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Electronic copy available at: http://ssrn.com/abstract=2217772
University of Oslo
University of Oslo Faculty of Law Legal Studies Research Paper Series
No. 2013-10
Edward Anderson, University of East Anglia
Malcolm Langford, University of Oslo
A Distorted Metric: The MDGs and State Capacity
Electronic copy available at: http://ssrn.com/abstract=2217772Electronic copy available at: http://ssrn.com/abstract=2217772
1
A Distorted Metric: The MDGs and State Capacity
Edward Anderson * and Malcolm Langford**
Abstract. The Millennium Development Goals (MDGs) have been commonly understood as national targets. This interpretation has fostered the critique that the framework favours complacent middle-income countries, discriminates against low-income countries, provides a poor national planning tool and generally fails to conform to the more nuanced obligations of states under international human rights law, such as the duty to use the maximum available resources to realise socio-economic rights. The result is that the current MDGs framework (and its likely successor in 2015) may be an unreliable and misleading indicator of progress when used as a cluster of national benchmarks. This paper tests this potential bias in the framework by measuring performance on two MDG targets, water and sanitation, from the perspective of state capacity. A number of proxy indicators are used to capture the relevant resources: GDP per capita; the ratio of ‘disposable national income’ (DNI) to GDP; total population; land area; urbanisation and the dependency ratio. The relationship between this capacity and actual progress on access to water and sanitation is measured for both levels and changes in resources between 1990 and 2010. The resource-adjusted performance of states is then ranked according to two alternative methods and compared with the ranking generated by the standard MDG metric. The paper concludes by arguing that the post-2015 agenda needs to address the distortion and disincentives created by the MDG framework and suggests a number of practical means of doing so.
Comments on the paper welcome
Lecturer, School of International Development, University of East Anglia. Email: [email protected] ** Research Fellow, Norwegian Centre for Human Rights, University of Oslo. Email: [email protected] Acknowledgment: The initial research findings were first presented at the Third Annual Meeting of Human Rights Metrics, in collaboration with the Center for Economic and Social Rights,, Madrid, 22-23 March 2012. We would like to sincerely thank participants for their feedback.
Electronic copy available at: http://ssrn.com/abstract=2217772Electronic copy available at: http://ssrn.com/abstract=2217772
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1. Introduction
Within a short period of time, the Millennium Development Goals (MDGs) were understood
and established as national benchmarks. Whether it was the halving income poverty, reducing
maternal mortality by two-thirds, or ensuring full access in universal primary education, each
country was given the same target. With the assistance of UNDP, States soon began
producing national reports on progress which were collated into comparative reports by the
UN, World Bank and others. They indicated which countries were ‘on-track’ or ‘off-track’.
This methodological nationalism was reinforced by efforts to use the targets as national
planning tool. A model of MDG-based planning and costing with 2015 set as the end date was
encouraged and piloted (see for a range of discussion Atkinson, 2006; Bourguignon et al.,
2008; Chakravarty and Majumber, 2008).
The approach has been subject to critique. One of the architects of the MDGs argued that it
was ‘denial’ of their very intention: “The quantitative targets were set in line with global
trends, not on the basis of historical trends for any particular region or specific country”
(Vandemoortele, 2007: 1).1 Easterly (2009) complains that Africa was set up for failure: the
goals had the “unfortunate effect” of making the “successes” of this region “look like
failures”. The problem with the MDGs metric becomes clear once the magnitude of a
proportional reduction is compared across countries. The baseline proportion of people living
in poverty in South Africa was 24.3 per cent: for Tanzania it was 72.6 per cent.2 In other
words, South Africa was required to reach a quarter of its population; Tanzania three-quarters.
Moreover, the baseline was set in the year 1990 (with most baseline data coming from
surveys in the early 1990s). This favoured countries which made significant progress before
the new millennium commenced. It led to the rather odd situation where upon the adoption of
the MDGs, China was announce that it had already met the MDG income poverty target, in
1999 (Pogge, 2004). It was not alone in trumpeting its rapid achievements. A number of other
middle and high-income States soon announced they reached the target or were ‘on-track’
(OHCHR, 2010).
1 See also Vandemoortele (2011) for an expanded argument. 2 See data at: http://www.cgdev.org/section/topics/poverty/mdg_scorecards
3
Tabatabai (2007) has responded to this criticism by noting that conflating the basis of
measurement with the intention of the MDGs would be rather meaningless: meeting historical
trends would make a weak case for adopting the framework in the first place. Rather, the
function of the MDGs was to “accelerate trends” which would “encourage weak performers to
lift themselves up to the average level”. However, Tabatabai conceded that it would be a
mistake to use the MDGs yardstick to classify some countries as failures, as such an
assessment can only be made by assessing the circumstances for the country. Tabatai also
notes that some countries had adopted more ambitious targets at the national level, in order to
compensate for “a conservative interpretation of the MDGs”. 3 However, Sumner and
Melamed (2010) find that only 24 states had adopted some form of ‘MDG-Plus’ model
although the extent of these adaptions varies considerably – and to that could be added the
reporting on them.
The MDGs thus contained an internal contradiction. The discourse and intentions were
forward-looking, as Tabatabai rightly states. But once the targets were applied as a singular
template to all counties the monitoring framework there was a risk that they would become
biased towards past achievements: rewarding previous performance.
The MDGs monitoring framework also faced criticism from the perspective of human rights.
The targets over-shadowed the more nuanced and national-sensitive human rights framework
(Amnesty, 2010; Langford, 2010; UN-OHCHR, 2008). 4 For instance, widely ratified
international treaties such as the International Covenant on Economic, Social and Cultural
Rights (ICESCR) and the Convention on the Rights of the Child instead require states to use
their “maximum available resources” to “progressively achieve” economic, social and cultural
rights.5 Thus the pace of expected achievement is dependent on capacity. This is not to
3 See for example, UNDP, MDG-Plus: a case study of Thailand, (New York: UNDP, undated). Similarly, in Latin America and the Caribbean, the region went beyond the MDG target of universal primary education to set a secondary education target of 75 per cent of children by 2010. See Inter-American Development Bank, The Millennium Development Goals in Latin America and the Caribbean: Progress, Priorities and IDB Support for Their Implementation (Washington: IADB, 2005), p. 24. 4 Thus, the human rights critique was not simply about whether the global goals were under-ambitious or not (cf. Alston, 2005; Pogge, 2004) or excluded key human rights or issues (Antrobus, 2003; UNIFEM, 2004), but about whether they reflected the more qualitative and heavily-negotiated gradation in international law. 5 Article 2(1) of ICESCR reads: “Each State Party to the present Covenant undertakes to take steps, individually and through international assistance and co-operation, especially economic and technical, to the maximum of its available resources, with a view to achieving progressively the full realization of the rights recognized in the present Covenant by all appropriate means, including particularly the adoption of legislative measures.” Article 4 of the Convention on the Rights of the Child reads: “States Parties shall undertake all appropriate legislative,
4
overshadow the fact that these treaties have been interpreted to contain a minimal standard of
achievement which might correspond to some of the MDGs (Alston, 2005). In General
Comment No. 3, the UN Committee on Economic, Social and Cultural Rights (CESCR)
stated: “a State party in which any significant number of individuals is deprived of essential
foodstuffs, of essential primary health care, of basic shelter and housing, or of the most basic
forms of education is, prima facie, failing to discharge its obligations under the Covenant”. 6
However, the Committee noted that poorer or fragile countries could plead insufficient
resources if they could establish that “every effort has been made”.7 Further, and more
importantly, countries with greater capacity were required to make brisker progress in terms
of achieving satisfactory level of the rights for all.8
Given that the national-based approach to monitoring remains dominant, it is important to
examine to what extent does the MDGs metric distort the picture of progress.
In this paper, we set out an approach to assessing state performance with regard to MDG
outcomes which takes into account differences in state capacity. This issue has been at the
core of the concerns over the MDGs framework and also fits with the duty of states to use
their maximum available resources to realise the relevant rights under international treaty law.
We apply this approach to access to water and sanitation, which feature both as MDG targets
and as universally accepted human rights. In Millennium Development Goal 7, states
committed to halve the proportion of those without access to an improved source water and
sanitation by 2015. Since 1977, water and sanitation have been recognised as human rights in
a range of international instruments9 and authoritatively so by the UN General Assembly and
UN Human Rights Council in 2010.10 The CESCR has affirmed that these rights attract the
administrative, and other measures for the implementation of the rights recognized in the present Convention. With regard to economic, social and cultural rights, States Parties shall undertake such measures to the maximum extent of their available resources and, where needed, within the framework of international co-operation.” 6 General Comment 3, The nature of States parties' obligations, (Fifth session, 1990), U.N. Doc. E/1991/23, annex III at 86 (1991), para. 10. For legal commentary, see Alston (1992); Langford and King (2008). 7 “In order for a State party to be able to attribute its failure to meet at least its minimum core obligations to a lack of available resources it must demonstrate that every effort has been made to use all resources that are at its disposition in an effort to satisfy, as a matter of priority, those minimum obligations.” Ibid. para. 3. 8 The Committee stated that progressive realisation “imposes an obligation to move as expeditiously and effectively as possible”. Ibid. para. 9. 9 For an overview, see Langford and Russell (2013). 10 In July 2010, the UN General Assembly recognised “the right to safe and clean drinking water and sanitation as a human right that is essential for the full enjoyment of life and all human rights”. U.N. General Assembly, The human right to water and sanitation (Sixty-fourth session, 2010) U.N. Doc A/64/L.63/Rev.1, para. 1.
5
duty of progressive realisation within maximum available resources in accordance with
Article 2(1) of the ICESCR.11 By focusing on water and sanitation only, we are able to go
beyond the use of GDP as a single indicator of state capacity and build a more fine-grained set
of indicators that reflect the relevant state capacity for realising particular targets.12
The paper proceeds as follows. In section 2 we set out our approach to assessing state
performance, taking state capacity into account, and present the indicators we use to measure
state capacity in relation to access to water and sanitation. In section 3 we apply this approach
to comparisons of performance across countries, using data for 2010 or 2005, and compare
the rankings generated by our approach with those generated by the SERF Index.13 In section
4 we apply our approach to comparisons of trends in performance between 1990 and 2010 (or
2005, and compare the rankings generated by our approach with the rankings generated by a
traditional MDG metric. In section 6, we conclude by arguing that the post-2015 agenda
needs to address the distortion and disincentives created by the MDG framework and suggest
a number of practical means of doing so.
However, a high number of States abstained from voting for this resolution: 122 voted in favour to none against, with 41 abstentions (including a number of EU member states). The majority of abstaining States noted that their vote was based on procedural grounds: the matter was also being handled by the UN Human Rights Council. Three months later in September 2010, the UN Human Rights Council affirmed the recognition of the right to water and sanitation, and its legal etymology, without the need to hold a vote: “(The Council) Affirms that the human right to safe drinking water and sanitation is derived from the right to an adequate standard of living and inextricably related to the right to the highest attainable standard of physical and mental health, as well as the right to life and human dignity.” U.N. Human Rights Council, Human rights and access to safe drinking water and sanitation (Fifteenth session, 2010) U.N. Doc. A/HRC/15/L.14. 11 See Committee on Economic, Social and Cultural Rights, General Comment 15, The right to water (Twenty-ninth session, 2002), U.N. Doc. E/C.12/2002/11 (2003); UN Committee on Economic, Social and Cultural Rights, Statement on the Right to Sanitation, (Forty-fifth session, 2010), UN Doc. E/C.12/2010/ 1. 12 The paper builds on earlier work in Dugard, Langford and Anderson (2013) where we compared performance of municipalities across South Africa based on their respective capacities. 13 See Randolph, Fukuda-Parr and Lawson-Remer (2010) for a discussion of the SERF Index.
6
2. Assessing water and sanitation performance: methodology
2.1 Overall approach
Our aim is to assess the performance of national governments in raising access to water and
sanitation, taking into account their ‘capacity’: i.e., their ability to raise access through policy
interventions and spending programmes financed by internal and/or external resources. We
assume that access is determined partly by government capacity, but also by government
performance, defined broadly to cover both the degree of priority attached by the government
to water and sanitation, relative to other goals, and the efficiency with which the government
makes use of resources.
Our approach works in two main stages. We first estimate a linear regression of the form:
ik
ikki uXA ++= ∑ββ0 (1)
where Ai is a measure of access, Xik is a set of indicators thought to affect government
capacity (e.g. GDP per capita, foreign aid), and ui is an error term. We then measure
performance by the difference between the country’s actual level of access and its expected
level of access, given the capacity indicators. Denoting performance by zi and the predicted
level of access by iA , our performance measure is therefore:
iii AAz ˆ−= . (2)
where
∑+=k
ikki XA ββ ˆˆˆ0 (3)
and 0β and kβ are the estimated values of 0β and kβ obtained from the regression analysis.
To see how this approach works, consider a performance comparison between two countries
m and n. The difference between the two countries in our performance measure is given by:
( ) ∑ −−−=−k
nkmkknmnm XXAAzz )(β (4)
7
Our approach therefore adjusts the difference in actual levels of access ( nm AA − ) by an
amount depending on the differences in each capacity indicator ( nkmk XX − ). This is designed
to give a more accurate estimate of the difference in performance than would be the case if
focusing only on actual levels of access. Two main points are worth noting however.
First, while the approach can assess whether performance in one country is better than in
another, it does not establish whether performance is good or bad in an absolute sense. From a
human rights standpoint, this might be considered a limitation: the obligation on governments
is to devote maximum available resources to economic and social rights, and their
performance should be judged relative to this absolute benchmark rather than relative to other
countries.14 Assessing compliance with this obligation is however a complicated task which
may require a range of different tools and methods (see for example Felner, 2009)). Relative
assessments of performance can still play a useful role, not least since countries shown to be
performing much worse than others provide obvious cases for suspicion, meriting further
more detailed analysis at the country-level.
Second, our approach is subject to error, because the actual level of access can differ from the
expected level for reasons other than good or poor government performance. One possible
reason is unobserved influences on government capacity. For example, one country may have
topographical features (e.g. hilly terrain) which make it more costly to provide water and
sanitation services. Its level of access might therefore be lower than expected on the basis of
our observable capacity indicators, but this would be a reflection of low unobserved capacity
rather than poor performance. Another possible reason is measurement error in the official
estimates of water and sanitation access. For example, official figures might over-estimate the
true percentage of the population with access to water and sanitation, perhaps because of poor
survey design. The level of access might therefore be higher than expected on the basis of the
capacity indicators, but this would be a reflection of poor data rather than good performance.
In this paper, we are unable to do much about the problem of measurement error; our access
figures are taken from the WHO-Joint Monitoring Programme (JMP) dataset, which is used
14 In Appendix 1 we discuss different ways of defining an absolute performance benchmark in relation to this human rights obligation, but we do not make use of any absolute performance benchmarks in the main body of the paper.
8
for current MDG monitoring and is arguably the most reliable source available – although it
faces some clear construct and statistical validity limitations.15 We do however seek to limit
errors caused by unobserved influences on government capacity, by including a larger set of
capacity indicators is than has been the case in previous work. This is discussed further in the
next sub-section.
2.2 Data and indicators
In implementing our approach, we measure access by the ‘log-odds ratio’, i.e. the ratio of the
percentage of the population with access to the percentage of the population without access,
expressed in logarithmic units. This tended to yield the best fit when estimating equation (1);
it implies that the relationship between access as a percentage of the population and any one
capacity indicator takes the form of an S-shaped curve (see Figure 1). This shape is consistent
with the argument that it becomes increasingly difficult to raise access to water and sanitation
as the percentage with access approaches 100, since this requires providing services to areas
with less favourable geographical and economic conditions and therefore higher costs (e.g.
Krause 2009). Countries with moderate historical levels of access should therefore find it
easier to raise access by a certain amount (in percentage points). It is also consistent with the
finding that countries with historically low levels find it harder to get moving (Anand 2006),
suggesting that economies of scale might be a challenge for countries at very low levels of
access.16 It is also possible that the flattening out effect as access approaches 100 per cent may
have something to do with political will – not providing access to an ethnic minority like the
Roma for example; we need to be careful that lack of political will is not turned into a
capacity constraint.
15 Former UN officials have noted that the definition for access to water is too permissive and sanitation too strict (Bartram, 2008: 283) while others have demonstrated that the water supply may be irregular or not potable (Mboup, 2005), not affordable (Smets, 2009) or culturally unacceptable (Singh, 2013). 16 Anand does not offer a hypothesis as to why this might be the case. He states: “There is strong evidence to suggest that legacy in terms of the starting point matters and as such there is a bigger mountain to climb for those countries which are starting with a lower base.” (p. 19). However, he also notes that a significant number of countries are able to break with that legacy suggesting it is not a hard constraint. However, one possible reason might be economies of scale. In countries with dated infrastructure and high population growth, significant infrastructural and water management investment is needed to reach the majority of the population. It is not a case of just extending networks or water supply projects.
9
Figure 1
Notes: The graph shows the assumed relationship between each capacity indicator and access as a percentage of
the population, i.e. holding other capacity indicators constant. Note that some of our capacity indicators (e.g.
GDP per capita) are measured in logarithmic units, so the relationship with look different from Figure 1 if these
indicators are expressed in their standard units.
Because the log-odds ratio is not defined when access is equal to 100 per cent, our
performance measure cannot be calculated for countries where access has reached this level.
We not consider this to be a problem however. On the one hand, it would be unfair to judge
the performance of such countries as worse than another country, since it is impossible to
raise access beyond 100 per cent. But on the other hand, we cannot say that their performance
as better than all other countries. Thus the only remaining option is to omit them from the
analysis. The performance of states which have already achieved 100 per cent access should
instead be done on the basis of other outcome indicators – for example, the percentage of the
population with access to piped water, as opposed to just ‘improved’ water sources. This is
discussed further in the conclusion.
10
We assume that government capacity is determined by six main indicators, each of which is
beyond government control and influence, at least in the short to medium-term over which
performance is being assessed. The indicators are:
• GDP per capita (constant prices, at PPP exchange rates);
• the ratio of ‘disposable national income’ (DNI) to GDP;
• total population (millions)
• land area (km2)
• urbanisation (% of total population)
• the dependency ratio (the share of population aged 15-64 to the sum of the shares aged
0-14 and 65+).
GDP per capita serves as a proxy indicator for the domestic resources which are available to
the government: ceteris paribus, higher GDP per capita means that the government can raise
more in terms of fiscal revenue which can in turn be used to fund policy interventions and
spending programmes. GDP per capita is of course affected by other circumstances beyond a
state’s control, such as natural disasters or armed conflict, and thus also offers a proxy for the
effects of these types of events, at least partly. The ratio of DNI to GDP is a proxy for the
extent to which additional external resources are available to the government.17 Values greater
than one indicate that additional external resources are available (e.g. migrants’ remittances,
foreign aid), while values less than one indicate that (on balance) some domestic resources are
transferred abroad and therefore not available to the government (e.g. interest payments on
foreign debt, repatriated profits by foreign-owned firms). The two resource variables are
included separately since their effect on capacity may differ. In particular, external resources
may be more easily translated into government resources than domestic resources: for
example, foreign aid is often paid directly into the government’s budget, while payments on
official foreign debt come directly out of government’s budget.
Population and land area are included as potential determinants of the costs of delivering
water and sanitation services. Ceteris paribus, a larger population relative to land area implies
higher population density; this is expected to lower average costs of delivery, thereby raising
17 DNI adjusts GDP to take account of external income flows into and out of a country; in particular, it adds so-called “net factor payments” and “net transfers” to GDP. It differs from GNI, which only adds net factor payments to GDP.
11
governments’ ability to raise access to water and sanitation. Population size may also affect
costs because of diseconomies of scale: ceteris paribus, it may be more difficult to reach an
access level of (say) 90% in a country of 100 million people than one of 1 million, because
the absolute number of people that must be connected to the network is much greater (JMP
2012).18 Urbanisation is expected to have a similar effect to population density, lowering
average costs of service delivery.
The dependency ratio is included for two reasons. On the one hand, higher dependency ratios
mean a smaller working age population, which can make it harder for the government to raise
revenue – since most taxes are drawn from people of working age. On the other hand, higher
dependency ratios can imply higher demands on other areas of government responsibility (e.g.
health, education, social pensions), which reduces the availability of domestic and/or external
resources for water and sanitation. Both reasons indicate that higher dependency ratios reduce
government capacity to raise access to water and sanitation, ceteris paribus.
All of our six main capacity indicators are available from the World Bank’s World
Development Indicators database for a large number of countries and time periods. We also
considered four other potential indicators. The first is average years of schooling in the adult
population. This is a proxy for the availability of human resources in the population, which
would be expected to raise capacity. The drawback with this indicator is that is not as widely
available as the others, and reduces quite substantially the number of countries and years for
which our performance measures can be calculated. The second is availability of domestic
freshwater resources, which would also be expected to raise capacity. The drawback in this
case is that, similar to other authors (e.g. Anand 2006, Krause 2009), we found little evidence
of a positive relationship between this variable and observed levels of access. The other two
indicators are the ratio of tax revenue to GDP, and a measure of government effectiveness
taken from the Kaufmann et al (2012) database. These indicators are positively correlated
with access, but are problematic as capacity indicators since they would normally be
considered firmly within the control of the government. However, one might argue that in
some cases it is quite difficult for governments to raise their effectiveness or the tax revenue
18 The 2012 JMP report argues that countries with rapid population growth have to work harder to meet the MDG target of halving the proportion of the population lacking access to water and sanitation, since the absolute number of people with access to water and sanitation facilities must increase more rapidly.
12
share, or at least in the short run. We therefore carry out sensitivity analysis to test the extent
to which our results change when these indicators are included among the capacity indicators.
It might be objected that our capacity indicators ignore other important causal determinants of
access to water, such as governance, participation, the nature of delivery (public, private,
public-private), general social sector spending, and corruption. For example, Krause (2009)
finds that specific water governance (including user participation and presence of civil society
groups) is highly significant (and more important than GDP), although the presence of
private-public partnerships was insignificant. Wolf (2007) finds that a number of variables not
connected with resource capacity were significant such as press freedom although the overall
effect was limited. Health expenditure per capita (as a proxy for social sector spending) in
Anand (2006) was highly significant in some models, although the general correlation
between rising GDP and health expenditure suggests that the variable needs to be adjusted to
distinguish political will from redistributive capacity.19
We do not consider this a problem for two reasons. The first is that our analysis is normative
rather than explanatory in orientation – seeking to establish a metric for compliance.
Beginning from the standpoint of both economics and human rights law, it is reasonable to
graduate expectations for states according to their resource capacity; more generally,
according to factors which are beyond their control. By contrast, it is less reasonable to
graduate expectations on factors such as water sector governance or press freedom, which are
more clearly within a state’s control and under its policy-making function. The second is that
explanatory-based analyses of the determinants of access to water and sanitation generally do
still find a strong relationship between measures of resource capacity (e.g. GDP per capita)
and water and sanitation outcomes (Anand, 2006); thus ‘providence’ and ‘policy’ are both
important. The task at hand is to try and separate the two factors in order that we can evaluate
states on the basis of factors under their control rather than their mere capacity.
A final possible objection is that some our capacity indicators are themselves partially under
government control – in particular, GDP per capita. There is a criticism not just our approach
but of other similar exercises which use GDP per capita as a proxy for state capacity (see
19 Wolf (2007) excludes GDP from her analysis since it is highly correlated with variables of interest to her: health sector spending per capita and corruption. Interestingly, in their analysis of health and education outcomes, Rajkumar and Swaroop (2002) find that relevant sectoral expenditure only has an effect as governance improves. This suggests that the interaction of various policy-related variables is important.
13
Section 2.2 below). This problem is hard to resolve because the extent to which GDP is under
government control is context specific: clearly, a government which has been in power for
five years cannot be held responsible for the prevailing level of GDP to such an extent that a
government in power for twenty five years can. For this reason, it is clearly important to
combine the results of cross-country performance rankings with more detailed country-level
analysis – a point we return to in Section 6. The best that can be said here is that the problem
is less severe in our approach compared to others, since we do not rely only on GDP per
capita, and our other capacity indicators are more clearly beyond the control of government.
2.3 Comparison with existing approaches
We apply our approach in two main ways. First, we rank countries on the basis of the level of
their performance in a recent year. Applied in this way, our approach is similar to the Socio
Economic Rights Fulfilment (SERF) index set out by Fukuda-Parr et al (2009) and Randolph
et al (2012). This also ranks states on the basis of their performance at a particular point in
time, adjusting for state capacity; it considers 13 different outcome indicators, of which access
to water and access to sanitation are two. There are however two key differences between our
approach and the SERF index.
First, while the SERF index measures state capacity by a single indicator – GDP per capita –
our approach uses multiple indicators of state capacity, including but not limited to GDP per
capita. Our reasoning is that while GDP per capita may be a good proxy for the domestic
resources potentially available to a state, it does not reflect differences in the external
resources that states have access to – captured in our analysis by the ratio of DNI to GDP. In
addition, capacity also depends on other factors beyond direct government control – e.g.
population density, or urbanisation – via their effects on the costs of service delivery.
Although we find that GDP per capita is the single most important capacity indicator, adding
these other indicators does nonetheless make a difference to the rankings (see Section 3
below).
Second, the performance benchmarks used by the SERF index are designed to reflect the
highest level of access achieved at any given level of GDP per capita. They are calculated by
first identifying the ‘outer envelope’ of observations in a scatter plot between GDP per capita
and levels of access, and then estimating the relationship between GDP per capita and access
14
among these observations alone. By contrast, our benchmarks are designed to reflect the
average level of access for any combination of the outcome indicators, and are estimated
using regression analysis of the entire sample. If the shape of the relationship between the
highest level of access and GDP per capita was the same as that between the average level of
access, this difference would not matter much. Performance measured by the SERF index
would then be equal to performance measured by our approach, plus some constant, and the
country rankings (if not the performance scores themselves) would be identical. However, the
shapes of these relationships do differ, causing different rankings.
To illustrate, consider Figure 2 which compares the SERF benchmarks with those generated
by a partial version of our approach which for comparative purposes uses GDP per capita as
the only capacity indicator. As expected, the SERF benchmarks are higher than our
benchmarks at all levels of GDP per capita; however, the extent of the difference is much
larger at lower levels of GDP per capita than at higher levels.20 This difference in slope in turn
leads to different rankings. For example, access to water in the Central African Republic in
2010 was (at 67 per cent) above average for its level of GDP per capita, but in Chile it was (at
96 per cent) roughly equal to the average. Thus by our approach, the Central African Republic
is performing better than Chile, controlling for capacity (in this case, just GDP per capita).
However, Chile is closer to its estimated highest achievable level of access than the Central
African Republic. Thus according to the SERF index, Chile is performing better than the
Central African Republic.
20 Note that the SERF bencharks assume a quadratic relationship between the highest level of access and GDP per capita, in contrast to our assumption of a logistic relationship between average levels of access and GDP per capita. However, if we also assume a quadratic relationship, our benchmarks are very similar to those derived under the assumption of a logistic relationship (see Figure 2). Note also that since our dataset differs somewhat from that used by Randolph et al (2010), we re-estimated the relationship between GDP per capita and the highest achievable level of access, using an otherwise identical methodology; the results are again very similar (see Figure 2).
15
Figure 2. Comparing benchmarks for water performance
Notes: 1=calculated using coefficients in Randolph et al (2010: 239); 2=calculated using equivalent coefficients
estimated from our dataset; 3=calculated using logistic relationship (equation 1 in main text); 4=calculated using
quadratic relationship. Source: Authors’ calculations using JMP (2012) and World Bank (2012)
Our argument for using average levels of access is that these are based on the full sample of
observations and therefore less likely to be biased by measurement error in the access data, or
by differences between observed capacity and actual capacity. Randolph et al (2010) do take
steps to reduce this bias, by excluding from the outer envelope any country with a major
conflict in the past 10 years, by adjusting the GDP per capita data for transitional countries,
and by requiring that the envelope includes at least four different countries. Nonetheless, two
of the countries making up the envelope for water have populations of less than 0.5 million
(Comoros and Tonga), and one might question whether their achievements can be applied to
countries with populations of (say) 50 or 100 million (the other two countries in the outer
envelope for water are Burundi and Nepal, which have populations of 6 and 24 million
respectively). A further problem is that identifying the ‘outer envelope’ becomes more of a
practical challenge when there are multiple capacity indicators – in particular, graphical
methods can no longer be used.
16
Of course, the average level of performance could be below the level of access that a country
could achieve if it took its commitments seriously. However, the same also applies to states
making up the outer envelope. While they are obviously doing better than other countries,
given their capacity, we cannot necessarily assume that they are doing as well as they could
be: if, for instance, they are using their ‘maximum available resources’ in order to achieve the
highest possible level of access to water and sanitation, given their capacity.
The second way in which we apply our approach is to rank countries on the basis of their
trends in performance since 1990, the starting date for the MDG targets. The trend in
performance is given by:
∑−
−−
=−
k
ikTikk
iTiiTi
nXX
nAA
nzz )(ˆ 90,,90,,90,, β (5)
where T is the latest available year of data and n is the length of the period in years. The trend
in performance is therefore the sum of two parts: the actual change in access over the period
minus an adjustment for changes in each capacity indicator over the period.
Applied in this way, our approach has parallels with MDG performance assessments which
compare countries in terms of whether and to what extent they are ‘on-track’ or ‘off-track’
towards the goal of halving the proportion of the population lacking access to water and
sanitation between 1990 and 2015. This target translates into a required rate of reduction of
2.8 per cent per year; thus countries with higher actual rates of reduction since 1990 are
considered to be good performers while countries with lower rates of reduction are considered
poor performers – in each case, increasingly so as rates of reduction lie above or below this
threshold. The implicit performance measure is therefore:
⎟⎟⎠
⎞⎜⎜⎝
⎛=
Ti
iMDGi s
sn
z,
90,ln1 (6)
where si is the percentage of the population without access.
There are however two key differences between this second application of our approach and
MDG performance assessments. First, our approach adjusts for trends in state capacity,
whereas MDG performance assessments do not. Thus countries in which the capacity
17
indicators have grown strongly since 1990 will tend to be ranked lower by our measure than
by an MDG assessment, ceteris paribus. Second, unlike MDG assessments, our approach is
not limited to rates of reduction in the proportion of the population lacking access to water
and sanitation; we also consider rates of increase in the proportion with access. To see this,
note that the annualised change in access measured as a log-odds ratio can be re-written as:
⎟⎟⎠
⎞⎜⎜⎝
⎛+⎟⎟⎠
⎞⎜⎜⎝
⎛=
−
Ti
i
i
TiiTi
ss
naa
nnAA
,
90,
90,
,90,, ln1ln1 (7)
where ai is the percentage of the population with access. Thus if capacity remains constant,
performance is equal to the proportional increase in the proportion with access plus the
proportional decrease in the proportion without access. This is of relevance since the MDG
target for water and sanitation has been criticised for focusing only on the proportional
reduction in the proportion without access, since this disadvantages regions (e.g. Sub Saharan
Africa) where the proportion without access is higher. For example, according to Easterly
(2008: 32), “percentage changes are higher when one starts from a lower base, which gives
the advantage to other regions on WITHOUT [access] and the advantage to Africa on WITH
[access].”
18
3. Comparing performance across countries
3.1 Regression results: access and capacity
We first report estimates of the effects of the six capacity indicators discussed in Section 2 on
access to water and sanitation. Sample information and descriptive statistics for each variable
are shown in Table 1. Our sample includes data for 138 countries, with data at 5-year intervals
between 1990 and 2010 (maximum of five observations per country). Observations where
access is equal to 100 per cent are excluded since the log-odds ratio is not defined in these
cases. The estimation method is ordinary least squares.
Table 1. Descriptive Statistics
Variable code Variable name Obs Mean Std. Dev. Min Max
lrwater Access to water, log odds ratio 506 1.74 1.38 -1.82 4.60 lrsanit Access to sanitation, log odds ratio 511 0.71 1.81 -3.48 4.60
ly GDP per capita (US$ PPP, 2005 prices), log units 506 8.10 1.03 5.82 10.80
lexflow Ratio of DNI to GDP, log units 506 0.04 0.12 -0.40 0.81 lpop Total population, log units 506 15.97 1.82 11.51 21.01 larea Land area, log units 506 12.05 2.06 5.70 16.61 pop_urban Urban population share (%) 506 46.22 21.18 5.40 98.30 dep Dependency ratio 506 0.72 0.18 0.38 1.22 ysch_tot Years of schooling in adult population 356 6.49 2.53 0.90 12.00
lwatres Renewable internal freshwater resources (million cubic meters), log units 480 10.68 2.27 3.00 15.51
tax_sh Tax revenues (% of GDP) 235 15.20 6.54 0.18 47.86 gef Government effectiveness (z-score) 303 -0.33 0.64 -1.66 1.86
The results are shown in Tables 2 and 3. The coefficients for our six main capacity indicators
are signed according to expectation and statistically significant at the 5% level or below in the
majority of cases. The coefficient for the DNI-GDP ratio is larger than that for GDP per capita,
particularly for water, while the coefficient for population is smaller in absolute terms than
that of land area.21 As discussed above, a possible explanation for the former result is that
external resources are more easily translated into government spending, which in turn leads to
higher levels of access to water. A possible explanation for the latter is that population size
21 A Wald test rejects the hypothesis that these two coefficients are equal at the 1% significance for water but only at the 10% level for sanitation. The hypothesis that the coefficients for population and land area are equal in absolute terms is rejected at the 1% level for both water and sanitation.
19
has an additional negative effect on access, above and beyond its positive effect via
population density, due to diseconomies of scale. However, the standardised (beta)
coefficients show that GDP per capita has the largest effect on access by far, followed by the
dependency ratio and land area.22
In terms of the other potential capacity indicators, the coefficient for educational attainment is
positive and statistically significant for sanitation, but smaller in size and not statistically
significant for water. The coefficient for domestic resources is negative and statistically
significant for both water and sanitation, which is contrary to expectation. The coefficient for
government effectiveness is positive and statistically significant for access to water, as
expected, but not statistically significant for sanitation. The coefficient for tax revenues is
positive but not statistically significant for water or sanitation. Of these additional indicators,
government effectiveness has the largest impact in standardised terms (at least for water),
although even in this case the effect is smaller than that of GDP per capita, the dependency
ratio or land area. For this reason, including these additional capacity indicators do not
substantially affect our performance rankings (see Section 3.2 below).
22 The standardised (beta) coefficients show the change in access in standard deviations when each capacity indicator changes by one standard deviation.
20
Table 2. Regression results, access to water (levels)
1 2 3 4 5 Dependent variable lrwater lrwater lrwater lrwater lrwater Explanatory variables ly 0.676** 0.554** 0.640** 0.526** 0.767**
0.056 0.073 0.059 0.088 0.081
lexflow 1.773** 1.170** 1.744** 1.563** 2.020**
0.249 0.309 0.277 0.340 0.397
dep -2.301** -2.340** -2.453** -2.422** -1.802**
0.267 0.392 0.275 0.330 0.349
lpop 0.123** 0.123** 0.146** 0.064 0.166**
0.030 0.036 0.033 0.037 0.049
larea -0.219** -0.248** -0.184** -0.158** -0.241**
0.028 0.032 0.032 0.033 0.046
pop_urban 0.010** 0.014** 0.010** 0.010** 0.009*
0.002 0.003 0.002 0.003 0.004
ysch_tot
0.032
0.021
lwatres
-0.057*
0.023
gef
0.320**
0.089
tax_sh
0.007
0.008
N 506 356 480 303 235 R2 0.75 0.77 0.74 0.76 0.72 Standardised coefficients: ly 0.505 0.418 0.480 0.398 0.559 lexflow 0.151 0.092 0.146 0.133 0.179 dep -0.307 -0.307 -0.336 -0.316 -0.243 lpop 0.163 0.151 0.186 0.085 0.219 larea -0.329 -0.350 -0.265 -0.238 -0.332 pop_urban 0.153 0.212 0.160 0.151 0.143 ysch_tot 0.060 lwatres -0.097 gef 0.150 tax_sh 0.031
Notes: Standard errors are shown below each coefficient; ** statistically significant at the 1% level; *
statistically significant at the 5% level. The standardised coefficients show the effect of a change of one standard
deviation in each capacity indicator on the dependent variable.
21
Table 3. Regression results, access to sanitation (levels)
1 2 3 4 5 Dependent variable lrsanit lrsanit lrsanit lrsanit lrsanit Explanatory variables ly 1.108** 0.860** 1.007** 1.086** 1.037**
0.084 0.103 0.080 0.127 0.125 lexflow 1.997** 0.150 1.008* 1.449* 0.827
0.584 0.587 0.468 0.643 0.703 dep -1.992** -1.219* -2.356** -2.229** -1.735**
0.357 0.499 0.352 0.487 0.520 lpop 0.104* 0.160** 0.143** 0.125* 0.089
0.041 0.044 0.044 0.054 0.067 larea -0.252** -0.326** -0.217** -0.240** -0.282**
0.039 0.039 0.041 0.053 0.062 pop_urban 0.007* 0.008* 0.008** 0.006 0.015**
0.003 0.004 0.003 0.004 0.005 ysch_tot 0.119** 0.031 lwatres -0.077** 0.028 gef -0.076 0.125 tax_sh 0.012
0.013
N 511 363 480 314 238 R2 0.69 0.72 0.70 0.68 0.73 Standardised coefficients: ly 0.608 0.500 0.570 0.619 0.550 lexflow 0.125 0.009 0.063 0.097 0.051 dep -0.202 -0.122 -0.250 -0.225 -0.175 lpop 0.103 0.152 0.136 0.128 0.083 larea -0.292 -0.359 -0.237 -0.284 -0.289 pop_urban 0.079 0.093 0.095 0.072 0.173 ysch_tot 0.175 lwatres -0.097 gef -0.027 tax_sh 0.039
Notes: As Table 2.
22
3.2 Country rankings
In this section we present our country rankings in terms of levels of performance in the most
available year of data: either 2010 or 2005. Our performance measure is given by the
difference between the actual level of access and the predicted level of access on the basis of
the observable capacity indicators (equation 2 in Section 2). As discussed in Section 2, we
exclude from the calculations any countries in which access is equal to 100 per cent; some
countries are also excluded due to missing data. Overall we are able to calculate our
performance measure for 114 countries for water and 121 countries for sanitation, of which 81
and 84 have data for 2010.23
The full set of results for each country is shown in Appendix 2; here we focus on the key
overall findings. First, our performance rankings have only a moderate correlation with
rankings based on observed levels of access: the Spearman rank correlation coefficients are
0.49 for water and 0.56 for sanitation. The absence of a strong correlation is due to two
factors. On the one hand, there are countries, predominantly in sub-Saharan Africa, in which
access is relatively low but predicted access is also low if not lower. These countries rank
much higher by our measure than they do by access alone; examples include Niger, Malawi,
Mali, and Uganda (see Table 4). On the other hand, there are countries, predominantly
middle-income, in which access is relatively high but predicted access si even higher. These
countries rank much lower by our measure than by their unadjusted level of access; examples
include Kuwait, South Korea, Trinidad and Tobago, and Lithuania (see again Table 4).
Overall, controlling for capacity clearly makes a significant difference to how we judge
‘good’ performance.
23 We found no tendency for levels of access to be higher in 2010 than in 2005, controlling for the capacity indicators. This suggests that there is no bias in our rankings against the countries in our samples which do not yet have data for 2010.
23
Table 4. Country rankings, actual vs. performance
Actual access (%)
Predicted access (%)
Ranking, actual access
Ranking, performance
Difference in rank
Water Niger 46 31 111 25 86
Malawi 73 51 84 11 73 Mali 55 43 106 36 70 Uganda 72 53 86 17 69 Burkina Faso 70 54 90 23 67 Guinea 74 56 83 19 64 Zambia 61 49 99 37 62 Burundi 72 61 86 35 51 Nepal 86 72 63 14 49 Cote d'Ivoire 79 69 80 32 48 Sanitation
Malawi 48 86 18 9 77 Burundi 46 90 20 14 76 Zambia 48 86 19 11 75 Niger 9 121 8 48 73 Mali 20 107 13 35 72 Uganda 34 92 23 31 61 Yemen, Rep. 47 89 35 33 56 Mozambique 18 110 19 55 55 Madagascar 14 114 17 65 49 Guinea 18 110 22 63 47 Water
Kuwait 99 99 1 92 -91 Korea, Rep. 98 99 11 94 -83 Trinidad and Tobago 93 98 38 109 -71 Lithuania 92 97 42 107 -65 Romania 89 96 55 108 -53 Jamaica 93 96 38 90 -52 Dominican Republic 86 96 63 113 -50 Oman 86 96 63 112 -49 El Salvador 88 96 58 106 -48 Venezuela, RB 92 95 42 88 -46 Sanitation
Trinidad and Tobago 92 30 97 103 -73 Mauritius 89 40 97 111 -71 Latvia 78 56 95 117 -61 Lithuania 86 44 95 104 -60 Macedonia, FYR 88 42 96 101 -59 St. Lucia 65 70 95 120 -50 Jamaica 80 53 93 102 -49 Poland 90 36 95 83 -47 Romania 73 62 92 109 -47 Azerbaijan 82 51 93 96 -45
24
Second, our performance rankings show no significant correlation with any of the six main
capacity indicators. There is for example no tendency for countries with higher levels of GDP
per capita to receive higher performance scores: the rank correlation is less than 0.10 in all
cases, and is never statistically significant. Relatedly, we find no evidence that rankings differ
significantly between low-income, middle-income and high-income countries: an F-test of the
equality of the average ranks across income groups cannot be rejected at the 10% level for
both water and sanitation. Put more simply, state performance and state capacity are in our
approach unrelated: there is no tendency for states with greater capacity to perform better than
states with lesser capacity.
Third, our use of multiple capacity indicators does make a difference. The correlation between
our rankings and those generated by an identical approach with GDP as the only capacity
indicator is high – 0.78 for water and 0.85 for sanitation – but clearly not perfect. For example,
Zambia is ranked 89th out of 114 countries in terms of its water performance when including
only GDP per capita, but it is ranked 37th when adding the other five capacity indicators. One
of the reasons for this difference is that disposable national income (DNI) in Zambia is quite a
bit below its GDP, because of repatriated earnings on foreign direct investment. Similarly, the
Republic of Congo rises from 103rd to 44th place, again mainly because of its low level of DNI
relative to GDP.
Finally, we re-estimated our rankings including each of the other possible capacity indicators
discussed in Section 2: education attainments, domestic water resources, tax revenues, and
government effectiveness. In each case, the rankings are very similar to those based on our six
main capacity indicators: the Spearman rank correlation coefficients are all above 0.97. This
reflects the finding of the regression analysis in Section 3.1, namely that the effect of these
other indicators on average levels of access is smaller, in standardised terms, than our main
capacity indicators. Thus while state capacity can never be measured exactly, our rankings do
at least appear to be robust to the particular indicators of capacity chosen from the set of
potential indicators available in standard international datasets.
3.3 Comparison with SERF index
We now compare our rankings with those generated by the SERF index (Randolph, Fukuda-
Parr and Lawson-Remer, 2010). As noted in Section 2, this index also ranks states on the
25
basis of levels of performance, adjusting for state capacity, but differs from our approach in
certain key ways: it uses a single capacity indicator, GDP per capita, and estimates
benchmarks on the basis of highest observed level of access at any given level of GDP per
capita, rather than the average level. We confine our comparison to the SERF performance
indices for water and sanitation, which are just two of the 13 outcome indicators considered
by the overall SERF index.
The benchmarks for water and sanitation used by the SERF index are estimated by regression
analysis applied to the outer envelope of observations. This method yields the following
formulae:
2
210 lnln ⎥⎦
⎤⎢⎣
⎡⎟⎠
⎞⎜⎝
⎛+⎟⎠
⎞⎜⎝
⎛+=POPGDP
POPGDPAi χχχ (water)
1
0
δ
δ ⎟⎠
⎞⎜⎝
⎛=POPGDPAi (sanitation)
with coefficients 88.1510 −=χ , 14.561 =χ , 10.32 −=χ and 04.90 =δ and 29.01 =δ (Randolph et al 2010: 239). The benchmarks are capped at 100 per cent in each case. Since
our dataset differs somewhat from that used by Randolph et al (2010), we re-estimated the
coefficients using our dataset and an otherwise identical methodology, yielding coefficients
49.2,13.47,0.116 210 −==−= χχχ and 381.0,824.4 10 == δδ . In what follows we report
the comparison using the original coefficients reported by Randolph et al, but the results are
very similar using our re-estimated coefficients.
The rank correlation between our measure and the SERF index is 0.59 for water and 0.71 for
sanitation. Part of the reason for the difference is our use of multiple capacity indicators: if we
compare the partial version of our measure, which includes GDP per capita as the single
capacity indicator, the rank correlations rise to 0.81 for water and 0.83 for sanitation. Given
the uncertainties of any performance exercise, some differences in rankings between
alternative approaches are of course to be expected. Nevertheless, the differences between our
approach and the SERF index are systematic: countries with lower GDP per capita tend to be
ranked substantially higher by our measure compared with the SERF index. This is shown by
Figures 3 and 4, which plot the difference in country rankings between the two measures
26
(with positive values indicating a higher rank for our measure compared to the SERF index,
and negative values the opposite). There is a clear negative relationship: countries with per
capita GDP above around US$4,000 (8.3 in log units) tend to be ranked higher by our
measure, while countries with per capita GDP below this level tend to be ranked higher by the
SERF index. This is confirmed by a regression of the difference in ranks between our measure
and the SERF index on the logarithm of GDP per capita yields an estimated slope of -18 for
both water and sanitation, statistically significant at the 1% level in each case.
The pattern shown in Figures 3 and 4 reflects the fact the SERF index tends to be lower for
countries with lower GDP per capita; as already noted, there is no correlation between our
performance measure and GDP per capita. This in turn reflects the different ways in which the
national benchmarks are calculated: highest observed levels of access for the SERF index, and
average levels of access for our measure. Our argument is that benchmarks are estimated on
the basis of all observations are less likely to be affected by measurement error in the access
data, and/or by the imperfect nature of the capacity indicators, than is the case when focusing
on the ‘outer envelope’ of observations only.
27
Figure 3. Comparing performance rankings, water
Figure 4. Comparing performance rankings, sanitation
28
4. Comparing performances over time
4.1 Regression results: trends in access and capacity
We now report estimates of the effect of trends in the capacity indicators on trends in access
to water and sanitation. Sample information and descriptive statistics for each variable are
shown in Table 5. Each variable is expressed in first-differences, i.e. changes over successive
five-year periods (1990-95, 1995-2000 etc). The estimation method is ordinary least squares.
Table 5. Descriptive Statistics (trends)
Variable code Variable name Obs Mean Std. Dev. Min Max
lrwater Access to water, log odds ratio 372 0.23 0.25 -1.03 1.31 lrsanit Access to sanitation, log odds ratio 373 0.18 0.21 -0.70 1.42
ly GDP per capita (US$ PPP, 2005 prices), log units 372 0.12 0.15 -0.39 0.96
lexflow Ratio of DNI to GDP, log units 372 0.00 0.06 -0.43 0.39 lpop Total population, log units 372 0.08 0.06 -0.24 0.37 pop_urban Urban population share (%) 372 1.99 1.74 -2.00 7.10 dep Dependency ratio 372 -0.05 0.04 -0.23 0.04 ysch_tot Years of schooling in adult population 269 0.46 0.49 -1.00 2.00 tax_sh Tax revenues (% of GDP) 128 0.06 3.26 -14.07 9.98 gef Government effectiveness (z-score) 182 0.03 0.25 -0.62 0.87
Notes: All variables are defined in terms of first differences. Land area and water resources are omitted since
these variables are time-invariant.
The results are shown in Table 6. The coefficients are in this case much smaller in magnitude
than the equivalent coefficients in Section 3, and levels of statistically significance much
lower. For water, only one capacity indicator is statistically significant at the 10% level:
population; for sanitation, three indicators are statistically significant: GDP per capita,
dependency ratio and urban population (all at the 1% level). The R2 values are also
correspondingly lower: just 0.03 for water and 0.11 for sanitation. In addition, none of the
other potential capacity indicators are statistically significant.
This difference in results with those in Section 3 suggests that there are unobserved country-
specific, time-invariant factors which affect access and which are at the same time correlated
with our capacity indicators. Possible examples include topography, climatic conditions (e.g.
rainfall), and the distribution of the population across cities of different sizes. Thus the
coefficients in Tables 2 and 3 reflect not only the ‘direct’ effect of each capacity indicator, but
29
also an ‘indirect’ effect due to their correlation with the unobserved, time-invariant factors.
But since taking first differences automatically controls for the time-invariant effects, the
coefficients in Table 6 reflect only the direct effect. Another way of controlling for country-
specific, time-invariant effects is to estimate a fixed effects model, e.g. the LSDV model. We
estimated this model and found very similar coefficients to those reported above.
Table 6. Regression results, access to water and sanitation (trends)
1 2 Dependent variable lrwater lrsanit Explanatory variables ly 0.124 0.194*
0.080 0.072 lexflow -0.109 -0.034
0.176 0.200 dep -0.131 -0.793*
0.314 0.298 lpop -0.523 0.180
0.291 0.208 pop_urban 0.009 0.026*
0.008 0.007 N 372 373 R2 0.0338 0.1143 Standardised coefficients: ly 0.074 0.136 lexflow -0.027 -0.010 dep -0.022 -0.155 lpop -0.127 0.051 pop_urban 0.060 0.212
Notes: As Tables 2-3. The coefficients for the other potential capacity indicators (gef, tax-sh and ysch_tot) were
not statistically significant for either water or sanitation and are therefore not reported here (details available on
request).
4.2 Country rankings
In this section we rank countries on the basis of the change in performance between 1990 and
the latest available year of data, as given by equation (4) in Section 2. We use the coefficients
in Table 6 for the estimates of kβ , on the grounds that they are less likely to reflect the effects
of correlation with time-invariant factors (e.g. climatic conditions), and for assessing
performance over time it clearly makes sense to eliminate such factors. We exclude from the
30
analysis any countries with levels of access equal to 100 per cent in the latest available year;
some countries are also excluded due to missing data. Overall, we are able to calculate
changes in performance for 80 countries for water and 79 for sanitation; the latest available
year is 2010 in 58 and 57 cases respectively; in all other cases it is 2005. The full rankings are
shown in Appendix 2; here we focus on the key overall findings.
The main result is that our rankings of countries on the basis of changes in performance are
much less affected by capacity than those in Section 3: our rankings are quite similar to those
yielded simply on the basis of the actual changes in access over time: 0.92 for sanitation and
0.98 for water. This reflects the more limited explanatory power that our capacity indicators
have in explaining changes in levels of access over time, as evidenced in the relatively low R2
figures for the regressions in first difference form in Table 6. Nevertheless, we still find some
significant differences between our rankings and those generated by MDG performance
assessments.
4.3 Comparison with MDG performance assessments
As noted in Section 2, the implicit performance measure for water and sanitation in the
current MDG framework is the average annual rate of reduction in the proportion of the
population lacking access to water or sanitation between 1990 and the latest available year of
data. Our measure of the trend in performance differs for two reasons: first, it adjusts for
changes in capacity, and second it includes the proportional increase in the proportion of the
population with access as well as the proportional decrease in the proportion of the population
without access.
As we have just seen, adjusting for capacity makes relatively little difference to country
rankings when considering performance over time. However, including proportional increases
in access does make a difference. In particular, there is a clear tendency for countries with
lower initial levels of access to rank higher according to our measure than the MDG measure.
This is shown in Figure 5: on average, a country with an initial level of access of 20 per cent
tends to rank 10 places higher by our measure than the MDG measure for water and 30 places
higher for sanitation. By contrast, a country with an initial level of access of 80 per cent tends
to rank 5 places lower by our measure than the MDG measure for water and 10 places lower
for sanitation. To give a specific example, Ethiopia increased access to sanitation from 3 per
31
cent in 1990 to 21 per cent in 2010. This ranks in only 45th place (out of 79 countries)
according to the MDG measure of performance, the percentage reduction in the proportion of
the population without access (just 1 per cent per year between 1990 and 2010). But Ethiopia
ranks 2nd out of 79 countries by our approach, which factors in the percentage increase in the
proportion of the population with access (10 per cent year between 1990 and 2010). This
gives an indication of the amount of bias resulting from the apparently arbitrary focus by the
MDGs on rates of decrease in access shortfalls, and the exclusion of information on rates of
increase in access.
Figure 5. Comparing performance rankings, water and sanitation (trends)
There are also systematic differences between our rankings and MDG rankings when we
compare countries by income group and by region. Using the World Bank’s classification
system, low-income countries tend to be ranked higher by our measure than the MDG
measure; by contrast, lower and upper middle income countries tend to be ranked lower by
our measure than the MDG measure. These differences mainly reflect the fact that low-
income countries had (on average) lower levels of access in 1990; lower rates of economic
growth among low-income countries also play a role in explaining the differences, but only
for sanitation. The same conclusion applies if we group countries into quintiles based on the
32
level of GDP per capita; lower-income groups tend to be ranked higher by our measure than
the MDG measure, and vice versa for higher-income groups. For the results by region,
countries in Sub-Saharan Africa tend to be ranked higher according to our measure than the
MDG measure. This again mainly reflects the lower average level of access in Sub-Saharan
Africa in 1990, and partly (at least for sanitation) the slower average rate of economic growth
since 1990. In the last few years, economic growth has accelerated in sub-Saharan the Africa
and it will be interesting to reassess the resource-adjusted performance in the coming years.
33
5. Conclusion: Towards the Post-‐2015 Agenda
The MDG monitoring framework has been critiqued for penalising low-income countries and
favouring middle-income countries. The former are burdened with a more constrained
resource envelope and the task of halving or eliminating significantly higher shortfalls. Even
if unintended, this bias in the MDGs metric becomes clear once it is subjected to quantitative
analysis. As our analysis makes clear in the case of water and sanitation, an adjustment for
capacity reveals a partial bias against low-income countries. The ranking of countries shifts
with a number of low-income countries rising up the performance ladder and a number of
middle-income countries fall. When we focus on changes in performance, we reveal a bias
against countries with low levels of access – mainly low income countries. While we would
be cautious about replacing the existing MDGs rankings with our approach – one needs to be
careful about the use of global rankings - the results point to an underlying problem when the
MDGs framework is used as a cross-national monitoring measure.
In the emerging discussions on a new global development agenda, there is a recognition that
any post-2015 goals will need to fairer across countries. This will be crucial for their
legitimacy. In the 2012 Rio Declaration, States set parameters for establishing Sustainable
Development Goals, which can be read as applicable for the post-2015 agenda. One of the
criteria is that they must be “universally applicable to all countries while taking into account
different national realities, capacities and levels of development”. 24 There is thus a challenge
as to how national capacities are built into the universal benchmarks.
Potentially, one could design a target based on the alternative metrics presented above. If we
took the SERF approach, a country could be on target if it has closed by 50 or 100 per cent the
gap between its current level of access and the maximum level of access for its level of GDP.
However, if the target is ambitious (e.g., over 50 per cent) there will be strong scrutiny and
controversy as to whether its achievement possibility frontier is truly realistic for all countries.
Using an average-based approach as we do, countries could be required to at least achieve the
24 Para. 247. The full sentence reads that the framework should be “action-oriented, concise and easy to communicate, limited in number, global in nature and universally applicable to all countries while taking into account different national realities, capacities and levels of development and respecting national policies and priorities”. It should also “be consistent with international law”, incorporate all dimensions of sustainable development in a balanced and coordinated manner and be implemented “with the active involvement of all relevant stakeholders” (Paras. 246-7).
34
average performance at their level of GDP for the last two decades. But this risks being under-
ambitious. Therefore, a compromise may be to set a standard whereby States are expected to
achieve the average pace of progress for say the five top performers in their general income
bracket.
However, the problem with such proposals is that rely on coefficients generated by
multivariate regression analysis – and GDP is shifting constantly affecting the relevant target.
This complicates standard-setting and can generate different interpretations and disputes over
statistical methods. A better approach may be to take heed of these quantitative results for
resource-adjustment. A bias exists in the measurement framework that needs to be taken into
account. The task is to design simpler but nuanced targets that take resource constraints into
account. One approach might be to graduate targets according to regions or income-brackets.
For instance, we might expect a halving of the water and sanitation gap in low-income
countries or Sub-Saharan Africa by 2030, but a 75 per cent reduction in all other countries. In
other words, one globalises the MDG-plus approach for wealthier countries.
A second approach to differential capacity is to question whether all countries should be
aiming for the ‘improved’ water and sanitation supply standard set by WHO and UNICEF,
and which informs the basis of the MDG measurement. The water and sanitation indicators
used for the MDGs are fairly minimalistic and wealthier countries could have been pushed to
achieve a higher standard, for example piped access. It is clear that for Sub-Saharan Africa, a
clear challenge remains in simply elevating people from unimproved to the minimalist
‘improved’. However, in the Asian regions, there is at least an equal challenge in moving
from ‘improved’ to piped access.25 In Northern Africa and Latin America, there has been a
dramatic and positive change on piped access while in the former communist states of the CIS
there has been regression. Interestingly, in the MDGs framework, many of these poorly
performing wealthier States would be marked as ‘on target’ as the unimproved gap has been
reduced by 50 per cent. Even in developed countries, access to piped water is notably below
universal access. There is therefore a strong case for raising the threshold requirements for
25 For instance, in South-Eastern Asia, between 2000 and 2008, the unimproved number was halved and the number of piped doubled, but a slight majority of the population still sit with improved access. In Southern Asia and Western Asia, there has been no progress in piped access at all (with the number in the improved category remaining constant).
35
water and sanitation access, particular in middle-income countries.26 They might be expected
to halve or close the gap on lack of access to piped water.27
The capacity-based assessment may suggest that standards should be lowered in poor
countries. For example, WHO and UNICEF (2008: 284) demonstrate positive developments
for a range of poorer countries if one uses a ladder of progress instead of a binary cut-off.
Open defecation declines in all regions (24 per cent to 18 per cent) and in Sub-Saharan Africa
(36 to 28 per cent). Half of decline captured in rise of shared facilities, which is not covered
by the MDGs framework (Bartram, 2008).28 Thus, one could develop a target that tracks and
rewards progress over a ladder from below basic access through to adequate access. For
example, a target could be to ensure that 50 per cent of households move up one ladder rung
(except the top) within 5 to 10 years. However, it may be prudent not to shift the sanitation
standard. It may be more appropriate now given higher levels of growth, the recognition of
sanitation as a human right by the General Assembly in 2010, and the enormous health and
economic benefits generated by investments in sanitation.29
Moreover, it is questionable whether one should even consider lowering the standard for
water. There is a strong argument that the water standard should be raised in poor countries:
not to ensure piped access to homes but rather to increase quantity of water secured. The
current means of measurement presume that an individual is able to secure roughly 20 lcpd
given the distance from the home to a water point (Howard and Bartram, 2003). Both the
MDGs and current international human rights law assume that basic household uses will be
prioritised over other water uses. However, research in rural areas suggests that communities
rank uses quite differently: for example, water for key livestock and kitchen gardening is
prioritised over many household uses (Van Koppen, 2013). One possible approach could be to
26 As Bartram (2008: 284) notes: “The evidence base for the health gains from potential sanitation benchmarks remains appallingly weak. Public toilets may be considered a key intermediate step and a means to ensure at least some dignity and safety. But some speak of dangers, especially to women, of rape and assault; and poorly maintained facilities are themselves a danger to health.” He concedes that some wealthier countries may resist this movement, but points out that some of their policymakers have expressed frustration about the irrelevance of the current MDGs framework. 27 Such a target might also be instrumental in pushing progress on other rights and goals, particularly slum upgrading. The current MDG indicators hide the problems of moving to an adequate level of access. However, once data is introduced on access to piped water or toilets, the failure of many States to make (any) progress on securing urban land tenure and support slum-upgrading (Langford, Bartram and Roaf, 2013; Tissington, 2008) is glaringly revealed. 28 Although there is a question of sustainability with overloaded pits. 29 For every dollar invested in sanitation the resulting benefits are estimated to be between 9 and 34 dollars: Albuquerque (2009) and UNDP (2007).
36
set goals for piped water in middle-and high- income countries, but introduce a multiple-use
perspective into the “improved water” standard for lower-income countries, so that productive
uses are captured.
A further alternative or complementary approach is to pay greater attention to equality. In
middle and high-income countries, the core of social challenge is often not scaling up
programmes to generate broad access to basic levels of economic and social rights. Rather, it
is ensuring that the most marginalised members of society share in this distribution: i.e.,
equality. However, only one target in the MDGs framework pays attention to equal access
(Target 3A on gender equality in schooling) and only one target requires universal access
(primary education).30 The result is that these States can pick the ‘low-hanging fruit’ in
meeting the more relative and average-based targets without having to address the most
deeply excluded individuals and groups. Most disadvantaged groups are heavily
overrepresented in poverty statistics. Thus, a target that directs attention to low-income
groups will invariably require addressing individuals facing others forms of discrimination.
There are various ways in which this could be done. The most practical approach would be to
measure progress for lower income quintiles or deciles. One could replace the current
average-based target with an equality-based target that requires the gap for a lower-income
group (e.g., the bottom quintile) to be halved or eliminated. An alternative approach is to
maintain the current average-based targets, but require proportional improvement for lower-
income groups.
A final alternative metric could be the degree of post-2015 acceleration. If goals and targets
are primarily aimed at boosting performance (Langford, 2012), it might be a useful measure.
Fukuda-Parr and Greenstein (2010) test whether this occurred post-2000 and reveal the
(unfortunate) fact that the majority of countries decelerated but also demonstrate that many
poorer countries have made faster progress on water and sanitation than middle-income
countries. In a separate piece, the authors argues that such an approach not only coheres with
the underlying purpose of the MDGs but is consistent with the duty of progressive realisation
in economic, social and cultural rights (Fukuda-Parr and Greenstein, 2013). However, the
difficult with this approach is the time period. For instance a country that achieved very fast
progress between 2000 and 2015 would face a more demanding target than countries that
30 Although the measures for the latter often fail to pick up excluded children with disabilities
37
were more sluggish. Unlike the resource-based approach, it risks being too backward–looking
in determining the measure of performance. This could be overcome by using on the average
rate of performance for various bands of GDP.
In summary, frameworks like the MDGs function best when they blend ambition with realism:
set at standard high enough to inspire action but low enough to be achievable. This is the
baseline provided by international human rights law although failure to reach a bare minimum
for all still requires a particularly strong justification. This suggests that the standard for
access to water and sanitation should be raised in the future for middle-income countries. But
it does not necessarily mean it should be dropped for low-income countries.
38
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41
Appendix 1. Absolute vs. relative performance benchmarks
What might constitute an absolute performance benchmark? This section sets out two possible
options. One is the ‘maximum achievable’ level of access: an achievement ceiling beyond
which further achievements are not possible. At least in theory, this will be achieved if raising
access to water and sanitation is the government’s only objective. The other is the ‘minimum
reasonable’ level of access: an achievement floor which will be exceeded if raising access is
one of the government’s objectives, alongside a set of other legitimate objectives (e.g. better
health, education). This level of access will be higher, the greater are complementarities
between access to water and sanitation and other social goals – for example, the extent to
which better access to water and sanitation contributes to better health and higher economic
productivity. For instance, Hutton and Haller (2004) estimate that in developing regions, for
every dollar invested in safe drinking water and sanitation, there were economic and health
returns of $US5 to $US28, depending on the region, technology and cost assumptions.31
The maximum achievable and minimum reasonable levels of access can be illustrated
graphically using an adapted version of the production possibility frontier, a widely used
diagrammatic tool in economics (see Figure A1). The vertical axis shows access to water and
sanitation, while the horizontal axis shows achievements in terms of other legitimate social
goals. The frontier (i.e., the curved line) illustrates the highest possible level of access to
water and sanitation at any given level of achievement in the other goals. The maximum
achievable level of access (denoted maxA ) is given by the highest point of the frontier; the
minimum reasonable level of access (denoted minA ) is given by the level of access at which
the other social goals are maximised. Complementarity between water and sanitation and
other social goals is illustrated by the upward sloping sections of the frontier. In the extreme
case of perfect complementarity, the frontier is an upward sloping line (of fixed length), and minmax AA = ; if there is no complementarity, the possibility curve is always downward sloping
and 0min =A .The shape of the frontier in Figure A1 indicates substantial but not perfect
complementarity.
31 The lower figure is commonly quoted as a global figure. In his cross-country analysis, Anand (2006: 19) concluded that access to water and sanitation has a “highly significant impact on child mortality rate” and to a lesser degree on maternal mortality and incidence of malaria. Rajkumar and Swaroop find a similar but not significant relationship with infant mortality while Wolf (2007) strikingly finds the opposite relationship.
42
Figure A1. A production possibility frontier for water and sanitation
In theory, either the maximum achievable or the minimum reasonable level of access could be
used to benchmark a state’s performance. For example, one could say that performance is
‘good’ if access is close to the maximum achievable level, and ‘poor’ otherwise; alternatively,
performance is ‘good’ if access is above the minimum reasonable level, and ‘poor’ otherwise.
Thus either could be used to provide an absolute benchmark of performance. In practice
however, neither the maximum achievable level of access nor the minimum reasonable level
of access is directly observable.
For this reason, this paper focuses only on relative assessments of performance which do not
require an absolute benchmark. These can indicate whether one country is closer to its
maximum achievable level of access than another, but cannot establish how close either is to
this level.
Other social goals
maxA
minA
Wat
er a
nd
sani
tatio
n
d sa
nita
tion
sani
ot
sani
tatio
n
43
Appendix 2. Country rankings
Table A1. Access to water (levels)
Country Year Actual access (%)
Actual rank
Predicted access (%)
Performance rank*
Albania 2010 95 32 96 74 Algeria 2005 85 70 92 93 Angola 2010 51 109 74 103 Argentina 2005 96 24 94 42 Armenia 2010 98 11 96 21 Azerbaijan 2010 80 78 96 114 Bangladesh 2010 81 77 89 91 Belize 2010 98 11 91 2 Benin 2005 70 90 66 48 Bolivia 2010 88 58 86 45 Bosnia and Herzegovina 2010 99 1 97 5 Botswana 2010 96 24 94 39 Brazil 2010 98 11 95 10 Burkina Faso 2005 70 90 54 23 Burundi 2010 72 86 61 35 Cambodia 2010 64 97 82 102 Cameroon 2010 77 82 77 55 Cape Verde 2010 88 58 95 100 Chile 2010 96 24 97 63 China 2010 91 49 94 80 Colombia 2010 92 42 94 79 Congo, Rep. 2005 71 88 65 44 Costa Rica 2010 97 20 97 51 Cote d'Ivoire 2005 79 80 69 32 Croatia 2010 99 1 97 9 Djibouti 2005 86 63 90 78 Dominican Republic 2010 86 63 96 113 Ecuador 2010 94 35 94 57 Egypt, Arab Rep. 2010 99 1 91 1 El Salvador 2010 88 58 96 106 Estonia 2010 98 11 97 43 Ethiopia 2010 44 112 62 96 Fiji 2010 98 11 93 3 Gabon 2005 86 63 91 87 Gambia, The 2010 89 55 80 22 Georgia 2010 98 11 94 7 Ghana 2010 86 63 79 33 Guatemala 2010 92 42 87 31 Guinea 2010 74 83 56 19
44
Country Year Actual access (%)
Actual rank
Predicted access (%)
Performance rank*
Guinea-‐Bissau 2005 57 104 68 85 Guyana 2010 94 35 83 6 Haiti 2005 66 95 85 105 Honduras 2010 87 62 89 66 India 2010 92 42 87 30 Indonesia 2010 82 75 91 97 Iraq 2005 80 78 79 52 Jamaica 2010 93 38 96 90 Jordan 2010 97 20 94 18 Kazakhstan 2010 95 32 92 38 Kenya 2010 59 100 67 76 Korea, Rep. 2010 98 11 99 94 Kuwait 2005 99 1 99 92 Kyrgyz Republic 2010 90 53 88 46 Lao PDR 2010 67 94 80 95 Latvia 2005 99 1 97 8 Lesotho 2010 78 81 86 89 Liberia 2010 73 84 75 59 Lithuania 2005 92 42 97 107 Madagascar 2005 42 113 50 75 Malawi 2005 73 84 51 11 Maldives 2010 98 11 97 28 Mali 2005 55 106 43 36 Mauritius 2010 99 1 98 29 Mexico 2010 96 24 96 50 Moldova 2010 96 24 95 49 Mongolia 2010 82 75 85 65 Morocco 2010 83 73 92 101 Mozambique 2010 47 110 54 71 Namibia 2010 93 38 85 16 Nepal 2005 86 63 72 14 Nicaragua 2010 85 70 88 73 Niger 2005 46 111 31 25 Nigeria 2010 58 101 75 98 Oman 2005 86 63 96 112 Pakistan 2010 92 42 85 24 Panama 2005 93 38 95 77 Papua New Guinea 2010 40 114 70 111 Paraguay 2010 86 63 89 69 Peru 2010 85 70 93 99 Philippines 2010 92 42 92 56 Portugal 2010 99 1 98 15
45
Country Year Actual access (%)
Actual rank
Predicted access (%)
Performance rank*
Romania 2005 89 55 96 108 Russian Federation 2010 97 20 95 34 Rwanda 2010 65 96 73 81 Samoa 2010 96 24 91 13 Sao Tome and Principe 2010 89 55 87 47 Senegal 2005 68 93 71 62 Serbia 2010 99 1 97 4 Sierra Leone 2010 55 106 66 86 Slovenia 2010 99 1 98 27 Solomon Islands 2005 70 90 73 61 South Africa 2010 91 49 94 83 Sri Lanka 2010 91 49 93 68 St. Lucia 2010 96 24 97 67 Sudan 2010 58 101 67 82 Suriname 2005 91 49 92 58 Swaziland 2010 71 88 89 110 Syrian Arab Republic 2010 90 53 90 54 Tajikistan 2010 64 97 84 104 Tanzania 2010 53 108 57 64 Thailand 2010 96 24 94 40 Togo 2005 58 101 64 72 Trinidad and Tobago 2005 93 38 98 109 Tunisia 2005 94 35 95 60 Turkey 2005 97 20 96 41 Uganda 2010 72 86 53 17 Ukraine 2010 98 11 95 12 United States 2010 99 1 98 20 Vanuatu 2005 83 73 82 53 Venezuela, RB 2005 92 42 95 88 Vietnam 2010 95 32 91 26 West Bank and Gaza 2005 88 58 92 84 Yemen, Rep. 2005 57 104 63 70 Zambia 2010 61 99 49 37
Notes: *Performance rankings are based on the difference between actual and predicted access, measured in each
case by the log odds ratio. These may differ slightly from rankings based on the difference between actual and
predicted access measured in percentage terms.
46
Table A2. Access to sanitation (levels)
Country Year Actual access (%)
Actual rank
Predicted access (%)
Performance rank*
Albania 2010 94 24 93 47 Algeria 2005 94 24 81 13 Angola 2010 58 75 53 45 Argentina 2005 90 36 88 41 Armenia 2010 90 36 90 54 Azerbaijan 2010 82 51 93 96 Bangladesh 2010 56 77 66 70 Belarus 2010 93 28 95 68 Belize 2010 90 36 84 30 Benin 2005 11 119 34 107 Bolivia 2010 27 100 67 116 Bosnia and Herzegovina 2010 95 17 93 39 Botswana 2010 62 73 90 112 Brazil 2010 79 54 87 79 Burkina Faso 2005 14 114 22 74 Burundi 2010 46 90 20 14 Cambodia 2010 31 98 56 97 Cameroon 2010 49 83 46 50 Cape Verde 2010 61 74 88 110 Chile 2010 96 13 93 29 China 2010 64 71 84 98 Colombia 2010 77 58 87 82 Congo, Rep. 2005 19 108 37 92 Costa Rica 2010 95 17 94 44 Cote d'Ivoire 2005 23 105 36 81 Croatia 2010 99 1 96 10 Czech Republic 2010 98 4 97 43 Djibouti 2005 54 79 71 85 Dominican Republic 2010 83 49 92 91 Ecuador 2010 92 30 87 34 Egypt, Arab Rep. 2010 95 17 79 8 El Salvador 2010 87 43 91 71 Estonia 2010 95 17 96 60 Ethiopia 2010 21 106 26 64 Fiji 2010 83 49 84 56 Gabon 2005 33 94 85 121 Gambia, The 2010 68 68 51 26 Georgia 2010 95 17 87 18 Ghana 2010 14 114 47 114 Greece 2010 98 4 97 36 Grenada 2010 97 9 95 28
47
Country Year Actual access (%)
Actual rank
Predicted access (%)
Performance rank*
Guatemala 2010 78 56 74 40 Guinea 2010 18 110 22 63 Guinea-‐Bissau 2005 17 113 35 94 Guyana 2010 84 48 64 17 Haiti 2005 19 108 58 118 Honduras 2010 77 58 75 51 India 2010 34 92 66 106 Indonesia 2010 54 79 74 90 Iraq 2005 71 63 54 25 Ireland 2010 99 1 97 19 Jamaica 2010 80 53 93 102 Jordan 2010 98 4 86 4 Kazakhstan 2010 97 9 84 6 Kenya 2010 32 95 34 57 Kyrgyz Republic 2010 93 28 68 5 Lao PDR 2010 63 72 54 37 Latvia 2005 78 56 95 117 Lebanon 2005 98 4 96 24 Lesotho 2010 26 101 66 115 Liberia 2010 18 110 32 87 Libya 2005 97 9 90 12 Lithuania 2005 86 44 95 104 Macedonia, FYR 2010 88 42 96 101 Madagascar 2005 14 114 17 65 Malawi 2005 48 86 18 9 Malaysia 2010 96 13 93 27 Maldives 2010 97 9 93 22 Mali 2005 20 107 13 35 Mauritius 2010 89 40 97 111 Mexico 2010 85 45 91 78 Moldova 2010 85 45 87 59 Mongolia 2010 51 82 63 72 Morocco 2010 70 66 80 75 Mozambique 2010 18 110 19 55 Namibia 2010 32 95 72 113 Nepal 2005 26 101 37 73 Nicaragua 2010 52 81 70 86 Niger 2005 9 121 8 48 Nigeria 2010 31 98 45 80 Oman 2005 95 17 94 42 Pakistan 2010 48 86 62 76 Panama 2005 68 68 90 108
48
Country Year Actual access (%)
Actual rank
Predicted access (%)
Performance rank*
Papua New Guinea 2010 45 91 42 49 Paraguay 2010 71 63 74 58 Peru 2010 71 63 84 89 Philippines 2010 74 61 78 61 Poland 2005 90 36 95 83 Romania 2005 73 62 92 109 Russian Federation 2010 70 66 90 105 Rwanda 2010 55 78 41 32 Samoa 2010 98 4 83 3 Sao Tome and Principe 2010 26 101 69 119 Senegal 2005 49 83 41 38 Serbia 2010 92 30 94 67 Sierra Leone 2010 13 117 29 95 Solomon Islands 2005 32 95 49 84 South Africa 2010 79 54 87 77 Sri Lanka 2010 92 30 84 23 St. Lucia 2010 65 70 95 120 Sudan 2010 26 101 34 69 Suriname 2005 82 51 82 53 Swaziland 2010 57 76 80 100 Syrian Arab Republic 2010 95 17 77 7 Tajikistan 2010 94 24 61 2 Tanzania 2010 10 120 24 99 Thailand 2010 96 13 88 15 Togo 2005 13 117 28 93 Tonga 2005 96 13 88 16 Trinidad and Tobago 2005 92 30 97 103 Tunisia 2005 85 45 88 62 Turkey 2005 89 40 92 66 Uganda 2010 34 92 23 31 Ukraine 2010 94 24 87 21 Uruguay 2005 99 1 90 1 Vanuatu 2005 49 83 68 88 Venezuela, RB 2005 91 34 90 46 Vietnam 2010 76 60 75 52 West Bank and Gaza 2005 91 34 81 20 Yemen, Rep. 2005 47 89 35 33 Zambia 2010 48 86 19 11
Notes:* See Table A1 above.
49
Table A3. Access to water (trends)
country Latest year
Access, 1990 (%)
Access, latest year
(%)
Actual change in access1
MDG progress2
Country ranking, our
approach
Country ranking, MDG
approach
Angola 2010 42 51 1.8 0.8 62 66
Albania 2010 97 95 -‐2.7 -‐2.6 79 79
Argentina 2005 94 96 2.8 2.7 55 43
Burundi 2010 70 72 0.5 0.3 66 67
Benin 2005 57 70 3.8 2.4 37 47
Burkina Faso 2005 43 70 7.5 4.3 14 27
Bangladesh 2010 77 81 1.2 1.0 67 63
Belize 2010 74 98 14.2 12.8 1 1
Bolivia 2010 70 88 5.7 4.6 29 23
Brazil 2010 89 98 9.0 8.5 9 5
Botswana 2010 93 96 3.0 2.8 58 41
Chile 2010 90 96 4.9 4.6 38 23
China 2010 67 91 8.0 6.5 25 12
Cote d'Ivoire 2005 76 79 1.1 0.9 63 65
Cameroon 2010 49 77 6.2 4.0 23 29
Colombia 2010 89 92 1.8 1.6 65 54
Costa Rica 2010 93 97 4.4 4.2 39 28
Dominican Rep. 2010 88 86 -‐0.9 -‐0.8 76 76
Algeria 2005 94 85 -‐6.8 -‐6.1 80 80
Ecuador 2010 72 94 9.0 7.7 7 7
Egypt, Arab Rep. 2010 93 99 10.0 9.7 4 4
Ethiopia 2010 14 44 7.9 2.1 10 50
Fiji 2010 84 98 11.2 10.4 3 3
Ghana 2010 53 86 8.5 6.1 11 16
Guinea 2010 51 74 5.0 3.2 28 36
Gambia, The 2010 74 89 5.2 4.3 31 26
Guinea-‐Bissau 2005 36 57 5.7 2.7 22 44
Guatemala 2010 81 92 5.0 4.3 32 25
Honduras 2010 76 87 3.7 3.1 41 40
Indonesia 2010 70 82 3.3 2.6 60 45
India 2010 69 92 8.2 6.8 15 10
Jamaica 2010 93 93 0.0 0.0 73 68
Jordan 2010 97 97 0.0 0.0 69 68
Kenya 2010 44 59 3.0 1.6 43 55
St. Lucia 2010 94 96 2.1 2.0 57 51
Sri Lanka 2010 67 91 8.0 6.5 16 12
Lesotho 2010 80 78 -‐0.6 -‐0.5 75 75
Morocco 2010 73 83 3.0 2.3 54 49
Madagascar 2005 29 42 3.8 1.3 33 57
Mexico 2010 85 96 7.2 6.6 17 11
Mali 2005 28 55 7.6 3.1 12 39
50
country Latest year
Access, 1990 (%)
Access, latest year
(%)
Actual change in access1
MDG progress2
Country ranking, our
approach
Country ranking, MDG
approach
Mongolia 2010 54 82 6.8 4.7 21 22
Mozambique 2010 36 47 2.3 0.9 61 64
Mauritius 2010 99 99 0.0 0.0 72 68
Malawi 2005 41 73 9.1 5.2 6 20
Namibia 2010 64 93 10.1 8.2 5 6
Niger 2005 35 46 3.1 1.2 36 61
Nigeria 2010 47 58 2.2 1.2 56 62
Nicaragua 2010 74 85 3.4 2.8 47 42
Nepal 2005 76 86 4.4 3.6 35 32
Oman 2005 80 86 2.9 2.4 53 48
Pakistan 2010 85 92 3.5 3.1 44 37
Panama 2005 84 93 6.2 5.5 30 17
Peru 2010 75 85 3.2 2.6 49 45
Philippines 2010 85 92 3.5 3.1 48 37
Papua New Guinea 2010 41 40 -‐0.2 -‐0.1 68 72
Portugal 2010 96 99 7.1 6.9 26 9
Paraguay 2010 52 86 8.7 6.2 8 15
Romania 2005 75 89 6.6 5.5 27 18
Rwanda 2010 66 65 -‐0.2 -‐0.1 74 73
Sudan 2010 65 58 -‐1.5 -‐0.9 77 77
Senegal 2005 61 68 2.0 1.3 51 59
Sierra Leone 2010 38 55 3.5 1.6 40 53
El Salvador 2010 74 88 4.7 3.9 42 30
Swaziland 2010 39 71 6.7 3.7 20 31
Syrian Arab Rep. 2010 86 90 1.9 1.7 59 52
Togo 2005 49 58 2.4 1.3 52 60
Thailand 2010 86 96 6.8 6.3 24 14
Trinidad & Tobago 2005 88 93 4.0 3.6 50 32
Tunisia 2005 81 94 8.7 7.7 13 8
Turkey 2005 85 97 11.6 10.7 2 2
Tanzania 2010 55 53 -‐0.4 -‐0.2 70 74
Uganda 2010 43 72 6.1 3.6 19 34
United States 2010 99 99 0.0 0.0 71 68
Venezuela, RB 2005 90 92 1.6 1.5 64 56
Vanuatu 2005 62 83 7.3 5.4 18 19
Samoa 2010 89 96 5.4 5.1 34 21
Yemen, Rep. 2005 67 57 -‐2.8 -‐1.8 78 78
South Africa 2010 83 91 3.6 3.2 45 35
Zambia 2010 49 61 2.4 1.3 46 58
Notes: 1 equal to the annualised change in the log-odds ratio, multiplied by 100, which is also equal to the annual rate of
increase (% per year) in the proportion of the population with access plus the annual rate of reduction (% per year) in the
proportion of the population without access (see equation 6, Section 2); 2 equal to the annual rate of reduction (% per year) in
the proportion of the population without access.
51
Table A4. Access to sanitation (trends)
country Latest year
Access, 1990 (%)
Access, latest year
(%)
Actual change in access1
MDG progress2
Country ranking, our
approach
Country ranking, MDG
approach
Angola 2010 29 58 6.1 2.6 24 22
Albania 2010 76 94 8.0 6.9 9 4
Argentina 2005 90 90 0.0 0.0 72 68
Burundi 2010 44 46 0.4 0.2 67 64
Benin 2005 5 11 5.7 0.4 15 56
Burkina Faso 2005 8 14 4.2 0.4 26 55
Bangladesh 2010 39 56 3.4 1.6 44 39
Belize 2010 77 90 4.9 4.2 22 14
Bolivia 2010 18 27 2.6 0.6 49 49
Brazil 2010 68 79 2.9 2.1 46 30
Botswana 2010 38 62 4.9 2.4 43 25
Chile 2010 84 96 7.6 6.9 7 4
China 2010 24 64 8.6 3.7 12 18
Cote d'Ivoire 2005 20 23 1.2 0.3 61 62
Cameroon 2010 48 49 0.2 0.1 75 67
Colombia 2010 67 77 2.5 1.8 41 35
Costa Rica 2010 93 95 1.8 1.7 71 38
Dominican Republic 2010 73 83 3.0 2.3 58 27
Algeria 2005 88 94 5.1 4.6 37 11
Ecuador 2010 69 92 8.2 6.8 8 8
Egypt, Arab Rep. 2010 72 95 10.0 8.6 3 2
Ethiopia 2010 3 21 10.8 1.0 2 45
Fiji 2010 61 83 5.7 4.2 19 15
Ghana 2010 7 14 3.9 0.4 39 59
Guinea 2010 10 18 3.4 0.5 30 54
Greece 2010 97 98 2.1 2.0 32 31
Grenada 2010 97 97 0.0 0.0 70 68
Guatemala 2010 62 78 3.9 2.7 29 21
Honduras 2010 50 77 6.0 3.9 16 16
Indonesia 2010 32 54 4.6 2.0 50 33
India 2010 18 34 4.3 1.1 28 43
Ireland 2010 99 99 0.0 0.0 73 68
Jamaica 2010 80 80 0.0 0.0 68 68
Jordan 2010 97 98 2.1 2.0 63 31
Kenya 2010 25 32 1.7 0.5 55 52
St. Lucia 2010 58 65 1.5 0.9 47 47
Sri Lanka 2010 70 92 8.0 6.6 4 9
Morocco 2010 53 70 3.6 2.2 36 29
Madagascar 2005 9 14 3.3 0.4 23 60
Mexico 2010 64 85 5.8 4.4 14 13
Mali 2005 15 20 2.3 0.4 53 58
52
country Latest year
Access, 1990 (%)
Access, latest year
(%)
Actual change in access1
MDG progress2
Country ranking, our
approach
Country ranking, MDG
approach
Mozambique 2010 11 18 2.9 0.4 62 57
Mauritius 2010 89 89 0.0 0.0 66 68
Malawi 2005 39 48 2.4 1.1 34 44
Malaysia 2010 84 96 7.6 6.9 20 4
Namibia 2010 24 32 2.0 0.6 64 50
Niger 2005 5 9 4.2 0.3 13 61
Nigeria 2010 37 31 -‐1.3 -‐0.5 78 77
Nicaragua 2010 43 52 1.8 0.9 60 48
Nepal 2005 10 26 7.7 1.3 10 41
Oman 2005 82 95 9.5 8.5 5 3
Pakistan 2010 27 48 4.6 1.7 25 37
Panama 2005 58 68 2.9 1.8 69 34
Peru 2010 54 71 3.7 2.3 27 28
Philippines 2010 57 74 3.8 2.5 42 23
Papua New Guinea 2010 47 45 -‐0.4 -‐0.2 65 76
Paraguay 2010 37 71 7.1 3.9 11 17
Romania 2005 71 73 0.7 0.5 48 53
Rwanda 2010 36 55 3.9 1.8 38 36
Sudan 2010 27 26 -‐0.3 -‐0.1 77 75
Senegal 2005 38 49 3.0 1.3 31 42
Sierra Leone 2010 11 13 0.9 0.1 54 66
El Salvador 2010 75 87 4.0 3.3 35 20
Swaziland 2010 48 57 1.8 1.0 57 46
Syrian Arab Rep. 2010 85 95 6.0 5.5 17 10
Togo 2005 13 13 0.0 0.0 74 68
Thailand 2010 84 96 7.6 6.9 6 4
Tonga 2005 96 96 0.0 0.0 59 68
Trinidad & Tobago 2005 93 92 -‐1.0 -‐0.9 76 78
Tunisia 2005 74 85 4.6 3.7 33 19
Turkey 2005 84 89 2.9 2.5 51 24
Tanzania 2010 7 10 1.9 0.2 52 65
Uganda 2010 27 34 1.7 0.5 45 51
Uruguay 2005 94 99 12.3 11.9 1 1
Venezuela, RB 2005 82 91 5.3 4.6 21 11
Samoa 2010 99 98 -‐3.5 -‐3.5 79 79
Yemen, Rep. 2005 24 47 6.9 2.4 18 26
South Africa 2010 71 79 2.1 1.6 56 40
Zambia 2010 46 48 0.4 0.2 40 63
Notes: See Table A3.