675 research outputs found

    Robust Multidimensional Spatial Poverty Comparisons in Ghana, Madagascar, and Uganda

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    We investigate spatial poverty comparisons in three African countries using multidimensional indicators of well-being. The work is analogous to the univariate stochastic dominance literature in that we seek poverty orderings that are robust to the choice of multidimensional poverty lines and indices. In addition, we wish to ensure that our comparisons are robust to aggregation procedures for multiple welfare variables. In contrast to earlier work, our methodology applies equally well to what can be defined as "union", "intersection", or "intermediate" approaches to dealing with multidimensional indicators of well-being. Further, unlike much of the stochastic dominance literature, we compute the sampling distributions of our poverty estimators in order to perform statistical tests of the difference in poverty measures. We apply our methods to two measures of well-being, the log of household expenditures per capita and children's height-for-age z-scores, using data from the 1988 Ghana Living Standards Survey, the 1993 EnquĂȘtes Permanente auprĂšs des MĂ©nages i Madagascar, and the 1999 National Household Survey in Uganda. Bivariate poverty comparisons are at odds with univariate comparisons in several interesting ways. Most importantly, we cannot always conclude that poverty is lower in urban areas from one region compared to rural areas in another, even though univariate comparisons based on household expenditures per capita almost always lead to that conclusion.Multidimensional Poverty, Stochastic Dominance, Ghana, Madagascar, Uganda

    The Global Joint Distribution of Income and Health

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    We investigate the evolution of global welfare in two dimensions: income per capita and life expectancy. First, we estimate the marginal distributions of income and life expectancy separately. More importantly, in contrast to previous univariate approaches, we consider income and life expectancy jointly and estimate their bivariate global distribution for 137 countries during 1970 - 2000. We reach several conclusions: the global joint distribution has evolved from a bimodal into a unimodal one, the evolution of the health distribution has preceded that of income, global inequality and poverty has decreased over time and the evolution of the global distribution has been welfare improving. Our decomposition of overall welfare indicates that global inequality would be underestimated if within-country inequality is not taken into account. Moreover, global inequality and poverty would be substantially underestimated if the dependence between the income and health distributions is ignored.Income; Health; Global Distribution; Inequality; Poverty

    The Global Joint Distribution of Income and Health

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    We investigate the evolution of global welfare in two dimensions: income per capita and life expectancy. First, we estimate the marginal distributions of income and life expectancy separately. More importantly, in contrast to previous univariate approaches, we consider income and life expectancy jointly and estimate their bivariate global distribution for 137 countries during 1970 - 2000. We reach several conclusions: the global joint distribution has evolved from a bimodal into a unimodal one, the evolution of the health distribution has preceded that of income, global inequality and poverty has decreased over time and the evolution of the global distribution has been welfare improving. Our decomposition of overall welfare indicates that global inequality would be underestimated if within-country inequality is not taken into account. Moreover, global inequality and poverty would be substantially underestimated if the dependence between the income and health distributions is ignored.income, health, global distribution, inequality, poverty

    Multidimensional Poverty Dominance: Statistical Inference and an Application to West Africa

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    This paper tests for robust multidimensional poverty comparisons across six countries of the West African Economic and Monetary Union (WAEMU). Two dimensions are considered, nutritional status and assets. The estimation of the asset index is based on two factorial analysis methods. The first method uses Multiple Correspondence Analysis; the second is based on the maximization of a likelihood function and on bayesian analysis. Using Demographic and Health Surveys (DHS), pivotal bootstrap tests lead to statistically significant dominance relationships between 12 of the 15 possible pairs of the six WAEMU countries. Multidimensional poverty is also inferred to be more prevalent in rural than in urban areas. These results tend to support those derived from more restrictive unidimensional dominance tests.Stochastic dominance, factorial analysis, bayesian analysis, multidimensional poverty, empirical likelihood function, bootstrap tests

    Multivariate Discrete First Order Stochastic Dominance

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    This paper characterizes the principle of first order stochastic dominance in a multivariate discrete setting. We show that a distribution f first order stochastic dominates distribution g if and only if f can be obtained from g by iteratively shifting density from one outcome to another that is better. For the bivariate case, we develop the theoretical basis for an algorithmic dominance test that is easy to implement.multidimensional first degree distributional dominance; robust poverty gap dominance; majorization; generalized equivalence result

    Measuring Poverty in a Multidimensional Perspective: a Review of Literature

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    Recent pioneer papers of Sen (1981, 1985, 1992) have emphasized that poverty is a multidimensional issue. Hence, it should be seen in relation to the lack of important "basic needs" or "basic capabilities". This recommendation has motivated many researchers to focus on the way multidimensional aspect of poverty should be measured and aggregated. This survey synthesizes the contribution of the main approaches to measuring poverty in its various dimensions to better understand the theoretical framework and the limitations of each. This should help one choose which approach to adopt based on the circumstances and the constraints of the study to be conducted.Multidimensional Poverty Measures, Robustness Analysis

    Comparing Multidimensional Poverty between Egypt and Tunisia

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    It is common to argue that poverty is a multidimensional issue. Yet few studies have included the various dimensions of deprivation to yield a broader and fuller picture of poverty. The present paper considers the multidimensional aspects of deprivation by specifying a poverty line for each aspect and combines their associated one-dimensional poverty-gaps into multidimensional poverty measures. An application of these measures to compare poverty between Egypt and Tunisia is illustrated using robustness analysis and household data from each country.Multidimensional poverty indices, Robustness analysis, Egypt, Tunisia

    Multidimensional Poverty Dominance: Statistical Inference and an Application to West Africa

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    This paper tests for robust multidimensional poverty comparisons across six countries of the West African Economic and Monetary Union (WAEMU). Two dimensions are considered, nutritional status and assets. The estimation of the asset index is based on two factorial analysis methods. The first method uses Multiple Correspondence Analysis; the second is based on the maximization of a likelihood function and on bayesian analysis. Using Demographic and Health Surveys (DHS), pivotal bootstrap tests lead to statistically significant dominance relationships between 12 of the 15 possible pairs of the six WAEMU countries. Multidimensional poverty is also inferred to be more prevalent in rural than in urban areas. These results tend to support those derived from more restrictive unidimensional dominance tests

    Multidimensional Poverty in Kenya: Analysis of Maternal and Child Wellbeing

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    This paper generates multidimensional poverty profiles for women and children over a ten-year period from 1993 to 2003.Data from the national Demographic and Health Survey are used to improve measurement of poverty in Kenya in four ways: First, the paper constructs a composite wealth index (CWI). Second, it applies the Alkire and Foster (2007) approach to the measurement of multidimensional poverty based on the CWI and health status. Third, stochastic dominance approaches are used to make poverty orderings across groups. Fourth, the probability of being poor in assets, health or both is explored using a bivariate probit model. The results show that the distribution of poor women and children differs across groups, space and time. We also find that the CWI and residence in a rural area respectively contribute more to multidimensional poverty than health and residence in an urban area. The results further suggest that understanding the correlates of wellbeing in a multidimensional context can generate policy insights for improving human capital investments.Multidimensional poverty, composite wealth indicator, child health, stochastic dominance, Kenya

    Child Survival, Poverty and Policy Options from DHS Surveys in Kenya: 1993-2003

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    This paper analyses multidimensional aspects of child poverty in Kenya. We carry out poverty and inequality comparisons for child survival and also use the parametric survival model to explain childhood mortality using DHS data. The results of poverty comparisons show that: children with the lowest probability of survival are from households with the lowest level of assets; and poverty orderings for child survival by assets are robust to the choice of the poverty line and to the measure of wellbeing. Inequality analysis suggests that there is less mortality inequality among children facing mortality than children who are better off. The survival model results show that child and maternal characteristics, and household assets are important correlates of childhood mortality. The results further show that health care services are crucial for child survival. Policy simulations suggest that there is potential for making some progress in reducing mortality, but the ERS and MDG targets cannot be achieved.Child survival, multidimensional poverty, inequality, stochastic dominance, childhood mortality, asset index, Kenya
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