Multidimensional Measures of Well-Being: Standard of Living and Quality of Life Across Countries

Home , Standard of living

World Development Vol. 35, No. 7, pp. 1259–1276, 2007 Ó 2007 Elsevier Ltd. All rights reserved 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev doi:10.1016/j.worlddev.2006.10.011 Multidimensional Measures of Well-Being: Standard of Living and Quality of Life Across Countries

VALE´ RIE BE´ RENGER Universite´ de Nice-Sophia Antipolis, CEMAFI (Centre d’Etudes en Macroe´conomie 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 well- being—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 countries, we adopt and compare two recent methodologies, Totally Fuzzy Analysis (TFA) International organizations now recognize and Factorial Analysis of Correspondences that human development goes beyond eco- (FAC), to analyze the usefulness of these indi- nomic growth and is a multidimensional phe- ces. Our broader well-being indices are com- nomenon covering all aspects of well-being. pared with the HDI and GDP per capita to This partly dates from Sen’s work on social jus- tice and inequalities (Sen, 1985, 1992), which inspired a new concept of development. Sen’s * Dr. Vale´rie Be´renger is from the CEMAFI (Centre capability approach contributed to the design d’Etudes en Macroećonomie et Finance Internationale), of the UNDP Human Development Index Universite´ de Nice-Sophia Antipolis, and Dr. Audrey (HDI) in 1990, which was intended as a more Verdier-Chouchane is from the Development Research comprehensive indicator than per capita in- Department, African Development Bank. We thank come for comparing the well-being of coun- Bernhard Gunter and four anonymous reviewers for tries. However, the HDI’s critics 1 say its their very helpful comments and valuable inputs as well indicators are too few and too arbitrarily cho- as Louis Kasekende, Temitope Waheed Oshikoya, and sen and that its definition is still inadequate De´sire´ Vencatachellum for their significant contributions and does not allow the capability approach to to this work. Greg Chamberlain and Diane Brothwell work. provided excellent assistance in editing the manuscript. This article defines two composite indices— Financial support from the African Development Bank SL (Standard of Living) and QL (Quality of is gratefully acknowledged. We retain responsibility for Life)—across 170 countries, supporting Sen’s any remaining errors. Final revision accepted: October capability approach. Focusing on 52 African 2, 2006. 1259 1260 WORLD DEVELOPMENT examine the cogency of the HDI and the redun- are all goods and services, not just merchan- dancy issues. dise. They can include transfers in kind and Section 2 here deals with the concepts and make possible the ‘‘functionings,’’ which take justification for the two indices (SL and QL), into account achievements of individuals— while Section 3 presents the two methodologies what they ‘‘are’’ and what they ‘‘make’’ with and the obtained results in Africa. Policy rec- their resources—and reflects life-style. ommendations and discussion are presented in The concept of ‘‘capabilities’’ is related to Section 4. Section 5 presents our conclusions. ‘‘functionings’’ but also includes notions of opportunity and freedom—the range of opportunities a person has and can choose from. 2. CONCEPTS FOR STANDARD OF ‘‘Capabilities’’ are various combinations of LIVING AND QUALITY OF LIFE functionings (beings and doings) that the person can achieve. ‘‘Capability is, thus, a set of GDP per capita is the most commonly used vectors of functionings, reflecting the person’s indicator to compare wealth among countries 2 freedom to lead one type of life or another and is a measure of well-being and development (...) to choose from possible livings’’ (Sen, exclusively based on material wealth. However, 1992, p. 40). A functioning is an achievement, insufficient income is merely one dimension of while capability is about the ability to produce under-development, so development cannot be it. Functionings are thus more directly related understood by only taking into account eco- to living conditions, while capability is a con- nomic performance. Attempts were made in cept of freedom, in a positive sense. According the 1970s to construct socio-economic indica- to Sen’s definition, the UNDP (1997) defines tors as an alternative to GDP per capita, which human development as increasing people’s was criticized as capturing neither distribu- choices by expanding their human capabilities tional aspects nor social and human welfare and opportunities. Under-development is thus dimensions (Desai, 1991). The standard human not a deprivation of basic needs, but is a depri- development concept dates from the 1990 vation of basic capabilities or freedoms that Human Development Report (UNDP, 1990). would allow an individual to have the kind of It drew on Sen’s work and led to growing life he/she wants. 5 Sen’s (1999) approach is acknowledgement of the multidimensional nat- qualitative and multidimensional. He puts hu- ure of development and to different strategies man beings at the center of the development for moving from promotion of growth to pro- concept. The aim of development is to enhance motion of well-being. It also highlighted the human capabilities so as to lead full, productive need to construct alternative composite indices, and satisfying lives. A higher income is neces- including non-monetary indicators, to assess sary but not sufficient and thus calls for wider the achieved development levels. The HDI measures of well-being. The HDI was supposed was launched in 1990 to represent the broad to use Sen’s approach to make international ideas included in the human development con- comparisons. It has been improved, in particu- cept. 3 lar, for GDP calculation and extremes fixing. 6 So the capability approach has helped en- It has also led to other indices such as the Hu- large the concept of human development. Inter- man Poverty Index (HPI) which assesses hu- national organizations such as the World Bank man development not on average national (2006) have adopted notions of ‘‘quality of achievement but on how many people live in growth’’ and ‘‘pro-poor growth’’ that reflect deprivation. Several heterodox World Bank greater concern about non-monetary dimen- studies consider indicators of inequality for sions of well-being. analysis of true development. But Easterly (2002), who examines inequality as a barrier (a) Sen’s capability approach and the need for to prosperity and growth, and Pritchett, Sur- going beyond the HDI yahadi, and Sumarto (2000), who look at vulnerability to poverty, use household data. Sen’s capability approach (1985) proposes a After GDP per capita, the HDI is the most normative framework to evaluate individual discussed measure of well-being. The literature well-being, social relationships and changes in seems to take two approaches that are not inev- society. 4 Its main components are the ‘‘com- itably exclusive of each other. modities’’ or resources, the ‘‘functionings’’ In the first, the main criticism of the HDI re- and the ‘‘capabilities.’’ The ‘‘commodities’’ lates to its very narrow definition of human MULTIDIMENSIONAL MEASURES OF WELL-BEING 1261 well-being. New indices, sometimes excluding aggregated data to allow international compar- the income component, have been proposed isons as our indices permit. Slottje (1991) uses without having content necessarily justified or 20 indicators to build a composite index of based on an explicit theoretical approach of well-being for 126 countries. Baliamoune well-being. The Physical Quality of Life Index (2003) explicitly uses Sen’s capability approach (PQLI) developed by Morris (1979) takes into and proposes classifying countries according to account life expectancy, infant mortality, and new indicators close to the concept of freedom literacy. The Quality of Life Index of Dasgupta conveyed by ‘‘capability.’’ and Weale (1992) adds civil liberties and political rights to the HDI. The Index of Economic (b) Alternative applications of the capability Well-Being proposed by Osberg and Sharpe approach (1998) is similar, though it also takes into account economic aspects of well-being neglected Studies using Sen’s approach have flaws. by GDP per capita (such as production stocks, They do not measure ‘‘capability.’’ With lim- unequal income distribution and uncertainty ited data available, usually only ‘‘functionings’’ about future income). Rahman, Mittelhammer, carried out are used as a proxy of ‘‘capabili- and Wandschneider (2003) propose a compos- ties.’’ These attempts are also sometimes far ite index of well-being based on eight social from the conceptual framework they are sup- dimensions, each including indicators for social posed to be linked to because the composite relationships, emotions, health, work, material indices rely on a combination of indicators that well-being, civil, and political liberties, personal are different by nature, some corresponding to security, and environment quality. The index is ‘‘capabilities’’ (civil liberties and political applied to 43 countries using the Borda rule 7 rights), others to ‘‘functionings’’ (literacy) and and the principal components analysis method. others still to resources or assets (such as the Several other studies (Ivanova et al., 1999; number of telephones per capita). Well-being, Ogwang & Abdou, 2003; Qizilbash, 2004) point standard of living and quality of life are gener- out the difficulties and risks of creating indices, ally not differentiated in these studies and they in view of the multidimensional aspects of well- apply different concepts and realities, raising being, the redundancy of variables and the two questions—which and how many indica- measurement sensitivity to any weighting sys- tors to use to move towards the concept of tem. McGillivray (1991) and McGillivray and human development, and the matter of new White (1993) highlight the redundancy between and more complete indices to make the distinc- the HDI and its components and say the HDI tion between the various concepts of Sen’s is ‘‘yet another redundant index’’ and that its approach. significantly high correlation with GDP per ca- Sen gives no list of ‘‘capabilities’’ to take into pita demolishes the argument that it would pro- account for constructing well-being indices and duce different rankings of countries. To allows multiple proposals (Alkire, 2002). The overcome this drawback, McGillivray (2005) three HDI components could be justified con- has recently applied principal components anal- ceptually as being universal, basic to life and ysis and regression estimates to the HDI com- measurable but they raise the non-inclusion of ponents to extract an aggregate measure of other dimensions. For example, Dasgupta non-economic aspects of human well-being. (1990, 1992) criticizes the HDI for neglecting Cahill (2005) analyses the implications of such human rights. The choice of indicators suggests correlations for the choice of weighting. The that if ‘‘capabilities’’ were carried out in these main conclusion is that high correlations be- three basic dimensions, it would be done in tween HDI components would produce a com- the other dimensions of human development. posite index insensitive to the weighting. With the capability approach, while the educa- In the second approach, the HDI’s reduction- tion and life expectancy indicators refer to ist nature is also criticized but bigger questions ‘‘functionings,’’ the income per capita compo- are raised, such as the relationship between the nent seems to be a ‘‘commodity.’’ capability approach and the concept of human As the human development concept has development (Gasper, 2002), or broader ones, emerged from the GDP limitations, it seems about the content and empirical measurement inappropriate to include an income component of ‘‘capabilities.’’ Most studies supporting the in an index of well-being. As Anand and Sen capability approach use disaggregated data (2000) themselves argue, income can be an indi- from household surveys 8 and very few use rect indicator of some capabilities. However, 1262 WORLD DEVELOPMENT well-being is not determined by possession of nation of ‘‘functionings’’ and/or ‘‘capabilities’’ resources but by their transformation into indicators within the meaning of freedoms. ‘‘functionings’’ which depends on personal, so- They are result indicators that refer to output cial and environmental factors. GDP per capita within a transformation system of ‘‘commodi- is necessary but not sufficient for human devel- ties,’’ as Sen suggests. opment as shown by countries where high and SL involves nine indicators in three domains growing GDP per capita has not led to enrich- (education, health, and material well-being). ment of human lives (Sen, 1999). The income Public expenditure as a percentage of GDP in component affects the purity of the HDI as a education and health take into account the capability-based measure. First, the level of money allocated to these social services. The GDP per capita is a poor indicator of the number of doctors/physicians is an indicator means of a group of people and its usefulness of health facilities. Access to safe water reflects for the expansion of social services and infra- public facilities available and a means to pre- structure development is not clear (Anand & vent illness and epidemics. The age dependency Ravallion, 1993; Anand & Sen, 2000; Sen, ratio and net primary school enrollment indica- 1981, 1999). Means indicators that are determi- tors are more difficult to justify as means indi- nants of well-being and part of the standard of cators and have been chosen on the basis of living index are needed. Second, GDP per capi- data availability. The teacher/student ratio ta is a bad proxy of freedoms and quality of would have been a better means indicator but life. was not available for many countries. The age Like our indices, the PQLI and the Capabil- dependency ratio is the demographic pressure. ity Poverty Measure (CPM), used in the 1996 It is a summary of ageing that includes both Human Development Report (UNDP, 1996) young and old in relation to the potentially ac- and replaced the following year by the HPI tive population, so is a rough indicator of edu- (with a variant for developing and industrial- cation. ized countries), are examples of non-income QL focuses on measures of well-being or hu- measures. The HPI replaces the income compo- man outcomes that include explicit references nent by several means indicators, while the to human freedoms. It tries to take into ac- CPM focuses on human capabilities, function- count the capability of participating in commu- ings and outcomes. Unlike the HDI, these indi- nity life that was dropped from the HDI. The ces are headcount proxies measuring the main indicators emphasize the ‘‘being and percentage of people in each country who lack doings’’ of a population, their opportunities basic human capabilities. as well as their non-opportunities. Capability failure can stem from violation of personal (c) Justification of Standard of Living and rights or absence of positive freedoms (Sen, Quality of Life Indices 1992). The QL composite index is a combination of nine indicators covering three domains: Standard of Living (SL) and Quality of Life health, education, and environment in a broad (QL) are not new indices of well-being, but past sense. Education quality includes capabilities indices differ from ours in that they capture dif- that education can provide. Adult literacy is ferent dimensions of well-being, use different accompanied by two other indicators measur- methods of aggregating and have different the- ing children’s and women’s capabilities. Child oretical foundations. labor indicates denied opportunities to acquire To take other aspects of development into ac- human capabilities needed in productive and count, while distinguishing between the con- social life. Female labor captures the intensity cepts, we define the composite indices of SL of gender equality in productive activity. It and QL as based on ‘‘commodities’’ on the may also depict to a lesser extent ‘‘outcomes’’ one hand and the ‘‘functionings’’ and ‘‘capabil- of education as capability of entering the labor ities’’ on the other. SL corresponds to the quan- market. Life expectancy is an indicator of tity of goods and services and to the services the capacity to live a long life, maternal mortality GDP produces. It includes several means indi- accounts for capacity to give birth in good cators that correspond to ‘‘commodities’’ that health conditions and the percentage of under- could be called inputs. QL includes (unlike weight or stunted children accounts for the po- SL) more intangible or qualitative aspects such tential for being well nourished. Civil rights and as quality of education, extent of child labor political freedoms refer to the aim of develop- and quality of the environment. It is a combi- ment relating to the actual freedom enjoyed MULTIDIMENSIONAL MEASURES OF WELL-BEING 1263 by the population involved. Trade openness in- relationships with other members of society. volves the capability to exchange goods with Several lists of ‘‘basic capabilities’’ are pro- partners and contribute to better quality posed in the literature (Alkire, 2002) to ap- products. Carbon dioxide (CO2) is not directly proach the concept of human development related to air pollution but to resource deple- but their use in identifying indicators is mostly tion. However, all fuels produce CO2 when limited to the disaggregated household data. burned but sometimes also other pollutants Several indicators can be interpreted at the (nitrogen oxides, gaseous hydrocarbons, and same time as a ‘‘commodity,’’ a ‘‘functioning’’ carbon monoxide). The more CO2 emissions, or a ‘‘capability.’’ So it is hard to produce a the more likely are other dangerous emissions division of indicators that is not contestable. and worse quality of air. The choice of indicators in Table 1 is justified by arguments above but is also limited by avail- 3. CONSTRUCTION OF WELL-BEING ability of data to compile SL and QL indices INDICES IN AFRICAN COUNTRIES for at least as many countries as there is data for the HDI and GDP per capita (we have (a) Methodologies used for analysis of Standard 170 countries). Division of indicators between of Living and Quality of Life SL and QL is not always easy to establish. The concept of ‘‘capability’’ is difficult to dis- The construction of SL and QL indices and cern from country data because it is initially de- measuring the degree of deficiency or ‘‘depriva- fined in reference to individuals and their tion’’ 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. 1264 WORLD DEVELOPMENT requires a suitable methodology. Well-being is development can be defined as an accumulation multidimensional so we shall use and compare of ‘‘deprivations’’ or ‘‘shortfalls.’’ Conversely, the methodology from the fuzzy sets approach the index value can be interpreted as an accu- (Totally Fuzzy Analysis) and from the simple mulation of ‘‘effective achievements.’’ The high- Factorial Analysis of Correspondences. 9 er the SL (or the QL), the closer the value index is to zero or the closer the value is to one, the (i) Measuring Standard of Living and Quality of higher the degree of deprivation relative to Life using the fuzzy sets approach the SL (or the QL). As components of human well-being, Stan- Application of fuzzy sets methodology to dard of Living and Quality of Life are multidi- indicators yields country rankings according mensional and vague concepts. The fuzzy sets to the SL and QL indices. Due to the addition- theory, invented by Zadeh (1965) and devel- ally decomposable nature of fuzzy indices, SL oped by Dubois and Prade (1980), is a suitable and QL can be broken down to obtain several mathematical tool to analyze phenomena that sub-indices by domain (such as education and are hard to place in a set. Use of this methodol- health). The breakdown provides key informa- ogy in economics is quite new. The best-known tion about the level and structure of well-being studies based on the fuzzy sets approach are and particularly about domains that contribute multidimensional analyses of poverty. 10 How- most to deprivation and under-development. It ever, these are mostly based on micro-level data also offers information for policymakers from census and household surveys, rarely on designing structural socio-economic policies to macro-level data. To our best knowledge, Bali- eradicate the main causes of under-develop- amoune (2003) has been the pioneer in the use ment. Identification of areas where structural of fuzzy-set theory to construct well-being mea- intervention is necessary could lead to capabil- sures at the macro-level. Compiling several hu- ity-building policies. man well-being indices, Baliamoune (2003) At first glance, the TFA method cannot say yields rankings for 48 countries measuring their which countries could receive aid to help build achievements in education, life expectancy and socio-economic structures in a given area, ex- political and civil rights. The non-linear mem- cept by arbitrarily fixing a cut-off line. But bership function used to assess well-being a dichotomous approach can obtain a critical across countries includes rules and goals and value from cumulative distribution of the depri- incorporates the idea of lower and upper lim- vation index in terms of SL, QL and their com- its. 11 Using the same methodology in 14 Paci- ponents. This critical value is a threshold to fic Asian countries, Baliamoune-Lutz and estimate the number of countries with genuine McGillivray (2006) provide a fuzzy representa- deprivation in a particular domain (see Appen- tion of the HDI and its three components. dix A). Comparisons with non-fuzzy estimates suggest TFA offers various means of capturing infor- that fuzzy measures should be used more mation from well-being indicators among widely to assess well-being outcomes. countries. The most common criticism of this Our composite indices using the fuzzy sets kind of method concerns choice of a weighting approach are constructed in two stages (see system. One way to test the robustness of rank- Appendix A). The first involves definition of ings is to compare results obtained with TFA the membership function of a given set associ- with those produced by FAC. ated with each country and indicator. The function can take several forms (Baliamoune, 2003; (ii) Charts of SL and QL obtained by Factorial Lelli, 2001), but we consider the ‘‘Totally Fuzzy Analysis of Correspondences Analysis (TFA),’’ as defined by Cerioli and FAC is a descriptive method for qualitative Zani (1990), in contrast to the ‘‘Totally Fuzzy data suggested by Benzećri et al. (1973) to study and Relative’’ approach (TFR) of Cheli and contingency tables (see Appendix B) and dis- Lemmi (1995). The value of the membership cover simple patterns in the relationships be- function will provide a country-specific depri- tween variables. It provides orthogonal basis vation degree relative to a given indicator, vectors of the data space by computing the increasing linearly between zero and one. eigenvalues of a matrix drawn from the data, The second stage involves the different de- reducing the dimensionality of the input data grees of deprivation in each country, with each by extracting the most important features (the indicator aggregated to obtain composite SL factors or principal components). These are and QL indices for each country. So under- sorted such that the first captures the maximum MULTIDIMENSIONAL MEASURES OF WELL-BEING 1265 variance of the data, and the others capture the the breaking values, suggesting that Africa decreasing amount of variance. has handicaps in all SL dimensions. The conti- With our well-being indices, where there are nent has deficiencies in all considered QL do- nine indicators (dimensions) for each country mains, but the percentage of countries with a and 170 countries (dimensions) for each indica- deficit is much smaller than for SL. Quality of tor, the two dimension-charts from FAC pres- education is the area in which the continent ent the projections of all variables as close as has most deficit countries (59.6%) and 57.7% possible from observation. This interprets the have a quality of health less than the breaking proximity between countries and indicators value. 12 and also between both kinds of variables (see Appendix B). Each indicator has a contribution (c) Graphical analysis for African countries (weight) in definition of the main axis (axis 1) that we shall use further. FAC was initially done at world level (170 We applied these two methods to 170 coun- countries). Charts have been created for the tries (see Appendix C) for the year 2000, using 52 African countries, so their coordinates for the indicators in Table 1. We now present the SL and QL indices are analyzed in comparison general results and then focus on the 52 African with data for 170 countries. countries available (Sao Tome & Principe is The most representative SL indicators (Fig- missing due to lack of data). ure 1) on axis 1 are for access to safe water, education, and transport. These are the indica- (b) Indices and sub-indices for Standard of tors whose contributions and square cosines are Living and Quality of Life highest. On the left are countries with a low public education expenditure and a problem TFA enables us to calculate SL and QL as with the access to safe water. Almost all the well as the various sub-indices for each of the African countries have negative coordinates 170 countries. Table 2 shows the statistical indi- and are behind the center of gravity, which cators of these indices and sub-indices. would be the international ‘‘average.’’ On axis Determining the breaking value in relation to 2, material well-being (vehicles and TV) is more the international average score enables calcula- important in the top quadrant (especially for tion of the percentage of countries by geo- North African countries). graphical region (Rest of the World and The same analysis for QL shows that African Africa) with a deficit in each domain (Table countries were ranked primarily according to 3). Material well-being is the area, internation- trade openness, life expectancy, and literacy. ally, where deprivation is highest. Again, the ‘‘richest’’ countries are in the top The average score is highest in Africa, mak- right quadrant. Contrary to SL, several coun- ing it the world’s poorest region. It has high tries have positive coordinates on axis 1, which SL deprivation, with 96.2% of its 52 countries shows that their quality of life is higher than the having an insufficient score. Where countries international average. This is so with North can be described as ‘‘rich’’ or ‘‘poor,’’ those in African countries, but also with some of the is- Africa would express deficiencies in each do- lands (Cape Verde, Mauritius, and the Sey- main. All the average scores are higher than chelles). 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 0.421 0.186 0.397 0.431 Standard of health 0.380 0.208 0.349 0.427 Standard of education 0.365 0.180 0.307 0.374 Material well-being 0.622 0.262 0.668 0.527 Quality of Life 0.217 0.130 0.179 0.341 Quality of health 0.218 0.191 0.136 0.391 Quality of education 0.249 0.183 0.215 0.398 Quality of environment 0.184 0.099 0.162 0.253 Source: Authors’ calculations based on UNDP (2002) and World Bank (2002) data. 1266 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 0.343 50.8 0.599 96.2 Standard of health 0.305 22.0 0.549 75.0 Standard of education 0.287 16.1 0.540 80.8 Material well-being 0.533 47.5 0.826 94.2 Quality of Life 0.162 8.5 0.335 51.9 Quality of health 0.131 5.9 0.415 57.7 Quality of education 0.173 10.2 0.422 59.6 Quality of environment 0.181 20.4 0.191 13.5 Source: Authors’ calculations based on UNDP (2002) and World Bank (2002) data.

Standard of Living 2000

Sudan0.53

Gabon

0.33 Ghana

Eq. Guinea Egypt Za mbia TV Mauritania Zimba bw e Mauritius PHYSICIANS Tunisia 0.13 Morocco Nigeria Guinea ROADS Niger Djibouti Eritrea -1.4 -1.2 -1 -0.8 -0.6Algeria -0.4 -0.2Seychelles 0 0.2 0.4 Benin WATER Somalia -0.07 Senegal SCHOOLENROL Swaziland Burundi Cote d'ivoire Cameroon Tanzania EDUCAT HEALTH Uganda Madagascar Mali AGEDEPBotswana Liberia Ethiopia S. Leone Cape Verde Kenya Togo Mozambique -0.27 CAR Comoros South Africa Angola Malawi Gambia Lesotho VEHICLES Congo Rwanda Chad Guinea Bissau Botswana Libya DRC Namibia -0.47

indicators African countries

Figure 1. Standard of Living in African countries. to each other and form a fairly homogeneous scaling, weighting, and aggregative procedure group where life expectancy is weak and liter- (for an overview, see Booysen, 2002), but the acy is low (Figure 2). crucial problem is to assign suitable weights to the indicators. Information can be aggregated into a single measure in two main ways. 4. DISCUSSION AND POLICY One is by bringing into play the arbitrariness IMPLICATIONS and beliefs of the researcher and may involve public and expert judgments. With the HDI, (a) Aggregation in multidimensional indices and PQLI, and Borda rule, the most-used method choice of weighting system is giving equal weights to the attributes of the composite index assuming they are equally Composite indices are usually constructed in important in capturing the several aspects of several stages, including choice of variables, the concept. The other main way of aggregating MULTIDIMENSIONAL MEASURES OF WELL-BEING 1267

Quality of Life 2000

TRADE

0.39 Seychelles

0.29 Swaziland Mauritius

0.19 Ghana Angola Liberia

Eq. Guinea Djibouti Lesotho Namibia Ga m bia Congo Mauritania 0.09 Guinea Bissau Tunisia Guine a Cote d'ivoire Nigeria Senegal Togo Botswana S. LeoneMozambique Eritrea Morocco MalawiMATERMORT Zimbabwe ZambiaMali Rwanda Somalia Ga bon -0.01 Cape Verde Ethiopia Niger Benin -0.85Kenya -0.35UNDERWEIGHT 0.15 0.65 1.15 Burundi ChadComoros CAR DRC Cameroon Algeria Burkina FasoMadagascar CHILDLABOR LIBERTY Tanzania South Africa Uganda Sudan -0.11 Libya Egypt FEMLABOR LITERACY

-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 The weight xj is an inverse function of the pro- and uses multivariate techniques for determin- portion of countries that are deprived relative ing the weights. Most common is the principal to indicator j. It implicitly relies on the belief component analysis (Rahman et al., 2003; that the smaller is the proportion of countries Ram, 1982; Slottje, 1991) in which the attri- scoring low on a specific indicator, the larger butes are weighted with the degree of variance is the weight attributed to it in the aggregate from the original set of variables explained by set. For example, in the SL index, the biggest the first principal component. weight is attributed to safe water and a small The choice of aggregation function is also weight to cars. So a high deprivation of cars crucial as it affects the compensability of addi- would have less importance than low access tive aggregations. The derived indices can be to safe water in assessing the relative perfor- either additive or functional, depending on mance of countries according to SL. FAC, like the context of analysis. Morris (1979) says that principal component analysis, attributes a if the aim is to measure relative performance of weight objectively to the components according individual countries, the simplest technique is to their percentage of variance in calculation of the additive aggregation method. Although the first axis. methodologies can lead to rankings sensitivity To compare the two methods, we ranked according to a particular weighting scheme, countries in descending order of well-being. the recent study of Cahill (2005) suggests this The two methods of analysis do not give ex- is not always the case. actly the same results. 13 These differences are Well-being measures are clearly not robust to mainly due to the weights given to the indica- alternative methods of aggregation, but the use tors (Table 4). of fuzzy set theory and factorial analysis allows So TFA gives the biggest weight to standard for less arbitrary assignment of weights to com- of education (more than 43%) in calculation of ponents of the indices. The two methods are SL and to quality of health in QL (more than complementary as they each use one way of 35%). FAC gives the biggest weight to material aggregation. For TFA, weights are based on well-being (more than 45%) and quality of frequency of symptoms of under-development. health (more than 58%). 1268 WORLD DEVELOPMENT

Table 4. Weights of indicators using diﬀerent methods Standard of Standard of Material Total Quality of Quality of Quality of Total health education well-being health education environment Weights TFA 0.3526 0.4486 0.1988 1.0 0.3540 0.3279 0.3181 1.0 Weights FAC 0.2507 0.2932 0.4561 1.0 0.5804 0.2172 0.2024 1.0 Source: Authors’ calculations based on UNDP (2002) and World Bank (2002) data.

The weighting system significantly changes correlations between our indices using two dif- the results but more comprehensively, the ma- ferent methods and weighting schemes. The trix of the rank correlations (Appendix D) matrix of correlations (Appendix F) also shows proves that differences in ranking are not signif- that GDP per capita is more strongly correlated icant and, on the contrary, with TFA and FAC with SL than with QL, which can be considered methodologies, are statistically correlated. through a capability approach. The coefficients Another set of interesting results comes from of correlation between our two indices and the the rank correlation between SL and QL deter- HDI are also far higher than with GDP per ca- mined by GDP per capita, and the HDI. The pita, which shows that our indices are closer to correlation coefficients of the two methods are the HDI than to GDP per capita. SL is most systematically lower with GDP per capita than strongly correlated with standard of education with the HDI, but remain generally high, mean- and QL with quality of education, which con- ing economic performance is strongly related to firms that ‘‘education’’ plays an important part well-being in all its other dimensions. Also, a in defining a country’s human development high correlation is found between rankings of performance. Education should thus be one of countries according to GDP per capita and the three mega-variables in the HDI. This sup- the HDI (R2 = 0.94), a result McGillivray ports the recent finding of McGillivray (2005) (1991) explains by the high correlations be- that adult literacy best captures the non-eco- tween HDI components and GDP per capita. nomic dimension of well-being. In contrast, And the strong correlations between SL and Dasgupta and Weale (1992) and Rahman QL indices with the HDI indicate that, despite et al. (2003) find that health (such as life expec- the serious criticism of the HDI’s reductionist tancy) is the closest measure of well-being. approach to human well-being, it reflects well The ability to read and write is very impor- the basic dimensions of human development. tant and allows a person to stand up for his/ Our results are robust and reinforce each her rights. Education also helps to overcome other since the country rankings according to negative processes such as child labor or social TFA, FAC, HDI, and GDP per capita are barriers and to empower disadvantaged groups. not very different. This is not surprising, though An educated person may also survive better or detailed study of the rank variations reveals longer and participate in political change. nuances in this simple idea. 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 (le- (b) Analysis of correlations and redundancies vel 2) and of 0.9 (level 1). Correlation coeffi- between indicators cients (Appendix F) are thus mainly level 2 of redundancy. But the correlation between QL The correlations between SL, QL and their and the GDP per capita, at 95% level, indicates components (Appendix E) show quality of absence of redundancy (in terms of value). This environment as the only indicator statistically striking result brings empirical support to the correlated neither with SL and QL nor the conceptual justification of QL. Indeed, the QL other indicators. The main reason is the heter- index measures ‘‘the end’’ rather than ‘‘the ogeneity of the quality of environment compo- means,’’ as does the GDP per capita. Though nents (trade openness, CO2 emissions and they are conceptually distinct, the high redun- liberties, and freedoms). More generally, the dancy between SL and QL was expected as indicators are statistically and significantly cor- the means and outcomes indicators can be seen related with each other. According to Cahill as inputs and outputs of a production or wel- (2005), this result might explain the high rank fare function. MULTIDIMENSIONAL MEASURES OF WELL-BEING 1269

(c) Policy recommendations for African tribute to development through enhancing indi- countries vidual freedoms. They are extremely important agents of Africa’s future development. One way of understanding well-being in Afri- ca is to look at the rank changes in countries in the SL and QL indices, GDP per capita and the 5. CONCLUSION HDI. GDP per capita has very many outlier cases in both directions: low wealth but fairly The two methods of well-being measurement, high well-being and high wealth but low well- TFA and FAC, take into account several dimen- being (Appendix G). So a country can be much sions of well-being (such as liberties, child labor, better or much worse ranked, according to the or the number of vehicles) and enable two indices index applied. The very large rank variations to be constructed according to Sen’s capability then give an indication of the deficiencies of approach. Unlike the HDI, the two indices do some indices. For example, it is preferable to not mix measures of resource availability and use SL rather than QL to rank North African of functioning and capability. The results for countries (Egypt, Libya, Algeria, and Tunisia). SL and QL show that African countries have The rank is higher according to GDP per capita substantial deficiencies in many areas, except for Equatorial Guinea, Gabon, Namibia, Bots- perhaps quality of environment, and highlight wana, and South Africa, suggesting that these the importance of education as a key variable countries are richer than they are developed. in a country’s multidimensional development. QL is more important than other measures in We also compare monetary performances with Ghana, Tanzania, and Zambia. the HDI and the SL and QL indices. Although According to Sen’s capability approach, we most components might be dependent on in- could say whether standards of health and edu- come, SL and QL completely exclude any mon- cation are associated with good functionings etary indicator. Low correlation between QL (or quality) in each sector. In health, we could and GDP per capita gives empirical support to check whether public expenditure, access to the conceptual distinction. Detailed analysis of water and the number of doctors allow mothers indices and sub-indices also makes it possible and children to live in healthy conditions and to distinguish between ‘‘lack of resources’’ and the population to have high life expectancy at ‘‘weak functioning’’ by domain. birth (as in Algeria, Egypt, Mauritius, Moroc- The weighting systems substantially change co, the Seychelles, Swaziland, and Tunisia). In the rankings, but the two methods are comple- education, the same would indicate whether mentary as they each use a different way of public spending, school enrollment and the aggregation. For TFA, weights are based on number of ‘‘dependents’’ allows females and allegation. For FAC, weights are objectively children to work in good conditions and adults calculated. However, by considering the rank to be literate (as in Algeria, Botswana Cape correlations, the differences between the two Verde, Congo, Gabon, Lesotho, Libya, Mauri- methods are not significant. Though the HDI tius, Namibia, the Seychelles, South Africa, seems restrictive, it does in fact take the essen- Swaziland, Tunisia, and Zimbabwe). In other tial indicators into account, since it establishes countries, either small resources (a low stan- country rankings very close to those of our dard) or weak functionings (a low quality) pre- two indices. But due to their diversity and orig- vent the two sectors from being efficient. inality, the SL and QL indices cover a much These results could be used to make better greater area than the HDI. They can be disag- and more efficient public expenditure allocation gregated and information can be better used to sectors. African countries should increase to establish social and economic policies to their investment in human capital and infra- fight structural under-development. They are structure to improve health, education, and based on Sen’s capability approach, allow material well-being. To enable efficient trans- assessment of two measurements of human formation of resources into capabilities, Africa well-being and create a clear framework for has to give high priority to improving gover- evaluating development policies and processes. nance, maintaining peace and security and Sen (1999) argues that development occurs increasing democratization and human rights. when people are able to achieve what makes Sen argues that development is freedom, so a their life valuable. Good governance, democra- variety of institutions (such as NGOs, media tization and respect for human rights are key is- and international and local communities) con- sues in Africa’s development. 1270 WORLD DEVELOPMENT

NOTES

1. Ivanova, Arcelus, and Srinivasan (1999), Kelley ordinal information, the Borda method simply involves (1991), McGillivray (1991) and Srinivasan (1994) have assigning a rank order score to each component index criticized the HDI. and adding up the rank order scores to obtain the Borda score. 2. Even though GDP per capita is a flow concept, it is generally used as a measure of a stock concept. 8. See Schokkaert and van Ootegem (1990), Brandolini and d’Alessio (1998), Chiappero Martinetti (2000), Lelli 3. The HDI is the unweighted average of three sub- (2001), and Qizilbash (2002). indices of basic well-being: life expectancy at birth, educational attainment and GDP per capita expressed in 9. These two methods were used in the context of real terms and purchasing power parity. Each indicator individual data by Lelli (2001). is normalized, so it takes values between zero and one. Scaling makes it possible to compare countries’ relative 10. Cerioli and Zani (1990) were the first to use it in performances in each dimension. The HDI does not this field. Others followed, such as Cheli and Lemmi measure absolute but relative levels of human develop- (1995), Chiappero Martinetti (1996, 2000), Lelli (2001), ment by ranking countries. and Qizilbash (2002).

4. His approach has not only been used in development 11. Applying fuzzy sets theory to macro-economic economics, but also in other analyses such as political data, Von Furstenberg and Daniels (1991) and Bali- philosophy, social policy, and welfare economics. amoune (2000) explain why this methodology makes it possible to avoid using dichotomic variables and allows 5. The distinction refers to the concept of ‘‘basic needs’’ a gradual transition from one state to another. 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). 12. For results in European, Arabian, or Mediterra- The concept was then reintroduced by the World Bank nean countries, see Be´renger and Verdier-Chouchane (2001), but it is currently very close to the concept of (2004). ‘‘basic capabilities.’’ 13. For example, out of 52 African countries, FAC and 6. Jahan (2002) presents the refinements in the HDI TFA both rank Morocco 8th in SL, while Cameroon is methodology over time. ranked 32nd with FAC and 30th with TFA. But the spread can be wider, as with the Central African 7. The method has been used by Dasgupta and Weale Republic (50th and 36th). In QL, FAC puts the (1992) as an implicit criticism of the cardinal informa- Seychelles first and Mauritius second, while TFA gives tion base that underlies the HDI. Relying exclusively on the reverse order. Coˆte d’Ivoire is 26th and 31st.

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Carnegie-Rochester Conference Series on Public Pol- Second step: weighted indices of SL and QL icy, 35, 267–307. World Bank (2001). World Development Report Following Cerioli and Zani (1990), composite 2000/01 – Attacking Poverty. New York: Oxford University Press. indices are defined by taking the weighted arith- World Bank (2002). World Development Indicators. metical mean of the membership functions Washington, DC. according (respectively) to the component M World Bank (2006). World Development Report—Equity and M0 indicators: and development. New York: Oxford University XM Press. l ðiÞ¼ x l ðiÞ; Zadeh, L. A. (1965). Fuzzy sets. Information and SL j j j 1 Control, 8(3), 338–343. ¼ XM0 0 0 lQLðiÞ¼ xjljðiÞ j0¼1 P M with xj P 0 and j¼1xj ¼ 1, where xj and xj0 APPENDIX A. TOTALLY FUZZY are the weights attributed, respectively, to indi- ANALYSIS cators j and j0. Chiappero Martinetti (1996) says the func- First step: degree of deprivation for tion must have a value between maximum each indicator and minimum and must allow interaction between the indicators. Assume i 2 [1, N] countries, j 2 [1, M] indica- The weights are defined as 0 0 tors of Standard of Living (SL) and j [1, M ] 1 2 ln XN indicators of Quality of Life (QL). Consider lj 1 xj ¼ P with lj ¼ ljðiÞ: X ={x /j =1,..., M} and X 0 x 0 =j 1; M 1 N j j j ¼f j ¼ ln i¼1 ...; M 0g vectors of components respectively of j¼1 lj i i SL and QL. Variables xj and xj0 are the values The weight xj is an inverse function of the taken by indicators j and j0 for the ith country. mean deprivation level relative to the indicator When ranking values of j and j0 by increasing j. The logarithmic curve function introduced order (the lower the value of a given indicator, into the weighting system shows that well-being the higher the deprivation), functions lj(i) and does not vary in a linear way. lj0 ðiÞ are defined as: 8 Third step: definition of a critical value > 1ifxi 6 xmin; <> j j xmaxxi The critical value (or breaking value) ljcrit l ðiÞ¼ j j if xmin < xi < xmax; j > xmaxxmin j j j associated to indicator j can be defined as: > j j : i max 0ifxj P xj F ðljcritÞ¼1 lj

min i max i with F the cumulative distribution function and with xj ¼ MiniðxjÞ and xj ¼ MaxiðxjÞ. lj the average value of indicator j which indi- Functions lj(i) and lj0 ðiÞ provide the depriva- cates, dichotomously, the proportion of un- tion degrees of the ith country relative to indi- der-developed countries according to j. cators j and j0. Inversely, if indicators are rearranged by decreasing value (which is the case for CO2 APPENDIX B. FACTORIAL ANALYSIS emission), functions lj(i) and lj0 ðiÞ are then de- OF CORRESPONDENCES fined as: 8 Data are presented in a N table in which the > 1ifxi P xmax; IJ <> j j rows (countries) are numbered by i =1,..., p i min xjxj and the columns (indicators) by j =1,..., q. l i if xmin < xi < xmax; jð Þ¼ xmaxxmin j j j > j j Alternatively, the PIJ table, whose general term :> i 6 min is pij = nij/n, provides the two marginal distri- 0ifxj xj : butions: pi = ni/n and pj = nj/n as well as the These functions are increasing linearly be- conditional distributions in rows and columns, tween zero and one according to the degree of called ‘‘row’’ profile and ‘‘column’’ profiles, i j deprivation. respectively: pj ¼ pij=pi and pi ¼ pij=pj. MULTIDIMENSIONAL MEASURES OF WELL-BEING 1273

First step: calculation of distances ple is essential but a difficult property whose interpretation is delicate. For details, see Casin To measure the distance between two pro- (1999) and Foucart (1997). 1 files, we compare the same rank terms (e.g., pj 2 2 and pj ). The v distance between the row pro- Third step: interpretation parameters files is dP(i, i0) whose square is given by 2 0 q i i0 2 d ði; i Þ¼ j¼1ðpj pj Þ =pj. The clouds of points N(I) and N(J) on the The v2 distance between the column profiles principal axes correspond to projections as is similarly defined: close as possible from observation. Two values Xp for interpretations are calculated: the contribu- 2 0 j j0 2 d ðj; j Þ¼ ðpi pi Þ =pi: tion (with variance taken as the indicator) and i¼1 the quality of representation (with squared cosine taken as the indicator). Second step: calculation of principal axes Contributions are used to measure the influence (weight) of a point (e.g., of a country i)in FAC provides orthogonal basis vectors— defining a principal axis. The sum of the contri- principal axes—preserving the distances and butions for each axis equals one. The proximity calculated according to the least squared meth- between projections does not always reflect the od. The origin of the axes (0, 0) shows the mar- proximity between the profiles. Some points ginal distribution, the center of gravity or the can be badly shown or moved away from the ‘‘average country’’. profiles they represent. Angle h measures the The duality principle makes it possible to proximity between the point in space and its show the two ‘‘profiles’’ on the same chart, projection on the plan. So a weak angle indi- interpret proximity between a row and a col- cates good proximity (the square cosine is close umn profiles and thus explain the connection to one) and an angle close to 90° (the square co- between two variables. The FAC duality princi- sine is close to zero) indicates a bad one.

APPENDIX C. LIST OF COUNTRIES Afghanistan Burundi Equatorial Guinea Albania Cambodia Eritrea Algeria Cameroon Estonia Angola Canada Ethiopia Argentina Cape Verde Fiji Armenia Central African Republic Finland Australia Chad France Austria Chile Gabon Azerbaijan China Gambia, The Bahamas, The Colombia Georgia Bahrain Comoros Germany Bangladesh Congo, Dem. Rep. Ghana Barbados Congo, Rep. Greece Belarus Costa Rica Guatemala Belgium Cote d’Ivoire Guinea Belize Croatia Guinea-Bissau Benin Cuba Guyana Bhutan Cyprus Haiti Bolivia Czech Republic Honduras Bosnia and Herzegovina Denmark Hong Kong, China Botswana Djibouti Hungary Brazil Dominican Republic Iceland Brunei Ecuador India Bulgaria Egypt, Arab Rep. Indonesia Burkina Faso El Salvador Iran, Islamic Rep. (continued on next page) 1274 WORLD DEVELOPMENT

APPENDIX C—continued

Iraq Morocco Somalia Ireland Mozambique South Africa Israel Myanmar Spain Italy Namibia Sri Lanka Jamaica Nepal Sudan Japan Netherlands Suriname Jordan New Zealand Swaziland Kazakhstan Nicaragua Sweden Kenya Niger Switzerland Korea, Rep. Nigeria Syrian Arab Republic Kuwait Norway Tajikistan Kyrgyz Republic Oman Tanzania Lao PDR Pakistan Thailand Latvia Panama Togo Lebanon Papua New Guinea Trinidad and Tobago Lesotho Paraguay Tunisia Liberia Peru Turkey Libya Philippines Turkmenistan Lithuania Poland Uganda Luxembourg Portugal Ukraine Macedonia, FYR Qatar United Arab Emirates Madagascar Romania United Kingdom Malawi Russian Federation United States Malaysia Rwanda Uruguay Maldives Samoa Uzbekistan Mali Saudi Arabia Vanuatu Malta Senegal Venezuela, RB Mauritania Seychelles Vietnam Mauritius Sierra Leone Yemen, Rep. Mexico Singapore Zambia Moldova Slovak Republic Zimbabwe Mongolia Slovenia

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 0.71765 1 (<0.0001) (<0.0001) QL (FAC) 0.8960 0.75068 0.843957 1 (<0.0001) (<0.0001) (<0.0001) GDP per capita 0.83276 0.68621 0.867768 0.864837 1 (<0.0001) (<0.0001) (<0.0001) (<0.0001) HDI 0.91287 0.79406 0.887808 0.926069 0.94121 1 (<0.0001) (<0.0001) (<0.0001) (<0.0001) (<0.0001) MULTIDIMENSIONAL MEASURES OF WELL-BEING 1275

APPENDIX E. CORRELATIONS OF INDICES AND SUB-INDICES

Standard Standard Material Quality Quality Quality Standard Quality of of Well-being of of of of of Health Education Health Education Environment Living Life Standard of 1 Health Standard 0.72889 Education (<0.0001) 1 Material 0.74575 0.66131 1 well-being (<0.0001) (<0.0001) Quality 0.78611 0.80929 0.73164 1 Health (<0.0001) (<0.0001) (<0.0001) Quality 0.71107 0.82384 0.68980 0.79712 1 Education (<0.0001) (<0.0001) (<0.0001) (<0.0001) Quality of 0.16860 0.14462 0.03223 0.09053 0.21796 1 Environment (0.028) (0.0599) (0.6765) (0.2403) (0.0043) Standard of 0.85466 0.89685 0.88348 0.86252 0.86198 0.10992 1 Living (<0.0001) (<0.0001) (<0.0001) (<0.0001) (<0.0001) (0.1536) Quality of 0.71498 0.77968 0.61419 0.79214 0.93196 0.50722 0.81180 1 Life (<0.0001) (<0.0001) (<0.0001) (<0.0001) (<0.0001) (0.0001) (<0.0001)

APPENDIX F. CORRELATIONS WITH GDP PER CAPITA AND HDI Standard of Living Quality of Life GDP per capita HDI Standard of Living 1 Quality of Life 0.8096 1 (<0.0001) GDP per capita 0.71151 0.50248 1 (<0.0001) (<0.0001) HDI 0.92149 0.80397 0.75804 1 (<0.0001) (<0.0001) (<0.0001) 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 Between SL Between SL Between QL and QL and GDP and GDP According to: QL GDP GDP % of displaced 32.7% 42.9% 32.7% African countries Most important declines Egypt (56) Zambia (48) Libya (36) Egypt (36) Tanzania (42) Algeria (31) Congo Rep. (25) Malawi (42) Tunisia (23) Zambia (22) Congo Rep (42) Comoros (21) Comoros (21) Ghana (41) Rwanda (21) Rwanda (18) Madagascar (32) (continued on next page) 1276 WORLD DEVELOPMENT

APPENDIX G—continued Rank variations Between SL Between SL Between QL and QL and GDP and GDP According to: QL GDP GDP % of displaced 32.7% 42.9% 32.7% African countries Greatest improvements Equ. Guinea (113) Ghana (57) Equ. Guinea (107) South Africa (52) Djibouti (49) Gabon (54) Libya (50) Liberia (44) Namibia (53) Botswana (46) Tanzania (37) Botswana (46) Gabon (46) Nigeria (29) South Africa (38) Namibia (43)

Rank variations Between GDP Between SL Between QL and HDI and HDI and HDI According to: HDI HDI HDI % of displaced 64.0% 57.1% 65.3% African countries Most important declines Equ. Guinea (70) Egypt (52) South Africa (55) Zimbabwe (43) Zambia (32) Botswana (55) Algeria (34) Djibouti (29) Namibia (50) Botswana (27) Ghana (28) Gabon (41) Malawi (25) Benin. (27) Swaziland (37) Congo Rep. (19) Cape Verde (22) Greatest improvements Congo Rep. (25) Equ. Guinea (37) Libya (50) Madagascar (14) Libya (32) Equ. Guinea (43) Tanzania (14) Ghana (15) Seychelles (17) Malawi (10) Gabon (14) Rwanda (15) Comoros (9) Madagascar (14) Eritrea (14)