Specifically, using housing budget surveys for all SSA countries for which such data are available<sup>4</sup> , this paper examines the tradeoffs that households make between housing quality, housing consumption, consumption of transport services, and consumption of other goods (see Box 1 for a broad description of the data). These allow for an examination of relative spending levels between potentially competing budget items. Specifically, in the next section of this paper, consumption of housing, transportation, health and education are compared across income quintiles. As a first step, this paper follows a descriptive strategy and provides a thorough overview of housing expenditures in SSA countries, and within countries across income quintiles.

In section three, an overview of average housing characteristics for SSA countries is provided in order to explore how existing housing quality reflects expenditure levels. This section focuses on the prevalence of housing shortages and the limited availability of basic housing amenities both at regional and national levels, beginning with a broad overview before focusing exclusively on urban areas. Set against global benchmarks, critical descriptive statistics on housing characteristics and household expenditures on nonfood expenses highlight the housing policy areas that deserve greater focus.

Section four delves into four capital cities from the countries studied to better understand the tradeoffs that households make in choosing their housing structures. This section uses hedonic models<sup>5</sup> to obtain an estimate of the value that households assign to specific characteristics, such as the material of the walls, or having a bathroom inside the house. The paper concludes by providing estimates for the income elasticity of housing expenditures in three African cities, discussing the implications of the findings for policy design in SSA.

# **<u>Box 1. The Data</u>**

The data used in this paper are from the World Bank’s Africa Region—Poverty Reduction and Economic Management Statistics Practice Group’s Survey-based Harmonized Indicator Program (SHIP), unless otherwise indicated. These surveys are representative, cross-sectional surveys at the country level. Annex 1 provides a full list of countries, survey years<sup>6</sup> used, and the number of households included in the analysis.

Cross-country analysis of the housing data in SSA provides insight into regional patterns of housing shortages faced by households as well as the relative level of household expenditures dedicated to housing. The use of standardized data allows for the development of a clear and consistent picture of housing issues across the region and complements the localized, less generalizable knowledge on housing that currently exists.

For purposes of this paper, areas<sup>7</sup> are categorized into three urbanization types: 1) the largest city; 2) other urban areas, and 3) rural areas. The largest city is identified using the smallest geographical unit available, which varied by country and could range from region to district. Using this geographical unit, urban households were identified and counted as residing in the country’s largest city.<sup>8</sup>

> 4 The analysis uses data for a total of 20 countries. See Annex 1 for a full list of countries and years examined.

> 5 Hedonic models are a standard technique used in the literature to examine what households are “willing to pay” for different amenities.

> 6 Because the data are collected by each country’s respective national governments, the survey years are not always comparable across countries or obtained at regular intervals.

> 7 This report recognizes that there is variation in national criteria used to designate urban areas. For example, in Kenya, areas are considered urban if there are more than 2,000 inhabitants residing there. Nigeria, on the other hand, an area is considered urban if it has more than 20,000 inhabitants. For a better understanding of the challenges associated with urban designation see Cohen (2004).

> 8 In many instances in Sub-Saharan African countries, large geographic units such as regions only contain one urban agglomeration so the issue of the misidentification of the largest city is minimized.

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