UNITED NATIONS CONFERENCE ON TRADE
AND DEVELOPMENT
UN Commission on Science and Technology
for Development
Panel on
INDICATORS OF TECHNOLOGY DEVELOPMENT
Geneva
22-24 May 2002
NOT TO BE CITED
Advanced Unedited Copy
Paper No. II
Indices of Technological Development
Prepared by the UNCTAD Secretariat
UNCTAD/ITE/TEB/MISC.2 (VOL. II)
Indices of Technological Development
PAPER II: INDICES OF TECHNOLOGICAL DEVELOPMENT
PAPER II CONTENTS
Executive Summary
1. Introduction
2. Theoretical Framework for Technology Indicators
3. Indices of Technological Development
4. Discussion of Results
5. Conclusions & Way Forward
6. Appendices:
Appendix 1 – Index of Technological Development;
Appendix 2 – Correlation Tables of component indices;
Appendix 3 – Comparison of R&D Expenditure and High-Tech Exports Indices;
Appendix 4 - Comparison of R&D Expenditure and Human Capital Indices.
7. Bibliography
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LIST OF ABBREVIATIONS
BERD
Business Expenditure on Research and Development
CEE
Central and Eastern Europe
CSTD
Commission on Science and Technology for Development
CU
'Catching Up'
ECI
Economic Creativity Index (World Economic Forum)
FDI
Foreign Direct Investment
GA
'Getting Ahead'
GDP
Gross Domestic Product
GIT
Georgia Institute of Technology
GNI
Gross National Income
HDI
Human Development Index
HTE
High Technology Exports
HTI
High Technology Indicators (GIT)
ICTs
Information and Communication Technologies
ITU
Information and Telecommunications Union
KU
'Keeping Up'
MNC
Multi-National Corporation
OECD
Organisation for Economic Cooperation and Development
PC
Personal computer
R&D
Research and Development
R&L
Royalty and License (fees)
TNC
Trans-National Corporation
UNCSTD
United Nations Commission on Science and Technology for Development
UNCTAD
United Nations Conference on Trade and Development
UNDP
United Nations Development Program
UNESCO
United Nations Educational, Scientific and Cultural Organization
UNIDO
United Nations Industrial Development Organization
UNSD
United Nations Statistical Division
WDI
World Development Indicators (World Bank)
WEF
World Economic Forum
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Indices of Technological Development
EXECUTIVE SUMMARY
The Commission on Science and Technology for Development (CSTD), at its fifth session
held in Geneva from 28 May - 1 June 2001, selected as the substantive theme for its intersessional period 2001-2003 “Technology development and capacity-building for
competitiveness in a digital society”. The work programme during this inter-sessional period
will be carried out through three panels focusing on different aspects of the main substantive
theme. This paper is the second in a set of three papers, which address the key issues of
Panel I. The objectives of Panel I are to: (i) review and select indicators to take stock of
technology development levels across countries, with a specific focus on ICTs as pervasive
technologies of global impact; (ii) classify countries as 'catching up', 'keeping up' and 'getting
ahead'; (iii) provide input for Panel II and Panel III in terms of policy analysis to facilitate
countries' upward movement between stages. To this end, Paper II presents a theoretical
framework based on Paper I and develops an 'Index for Technological Development',
comprising indicators for research and development, human capital and export performance.
It then classifies countries as 'catching up', 'keeping up' and 'getting ahead' on the basis of
index measurements, and reviews results to identify interesting trends.
We present a cross-country analysis of technological development for 84-92 countries in
terms of financial resources for research (R&D expenditure as a proportion of national
income), human capital (tertiary enrolments and number of personnel engaged in R&D), as
well as export performance (high-tech exports as a proportion of total merchandise exports).
These different aspects are related, with high correlations observed between R&D, human
capital and export performance for 1995-1999. Classification of countries as 'catching up',
'keeping up' and 'getting ahead' on the basis of rankings in these indicators shows that
rankings are stable over time, with some regional influences apparent. As a broad
generalisation, Latin American and transition economies are classified as 'keeping up' and
OECD countries and some South-East Asian Tigers as 'getting ahead'. However, this
generalisation masks considerable diversity in countries' experiences, with transition
economies in particular displaying notable variation in all indicators except education and
human capital, where they are consistently strong. Data limitations meant that African and
South Asian countries are largely omitted from this analysis. Cross-country analyses
generally lack the depth of insight required for policy purposes. We have omitted measures
of policy and national strategy, since the variety of technology policy options open to
governments is difficult to capture in cross-country indices. Consideration of policy will be
undertaken in Panels II and III through case study analysis to give added depth of insight.
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1. INTRODUCTION
'Technological development' was defined at CSTD Preparatory Panel Meeting as "the result
of knowledge produced by scientific and applied research that culminated in new processes,
designs, products and consumer goods". However, different definitions of technology have
been proposed. Schumpeter (1939)'s theory of "creative destruction", for instance,
emphasised views of technology as an ongoing process embedded in a socio-economic
context, accompanied by and inspiring socio-economic change. UNCTAD (1991), on the
other hand, defines technology as "the application of knowledge to produce goods and
services… [It] involves the dynamic interaction of human skills, physical assets, and
organisational and institutional settings". UNCTAD further notes that "the process of
technological change includes complex interdependencies among the various stages of
innovative activity, where the supply of technology and demand for technology are
interrelated… These features of technology and technological change have important
implications for the measurement and interpretation of technology indicators".
Measurement of technology, both as a complex process and as clusters of associated subtechnologies, is thus problematic. UNIDO (forthcoming), for instance, reviews trends in and
drivers of export performance as measures of industrial performance, whereas GIT (1987
onwards) studies seven aspects to national technology-based competitiveness, namely four
input indicators of national orientation, socio-economic infrastructure, technological
infrastructure and productive capacity; and three output indicators relating to technological
exports, and trends within these.
Technological development can thus be defined and measured in terms of:
• several aspects (innovation, human capital, export performance, infrastructure);
• different levels (sudden creative innovations, ongoing incremental change, productive
activities, goods and services or wider socio-economic processes);
• various methods (index rankings, regressions, case studies);
• different purposes (economic, commercial and policy purposes).
This broad conceptualisation of technological development is central and pervasive to Panel
I's benchmarking nations' technological development to classify countries for policy analysis.
Despite the different definitions and measurements possible, there are several key features to
the global diffusion of technology. Firstly, advanced technology and complex industrial
structures are important for growth, development and competitiveness (UNIDO,
forthcoming). Secondly, the rate of technological change is accelerating. UNDP (2001)
cites Moore's Law, which states that (microprocessor) technology is accelerating. However,
technological diffusion is far from even. UNIDO (forthcoming) finds evidence of uneven
diffusion in technological development, with divergence and marginalisation strong features
of the industrial scene. UNDP (2001) notes that uneven diffusion of technology is not new –
'older' innovations such as telephony/electricity are still far from evenly diffused.
Developing countries risk being left behind in terms of income, rising inequality,
development, voice and presence in an increasingly technological world.
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Indices of Technological Development
2. THEORETICAL FRAMEWORK FOR TECHNOLOGY INDICATORS
Paper I's review of existing work carried out to date to evaluate countries' technological
development reveals consistent aspects of research and innovation, human capital,
connectivity, economic/sectoral composition and export structure across most studies,
irrespective of the viewpoint from which they are written, as illustrated in the table below.
We adopted this framework in the formulation of our Indices of Technological Development.
Technology indicators and the components selected for our Indices are discussed below.
Index of Technological Development
UNCTAD (1991) defines technology as "the application of knowledge to produce goods and
services… [It] involves the dynamic interaction of human skills, physical assets, and
organisational and institutional settings". We thus formulated the Index of Technological
Development to include measures of human skills, financial resources and economic
structure (both an outcome and a setting). The Index of Technological Development is a
simple additive average of scores on quantitative variables (R&D, human capital and hightech exports).
Index/Dimension
Indicators
Sources
1. Innovation and R&D
R&D expenditure (as a percentage of GNI).
World Bank, UNIDO.
2. Human capital
Number of scientists, engineers and technicians in
R&D (per million people).
World Bank.
Tertiary enrolment, as a percentage of population.
3. Export structure
High-tech exports, as a proportion of total
merchandise exports.
World Bank.
We have included two input indicators, measuring the resources devoted to the generation of
scientific and technological knowledge, and one indicator of technology-related economic
performance. These indicators represent only a selective subset of the comprehensive set of
indicators possible. Lack of data availability meant that we could not include an output
indicator for an adequate number of countries (e.g. patents and bibliometrics, measuring
outcomes of scientific and research activities). Patent data have only limited availability for
developing countries (there are often difficulties of domestic registration, as opposed to
registration overseas e.g. with the U.S. Patent Office). Other indicators were omitted owing
to difficulties in their measurement (e.g. difficulties in quantification of linkages between
firms and industry, and of strength of institutions and governance).
Appendix 1 presents the Index of Technological Development and country rankings for 1999
for 89 countries with data available. 1995, 1996, 1997 and 1998 have also been calculated
and are analysed in Appendices 3 and 4.
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Indices of Technological Development
Study/
Attribute
UNDP
UNIDO
(2001)
(forthcoming)
WEF
(2000)
GIT
UNCSTD
(1987 onwards)
(forthcoming)
Perspective
Developmental
Developmental
Commercial
Academic
Developmental
Item measured
Technological achievement
Industrial performance and its
determinants
National competitiveness
Technology-based national
competitiveness
Technological development
1. Research,
Innovation
and R&D
Patents
R&L fees
Local technological effort
(R&D as a percentage of
GNP); FDI – internal;
R&L – external tech. effort.
ECI, Innovation Index
Tech. Transfer Index,
Start-up Index, Technol.
Category of indicators
R&D included in
technological infrastructure
R&D (as a percentage of GNI)
Tertiary enrolment;
Mean years schooling;
R&D staff.
Skills
(Harbison-Myers Index)
Separate category of
indicators for human capital
(education/health)
Socio-economic infrastructure
Productive capacity
Personnel engaged in R&D.
Tertiary enrolment, as a
percentage of population.
Fertiliser consumption;
Tractors in use;
Low-tech exports;
Medium-tech exports;
High-tech exports (HTE).
Manufacture value-added;
Technological structure of
MVA;
Manufactured exports & their
technological structure.
Technological standing
Technological emphasis
(ratio of HTE);
Rate of technological change
(rate of change HTE).
High-tech exports (HTE).
2. Human
Capital
3.Industrial
and export
performance
4.Connectivity Telephone mainlines;
Infrastructure
and ICTs
Cellular subscribers;
Internet hosts;
Cost of a local call;
Waiting lists for mainlines.
Infrastructure;
traditional energy use;
modern ICTs.
Separate category of
indicators for infrastructure.
Venture capital, start-ups
Additional
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Connectivity Index,
component of Index of ICT
diffusion:
Panel I, Paper III.
National orientation
Indices of Technological Development
1. Innovation and R&D
Innovation has been defined as the process that imitates, produces, imports, adapts or
diffuses new technology to sustain economic development. R&D has been defined as "any
systematic and creative work undertaken to increase the stock of knowledge, including
knowledge of man, culture and society, and the use of this knowledge to devise new
application" according to UNESCO.
A country's "innovation and learning system consists of a complex mix of skills, knowledge,
routines, interactions and business practices" (UNIDO, forthcoming). Innovation is a
complex process requiring a multitude of other factors to be in place, such as policy
incentives, trade openness, investment level and maturity of the financial system. It is
necessary, but not sufficient, to have the innovation; that innovation must also be brought to
market – hence WEF (2000)'s emphasis on venture capital with their Start-up Index.
The "complex interdependencies" noted by UNCTAD (1991) mean that a multitude of
factors must be in place for successful technological development to realise potential
synergies. A weakness in any of these factors can prevent realisation of the full benefits and
productive outcomes of R&D and the economic potential of technology.
We sought to formulate an index based on a focused set of indicators as a narrow selection of
important factors. Given such a multitude of supporting factors, it may be difficult to define
and justify narrow measures of innovation. However, we selected as the most representative
measures input indicators of expenditure (capturing financial resources) and human resources
engaged in R&D (see Section 2: Human Capital). This omits consideration of research
outputs (e.g. patents, bibliometrics) and the process of bringing outputs to market (e.g.
venture capital, start-ups).
R&D Expenditure (RDE, as % GNI)
Expenditures for R&D are defined as current and capital expenditures on creative, systematic
activity intended to increase the stock of knowledge, including fundamental and applied
R&D leading to new devices, products, or processes [World Bank WDI, 2001].
UNIDO (forthcoming) note that R&D focuses only on the "tip of the technological activity
iceberg", but considers that "the intensity of R&D may provide an indication of abilities to
master and use new technologies". UNIDO (forthcoming) use R&D expenditure financed by
productive enterprises as a measure of "local technological effort" and as a determinant of
industrial performance. The U.S. Council for Competitiveness used R&D expenditure as an
explanatory variable in their Innovation Index [Porter (1999)]; WEF (2000) use qualitative
variables for BERD as measures of technological development.
Increasing R&D expenditure implies increased capacity to conduct R&D, and may imply
increased R&D output. It is noticeable that some highly successful economies (e.g.
Singapore, Malaysia) that have significantly improved their export performance have done so
with increased prioritisation of R&D and increased R&D expenditures.
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However, difficulties include:
•
R&D output has only a tentative relationship with R&D inputs, as creativity is
notoriously independent of the resources allocated to it.
•
The complex nature of R&D: R&D needs to be embedded within the wider context of
an economy and sustained by supporting infrastructure and technical expertise. R&D
is not an independent, stand-alone activity. It requires linkages and productive
partnerships between universities and firms. However, these are very difficult to
measure.
•
Problems in definition and measurement: aggregate indicators of R&D expenditure
do not describe the type of research, the sector in which the activities occur
(productive sector, higher education, general services) or branch in which economic
activities occur.
•
Data availability problems meant that we used the most recently available data as
substitute data for 1998 and 1999. For most countries, the most recent data available
derive from 1995.
2. Human Capital (H.C.)
Human capital is evident at several levels. Basic educational skills of literacy and numeracy
are relevant to everyday abilities, quality of life (literacy and schooling are included in HDI)
and also ICT access (literacy is included in our Index of Access in Paper III). Evidence
suggests that even small amounts of education may be beneficial to productivity (e.g.
Kingdon, 1997).
In this paper, we are primarily concerned with advanced human capital skills in technical
areas, which may be of additional productive importance to an economy (although basic
education is considered implicitly, as an essential prerequisite to these). These include:
•
Number of R&D personnel engaged in research;
•
Tertiary enrolments (as a percentage of the population).
These represent a narrow subset of advanced human capital, and do not include broader
measures considered by UNDP (2001) or the Mosaic Group (for ICTs). Please see Paper I.
Domestic human resource development and indigenous technological capabilities are
essential for indigenous R&D, as well as the effective utilisation of imported technology.
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1. Number of R&D personnel
Number of R&D personnel is defined as the total number of scientists, engineers and
technicians engaged in R&D (as the full time equivalent), which may be expressed as a share
of:
•
•
total scientists and engineers (measures the proportionate importance of scientific and
research activities relative to the skilled human capital base);
per total employees in the corresponding sector (measures intra-industry research,
enabling inter-industry comparisons);
•
per million capita (measures research activities relative to the population).
Increasing R&D personnel implies increased capacity to conduct R&D and may imply
increased R&D output. As noted previously, some economies that have significantly
improved their export performance have done so through increased R&D, with increased
R&D expenditure and human resources devoted to this area.
However, difficulties include:
•
As noted previously, R&D output has only a tentative relationship with R&D inputs,
as creativity is notoriously independent of the resources allocated to it.
•
Despite UNESCO guidelines, non-uniform definitions of scientists and engineers
across countries result in inaccurate personnel counts and non-comparability in
international comparisons. Scientists/engineers are defined as those trained to work
in science engaged in professional R&D activity (requiring completion of tertiary
education). Technicians have been defined as those who have received vocational or
technical training in any branch of knowledge or technology (most of these jobs
require three years beyond the first stage of secondary education). However, in
practice these are used as guidelines rather than definitions and may be open to
different interpretations across countries.
•
Research personnel estimates include only those engaged in formal R&D and omit
informal R&D (e.g. production engineering departments in large firms) and part-time
innovative activities in small firms. Both of these activities contribute towards the
incremental technical change that may account for a considerable proportion of
technological innovation. R&D indicators are thus likely to underestimate research
inputs.
•
There were also some problems in data coverage with R&D personnel, with data
available for between 19 (1998) and 50 (1995) countries. Due to substantial
fluctuations in R&D personnel year on year, it was not felt appropriate to use latest
available data for this.
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2. Tertiary Enrolments
This includes tertiary level students in higher education, as a percentage of total population.
UNCTAD (1991) considers that "proportional shares of secondary and tertiary educational
levels give a valuable indication of existing levels of higher skills and pools of more
specialised skills".
Studies have primarily used this variable incorporated into the Harbison-Myers Index, which
measures the percentage of population attaining secondary and higher educations e.g.
UNIDO (forthcoming).
Increasing tertiary enrolments implies increased quantity and scientific quality of human
capital and thus an increased capacity to conduct R&D.
However, the coverage and quality of the data are not fully comparable across countries.
Definitions, degrees and degree content are non-uniform across countries. Cross-country
data may be particularly limited for statistics on higher education, compared to statistics on
primary and secondary education. This data was still more widely available than R&D
personnel however, with between 88 (1996) and 117 (1998) countries with data available.
Important forms of training, such as on-the-job training and the relevance of this training to
industrial needs, are omitted. Despite the omission of other training, tertiary education may
still represent a useful yardstick and proxy measure of technical expertise, so we included it
in the Index of Human Capital.
3. Export Structure
1. High-tech exports (HTE)
HTE are defined as products with high R&D intensity. They include high-tech products such
as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery
(World Bank, WDI, 2001), although other definitions are possible (e.g. OECD) and
definitions may change from year to year. In general, however, they comprise advanced and
fast-changing technologies, complex skill requirements and large R&D investments.
UNIDO (forthcoming) considers that "initial export structure captures the 'positioning' of
countries with dynamic segments of trade, while changes in export structure capture the
effects of technological upgrading". UNIDO concludes "the technological structure of
industry matters…complex [industrial] structures offer greater learning potential and
spillover benefits…[and] are likely to…be more able to handle new technologies".
Work has primarily used these variables as highly representative variables of technological
development in the wider context of competitiveness in global trade e.g. UNIDO
(forthcoming), GIT (1987 onwards). By comparing countries' performance across a
technology-intensive scale of trade, conclusions may be drawn as to their relative ability to
upgrade their comparative advantage towards more knowledge- and skill-intensive
industries. A country's ability to excel in high-technology or R&D-intensive industries can
be viewed as an indication of its competitiveness in industries with high growth potential.
Increasing HTE as a proportion of total exports implies improved performance and a change
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in the structure of an economy. This introduces wider considerations of competitiveness, in
relation to the global economy. UNCTAD (1991) notes that technology-intensive exports are
only one class of indicators of trade performance (e.g. other classes include trade balance and
trade orientation). Consideration of export performance introduces other, non-technological
factors such as trade policy, domestic economic policies, balance-of-payments, demand
variations and imports, which may influence HTE and make them less representative.
UNCTAD (1991) distinguishes between domestic and export performance indicators. It may
be argued that domestic performance and success in the home market influences
technological competitiveness and export performance, implying a direction to and degree of
causality. However, UNIDO (forthcoming) considers that the "ability of countries to link
effectively to foreign resources and markets affects how they leverage domestic resources to
build and upgrade industrial competitiveness". The direction of causation between domestic
and export-related performance is therefore unclear and may be circular and reinforcing.
UNIDO (forthcoming) argues that "data on capabilities [i.e. determinants of R&D
expenditure, human capital, infrastructure] can be used to relate the complexity of export
structures to the intensity of domestic technological activity".
It is argued that in today's increasingly integrated international trading system, it is primarily
export performance and access to larger, international markets that are increasingly
important. Benchmarking to global standards is necessary to evaluate the ability of national
industries to keep up with technical changes in exported products.
Furthermore, UNCTAD (1991) notes that it may be difficult to define the technological
intensity of products, as technological needs, industrial profiles and specialisation differ
across countries. Such definitions depend upon the sample of countries covered. They are
also defined at the industry level and do not therefore recognise intra-industry diffusion of
innovations. This may be particularly important omission for developing countries, which
may often be technological followers and adaptors capable of modifying and adapting
technologies, rather than innovators. Such classifications of technological intensity
recognise structural changes within an economy, rather than change or technological
upgrading within industries.
Further difficulties arise in respect of the reassembly and local production of intermediate
parts by TNCs in countries with low-wage advantages. UNIDO (forthcoming) note that
"most of the highly export-orientated countries have strong TNC presence". Export
performance implicitly reflects underlying variables of FDI and TNC presence.
Consideration of export figures in isolation fails to reflect local assembly of imported
manufactured parts, which is thought to give rise to only shallow technological capabilities.
Since the production of intermediate parts usually falls into the same category as the final
good, low-wage countries may appear to have relatively large shares of HTE in their total
exports. This does not reflect these countries' capacities to produce high-tech products, but
rather their patterns of comparative advantage and implicitly, global production patterns. It
is also difficult to be certain whether consistent definitions are applied across data categories.
UNIDO (forthcoming) note that "it is not possible to distinguish between technological
structures based on genuine technological capabilities, from those reflecting assembly
activities in high-tech industries… Countries with a high proportion of exports from hightech assembly (e.g. the Philippines) can appear as advanced industrial performers".
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3. INDICES OF TECHNOLOGICAL DEVELOPMENT
Index2 Methodology
Press (1999) observes that "in tracking diffusion of [technology], one must choose a balance
between breadth and depth". He concludes that "an index may be more robust than a [single]
indicator in measuring a qualitative concept" (Press, 1999, p.5).
Mosaic Group (1996, 1998) suggests that individual technologies need to be evaluated, since
countries seldom exhibit uniform capabilities across the broad spectrum of technological
capabilities. Measures of breadth and depth are needed; a dilemma that Mosaic resolves by
the use of Kiriat diagrams [Kiriat, 1973] to reflect technology as a 'multi-faceted concept'.
UNCTAD (1991) also notes that, with regard to broad concepts of technology, "indicators
measure only aspects of technology, rather than bodies of knowledge. Thus, they may reflect
elements of science and technology, such as human resources, physical assets, organisational
settings or the economic and legal environment, but they do not cover the entire constellation
of elements that constitute technology. Accordingly, there is no unique or superior
indicator". Indicators measure only aspects of the multi-dimensional concept of technology,
with different aspects appropriate to different needs. Indeed, the relative importance of
different aspects and their interpretation varies according to the level of development and
different economic policies between countries. UNCTAD (1991) notes that "technical
assistance is a major source of technology for LDCs, while for higher income developing
countries, it is of little relevance. Growing inflows of capital goods or FDI may be
interpreted as increased availability of technology or growing dependency on foreign
technology".
UNDP (2001) and this study seek to reflect this balance between breadth and depth through
use of an aggregate index with component sub-indices. However, there are dangers in using
a disaggregated index. Mosaic Group (1997) observes "while it is tempting to derive a single
index to reflect a country's IT capability, such an approach is unlikely to provide the depth of
understanding needed for policy decision-making". Press (1997) explicitly warns about the
dangers of averaging, or "reducing a [multi-faceted] capability diagram down to a single
number" (area), since capability diagrams with the same total area may have different shapes
i.e. countries exhibit different profiles across the spectrum of technological capabilities.
Press (1999) notes further challenges for sets of indices: [they] "should be orthogonal, each
measuring an independent aspect of the state of the Internet [technology] in a nation, but it is
difficult to define indices that are both comprehensive and uncorrelated".
Simple averaging of indicators in an index implicitly assumes equal weighting of indicators
and the possibility of offset of one indicator by another (i.e. R&D expenditure is assumed
equivalent to and given equal weighting to human capital and export performance). GIT
(2000) note that an "additive model implies that strength on any one of these dimensions
could compensate for weakness on another".
2
Edgeworth (1925) defines an index as "an index number [that] shows by its variations the changes in a
magnitude, which is not susceptible either [to] accurate measurement itself or [to] direct valuation in practice".
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In fact, determinants do not all have the same or equivalent influence over technological
capabilities. While there may be no well-recognised limiting factors for technology (in
contrast to the 'limiting factor' of ICT infrastructure for ICT capabilities), a multitude of
factors such as education and human capital, economic structure, physical infrastructure,
good institutions and government policy are recognised as important. We use the aggregated
index approach with component indices (similar to UNDP's HDI) to allow countries' overall
scores to be disaggregated into component indices, permitting finer discernment between
nations with different profiles across the spectrum of technological capabilities. Attention
should not focus on final index scores, but across country profiles.
Indicator Scores Methodology
Index scores derive from the maximum and minimum achieved by a country in an indicator:
Index score = (Value – Minimum)/(Maximum – Minimum)
Since minimum values are close to zero3, scores amount to a percentage of maximum values:
Index score: = (Value – 0)/(Maximum – 0) = Value / Maximum
Appendix 1 presents Indices of Technological Development for 1999 for 89 countries for
which data are available. On the basis of rankings from these indices, countries are classified
into 'catching up' (CU), 'keeping up' (KU) and 'getting ahead' (GA) corresponding to the first,
second and last thirds in rankings. Segmental analysis is carried out in Appendices 3 and 4,
dividing countries into these categories on the basis of rankings, rather than scores.
Countries are categorised on the basis of rankings, since countries may group or 'bunch'
together, so scoring intervals have different significance across the range of countries' scores.
This permits categorical analysis of results, by income level, region or culture and over time
for 1995-1999. It also allows analysis of the scatter of observations (frequency in brackets).
Time Period
We calculated indicators for 1995-19994. Limited data availability for 1998 and 1999 meant
that these years are only partly comparable e.g. R&D expenditure and R&D personnel data
are limited. Most recent data available were used for 1998 and 1999. UNIDO (forthcoming)
calculates indices of industrial performance for 1985-1998, which enables them to compare
the evolution of performance over this thirteen-year period defined by its endpoints. We
adopt a different approach in studying the evolution of these indices over a shorter, more
recent period of time. The long-term evolution of technological development is interesting;
however, it may be argued that short-term trends are increasingly important in view of new
fast-changing technologies and accelerating technological change.
3
Irving Fisher (1922)'s statistically desirable property of 'reversibility' (i.e. that the index calculated forwards and the index calculated
backwards should be reciprocals of each other) is not fulfilled due to use of arithmetic averages in the indices. Use of 'zero' minimum values
means that this 'reversible property' yields mathematically undefined answers (reciprocals of zero). However, this does not have any
significant consequences for this index.
4
this period includes the S.E.Asian crisis and ongoing reform for FUSSR and transition economies.
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Unit of analysis
Our units of analysis are nation states, countries or territories defined by national boundaries.
Technological hubs, or "centres of excellence" with extensive hinterlands [Telegeography
survey (2001), quoted in UNDP's HDR (2001)] are aggregated into national level statistics
and it is important to be aware of the significant implicit averaging effect this has on our
results. Adoption of nations and territories as our unit of analysis gives added pre-eminence
to Singapore, as both a nation state and a "large city" (ITU, 2001), compared to e.g. a lower
ranking for India, comprising Bangalore as a technological hub. Were New York or
Bangalore to be separated out from their hinterlands, very different results would emerge.
Telegeography's survey (2001) gives some indication of what a ranking by cities looks like.
There remains a role for national policy in promoting technology on a national basis.
Important policies include policies on imports, FDI, MNCs and technology transfer,
industrial policy, education and research, as well as stable macro-fundamentals and legal and
regulatory environment. The role of government policy in the success of Asian Tigers has
received much attention e.g. for Singapore, Malaysia, Korea (UNCTAD, forthcoming).
Analysis of technology along national lines is necessary to measure "national differences"
(including government policy) in the adoption and absorption of technology. However,
whether such differences are national or cultural may be indeterminate (boundaries of nation
states and culture may coincide, but this is not always the case). Expatriate communities are
often important in promoting technological adoption in their homelands (e.g. investment
from overseas communities; human capital of Indian software specialists in US).
National Size Effects
GIT (2000) note that Porter et al (1999)'s Innovation Index "is normalised (per capita
measures), whereas [GIT's] is not (most of the statistical components reflect national totals).
HTI address national technological competitiveness without particular concern for an
economy's size". However, they do not explore the consequences of this for their results.
In fact, this may introduce a degree of bias into results. UNIDO (forthcoming) notes that
"the use of a population deflator works against large countries, but remains a good way to
adjust for country size". It is unlikely that we will be able to correct for these effects;
however, it is important to remain aware of their existence and the fact that averaging
measures across per capita population may implicitly work against larger countries, lowering
their relative rankings.
Averaging
Use of averages across component index scores results in averaging effects. GIT (2000)
recognises "in that a given indicator combines several scores, typically no country will score
100 on the resulting indicators". It is unlikely that countries will score the maximum and
minimum possible; in general, distributions are averaged or 'regress' towards the centre or
mean of the scoring range. Averaging effects are also noted by UNIDO (forthcoming), who
recognises the possibility of "offset… at least for some countries [where] use of two
benchmarks together biases the results in that their average capabilities appear lower"
(reinforcement is also possible).
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Data Omission Effects
Data omissions pose a particular problem for this study, the extent of which is only partially
apparent. Our sample consists of 78-94 countries in any year, as those countries for which at
least two data observations were available. In calculating the Index of Technological
Development presented in Appendix 1, only those countries with at least two observations of
the three component indices were included. Inclusion of countries with only one observation
in any of the three indices would have greatly improved our sample size, but led to a curious
phenomenon of data availability effects combined with averaging, as discussed above.
Where there is only one observation, countries tend to 'polarise' towards the extremes of the
distribution (e.g. FUSSR countries tend to score highly on tertiary education and consistently
rank in the top quartile for this variable). However, where there is an additional data
observation for R&D personnel, the averaging effect of the two variables may result in
'regression towards the mean' and the possibility of 'offset' noted by UNIDO (forthcoming).
This data availability effects combined with averaging tends to move countries for which
multiple observations are available towards the centre of the aggregate index distribution.
The relative rankings partially reflect underlying patterns of data availability and averaging.
We therefore calculated the overall Index of Technological Development for countries where
at least two data observations are available in at least two or three of the component indices
to eliminate more extreme 'polarised' values for countries with only one data observation
available.
The omission of mostly poorer countries with low data availability from consideration in our
study means that absent/negligible observations are excluded. Appendices 3 and 4 largely
omit Africa and S.Asia. Our sample comprises those countries with a degree of
technological capability in the first instance. This introduces bias from sample truncation
into our results.
Relative movements
UNCTAD (1991) note that "due to the complex interactions between the different elements
of technology, it is difficult to trace causalities among technology indicators… technological
change is not synonymous with capital accumulation, or scientific advances, even if it
overlaps with them". Indices reflect the outcomes of, but do not address, causation.
Movements in index rankings are in fact relative, rather than absolute. GIT (2000) note that
indices are "a relative scaling, so an apparent 'decline' over time or low score is only relative
to other countries". GIT's HTI "are relative indicators. A 'decline' in an indicator does not
imply an actual drop, just that competing countries have advanced faster". Thus, "Germany is
considerably closer to other leading nations than to the U.S. and Japan… this distancing is
not due to any decline in Germany, but rather to the remarkable gains by the U.S." (GIT
2000). UNIDO (forthcoming) observes: "Movements in rankings are relative, not absolute.
Many [countries] like Kenya are not particularly technology-intensive exporters – they move
up the scale because their exports are more complex than their other measures relative to
other countries in their vicinity".
In general, it is more meaningful to talk in terms of country rankings, rather than a country's
index score. Countries tend to group or 'bunch' together (particularly around the centre of the
index distribution), where a score interval of 0.1 may be equivalent to several places in the
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rankings. Conversely, countries that stand out or fall behind may have a relatively large lead,
such that a significant improvement in index score is necessary to catch up leaders, or for
those behind to catch other countries up.
This observation describes the problem of an outlying 'star performer', which is also
illustrated in GIT's work i.e. where the reference country with the maximum value in the lead
forges ahead. "The U.S. increased [its electronics production] by $71B from 1996 to 1999.
The U.S. position is so strong that even China's remarkable doubling of electronics
production from $33B to $65B increases its score only from 12 to 19" (out of 100). Fixed
reference values, as with UNDP's HDI could resolve this. However, with fast-changing
technological indicators, it is not evident what these fixed reference values should be
(compared to life expectancy/literacy, where relatively well-established upper ceiling values
exist).
4. DISCUSSION OF RESULTS
Results in this section are discussed by:
1. Income (UNDP codes of high-, middle- and low-income, others); and
2. Regional/cultural groupings (UNDP codes of E. Europe and CIS, OECD, Arab states,
E.Asia, S.Asia, Latin America & Caribbean, Sub-Saharan Africa, others).
Comparisons are also made between:
3. R&D Expenditure and High-Tech Export Indices and their relation in Appendix 3;
4. R&D Expenditure and Human Capital Indices and their relation in Appendix 4;
5. Evolution of indices over time.
Comparison of Human Capital and High-Tech Export Indices appears less meaningful, given
the lower correlations observed between these two component indices in Appendix 2 of
approximately 0.3. Positive correlations between sub-indices in Appendix 2 are illustrated
by 'over-weight' positive diagonals in Appendices 3 and 4. Random scatters of observations
would yield equal weightings across boxes. However, the frequency of observations along
the positive diagonal (given by the figures in brackets) illustrates a positive correlation in
weightings. This pattern is partly attributable to data omissions, which pose a particular
problem for this index. Data omissions principally affect the LDCs, with African and Asian
subcontinent countries mostly absent (it is presumed that these countries would mostly
populate the lower row of 'CU RDE', which explains the lower incidence of observations in
these segments).
The high correlations in Appendix 2 suggest that we are measuring consistent indicators of
central 'technological development'. However, these indicators may also represent proxy
variables for key drivers underlying technological development (e.g. average income and/or
level of development, investment and education). Causation cannot be determined by
correlations and it is proposed to investigate causation by regression work at a later date.
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1. Income
UNDP codes were used to classify between 84-92 countries in any year into four categories
of high-/middle-/low-income and 'others'. Analysis of rankings reveals that:
Index of Tech.
1999
1998
1997
1996
1995
Development
(88 countries)
(85 countries)
(92 countries)
(84 countries)
(86 countries)
High: Best
Worst
Average
U.S. 1
Kuwait 71
18
U.S. 1
Kuwait 70
19
U.S. 1
Kuwait 76
19.6
Japan 1
Kuwait 68
18.9
Japan 1
Kuwait 68
18.5
Middle: Best
Worst
Average
Korea, Rep 2
Ecuador 86
50
Philippines 2
Ecuador 80
48
Philippines 2
Ecuador 86
50.7
Malta 2
Ecuador 82
47.5
Malta 3
Ecuador 79
47.7
Low: Best
Worst
Average
Moldova 42
Pakistan 88
72
Moldova 35
Bangladesh 85
71
Moldova 33
Tanzania 92
74.5
Moldova 28
Bangladesh 84
67
Moldova 35
Senegal 86
69.9
Others: Best
Worst
Average
Macao 52
Macao 48
Macao 46
Macao 40
---
Rankings are fairly stable between 1995-1999, with consistent 'best-performers' (e.g.
Moldova, U.S.) and 'worst-performers' (e.g. Kuwait, Ecuador). Average rankings are also
consistent, although variations in sample size influence rankings (e.g. an increased sample
size of 92 in 1997 lowers 1997 worst and average rankings relative to 1996 and should not
necessarily be interpreted as a decline in performance over this period. See 'Relative
Movements', Section 4).
Average rankings conform to expectations. 'High' income countries led by U.S. and Japan
capture the top rankings, with a range of 1-76 and average ranking of 18-21. This average
ranking is approximately 30 places ahead of 'middle' income countries, which range between
2-86 with a roughly constant average ranking of 50.7-47.5 from 1995-1999. 'Low' income
countries range between 28-last place, and show an average ranking over this period
fluctuating between 67 and 74.5. 'Others' consists solely of Macao and is too limited a
category to yield meaningful conclusions.
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2. Cultural Groupings
UNDP codes were used to classify between 84-92 countries into eight categories of E.
Europe and CIS, OECD, Arab states, E.Asia, S.Asia, Latin America & Caribbean, SubSaharan Africa and 'others'. Analysis of these categories reveals that:
Index of Tech.
1999
1998
1997
1996
1995
Development
(88 countries)
(85 countries)
(92 countries)
(84 countries)
(86 countries)
1. OECD: Best
Worst
Average
U.S. 1
Turkey 55
21
U.S. 1
Turkey 55
21
U.S. 1
Turkey 59
21.8
Japan 1
Turkey 60
22.6
Japan 1
Turkey 54
21.7
2. E. Asia: Best
Worst
Average
Singapore 5
Indonesia 64
34.5
Philippines 2
Indonesia 61
32
Philippines 2
Indonesia 62
33.5
Singapore 7
Mongolia 56
38.8
Singapore 7
Indonesia 59
34.5
3. EE & CIS: Best
Worst
Average
Russia 19
Kyrgyz Rep. 67
41
Russia 17
Albania 65
42
Russia 18
Albania 70
42
Russia 18
Kazakhstan 70
39
Russia 8
Romania 49
35.3
4. LAC: Best
Worst
Average
Costa Rica 13
Ecuador 86
58
Barbados 34
Ecuador 80
59
Barbados 37
Ecuador 86
62
Panama 38
Ecuador 82
56.3
Barbados 32
Ecuador 79
58.7
5. Arab: Best
Worst
Average
Jordan 56
Syria 82
68
Jordan 58
Kuwait 70
65
Jordan 60
Syria 82
71.7
Saudi Arabia 57
Egypt 75
66.2
Jordan 56
Kuwait 68
62.9
6. S. Asia: Best
Worst
Average
India 58
Pakistan 88
77.5
Iran 60
Bangladesh 85
73
Iran 64
Bangladesh 90
77
India 62
Bangladesh 84
73
India 62
Bangladesh 81
71.5
7.SubSaharan
Africa: Best
Worst
Average
S. Africa 54
Tanzania 87
77.5
S. Africa 52
Tanzania 84
76
S. Africa 54
Tanzania 92
81
Uganda 71
Senegal 83
77
S. Africa 53
Senegal 86
77
Others: Best
Worst
Average
Israel 17
Macao 51
34
Malta 6
Macao 47
23
Malta 7
Macao 46
23
Malta 2
Macao 40
19.7
Malta 3
Israel 15
9
Again, rankings are fairly stable between 1995-1999, with consistent 'best-performers' (e.g.
U.S., Russia, Jordan) and 'worst-performers' (e.g. Indonesia, Bangladesh, Ecuador) within
categories. Average rankings are also relatively consistent, although variations in sample
size again influence rankings (e.g. the increased sample size of 92 in 1997 generally lowers
1997 worst and average rankings relative to 1996 and should not necessarily be interpreted as
a decline in performance over this period. See 'Relative Movements', Section 4).
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Average rankings conform to expectations. OECD countries capture the top rankings, with
the U.S. and Japan first and Turkey last (compared to Mexico in ICT indicators). OECD
average rankings vary between 21 and 22.6 from 1995 to 1999, around places ahead of
'E.Asian countries' with an average ranking that fluctuates between 32-38.8. This is a diverse
category, from Asian Tigers (e.g. Philippines at 2nd and Singapore at 5th and 7th places) to
Mongolia and Indonesia as countries tasked with 'catching up'. 'E.Europe and CIS' average
rankings range from 35.3 to 42 from 1995-1999, while LAC countries' average ranking
varies between 56-62. 'Arab' countries are also diverse, with countries such as Jordan,
Kuwait and Syria, and an average ranking between 71.7 in 1997 and 65 in 1998. South Asia
has an average of 77 in 1997 to 72 in 1999. Sub-Saharan Africa's average ranking is last and
ranges between 81 in 1997 to 76 in 1998, although best-performer South Africa ranks as
high as 52-54. 'Others' is too limited a category to yield meaningful conclusions.
3. R& D Expenditure and High-Tech Export Indices – Appendix 3
Appendix 3 compares the input indicator of R&D expenditure and the performance-related
indicator of high-tech exports. The R&D Expenditure and High-Tech Export Indices show
reasonable correlations of 0.4463 (1999), 0.6196 (1998), 0.6356 (1997), 0.5325 (1996) and
0.497 (1995), as shown in Appendix 2. However, it is not possible to make conclusions as to
causation on the basis of these correlations.It is expected that R&D expenditure provides a
foundation for and could enhance high-tech exports (average GDP income could also be an
important underlying factor in creating effective demand for high-tech exports, or an
outcome of a country specialised in and exporting a high proportion of high-tech products).
Although increased R&D expenditure may result in more successful R&D outcomes and
improve high-tech exports in quality, value and quantity, a propensity towards high-tech
imports and exports may itself foster more sophisticated demand, encourage reverse
engineering and could facilitate R&D.
It is important to be aware that the High-Tech Export Index also captures countries that have
apparently substantial HTE capacity but in fact specialise in local production and assembly in
the absence of substantial R&D capabilities ('KU RDE-GA HTE' and 'CU RDE-GA HTE'
segments in Appendix 3 for e.g. China, Mexico and Thailand).There are however no
countries that spend relatively large amounts on R&D in the absence of a strong high-tech
export capacity (no countries found in 'GA RDE-CU HTE' segment).
Appendix 3 illustrates the reasonable correlations observed between these two indices, with
countries lying mainly on the positive correlation diagonal and less so on the inverse
diagonal (data omissions principally affect the lower row three segments of 'CU RDE').
Substantial high-tech exports despite low R&D expenditure (segments in the bottom right
corner) illustrates the case of local production and assembly – seven countries experience
good high-tech exports ('GA HTE': Italy, Hungary, Lithuania, China, Estonia, Mexico and
Thailand) with only adequate or relatively low R&D expenditure. These countries,
particularly China, Mexico and Thailand, illustrate the possibilities for assembly (import
patterns not considered here). Transition economies exhibit a highly varied experience, from
the success enjoyed by the Czech Republic, to Moldova which is spending relatively more on
R&D than its export structure might suggest, and which has yet to reap the benefits of its
higher RD expenditure. Data omissions principally affect the LDCs, so African and Asian
subcontinent countries are mostly absent (it is presumed that these countries would mainly
populate the lower row of 'CU RDE', which explains the lower incidence of observations in
these segments).
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4. R&D Expenditure and Human Capital Indices – Appendix 4
R&D Expenditure and Human Capital index scores show the highest correlations in
Appendix 2 of 0.6972 (1999), 0.6914 (1998), 0.6936 (1997), 0.7328 (1996) and 0.7984
(1995). This is to be expected, since R&D Expenditure and Human Capital Indices
constitute input indicators for technology and are inter-related. The Human Capital Index is
composed of two variables: personnel engaged in R&D and tertiary enrolments in further
education.
(Insofar as further education is a prerequisite to working in R&D, these two variables are
themselves related). R&D Expenditure partly pays for R&D personnel. However, it is not
proposed to analyse causation in this inter-related set of variables here. The correlations
between these variables are high, as illustrated by the positive diagonal in Appendix 4, which
is again overweight in country observations, although this pattern is again partly explained
by data omissions, which detract from the frequency of observations in the lower row.
The segmental analysis of rankings presented in Appendix 4 illustrates some regional
groupings. The 'GA RDE-GA HC' box contains 17-19 mostly OECD countries in any year,
engaged in a 'virtuous circle' of good education and high R&D expenditure. The higher
frequency of observations in the 'GA RDE-KU HC' segments likely reflects the prioritisation
of education, particularly in FUSSR and transition economies, which notably all fall in the
'KU HC' and 'GA HC' columns. This illustrates their relative strength in education and
human capital. However, 'HC' as a composite index also includes R&D personnel, which
accounts for the allocation of countries between these categories (e.g. why Russia is
classified as 'GA HC' due to its relatively high R&D staffing, while Hungary and the Czech
Republic are 'KU HC' due to their relatively lower R&D personnel.
The 'KU RDE-CU HC' box contains China, that may have yet to witness the full benefits of
its adequate R&D expenditure in terms of matching human capital and R&D personnel.
However, countries in the 'KU RDE-GA HC' and 'CU RDE-GA HC' boxes (e.g. Russia,
Latvia, Estonia, Kazakhstan) offer the most potential. These are countries that have a pool of
well-educated human capital or relatively high R&D personnel, the potential of which may
have yet to be fully realised by increased R&D expenditure.
Data omissions principally affect the LDCs, so African and Asian subcontinent countries are
mostly absent (it is presumed that these countries would mainly populate the lower row of
'CU RDE', which explains the lower incidence of observations in these segments).
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Indices of Technological Development
5. Evolution over time
Comparison of the tables in Appendices 3 and 4 for 1995-1999 reveals that rankings are
relatively stable year on year. This is consistent with high correlations observed between
years:
Index of Tech. Dev't
1999
1999
1
1998
1998
1997
1996
0.9695
0.9642
0.9179
0.9496
1
0.9947
0.9439
0.9493
1
0.9513
0.9508
1
0.9646
1997
1996
1
1995
HTE Index
1999
1998
1995
1999
1
1998
1997
1996
1995
0.8622
0.7829
0.7729
0.8048
1
0.9760
0.9630
0.8558
1
0.9869
0.8884
1
0.9329
1997
1996
1
1995
(RDE and HC Indices not comparable, as latest available data means 1997 equivalent to
1998-9).
The stability of these high correlations is consistent with UNIDO's (forthcoming)
"considerable stability in Industrial Performance Scoreboard rankings [of the technological
structure of exports and MVA]… supporting the argument that capability building is a slow
and incremental process". UNCTAD (1991) observe that "the cumulative and firm-specific
character of technological change implies that technological strengths of firms and countries
do not change rapidly over time". These correlations could thus reflect the long-term nature
of technological change. It also suggests that it may be difficult to break out of a 'vicious
circle', but that benefits conferred by establishing a 'virtuous circle' may be long-term.
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5. CONCLUSIONS & WAY FORWARD
There are different aspects to 'multi-faceted' technology clusters, and different ways of
examining and measuring technological development from various perspectives.
Measurement of countries' technological capabilities across multiple aspects is necessary, to
give rounded country profiles across the broad spectrum of technologies. Based on our
review of previous work in Paper I, we selected the aspects of R&D, human capital and
export performance as key components in the measurement of technological development
across countries. We measured technological profiles across these dimensions in a
disaggregated Index of Technological Development, with component indices for R&D
expenditure (RDE), human capital (HC) and high-tech exports (HTE).
These different aspects are related, with strong positive correlations observed between R&D
expenditure and human capital and, to a lesser extent, R&D expenditure and high-tech
exports, which are stable over time. This suggests that we are measuring some central
measure of 'technological development', although the question of causation cannot be
addressed with indices - for example, we may (also) be implicitly measuring proxy variables
of income and/or development. Average income may be an important underlying variable
e.g. by creating effective demand for high-tech products by consumers and providing the
resources necessary for R&D.
Classification of countries as 'catching up', 'keeping up' and 'getting ahead' in Appendices 3
and 4 on the basis of rankings in these indicators shows consistent and stable rankings over
time, with high correlations between years. This likely reflects the long-term nature of
technological change. There are some regional influences apparent (although to a lesser
extent than with the fuller sample of 200 countries in the Indices of ICTs presented in Paper
III). As a broad generalisation, Latin American and transition economies are classified as
'keeping up' and OECD countries and some South-East Asian Tigers as 'getting ahead'. Data
limitations meant that African and South Asian countries are largely omitted from this
analysis, which only covers 84-92 countries (compared to 200 countries in the Indices of
ICTs). However, this generalisation masks considerable diversity in countries' experiences,
with transition economies in particular displaying notable variation in all indicators except
education and human capital, where they are consistently strong.
Cross-country analyses form a useful first step in benchmarking countries' levels of
technological development, which provides important input to policy analysis in terms of the
stage countries are currently at. However, they generally lack the depth of insight required
for policy analysis, which is difficult to achieve using cross-country indicators.
Nevertheless, our human capital index, which includes education as a fundamental policy
priority, is a significant input for further policy analysis. We have omitted other measures of
policy and national strategy, as the variety of technology policy options open to governments
is difficult to capture in cross-country indices. A fuller consideration of policy will be
undertaken in Panels II and III through case study analysis for added depth of insight into
policy frameworks.
It would be interesting to compare our indices and the changes in rankings therein with other
indices [e.g. UNIDO (forthcoming)'s Industrial Performance Scoreboard, GIT (1987
onwards)'s high-tech export indices]. In future work, the challenge will be to address
causation in the reinforcing 'virtuous circles' observed in categorical analysis of country
rankings as 'catching up', 'keeping up' and 'getting ahead' with panel regression analysis.
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Regression work should include consideration of economies' sectoral composition, as
reflected in the technological structure of exports, and FDI as important determinants of and
influences on countries' uptake and absorption of new technologies.
6. APPENDICES
The following appendices are presented:
1. Appendix 1 – Indices of Technological Development (by country and by ranking);
2. Appendix 2 – Correlations of component indices (RDE, HC, HTE);
3. Appendix 3 – Comparison of R&D Expenditure/High-Tech Export Indices;
4. Appendix 4 – Comparison of R&D Expenditure /Human Capital Indices.
Appendix 1 presents Indices of Technological Development for 1999 calculated as discussed
above in the section on Index Methodology.
Appendix 2 presents correlation tables for the three main component indices (R&D
Expenditure, Human Capital and High-Tech Export Indices) calculated using the correlation
function in Excel for the five years 1995, 1996, 1997, 1998 and 1999.
Appendices 3 and 4 compare R&D Expenditure/High-Tech Export Indices and R&D
Expenditure /Human Capital Indices respectively by categorising them as 'catching up' (CU),
'keeping up' (KU) and 'getting ahead' (GA), consistent with UNCSTD recommendations. For
each index, countries were divided into thirds on the basis of rankings, with the first third
classified as GA, the second third classified as KU and the last third as CU. This allows the
segmental classification and analysis of R&D Expenditure with the High-Tech Export Index
and R&D Expenditure with Human Capital Index.
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6. Appendix 1: Indices of Technological Development for 1999
COUNTRY
1999 RD Exp
..
1999 Human Cap
..
Albania
..
0.136674
..
..
Algeria
..
American Samoa
..
0.136674
..
0.00249
..
0.069582
..
Andorra
..
..
..
..
Angola
..
..
..
..
Antigua and Barbuda
..
..
..
..
0.101064
..
..
0.04561
0.073337
..
0.267948
..
0.025546
..
0.146747
..
Australia
0.478723
0.908884
0.055332
0.48098
Austria
0.406915
0.550114
0.383066
Azerbaijan
0.055851
..
0.198178
..
0.19217
..
Afghanistan
Argentina
Armenia
Aruba
Bahamas, The
1999 HT Exps
..
..
1999 TECH DEVT
..
0.127014
..
..
..
..
..
..
..
..
Barbados
0.007979
..
0.326879
0.226674
0.276777
Belarus
0.284574
0.498861
Belgium
Belize
0.425532
..
0.64123
..
0.050587
..
Benin
..
Bermuda
..
Bhutan
Bolivia
Bahrain
Bangladesh
0.278008
..
0.533381
..
0.035308
..
..
..
..
..
..
..
..
..
0.132979
..
0.144116
..
0.541127
..
0.27274
..
..
0.066059
..
..
0.215426
..
0.165148
Brunei
0.075171
0.137403
..
0.172659
..
Bulgaria
0.151596
0.469248
..
0.310422
Burkina Faso
0.050532
..
0.009066
..
..
..
0.029799
..
Cambodia
..
..
..
0.013667
..
..
Cameroon
..
..
0.441489
..
1
..
0.191768
..
0.544419
..
Cayman Islands
..
..
..
..
Central African Republic
..
..
..
..
Chad
..
0.006834
..
..
Chile
0.180851
0.35877
..
0.26981
China
0.175532
0.069476
0.28991
0.178306
Colombia
0.106383
..
0.190205
0.047621
..
0.114736
..
..
..
Bosnia and Herzegovina
Botswana
Brazil
Burundi
Canada
Cape Verde
Comoros
Congo, Dem. Rep.
..
0.006834
..
Congo, Rep.
..
..
..
..
0.319149
..
0.345102
..
0.786271
..
0.483507
..
0.273936
..
0.317768
0.120938
..
0.237547
..
Costa Rica
Cote d'Ivoire
Croatia
Cuba
Cyprus
Czech Republic
..
0.14123
..
0.319149
0.279755
24
0.016344
0.152324
..
0.250409
UNCTAD, Geneva
Indices of Technological Development
COUNTRY
Denmark
Djibouti
1999 RD Exp
1999 Human Cap
1999 HT Exps
1999 TECH DEVT
0.518617
..
0.655428
0.252952
..
0.475666
..
Dominica
..
0.003417
..
..
0.26082
0.042781
..
..
Dominican Republic
0.005319
0.03487
0.010741
0.016977
Ecuador
..
Egypt, Arab Rep.
0.058511
0.230068
0.001857
0.096812
El Salvador
0.021277
..
0.202733
..
0.06281
..
0.095607
..
Eritrea
..
0.01139
..
..
Estonia
0.151596
..
0.525598
0.175832
..
0.284342
..
..
..
Equatorial Guinea
Ethiopia
Faeroe Islands
..
0.009112
..
Fiji
..
..
..
..
Finland
0.739362
0.843964
0.391785
0.65837
France
0.598404
..
0.580866
..
0.35513
..
0.511467
..
Gabon
..
..
..
..
Gambia, The
..
..
..
..
Georgia
..
0.47836
..
..
Germany
0.640957
..
0.537585
..
0.26522
0.033964
0.481254
..
0.125
..
0.53303
..
0.082997
..
0.247009
..
French Polynesia
Ghana
Greece
Greenland
Grenada
Guatemala
Guinea
..
..
..
..
0.042553
..
0.096811
0.05684
..
0.065402
..
0.014806
..
Guinea-Bissau
..
Guyana
..
Honduras
..
0.129841
..
..
..
..
..
Hong Kong, China
..
0.00927
..
..
0.249431
Hungary
0.180851
0.265636
0.369568
0.272018
Iceland
0.412234
0.706866
0.194149
0.078588
0.039846
..
0.386315
India
Indonesia
0.018617
0.128702
0.107227
0.084849
Iran, Islamic Rep.
Iraq
0.12766
..
0.200456
..
0.001098
..
0.109738
..
Ireland
0.428191
0.46697
0.758055
0.551072
0.625
0.465831
0.343957
0.478263
0.534169
Jamaica
0.587766
..
0.142817
..
0.421584
..
Japan
0.744681
0.730638
0.069149
0.203872
0.477399
..
0.650906
Jordan
Kazakhstan
0.379271
..
0.044461
Kenya
0.085106
..
Kiribati
..
..
0.016253
..
0.169613
..
Korea, Dem. Rep.
..
..
..
..
0.75
0.771071
0.54711
0.689394
Israel
Italy
Korea, Rep.
0.088838
..
0.136368
0.136511
..
Kuwait
0.042553
0.145853
0.005527
0.064645
Kyrgyz Republic
0.053191
..
0.143478
0.021178
..
0.072616
..
0.044078
..
0.171753
..
..
..
..
..
Lao PDR
Latvia
Lebanon
0.114362
..
Lesotho
..
Liberia
..
0.031891
0.35682
0.307517
0.027335
..
25
UNCTAD, Geneva
Indices of Technological Development
COUNTRY
1999 RD Exp
..
1999 Human Cap
..
1999 HT Exps
..
1999 TECH DEVT
..
..
..
..
..
0.18617
..
0.357631
0.239557
..
0.26112
..
Macao, China
..
0.316629
Macedonia, FYR
..
0.222096
0.014155
..
0.165392
..
0.047872
..
0.022779
..
0.006834
..
0.035326
..
0.06383
..
0.133257
..
0.904747
..
0.367278
..
Mali
..
0.015945
..
..
Malta
..
..
..
Marshall Islands
..
0.333713
..
..
..
Mauritania
..
0.04328
..
..
Mauritius
0.106383
0.069476
0.01664
0.064166
Mexico
0.087766
..
0.182232
..
0.336382
..
0.202127
..
0.239362
..
0.386283
Mongolia
0.193622
0.021095
..
0.21558
..
Morocco
..
0.126424
..
..
Mozambique
..
..
..
Myanmar
..
0.005695
..
..
..
Namibia
..
0.092255
..
..
Nepal
..
0.05467
..
..
0.553191
..
0.538724
..
0.380621
..
0.490846
..
Libya
Liechtenstein
Lithuania
Luxembourg
Madagascar
Malawi
Malaysia
Maldives
Micronesia, Fed. Sts.
Moldova
Netherlands
Netherlands Antilles
0.111617
..
..
..
..
New Zealand
0.276596
0.712984
0.093789
0.361123
Nicaragua
0.031915
..
0.134396
..
0.008056
..
0.058122
..
Nigeria
..
..
0.00292
..
Norway
0.420213
..
0.70615
0.085621
0.403995
0.091116
0.031081
0.061099
Pakistan
..
0.020301
0.004755
0.012528
Panama
0.35877
0.003702
0.153625
Paraguay
0.098404
..
0.117312
0.008316
0.062814
Peru
0.015957
0.174749
0.014406
0.068371
Philippines
0.058511
0.330296
0.441999
0.276935
Poland
0.204787
0.281321
0.040436
0.175515
Portugal
0.164894
..
0.441913
..
0.084889
..
0.230565
..
New Caledonia
Niger
Oman
Puerto Rico
..
0.302961
..
..
0.191489
0.256264
0.05314
0.166965
Russian Federation
0.234043
Rwanda
0.010638
..
1
..
0.074545
..
0.436196
..
..
..
..
..
..
..
0.185649
..
..
..
..
0.064222
..
0.033441
..
..
..
..
Qatar
Romania
Samoa
Sao Tome and Principe
..
Saudi Arabia
..
Senegal
Seychelles
Sierra Leone
0.00266
..
..
Singapore
0.489362
0.438497
1
0.642619
Slovak Republic
0.279255
0.251708
0.070286
0.200416
Slovenia
0.388298
0.411162
0.070668
0.290042
26
UNCTAD, Geneva
Indices of Technological Development
COUNTRY
1999 RD Exp
..
1999 Human Cap
..
1999 HT Exps
..
1999 TECH DEVT
..
Somalia
..
..
..
..
South Africa
..
0.215262
0.075492
0.145377
0.239362
..
0.585421
0.120503
0.315096
Solomon Islands
Spain
Sri Lanka
St. Kitts and Nevis
..
0.058087
..
0.045315
..
0.051701
..
St. Lucia
..
..
..
..
St. Vincent and the Grenadines
..
..
..
..
Sudan
..
..
..
..
Suriname
..
..
..
..
Swaziland
..
0.068337
..
..
0.637586
Sweden
1
0.572893
0.339864
Switzerland
0.691489
0.371298
Syrian Arab Republic
0.013136
Tajikistan
0.053191
..
0.387086
..
0.232346
..
0.033164
..
Tanzania
..
0.006834
0.019522
0.013178
Thailand
0.034574
..
0.251708
0.458017
0.2481
0.041002
0.002506
0.021754
..
0.093394
0.017548
0.055471
Tunisia
0.079787
0.099752
0.04025
0.073263
Turkey
0.119681
..
0.23918
..
0.065458
..
0.141439
..
0.151596
..
0.015
0.565391
0.006299
..
0.057631
..
..
0.135535
..
..
United Kingdom
0.518617
0.595672
0.475426
0.529905
United States
0.699468
0.921412
0.506289
0.709056
Uruguay
0.055851
..
0.335991
..
0.017644
..
0.136495
..
Togo
Trinidad and Tobago
Turkmenistan
Uganda
Ukraine
United Arab Emirates
Uzbekistan
Vanuatu
Venezuela, RB
Vietnam
0.483291
..
..
..
..
0.039894
..
..
0.006823
..
0.023358
..
Virgin Islands (U.S.)
..
0.078588
..
..
..
Yemen, Rep.
..
0.047836
..
..
Yugoslavia, FR (Serbia/Montenegro)
..
..
Zambia
0.249431
..
..
..
..
..
Zimbabwe
..
0.075171
0.009247
0.042209
27
UNCTAD, Geneva
Indices of Technological Development
COUNTRY
1999
1999
1999
1999 TECH
RD EXP
HUM.CAP
HT EXPS
DEVELOP'T
0.6995
0.5063
COUNTRY
1999
1999
1999
1999 TECH
RD EXP
HUM.CAP
HT EXPS
DEVELOP'T
0.1755
0.0695
1
United States
0.7091
45
China
0.2899
0.1783
2
Korea, Rep.
0.7500
0.7711
0.5471
0.6894
46
Poland
0.2048
0.2813
0.0404
0.1755
3
Finland
0.7394
0.8440
0.3918
0.6584
47
Brazil
0.2154
0.1651
0.1374
0.1727
4
Japan
0.7447
0.7306
0.4774
0.6509
48
Latvia
0.1144
0.3568
0.0441
0.1718
5
Singapore
0.4894
0.4385
1.0000
0.6426
49
Kazakhstan
0.0851
0.3793
0.0445
0.1696
6
Sweden
1.0000
0.5729
0.3399
0.6376
50
Romania
0.1915
0.2563
0.0531
0.1670
7
Ireland
0.4670
0.7581
8
Canada
0.4415
1.0000
0.1918
9
Belgium
0.4255
0.6412
10
United Kingdom
0.5186
0.5957
11
France
0.5984
12
Netherlands
13
14
0.4282
0.9214
RANKING
RANKING
Index of Technological Development by Ranking
0.5511
51
Macao, China
0.5444
52
Panama
0.3588
0.0037
0.1536
0.5334
53
Armenia
0.2679
0.0255
0.1467
0.4754
0.5299
54
South Africa
0.2153
0.0755
0.1454
0.5809
0.3551
0.5115
55
Turkey
0.1197
0.2392
0.0655
0.5532
0.5387
0.3806
0.4908
56
Jordan
0.0691
0.2039
Costa Rica
0.3191
0.3451
0.7863
0.4835
57
Uruguay
0.0559
0.3360
Switzerland
0.6915
0.3713
0.3871
0.4833
58
India
0.1941
0.0786
0.1364
15
Germany
0.6410
0.5376
0.2652
0.4813
59
Azerbaijan
0.0559
0.1982
0.1270
16
Australia
0.0553
0.4810
60
Colombia
0.1064
0.1902
0.0476
17
Israel
0.6250
0.4658
0.3440
0.4783
61
Iran, Islamic Rep.
0.1277
0.2005
0.0011
0.1097
18
Denmark
0.5186
0.6554
0.2530
0.4757
62
Egypt, Arab Rep.
0.0585
0.2301
0.0019
0.0968
19
Russian Federation
0.2340
1.0000
0.0745
0.4362
63
El Salvador
0.0213
0.2027
0.0628
0.0956
20
Italy
0.5878
0.5342
0.1428
0.4216
64
Indonesia
0.0186
0.1287
0.1072
0.0848
21
Norway
0.4202
0.7062
0.0856
0.4040
65
Argentina
0.1011
22
Iceland
0.4122
0.7069
0.0398
0.3863
66
Tunisia
0.0798
0.0998
23
Austria
0.1922
0.3831
67
Kyrgyz Republic
0.0532
0.1435
24
Malaysia
0.0638
0.1333
0.9047
0.3673
68
Algeria
25
New Zealand
0.2766
0.7130
0.0938
0.3611
69
Peru
26
Spain
0.2394
0.5854
0.1205
0.3151
70
Guatemala
0.0426
0.0968
0.0568
0.0654
27
Bulgaria
0.1516
0.4692
0.3104
71
Kuwait
0.0426
0.1459
0.0055
0.0646
28
Slovenia
0.3883
0.4112
0.0707
0.2900
72
Mauritius
0.1064
0.0695
0.0166
0.0642
29
Estonia
0.1516
0.5256
0.1758
0.2843
73
Paraguay
0.1173
0.0083
0.0628
30
Belarus
0.2846
0.4989
0.0506
0.2780
74
Oman
0.0311
0.0611
31
Philippines
0.0585
0.3303
0.4420
0.2769
75
Nicaragua
0.0319
0.1344
0.0081
0.0581
32
Barbados
0.3269
0.2267
0.2768
76
Uganda
0.1516
0.0150
0.0063
0.0576
33
Bolivia
0.1330
0.1441
0.5411
0.2727
77
Trinidad and Tobago
0.0934
0.0175
0.0555
34
Hungary
0.1809
0.2656
0.3696
0.2720
78
Sri Lanka
0.0581
0.0453
0.0517
35
Chile
0.1809
0.3588
0.2698
79
Zimbabwe
0.0752
0.0092
0.0422
36
Lithuania
0.1862
0.3576
0.2396
0.2611
80
Madagascar
0.0479
37
Czech Republic
0.3191
0.2798
0.1523
0.2504
81
Senegal
0.0027
0.0642
0.0334
38
Thailand
0.0346
0.2517
0.4580
0.2481
82
Syrian Arab Republic
0.0532
0.0131
39
Greece
0.1250
0.5330
0.0830
0.2470
83
Burkina Faso
0.0505
0.0091
40
Croatia
0.2739
0.3178
0.1209
0.2375
84
Venezuela, RB
0.0399
41
Portugal
0.1649
0.4419
0.0849
0.2306
85
Togo
42
Moldova
0.2394
0.3863
0.0211
0.2156
86
Ecuador
43
Mexico
0.0878
0.1822
0.3364
0.2021
87
Tanzania
0.0068
0.0195
0.0132
44
Slovak Republic
0.2793
0.2517
0.0703
0.2004
88
Pakistan
0.0203
0.0048
0.0125
0.4787
0.4069
0.9089
0.5501
28
0.3166
0.0984
0.0160
0.1654
0.1414
0.1365
0.0176
0.1365
0.1147
0.0456
0.0733
0.0402
0.0733
0.0212
0.0726
0.1367
0.0025
0.0696
0.1747
0.0144
0.0684
0.0911
0.0053
0.0142
0.0228
0.0353
0.0332
0.0298
0.0068
0.0234
0.0410
0.0025
0.0218
0.0349
0.0107
0.0170
UNCTAD, Geneva
Indices of Technological Development
6. Appendix 2: Correlations of component Indices
R&D Expenditure
1999
R&D Expenditure
1
Human Capital
Human Capital
0.6972
0.4463
1
0.2734
High-Tech Exports
1
R&D Expenditure
1998
R&D Expenditure
1
Human Capital
Human Capital
0.6196
1
0.2388
1
R&D Expenditure
1997
1
Human Capital
Human Capital
0.6356
1
0.2805
1
R&D Expenditure
1996
1
Human Capital
Human Capital
0.5325
1
0.2965
1
R&D Expenditure
1995
Human Capital
High-Tech Exports
0.7328
High-Tech Exports
R&D Expenditure
High-Tech Exports
0.6936
High-Tech Exports
R&D Expenditure
High-Tech Exports
0.6914
High-Tech Exports
R&D Expenditure
High-Tech Exports
1
Human Capital
High-Tech Exports
0.7984
0.4971
1
0.3762
High-Tech Exports
1
29
UNCTAD, Geneva
Indices of Technological Development
6. Appendix 3: Comparison of R&D Expenditure/HTE Indices
1999 R&D EXPENDITURE VERSUS HTE PLOT OF RANKINGS
Key data omissions: Chile, Belgium, Barbados, Madagascar.
GA RDE – CU HTE
GA RDE – KU HTE
GA RDE – GA HTE
(0)
(4)
(16)
Norway, Slovenia, Iceland,
Belarus.
Sweden, Japan, Korea Rep.,
Finland, U.S., France,
Germany, Israel,
Netherlands, Denmark,
United Kingdom, Singapore,
Canada, Ireland, Austria,
Czech Republic.
KU RDE – CU HTE
KU RDE – KU HTE
KU RDE – GA HTE
(1)
(10)
(4)
Moldova.
Australia, Slovak Rep., New
Zealand, Croatia, Spain,
Brazil, Russian Federation,
Poland, Romania, Portugal.
Italy, Hungary, Lithuania,
China.
CU RDE – CU HTE
CU RDE – KU HTE
CU RDE – GA HTE
(5)
(6)
(3)
Uganda, Egypt, Kyrgyz Rep., Latvia, Argentina, Turkey,
Kuwait, Ecuador.
Tunisia, Kazakhstan,
Senegal.
30
Estonia, Mexico, Thailand.
UNCTAD, Geneva
Indices of Technological Development
1998 R&D EXPENDITURE VERSUS HTE PLOT OF RANKINGS
Key data omissions: Singapore, Belarus, and Kyrgyz Republic.
GA RDE – CU HTE
GA RDE – KU HTE
GA RDE – GA HTE
(0)
(3)
(16)
Norway, Slovenia, Iceland.
Sweden, Japan, Korea Rep.,
U.S., Finland, France,
Germany, Israel,
Netherlands, Denmark,
United Kingdom, Canada,
Ireland, Belgium, Austria,
Czech Republic.
KU RDE – CU HTE
KU RDE – KU HTE
KU RDE – GA HTE
(1)
(10)
(4)
Chile.
Moldova, Slovak Rep., New
Zealand, Spain, Brazil,
Russian Federation, Poland,
Romania, Lithuania,
Portugal.
Italy, Croatia, Hungary,
China, Estonia.
CU RDE – CU HTE
CU RDE – KU HTE
CU RDE – GA HTE
(5)
(5)
(4)
Uganda, Egypt, Kuwait,
Bangladesh, Ecuador.
Latvia, Argentina, Turkey,
Kazakhstan, Senegal.
31
Mexico, Tunisia,
Madagascar, Thailand.
UNCTAD, Geneva
Indices of Technological Development
1997 R&D EXPENDITURE VERSUS HTE PLOT OF RANKINGS
Key data omissions: Singapore, Belarus, Uganda, Australia.
GA RDE – CU HTE
GA RDE – KU HTE
GA RDE – GA HTE
(0)
(3)
(17)
Slovenia, Iceland, Norway.
Sweden, Japan, Finland,
Korea Rep., U.S., Germany,
Israel, France, Italy,
Netherlands, Denmark,
United Kingdom, Canada,
Ireland, Belgium, Austria,
Czech Republic.
KU RDE – CU HTE
KU RDE – KU HTE
KU RDE – GA HTE
(1)
(9)
(6)
Chile.
Slovak Rep., New Zealand,
Brazil, Russian Federation,
Poland, Romania, Lithuania,
Portugal, Latvia.
Croatia, Moldova, Spain,
Hungary, China, Estonia.
CU RDE – CU HTE
CU RDE – KU HTE
CU RDE – GA HTE
(6)
(4)
(2
Egypt, Madagascar, Kuwait,
Bangladesh, Ecuador,
Senegal.
Argentina, Turkey,
Kazakhstan, Tunisia.
32
Mexico, Thailand.
UNCTAD, Geneva
Indices of Technological Development
1996 R&D EXPENDITURE VERSUS HTE PLOT OF RANKINGS
Key data omissions: Belarus, Moldova.
GA RDE – CU HTE
GA RDE – KU HTE
GA RDE – GA HTE
(0)
(3)
(19)
Australia, Norway, Iceland.
Sweden, Korea Rep., Japan,
Finland, U.S., Switzerland,
France, Germany, Israel,
Netherlands, Denmark,
United Kingdom, Canada,
Ireland, Belgium, Austria,
Slovenia, Singapore, Czech
Republic.
KU RDE – CU HTE
KU RDE – KU HTE
KU RDE – GA HTE
(1)
(12)
(6)
Uganda.
Slovak Rep., New Zealand,
Russian Federation, Brazil,
Poland, Romania, Lithuania,
Chile, Hungary, Bulgaria,
Bolivia, Latvia.
Italy, Croatia, Spain,
Portugal, China, Estonia.
CU RDE – CU HTE
CU RDE – KU HTE
CU RDE – GA HTE
(6)
(5)
(3)
Egypt, Madagascar, Kuwait,
Bangladesh, Senegal,
Ecuador.
Latvia, Turkey, Argentina,
Kazakhstan, Tunisia.
33
Mexico, Thailand, Kyrgyz
Republic.
UNCTAD, Geneva
Indices of Technological Development
1995 R&D EXPENDITURE VERSUS HTE PLOT OF RANKINGS
Key data omissions: Russian Federation, Senegal.
GA RDE – CU HTE
GA RDE – KU HTE
GA RDE – GA HTE
(0)
(3)
(17)
Norway, Slovenia, Iceland.
Sweden, Japan, Korea Rep.,
U.S., Finland, France,
Germany, Israel,
Netherlands, Denmark,
United Kingdom, Canada,
Ireland, Belgium, Austria,
Singapore, Czech Republic.
KU RDE – CU HTE
KU RDE – KU HTE
KU RDE – GA HTE
(1)
(7)
(8)
Chile
Moldova , Slovak Rep., New
Zealand, Brazil, Poland,
Romania, Kazakhstan.
Italy, Croatia, Spain,
Hungary, Lithuania,
Portugal, China, Estonia.
CU RDE – CU HTE
CU RDE – KU HTE
CU RDE – GA HTE
(6)
(3)
(4)
Uganda, Egypt, Kuwait,
Madagascar, Bangladesh,
Ecuador.
Argentina, Turkey, Tunisia.
Latvia,
Mexico,
Kyrgyz
Rep., Thailand.
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Indices of Technological Development
6. Appendix 4: Comparison of R&D Expenditure/H.C. Indices
1999 R&D EXPENDITURE VERSUS HUMAN CAPITAL RANKINGS
Key data omissions:Argentina, Bangladesh, Rwanda, Senegal.
GA RDE – CU HC
GA RDE – KU HC
GA RDE – GA HC
(0)
(1)
(19)
Czech Republic.
Sweden, Korea Rep., Japan,
United States, Switzerland,
Finland, France, Germany,
Israel, Netherlands,
Denmark, United Kingdom,
Norway, Slovenia, Canada,
Ireland, Belgium, Iceland,
Austria, Singapore, Belarus.
KU RDE – CU HC
KU RDE – KU HC
KU RDE – GA HC
(1)
(6)
(8)
Slovak Rep., Croatia, Brazil,
Hungary, Poland, Romania.
Moldova, New Zealand,
Italy, Spain, Russian
Federation, Lithuania, Chile,
Portugal.
CU RDE – CU HC
CU RDE – KU HC
CU RDE – GA HC
(5)
(7)
(3)
China
Uganda, Tunisia,
Madagascar, Burkina Faso,
Ecuador.
Turkey, Mexico, Egypt,
Kyrgyz Rep., Kuwait,
Azerbaijan, Thailand.
35
Estonia, Latvia, Kazakhstan.
UNCTAD, Geneva
Indices of Technological Development
1998 R&D EXPENDITURE VERSUS HUMAN CAPITAL RANKINGS
Key data omissions: Japan, Argentina, Bangladesh, Rwanda, Senegal, Belarus.
GA RDE – CU HC
GA RDE – KU HC
GA RDE – GA HC
(0)
(1)
(18)
Czech Republic.
Sweden, Korea Rep., United
States, Finland, France,
Germany, Israel,
Netherlands, Denmark,
United Kingdom, Norway,
Slovenia, Canada, Ireland,
Belgium, Iceland, Austria,
Singapore.
KU RDE – CU HC
KU RDE – KU HC
KU RDE – GA HC
(1)
(6)
(8)
Slovak Rep., Croatia, Brazil,
Hungary, Poland, Romania.
Moldova, New Zealand,
Italy, Spain, Russian
Federation, Lithuania, Chile,
Portugal.
CU RDE – CU HC
CU RDE – KU HC
CU RDE – GA HC
(5)
(7)
(3)
China
Uganda, Tunisia,
Madagascar, Burkina Faso,
Ecuador.
Turkey, Mexico, Egypt,
Kyrgyz Rep., Kuwait,
Azerbaijan, Thailand.
36
Estonia, Latvia, Kazakhstan.
UNCTAD, Geneva
Indices of Technological Development
1997 R&D EXPENDITURE VERSUS HUMAN CAPITAL RANKINGS
Key data omissions: Japan, Argentina, Bangladesh, Rwanda, Senegal.
GA RDE – CU HC
GA RDE – KU HC
GA RDE – GA HC
(0)
(1)
(19)
Czech Republic.
Sweden, Finland, Korea
Rep., United States,
Germany, Israel, France,
Italy, Netherlands, Denmark,
United Kingdom, Slovenia,
Canada, Ireland, Belgium,
Iceland, Norway, Austria,
Singapore.
KU RDE – CU HC
KU RDE – KU HC
KU RDE – GA HC
(2)
(6)
(10)
Slovak Rep., Croatia, Brazil,
Hungary, Poland, Romania.
Belarus, New Zealand,
Moldova, Spain, Russian
Federation, Lithuania, Chile,
Portugal, Estonia, Latvia.
CU RDE – CU HC
CU RDE – KU HC
CU RDE – GA HC
(5)
(7)
(1)
China, Uganda.
Tunisia, Syria, Burkina Faso,
Madagascar, Ecuador.
Turkey, Mexico, Egypt,
Kyrgyz Rep., Azerbaijan,
Kuwait, Thailand.
37
Kazakhstan.
UNCTAD, Geneva
Indices of Technological Development
1996 R&D EXPENDITURE VERSUS HUMAN CAPITAL RANKINGS
Key data omissions: U.S., Israel, Canada, Belgium, Poland, Portugal, Argentina, Kazakhstan,
Egypt, Rwanda, Bangladesh, Ecuador.
GA RDE – CU HC
GA RDE – KU HC
GA RDE – GA HC
(0)
(1)
(17)
Czech Republic.
Sweden, Korea Rep., Japan,
Finland, Switzerland, France,
Germany, Netherlands,
Denmark, United Kingdom,
Australia, Norway, Ireland,
Iceland, Austria, Slovenia,
Singapore.
KU RDE – CU HC
KU RDE – KU HC
KU RDE – GA HC
(3)
(6)
(11)
China, Uganda, Bolivia.
Moldova, Brazil, Romania,
Hungary, Latvia, Turkey.
Belarus, Italy, Slovak Rep.,
New Zealand, Croatia, Spain,
Russian Federation,
Lithuania, Chile, Bulgaria,
Estonia.
CU RDE – CU HC
CU RDE – KU HC
CU RDE – GA HC
(5)
(3)
(1)
Tunisia, Kyrgyz Rep.,
Madagascar, Burkina Faso,
Senegal.
Mexico, Kuwait, Thailand.
38
Azerbaijan.
UNCTAD, Geneva
Indices of Technological Development
1995 R&D EXPENDITURE VERSUS HUMAN CAPITAL RANKINGS
Key data omissions: Australia, Switzerland, Bolivia Bulgaria, Belarus.
GA RDE – CU HC
GA RDE – KU HC
GA RDE – GA HC
(0)
(1)
(19)
Czech Republic.
Sweden, Japan, Korea Rep.,
U.S., Finland, France,
Germany, Israel,
Netherlands, Denmark,
United Kingdom, Norway,
Slovenia, Canada, Ireland,
Belgium, Iceland, Austria,
Singapore.
KU RDE – CU HC
KU RDE – KU HC
KU RDE – GA HC
(2)
(6)
(8)
Brazil, China.
Moldova, Hungary, Poland,
Romania, Lithuania, Chile.
Slovak Rep., New Zealand,
Italy, Croatia, Spain, Russian
Federation, Portugal,
Estonia.
CU RDE – CU HC
CU RDE – KU HC
CU RDE – GA HC
(8)
(8)
(2)
Uganda, Tunisia,
Madagascar, Burkina Faso,
Rwanda, Bangladesh,
Ecuador, Senegal.
Latvia, Argentina, Turkey,
Kazakhstan, Azerbaijan.
Mexico, Egypt, Kyrgyz Rep.,
Kuwait, Thailand.
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Indices of Technological Development
Data Appendix
Variable Sources and Definitions:
1. R&D Expenditure: Expenditures for R&D are defined as current and capital expenditures
on creative, systematic activity intended to increase the stock of knowledge, including
fundamental and applied R&D leading to new devices, products, or processes.
Source: World Bank.
2. R&D Personnel: Scientists/engineers are defined as those trained to work in science
engaged in professional R&D activity (requiring completion of tertiary education).
Technicians have been defined as those who have received vocational or technical training in
any branch of knowledge or technology (most of these jobs require three years beyond the
first stage of secondary education). Source: World Bank.
3. Tertiary Enrolment: This includes tertiary level students in higher education by
technical field (engineering, science, maths and computers, and social/behavioural sciences)
as a percentage of total population. Source: World Bank.
4. High-Tech Exports: HTE are defined as products with high R&D intensity. They include
high-tech products such as in aerospace, computers, pharmaceuticals, scientific instruments,
and electrical machinery. Source: World Bank.
5. Merchandise: Merchandise exports show the f.o.b. value of goods provided to the rest of
the world valued in U.S. dollars. Data are in current U.S. dollars. Source: World Bank.
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Indices of Technological Development
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