metadata: authors: 0: author: Bogetic,Zeljko
1: author: Fedderke,Johannes Wolfgang
count: Namibia,Lesotho,Botswana,Swaziland,South Africa
docna: 0: docna: International benchmarking of infrastructure
            performance in the Southern African Customs Union Countries
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,Transport Services,Town Water Supply and Sanitation
teratopic: Macroeconomics and Economic Growth,Transport,Energy,Infrastructure Economics and Finance
abstracts: cdata!: The paper provides a first, systematic
            benchmarking of infrastructure performance in the Southern
            African Customs Union (SACU) countries (South Africa,
            Botswana, Lesotho, Namibia, and Swaziland) in four major
            sectors-electricity, water and sanitation, information and
            communication technology, and transportation-against the
            relevant group of comparator countries using a new World
            Bank international data base with objective and
            perception-based indicators of infrastructure performance
            from over 200 countries. The analysis suggests important
            comparative gaps in all major infrastructure sectors,
            although performance varies widely across the SACU region.
            Performance shortfalls are particularly acute in rural areas
            where most of the poor live. The benchmarking is envisaged
            as a comparative input into deeper analyses of
            infrastructure performance, especially in the context of the
            ongoing scaling-up efforts (for example, South Africa,
            Lesotho, and Botswana).
geo_reg_key: 517195,517191
repnme: repnme: International benchmarking of
            infrastructure performance in the Southern African Customs
            Union Countries
display_title: International benchmarking of
            infrastructure performance in the Southern African Customs
            Union Countries
listing_relative_url: /research/2006/08/6969719/international-benchmarking-infrastructure-performance-southern-african-customs-union-countries
topicv3: Sanitation m1326453 2000,Water m1326522 137,Transport m1326661 135,Power and Electricity Sector m1327094 515,Electricity Access m1503452 3922
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Transport,Energy,Infrastructure Economics and Finance
no_of_pages: 62
author_keys: 0: author_key: 94115
1: author_key: 675021
model_extractions: WPS3987

# International Benchmarking of Infrastructure Performance in the Southern African Customs Union Countries

## by

## Željko Bogetić and Johannes W. Fedderke

Summary: The paper provides a first, systematic benchmarking of infrastructure performance in the SACU countries (South Africa, Botswana, Lesotho, Namibia, and Swaziland) in four major sectors––electricity, water and sanitation, information and communication technology, and transportation––against the relevant group of comparator countries using a new World Bank international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. The analysis suggests important comparative gaps in all major infrastructure sectors, although performance varies widely across the SACU region. Infrastructure performance shortfalls are particularly acute in rural areas where most of the poor reside. The benchmarking is envisaged as a comparative input into deeper analyses of infrastructure performance, especially in the context of the ongoing scaling-up efforts (e.g., South Africa, Lesotho, Botswana).

## JEL classification: H54, H, R, O, O57

World Bank Policy Research Working Paper 3987, August 2006 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ Authors: Bogetić is a lead economist at the World Bank, Africa Region, Washington D.C. (Email: zbogetic@worldbank.org) and Fedderke is professor of economics at the University of Cape Town, School of Economics (Email: <u>jfedderk@commerce.uct.ac.za).</u>
# **TABLE OF CONTENTS**

|**Introduction........................................................................................................................**1|
|---|
|The Benchmarking Database**...................................................................................................**2|
|Infrastructure Performance in the SACU: A Synthetic Overview**...........................................**4|
|Energy––electricity: An Overall assessment for SACU Countries**........................................**5|
|Water & Sanitation: An Overall Assessment for SACU Countries**.........................................**7|
|Information and Communications Technology (ICT): An Overall Assessment for SACU**....**8|
|Transport: An Overall Assessment for SACU Countries**......................................................**12|
|Reflections on Regional Integration and Regional Infrastructure Policy**..............................**14|
|**South Africa......................................................................................................................**16|
|Energy––electricity............................................................................................................ 16|
|Water & sanitation............................................................................................................. 19|
|Information and Communications Technology (ICT)....................................................... 21|
|Transport............................................................................................................................ 24|
|Large differences from the benchmark.............................................................................. 27|
|The rural-urban divide ....................................................................................................... 30|
|Conclusion ......................................................................................................................... 31|
|**Botswana...........................................................................................................32**|
|Energy – electricity............................................................................................................ 32|
|Water and sanitation .......................................................................................................... 33|
|Information and Communications Technology (ICT)....................................................... 36|
|Transport............................................................................................................................ 37|
|Large differences from the benchmark.............................................................................. 38|
|Conclusion ......................................................................................................................... 39|
|**Lesoto ................................................................................................................40**|
|Energy – electricity............................................................................................................ 40|
|Water and sanitation .......................................................................................................... 40|
|Information and Communications Technology (ICT)....................................................... 41|
|Transport............................................................................................................................ 44|
|Large differences from the benchmark.............................................................................. 44|
|Conclusion ......................................................................................................................... 46|
|**Namibia.............................................................................................................47**|
|Energy – electricity............................................................................................................ 47|
|Water and sanitation .......................................................................................................... 48|
|Information and Communications Technology (ICT)....................................................... 50|
|Transport............................................................................................................................ 51|
|Large differences from the benchmark.............................................................................. 52|
|<br>Conclusion ......................................................................................................................... 53|
|**Swaziland..........................................................................................................54**|
|Energy – electricity............................................................................................................ 54|
|Information and Communications Technology (ICT)....................................................... 57|
|Transport............................................................................................................................ 57|
|Large differences from the benchmark.............................................................................. 58|
|Conclusion ......................................................................................................................... 58|
|**References.........................................................................................................59**|

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International Benchmarking of Infrastructure Performance in the Southern African Customs Union Countries Željko Bogetić and Johannes W. Fedderke*

# **INTRODUCTION**

**The quantity, quality, and performance of a country’s infrastructure affect its level of economic activity and growth prospects.** This old theme in development economics is now back center stage on the development agenda, especially in Sub-Saharan Africa. At the same time, there is considerable rethinking of the experience and role of infrastructure in development at the World Bank (Bourguignon 2006). The generally strong association between infrastructure and growth is now well established in recent empirical and broader, comparative analyses (e.g., Canning 2006, Estache 2006, Fedderke and Bogetić 2006, Calderon and Serven 2004; World Development Report 1994, 2005; Aschauer 1989). But during the 1990s, underinvestments in infrastructure in Latin America and Sub-Saharan Africa have led to lower economic growth (Esfahani and Ramirez 2003; and Easterly and Servén 2002), contributing to the “lost decade” for economic and social progress. More broadly, infrastructure affects human welfare, especially of the poor, directly and indirectly via access to and quality of basic services such as water and sanitation. Finally, infrastructure is also associated with child health, human capital accumulation and the achievement of the Millenium Development Goals (MDGs) (Leipziger et al. 2003).

**In the Southern African Customs Union (SACU) region, recent research for South Africa, for example, has shown that infrastructure strongly affects economic growth** (see, for example, Perkins, Fedderke and Luiz 2005; and Fedderke, Perkins and Luiz 2005). In Lesotho, infrastructure deficiencies have been found to directly constrain private sector activity and economic growth, and the performance of several infrastructure utilities in Swaziland has been seriously lacking (World Bank 2005, 2006). Even in Botswana and South Africa, the SACU countries with the highest gross national income (GNI) per capita and strong overall economic management, new debates have resurfaced on the adequacy of infrastructure performance in the context of the need to sustain and accelerate economic growth. Infrastructure scale-up and modalities of its implementation in the context of the government’s new Accelerated and Shared Growth Initiative (ASGI-SA) is currently a key policy issue in South Africa (Manuel 2005, 2006). In a word, concern with infrastructure performance is a critical policy question in the SACU countries, as well as in the African region as a whole.

___________________________________________________________________________________________

> * Bogetić is a lead economist at the World Bank, Africa Region, Washington D.C. (E-mail: <u>zbogetic@worldbank.org)</u> and Fedderke is professor of economics at the University of Cape Town, School of Economics (E-mail: jfedderk@commerce.uct.ac.za). The paper synthesizes and extends to the whole SACU region earlier benchmarking exercises for South Africa (Bogetić and  Fedderke, 2006b) and Lesotho (Bogetić 2006). Helpful discussions with Ritva Reinikka, Emmanuel Akpa, Anton Eberhard, Ioannis Kessides, Luiz Maurer, Antonio Estache, Cecilia Briceno-Garmendia, Luis Guasch and Vivien Foster are gratefully acknowledged. Preliminary findings of this paper were presented at a seminar at the Africa Region (AFTP3) of the World Bank in April 2006. The authors also wish to express gratitude for excellent research assistance by Atsede Aemro-Selassie.

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**Effective infrastructure scale-up, however, requires an assessment of performance of various infrastructure sectors.** Yet surprisingly, detailed and sufficiently extensive comparative data on multiple dimensions of infrastructure performance are only now emerging, and there are few systematic, comparative studies of infrastructure performance that would help the policymakers put their sector performance in perspective as a first step in charting future infrastructure policy. In particular, analyzing and _benchmarking international comparative performance_ can be a useful part of such assessments in order to identify dimensions (quantitative and qualitative) of infrastructure performance where SACU countries score better, on par, or worse than their relevant comparators. This paper aims to fill this gap in comparative literature on infrastructure performance. In large part, it synthesizes and extends earlier exercises for South Africa and Lesotho to the entire SACU region (Bogetić and Fedderke 2006b, and Bogetić 2006c). In addition, the paper speculates about the implications of the analysis for the regional infrastructure policy in the SACU countries.

**The purpose of this paper, therefore, is to systematically benchmark the SACU countries’ infrastructure performance in four major sectors––electricity, water and sanitation, information and communication technology, and transportation.** The comparison is made against the relevant group of comparator countries using a new international data base with objective and perception-based indicators of infrastructure performance from over 200 countries. Specifically, we seek to answer a number of relevant questions: How do SACU countries compare on major indicators of infrastructure sector performance against their relevant country groups? What do outcome indicators tell us about the relative strengths and weaknesses of SACU countries’ infrastructure vis-à-vis various income and geographical comparator groups of countries? Where are the largest deviations—positive and negative–– from the benchmarks and other comparators? And how does one interpret some of these comparisons to be useful for policy purposes?  The answers to these questions are likely to provide important building blocks towards a more complete assessment of the state of infrastructure in the SACU countries, its performance, and any need for reform. It would also help frame the ongoing discussion in the sub-region in a comparative perspective and based on well-defined and widely accepted performance indicators.

**The organization of the paper is the following.** In the next section, we first provide a brief description of the data and its caveats, and discuss the choice of the comparator groups of countries. Then, we provide a broad, _SACU-wide synthetic summary_ of individual country comparative exercises using average indicators for SACU countries and comparing them with all the world regions and the relevant income level country groupings (middle-income and lowincome countries). We also offer some reflections on the regional implications of such exercises.  Finally, in the following five detailed sections, we present individual benchmarking case studies for South Africa, Botswana, Lesotho, Namibia, and Swaziland. These studies provide international comparative assessments of these countries’ infrastructure performance in electricity, water and sanitation, information and communications technology, and transportation.

# **THE BENCHMARKING DATABASE**

**The data used for comparing SACU countries’ infrastructure performance comes from the World Bank research data base that was recently developed with a specific objective**

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**to be used for this type of benchmarking exercise** (Eustache and Goicoechea, 2005). The database pulls together the latest available observations from a number of well documented, specialized data sources that contain more detailed data with longitudinal information. The data base aims to measure _sector performance_ by focusing on sector _outcomes_ — **_access, affordability/pricing, and quality_** (technical or perceived).  It also provides information on sector institutional reform. Data cover 207 countries (although coverage varies by sector and indicator)<sup>1</sup> with a limited number of variables<sup>2</sup> measuring sector outcomes.

**For the purpose of benchmarking SACU countries, we compare each SACU country (and, in the synthetic summary below, the SACU country average indicators) with all the world geographical comparator groups and four groups of countries clustered by level of income per capita.** Geographical groupings encompass Sub-Saharan Africa, OECD, South Asia, East Asia & Pacific, Middle East & North Africa, Latin America & the Caribbean, Europe and Central Asia, and the world—and—income groupings cover low-income, middleincome, upper middle-income, and OECD countries. Therefore, both region and income level are used as comparator criteria. The main benchmarking group on which much of the individual country analysis is focused is the specific SACU country per capita income group ( _upper-middle-income countries for South Africa and Botswana, lower-middle-income country group for Namibia and Swaziland, and low-income country group for Lesotho)_ . Because of the strong association between infrastructure and income level, these are economically the most meaningful group comparator for each country. This is supplemented by relevant comparisons with other income groups and major regions of the world to provide a wider, geographical comparison of the SACU countries infrastructure performance.

**As with any comparative indicators summarizing performance in a vast number of different countries, interpretation of comparisons must be made carefully and taken with caution.** Some of the variations between the indicators may reflect other factors that may make single indicator comparison less revealing, or even misleading. The database itself has its limitations detailed in the cited World Bank analysis by Estache and Goicoechea.<sup>3</sup> In addition, where appropriate, we explain or qualify how specific circumstances in specific SACU countries may affect a specific indicator comparison. More broadly, benchmark values of specific performance indicators should not necessarily be construed as “optimal” in the economic sense; in some cases, the benchmark values for comparator group of countries may display “excessive” or “limited” performance in particular indicator due to other, unobservable factors. If so, this would imply that observed “shortfalls” from such biased benchmark values would not be useful for policy purposes. This is a generic problem with all benchmarking. It implies that the results should be taken as indicative––not definitive––of the observed shortfalls from the benchmarks, especially when considering policy implications such as scaling up. Nevertheless, since the benchmarks used are averages of a large number of

> 1 The percentage of available country observations from the total number of countries (207) varies by indicators from close to 90% for measures of access to roads, 85-95% for ICT access and pricing measures, 75-85% for access to water, and 60% for access to electricity.  The quality indicators generally show lower degrees of completeness than those of access.

> 2 The total numbers of indicators in the database in each sector are as follows: 7 in energy, 4 in water & sanitation, 12 in ICT, and 12 in transport. The availability of indicators and observations varies by country, and is noted in specific SACU countries case studies below.

> 3 For example, the database reports the latest information available between 1997 and 2002, corresponding to arithmetic averages of available data.  In some cases, more recent within country information may be available that is not yet reflected in the database. Also, efforts to weight the raw data by population could help enhance the quality of the cross-country comparisons.

3
countries with the same income level as South Africa, the risk of such bias, while difficult to identify, should be minimized. Also, the indicators presented here are widely accepted and fairly unambiguous representations of major dimensions of sector performance. For these reasons, we believe that the benchmarking and comparisons are sufficiently meaningful and could prove useful in both analytical and policy studies of SACU countries’ infrastructure performance.

# **INFRASTRUCTURE PERFORMANCE IN THE SACU: A SYNTHETIC OVERVIEW**

**Overall, the SACU countries, on average, lag behind their comparator groups of countries in several major indicators of** **_access_ to basic infrastructure services (electricity, water & sanitation, ICT, and transport), though this performance varies widely across the SACU region.** Access performance is particularly lacking in the case of Swaziland whose indicators are more akin to a low-income country rather than a middle-income country that it is. In Lesotho, the only low-income country in the SACU region, access also lags behind its income group of countries. But even in South Africa, despite major gains in recent years in expanding access to electricity to the previously unserviced areas, further extension of access to poor areas is very much needed.

**Looking at the SACU region countries’ main infrastructure sectors, telephony and electricity are generally performing better in comparative terms, while access is particularly lacking in water and sanitation sectors.** This is especially true in Lesotho and Swaziland. Where data are available (e.g., South Africa; and for water and sanitation in all countries), the comparison shows a stark rural-urban divide with access in rural areas significantly behind the cities. Botswana’s low access to electricity (10%) is particularly stark compared with its benchmark income group (87%). But aggregate indicators may also hide inequities within larger urban areas where many peri-urban communities (e.g., in South Africa) continue to experience access problems with respect to basic infrastructure services. In the telecom sector, technical progress favoring cellular telephony already brings opportunities to SACU countries “leapfrog” from fixed lines to cellular telephony, much more rapidly catching up with comparator countries.

**The technical indicators of the quality of infrastructure service (e.g., system losses in electricity; phone fault rates; percentage of paved roads) in the SACU countries vary widely.** While some sectors (e.g., electricity) and countries (e.g., South Africa, Namibia) perform well, others (e.g., some dimensions of ICT performance; and even roads), especially in the poorer SACU countries, fall short of expected performance. Nevertheless, subjective perceptions of the quality of service are broadly favorable, but this may well reflect the urban bias in the surveys used to collect information for this indicator.

**While data performance indicators in rural vs. urban areas are not available for all countries and sectors, they (and other, supplemental information) do suggest a strong “urban bias” in infrastructure performance in all SACU countries.** In South Africa, for example, the available indicators on electricity, water and sanitation, and telephone services suggest that while urban areas score relatively well, there is a significant shortfall in access to improved water and sanitation in rural areas compared with the benchmark upper-middleincome countries. In the poorer SACU countries, some of the infrastructure services that matter most to the poor––water and sanitation––are lacking in rural areas. In Swaziland, for example,

4
rural access to improved water sources is 42%, almost half the level of lower-middle-income group and lower than all other groups.

**A more detailed synthesis of SACU performance in four major infrastructure sectors is provided below.** It is then followed by more in-depth, individual country benchmarking exercises for South Africa, Botswana, Namibia, Lesotho, and Swaziland.

# **ENERGY––ELECTRICITY: AN OVERALL ASSESSMENT FOR SACU COUNTRIES**

**_Access—_ SACU region access to electricity is poor when compared to the middle-income countries but slightly better than the average for Sub-Saharan Africa (SSA), but access varies widely across SACU countries** . The SACU country average indicator for access of 24 percent is low compared to over 80 percent in middle-income countries and 15 percent in SSA. Not surprisingly, South Africa’s access to electricity network is superior than the other countries in SACU, but still lags its comparable income group of upper-middle-income countries significantly – 66 percent access compared to 87 percent.  Lesotho and, perhaps surprisingly, Botswana are the poorest performers with only 6 and 10 percent of the population having access to electricity, much lower than their comparator income groups. Access in Namibia and Swaziland is slightly above the SSA average, but below that of low-income countries.

**_Affordability—pricing._ Average end-user prices for electricity (in US cents/kwh), both residential and non-residential, in the SACU region are one of the cheapest in the world with South Africa having the lowest rate in the SACU region** – 3 cents for residential and 2 cents for non-residential customers.<sup>4</sup> SACU average end-user prices are 5 cents for residential and 3 cents for non-residential customers. In contrast, average prices in middle-income countries are 8 and 7 cents, respectively and 6 and 5 cents in SSA.  Lesotho has the highest charge for residential customers at 8 cents. These level comparisons must of course be taken with caution. Such information needs to be complemented with data on costs to assess the adequacy of pricing and ascertain any possible cross-subsidization.

**_Quality—perceptions._ In terms of commercial perception of electricity service, the SACU region scores better than all the comparable regions** with a 5.4 rating out of 7 (7=best, 1=worst).  In contrast commercial perception of electricity services was the lowest in the lowincome countries (2.8) and SSA (4.3) and slightly better in upper-middle-income countries at 5.2.  One should be cautious with interpreting this number as it is heavily influenced by South Africa and Namibia in the absence of scores for Lesotho and Swaziland. Furthermore, it may also reflect a selection bias in that the only customers with access to service would normally respond to the question and thus have favorable perceptions.

> 4 In South Africa, the low user prices do not seem to reflect underpricing, as is the case in many developing countries with the attendant quasi-fiscal losses. Instead, it is a consequence of the low cost structure of what is considered a well-run state electricity company (ESKOM) and the fact that part of the capital stock has already been depreciated; hence, the fixed cost component of the electricity cost/price in South Africa is very low.

5
|Energy|SACU|Botswana|Lesotho|Namibia|Sout!<br>sien|Swaziland|Upper<br>Middle<br>Income|Lower<br>Middle<br>Income|Low<br>Ieee|SSA|
|---|---|---|---|---|---|---|---|---|---|---|
|auaaato ElectricityNetwork<br>(%o ofPopulation)|4|10|6|0|66|a0|37|32|3]|re|
|ar ReportingAccess to Electricity<br>(%o ofhouseholds)|65|.|.|.|65|.|74|84|33|4|
|Energy Use PerPPP GDP<br>*<br>(kg of oil equivalent/1000 PPP dollars, Constant 2000)|178|“|“|98|257|os|249|242|374|.<br>364|
|ayAverageahsPrices (UScents/k<br>Wh) -<br>(Residential/Non-Residential)|5/3|4/3|8/3|4/2|3/2|4/3|0/7|8/7|6/6|6/5|
|CommercialPerception ofElectricity service<br>(l=worst 7=best)|54|48|.|56|58|.|52|42|18|43|
|fcommercialPerceptionofPublicAgency ElectricityProvider<br>(l=worst 7=best)|6.0|57|.|63|6.0|.|53|50|40|43|
|oePowerTransmissionandDistribution Losses<br>(%of totaloutput)|80|7|7|7|8.0|.|140|15|0|190|

Note: Data used isfrom the latest observation available between 1997 and 2003, Source: Esiache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005,
|Water<br>——|SACU|Botswana|Lesotho|Namibia|south<br>|<br>;<br>Africa|Swazlind|Upper<br> Midde<br>Income|Lower<br>Middle<br>Income|Low<br>Income|SeA|
|---|---|---|---|---|---|---|---|---|---|---|
|Access to Improved Water Sources<br>D<br>(% ofpopulation)|78|95|.<br>76|$0|$7|52|93|35|.<br>as|.<br>o4|
|Access to Improved Sanitation<br>;<br>(% ofpopulation)|45|41|a7|30|.<br>a7|5)|.<br>80|72|4]|37|
|Commercial Perception ofWater Service<br>-<br>I<br>(l=worst 7=hest)|59|57|.|.<br>62|57|.|50|48|40|43|
|Households UsingPiped or Well Water as Main Source of<br>a<br>DrinkingWater<br>("ofhouseholds)|s|t.|.|-|8|.|0|8|B|B|

Note: Data used isfront the latest observation available between 1997 and 2603, Source: Estache, Antonio and Anta Goicoechea (2005), “A ‘Research’ Database on Infrastructure Econontic Performance,” Policy Research Paper No. 3643, infrastructure Network, Office ofthe Vice President, June 2008,
**Figure 2: Access to Improved Water Sources (% of Population)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>SACU Botswana Lesotho Namibia South Africa Swaziland<br><!-- End of picture text -->

**Figure 3: Access to Improved Sanitation (% of Population)**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>SACU Botswana Lesotho Namibia South Africa Swaziland<br><!-- End of picture text -->

# **INFORMATION AND COMMUNICATIONS TECHNOLOGY (ICT): AN OVERALL ASSESSMENT FOR SACU**

**_Access._ In terms of** **_mainline teledensity_ , the SACU region average scores significantly better than SSA and low-income country average, but it is significantly behind upper and lower-middle-income comparators, albeit less so in** **_cellular teledensity_ .** There are only 62 telephone subscribers per 1,000 people in the SACU region compared to 261 in upper-middleincome countries and 126 in lower-middle-income group.  The SACU average is double the penetration for low-income and SSA. In terms of cellular teledensity, there are 182/1000 subscribers in SACU region compared to 381 in upper-middle-income countries and 179 in lower-middle-income countries.  Not surprisingly, South Africa has the highest penetration of both mainline and cellular telephony in the SACU region, followed by Botswana and Namibia.

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With the gradual technological shift towards cellular telephony now underway, however, SACU countries may “leapfrog” from fixed line to cellular telephony in the near future, leaving behind the low mainline teledensity––and appropriately so.

**_Affordability—pricing_** _._ **A 3-minute local call in U.S. cents is one of the most expensive in the world in the SACU region at 10 cents, largely driven by 15 cents in South Africa and 22 cents in Lesotho.** In contrast, the cost of a 3-minute call to the US is the lowest among the comparable groups.  It costs 265 cents in the SACU region, which is almost half the lowincome and SSA region and much lower than in the middle-income group.  The picture is similar when one looks at the cost of three off-peak minute cellular local call which costs 30 cents, compared to 54 cents in upper middle income countries and 42 cents in SSA.  South Africa and Namibia have the cheapest rates in the region with Swaziland and Botswana having the most expensive cellular rates.

**_Quality—technical._ The SACU average records more than twice as many faults as uppermiddle-income countries is more in line with low-income and SSA average.** Reported faults per 100 telephone mainlines (per year) are very high in the SACU region (54), with Lesotho (75) having the worst record and Botswana (37) being the best performer in the region.

**_Quality—perception._ In spite of the SACU region’s relatively poor showing in most of the categories in the ICT sector, commercial perceptions are more closely aliened to the better performing middle-income countries.** _Commercial perception of telephone infrastructures_ gets a rating of 4.9 compared to 5.6 in upper-middle-income countries and 4.9 in lower-middle-income countries, while _perception of availability of mobile telephone_ is even better at 6 which is matched by upper-middle-income countries and slightly higher than lowermiddle-income countries. Situation is also same in terms of _commercial perception of postal efficiency_ with the SACU region having a rating of 4.2 compared to 4.4 for upper-middleincome and 3.5 for lower-middle-income group. Commercial perceptions in SSA and lowincome countries were ranked lower than SACU across the board.

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|ICT<br>—_—|SACU|Botswana|Lesotho|Namibia|Sontt<br>Attica|Swazihnd|Upper <br> Middle<br>Income|—Lower<br>Middle<br>Income|Low<br>=<br>Income|ga|
|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity<br>:<br>(totaltelephone subscribers/1000 people)|Bl|372|.<br>3<br>0|1<br>B|408|19<br>!|.<br>35<br>0|3<br>ol|.<br>o|99|
|Mainlines Teledensity<br>,<br>(mainlines <sup>subscribers’</sup><br>1000 people)|Me|75|.<br>7|.<br>00|7<br>0|ba|;<br>1<br>7|.<br>}<br>i|19|3<br>l|
|Cellular Teledensity<br>.<br>(cellular <sup>subscribers!</sup><br>L000 people)|8)|5<br>197|7<br>4|.<br>Ho|.<br>3<br>4|4|3<br>8|79<br>l|47|73|
|Households<br>with Own Teleph<br>ouseliolds wath wi Telephone<br>(6 ofhouseholds}|28|;|;|;|18|;|a|31|F|4|
|CostofLocalPhone Call<br>(US cents/3minutes)|u|)|1|:|5<br>:|5|9|j<br>0|:|0|
|Cost of Phone Callto the<br>US<br>nan<br>(US cents/3 mimutes)|ws|0d|(aC|(|a|||S|T||
|CostofCellularLocalCall<br>(UScents/3 off-peakminutes)|a0|3|16<br>0|3<br>0|25|3<br>4|5<br>4|“4|40|}<br>4|
|Cc<br>rcial Perception<br>ofTelephone/Fax Infrastructur<br>canner ialPerception 0<br>elephone/Fax Infastructure<br>quality<br>(1=warst 7=best)|40|46|;|5]|3]|;|54|40|44|43|
|“omumnercial PerceptionofAvailability ofMobileor<br>Cellular<br>Telephone<br>(l=worst 7=hest)|60|57|;|50|63|;|60|58|50|57|
|C<br>rcial Perception<br>**of** Inter**n**e**t**<br>Access inSchool:<br>omer<br>erception<br>I ernet<br>Access<br>in Schools<br>(l=worst 7=best)|37|0|;|44|46|;|33|40|1|18|
|SommertalPerceptionofPostalFfficiency<br>(l=worst 7=best}|42|5)|;|4]|43|;|44|48|31|47|
|PhoneFaults<br>(reportedfiults/100mainlines)|5<br>‘|37|fi|a|a|fi<br>:|7|33|}<br>Sy|57|
|UnmetDemand<br>(%ofmamtelephonelinesinoperation)|u|9<br>K|%|2|;|9<br>4|4|0|7<br>4|5<br>0|

Note: Data used isfrou the latest observation available between 1997 and 2003, Source: Estache, Anvionto and Ana Goicoechea (2005), “A ‘Research’ Database on Infrastructure Econontic Performance,” Policy Research Paper No. 3643, hufrastructure Network, Office ofthe Vice President, June 2005.
# **Figure 4: Teledensity (total telephone subscribers/1000 people)**

<!-- Start of picture text --> 500<br>400<br>300<br>200<br>100<br>0<br>SACU Botswana Lesotho Namibia South Africa Swaziland<br><!-- End of picture text -->

**Figure 5: Phone Faults (reported faults/100 mainlines)**

<!-- Start of picture text --> 80<br>70<br>60<br>50<br>40<br>30<br>20<br>SACU Botswana Lesotho Namibia South Africa Swaziland<br><!-- End of picture text --> 11
# **TRANSPORT: AN OVERALL ASSESSMENT FOR SACU COUNTRIES**

**_Access – Road density in terms of population (road-kilometers per 1,000 people)_ in the SACU region is slightly lower than upper-middle-income countries, but significantly higher than lower-middle-income and more than double the low-income and SSA averages.** The SACU region has a density of 8 road-km per 1,000 people compared to 4.9 in lower-middle-income countries. Within SACU, Namibia has the highest road density at 21.3 road-km per 1,000 people with the smaller counties, Lesotho and Swaziland, having the lowest density of around 3 road-km per 1,000 people. In contrast, _road density in terms of land_ is very low in the SACU region when compared to the four comparators. The SACU region has 135 road-km per 1,000 sq km, compared to 1,076 for upper-middle-income countries and 156 for SSA. Not surprisingly, the larger countries, Botswana and Namibia, have the lowest density with Swaziland and Lesotho having road density of around 200 road-km per 1,000 sq km.

**_Quality—technical._ The SACU region has the lowest percentage of paved roads at 27 percent when compared to the other income groups and only manages to slightly outperform SSA average in this category, but this may partly reflect the region’s geography and its spatial distribution of the population** .  Upper-middle-income average is almost twice the SACU average. Within the SACU region, Botswana has the highest percentage of paved roads at 55 percent, with Namibia having the worst at 13 percent. This indicator, however, must be taken with caution and supplemented with other indicators of road quality, which are unfortunately not uniformly available for large number of countries. For example, the SACU region’s largely semi-arid climate may require a lower percentage of paved roads than in a rainier climate, and the quality of unpaved roads may be better, perhaps significantly so, than in an average country in the SSA region as a whole.

**_Quality—perception._ The SACU region has one of the shortest travel times to work at 23 minutes when compared to upper-middle-income countries (29 minutes), lower-middleincome countries (40 minutes) and SSA at 34 minutes.** Within SACU, South Africa has the longest travel time at 35 minutes compared to 15 minutes for Lesotho. These results must be used with caution because, for this indicator, data are available only for Lesotho, Namibia and South Africa.

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|Transport|SACU|Botswana|Lesotho|Namibia|,<br>5"<br>Affica|gyazitand|Upper<br>Middle<br>Income|——Lower<br>Middle<br>Income|;<br>1 —<br>Income|gga|
|---|---|---|---|---|---|---|---|---|---|---|
|RoadDensityinTerms ofPopulation<br>(road-k1v/1000 people)|80|62|34|213|6.1|40|93|40|40|43|
|RoadDensityinTerms ofLand<br>(road-kr/1000 sqkm)|135|Is|198|.7|227|181|107%|328|181|156|
|RailLines DensityinTerms ofPopulation<br>(rail-km/1000 people)|08|.|.|123|odd|.|051|030|O13|o.18|
|RailLines DensityinTerms ofLand<br>(rail-km/1000 sqkm)|9.7|.|.|29)|«6S|.|313|.0|8|3.7|
|WealaetoWork inuiCities<br>(minutes/one-way work trip)|4|.|15|20|35|;|20|40|33|34|
|CommercialporeeRug ofServices DeliveredbyRoad<br>Department/Public Works<br>(l=worst 7=best)|55|53|-|50|53|-|41|42|34|37|
|CommercialPerception ofPortFacilities<br>(l=worst 7=best)|42|i0|.|50|47|;|38|35|16|38|
|Ccomamer cialPerceptionofRailroad Services<br>(l=worst 7=best)|4]|40|.|4]|43|;|30|16|7|43|
|CommercialPerception ofAirTransportServices<br>(l=worst 7=best)|49|43|.|47|56|;|45|42|3.6|45|
|PavedRoads<br>(%ooftotalroads)|27|55|18|B|1|.|57|47|30|28|

Note: Data used isfrom the latest observation available between 1997 and 2663.

Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office ofthe Vice President, June 2005,
<!-- Start of picture text --> Fihure 7: Paved Roads<br>(% of total roads)<br><!-- End of picture text --> <!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>N/A<br>0<br>SACU Botswana Lesotho Namibia South Africa Swaziland<br><!-- End of picture text -->

# **REFLECTIONS ON REGIONAL INTEGRATION AND REGIONAL INFRASTRUCTURE POLICY**

**The above synthesis shows (and individual country case studies strongly suggest) that there are significant infrastructure shortfalls in SACU countries in several major indicators of** **_access_ to basic infrastructure services (electricity, water & sanitation, ICT, and transport).** Performance in Swaziland and Lesotho lags particularly behind their comparator group of countries. But even in South Africa, despite major gains in recent years in expanding access to electricity to the previously unserviced areas, further extension of access to poor areas is very much needed.

**But how SACU countries decide to go about meeting these comparative shortfalls in infrastructure performance (especially access)––within purely national or wider, regional frameworks––matters for the success of their infrastructure scale-up efforts.** At one extreme, each country could seek to meet this challenge from the narrow view point of its own “national” interest that may include elements of energy security, self-reliance, etc. But this approach may be costly, not only from the point of view of the smaller SACU members but also for South Africa that is increasingly looking to expand its economic, trade, and transportation links into the rest of the broader African continent. At the other extreme, therefore, the SACU countries may be seen as an integrated economic, not only customs, space featuring potentially lower trade, transportation and transaction costs within the region and also vis-à-vis other neighboring countries with significant geographic links (e.g., Mozambique).

**Moreover, there is evidence of an increasingly tight supply-demand balance for basic infrastructure services in the SACU region (e.g., electricity, telecom, and transport and water and sanitation) so that infrastructure is becoming a binding constraint for growth.** In South Africa, for example, recent forecasts of demand for electricity and telecom and associated investment needs in an accelerated growth scenario suggest annual requirements of the order of $5 billion in just these two sectors. In Botswana (World Bank and BIDPA 2005c), Lesotho (World Bank 2005b) and Swaziland (World Bank 2006), in particular, there is also a growing recognition of infrastructure deficiencies and their potential impact on growth.

**The basic geographic and economic structure of SACU––with one very large economy (South Africa) and four small states––suggests considerable benefits from greater**

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**_infrastructure connectivity and market integration_ , especially for the smaller, landlocked members.** For smaller economies, it is not the level of the infrastructure capital stock _per se_ that matters as much as its connectivity to their wider economic areas. Lesotho and Swaziland are already substantially integrated into the wider South African economy, and Botswana’s largest concentration of the population and economic activity lies close to the South African border. There are also large movements of labor across borders, and remittances from South Africa are important source of income in Swaziland, Lesotho and Botswana. Moreover, recent research has demonstrated significant geographic and infrastructure related disadvantages of landlockedness in Africa (Collier and O’Connell 2004; Limao and Venables 2001).<sup>5</sup> As a result, further reduction in the costs of the “border effect”, in part associated with still not fully exploited infrastructure connectivity and market integration in SACU, seems warranted (Ndulu 2006).

**Effective infrastructure connectivity and market integration in the SACU is likely to require mobilization of both private and public sector resources.** While key infrastructure sectors in the region remain in public sector hands, there is scope for greater private participation in infrastructure (PPI), which has so far been limited in Africa. Given the large investment requirements of the scale up efforts, PPI is likely to be an attractive financing option, especially for smaller SACU states. The Government of Botswana, for example, has already identified toll roads, power generation, railways and telecommunications as the main areas for potential private investment. Botswana’s Privatization Master Plan was approved in March 2005 aiming to eventually privatize most of the major utilities (World Bank 2006b). The experience of East Asia is also instructive in this regard. In that region, cross-border integration was private sector, market-driven process of emergence of regional production networks; it is these production networks that contributed significantly to greater regional infrastructure connectivity in East Asia (Kuroda 2006).

**Finally, there are important, successful examples of regional projects that significantly facilitated transport, trade, and investment in the broader region.** These multi-country projects demonstrate the feasibility and potential benefits of infrastructure connectivity on regional integration in the SACU. Once in place, such regional projects may also prove more resilient to various national economic shocks that individual countries may be subject to and would affect more directly local projects. These include the following (Kritzinger van Niekerk, Ndulu and Reinikka 2006):

- Lesotho Highlands Project

- Southern Africa Power Pool

- South Africa-Maputo Toll Road

- Transit facilitation projects, e.g. Trans-Kalahari Corridor

With this broad summary of SACU-wide findings in mind, we now turn to in-depth benchmarking of infrastructure performance in individual SACU countries: South Africa, Botswana, Lesotho, Namibia and Swaziland.

> 5 Limao and Venables (2001), for example, estimate that landlocked countries, on average, face 50 percent higher transport costs and about equally lower trade volumes in coastal economies, but landlocked countries are able to offset these costs significantly by building better infrastructure and connectivity to the coastal areas.

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# **SOUTH AFRICA**

The international benchmarking of infrastructure performance in South Africa presented in this section draws almost entirely on a recent exercise for South Africa by Bogetić and Fedderke (2006b). For South Africa, we use from the aforementioned data base the available indicators for electricity (7), water & sanitation (4), and most of the broader group of indicators for information communication technology (13) and transport (10). For our purpose, this country’s benchmarking is limited to _sector performance_ indicators. Detailed benchmarking is presented in Tables 1-4. We do not use nor discuss institutional reform indicators that are also available in the database, and are subject of a separate exercise. In the following discussion, we focus on some of the most important conclusions.

## **Energy––electricity**

**Overall assessment.** Overall, despite important gains in recent years, South Africa’s performance in the electricity sector (measured by the seven indicators discussed below) compared to the upper-middle-income country benchmark is relatively weak in terms of access, but favorable in terms of technical efficiency (i.e., percentage of losses), pricing, and perceptions of service. Since 1994, a significant area of policy success has been to increase service access, but the level remains behind the average expectation for upper middle-income countries. Low aggregate losses reflect solid internal technical efficiency of the electricity network, but this hides the problem of occasional outages in specific areas, even in large cities such as Johannesburg. Low prices reflect the comparatively low cost of South Africa’s electricity (produced almost exclusively from coal) and the fact that a good part of the capital stock has already been depreciated as there has been little investment in the sector over the past two decades.<sup>6</sup> Finally, local perceptions of service have been rather favorable, but this may have reflected a selection bias in that only customers that do have service provision responded to this question. The overall picture, therefore, suggests that despite recent gains, the main weakness in South Africa’s electricity sector lies in access, which remains limited, particularly in terms of service delivery to the poor (Table 1 and Figures 1-2).

**_Access_** .  Not surprisingly, South Africa’s access to the electricity network is superior to the rest of Sub-Saharan Africa (SSA) with 66% of the _population_ having access, compared to 15% for region as a whole.<sup>7</sup> Access has also risen substantially in South Africa, from some 30% ten years ago reflecting the success to date of the policy of widening access to the electricity network. Nevertheless, South Africa’s level of overall access remains significantly behind—by about a third—its own income group of uppermiddle-income countries (87%). A related (and corroborating) statistic focusing on _households_ indicates that about 65% of all households in South Africa report having access to electricity compared to 23% for the SSA region and 74% upper-middleincome countries. Compared with other regions of the world, South Africa performs better than South Asia and East Asia Pacific.

> 6 With significant new investments being planned by ESKOM over the coming years, prices are likely to increase. 7 The database observation for South Africa is from 2002. More recent country data suggests an access rate of 70% (UNDP/WorldBank 2005), but the overall conclusion remains the same.

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**_Affordability—pricing._** South African average end-user prices (US cents/kwh) are one of the cheapest in the world at 3 cents for residential and 2 cents for non-residential customers. By comparison, these user prices in upper-middle-income countries are, on average, about 9 and 7 cents, respectively, while SSA rates are 6 and 5 cents. The world average is 9 cents for residential customers and 6 cents for non-residential customers. In South Africa, the low user prices do not seem to reflect obvious, overall underpricing, as is the case in many developing countries with the attendant quasi-fiscal losses. Instead, it is a consequence of the low cost structure of what is considered a well-run state electricity company (ESKOM) and the fact that part of the capital stock has already been depreciated as there has been little investment in the sector over the past two decades; hence, the fixed cost component of the electricity cost/price in South Africa is very low.<sup>8</sup>

**_Quality—technical._** Measured by the percentage of aggregate transmission and distribution losses, technical efficiency of the electricity sector is strong. Electric power transmission and distribution losses account for 8% of total output in South Africa, which outperforms both SSA (19%) and upper-middle-income countries (14%). Indeed, this aggregate performance is more comparable to the high-income group of OECD countries (6%). However, this aggregate picture must be qualified: for the 30% of the population not having access, this aggregate technical efficiency does not matter. In addition the increased prevalence of power outages in specific communities, even within Johannesburg, associated with the poor maintenance and the state of the distribution network under the control of local governments further qualifies the technical efficiency of the distribution network.<sup>9</sup>

**_Quality—perceptions._** In terms of commercial perception of electricity service, South Africa ranks amongst the best with a 5.8 rating out of 7 (7=best, 1=worst). This compares favorably with high income OECD countries (6.3) and is higher than upper middle income countries (5.2).  South Africa also performs well on commercial perception of the public agency electricity provider with a ranking of 6 out of 7. This ranking is only matched by Middle East and North Africa (MENA) and is higher than upper-middle-income (5.3) and high-income OECD (5.3).  As noted above, however, this may reflect a selection bias in that only customers with access to service would normally respond to this question. In that sense, the indicators of the lack of access (about 34% of the population) and the generally favorable perceptions of service cover essentially different segments of the population.

> 8 However, this static picture is going to change soon. The South African rates are likely to increase over the coming years as the electricity company, ESKOM, implements its major investment upgrading program aimed at rehabilitating and expanding its existing capacity and the distribution network. Part of the financing of this investment program will need to come from increased rates to accommodate higher fixed costs arising from the new investments. When it comes to pricing, this will require careful thought to the rebalancing/design of tariffs to balance the objectives of access, efficiency, revenue and affordability.

> 9 In South Africa, about half of the distribution network is under the control of the main utility ESKOM, and the rest is under the control of local governments. Since the local governments face major expenditure pressures against limited revenue sources available, maintenance of their electricity distribution network has often been inadequate.

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|Ener:<br>ahersy|South<br>Africa|U<br>Middle<br>Income|SSA|Middle<br>East<br>and<br>‘<br>Noth<br>.<br>Africa|South<br>Asia|East<br>Asia<br>"<br>Pacific|Latin<br>,<br>America<br>&<br>_<br>Caribbean|Europe<br>&<br>Central <br>.<br>Asia|Low<br> Income|Middle <br> Income|Hi<br> Income <br>\<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Access to ElectricityNetwork<br>(% ofPopulation)|66|87|15<br>~|38|a1|4<br>.|70<br>:|09|41|85<br>~|.|«0|
|Households ReportingAccess to Electricity<br>(% ofhouseholds)|6s<br>-|“4|4|80|30|62|or<br>:|09|32|70<br>;|“|45<br>7|
|EnergyUsePerPPP GDP<br>(kg ofoil equivalent/1000 PPP dollars, Constant2000)|257|249|304|249|184|212|207|375|374|246|188|275|
|ElectricityAverage End-UserPrices (U8 cents/kWh) -<br>(Residential/Non-Residential)|3/2|o/7|o/s|“|7|5/5|10/8|6/6|6/6|8/7|3/6|9/6|
|CommercialPerception ofElectricity service<br>(1=worst 7=best)|58<br>5.|53<br>.|43<br>.|5]<br>5.|26<br>:|43<br>.|43<br>.|48<br>;|28<br>:|47<br>4|63<br>.|50<br>51|
|CommercialPerception ofPublicAgencyElectricityProvider<br>(1=worst 7=best)|60<br>;|53<br>.|43<br>.|60<br>;|48<br>:|50<br>51|48<br>;|48<br>;|40<br>!|63<br>5.|53<br>5.|48<br>;|
|ElectricPowerTransmussionand Distribution Losses<br>(%oftotaloutput)|80|140|90|140|220|120|180|180|220|150|60|140|

Note: Data used isfrom the latest observation available between 1997 and 2602, Source: Estache, Antonio and Ana Goicoechea (2005), “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, bufrastructure Network, Office ofthe Vice President, June 2005.
# **Water & sanitation**

**Overall assessment.** Overall, South Africa’s performance in access to water lags behind its benchmark upper-middle-income group, essentially because of the much lower access in rural areas. This is even more notable with respect to sanitation. In terms of the overall average, the quality of drinking water as measured by the percentage of households using piped or welled water in the aggregate is better than in its benchmark group, but this is because of the very high performance on this indicator in urban areas. (The database does not have information on affordability/pricing).

By contrast, rural households score lower than the benchmark and also lower than almost all world regions, including Sub-Saharan Africa. Apparently, while access and quality do not appear to be major problems in urban areas, the performance in these dimensions in rural areas lags seriously behind most comparator groups. This favorable urban picture should also be qualified by the fact that in South Africa, some communities in urban areas, referred to as “peri-urban” areas (e.g., parts of Soweto, Diepsloot etc.) that may or may not belong formally or statistically to their larger urban agglomerations, often lack quantity and quality of basic service, such as water and sanitation (Table 2 and Figures 3-4).

**_Access_** .  Overall access to water is below the benchmark upper-middle-income countries, because of the significantly lower access in rural areas.  Specifically, population with access to improved **_water_** is 87%, which is significantly higher than SSA (64%) but lags behind upper-middle-income countries (93%). South Africa compares well to the broader group of middle-income countries (both lower and uppermiddle income) (89%), but is noticeably below high-income OECD (99%).  However, looking at the rural/urban differences, rural South Africa (73%) lags far behind the upper-middle-income rural average (85%), while South African urban areas (98%) do better than other upper-middle-income countries (96%). About 67% of the population has access to improved **_sanitation_** , which is almost twice the SSA level and slightly above the world average (64%). However it is still far behind upper-middle-income countries (86%) and high-income OECD countries (100%).

**_Quality—technical_** . The percentage of households using piped or welled water as main source of drinking water is 85 for South Africa, which is higher than in upper-middleincome group (82%), East Asia and Pacific (80%), and SSA (78%), but below both Middle East & North Africa (89%) and South Asia (89%). Once again, however, this figure masks a stark contrast between _urban and rural areas_ .  The share of rural households in South Africa using piped or welled water as main source of drinking water is only 65% compared to 98% for urban areas. Significantly, the rural area percentage for South Africa is _lower_ than the comparable rural household percentages in all the world regions, including SSA (71%), except for East Asia & Pacific and Latin America & the Caribbean which have very similar performance (64% each); the latter is the region that has been documented as having a significant backlog of infrastructure deficiencies that, inter alia, contributed to its low growth (Leipziger, 2001). By contrast, South Africa’s performance on this indicator in urban areas (98%) is the highest (together with South Asia) among all world regions and low and middle-incomecountries.

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|P<br>Water<br>a|South<br>;<br>Africa|Upper<br>th<br>Middle<br>Income|SSA|Middle<br>East<br>and<br>Noth<br>.<br>Africa|South<br>;<br>Asia|East<br>..<br>Asia<br>.<br>Pacif|Latin<br>‘<br>America<br>&<br>ic.<br>Caribbean|Europe<br>&<br>;<br>Central <br>.<br>Asia|Low<br> Income|Middle<br> Income|High<br>a<br>Income <br>\<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Access<br>to<br>Improved<br>Water<br>Source:<br>setae<br>inact<br>ile aa<br>(% ofpopulation)|87|8||8s|2|7|90|87|65|89|99|80|
|Access<br>to<br>Improved<br>Sanitati<br>eeess<br>fo Proveg ues<br>(% ofpopulation)|67|8H|T|TT|a||TT|3%|41|2|100|64|
|‘S<br>rcial Perception<br>ofWater<br>Servi<br>es<br>a cnn<br>(1=worst 7=best)|57|50|42|«56|064||OT|46-|400|4|953|s|
|Households Using PipedorWellWateras MainSource of<br>DrinkingWater<br>(%ofhouseholds)|5<br>a|at|7<br>ia|ae|ct|3<br>:|75|a|7<br>i|iP|7|7<br>is|

Note: Data used isfrom the latest observation available between 1997 and 2602, Source: Esiache, Antonio and Ana Goicoechea (2005), “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, lufrastructure Network, Office ofthe Vice President, June 2005.
## **Figure 4: Households Using Piped or Well Water as Main Source of Drinking Water   (% of households)**

<!-- Start of picture text --> 90<br><!-- End of picture text --> <!-- Start of picture text --> 85<br>85<br>81<br>80<br>80<br>79<br>78<br>75<br>70<br>South Africa Upper Middle Income SSA Middle Income World<br><!-- End of picture text -->

# **Information and Communications Technology (ICT)**

**Overall assessment.** In general, the ICT sector in South Africa displays performance akin to that of the energy and water & sanitation sectors, with some interesting nuances emerging from the pricing of various services. At first glance, the overall indicator of access appears good, but supplementary indicators suggest less than expected quality and major gaps in service delivery, quality and even access in rural areas. For example, on the surface, South African households pay some of the highest local rates in the world, and the low affordability is reflected in low telephone ownership in rural areas. By contrast, off-peak cellular service and international calls to the United States are provided at low, competitive prices, catering largely the wealthier segments of the population. But this has to be qualified. The poor segments of the population may now be effectively by-passing the land-line network, and are switching straight into the cellular network. So the lower rates may not be exclusively benefiting the wealthy. (See the discussion below on the perception of cellular access.) Nevertheless, in the local debate on the cost issue, there are indications that high international prices prevail for calls to many other destinations, and that broadband access is both limited and expensive.<sup>10</sup> Moreover, quality, as measured by reported faults per year per 100 mainlines, appear surprisingly low for an upper middle-income country (Table 3 and Figures 5-6).

**_Access._** In terms of _mainline teledencity_ , the picture is similar to energy and water whereby South Africa has better penetration than SSA, Middle East & North Africa, South Asia, and East Asia Pacific countries but lags somewhat behind middle-income countries and Latin America & the Caribbean.  However _cellular teledencity_ is reasonably comparable to upper-middle-income countries though it still lags behind high-income OECD levels.  In terms of household _ownership of telephones_ , only 28% of South African households own a telephone, which is much higher than the uppermiddle-income benchmark of 13% and comparable figures for SSA and South Asia regions, both at 4%, and East Asia Pacific (9%). But it is still a long way from Europe and Central Asia (ECA) at 43%, suggesting considerable remaining room for growth.

> 10 See Truen and Hodge (2005).

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The ownership rates in rural areas, however, are seven times lower (6%) than in urban areas (43%) in South Africa, making the rural rate more comparable to those in entire group of middle-income countries.

**_Affordability—pricing_** _._ A 3-minute local call in U.S. cents is one of the most expensive in the world in South Africa at 15 cents, which is three times the cost in South Asia and East Asia Pacific and equal to the high-income OECD countries. This reflects monopolistic market structures in local services. Local debate in South Africa has also emphasized the high cost structure, including on the international call market segment.  Nevertheless, within the cellular and international segment, some calls are very competitive. For example, the database used for this benchmarking shows that in terms of the cost of three off-peak minute cellular local calls (25 cents), South Africa has one of the cheapest rates when compared with upper-middle-income (54 cents) and high-income OECD (59 cents) countries where costs are twice as much. In addition, according to the database (with original data from the ITU—World Telecommunications Development Report) an international, 3-minute, peak rate, fixed line call to the United States in 2001 appeared lower than in upper-middle-income countries and high-income OECD countries, but this needs to be checked against most recent data within the country and a more detailed look at the rate structure before making definitive conclusions.

**_Quality—technical._** Reported faults per 100 telephone mainlines (per year) are very high (48) by upper-middle-income (18) country standards and are closer to the SSA average (57). High income OECD country rates—towards which South Africa may aspire in this highly globalized sector--are more than four times lower (11%).

**_Quality—perception._** Interestingly, perceptions of ICT are more closely aligned to the other indicators of performance. Commercial perception of _telephone infrastructures_ rating is 5.1 out of 7 compared to 6.6 for high income OECD countries and 5.6 in upper-middle-income countries. Commercial perception of availability of mobile telephones is much higher at 6.3 (vs. 6 for upper-middle-income group), which is only better in high-income OECD at 6.6. Commercial perception of _internet access_ in schools is also low by high income OECD standards, 3.6 vs. 5.4 and slightly lower than upper-middle-income countries (3.8). Commercial perception of _postal efficiency_ is even worse at 3.3 which is only just above the average for low-income countries (3.1), and almost half the rating given to high-income OECD, and compared to 4.4 for uppermiddle-income countries.

22
<!-- Start of picture text --> Middle 5<br>Upper East East oi tet High<br>ICT Soutiy Middle SSA and Soutly Asia amnerice . od Pow me wuddle Income World<br>a Africa Asia . & Central Income Income a<br>Income Noth; Pacific Caribbean. Asia. OECD<br>Africa<br>Teledensity 7 7 547 5<br>(total telephone subscribers/1000 people) 408 635 °9 292 2 12 433 4 o4 408 1393 ol<br>Mainlines Teledensity<br>(mainlines subscribers/1000. people) Cd 7 at a vs2! a5 7: o 2 22a 2ss oi 572 2<br>Cellular Teledensity 7. 7 7 2: 5 7 27<br>(cellular subscribers/1000 people) iad sab -s oI as si aa sd Soa 7 Sse<br>Households with Own Telephone 2 2 2 22<br>(% of households) aS ue bs a A 2 a a 5 “ “ os<br>Cost of Local Phone Call 5 5 7 7 7 5<br>(US cents/3 minutes) a “ ao S be ~ sd Z y<br>Cost(US cents/3of Phoneminutes)Call to the US 58 305 «497° «281.336.4803 326 «504 «309 s«d28 338<br>Cost of Cellular Local Call 25 2 52 7 2 57 57<br>(US cents/3 off-peak minutes) ~ = af ~ Z a a ao na ne<br>Commercial Perception of Telephone/Fax Infrastructure 51 56 43 58 43 52 46 53 34 53 6.6 55<br>quality (l=worst 7=best)<br>Commercial Perception i ad of Mobile or Cellular 63 60 57 59 59 57 55 60 50 59 6.7 61<br>Telephone (l=worst 7=best)<br>Commercial Perception of Internet Access in Schools 3.6 38 28 35 37 34 28 3.7 a1 34 54 40<br>(l=worst 7=best)<br>Commercial Perception of Postal Efficiency 33 44.937 #49 33 42 3.1 43 31 39 62 46<br>(l=worst 7=best)<br>Phone Faults 57 2 7 2 25 7<br>(reported faults/100 mainlines) ae ue . = d ae 2 af aS a a<br>Unmet Demand 5 2 7 7<br>(% of main telephone lines in operation) 1 4 so 26 4 13 1 8 4 2 o 71<br><!-- End of picture text --> Note: Data used isfrom the latest observation available between 1997 and 2003.

Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office ofthe Vice President, June 2005.
**Figure 6:  Phone Faults (reported faults/100 mainlines)**

<!-- Start of picture text --> 60<br>57<br>48<br>40<br>37<br>25<br>20 18<br>11<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

# **Transport**

**Overall assessment.** The transport sector’s overall performance indicators show South Africa lagging behind its comparator country groups and several other regions. This finding must be taken with caution though, reflecting at least two factors at play. First, it may simply reflect a more idiosyncratic territorial distribution of population and economic activity in a large, coastal, resource rich country such as South Africa than is the case for the average country group. Second, it may reflect some peculiarities in the type of road network that is appropriate for a country with semi-arid climate, and with a large proportion of its land surface carrying low population densities. However, it also reflects the legacy of the Apartheid era and wide inequalities in infrastructure service delivery between the privileged group and the majority of the population.  Perceptions of the quality of service delivery are relatively favorable (Table 4 and Figures 7-8).

**_Access. Road_** _density in terms of population (road-kilometers per 1,000 people)_ is twice the average for low-income countries and higher than Middle East & North Africa, and East Asia Pacific. However, at 6.1 road-km per 1000 people it is only a third of the density of high-income OECD countries and two thirds of upper-middleincome countries. Interestingly, Brazil has significantly higher road density in terms of population (10), while Indonesia and Malaysia score lower (1.7 and 2.8).  Road _density in terms of land (road-kilometers per 1,000 sq km)_ is low (227) when compared with many other groups and is only higher than that of SSA (155) and low-income countries (181). It is much lower than in benchmark upper-middle-income countries (328).  At the same time, it is important to recognize that these road density indicators for South Africa may be somewhat misleading in comparative perspective because of the specific territorial configuration of population density that is concentrated in comparatively small parts of an otherwise large national territory. To the extent that South Africa has lower than average population density and greater differences in the concentration of population and economic activity (parts of the country are very poorly inhabited with

24
very little or no commercial activity) in different regions, the indicator may overstate the gap with comparator countries. For example, South Africa performance on density in terms of land appears similar to Brazil (203 road kilometers per 1,000 sq km), another resource rich, coastal country with vast segments of the territory sparsely populated; similarly Indonesia and Malaysia also have very similar road density in terms of land (203 and 200 road kilometers per 1,000 sq km, respectively).<sup>11</sup> **_Rail_** _lines density in terms of population_ , by contrast, is very high when compared to SSA, Middle East & North Africa, South Asia, East Asia Pacific, and Latin America & the Caribbean. It is slightly higher than the average for all middle-income and just below the benchmark upper-middle-income group. South Africa’ _rail lines density in terms of land,_ however, is far behind those in middle-income and high-income countries and is also behind South Asia but ahead of other groups. Similar qualification for the interpretation of road densitiy applies here as well.

**_Quality—technical._** According to the database, South Africa has one of the lowest percentages of paved roads at 21%. This is lower than SSA (25%) and low-income countries (30%) and far behind upper-middle-income (57%) and high-income countries (82%). This may reflect the legacy of the strong urban bias and the economic configuration of economic activity inherited from the Apartheid era. Four important caveats are in order here. First, local information on paved roads show a higher percentage (31%) that was already achieved in 1994 (Perkins et al. 2005); as of writing, we could not reconcile this with the number from the benchmarking database that on this indicator originates in the World Bank’s World Development Report 2004. But even if the 31% figure is taken at face value, while it would imply better performance in South Africa than in SSA region, it would remain worse than in upper-middleincome countries. Second, the quality of the paved roads variable itself probably hides considerable cross-country variation: paved roads in South Africa may be of higher quality than those in most SSA countries, but this variation would presumably be much less relevant for the upper-middle-income group. Third, the proportion of roads paved may hide a relatively well developed road network in terms of road kilometers (thus inflating the denominator of the proportion), though the preceding discussion puts some limits on this possibility. Fourth, given the semi-arid climate of South Africa, and the low population densities in large parts of South Africa’s geography, it is not clear what the optimal pave roads proportion might be. Nevertheless, irrespective of which proportion is used (21% or 31%), we note that that South Africa reports a lower proportion than its benchmark group of upper-middle-income countries, and significantly lower than high-income OECD countries.

**_Quality—perception._** South Africa has one of the longest travel times to work in main cities (35 minutes) when compared to upper-middle-income (29 minutes), high-income OECD (32 minutes) and SSA (34 minutes) although the difference is small. The world average is 31 minutes. While the small difference makes it difficult to infer much of this comparison, the long commutes in South Africa may also reflects the inherited road network from the pre-1994 era that now results in the majority of the population often facing long and costly commutes; in some communities, commuting costs may reach prohibitive levels as proportion of the prospective wage, thereby discouraging job

> 11 Euchache and Goicoechea (2005), Table A4-2, pp. 105-07.

25
|Transport<br>stansport|South<br>Africa|Upper<br>|piiddle<br>Income|$84<br>_|Middle<br>East<br>and<br>“<br>Noth<br>.<br>Aftica|SOM<br>Asia|East<br>E)<br>.<br>Pacific|Latin<br>mL<br>America<br>&<br>Caribbean|E.<br>urope<br>Central <br>.<br>Asia|Low <br> Income|Mile <br> Income|5<br>H<br> taco <br>.<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|RoadDensityinTerms ofPopulation<br>(road-km/1000 people)|61|04|33|48|44|42|57|86|50|70|173|67|
|Road DensityinTerms ofLand<br>(road-kiw/1000 sqkm)|27|«106|«SSG|s|si|(2H—i|(‘ité‘sTdS|SCtSSC*dS|'S“(<Gs|isiTOD:«|C«CdiGA.|SAL.|
|RailLines DensityiTerms ofPopulation<br>(vail-kia/1000 people)|044|O51|O18|O1|OOF|O14|O33|O47|O13|O4|083|033|
|RailLines DensityinTerms ofLand<br>(rail-km/1000 sqkin)|65|313|3.7|055|OSS|8|9S|3|2|8|GD|B|
|Bevifine toWorkinWECities<br>(oinutes/one-waywork trip)|35|20|34|35|37|36|30|40|33|20|3|31|
|Commercial PerceaaanofServices Debiveredby Road<br>Department/Public Works<br>(1=worst 7=best)|53|41|37|55|39|44|40|36|34|41|43|40|
|ComercialPerception ofPort Facilities<br>(l=worst 7=hest)|47|38|38|41|44|46|4]|5.6|46|46|54|42|
|Commercial PerceptionofRailroad Services<br>(1=worst 7=hest)|43|29|32|35|36|31|1S|37|17|17|48|34|
|§omunercialPerceptionofAirTransportServices<br>(l=worst 7=best)|56|45|45|49|42|44|42|30|36|44|57|48|
|PavedRoads<br>(0of totalroads)|a|57|25|5H|(|8D|36|6|0|3|0|S|

Note: Data used isfrom tha latast observation available between 1997 and 2663.

Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643,Infrastructure Network, Office of the Vice President, June 2005,
**Figure 7: Road Density in Terms of Population -** **<u>(road km/1000 people)</u>**

<!-- Start of picture text --> 20<br>17.3<br>15<br>10 9.2<br>7<br>6.7<br>6.1<br>5<br>3.3<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

**Figure 8: Paved Roads (% of total roads)**

<!-- Start of picture text --> 100<br>82<br>80<br>60 57<br>52<br>50<br>40<br>25<br>21<br>20<br>0<br>South Africa Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text -->

# **Large differences from the benchmark**

As a conclusion to the detailed benchmarking of South Africa’s infrastructure performance discussed above, it might be useful to highlight some of the _large_ differences—positive and negative—against the areas where performance differences are small between South Africa and comparator groups of countries, especially the benchmark upper-middle-income group. With 27
appropriate caveats, this selection of indicators showing particularly “large deviations” (which we define as plus or minus 10% from the benchmark) from the benchmark can be taken as indicative of the possible significant strengths and weaknesses in infrastructure performance. We also note the areas where performance has been broadly on par (within the +/10% deviation from the benchmark) with that in upper middle income countries. Together with the additional, country specific indicators and analysis of sectors, it is hoped that this could help focus the attention of policymakers on those areas where performance improvements are most needed (Table 5).

In **electricity,** comparative indicators suggest that access remains a major issue, despite important gains in expanding access in recent years, while technical efficiency for the served population is relatively high. Indicators of internal technical efficiency (e.g., losses in distribution/transmission) and low prices are favorable compared with the benchmark country group as well as other comparators. These are comparatively strong dimensions of South Africa’s performance in electricity. Also, the energy intensity, measured by the energy use per PPP GDP, appears broadly on par with that of the benchmark upper-middle-income countries. However, indicators of access to network and reported access to service by households fall significantly short of what would be expected of a well-run sector in an upper-middle-income country. While much was done in recent years to extend the service to the previously unserved population, much more remains to be done, especially in rural (and peri-urban) areas.

In **water and sanitation** , access is much lower than in benchmark upper-middle-income countries, especially in rural areas. Quality indicators also indicate relative shortfalls. To the extent that the shortfalls reflect the internal imbalances between supply and demand for this vital service that is closely monitored internationally under the Millennium Development Goals (MDGs) because of its multiple links with health and the general wellbeing, expanding access and quality in this sector may be particularly important.

In **information and communications technology,** pricing of services catering the wealthier segments of the population and the large, internationally oriented enterprise sector—cellular calls and some international calls (to the U.S., for example)—reflect generally good and competitively provided services. We note, however, that in the current debate, broadband speed and access in particular remain a strong area of concern. Arguably, broadband services are of particular concern to economic activity.<sup>13</sup> Further, local service rates, which may be used relatively heavily by the middle class and the poorer segments of the population, are among the highest in the world. Finally, access rates, especially in poor, rural areas are much lower compared with the benchmark. The implication is that more could be done to raise competitiveness in the local service segment and generally expand access to the rural areas.

Finally, in **transport** , both road and rail, all basic indicators of performance show worse performance than the benchmark upper middle-income countries, even when accounting for some uncertainty about specific indicators and the caveats of interpretation. While this in part may reflect the peculiar territorial distribution of economic activity, population, markets, and transportable goods, it also reflects the legacy of Apartheid. While more in-depth comparison of a more detailed battery of road indicators of quality and quantity of service would be needed to draw firm conclusions, the indicators presented do suggest the need for a deeper look at the

> 13 See Truen and Hodge (2005) and Reynolds (2005).

28
<!-- Start of picture text --> Benchmark<br>south Upper tet . south Africa's<br>Affica Middle SBA ithe performance vs.<br>Income Benchmark<br>Deviation of +10% or more from Benclunark<br>Electricity: Average End-User Prices (US cents/k Wh) - . . _, ve<br>(Residential/Non-Energy: Residential) aa 6 6 reeled a<br>Energy: Electric Power Transmission and Distibution Losses<br>(% of total output) 3.0 14.0 19.0 14.0 Outperform<br>ICT: Cost of Phone Call to the US 5 5 OF 335<br>(US cents’/S minutes) a a 7 Masala<br>ICT: Cost of Cellular Local Call 25 5 2 9<br>(US cents/3 off-peak minutes) a . 2 peste<br>Deviation of between -10% and +10%o from Benchmark<br>Enerty: Energy Use Per PPP GDP 4 .<br>(kg of. oil equivalent/1000a PPP dollars, Constant 2000) 257 240 3o4 275 AA ver aage<br>Water:(%7 ofer population)SGAccesstoo TmprovedLmproved WaterWater SourcesSource: 87 03 64 80 Average<br>Water: Households Using Piped or Well Water as Main Source of<br>Drinking Water = (% of households)- .5 a 7. 79 at)Avera<br>ICT: Cellular Teledensity - -<br>(celhilar subscribers/1000" people) 3o4 381 73 29® AA ver aage<br>Deviation of -10% or more from Benchmark<br>Energy: aeaead to Electricity Network 66 87 15 60 Underperform<br>(% of Population)<br>Energy: Households Reporting Access to Electricity 6s 74 33 45 Underperform<br>(%o of households)<br>a Acces to Improved Sanitation 67 86 37 64 Underperform<br>(%o of population)<br>ICT: Mamlines Teledensity<br>(Mainlines subscribers/1O00. people) 107 261 31 213 Undi eafi<br>ICT: Cast of Local Phone Call 15 9 10 Pa Underperform<br>(US cents/3 minutes) ~ . ¥<br>ICT: Phone Faults 48 18 57 37 Underperform<br>(reported faults/100 maimlimes) ¥<br>Te Cite Road Density in Terms of Population 61 02 33 6.7 Underperform<br>(voad-km‘1000 people)<br>Transport: Road Density in Terms of Land EG, 1076 156 841 Underperform<br>(road-kimv/'1000 sq km) a ; ~ ¥<br>Transport: Rail Lines Density in Terms of Land<br>(vail-km’1000 sq km) . 17 31 4 23 Underperform¥<br>Transport: Travel Time to Work in Main Cities 35 20 a4 31 Underperform<br>(minutes/one-way work trip) ~ 7 7 7<br>Transport: Paved Roads a1 57 a5 =n Underperform<br>(% of total roads) 7 - ~~ ~<br><!-- End of picture text --> Note: Data used isfrou the latest observation available between 1997 and 2045, Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
||||Inc|ome|||
|---|---|---|---|---|---|---|
||Rural|Urban|Rural|Urban|Rural|Urban|
|Households Reporting Access to Electricity<br>eee<br>(%o ofhouseholds)|37|84|30|90|8|54|
|Access to Improved Water Sources<br>P<br>(%o ofpopulation)|73|98|85|96|54|83|
|Access to Improved Sanitation<br>:<br>(% ofpopulation)|44|86|76|91|28|54|
|Households with Own Telephone<br>H<br>(%oofhouseholds)|6|43|1|17|1|10|

Note: Data used isfrom the fatest observation available between 1997 and 2003.

Source: Estache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Dufrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
# **Conclusion**

In sum, we have benchmarked South Africa’s infrastructure performance on all four major infrastructure sectors in terms of key indicators of access, affordability-pricing, and quality using the new World Bank database of infrastructure sector outcome indicators from 207 countries. The broad results suggest the following: access remains a major issue in electricity and, especially, water and sanitation (particularly in rural areas), and so does performance in local telecom services. Even transport performance appears comparatively less strong than would be expected, though more in-depth analysis of comparative performance of transport may be warranted to develop a more nuanced picture, especially across different modes of transport.

More specifically, compared to the benchmark and other country groupings, South Africa’s utilities provide solid service at reasonable quality and, in some cases, very competitive prices. But there remain significant shortfalls relative to benchmarks in all infrastructure sectors, largely related to limited access and less than expected quality, especially in rural areas, where large numbers of South Africa’s poor live with limited or no access to service. To _catch up_ with its income group in terms of these indicators, South Africa will need to improve access to and quality of electricity, and water and sanitation services in presently unserved areas. Indicators of transport performance, while less definitive, potentially also point in the same direction. Even in telecom, the most globalized and “modern” infrastructure sector, access in rural areas can be improved and pricing of local services remains uncompetitive. Moreover, to put it simply, if South Africa is to strengthen its overall infrastructure performance, it will need to improve these dimensions’ performance in the sectors _faster_ than the average country in the upper-middle-income group while maintaining or improving its position in the already strong areas of performance. This will not be easy in competition within a group of countries all aspiring to move to much higher levels of income and with ambitious poverty reduction targets. But catching up with these _relative_ shortfalls may also help South Africa reduce its economic inequities and poverty rates and, therefore, meet the infrastructure related Millennium Development Goals (MDGs). It could also help address some of the concerns about South Africa’s external and industrial competitiveness and service delivery that in good part depends on the efficiency and capacity of network utilities to deliver basic services to population and businesses alike.

31
|Energy|Botswana|Main<br>Benchinark<br>Upper<br>Middle<br>Income|SSA|Middle<br> ee <br>.<br>Africa|we<br>"Pa|Fast<br>Asia <br>cific|Latin<br>,—_<br> ~<br>Caribhean|Europe&<br>Central <br>Asa|an <br>OMe|.<br> en <br>ENE|High<br> Income <br> OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Access to Electricity Network(% ofPopulation)|10|a7|15|88|i|54|a|99||)|||||
|ElectricityAverage End-User Prices (US cents/kWh) -<br>vy<br>—<br>(ResidentialNon-Residential)|4/3|OT|ils|.|.|ve|e|e||e||e|
|Commercial Perception ofElectricity service (I=worst 7hest}}|4.8|52|43|51|260|43|42|48|28|6470|«89|40|
|Commercial PerceptionofPubkeAgencyElectneityProvider<br>(1=worst7-best)|5a|53|ie|A|m|a|oe|1m|A|Bl|Sa|AG|

Note: Data used isfrome the latest observation available between 1997 and 2003.

Source: A "Research" Database ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005.
<!-- Start of picture text --> Main Middle Latin<br>Benchmark Est ,—_Eupe& ay High<br>Water Boisvara Upper 94 ‘stam South America aty HOM came Worl<br>— Middle Noth = Asia Pacific & isa Income Income OCD<br>Tneomte Attica Carithean<br>Access to lnpoved Water Sources (Mo of population) 5 3 é4 i n i il i i 8 ©<br>Access to Improved Sanitation (6 of population) I if F i & él i 2 4 it 4<br>Commercial Perception of Water Service (L=worst 7=best) iV i 42 jf rr 47 4p 40 49 DT<br><!-- End of picture text --> Note: Data used isfrom the latest observation available between 1997 aud 2083, Source: A "Research" Database ofInfrastructure Econontic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
**_Quality – perceptions:_** Commercial perception of water service is one of the highest ratings of all groups at 5.7 out of 7, while the rating for upper-middle-income is 5 and SSA is 4.2. Botswana also has higher rating than OECD at 5.2.  Given the level of access level, the high ranking is not a surprising but the fact that it is higher than OECD may reflect a slight “urban bias” of the perception indicator.

34
<!-- Start of picture text --> Access to Impoved Water Sources<br>(% of population)<br>Access to Electricity Network 110<br>(% of population)<br>100<br>87 85 100 99<br>95<br>80 93<br>90 89<br>60<br>60<br>80<br>80<br>40<br>70<br>64<br>20<br>15 60<br>10<br>0 50<br>Botswana Upper Middle Income SSA Middle Income World Botswana Upper Middle SSA Middle Income High Income OECD World<br>Income<br>Road Density in Terms of Population<br>(road-km/1000 people)<br>Teledensity 20<br>(total telephone subscribers/1000 people)<br>1600<br>17.3<br>1393<br>15<br>1200<br>800 10 9.2<br>635<br>7 6.7<br>468 501 6.2<br>400 372 5<br>3.3<br>99<br>0<br>Botswana Upper Middle SSA Middle Income High Income OECD World 0<br>Income Botswana Upper Middle SSA Middle Income High Income OECD World<br>Income<br><!-- End of picture text --> 35
|ICT<br>—|Boiswana|Main<br>Benchmark<br>| Upper<br>Middle<br>Income|SS‘|Middle<br>Fastand<br>Noth<br>Africa|South<br>Asin|East<br>gi<br>pacific|Latin<br>._—«<br>America<br>Caribbean|Europe&<br>Gontral<br>gig|LOW<br>Tieme|.<br>Middle <br> Income|High<br> come <br> ry|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity (totaltelephone subscribers/1000people)|372|633|99|292|72|172|43|54?|4|488|1393|301|
|Maiilines Teledensity(mainlines subscribers/1000 people)|75|261|41|129|35|76|192|224|29|193|3720|213|
|CellularTelecensity(cellular subaciibers/1000people)|27|381|73|167|cy|96|249|3a3|cy|280)|aap|296|
|Cast of Local Phone Call (US centa/3 muimutes)|2|9|10|6|4|5|7|7|8|7|15|9|
|Cost ofPhone Call to theUS(U8 cents/3 minutes)|364|305|oT|281|336|40|325|326|304|309|128|(333|
|Cost ofCellularLocalCall(08 cents/3 off-peakmites)|3|54|A2|52|17|A2|a)|4|4|4|a)|49|
|CotumercialPerception ofTelephone/FaxInfrastructure quality<br>f=unrst.T=hest|46|56|43|58|43|52|46|53|34|53|66|55|
|Commercial PerceptionofAvailability ofMobile orCellular<br>Telephone<br>(l=vrorst7=hest)|57|60|57|59|59|57|55|60|50|59|67|él|
|CottumercialPerception ofInternetAccessinSchools(1=worst<br>T=hbest)|42|38|22|35|7|34|22|37|7|34|54|40|
|CommercialPerception ofPostalEfficiency(1=worst7=hest)|52|44|a7|49|43|42|Al|43|Al|49|62|46|
|Phone Faults (reportedfaults/100 mainlines)|cy|18|i|23|oF|9|24|34|64|23|ll|ct|
|UninetDemand(ofmaintelephonelinesinoperation)|19|4|i|26|24|13|7|8|4}|12|0|al|

Note: Data used isfrom the laiest observation available between 1997 and 2663.

Source: A "Research" Datahase ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005.
|Transport|Boiswara|Main<br>Benchiark<br>*<br> = Upper =<br>Middle<br>Thcome|S8A|Middle<br>Basiad<br>Noh<br>;<br>Africa|Sout<br>Ash|East<br>Asia<br>Pacific|Latin<br>america<br>&<br>Caribbean|Europe<br>&<br>Central <br>Asia|"oy <br> tncame|atte<br> Income|igh<br> Income <br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|RoadDensityinTetms ofPopulation(toad-km/1000 people)|62|92|34|4g|74|Aa|i|B64|300|070|WT|B|
|RoadDensityinTetms ofLand(road-hiw/1000 sqkan)|Ig|Wid|157|ABD||T|A|T||R|A||
|CommercialPerceptionofServicesDeliveredbyRoad<br>Department/PubheWorks(l=worstbest)|33|4]|i|33|39|da|40|34|34|Al||AD|
|CommercialPerceptionofPortFacihties (l=worst7=best)|29|34|38|4]|34|038|i|34|2600|48|SA||
|CommercialPerception of Railroad Services(l=worst7=best)|40|29|32|33|36|03|13|37|2700|a7|48|94|
|CommercialPerception ofAirTransportServices(l=worst/=best)|43|45|45|4g|42|44|42|39|3600C|4A||AB|
|Paved Roads(4oftotalroads)|35|i|3|if|x|2|46|76|H|2|@||
|<br>Note: Datausedisfrontthe latestobservationavailablebetween 1<br>Source:A"Research"DatahaseofInfrastructureEconomicPerform|997 and2003.<br>ance,Antonio|EstacheandA|na Goico|echea,World|BankIn|frastructu|reNeiwork,|Officeofth|eVice Pr|esident,|June2005|.|
|O||Income|||performancevs.<br>Benchmark|
|---|---|---|---|---|---|
|Deviation of-10% or more from Benchmark||||||
|Energy: Access to ElectricityNetwork(% ofPopulation)|10|a7|15|60|Underperform|
|ICT: Mainlines Teledensity(mainlines subscribers/1000 people)|TS|261|31|213|Underperform|
|ICT: Phone Faults (reported faults/100 mainlines)|37|Ig|at|37|Underperform|
|ICT:Teledensity (totaltelephone subseribers/1000 people)|a7|6i5|90|201|Underperform|
|ICT: Cost ofPhone Call to theUS (U3 cents/3 minutes)|364|365|407|335|Underperform|
|Transport: RailLines DensityinTerms ofLand(rail-lon/1000 eqkon)|18|aT|3.63|23.12|Underperform|
|Transport: RoadDensity<br>in Terms of Land (road-kmv‘1000 sqke)|227|L076|1537|$40.6|Underperform|
|Transport: RoadDensity<br>in Terms ofPopulation(road-km1000 people)|62|92|33|67|Underperform|
|Water: Access to Improved Sanitation( ofpopulation)|41|a6|at|é4|Underperform|
|Deviation ofbetween-10% and +10% from Benchmark||||||
|Transport: PavedRoads(% oftotalroads)|35|Ss?|25|40|Average|
|Water: Access toImproved Water Sources (%6 ofpopulation)|95|of|64|#0|Average|
|Deviation of +10°%o or more from Benchmark||||||
|ElectricityAverage End-UserPrices(U3 cents‘kWh) - (Residential/Non-Energy: Residential|4g|OFF|6s|6|Outperform|
|ICT:Costof LocalPhone Call(US cente/3 minutes)|2|9|id|g|Outperform|
|ICT:CostofCellularLocalCall(U3cente/3off-peakminutes)|33|ja|42|49|Outperform|
# **Conclusion**

Surprisingly for one of the highest income countries in Sub-Saharan Africa, benchmarking indicators for Botswana show unfavorable performance in important dimensions in all four infrastructure sectors: electricity, ICT, transport, and water and sanitation. For example, access to electricity of 10% of the population is extremely low compared with its benchmark group of middle-income countries (87%) and other geographical groups, even when spatial peculiarities of the country are taken into account. Access to improved sanitation (41% of the population) is also only about half its benchmark level (86%). The phone fault rates are twice higher than in the benchmark countries, although more favorable than in the SSA region as a whole. In transport and water, indicators are about on par with the benchmark. The only indicators where Botswana scores well are related to the cost of some services such as cellular phones and electricity. This suggests the need to dig deeper into the causes and consequences of this _comparatively_ unfavorable performance of Botswana’s infrastructure as a further step towards formulating appropriate infrastructure policy response.

39
|Energy|Lesothy|Pemclmark<br>Lowlicome|coy|Middle<br>Fastand <br>Noth <br>Africa|Sou<br>= Asia|Fast<br>ths <br>Pacific|Latin<br>,<br> America <br>&<br>Caribhea|Europe&<br> central <br>Asia|MMe<br>Income|Lower <br>rsadle<br> Income|— Upper<br>Middle <br>Income||World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Access to ElectticityNetwork(% ofPopulation)|i|31|15|88|31|id|9|9|85|82|a7|é0|
|NEENGL.End-UserPrices (03cents/k/h)<br>-(Residential/Non<br>Residentia|8/3|ar|ar|;|;|5/5|10/8|B|OR|E|T|g|

Nowa: Data used isfrom the latest observation available between 1997 and 2603.

Source: A "Research" Database of Infrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
<!-- Start of picture text --> i Latin<br>East America Europe & Lower Upper<br>Water; Lesviho Benchotarktame °° KastandNoth Southen SS & Central”Middle Middle Middle World<br>_— " "Pacific Caribhea Asia" Income Income<br>Africa<br>nh<br>Access to Improved Water Sources (% ofpopulation) 76 63 64 a T2 73 90) af a a3 93 a<br>Access to Improved Sanitation (% of population) ci 4 ci TF & 60 TF 78 79 2 86 64<br><!-- End of picture text --> Note: Daia used isfrom the latest observation available between 1997 and 2603.

Source: A "Research" Datahase ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,

|ICT|Lesotho|mas<br>owencome|SSA|Middle<br> ie <br>-<br>Africa|ee<br>1A|Fast<br>Asia <br>Pacific|Latin<br>America <br> «=&<br> Caribbea<br>1|Europe&<br>Central <br>Asia|||,<br> ule<br>ome|Lower<br>Middle<br>Income|=Upper<br>Middle<br>Income|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity (totaltelephone subscribers/1000peoplel|63|é4|oo|202|Ta|fa|43|54?|468|301|633|5al|
|Mainlines Teledensity(mainlines subscribers/1000 people)|16|2|al|129|35|76|192|aa4|195|126|261|ala|
|CellularTeledensity (cellular subscribers/1000 people)|47|7|7a|167|7|96|249|325|280|179|381|296|
|Cost of LocalPhone Call(US cents/3minutes)|Za|8|10|6|4|5|?|?|?|6|9|9|
|Cost ofPhone Callto theUS (U8 cente/3 minutes)|231|504|497|281|336|450|323|326|309|314|305|335|
|Cost ofCellularLocalCall(U8 cente/S offpeak minutes)|26|4|4a|ia|iF|42|it|4|49|44|54|49|
|Phone Faults (reported faults/100 mainlines)|75|f4|57|23|oF|39|24|34|25|33|18|37|
|UnmetDemand(%ofmaintelephonelinesinoperation)|73|4|30|26|24|13|17|8|12|20|4|21|

Note: Data used isfrom the latest observation available between 1997 and 2663, Source: A "Research" Database of Infrastructure Economic Performance, Antonio Esiache and Ana Goicoechea, World Bank Infrastructure Network, Office of<sup>theVicePresident, June2005.</sup>
**_Quality – technical:_** Reported faults per 100 telephone mainlines are very high in Lesotho at 75%, much higher than low-income (64) and SSA (57) but lower much lower than the South Asia where 97% faults are reported. Unmet demand is also very high with around 73% of people registered for connection not being connected.

42
<!-- Start of picture text --> Access to Electricity Network Access to Impoved Water Sources<br>(% of population) (% of population)<br>100 110<br>87<br>82 100<br>80<br>93<br>90<br>60 60 85<br>80<br>80<br>76<br>40<br>31 70<br>65 64<br>20<br>15 60<br>6<br>0 50<br>Lesotho Low Income SSA Lower Middle Upper Middle World Lesotho Low Income SSA Lower Middle Upper Middle World<br>Income Income Income Income<br>Teledensity Road Density in Terms of Population<br>(total telephone subscribers/1000 people) (road-km/1000 people)<br>800 10.0<br>9.2<br>635<br>6.7<br>501<br>4.9<br>400 5.0<br>301<br>3.4 3.3<br>3.0<br>99<br>63 64<br>0 0.0<br>Lesotho Low Income SSA Lower Middle Upper Middle World Lesotho Low Income SSA Lower Middle Upper Middle World<br>Income Income Income Income<br><!-- End of picture text --> 43
|Transport|Lesotho|saneeinail<br>ow<br>mene|SSA|Middle<br> adi <br>“<br>Africa|ae<br>"Pa|East<br>Asia <br>cific|Latin<br>America <br> «=&<br> Carithea<br>hn|Europe& <br>(Central <br>Asia|.<br>|.<br> Ihe<br>ome|Lower<br>Middle<br>Income|=‘Upper<br>Middle<br>Income|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|RoadDensityinTerms ofPopulationfroad-k/1000 people)|34|3.0|33|48|24|42|a7|$4|Td|49|92|67|
|RoadDensityinTerms ofLand(road-dew/1000 sq keert)|195.7|181|155.7|6089|54460-|27640|F127|580.3|702|327.7|10764|=840.8|
|TravelTime toWorkinMainCities (minutes/one-wayworktrip)|15|33|34|23|a?|36|23|23|23|40|23|31|
|Paved Roads (% oftatalroads)|18|30|25|56|38|32|36|76|5a|47|it|50|
|Note:Datausedisfromthelatestobservationavailablebetween1|997and2663,||||||||||||

Source: A "Research" Database of Infrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrasiructure Network, Office of the Vice President, June 2005.
|e|e<br>Lesotho|e<br>LowLacome|S54|World|performancevs.<br>Benchmark|
|---|---|---|---|---|---|
|Deviaton of -10%o or more from Benchmark||||||
|Energy: 4uccess to Electricity<br>NMetvrork(> ofPopulation)|6|iF|13|60|Underperform|
|ElectricityAverage End-User Prices (U5 cents/k¥h) - (Residential’Mon-Energy: Residential|a3|676|avs|a6|Underperform|
|ICT: WMlainlines Teledensity (mainlines subscribers/1000 people)|16|ao|31|213|Underperform|
|ICT: Cost of LocalPhone Call(U5 cente/S minutes)|22|z|10|a|Underperform|
|ICT: Phone Faults (reported faults/100 mainlines)|FS|ot|37|37|Underperforin|
|Transport: Paved Roads (% of totalroads)|12|ig|23|30|Underperform|
|Water: Access to Improved Sanitation (% ofpopulation)|37|ar|37|64|Underperform|
|Deviaton ofbetween -10%o and +10%o from Benchmark||||||
|Deviation of +10%o or more from Benchmark||||||
|ICT: Celhilar Teledensity (cellular subscribers/1000 people|Ay|a7|ac|296|Cutperform|
|ICT: Cost ofPhone Callto the US (CUS cents/3 minutes)|231|Fhe|4o7|333|Outperform|
|ICT: Cost ofCellularLocal CallCUS cente/S off peakminutes)|26|ad|4a|4a|Outperform|
|Water: Access to ImprovedVaterSources (*6 ofpopulation)|ea|oF|64|#0|Outperform|
|Transport: Road Density inTerms ofPopulation(road-low1000 people)|3.4|i.e|3.3|6.7|Outperform|
|Transport: RoadDensity inTerms ofLandfroad-hm1000 sqkt|196|rar|153.7|240.6|Outperform|
|Transport:TravelTimetoVorkinMainCitiesGminutes/one-wayworktrip)|13|IF|34|31|Outperform|
# **Conclusion**

In sum, we benchmarked Lesotho’s infrastructure performance using available comparative indicators of various dimensions of performance. The indicators come from the recent World Bank research data base specifically developed for this purpose. The results are revealing of the several major, comparative deficiencies in infrastructure performance in Lesotho: (i) extremely low access to electricity and its affordability; (ii) poor coverage, quality and the cost of local (non-cellular) telephony; (iii) poor quality of roads, and (iv) very low access of the population to improved sanitation. Infrastructure service delivery in these areas is clearly well below what would be expected on average for a country in Lesotho’s income group.

These infrastructure shortfalls are likely to adversely affect not only the welfare of the poor but also the cost competitiveness and growth prospects of a range of economic sectors (e.g., tourism, trade) that depend critically on a stable and competitive supply of basic infrastructure service. As such, these shortfalls are also indicative of the infrastructure sectors and performance dimensions that need improvement if Lesotho is to catch up with competitive performance of its peer group of countries. Within the SACU sub-region, Lesotho’s infrastructure shortfalls are also likely to constrain its regional economic integration in several areas (e.g., Southern Africa power pool, road network) that are important for long-term economic prosperity. Finally, this simple, international comparison of Lesotho’s infrastructure performance should, of course, be combined with more in-depth, sector analyses for a more complete picture of the overall infrastructure performance. Taken together, such analyses could provide policymakers in Lesotho a useful guide to the areas of infrastructure performance requiring urgent attention.

46
|Energy<br>;|._<br>Namibia|Main<br>Lower<br>.<br>Middle<br>Income|554|Middle<br>Bastard<br>Noth =<br>.<br>Africa|South<br>\<br> Asia|"am<br>Asia<br>:<br>Pacific|Latin<br>erica <br>&<br>Carthhean|Mo<br>Central<br>j<br>Asia|y <br>Income|adie <br>Income|e<br>Middle<br>Income|;<br>h<br>Income<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|AccesstoElecticityNetwrotk(4ofPopulation)|20|$2|15|8|cil|i|9|99|3|Hs)|ay|.|él|
|EnergyUsePetPPPGDPckgofoilequivalent/1000PPP dollars,Constant2000)||<br>98|242|364|240|184|212|27|ci)|34|||LBB|275|
|FlectncityAverageEnd-UserPrices(UScents/kWh) -<br>(ResidentialMon-Residential)|Aid|iT|re)|.|.|3/5|108|6/6||e|e||er|
|CommercialPerception ofElecticity service(1=yorst7=best)|36|42|43|3d|26|43|42|48|28|43|32|63|30|
|CommercialPerceptionofPublicAgencyElectricityProvider(1=worst7=best)|63|30|43|60|38|30|48|48|40|32|33|33|48|

Note: Data used isfront tke latest observation available between 1997 aud 2663.

Source: A "Research" Database ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
|Water|Namibia|Main<br>Benelonark<br>Tomer<br>Middle<br>Theome|94|Middle<br> tM<br>“alt<br>Africa|South<br>AS|East<br> ite|Latin<br> <br>AME <br>Cartthean|Enpe<br> ry<br>gy|etme|oy,<br>Mie <br> Beem|Upper<br> ide <br> me|High<br> come <br> OECD|Wl|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Accesstolnproved Water Sources(4 of population)|il|9)|fd|i|1|h|0|a|“iH|8|8||al|
|Access to lnproved Samtation(oof population)|i|Tt|q|q|£|if|Ff|i|4|8|&|Mm|#|
|CottunerctalPerceptionofWaterService(l=worstJ=best)|f2|4p|4)|jo|4]|46|4]|df|4)|49||4|4}|

Note: Data used dsfron the latest observation available betwean 1997and 2002, Hource: A “Research” Datahase oflifrastructure Economic Performance, Antero Estache andAna Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
<!-- Start of picture text --> Access to Electricity Network Access to Impoved Water Sources<br>(% of population) (% of population)<br>100 110<br>87<br>85<br>82 100<br>80<br>93<br>90 89<br>60 60 85<br>80 80<br>80<br>40<br>70<br>64<br>20<br>20<br>15 60<br>0 50<br>Namibia Lower Middle SSA Middle Income Upper Middle World Namibia Lower Middle SSA Middle Income Upper Middle World<br>Income Income Income Income<br>Teledensity<br>(total telephone subscribers/1000 people)<br>800 Road Density in Terms of Population<br>(road-km/1000 people)<br>25<br>635 21.3<br>20<br>501<br>468<br>15<br>400<br>301<br>10 9.2<br>7 6.7<br>182<br>4.9<br>5<br>99 3.3<br>0 0<br>Namibia Lower Middle SSA Middle Income Upper Middle World Namibia Lower Middle SSA Middle Income Upper Middle World<br>Income Income Income Income<br><!-- End of picture text --> 49
|ICT<br>a|Namibia|Main<br>Benchitark<br>Lower<br>Middle<br>Tncorie|84|Middle<br>stand<br>Noth<br>Africa|South <br>Asia|East<br> i<br>pacific|Latin<br>.<br>Aamenica<br>Caribhean|Europe&<br>Central<br>sig|HE|.<br>Mile <br> EMME|Upper<br> sidle<br> coe|High<br>Income<br> OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity (totaltelephone subscuibers/L000people)|182|301|99|272|R|172|43|34]|od|45|35|1393|a0|
|Mainlines Teledensity (mamlines subscribers/1000 people)|66|124|Al|129|4|76|192|224|rv)|13|26|a|213|
|CellularTeledensity(cellular subseribers/1000 people)|16|179|13|167|cf)|96|24|325|cf)|2300|Bl|aa?|296|
|Cost of Local Phone Call(US cente/3 minutes)|4|6|10|6|4|i|7|}|8|7|9|1h|9|
|CostofPhone CalltotheUS(US cents/3 minutes)|As|a4|AT|281|336|450|325|326|M4|39|3|1B|335|
|Cost ofCellularLocal<br>Call(U8 cents/3 off-peakmunutes)|cil|4|a|By)|7|4|By|4|4|4|a4|i|4|
|Commercial PerceptionofTelephone/FaxInfrastructure quality(L=voret<br>best)|54|49|43|58|43|52|46|53|a4|53|56|6|55|
|Commercial Perception ofAvailabilityofMobile ot Cellular Telephone<br>(l=wotst7=best)|39|38|47|39|39|37|55|Ai|50|39|‘ll|Fy|él|
|CommercialPerception ofInternetAccess inSchools (1=wrotst7=best)|34|30|28|43|27|34|28|cn|21|34|38|34|40|
|Commercial Perception of PostalEfficiency(l=worst7=best)|41|45|a7|49|43|42|31|43|31|49|44|2|46|
|FhoneFaults (reportedfaulte/100mainlines)|4|3|Hv|B|Es|39|a4|34|é4|ra)|18|il|37|
|UnrnetDemand(0ofmaintelephonelinesinoperation)|2|2|j0|26|24|13|7|8|4|12|4|Q|al|

Note: Data used isfront the latest observation available between 1997 and 2083.

Source: A "Research" Datahase ofinfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank bufrasiructure Network, Office ofthe Vice President, June 2005.
|Transport|Namibia|Main<br>a<br>Lower<br>Middle<br>Tneome|SSA|Middle<br>Fasiand<br>Noth<br>.<br>Africa|South <br>Asta|"er<br>Asia<br>Pacific|Latin<br>ica <br>&<br>Caribbean|TI<br>Central<br>Asia|toy <br>Income|ite <br> Income|ret<br> Middle<br>Income|;<br>Bish<br>Income<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|RoadDensityinTerms ofPopulation (road kev'l000 people)|a3|49|43|4g|24|42|a|afi|30|70|92|173|67|
|RoadDensityinTerms ofLand(road-kiv'l000 sqkin)|iS|Sar|57|0 089|SAG|A|PL?|B03|181|m2|(14|1344|bald|
|RailLinesDensityinTetms ofPopulation(rail-km/1000 people)|123|03|O18|ol|O05|O14|033|oat|O13|O04|Ol|08|033|
|RailLinesDensityirrTetms ofLand(tail-lo/L000 ag kant)|229|152|46|a5|1379|8BS|AB|Sila|933|326|33|AR|B|
|TravelTine toWorkinMainCities (tuinutes/one-wayworktrip)|20|4|4|25|a|36|2)|2o|3|a|2g|32|jl|
|CommercialPerception ofServices Delrvered<br>by RoadDepartment/Pubhec<br>Works (1=worstT=best)|59|43|47|55|49|44|a|16|id|44|44|43|a|
|CommercialPerceptionof Port Facilities(1=worst7=best)|30|ci)|38|4l|34|36|Al|46|26|46|38|a4|42|
|CommercialPerception ofRailroad Services (1=worst7=best)|4l|26|42|43|44|al|15|47|a|2]|29|43|44|
|CommercialPerception ofAirTransportServices (1=yorst7=best)|4?|42|435|49|42|44|4)|49|36|440|45|a7|43|
|PavedRoads(%oftotalroads)|13|i|re)|i|8|Q|46|f6|“0|2|a]|82|sil|

Note: Data used isfront the latest observation available between 1997 and 1002, Source; A "Research" Datahase ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
|e||Income|||performancevs.<br>Benclonark|
|---|---|---|---|---|---|
|Deviation of-10%o or more from Benchmark||||||
|Energy: Access to ElectricityNetwork(% ofPopulation)|20|g2|15|60|Underperform|
|ICT: Cost ofPhone Call to the US (U5 cents/$ minutes)|428|ai4|497|335|Underperform|
|ICT: Mainlines Teledensity(mainlines subscribers/1000 people)|66|i226|31|213|Underperform|
|ICT:Phone Faults (reportedfaults/100 mairilines)|40|ae|SF|a7|Underperform|
|ICT: Teledensity (totaltelephone subscribers/1000 people)|182|sel|oo|S01|Underperform|
|Transport: PavedRoads (% oftotalroads)|13|47|25|50|Underperform|
|Transport: RailLines DensityinTerme ofLand (rail-kinw'l000 eqkin)|29|is|a7|23.1|Underperform|
|Transport: RoadDensityinTerms ofLand(road-km/1000 sq kn)|31|32%|156|S41|Underperform|
|Water: Access tolmprovedSanitation(% ofpopulation)|30|72|a7|64|Underperform|
|Deviation ofbetween -10% and +10% from Benchmark||||||
|Water: Access to ImprovedWaterSources (% ofpopulation)|30|as|64|30|Average|
|Deviation of +10% or more from Benchmark||||||
|ElectricityAverage End-UserPrices (US cente/kVWh) - (Residential‘N on-Energy: Residential)|4fa|RSF|6/3|of|Outperform|
|ICT: Cost ofCellularLocalCall(US cents/3 off-peakminutes)|30|44|42|49|Outperform|
|ICT: Cast of LocalPhone Call(U3 cente/3 minutes)|4|6|10|a|Outperform|
|Transport: RoadDensity inTerms ofPopulation (road-kmv/‘1000 people)|213|49|33|a7|Outperform|
|Transport:TravelTimetoWorkinMainCitiesGnirutes/one-wayworktrig)|20|dé|34|31|Outperform|
# **Conclusion**

Perhaps surprisingly as one of the better off countries in the SACU region, benchmarking indicators for Namibia show unfavorable performance in several dimensions of infrastructure performance, suggesting important infrastructure deficiencies. In some ways, the overall picture is not dissimilar to that of Botswana, a SACU country with even higher income level but with clear gaps in performance in some infrastructure sectors. In Namibia, ICT and sanitation indicators also lag significantly behind the benchmarks. Strong dimensions of Namibia’s infrastructure performance appear to be the cost of some ICT services and the roads density in terms of population.

53
<!-- Start of picture text --> Main Middle Latin ;<br>Energy Swaziland_ | Lower S84 Festa South; Asia gyorica FUMPEECentral toy dae Middle«Tope IncomeHl World<br>; Moth == Asia : & . ‘Tneome Income<br>Middle ; Pacific Asia Income OECD<br>Income Africa Caribhean<br>Access to Flecticity Network (4 ofPopulation) 0 0 5 98 "i 4 19 9 "i 85 Fa ; 60<br>Electricity Average End-User Prices (1S cents/kW'h) -<br>(Residential Non-Residential 43 aff 6/3 . . 5) WR 6/6 ofp | BY oT 13/6 Of6<br><!-- End of picture text -->

Note: Data used isfront tke latest observation available beiweer 1997 and 2682, Source: A "Research" Database ofInfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
<!-- Start of picture text --> Main Middle Latin<br>Benchmark East .—_ Europe& . Upper High<br>Water Svazild Lower 9Sq Pastand South go, Aterich Confra] Le Mle Middle Tneome World<br>~~ Wile Nath AS patie © gaig sont come come OECD<br>Thome Africa Caribbean<br>Access to ImprovedWater Soutces (% of population) i s) 4 s) n Ni 0 ay is) a % a0<br>Access to lmproved Satutation (M4 of population) i 7 F T s él h B 4 4 86 100 4<br><!-- End of picture text --> Note: Data used isfrowe the latest observation available between 1997 and 2003.

Source: A “Research” Database ofInfrastructure Economic Performance, Amtonio Estache and Ana Goicoechea, World Bank Infrastructure Network, Office ofthe Vice President, June 2005,
<!-- Start of picture text --> Access to Electricity Network Access to Impoved Water Sources<br>(% of population) (% of population)<br>100 110<br>87<br>85<br>82 100<br>80<br>93<br>90 89<br>60 60 85<br>80<br>80<br>40<br>70<br>64<br>20<br>20<br>15 60<br>52<br>0 50<br>Swaziland Lower Middle SSA Middle Income Upper Middle World Swaziland Lower Middle SSA Middle Income Upper Middle World<br>Income Income Income Income<br>Teledensity Road Density in Terms of Population<br>(total telephone subscribers/1000 people) (road-km/1000 people)<br>800 10<br>9.2<br>635<br>7<br>6.7<br>501<br>468<br>4.9<br>400 5<br>301<br>3.3<br>3<br>129<br>99<br>0 0<br>Swaziland Lower Middle SSA Middle Income Upper Middle World Swaziland Lower Middle SSA Middle Income Upper Middle World<br>Income Income Income Income<br><!-- End of picture text --> 56
|ICT<br>a|Swaziland|Main<br>Benchmark<br>Lower<br>Midile<br>Income|ssa|Middle<br>Pastamd<br>Noth<br>Africa|South<br>Asa|East<br>45,<br>pacific|Latin<br>.<br>_<br>America<br>©<br>Caribhean|-Europe&<br>Contry<br>Asia|LOW<br>Tievme|.<br>Middle<br>Ticome|Upper<br>iddle <br>iacome|High<br>© Tncome<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Teledensity (totaltelephone subscribers/1000 people)|129|301|99|292|Td|172|433|347|64|468|635|1393|501|
|Maitdines Teledensity(mainlines subscribers/1000 people)|4|126|31|129|33|76|19d|224|2|193|261|57a|213|
|CellularTeledensity(cellular subscribers/LO00 people)|a4|179|rx|1a?|cy|Oe|240|a25|cy|al)|331|aa|256)|
|Cost ofLocalPhone Call(U5 cents’3minutes)|i|i|10|i|4|i|fi|fi|a|7|9|15|9|
|Cost ofFhoneCalltotheUS(US centa/3minutes)|242|414|497|231|336|450|325|326|j04|309|305|128|335|
|Cost ofCellularLocalCall(US cerits/3 off-peakminutes)|4|44|42|5a|Vv|42|7|4|4|4|54|iT|4|
|Phone Faults (reported faults/100 mainlines)|70|3|a|3|W|ci|24|4|64|25|18|ll|cH|
|UnmetDemand(%ofmaintelephonelinesinoperation)|4|20|30|26|24|13|17|8|4}|12|4|il|21|

Note: Data used isfrom tke latest observation available between 1997 and 2662.

Source: A "Research" Datahase of Idfrastructure Economic Performance, Antonio Estache and Ana Goicoechea, World Bank bifrastructure Network, Office of the Vice President, June 2005.
||Main<br>Benchmark<br><br><br><br><br>|Middle<br>||Latin<br>East<br>.<br><br><br>|Europe&<br>||.<br>Up<br><br><br>|pe<br>|.<br>r<br>= High<br><br>||
|---|---|---|---|---|---|---|---|---|---|---|
|Transpo|:<br>t<br>Swaziland<br>Lower<br>554<br>.<br>Middle<br>Income|Fastand<br>Noth =<br>.<br>Africa|South<br>;<br> Asia|_<br>_Attierica<br>Asia<br>.<br>&<br>Pacific<br>Caribhean|Central<br>.<br>Asia|Low<br>—sTcome|Middle<br>,<br>Mid<br>Income<br>Inc|dl<br>om|e<br>Income<br>e<br>OECD|World|
|RoadDensity|inTerms ofPopulation(road-tan/1000people)<br>30<br>49<br>33|Ag|24|4a<br>il|86|30|70<br>9|2|13|iy|
|RoadDensity<br>Note: Datau<br>Source:A"R|inTerms ofLand(road-krw'L000 sq km)<br>181<br>328<br>136<br>sed isfronthelatestobservation avatlabia between 1997 and2682.<br>esearch"DatahaseofinfrastructureEconotticPerformance,Antonio Estache and AnaGoicoech|609<br>ea,WorldB|i)<br>ankIufra|78<br>713<br>structureNetwork, O|30<br>fficeoftheV|it<br>icePreside|10<br>nt,June2005.|76|4)|al|
|||||Be<br>|nchmark<br>||||||
|||||.<br>Swaziland<br><br>|Lower<br>Middle<br>Income|SSA|World||Swaziland'’s<br>peri<br>ev<br>Benchmark|s.|
||Deviation of-10%o or more from Benchmark<br>Energy: Access to ElectricityNetwork(> ofPopulation)|||20|a2|15|60||Underperfor|m|
||ICT: Teledensity (total telephone subscribers/1000 people)|||129|tai|oo|301||Underperfor|m|
||ICT: Mainlines Teledensity (mainlines subscribers/1000 people)|||44|26|31|213||Underperfor|m|
||ICT: Phone Faults (reported faults/100 mainlines)|||70|at|a|3F||Underperfor|m|
||Transport:RoadDensityinTerms ofPopulation (Groad-ke/1000 people)|||3.0|4.2|33|a?||Underperfor|m|
||Transport: RoadDensityinTerms of Land (road-bm/1000 sqken|||lal|328|1557|840.6||Underperfor|m|
||Water. Access to ImprovedWaterSources (* ofpopulation)|||5a|as|64|30||Underperfor|m|
||Water: Access to ImprovedSanitation(% ofpopulation|||3a|2|37|64||Underperfor|m|
||Deviation of +10%o or more from Benchmark||||||||||
||ElectricityAverage End-User Prices (US centa/kVi"h) - (Residential‘N on-Energ|y: Resident|ial)|4f3|B47|6f5|of||Qhatperform||
||ICT: Cost of Local Phone Call(US cente/3 mirnates)|||3|6|10|Q||Qhatpefor||
||ICT: Cost ofPhone Callto the US (U5 centes3 minutes)<br>|||242<br>|314<br>|4g7<br>|335<br>||Outperform<br>||
||ICT:CostofCellularLacalCall(UScents/3offpeakminutes)|||34|tet|42|49||Outperform||

<!-- Start of picture text --> Benchmark<br>. Lower Swaziland'’s<br>Swaziland Middle SSA World peri evs.<br>Income Benchmark<br>Deviation of -10%o or more from Benchmark<br>Energy: Access to Electricity Network (> of Population) 20 a2 15 60 Underperform<br>ICT: Teledensity (total telephone subscribers/1000 people) 129 tai oo 301 Underperform<br>ICT: Mainlines Teledensity (mainlines subscribers/1000 people) 44 26 31 213 Underperform<br>ICT: Phone Faults (reported faults/100 mainlines) 70 at a 3F Underperform<br>Transport: Road Density in Terms of Population (Groad-ke/ 1000 people) 3.0 4.2 33 a? Underperform<br>Transport: Road Density in Terms of Land (road-bm/1000 sq ken lal 328 1557 840.6 Underperform<br>Water. Access to Improved Water Sources (* of population) 5a as 64 30 Underperform<br>Water: Access to Improved Sanitation (% of population 3a 2 37 64 Underperform<br>Deviation of +10%o or more from Benchmark<br>Electricity Average End-User Prices (US centa/kVi"h) - (Residential‘N on-Energy: Residential) 4f3 B47 6f5 of Qhatperform<br>ICT: Cost of Local Phone Call (US cente/3 mirnates) 3 6 10 Q Qhatp efor<br>ICT: Cost of Phone Callto the US (U5 centes3 minutes) 242 314 4g7 335 Outperform<br>ICT: Cost of Cellular Lacal Call (US cents/3 offpeak minutes) 34 tet 42 49 Outperform<br><!-- End of picture text -->