metadata: authors: 0: author: Wagstaff, Adam;Wagstaff,Robert Adam Stephen
1: author: Bredenkamp, Caryn
2: author: Buisman, Leander R.
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docna: 0: docna: Progress toward the health MDGs : are the
            poor being left behind ?
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Reproductive Health,Early Child and Children's Health,Nutrition,Cholera,Malaria,Communicable Diseases,Leprosy,Law and Justice Institutions
teratopic: Poverty Reduction,Health, Nutrition and Population
abstracts: cdata!: This paper looks at differential
            progress on the health Millennium Development Goals between
            the poor and better-off within countries. The findings are
            based on original analysis of 235 Demographic and Health
            Surveys and Multiple Indicator Cluster Surveys, spanning 64
            developing countries over the period 1990-2011. Five health
            status indicators and seven intervention indicators are
            tracked for all the health Millennium Development Goals. In
            most countries, the poorest 40 percent have made faster
            progress than the richest 60 percent. On average, relative
            inequality in the Millennium Development Goal indicators has
            been falling. However, the opposite is true in a sizable
            minority of countries, especially on child health status
            indicators (40-50 percent in the cases of child malnutrition
            and mortality), and on some intervention indicators (almost
            40 percent in the case of immunizations). Absolute
            inequality has been rising in a larger fraction of countries
            and in around one-quarter of countries, the poorest 40
            percent have been slipping backward in absolute terms.
            Despite reductions in most countries, relative inequalities
            in the Millennium Development Goal health indicators are
            still appreciable, with the poor facing higher risks of
            malnutrition and death in childhood and lower odds of
            receiving key health interventions.
repnme: repnme: Progress toward the health MDGs : are the
            poor being left behind ?
display_title: Progress toward the health MDGs :
            are the poor being left behind
listing_relative_url: /research/2014/05/19581319/progress-toward-health-mdgs-poor-being-left-behind
topicv3: Infant and Child Mortality m1326544 1446,Malaria m1327042 1420,Mortality m1504365 3661,Health m1504661 3480,Malnutrition m1627088 4737
publishtoextweb_dt: 2014-05-01T00:00:00Z
historic_topic: Poverty Reduction,Health, Nutrition and Population
no_of_pages: 37
author_keys: 0: author_key: 142270
model_extractions: WPS6894 Policy Research Working Paper 6894 Progress Toward the Health MDGs Are the Poor Being Left Behind?

_Adam Wagstaff Caryn Bredenkamp Leander R. Buisman_ The World Bank Development Research Group Human Development and Public Services Team May 2014
Policy Research Working Paper 6894

# **Abstract**

This paper looks at differential progress on the health Millennium Development Goals between the poor and better-off within countries. The findings are based on original analysis of 235 Demographic and Health Surveys and Multiple Indicator Cluster Surveys, spanning 64 developing countries over the period 1990–2011. Five health status indicators and seven intervention indicators are tracked for all the health Millennium Development Goals. In most countries, the poorest 40 percent have made faster progress than the richest 60 percent. On average, relative inequality in the Millennium Development Goal indicators has been falling. However, the opposite is true in a sizable minority of countries, especially on child health status indicators (40–50 percent in the cases of child malnutrition and mortality), and on some intervention indicators (almost 40 percent in the case of immunizations). Absolute inequality has been rising in a larger fraction of countries and in around one-quarter of countries, the poorest 40 percent have been slipping backward in absolute terms. Despite reductions in most countries, relative inequalities in the Millennium Development Goal health indicators are still appreciable, with the poor facing higher risks of malnutrition and death in childhood and lower odds of receiving key health interventions.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at awagstaff@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Progress Toward the Health MDGs: Are the Poor Being Left Behind?

Adam Wagstaff<sup>a*</sup> , Caryn Bredenkamp<sup>b</sup> and Leander R. Buisman<sup>c</sup> a Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA b Human Development Network, The World Bank, 1818 H Street, NW, Washington DC 20433, USA c Institute of Health Policy and Management, Erasmus University Rotterdam, PO Box 1738, 3000 DR Rotterdam, The Netherlands

## **Acknowledgments**

The research reported in this paper was supported by the World Bank’s Rapid Social Response Multi-Donor Trust Fund (RSR-MDTF). We are grateful to Nicole Klingen, Gayle Martin, Owen Smith, Aparnaa Somanathan, Owen O’Donnell, Ellen Van de Poel, Jos Verbeek, and Xiaoqing Yu for advice and useful comments on earlier versions of the paper.

> * Corresponding author: Adam Wagstaff. Development Research Group, The World Bank, 1818 H Street, NW, Washington DC 20433, USA. Tel: +1 202 473 0566, awagstaff@worldbank.org
# **Introduction**

With barely more than 500 days to go until the December, 31 2015 target date for the attainment of the Millennium Development Goals (MDGs), global and country-specific progress toward the health-related MDGs is being fervently discussed – by ministries of health, development partners, civil society organizations, and health advocates. Recent estimates (Go and Quijada 2012; United Nations 2012; World Bank 2013) show that while progress toward most non-health MDG targets is largely on-track, progress toward the health MDG targets is not.

Largely absent from the discussion on the health MDGs has been the question raised by Gwatkin (2005) nearly 10 years ago, namely whether progress within countries has been pro-poor (i.e. faster among the poor and hence inequality-reducing) or pro-rich.<sup>1</sup> Only three studies have shed light on this question; as we show below, these studies reached different conclusions. As a result, it is unclear right now whether progress to date on the health MDGs has been pro-poor or pro-rich – for both interventions (e.g. immunization) and outcomes (e.g. under-five mortality). In this paper we re-analyze this unresolved question, using a larger number of MDG indicators, more countries, more survey years, more recent data, and more measures of inequality than previous studies.

# **What did previous studies find about whether progress towards the health MDGs has been pro-poor?**

Table 1 indicates, for each of the main four health-related MDGs, the goal, the targets, the official indicators, and the additional core intermediate indicators recommended by a technical consultation between the UN technical agencies and the World Bank in 2001.

> 1 Throughout this paper we mean by “pro-poor” whether changes in MDG health indicators are such that there is a reduction in inequality (in the indicator) between the poor and better off. This is in the spirit of the definition of pro-poor growth proposed by Kakwani and Pernia (2000) (growth that reduces income inequality) and the definition of pro-poorness used in the context of the redistributive effects of taxes and transfers (taxes and transfers that reduce income inequality) (Lambert 2001).

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Table 2 summarizes the three previous studies that shed light on the issue of whether progress toward the health MDGs has been pro-poor at the country level. The studies vary in the time period under analysis, and in the number of countries studied. Two use only the Demographic and Health Survey (DHS), while one uses both the DHS and the Multiple Indicator Cluster Survey (MICS). One study focuses on just one MDG and one official indicator, another looks at two MDG and one official indicator of each, and a third looks at three MDGs and six official indicators.

In the absence of consumption data (the preferred measure of household living standards in developing countries (Deaton 1997)), all three studies distinguish between poor and betteroff households using the ‘wealth index’ developed by Filmer and Pritchett (2001) and included in each public release DHS and MICS dataset.<sup>2</sup> Two of the previous three studies address the inequality issue by looking at changes in the ratio of the outcome among the poorest quintile to the outcome among the richest quintile. The other uses the concentration index (Kakwani, Wagstaff and van Doorslaer 1997), analogous to the Gini coefficient except that when looking at inequality in, say, underweight, individuals are ranked by their household’s score on the wealth index, rather than by underweight. Both approaches speak to the issue of relative – rather than absolute – inequality, and shed light on whether progress has been pro-poor in a relative sense (in which case relative inequality is said to have fallen).

The findings of the three previous studies are mixed. Looking at under-five mortality (U5MR) between 1990 and 2001, Moser et al. (2005) find no clear trend , while Suzuki et al.

> 2 This uses principal components analysis to construct a weighted average of a variety of indicators capturing the ownership of assets (e.g. car and television), and the characteristics of the family’s home (e.g. type of floor and roof). Subsequent to the work of Gwatkin et al. (2000; 2007), who published tabulations by ‘wealth quintile’ of a large battery of health, nutrition and population indicators for all DHS surveys, the organization responsible for the DHS (then Macro International) decided to include the Filmer-Pritchett wealth index in each public-release DHS dataset; UNICEF, which is responsible for the MICS, subsequently decided to the same with the MICS.

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(2012) find that progress between 1995 and 2007 on MDGs 1, 4 and 5 outcomes was generally pro-rich. On interventions, both Suzuki et al. (2012) and Victora et al. (2012) mostly find pro-poor progress, except on skilled birth attendance (SBA) where Suzuki et al. (2012) find progress has been pro-rich.

# **Data and indicators**

# _Data_

Like Suzuki et al. (2012), we take our data from both the DHS and the MICS surveys. These global household survey programs are the only regular source of comparable household-level data on the health MDG monitoring indicators. We analyzed the raw data from 164 DHS surveys and 71 MICS surveys, covering a total of 91 countries between 1990 and 2011. After eliminating countries with only one period of data, and one dataset with some implausible values, we were left with 64 countries with at least two periods of data. Depending on the MDG indicator (see below for selection criteria and definitions), we have data for between 9 and 63 countries, with an average of 40 countries per indicator (see Table 3). There are some indicators with up to six surveys per country, but the average is 2.7.

We thus cover more countries than previous studies (64 compared to the 22 analyzed by Moser et al. (2005), the 40 analyzed by Suzuki et al. (2012), and the 35 analyzed by Victora et al. (2012)), and unlike previous studies whose trends have been estimated from just two surveys per country per indicator, ours are estimated from several surveys per country per indicator, for an average of 2.7.

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# _Indicators_

Table 4 shows the MDG indicators and their definitions used in the present study. Our list includes five health status indicators (stunting, underweight, infant mortality, under-five mortality, and HIV prevalence); the rest are intervention indicators. Our aim was to include as many of the official indicators as possible for each MDG.<sup>3</sup> We have included in our analysis five additional indicators that are almost always used in MDG monitoring exercises, drawn from the additional core intermediate indicators listed in Table 1. Specifically, we include the prevalence of stunted children (MDG1), the percentage of children aged one who have been fully immunized (MDG4)<sup>4</sup> , the percentage of children in malaria-endemic countries sleep under an insecticide-treated bednet (MDG4 and MDG6), antenatal visit coverage (MDG5), and whether or not a condom was used during sexual intercourse with a high-risk partner (MDG6).

Our study thus extends the range of indicators studied compared to previous studies. Unlike Moser et al. (2005) who focused on MDG outcomes (actually just one outcome: under-five mortality) and Victora et al. (2012) who focused on MDG interventions, but like Suzuki et al. (2012) we analyze data on both MDG outcomes and MDG interventions. While Suzuki et al. (2012) analyze more indicators than we do, we analyze more MDG indicators.<sup>5</sup>

> 3 Some official indicators – such as the number of tuberculosis (TB) cases detected and treated under DOTS – are not covered by the DHS or MICS, or in any other global household survey exercise. Deaths from malaria and TB are also not captured by regular household surveys like the DHS and MICs, partly because of sample size issues, but also because of the difficulty of ascertaining after the fact the cause of death. Maternal mortality is often computed using variables from the DHS, but maternal deaths are so rare that quintile-level analysis would be challenging.

> 4 The official indicator captures only measles immunization.

> 5 We include two additional MDG6 indicators – condom use and HIV prevalence.

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# _Stratification by ‘wealth’_

As in previous studies, we stratify households by the Filmer-Pritchett wealth index. Where available, we use the wealth index variable in the DHS or MICS dataset. For a handful of MICS surveys, we had to construct the wealth index ourselves. In each case, when using wealth quintiles, we sorted _households_ into quintiles; individuals then acquire the quintile of their household, so that in an analysis of, say, child mortality, the ‘poorest quintile’ may account for more than 20 percent of births.

# **Results**

We ask first how well countries _as a whole_ are progressing on the MDG indicators, and display our results in such a way as to show the full variation across countries and across indicators in rates of progress. We then examine progress among the poorer wealth quintiles (we focus on the poorest two<sup>6</sup> ), both in absolute terms and relative to the rest of the population. Finally, we explore more systematically how wealth-related inequalities in the MDG indicators have changed over time, presenting results for both relative and absolute inequality. Table 5 pulls together the key summary statistics that emerge from our analysis. Figures 1-8 give much more detail, and highlight the heterogeneity across developing countries.

> 6 The poorest 40 percent is the target group in the World Bank’s new goal of “promoting shared prosperity”, see e.g. http://www.worldbank.org/content/dam/Worldbank/document/WB-goals2013.pdf, where the Bank says: “we will monitor progress in shared prosperity using the income growth of the bottom 40 percent of a nation’s population. This implies a direct focus on the income of the less well-off, as opposed to the common practice of focusing only on growth of GDP per capita and implicitly relying on the “trickle down” impact of growth on the bottom of the distribution. The measure captures the two elements central to the notion of shared prosperity: a growing economy and a fundamental concern for equity.”

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# _How fast is the developing world progressing toward the health MDGs?_

Figure 1a shows the average annual growth rates for all countries (ranked in ascending order of their growth rate) on each of our MDG indicators; see also columns 1 and 2 of Table 5.<sup>7</sup> The growth curves in Figure 1a also allow us to see at a glance the median annual growth rate for each indicator, _and_ the fraction of countries experiencing negative and positive growth. For the health status (intervention) indicators, progress entails a country having a negative (positive) value of average annual growth in Table 5 and being below (above) the zero line on the y-axis in Figure 1a.

On all indicators except contraception, the majority of countries have made progress. For example, reading across the zero line on the y-axis in the stunting chart, we see that around 65 percent of countries have reduced the prevalence of stunting; for underweight, the figure is 80 percent. All but 22 per cent of countries have seen increases in ANC coverage. Reading up the median line on the x-axis in the IMR chart, we see that the median annual rate of reduction of IMR is around 0.03 or three percent; the median rate annual rate of increase of measles immunization is around two percent.

Several results shown in Figure 1a are worth noting. First, despite progress for the developing world as a whole, not all countries have made progress toward the MDGs; and, for some MDGs, the fraction not making progress is very high. Over half of countries have seen no progress in contraceptive prevalence, one quarter of countries have not recorded increases in immunization coverage, and one fifth of countries have not made progress in

> 7 Growth rates are computed using information from all available survey years. We assume a compound growth process, and estimate the average annual percentage change by regressing – for each country and each indicator, separately for the population and the poorest 40 percent – the natural logarithm of the indicator on the date of the survey. The average annual rate of growth is equal to the exponential of the (regression) - coefficient on the year variable minus one (see e.g. <u>http://data.worldbank.org/about/data overview/methodologies).</u>

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reducing underweight, with the figure for stunting as high as one third. Moreover, it is not just a question of some countries not making progress; there are countries that are actually going backwards. In some cases, such as contraception, the countries going backwards are doing so fairly slowly; in others, such as infant- and under-five mortality, immunization and malaria nets, the rates of negative progress are quite high.

A second noteworthy feature of Figure 1a is the variation _across indicators_ in the amount of progress made. The conclusion we draw on this depends somewhat on whether we look at median rates of growth or the fraction of countries making progress. For example, the median country recorded roughly similar rates of reduction for underweight, IMR and U5MR; but 93 percent of countries saw progress on IMR while only 80 percent did on underweight. Overall, though, progress has been more on child mortality (Goal 4) than on malnutrition (Goal 1) and maternal health (Goal 5).

A final feature of Figure 1a worth noting is how the indicators vary in how evenly progress is spread _across countries_ . This is clearer in Fig 1b which imposes the same range on the y- axis on each chart. The measles immunization growth is very flat: very few countries record an exceptionally high growth rate, and none achieved a growth rate of more than 20 percent. The SBA curve is also fairly flat, but has a steeper and more extended right tail: around five percent of countries averaged growth in excess of 10 percent, some recording growth rates substantially higher than 10 percent. The malaria net growth curve shows much more variation in the spread of growth rates across countries.

_How fast are the poor progressing toward the health MDGs?_ Figure 2 shows the growth curves for the poorest 40 percent and the richest 60 percent within each country; see also columns 3 and 4 of Table 5. The first conclusion is that for all 8
MDG indicators, except contraception, the poorest 40 percent have made progress, whether we measure this in terms of the median growth rate or the fraction of countries where progress among the poorest 40 percent has occurred. We also see similar broad patterns in the data of the poorest 40% to those for the population as a whole: negative progress among some countries, variations across indicators in the speed of progress, and small pockets of rapid progress on some indicators.

While the poorest 40 percent have, broadly-speaking, shared in the progress toward the health MDGs of the population as a whole, Figure 2 does reveal some differences in the speed of progress between the poorest 40 percent and richest 60 percent. The degree to which progress has been pro-poor or pro-rich is also shown in Figure 3 (and column 5 of Table 5) which shows how countries vary in excess growth among the poorest 40 percent, i.e. the excess of the growth rate among the poorest 40 percent over the growth rate among the richest 60 percent. For an intervention indicator, such as immunization, positive excess growth among the poorest 40 percent means faster progress among the poor, i.e. pro-poor progress. For health status indicators, such as underweight and mortality, the opposite is the case: positive excess growth among the poorest 40 percent means pro-rich progress.

On the MDG outcomes, the picture varies somewhat by indicator. On HIV prevalence, progress has been markedly pro-rich. The median rate of reduction for the richest 60 percent was around three percentage points higher than that for the poorest 40 percent. And while 90 percent of countries saw a reduction in HIV prevalence among the richest 60 percent, only around 55 percent of countries saw a reduction in HIV prevalence among the poorest 40 percent. Figure 3 shows that in the case of HIV prevalence, excess (negative) growth among the poorest 40 percent was recorded in only roughly 35 percent of countries.

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By contrast, no striking differences in progress between the poor and better off emerge for malnutrition and child mortality. The growth curves for the poorest 40 percent and richest 60 percent are close to one another, and actually intersect several times; and the excess growth curves in Figure 3 all intersect the median line on the x-axis around zero on the y- axis. For both malnutrition indicators, the median rate of reduction among the richest 60 percent is very slightly larger than that among the poorest 40 percent, and the fraction of countries where the richest 60 percent saw reductions in malnutrition is very slightly higher than the fraction of countries where the poorest 40 percent saw reductions. In Figure 3 it emerges that for malnutrition the fraction of countries seeing negative excess growth among the poorest 40 percent is only slightly less than 50 percent, and the median excess growth rate is almost zero. On the excess growth measure, progress on child mortality emerges as neither pro-poor nor pro-rich.

On MDG intervention indicators, a distinctly pro-poor picture emerges. In Figure 2, for all indicators except contraception, the growth curve for the poorest 40 percent lies above that for the richest 60 percent, so that whether one looks at the difference in median growth rates or at the differences in the fraction of countries recording progress, we get a pro-poor result. We get the same pro-poor picture for MDG intervention indicators in Figure 3, where all curves except that for contraception intersect the zero line on the y-axis to the left of the median line on the x-axis. Thus, whether we look at the median rate of excess growth among the poorest 40 percent, or at the fraction of countries where there is positive excess growth among the poorest 40 percent, we see the same pro-poor picture. Using the latter indicator of pro-poorness, Figure 3 suggests progress has been especially pro-poor for antenatal care and condom use, while progress on use of contraception has been neither pro-poor nor pro-rich.

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Figure 3 points to large differences across countries in the degree to which progress toward the health MDGs has been pro-poor. For example, while on average progress has been broadly similar for the poorest 40 percent and the richest 60 percent for malnutrition and child mortality, there are some countries (to the left of Figure 3) where progress has been dramatically pro-poor (growth rates have been more negative for the poorest 40 percent than for the richest 60 percent), but also some countries (to the right of Figure 3) where progress has been markedly pro-rich (growth rates have been less negative for the poorest 40 percent). Likewise, while, on average, progress on intervention indicators has been propoor, Figure 3 shows that in plenty of countries, progress has been slower for the poorest 40 percent than for the richest 60 percent.

The maps in Figure 4 show, for a subset of indicators, where progress has been most propoor. For most indicators, most continents have a mix picture – some countries have achieved pro-poor progress, but not all. This is less true of Asia and the Americas: on antenatal care and skilled birth attendance, almost all countries in these regions have achieved pro-poor progress; by contrast, in almost all countries in Asia, progress on underweight has been pro-rich.

To what extent is overall pro-poor progress attributable to some countries improving systematically across all MDG indicators? Treating each country-indicator combination as an observation, and defining the outcome as whether the country recorded excess growth among the poorest 40 percent for that indicator, we performed a two-way analysis of variance; the 12 indicator and 64 country codes were entered as independent variables. We found that two thirds of the explained variance is attributable to variation across countries, holding indicators constant. In other words, two thirds of the explained variation is 11
explained by some countries systematically doing better than others in achieving pro-poor progress _across all indicators_ .

_How have wealth-related inequalities in the health MDGs been changing?_ Faster growth among the poorer wealth groups implies a reduction in wealth-related inequalities in the MDG health indicators. Figures 2 and 3 are thus _suggestive_ of inequality reduction for the intervention indicators, and of little change in inequality for the outcome indicators. But the charts do not give us a definitive answer on inequality trends; it could be argued that splitting the population into two at the 40 percent mark is a somewhat arbitrary way of tracking inequality. Moreover, they do not give us a sense of _by how much_ inequality has been reduced, or of the amount of inequality left at the time of the latest survey. Finally, pro-poor progress – in the sense of faster growth among the poor – implies a reduction in _relative_ inequality, but not necessarily in _absolute_ inequality. Equal increases in, say, income for everyone implies less relative inequality, but no change in absolute inequality. The choice between the two concepts is entirely a value judgment; given this, it seems worth presenting evidence on both.

A simple – and very incomplete – relative inequality measure is the ratio of the poorest quintile’s value to that of the richest quintile. Figure 5 shows this measure for the earliest survey in each country and the latest survey. A more complete measure of wealth-related health status and health intervention inequality is the concentration index (Kakwani, Wagstaff and van Doorslaer 1997). A positive value indicates that the better off, on average, have higher values of the outcome variable (good in the case of an intervention, bad in the case of an indicator like malnutrition), while a negative value indicates they have, on average lower values (bad in the case of an intervention, good in the case of an indicator 12
like malnutrition). With a binary outcome, like the variables being used in the present paper, the bounds of the concentration index are μ -1 and 1- μ , where μ is the mean of the outcome variable (Wagstaff 2005); thus as the mean increases, the range of possible values shrinks, tending to zero as the mean goes to one. Figure 6 shows the population mean and the concentration index for the earliest and latest surveys for all countries for all MDG indicators; see also column 6 of Table 5.<sup>8</sup> Four points are worth noting in Figures 5 and 6. First, reinforcing earlier studies, we find in both charts inequalities on all but one indicator to the disadvantage of the poor: the poor, on average, have higher rates than the better off on all the MDG health status indicators except HIV, and lower rates on the MDG intervention indicators. Second, for the intervention indicators, we see the ratios in Figure 5 getting closer to one and the concentration indices in Figure 6 approaching zero as the population average gets closer to one. All the charts in Figure 6 have something of a half-arrowhead appearance, reflecting the fact that the bounds shrink as the population mean rises towards one, and that inequality is almost everywhere pro-rich (so we see only the left or right of the arrowhead). Third, in the case of the intervention indicators the markers in both charts seem to be rising over time (population averages are improving), and closing in on the vertical line on the x-axis (inequalities are falling). For the malnutrition and child mortality indicators, we see the markers falling over time, but no clear pattern in terms of inequality. We come back to this in a moment. Fourth, inequalities favoring the better off are still very much in evidence even in the latest round of surveys.

> 8 Concentration indices were computed from the individual-level data not the quintile data using the convenient covariance method (see e.g. Jenkins 1988), except in the case of the mortality variables whose concentration indices were computed from quintile data (see e.g. Kakwani, Wagstaff and van Doorslaer 1997).

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The analogue of the concentration index for absolute inequality measurement is the absolute concentration index, equal to the mean multiplied by the standard concentration index.<sup>9</sup> A positive value indicates higher values, on average, among the better off, and a negative value indicates higher values, on average, among the less well off. In the case of a binary outcome, the bounds of the absolute concentration index start at zero when the population mean is zero, grow as the mean rises above zero reaching a maximum and minimum widen, increase as the population average rises above zero, reaching minimum and maximum values of -0.25 and +0.25 as the mean reaches one half; as the population mean rises further, the bounds shrink, reaching zero again as the mean reaches one.<sup>10</sup> Figure 7 is the analogue of Figure 6 but for the absolute concentration index; see also column 10 of Table 5. Five features of Figure 7 are worth highlighting. First, we see that absolute inequality also favors the better off. Second, we see that as the population average rises, absolute inequality does not always decrease; instead, there is a sign – especially visible for the ANC and SBA indicators – of inequalities growing as the population average increases, before diminishing. This is consistent with the bounds of the absolute concentration index having a rotated ellipse shape with minima and maxima of -0.25 and +0.25. Third, the smaller bounds of the absolute concentration index explain why the values for the absolute concentration index are smaller than those for the regular concentration index. Fourth, as with relative inequality, it appears that the upward drift in the intervention markers over time has been associated with less absolute inequality, while for the malnutrition and mortality indicators, the pattern is less clear. Fifth, as with relative inequality, we still see evidence of inequalities even in the latest data.

> 9 The absolute concentration index is the same as the generalized concentration index, the former being derived from the absolute concentration curve, the latter from the generalized concentration curve. On absolute inequality, see Jenkins and Jäntti (2005 p.14),  Moyes (1987 p.205) and Lambert (2001 p.56). In the health context, see Wagstaff et al. (1991) and Wagstaff (2009). 10 This follows from the fact that the absolute concentration index is equal to the mean times the standard concentration index.

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Figure 8 shows the global distribution, for each indicator, of annual average changes in the regular and absolute concentration indices; see also columns 7, 8, 9, 11, 12 and 13 of Table 5. Two points are worth highlighting. First, reductions in relative inequality are more evident for the intervention indicators than for the health status indicators. For IMR, roughly half of countries saw relative inequality increase; half saw it fall. For the other four health status indicators, only a slight majority of countries saw a reduction in relative inequality, and the (absolute) percentage change per year in relative inequality was small (typically less than one percent). The health status indicator that bucks this trend is HIV prevalence, where 67 percent of countries saw relative inequality fall, and the regular concentration index fell by 2.3 percent per year. Keep in mind, though, that in the initial round of surveys, HIV was more prevalent among the better off, not the poor; so while a reduction in inequality occurred, it was not a pro-poor change. On the intervention indicators, reductions in inequality are more pronounced. On average, nearly three quarters of countries record reductions in relative inequality on the intervention indicators, and the annual reduction in the concentration index averages around four percent.

Second, we see fewer countries reducing absolute inequality than relative inequality, and smaller median reductions in absolute inequality. On the malnutrition and child mortality indicators, we actually see _increases_ in absolute inequality in the majority of countries, with changes in the absolute concentration index for these indicators averaging three percent. On the intervention indicators we see reductions in absolute inequality, but the percentage reductions in absolute inequality are, on average, half as large as the percentage reductions in relative inequality. The different behaviors of the relative and absolute inequality measures reflects the fact that an improvement to someone in the richest half of the population will cause absolute inequality to rise, but it need not cause relative inequality to 15
rise; depending on the initial concentration index value, improvements to people well above the mid-point of the wealth distribution could still be compatible with a reduction in relative inequality.<sup>11</sup>

# **Discussion**

The developing world as a whole is making progress toward all MDG indicators. Unsurprisingly perhaps, given the different starting points, progress has been slower on some indicators (for example, immunization, which had higher initial rates of coverage) than on others (for example, antenatal care coverage, which had lower initial rates). More surprising is the fact that for every indicator there are several countries that have gone backwards. For some indicators, the fraction of such countries is quite high: one quarter for immunization; and a fifth and a third for underweight and stunting.

The main focus of the paper, however, has been on differential progress between the poor and better off. There are three key messages of this paper. First, in a majority of countries the poorest 40 percent are making faster progress than the richest 60 percent, and on average relative inequality in the MDG indicators has been falling. Second, in an appreciable fraction of countries, the poorest 40 percent have not been progressing in absolute terms. Moreover, in a sizable fraction of countries the poorest 40 percent have progressed less quickly than the richest 60 percent, and relative inequality has been growing. This is especially true of the child health status indicators (for child malnutrition and mortality, the fraction in this latter category is 40-50 percent), but it is also true of some intervention indicators (for immunization, the figure is almost 40 percent). Third,

> 11 Wagstaff (2009) proves both results. An increase in individual _i_ ’s outcome (holding everyone else’s constant) will raise absolute inequality if person _i_ is in the upper half of the wealth distribution. The same increase will raise relative inequality only if the person’s rank in the wealth distribution exceeds 1⁄2(CI+1) where CI is the value of the (initial) concentration index. So if CI is 0.25, person _i_ ’s rank in the wealth distribution would have to be above 0.625 for relative inequality to increase.

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while on average relative inequality has been falling, appreciable inequalities remain. In the latest round of surveys used in this study (of which all but two are from the 2000s), the richest 60 percent of children in the developing world are, compared to the poorest 40 percent and based on a comparison of odds ratios, 1.2 times as likely to sleep under a bednet, 1.5 times as likely to be immunized, and 3.7 times as likely to have been delivered by a skilled birth attendant.

On the subset of issues that we address and previous authors have addressed, we see some similarities in results, but also some differences. Our results are broadly consistent with those of Victora et al. (2012) and Suzuki et al. (2012) who found pro-poor progress on most of the MDG intervention indicators that were also examined here; the exception is SBA which Suzuki et al. (2012) concluded had been pro-rich. On inequalities in health status indicators, our results on under-five mortality are consistent with those of Moser et al. (2005) who found that progress had been neither pro-poor nor pro-rich on balance, but our results on underweight are less encouraging than those of Suzuki et al. (2014) who found pro-poor progress in over 60 percent of countries. Differences in findings may be due to variations in the methodological approaches adopted, such as differences in the number of countries included, time period examined the definition of poor and non-poor and the measure of inequality.

Our first finding – that in most countries the poor have not been left behind by the health MDGs – will be reassuring to donors, international development and technical agencies, national governments, NGOs, program implementers, and health care professionals. But our other two findings – that relative inequalities have grown in a sizable minority of countries especially on health status indicators, and that, despite reductions in most countries, inequalities are still appreciable – will be a cause for concern. Governments, 17
donors and NGOs ought, after all, to be able to exert a fairly direct influence over the coverage of immunization, skilled birth attendants, bednets, etc. Our findings suggest that Gwatkin (2005) was right to warn that programs that improve population averages may not necessarily disproportionately benefit the poor. Our findings also suggest that it is indeed important to monitor progress not only on population averages but also on inequalities – a point made by the UN’s 2013 high-level panel report on the post-2015 development agenda (United Nations 2013). Our results ought also to prompt yet further discussion of appropriate policies to reduce inequalities in intervention coverage and health status. For example, are universalist policies the answer, or is there a greater need for targeting of efforts toward the poor? Do ‘new’ initiatives such as conditional cash transfers and pay-forperformance stand a better chance of reducing inequalities than ‘traditional’ approaches like pure input-financing?

Our finding that progress on interventions has been considerably more pro-poor than progress on health status is a puzzle that merits further research. One hypothesis is that the quality of health care is worse for lower socioeconomic groups, so that even though they may have experienced a larger percentage increase in, say, antenatal visits, babies in the poorest 40 percent of the population have not had the same improvement in their survival prospects. If true, this would point to the need for a monitoring framework that captures not only the quantity of care (as is currently the case) but also its quality. Another hypothesis is that while inequalities in the official monitoring indicators have been narrowing, inequalities in at least some of the interventions not monitored may have been widening. Examples include health interventions delivered by caregivers, like breastfeeding, hand-washing, and antibiotics for a child suffering from dysentery or pneumonia – indicators that a UN technical consultation suggested be tracked but which 18
never obtained official indicator status; as a result, the interventions in question may have been under-promoted. If true, this would point to the need for a broadening of the monitoring framework to capture interventions delivered by caregivers, as well perhaps as a policy shift in the health sector toward preventive and curative interventions delivered in the home and in the community.

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# **Table 1: MDG health goals, targets, and indicators**

|**MDG goal**|**Targets**|**Official indicators**|**Additional core intermediate monitoring indicators**<sup>**§**</sup>|
|---|---|---|---|
|**Goal 1:**<br>**Eradicate**<br>**extreme poverty**<br>**and hunger**|Halve, between 1990 and<br>2015, the proportion of<br>people who suffer from<br>hunger.|Prevalence of underweight<br>children under five years of age<br>Proportion of population below<br>minimum level of dietary<br>energy consumption|Percentage of children 6 to 59 months who received one dose of vitamin A in the past six<br>months, proportion of infants under six months who are exclusively breastfed.|
|**Goal 4: Reduce**<br>**child mortality**|Reduce by two thirds,<br>between 1990 and 2015,<br>the under-five mortality<br>rate|Under-five mortality rate<br>Infant mortality rate<br>Measles immunization among<br>children under one|Proportion of infants under six months who are exclusively breastfed, proportion of<br>surviving infants who have received a dose of measles vaccine by their first birthday,<br>proportion of children with fast or difficult breathing in the past two weeks who received<br>an appropriate antibiotic, proportion of children with diarrhea in the past two weeks who<br>received oral rehydration therapy (ORT), proportion of children under five who slept<br>under an insecticide-treated net the previous night (in malarious areas), proportion of<br>children with fever in the past two weeks who received an appropriate anti-malarial (in<br>malarious areas)|
|**Goal 5: Improve**<br>**maternal health**|Reduce by three<br>quarters, between 1990<br>and 2015, the maternal<br>mortalityratio|Maternal mortality ratio<br>Proportion of births attended<br>by skilled health personnel|Contraceptive prevalence rate, percentage of women with any antenatal care, provision of<br>emergency obstetric care, syphilis in pregnant women, and proportion that are properly<br>treated, percentage of women receiving antenatal care who receive at least two to three<br>intermittent preventivemalaria treatments during pregnancy (in malarious areas)|
|**Goal 6: Combat**<br>**HIV/AIDS,**<br>**malaria and**<br>**other diseases**|Have halted by 2015 and<br>begun to reverse the<br>spread of HIV/AIDS|HIV prevalence among 15-24-<br>year old pregnant women<br>Condom use rate of the<br>contraceptive prevalence rate<br>Number of children orphaned<br>byHIV/AIDS|Percentage of persons using a condom at last higher-risk sex, percentage of sexually<br>transmitted infection clients who are appropriately diagnosed and treated according to<br>guidelines, percentage of HIV-positive women receiving anti-retroviral treatment during<br>pregnancy to prevent mother-to-child transmission of HIV|
||Have halted by 2015 and<br>begun to reverse the<br>incidence of malaria and<br>other major diseases|Prevalence of and death rate<br>associated with malaria<br>Proportion of population in<br>malaria risk areas using<br>effective malaria prevention<br>and treatment measures|Percentage of patients with uncomplicated malaria who received treatment within 24<br>hours of onset of symptoms, percentage of children under five sleeping under insecticide-<br>treated nets, percentage of pregnant women sleeping under insecticide-treated nets,<br>percentage of pregnant women who have taken chemoprophylaxis or drug treatment for<br>malaria|
|||Prevalence of and death rates<br>associated with tuberculosis<br>Proportion of tuberculosis<br>cases detected and cured under<br>directly observed treatment,<br>short-course (DOTS)|Percentage of estimated new smear-positive tuberculosis cases that were registered under<br>the DOTS approach|

Source: Wagstaff and Claeson (2004).<sup>§</sup> Additional core intermediate monitoring indicators are those recommended by a technical consultation between the World Bank and UN technical agencies, and are as reported in Table 2.1 of Wagstaff and Claeson (2004). That table also lists optional indicators.

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**Table 2: Previous studies of differential progress by wealth toward the health MDGs**

||Moser, Leon and Gwatkin<br>(2005)|Suzuki, Sharan and Bos (2012)|Victora et al. (2012)|
|---|---|---|---|
|Time period|1990-2001|1995-2007|1990-2007|
|No. countries|22|40|35|
|Data source|Demographic and Health<br>Survey (DHS)|Demographic and Health Survey<br>(DHS), Multiple Indicator Cluster<br>Survey (MICS)|Demographic and<br>Health Survey (DHS)|
|No. surveys per<br>country|2|2|2|
|Official indicators|Under-five mortality|Prevalence of underweight children<br>under five years of age<br>Under-five mortality rate<br>Infant mortality rate<br>Measles immunization among children<br>under one<br>Proportion of births attended by<br>skilled health personnel|Skilled birth attendant<br>Measles immunization|
|Additional core<br>intermediate<br>monitoring indicators|None|Use of insecticide-treated bednets by<br>under-five children<br>Proportion of infants under six months<br>who are exclusively breastfed<br>Proportion of children with diarrhea in<br>the past two weeks who received oral<br>rehydration therapy (ORT)<br>Proportion of children with fever in the<br>past two weeks who received an<br>appropriate anti-malarial (in<br>malarious areas)<br>Contraceptive prevalence rate|Use of insecticide-<br>treated bednets by<br>under-five children|
|Stratifying variable|Wealth index|Wealth index|Wealth index|
|Summary statistic|Changes in ratio of the richest<br>20 percent to the poorest 20<br>percent|Changes in ratio of the richest 20<br>percent to the poorest 20 percent|Changes in<br>concentration index|
|Findings|An approximately equal<br>number of countries with<br>widening and narrowing<br>inequalities|Pro-poor progress on underweight<br>Pro-rich progress on under-five<br>mortality<br>Pro-poor progress on most intervention<br>indicators except skilled birth<br>attendance|Inequalities have been<br>falling over time|

Source: Authors’ compilation based on listed studies.

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**Table 3: Details of datasets used in the current study**

|MDG|Indicator|No. countries|No. surveys|Surve<br>Average|ys per country<br>Minimum|Maximum|
|---|---|---|---|---|---|---|
|1|Stunting|53|156|2.9|2|5|
|1|Underweight|54|158|2.9|2|5|
|4|IMR|41|126|3.1|2|5|
|4|U5MR|41|125|3.0|2|5|
|4|Full immunization|60|178|3.0|2|6|
|4|Measles immunization|63|188|3.0|2|6|
|5|ANC4+|41|127|3.1|2|5|
|5|SBA|41|127|3.1|2|5|
|5|Contraceptive prevalence|38|87|2.3|2|4|
|6|Condom use in risky intercourse|15|32|2.1|2|3|
|6|HIV prevalence|9|18|2.0|2|2|
|6|Malaria nets (children)|23|54|2.3|2|3|
||Average|40|115|2.7|2|4.5|

Source: Authors’ calculations from original DHS and MICS datasets.

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# **Table 4: MDG indicators used in current study**

|MDG goal|Indicator|Definition|
|---|---|---|
|Goal 1: Eradicate extreme<br>poverty and hunger|Underweight|Percentage of children with a weight-for-age z-score <-2 standard<br>deviations from the reference median<sup>§</sup>|
||Stunting|Percentage of children with a height-for-age z-score <-2 standard<br>deviationsfromthereferencemedian<sup>§</sup>|
|Goal 4: Reduce child<br>mortality|Infant mortality rate<br>(IMR)|Number of deaths among children under 12 months of age per 1,000<br>live births<sup>$</sup>|
||Under-five mortality<br>rate (U5MR)|Number of deaths among children under 5 years of age per 1,000 live<br>births<sup>$</sup>|
||Measles immunization|Percentage of children aged 12-23 months who received measles<br>either verified by card or by recall of respondent|
||Full immunization|Percentage of children aged 12-23 months who received BCG,<br>measles, and three doses of polio and DPT, either verified by card or<br>byrecallof respondent|
||Malaria nets (children)|Percentage of children under 5 sleeping under an (ever) insecticide-<br>treated bednet the previous night|
|Goal 5: Improve maternal<br>health|Skilled birth attendant<br>(SBA)|Percentage of births of births to mothers aged 15-49 that were<br>attended by skilled health attendant (SBA)|
||Antenatal care visits – 4<br>or more (ANC4+)|Percentage of mothers aged 15 to 49 who received at least four<br>antenatal care (ANC4+) visits from any skilled personnel (as defined<br>in the country’s DHS or MICS)|
||Contraceptive<br>prevalence|Percentage of women aged 15 to 49 who currently use a modern<br>method of contraception|
|Goal 6: Combat HIV/AIDS,|HIV prevalence|Percentage of adults aged 15 to 49 testing positive for HIV 1 or 2|
|malaria and other diseases|Condom use in risky<br>intercourse|Percentage of women aged 15 to 49 who had more than one partner<br>inthe past yearand used a condomduringlast sexual intercourse|
||Malaria nets (children)|Percentage of children under 5 sleeping under an (ever) insecticide-<br>treated bednet the previousnight|

§ WHO 2006 child growth standards used to calculate z-score $ Mortality rate calculated using the true cohort life table approach. The DHS reports use the synthetic cohort life table approach 23
**Table 5: Key summary statistics on differential progress by wealth toward the health MDGs**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||An<br>%|nual grow|th<br>%<br>countries|Median||Relative i|nequality|as % of||Absolute<br>%|inequality|as % of<br>median|
|||countries<br>|Median<br>|where<br>|excess<br>||||median<br>||countries<br>|Median<br>|initial<br>|
||Median|where|growth|mean for|growth||%|Median|initial|Median|with fall|change|absolute|
||growth|pop|rate|poorest|of|Median|countries|change|CI<sup>§</sup>|initial|in|in|CI<sup>§</sup>|
|Indicator|rate<br>pop|mean<br>improved|poorest<br>40%|40%<br>improved|poorest<br>40%|initial<br>CI<sup>§</sup>|with fall<br>in CI<sup>§</sup>|in CI<sup>§</sup><br>p.a.|(absolute<br>value)|absolute<br>CI<sup>§</sup>|absolute<br>CI<sup>§</sup>|absolute<br>CI<sup>§</sup>p.a.|(absolute<br>value)|
|Stunting|-0.0084|64|-0.0087|64|0.0023|-0.1035|58|-0.0009|0.8|-0.0406|45|0.0001|0.3|
|Underweight|-0.0269|80|-0.0239|74|0.0064|-0.1345|56|-0.0012|0.9|-0.0246|43|0.0004|1.8|
|IMR|-0.0281|93|-0.0283|93|-0.0004|-0.1039|49|0.0010|0.9|-0.0080|34|0.0003|3.4|
|U5MR|-0.0307|88|-0.0328|90|0.0019|-0.1209|61|-0.0009|0.7|-0.0143|34|0.0003|2.1|
|Full immunization|0.0242|73|0.0295|72|0.0105|0.0934|68|-0.0040|4.2|0.0419|58|-0.0008|1.9|
|Measles|0.0169|78|0.0217|78|0.0091|0.0655|75|-0.0025|3.8|0.0386|73|-0.0012|3.1|
|ANC4+|0.0310|78|0.0445|78|0.0164|0.2001|78|-0.0049|2.4|0.0571|44|0.0003|0.6|
|SBA|0.0183|83|0.0277|80|0.0130|0.2807|73|-0.0043|1.5|0.1080|49|0.0004|0.3|
|Contraception|-0.0143|41|0.0065|50|-0.0009|0.0691|53|-0.0015|2.2|0.0097|53|-0.0005|5.6|
|HIV-prevalence|-0.0101|67|-0.0026|56|0.0198|0.1526|67|-0.0035|2.3|0.0066|89|-0.0004|5.4|
|HIV-condom|0.0740|93|0.1095|100|0.0412|0.2446|93|-0.0113|4.6|0.0625|57|-0.0002|0.4|
|Malaria-net children|0.1691|78|0.3353|74|0.0480|0.2950|65|-0.0304|10.3|0.0171|52|-0.0001|0.8|
|Av. health status|-0.0208|78|-0.0193|75|0.0060|-0.0620|58|-0.0011|1.1|-0.0162|49|0.0002|2.6|
|Av. interventions|0.0456|75|0.0821|76|0.0196|0.1783|72|-0.0084|4.2|0.0478|55|-0.0003|1.8|

Source: Authors’ calculations from original DHS and MICS datasets.<sup>§</sup> CI is concentration index – see text for definition.

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# **Figure 1a: Progress toward the health MDGs for the entire population**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>.08 .1 .1 .08<br>.06 .08 .06<br>.04 .05 .06 .04<br>.04<br>.02 0 .02 .02<br>0 0<br>Av. growth p.a. -.02 Av. growth p.a. -.05 Av. growth p.a. 0-.02 Av. growth p.a. -.02<br>-.04 -.1 -.04 -.04<br>-.06 -.06 -.06<br>-.08 -.15 -.08 -.08<br>.4 .5 .7<br>.2<br>.6<br>.3 .4<br>.15 .5<br>.2 .3 .4<br>.1<br>.1 .2 .3<br>.05 .2<br>Av. growth p.a. 0 Av. growth p.a. Av. growth p.a. .1 Av. growth p.a.<br>0 .1<br>-.1 0 0<br>-.2 -.05 -.1 -.1<br>1.4 .01 .16 .6<br>1.2 0 .14 .5<br>1 -.01 .12 .4<br>.8 -.02 .1 .3<br>.2<br>.6 -.03 .08<br>.1<br>.4 -.04 .06 0<br>Av. growth p.a. .2 Av. growth p.a. -.05 Av. growth p.a. .04 Av. growth p.a. -.1<br>0 -.06 .02 -.2<br>-.2 -.07 0 -.3<br>-.4 -.08 -.02 -.4<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

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**Figure 1b: Progress toward the health MDGs for the entire population – common growth rate scales**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>1.4 1.4 1.4 1.4<br>1.2 1.2 1.2 1.2<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2<br>0 0 0 0<br>-.2 -.2 -.2 -.2<br>-.4 -.4 -.4 -.4<br>1.4 1.4 1.4 1.4<br>1.2 1.2 1.2 1.2<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2<br>0 0 0 0<br>-.2 -.2 -.2 -.2<br>-.4 -.4 -.4 -.4<br>1.4 1.4 1.4 1.4<br>1.2 1.2 1.2 1.2<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2 Av. growth p.a. .2<br>0 0 0 0<br>-.2 -.2 -.2 -.2<br>-.4 -.4 -.4 -.4<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

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**Figure 2: Progress toward the health MDGs among the poorest 40 percent and richest 60 percent**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a. Cumul. fraction of countries ranked by av. growth p.a.<br>Poorest 40% Richest 60%<br>.1 .1 .15 .25<br>.2<br>.05 .05 .1<br>.15<br>0 0 .05 .1<br>0 .05<br>-.05 -.05 0<br>Av. growth p.a. Av. growth p.a. Av. growth p.a. -.05 Av. growth p.a.<br>-.1 -.1 -.05<br>-.1<br>-.1<br>-.15 -.15 -.15 -.15<br>.5 .3 .6 .7<br>.4 .25 .5 .6<br>.3.2 .2.15 .4 .4.5<br>.1 .3 .3<br>.1<br>.05 .2 .2<br>Av. growth p.a. 0 Av. growth p.a. 0 Av. growth p.a. Av. growth p.a. .1<br>-.1 -.05 .1 0<br>-.2 -.1 0 -.1<br>-.3 -.15 -.1 -.2<br>1.4 .08 .8<br>.25<br>1.2 .06<br>1 .04 .2 .6<br>.8 .02 .4<br>.6 0 .15<br>-.02 .2<br>.4 -.04 .1<br>Av. growth p.a. .2 Av. growth p.a. -.06 Av. growth p.a. Av. growth p.a. 0<br>0 -.08 .05 -.2<br>-.2 -.1<br>-.4 -.12 0 -.4<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

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# **Figure 3: Incidence of excess growth among the poorest 40 percent on health MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth Cumul. fraction of countries ranked by excess growth<br>.12 .2 .12 .25<br>.1 .1<br>.15 .2<br>.08 .08<br>.06 .1 .06 .15<br>.04.02 .05 .04.02 .1<br>0 0 0 .05<br>-.02-.04 -.05 -.02-.04 0<br>Excess growth among poorest 40% p.a. -.06 Excess growth among poorest 40% p.a. -.1 Excess growth among poorest 40% p.a. -.06 Excess growth among poorest 40% p.a. -.05<br>.15 .08 .3 .1<br>.1 .06 .25 .05<br>.04<br>.05 .02 .2 0<br>0 0 .15<br>-.05<br>-.05 -.02 .1<br>-.04 -.1<br>-.1 -.06 .05<br>-.15 -.08 0 -.15<br>Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a. -.1 Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a.<br>-.2 -.05 -.2<br>.1 .1 .15 .5<br>.05 .08 .4<br>0 .06.04 .1 .3.2<br>-.05 .02 .05 .1<br>-.1 0-.02 0 0<br>-.15-.2 -.04-.06 -.05 -.1-.2<br>Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a. Excess growth among poorest 40% p.a.<br>-.25 -.08 -.1 -.3<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The annual growth is as a fraction, so e.g. 0.02 means 2% annual growth.

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<!-- Start of picture text --> Underweight Under-five mortality Excess growth p.a.<br>: 7 7 an . - : = . aoe among poorest<br>¥ "| \\ sPe rd 1 Aag ld“ j "| 4 1 . . - r ¥4 4J iPie = 3 4s¢ =e a j \ 4 y . ~ health40 percent status<br>aa Paty ~" oe ff - - > > ber at. ; a fF E wth pa.<br>mm.’ W mh, rir. x Ay+ > ' ¥ ru 4 h, . << 1, B » Vv ~ interventiona percent coverage<br>K i 7 , ‘j ‘Cm * x t * 4 4 Oca 5.00 500<br>rw = ‘ tk ey | i oe te<br>‘About Tablnas maps: wmw:tabisousoftwars. comvmapdata LA About Tableau mags: www.tablsausoftewam.com/mapaata iy ry<br>Measles immunization Antenatal care<br>— ne 4 ‘ » ¥ rf = ¥ 4 » ¥<br>About Tabluau maps: wewwtablaausofteare.com/mapdata [! by About Tableau maps:wwetabkausoftearm.com/mapidata /" by /<br>Skilled birth attendance Bednets<br>. ee. va ® 4, is ‘| a, * in<br>4... ¢ “ M oe ey is nt<br>m 2 Pu, a ‘i * ; Fic 4 ‘ . a + |<br>* ”<br>. = / P| ( . }<br>‘About Tabinau maps: www:tabicousoftware.com/mapetata a bg About Tablsau maps: www.tablausoferara.com/'mapdata Fa<br><!-- End of picture text -->
**Figure 5: Trends in ratio of poorest quintile’s MDG indicator value to richest quintile’s value**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10<br>q1/q5 q1/q5 q1/q5 q1/q5<br>Full immunization Measles ANC4+ SBA<br>0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10 0 2 4 q1/q5 6 8 10<br>Earliest survey Latest survey<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Population average Population average Population average Population average<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Population average Population average Population average Population average<br>.2 .2 .2 .2<br>0 0 0 0<br>1 1 1 1<br>.8 .8 .8 .8<br>.6 .6 .6 .6<br>.4 .4 .4 .4<br>Population average Population average Population average Population average<br>.2 .2 .2 .2<br>0 0 0 0<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The population rate is as a fraction, so e.g. 0.2 means a population average of 20%. This is true also for IMR and U5MR which are typically expressed per 1,000.

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# **Figure 6: Trends in the concentration index of MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>-.5 0 .5 1 -.5 0 .5 1 -.5 0 .5 1 -.5 0 .5 1<br>Concentration index Concentration index Concentration index Concentration index<br>Full immunization Measles ANC4+ SBA<br>-.5 0 .5 1 -.5 0 .5 1 -.5 0 .5 1 -.5 0 .5 1<br>Concentration index Concentration index Concentration index Concentration index<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>-.5 0 .5 1 -.5 0 .5 1 -.5 0 .5 1 -.5 0 .5 1<br>Concentration index Concentration index Concentration index Concentration index<br>Earliest survey Latest survey<br>.6 .2 .25<br>.5<br>.5 .2<br>.4 .15<br>.4 .3 .15<br>.1<br>.3 .2 .1<br>Population average Population average Population average Population average<br>.2 .1 .05 .05<br>.1 0 0 0<br>1 1 1 1<br>.8 .8 .8<br>.8<br>.6 .6 .6<br>.6<br>.4 .4 .4<br>Population average Population average Population average Population average<br>.4<br>.2 .2 .2<br>0 .2 0 0<br>.8 .25 .6 .8<br>.2<br>.6 .6<br>.4<br>.15<br>.4 .4<br>.1<br>Population average Population average Population average .2 Population average<br>.2 .05 .2<br>0 0 0 0<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. . The population rate is as a fraction, so e.g. 0.2 means a population average of 20%. This is true also for IMR and U5MR which are typically expressed per 1,000.

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**Figure 7: Trends in the absolute concentration index of MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>-.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2<br>Absolute concentration index Absolute concentration index Absolute concentration index Absolute concentration index<br>Full immunization Measles ANC4+ SBA<br>-.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2<br>Absolute concentration index Absolute concentration index Absolute concentration index Absolute concentration index<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>-.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2 -.1 0 .1 .2<br>Absolute concentration index Absolute concentration index Absolute concentration index Absolute concentration index<br>Earliest survey Latest survey<br>.6 .2 .25<br>.5<br>.5 .2<br>.4 .15<br>.4 .3 .15<br>.1<br>.3 .2 .1<br>Population average Population average Population average Population average<br>.2 .1 .05 .05<br>.1 0 0 0<br>1 1 1 1<br>.8 .8 .8<br>.8<br>.6 .6 .6<br>.6<br>.4 .4 .4<br>Population average Population average Population average Population average<br>.4<br>.2 .2 .2<br>0 .2 0 0<br>.8 .25 .6 .8<br>.2<br>.6 .6<br>.4<br>.15<br>.4 .4<br>.1<br>Population average Population average Population average .2 Population average<br>.2 .05 .2<br>0 0 0 0<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. . The population rate is as a fraction, so e.g. 0.2 means a population average of 20%. This is true also for IMR and U5MR which are typically expressed per 1,000.

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**Figure 8: Variations across countries in trends in relative and absolute wealth-related inequality in MDG indicators**

<!-- Start of picture text --> Stunting Underweight IMR U5MR<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI<br>Full immunization Measles ANC4+ SBA<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI<br>Contraception HIV-prevalence HIV-condom Malaria-net children<br>0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1<br>Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI Cumul. fraction of countries ranked by change in CI<br>Concentration index Absolute concentration index<br>.03 .04 .01 .02<br>.02 .03.02 0 .010<br>.010 .010 -.01 -.01-.02<br>-.01 -.02<br>-.01 -.02 -.03<br>-.02 -.03 -.03 -.04<br>Change in conc. index p.a. -.03 Change in conc. index p.a. -.04-.05 Change in conc. index p.a. -.04 Change in conc. index p.a. -.05-.06<br>-.04 -.06 -.05 -.07<br>.04 .015 .02 .03<br>.03 .01 .01<br>.02 .005 0 .02<br>.01 0 -.01 .01<br>0 -.005-.01 -.02-.03 0<br>-.01<br>-.015 -.04 -.01<br>Change in conc. index p.a. -.02 Change in conc. index p.a. -.02 Change in conc. index p.a. -.05 Change in conc. index p.a.<br>-.03 -.02<br>-.025 -.06<br>-.04 -.03 -.07 -.03<br>.1 .01 .005 .1<br>.08.06 .0050 0-.005-.01 .08.06.04<br>.04 -.005 .02<br>.02 -.01 -.015-.02 0<br>Change in conc. index p.a. 0 Change in conc. index p.a. -.015 Change in conc. index p.a. -.025 Change in conc. index p.a. -.02-.04<br>-.02 -.02 -.03 -.06<br>-.04 -.025 -.035 -.08<br><!-- End of picture text --> Source: Authors’ calculations from original DHS and MICS datasets. The vertical axis measures the annual average change in the index, not the percentage change.

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