FCND DP No. 147

FCND DISCUSSION PAPER NO. 147

Food Consumption and Nutrition Division

International Food Policy Research Institute 2033 K Street, N.W.

Washington, D.C. 20006 U.S.A. (202) 862�5600

Fax: (202) 467�4439

April 2003 FCND Discussion Papers contain preliminary material and research results, and are circulated prior to a full

peer review in order to stimulate discussion and critical comment. It is expected that most Discussion Papers

will eventually be published in some other form, and that their content may also be revised.

ARE NEIGHBORS EQUAL? ESTIMATING LOCAL

INEQUALITY IN THREE DEVELOPING COUNTRIES

Chris Elbers, Peter Lanjouw, Johan Mistiaen, Berk Özler, and Kenneth Simler

ii

Abstract

A methodology to produce disaggregated estimates of inequality is implemented

in three developing countries: Ecuador, Madagascar, and Mozambique. These inequality

estimates are decomposed into progressively more disaggregated spatial units and the

results in all three countries are suggestive that even at a very high level of spatial

disaggregation, the contribution of within-community inequality to overall inequality

remains very high. The results also indicate there is a considerable amount of variation

across communities in all three countries. The basic correlates of local-level inequality

are explored, and it is consistently found that geographic characteristics are strongly

correlated with inequality, even after controlling for demographic and economic

conditions.

iii

Contents

Acknowledgments............................................................................................................... v 1. Introduction.................................................................................................................... 1 2. An Overview of the Methodology ................................................................................. 4

Definitions....................................................................................................................... 4 Estimating Error Components......................................................................................... 6

Idiosyncratic Error ...................................................................................................... 6 Model Error................................................................................................................. 6 Computation Error ...................................................................................................... 7

3. Data ................................................................................................................................ 7 4. Implementation .............................................................................................................. 9 5. Stratum-Level Comparisons Between Survey and Census.......................................... 12 6. Decomposing Inequality by Geographic Subgroups ................................................... 15 7. Correlates of Local Inequality: Does Geography Matter?.......................................... 21 8. Conclusions.................................................................................................................. 30 References......................................................................................................................... 32

Tables 1 Main data sources ..........................................................................................................7

2 Average expenditure, poverty, and inequality in Ecuador, by region (stratum)..........13

3 Average expenditure, poverty, and inequality in Madagascar, by provine and sector .....................................................................................................................14

4 Average expenditure, poverty, and inequality in Mozambique, by province..............14

5 Decomposition of inequality, by regional subgroup (GE0).........................................17

6a Correlates of mean Log deviation (GE0) in rural Ecuador (parroquia-level regression (915 parroquias) .........................................................................................24

iv

6b Correlates of mean Log deviation (GE0) in urban Ecuador (zona-level regression (660 zonas).................................................................................................26

6c Correlates of mean Log deviation (GE0) in rural Madagascar (firaisana-level regression (1,117 firaisanas)........................................................................................27

6d Correlates of mean Log deviation (GE0) in urban Madagascar (firaisana-level regression (131 firaisanas)...........................................................................................28

6e Correlates of mean Log deviation (GE0) in Mozambique (administrative post-level regression (464 administrative posts) .................................................................29

Figures

1 Distribution across parroquias of parroquia-level inequality, rural Ecuador: GE(0)...20

2 Distribution across zonas of zona-level inequality, urban Ecuador: GE(0).................20

3 Distribution across firaisanas of firaisana-level inequality, rural Madagascar: GE(0)......................................................................................................21

4 Distribution across firaisanas of firaisana-level inequality, urban Madagascar: GE(0)......................................................................................................22

5 Distribution across administrative posts of post-level inequality, Mozambique: GE(0) ....................................................................................................22

v

Acknowledgments

We are grateful to the Instituto Nacional de Estadística y Censos (INEC),

Ecuador, Institut National de la Statistique (INSTAT), Madagascar, and the Instituto

Nacional de Estatística, Mozambique, for access to their unit record census data and the

World Bank Netherlands Partnership Program (BNPP) for financial support. Helpful

comments were received from Jenny Lanjouw, Stanley Wood, and participants at a

PREM Inequality Thematic Group seminar at the World Bank. The views in this paper

are our own and should not be taken to reflect those of the World Bank or any of its

affiliates. All errors are our own.

Chris Elbers Vrije (Free) University, Amsterdam Peter Lanjouw, Johan Mistiaen, Berk Özler World Bank Kenneth Simler International Food Policy Research Institute

1

1. Introduction

The 1990s have witnessed a resurgence in theoretical and empirical attention by

economists to the distribution of income and wealth.1 One important strand of research

in the area of political economy and public policy has focused on the appropriate level of

government to which can be devolved financial and decisionmaking power regarding

public service provisioning and financing. The advantage of decentralization to make use

of better community-level information about priorities and the characteristics of residents

may be offset by a greater likelihood that the local governing body is controlled by

elites�to the detriment of weaker community members. In a recent paper, Bardhan and

Mookherjee (1999) highlight the roles of both the level and heterogeneity of local

inequality as determinants of the relative likelihood of capture at different levels of

government. As most of the theoretical predictions are ambiguous, they stress the need

for empirical research into the causes of political capture�analysis that to date remains

relatively scarce.2 In addition to questions of political capture, decentralization also has

the potential weakness that community level decisions may be less likely to reflect social

and economic costs and benefits across larger spatial scales.

Detailed information on local-level inequality has traditionally been available

only from case studies that focus on one or two specific localities.3 Such studies do not

provide a basis for generalizations about local level inequality across large numbers of

communities. Construction of comprehensive �geographic profiles� of inequality across

localities has been held back by limitations with conventional distributional data.

1 In their introductory chapter to the Handbook of Income Distribution, Atkinson and Bourguignon (2000) welcome the marked expansion of research on income distribution during the 1990s, but underscore that much ground remains to be covered. 2 Although see Galasso and Ravallion (2002), Ravallion (1999, 2000), and Tendler (1997). 3 Lanjouw and Stern (1998) report on a detailed analysis of the evolution of poverty and inequality in a north Indian village over five decades. As their study covered the entire population of the village in all survey years, their measures of income inequality describe the true distribution of income in the village. Such studies are rare. More common are village or community studies that estimate inequality across (often small) samples of households within the village.

2

Detailed household surveys that 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. In the three developing countries

examined here�Ecuador, Madagascar, and Mozambique�the lowest level of

disaggregation possible using sample survey data is to regions that encompass hundreds

of thousands of households. At the same time, census (or large sample) data of sufficient

size to allow disaggregation either have no information about income or consumption, or

measure these variables poorly.

This paper provides, in the next section, a brief description of 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 (Elbers, Lanjouw, and Lanjouw, 2002, 2003; Demombynes et al.

2002; Hentschel et al. 2000). Using a household survey to impute per capita

expenditures, y, for each household enumerated in the census we estimate inequality at a

finely disaggregated level. The idea is straightforward. First a model of y is estimated

using the sample survey data, restricting explanatory variables to those either common to

both survey and census, or variables in a tertiary data set that can be linked to both of

those data sets. 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 well being, such as

assets, income, or employment.

Applying this methodology to the three developing countries mentioned above,

we examine how well our census-based estimates match estimates from the

corresponding household surveys at the level of disaggregation at which the household

surveys are representative. Following a description of our data in Section 3, and a

discussion of implementation of the method in Section 4, we find in Section 5 that despite

the variation in levels of development, geographical context, quality, and organization of

data, the method seems to work well in all three countries we examine.

3

In Section 6 we turn to a detailed examination of local-level inequality in our

three countries. We first examine the importance of local-level inequality by

decomposing national inequality in all three countries into a within-community and

between-community component, where we successively redefine community to

correspond to lower levels of disaggregation. We find that in all countries, the within-

community share of overall inequality remains dominant even after we have

disaggregated the country into a very large number of small communities (corresponding

to the third administrative level�often representing an average of no more than 1,000-

2,000 households). These results might be construed to suggest that there is no basis for

expecting communities to exhibit a greater degree of homogeneity than larger units of

aggregation. To the extent that local-level inequality is correlated with factors, such as

elite-capture, that might threaten the success of local-level policy initiatives such as

decentralization and community-driven development, this finding sends a cautioning note

where initiatives in local-level decisionmaking are being explored.

However, it is important to carefully probe these decomposition results.

Decomposing inequality into a within-group and between-group component effectively

produces a summary statistic that can mask important differences. Upon closer

examination of the distribution of communities in our data sets, we find that in all three

countries considered, a very high percentage share of within-community inequality is

perfectly consistent with a large majority of communities having levels of inequality well

below the national level of inequality. We illustrate how this seemingly paradoxical

finding is in fact fully consistent with the decomposition procedure.

Given that in our three countries we observe a significant degree of heterogeneity

in inequality levels across communities, we explore in Section 7 some simple correlates.

Our aim is not so much to explain local inequality (in a causal sense) but rather to explore

the extent to which inequality is correlated with geographic characteristics, and whether

this correlation survives the inclusion of some basic economic and demographic controls.

In Section 8 we offer some concluding remarks.

4

2. An Overview of the Methodology

The survey data are first used to estimate a prediction model for consumption and

then the parameter estimates are applied to the census data to derive welfare statistics.

Thus, a key assumption is that the models estimated from the survey data apply to census

observations. This is most reasonable if the survey and census years coincide. In this

case, simple checks can be carried out by comparing the estimates to basic poverty or

inequality statistics in the sample data. If different years are used but the assumption is

considered reasonable, then the welfare estimates obtained refer to the census year,

whose explanatory variables form the basis of the predicted expenditure distribution.

An important feature of the approach applied here involves the explicit

recognition that the poverty or inequality statistics estimated using a model of income or

consumption are statistically imprecise. Standard errors must be calculated. The

following subsections briefly summarize the discussion in Elbers, Lanjouw, and Lanjouw

(2002, 2003).

Definitions

Per capita household expenditure, yh, is related to a set of observable

characteristics, xh4:

ln yh = E[ln yh | xh ] + uh . (1)

Using a linear approximation, we model the observed log per capita expenditure for

household h as

hhh uy +′= βxln , (2)

where β is a vector of parameters and uh is a disturbance term satisfying

4 The explanatory variables are observed values and need to have the same degree of accuracy in addition to the same definitions across data sources.

5

E[uh | xh] = 0.

In applications we allow for location effects and heteroskedasticity in the distribution of

the disturbances.

The model in equation (2) is estimated using the household survey data. We are

interested in using these estimates to calculate the welfare of an area or group for which

we do not have any, or insufficient, expenditure information. Although the

disaggregation may be along any dimension�not necessarily geographic�we refer to

our target population as a �county.� Household h has mh family members. While the unit

of observation for expenditure is the household, we are more often interested in welfare

measures based on individuals. Thus we write W (m, X, β, u), where m is a vector of

household sizes, X is a matrix of observable characteristics, and u is a vector of

disturbances. Because the disturbances for households in the target population are

always unknown, we estimate the expected value of the indicator, given the census

households� observable characteristics and the model of expenditure in equation (2).5 We

denote this expectation as

µ = E[W | m, X, ξ ] , (3)

where ξ is the vector of all model parameters, i.e., β and the parameters describing the

distribution of u. In constructing an estimator of µv we replace the unknown vector ξ

with consistent estimators, ξ� , from the first-stage expenditure regression. This yields

µ� = E[W | m, X, ξ� ].

This expectation is generally analytically intractable, so we use Monte Carlo simulation

to obtain our estimator, µ~ .

5 If the target population includes sample survey households, then some disturbances are known. As a practical matter, we do not use these few pieces of direct information on y.

6

Estimating Error Components

The difference between µ~ , our estimator of the expected value of W for the

county and the actual level of welfare for the county may be written:

)~�()�()(~ µµµµµµ −+−+−=− WW . (4)

Thus the prediction error has three components: the first due to the presence of a

disturbance term in the first-stage model, which implies that households� actual

expenditures deviate from their expected values (idiosyncratic error); the second due to

variance in the first-stage estimates of the parameters of the expenditure model (model

error); and the third due to using an inexact method to compute µ� (computation error).6

Idiosyncratic Error

The variance in our estimator due to idiosyncratic error falls approximately

proportionately in the number of households in the county. That is, the smaller the target

population, the greater is this component of the prediction error, and there is thus a

practical limit to the degree of disaggregation possible. At what population size this error

becomes unacceptably large depends on the explanatory power of the expenditure model

and, correspondingly, the importance of the remaining idiosyncratic component of the

expenditure equation (2).

Model Error

The part of the variance due to model error is determined by the properties of the

first-stage estimators. Therefore it does not increase or fall systematically as the size of

the target population changes. Its magnitude depends on the precision of the first-stage

coefficients and the sensitivity of the indicator to deviations in household expenditure.

For a given county, its magnitude will also depend on the distance of the explanatory

6 Elbers et al. (2001) use a second survey in place of the census, which then also introduces sampling error.

7

variables for households in that county from the levels of those variables in the sample

data.

Computation Error

The variance in our estimator due to computation error depends on the method of

computation used and can be made as small as desired by increasing the number of

simulations.

3. Data

In all three of the countries examined here, household survey data were combined

with unit record census data; details of these data sources are summarized in Table 1. In

Ecuador the poverty map is based on census data from 1990, collected by the National

Statistical Institute of Ecuador (Instituto Nacional de Estadística y Censos, INEC)

combined with household survey data from 1994. The census covered roughly 2 million

households. The sample survey (Encuesta de Condiciones de Vida, ECV) is based on the

Living Standards Measurement Surveys approach developed by the World Bank, and

covers just under 4,500 households. The survey provides detailed information on a wide

range of topics, including food consumption, nonfood consumption, labor activities,

agricultural practices, entrepreneurial activities, and access to services such as education

and health. The survey is clustered and stratified by the country�s three main

agroclimatic zones and a rural-urban breakdown. It also oversamples Ecuador�s two

main cities, Quito and Guayaquil. Hentschel and Lanjouw (1996) develop a household

Table 1 Main data sources

Survey Census

Country Year Sample size (households) Year

Population (millions)

Households (millions)

Ecuador 1994 4,391 1990 10.2 2.0 Madagascar 1993-94 4,508 1993 11.9 2.4 Mozambique 1996-97 8,250 1997 16.1 3.6

8

consumption aggregate adjusted for spatial price variation using a Laspeyres food price

index reflecting the consumption patterns of the poor. The World Bank (1996)

consumption poverty line of 45,476 sucres per person per fortnight (approximately $1.50

per person per day) underlies the poverty numbers reported here. Although the 1994

ECV data were collected four years after the census, we maintain the assumption that the

model of consumption in 1994 is appropriate for 1990. The period 1990-94 was one of

relative stability in Ecuador. Comparative summary statistics on a selection of common

variables from the two data sources support the presumption of little change over the

period. Additional details on these data may be found in Hentschel et al. (2000).

Three data sources were used to produce local level poverty estimates for

Madagascar: first, the 1993 unit record population census data collected by the Direction

de la Démographie et Statistique Social (DDSS) of the Institut National de la Statistique

(INSTAT). Second, a household survey, the Enquête Permanente Auprès des Ménages

(EPM), fielded to 4,508 households between May 1993 and April 1994, by the Direction

des Statistique des Ménages (DSM) of INSTAT. Third, a set of spatial and

environmental outcomes at the Fivondrona level (second administrative level or

�districts�) was used with the help of GIS.7 The consumption aggregate underpinning the

Madagascar poverty map includes components such as an imputed stream of

consumption from the ownership of consumer durables. Further details are provided in

Mistiaen et al. (2002).

The Mozambique survey data used in this analysis are from the Inquérito

Nacional aos Agregados Familiares sobre as Condições de Vida, 1996-7 (IAF96). The

survey is a multipurpose household and community survey following the World Bank�s

LSMS format and covering 8,250 households living throughout Mozambique. The

sample is designed to be nationally representative, as well as representative of each of the

ten provinces, the city of Maputo, and along the rural/urban dimension. As the survey

was fielded over a period of 14 months, and there is significant temporal variation in food

7 These data were provided to this project by the nongovernmental organization CARE.

9

prices corresponding to the agricultural season, nominal consumption values were

deflated by a temporal price index. Similarly, spatial differences in the cost of living

were addressed by using a spatial deflator based on the cost of region-specific costs of

basic needs poverty lines (Datt et al. 2000). In this study, the IAF96 is paired with the II

Recenseamento Geral de População e Habitação (Second General Population and

Housing Census) conducted in August 1997. In addition to providing the first complete

enumeration of the country�s population since the initial post-independence census in

1980, the 1997 census collected information on a range of socioeconomic variables.

These include educational levels and employment characteristics of those older than six

years, dwelling characteristics, and ownership of some consumer durables and productive

assets. The 1997 census covers approximately 16 million people living in 3.6 million

households. Further details on the Mozambique data can be found in Simler and Nhate

(2002).

4. Implementation

The first-stage estimation is carried out using the household sample survey. For

each of the three countries considered in this paper, the household survey is stratified into

a number of regions and is representative at that level. Within each region there are one

or more levels of clustering. At the final level, households are randomly selected from a

census enumeration area. Such groups we refer to as �cluster� and denote by a subscript

c. Expansion factors allow calculation of regional totals.

Our first concern is to develop an accurate empirical model of household

consumption. Consider the following model:

chc

T

chch

T

chchch xuxyEy εη ++=+= β]|[lnln , (5)

where η and ε are independent of each other and uncorrelated with observable

characteristics. This specification allows for an intracluster correlation in the

disturbances. One expects location to be related to household income and consumption,

10

and it is certainly plausible that some of the effect of location might remain unexplained

even with a rich set of regressors. For any given disturbance variance, 2chσ , the greater

the fraction due to the common component ηc, 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.

Thus the first goal is to explain the variation in consumption due to location as

much as possible with the choice and construction of explanatory variables. We tackle

this in four ways.

• We estimate different models for different strata in the countries� respective

surveys.

• We include in our specification household-level indicators of access to various

networked infrastructure services, such as electricity, piped water, networked

waste disposal, telephone, etc. To the extent that all or most households within a

given neighborhood or community are likely to enjoy similar levels of access to

such networked infrastructure, these variables might capture unobserved location

effects.

• We calculate means at the enumeration area (EA) level in the census (generally

corresponding to the �cluster� in the household survey) of household-level

variables, such as the average level of education of household heads. We then

merge these EA means into the household survey and consider them for inclusion

in the first-stage regression specification.8

• Finally, in the case of Madagascar, we have merged a Fivondrona-level data set

provided by CARE and considered these spatially referenced environmental

variables, such as droughts and cyclones, for inclusion in our household

expenditure models. The final models for Madagascar included Fivondrona-level

8 In Madagascar the EA in the household survey is not the same as that in the census. The most detailed spatial level at which we can link the two data sets is the firaisana (�commune�). Thus, firaisana-level means were used.

11

GIS variables for flood risk and how many times the eye of a cyclone had passed

over the Fivondrona.

To select variables to reduce location effects, we regress the total residuals, u� , on

cluster fixed effects. We then regress the cluster fixed-effect parameter estimates on our

location variables and select a limited number that best explain the variation in the cluster

fixed-effects estimates.9 These location variables are then included in the first-stage

regression model.

A Hausman test described in Deaton (1997) is used to determine whether to

estimate with household weights. 2R 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.10

We next model the variance of the idiosyncratic part of the disturbance, 2,chεσ .

The total first-stage residual can be decomposed into uncorrelated components as

follows:

chccchcch euuuu +=−+= η�)��(�� .. , (6)

where a subscript �.� indicates an average over that index. Thus the mean of the total

residuals within a cluster serves as an estimate of that cluster�s location effect. To model

heteroscedasticity in the household-specific part of the residual, we choose somewhere

between 5 and 20 variables, zch, that best explain variation in 2che out of all potential

explanatory variables, their squares, and interactions.11

9 As degrees of freedom with cluster-level variables are very limited, only the five or six location variables with the best explanatory power are usually selected. 10 Again, see Elbers, Lanjouw, and Lanjouw (2002), Mistiaen et al. (2002), and Simler and Nhate (2002) for details. 11 The zch variables are selected by a stepwise procedure using a bounded logistic functional form. When this yields more than 20 variables, we limit the number of explanatory variables to be cautious about overfitting.

12

Finally, we determine the distribution of η and ε using the cluster residuals cη�

and standardized household residuals

]�

1[

� ,,

*

ch

ch

ch

ch

ch

ch

e

H

ee

εε σσ∑−= ,

respectively, where H is the number of households in the survey. We use normal or t

distributions with varying degrees of freedom (usually five), 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. For a description of

different approaches to simulation, see Elbers, Lanjouw, and Lanjouw (2000).

5. Stratum-Level Comparisons Between Survey and Census

In this section we examine the degree to which our census-based estimates match

estimates from the countries� respective surveys at the level at which those surveys are

representative.12 Table 2 presents estimates for Ecuador of average per capita

consumption, the headcount poverty rate, and the Gini-coefficient inequality measure

from both the household survey and census at the level of the eight strata at which the

household survey is representative. 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 (as described

above). In nearly every case, the estimates across the two data sources are within each

other�s 95 percent confidence interval. In fact, it is striking how closely the point

estimates match, particularly for the average consumption and headcount rates. In the

case of the inequality measure, we can see that the census estimates tend to be higher

12 For a similar analysis, focusing specifically on poverty, see Demombynes et al. (2002).

13

than the survey-based estimates, although not generally to such an extent that one can

reject that they are the same. The propensity to produce higher estimates of inequality

from the imputed census data arises from the fact that inequality measures tend to be

sensitive to the tails in the distribution of expenditure. Since the tails are typically not

observed in the survey (because of its small size), the survey underestimates inequality.

Table 2 Average expenditure, poverty, and inequality in Ecuador, by region (stratum)

Survey estimate Census-based estimate

Region Mean

expenditure Headcount

index Gini

coefficient Mean

expenditure Headcount

index Gini

coefficient

Quito 126,098 (11344) 0.25 (0.033) 0.490 (0.023) 125,702 (8026) 0.23 (0.024) 0.465 (0.012)

Urban Sierra 121,797 (8425) 0.19 (0.026) 0.436 (0.020) 122,415 (4642) 0.22 (0.017) 0.434 (0.011)

Rural Sierra 66,531 (4067) 0.43 (0.027) 0.393 (0.034) 63,666 (2213) 0.53 (0.019) 0.457 (0.013)

Guayaquil 89,601 (5597) 0.29 (0.027) 0.378 (0.014) 77,432 (2508) 0.38 (0.019) 0.416 (0.011)

Urban Costa 86,956 (3603) 0.25 (0.030) 0.359 (0.015) 90,209 (2391) 0.26 (0.015) 0.382 (0.011)

Rural Costa 57,617 (4477) 0.50 (0.042) 0.346 (0.036) 61,618 (2894) 0.50 (0.024) 0.400 (0.015)

Urban Oriente 110,064 (9078) 0.20 (0.050) 0.398 (0.035) 174,529 (56115) 0.19 (0.02) 0.563 (0.104)

Rural Oriente 47,072 (4420) 0.67 (0.054) 0.431 (0.034) 59,549 (3051) 0.59 (0.025) 0.478 (0.014)

Tables 3 and 4 present results analogous to those presented in Table 2 for

Madagascar and Mozambique, respectively. Again, the results indicate that at the

stratum level there is little basis for rejecting equality of the survey- and census-based

estimates of average per capita consumption, poverty, and inequality in the two countries.

In Madagascar, standard errors on the survey estimates are quite high, indicating that

while the household survey may be representative at the province and sector level, the

sample size in these strata is rather small so that estimates are imprecise. Nonetheless,

for our purposes it is encouraging to note that point estimates across all three welfare

indicators are often remarkably close. In Mozambique, as in Ecuador (but less markedly

so in Madagascar), inequality estimates tend to be higher than the survey estimates. In

some provinces, such as Sofala and Maputo Provinces, and Maputo City, the estimates

are not only very high, but are also quite imprecisely estimated in the census. Although

these census-level standard errors are large, it is due primarily to model error. As a

14

Table 3 Average expenditure, poverty, and inequality in Madagascar, by province and sector

Survey estimate Census-based estimate

Province Mean

expenditure Headcount

index Gini

coefficient Mean

expenditure Headcount

index Gini

coefficient

Urban Antananarivo 513,818 (48,455) .544 (.048) .492 (.027) 576,470 (23,944) .462 (.015) .469 (.012)

Fianarantsoa 360,635 (42,613) .674 (.059) .430 (.038) 372,438 (21,878) .646 (.027) .426 (.015)

Taomasina 445,514 (73,099) .599 (.086) .434 (.042) 417,823 (15,406) .599 (.018) .402 (.015)

Mahajanga 613,867 (74,092) .329 (.072) .371 (.027) 580,775 (31,025) .378 (.028) .392 (.016)

Toliara 343,111 (76,621) .715 (.086) .514 (.052) 321,602 (32,193) .713 (.036) .504 (.030)

Antsiranana 504,841 (46,148) .473 (.087) .362 (.025) 693,161 (93,437) .344 (.031) .433 (.039)

Rural Antananarivo 312,553 (23,174) .767 (.037) .376 (.023) 324,814 (14,378) .738 (.019) .404 (.015)

Fianarantsoa 319,870 (45,215) .769 (.049) .470 (.050) 251,312 (18,091) .820 (.025) .437 (.018)

Taomasina 275,943 (22,832) .810 (.035) .352 (.036) 279,239 (15,838) .786 (.026) .362 (.017)

Mahajanga 325,872 (30,209) .681 (.065) .320 (.026) 321,398 (19,385) .695 (.039) .306 (.015)

Toliara 233,801 (22,174) .817 (.042) .383 (.029) 259,537 (16,222) .800 (.027) .377 (.017)

Antsiranana 486,781 (91,181) .613 (.073) .518 (.110) 442,431 (54,869) .581 (.046) .453 (.048)

Notes: All figures based on a poverty line of 354,000 Malagasy francs per capita. Household survey figures are calculated using weights that are the product of household survey weights and household size. Census-based figures are calculated weighting by household size.

Table 4 Average expenditure, poverty, and inequality in Mozambique, by province

Survey estimate Census-based estimate

Province Mean

expenditure Headcount

index Gini

coefficient Mean

expenditure Headcount

index Gini

coefficient

Niassa 4,660 (355) 0.71 (0.038) 0.355 (0.020) 5,512 (484) 0.67 (0.042) 0.402 (0.025)

Cabo Delgado 6,392 (416) 0.57 (0.042) 0.370 (0.025) 6,586 (433) 0.56 (0.036) 0.413 (0.021)

Nampula 5,315 (287) 0.69 (0.032) 0.391(0.026) 5,547 (279) 0.65 (0.024) 0.400 (0.020)

Zambezia 5,090 (208) 0.68 (0.026) 0.324 (0.017) 5,316 (274) 0.67 (0.029) 0.366 (0.012)

Tete 3,848 (267) 0.82 (0.032) 0.346 (0.019) 4,404 (176) 0.77 (0.016) 0.394 (0.018)

Manica 6,299 (741) 0.63 (0.059) 0.413 (0.036) 6,334 (527) 0.62 (0.044) 0.449 (0.020)

Sofala 3,218 (191) 0.88 (0.015) 0.405 (0.031) 4,497 (379) 0.78 (0.017) 0.529 (0.032)

Inhambane 4,215 (359) 0.83 (0.024) 0.382 (0.037) 4,177 (134) 0.81 (0.013) 0.398 (0.012)

Gaza 6,024 (356) 0.65 (0.033) 0.380 (0.024) 6,521 (355) 0.59 (0.021) 0.421 (0.023)

Maputo Province 5,844 (613) 0.66 (0.054) 0.424 (0.029) 8,559 (745) 0.55 (0.024) 0.518 (0.029)

Maputo City 8,321 (701) 0.48 (0.041) 0.444 (0.033) 11,442 (4956) 0.49 (0.047) 0.560 (0.108)Notes: All figures based on a poverty line of 5,433 meticais daily per capita. Survey figures are calculated using

weights that are the product of household survey weights and household size. Census-based figures are calculated weighting by household size.

15

result, and as we shall see below, there is no evidence that estimates become even more

noisy at lower levels of aggregation.

6. Decomposing Inequality by Geographic Subgroups

We turn in this section to the important question of how much of overall

inequality in a given country is attributable to differences in average consumption across

localities as opposed to inequality within localities. It is clear that where national

inequality is largely due to differences in mean income across regions, the policy

implications are very different from the situation where subregions themselves are

unequal and national inequality is simply an expression at the country level of a degree of

heterogeneity that already exists at the more local level. Decomposing inequality by

subgroups enjoys a long tradition in the empirical analysis of inequality, in both

developed and developing countries. We decompose inequality using the General

Entropy class of inequality measures, a class of measures that is particularly well-suited

to this exercise.13 This class of measures takes the following form:

]1)[()1(

1−∑

−= ci

ii

c

yf

ccI

µ for c ≠ 0,1

)log(µ

ii

i

yf∑−= for c = 0

)log(µµ

iii

i

yyf∑= for c = 1 ,

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

13 Following 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 subgroup decomposition. Sen and Foster (1997) and Kanbur (2000) discuss some of the difficulties in interpreting results from such decompositions.

16

user.14 This class of inequality measures can be decomposed into a between- and within-

group component along the following lines:

cj

jjj

cj

jj

c gIgcc

I )(])[(1[)1(

1

µ

µ

µ

µ∑+∑−

−= for c ≠ 0,1

jjj

j

jc gIgI ∑+= )]log([µ

µ for c = 0

)()]log()([µ

µ

µ

µ

µ

µ j

jjj

jj

jj

c gIgI ∑+∑= for c = 1

where j refers to subgroups, gj refers to the population share of group j, and Ij 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 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 Ij. 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.

In Table 5 we examine how within-group inequality evolves at progressively

lower levels of regional disaggregation in our three countries. At one extreme, when a

country-level perspective is taken, 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. But how rapidly does the within-group

share fall? Is it reasonable to suppose that at a sufficiently low level of disaggregation,

such as the third administrative level in our three countries (with about 1,000-10,000 14 Lower values of c are associated with greater sensitivity to inequality among 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.

17

households), differences within groups are small, and most of overall inequality is due to

differences between groups?

Table 5 Decomposition of inequality, by regional subgroup (GE0)

Level of decomposition Number of subgroups Within-group (%) Between-group (%) Ecuador Rural National 1 100 0 Region 3 100 0 Province 21 98.7 1.3 Canton 195 94.1 5.9 Parroquia 915 85.9 14.1 Household 960,529 0 100 Urban National 1 100 0 Regiona 5 100 6.6 Provincea 19 98.7 7.3 Cantona 87 94.1 8.6 Zonas 664 85.9 23.3 Household 880,001 0 100 Madagascar Urban 1 100 0 Faritany 6 92.3 7.7 Fivondrona 103 78.3 21.7 Firaisana 131 76.7 23.2 Rural 1 100 0 Faritany 6 95.2 4.8 Fivondrona 104 84.6 15.4 Firaisana 1,117 81.9 18.1 Mozambique National 1 100 0 Province 11 90.7 9.3 District 146 81.6 18.4 Administrative Post 424 78.0 22.0 a Quito and Guayaquil are treated as independent geographic areas.

We decompose inequality in our three countries on the basis of the GE(0)

measure.15 In rural Ecuador we see that when we have disaggregated down to the level of

915 parroquias (with an average number of households of a little over 1,000), some 86

percent of overall inequality remains within-group. In urban areas of Ecuador, the within-

group share, across 664 urban zonas (with 1,300 households, on average), is only slightly

lower at 77 percent. 15 Results remain virtually identical for other values of c.

18

The same pattern obtains in Madagascar and Mozambique (Table 5). In all three

countries no less than three quarters of all inequality is attributable to within-community

differences, even after one has disaggregated down to a very low level (corresponding, in

our countries, to the lowest level of central government administration). At first glance, one

might understand these results as suggesting that even within local communities, there exists

a considerable heterogeneity of living standards. Such a conclusion might have implications

regarding the likelihood of political capture, the feasibility of raising revenues locally, and

the extent to which residents in these localities can be viewed as having similar demands

and priorities.

However, a blanket statement about the degree of inequality within communities

does not follow directly from the above decomposition results. It is important to recognize

that the decomposition exercise indicates that, on average, inequality does not fall much

with aggregation level. In other words, it is very well possible that at low levels of

aggregation, the population is characterized by both highly equal and highly unequal

communities. A simple example can illustrate this. Consider a population of eight

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 the decomposition exercise is always zero (and thus the within-

group share is 100 percent 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 higher and the other lower inequality than at the national level. As can be

readily seen from the expressions for decomposing the General Entropy class of

inequality measures provided above, when average consumption levels are the same for

all communities, overall inequality is calculated by taking a population-weighted average

of community-level inequality rates. Finding a high within-group share from a

decomposition exercise across a large number of communities is thus perfectly consistent

with great heterogeneity in inequality levels across communities.

19

In a situation, such as ours, where the decomposition exercise is carried out across a

very large number of communities, it is important to check for variation in the degree of

inequality across communities. Are all communities as unequal as the country as a whole?

Such a finding would certainly generate a large within-group contribution in a

decomposition exercise. Or do communities vary widely in their degree of inequality? That

could also yield a high within-group share. In Figures 1-5, we plot community-level

inequality estimates and compare these against national-level inequality. Communities are

ranked from most equal to most unequal, and 95 percent 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 exercise was as high as 86 percent,

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 percent 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 small

number of parroquias are considerably more unequal.16 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

16 Note 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 within-group inequality (in the example above, suppose the eight person population were divided into two groups of four persons one with incomes 1,1,2,2, and the other with incomes 4,4,5,5) then total, national inequality would be higher than inequality in all of the individual communities (equal in the example to the common within-group inequality plus that attributable to the difference between the two groups).

20

Figure 1 Distribution across parroquias of parroquia-level inequality, rural Ecuador: GE(0)

Figure 2 Distribution across zonas of zona-level inequality, urban Ecuador: GE(0)

21

somewhat higher than in rural areas; accordingly, more zonas lie unambiguously below the

national inequality level.

In rural and urban Madagascar (Figures 3 and 4) and in Mozambique (Figure 5)

the picture is very similar. In all of the countries considered in this study, there is a clear

and 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.

7. Correlates of Local Inequality: Does Geography Matter?

We have found empirical support for both the view that at the local level,

communities are more homogeneous than society as a whole, and the view that local

communities are as heterogeneous as society as a whole. The question then arises as to

Figure 3 Distribution across firaisanas of firaisana-level inequality, rural Madagascar: GE(0)

22

Figure 4 Distribution across firaisanas of firaisana-level inequality, urban Madagascar: GE(0)

Figure 5 Distribution across administrative posts of post-level inequality, Mozambique:

GE(0)

23

whether it is possible to readily distinguish between communities on the basis of some

simple indicators. In particular, we are interested to know whether there are discernable

geographic patterns of inequality.

In Tables 6a-6e, we provide results from OLS regressions of inequality on a set of

simple community characteristics. We ask whether inequality levels are correlated with

location, controlling for both demographic characteristics of the communities (population

size and demographic composition), and mean per capita consumption. Table 6a for rural

Ecuador finds strong evidence that inequality in the parroquias of the eastern, Oriente,

region is significantly higher than the province of Pichincha in the central, mountainous,

Sierra, region. Communities located in provinces in the western, coastal, Costa, region tend

to be more equal, significantly so in the provinces of Manabi, Los Rios, Guayas, and El Oro.

Relatively few differences are discernable across provinces within the Sierra region.17

Understanding these geographic patterns of inequality is beyond the scope of this paper, but

the evidence is consistent with historical and anecdotal accounts of a very divergent

evolution of society and economic structures in the mountainous Sierra vis-à-vis the Costa

and Oriente.18 In rural Ecuador, there is evidence that larger parroquias tend to be more

unequal. An interesting finding is that parroquias with a larger proportion of elderly,

relative to the population share of 20-40-year olds, are more unequal. This pattern is

consistent with the findings of Deaton and Paxson (1995) regarding the positive association

between an aging population and inequality. The quantitative importance and statistical

significance of both geographic and demographic characteristics remains broadly unchanged

when mean per capita consumption (and its square) are added to the model. In rural

Ecuador, inequality is positively associated with higher consumption levels. While there is

some suggestion of a turning point (at around $2,800 per capita per month)�the

17 We can reject with 95 percent confidence, for both rural and urban Ecuador, the null hypothesis that parameter estimates on province dummies within their respective regions are all equal. 18 See, for example, �Under the Volcano,� The Economist, November 27, 1999, p. 66.

24

Table 6a Correlates of mean Log deviation (GE0) in rural Ecuador

Parroquia-level regression (915 parroquias)

Basic regression + expenditure

Log population 0.0169 (0.002)*** 0.010 (0.002)***

Percent aged 0-10 -0.139 (0.079)* 0.321 (0.080)***

Percent aged 10-20 -0.375 (0.104)*** -0.084 (0.096)

Percent aged 40-60 -0.246 (0.130)* 0.053 (0.120)

Percent aged 61+ 0.269 (0.123)*** 0.392 (0.112)***

Log mean per capita expenditure 0.222 (0.085)***

(Log mean per capita expenditure)2 -0.014 (0.010)

Oriente

Sucumbios 0.036 (0.013)*** 0.036 (0.012)***

Napo 0.051 (0.012)*** 0.056 (0.011)***

Pastaza 0.071 (0.015)*** 0.077 (0.013)***

Morona_Santiago 0.040 (0.011)*** 0.036 (0.010)***

Zamora_Chinchipe 0.034 (0.013)** 0.037 (0.012)***

Costa

Esmeraldas -0.012 (0.010) -0.036 (0.010)***

Manabi -0.060 (0.010)*** -0.057 (0.009)***

Los Rios -0.041 (0.013)*** -0.025 (0.012)**

Guayas -0.050 (0.010)*** -0.035 (0.009)***

El Oro -0.022 (0.010)** -0.020 (0.009)**

Galápagos 0.027 (0.023 ) -0.000 (0.021)

Sierra

Carchi -0.002 (0.012) 0.014 (0.010)

Imbabura 0.024 (0.010)** 0.037 (0.011)***

Cotopaxi -0.013 (0.011) -0.001 (0.010)

Tungurahua -0.025 (0.010)** -0.010 (0.009)

Bolivar -0.0002 (0.012) 0.002 (0.011)

Chimborazo -0.010 (0.010) 0.006 (0.010)

Canar 0.003 (0.012) 0.007 (0.011)

Azuay 0.011 (0.010) 0.014 (0.009)

Loja 0.024 (0.009)** 0.036 (0.008)***

Constant 0.296 (0.060) -0.571 (0.192)

Observations 915 915

R-squared 0.24 0.38 Notes: Standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent. *** significant at 1

percent. Excluded groups are Pichincha and percent population age 20-40.

25

well-known �inverted U-curve��the statistical support for this is weak. The correlation

between inequality and the population share of young children, relative to 20-40-year olds,

switches in sign from negative to positive, depending on whether per capita consumption is

included in the specification. It seems clear that the share of young children is likely to be

(negatively) correlated with per capita consumption so that the coefficient on this variable is

capturing the consumption effect, when average expenditures are excluded from the

specification. Once consumption expenditures are controlled for, the correlation between

inequality and the share of children in the population becomes positive. Possibly there

exists greater heterogeneity in household size in those parroquias with large population

shares of young children and that this translates into greater inequality of per capita

consumption.

In urban Ecuador (Table 6b) the relatively low inequality in the Costa region is again

observed. Relative to the zonas in the capital Quito, inequality in all zonas of the Costa

region tends to be significantly lower. Other urban areas in the Sierra are again not

noticeably less or more equal than Quito. In urban areas, in contrast to rural areas,

population size of the zona does not appear to be significantly correlated with its inequality

level.19 Also in contrast to rural areas, conditioning on mean consumption levels does not

add much explanatory power: there is no evidence that poorer zonas are also more equal.

Zonas with large dependency ratios (irrespective of whether these are due to many young

children or of a large proportion of elderly) are associated with higher inequality levels,

irrespective of controlling for consumption.

Tables 6c and 6d provide analogous results for Madagascar. The broad conclusions

are quite similar to those found in Ecuador. As in rural Ecuador, in rural Madagascar

population size is positively associated with inequality, and the larger the percentage of

elderly in the firaisana, the more unequal the community. As in Ecuador, inequality rises

with mean consumption (in the Madagascar case the inverted U curve is more clearly

19 Although zonas vary less in population size than parroquias, they still range between 800-1,900 households.

26

Table 6b Correlates of mean Log deviation (GE0) in urban Ecuador

Zona-level regression (660 zonas)

Basic regression + expenditure

Log population -0.013 (0.015) -0.003 (0.014)

Percent aged 0-10 0.231 (0.118)* 0.253 (0.119)**

Percent aged 10-20 0.283 (0.098)*** 0.791 (0.112)***

Percent aged 40-60 0.001 (0.141) -0.673 (0.162)***

Percent aged 61+ 0.704 (0.162)*** 1.084 (0.161)***

Log mean per capita expenditure 0.025 (0.075)

(Log mean per capita expenditure)2 0.005 (0.008)

Oriente

Pastaza 0.052 (0.033) 0.049 (0.031)

Morona_Santiago 0.457 (0.046)*** 0.381 (0.045)***

Zamora_Chinchipe 0.031 (0.046) 0.004 (0.044)

Costa

Esmeraldas -0.073 (0.013)*** -0.066 (0.012)***

Manabi -0.084 (0.007)*** -0.069 (0.007)***

Los Rios -0.077 (0.010)*** -0.049 (0.011)***

Guayas -0.097 (0.008)*** -0.064 (0.008)***

El Oro -0.094 (0.009)*** -0.081 (0.009)***

Guayaquil -0.087 (0.005 )*** -0.054 (0.007)***

Sierra

Carchi -0.009 (0.017) 0.012 (0.017)

Imbabura 0.022 (0.014) -0.008 (0.013)

Cotopaxi 0.007 (0.016) 0.006 (0.015)

Tungurahua -0.008 (0.014) -0.003 (0.013)

Pichincha -0.011 (0.010) -0.000 (0.010)

Chimborazo -0.025 (0.015)* -0.026 (0.014)*

Canar -0.012 (0.024) -0.018 (0.022)

Azuay -0.013 (0.010) -0.018 (0.010)*

Loja -0.003 (0.013) -0.010 (0.012)

Constant 0.272 (0.140) -0.076 (0.242)

Observations 660 660

R-squared 0.52 0.57 Notes: Standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent. *** significant at 1

percent. Excluded groups are Quito and percent population age 20-40.

27

Table 6c Correlates of mean Log deviation (GE0) in rural Madagascar

Firaisana-level regression (1,117 firaisanas)

Basic regression + expenditure

Log population 0.010 (0.002)*** 0.012 (0.002)***

Percent aged 0-5 -0.768 (0.085)*** -0.700 (0.086)***

Percent aged 6-11 -0.226 (0.127)* -0.091 (0.126)

Percent aged 12-14 0.193 (0.241) 0.236 (0.242)

Percent aged 50-59 -1.757 (0.292)*** -1.747 (0.286)***

Percent aged 60+ 0.462 (0.152)** 0.696 (0.152)***

Log mean per capita expenditure 0.886 (0.118)***

(Log mean per capita expenditure)2 -0.034 (0.005)***

Provinces

Antananarivo -0.068 (0.006)*** -0.065 (0.006)***

Fianarantsoa 0.011 (0.005)** 0.020 (0.006)***

Toamasina -0.059 (0.006)*** -0.054 (0.006)***

Mahajanga -0.115 (0.006)*** -0.116 (0.006)***

Toliara -0.046 (0.005)*** -0.042 (0.006)***

Constant 0.430 (0.041)*** -5.356 (0.765)***

Observations 1,117 1,117

R-squared 0.53 0.55 Notes: Standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1

percent. Excluded groups are Antsiranana and percent population age 15-49.

discernable) and geography is strongly and independently significant. Relative to the

population share aged 15-50, the higher the share of children and the share of population

aged 50-59, the more equal the community, whether or not one controls for consumption. In

Madagascar, it seems that communities with large population shares of children are not

markedly more heterogeneous in household size. For rural Madagascar, the simple

specification employed here yields an R2 as high as 0.55 when all variables are included.

In urban Madagascar, the explanatory power is even greater (Table 6d). Here,

unlike rural areas, population size is significantly negatively associated with inequality. As

in rural areas, the larger the percentage of children, the lower is inequality. As in urban

Ecuador, mean per capita consumption is not significantly associated with inequality�there

is no presumption that a poorer urban firaisana is more homogeneous than a rich one.

Geographic variables remain independently significant, with urban areas in Antananarivo

28

Table 6d Correlates of mean Log deviation (GE0) in urban Madagascar

Firaisana-level regression (131 firaisanas)

Basic regression + expenditure

Log population -0.014 (0.005)*** -0.011 (0.005)**

Percent aged 0-5 -1.253 (0.202)*** -1.053 (0.243)***

Percent aged 6-11 0.166 (0.464) 0.147 (0.465)

Percent aged 12-14 -0.965 (0.777) -0.551 (0.826)

Percent aged 50-59 -2.602 (0.882)*** -2.543 (0.882)***

Percent aged 60+ 1.183 (0.396)*** 1.355 (0.417)***

Log mean per capita expenditure 0.117 (0.143)

(Log mean per capita expenditure)2 -0.004 (0.013)

Provinces

Antananarivo 0.079 (0.015)*** 0.080 (0.015)***

Fianarantsoa 0.059 (0.014)*** 0.065 (0.015)***

Toamasina -0.012 (0.014) -0.007 (0.015)

Mahajanga -0.025 (0.014)* -0.027 (0.014)*

Toliara 0.117 (0.013)*** 0.125 (0.014)***

Constant 0.717 (0.106) -0.270 (2.245)

Observations 131 131

R-squared 0.78 0.79 Notes: Standard errors in parentheses. * significant at 10 percent; ** significant at 5 percent; *** significant at 1

percent. Excluded groups are Antsiranana and and population age 15-49.

(the capital province), Fianarantsoa, and Toliara more unequal than the urban areas in the

rest of the country.

Table 6e confirms that in Mozambique, too, geographic variables are key

indicators of local-level inequality, controlling for population characteristics, mean

expenditure levels, and urban/rural differences. Compared with Maputo City, the rest of

the country has significantly less inequality. There is more inequality in urban areas, an

increasing association with mean consumption (but no Kuznets curve), and areas with a

higher percentage of 17-30-year-olds seem to have higher inequality.

We have not attempted here to identify the best possible set of correlates of local

inequality for each of the three countries we are examining. We have chosen to employ a

parsimonious, and broadly similar, specification in the three countries in order to ask

whether there are any common patterns across countries that in other respects resemble

29

Table 6e Correlates of mean Log deviation (GE0) in Mozambique

Administrative post-level regression (464 administrative posts)

Basic regression + expenditure + urban

Percent aged 0-5 -0.002 (0.004) 0.000 (0.003) 0.001 (0.003) Percent aged 6-10 0.017 (0.005)** 0.015 (0.004)** 0.014 (0.004)**

Percent females aged 11-16 0.027 (0.009)** 0.020 (0.008)** 0.015 (0.008)

Percent males aged 11-16 -0.000 (0.009) 0.001 (0.008) 0.002 (0.008)

Percent females aged 17-30 0.016** (0.004) 0.012 (0.003)** 0.011 (0.003)**

Percent males aged 17-30 0.015 (0.005)** 0.010 (0.004)* 0.009 (0.004)*

Percent females aged 31-60 0.005 (0.006) 0.005 (0.006) 0.007 (0.006)

Percent males aged 31-60 0.007 (0.004) 0.005 (0.004) 0.004 (0.004)

Log (population of posto) 0.001 (0.004) -0.003 (0.003) -0.005 (0.004)

Niassa -0.200 (0.036)** -0.138 (0.034)** -0.136 (0.033)**

Cabo Delgado -0.204 (0.034)** -0.163 (0.031)** -0.158 (0.031)**

Nampula -0.204 (0.035)** -0.143 (0.032)** -0.143 (0.032)**

Zambézia -0.215 (0.035)** -0.154 (0.032)** -0.149 (0.032)**

Tete -0.212 (0.036)** -0.133 (0.033)** -0.127 (0.033)**

Manica -0.135 (0.035)** -0.095 (0.032)** -0.089 (0.032)**

Sofala -0.118 (0.035)** -0.005 (0.032) -0.006 (0.032)

Inhambane -0.178 (0.035)** -0.088 (0.032)** -0.090 (0.032)**

Gaza -0.189 (0.035)** -0.136 (0.032)** -0.135 (0.031)**

Maputo Province -0.088 (0.036)* -0.045 (0.032) -0.044 (0.032)

Log (mean expenditure) -0.406 (0.216) -0.324 (0.217)

[Log (mean expenditure)]squared 0.031 (0.013)* 0.025 (0.013)*

Urban 0.037 (0.014)**

Constant -0.504 (0.321) 0.856 (0.962) 0.605 (0.960) Observations 424 424 424

R-squared 0.465 0.595 0.601

Notes: Standard errors in parentheses. * significant at 5 percent; ** significant at 1 percent. Excluded groups are Maputo city and percent persons older than 60 years.

each other very little (particularly the comparison between Ecuador and the two Sub-

Saharan African countries). We have indeed found that in all three countries we

consider, in both rural and urban areas, geographic location is a good predictor of local-

level inequality, even after controlling for some basic demographic and economic

characteristics of the communities. With respect to other characteristics, there appear to

be clear differences between urban and rural areas (best seen in the models for Ecuador

30

and Madagascar). In rural areas, inequality tends to be higher in communities with larger

populations, a higher share of the elderly in the total population, and in communities with

higher mean consumption levels. In urban areas, mean consumption is not independently

correlated with inequality, and inequality is not typically higher in communities with

larger populations. High population shares of elderly are clearly associated with higher

inequality, but the correlation with population shares of children depends on the country.

8. Conclusions

This paper has taken three developing countries, Ecuador, Madagascar, and

Mozambique, and has implemented in each a methodology to produce disaggregated

estimates of inequality. The countries are very unlike each other�with different

geographies, stages of development, quality and types of data, and so on. The

methodology works well in all three settings and produces valuable information about the

spatial distribution of poverty and inequality within those countries�information that

was previously not available.

The methodology is based on a statistical procedure to combine household survey

data with population census data, by imputing into the latter a measure of economic

welfare (consumption expenditure in our examples) from the former. Like the usual

sample-based estimates, the inequality measures produced are also estimates and subject

to statistical error. The paper has demonstrated that the mean consumption, poverty, and

inequality estimates produced from census data match well the estimates calculated

directly from the country�s surveys (at levels of disaggregation that the survey can bear).

The precision of the inequality estimates produced with this methodology depends on the

degree of disaggregation. In all three countries considered here, our inequality estimators

allow one to work at a level of disaggregation far below that allowed by surveys.

We have decomposed inequality in our three countries into progressively more

disaggregated spatial units, and have shown that even at a very high level of spatial

disaggregation, the contribution to overall inequality of within-community inequality is

31

very high (75 percent or more). We have argued that such a high within-group

component does not necessarily imply that there are no between-group differences at all

and that all communities in a given country are as unequal as the country as a whole. We

have shown that in all three countries, there is a considerable amount of variation in

inequality across communities. Many communities are rather more equal than their

respective country as a whole, but there are also many communities that are not clearly

more homogeneous than society as a whole, and may even be considerably more unequal.

We have explored some basic correlates of local-level inequality in our three

countries. We have found consistent patterns across all three countries. Geographic

characteristics are strongly correlated with inequality, even after controlling for

demographic and economic conditions. The correlation with geography is observed in

both rural and urban areas. In rural areas, population size and mean consumption at the

community level are positively associated with inequality, while in urban areas that is not

the case. In both rural and urban areas, populations with large shares of the elderly tend

to be more unequal. In Madagascar, populations with large shares of children and large

shares of individuals aged 50-59 are consistently more equal. In Ecuador, this is true

only in rural areas.

32

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126 Health Care Demand in Rural Mozambique: Evidence from the 1996/97 Household Survey, Magnus Lindelow, February 2002

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124 The Robustness of Poverty Profiles Reconsidered, Finn Tarp, Kenneth Simler, Cristina Matusse, Rasmus Heltberg, and Gabriel Dava, January 2002

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120 Control and Ownership of Assets Within Rural Ethiopian Households, Marcel Fafchamps and Agnes R. Quisumbing, August 2001

119 Assessing Care: Progress Towards the Measurement of Selected Childcare and Feeding Practices, and

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118 Is PROGRESA Working? Summary of the Results of an Evaluation by IFPRI, Emmanuel Skoufias and Bonnie McClafferty, July 2001

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113 Measuring Power, Elizabeth Frankenberg and Duncan Thomas, June 2001

112 Effective Food and Nutrition Policy Responses to HIV/AIDS: What We Know and What We Need to Know, Lawrence Haddad and Stuart Gillespie, June 2001

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110 Evaluating Transfer Programs Within a General Equilibrium Framework, Dave Coady and Rebecca Lee Harris, June 2001

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108 How Efficiently Do Employment Programs Transfer Benefits to the Poor? Evidence from South Africa, Lawrence Haddad and Michelle Adato, April 2001

107 Rapid Assessments in Urban Areas: Lessons from Bangladesh and Tanzania, James L. Garrett and Jeanne Downen, April 2001

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105 The Nutritional Transition and Diet-Related Chronic Diseases in Asia: Implications for Prevention, Barry M. Popkin, Sue Horton, and Soowon Kim, March 2001

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103 Targeting the Poor in Mexico: An Evaluation of the Selection of Households for PROGRESA, Emmanuel Skoufias, Benjamin Davis, and Sergio de la Vega, March 2001

102 School Subsidies for the Poor: Evaluating a Mexican Strategy for Reducing Poverty, T. Paul Schultz, March 2001

101 Poverty, Inequality, and Spillover in Mexico�s Education, Health, and Nutrition Program, Sudhanshu Handa, Mari-Carmen Huerta, Raul Perez, and Beatriz Straffon, March 2001

100 On the Targeting and Redistributive Efficiencies of Alternative Transfer Instruments, David Coady and Emmanuel Skoufias, March 2001

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27 "Bargaining" and Gender Relations: Within and Beyond the Household, Bina Agarwal, March 1997

FCND DISCUSSION PAPERS

26 Why Have Some Indian States Performed Better Than Others at Reducing Rural Poverty?, Gaurav Datt and Martin Ravallion, March 1997

25 Water, Health, and Income: A Review, John Hoddinott, February 1997

24 Child Care Practices Associated with Positive and Negative Nutritional Outcomes for Children in

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22 Alternative Approaches to Locating the Food Insecure: Qualitative and Quantitative Evidence from South

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20 Macroeconomic Crises and Poverty Monitoring: A Case Study for India, Gaurav Datt and Martin Ravallion, November 1996

19 Food Security and Nutrition Implications of Intrahousehold Bias: A Review of Literature, Lawrence Haddad, Christine Peña, Chizuru Nishida, Agnes Quisumbing, and Alison Slack, September 1996

18 Care and Nutrition: Concepts and Measurement, Patrice L. Engle, Purnima Menon, and Lawrence Haddad, August 1996

17 Remittances, Income Distribution, and Rural Asset Accumulation, Richard H. Adams, Jr., August 1996

16 How Can Safety Nets Do More with Less? General Issues with Some Evidence from Southern Africa, Lawrence Haddad and Manfred Zeller, July 1996

15 Repayment Performance in Group-Based credit Programs in Bangladesh: An Empirical Analysis, Manohar Sharma and Manfred Zeller, July 1996

14 Demand for High-Value Secondary Crops in Developing Countries: The Case of Potatoes in Bangladesh and

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11 Rural Financial Policies for Food Security of the Poor: Methodologies for a Multicountry Research Project, Manfred Zeller, Akhter Ahmed, Suresh Babu, Sumiter Broca, Aliou Diagne, and Manohar Sharma, April 1996

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09 Gender and Poverty: New Evidence from 10 Developing Countries, Agnes R. Quisumbing, Lawrence Haddad, and Christine Peña, December 1995

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07 A Food Demand System Based on Demand for Characteristics: If There Is "Curvature" in the Slutsky Matrix,

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05 Gender Differences in Agricultural Productivity: A Survey of Empirical Evidence, Agnes R. Quisumbing, July 1995

04 Market Development and Food Demand in Rural China, Jikun Huang and Scott Rozelle, June 1995

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02 Determinants of Credit Rationing: A Study of Informal Lenders and Formal Credit Groups in Madagascar, Manfred Zeller, October 1994

01 Agricultural Technology and Food Policy to Combat Iron Deficiency in Developing Countries, Howarth E. Bouis, August 1994