So, lacking vital data, I excluded them, but they will be included in some of the total data analysis for SS Africa used, rather than spend time in calculating the sums for the countries available. Angola was also excluded from the list of countries because the data for 1989 or nearest years were simply inadequate. Coefficients above.4000 have been identified as probably having some significance in indicating relationship possibilities, although some of the variables, particularly those with a financial base such as the valueadded in agriculture, admittedly have somewhat skewed distributions. Obviously in some countries the rural poor will survive better than in others, possibly owing to better access to food. In order to examine the way in which these variables group together, principal components have been identified.
Negative values link to rural poverty, deaths of infants, and rural population percentage.
This would appear to be a component of development, but with no particular link to general GDP growth or agricultural growth. It is also linked positively to the index of food production per capita and to a lesser extent to the export of minerals, metals, and fuels, but negatively to food import dependence and the percentage of rural population below the poverty line. Component 8 is also linked to population growth, inflation, and debt percentage of GNP, and is low on imports of energy and of machinery and transport, and on value-added in agriculture. It would seem that this component represents rural poverty linked to arable production coupled with poor economic performance and financial problems. The more significant components from this data set, apart from the first, reflected mainly urban growth, well-developed commercial agriculture, aid, and trade, and tended to suggest relationships of interest in a context outside the scope of this paper.