ADB Economics
Working Paper Series
Infrastructure and Growth
in Developing Asia
Stéphane Straub and Akiko Terada-Hagiwara
No. 231 | November 2010
ADB Economics Working Paper Series No. 231
Infrastructure and Growth
in Developing Asia
Stéphane Straub and Akiko Terada-Hagiwara
November 2010
Stéphane Straub is Professor at the Toulouse School of Economics, Arqade; Akiko Terada-Hagiwara is
Economist in the Macroeconomics and Finance Research Division, Economics and Research Department,
Asian Development Bank. This paper was prepared as a background material for the theme chapter, “The
Future of Growth in Asia” in the Asian Development Outlook 2010 Update, available at www.adb.org/
economics. The authors thank Douglas Brooks, Biswanath Bhattacharyay, and other participants in the
workshops in Manila for their helpful comments; and Shiela Camingue for her superb assistance.
Asian Development Bank
6 ADB Avenue, Mandaluyong City
1550 Metro Manila, Philippines
www.adb.org/economics
©2010 by Asian Development Bank
November 2010
ISSN 1655-5252
Publication Stock No. WPS102881
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Contents
Abstract
v
I. Infrastructure: A Review of Issues
1
II. Where Does Developing Asia Stand?
3
A.
B.
3
8
III. Theory and Existing Evidence
Stylized Facts
Demand for and Gaps in Infrastructure Services
10
A.Theory
B.Empirics
10
12
IV. Empirical Analysis
18
A.
B.
18
32
V.
Conclusion
Cross-country Regressions
Growth Accounting
39
Appendix
41
References
43
Abstract
This paper presents the state of infrastructure in developing Asian countries.
It applies two distinct approaches (growth regressions and growth accounting)
to analyze the link between infrastructure, growth, and productivity. The main
conclusion is that a number of countries in developing Asia have significantly
improved their basic infrastructure endowments in the recent past, and this
appears to correlate significantly with good growth performances. However, the
evidence seems to indicate that this is mostly the result of factor accumulation (a
direct effect), while the impact on productivity is inconclusive.
I. Infrastructure: A Review of Issues
The relevance of infrastructure for development outcomes comes from the fact that
it provides both final consumption services to households and key intermediate
consumption items for production. Crude estimates from the literature indicate that
between one third and one half of infrastructure services are used as final consumption
by households (Prud’homme 2005, Fay and Morrison 2007), while the rest corresponds to
firms.
Infrastructure should thus play an important role to support growth and poverty reduction.
On the supply side, there is both a direct channel (infrastructure capital stock serves as
a production factor), and an indirect one (improved infrastructure affects technological
progress). An increase in the stock of infrastructure capital is argued to have a direct,
increasing effect on the productivity of the other factors. It is also believed to generate
important externalities across a range of economic activities, which could possibly have
a larger net effect than what is expected from a simple factor accumulation effect. These
indirect effects could operate through various channels; among others, through labor
productivity gains resulting from improved information and communication technologies;
reductions in time wasted commuting to work and stress; improvements in health and
education; and through improvements in economies of scale and scope throughout the
economy.
From a demand side point of view, infrastructure provides people with services they
need and want—water and sanitation; power for heat, cooking, and light; telephone and
computer access; and transport. The absence of some of the most basic infrastructure
services is an important dimension of what we often mean when we talk about poverty.
Increasing level of infrastructure stock, therefore, has direct implication for poverty
reduction.
Infrastructure appears significantly related to per capita gross domestic product (GDP)
growth in the past decades mainly through accumulating infrastructure capital stock as a
production factor. Yet, the assumed link between infrastructure and growth relies mostly
on the assumption that variations of infrastructure services availability and quality are one
of the main drivers of differences in firms’ productivity across regions and countries. The
link between the two, however, is not particularly clear from the data (Figure 1). Linking
the availability and the quality of infrastructure with economic growth or productivity is,
therefore, still subject to considerable debate and uncertainty.
2 | ADB Economics Working Paper Series No. 231
Figure 1: Average Productivity Growth, 2005–2009 versus Infrastructure Quality, 2005
Average Annual Change in Factor Productivity, 2005–2009
10
ARM
PRC
5
MONPHI
0
KGZ
SRI
VIE
SIN
THA
IND
BAN PAK
CAM
MAL
HKG
KOR TAP
INO
-5
-10
2
3
4
5
6
7
Quality of Overall Infrastructure, 2005
Developing Asia
Rest of the World
ARM = Armenia; BAN = Bangladesh; CAM = Cambodia; HKG = Hong Kong, China; IND = India; INO = Indonesia;
KGZ = Kyrgyz Rep.; KOR = Rep. of Korea; MAL = Malaysia; MON = Mongolia; PAK = Pakistan; PHI = Philippines;
PRC = People’s Rep. of China; SIN = Singapore; SRI = Sri Lanka; TAP = Taipei,China; THA = Thailand; VIE = Viet Nam.
Sources:Authors’ calculations using data from World Economic Forum (2005), and Barro and Lee (2010).
There are many ways infrastructure shortcomings translate into productivity efficiency
losses. For example, access to markets and interactions with potential clients rely on the
existence and reliability of transport and telecommunication networks, and when these
fail, firms may suffer from lack of access to market opportunities, higher logistic costs and
inventory levels, or information losses.1 Similarly, investment and technological choices
may be affected by the efficiency of electricity networks, in the sense that frequent power
outages and unstable voltage induce high costs and greater risk of machinery breakdown.
Growing evidence shows that firms respond by making suboptimal technological choices,
by investing in remedial equipment such as power generators, or by deferring some types
of investments (Alby, Dethier, and Straub 2009).
1
See for example Guasch and Kogan (2001), Li and Li (2009), and Jensen (2007) .
Infrastructure and Growth in Developing Asia | 3
The objective of the present paper is twofold. First, it presents a detailed update of
the state of infrastructure in developing Asian countries. Then it applies two distinct
approaches (growth regressions and growth accounting) to analyze the link between
infrastructure, growth, and productivity. The main conclusion is that a number of countries
in developing Asia have significantly improved their basic infrastructure endowments in
the recent past, and this appears to correlate significantly with good growth performances.
However, the evidence seems to indicate that this is mostly the result of factor
accumulation (a direct effect), while the impact on productivity is rather inconclusive.
The rest of the paper is organized as follows. The next section provides an overview
of past developments in infrastructure capital accumulation in developing Asia. Section
III reviews the existing empirical literature.2 Section IV presents empirical results
investigating the theoretical effects discussed in Section III. Section V concludes.
II. Where Does Developing Asia Stand?
A.
Stylized Facts
While some developing Asian countries have far better infrastructure than others, overall,
the region remains below the world average in terms of both its quantity and quality.
Quality of infrastructure as well as access to infrastructure services is also uneven across
region and country. Except for the five relatively advanced economies—Hong Kong,
China; the Republic of Korea; Malaysia; Singapore; and Taipei,China (or Asia-5)—the
quality of infrastructure in developing Asia is largely lagging behind that of industrialized
economies (Figure 2).
2
This is by no mean an exhaustive review of empirical contributions. For surveys of the empirical literature, see
Gramlich (1994), Sturm et al. (1998), Romp and de Haan (2005), and Straub (2008 and 2010).
4 | ADB Economics Working Paper Series No. 231
Figure 2: Quality of Overall Infrastructure and GDP per Capita, Selected Economies, 2008
8
Quality of Overall Infrastructure, 2008
SIN
HKG
6
MAL KOR
TAP
THA
BRU
SRI PRC
4
CAM
2
NEP
IND
BAN
PHI
GEO
VIE INO
TIM MON
0
6
8
10
12
Log per Capita GDP, 2008
Developing Asia
Rest of the World
OECD
BAN = Bangladesh; BRU = Brunei Darussalam; CAM = Cambodia; GEO = Georgia; HKG = Hong Kong, China; IND = India;
INO = Indonesia; KOR = Rep. of Korea; MAL = Malaysia; MON = Mongolia; NEP = Nepal; PHI = Philippines;
PRC = People’s Rep. of China; SIN = Singapore; SRI = Sri Lanka; TAP = Taipei,China; THA = Thailand;
TIM = Timor-Leste; VIE = Viet Nam.
Note: Per capita GDP is based on purchasing power parity (PPP) in current international dollars. Quality of overall infrastructure
refers to assessments of the quality of general infrastructure (e.g., transport, telephony, and energy) in an economy.
1 indicates extremely underdeveloped while 7 indicates extensive and efficient by international standards. Data cover 132
economies.
Sources:Author's calculation using data from World Economic Forum (2009) and International Monetary Fund (2010).
While aggregate measures of infrastructure stock are not systematically available, the
quality measure based on a perception survey (qualitative data) from World Economic
Forum (WEF 2009) suggests a significant improvement over the past 4 years. In
particular, significant upgrading in the quality of transport, telephony, and energy
infrastructure was reported for Cambodia, Georgia, and Sri Lanka. But there are countries
such as Bangladesh and Mongolia that registered worsening of the overall infrastructure
quality over the same period despite the fact that these countries should only improve
given the very low level of existing infrastructure stock (Figure 3). Further, the same
survey indicates a worrisome situation in terms of the quality of electricity supply in
developing Asia. Close to half of the 25 surveyed Asian countries reported that the
quality of electricity supply worsened since 2005. The worsening of the situation appears
particularly severe in some South Asian economies including Bangladesh, Nepal, and
Pakistan.
Infrastructure and Growth in Developing Asia | 5
Figure 3: Change in Quality of Overall Infrastructure, 2009 versus 2005
GEO
SRI
CAM
AZE
PRC
ARM
KOR
KGZ
PHI
HKG
TAP
NEP
IND
KAZ
TIM
VIE
PAK
TAJ
THA
SIN
MON
BAN
INO
MAL
World
JPN
USA
-1
0
1
Percentage Points
2
ARM = Armenia; AZE = Azerbaijan; BAN = Bangladesh; CAM = Cambodia; GEO = Georgia;
HKG = Hong Kong, China; IND = India; INO = Indonesia; KAZ = Kazakhstan; KGZ = Kyrgyz Rep.;
KOR = Rep. of Korea; JPN = Japan; MAL = Malaysia; MON = Mongolia; NEP = Nepal; PAK = Pakistan;
PHI = Philippines; PRC = People’s Rep. of China; SIN = Singapore; SRI = Sri Lanka; TAJ = Tajikistan;
TAP = Taipei,China; THA = Thailand; TIM = Timor-Leste; USA = United States; VIE = Viet Nam.
Note: Quality of overall infrastructure refers to assessments of the quality of general infrastructure
(e.g., transport, telephony, and energy) in an economy. 1 indicates extremely underdeveloped
while 7 indicates extensive and efficient by international standards. 2009 data cover 133 economies,
while that of 2005 covers 117 countries.
Source: World Economic Forum (2005 and 2009).
A closer look at different types of infrastructure capital over a longer period reveals
a similar trend—robust growth but uneven across countries and regions. Electricity
generation capacity in developing Asia grew by 4.3% annually (Figure 4) and more than
doubled during the period between 1990 and 2007. But there is a divergence within
developing Asia. Central Asia and Pacific economies were an exception to the rapid
growth trend and achieved only marginal growth, if not contraction, during this period.
6 | ADB Economics Working Paper Series No. 231
Among the good performers, particularly the Asia-5, capacity growth slowed down by
2000, as the economies matured. Meanwhile some lower-income countries such as
Cambodia (since 1995), the People's Republic of China (PRC), and Viet Nam continued
with a robust expansion in generation capacity in the 2000s (Figure 5).
Figure 4: Annual Growth in Electricity Generation, 1990–2007
World
Advanced Economies
Rest of the World
Developing Asia
Central Asia
East Asia
South Asia
Southeast Asia
Asia-5
-5
0
5
10
Average Annual Change (percent)
Note: Starting year for Cambodia is 1995. Asia-5 refers to Hong Kong, China; the Republic of Korea; Malaysia; Singapore; and
Taipei,China.
Source: Authors’ calculations using data from World Development Indicators online database, accessed 19 August 2010 (World Bank
2010).
Increase in other infrastructure stocks such as in telecommunication and internet
connections has also been significant. Internet users, for example, more than tripled since
2000, or from five users per 100 persons in 2000, to 16 users by 2008 (Figure 6). This
growth, however, has again been largely driven by the Asia-5. Nevertheless, the current
level lags far behind that of Latin American economies, where for example, 28 people out
of 100 have access to the internet in 2008, a level comparable to the world average.
Share of paved road (as a percentage of the total) increased from less than half in 1990
to reach almost 60% of the total roads by mid-2000 (Figure 7). But again the dispersion
of this infrastructure stock index is huge across economies. Many of developing Asia’s
roads such as in Bangladesh, Cambodia, Mongolia, and Papua New Guinea are
hardly paved. Meanwhile almost all roads are paved in Armenia; Hong Kong, China;
Kazakhstan; and Singapore.
Infrastructure and Growth in Developing Asia | 7
Figure 5: Electricity Growth by Selected Asian Developing Economies, 1990–2007
Cambodia*
Viet Nam
China, People's Rep. of
Malaysia
Indonesia
Korea, Rep. of
Thailand
Brunei Darussalam
India
Myanmar
Singapore
Philippines
Hong Kong, China
0
5
10
15
20
Average Annual Change (percent)
*Change over 1995.
Source: Authors’ calculations using data from World Development Indicators online database, accessed 19 August 2010 (World Bank
2010).
Figure 6: Internet Users by Region, 2008
World
Advanced Economies
Rest of the World
Developing Asia
Central Asia
East Asia
South Asia
Southeast Asia
Pacific
Asia-5
0
20
40
60
Number per 100 Inhabitants
80
Note: Asia-5 refers to Hong Kong, China; the Republic of Korea; Malaysia; Singapore; and Taipei,China.
Source: Authors’ calculation using data from World Development Indicators online database, accessed 19 August 2010 (World Bank
2010).
8 | ADB Economics Working Paper Series No. 231
Figure 7: Paved Roads by Region, 2006
World
Advanced Economies
Rest of the World
Developing Asia
Central Asia
East Asia
South Asia
Southeast Asia
Pacific
Asia-5
0
20
40
60
80
100
Percent of Total Roads
Note: Asia-5 refers to Hong Kong, China; the Republic of Korea; Malaysia; Singapore; and Taipei,China. Data for the Pacific pertain
to share in 2000. Paved roads are those surfaced with crushed stone and hydrocarbon binder or bituminized agents, with
concrete, or with cobblestones, measured in length and as a percentage of all the country’s roads.
Source: Authors’ calculation using data from World Development Indicators online database, accessed 19 August 2010 (World Bank
2010).
Are these developments sufficient to improve productivity? WEF (2009) finds that the
inadequate supply of infrastructure is a problem for doing business in countries such
as Bangladesh, Nepal, and Viet Nam. This closer look at various types of infrastructure
indexes in developing Asia reveals that overall the improvement has not been enough to
catch up with developed economies, and is not sufficient to translated into an important
productivity-enhancing factor in many developing Asian countries.
B.
Demand for and Gaps in Infrastructure Services
1.Urbanization
The demand for infrastructure is increasing and expected to soar further, in particular
because of increasing urbanization. Except for Hong Kong, China; the Republic of Korea;
and Singapore where more than 80% of the total population already live in urban areas,
a majority of people in developing Asia live in rural areas. As economies grow rapidly and
urbanization advances, however, the demand for more and highquality infrastructure is
expected to further accelerate.
Infrastructure and Growth in Developing Asia | 9
The proportion of a country’s population living in urban areas is highly correlated with its
level of income (Bloom et al. 2008). As economies grow, the urban share of developing
Asia’s population is rising exponentially (Figure 8). From 28.9% in 1960 to 43.0% in 2000,
it is expected to rise to 56.9% by 2030 (data from United Nations population projections).
Urban areas offer richer market structures, and there is strong evidence that workers in
urban areas are individually more productive and earn more than rural workers. Densely
populated urban areas provide markets for output, inputs, labor, and other services and
allow firms to profit from economies of scale and scope, specialization, and rapid diffusion
of knowledge innovation.
Figure 8: Urbanization and Income per Capita, Selected Economies, 2005
Per Capita GDP, PPP (constant 2005 international $)
80,000
60,000
SIN
BRU
40,000
HKG
KOR
20,000
MAL
THA
SRI
VIE IND
0
0
20
KAZ
PRC
PAK
40
INO
PHI
60
80
100
Urban Population Shares (percent)
Developing Asia
Rest of the World
BRU = Brunei Darussalam; HKG = Hong Kong, China; IND = India; INO = Indonesia; KAZ = Kazakhstan; KOR = Rep. of Korea;
MAL = Malaysia; PAK = Pakistan; PHI = Philippines; PRC = People’s Rep. of China; SIN = Singapore; SRI = Sri Lanka;
THA = Thailand; VIE = Viet Nam.
Note: Data cover 181 economies.
Source: World Development Indicators online database (accessed 19 August 2010).
Challenges are mounting, however. Rapid urbanization is associated with crowding,
environmental degradation, and other impediments to productivity unless accompanied
by appropriate infrastructure services. Poorly conceived investments today will have
costly environmental, economic, and social impacts later. If appropriately designed
and implemented, infrastructure should play an enormous role in maintaining the
competitiveness of cities.
10 | ADB Economics Working Paper Series No. 231
In particular, developing Asia needs to connect cities to international markets through
enhanced transport infrastructure, telecommunications, and logistic services. Exploiting
the region’s comparative advantage in high-value services and high-tech industry requires
providing advanced communications and just-in-time delivery for integrated production
chain to spread across a number of countries in developing Asia (Brooks and Hummels
2009). The urban investment climate, and hence employment prospects, can depend
critically on the quality of urban infrastructure.
2.
Urban–Rural Divide
While developing Asia is rapidly urbanizing, about 62.6% (or 2.2 billion people) still lived
in rural areas as of 2008. Moreover, developing Asia’s poverty is overwhelmingly rural,
and rural–urban disparities—across income as well as access to services—provoke
political concerns and demands for inclusion in economic development.
In rural areas, poor road conditions for example restrict the all-weather access of
local communities within and between towns/townships, while the lack of clean and
reliable water supply inhibits economic growth and poverty alleviation. The proportion
of population using an improved drinking water source in 2006, for example, is 90%
for those living in urban areas as opposed to 68% for those in rural areas. The gap is
particularly significant in economies such as the Lao People's Democratic Republic,
Mongolia, Papua New Guinea, and Vanuatu. Urban–rural integrated infrastructure
development is crucial to achieve inclusive growth. Failing to provide basic needs in
infrastructure means disconnect from markets, basic education, medical services, and
jobs, which often leads to a poverty trap.
III. Theory and Existing Evidence
This section reviews theory underlying the link between the infrastructure capital stock
and growth and productivity, and also discusses the existing empirical literature.
A.Theory3
In a standard production function where factors are gross complements, an increase
in the stock of infrastructure capital would have a direct, increasing effect on the
productivity of the other factors. This is particularly clear if one thinks of cases of strong
complementarities,4 for example, if roads or bridges investment provide access to
previously inaccessible areas, enabling productive investment there, or if improvements
of the electricity or telecommunications networks make the use of certain types of
3
4
See Straub (2010) for a detailed review.
The theoretical formulation is in Kremer (1995) O-ring production function.
Infrastructure and Growth in Developing Asia | 11
machineries possible. But because infrastructure capital is also believed to generate
important externalities across a range of economic activities, it is possible that its net
effect is larger than expected from a simple factor accumulation effect. The theoretical
literature has discussed a number of channels for these indirect effects.
The first one is maintenance, private capital durability, and adjustment costs. There
is growing evidence that infrastructure policy is biased toward the realization of new
investments at the detriment of the maintenance of the existing stock. The main reasons
appear to be political economy ones.5 As a consequence, the life span of the stock of
both the infrastructure itself and of private capital that makes use of it such as trucks
operating on low-quality roads or machines connected to unstable voltage lines is
reduced, and operating costs increase.6 The case of palliative private investments in
devices such as electricity generators is an extreme example of this.
Second, infrastructure appears to have a microeconomic impact through a number of
channels, including labor productivity gains resulting from improved information and
communication technologies, reductions in time wasted commuting to work and stress,
and improvements in health and education among others. Moreover, such improvements
are likely to induce additional investment in human capital in the medium and long term.
Finally, infrastructure may be the source of economies of scale and scope throughout
the economy. For example, as roads and railroads improve, lowering transport costs,
private firms benefit from economies of scale and more efficient inventory management.7
Similarly, enhanced access to communication devices, as was the case across the
developing world in the last 2 decades with the growth of mobile telephony, is likely to
result in efficient market clearing and enhanced competition as a result of improved
information flows.8 Economic geography tells us that, as a result, we should expect
different patterns of agglomeration, as well as changes in the pattern of specialization
of agents and in their incentives to innovate (see for example Baldwin et al. 2003). In
other words, changes in the nature and availability of key types of infrastructure are likely
to have profound structural effects on the whole economy through agents’ and firms’
decisions.
Infrastructure investments, however, do not occur in isolation from other economic
constraints. Unlocking some of the direct and indirect effects mentioned above may be
conditioned by a number of practical issues, which are at present less well understood.
First of all, infrastructure investments are often at least partly publicly financed, either
through taxation or borrowing on financial markets, with the consequent risk of crowding
out private investments. Another key question refers to sequencing. Which type of
5
6
7
8
Either on the financing side or linked to pork-barrel arguments. See Rioja (2003), Maskin and Tirole (2006), and
Dewatripont and Seabright (2006), among others.
See Engel, Fischer, and Galetovic (2009) for a detailed analysis of the case of roads.
See Li and Li (2009) for evidence in the case of the PRC.
See Jensen (2007) for a striking example in the case of Indian fishermen.
12 | ADB Economics Working Paper Series No. 231
infrastructure is more effective in supporting growth and should be prioritized? And
which type of reforms supporting these investments, such as privatization, restructuring
measures, regulation and introduction of competition, should be pursued and how?
Because of their obvious context-dependence, the answers to these questions have to be
empirical.
The next section reviews the existing empirical literature, with a special emphasis on
contributions focusing on Asian countries.9 It discusses the theoretical effects identified
above, including practical issues of financing and investment sequencing, showing that on
all these aspects empirical evidence is somewhat lagging.
B.Empirics
1.
Macroeconomic Level
The first generation of studies applying a production function approach augmented with
some measure of infrastructure capital to state-level data for the United States (US)
provided estimates of the output elasticity of infrastructure capital, varying between 0.3
and 0.5.10 Because these numbers imply a marginal product of around 100%, they have
often been dismissed as unrealistic.
The subsequent literature has responded to these critics in several ways. On one hand,
focus shifted to industry-level studies, such as Fernald (1999), who analyzed the impact
of the large US interstate highway network built in the 1950s and 1960s. Although
Fernald (1999) finds an output elasticity of road investment of around 0.35 for US
industries that use roads more intensively, he also argues that this was a one-off effect,
corresponding to a one-time boost in productivity and explaining the post-1973 slowdown
in productivity.11
On the other hand, there were a number of econometric weaknesses in the early
contributions that were pointed out by later studies.12 These weaknesses include the
existence of state- or region-level unobserved effects, and potential reverse causality
between output and infrastructure investment, which may generate an upward bias in
the estimated coefficients. Unsurprisingly, a second generation of studies taking these
concerns into account came up with significantly smaller estimates. For example, the
survey by Romp and de Haan (2005) reports elasticities between 0.1 and 0.2; and Bom
and Ligthart (2008) in their meta-analysis of 67 studies using public capital measures
report an unconditional output elasticity of public capital of around 0.15. A more recent
9
This is by no mean an exhaustive review of empirical contributions. For surveys of the empirical literature, see
Gramlich (1994), Sturm et al. (1998), Romp and de Haan (2005), and Straub (2008 and 2010).
10 The most widely quoted paper is Aschauer (1989). Gramlich (1994) provides a review of this early empirical
literature.
11 Fernald estimates a 1% boost in productivity, while the post-1973 productivity slowdown was 1.3%.
12 See Straub (2010) for a detailed analysis of these aspects.
Infrastructure and Growth in Developing Asia | 13
paper by Calderón et al. (2009) estimates the output elasticity of a synthetic infrastructure
index to be between 0.07 and 0.10.13
This evolution in the magnitude of estimates is also partly explained by the fact that
physical indicators of infrastructure have progressively replaced monetary measures
of public infrastructure capital investment as the main proxies used in the empirical
literature. The growing share of private investment in infrastructure and the large
measurement and reliability problems of public capital indicators14 were probably
instrumental in favoring the emergence of physical indicators databases, such as the one
initially developed by Canning (1998), which tracks country-level kilometers of road and
railroads, number of phone lines, and electricity generating capacity.
In this strand of literature, specific evidence on East Asia can be found in both Seethepalli
et al. (2008) and Straub et al. (2008). Seethepalli et al. (2008) do find a positive effect
of all dimensions of infrastructure stocks on growth, using standard growth regressions
in a panel of 16 East Asian countries at 5-year intervals. They also conclude that these
significant effects vary with a number of country-level characteristics. For example
telecom and sanitation are found to have a greater effect in countries with better
governance, higher income level, and low inequality in the access to infrastructure. In
contrast, Straub et al. (2008) find much weaker results in their cross-country growth
regressions, when using a sample of 93 developing or emerging countries, including
16 East Asian countries. The number of phone lines has a positive effect on growth,
and some evidence emerges of an above-average effect for East Asia and high
income countries. However, most results are not robust to using panel techniques or to
controlling for an endogenous response of infrastructure to growth. As a matter of fact,
while Seethepalli et al. (2008) argue that the use of infrastructure stocks rather than
flows alleviates the problem of reverse causation, they fail to control for the potential
endogeneity of infrastructure stocks due to countries’ unobserved characteristics, leading
them both to have higher infrastructure stocks and higher growth; and do not include
country fixed effects (see Holtz-Eakin 1994).
The existence and magnitude of indirect effects is a more complex issue at the empirical
level, which as a result has been rarely addressed. Most of the existing contributions
have used a growth accounting framework, for example Hulten et al. (2000 and 2005)
and La Ferrara and Marcelino (2000). Assuming that the share of output of intermediate
inputs is constant over time, Hulten and Schwab (2000) conclude to the absence of
infrastructure externalities on growth in the US case, while Hulten et al. (2005) find
highways and electricity to account for about half of total factor productivity (TFP) growth
across Indian states in the period 1972–1992. Straub et al. (2008) growth accounting
exercise on five East Asian countries yields few significant results: telecommunications
investment has contributed to TFP growth more than other types of capital in Indonesia
13
14
Note that such estimates still imply high rates of return, between 25% and 50%.
See for example Pritchett (1996).
14 | ADB Economics Working Paper Series No. 231
and the Philippines, while roads have had a positive influence only in Thailand. No
significant effect is found in the Republic of Korea and Singapore.
Indirect effects matter because they are instrumental in determining whether the effects
of infrastructure investments are permanent or transitory. The belief that indirect effects
may be sufficient to generate constant returns on aggregate and lead to endogenous
growth underlies for example the claim in Calderón and Servén (2004) that raising Latin
American quantities and qualities of infrastructure stocks to East Asian tigers’ level
would have generated long-term per capita growth gains of around 3%. Straub (2010)
claims that an alternative take on these numbers is that the different overall incentive
structures prevailing in Latin America and East Asian countries imply that their economies
display, in equilibrium, important gaps both in their infrastructure stocks and in output
growth. However, it is not clear which part of these incentive differences has to do with
infrastructure, and the lack of an empirical method to disentangle externalities from
different sources imply that Calderón and Servén (2004) estimates might be biased
upward. Indeed, several contributions have shown that infrastructure investments returns
were highly sensitive to the quality of the regulatory or contractual framework, to the
political economy environment, including aspects such as the efficiency of the local
bureaucracy and potential corruption. Guasch, Laffont, and Straub (2007 and 2008) show
that concession renegotiations in Latin America in the 1990s were to a large extent due to
regulatory and institutional failures, and there are reasons to believe that this contributed
to the subsequent backlash against private participation and discouraged private
investment in infrastructure there in more recent years. The fact that East Asia focused
mostly on build–operate–transfer (BOT) schemes for wholesale facilities, instead of
concessioning of retail and distribution facilities as in Latin America, and could mobilize its
higher savings, may have created fewer incentive and information problems and reduced
to some extent direct political concerns.
2.
Microeconomic Level
Microeconomic data first provide a wealth of descriptive evidence. For example, firm-level
surveys on the investment climate provide a window on the extent to which infrastructure
deficiencies constitute barriers to entrepreneurial development. For example, the
World Bank Enterprise Surveys (World Bank 2009) indicate that a large proportion of
respondents (between 20% in East Asia and the Pacific, and 55% in the Middle East
and North Africa, as well as Latin America) view any of electricity, telecommunications,
or transport as a major or severe obstacle to doing business. In Asia, electricity is
considered as a major constraint by 78% of firms in Bangladesh, 76% in Nepal, 75% in
Pakistan, 68% in Afghanistan, 61% in Timor-Leste, 44% in Samoa, 32% in India, and
30% in the PRC, among others. For transport, high proportions of firms viewing it as a
major problem are found in Nepal (33%), Afghanistan (30%), Samoa (29%), and Thailand
(21%), among others.
Infrastructure and Growth in Developing Asia | 15
Moreover, a growing amount of microeconometric evidence is providing insights into a
number of specific channels linking infrastructure investment and development outcomes,
such as household income and poverty, welfare, health issues such as child mortality, and
gender empowerment, for example, in the labor market.
Transportation infrastructure development has been shown to favor poverty reduction.
Gibson and Rozelle (2003), using the timing of progressive coast to inland highway
penetration in Papua New Guinea to instrument for road density, show that reducing
access time to less than 3 hours where it was above this threshold leads to a decrease
of 5.3% in the headcount index. Fan, Nyange, and Rao (2005) similarly find very positive
effects of public investment and roads on household level income and poverty using
Tanzanian household survey data. In an historical context, Donaldson (2009) shows
that railroad development in colonial India significantly reduced trade costs, increased
commercial exchanges between regions, with large overall welfare effects.
Thomas and Strauss (1992) analyze the determinants of child height in Brazil, a standard
development outcome, and include electricity, water and sewerage connections as
explanatory variables. They find the number of electricity connections per capita in
the community to be positively correlated with height of babies, with a complementary
effect of mother education. Positive results are also found for water and sanitation, with
variations by children’s age and level of mother’s education. Dinkelman (2009) uncovers
large effect on women employment of rural electrification in South Africa, which she
relates to an increase of their labor supply as a result of more efficient home production
technologies, and of a job expansion in new, small-scale market-based services.
In the case of water, in a widely quoted paper, Galiani et al. (2005) show that in
Argentina, increased household access to the water network following privatization
significantly reduced the incidence of water-borne diseases related child mortality. Duflo
and Pande (2007) analyze the link between irrigation dams and agricultural production
and poverty in India using geographic information system data on land inclination as
instruments for dams placement choices, and claim that dams have low rates of return
and negative distributional effects. Ilahi and Grimard (2000) find a significant impact of
water infrastructure development on women’s time allocation in Pakistan, while Koolwal
and van de Walle (2010), looking at micro data across nine countries, including three in
South Asia (India, Nepal, and Pakistan), find that better access to water improves both
boys’ and girls’ enrollments in countries with substantial gender gaps in schooling (among
which Nepal and Pakistan), but no significant effect on women’s off-farm work.
Finally, a few papers, such as the study by Alby, Dethier, and Straub (2009) on the
impact of electricity deficiencies have used enterprise survey data to assess the
impact of infrastructure constraints on firms’ choices and performance.15 Additionally,
microeconomic firm-level data are interesting because they can be used to analyze the
15
Dethier, Hirn, and Straub (2008) review this literature.
16 | ADB Economics Working Paper Series No. 231
sequencing of reforms supporting infrastructure investment, in particular when the private
sector is involved, and these reforms may include restructuring measures, regulation
and the introduction of competition (see for example Andres et al. 2007). Of course,
these studies generally deal with outcomes such as operating performance, quality,
employment, prices, output and coverage, while policy makers care about final outcomes,
such as output growth, welfare, and poverty reduction. This signals the need for more
large-scale welfare assessments based on micro-data, as performed for example by
McKenzie and Mookherjee (2003) in the case of four Latin American countries.
3.
Geographic Evidence
It is difficult to underestimate the importance of the spatial nature of infrastructure
investment. First, investment decisions imply rival choices on the geographical areas to
be served. Second, spatial variations in the availability and quality of infrastructure are
likely to have an impact on individuals’ and firms’ decisions, such as migration, location of
new firms, etc.
Some contributions indirectly account for the spatial implications of public capital
investment. Jacoby (2000) analyzes the distributional consequences of rural roads in
Nepal, a country in which a large proportion of farmers live far from the main road giving
access to market centers, using household survey and plot value data. It concludes that
a 10% increase in travel time to market reduces the value of land by 2.2%, but that these
benefits are “more like a tide that lifts all boats rather than a highly effective means of
reducing income inequality”.
Recently, empirical contributions directly inspired by the new economic geography
literature have explicitly included spatial variables. They are based on the use of an
accessibility indicator, which measures, for households and firms in a given geographic
area, the opportunities available at other locations in terms of employment opportunities
or market potential by inversely weighting the sum of some destinations’ indicators (GDP
or employment for example) by a proxy of the costs involved in reaching them.
Examples for developing countries include Deichman, Fay, Koo and Lall (2002) for
Southern Mexico; Lall, Funderburg, and Yepes (2004) for Brazil; Lall, Shalizi, and
Deichmann (2004) for India; Deichmann, Kaiser, Lall, and Shalizi (2005) for Indonesia;
and Lall, Sandefur, and Wang (2009) for Ghana. All these studies find that accessibility is
a major determinant of firm productivity.
Looking at Asian countries in more detail, Lall, Shalizi, and Deichmann (2004) examine
the effects of agglomeration economies on plant level productivity, distinguishing
between economies of scale at the plant level (related to increases in market access),
at the industry level (from localization economies), and at the regional level (from
urbanization economies across industries). They find that market access and proximity to
transport hubs provide net benefits in four out of 11 industrial sectors. As for localization
Infrastructure and Growth in Developing Asia | 17
economies, net benefits are found in only two industrial sectors. Finally, firms’ benefits
of locating in dense urban areas appear be smaller than associated costs, a finding that
the authors attribute to the unequal spatial distribution of transport infrastructure, which
makes it difficult for firms to locate in cheaper secondary urban centers while maintaining
linkages with final buyers and suppliers.
Deichmann, Kaiser, Lall, and Shalizi (2005) estimate the impact of factors influencing
location choice at the firm level to explain the aggregate and sectoral geographic
concentration of manufacturing industries in Indonesia. They distinguish between natural
advantage, such as infrastructure endowments, wage rates, and natural resource
endowments, and production externalities linked to the co-location of firms in the same
or complementary industries. They conclude that because of the strong relevance of
agglomeration economies in explaining location choices, investment in infrastructure
in lagging regions would have failed to attract firms away from established “leading”
regions, especially in mainstream sectors characterized by a high concentration in these
leading regions. One of the policy lessons is that the best strategy may not be to divert
industries already concentrated in large agglomerations away to lagging regions, but
rather to exploit existing location endowments to stimulate the development of alternative
industries.
4.
Practical Issues
Public financing often raises the concern of the potential crowding out of private
investment. Of course, the extent to which this happens is an empirical issue, and should
in particular be sensitive to the business cycle. One potential channel for this is the
limited capacity of domestic financial markets, which might be overburdened by public
borrowing, especially in times of crisis when spending on infrastructure is likely to build
up, and the consequent deterioration of banks’ portfolios. The optimistic view is that in an
economic downturn, public financing for large infrastructure projects of the type sustained
by most large stimulus plans would in fact sustain the credibility of the projects and may
crowd-in private investors (Lardy 2010). Unfortunately, systematic empirical evidence has
yet to be produced.
As for sequencing of investments and reforms, little guidance is available for policy
makers. While virtually no evidence exists to assess the comparative growth dividends
of investments in different infrastructure sectors, insights about the sequencing of
reforms supporting infrastructure investment are found in relation with discussions of
private sector involvement. In this case, the accumulated wisdom points to the need of
restructuring and the implementation of a regulatory framework prior to the transfer to the
private partners. Moreover, institutional arrangements aiming at ensuring transparency
and the independence of the agencies in charge seem warranted. Finally, competition
should be introduced whenever the characteristics of the activity make this possible
(World Bank 2004).16
16
See also Straub (2008) for more discussion.
18 | ADB Economics Working Paper Series No. 231
IV. Empirical Analysis
In this section, we extend the analysis in Straub et al. (2008) along several lines. The
first part performs a growth regression analysis to examine specifically interactions with
several subgroups of Asian countries. The second part performs a growth accounting
analysis, including longer TFP time series and more countries (14). The description of the
theoretical framework used in both sections is in the Appendix.
A.
Cross-country Regressions
1.Data
We use physical infrastructure indicators used extensively in recent literature. This allows
direct comparisons with the results from the growth accounting exercise below.
Physical indicators for four different sectors (telecommunications, energy, transport, and
water) are taken from the World Development Indicators (WDI) database (unless specified
otherwise) covering the 1971–2006 period. Specifically, we use the following series:
(i)Telecommunications
–
–
–
main telephone lines
number of mobile phones
internet coverage: users per 100 inhabitants (from the International
Telecommunication Union website, www.itu.int/ITU-D/icteye/
Indicators/Indicators.aspx#, downloaded 3 August 2010).
(ii)Energy
–
a “quality proxy” is computed using electricity generating capacity
(in million kilowatts) and electric power transmission and distribution losses
(as a percentage of output).
(iii) Transport
–
–
–
road total network (in kilometers)
paved roads (as a percentage of total network), as a quality proxy
rail route length (in kilometers)
(iv) Water
–
improved water source (percent of population with access)
Infrastructure and Growth in Developing Asia | 19
Additional variables used include GDP per capita, gross fixed capital formation
(investment/GDP), and primary and secondary school enrolment (all from WDI).
2.Sample
We rely on a sample of 102 developing or emerging countries, of which 17 (the PRC; the
Fiji Islands; Hong Kong, China; Indonesia;the Republic of Korea; Democratic People's
Republic of Korea; the Lao PDR; Malaysia; Mongolia; Myanmar; Papua New Guinea; the
Philippines; Singapore; Thailand; Tonga; Vanuatu; Viet Nam) belong to the East Asia and
Pacific region. Five (Bhutan, India, Nepal, Pakistan, Sri Lanka) belong to the South Asia
area.
3.Results
In what follows we present the results from cross country estimations based on the
collapsed dataset17 for 1975–2006 for fixed plus mobile telephony (1980–2006 for mobile
telephony alone and 1999–2006 for internet usage); 1980–2006 for rail; 1990–2006
for roads; and 1990–2006 for water, using the rate of growth of GDP per capita as
dependent variable and standard controls (initial level of GDP, investment, proxies
for human capital). Note that we introduce infrastructure proxies in two ways: first we
use the average levels over the relevant period; second, we use the average growth
rates of these variables over the same period. In each case, after testing simple OLS
specifications, we instrument potentially endogenous infrastructure indicators and perform
related tests. In all cases, the instruments are beginning of the period indicators for the
relevant infrastructure variable, the share of agriculture over GDP, population density, and
total population.
17
Using averages over the period.
20 | ADB Economics Working Paper Series No. 231
We also test specifications with interactions between both an East Asia and Pacific (EAP)
and a South Asia (SA) dummy and the alternative infrastructure indicators mentioned
above. Results for each infrastructure dimension are included in a separate table: Table 1
for electricity, Table 2 for telecommunications (both fixed + mobile phone lines and mobile
phones alone), Table 3 for internet, Table 5 for railroads, Table 6 for roads, and Table 7
for water.18
Note first that the control variables yield results consistent with the empirical growth
literature present: initial per capita GDP is negative and significant, which denotes
convergence conditional on the other variables, while education and investment variables
are positive and generally significant throughout Tables 1 to 7. Table 1 presents the
specific results for electricity.
Table 1: Cross-section, Electricity
(1)
pcgdp71
School enrollment
secondary
School enrollment
primary
Inv/gdp
pcegc
pcegc_gr
(2)
(3)
(6)
(0.000149)
-0.000293
(0.000138)
-0.000154
(0.000165)
-0.000168
(0.000121)
-0.000115
(0.000799)
-0.000293
(0.000296)
-7.43e-05
(0.000186)
0.149***
(0.0523)
0.00121
(0.00187)
(0.000188)
0.110**
(0.0482)
(0.000200)
0.120**
(0.0520)
-5.49e-05
(0.00164)
(0.000151)
0.0687*
(0.0390)
(0.000580)
0.149
(0.122)
-0.0170
(0.0209)
(0.000371)
0.00608
(0.107)
0.222***
(0.0646)
pcegc_sa
pcegc_greap
0.00765***
(0.00258)
0.0476***
(0.0132)
pcegc_grsa
Observations
R-squared
Wu-Hausman F
test, p-value
(5)
pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth
-1.57e-06*** -1.24e-06*** -1.16e-06*** -1.07e-06***
2.48e-06
-9.96e-07*
(4.91e-07)
(1.85e-07)
(4.21e-07)
(1.90e-07)
(4.87e-06)
(5.59e-07)
0.000577***
0.000485***
0.000482***
0.000420***
0.00119
0.000215
pcegc_eap
Constant
(4)
-0.0149
(0.0162)
48
0.509
-0.248***
(0.0713)
46
0.635
-0.0177
(0.0170)
48
0.582
0.181***
(0.0444)
0.0198***
(0.00664)
0.0155***
(0.00403)
-0.201***
(0.0528)
46
0.763
-0.0220
(0.0361)
0.281
(0.251)
0.245
(0.203)
-0.0353
(0.0430)
38
0.0475
(0.0563)
-0.00262
(0.0562)
-0.252
(0.234)
37
0.001
0.377
*** p<0.01, ** p<0.05, * p<0.1.
pcegc = per capita electricity generation capacity, net of transmission and distribution losses; pcegc_gr = rate of growth; pcegc_eap
= interaction with East Asian and Pacific dummy; pcegc_sa = interaction with South Asia dummy.
Note: Robust standard errors in parentheses.
18
Note that results from panel regressions on 5-year subperiod averages, using alternatively fixed and random
effects, as well as instrumental variable estimations, yield not significant results overall. These results are omitted
for the sake of space.
Infrastructure and Growth in Developing Asia | 21
Source: Authors' calculations.
The per capita electricity generating capacity appears to have no significant effect on
growth in column 1, while in column 2 its growth rate has a positive and significant effect,
such that an additional point in the average growth rate of electricity generating capacity
net of losses results in 0.22 additional average per capita growth over the period in our
sample of developing countries. The introduction of the interactions with the EAP and the
SA dummies in columns 3 and 4 show that positive and significant effects arise for both
groups of countries. As for electricity generating capacity, the effects are now 0.008 for
EAP and 0.05 for SA. The effects when variables are expressed in growth rates are 0.2
for EAP and 0.197 for SA.19 These numbers mean that an additional point in the average
growth rate of electricity generating capacity net of losses results in 0.18 additional
average per capita growth over the period in our sample of developing countries, but that
this effect is about 11% stronger among Asian countries, indicating that investments in
electricity across the region have been effective in relieving infrastructure bottlenecks and
complementing productive investment. In columns 5–6, a Wu-Hausman endogeneity test
rejects exogeneity for the electricity variable in level, which probably corresponds to the
fact that countries have characteristics unobserved to the econometrician such that they
have both faster growth and higher electricity generation. When instrumented, electricity
in level loses significance. On the other hand, exogeneity is accepted for the growth rate,
suggesting that these results are more robust than those in levels.
Table 2 presents the results for the telecommunications variables, which are respectively
the sum of fixed and mobile phone lines in columns 1–6, and the number of mobile
phones lines alone in columns 7 to 9.20
19
20
Marginal effects are computed as follows: for example the effect in growth rates for EAP = 0.181 + 0.0198 ≈ 0.2.
The series of mobiles phone growth rates are characterized by very high rates in the first years as they start from
very low levels, so we do not use it. The growth effect resulting from the introduction of mobile phones in the
1980s is however reflected in the growth of the total number of fixed and mobile phones.
(3)
(4)
(5)
(6)
-0.00926
(0.0114)
66
0.469
(0.000136)
0.109***
(0.0310)
(0.000134)
0.110***
(0.0293)
0.0367**
(0.0172)
-0.276***
(0.0953)
57
0.545
0.231***
(0.0775)
(0.000110)
-0.000243*
(0.000133)
-0.000186
-0.00994
(0.0113)
66
0.531
(0.000130)
0.0986***
(0.0285)
0.0173
(0.0127)
0.0418***
(0.0145)
0.533***
(0.197)
(0.000122)
-0.000158
-0.236***
(0.0630)
57
0.640
0.197***
(0.0520)
0.0157***
(0.00568)
0.0120**
(0.00500)
(0.000111)
0.0907***
(0.0297)
(9.87e-05)
-0.000210*
0.0423
(1.459)
46
0.08
0.001
0.000425
(1.172)
0.197
(0.546)
-0.157
(0.513)
(0.00127)
-0.101
(0.583)
(0.00204)
-0.000264
-0.0331
(0.102)
48
(0.00220)
0.285
(0.539)
0.0694
(0.702)
-0.869
(2.411)
8.928
(19.95)
(0.00287)
-0.000880
*** p<0.01, ** p<0.05, * p<0.1.
pctel = per capita number of fixed plus mobile phones lines; pctel_gr = rate of growth; pctel_eap = interaction with East Asian and
Pacific dummy; pctel_sa = interaction with South Asia dummy. pcmob = per capita number of mobile phones lines; pcmob_eap =
interaction with East Asian and Pacific dummy; pcmob_sa = interaction with South Asia dummy.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
Observations
R-squared
Wu-Hausman F
test, p-value
Constant
pcmob_sa
pcmob_eap
pcmob
pcgdp80
pctel_grsa
pctel_greap
pctel_gr
pctel_sa
pctel_eap
pctel
Inv/gdp
School enrollment
primary
School enrollment
secondary
pcgdp75
(2)
(7)
(8)
(9)
-0.0136
(0.0121)
72
0.500
-1.46e-06***
(3.33e-07)
0.0824**
(0.0311)
(0.000129)
0.135***
(0.0231)
(0.000108)
-0.000159
(0.00152)
0.119
(0.209)
(0.00641)
0.000403
-1.25e-06*** -1.47e-05
(2.98e-07)
(3.60e-05)
0.0550**
2.467
(0.0237)
(6.058)
0.0697**
-1.475
(0.0275)
(3.805)
0.906**
23.25
(0.387)
(45.92)
-0.0133
-0.0503
(0.0119)
(0.113)
72
60
0.552
0.000
(0.000126)
0.120***
(0.0228)
(9.93e-05)
-0.000128
pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth
-1.56e-06*** -1.11e-06*** -1.27e-06*** -9.37e-07**
-2.27e-06
-3.99e-07
(2.75e-07)
(3.81e-07)
(2.19e-07)
(3.90e-07)
(8.01e-06)
(5.28e-06)
0.000304**
0.000605***
0.000313**
0.000533***
0.00135
-3.66e-05
0.000205*
0.000200**
-0.00240
(1)
Table 2: Cross-section, Telecommunications
22 | ADB Economics Working Paper Series No. 231
Infrastructure and Growth in Developing Asia | 23
This total telecom variable is positive and significant both when introduced in level
in column 1 and in growth rate in column 2 (with a marginal effect indicating that 1
additional point in the average growth rate of the number of per capita phone lines
generates 0.23 additional average per capita growth over the period). The interactions
with the regional dummies show much stronger and significant effects for Asian countries,
both in levels and in growth rates. Focusing on these last ones, the overall effect is
0.213 for EAP countries and 0.209 for SA countries, compared with only 0.197 for the
overall sample. These results are supported by the outcome of the estimations focusing
on mobile phones in columns 7 and 8. The positive and significant effect on growth
remains, and again is much stronger in the EAP and SA subsamples (0.125 and 0.961
respectively, versus 0.055 in the overall sample). Together, these results support the idea
that telecom development was instrumental in boosting growth in the Asian region, and
especially the rapid spread of mobile telephony, probably facilitated by well-designed
regulatory frameworks.21
The introduction of internet services in the 1990s is an additional aspect that is often
thought to have been instrumental for private sector development. To test this hypothesis,
we perform a similar analysis using as independent variable the average growth rate of
the number of internet users, as provided by the International Telecommunication Union
(ITU).22 Table 3 shows that while the effect across the whole sample is negative, the
net effect for the two groups of Asian countries under study is positive and significant.
Specifically, an additional point in the average growth rate of internet coverage implied an
additional 0.01 average per capita growth over the period for EAP countries, and 0.003
for SA countries, respectively. Given that the average annual growth of the number of
internet users over this period was 18% worldwide, and close to 35% for the subsample
of available Asian and Pacific countries (see Table 4), these elasticities are far from
negligible. Exogeneity of the internet usage growth rate is not rejected.
In Table 5, we introduce the railroads variable. While no results are significant in the
overall sample, the interactions with the regional dummies show again much stronger
and significant effects for Asian countries, both in levels and in growth rates. For example
in column 4, an additional point in the average growth rate of railroads line results in
0.25 additional average per capita growth over the period in our sample of developing
countries, but this effect is about 10%–12% stronger among Asian countries (0.283
and 0.276 for EAP and SA respectively, versus 0.248 in the overall sample). Note
that exogeneity is not rejected by the Wu-Hausman test instrumented for the railroads
variables in levels and in growth rates.
21
Exogeneity is rejected by the Wu-Hausman test for both the variables in level and in growth rate, although only
marginally in this last case. However, when instrumented, they are no longer significant, which may indicate that
our instruments are weak.
22 Other indicators, such as the number of broadband subscriptions, were not available for a long enough period to
perform this analysis.
24 | ADB Economics Working Paper Series No. 231
Table 3: Cross-section, Internet
pcgdp99
School enrollment secondary
School enrollment primary
Inv/gdp
pcinternet_gr
pcinternet_greap
(1)
(2)
(3)
pcgdpgrowth
-5.56e-08
(5.17e-07)
9.34e-05
(0.000116)
-0.000187
(0.000150)
0.150***
(0.0368)
-0.00271
(0.0117)
pcgdpgrowth
-9.63e-08
(4.04e-07)
8.44e-05
(0.000112)
-0.000218
(0.000151)
0.117***
(0.0334)
-0.00420
(0.0116)
0.0154***
(0.00317)
0.00706*
(0.00421)
0.0161
(0.0199)
77
0.372
Pcgdpgrowth
-1.03e-07
(1.12e-06)
-1.07e-05
(0.000193)
-0.000143
(0.000277)
0.0820
(0.0689)
-0.0458
(0.0568)
0.0119
(0.0204)
0.0625
(0.0488)
0.0784
(0.0882)
75
pcinternet_grsa
Constant
Observations
R-squared
Wu-Hausman F test, p-value
0.00570
(0.0202)
77
0.272
0.12
*** p<0.01, ** p<0.05, * p<0.1.
pcinternet_gr = rate of growth in percent of number of users per 100 inhabitants between 1999 and 2006; pcinternet_greap =
interaction with East Asian and Pacific dummy; pcinternet_grsa = interaction with South Asia dummy.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
Table 4: Average Growth of Internet Usage, 1999–2006
Economy
China, People’s Rep. of
Fiji Islands
Hong Kong, China
India
Indonesia
Iran
Korea, Rep. of
Lao PDR
Malaysia
Nepal
Pakistan
Papua New Guinea
Philippines
Singapore
Thailand
Tonga
Vanuatu
Viet Nam
World
Growth of Internet Users
40.1
33.7
15.0
34.0
34.7
58.8
15.4
52.5
19.6
28.9
81.5
12.8
19.0
12.0
27.7
24.4
34.7
84.2
18.0
Source: International Telecommunication Union (2010).
Infrastructure and Growth in Developing Asia | 25
Table 5: Cross-section, Railroads
(1)
(2)
(3)
(4)
(5)
(6)
pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth
pcgdp80
School enrollment
secondary
School enrollment
primary
Inv/gdp
pcrail
-1.65e-06**
-2.54e-06***
-1.08e-06
-1.91e-06**
1.53e-06
-1.03e-06
(6.22e-07)
(7.07e-07)
(6.67e-07)
(8.43e-07)
(2.06e-05)
(1.72e-06)
0.000393*
0.000246
0.000282
0.000332
0.00126
0.000337
(0.000230)
(0.000279)
(0.000179)
(0.000222)
(0.00610)
(0.000357)
-0.000128
-0.000260
-2.32e-05
-0.000113
-0.000194
0.000204
(0.000239)
(0.000318)
(0.000203)
(0.000327)
(0.00263)
(0.000574)
0.155**
0.161**
0.0954*
0.0694
0.778
0.0312
(0.0613)
(0.0738)
(0.0509)
(0.0614)
(4.105)
(0.100)
3.968
9.105
-36.46
(7.129)
(6.600)
(320.1)
pcrail_eap
pcrail_sa
443.8***
-5,175
(146.4)
(33,066)
306.8***
3,457
(87.71)
pcrail_gr
0.248
-0.199
(0.272)
(0.259)
(0.674)
pcrail_greap
pcrail_grsa
Constant
Observations
R-squared
Wu-Hausman F
test, p-value
(17,518)
0.397
0.0346***
0.0489
(0.0114)
(0.0379)
0.0281***
0.0390
(0.00763)
(0.0435)
-0.0257
-0.391
-0.0243
-0.253
-0.183
0.159
(0.0217)
(0.258)
(0.0204)
(0.237)
(0.933)
(0.623)
39
26
39
26
32
24
0.456
0.508
0.650
0.726
0.13
0.58
*** p<0.01, ** p<0.05, * p<0.1.
pcrail = per capita kilometer of railroad lines; pcrail_gr = rate of growth; pcrail_eap = interaction with East Asian and Pacific dummy;
pcrail_sa = interaction with South Asia dummy.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
Results using the kilometers of roads per capita are in Table 6. Most of the results
fail to be significant, with two exceptions. In column 4, the interaction of the quality
proxy (percentage of the road network that is paved) with the EAP and SA dummy
indicates that the quality of the road network has a positive effect in the Asia region: the
implied marginal effects are that an increase of 10% in the proportion of paved roads
26 | ADB Economics Working Paper Series No. 231
corresponds to an additional average per capita growth over the period of 0.3% in EAP
and 0.4% in SA. In column 5, an additional point in the average growth rate of roads
results in significantly higher average per capita growth over the period for EAP and SA
than in the overall sample of developing countries (0.071, versus 0.056 in the overall
sample).23
Finally, Table 7 reports the results using percentage of the population with access to an
improved water source. While overall results are not significant, interactions with the EAP
and the SA dummies are again positive and significant, supporting the idea of a positive
link between the number of connections and growth in the Asian region. In levels, the
results indicate that an additional 10% in the rate of population coverage translates into
0.06% additional per capita growth in the EAP region and 0.04% in SA. In growth rates,
an additional point in the average growth rate of water coverage results in significantly
higher average per capita growth over the period for SA than in the overall sample of
developing countries (0.172, versus 0.158 in the overall sample). This is in line with
some previous studies that found that countries with a well-functioning water sector also
experimented stronger growth. Possible channels include an indirect impact through
external effects such as better health and better productivity of workers.
23
Note that exogeneity is rejected by the Wu-Hausman test for roads, both when considering level and growth rate,
but instrumental estimations fail to be significant for growth rates.
-0.0233**
(0.0116)
79
0.468
(0.000143)
0.154***
(0.0368)
(0.000132)
0.140***
(0.0272)
-0.518
(0.561)
-0.0236**
(0.0116)
79
0.469
-0.113
(0.0774)
59
0.448
0.0956
(0.0775)
(0.000122)
-5.99e-05
(0.000113)
3.24e-05
3.29e-05
(6.66e-05)
pcgdpgrowth
-5.54e-07
(6.86e-07)
0.000188
(3)
pcgdpgrowth
-7.03e-07*
(3.98e-07)
0.000198*
(2)
-0.0181
(0.0128)
79
0.523
(0.000135)
0.116***
(0.0306)
-0.357
(0.552)
-0.847
(1.742)
-1.797
(2.598)
-3.98e-05
(6.60e-05)
0.000241**
(0.000106)
0.000345*
(0.000184)
(0.000114)
1.41e-05
pcgdpgrowth
-7.61e-07**
(3.78e-07)
0.000233**
(4)
0.0562
(0.0631)
0.0149**
(0.00649)
0.0145**
(0.00605)
-0.0708
(0.0631)
59
0.532
3.93e-05
(7.09e-05)
(0.000145)
0.113***
(0.0301)
(0.000126)
-3.71e-05
pcgdpgrowth
-5.82e-07
(5.83e-07)
0.000175
(5)
*** p<0.01, ** p<0.05, * p<0.1.
pcroad = per capita kilometer of road lines; pcroad_gr = rate of growth; pcroad_eap = interaction with East Asian and Pacific
dummy; pcroad_sa = interaction with South Asia dummy; roadqual = paved roads in percent of total network; roadqual_eap =
interaction with East Asian and Pacific dummy; roadqual_sa = interaction with South Asia dummy.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
Observations
R-squared
Wu-Hausman F
test, p-value
Constant
pcroad_grsa
pcroad_greap
pcroad_gr
roadqual_sa
roadqual_eap
roadqual
pcroad_sa
pcgdp90
pcgdpgrowth
-6.44e-07*
(3.66e-07)
School enrollment 0.000213*
secondary
(0.000108)
School enrollment 2.69e-05
primary
(0.000130)
Inv/gdp
0.144***
(0.0258)
pcroad
-0.570
(0.568)
pcroad_eap
(1)
Table 6: Cross-section, Roads
(7)
0.033
-0.00981
(0.0596)
67
0.001
(0.000188)
0.0806
(0.0934)
(0.000231)
-8.71e-06
pcgdpgrowth
-1.64e-06
(2.08e-06)
0.000312
-0.217
(0.421)
0.0296
(0.0377)
0.0645
(0.0407)
0.197
(0.412)
56
(0.00159)
-0.0904
(0.369)
-4.587
(7.252)
-34.80
(110.3)
61.08
(107.4)
(0.000897)
0.000690
pcgdpgrowth
9.91e-07
(3.97e-06)
-4.55e-05
(6)
Infrastructure and Growth in Developing Asia | 27
28 | ADB Economics Working Paper Series No. 231
Table 7: Cross-section, Water
(1)
(2)
(3)
(4)
(5)
(6)
pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth pcgdpgrowth
pcgdp90
School enrollment
secondary
School enrollment
primary
-7.57e-07*
-5.99e-07*
-5.30e-07
-3.79e-07
3.75e-07
4.85e-07
(4.12e-07)
(3.54e-07)
(3.82e-07)
(4.05e-07)
(1.21e-06)
(2.12e-06)
0.000213*
0.000217*
0.000233**
0.000211
0.000406
0.000271
(0.000117)
(0.000122)
(0.000111)
(0.000127)
(0.000338)
(0.000337)
-4.23e-06
-8.94e-05
-1.47e-05
(0.000159)
(0.000263)
(0.000327)
3.10e-05
(0.000132)
Inv/gdp
pcwater
pcwater_eap
0.149***
(0.0276)
-5.74e-05
(0.000126)
-2.32e-05
(0.000159)
(0.000128)
0.213***
0.120***
0.183***
(0.0363)
(0.0283)
-0.000122
(0.000132)
0.000179**
(7.07e-05)
0.000164***
(5.92e-05)
(0.0416)
pcwater_sa
pcwater_gr
2.95e-05
0.111
(0.141)
pcwater_greap
pcwater_grsa
Constant
Observations
R-squared
Wu-Hausman F test,
p-value
0.158
(0.143)
0.00897
(0.00674)
0.0142***
(0.00363)
0.0663
(0.224)
-0.000390
(0.000420)
0.000716
(0.00117)
0.000366
(0.000675)
-0.0595
(0.518)
0.319
(0.531)
0.0975
(0.193)
0.00458
(0.105)
-0.0226*
-0.146
-0.0150
-0.192
0.0120
-0.319
(0.0129)
77
(0.149)
60
(0.0140)
77
(0.150)
60
(0.0662)
61
(0.497)
57
0.461
0.550
0.518
0.580
0.094
0.202
*** p<0.01, ** p<0.05, * p<0.1.
pcwater = percentage of population with access to an improved water source; pcwater_gr = rate of growth; pcwater_eap =
interaction with East Asian and Pacific dummy; pcwater_sa = interaction with South Asia dummy.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
One possibility that is often mentioned is that infrastructure would require a suitable
institutional environment to generate sizable growth dividends (e.g., see De 2010).
To test this hypothesis in the context of Asian countries, we augment our standard
specifications by introducing several indices capturing institutional quality. The first one
is an index of regulatory quality taken from Kaufmann, Kraay, and Mastruzzi (2009).
We focus on this index because the overall quality of the regulatory environment should
matter for infrastructure development. Alternatively, we use, from the same source, an
index computed as the average of government effectiveness, rule of law, and control of
corruption. This is meant to capture the overall institutional quality prevailing in a given
country. These indicators are measured in units ranging from about –2.5 to 2.5, with
higher values corresponding to better governance outcomes. We use 2006 values.24
24
Unfortunately, these indices are only available from 1996, preventing us from using similar period-average values
as for other variables.
Infrastructure and Growth in Developing Asia | 29
We also experiment with an index of infrastructure performance taken from the World
Bank. The specific infrastructure subcomponent of this index measures the “quality
of trade and transport related infrastructure (e.g., ports, railroads, roads, information
technology)”. Consistently with its definition, this index is used only for estimation
including transportation indicators (railroads and roads). The scores are from 1 to 5,
with 1 being the worst performance. Table 8 displays Asian countries scores, as well as
regional and income groups averages.
Table 8: Infrastructure Performance Index
Economy
Singapore
Hong Kong, China
Korea, Rep. of
China, People’s Rep. of
Malaysia
Thailand
India
Philippines
Viet Nam
Indonesia
Pakistan
Fiji Islands
Lao PDR
Mongolia
Myanmar
Papua New Guinea
Sri Lanka
Bhutan
Nepal
East Asia and Pacific average
South Asia average
High income: all
Upper middle income
Lower middle income
Low income
Infrastructure Performance
Index
4.22
4.00
3.62
3.54
3.50
3.16
2.91
2.57
2.56
2.54
2.08
1.98
1.95
1.94
1.92
1.91
1.88
1.83
1.80
2.46
2.13
3.56
2.54
2.27
2.06
Source: World Bank (2010), availble: info.worldbank.org/etools/tradesurvey/mode1b.asp, downloaded 3 August 2010.
Table 9 presents results where the standard specifications have been extended to include
our institutional indices of interest, as well as additional interactions with our Asian
country groups, i.e., we introduce, on top of the standard interactions of previous tables,
triple interactions between infrastructure indices, regional dummies, and institutional
indices. A significant coefficient would therefore indicate that the additional effect of
infrastructure in the Asian subgroups, as implied by the significant interactions found in
most previous regressions, can be partly attributed to institutional quality in the sense that
within EAP or SA country groups, countries with better environment would experiment
stronger links between infrastructure investment and growth.
0.163***
(0.0458)
0.0171**
(0.00809)
0.0104**
(0.00446)
-0.0288
(0.0597)
-0.00350
(0.00706)
-0.0150
(0.0181)
-0.179***
(0.0551)
46
0.813
pcinfra_gr
0.195***
(0.0486)
0.0120*
(0.00625)
0.00518
(0.00713)
0.0890
(0.0707)
0.00206
(0.00542)
-0.0268
(0.0188)
-0.229***
(0.0622)
57
0.716
0.000372***
(0.000112)
-0.000149
(0.000134)
0.0924***
(0.0239)
-0.0902
(0.0770)
-1.10e-06***
(3.37e-07)
(3)
0.287
(0.227)
0.0456***
(0.00898)
0.0332***
(0.00615)
-0.551
(0.331)
-0.0430**
(0.0179)
0.0346
(0.0230)
-0.251
(0.209)
26
0.862
0.000242
(0.000226)
-0.000348
(0.000292)
0.0375
(0.0481)
0.551
(0.324)
-2.39e-06**
(8.05e-07)
pcgdpgrowth
Railroads
Regulatory
quality
(4)
0.831
(0.706)
2.696
(2.156)
-0.117
(0.145)
0.0392
(0.0382)
-0.846
(0.718)
0.0450
(0.0441)
-0.00481
(0.0149)
-2.656
(2.104)
25
0.761
0.000114
(0.000326)
5.88e-05
(0.000345)
0.0470
(0.0706)
-1.88e-06
(1.20e-06)
pcgdpgrowth
Railroads
Infrastructure
performance
(5)
0.112
(0.0719)
0.0132**
(0.00580)
0.0250***
(0.00544)
-0.251**
(0.124)
0.00195
(0.00738)
0.0490***
(0.0146)
-0.109
(0.0692)
59
0.604
-1.41e-06**
(6.07e-07)
0.000122
(0.000144)
-0.000131
(0.000164)
0.116***
(0.0306)
0.259**
(0.122)
pcgdpgrowth
Roads
Regulatory
quality
*** p<0.01, ** p<0.05, * p<0.1.
pcinfra _gr = rate of growth of per capita infrastructure indicator; pcinfra_gr*eap = interaction with East Asian and Pacific dummy;
pcinfra_gr*sa = interaction with South Asia dummy; pcinfra_gr*inst_index = interaction of pcinfra _gr with institutional indicator;
pcinfra_gr*eap*inst_index and pcinfra_gr*sa*inst_index = interactions of pcinfra_gr*inst_index with East Asian and Pacific dummy,
and South Asia dummy.
Note: For each column, the infrastructure and the institutional indicators used are indicated above. Robust standard errors in
parentheses.
Source: Authors' calculations.
Observations
R-squared
Constant
pcinfra_gr*sa*inst_index
pcinfra_gr*eap*inst_index
pcinfra_gr*inst_index
pcinfra_gr*sa
pcinfra_gr*eap
Infrastructure performance
Regulatory Quality
Inv/gdp
School enrollment primary
0.000230
(0.000156)
-5.01e-05
(0.000204)
0.0830**
(0.0374)
0.0379
(0.0600)
School enrollment secondary
pcgdp90
pcgdp80
pcgdp75
pcgdp71
Infrastructure indicator
Institutional index
(2)
pcgdpgrowth
Phones
Regulatory
quality
(1)
pcgdpgrowth
Electricity
Regulatory
quality
-1.06e-06***
(2.17e-07)
Table 9: Institutional Indicators
(6)
0.113
(0.101)
0.407
(0.331)
-0.0231
(0.0197)
-0.0118
(0.0225)
-0.106
(0.1000)
0.0114
(0.00805)
0.00851
(0.00934)
-0.434
(0.325)
52
0.622
-1.07e-06
(7.80e-07)
6.96e-05
(0.000163)
-5.23e-05
(0.000189)
0.144***
(0.0388)
pcgdpgrowth
Roads
Infrastructure
performance
(7)
0.253
(0.298)
0.00916
(0.00651)
0.0150**
(0.00648)
0.176
(0.327)
-0.00559
(0.00783)
0.00589
(0.0203)
-0.274
(0.304)
60
0.637
-7.95e-07**
(3.28e-07)
8.49e-05
(0.000148)
2.12e-05
(0.000163)
0.159***
(0.0401)
-0.166
(0.329)
pcgdpgrowth
Water
Regulatory
quality
30 | ADB Economics Working Paper Series No. 231
Infrastructure and Growth in Developing Asia | 31
Table 9: continued.
Infrastructure indicator
Institutional index
pcgdp71
pcgdp75
(8)
(9)
(10)
(11)
(12)
pcgdpgrowth
Electricity
Institutional
quality
-1.15e-06***
(1.97e-07)
pcgdpgrowth
Phones
Institutional
quality
pcgdpgrowth
Railroads
Institutional
quality
pcgdpgrowth
Roads
Institutional
quality
pcgdpgrowth
Water
Institutional
quality
-7.45e-07**
(3.44e-07)
4.65e-05
(0.000160)
7.04e-05
(0.000182)
0.147***
(0.0413)
-0.523
(0.568)
0.491
(0.369)
0.0114*
(0.00673)
0.0110**
(0.00452)
0.532
(0.564)
0.000994
(0.00774)
-0.00281
(0.00841)
-0.512
(0.373)
60
0.638
pcgdp80
-1.15e-06***
(3.26e-07)
pcgdp90
School enrollment
secondary
School enrollment primary
Inv/gdp
Institutional quality
pcinfra_gr
pcinfra_gr*eap
pcinfra_gr*sa
pcinfra_gr*inst_index
pcinfra_gr*eap*inst_index
pcinfra_gr*sa*inst_index
Constant
Observations
R-squared
-2.55e-06***
(7.52e-07)
0.000116
0.000239*
0.000128
-1.34e-06**
(5.97e-07)
0.000135
(0.000201)
2.75e-05
(0.000247)
0.0638*
(0.0338)
0.0527
(0.0644)
0.159***
(0.0420)
0.0170**
(0.00678)
0.0112***
(0.00251)
-0.0405
(0.0642)
-0.00262
(0.00599)
-0.00805
(0.00976)
-0.171***
(0.0541)
46
0.819
(0.000131)
-5.88e-05
(0.000149)
0.0668***
(0.0222)
-0.0784
(0.0631)
0.213***
(0.0501)
0.0132**
(0.00509)
0.00762
(0.00488)
0.0813
(0.0580)
0.00154
(0.00450)
-0.0117
(0.00849)
-0.243***
(0.0629)
57
0.741
(0.000262)
-0.000284
(0.000274)
0.0247
(0.0493)
0.428
(0.314)
0.195
(0.266)
0.0432***
(0.0129)
0.0219***
(0.00482)
-0.423
(0.322)
-0.0360*
(0.0201)
0.00512
(0.00886)
-0.155
(0.253)
26
0.854
(0.000154)
-8.41e-05
(0.000178)
0.104***
(0.0324)
0.231**
(0.108)
0.0953
(0.0698)
0.0153**
(0.00575)
0.0170***
(0.00392)
-0.227**
(0.109)
0.00605
(0.00638)
0.0181**
(0.00708)
-0.0955
(0.0673)
59
0.588
*** p<0.01, ** p<0.05, * p<0.1.
pcinfra _gr = rate of growth of per capita infrastructure indicator; pcinfra_gr*eap = interaction with East Asian and Pacific dummy;
pcinfra_gr*sa = interaction with South Asia dummy; pcinfra_gr*inst_index = interaction of pcinfra _gr with institutional indicator;
pcinfra_gr*eap*inst_index and pcinfra_gr*sa*inst_index = interactions of pcinfra_gr*inst_index with East Asian and Pacific dummy,
and South Asia dummy.
Note: For each column, the infrastructure and the institutional indicators used are indicated above. Robust standard errors in
parentheses.
Source: Authors' calculations.
32 | ADB Economics Working Paper Series No. 231
Note that the Kaufmann et al. (2009) indices are commonly used in cross-country
applications and are built by aggregating most of the existing country-level institutional
indicators along each dimension (corruption, regulatory quality, government effectiveness,
etc.). As such, the results obtained here can be considered pretty reliable, in the sense
that using alternative indicators would most probably produce very consistent outcomes.
We focus on interactions with infrastructure indicators rates of growth, as these have
both shown consistently significant results and are also much less subject to endogeneity
problems than indicators in levels.
The results are disappointing, as almost no significant effects are found. The
interpretation is that the comparatively higher impact of infrastructure on per capita
growth in the EAP and SA groups is not particularly driven by the subset of countries in
these regions that have better institutional environment. The only exception is the case
of roads in the South Asia region, which therefore indicates that the fact that the growth
of the roads network has a stronger effect on per capita growth in this group of countries
than in the whole sample is driven by the countries in the group with a better institutional
environment (therefore Bhutan, India, and Sri Lanka). The interpretation cannot be
pushed too far, however, as only five South Asian countries are included in the analysis.
Next, we turn to the growth accounting analysis to delve further into the interpretation of
our results.
B.
Growth Accounting
1.
Data and Estimation
There are two main options for estimating the impact of infrastructure on TFP growth as
summarized in equations (7) or (8) in the Appendix. One is based on regional panel data,
while the other one is a country-per-country approach based on time series data.
We first perform individual country estimations, which more realistically do not assume
that there is a common underlying technology for all countries. This has been the
approach used by most noninfrastructure growth accounting studies (see for example
Barro and Sala-i-Martin 2005). The panel estimation technique, on the other hand, rests
on the assumption that a common production function exists for the countries under
analysis, with individual country effects to be controlled for. While this approach has
been extensively used with state/provincial panel data for India (Hulten et al. 2005), Italy
(La Ferrara and Marcellino 2000), and the US (Holtz-Eakin 1994), it remains to be seen
whether it can work when applied to a set of countries, albeit in the same region. We
report below panel estimations suggesting that indeed this modeling of growth accounting
runs into the problem of country-level heterogeneity and adds little to individual country
estimations.
Infrastructure and Growth in Developing Asia | 33
Concerning any possible simultaneity in the estimation of (8) we cannot rule out a
X
priori an influence of TFP growth on investment in infrastructure, X . Possible causes
of simultaneity include endogenous responses of infrastructure policies to TFP growth,
making it necessary to test the presence of reverse causation in the data.
Country-specific estimations, as opposed to panel estimations, call for longer time series
in order to produce efficient estimators. We concentrate again on physical indicators of
infrastructure that allow for longer time coverage.
With respect to explanatory variables, we use data from WDI, covering a time period up
to 2006:
(i)
Number of mobile and fixed-lines telephone subscribers.
(ii)
Electricity: Computed from electricity generation in kilowatt-hours, net
of electricity power transmission and distribution losses; this provides a
quality-adjusted time series, which is used in all the estimations below.
(iii)
Railway lines, total route in kilometers.
(iv)
Roads data, total route in kilometers. Note that this variable could only be
used for two countries.
(v)
Water data could not be used as the corresponding time series are too
short for all Asian countries.
The TFP growth rates, to be used as the dependent variable as in equation (8), were
collected from two sources:
25
(i)
Asian Productivity Organization (APO 2010) provides TFP growth rates for
six Asian and Pacific countries (the PRC, Indonesia, the Fiji Islands, the
Republic of Korea, the Philippines, Thailand) over the period 1970–2007.
These are calculated (not estimated) TFP growth rates following the
standard methodology in equation (3) in the Appendix and, in addition,
taking into account changes in labor quality.
(ii)
UNIDO’s World Productivity Database (WPD) (UNIDO 2010) provides
information on levels and growth of aggregate TFP for eight other Asian
and Pacific countries between 1960 and 2000 (see Isaksson 2008). We
use TFP growth estimated with a nonparametric technique using Data
Envelopment Analysis with Long Memory (3).25
See Isaksson (2008) and Noumba et al. (2009) for details. Essentially the LM DEA method relies on linear
programming estimators.
34 | ADB Economics Working Paper Series No. 231
Crossing available infrastructure data series, which in general are good for telephones
(fixed and mobiles) and electricity (production); mediocre to bad for railroads and roads;
and inexistent for water, with TFP data, we get the following coverage:
(i)
For the period 1970–2007 (APO data):
(a)
(b)
(c)
(d)
(e)
(f)
(ii)
For the period 1961–2000 (UNIDO data):
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
the PRC (telecom, electricity, railroads)
Indonesia (telecom, electricity)
Philippines (telecom, electricity)
Thailand (telecom, electricity, railroads)
Fiji Islands (telecom)
Republic of Korea (telecom, electricity, railroads, roads)
Hong Kong, China (telecom, electricity)
India (telecom, electricity, railroads)
Nepal (telecom, electricity)
Pakistan (telecom, electricity, railroads, roads)
Sri Lanka (telecom, electricity)
Malaysia (telecom, electricity, railroads)
Singapore (telecom, electricity)
Papua New Guinea (telecom)
2.Results
Tables 10a, 10b, and 11 report the results from individual growth accounting regressions.
The results of regressions including only the electricity and telecom variables for the 14
countries included in our sample are in Tables 10a and 10b, while Table 11 adds railroads
and roads when available.
First, note that in 11 out of 14 countries, we cannot reject the hypothesis that the
coefficients of both electricity and telecom are zero. Again, recall that the interpretation for
this result is not that infrastructure is not productive but rather that there is no evidence
that it is more productive than other types of capital. This conclusion is not modified for
the two countries of this group also included in Table 3, where neither railroads nor roads
are significant.
Infrastructure and Growth in Developing Asia | 35
Table 10a: Individual Country Regressions (dependent variable: TFP growth rate)
Growth rate of mobile
and fixed-line telephony per
capita
Growth rate of per capita
electricity production net of
losses
Constant
(1)
(2)
(3)
(4)
(5)
(6)
(7)
China,
People’s
Rep. of
Fiji
islands
Hong
Kong,
China
India
Indonesia
Korea,
Rep. of
Malaysia
0.0497*
0.0913
-0.280
0.0230
0.0750
-0.0757
-0.00822
(0.0272)
(0.107)
(0.234)
(0.0818)
(0.0537)
(0.0820)
(0.101)
0.346**
0.113
0.288
0.0491
0.279**
0.0311
(0.152)
(0.0827)
(0.367)
(0.111)
(0.126)
(0.123)
-0.00374
-0.00860
0.0344*
-0.00144
-0.0181
-0.00622
0.0131
(0.0182)
(0.0138)
(0.0197)
(0.0314)
(0.0265)
(0.0153)
(0.0176)
Observations
31
31
25
25
31
28
23
R-squared
0.234
0.030
0.175
0.077
0.053
0.228
0.003
*** p<0.01, ** p<0.05, * p<0.1.
TFP = total factor productivity.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
Table 10b: Individual Country Regressions
Growth rate of
mobile and fixed-line
telephony per capita
Growth rate of per
capita electricity
production net of
losses
Constant
(8)
(9)
(10)
(11)
(12)
(13)
(14)
Nepal
Pakistan
Papua
New
Guinea
Philippines
Singapore
Sri Lanka
Thailand
-0.00635
-0.0515
-0.0395
0.0675
-0.0666
0.000378
0.0238
(0.0347)
(0.0536)
(0.212)
(0.0485)
(0.104)
(0.0405)
(0.0435)
0.00182
0.0719
0.107
0.314
-0.151
0.498**
(0.0277)
(0.106)
(0.192)
(0.243)
(0.121)
(0.197)
0.00543
0.0160
-0.00398 -0.0193
0.0104
0.00894
-0.0285
(0.0109)
(0.00954)
(0.0126)
(0.0125)
(0.0158)
(0.00880)
(0.0253)
Observations
25
25
25
31
20
25
31
R-squared
0.001
0.043
0.001
0.118
0.074
0.051
0.253
*** p<0.01, ** p<0.05, * p<0.1.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
36 | ADB Economics Working Paper Series No. 231
As for the significant results, they concern the PRC, the Republic of Korea, and
Thailand. For the PRC, the total number of telephones (fixed plus mobiles) has a positive
coefficient of 0.02, significant at the 1% level, while electricity generating capacity
adjusted for losses also appears to be more productive than standard capital, with a 5%
level of significance. This can be interpreted as an externality effect expressed as an
output elasticity of 0.12 for telecom and 0.35 for electricity.
In the Republic of Korea and Thailand, the electricity variable also has a positive
coefficient of 0.28 and 0.50 respectively, significant at the 5% level, again supporting
externalities from this variable. Finally, note that in these three countries, the growth of
infrastructure indicators explains close to one fourth of the TFP growth (R2 of 0.23 for the
PRC, 0.23 for the Republic of Korea, and 0.25 for Thailand).
Table 11: Individual Country Regressions with Railroads and Roads (dependent variable:
TFP growth rate)
Growth rate of mobile and fixed-line
telephony per capita
Growth rate of per capita electricity
production net of losses
Growth rate of per capita kilometer rail
(1)
(2)
(3)
(4)
(5)
(6)
China,
People’s
Rep. of
India
Korea,
Rep. of
Malaysia
Pakistan
Thailand
0.0395
-0.0452
-0.211***
-0.123
0.00528
0.0228
(0.0282)
(0.0545)
(0.0525)
(0.111)
(0.0806)
(0.0496)
0.274**
0.0356
0.216*
0.0956
0.0175
0.461**
(0.123)
(0.249)
(0.111)
(0.113)
(0.0710)
(0.221)
-0.906
0.196
0.0460
-0.614
-0.0809
-0.170
(0.539)
(0.473)
(0.0617)
(0.939)
(0.0947)
(0.418)
Growth rate of per capita kilometer
roads
Constant
Observations
R-squared
0.00146
0.0346
(0.0293)
(0.104)
0.00498
0.0290
0.0118
0.00477
-0.00242
-0.0263
(0.0170)
(0.0202)
(0.00882)
(0.0335)
(0.0169)
(0.0291)
26
20
12
16
10
26
0.191
0.044
0.758
0.160
0.058
0.233
*** p<0.01, ** p<0.05, * p<0.1.
TFP = total factor productivity.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
In Table 3, the results on electricity are robust to including the railroads variable in
the specifications, while the telecom variable becomes insignificant for the PRC and
negative for the Republic of Korea. However, this could be due to the fact that including
Infrastructure and Growth in Developing Asia | 37
railroads (and roads in the case of the Republic of Korea) reduces the number available
observations, from 31 to 25 for the PRC, and from 28 to 12 for the Republic of Korea.
These individual country regressions could be missing cross-country variations explaining
differences in TFP growth. Table 12 reports the results from panel regressions.
Table 12: Panel Regressions
(1)
Growth rate of per capita electricity
production net of losses
Growth rate of mobile and fixed-line
telephony per capita
Growth rate of per capita kilometer rail
Growth rate of electricity production,
net of losses
Growth rate of mobile and fixed-line
telephony
Growth rate of kilometer rail
Constant
Observations
Number of id_country
R-squared (overall)
Rho
Hausman test (FE vs RE) p-value
(2)
(3)
tfp_g
0.0910**
tfp_g
0.0924**
tfp_g
0.228***
(0.0462)
(0.0471)
0.0283*
(0.0722)
0.00663
(0.0160)
(0.0186)
-0.0460
(0.0500)
(4)
(5)
(6)
tfp_g
tfp_g
tfp_g
0.0653
0.0643
0.220***
(0.0458)
(0.0522)
0.0293*
(0.0556)
0.0102
(0.0161)
(0.0189)
-0.0281
(0.0590)
0.00468*
0.00134
-0.00410
0.00514
0.00116
-0.00765
(0.00283) (0.00426) (0.00889) (0.00383) (0.00594) (0.00811)
378
320
178
378
320
178
12
12
9
12
12
9
0.0266
0.0362
0.0730
0.0183
0.0259
0.0654
0.0454
0.0734
0.152
0.0461
0.0727
0.128
0.85
0.86
0.52
0.78
0.84
0.00*
*** p<0.01, ** p<0.05, * p<0.1.
Note: Full set of period dummies included. Robust standard errors in parentheses.
Source: Authors' calculations.
The electricity variable comes out positive and significant in columns 1–3, i.e., when per
capita growth rate of the infrastructure indicators is used. Additionally, in column 2 the
telecom variable is also positive and significant. In columns 4–6, similar albeit slightly
weaker results are obtained when using aggregate rather than per capita growth rates.
Note that a Hausman specification test supports the random effect specification in all but
the last column, which is not surprising considering that the countries included (between
9 and 12) are drawn from a larger population of countries. However, the fixed effect
estimation of the specification in column 6 yields very similar results and is not reported.
Also, the overall R² (measuring goodness of fit for both between and within variations) is
rather small in all specifications.
38 | ADB Economics Working Paper Series No. 231
Overall, the rho-statistic and the results from a between-effect version of the panel
regressions in Table 12, not shown here to save space, indicate that most of the
significant results are driven by the “within” (that is, individual country-level) variation. The
panel analysis does not add anything to the results in Tables 10a, 10b, and 11, probably
because the assumption needed to run such estimations, i.e., that countries in the sample
share some common technology, is not warranted in our sample, which contains very
heterogenous countries, from large emerging Asian countries such as the PRC, to small
Pacific island economies.
Finally, because TFP series are by construction noisy, in Table 13 we turn to 5-year
averages to smooth them out. The resulting panel size is reduced to between 38 and
77 observations. The results corresponding to the total number of phone lines are
maintained, while electricity is no longer significant. However, the small panel size (N
between 9 and 12, t between 3 and 7) makes these results obviously fragile.
Table 13: Panel Regressions, 5-year Averages
(1)
Growth rate of per capita electricity
production net of losses
(2)
(3)
(4)
(5)
(6)
tfp_g
tfp_g
tfp_g
tfp_g
tfp_g
tfp_g
0.0734
0.0122
0.0279
(0.0655)
(0.0772)
(0.137)
0.0609*
0.0582
(0.0318)
(0.0393)
Growth rate of mobile and fixed-line
telephony per capita
Growth rate of per capita kilometer rail
0.167
(0.358)
Growth rate of electricity production,
net of losses
0.0699
0.00902
0.0327
(0.0646)
(0.0882)
(0.134)
0.0613**
0.0571
(0.0309)
(0.0414)
Growth rate of mobile and fixed-line
telephony
Growth rate of kilometer rail
0.139
(0.462)
Constant
Observations
0.0182***
0.00836
0.00894
0.0175***
0.00738
0.00603
(0.00271)
(0.00959)
(0.00782)
(0.00356)
(0.0108)
(0.00932)
77
65
38
77
65
38
Period dummies
Number of id_country
Yes
12
Yes
12
Yes
9
Yes
12
Yes
12
Yes
9
R-squared (overall)
0.180
0.222
0.329
0.181
0.221
0.310
Rho
0.0998
0.217
0.172
0.104
0.218
0.161
*** p<0.01, ** p<0.05, * p<0.1.
Note: Robust standard errors in parentheses.
Source: Authors' calculations.
Infrastructure and Growth in Developing Asia | 39
V.Conclusion
Infrastructure stocks in developing Asia have been growing at a significant pace. We
find, however, that their levels remain well below corresponding world averages in
terms of both quantity and quality. As infrastructure-stock accumulation (in electricity,
telecommunications, transport, and water supply) has a positive impact on economic
growth, a massive build-up of these stocks is needed, but may well be beyond the
financing capacities of many governments.
Moreover, demand for infrastructure services is expected to soar in cities due to
rapid urbanization. In order to keep cities in developing Asia competitive, investments
in infrastructure need to be designed to take account of congestion, environmental
degradation, and other impediments to productivity that are associated with urban
agglomeration. The urban–rural divide in access to infrastructure services detracts from
the inclusiveness of growth in developing Asia. Improving access to basic infrastructure
services, such as the provision of potable water and sanitation, in rural areas is crucial for
poverty alleviation.
After reviewing the state of infrastructure development in developing Asia and the
literature on infrastructure and development, we have performed a number of empirical
exercises to assess the contribution of infrastructure to growth and productivity across a
number of dimensions (electricity, telecommunications, railroads, roads and water).
Cross-country estimations show that for most infrastructure indicators, the growth rate of
stocks has a positive and significant impact on per capita GDP average growth rate in
the subgroups of EAP and SA countries. The growth accounting exercise, on the other
hand, shows that positive and significant effects of infrastructure on TFP growth are only
observed in a few countries (the PRC, the Republic of Korea, Thailand), for telecom
and electricity indicators. At face value, a possible interpretation is that in most Asian
countries, the observed effect on growth was simply the results of higher than average
infrastructure capital accumulation (a “direct effect”), but that additional productivity
enhancing (“indirect”) effects were rather rare.
East Asia’s economic history seems to give credit to that claim. As discussed in
Straub, Vellutini, and Warlters (2008), between 1975 and 1995 East Asia accumulated
infrastructure at a rate that outpaced other regions (see Table 14). The PRC and Viet
Nam, the two fastest-growing economies in the region invest around 10% of GDP in
infrastructure, and other countries, for example those in the Greater Mekong countries
(the PRC, Cambodia, Indonesia, the Lao PDR, Myanmar, Thailand, and Viet Nam) are
planning to reach investment levels above 5%–6%.
40 | ADB Economics Working Paper Series No. 231
Table 14: 1995 infrastructure Stocks as Multiples of 1975 Levels
East Asia
South Asia
Middle East and North Africa
Latin America and Caribbean
OECD
Pacific
Sub-Saharan Africa
Eastern Europe
Electricity
5.9
4.4
6.1
3.0
1.6
2.0
2.6
1.6
Roads
2.9
2.5
2.1
1.9
1.4
1.7
1.2
Telecommunications
15.5
8.2
7.2
5.1
2.2
4.3
3.9
6.9
OECD = Organisation for Economic Co-operation and Development.
Note: Electricity = generating capacity in megawatts; roads = paved roads in kilometers; Telecommunications = number of main
lines. See Appendix for construction.
Sources:Straub, Vellutini, and Warlters (2008); World Development Indicators (World Bank, various years); Canning (1998).
One must be cautious however, because due to the limited availability of data, there
is no perfect match between the samples and the indicators used in both exercises. In
particular, not all countries have long enough TFP data, and not all infrastructure series
are long enough to be included in the growth accounting estimations.
One final comment can be made regarding convergence in the region. As shown by the
cross-country regressions, initial values of per capita GDP are consistently negative and
significant, indicating indeed the existence of convergence in our sample. Furthermore,
specific results regarding the interaction between income levels and infrastructure
endowments from Straub et al. (2008) are relevant. As shown there, infrastructure
indicators appear to have a significantly lower impact among low- and middle-income
countries, compared to high-income ones. That means that when we interact the
specific infrastructure indicators with dummy variables for low-, middle-, and high-income
countries, the results show that the net effect of infrastructure is lower in the low- and
middle-income groups than in the high-income one (being actually negative in some
cases for low-income countries).
The first possibility, consistent with some of the existing literature on telecommunication
(for example, see Röller and Waverman 2001), is simply a network effect type of
explanation. In the case of roads, a similar argument could maybe be made referring to
the importance of regional integration to potentiate physical infrastructure investments
among the fastest growing countries, for example in the Greater Mekong subregion (see
Stone et al. 2009). Another line of explanation is that more developed countries also have
a more favorable institutional environment, e.g., better property rights, which boosts the
impact of infrastructure investments or facilitate their implementation. Although our data
does not allow us to isolate such an effect, it may be the case that a more microeconomic
approach to institution and infrastructure measurement is needed to illustrate such
channels.
Infrastructure and Growth in Developing Asia | 41
Appendix
Cross-country Regressions
This section is adapted from Straub et al. (2008). Cross-country regressions use as dependent
variable real per capita GDP growth, and as explanatory variables the initial level of real per capita
GDP and other additional factors such as physical investment, human capital, etc.26 Infrastructure
capital can be included as an additional explanatory factor, yielding a specification of the form:
(1)
where gi is the growth rate of real per capita GDP for country i, yi0 is initial income (possibly in log
form), KIi is a measure of infrastructure capital, and Zi is a vector of controls.27
gi = α yi0 + β K iI + Zi γ + ν i
Growth Accounting Framework
The growth accounting framework is based on a standard production function of the type
Y = A.F (K , L )
(2)
where Y is aggregate GDP, A is the time-varying total factor productivity (TFP) and K and L are
(total) capital and labor, respectively. Taking logs and differentiating with respect to time yields
Y A FL .L L FK .K K
= +
+
Y A
F L
F K (3)
Assuming that marginal factor productivities equal factor prices, we get the standard formula for
growth accounting, where the growth of TFP is computed as the residual between the growth of
GDP and the growth of factors:
A Y
L
K
= − SL − SK
A Y
L
K (4)
In this equation SL and Sk are the respective observed shares of income. Importantly, equation (4)
is typically implemented through direct calculation, as all the variables on the right-hand side are
observed, rather than through econometric estimation, and is commonly used in country-specific
studies to calculate TFP growth (see Barro and Sala-i-Martin 2005).
Assuming that infrastructure (denoted X) influences output through two channels, it can be added
straightforward to this framework in the following way (Hulten et al. 2005). First, it impacts TFP
through
A = A( X ) = A . X η (5)
26
27
See for example Barro and Sala-i-Martin (2005).
See Straub (2010) for a discussion of the limitations of such estimations in the context of infrastructure.
42 | ADB Economics Working Paper Series No. 231
where A is the « true » TFP and η is the elasticity of A with respect to X. This channel captures
the externality aspect of infrastructure, as the output-increasing effect occurs without any payments
by firms for infrastructure services. Note that η is therefore not observable.
Second, infrastructure enters the production function as an additional factor of production:
Y = A . X η .F (K , L, X ) (6)
where K is the stock of noninfrastructure capital. The presence of infrastructure as one more factor
reflects its market-mediated (“direct”) impact, whereby firms pay for infrastructure services. This
implies:
&
Y& A%
X&
X&
L&
%
= +η + SX
+ SL + SK
Y A%
X
X
L
&
K%
K%
(7)

where S X is the share of GDP accruing to market-mediated infrastructure and S K the share of
revenue that accrues to noninfrastructure capital. If S X was observable we could disentangle the
direct influence of infrastructure from its indirect effect. In practice data on infrastructure prices are
not available in a consistent way and available data on capital do not distinguish between different
types of capital, such as infrastructure. We can rewrite the model so as to fit the available data,
as:
Y = A . X η .F (K , L )
(6’)
which implies
&
Y& A%
X&
L&
K&
(7’)
= + η + SL + SK + ε
Y A%
X
L
K
Substituting equation (3) into (6’), and appending an error term, we get:
&
A& A%
X&
= % +η + ε
A A
X
(8)
The left-hand side of equation (8) is TFP growth computed (not estimated) from the standard
growth accounting approach. Rather than performing a full estimation of equation (7’), we can
estimate the reduced form equation (8), using the (year by year) results of equation (3) for TFP
A
growth rates ( A ), which are usually available from standard growth accounting exercises for a
number of countries.
Either equation (8) or equation (7’) provide an estimation of η, the externality effect of
infrastructure, as opposed to the full elasticity of output with respect to infrastructure. If an
estimation of equation (8) produces a value for η not significantly different from zero, we conclude
that infrastructure is not more productive than other types of capital, in the sense that it exerts no
specific externality.
Infrastructure and Growth in Developing Asia | 43
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About the Paper
Stéphane Straub and Akiko Terada-Hagiwara present the state of infrastructure in
developing Asian countries, and analyze the link between infrastructure, growth, and
productivity. The main conclusion is that a number of countries in developing Asia have
significantly improved their basic infrastructure endowments in the recent past, and this
appears to correlate significantly with good growth performances. However, the evidence
seems to indicate that this is mostly the result of factor accumulation (a direct effect), while
the impact on productivity is rather inconclusive.
About the Asian Development Bank
ADB’s vision is an Asia and Pacific region free of poverty. Its mission is to help its developing
member countries substantially reduce poverty and improve the quality of life of their
people. Despite the region’s many successes, it remains home to two-thirds of the world’s
poor: 1.8 billion people who live on less than $2 a day, with 903 million struggling on
less than $1.25 a day. ADB is committed to reducing poverty through inclusive economic
growth, environmentally sustainable growth, and regional integration.
Based in Manila, ADB is owned by 67 members, including 48 from the region. Its
main instruments for helping its developing member countries are policy dialogue, loans,
equity investments, guarantees, grants, and technical assistance.
Asian Development Bank
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