Statistique, Développement et Droits de l‘Homme
Session C-Pa 6c
International Comparison Theory - Human
Development
György SZILÁGYI
Montreux, 4. – 8. 9. 2000
Statistique, Développement et Droits de l‘Homme
International Comparison Ttheory - Human Development
György SZILAGYI
President, Hungarian Statistical Association
PO Box 51
1525 Budapest, Hungary
T. + 36 1 200 1185 / 345 6735 F + 36 1 345 6683
gyorgy.szilagyi@office.ksh.hu
ABSTRACT
International Comparison Theory - Human Development
In this paper the Human Development Index of the UNDP is confronted with the theory of
international comparisons. Human development belongs to the family of social-economic terms, to
which no á priori target indicator can be ordered, therefore the procedure of its comparison
consists of selection, arrangement and combination of an indicator set.
The paper analyses the composition of the set of indicators and the operations carried out
with them, up to obtaining an overall measure (index). It seems that major components of human
development are missing from the actual list of indicators and the size of the set is too small.
Comments are made regarding the treatment of GDP as one of the key variables. The paper
concludes with the wish to take into account the limits of reliability in the comments made on the
basis of the numerical results.
RESUME
Théorie comparative internationale – Développement humain
Dans cette communication, l’indice de développement humain du PNUD est confronté à la
théorie des comparaisons internationales. Le développement humain appartient à la famille des
termes socio-économiques qui ne peuvent être définis suivant un indicateur cible prédéfini, de sorte
que la procédure de sa comparaison consiste en une sélection, un arrangement et une combinaison
d’une série d’indicateurs.
Cette communication analyse la composition de la série d’indicateurs et des opérations
effectuées avec leur aide, afin d’obtenir une mesure globale (indice). Il semble que la majorité des
composants du développement humain ne figure pas sur la liste des indicateurs et que la taille de
cette série est trop restreinte. Des commentaires sont faits sur le traitement du GDP comme l’une
des variables clefs. La communication conclut par le souhait de prendre en considération les
limites de la fiabilité des commentaires faits sur la base des résultats numériques.
1. Introduction
In this paper International comparison theory is meant by a system of theoretical
considerations based on experiences of international comparisons. In the last 3-4 decades growing
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number of comparisons of different types were carried out on different subjects. They were
exercises in the framework of international organisations, as well as “armchair” works by
individuals and national institutes. International comparison theory is therefore derived from
methodological and analytical lesson of those works.
International comparison of human development have been carried out and published
regularly in Human Development Report by UNDP. In that Report Human Development Index
(HDI) is considered as aggregate measure of Human Development. The present paper focuses on
HDI, i.e. no other aspects of the Human Development Report are taken into account, despite their
rich and valuable conclusions.
Human Development Index is therefore set against the background of international
comparison theory. In this overview the 1999 issue of the Human Development Report is being
considered, with HDI data of 1997. The discussion is basically of theoretical and methodological
character, rather than consideration of actual figures (this had been done by other studies, e.g.
Castles 2000).
There are several reasons of paying attention to HDI:
• It has been calculated for a considerable number of countries (174).
• The last issue of the HDR accounts on substantial improvements of its data base and
methods.
• The United Nation Statistical Commission at its thirty-first session (March 2000)
considered HDR matters and requested the appointment of a group of statistical experts to
prepare, in conjunction with UNDP, a report on the accuracy of the statistical information
in the HDR, focusing on the points raised by the room document referred to above
(Castles 2000). Unfortunately the outcome of the investigation of the expert group is not
available at the time of drafting this paper.
2. A Simple Ttypology of Socio-economic Categories
The choice of the proper method of measurement and international comparison depends on
the type of term being measured. In this respect three types of social-economic categories can be
identified (Szilágyi 1984):
• Social-economic categories that can be expressed by a single indicator (target variable)
unambiguously; e.g. household consumption, industrial production etc. There exist
standard methods for international comparisons of such indicators, e.g. purchasing power
parities (PPP).
• Categories which, in pure theoretical terms, cannot be quantified in a scalar term, but there
are conventions regarding a generally accepted indicator; e.g. GDP is such a measure of
economic level (irrespective of its widely discussed deficiencies). Methods of
international comparisons are partly the same as above (PPP), partly procedures tailored to
this type of subjects: regression methods, factor analysis etc.
• Socio-economic categories to which no a priori target variable can be ordered; e.g.
welfare, infrastructure, technical level etc.
Human development belongs to this third type of socio-economic terms, therefore the
discussion in this paper focuses on the properties of such comparison.
Theory and practice of international comparisons developed a number of methods for
quantification of socio-economic categories having no a priori target variable. A common property
of these methods is the selection of a set of partial variables, followed by their proper arrangement
and combination. The variables are elements of the term being compared (in the case of human
development e.g. infant mortality, number of hospital beds, calorie intake, animal protein intake,
unemployment rate etc.). This family of methods is large and manifold. The individual procedures
differ in two basic properties:
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•
•
composition of the set of variables,
operations carried out with them
3. Composition of the Set of Variables
3.1. Size
Generally speaking, reliability of the calculations can be improved by the increase of the size
of the set of variables. The more complex the target variable, the larger set be necessary. Studies
estimating standard of living use 20 or more (e.g. Bennet 1951, UNRISD 1970, OECD 1982), as
well as comparisons of infrastructure (Csernok-Ehrlich Szilágyi 1980)
Towards this end the size of the variable set of the Human Development Index seems to be
scanty with no more than four variables: life expectancy, adult literacy rate, combined enrolment
ratio and adjusted per capita GDP. No doubt, limits of availability, reliability and comparability
may impede the establishment of an optimal size of indicators, especially when a large set of
countries is being compared. The slogan of Richard Stone „Working with what we have” is a useful
guidance. Also the statement in the 1999 HDR “the concept of human development is much deeper
and richer than what can be captured in any composite index or even by a detailed set of indicators”
(page 127) is true. Nevertheless the data stock put forward on the numerous tables of the Human
Development Report permits the enlargement of the HDI variables.
Insufficient number of variables may result in essential components (e.g. nutrition, housing,
security) being neglected. Another drawback of a small set is that missing data, outliers or errors
may cause substantial distortion in the final score of the respective countries (Castles 2000).
Coming back to the theory, let us consider now a set of all possible variables (and disregard
the limits of availability etc.). Such a set can be very large, to the extent that a reasonable reduction
might be desirable. Theoretically, an optimal set is that, that minimises the size and maximises the
information value. Such a set should meet two requirements:
• minimal correlation among the selected variables (to avoid multicollinearity);
• maximal correlation of the selected variables with the non-selected variables (in order to
select the most characteristic estimators).
These theoretical considerations can hardly be applied in everyday praxis, due to the lack of
the necessary information. For example no correlation matrix of the possible variables can be drawn
up. A reasonable substitute is the arrangement of the variables into components or rather identify
the main components of the target term and order subsets of variables to each component.
3.2.Components
The proper selection of components requires an exact definition of the term being compared.
What is exactly human development and what they components are, is not exactly defined by the
HDR. When it comes to the content of the term, different approaches can be found in various places
of the report. E.g. components are listed on page 22 in the following way: health, education, food
and nutrition, income, women, children, environment, human security. A more ambitious list can be
found on page 16-17 (Paul Streeten: Ten years of human development): nutrition, safe water at
hand, medical services, schooling, transport, shelter, employment and working conditions, secure
livelihoods, satisfying jobs …
The actual list of components of the HDI is smaller than any of the definitions above; no
more than three are taken into accounts: longevity, knowledge and standard of living. There is no
doubt about their proper place in the list, but other substantial components are obviously missing. In
addition, only one component of the three – knowledge – is described by more than one variable;
longevity and standard of living remain one-dimensional.
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Table 1. Summary of the HDI Indicator System
COMPONENTS
INDICATORS
Longevity
Knowledge
Life expectancy at birth
Adult literacy rate
Combined gross enrolment ratio (1)
Adjusted per capita GDP (2)
Standard of living
(1) Combined first, second and third level
(2) Discussion of this variable in Section 5
4. Operation with the Variables
In order to estimate an overall measure, a sort of synthesis of the information provided by the
individual variables is necessary. In this type of comparison the synthesis cannot be obtained in
monetary term, as most of the variables are expressed in measurement unit. other than money, in
addition, the units may differ from variable to variable (year, percentage ratio etc.)
Given the arrangement of the variables outlined above, the operations shall be considered in
two steps:
• operations with the individual variables within the components;
• operations with the components to obtain an overall measure.
In this section the intra-component operations are discussed, the inter-component steps in
section 6.
Two kinds of operation are necessary with the individual variables: (i) standardisation, in
order bring them into a uniform measurement unit; (ii) combination of the standard values up to
component level.
4.1 Standardisation
Various methods of standardisation have been used so far in different studies; for example
• the ratio of the country j value to the value of the country which has the maximum in the
given variable (Bennet 1951; Csernok-Ehrlich-Szilágyi 1980) ;
• normalisation, i.e. transformation of the variables into values with average=0 and
variance=1 (Csahók-Szilágyi 1979).
The standardisation procedure of the HDI is different. First a minimum and a maximum value
is estimated for each variable and the standard value (“index”) of variable X for country j is
derived:
(1)
__Xj – min X__
max X – min X
A minor problem may arise in estimating maximal and minimal values (they are not upper or
lower limits in the actual country set). Some valuables have á priori limits (literacy rate, enrolment
ratio 0% and 100%), in other cases, however arbitrary limit setting is unavoidable. For example 2585 years for life expectancy at birth, 100$-40000$ for per capita GDP.
The choice of limits affects the measuring scale of the given variable. It has no impact to the
country ranking in terms of that variable, but it may modify the overall score and ranking. The more
variables, the smaller this effect.
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4.2 Synthesis
Standard values are combined in order to obtain a score for the respective component. The
most obvious way is, naturally averaging the individual indexes produced by (1). In the case of HDI
“knowledge” (educational attainment) is the sole component where synthesis be relevant, as this is
the component with more than one indicator. HDI provides a weighted arithmetical average of the
two, assigning weight 2 to adult literacy and 1 to combined gross enrolment. Here, again, a minor
comment can be made regarding the higher weight of adult literacy: this indicator reached already
the upper limit in the developed countries, e.g. the value of this indicator in 19 out of the first 20
countries in the ranking by per capita GDP (on PPP basis according to the 1996 ECE-OECDEUROSTAT comparison) is 99.0. Consequently, “knowledge” index in this subset of countries
produces rather incidental than significant differences.
5. Standard of Living – The Treatment of GDP
Special attention should be devoted to the component “Standard of living”, for which per
capita GDP in USD according to Purchasing Power Parities is used as indicator, adjusted by a
logarithmic transformation. This procedure seems to be the most problematic (in some respects
vulnerable) operation of the system.
Towards this end the following issues need consideration:
• Data sources
• Further transformation
• GDP as a standard of living indicator
• Consequent treatment of GDP
5.1 Data Sources
It is very likely that not the optimal data sources have been used. Benchmark estimates of
Purchasing Power Parities are made in the ECE-OECD region on regular basis in three-yearintervals, the most recent being 1996, preceded by the 1993 and 1990 projects. In other continents
such exercises are less frequent. GDP in terms of Purchasing Power Parities (PPP) for nonbenchmark years are obtained by updating procedures with the help of national volume and price
indices. According to a general rule, reliability of the estimations decreases along with the length of
the period between benchmark year and reference year. Per capita GDP of the OECD countries in
USD by PPP for 1997 differ in HDI from the OECD estimates. The reason of the differences is
presumably, that HDI used data that result from longer updating period i.e. earlier benchmark year
(1993 or 1990) than OECD (1996). As Table 2 shows, differences are significant for a number of
countries.
Table 2. Per Capita GDP 1997 in USD by Purchasing Power Parities according to HDI and the
OECD Comparison
Country
HDI
OECD(1)
Percentage difference HDI–OECD
Mexico
France
Sweden
Germany
Austria
Italy
8370
22030
19790
21260
22070
20290
7697
21293
20439
22049
23077
21265
+ 8.74
+ 3.46
- 3.18
- 3.58
- 4.36
- 4.58
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Canada
Korea Rep.
Luxembourg
Denmark
Greece
Norway
Poland
Czech Rep.
22480
13590
30863
23690
12769
24450
6520
10510
23761
14477
33119
25514
13912
26771
7487
13087
- 5.39
- 6.13
- 6.81
- 7.15
- 8.22
- 8.67
-12.92
-19.69
(1) OECD 1999
In connection with this indicator an additional transformation has been made. “The basic
approach in the treatment of income has been driven by the fact that achieving a respectable level of
human development does not require unlimited income. To reflect this, income has been …
discounted in calculating the HDI” (Human Development Report p. 159.) To do this, per capita
GDP data have been adjusted in terms of a degressive function, namely their logarithms enter all
positions of (1), the general transformation formula. It should be acknowledged that this procedure
does not discount GDP as severely, as that used in previous issues of HDI.
5.2 GDP as Standard of Living Indicator
Is GDP an indicator of standard of living? As a matter of fact, it is not. GDP as an income
measure includes the primary income of all resident institutional units, i.e. not only household
incomes, but incomes of financial and non-financial enterprises and general government as well;
incomes of those sectors have nothing to do with standard of living. GDP as sum of final uses
(expenditures) includes not only household consumption, but also items, not connected to standard
of living: collective consumption of government, gross fixed capital formation, change in stocks
and net exports. Hard to understand why HDI did not resort to direct and explicit standard of living
indicators as household consumption, especially because PPP based data are available in the sources
otherwise used for its calculations. In addition, one may ask, whether the same transformation (5.2.
above) would be appropriate, if household consumption, rather than GDP were used as indicator for
standard of living.
5.3 Consequent Treatment of GDP
Apart from the problem “GDP versus Household consumption” (5.3. above), per capita GDP
converted to USD by PPP is playing a major role all over the Human Development Report. It is
however astonishing that time and again GDP is mixed up with GNP and PPP conversion with
official exchange rates. Both kinds of mixture can be found for example in the chapter “What do the
human development indices reveal?”, where Sri Lanka and Ivory Coast are compared in Figure 2
(“Similar income, different human development” – page 129). Here the three components of HDI
are shown, but GDP is replaced by GNP, and the income data in dollars are expressed by exchange
rate rather than PPP. The differences are drastic:
• Sri Lanka: GDP (GNP?) 2490$ by PPP, 800$ at exchange rate.
• Ivory Coast: GDP (GNP?) 1840$ by PPP, 710$ at exchange rate.
6. The Overall HDI Index and Some Cconcluding Remarks
The overall HDI index is obtained as the simple arithmetic average of the indexes of the three
components. It has the advantage of easy interpretation, with 0 as lower and 1 as upper limit.
It should be reminded however, that two of the four variables show small or even negligible
differences for the developed countries: adult literacy (see subsection 4.2.) and per capita GDP
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because of the discounted GDP (5.2.). Literacy has 2/3 weight in the component “Knowledge”, i.e.
2/9 in the overall index. With 1/3 weight of GDP, 5/9, more than a half is the share of variables that
make little or negligible differences in the scope of those countries.
It is stated in HDR – in a very correct and appreciable manner - that HDI is a rough approach.
“…HDI, which is inescapably a crude index, must not be seen anything other than an introductory
move in getting people interested in the rich collection of information that is present in the Human
Development Report.” (page 23.). Though confidence degree is not specified in the Report, it is
obvious that, for example a difference between 0.93 (Canada with rank 1) and 0.92 (United
Kingdom with rank 10) cannot be taken as significant; therefore we cannot say that one country is
by ten positions lower in terms of human development, than the other. Anyway, it would be useful
to take into account the limits of reliability in the conclusions drawn from the figures.
REFERENCES
Bennet M.K. (1951): International disparities in consumption levels. The American Economic
Review, 4, 1951.
Castles, I. (2000). The Human Development Report 1997. Room Document submitted to the thirtyfirst session of the United Nation Statistical Committee.
Csahók, G. – Szilágyi, G. (1979). Measuring scales for international comparisons. Acta
Oeconomica, Budapest, Vol. 22. (3-4)
Csernok A. – Ehrlich É. – Szilágyi G. (1980). Ein Verfahren für den internationalen Vergleich der
Infrastruktur. (A procedure for international comparison of infrastucture) In: Probleme
internationaler wirtschafts- und sozialstatistischer Vergleiche. Bund-Verlag.
OECD (1982). The OECD list of social indicators. Paris.
OECD (1999). National Accounts –Main Aggregates 1960-1997. Volume I.
Szilágyi, G. (1984). International comparisons – Types and methods. Statistical Journal of the
United Nations ECE, 2.
Szilágyi, G. (1995). International Comparison of the Human Development (in Hungarian).
Statisztikai Szemle (Statistical Review). 8-9.
UNDP (1999). Human Development Report 1999. New York, Oxford. Oxford University Press.
UNRISD (1980). Content and measurement of socio-economic development: an empirical enquiry.
Geneva.
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