POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS In Dakar, at first glance and somewhat erroneously, the poor seem to have better tenure indicators than the nonpoor. For instance, the poor are more likely (73.5 percent versus 59.9 percent) to own land/structure and are less likely (23.7 percent versus 36.4 percent) to be tenants than nonpoor households. Further, the poor have resided in their slum settlement for about five years longer than the nonpoor. These indicators are influenced, however, by the national government’s definition of poverty— because rent is included in the government’s expenditure-based poverty threshold, rent-paying tenants are more likely to rise above the poverty line than homeowners are. Additional analyses are required, therefore, to understand the links between poverty and tenure for the case of Dakar.

Among those who rent, the poor are more likely to have no agreement (at times suggesting a squatter status) and are less likely to have formal written agreement than their nonpoor counterparts in both cities. Finally, we do not find a significant difference between poor and nonpoor households in either city regarding perceptions of house tenure security.

At a broader level, it is important to test whether tenure influences (or is related to) some of the outcomes that we have been examining. We find that two sets of variables do exhibit statistically significant correlations with tenure status (that is, difference between tenant and nontenant households has a _p_ value <0.05). First, data from Nairobi, Dakar, and Johannesburg show that homeowners have larger families (household size) and live in larger homes. Second, tenure affects infrastructure connections. When present, the effect is large in magnitude, but strikingly the sign of the effect varies across cities. In Nairobi and Dakar, owners are more likely to have electricity and water connections; in Johannesburg the opposite is true. The relationship between tenure and the likelihood of having basic infrastructure— particularly water and electricity connections—is further explored in Section 4.

## **Neighborhood safety and facilities: mo welfare gap**

We do not find significant differences between poor and nonpoor households in either city regarding their perceptions of neighborhood safety as well as the actual incidence of crimes (personal and property).

With respect to school facilities, cross-tabulations suggest that nonpoor households are more likely to have public and private schools in their neighborhood. When controlling for other factors, using logistics regression analyses, the results are different. We find that the likelihood of a household having public or private school in its vicinity depends not on the household’s poverty level but on the neighborhood; this is true in both cities.

The story regarding availability of health clinics is similar. A regression analysis shows that the likelihood of a household having a nearby health clinic, in either city, depends not on the poverty level of the household but on the neighborhood.

# The infrastructure polygon: the poor are worse off

Now we turn to the fourth dimension of the living conditions diamond—infrastructure. In this section we examine six different infrastructure services and find that, compared to the nonpoor, the poor are systematically worse off (appendix tables 15.1–15.5). Although this makes intuitive sense in general, it is remarkable that these differences manifest themselves even among slum residents, who are already deprived relative to other residents. We also find that, compared with the nonpoor, poor slum households 17