METHODOLOGY AND WORLD BANK OPERATIONS Derek H. C. Chen* and Carl J. Dahlman** Abstract This paper highlights the importance of knowledge for… [628504]

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METHODOLOGY AND WORLD BANK OPERATIONS Derek H. C. Chen* and Carl J. Dahlman** Abstract This paper highlights the importance of knowledge for… [628504]

Byadmin ianuarie 1, 2024

THE KNOWLEDGE ECONOMY, THE KAM
METHODOLOGY AND WORLD BANK OPERATIONS

Derek H. C. Chen* and Carl J. Dahlman**

Abstract

This paper highlights the importance of knowledge for long-term economic growth. It presents the
concept of the knowledge economy, an economy wher e knowledge is the main engine of economic
growth. The paper also introduces the knowledge economy framework, which asserts that sus-
tained investments in education, innovation, information and communication technologies, and a conducive economic and institutional environment will lead to increases in the use and creation of
knowledge in economic production, and consequently result in sustained economic growth. In or-
der to facilitate countries trying to make th e transition to the knowledge economy, the Knowledge
Assessment Methodology (KAM) was developed. It is designed to provide a basic assessment of
countries’ readiness for the knowledge economy, and identifies sectors or specific areas where policymakers may need to focus more attention or future investments. The KAM is currently being
widely used both internally and externally to the World Bank, and frequently facilitates engage-
ments and policy discussions with government officials from client countries.

World Bank Institute
Washington, D.C.

* Economist, Knowledge for Development Program, World Bank Institute.
** Luce Professor of International Affairs and Information Age Technologies, Georgetown University, and
Expert Consultant, Knowledge for Development Program, World Bank Institute. The views expressed in this
paper do not necessarily represent those of the World Bank. We are grateful to Robert Vitro, Aimilios
Chatzinikolaou, Anuja Utz, Alexey Volynets and Yevgeny Kuznetsov for very helpful comments and sug-
gestions.

Copyright © 2006
The International Bank for Reconstruction
and Development/The World Bank
1818 H Street, N.W.
Washington, D.C. 20433, U.S.A.

The World Bank enjoys copyright under protocol 2 of the Universal Copyright Convention. This
material may nonetheless be copied for research, e ducational, or scholarly purposes only in the
member countries of The World Bank. Material in this series is subject to revision. The findings,
interpretations, and conclusions expr essed in this document are enti rely those of the author(s) and
should not be attributed in any manner to the Wo rld Bank, to its affiliated organizations, or the
members of its Board of Executive Directors or the countries they represent.

The Knowledge Economy, The KAM Methodology And World Bank Operations
Derek H. C. Chen and Carl J. Dahlman
2006. 42 pages. Stock No. 37256

Contents

1. Introduction 1
2. Knowledge and Economic Development 2
2.1 Knowledge Revolution and Global Competition 2
2.2 The Knowledge Economy Framework 4
2.3 The Pillars of the Knowledge Economy 5
3. The Knowledge Assessment Methodology (KAM) 9
3.1 The Basic Scorecard 10
3.2 The Knowledge Economy Index 11
3.3 Custom Scorecards 12
4. The KAM and World Bank Operations 1 4
5. Conclusion 1 6
Annexes:
Annex 1 KAM Normalization Procedure 17
Annex 2 Decomposition of Economic Growth for South Korea 18
Annex 3 Real GDP per Capita Projections for Mexico 19
References 2 1
Figures and Tables 2 4

iii

1. Introduction
In the past decade or so, much research has been conducted on pro ductivity-led economic growth and
its determinants. A major reason is the widespread belief that economic growth due to rapid factor
accumulation is subject to diminishing returns, and he nce is not sustainable. Recently, there has been
a growing interest in the contribution of knowledge to total factor productivity growth, and conse-
quently to sustainable long- term economic development.

This paper highlights the importance of the use and creation of knowledge for long-term economic
growth. It discusses the concept of the know ledge economy, which is essentially an economy where
knowledge is the main engine of economic growth. The paper introduces the knowledge economy
framework, which holistically encomp asses elements or pillars such as education and training, innova-
tion and technological adoption, the information infrastructure, and a conducive economic incentive
and institutional regime. The framework asserts that sustained investments in these knowledge econ-
omy pillars will lead to the availability of knowle dge and its effective use for economic production.
This would tend to increase the growth rate of total factor productivity, and consequently result in sus-
tained economic growth.

This paper also introduces a simple knowledge economy benchmarking tool, the Knowledge Assess-
ment Methodology (KAM), which was developed by the World Bank Institute. The KAM is a user-
friendly interactive Internet-based tool that provi des a basic assessment of countries’ and regions’
readiness for the knowledge economy. It is designe d to help client countries identify problems and
opportunities that they may face, and where it may n eed to focus policy attention or future invest-
ments, with respect to making the transition to the knowledge economy. The unique strength of the
KAM lies in its cross-sectoral approach that allows a holistic view of the wide spectrum of factors relevant to the knowledge economy. This, together with its transparency, simplicity and versatility,
has led to the KAM being widely used both internally and externally to the World Bank, and it is fre-
quently use for facilitating engagements and policy di scussions with government officials from client
countries.

This paper is organized as follows: Section 1 underscores the importance of knowledge to economic development. It also presents the knowledge econom y framework and provides a brief survey of the
literature showing the importance of the knowledge economy pillars for economic growth. Section 2
introduces the Knowledge Assessment Methodology and provides examples of its various modes us-ing an array of countries from around the world. Following this, the features of the KAM that have led to its widespread use, especially in terms of facilitating policy dialogue with country clients are described in detailed in Section 3. Secti on 4 highlights the key points of the paper.

1

2 Derek H. C. Chen and Carl J. Dahlman

2. Knowledge and Economic Development
2.1 Knowledge Revolution and Global Competition
Over the past quarter century, the rate of knowledge creation and dissemination has increased signifi-
cantly. One reason is due to the rapid advan ces in information and communications technologies
(ICTs) that have significantly decreased the costs of computing power and electronic networking.
With the increased affordability, th e usage of computing power and electronic networking has surged,
along with the efficient dissemination of existing know ledge. Modern ICTs also enable researchers in
different locations to work together, which conse quently enhance the productivity of researchers, re-
sulting in rapid advances in research and development and the generation of new knowledge and tech-
nologies. One indicator of the creation of new know ledge and technologies is the number of patents
granted by the United States Patent and Trademar k Office (USPTO) each year. From Figure 1, it can
be seen that the total number of patents grante d by the USPTO increased from 71,114 in 1981 to
187,053 in 2003. Note that the share of patents granted to inventors outside of the United States has also grown from 39 percent in 1981 to 47 percent in 2003. The increased rate of creation of new
knowledge and technologies thus reflects a recent global trend.

The increased speed in the creation and dissemination of knowledge has led to the rapid spread of
modern and efficient production techniques, plus the increased probability of leapfrogging, which has
consequently resulted in the world economy becoming much more competitive. The share of world trade (exports and imports) in world GDP, which is an indicator of globalization and competition in
the global economy, has increased from 24 percent in 1960 to 47 percent in 2002 (Figure 2).
1 Thus,
the knowledge revolution, together with increased globalization, presents significant opportunities for promoting economic and social development. Howeve r, countries also face the very real risk of fal-
ling behind if they are not able to keep up with the pace of rapid change.

In addition to the higher level of competition, the na ture of competition has been changing. It has
evolved from one that was just based on cost, to one where speed and innovation are also essential.
Commodity production is usually allocated to lowest cost producers, but intense competition resulting
from globalization tends to drive profits from commodity production to nearly zero. As such, it has become crucial to derive additio nal value added from various means of product differentiation via in-
novative designs, effective marketing, efficient distri bution, reputable brand names, etc. Thus, to
prosper it is critical to be able to contribute prod uctively to global value chains and to generate own
new value chains, and the key part of which is not necessarily production, but innovation and high-value services.

In light of the above, sustained economic growth in the era of this new world economy depends on
developing successful strategies that involve the su stained use and creation of knowledge at the core
of the development process. At lower levels of de velopment, which typically implies lower levels of
science and technology capability, knowledge strate gies typically involve the tapping of existing

1 International trade increases the numb er of consumers and producers participating in the market and hence in-
creases the level of competition.

The Knowledge Economy, the KAM Methodology and World Bank Operations 3

global knowledge and adoption of such foreign tech nologies to local conditions in order to enhance
domestic productivity. At higher leve ls of development, which typica lly implies higher levels of sci-
ence and technology capability, knowledge strategi es also hinges critically on domestic innovative
effort and underlie the move to produce products and services that higher value-added in order to be
consistent with the high wages that are characteristic of these economies.

Figure 3 presents the decomposition of South Korea’s economic growth over the past four decades,
and clearly highlights the contribution of knowledge , represented here by total factor productivity
(TFP), to South Korea’s economic miracle.2 In 1960, Korea’s real GDP per capita was around
US$1,110, and increased by eleven-fold to US$12,200 in 2003. In contrast, Mexico’s real GDP per capita experienced a slightly more than two-fold increase, from US$2,560 to US$5,800 over the same
period. Note that without the contribution of k nowledge, Korea’s real GDP per capita in 2003 would
still be below that Mexico’s.
3

Similarly, Figure 4 demonstrates the enormous potential of knowledge use and creation in sustaining long-term economic growth by presenting alternative projections real GDP per capita for the years 2004 to 2020, assuming different TFP growth rates for Mexico. It can be seen that with a TFP growth
rate of 3 percent per annum, Me xico would attain South Korea’s 2003 real GDP per capita by 2020.
4

2 It is well accepted in the economics literature that to tal factor productivity depends on the availability of
knowledge. For example, Romer (1986, 1990) and Lucas (1 988) argued that TFP levels depend on the stock of
knowledge or human capital. Grossman and Helpman (1991) postulated that imported goods embodied foreign
technology and hence imports would lead to increases in TFP. Similarly, Coe and Helpman (1995) found that
for a sample of developed countries both domestic and foreign R&D had significant impact on TFP.
3 Technical details regarding the growth decomposition illustrated in Figure 3 are presented in the Annex.
4 Note that for all 4 projections, capital, labor and population were all assumed to grow at their 1991-2003 aver-
age annual growth rates for Mexico, wh ich are 3.68 percent, 2.70 percent a nd 1.59 percent, respectively. Tech-
nical details regarding the TFP and real GDP per capita pr ojections illustrated in Figure 4 are presented in the
Annex.

4 Derek H. C. Chen and Carl J. Dahlman

2.2 The Knowledge Economy Framework
With sustained use and creation of knowledge at the center of the economic development process, an
economy essentially becomes a Knowledge Economy. A Knowledge Economy (KE) is one that util-
izes knowledge as the key engine of economic grow th. It is an economy where knowledge is ac-
quired, created, disseminated and used eff ectively to enhance economic development.5

It has been found that the successful transition to the Knowledge Economy typically involves elements
such as long-term investments in education, deve loping innovation capability, modernizing the infor-
mation infrastructure, and having an economic environment that is conducive to market transactions. These elements have been termed by the World Bank as the pillars of the Knowledge Economy and
together they constitute th e Knowledge Economy framework.

More specifically, the four pillars of the Knowledge Economy (KE) framework are:

• An economic incentive and institutional regime that provides good economic policies and institu-
tions that permit efficient mobilization and alloca tion of resources and stimulate creativity and in-
centives for the efficient creation, dissemin ation, and use of existing knowledge.
• Educated and skilled workers who can continuously upgrade and adapt their skills to efficiently
create and use knowledge.
• An effective innovation system of firms, research centers, universities, consultants, and other or-
ganizations that can keep up with the knowledge revolution and tap into the growing stock of
global knowledge and assimilate and adapt it to local needs.
• A modern and adequate information infrastructure that can facilitate the effective communication,
dissemination, and processing of information and knowledge.

The Knowledge Economy framework thus asserts that investments in the four knowledge economy pillars are necessary for sustaine d creation, adoption, adaptation and use of knowledge in domestic
economic production, which will consequently result in higher value added goods and services. This
would tend to increase the probability of economic success, and hence economic development, in the
current highly competitive and globalized world economy.

5 Contrary to some beliefs, the concept of the Knowle dge Economy does not necessarily revolve around high
technology or information technology. For example, the application of new techniques to subsistence farming
can increase yields significantly or th e use of modern logistical services can enable traditional craft sectors to
serve broader markets than before.

The Knowledge Economy, the KAM Methodology and World Bank Operations 5

2.3 The Pillars of the Knowledge Economy
We elaborate in detail on each of the knowledge economy pillars in this section. We also briefly re-
view empirical literature that shows that all of the pillars are important determ inants of long-term eco-
nomic growth, thereby lending empirical support to the knowledge economy framework.

Educated and Skilled Labor Force
A well-educated and skilled population is essential to the efficient creation, acquisition, dissemination
and utilization of relevant knowledge , which tends to increase total factor productivity and hence eco-
nomic growth.

Basic education is necessary to increase peoples’ capacity to learn and to use information. On the
other hand, technical secondary-level education, a nd higher education in engineering and scientific
areas is necessary for technological innovation. Note that the production of new knowledge and its
adaptation to a particular economic setting is gene rally associated with higher-level teaching and re-
search. For example, in the industrial economies, university research accounts for a large share of
domestic R&D. Technical secondary-level education is also required for the process of technological
adaptation of foreign technologies for use in dom estic production processes. Such training is neces-
sary to monitor technological trends, assess what is relevant for the firm or economy, and assimilate
new technologies. A more educated population also tends to be relatively more technologically so-
phisticated. This generates local quality sensitive demand for advanced goods, which in turns tends to
stimulate local firms to innovate and design tec hnologically sophisticated goods and production tech-
niques.

Most empirical cross-country studies of long-run growth now include some measure of human capital
and recent studies of international differences in output per worker6 and economic growth rates have
focused the role of human capital in economic development7. Regardless of the underlying model, it
is a fairly robust finding that a country’s human cap ital is almost always id entified as an essential in-
gredient for achieving growth. For example, Ba rro (1991), using cross-section data for 98 countries
for the period 1960 to 1985 and the 1960 values of school enrollment rates at the secondary and pri-
mary levels as proxies for initial human capital, f ound that both school enrollment rates had statisti-
cally significant positive effects on growth of per cap ita real GDP. Similarly, Cohen and Soto (2001),
using cross-country time-series data on educational a ttainment or average years of school, finds statis-
tically significant positive effects of education on economic growth. Ha nushek and Kimko (2000)
take an alternative approach by focusing on the effects of educational quality on economic growth.
Using international test scores as a proxy for the qua lity of educational systems, they find that educa-
tional quality does exert positive effects on economic growth.

6 See Temple (1999), Krueger and Lindal (2000).
7 See Mankiw et al. (1992), Benhabib and Spiegel (1994), Hall and Jones (1999).

6 Derek H. C. Chen and Carl J. Dahlman

An Effective Innovation System
Economic theory indicates that technical progress is a major source of productivity growth and an ef-
fective innovation system is key for such technical advancement.8 An innovation system refers to the
network of institutions, rules and procedures that in fluences the way by which a country acquires, cre-
ates, disseminates and uses knowledge . Institutions in the innovati on system include universities, pub-
lic and private research centers and policy think tanks. Non-governmental organizations and the gov-
ernment are also part of the innovation system to the extent that they also produce new knowledge. An effective innovation system is one that provid es an environment that nurtures research and devel-
opment (R&D), which results in new goods, new pr ocesses and new knowledge, and hence is a major
source of technical progress.
9

There have been a number of studies that show that innovation or the generation of technical knowl-edge has substantial positive effect s on economic growth or productivity growth. For example, Led-
erman and Maloney (2003), using re gressions with data panels of fi ve-year averages between 1975 to
2000 over 53 countries, finds that a one-percentage point increase in the ratio of total R&D expendi-ture to GDP increases the growth rate of GDP by 0.78 percentage points. Guellec and van Pottels-
berghe (2001) investigated the long-term effects of various types of R&D on multifactor productivity
growth using panel data for the OE CD over the period 1980-98. They find that business, public and
foreign R&D all have statistically significant positive effects on productivity growth
10. Adams
(1990), using the number count of academic scien tific papers of various scientific fields11 to proxy for
the stock of knowledge, finds that technical knowle dge contributed significantly to the total factor
productivity growth of U.S. manufacturing industries for the period 1953-1980.

Currently, the majority of technical knowledge is produced in the developed countries: more than 70
percent of patenting and production of scientific and technical papers are accredited to researchers in
industrialized countries. The disparity in the pr oduction of technical knowledge per capita between
developed and developing countries is even greater th an the disparity in income. However, note that
domestic technological innovation is not the sole source of generation of technical knowledge. There are many ways for developing countries to avoid re inventing the wheel and tap into, adopt and adapt
technical knowledge that was created in other devel oped countries. Therefore, a key element of a de-
veloping country’s innovation strategy is to find the best ways to tap into the growing global knowl-edge base and to decide where and how to deploy its domestic R&D capability.

8 See Solow (1957) and Romer (1986, 1990).
9 The OECD defines R&D to “comprise of creative work u ndertaken on a systemic basis in order to increase the
stock of knowledge and the use of this stock of knowledge to devise new applications” (OECD, 1993).
10 Guellec and van Pottelsberghe (2001) define public R&D as R&D performed by government and higher edu-
cation sectors, and foreign R&D as business R&D performed in other 15 OECD countries.
11 Adams (1990) used worldwide annual counts of publications in nine sciences: agriculture, biology, chemistry,
computer science, engineering, geology, math ematics and statistics, medicine, and physics.

The Knowledge Economy, the KAM Methodology and World Bank Operations 7

An Adequate Information Infrastructure
Information and communications technologies (ICT) in frastructure in an economy refers to the acces-
sibility, reliability and efficiency of computers, pho nes, television and radio sets, and the various net-
works that link them. The World Bank Group defines ICT to consist of hardwa re, software, networks,
and media for collection, storage, processing transmis sion, and presentation of information in the form
of voice, data, text, and images. They range from the telephone, radio and television to the Internet
(World Bank, 2003a and 2003b).

ICTs are the backbone of the knowledge economy and in recent years have been recognized as an ef-
fective tool for promoting economic growth and sust ainable development. With relatively low usage
costs and the ability to overcome distance, ICTs have revolutionized the transfer of information and
knowledge around the world. Over the past decade, there has been a series of studies that show that
both ICT production and ICT usage have contributed to economic growth12. ICT producing sectors
have experienced major technological advancements , which have showed up as large gains in total
factor productivity at the level of the economy. As for the non-ICT producing sectors, investment in
ICT has resulted in capital deepening, and hen ce increases in labor productivity. More importantly,
various studies have produced empirical evidence s uggesting that substantial productivity gains have
been experienced from ICT usage13.

One of the most obvious benefits associated with ICT usage is the increased flow of information and knowledge. Because ICTs allow information to be tr ansmitted relatively inexpensively and efficiently
(in terms of cost), ICT usage tends to reduce uncertain ty and transactions costs of participating in eco-
nomic transactions. This, in turn, tends to lead to an increase in the volume of transactions leading to
a higher level of output and productivity. Moreover, with the increased flow of information, technolo-
gies can be acquired and adapted more easily agai n leading to increased innovation and productivity.

Apart from increasing the supply of information a nd knowledge, ICTs are able to overcome geo-
graphic boundaries. Therefore, international buyers and sellers are increasingly able to share informa-
tion, reduce uncertainty, reduce transactions costs, and increase competitiveness across borders, all of which results in a more efficient global marketpl ace. Also, production processes can be outsourced,
based on comparative advantage, across national bo undaries resulting in further global efficiency
gains. Market access and coverage also tend to expand, along with increased access to global supply
chains.

12 See Pilat and Lee (2001), Jorgenson and Stiroh (2000), Oliner and Sichel (2000), Whelan (2000), and Schreyer
(2000).
13 Some national studies point to the use of ICT as an important factor in improved TFP growth. For example,
see the Economic Report of the President (Council of Economic Advisors, 2000, 2001), Whelan (2000), Oliner
and Sichel (2000), and Jorgenson and Stiroh (2000).

8 Derek H. C. Chen and Carl J. Dahlman

A Conductive Economic and Institutional Regime
The final pillar of the knowledge economy framework , but by no means the least, is the economic and
institutional regime of the economy. The economic and institutional regime of an economy needs to
be such that economic agents have incentives for th e efficient use and creation of knowledge, and thus
should have well-grounded and tran sparent macroeconomic, competition and regulatory policies.

A “knowledge-conducive” economic regime should be in general one that has the minimal number of
the price distortions. For example, it should be ope n to international trade and be free from various
protectionist policies in order to foster competiti on, which in turn will encourage entrepreneurship14.
Government expenditures and budget de ficits should be sustainable, a nd inflation should be stable and
low15. Domestic prices should also be largely free fro m controls and the exchange rate should be sta-
ble and reflect the true value of the currency. The fi nancial system should be one that is able to allo-
cate resources to sound investment opportunities an d redeploy assets from failed enterprises to more
promising ones.16

Features of a conducive institutional regime include an effective, accountable and corrupt-free gov-
ernment and a legal system that supports and enfo rces the basic rules of commerce and protects prop-
erty rights. Intellectually property rights should be also protected and strongly enforced. If intellec-tual property rights are not adequately protected a nd enforced, then researchers/scientists will have
less incentive to create new technological knowledge a nd even in the event that knowledge is created,
the lack of intellectual property rights protection will greatly hamper dissemination of such new knowledge.
17

14 See Sachs and Warner (1995) and Bosworth and Collins (2003).
15 See Barro (1991).
16 See Levine et al. (2000).
17 See Knack and Keefer (1995) and Kaufmann et al. (2002, 2003)

The Knowledge Economy, the KAM Methodology and World Bank Operations 9

3. The Knowledge Assessm ent Methodology (KAM)
The transition to becoming a knowledge economy requir es long-term strategies that focus on develop-
ing the four KE pillars. Initially this means th at countries need to understand their strengths and
weaknesses, and then act upon them to develop appr opriate policies and investments to give direction
to their ambitions and mechanisms to enable the policy makers and leaders to monitor progress against
the set of goals.

To facilitate this transition process, the World Bank Institute’s Knowledge for Development (K4D)
Program has developed the Knowledge Assessme nt Methodology (KAM – www.worldbank.org/kam),
which is an Internet-based tool that provides a basic assessment of countries’ and regions’ readiness
for the knowledge economy. The KAM is a use r-friendly interactive dia gnostic and benchmarking
tool that is designed to help client countries unde rstand their strengths and weaknesses by comparing
themselves with neighbors, competitors, or other countries that they may wish to emulate based on the
four KE pillars. The KAM is therefore useful for identifying problems and opportunities that a coun-
try may face, and where it may need to focus policy at tention or future investments, with respect to
making the transition to the knowledge economy. Th e unique strength of the KAM lies in its cross-
sectoral approach that allows a holistic view of the wide spectrum of factors relevant to the knowledge
economy.

Comparisons in the KAM are made on the basis of 80 structural and qualitative variables that serve as proxies for the four knowledge economy pillars. Currently, there are 128 countries and 9 regional groupings that are available in the KAM and these are listed in Table 1. The comparisons are pre-sented in a variety of charts and figures that visi bly highlight similarities and differences across coun-
tries and these will be discussed in some detail belo w. The data on which the KAM is based are all
published by reputable institutions that are at the fo refront of gathering and producing country statis-
tics that is reliable and internationally consistent . The data are continuously updated and the country
coverage is expanded whenever possible.

The most recent version of the KAM, KAM 2005, is able to provide assessments of a country or region
position in terms the Knowledge Economy on:

• A global scale, when compared to all 128 count ries that are available in the KAM database;
• A regional scale, when compared with countries in the same region
• The basis of human development, when compared with other countries in the same category of human development
18 and
• The basis on income levels, when compared w ith other countries of the same income level
category.19

18 The categories for human developm ent are as follows: High human development (HDI >= 0.800); Medium
human development (0.799 <= HDI <= 0.500); and Low human development (HDI < 0.500).
19 Income-level categories are based on the 2004 World Development Indicator categories, which use the World
Bank estimates of 2002 GNI per capita. The groupings are as follows: low income ($735 or less); lower middle
income ($736-$2,935); upper middle income ($2,936-$9,075) and high income ($9,076 or more).

10 Derek H. C. Chen and Carl J. Dahlman

Because the 80 variables that are contained in the KAM span over different ranges of values, all vari-
ables are normalized from 0 (weakest) to 10 (stronge st) and the 128 countries and 9 regions are ranked
on an ordinal scale. The normalization procedure for the KAM 2005 is presented in the Annex.

Given its ease of use, transparency, accessibility over the Internet, the KAM has been widely used by
government officials, policy makers, researchers, representatives of civil society, and the private sec-
tor. The KAM has also been used by multilateral and bilateral aid ag encies, research institutions, con-
sultants and others to undertake preliminary si ngle or multi-country know ledge economy assessments.

3.1 The Basic Scorecard
One of the more frequently used modes of the KAM is the basic scorecard. The KAM basic scorecard
provides an overview of the performance of a specific country or region in terms of all 4 pillars of the
knowledge economy. It includes 14 standard variab les: two performance variables and 12 knowledge
variables, with 3 variables representing each of the 4 pillars of the knowledge economy (Table 2). While there may be more robust data describi ng a country's preparedness for a knowledge-based
economy, the 12 selected variables are generally availa ble for a larger time series and remain regularly
updated for the vast majority of the countries that are assessed by the KAM. The comparisons for the
14 basic scorecard variables can be made for the year 1995 or for the most recent period, or for both in
order to show the movement over time.

There are various ways available to the use to illustrate the KAM basic scorecard, which includes the spider, diamond, and bar charts. Figure 5a illustra tes the basic scorecard spider chart with Finland as
an example. The center of the chart denotes the mi nimum normalized value of 0, while the outer pe-
rimeter of the chart denotes the maxi mum normalized value of 10. Thus, a “bigger” or “fuller” spider
chart implies that the country or region is better positioned in terms of the k nowledge economy. Both
values for 1995 and the most recent year, which is cu rrently 2002, are shown in Figure 5a. The actual
or raw values of the variables for most recent year are provided in the parentheses.
20

Finland is overall very strong in many of the knowledge indicators. For example, it is very strong in terms of regulatory quality with a normalized value of 9.92, which implies that Finland ranks in the
99th percentile in terms of regulatory quality. On th e other hand, it is not as strong in terms of tariff
and nontariff barriers with a normalized value of 6.59, implying that it ranks only in the 65
th percen-
tile. The innovation pillar is probably the strongest pillar for Finland, with rankings above the 90th
percentile in all three innovation indicators. In terms of changes over time, Finland has made im-provements in innovation pillar but has lost some ground for the ICT pillar.

Note that, because countries are ranked on an ordina l scale, the KAM illustrates the relative perform-
ance of a country as compared to other countries in the KAM database. As such, when a country’s
performance in a specific variable is indicated to have declined, it could have occurred for two rea-
sons. First, the country’s performance in that variable declined, resulting in lower values in absolute

20 The KAM basic scorecard provides the option of displaying the actual, normalized or no values in the chart.

The Knowledge Economy, the KAM Methodology and World Bank Operations 11

terms. Alternatively, the country’s performance c ould have improved and resulted in large absolute
values, but other countries experienced even larger improvements, leading to the country’s ordinal
ranking falling and resulting in a lower value in relative terms.21

Figure 5b presents the development of Slovakia in terms of the knowledge economy using the basic
scorecard plotted with the diamond chart. Here onl y aggregate performance in each of the four KE
pillars is shown. The value for each pillar is cons tructed as the simple average of the normalized val-
ues of the 3 knowledge indicators that proxy for each pillar in the basic scorecard. As it can been
seen, Slovakia’s performance in terms of the knowledg e economy is relatively strong, with all of pil-
lars ranking well above the 50th percentile. Slovakia’s strongest pillar is the ICT pillar with its per-
formance ranking above the 70th per centile, while its weakest is the economic incentive regime with a
ranking around the 57th percentile. It also can be said that Slovakia has made significant progress to-
wards the knowledge economy since 1995, especially in terms of the innovation and ICT pillars.

Another mode of the KAM enables the basic scorecards of up to three countries or regions to be plot-
ted on one chart. Figure 5c illustrates this mode using the most recent data for Singapore, Malaysia
and Indonesia as examples.

As can be seen, Singapore is the most developed in terms of the knowledge economy among the three East Asian countries, with all of its knowledge indicators being ranked in the 80
th percentile or higher,
except for those in the education pillar. Malaysia comes in next with its indicators coming in between the 30
th and 80th percentiles. The ICT pillar appears to be Malaysia’s strong point with all of the indi-
cators being in the 60th to 80th percentile range. Indonesia is the weakest in terms of the knowledge
economy, with all of its indicators ranking below the 45th percentile.

3.2 The Knowledge Economy Index
The KAM Knowledge Economy Index (KEI) is an aggr egate index that represents the overall level of
development of a country or region in the Knowle dge Economy. It summarizes performance over the
four KE pillars and is constructed as the simple av erage of the normalized values of the 12 knowledge
indicators of the basic scorecard. The basic scorecard can be thus seen as a disaggregated representa-tion of the Knowledge Economy Index.

While there are several ways to illustrate perfo rmance in the KEI, the Global Knowledge Economy
Comparisons mode presents a simple way to visua lize and comparing countries and regions, in terms
of their development towards a knowledge economy, by plotting them in a scatter plot based on their
relative performance in the KEI for two points in time: 1995 and most recent (Figure 6).22 The hori-
zontal axis plots countries’ and regions’ performance in the KEI in 1995, while the vertical axis plots

21 For this reason, both actual and normalized valu es are available for each variable in the KAM.
22 The user may opt to demonstrate performance in th e aggregate Knowledge Economy Index (KEI) or the indi-
vidual pillars that define them: Economic Incentive Re gime, Education, Innovation and Information Infrastruc-
ture. Values for each pillar are constructed as the simple average of the normalized values of the respective 3
variables in the basic scorecard.

12 Derek H. C. Chen and Carl J. Dahlman

countries’ and regions’ performance in the KEI for th e most recent year, currently 2002. The diagonal
line represents the locus of points where the KEI values in 1995 and in the most recent year are equal.
As such, countries and regions that appear above th e diagonal line have made an improvement in the
KEI since 1995, and countries that appear below dia gonal line have experienced deterioration in terms
of the KEI.

The countries that appear in the KEI scatter plot can be loosely grouped into three broad categories in
terms of their development towards the knowledge economy. Firstly, located near the top-right corner
of the scatter plot, are a group of countries that are in the advance stages of development in terms of
the knowledge economy. These are mostly the economies of the OECD and those of the East Asian Newly Industrializing Economies (NIEs). Next, arou nd the center of the scatter plot are a group of
countries that are midway through the transition to the knowledge economy. Majority of the countries
are in this category which typically includes the middle income countries from Europe and Central Asia, East Asia, Middle East and North Africa, and Latin America. Lastly, countries that have just embarked on the path to becoming a knowledge ec onomy appear around the bottom-left portion of the
scatter plot, and these typically include the low-income economies from Africa and South Asia.

Figure 6 highlights the relative KEI performance of a number of countries from the Middle East and North Africa region
23, and it can be seen that all of them fall between the 15thth and 60th percentile for
both 1995 and the most recent year. In addition, note that Morocco, Tunisia, Egypt, Jordan and Saudi Arabia appear above the diagonal line, indicating that they have improved in the KEI since 1995. In contrast, Pakistan, Turkey and Lebanon appear belo w the diagonal line, indicating that their perform-
ance in the KEI has worsened since 1995.

3.3 Custom Scorecards
Apart from the basic scorecard, the KAM also provides the user with the flexibility to customize vari-ous combinations of variables to be included in benchmarking comparisons. The “Create Your Own
Scorecard” mode allows the user to compare any tw o countries or regions for any of the 80 variables
included in the KAM database (See Table 3 for a list of the 80 variables). Very frequently, this mode is used to generate scorecards that focus solely on individual pillars or sectors of the knowledge econ-
omy.

For example, Figure 7 presents all the available va riables for the economic and institution regime for
Brazil. We see that Brazil is relatively strong and performing better than the 50th percentile for indica-
tors such as intellectually property protection, soundness of banks, local competition, voice and ac-countability, and press freedom. On the other hand, Brazil is relatively weak in areas such reduction
in tariff and non-tariff barriers, and exports of goods and services. Figure 8 illustrates the KAM vari-
ables for education and training for Uruguay, and we see that Uruguay is relatively strong in indicators
such as average years of schooling, secondary and tert iary enrollments. Ecuador’s performance in the
innovation and technological adoption pillar are shown in Figure 9. For most of the variables, Ecua-

23 In the KAM Global Knowledge Economy Comparisons mode, the user can select up to five countries, in addi-
tion to a default selected group of countries and regions, to be plotted in the scatter plot.

The Knowledge Economy, the KAM Methodology and World Bank Operations 13

dor ranks below the 50th percentile, with exceptions being the cost of registering a business, the level
of foreign direct investment and the amount of ro yalty payments. Lastly, we use Venezuela as an ex-
ample to illustrate the ICT pillar scorecard (Figure 10). As can be seen, Venezuela performs relatively
well for e-government and the circulation of newspapers, and ranks at or below the 50th percentile for
the rest of the ICT variables.

14 Derek H. C. Chen and Carl J. Dahlman

4. The KAM and World Bank Operations
The KAM has successfully been used in facilitating engagements with World Bank country teams as
well as policy discussions with government officials from client countries. Moreover, the KAM has
been broadly applied to various economic and sector work such as those for China, India, South Ko-
rea, Japan, Finland, Mexico, Argentina, Chile and Sl ovakia. We highlight in this section the features
of the KAM as a tool that allows it to play a critical role in World Bank country operations.

Firstly, the KAM is based on the knowledge econom y framework, which is holistic in nature as it
identifies and integrates together four areas that are crucial for knowledge to contribute effectively to
sustained economic growth. The KAM and this fr esh approach to economic development tends to
bring together specialists and policymakers in the fields of education and life long-learning, R&D and
innovation, ICT infrastructure, and economic environm ent and institutions to work together on formu-
lating integrative developmental strategies. In a ddition, the World Bank takes conscious efforts to
include private sector executives, academics and re presentatives from think-tanks, so as to maximize
civil participation in discussions relating to economic developing strategies. Discussions relating to
the KAM and the knowledge economy therefore tend to be participated by diverse groups of individu-
als representing various fields of specialization a nd different facets of government and society. These
groups typically do not interact together in a polic y making environment. However, discussions relat-
ing to the KAM and knowledge economy approach presents an opportunity for these groups to come together to discuss, share and exchange ideas and viewpoints with the objective deriving coherent sets of policies or strategies that allow knowledge and its use to drive long-term economic development.

The user-friendliness of the KAM has certainly contributed to its widespread use. It requires virtually no training other than some basic familiarization that the users can undertake for themselves online.
As illustrated above, results from the KAM can be pr esented in a range of comparative charts, figures
and data tables that is clear and concise, with signi ficant visual impact. Furthermore, given that data
sources for all variables are clearly listed, the KAM is a very transparent tool that is constructed from
data that is published by reputable sources. A dherence to reputable data sources ensures a certain
level of consistency in the data collection acro ss countries. Also, the KAM’s ordinal normalization
and ranking procedure is relatively transparent, st raightforward and clearly described. These features,
together with the unrestricted on-line access have c ontributed to Bank countr y teams and country cli-
ents finding the KAM to be a very useful tool for discussions and for use in highlighting strengths and weaknesses in various country policy reports, especia lly when coupled with more in-depth economic
analysis. As such, results from the KAM have been routinely used to initiate policy dialogue within a
country and to identify issues for further investigation.

Recall that the KAM has the ability to perform analys is or benchmarking using variables or indicators
that are beyond the 14 pre-selected variables in the KAM basic scorecard. As it has been seen, the
user has the flexibility to choose to benchmark c ountries using any of the 80 variables in the KAM
database. This is an important feature as certain variables may be more relevant for some countries,
but less relevant for other countries. This option significantly increases the versatility KAM by allow-ing the user to select the variables that are the most relevant for the country being analyzed. In addi-

The Knowledge Economy, the KAM Methodology and World Bank Operations 15

tion, with this option, the KAM has the ability to pe rform analysis on a sectoral or individual KE pillar
basis. As such, while the KAM is based on the holistic knowledge economy framework, it is suffi-
ciently versatile to perform sectoral specific analysis.

Perhaps the most important feature of the KAM is its ability to place countries’ and regions’ perform-
ance in a global comparative context. The current version of the KAM, KAM 2005, has the ability to
benchmark countries contemporaneously either using data for the most recent period or that for 1995.
The ability to compare countries’ performance across the two time periods is also useful for highlight-ing whether countries are catching up or falling behind over time. The KAM by highlighting areas in which countries have fallen behind, or equivalently, areas in which other countries have surged ahead,
provides a reality check to countries with regard to their performance relative to other countries. Poli-
cymakers frequently, on realization of the relative global position in terms of the knowledge economy, bear a sense of urgency to develop coherent polic ies that place knowledge at the core of their devel-
opment strategies.

16 Derek H. C. Chen and Carl J. Dahlman

5. Conclusion
With the spread of modern and efficient information and communication technologies, the world
economy has become more competitive as well as inte rdependent. As such, economic survival made
it essential to have knowledge creation and use play a focal point in long-term developmental strate-
gies. In other words, it is critical for countries make the transition to become a Knowledge Economy.

This paper presents the Knowledge Economy framework thus asserts that investments in education and training, innovation and technological adoption, the information infrastructure, and a conducive
economic incentive and institutional regime are necess ary for sustained creation, adoption, adaptation
and use of knowledge in domestic economic production, which will consequently result in higher
value added goods and services. Th is would tend to increase the proba bility of economic success, and
hence economic development, in the current hi ghly competitive and globalized world economy.

In 1999, the Knowledge for Development Program of the World Bank Institute developed the Knowl-
edge Assessment Methodology (KAM) with the objective of helping country clients make the transi-tion to the knowledge economy. The KAM helps to identify problems and opportunities that a country
may face, and where it may need to focus policy attent ion or future investments, with respect to mak-
ing the transition to the knowledge economy. The unique strength of the KAM lies in its cross-sectoral approach that allows a holistic view of the wide spectrum of factors relevant to the knowledge
economy. In addition, because of its transparen cy, simplicity and versatility, the KAM has been
widely used and accepted for facilitating engagement s with World Bank country team and policy dis-
cussions with government officials from client countries.

The Knowledge Economy, the KAM Methodology and World Bank Operations 17

Annexes
Annex 1 KAM Normalization Procedure
The KAM consists of data for 128 countries for 80 va riables, describing the four pillars of the knowl-
edge economy, as well as economic and social performance, governance and gender issues. The nor-
malization procedure used in the KAM is as follows:

1. The raw data ( u) is collected from World Bank datasets a nd international literature for 80 vari-
ables and 128 countries.

2. Ranks are allocated to countries according based on the absolute values (raw data) that de-
scribe each and every one of the 80 variables (rank u). Countries with the same performance are allocated the same rank. Therefore, the rank equals 1 for a country that performs the best
among the 128 countries in our sample on a particular variable (that is, it has the highest score), the rank equals to 2 for a country that performs second best, and so on.

3. For each specific country, the number of countries that ranks lower or below it ( Nw) is calcu-
lated.

4. The following formula is used in order to normalize the scores for every country on every
variable according to their ranking and in relation to the total number of countries in the sam-ple (Nc) with available data:

() ⎟⎠⎞⎜⎝⎛=
NcNwu Normalized 10 ( A 1 )

5. The above formula allocates a normalized score from 0-10 for each of the 128 countries with
available data on the 80 variables. 10 is the t op score for the top performers and 0 the worst
for the laggards. The top 10% of performers gets a normalized score between 9 and 10, the
second best 10% gets allocated normalized scores between 8 and 9 and so on. As mentioned,
more than one country may be allocated either the top or worst of normalized scores. The 0-10
scale describes the performance of each country on each variable, relatively to the perform-
ance of the rest of the country sample.

18 Derek H. C. Chen and Carl J. Dahlman

Annex 2 Decomposition of Economic Growth for South Korea
For the growth decomposition exercise for South Kor ea illustrated in Figure 3, we considered a stan-
dard neoclassical aggregate production function that assumes a Cobb-Douglas specification together
with perfect competition and constant returns to scale:

α α−=1L K A Y (A2)

where
Y is the level of aggregate output
K is the level of the capital stock
L is the size of the labor force
A is total factor productivity
α is the share of capital in national income

Total factor productivity (TFP) was derived as the r esidual after accounting for the contribution of la-
bor and capital to aggregate output. More specifically,

α α−=1L KYA ( A 3 )

Real GDP (in constant 2000 U.S. dollars), labor force and population figures were taken from the
World Development Indicators 2005. The capital stoc k was constructed using gross fixed capital for-
mation24 (in constant 2000 U.S. dollars) also obtained from the World Development Indicators 2005.
The perpetual inventory method was used with an assu med depreciation rate of 5 percent. To calcu-
late the initial value of the capital stock, we used th e average growth rate of gross capital formation for
the first 5 years and applied the formula for the su m of an infinite geometric progressive series.

The estimates for labor share for South Korea and Mexico were 0.796 and 0.590, were taken from
Gollin (2001) and Bernanke and Gürkaynak (2001), respectively. Invoking the assumption of con-stant returns to scale, the capital shares were obtai ned by taking 1 and subtracting the respective labor
shares.

24 Gross fixed capital formation (formerly gross domestic fixed investment) includes land improvements (fences,
ditches, drains, and so on); plant, machinery, and equipment purchases; and the construction of roads, railways,
and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. According to the 1993 System of National Accounts (SNA), net acquisitions of valuables are also
considered capital formation.

The Knowledge Economy, the KAM Methodology and World Bank Operations 19

Annex 3 Real GDP per Capita Projections for Mexico
Different TFP growth rates were assumed to produce the alternative projections of real GDP per capita
for Mexico illustrated in Figure 4. We first deri ved the actual historical TFP growth rates by building
on the computations already performed described in Section A2. Mathematically, by taking logs and
time derivatives of equation (A3), and then rearrangi ng, we obtained the estimate of growth rate of
total factor productivity:

() L K Y A ˆ 1ˆ ˆ ˆ α α − − − = (A4)

where
Xˆ represents the growth rate of variable X

Table A1 presents the estimates of the growth rates of total factor productivity resulting from the
growth decomposition exercise. The annual growth rates of TFP were averaged to produce decade
averages.
Table A1
South Korea Mexico
1961-1970 2.08 1.02
1971-1980 1.48 0.90
1981-1990 4.28 -1.74
1991-2000 2.36 0.27
2001-2003 2.48 -2.39
1991-2003 2.38 -0.35Annual Growth Rates of
Total Factor Product ivity (in percent)

With reference to Figure 4, Projection 1 plots the path of Mexico’s real GDP per capita would take if
the TFP growth rate were to take its 1991-2003 average value, i.e. –0. 35 percent per annum. In this
scenario, the real GDP per capita increases from US$5,792 in 2003 to US$7,026 in 2020, a 21 percent
increase. Projection 2 plots the path of Mexico’s Real GDP per capita would take if the TFP growth
rate were to take 1 percent annum, which is close to the 1961-1970 and 1971-1980 decade averages
for Mexico. In this case, the real GDP per capita increases to US$8,828 in 2020. This represents a 52 percent increase.

Projection 3 plots the path of Mexico’s real GDP per capita would take if the TFP were to grow at
2.38 percent annum, which is the 1991-2003 average fo r South Korea. Here real GDP per capita in-
creases to US$11,118 in 2020, which represents an increase of 92 percent. Lastly, Projection 4 plots
the path of Mexico’s real GDP per capita would take if the TFP growth rate were to take a hypotheti-
cal 3 percent per annum. Based on this assumption, the real GDP per capita increases by 113 percent
to US$12,320 in 2020, and would allow Mexico to catch up with South Korea’s current real GDP per capita.

20 Derek H. C. Chen and Carl J. Dahlman

Note that for all 4 projections, capital, labor a nd population were all assumed to grow at their 1991-
2003 average annual growth rates for Mexico, which are 3.68 percent, 2.70 percent and 1.59 percent,
respectively.

The Knowledge Economy, the KAM Methodology and World Bank Operations 21

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24 Derek H. C. Chen and Carl J. Dahlman

Figure 1
USPTO Patent C ount (1981-2003)
20,00040,00060,00080,000100,000120,000140,000160,000180,000
1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003U.S.
Non U.S.
World
Source: Authors' construction based on data from the USPTO website.World
Non U.S.U.S.

The Knowledge Economy, the KAM Methodology and World Bank Operations 25

Figure 2
World Trade (1960 – 2003)
01020304050
1960 1965 1970 1975 1980 1985 1990 1995 2000Trade in goods and services (% of GDP)
Trade in goods (% of GDP)
Trade in services (% of GDP)
Source: Authors' construction based on data from the World Develo pment Indicators Trade in goods
and services
Trade in goods
Trade in services

26 Derek H. C. Chen and Carl J. Dahlman

Figure 3
Knowledge Makes the Difference
020004000600080001000012000
1960 1965 1970 1975 1980 1985 1990 1995 2000Real GDP per capita (2000 US$)South Korea
Mexico
Difference in output
due to growth in labor and capital in KoreaDifference in output due to TFP growth or knowledge
accumulation
in Korea

Source: Authors’ computations

The Knowledge Economy, the KAM Methodology and World Bank Operations 27

Figure 4
Mexico: Real GDP Per Capita – Alternative Projections 2004-2020
4,5005,5006,5007,5008,5009,50010,50011,50012,500
1990 1995 2000 2005 2010 2015 2020
Year2000 US$
Actual
Projection 1: -0.35% TFP Growth (Mexico 1991-2003)
Projection 2: 1% TFP Growth
Projection 3: 2.38% TFP Growth (Korea 1991-2003)
Projection 4: 3% TFP Growth
Projection 1Projection 2Projection 3Projection 4

28 Derek H. C. Chen and Carl J. Dahlman

Figure 5a
The Basic Scorecard (Spider Chart) – Finland