854 resultados para Sport, income inequality, performance, relative income


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This paper discusses the role of institutions and structural change in shaping income inequality. It is argued that while social expenditure and direct redistribution are crucial for improving income distribution, sustainable equality requires structural change to create decent jobs. The relative importance of these variables in different countries is analyzed and a typology suggested. It is argued that the most equal countries in the world combine strong institutions in favor of redistribution and knowledge-intensive production structures that sustain growth and employment in the long run. Both institutions and the production structure in Latin America fail to foster equality and this explains its extremely high levels of inequality. The last decade witnessed significant advances in reducing inequality in Latin America, but these advances are threatened by slow productivity growth and weak structural change.

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In 2000, the United Nations adopted the Millennium Development Goals which set targets for raising living standards in low-income countries. The first goal was to “eradicate extreme poverty and hunger” (United Nations). The World Bank defines extreme poverty as income of less than $1.25 per day (World Bank, 2010a). Based on this definition, the World Bank estimates that the percentage of the population in China living in extreme poverty has fallen from 84 percent in 1981 to about 16 percent in 2005, a period during which China’s population grew by more than 300 million people (see Table 1 on last page). Because China is a very large country with a current population approaching 1.4 billion (more than four times the United States population), its dramatic reduction in poverty over the past 30 years has had a profound effect on global poverty measures. In fact, poverty reduction in China is the main reason that the incidence of extreme poverty in developing countries has fallen from about 52 percent in 1981 to 25 percent in 2005 (Table 1). While the absolute number of poor in China fell by some 627 million, the number of poor in other developing countries actually grew slightly (from 1,065 million to 1,166 million). These figures represent a decline in the percentage of the total population in poverty in other developing countries because of general population growth over that 25-year period (World Bank, 2010b).

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Purpose: To test the association between income inequality and elderly self-rated health and to propose a pathway to explain the relationship. Methods: We analyzed a sample of 2143 older individuals (60 years of age and over) from 49 distritos of the Municipality of Sao Paulo, Brazil. Bayesian multilevel logistic models were performed with poor self-rated health as the outcome variable. Results: Income inequality (measured by the Gini coefficient) was found to be associated with poor self-rated health after controlling for age, sex, income and education (odds ratio, 1.19; 95% credible interval, 1.01-1.38). When the practice of physical exercise and homicide rate were added to the model, the Gini coefficient lost its statistical significance (P>.05). We fitted a structural equation model in which income inequality affects elderly health by a pathway mediated by violence and practice of physical exercise. Conclusions: The health of older individuals may be highly susceptible to the socioeconomic environment of residence, specifically to the local distribution of income. We propose that this association may be mediated by fear of violence and lack of physical activity. (C) 2012 Elsevier Inc. All rights reserved.

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Background Support for the adverse effect of high income inequality on population health has come from studies that focus on larger areas, such as the US states, while studies at smaller geographical areas (eg, neighbourhoods) have found mixed results. Methods We used propensity score matching to examine the relationship between income inequality and mortality rates across 96 neighbourhoods (distritos) of the municipality of Sao Paulo, Brazil. Results Prior to matching, higher income inequality distritos (Gini >= 0.25) had slightly lower overall mortality rates (2.23 per 10 000, 95% CI -23.92 to 19.46) compared to lower income inequality areas (Gini <0.25). After propensity score matching, higher inequality was associated with a statistically significant higher mortality rate (41.58 per 10 000, 95% CI 8.85 to 73.3). Conclusion In Sao Paulo, the more egalitarian communities are among some of the poorest, with the worst health profiles. Propensity score matching was used to avoid inappropriate comparisons between the health status of unequal (but wealthy) neighbourhoods versus equal (but poor) neighbourhoods. Our methods suggest that, with proper accounting of heterogeneity between areas, income inequality is associated with worse population health in Sao Paulo.

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The supposed rise of inequality in income and wealth is a much-discussed issue. Since in a number of industrialized countries a trend towards more inequality was observed over the last decades, it is often assumed that Switzerland has experienced a similar development. Yet, although a variety of studies exists that provide inequality estimates for the Swiss society at different points in time, no conclusive picture of the changes in inequality can be drawn from these studies. For example, recent estimates by the Swiss Federal Statistical Office indicate that – against expectations – inequality in disposable equivalent-incomes has not risen since the end of the 1990ies, whereas other studies indicate that earnings from employment – especially top salaries – have become more unequal. The reasons for the inconclusive picture are manifold. For example, trends might have been different for different income types and results might strongly depend on the quality of the used data. To close the knowledge gap in inequality research in Switzerland a new Swiss National Science Foundation project by the University of Bern and the Bern University of Applied Sciences has been started in 2013. Individual tax data from cantons will be analyzed along with aggregate data from the Swiss Federal Tax Administration, covering a period from the early 1970ies to the present. The goal is to gain a systematic overview of the development of inequality in income and wealth in Switzerland as a whole and within cantons, and to determine how changes can be explained. In our talk we will present first results from this project.