metadata: authors: 0: author: Straub,Stephane
1: author: Vellutini,Charles
2: author: Warlters,Michael Ian
count: East Asia and Pacific
docna: 0: docna: Infrastructure and economic growth in East Asia
docty: Policy Research Working Paper
subtopic: Economic Theory & Research,Industrial Economics,Energy Policies & Economics,Economic Growth,Energy Demand,Energy and Environment,Energy and Mining,Transport Services
teratopic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,Poverty Reduction,Transport
abstracts: cdata!: This paper examines whether
            infrastructure investment has contributed to East
            Asia's economic growth using both a growth accounting
            framework and cross-country regressions. For most of the
            variables used, both the growth accounting exercise and
            cross-country regressions fail to find a significant link
            between infrastructure, productivity and growth. These
            conclusions contrast strongly with previous studies finding
            positive and significant effect for all infrastructure
            variables in the context of a production function study.
            This leads us to conclude that results from studies using
            macro-level data should be considered with extreme caution.
            The Authors suggest that infrastructure investment may have
            had the primary function of relieving constraints and
            bottlenecks as they arose, as opposed to directly
            encouraging growth.
repnme: repnme: Infrastructure and economic growth in East Asia
display_title: Infrastructure and economic growth
            in East Asia
listing_relative_url: /research/2008/04/9354731/infrastructure-economic-growth-east-asia
topicv3: Economic Growth m1326241 471,Energy and Extractives m1326595 507,Transport m1326661 135,Power and Electricity Sector m1327094 515,Population, Aging and Health m1327736 640
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,Poverty Reduction,Transport
no_of_pages: 47
author_keys: 0: author_key: 632159
1: author_key: 636171
2: author_key: 142680
model_extractions: WPS4589 Policy ReseaRch WoRking PaPeR 4589 Infrastructure and Economic Growth in East Asia _Stéphane Straub Charles Vellutini Michael Warlters_ The World Bank East Asia and Pacific Sustainable Department Policy Unit April 2008
Policy ReseaRch WoRking PaPeR 4589

# **Abstract**

This paper examines whether infrastructure investment has contributed to East Asia’s economic growth using both a growth accounting framework and cross-country regressions. For most of the variables used, both the growth accounting exercise and cross-country regressions fail to find a significant link between infrastructure, productivity and growth. These conclusions contrast strongly with previous studies finding positive and significant effect for all infrastructure variables in the context of a production function study. This leads us to conclude that results from studies using macro-level data should be considered with extreme caution. The Authors suggest that infrastructure investment may have had the primary function of relieving constraints and bottlenecks as they arose, as opposed to directly encouraging growth.

This paper—a product of the Operations and Policy Unit, East Asia and Pacific Sustainable Department—is part of a larger effort in the department to examine the relationship between infrastructure and economic growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at stephane. struab@ed.ac.uk, charles.vellutini@ecopa.fr, or mwarlters@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
# **Infrastructure and Economic Growth in East Asia**

Stéphane Straub,<sup>†</sup> Charles Vellutini,<sup>‡</sup> and Michael Warlters<sup>♫</sup>

> † University of Edinburgh. Email: stephane.Straub@ed.ac.uk

> ‡ Toulouse School of Economics (Arqade) and ECOPA. Email: charles.vellutini@ecopa.fr ♫ World Bank
# **1. Introduction**

Policy-makers in developing East Asia see infrastructure investment as an essential determinant of growth.<sup>1</sup> The two fastest-growing economies in the region, China and Vietnam, are investing around 10 percent of GDP in infrastructure, and even at that rate they are struggling to keep pace with demand for electricity and telephones, and to install major transport networks. Hopes for a significant contribution to growth in the Greater Mekong countries – Laos, Cambodia, Thailand, Vietnam, Myanmar, and China – are centered on plans for greater integration of transport and energy markets. Since its election in late 2004, the new Indonesian government has made infrastructure a national priority, seeking to restore investment to its pre-crisis level of 5-6 percent of GDP.

The current emphasis on infrastructure draws its inspiration from East Asia’s economic history, including the experience of countries such as Japan, South Korea, Malaysia and Taiwan, China, which also made large investments in infrastructure. East Asia’s accumulation of infrastructure stocks has outpaced infrastructure investment in other regions (Table 1). And East Asia’s economic growth has outpaced the growth of other world regions. Between 1975 and 2005, East Asia’s GDP increased ten-fold; South Asia’s GDP increased five-fold; and all other regions’ economies grew by factors of between two and three.<sup>2</sup> For most policy-makers this is no coincidence.

**Table 1: Growth of GDP and Infrastructure Stocks**

|**19**|**95 levels as mul**<br>|**tiples of 1975 levels**|||
|---|---|---|---|---|
||GDP|Electricity|Roads|Telecoms|
|East Asia|4.8|5.9|2.9|15.5|
|South Asia|2.6|4.4|2.5|8.2|
|Middle East & North Africa|1.8|6.1|2.1|7.2|
|Latin America & Caribbean|1.8|3.0|1.9|5.1|
|OECD|1.8|1.6|1.4|2.2|
|Pacific|1.7|2.0||4.3|
|Sub-Saharan Africa|1.4|2.6|1.7|3.9|
|Eastern Europe|1.0|1.6|1.2|6.9|

GDP – PPP constant 2000 international $; Electricity - MW of generating capacity; Roads – km of paved road; Telecoms – number of main lines. See Annex 1 for construction. Sources: World Development Indicators and Canning (1998)

> 1 See ADB, IBRD, WB, JICA, (2005). 2

> Difference in GDP (PPP) in constant 2000 dollars between 1975 and 2005.

2
But academics aren’t so sure. Perhaps it is East Asia’s growth success that has driven the high rate of infrastructure investment, rather than the other way around. In the neoclassical growth model, exogenous shocks, such as new technology, increase the rate of return to capital, inducing investment. Investment increases the stock of capital, thereby reducing the rate of return to capital and restoring equilibrium at the initial capital-labor ratio and a higher level of output.<sup>3</sup> Within this framework, if infrastructure is merely another form of capital with decreasing returns, infrastructure investment does not “cause” long-term growth, it is an inevitable consequence of growth, but the sources of growth must be found elsewhere.

Decreasing returns to infrastructure investment can certainly be observed. For example, electricity supply capacity that exceeds demand growth provides a poor return on investment, as several countries found in the aftermath of the 1997 financial crisis when economies and electricity demand contracted. And most of East Asia’s infrastructure investment has occurred as a reaction to emerging constraints. So there are certainly arguments that infrastructure has the same properties as assumed in the neoclassical framework for other forms of capital.

But the neoclassical growth model also assumes that investment responds automatically to changes in rates of return. In fact, most infrastructure services are not provided in freely functioning markets. Government regulation, market power and externalities mean that infrastructure services are rarely provided at prices that represent the cost of inputs or their marginal social value. And infrastructure investments are dominated by government decision-making (e.g. public investment) and regulatory constraints (e.g. spatial planning, environmental considerations, etc). If the link between high rates of return and investment is blocked, the economy will not grow in accordance with the neoclassical model’s predictions.

Complete non-responsiveness of infrastructure investment could be a partial explanation for differences in observed long-run growth rates across countries.<sup>4</sup> Mere differences in the speed with which infrastructure investment responds to infrastructure constraints would only affect the speed with which economies return to the long-run equilibrium growth path following a shock, and would not determine countries’ long-run growth rates. But for

> 3 See Barro and Sala-i-Martin (2005).

> 4 It is assumed that infrastructure services are strongly complementary with modern technologies – that is other forms of capital investment cannot substitute for infrastructure services.

3
practical policy purposes, such “transitory” growth rates are just as important as long-run growth. In a developing economy with chronic under-supply of infrastructure, transitory growth could conceivably last for decades.

Following this line of argument, infrastructure policies might play a role in explaining East Asia’s relative growth success if East Asia is more effective than other regions in relieving infrastructure constraints as they emerge. A small piece of evidence to this effect may be seen in the results of enterprise surveys (Table 2), which indicate that new connections are provided to firms more quickly and that service interruptions are lest costly in East Asia than in most developing regions.

||**Table 2: Impact**<br>|**of Infrastructure Shor**<br>|**tages on Firms**<br>||
|---|---|---|---|---|
|Region|Electricity<br>connection delay<br>(days)|Value lost to power<br>outages (% of sales)|Water connection<br>delay (days)|Mainline telephone<br>connection delay<br>(days)|
|East Asia & Pacific|21|2.6|18|16|
|Europe & Central Asia|15|3.0|9|16|
|Latin America &<br>Caribbean|34|4.1|35|36|
|Middle East & North<br>Africa|62|4.3|44|49|
|South Asia|49|7.4|29|50|
|Sub-Saharan Africa|38|5.9|42|54|
|OECD|10|2.3|—|9|

Source: The data are derived from World Bank Investment Climate Assessments, and reported at <u>www.enterprisesurveys.org</u> (last visited January 10, 2008) However, this could again reflect causality running the other way round, as economies with stronger growth have readily available resources to address such bottlenecks as they become apparent.

Beyond the neoclassical growth framework, endogenous growth theory envisages instances where an aggregate economy may exhibit increasing returns to scale, notwithstanding the presence of diminishing or constant returns to individual factors.<sup>5</sup> If infrastructure stocks play a role in the realization of these economies of scale, infrastructure policy has a role in determining long-run growth.

An important feature East Asia’s infrastructure history has been the construction of major transport links between cities. Korea’s Seoul-Pusan highway built in the 1960s, Malaysia’s road network built in the 1970s and 1980s, China’s rail network and more recent expressways

> 5 See Aghion and Howitt (1998) and Barro and Sala-i-Martin (2005) for a review of endogenous growth models.

4
development, and Vietnam’s Hanoi-Ho Chi Minh City and Hanoi-Haiphong highways have all enlarged and integrated domestic markets, as well as providing the logistical connections for access to ports and international markets. Further investment in these transport networks may not give the same boost to productivity, but it is possible that the larger markets they create facilitates the exploitation of economies of scale within firms, the production of more specialized goods and services, and better and more specialized skills matches between employers and workers. That is, notwithstanding the presence of diminishing returns to infrastructure investment, the creation of infrastructure networks could contribute to the rate of innovation and technological advance in the economy, and thereby lift the long-term growth rate.

An alternative possible source of ongoing growth may lie in knowledge externalities. Cities play an important role in facilitating the exchange of ideas and innovation, and hence advancing the technological frontier. To the extent that infrastructure services affect the efficiency of cities and the effectiveness with which knowledge is shared, infrastructure services may influence the rate of productivity growth.<sup>6</sup> Moreover, this raises the question of whether infrastructure investment should be directed in priority to large urban areas or to lagging regions. It has been hypothesized (Williamson, 1965), that poor countries would go first through a process of concentration, industrialization and regional divergence, in which infrastructure investment is if anything following development, but that, as congestion in cities becomes too important, a reversed process of deconcentration and regional convergence occurs, which could be sustained by regional infrastructure investment. If these linkages are important, understanding the dynamic of cities should play a particularly important role in analyzing the sources of East Asia’s growth. Overall, however, the evidence on the link between urbanization, infrastructure and growth is still very limited. East Asia is one of the least urbanized regions in the world. But its rate of urbanization is one of the fastest and the East Asian mega-cities are comparably large and more densely populated. Average urban densities in East Asia range from 10,000 to around 15,000 persons per sq km – about double the urban densities of Latin America; triple those of Europe; and ten times those of US cities. On the Williamson’s hypothesis, some corroborating evidence has been found for Korea (see Henderson, Shalizi and Venables, 2001), but more work is still due to guide policies.<sup>7</sup>

> 6 See Henderson (2005) on urbanization and growth.

> 7 See Straub (2008) for a detailed discussion of economic geography and urbanization issues in the context of infrastructure policy.

5
While not a channel that has been greatly explored in modern growth theory, it is plausible that growth is enhanced in countries with lower poverty, all else equal. Poverty reduction could serve to increase market size (e.g. greater disposable income), enhance labor productivity (e.g. health improvements), and enhance innovation through improved human capital (e.g. less poor populations might invest more in education; there may be less scope for innovation in an agrarian society, etc.). If such linkages are important, ensuring that all sections of the population are provided with infrastructure could indirectly boost growth by reducing poverty. It is notable that East Asia has been more successful in providing rural access to all-weather roads than other developing regions. Access to roads has been shown in numerous studies to have a significant effect on rural poverty (Jacoby, 2000; Gibson and Rozelle, 2003).

|**Table 3: Proportion of rural population living**|**within two kilometers of an all-weather road**|
|---|---|
|Sub-Saharan Africa|30|
|Middle East & North Africa|34|
|Latin America & Caribbean|38|
|South Asia|58|
|Europe & Central Asia|75|
|East Asia|94|
|Source: Roberts et al (2006).||

Theoretical speculation on the relationship between infrastructure and growth should be tested against empirical observations. Examining 80 econometric specifications from 30 studies using macro-level data, Straub (2007) reports a significant positive effect of infrastructure on output or growth in 56 percent of specifications, no significant effect in 38 percent, and a significant negative effect in 6 percent. Among the studies that do find positive effects there is wide variation in their estimated magnitude.

There are several possible reasons for the variation in empirical results. It seems quite likely that the effects of infrastructure investment do, indeed, vary from location to location, and across different stages of economic development. A further source of variation is the theoretical framework used. Straub (2007) observes that a positive effect of infrastructure on growth is more likely to be detected in studies based on a production function than studies using cross-country regressions. The empirical literature frequently fails to set out the theoretical issues that are being tested so that results may not be strictly comparable, a number of methodological problems are either not considered or cannot be addressed with macro6
level data, and above all, aggregate data are simply not adequate to address the important policy issues.

To illustrate this, our paper examines whether infrastructure investment has, indeed, contributed to East Asia’s economic growth using both a growth accounting framework and cross-country regressions. Our results are then contrasted with the results of Seethepalli, Bramati, and Veredas (2007), who use a production function specification to examine the impact of infrastructure on East Asia’s growth. With all three methodologies focused on the same region and the same time-frame, any significant findings that recur across methodologies would shed light on whether infrastructure investment has indeed been a cause of economic growth in East Asia.

Two main conclusions emerge. First of all, for most of the variables used both the growth accounting exercise and cross-country regressions fail to find a significant link between infrastructure, productivity and growth. When they do, they produce rather contradictory conclusions, as growth accounting indicates no contribution of infrastructure to productivity in the richer countries (South Korea and Singapore), and some contribution in the relatively poorest countries (of telecommunications in Indonesia and Philippines, and of roads in Thailand), while cross-country growth regressions tend to indicate that the effects are generally negative for low-income countries and positive only for the high-income ones.

Second, these conclusions contrast strongly with those of Seethepalli, Bramati and Veredas (2007), who find positive and significant effects for all infrastructure variables in the context of a production function study. This leads us to conclude that results from studies using macro-level data should be considered with extreme caution. Given that macroeconomic data give only limited support to the notion that infrastructure investment has driven growth in East Asia, we conclude by speculating on other aspects, in particular the idea that infrastructure investment may have had the primary function of relieving constraints and bottlenecks as they arose.

The structure of the paper is as follows. Section 2 presents the growth accounting exercise. Section 3 turns to cross-country growth regressions. Finally, Section 4 discusses the results, compares them to other related studies and concludes.

7
# **2. Growth accounting**

## **2.1. Methodology**

### **Standard growth accounting**

The formal framework of growth accounting is the production function where _Y_ is aggregate GDP, _A_ is the time-varying total factor productivity (TFP) and _K_ and _L_ are respectively (total) capital and labor. Taking logs and differentiating with respect to time yields 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: In this equation _S L_ and _S K_ are therefore the respective observed shares of income. Importantly, (3) is typically not implemented through econometric estimation but rather through direct calculation: all the variables on the right-hand side are observed. As reported in Barro and Sala-i-Martin, (3) has been used in many country-specific studies with the objective of calculating TFP growth.

8
# **Growth accounting with infrastructure**

Assume, as in Hulten et al. (2005) that infrastructure (denoted _X_ in the following equations) influences output through two channels. First, it impacts TFP through where _A_<sup>~</sup> is the « true » TFP and η is the elasticity of _A_ with respect to _X_ . Here, infrastructure raises output without any payments by firms for infrastructure services. This channel captures the externality aspect of infrastructure.

Second, infrastructure can enter the production function as an additional production factor: where _K_<sup>~</sup> is the stock of non-infrastructure capital.

The presence of infrastructure as one more factor reflects its market-mediated impact, whereby firms pay for infrastructure services.

This leads to: where _S X_ is the share of GDP that accrues to market-mediated infrastructure and _S K_ ~ the share of revenue that accrues to non-infrastructure capital.

A few remarks are in order. First, η , the elasticity of TFP with respect to infrastructure, is not observable as it captures the externality dimension of infrastructure: there are no payments involved, and therefore no income and price data can be used. Second, (6) shows that should data on _S X_ be available, that relationship would enable us to disentangle the market-mediated influence of infrastructure from its externality incidence. However, even though in principle the market-mediated part of infrastructure could be tracked by the corresponding payments 9
and prices, in practice data on infrastructure prices are not available in a consistent way for the countries under analysis. In addition, available data on capital do not distinguish between different types of capital, including infrastructure. Instead of having data on _K_<sup>~</sup> , we have data on _K_ .

Because of this, it is clearer to rewrite to model so as to fit the available data, as: which leads to Finally, the trick is to substitute (3) into (6’), so that we get (appending an error term): The left-hand side of (7) is TFP growth as computed (not estimated) in the standard growth accounting approach. An alternative route to a full estimation of (6’) is thus to estimate the _A_<sup>&</sup> reduced form (7) using the (year by year) results of (3) in terms of TFP growth rates ( ), _A_ which is convenient as these are available from standard growth accounting exercises for a number of countries.

Either (7) or (6’) provide an estimation of η , the pure externality effect of infrastructure, as opposed to the full elasticity of output with respect to infrastructure. For example, if an estimation of (7) produces a value for η not significantly different from zero, it suggests that infrastructure has no externality role in that particular economy. However, because _K_ includes _X_ , it does not imply that infrastructure is not productive: it is just not more productive than other types of capital.

~ & _A_ Finally note that (7) or (6’) also provide a basis for estimating ~~~~~ , the “true” TFP growth. _A_ 10
# **2.2. Data and estimation**

There are two main options for estimating (7). One is based on regional panel data, while the other one is a country-per-country approach based on time series data.

The panel estimation technique rests on the assumption that a common production function exists for the Asian 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)), the above assumption is dubious when applied to a set of countries as diverse as those in our sample. We report below tentative panel estimations that confirm such crosscountry heterogeneity.

We therefore give priority to 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 non-infrastructure growth accounting studies.<sup>8</sup> Concerning any possible simultaneity in the estimations, note again that the left hand-side quantity in (8) – TFP growth – is computed directly from data, not estimated. In particular, we do not have to worry about the typical simultaneity problem in regression-based growth accounting studies, namely reverse causation from the growth rates of GDP to _K_ . The only possible remaining source of simultaneity would be an influence of TFP growth on _X_<sup>&</sup> investment in infrastructure, . Possible causes of simultaneity include endogenous _X_ 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. Two sets of long time series can be considered. First, physical indicators of infrastructure stocks have been used in the literature. Canning (1999) uses indicators of telephones lines availability, electricity generating power and length of

> 8 See for example Barro and Sala-i-Martin (2004).

11
paved roads and railways to estimate an aggregate production function. This dataset includes time series of usable length for key infrastructures (excluding water) for all countries included in our exercise. Second, it is in theory possible to build time series of infrastructure stocks based on investment data together with the perpetual inventory method – just as time series of _K_ are normally constructed. Unfortunately, in practice financial data on infrastructure (in monetary terms or as percentage of GDP) are scarce for the sample countries. Also, some authors (see Pritchett, 1996) have warned against the poor quality of financial indicators of public investment. For these reasons, we concentrate on physical indicators of infrastructure.

With respect to explanatory variables, we have used data from the Canning database in five countries: Indonesia, Philippines, Thailand, South Korea and Singapore. The following series of physical infrastructure indicators from the Canning (1998) database, as extended with the World Bank’s infrastructure database, have been used:

- Number of telephones and telephone main lines;

- Electricity generating capacity;

- Total roads (railways and paved roads).

Finally, the TFP growth rates calculated by the Asian Productivity Organization (APO, 2004) for the five countries under analysis have been used as the dependent variable, as in (7). The APO has calculated (not estimated) TFP growth rates following the standard methodology that is, following equation (3) and, in addition, taking into account changes in labor quality.

# **2.3. Results**

The main results from individual growth accounting regressions are reported in Table 4. First, in South Korea and Singapore, which are the two most developed economies in our sample (Figure 1), we cannot reject the hypothesis that the coefficients on the three infrastructure variables are zero. Again, recall that the interpretation for this result is not that infrastructure is not productive but rather that there is no evidence from this exercise that it is more productive than other types of capital.

12
Second, in Indonesia, Thailand and Philippines we report preliminary evidence that some infrastructure variables are significantly more – or less – productive than other types of capital.

In Indonesia, the number of telephones has a positive coefficient of 0.12, significant at the 90% level. This suggests a productivity level above that of the rest of capital, specifically an externality effect expressed as an output elasticity of 0.12. However, in the same country electricity generating capacity appears to be _less_ productive, at the 95% level of significance. With a R<sup>2</sup> of 0.59, it is interesting to note that the growth of the two significant infrastructure indicators seem to explain a large share of the standard TFP growth. Since the electricity generating capacity variable carries a negative coefficient, it implies that the bulk of TFP growth has rested on the increase in the number of telephones. The estimate of the “true” TFP growth (after accounting for infrastructure growth) is only 0.0430% per year.

In the Philippines, the telephone variable also has a positive coefficient, significant at the 90% level, again supporting externalities from this variable.

The road variable is significant in only one country, Thailand, at the 95% level. But with a R<sup>2</sup> of 0.49, this variable alone explains a lot of the standard TFP growth. The estimate of the true TFP growth is a negative -0.3964% per year – suggesting that roads have been a primary driving force of productivity growth. With 11 observations only in Thailand, however, caution is warranted in interpreting this result.

One possible interpretation for the presence of two groups, with the most developed countries (South Korea and Taiwan, China) exhibiting no specific impact of infrastructure, is that infrastructure is not a binding constraint in these countries because it has been tailored to the needs of the economy, whereas it is in developing countries such as Philippines, Thailand and Indonesia, infrastructure has yet to catch up with the economy’s needs and could still be a bottleneck. The negative impact of electricity generating capacity in Indonesia could possibly be interpreted in this context as the result of the instability of infrastructure needs in a rapidly changing economy.

However, this interpretation, which is impossible to test with a sample of 5 countries, is clearly not consistent with the results reported in the next section on growth regressions, 13
which are based on a broader sample of countries. Tables 9 to 12 show that the interaction terms between the Low-Income dummy and infrastructure variables often carry a significantly _negative_ coefficient. This suggests that the explanation for the mixed outcome from our growth accounting regressions could be related to factors which are orthogonal to GDP, for example if the productivity impact of the infrastructure stock is conditional on complementary factors such as the quality of regulation and governance in the sector.

**Figure 1**

<!-- Start of picture text --> GDP per capita (US$)<br>20000<br>18000<br>16000<br>Taiwan<br>14000<br>12000 Korea, Republic of<br>10000<br>Thailand<br>8000<br>6000 Indonesia<br>4000<br>Philippines<br>2000<br>0<br>1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000<br><!-- End of picture text --> The simple OLS specification based on equation (7) has been tested in several directions. First, the regressions have been tested for the presence of endogeneity. For each country, Hausman tests using various lags of the explanatory variables as instruments have been performed, all rejecting endogeneity. However, for several countries autocorrelation for some of the exogenous variables is rejected, making the latter invalid instruments for Hausman tests. In those specific cases, we follow the literature<sup>9</sup> in using population and population density (both contemporary and lagged) as instruments for Hausman test, which also leads to the rejection of endogeneity.

Second, time dummies were tentatively introduced as explanatory variables in each of the regressions above. The objective of this introduction was to test for possible time-varying effects on TFP growth, for example the role of the 1997 crisis in Asia. We do not report the

> 9 See Straub (2007).

14
results of these estimations as the time variable is never a significant determinant of TFP growth.

Thirdly, the individual country regressions used above have the obvious shortcoming that they cannot account for cross-country variations. Could not pooled data reveal cross-country regularities masked by individual estimations? With the important caveat noted above – a common technology in all five country is a strong assumption – pooled/panel estimations were performed under various specifications. Table 5 reports the outcome of these estimations, with none of the infrastructure significantly different from zero and very low R<sup>2</sup> .

Next, we turn to the results from cross-country regressions.

# **3. Growth regression**

This section applies growth regression techniques to the study of the link between infrastructure and growth in the case of East Asian countries.

## **3.1 Standard framework**

Standard cross-country regressions in general start from a specification that intends to explain real per capita GDP growth by the initial level of real per capita GDP and explanatory factors such as physical investment, human capital (for example proxied by enrollment in different education levels) and additional factors that vary across studies. Indeed, approximately 60 different variables have been used in this abundant literature (Romp and de Haan, 2005), of which varying subsets have been deemed “robust” by different authors.<sup>10</sup> Adding infrastructure capital to this framework yields the following reduced form equation to be estimated:

> 10 See Levine and Renelt (1992), Sala-i-Martin (1997), and Temple (2000) for a discussion.

15
where _gi_ is the growth rate of real per capita GDP for country _i_ , _yi0_ is initial income (possibly in log form), _K_<sup>_I_</sup> _i_<sup>is a measure of infrastructure capital, and</sup><sup>_Z_</sup> _i_<sup>is a vector of covariates as</sup> mentioned above.

# **3.2. Data**

We opt for physical infrastructure indicators. Three specific reasons support this choice.<sup>11</sup>

1. As mentioned above, public investment data are subject to a lot of problems, which make them unlikely to capture infrastructure stock or availability properly.

2. Physical indicators allow for a longer time frame and a higher number of countries.

3. They will allow for direct comparisons with the results from the growth accounting exercise.

Physical indicators for three different sectors (telecom, energy and transport) are taken from Canning’s database, covering the 1971-1995 period. Specifically, we use the following series:

- Main telephone lines per 1,000 people. This series is extended up to 2002/2003 using Estache and Goicoechea (2005).

- Electricity generating capacity in million kilowatt per 1,000 people.

- Rail route length in km per 1,000 people.

- Paved road length in km per 1,000 people.

Additionally, we perform some tests with alternative variables: Telephone mainlines (per 1,000 people) from the World Bank World Development Indicator (WDI), fixed line and mobile phone subscribers (per 1,000 people) also from WDI, to capture the rise in mobile connections in the second half of the 1990s, roads total network and percentage of paved roads from WDI, which is used here as a quality proxy. We introduce additional proxies for

> 11 See Straub (2007) for a more general discussion of public investment versus physical infrastructure indicators.

16
the quality of the other services under study, namely telephone faults (per 100 mainlines) and electric power transmission and distribution losses in % of output, both from WDI. Other general data include (from WDI, unless mentioned otherwise) measures of GDP per capita, gross fixed capital formation, primary and secondary school enrollment (from Barro and Lee, 2000), primary and secondary schooling expenditures, government stability and Corruption (from Political Risk Service, International Country Risk Guide), life expectancy, M2/GDP (as a measure of financial development), imports/GDP and inflation.

# **3.3. Sample**

We rely on a sample of 93 developing or emerging countries. Of these 40 are classified by the World Bank as low income, 25 as lower middle income, 19 as upper middle income and 9 as high income. Note that this last category includes Hong Kong, Korea, Singapore and a number of oil producing countries. Overall, 16 East Asian and Pacific countries are included: China, Fiji, Hong Kong, Indonesia, Korea, Lao PDR, Malaysia, Mongolia, Myanmar, Papua New Guinea, Philippines, Singapore, Thailand, Tonga, Vanuatu, Vietnam.

# **3.4. Techniques**

In what follows we present two types of estimations. First, we perform simple cross country estimations based on the collapsed data set for 1971-1995 or 1984-1995 alternatively, using the rate of growth of GDP per capita as dependent variable and standard controls (initial level of GDP, investment, proxies for human capital). In each case, we instrument potentially endogenous infrastructure indicators and perform related tests. We also test specifications with different set of regional dummies (specific East Asian dummy, income groups), and the alternative infrastructure indicators mentioned above.

Then, we present panel regressions on 5-year subperiod averages with the same dependent variable. This frequency should result in enough variations in infrastructure indicators to allow the use of fixed effects. Following best practice in this type of exercise, we compare fixed vs. random effects and perform instrumental estimations. Finally, we use Arellano-Bond dynamic panel techniques.

17
# **3.5. Interpretation**

Table 6 presents the results from cross country regressions with the 1971-1995 averages. Overall, only the number of telephone lines per hab. is significant, with a positive sign of 0.022. This implies that an increase in 100 lines per hab., from the average level over the period of Venezuela (63) to that of Korea (163) would add 2.2 points to the average growth rate of per capita GDP. All other infrastructure variables are insignificant and the paved roads length one is of the wrong sign. In columns 5 and 6, we add measures of quality of infrastructure, namely the number of telephone faults and electricity losses. These measures are not significant and the main indicators’ coefficients are unchanged.

When considering instead the 1984-1995 period, in Table 7, which in particular enables us to introduce indices of government stability and corruption as additional control variables, we get even less conclusive results. The number of phone lines is now only significant when quality is controlled for and its coefficient is about half of the 1971-1995 one, while the paved roads variable is now negative and significant.

In Tables 8 and 9, we address the fact that infrastructure stocks may be determined simultaneously with output. Following previous contributions in the literature, we use beginning of the period (1971) values of the indicators themselves, as well as 1971 values of the level of population, population density and the share of agriculture in GDP. Overall, the results from IV estimations are similar to simple OLS. The coefficient for the number of phone lines is now larger, between 2.8 (1971-1995) and 4.7 (1984-1995). Note however that a Wu-Hausman test does not reject exogeneity in all but one of the 12 estimations.

Next, we test regional effects by interacting the infrastructure indicators with regional dummies. Tables 9 to 13 present the results for telecom, energy, railroad and roads respectively. In each case, we first use an East Asian dummy, then dummies for low and middle income countries respectively.

As for telecom, the group of East Asian countries does not display any significantly different behavior (the coefficient is negative but not significant), while income classification indicates that telecom impact is significantly lower in low income countries, a result that may appeal to 18
Röller and Waverman’s (1999) conclusions on network externalities in telecom kicking in at near universal coverage level.

In Table 10, we observe that the impact of energy is positive and significant for the subgroup of East Asian countries, suggesting that the development of the electric network may have been an important contributor to growth of per capita output during the period. To compare again the same countries as before, the difference between the period average electricity generating capacity of Korea (0.667 million kw per 1,000 hab.) and that of Venezuela (0.376) implies an additional 1.1% per capita GDP growth. As for the level of development, the impact of electricity generation appears lower in low and middle income countries.

In Table 10, the impact of the railroad network is positive and weakly significant for East Asian countries, and it is again lower for low and middle income countries (actually slightly negative for low income ones). Finally, a similar pattern is repeated in Table 13 with respect to paved roads. Note finally, that in all cases instrumental estimations fail to yield significant results, and that the Wu-Hausman test fails to reject exogeneity in all but one of the 8 specifications tested.

Overall, this exercise seems to provide two main insights. First, East Asian countries display positive and significant returns from infrastructure across most dimensions. Second, a pattern emerges that indicates low or possibly negative returns for low income countries, slightly higher returns for middle income ones and strongly positive returns for the richer countries in the sample. The type of data we use does not allow for a very detailed analysis of this result. One possibility is simply a network effect type of explanation, although it is not clear how this applies to roads for example. Alternatively, it may be the case that richer countries also display more favorable conditions along other dimensions (better incentive structure, more efficient political interactions) that provide the required conditions for a favorable effect of infrastructure investment.

Finally, we use alternative infrastructure indicators in Table 14. Using the number of fixed and mobile phone lines, we inquire whether the very quick surge in mobile telephony in the 1990s had a special effect on growth above the effect of traditional main lines, as suggested by Waverman, Meschi and Fuss (2005). In column 1, we use 1984-2003 averages, and introduce both the number of fixed line 1984-1995 and the number of fixed plus mobile lines 19
1996-2003. Mobile lines appear to have a significant and positive effect on GDP per capita growth and render the effect of fixed lines negative. This result loses significance when instruments are used, but again exogeneity is not rejected at usual levels.

In column 3, we combine the total length of the road network and the percentage of paved roads, which results in only the second indicator being significant. This indicates that it is the quality of the road network that mostly provides growth dividends. In column 4, an indicator of the number of vehicles per kilometer of road is added to the specification. This variable now shows up positive and significant at the 5% level, while road length and proportion of paved road fail to be significant. If anything, this seems to indicate that, because it is usage of infrastructure that ultimately drives aggregate growth benefit, a proxy for the average use of roads capture the benefits from the extension and the quality of the network. Again, IV estimations yield no clear results and endogeneity is rejected.

Next, we perform panel estimations using 5 year averages of the different indicators. The results from fixed effects vs. random effects estimations are shown in Table 15, and a Hausman test is performed to decide which estimation technique is more suited. In all cases, a full set of time dummies is included. The main conclusions are that none of the infrastructure indicators introduced individually is significant, except negative and significant signs for electricity in the random effect specification and for paved roads in the fixed effects one respectively. Fixed effect estimations are supported by the test in 2 out of 4 cases (telecom and roads).

In columns 9 and 10, we introduce all four indicators together. The number of phone lines is positive and significant, while electricity and roads remain negative and significant. In this case, the Hausman test favors fixed effect estimation. Again, the interpretation of the signs of the coefficients, and especially the negative ones, is made difficult by the nature of the data. Several lines may be relevant, among which an “optimal stock” type of argument (returns may become negative in case of over accumulation), or arguments about investment decisions being politically driven and therefore departing significantly from efficiency.

In Table 16, we address the issue of endogeneity. Instruments are now the lagged value of infrastructure indicators, as well as the ones used previously (1971 values of the share of agriculture in GDP, population, and population density). Following the outcome of the test in 20
column 9 and 10 of Table 15, we chose a fixed effect specification and introduce a full set of time period dummies. Overall, few results are again significant, with only electricity generating capacity being positive and significant. This holds true when all indicators are introduced together. Note that exogeneity is now rejected for the individual estimations with the number of phone lines and the electricity generating capacity.<sup>12</sup>

# **4. Conclusion**

Our results on growth accounting are mixed: in Indonesia and the Philippines telecommunications investment has generated externalities and has contributed to growth more than other types of capital. Roads have positively influenced TFP growth in only one country, Thailand. In South Korea and Singapore, however, two countries which have markedly higher GDP than the other countries in the sample, no significant effect of infrastructure on TFP growth has been detected.

Our cross country growth regressions provide relatively fragile results on the impact of infrastructure in per capita GDP growth, a conclusion that contrasts with previous studies that found robust results (Easterly and Servén, 1993; Calderón and Servén, 2004 among others). The number of phone lines appears positively related to growth in the cross country exercise, and some regional patterns emerge, showing above average effects for East Asia and high income countries. However, most results appear not to be robust when using panel techniques or when controlling for an endogenous response of infrastructure to growth.

Our growth accounting estimates indicate that infrastructure has contributed to TFP growth in poorer countries, while having no significant effect in other, richer economies. A possible explanation would be that poor countries have less developed infrastructure networks, and experience a one-off productivity dividend as they develop those networks. But in our crosscountry growth regressions, which draw on data extending beyond East Asia, the interaction terms between the Low-Income dummy and infrastructure variables (Table 10 to Table 13) carry significantly _negative_ coefficients. This suggests that the explanation for the mixed

> 12 Finally, we perform Arellano-Bond IV estimations similar to the one implemented in Calderón and Servén (2004). Two types of instruments are used: internal ones, constituted by the lagged values of the differenced explanatory variables including infrastructure indicators, and the external ones used above, namely 1971 values of the share of agriculture in GDP, population, and population density. Only electricity generating capacity is significant, and its coefficient is negative. Results are not shown here to save space.

21
outcome from our growth accounting regressions could be related to factors which are orthogonal to GDP, such as government policies and the quality of regulation and governance. The two results could be reconciled with a growth story in which infrastructure constraints, if left unaddressed by governments, can slow the transition towards the long-run growth path, but do not ultimately affect the long-run rate of growth. Governments of poor countries in East Asia may do a better job of addressing these constraints than governments of poor countries elsewhere.

It is interesting to compare our results with those of Seethepalli et al (2007). As in Estache et al. (2005), these authors compare a “benchmark” (without infrastructure) production function estimated at the steady state with the same specification including infrastructure variables (these include physical indicators for telecom, electricity, roads, sanitation and water).<sup>13</sup> Using data for East Asian countries<sup>14</sup> , they find that virtually all dimensions of infrastructure positively influence GDP per capita when controlling for education and investment. The cross-country regressions have similar controls, while our growth accounting estimations both investment and changes in the quality of labor are captured in the APO calculations of TFP growth rates, making comparison meaningful. Our growth accounting results in the Philippines, Indonesia and Thailand tends to support their result that telecom and road infrastructure enhances productivity conditional on investment and education, but not so for electricity. But our results from South Korea and Singapore, where no infrastructure impact has been found, suggest that individual countries could be at variance with the cross-country results of Seethepalli et al (2007). And our growth regressions provide much weaker results than those obtained by Seethepalli et al. One of the reasons might be the fact that they do not control for the potential endogeneity of infrastructure stocks. While they argue that the use of stocks rather than flows mitigates the problem of reverse causation, countries may have unobserved characteristics that lead them both to have higher infrastructure stocks and higher growth. The fact that fixed effects estimations are not carried out reinforces the concern that this may bias the results (Holtz-Eakin, 1994).

> 13 This approach parallels our interpretation of our results in terms of infrastructure productivity greater than or less than that of other types of capital.

> 14 Australia, Cambodia, China, Fiji, Indonesia, South Korea, Laos, Mongolia, Papua New Guinea, Philippines, Singapore, Thailand, Tonga, Vanuatu, Vietnam.

22
A first conclusion is therefore that the results from studies using aggregate data lack robustness, Indeed, as shown above, different techniques (production function, growth regressions, growth accounting) produce very different results, even when looking at similar set of countries. Moreover, similar techniques, when applied to slightly different samples, also fail to produce consistent results. For example, we were unable to reproduce the results from Calderón and Servén (2004) in our sample of 93 developing countries.

Keeping these caveats in mind, what are the potential lessons for East Asian economies? Overall, our results give only limited support to the notion that infrastructure investment has driven growth in East Asia. Our results do not seem to be inconsistent with a story in which infrastructure can constrain growth, when that growth potential is generated exogenously, and that East Asian countries have been relatively successful in addressing infrastructure constraints as they arise. But the weakness of our data and results do not permit any definitive conclusions about the theoretical channels by which infrastructure may have influenced growth in East Asia.

If indeed East Asia is more effective than other regions at responding to infrastructure constraints it would be useful to understand why. Various arguments could be mounted. For example, East Asia has high levels of savings, and the availability of financing may facilitate more rapid responses. East Asian countries have typically relied on powerful planning agencies, such as Japan’s MITI, etc. And to the extent that private investment in infrastructure has played a role in total investment, it is notable that the modalities employed in East Asia have differed from those employed elsewhere: for example, while East Asia focused on attracting investment at the wholesale level and greenfield sites (eg independent power producers), Latin America placed greater emphasis on the concessioning of existing retail systems. Testing such hypotheses is a subject for separate enquiry.

23
**Table 4. Results from growth accounting (single-country OLS estimations)**

|Dependent variable :<br>TFPgrowth rate|**Indonesia**|**Thailand**|**Philippine**<br>**s**|**South**<br>**Korea**|**Singapore**|
|---|---|---|---|---|---|
|Constant|0.000430<br>(0.012378)|-0.003964<br>(0.013298)|-0.032361<br>(0.016032)|-0.011897<br>(0.049837)|0.013270<br>(0.019744)|
|Number of telephones|0.122859*<br>(0.055356)|-0.082006<br>(0.048992)|0.282970*<br>(0.152046)|-0.071994<br>(0.123365)|-0.090847<br>(0.107652)|
|Total roads (railways and<br>paved roads)|-0.119878<br>(0.129726)|0.470495**<br>(0.196164)|-0.017326<br>(0.221852)|0.539977<br>(0.534860)|-0.191091<br>(0.300689)|
|Electricity generating<br>capacity|-0.047187**<br>(0.015937)|0.027691<br>(0.052692)|0.062823<br>(0.123118)|-0.026010<br>(0.083775)|-0.003853<br>(0.076603)|
|R<sup>~~2~~</sup>|0.590285|0.486470|0.153937|0.069669|0.092980|
|Number of observations|14|11|28|21|18|

Standard errors in parentheses. ** significant at the 5% level; * significant at the 10% level.

24
**Table 5. Results from growth accounting (panel estimations)**

|Dependent variable : TFP<br>growth rate|Pooled<br>regression,<br>unbalanced<br>sample|Pooled<br>regression,<br>balanced<br>sample|Fixed<br>effects,<br>unbalanced<br>sample|Fixed<br>effects,<br>balanced<br>sample|Random<br>effects,<br>unbalanced<br>sample|Random<br>effects,<br>balanced<br>sample|
|---|---|---|---|---|---|---|
|Constant|-0.008737<br>(0.007908)|-0.012026<br>(0.010743)|||-0.003989<br>(0.010032)|-0.013044<br>(0.010060)|
|Number of telephones|0.043324<br>(0.048094)|0.057099<br>(0.079778)|0.027650<br>(0.050246)|0.030315<br>(0.084314)|0.035615<br>(0.048686)|0.059584<br>(0.079787)|
|Total roads (railways and|0.154079|0.207674|0.029425|-0.042937|0.085562|0.226184|
|paved roads)|(0.107193)|(0.193470)|(0.125109)|(0.276359)|(0.115693)|(0.187275)|
|Electricity generating|-0.015537|-0.031110|-0.017844|-0.027643|-0.016328|-0.032020|
|capacity|(0.027578)|(0.034907)|(0.027756)|(0.037000)|(0.027313)|(0.034794)|
|R<sup>2</sup>|0.046681|0.091113|0.112922|0.167258|0.086519|0.080648|
|Number of observations|92|40|92|40|92|40|

Standard errors in parentheses. ** significant at the 5% level; * significant at the 10% level.

25
**Table 6. Cross section 1971-1995, OLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.528|-3.061|-3.657|-3.528|-2.665|-2.486|
||(0.970)**|(1.161)**|(1.239)***|(0.996)***|(1.265)**|(2.058)|
|GDPpc71|-0.001|-0.000|-0.000|-0.000|-0.001|-0.001|
||(0.000)***|(0.000)|(0.000)|(0.000)|(0.000)***|(0.000)***|
|prim_expen_70|-0.053|-0.028|-0.058|-0.041|-0.060|-0.025|
||(0.034)|(0.039)|(0.065)|(0.036)|(0.037)|(0.058)|
|second_expen_70|0.002|0.001|-0.000|0.001|0.002|-0.013|
||(0.001)|(0.001)|(0.004)|(0.002)|(0.002)|(0.009)|
|Invt_GDP7195|0.227|0.223|0.289|0.270|0.234|0.220|
||(0.042)***|(0.054)***|(0.054)***|(0.042)***|(0.053)***|(0.074)***|
|main7195|0.022||||0.022||
||(0.003)***||||(0.004)***||
|egc7195||2.871||||4.108|
|||(2.084)||||(2.577)|
|rail7195|||0.127||||
||||(1.687)||||
|pavroads7195||||-0.172|||
|||||(0.362)|||
|tel_faults7195|||||0.001||
||||||(0.003)||
|elec_loss7195||||||0.030|
|||||||(0.071)|
|Observations|51|48|41|51|47|33|
|R-squared|0.68|0.53|0.50|0.56|0.69|0.50|

Robust standard errors in parentheses. Significant at 10%; ** significant at 5%; *** significant at 1%.

26
# **Table 7. Cross section 1984-1995, OLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|OLS|OLS|OLS|OLS|OLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-5.691|-6.385|-6.289|-8.614|-4.570|-4.183|
||(1.813)***|(1.744)***|(1.919)***|(1.483)***|(2.326)*|(3.437)|
|GDPpc71|-0.000|0.000|-0.000|0.000|-0.000|0.000|
||(0.000)|(0.000)|(0.000)|(0.000)**|(0.000)|(0.000)|
|prim_enrol8495|0.007|0.014|0.013|0.020|-0.010|0.018|
||(0.013)|(0.013)|(0.013)|(0.011)*|(0.014)|(0.033)|
|Invt_GDP8495|0.261|0.272|0.297|0.281|0.238|0.290|
||(0.066)***|(0.063)***|(0.065)***|(0.054)***|(0.075)***|(0.086)***|
|Govstab8495|0.070|0.045|-0.002|0.533|0.344|-0.113|
||(0.305)|(0.317)|(0.320)|(0.293)*|(0.321)|(0.417)|
|Corrup8495|0.110|0.202|0.151|0.140|-0.478|0.107|
||(0.356)|(0.326)|(0.417)|(0.337)|(0.459)|(0.463)|
|main8495|0.005||||0.012||
||(0.004)||||(0.005)**||
|egc8495||-0.411||||-1.629|
|||(1.343)||||(1.777)|
|rail8495|||1.262||||
||||(1.720)||||
|roads8495||||-1.414|||
|||||(0.631)**|||
|tel_faults8495|||||0.006||
||||||(0.005)||
|elec_loss8495||||||-0.104|
|||||||(0.095)|
|Observations|55|54|47|49|48|42|
|R-squared|0.51|0.48|0.48|0.60|0.55|0.43|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%.

27
# **Table 8. Cross section 1971-1995, 2SLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.537|-1.983|-2.839|-3.184|-4.132|27.406|
||(1.109)**|(1.245)|(1.341)**|(1.322)**|(3.062)|(93.724)|
|GDPpc71|-0.001|0.000|-0.001|-0.000|-0.001|0.003|
||(0.000)***|(0.000)|(0.000)**|(0.000)**|(0.001)|(0.012)|
|prim_expen_70|-0.063|-0.075|-0.047|-0.047|-0.068|0.107|
||(0.047)|(0.055)|(0.072)|(0.050)|(0.051)|(0.614)|
|second_expen_70|0.002|0.001|-0.003|0.002|0.004|-0.073|
||(0.003)|(0.003)|(0.006)|(0.003)|(0.004)|(0.203)|
|Invt_GDP7195|0.230|0.230|0.248|0.250|0.253|0.245|
||(0.042)***|(0.053)***|(0.055)***|(0.051)***|(0.048)***|(0.416)|
|main7195|0.028||||0.028||
||(0.013)**||||(0.014)**||
|egc7195||-6.453||||-48.742|
|||(5.223)||||(164.983)|
|rail7195|||1.892||||
||||(1.522)||||
|pavroads7195||||0.464|||
|||||(0.429)|||
|tel_faults7195|||||0.012||
||||||(0.022)||
|elec_loss7195||||||-1.881|
|||||||(6.248)|
|Observations|44|44|36|41|41|30|
|Wu-Hausman F<br>test, p-value|0.70|0.13|0.11|0.18|0.83|0.28|

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: 1971 Infrastructure physical stock, 1971 share of agriculture in GDP, 1971 population density.

28
# **Table 9. Cross section 1984-1995, 2SLS.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||2SLS|2SLS|2SLS|2SLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.785|-6.569|-6.276|-10.275|-3.602|3.534|
||(2.091)*|(3.047)**|(1.999)***|(2.832)***|(2.562)|(17.124)|
|GDPpc71|-0.000|0.000|-0.000|0.000|-0.000|0.003|
||(0.000)|(0.001)|(0.000)|(0.000)|(0.000)|(0.004)|
|prim_enrol8495|-0.011|0.030|0.017|0.023|-0.052|0.222|
||(0.022)|(0.045)|(0.023)|(0.020)|(0.032)|(0.285)|
|Invt_GDP8495|0.245|0.180|0.248|0.264|0.286|0.122|
||(0.070)***|(0.083)**|(0.083)***|(0.078)***|(0.087)***|(0.307)|
|Govstab8495|0.019|0.511|0.190|0.935|0.387|1.065|
||(0.395)|(0.924)|(0.417)|(0.527)*|(0.497)|(2.511)|
|Corrup8495|-0.130|-0.303|0.003|0.065|-1.030|-1.900|
||(0.368)|(1.085)|(0.407)|(0.359)|(0.591)*|(3.664)|
|main8495|0.024||||0.047||
||(0.017)||||(0.021)**||
|egc8495||-5.502||||-38.323|
|||(14.823)||||(51.504)|
|rail8495|||1.393||||
||||(1.892)||||
|roads8495||||-1.789|||
|||||(1.217)|||
|tel_faults8495|||||0.028||
||||||(0.022)||
|elec_loss8495||||||-1.380|
|||||||(1.764)|
|Observations|48|47|44|40|43|37|
|Wu-Hausman F<br>test, p-value|0.29|0.77|0.14|0.29|0.33|0.05|

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: 1971 Infrastructure physical stock, 1971 share of agriculture in GDP, 1971 population density.

29
**Table 10. Cross section, Telecom, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.577|-1.769|-0.034|2.113|
||(0.980)**|(0.913)*|(3.507)|(5.107)|
|GDPpc71|-0.001|-0.001|-0.001|-0.001|
||(0.000)***|(0.000)***|(0.001)|(0.001)*|
|prim_expen_70|-0.055|-0.063|-0.083|-0.084|
||(0.034)|(0.034)*|(0.097)|(0.090)|
|second_expen_70|0.002|0.003|0.004|0.002|
||(0.001)|(0.001)*|(0.006)|(0.005)|
|Invt_GDP7195|0.229|0.220|0.065|0.148|
||(0.043)***|(0.040)***|(0.197)|(0.113)|
|main7195|0.029|0.022|0.027|0.000|
||(0.007)***|(0.003)***|(0.025)|(0.000)|
|EA*main|-0.006||0.313||
||(0.005)||(0.339)||
|LI*main||-0.222||-1.188|
|||(0.066)***||(1.224)|
|MI*main||0.004||0.009|
|||(0.003)||(0.030)|
|Observations|51|51|44|44|
|R-squared|0.68|0.75|||
|Wu-Hausman F test,<br>p-value|||0.22|0.47|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

30
**Table 11. Cross section, Energy, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-2.058|-1.085|-0.784|-0.646|
||(1.159)*|(1.150)|(1.752)|(1.542)|
|GDPpc71|-0.000|-0.000|0.000|-0.000|
||(0.000)|(0.000)**|(0.000)|(0.001)|
|prim_expen_70|-0.048|-0.079|-0.053|-0.093|
||(0.038)|(0.037)**|(0.064)|(0.048)*|
|second_expen_70|0.000|0.002|0.001|0.002|
||(0.002)|(0.001)|(0.003)|(0.003)|
|Invt_GDP7195|0.197|0.188|0.106|0.182|
||(0.049)***|(0.047)***|(0.128)|(0.060)***|
|egc7195|-0.899|4.450|-3.038|-0.057|
||(2.244)|(1.454)***|(6.514)|(6.997)|
|EA*egc|4.625||32.843||
||(1.361)***||(30.305)||
|LI*egc||-18.451||-19.050|
|||(3.343)***||(16.486)|
|MI*egc||-3.719||0.000|
|||(1.186)***||(0.000)|
|Observations|48|48|44|44|
|R-squared|0.60|0.70|||
|Wu-Hausman F test,<br>p-value|||0.25|0.87|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

31
**Table 12. Cross section, Rail, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.245|-2.387|-0.320|-0.489|
||(1.257)**|(1.138)**|(2.900)|(1.867)|
|GDPpc71|-0.000|-0.000|-0.000|-0.001|
||(0.000)|(0.000)|(0.000)|(0.000)***|
|prim_expen_70|-0.043|-0.046|0.001|-0.049|
||(0.061)|(0.061)|(0.121)|(0.081)|
|second_expen_70|0.001|0.004|0.000|0.012|
||(0.004)|(0.004)|(0.010)|(0.010)|
|Invt_GDP7195|0.241|0.224|0.024|0.172|
||(0.058)***|(0.049)***|(0.194)|(0.071)**|
|rail7195|0.661|52.269|4.073|-19.234|
||(1.771)|(16.292)***|(2.968)|(9.942)*|
|EA.rail|22.630||101.633||
||(11.833)*||(78.180)||
|LI.rail||-57.714||0.000|
|||(16.315)***||(0.000)|
|MI.rail||-50.803||23.624|
|||(16.076)***||(10.950)**|
|Observations|41|41|36|36|
|R-squared|0.55|0.61|||
|Wu-Hausman F test,<br>p-value|||0.04|0.12|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

32
**Table 13. Cross section, Roads, regional dummies interactions.**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.045|-1.686|-2.085|2.604|
||(1.043)***|(1.011)|(2.169)|(7.157)|
|GDPpc71|-0.000|-0.000|-0.000|-0.001|
||(0.000)|(0.000)|(0.000)|(0.001)|
|prim_expen_70|-0.043|-0.062|-0.062|-0.087|
||(0.036)|(0.034)*|(0.058)|(0.084)|
|second_expen_70|0.002|0.002|0.002|0.001|
||(0.002)|(0.001)|(0.003)|(0.004)|
|Invt_GDP7195|0.236|0.209|0.187|0.097|
||(0.048)***|(0.043)***|(0.109)*|(0.196)|
|pavroads7195|-0.101|4.250|0.353|-8.633|
||(0.350)|(1.621)**|(0.480)|(10.902)|
|EA*pavroads7195|1.746||2.914||
||(0.891)*||(4.421)||
|LI*pavroads7195||-6.112||0.000|
|||(1.736)***||(0.000)|
|MI*pavroads7195||-4.037||9.525|
|||(1.461)***||(11.397)|
|Observations|51|51|41|41|
|R-squared|0.59|0.65|||
|Wu-Hausman F test, p-<br>value|||0.29|0.32|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Regional dummies: EA (East Asia), LI (low income countries), MI (middle income countries). Instruments: see Table 3 and 4.

33
**Table 14. Cross section, Alternative infrastructure indicators.**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||OLS|2SLS|OLS|OLS|2SLS|2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-3.083|-1.456|-3.770|-3.544|-2.077|-9.343|
||(1.624)*|(4.275)|(1.549)**|(1.627)**|(6.697)|(11.441)|
|GDPpc71|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000|
||(0.000)***|(0.000)|(0.000)|(0.000)*|(0.000)|(0.000)|
|prim_enrol8403|-0.018|-0.026|||||
||(0.016)|(0.033)|||||
|second_enrol8403|0.021|-0.012|||||
||(0.011)*|(0.050)|||||
|Invt_GDP8403|0.073|-0.015|||||
||(0.377)|(0.554)|||||
|Invt_GDP8495|||0.204|0.202|0.154|0.321|
||||(0.054)***|(0.072)***|(0.143)|(0.339)|
|prim_enrol8495|||0.007|0.007|-0.010|0.061|
||||(0.012)|(0.013)|(0.058)|(0.109)|
|Govstab8495|(0.052)|(0.157)|-0.114|0.086|-0.041|0.351|
||0.412|0.488|(0.270)|(0.304)|(0.700)|(1.133)|
|Corrup8495|(0.252)|(0.573)|-0.066|-0.370|-0.489|0.196|
||-0.102|-0.155|(0.289)|(0.347)|(0.853)|(1.389)|
|telmain_8495|-0.014|-0.080|||||
||(0.008)*|(0.092)|||||
|fix+mob_9603|0.008|0.038|||||
||(0.003)**|(0.039)|||||
|roads_tot_8495|||0.000|0.000|0.000|-0.000|
||||(0.000)|(0.000)|(0.000)|(0.000)|
|pavroads%_8495|||0.028|0.007|0.057|-0.076|
||||(0.016)*|(0.021)|(0.087)|(0.175)|
|vehicles8495||||0.018||0.063|
|||||(0.009)**||(0.095)|
|Observations|46|41|53|52|41|40|
|R-squared|0.64||0.48|0.52|||
|Wu-Hausman F<br>test, p-value||0.34|||0.82|0.42|

Robust standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: see Table 3 and 4.

34
# **Table 15. Panel 5 years average, Fixed and random effects.**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|---|---|---|---|---|---|---|---|---|---|---|
||pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|pcGDPgrowt<br>h|
||Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|Fixed Effects|Random<br>Effects|
|Constant|-3.624|-3.580|-4.288|-5.847|-2.489|-2.503|-4.853|-8.293|3.691|-3.969|
||(2.595)|(1.093)***|(6.055)|(1.477)***|(6.391)|(1.833)|(6.059)|(1.475)***|(6.833)|(1.707)**|
|GDPpc|-0.000|-0.000|-0.000|-0.000|0.000|-0.000|-0.000|-0.000|-0.000|0.000|
||(0.000)***|(0.000)**|(0.000)***|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)***|(0.000)|(0.000)|
|Invt/gdp|0.111|0.145|0.093|0.109|0.147|0.196|0.087|0.134|0.113|0.186|
||(0.030)***|(0.023)***|(0.061)|(0.036)***|(0.069)**|(0.043)***|(0.053)|(0.034)***|(0.075)|(0.040)***|
|lifeexpect|0.091|0.045|0.102|0.099|0.062|0.041|0.120|0.091|-0.006|0.079|
||(0.042)**|(0.020)**|(0.105)|(0.029)***|(0.120)|(0.036)|(0.103)|(0.029)***|(0.129)|(0.032)**|
|m2/gdp|-0.054|-0.006|-0.032|0.015|-0.049|-0.002|-0.020|-0.000|-0.006|0.014|
||(0.014)***|(0.008)|(0.028)|(0.014)|(0.030)|(0.016)|(0.030)|(0.014)|(0.032)|(0.015)|
|imports/gd<br>|0.030|0.007|0.029|0.003|0.040|-0.012|-0.003|0.014|0.063|-0.028|
|p|||||||||||
||(0.019)|(0.009)|(0.042)|(0.013)|(0.040)|(0.015)|(0.035)|(0.012)|(0.047)|(0.017)*|
|inflation|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|-0.001|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)***|
|main|-0.003|0.001|||||||0.023|0.012|
||(0.003)|(0.003)|||||||(0.011)**|(0.006)*|
|egc|||-2.427|-1.325|||||-6.563|-4.407|
||||(1.516)|(0.709)*|||||(2.014)***|(1.258)***|
|rail|||||-3.314|-1.242|||-4.596|-0.642|
||||||(5.158)|(1.093)|||(5.445)|(1.083)|
|pav|||||||-2.823|0.116|-4.053|-0.851|
||||||||(1.084)***|(0.188)|(1.776)**|(0.448)*|
|Obs|497|497|313|313|237|237|276|276|202|202|
|R-squared|0.23||0.20||0.27||0.24||0.35||
|Haus test<br>FEvsRE|51.06***||12.43||11.48||18.41**||19.79*||

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Full set of period dummies included.

35
# **Table 16. Panel 5 years average, 2SLS estimations.**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Fixed Effects,<br>2SLS|Fixed Effects,<br>2SLS|Fixed Effects,<br>2SLS|Fixed Effects,<br>2SLS|Fixed Effects,<br>2SLS|
||pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|pcGDPgrowth|
|Constant|-5.587|-17.221|-7.866|-20.282|-8.576|
||(2.496)**|(6.732)**|(7.535)|(6.758)***|(11.968)|
|GDPpc|-0.000|0.000|-0.000|-0.000|0.004|
||(0.000)**|(0.000)|(0.000)|(0.000)|(0.002)*|
|Invt/gdp|0.143|0.267|0.216|0.043|0.205|
||(0.031)***|(0.070)***|(0.065)***|(0.054)|(0.104)**|
|lifeexpect|0.106|0.239|0.094|0.340|0.071|
||(0.042)**|(0.109)**|(0.124)|(0.106)***|(0.192)|
|m2/gdp|-0.057|-0.040|-0.043|-0.030|-0.081|
||(0.017)***|(0.033)|(0.031)|(0.033)|(0.053)|
|imports/gdp|0.038|-0.070|0.023|0.045|-0.025|
||(0.021)*|(0.050)|(0.039)|(0.039)|(0.076)|
|inflation|-0.001|-0.001|-0.001|-0.001|-0.001|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|(0.000)**|
|main|-0.002||||-0.082|
||(0.005)||||(0.052)|
|egc||6.211|||38.411|
|||(2.714)**|||(20.499)*|
|rail|||2.650||-11.985|
||||(7.567)||(14.405)|
|pav||||-0.459|-6.015|
|||||(1.889)|(5.749)|
|Observations|362|218|177|202|148|
|Haus test endog<br>p-value|60.17***|38.84***|0.15|13.12|7.28|

Standard errors in parentheses. * significant at 10%; ** significant at 5%; *** significant at 1%. Instruments: Lagged infrastructure, agri/gdp71, population density 71, population 71. Full set of period dummies included.

36