 )
89860
7502
(
15
)
(
5
)
Japan
548751
1358662095( 24 )
Korea
248180 ( 25 )
58636
2641
(
1
)
(
1
)
Luxembourg
9175292134256092( 29 )
Mexico131384 ( 30 )
21320
6641
(
10
)
(
10
)
Netherlands
59354610965813193( 21 )New Zealand
414114 ( 20 )
76281
52980
(
3
)
(
2
)
Norway861798183078110163( 30 )
Poland135941 ( 29 )
20526
8894
(
24
)
(
23
)
Portugal
305831
59939
4204
(
34
)
(
32
)
Russian Federation
73167
17712
31318
( 28 )
Slovak Republic
142372 ( 28 )
31954
4979
(
21
)
(
20
)
Spain
40838482194
7471( 16 )
Sweden627951 ( 8 )
92488
15674
Switzerland
736795 ( 5 ) 165561 ( 3 )9410( 33 )
Turkey114830 ( 31 )
13895
5356
(
7
)
(
19
)
United Kingdom66262584861
6263
United States734195 ( 6 ) 100075 ( 11 )
13821

22

intangible
capital( 3 )367156( 16 )
448791( 29 )
458610( 10 )
52834( 4 )411961( 8 )63763( 31 )
131971( 6 )592512( 7 )454469( 18 )
484219( 26 )
443200( 19 )
310598( 25 )
131871( 13 )
753128( 14 )
475552( 30 )
275397( 20 )
400915( 34 )
410790( 33 )
186903( 24 )
698012( 22 )
103423( 12 )
470695( 2 )284853( 1 )568557( 17 )
106521( 32 )
241688( 5 )24137( 28 )
105439( 21 )
318719( 9 )519789( 15 )
561824( 27 )
95579( 23 )
571501( 11 )
620299( 19 )( 14 )( 12 )( 33 )( 16 )( 32 )( 26 )(

4 )( 13 )(

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