Fuzzy Set Approach to Poverty Reduction Compared with Growth Modelling

The fuzzy set method is used to examine economic poverty reduction in selected countries 1992-2002. We summarise the fuzzy set method as a socio-economic research protocol. We compare our findings with some explanations offered by growth economists for poverty reduction trajectories. Econometric growth models operate under the assumption that their core economic theories of growth are explanatorily adequate. However even in growth models, non-economic variables are effective because they offer complementary explanatory power. Critics of the linear additivity of causes suggest that fuzzy set analysis has some advantages over statistical regression analysis in this kind of context. Using poverty reduction as a dependent variate, we compare the two methods. Gender and human capital variables are important to growth outcomes. But other factors influence inequality, notably the absence of HIV AIDS virus rates, and size of government spending as causal mechanisms. This paper supports the hypothesis that women???s labour force participation positively helps poverty reduction. But we also find that the deeper causes of economic change, such as state intervention, are critical to human development trajectories. Our findings are consistent with the Weberian approach to the quality of state intervention. Using realist methodology, we ask ???what would have to have been true for the fuzzy set patterns to emerge as they did during 1992-2002???? The changes in poverty over time are very diverse so fuzzy set analysis proves useful in identifying small sets of countries whose characteristics helped them to achieve poverty reduction in that decade.