**House Ownership** 3 5.16 0.00 **Rejected Position in occupation** 7 5.58 0.00 **Rejected** _Source:_ Own calculations based on ECV Survey.

Annex 1 presents the results for the three regions chosen in the analysis using equations (1’) and (2’). The Oriente region (mainly the rural part) is very different to the rest of the regions in characteristics and on returns. By contrast the Sierra (including Quito) and Costa (including Guayaquil) appear to be more similar on characteristics and returns.

# 4. Decompositions at the National Level

In light of the salient differences in per capita income between rural-urban areas and across provinces, the following analysis considers the contribution of household characteristics to per capita income differences. These differences may come from differences in characteristics (for example, a lower level of education in rural areas) or from differences in the returns to characteristics (for example, a lower impact of education on earnings and thereby a lower per capita income in rural areas). In some cases, the differences in characteristics and in the returns to characteristics reinforce each other, but in other cases they might not. This is analyzed using the Oaxaca-Blinder decomposition of the income gap between assets and returns to those assets (Blinder 1973 and Oaxaca 1973). The estimates from equations (1) and (2) are used to analyze the comparisons. Three types of geographic comparisons are examined: i) the difference in mean welfare ratios between urban and rural areas, ii) the difference within the urban and rural sectors across provinces, and iii) the difference between urban and rural areas within a given province.

The first questions concern the overall differential in living standards between urban and rural areas at the national level. This entails a comparison of urban-rural differentials in mean welfare ratios, given by (3):

> where **X** _U_ and **X** _R_ are the sample means for urban and rural areas respectively, and _pUj_ and _pRj_ are the

> proportions of province _j_ ’s population in each area.

Table 4 shows the result obtained for equation (3) using the coefficients showed in Table 2 and the means reported in Annex Table A1. The difference in the intercepts, -0.057, gives the difference between the fitted log welfare ratio for a married couple, both illiterate, with a male indigenous household head who owns a house and is an employee living in the urban Pichincha area, and a household with the same characteristics located in the rural Pichincha area. Among the non-geographic variables that could cause the differential impact in urban and rural areas, house ownership is a minor factor. The next variable in order of significance is the occupation of household head, while most of the differential is due to demographic variables and education. In fact, demographics accounts for around 32 percent of the differential in log welfare ratio and education for 68.5 percent approximately, which is explained by both higher levels and higher returns to education in urban sector. The difference due to the geographic variable is small, which indicates that, controlling for other characteristics, the gap between Pichincha province and all other provinces in urban areas is almost of the same order in magnitude as the gap in rural areas.

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