# **_Associations between household ownership of assets and welfare_**

Another characteristic of poor Burkinabes is their lack of physical assets. Here, we focus on the association between household ownership of assets and welfare.<sup>24</sup> The asset variable is constructed at the household level, by creating a dichotomized asset vector which assigns value A _=_ I to households possessing a given asset and 0 otherwise. The sum across these variables produces the asset score which ranges between 0 (no asset) and 6 (all key assets selected).<sup>25</sup> The distribution of poor on the assets score is inversely related to the assets scores- the share of poor declines with rising asset scores, and there is no poor household beyond an asset score of three (Annex V-B). On the other hand, the distribution of non poor is directly proportional to increasing assets score. Households with large possession of assets are likely to be non poor.

This distributional pattern persisted in 1998. A relatively large share of households with no asset continues to be poor in rural areas (63%), at the national level (60%), and a much lower percentage in urban areas where asset ownership is more important (33%). Likewise, beyond the asset score of three, the total weight is allocated to non poor, suggesting some form of association between household asset ownership and welfare. In a related study on Thailand, Fofack and Zeufack (1999) found assets ownership to be one of the key determinants of income inequality.

We estimate the direction and strength of association between household assets ownership and welfare using the measured correlation coefficients. The unconditional correlation shows a relatively strong and positive association over time, _p(94)(A,W)_ =.936, and _p(98 )(A,W)_ =.938, implying that over 87% of proportional variance of welfare is explained by household assets ownership in 1994 and 1998, respectively. This association is statistically significant at the I percent level.

It is also affected by household SEG and spatial location. The estimated measure ranges between (.7508 < p(94 )(A,W _SEG) <_ .916), taking the lowest value (p(9 4)(A,W _SEG)_ =.7508) for public sector wage earners and the highest value _p(94 )(A,W I SEG) =_ .916 for Traders. These associations are statistically significant at 10 and I percent level. Figure 5 below shows smoothed curves of measured coefficients of correlation between household assets ownership and welfare in a cross section of SEG and over time. These graphs exhibit a much larger variability 1998, (Annex VI).

There are also important variations across geographical regions (Figure 6). The solid, darker and thicker line represents the variations in 1998, while the solid, gray and less thick represents the variations in 1994; the smallest lines, dashed and solid represent unconditional correlation measures during the two periods. Again, over time changes across economic regions appear more significant than the variations across SEG. Irrespective of time dimension and household spatial location, household assets ownership is positively correlated with household welfare. This association is much stronger in urban areas where assets distribution is more evenly spread, and even increased in OuagaBobo to _p( 98 )(A,W Quaga-Bobo)=.967._ This measured coefficient is large and significant across most economic regions, (Annex VII). The relative similarity in the pattern of the smoothed curves over time may further corroborate the fact that household asset ownership did not improve.

25 These key assets include: television, refrigerator, gas stove, radio, improved stove, and sewing machine.

24 The latest World Development Report identifies lack of assets as one of the major causes of poverty, see World Bank (2001).

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