We consider an asset-based alternative to the standard use of expenditures in defining well-being and poverty. Our motivation is to see if there exist simpler and less demanding ways to collect data to measure economic welfare and rank households. This is particularly important in poor regions where there is limited capacity to collect consumption, expenditure and price data. We evaluate an index derived from a factor analysis on household assets using multipurpose surveys from several countries. We find that the asset index is a valid predictor of a crucial manifestation of poverty-child health and nutrition. Indicators of relative measurement error show that the asset index is measured as a proxy for long-term wealth with less error than expenditures. Analysts may thus prefer to use the asset index as an explanatory variable or as a means of mapping economic welfare to other living standards and capabilities such as health and nutrition. Copyright 2003 Blackwell Publishing Ltd..

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We investigate how to make poverty comparisons using multidimensional indicators of well-being, showing

This study explores global inequality in health status, and decomposes it into within-and between-country inequality. We rely on standardized height indicators as our health indicator since they avoid the measurement pitfalls of more traditional measures of health such as morbidity, mortality and life expectancy. They also avoid measurement problems associated with using incomes across time or place to compare welfare. Our calculation of world height inequality indicates that in contrast with similar research on income inequality, within-country variation is the source of most inequality, rather than the differences between countries.

We investigate how to make poverty comparisons using multidimensional indicators of well-being, showing

In this paper we examine the relative importance of rural versus urban areas in terms of monetary poverty and seven other related living standards indicators. We present the levels of urban-rural differences for several African countries for which we have data and find that living standards in rural areas lag far behind those in urban areas. Then we examine the relative and absolute rates of change for urban and rural areas and find no overall evidence of declining differences in the gaps between urban and rural living standards. Finally, we conduct urban-rural decompositions of inequality, examining the within versus between (urban and rural) group inequality for asset inequality, education inequality, and health (height) inequality.

This paper examines the pattern of health care demand in rural Tanzania. We distinguish between hospital and clinic‐based care, in both the public and private sector using a two‐level nested multinomial logit model. Own price elasticities of demand for all health care options are high, although less so for public clinics and dispensaries than other choices. However, there is a high degree of substitution between public and private care. Consequently, price increases or user fees will result in small percentage of people opting for self‐treatment. Another important contribution of this paper is that the quality of medical care has large effects on health demand. This applies to the quality and availability of doctors/nurses, drugs and the clinic environment.