metadata: authors: 0: author: Bramati, Maria Caterina
1: author: Seethepalli,Kalpana
2: author: Veredas,David
count: East Asia and Pacific
docna: 0: docna: How relevant is infrastructure to growth in
            East Asia ?
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Private Sector Economics,Industrial Economics,Urban Solid Waste Management,Sanitation and Sewerage,Engineering,Water and Human Health,Hydrology,Private Sector Development Law,Economic Growth,Water Supply and Sanitation Economics,Marketing,Sanitary Environmental Engineering,Small Private Water Supply Providers,Environmental Engineering,Health and Sanitation,Town Water Supply and Sanitation
teratopic: Governance,Urban Development,Communities and Human Settlements,Finance and Financial Sector Development,Transport
abstracts: cdata!: This paper seeks to shed some light on
            the extent to which infrastructure sub-sectors - energy,
            telecommunications, water supply, sanitation, and transport
            - contributed to growth in East Asia during 1985-2004. It
            also attempts to provide additional insights on whether the
            relationship between infrastructure and growth depends on
            five additional variables: the degree of private
            participation in infrastructure, the quality of governance,
            the extent of rural-urban inequality in access to
            infrastructure services, country income levels, as well as
            geography. The findings show that greater stocks of
            infrastructure were indeed associated with higher growth.
            However, a more nuanced look at the sensitivity of
            infrastructure impacts on the five additional variables
            yields different results, with some sectors supporting
            conventional expectations and others yielding mixed or
            counter-intuitive results. In particular, the telecom and
            sanitation sectors yield statistically significant results
            supporting the a priori hypotheses; electricity and water
            infrastructure provide mixed results; and road
            infrastructure consistently contradicts a priori
            expectations. The results are consistent with the
            widely-accepted idea in policy research that infrastructure
            plays an important role in promoting growth, as well as with
            the viewpoint that certain countries' endowments
            influence the growth-related impacts of infrastructure.
repnme: repnme: How relevant is infrastructure to growth in
            East Asia ?
display_title: How relevant is infrastructure to
            growth in East Asia
listing_relative_url: /research/2008/04/9365206/relevant-infrastructure-growth-east-asia
topicv3: Private Sector Development m1326060 1921,Economic Growth m1326241 471,Sanitation m1326453 2000,Water m1326522 137,Regional Integration m1504278 2576
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Governance,Urban Development,Communities and Human Settlements,Finance and Financial Sector Development,Transport
no_of_pages: 42
author_keys: 0: author_key: 142908
1: author_key: 781808
model_extractions: WPS4597 Policy ReseaRch WoRking PaPeR 4597 How Relevant Is Infrastructure to Growth in East Asia?

_Kalpana Seethepalli Maria Caterina Bramati David Veredas_ The World Bank East Asia & Pacific Sustainable Development Department Operations and Policy Division April 2008
Policy ReseaRch WoRking PaPeR 4597

# **Abstract**

This paper seeks to shed some light on the extent to which infrastructure sub-sectors – energy, telecommunications, water supply, sanitation, and transport – contributed to growth in East Asia during 1985-2004. It also attempts to provide additional insights on whether the relationship between infrastructure and growth depends on five additional variables: the degree of private participation in infrastructure, the quality of governance, the extent of rural-urban inequality in access to infrastructure services, country income levels, as well as geography.

The findings show that greater stocks of infrastructure were indeed associated with higher growth. However, a more nuanced look at the sensitivity of infrastructure impacts on the five additional variables yields different results, with some sectors supporting conventional expectations and others yielding mixed or counterintuitive results. In particular, the telecom and sanitation sectors yield statistically significant results supporting the a priori hypotheses; electricity and water infrastructure provide mixed results; and road infrastructure consistently contradicts a priori expectations. The results are consistent with the widely-accepted idea in policy research that infrastructure plays an important role in promoting growth, as well as with the viewpoint that certain countries’ endowments influence the growthrelated impacts of infrastructure.

This paper—a product of the Operations and Policy Division, East Asia & Pacific Sustainable Development (EASSD) Department—is part of a larger effort in the department to better understand the extent to which infrastructure subsectors have contributed to growth in East Asia. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The author may be contacted at kseethepalli@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **How Relevant Is Infrastructure to Growth in East Asia?***

## Kalpana Seethepalli

Operations and Policy Unit, East Asia Sustainable Development Department The World Bank <u>kseethepalli@worldbank.org</u>

## Maria Caterina Bramati

Belgian National Institute of Statistics <u>Maria-Caterina.Bramati@economie.fgov.be</u>

## David Veredas

ECARES, Université Libre de Bruxelles CORE, Université catholique de Louvain <u>david.veredas@ulb.ac.be</u>

* We have benefited from valuable comments and suggestions from Michel Kerf, Michael Warlters, and Antonio Estache. Any shortcoming is however ours and should not be attributed to anyone else, in particular to the institutions we are affiliated with.
# **1. Infrastructure and Growth in East Asia**

Despite the financial crisis of 1997, East Asia<sup>1</sup> has seen strong growth and poverty reduction outcomes. Output has increased by a yearly average of more than 7% over the last 15 years, and investment levels have generally been high, averaging more than 30% of GDP since the 1990s. Infrastructure has played a central part in the East Asian development model; it may be no coincidence that China and Vietnam, with average annual per capita growth rates of 8.7% and 5.8% respectively over the 1990-2005 period (among the highest in developing East Asia), had made investments in infrastructure of 7% and 9% of GDP respectively over the same period.

Infrastructure works to reduce poverty by supporting economic growth and providing access to key services (ADB, JCIB, World Bank, 2005). Infrastructure can, in addition, lower income inequality and help raise the income of the poor more than proportionately (Calderon & Serven, 2005), suggesting that infrastructure development in an essential ingredient for poverty reduction. This paper examines the first part of the infrastructure-growth-poverty reduction equation. It seeks to understand the extent to which different infrastructure sub-sectors – i.e. energy, telecommunications, water supply, sanitation, and transport - have contributed to growth in East Asia<sup>2</sup> over the period 1985-2004, and presents a similar analysis to that conducted for 41 countries in Sub-Saharan Africa by Estache, Speciale, & Veredas (2005) to the East Asian context. As in the Estache et al (2005) paper, we use an augmented Solow growth model to compare the significance of infrastructure to growth after taking into account the effect of other sectoral expenditures such as those pertaining to education and capital formation.

In order to anchor this analysis in the context of ongoing policy debates, we attempt to provide for additional insights on the importance of infrastructure subsectors to growth depending on the quality of governance, degree of private participation in infrastructure (PPI), country income levels, extent of rural-urban inequality in access to infrastructure services, as well as geography (island vs. non-island countries):

> 1 Reference to East Asia throughout the paper includes the 9 Pacific Islands Countries (PICs), and corresponds to the Bank’s definition of East Asia and Pacific Region.

> 2 For a comprehensive review of the empirical literature on infrastructure’s impact on growth, see Straub & Vellutini (2006).

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- _Governance._<sup>3</sup> Good governance is associated with better provision of infrastructure services. It acts by reducing corruption and incentives of political actors to capture monopoly rents, increasing transparency of public and private transactions and regulatory structures while guaranteeing investors an adequate rate of return and protecting them from arbitrary action by government as well as protecting consumers from abuse by firms with substantial market power (Lovei and McKechnie, 2000; Jacobs, 2004; World Development Report, 2005). Additionally, better governance quality in a country helps to define its business environment and is associated with higher investment flows (Globerman and Shapiro, 2002), thereby creating favorable conditions for economic growth (Kaufmann and Kraay, 2002; Mauro, 1995; Kaufmann, Kraay and ZoidoLobaton, 1999; Wei, 1997). Taken together, we expect good governance to enhance the growth enhancing effects of infrastructure. More specifically, we hypothesize that good governance contributes to growth by supporting better provision of infrastructure services, and a priori, expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon its quality of governance.

- _Private Participation in Infrastructure._ The move towards private provision of infrastructure services across developing countries has been motivated by disenchantment with the inefficiency and poor performance of state-owned monopolies, the need for new investments and modernization to meet rapid growth in demand, and fiscal constraints, along with the desire to extend service access to the poor (Besant-Jones, 2006). In the East Asian context, rapid growth continues to place a strain on existing infrastructure stocks, and many governments in the region are looking to increase the role that Public Private Partnerships (PPPs) play in meeting critical infrastructure needs (ADB, JBIC, World Bank, 2005). Evidence suggests that the introduction of the private sector has led to improvements in efficiency and, very often, an expansion in, and quality of, services, with the poor benefiting from private participation compared to the situation prevailing under public provision; moreover, improvements in efficiency that occur can also lead to reductions in prices (Harris, 2003). Overall, PPI can expand access and quality of infrastructure services, with attendant increases in labor and service productivity (Ramamurti, 1996; Agénor and Moreno-Dodson, 2006; ILO, 2002), support efficiency

> 3 We follow Kaufmann, Kraay and Zoido-Lobaton’s (1999) definition of governance that includes (i) the process by which governments are selected, monitored and replaced; (ii) the capacity to effectively formulate and implement sound policies; and (iii) the respect of the citizens and the state for the institutions that govern economic and social interactions among them.

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improvements and deployment of new technology in utilities, introduce alternative mechanisms to finance infrastructure and free scare resources to channel towards growth and other development policy priorities – all of which may positively impact growth. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon prevalence of PPI.

- _Income levels_ . Countries with higher income levels have better rates of access to infrastructure services. Moreover, rich countries have higher levels of accumulated capital stock and savings rates (Cass, 1965; Koopmans, 1965), technology (Romer, 1986), and human capital (Lucas, 1988) to channel towards better provision of infrastructure services. Additionally, richer countries tend to use resources spent on infrastructure more effectively (Hulten, 1996) to deliver better infrastructure service quality (Calderon and Serven, 2004) that is closely related to higher competitiveness (Global Competitiveness Report, various years) and higher rates of economic growth. Based on this intuition, we investigate whether the contribution of infrastructure to growth is systematically greater in richer countries than in poorer ones. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon countries’ income levels.

- _Inequality of access to infrastructure._ A priori, one could argue that greater inequality of access to infrastructure services would lead to lower growth by systematically excluding groups of individuals who, with better access to infrastructure, could have made a greater contribution to growth. Indeed, several studies have argued that access to infrastructure services helps poorer individuals and underdeveloped areas to get connected to core economic activities, allowing them to access additional productive opportunities (Estache, 2003), enhancing their human capital and therefore income prospects (Leipziger, Fay and Yepes, 2003), as well as reducing production and transaction costs (Gannon and Liu, 1997). Together, these benefits of equitable access to infrastructure services can play a key role not only in helping reduce income inequality (Calderon and Serven, 2004; Estache, Foster and Wodon, 2002; Lopez, 2004), but also in fostering economic growth (Alesina and Rodrick, 1994; Alesina and Perotti, 1996; Galor and Zeira, 1993). We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon inequality in access to infrastructure services.

4
- _Geography:_ The goal here is to investigate whether infrastructure provision in Pacific Island countries (PICs) is predisposed to yield systematically different results than could be expected in non-PIC settings.

In addition, this paper serves as a benchmark against which to re-examine, at a future date, the relationship between infrastructure and growth in the context of the sweeping changes in East Asia’s political economy<sup>4</sup> . Among the principal elements influencing contemporary East Asia, and which no doubt will shape its future, are decentralization, rapid urbanization and regional integration (ADB, JBIC, World Bank, 2005). Despite their relevance to East Asia’s growth, we could not examine the differential impact of infrastructure depending on the extent of decentralization, urbanization and regional integration, since these trends are still nascent, with little variation over the 1985-2004 interval on which we focus, to make for meaningful econometric analysis. The present paper provides an estimate of the impact of infrastructure on growth implicitly<sup>5</sup> taking as a given present levels of decentralization, urbanization and regional integration. Such an estimate provides a benchmark against which to judge, through future analysis using a larger number of independent variables, the extent to which variations in levels of decentralization, urbanization and regional integration (for example) influence the magnitude of infrastructure’s impact on growth.

_Limitations of growth theory_ . Before proceeding any further, a restatement of some of the well documented limitations of the research into economic growth is called for<sup>6</sup> . The new growth literature, using both neoclassical (Solow, 1956; Swan, 1956) and endogenous growth (Romer, 1985; Lucas, 1988) models, has intensively studied physical and human capital, technological change, and national economic policies (such as those relating to macroeconomics and trade), as key factors influencing long run growth. Yet, there are few policies about which we can say with certainty that they deeply and positively affect growth (Harberger, 2003; Rodrik, 2004<sup>7</sup> ; Easterly,

> 4 See “Connecting East Asia: A New Framework for Infrastructure” for a discussion of the economic, spatial and demographic, environmental, political and funding dimensions of infrastructure service provision.

> 5 Lack of sufficient (variation in) data on decentralization, urbanization and regional integration prevented us from explicitly including these as control variables in the econometric specifications examined in this paper.

> 6 For an excellent review of the history of modern growth theory, see Barro and Sala-i-Martin (2004).

> 7 A related issue is that igniting economic growth and sustaining it are somewhat different enterprises. The former generally requires a limited range of (often unconventional) reforms that need not overly tax the institutional capacity of the economy. The latter challenge is in many ways harder, as it requires

5
2004). For instance, although extremely bad policy can probably destroy any chance of growth, it does not follow that good macroeconomic or trade policy alone can create the conditions for high steady-state growth. Second, research into the effects of economic policies on growth usually focus on the years following 1960, for which national accounts data started to become available for a large group of countries<sup>8</sup> . However, given that the correlation of per capita income in 1960 with per capita income in 1999 is 0.87 (Easterly, 2004), countries’ relative performance may largely be explained by the point they had already reached by 1960, suggesting that the role of post-1960 policies in determining development outcomes can only be limited. The role of policies in explaining post-1960 growth is further constrained once we realize that policy variables are much more stable over time than are growth rates (Easterly, Kremer and Pritchett, 1993). Third, research devoted to economic growth does not always take into account the role of externalities, which would otherwise explain why many countries appear to share a common long run growth rate despite persistently different rates of investment in physical and human capital (Klenow and Rodriguez-Clare, 2004). Finally, growth regressions require large samples to address issues such as the identification of empirically salient determinants of growth when the range of potential factors is large relative to the number of observations (Durlauf, 2004), and the requirements of panel data methods to detect growth patterns in a dataset comprised of heterogeneous countries (Islam, 1995; Durlauf, 2004). Similarly, some methods for addressing other problems, such as measurement error, are only useful in samples larger than those that are usually available to growth researchers. It follows that conclusions derived from empirical growth research based on small samples would rightly be regarded as questionable.

Despite the many difficulties that arise in empirical growth research, researchers have uncovered stylized facts – such as the massive divergence in living standards that has occurred over the last several centuries, the lack of sustained productivity growth in the poorer regions of the world, the unprecedented growth rates in some developing countries after the 1960s – and linked them to substantive economic arguments. This paper attempts to contribute in a small way to the understanding of the contribution of one set of factors, namely infrastructure stocks, to economic growth, in one region of the world, namely East Asia. It should be stated at the outset that the dataset constructed for the analysis is small given the relatively low number of countries and the

> constructing a sound institutional underpinning to maintain productive dynamism and endow the economy with resilience to shocks over the longer term. Ignoring the distinction between these two tasks leaves reformers saddled with impossibly ambitious, undifferentiated, and impractical policy agendas (Rodrik, 2004).

> 8 Another reason for this starting point is that many colonies did not gain independence until the 1960s (Durlauf, 2004; Easterly, 2004).

6
relatively short time interval of 1985-2004. This could dilute the veracity of the results presented in this paper. In order to address this issue, we conducted a robustness check of the estimation results using the bootstrapping technique (Cameron & Trivedi, 2005), and found evidence confirming our results.

The paper is organized as follows. Section 2 lays out the theoretical framework underlying the analysis of the impact of infrastructure on growth. Section 3 discusses the data underlying this analysis; it includes a description of dependent and independent variables as well as a discussion of data limitations. Section 4 presents the results of the econometric exercise stemming from the theoretical framework. Section 5 presents concluding remarks and draws on Estache et al (2005) to provide a brief comparison of the extent to, and manner in which infrastructure has contributed to growth across the East Asia and Sub-Saharan Africa regions.

# **2. A Theoretical Framework: An Augmented Solow Model with Infrastructure**<sup>**9**</sup>

As mentioned above, we employ an augmented Solow growth model, developed in Estache et al (2005), as the theoretical framework to organize our analysis of the relative importance of different infrastructure sub-sectors to growth in the East Asian context. The model builds on that proposed by Solow (1956) and further developed by Mankiw, Romer and Weil (1992), and includes the accumulation of human as well as physical capital to explain the international variation in growth rates across East Asian countries. In addition, the model introduces an infrastructure index (Inf Inx hereafter) in the production function. As in the Estache et al (2005) paper, the Solow growth model used here relies on a Cobb-Douglas production function with labor augmenting technological progress, human capital and the infrastructure index.

where _Y_ is the income, _I_ is the Inf Inx, _K_ is the stock of physical capital, _H_ is the stock of human capital, _A_ is technology and _L_ is labour. _α_ and _β_ are the physical and human capital shares of income respectively. The parameter constraint 0 < α + β < 1 ensures decreasing returns to capital. We define _y_ = _Y_ / _AL_ , _k_ = _K_ / _AL_ and _h_ = _H_ / _AL_ to be the income-, and stocks of

> 9 For more details about the development of the Augmented Solow Model with Infrastructure, see Estache et al (2005).

7
human and physical capital, per effective unit of labour. Further,<sup>_s_</sup> _k_ and<sup>_s_</sup> _h_ are the fractions invested in human and physical capital. Labour and technological progress are assumed to grow exogenously at rates _n_ and _g_ : _L_ ( _t_ ) = _L_ (0) _ent_ and _A_ ( _t_ ) = _A_ (0) _egt_ . Therefore the number of effective units of labour, _L_ ( _t_ ) _A_ ( _t_ ) grows at rate _g+n_ . Capital depreciates at rate _δ_ . The growth rate of technology and depreciation rates are assumed to be constant and both sum 0.05, i.e. 10 _g+δ=0.05_ . _I,_ the Inf Inx, represents the influence of an economy’s infrastructure. Inf Inx is measured by infrastructure indicators, denoted by _ω_ , such as kilometres of paved roads, number of phone lines, percentage of population with access to improved water and sanitation facilities, etc. The index is _I_ = πγ where γ reflects the elasticity of GDP with respect to infrastructure. To a large extent, the exercise conducted in this paper consists in demonstrating that γ is statistically significant as evidence that infrastructure matters to growth. In per capita terms, The evolution of _k_ and _h_ is governed by Equating the right hand side to zero to arrive at steady states, substituting _y_ and solving we get Introducing these steady states in _y_ ( _t_ ) = _Ik_ ( _t_ )<sup>α</sup> _h_ ( _t_ )<sup>β</sup> and taking logs we get

> 10 This assumption is consistent with that adopted in Mankiw, Romer and Weil (1992, pp. 413, Footnote 6) and Islam (1995, pp. 1139) for all countries analyzed (although it is important to point out that this value is only valid for countries comparable to the United States) and is similar to the value 0.03-0.04 used in Romer (1990). Furthermore, the assumption _g+δ=0.05_ has been used in Estache et al (2005) for the SubSaharan Africa dataset. Given that this assumption has been made in the context of diverse datasets, we feel comfortable doing the same for the East Asian sample.

8
This equation shows how income per capita depends on labour growth, accumulation of physical and human capital, technology and Inf Inx. Following Mankiw, Romer and Weil (1992), this equation may be rewritten in terms of the steady state of human capital This model nests the augmented Solow model developed by Mankiw, Romer and Weil (1992), in which Inf Inx equals one. In their augmented Solow model, only effective labour, and physical and human capital affect income: The steady states of this model is In our estimations we will consider this augmented version as the benchmark model.

There are several possible econometric techniques to estimate the benchmark model, namely panel data, pooled regression and cross-section regression (over long time spans). With respect to panel data, we can choose the appropriate estimator from random and fixed effect methodologies. Panel estimators relying on the random effects assumption do not seem to be appropriate since it is difficult to argue that individual (country) effects are uncorrelated with the exogenous variables (Greene, 2003). Another option is to consider the Least Squares Dummy Variable (LSDV) estimator, which is based on the fixed effects assumption. Country (fixed) effects would then be included in the regression and treated as parameters to be estimated together with those of the exogenous variables. However, while the LSDV estimator has the advantage to control for unobserved heterogeneity, it is not a suitable estimator for our model for two main reasons. First, a simple ANOVA analysis showed how within-country variation is negligible. There are more differences between countries than within countries, with between country differences representing 98% of the source of total variance. A fixed effects estimator would thus be of little 9
use in studying differences between countries because it uses only the time variation within each cross-section. And the severe unbalanced nature of our panel due to missing data reduces the number of time periods to very few, as a result of which the within cross-section variation becomes unimportant. Second, fixed effects estimates would exacerbate measurement errors problems (Wooldridge, 2002).

We therefore rely on the pooled regression technique. Pooled regression results are very similar to single cross-section results (Islam 1995, pp. 1141). Since our study focuses on East Asian economies, for which little data is available, a further advantage of a pooled regression over a single cross-section is the increased sample size it affords. We therefore specify the following econometric model for our benchmark model: ln _GDPit_ = _b_ 0 + δ _t_ + _b_ 1 ⎡⎣ ln _Total Investment_ − ln ( _Population growth_ − 0.05 )⎤⎦ _it_ + _b_ 2 ln _Educationit_ + ε _it_ ...(4) and the following econometric model for our Infrastructure model:

> where _i_ = 1,..., _N_ , _t_ = 1,..., _T_ , _N_ is the number of countries, _T_ the number of periods and<sup>δ</sup> _t_<sup>is a</sup> set of time dummies.

It is important to clarify that the parameters that are estimated are those of the econometric model needed to generate the implicit parameters of the economic model – in particular γ (the elasticity of GDP with respect to infrastructure), and _α_ and _β_ (the physical and human capital shares of income respectively). The total investment parameter, the education parameter and the − 1 infrastructure parameter can be computed from the estimated model using α = _b_ 2 (1 + _b_ 2 ) , β = _b_ 3<sup>(1</sup> −α ) and γ = _b_ 1<sup>(1</sup> −α ) respectively. This means that the statistical significance of the various parameters of the econometric model will tell us whether infrastructure matters or not. If _b1_ is not statistically significant, infrastructure does not matter. If, by contrast,<sup>_b_</sup> 2 is not statistically significant, α = 0 and hence the implied parameters for education and infrastructure can be read directly from the estimated econometric model, i.e. β =<sup>_b_</sup> 3 and γ =<sup>_b_</sup> 1 (Estache et al, 2007).

10
# **3. Data and Estimation**

_Data_ . In order to conduct the econometric analysis discussed, we constructed a database of all East Asian countries drawing on multiple data sources including, World Development Indicators (WDI), PPI database and the database of Governance Indicators maintained by the World Bank. The database allowed us to cover 16 countries in East Asia: Australia, Cambodia, China, Fiji, Indonesia, South Korea, Laos, Malaysia, Mongolia, Papua New Guinea, Philippines, Singapore, Thailand, Tonga, Vanuatu, and Vietnam. Owing to limitations of data availability, we had to drop Kiribati, Marshall Islands, Micronesia, Myanmar, Palau, Solomon Islands, and Timor-Leste from the analysis.

_Variables_ . The time horizon considered in the paper covers 20 years from 1985 to 2004<sup>11</sup> . The _dependent variable_ is growth in the East Asian countries studied, as measured by the evolution of GDP per capita in constant 2000 prices. We consider a set of infrastructure stocks as _explanatory variables_ . Specifically, our analysis takes into account the following measures of infrastructure in 5 key sectors:

- (i) Telecommunications – telephone mainlines per 1000 people; mobile subscribers per 1000 people;

- (ii) Electricity – electric power consumption per capita (kwh);

- (iii) Roads – kilometres of paved roads per capita; percentage of road network paved;

- (iv) Water – percentage of population with access to an improved water source;

- (v) Sanitation – percentage of population with access to improved sanitation facilities.

As discussed in the econometric model developed above, we _control_ for the level of investment and of human capital, proxied by Gross Fixed Capital Formation (% of GDP) and secondary school enrolment (% gross)<sup>12</sup> , respectively. Natural logarithms are taken for both infrastructure and control variables. Additionally, we consider the five variables mentioned above (i.e. PPI; governance; inequality in infrastructure access; country income levels; and Pacific Island<sup>13</sup> vs.

> 11 Although data were available for the year 2005 for a few variables, our time series spanned the years 1985-2004 in the interest of consistency across all variables.

> 12 Gross (secondary) enrollment ratio is the ratio of total (secondary) enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown.

> 13 The 9 Pacific Island Countries (PICs) include Fiji, Federated States of Micronesia (FSM), Kiribati, Marshall Islands, Palau, Samoa, Solomon Islands, Tonga, and Vanuatu.

11
non island countries). Each variable is incorporated into the econometric model as a dummy variable. Details of variable definitions, sources and dummy variable construction are provided in Appendix 1.

By way of illustration, Figure 1 shows the scatter plots between per capita growth and the various infrastructure sub-sectors as well as education from the estimation of a linear trend in East Asia over the period 1985-2004.

**Figure 1. Scatter Plots: The Impact of Education and Infrastructure on Growth**

<!-- Start of picture text --> ln(GDP)=2.14+1.25ln(Education) ln(GDP)=0.51+1.01ln(Electricity)<br>R^2=0.33 R^2=0.94<br>12 12<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>0 1 2 3 4 5 6 0 2 4 6 8 10<br>ln(EDUCATION) ln(ELECTRICITY)<br>ln(GDP)=12.38+0.75ln(Roads) ln(GDP)=-0.49+1.88ln(Sanitation)<br>R^2=0.38 R^2=0.56<br>10 10<br>8 8<br>6 6<br>4 4<br>2 2<br>0 0<br>-10 -8 -6 -4 -2 0 0 1 2 3 4 5<br>ln(ROADS) ln(SANITATION)<br>ln(GDP)=4.48+0.70ln(Telecoms) ln(GDP)=-3.27+2.44ln(Water)<br>R^2=0.75 R^2=0.36<br>10<br>10<br>8<br>8<br>6 6<br>4 4<br>2<br>2<br>0<br>0<br>-1 0 1 2 3 4 5 6 7 0 1 2 3 4 5<br>ln(TELECOMS) ln(WATER)<br>ln(GDP) ln(GDP)<br>ln(GDP) ln(GDP)<br>ln(GDP) ln(GDP)<br><!-- End of picture text --> The simplistic univariate linear regression estimation above allows for visualizing the possible links between growth and infrastructure, and suggests that the impact of any infrastructure subsector on GDP per capita is clearly positive (reported in row 1 of Table 2).

_Missing data_ . Several variables in our dataset suffered from moderate to sever missing data problems. Compounding the missing data problem is the fact that East Asia as a region comprises a smaller number of countries than other regions resulting in a rather modest number of potential observations per variable to begin with. Table 1 summarizes the size of the database for all of the variables used in the paper. It shows the number of annual observations and the proportion of 12
missing values. Two facts emerge from the table. First, all the variables have missing values. Second, and most important, for five out of the eight variables more than half of the data cells are empty.

**Table 1: Observations and missing values**

||**5year**<br> **Number of obs**|**s interval**|**Ann**<br>**Number of obs**|**ual**|
|---|---|---|---|---|
||<br>**available**|**% missing obs**|<br>**available**|**% missing obs**|
|GDP per capita|63|2%|310|3%|
|Education|44|31%|113|65%|
|Investment|62|3%|278|13%|
|Telecoms|64|0%|313|2%|
|Mobile Phones|57|11%|302|6%|
|Electricity|36|44%|171|47%|
|Roads|46|28%|205|36%|
|Water|28|56%|28|91%|
|Sanitation|25|61%|26|92%|

In order to address this problem, at least partially, we aggregated the observations over intervals of five years. The number of observations and the proportion of missing values for each sector are provided in Table 1. The manner in which such an aggregation is made (i.e., average, sum of observations, or usage of the last available observation) depends on the nature of the variable. For the variables measured as a stock (investments, education and all infrastructure variables except electricity) the last observation over the 5-year period was taken. For the variables measured as a flow (per capita GDP and electricity), averages over the 5-year periods were taken. In all, we considered four intervals of time: 1985− 1989, 1990−1994, 1995− 1999, 2000 − 2004.

Table 1 shows that if data is aggregated to capture 5 years intervals, only two variables have more than half observations missing. While the missing data problem is not entirely circumvented, data aggregation is a definite improvement over the use of annual data. The use of data aggregation over five years is standard practice in the literature, e.g. Islam (1995), in particular when there is a concern that annual observations are too noisy. Additionally, the five-year time-span can mitigate the impact of business cycles on observations in the dataset.

_Multicollinearity_ . Table 2 provides a correlation matrix between dependent and explanatory variables used in the analysis. It is also useful to get a sense of the simple correlation between the various subsectors since it would provide an early hint of any possible multicollinearity problems for the econometric work presented later in the paper. A few observations are called for on the basis of the analysis in Table 2. First, the first row of Table 2 suggests that there is a high and 13
statistically significant correlation between growth and infrastructure. Second, education is highly correlated with all the infrastructure subsectors except roads<sup>14</sup> . The final observation is that the various infrastructure subsectors are highly correlated among themselves.

Overall, these results suggest that there is a strong likelihood of multicollinearity, which if confirmed, needs to be addressed as part of our econometric work. In particular, multicollinearity has the effect of significantly increasing standard errors resulting in low t-statistics. In other words, including all independent variables in the model at the same time could result in many of them not being significant, even though their respective relationships with the dependent variable may be significant when considered individually. To avoid this problem, we regress the infrastructure variables one by one in model (5)<sup>15</sup> . We may then compare model (5)’s goodness of fit with that of the benchmark model (4).

**Table 2: Sample pair-wise correlations between dependent and explanatory variables (p-values in parenthesis)**

||**GDPpc **|**Education**|**Telecom**|**Electricity**|**Roads**|**Water**|**Sanitation**|
|---|---|---|---|---|---|---|---|
|**GDPpc**|1|0.63|0.93|0.98|0.58|0.51|0.60|
|||(.0000)|(.0000)|(.0000)|(.0000)|(.0056)|(.0015)|
|**Education**||1|0.68|0.80|0.23|0.61|0.67|
||||(.0000)|(.0000)|(.1331)|(.0006)|(.0002)|
|**Telecoms**|||1|0.93|0.46|0.56|0.67|
|||||(.0000)|(.0013)|(.0019)|(.0002)|
|**Electricity**||||1|0.59|0.64|0.73|
||||||(.0002)|(.0002)|(.0000)|
|**Roads**|||||1|0.36|0.45|
|||||||(.0599)|(.0240)|
|**Water**||||||1|0.81<br>(.0000)|
|**Sanitation**|||||||1|

We formally assess the presence of multicollinearity by examining tolerance and its inverse, variance inflation factors (VIFs), which give a sense of the magnitude by which the standard error of an independent variable is “inflated” when it is predicted by other independent variables in the

> 14 One would expect a superior endowment of roads to be associated with ease of access to education by facilitating an individual’s travel to school. The statistically insignificant pairwise correlation coefficient between education and roads (correlation coefficient of 0.23, with a _p_ -value of 0.1331) is therefore somewhat counter-intuitive. A possible reason for this could lie in the definition of our roads infrastructure variable as kilometers per capita of paved roads. In other words, roads that are not paved are not accounted for in the analysis, which in turn could lower the correlation between education and roads infrastructure. 15 An alternative is to rely on a synthetic index of the various infrastructure services as done by Calderon and Serven (2004). This is a useful solution when the focus is on the aggregate level of infrastructure. Here the emphasis in on the relative relevance of the various infrastructure sub-sectors and hence we discarded the synthetic index approach.

14
model<sup>16</sup> . As a rule of thumb, a tolerance value of 0.10, which corresponds to a VIF value above 10 suggests that multicollinearity among independent variables is a serious problem (Hair, Anderson, Tatham, and Black, 1998, pp. 193). Table 3 below shows that every infrastructure variable, with the exception of per capita availability of paved roads, is associated with VIFs that far exceed the widely accepted cutoff of 10, confirming that the infrastructure (i.e., independent variables in our analysis) are multicollinear. As mentioned above, we address this problem by regressing the infrastructure variables one at a time rather than incorporating them together in our empirical model.

**Table 3: Tolerance and Variance Inflation Factor for infrastructure variables**

||**Telecoms**|**Electricity**|**Roads**|**Water**|**Sanitation**|
|---|---|---|---|---|---|
|**Tolerance**|.01|.01|.14|.02|.03|
|**Variance Inflation Factor**|185.19|200.00|7.02|41.15|36.10|

_Endogeneity_ . Another concern to deal with is the possible presence of endogeneity. In our paper, endogeneity would be present when both our variables of interest, GDP per capita and its infrastructure variables are flows. However, endogeneity does not arise when the explanatory variables are stocks, as it mitigates the possibility of reverse causality. For instance, the accumulated stock of roads today dates back a few decades. While it may be reasonable to say that roads constructed 20 years ago are still causing growth, it would be difficult to argue the reverse, i.e., roads constructed 20 years ago could not have been “caused” by today’s growth. All the infrastructure variables in our paper are measured as stocks, except electricity. We conducted a Hausman test for endogeneity, which did not reject the null hypothesis of exogeneity of electricity<sup>17</sup> .

> 16 The tolerance and its inverse, the variance inflation factor (VIF), are among the most common measures for assessing multicollinearity. Tolerance is the amount of variability of the selected independent variable not explained by other independent variables. Small tolerance values, and therefore large VIFs, denote high multicollinearity (Hair, Anderson, Tatham, and Black, 1998).

17 In order to test for endogeneity of electricity, we conduct a Hausman test (Wooldridge, 2002) that involves the two stage Least Squares procedure. In the first stage, we introduce total investment and education, at both current and lagged values, as additional regressors on electricity. The first stage F statistic is equal to 5.87. In the second stage, we introduce the residuals of the first stage as an additional variable to the Solow regression for electricity. The regression coefficient for the first stage residuals is -0.18 with t-statistic equal to -1.03. Therefore, the test does not reject the exogeneity of electricity. Additionally, we conducted a Hausman test for endogeneity of country income levels (for results displayed in Table 7) in the infrastructure model for electricity. The regression coefficient for the first stage residuals is 0.01, with a t-statistic equal to 0.03. We therefore conclude that the null hypothesis of exogeneity of electricity when dummies on income are added to the infrastructure model is not rejected.

15
_Limitations_ . A note on the limitations of the analysis is called for. First, while missing data issues have been addressed in the manner described above, the overall dataset available for the analysis is small, which could affect the robustness of our results. In order to assess the robustness of our estimation results, we computed standard errors across all specification reported using the bootstrapping technique with replacement (Cameron & Trivedi, 2005) and 100 replications, and found evidence confirming the results. Second, as pointed out above, in order to address the issue of endogeneity, we defined our dependent variable as per capita GDP and regressed this dependent variable on the stocks of infrastructure at different points in time, rather than regressing change in GDP per capita to a change in infrastructure stocks. Had we been able to use dependent and independent variables defined in terms of “changes”/“increases”, we could have conducted a more sophisticated analysis of how growth responds to increments in infrastructure, including an investigation of the lags with which growth responds to expansions of infrastructure stocks.<sup>18</sup> Third, in addition to being directly consumed by households and firms, infrastructure services have efficiency-enhancing externalities – higher quality electricity supply makes possible the use of more sophisticated machines; better transport infrastructure, by lowering transport costs, promotes access to markets, economies of scale, and different patterns of agglomeration. Interesting and important as the issue may be, our analysis does not separate the direct and efficiency-enhancing effects of infrastructure. Finally, while our analysis attempts to tell a preliminary story about the comparative influence of infrastructure sectors on per capita growth across East Asian countries (and in different contexts regarding governance, PPI, inequality in access to infrastructure, income levels and geographic characteristics), it does not address the specific priorities of individual countries. Country-specific assessments would be required to formulate country-specific policy priorities.

18 The usage of these metrics of growth would have required us to address endogeneity by employing instrumental variables (IVs), i.e., variables that are correlated with the endogenous independent variable (infrastructure, in our analysis), but uncorrelated with the dependent variable (growth) and the error term. However, this approach is not without its limitations. For instance, the IV-estimator is highly biased if a slight correlation exists between the IV and the error term (Bartels, 1991) and, since the error term is unobservable, there is no effective test to reveal such correlation. In addition, large finite sample biases can occur when instrumental variables are weak (i.e., weakly correlated with the included endogenous variables). In particular, when the number of time series observations is small as is the case in our sample, lagged levels of the variables are only weak instruments for subsequent first-differences (Nelson and Startz, 1990a, 1990b; Staiger and Stock, 1997). Overall, it is advisable to select reliable instrumental variables based on subjective judgment about cause-effect relationships in the domain. On balance we favored using infrastructure stocks as explanatory variables to address the endogeneity problem over the use of IVs.

16
# **4. Results**

In this section we provide the results of our econometric analysis. We begin by discussing the augmented Solow model with education and total investment, which serves as a benchmark. Against this benchmark model, we include each infrastructure subsector, one at a time, to assess the additional variance in growth that is now captured by each of the infrastructure variables, respectively. We then estimate how variations in each one of the five factors mentioned above (governance, PPI, inequality in access to infrastructure, income levels and geographic characteristics) influence the relationship between infrastructure and growth.

Estimation results of models (4) and (5) are shown in Tables 4-9, in which the rows report on the explanatory variables: education, total investment and infrastructure; subsectors are specified in the columns of these tables. Table 9 summarizes the results.

# _How relevant is infrastructure to growth?_

Table 4 compares estimation results of the augmented Solow model accounting for human capital exclusively (our benchmark model) with the augmented model including infrastructure variables. As explained in Section 3, our dataset suffers from missing observations; additionally, each infrastructure variable has different missing observations. In order to address this problem, we estimated the benchmark and infrastructure models using five sets of observations corresponding to the five infrastructure subsectors. This allowed us to compute the fraction of the total variance explained by each infrastructure variable, separately. It also allowed us to show the size of the bias of the estimates for the models that did not include the infrastructure variables.

All the infrastructure variables significantly affect GDP per capita, after controlling for education and total investment<sup>19</sup> . The adjusted _R_<sup>_2_</sup> also tells us that growth can be better explained when infrastructure is included in the model; indeed, the adjusted _R_<sup>_2_</sup> increases in all cases. The magnitude of increase is greatest for telecoms and electricity, with these two infrastructure subsectors explaining 54% and 53%, respectively, of the total variance after accounting for the contribution of education and total investment to growth. Among the other infrastructure

> 19 As a robustness check of the estimation results we computed the standard errors using the bootstrapping technique with replacement and 100 replications. Results, available upon request, do not change qualitatively and confirm the significance of the estimated parameters.

17
subsectors, sanitation explained 31%, water 17% and roads 14%, of the variance in growth over and above the benchmark model.

**Table 4: Augmented Solow model with infrastructure variables**

||Tele<br>|coms<br>|Elect<br>|ricity<br>|Ro<br>|ads<br>|San<br>|itation<br>|Wat<br>|er<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Ben<br>ch|st<br>Infra|Bench|st<br>Infra|Benc<br>h|rast<br>Inf|Ben<br>ch|t<br>Infras|Be<br>nch|st<br>Infra|
|Education|1.24|−.31|2.49|−.04|.73|.38|1.23|.28|1.02|.31|
|t-stat|3.65|−1.35|4.14|−.16|1.40|.92|2.73|.63|2.21|.62|
|)<br>Elast(β|1.24|.00|2.49|.00|.73|.00|1.23|.00|1.02|.00|
|Total Invst|.03|−<br>.83|.36|−<br>.35|.72|.43|−.39|−<br>.22|−.11|−<br>.67|
|t-stat|.08|−<br>3.64|.61|−<br>1.72|1<br>.40|.89|−.65|−.46|−.16|−<br>1.08|
|)<br>Elast(α|.00|−4.88|.00|.00|.00|.00|.00|.00|.00|.00|
|Infrast||.94||1.04||.55||1<br>.72||2.53|
|t-stat||10<br>.39||1<br>2.72||2<br>.53||3.64||2.57|
|γ)<br>Elast (||5.50||1.04||.55||1.72||2.53|
|R<sup>2</sup>|.29|.83|.41|.94|.24|.38|.20|.51|.14|.31|
|. Obs<br>No|42|42|23|23|28|28|23|23|25|25|

_Note_ : This Table shows the human capital augmented Solow models (denoted by Benchmark) and the infrastructure and human capital augmented Solow models (denoted by Infrast). Constants are not reported. In order to compare the Benchmark and the Infrast and avoid the effect of missing observations, Bechmark is estimated for the 5 different set of observations (each infrastructure variable has different missing observations). Total Invst stands for Total Investment minus population growth minus depreciation. Infrast stands for the infrastructure variable. Elast stands for the implied estimated elasticities of the economic model. Parameters are estimated by pooled OLS and standard errors estimators are heteroskedastic-consistent. _R_<sup>_2_</sup> stands for the adjusted goodness of fit.

Education has a positive and significant impact on growth across all subsectors in the benchmark model. However, education loses significance across all models once we include the infrastructure variables, implying that the omission of these infrastructure variables biases upward the impact of education on growth. Also noteworthy is the fact that total investment seems statistically unrelated to growth in the electricity, roads, sanitation and water subsectors (for both the benchmark and infrastructure models). It gains significance only when telecom is incorporated in the regression model, although somewhat counter-intuitively, it is then negatively associated with growth. In other words, Table 4 suggests that when considered alongside telecom infrastructure (which has a significant and positive impact on growth), non-telecom investments are negatively associated with growth. A possible reason for this finding could be that the configuration of telecom infrastructure and non-telecom investments is sub-optimal. To the extent that key infrastructure services, such as transport, energy and telecom, enter as complementary inputs in production and/or consumption processes, a bottleneck and negative elasticities may arise if one or more of these complementary inputs is (are) under-provided.

An important policy implication that emerges from Table 4 is that infrastructure subsectors across the board have a positive and significant impact on growth in East Asia, and are shown to be statistically significant engines of growth. Notably, education loses relevance to growth once 18
infrastructure is taken into account. The mechanics underlying the relationship between education and infrastructure would clearly be of interest to policy makers, and as such call for further research.

While Table 4 shows that infrastructure matters to East Asia’s growth, it does not specify whether the relationship is affected by the countries’ institutional or physical endowments. This subsection and those that follow incorporate the five “variables of interest” discussed above in the infrastructure model. Doing so allows us to comment on how the relevance of infrastructure to growth is affected by explicitly accounting for each of these variables.

# _Quality of governance_

Good governance tends to be associated with better provision of infrastructure services. It acts by reducing corruption and incentives of political actors to capture monopoly rents, increasing transparency of public and private transactions and regulatory structures while guaranteeing investors an adequate rate of return and protecting them from arbitrary action by government, improving a country’s business environment, as well as protecting consumers from abuse by firms with substantial market power (Lovei and McKechnie, 2000; Jacobs, 2004; World Development Report, 2005; Globerman and Shapiro, 2002), thereby creating favorable conditions for economic growth (Kaufmann and Kraay, 2002; Mauro, 1995; Kaufmann, Kraay and ZoidoLobaton, 1999. Poor governance and corruption on the other hand, whether through grand corruption in the allocation of lucrative monopolies, or petty corruption in meter reading and billing contributes to weak operational and financial performance and, for the poor in particular, declining service quality or reduced chances of ever accessing network services (Lovei and McKechnie, 2000). We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon its quality of governance.

In order to test for the relevance of this dimension, we include a dummy variable in the model to account for differences between high, medium and low quality of governance. Table 5 displays the estimation results of the augmented Solow model. In three of the five infrastructure subsectors (telecom, electricity and sanitation), investments in infrastructure have a significantly higher payoff in countries with high quality of governance than in countries with medium or low quality of governance. The results are particularly striking for sanitation with the elasticity of GDP with respect to sanitation climbing from 0.90 in low and medium governance settings to 1.38 in high 19
governance settings. In telecom and electricity, elasticity figures rise from 0.60 and 0.63 in low and medium governance settings to 0.83 and 0.75 in high governance settings respectively.

**Table 5: Augmented Solow model with infrastructure variables and the effect of the quality of governance: low, medium or high quality governance**

||Tel<br>e<br>|coms<br>|Elec<br>|tricity<br>|Ro<br>|ads<br>|Sani<br>|ation<br>t<br>|W<br>|ater<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Bench|Infrast|Bench|st<br>Infra|Bench|st<br>Infra|Bench|st<br>Infra|Bench|st<br>Infra|
|Education|1.24|−0.18|2.49|0.26|0.73|0.15|1.23|0.09|1.02|−0. 02|
|t-stat|3.65|−1.03|4.14|0.85|1.40|0.55|2.73|0.33|2.21|−0. 05|
|Elast(β)|1.24|0.00|2.49|0.00|0.73|0.00|1.23|0.00|1.02|0.00|
|st<br>Total Inv|0.03|−0.54|0.36|−<br>0.15|0.72|0.01|−<br>0.39|0.05|−<br>0. 11|−0. 15|
|t-stat|0.08|−<br>3.06|0.61|−<br>0.78|1.40|0.01|−<br>0.65|0.16|−<br>0. 16|−<br>0.38|
|Elast(α)|0.00|−1.17|0.00|0.00|0.00|0.45|0.00|0.00|0.00|0.00|
|Low|||||||||||
|Infrast||0.60||0.63||0.35||0.90||1.04|
|t-stat||6.48||3.53||2.33||2.80||1.65|
|)<br>Elast (γ||1.30||0.63||0.35||0.90||.00|
|Medium|||||||||||
|Infrast||0.60||0.63||0.35||0.90||1.04|
|t-stat||6.48||3.53||2.33||2.80||1.65|
|)<br>Elast (γ||1.30||0.63||0.35||0.90||0.00|
|High|||||||||||
|Infrast||0.83||0.75||0<br>.03||1.38||0.55|
|t-stat||5.28||2.58||−<br>4.77||6.13||6.54|
|)<br>Elast (γ||1.80||0.75||0.03||1.38||0.55|
|R <sup>2</sup>|0.29|0.90|0.41|0.95|0.24|0.72|0.20|0.84|0.14|0.77|
|No. Obs|42|42|23|23|28|28|23|23|25|25|
|e<br>Differenc||Y<br>ES||Y<br>ES||Y<br>ES||Y<br>ES||YES|

Somewhat counter-intuitively, the elasticity of GDP with respect to roads decreases from 0.35 in countries with low and medium governance to 0.03 in countries with high governance quality. This interpretation must however be treated with caution since there is a risk of encountering an omitted variable bias in comparing the results across countries with different levels of governance. For instance, there could be a variable correlated with high quality of governance such as better air transportation, which could potentially decrease the sensitivity of GDP to road infrastructure. Note that in this table and in those that follow, when the coefficients for the dummies are not significant (i.e. when there are no differences with respect to the coefficients associated with different levels of the variable of interest, such as “low” and “medium” quality of governance), we leave identical coefficients and t-values.

20
_Private participation in infrastructure_ PPI can expand access and quality of infrastructure services, with attendant increases in labor and service productivity (Ramamurti, 1996; Agénor and Moreno-Dodson, 2006; ILO, 2002), support efficiency improvements and deployment of new technology in utilities, introduce alternative mechanisms to finance infrastructure and free scare resources to channel towards growth and other development policy priorities (Besant-Jones, 2006; ADB, JBIC, World Bank, 2005; Harris, 2003) – all of which may positively impact growth. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon prevalence of PPI. In this paper, the dummy variable “private” summarizes the information on the timing of private participation in specific infrastructure subsectors in a country. The variable takes the value 0 before the first year of private entry and the value 1 from that year on. Overall, private investors have invested approximately US$ 224 billion since 1990 (World Bank PPI Database) in East Asia’s infrastructure subsectors across 18 countries<sup>20</sup> .

**Table 6: Augmented Solow model with infrastructure variables and the effect of privatization**

||Telec|oms|Elect|ricity|Ro|ads|Sani|tation|W|ater|
|---|---|---|---|---|---|---|---|---|---|---|
||Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|
|Education|1.24|.53|2.49|.26|.73|.55|1.23|.33|1.02|.37|
|t-stat|3.65|1.68|4.14|1.17|1.40|1.33|2.73|.83|2.21|.79|
|Elast(β)|1.24|.00|2.49|.00|.73|.00|1.23|.00|1.02|.00|
|Total Invst|.03|.62|.36|−.07|.72|1.08|−. 39|−.20|−. 11|−.70|
|t-stat|.08|2.99|.61|−.53|1.40|2.85|−. 65|−.75|−. 16|−1.27|
|Elast(α)|.00|.38|.00|.00|.00|.52|.00|.00|.00|.00|
|No private|||||||||||
|Infrast||.29||.99||.13||1.59||2.16|
|t-stat||1.89||12.29||.92||3.96||2.62|
|Elast (γ)||.18||.99||.00||1.59||2.16|
|Private|||||||||||
|Infrast||.48||.99||.13||1.75||2.16|
|t-stat||2.17||12.29||.92||1.86||2.62|
|Elast (γ)||.30||.99||.00||1.75||2.16|
|R <sup>2</sup>|.29|.57|.41|.93|.24|.33|.20|.58|.14|.33|
|No. Obs|42|42|23|23|28|28|23|23|25|25|
|Difference||YES||NO||NO||YES*||NO|

_Note_ : “*” means result significant only at 10% probability level.

Table 6 shows that PPI, when measured as a dummy variable, did not influence East Asia’s growth, except for telecom and sanitation infrastructure. Specifically, the elasticity of GDP with

> 20 Electricity, roads, water supply and sanitation, and telecom accounted, respectively, for 34%, 18%, 16% and 7% of the number of PPI projects in the region. However, the distribution of projects has been skewed across East Asian countries, with over 77% of water and sanitation, 68% of roads, and 42% of electricity projects located in China alone.

21
respect to telecom investments increased from 0.29 in countries without private participation in telecom to 0.48 in countries where private participation was allowed in the telecom sector. Similar results were obtained for sanitation, albeit at a lower level of significance. However, there seems to be no impact of PPI in the other three sectors, despite a relatively large proportion of PPI projects devoted to these subsectors.

# _Income level_

Countries with higher income levels have better rates of access to infrastructure services. Moreover, rich countries have higher levels of accumulated capital stock and savings rates (Cass, 1965; Koopmans, 1965), technology (Romer, 1986), and human capital (Lucas, 1988) to channel towards better provision of infrastructure services. Additionally, richer countries tend use resources spent on infrastructure more effectively (Hulten, 1996) to deliver better infrastructure service quality (Calderon and Serven, 2004) that is closely related to higher competitiveness (Global Competitiveness Report, various years) and higher rates of economic growth. Based on this intuition, we investigate whether the contribution of infrastructure to growth is systematically greater in richer countries than in poorer ones. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon countries’ income levels.

Table 7 shows that an increase in countries’ income levels (i.e., from low to medium to high levels) is associated with a greater positive and significant impact of telecom, sanitation and water infrastructure on growth. Specifically, the elasticity of per capita GDP with respect to telecom increases from 0.37 for low-income countries to 0.54 in medium incomes countries and 0.91 in high-income countries. Similar results are obtained for sanitation. There was no significant growth effect associated with water infrastructure in low-income countries. However, the growth effect of water infrastructure differed between medium and high income countries, with an elasticity of growth with respect to water of 0.94 in high income countries, more than four times time higher than the elasticity in medium income countries (0.23). The policy implication emerging from these results is that growth-related results arising from expansion of stocks of telecom and sanitation infrastructure may be expected to increase as the country’s income level rises. In the water sector however, it appears that a certain threshold of income needs to be crossed (i.e., the transition from low to medium income) before the benefits of increasing water sector infrastructure begin to increase as the country’s income level rises further.

22
However, the elasticity of GDP with respect to roads is higher in poor countries (0.29) than in medium income countries (0.15), which in turn is higher than in high-income level countries (-2.6). One possible reason could lie in the definition of the variable as kilometers of paved roads per capita. This means that roads that are not paved (see the Appendix for the precise meaning of “paved”) are not taken into account. Poor countries have the lowest volume of paved roads per capita. In these countries with a limited supply of paved roads, the marginal contribution of a km of paved road to growth can be expected to be higher than in countries with a denser network of paved roads. Additionally, as discussed in the interpretation of the Governance model (Table 5), this result could perhaps be explained by an omitted variable bias: the widespread use of alternative modes of transportation such as air transport as countries’ income level rises is not considered in our analysis and such alternative modes could potentially decrease the sensitivity of GDP to roads infrastructure in richer countries. Finally, while electricity is associated with a positive and significant influence on growth in East Asia, its impact does not differ by countries’ income levels.

**Table 7: Augmented Solow model with infrastructure variables and the effect of the income level: low, medium or high-income countries**

||Tele<br>|coms<br>|Elec<br>|tricity<br>|Ro<br>|ads<br>|Sani<br>|tation<br>|W<br>|ater<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|Bench|Infrast|
|Education|1.24|−.17|2.49|.00|.73|−. 09|1.23|−.10|1.02|.09|
|t-stat|3.65|−1.18|4.14|−. 01|1.40|−.39|2.73|.07|2.21|.38|
|Elast(β)|1.24|.00|2.49|.00|.73|.00|1.23|.00|1.02|.00|
|Total Invst|.03|−.40|.36|−. 22|.72|.12|−. 39|.02|−.11|.03|
|t-stat|.08|−2.73|.61|−1.01|1.40|.46|−. 65|.07|−.16|.11|
|Elast(α)|.00|−.67|.00|.00|.00|.00|.00|.00|.00|.00|
|Low|||||||||||
|Infrast||.37||.89||.29||.68||−.12|
|t-stat||4.15||4.07||2.47||2.50||−. 21|
|Elast (γ)||.62||.89||.29||.68||.00|
|Medium|||||||||||
|Infrast||.54||.89||.15||.86||.23|
|t-stat||4.29||4.07||−3.96||3.00||3.60|
|Elast (γ)||.90||.89||.15||.86||.23|
|High<br>|||||||||||
|Infrast||.91||.89||−.26||1.46||.94|
|t-stat||7.80||4.07||−8.39||7.58||8.53|
|Elast (γ)<br>||1.52||.89||−.26||1.46||.94|
|R <sup>2</sup>|.29|.93|.41|.94|.24|.84|.20|.88|.14|.85|
|No. Obs|42|42|23|23|28|28|23|23|25|25|
|Difference||YES||NO||YES||YES||YES|

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# _Inequality in access to infrastructure_

A priori, one could argue that greater inequality of access to infrastructure services would lead to lower growth by systematically excluding groups of individuals who, with better access to infrastructure, could have made a greater contribution to growth. Indeed, several studies have argued that access to infrastructure services helps poorer individuals and underdeveloped areas to get connected to core economic activities, allowing them to access additional productive opportunities (Estache, 2003), enhancing their human capital and therefore income prospects (Leipziger, Fay and Yepes, 2003), as well as reducing production and transaction costs (Gannon and Liu, 1997). Together, these benefits of equitable access to infrastructure services play a key role in helping reduce income inequality (Calderon and Serven, 2004; Estache, Foster and Wodon, 2002; Lopez, 2004), with its positive impact on economic growth (Alesina and Rodrick, 1994; Alesina and Perotti, 1996; Galor and Zeira, 1993). We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon inequality in access to infrastructure services. We use a derived index of rural-urban inequality in access to infrastructure to test this hypothesis (see details of how the index was constructed in the Appendix).

Table 8 summarizes the estimation results, which partially support our hypothesis. There seems to be some inequality effect in the impact of telecom infrastructure on growth. Comparing elasticities of growth with respect to telecom, the figure for medium access inequality is somewhat higher (1.15) than that for high access inequality (1.05); there is no difference in telecom’s impact on growth between countries with low- and medium access inequality to telecom infrastructure. Similar results are obtained for sanitation, with a reduction in elasticity from 1.32 in environments of low and medium access inequality to 1.09 in high inequality settings, albeit at a weaker level of significance.

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**Table 8: Augmented Solow model with infrastructure variables and the effect of the level of inequality: low, medium or high inequality**

||Tele<br><br>|coms<br>|Elec<br>|tricity<br>|Ro<br><br>|ads<br>|San<br><br>|itation<br>|W<br><br>|ater<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Ben<br>ch|st<br>Infra|Bench|st<br>Infra|Benc<br>h|rast<br>Inf|Ben<br>ch|t<br>Infras|Benc<br>h|rast<br>Inf|
|Education|1.38|−.52|1.82|−2.25|−|−|1.25|.50|1.01|.21|
|t-stat|3.27|−<br>2.18|1.61|−1.64|−|−|2.48|1<br>.13|1.85|.34|
|)<br>Elast(β|1.38|−3.25|.00|.00|−|−|1.25|.00|.00|.00|
|st<br>Total Inv|−.11|−.84|−<br>.22|.50|−|−|−. 27|−<br>.14|.02|−<br>.12|
|t-stat|−.23|−<br>4.15|−.25|.65|−|−|−. 42|−.30|.03|−.14|
|)<br>Elast(α|.00|−5.25|.00|.00|−|−|.00|.00|.00|.00|
|Low|||||||||||
|Infrast||1.15||2.64||−||1.32||1.51|
|t-stat||1<br>1.56||2.81||−||2.39||.73|
|γ)<br>Elast (||7.19||2.64||−||1.32||.00|
|Medium|||||||||||
|Infrast||1.15||2.64||−||1.32||1.51|
|t-stat||1<br>1.56||2.81||−||2.39||.73|
|γ)<br>Elast (||7.19||2.64||−||1.32||.00|
|High|||||||||||
|Infrast||1.05||2.16||−||1.09||1.51|
|t-stat||−<br>2.28||−<br>2.37||−||−<br>1.85||.73|
|γ)<br>Elast (<br>||6.56||2.16||−||1.09||.00|
|R <sup>2</sup>|.24|.88|.13|.92|−|−|.19|.58|.11|.35|
|Obs<br>No.|34|34|9|9|−|−|20|20|21|21|
|e<br>Differenc||YE<br>S||YE<br>S||−||YES*||NO|

_Note_ : “*” means result significant only at 10% probability level.

In the case of electricity, results do suggest that growth is lower in countries with high inequality, than in countries with medium inequality. However, such a result should be interpreted with extreme caution, since only 9 observations were available on rural and urban access to electricity; moreover, the adjusted R<sup>2</sup> in the benchmark model is very low. There is no inequality effect in the impact of water on growth. Finally, we were not able to obtain regression estimates for roads infrastructure owing to the lack of data on rural and urban access to roads (see details of how the index was constructed in the Appendix).

# _The effect of geography – Pacific Island vs. non-island countries_

The goal here was to investigate whether infrastructure provision in Pacific Island countries (PICs) was predisposed to yield systematically different results than could be expected in nonPIC settings. This exercise did not yield any significant findings. The results obtained suggest that a country’s geographic status in East Asia measured as a dummy variable did not significantly influence its growth. A part of these results may be attributed to the severe limitations in the data underlying this specification. Although there are 9 PICs in the region, we included only Fiji, Tonga and Vanuatu in the dataset constructed for this study due to severe 25
missing data problems encountered in the remaining PICs on a number of variables. Additionally, the electricity subsector could not be analyzed for lack of data on electricity for the PICs included in the sample.

# **5. Conclusions**

Overall, the results of our analysis indicate that greater stocks of infrastructure were indeed associated with higher growth, proxied by GDP per capita, in East Asia over the interval 19852004 (benchmark model, Table 4). Moreover, consistently higher R<sup>2</sup> values suggest that growth can be better explained when infrastructure is included in the model. The magnitude of the increase in R<sup>2</sup> values is greatest in the telecom and electricity sectors.

In order to anchor this analysis in the context of ongoing policy debates, we attempt to provide for additional insights on the importance of infrastructure sub-sectors to growth depending on the degree of private participation in infrastructure (PPI), quality of governance, extent of rural-urban inequality in access to infrastructure services, country income levels, as well as geography. Among the various infrastructure sub-sectors, telecom and sanitation are those that conform most closely to our a priori hypotheses. For example, a given level of telecom and sanitation infrastructure may be expected to provide a greater boost to growth in East Asian countries with better governance, low inequality in access to infrastructure and higher income levels. Electricity and water infrastructure yield mixed results, while the impact of roads on GDP per capita consistently contradicts a priori expectations, for e.g., the impact of roads infrastructure on per capita growth decreases as the quality of governance improves. Taken together, our analysis indicates that while infrastructure matters to growth across the board, a more nuanced look at the sensitivity of growth to various institutional and physical endowments can yield starkly differing results, with some sectors supporting conventional expectations and others yielding mixed or counter-intuitive results.

Among the additional variables of interest, the quality of governance and country income level, by and large, consistently influence the sensitivity of growth to the level of infrastructure stocks. Better governance (country income level) seems to engender a greater impact of telecom, electricity and sanitation (telecom, sanitation and water) infrastructure on growth. Greater inequality in access to infrastructure for its part dampens the growth-related impact of infrastructure in the telecom sector. Similar results were found for water, albeit at a lower level of 26
significance. While the same was observed for electricity, the very small number of observations compromises the reliability of the result. PPI in the telecom and sanitation sub-sectors had a high payoff in terms of growth. PPI in electricity and water on the other hand did not have any specific affect on growth. The result in the case of roads was not statistically significant. Finally, our analysis found no evidence suggesting that infrastructure provision in PICs was predisposed to yield systematically different results than could be expected in non-PIC settings.

Overall, our results are consistent with the widely-accepted idea in policy research that infrastructure plays an important role in promoting growth, as well as with the viewpoint that certain countries’ endowments influence the growth-related impacts of infrastructure. These findings suggest that leveraging the benefits of infrastructure investments may be contingent on an optimal configuration of various factors, rather than on the undertaking of infrastructure investments in isolation. However, the results should be regarded as indicative and more analysis needs to be undertaken before translating these results into policy priorities.

It is instructive to compare these indicative results from our East Asia sample to those that were obtained, using the same methodological framework, in the context of Sub-Saharan Africa (Estache et al, 2005). Two specifications – the benchmark model and the model examining the impact of private participation in infrastructure on infrastructure’s impact on growth – are common across the Sub-Saharan Africa and East Asia analyses<sup>21</sup> . As in East Asia, infrastructure significantly effects growth across various infrastructure sectors in Sub-Saharan Africa. In SubSaharan Africa the impact is largest for telecom, electricity and roads; it appears inexistent for sanitation however. In East Asia, as we have seen, the impact of telecom and electricity is large also, but the impact of roads is low while that of sanitation is significant. Private participation in infrastructure, on the other hand, did not matter at all to Sub-Saharan Africa’s growth. Estache et al (2005) attribute this finding to the fact that little private investment took place in Africa’s infrastructure, and that when it did take place it was highly concentrated. Consequently, the contribution of the private sector to growth was not sufficient to make any difference. In the East Asian sample, there was some evidence that growth-related benefits of infrastructure were significantly higher when PPI was adopted in telecom and, to a lesser extent, in sanitation.

> 21 Other variables of interest in Estache et al (2005) were the legal inheritance of a country (i.e., whether or not a country was a former British colony), and geography (i.e., whether a country was a coastal or landlocked country).

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In terms of empirical analysis, the Sub-Saharan Africa study may stand on firmer ground given the larger sample size with numbers of observations across specifications ranging from 83 to 176, compared to a corresponding range of 9 to 42 in East Asia. As mentioned earlier, our analysis tells a preliminary story about the comparative influence of infrastructure sectors on per capita growth across East Asian countries, and in different institutional and physical settings. Clearly, more detailed and better quality data would be useful to further test and confirm this preliminary story. Finally, as already pointed out, country-specific assessments are required to formulate country-specific policy priorities.

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**Table 9. How Relevant Is Infrastructure to Growth in East Asia: Summary of Results**

||**Telecom**|**Electricity**|**Roads**|**Sanitation**|**Water**|
|---|---|---|---|---|---|
|**Benchmark model –**<br>**impact of infrastructure**<br>**on growth**|Hypothesis supported<br>.High<br>education and total investm<br>model.|er infrastructure stocks asso<br>ent. Consistently higher R<sup>2</sup>i|ciated with significant grow<br>ndicates that growth can be|th in GDP per capita (GDPpc<br>better explained when infrast|), after controlling for<br>ructure is included in the|
|**Quality of governance**|Hypo supported<br>.Better<br>the governance, greater<br>the elasticity of GDPpc<br>with respect to telecom.|Hypo supported<br>.Better<br>the governance, greater<br>the elasticity of GDPpc<br>with respect to<br>electricity.|Hypothesis not<br>supported<br>.Better the<br>governance, lower the<br>elasticity of GDPpc with<br>respect toroads.|Hypo supported<br>.Better<br>the governance, greater<br>the elasticity of GDPpc<br>with respect to sanitation.|Hypothesis not<br>supported<br>.Better the<br>governance, lower the<br>elasticity of GDPpc with<br>respect to water.|
|**Privatization**|Hypo supported<br>.Greater<br>elasticity of GDPpc with<br>respect to telecom when<br>PPI in telecom (compared<br>tonoPPI).|Hypothesis not<br>supported<br>.|Results not significant<br>.|Hypo supported<br>.Greater<br>elasticity of GDPpc with<br>respect to sanitation<br>when PPI in sanitation<br>(compared tonoPPI).|Hypothesis not<br>supported<br>.|
|**Income level**|Hypo supported<br>.Greater<br>the income level, greater<br>the elasticity of GDPpc<br>with respect to telecom.|Hypothesis not<br>supported<br>.|Hypothesis not<br>supported<br>.Greater the<br>income level, lower the<br>elasticity of GDPpc with<br>respect toroads.|Hypo supported<br>.Greater<br>the income level, greater<br>the elasticity of GDPpc<br>with respect to sanitation.|Hypo supported<br>.Greater<br>the income level, greater<br>the elasticity of GDPpc<br>with respect to water.|
|**Level of inequality of**<br>**access to infrastructure**|Hypo supported<br>.Greater<br>the level of inequality,<br>lower elasticity of GDPpc<br>with respect to telecom.|Hypo supported<br>.Greater<br>the level of inequality,<br>lower elasticity of<br>GDPpc with respect to<br>electricity;_BUT only 9_<br>_observations_.|Data not available<br> to<br>analyze model.|Hypo supported<br>.Greater<br>the level of inequality,<br>lower elasticity of<br>GDPpc with respect to<br>sanitation;_BUT result_<br>_weakly significant._|Result not significant<br>.|
|**Island vs. non-island**|Hypothesis not supported<br>. <br>Additionally,no analysis co|No significant impact of cou<br>uld be carried outforelectri|ntry’s geography on the relat<br>city due tolackofdata.|ionship between infrastructu|re and growth.|
|**Overall**|By and large, telecom<br>found to have impact on<br>infrastructure, supporting<br>hypotheses - i.e., telecom<br>infrastructure expected to<br>have most impact on<br>growth in countries with<br>better governance, PPI in<br>telecom, higher income<br>level, and low levels of<br>inequality of infra access.|Results indicate that<br>better governance<br>engenders higher growth-<br>related benefits of<br>electricity infrastructure.<br>Other hypotheses not<br>supported; poor data<br>quality.|Results consistently<br>contrary to hypotheses<br> -<br>i.e., better governance<br>and higher income<br>associated with lower<br>impact of roads<br>infrastructure on growth.<br>Poor data quality.|By and large, sanitation<br>found to have impact on<br>infrastructure, supporting<br>hypotheses – i.e.,<br>sanitation infrastructure<br>expected to have most<br>impact on growth in<br>countries with better<br>governance, PPI in<br>sanitation, higher income<br>level, and low levels of<br>inequality of infra access.|Results indicate that<br>greater income level<br>engenders higher growth-<br>related benefits of water<br>infrastructure.<br>Other hypotheses not<br>supported; or result not<br>significant.|

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