information and technology of singapore.pdf

Information and Communication Technology (ICT) and
Singapore’s economic growth
Khuong M. Vu ⇑
National University of Singapore, 469C Bukit Timah Road, Singapore 259772, Singapore
a r t i c l e i n f o
Article history:
Received 7 January 2012
Received in revised form 12 August 2013
Accepted 28 August 2013
Available online 25 September 2013
JEL classification:
O40
O47
O53
Keywords:
ICT
Singapore
I–O tables
Growth decomposition
Productivity
ICT manufacturing
a b s t r a c t
Singapore’s remarkable success in economic development has been strongly associated
with the country’s vigorous efforts to embrace the Information and Communication Tech-
nology (ICT) revolution to promote economic growth. This study provides a comprehensive
investigation of the contributions of ICT to Singapore’s economic growth during the 1990–
2008 period. It documents three key findings. First, there is a strong positive association
between the intensity of ICT use and value-added and labor productivity growth at the sec-
tor level. Second, ICT investment contributed approximately 1 percentage point to Singa-
pore’s GDP during 1990–2008, and its role in driving economic growth has become
increasingly important over time. Third, the contribution of the ICT manufacturing sector
to Singapore’s growth was notable, but it was on the decline and faced difficult restructur-
ing challenges. This paper also provides valuable policy lessons and strategic insights for
governments in both developed and developing countries that aspire to embrace ICT to
promote economic growth.
Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction
Embracing global trends and proactively seizing oppor-
tunities brought about by emerging technologies has en-
abled Singapore to achieve outstanding economic
performance since its independence in 1965. With its per
capita GDP growing at an average rate of more than 6%
per year in the past four and a half decades (1965–2010),
Singapore has transformed itself from a third world coun-
try into a prosperous developed nation.1
In achieving and
sustaining this success, Information and Communication
Technology (ICT) has been a top priority and strategic lever
of Singapore’s development strategy and policy. Examining
the contributions of ICT to Singapore’s growth provides
valuable insights and policy implications for efforts to em-
brace ICT to promote economic growth.
There has been a rich literature examining the contribu-
tions of ICT to economic growth at the national level.2
These studies, however, primarily focused on the US and
European countries.3
Initiated by the pioneering studies of
Jorgenson and Stiroh (1995, 1999) and Oliner and Sichel
(1994, 2000), a large volume of studies on this topic has
emerged. Examples of such studies include Jorgenson
(2001), Stiroh (2002), Oliner and Sichel (2003), Jorgenson
et al. (2003a, 2008), and Martínez et al. (2010) on the US;
0167-6245/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.infoecopol.2013.08.002
⇑ Tel.: +65 65168695.
E-mail address: sppkmv@nus.edu.sg
1
In 2010, Singapore’s per capita GDP was PPP $56,694, ranking third
among the 183 economies listed in the World Economic Outlook Database-
September 2011 of the International Monetary Fund (available at http://
www.imf.org/external/pubs/ft/weo/2011/02/weodata/index.aspx).
2
Van Reenen et al. (2010) and Cardona et al. (2013) provided excellent
surveys of the effects of ICT on economic and productivity growth.
3
It is worth noting, however, that Jorgenson and Vu (2005, 2010, 2011)
and Vu (2011a) provided a broad picture of the contribution of ICT
investment to economic growth in more than 100 economies worldwide.
Information Economics and Policy 25 (2013) 284–300
Contents lists available at ScienceDirect
Information Economics and Policy
journal homepage: www.elsevier.com/locate/iep

Oulton (2002) and Correa (2006) on the UK; Jorgenson and
Motohashi (2005) on Japan; Jalava and Pohjola (2002,
2007) on Finland; Atzeni and Carboni (2005) on Italy; Martí-
nez et al. (2008) on Spain; Antonopoulos and Sakellaris
(2009) on Greece; Colecchia and Schreyer (2001), Van Ark
et al. (2002), Daveri (2002), and Timmer et al. (2003) on
EU economies; Jorgenson (2003) on the G7 economies; and
Dimelis and Papaioannou (2011) on industry-level compar-
isons between the EU and the US.
This paper examines the contributions of ICT to growth
in Singapore, where ICT diffusion and production have
been promoted with rigorous policy initiatives. Three fac-
tors motivate this study. First, Asia is a vibrant region for
ICT diffusion and production, but research on the contribu-
tions of ICT to growth is scarce. Therefore, there is a need
for studies on this topic in countries of the region, espe-
cially for those where high-quality data are available, such
as Singapore. Second, Singapore has been highly proactive
and effective in embracing ICT to foster economic growth.
Examining the case of Singapore can provide a comprehen-
sive understanding of the contributions of ICT to growth,
which come from ICT use, ICT investment, and ICT produc-
tion. Third, the fluctuation in the performance of Singa-
pore’s ICT manufacturing sector and its rapid structural
change provide valuable policy insights into the challenges
faced by the government in its efforts to promote this
industry.
This paper examines ICT development and growth in
Singapore since 1980, with a detailed analysis of the period
between 1990 and 2008, for which data on ICT investment
and production are available. It is also worth noting that
the in-depth analysis of the period 1990–2008 also pro-
vides meaningful insights because 1990 marked a mile-
stone in Singapore’s economic growth process. In the
period prior to 1990, Singapore’s economic growth was
characterized by the government’s export-led efforts to
promote quantitative growth through the rapid accumula-
tion of capital. Since 1990, the government has shifted its
strategic focus to qualitative development to transform
Singapore into a developed nation.4
Furthermore, the peri-
od 1990–2008 was also characterized by the accelerating
pace of globalization, fueled by the end of the Cold War in
1991, the rise of China and India, and the rapid penetration
of the ICT revolution across nations.
This paper primarily uses data compiled from Singa-
pore’s Department of Statistics (DOS), of which the data
from the I–O tables for 1995, 2000, and 2005 are particu-
larly valuable.5
Additional sources of data include the Min-
istry of Trade and Industry (MTI), Monetary Authority of
Singapore (MAS), World Bank’s World Development Indica-
tors (WDI), and the EU KLEMS Project.
The remainder of this paper proceeds as follows. Sec-
tion 2 introduces Singapore’s strategy and policy initiatives
in its effort to embrace the ICT revolution to foster economic
growth. Section 3 presents evidence on the associations
between ICT use intensity and growth at the sector level.
Section 4 estimates the contribution of ICT investment,
among other sources, to Singapore’s economic growth.
Section 5 examines the contributions of the ICT manufac-
turing sector. Section 6 summarizes the findings and draws
policy lessons.
2. Singapore and the ICT revolution
Singapore initiated its strategy to embrace the ICT rev-
olution to promote economic growth and development in
the early 1980s, as soon as the first generation of personal
computers demonstrated its substantial potential. This en-
deavor has been concentrated on two fronts: fostering ICT
adoption and promoting ICT production. Singapore’s
achievements in these efforts are remarkable. However,
some critical challenges have also emerged. This section
highlights these issues.
2.1. Fostering ICT adoption
The efforts of the Singaporean government to foster ICT
adoption can be characterized by two prominent features.
One is a proactive ICT strategy with a clear master plan for
each stage of development, and the other is the govern-
ment’s pioneering role in developing e-government that
leverages ICT to enhance its efficiency and effectiveness.
Singapore’s journey over the past three decades has
evolved according to six master plans that set out the main
points of focus and priorities to support the country’s ICT
readiness and realize its ICT-enabled potential (Table 1).
The first master plan, entitled ‘‘National Computerization
Plan’’, was implemented from 1980 to 1985 and focused
on ICT capacity building, including computerization and
ICT manpower, and investment in the ICT industry. The
second master plan (‘‘National IT Plan’’, 1986–1991) aimed
to enhance communications between government agen-
cies and businesses by extending the government’s ICT
system into the private sector. The third master plan
(‘‘IT2000’’, 1992–1999) embraced the emergence of the
Internet with a focus on connectivity and Internet-enabled
services. The fourth master plan (‘‘Infocomm 21’’,
2000–2003) emphasized convergence, fostering the pene-
tration of ICT across economic sectors and in society at
large. The fifth master plan (‘‘Connected Singapore’’,
2003–2006) sought to unleash the potential of ICT to
create value and increase capabilities. The sixth master
plan (‘‘iN2015’’, 2006–2015) aspired to embrace ICT for
innovation, social and economic integration, and interna-
tional collaboration. With its effective strategies and vigor-
ous implementation initiatives in fostering ICT adoption,
Singapore has become a leading country in ICT-readiness
and e-government performance.
2.2. Promoting ICT production
Singapore has proactively promoted ICT production,
especially the ICT manufacturing sector. With strong sup-
port from the government through its industrial policy,
the industry has rapidly expanded since 1980. The
4
Ministry of Trade and Industry, The Strategic Economic Plan Towards a
Developed Nation, Report of the Economic Planning Committee, 1991.
5
The DOS produces Singapore’s I–O Tables every five years. The I–O
Tables since 1995 provide information related to the sales and purchase of
the ICT sector. The I–O Tables for 2010 have not been published.
K.M. Vu / Information Economics and Policy 25 (2013) 284–300 285

industry’s growth was driven by an influx of multinational
companies (MNCs) manufacturing ICT hardware products,
such as disk drives, computer peripherals, computer sys-
tems, and integrated circuits (ICs).
The government has also made notable efforts to pro-
mote local businesses and enhance their linkages with
MNCs through the Local Industry Upgrading Program
(LIUP) introduced by the Economic Development Board
(EDB) in 1986. Under this program, MNCs such as Motoro-
la, IBM, and Intel were encouraged to enter long-term con-
tracts with their local suppliers to help these local firms
improve their operational efficiency, organizational man-
agement, and technical capabilities. This program has been
helpful in enabling local suppliers to take on original
equipment manufacturer (OEM) production as MNCs
increasingly engage in outsourcing.6
Singapore’s ICT manufacturing sector consists of five
industries: Semiconductors; Computer Peripherals; Data
Storage; Infocomms & Consumer Electronics; and Other
Electronics Components. The shares of these five industries
in value-added and employment of the sector during
1985–2008 are described in Fig. 1.
Thanks to the surge in the global market demand and
the government’s proactive policy initiatives, Singapore’s
ICT manufacturing sector has rapidly become a major pillar
of the economy. The sector accounted for 5–8% of GDP and
30–50% of the country’s total exports in most years during
the 1990–2010 period.7
The development of Singapore’s ICT manufacturing sec-
tor is characterized by two main features. First, the sector’s
heavy reliance on exports makes it extremely vulnerable to
fluctuations in global demand. Positioning itself as a hub of
the regional ICT production network,8
Singapore relies
almost exclusively on external markets for the development
of its ICT manufacturing sector. For example, in 2005, Singa-
pore’s ICT manufacturing sector exported S$75.2 billion, or
95.5% of its total output of S$78.7 billion.9
As a result, the
performance of Singapore’s ICT manufacturing sector is
highly vulnerable to fluctuations in the global ICT market.10
Second, Singapore’s ICT manufacturing sector has under-
gone notable structural change since the late 1980s, shifting
toward more capital- and technology-intensive operations
and higher value-added activities.Thisdynamic has been dri-
venbyrapid technological change andincreasingglobal com-
petition, especially from emerging low-cost producers in the
region. Singaporean producers responded by automating
manufacturing operations and shifting their focus to higher
value-added products and activities such as semiconductors,
product design, and R&D; while they simultaneously relo-
cated their labor-intensive operations to countries in the re-
gion with lower labor costs (Chia and Lim, 2003). This
structural change intensified after the 1997–1998 Asian
financial crisis. The rapid expansion of the semiconductor
industry’s share in the ICT manufacturing sector, as shown
in Fig. 1A, serves as evidence of this shift.
Fig. 2 below describes the dynamics of Singapore’s ICT
manufacturing sector in terms of its contributions to the
economy’s GDP and employment during 1985–2010. Sev-
eral observations stand out. First, the employment share
of the sector in the economy has steadily declined since
1990, while its GDP share was rather stable at approxi-
mately 7–8% until 2005. This reflects Singapore’s continual
efforts to shift the sector towards activities with higher va-
lue-added and labor productivity since the late 1980s.
Third, the share of the sector’s value-added in GDP
peaked at an extraordinarily high level in 2000. This was
driven by the dynamics of the worldwide ICT hardware
market, which grew rapidly during the 1990s and reached
a peak in 2000 before it plunged in 2001 and 2002 due to
the dotcom crash (OECD, 2006). Fig. 3, which depicts the
worldwide semiconductor market – the bellwether of the
ICT industry, provides a clear picture of these dynamics.
Table 1
Singapore’s ICT strategy and policy initiatives, 1980–2015. Source: Infocomm Development Authority (IDA).
Period Name Main focus Priorities/programs E-government
initiatives
1980–1985 National
Computerization Plan
Computerization Computerizing civil services
Developing IT industry & IT manpower
Civil Service
Computerization
Program
1986–1991 National IT Plan Communications Extending government systems to private sector, e.g.,
TradeNet, MediNet, LawNet
1992–1999 IT2000 Connectivity and
Content
Transforming Singapore into an intelligent island
2000–2003 Infocomm 21 Convergence Developing Singapore as a global Infocomm Capital, e-
Economy and e-Society
E-government Action
Plan
2003–2006 Connected Singapore Connectedness Unleashing potential of Infocomm to create new values,
realize possibilities and enrich lives
E-government Action
Plan II
2006–2010 iN2015 (Intelligent
Nation)
Creation Leveraging Infocomm for Innovation, Integration and
Internationalization
igov2010
2010–2015 egov2015
6
The LIUP is a long-term policy initiative and has been expanded and
tailored to the services sector. For example, the Infocomm Local Industry
Upgrading Programme (iLIUP) established in 1999 aimed to promote
strategic and mutually beneficial partnerships between infocomm local
enterprises (iLEs) and infocomm MNCs. (Source: Infocomm Development
Authority.)
7
Source: Based on MTI data.
8
From the highlight of the electronics sector by the Singapore Economic
Development Board (EDB), the leading government agency for planning and
executing economic strategies, available at http://www.edb.gov.sg/content/
dam/edb/en/industries/Electronics/downloads/Electronics.pdf, retrieved
May 20, 2013.
9
Source: Singapore I–O tables 2005.
10
It is worth noting that the semiconductor industry, which plays a
dominant role in Singapore’s ICT manufacturing sector, is a bellwether of
the demand for ICT hardware products (OECD, 2006).
286 K.M. Vu / Information Economics and Policy 25 (2013) 284–300

Fourth, the share of Singapore’s ICT manufacturing sec-
tor in GDP declined sharply after 2005. This declining trend
was caused by several factors. One factor is the sector’s
continual restructuring, in which labor-intensive and low
value-added industries were scaled down (Fig. 1B). An-
other factor is Singapore’s shift toward non-manufacturing
sectors for driving growth. This shift reduced the share of
the manufacturing sector in GDP from 26% in 2005 to
23% in 2007 and 20% in 2008. Moreover, the 2007–2009
global ﬁnancial crisis that erupted in 2007 also had an ad-
verse effect on the world ICT market (ITU, 2009) and hence
on the growth of Singapore’s ICT manufacturing sector in
2007–2009.
3. Contributions of ICT use to sector-level growth
This section investigates the effect of ICT use intensity
on growth at the sector-level. The data for this analysis
are derived from Singapore’s I–O Tables, which are pub-
lished every 5 years. Using Singapore’s I–O Tables for
1995, 2000, and 200511
provides consistent data on value-
added, employment, and ICT product purchases for the 18
sectors of the economy in these 3 years (see Appendix A
for a description of these 18 sectors). The panel data for this
examination consist of 36 observations for 18 sectors over
two 5-year periods, 1995–2000 and 2000–2005.
3.1. Model
Due to data availability, the analysis is based on the fol-
lowing parsimonious regression model:
Z grit ¼ b0 þ b1 ln Z 0it þ b2EMP grit þ b3ICTI avgit
þ di þ gt þ eit; ð1Þ
where the subscripts i and t indicate sector i in period t; Z is
one of the two dependent variables of interest, value-
added (VA) or average labor productivity (ALP); Z gr is
Data sources: Ministry of Trade and Industry
Fig. 1B. Singapore’s ICT manufacturing sector: Employment by industry, 1985–2008.
Data sources: Ministry of Trade and Industry
Fig. 1A. Singapore’s ICT manufacturing sector: Value-added by industry, 1985–2008.
11
Singapore’s I–O Tables for 1990 and earlier years do not have
information on ICT. The I–O Tables for 2010 have not been published.

the average growth rate of variable Z; ln Z 0 is the log of
the beginning-of-period value of Z; EMP gr is the average
growth rate of employment; ICTI avg is the average level
of ICT use intensity12
; di and gt capture, respectively, sector
and time fixed effects; and eit is the random error term.13
The salient econometric issues related to the regression
model are addressed below.
First, this model does not suffer from collinearity prob-
lems. As shown in Table 2, the three explanatory variables
in Eq. (1) are not highly correlated. In addition, the vari-
ance inflation factor (VIF) test, which is used to detect mul-
ticollinearity among the explanatory variables, shows that
collinearity problems do not exist.14
Second, the model must address endogeneity bias be-
cause the dependent variable Z gr may have reverse effects
on the explanatory variables EMP gr and ICTI avg. To over-
come this endogeneity bias, it is necessary to employ
instrument techniques such as the two-stage least-squares
(2SLS) or generalized method-of-moments (GMM) ap-
proaches. Note that GMM is identical to 2SLS if the number
of instrumental variables (IVs) is equal to the number of
endogenous variables. However, GMM is more efficient
than 2SLS if the number of IVs exceeds the number of
endogenous variables and heteroskedasticity is present
(Baum, 2006, pp. 195–197). The three IVs chosen for the
two endogenous variables of the model – EMP gr and
ICTI avg – are the lags of three variables: employment,
ICT use intensity, and average wage.15
As shown in Tables
3A and 3B in Section 3.2, this set of IVs passes the tests for
instrument relevance and exogeneity.
Third, fixed effects (FE) rather than random effects (RE)
should be used for this estimation. In panel data regression
analysis, it is necessary to choose between the FE and RE
estimators. RE is typically more efficient (producing smal-
ler standard errors) than FE (Allison, 2009). However, RE is
inconsistent if the assumption that the sector-specific fixed
effects are uncorrelated with the vector of explanatory
variables does not hold (Caselli and Coleman, 2001, pp.
330). The result of the Hausman test indicates that the con-
ditions required for the RE estimator do not hold. This
means that FE should be employed in this analysis rather
than RE.
When employing cross-sectional regression to analyze
economic growth, it is important to understand its main
strengths and weaknesses. Levine and Zervos (1993) and
Durlauf (2009) suggested that cross-sectional regressions
should not be considered a perfect tool for growth analysis.
Instead, they should be viewed as a way to evaluate the
strength of partial correlations or capture stylized facts
that may suggest certain policy–growth relationships. In
this spirit, the model described by Eq. (1) can produce
meaningful results.
Data sources: Ministry of Trade and Industry; Department of Statistics; Ministry of Manpower
Fig. 2. Singapore’s ICT manufacturing industry: shares in GDP and employment.
12
The ICT use intensity of a sector is defined as the share of its purchase of
goods and services from the communications sector, which includes
telecommunications services, network operators and data communications
services, web hosting services, computer time-sharing services, and data
processing, hosting, and related activities, in its total intermediate inputs.
13
The model does not include time effects because they are not jointly
significant in any regressions in which they are included. Note that there
are only two periods in the panel data.
14
The VIFs from the test range from 1.07 to 1.40 (for the regression with
VA) and from 1.08 to 1.15 (for the regression with ALP). Baum (2006, pp.
85) suggested the rule of thumb that a regression may suffer from
collinearity problems if the largest VIF of its explanatory variables is greater
than 10.
15
Temple (1999) suggested that using panel data with lags as instru-
ments is a good way to avoid endogeneity problems when estimating cross-
sectional growth. A number of studies have employed this approach. For
example, see Acemoglu et al. (2008).

3.2. Empirical results
Results from regressions based on Eq. (1) are reported
in Tables 3A (for VA) and 3B (for ALP). In each of the two
tables, columns (1) and (2) report OLS results, and columns
(3) and (4) report GMM results.
As discussed above, the OLS estimates suffer from the
endogeneity bias caused by the reverse effect of the depen-
dent variables ðVA gr and ALP gr) on EMP gr and ICTI avg.
Wooldridge (2009, pp. 552) remarked that it is generally
complicated to obtain the direction of the endogeneity bias
in OLS coefficients. This direction may be downward in
some circumstances, as observed in a number of previous
studies.16
This downward bias is also salient in this investi-
gation. As shown in Tables 3A and 3B, the coefficients on
ICTI avg and EMP gr from OLS are positive but notably lower
than those from GMM.
The test of joint hypothesis indicates that sector fixed
effects are highly significant, whereas time fixed effects
are not. Therefore, time effects can be dropped; hence,
regression (3) is expected to be the most appropriate
estimation.
The GMM coefficient on ICTI avg is positive and statis-
tically significant for VA growth (Table 3A) and ALP growth
(Table 3B). This means that ICT use intensity has a strong
positive association with VA and ALP growth.17
This finding
is consistent with the results obtained in previous studies.
For example, Jorgenson et al. (2005, pp. 127, 353) found that
among ICT-using industries in the US, industries with more
intense ICT use experienced notably higher growth in VA
and ALP during 1977–2000, especially during 1995–2000.
In their study of industries in the US and EU for 1980–
2000, Dimelis and Papaioannou (2011) also found that the
effects of ICT on growth were more notable among heavy
ICT users.
However, as suggested by Levine and Zervos (1993) and
Durlauf (2009), the association between an explanatory
variable and the dependent variable should not be inter-
preted as a causal link but rather a suggestion of a mean-
ingful relationship between them. In this analysis, the
robust significance of the coefficients on ICTI avg suggests
Unit: USD billions, current prices
Source: OECD (2006)
Fig. 3. Worldwide semiconductor market by segment, 1990–2005.
Table 2
Pairwise correlations of explanatory variables in regressions for VA and ALP.
In VA regressions
VA_gr LnVA_0 EMP_gr ICTI_avg
VA_gr 1.000
LnVA_0 0.065 1.000
EMP_gr 0.394 0.160 1.000
ICTI_avg 0.286 0.449 0.253 1.000
In ALP regressions
ALP_gr LnALP_0 EMP_gr ICTI_avg
ALP_gr 1.000
LnALP_0 0.102 1.000
EMP_gr 0.217 0.301 1.000
ICTI_avg 0.140 0.159 0.255 1.000
16
For example, see Frankel and Romer (1999) for the relationship
between openness and growth, Hall and Jones (1999) for social infrastruc-
ture and income, Raphael and Winter-Ebmer (2001) for unemployment and
crime, and Rodrik et al. (2004) for institutions and development.
17
It is worth noting that a negative association between non-ICT use
intensity (which is defined as 100% less ICT use intensity) and VA and ALP
growth is found in this investigation. These results are not reported here.

that sectors with higher ICT use intensity are likely to have
better opportunities to grow in terms of value-added and
labor productivity, controlling for other explanatory vari-
ables and sector fixed effects. Similarly, the significance
of the coefficients on EMP gr in the GMM regressions in
Tables 3A and 3B implies that sectors with higher employ-
ment growth in Singapore tend to be those with stronger
value-added and labor productivity growth, controlling
for other factors.
4. ICT investment as a source of growth
This section estimates the contribution of ICT invest-
ment to Singapore’s GDP and ALP growth during 1990–
2008. The exercise divides this period into three 6-year
sub-periods: 1990–1996, 1996–2002, and 2002–2008.
The first sub-period (1990–1996) witnessed the rapid
growth of the Singaporean and other East Asian economies.
The second sub-period (1996–2002) was marked by three
major external shocks: the Asian financial crisis of 1997–
1998, the dotcom crash in 2000, and the ‘‘9–11’’ terrorist
attack in 2001, all of which dealt severe blows to Singa-
pore’s economy. In the last sub-period (2002–2008), Singa-
pore bounced back with high growth before it was hit by
the global economic crisis of 2008–2009.
4.1. Growth decomposition framework
The decomposition framework is based on the extended
production possibility frontier (PPF) model used by Jorgen-
son et al. (2003a):
Y ¼ A XðKnict; Kict; H; LQ Þ; ð2Þ
where the gross domestic product Y is produced from the
aggregate input function X of capital and labor services.
The capital services are rendered by non-ICT capital Knict
and ICT capital Kict. The non-ICT capital Knict consists of
three non-ICT capital vintages: non-residential buildings
and structures, transport equipment, and machinery and
equipment. The ICT capital Kict comprises computer hard-
ware, computer software, and telecommunications equip-
ment. The labor services are from the labor input L,
which is a product of the total hours worked H and the la-
bor quality index LQ (L = H LQ). The total factor productiv-
ity (TFP) A represents a Hicks-neutral augmentation of the
aggregate input function.
Under the neoclassical assumptions of competitive
markets and constant returns to scale, Eq. (2) can be trans-
formed into a growth-accounting decomposition18
:
D ln Y ¼
mKict
D ln Kict þ
mKnict
D ln Knict þ
mLD ln H
þ
mLD ln LQ þ D ln A; ð3Þ
where
m is the two-period average share in the total factor
income of the subscripted input. All variables are ex-
pressed in logarithmic first differences (Dln) to represent
their growth rates. The assumption of constant returns to
scale of the aggregate input function implies that:

mK ¼
mKict
þ
mKnict
¼ 1
mL:
According to Eq. (3), GDP growth can be decomposed as
follows:
Table 3A
ICT use intensity and sectoral value-added growth.
Dependent variable: VA growth per period ðVA grÞ
Explanatory variable OLS (FE) GMM
(1) (2) (3) (4)
Average ICT use intensity ðICTI avgÞ 0.179 0.135 0.332***
0.280***
(0.126) (0.109) (0.100) (0.105)
Average employment growth per period ðEMP grÞ 1.013***
0.870**
2.435**
2.096**
(0.308) (0.302) (0.998) (0.916)
Log of value-added at the beginning of period ðLnVA 0Þ 0.099*
0.166**
0.072 0.115*
(0.051) (0.072) (0.050) (0.066)
Sector effects Yes Yes Yes Yes
Time effects No Yes No Yes
Tests of joint effects: F-statistics and p-values (in square brackets)
Sector effects 7.72***
2.71**
61.92***
66.74***
[0.000] [0.033] [0.000] [0.000]
Time effects – 2.64 [0.126] – 1.47 [0.225]
Tests of instrument validity: test statistics and p-values (in square brackets)
Relevance (Anderson’s LR statistics) – – 6.703**
5.950**
[0.035] [0.051]
Exogeneity (Hansen J-statistic) – – 0.204 0.004
[0.651] [0.947]
R-squared 0.71 0.76 0.72 0.78
N 36 36 36 36
Notes: Robust standard errors are in parentheses. R-squared for the GMM regressions are Uncentered R2.
*
p 10%.
**
p 5%.
***
p 1%.
18
These assumptions have been used as the gold standard in growth
accounting exercises. For example, see OECD (2001, Annex 3), Jorgenson
et al. (2003a, 2003b), and Timmer and Van Ark (2005).

The contribution of capital input ð
mKict
D ln Kict
þ
mKnict
D ln KnictÞ, which consists of the contribution of
ICT capital ð
mKict
D ln KictÞ and the contribution of
non-ICT capital ð
mKnict
D ln KnictÞ.
The contribution of labor input ð
mLD ln H þ
mLD ln LQ Þ,
which consists of the contribution of total hours worked
ð
mLD ln HÞ and the contribution of labor quality
improvement ð
mLD ln LQ Þ.
TFP growth (Dln A).
Eq. (3) can also be rewritten to decompose the growth
in ALP as
D ln y ¼
mKict
D ln kict þ
mKnict
D ln knict þ
mLD ln LQ
þ D ln A; ð4Þ
where y = Y/H is ALP, and k = K/H is capital deepening.
ALP growth, therefore, can be decomposed into
The contribution of ICT and non-ICT capital deepening
ð
mKict
D ln kict þ
mKnict
D ln knictÞ.
The contribution of labor quality improvement
ð
mLD ln LQ Þ.
TFP growth (Dln A).
The GDP and ALP growth decomposition exercises re-
quire the estimation of a number of variables, which is
elaborated in Appendices B and C. Appendix B computes
the labor income share and constructs a labor quality in-
dex. Appendix C estimates investment flows in ICT capital,
ICT and non-ICT capital stocks, and ICT and non-ICT capital
services.
4.2. Growth decomposition results
Results from the growth decomposition exercises above
are reported in Tables 4 and 5. As shown in Table 4 (GDP
growth), Singapore achieved a high GDP growth over
1990–2008, with an outstanding rate of 8.1% in 1990–
1996, a resilient rate of 4.0% during the Asian financial cri-
sis and dotcom crash period (1996–2002), and a robust
rate of 6.4% over 2002–2008. Capital input played a major
role in this performance with an average share of nearly
60% for 1990–2008. With its contribution to GDP growth
remaining stable at approximately 1.0 percentage point
during the three 6-year sub-periods, ICT capital increased
its relative share in the contribution of capital inputs to
GDP growth from 22.2% in 1990–1996 to 31.4% in 1996–
2002 and to 33.3% in 2002–2008 (Table 4, Fig. 4). It is also
worth noting that labor input significantly increased its
share in GDP growth over the three sub-periods from
24.1% in 1990–1996 to 32.4% in 1996–2002 and to 46.9%
in 2002–2008 (Table 4). This increase was driven by labor
quality growth and employment expansion that were en-
abled by the government’s policy of attracting foreign
workers.19
Singapore’s ALP growth over 1990–2008, however, was
less impressive. Its average growth rate was 2.8% for
1990–2008 and decreased from 4.3% in 1990–1996 to
2.0% in 1996–2002 and 2.3% in 2002–2008. ICT capital
deepening played an important role in Singapore’s ALP
growth, with a contribution of 0.8 percentage points or
a contribution share of 28.1% for 1990–2008 that re-
mained robust throughout the three sub-periods: 0.8 per-
centage points in 1990–1996, 1.0 percentage point during
the tumultuous period of 1996–2002, and 0.6 percentage
points in 2002–2008. The contribution of non-ICT capital
deepening, however, declined sharply from 1.9 percent-
age points in 1990–1996 to 1.6 percentage points in
1996–2002 and 0.004 percentage points in 2002–2008
Table 3B
ICT use intensity and sectoral ALP growth.
Dependent variable: ALP growth per period ðALP grÞ
Explanatory variable OLS (FE) GMM
(1) (2) (3) (4)
Average ICT use intensity ðICTI avgÞ 0.143 (0.124) 0.103 (0.111) 0.334***
(0.104) 0.295**
(0.123)
Average employment growth per period ðEMP grÞ 0.288 (0.416) 0.309 (0.438) 2.216**
(0.884) 2.248***
(0.799)
Log of ALP at the beginning of period ðLnALP 0Þ 0.094 (0.063) 0.140*
(0.068) 0.073 (0.060) 0.109 (0.088)
Sector effects Yes Yes Yes Yes
Time effects No Yes No Yes
Tests of joint effects: F-statistics and p-values (in square brackets)
Sector effects 12.21***
[0.000] 5.98***
[0.001] 69.62***
[0.000] 65.60***
[0.000]
Time effects – 1.25 [0.282] – 0.46 [0.495]
Tests of instrument validity: test statistics and p-values (in square brackets)
Relevance test (Anderson’s LR statistics) – – 10.59***
[0.005] 10.77***
[0.005]
Exogeneity test (Hansen J-statistic) – – 0.183 [0.669] 0.058 [0.810]
R-squared 0.63 0.65 0.44 0.45
N 36 36 36 36
Notes: Robust standard errors are in parentheses. R-squared for the GMM regressions are Uncentered R2.
*
p 10%.
**
p 5%.
***
p 1%.
19
As evidence, the share of foreign workers in Singapore’s labor force
increased from 16.1% in 1990 to 28.1% in 2000 and 34.7% in 2008. (Source:
Ministry of Manpower.)

(Table 5). As a result, the relative share of ICT in the con-
tribution of capital deepening to ALP growth rose from
28.9% in 1990–1996 to 37.7% in 1996–2002 and to 100%
in 2002–2008 (Fig. 4). The sharp decline in the contribu-
tion of non-ICT capital deepening was, in part, caused by
the rapid employment expansion associated with the in-
ﬂux of foreign workers discussed above. In this context,
the robust contribution of ICT capital deepening suggests
that Singapore’s ICT investment was well in excess of its
employment expansion.
5. Contributions of the ICT manufacturing sector to
growth
As described in Section 2.2, Singapore has made tre-
mendous efforts to promote the ICT manufacturing sector.
To provide a comparison, Singapore far exceeds other
OECD countries in the relative share of the ICT manufactur-
ing sector in the economy (Fig. 5). This section analyzes the
contribution of the ICT manufacturing sector and its seg-
ments to Singapore’s growth over the period 1990–2008.
Table 4
Sources of GDP growth, 1990–2008.
Contribution to growth (percentage points per annum) Contribution share in growth (%)
1990–08 1990–96 1996–02 2002–08 1990–08 1990–96 1996–02 2002–08
Real GDP growth 6.2 8.1 4.0 6.4 100 100 100 100
Capital inputs 3.6 4.5 3.5 2.7 57.8 56.0 88.3 41.5
ICT capital 1.0 1.0 1.1 0.9 15.7 12.0 26.9 13.6
Non-ICT capital 2.6 3.6 2.4 1.8 42.1 44.0 61.4 27.8
Labor input 2.1 1.9 1.3 3.0 33.8 24.1 32.4 46.9
Labor quality 0.3 0.1 0.2 0.9 5.6 1.3 5.6 14.2
Hours worked 1.7 2.1 1.1 2.1 28.2 25.4 26.8 32.7
TFP 0.5 1.6 0.8 0.7 8.3 19.9 20.7 11.6
Table 5
Sources of ALP growth, 1990–2008.
Contribution to growth (percentage points per annum) Contribution share in growth (%)
1990–08 1990–96 1996–02 2002–08 1990–08 1990–96 1996–02 2002–08
ALP growth 2.8 4.3 2.0 2.3 100.0 100.0 100.0 100.0
Capital deepening 2.0 2.8 2.6 0.6 69.7 64.7 129.9 26.3
ICT capital 0.8 0.8 1.0 0.6 28.1 18.9 49.4 26.4
Non-ICT capital 1.2 1.9 1.6 0.004 41.7 45.7 80.5 0.2
Labor quality 0.3 0.1 0.2 0.9 12.1 2.5 11.2 40.6
TFP 0.5 1.6 0.8 0.7 18.1 37.8 41.0 33.2
Source: Author’s calculations
Fig. 4. Relative share of ICT in the contribution of capital input to growth, 1990–2008.

5.1. Contribution of the ICT manufacturing sector to GDP
growth
The direct contribution of the ICT manufacturing sector
to GDP growth can be estimated from the following equa-
tion (Jorgenson et al., 2005, pp. 363–379):
D ln Y ¼
wICT D ln YICT þ
wOD ln YO þ RE; ð5aÞ
where Y is GDP, which consists of the value-added YICT
generated by the ICT manufacturing sector and the value-
added YO produced by other economic sectors;
w is the
value-added share in the GDP of the subscripted sector;
and RE is the value-added reallocation effect associated
with the reallocation of resources within the economy.
The direct contribution of the ICT manufacturing sector
to GDP growth is estimated as
wICT D ln YICT , whereas the
contribution of the rest of the economy is the residual
ð
wOD ln YO þ REÞ.
The ICT manufacturing sector’s direct contribution to
Singapore’s GDP growth can be further decomposed into
the contributions of its five segments (Semiconductors
(ICTS), Computer Peripherals (ICTC), Data Storage (ICTD),
Infocomms and Consumer Electronics (ICTI), and Other
Electronics Components (ICTO)) as

wICT D ln YICT ¼
wICTSD ln YICTS þ
wICTCD ln YICTC
þ
wICTDD ln YICTD þ
wICTID ln YICTI
þ
wICTOD ln YICTO þ REICT ; ð5bÞ
where REICT is the value-added reallocation effect associ-
ated with the reallocation of resources within the ICT man-
ufacturing sector.
The results provided in Table 6 show that the ICT man-
ufacturing sector, on average, contributed 0.29 percentage
points to GDP growth over 1990–2008. This contribution,
however, declined from 0.73 percentage points in 1990–
1996 to 0.26 percentage points in 1996–2002 and 0.10
percentage points in 2002–2008. In contrast, the contribu-
tion of the Semiconductors segment was 0.32 percentage
points in 1990–2008 and was robust in all three sub-peri-
ods: 0.25 percentage points in 1990–1996 to 0.32 percent-
age points in 1996–2002 and 0.26 percentage points in
2002–2008. The contributions of the other ICT manufactur-
ing segments were negligible in 1990–2008 and tended to
deteriorate over the three sub-periods. With respect to
structural change, the Semiconductor segment’s value-
added share in GDP rose significantly as the shares of the
other segments were shrinking, especially in the last sub-
period of 2002–2008, from 1.3% in 1990–1996 to 2.5% in
1996–2002 and 3.5% in 2002–2008. These results suggest
that the industry was experiencing notable restructuring
during 1990–2008, especially in the last sub-period.
5.2. Contributions of the ICT manufacturing sector to ALP
growth
The contribution of the ICT manufacturing sector to ALP
growth can be estimated from the following equation
(Jorgenson et al., 2005, pp. 372–373):
D ln y ¼
wICT D ln yICT þ
wOD ln yO þ RE y ð6Þ
where y, yICT, and yO are, respectively, the ALP of the econ-
omy, the ICT manufacturing sector, and the remaining sec-
tors of the economy. RE y is the ALP reallocation effect
associated with the shift of resources within the economy.
As such, economy-wide ALP growth can be decomposed
into the direct contributions of the ICT manufacturing sec-
tor ð
wICT D ln yICT Þ and the rest of the economy
ð
wOD ln yO þ RE yÞ.
The ICT manufacturing sector’s direct contribution to
ALP growth can be further decomposed into the contribu-
tions of its five segments, as follows:
Data source : EU KLEMS database, updated March 2011, for OECD countries
Fig. 5. ICT manufacturing’s value-added share in GDP and growth: Singapore vs. OECD countries, 1995–2005.

wICT D ln yICT ¼
wICTSD ln yICTS þ
wICTCD ln yICTC
þ
wICTDD ln yICTD þ
wICTID ln yICTI
þ
wICTOD ln yICTO þ RE yICT ð7Þ
where RE yICT is the ALP reallocation effect associated with
the shift of resources within the ICT manufacturing sector.
The results reported in Table 7 show that the direct con-
tribution of the ICT manufacturing sector to ALP growth
was 0.41 percentage points in 1990–2008. This contribu-
tion was strong in 1990–1996 (0.67 percentage points)
and 1996–2002 (0.72 percentage points) but slightly nega-
tive in 2002–2008 (0.09 percentage points). Although the
Semiconductor segment had the highest contribution, the
contributions of the other segments were also notable,
especially in the ﬁrst two sub-periods, 1990–1996 and
1996–2002. These results also revealed signiﬁcant restruc-
turing efforts in the segments of the ICT manufacturing
sector that aimed to enhance their ALP growth. However,
in the last sub-period, 2002–2008, only the Semiconductor
segment managed to positively contribute to ALP growth.
Table 6
ICT manufacturing industry’s contribution to GDP growth, 1990–2008.
1990–2008 1990–1996 1996–2002 2002–2008
GDP growth (%) 6.2 8.1 4.0 6.4
Contribution in percentage points
Total ICT Manufacturing 0.29 0.73 0.26 0.10
Semiconductors 0.32 0.25 0.32 0.26
Computer Peripherals 0.01 0.04 0.07 0.15
Data Storage 0.00 0.17 0.05 0.07
Infocomms Consumer Electronics 0.04 0.20 0.08 0.11
Other Electronics Components 0.00 0.08 0.00 0.04
Within-industry Reallocation Effects 0.02 0.01 0.00 0.00
The Rest of the Economy 5.87 7.35 3.70 6.55
Addendums
Value-added growth (%)
Data Storage 0.0 11.2 2.8 8.3
Value-added share in GDP (%)
Data Storage 1.4 1.5 1.8 0.8
Table 7
ICT manufacturing industry’s contribution to ALP growth, 1990–2008.
1990–2008 1990–1996 1996–2002 2002–2008
Economy-wide ALP Growth (%) 2.8 4.3 2.0 2.3
Contribution in percentage points
ICT Manufacturing 0.41 0.67 0.72 0.09
Data Storage 0.05 0.11 0.12 0.02
Within-industry Reallocation Effects 0.05 0.03 0.05 0.09
The Rest of the Economy 2.43 3.59 1.27 2.35
Addendums
ALP Growth (%)
Data Storage 3.6 7.1 6.6 2.7

5.3. Contribution of the ICT manufacturing industry to TFP
growth
Jorgenson et al. (2003b) found that the ICT-producing
sector’s20
contribution to aggregate TFP growth was sub-
stantial in the US (0.27 percentage points for the 1977–
2000 period). In Finland, Jalava and Pohjola (2007) esti-
mated that the contribution of the ICT-producing sector to
TFP growth to be 1.41 percentage points in 1995–2005. Tim-
mer and Van Ark (2005) arrived at positive estimates for this
contribution in the EU countries by assuming that the rates
of TFP growth in the US’ ICT-producing industries also apply
to the EU. Based on this assumption, the study found that
the contribution of the ICT-producing sector to TFP growth
during 1995–2001 for 14 EU countries ranged from 0.01 per-
centage points in Greece to 0.67 percentage points in Fin-
land and 3.62 percentage points in Ireland. However, the
authors also cautioned that their results should be consid-
ered upper-bound estimates because ICT production in Eur-
ope may consist, to a large extent, of lower value-added
activities relative to the US; hence, the TFP growth rates
are likely to be lower. This caution about lower TFP growth
in ICT-producing sector should also be taken when examin-
ing the case of Singapore for the same reason. The share of
value-added in total output of the ICT manufacturing sector
was much lower for Singapore, which averaged at 20% dur-
ing the 2000s, compared to 47% for the US.21
In addition, Sin-
gapore’s ICT manufacturing sector suffers from high
volatility due to its heavy reliance on external demand.
Moreover, this volatility tended to intensify over time. The
coefficient of variation (CV)22
of the sector’s value-added
growth rate in its absolute value rose from 4.5 in 1990–
1996 to 7.6 in 1996–2002 and 10.7 in 2002–2008 (Table 8).
This high volatility in the sector’s performance, therefore,
should have adverse effects on its efficiency because the sec-
tor relies on high skilled labor and large amount of fixed cap-
ital assets, which are not easily adjusted to demand
fluctuations.
This subsection provides a rough estimate of the contri-
bution of Singapore’s ICT manufacturing sector to the
economy’s TFP growth during the 1990–2008 period and
its three sub-periods, 1990–1996, 1996–2002, and 2002–
2008. The estimation method for this exercise is obtained
from Jorgenson et al. (2005, pp. 295, 298, and 375), which
is elaborated in Appendix D.
Because growth in Singapore’s ICT manufacturing sec-
tor was heavily driven by large capital investment flows,23
its TFP growth, and hence its direct contribution to econ-
omy-wide TFP growth, is sensitive to the assumption
regarding SICT
K – the share of capital in the sector’s income:
the higher SICT
K , the lower the sector’s contribution to econ-
omy-wide TFP growth. Table 9 provides results on the direct
contribution of the sector to the economy’s TFP growth for
three assumptions regarding SICT
K . In the first scenario, SICT
K
is assumed to be 1/3, which is textbook assumption for an
average economy24
(Weil, 2009, pp. 54). In the second sce-
nario, SICT
K is assumed to be 0.5. In the third scenario, SICT
K is
Table 8
Growth fluctuation, 1990–2008: ICT manufacturing sector vs. overall economy. Source: Author’s calculation from MTI data.
1990–2008 1990–1996 1996–2002 2002–2008
Growth (%) CV Growth (%) CV Growth (%) CV Growth (%) CV
Economy (GDP) 6.2 0.58 8.1 0.24 4.0 1.18 6.4 0.45
ICT Manufacturing (VA) 3.8 4.5 9.7 1.1 3.1 7.6 1.4 10.7
Note: The coefficient of variation (CV), which is defined as standard deviation divided by mean, captures the fluctuation of growth during a given period
(sub-period).
Table 9
Contribution of the ICT manufacturing industry to economy-wide TFP growth, 1990–2008.
1990–2008 1990–1996 1996–2002 2002–2008
Contribution of the sector to the economy’s TFP growth (percentage points)a
If SICT
K ¼ 1=3 0.01 0.34 0.18 0.30
If SICT
K ¼ 0:5 0.11 0.17 0.09 0.40
If SICT
K ¼ 0:7 0.33 0.02 0.42 0.53
Addendums
Economy-wide TFP growth (%) 0.5 1.6 0.8 0.7
Average value-added share of the ICT manufacturing sector in GDP (%) 7.6 7.5 8.3 7.0
a
The contribution of the ICT manufacturing to the economy’s TFP growth depends on the assumption on the share of capital in its income ðSICT
K Þ.
20
The authors defined this sector to include the industries that produce
semiconductors, computers, communication equipment, and software.
21
Sources: Data from MTI for Singapore; Bureau of Economic Analysis
(BEA) for the US.
22
The CV of the annual growth rate of a variable during a period is
calculated as its standard deviation divided by its mean. A higher CV
implies a larger fluctuation of growth in the period.
23
The average ratio of gross fixed capital investment to value-added
during 1990–2008 was 33.4% for the sector and 62.1% for its semiconductor
industry.
24
This assumption is supported by the results from Bernanke and
Gürkaynak (2002), which showed that the income shares of capital for a
large sample of economies are rather stable by country. The share ranges
between 0.2 and 0.4, with an average value of approximately 0.35.

assumed to be 0.7. It should be noted that the estimates of
SICT
K from the I–O tables for 1995, 2000, and 2005, which
range from 0.65 to 0.75, tend to support the assumption
used in the third scenario.
As shown in Table 9, the contribution of the ICT manu-
facturing sector to the economy’s TFP growth during the
1990–2008 period was 0.01 percentage point in the first
scenario ðSICT
K ¼ 1=3Þ but was negative in the second
ðSICT
K ¼ 0:5Þ and third ðSICT
K ¼ 0:7Þ. This sensitivity analysis
suggests that although it is difficult to arrive at an accurate
estimate of the contribution of the ICT manufacturing sec-
tor to Singapore’s TFP growth, it is plausible to conclude
that this contribution of the sector was modest and tended
to decline rapidly during 1990–2008.
6. Conclusion
Singapore has embraced the ICT revolution with tre-
mendous efforts to promote economic growth and has
achieved remarkable success. This paper examines the
contribution of ICT adoption and production to Singapore’s
economic growth over the period between 1990 and 2008.
The evidence of the contribution of ICT adoption to
growth is solid in both an econometric examination and
a growth accounting exercise. Using an econometric exam-
ination, this study found that the intensity of ICT use has a
significant positive link with value-added and ALP growth
at the sector level. The growth accounting exercise re-
vealed that ICT capital played a substantial role in Singa-
pore’s growth, contributing 1.0 percentage point to GDP
growth and 0.8 percentage points to ALP growth in
1990–2008. The growth decomposition exercise also re-
vealed that the rate of capital deepening in Singapore’s
economy was far more robust for ICT than for non-ICT as-
sets. The share of ICT capital in the contribution of total
capital input to growth rose sharply over the three sub-
periods of 1990–2008. This indicator for GDP growth was
22% in 1990–1996, 31% in 1996–2002, and 33% in 2002–
2008, whereas the figure for ALP growth was 29%, 38%,
and 100% for each of the three sub-periods, respectively.
Singapore’s ICT manufacturing sector was established
in the early stage of Singapore’s development and has been
vigorously promoted, especially since 1980. The sector has
played an important role in Singapore’s economy, with
very large shares in GDP and total exports. Between 1990
and 2008, the sector’s direct contribution was 0.29 per-
centage points for GDP growth and 0.41 percentage points
for ALP growth. The sector’s contribution to Singapore’s
growth, however, tended to decline over time in all three
measures: Value-added, ALP, and TFP. The declining trend
in the sector’s performance and its contributions to the
economy is likely caused by external factors: the rapid pro-
gress of ICT technology; intensifying global competition,
especially from lower-cost countries in the region; and
the high volatility of the global demand for ICT hardware.
This study offers several important policy insights,
especially for developing countries’ efforts to embrace
ICT for economic growth. First, ICT use offers solid contri-
butions to growth. A country’s government plays a critical
role in fostering ICT adoption. To this end, an effective
strategy, well-considered policy initiatives for each stage
of ICT development, and the government’s role as a pioneer
are critical ingredients for success.
Second, ICT production may not always robustly con-
tribute to growth, especially TFP growth. Because of rapid
technological change and intensified global competition,
the industry faces tremendous pressure to maintain its
performance. Given the constraints on its resources, a
developing country should assign a much higher priority
to fostering ICT adoption than to investing in ICT produc-
tion. This point is supported by some earlier studies. Wong
(2002) observed that some developing East Asian countries
may have over-emphasized industrial policy in favor of
electronics manufacturing at the expense of promoting
ICT diffusion across sectors in the economy. He argued that
in such a case, ICT production may divert resources away
from ICT diffusion activities rather than being complemen-
tary to them. Dedrick and Kraemer (1998) also warned that
East Asian countries may become trapped in low-margin
electronics manufacturing and fail to develop the ability
to move into high-margin, IT-enabled service sectors.
Third, an industrial policy that worked well in the past
may no longer work well in a new global market landscape,
in which the rise of China and, to some extent, India, is
transforming the global dynamics of competition and pro-
duction networks. As a result, policy makers need to re-
think and reform their development strategies, especially
in their efforts to reap the benefits of the ICT revolution
to promote economic growth.
Acknowledgements
I am indebted to two anonymous referees and editors
for comments and suggestions, which were very valuable
for me to improve the paper. I acknowledge the academic
research support grants provided by the Lee Kuan Yew
School of Public Policy and thank Nguyen Chi Hieu for his
excellent research assistance.
Appendix A. Singapore’s 18 economic sectors and their
ICT use intensity
Sector
code
Sector Average ICT use
intensitya
(%)
1995–
2000
2000–
2005
1 Food, Beverages Tobacco 0.22 0.27
2 Petroleum, Chemical,
Pharmaceutical, Rubber
Plastic Products
0.13 0.11
3 Fabricated Metal Products 0.29 0.28
4 Machinery Equipment 0.42 0.33
5 Electrical Products 0.27 0.32
6 Electronic Products 0.15 0.19
7 Medical Precision 0.25 0.24
continued on next page

Appendix A (continued)
Sector
code
Sector Average ICT use
intensitya
(%)
1995–
2000
2000–
2005
Instruments
8 Transport Equipment 0.31 0.32
9 Other Manufacturing
Industries
0.28 0.30
10 Construction 0.13 0.28
11 Wholesale Retail Trade 2.25 2.18
12 Transport, Storage
Communications
1.32 1.60
13 Hotels Restaurants 0.88 0.57
14 Financial Services 1.92 2.40
15 Insurance Services 0.83 0.94
16 Information, Real Estate
Leasing Services; Professional,
Administrative Support
Services
1.69 2.30
17 Education Public
Administration
0.99 1.16
18 Others 0.42 0.63
Total economy 0.85 1.00
Sources: Singapore I–O Tables 1995, 2000, 2005.
a
The ICT use intensity of a sector is defined as the share of its purchase
of goods and services from the communications sector, which includes
telecommunications services, network operators and data communica-
tions services, web hosting services, computer time-sharing services, and
data processing, hosting, and related activities, in its total intermediate
inputs.
Appendix B. Labor income share and labor quality
index25
B.1. Labor income share
We have (from Singapore’s Annual Yearbook of
Statistics)
Y ¼ CE þ GOS þ GOSO þ TPI þ SD; ðB1Þ
where Y is GDP, CE is ‘Compensation of Employees’, GOS is
‘Gross Operating Surplus’, GOSO is ‘Gross Operating Surplus
of Others’, which capture the income of self-employed
workers, TPI is ‘Taxes on Production and Imports’, and SD
is ‘Statistical Discrepancy’.
The labor income share in Singapore’s economy can be
estimated based on the formula adopted by Bernanke
and Gürkaynak (2002), Gomme and Rupert (2004) and Ke-
hoe and Prescott (2007) as follows.
vL ¼
CE
Y GOSO TPI SD
¼
CE
CE þ GOS
: ðB2Þ
This formula is based on the assumptions that the shares of
labor income in the three mixed income items – income of
self-employed workers, which is reported as ‘Gross Operat-
ing Surplus of Others’ (GOSO), ‘Taxes on Production and
Imports’ (TPI), and ‘Statistical Discrepancy’ (SD), are the
same as the labor income share in the rest of the economy.
Gollin (2002) remarked that the formula above, which is
based on these assumptions, is one of the most plausible
ways to estimate the labor income share.
Under the neoclassical assumption of constant returns
to scale, the income share of capital input can simply be
computed as vK = 1 vL.
B.2. Labor quality
Constructing a labor quality index for Singapore is
based on the approach introduced by Jorgenson et al.
(2005) in constructing a labor quality index. Given the
available data, we divide Singapore’s employment into 12
worker groups according to two dimensions: educational
attainment and gender. Educational attainment consists
of six levels of education: Primary and Below, Lower Sec-
ondary, Secondary, Post Secondary, Diploma, and Degree.
Gender includes male and female categories.26
The aggregate volume of labor input Lt for year t is de-
fined as a Tornqvist index of the individual components:
Lt ¼
Y
i
ðHl;tÞ

vl;t
; ðB3Þ
where Hl;t is the total hours worked by worker group
l ðl ¼ 1; 2; 3; . . . ; 12Þ in year t;
ml;t is the two-period average
weight of worker group l in time t. The weight of worker
group l is defined as its share of the value of labor compen-
sation, which is computed as
ml;t ¼
Wl;tHl;t
X
l
Wl;tHl;t
; ðB4Þ
and hence,

ml;t ¼
1
2
ml;t þ ml;t1

; ðB5Þ
where Wl,x is the average wage of worker group l.
The labor quality index is obtained from the ratio of the
volume of labor input to the total hours worked:
LQt ¼
Lt
Ht

: ðB6Þ
Appendix C. ICT investment series, capital stocks, and
capital services
C.1. Estimating ICT investment series
ICT investment is the major measure of ICT diffusion in
a country. ICT investment is divided into three ICT capital
25
Note that Appendices B and C are adapted from Vu (2011b).
26
Employment data are estimated by the Economy Policy Group, MAS.
The shares of the employed persons in the twelve worker groups are
calculated using survey-based data from the Labor Force Survey (LFS) and
applied to administrative employment records from the Central Provident
Fund. Median wage data were obtained directly from the LFS. For
population census years (1990, 1995, 2000 and 2005) when the LFS was
not conducted, a simple interpolation method is applied.

goods: computer hardware, telecommunications equip-
ment, and software.27
To estimate current-price ICT investment flows in an
ICT asset type, we follow a method based on the approach
used by Timmer and Van Ark (2005) and including two
main steps. In the first step, the Singapore Input–Output
Tables 2000 are used to derive current-price investment
(defined as gross fixed capital formation) in each of the
three ICT asset types for the year 2000.28
In the second step, we project an annual ICT investment
series for each ICT capital asset. We base this projection on
the investment figures for the year 2000 and the assump-
tion that the nominal growth of investment in each ICT as-
set type is proportional to the growth in the domestic
market for that product category.
The following formula for estimating investment Ii,t in
ICT asset i for year t is a modification of the formula pro-
posed by Timmer and Van Ark (2005):
Ii;t ¼
Ri;t
Ri;2000
a
:Ii;2000; ðC1Þ
where Ri,t is Singapore’s domestic spending on ICT asset i in
year t. The parameter a ð0 aÞ is used to adjust the
growth rate of spending by a specific business sector rela-
tive to that of the overall domestic market. To be conserva-
tive, we choose a = 0.5.
C.2. Harmonized ICT deflation
Schreyer (2002) introduces methods to deflate nominal
ICT investment flows (in current local currency) to con-
stant price series. These methods use the US hedonic price
index as a base to construct the deflator for that asset for a
non-US country.29
Because Singapore is an open economy, we chose the
exchange rate-based approach from the methods sug-
gested by Schreyer, which implies:
D ln PSingapore
ict ¼ D ln PUS
ict þ D ln eSingapore
US : ðC2Þ
That is, for a given ICT asset type, its Singapore price
change over a period of interest is equal to its US price
change plus the change in the Singapore–US exchange rate.
C.3. Capital stocks
The quantity of capital stock for asset30
i is constructed
based on the ‘‘perpetual inventory method’’ (PIM) as:
Si;T ¼ Si;T1ð1 diÞ þ Ii;T ¼
X
1
t¼0
ð1 diÞt
Ii;Tt ðC3Þ
where Si,T is the capital stock in year T for asset type i, di is
the constant rate of depreciation31
and Ii,Tt is the constant
price investment flow in year T t.
C.4. Capital Services
The steps to estimate capital services for each of the six
capital assets follow Jorgenson et al. (2005).
C.4.1. Capital services quantity
The quantity of capital services rendered by capital as-
set i in year T is defined as:
Ki;T ¼
ðSi;T þ Si;T1Þ
2
: ðC4Þ
C.4.2. Rental price of capital services
The rental price ci,T of capital services from capital good
i in period T is obtained based on the assumption that the
typical investor in period T 1 who invests in this capital
asset at price pi,T1 would get a return rate that must justify
the nominal rate of return rT observed for the economy and
the remaining market price of the asset. Under the market
equilibrium condition, we have:
pi;T1ð1 þ rT Þ ¼ ci;T þ ð1 diÞpi;T : ðC5Þ
Eq. (C5) suggests the formula for computing the rental
price, ci,T:
ci;T ¼ rT pi;T1 þ dipi;T pi;T pi;T1; ðC6Þ
where pi,T = (pi,T pi,T1)/pi,T1 is the asset’s price change
over the period.
C.4.3. Income share of a capital good
The income share mi,T of capital services from capital
good i in year T is computed as
mi;T ¼
Ki;T
YT
ci;T ; ðC7Þ
where YT is GDP in current prices in year T.
C.4.4. Nominal rate of return
The income capital share vK computed from Appendix B
can be expressed as
vK ¼
X
i
mi;T : ðC8Þ
Combining Eqs. (C6)–(C8) yields
vK ¼
X
i
Ki;T
YT
rT pi;T1 þ dipi;T pi;T pi;T1

: ðC9Þ
27
Computer hardware comprises the products included in Industry 30
and telecommunication equipment comprises the products in Industry 32
in the International Standard Industrial Classification System (ISIC) rev. 3
(Timmer and Van Ark, 2005).
28
I–O codes for the three ICT asset types are as follows: Computer
hardware: 64, 65, 67–70; Telecommunications equipment: 66, 71, 72, 120;
Software (and related IT services): 128.
29
The hedonic price index for the ICT assets (computer hardware,
telecommunication equipment, and software) is provided by the Bureau
of Economic Analysis (BEA).
30
The six asset types examined in this exercise are: computer hardware,
computer software, telecommunication equipment, non-residential build-
ings and other structures, transport equipment, and non-ICT machinery.
31
The geometric depreciation rates are from Jorgenson et al. (2005) and
Timmer et al. (2003): 0.315 for computer hardware and computer software,
0.11 for telecommunication equipment, 0.132 for non-IT machinery, 0.191
for transportation equipment, and 0.028 for non-residential buildings and
other structures.

The nominal rate of return rT (based on the ex-post ap-
proach), therefore, can be estimated from Eq. (C9) as
rT ¼
vK YT þ
P
iKi;T pi;T pi;T1
P
iKi;T dipi;T

P
iKi;T pi;T1
ðC10Þ
Appendix D. Direct contribution of ICT manufacturing
industry to TFP growth
The method for estimating the contribution of an indus-
try to TFP growth is derived from Jorgenson et al. (2005, pp.
295, 298). Under a constant return to scale and competitive
markets, the TFP growth vT,j for industry j is defined as
vT;j ¼ D ln Yj
vK;jD ln Kj
vL;jD ln Lj
vX;jD ln Xj; ðD1Þ
where
v represents the two-period average shares of the
subscripted inputs (capital input K, labor input L and other
intermediate input X) in the nominal values of gross output
Y. Subscript T indicates a measure of efficiency.
At the same time and under the same assumptions,
when the gross output and intermediate inputs are not
available, the value-added TFP growth vVA
T;j is estimated as
vVA
T;j ¼ D ln Vj
uK;jD ln Kj
uL;jD ln Lj; ðD2Þ
where
u represents the two-period average value-added
shares of the subscripted inputs in the value-added V gen-
erated by the industry.
The relationship between vVA
T;j and vT,j is
vT;j ¼
vV;jvVA
T;j; ðD3Þ
where
vV;j is the share of the industry’s value added to its
gross output.
The TFP growth for an economy vT can be decomposed
as follows:
vT ¼ D ln V
vK D ln K
vLD ln L; ðD4Þ
vT ¼
X
j

wj

vV;j
vT;j
!
þ
X
j

wj

vK;j

vV;j
D ln Kj
vK D ln K
!
þ
X
j

wj

vL;j

vV;j
D ln Lj
vLD ln L
!
; ðD5Þ
vT ¼
X
j

wj

vV;j
vT;j
!
þ REALLK þ REALLL; ðD6Þ
vT ¼
X
j

wjvVA
T;j
!
þ REALLK þ REALLL: ðD7Þ
Therefore, the direct contribution of industry j to the econ-
omy’s TFP growth can be estimated as
wjvVA
T;j.
References
Acemoglu, D., Johnson, S., Robinson, J., Yared, P., 2008. Income and
democracy. American Economic Review 98 (3), 808–842.
Allison, P., 2009. Fixed Effects Regression Models, first ed. Sage
Publications, Thousand Oaks, CA.
Antonopoulos, C., Sakellaris, P., 2009. The contribution of information and
communication technology investments to Greek economic growth:
an analytical growth accounting framework. Information Economics
and Policy 21 (3), 171–191.
Atzeni, G., Carboni, O., 2005. ICT Productivity and Firm Propensity to
Innovative Investment: Learning Effect Evidence from Italian Micro
Data. Working Paper CRENoS 200414. Centre for North South
Economic Research, University of Cagliari and Sassari.
Baum, C., 2006. An Introduction to Modern Econometrics Using Stata.
Stata Press, College Station, TX.
Bernanke, B., Gürkaynak, R., 2002. Is growth exogenous? Taking Mankiw,
Romer, and Weil seriously. In: National Bureau of Economic Research,
NBER Macroeconomics Annual 2001, vol. 16, pp. 11–72.
Cardona, M., Kretschmer, T., Strobel, T., 2013. ICT and productivity:
conclusions from the empirical literature. Information Economics and
Policy 25 (3), 109–125.
Caselli, F., Coleman, W., 2001. Cross-country technology diffusion: the
case of computers. American Economic Review 91 (2), 328–335.
Chia, S., Lim, J., 2003. Singapre: a regional hub in ICT. In: Masuyama, S.,
Vandenbrink, D. (Eds.), Towards a Knowledge-Based Economy: East
Asia’s Changing Industrial Geography. ISEAS Publishing, Singapore,
pp. 259–298.
Colecchia, A., Schreyer, P., 2001. The Impact of Information
Communications Technology on Output Growth. STI Working Paper
2001/7. OECD, Paris.
Correa, L., 2006. The economic impact of telecommunications diffusion on
UK productivity growth. Information Economics and Policy 18 (4),
385–404.
Daveri, F., 2002. The new economy in Europe, 1992–2001. Oxford Review
of Economic Policy 18 (3), 345–362.
Dedrick, J., Kraemer, K., 1998. Asia’s Computer Challenge: Threat or
Opportunity for the United States and the World? Oxford University
Press, New York.
Dimelis, S., Papaioannou, S., 2011. ICT growth effects at the industry level:
a comparison between the US and the EU. Information Economics and
Policy 23 (1), 37–50.
Durlauf, S., 2009. The rise and fall of cross-country growth regressions.
History of Political Economy 41 (1), 315–333.
Frankel, J., Romer, D., 1999. Does trade cause growth? American Economic
Review 89 (3), 379–399.
Gollin, D., 2002. Getting income shares right. Journal of Political Economy
110 (2), 458–474.
Gomme, P., Rupert, P., 2004. Measuring Labor’s Share of Income. FRB of
Cleveland Policy Discussion Paper No. 7. http://dx.doi.org/10.2139/
ssrn.1024847.
Hall, R., Jones, C., 1999. Why do some countries produce so much more
output per worker than others? Quarterly Journal of Economics 114
(1), 83–116.
ITU (International Telecommunications Union), 2009. Confronting the
Crisis: Its Impact on the ICT Industry. Report by the International
Telecommunications Union in Preparation for the World
Telecommunication Policy Forum, Lisbon, 21–24 April 2009.
Jalava, J., Pohjola, M., 2002. Economic growth in the new economy:
evidence from advanced economies. Information Economics and
Policy 14 (2), 189–210.
Jalava, J., Pohjola, M., 2007. ICT as a source of output and productivity
growth in Finland. Telecommunications Policy 31 (8–9), 463–472.
Jorgenson, D., 2001. Information technology and the US economy.
American Economic Review 91 (1), 1–32.
Jorgenson, D., 2003. Information technology and the G7 economies. World
Economics 4 (4), 139–169.
Jorgenson, D., Ho, M., Stiroh, K., 2003a. Lessons from the US growth
resurgence. Journal of Policy Modeling 25 (5), 453–470.
Jorgenson, D., Ho, M., Stiroh, K., 2003b. Growth of US industries and
investments in information technology and higher education. Taylor
and Francis Journals 15 (3), 279–325.
Jorgenson, D., Ho, M., Stiroh, K., 2005. Productivity: Information
Technology and the American Growth Resurgence, vol. 3. MIT Press,
Cambridge, MA.
Jorgenson, D., Ho, M., Stiroh, K., 2008. A retrospective look at the U.S.
productivity resurgence. Journal of Economic Perspectives 22 (1), 3–
24.

Jorgenson, D., Motohashi, K., 2005. Information technology and the
Japanese economy. Journal of the Japanese and International
Economies 19 (4), 460–481.
Jorgenson, D., Stiroh, K., 1995. Computers and growth. Economics of
Innovation and New Technology 3 (3–4), 295–316.
Jorgenson, D., Stiroh, K., 1999. Information technology and growth.
American Economic Review 89 (2), 109–115.
Jorgenson, D., Vu, K., 2005. Information technology and the world
economy. Scandinavian Journal of Economics 107 (4), 631–650.
Jorgenson, D., Vu, K., 2010. Potential growth of the world economy.
Journal of Policy Modeling 32 (5), 615–631.
Jorgenson, D., Vu, K., 2011. The rise of developing Asia and the new
economic order. Journal of Policy Modeling 33 (5), 698–716.
Kehoe, T., Prescott, E., 2007. Great Depressions of the Twentieth Century.
Federal Reserve Bank of Minneapolis, Minneapolis.
Levine, R., Zervos, S., 1993. What we have learned about policy and
growth from cross-country regressions. American Economic Review
83 (2), 426–430.
Martínez, D., Rodríguez, J., Torres, J., 2008. The productivity paradox and
the new economy: the Spanish case. Journal of Macroeconomics 30
(4), 1569–1586.
Martínez, D., Rodríguez, J., Torres, J., 2010. ICT-speciﬁc technological
change and productivity growth in the US: 1980–2004. Information
Economics and Policy 22 (2), 121–129.
OECD, 2001. Measuring Productivity – OECD Manual. OECD Publishing,
Paris.
OECD, 2006. Information Technology Outlook 2006. OECD Publishing,
Paris.
Oliner, S., Sichel, D., 1994. Computers and output growth revisited: how
big is the puzzle? Brookings Papers on Economic Activity 2, 273–317.
Oliner, S., Sichel, D., 2000. The resurgence of growth in the late 1990s: is
information technology the story? Journal of Economic Perspectives
14 (4), 3–22.
Oliner, S., Sichel, D., 2003. Information technology and productivity:
where are we now and where are we going? Journal of Policy
Modeling 25 (5), 477–503.
Oulton, N., 2002. ICT and productivity growth in the UK. Oxford Review of
Economic Policy 18 (3), 363–379.
Raphael, S., Winter-Ebmer, R., 2001. Identifying the effect of
unemployment of crime. Journal of Law and Economics 44 (1), 259–
283.
Rodrik, D., Subramanian, A., Trebbi, F., 2004. Institutions rule: the primacy
of institutions over geography and integration in economic
development. Journal of Economic Growth 9 (2), 131–165.
Schreyer, P., 2002. Computer price indices and international growth and
productivity comparisons. Review of Income and Wealth 48 (1), 15–
31.
Stiroh, K., 2002. Information technology and the US productivity revival:
what do the industry data say? American Economic Review 92 (5),
1559–1576.
Temple, J., 1999. The new growth evidence. Journal of Economic
Literature 37 (1), 112–156.
Timmer, M., Van Ark, B., 2005. Does information and communication
technology drive productivity growth differentials? A comparison of
the European Union countries and the United States. Oxford
Economic Papers 57 (4), 693–716.
Timmer, M., Ypma, G., Van Ark, B., 2003. IT in the European Union: Driving
productivity divergence? Research Memorandum GD-67. Groningen
Growth and Development Centre.
Van Ark, B., Melka, J., Mulder, N., Timmer, M., Ypma, G., 2002. ICT
Investment and Growth Accounts for the European Union, 1980–
2000. Research Memorandum GD-56, revised March 2003, Groningen
Growth and Development Centre.
Van Reenen, J., Bloom, N., Draca, M., Kretschmer, T., Sadun, R., 2010. The
Economic Impact of ICT. SMART N. 2007/0020 Final Report, Centre for
Economic Performance, London School of Economics.
Vu, K., 2011a. ICT as a source of economic growth in the information age:
empirical evidence from the 1996–2005 period. Telecommunications
Policy 35 (4), 357–372.
Vu, K., 2011b. Sources of Singapore’s economic growth, 1965–2008:
trends, patterns and policy implications. ASEAN Economic Bulletin 28
(3), 315–336.
Weil, D., 2009. Economic Growth, second ed. Pearson Addison Wesley,
Boston.
Wong, P., 2002. ICT production and diffusion in Asia: digital dividends or
digital divide? Information Economics and Policy 14 (2), 167–187.
Wooldridge, J., 2009. Introductory Econometrics: A Modern Approach.
Cengage Learning, Stamford, CT.

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