We can also trace some differences between urban and rural areas. For example, the employment of the head of the household takes different signs in urban and rural areas and so do most modalities for the type of accommodation and for used water. There are also differences in signs for at least one modality of other variables such as education and health variables.

Overall, we can safely argue that the first axis describes a situation of welfare and that this welfare is multidimensional in that many different aspects of welfare such as health and education exhibit very similar behaviors towards this axis. However, much more can be done in further refining the model by removing or adjusting variables so as to better meet the fundamental properties of the MCA approach.

**Step 2** . Next, we review the behavior of each variable to test whether the model is robust to the underlying properties discussed. The MCA provided us with the basic elements to select the variables that will be used in the construction of the CWI.  The main criterion used to reduce the number of variables without losing substantial consistency is the First Axis Ordering Consistency (FAOC-I).  The variables that have the FAOC-I property are those obeying the rule that welfare deteriorates from a position of wealth to a position of poverty throughout the first axis.<sup>6</sup> We therefore proceeded to eliminate some variables based on FAOC-I. These variables include the employment of the head of the household in both urban and rural areas. This variable had a counter-intuitive sign and was clearly a substitute for other employment variables. Other categorical variables did not meet the FAOC-I property but were kept and restructured so as to meet the property. These variables are access to health care, state of health and medical coverage, type of housing, children's nutritional status and source of drinking water. Categorical variables representing the proportions of employed in the household and those of the unemployed were eliminated altogether failing to meet FAOC-I. Possession of a freezer in rural areas responds to FAOC-I but the variable was eliminated because of the low frequency of one of the modalities (less than 1%).

Tables 2 and 3 also report results for the final MCA exercise. For urban areas (Table 2), the MCA performed on the remaining variables led to an increase in the explanatory power of the first factor, which rose from 52% to 69%. In the final analysis the poor are situated on the left and the rich on the right so that well-being increases from left to right. In this new plan and as shown in Table 5, all variables have the FAOC-I property of ordinal consistency on the first axis. We can also see that all variables show the same direction vis-à-vis the first axis satisfying FAOC-G. There is therefore a clear separation of rich and poor households who are opposed to the first factorial axis. We can conclude that the household score generated by the MCA on the first axis is a consistent indicator that can be used for ordering purposes to

> 6 Note that other criteria also used to select relevant variables such as the spread on the first axis, the high frequency of nonresponses or low frequencies of responses on certain modalities.

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