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The relationship between poverty and food weights was looked at in general terms in Section 4.1 above. However, to demonstrate this association, data for both poverty and food weights in different countries were collected. The international poverty line measure was used in this case to ensure cross comparison of estimated poverty figures, since countries have different measures and definitions of poverty. Local poverty lines have higher purchasing power than in

rich countries, where generous standards are used compared to poor ones (World Bank, 2005). International poverty line data, which uses $1 a day expenditure standard, is obtained from the World Development Indicators 2005, and is used to determine the relationship which the World Bank (2005) states is a standard measure to hold the real value of the poverty line constant across countries. In addition to the above, data inflation food weights were collected for each country from their official statistical authority websites.

Care was taken in obtaining this data to ensure that the base year of the poverty survey contained in the World Development Indicators 2005 corresponded with the year in which the household consumer survey data for the weights was carried out. Two years either side of the poverty survey was allowed when accepting the household survey for this purpose thus only eighteen out of ninety countries (data points) were chosen using this decision rule. The choice of two years either side of the survey was based on the recommendations made by Morrow (1986) as well as the rationale that, within a fairly stable economy, poverty levels would not decline dramatically over two years. In the UK, surveys are carried out every year and this is the norm in most developed countries; hence the use of the two years in environments where baskets are reviewed more than five years apart is considered here as an acceptable compromise. Figure 4.1 below presents the scatter diagram for the relationship between poverty and food weights on the basis of $1 per day poverty line.

Poverty and Weights Chart 0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 0.0 20.0 40.0 60.0 80.0 100.0

people living below poverty line (%)

F o o d w e ig h ts ( % )

Figure 4.1: $1 per day poverty line and food inflation weights chart

The diagram shows a slight positive relationship between poverty and weights, but this is not conclusive as there are many data points clustered to the top left. The reason for this is that the percentage of people in these countries living on $1 per day is small, indicating that more people live on more than this amount. The correlation coefficient for this relationship is 0.2 and demonstrates that food weights and poverty as defined by the $1 per day poverty line move in the same positive direction. However, this calculated statistic is not convincing, and a formal regression is presented to ascertain the significance of the relationship. Equation 4.1 below is a regression that shows the relationship between poverty and food weights in a more precise form.

pv

fw



44

.59



0.12

where fw represents food weights and pvthe poverty level as defined by the $1 per day

poverty line. The coefficient for the poverty variable in this equation has a reported probability of 0.422 as well as a t-statistic of 0.8242, and this clearly shows the statistical insignificance of

the $1 per day poverty line in explaining food weight changes. However, R-squared is 0.0407, revealing that only 4.1% of changes in weights are explained by movements in poverty levels as defined by $1 per day poverty line. R-squared further confirms that poverty levels as defined by the $1 per day poverty line are not important in explaining food inflation weights, considering that the regression has an insignificant F-statistic of 0.6793.

The other measure of poverty recommended by the World Bank is the International poverty line as defined by $2 per day expenditure. Figure 4.2 is a scatter diagram based on the relationship between the $2 per day poverty line and food inflation weights.

Poverty and Weights Chart

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 0.0 20.0 40.0 60.0 80.0 100.0 120.0 people living below poverty line (%)

F o o d w e ig h ts ( % )

Figure 4.2: $2 per day poverty line and food inflation weights chart

The $2 per day poverty line data and inflation food weights relationship seems to be more linear, as most of the data points are spread such that a positive relationship is clearer than in the case of the $1 per day poverty line. The correlation coefficient for the two variables is 0.5, a figure which is positive and higher compared to the earlier relationship. This submission is further supported by Equation 4.2, which has a 0.223 R-squared indicating that the equation explains over 22.3% of movements in food inflation weights.

pv

fw



33

.71



0.24

The impact of the $2 per day poverty line is confirmed as an explanatory variable in food inflation weights at 5% level of significance, since its probability is 0.0478. The F-statistic of 4.5923 for this regression is also significant at the 5% level. Thus a 1% increase in poverty would warrant a 0.24% movement in food inflation weight adjustment.

If poverty in any of the African countries is increasing there is need to have a more regular consumer consumption survey to ensure accuracy in the calculation of inflation. This situation is more compelling where food inflation is increasing at a higher rate than non-food inflation. The challenge with using this approach, though, is that it assumes food to be the only biological and physiological need while it is not. In fact, it might be that food; clothing and shelter expenditure weights increase together as poverty soars. However, people can live in shacks in Africa without paying rent and can buy second-hand clothing, which is not accounted for in the inflation basket, but they cannot survive without food, hence the rationale for this approach. Moreover, the remainder of the non-food inflation weights could be apportioned such that the biological and physiological needs get a bigger share, thereby reducing or foregoing expenditure on such items as transport, education and eating out.