POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# Appendix A   Spatial heterogeneity in service access in Johannesburg

This section examines differences in access across space within Johannesburg. As noted earlier in Section 4 in the paper, we discuss this analysis for Johannesburg separately from that of Nairobi and Dakar because of lack of comparable underlying data in two ways. First, unlike for Nairobi and Dakar, the data for Johannesburg are not exclusively for slum settlement but for selected underserved urban areas. Second, we do not have a household poverty-level measure for Johannesburg, as we do for Nairobi and Dakar. We still wanted to glean some insights, however, from the spatial heterogeneity in infrastructure access within Johannesburg based on an analogous type of analysis.

Accordingly, here we rely on the 11 divisions and 253 constituent enumeration areas (EAs) that were used to design the sampling frame for Johannesburg underserved areas. We examine the same two categories of infrastructure: home electricity and piped-water connections as we did for Nairobi and Dakar. As for the logistic regressions at the household level to investigate whether the supply and demand side factors do play roles in explaining the observed variations in coverage levels of these two types of infrastructure, we use whether a household resides in a shack-type dwelling or not as the relative measure of household poverty level in absence of direct data on poverty level. In all other respects, the analysis here follows the same approach as we used for Nairobi and Dakar. The results are discussed below.

## Electricity connections

Table A1a summarizes how the electricity coverage varies _across_ the 11 divisions in the city and _within_ each division across its constituent EAs. All the divisions report at least some households with electricity suggesting the presence of trunk infrastructure in all divisions. However, the coverage levels do vary considerably across divisions with a mean of 71 percent and s.d. of 39. The minimum level coverage is 49 percent (Johannesburg South) and the maximum level is 94 percent (Sandton/Rosebank). We also find that 64 percent (7 out of 11) of the divisions have at least one or more EAs that completely lack access to electricity and about 16 percent (41 out of 253) of all EAs have no access.

Table A1b presents the results from the household-level logistic regression results that shed insight into the extent of roles by demand and supply side factors in explaining the observed spatial differences in electricity coverage levels within the city. The results show (at 10 percent significance level or less) that tenancy status does not play much of a role in terms of a household’s access to electricity. However, in terms of whether the household lives in a shack or not, it does appear to affect the probability of access. Specifically, the likelihood of a shack household having access to electricity is only about 14 percent of that of a nonshack household. We also find that several location odds ratios are statistically significant. That indicates that, after controlling for demand side factors, residential location also matters greatly in determining access—a likely reflection of differences in supply side bottlenecks across locations. For example, the likelihood of households residing in Roodepoort of having access to electricity is only 10 percent of those residing in Alexandra division.

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