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A New Indicator of Technological Capabilities for Developed and Developing Countries (ArCo)


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This paper devises a new indicator (ArCo) of technological capabilities that aims at accounting for developed and developing countries. Building on similar attempts as those devised by UN Agencies, including the UNDP Human Development Report's Technology Achievement Index (TAI) and UNIDO's Industrial Performance Scoreboard, this index takes into account a number of other variables associated with technological change. Three main components are considered: the creation of technology, the technological infrastructures and the development of human skills. Eight sub-categories have also been included. ArCo also allows for comparisons between countries over time. A preliminary attempt to correlate ArCo to GDP is also presented.
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A New Indicator of Technological Capabilities
for Developed and Developing Countries (ArCo)
London School of Economics and Political Science, UK
Italian National Research Council, CNR, Rome, Italy
Bank of Italy, Rome, Italy
eCatholique de Louvain la Neuve, Belgium
Summary. — This paper devises a new indicator (ArCo) of technological capabilities that aims at
accounting for developed and developing countries. Building on similar attempts as those devised
by UN Agencies, including the UNDP Human Development Report’s Technology Achievement
Index (TAI) and UNIDO’s Industrial Performance Scoreboard, this index takes into account a
number of other variables associated with technological change. Three main components are
considered: the creation of technology, the technological infrastructures and the development of
human skills. Eight subcategories have also been included. ArCo also allows for comparisons
between countries over time. A preliminary attempt to correlate ArCo to GDP is also presented.
Ó2004 Elsevier Ltd. All rights reserved.
Key words — technology creation, infrastructures, human skills, development index
Technological capabilities have always been
a fundamental component of economic growth
and welfare. One of their key characteristics is
that they are far from being uniformly distri-
buted across countries, regions and firms.
Knowledge production is largely concentrated
in a few highly industrialized countries. The
access to new and old knowledge, in spite of
international trade, communications, foreign
direct investment, public policies promoting
scientific cooperation and many other channels
of technology transfer, is a long way away
from being geographically homogenous. A few
countries constantly upgrade their knowledge-
base while the majority of them lag behind and
have many difficulties absorbing capabilities
that are already considered obsolete in other
parts of the world.
The determinants of the generation, trans-
mission and diffusion of technological inno-
vation have been studied both from the
theoretical and empirical viewpoint in a large
body of literature (Pietrobelli, 2000). But the
World Development Vol. 32, No. 4, pp. 629–654, 2004
Ó2004 Elsevier Ltd. All rights reserved
Printed in Great Britain
0305-750X/$ - see front matter
Preliminary versions of this paper were presented at
the Workshop on Economic Impact of Innovation and
Globalization, Turin, 13 June 2002, at the Master in
Science, Technology and Society, University of Buenos
Aires Quilmes, October, 8 2002, at the Doctoral Pro-
gram on Economics and Management of Technological
Change, University of Madrid Complutense, January
28, 2003, at the ECA Knowledge Economy Unit, the
World Bank, Washington, DC, May 8, 2003. We also
wish to thank Kim Bizzarri, Liliana Herrera Enriquez,
Francesco Lissoni, Richard Nelson, Mario Pianta, Carlo
Pietrobelli, Giuseppe Zampaglione and two anonymous
referees for detailed comments to previous versions. We
also wish to thank a large number of national statistical
experts who provided information, statistics, and com-
ments to complete and/or corroborate the ArCo data-
base. Financial support from MIUR Project 2001, on
Technological innovation and economic performance,
No. 2001133591, University of Urbino, is gratefully
acknowledged. Final revision accepted: 7 October 2003.
current understanding of the devices of tech-
nology creation and transfer is still inadequate,
in part due to the lack of detailed indicators of
technological change. This paper presents a
new index of technological capabilities, ArCo,
for a large number of countries. It builds on
many lessons learned of the nature of techno-
logical change and on other previous attempts
to measure it, including the latest Technology
Achievement Index (TAI) presented by the
UN’s Human Development Report (UNDP,
2001) and the UN Industrial Performance
Scoreboard (UNIDO, 2003).
Among the lessons learned on the measure-
ment of technological capabilities, we wish to
recall the following:
––The technological capabilities of a country
are composed of a variety of sources of
knowledge and of innovation. A comprehen-
sive measure should be able to account for
the activities that are codified as well as for
those that are tacit (Lundvall, 1992). Some
of the capabilities are disembodied, such as
new ideas and inventions. Others are embod-
ied in equipment, machinery and infrastruc-
tures, while still others are embodied in
human skills (Evangelista, 1999; Pianta,
1995; Smith, 1997).
––Technological capabilities are composed
of clusters of innovations associated with
different waves of industrial development
(Freeman & Louta, 2001).
––The integration of new technology sys-
tems requires the mastering of previous tech-
nologies, allowing economic agents to build
competencies in a cumulative manner (Bell
& Pavitt, 1997; Pavitt, 1988a). Often new
systems make previous ones obsolete (Juma
& Konde, 2002). As Schumpeter remarked,
‘‘add as many mail-coaches as you please,
you will never get a railroad by doing so.’’
––The various sources of technological cap-
ability are more likely to be complementary
rather than interchangeable. First rate infra-
structures devoid of a sufficiently qualified
labor force will be useless and vice versa (Ab-
ramovitz, 1989, Maddison, 1991). Moreover,
successful integration among the various
waves of innovations has the effect of multi-
plying its economic and social impact (Anto-
nelli, 1999; Amable & Petit, 2001).
––The creation and improvement of techno-
logical capabilities involve a crucial element
of technological ‘‘effort.’’ Access to ad-
vanced technology is a necessary condition,
but it needs to be accompanied by substan-
tial and purposeful investments for it to be
absorbed, adopted and learned (Pietrobelli,
1994; Lall, 2001a).
––Since the differences across countries’
technological capabilities are colossal, a
measure to account for them meaningfully
should consider the components that are
specific to both developed and developing
countries (Lall, 2001a).
Our work has been inspired by a variety of
attempts to generate measures of technological
capabilities. Even when we departed from
previous statistical exercises, we benefited
from their methodology. In particular, we
wish to mention, besides the already cited
Technology Achievement Index (UNDP,
2001) and the Industrial Development Score-
board (Lall & Albaladejo, 2001; UNIDO,
2003), also the Technology Index of the World
Economic Forum’s Global Competitiveness
Report (WEF, 2002), and the critical analysis
by Lall (2001b). Throughout the paper, we
specify when we have followed these approa-
ches and when, and why, we have opted for
alternative paths.
It should be noted that statistics of techno-
logical activities for the restricted group of the
30 most developed countries could be much
more sophisticated in terms of coverage and
significance. For this group of leading coun-
tries, many more indicators are available (and
the quality of the data is much more satisfac-
tory than for other countries). If we were to
limit our analysis to this restricted number of
countries, we would have used different indi-
cators and methodology (for a discussion of
the various attempts to measure scientific and
technological capabilities of advanced coun-
tries see Archibugi & Pianta, 1992; Patel &
Pavitt, 1995). It is hardly surprising that data
for the selected number of countries that con-
centrate the bulk of inventive and innovative
activities are much richer. The attempt here is
to provide measures for a much larger group of
countries which, as a whole, have a much more
limited level of technological capabilities.
Monitoring the existing capabilities will permit,
to identify of the nature and intensity of the
technology gap and the appropriate strategies
to bridge it.
This analysis is based upon a number of
assumptions. First, we assume that a compar-
ative analysis across countries is meaningful
(Sirilli, 1997). In spite of the enormous differ-
ence across countries (how can one describe in
a single number the technology gap between
Switzerland and Somalia?), countries can be
compared. But we also assume that a battery of
indicators could provide a more comprehensive
picture of the differences than a single indicator
would. The statistics produced achieve greater
significance when considering homogeneous
groups of countries and allow comparisons
between countries geographically, culturally
and economically close to each other, (such as,
for example, Switzerland and Germany,
Somalia and Ethiopia. For a discussion, see
Pietrobelli, 1994).
Second, we assume that a country-level
analysis still proves useful despite the enormous
differences found within countries. Synthetic
indicators for countries as large as China or
India inevitably overestimate the technological
capabilities of certain areas and underestimate
the capabilities of others. This also applies
to countries with much higher technological
capabilities such as, for example, the United
States and Japan. Moreover, recent research on
technological agglomerations (Cantwell &
Iammarino, 2003) showed that technological
activities tend to cluster in a few hubs even in
the most technologically advanced countries.
Still, the notion of national systems of inno-
vation (see Andersen, Lundvall, & Sorrn-
Friese, 2002; Edquist, 1997; Freeman, 1997;
Lundvall, 1992; Nelson, 1993) indicates that it
makes sense to analyze the technological
capabilities of territorial states, since these
provide one of the main institutional settings
for know-how generation and diffusion The
same analysis has already been successfully
applied to developing countries (see Cassiolato
& Lastres, 1999, and Sutz, 1997, for Latin
America; Hobday, 1995, for Asia; Lall &
Pietrobelli, 2002 for Africa).
Third, although we measured technological
capabilities with a variety of indicators, we
made an attempt to provide a synthetic
indicator. Other exercises made an effort to
estimate countries’ technological capabilities
by aggregating data at the firm level. Unfor-
tunately, this approach has not yet been able
to generate data for larger groups of coun-
tries. Our measure is typically a macro-eco-
nomic one and, at the country level, it is
composed of a selected number of indicators.
In spite of the limitations of a synthetic
indicator, we share with the UNDP, UNIDO
and WEF the belief that the various compo-
nents singled out could be added up in order
to provide a more comprehensive measure of
technological activities.
We built upon the TAI attempt developed by
UNDP (Desai, Fukuda-Parr, Johansson, &
Sagasti, 2001; UNDP, 2001), and the Indus-
trial Development Scoreboard developed by
UNIDO (Lall & Albaladejo, 2001; UNIDO,
2003). The TAI takes into account many indi-
cators, by classifying them in four categories:
the creation of technology, the diffusion of new
technology, the diffusion of old technology, and
human skills. We considered this a more effec-
tive starting point than the index suggested
by the WEF (2002). The UNIDO Industrial
Development Scoreboard divides a battery of
indicators into two broad groups: the first
deals with competitive industrial performance
(including manufacturing value-added per
capita, manufactured exports per capita, share
of medium- and high-tech industries in manu-
facturing value-added and share of medium-
and high-tech in manufactured exports); the
second concerns industrial capabilities (includ-
ing foreign direct investment per capita, foreign
royalty payments per capita, tertiary techni-
cal enrolments, enterprise financed R&D per
capita, and the infrastructure as measured by
telephone main lines). The main modifications
we introduced to these two indexes are the
(a) Enlarge the number of countries examined
In order to enlarge the number of countries
examined, without losing data and source
coherence, we focused on indicators whose
coverage was more satisfactory. We took into
account both the availability of data and the
dimension of population: we neglected coun-
tries with less than 500,000 inhabitants, except
for those countries (Luxembourg, Malta,
Cyprus and Suriname) for which we retained
sufficient data. For those countries for which
data proved analytically insufficient (as for
most African countries), missing values were
estimated on the basis of national sources,
interviews with country experts, and perfor-
mance in comparatively similar countries and
indicators. In extreme cases, minimum values
were taken for groups of comparable countries
(often equivalent to zero, due to the conditions
of extreme poverty of some of the countries
analyzed). Our pool is comprised of 162 coun-
tries in total.
(b) Allowing comparisons over time
In addition to crosscountry comparisons, we
attempted time-series comparisons. The pur-
pose of the TAI was not to compare countries
at different time points but to perform cross-
country comparisons at particular time points.
Standardized indicators from 0 to 1 were built
according to the following formula:
Observed value Minimum observed value
Maximum observed value Minimum observed value:
In TAI, all observed values referred to the same
time period. Since maximum and minimum
observed values are subject to change over
time, time comparisons are impossible. In
addition, the Industrial Development Score-
board presents a time-series comparison for
In order to allow for time-series compari-
sons, a maximum and a minimum value were
fixed for ArCo, so that both would result
identical for both the time points considered (a
current period which oscillates from 1997 to
2000 and a past period from 1987 to 1990).
Given that during the two time points consid-
ered the majority of countries under observa-
tion experienced progress of some kind, the
minimum observed value was taken from the
past period, while the maximum observed value
was taken from the most current one. Conse-
quently, homogeneous indicators for all time
periods were devised with the certainty that no
country would express a passed minimum value
higher than the more recent one. In other
words, no index in the past could ever over-
come the value of 1. The formula for this new
indicator can be summarized as:
Ix¼Obspresent Minpast
Maxpresent Minpast
Since the literacy rate indicator is known to
oscillate between the values of 0% and 100%,
these were taken automatically as the minimum
and maximum goalposts (therefore eliminating
the need for minimum and maximum observed
values for this indicator).
Three main dimensions of technological
capabilities were considered:
––the creation of technology;
––the technological infrastructures;
––the development of human skills.
The choice was based on the assumption that
the three components play a comparative role
in the making of a country’s technological
capabilities. Thus, the overall Technology
Index (ArCo) has been built upon the equal
weighting of the three mentioned categories
(each of which is indexed).
The ArCo index
formula can therefore be sketched as:
where Iirepresents the three indexes (technol-
ogy creation, actual technology infrastructures
and actual human skills) for each country and
kiare the constants of 1/3.
The index of each category is calculated by
the same procedure used for the overall index,
that is, through the simple mean of certain
subindicators. In total we considered eight
basic indicators: two for the first category and
three for the second and the third. The eight
subindexes are the following:
(a1) patents;
(a2) scientific articles;
(b1) Internet penetration;
(b2) telephone penetration;
(b3) electricity consumption;
(c1) tertiary science and engineering enrol-
(c2) mean years of schooling;
(c3) literacy rate.
The following is a detailed explanation of
each indicator:
(a) Creation of technology
(i) (a1) Patents
Patents are one measure of accounting for
the technological innovations generated for
commercial purposes. They represent a form
of codified knowledge generated by profit-
seeking firms and organizations. Among the
various patent sources (for surveys on patents
as internationally comparable indicators, see
Archibugi, 1992; Pavitt, 1988b), we considered
patents granted in the United States. Since the
latter is the largest and technologically more
developed market of the world, it is reasonable
to assume that important inventions and
innovations are legally protected in the US
market. The TAI considers those patents that
are taken out by individuals in their home
country. Such data were not used here since
countries exhibit significant legal differences––
for example, the very high number of patented
inventions registered by Japanese and Korean
inventors at their national patent offices is also
associated with the legal practice that requires
inventors to file an application for each claim.
The patent index is based on utility patents
(that is, invention patents) registered at the US
Patent and Trademark Office (USPTO, 2002).
Patents taken out in the United States by the
inventor’s country of residence were consid-
ered. The USPTO receives a greater number of
foreign patent applications than any other
patent office. Despite the fact that many
inventions are never patented, especially in
developing countries, patents represent never-
theless a good proxy for commercially exploit-
able and proprietary technological inventions.
The propensity of US inventors to register
inventions in their own national patent office is
higher than that of foreign inventors. To elim-
inate the bias toward US domestic patents, we
replaced the effective number of domestic pat-
ents with our own estimation. The latter is
based on a comparison between the Japanese
and the US patents registered at the European
Patent Office (EPO), which represents a foreign
institution both for Japanese and American
inventors. We used the following estimation:
Estimated US domestic patents
where JAPUSA is the effective number of patents
granted to Japanese inventors in the United
States, and USAEPO and JAPEPO are the effective
number of patents granted to US and Japanese
inventors at the European Patent Office. Pro-
portions for patents granted in Japan to Euro-
pean inventors were also estimated and
appeared not to exhibit any major differences.
The number of patents for each country was
normalized by dividing it for the country’s
respective population (the number of patents
was expressed for a million people). In order to
account for the effects that yearly fluctuations
might have on the results obtained from small-
and medium-sized countries, a four-year mov-
ing average for 1987–90 and 1997–2000 was
The goal posts were set as the maximum and
the minimum observed value for 1997–2000
(230 for the maximum value––corresponding to
Japanese patents for a million people––and
zero for the minimum value) and the stan-
dardized patent activity index was constructed
by application of the general formula, with
values oscillating between zero and one. As
explained above, in order to allow for com-
parisons to be made across time as much as
across geographical borders, the same goal-
posts were kept for the previous years, so that a
comparable index for 1987–90 could be calcu-
lated while allowing us to evaluate each coun-
try’s growth rate during the two points in time.
(ii) (a2) Scientific articles
Scientific literature is another important
source of codified knowledge. It represents the
knowledge generated in the public sector, and
most notably in universities and other publicly
funded research centres, although researchers
working in the business sector also publish a
significant share of scientific articles.
There is no single source of information
concerning all the scientific literature published
in the world. We were forced to rely on the
available, if limited, sources. Among them, the
most comprehensive and validated is the Sci-
ence Citation Index generated by the Institute
for Scientific Information. The index reports
information concerning the scientific and tech-
nical articles published in a sample of about
8,000 journals selected among the most presti-
gious in the world. The fields covered are:
physics, biology, chemistry, mathematics, clin-
ical medicine, biomedical research, engineering
and technology, and earth and space sciences.
It is often argued that the journals in this
sample are biased toward English-speaking
countries. Although there is some evidence
supporting this claim, it might be more accu-
rate to state that journals reflect the most visi-
ble part of the scientific literature, while they
ignore other important components in both
developed and developing countries––though
we believe the data source do not discriminate
heavily against developing countries. It is
certainly significant that late industrializing
countries have begun to be active in both pat-
enting and scientific publications (see Amsden
& Mourshed, 1997).
Data were taken from the US National
Science Foundation’s most recent publications
(NSF, 2000, 2002) and the World Bank’s
Article counting was based on
fractional assignments: for example, an article
written by two authors in two different coun-
tries was counted as one-half article to each
Switzerland scored the highest
number of articles for 1997–99 with 977 annual
articles per million people, while the minimum
goal post was zero for many countries with no
published scientific articles.
Data on R&D would have nicely comple-
mented the measure of national technological
creation, especially since they document devel-
oping countries’ learning effort for acquiring
scientific and technological expertise. This
source however, was not employed due to a
lack of available data for all countries (see
UNESCO, 2002; World Bank, 2003, Table
5.12). UNIDO (2003) reported these data for
87 countries only, and for 16 of them the values
prove negligible. Moreover, some developing
countries tend to include some activities in
R&D statistics that do not fit the standard
OECD Frascati Manual definitions (OECD,
2002). The advantage of using patents and
scientific articles consists of both sets of data
being validated by external sources as much as
by national ones (the US Patent Office in the
first case, and the academic journals monitored
by the Institute for Scientific Information in
the second). This guarantees that individual
observations are collected according to stan-
dard criteria. A rank correlation was calculated
between the hierarchy of countries according to
US patents per million population and the
enterprise financed R&D per capita (employed
in UNIDO, 2003). The result for the 61 coun-
tries with available data proved very high, with
a value of 0.92 (Archibugi & Coco, 2004),
demonstrating that a combination of patents
and scientific articles provide a robust measure
of national technological efforts also compris-
ing R&D inputs.
(b) Technological infrastructures
We considered three different indicators of
technological infrastructures: Internet, tele-
phony and electricity. They correspond to three
major industrial revolutions of the 20th century
(Freeman & Louta, 2001). They are basic
infrastructures for economic and social life.
Although they are not necessarily connected to
industrial capabilities, production knowledge is
strongly associated to their availability and
(i) (b1) Internet penetration
The Internet is a vital infrastructure not only
for business purposes, but also for access to
knowledge. Internet users access a worldwide
network. They differ from Internet hosts, which
are computers with active Internet Protocol
(IP) addresses connected to the Internet. The
data on users, when available, are preferable to
those on hosts for two reasons: first, they give a
more precise idea about the diffusion of Inter-
net among the population; second, some hosts
do not have a country code identification and
in statistics are assumed to be located within
the United States, therefore causing a bias. The
source here used was the World Bank (see also
World Bank, 2003, Table 5.11), which extracted
the data from ITU (2001) (the same data are
employed in UNDP, 2001).
In order to compare the penetration of the
Internet among the different countries we divi-
ded the number of users by population. The
maximum goal post is 540 per 1,000 people,
value belonging to Iceland, while the minimum
is zero, observed both in the recent and in the
past period for some very poor countries. The
internet is a new technology that has quickly
become the keystone of the Information and
Communication Technology, but it was not yet
commercially available in 1989–90. For this
reason, we postponed the past period to 1994
so that data referred to a time interval of five
instead of 10 years.
(ii) (b2) Telephone penetration
Telephony, besides its civilian component, is
also a fundamental infrastructure for business
purposes, and it allows tracing populations
with human skills and acquiring technical
information. Telephone mainlines are tele-
phone lines connecting a customer’s equipment
to the public switched telephone network. They
are another fundamental infrastructure for
economic and social life. Data are presented
per 1,000 people for the entire country (for
more information, see World Bank, 2003,
Table 5.10) both by World Bank database and
UNDP (2001), which both collected the data
from ITU (2001). To main lines, we added
mobile phones per 1,000 people, since they
represent the natural evolution of telecommu-
nication. An equal weight was assigned to older
and newer telephonic component since they
share the same function despite incorporating
different degrees of technology.
As telephony represents a definitively
acquired form of technology for a large number
of countries (the developed ones), we expressed
the sums between fixed and mobile lines in
natural logarithms. This ensures that, as the
level of telephony increases (therefore as we
move toward the more developed countries),
the difference between the new and the old
(lower) value expressed in logarithms decreases,
consequently reducing the gap among coun-
tries, for the exception of those countries with
very low initial values. In other words, the use
of log creates a threshold above which the
technological capacity of a country is no longer
enriched by the use of telephones.
Furthermore, since many countries can said
to have reached the desired level of telephony
penetration, the chosen goal value for the cal-
culus of the index was not taken as the maxi-
mum observed value, but the OECD average
(960 telephones for 1,000 people). This not only
increases the index for all countries, but also
allows to eliminate useless differences among all
those countries whose telephony share is supe-
rior to the mean one (they all get the value one).
Therefore, as the minimum observed value is
zero (transformed to one due to the use of
logarithms), the formula becomes:
Ln ðobserved valueÞ
Ln ðOECD averageÞ:
(iii) (b3) Electricity consumption
Electric power consumption (kilowatt per
hour per capita) measures the production of
power plants and combined heat and power
plants, less distribution losses, and own use by
heat and power plants (for more information,
see World Bank, 2003, Table 5.10). This indi-
cator accounts for the oldest technological
infrastructure. Electricity consumption is also a
proxy measure for the use of machinery and
equipment, since most of it is generated by
electric power. Although we are aware that this
is likely to be larger for capital-intensive
industries than for services, we believe that the
use of logs provides values that respond to the
real use of machinery and equipment. Other
valuable measures of industrial capacity devel-
oped, for example, by Lall and his colleagues
(see Lall & Albaladejo, 2001; UNIDO, 2003)
are available for a smaller number of countries
The observations on the telephony index over
the use of logarithms and the adoption of the
OECD average as the maximum goalpost,
apply a fortiori for the electricity consumption
index. The OECD average corresponded to
8,384 kwh per capita, whilst Ethiopia (1989–90)
produced the minimum value of 17 kwh per
capita. For those other low-income countries
whose data were not available a minimum
estimate was calculated.
Data on high technology production and
trade were not included. Although various
sources provide this kind of data (UNDP, 2001;
UNIDO, 2003; World Bank, 2003), some
problems emerge. Concerning high-tech pro-
duction, data for many countries are missing.
Moreover, available data are not always reli-
able, especially concerning production, since
they are derived from national sources, which
often apply different criteria for defining high-
tech sectors. Concerning high-tech trade, high
exports can simply imply high imports (as in
the case of Singapore and Hong Kong).
Moreover trade, including high-tech, is strongly
associated to the size of a country’s economy:
large countries have a lower propensity to trade
than small ones do, and vice versa. It was not
possible to produce an index able to account
for intraindustry trade and size, however a
comparison of ArCo with high-tech imports
data is attempted in Section 7.
Measures of capital equipment and ma-
chinery were not included either, despite these
representing a key component of embodied
technological capacity vital both for developed
and developing countries (Evangelista, 1999;
Pianta, 1995; Scott, 1989). The closest substi-
tute would be gross fixed capital formation,
which is also available for a large number of
countries in the World Bank data base (World
Bank, 2003, Table 4.9). This measure, however,
was not accounted for either since: (i) it is not
possible to separate the component of gross
capital formation devoted to investment in
capital equipment and machinery from other
forms of investment; and (ii) the indicator is
expressed in monetary values, which would
make it difficult to link ArCo to other currency-
based economic variables.
(c) The development of human skills
Technological capabilities are strongly asso-
ciated with human skills. Disembodied knowl-
edge (as measured by patents and scientific
literature) and technological infrastructures (as
measured by the Internet, telephony and elec-
tricity) have little value unless used by experi-
enced people. To complement our index, we
took into account three different measures of
human skills.
(i) (c1) Tertiary science and engineering
The indicator considered the share of uni-
versity students enrolled in science and engi-
neering related subjects in the population of
that age group. This indicator provides an
estimate of the science and technology human
capital, through the creation of a skilled human
base. It is obtained by multiplying two per-
centages, which are gross tertiary enrolment
ratio and percentage of tertiary students in
science and engineering.
The gross tertiary enrolment ratio is the ratio
of total enrolment at the tertiary level, regard-
less of age, to the population of the age group
that officially corresponds to the level of edu-
cation considered. Tertiary education, whether
or not to an advanced research qualification,
normally requires, as a minimum condition for
admission, the successful completion of educa-
tion at the secondary level (for more informa-
tion, see World Bank, 2003, Table 2.12). Data
were gathered from the World Bank data set––
originally produced by UNESCO (2002).
Science and engineering students include
students at the tertiary level in the following
fields: engineering, natural science, mathemat-
ics and computers, and social and behavioral
science. By multiplying the two percentages, we
obtained the desired indicator. The maximum
value was scored by Finland in 1998 with a
value of 32.6%, while the minimum value
scored was zero for more than one country.
This indicator rests on an implicit assumption,
namely that the quality of education pro-
vided across countries is comparable. On the
contrary, we are aware that the quality of
education, and the successful completion of
education, is subject to great variation across
countries. The capability of developing coun-
tries is probably overestimated in our analysis,
while the capability of developed countries
is probably subject to underestimation. The
completion of courses is not accounted for
since it is assumed that enrolment in science-
and engineering-related subjects contributes to
the technological capability of a country inde-
pendently as to whether courses are completed.
(ii) (c2) Mean years of schooling
They represent the average number of years
of school completed in the population over
14. Although this indicator does not consider
differences in the quality of schooling, it gives
an indication of the human skill level (the
‘‘stock’’). The sources are the UNDP (2001),
which collected an elaboration by Barro and
Lee (2001),
and World Bank (2003, Table
2.13). The maximum goalpost is 12 and corre-
sponds to United States’ mean years of
schooling, while the minimum value ð0;7Þwas
observed in Mali (zero index was extended to
other poor countries without available data).
Even for this indicator we had to implicitly
assume the level of education to be comparable
across countries.
(iii) (c3) Literacy rate
Literacy rate represents the percentage of
people over 14 who can, with understanding,
read and write a short, simple statement about
their everyday life. Data were collected from
World Bank (2003) and UNDP (2001) (for
more information, see World Bank, 2003, Table
2.14). This indicator allows performing a better
distinction between the less-developed coun-
tries. We considered the literacy rate as a nec-
essary condition for the development of human
ability. In this case the index oscillates between
zero and 100%, which consequently represent
the minimum and the maximum goalpost.
A final note about population, which is the
base for the calculus of the pro capita indexes. It
is based on the de facto definition of population,
which counts all residents regardless of legal
status or citizenship, except for refugees not
permanently settled in the country of asylum,
who are generally considered part of the popu-
lation of their country of origin (for more
information, see World Bank, 2003, Tables 1.1
& 2.1).
An interesting feature of the indicator here
devised is that none of the eight individual
components is based, directly or indirectly, on
monetary values. This means that it could be
matched by indicators expressed in monetary
value without any risk of collinearity. For
instance, it could be compared to indicators
such as international trade (including trade in
high-tech products), value added per employee
(which is often used as a measure for produc-
tivity), gross capital formation (a measure of
investment, including investment in capital
goods), and, of course, GDP and its growth. The
full database can be freely downloaded at http://
Results do not differ in a revolutionary
manner from other similar studies, but a num-
ber of fresh considerations can be made. First of
all, we tried, as in the TAI case, to group the 162
examined countries in different blocks, by clas-
sifying them along with the level of the overall
Table 1. A composite index of technological capabilities across countries (ArCo), 1990–2000
Current ArCo
Technology Index
Past ArCo
Technology Index
Growth rate from the
last decade (%)
1 Sweden 0.867 0.681 2 27.2
2 Finland 0.831 0.614 6 35.2
3 Switzerland 0.799 0.735 1 8.7
4 Israel 0.751 0.669 4 12.2
5 United States 0.747 0.663 5 12.6
6 Canada 0.742 0.678 3 9.4
7 Norway 0.724 0.581 9 24.6
8 Japan 0.721 0.569 12 26.8
9 Denmark 0.704 0.584 8 20.6
10 Australia 0.684 0.561 14 21.9
11 Netherlands 0.683 0.571 10 19.7
12 Germany 0.682 0.593 7 15.0
13 United Kingdom 0.673 0.562 13 19.8
14 Iceland 0.666 0.484 18 37.8
15 Taiwan 0.665 0.436 22 52.6
16 New Zealand 0.645 0.570 11 13.3
17 Belgium 0.642 0.523 15 22.7
18 Austria 0.619 0.502 16 23.4
19 Korea, Rep. 0.607 0.415 31 46.3
20 France 0.604 0.499 17 21.0
21 Singapore 0.573 0.397 37 44.5
22 Hong Kong, China 0.569 0.435 24 30.8
23 Ireland 0.567 0.450 20 26.0
24 Italy 0.526 0.444 21 18.5
25 Spain 0.516 0.410 34 25.8
26 Slovenia 0.507 0.412 33 23.1
27 Greece 0.489 0.416 30 17.5
28 Luxembourg 0.486 0.426 27 13.9
29 Slovak Republic 0.481 0.428 26 12.3
30 Russian Federation 0.480 0.464 19 3.4
31 Czech Republic 0.475 0.432 25 9.9
32 Estonia 0.472 0.413 32 14.4
33 Hungary 0.469 0.402 36 16.8
34 Poland 0.465 0.393 39 18.3
35 Portugal 0.450 0.346 53 30.0
36 Bulgaria 0.449 0.435 23 3.2
37 Cyprus 0.440 0.384 41 14.4
38 Latvia 0.439 0.423 29 3.7
39 Belarus 0.431 0.403 35 6.8
40 Argentina 0.426 0.379 45 12.5
41 Chile 0.424 0.336 57 26.2
42 Ukraine 0.417 0.426 28 )2.2
43 Uruguay 0.417 0.348 52 19.9
44 Croatia 0.414 0.376 46 10.3
45 Bahrain 0.410 0.355 49 15.4
46 Lithuania 0.408 0.380 43 7.4
47 Kuwait 0.405 0.380 44 6.7
48 Moldova 0.395 0.394 38 0.2
49 United Arab Emirates 0.394 0.321 63 23.1
50 Romania 0.393 0.383 42 2.5
51 Panama 0.382 0.337 56 13.3
(continued next page)
Table 1—(continued)
Current ArCo
Technology Index
Past ArCo
Technology Index
Growth rate from the
last decade (%)
52 Kazakhstan 0.381 0.393 40 )2.8
53 Trinidad and Tobago 0.380 0.348 51 9.3
54 Qatar 0.380 0.353 50 7.6
55 Georgia 0.379 0.371 47 2.3
56 South Africa 0.372 0.334 58 11.1
57 Lebanon 0.370 0.292 72 26.5
58 Malaysia 0.369 0.295 69 25.2
59 Venezuela, RB 0.369 0.328 60 12.4
60 Costa Rica 0.361 0.322 62 12.2
61 Malta 0.361 0.325 61 10.9
62 Yugoslavia, Fed. Rep. 0.358 0.334 59 7.2
63 Mexico 0.358 0.320 64 11.8
64 Tajikistan 0.356 0.369 48 )3.6
65 Turkey 0.347 0.286 75 21.4
66 Jamaica 0.346 0.264 85 30.8
67 Peru 0.345 0.292 74 18.2
68 Thailand 0.342 0.278 80 23.3
69 Jordan 0.341 0.300 67 13.6
70 Azerbaijan 0.337 0.342 54 )1.4
71 Colombia 0.331 0.286 76 15.6
72 Brazil 0.330 0.280 77 17.6
73 Armenia 0.326 0.339 55 )3.6
74 Puerto Rico 0.326 0.293 71 11.4
75 Saudi Arabia 0.326 0.280 78 16.4
76 Paraguay 0.323 0.269 84 20.0
77 Philippines 0.322 0.277 81 16.4
78 Cuba 0.322 0.313 65 2.8
79 Ecuador 0.319 0.294 70 8.3
80 Uzbekistan 0.319 0.313 66 1.9
81 Iran, Islamic Rep. 0.313 0.241 90 29.9
82 Libya 0.312 0.274 83 13.7
83 El Salvador 0.311 0.236 93 31.9
84 Dominican Republic 0.308 0.258 86 19.4
85 China 0.306 0.227 97 34.7
86 Kyrgyz Republic 0.306 0.300 68 1.9
87 Bolivia 0.305 0.254 88 19.8
88 Fiji 0.304 0.278 79 9.1
89 Oman 0.300 0.238 91 26.0
90 Macedonia, FYR 0.300 0.276 82 8.5
91 Turkmenistan 0.289 0.292 73 )1.2
92 Tunisia 0.288 0.227 98 26.8
93 Mauritius 0.285 0.231 95 23.6
94 Syrian Arab Republic 0.282 0.256 87 10.2
95 Sri Lanka 0.280 0.227 96 23.0
96 Zimbabwe 0.279 0.248 89 12.2
97 Algeria 0.277 0.221 100 25.1
98 Guyana 0.271 0.226 99 20.0
99 Egypt, Arab Rep. 0.269 0.219 101 22.6
100 Indonesia 0.265 0.190 108 39.7
101 Suriname 0.264 0.219 102 20.1
102 Honduras 0.258 0.218 103 18.3
103 Botswana 0.255 0.189 109 34.8
104 Albania 0.251 0.231 94 8.5
Table 1—(continued)
Current ArCo
Technology Index
Past ArCo
Technology Index
Growth rate from the
last decade (%)
105 Iraq 0.246 0.238 92 3.4
106 Zambia 0.240 0.213 104 12.3
107 Vietnam 0.239 0.164 118 45.5
108 Nicaragua 0.238 0.202 106 17.8
109 Guatemala 0.234 0.187 110 25.2
110 Gabon 0.231 0.204 105 13.1
111 India 0.225 0.169 116 32.9
112 Swaziland 0.222 0.184 111 20.4
113 Morocco 0.217 0.169 117 28.5
114 Namibia 0.217 0.184 112 17.6
115 Congo, Rep. 0.207 0.195 107 6.4
116 Kenya 0.204 0.177 114 15.1
117 Ghana 0.203 0.163 119 24.3
118 Mongolia 0.197 0.176 115 11.6
119 Cameroon 0.192 0.163 120 18.0
120 Pakistan 0.191 0.158 121 20.9
121 Korea, Dem. Rep. 0.187 0.179 113 4.9
122 Myanmar 0.179 0.135 123 32.2
123 Lesotho 0.178 0.154 122 15.4
124 Tanzania 0.155 0.126 124 23.2
125 Senegal 0.151 0.109 130 38.1
126 Papua New Guinea 0.146 0.119 125 22.4
127 Togo 0.145 0.097 133 48.8
128 Nigeria 0.141 0.114 127 23.6
129 Sudan 0.140 0.096 136 46.3
130 Yemen, Rep. 0.140 0.112 128 24.2
131 C^
ote d’Ivoire 0.136 0.080 141 69.8
132 Malawi 0.134 0.106 131 26.4
133 Uganda 0.133 0.097 134 37.6
134 Haiti 0.129 0.117 126 10.4
135 Congo, Dem. Rep. 0.125 0.110 129 13.6
136 Gambia 0.123 0.070 146 76.1
137 Bangladesh 0.123 0.086 138 43.2
138 Djibouti 0.122 0.099 132 22.3
139 Nepal 0.121 0.070 145 72.9
140 Madagascar 0.116 0.096 135 20.8
141 Benin 0.114 0.078 143 46.3
142 Rwanda 0.113 0.081 140 39.5
143 Mauritania 0.111 0.077 144 43.6
144 Central African
0.110 0.081 139 36.1
145 Angola 0.107 0.088 137 21.7
146 Bhutan 0.103 0.063 148 65.2
147 Lao PDR 0.098 0.057 151 73.6
148 Mozambique 0.098 0.069 147 41.6
149 Cambodia 0.096 0.047 156 103.3
150 Liberia 0.095 0.079 142 20.5
151 Eritrea 0.093 0.048 154 92.8
152 Guinea 0.079 0.045 158 73.9
153 Burundi 0.078 0.057 152 38.2
154 Guinea-Bissau 0.076 0.061 149 26.2
155 Sierra Leone 0.075 0.060 150 24.4
(continued next page)
ArCo Technology Index (Table 1). We identi-
fied four groups,
according to the existence of
a significant gap among the last country of a
group and the first of the subsequent
––leaders (from 1 to 25 ranking);
––potential leaders (from 26 to 50);
––latecomers (from 51 to 111);
––marginalized (from 112 to 162).
(a) Leaders (from 1 to 25 ranking)
The first group includes those countries able
to create and sustain technological innovation.
This is the group that concentrates the bulk of
the creation of technology. Seven consider-
ations can be made:
(i) What can be immediately noted is the
excellent performance of Nordic Euro-
pean countries: Sweden ranks first, Finland
second, Norway seventh. These countries
hold extraordinary technological infra-
structures, and highly qualified human
resources. In addition to the static picture,
is noteworthy their trend: all but Den-
mark improved their ranking with respect
to a decade ago, with rates of growth beyond
(ii) Still more pronounced is the growth
of Newly Industrialized Countries, the so-
called Asian tigers: Taiwan, South Korea,
Hong Kong, Singapore. In a decade, their
index has grown by 52% in Taiwan and 31%
in Hong Kong. A huge growth occurred in
the category of the creation of technol-
ogy (1100% in South Korea and 200% in Sin-
(iii) North American countries are more or
less stable in the top positions: the United
States ranks fifth and Canada sixth, losing
a few positions. The United States has a
more prominent position in the creation of
technology than it did in the other two cate-
(iv) Japan occupies the eighth place (gaining
four positions in a decade), fruit of an excel-
lent performance in technology creation,
very good in technological infrastructures,
and relatively poor in human skills.
(v) Western Europe shows a slowdown: Ger-
many, France, Belgium, Austria, and Italy
fell behind during the decade, not so much
due to a slow growth, as much as due to
better performance by other countries (this
is particularly the case in technological infra-
structures). Switzerland ranked first a decade
ago and now finds itself in third position.
Germany is now 12th, losing five positions.
The United Kingdom is stable at the 13th
position, while Ireland (23rd) lost two ranks.
Only Spain gained a few positions, resting
on the borderline (25th rank), between the
first and the second grouping.
(vi) Australia and New Zealand almost ex-
changed places: the first gained rank (from
14th to 10th) while the second lost rank
(from 11th to 16th).
(vii) Finally, Israel ranks fourth, even ahead
of the United States. This apparently sur-
prising result is attributable to the high num-
ber of patents granted in the United States,
accompanied by an excellent achievement
in the formation of human capital.
(b) Potential leaders (from 26 to 50 ranking)
The second group comprises countries that
have, on the one hand, invested in the forma-
tion of human skills and developed standard
technological infrastructures, and on the other
they have achieved little innovation.
Table 1—(continued)
Current ArCo
Technology Index
Past ArCo
Technology Index
Growth rate from the
last decade (%)
156 Chad 0.071 0.050 153 42.6
157 Ethiopia 0.067 0.047 155 41.1
158 Mali 0.066 0.032 159 108.2
159 Afghanistan 0.056 0.046 157 20.5
160 Burkina Faso 0.050 0.028 160 79.2
161 Niger 0.031 0.017 162 84.0
162 Somalia 0.028 0.024 161 13.9
Sources: CSRS (1996a, 1996b), EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and
World Bank (2003).
(i) The largest number of countries in this
group comes from the former Socialist East-
ern European countries. Predictions here are
particularly risky, especially since the eco-
nomic and social conditions of these coun-
tries have been particularly turbulent. Data
and trends for the ex-Soviet or ex-Yugosla-
vian new states are not entirely reliable. In
spite of turmoil, these countries show a good
performance in human skills. Russia lost
position considerably in the last decade in
all three categories as a consequence of the
transition to a market economy. Bulgaria
and Romania lost meaningful positions
too, while Hungary and Poland have gained
a few positions.
Greece and Portugal, the countries to have
always lagged behind in technological cap-
abilities within the European Union, are
slowly bridging the gap. The latter, with a
growth rate of 30%, climbed from the
53rd up to the 35th rank. Greece gained
a few positions by reaching the 27th posi-
(ii) Some South American countries have
also gained positions during the decade:
Argentina, Uruguay and especially Chile
had a grow rate of 26%, with Argentina
reaching 40th.
(iii) Within the Arab countries the perfor-
mance of United Arab Emirates is noteable:
thanks to a good availability of infrastruc-
tures it gained 14 positions and almost
reached Kuwait, which remains the leader
of the Arabic countries for technological
progress at the 47th place.
(c) Latecomers (from 51 to 111 ranking)
The third group, the largest, is composed of
countries which, in one way or another, try to
stimulate their technology growth parallel to
their development efforts: technological infra-
structure and formation of human skills.
(i) Central and South American countries
deserve a special comment since none of
them, with the exception of Cuba, have
shown a downgrading trend compared to a
decade ago (Panama, Venezuela, Costa
Rica, Mexico, Jamaica, Peru, Colombia,
Brazil, Paraguay and Bolivia). These coun-
tries have developed particularly good tech-
nological infrastructure (growth rates
around 20%), though human skills have
not grown as effectively (not superior to
(ii) A similar trend can be observed among
Asian countries, where Malaysia and Thai-
land (both with a growth rate beyond 20%)
are in the top positions, followed by the Phil-
ippines (growth rate of 16%). Although
placed at the bottom of this list (100th),
Indonesia shows the highest growth rate
since the previous decade (40%).
(iii) In Asia, China and India deserve a sepa-
rate comment: China has shown an extraor-
dinary growth rate of technological
infrastructures (71%) but has remained al-
most stable in human skills wise. Overall, it
has shown one of the highest growth rates
in the last 10 years (35%, second only to Indo-
nesia), by gaining 12 positions (from 97th to
(iv) India closes the third grouping by rank-
ing at 111th. This may seem unfair but,
apart from some African countries and Viet-
nam––which do not have reliable data relat-
ing to the past––India is the country that
shows the highest growth rate (33%), driven,
like China, by the development of techno-
logical infrastructure.
(v) In the Middle East, Lebanon climbed to
the 57th position (growth rate of 26%), plac-
ing behind Qatar (54th) and ahead of Jordan
(69th), while Saudi Arabia increased its rank
to the 75th position.
(vi) Finally, a restricted set of African
countries showed signs of catching up,
with South Africa (56th) in the lead and
North African countries Tunisia (92nd),
Algeria (97th) and Egypt (99th) just
behind. These countries show a delay in
the development of technology infrastruc-
tures, but are growing in terms of human
(d) Marginalized (from 112 to 162 ranking)
The fourth and last group is composed of
marginalized countries, which do not have
large access even to the oldest technologies,
such as electricity and telephony. In this group,
relative position is not particularly meaningful,
due mainly to the lack of available data. Even
high growth rates can simply be due to the very
low values in both periods. These countries are
practically lacking the first category––creation
of technology––and have poor technological
infrastructures and human skills. Many African
countries fall within this grouping where the
low technological level is associated to the very
low income levels.
After having commented the results at the
country level, we wish to report some simple
statistics about the indicators. In Table 2 we
calculated the correlation matrix across the
eight indicators presented. As expected, all
correlation coefficients are positive. The values
are different, however, indicating that the var-
ious indicators taken into account highlight
different aspects of technological capabilities.
As predictable, the correlation is greater
across indicators belonging to the same cate-
gory of technology (creation, infrastructures or
human skills), but with some exceptions. For
example, the correlation between Internet users
and scientific publications is high. At the same
time the Internet is less correlated with the
traditional infrastructures (telephony and elec-
tricity). The latter are more highly correlated
with literacy rate and years of schooling. So it
would appear that more traditional forms of
technology remain closer to each other: the
indices of technology creation (patents, scien-
tific articles) have little correlation with literacy
rate, telephony and electricity.
It is also interesting to note the degree to
which each indicator is correlated with the final
ArCo Technology Index. Since the ArCo
Technology Index represents the mean of the
eight components, it is natural to expect a high
correlation between them. This is indeed the
case, although patents show the weakest cor-
relation and schooling the strongest.
Different results emerge if we consider the
correlation within each group. In particular,
differences emerged within the group of poten-
tial leaders (Table 3): composed mainly of East
European countries, it shows a negative corre-
lation (although very weak) between indicators
of human skills and those of technological
infrastructure. In this group of countries there
is no correlation between education perfor-
mance on the one hand, and infrastructures and
patenting activity on the other. Moreover, there
is no connection between scientific articles and
patents, confirming that the sources of codified
knowledge creation from the business sector
and the academic community are not neces-
sarily complementary.
Table 4 reports the correlation matrix for the
latecomers, which signals a practical indepen-
dence between indicators of human skills and
indicators of creation of technology. The
former also exhibit little correlation with the
technological infrastructures, although a posi-
tive correlation is found between indicators of
creation and indicators of technology infra-
structure. Correlation within the leaders group
or within the marginalized group was not
reported. While for the latter group data can-
not be considered sufficiently reliable, countries
within the group of leaders have already
reached the maximum level for more than one
indicator. The linear correlation coefficients
would therefore prove less informative.
Table 5 reports the correlation matrix for the
three category indexes. The category of tech-
nology creation is a little less correlated with
the other two as well as with the final ArCo
index. The intragroup analysis does not reveal
any new information, though it is interesting to
look at the indicators’ coefficients of variation
(Table 6), which signal different levels of
polarization of technological capabilities across
the 162 countries. As expected, the most sig-
nificant dispersion occurs in the case of the
generation of technology, which is very highly
concentrated in a small cluster of countries. In
addition, Internet users and, to a lesser extent,
the scientific tertiary formation, are concen-
trated in just a small number of countries.
Concerning infrastructures, we note that the
older the technology is, the less its utilization is
polarized. Literacy is the least dispersed indi-
Historians who have taken into account the
geographical location of inventions over 3,000
years would not be surprised that the genera-
tion of inventions and innovations is strongly
concentrated in certain areas. They have in fact
shown that in the past inventive activity was
concentrated in what now we would call
‘‘hubs’’ such as the Greek cities, the Italian
Renaissance republics and Britain during the
industrial revolution (see Smithsonian Visual
Timeline of Inventions, 1994). Today some-
thing similar is happening in Silicon Valley as
well as in the Balgalore district. What might
appear surprising to an historian is the geo-
graphical diffusion of contemporary innovation
compared to its concentration in the past.
A comparison of the variation coefficients
across the two periods allows also to test
whether the 162 countries are somehow con-
verging or diverging in their technological
capabilities. All the indicators show a certain
convergence from the past (that is, a reduction
of the divergence signalled by the coefficients),
especially with regard to Internet (many coun-
tries in the past did not possess it at all, while
Table 2. Correlation coefficients across the various indexes of technological capabilities (all countries)a
ArCo technology
Patent index 1.000 0.791 0.692 0.446 0.445 0.537 0.530 0.320 0.705
Articles index 0.791 1.000 0.833 0.571 0.567 0.699 0.665 0.420 0.828
Internet index 0.692 0.833 1.000 0.607 0.594 0.618 0.659 0.431 0.805
Telephony index 0.446 0.571 0.607 1.000 0.843 0.713 0.819 0.818 0.890
Electricity index 0.445 0.567 0.594 0.843 1.000 0.674 0.744 0.712 0.854
Tertiary index 0.537 0.699 0.618 0.713 0.674 1.000 0.707 0.617 0.837
Schooling index 0.530 0.665 0.659 0.819 0.744 0.707 1.000 0.805 0.903
Literacy index 0.320 0.420 0.431 0.818 0.712 0.617 0.805 1.000 0.788
Sources: CSRS (1996a, 1996b); EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and World Bank (2003).
––Patent index: patents granted at the USPTO by country per million people (annual average for 1997–2000).
––Articles index: scientific articles by country per million people (annual average for 1997–99).
––Internet index: Internet users by country per million people (1999).
––Telephony index: fixed and mobile telephone lines by country per million people (1999).
––Electricity index: electricity consumption by country per million people (annual average for 1997–98).
––Tertiary index: gross tertiary science and engineering enrolment by country (annual average for 1996–98).
––Schooling index: mean years of schooling by country (2000).
––Literacy index: adult literacy rate by country (2000).
––ArCo technology index: weighted mean of the previous indexes.
Table 3. Correlation coefficients across the various indexes of technological capabilities for the potential leaders (countries from 26 to 50)a
ArCo technology
Patent index 1.000 )0.018 0.519 0.385 0.401 )0.318 )0.258 0.095 0.325
Articles index )0.018 1.000 0.362 0.530 0.342 0.067 0.143 0.191 0.798
Internet index 0.519 0.362 1.000 0.768 0.580 )0.532 )0.194 )0.236 0.447
Telephony index 0.385 0.530 0.768 1.000 0.606 )0.435 )0.188 )0.207 0.531
Electricity index 0.401 0.342 0.580 0.606 1.000 )0.309 )0.475 )0.575 0.325
Tertiary index )0.318 0.067 )0.532 )0.435 )0.309 1.000 )0.106 0.396 0.220
Schooling index )0.258 0.143 )0.194 )0.188 )0.475 )0.106 1.000 0.489 0.199
Literacy index 0.095 0.191 )0.236 )0.207 )0.575 0.396 0.489 1.000 0.438
Sources: CSRS (1996a, 1996b), EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and World Bank (2003).
––Patent index: patents granted at the USPTO by country per million people (annual average for 1997–2000).
––Articles index: scientific articles by country per million people (annual average for 1997–99).
––Internet index: Internet users by country per million people (1999).
––Telephony index: fixed and mobile telephone lines by country per million people (1999).
––Electricity index: electricity consumption by country per million people (annual average for 1997–98).
––Tertiary index: gross tertiary science and engineering enrolment by country (annual average for 1996–98).
––Schooling index: mean years of schooling by country (2000).
––Literacy index: adult literacy rate by country (2000).
––ArCo technology index: weighted mean of the previous indexes.
Table 4. Correlation coefficients across the various indexes of technological capabilities for the latecomers (countries from 51 to 111)a
ArCo Technology
Patent index 1.000 0.508 0.631 0.476 0.374 )0.012 0.001 0.161 0.431
Articles index 0.508 1.000 0.437 0.511 0.447 0.159 )0.008 0.094 0.501
Internet index 0.631 0.437 1.000 0.723 0.236 0.035 0.097 0.141 0.500
Telephony index 0.476 0.511 0.723 1.000 0.244 0.311 0.087 0.332 0.686
Electricity index 0.374 0.447 0.236 0.244 1.000 0.169 0.079 0.022 0.627
Tertiary index )0.012 0.159 0.035 0.311 0.169 1.000 0.114 0.189 0.507
Schooling index 0.001 )0.008 0.097 0.087 0.079 0.114 1.000 0.421 0.528
Literacy index 0.161 0.094 0.141 0.332 0.022 0.189 0.421 1.000 0.586
Sources: CSRS (1996a, 1996b), EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and World Bank (2003).
––Patent index: patents granted at the USPTO by country per million people (annual average for 1997–2000).
––Articles index: scientific articles by country per million people (annual average for 1997–99).
––Internet index: Internet users by country per million people (1999).
––Telephony index: fixed and mobile telephone lines by country per million people (1999).
––Electricity index: electricity consumption by country per million people (annual average for 1997–98).
––Tertiary index: gross tertiary science and engineering enrolment by country (annual average for 1996–98).
––Schooling index: mean years of schooling by country (2000).
––Literacy index: adult literacy rate by country (2000).
––ArCo technology index: weighted mean of the previous indexes.
it was already a common infrastructure in
others), telephony and literacy rate. It also
emerges that the propensity toward conver-
gence is much faster in infrastructure, including
new ones such as Internet, than in the creation
of technology.
For the coefficients of variation we also
decomposed the analysis at the group level,
and we found clear evidence that within the
groups there is more homogeneity than for
the overall 162 countries. The ratios inside
the groups are lower for every indicator, and
this is particularly true for the final ArCo
Index, which shows not only a lower absolute
value, but also a faster rate of convergence at
the group level with respect to the aggregate
So far, the ArCo has considered each country
as if it were a closed economy. Of course, in a
highly globalized world this is hardly the case
(the relationship between globalization and
technology is discussed in Archibugi &
Lundvall, 2001; Archibugi & Michie, 1997). It
is certainly an advantage for a country to
receive information and know-how from other
countries. We assumed that these exchanges
should have an effect on some of the eight
variables included in ArCo. In this section we
try to take into account, in a separate manner,
the contribution provided by import technol-
ogy to national technological capabilities by
adding a fourth category.
Following the suggestions of a referee, and
the method applied by Lall and Albaladejo
(2001), we added a measure of import tech-
nology. This measure is composed of three
subindices: inward Foreign Direct Invest-
ment (FDI), technology licensing payments,
and import of capital goods. We relied on a
combined index of these three variables as
developed by Lall and Albaladejo (2001, Table
9). The results are reported in column 2 of
Table 7, with data available for 86 coun-
tries only (therefore, we confine here our anal-
ysis to this subset of countries). According
to this measure, the countries with the high-
est import of technology are Singapore and
We therefore added this component of
‘‘Import technology’’ as a fourth dimension to
the ArCo Index. We gave it equal weight
compared to the other three, that is one-quar-
ter. The results are reported in column 4 of
Table 7, while column 5 reports the new rank-
ing, and column 6 the difference between the
original ArCo and this more comprehensive
measure of technological capabilities. The
ranking of world countries according to this
index does not differ substantially from the
previous one. The first three positions remain
unchanged. Very significant differences emerge
for two countries only: Singapore, the top
importer of technology, which gains 16 posi-
tions and reach the fourth place, and Ireland,
which gains 10 positions moving its ranking
from 22 to 12.
The largest economies lose some position:
United States, Israel, Japan, Germany, Aus-
tralia and United Kingdom downgrade. On
the other hand, a few small and dynamic
economies––Netherlands, Norway, Belgium––
gain position. This reinforces the impression
that this measure of global technology is
affected by the size of the economy and, as
is well known in international trade theory,
small countries are more open to technology
imports. As we move to the bottom part of
Table 5. Correlation coefficients across the category indexes of technological capabilities
Technology creation
Technology diffusion
skills index
ArCo technology
Technology creation index 1.000 0.667 0.627 0.819
Technology diffusion index 0.667 1.000 0.894 0.956
Human skills index 0.627 0.894 1.000 0.937
Sources: CSRS (1996a, 1996b), EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and
World Bank (2003).
––Technology creation index: simple mean of Patent index and articles index.
––Technology diffusion index: simple mean of Internet index, telephony index and electricity index.
––Human skills index: simple mean of tertiary index, schooling index and literacy index.
––ArCo technology index: simple mean of the previous indexes.
Table 6. Coefficients of variation of the various indexes of technological capabilities
Current Past Growth rate (%)
Patent index
All countries 2.787 3.087 )9.7
Leaders 0.705 0.935 )24.6
Potential leaders 3.251 3.374 )3.6
Latecomers 1.822 2.684 )32.1
Articles index
All countries 1.999 2.172 )8.0
Leaders 0.420 0.626 )33.0
Potential leaders 0.654 0.672 )2.6
Latecomers 1.004 1.227 )18.2
Internet index
All countries 1.831 2.642 )30.7
Leaders 0.459 0.838 )45.3
Potential leaders 0.737 1.330 )44.6
Latecomers 1.158 4.108 )71.8
Telephony index
All countries 0.435 0.550 )20.9
Leaders 0.010 0.039 )73.7
Potential leaders 0.082 0.100 )18.1
Latecomers 0.175 0.285 )38.6
Electricity index
All countries 0.497 0.536 )7.4
Leaders 0.039 0.071 )44.2
Potential leaders 0.109 0.109 )0.2
Latecomers 0.286 0.338 )15.5
Tertiary index
All countries 1.018 1.034 )1.5
Leaders 0.319 0.369 )13.4
Potential leaders 0.501 0.664 )24.6
Latecomers 0.665 0.765 )13.0
Schooling index
All countries 0.549 0.590 )7.0
Leaders 0.161 0.187 )14.2
Potential leaders 0.209 0.245 )14.5
Latecomers 0.288 0.327 )11.8
Literacy index
All countries 0.279 0.352 )20.8
Leaders 0.018 0.029 )38.1
Potential leaders 0.062 0.079 )22.2
Latecomers 0.132 0.183 )27.8
Technology creation index
All countries 2.151 2.289 )6.0
Leaders 0.435 0.630 )31.0
Potential leaders 0.707 0.712 )0.8
Latecomers 1.006 1.249 )19.4
Technology diffusion index
All countries 0.561 0.586 )4.2
Leaders 0.100 0.065 54.4
(continued next page)
the ranking, the differences vanish. Both linear
correlation coefficient and rank correlations
are very high, and equal to 0.990 and 0.995. We
can deduce that, as a method to rank coun-
tries’ technological capabilities, ArCo is a suf-
ficiently robust measure even without including
a separate category devoted to import tech-
An important application of ArCo is to allow
the investigation of the role played by techno-
logical capabilities in economic growth (for a
review of the literature, see Fagerberg, 1994).
In future research we will use a wider battery of
statistical and econometric methods to explore
this relationship. Here we limit ourselves to a
preliminary analysis by linking the ArCo
index to the economic growth proxied by the
GDP per capita. Table 8 reports two sets of
regressions designed to check the extent to
which the two sets of data overlap. First, we
considered the absolute levels, by regressing
per capita current GDP expressed in US dollars
at Purchasing Power Parities on the current
ArCo index value; then we investigated the
dynamics in the last decade, by regressing
Table 6—(continued)
Current Past Growth rate (%)
Potential leaders 0.119 0.091 31.0
Latecomers 0.190 0.268 )28.9
Human skills index
All countries 0.439 0.475 )7.5
Leaders 0.097 0.108 )10.3
Potential leaders 0.130 0.154 )15.1
Latecomers 0.166 0.219 )24.2
ArCo Technology Index
All countries 0.578 0.589 )1.9
Leaders 0.133 0.177 )24.6
Potential leaders 0.077 0.089 )3.1
Latecomers 0.144 0.196 )26.7
Sources: CSRS (1996a, 1996b), EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and
World Bank (2003).
––Patent index: patents granted at the USPTO by country per million people (annual average for 1997–2000 for the
actual value and for 1987–90 from the past one).
––Articles index: scientific Articles by country per million people (annual average for 1997–99 for the actual value
and for 1987–89 for the past one).
––Internet index: Internet users by country per million people (year 1999 for the actual value and year 1994 for the
past one).
––Telephony index: fixed and mobile telephone lines by country per million people (year 1999 for the actual value
and year 1989 for the past one).
––Electricity index: electricity consumption by country per million people (annual average for 1997–98 for the
actual value and annual average for 1988–89 for the past one).
––Tertiary index: gross tertiary science and engineering enrolment by country (annual average for 1996–998 for the
actual value and annual average for 1987–89 for the past one).
––Schooling index: mean years of schooling by country (year 2000 for the actual value and year 1990 for the past
––Literacy index: adult literacy rate by country (year 2000 for the actual value and year 1990 for the past one).
––Technology creation index: simple mean of patent and articles indexes.
––Technology diffusion index: simple mean of Internet, telephony and electricity indexes.
––Human skills index: simple mean of tertiary, schooling and literacy indexes.
––ArCo Technology Index: simple mean of the three previous (category) indexes.
––Coeff. of variation: ratio between standard deviation and simple mean of the observations. It signals the internal
variability of each index.
Table 7. Import Technology Index and its divergence to ArCo Technology Index: a comparison for 86 countriesa
12345 6
between ArCo and
GTI ranking
Sweden 1 0.193 6 0.698 10
Finland 2 0.091 15 0.646 20
Switzerland 3 0.172 7 0.642 30
Singapore 20 0.777 1 0.624 416
Norway 7 0.161 8 0.583 52
Canada 6 0.098 13 0.581 60
Israel 4 0.065 19 0.580 7)3
United States 5 0.066 18 0.576 8)3
Netherlands 11 0.199 5 0.562 92
Denmark 9 0.129 10 0.560 10 )1
Japan 8 0.027 33 0.547 11 )3
Ireland 22 0.480 2 0.545 12 10
Belgium 16 0.232 4 0.539 13 3
Australia 10 0.092 14 0.536 14 )4
United Kingdom 13 0.101 12 0.530 15 )2
Germany 12 0.052 21 0.525 16 )4
New Zealand 15 0.141 9 0.519 17 )2
Taiwan 14 0.060 20 0.514 18 )4
Hong Kong 21 0.306 3 0.503 19 2
Austria 17 0.112 11 0.492 20 )3
France 19 0.085 16 0.474 21 )2
Korea, Rep. 18 0.035 28 0.464 22 )4
Italy 23 0.031 30 0.402 23 0
Spain 24 0.051 22 0.400 24 0
Slovenia 25 0.044 25 0.391 25 0
Greece 26 0.030 31 0.374 26 0
Czech Republic 28 0.040 27 0.366 27 1
Hungary 29 0.047 23 0.364 28 1
Russia 27 0.004 63 0.361 29 )2
Poland 30 0.020 36 0.353 30 0
Portugal 31 0.044 24 0.348 31 0
Chile 33 0.043 26 0.329 32 1
Argentina 32 0.029 32 0.327 33 )1
Uruguay 34 0.013 42 0.316 34 0
Bahrain 35 0.010 50 0.310 35 0
Malaysia 39 0.079 17 0.296 36 3
Romania 36 0.006 55 0.296 37 )1
Panama 37 0.032 29 0.295 38 )1
South Africa 38 0.012 44 0.282 39 )1
Venezuela 40 0.016 37 0.281 40 0
Costa Rica 41 0.023 34 0.276 41 0
Mexico 42 0.021 35 0.274 42 0
Jamaica 44 0.015 40 0.263 43 1
Peru 45 0.016 38 0.263 44 1
Turkey 43 0.008 52 0.263 45 )2
Thailand 46 0.016 39 0.260 46 0
Jordan 47 0.006 54 0.257 47 0
Colombia 48 0.012 45 0.251 48 0
Brazil 49 0.011 46 0.250 49 0
(continued next page)
the variation of GDP over 1990–2000 on the
variation of the ArCo values in the same
The first part of the table signals a high
correlation between the two indicators for the
whole set of countries. The differences across
countries are so wide that it is not surprising
that there is a very strong association between
per capita technological capabilities and GDP.
But this relationship becomes weaker when we
Table 7—(continued)
12345 6
between ArCo and
GTI ranking
Saudi Arabia 50 0.008 53 0.246 50 0
Paraguay 51 0.010 48 0.245 51 0
Philippines 52 0.006 56 0.243 52 0
Ecuador 53 0.010 47 0.242 53 0
El Salvador 54 0.003 66 0.234 54 0
Bolivia 56 0.009 51 0.231 55 1
China 55 0.005 59 0.231 56 )1
Oman 57 0.014 41 0.229 57 0
Tunisia 58 0.010 49 0.218 58 0
Mauritius 59 0.013 43 0.217 59 0
Sri Lanka 60 0.002 69 0.210 60 0
Zimbabwe 61 0.003 67 0.210 61 0
Algeria 62 0.002 70 0.208 62 0
Egypt, Arab Rep. 63 0.004 60 0.203 63 0
Indonesia 64 0.005 58 0.200 64 0
Honduras 65 0.004 61 0.194 65 0
Albania 66 0.004 65 0.189 66 0
Zambia 67 0.001 71 0.180 67 0
Nicaragua 68 0.004 62 0.180 68 0
Guatemala 69 0.004 64 0.176 69 0
India 70 0.001 81 0.169 70 0
Morocco 71 0.005 57 0.164 71 0
Kenya 72 0.001 74 0.153 72 0
Ghana 73 0.001 76 0.153 73 0
Cameroon 74 0.001 80 0.144 74 0
Pakistan 75 0.001 75 0.144 75 0
Tanzania 76 0.001 77 0.116 76 0
Senegal 77 0.001 72 0.114 77 0
Nigeria 78 0.002 68 0.107 78 0
Yemen, Rep. 79 0.001 73 0.105 79 0
Malawi 80 0.000 83 0.100 80 0
Uganda 81 0.001 79 0.100 81 0
Bangladesh 82 0.000 84 0.092 82 0
Nepal 83 0.000 85 0.091 83 0
Madagascar 84 0.000 82 0.087 84 0
Mozambique 85 0.001 78 0.073 85 0
Ethiopia 86 0.000 86 0.050 86 0
Linear correlation coefficient (n¼86) between the Arco Index and Global Technology Index ¼0.990.
Correlation coefficient (n¼86) between Arco ranking and Global Technology ranking ¼0.995.
(1) Ranking ArCo slightly differs from the values reported in Table 1 because we consider here 86 countries. (2)
Data taken from Lall and Albaladejo (2001, Table 9). Period coveres: 1995–98. (4) Global Technology Index is the
arithmetic mean of four components: the three from ArCo plus Import Technology Index.
look at more homogeneous groups: once we
consider countries comparable in terms of
technological capabilities, a larger variety of
income levels emerges. The beta coefficients are
all significant, although the square-Rdecreases
as we focus on less-developed countries.
The bottom part of the table considers the
dynamics: how is the variation in technological
capabilities over a decade related to GDP var-
iation? In this case, the relationship is weak
for the full set of countries and the coefficient is
not meaningful. But it becomes significant for
every subgroup, especially for potential leaders
and latecomers: improved technological capa-
bilities are strongly associated to GDP growth.
Of course, none of the results so far reported
provide a unique interpretation on the causal-
ity between the two variables. Nor do they
shed light on the impact of each component of
the technological index (each subindex) on
the GDP level and growth. The exploration
of these links will be addressed in future
Elsewhere (see Archibugi & Coco, 2004) we
ran a regression of the ArCo index on gross
capital formation to explore whether the evo-
lution of investments affected the technological
capabilities in the different countries. The
results show a slightly negative correlation,
because the countries which invested more in
the last decade are the poorer ones, therefore
the ones with a lesser dowry of scientific and
technological capability.
It is generally assumed that technological
capabilities are a fundamental component for
achieving substantive goals such as a satisfac-
tory quality of life or a higher income. But in
order to understand properly the role of tech-
nological capabilities in social and economic
development, this should be conceptualized and
quantified. As Kula (1986) showed, the con-
ceptualization is necessarily associated to the
quantification, and vice versa.
This paper presents a fresh attempt to
develop an index of technological capabilities
for a large number of countries and for two
periods of time. It follows other similar
attempts, although we some what modified the
methodology. Our aim was to include a larger
number of countries, and to rely on dependable
data sources. This led to the inclusion of some
indicators and to the exclusion of others. In the
case of technology creation, resources devoted
to R&D represent perhaps a better indicator
than the combination of patents and scientific
papers, but data for the majority of developing
countries are either not reliable or not avail-
able. Further, we reported data on three tech-
nological infrastructures such as Internet,
telephony and electricity, but we did not pro-
vide information about the stock of capital
goods such as machinery and equipment. A
careful scrutiny of the data indicates that they
are either not available or not reliable for the
Table 8. Link between ArCo Technology Index and GDP per capita
Constant Regression
t-Statistic R2
Regression of current GDP per capita in PPP $ (99-01) on actual ArCo Technology Index (2000)
All countries 0.83 )5007 40,518 5,162 7.85 0.69
Leaders 0.26 16,764 11,588 3,971 2.92 0.07
0.31 )25,722 87,105 9,261 9.41 0.10
Latecomers 0.29 )2,555 26,117 3,880 6.73 0.08
Regression of the variation of GDP per capita in PPP $ in the last decade (1990–2000) on the variation of ArCo
Technology Index in the same period
All countries 0.28 0.207 0.472 0.325 1.85a0.08
Leaders 0.46 0.207 1.082 0.213 5.08 0.21
0.65 )0.097 3.044 0.297 10.25 0.43
Latecomers 0.63 )0.015 2.098 0.294 7.14 0.39
Sources: CSRS (1996a, 1996b), EPO (2002), ITU (2001), NSF (2000, 2002), UNESCO (2002), USPTO (2002) and
World Bank (2003).
The regression coefficient is not significant at a 5% confidence level.
number of countries we considered: on the one
hand, we hope that electricity consumption can
be a good proxy for capital machinery and
equipment; on the other hand, this allowed us
to keep ArCo entirely independent from any
indicator expressed in monetary value. Finally,
as regards human resources, an ideal indica-
tor would have been the job qualifications,
allowing us to capture learning-by-doing and
learning-by-using in the working process (Ar-
chibugi & Lundvall, 2001). But, again, these
data are available for a much more restricted
number of countries and they are hardly com-
We are aware of the limitations of each of the
indicators employed, but we believe that they
provide a faithful picture of the capabilities of
each country. Overall, the results achieved
confirm expectations. A great deal can be done
in order to improve the quality of the data and
to refine the index. We hope that this attempt
will be a further incentive to promote the pro-
duction of statistics on science and technology,
especially from those institutions, such as
World Bank and others, that pioneered and
generated data in the field. In future research,
we will test the similarities and differences
between the measure here presented and other
comparable technological indicators. The
database will also allow mapping countries
according to their technological characteristics
(besides their aggregate technological level),
and this will hopefully help science and tech-
nology policy analysis for development.
The creation of a database is a preliminary
condition to study the determinants and the
impact of technological change. We know that
technological capabilities are multifarious, and
that aggregate and macroeconomic measures
do not provide a faithful account. But this
database might help test a few hypotheses often
discussed in the literature.
First, it might contribute to the vast literature
on how technological capabilities are associated
to economic growth. A large number of
hypotheses discussed in the literature (see the
review by Fagerberg, 1994) can be tested, and
ours is but a preliminary attempt. It is widely
debated whether the technological capabilities
are a determinant or an effect of economic
growth. As with the chicken and the egg
dilemma, it is difficult to determine with a single
answer. We expect the various sources of tech-
nological capability to have a different impact on
economic growth, and this will also depend on
the income level achieved by each country.
Certainly the same component will have a dif-
ferent impact across countries with such a large
differences in income level.
Second, it might be possible to relate this
indicator to economic aspects such as produc-
tion and employment. Again, there will be no
overlap between the ArCo Technology Index
and measures for these economic activities. The
index could also allow relate international trade
to technological capabilities since no trade
indicators are included. This should be under-
stood in two ways: the first is to explore how
economic and social openness helps the devel-
opment of technological capabilities, the second
is how technological capabilities can be seen
as a determinant of international competitive-
1. In a companion paper (Archibugi & Coco, 2004), we
explore the similarities and differences between ArCo
and these measures. In order to carry out these compar-
isons, we had to restrict the number of countries in the
sample. While the overall ranking of countries is broadly
comparable, a few significant differences emerge. This is
associated to both the statistical method and indicators
used and to the slightly different purposes of the various
2. In principle, this implies that the three categories can
be perfect substitutes: a reduction in the level of
technology creation, for example, independently from
the starting level, can be perfectly compensated by an
equal increase in the level of human skills. The
arithmetic mean does not take into account the dis-
persion of the three subindexes. If we wanted to consider
this aspect, we could use the geometric mean, which
assumes as much higher values as closer the three
subindexes are. Anyway we maintained the aggrega-
tion criteria of arithmetic mean used by other estab-
lished indicators (including the Human Development
Index), even because the geometric one results are too
sensitive to code values, often caused by an incomplete-
ness of data for some indicator and for the poorest
3. See World Bank (2003). Data are reported in greater
detail in the World Bank web site. In this paper, we will
refer to the World Bank Report, although some of the
information used is reported in the web site only.
4. The former USSR is the combination of the former
republics. In 1986–88 we assigned articles to the ex-
Soviet Republics according to their shares of the 1995–
97 period; the same is true for Croatia, Slovenia, and
Macedonia inside the ex-Yugoslavia and for Czech
Republic and Slovakia inside the ex-Czechoslovakia.
German data are combined for all years.
5. The data were obtained by multiplying in each
country the proportion of the population over 14 who
completed the primary, secondary and tertiary education
by the duration of the respective education’s levels. Not
all the countries could be analyzed due to a shortage of
data; we proceeded to estimate the data for Russia, by
using Unesco data and the data made available by
Russian Centre for Science Research and Statistics
(CSRS, 1996a, 1996b). In Russia, three years of primary
school, seven years of secondary school and from 6 to 9
years of higher education are contemplated. We used the
gross enrolment ratio to the secondary level (93%) as a
proxy of the proportion of the population who com-
pleted the primary school, and the enrolment to the
tertiary level (58%) as a proxy of the population who
completed the secondary school; finally we calculated
the average between the proportion of graduated over
the population and the proportion of enrolled at
University in the population (1.2%). With these data
we estimated the mean years of schooling for Russia
according to the following expression:
MS ¼30:93 þ70:58 þ90:012 ¼6:96:
In a similar manner, we estimated the other missing val-
ues, for some African, Asian and ex-USSR countries.
6. The classification of countries according to the
ArCo values is, of course, arbitrary. But since this is the
first presentation of our index, we show the ranking
produced by this measure. In future research, we plan to
take into account aggregations according to other
criteria (regions, high, medium and low income, high-
medium- and low-human development, etc.). We also
plan to relate the technological position of countries, as
measured by ArCo, with other measures of technological
activity (Archibugi & Coco, 2004) as well as with other
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... The technology-related capability of a country is assessed according to the implementation of new technology, technology infrastructure, and human development (Archibugi and Coco, 2004). Strategic planning, Research and Development (R&D), human resources, and technology infrastructure influence the technological capabilities of firms in developing countries (Madanmohan, Kumar, and Kumar, 2004). ...
... Three key components of technological capability have been identified; these are the creation of new technologies, technology infrastructure, and human skill development (Archibugi and Coco, 2004). These components are vital for the development of technological capabilities According to (Madanmohan, Kumar, and Kumar, 2004), the transfer of international technologies is considered the preferred method for acquiring technological capability. ...
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Indonesia is the world’s largest producer of Crude Palm Oil (CPO). Accordingly, it is vital that Indonesian CPO industries diversify and add value to downstream products. Technological capability is a key component for agricultural-based industries to retain their global competitiveness and holds immense importance in facilitating product-based diversification. In terms of technological capabilities that are specific to agricultural-based industries, unique indicators and parameters have been identified and include research and development, human resources, strategic planning, technology infrastructure, and manufacturing. Having knowledge of organizational technological capabilities can contribute to expanding the range of available downstream Crude Palm Oil (CPO) products. However, the income generated from CPO in Indonesia is currently lower compared to that achieved by its competitors. The current study was conducted on 11 CPO companies in Sumatera Utara Province, Indonesia. The purpose of the study is to develop an assessment tool. The tool is proposed to measure the capability of technology in crude palm oil- based industries. The device developed is also to obtain empirical data in order to determine the capability of technology in CPO companies. The descriptive analysis approach is applied in this study.
... The technology achievement of nations was measured by using data from 72 countries to calculate the TAI [5]. As in [6], Archibugi and Coco improved the TAI for a large number of countries, as well as advocated new indicators. TAI also be calculated with the data of 2012 for 34 Muslim countries [2]. ...
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The Technical Achievement Index (TAI) is one way of assessing the technical achievement of regions such as countries, continents, and historical periods. In our study, the entropy weight method (EWM) was employed to compute the weighting of TAI indicators and to create the provincial TAI from 2010 to 2020 using updated variables and datasets. To the best of our knowledge, this is the first time the provincial TAI has been calculated. The key findings suggested that the dissemination of recent inventions was at the top, while the creation of technology was at the bottom of the four TAI dimensions. The TAI in the Eastern Region of China was rated second, but it has been gradually expanding since 2014. the TAI in the Northeast region of China has declined significantly since 2017 which needs to give more special attention. Provincial TAI rankings included two provinces as leaders, four provinces as prospective leaders, fourteen provinces as active adopters, and eleven provinces as marginalized in China. This study provides evidence for China's TAI's unequal and inadequate development and gives helpful information for policymakers to establish appropriate strategies for improving innovative provinces.
... These are only a few examples of the enormous multidisciplinary academic production on this topic. CIs are particularly useful in facilitating the reading of phenomena and the comparison and evaluation of performance of different statistical units (Archibugi and Coco 2004;Filippetti and Peyrache 2011;Sehnbruch et al. 2020;Masset and García-Hombrados 2021). Consequently, they are a key tool in decision making and policy evaluation. ...
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Over the years, the Human Development Index has become a reference measure of quality of life and well-being. Its growing importance has been accompanied by a lively debate in the literature concerning the pros and cons of this index. Many works have attempted to provide solutions to Human Development Index related problems. In this paper, we will focus on some of these problems, which are typical not only for the measurement of human development, but for the construction of composite indicators. We will try to provide an answer by proposing two new methodological tools, the \(Min-BoD\) interval of synthesis and the mid aggregation point, which present interesting potentialities to be used in empirical analyses and for policy evaluations, not only in the human development measurement. The proposed tool have been applied to the Human Development Index data collected for 189 countries in 2019.
... Technological readiness relates to the belief of consumer concerning the resources and support available for the full execution of the behavior (e.g., availability of required technical infrastructure, facilities, and support to ease and speed up the service use) (Venkatesh et al., 2012), and innovation adoption appears to be influenced by the technological environment (Archibugi & Coco, 2004;Bhatt & Bhatt, 2016;Frimpong et al., 2020). The positive effect of technical conditions on new technology adoption has been reported (Gerlach & Lutz, 2019;Oliveira & Martins, 2010), and so, the readiness of the required infrastructure like the Internet connectivity will stimulate new technology adoption. ...
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The effect of contextual factors namely information quality, service quality, system quality, Technological readiness, trust in applications (app) and COVID-19 health anxiety, on the intention and consequently the actual use of Mobile Payment (MP) app was examined in this study. Trust, as mediator to the relationship between technological readiness and intention to use MB-app was examined also. Data were obtained from 740 Jordanian Mobile Banking (MB) app users through an online survey. The relationship between service quality, system quality, information quality, trust in the app, COVID-19 health anxiety, Technological readiness and the intentions to use MB-app and the actual use of MB-app was empirically examined. The results showed a positive relationship between service quality, system quality, information quality, trust in the app and COVID-19 health anxiety, and the intentions to use MB-app, and in turn he actual use of MB-app, and a positive mediation of trust on the relationship between COVID-19 health anxiety and the intentions to use MB-app.
... It is well-known that there exist significant knowledge gaps between students from developed and developing countries. (See Archibugi & Coco, 2004;Khan et al., 2012;and Lavy, 2015.) Participation in an international competition that includes students from technologically advanced countries can thus be quite daunting for students from developing countries. ...
Conference Paper
This paper presents an account of the launch of the ISLP Poster Competition in Pakistan for the very first time in 2020–2021. The first phase of the competition, the ‘national competition,’ attracted 23 posters from undergraduate students of various higher education institutions within the country, and in the second and final phase, the ‘international competition,’ the national winner from Pakistan was selected to be the international winner as well. Based on this positive experience, it is recommended that developing nations engage with such activities without any reservations because these are likely to promote not only self-confidence in their students enrolled in various disciplines but also the urge for learning the basics of statistics that will promote long-lasting appreciation of the subject.
... Before calculating a set of technological capability indices, we first imputed missing values in the data using a conventional methodology. Then each indicator was rescaled to a value between 0 and 1 over the analysis period using the min-max method (Archibugi and Coco 2004;Desai et al. 2002;Filippetti and Peyrache 2011;Freudenberg 2003;Nasir et al. 2011;UNIDO 2017). After that, the two indices of implementation capability (IC) and design capability (DC) were calculated as an equal-weighted sum of five normalized variables. ...
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This book synthesizes and interprets existing knowledge on technology upgrading failures as well as lessons from successes and failures in order to better understand the challenges of technology upgrading in emerging economies. The objective is to bring together in one volume diverse evidence regarding three major dimensions of technology upgrading: paths of technology upgrading, structural changes in the nature of technology upgrading, and the issues of technology transfer and technology upgrading. The knowledge of these three dimensions is being synthesized at the firm, sector, and macro levels across different countries and world macro-regions. Compared to the old and new challenges and uncertainties facing emerging economies, our understanding of the technology upgrading is sparse, unsystematic, and scattered. While our understanding of these issues from the 1980s and 1990s is relatively more systematized, the changes that took place during the globalization and proliferation of GVCs, the effects of the post-2008 events, and the effects of the current COVID-19 and geopolitical struggles on technology upgrading have not been explored and compared synthetically. Moreover, the recent growth slowdown in many emerging economies, often known as a middle-income trap, has reinforced the importance of understanding the technology upgrading challenges of catching-up economies. We believe that the time is ripe for “taking stock of the area” in order to systematize and evaluate the existing knowledge on processes of technology upgrading of emerging economies at the firm, sector, and international levels and to make further inroads in research on this issue. This volume aims to significantly contribute towards this end.
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This paper explore how export competitiveness in the Malaysian palm oil downstream industry is affected by European Union (EU) environmental regulation. Porter (1990) suggests that environmental policies may foster international competitiveness. To investigate the impact of EU environmental policies on trade competitiveness in the Malaysian palm oil downstream industry, the dynamic generalized method of moments (DGMM) is employed. The final results reveal that EU environmental regulations have a positive impact on palm oil industry competitiveness. This result is consistent with the Porter Hypothesis, which argues that a more stringent environmental regulation can trigger innovation to non-compliance cost. Palm oil downstream innovation is crucial to improve the overall competitiveness of the industry, including the smallholders’ sector. This implies that the Malaysian government may want to introduce certain measures, such as energy taxes to promote the use of renewable energy. This may lead to more sustainable palm oil production which may improve the overall competitiveness of the palm oil downstream industry.
Today’s industrial firms are seeking to adopt strategies in which new technologies play key role in the sustainability of the industry and growth. The record on growth, as the most important measure for the long-run success in economy, requires continued innovation in the wide range of products, services as well as advanced methods on the production process and delivery. Furthermore, the importance of the technologic advancement has been confirmed by many studies, especially by emphasizing intellectual human capital and university science on the commercialization of scientific work and discoveries. The study gives an insight on the R&D investments: types, phases, and outcomes from the investments, as well as analysis on the literature for the R&D aspects, innovation, and technological improvement. Besides, evidence is given from the 30 world countries on R&D investments as share of GDP. Comparison among countries are made, as well as among three types of the group composed of 10 countries: (a) countries with the highest GDP in the world, (b) countries with the highest GDP per capita, and (c) Balkan countries. The estimation is done based on the data for year 2017. Additionally, there are analyzed and elaborated policies for investments in R&D at the European level, and evidence is provided also for the world top R&D investments distribution.
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The perhaps broadest approach to economic performance at the level of a country is the concept of national systems of innovation. Despite the emergence of a compelling literature identifying the persistence of innovative activities and country specific institutional effects, evidence on the nature of national systems of innovation is still missing and a number of crucial questions and answers remain unanswered. To shed light on these issues, as of leading scholars of entrepreneurship and innovation was assembled from around the world for a conference on “National Systems of Entrepreneurship and Innovation” at the ZEW Mannheim in November 2014. This article draws on the NSI framework, sets it in a larger context, examines the logic of the approach and introduces the special issue by summarizing the papers presented at the conference and selected for this special issue.
Advances in information technology have increased the actual and potential uses of patent statistics as a proxy measure of inventive and innovative activities. Analytical contributions have come out of economics, bibliometrics, and descriptive comparisons for policy purposes. They show achievement and promise in describing and explaining1 international patterns of technological activity and their effects on the economic performance2 the volume, sectoral pattern, geographical location and dynamics of technological activity in specific firms, and their effects on competitive performance3 links between science and technology.