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World Development Vol. 35, No. 7, pp. 1259–1276, 2007
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doi:10.1016/j.worlddev.2006.10.011
Multidimensional Measures of Well-Being: Standard
of Living and Quality of Life Across Countries
VALÉRIE BÉRENGER
Université de Nice-Sophia Antipolis, CEMAFI
(Centre d’Etudes en Macroéconomie et Finance Internationale), Nice, France
and
AUDREY VERDIER-CHOUCHANE *
African Development Bank, Tunis, Tunisia
Summary. — Using Sen’s capability approach, we propose to measure two components of wellbeing—standard of living and quality of life. Unlike the UNDP Human Development Index, the
two indices do not mix measures of resource availability and of functioning and capability. The
empirical results for 170 countries are based on two multidimensional analyses, the Totally Fuzzy
Analysis and the Factorial Analysis of Correspondences. The paper also compares our results with
the HDI and GDP per capita. It focuses on Africa, presents policy implications, and discusses
aggregation and redundancy in multidimensional indices.
Ó 2007 Elsevier Ltd. All rights reserved.
Key words — Africa, totally fuzzy analysis, factorial analysis of correspondences, multidimensional
indices, Sen’s capability approach
1. INTRODUCTION
International organizations now recognize
that human development goes beyond economic growth and is a multidimensional phenomenon covering all aspects of well-being.
This partly dates from Sen’s work on social justice and inequalities (Sen, 1985, 1992), which
inspired a new concept of development. Sen’s
capability approach contributed to the design
of the UNDP Human Development Index
(HDI) in 1990, which was intended as a more
comprehensive indicator than per capita income for comparing the well-being of countries. However, the HDI’s critics 1 say its
indicators are too few and too arbitrarily chosen and that its definition is still inadequate
and does not allow the capability approach to
work.
This article defines two composite indices—
SL (Standard of Living) and QL (Quality of
Life)—across 170 countries, supporting Sen’s
capability approach. Focusing on 52 African
countries, we adopt and compare two recent
methodologies, Totally Fuzzy Analysis (TFA)
and Factorial Analysis of Correspondences
(FAC), to analyze the usefulness of these indices. Our broader well-being indices are compared with the HDI and GDP per capita to
* Dr. Valérie Bérenger is from the CEMAFI (Centre
d’Etudes en Macroéconomie et Finance Internationale),
Université de Nice-Sophia Antipolis, and Dr. Audrey
Verdier-Chouchane is from the Development Research
Department, African Development Bank. We thank
Bernhard Gunter and four anonymous reviewers for
their very helpful comments and valuable inputs as well
as Louis Kasekende, Temitope Waheed Oshikoya, and
Désiré Vencatachellum for their significant contributions
to this work. Greg Chamberlain and Diane Brothwell
provided excellent assistance in editing the manuscript.
Financial support from the African Development Bank
is gratefully acknowledged. We retain responsibility for
any remaining errors. Final revision accepted: October
2, 2006.
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WORLD DEVELOPMENT
examine the cogency of the HDI and the redundancy issues.
Section 2 here deals with the concepts and
justification for the two indices (SL and QL),
while Section 3 presents the two methodologies
and the obtained results in Africa. Policy recommendations and discussion are presented in
Section 4. Section 5 presents our conclusions.
2. CONCEPTS FOR STANDARD OF
LIVING AND QUALITY OF LIFE
GDP per capita is the most commonly used
indicator to compare wealth among countries 2
and is a measure of well-being and development
exclusively based on material wealth. However,
insufficient income is merely one dimension of
under-development, so development cannot be
understood by only taking into account economic performance. Attempts were made in
the 1970s to construct socio-economic indicators as an alternative to GDP per capita, which
was criticized as capturing neither distributional aspects nor social and human welfare
dimensions (Desai, 1991). The standard human
development concept dates from the 1990
Human Development Report (UNDP, 1990).
It drew on Sen’s work and led to growing
acknowledgement of the multidimensional nature of development and to different strategies
for moving from promotion of growth to promotion of well-being. It also highlighted the
need to construct alternative composite indices,
including non-monetary indicators, to assess
the achieved development levels. The HDI
was launched in 1990 to represent the broad
ideas included in the human development concept. 3
So the capability approach has helped enlarge the concept of human development. International organizations such as the World Bank
(2006) have adopted notions of ‘‘quality of
growth’’ and ‘‘pro-poor growth’’ that reflect
greater concern about non-monetary dimensions of well-being.
(a) Sen’s capability approach and the need for
going beyond the HDI
Sen’s capability approach (1985) proposes a
normative framework to evaluate individual
well-being, social relationships and changes in
society. 4 Its main components are the ‘‘commodities’’ or resources, the ‘‘functionings’’
and the ‘‘capabilities.’’ The ‘‘commodities’’
are all goods and services, not just merchandise. They can include transfers in kind and
make possible the ‘‘functionings,’’ which take
into account achievements of individuals—
what they ‘‘are’’ and what they ‘‘make’’ with
their resources—and reflects life-style.
The concept of ‘‘capabilities’’ is related to
‘‘functionings’’ but also includes notions of
opportunity and freedom—the range of opportunities a person has and can choose from.
‘‘Capabilities’’ are various combinations of
functionings (beings and doings) that the person can achieve. ‘‘Capability is, thus, a set of
vectors of functionings, reflecting the person’s
freedom to lead one type of life or another
(. . .) to choose from possible livings’’ (Sen,
1992, p. 40). A functioning is an achievement,
while capability is about the ability to produce
it. Functionings are thus more directly related
to living conditions, while capability is a concept of freedom, in a positive sense. According
to Sen’s definition, the UNDP (1997) defines
human development as increasing people’s
choices by expanding their human capabilities
and opportunities. Under-development is thus
not a deprivation of basic needs, but is a deprivation of basic capabilities or freedoms that
would allow an individual to have the kind of
life he/she wants. 5 Sen’s (1999) approach is
qualitative and multidimensional. He puts human beings at the center of the development
concept. The aim of development is to enhance
human capabilities so as to lead full, productive
and satisfying lives. A higher income is necessary but not sufficient and thus calls for wider
measures of well-being. The HDI was supposed
to use Sen’s approach to make international
comparisons. It has been improved, in particular, for GDP calculation and extremes fixing. 6
It has also led to other indices such as the Human Poverty Index (HPI) which assesses human development not on average national
achievement but on how many people live in
deprivation. Several heterodox World Bank
studies consider indicators of inequality for
analysis of true development. But Easterly
(2002), who examines inequality as a barrier
to prosperity and growth, and Pritchett, Suryahadi, and Sumarto (2000), who look at vulnerability to poverty, use household data.
After GDP per capita, the HDI is the most
discussed measure of well-being. The literature
seems to take two approaches that are not inevitably exclusive of each other.
In the first, the main criticism of the HDI relates to its very narrow definition of human
MULTIDIMENSIONAL MEASURES OF WELL-BEING
well-being. New indices, sometimes excluding
the income component, have been proposed
without having content necessarily justified or
based on an explicit theoretical approach of
well-being. The Physical Quality of Life Index
(PQLI) developed by Morris (1979) takes into
account life expectancy, infant mortality, and
literacy. The Quality of Life Index of Dasgupta
and Weale (1992) adds civil liberties and political rights to the HDI. The Index of Economic
Well-Being proposed by Osberg and Sharpe
(1998) is similar, though it also takes into account economic aspects of well-being neglected
by GDP per capita (such as production stocks,
unequal income distribution and uncertainty
about future income). Rahman, Mittelhammer,
and Wandschneider (2003) propose a composite index of well-being based on eight social
dimensions, each including indicators for social
relationships, emotions, health, work, material
well-being, civil, and political liberties, personal
security, and environment quality. The index is
applied to 43 countries using the Borda rule 7
and the principal components analysis method.
Several other studies (Ivanova et al., 1999;
Ogwang & Abdou, 2003; Qizilbash, 2004) point
out the difficulties and risks of creating indices,
in view of the multidimensional aspects of wellbeing, the redundancy of variables and the
measurement sensitivity to any weighting system. McGillivray (1991) and McGillivray and
White (1993) highlight the redundancy between
the HDI and its components and say the HDI
is ‘‘yet another redundant index’’ and that its
significantly high correlation with GDP per capita demolishes the argument that it would produce different rankings of countries. To
overcome this drawback, McGillivray (2005)
has recently applied principal components analysis and regression estimates to the HDI components to extract an aggregate measure of
non-economic aspects of human well-being.
Cahill (2005) analyses the implications of such
correlations for the choice of weighting. The
main conclusion is that high correlations between HDI components would produce a composite index insensitive to the weighting.
In the second approach, the HDI’s reductionist nature is also criticized but bigger questions
are raised, such as the relationship between the
capability approach and the concept of human
development (Gasper, 2002), or broader ones,
about the content and empirical measurement
of ‘‘capabilities.’’ Most studies supporting the
capability approach use disaggregated data
from household surveys 8 and very few use
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aggregated data to allow international comparisons as our indices permit. Slottje (1991) uses
20 indicators to build a composite index of
well-being for 126 countries. Baliamoune
(2003) explicitly uses Sen’s capability approach
and proposes classifying countries according to
new indicators close to the concept of freedom
conveyed by ‘‘capability.’’
(b) Alternative applications of the capability
approach
Studies using Sen’s approach have flaws.
They do not measure ‘‘capability.’’ With limited data available, usually only ‘‘functionings’’
carried out are used as a proxy of ‘‘capabilities.’’ These attempts are also sometimes far
from the conceptual framework they are supposed to be linked to because the composite
indices rely on a combination of indicators that
are different by nature, some corresponding to
‘‘capabilities’’ (civil liberties and political
rights), others to ‘‘functionings’’ (literacy) and
others still to resources or assets (such as the
number of telephones per capita). Well-being,
standard of living and quality of life are generally not differentiated in these studies and they
apply different concepts and realities, raising
two questions—which and how many indicators to use to move towards the concept of
human development, and the matter of new
and more complete indices to make the distinction between the various concepts of Sen’s
approach.
Sen gives no list of ‘‘capabilities’’ to take into
account for constructing well-being indices and
allows multiple proposals (Alkire, 2002). The
three HDI components could be justified conceptually as being universal, basic to life and
measurable but they raise the non-inclusion of
other dimensions. For example, Dasgupta
(1990, 1992) criticizes the HDI for neglecting
human rights. The choice of indicators suggests
that if ‘‘capabilities’’ were carried out in these
three basic dimensions, it would be done in
the other dimensions of human development.
With the capability approach, while the education and life expectancy indicators refer to
‘‘functionings,’’ the income per capita component seems to be a ‘‘commodity.’’
As the human development concept has
emerged from the GDP limitations, it seems
inappropriate to include an income component
in an index of well-being. As Anand and Sen
(2000) themselves argue, income can be an indirect indicator of some capabilities. However,
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WORLD DEVELOPMENT
well-being is not determined by possession of
resources but by their transformation into
‘‘functionings’’ which depends on personal, social and environmental factors. GDP per capita
is necessary but not sufficient for human development as shown by countries where high and
growing GDP per capita has not led to enrichment of human lives (Sen, 1999). The income
component affects the purity of the HDI as a
capability-based measure. First, the level of
GDP per capita is a poor indicator of the
means of a group of people and its usefulness
for the expansion of social services and infrastructure development is not clear (Anand &
Ravallion, 1993; Anand & Sen, 2000; Sen,
1981, 1999). Means indicators that are determinants of well-being and part of the standard of
living index are needed. Second, GDP per capita is a bad proxy of freedoms and quality of
life.
Like our indices, the PQLI and the Capability Poverty Measure (CPM), used in the 1996
Human Development Report (UNDP, 1996)
and replaced the following year by the HPI
(with a variant for developing and industrialized countries), are examples of non-income
measures. The HPI replaces the income component by several means indicators, while the
CPM focuses on human capabilities, functionings and outcomes. Unlike the HDI, these indices are headcount proxies measuring the
percentage of people in each country who lack
basic human capabilities.
(c) Justification of Standard of Living and
Quality of Life Indices
Standard of Living (SL) and Quality of Life
(QL) are not new indices of well-being, but past
indices differ from ours in that they capture different dimensions of well-being, use different
methods of aggregating and have different theoretical foundations.
To take other aspects of development into account, while distinguishing between the concepts, we define the composite indices of SL
and QL as based on ‘‘commodities’’ on the
one hand and the ‘‘functionings’’ and ‘‘capabilities’’ on the other. SL corresponds to the quantity of goods and services and to the services the
GDP produces. It includes several means indicators that correspond to ‘‘commodities’’ that
could be called inputs. QL includes (unlike
SL) more intangible or qualitative aspects such
as quality of education, extent of child labor
and quality of the environment. It is a combi-
nation of ‘‘functionings’’ and/or ‘‘capabilities’’
indicators within the meaning of freedoms.
They are result indicators that refer to output
within a transformation system of ‘‘commodities,’’ as Sen suggests.
SL involves nine indicators in three domains
(education, health, and material well-being).
Public expenditure as a percentage of GDP in
education and health take into account the
money allocated to these social services. The
number of doctors/physicians is an indicator
of health facilities. Access to safe water reflects
public facilities available and a means to prevent illness and epidemics. The age dependency
ratio and net primary school enrollment indicators are more difficult to justify as means indicators and have been chosen on the basis of
data availability. The teacher/student ratio
would have been a better means indicator but
was not available for many countries. The age
dependency ratio is the demographic pressure.
It is a summary of ageing that includes both
young and old in relation to the potentially active population, so is a rough indicator of education.
QL focuses on measures of well-being or human outcomes that include explicit references
to human freedoms. It tries to take into account the capability of participating in community life that was dropped from the HDI. The
main indicators emphasize the ‘‘being and
doings’’ of a population, their opportunities
as well as their non-opportunities. Capability
failure can stem from violation of personal
rights or absence of positive freedoms (Sen,
1992). The QL composite index is a combination of nine indicators covering three domains:
health, education, and environment in a broad
sense. Education quality includes capabilities
that education can provide. Adult literacy is
accompanied by two other indicators measuring children’s and women’s capabilities. Child
labor indicates denied opportunities to acquire
human capabilities needed in productive and
social life. Female labor captures the intensity
of gender equality in productive activity. It
may also depict to a lesser extent ‘‘outcomes’’
of education as capability of entering the labor
market. Life expectancy is an indicator of
capacity to live a long life, maternal mortality
accounts for capacity to give birth in good
health conditions and the percentage of underweight or stunted children accounts for the potential for being well nourished. Civil rights and
political freedoms refer to the aim of development relating to the actual freedom enjoyed
MULTIDIMENSIONAL MEASURES OF WELL-BEING
by the population involved. Trade openness involves the capability to exchange goods with
partners and contribute to better quality
products. Carbon dioxide (CO2) is not directly
related to air pollution but to resource depletion. However, all fuels produce CO2 when
burned but sometimes also other pollutants
(nitrogen oxides, gaseous hydrocarbons, and
carbon monoxide). The more CO2 emissions,
the more likely are other dangerous emissions
and worse quality of air.
The choice of indicators in Table 1 is justified
by arguments above but is also limited by availability of data to compile SL and QL indices
for at least as many countries as there is data
for the HDI and GDP per capita (we have
170 countries). Division of indicators between
SL and QL is not always easy to establish.
The concept of ‘‘capability’’ is difficult to discern from country data because it is initially defined in reference to individuals and their
1263
relationships with other members of society.
Several lists of ‘‘basic capabilities’’ are proposed in the literature (Alkire, 2002) to approach the concept of human development
but their use in identifying indicators is mostly
limited to the disaggregated household data.
Several indicators can be interpreted at the
same time as a ‘‘commodity,’’ a ‘‘functioning’’
or a ‘‘capability.’’ So it is hard to produce a
division of indicators that is not contestable.
3. CONSTRUCTION OF WELL-BEING
INDICES IN AFRICAN COUNTRIES
(a) Methodologies used for analysis of Standard
of Living and Quality of Life
The construction of SL and QL indices and
measuring the degree of deficiency or ‘‘deprivation’’ for each country in these domains
Table 1. List of selected indicators
Standard of living
Standard of health
Public health expenditure (% of GDP)
Improved water source (% of population with access)
Physicians (per 1,000 people)
Standard of education
Age dependency ratio (dependents to working-age population)
Public spending on education, total (% of GDP)
Net primary enrolment (%)
Material well-being
Vehicles (per 1,000 people)
Roads paved (% of total roads)
Television sets (per 1,000 people)
Quality of life
Quality of health
Under-weight or under-height children under age five (%)
Life expectancy at birth (years)
Maternal mortality reported (per 100,000 live births)
Quality of education
Literacy rate, adult total (% of people aged 15 and above)
Labor force, children 10–14 (% of age group)
Labor force, female (% of total labor force)
Quality of environment
Openness (trade, % of GDP)
CO2 emissions (metric tons per capita)
Political rights and civil liberties (index)a
Source: UNDP (2002) and World Bank (2002) data.
a
Indices of political rights and civil liberties are available on the House of Freedom website: www.freedomhouse.org/
ratings/index.htm.
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WORLD DEVELOPMENT
requires a suitable methodology. Well-being is
multidimensional so we shall use and compare
the methodology from the fuzzy sets approach
(Totally Fuzzy Analysis) and from the simple
Factorial Analysis of Correspondences. 9
(i) Measuring Standard of Living and Quality of
Life using the fuzzy sets approach
As components of human well-being, Standard of Living and Quality of Life are multidimensional and vague concepts. The fuzzy sets
theory, invented by Zadeh (1965) and developed by Dubois and Prade (1980), is a suitable
mathematical tool to analyze phenomena that
are hard to place in a set. Use of this methodology in economics is quite new. The best-known
studies based on the fuzzy sets approach are
multidimensional analyses of poverty. 10 However, these are mostly based on micro-level data
from census and household surveys, rarely on
macro-level data. To our best knowledge, Baliamoune (2003) has been the pioneer in the use
of fuzzy-set theory to construct well-being measures at the macro-level. Compiling several human well-being indices, Baliamoune (2003)
yields rankings for 48 countries measuring their
achievements in education, life expectancy and
political and civil rights. The non-linear membership function used to assess well-being
across countries includes rules and goals and
incorporates the idea of lower and upper limits. 11 Using the same methodology in 14 Pacific Asian countries, Baliamoune-Lutz and
McGillivray (2006) provide a fuzzy representation of the HDI and its three components.
Comparisons with non-fuzzy estimates suggest
that fuzzy measures should be used more
widely to assess well-being outcomes.
Our composite indices using the fuzzy sets
approach are constructed in two stages (see
Appendix A). The first involves definition of
the membership function of a given set associated with each country and indicator. The function can take several forms (Baliamoune, 2003;
Lelli, 2001), but we consider the ‘‘Totally Fuzzy
Analysis (TFA),’’ as defined by Cerioli and
Zani (1990), in contrast to the ‘‘Totally Fuzzy
and Relative’’ approach (TFR) of Cheli and
Lemmi (1995). The value of the membership
function will provide a country-specific deprivation degree relative to a given indicator,
increasing linearly between zero and one.
The second stage involves the different degrees of deprivation in each country, with each
indicator aggregated to obtain composite SL
and QL indices for each country. So under-
development can be defined as an accumulation
of ‘‘deprivations’’ or ‘‘shortfalls.’’ Conversely,
the index value can be interpreted as an accumulation of ‘‘effective achievements.’’ The higher the SL (or the QL), the closer the value index
is to zero or the closer the value is to one, the
higher the degree of deprivation relative to
the SL (or the QL).
Application of fuzzy sets methodology to
indicators yields country rankings according
to the SL and QL indices. Due to the additionally decomposable nature of fuzzy indices, SL
and QL can be broken down to obtain several
sub-indices by domain (such as education and
health). The breakdown provides key information about the level and structure of well-being
and particularly about domains that contribute
most to deprivation and under-development. It
also offers information for policymakers
designing structural socio-economic policies to
eradicate the main causes of under-development. Identification of areas where structural
intervention is necessary could lead to capability-building policies.
At first glance, the TFA method cannot say
which countries could receive aid to help build
socio-economic structures in a given area, except by arbitrarily fixing a cut-off line. But
a dichotomous approach can obtain a critical
value from cumulative distribution of the deprivation index in terms of SL, QL and their components. This critical value is a threshold to
estimate the number of countries with genuine
deprivation in a particular domain (see Appendix A).
TFA offers various means of capturing information from well-being indicators among
countries. The most common criticism of this
kind of method concerns choice of a weighting
system. One way to test the robustness of rankings is to compare results obtained with TFA
with those produced by FAC.
(ii) Charts of SL and QL obtained by Factorial
Analysis of Correspondences
FAC is a descriptive method for qualitative
data suggested by Benzécri et al. (1973) to study
contingency tables (see Appendix B) and discover simple patterns in the relationships between variables. It provides orthogonal basis
vectors of the data space by computing the
eigenvalues of a matrix drawn from the data,
reducing the dimensionality of the input data
by extracting the most important features (the
factors or principal components). These are
sorted such that the first captures the maximum
MULTIDIMENSIONAL MEASURES OF WELL-BEING
variance of the data, and the others capture the
decreasing amount of variance.
With our well-being indices, where there are
nine indicators (dimensions) for each country
and 170 countries (dimensions) for each indicator, the two dimension-charts from FAC present the projections of all variables as close as
possible from observation. This interprets the
proximity between countries and indicators
and also between both kinds of variables (see
Appendix B). Each indicator has a contribution
(weight) in definition of the main axis (axis 1)
that we shall use further.
We applied these two methods to 170 countries (see Appendix C) for the year 2000, using
the indicators in Table 1. We now present the
general results and then focus on the 52 African
countries available (Sao Tome & Principe is
missing due to lack of data).
(b) Indices and sub-indices for Standard of
Living and Quality of Life
TFA enables us to calculate SL and QL as
well as the various sub-indices for each of the
170 countries. Table 2 shows the statistical indicators of these indices and sub-indices.
Determining the breaking value in relation to
the international average score enables calculation of the percentage of countries by geographical region (Rest of the World and
Africa) with a deficit in each domain (Table
3). Material well-being is the area, internationally, where deprivation is highest.
The average score is highest in Africa, making it the world’s poorest region. It has high
SL deprivation, with 96.2% of its 52 countries
having an insufficient score. Where countries
can be described as ‘‘rich’’ or ‘‘poor,’’ those in
Africa would express deficiencies in each domain. All the average scores are higher than
1265
the breaking values, suggesting that Africa
has handicaps in all SL dimensions. The continent has deficiencies in all considered QL domains, but the percentage of countries with a
deficit is much smaller than for SL. Quality of
education is the area in which the continent
has most deficit countries (59.6%) and 57.7%
have a quality of health less than the breaking
value. 12
(c) Graphical analysis for African countries
FAC was initially done at world level (170
countries). Charts have been created for the
52 African countries, so their coordinates for
SL and QL indices are analyzed in comparison
with data for 170 countries.
The most representative SL indicators (Figure 1) on axis 1 are for access to safe water,
education, and transport. These are the indicators whose contributions and square cosines are
highest. On the left are countries with a low
public education expenditure and a problem
with the access to safe water. Almost all the
African countries have negative coordinates
and are behind the center of gravity, which
would be the international ‘‘average.’’ On axis
2, material well-being (vehicles and TV) is more
important in the top quadrant (especially for
North African countries).
The same analysis for QL shows that African
countries were ranked primarily according to
trade openness, life expectancy, and literacy.
Again, the ‘‘richest’’ countries are in the top
right quadrant. Contrary to SL, several countries have positive coordinates on axis 1, which
shows that their quality of life is higher than the
international average. This is so with North
African countries, but also with some of the islands (Cape Verde, Mauritius, and the Seychelles). The other countries are rather close
Table 2. Statistical indicators for SL and QL components at international level
Average
Standard deviation
Median
Breaking value
Standard of Living
Standard of health
Standard of education
Material well-being
0.421
0.380
0.365
0.622
0.186
0.208
0.180
0.262
0.397
0.349
0.307
0.668
0.431
0.427
0.374
0.527
Quality
Quality
Quality
Quality
0.217
0.218
0.249
0.184
0.130
0.191
0.183
0.099
0.179
0.136
0.215
0.162
0.341
0.391
0.398
0.253
of Life
of health
of education
of environment
Source: Authors’ calculations based on UNDP (2002) and World Bank (2002) data.
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WORLD DEVELOPMENT
Table 3. Average score and percentage of countries with deficits
Rest of the world
African countries
Average score
%
Average score
%
Standard of Living
Standard of health
Standard of education
Material well-being
0.343
0.305
0.287
0.533
50.8
22.0
16.1
47.5
0.599
0.549
0.540
0.826
96.2
75.0
80.8
94.2
Quality
Quality
Quality
Quality
0.162
0.131
0.173
0.181
8.5
5.9
10.2
20.4
0.335
0.415
0.422
0.191
51.9
57.7
59.6
13.5
of Life
of health
of education
of environment
Source: Authors’ calculations based on UNDP (2002) and World Bank (2002) data.
Standard of Living 2000
0.53
Sudan
Gabon
0.33
Ghana
Egypt
Eq. Guinea
TV
Zambia
Mauritania
Zimbabwe
Mauritius
Tunisia
0.13
Morocco
Nigeria
Guinea
-1.4
-1.2
-1
Somalia
Eritrea
ROADS
Djibouti
Niger
-0.8
-0.6
Benin
Burundi Senegal
PHYSICIANS
Seychelles 0
Algeria-0.4
WATER
SCHOOLENROL
Cote d'ivoire
Cameroon
Tanzania
EDUCAT
Madagascar
Uganda
AGEDEPBotswana Liberia
Mali
Ethiopia
S. LeoneKenya
Togo
Cape Verde
Mozambique
Comoros
CAR
Angola
Gambia Lesotho
Malawi
Congo
Rwanda
Botswana
Guinea Bissau
Chad
DRC
Namibia
-0.2
Swaziland
0.2
0.4
-0.07
HEALTH
-0.27
South Africa
VEHICLES
Libya
-0.47
indicators
African countries
Figure 1. Standard of Living in African countries.
to each other and form a fairly homogeneous
group where life expectancy is weak and literacy is low (Figure 2).
4. DISCUSSION AND POLICY
IMPLICATIONS
(a) Aggregation in multidimensional indices and
choice of weighting system
Composite indices are usually constructed in
several stages, including choice of variables,
scaling, weighting, and aggregative procedure
(for an overview, see Booysen, 2002), but the
crucial problem is to assign suitable weights
to the indicators. Information can be aggregated into a single measure in two main ways.
One is by bringing into play the arbitrariness
and beliefs of the researcher and may involve
public and expert judgments. With the HDI,
PQLI, and Borda rule, the most-used method
is giving equal weights to the attributes of the
composite index assuming they are equally
important in capturing the several aspects of
the concept. The other main way of aggregating
MULTIDIMENSIONAL MEASURES OF WELL-BEING
1267
Quality of Life 2000
TRADE
0.39
Seychelles
0.29
0.19
Angola
Mauritius
Swaziland
Ghana
Liberia
Namibia
Djibouti Lesotho
Eq. Guinea
Gambia
0.09
Mauritania
Congo
Guinea Bissau
Cote
d'ivoire
Togo
Nigeria Guinea
Botswana
S. LeoneMozambique Eritrea Senegal
Morocco
MalawiMATERMORT Zimbabwe
Mali
Zambia
Rwanda Somalia
-0.01
Gabon
Niger
Benin -0.35
UNDERWEIGHT
-0.85 Ethiopia
0.15
Kenya
Comoros
Burundi CAR DRCChad
Cameroon
Algeria
Madagascar
Burkina Faso
CHILDLABOR
Tanzania
South Africa
-0.11
Uganda Sudan
Egypt
Tunisia
Cape Verde
0.65
1.15
LIBERTY
Libya
LITERACY
FEMLABOR
-0.21
LFEXPECIT
-0.31
CO2
-0.41
indicators
African countries
Figure 2. Quality of Life in African countries.
supports the objectivity of weighting schemes
and uses multivariate techniques for determining the weights. Most common is the principal
component analysis (Rahman et al., 2003;
Ram, 1982; Slottje, 1991) in which the attributes are weighted with the degree of variance
from the original set of variables explained by
the first principal component.
The choice of aggregation function is also
crucial as it affects the compensability of additive aggregations. The derived indices can be
either additive or functional, depending on
the context of analysis. Morris (1979) says that
if the aim is to measure relative performance of
individual countries, the simplest technique is
the additive aggregation method. Although
methodologies can lead to rankings sensitivity
according to a particular weighting scheme,
the recent study of Cahill (2005) suggests this
is not always the case.
Well-being measures are clearly not robust to
alternative methods of aggregation, but the use
of fuzzy set theory and factorial analysis allows
for less arbitrary assignment of weights to components of the indices. The two methods are
complementary as they each use one way of
aggregation. For TFA, weights are based on
frequency of symptoms of under-development.
The weight xj is an inverse function of the proportion of countries that are deprived relative
to indicator j. It implicitly relies on the belief
that the smaller is the proportion of countries
scoring low on a specific indicator, the larger
is the weight attributed to it in the aggregate
set. For example, in the SL index, the biggest
weight is attributed to safe water and a small
weight to cars. So a high deprivation of cars
would have less importance than low access
to safe water in assessing the relative performance of countries according to SL. FAC, like
principal component analysis, attributes a
weight objectively to the components according
to their percentage of variance in calculation of
the first axis.
To compare the two methods, we ranked
countries in descending order of well-being.
The two methods of analysis do not give exactly the same results. 13 These differences are
mainly due to the weights given to the indicators (Table 4).
So TFA gives the biggest weight to standard
of education (more than 43%) in calculation of
SL and to quality of health in QL (more than
35%). FAC gives the biggest weight to material
well-being (more than 45%) and quality of
health (more than 58%).
1268
WORLD DEVELOPMENT
Table 4. Weights of indicators using different methods
Standard of
health
Weights TFA
Weights FAC
0.3526
0.2507
Standard of Material Total Quality of
education well-being
health
0.4486
0.2932
0.1988
0.4561
1.0
1.0
0.3540
0.5804
Quality of
education
Quality of
environment
Total
0.3279
0.2172
0.3181
0.2024
1.0
1.0
Source: Authors’ calculations based on UNDP (2002) and World Bank (2002) data.
The weighting system significantly changes
the results but more comprehensively, the matrix of the rank correlations (Appendix D)
proves that differences in ranking are not significant and, on the contrary, with TFA and FAC
methodologies, are statistically correlated.
Another set of interesting results comes from
the rank correlation between SL and QL determined by GDP per capita, and the HDI. The
correlation coefficients of the two methods are
systematically lower with GDP per capita than
with the HDI, but remain generally high, meaning economic performance is strongly related to
well-being in all its other dimensions. Also, a
high correlation is found between rankings of
countries according to GDP per capita and
the HDI (R2 = 0.94), a result McGillivray
(1991) explains by the high correlations between HDI components and GDP per capita.
And the strong correlations between SL and
QL indices with the HDI indicate that, despite
the serious criticism of the HDI’s reductionist
approach to human well-being, it reflects well
the basic dimensions of human development.
Our results are robust and reinforce each
other since the country rankings according to
TFA, FAC, HDI, and GDP per capita are
not very different. This is not surprising, though
detailed study of the rank variations reveals
nuances in this simple idea.
(b) Analysis of correlations and redundancies
between indicators
The correlations between SL, QL and their
components (Appendix E) show quality of
environment as the only indicator statistically
correlated neither with SL and QL nor the
other indicators. The main reason is the heterogeneity of the quality of environment components (trade openness, CO2 emissions and
liberties, and freedoms). More generally, the
indicators are statistically and significantly correlated with each other. According to Cahill
(2005), this result might explain the high rank
correlations between our indices using two different methods and weighting schemes. The
matrix of correlations (Appendix F) also shows
that GDP per capita is more strongly correlated
with SL than with QL, which can be considered
through a capability approach. The coefficients
of correlation between our two indices and the
HDI are also far higher than with GDP per capita, which shows that our indices are closer to
the HDI than to GDP per capita. SL is most
strongly correlated with standard of education
and QL with quality of education, which confirms that ‘‘education’’ plays an important part
in defining a country’s human development
performance. Education should thus be one of
the three mega-variables in the HDI. This supports the recent finding of McGillivray (2005)
that adult literacy best captures the non-economic dimension of well-being. In contrast,
Dasgupta and Weale (1992) and Rahman
et al. (2003) find that health (such as life expectancy) is the closest measure of well-being.
The ability to read and write is very important and allows a person to stand up for his/
her rights. Education also helps to overcome
negative processes such as child labor or social
barriers and to empower disadvantaged groups.
An educated person may also survive better or
longer and participate in political change.
According to Sen, education expands the real
freedoms that people value.
For redundancy, we use McGillivray and
White (1993) criteria for redundancy of 0.7 (level 2) and of 0.9 (level 1). Correlation coefficients (Appendix F) are thus mainly level 2 of
redundancy. But the correlation between QL
and the GDP per capita, at 95% level, indicates
absence of redundancy (in terms of value). This
striking result brings empirical support to the
conceptual justification of QL. Indeed, the QL
index measures ‘‘the end’’ rather than ‘‘the
means,’’ as does the GDP per capita. Though
they are conceptually distinct, the high redundancy between SL and QL was expected as
the means and outcomes indicators can be seen
as inputs and outputs of a production or welfare function.
MULTIDIMENSIONAL MEASURES OF WELL-BEING
(c) Policy recommendations for African
countries
One way of understanding well-being in Africa is to look at the rank changes in countries in
the SL and QL indices, GDP per capita and the
HDI. GDP per capita has very many outlier
cases in both directions: low wealth but fairly
high well-being and high wealth but low wellbeing (Appendix G). So a country can be much
better or much worse ranked, according to the
index applied. The very large rank variations
then give an indication of the deficiencies of
some indices. For example, it is preferable to
use SL rather than QL to rank North African
countries (Egypt, Libya, Algeria, and Tunisia).
The rank is higher according to GDP per capita
for Equatorial Guinea, Gabon, Namibia, Botswana, and South Africa, suggesting that these
countries are richer than they are developed.
QL is more important than other measures in
Ghana, Tanzania, and Zambia.
According to Sen’s capability approach, we
could say whether standards of health and education are associated with good functionings
(or quality) in each sector. In health, we could
check whether public expenditure, access to
water and the number of doctors allow mothers
and children to live in healthy conditions and
the population to have high life expectancy at
birth (as in Algeria, Egypt, Mauritius, Morocco, the Seychelles, Swaziland, and Tunisia). In
education, the same would indicate whether
public spending, school enrollment and the
number of ‘‘dependents’’ allows females and
children to work in good conditions and adults
to be literate (as in Algeria, Botswana Cape
Verde, Congo, Gabon, Lesotho, Libya, Mauritius, Namibia, the Seychelles, South Africa,
Swaziland, Tunisia, and Zimbabwe). In other
countries, either small resources (a low standard) or weak functionings (a low quality) prevent the two sectors from being efficient.
These results could be used to make better
and more efficient public expenditure allocation
to sectors. African countries should increase
their investment in human capital and infrastructure to improve health, education, and
material well-being. To enable efficient transformation of resources into capabilities, Africa
has to give high priority to improving governance, maintaining peace and security and
increasing democratization and human rights.
Sen argues that development is freedom, so a
variety of institutions (such as NGOs, media
and international and local communities) con-
1269
tribute to development through enhancing individual freedoms. They are extremely important
agents of Africa’s future development.
5. CONCLUSION
The two methods of well-being measurement,
TFA and FAC, take into account several dimensions of well-being (such as liberties, child labor,
or the number of vehicles) and enable two indices
to be constructed according to Sen’s capability
approach. Unlike the HDI, the two indices do
not mix measures of resource availability and
of functioning and capability. The results for
SL and QL show that African countries have
substantial deficiencies in many areas, except
perhaps quality of environment, and highlight
the importance of education as a key variable
in a country’s multidimensional development.
We also compare monetary performances with
the HDI and the SL and QL indices. Although
most components might be dependent on income, SL and QL completely exclude any monetary indicator. Low correlation between QL
and GDP per capita gives empirical support to
the conceptual distinction. Detailed analysis of
indices and sub-indices also makes it possible
to distinguish between ‘‘lack of resources’’ and
‘‘weak functioning’’ by domain.
The weighting systems substantially change
the rankings, but the two methods are complementary as they each use a different way of
aggregation. For TFA, weights are based on
allegation. For FAC, weights are objectively
calculated. However, by considering the rank
correlations, the differences between the two
methods are not significant. Though the HDI
seems restrictive, it does in fact take the essential indicators into account, since it establishes
country rankings very close to those of our
two indices. But due to their diversity and originality, the SL and QL indices cover a much
greater area than the HDI. They can be disaggregated and information can be better used
to establish social and economic policies to
fight structural under-development. They are
based on Sen’s capability approach, allow
assessment of two measurements of human
well-being and create a clear framework for
evaluating development policies and processes.
Sen (1999) argues that development occurs
when people are able to achieve what makes
their life valuable. Good governance, democratization and respect for human rights are key issues in Africa’s development.
1270
WORLD DEVELOPMENT
NOTES
1. Ivanova, Arcelus, and Srinivasan (1999), Kelley
(1991), McGillivray (1991) and Srinivasan (1994) have
criticized the HDI.
2. Even though GDP per capita is a flow concept, it is
generally used as a measure of a stock concept.
3. The HDI is the unweighted average of three subindices of basic well-being: life expectancy at birth,
educational attainment and GDP per capita expressed in
real terms and purchasing power parity. Each indicator
is normalized, so it takes values between zero and one.
Scaling makes it possible to compare countries’ relative
performances in each dimension. The HDI does not
measure absolute but relative levels of human development by ranking countries.
4. His approach has not only been used in development
economics, but also in other analyses such as political
philosophy, social policy, and welfare economics.
5. The distinction refers to the concept of ‘‘basic needs’’
proposed by the International Labour Organisation in the
1970s and by Hicks and Streeten (1979) and defined as
physical necessities (food, shelter, and public goods).
The concept was then reintroduced by the World Bank
(2001), but it is currently very close to the concept of
‘‘basic capabilities.’’
6. Jahan (2002) presents the refinements in the HDI
methodology over time.
7. The method has been used by Dasgupta and Weale
(1992) as an implicit criticism of the cardinal information base that underlies the HDI. Relying exclusively on
ordinal information, the Borda method simply involves
assigning a rank order score to each component index
and adding up the rank order scores to obtain the Borda
score.
8. See Schokkaert and van Ootegem (1990), Brandolini
and d’Alessio (1998), Chiappero Martinetti (2000), Lelli
(2001), and Qizilbash (2002).
9. These two methods were used in the context of
individual data by Lelli (2001).
10. Cerioli and Zani (1990) were the first to use it in
this field. Others followed, such as Cheli and Lemmi
(1995), Chiappero Martinetti (1996, 2000), Lelli (2001),
and Qizilbash (2002).
11. Applying fuzzy sets theory to macro-economic
data, Von Furstenberg and Daniels (1991) and Baliamoune (2000) explain why this methodology makes it
possible to avoid using dichotomic variables and allows
a gradual transition from one state to another.
12. For results in European, Arabian, or Mediterranean countries, see Bérenger and Verdier-Chouchane
(2004).
13. For example, out of 52 African countries, FAC and
TFA both rank Morocco 8th in SL, while Cameroon is
ranked 32nd with FAC and 30th with TFA. But the
spread can be wider, as with the Central African
Republic (50th and 36th). In QL, FAC puts the
Seychelles first and Mauritius second, while TFA gives
the reverse order. Côte d’Ivoire is 26th and 31st.
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Second step: weighted indices of SL and QL
Following Cerioli and Zani (1990), composite
indices are defined by taking the weighted arithmetical mean of the membership functions
according (respectively) to the component M
and M 0 indicators:
M
X
xj lj ðiÞ;
lSL ðiÞ ¼
j¼1
lQL ðiÞ ¼
M0
X
x0j l0j ðiÞ
j0 ¼1
APPENDIX A. TOTALLY FUZZY
ANALYSIS
First step: degree of deprivation for
each indicator
Assume i 2 [1, N] countries, j 2 [1, M] indicators of Standard of Living (SL) and j 0 2 [1, M 0 ]
indicators of Quality of Life (QL). Consider
Xj = {xj/j = 1, . . . , M} and X j0 ¼ fxj0 =j ¼ 1;
. . . ; M 0 g vectors of components respectively of
SL and QL. Variables xij and xij0 are the values
taken by indicators j and j 0 for the ith country.
When ranking values of j and j 0 by increasing
order (the lower the value of a given indicator,
the higher the deprivation), functions lj(i) and
lj0 ðiÞ are defined as:
8
>
1
if xij 6 xmin
j ;
>
>
< xmax xi
j
j
min
lj ðiÞ ¼ xmax xmin if xj < xij < xmax
;
j
>
j
j
>
>
:0
if xij P xmax
j
with xmin
¼ Mini ðxij Þ and xmax
¼ Maxi ðxij Þ.
j
j
Functions lj(i) and lj0 ðiÞ provide the deprivation degrees of the ith country relative to indicators j and j 0 .
Inversely, if indicators are rearranged by
decreasing value (which is the case for CO2
emission), functions lj(i) and lj0 ðiÞ are then defined as:
8
;
1
if xij P xmax
>
j
>
>
<
xij xmin
< xij < xmax
;
lj ðiÞ ¼ xmax xj min if xmin
j
j
j
j
>
>
>
:0
if xij 6 xmin
j :
These functions are increasing linearly between zero and one according to the degree of
deprivation.
P
0
with xj P 0 and M
j¼1 xj ¼ 1, where xj and xj
are the weights attributed, respectively, to indicators j and j 0 .
Chiappero Martinetti (1996) says the function must have a value between maximum
and minimum and must allow interaction between the indicators.
The weights are defined as
N
ln l1j
1 X
with l
j ¼
l ðiÞ:
xj ¼ P
M
1
N i¼1 j
j¼1 ln l
j
The weight xj is an inverse function of the
mean deprivation level relative to the indicator
j. The logarithmic curve function introduced
into the weighting system shows that well-being
does not vary in a linear way.
Third step: definition of a critical value
The critical value (or breaking value) ljcrit
associated to indicator j can be defined as:
j
F ðlj critÞ ¼ 1 l
with F the cumulative distribution function and
j the average value of indicator j which indil
cates, dichotomously, the proportion of under-developed countries according to j.
APPENDIX B. FACTORIAL ANALYSIS
OF CORRESPONDENCES
Data are presented in a NIJ table in which the
rows (countries) are numbered by i = 1, . . . , p
and the columns (indicators) by j = 1, . . . , q.
Alternatively, the PIJ table, whose general term
is pij = nij/n, provides the two marginal distributions: pi = ni/n and pj = nj/n as well as the
conditional distributions in rows and columns,
called ‘‘row’’ profile and ‘‘column’’ profiles,
respectively: pij ¼ pij =pi and pji ¼ pij =pj .
MULTIDIMENSIONAL MEASURES OF WELL-BEING
First step: calculation of distances
To measure the distance between two profiles, we compare the same rank terms (e.g., p1j
and p2j ). The v2 distance between the row pro0
files is d(i,
P i ) whose square is given by
d 2 ði; i0 Þ ¼ qj¼1 ðpij pi0j Þ2 =pj .
The v2 distance between the column profiles
is similarly defined:
p
X
0
ðpji pji Þ2 =pi :
d 2 ðj; j0 Þ ¼
i¼1
Second step: calculation of principal axes
FAC provides orthogonal basis vectors—
principal axes—preserving the distances and
calculated according to the least squared method. The origin of the axes (0, 0) shows the marginal distribution, the center of gravity or the
‘‘average country’’.
The duality principle makes it possible to
show the two ‘‘profiles’’ on the same chart,
interpret proximity between a row and a column profiles and thus explain the connection
between two variables. The FAC duality princi-
1273
ple is essential but a difficult property whose
interpretation is delicate. For details, see Casin
(1999) and Foucart (1997).
Third step: interpretation parameters
The clouds of points N(I) and N(J) on the
principal axes correspond to projections as
close as possible from observation. Two values
for interpretations are calculated: the contribution (with variance taken as the indicator) and
the quality of representation (with squared cosine taken as the indicator).
Contributions are used to measure the influence (weight) of a point (e.g., of a country i) in
defining a principal axis. The sum of the contributions for each axis equals one. The proximity
between projections does not always reflect the
proximity between the profiles. Some points
can be badly shown or moved away from the
profiles they represent. Angle h measures the
proximity between the point in space and its
projection on the plan. So a weak angle indicates good proximity (the square cosine is close
to one) and an angle close to 90° (the square cosine is close to zero) indicates a bad one.
APPENDIX C. LIST OF COUNTRIES
Afghanistan
Albania
Algeria
Angola
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahamas, The
Bahrain
Bangladesh
Barbados
Belarus
Belgium
Belize
Benin
Bhutan
Bolivia
Bosnia and Herzegovina
Botswana
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi
Cambodia
Cameroon
Canada
Cape Verde
Central African Republic
Chad
Chile
China
Colombia
Comoros
Congo, Dem. Rep.
Congo, Rep.
Costa Rica
Cote d’Ivoire
Croatia
Cuba
Cyprus
Czech Republic
Denmark
Djibouti
Dominican Republic
Ecuador
Egypt, Arab Rep.
El Salvador
Equatorial Guinea
Eritrea
Estonia
Ethiopia
Fiji
Finland
France
Gabon
Gambia, The
Georgia
Germany
Ghana
Greece
Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary
Iceland
India
Indonesia
Iran, Islamic Rep.
(continued on next page)
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WORLD DEVELOPMENT
APPENDIX C—continued
Iraq
Ireland
Israel
Italy
Jamaica
Japan
Jordan
Kazakhstan
Kenya
Korea, Rep.
Kuwait
Kyrgyz Republic
Lao PDR
Latvia
Lebanon
Lesotho
Liberia
Libya
Lithuania
Luxembourg
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Malta
Mauritania
Mauritius
Mexico
Moldova
Mongolia
Morocco
Mozambique
Myanmar
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines
Poland
Portugal
Qatar
Romania
Russian Federation
Rwanda
Samoa
Saudi Arabia
Senegal
Seychelles
Sierra Leone
Singapore
Slovak Republic
Slovenia
Somalia
South Africa
Spain
Sri Lanka
Sudan
Suriname
Swaziland
Sweden
Switzerland
Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago
Tunisia
Turkey
Turkmenistan
Uganda
Ukraine
United Arab Emirates
United Kingdom
United States
Uruguay
Uzbekistan
Vanuatu
Venezuela, RB
Vietnam
Yemen, Rep.
Zambia
Zimbabwe
APPENDIX D. RANK CORRELATIONS
SL (fuzzy) QL (fuzzy) SL (FAC) QL (FAC) GDP per capita HDI
SL (fuzzy)
1
QL (fuzzy)
0.82894
(<0.0001)
1
SL(FAC)
0.832065
(<0.0001)
0.71765
(<0.0001)
1
QL (FAC)
0.8960
(<0.0001)
0.75068
(<0.0001)
0.843957
(<0.0001)
1
GDP per capita
0.83276
(<0.0001)
0.68621
(<0.0001)
0.867768
(<0.0001)
0.864837
(<0.0001)
1
HDI
0.91287
(<0.0001)
0.79406
(<0.0001)
0.887808
(<0.0001)
0.926069
(<0.0001)
0.94121
(<0.0001)
1
MULTIDIMENSIONAL MEASURES OF WELL-BEING
1275
APPENDIX E. CORRELATIONS OF INDICES AND SUB-INDICES
Standard Standard Material
of
of
Well-being
Health Education
Quality
of
Health
Standard of
Health
1
Standard
Education
0.72889
(<0.0001)
Material
well-being
0.74575
0.66131
(<0.0001) (<0.0001)
1
Quality
Health
0.78611
0.80929
(<0.0001) (<0.0001)
0.73164
(<0.0001)
Quality
Education
0.71107
0.82384
(<0.0001) (<0.0001)
0.68980
0.79712
(<0.0001) (<0.0001)
Quality of
Environment
Quality
Quality
Standard Quality
of
of
of
of
Education Environment Living
Life
1
0.16860
(0.028)
0.03223
(0.6765)
0.14462
(0.0599)
1
0.09053
(0.2403)
1
0.21796
(0.0043)
1
Standard of
Living
0.85466
0.89685
(<0.0001) (<0.0001)
0.88348
0.86252
0.86198
(<0.0001) (<0.0001) (<0.0001)
0.10992
(0.1536)
1
Quality of
Life
0.71498
0.77968
(<0.0001) (<0.0001)
0.61419
0.79214
0.93196
(<0.0001) (<0.0001) (<0.0001)
0.50722
(0.0001)
0.81180
(<0.0001)
1
APPENDIX F. CORRELATIONS WITH GDP PER CAPITA AND HDI
Standard of Living
Standard of Living
Quality of Life
GDP per capita
HDI
1
Quality of Life
0.8096
(<0.0001)
1
GDP per capita
0.71151
(<0.0001)
0.50248
(<0.0001)
1
HDI
0.92149
(<0.0001)
0.80397
(<0.0001)
0.75804
(<0.0001)
1
Calculations based on 166 countries. GDP per capita is in current dollars based on purchasing power parity, identical
to the one UNDP uses for calculating the HDI. The negative correlation coefficients are because high levels of SL and
QL indices correspond to values close to zero, contrary to GDP per capita and the HDI.
APPENDIX G. RANK VARIATIONS ACCORDING TO VARIOUS INDICES IN AFRICA
Rank variations
According to:
% of displaced
African countries
Most important declines
Between SL
and QL
QL
32.7%
Between SL
and GDP
GDP
42.9%
Between QL
and GDP
GDP
32.7%
Egypt (56)
Libya (36)
Algeria (31)
Tunisia (23)
Comoros (21)
Rwanda (21)
Egypt (36)
Congo Rep. (25)
Zambia (22)
Comoros (21)
Rwanda (18)
Zambia (48)
Tanzania (42)
Malawi (42)
Congo Rep (42)
Ghana (41)
Madagascar (32)
(continued on next page)
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WORLD DEVELOPMENT
APPENDIX G—continued
Rank variations
According to:
% of displaced
African countries
Between SL
and QL
QL
32.7%
Between SL
and GDP
GDP
42.9%
Between QL
and GDP
GDP
32.7%
Ghana (57)
Djibouti (49)
Liberia (44)
Tanzania (37)
Nigeria (29)
Equ. Guinea (107)
Gabon (54)
Namibia (53)
Botswana (46)
South Africa (38)
Equ. Guinea (113)
South Africa (52)
Libya (50)
Botswana (46)
Gabon (46)
Namibia (43)
Greatest improvements
Rank variations
According to:
% of displaced
African countries
Most important declines
Greatest improvements
Between GDP
and HDI
HDI
64.0%
Between SL
and HDI
HDI
57.1%
Between QL and HDI
Equ. Guinea (70)
South Africa (55)
Botswana (55)
Namibia (50)
Gabon (41)
Swaziland (37)
Egypt (52)
Zimbabwe (43)
Algeria (34)
Botswana (27)
Malawi (25)
Congo Rep. (19)
Zambia (32)
Djibouti (29)
Ghana (28)
Benin. (27)
Cape Verde (22)
Congo Rep. (25)
Madagascar (14)
Tanzania (14)
Malawi (10)
Comoros (9)
Equ. Guinea (37)
Libya (32)
Ghana (15)
Gabon (14)
Madagascar (14)
Libya (50)
Equ. Guinea (43)
Seychelles (17)
Rwanda (15)
Eritrea (14)
HDI
65.3%