because some types of assets and housing characteristics are complementary to public investments. This is the case, for example, with electricity, refrigerators, televisions, piped water, and flush toilets. For any level of consumption, these types of assets are therefore more likely to be found in households in urban and more economically advanced areas. Second, if the health variable of interest is correlated with the spatial domain, and the if the use of domain-specific weights for assets and housing characteristics has a big impact on the relative position of households in the ranking, the re-ranking that results from moving from one welfare measure to the other can have a big impact on measured inequality.

# **3. Data and Variables**

The following analysis is based on the 1996/97 Mozambique National Household Survey on Living Conditions (IAF) _._<sup>17</sup> The survey was designed and implemented by the National Statistics Institute in Mozambique, and was conducted from February 1996 to April 1997. The sample covers approximately 43,000 individuals living in 8,250 households.<sup>18</sup> It was selected in three stages and is geographically stratified to ensure representativeness at both at provincial level and for urban/rural areas. The analysis is based on individual-level data and uses three types of variables: (i) consumption; (ii) asset index; and, (iii) health service indicators.

# **3.1 Consumption**

Following the approach set out above, per capita consumption is calculated as the person average of total estimated household consumption.<sup>19</sup> Similar to most survey data from developing country, household consumption is measured in the IAF on the basis of recall data on expenditures and consumption collected as part of the survey. It includes expenditures and consumption of home-produced food and nonfood items, as well as imputed use values for owner-occupied housing and household durable goods.<sup>20</sup> Household consumption has been deflated using spatial price indices. These were defined for 13 geographic areas in the original data analysis, distinguishing urban and rural areas in provinces or groupings of provinces. The spatial price indices reflect the cost of attaining the same minimum standard of living in the respective spatial domains, considering spatial differences in consumption patterns to meet the minimum standard and in prices. It

> 17 Inquérito Nacional aos Agregados Familiares Sobre as Condições de Vida (IAF). Details concerning the survey can be found in Datt and others (2000) and Ministry of Planning and Finance (1998).

18 For the purposes of the analysis in this paper, a number of observations had to be dropped due to missing values in the variables of interest. The resulting sample contains 41,856 observations.

19 Simple per capita normalization of consumption entails an assumption of no household economies of scale. This assumption is common in contexts where food makes up a large proportion of total consumption (Deaton 1997). In order to assess the sensitivity of the results to this assumption, the impact of applying commonly used equivalence on the distribution of service use across income quintiles was considered. The application of equivalence scales does not have a notable impact on the findings and this analysis is not reported here.

20 Data on major food items and some typical nonfood item were collected with a seven-day recall period (collected on three separate visits); data on regular nonfood expenditures were collected with onemonth recall; data on major nonfood expenditures refer to a three-month recall period.

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