Measurement of Poverty, Undernutrition and Child Mortality

Misselhorn, Mark Book — Digitized Version Measurement of Poverty, Undernutrition and Child Mortality Göttinger Studien zur Entwicklungsökonomik / Göttingen Studies in Development Economics, No. 24 Provided in Cooperation with: Peter Lang International Academic Publishers Suggested Citation: Misselhorn, Mark (2008) : Measurement of Poverty, Undernutrition and Child Mortality, Göttinger Studien zur Entwicklungsökonomik / Göttingen Studies in Development Economics, No. 24, ISBN 978-3-631-75363-7, Peter Lang International Academic Publishers, Berlin, https://doi.org/10.3726/b13885 This Version is available at: https://hdl.handle.net/10419/182897 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Measurement of Poverty, Undernutrition and Child Mortality GÖTTINGER STUDIEN ZUR ENTWICKLUNGSÖKONOMIK / GÖTTINGEN STUDIES IN DEVELOPMENT ECONOMICS Mark Misselhorn Although the world has seen a strong increase in global incomes in the last two decades and consequently a decline in global poverty rates, the number of persons living in absolute poverty remains on unacceptably high levels. Besides rising incomes can not distract from the fact that resources to fight global problems remain scarce. These resources have to be devoted to the fight against different global problems like the fight against communicable and non-communicable diseases (especially HIV/AIDS and Malaria) or the fight against global warming. The main precondition to achieve the best results with these limited resources is a good knowledge about the determinants and the best policies to fight each problem. But before being able to analyze the determinants of the different global problems and especially of poverty, it is of fundamental importance to find the right indicators for each phenomenon. This book contributes to the discussion of appropriate poverty indicators for the different dimensions of poverty like income poverty, undernutrition and child mortality and proposes a multidimensional poverty indicator that takes the income distribution into consideration. Mark Misselhorn, born in Munich in 1977, studied economics and political science at the Ludwig-Maximilians-University, Munich, the Hochschule für Politik in Munich and the York University in Toronto (Canada). As a Ph.D. student at the Chair for Development Economics he also worked as a consultant for several international development agencies in various countries in Africa, Latin America and Asia. GÖTTINGER STUDIEN ZUR ENTWICKLUNGSÖKONOMIK / GÖTTINGEN STUDIES IN DEVELOPMENT ECONOMICS Mark Misselhorn Measurement of Poverty, Undernutrition and Child Mortality Measurement of Poverty, Undernutrition and Child Mortality Gottinger Studien zur Entwicklungsokonomik Gottingen Studies in Development Economics Herausgegeben von/Editecl by Hermann Sautter und/and Stephan Klasen Bd./Vol. 24 • PETER LANG Frankfurt am Main · Berlin · Bern · Bruxelles · New York · Oxford · Wien Mark Misselhorn Measurement of Poverty, Undernutrition and Child Mortality PETER LANG lnternationaler Verlag der Wissenschaften Open Access: The online version of this publication is published on www.peterlang.com and www.econstor.eu under the international Creative Commons License CC-BY 4.0. Learn more on how you can use and share this work: http://creativecommons. org/licenses/by/4.0. This book is available Open Access thanks to the kind support of ZBW – Leibniz-Informationszentrum Wirtschaft. ISBN 978-3-631-75385-9 (eBook) Bibliographic Information published by the Deutsche Natlonalblbllothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available in the internet at <http://www.d-nb.de>. :£ Zugl.: Gottingen, Univ., Diss., 2007 Cover illustration by courtesy of the lbero-Amerika-lnstitut fur Wirtschaftsforschung, Gottingen. D7 ISSN 1439-3395 ISBN 978-3-631-57659-5 © Peter Lang GmbH lnternationaler Verlag der Wissenschaften Frankfurt am Main 2008 All rights reserved. All parts of this publication are protected by copyright. Any utilisation outside the strict limits of the copyright law, without the permission of the publisher, is forbidden and liable to prosecution. This applies in particular to reproductions, translations, microfilming, and storage and processing in electronic retrieval systems. Printed in Germany 1 2 3 4 5 7 www.peterlang.de To Sabine For all your support without which I couldn't have accomplished this. List of Tables 1.1 Absolute change in headcount poverty rate during growth spell 17 1.2 Relative change in headcount poverty rate during growth spell 18 1.3 Absolute change in poverty gap ratio during growth spell . . . 20 1.4 Relative change in poverty gap ratio during growth spell . . . 21 1.5 Absolute change in squared poverty gap ratio during growth spell . 22 1.6 Relative change in squared poverty gap ratio during growth spell . 23 I. 7 Poverty/Growth semi-elasticity as a function of mean income and income inequality . . . . . . . . . . . . . . . . . . . . . . 24 1.8 Country Comparisons of Elasticities and Semi-Elasticities 24 2.1 Data Sources: Demographic and Health Surveys (DHS) . . 28 2.2 Prevalence rates of undemutrition . . . . . . . . . . . . . 30 2.3 Mobility matrix of NCHS/WHO to WHO reference standard 31 2.4 Composition of group of underweight children . 39 2.5 Composition of group of underweight children . 39 2.6 Anthropometric indicators over time . . . . . . 46 3.1 Infant Mortality and Anthropometric Indicators 65 3.2 Summary Statistics . . . . . . . . . . . . . . . 66 3.3 Mortality and Stunting by Asset and Access to Health Facility Index 69 3.4 Regression Results of Infant Mortality . . . . . . . . . . . 79 3.5 Regression Results of Stunting (Old Reference Standard) . 80 3.6 Regression Results of Stunting (New Reference Standard) 3.7 Global Regression of Infant Mortality and Stunting 4.1 Quintile specific HDI by country) . . . . . . . . . A. I Poverty/Growth elasticity as a function of mean income and in- 81 82 100 come inequality(assumption: no change in distribution) . . . . . . 107 A.2 Poverty/Distribution change elasticity as a function of mean income and income inequality(assumption: no change in distribution) I 08 xiv A.3 A.4 A.5 A.I A.2 A.3 A.4 A.5 A.6 A.7 A.8 A.I A.2 A.3 A.4 LIST OF TABLES Theoretical values of headcount poverty as a function of mean income and income inequality(assumption: no change in distribution) 108 Poverty/Distribution change semi-elasticity as a function of mean income and inequality(assumption: zero growth of mean income) . I 09 Descriptives of Regions . . . . . . . . . . . . . . . . . . . . 110 Scoring Coefficients . . . . . . . . . . . . . . . . . . . . . . 117 Regression Results of Infant Mortality (Logistic Regression) 118 Regression Results of Stunting (Logistic Regression) 119 Regression Results of Stunting (Logistic Regression) . . . 120 Regression Results of Stunting (OLS Regression) . . . . . 121 Regression Results of Stunting Z-Scores (OLS Regression) 122 Regression Results of Stunting Z-Scores (Multilevel Regression) 123 Regression Results of Stunting Z-Scores (Multilevel Regression) 124 Data sources for developing countries . . . . .- . . 125 Quintile specific life expectancy indices by country 126 Quintile specific education indices by country 127 Quintile specific GDP indices by country 128 List of Figures l. l Decomposition . . . . . . . . . . . . . . . . . . . . . . 12 2. l Wasting/Stunting Combinations for Underweight of -2.0 34 2.2 Wasting/Stunting Combinations for Underweight of -2.0 35 2.3 Stunting/Wasting Combinations for Underweight of -2.0 36 2.4 Stunting/Wasting Combinations for Underweight of -2.0 37 2.5 Composition of undemutrition by countries (Group l) . 40 2.6 Composition of undemutrition by countries (Group 2) . . 41 2.7 Composition of undemutrition by countries (Group 3) . . 42 2.8 Composition of undemutrition in India by asset index quintiles 43 2.9 Composition of undemutrition in Uganda by nutrition status of mother. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 2. l 0 Composition of undemutrition in India and Tanzania by survey year 47 3. l Mean Stunting Z-Scores By Age . . . . . . . . 75 4. l A human development index by income groups IO l 4.2 Correlation between the overall HDI and the ratio between the QHDI for the richest and the poorest quintile . . . . . l 03 A.l Stunting/Wasting Combinations for Underweight of -2.0 ..... l l l A.2 Stunting/Wasting Combinations for Underweight of -2.0 . . . . . 112 A.3 Composition of undemutrition in Burkina Faso by asset index quintiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 A.4 Composition of undemutrition in Bolivia by asset index quintiles . l l 4 A.5 Composition of undemutrition in Chad by the nutrition status of mother ................................ 115 A.6 Composition of undemutrition in India by the nutrition status of mother ................................ 116 Introduction and Overview The State of Global Poverty Over the past decades a mix of technology and economic integration transforming the world has lead to unprecedented increases in material wealth and prosperity. Between 1980 and 2005 the world economy grew at a steady pace despite several major disruptions including the Latin American debt crisis, the demise of the Soviet Union, the East Asia crisis, two global downturns, and the tragedy of September 11, 2001. Fortunately strong income growth was not only limited to developed countries. Income growth was especially strong in South and East Asia, but other developing regions were also able to realize strong increases in output. As a consequence the percentage of people living on less than $1 a day fell according to World Bank estimates from almost 27.9% of the population of developing countries to 18.4% (World Bank 2007). Although these percentage reductions in poverty rates are important improvements, it is the absolute number of poor persons that is recognized in the public. The still high total number of poor persons often leads to the impression that global inequality has increased in the past decades. It is therefore very important to note that despite strong population growth, the absolute number of poor persons has decreased from more than 1.2 billion people in 1990 to 984 million in 2004. For the first time the number of people that live on less than $1 a day is below a billion. But this can not distract from the fact that still some 2.6 billion people, or almost half the developing world's population, remain below the $2 a day poverty line (World Bank 2007). It is also extremely important to keep in mind that poverty is not limited to the income dimension but encompasses aspects such as a low life expectancy, high child mortality and undernutrition rates. Differences in these dimensions between developing and developed regions are enormous as well. While in rich countries fewer than I child in l 00 does not reach its fifth birthday, in the poorest countries as many as a fifth of children do not. And while in rich countries fewer than 5 percent of all children under five are malnourished, in poor countries as many as 50 percent are. 2 INTRODUCTION The Fight against Poverty In times of increasing opulence in the developed countries as well as in certain population subgroups in developing countries, the persistence of significant percentages of world population in poverty becomes more and more unacceptable. Consequentially this has lead to a lot of political activism. Numerous national and international organizations have formulated goals on how much poverty has to be reduced within the next decades, politicians have demanded time and again that development aid has to be increased dramatically, "Live-8"-Concerts have received a lot of public attention and celebrities as Bono are considered as "poverty experts". Although globalization promises to improve the lot of humanity as a whole incalculably, there are signs of a backlash abound. The strong opposition against globalization is on the one hand due to the fact that in the rich world labor's share of GDP has fallen to historic lows, while profits are soaring, and on the other hand due to the persistence of absolute poverty in developing countries. Reductions in poverty rates are consequently not only a very worthy goal on their own, but an equitable distribution of globalization's profits is also the precondition for the general acceptance of liberal market economies. Unfortunately not only the views of many adversaries of globalization but also most demands publicly announced by well-meaning politicians and celebrities are neither very realistic nor very likely to have the assumed positive effects on the poor. Although poverty in its many dimensions is clearly a very emotional topic, the fight against poverty should not be dominated by those expressing the most ambitious goals but by realism and scientific insights into the determinants of poverty. Besides it is important to keep in mind that despite large income increases in the past and likely further increases in the future, that have the potential to lift a lot of of people out of poverty, it cannot be neglected that resources are scarce and will remain so in the future. This is especially the case when we take into account that poverty is not the only problem that has to be tackled in the coming decades. Considerable resources have to be devoted for example to the fight against communicable and non-communicable diseases like HIV/AIDS and Malaria or the fight against global warming. Prioritization and Efficiency Due to the scarcity of resources a certain degree of prioritization is necessary. Although prioritization is certainly necessary, the resentment against this idea is widespread. Mainly this is due to the general notion that we shouldn't have to prioritize. Whereas all demands for improvement in certain or all areas are un- INTRODUCTION 3 controversial, efforts for prioritization not only say where we should do more, but also where we shouldn't increase our efforts for the time being. This is then often seen as cynical. 1 To be able to prioritize appropriately it is necessary to implement a cost-benefit analysis to be able to find the most efficient usage of the limited resources. The main precondition for this is a good knowledge about the determinants and the best policies to fight each problem. But before being able to analyze the determinants of the different global problems and especially of poverty it is of fundamental importance to find the right indicators for each phenomenon. Due to the multidimensionality of poverty there is a very large number of potential indicators and measurement issues are far from solved. This thesis will try to contribute to the discussion of appropriate poverty indicators. All four essays are concerned with measurement issues of the different dimensions of poverty or are contributing to the literature on the determinants of poverty. While the first essay is focused on the appropriate measurement of changes in income poverty, namely changes in the Foster-Greer-Thorbecke (FGT) indicators of poverty, and the determinants of the size of these changes, the second essay is concerned with finding a meaningful indicator for undernutrition. Generally a large set of potential indicators for the measurement of undernutrition exists, but almost all suffer from certain biases and limitations. The second essay will try to shed some light on these problems and will give a clear recommendation on what indicator should be used to measure undernutrition and its changes over time. Besides income poverty and undernutrition, child mortality is a very important third dimension of poverty. Although the measurement of infant or child mortality is less controversial, knowledge on the determinants of this phenomenon can still be improved. The same is true for the determinants of undernutrition. Besides it is unclear how closely these two different dimensions of poverty -undernutrition and child mortality -are related. To be able to track progress in different poverty dimensions and to give a better picture of the general development status of a country different multidimensional indicators were developed. Probably the most prominent example of such an indicator is the Human Development Index. But even this often cited indicator is far from universally accepted and it is criticized in different ways. One of the most controversial aspects of the HDI is the use of average values for each country. The fourth and last essay tries to allow for such criticism and develops a methodology to create an distribution sensitive Human Development Index. 1 There have been very laudable efforts by the Copenhagen Consensus Project to establish a framework in which solutions to problems are prioritized based upon economic and scientific analysis of distinct subjects. The final IO challenges found to hold the most promising opportunities include different dimensions of poverty, like communicable diseases, access to education, malnutrition and hunger and sanitation and access to clean water. 4 INTRODUCTION The Measurement of Changes in Income Poverty Clearly one of the most important measurement issues of poverty is related to the measurement of poverty on the aggregate or macro level. The measures most often used to calculate the prevalence of poverty are the FGT poverty indicators. To be able to asses the impact of growth and distributional change on poverty reduction in a comparable manner for all countries, the relationship between growth, distributional change, and poverty reduction must be studied in a way that allows for country heterogeneity but remains tractable. Although the usual measure of choice to analyze changes of poverty over time is the poverty elasticity, we propose an alternative measure to calculate the effects of income growth and distributional changes on poverty. Instead of studying the determinants of the percentage change in poverty (and the associated poverty elasticity of growth and distributional change), it is proposed to study the percentage point change in poverty (and the associated poverty semi-elasticity of growth and distributional change). It is argued that there are two distinct advantages to study absolute rather than proportionate poverty reductions. The first set of arguments is conceptual and relate to the fact that a strong bias is inherent in growth and distribution elasticities and that policy-makers are likely to be more interested in the percentage point changes in poverty rather than percent changes. The second set of arguments is empirical. It is shown that the estimation of semi-elasticities of growth and distributional changes on poverty rates is more precise. Besides using semi-elasticities avoids some arbitrary assumptions about excluding data from countries with low poverty incidence. The Measurement of Undernutrition A second dimension of poverty is the insufficient intake of energy through food and consequently the incidence of undernutrition. As undernutrition is a very severe and unacceptable dimension of poverty, the world community committed itself in the Millennium Development Goals (MDG) to reduce the number of people who suffer from hunger by half until 2015. The decision was made to use two different indicators to track progress with respect to the incidence of undernutrition. The first indicator is the FAO measure of access to an insufficient amount of calories. Unfortunately, there are considerable methodological as well as conceptual doubts about this FAO measure. The second measure used in the MDGs is 'underweight', which is an anthropometric indicator that measures the weight of a child for a certain age and compares it to a reference standard to be able to categorize a child as undernourished or not. As Essay 2 argues, the choice of underweight as main indicator for the hunger dimension of poverty is not very fortunate. On the one hand, there are doubts INTRODUCTION 5 about the general construction and interpretation of underweight that make it not very suitable to be used as a summary indicator. On the other hand, and this will be the main focus of this section, we can observe a bias in the development of underweight prevalence rates that is due to the large changes in the nutritional composition of diets in developing countries that are taking place. This so called 'nutrition transition' is characterized in large increases in the consumption of processed and semi-processed foods, that contain higher percentages of cheap fatty acids. Although this certainly means that total energy amounts taken up by children are increasing this should not be equalized with real improvements in their nutritional situation. This bias could lead to wrong conclusions concerning the fulfillment of the undernutrition aspect of MDG I. Besides in 2006 a new multi-country reference standard was published by WHO. It is very likely that future progress in the fight against undernutrition will be tracked by using this new standard. The use of the new reference standard will result in clear changes in the prevalence and composition of undernutrition. Essay 2 therefore argues that this opportunity should be used to switch to stunting or a Composite Index of Anthropometric Failure instead of underweight as the main indicator to measure progress in the fight against hunger. The Relationship between Undernutrition and Child Mortality Poverty and changes in poverty are determined by various household, individual socio-economic and demographic characteristics as well as by various environmental factors. Essay 3, which is based on joint work with Kenneth Harttgen, is concerned both with the regional differences and the interdependencies of the outcomes and determinants of two of the most important poverty dimensions, namely child mortality and child undernutrition in South Asia and Sub-Saharan Africa. Child mortality and undernutrition remain still on a high level both in South Asia and Sub-Saharan Africa. Arguing that child mortality and undernutrition are highly correlated, i.e that a bad nutritional status of the child strongly increases the child's mortality risk (see e.g. Pelletier et al, I 995), a puzzle arises when comparing the two regions regarding the outcomes of both phenomena. Anthropometric outcomes of children are considerably better (but still on a very low level) in Sub-Saharan Africa than in South Asia. In contrast to the severe anthropometric failure in South Asia, Sub-Saharan African countries suffer from relatively high rates of child mortality (see e.g. Klasen, 2007; Ramalingaswami et al, 1996). This regional puzzle of child mortality and undernutrition between both regions is called the South Asia -Sub-Saharan Africa Enigma. To shed more light on this puzzle and the underlying reasons is of particular relevance. First, it would allow a much more detailed assessment of what is needed to reduce child mortality and undernutrition in these two regions. Second, it could show how strong 6 INTRODUCTION child mortality and undernutrition are correlated and whether it is really sufficient to reduce undernutrition in order to reach the goal of reducing child mortality. Approaches using macro-data have not been able to explain the South Asia -Sub- Saharan Africa Enigma appropriately, however, and less attention has been paid so far to the analysis of determinants of undernutrition and child mortality based on micro-data, i.e population based household survey data. Essay 3 analyzes the determinants of child mortality as well as of child undernutrition based on large-scale Demographic and Health Surveys (DHS) data for a sample of five developing countries in South Asia and Sub-Saharan Africa, namely Bangladesh, India, Uganda, Mali, and Zimbabwe. In particular, Essay 3 investigates the effects of a set of individual, household and cluster socioeconomic characteristics both on child mortality and undernutrition based on the analytical framework proposed by Mosley and Chen ( 1984 ). The aim of the paper is helping to explain the South Asia -Sub-Saharan Africa Enigma. To achieve this, first, Essay 3 analyzes the relationships between child mortality and undernutrition. The aim of this analysis is, first, to identify determinants that affect child mortality and undernutrition in different ways, which would help to explain the South Asia Sub-Sahara Africa Enigma. Second, analyzing the determinants of child mortality and undernutrition, Essay 3 concentrates on region-specific and country-specific differences both in the outcomes and determinants of both phenomena. This allows one to identify major differences that drives the puzzle of child mortality and undernutrition in the two regions and between countries. The main result of Essay 3 is the identification of several determinants that differ significantly from each other regarding their impact on child mortality and undernutrition, regarding the two regions of South Asia and Sub-Saharan Africa, and also regarding countries within the two regions. Whereas the access to health infrastructure is relatively more important ro reduce the risk of child mortality than the reduce the risk of undemutrition, the nutritional status of the mother, which is worse in South Asia than in Sub-Saharan Africa, has a much higher impact on child undernutrition than on child mortality, which can partly explain the Enigma. 1.2. INFLUENCE OF INCOME AND DISTRIBUTION CHANGES 13 poverty can be decomposed into a) a "growth effect" that is the result of a proportional change in all incomes that leaves the distribution of relative incomes unaffected and b) a "distributional effect" that is only due to a change in the distribution of relative incomes leaving the mean income constant. These two effects are shown in Fig. 1.1. It is discemable that the estimation of the two effects will exhibit path dependence. Formally the change in headcount poverty can be explained by the following decomposition identity: Ml =H 1 , -H, = [ti (y:, )-ti (i)] +[ti,(;, )-ti(;,)] (I.I) Using the empirically plausible assumption proposed by Bourguignon (2003) that incomes are lognormally distributed, we no longer need to know the total distribution of individual incomes to calculate headcount poverty. The only information necessary is the mean income yt, the constant international poverty line z (e.g. the $1 a day criterion) and the standard deviation of the lognormal distribution: - _ [log(z/y,) 1 ] H, = Fi(/og(z/y,) = n cr + 2 cr . (1.2) wherein TT is the cumulative distribution function of the standard normal. The standard deviation of the lognormal distribution can be calculated from the Gini coefficient by the following equation: (1.3) Besides the headcount poverty ratio at a certain point in time, relative and absolute changes in poverty due to "growth effects" and "distributional effects" can be formally studied by considering the poverty impact of changes in mean income and changes in the income distribution. When considering relative changes in the headcount poverty ratio, the growth elasticity of poverty reduction is given by eH = Ml = _!_ 1 [log(z/y,) + ~cr] Y filog(y)H, cr cr 2 (1.4) where A is the hazard rate, which is the ratio of density function to the cumulative density function of the standard normal. Similarly the distribution elasticity of poverty reduction is given by eH = A [log(z/y,) + ~cr] . [~ _ log(z/y, )] . a cr 2 2 cr 2 ( 1.5) 14 I. THE SEMI-ELASTICITY OF POVERTY REDUCTION In contrast to Bourguignon, our focus will be on absolute (i.e. percentage point) changes in the headcount poverty ratio and therefore on semi-elasticities. As will be argued below this is a less misleading measure than elasticities. Using equation (I) the growth semi-elasticity of poverty reduction is ,c = _!_,r [log(z/y,) + ~er] y (J' CJ' 2 (1.6) and the semi-elasticity due to distributional changes in relative incomes is given by _ [log(z/y1) ~ ] • [~ _ log(z/y1 )] ICa - ,r er + 2 CJ' 2 cr2 ' (1.7) where ,r is the density function of the standard normal. When combined with the growth rate and the percentage change in the standard deviation, respectively these theoretical values of the semi-elasticities will identify the percentage point changes in the headcount poverty ratio either due to growth in mean income (5) or due to changes in the distribution of relative incomes (6) depending on the level of development and the existing distribution of incomes. As mentioned before, it is also possible to calculate the elasticities and semielasticities for the other FGT-measures. According to formulas derived by Kakwani ( 1993) the elasticity 11Pa of FGT-measure Pa with respect to changes in mean income is DPa µ a[Pa-1 -Pa] 11Pa = <5µ Pa = - Pa (1.8) The elasticity £pa of a FGT-measure with respect to a change in the distribution leaving the mean income unaffected can be denoted by the following equation aµPa-l £pa = 11Pa + P. (1.9) Z a In combination with the assumption of lognormally distributed incomes this means that the elasticity of the poverty gap with respect to changes in mean come is the following and depends partly on the mean income of the poor y7 3: II[log~y,) + ½er] ( J.-). II[Iog(z/y,) + lcr] _ II[log(z/Yt) + lcr] Y7 a 2 a 2 (1.10) 3It should be noted that identity 1.10 differs from the formula cited by Bourguignon (2003), in which the mean income of the poor is not explicitly taken into consideration. 1.3. GROWTH ELASTICITY VERSUS SEMI-ELASTICITY 15 Using the formulas derived by Kakwani (1993) we can also generate values for the semi-elasticities of the FGT-measures, which are with respect to income IC{a = e;,a *Pa= -a[Pa-l -Pa] and with respect to changes in distribution ~a= aPa + a(!!_ - l)Pa-1· z 1.3 Growth Elasticity versus Semi-Elasticity (I.I I) (1.12) Economists usually tend to use elasticities to measure the influence of income/ consumption growth on poverty changes. Although this information is clearly of some relevance it is actually absolute changes in poverty measures and therefore semi-elasticities that policy makers at the national and international level are interested in. The amount of persons leaving or entering poverty measured as a percentage of the total population is clearly of more interest than the same amount measured as a percentage of the poor. Stated differently the reduction of the percentage of the population that is living below the poverty line by IO percentage points is clearly a lot. But the reduction of headcount poverty by I 0% can be a lot, if the poverty rate is currently around 60%, but if it is only at 6% it is not really that much (only another 0.6% of the population are leaving poverty). 4 Moreover, as shown in the formulas above, the growth elasticity of poverty reduction is highly sensitive to the location of the poverty line relative to mean incomes. For example the continuous progress in the economic development of developing countries will lead to an increase in the distance between poverty line and mean incomes, equivalent to a reduction in headcount poverty. This reduction of the level of poor will lead to general increases in the elasticity due to the lower base by which absolute poverty changes are divided. This may lead policy makers to the conclusion that policies that were implemented in times with lower poverty rates were more successful in poverty reduction than policies that were implement during times of very high poverty rates, although these changes are purely a consequence of the way elasticities are calculated. To give an easy imaginary example, future economists might find that growth elasticities between 1980 and 2000 were a lot lower than in the following two decades. Therefore they might falsely come to the conclusion that the growth enhancing policies implemented in the last two decades were less successful than growth policies that are to be implemented in the future. In contrast, the semi-elasticity formulation will not 4 0ne could also focus on the absolute number of poor persons, which is clearly of considerable policy relevance. 16 1. THE SEMI-ELASTICITY OF POVERTY REDUCTION have an in-built increase in the poverty impact of growth. In fact, the opposite occurs. As countries grow richer, the ability of growth to achieve the same absolute poverty reduction becomes increasingly smaller which seems more plausible as it becomes increasingly difficult to improve the plight of few remaining desperately poor in a society. From an empirical point of view, there are further advantages to estimating the determinants of absolute rather than proportionate changes in poverty. In estimating the determinants of proportionate changes in welfare, Bourguignon states that he had to 'eliminate all spells where the percentage change in poverty headcount was abnormally large in relative value' (Bourguignon 2003). Also, all observations where poverty was O in the first period can also not be considered. Using the semi-elasticity, we can include all observations that are available and are not bound by such an arbitrary decision. In our case we can increase the number of growth and poverty spells from 102 to 125. In particular, we are able to include many growth spells from Eastern Europe and Central Asia which would otherwise be under-represented in the dataset. Another positive consequence of using semi-elasticities is a better fit when considering other poverty measures such as the poverty gap and squared poverty gap. This is an issue that will become apparent in the empirical findings below. 1.4 Empirical Results In the empirical section we test our ability to explain the determinants of absolute and relative poverty change using the above formulas. We do this using a slightly different data-set which is an updated version of the World Bank Poverty Monitoring data set also used by Adams (2004). To make our results easily comparable with those of Bourguignon we have used the same set of regressions and given them the same names. In Tab. 1.1 -1.6 our first regression is the nai've model that tries to explain changes in poverty measures by changes in mean incomes only. In all cases growth clearly has a significant poverty reducing effect but only a small part of the variation in poverty changes can be explained by a linear influence of mean income growth. The second regression in Tab. 1.1 -1.6 is the so called standard model that also takes changes in the distribution of incomes (i.e. variations in the Gini coefficient) into consideration and improves the fit of all models. As shown in the formulas above, both changes in mean incomes as well as changes in distribution have a non-constant influence on changes in poverty measures. As the formulas show, the size of the effects depends on the position of the poverty line relative to mean income and on initial inequality. This non-linear influence of growth in mean incomes is considered by interacting growth with the initial poverty-line/mean-income ratio and the initial Gini coefficient (Improved 1.4. EMPIRICAL RESULTS 17 Table 1.1: Absolute change in headcount poverty rate during growth spell Improved Improved Naive Standard Standard Standard Identity Identity Model Model Model I Model 2 Model I Model2 Intercept -0.0035 -0.0123 0.0001 0.0069 0.0007 0.0096 -0.50 -1.92 0.01 1.41 0.15 2.08 Y = % change in mean income -0.1769 -0.1462 0.2837 0.1612 -6.53 -5.94 4.()6 2.54 OOini = Variation in Gini 0.1947 0.2569 0.0168 0.2443 5.87 9.31 0.38 9.83 Y * pov.line/mean-inc. -0.8954 -0.8617 -6.95 -7.73 Y * initial Gini -0.5602 -0.3036 -2.88 -1.75 DGini * poverty-line/mean-income 0.0088 2.26 DGini * initial Gini 0.0123 3.01 Y * theoretical value of gse* -0.9901 -1.0419 (lognormal assumption) -12.09 -13.29 DSigma * theor. value of pise** 0.7330 (lognormal assumption) 10.87 R2 0.2571 0.4207 0.6368 0.7331 0.6601 0.6906 Adj. R2 0.2511 0.4112 0.6247 0.7196 0.6546 0.6856 Obs. 125 125 125 125 125 125 Notes: *gse = growth semi-elasticity. **pise = poverty inequality semi-elasticity. Standard Model I). By interacting changes in the Gini coefficient with the same two factors we also take account of the non-linear influence of changes in the distribution of incomes (Improved Standard Model 2). When taking these nonlinear influences of growth and distribution changes into consideration we are able to explain more than 70% of the variation in absolute changes in headcount poverty (Table 1.1) and about 50% of the variation in relative changes in headcount poverty (Table 1.2). This is a considerably improvement. The greater explanatory power of the regressions of the absolute poverty change is also true if we restricted the data set to the I 02 observations used in the relative regression. While in these first four regressions no assumptions are made on how income growth interacts with the distance of the poverty line to mean income and the initial degree of inequality the fifth regression (Identity Model I) assumes a joint effect of these three variables according to the theoretical (semi-)elasticity men- 18 l. THE SEMI-ELASTICITY OF POVERTY REDUCTION Table 1.2: Relative change in headcount poverty rate during growth spell Intercept Y = % change in mean income DGini = Variation in Gini Y * poverty-line/mean-income Y * initial Gini DGini * poverty-line/mean-income DGini * initial Gini Y * theoretical value of gse* (lognormal assumption) DSigma * theor. value of pise** (lognormal assumption) R2 Adj. R2 Obs. Naive Model 0.5266 2.46 -4.1283 -4.28 0.1546 0.1462 102 Improved Standard Standard Model Model 1 0.4782 0.3429 2.37 1.76 -4.5748 -21.3351 -4.99 -4.37 5.3861 5.7352 3.71 4.13 16.2323 2.98 28.3531 2.82 0.2580 0.3491 0.2430 0.3223 102 102 Improved Standard Identity Identity Model2 Model 2 Model 1 0.2278 1.23 -22.3100 -5.20 36.5913 5.96 17.2438 3.61 29.1387 3.30 -30.5798 -4.63 -44.7220 -3.55 0.51 IO 0.4801 102 0.3877 0.1903 2.18 1.58 5.6641 4.42 -2.0519 -7.72 0.4201 0.4084 102 -1.6908 -9.48 1.1989 12.76 0.7374 0.7321 102 Notes: *gse = growth semi-elasticity. **pise = poverty inequality semi-elasticity. tioned in section 1.3. The last regression model (Identity Model 2) further assumes a joint effect of a change in the distribution, the development level and the initial degree of inequality according to the theoretical (semi-)elasticity. As seen in Table 1.1 and Table 1.2 the assumption of a lognormal distribution fits the data very well. Multiplying growth in mean incomes with the respective theoretical value for the (semi-)elasticity and multiplying a change in the distribution of incomes with its respective theoretical value for the (semi-)elasticity can explain in both cases about 70% of the variation in absolute/relative changes in headcount poverty rates. Whereas the results for headcount poverty are very similar between the last regressions in Tables 1.1 and 1.2, the goodness of fit is a lot better when looking at absolute changes in poverty gap and squared poverty gap and therefore when 1.4. EMPIRICAL RESULTS 19 semi-elasticities are considered (Tables 3 and 5). The R2 values in Tables 1.3 to 1.6, where relative changes in poverty gap and squared poverty gap are considered, respectively, are quite modest, suggesting that the lognormal assumption is no longer as suitable because the explanatory power of the Identity Models are in both cases considerably smaller than those of the Improved Standard Models. The likely reason for the poor fit when considering depth and distributionsensitive poverty measures is that the increasing importance of the left tail of the distribution in these poverty measures where observations from countries with low poverty incidence are particularly influential and particularly prone to measurement error in the left tail of the distribution. An alternative way of phrasing the issue is that the assumption of log-normality is probably particularly errorprone the more one moves into the left tail of the distribution. In the relative poverty change regressions, the left tail from low incidence countries are particularly influential and this might therefore explain the poor fit. In contrast, it is very encouraging to see that we are able to explain changes in the absolute poverty gap and poverty severity very well still with the log-normal assumption. Thus our simplifying assumption of log-normality works particularly well when trying to explain absolute changes in poverty. The preceding empirical results are very encouraging and allow us to generate tables for policy makers that could give a clear impression as to what percent point reduction in headcount poverty a l % growth in mean incomes yields depending on the initial Gini coefficient and the level of development (Table 1. 7). Similar tables can also be generated for changes in the distribution of incomes as well as for other FGT-measures. These tables are shown in Tables Al to A4 in Appendix A Table 1.7 shows that the impact of growth on absolute (percentage point) poverty reduction is particularly large for countries where the poverty line is close to mean incomes and the level of inequality is low. Table 1.8 shows the elasticities and semi-elasticities for a number of individual countries to illustrate the difference with concrete country examples. When we study elasticities, by far the largest effects are found in the transition countries where poverty incidence is very low. In contrast, the highest semi-elasticities are found in Bangladesh, Ethiopia, Pakistan, and India. Thus we could expect that growth and pro-poor distributional change will lead to the largest effects on absolute poverty reduction in these countries and thus to the largest impact on poverty reduction at the global level. This not only generates a totally different picture on the impact of growth on poverty than suggested by the elasticities but it also puts into perspective recent debates about India's success in reducing poverty (e.g. Bhalla 2002; Bhalla 2003; Deaton 2003a, Deaton 2003b ). It appears that India was benefiting from being precisely in the situation where its growth will have the largest absolute impact on poverty. 20 1. THE SEMI-ELASTICITY OF POVERTY REDUCTION Table 1.3: Absolute change in poverty gap ratio during growth spell Improved Improved Naive Standard Standard Standard Identity Identity Model Model Model I Model2 Model I Model2 Intercept -0.1893 -0.6600 0.1307 0.4616 0.0261 0.8068 -0.46 -1.72 0.43 1.85 0.08 3.30 Y = % change in mean income -8.0477 -6.4003 19.9626 10.6787 -5.06 -4.33 4.96 3.31 DGini = Variation in Gini 10.4652 14.3797 -1.8911 12.1874 5.25 9.06 -0.84 7.95 Y * poverty-line/mean-income -59.2421 -56.7783 -8.00 -10.02 Y * initial Gini -31.0163 -11.8068 -2.77 -1.34 DGini * poverty-line/mean-income 1.0293 5.21 DGini * initial Gini 0.4677 2.24 Y * theoretical value of gse* -I.I 187 -1.1476 (lognormal assumption) -10.25 -12.82 DSigma * theor. value of pise* * 1.7043 (lognormal assumption) 12.39 R2 0.1721 0.3247 0.6117 0.7771 0.5813 0.7185 Adj. R2 0.1653 0.3137 0.5987 0.7657 0.5745 0.7139 Obs. 125 125 125 125 125 125 Notes: *gse = growth semi-elasticity. **pise = poverty inequality semi-elasticity. 1.5 Conclusion To summarize our results strong support is found for the assumption of lognormally distributed incomes. On the other hand the results show that the use of semi-elasticities instead of elasticities has considerable advantages aside from the fact that semi-elasticities are less prone to misinterpretations. By looking at absolute changes (i.e. percentage point changes) in headcount poverty, poverty gap and squared poverty gap we can increase the number of observations by about 20%. The generation of semi-elasticities needs no additional information and can be achieved by simple modifications of the formulas derived in Kakwani (1993). Besides the use of semi-elasticities leads to very high R2 values even for distributionally sensitive measures like poverty gap and squared poverty gap. With our measure we come to drastically different interpretations of the prospects for 1.5. CONCLUSION 21 Table I .4: Relative change in poverty gap ratio during growth spell Improved Improved Naive Standard Standard Standard Identity Identity Model Model Model 1 Model2 Model I Model2 Intercept 0.8454 0.8022 0.7712 0.7688 0.7089 0.7008 2.72 2.63 2.46 2.24 2.26 2.22 Y = % change in mean income -4.5943 -4.9930 -2.4044 -2.6994 -3.28 -3.60 -0.31 -0.34 DGini = Variation in Gini 4.8088 4.5001 7.2457 4.5035 2./9 2.02 1.18 /.99 Y * poverty-line/mean-income 5.2923 5.5254 0.60 0.63 Y * initial Gini -l0.7182 -l0.3106 -0.66 -0.63 OOini * poverty-line/mean-income -0.1799 -0.60 DGini * initial Gini 0.1376 (J.05 Y * theoretical value of gse* -0.7561 -0.6978 (lognormal assumption) -2.52 -2.33 DSigma • theor. value of pise** 1.7376 (lognormal assumption) 1.73 R2 0.0970 0.1388 0.1480 0.1516 0.0848 0.0761 Adj. R2 0.0880 0.1214 0.1129 0.0980 0.0664 0.0574 Obs. 102 102 102 102 102 102 Notes: *gse = growth semi-elasticity. **pise = poverty inequality semi-elasticity. poverty reduction in the future as well as on explaining the record of poverty reduction in different countries. But at the same time it has to be kept in mind, that the results are not directly about policies. Therefore they give no hint as to what policies are of particular importance to the reduction of poverty rates. Besides the differentiation between growth and distribution effects is somehow artificial since almost no policy influences only growth or only the distribution of incomes. But despite these caveats the above elucidated method to assess the impacts on poverty rates across countries seems to be better suited than prior methods. 22 l. THE SEMI-ELASTICITY OF POVERTY REDUCTION Table 1.5: Absolute change in squared poverty gap ratio during growth spell Improved Improved Naive Standard Standard Standard Identity Identity Model Model Model 1 Model2 Model 1 Model2 Intercept -0.0634 -0.3592 0.1867 0.3657 0.0322 0.6071 -0.21 -1.25 0.79 1.84 0./3 2.89 Y = % change in mean income -4.8698 -3.8330 14.0963 7.616 -4.28 -3.54 4.63 3.03 DGini = Variation in Gini 6.5542 9.2I03 -1.5160 7.3959 4.48 7.65 -0.86 6.11 Y * poverty-line/mean-income -40.5948 -38.7195 -7.22 -8.74 Y * initial Gini -20.8864 -7.6895 -2.46 -1./2 DGini * poverty-line/mean-income 0.8811 5.62 DGini * initial Gini 0.1419 0.86 Y * theoretical value of gse* -1.0922 -1.1214 (log normal assumption) -7.96 -9.29 DSigma * theor. value of pise** 1.4229 (lognormal assumption) 9.08 R2 0.1342 0.2611 0.5460 0.7232 0.4692 0.5892 Adj. R2 0.1268 0.2485 0.5302 0.7085 0.4602 0.5821 Obs. 120 120 120 120 120 120 Notes: *gse = growth semi-elasticity. **pise = poverty inequality semi-elasticity. 2.3. THE NEW WHO CHILD GROWTH STANDARD 29 nately the surveys do not contain information on income or expenditure. Therefore an asset-based approach is used to define well-being (Sahn and Stifel, 2001). In order to construct an asset index for DHS households, first, a set of household assets was identified, which were the ownership of a radio, TV, refrigerator, bicycle, motorized vehicle, floor material of housing, type of toilet, type of water source and some other assets depending on the country. Afterwards, these assets were aggregated into one single metric index for each household using the first component of principal component analysis, or, alternatively, the closely related factor analysis (see Filmer and Pritchett (2001) and Sahn and Stifel (2001)). In this case principal component analysis was used. Once the asset index is built, one can construct the cumulative distribution function of the asset index and, hence, households in the DHS can be classified into asset quintiles. 2.3 The new WHO Child Growth Standard As is well known, there are a large number of limitations of the NCHS/WHO reference and these limitations have been documented by different authors (notably WHO Working Group on Infant Growth, 1994; de Onis and Yip, 1996; de Onis and Habicht, 1996). Between others the data used to construct the reference covering birth to two years of age were derived from a longitudinal study of children of European ancestry from a single community in the USA. Besides these children were measured only every three months, which is an inadequate way to describe the rapid and changing rate of growth in early infancy. Another aspect is that statistical methods have developed further in the last decades and are therefore better able to correctly model the pattern and variability of growth of children during their first five years. As a consequence the WHO implemented the Multicentre Growth Reference Study (MGRS) which included children from six different countries: Brazil, Ghana, India, Norway, Oman and the USA. To make sure that only children were considered that are likely to have achieved their full genetic growth potential, only mothers from high socio-economic backgrounds were considered that engaged in fundamental health-promoting practices, namely breastfeeding and not smoking (de Onis et al., 2004). By selecting only privileged, healthy populations the study reduced the impact of environmental variation and is therefore very suitable to construct a truly international reference standard. 2 These aspects make it very likely that the new WHO reference standard will be adopted as the main indicator to track the progress in reductions in child undernutrition according to MDG I. One first consequence will be that initial un- 2For a closer look at the appropriateness of using elites for the construction of a reference standard attention is drawn to Klasen and Moradi (2000). 30 2. UNDERNUTRITION AND THE NUTRITIONiTRANSITION demutrition rates using the new WHO reference standard will decrease compared to undemutrition rates using the old NCHS/WHO reference standard, when underweight is used as the only indicator. Tab. 2.2 shows the prevalence rates for the four different anthropometric indicators using both reference standards for the eleven countries considered in this study. Table 2.2: Prevalence rates of undemutrition (percentage) Country Reference Stunting Underweight Wasting CIAF Burkina Faso 2003 NCHS/WHO 38.59 39.16 19.86 55.94 Burkina Faso 2003 WHO2006 43.72 34.83 22.68 59.30 Bolivia 2003 NCHS/WHO 27.75 7.89 1.51 29.90 Bolivia 2003 WHO2006 33.58 5.61 1.92 35.33 Chad 2004 NCHS/WHO 39.28 36.84 14.86 52.40 Chad 2004 WHO2006 42.80 32.85 17.44 54.85 Cameroon 2004 NCHS/WHO 31.72 17.42 4.77 37.59 Cameroon 2004 WHO2006 36.62 13.42 5.27 40.75 Colombia 2005 NCHS/WHO 12.06 7.63 1.67 15.40 Colombia 2005 WHO2006 16.24 5.38 1.99 18.45 Egypt 2003 NCHS/WHO 15.78 8.62 3.68 20.76 Egypt 2003 WHO2006 19.47 7.82 4.75 24.25 Ghana 2003 NCHS/WHO 30.90 23.43 7.69 40.17 Ghana 2003 WHO2006 36.75 19.42 8.99 44.02 India I 998/99 NCHS/WHO 43.36 43.74 14.83 57.33 India I 998/99 WHO2006 49.13 39.53 18.83 61.25 Tanzania 2004 NCHS/WHO 36.46 22.67 3.69 41.95 Tanzania 2004 WHO2006 42.48 16.97 4.40 46.14 Uganda 2001/02 NCHS/WHO 37.88 21.50 3.93 42.57 Uganda 2001/02 WHO2006 43.87 17.43 4.78 47.39 Zambia 2001/02 NCHS/WHO 47.55 28.64 4.84 53.39 Zambia 2001/02 WHO2006 54.09 23.26 5.84 58.25 Source: DHS datasets; Own calculations. Although the numbers in Tab. 2.2 show increases in stunting, wasting and the Composite Index of Anthropometric Failure (CIAF), underweight rates fall in all cases. This puzzling phenomenon will be discussed in more detail in Section 2.5. Besides these general trends, the numbers hide that changes in undemutrition rates due to the adoption of the new reference standard are much more complex than just a simple increases or decreases. This can be seen in Table 2.3 which is a mobility matrix that shows that the composition of undemutrition changes considerably. Looking for example at the first row of Tab. 2.3 one can observe that over 88% of children that were not malnourished according to the WHO/NCHS reference standard are also not malnourished according to the new WHO reference standard. The remaining around 11 % of the children are now considered as 2.4. THE NUT.liUTION TRANSITION 31 only stunted (6.89% ), stunted and underweight (0.64% ), undernourished according to all three indicators (0.21 %), only wasted (1.75% ), wasted and underweight (0.88%) and only underweight (0.73% ). But as mentioned before, the changes are not limited to increases in undernutrition rates, but a large number of individuals is now counted as not malnourished according to the new WHO reference standard that were categorized as malnourished using the NCHS/WHO reference standard. For example more than 16% of children that were only wasted according to the old reference standard are now considered as not malnourished and 39% of children that were only underweight according to the NCHS/WHO reference standard have z-scores above -2.0 according to the new standard. 3 Table 2.3: Mobility matrix ofNCHS/WHO to WHO reference standard (percentages) A B C D E F G A - "Not malnourished" 88.88 6.89 0.64 0.21 1.75 0.88 0.73 B - "Only Stunted" 1.44 95.72 2.83 0.00 0.00 0.00 0.01 C - "Stunted and Underweight" 0.25 17.63 76.25 5.69 0.00 0.07 0.11 D - "All Indicators" 0.00 0.00 7.35 91.56 0.00 0.97 0.12 E - "Only Wasted" 16.42 0.00 0.00 O.D3 71.30 12.25 0.00 F - "Wasted and Underweight" 5.02 0.00 I.II 10.04 11.47 68.47 3.89 G - "Only Underweight" 39.06 I 1.00 16.77 3.30 0.91 9.72 19.25 Note: *In each row the 100% of the respective category of the NCHS/WHO reference standard are divided into the same categories according to the new WHO reference standard. The percentages are generated from a pooled data set of all 22 data sets used in the present study. These complex changes necessitate the complete revision of the nutrition aspect of MDG I using the new reference standard as basis for the measurement of progress in the reduction in undernutrition. This opportunity should be used to switch to stunting as alternative indicator, since all weight-based measures are inherently biased by the consequences of the Nutrition Transition as the following Section 2.4 will show. 2.4 The Nutrition Transition Changes in diet and activity patterns are not limited to the developed countries but are rapidly taking place in the developing regions as well. For a large set of countries marked shifts in the structure of the diet have been documented (e.g. Kim et al. 2000, Monteiro et al. 1995, Popkin 1994, Popkin 1998). Major dietary 3For a closer description of the z-scores, the way they are generated and how one has to interpret them, attention is drawn to WHO ( 1995). 32 2. UNDERNUTRITION AND THE NUTRITION;,TRANSITION changes include large increases in the consumption of fat and added, sugar in the diet and often a significant increase in animal food products. This is contrasted with a fall in total cereal intake and fiber. Although there is a great heterogeneity in the diet shifts, there seems to be a general shift to the higher fat Western diet, which is reflected by a large proportion of the population consuming over 30% of energy from fat. These diet and activity patterns are fueling the obesity epidemic that is also rapidly proceeding in the developing countries. As a consequence large increases in diet-related chronic diseases such as diabetes and cardiovascular diseases are discernable. In fact the WHO estimates that two thirds of deaths due to chronic disease worldwide now occur in developing countries and that obesity is a primary risk factor in this context (WHO 2004). The nutrition transition and its related disease pattern might lead to the misconception that diets are moving entirely away from undernutrition toward problems of excess. Unfortunately the rapid increase in obesity does not come along with an equally rapid decrease in malnutrition or undernutrition. This could be the case due to the fact that not all individuals in a given society profit in the same way from increases in energy availability. During this transition, symptoms of under- and overnutrition logically coexist at the population level, with wealthier households exhibiting diseases of affluence including obesity, and poorer households exhibiting food insecurity and malnutrition. Recent work indicates that under- and overnutrition can even coexist in the same household (Doak et al. 2000, 2002; Monteiro et al. 1997). In fact the prevalence of stunted child-overweight mother pairs is not as seldom as one could assume, with the highest level being recorded for Egypt, where 14 percent of children live in stunted child-overweight mother pairs (Garrett and Ruel 2003). Besides Kandala et al. (2001) find a nonlinear influence of the BMI of the mother that might indicate that not only parental undernutrition, but also parental malnutrition might also have negative effects on the nutritional status of children. As is argued in the following the coexistence of under- and overweight in the same household is not due to a very unequal distribution of diets within households but the fact that increases in total energy intake do not coincide with equal increases in all vital micronutrients and, therefore, children might on average gain weight while still being malnourished. It is therefore possible, that the nutrition transition will have two detrimental effects on policies concerning child undernutrition. On the one hand the emerging obesity epidemic might lead to the misconception that undernutrition is a phenomenon of the past. On the other hand using the 'wrong' indicator for undernutrition might also lead to wrong conclusions concerning the prevalence of undernutrition in developing countries. On the latter aspect will be the prime focus of this article. It is argued that the use of weight-based anthropometric measures is biased by the nutrition transition that leads to increases in the weight of children that do not necessarily reflect improvements in their nutritional status. They might 2.5. RESUkTS 33 still suffer from micronutrient malnutrition that will have severe long-term effects (Eckhardt 2006) and that is reflected in reduced long term growth of children. This micronutrient malnutrition can be accounted for when stunting is used as the anthropometric indicator for child undernutrition or alternatively the Composite Index of Anthropometric Failure (CIAF). The fact that such a bias is present in the underweight measure will be shown in the following sections. Although it is argued that stunting is a much better measure for undernutrition than underweight or wasting, one has to keep in mind that a certain degree of overestimation is inherent in stunting, especially in comparison to a measure of average calorie availability like the FAQ measure. This results from the fact that stunting measures the effective nutritional status and an inadequate access to calories is only one of the reasons why the growth of a child can falter. Other main reasons are frequent, prolonged and untreated illness that reduce the appetite and the absorption of energy in the body. Energy may also by diverted by intestinal parasites (Svedberg 2002). A second aspect that has to be kept in mind is the fact that comparisons between countries and regions are made by using one general reference standard, i.e. using identical height and weight norms. It has been claimed that the genetic potential for growth in children is not the same for all regions (Bogin 1988, Davies 1988, Eveleth and Tanner 1990). Besides Klasen (2007) demonstrates that even minor differences of 1-3% in median height of children at age 5 between regions can lead to significant measurement biases, that result in an overestimation of the incidence of undernutrition in regions with a growth pattern of slightly lower growth. Still the consensus view seems to be that there are no or only very small genetic differences between populations in their growth and weight development between O and 5 years. This view is backed up by a variety of studies that showed that differences in growth and weight patterns between affluent groups of various countries are extremely similar (e.g. Graitcer and Gentry 1981, Ramalingaswami et al. 1996, WHO 1995, Bhandari et al. 2002). It is important to keep in mind that these two confinements are not limited to stunting but general limitations of the three anthropometric measures. They can therefore not be used to favor any of these measures. 2.5 Results The following section will first present some general aspects considering the construction of the underweight measure and especially point to the changes in prevalence rates between the old NCHS/WHO and the new WHO reference standard. Afterwards some empirical results with respect to the composition of undernutri- 34 2. UNDERNUTRITION AND THE NUTRITIONlfRANSITION tion and especially the composition over time will be discussed:!'Alt'results are based on own calculations using the aforementioned DHS surveys. 2.5.1 The theoretical composition of the underweight indicator As mentioned before, undemutrition can be measured by three different anthropometric measures, namely stunting, wasting and underweight. The classical idea of undemutrition, a low weight for height, is measured by wasting which is therefore a measure of immediate undemutrition. Contrary to that a long run supply of insufficient amounts of energy will result in growth retardations which are measured by stunting (low height for age). Although it is well known that weight is more sensitive than height to seasonal influences, but height generally more responsive than weight to improved food intake in the long term (WHO 1994 ), the weight-based measure underweight (low weight for age) is used to track long term changes in child undemutrition. This derives from the intention to take account of both types of undemutrition, long-term as well as immediate. Figure 2.1: Wasting/Stunting Combinations for Underweight of -2.0 (WHO reference standard) oo+--------------------------- .1~- ~---'>-....._..__ _______ ,...._._....- •• ---::-:~--.:::::-::-:.,-:-:::::-:-:,='='==='==='=;- ;··'/:.:.:·..:.. .. ;; ..... ,.. ....... ~-.-:. ~-,..-= .,,...; ::..-.:.: ;;·.:.:~-..:. ".:::..: ·;::.:. ·_.r.;,::.: ·.:.: ~~ .... \&'&: '::.-:.::..: ·:..· 0 10 20 30 40 50 60 Age (months) --- WHO Z-Score Boys {Stunting= O) - · - · - · - WHO Z-Score Girls (Stunting= 0) - - - - - WHO Z-Score Boys (Stunting= -1) - · - · • · - · - · • WHO Z-Score Girls (Stunting= -1) --------- WHO Z-Score Boys (Stunting= -2) ---WHO Z-Score Girls (Stunting= -2) Source: WHO reference standard~ own calculations. 2.5. RESULTS 35 Theoretically there are two reasons for a low weight for age. First, a value below 2 standard deviations of the reference standard can occur due to a very low weight for height. Second a underweight z-score of less than -2.0 can occur due to a very low height for age. Unfortunately underweight does not capture all individuals that are undernourished according to any of the other two measures. To demonstrate what children are really considered as 'underweight' different pairs of stunting and wasting z-scores are shown in Fig. 2.1 to 2.4 using both the NCHS/WHO and the WHO reference standard. These figures show that the relationship between stunting, wasting and underweight is rather complex. Besides the figures show that the underweight measures does not really capture what it is supposed to do. 0 ~ Figure 2.2: Wasting/Stunting Combinations for Underweight of -2.0 (NCHS/WHO reference standard) co-+--------------------------- 0 10 20 30 40 50 60 Age (months) --- NCHS/WHO Z-Score Boys (Stunting= 0) - · - · - ·.. NCHS/WHO Z-Score Girls (Stunting= 0) - - - - - NCHS/WHO Z-Score Boys {Stunting= -1) • · - · - · •· - .. NCHS/WHO Z-Score Girls (Stunting= -1) -- - - ----- NCHS/WHO Z-Score Boys (Stunting= -2) - · - · • NCHS/WHO 2-Score Girls (Stunting= -2) Source: NCHS/WHO reference standard; own calculations. If underweight was really suitable as a summary indicator than for example every child that has a normal height for its age and a wasting z-score of below -2.0 would be counted as underweight. In fact as Fig. 2.1 and Fig. 2.2 show if it is assumed that a child has a height for age that corresponds to the new WHO reference standard (i.e. a stunting z-score of 0) has to have a wasting z-score of about-3 or below to have an underweight z-score of below -2.0. That means a boy 36 2. UNDERNUTRITION AND THE NUTRITION TRANSITION or girl with normal height has to be severely wasted to be counted as underweight. When a child has a stunting z-score of -2 it still has to be slightly wasted and has to have a wasting z-score below - l ( depending on its exact age in months). Using the old NCHS/WHO reference standard the relationship between stunting, wasting and underweight is even more complex, with a clear jump being discernable when the reference switches between length and height measures. This jump is not existent in the new WHO reference standard. But what is even more important is the fact that the z-score requirements for wasting (for given stunting z-scores) are significantly lower in the NCHS/WHO reference standard than in the new WHO reference standard. A child with a height according to the old reference standard is counted as underweight, when it has an wasting z-score of about -2.5 or below. The differing z-score requirements between the two reference standards are easily observable in Tab. A. I and A.2 in the Appendix B. Figure 2.3: Stunting/Wasting Combinations for Underweight of -2.0 (Boys) co+--------------------------- / ---·- . ,.-·-,..,·-·-·-·-·-·-·-·-·- ,,.,, . - - ,.~-"< ........ '.' - . - . -'~----_,,,......:.:.:...__:~=----"--....:.. . ,\ .. """",..._ ... ,................... .. .... .. "'..._ .. I -~..:--'--.,--.,;•,:.:":,:.'°.:.:-.•-• '-" ~-..... ~'- ... - .. -- .. ___ ,..,--' "' ..... , , ..... ,,,,,. .... _,.., '-..... '- .......... ....._ ..................... ,---- ........... ------ .... -...:., ........ ..... , ......... -.. -c..:.:. /" - ,/ _, ........................................... ,----~ -....--. '""- 0 10 20 30 40 50 60 Age (months) --- WHOZ-Score Boys (Wasting= -1) - · - · - · - NCHS/WHOZ-Score Boys(Wasting= -1 - - - - - WHO Z-Score Boys (Wasting= 0) - · - · - · - · - · • NCHS!WHO Z-Score Boys (Wasting= O) --------- WHO Z-Score Boys (Was1ing = 1) - · - · - NCHS/WHO Z-Score Boys (Wasting= 1) Source: WHO reference standard~ own calculations. Although it is not entirely clear how this shifts comes about, one possible reason could be that children with 'unhealthy weights for length/height', i.e. observations falling above +3SO and below -3SO of the sample median were excluded 2.5. RESUL-S 37 prior to constructing the WHO reference standard. For the cross-sectional sample the +2 SD cut-off was applied (WHO Multicentre Growth Reference Study Group 2006). The exclusion of this observations shifts the values of the weight standard downwards and therefore increases the weight shortfall requirements. q 'l' Figure 2.4: Stunting/Wasting Combinations for Underweight of -2.0 (Girls) oo-+-------------------------- i i':~ ~I C: ::, j')J 2 ~7 \? I. f"f E ~ ju;> C) 'i: ...... -,.- ....... ·' ,., /'·-·-·-·-.,. _________ .- ....... ___ ,_,_ ~~ "\.····•., • ' ... - , J_/_,.....__....._,,,..... _ _,. _______ ~ -v,~~:?~~·=:::::~:=:=~;; 0 10 20 30 40 50 60 Age (months) --- WHO Z-Score Girls (Wasting = -1) - · - · - · - NCHS/WHO Z-Score Girls (Wasting = -1 ) - - - - - WHO Z-Score Girls (Wasting= 0) - · - · - · - · - · • NCHS/WHO Z-Score Girls (Wasting= 0) --------- WHOZ-Score Girls (Wasting= 1) - · - · - NCHSIWHO Z-Score Girls (Wasting= 1) Source: NCHS/WHO reference standard; own calculations. As Fig. 2.3 and 2.4 show, the requirements for stunting z-scores, given a 'normal' nutrition status according to wasting, are even more restrictive when the WHO reference standard is used. If boys or girls have a wasting z-score of 0, children have to have a stunting z-score of significantly below -3 to be counted as underweight. Only in the first eight month a stunting z-score between -2.5 and -3 is low enough to be also considered as underweight. If a child is mildly wasted ( wasting z-score of -1) a child still has to be considerably stunted (stunting z-score of below -2) to be counted as underweight. When looking at Fig. 2.3 and 2.4, we can again discover that the NCHS/WHO reference standard is not as restrictive as the WHO reference standard. The only exception is the scenario in which a wasting z-score of+ I is assumed. In this case only children with extremely low stunting z-scores do potentially fall in the group of underweight children. As Fig. ?? also shows, the NCHS/WHO curve is up to 38 2. UNDERNUTRITION AND THE NUTRITION TRANSITION the age of 26 months above and afterwards below the WHO curve. Fig. A. I and A.2 in the Appendix B show the curves for the old and new reference standard separately. It is important to keep in mind, that increases in wasting z-scores are very likely in the developing world due to the aforementioned nutrition transition. The threshold be counted as underweight is therefore becoming more and more restrictive. As the preceding figures have shown, concentrating on a measure like underweight risks neglecting a large number of undernourished children. Although this is also the case for the other two measures, -just as the concentration on any of the other two indicators does. The only possibility to really capture all undernourished children is by using the Composite Index of Anthropometric Failure (CIAF) proposed by Svedberg (2001) and modified by Nandy et al. (2005).4 This index indicates whether a child is undernourished according to any of the three anthropometric indicators. 2.5.2 The empirical composition of the underweight indicator Knowing the theoretical requirements for the underweight indicator it is of considerable interest to have a closer look at its actual composition. As Tab. 2.4 shows, more than 42% of children with an underweight z-score of below -2.0 exhibit stunting z-scores below -3.0. This already points to the strict requirements for underweight, when undernutrition is limited to long-term undernutrition and a child has a normal weight for its height. At the same time Tab. 2.4 shows that some children that are considered neither as stunted nor wasted can at the same time be indicated as underweight. In fact more than 12% of the 47 ,2222 children that are underweight according to the NCHS/WHO reference standard stem from categories C and D in both indicators at the same time and are therefore neither considered as stunted nor wasted. It is therefore unclear whether it is really appropriate to consider these children as undernourished. The percentage of children that are only underweight and neither wasted nor stunted is much lower when the new WHO reference standard is used (see Tab. 2.5). In this case only 4.84% of the remaining 42,424 children are at the same time in categories C and D for both indicators. This reduction is entirely due to the fact that the nutrition status has to be worse to get an underweight z-score of below -2.0. This is demonstrated by the drop in the total number of underweight children as well as by the fact that fraction of children that have severe growth failures has increased to more than 54%. 4Nandy et al. (2005) that the CIAF consists of six subgroups contrary to the five groups proposed by Svedberg (2002), with the last group being children that are undernourished according to the underweight indicator only. 2.5. RESULTS 45 undemutrition. On the contrary these improvements are mainly limited to the weight-based measures and stunting remains almost unaffected. The observed patterns are not consistent with an explanation that increases in the BMI of mothers are a proxy for improvements in the wealth of households and therefore lead to general improvements in child anthropometry. The pattern is more consistent with a theory that states that the BMI of the mother is a good proxy for the dietary composition of a household. A higher BMI is consequently an indicator for a diet in the household that is rich in cheap fatty acids. A higher weight or BMI of the mother is therefore correlated with a higher weight of the child which does not necessarily reflect a better nutritional status. As mentioned before, higher amounts of energy from foods do not necessarily coincide with sufficient amounts of vital micronutrients. Restrictively it has to be stated that the BMI of the mother is susceptible to a number of biases. For example, a bad nutritional status of the mother during her childhood and the resulting growth retardations can increase the likelihood of being overweight according to the BMI because of the small body height. 2.5.5 Changes in Undernutrition over Time Probably the clearest and best way to show how focusing on the 'wrong' indicator for undemutrition will lead to a considerable bias in measurement is by comparing prevalence rates over time. Here, the secular downward trend in the prevalence of weight-based undemutrition, that is not reflected in a similar improvement in stunting or the Composite Index of Anthropometric Failure, is extremely obvious. Looking at the following Tab. 2.6, it is discemable that changes over time are not the same for all three indicators of undemutrition. In fact, the magnitude of changes does not only differ between stunting, wasting and underweight but also the direction of change differs. On the one hand, we can observe decreases in all three measures in Colombia, Egypt, India and Tanzania, no significant changes in any measure are observable in Chad and increases in all three measures are found in Burkina Faso and Zambia. On the other hand, the directions of change differ in Bolivia, Cameroon, Ghana and Uganda. In all these countries reductions in wasting and underweight took place, while stunting increased. It is clear that decreases in weight-based anthropometric measures that come along with increases in stunting are only very extreme examples. Fortunately in most countries the prevalence of undemutrition was reduced according to all measures. But even in these countries the magnitude of the change is not the same for all three anthropometric indicators. A stronger decrease in weight-based measures compared to stunting are again observable when we compare the two 46 2. UNDERNUTRITION AND THE NUTRITION TRANSITION Table 2.6: Anthropometric indicators over time (WHO 2006 reference standard) Survey (Year) Stunting Underweight Wasting CIAF Differing Directions Bolivia (I 993194) 33.4% 12.2% 6.1 % 38.1% Bolivia (2003) 33.7% 5.5% 1.9% 35.4% Cameroon (1998) 34.0% 16.1% 8.0% 39.6% Cameroon (2003) 36.6% 13.3% 5.5% 40.9% Ghana (1993) 33.0% 24.7% 14.6% 44.2% Ghana (2003) 36.7% 19.4% 9.0% 44.0% Uganda (1995) 41.5% 19.3% 6.8% 45.9% Uganda (200010 I) 43.8% 17.2% 4.8% 47.3% Same Directions Burkina Faso ( 1998/99) 43.5% 31.7% 15.4% 54.6% Burkina Faso (2003) 43.6% 34.5% 22.7% 59.4% Chad (1996/97) 43.3% 32.4% 16.2% 53.7% Chad (2004) 42.6% 32.3% 17.4% 54.9% Colombia (1995) 19.8% 6.2% 1.7% 21.7% Colombia (2005) 16.3% 5.3% 2.0% 18.5% Egypt (1995) 34.7% 11.8% 7.3% 40.9% Egypt (2003) 19.7% 7.7% 4.8% 24.5% India ( 1992/93) 54.1% 43.8% 19.0% 65.7% India (1998199) 48.9% 38.8% 18.7% 61.1% Tanzania (1996) 50.1% 26.6% 8.7% 56.0% Tanzania (2004) 42.6% 16.9% 4.4% 46.3% Zambia (1996) 50.2% 20.1% 5.4% 54.2% Zambia (2001102) 53.8% 22.9% 6.0% 58.2% Note: *Children are considered as wasted, stunted or underweight if the respective z-scores are below -2 standard deviation from the median of the reference category. surveys for each country. Below the development over time is shown for India and Tanzania in Fig. 2.10. 2.5. RESULTS 47 Figure 2.10: Composition of undemutrition in India and Tanzania by survey year 0 Composition of undernutrition in India by Survey Year 1992/93 -Only Stunted -All Indicators -Wasted and Underweight -Stunted and Underweight On ly Wasted Only Underweight Composition of undernutrition in Tanzania by Survey Year 1996 -Only Stunted -All Indicators -Wasted and Underweight 2004 -Stunted and Underweight 1111111 Only Wasted Only Underweight Sourl'e: OHS datasets; own calculations. 48 2. UNDERNUTRITION AND THE NUTRITION TRANSITION The analysis of changes over time confirms therefore the results of the analysis of the general composition of undernutrition and the analysis of different subgroups. In all cases can be shown that the two weight-based show stronger reductions than stunting, which is by far the most persistent indicator of child undernutrition with the lowest rates in improvement. This is important to keep in mind because if the falling prevalence rates in the two weight-based measures where really due to considerable improvements in the nutrition status of children than stunting rates would fall in a similar manner. Since this is not the case, it is reasonable to assume that weight increases due to changes in dietary composition play an important role in these changes. These weight gains should not be counted as improvements per se and it would therefore make sense to use stunting instead of underweight as the indicator of choice. 2.6 Conclusion As the preceding chapters have demonstrated it is very helpful to have a closer look at the exact composition of undernutrition. Although the general pattern is very durable in each country across subgroups there are large differences between countries that will lead to different country rankings depending on which indicator is used. Besides this general aspect, it was argued that especially the use of underweight as the measure of choice to track the progress in the fight against undernutrition is highly problematic. Due to the way this indicator is defined, children either have to be extremely undernourished according to one indicator or have to be moderately undernourished according to both indicators. Consequently a large number of children are not considered as underweight that are significantly stunted or wasted. And some children are considered as underweight that are neither stunted nor wasted. Since the interpretation of the underweight indicator on its own is not very straightforward, there are considerable doubts whether it is a useful indicator at all. Further it was demonstrated that the use of the new WHO reference standard alone results in significant reductions in the prevalence of underweight, although the other two indicators show increases. Adding to this reduction is the general downward trend in all weight based measures, such as underweight and wasting, that is due to changes in the nutritional composition of diets in developing countries. This secular reduction does not necessarily coincide with real improvements in the health of the affected children due to still lacking micronutrients. This bias makes the interpretation of reductions in the prevalence rates of undernutrition even more problematic and could lead to wrong conclusions concerning the fulfillment of the undernutrition aspect of MDG I. 2.6. CONCLUSION 49 While on a scientific level it is very useful to have a closer look at the exact composition of undemutrition, it is acknowledged at the same time, that the necessity exists on a political level to have a single indicator to count the number of undernourished persons. Consequently the opportunity of the introduction of the new WHO reference standard should be used to switch to stunting or the Composite Index of Anthropometric Failure (CIAF) instead of underweight as the indicator to measure progress in the field of undemutrition. Both measures do not suffer from biases that are introduced due to the nutrition transition that is taking place in the developing countries and are much easier to interpret. Essay 3 A Regional Puzzle of Child Mortality and Undernutrition Abstract: While undemutrition among children is very pervasive both in Sub-Saharan Africa and South Asia, child mortality is rather low in South Asia. In contrast to that, Sub-Saharan African countries suffer by far the worst from high rates of child mortality. This different pattern of child mortality and undernutrition in both regions is well known, but approaches using aggregated macro-data have not been able to explain it appropriately. In this paper, we analyze the determinants of child mortality as well as child undernutntion based on OHS data sets for a sample of five developing countries in South Asia and Sub-Saharan Africa. We investigate the effects of individual, household, and cluster socio-economic characteristics using a multilevel model approach and examine their respective influences on both phenomena. The results show si1fnificant differences in outcomes of child mortality and undernutrition and in their respective determinants between the two regions and between countries. Whereas the access to health infrastructure is more important for child mortality than for undemutrition, the nutritional status of the mother, which is worse in South Asia than in Sub-Saharan Africa, has a much higher impact on child undernutrition than on child mortality. based on joint work with Kenneth Harttgen. 52 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDER.NUTRITION 3.1 Introduction 3.1.1 Child Mortality and Undernutrition Despite the overall decline in the prevalence of undernutrition and child mortality in developing countries, both phenomena are still at unacceptably high levels and, therefore, remain big challenges in the fight against lacking capabilities and reaching the MDGs. Concerning the childrens' anthropometric failure, the WHO (2002) estimated that almost 27 percent (168 million) of children under five years of age are underweight. And looking at the threat of child mqrtality, nearly 11 million children died in the year 2003 before reaching the age of five. Around 98 percent of the deaths occur in developing countries (UN, 2005). Several papers have studied the socio-economic determinants of child mortality and undernutrition. Examples for empirical studies of child mortality are Subbaro and Rany (1995), Pritchett and Summers (1996), Ssewanyana and Younger (2004), and for undernutrition Gillespie et al (1996), Osmani (1997), and more recently Smith and Haddad (2000). As stated in numerous studies in this field, one of the major causes of child mortality is undernutrition itself. Most studies cite this result by referring to a study by Pelletier et al. (1995), which finds that more than 50 percent of child mortality is attributable to mild, moderate, and severe undernutrition. In addition, a study of Pelletier et al. (2002) measures the effect of malnutrition on changes in child mortality for 59 developing countries using aggregate longitudinal data from 1966 to 1996, finding that reducing malnutrition by 5 percent could reduce under-five child mortality by 30 percent. Although, intuitively it seems to be clear that being malnourished increases the risk of child mortality, considerable doubts concerning the closeness of the relationship exist. 3.1.2 The South Asia -Sub-Saharan Africa Enigma Assuming a close relationship between child mortality and undernutrition, two glaring puzzles exist when the two regions of South Asia and Sub-Saharan Africa are compared. The first puzzle is the so called South Asian Enigma. The anthropometric outcomes are considerably better in Sub-Saharan Africa than in South Asia. Almost half of the children in South Asia are malnourished. Compared to Sub- Saharan Africa the anthropometric shortfall is almost 70 percent higher in South Asia (WHO, 2005), despite higher per capita calorie availability and better provision of health care, water, and sanitation (Ramalingaswami et al., 1996; Osmani, 1997; Svedberg, 2002). The second puzzle concerns the existing child mortality reversals between these two regions (Svedberg, 2000; Klasen, 2003, 2007). In contrast to the severe anthropometric failure in South Asia, Sub-Saharan African countries suffer by far the worst from high rates of child mortality. In Sub-Saharan 3.1. INTROmJCTION 53 Africa, 174 children out of I 000 die before reaching the age of five, while 97 die in South Asia (UNICEF, 2004). Together, these two puzzles can then be defined as the South Asia -Sub-Saharan Africa Enigma of anthropometric failure and mortality reversals. Various possible explanations for the Enigma exists. First, the level of income poverty is a obvious and major cause both for child mortality and undernutrition, but this cannot explain the regional differences as the average incidence of poverty is quite similar in the two regions. Second, the high magnitude of undernutrition is a result of how undernutrition is measured. For example, Klasen (2003, 2007) argues that the US-based reference standard for international comparison of undernutrition proposed by the WHO ( 1995) leads to an overestimation of undernutrition in South Asia. This overestimation could be due to different genetic potential in growth between the population in these two regions. The high level of undernutrition in South Asia might then appear because of genetic differences in height and weight, i.e. that children in South Asia are genetically shorter and/or lighter compared to the reference population and are, therefore, spuriously considered as malnourished. But even if this is the case, this could explain only a part of the large differences in the anthropometric outcomes between South Asia and Sub-Saharan Africa. However, also the use of the new reference standard by the WHO (WHO, 2006) that is based on child growth data from six different developed and developing countries 1 that takes explicitly into account the growth potential of children by selecting children from well-doing households, has not been able to solve the Enigma. In particular, using the new reference standard only leads to an upward shift in the level of anthropometric measures compared to the old reference standard, but it does not provide any changes in the ratio of the outcomes of anthropometric measures between South Asia and Sub-Saharan Africa (see also Klasen, 2007). Besides, several authors have demonstrated evidence that no real genetic differences exist between childrens' growth paths below the age of five in South Asia (see e.g. Gopalan, 1992; Eveleth and Tanner, 1990; Svedberg, 2000; Svedberg, 2002), which suggests that these differences are caused by other factors, although a final conclusion concerning the influence of genetic factors on childrens' growth paths is not yet possible. Third, the relative higher rates of child mortality in Sub-Saharan Africa than in South Asia can partly be explained through the fact that Sub-Saharan Africa is much more affected by diseases, among other things also due to climatic reasons. In addition, the high incidence of HIV /AIDS and Malaria can potentially explain a part of the Enigma, but a further assessment of this effect is strongly constraint by data availability. Fourth, the primary health care provision and other public services are possible 1 Brazil, Oman, Ghana, India, USA, and Norway. For more information on the new reference standard see Chapter 2. 54 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDER-NUTRITION explanations, which is less adequately provided in Sub-Saharan Africa (Svedberg, 1999; Ramalingaswami et al., 1996). Fifth, a further explanation i11 that the same determinants of child mortality and undemutrition may have different impacts in the two regions or that both phenomena are not as closely related as generally assumed (see e.g. Seckler, 1982; Messer, 1986). 2 Explaining the different relationships of child mortality and undemutrition between these two forms of deprivation within a country and also between countries and regions has important policy implications, as it supports a much more detailed assessment of required policy interventions to fight child mortality and undemutrition in order to reach the MDGs. But approaches using aggregated•macro-data have not been able to explain this regional puzzle appropriately. So far, we find no attempts to explain the South Asia -Sub-Saharan Africa Enigma from a microeconomic perspective that have analyzed the socio-economic determinants simultaneously for child mortality and undemutrition with the focus on their differences and similarities using micro-data. This paper analyzes the regional puzzle of child mortality and undemutrition between South Asia and Sub-Saharan Africa. The aim of the paper is helping to explain the South Asia -Sub-Saharan Africa Enigma using micro-data. To achieve this, we address three main issues concerning the explanation of the Enigma. First, we analyze the relationship between child mortality and undemutrition. We simultaneously try to find socio-economic determinants that affect child mortality and undemutrition. In particular, we try to find out, which determinants drive undemutrition as well as child mortality in a similar way and what factors have differing effects on both phenomena. Identifying determinants that drive both phenomena in a different way can than help to explain the Enigma. Second, analyzing the determinants of child mortality and undemutrition, we concentrate also on region-specific differences both in the outcomes and determinants of both phenomena. This allows us to identify major differences that drives the puzzle of child mortality and undemutrition in the two regions. Third, we also focuss on country-specific differences. Especially, if countries differ in the outcomes of socio-economic characteristics that have different impacts on child mortality and undemutrition. In addition to these three issues, we argue that sCK.io-economic characteristics at the community level (e.g. infrastructure) play an•important role both for child mortality and undemutrition, but standard regression models do not allow to incorporate these higher-level information appropriately. Therefore, in contrast to most cross-country studies that investigate the determinants of child mortality and undemutrition, we introduce the methodology of multilevel mod- 2In particular, the assumed small relattonship between child mortality and UljQ~mutritton goes back to the so-called 'small but healthy' hypothesis, which claims that populatiQns adapt to different physical and socio-economic environments and that individuals can adapt to lower levels of energy and protein intakes without suffering from functional deteriorations (Seckler, 1982). 3.3. EMPIRl{)AL ANALYSIS 61 3.3 Empirical Analysis 3.3.1 Data Description To obtain possible explanations about the regional differences in child mortality and undernutrition between South Asia and Sub-Saharan Africa, we analyze a sample of five countries from these regions. We use nationally representative DHS data that provide information on anthropometric outcomes of children, information about access to the health system, and other information about the socioeconomic status of children below the age of five and the mothers (aged between 15 and 49). The DHS data sets also contain information on cluster characteristics, especially on infrastructure. This information is included in the service availability recodes that are available for the South Asian countries Bangladesh (2000) and India (1999) and in Sub-Saharan Africa for Mali (2001), Uganda (1995), and Zimbabwe (1994). In total, our sample contains more than 53.000 children in South Asia and more than 29.000 children in Sub-Saharan Africa. The underlying theoretical framework for the choice of the dependent and independent variables to study child mortality and undemutrition, i.e. the underlying determinants, closely follows the analytical framework proposed by Moisey and Chen (1984) to study child survival. The idea of this framework is the assumption that social, economic, demographic, and medical determinants, i.e. the proximate determinants, affect the survival probability of the children through a set of biological mechanism. The proximate determinants are grouped at different hierarchial levels, i.e. the individual, household, and community level. In this analysis, the Mosley and Chen (1984) framework is combined with the conceptual framework to study the causes of child undemutrition proposed by the United Nations Childrens' Fund (UNICEF, 1990), which is based on assumptions similar to the Mosley and Chen ( 1984) framework, and the subsequent extended model of Engle et al. ( 1999), which implements also the provision of health care capacities of households into the analysis of childrens' welfare. As dependent variables, we use two dummy variables. For child mortality, the dummy is used whether the child died in the first year of life. 13 To measure child undemutrition, the DHS data sets provide information on several anthropometric outcomes of children, in particular the z-scores for weight for age, weight for 13 To capture the whole birth history of the children, we do not consider child mortality of children below the age of five because this throws out to many observations. We do not explicitly separate between neonatal deaths (child died in the first month) and post-neonatal death (child died between the first month and the first year of life (Adebayo et al., 2004) because this did not change the results. 62 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION height, and height for age. 14 In line with the dependent variable for child mortality, as dependent variable for child mortality, we use a dummy variable whether the child is stunted, that is, whether the stunting z-score (height for age) is below -2 standard deviations from the median of the reference population (WHO, 1995, 2006). 15 In addition, we also use the stunting z-scores as the dependent variable to study the determinants of undemutrition. In the empirical model, we include a set of several individual and household characteristics as well as cluster characteristics that might have an effect on the two outcome variables. For the individual characteristics, besides the household size and the number of children in the household, we include the age and sex of the child in the regression equation. The nutritional status is supposed to worsen non-linearly with increasing age of the child, and with the sex variable we control for sex differentials in mortality and undemutrition in our countries as is often to be found in the empirical literature on child mortality and undemutrition (for example, see Marcoux, 2002; Klasen, 1996). Another major determinant focussing especially on child mortality is the question whether the child was immediately breastfed after birth. Breastfeeding in the first month of life plays an important role for the development of the child because the breastmilk meets most of the childs' nutritional needs and increases the childs' resistance against diseases (Ramalingaswami et al., 1996). To avoid the problem of endogeneity when including breastfeeding and the birth order number of the child in our regression model, i.e. that these variables are affected by the age of the child, we include a dummy whether the child was breastfed immediately after birth and a dummy whether the child is the first born child in the household. In addition, we include also a variable that shows the preceding birth interval, and a variable that indicates whether the vaccination process of the child is completed, which is expected to decrease the mortality risk of the child. To avoid the problem of endogeneity, the dummy whether the vaccination process is completed is defined as follows: the first 2 month after birth are not considered as incomplete if no vaccinations were received, for the age between 3 and 6 months the dummy is one if the child has received at least 3 vaccinations, for the age between 7 and 9 months if the child has received at least 6 vaccinations and between l O and 12 months if the child has received all 8 vaccinations. 14 The z-score is defined as: z = AI;~aMAJ, where Al; refers to the individual anthropometric indicator (height for age -stunting, weight for height -underweight, weight for age -wasting), MAI refers to the median of the reference population, and CJ refers to the standard deviation of the reference population. For example, the stunting z-scores are the outcomes of the ratio of height over age minus the median of the reference population and the standard deviation of the reference population (see e.g. Klasen, 2003, 2007; Smith and Haddad, 2000). 15 We also consider the case of extreme stunted children where the z-score is below -3 standard deviations of the height for age reference. 3.3. EMPIRICAL ANALYSIS 63 Concerning the mother, the educational level of the mother enters the regression equation. The argument here is twofold. First, more-educated women might be able to better process information and to acquire skills in order to take care of the children, for example in the case of illness, and second, better-educated women are in a better position to earn money. In addition, the nutritional status of the mother is included, which is supposed to strongly affect the nutritional status of the child. 16 In particular, a bad nutritional status is expected to have a severe negative impact especially on the nutritional status of the child. Strong empirical evidence exist that show a high risk of low birth weight and birth height for children whose mother has suffered by a bad nutritional status (see e.g. Rao et al., 200 I; Hasin et al., 1996; Smith and Haddad, 2000). As a proxy for the status of women within the household, we include the age of the mother at the time of the survey. To take into account the household structure, we include the household size into the model. As a strong argument can be made that the household size is endogenous, the household size enters based on an instrumental variable approach, where the mean household size in the respective cluster is used as instrument. As we do not have information on income or expenditure in the DHS, we consider an asset-based approach in defining well-being (Sahn and Stifel, 2001). For this, we use a principal component analysis on several household assets proposed by Filmer and Pritchett (2001) to derive an index that indicates the material status of a household. In particular, as the components for the asset index, we include dummies whether the following assets exist or not: radio, TV, refrigerator, bike, motorized transport, low floor material, toilet, drinking water. Of course, one could include the assets separately into the regression, but the use of an aggregate index has two main reasons: First, it provides an income proxy of the household, which can be used to analyze distributional differences of outcomes in child mortality and undernutrition. Second, as the assets are correlated, their coefficients are likely to provide no significant effects if they are included separately, which would however lead to misleading interpretations of the estimation results. We then introduce another index into the analysis, which includes information on the access to health facilities of the household. Again, this is based on a principal component analysis using dummies whether the mother has received a tetanus vaccination before birth, whether the mother has received prenatal care, and whether the child was born at home without assistance of a doctor or a nurse. We assume that the access to health facilities is a crucial determinant both for child mortality and undernutrition. This index captures both the potential access 16 The recommend method to measure the nutritional status of adults is the body mass index (BM!), which is calculated by BM/= weight(kg)/height 2(m2 ). A mother is considered as malnourished if her BM! is less then 18.5 64 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION opportunities to the health system as well as real outcomes, which means that the child or the mother have really benefited from the services. To make the coefficients of the asset index and the access to health facility index comparable across regions and countries, the calculation of both indices are based on a global data set, which includes all country samples. Besides the individual and household characteristics, we include cluster variables. 17 In this context, the multilevel model distinguishes two different kinds of variables, namely contextual variables and global variables. Contextual variables at higher levels are variables that are simply the aggregates of the covariates at the individual level for each cluster. For example, we include the percentage of women with secondary education per cluster and the percentage of children that had recently suffered from fever per cluster. The global variables are part of the service availability recode and are not drawn from information of the individual level. In our case, these global variables provide information about the infrastructure in the cluster. We include the distance to the next health facility, which might be important for the access to heath services, and a public infrastructure index that is based on the availability of general facilities like a bank, a cinema, a post office, primary and secondary schools, a telephone, and public transportation. The weights for the index again are determined by a principal component analysis. 3.3.2 Descriptive Statistics As can be seen in Table 3.1, the South Asia -Sub-Saharan Africa Enigma of anthropometric failure and mortality reversals is clearly discemable in our five data sets. Relatively higher undemutrition rates in both South Asian countries coincide with relatively lower infant mortality rates than in the three Sub-Saharan African countries. For example, whereas the infant mortality rate in Mali is 1.9 times higher than in India ( 149 compared to 79), the stunting rate in India is 15 percent higher than in Mali ( 43.17 compared to 37 .61 ). This result is independent of the measure for undemutrition (i.e. stunting, wasting, underweight or the composite index of (severe) anthropometric failure (CIAF/CISAF) that indicates undemutrition by any of the preceding measures). 18 For example, whereas Bangladesh and Zimbabwe show almost equal mortality rates (80 compared to 75), the rate of severely underweight children is four times higher in Bangladesh than in Zimbabwe (13.12 and 3.26, respectively). This picture is not changed by the use of the new multi-country growth reference standard that was published by the WHO 17 1n the case of India, the service availability recode contains information on districts instead of clusters. 18 In particular, CIAF and CISAF indicate whether a child is (severely) undernourished by either stunting, wasting, or underweight. 3.3. EMPIRICAL ANALYSIS 65 in 2006. Prevalence rates using this new reference standard are shown in parentheses. Table 3.1 shows that the anthropometric indicators that are based on the new reference standard lie all above the indicators based on the old reference standard. For example, the stunting rate in Bangladesh increases from 44. 12 percent to 50.82 percent. However, this level effect does not change the picture of the Enigma, because the ratios of anthropometric indicators between South Asia and Sub-Saharan Africa do not change very much. For instance, the stunting ratio between India and Mali is 1.16 for the old standard and 1.17 for the new standard. This result suggests that the use of new reference standard instead of the old reference standard is not able to solve the Enigma alone. Table 3.1: Infant Mortality and Anthropometric Indicators (percentage) Bangladesh India Mali Uganda Zimbabwe 2000 1999 2001 1995 1994 Infant mortality Infant mortality* 80 79 149 99 75 U ndemutrition* * Stunting 44.12 43.17 37.61 35.19 22.24 (50.82) (50.75) (43.06) (42.37) (30.09) Wasting IO.SO 14.99 10.97 5.11 5.56 (13.47) (20.09) (13.83) (7.34) (7.30) Underweight 47.32 43.77 34.14 23.38 16.30 (41.48) (40.62) (30.62) (19.81) (12.54) Severe stunting 18.12 21.39 19.01 13.26 6.18 (24.39) (30.31) (24.44) (19.20) (10.28) Severe wasting I.OS 2.84 1.72 0.85 0.86 (3.36) (8.49) (5.48) (3.02) (2.99) Severe underweight 13.12 15.92 11.66 6.12 3.26 (14.41) (18.21) (12.97) (7.19) (3.39) CIAF*** 56.63 57.21 47.86 41.21 29.82 (61.10) (63.58) (52.05) (47.01) (36.09) CISAF 22.16 27.33 22.85 15.39 8.05 (29.64) (38.37) (29.42) (22.37) (13.26) Source: Demographic and Health Surveys (DHS); own calculations. Note: *Child mortality shows the number of dead children per 1000 of children under one year of age who died within the last 12 month. **Children are considered a~ wasted, stunted, or underweight if the respective zwscores are below -2 standard deviation from the median of the reference category (WHO. 1995). If the z-scores are below -3, children are considered as severely undernourished. The numbers in parentheses refer to the new reference standard for child anthropometric failure that was published by WHO in 2006 (WHO, 2006). ***CIAF and CISAF refer to the composite index of (severe) anthropometric failure that indicates wbether a child is (severely) undernourished by either stunting, wasting, or underweight. Table 3.2 presents summary descriptive statistics of individual, household, and community characteristics four the five countries of our sample. Besides some 66 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION Table 3.2: Summary Statistics for Individual, Household and Community Characteristics Bangladesh India Mali Uganda Zimbabwe 2000 1999 2001 1995 1994 Total number of children in OHS 6.944 46.569 14.328 5.799 2.438 Individual characteristics Age (month) 28.79 17.14 28.56 22.63 17.50 Immediate breastfeeding ( % ) 35.05 25.81 42.40 59.13 53.08 Household characteristics Household size 6.79 7.41 7.35 6.59 6.89 Total number children 3.11 2.90 4.67 4.16 3.54 Female headed household (%) 5.36 6.53 8.70 20.28 32.77 Household has TV(%) 17.98 38.37 16.07 5.87 12.29 Radio(%) 31.52 41.73 73.06 49.19 43.34 Flush toiled(%) I0.93 25.32 7.19 3.21 22.38 Piped drinking water(%) 6.37 40.04 27.06 12.58 31.27 Mothers· education (years) 3.19 3.90 0.91 4.13 6.38 Mother has no education ( % ) 45.36 50.14 83.68 26.38 12.92 Mother has primary education(%) 28.96 16.21 11.33 57.76 51.27 Mother has secondary education (%) 21.18 24.45 4.62 15.67 34.58 Age at first birth 17.67 19.35 18.21 18.11 18.96 BM! of mother 19.42 19.83 22.07 21.92 23.00 BMI of mother <18.5 (%) 41.63 34.85 8.33 7.77 5.28 Community characteristics Number of vaccinations 5.44 4.65 3.83 5.03 5.82 Birth assistance Assistance at birth**(%) 14.31 44.39 22.43 44.16 67.80 Prenatal care(%) 22.51 62.88 20.88 90.46 93.55 Tetanus vaccination (%) 62.37 75.79 31.37 81.25 82.53 Born home w/o assist.(%) 85.24 55.08 60.85 22.77 31.17 Distance to health facility*** 47.67**** I0.05 7.87 8.69 16.19 Children had fever recently(%) 37.81 30.27 31.08 48.17 37.73 Source: Demographic and Health Surveys (DHS); own calculations. Notes: *Child was breastfed immediately after birth. **By doctor or nurse. ***Distance to hospital and clinic in kilometers. ****As the distance to the next health facility is not included in the data set for Bangladesh the time in minutes to next health facility is used. observed similarities in the household and child characteristics, Table 3.2 shows also large differences in the covariates both between South Asia and Sub-Saharan Africa and within these regions between countries. First, looking at regional differences in the individual, household, and community characteristics between South Asia and Sub-Saharan Africa, they provide first insights of possible explanations of the Enigma. For example, Table 3.2 shows that both the share of mothers who breastfed their children immediately after birth and the share of female headed households are considerably higher in Sub-Saharan Africa than in 3.3. EMPIRICAL ANALYSIS 67 South Asia. If female headed households are less able to care for their children, than the high share in Sub-Saharan Africa can contribute to the existing relative high rates of child mortality. In addition, Table 3.2 shows also very large regionspecific differences in the nutritional status of the mothers, which is much worse in South Asia than in Sub-Saharan Africa. In particular, whereas in Bangladesh 41.63 percent of mothers have a BMI less than 18.5, in Zimbabwe 'only' 5.28 percent are malnourished. This regional disparity is also reflected in the low mean values of the BMI of mothers in South Asia compared to countries in Sub-Saharan Africa. This regional pattern is very interesting since the nutritional status of the mother is expected to have a strong negative impact on the nutritional status of the child, which would then help to explain the Enigma. Various empirical studies exist, which analyze the reasons for the high prevalence of malnourished mothers in South Asia. Primarily, gender differences in the socio-economic status of women are identified as the most important determinants of the low BMI of mothers in Bangladesh, particularly in urban areas (see e.g. Ahmed et al., 1998). In India, women in rural areas are more likely to work full-time in farming activities than men and carry also the burden of the work in the household, which often results in chronic fatigue and undemutrition (see e.g. Barker et al., 2006). If the nutritional status of the the mother increases the risk of child undemutrition, this would then contribute to explain the higher rates of stunting in South Asia compared to Sub-Saharan Africa and contributes to explain the Enigma. Second, country specific differences between covariates provide also interesting information regarding the explanation of the Enigma. For example, although an overall low level of mothers' education is observed for all five countries, in Mali, which was found to be country with highest mortality rates in our sample, the mean years of education of mothers is less than I year, compared to 6.38 years in Zimbabwe, where mortality rates and rates of child undemutrition were found to be rather low. Hence, given that Bangladesh, India, and Zimbabwe show relatively high levels of education and low levels of child mortality, this suggests that the educational attainment might be an important determinant for the survival probabilities of children. However, as argued in the previous section, the educational level of mothers is also expected to have an important impact on the nutritional status of the child, but Bangladesh and India show the highest rates of undemutrition. Other glaring differences between countries are found for some community characteristics, particular the access to health infrastructure. Bangladesh has the lowest rates of assistance at birth ( 14.31) and of prenatal care (22.51 ), Zimbabwe has the best access to birth assistance in the sample, with 67.8 percent of mothers who have received assistance at birth and even 93.55 percent who received prenatal care. Looking at the difference in stunting and child mortality, this result suggests a strong influence of access to health care on both outcomes of childrens' welfare. 68 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION As mentioned before, the lack of income data necessitates the use of a wealth index as a proxy for incomes or consumption. To avoid using arbitrary weights, we use a principal component analysis, which implies that the weights are equivalent to a measure of the degree of correlation between each factor and a hidden component (in our case wealth). First, the results of the principal component analysis, which are presented in Table A. I in Appendix A, show that all weights for the factors have the assumed sign, giving positive values to durable goods like TV and radio and negative values to the lack of a toilet facility or the use of surface drinking water. Second, also when we look at the weights of our health facility index, it can be seen that the principal component analysis determines weights with the 'right' signs, which is shown in Table A. I in Appendix A. Positive weights are generated, therefore, for the dummies for a tetanus vaccination of the mother before birth, and for prenatal care. A negative value is generated for the dummy whether a child was born at home without the assistance of a doctor or a nurse. In addition, the results of the principal component analysis show that both factors, wealth and access to health facilities are strongly correlated with child mortality and undernutrition. First, Table 3.3 reflects the differences in the levels of child mortality and undernutrition between the two regions and between countries, and confirms the Enigma. Second, Table 3.3 show that both phenomena are a lot more prevalent in the lower quintiles of both indices meaning that the poor population is much more affected by child mortality and undernutrition than the non-poor population. As the distribution of both phenomena over the indices shows a similar pattern across regions and countries, a particularly strong connection is observable between access to health facilities and child mortality indicating that the development of the health care system is a very strong determinant for probability of the child to survive. Here, for example, the ratio of the first to the fifth quintile in Bangladesh is 9.70, and India, Uganda, and Mali even show no child mortality for their respective fifth quintile. 3.3.3 Regression Results This section presents the regression results and discusses possible explanations of the South Asia -Sub-Saharan Africa Enigma that were asked in Section 3.1.2. First, it starts with comparing the multilevel approach to standard regression models when analyzing child mortality and undernutrition. Second, to shed more light on the South Asia -Sub-Saharan Africa Enigma, this section discusses the differences in the determinants of child mortality and undernutrition to explain the relationship between both phenomena. In particular, the results provide us with information about socio-economic characteristics that determine child mortality and undernutrition in a similar way and characteristics that determine both phenomena in different ways, which helps to explain the Enigma. Third, regional dif- 3.3. EMPIRICAL ANALYSIS 69 Table 3.3: Mortality and Stunting by Asset and Access to Health Facility Index (percentage) Ratio Quintile I Quintile 2 Quintile 3 Quintile 4 Quintile 5 1/5 Asset index Infant monality Bangladesh 2000 9.26 8.21 8.03 8.12 6.33 1.46 India 1999 10.32 8.43 7.64 7.20 5.69 1.81 Mali 2001 16.54 16.41 15.12 14.41 11.92 1.39 Uganda 1995 11.27 11.16 9.86 9.21 8.46 1.33 Zimbabwe 1994 8.98 7.74 6.50 6.50 8.05 1.12 Stunting Bangladesh 2000 54.49 49.46 43.01 32.81 16.74 3.26 India 1999 53.11 47.48 44.21 41.16 33.15 1.60 Mali 2001 46.84 40.70 40.62 37.20 23.83 1.97 Uganda 1995 41.57 37.98 31.10 21.77 16.48 2.52 Zimbabwe 1994 22.13 28.12 25.40 20.90 12.43 1.78 Access to health facilities index Infant monality Bangladesh 2000 21.72 7.25 7.51 0.81 2.24 9.70 India 1999 15.16 10.80 8.97 3.80 0 n.c. Mali 2001 21.59 17.29 16.07 11.15 8.08 2.67 Uganda 1995 19.98 16.99 11.12 1.07 0 n.c. Zimbabwe I 994 23.84 6.52 6.50 0 0 n.c. Stunting Bangladesh 2000 56.62 45.90 46.73 31.90 17.51 3.23 India 1999 56.21 54.31 44.64 37.66 31.89 1.76 Mali 2001 43.06 39.85 35.21 21.27 17.16 2.51 Uganda 1995 36.96 39.54 36.32 34.36 33.94 1.09 Zimbabwe I 994 27.71 26.60 21.61 22.52 21.25 1.30 Source: Demographic and Health Surveys (DHS); own calculations. Notes: Infant monality shows the percentage of children under I year of age who died within the last 12 months compared to all children under one year of age living in the respective quintile. The stunting rate shows the percentage of stunted children in the respective quintile compared to all children under 5 years of age. A child is considered as stunted if the height over age z-score is below -2 standard deviations from the reference category. The asset index is calculated based on a principal component analysis. As variables to calculate the asset index, dummies are included whether the following assets exist or not in a household: radio, TV, refrigerator, bike, motorized transport, low floor material, toilet, drinking water. Quintile I corresponds to the poorest and quintile 5 to the richest population sub-group. ferences between South Asia and Sub-Saharan Africa and also country-specific differences in the regression results are discussed concerning possible explanations of the Enigma. As mentioned before, we use a multilevel model approach to examine the influence of individual, household, and community socio-economic characteristics on child mortality (Table 3.4) and child undernutrition (Tables 3.5 and 3.6). The use of a multilevel approach instead of standard regression models insures that we avoid misleading significance effects due to violations of the assumption 70 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION of independent errors with a constant variance. This effect is confirmed in our regression results in which the multilevel regressions display lower levels of significance compared to the OLS regression and the logit regression with the same model specification, which are presented in Tables A.2-A.6 in Appendix A. The standard errors for the multilevel regression are higher than the standard errors for the logit and OLS regressions for mortality and for undernutrition both using a dummy whether the child is stunted and also using the stunting z-scores as dependent variable. Especially, in the case of community characteristics, a strong reduction in significance levels is observable, whereas for the individual and household characteristics the differences in significance of the coefficients are rather low. This means that the multilevel approach allows a more reliable analysis of the determinants of our household survey data through explicitly incorporating the hierarchical data structure of the data and community information. Turning to the estimation results and possible explanations of the South Asia -Sub-Saharan Africa Enigma, Tables 3.4-3.6 show the regression results for infant mortality and stunting both for the old and the new reference standard. As expected, the age of the mother has a significant non-linear negative influence on child mortality in all cases, meaning that the number of child deaths decreases non-linearly with age, which reflects the increasing experience of mothers to take care of their children when they got older compared to very young mothers. At the same time, the age of the child influences undernutrition positively in a well known non-linear way in all countries as is also shown in Figure 3.1, which presents the stunting z-score by age for the respective country and reference standard.19 Figure 3.1 shows that the stunting z-scores strongly decrease within the first two years of live and then remain constant or starts to raise slowly. This pattern of decreasing z-scores in early childhood can be explained by the critical phase for the child when the mother replaces breast milk to complementary food or liquids. If the mother stops breastfeeding, the child is exposed to malnutrition and diseases, particularly if the complementary food is nutritionally inadequate or not hygienic (see e.g. UNICEF, 1998; Klasen, 2007). Again, as described in Section 3.3.2, Figure 3.1 demonstrates the differences in the levels of the z-scores between the old and the new reference standard to calculate the z-scores. Concerning the relationship between child mortality and undernutrition and their respective determinants, Tables 3.4 through 3.6 show that mortality and undernutrition have both very similar determinants across countries and also determinants that affect both phenomena in different way. The results are confirmed when using the stunting z-scores as dependent variable. Very similar effects across countries are found when looking at individual characteristics like immediate 19 For India and Zimbabwe, the OHS provide information on anthropometric indicators for children only until the age of 3 years. 3.4. CONCLUSION 77 erted by the access to health facilities. Although the influence on stunting was also very significant, the magnitude was by far not as large. At the same time, we confirmed the preceding results that increases in material wealth will result in significant reductions of undernutrition and mortality. Even stronger improvements in the incidence of undernutrition could be generated by increases in the level of education of mothers, which has only a limited positive effect on changes in mortality rates by it own. 3.4 Conclusion In this paper, we analyzed the regional puzzle of child mortality and undernutrition between South Asia and Sub-Sahara Africa. We investigated the effects of individual, household and community socio-economic characteristics on child mortality and undernutrition using a multilevel approach for a set of five developing countries in South Asia and Sub-Saharan Africa. We find strong evidence supporting the existence of the South Asia Sub-Saharan Africa Enigma using micro-data. The results show large differences in the detenninants and their significance levels between mortality and undernutrition, across the two regions, and also between countries. While finding very similar patterns across countries indicating a close relationship between child mortality and undernutrition, we also find that the detenninants of mortality and undernutrition differ significantly from each other, which helps to explain the Enigma. And although very similar patterns in the detenninants of each phenomenon are discernable, there are large differences in the magnitude of the coefficients. Accounting for the high rates of child mortality in Sub-Saharan Africa, strong evidence is found that the access to health infrastructure is more important for mortality than for undernutrition and the impact is of greater magnitude in Sub-Saharan Africa than in South Asia, whereas the individual and household characteristics like wealth and educational attainment and nutritional characteristics of mothers play a larger role for anthropometric shortfalls. Especially, the nutritional status of mothers has a greater effect in South Asia accounting partly for the high rates of undernutrition, since the overall nutritional status of the mother is even worse in South Asia than in Sub-Saharan Africa. As our study has also shown, there are detenninants at the community level that have a significant influence on mortality as well as on undernutrition like the percentage of children with fever or public infrastructure. Besides, regressions using a combined data set of all five countries show that there are still significant unexplained differences between the two regions although taking account of a large set of covariates. Therefore, our results are only partly be able to solve the Enigma. One hypothetical explanation for the regional differences remains the quality of the data. There might be biases and errors especially in the African data sets. 78 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION However, these biases cannot account for the differences in the determinants of both phenomena, since the same data sets and explanatory variables are used for the explanation of child mortality and undemutrition in all countries. Explaining the high rates of child mortality in Sub-Saharan Africa, clearly unobserved socio-economic characteristics play an important role. For example Klasen (2003) emphasizes the importance of the mortality decreasing effect of lower fertility. Another possible explanation for the Enigma might lie in the different prevalence of diseases like HIV/AIDS and Malaria, which heavily affects countries in Sub-Saharan Africa. Therefore, given the data constraints on infectious diseases and their consequences at the individual level, further studies should try to estimate the impact of HIV/ AIDS and other diseases on mortality rates. Future research should also try to capture differences in the quality of health facilities and in the composition of nourishments. Accounting for the high rates of undemutrition in South Asia, one possible explanation might still be how undemutrition is measured. Klasen (2007) suggests that measurement issues play a significant role for the high rates of undemutrition in South Asia. He identifies both the arbitrary cut-off of a z-score of -2 and the reference standard by the WHO (old and new) to calculate the z-scores as two important issues that might lead to an overestimation of undemutrition in South Asia. In particular, he emphasizes that already small genetic differences in the growth potential of children in South Asia considerably overestimate undemutrition in South Asia. Therefore, a closer investigation of the role of the genetic differences as a possible explanation for the high rates of undemutrition in South Asia is of high priority. Another reason for the high rates of undemutrition in South Asia could also be the effects of past undemutrition of the mothers during their childhood, which might has an impact on the status of their children, even if they grow up an a healthy and wealthy environment (Klasen, 2007). To investigate further explanations of the Enigma, high research priority should also be given on other factors like interactions and non-linearities between risk factors and that they might effect child mortality and undemutrition in a rather multiplicative than additive way (see e.g. Pelletier, 1994 ). Finally, one part of the explanation of the South Asia Sub-Saharan Africa Enigma could be the insight that child mortality and undemutrition are not as closely correlated as generally assumed. Our study finds considerable evidence for large differences in the determinants of both phenomena. These differences make it highly unlikely that child mortality and undemutrition are as closely correlated as found by the studies of Pelletier et al. (1995, 2002) and cited by numerous other publications. And in order to achieve both Millennium Development Goals concerning child mortality and undemutrition it is, therefore, important that both phenomena are taken into account as separate goals, which are to be achieved by different policy measures. 3.4. CONCLUSION 79 Table 3.4: Regression Results of Infant Mortality (Multilevel Regression) Bangladesh India Mali Uganda Zimbabwe 2000 1999 2001 1995 1994 Constant -3.188** -2.591 ** -2.109** -2.234** -2.790** (0.111) (0.043) (0.061) (0.118) (0.222) Age (mother) -0.116** -0.224** -0.152** -0.094 -0.182 (0.062) (0.031) (0.034) (0.067) (0.157) Age2 (mother) 0.170* 0.352** 0.241 ** 0.133* 0.353 (0.106) (0.054) (0.053) (0.111) (0.260) Sex of child ( I =female) -0.158 0.055 -0.112* -0.070 -0.410* (0.106) (0.047) (0.061) (0.110) (0.231) Immediate breastfeeding (=I) -0.429** -0.491** -0.179** -0.061 -0.501* (0.125) (0.068) (0.064) (0.115) (0.231) Complete vaccination (=I) -2.458** -2.531 ** -2.008** -2.003** -3.683** (0.227) (0.070) (0.130) (0.199) (0.393) First born (=I) 0.295 -0.057 -0.095 0.153 0.299 (0.187) (0.084) (0.116) (0.200) (0.403) Preceding binh interval -0.005 -0.006 -0.015** -0.009* 0.013* (0.003) (0.002) (0.002) (0.005) (0.005) Household size (IV) 0.200** 0.105** 0.016** 0.067 0.096 (0.053) (0.024) (0.005) (0.049) (0.088) Female headed household (=I ) 0.055 0.100 -0.079 -0.076 -0.012 (0.258) (0.100) (0.111) (0.147) (0.251) Asset index -0.167 -0.098** -0.053 -0.145 -0.015 (0.114) (0.024) (0.046) (0.111) (0.147) BMI of mother -0.042* 0.051 0.026* -0.045 0.008 (0.025) (0.010) (0.012) (0.023) (0.040) BM12/l 00 of mother 0.484 0.040 -0.281* 0.243 -0.056 (0.280) (0.122) (0.159) (0.318) (0.360) Mother has sec. education (=I) -0.174 -0.084 -0.313 -0.523** 0.057 (0.159) (0.064) (0.228) (0.207) (0.003) Health facility index -0.647** -0.326** -0.400** -0.287** -0.186** (0.090) (0.030) (0.048) (0.086) (0.179) Community characteristics Distance to health facility*** -0.003 0.001 0.002 0.009 0.003 (0.002) (0.004) (0.003) (0.005) (0.007) Secondary education (%) -0.661 -0.591* -0.394 0.173 -0.647 (0.458) (0.301) (0.528) (0.472) (0.658) Children had fever(%) 0.775* -0.330 0.124 0.542* 0.545 (0.370) (0.357) (0.220) (0.301) (0.573) Public infrastruct. index 0.046 -0.002 0.012 0.167* 0.317* (0.070) (0.036) (0.046) (0.079) (0.149) crJ 0.169 0.255 0.172 0.274 0.698 (0.086) (0.042) (0.041) (0.108) (0.375) R2 0.099 0.128 0.068 0.068 0.159 Obs. (level I ) 5381 28539 9852 4232 1.568 Obs. (level 2) 339 426 371 357 229 Source: Demographic and Health Surveys (DHS); own calculations. Notes: *P-value<O. I. **P-value<0.01. For details about the variables, see Section 3.3.1. er,; refers to the variance of the residual errors of the intercepts at the household level (level 2). ***In the case of Bangladesh distance is measured in time (hours). The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used. 80 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION Table 3.5: Regression Results of Stunting (Old Reference Standard) (Multilevel Regression) Bangladesh India Mali Uganda Zimbabwe 2000 1999 2001 1995 1994 Constant -0.601** -0.326** -0.644** -0.776** -1.459** (0.050) (0.030) (0.049) (0.075) (0.l02) Age (mother) 0.l08** 0.247** 0.156** 0.169** 0.305** (0.008) (0.006) (0.006) (0.012) (0.031) Age2 (mother) -0.001 •• -0.005** -0.002** -0.003** -0.006** (0.000) (0.000) (0.000) (0.000) (0.001) Sex of child (I =female) 0.027 0.008 -0.071 -0.241** -0.049 (0.061) (0.025) (0.049) (0.069) (0.115) Immediate breastfeeding (=I ) -0.131* -0.010* -0.120* -0.052 -0.072 (0.065) (0.031) (0.052) (0.071) (0.118) Complete vaccination (=I) -0.051 -0.091 -0.111 -0.568 -0.189 (0.139) (0.434) (0.104) (0.134) (0.297) First born (=I) -0.241* -0.291** -0.335** -0.213* -0.254 (0.100) (0.041) (0.088) (0.118) (0.196) Preceding binh interval -0.008** -0.005** -0.010** -0.007** -0.004 (0.002) (0.001) (0.002) (0.002) (0.003) Household size (IV) 0.034 0.058** -0.003 -0.047* 0.055 (0.029) (0.016) (0.005) (0.027) (0.039) Female headed household (=I ) -0.161 -0.140** -0.049 0.057 -0.126 (0.137) (0.052) (0.089) (0.089) (0.126) Asset index -0.430** -0.178** -0.090* -0.203** -0.127* (0.064) (0.013) (0.039) (0.069) (0.070) BM! of mother -0.090** -0.065** -0.075** -0.073** -0.060** (0.0!5) (0.005) (0.010) (0.015) (0.020) BMI21100 of mother 0.309 -0.084* -0.130 -0.092 0.294 (0.217) (0.082) (0.145) (0.258) (0.253) Mother has sec. education (=I) -0.354** -0.251 •• -0.561 •• -0.291* -0.346* (0.086) (0.031) (0.163) (0.116) (0.156) Health facility index -0.246** -0.243** -0.023 -0.061 -0.248** (0.047) (0.017) (0.036) (0.059) (0.086) Community characteristics Distance to health facility*** 0.001 -0.004 0.005* 0.006* 0.001 (0.001) (0.003) (0.003) (0.003) (0.003) Secondary education (%) -0.162 -1.520** -2.403** -1.280** -0.290 (0.260) (0.203) (0.484) (0.297) (0.345) Children had fever ( % ) 0.215 -0.582* 0.160* -0.421 * -0.206 (0.218) (0.268) (0.227) (0.186) (0.288) Public infrastruct. index 0.054 -0.021 -0.094 -0.074 -0.025 (0.042) (0.024) (0.046) (0.054) (0.081) crJ 0.097 0.252 0.326 0.134 0.082 (0.033) (0.025) (0.045) (0.042) (0.080) R2 0.109 0.140 0.118 0.106 0.121 Obs. (level I ) 5284 34797 9258 4518 2057 Obs. (level 2) 339 424 368 354 227 Source: Demographic and Health Surveys (DHS); own calculations. Notes: *P-value<O. l. **P-value<0.01. For details about the variables, see Section 3.3.1. crJ refers to the variance of the residual errors of the intercepts at the household level (level 2). ***In the case of Bangladesh distance is measured in time (hours). The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used. 3.4. CONCLUSION 81 Table 3.6: Regression Results of Stunting (New Reference Standard) (Multilevel Regression) Bangladesh India Mali Uganda Zimbabwe 2000 1999 2001 1995 1994 Constant -0.243** 0.089** -0.340** -0.356** -0.925** (0.046) (0.030) (0.046) (0.069) (0.091) Age (child) 0.107** 0. 152** 0.135** 0.127** 0.171** (0.007) (0.005) (0.005) (0.010) (0.023) Age2 (child) -0.001** -0.002** -0.002** -0.002** -0.003** (0.000) (0.000) (0.000) (0.000) (0.001) Sex of child (I =female) -0.060 -0.132** -0.162** -0.344** -0.209 (0.059) (0.023) (0.046) (0.065) (0.105) lmmediale breastfeeding (=I) -0.130* -0.039* -0.132* -0.093* -0.011* (0.063) (0.029) (0.049) (0.067) (0.107) Complete vaccination (=I ) 0.090 0.047 0.204 -0.012 0.177 (0.130) (0.038) (0.092) (0.198) (0.244) First born (=I) -0.173* -0.222** -0.204* -0.187* -0.309* (0.()96) (0.038) (0.082) (0.111) (0.175) Preceding birth inlerval -0.007** -0.004** -0.008** -0.008** -0.005 (0.001) (0.001) (0.001) (0.002) (0.003) Household size (IV) 0.012 0.044** -0.008 -0.043* 0.022 (0.029) (0.016) (0.005) (0.026) (0.038) Female headed household (=I ) -0.112 -0.084 -0.127* -0.066 -0.126 (0.132) (0.048) (0.084) (0.084) (0.114) Asset index -0.387** -0.160** -0.109** -0.202** -0.157** (0.061) (0.012) (0.036) (0.065) (0.068) BM! of mother -0.072** -0.056** -0.059** -0.064** -0.049** (0.014) (0.005) (0.009) (0.014) (0.018) BMJ2/JOO of mother 0.362* 0.000 -0.126 -0.404* 0.157 (0.155) (0.001) (0.129) (0.244) (0.216) Mother has sec. education (=I) -0.290** -0.223** -0.466** -0.175 -0.260* (0.081) (0.029) (0.143) (0.l07) (0.138) Health facility index -0.219** -0.222** -0.040 -0.085 -0.184* (0.046) (0.016) (0.034) (0.055) (0.080) Community characteristics Distance to health facility*** 0.001 -0.006* 0.004 0.010** 0.002 (0.001) (0.003) (0.003) (0.003) (0.003) Secondary education (%) -0.253 -1.278** -1.740** -1.042** -0.271 (0.258) (0.205) (0.437) (0.277) (0.325) Children with fever(%) 0.276 -0.586* 0.194 -0.470** -0.124 (0.217) (0.273) (0.218) (0.178) (0.276) Public infrastruct. index -0.005 -0.020 -0.060 -0.121* -0.042 (0.041) (0.024) (0.043) (0.051) (0.078) er,; 0.113 0.284 0.312 0.137 0.167 (0.034) (0.027) (0.042) (0.040) (0.079) R2 0.100 0.114 0.100 0.094 0.097 Obs. (level I ) 5503 37880 9750 4771 2114 Obs. (level 2) 339 424 368 355 227 Source: Demographic and Health Surveys (DHS); own calculations. Notes: *P-value<O. l. **P-value<0.01. For details about the variables, see Section 3.3.1. er,; refers to the variance of the residual errors of the intercepts at the household level (level 2). ***In the case of Bangladesh distance is measured in time (hours). The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used. 82 3. A REGIONAL PUZZLE OF CHILD MORTALITY AND UNDERNUTRITION Table 3.7: Global Regression oflnfant Mortality and Stunting (Multilevel Regression) Stunting Stunting Infant mortality (old ref. standard) (new ref. standard) Constant -2.694** (0.054) -0.445** (0.029) -0.036** (0.028) Age -0.158** (0.019) 0.147** (0.003) 0.126** (0.002) Age2 0.245** (0.031) -0.002** (0.000) -0.002** (0.000) Sex of child (!=female) -0.033 (0.033) -0.023 (0.019) -0.141** (0.018) Immediate breastfeeding (=I) -0.472** (0.057) -0.089** (0.025) -0.104** (0.024) Complete vaccination (=I) -2.438** (0.063) -0.102** (0.038) -0.020 (0.035) First born (=I) 0.025 (0.072) -0.297** (0.036) -0.235** (0.033) Preceding birth interval -0.005** (0.001) -0.005** (0.001) -0.004** (0.001) Household size (IV) 0.082** (0.014) 0.066** (0.008) 0.058** (0.007) Female headed household (=I) 0.036 (0.091) -0.143** (0.047) -0.096* (0.043) Asset index -0.070** (0.022) -0.167** (0.01 I) -0.156** (0.011) BMI of mother -0.038** (0.008) -0.076** (0.004) -0.062** (0.004) BMJ2/IOO of mother 0.008 (0.082) 0.020 (0.061) 0.D28 (0.052) Mother has sec. education (=I) -0.224** (0.055) -0.272** (0.026) -0.233** (0.025) Health facility index (global) -0.353** (0.022) -0.221** (0.013) -0.202** (0.012) Sec. education in cluster(%) -0.463* (0.182) -1.136** (0.089) -1.076** (0.084) Child. w. fever in cluster(%) 0.083 (0.190) -0.364** (0.100) -0.380** (0.095) Public infras. index -0.007 (0.023) -0.008 (0.011) -0.004 (0.011) Region fixed effects Sub-Saharan Africa (=I) 0.664** (0.080) -0.363** (0.126) -0.411 •• (0.074) Region interaction effects SSA • household size -0.070** (0.015) -0.070** (0.008) -0.065** (0.008) SSA * female headed hh -0.140 (0.121) 0.097 (0.070) -0.034 (0.066) SSA * vaccination 0.409** (0.116) 0.329** (0.077) 0.176* (0.069) SSA • first born -0.109* (0.111) 0.054 (0.072) 0.091 (0.067) SSA • preceding birth int. -0.004 (0.002) -0.002 (0.001) -0.001 (0.001) SSA * breastfeeding -0.342** (0.077) -0.013 (0.045) -0.003 (0.042) SSA • asset index (global) 0.002 (0.066) -0.094* (0.046) -0.152** (0.042) SSA * mothers' BMI -0.036** (0.032) 0.008 (0.008) 0.004 (0.007) SSA • health facility index -0.127** (0.062) -0.055 (0.043) 0.001 (0.040) SSA • sec. education (%) 0.008 (0.306) 0.335* (0.192) 0.510** (0.174) SSA • children w. fever(%) 0.140 (0.232) 0.081 (0.139) 0.110 (0.130) SSA • infra,. index (global) 0.044 (0.040) -0.030 (0.027) -0.037 (0.025) R2 0.110 0.125 0.110 Obs. 49572 55914 60018 Source: Demographic and Health Surveys (OHS); own calculations. Notes: *P-value<().I. **P-value<().01. The variables 'age' and 'age2• denote the age of the mother in the child mortality regression and the age of the child in the two undemutrition regressions. For more details on the variables, see Section 3.3.1. Essay 4 A Human Development Index by Income Groups Abstract: One of the most often heard 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 14 low and middle income countries shows that inequality in human development within countries is indeed high. The results also show that the level of inequality is not directly linked to the level of human development itself. based on joint work with Michael Grimm, Kenneth Harttgen and Stephan Klasen. 84 4. A HUMAN DEVELOPMENT INDEX BY INCOME GROUPS 4.1 Introduction The Human Development Index (HDI) is a composite index that measures the average achievement in a country in three basic dimensions of human development: a long and healthy life, as measured by life expectancy at birth; knowledge, as measured by the adult literacy rate and the combined gross enrollment ratio for primary, secondary and tertiary schools; and a decent standard of living, as measured by GDP per capita in purchasing power parity US dollars (UNDP, 2006). Based on available statistics UNDP was able to provide an HDI for 177 countries in the latest Human Development Report (UNDP, 2006). The HDI is today widely used in academia, the media and in policy circles to measure and compare progress in human development between countries and over time. Despite its popularity, which is among other things due to its transparency and simplicity, the HDI is criticized for several reasons. 1 First, it neglects several other dimensions of human well-being, such as for example human rights, security and political participation (see e.g. Anand and Sen (l 992), Ranis, Stewart and Samman (2006)). Second, it implies substitution possibilities between the three dimension indices, e.g. a decline in life expectancy can be off set by a rise in GDP per capita.2 Related to that critique is the third point, which charges that the HDI uses an arbitrary weighting scheme (see e.g. Kelley (l 99 l ), Srinivasan (1994) and Ravallion (1997)). For instance, why should education be worth as much as income or health? Finally and fourth, the HDI is often criticized because it only looks at average achievements and, thus, does not take into account the distribution of human development within a country (see e.g. Sagar and Najam (l 998)). When constructing distribution-sensitive measures of human development, limited data availability on the distribution of human development achievements seriously constrains the analysis. Household income surveys are today widely undertaken and, hence provide data on income distribution, but it is much more difficult to get data on inequality in life-expectancy, educational achievements and literacy. Inequality in these dimensions seems, at least in developing countries, also to be very high. There is also broad empirical evidence that mortality as well as educational attainment vary with income and wealth in both rich and poor countries (see e.g. Cutler, Deaton and Lleras-Muney (2006) and Filmer and Pritchett (l 999)). In the past several attempts have been made to integrate inequality into the human development index. Anand and Sen (l 992) and Hicks (l 997) suggested to discount each dimension index by one minus the Gini coefficient for that dimension before the arithmetic mean over all three is taken. Therefore, high inequality 1 For a critical review, see e.g. Sagar and Najam (l 998). 2Moreover, if poor people face higher mortality, their deaths would increase per capita incomes of the survivors, generating a further distortion, particularly in HDI trends over time. 4. I. INTRODUCTION 85 in one dimension lowers the index value for that dimension and, hence its contribution to the HDI. Although the idea of such a discount factor is rather intuitive, the Gini-corrected HDI has not been widely used. 3 One reason might be that it is not easy to compute the Gini coefficient for education and life-expectancy due to data limitations and conceptual problems. Another reason might be that it is not clear how to interpret the interaction between the Gini coefficient and the average achievement in a component. The gender related development index, or GDI, was another attempt in that direction. Its motivation was the I 995 Human Development Report's emphasis on gender inequalities. The GDI adjusts the HDI downward by existing gender inequalities in life-expectancy, education and incomes. The GDI calculates each dimension index separately for men and women and then combines both by taking the harmonic mean, penalizing differences in achievement between men and women. The overall GDI is then calculated by combining the three genderadjusted dimension indices by taking the arithmetic mean. This concept could of course also be applied using other segmentation variables than gender, such as different ethnic or income groups. However for gender in particular, it is not clear how gender related inequality in income can reasonably be measured.4 In most cases men and women pool incomes in households. Usually not much information is available how the pooled income is then allocated among household members. That and other critical issues related to the GDI are discussed in detail by Klasen (2006a, 2006b ). Another attempt was undertaken by Foster, L6pez-Calva and Szekely (2003). They chose an axiomatic approach to derive a distribution sensitive HDI. They suggest a three-step procedure. First, each dimension index is calculated on the lowest possible aggregation level, given the data availability. For instance, income at the level of households and life-expectancy at the level of municipalities (taken from census data). Second, for each dimension an overall index is computed by taking the generalized mean µq, The formula for the generalized mean is µq = [(x'( + ... +x;{)/n] l/q_ For q = I,µ yields the arithmetic mean, but for negative values for q, µ gives more emphasis on lower levels of x. The higher the absolute value of q, the more weight is given to low levels of x. Third, the overall HDI is computed by taking again the generalized mean instead of the simple arithmetic mean. The advantage of this approach is its axiomatic foundation. For instance, the index is decomposable by sub-groups, which is not the case for the Ginicorrected HDI. The problem with this approach is, however, that the generalized mean may not seem very intuitive for many users of the HDI. It obviously also 3See Griin and Klasen (2006) for an analysis of a Gini-adjusted GDP measure. 4Generally, the GDI uses information on earned income of males and females, based on sexspecific labor force participation rates and earnings differentials (UNDP, 2006). 86 4. A HUMAN DEVELOPMENT INDEX BY INCOME GROUPS raises the question of how to determine the 'right' inequality aversion parameter q. An additional problem is, that again no generally applicable methodology is suggested, which could help to compute the three dimension indices on the lowest disaggregation level. The approach chosen in this paper differs from the others in that, first, we focus of inequality in human development across the income distribution and, second, we do not try to incorporate the aggregate well-being costs associated with existing inequalities, but rather generate a separate HDI for different segments of the income distribution. More precisely, we take household income and demographic data to compute the three dimension indices for quintiles of the income distribution. This allows on the one hand to track the progress in human development separately for 'the poor' and 'non-poor' and on the other hand to compare the level of human development of the poor to the level of the average population and the level of the non-poor. In contrast to previous attempts, we also present, at least for developing countries, a clear methodology how the three dimension indices for different segments of the income distribution can be calculated with commonly available data sources. Applying our methodology to developed countries entails some data availability and comparability problems which we discuss below. Due to these problems, we are only able to provide rough estimates for two developed countries. The objective of this paper is first of all illustrative. We will show that our methodology has also some shortcomings, and, hence, all presented results should be interpreted with caution and in the light of our assumptions. The reminder of this paper is organized as follows. Section 2 presents our methodology. Section 3 presents the sample of countries for which we illustrate it. Section 4 discusses the results. Section 5 offers a critical assessment of our methodology. Section 6 concludes. 4.2 Methodology 4.2.1 General idea and overview The basic idea of our method is to use disaggregated data to calculate the three dimension indices which constitute the HDI for different segments of the income distribution. This will allow us to get an idea of the heterogeneity and inequality in human development which exists within a country. As data sources, we use household surveys. As segments of the income distribution, we define income quintiles. Since the early nineties, two types of surveys are undertaken in almost all developing countries. First, there are so-called Living Standard Measurement 4.2. METHODOLOGY 93 Then we calculate the quintile specific adult literacy index, AQ, using again the corresponding usual minimum and maximum values employed in the HDI: Q _ aQ-o - A - VQ-1,2, ... ,5. 1-0 (4.8) The aggregate adult literacy index A can be calculated using a instead of aQ. In a last step, we rescale again linearly AQ and A to achieve consistency with the aggregate HDI calculated by UNDP for the relevant year. As rescaling factor we use the ratio between our aggregate literacy index A and the aggregate literacy index calculated by UNDP. Calculating the enrollment index To calculate the quintile specific gross enrolment index, we calculate first the combined gross enrolment rate for each quintile. Each individual attending school or university whether general or vocational is considered as enrolled. We define this rate over all individuals of the age group five to 23 years old. Age is for each individual the age at the date of the interview. This yields: 1 gQ=Q L I(gf>O) VQ=l,2, ... ,5, n i(V5'.Sj'.S23) (4.9) where nQ is the total number of individuals of age five to 23 in quintile Q and / is an indicator function which takes the value one if an individual i independent of age, is enrolled, i.e. g; > 0. We calculate also the aggregate gross enrolment rate g. Then we calculate the quintile specific gross enrollment index, GQ using the usual minimum and maximum values used for the calculation of the HDI: gQ-o GQ=-- 0 VQ= 1,2, ... ,5. I- (4.10) The aggregate gross enrollment index G can be calculated by using g instead of gQ. Finally, we also rescale GQ and G to the level of the HDI enrollment index. Calculating the education index The quintile specific education index EQ is calculated using the same weighted average as the HDI: EQ=(2/3)xAQ+(l/3)xGQ VQ= 1,2, ... ,5. ( 4.11) 94 4. A HUMAN DEVELOPMENT INDEX BY INCOME GROUPS The aggregate education index E can be calculated by using A and G instead of AQ and GQ. 4.2.5 Calculating the GDP index by income quintiles To calculate the GDP index by income quintile, we use our income variable from the HIS. One main difference with the two other dimension indices is that mean income calculated from the HIS can be very different from GDP per capita derived from National Accounts data, which is used for the GDP index in the general HDI. This has two reasons: first, because of conceptual differences and, second, because of measurement error on both levels. GDP measures the value of all goods and services produced for the market within a year in a given country evaluated at market prices. Income in the household survey is either measured, as mentioned above, via household expenditure (including self-consumed production) or via the sum of earned and unearned household income. Therefore, non distributed profits of enterprises, property income and so on will not be included in the household income variable. Moreover, on the household survey side, there may be measurement errors, because it is difficult to get accurate responses from households concerning wages, profits and expenditures. On the National Accounts side, while supply-side information on output and income for some sectors is based on highquality surveys or census data for agriculture and industry, information about subsistence farmers and informal producers is harder to obtain and usually of lower quality.8 We proceed as follows. First, to eliminate differences in national price levels we express household income per capita Yh calculated from the HIS, in USO PPP using the conversion factors based on price data from the latest International Comparison Program surveys provided by the World Bank (2005): yfPP = Yh X PPP. (4.12) Second, we rescale 1,;PP using the ratio between yPP and GDP per capita expressed in PPP (taken from the general HDI), i.e. we only take the information on the distribution of income from the HIS and stick with GDP per capita as the level of income: PPP - PPP [GDPPCPPP] ryh - Yh X -ppp . y (4.13) 8 A detailed discussion of all these problems can be found in Ravallion (2001) and Deaton (2005). 4.2. METHODOLOGY 95 Once, theses adjustments are done, it is straightforward to calculate the quintile specific GDP index, again using the usual minimum and maximum values of the HDI: YQ = log ryQ,PPP -log( 100) log( 40,000) -log( JOO) 'v Q = I, 2' ... '5' (4.14) where ryQ,PPP is the quintile specific arithmetic mean of the rescaled household income per capita. It should be noted that in richer countries the GDP per capita measure for the richest quintile, ry5,PPP, could easily exceed 40,000 USD PPP and, hence, the index could take a value greater than l. 4.2.6 Calculating the overall HDI and the HDI by income quintiles Once the quintile specific dimension indices have been calculated, determining the QHDI is straightforward. It is the simple average of the three dimension indices: HDIQ = (1/3) x LQ + (1/3) x EQ + (1/3) x yQ 'vQ= 1,2, ... ,5. (4.15) The aggregate HDI is as usual given by: HD/= (1/3) x L+ (1/3) x E + (1/3) x Y. (4.16) To get a sense of the inequality in human development within a country, one may compute the ratio between the HDI for the richest quintile and the poorest quintile: HDJQ=S RQHDJs,1 = HDJQ=I, or the ratio of the quintile specific HDI to the aggregate HDI: H D/Q= 1 H DJQ=S RQHD[l,mean = ___ and RQHDJS,mean = __ _ HD/ HD/ (4.17) (4.18) All these indicators can of course also be calculated for each dimension index. Hence, the QHDI cannot only be used to inform about the level of human development of the poor, the rich and the groups in-between, but also about the inequality in human development within a society. Moreover, the quintile specific indices can be compared across countries. This may lead to results where country A has a higher overall HDI than country B, but that in country B human development of the poor is on a substantial higher level than in country A. 96 4. A HUMAN DEVELOPMENT INDEX BY INCOME GROUPS 4.2.7 Calculating the HDI by income quintiles for OECD countries The application of our approach to OECD countries entails some additional problems. The data availability is very different in developing and industrialized countries. Whereas for a long time access to disaggregated and harmonized income, education and health data was much better in industrialized countries than in developing countries is seems today to be the other way around. For many developing countries there exist today at least roughly comparable income, education and health data thanks to the household income surveys and Demographic and Health Surveys. In many industrialized countries, such standardized surveys are either absent or not easily accessible. Moreover, due to very low infant and child mortality levels in rich countries, we could not easily apply our methods of deducing life expectancy from infant or child mortality rates available in household survey data as the absolute number of infant and child deaths are too low in such surveys to calculate life expectancies (and its differential by income) with any reliability. Thus we will briefly discuss data availability and outline an approach to construct quintile-specific HDis in rich countries and illustrate it for Finland and the USA. However, these calculations are not fully comparable to the calculations for developing countries and thus should be viewed as tentative. Matters are easiest for the income component. Here we can rely on the Luxemburg Income Study (LIS), which produces harmonized micro data sets on income, demographics, labor market status and expenditures on the level of households and individuals for 30 OECD countries.9 These data are of very high quality and probable more reliable than the income/expenditure data available in many developing countries. For our examples, Finland and the USA, we took the LIS income data for the year 2000 and simply rescaled it to fit UNDP's GDP index. Unfortunately, the data sets contained in LIS do not have educational enrolment or adult literacy information and only provide information on educational achievements by levels of education passed. Therefore, for Finland and the USA, we assume no inequality in adult literacy and use the schooling achievement differential by income for 2000 as reported in the Luxembourg Income Study to estimate income differentials in enrolments, after which we rescale again. Alternatively, enrolment rates by income quintile could probably be generated from national household income surveys (or coordinated surveys such as the European Household Panel Survey) but this would mean that we rely on two different income measures to calculate the two different components (as we had to do with the HIS and the DHS for developing countries). 9For details see: http://www.lisproject.org. 4.2. METHODOLOGY 97 A different approach would be to use data from the 'International Adult Literacy Survey (IALS)' for the education component. This is an international comparative study designed to provide information about the skills of the adult populations. It was conducted in three phases (1994, 1996 and 1998) in 20 nations. 10 There exists also a follow up survey called 'Adult Literacy and Lifeskills (ALL) Survey' but which exists so far only in six countries. A problem with using that information would be that it is not directly comparable to the literacy and enrolment measures used for all the other countries. By far the most difficult issues arise however with the life expectancy component. As already stated, using quintile specific child mortality to derive an estimate of quintile specific life expectancy from household surveys would not be possible as child mortality in most OECD countries is so low that no meaningful differentials by income could be identified. Moreover, child mortality in these countries is much related to premature births, genetic defects, complications during birth and due to accidents all of which not closely related to income. In fact, it is likely that existing income differentials in life expectancy in rich countries are largely due to mortality beyond childhood. In principle, one could try to rely on census or census-like sample surveys with large numbers of observations. An alternative would be to rely on death registrations. These data sources are generally used in rich countries to calculate mortality rates and associated life expectancy statistics. But these data sources usually do not include incomes and cannot be used to calculate income differentials. Two exceptions are the USA and Finland where specialized analyses on the link between incomes and mortality were undertaken. We therefore use the results from Rogol et al. (1997) and Martikainen et al. (200 I) on the life expectancy differential by incomes. These data are based on linked income survey data with vital registration data and are covering the adult mortality experience for 1979-85 for the USA, and 1991-96 for Finland. Through matching the mortality experience by income quintile with the Model Life Tables 'North' (Coale and Demeny, 1983), we derive life expectancy at birth for the two countries, after which we re-scale as described above. 11 An alternative way would be to use similar data matching techniques that we used above to impute incomes into the DHS to impute incomes into census data and then generate life expectancy information by income quintile. That presupposes access to census data (which are not available or accessible in some countries) and a detailed analysis of the potential of such a method. IOFor details see: http://nces.ed.gov/surveys/all/. 11 The 'income' that is referred to in these studies does not closely match annual household per capita income that we would use for the income component which causes a further complication. See also discussion below. 98 4. A HUMAN DEVELOPMENT INDEX BY INCOME GROUPS Given these caveats, we included only Finland and the USA in our analysis and focus otherwise solely on low and middle income countries and leave the calculation of a QHDI for OECD countries for future work. 4.3 Illustrating sample of countries Besides Finland and the USA, we illustrate our approach for a sample of 13 developing countries: seven countries from Sub-Saharan Africa (Burkina Faso, Cote d'Ivoire, Cameroon, Guinea, Madagascar, Mozambique, South-Africa and Zambia), three countries from Latin America (Bolivia, Colombia, and Nicaragua), and two countries from South-East Asia (Indonesia and Vietnam). These countries are listed in Table A. I (Appendix D). We tried to restrict the sample to countries where a HIS and DHS were undertaken within a two-year time period. For two countries both surveys were undertaken in the same year. For three countries there is a gap of one year and for four countries a gap of two years. Only in three countries (Guinea, Indonesia, and Madagascar) we were not able to follow this rule and have actually a gap between both surveys of three to four years. Moreover, we tried to include countries where both surveys are not older than five years. This was however not possible for four countries (Cote d'Ivoire, Guinea, Madagascar, South-Africa), where the HIS or the DHS (or both) were undertaken at the end of the 1990s. The survey dates should also be taken into account when comparing our unscaled QHDI with the usual HDI. The published HDI in the UNDP's Human Development Report 2005 (UNDP, 2005) refers to the year 2003. But a closer look at the data sources shows that literacy rates and life-expectancy estimates were usually based on censuses or surveys conducted between 2000 and 2004. In several countries the data sources even stem from data collected in the 1990s (e.g. Belarus, Burkina Faso, Kazakhstan, Mali). Hence, time consistency between the different dimension indices and actuality of the data is not a problem specific to our approach, but rather is present for both the usual HDI and the QHDI. 4.4 Results Table 1 shows the HDI by income quintile, the HDI, and the ratio of the HDI for the richest quintile to the poorest quintile and the HDI ranking for the richest and poorest quintile (using the HDI values from the 2005 report) for the sample countries. The countries are sorted by descending HDI. The results reveal very stark differences in human development between the richest and the poorest quintile. For example, in Guinea, Burkina Faso, Zambia, and Madagascar, the HDI 4.4. RESULTS 99 for the richest income quintile is about twice as high as in the poorest quintile. In a second group of countries, including Bolivia, Cameroon, Nicaragua, Cote d'Ivoire, Mozambique, and South Africa, the gap between the rich and the poor is also very large, between 50% and 65%. In a third group of countries, comprising Colombia, Vietnam, and Indonesia, the differential in the HDI for the richest and poorest quintile is smaller but still substantial at about 30%-50%. In the two rich countries included, the differences are smaller than in developing countries but large differences remain between the quintiles, particularly in the USA. The rank positions of the different quintiles further illustrate this point. For example, the richest quintile in Bolivia is at rank 34, i.e. among the countries with high human development, actually at the same level as Poland, whereas the poorest quintile is at rank 132. The average HDI Bolivia was in the last year's report at rank 112. In some Sub-Saharan African countries such as Cameroon, Guinea and Madagascar the richest quintile achieves a level similar to those countries with medium human development, i.e. far above the threshold of 0.5. In contrast the poorest quintiles of these countries all rank among the 15 countries with the lowest HDI. Put differently, the differences within countries are as high as the differences between high and medium as well as medium and low human development countries. Also among rich countries, the differences are sizable. While the richest quintile in the USA (followed by Finland) would top the list of human development achievements, the poorest quintile in the USA only achieves rank 48, considerably worse off than the richest quintile in South Africa, Colombia, Bolivia, or Indonesia. These differences are also nicely illustrated in Figure 4.1. When examining the individual components (see Tables A.2 A.3, A.4 in Appendix), it becomes clear that the biggest effect of inequality on the quintilespecific HDI is in the income component. As Table A.4 shows, in many countries the richest quintile has an income index (Y) that is often more than twice or even up to five times as high as among the poorest quintile. Here many of the Sub-Saharan African countries have the highest inequality, followed closely by the Latin America while the two East Asian countries have ratios of less than 2. This may seem surprising since it is well-known that Latin American countries have, on average, (slightly) higher income inequality than Sub-Saharan African countries. The reason why this is not reflected here is that the income index uses a logarithmic transformation of incomes under the assumption that the well-being effects of higher incomes among the rich is declining with higher incomes. Thus what is being measured here is not the differential in incomes but, in line with the general treatment of the income component in the HDI, the differential in important aspects of quality of life such as nutrition, housing, clothing, and other aspects that are closely correlated with incomes. In that sense it is particularly worrying that the differential is so stark in Africa and Latin America. Table 4.1: Quintile specific HDI by country) (LQ computed using asset index) Country Q=I Q=2 Q=3 Q=4 Q=5 Overall RatioQ5/Ql Ranking Ranking Ranking HD! Q5/Ql All Q=l Q=5 Industrialized Countries USA (2000) 0.837 0.893 0.927 0.957 I.Oil 0.940 1.208 15 48 Finland (2000) 0.870 0.897 0.919 0.944 0.989 0.930 1.137 20 32 Developing Countries Colombia (2003/2005) 0.673 0.741 0.800 0.857 0.927 0.790 1.377 66 115 22 Vietnam (2004/2002) 0.627 0.680 0.718 0.765 0.828 0.713 1.321 108 125 51 Indonesia (2000/2003) 0.593 0.651 0.700 0.764 0.874 0.701 1.474 110 129 31 South Africa (2000/1998) 0.561 0.640 0.700 0.743 0.879 0.691 1.567 112 132 30 Bolivia (2002/2003) 0.550 0.640 0.704 0.741 0.863 0.690 1.570 113 132 34 Nicaragua (2001/2001) 0.531 0.629 0.678 0.720 0.830 0.667 1.563 116 135 51 Cameroon (200112004) 0.417 0.477 0.529 0.553 0.644 0.523 1.544 137 165 123 Madagascar (2001/1997) 0.343 0.463 0.496 0.563 0.684 0.488 1.994 152 173 114 Guinea ( 1995/1999) 0.340 0.457 0.490 0.594 0.696 0.467 2.047 156 174 Ill Cote d'Ivoire (1998/1999) 0.343 0.416 0.434 0.515 0.561 0.430 1.636 163 173 132 Zambia (2002/2002) 0.317 0.390 0.431 0.476 0.583 0.426 1.839 164 175 129 Mozambique (2002/2003) 0.305 0.355 0.380 0.417 0.504 0.387 1.652 167 176 146 Burkina Faso (2003/2003) 0.257 0.306 0.331 0.365 0.489 0.348 1.903 172 178 151 Note: For developing countries the years in brackets refer to the respective survey years. The first year refers to the HIS data set, the second to the DHS data set. All indices are rescaled to UNDP's reponed HD! value of the second survey year. Source: Household Income Survey (HIS) and Demographic and Health Surveys (DHS) (see Table A.I), Human Development Repons; calculations by the authors. - 8 .l',. > :I: c:: :s: • z t:I tTl < tTl r 0 "C :s: tTl z ..., -z 0 tTl :>< Ol -< - z (') 0 :s: tTl C) :,0:, 0 c:: "C en Figure 4.1: A human development index by income groups HDI global scale 0.9 0.8 0.7 0.6 ~ 0.5 0.4 0.3 0.2 0.1 0 Wortd BUF MOZ ZAM CIV GUI MAD CAM NIC BOL SAF IND VIE COL FIN USA 2003 2003 2002 1999 1999 1997 2004 2001 2003 1998 2003 2002 2005 2000 2000 Source: Computations by the authors. HDI global scale (HDR 2006). .j::,. ~ ~ tTl en C ti en 0 102 4. A HUMAN DEVELOPMENT INDEX BY INCOME GROUPS The differential in educational achievements (E) between the richest and the poorest quintile are also sizable, but smaller than in the income index (see Table A.3). In some Sub-Saharan African countries such as Burkina Faso and Madagascar the rich have nearly twice the educational achievement of the poor. But in many other countries such as South Africa, Vietnam, Nicaragua and Colombia, the differentials are not very large reflecting substantial efforts to improve education across the entire income spectrum. One should note, however, that education is only using literacy and enrolment rates and says little about educational quality which is likely to differ much more strongly between the rich and the poor. The differential in life expectancy achievements (L) between the richest and poorest quintile are also substantial, but generally the smallest of the three components. In Appendix we present the results for both matching approaches, the income regression based approach (see Table A.2) and the asset index based approach (see Table A2b). While one reason for the smaller inequality in the lifeexpectancy index compared to the two other dimension indices may be related to data quality issues and the assumptions that were made in order to derive at these estimates (see also Section 4.5), it appears that inequality in life expectancy is indeed smaller in the developing countries we consider than other forms of inequality. Three cautionary notes are important, however. To some extent, such smaller inequality is to be expected given that life expectancy is effectively bounded above, i.e. there are limits to life expectancy that even high income people run up against. Second, the differences in actual life expectancy (rather than the life expectancy index) are still substantial with gaps between the poorest and richest quintile amounting to more than 10 years in 5 countries. Third, even seemingly smaller differentials in life expectancy may be seen as just as important, or even more important, than larger differentials in the other components. After all, the chance to live and be free from the fear of premature mortality is a fundamental precondition for all other aspects of life. Among rich countries, all three differentials are considerably smaller. Income differentials (especially when expressed using the logarithmic transformation) are considerably smaller suggesting smaller differentials in income-sensitive human development achievements than elsewhere. Education differentials are, as expected, also smaller as schooling up to secondary level and thus basic literacy is near universal and only slight differentials exist at the post-secondary level. Also life expectancy differentials by income (based on cause of death information for the 1980s or early 1990s) are smaller in developing countries but remain sizable. In both the USA and Finland, the top quintiles enjoys about five more years of life than the poorest quintile. Given the wealth of these countries and the ability to provide health case to all, such differentials seem still unacceptably large. The correlation between the level of the HDI and inequality in human development seems to be negative but only weakly so as Figure 4.2 illustrates. APPENDICES 109 Table A.4: Poverty/Distribution change semi-elasticity as a function of mean income and inequality(assumption: zero growth of mean income) Poverty line as a proportion of mean income Gini 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 0.20 0.00 0.00 0.02 0.18 0.50 0.82 0.94 0.81 0.52 0.20 0.25 0.00 0.01 0.13 0.39 0.66 0.80 0.77 0.61 0.41 0.19 0.30 0.00 0.06 0.28 0.53 0.69 0.71 0.63 0.49 0.34 0.19 0.35 0.01 0.16 0.41 0.59 0.65 0.62 0.53 0.41 0.30 0.19 0.40 0.04 0.27 0.49 0.59 0.60 0.54 0.45 0.36 0.27 0.19 0.45 0.10 0.37 0.53 0.57 0.54 0.48 0.40 0.32 0.25 0.18 0.50 0.18 0.43 0.53 0.53 0.49 0.42 0.35 0.29 0.23 0.18 0.55 0.27 0.47 0.52 0.49 0.44 0.04 0.32 0.26 0.21 0.17 0.60 0.35 0.49 0.49 0.45 0.39 0.34 0.29 0.24 0.20 0.17 0.65 0.40 0.49 0.46 0.41 0.35 0.30 0.26 0.22 0.19 0.16 0.70 0.44 0.47 0.42 0.37 0.32 0.27 0.24 0.20 0.18 0.15 Table A.5: Descriptives of Regions Headcount Headcount Gini Mean PO poverty poverty Coeff. Income relative Region (theo.) growth East Asia and Pacific 16.46 20.68 39.74 ll05 -3.45 Europe and Central Asia 2.47 2.55 29.41 2235 1.70 Latin America and Caribbean 14.77 16.26 51.47 2011 -0.14 Middle East and North Africa 1.93 4.74 37.44 1909 -0.16 South Asia 34.35 32.09 32.44 646 -1.68 Sub-Saharan Africa 35.88 34.51 43.20 900 2.39 PO Income absolute growth growth -7.93 11.48 220.04 -12.97 47.92 3.10 -11.43 -9.14 -1.04 3.92 21.10 -7.08 Gini growth 2.53 18.20 0.96 -8.16 4.20 -0.76 10 > "O "O m z 0 n m "' Appendix B Figure A. I: Stunting/Wasting Combinations for Underweight of -2.0 (WHO reference standard) oo+--------------------------------- 0 10 __ .,,,._ -·-·-·-. 20 30 Age (months) WHO Z-Score Boys {Wasting= -1) WHO Z-Score Boys (Wasting= O) WHO 2-Score Boys (Wasling = 1) Source: WHO and NCHS/WHO reference standard; own calculations. 40 50 WHO Z-Score Girls (Wasting= -1) WHO Z-Score Girls (Wasting= O) WHO Z-Score Girls (Wasting= 1) 60 112 0 " I 150 :E C> ·a; ~- a, I § ""' ilj I ,:: .9 ~<j' 'i N :1 C ~ iu;> 'a, :i: APPENDICES Figure A.2: Stunting/Wasting Combinations for Underweight of -2.0 (NCHS/WHO reference standard) 0 10 20 30 Age (months) NCHS/WHO Z-Score Boys (Wasting= -1 NCHS/WHO Z-Score Boys {Wasting= 0) NCHS/WHO 2-Score Boys (Wasting= 1) 40 50 60 NCHS/WHO Z-Score Girls (Wasting= -1) NCHS/WHO 2-Score Girls (Wasting= 0) NCHS/WHO Z-Score Girls (Wasting= 1) Source: WHO and NCHS/WHO reference standard; own calculations. APPENDICES 113 Figure A.3: Composition of undemutrition in Burkina Faso by asset index quintiles Composition of undernutrition in Burkina Faso by asset index quintiles 0 ...,__ -- - -- --~ -- Quintile 1 Quintile 2 Oui ntile3 Qu intile 4 Quintile 5 -Only Stunted - All Indicators -Wasted and Underweight Source: OHS dataset (Burkina Faso 2003); own calculations. - S tu nted and Underweight Only Wasted Only Underweight 114 APPENDICES Figure A.4: Composition of undemutrition in Bolivia by asset index quintiles "' Q) g, ~N 0 • ai 0.. 0 Composition of undernutrition in Bolivia by asset index quintiles Quintile 1 Quintile 2 -Only Stunted -Stunted and Underweight -All Indicators -Only Wasted -Wasted and Underweight Only Underweight Source: DHS dataset (Bolivia 2003); own calculations. APPENDICES 115 Figure A.5: Composition of undemutrition in Chad by the nutrition status of mother "! 0 Composition of undernutrition in Chad by Nutrition Status of Mother Mother Underweight Normal Weight Mother Overweight -Only Stunted -All Indicators -Wasted and Underweight -Stunted and Underweight Only Wasted Only Underweight Source: OHS dataset (Chad 2003); own calculations. 116 APPENDICES Figure A.6: Composition of undemutrition in India by the nutrition status of mother II) Q) Ollll "'~ c· Q) ~ Q) ~ Q. . 0 Composition of undernutrition in India by Nutrition Status of Mother Mother Underweight Normal Weight Mother Overweight -Only Stunted - All Indicators -Wasted and Underweight -Stunted and Underweight Only Wasted Only Underweight Source: DHS dataset (India 1998/99); own calculations. Appendix C Table A. I: Scoring Coefficients for Asset Index and Access to Health Facilities Index (Principal Component Analysis) Bangla- India Mali Uganda Zim- Global desh babwe value 2000 1999 2001 1995 1994 Asset index Radio 0.191 0.173 0.135 0.173 0.141 0,221 TV 0.284 0.270 0.272 0.245 0.195 0.332 Fridge 0.239 0.249 0.182 0.167 Bike 0.093 0.077 0.021 -0.002 0.036 0.095 Motorized transport 0.143 0.229 0.205 0.177 0.128 0.263 Low floor material -0.300 -0.255 -0.274 -0.184 No toilet facility -0.125 -0.265 -0.144 -0.118 -0.172 -0.220 Flush toilet 0.273 0.282 0.105 0.195 0.221 0.308 Piped drinking water 0.192 0.196 0.206 0.243 0.203 0.268 Surface drinking water -0.048 -0.070 -0.085 -0.143 -0.086 -0.142 Access to health facility index Tetanus vaccination 0.393 0.349 0.344 0.480 0.403 0.358 Prenatal care 0.450 0.367 0.347 0.487 0.442 0.376 Born w/o assistance -0.357 -0.312 -0.335 -0.252 -0.307 -0.286 Source: Demographic and Health Surveys (OHS); own calculations. 118 APPENDICES Table A.2: Regression Results of Infant Mortality (Logistic Regression) Bangladesh India Mali Uganda Zimbabwe 2000 1999 2001 1995 1994 Constant -3.098** -2.479** -2.042** -2.140** -2.544** (0.099) (0.028) (0.053) (0.104) (0.167) Age (mother) -0.119* -0.214** -0.145** -0.080* -0.176 (0.061) (0.030) (0.033) (0.065) (0.150) Age2 (mother) 0.178* 0.337** 0.231 ** 0.110 -0.337 (0.104) (0.032) (0.052) (0.107) (0.248) Sex of child ((=female) -0.156 0.060 -0.107* -0.077 -0.414* (0.104) (0.046) (0.059) (0.107) (0.219) Immediate brea,tfeeding (=I ) -0.413** -0.473** -0.176** -0.040 -0.499* (0.121) (0.062) (0.062) (0.110) (0.219) Complete vaccination (=I ) -2.396** -2.453** -1.961 ** -1.928** -3.454** (0.219) (0.066) (0.126) (0.187) (0.344) First born(= I) 0.273 -0.076 -0.116 0.134 -0.317 (0.185) (0.081) (0.114) (0.195) (0.382) Preceding birth interval -0.006* -0.007** -0.015** -0.003* 0.012* (0.003) (0.001) (0.002) (0.004) (0.005) Household size (IV) 0.181** 0.068** 0.014** 0.064* O.Q35 (0.049) (0.016) (0.004) (0.042) (0.073) Female headed household (=I ) O.Q28 0.037 -0.107 -0.088 -0.002 (0.254) (0.097) (0.108) (0.142) (0.235) Asset index -0.132 -0.086** -0.066 -0.160 -0.013 (0.111) (0.026) (0.042) (0.103) (0.128) BMI of mother -0.039* 0.048** 0.026* -0.041* -0.003 (0.024) (0.010) (0.012) (0.022) (0.037) BMJ2/JOO of mother 0.469 0.459 -0.253 0.265 -0.370 (0.271) (0.120) (0.155) (0.310) (0.366) Mother has sec. education (=I) -0.180 -0.210* -0.305** -0.507 0.053 (0.160) (0.063) (0.218) (0.306) (0.293) Health facility index -0.630** -0.301 ** -0.384** -0.284** -0.394** (0.088) (0.028) (0.046) (0.084) (0.135) Community characteristics Distance to health facility*** -0.003* 0.001 0.000 -0.018 -0.033 (0.001) (0.002) (0.000) (0.049) (0.095) Secondary education(%) -0.587 -0.614* -0.319 -0.006 -1.525* (0.418) (0.204) (0.463) (0.431) (0.595) Children had fever(%) 0.753* -0.297 0.062 0.479* 0.333 (0.418) (0.255) (0.181) (0.252) (0.480) Public infrastruct. index 0.041* -0.014 0.003 0.129* 0.261* (0.065) (0.025) (0.039) (0.067) (0.127) Pseudo R2 0.099 0.128 0.068 0.068 0.159 Obs. 5381 28539 9852 4232 1568 Source: Demographic and Health Surveys (DHS); own calculations. Notes: *P-value<().I. **P-value<().01. For details about the variables, see Section 3.3.1. ***In the case of Bangladesh distance is measured in time (hours). The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used. AppendixD Table A. I: Data sources for developing countries Country Year Type of survey Burkina Faso 2003 Demographic and Health Survey (DHS) 2003 Enquete Prioritaire sur !es Conditions de Vie des Menages EP) Bolivia 2003 Demographic and Health Survey (DHS) 2002 Living Standard Measurement Survey (LSMS) Cote d'Ivoire 1999 Demographic and Health Survey (DHS) 1998 Enquete de Niveau de Vie des Menages (ENV) Cameroon 2004 Demographic and Health Survey (DHS) 2001 Enquete Camerounaise aupres des Menages (ECAM) Colombia 2005 Demographic and Health Survey (DHS) 2003 Encuesta de Calidad de Vida Guinea 1999 Demographic and Health Survey (OHS) 1995 Enquete lntegrale avec Module Budget et Consummation Indonesia 2003 Demographic and Health Survey (DHS) 2000 Indonesian Family Life Survey (3rd wave) (IFLS) Madagascar 1997 Demographic and Health Survey (OHS) 2001 Enquete aupres des Menages (EPM) Mozambique 2003 Demographic and Health Survey (DHS) 2002 Inquerito Nacional aos Agregados Familiares sobre as Condicoes de Vida Nicaragua 2001 Demographic and Health Survey (DHS) 2001 Encuesta Nacional de Hogares sobre Medicion de Nivel de Vida (EMNV) South Africa 1998 Demographic and Health Survey (DHS) 2000 Income and Expenditure Survey Vietnam 2002 Demographic and Health Survey (DHS) 2004 Living Standard Measurement Survey (LSMS) Zambia 2002 Demographic and Health Survey (DHS) 2002 Living Conditions Monitoring Survey (LCMS) 126 APPENDICES Table A.2: Quintile specific life expectancy indices by country Q=l Q=2 Q=3 Q=4 Q=5 All Ratio Q5/QI (a) LQ computed using predicted income Industrialized Countries USA(2000) 0.82 0.86 0.88 0.89 0.90 0.87 1.098 Finland (2000) 0.85 0.87 0.89 0.91 0.93 0.89 1.094 Developing Countries Colombia (2003/2005) 0.814 0.781 0.801 0.799 0.788 0.797 0.968 Vietnam (2004/2002) 0.713 0.707 0.825 0.812 0.849 0.764 1.191 Indonesia (2000/2003) 0.650 0.651 0.711 0.711 0.799 0.697 1.229 South Africa (2000/1998) 0.416 0.468 0.532 0.542 0.523 0.481 1.257 Bolivia (2002/2003) 0.632 0.622 0.666 0.691 0.681 0.651 1.078 Nicaragua (2001/2001) 0.730 0.700 0.753 0.756 0.783 0.735 1.073 Cameroon (2001/2004) 0.370 0.335 0.337 0.323 0.342 0.344 0.924 Madagascar (2001/1997) 0.509 0.432 0.463 0.574 0.574 0.500 1.128 Guinea ( 1995/ 1999) 0.532 0.491 0.473 0.455 0.415 0.479 0.780 Cote d'Ivoire (1998/1999) 0.369 0.394 0.296 0.306 0.427 0.364 1.158 Zambia (2002/2002) 0.214 0.200 0.205 0.209 0.211 0.208 0.986 Mozambique (2002/2003) 0.329 0.309 0.263 0.226 0.219 0.281 0.666 Burkina Faso (2003/2003) 0.385 0.386 0.382 0.359 0.359 0.375 0.932 (b) LQ computed using asset index Colombia (2003/2005) 0.787 0.791 0.831 0.870 0.777 0.797 0.987 Vietnam (2004/2002) 0.684 0.751 0.799 0.835 0.877 0.764 1.282 Indonesia (2000/2003) 0.616 0.631 0.679 0.764 0.890 0.697 1.445 South Africa (2000/1998) 0.405 0.476 0.530 0.504 0.602 0.481 1.486 Bolivia (2002/2003) 0.600 0.627 0.682 0.667 0.811 0.651 1.352 Nicaragua (2001/2001) 0.678 0.745 0.756 0.759 0.828 0.735 1.221 Cameroon (2001/2004) 0.328 0.328 0.365 0.330 0.391 0.344 1.192 Madagascar (2001/1997) 0.429 0.509 0.498 0.556 0.567 0.500 1.322 Guinea (1995/1999) 0.415 0.458 0.431 0.562 0.624 0.479 1.504 Cote d'Ivoire (1998/1999) 0.317 0.384 0.340 0.464 0.384 0.364 1.211 Zambia (2002/2002) 0.185 0.215 0.209 0.196 0.246 0.208 1.330 Mozambique (2002/2003) 0.264 0.276 0.277 0.310 0.312 0.281 1.182 Burkina Faso (2003/2003) 0.345 0.386 0.373 0.368 0.420 0.375 1.217 Note: For developing countries the years in bracket~ refer to the respective survey years. The first year refers to the HIS data set, the second to the DHS data set. All indices are rescaled to UNDP's reported HDI value of the second survey year. Source: Household Income Survey (HIS) and Demographic and Health Surveys (DHS) (see Table A I), Human Development Repons; calculations by the authors. APPENDICES 127 Table A.3: Quintile specific education indices by country Q=l Q=2 Q=3 Q=4 Q=5 All Ratio Q5/Ql Industrialized Country USA 0.94 0.96 0.98 0.99 1.02 0.98 1.085 Finland 0.97 0.97 0.98 0.99 1.02 0.99 1.052 Developing Country Colombia (2003/2005) 0.788 0.834 0.866 0.887 0.932 0.863 1.18 Vietnam (2004/2002) 0.783 0.807 0.821 0.867 0.880 0.831 1.12 Indonesia (2000/2003) 0.730 0.789 0.821 0.855 0.900 0.814 1.23 South Africa (2000/1998) 0.814 0.818 0.823 0.824 0.824 0.821 1.01 Bolivia (2002/2003) 0.721 0.833 0.888 0.922 0.954 0.870 1.32 Nicaragua (2001/2001) 0.621 0.635 0.666 0.688 0.722 0.665 1.16 Cameroon (200 I /2004) 0.579 0.664 0.716 0.752 0.801 0.713 1.38 Madagascar (2001/1997) 0.462 0.598 0.612 0.648 0.822 0.593 1.78 Guinea ( 1995/1999) 0.304 0.433 0.442 0.486 0.462 0.410 1.52 Cote d'Ivoire (1998/1999) 0.367 0.417 0.448 0.490 0.546 0.443 1.49 Zambia (2002/2002) 0.586 0.657 0.707 0.771 0.831 0.704 1.42 Mozambique (2002/2003) 0.433 0.459 0.460 0.464 0.524 0.471 1.21 Burkina Faso (2003/2003) 0.194 0.207 0.228 0.259 0.373 0.260 1.92 Nme: For developing countries the years in bracket, refer to the respective survey years. The first year refers to the HIS data set, the second to the OHS data set. All indices are rescaled to UNDP's reported HDI value of the second survey year. Source: Household Income Survey (HIS) and Demographic and Health Surveys (OHS) (see Table A I), Human Development Repons; calculations by the authors. 128 APPENDICES Table A.4: Quintile specific GDP indices by country Q=I Q=2 Q=3 Q=4 Q=5 All Ratio Q5/QI Industrialized Country USA 0.75 0.87 0.93 0.99 I.II 0.97 1.480 Finland 0.82 0.88 0.92 0.96 1.04 0.94 1.146 Developing Country Colombia (2003/2005) 0.444 0.598 0.702 0.815 1.072 0.711 2.41 Vietnam (2004/2002) 0.415 0.482 0.534 0.592 0.726 0.543 1.75 Indonesia (2000/2003) 0.433 0.533 0.599 0.673 0.832 0.593 1.92 South Africa (2000/1998) 0.462 0.624 0.748 0.901 1.211 0.773 2.62 Bolivia (2002/2003) 0.329 0.459 0.543 0.634 0.825 0.548 2.51 Nicaragua (2001/2001) 0.296 0.507 0.611 0.714 0.939 0.599 3.18 Cameroon (2001/2004) 0.345 0.439 0.505 0.576 0.738 0.513 2.14 Madagascar (2001/1997) 0.139 0.281 0.377 0.484 0.664 0.370 4.79 Guinea (1995/1999) 0.300 0.480 0.598 0.735 1.002 0.514 3.34 Cote d'Ivoire (1998/1999) 0.344 0.446 0.515 0.591 0.753 0.483 2.19 Zambia (2002/2002) 0.179 0.297 0.376 0.462 0.672 0.366 3.75 Mozambique (2002/2003) 0.218 0.329 0.401 0.477 0.676 0.409 3.10 Burkina Faso (2003/2003) 0.232 0.325 0.393 0.470 0.675 0.409 2.91 Nole: For developing countries the years in brackets refer to the respective survey years. 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