metadata: authors: 0: author: Foster,Vivien
1: author: Pierce,Justin
2: author: Yepes Delgado,Tito
count: Africa
docna: 0: docna: Making sense of Africa's infrastructure
            endowment : a benchmarking approach
docty: Policy Research Working Paper
subtopic: Sanitation and Sewerage,Engineering,Electric Power,Energy Policies & Economics,Water and Human Health,Hydrology,Water Supply and Sanitation Economics,Sanitary Environmental Engineering,Small Private Water Supply Providers,Environmental Engineering,Health and Sanitation,Global Environment,Town Water Supply and Sanitation
teratopic: Transport,Infrastructure Economics and Finance,Urban Development,Finance and Financial Sector Development,Communities and Human Settlements
abstracts: cdata!: The paper's objective is to explain
            factors underlying Africa's weak infrastructure
            endowment and to identify suitable infrastructure goals for
            the region based on benchmarking against international
            peers. The authors use a dataset covering the stocks of key
            infrastructure-including information and communication
            technology (ICT), power, roads, and water-across 155
            developing countries over the period 1960 to 2005. The paper
            also examines subregional differences within Africa. They
            make use of regression techniques to control for a
            comprehensive set of economic, demographic, geographic, and
            historic conditioning factors, as well as adjusting for
            potential endogeneities. Results show that Africa lags
            behind all other regions of the developing world in its
            infrastructure endowment, except in ICT. By far the largest
            gaps arise in the power sector, with generating capacity and
            household access to electricity at half the levels observed
            in South Asia. While it is often assumed that Africa's
            infrastructure deficit is largely a reflection of its
            relatively low income levels, the authors find that African
            countries have much more limited infrastructure than income
            peers in other parts of the developing world. Countries that
            face the most challenging environment, with low population
            density, weak governance, and history of conflict, have the
            poorest infrastructure endowments. At the outset of the data
            series, Africa was doing significantly better than other
            developing regions for road density, generation capacity,
            and fixed-line telephones, but Africa's relative
            position has deteriorated over time. The most dramatic loss
            of ground has come in electrical generating capacity, which
            has stagnated since 1980.
repnme: repnme: Making sense of Africa's
            infrastructure endowment : a benchmarking approach
display_title: Making sense of Africa's
            infrastructure endowment : a benchmarking approach
listing_relative_url: /research/2009/04/10509356/making-sense-africas-infrastructure-endowment-benchmarking-approach
topicv3: Sanitation m1326453 2000,Water m1326522 137,Power and Electricity Sector m1327094 515,Environment, Natural Resources and Blue
            Economy m1327391 566,Electricity Access m1503452 3922
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Transport,Infrastructure Economics and Finance,Urban Development,Finance and Financial Sector Development,Communities and Human Settlements
no_of_pages: 42
author_keys: 0: author_key: 452278
1: author_key: 797417
2: author_key: 635686
model_extractions: WPS4912

# Policy ReseaRch WoRking PaPeR 4912

# A Benchmarking Approach

_Tito Yepes Justin Pierce Vivien Foster_ The World Bank Africa Region African Sustainable Development Front Office April 2009
Policy ReseaRch WoRking PaPeR 4912

# **Abstract**

The paper’s objective is to explain factors underlying Africa’s weak infrastructure endowment and to identify suitable infrastructure goals for the region based on benchmarking against international peers. The authors use a dataset covering the stocks of key infrastructure— including information and communication technology (ICT), power, roads, and water—across 155 developing countries over the period 1960 to 2005. The paper also examines subregional differences within Africa. They make use of regression techniques to control for a comprehensive set of economic, demographic, geographic, and historic conditioning factors, as well as adjusting for potential endogeneities.

Results show that Africa lags behind all other regions of the developing world in its infrastructure endowment, except in ICT. By far the largest gaps arise in the power sector, with generating capacity and household access to electricity at half the levels observed in South Asia. While it is often assumed that Africa’s infrastructure deficit is largely a reflection of its relatively low income levels, the authors find that African countries have much more limited infrastructure than income peers in other parts of the developing world. Countries that face the most challenging environment, with low population density, weak governance, and history of conflict, have the poorest infrastructure endowments.

At the outset of the data series, Africa was doing significantly better than other developing regions for road density, generation capacity, and fixed-line telephones, but Africa’s relative position has deteriorated over time. The most dramatic loss of ground has come in electrical generating capacity, which has stagnated since 1980.

This paper—a product of the African Sustainable Development Front Office, Africa Region—is part of a larger effort in the region to gauge the status of public expenditure, investment needs, financing sources, and sector performance in the main infrastructure sectors for 24 African focus countries, including energy, information and communication technologies, irrigation, transport, and water and sanitation. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at jirving@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
# Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach

Tito Yepes, Justin Pierce, and Vivien Foster

## **Acknowledgments**

This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. Financing for AICD is provided by a multi-donor trust fund to which the main contributors are the Department for International Development (United Kingdom), the Public Private Infrastructure Advisory Facility, Agence Française de Développement, and the European Commission. Inquiries concerning the availability of datasets should be directed to vfoster@worldbank.org.

The authors thank Cesar Calderón for generously providing some of the data used in our analysis of the interplay of infrastructure and growth.
# Contents

|**1 Methodological framework**|**1**|
|---|---|
|**2 The impact of environmental factors on infrastructure endowments**|**5**|
|Variations across regions|5|
|Variations across types of countries|10|
|**3 The impact of historical trends on infrastructure endowments**|**17**|
|Variations over time|17|
|Panel data models|23|
|Benchmarking|26|
|**4 Conclusions**|**28**|
|**References**|**29**|
|**Annex A Alternative regression models**|**31**|
|**Annex B Country-specific deviations from regression models**|**32**|
|**Annex C Regional infrastructure endowment**|**36**|
|**About AICD**|**38**|

## **The authors**

Tito Yepes is an economist in the World Bank’s Latin America and Caribbean Region. Justin Pierce (justinrpierce@gmail.com) is a doctoral student in economics at Georgetown University. Vivien Foster (vfoster@worldbank.org) is a lead economist in the World Bank’s Africa Region and the staff director of the Africa Infrastructure Country Diagnostic.

iii
T he international community has recently committed itself to scaling up development assistance to Africa, in part to address the continent’s major infrastructure deficit. But given its income and other constraints, what level of infrastructure should Africa aim for? A related issue is how well African institutions currently perform in sustaining and expanding infrastructure stocks based on the resources that they have at their disposal. Both questions are amenable to analysis through cross-country benchmarking of infrastructure stocks.

The objective of this paper is to shed light on Africa’s current infrastructure endowment and clarify its future infrastructure goals. We pursue that objective by benchmarking the region’s stock of key infrastructure—including information and communication technology (ICT), power, roads, and water—against a dataset comprising 155 countries over the period 1960 to 2005. The paper also sheds light on subregional differences, by comparing infrastructure stocks between southern, central, eastern, and western Africa. In addition to pinpointing the factors that have contributed to low infrastructure development on the continent, the benchmarking models serve to identify suitable infrastructure targets that take into account the environmental difficulties that countries face.

The paper is divided into three main sections. Section 1 lays down the methodological framework used in the paper and relates it to the academic literature. Section 2 undertakes a cross-sectional analysis that identifies the extent to which differences in infrastructure stocks across countries can be explained in terms of differences in the geographical, demographic, and economic environment that they face. Section 3 presents a panel data analysis that incorporates the evolution of infrastructure stocks over time and thus clarifies the role of historical factors in determining today’s infrastructure endowments.

# 1   Methodological framework

Benchmarking is a technique widely used in the management and regulatory fields to compare the performance of firms against relevant peer groups. The benchmarking technique employed in this paper was first proposed by Shleifer (1985), as a means of regulating so-called “natural monopolies.” Shleifer argued that regulators should select socially optimal prices for markets served by natural monopolies based on cost data from “similar” firms. Our approach also resembles that used by Battese and Coelli (1993) to determine levels of “technical inefficiency” among firms in a particular industry. Battese and Coelli estimated industry-level production functions based on firm-level data and defined the distance between a firm’s actual and predicted levels of production as its “technical inefficiency.” More recently, the World Bank’s annual _Doing Business_ report has illustrated the application of benchmarking techniques at the country level. By creating a global database of investment climate indicators and conducting crosscountry comparisons, _Doing Business_ has prompted a global debate about how to reduce red tape and motivated policy makers to improve their competitiveness. A similar approach has been applied to the infrastructure sectors in recent World Bank work at the country level (World Bank, 2004, 2005a, 2005b). Research in this area has benefited greatly from the availability of an international panel of data on infrastructure stocks provided in Estache and Goicoechea (2005).
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH Here we perform a country-level benchmarking exercise for infrastructure stocks in SubSaharan Africa, comparing them with a global peer group of developing countries. The paper extends and deepens the work of Bogetic (2006) for the countries of the Southern African Development Community (SADC), in particular by using regression techniques to control for a wide range of differences in the operating environment faced by particular countries. In this way, the benchmark against which each country is compared is individually adjusted to control for a comprehensive set of economic, demographic, geographic, and historic conditioning factors.

The analysis is based on a panel dataset of all developing countries for the years 1960 to 2004. Developing countries are defined to be those in the low-income, lower-middle-income, and upper-middle-income categories as defined by the World Bank. Data availability varies over the period according to the specific infrastructure variable under consideration. Thus, road data are available from 1960, ICT data from 1970, and electricity generation capacity data from 1980. In the case of water and sanitation, only two time periods are available (1990 and 2002), while for access to electricity no consistent time series could be found. The data are drawn from a variety of global sources, including the World Bank’s _World Development Indicators_ , the International Telecommunications Union, and the World Economic Forum’s _Global Competitiveness Report_ , among others.

Our study contributes to the international benchmarking literature in three ways. In terms of scope, it is the first study to benchmark the entire region of Sub-Saharan Africa, with an emphasis on comparing regions of the developing world as well as the diverse subregions of Africa. It is also the first study to base its predicted infrastructure levels on a panel dataset and to control for potential endogeneity of regressors.

The benchmarking exercise compares each country’s actual infrastructure endowments to an expected, or predicted, value based on its socioeconomic structure. The predicted values are derived from an econometric model that explains variation in infrastructure levels among developing countries based on a set of economic, demographic, and structural variables. This exercise produces a measure of infrastructure endowment that controls for differences in all of the socioeconomic variables included in the model. Thus, for example, it would be incorrect to explain a lower-than-predicted infrastructure endowment for a particular country in terms of the country’s low income, because the model includes income as an independent variable. Hence the result already controls for differences in income. Note that here the concept of an expected, or predicted, infrastructure level does not refer to any concept of demand, since actual levels of infrastructure may also be driven by supply factors. Moreover, the expected value should not be treated as an ideal; it simply expresses the average endowment of countries with comparable characteristics.

Each country’s infrastructure endowment is measured by the deviation between its actual endowment and the endowment predicted by the model (equation 1). A positive deviation indicates that the country outperforms the benchmark provided by the econometric model (i.e., the average for the relevant peer group) and vice versa. The larger the absolute size of the deviation, the greater the extent of the corresponding over- or underachievement.

2
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH Separate econometric models are estimated for nine different infrastructure variables—ICT (fixed and mobile telephone lines, Internet connections), power (generating capacity and access to electricity), roads (total and paved), water, and sanitation. All variables are normalized to facilitate comparisons across countries. Roads are measured in terms of their density across the country’s surface area. However, to allow for the fact that some countries include large areas of uninhabitable wilderness, the total land area and total arable area are used as alternative means of normalization. Generation capacity is normalized per million inhabitants; ICT variables are normalized per thousand inhabitants. Electricity, water, and sanitation are expressed as percentage household access rates. Access to water and sanitation correspond to the definitions of improved water and sanitation specified in the Millennium Development Goals (United Nations 2007).

Our data reflect only the _quantity_ of infrastructure; they say nothing of the quality and hence economic value of those stocks. For example, two countries may have the same paved road density, but one network may be well maintained and the other nearly impassable. Unfortunately, there is no global dataset available that documents the quality of infrastructure stocks, although some research does indicate a close correspondence between quality and quantity of infrastructure (Calderon and Serven 2004). **[** The first step is to estimate a simple, cross-sectional, ordinary least squares (OLS) model, based on the most recent year of data available for each of the nine forms of infrastructure . This is done following the specification given in equation (2), where _y_ is an infrastructure stock, _X_ is a vector of independent variables (including economic, demographic, and environmental variables as discussed below), and  is an error term.

Our approach extends the work of Canning (1998) to include a much wider set of explanatory variables. In his seminal paper, Canning found that a significant portion of cross-country variation in infrastructure endowments could be explained by economic and demographic variables, such as per capita income, population density, urbanization rate, and growth of urban population. To Canning’s set of regressors we add several demographic, public sector, and structural variables. Ethnic fractionalization is also included as a regressor, since competition between ethnic groups may affect infrastructure building programs. Similarly, a governance term based on Transparency International’s Corruption Perceptions Index accounts for the impact of wasteful or corrupt government management of infrastructure projects. A per capita measure of foreign aid designated for infrastructure controls for significant variations in aid activity between countries. Lastly, structural variables such as the share of manufacturing, agriculture, and exports in GDP are incorporated, since the structure of the economy may affect demand for specific infrastructure services. Our choice of explanatory variables draws upon an extensive exploratory data analysis presented in the next section of the paper.

3
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH Several important limitations of this approach call for the use of panel data models. The simple OLS cross-section is of some interest in that it replicates the results of earlier literature (Canning 1998) and serves to isolate the effect of specific environmental variables that can be identified as relevant from the exploratory data analysis. Nevertheless, it is subject to important methodological limitations. First, the environmental variables only imperfectly control for the myriad differences that arise in different country situations, which can be fully reflected only by means of country-specific factors. Second, the long lag times in the development of infrastructure stocks mean that historic trends play an important role in explaining a country’s current infrastructure endowment. Third, the potential reverse causality from per capita income to infrastructure stocks raises the possibility of endogeneity. All of these issues can be addressed using panel data models that analyze a repeated cross-section for the same countries across a number of years.

Our second step is therefore to estimate an OLS panel model that controls for country differences (fixed effects) that affect the ease or difficulty of providing infrastructure services. This is represented by equation (3), where _yit_ is defined as the infrastructure level for country _i_ at time _t_ , _Xit_ is a matrix of socioeconomic and structural explanatory variables, and _ηi_ is a timeinvariant country-specific fixed effect.

Because the OLS fixed-effects model does not correct for the potential endogeneity of the explanatory variables, our third step is to control for potential endogeneity of relevant regressors using instrumental variables. It seems likely that per capita gross domestic product (GDP) will be endogenous in the specification given in (3). In fact, there is already a large literature based on the concept that causation lies in the opposite direction—that is, that expansion of infrastructure services increases income and income growth. This direction of causality has been examined extensively in Easterly and Serven (2003) and Calderon and Serven (2003, 2004). We draw on this literature to choose appropriate instruments for per capita GDP. Specifically, we employ some of the standard growth regressors from Calderon and Serven (2004), including trade openness (trade as share of GDP), inflation, political risk index, government involvement in economy (government consumption as share of GDP), domestic credit available to the private sector (as share of GDP), and the terms of trade index. Adding these regressors gives us the following equation: _Xit_ contains the same set of regressors included in the cross-sectional OLS study. Per capita GDP is instrumented using the standard growth variables described above. Lastly, it is important to note that any time-invariant variables from the OLS regressions are dropped once country fixed effects are included, which accounts for the smaller sets of coefficients reported for panel data models later in the paper.<sup>1</sup> 1 The regressors used in (4) were also considered in a dynamic panel specification, using the technique developed by Blundell and Bond (1998). These results are reported in Annex A.1 for comparison purposes, 4
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

# 2   The impact of environmental factors on infrastructure endowments

Here we examine cross-country variations in today’s infrastructure stocks in the context of the very different operating environments that countries face. We begin with a simple benchmarking of infrastructure stocks by regions, subregions, income groups, and other subcategories. We then use a cross-sectional OLS model to isolate the impact of individual factors on infrastructure stocks in a multivariate framework. All averages presented are simple unweighted averages across countries, as opposed to population-weighted averages for the different country groupings. As a result, regional summary statistics may appear to differ from commonly reported regional averages, which typically are based on a population weighting of countries.

## Variations across regions

Africa lags behind all other regions of the developing world in its infrastructure endowment, except in ICT (table 2.1). This finding holds across a wide range of indicators including the density of roads and paved roads, per capita capacity to generate electricity, and household access to electricity, water, and sanitation. By far the largest gaps arise in the power sector, with generating capacity and household access to electricity in Africa at around half the levels observed in South Asia, and about a third of the levels observed in East Asia. The conclusion on paved road density differs depending on whether one is considering total land area (in which case Africa comes in last) or only arable area (in which case Africa comes in ahead of South Asia and East Asia). In ICT, Africa significantly outperforms South Asia in density of mobile telephones and Internet connections and comes close in terms of fixed-line density.

but they should be treated with caution because the slow adjustment process for infrastructure stocks may overstate the significance of the lagged dependent variable.

5
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.1 Infrastructure endowments by world region**

|Sector and measure|Sub-<br>Saharan<br>Africa|South Asia|East Asia<br>and Pacific|Europe and<br>Central Asia|Latin<br>America and<br>Caribbean|Middle East<br>and North<br>Africa|
|---|---|---|---|---|---|---|
|_Transport_|||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|49|149|59|335|418|482|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|1,087|675|588|1,208|4,826|6,890|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|152|306|237|576|740|599|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|2,558|1,400|5,385|2,160|8,850|30,319|
|_Information and communication technology_|||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|33|39|90|261|197|100|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|101|86|208|489|350|224|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.8|1.7|6.6|16.4|14.1|10.1|
|_Energy_|||||||
|Electrical generating capacity (MW per 1<br>million people, 2003)|70|154|231|970|464|496|
|Access to electricity<br>(% of households with access, 2004)|18|44|57|—|79|88|
|_Water and sanitation_|||||||
|Water<br>(% of households with access, 2002)|63|72|75|87|90|85|
|Sanitation<br>(% of households with access, 2002)|35|48|60|78|77|77|

_Sources_ : For transport, Easterly & Serven (2003); for ICT, International Telecommunications Union; for energy, Energy Information Agency, U.S. Department of Energy); for water and sanitation, World Development Indicators.

Africa’s deficit remains even when the countries of the region are compared with others in the same income bracket (table 2.2). It is often assumed that Africa’s infrastructure deficit is largely a reflection of its relatively low income levels. But the comparison with other developing countries in the same income bracket shows that income does not tell the whole story. Africa’s low-income countries (LICs) lag substantially behind those in other regions, while the same is true for lower-middle-income countries (LMCs) and upper-middle-income countries (UMC). The divergence is particularly striking for power and paved roads. Electrical generating capacity and access to electricity in Africa are less than a third of the levels found in other UMCs around the world.

6
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.2 Infrastructure endowments by income group, Sub-Saharan Africa vs. other world regions**

||L<br>|ow-incom<br>|e<br>|Co<br>Lower<br>|untry gro<br>-middle-i<br>|up<br>ncome<br>|Upper<br>|-middle-i<br>|ncome<br>|
|---|---|---|---|---|---|---|---|---|---|
||Africa|Other|Ratio|Africa|Other|Ratio|Africa|Other|Ratio|
|Sector and measure|(A)|(B)|(C=B/<br>A)|(D)|(E)|(F=E/<br>D)|(G)|(H)|(I=H/<br>G)|
|_Transport_||||||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|31|134|4.3|94|141|1.5|238|781|3.3|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|290|728|2.5|1,176|1,919|1.6|11,086|7,415|0.7|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|137|211|1.5|215|343|1.6|293|1,171|4.0|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,535|2,194|1.4|4,233|10,624|2.5|14,179|13,375|0.9|
|_Information and communication technology_||||||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|10|78|7.7|106|131|1.2|120|274|2.3|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|55|86|1.6|201|298|1.5|422|554|1.3|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.0|3.2|1.6|5.1|8.0|1.6|10.3|26.2|2.5|
|_Energy_||||||||||
|Electrical generating capacity (MW per 1<br>million people, 2003)|37|326|8.8|256|434|1.7|246|861|3.5|
|Access to electricity<br>(% of households with access, 2004)|16|41|2.6|35|80|2.3|28|95|3.4|
|_Water and sanitation_||||||||||
|Water<br>(% of households with access, 2002)|60|72|1.2|75|86|1.2|90|93|1.0|
|Sanitation<br>(% of households with access, 2002)|34|51|1.5|48|74|1.5|39|90|2.3|

_Sources_ : As for table 2.1.

Nevertheless, the African average masks significant variations within the region (table 2.3). To compare subregional endowments, we used four country groupings. They are SADC (all the SADC countries except Tanzania); the East African Community (EAC) of Kenya, Tanzania, and Uganda; the Economic Community of West African States (all the ECOWAS countries); and Central Africa, a default category comprised of Burundi, Cameroon, the Central African Republic, Chad, Comoros, Congo, Rep., Equatorial Guinea, Eritrea, Ethiopia, Gabon, Madagascar, Mauritania, Mozambique, Rwanda, Sao Tome and Principe, Somalia and Sudan.

Comparing infrastructure endowments across these subregions reveals that the SADC countries have a substantial advantage over the others. That advantage is most pronounced in the case of paved roads, ICT, and power, where SADC is ahead of the other regions by several multiples. Generating capacity (per capita) in SADC, for example, is more than five times that reported in other parts of Africa, although, strikingly, household access to electricity is relatively similar. At the other end of the spectrum, EAC has the lowest infrastructure endowment on most measures. Western and central Africa are similar in their results. The least divergence in stocks 7
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH across subregions is seen in water and sanitation, with all regions reporting water access in the 60–70 percent range and sanitation access in the 30–45 percent range.

**Table 2.3 Infrastructure endowments by African subregion**

|||African sub<br>|region<br>||
|---|---|---|---|---|
||ECOWAS|EAC|SADC|Central|
|_Transport_|||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|38|8|92|41|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|301|93|3,636|416|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|144|105|214|132|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,279|1,286|6,164|1,790|
|_Information and communication technology_|||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|28|6|74|13|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|72|54|180|74|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.4|2.1|5.5|1.7|
|_Energy_|||||
|Electrical generating capacity (MW per 1 million<br>people, 2003)|31|24|175|44|
|Access to electricity<br>(% of households with access, 2004)|18|7|21|18|
|_Water and sanitation_|||||
|Water<br>(% of households with access, 2002)|63|64|71|58|
|Sanitation<br>(% of households with access, 2002)|35|45|43|28|

_Sources_ : As for table 2.1.

East Asia is the closest comparator for the SADC countries; South Asia for the other African subregions. Figure 2.1 brings together the regional and subregional analysis by plotting infrastructure stocks against income for all of the regions and subregions we have considered so far. In income terms, the SADC countries are most comparable to East Asia and the Pacific. Nevertheless, the SADC countries have less infrastructure than the countries of East Asia and the Pacific by all measures, particularly access to sanitation and electricity. The only exception is paved road density, where SADC has some edge over EAP. By contrast, all other African subregions are, in income terms, similar to each other and to South Asia. On the whole, their endowments do not diverge substantially from that of the whole of Sub-Saharan Africa, although the Central African countries lag behind in terms of water and sanitation.

8
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Figure 2.1   Infrastructure endowments plotted against national income, by world region**

<!-- Start of picture text --> a. Percentage of households with improved water b. Percentage of households with improved sanitation<br>LCR<br>ECA ECALCR MNA<br>MNA<br>EAP<br>EAP<br>SAS<br>SADC SAS<br>EAC<br>SADC<br>EACECOWASSSA<br>ECOWASSSA<br>CENTRAL CENTRAL<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Income Per-Capita Income Per-Capita<br>c. Percentage of households with electricity d. MW installed generating capacity per 1 million people<br>MNA<br>ECA<br>LCR<br>EAP<br>SAS MNA<br>LCR<br>SADC EAP<br>ECOWASCENTRALSSA<br>SAS SADC<br>EAC SSA<br>EACECOWASCENTRAL<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Income Per-Capita Income Per-Capita<br>e. Total roads (km/km2) f. Paved roads (km/km2)<br>MNA<br>LCR<br>LCR<br>MNA<br>ECA<br>ECA<br>SAS<br>EAP SAS<br>SADC<br>ECOWASCENTRALSSA SADC<br>EAC ECOWASCENTRALSSA EAP<br>EAC<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Income Per-Capita Income Per-Capita<br>90 80<br>70<br>80<br>60<br>50<br>70<br>40<br>% HHolds With Improved Water<br>% HHolds With Improved Sanitation<br>60<br>30<br>1000<br>80<br>800<br>60<br>600<br>40<br>400<br>MW per 1 Mil. People<br>% HHolds With Electricity 20<br>200<br>0 0<br>800 500<br>400<br>600<br>300<br>400<br>200<br>Tot. Road (km/km^2)<br>200 Paved Road (km/km^2)<br>100<br>0 0<br><!-- End of picture text --> 9
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Figure 2.1   Infrastructure endowments plotted against national income, by world region, continued**

|g. Mobile telephone subscribers per 1,000 inhabitants<br>|h. Landline telephone|subscribers per 1,000 inhabitants|
|---|---|---|
|CENTRAL<br>EAC<br>ECOWAS<br>SADC<br>EAP<br>ECA<br>LCR<br>MNA<br>SAS<br>SSA<br>0<br>100<br>200<br>300<br>400<br>500<br>Mobile Subscribers Per 1000 Inhabitants|CEN<br>EAC<br>ECOW<br>SA<br><br>0<br>50<br>100<br>150<br>200<br>250<br>Mainline Subscribers Per 1000 Inhabitants|TRAL<br>AS<br>SADC<br>EAP<br>ECA<br>LCR<br>MNA<br>S<br>SSA|
|0<br>1000<br>2000<br>3000<br>4000<br>Income Per-Capita<br>_Source_: As for table 2.1.<br>Variations across types of countries<br>Relative to other developing regions, Sub-Saharan<br>development of infrastructure, and the region’s infrastr<br>environmental disadvantages (table 2.4). Building infra<br>countries characterized by low population density and l<br>governance, high incidence of conflict, and geographic<br>Africa from other developing regions. Africa has the lo<br>governance index, and the second-lowest urbanization<br>incidence of conflict in Africa is similar to that in other<br>absolute number of countries in conflict is higher in Af<br>**Table 2.4 Difficulty of infrastructure environment across regi**<br>n|0<br>1000<br>Africa faces a dif<br>ucture shortfall m<br>structure tends to<br>ow levels of urba<br>al isolation—all o<br>west population d<br>rate of all develop<br>regions in percen<br>rica than in any ot<br>**ons**<br>d|2000<br>3000<br>4000<br>Income Per-Capita<br>ficult environment for the<br>ay be traceable to those<br>be more difficult in<br>nization, weak<br>f which distinguish<br>ensity, the lowest<br>ing regions. The<br>tage terms, but the<br>her region.<br> <br> <br>a<br>an<br>|
|ahara<br>|Asia<br>sia an<br>c|e and<br>al Asia<br>Americ<br>aribbe<br>e East<br>orth<br>|
|b-S<br>ica|uth<br>st A<br>cifi|rop<br>ntr<br>in<br>d C<br>dl<br>d N<br>ica|
|Regional characteristic<br>Su<br>Afr|So<br>Ea<br>Pa|Eu<br>Ce<br>Lat<br>an<br>Mid<br>an<br>Afr|
|Population density (pop./km2)<br>67|436<br>121|71<br>133<br>150|
|Urbanization rate (%)<br>37|23<br>42|57<br>63<br>69|
|Governance index (0=most corrupt; 10=least corrupt)<br>3.7|4.3<br>4.2|3.9<br>3.8<br>4.5|
|Landlocked countries (%)<br>30|38<br>9|29<br>6<br>0|
|Countries in conflict (%)<br>23|25<br>14|25<br>12<br>25|
|Income per capita (US$)<br>999|842<br>1,801|3,628<br>3,649<br>4,193|

_Sources_ : As for table 2.1.

To explore the potential impact of environmental variables, we compare the infrastructure endowment of countries _within the region_ according to the difficulty of their environment. As 10
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH expected, countries facing a more challenging environment perform systematically worse on infrastructure (table 2.5). The differences in endowment are largest for landlocked countries, countries in conflict, and countries with low population density. Countries with low urbanization perform worse on service coverage, but better on road density. Countries with poor governance also perform worse than their comparators, but the differences are less pronounced. Overall, paved road density is the infrastructure variable that shows the largest variation across types of countries, while water and sanitation show the least variation.

Striking differences also emerge when countries are grouped according to their oil-exporting status and language (table 2.6). Oil-exporting countries score systematically and substantially _worse_ than oil-importing countries, suggesting that oil revenues are not being channeled into infrastructure investments. With respect to language groupings, francophone countries have the lowest infrastructure stocks overall. Once again, the largest differences are to be found in the area of paved road density, and the smallest in water and sanitation.

11
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.5 Infrastructure endowments by demographic, political, and geographic variables**

||Popul<br>den<br>|ation<br>sity<br>|Urbani<br>|zation<br>|Govern<br>|ance<br>|Con<br>stat<br>|flict<br>us<br>|Coas<br>|tline<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|Sector and measure|Low|High|Low|High|Poor|Good|Yes|No|No|Yes|
|_Transport_|||||||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|11|131|51|21|45|54|12|62|16|65|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|273|2,829|1,144|308|354|1,819|212|1,378|218|1,492|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|79|315|155|112|178|126|135|158|148|154|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,878|4,065|2,585|2,178|1,532|3,631|1,927|2,762|1,174|3,183|
|_Information and communication technolo_|_gy_||||||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|21|56|33|28|10|51|15|36|14|50|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|92|133|82|321|68|128|44|115|49|131|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|2.2|4.1|2.7|3.8|2.6|3.0|1.0|3.3|1.3|3.5|
|_Energy_|||||||||||
|Electrical generating capacity (MW per<br>1 million people, 2003)|79|49|46|410|47|92|39|79|40|83|
|Access to electricity<br>(% of households with access, 2004)|18|17|16|49|15|21|10|21|9|22|
|_Water and sanitation_|||||||||||
|Water<br>(% of households with access, 2002)|59|72|62|77|63|64|52|67|61|64|
|Sanitation<br>(% of households with access, 2002)|34|39|34|48|35|35|27|38|32|36|

_Sources_ : As for table 2.1.

While the preceding analysis is suggestive, it does not take into account the high correlations that exist among these different variables, and with income. Indeed, correlation coefficients between these different indicators of “difficulty” range in absolute value between 0.3 and 0.6. In order to isolate the effect of individual factors we must perform a multivariate regression analysis that looks at all of the effects simultaneously.

As described in equation (1) above, we perform simple OLS cross-sectional analysis for each category of infrastructure. Many control variables are considered—among them income per capita; demographic measures (population density, urbanization, urban growth rates, and ethnic fractionalization); measures of the quantity and quality of public spending on infrastructure (proxied by governance and infrastructure aid per capita); measures capturing the geographical and cultural heritage of the country (including language group, location, and natural resources); and indicators of the structure of the economy (such as the share of exports, agriculture, and manufacturing in GDP). In addition, an Africa dummy is used to see if any specific disadvantages are associated with the continent as a whole.

12
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.6 Infrastructure endowments by historical variables**

||Oil-exporter|Not oil<br>exporter|Lusophone|Anglophone|Francophone|
|---|---|---|---|---|---|
|_Transport_||||||
|Density of paved road network<br>(km/1,000 km<br>2, 2001)|14|57|95|84|31|
|Density of paved road network<br>(km/1,000 arable km<br>2, 2001)|246|1,273|1,315|2,705|188|
|Density of total road network<br>(km/1,000 km<br>2, 2001)|70|173|152|240|136|
|Density of total road network<br>(km/1,000 arable km<br>2, 2001)|1,909|2,720|2,295|4,861|1,425|
|_Information and communication technology_||||||
|Density of fixed-line telephones<br>(subscribers per 1,000 people, 2004)|16|38|148|48|4|
|Density of mobile telephones<br>(subscribers per 1,000 people, 2004)|118|97|86|139|46|
|Density of Internet connections<br>(subscribers per 100 people, 2004)|1.7|3.1|3.7|4.8|1.5|
|_Energy_||||||
|Electrical generating capacity (MW per 1 million<br>people, 2003)|66|71|49|145|22|
|Access to electricity<br>(% of households with access, 2004)|26|16|15|23|15|
|_Water and sanitation_||||||
|Water<br>(% of households with access, 2002)|59|64|59|72|60|
|Sanitation<br>(% of households with access, 2002)|34|35|35|46|29|

_Sources_ : As for table 2.1.

The OLS regression analysis reveals that levels of infrastructure stocks are primarily related to per capita income and demographic variables, and less so to other variables (table 2.7). Income is statistically significant in almost all cases, except for household access to water and electricity. Demographic variables also seem to be important. Higher levels of urbanization are associated with significantly higher rates of household access to water, sanitation, electricity. By contrast, higher rates of urban growth significantly hold back rates of access to services, suggesting difficulties in keeping up with the rate of urban expansion. Moreover, countries with higher population density seem to have significantly less road area, although this difference disappears when arable land is used to measure road density.

None of the other geographical, cultural, or structural variables prove to be statistically significant in explaining infrastructure stocks. This suggests that in the earlier analysis, the seeming effect of geographical and cultural variables was likely being confounded with the effect of income and demographic variables with which they are associated. Finally, it is noteworthy that the coefficients for infrastructure aid per capita and governance are rarely significant and do not always have the expected signs.

An alternative log-log specification of the model, designed to test the sensitivity of the estimates, yields similar results, while revealing relatively low income elasticities and somewhat 13
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH higher urbanization elasticities (table 2.8). The main differences from the first OLS model are these: In the log-log model, income is no longer significant for road density, and some of the structural variables (exports, agriculture, manufacturing) become more significant. Overall, the fit of the model is also somewhat improved. The log-log specification also allows the coefficients on continuous variables to be interpreted as elasticities. The results suggest that the income elasticity of infrastructure stocks is generally well below unity, with the highest values (around 0.5 to 0.7) found for ICT and generating capacity and the lowest (around 0.1 to 0.2) for access to water, sanitation, and electricity, and for roads. The elasticity of urbanization is somewhat higher, particularly for access to electricity and ICT services.

In summary, Africa presents a major infrastructure deficit relative to other developing regions that appears to reflect low urbanization as much as low income. The region lags behind all others in the developing world in almost all areas of infrastructure. The strongest endowments are found in the ICT sector, where Africa is somewhat ahead of South Asia. By far the lowest endowment is in the power sector. Rates of access to electricity in the middle-income countries of Africa, for example, are a fraction of those found in middle-income countries in other regions.

The regional average masks significant geographic variations in infrastructure endowments from country to country. The SADC countries are substantially ahead of the others, while the EAC countries are significantly behind.

Africa’s deficit remains even when countries are compared with others in the same income bracket. The reason appears to be that—compared with other regions of the world—Africa is a difficult environment in which to develop infrastructure. The proof is that infrastructure is weakest in those countries that face the most challenging environment. However, some environmental factors are more important than others. When all environmental variables are considered simultaneously, demographic variables (notably urbanization) appear to have the most substantial effect on infrastructure endowments.

14
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.7 Effect of country variables on infrastructure endowments, part 1**

<u>Cross-sectional OLS analysis, data in levels</u>

|Variable|Density of<br>roads|Density of<br>roads<br>(arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to<br>water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Income per|0.0001|0.002|0.0001|0.001|0.001|0.003|0.001|0.0001|0.003|0.056|0.021|
|<br>capita|(5.73)**|(3.07)**|(4.20)**|(3.15)**|–0.66|(2.76)**|–0.84|(4.87)**|(5.25)**|(5.75)**|(5.37)**|
||–0.515|–8.845|–0.288|–2.697|26.93|40.957|63.355|0.345|3.878|105.594|97.429|
|Urbanization|–1.66|–1.16|–1.06|–0.85|(2.66)**|(3.31)**|(4.81)**|–1.55|–0.59|–0.82|–1.89|
|Population|0.001|–0.007|0.002|0.003|0.031|0.007|0.019|–0.0001|0.01|–0.004|0.074|
|density|(4.29)**|–0.87|(5.53)**|–0.84|(3.04)**|–0.55|–1.4|–0.56|–1.49|–0.03|–1.36|
|Ub th|–0.052|0.451|–0.061|–0.052|–2.081|–4.273|–3.097|–0.095|–0.485|–16.292|–15.805|
|ran grow|–1.92|–0.67|(2.55)*|–0.19|(2.23)*|(3.66)**|(2.11)*|(4.62)**|–0.82|–1.43|(3.34)**|
|Fractionali-|0.036|–1.447|–0.048|–2.14|0.567|4.632|14.682|0.297|–0.583|71.167|–9.624|
|zation|–0.23|–0.38|–0.36|–1.37|–0.11|–0.72|(2.23)*|(2.56)*|–0.18|–1.08|–0.36|
||0.003|–0.197|0.005|0.198|0.507|–0.4|–0.717|–0.011|0.283|–6.042|0.024|
|Governance|–0.15|–0.39|–0.29|–0.96|–0.74|–0.46|–0.73|–0.74|–0.66|–0.74|–0.01|
|Infrastructure<br>|0.002|0.003|–0.002|–0.032|0.035|0.263|0.231|0.0004|–0.059|0.635|0.55|
|aid (per<br>capita)|–0.89|–0.07|–1.34|–1.81|–0.62|(3.76)**|(2.15)*|–0.33|–1.38|–0.72|–1.69|
|Afi|–0.119|–1.954|0.034|1.226|–7.198|–19.246|–35.293|–0.198|–2.237|–73.992|–29.09|
|rca|–1.03|–0.69|–0.34|–1.05|–1.95|(4.27)**|(7.13)**|(2.37)*|–0.89|–1.39|–1.42|
||0.141|3.394|–0.001|1.001|–3.101|7.351|–5.68|–0.103|0.088|8.696|–1.339|
|Anglophone|–1.1|–1.08|–0.01|–0.78|–0.73|–1.41|–1.04|–1.08|–0.03|–0.15|–0.06|
||0.043|–0.113|0.061|0.01|–6.511|–0.918|1.79|–0.034|1.266|28.44|–2.927|
|Francophone|–0.36|–0.04|–0.59|–0.01|–1.68|–0.2|–0.39|–0.39|–0.5|–0.55|–0.14|
||0.012|–0.864|0.041|–0.397|–1.434|–2.953|–4.667|0.075|–0.131|20.189|–0.019|
|Landlocked|–0.12|–0.36|–0.49|–0.4|–0.44|–0.75|–1|–1.06|–0.06|–0.47|0|
|Oili|–0.109|0.698|0.037|0.213|–3.356|–6.323|5.269|0.001|–3.03|–53.872|–15.892|
|-exportng|–1.25|–0.33|–0.48|–0.24|–1.15|–1.79|–1.35|–0.01|–1.65|–1.48|–1.08|
|Conflict|0.09|0.104|0.003|–0.173|–6.223|5.548|–2.748|–<br>0.0001|–2.37|4.995|–2.097|
||–0.85|–0.04|–0.03|–0.16|–1.84|–1.36|–0.58|0.0766|–1.08|–0.11|–0.12|
|Exports|–0.0003|0.07|0.0004|0.038|–0.099|–0.068|–0.131|0.001|0.114|1.497|–0.029|
|(% GDP)|–0.15|–1.29|–0.2|–1.75|–1.38|–0.71|–1.44|–0.63|(2.45)*|–1.62|–0.08|
|Agriculture|0.002|–0.056|0.003|–0.004|–0.164|0.192|–0.196|0.006|0.052|–1.379|0.578|
|<br>(% GDP)|–0.52|–0.49|–0.71|–0.09|–1.05|–1.02|–1|–1.63|–0.52|–0.64|–0.73|
|Manufacturing|–0.008|–0.275|–0.006|–0.062|–0.294|0.113|0.572|0.002|0.138|2.601|0.304|
|<br>(% GDP)|–1.21|–1.79|–1.04|–0.96|–1.47|–0.46|–1.98|–0.56|–1.05|–0.97|–0.29|
||0.343|7.331|0.126|0.385|81.37|45.274|38.376|0.092|–4.259|44.459|41.711|
|Constant|–1.13|–0.99|–0.48|–0.13|(8.18)**|(3.51)**|(2.93)**|–0.42|–0.66|–0.35|–0.84|
|Observations|105|104|104|103|101|99|82|110|108|93|107|
|R–squared|0.62|0.28|0.55|0.35|0.61|0.74|0.87|0.69|0.63|0.74|0.74|

_Note_ : * = significant at 5%; ** = significant at 10%. T-statistics are reported below coefficients. Parentheses denote that T- statistics have been reported in absolute value.

15
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 2.8  Effect of country variables on infrastructure endowments, part 2**

|Cross-sectio|nalOLS an<br>|alysis, data<br>|in logs (e<br> <br>s|xcept dum<br> <br>s|myvariable|s,which|arein leve|ls)<br>|s|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|Variable|Density of<br>roads|Density of<br>roads<br>(arable)|Density of<br>paved road|Density of<br>paved road<br>(arable)|Access to<br>water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connection|Mobile<br>telephones|Fixed-line<br>telephones|
|Income per|0.112|0.262|0.082|0.222|0.135|0.041|–0.061|0.451|0.693|0.492|0.748|
|capita|(2.87)**|(2.93)**|–0.57|–1.65|–0.75|–0.2|–0.27|(2.30)*|(5.19)**|(3.05)**|(5.13)**|
|Urbanization|0.188|0.461|0.991|0.341|0.426|–0.126|–0.023|0.942|0.885|0.859|0.54|
||(3.19)**|(3.40)**|(4.93)**|–1.62|–1.5|–0.38|–0.06|(3.05)**|(4.07)**|(3.53)**|(2.29)*|
|Population|0.071|0.081|0.09|0.729|1.111|–0.036|0.341|0.193|0.062|–0.048|0.121|
|density|(4.80)**|(2.38)*|–1.72|(14.12)**|(16.12)**|–0.44|(3.95)**|(2.57)*|–1.18|–0.75|(2.11)*|
||–0.07|–0.149|–0.181|–0.208|–0.598|0.201|–0.205|–0.243|–0.138|–0.488|–0.346|
|Urban growth|(2.26)*|(2.04)*|–1.46|(2.23)*|(4.76)**|–1.39|–1.31|–1.72|–1.53|(4.42)**|(3.28)**|
|Fractionali-|–0.011|–0.018|0.047|–0.088|–0.013|–0.146|–0.073|–0.019|–0.022|0.08|–0.08|
|zation|–0.69|–0.51|–0.88|–1.61|–0.18|–1.72|–0.81|–0.24|–0.4|–1.28|–1.37|
|Governance|0.035|0.034|0.086|–0.308|–0.122|–0.259|–0.05|–0.191|–0.339|0.016|0.035|
||–0.81|–0.33|–0.55|(2.09)*|–0.62|–1.13|–0.2|–0.88|(2.43)*|–0.09|–0.22|
|Infrastructure|–0.006|0.023|0.033|0.008|–0.006|0.025|0.009|0.002|0.067|–0.016|0.019|
|aid (per capita)|–0.67|–1.05|–0.99|–0.27|–0.14|–0.54|–0.17|–0.04|(2.24)*|–0.45|–0.57|
|Africa|–0.05|–0.251|–0.871|–0.097|–0.029|–0.249|–0.169|–0.042|–0.003|–0.998|–0.451|
||–1.01|(2.19)*|(5.14)**|–0.55|–0.12|–0.9|–0.57|–0.16|–0.02|(4.93)**|(2.26)*|
||–0.024|0.265|–0.135|0.526|–0.214|0.565|–0.144|0.039|0.269|0.246|–0.364|
|Anglophone|–0.44|(2.13)*|–0.73|(2.81)**|–0.85|–1.94|–0.46|–0.14|–1.4|–1.08|–1.67|
||–0.153|–0.23|–0.075|–0.11|–0.322|–0.347|–0.53|–0.331|–0.057|–0.621|–0.825|
|Francophone|(3.01)**|–1.96|–0.45|–0.61|–1.33|–1.24|–1.76|–1.26|–0.31|(2.95)**|(4.19)**|
|Landlocked|0.018|0.059|–0.221|0.314|–0.107|0.016|–0.474|–0.04|0.142|0.107|–0.076|
||–0.36|–0.52|–1.18|–1.92|–0.48|–0.06|–1.67|–0.16|–0.89|–0.54|–0.42|
|Oil i|–0.017|–0.033|0.195|–0.453|–0.001|–0.305|0.156|–0.294|–0.014|0.005|0.002|
|exportng|–0.41|–0.34|–1.34|(3.06)**|0|–1.32|–0.63|–1.37|–0.1|–0.03|–0.01|
||–0.091|0.011|–0.109|0.088|–0.347|0.002|–0.425|–0.272|0.171|–0.169|–0.286|
|Conflict|–1.98|–0.11|–0.66|–0.54|–1.58|–0.01|–1.55|–1.15|–1.08|–0.89|–1.62|
|Exports|–0.078|0.079|–0.015|0.365|0.479|0.585|0.666|0.444|0.474|0.285|0.034|
|(% GDP)|(2.18)*|–0.92|–0.12|(2.86)**|(2.82)**|(2.92)**|(3.12)**|(2.42)*|(3.88)**|–1.93|–0.24|
|Agriculture|0.081|0.296|0.07|–0.031|–0.124|–0.759|–0.864|0.114|0.231|0.265|0.344|
|<br>(% GDP)|–1.88|(2.97)**|–0.47|–0.21|–0.63|(3.33)**|(3.52)**|–0.53|–1.57|–1.45|(2.13)*|
|Manufacturing|–0.064|0.04|0.142|–0.148|–0.193|–0.367|–0.428|0.014|0.089|0.035|–0.033|
|(% GDP)|–1.79|–0.47|–1.12|–1.17|–1.13|–1.86|(2.01)*|–0.07|–0.67|–0.24|–0.23|
||3.706|1.111|3.526|–6.44|–8.298|1.309|–0.262|–3.128|–1.791|–5.408|–2.015|
|Constant|(9.19)**|–1.16|(2.41)*|(4.59)**|(4.39)**|–0.6|–0.11|–1.56|–1.35|(3.19)**|–1.34|
|Observations|94|92|82|95|95|94|94|98|84|98|97|
|R–squared|0.71|0.69|0.81|0.85|0.87|0.55|0.69|0.7|0.87|0.86|0.87|
|_Note_: * = sig<br>statistics ha|nificant at 5<br>ve been rep|%; ** = sig<br>orted in ab|nificant at<br>solute val|10%. T-sta<br>ue.|tistics are r|eported b|elow coef|ficients. P|arentheses|denote th|at T-|

16
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

# 3   The impact of historical trends on infrastructure endowments

## Variations over time

Africa’s present-day infrastructure is strongly influenced by the endowment that the countries inherited at independence. Because infrastructure is costly to build, stocks usually change slowly over time. Hence, differences across developing regions will reflect differences in their history. The current dataset contains time series data going back as far as 1960 for roads, 1970 for fixed telephone lines, 1980 for generating capacity, and 1990 for water and sanitation. No consistent time series data are available for access to electricity. The data points from the 1960s and 1970s are of particular interest for Africa, since they describe the situation at around the time of independence.

Overall, the data show some evidence of worldwide convergence in infrastructure levels (table 3.1). They also reveal the divergent starting points and differing rates of infrastructure growth in world regions. Convergence requires, of course, that regions with a low starting point grow faster than regions with a high starting point. We computed correlation coefficients between starting levels and growth rates. These coefficients are uniformly negative and always smaller than –0.28, suggesting that convergence is underway. The strongest evidence of convergence is found for water and sanitation (with correlation coefficients between –0.6 and –0.7) and roads (with correlation coefficients between –0.45 and –0.55). The lowest is for generating capacity (with a correlation coefficient of –0.28).

Bucking the trend toward worldwide convergence, the infrastructure gap between Africa and other developing regions is larger today than it was some decades ago. Slow convergence has occurred for some forms of infrastructure. At the beginning of the data series for paved roads and access to water and sanitation, Africa had the lowest endowment of any developing region. The region managed to achieve relatively high growth rates in each category, but they were not great enough to compensate for the region’s starting position. On the other hand, at the outset of the data series, Africa was doing significantly better than other developing regions with regard to overall road density, generation capacity, and fixed-line telephones. But growth rates in these categories have been slower than in other regions, so that by 2000 Africa had lost substantial ground relative to the other regions.

Africa’s failure to converge with the rest of the developing world is most clearly illustrated by the comparison between Africa and South Asia. At the outset of the period under study, South Asia was ahead of Africa in all forms of infrastructure except for fixed-line telephones and electrical generating capacity (figure 3.1). Their positions have since been reversed. The record on generating capacity is particularly stark. In 1980, Africa had almost three times as much generating capacity (per million people) as South Asia. Since then, capacity in South Asia has expanded at an average annual rate of 9 percent, while in Africa it has stagnated. Consequently, 17
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH by 1990 South Asia had overtaken Africa; by 2000 it had almost twice the generating capacity (per million people) of Africa. Indeed, Africa had the slowest rate of growth in generating capacity of any region in the developing world. The story for telephone lines is similar, if less dramatic. In 1970, Africa had twice the teledensity of South Asia. However, with faster average annual growth rates in South Asia (9 percent against 6 percent in Africa), the two regions had converged by 2000. Africa has now fallen behind South Asia with respect to fixed-line telephones, but in mobile telephony and Internet connections Africa has been growing more rapidly than South Asia and currently maintains a lead.

Indexing infrastructure stocks in each region and plotting the evolution of the indexes over time sheds light on the sequencing and relative growth path of different aspects of infrastructure. In all regions, fixed-line telephones have been by far the fastest-growing component of infrastructure since the 1980s (albeit from low starting points), but the growth varies substantially across regions from around tenfold in Africa, Latin America, and the Middle East, to more than forty-fold in East Asia (figure 3.2). After telephone lines, paved roads generally have expanded most quickly, particularly in East Asia and Eastern Europe. Generating capacity (per million people) has grown only very slowly across all regions, except in South Asia in the late 1980s, when a significant expansion took place.

Within Africa, the SADC countries started with a larger infrastructure endowment than the other subregions and extended it more rapidly (table 3.2). At independence there already were substantial variations in infrastructure endowment across the continent—particularly with respect to paved roads, generating capacity, and telephone lines. By 1980, the SADC countries had more than three times the generating capacity of other subregions. By 1970, they had five times the teledensity. In the case of roads, ECOWAS was in a much stronger position than the other subregions in the 1960s, but was overtaken by SADC after 1980. In water and sanitation, the differences between subregions have been relatively small.

18
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.1 Trends in infrastructure endowments, by world region, 1960–2000**

||Region|1960|1970|1980|1990|2000<br>a|Average<br>annual<br>growth<br>b<br>(percent)|
|---|---|---|---|---|---|---|---|
||SSA|6|8|21|26|39|5.0|
||SAS|60|76|115|113|146|2.3|
|Density of paved road network<br>|EAP|12|21|37|39|54|4.0|
|(km/1,000 km<br>2)|ECA|54|239|312|362|351|4.9|
||LAC|132|185|59|274|361|2.6|
||MNA|14|19|26|39|45|3.1|
||SSA|71|120|544|652|1,037|7.1|
||SAS|477|434|585|711|670|0.9|
|Density of paved road network<br> <br>2|EAP|239|392|563|453|556|2.2|
|(km/1,000 arable km<br>)|ECA|110|498|687|800|1,197|6.3|
||LAC|472|808|527|1,973|3,958|5.6|
||MNA|196|320|3,283|4,594|5,729|9.0|
||SSA|73|80|106|119|143|1.7|
||SAS|104|145|204|204|303|2.8|
|Density of total road network|EAP|55|75|144|157|175|3.0|
|<br>(km/1,000 km<br>2)|ECA|298|576|567|630|620|1.9|
||LAC|267|198|241|516|583|2.0|
||MNA|45|50|77|92|104|2.2|
||SSA|1,525|1,577|2,106|2,343|2,556|1.3|
||SAS|757|699|888|1,084|1,408|1.6|
|Density of total road network|EAP|3,064|2,671|3,244|3,063|3,252|0.2|
|<br>(km/1,000 arable km<br>2)|ECA|651|1,188|1,261|1,543|2,045|3.0|
||LAC|1,962|2,138|2,743|4,929|7,021|3.3|
||MNA|558|760|33,263|28,850|30,704|10.8|
||SSA|—|4|7|11|22|5.6|
||SAS|—|2|2|5|21|8.9|
|Density of fixed-line telephones|EAP<br>|—|5|10|21|58|8.9|
|(subscribers per 1,000 people)|ECA|—|32|76|134|229|6.8|
||LAC|—|24|44|75|155|6.4|
||MNA|—|9|18|41|84|7.7|
||SSA|—|—|71|82|73|0.1|
||SAS|—|—|26|127|137|8.6|
|Electrical generating capacity|EAP|—|—|123|178|229|3.2|
|(MW per 1 million people)|ECA|—|—|587|786|1,022|2.8|
||LAC|—|—|309|409|495|2.4|
||MNA|—|—|207|421|413|3.5|
||SSA|—|—|—|51|63|1.0|
||SAS|—|—|—|72|70|–0.2|
|Water|EAP|—|—|—|73|73|–0.03|
|(% of households with access)|ECA|—|—|—|93|89|–0.4|
||LAC|—|—|—|81|89|0.7|
||MNA|—|—|—|83|83|–0.02|

19
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

|Sanitation<br>|SSA|—|—|—|30|35|0.4|
|---|---|---|---|---|---|---|---|
|(% of households with access)|SAS|—|—|—|31|47|1.3|
||EAP|—|—|—|57|53|–0.4|
||ECA|—|—|—|86|80|–0.5|
||LAC|—|—|—|64|76|1.0|
||MNA|—|—|—|69|75|0.5|

Sources: As for table 2.1.

- Note: SSA = Sub-Saharan Africa, SAS = South Asia, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and Caribbean, MNA = Middle East and North Africa.

- a. Water and sanitation data are from 2002.

- b. Calculated over entire period available for each sector.

- = data not available.

20
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.2 Trends in infrastructure endowments by African subregion, 1960–2000**

|||1960|1970|1980|1990|2000<br>a|Average<br>annual<br>growth<br>b<br>(percent)|
|---|---|---|---|---|---|---|---|
||Central Africa|1|2|5|8|9|6|
|Density of paved road network<br>|EAC|3|6|12|13|8|3|
|(km/1,000 km<br>2)|ECOWAS|11|14|21|24|38|3|
||SADC|4|9|46|53|92|8|
||Central Africa|19|75|156|161|159|6|
|Density of paved road network|EAC|46|106|135|148|95|2|
|<br>(km/1,000 arable km<br>2)|ECOWAS|122|161|213|237|305|2|
||SADC|64|123|1,715|2,015|3,649|11|
||Central Africa|57|60|82|94|101|1|
|Density of total road network<br>|EAC|56|77|82|94|105|2|
|<br>(km/1,000 km<br>2)|ECOWAS|84|93|109|121|144|1|
||SADC|81|90|140|154|211|2|
||Central Africa|2,101|1,720|1,776|1,677|1,587|–1|
|Density of total road network<br>|EAC|1,053|1,201|1,073|1,124|1,297|1|
|(km/1,000 arable km<br>2)|ECOWAS|1,137|1,259|1,391|1,319|1,300|0.3|
||SADC|1,588|2,058|4,002|5,045|6,174|4|
||Central Africa|—|1|2|4|7|5|
|Density of fixed-line telephones|EAC|—|2|3|4|6|4|
|(subscribers per 1,000 people)|ECOWAS|—|2|3|5|17|8|
||SADC|—|10|18|32|56|6|
||Central Africa|—|—|49|57|48|–0.1|
|Electrical generating capacity|EAC|—|—|21|20|23|0.4|
|<br>(MW per 1 million people)|ECOWAS|—|—|38|34|31|–1|
||SADC|—|—|177|204|185|0.2|
||Central Africa|—|—|—|46|56|1|
|Water|EAC|—|—|—|42|64|2|
|(% of households with access)|ECOWAS|—|—|—|51|63|1|
||SADC|—|—|—|60|73|1|
||Central Africa|—|—|—|22|27|0.5|
|Sanitation|EAC|—|—|—|44|45|0.1|
|(% of households with access)|ECOWAS|—|—|—|28|35|1|
||SADC|—|—|—|37|42|0.4|

_Sources_ : As for table 2.1.

a. Water and sanitation data are from 2002.

b. Calculated over entire period available for each sector.

- = data not available.

21
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

# **Figure 3.1 Index of infrastructure growth by sector and by region**

<!-- Start of picture text --> Tot. Rd. Density - Indexes Relative to SSA<br>1,600<br>1,400<br>1,200 SSA<br>1,000 EAP<br>800 ECA<br>600 LCR<br>400 MNA<br>200 SAS<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Mainline Density - Indexes Relative to SSA<br>7,000<br>6,000<br>5,000 SSA<br>4,000 EAP<br>ECA<br>3,000<br>LCR<br>2,000 MNA<br>1,000 SAS<br>0<br>1975 1980 1985 1990 1995 2000<br>Internet Density - Indexes Relative to SSA<br>16,000<br>14,000<br>12,000 SSA<br>10,000 EAP<br>8,000 ECA<br>6,000 LCR<br>4,000 MNA<br>2,000 SAS<br>0<br>Water Access - Indexes Relative to SSA<br>200<br>180 1990<br>160 2002<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>SSA EAP ECA LCR MNA SAS<br>1993 199419951996 19971998 19992000 200120022003 2004<br><!-- End of picture text --> <!-- Start of picture text --> Paved Rd. Density - Indexes Relative to SSA<br>25,000<br>20,000<br>SSA<br>15,000 EAP<br>ECA<br>10,000<br>LCR<br>MNA<br>5,000<br>SAS<br>0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Mobile Density - Indexes Relative to SSA<br>350,000<br>300,000<br>250,000 SSA<br>200,000 EAP<br>ECA<br>150,000<br>LCR<br>100,000<br>MNA<br>50,000 SAS<br>0<br>Generation Capacity - Indexes Relative to SSA<br>1,600<br>1,400<br>1,200 SSA<br>1,000 EAP<br>800 ECA<br>600 LCR<br>400 MNA<br>SAS<br>200<br>0<br>1980 1985 1990 1995 2000<br>Sanitation Access - Indexes Relative to SSA<br>350<br>1990<br>300<br>2002<br>250<br>200<br>150<br>100<br>50<br>0<br>SSA EAP ECA LCR MNA SSA<br>1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> _Sources_ : As for table 2.1.

22
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Figure 3.2 Indexes of infrastructure growth by region, 1960–2003**

<!-- Start of picture text --> Index of Infrastructure Growth - SSA Index of Infrastructure Growth - SAS<br>2,000 2,000<br>1,800 Paved Road 1,800 Paved Road<br>1,600 Tot. Road 1,600 Tot. Road<br>1,400 Gen. Cap. 1,400 Gen. Cap.<br>1,200 Mainline 1,200 Mainline<br>1,000<br>800 1,000<br>600 800<br>400 600<br>200 400<br>0 200<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Index of Infrastructure Growth - EAP Index of Infrastructure Growth - ECA<br>2,000 2,000<br>1,800 Paved Road 1,800 Paved Road<br>1,600 Tot. Road 1,600 Tot. Road<br>1,400 Gen. Cap.<br>1,2001,000 Mainline 1,4001,200 Gen. Cap.Mainline<br>800 1,000<br>600 800<br>400 600<br>200 400<br>0 200<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br>Index of Infrastructure Growth - LCR Index of Infrastructure Growth - MNA<br>2,000 2,000<br>1,800 Paved Road 1,800 Paved Road<br>1,600 Tot. Road 1,600 Tot. Road<br>1,4001,2001,000 Gen. Cap.Mainline 1,4001,200 Gen. Cap.Mainline<br>800 1,000<br>600 800<br>400 600<br>200 400<br>0 200<br>1960 1965 1970 1975 1980 1985 1990 1995 2000 0<br>1960 1965 1970 1975 1980 1985 1990 1995 2000<br><!-- End of picture text --> _Sources_ : As for table 2.1.

# Panel data models

The preceding exploratory data analysis illustrated the importance of historical perspective in understanding countries’ present-day infrastructure position. We use panel data models to integrate that perspective into our formal analysis. As described in the first section of this paper, two different panel model specifications are estimated: an OLS fixed effects model and an instrumental variables specification. Unfortunately, it is not possible to apply either of the panel data models to access to electricity, for which only a single cross-section is available. (However, we do apply a cross-sectional instrumental variables specification to the electricity access data so as to compare them to the OLS estimates. That comparison is reported in table 3.4.) The fixed effects model confirms the importance of income, demography, and economic structure in driving stocks of household services and broader economic infrastructure (table 3.3).

23
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH The income and demographic variables that proved statistically significant in the cross-sectional OLS results were also statistically significant in the fixed effects panel results. To a greater extent than before, however, variables capturing the economic structure of the economy (export orientation, and shares of agriculture and manufacturing compared with services) become statistically significant in explaining infrastructure stocks, particularly in the case of roads and ICT. It is important to remember, however, that these results do not yet account for dynamics in infrastructure provision, nor have they been corrected for endogeneity.

**Table 3.3 Results of OLS fixed effects panel data model**

|Datain logs<br>Variable|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
|I|0.051|0.079|0.196|0.227|–0.022|0.126|0.371|0.247|1.768|2.059|0.633|
|ncome|(3.25)**|(4.49)**|(10.60)**|(11.15)**|–0.47|–1.33|(2.52)*|(11.87)**|(7.73)**|(11.77)**|(27.68)**|
|Ubiti|0.244|0.131|0.477|0.365|0.664|0.141|1.096|0.504|2.316|3.158|0.986|
|ranzaon|(3.52)**|–1.69|(5.90)**|(4.10)**|(3.30)**|–0.36|(5.11)**|(5.50)**|(2.88)**|(4.72)**|(10.98)**|
|Population|0.366|0.094|0.587|0.312|0.103|1.01|0.119|0.062|21.884|16.909|1.524|
|density|(6.43)**|–1.48|(8.76)**|(4.24)**|–0.64|(3.12)**|(2.18)*|–0.84|(26.44)**|(25.86)**|(20.14)**|
|Infrastructure aid|–0.005|–0.011|–0.011|–0.017|–0.004|0.019|0.006|0.003|–0.019|0.08|–0.003|
|(per capita)|–1.87|(3.90)**|(3.76)**|(5.08)**|–0.54|–1.25|–0.17|–0.96|–0.8|(3.98)**|–0.85|
|Exports|0.001|0|0.001|0.001|0.001|0|0.04|0.004|0.073|0.034|0.006|
|<br>(% GDP)|–1.13|–0.35|(2.20)*|–1.82|–0.35|–0.01|–0.29|(5.98)**|(11.28)**|(6.85)**|(8.60)**|
|Agriculture|–0.004|–0.003|0.001|0.002|–0.001|0.009|0.188|–0.005|–0.112|–0.154|–0.01|
|(% GDP)|(3.40)**|(2.58)**|–0.95|–1.38|–0.49|–1.43|–1.17|(3.29)**|(7.11)**|(11.30)**|(5.69)**|
|Manufacturing|0.001|0.002|–0.001|0.001|–0.007|0.01|0.398|0.004|–0.151|–0.205|–0.002|
|<br>(% GDP)|–0.85|–1.43|–0.37|–0.27|–1.56|–1.13|(2.85)**|(2.23)*|(7.55)**|(13.39)**|–0.99|
|C|–3.401|–0.185|–6.602|–3.424|4.764|–1.021|–0.22|–10.59|–92.962|–70.033|–5.856|
|onstant|(11.67)**|–0.57|(19.38)**|(9.13)**|(5.72)**|–0.61|–0.15|(27.63)**|(23.72)**|(23.53)**|(14.93)**|
|Observations|1919|1910|1875|1866|167|165|85|2223|1100|1230|2323|
|Plant fixed<br>effects|0.21|0.07|0.39|0.24|0.67|0.52|0.68|0.22|0.68|0.76|0.73|

_Sources_ : As for table 2.1.

The results from the instrumental variables estimation indicate endogeneity bias in the OLS specification. If—as suggested by Calderón and Servén (2004) and others that expansion of infrastructure is associated with higher levels of income and growth, then we would expect the OLS coefficient on per capita GDP to be upwardly biased. Indeed, the results of our instrumental variables estimation show that the magnitude of the coefficient on per capita GDP decreased substantially once we controlled for endogeneity using the standard regressors from the growth literature, as discussed above. The traditional Hausman test of the exogeneity of our instruments indicates that they are appropriate in most infrastructure sectors. The exception is in the estimates for access to water and sanitation, where the limited number of observations (from just two years of data) may have affected the results. But it is clear that addressing the endogeneity of per capita GDP is critical to obtaining consistent coefficient estimates.

24
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.4 Fixed effects with instrumental variables (data in logs)**

|Variable|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
||–0.065|0.0002|0.00002|0.0002|0.001|0.003|0.001|0.0000001|0.004|0.103|0.03|
|Income|–1.15|–1.43|(3.35)**|(2.91)**|–0.23|–1.11|–0.3|(8.70)**|(8.12)**|(10.97)**|(12.32)**|
|Ubiti|0.042|6.246|–0.041|3.011|40.797|–12.423|57.96|0.0002|–7.384|–30.302|31.93|
|ranzaon|–0.46|(5.32)**|–0.57|(4.96)**|–1.02|–0.32|(2.12)*|(2.09)*|–0.67|–0.18|–1.16|
|Population|–0.145|0.00001|0.001|0.0002|–0.002|0.093|0.01|–0.0000002|–0.076|–1.312|–0.128|
|density|–0.8|–0.01|(4.75)**|–0.23|–0.04|(2.04)*|–0.51|–1.35|(3.28)**|(4.23)**|(2.97)**|
|Infrastructure|0.0003|–0.0005|0.00005|–0.0004|–0.256|–0.018|–0.047|0.0000003|–0.007|0.222|0.009|
|aid (per capita)|–0.09|–0.22|–0.37|–0.34|(2.05)*|–0.14|–0.16|–1.74|–0.54|–1.16|–0.18|
|Exports|–0.001|0.001|0.0002|0.001|0.016|0.037|–0.03|2.92E–06|0.228|3.58|0.699|
|(% GDP)|–0.64|–0.3|–0.67|–0.69|–0.12|–0.25|–0.22|(8.16)**|(8.43)**|(8.05)**|(7.15)**|
|Agriculture|–0.006|–0.008|0.001|0.001|–0.213|0.097|–0.791|0.000002|0.224|5.128|–0.011|
|(% GDP)|(2.38)*|–0.82|–1.47|–0.26|–1.03|–0.49|(2.20)*|(2.80)**|(2.83)**|(3.67)**|–0.05|
|Manufacturing|0.009|0.025|0.0004|0.011|–0.27|0.206|2.369|0.000002|0.204|1.001|–0.384|
|<br>(% GDP)|(4.38)**|(2.47)*|–0.65|(2.08)*|–0.89|–0.69|(5.60)**|(2.85)**|(2.58)**|–0.75|–1.63|
|C|–1.186|–0.382|–0.006|–1.046|63.256|46.968|6.852|–0.0001|–9.948|–243.622|–25.85|
|onstant|–1.19|–0.53|–0.13|(2.85)**|(3.07)**|(2.09)*|–0.39|(2.43)*|–1.37|(2.06)*|–1.59|
|Observations|1,247|1,299|1,308|1,296|119|116|78|1,474|765|892|1,525|
|Plant fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|Yes|
|Year fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|No|Yes|Yes|Yes|Yes|
|Hausman p–<br>value|0.0523|0.0405|0.0001|0.0000|0.7338|0.7772|N/A|0.0000|0.0000|0.0000|0.0000|

_Sources_ : As for table 2.1.

When we model our data using the instrumental variables estimation, per capita GDP, urbanization, population density, and certain structural variables continue to explain the most country-to-country variance in infrastructure stocks (table 3.4), although the magnitude of their coefficients changes from the basic fixed effects specification. For the most part, per capita GDP remains highly significant in explaining national variations in infrastructure, although the magnitude of the coefficients decreased from the simpler model. Urbanization becomes slightly less important, with significance in only four sectors, compared to eight under the first fixed effects specification. Population density, too, loses significance in several sectors. The significance of structural variables varies by sector, but export share of GDP is highly significant with respect to generating capacity and ICT.

25
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

# Benchmarking

The main objective of the regression models is to predict expected levels of infrastructure stocks for the purposes of benchmarking. As described in the first section of the paper, benchmarking is done by comparing the infrastructure stock in each country with the value predicted by the regression model and calculating the deviation. The value predicted by the model takes into account the effect of each country’s history and environment in determining the level of infrastructure stocks the country might be expected to have. A negative deviation indicates that the country has performed below the benchmark, while a positive one indicates the opposite. The deviations are averaged across countries to produce unweighted average deviations at the regional and subregional level. For the purposes of sensitivity analysis, deviations are calculated for each of the two model specifications described above. Following Bogetic (2006), a deviation of less than 10% in absolute value is considered not to constitute a major divergence from the benchmark. Hence, attention focuses on larger deviations of more than plus or minus 10% that are taken to be indicative of a substantial degree of over or underachievement.

**Table 3.5 Mean deviations from predicted values, OLS**

|Region|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Subregions of A_|_frica_|||||||||||
|Central|–6|–6|–12|–13|2|4|–7|–18|–62|–39|–10|
|EAC|–12|–10|–55|–52|5|–13|–26|–11|–117|–57|–72|
|ECOWAS|–7|–14|–13|–20|–0.1|–1|–10|–9|–143|–30|–4|
|SADC|9|12|13|16|0.2|–3|–11|–21|9|7|–17|
|_World regions_||||||||||||
|SSA|–4|–5|–6|–8|1|–1|–11|–17|–78|–25|–15|
|SAS|24|25|13|14|–0.4|16|30|29|–36|35|35|
|EAP|–7|–6|–4|–4|–2|–1|9|–2|–33|–20|11|
|ECA|6|9|–6|–3|0.02|–0.5|N/A|–7|75|95|–18|
|LAC|–7|–7|–0.3|0.4|–0.1|–1|3|16|–11|30|9|
|MNA|0.1|3|1|4|–3|–3|10|4|–2|–2|5|

Note: SSA = Sub-Saharan Africa, SAS = South Asia, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and Caribbean, MNA = Middle East and North Africa.

26
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Table 3.6 Mean deviations from predicted values, instrumental variables**

|Region|Density of<br>roads|Density of<br>roads (arable)|Density of<br>paved roads|Density of<br>paved roads<br>(arable)|Access to water|Access to<br>sanitation|Access to<br>electricity|Generating<br>capacity|Internet<br>connections|Mobile<br>telephones|Fixed-line<br>telephones|
|---|---|---|---|---|---|---|---|---|---|---|---|
|_Subregions_|_of Africa_|||||||||||
|Central|57|–57|–63|–93|–11|–48|–58|–81|–110|–79|–44|
|EAC|11|106|–87|20|–7|–20|–39|–77|–48|–54|–60|
|ECOWAS|21|–4|–50|–59|–11|–37|–24|–81|–12|–49|–64|
|SADC|6|84|–18|85|–1|–20|–69|–38|–76|–89|–38|
|_World regio_|_ns_|||||||||||
|SSA|44|0.3|–61|–7|–10|–34|–41|–66|–50|–72|–64|
|SAS|–31|–53|–11|215|20|–42|–8|–51|–108|–118|40|
|EAP|32|31|–47|–42|–8|–1|24|–29|–29|–24|–34|
|ECA|–25|–43|173|57|12|38|81|167|–10|27|105|
|LAC|14|–34|–62|–62|1|34|–1|16|–14|–26|26|
|MNA|29|9|–14|–45|5|36|42|–11|–11|–10|29|

Note: SSA = Sub-Saharan Africa, SAS = South Asia, EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and Caribbean, MNA = Middle East and North Africa.

Relative to the levels predicted by the instrumental variables model, Africa performs well in total road density (paved and unpaved). In the remaining sectors, it performs significantly below the levels predicted based on the region’s economic, demographic, and structural characteristics. In fact, it is the worst-performing world region (that is, the region with the largest negative deviation as shown in table 3.6) in access to electricity, electrical generating capacity, and density of fixed-line telephones. Africa also exhibits large negative deviations in paved road density, sanitation access, mobile telephone density, and Internet density.

Once historic and environmental factors are taken into account and steps are taken to control for the endogeneity of per capita GDP, the relative standing of Africa’s subregions is not as clear cut as it first appeared. The initial comparison of infrastructure stock indicators by subregions (see table 1.3) revealed a very clear ranking: SADC was the top performer on all dimensions and EAC the worst performer on nearly all dimensions, with Central Africa and ECOWAS somewhere in between. The picture is more nuanced once the regions are compared in terms of the magnitude of their deviation from benchmarks.

Except in road density (paved and unpaved densities and arable paved density), every African subregion underperformed relative to its expected value in every infrastructure sector. This underachievement is most severe in Central Africa, which exhibits the largest average negative deviation in Africa in water access, sanitation access, electricity generation capacity, and Internet density. While SADC—like the other African subregions—performs worse than predicted by the model in nearly every sector, its deviation from expectations is less great than that of other subregions, with the exception of electricity access and mobile telephone density, where it ranks last. ECOWAS and ECA generally fall somewhere between SADC and Central Africa, although ECOWAS exhibits the largest negative deviation in density of fixed-line telephones and ECA ranks last in paved road density.

27
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH History has a large influence. Deficiencies present at the beginning of our time series have become more pronounced over time. The historical data show that at the outset of the series, Africa was behind all other regions with respect to paved road density, water, and sanitation, but was doing significantly better than other regions in terms of road density, generating capacity, and telephone lines. Over time, Africa has lost ground, so that today it is the lowest-performing region in all areas except ICT. The most dramatic loss of ground has come in electrical generating capacity, which has largely stagnated since 1980. Comparing subregions within Africa reveals that most SADC countries started with a larger infrastructure endowment and have extended it more rapidly than other subregions on the continent.

Given the importance of historical trends, panel models provide the appropriate analytical framework for a benchmarking exercise. The OLS fixed effects specification underscores the importance of income and demographic variables in driving infrastructure endowments. The importance of these variables is confirmed by the instrumental variables model, which controls for potential endogeneity of per capita GDP. When Africa’s actual infrastructure levels are compared to those predicted by the instrumental variables model, it is clear that Africa underperforms relative to its benchmark in nearly every sector. In fact, Africa tends to have the largest magnitude of underachievement relative to other regions in many sectors, with the most severe underachievement found in Central Africa.

# 4   Conclusions

We have tried to shed light on the status of infrastructure in Sub-Saharan Africa by benchmarking the region against other developing countries and against the level of development predicted by our models based on the region’s income, demography, and economic structure. Our analysis has shown that with but one exception Africa has the worst infrastructure endowment of any developing region today, particularly with respect to electrical generating capacity. (The exception is the modern ICT sector.) African countries even perform poorly compared with their peers in the same income group in other parts of the world. Part of the explanation seems to lie in difficult environmental factors that complicate the development of infrastructure services, in particular low rates of urbanization. The region’s very low level of infrastructure at the time of independence a half centry ago is another explanation for today’s results.

Today, the SADC countries are far ahead of the rest of Sub-Saharan Africa—their infrastructure situation is more similar to that of East Asia than to other African subregions. EAC has the weakest infrastructure situation today; its position, like that of Central Africa and ECOWAS, most closely resembles that of South Asia.

When controlling for income, demographic, and structural variables, Central Africa misses its benchmark level of predicted infrastructure achievement by the largest margin of any African subregion, while SADC comes closest, despite generally falling below the predicted values.

28