Measuring the Impact of ICT Investments on
Economic Growth
By Khuong Vu
Abstract
Measuring the impact of ICT investment on economic growth has been thoroughly
examined for a number of industrialized countries. This study provides a cross-country
view on this issue by assessing the impact of ICT on economic growth for 50 major ICTspending countries, which together account for over 90% of the global ICT market. We
find that the key determinants of the variance of ICT contribution to growth across
economies include education, institutional quality, openness, and English fluency.
Furthermore, ICT investment has a significant impact on economic growth not only as
traditional investment, but also as a boost to efficiency in growth: a higher level of ICT
capital stock per capita allows an economy to achieve a higher growth rate for given
levels of growth in labor and capital inputs.
JEL Classification: O3, O4
Key words: ICT, Economic Growth, Capital Stock

Program on Technology and Economic Policy, Harvard Kennedy School of Government; email:
[email protected]. I am grateful to Dale Jorgenson, Dwight Perkins, Robert Jensen for advising
me on this research. I would also like to thank seminar participants at the Conference Board, the World
Bank E-Development Group, and the Economic and Social Research Institute (Japan) for their helpful
comments.
1
Table of Contents
I. INTRODUCTION..................................................................................................................................... 3
II. DECOMPOSING THE SOURCES OF ECONOMIC GROWTH ..................................................... 4
II.2. MEASURING ICT INVESTMENT FLOWS ................................................................................................ 5
II.3. CAPITAL STOCK .................................................................................................................................. 6
II.4. CAPITAL SERVICES .............................................................................................................................. 8
II.5.THE SOURCES OF OUTPUT GROWTH .................................................................................................. 11
III. ICT CONTRIBUTION TO ECONOMIC GROWTH...................................................................... 14
III.1.THE DYNAMICS ................................................................................................................................ 14
III.2.THE SHARE OF ICT CONTRIBUTION IN THE RATE OF OUTPUT GROWTH ........................................... 17
III.3. DETERMINANTS OF ICT CONTRIBUTION TO ECONOMIC GROWTH ................................................... 17
IV. IMPACT OF ICT INVESTMENT ON ECONOMIC GROWTH ................................................... 25
IV.1. ICT AS A DETERMINANT OF OUTPUT GROWTH................................................................................ 25
IV.2. IMPACT OF ICT PENETRATION ON THE EFFICIENCY OF ECONOMIC GROWTH................................... 28
V. CONCLUSION ...................................................................................................................................... 31
BIBLIOGRAPHY....................................................................................................................................... 33
APPENDIX: ESTIMATING ICT INVESTMENT FLOWS .................................................................. 37
A-I. WITSA/IDC ICT SPENDING DATA ................................................................................................... 37
A-II. PROJECTING BACKWARD WITSA ICT EXPENDITURE FLOWS .......................................................... 37
A-III. EXAMINING THE U.S. PATTERN ...................................................................................................... 38
A-IV. ESTIMATING ICT INVESTMENT FLOWS ........................................................................................... 39
2
I. Introduction
No one has any doubt that information and communication technologies (ICT) have had a
significant impact on most countries in the world, especially in the ways of
communication, working, and learning. However, it is still a challenge to assess how and
how much ICT has contributed to economic growth at the country as well as the global
levels.
Examining the contribution of ICT to output and productivity growth was initiated by the
studies of Oliner and Sichel (1994) and Jorgenson and Stiroh (1995, 1999), which
focused on the U.S. economy. A series of studies has followed this approach, inspecting
individual countries or group of countries. Besides the follow-up studies by Jorgenson
and Stiroh (2000), and Oliner and Sichel (2001, 2002) for the U.S. economy, the notable
studies on individual countries include (Oulton, 2001) for the United Kingdom, (Jalava
and Pohjola, 2002) for Finland, (Van der Wiel, 2002) for the Netherlands, (Parham et al,
2001) for Australia, (Armstrong et al, 2002) and (Khan and Satos, 2002) for Canada,
(RWI and Gordon, 2002) for Germany, and (Cette et al, 2002) for France. The significant
studies for a group of countries include Schreyer (2000), Colecchia and Schreyer (2001),
Ark et al (2002), and Daveri (2002) for most EU economies; and Jorgenson (2003) for
the G7 economies.
This study aims to investigate the impact of ICT on economic growth on a global basis by
examining all countries with significant expenditure on ICT in the past decade. The paper
investigates 50 major ICT-spending countries, which together account for over 90% of
the global ICT market.
The paper focuses on three issues: (i) measuring the contribution of ICT investment to
growth for each of the 50 economies and analyzing the determinants of variation in the
magnitude of the ICT contribution across countries; (ii) assessing the impact of ICT
investment on boosting efficiency of economic growth; and (iii) discussing policy
implications related to ac
ount
r
y
’
s efforts to promote investment in ICT.
3
Data for this study are mainly compiled from the Penn-World Table (version 5.6) and the
World Bank Development Indicators (on-line version). The data on ICT expenditure is
extracted from the Digital Planet Reports (published by WITSA1).
The remainder of this paper is organized as follows. Section II decomposes the sources of
output growth, including the contribution of ICT for the 50 individual economies over
two periods, 1990-1995 and 1995-2000, and examines the determinants of variation in
the magnitude of ICT contribution to growth across economies. Section III investigates
the impact of ICT on economic growth. Section IV concludes.
II. Decomposing the Sources of Economic Growth
To decompose the sources of growth, I employ the Production Possibility Frontier (PPF)
model2, which takes the following form:
[II-1]
Y= A.X(Kn, Kc, Ks, Kt, L),
where Y is aggregate output, X is a function of capital (K) and labor services (L), and A
is the “
Hicks-ne
ut
r
a
l
”t
e
c
hnol
og
i
c
a
la
ug
me
nt
a
t
i
on of aggregate input or Total Factor
Productivity (TFP). Capital services are divided by the asset types of interest: non-ICT
capital (Kn), hardware (Kc), software (Ks), and telecommunication (Kt).
Suppose that capital Ki has rental price ca (the subscr
i
pt“
a”i
ndi
c
a
t
e
st
het
y
peofc
a
pi
t
a
l
,
whi
c
hi
s“
n” f
or non-I
CT, “
c
”f
or ha
r
dwa
r
e
,“
s
”f
or s
of
t
wa
r
e
,a
nd “
t
”f
or
telecommunication equipment), and labor L (measured as hours worked) earns the
average wage of w. Then, in nominal output, the share of capital Ka is computed as sKa =
1
WITSA stands for World Information Technology and Services Alliance –a private consortium of 48
global ICT industries. The WITSA data on ICT are based on the work of International Data Corporation
(IDC), which is a global market intelligence company specializing in information and telecommunication
technology with offices in 50 major ICT-spending countries.
2
The PPF model was introduced by Jorgenson (1966) and first employed by Jorgenson and Griliches
(1967). The model was introduced into productivity measurement by Jorgenson (1996) and has been used
in numerous studies in the field. This model is an improvement of the Solow Aggregate Production model,
which was introduced by Solow (1957, 1960).
4
ca Ka/YP and the share of labor is sL = wL/YP, where P is the price of output Y.
Differentiating equation II-1 with respect to time yields
[II-2]
 



Y= sKn K
n + sKc K c + sKs K s + sKt K t + sL L + TFP
where a dot above each variable indicates its growth rate. In particular, TF
P (or A) is
the growth rate of technology or Total Factor Productivity (TFP).
Equation II-2] thus indicates that the growth rate of output can be decomposed into the
contributions of major types of capital assets, labor, and productivity growth:
 hardware, sKs K- sKn K
n , the contribution of the non-ICT asset, sKc K c –
s
software, and sKt K
–telecommunication equipment;
t
 sL L, the contribution of the labor input; and
 TF
P , the contribution of TFP growth.
II.2. Measuring ICT Investment Flows
Efforts to analyze the impact of ICT on economic growth with a cross-country approach
face two major statistical challenges: the availability and comparability of data on
investment flows into ICT assets. For developing countries, these data are virtually
unavailable, especially from official sources. For some developed countries, the data have
been collected but the statistical methods employed for assembling the data differ
considerably across countries3. Therefore, it is impossible to expect to have an official
comprehensive dataset on ICT investment series for a broad set of countries, including
developing economies.
A cross-country analysis of the impact of ICT on economic growth, however, requires an
ICT dataset that meets the following two principal criteria: (i) the dataset covers a large
number of countries that well represent the global market; (ii) the dataset is assembled
based on a consistent methodology, which allows meaningful cross-country comparisons.
3
See Colecchia and Schreyer (2001) and Ahmad, Schreyer, and Wolfl (2004) for helpful discussions
5
The ICT data provided by the Digital Planet reports published by WITSA (World
Information Technology and Services Alliance, a private consortium of 48 ICT
industries) best satisfy the above two requirements. TheI
CTda
t
af
r
om WI
TSA’
sDi
g
i
t
a
l
Planet reports are compiled based on market studies conducted by International Data
Corporation (IDC), a global market intelligence company specializing in information and
telecommunication technology with offices in 50 major ICT-spending economies. The 50
economies together account for about 98 percent of the global ICT market4 and hence
5
well represent the ICT-i
nf
l
ue
nc
e
dwor
l
d.WI
TSA’
sDi
g
i
t
a
lPl
a
ne
tr
e
por
t
s
cover ICT data
for these 50 economies since 1992. WITSA/IDC data are collected and compiled based
on a consistent methodology adopted by IDC for all the economies. WITSA/IDC ICT
data have been widely used by researchers for analyzing the contribution of ICT to
growth in a group of economies6. Furthermore, the World Bank, IMF, and the UN, in
their recent major reports, used WITSA/IDC data to monitor global ICT development.7
The WITSA data source, however, provides only ICT data related to total spending
without a breakdown into investment and consumption. Therefore my paper estimates
ICT investment flows from the WITSA ICT spending series. The relationship between
the ICT investment flows8 and the WITSA ICT spending data for the U.S. is used as the
main benchmarks for estimating ICT investment flows for other economies. Details of
the estimation are presented in Appendix B.
II.3. Capital Stock
The Perpetual Inventory Method (PIM)
The Perpetual Inventory Method (PIM) allows one to calculate the capital stock of an
asset type as the accumulated sum of its past real investment flows, weighted to reflect
4
(WITSA, 2002)
5
WI
TSA’
sDi
g
i
t
a
lPl
a
n
e
tr
e
por
t
swe
r
epu
bl
i
s
h
e
di
n1998,2
00
0,a
n
d200
2.
6
Schreyer (2000), Daveri (2002), and Lee and Khatri (2003) are among the most notable examples.
7
For examples, OECD (2000, 2001, 2002), World Bank (2000, 2001), IMF(2001), UN(2001).
8
This data is compiled by BEA (Bureau of Economic Analysis, website www.BEA.gov)
6
the loss of productive efficiency of the installed asset over time. The stock of capital a at
period t can be estimated as

Sa,t = =0,  (1-
a) Ia, t-
[II-3]
where Ia,t-is the flow of investment in capital a at time t-
. Supposing that the average
service life of the asset is m years, we can assume that this asset will be discarded after m
years in service. That is, investment in asset a in year t-m or earlier are eradicated from
the capital stock of asset a in year t. Therefore, Equation [II-3] can be simplified into
Sa,t = =0, m-1 a ,Ia, t-
[II-4]

where a =(1-
a) is the rate of efficiency remaining at year t of asset a invested in year
t-for 
=1, 2,.., m. The value of a 0 needs to be adjusted for the earlier years to take into
account the assumption that the asset is discarded when its age reaches m years. For
computational convenience, the remaining efficiency of the asset when it is discarded
m
after its mth year in service, (1-
should be added to the efficiency of the asset in its
a)
m
first year in use, that is, a, 0 = 1 + (1-
a) .
I start to compute the capital stock of the four types of asset, including three types of ICT
asset–hardware, software, and telecommunication; and the aggregate non-ICT asset.
Following widely-used practice, originating from the work of Fraumeni (1997), we
assume the service lives and depreciation rates of the above assets9 as follows:
 The service life is 7 years for hardware, 5 years for software, 11 years for
telecommunication equipment, and 30 years for other assets (aggregated as nonICT assets).
 These service lives can be approximated by a geometric depreciation rate of 31.5
percent for hardware and software, 11 percent for telecommunication equipment,
and 7.5 percent for the non-ICT assets.
Consider asset i with an average service life of m years. Measuring the capital stocks for
this asset at year 1990 requires the investment flow into this asset for m years, from
9
These assumptions are widely used in studies on the contribution of ICT to economic growth.
7
1990-m-1 to 1990. Specifically, one needs to estimate investment flow starting at 1984
for hardware (m=7), 1986 (m= 5) for software, 1980 for telecommunication equipment
(m=11), and 1961 for the aggregate asset (m=30).
Investment Deflators
The investment flows {Ia,t-} in equation II-4 are measured in their real values. For nonICT assets, this investment flow is the GFCF flow in constant units. For an ICT asset type
(hardware, software, and telecommunication equipment), its nominal investment flow
estimated from the previous section is deflated using hedonic price indices provided by
BEA10.
II.4. Capital Services
Capital Services Methodology
When a firm hires a worker, the wage paid to this worker is a measure of his/her labor
service and is added to GDP as value-added. Similarly, when a firm purchases or rents a
piece of equipment, the capital services rendered by this equipment should be added to
GDP. The capital services methodology is based on the economic theory of production
and has been comprehensively described in Jorgenson and Stiroh (2000) and OECD
(2001a, 2001b).
Capital services are defined as the productive inputs, per period, that flow to production
from a capital asset. The value of capital services rendered by an asset is the quantity of
services provided by the asset multiplied by the price of those services. The framework
for computing capital services is as follows11:
10
I use the price indexes for computer, software, and communication equipment for private fixed
investment, available on the BEA website,
http://www.bea.gov/bea/dn/nipaweb/TableView.asp?SelectedTable=127&FirstYear=2003&LastYear=2005
&Freq=Qtr
11
See Jorgenson and Stiroh (2000) for more details.
8
 The quantity of capital services of asset a in period t is proportional to the average
of the stocks of this asset available at the end of periods t and t-1; that is
[II-5]
Ka,t=qa*( Sat + Sa,t-1)/2
where Ka,t and Sa,t are, respectively, quantity of capital services and capital stock
measured for asset a at time t, and qa denotes the constant of proportionality,
which can be set equal to unity without losing generality.
 The capital service price is the unit cost for the use of a capital asset for one
period; that is, the price for employing or obtaining one unit of capital services.
Thes
e
r
vi
c
epr
i
c
ei
sa
l
s
or
e
f
e
r
r
e
dt
oa
st
he“
r
e
nt
a
lpr
i
c
e
”ofac
a
pital good or the
“
us
e
rc
os
tofc
a
pi
t
a
l
”
.Toi
nf
e
rt
hec
a
pi
t
a
ls
e
r
vi
c
epr
i
c
eofac
a
pi
t
a
lg
ood,o
ne
assumes that a typical investor in period t-1 makes her decision by considering
only two alternatives: earning a nominal interest rate of return, rt, on her money,
Pa, t-1, in the money market to get (1+rt)Pa, t-1, or buying a unit of capital, collecting
a rental fee, ca,t, and then selling the depreciated asset in period t for (1-
a)Pa, t.
Under the equilibrium condition, the investor is indifferent between the two
alternatives; that is
[II-6]
(1+rt)Pa, t-1=ca,t + (1-
a)Pa, t
where rt is the nominal interest rate at time t, Pa, and ca, are the prices of capital
stock and capital services, respectively, of asset a at time 
, and 
a is the
geometric depreciation rate for asset a.
Rearranging equation [II-6] provides a formula for computing rental price ca,t
[II-7]
ca,t = rtPa, t-1- a, t Pa, t-1+ 
aPa,t
where a, t = (Pa, t - Pa,t-1)/Pa,t-1 is the investment deflator or capital gain/loss for
owning asset a over period t.
 Normalizing the price of output to 1, the income share sKa of capital a in output at
time t is computed as
[II-8] sKa, t = Ka, t*ca, t /Yt = Ka, t* (rtPa, t-1- a, t Pa, t-1+ 
aPa,t) /Yt
9
The interest rate rt can be computed from the following equation:
[II-9]
sK =shw+ssw+stel+snict
which shows that the income share of aggregate capital (sK) equals the sum of the
income shares of the four types of capital under discussion: hardware (shw),
software (ssw), telecom equipment (stel), and non-ICT (snict).
Plugging sK a, t from equation [II-8] into equation [II-9] leads to the equation for
computing the interest rate rt as follows:
[II-10] rt={sK Yt+
 [Ka, t a, t Pa, t-1] -  [Ka, t aPa,t]} /  [Ka, t* Pa, t-1]
where the summation symbol in front of each component in [ ] indicates the sum
of this component across the four types of capital, a{n, c, s, t}, and the income
share of capital is assumed to be constant over the period under consideration.
User Cost of Capital
Computing capital services requires not only capital service quantity, which is derived
from capital stock, but also the capital service price or the user cost of capital. There are
three important elements in equation [II-7], which is used for computing the user cost of
capital asset a at time t: interest rate rt,t
hea
s
s
e
t
’
sde
pr
e
c
i
a
t
i
onr
a
t
e
,
a, t, and its capital
gain, a, t.
The depreciation rate, 
a, t , for capital goods a at time t, as discussed in section II.3, is
assumed to be constant at 31.5 percent for computer hardware and computer software,
and 11 percent for business telecommunication equipment.
The capital gain a, t can be computed straightforwardly from its definition,
a, t = (Pa, t - Pa, t-1)/Pa, t-1, where Pa, t and Pa, t-1 are the price indices of asset a at times t and
t-1, respectively.
The interest rate, rt, which is applied to all types of capital goods, is computed from
Equation [II-10]. In this equation, only the income share of aggregate capital, sK, needs to
be estimated. This study assumes that the capital share, sK, equals 0.35 across economies
(and therefore, their labor share, sL, is 0.65). The interest rate, therefore, can be computed
from Equation [II-10].
10
II.5.The Sources of Output Growth
Equation II-2 indicates that the sources of output growth can be grouped into three main
channels:
 Contribution of capital input, which consists of non-ICT (sKn K
n ) and ICT
capital. The contribution of ICT capital comes from its three main components:
hardware (sKc K
), software (sKs Ks ), and telecom (sKt K
),
c
t
 Contribution of labor input (sL L), and
 Contribution of TFP growth: TF
P
The sources of output growth for the 50 economies over the two periods 1990-1995 and
1995-2000 are reported in Table 1.
11
Table 1. Sources of Output Growth, 1990-1995 and 1995-2000
Country
GDP
Growth
G7 Economies
Canada
France
Germany
Italy
Japan
UK
US
Group
1.72
1.06
1.59
1.26
1.39
1.59
2.36
1.84
Period 1990-1995
Sources of Growth (p.p.a)
Capital
Labor
TFP
ICT Non-ICT
Growth
Period 1995-2000
Sources of Growth (p.p.a)
Capital
Labor
TFP
ICT Non-ICT
0.53
0.50
0.50
0.34
0.97
0.40
0.49
0.57
0.66
0.24
-0.01
0.54
0.51
0.00
1.00
0.62
0.27
0.16
0.94
0.28
-0.25
0.97
0.48
0.38
3.59
2.45
1.73
1.87
1.44
2.77
4.10
2.99
0.65
0.33
0.33
0.22
0.36
0.49
0.78
0.56
0.72
0.38
0.32
0.40
0.66
0.53
0.92
0.70
1.24
0.78
-0.03
0.38
0.04
0.91
1.36
0.83
0.97
0.97
1.10
0.88
0.39
0.84
1.04
0.90
Non-G7 Industrialized Economies
Australia
3.33
0.33
0.45
Austria
2.03
0.12
0.70
1.48
0.19
0.60
Denmark
1.95
0.14
0.22
Finland
-0.68
0.14
-0.15
Greece
1.24
0.08
0.17
Ireland
4.55
0.23
0.53
Israel
6.36
0.31
1.46
Netherlands
2.08
0.25
0.41
New Zealand
3.00
0.33
0.11
Norway
3.62
0.15
0.05
Portugal
1.74
0.14
0.70
Spain
1.33
0.10
0.72
Sweden
0.59
0.18
0.15
Switzerland
-0.08
0.21
0.49
Group
1.90
0.18
0.49
0.90
0.59
0.22
0.41
-1.77
0.46
1.53
3.95
0.73
1.54
0.78
0.05
-0.40
-0.67
0.41
0.34
1.65
0.62
0.47
1.17
1.11
0.53
2.27
0.64
0.69
1.02
2.64
0.85
0.91
0.93
-1.18
0.88
3.92
2.44
2.72
2.63
4.97
3.26
9.22
3.74
3.45
2.27
3.01
3.53
3.69
2.84
1.76
3.44
0.60
0.24
0.37
0.38
0.48
0.20
0.50
0.50
0.54
0.56
0.39
0.39
0.22
0.62
0.45
0.41
0.78
0.65
0.62
0.62
0.02
0.41
1.29
1.26
0.52
0.53
0.45
0.96
0.91
0.17
0.33
0.67
1.28
0.10
0.63
0.24
0.99
0.19
3.03
2.14
0.93
0.98
0.87
0.87
2.08
0.65
0.46
1.13
1.26
1.45
1.10
1.39
3.48
2.45
4.40
-0.16
1.45
0.20
1.30
1.31
0.47
1.39
0.53
1.23
0.98
1.15
1.64
2.17
1.91
2.65
1.99
2.00
1.39
1.61
1.54
1.45
0.00
8.09
2.24
1.97
3.63
2.74
3.81
-0.63
4.76
3.33
4.28
4.99
4.77
7.92
3.35
5.57
0.68
4.66
4.57
3.49
6.15
5.61
0.24
6.48
5.89
0.44
0.51
0.18
0.08
0.46
0.48
0.19
0.69
0.42
0.14
0.41
0.34
2.45
1.51
1.70
1.71
1.78
1.92
0.82
1.85
2.17
0.85
2.25
2.01
0.72
1.53
1.65
1.57
1.03
2.08
1.65
1.40
0.66
0.64
1.27
1.09
0.00
4.31
-0.20
2.05
-2.68
1.38
0.09
0.84
2.21
2.35
-1.39
2.55
2.44
Developing Asia
China
11.38
Hongkong
5.21
India
5.05
Indonesia
7.57
Korea
7.19
Malaysia
9.05
Philippines
2.14
Singapore
8.72
Taiwan
6.89
Thailand
8.30
Vietnam
7.91
Group
8.22
0.26
0.16
0.16
0.11
0.17
0.22
0.39
0.27
GDP
0.17
0.28
0.08
0.09
0.26
0.26
0.10
0.33
0.20
0.10
0.15
0.15
2.15
1.54
1.36
1.69
2.28
2.33
0.68
1.63
1.97
2.32
1.24
1.85
12
Table 1. (Continued)
Country
GDP
Growth
Latin America
Argentina
Brazil
Chile
Colombia
Mexico
Venezuela
Group
Period 1990-1995
Sources of Growth (p.p.a)
Capital
Labor
TFP
ICT Non-ICT
GDP
Growth
Period 1995-2000
Sources of Growth (p.p.a)
Capital
Labor
TFP
ICT Non-ICT
6.35
3.09
8.33
4.43
1.52
3.39
3.46
0.10
0.10
0.22
0.14
0.14
0.11
0.12
0.29
0.28
1.64
0.95
0.70
0.14
0.50
-0.48
1.14
2.02
2.35
1.91
2.32
1.32
6.44
1.57
4.45
1.00
-1.24
0.82
1.52
2.59
2.25
4.44
0.92
5.36
0.61
3.01
0.18
0.31
0.37
0.41
0.18
0.23
0.27
0.33
0.40
1.95
0.48
0.77
0.03
0.54
2.71
0.81
0.66
0.06
2.41
1.53
1.47
-0.64
0.73
1.46
-0.03
2.00
-1.18
0.74
-2.65
-0.97
-2.39
2.17
-2.15
-9.52
-3.03
-0.59
-6.38
0.13
0.22
0.22
0.11
0.04
0.07
0.20
0.15
0.10
-0.74
-0.32
0.03
0.11
-0.84
-0.60
-0.31
-0.89
-0.49
-2.92
0.01
-1.16
-0.68
-0.71
-1.45
-0.37
-0.24
-1.20
0.88
-0.88
-1.49
2.63
-0.64
-7.54
-2.56
0.39
-4.78
-1.31
0.95
3.93
5.01
-1.60
1.13
4.02
4.25
1.72
0.23
0.43
0.42
0.34
0.10
0.10
0.37
0.32
0.19
-0.84
0.19
0.35
1.12
-0.32
-1.77
0.21
0.14
-0.88
-0.35
-0.41
0.60
-0.09
-0.31
-0.19
-0.32
0.17
-0.16
-0.35
0.75
2.56
3.64
-1.07
2.99
3.76
3.63
2.56
Other Economies
Egypt
3.34
S. Africa
0.86
Turkey
3.14
Group
2.26
0.10
0.20
0.08
0.13
0.17
-0.06
1.71
0.70
1.59
2.36
2.24
2.17
1.49
-1.65
-0.88
-0.74
5.30
2.42
3.70
3.54
0.19
0.42
0.22
0.29
0.57
0.23
1.69
0.91
2.84
0.17
1.47
1.25
1.71
1.61
0.33
1.10
Eastern Europe
Bulgaria
Czech
Hungary
Poland
Romania
Russia
Slovakia
Slovenia
Group
All Sample
(50 Econ.)
2.68
0.21
0.74
0.73
1.01
3.70
0.44
0.93
0.93
1.40
13
III. ICT Contribution to Economic Growth
III.1.The Dynamics
a) A Surge in the Second Period (from Table 1)
 The contribution of ICT to output growth was positive for all economies in the two
periods 1990-1995 and 1995-2000. Figure 1 shows a decisive shift of the distribution
of economies by the magnitude of ICT contribution to output growth, with the
average surging from 0.17 in the first period to 0.37 percentage points in the second.
For nearly two thirds of economies, the magnitude of ICT contribution at least
doubled over the two periods.
 At the group level, the weighted average of the ICT contribution to output growth
increased from 0.27 percentage points during 1990-1995 to 0.56 during 1995-2000
for the G7; from 0.18 to 0.41 for the Non-G7; from 0.15 to 0.34 for Developing Asia;
from 0.12 to 0.27 for Latin America; from 0.10 to 0.19 for Eastern Europe; from 0.13
to 0.29 for Other-3;a
ndf
r
om 0.
21t
o0.
29f
ort
he“
Gl
oba
lEc
onomy
”
.
Figure 1: Distribution of the 50 Economies by the Magnitude of
ICT Contribution to Output Growth, 1995-2000 vs. 1990-1995
Period 1990-95
Period 1995-00
density, % of Countries
10
5
0
.05
.1
.17 .2
.3
.37 .4
.5
ICT Contb. to Output Grow th, ppa
.6
.7
.8
14
b) ICT vs. Non-ICT capital
 Non-ICT capital remains a major source of output growth for all groups except
Eastern Europe. However, unlike ICT capital, the contribution of Non-ICT capital did
not show a solid increasing trend across economies; in fact, it decreased notably in
Germany, France, Japan, Korea, Malaysia, Thailand, Colombia, and Venezuela.
 ICT and Non-ICT contribute to growth via capital input, of which the share of ICT
rose considerably from 1990-1995 to 1995-2000 across groups, from 32% to 45% for
the G7; 27% to 38% for the Non-G7; 7% to 15% for Developing Asia; 19% to 33%
for Latin America; 16% to 24% for Other-3; a
ndf
r
om 22% t
o32% f
ort
he“
g
l
oba
l
e
c
onomy
”(Figure2). For the Eastern Europe group, while the contribution of NonICT capital was negative and declining from the first to the second period, the
contribution of ICT capital was positive and substantially increasing.
Figure 2: Share in Capital Input Contribution to Growth by Region/Group:
ICT vs. Non-ICT
120
100
80
55
62
67
68
68
73
76
60
81
78
84
85
93
40
45
20
38
33
32
32
27
24
19
22
16
15
7
0
1990-1995
1995-2000
G7
1990-1995
1995-2000
Non-G7
1990-1995
1995-2000
Developing Asia
1990-1995
1995-2000
Latin America
0.10
-0.49
0.19
-0.88
1990-1995
1995-2000
Eastern Europe
1990-1995
1995-2000
Other-3
1990-1995
1995-2000
All Sample
-20
ICT
Non-ICT
15
c) The Global Picture of ICT Contribution to Growth
 The global picture of ICT contribution to output growth is a mix of static and
dynamic features. Figure 3 shows that the magnitude of the ICT contribution to
output growth for an economy in the first period highly predicts that in the second
period (the 50 economies lie along the upward diagonal line in the graph). The figure,
however, also indicates that a number of economies significantly deviate from the
diagonal, either above or below. The economies lying below the diagonal (such as
Canada, Sweden, Finland, China, Colombia, and Brazil) made a substantial
improvement in the second period relative to the first period.
Figure 3: ICT Contribution to Output Growth: 1995-2000 vs. 1990-1995
.4
US
New Zealand
Australia
Singapore
Israel
.3
Hongkong
Malaysia
Kor ea
Canada
1990-95, p.p.a
Netherlands
Chile
Slovakia
.2
Ireland
Czech
UK
Hungary
Switzer land
S.Taiwan
Afr ica
Belgium
Sweden
.17
Mexico
Bulgaria
.1
Indonesia
Austria
Italy
Venezuela
Thailand Philippines
Egypt Spain
Argentina
China
Japan
Germany
France
Slovenia
Norway
Vietnam
Denmark
Portugal
Finland
Colombia
Poland
Brazil
India
Greece
Tur key
Russia
Romania
0
0
.1
.15
.3
.37
.45
1995-00, p.p.a
.6
.75
.8
ICT Contribution to Output Grow th, 1995-00 vs. 1990-95
16
III.2.The Share of ICT Contribution in the Rate of Output Growth
Not only the magnitude but also the share of overall output growth attributable to ICT
contribution rose significantly from 1990-1995 to 1995-2000.
 Figure 4 shows this trend at the group and world levels. The share of ICT contribution
in overall output growth jumped from 14.5% to 18.8% for the G7, 9.8% to 12.0% for
the Non-G7, 1.8% to 5.8% for Developing Asia, 3.4% to 8.8% for Latin America,
5.8% to 8.1% for Other-3,a
ndf
r
om 7.
7% t
o12.
0% f
ort
he“
g
l
oba
le
c
onomy
”
;f
or the
Eastern Europe group, the share of ICT contribution in its overall output growth was
11.1% for the second period when its output growth turned positive.
Figure 4: The Share of ICT Contribution in Overall Ouput Growth
100%
81.2
85.5
18.8
14.5
90.2
88.0
9.8
12.0
0%
98.2
94.2
96.6
1.8
5.8
3.4
88.9
91.2
8.8
For Eastern Europe, the output
growth was negative in the first
period; hence, the label values are
not the shares but the actual
contribution of ICT and other
sources to output growth (in
percentage points)
11.1
0.10
94.2
91.9
92.3
88.0
5.8
8.1
7.7
12.0
-6.47
-100%
1990-1995 1995-2000 1990-1995 1995-2000 1990-1995 1995-2000 1990-1995 1995-2000 1990-1995 1995-2000 1990-1995 1995-2000 1990-1995 1995-2000
G7
Non-G7
Developing Asia
Latin America
The Share of ICT Contribution
Eastern Europe
Other-3
All Sample
The Share of Others
III.3. Determinants of ICT Contribution to Economic Growth
The previous section has revealed the important features of the magnitude of ICT
contribution to output growth, especially its large and increasing variation across
economies over the two periods, 1990-1995 and 1995-2000. This section investigates the
determinants of the variation of ICT contribution to output growth.
17
To investigate the determinants of ICT contribution to growth I look at two sets of
factors; the factors in the first set underlie the pace of ICT diffusion and those in the
second set have proved to be important for economic growth.
With regards to the first set, previous studies have identified that income level, costs of
ICT, education, openness, and institutional quality are the most important factors. Income
level appears to be a major determinant of ICT diffusion. Pilat and Devlin (OECD, 2004,
Section I) point out that firms in countries with higher levels of income and productivity
have greater incentive to invest in ICT. Furthermore, the costs of investment in, and use
of, ICT are significant. Quibria et al (2002) and Baliamoune (2002) conclude that income
level is a major determinant of ICT diffusion.
Pohjola (2003) makes a similar
conclusion, finding that income per capita is positively associated with computer
hardware spending per capita.
Education is a key factor influencing the capability to adopt a new and knowledgeintensive technology such as ICT.
Caselli and Coleman (2001) find that computer
adoption across 43 countries over 1970-1990 strongly depends on the levels of education
of their labor force. Lee (2000) points out that the level of secondary education is
important for the adoption of both main lines and mobile phone. Quibria et al (2002)
reveal that tertiary education has a significant impact on the penetration of PC and
Internet use. Kiiski and Pohjola (2002) find that tertiary education has a positive and
statistically significant impact on ICT diffusion for a sample of developing and OECD
countries. Furthermore, Pohjola (2003) shows that education, which is measured as
average years of schooling, has a positive and statistically significant influence on the
adoption of ICT among the 50 ICT-spending economies covered by the WITSA.
However, there are also studies that cast doubt on the role of education in promoting ICT
diffusion; for example, Hargittai (1999) and Norris (2000) show that education does not
have any predictive power in explaining Internet diffusion.
Openness to international trade facilitates and fosters ICT diffusion. Firms and
individuals that are involved in international competition and have access to global
18
resources have a more pressing demand for, and better access to, investment in ICT
assets. Caselli and Coleman (2001) find that computer adoption significantly depends on
the extent of manufacturing import. Baliamoune (2002) concludes that openness has
some influence on ICT diffusion over 1998-2000. Pohjola (2003), however, finds no
supporting evidence for the positive impact of openness on computer hardware spending
per capita.
Institutional quality plays a crucial role in facilitating ICT diffusion. Moreover, a good
economic environment also enhances the certainty of making profits from investments in
new technology. Baliamoune (2002) discusses the institution determinant by examining
its two related factors, civil liberties and political freedom. The study finds mixed results:
these two factors have a strong influence on the penetration of mobile phones and
Internet hosts but their impacts are not significant for PC and Internet use.
The second set of factors that have a significant impact on economic growth includes
income level, education, health, institutional quality, and openness (Barro, 1997; Sachs
and Warner, 1995; Rodrik et al, 2002). Except for income level, which has a negative
e
f
f
e
c
tone
c
onomi
cg
r
owt
hduet
ot
he“
c
onve
r
g
e
nc
ee
f
f
e
c
t
”(
Ba
r
r
oa
ndSa
l
a
-I-Martin,
1995), the other three factors have solid positive impact on growth. In particular, Sachs
and Warner (1995) argue that countries that are open to trade experience unconditional
convergence to the income levels of the wealthy nations; Rodrik and his coauthors
(Rodrik et al, 2002; Rodrik, 2003) provide convincing evidence that the quality of
institutions is even more important than openness and other factors as a deep determinant
of growth.
The results of the determinants of ICT diffusion and of economic growth from previous
studies, suggest that education, openness, and institutional quality are expected to be
among the major determinant of the variation in ICT contribution to output growth across
economies. In addition to these factors, I expect that English proficiency is also an
important determinant of ICT contribution to growth because the countries with high
19
degree of English fluency can reap huge benefits from the Internet, for which English is
the dominant language.
Because the sample (covering the 50 economies) exhausts the population (which includes
the 50 largest ICT-spending economies), it is appropriate to use the fixed effect model to
isolate the factors underlying variations in the magnitude of ICT contribution to growth.
The model takes the following form:
[III-1]
it
Cit=+ Xi + 
Di + 
T + Xi * T+ 
where Cit is the magnitude of ICT contribution to output growth (measured in percentage
poi
nt
s
)f
ore
c
onomyi(
i
=1,2,…,50)i
npe
r
i
odt(
t
=1 for the fist period, 1990-1995, and
=2 for the second one, 1995-2000); Xi is a set of the observed characteristics of economy
i; Di i
sadummyf
ore
c
onomyit
oc
a
pt
ur
et
hee
c
onomy
’
sunobs
e
r
ve
dc
ha
r
a
c
t
e
r
i
s
t
i
c
s
;Ti
s
the time period dummy for the second period (T=1 if t=2, and =0 if t=1; this dummy is
also named as After95); Xi * T is a set of interaction terms between the set of the observed
it
characteristics of economy i, Xi, and the time period dummy T; and finally, 
is a
random effect, independently and identically distributed among economies and time
periods (i=1,..,50; t=1, 2).
The set of variables for the observed characteristics X
The set of variables representing the observed characteristics of each individual economy
includes the following variables:
 Developed: This variable is a dummy, which takes the value 1 if the economy is
among the 22 industrialized economies; otherwise it takes the value 0. The
“
de
ve
l
ope
d”s
t
a
t
usi
ss
t
r
ong
l
yc
or
r
e
l
a
t
e
dwi
t
ht
hei
nc
omel
e
ve
l
; therefore, its
impact on the ICT contribution to growth is the net of impacts of the opposite
effects: on the one hand, the high income level facilitates ICT diffusion, and
hence fosters ICT contribution to growth; on the other hand, the convergence
effect makes it harder for a high income economy to generate one percentage
point of output growth than for a poor economy.
20
 Education: this variable is defined as the educational attainment of the population
aged 25 or over in 1995 (Barro and Lee, 1996). The Education variable is
expected to positively associate with ICT contribution to growth.
 Institution (Institutional quality): a good measure for this variable is the Rule of
Law index produced by the World Bank (WB, 2003). The Rule of Law index
captures the effectiveness of an economy in law enforcement, and therefore is a
good proxy for the quality of investment climate in an economy. The Rule of Law
index for 1996 is used to define the Institution variable. The variable Institution is
expected to positively associate with ICT contribution to growth.
 Openness: this variable openness is measured as the trade-to-GDP ratio averaged
for 1990-2000.
 English (English fluency): this dummy variable categorizes the economies that
use English as the major language.12 The variable takes the value 1 if an economy
belongs to this group and 0 otherwise. One may expect that English proficiency is
an important determinant of ICT diffusion as well as ICT contribution to growth.
The countries with English as the major language have a superior advantage in
adopting ICT and reaping its benefits because they have more effective
communications with the US market, the center of the ICT revolution and the
major export market for most countries; moreover, English is the dominant
language in the Internet.13
The above observed characteristics of each individual economy are assumed to be stable
through the two periods 1990-1995 and 1995-2000. These characteristics will be
investigated to assess the significance of the determinants of ICT contribution to growth.
12
In our sample of 50 countries, this group consists of nine countries: the US, UK, Ireland, Canada,
Australia, New Zealand, South Africa, Hong Kong, and Singapore. For other two countries, India and the
Philippines, English is also an official language but it is not spoken by a majority of their populations (from
Microsoft Encarta Encyclopedia, 2002 and Gunnemark, 1991).
13
According to a survey completed in January 2000 by NEC Research Institute and Inktomi (NCSE, 2000)
the Web has more than one billion unique pages, of which nearly 87 percent are in English.
21
The dummies for individual economies and time periods
 Each individual economy i (i=1, 2,…, 50) is assigned a dummy variable Di, which
takes the value 1 if the observation is for economy i and 0 otherwise. The dummy
Di is included in the model to capture the fixed characteristics unique to each
individual economy. Practically, only 49 economy dummies (Di,f
ori
=1,2
,
…,49)
are needed and the remaining economy is the base case).
 The two time periods, 1990-1995 and 1995-2000, are represented by dummy
variable T, which takes the value 1 if the observation is for the second period and
0 otherwise. The time dummy variable T captures the unobserved fixed effects of
the second period, which are expected to be distinct from those of the first period
due to the prominent emergence of the Internet and the accelerated pace of the
ICT revolution.Thet
i
medummyTi
sa
l
s
ona
me
da
s“
Af
t
e
r
95”
.
The interaction terms M
In anticipation of possible accelerated effects of the key explanatory variables, including
Income, Education, Institution, Openness, and English, I create the interaction terms for
each of these variables with the time dummy T (After95) as follows:
 Developed_After95 = Developed * After95
 Education_After95 = Education * After95
 Instituion_After95 = Institution * After95
 Openness_After95 = Openness * After95
 English_After95 = English * After95
One may expect that the coefficients of these interaction terms are positive and some of
them could be statistically significant because these five factors, income level, education,
institutional quality, openness, and English language, to some extent, seemed to have
accelerated impact on ICT diffusion in the second half of the decade.
Results:
 The data for examining the determinants of ICT contribution to growth is a set of 100
observations of ICT contribution to growth of the 50 economies for the two time
22
periods 1990-1995 and 1995-2000. The regression, based on model III-1, which uses
the robust estimator of variance, produces results reported in Table 3.14
Table 2: Determinants of ICT Contribution to Output Growth
Dependent Variable: ICT Contribution to Output Growth
Explanatory
Variables
Coefficient
t-statistics
p-value
Developed
0.035
0.81
0.420
Education
0.020**
2.54
0.015
Institution
0.074***
2.93
0.005
Openness
0.031
1.34
0.189
English
0.095***
3.46
0.001
After95
0.118**
2.19
0.034
Developed_After95
-0.022
-0.68
0.500
Education_After95
0.002
0.27
0.789
Institution_After95
0.061**
2.43
0.019
Openness_After95
0.013
0.67
0.508
English_After95
0.071**
2.34
0.024
R-squared
0.95
Number of Observations
100
Notes: *, **, and *** indicate, respectively, the 10-percent, 5-percent, and 1-percent significance
level.
The following findings stand out:
1) Education, Institutional quality, English fluency have solid positive impact on ICT
contribution to growth:
 The coefficient of the variable Education is positive and significant at the 5 percent
significance level. The significance of the Education variable suggests that
educational attainment is important determinant of ICT contribution to growth. The
coefficient of the interaction term Edu_After95 is positive but not statistically
significant, which suggests that impact of education in the second half of the 1990s
was somewhat accelerated but the accelerated impact was not statistically significant.
14
The results for the 49 economy dummies are not reported to save space.
23
 The coefficient of the variable Institution is large and significant at the 1-percent
level. This implies that institutional quality plays a very important role in enhancing
the ICT contribution to growth. Furthermore, the coefficient of the interaction term
Institution_After95 is positive and significant at the 5-percent level, which indicates
that the institutional quality has a significant accelerated effect on the ICT
contribution to growth. The finding is consistent with the observation that
governments in a number of nations have become highly involved in embracing the
ICT revolution to promote economic growth. The quality of institutions is an
important factor underlying the effectiveness of the formulation and implementation
of their ICT national agenda.
 The coefficient of variable English is positive and significant at the 1-percent level;
furthermore, the coefficient of the interaction term English_After1995 is also positive
and significant at the 5-percent level. The results indicate that English fluency has a
solid impact on ICT contribution to growth and its impact was significantly
accelerated in the second period. That is, the countries with English fluency have a
superior advantage in reaping the benefits of the ICT revolution; especially the
Internet.
2)Ope
nne
s
sandt
he“De
v
e
l
ope
d”s
t
at
ushav
eapos
i
t
i
v
ebutnotstatistically significant
impact on ICT contribution to output growth:
 The coefficient of variable Openness is positive but not statistically significant (pvalue=19%). The coefficient of the interaction term Openness_After95 is also
positive but not statistically significant. The results imply that that openness has some
positive impact on ICT contribution to output growth and this impact was accelerated
in the second half of the decade. However, this impact is not statistically significant.
Regarding this finding, one should bear in mind the drawback of the variable
Openness, which does not well capture the openness of very large economies such as
the U.S. Therefore, the implication of this finding is limited.
 The coefficient of the variable “
developed”is positive but not statistically significant;
moreover, the coefficient of the interaction term Developed_ After95 is negative
(although it is not statistically significant). There are several reasons explaining this
24
finding: (i) the positive effect of high-income on ICT diffusion does not far outweigh
t
he“
c
onve
r
g
e
nc
e
”e
f
f
e
c
ta
sdi
s
c
us
s
e
di
npr
e
vi
ouss
e
c
t
i
ons
;a
nd(
i
i
)“
de
ve
l
ope
d”
status does not perfectly determine the income level; several economies, such as
Singapore or Hong Kong, have a higher income level than some developed nations.
3) The distinct effect of the second period, 1995-2000
 The coefficient of the variable After95 is positive and significant at the 5-percent
level. In addition, the magnitude of the coefficient is much larger than those of other
coefficients. This evidence confirms that there was a boost in ICT contribution to
growth in the period 1995-2000 across economies. The finding is consistent with the
fact that the period 1995-2000 was characterized by the prominent emergence the
Internet and rapid progress in the ICT revolution. The Internet and new ICT products
have remarkably enhanced the benefits and lowered the costs of investing in ICT.
IV. Impact of ICT Investment on Economic Growth
The previous section confirms that ICT accounts for an increasing share in economic
growth from the period 1990-1995 to the period 1995-2000 in most economies. These
results, however, are not sufficient to judge how significant ICT has impacted output
growth across economies. This section examines if ICT accumulation is a significant
determinant of the variation in growth performance across economies and if ICT is
superior to Non-ICT in promoting output growth.
IV.1. ICT as a Determinant of Output Growth
Model
Start with the basic Cobb-Douglass production function:
[IV-1.A]
Yit = AitKcit1 Kncit2 Lit
where Yit is output, Ait - the technology level, Kcit and Kncit - the ICT and non-ICT
capital stocks, and Lit is labor stock (hours worked); subscripts i and t indicate that the
observation is for country i in year t.
25
Thet
e
c
hnol
ogyl
e
ve
lofe
a
c
hi
ndi
vi
dua
lc
ount
r
yi
saf
unc
t
i
onoft
hec
ount
r
y
’
sfixed
characteristics and the global technology level shared by all countries. Suppose that this
function takes the form Ait = A0eλtDi, where A0eλtis the global technology level shared by
all countries (is the pace of global technology progress over time) and Di represents the
fixed characteristics of country i. Therefore, equation IV-1.A can be rewritten as
Yit = A0eλtDi Kcit1 Kncit2 Lit
[IV-1.B]
Taking the logarithm for equation V-2 and rearranging the terms lead to equation
[IV-2]
ln(Yit)= 1ln(Kcit) + 2ln(Kncit) + ln(Lit)+ t + ln(A0) + ln(Di)
First-differencing equation [IV-2] yields an equation for the rate of output growth as the
following:
[IV-3]
dYit= 1dKcit + 2dKncit + dLit+ + 
it
where dYit=Ln(Yit)-Ln(Yit-1) is the growth rate of output (from year t-1 to year t);
dKcit=Ln(Kcit)-Ln(Kcit-1) and dKncit=Ln(Kncit)-Ln(Kncit-1) –the growth rates of ICT and
non-ICT capital stocks; dLit=Ln(Lit)-Ln(Lit-1) –the growth rate of hours worked; is the
constant term of technological progress over the period 1990-2000, and 
it is the random
noise, identically and independently distributed among economies and years.
Results:
Because the growth of output and capital and labor inputs may have simultaneous effects
on each other, the estimates produced by the OLS regressions tend to be upward-biased
and should be taken with caution. IV regressions are designed to surmount this
simultaneity problem. The instrumental variables used for the IV regressions are the 1period and 2-period lags of the independent variables and the time dummy.
The OLS and IV regressions are based on model [IV-3] and both use the robust estimator
of variance to correct for heteroskedasticity of the variance. Table 4 provides results of
the OLS and IV regressions for the whole sample of 50 economies and for the subsample
of 22 industrialized economies.
26
Table 3: ICT Capital as a Determinant of Output Growth
Dependent Variable: dY (output growth rate)
Coefficient
DKnc
DKc
Dl
Constant
N
R2
0.44***
0.10***
0.87***
-0.01
dKnc
dKc
dL
Constant
N
R2
0.06
0.08***
0.97***
0.00
OLS Results
t-statistics
p-value
Coefficient
All Sample (50 Economies)
9.64
0.000
0.34***
3.90
0.000
0.05**
10.02
0.000
0.74***
-2.39
-0.017
0.00
550
0.51
Industrialized Group (22 Economies)
0.77
0.439
-0.14
3.27
0.001
0.15***
10.33
0.000
1.08***
1.08
0.280
0.00
242
0.57
IV Results
t-statistics
p-value
5.91
1.97
4.57
0.88
550
0.49
0.000
0.049
0.000
0.378
-1.61
3.58
7.83
0.04
242
0.54
0.110
0.000
0.000
0.966
There are notable observations:
 For the entire sample of the 50 economies, the ICT capital variable is significant at
the 1 percent level in the OLS regression and 5-percent in the IV regression. The
results indicate that ICT is a significant determinant for the variation in output growth
across the 50 economies during the last decade (1990-2000). Furthermore, the
coefficient of the ICT capital variable, dKc, in the IV regression implies that, on
average, a 10 percent increase in the ICT capital stock adds about 0.45 percent points
to output growth. The results also show that Non-ICT capital and labor are important
determinants of output growth: Non-ICT capital and labor variables are all significant
at the 1 percent level in both OLS and IV regressions.
 For the subsample that includes only 22 developed economies, the ICT variable is
significant at the 1 percent level in both OLS and IV regressions. Moreover, the
magnitude of its coefficient is much larger for the subsample than the entire sample
(0.153 vs. 0.045). The results indicate that ICT plays a more important role in
determining the output growth for the developed economies than for the developing
ones. For an average developed economy, a 10 percent increase in the ICT capital
stock adds 0.8 percentage points to output growth. Regarding the Non-ICT capital
27
and labor, the results show that labor variable is significant at the 1 percent level in
both OLS and IV regressions, while non-ICT capital is not statistically significant in
either of the two regressions.
The findings, thus, confirm that the accumulation in ICT capital has a solid causal effect
on output growth and this impact is even stronger for the industrialized group.
IV.2. Impact of ICT Penetration on the Efficiency of Economic Growth
Thet
e
r
m“
efficiency ofe
c
on
omi
cgr
owt
h”is defined as follows: an economy with a
higher efficiency of growth can achieve a higher output growth for given growth in
aggregate capital and labor inputs.
Previous studies have found that ICT has a positive impact on the efficiency of growth.
At the firm level, Brynjolfsson and Hitt (1995, 1996, 2003) find a positive relationship
between IT investment and firm productivity levels; firms investing more in IT produce
more output per unit of input. Lichtenberg (1995) estimate firm production functions and
finds an excess return on IT equipment and labor. Lehr and Lichtenberg (1999) inspects
firm-level data among service industries and report evidence that personal computers
make a positive and significant contribution to productivity growth
At the industry level, Siegel (1997), examining the US manufacturing industries, reveals
that computerization has a significant positive effect on productivity growth. Stiroh
(2002), investigating the US 57 major industries, confirms a strong link between IT
accumulation and productivity growth; in particular, among the major input classes, only
IT-capital deepening is significantly associated with future productivity acceleration.
O’
Ma
honya
ndVe
c
c
hi(
2003)
,i
nve
s
t
i
ga
t
i
ngi
ndus
t
r
yda
t
af
ort
heUSa
ndt
heUK,une
a
r
t
h
that ICT has a positive and significant long-run impact on TFP.
At the economy level, Pohjola (2000), by examining IT investment in 39 countries over
the period of 1980-1995, concludes that net return on IT investment was much larger than
28
net return on non-IT investment; this implies that investment in IT fosters the efficiency
of growth.
While positive assessments of the role of ICT in productivity growth seem to prevail, the
opposite view remains challenging. Gordon (2002) claims that “
c
omput
e
rc
a
pi
t
a
ld
i
dnot
have any kind of magical or extraordinary effect—it earned the same rate of return as any
ot
he
rt
y
peofc
a
pi
t
a
l
”
.
To investigate the impact of ICT capital accumulation per capita on efficiency of growth,
I use a model, starting with the basic Cobb-Douglas production function:
Yit = AitKitLit
[IV-4.A]
where Yit, Ait, Kit, and L are, respectively, the levels of output, technology, aggregate
capital and labor inputs of country i in year t. The aggregate capital input Kit is
proportional to the aggregate capital stock, which can be computed from the flow of
gross fixed capital formation. Labor input is measured in hours worked.
Suppose that the technology level Ait of country i in year t is a function of global
technology level A0eλt(
whe
r
eλi
st
hepa
c
eofg
l
oba
ltechnology progress over time
shared by all countries), the country-specific effects Di, and a variable representing the
depth of ICT penetration such as the ICT capital stock per capita variable, kcit. Let’
s
assume that the technology level Ait takes the following form
Ait = A0eλtDi kcitγ
Equation [V-4.A], therefore, can be rewritten as
Yit = A0eλtDi kcitγKitLit
[IV-4.B]
Taking the logarithm for equation [V-4B], then, rearranging the terms lead to the
following equation:
[IV-5]
ln(Yit)= ln(Kit) + ln(Lit) +γ
l
n(
kc
t + ln(A0) + ln(Di)
it) + 
First-differencing equation [III-5] yields an equation for output growth rate as the
following:
[IV-6]
dYit= dKit + dLit +γ
dkc
it+ + 
it
29
where dYit = Ln(Yit)-Ln(Yit-1) is the growth rate of output (from year t-1 to year t); dKit =
Ln(Kit)-Ln(Kit-1) –the growth rate of aggregate capital input; dLit = Ln(Lit)-Ln(Lit-1) –the
growth rate of labor input; dkcit = Ln(kcit)-Ln(kcit-1) –the growth rate of the ICT capital
stock per capita level (which is also labeled as “
dkc
pe
r
c
a
p”t
o ma
kei
te
a
s
i
e
rt
o
remember),  is the constant term,
and 
it is the random noise, identically and
independently distributed among all economies and years.
Results
Both OLS and IV regressions are used to examine the impact of ICT on the efficiency of
growth. As mentioned in section IV.1, the IV regression is designed to surmount the
simultaneity problem. The instrumental variables used for the IV regressions are the 1period and 2-period lags of the independent variables and the time dummy. Both
regressions are based on model [IV-6] and use the robust estimator of variance to correct
for heteroskedasticity.
Table 4 provides results of the OLS and IV regressions for the entire sample of the 50
economies and for the subsample of 22 developed economies.
Table 4: Impact of ICT on the Efficiency of growth
Dependent Variable: dY (output growth rate)
OLS Results
Coefficient
t-statistics
p-value
Coefficient
All Sample (50 Economies)
0.44***
9.50
0.000
0.36***
Dk
0.90***
10.61
0.000
0.76***
Dl
3.87
0.000
0.04
Dkcpercap 0.10***
-0.10
-2.64
-0.009
0.00
Constant
N
550
R2
0.52
Industrialized Group (22 Economies)
0.10
1.19
0.235
-0.06
dK
0.97***
10.51
0.000
1.05***
dL
3.59
0.000
0.16***
Dkcpercap 0.90***
0.03
0.74
0.461
-0.00
Constant
N
242
R2
0.58
IV Results
t-statistics
p-value
5.92
4.94
1.49
0.95
550
0.49
0.000
0.000
0.136
0.343
-0.68
7.72
3.89
-0.20
242
0.55
0.500
0.000
0.000
0.838
30
The following findings stand out:
 For the entire sample of the 50 economies, the ICT stock per capita variable is
significant at the 1 percent significance level in the OLS regression but not
statistically significant in the IV regression. The OLS result implies that for the entire
group of the 50 economies, output growth across economies is strongly associated
with the level of ICT stock per capita, controlling for the growth in the aggregate
capital and labor inputs. However, this associated effect is not a causal effect because
there might be a simultaneous effect between growth in output and in the ICT capital
stock per capita. The IV regression result indicates that the coefficient of the ICT
stock per capita is positive but not statistically significant (p-value=0.136). The result
implies that the causal effect of the ICT capital stock per capita on the quality of
output growth is not solid for the whole sample of the 50 economies.
 For the subsample of the 22 developed economies, the ICT stock per capita variable
is significant at the 1 percent significance level in both OLS and IV regressions. The
results imply that for the developed economies, the ICT capital accumulation per
capita has a solid causal effect on the efficiency of growth; that is, for given growth
rates of aggregate capital (dK) and hours worked (dL), an increase in ICT capital
stock per capita clearly enhances the growth rate of output.
V. Conclusion
This paper examines the contribution of ICT to, and its impact on, global economic
growth.
The study finds that ICT contribution to economic growth is a global phenomenon, which
is evident not only in developed economies but also in developing ones. The ICT
contribution to growth in most economies drastically increased from the period 19901995 to the period 1995-2000, while its variance was also strikingly widened. The key
determinants of the variance in ICT contribution to growth across economies are
31
education, institutional quality, openness, and English fluency. Furthermore, the impacts
of institutional quality and English fluency are significantly accelerated over time.
The study confirms that ICT has a significant impact on economic growth. First, the
accumulation in ICT capital stock is a significant determinant of the variation in output
growth across economies. Second, ICT is superior to Non-ICT in enhancing the
efficiency of output growth: for given levels of growth in labor and capital inputs, a
higher level of ICT capital stock per capita allows a typical economy to achieve a higher
output growth rate.
32
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Appendix: Estimating ICT Investment Flows
A-I. WITSA/IDC ICT Spending Data
The WITSA/IDC digital planet reports provide data on ICT spending for the 50
individual countries. I estimate investment flows in each ICT asset type from the
expenditure flows on that asset, which are provided by the WITSA reports. The
WITSA/IDC data on ICT expenditure flows incur two limitations: 1) WITSA keeps track
of the data only for 1992 onwards; and 2) The expenditure data are not split into
investment and consumption.
To project WITSA expenditure series for years prior to 1992, I use an OLS model with a
high predictive power. To estimate the investment flows into an ICT asset type, I use on
the pattern observed for the U.S., capuring the relationship between investment and
expenditure, and the change in the penetration in a country for each ICT asset type. The
details are presented below.
A-II. Projecting backward WITSA ICT Expenditure Flows
For an ICT asset type c (c could be hardware, software, or telecommunication
equipment), WITSA spending data are observed for the 50 economies under investigation
over 10 years (1992-2001). These data allow one to project backwards the spending data
for the ICT asset c for earlier years, namely from 1960 to 1991. I employ the following
OLS log-log backward projection model:
[A-1]
ln(Eci t-1) = 0 + 1ln(Ec i t) + 2 ln(y i t-1)+ i t
where Eci t represents expenditure on ICT asset c (subscripts i and t indicate, respectively,
country i and year t), yi t is GDP per capita, 
i t is the error term. The model specifies that,
for a country i, spending on ICT asset c in year t-1 can be deduced from GDP per capita
in that year and the spending on the asset c in period t. Model A-1 has a high predictive
power for each ICT asset type (R2 equals 0.98 for hardware, 0.99 for software, and 0.99
for telecom). The model, therefore, is used to project the expenditure series on the three
types of ICT asset (hardware, software, and telecom) for the years prior to 1992 for the
50 countries/economics.
37
A-III. Examining the U.S. Pattern
In order to identify a systematic difference between the WITSA/IDC expenditure and the
actual investment data flows for an ICT asset type, one can examine the investment-toexpenditure for the U.S., where the data on ICT investment have been well recorded by
the Board of Economic Analysis (BEA)15. Tables A.1 depicts the ratios between
WITSA/IDC expenditure and the BEA investment for the three ICT asset types,
hardware, software, and telecommunication equipment over 22 years from 1980 to 2001
(the WITSA/IDC expenditure data for 1980-1991 are predicted values). The results
reveal the following pattern:
(i) Consistency: The ratio of investment/spending for each type of ICT good is consistent
over time. There are only some notable changes, especially for hardware, between the
two periods 1980-1989 and 1990-2001. The fact that the share of business investment in
total expenditure on each ICT vintage, especially computer hardware, was higher during
1980-1990 than 1991-2001 is understandable because the penetration of ICT in the
household sector relative to the business sector was less intensive in the earlier period
than in the later. Within each of these two periods, the ratio for an ICT asset type
(hardware, software, telecommunication equipment) is consistent. As a result, the 95
percent confidence intervals for the three ratios make up narrow ranges: (0.70, 0.82) over
1981-1990 and (0.55, 0.59) over 1991-2001 for hardware; (2.20, 2.30) and (2.01, 2.13)
for software; and (0.32, 0.34) and (0.29, 0.37) for telecommunication equipment in the
two periods, respectively. Because ICT products are nearly identical across countries, it is
reasonable to expect that the consistency of the investment-expenditure ratios holds
across countries over the two periods 1980-1989 and 1990-2001.
(ii) The Mean: The mean of the ratio of investment/expenditure is 0.76 over 1981-1990
and 0.57 over 1990-2001 for hardware, 2.25 and 2.07 for software in the two periods,
respectively, and 0.33 for telecom in both periods. Using these mean values allows one to
estimate investment flows into ICT assets for the U.S. with a high degree of accuracy.
15
Business sector investment series in ICT are provided on the BEA website (www.BEA.org).
38
(iii) The Compatibility between the U.S. pattern and the Global Market: The
information provided by the International Telecommunication Union on the global
telecommunication market over 1991-2001 allows one to compare the U.S. and global
pattern of the investment-to-spending ratio. Because investment includes equipment,
installation, and other related services, it is reasonable to assume that the investment is
about 1.3-1.4 times higher than the market value of the equipment at the global market
level. Table A.2 shows the ratio between equipment and total telecom market over 19912001. This ratio is also consistent over the period and its adjusted value (multiplied by
1.4 to take into account the cost of additional investment) is very similar to the U.S.
pattern. The investment-spending ratio at the global market level has a mean of 0.33,
which is the same as in the U.S. and its 95 percent confidence interval is (0.32, 0.34).
This observation implies that the U.S. pattern is relevant for the world global ICT market.
A-IV. Estimating ICT Investment Flows
The consistency of the investment-to-expenditure ratios revealed from the U.S. pattern is
assumed to hold across countries because the ICT assets are nearly identical all over the
world. This feature implies that it is plausible to use a constant ratio to estimate the
investment flow into an ICT asset from its WITSA/IDC expenditure for each of the two
time periods, 1981-1990 and 1991-2001. However, it is less plausible to assume that the
mean values of the ratios observed for the U.S. are similar across economies. One of the
notable reasons is the dissimilarity in the market efficiency and purchasing power parity
of US$ for ICT goods across countries. A calibration based on the change in the
penetration of each asset type provides a better estimate for its ratio for each individual
economy. The investment in each of the three ICT asset types now can be simply
computed from the WITSA spending series as Ic,a,t = c,a,t*Sc,a,t, where Ic,a,t, c,a,t, and Sc,a,t
are, respectively, investment, the estimated investment-to-spending ratio and
WITSA/IDC spending in year t for country c, and ICT asset a.
39
Table A.1: Relationship between Spending and Investment by Type of ICT Assets: US Patterns
Spending
(WITSA, $Billions)
Year
HW
SW
Investment
(BEA, $Billions)
TEL
HW
SW
Investment/Spending
Ratio
TEL
SW
TEL
1981
26.67
6.12
87.97
17.10
12.90
29.00
0.64
2.11
0.33
1982
29.17
7.03
93.82
18.90
15.40
31.10
0.65
2.19
0.33
1983
31.94
8.10
100.14
23.90
18.00
31.90
0.75
2.22
0.32
1984
34.94
9.33
106.75
31.60
22.10
36.60
0.90
2.37
0.34
1985
38.20
10.75
113.55
33.70
25.60
39.90
0.88
2.38
0.35
1986
41.75
12.40
120.65
33.40
27.80
42.10
0.80
2.24
0.35
1987
45.61
14.30
128.07
35.80
31.40
42.10
0.78
2.20
0.33
1988
49.81
16.52
135.82
38.00
36.70
46.70
0.76
2.22
0.34
1989
54.37
19.09
143.86
43.10
44.40
46.90
0.79
2.33
0.33
1990
59.34
22.09
152.24
38.60
50.20
47.50
0.65
2.27
0.31
0.76
2.25
0.33
0.06
0.05
0.01
Average for Period
1981-1990
Mean
Margin of Error (=0.05, 10 observations for 10 years)
95 percent Confidence Interval
Average for Period
1991-2001
HW
(0.70, 0.82)
(2.20, 2.30)
(.32, .34)
1991
64.77
25.59
161.03
37.70
56.60
45.70
0.58
2.21
0.28
1992
70.74
29.72
170.40
43.60
60.80
47.80
0.62
2.05
0.28
1993
80.97
33.02
181.33
47.20
69.40
48.20
0.58
2.10
0.27
1994
89.79
37.78
195.17
51.30
75.50
54.70
0.57
2.00
0.28
1995
105.67
40.67
205.58
64.60
83.50
60.00
0.61
2.05
0.29
1996
128.87
46.80
209.59
70.90
95.10
65.60
0.55
2.03
0.31
1997
138.61
54.01
220.07
79.60
116.50
73.70
0.57
2.16
0.33
1998
159.48
65.25
231.07
84.20
140.10
81.20
0.53
2.15
0.35
1999
169.19
75.01
242.62
90.40
162.50
93.70
0.53
2.17
0.39
2000
165.47
90.97
252.33
93.30
179.40
116.60
0.56
1.97
0.46
2001
136.05
96.56
265.95
74.20
180.40
90.60
0.55
1.87
0.34
0.57
2.07
0.33
0.02
0.06
0.04
Mean
Margin of Error (=0.05, 11 observations for 11 years)
(0.55, 0.59)
(2.01, 2.13)
(0.29, 0.37)
95` percent Confidence Interval
40
Table A.2: Global Telecom Market
Year
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
Market Revenue (current price, US$ Billions)
Invest/Spending Ratio
Total Market
Equipment
Services
Equip/Total
Ajusted
(T)
(E)
(S)
(E/T)
(E/T)*1.4
523
120
403
0.23
0.32
580
132
448
0.23
0.32
605
135
470
0.22
0.31
675
158
517
0.23
0.33
779
183
596
0.23
0.33
885
213
672
0.24
0.34
946
234
712
0.25
0.35
1015
248
767
0.24
0.34
1123
269
854
0.24
0.34
1210
290
920
0.24
0.34
1232
264
968
0.21
0.30
Mean
0.24
0.33
SE
0.006
0.008
95% Confidence Interval
(0.23, 0.24)
(0.32, 0.34)
Source: International Telecommunication Union, 2001
41