The EPSOC allowed us to examine the relationship between the _strata_ categorization of households and income per capita as an alternative welfare measure. Differences between the two could arise for two reasons: first, a less than perfect correlation between income per capita and the strata classification; second, considerable heterogeneity of living standards within _manzanas_ and the ‘average’ living standard not being meaningful at the individual, household level. Indeed, income and strata showed a relatively high variation. Based on the EPSOC, Table 3 includes a mapping of the _strata_ (row entries) by population income quintiles. Variation of strata by income is very high, especially for _stratas_ two and three – in _strata_ three, income is almost evenly distributed across all quintiles. This result cautioned against using _strata_ as the only identifying variable for geographic targeting of beneficiaries, especially when aimed to reach income-poor households.

We were also able to compute the distribution of poverty within the city. Originally, sampling for the EPSOC was designed to achieve representativeness for five different areas within the city.<sup>17</sup> However, poverty within these larger areas varied significantly so that we also derived poverty rates at the individual _comuna_ levels. Since this considerably reduced the number of observations available per geographic unit, it lowered precision of the poverty estimates and hence made it even more important to calculate the standard errors associated with each of the poverty rates. Results are presented in Table 4. As can be seen, the standard errors were in a reasonable range except for _comunas_ 3, 4 and 12. Consequently, extreme caution was warranted for city policy makers to draw conclusions on poverty in these three _comunas_ .

We found that poverty varied significantly within the five larger areas generally used for analysis within the city – and that heterogeneity even within _comunas_ was also considerable. Several _comunas_ (especially 7 and 13) showed very high extreme poverty rates but rather average poverty rates, indicating that pockets of extreme poverty in these _comunas_ existed.<sup>18</sup> The existence of such pockets of extreme income-poverty within _comunas_ calls for a good

> 17 The selection of the five geographic areas, for which the sample was to be representative, followed a commonly used classification in Cali.  First, the poor area in the East of the city is referred to as _Aguablanca_ ( _comunas_ 13, 14, 15 and 16). Twenty years ago, Aguablanca did not exist and in its place was a large lake.  But with massive migration from the rural countryside, the lake was drained and slum-settlements were set up and slowly upgraded.  The _comunas_ of Aguablanca have been heavily populated by black migrants from the pacific coast. The second area commonly identified as very poor is _ladera_ , comprising _comunas_ 1, 18, 20 (and 21) in the western part of the city.  Most of these _comunas_ are steeply sloped at the hill side and comprised of mainly poorer mestizo migrants.  The _comunas_ in the _north-eastern part of the city_ (6, 7) also include recent migrants and are mostly populated by the lower-middle income class. Lastly, a _corredor_ which runs from the north ( _comuna_ 2) through the center ( _comuna_ 19) to the south ( _comuna_ 17) are populated by the mediumand higher-income class are consequently show relatively low levels of poverty.

> 18 As before, we define ‘extreme poverty’ here as the population group in the lowest income quintile in Cali.

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