Measuring Health Inequality in US: A Composite Index Approach

Social Indicators Research - In this paper, we use the standardized mortality rates for 21 mutual exclusive causes of death to propose a composite index of US county-level health performances in...     

The Cost of Well-Being

It is by now common knowledge that in switching from GDP to alternative, multidimensional, measures of collective well-being one can provide a better account of a country’s socio-economic conditions. Such a gain, however, comes at the price of losing output-to-input type of link between well-being and the resources necessary to make it available. Since well-being measures are not meant to be only an exercise in documentation, but also to inform policies and priorities, we propose a method to build a measure of well-being in the form of a single index, as for GDP, which takes into account: (1) the social and environmental costs, not considered in the GDP, and (2) the use of conventional resources (capital and labour), not considered in the currently available multidimensional measures of well-being. We use a Data Envelopment Analysis type of model, integrated with Principal Component Analysis, to evaluate OECD countries’ relative efficiency in providing well-being. Our results show that the costs of producing well-being have a large and significant impact on the resulting index of well-being. Therefore, high efficiency in providing well-being and high income cannot be considered a proxy to each other. In addition, it is shown that countries react differently to the different costs of well-being: poor countries are, on average, more efficient in terms of conventional inputs (labour and capital), while rich countries have higher efficiency indices relative to social and environmental costs. The close to zero correlation between GDP and well-being indices for rich countries provides new support to the “Easterlin paradox”.     

Regional Well-Being and its Inequality in the OECD Member Countries

This paper analyses the inequality between the regions of the Organization for Economic Co-operation and Development (OECD) member countries by using stochastic multi-objective acceptability analysis and the associated multivariate Gini index. By considering a large number of possible combinations of weights, the distribution of the potential rankings for each region is used to measure multidimensional inequality both within and between countries. Our results show that beyond the expected two clubs of rich and poor countries, a third group of countries emerges that belongs neither to the top nor to the bottom of the ranking, an outcome that can be attributed to the presence of significant economic differences among regions within those countries. Most of the inequality lies between countries, but regional well-being also significantly varies within the same countries and we find an inverse U-shape connection between regional well-being and its inequality within the OECD member countries.