On the Unequal Inequality of Poor Communities
Chris Elbers, Peter F. Lanjouw, Johan A. Mistiaen, Berk Özler and Ken Simler*
February 5, 2004
Abstract: Important differences exist between communities with respect to their needs, capacities
and circumstances. As central governments are not able to discern these differences fully, they seek
to achieve their policy objectives by relying on decentralized mechanisms that utilize local
information. However, household and individual characteristics within communities can also vary
substantially. A growing theoretical literature suggests that inequality within communities can
influence policy outcomes, and that this influence could be harmful or helpful, depending on the
circumstances. Empirical investigations into the impact of inequality have, to date, largely been held
back by a lack of systematic evidence on community-level inequality. This paper uses household
survey and population census data to estimate per capita consumption inequality within communities
in three developing countries: Ecuador, Madagascar, and Mozambique. Communities are found to
vary markedly from one another in terms of the degree of inequality they exhibit. We also show that
there should be no presumption that inequality is less severe in poor communities. We argue that the
kind of community-level inequality estimates generated in this paper can be utilized in designing and
evaluating decentralized anti-poverty programs.
Key Words: Inequality, targeting.
JEL Classification Numbers: D63, H70, I38, R13
___________________________________
* Elbers is with Vrije (Free) University Amsterdam, Lanjouw, Mistiaen and Özler are with the
World Bank, and Simler is with the International Food Policy Research Institute. We are grateful to
Francois Bourguignon, Francisco Ferreira, Emanuela Galasso, Ravi Kanbur, Jenny Lanjouw,
Vijayendra Rao, Martin Ravallion and two anonymous referees for comments and/or useful
discussions. All errors are our own. The views presented here should not be taken to reflect those of
the World Bank or affiliated institutions. Correspondence: planjouw@worldbank.org.
1. Introduction
Governments in developing countries commonly implement decentralized anti-
poverty programs that are designed to distribute assets or cash to individuals or households.
In many such cases, the central government first distributes its poverty reduction budget to
communities, and these are then left to decide how to allocate that budget across individuals.
Social Funds projects provide a well-known example from the family of community based
development (CBD) initiatives, in which poor communities are required to identify, apply
for funding, design, implement and manage their projects (Mansuri and Rao, 2003).1 These
initiatives aim to improve poverty targeting and implementation of projects by making use
of information at the local level and by involving local participation. However, in practice
these potential benefits may be outweighed by the possibility of resources being captured by
local elites.2 For example, in their review of the CBD approach, Mansuri and Rao (2003)
argue that while potential gains from CBD efforts are large, there are also important risks
inherent in the basic precepts of the approach.
Uncertainty around the ultimate impact of such programs implies that a blanket
application of a given approach in all communities may not be appropriate. Again, Mansuri
and Rao (2003) caution against the wholesale scaling-up of best-practices identified in one,
or perhaps several, pilot settings, as the success of such pilot projects might depend crucially
on local conditions that are not observed elsewhere. At the same time, it is clear that
administrators of large projects, such as a country-wide cash transfer or Social Funds
program, are unable to take into account the full range of local characteristics that could
1Mansuri and Rao (2003) distinguish CBD from Community Driven Development (CDD), popularized by the
World Bank, in that the latter refer to projects where communities have direct control over key project decisions
as well as the management of investment funds. CBD can be thought of as a broader umbrella term that
accommodates but is not restricted to the World Bank's CDD concept.
3
possibly affect project performance. Hence, policymakers are confronted with the
challenging task of designing schemes that do take critical local information into account,
but are not prohibitively costly in terms of their implementation.
One way governments have traditionally broached this problem is to categorize
communities by easily observable characteristics and then adapt schemes for different
groups. For example, while local level data on poverty are generally unavailable,
government programs often draw on proxy indicators believed to be correlated with local
poverty conditions to determine eligibility of communities for various projects. As we
discuss below, there is emerging theoretical analysis and empirical evidence that suggests
that local inequality may also affect local development outcomes. However, such
information has not generally made its way into program design. There seem to be two
main reasons why local inequality is not explicitly considered in program design. First, until
recently estimates of local inequality measures have not been widely available. While basic
needs-type indices have been used in place of the missing income (or expenditure) poverty
measure, such proxies have not been available for income inequality.3 Second, when the
target of an intervention is a small, poor community, inequality may not be considered of
primary importance: it seems natural to assume that in the poorest communities in the
developing world livelihoods are at the subsistence level and so there is little scope for
pronounced variation in wellbeing across households and individuals.
This paper addresses these two issues. First, using data from Ecuador, Madagascar,
and Mozambique, we apply a newly developed methodology to estimate local-level welfare
2A vivid illustration of elite capture problems in practice and a theoretical treatment of this issue are provided
in Platteau and Gaspart (2003).
3McKenzie (2003) provides a recent attempt to proxy local inequality on the basis of easily observed correlates
of household income.
4
outcomes combining the detailed information available from a household survey with the
large-scale representation of the population census. We suggest that meaningful estimates
of income or expenditure inequality for small areas can be obtained for many countries on
the basis of these techniques. Second, we show that there is great heterogeneity in
inequality across these communities in each country. We find that this heterogeneity in
local inequality levels is still present when we focus our attention on the poorest
communities in rural areas. The combined implication of these findings is that information
on local inequality is available for use by program implementers and that this information
can help to categorize communities even after conditioning on local poverty and type of
area.
How Can Local Inequality Affect Welfare Outcomes?
Mansuri and Rao (2003) present a comprehensive overview of the theoretical and
empirical literature on the relationship between local inequality and development outcomes.
Two critical issues emerge: First, how does inequality within a community influence the
targeting impact of a particular project, and second, how does local inequality shape the
degree and nature of collective action within communities?
Recent theoretical analysis suggests that inequality may affect targeting outcomes of
social funds projects or anti-poverty transfer schemes by reducing the relative power of the
intended beneficiaries (Galasso & Ravallion, 2004; Bardhan and Mookherjee, 1999). The
advantage of such decentralized approaches to make use of better community-level
information about priorities and the characteristics of residents could be offset by the
possibility that the local governing body is controlled by elites who may have different
5
objectives than the poor within their communities. While the predictions from this
theoretical work are ambiguous, limited empirical evidence shows that both the pros and the
cons of decentralized decision-making are at work in various countries. For example,
Alderman (2002) finds in Albania that communities were able to improve targeting by using
information unavailable to the central government. On the other hand, Galasso & Ravallion
(2004) find that high levels of local inequality (measured in terms of landholdings) were
associated with worse targeting performance under the Food for Education program in
Bangladeshi villages.
A detailed case study of the north Indian village of Palanpur provides one illustration
of the manner in which local elites are able to appropriate for their own purposes resources
and opportunities made available to the community through public provisioning. Drawing
on information collected in this small village over the period spanning the late 1950s
through the early 1990s, Drèze, Lanjouw and Sharma (1998) document the introduction of
18 types of government-provided programs in the village. These include a public works
village-road building program, free schooling, free basic health care, old-age pensions, a
fair-price shop, a farmer's cooperative, and so on. The sobering diagnosis is that the large
majority of these programs were for all practical purposes non-functional, particularly
wherever there existed a redistributive component. Drèze, Lanjouw and Sharma (1998)
argue that a key explanation for this dispiriting record is that, at the village level, collective
institutions were dominated by privileged groups. This means that only those programs that
enjoyed strong backing from the politically advantaged segment of the village were allowed
to succeed. Drèze, Lanjouw and Sharma (1998) argue that "there is little prospect of major
6
improvement in the orientation and achievements of government intervention without a
significant change in the balance of political power, both at the state and at the local level".4
There is also a rich literature on the relationship between inequality and collective
action with implications on the provision of public goods, management of common pool
resources, and group participation (Olson, 1973; Balland and Platteau, 1999, 2001, 2003;
Dayton-Johnson and Bardhan, 2002, among others). This literature points to the possibility,
at least in principle, that some inequality may be necessary in order to mobilize the
collective action needed for group provision of a public good (Olson, 1973). The argument
is that if a community is large and homogeneous, no single individual could make any
significant difference in the provision of the public good, and hence all would want to free-
ride, resulting in no provision.
Again, the theoretical relationship between inequality, participation and collective
action is complex. However, most of the empirical evidence seems to point to a negative or
a U-shaped relationship, where increased inequality leads, at least initially, to a decline in
collective action (Dayton-Johnson, 2000; Bardhan and Dayton-Johnson (2002), Khwaja
(2001); Alesina and La Ferrara (2000), La Ferrara (2002)).
The growing literature on the relationship between local inequality and development
outcomes thus suggests that there are a number of ways in which development efforts, such
as those described in this paper, could be influenced by local inequality. The empirical
literature, while still far from complete, suggests that on balance inequality is likely to
hamper local development efforts. It is for this reason that incorporating information on
4The review by Drèze, Lanjouw and Sharma (1998) does not cover any specific CBD projects in Palanpur. It
is possible that performance of such projects might have been different. The review does indicate, however,
that any notion of the villagers in Palanpur all having the same objectives, interests, and influence would be
sorely mistaken. That villagers differed in terms of economic well-being was clearly discernable in the study:
7
inequality into the design of development efforts might be necessary. This paper argues that
such information can be obtained with data available in many developing countries.
In the next section we describe the methodology underlying the estimation of local
welfare indicators and our data, and section 3 discusses the plausibility and the precision of
our inequality estimates in Ecuador, Madagascar, and Mozambique. In section 4, we
examine the importance of local-level inequality by decomposing national inequality in each
of the three countries into a within-community and between-community component. Also
in this section, we argue that this decomposition exercise produces a summary statistic that
masks significant heterogeneity in inequality across communities. In section 5, we provide
evidence that this heterogeneity in inequality is evident even among poor rural communities.
Section 6 discusses implications for policy.
2. Data and Methodology
The data used in this study consist of a household survey and a population census
from Ecuador, Madagascar, and Mozambique. Table 1 presents the basics on each of the
data sources, such as year, sample size, stratification, etc. For more detail on the data, refer
to the studies listed in the References row in Table 1.
Construction of comprehensive geographic profiles of inequality across localities
has been constrained by the limitations of conventional distributional data. Detailed
household surveys, which include reasonable measures of income or consumption, are
samples and thus are rarely representative or of sufficient size at low levels of
disaggregation to yield statistically reliable estimates. At the same time, census (or large
income inequality within Palanpur was on the same orders of magnitude as measures of inequality for India as a
whole (Lanjouw and Stern, 1998).
8
sample survey) data of sufficient size to allow disaggregation either have no information
about income or consumption, or measure these variables poorly.5
Using a recently developed statistical procedure to combine data sources so as to
take advantage of the detailed information available in household sample surveys and the
comprehensive coverage of a census, this paper provides estimates of inequality at a level of
disaggregation previously unattainable in each of the three countries. The methodology is
developed in detail by Elbers, Lanjouw and Lanjouw (2002 and 2003a), and applications are
described in a series of papers (Demombynes et. al., 2002, Elbers et. al., 2002, and Mistiaen
et. al., 2002 among others), so we provide only the briefest description here.
First a model of log per capita household expenditures, y, is estimated using the
sample survey data, restricting the explanatory variables to those either common to both the
survey and the census, or variables in a tertiary dataset that can be linked to both of those
data sets.6 Then, letting W represent an indicator of poverty or inequality, we estimate the
expected level of W given the census-based observable characteristics of the population of
interest using parameter estimates from the `first-stage' model of y. The same approach
could be used with other household measures of wellbeing, such as assets, income, or
employment.
The first-stage estimation is carried out using the household sample survey. Our first
concern is to develop an accurate empirical model of household consumption. Consider the
following model:
ln ych = E[ln ych | xch] + uch xch +c + ch ,
T T
5See Alderman et al. (2003)
6As described in Elbers et. al (2003a), a separate model is estimated for each stratum, rather than forcing the
models and the parameter estimates to be the same for the whole country.
9
where household h is located in sample cluster c, and are uncorrelated with each other
and are uncorrelated with observables. This specification allows for an intracluster
correlation in the disturbances. For any given disturbance variance, ch , the greater the2
fraction due to the common component c, the less one benefits from aggregating over more
households. Welfare estimates become less precise. Further, failing to account for spatial
correlation in the disturbances could bias the inequality estimates.
A Hausman test described in Deaton (1997) is used to determine whether to estimate
with household weights. R 2's for our models are generally high, ranging between 0.45 and
0.77 in Ecuador, 0.29 to 0.63 in Madagascar, and 0.27 to 0.55 in Mozambique.7
We next model the variance of the idiosyncratic part of the disturbance, ,ch . To 2
model heteroskedasticity in the household-specific part of the residual, we choose between 5
and 20 variables, zch, that best explain variation in ech out of all potential explanatory
2
variables, their squares, and interactions.8
Finally, we determine the distribution of and using the cluster residuals ^c and
standardized household residuals. We use normal or t distributions with varying degrees of
freedom, or the actual standardized residual distribution mentioned above when taking a
semi-parametric approach. Before proceeding to simulation, the estimated variance-
covariance matrix is used to obtain final GLS estimates of the first stage consumption
model.
At this point we have a full model of consumption that can be used to simulate any
expected welfare measures with associated prediction errors.
7Again, see Elbers et al. (2002), Mistiaen et al. (2002) and Simler and Nhate (2002) for details.
10
3. Estimates of Local Inequality in Three Countries
In this section, we examine how our census-based estimates compare with estimates
from the countries' respective surveys at the level at which those surveys are representative.9
If the methodology we employ is applied properly, with proper attention to data
comparability issues, first-stage regression models and the error structures used in
simulating the inequality measures, then stratum level estimates should naturally correspond
closely to those in the household survey.
Table 2 presents estimates of average per capita consumption for each country from
both the household survey and census at the stratum level, for which the household survey is
representative. Indeed, in nearly every case we cannot reject that estimates of average per
capita consumption across the two data sources are the same (at the 95% confidence level).
With few exceptions point estimates match closely. Note that the standard errors of the per
capita consumption estimates in the census are almost always smaller than those in the
household survey. While the census estimates are predicted with error mainly due to the
imprecision of the first-stage regressions, they are free of sampling error, making them more
precise than their counterparts from the household survey.
Comparing stratum-level estimates of inequality across the census and survey is less
straightforward. Inequality measures tend to be sensitive to the tails in the distribution of
expenditure. Since far-off portions of the tails are typically not observed in the survey
(because of its small sample size), the survey estimates of inequality will often be below the
true level of inequality. Perhaps more importantly, non-response may be of some
8We limit the number of explanatory variables to be cautious about over-fitting and use a bounded logistic
functional form.
11
importance in a household survey, and to the extent that non-response can be expected to be
more prevalent among rich households, the resulting selection bias will lead to further
downward bias of survey-based estimates.10 To the extent that a census suffers less from
such problems of observation, and assuming that the expenditure model is correct, the
expenditure of rich households will be better represented in the census-based estimates of
inequality. These considerations lead one to expect higher inequality estimates from census-
bases imputation.
Table 3 presents estimates of the Gini-coefficient in our three countries. Standard
errors are presented for all estimates reflecting the complex sample design of the
household survey for the survey-based estimates, and our imputation procedure for the
census based estimates. For Ecuador and Mozambique, we can see that the census estimates
of consumption inequality tend to be higher than the survey based estimates, although not
generally to such an extent that one can reject that they are the same (Table 3).11 Note that
for some provinces in Mozambique, such as Sofala, Maputo Province and Maputo City, the
estimates from the census are not only higher than those in the survey, but also happen to be
quite imprecisely estimated.12
In Madagascar, it is the standard errors on the survey estimates of inequality that are
quite high (Table 3). This serves as a reminder that although stratum-level estimates of
welfare in household surveys are often referred to as representative, the sample size in these
strata can be rather small so that the accompanying welfare estimates are not always precise.
9For a similar analysis, focusing specifically on poverty, see Demombynes et al (2002).
10On this, see also Mistiaen and Ravallion (2003).
11These issues are subject of current research. If anything we expect the true difference between census-based
and survey-based inequality estimates to be even larger, since in the simulations underlying poverty maps we
regularly discard extreme draws of the error terms. Again, this might lead to an under-representation of high-
expenditure cases.
12
Nonetheless, for our purposes it is encouraging to note that point estimates of the Gini
coefficient between the survey and the census in Madagascar are often quite close.
Elbers, Lanjouw and Lanjouw (2002, 2003a) demonstrate that standard errors on
census-based estimates are inversely correlated with the size of the target population. Thus,
although they may look good at the stratum level, estimates of inequality for smaller
localities could become quite imprecise. Does this imply that at fine levels of
disaggregation such as firaisana in Madagascar, or parroquia or zona in Ecuador our
inequality estimates are too noisy to be useful? In the three countries we are working with
here, we have produced estimates of inequality at the third administrative level (the
firaisana in Madagascar, the parroquia in rural Ecuador, the administrative post in
Mozambique). Elbers et al (2002) document that standard errors correspond to about 5-15%
of point estimates of inequality for these localities (see also below). This is in the same
range of what is generally judged to be acceptable at the stratum level in household surveys.
Elbers et al (2002) also show that the explanatory power of simple descriptive OLS
regressions of inequality at the smallest administrative level on a set of simple community
characteristics is quite high in these three countries (R2 's ranging between 0.57 and 0.78 in
urban areas and between 0.38 and 0.57 in rural areas). If the inequality estimates produced
with this methodology were just noise, one would expect the explanatory power of these
regressions to be much lower.13
Based on the evidence presented in this section, we conclude that the estimation
technique used here can yield meaningful estimates of inequality for small areas. Next, we
12Fortunately, as we shall see below, there is no evidence that the census-based estimates become even noisier
at lower levels of aggregation in Mozambique.
13
focus our attention on inequality decompositions by administrative units and the
heterogeneity of inequality across communities.
4. Decomposing inequality by geographic sub-groups
We now turn to inequality decomposition by geographic sub-unit, which enjoys a long
tradition in the empirical analysis of inequality, in both developed and developing countries.
It is clear that when national inequality is attributable largely to differences in mean incomes
across localities, the policy implications may be quite different from the situation, where
sub-regions themselves are unequal and national inequality is basically an expression of
heterogeneity that already exists at the local levels. We decompose inequality using the
General Entropy class of inequality measures, a class of measures that is particularly well
suited for this exercise.14 This class of measures takes the following form:
GEc = 1 yi c
for c 0,1
c(c -1)
i fi -1
µ
= - fi log yi
for c=0
i µ
= fi log
yi yi
for c=1
i µ µ
13Elbers, Lanjouw and Lanjouw (2003b) argue that although the inequality measures included in these
regressions have been estimated, this does not invalidate their use for these purposes (although they do
advocate correcting standard errors for model error).
14Following Bourguignon (1979), Shorrocks (1980) and Cowell (1980). Cowell (2000) provides a useful
recent survey of methods of inequality measurement, including a discussion of the various approaches to sub-
group decomposition. Sen and Foster (1997) and Kanbur (2000) discuss some of the difficulties in interpreting
results from such decompositions.
14
where fi is the population share of household i, yi is per capita consumption of household i,
µ is average per capita consumption, and c is a parameter that is to be selected by the user.15
This class of inequality measures can be decomposed into a between and within-group
component along the following lines:
c
GEc = 1 g j
µj c
for c 0,1
c(c -1) 1- g + GE
j j
j µj
µ j µ
GEc = g j log
j µ
µj + GE for c=0
jg j
j
GEc = g µj
g GE for c=1
j j
j µj µj
µ log +µ j
j µ
where j refers to sub-groups, gj refers to the population share of group j and GEj refers to
inequality in group j. The between-group component of inequality is captured by the first
term to the right of the equality sign. It can be interpreted as measuring what would be the
level of inequality in the population if everyone within the group had the same (the group-
average) consumption level µj. The second term on the right reflects within-group
inequality, or what would be the overall inequality level if there were no differences in mean
consumption across groups but each group had its actual within-group inequality GEj.
Ratios of the respective components with the overall inequality level provide a measure of
the percentage contribution of between-group and within-group inequality to total
inequality.
15Lower values of c are associated with greater sensitivity to inequality amongst the poor, and higher values of
c place more weight to inequality among the rich. A c value of 1 yields the well known Theil entropy measure,
a value of 0 provides the Theil L or mean log deviation, and a value of 2 is ordinally equivalent to the squared
coefficient of variation.
15
In Table 4, we present the decomposition results in each of the three countries
examined in this paper. At one extreme, when inequality is measured at the national level,
all inequality is, by definition, within-group. At the other extreme, when each individual
household is taken as a separate group, the within-group contribution to overall inequality is
zero and all inequality is between-group. But where does the between-group component
start to outweigh the within-group component? Is it reasonable to suppose that at a
sufficiently low level of disaggregation, such as a village or community, inequality within
groups is small, and most of overall inequality is due to differences between groups?
The first row for each country in Table 4 contains the share of inequality within and
between communities, where community is defined as the third administrative level
(number of households ranging between 1,000-10,000) in each of our three countries. The
inequality measure we use is the mean log deviation, i.e. GE(0).16 The highest between-
group inequality is observed in Ecuador, at approximately 41%. In Madagascar and
Mozambique, the share of inequality that can be attributed to mean expenditure differences
between communities is much smaller, at 25% and 22%, respectively. There is also
evidence, particularly in Ecuador, that the observed between-community inequality is due
mainly to the differences between urban and rural communities. When we focus our
attention solely on rural communities in Ecuador, the between-group component of
inequality falls to under 15% of total inequality in rural Ecuador. Similarly in Madagascar,
the share of between-group inequality in rural areas is 18%, significantly lower than for the
combined rural and urban areas. In all three countries, overall inequality is mostly
16Results remain virtually identical for other values of c.
16
attributable to inequality within communities, even when the community is defined as the
lowest level of central government administrative unit.17 18
Interpretations of decompositions such as these are, however, not completely
straightforward. For example, the above decomposition results (documenting a large
within-group component) do not imply that local inequality levels are uniformly high, or
even that the majority of communities exhibit high levels of inequality. It is important to
recognize that the decomposition provides a summary statistic, suggesting that on
average within-group inequality is not particularly low at the third administrative level.
In other words, it is perfectly possible that a country is characterized by both highly equal
and highly unequal communities. A simple example can illustrate this. Consider a
population of 8 individuals with consumption values (1,1,2,2,4,4,5,5). This population
could be divided into two communities as (1,2,4,5) and (1,2,4,5); or as (1,1,5,5) and
(2,2,4,4). In both cases the two communities have the same average consumption. As a
result the between-group component from a decomposition exercise as has been carried
out above is always zero (and the within-group share is thus 100% in both cases).
However, in the first case inequality in the two communities is exactly equal to national
inequality, whereas in the second case one community has a higher and the other a lower
17Inequality estimates produced on the basis of the methodology described in section 2 are averages calculated
over a number of simulations (100 in our case). It is possible that a decomposition of inequality carried out
after this averaging procedure has occurred overstates the within-group component of inequality because
differences in inequality across communities have been smoothed out. To check this we carried out the
decomposition exercise for each of the 100 simulations and then averaged across the decomposition results.
We found that the between group component of inequality increased by at most 1-2% and that our qualititative
results were completely unchanged.
18We have no other reason to suspect that our methodology for estimating local level inequality is associated
with any built-in tendency to over state within-group inequality. One way to test this is to carry out the
imputation exercise described here into a dataset that also contains information on welfare that has been directly
collected, and to then compare decomposition results on the basis of imputed welfare against those on the basis
of observed welfare. Elbers, Lanjouw, Lanjouw and Leite (2003) undertake such an analysis in Brazil and
show that a decomposition of inequality based on imputed consumption reaches virtually identical conclusions
as a decomposition based on observed income.
17
level of inequality than at the national level. Hence, finding a high within-group share
from a decomposition exercise across a large number of communities is perfectly
consistent with great heterogeneity in inequality levels across those same communities.
It is then natural to ask, in our case, whether communities vary widely in their degree of
inequality.
In Figures 1-5, we plot community-level inequality estimates and compare these
with overall inequality. Communities are ranked from most equal to most unequal, and 95%
confidence intervals on each community-level estimate are included as scatter plots. Figure
1 compares parroquia level inequality in rural Ecuador against the overall inequality level in
rural areas. We see that although the within-group share from the decomposition was as
high as 86%, this summary statistic masks considerable variation in parroquia inequality
levels. A large majority of parroquia-level point estimates are well below the national level
in rural Ecuador. Even allowing for the imprecision around the parroquia-level estimates
(which are typically 5-15% of the point estimate), a sizeable proportion of parroquias are
unambiguously more equal than the picture at the national level. Another sizeable
proportion is not obviously less or more unequal than the country as a whole, and a smaller
number of parroquias are considerably more unequal.19 In urban Ecuador (Figure 2), the
proportion of zonas that have lower inequality than the national-level inequality rate is even
higher than in rural areas. The precision of point estimates in urban areas of Ecuador is
somewhat higher than in rural areas; accordingly, more zonas lie unambiguously below the
national inequality level.
19Note the reason that there are more communities with inequality below the national level than above the
national level is due to the fact that between-group inequality, while relatively small, is not absent. Differences
in average per capita consumption ensure that at least some of total inequality is attributable to differences
between groups. If there were no within-group inequality at all, or if all communities had the same level of
18
In rural and urban Madagascar (Figures 3 and 4) and in Mozambique (Figure 5) the
picture is very similar. In each of the three countries, there is clearly a sizeable subset of
communities with lower inequality than the country as a whole, another large group for
which inequality is not significantly different from inequality in the country as a whole, and
a small third group of communities with inequality higher than the national level.
5. Are Poor Communities More Equal than Others?
In the last section, we noted that while most of the inequality in Ecuador,
Madagascar, and Mozambique is attributable to inequality within communities, there is a lot
of heterogeneity in inequality across these communities within each country. In this section,
we ask whether inequality is less marked if we focus our attention on poor communities.
CBD programs are often targeted primarily to poor communities. If those communities
have low levels of inequality, it may be less important that policymakers incorporate
information on inequality into the design and implementation of CBD projects.
Unfortunately, it turns out that this is not the case for the countries examined in this paper.
Figures 6, 8, and 10 present the range of inequality (measured by the commonly
used Gini index) across communities in each country by quintiles of the imputed headcount
index (see Demombynes et al, 2003).20 The Gini index at the community level ranges from
0.299 to 0.501 in Ecuador, 0.231 to 0.466 in Madagascar, and 0.261 to 0.534 in
Mozambique.21 Interestingly, in all three countries, median inequality in the poorest quintile
within-group inequality, then overall inequality would be greater than or equal to inequality in each of the
individual communities.
20 It is possible that we would observe high inequality in high poverty areas simply because of the fact that
these two measures of welfare are highly correlated. However, the results presented in this section are the same
if we rank communities by their mean consumption levels instead of the headcount index.
21 These reported ranges exclude the top and bottom 1% of communities (in terms of the Gini index) in each
country.
19
is not lower than that in any of the richer quintiles. Furthermore, the range of inequality
levels across communities is among the widest in the poorest quintile. This observation
remains true even when we restrict our attention to rural communities only (Figures 7 & 9).
We conclude that a typical poor community in any of these three countries even if it is in a
rural area is at least as unequal as other communities, and that the range of inequality
among poor communities is not narrower than the country as a whole. The next section
discusses the possible policy implications of our findings.
6. Policy Discussion
There has been a massive increase in resources devoted to CDD programs in the past
10 years. The review by Mansuri and Rao (2003) suggests that between 1996 and 2003,
funding for CDD projects rose from around US$325 million to around $2 billion. While the
main goal is to achieve better outcomes by involving local communities in the decision-
making process and management of projects, governments nonetheless need some basic
indicators to target communities and tailor basic features of these projects to different types
of communities. So far, governments have commonly utilized type of area (urban/rural) and
proxy information on poverty at the community level for such purposes.
In this paper, we propose another measure of welfare, namely inequality at the
community level, as a possible additional indicator to inform the design of decentralized
anti-poverty programs and CDD projects. Recent theory and limited empirical evidence
suggests that inequality may be related to outcomes at the community level. It is possible
that inequality at the community level may lead to the capture of the intended benefits by the
local elite or inequality may simply be highly correlated with another (not easily observed)
20
factor that leads to elite capture. Collective action and the subsequent provision of public
goods may also be correlated with the level of inequality within communities.
A recently developed small area estimation technique can provide estimates of
inequality at the local level. In the three different countries examined here, we find that
although, on average, most of the consumption inequality in each of Ecuador, Madagascar,
and Mozambique is attributable to inequality within communities, local inequality varies
widely across communities. Furthermore, we find that inequality is highly heterogeneous
even in the poorest communities in these countries. Not only is inequality in a typical poor
community as high as in other communities, but the range of inequality levels among poor
communities is at least as wide as it is in richer communities. This finding remains true
even when we restrict our attention to rural areas.
Our findings suggest that local inequality can provide additional information even
after controlling for the type of area and the poverty levels of communities. It is possible
that use of such information can enhance desired outcomes. For example, for transfer
programs where it is intended that local communities identify poor beneficiaries, eligible
communities could broadly be categorized as low, middle, and high inequality. Random
audits and means-tested targeting by the central government (as are conducted, for example,
in Mexico's PROGRESA program) could then be considered to improve pro-poor targeting
in the middle and high inequality communities.
Clearly, a first priority is to undertake further and more systematic research into the
relationship between local inequality and various development outcomes. A critical
question concerns the manner and extent to which current development processes and
practices interact with local inequality. Better estimates of local level consumption
21
inequality made possible through application of the techniques described in section 2 of this
paper, as well as through other related approaches, offer important new opportunities for
analysis. At present, micro-level estimation of welfare based on the methodology described
here has been completed or is currently underway in some 25 developing countries. Such
estimates can be combined with detailed information on the operation of anti-poverty
programs and CBD projects in these countries, with an eye toward uncovering systematic
relationships, positive or negative.
22
References
Alderman, H. (2002) `Do Local Officials Know Something We Don't? Decentralization
of Targeted Transfers in Albania', Journal of Public Economics, Vol 83, 375-404.
Alesina, A. and La Ferrara, E. (2000) `Participation in Heterogeneous Communities'
Quarterly Journal of Economics, pg 847-904.
Baland, J-M. and Platteau, J-Ph (1999) `The Ambiguous Impact of Inequality on Local
Resource Management', World Development, Vol 27, No. 5.
Baland, J-M and Platteau, J-Ph (2001) `Collective Action and the Commons: The Role
of Inequality' forthcoming in, Baland, J-M, Bardhan, P. and Bowles, S.(eds)
Inequality, Cooperation and Environmental Sustainability (Princeton: Princeton
University Press).
Baland, J-M and Platteau, J-Ph (2003) `Institutions and the Efficient Management of
Environmental Resources', in Mahler, K.G. and Vincent, J.R. (eds) Handbook of
Environmental Economics Vol 1, (Amsterdam: Elsevier North Holland).
Bardhan, P. and Mookherjee, D. (1999) `Relative Capture of Local and Central
Governments', mimeo. Boston University.
Bourguignon, F. (1979) `Decomposable Income Inequality Measures' Econometrica
47:901-920
Cowell, F. (1980) `On the Stucture of Additive Inequality Measures' Review of
Economic Studies, 47521-531.
Cowell, F. (2000) `Measurement of Inequality' in Atkinson, A.B. and Bourguignon, F.
(eds) (2000) Handbook of Income Distribution Vol. 1, (North Holland: Elsevier
Science B.V.)
Dayton-Johnson, J. (2000) `Determinants of Collective Action on the Local Commons:
A Model with Evidence from Mexico', Journal of Development Economics, Vol. 62,
181-208.
Dayton-Johnson, J. and Bardhan, P. (2002) `Inequality and Conservation on the Local
Commons: A Theoretical Exercise', The Economic Journal, Vo1 112, 577-602.
Deaton, A. (1997) The Analysis of Household Surveys: A Microeconometric Approach to
Development Policy. The Johns Hopkins University Press for the World Bank:
Washington, D.C.
Demombynes, G., Elbers, C., Lanjouw, J.O., Lanjouw, P., Mistiaen, J. and Özler, B.
(2002) `Producing an Improved Geographic Profile of Poverty: Methodology and
Evidence from Three Developing Countries' in van der Hoeven, R. and Shorrocks, A.
(eds) Growth, Inequality and Poverty: Prospects for Pro-Poor Economic Development
(Oxford: Oxford University Press).
Demombynes, G. and Özler, B. (2002) `Crime and Local Inequality in South Africa'
Journal of Development Economics (forthcoming).
23
Drèze, J., Lanjouw, P. and Sharma, N. (1998) `Economic Development in Palanapur,
1957-1993' in Lanjouw, P. and Stern, N. (eds) Economic Development in Palanpur
Over Five Decades (Oxford: Oxford University Press).
Elbers, C., Lanjouw, J.O. and Lanjouw, P. (2003a) `Micro-Level Estimation of Poverty
and Inequality', Econometrica 71(1): 355-64.
Elbers, C., Lanjouw, J.O. and Lanjouw, P. (2003b) `Imputed Welfare Estimates in
Regression Analysis' mimeo, Development Economics Research Group, The World
Bank.
Elbers, C., Lanjouw, J.O., Lanjouw, P. and Leite, P. (2003) `Poverty and Inequality in
Brazil: New Estimates from Combined PPV-PNAD Data', paper presented at the
Conference on the 100th Anniversary of Jan Tinbergen, Erasmus University,
Rotterdam, Netherlands, April 7-11.
Elbers, C., Lanjouw, J.O. and Lanjouw, P. (2002) `Micro-Level Estimation of Welfare'
Policy Research Working Paper 2911, Development Research Group, the World Bank,
Washington D.C.
Elbers, C., Lanjouw, P., Mistiaen, J., Özler, B., and Simler, K. (2002) `Are Neighbors
Equal? Estimating Local Inequality in Three Developing Countries' forthcoming in
Spatial Inequality and Development, Ravi Kanbur & Tony Venables (eds.)
Galasso, E. and Ravallion, M. (2004) `Decentralized Targeting of an Anti-Poverty
Program', Journal of Public Economics (in press).
Hentschel, J. and Lanjouw, P. (1996) `Constructing an Indicator of Consumption for the
Analysis of Poverty: Principles and Illustrations with Reference to Ecuador', LSMS
Working Paper No.124, DECRG-World Bank: Washington DC.
Hentschel, J., Lanjouw, J.O., Lanjouw, P., and Poggi, J. (2000) `Combining Census and
Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador',
World Bank Economic Review 14(1)147-65.
Khwaja, A. (2001) `Can Good Projects Succeed in Bad Communities? Collective Action
in the Himalayas' mimeo, Harvard University.
La Ferrara, E. (2002) `Inequality and Participation: Theory and Evidence from Rural
Tanzania' Journal of Public Economics, v85, No. 2: 235-73.
Lanjouw, P. and Stern, N. (1998) `Inequality', in Lanjouw, P. and Stern, N. (eds)
Economic Development in Palanpur Over Five Decades (Oxford: Oxford University
Press).
Mansuri, G. and Rao, V. (2004) `Community Based (and Driven) Development: A
Review', World Bank Research Observer, (forthcoming)
McKenzie, D. (2003) `Measuring Inequality with Asset Indicators' BREAD Working
Paper No. 042, August 2003.
24
Mistiaen, J., Özler, B., Razafimanantena, T., and Razafindravonona, J. (2002) `Putting
Welfare on the Map in Madagascar'. Africa Region Working Paper Series on. 34.
Olson, M. (1973) The Logic of Collective Action: Public Goods and the Theory of
Groups (Cambridge: Harvard University Press).
Platteau, J.P. and Gaspart, F. (2003). The "Elite Capture" Problem in Participatory
Development. Working Paper No. 253 2003/14. FUNDP, The University of
Namur.
Sen, A.K. and Foster, J. (1997) On Economic Inequality: Expanded Edition with
Substantial Annexe (Oxford: Oxford University Press).
Shorrocks, A. (1980) `The Class of Additively Decomposable Inequality Measures'
Econometrica 48: 613-625.
Simler, K. and Nhate, V. (2002) `Poverty, Inequality and Geographic Targeting:
Evidence from Small-Area Estimates in Mozambique', mimeo, International Food
Policy Research Institute.
25
Table 1. Data Summary
Ecuador Madagascar Mozambique
Household Survey
Year 1994 1993-4 1996-7
Source Encuesta de Enquête Permanente Auprès Inquérito Nacional aos
Condiciones de Vida des Ménages (EPM) Agregados Familiares
(ECV) sobre as Condições de
Vida (IAF96)
Sample Size 4,500 Households 4,508 Households 8,250 Households
References Hentschel and Lanjouw Mistiaen, Özler, Simler and Nhate (2002)
(1996); and Razafimanantena and
Hentschel, Lanjouw, Razafindravonona (2002)
Lanjouw and Poggi
(2000)
Population Census
1990 1993 1997
Year
Coverage About 10 million about 11.9 million individuals about 16 million
individuals in 2 million in 2.4 million households individuals
households in 3.6 million households
26
Table 2. Comparison of Survey and Census-Based Average Per-Capita
Consumption Estimates at the Stratum Level
Ecuador Madagascar Mozambique
Sucres per capita Francs per capita Meticais per capita
Stratum Survey Census Stratum Survey Census Stratum Survey Census
Quito 126098 125702 Antananarivo 513818 576470 Niassa 4660 5512
(11344) (8026) Urban (48455) (23944) (355) (484)
Sierra 121797 122415 Fianarantsoa 360635 372438 Cabo 6392 6586
Urban (8425) (4642) Urban (42613) (21878) Delgado (416) (433)
Sierra 66531 63666 Toamasina 445514 417823 Nampula 5315 5547
Rural (4067) (2213) Urban (73099) (15406) (287) (279)
Guayaquil 89601 77432 Mahajanga 613867 580775 Zambezia 5090 5316
(5597) (2508) Urban (74092) (31025) (208) (274)
Costa 86956 90209 Toliara 343111 321602 Tete 3848 4404
Urban (3603) (2391) Urban (76621) (32193) (267) (176)
Rural 57619 61618 Antsiranana 504841 693161 Manica 6299 6334
Costa (4477) (2894) Urban (46148) (93437) (741) (527)
Oriente 110064 174529 Antananarivo 312553 324814 Sofala 3218 4497
Urban (9078) (56115) Rural (23174) (14378) (191) (379)
Oriente 47072 59549 Fianarantsoa 319870 251312 Inhambane 4215 4177
Rural (4420) (3051) Rural (45215) (18091) (359) (134)
Toamasina 275943 279239 Gaza 6024 6521
Rural (22832) (15838) (356) (355)
Mahajanga 325872 321398 Maputo 5844 8559
Rural (30209) (19385) Province (613) (745)
Toliara 233801 259537 Maputo 8321 11442
Rural (22174) (16222) City (701) (4956)
Antsiranana 486781 442431
Rural (91181) (54869)
All household survey estimates are computed using weights that are the product of household survey weights
and household size. The census-based estimates are calculated weighting by household size. Standard errors are
in parentheses.
27
Table 3. Comparison of Survey and Census-Based Inequality Estimates
(Gini) at the Stratum Level
Ecuador Madagascar Mozambique
Gini Gini Gini Gini Gini Gini
(s.e.) (s.e.) (s.e.) (s.e.) (s.e.) (s.e.)
Survey- Census- Survey- Census- Survey- Census-
Stratum Based Based Stratum Based Based Stratum Based Based
Quito 0.490 0.465 Antananarivo 0.492 0.469 Niassa 0.355 0.402
(0.023) (0.012) Urban (0.027) (0.012) (0.020) (0.025)
Sierra 0.436 0.434 Fianarantsoa 0.430 0.426 Cabo 0.370 0.413
Urban (0.020) (0.011) Urban (0.038) (0.015) Delgado (0.025) (0.021)
Sierra 0.393 0.457 Toamasina 0.434 0.402 Nampula 0.391 0.400
Rural (0.034) (0.013) Urban (0.042) (0.015) (0.026) (0.020)
Guayaquil 0.378 0.416 Mahajanga 0.371 0.392 Zambezia 0.324 0.366
(0.014) (0.011) Urban (0.027) (0.016) (0.017) (0.012)
Costa 0.359 0.382 Toliara 0.514 0.504 Tete 0.346 0.394
Urban (0.015) (0.011) Urban (0.052) (0.030) (0.019) (0.018)
Rural 0.346 0.400 Antsiranana 0.362 0.433 Manica 0.413 0.449
Costa (0.036) (0.015) Urban (0.025) (0.039) (0.036) (0.020)
Oriente 0.398 0.563 Antananarivo 0.376 0.404 Sofala 0.405 0.529
Urban (0.035) (0.104) Rural (0.023) (0.015) (0.031) (0.032)
Oriente 0.431 0.478 Fianarantsoa 0.470 0.437 Inhambane 0.382 0.398
Rural (0.034) (0.014) Rural (0.050) (0.018) (0.037) (0.012)
Toamasina 0.352 0.362 Gaza 0.380 0.421
Rural (0.036) (0.017) (0.024) (0.023)
Mahajanga 0.320 0.306 Maputo 0.424 0.518
Rural (0.026) (0.015) Province (0.029) (0.029)
Toliara 0.383 0.377 Maputo 0.444 0.560
Rural (0.029) (0.017) City (0.033) (0.108)
Antsiranana 0.518 0.453
Rural (0.110) (0.048)
All household survey estimates are computed using weights that are the product of household survey weights
and household size. The census-based estimates are calculated weighting by household size. Standard errors are
in parentheses.
28
Table 4. Decomposition of Inequality Between and Within Communities
Level of Number of Within-group Between-group
Decomposition Sub-Groups inequality (%) inequality (%)
Ecuador
All Communities 1579 58.8 41.2
Urban 664 76.7 23.3
Rural 915 85.9 14.1
Madagascar
All Communities 1248 74.6 25.4
Urban 131 76.7 23.2
Rural 1117 81.9 18.1
Mozambique
All Communities 424 78.0 22.0
Our communities in Ecuador are Zonas in urban areas and Parroquias in rural areas.
Communities are Firiasana (communes) in Madagascar and Administrative Posts in Mozambique.
29
Figure 1
Figure 2
30
Figure 3
Figure 4
31
Figure 5
32
Figure 6
Figure 7
33
Figure 8
Figure 9
34
Figure 10
35