**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