In this paper the theoretical tradition of coping strategies and capital portfolios is used as the basis for adaption and combination of existing methodologies to analyze well-being in rural households. Special attention is given to comparisons among different contexts. First we estimate a multidimensional measurement of poverty based on fuzzy logic for two areas of rural frontiers: Nang Rong, Thailand, and Altamira, in the Amazon Basin in Brazil. To enable a cross-contextual comparison we calculated a second estimate using a subset of shared measurements in the two areas. The findings suggest that the pattern of responses on a range of numerous key variables -including education, income and demographic dependency ratio -is robust for the model specification. It is concluded that comparative generalizations, useful in formulating cost-effective public policy interventions across contexts, could be satisfactorily identified in many situations. More generically, this approach provides researchers and policymakers with a framework for understanding the interaction of contexts with the subjective construction of well-being. The understanding of this interaction is useful for distinguishing stable corollaries of poverty from those that are volatile across contexts.
IntroductionThe concept of poverty has received renewed theoretical attention and revision in recent years (SEN;1985, 1999KAKWANI;SILBER, 2008a;ALKIRE, 2007). The result has been the emergence of a large literature seeking to identify specific dimensions of poverty that are argued to better capture the complex paths to well-being than existing unidimensional measures of monetary income (BIBI;2005). However, the literature on Multidimensional Poverty (MDP) presently does a poor job of acknowledging the new challenges that highinformation measures of MDP present to comparative cross-contextual studies of MDP. Using these more refined and data-intensive measures has the potential to reduce the ability to assess and compare poverty across contexts for reasons that range from mundane but important restrictions imposed by data collection and the use of secondary data sources to significant questions about the meaning, relevance, and significance of various dimensions to the overall assessment of poverty across contexts.Methodologically, differences across the data sets used in studies of MDP -in data collection strategy, rigor, and substantive purpose -create a host of challenges, some of which are error-related, and others of which take on theoretical importance. Based informally upon our own experience in attempting to construct high-information MDP measures for comparative work, the relationship between the number of indicators used in an MDP measure and the availability of that measure (or taking it to the next level, of any measures of a given dimension) in all study areas is a negative one. In short, the likelihood that all relevant measures exist across all comparison contexts is low, and any success in obtaining such a match is due mostly to good fortun...