POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS either an electricity connection or a mobile phone. Water-connection rates are similar for the two groups, but water-use is lower among poor households. We were encouraged to find no statistically significant difference in their educational attainment levels.

- Spatially, we find considerable variation across slum neighborhoods within a city. In Nairobi and Dakar, and for both electricity and water connections, neighborhood location is a powerful explanatory variable, even after controlling for household characteristics and poverty. This suggests that supply-side factors play an important role in determining infrastructure access.

- Finally, tenancy is a key variable in this issue of access to certain services—tenants are systematically less likely to have water and electricity connections as compared with homeowners. A tenant might be less willing or able to pay. The utilities might be less willing (or able) to connect. It might be a combination of the two.

The current paper also contributes to a methodological framework for a comparative analysis of slum residents’ experiences. We show how radar graphs of thematically grouped variables—the development diamond, the infrastructure polygon, and the living conditions diamond—provide a useful way to aggregate data and to examine differences within and across cities. To our knowledge, this is the first comparative study of African cities that draws on multisectoral random samples of slum residents (indeed, the three data sets that we analyze appear to be the only high-quality multisectoral household survey data available for urban slums in Africa).

The paper proceeds as follows. Section 2 presents cross-city comparisons that are organized around the development diamond, the infrastructure polygon, and the living conditions diamond. Section 3 uses this framework to compare poor and nonpoor households. The fourth section examines spatial heterogeneity and what it implies about supply and demand of key infrastructure, while Section 5 offers conclusions.

# Data

In February–March 2004, a World Bank-led research team conducted household surveys in the slums of Nairobi and Dakar. The questionnaires in the two cities were almost identical to allow for an apples-toapples comparative analysis and to establish a base for comparative studies including other cities. Although the sampling methodology differed somewhat for the two cities, both sought to produce a population-weighted stratified random sample of slums. The study covered 1,755 households in Nairobi and 1,960 households in Dakar.

In Nairobi the 1,755 households were selected from 88 Enumeration Areas (EAs). For census purposes, Kenya’s Central Statistics Bureau (CBS) has divided Nairobi into about 4,700 EAs, of which 1,263 are categorized as EA5, or slums, which are characterized by substandard housing and poor infrastructure. The 88 EAs in our sample were selected randomly from the subset of 1,263 EA5s and weighted by population. Because the household lists were not up-to-date, a complete relisting was conducted in each EA selected for study, and the sample households were chosen randomly from the revised lists. CBS collaborated with the World Bank’s team in designing the sampling frame of this study and also carried out the field-based relisting of households in the 88 EAs. Overall, the Nairobi data set is a 2