Child Malnutrition, Social Development and Health Services in the Andean Region

This paper analyzes the social, ethnic and regional determinants of child malnutrition, as well as the effects of access to health services in the Andean region, by comparing conditions in Ecuador, Peru and Bolivia. These three countries are marked by a high prevalence of stunting and by wide socioeconomic, regional and ethnic disparities. The analysis used Demographic and Health Survey (DHS) data from Peru (1992, 1996 and 2000) and Bolivia (1997), and Living Standards Measurement Study (LSMS) data for Ecuador (1998). The paper adopts an international comparative perspective, analyzing Ecuador in particular detail.

Estimation of household smoke-exposure risk using Demographic and Health Survey (DHS) data

We introduce Stata and R codes to estimate the household smoke-exposure risk (SER) variable using cooking fuel- and cooking place-related information obtained from country-level demographic and health survey (DHS) data. Two categories of cooking fuels (smoke-producing and non-smoke producing fuels) and two categories of cooking places (indoor and outdoor) are used to estimate the household SER. Finally, household SER is classified into four levels of risk: high (cooking indoor using smoke-producing fuels), medium (cooking outdoor using smoke-producing fuels), low (cooking indoor using non-smoke-producing fuels), and very low (cooking outdoor using non-smoke-producing fuels). An example of a household SER calculation using the DHS data and codes is provided for clarification. The available DHS data of over 90 countries contain both cooking fuel- and cooking place-related information, so the method of estimating household SER would be the same for these countries. We introduce Stata and R codes to estimate the household smoke-exposure risk (SER) variable using cooking fuel- and cooking place-related information obtained from country-level demographic and health survey (DHS) data. Two categories of cooking fuels (smoke-producing and non-smoke producing fuels) and two categories of cooking places (indoor and outdoor) are used to estimate the household SER. Finally, household SER is classified into four levels of risk: high (cooking indoor using smoke-producing fuels), medium (cooking outdoor using smoke-producing fuels), low (cooking indoor using non-smoke-producing fuels), and very low (cooking outdoor using non-smoke-producing fuels). An example of a household SER calculation using the DHS data and codes is provided for clarification. The available DHS data of over 90 countries contain both cooking fuel- and cooking place-related information, so the method of estimating household SER would be the same for these countries

Inequality Adjustment Criteria for the Human Development Index

Our goal is to analyse the inequality aspects of Human Development Index and to propose a new aggregation function that can adjust it by considering inequality penalisation. We take into account inequality across dimensions and across individuals and three laws of inequality penalisation: decreasing, constant and increasing. At the beginning, we describe the features of standard Human Development Index and after we survey main analytical contributions regarding the inequality adjustment of Human Development Index. Successively, we decline the basic properties of the human development indices and also we present specific properties enjoyed by the aggregation function proposed: the Inequality Adjusted Exponential Mean (IAEM). This function is a specific case of the generalised mean. Three are the innovative aspects of IAEM function not enjoyed by the others ones. Firstly, the domain of IAEM function is unlimited. Secondly, IAEM function enjoys the property of incomplete compensability. Thirdly, with IAEM function it is possible to build three different rating and ranking classification according to the laws of penalisation. Finally, we apply the IAEM function to the database with 32 countries, developing and developed. According to the results, the Inequality Adjusted Human Development Index built by the IAEM function is significantly different from the standard Human Development Index built by the arithmetic mean, especially for the cases of decreasing and increasing penalisation. Moreover there is a negative correlation between the level of standard Human Development Index and the Penalisation Index, both in terms of rating and ranking.inequality, human development index, aggregation functions

Orphans at risk in Sub-Saharan Africa: Evidence on educational and health outcomes

In this paper, we examine how orphanhood affects children's educational and health outcomes in eleven sub-Saharan African countries. Our analysis is based on a comparison of orphans and non-orphaned children living under the same conditions. We also examine the impacts of various family structures and compare social orphans (non-orphaned children not living with a biological parent) to orphans. Using household fixed-effects estimation, we provide evidence that children not living with a biological parent lag behind in education and are more often malnourished and stunted. Educational gaps are particularly evident among orphans and social orphans not living with a mother. The effect of paternal death or absence is rather modest in most countries. --Orphans,family structure,human capital,sub-Saharan Africa,fixed-effects

A Human Development Index by Income Groups

human development, water, sanitation

A Human Development Index by Income Groups

One of the most frequent critiques of the HDI is that is does not take into account inequality within countries. We suggest a relatively easy and intuitive approach which allows to compute the three components and the overall HDI for quintiles of the income distribution. This allows comparisons of the level in human development of the poor and non-poor within and across countries. An empirical illustration shows large discrepancies in human development within countries especially in Africa. These discrepancies are lower the higher the HDI, but only weekly so. Inequality in income is generally higher than inequality in education and life-expectancy. --Human Development,Income Inequality,Differential Mortality,Inequality in Education

A Human Development Index by Income Groups

One of the most frequent critiques of the HDI is that is does not take into account inequality within countries in its three dimensions. We suggest a relatively easy and intuitive approach which allows to compute the three components and the overall HDI for quintiles of the income distribution. This allows to compare the level in human development of the poor with the level of the non-poor within countries, but also across countries. An empirical illustration for a sample of 13 low and middle income countries and 2 industrialized countries shows that inequality in human development within countries is indeed high. The results also show that the level of inequality is only weakly correlated with the level of human development itself.Human Development, Income Inequality, Differential Mortality, Inequality in Education

A Human Development Index by Internal Migration Status

Migration continues to be a very important income diversification strategy, especially for poor populations in developing countries. However, while there has been much analysis on the economic consequences of migration for migrants and the receiving regions, whether internal migration improves or deteriorates human development is not easy to determine. This papers applies a recently development analytical framework that allows to calculate the HDI for subgroups of a population. We use this approach to calculate the HDI by internal migrational status to assess the differences between the levels of human development of internal migrants compared to non-migrants, and also across countries as well as by urban and rural areas. An empirical illustration for a sample of 16 low and middle income countries shows that, overall, internal migrants slightly achieve a higher level of human development than non-migrants. The results also show that differences in income between migrants and non-migrants are generally higher than differences in education and life-expectancy. Disaggregating the analysis by urban and rural areas reveals that urban internal migrants are better o® than urban non-migrants and rural migrants are better off than rural non-migrants.Human Development, Migration Income Inequality, Differential Mortality, Inequality in Education

Parental education and inequalitties in child mortality: a global systematic review and meta-analysis

The educational attainment of parents, particularly mothers, has been associated with lower levels of child mortality, yet there is no consensus on the magnitude of this relationship globally. We aimed to estimate the total reductions in under-5 mortality that are associated with increased maternal and paternal education, during distinct age intervals. This study is a comprehensive global systematic review and meta-analysis of all existing studies of the effects of parental education on neonatal, infant, and under-5 child mortality, combined with primary analyses of Demographic and Health Survey (DHS) data

Correlated mortality risks of siblings in Kenya

Random-effect models have been useful in demonstrating how unobserved factors are related to infant or child death clustering. Another potential hypothesis is state dependence whereby the death of an older sibling affects the risk of death of a subsequent sibling. Probit regression models incorporating state dependence and unobserved heterogeneity are applied to the 1998 Demographic and Health Survey (DHS) data for Kenya. We find that mortality risks of adjacent siblings are dependent: a child whose preceding sibling died is 1.8 times more likely to die. After adjusting for unobserved heterogeneity, the death of the previous child accounts for 40% of child death clustering. Further, eliminating state dependence would reduce infant mortality among second- and higher-order births by 12.5%.death clustering, dynamic Probit and Logit models, initial conditions problem, Kenya, sequence data, state dependence, unobserved heterogeneity