FOOD CONSUMPTION AND NUTRITION DIVISION November 2006

FCND Discussion Paper 211

Poverty Comparisons with Absolute Poverty Lines Estimated from Survey Data

Kenneth R. Simler and Channing Arndt

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ii

Abstract

The objective of measuring poverty is usually to make comparisons over time or

between two or more groups. Common statistical inference methods are used to

determine whether an apparent difference in measured poverty is statistically significant.

Studies of relative poverty have long recognized that when the poverty line is calculated

from sample survey data, both the variance of the poverty line and the variance of the

welfare metric contribute to the variance of the poverty estimate. In contrast, studies

using absolute poverty lines have ignored the poverty line variance, even when the

poverty lines are estimated from sample survey data. Including the poverty line variance

could either reduce or increase the precision of poverty estimates, depending on the

specific characteristics of the data. This paper presents a general procedure for

estimating the standard error of poverty measures when the poverty line is estimated from

survey data. Based on bootstrap methods, the approach can be used for a wide range of

poverty measures and methods for estimating poverty lines. The method is applied to

recent household survey data from Mozambique. When the sampling variance of the

poverty line is taken into account, the estimated standard errors of Foster-Greer-

Thorbecke and Watts poverty measures increase by 15 to 30 percent at the national level,

with considerable variability at lower levels of aggregation.

Key words: poverty measurement, bootstrap, Mozambique

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Contents

Acknowledgments.............................................................................................................. iv 1. Introduction.................................................................................................................... 1 2. Estimating Poverty......................................................................................................... 3 3. Data and Methods .......................................................................................................... 9

Data Collection.............................................................................................................. 9 Definition of the Welfare Metric................................................................................. 10 Setting Poverty Lines .................................................................................................. 11 Estimating Poverty Measures and Their Standard Errors ........................................... 14

4. Results.......................................................................................................................... 17 5. Conclusions.................................................................................................................. 23 References......................................................................................................................... 26

Tables

1 Outline of calculations included and excluded from bootstrap procedure ................. 17 2 Region-specific food, nonfood, and total poverty lines for Mozambique 2002-03.... 18 3 Estimates of poverty headcount index (P0) and standard errors, Mozambique

2002-03 ....................................................................................................................... 19 4 Estimates of poverty gap index (P1) and standard errors, Mozambique 2002-03 ...... 20 5 Estimates of Watts Index and standard errors, Mozambique 2002-03 ....................... 22

Figure

1 Illustration of poverty line error’s contribution to poverty estimate error.................... 8

iv

Acknowledgments

This research was supported through IFPRI’s cooperative policy research

program with the Mozambique Ministry of Planning and Development, with financing

from Danish International Development Assistance (Danida), the United Kingdom’s

Department for International Development (DfID), and the Swiss Agency for

Development and Cooperation (SDC).

We thank Dan Gilligan, Paul Glewwe, Dean Jolliffe, Martin Ravallion, and two

anonymous journal referees for their constructive comments on earlier drafts of this

paper. We also thank seminar participants at Eduardo Mondlane University, IFPRI, the

3rd Minnesota International Economic Development Conference, and the 26th

Conference of the International Association of Agricultural Economists. The authors are

solely responsible for any remaining errors.

Kenneth R. Simler International Food Policy Research Institute Channing Arndt Department of Agricultural Economics Purdue University

1

1. Introduction

A principal objective of poverty measurement is to make comparisons between

groups. Analysts and policymakers are generally interested less in the absolute level of

poverty at a given place and time than they are in knowing how measured poverty levels

compare to levels observed in other settings or at other points in time. Is poverty higher

in the hills or on the coast? Did poverty decline following implementation of a poverty

reduction program? These questions have gained an even higher profile in recent years.

Besides the high profile Millennium Development Goal of halving world poverty by

2015, country development programs and donor support are increasingly driven by the

Poverty Reduction Strategy Paper (PRSP) process, which requires close monitoring of

poverty levels and detectable progress in reducing poverty. For example, indications of a

recent increase in poverty in Uganda sparked a debate about whether the national

development strategy, which had steadily reduced poverty in the 1990s, needed an

overhaul (Kappel, Lay, and Steiner 2005).

There are many ways to define and measure poverty, but with few exceptions the

empirical basis for poverty comparisons is statistical, employing point estimates of

relevant poverty measures and their associated standard errors. These are generally

estimated from household survey data. Statistical tests are applied to assess whether

differences or changes in poverty levels are significant. Research over the past 15 years

has increasingly refined statistical inference methods for poverty measures. Kakwani

(1993) develops distribution-free asymptotic standard errors for several additively

decomposable poverty measures. Bishop, Chow, and Zheng (1995) provide asymptotic

theory for testing poverty measures decomposed by subgroup. Ravallion (1994a)

examines the effect of errors in consumption data on poverty comparisons, finding that

noisier data for some subgroups can lead to re-rankings of poverty measures, with the

exact nature of the re-ranking dependent upon the poverty measure used. These

approaches assume that the data are generated by simple random sampling, but Howes

and Lanjouw (1998) note that most poverty data come from stratified cluster sample

2

surveys. Using data from Pakistan and Ghana, they find that when complex sample

design is taken into account, estimated standard errors of FGT poverty measures increase

by 26 to 33 percent in Pakistan and 45 to 64 percent in Ghana.

Preston (1995) demonstrates that the precision of poverty estimates depends not

only on the sampling properties of the welfare measure, but also on the error associated

with the poverty line itself. He presents standard error formulae for poverty measures

that incorporate simple random sampling error in relative poverty lines (based on sample

quantiles) as well as the welfare measure. He observes that the two sources of error

could reinforce or offset one another, so that one cannot say a priori whether accounting

for sampling error in the poverty line will increase or reduce the precision of poverty

estimates.1 Zheng (2001) builds on this work to develop analytical expressions for

asymptotic distribution-free inference applicable to several additively decomposable

poverty measures when relative poverty lines are set as percentages of mean income or

percentages of quantiles, and allows for cluster sampling. In his empirical applications

with relative poverty lines, Zheng (1997, 2001) finds that the sampling error of the

poverty line always increases the standard error of poverty estimates.

Zheng (2001) states that the sampling variability of poverty lines is only relevant

for relative poverty measures, asserting that absolute poverty lines are not estimated from

sample survey data. However, a review of the absolute poverty literature shows that

absolute poverty lines are routinely estimated from sample survey data, especially in low

income countries over the past 10 to 20 years. For example, influential articles on

estimation of absolute poverty lines have been based on survey data (Greer and

Thorbecke 1986; Ravallion and Bidani 1994). Similarly, a recent “how to” manual by

the World Bank Institute (World Bank 2005) emphasizes the use of survey data for

determining poverty lines, citing examples from empirical work in a wide range of low

1More specifically, the standard error of the poverty estimates will be smaller when the poverty line sampling error is included if the covariance of the welfare measure and the poverty line is sufficiently negative to offset the additional variance contributed by the poverty line (i.e., when 2 2z yzσ σ< ).

3

income countries. Yet, none of the absolute poverty studies consider the effect of poverty

line variability on poverty estimates. This raises the possibility that the precision of most

estimates of absolute poverty has been overstated.

This paper makes three contributions. First, it articulates the argument for

incorporating the statistical error associated with absolute poverty lines in the calculation

of standard errors of poverty measures. Second, it proposes a method for estimating the

sampling error of absolute poverty lines estimated from survey data. Based on

bootstrapping, the method is extremely general and can be applied to a wide range of

poverty lines and poverty measures. Third, using recent household survey data from

Mozambique, the paper provides an initial estimate of the magnitude of the change in the

standard errors of poverty measures when the sampling properties of the poverty lines are

taken into consideration.

The remainder of the paper is structured as follows. Section 2 considers

challenges in estimating poverty and assessing the precision of estimated poverty

measures. This is followed by a description of the methods and data in Section 3.

Section 4 presents empirical results. Section 5 summarizes and concludes, including

remarks about the scope for wider application of this procedure.

2. Estimating Poverty

The measurement of poverty poses two fundamental questions (Sen 1976). First,

how does one identify the poor among the total population? Second, how does one

aggregate information on individuals and households into a scalar measure of poverty?

The first question has two components, namely, how do we measure individual welfare

and, using this same metric, how do we determine the threshold that separates the poor

from the nonpoor? Following Zheng (2000), one can formally write a generic poverty

measure P as

4

∫=z

ydFzyP0

)(),(φ , (1)

where y is a money-metric welfare measure, z is a monetary poverty line, and φ is a

poverty function that is decreasing in y, increasing in z, and homogenous of degree 0.

The poverty line can be considered as the expenditure function that corresponds to a

reference level of utility, uz, which defines the poverty threshold, or

),,( zuxpez = , (2)

where x is a vector of commodities consumed and p is the corresponding price vector.

It is possible to make interpersonal welfare comparisons over space or time by

defining money-metric utility, or what Blackorby and Donaldson (1987) call the welfare

ratio, as

y* = y / e(p, x, μz).

The poverty measure can then be written in terms of money-metric utility as the definite

integral

∫=1

0

** )()1,( ydFyP φ . (3)

It bears noting that when the poverty line is estimated from sample survey data, y* is the

ratio of two random variables whose distribution functions are not known. Computing

the variance of equation (3), where y* is an argument, thus poses some challenges, which

we discuss in greater detail in Section 3.

In empirical work, there are numerous options available with regard to

constructing the welfare metric, setting the poverty lines, and computing poverty

measures. For the purposes of the present illustration we use total consumption per

capita (Deaton and Zaidi 2002), Cost of Basic Needs (CBN) poverty lines (Ravallion

5

1994b, 1998), and the poverty measures proposed by Watts (1968) and Foster, Greer, and

Thorbecke (1984).

Consider the Foster-Greer-Thorbecke (FGT), or Pα, class of poverty measures. At

the household level, the general form of the FGT measure for household j can be written

,0,*

, ≥⎟⎟⎠

⎞⎜⎜⎝

⎛ −= α

α

α zyz

P j

j (4)

where *jy = yj if yj < z and *

jy = z if yj ≥ z. Poverty in a population of n households is the

weighted mean of equation (4) over all households, with the number of members in each

household (hj) as the weights, or

.

1

1,

∑

∑

=

== n

jj

n

jjj

h

PhP

α

α (5)

The poverty headcount index and poverty gap index are obtained when α = 0 and

1, respectively. In the case of non-self-weighting sample surveys, which is the typical

source of poverty data, sample weights (or expansion factors), wj, must be employed to

arrive at an unbiased estimator of individual-level poverty measures, written

.

1

1,

∑

∑

=

== n

jjj

n

jjjj

hw

PhwP

α

α (6)

By using hj as weights, equations (5) and (6) assume that poverty is distributed equally

within the household. Although this may be a strong assumption, it is difficult to avoid

because individual-specific information on the welfare metric is rarely available. If such

6

data were available then poverty could be measured by equation (6), but with j indexing

individuals instead of households and hj = 1.

Howes and Lanjouw (1998) define the estimator of the poverty measure in

equation (6) as πα = t/p, where p (the denominator in (6)) is the sample estimate of the

population size and t (the numerator in (6)) is the sample estimate of “total poverty.”

They show that under fairly weak assumptions that also conform well to the non-self-

weighting stratified multiple-stage cluster sampling procedures that are common among

household living standards surveys, a Taylor series expansion provides a consistent

estimator of the variance of πα. More specifically, for survey stratum k, cluster c, and nk

cluster samples drawn in the survey sample, a consistent estimator of the variance of πα is

[ ]),(ˆ2)(ˆ)(ˆ1)(ˆ 22 ptvoCpraVtraV

praV ααα πππ −+= , (7)

where

∑∑∑===

−−

==kn

ckkc

K

k kk

K

kk tt

nntraVtraV

1

2

11)(

)1(1)(ˆ)(ˆ , (8)

∑∑∑===

−−

==kn

ckkc

K

k kk

K

kk pp

nnpraVpraV

1

2

11)(

)1(1)(ˆ)(ˆ , (9)

and

∑∑∑===

−−−

==kn

ckkckkc

K

k kk

K

kkk pptt

nnptvoCptvoC

111))((

)1(1),(ˆ),(ˆ . (10)

The crux of our argument goes back to equation (1). Whereas the welfare metric

y is treated as a random variable with a sampling error, the absolute poverty line z is

routinely treated as a fixed constant, even though it is also estimated from the survey

data. Standard absolute poverty analyses ignore this variance component, leading to

7

incorrect estimates of the precision of poverty measures, and potentially misleading

poverty comparisons over time and space.

The intuition of the argument is seen in Figure 1. In both panels of the figure, the

horizontal axis is the welfare measure, the vertical axis is the proportion of the

population, the dark curved line is the empirical cumulative density function (CDF) of the

welfare measure (truncated at the upper end to focus on the region near the poverty line),

and the vertical line labeled z is the poverty line. The dotted lines on either side of the

CDF are an indicative confidence interval for the cumulative density of the welfare

measure. The point estimate of the poverty headcount, 0̂P , is read from the vertical axis,

at the level where the poverty line intersects the CDF. If the poverty line is assumed to

be fixed, then the confidence interval for 0̂P is the interval AB on the vertical axis,

corresponding to where the upper and lower bounds of the CDF confidence interval

intersect the poverty line.2 In the lower panel of Figure 1, the assumption of a fixed

poverty line is relaxed, and the dashed vertical lines represent the confidence interval

around the poverty line. If the estimated welfare metric and the estimated poverty line

are independent (i.e., the covariance is zero), then the confidence interval around 0̂P

would expand, as shown by the interval CD along the vertical axis of the lower panel. In

practice, the poverty line and the welfare metric are unlikely to be independent, and the

overall effect on the precision of the estimate of 0̂P will depend on the joint distribution of

the two random variables, including the possibility that the estimate of 0̂P will be more

precise when the variance of the poverty line is taken into account, as noted by Preston

(1995). Figure 1 also illustrates that whether or not the poverty line variance is included

in the poverty measure’s standard error, the precision of the poverty estimate also

depends on the location of the poverty line. Poverty lines that are closer to the mode of

2This is something of an oversimplification, but does capture the essence of the idea and is used here to illustrate the argument.

8

Figure 1—Illustration of poverty line error’s contribution to poverty estimate error

0

1C

umul

ativ

e pr

opor

tion

of s

ampl

e

zConsumption per capita

0

1

Cum

ulat

ive

prop

ortio

n of

sam

ple

zConsumption per capita

Po B

A

Po

D

C

9

the distribution, where the CDF has its steepest slope, will also tend to generate less

precise poverty estimates. It should also be noted that analogous illustrations could be

employed for higher orders of the FGT poverty measures, for example, by using the

poverty deficit curve (Ravallion 1994b; Deaton 1997) in place of the CDF for the

estimate of the average poverty gap, P1.

3. Data and Methods

This section describes our approach to incorporating the sampling error of the

poverty line in estimates of standard errors of poverty measures using household data

from Mozambique as a case study. Before describing the approach to calculating

standard errors specifically, we first describe the data collection process, the definition of

the welfare metric, and the setting of poverty lines. This is presented in some detail

because the individual steps determine not only the point estimates of the poverty lines

and poverty measures, but also the bootstrapped estimates of their standard errors.

Data Collection

We use data from the 2002–03 national Household Budget Survey in

Mozambique, also known by its Portuguese abbreviation IAF (for Inquérito aos

Agregados Familiares sobre Orçamento Familiar). Additional details about the survey

may be found in INE (2004). The survey was carried out from July 2002 through June

2003, visiting 8,700 households throughout the country. The sample had 21 strata:

separate rural and urban strata for each of Mozambique’s ten provinces, plus one for the

capital city of Maputo. A two-stage procedure was used to select sample households.

Within each stratum, primary sampling units (PSUs) (already defined on the basis of the

1997 Census) were selected with probability proportional to size. One month before the

launch of the survey, the survey teams carried out a complete listing of all households in

each of the 857 selected PSUs. In the second stage, households were randomly selected

within each PSU, with 12 households per urban PSU and 9 households per rural PSU.

10

The survey was limited to households residing in private residences, thus excluding those

living in institutions (e.g., prisons, boarding schools, military barracks), diplomatic

residences, and the homeless. The complex sampling structure implies unequal

probability of selection across PSUs, so sampling weights were calculated as the inverse

of the probability of selection.

The content of the 2002–03 IAF is similar to that of other household budget

surveys conducted in low income countries. Households were visited by interviewers at

least three times over a seven-day period. On the first visit, the interviewer and

household completed the module on general household characteristics, and collected

consumption information on food and selected common nonfood items with reference to

the preceding day (purchases, consumption from home production, and in-kind transfers

received). On subsequent visits other parts of the questionnaire were completed (monthly

expenditures, annual expenditures, and income), as well as daily consumption

information for the period since the previous interview.

Definition of the Welfare Metric

The approach used to calculate consumption follows closely the one described by

Deaton and Zaidi (2002) and Deaton and Grosh (2000), drawing from several modules of

the IAF. It measures the total value of consumption of food and nonfood items

(including purchases, home-produced items, and gifts received), as well as imputed use-

values for owner-occupied housing and household durable goods. Market purchases

were valued at the price paid, whereas nonmarket purchases were valued at the prevailing

market price in the area at that time. The only two significant omissions from the

consumption measure—both because of lack of data—are consumption of commodities

supplied by the public sector free of charge (or the subsidized element in such

commodities) and consumption of home produced services. For example, an all-weather

road, or a public market, or a public water tap, presumably enhances the well-being of the

11

people who use those facilities. Similarly, home-produced services, such as cooking and

cleaning, also add to welfare. These are often not captured by household surveys.

Food prices tend to follow a seasonal pattern, which implies that the purchasing

power of a given amount of money varies during the year. For example, to acquire the

same amount of food, a given household might have to spend twice as much in January

as it spends in June. If the household consumed the same amount in real (quantity) terms

in those months, it would appear to have a higher standard of living in January in nominal

monetary terms. To avoid this kind of inconsistency, an intra-survey temporal food price

index was developed from the survey data, and all nominal values of food consumption

were adjusted by the index to take these price fluctuations into account.

As larger households tend to have higher subsistence requirements than smaller

households, we divide total household consumption by household size and use

consumption per capita in our poverty comparisons. Alternative normalizations or

equivalency scales exist, but the per capita scale is sufficient for the purposes of the

present analysis. Adapting the method to other equivalence scales is straightforward.

Setting Poverty Lines

Poverty lines were set using the CBN approach (Ravallion 1994b). Mozambique

is a large country with poorly developed infrastructure and markets. High transactions

costs, combined with wide variation in agro-climatic conditions and production costs,

lead to wide spatial and temporal variation in the prices of basic goods. In particular,

differences in relative prices across space and time affect not only the total cost of

acquiring basic needs, but also the composition of the basic needs bundle, as households

adjust their consumption patterns in response to differences in relative prices.

As absolute poverty lines are supposed to represent the cost of achieving the same

standard of living across the domain of comparisons, it is necessary to establish region-

specific poverty lines. To define the poverty lines, the country was divided into 13

regions, based on an aggregation of the 21 survey strata that preserved the distinction

12

between rural and urban areas, but grouping adjacent strata with similar characteristics

(especially food prices and consumption patterns) if they had relatively few observations.

For each poverty line region, the food poverty line is constructed by an iterative

procedure that determines the caloric content of the typical diet of the poor in that region,

the average cost (at local prices) of a calorie when consuming that diet, and the food

energy intake requirements for the reference population (the poor). The food poverty

line—expressed in monetary cost per person per day—is the region-specific cost of

meeting the caloric requirements when consuming a food bundle comprised of goods that

the poor in the region actually consume.3 It bears emphasizing that the food bundle is not

determined by an externally imposed least-cost diet, but rather by the food consumption

characteristics of poor households as recorded in the survey, which contributes to the

sampling error of the poverty line.

The decision to allow the basic needs food bundles to vary by region was driven

by the large differences in relative food prices across the 13 poverty line regions, and

corresponding consumer behavior consistent with cost minimization. Within the 13

poverty line regions, relative prices and consumption patterns are fairly homogeneous.

Ravallion (1998) and Tarp et al. (2002) present arguments that allowing the food bundle

to vary by region can result in more consistent poverty comparisons than using a fixed

national bundle. Recent poverty studies that use region-specific poverty bundles and

prices include Tarp et al. (2002), Mukherjee and Benson (2003), Gibson and Rozelle

(2003), Ravallion and Lokshin (2003), Datt and Jolliffe (2005), and Arndt and Simler

(2005). Note that the same arguments in favor of allowing the bundle to vary over space

can also be applied to comparisons over time. 3The typical food bundle of the poor may contain more or less calories than the requirement for that region. This bundle is then proportionally scaled up or down until it yields exactly the pre-established caloric requirement, and the cost of this rescaled bundle at region-specific prices determines the food poverty line for that region. Also, it is recognized that food energy is only one facet of human nutrition, and that adequate consumption of other nutrients, such as protein, iron, vitamin A, and so forth, is also essential for a healthy and active life. However, like most multipurpose household surveys, the information on food consumption in the IAF data set is not sufficiently detailed to permit estimation of the intake and absorption of other nutrients. Use of energy requirements alone is also well established in the poverty measurement literature (Greer and Thorbecke 1986; Ravallion 1994b, 1998).

13

The relevant food bundles and associated prices were estimated for relatively poor

households using the iterative procedure described by Ravallion (1998). All households

were ranked in descending order by nominal consumption per capita, with the bottom X

percent identified as the relatively poor. The cutoff point may be considered as a

preliminary estimate of the poverty headcount, and can be chosen based on past poverty

assessments or other information. Preliminary poverty line bundles were constructed

using the consumption patterns of the relatively poor, and the nominal consumption

values converted to real terms (i.e., taking into account region-specific differences in the

cost of acquiring the basic needs bundles). Households were then re-ranked using this

first approximation of consumption per capita in real terms; households in regions with

high (low) price levels are poorer (richer) than indicated by nominal consumption, and

thus move down (up) when ranked in real terms. Revised food poverty line bundles were

constructed, producing a second estimate of food poverty lines, by which the households

were re-ranked again. The iterative process continues until it converges, meaning that the

same, or nearly the same, subsample of households appears below the cutoff point on

successive iterations. We experimented with several starting values, ranging from 40 to

65 percent, and found that all tended to converge on 48 percent (the poverty headcount

ratio), with convergence occurring after four or five iterations. This implies that the

poverty headcount estimate is robust to the choice of population subgroup that is used to

construct the food poverty line bundles.

Caloric requirements for moderately active individuals, disaggregated by age and

sex, were obtained from the World Health Organization (WHO 1985). Average per

capita requirements were allowed to vary by poverty line region, reflecting differences in

the average household composition across regions. In practice, the average daily food

energy requirement varies little across the 13 regions, averaging approximately 2,150

kilocalories per person.

Whereas physiological needs provide the conceptual underpinning of the food

poverty lines, no similar basis is readily available for defining nonfood needs. In

virtually all settings, even very poor households allocate a sizeable proportion of their

14

total consumption to nonfood items, such as shelter and clothing. We estimate the

nonfood poverty line by examining the proportion of total consumption allocated to

nonfoods among those households whose total expenditure is approximately equal to the

region-specific food poverty line (Ravallion 1994b, 1998; Ravallion and Bidani 1994).

The logic is that if a household’s total consumption is only sufficient to purchase the

minimum amount of calories using a food bundle typical for the poor, any expenditure

devoted to nonfoods is clearly a basic need, as it is displacing expenditure on basic food

items. Specifically, we estimate the nonfood component of the poverty line as the

average nonfood budget share of households whose total consumption is between 80 and

120 percent of the food poverty line, using a triangular kernel to give more weight to

those households closer to 100 percent of the food poverty line.

Estimating Poverty Measures and Their Standard Errors

After calculating the consumption variable and estimating the region-specific

poverty lines, obtaining point estimates of FGT poverty measures for the population and

subgroups requires nothing more than application of equation (6) to the survey data.

Obtaining consistent estimates of the standard errors of the poverty measures is less

obvious.

It should be clear from the description of constructing the poverty lines that the

poverty lines, as well as the welfare metric, are built from a series of estimates of

population characteristics from the sample survey data. Food energy requirements are

based on survey estimates of the population’s age and sex distributions. The expenditure

patterns that determine the basic needs food bundles are also estimates that are subject to

sampling error, as are the nonfood budget shares that determine the nonfood poverty line.

Similarly, the prices used to estimate the cost of the basic needs bundles come from the

survey. In this light, it seems difficult to justify the common assumption that the poverty

lines are not a source of sampling error in poverty estimates.

15

More formally, assume that the entire procedure for estimating Pα , including

estimation of the poverty line, is summarized as a function Hα(x) where x represents a

vector of exogenous inputs. As argued above, these inputs are random variables, which

we represent via distribution function g(x).4 The variance of Pα is, then,

∫Ω −=− dxxgxHExHxHExHE )(])])([)([(])])([)([( 22αααα , (11)

where Ω defines the domain of integration. Given the complexity of Hα(·), an analytical

solution to the integral does not exist. In these instances, an approximate solution to the

integration problem must be obtained numerically.

Problems of numerical integration have occupied mathematicians for centuries

with contributions by luminaries such as Gauss, Hermite, and Chebychev; and a wide

variety of numerical integration formulae are available. With modern computer power,

the Monte Carlo method has become popular. Under the Monte Carlo method, the

integrand is evaluated J times with each draw being a random selection from the domain

Ω of the distribution function g(x). The approximate solution is the simple average of all

evaluations. If J is large, the numerical approximation to the integral will have good

properties under extremely mild conditions on the integrand (Haber 1970).

More recently, econometricians, wishing to take advantage of the desirable

properties of Monte Carlo but lacking a specific form for g(x), have resorted to the

bootstrap (Efron 1979; Efron and Tibshirani 1993). The bootstrap approach is based on

repeated (J times) samples, drawn with replacement, of size K from the original sample

data, of size N, where K ≤ N. As the original sample size, N, increases, the bootstrap

approach converges to Monte Carlo for fixed K. The primary assumption behind the

bootstrap is that the distribution of the observed sample is a good approximation of the

distribution of the population.

4For simplicity, we are implicitly treating x as a continuously distributed random variable.

16

In sum, the bootstrap method can be applied to the problem of estimating the

variance of Pα in a manner analogous to the bootstrap estimate of the variance of an

econometrically estimated parameter. In both cases, resort to the bootstrap is made

because the estimate is derived from a complex, and hence analytically intractable,

estimation procedure.

In our application, the bootstrap samples are drawn in a manner that mimics the

stratified cluster sample design of the IAF survey. That is, within each stratum, K

clusters are randomly drawn, with replacement, where K is also the number of primary

sampling units in the stratum (i.e., K = N). When a cluster is drawn, all of the households

in that cluster are drawn. Because the bootstrap sampling is done with replacement, each

cluster (and household) may appear one or more times in a given bootstrap sample, or not

at all. The estimated poverty lines, poverty headcount, poverty gap, and Watts index are

calculated for each bootstrap sample. The process is repeated J = 1,000 times. The

standard deviation of a poverty measure over the 1,000 bootstrap replications is an

estimator of the standard error of that poverty measure. The point estimates of the

poverty measures are calculated from the original, non-bootstrapped sample (Efron and

Tibshirani 1993).

The process of estimating the poverty lines and poverty measures in each

bootstrap replication is summarized in Table 1, which is divided into three columns. The

first column lists processes that can be undertaken prior to the bootstrap loop. The

calculation of nominal consumption per capita for each household occurs at this step as

this measurement is (almost entirely) independent of the particular sample drawn.5 The

second column contains processes undertaken within the bootstrap loop. These are the

steps described earlier for calculating the poverty lines and the point estimates for the

poverty measures for each bootstrap sample. The third column shows post-bootstrap

5In the Mozambique case, hedonic regressions were used to impute use-values for owner-occupied housing. Obviously, the value obtained then depends upon the sample. Nevertheless, nominal use-values (rent foregone) for owner-occupied housing are in principle observable at the household level. The poverty line, in contrast, is not. Based on this distinction, we elect to treat estimates of use-value for owner occupied housing as data.

17

processing, which is simply the calculation of the standard deviations of the poverty

measures over the bootstrap replications.

Table 1—Outline of calculations included and excluded from bootstrap procedure Data collected or calculated before applying bootstrap

Calculations included in the bootstrap loop Post-bootstrap calculations

Household food and nonfood consumption expenditure

Identification of poorest households Standard deviation of estimated poverty measures over all replications as an estimator of the standard error of poverty measures

Value of consumption of home-produced items

Average household composition and calorie requirements per person

Value of transfers received Intra-survey temporal price index Use-value of durable assets Composition and cost of food poverty

line bundles

Use-value of owner-occupied housing

Nonfood budget share and poverty line

Total region-specific poverty lines Poverty measures

4. Results

The 13 region-specific food, nonfood, and total poverty lines are shown in

Table 2. The variation in the cost of basic needs is considerable across regions. Some

general patterns are evident, such as the higher poverty lines in urban areas of a given

province or province grouping, and the tendency for the poverty lines to increase (within

urban and rural zones) as one moves down the list, which is roughly ordered from

northern provinces to southern provinces.6 Table 2 also shows the estimated standard

errors of the total poverty line, estimated via the bootstrap process described earlier with

6It should be noted that these poverty lines, and the poverty measures presented in Tables 3 and 4, differ from the official poverty lines reported elsewhere (MPF 2004; Arndt and Simler 2005). The official poverty lines include a relatively novel entropy estimation adjustment to ensure that the basic needs food bundles satisfy revealed preference conditions across regions and over time. While we believe that revealed preference consistent poverty lines yield superior poverty measures, we elect to omit the revealed preference adjustment procedure in this presentation in order to focus on a commonly used approach for measuring poverty. It is straightforward to include the revealed preference adjustment procedure in the calculation of standard errors. With the Mozambique data the resulting standard errors are, on average, only slightly smaller than the results presented here. These results are available upon request.

18

1,000 replications. The poverty line standard errors range from 3 to 19 percent of the

point estimates, with most of them between 5 and 10 percent.

Table 2—Region-specific food, nonfood, and total poverty lines for Mozambique 2002-03

Poverty line

(meticais per person per day)

Poverty line region Food Nonfood Total Standard error of total poverty linea

Rural Niassa and Cabo Delgado 4,756 1,532 6,288 282 Urban Niassa and Cabo Delgado 7,717 2,838 10,555 1,164 Rural Nampula 2,752 913 3,665 399 Urban Nampula 3,749 1,370 5,119 982 Rural Sofala and Zambézia 3,548 1,195 4,743 302 Urban Sofala and Zambézia 5,902 2,177 8,079 750 Rural Tete and Manica 6,937 1,456 8,393 598 Urban Tete and Manica 9,656 3,575 13,231 1,056 Rural Inhambane and Gaza 5,438 1,930 7,368 497 Urban Inhambane and Gaza 6,613 3,025 9,638 762 Rural Maputo Province 12,584 5,385 17,969 1,755 Urban Maputo Province 13,741 7,810 21,551 1,467 Maputo City 13,211 8,022 21,232 694 Source: Authors’ calculations from the 2002–03 IAF. a Estimated by bootstrapping with 1,000 replications.

Table 3 presents estimates of the poverty headcount index at the national level

and for several subnational groupings. The national headcount ratio is 48 percent, and

ranges from 30 percent in Nampula Province to 76 percent in Maputo Province. The

column showing standard errors without poverty line error uses the Howes and Lanjouw

(1998) method described in section 2, which includes complex sample design effects and

is the method used most often in the current literature. At higher levels of aggregation,

such as the national level or estimates for rural and urban areas, the standard errors are 2

to 3 percent of the point estimate. As sample size decreases with further disaggregation,

the standard errors reach as high as 12 percent of the point estimates, although some of

the provincial estimates are still fairly precise (e.g., Inhambane and Maputo provinces).

19

Table 3—Estimates of poverty headcount index (P0) and standard errors, Mozambique 2002-03

Standard error

Region Sample

size Headcount

index Without poverty

line error With poverty

line errora Ratio of

standard errors National 8,700 0.4796 0.0128 0.0151 1.18 Urban 4,005 0.5239 0.0231 0.0252 1.09 Rural 4,695 0.4586 0.0165 0.0201 1.22 Northern 2,310 0.3977 0.0237 0.0322 1.36 Central 3,100 0.4456 0.0223 0.0259 1.16 Southern 3,290 0.6381 0.0146 0.0218 1.49 Northern

Niassa 816 0.4559 0.0501 0.0503 1.00 Cabo Delgado 738 0.5708 0.0355 0.0401 1.13 Nampula 756 0.3047 0.0349 0.0492 1.41

Central Zambézia 733 0.3514 0.0428 0.0443 1.04 Tete 756 0.7080 0.0377 0.0434 1.15 Manica 816 0.5853 0.0412 0.0465 1.13 Sofala 795 0.3093 0.0280 0.0360 1.29

Southern Inhambane 753 0.7509 0.0250 0.0333 1.33 Gaza 786 0.4709 0.0266 0.0429 1.61 Maputo Province. 828 0.7591 0.0277 0.0303 1.10 Maputo City 923 0.5804 0.0325 0.0339 1.04

Source: Authors’ calculations from the 2002–03 IAF. a Estimated by bootstrapping with 1,000 replications.

The next to last column of Table 3 shows the standard errors including the

sampling error of the poverty lines, as estimated using the bootstrap procedure described

in the preceding section. These standard errors are larger in all instances, despite the

possibility of poverty line error offsetting the error in the welfare measure that Preston

(1995) described. As seen in the rightmost column, the standard error of the national

headcount is 18 percent higher when poverty line sampling error is included. For other

levels of aggregation, including the poverty line as a source of variation increases the

standard error of the headcount estimate from a negligible amount in Niassa Province to

over 60 percent in Gaza Province. On average, including the poverty line sampling error

20

increases the estimated standard errors of the subnational poverty headcount estimates by

about 20 percent.

Table 4 shows the same set of results for the poverty gap index. At each level of

aggregation the standard errors of the poverty gap index are larger relative to the point

estimate than is observed for the headcount index. This is consistent with Kakwani’s

(1993) observation that the precision of FGT poverty measures (measured as the standard

error divided by the point estimate) tends to decrease for higher levels of α, a finding that

is corroborated by the results of Howes and Lanjouw (1998). Comparing the standard

errors estimated with and without poverty line sampling error, we see that in Zambézia

Province, including the poverty line sampling error marginally reduces the total standard

error of the poverty gap estimate. For all other estimates, the poverty line error increases

Table 4—Estimates of poverty gap index (P1) and standard errors, Mozambique 2002-03 Standard error

Region Sample size Poverty

gap index Without poverty

line error With poverty

line errora Ratio of standard

errors National 8,700 0.1754 0.0058 0.0074 1.27 Urban 4,005 0.1986 0.0101 0.0130 1.29 Rural 4,695 0.1644 0.0079 0.0094 1.20 Northern 2,310 0.1160 0.0081 0.0133 1.63 Central 3,100 0.1627 0.0102 0.0119 1.17 Southern 3,290 0.2709 0.0102 0.0156 1.53 Northern Niassa 816 0.1266 0.0122 0.0150 1.22 Cabo Delgado 738 0.1796 0.0162 0.0177 1.09 Nampula 756 0.0846 0.0126 0.0208 1.65 Central Zambézia 733 0.1017 0.0161 0.0159 0.99 Tete 756 0.3361 0.0268 0.0298 1.11 Manica 816 0.2439 0.0283 0.0322 1.14 Sofala 795 0.0785 0.0094 0.0125 1.33 Southern Inhambane 753 0.3519 0.0233 0.0325 1.39 Gaza 786 0.1421 0.0121 0.0199 1.65 Maputo Province 828 0.3612 0.0201 0.0258 1.28 Maputo City 923 0.2364 0.0159 0.0172 1.08 Source: Authors’ calculations from the 2002–03 IAF. a Estimated by bootstrapping with 1,000 replications.

21

the standard error of the poverty gap estimate, in some cases by as much as two-thirds.

On average, the inclusion of poverty line sampling error increases the standard errors of

the poverty gap estimates by about 30 percent, considerably more than the increase

observed for the poverty headcount index.

As noted earlier, the issue of poverty line variance is not limited to the FGT class

of measures, and the method presented here is readily adapted to other poverty measures.

The Watts index (Watts 1968) is one of the earliest summary measures of poverty.

Although it is not as widely used as the FGT class of poverty measures, it has been noted

for its favorable theoretical properties (Zheng 1993), and has also received considerable

attention recently in the “pro-poor growth” literature (see, for example, Ravallion 2004).

The Watts index may be written as

∫H

dppyz0

)](/[log , (12)

where H is the headcount index and y(p) is the quantile function, which is the inverse of

the cumulative distribution function p = F(x) at the p’th quantile. The Watts index was

estimated using the Mozambican data, with the results presented in Table 5. These are

qualitatively similar to the poverty gap results in Table 4. At the national level, the

standard error for the Watts index is 30 percent larger when the poverty line error is

included. In four instances (Tete, Manica, and Maputo provinces, plus Maputo City),

incorporation of the poverty line error reduces the standard error of the Watts index.

However, in most cases, it increases the standard error of the estimates of the Watts

index, varying widely from marginally higher in Inhambane Province to more than

double for the northern region.

How important is the increase in standard errors of the estimated poverty

measures when poverty line sampling error is included? One way of assessing this is to

put it in the context of the existing literature. As indicated earlier, Howes and Lanjouw

22

Table 5—Estimates of Watts Index and standard errors, Mozambique 2002-03 Standard error

Region Sample

size Watts index

Without poverty line error

With poverty line errora

Ratio of standard errors

National 8,700 0.2585 0.0100 0.0130 1.30 Urban 4,005 0.2859 0.0158 0.0195 1.23 Rural 4,695 0.2455 0.0139 0.0172 1.24 Northern 2,310 0.1521 0.0112 0.0234 2.09 Central 3,100 0.2476 0.0178 0.0238 1.34 Southern 3,290 0.4101 0.0194 0.0212 1.09 Northern

Niassa 816 0.1702 0.0173 0.0249 1.43 Cabo Delgado 738 0.2382 0.0231 0.0277 1.20 Nampula 756 0.1088 0.0171 0.0326 1.91

Central Zambézia 733 0.1374 0.0239 0.0338 1.42 Tete 756 0.5625 0.0554 0.0512 0.92 Manica 816 0.3845 0.0552 0.0507 0.92 Sofala 795 0.1023 0.0133 0.0189 1.42

Southern Inhambane 753 0.5573 0.0496 0.0515 1.04 Gaza 786 0.1871 0.0176 0.0238 1.35 Maputo Province 828 0.5639 0.0374 0.0352 0.94 Maputo City 923 0.3445 0.0254 0.0232 0.91

Source: Authors’ calculations from the 2002–03 IAF. a Estimated by bootstrapping with 1,000 replications.

(1998) found that accounting for sample stratification and clustering increased the

standard errors of estimated FGT poverty measures by 26 to 33 percent in Pakistan and

45 to 64 percent in Ghana. Adding the poverty lines as a source of error increases the

standard errors of the national-level poverty estimates in Mozambique by 18 to 30

percent. This suggests that accounting for poverty line sampling error may be nearly as

important quantitatively as accounting for complex sample design, although results from

other countries, and using alternative methods of setting the poverty lines, would be

needed before drawing a firm conclusion in this regard. It should also be noted that there

is no conflict between incorporating sample design and including poverty line error.

Rather, it is advisable to do both, as in the present example, in which the complex sample

design was also included in estimating the poverty line error. Even though the impact of

23

incorporating the variability of the poverty line might not be quite as dramatic as the

effect of complex sample design found by Howes and Lanjouw (1998), there is no good

reason to consistently overstate the precision of the poverty headcount by 15 to 20

percent, and the poverty gap or Watts indices by an even greater margin.

5. Conclusions

Poverty reduction is a fundamental objective of economic development, and

reducing poverty is a major focus of governments, international financial institutions, and

nongovernmental and community-based organizations. The success of policies,

programs, and development lending is increasingly judged in terms of poverty reduction.

There has been substantial progress over the past three decades in the measurement of

poverty, with the development of additively decomposable measures that reflect not only

the number of poor persons, but also the depth and severity of poverty for subgroups of

the population. As most poverty estimates come from sample survey data, the statistical

properties of poverty measures and appropriate inference procedures are important for

evaluating the precision of poverty estimates and the statistical significance of poverty

comparisons.

Studies of relative poverty have observed that there is sampling error associated

with both the welfare metric and relative poverty lines calculated from the survey data.

The recognition of poverty lines’ sampling error has not extended to absolute poverty

lines, even though they are also routinely estimated from sample survey data. This paper

addresses this gap by proposing a general method for including the sampling error of

poverty lines in the standard error of poverty measures, using CBN poverty lines and

FGT and Watts poverty indices as an illustration. The approach is based on bootstrap

methods that can be similarly applied to other methods of setting poverty lines (such as

the Food Energy Intake approach) and to other poverty measures.

Using recent data from Mozambique, we estimate that accounting for the

sampling error of poverty lines increases the standard errors of FGT poverty measures by

24

an average of 20 to 30 percent, with the standard errors increasing by up to 65 percent for

some subgroups, with similar results for the Watts index. Thus, to be considered

statistically significant, changes in poverty levels need to be larger than previously

believed.

Are there circumstances in which one can safely ignore the sampling error of

poverty lines, and treat them as fixed constants, without sampling error that contributes to

the error of the poverty measures? In our view, the only situation would be the case of

poverty lines that are determined exogenously, without reference to survey data. As

absolute poverty lines are supposed to reflect the same standard of living across the

domain of comparisons, and the cost of acquiring basic needs inevitably varies spatially

and temporally, it is highly improbable that one could divine utility-consistent poverty

lines without reference to data. Given a choice between arbitrarily specifying poverty

lines that are certain to be utility-inconsistent to an unknown degree, and accepting a

measurable loss in precision by estimating poverty lines from available data, the latter has

clear advantages.

Poverty analysts are increasingly employing stochastic dominance approaches to

make robust poverty comparisons across a range of plausible poverty lines, rather than a

single set of poverty lines pegged to a somewhat arbitrary level of utility (Atkinson 1987;

Davidson and Duclos 2000). This is intuitively appealing, and avoids the need to make

the (usually unconvincing) claim that the poverty line divides the population into discrete

states of poor and nonpoor. However, while stochastic dominance approaches usefully

sidestep the issue of the point estimates of poverty lines, they do not necessarily avoid the

issue of the variance of poverty lines. To make interpersonal welfare comparisons when

the cost of acquiring basic needs varies over time or space, nominal consumption must be

deflated by cost of living indices (Ravallion 1998). Establishing a common welfare

metric is typically accomplished by computing the welfare ratio, which is nominal

consumption divided by the relevant poverty line, or y*/z (Blackorby and Donaldson

1987). If these poverty lines or cost of living indices are estimated from survey data, then

the associated sampling error should be included in the confidence interval around the

25

empirical CDFs. To the extent that the poverty line error increases the total error of the

poverty estimates, the confidence regions around each CDF will be wider, and it will

become more difficult to reject a null hypothesis of no dominance.7 Adapting the

methods presented in this paper to stochastic dominance approaches to poverty

comparisons is an area for future research.

7Likewise, because the dollar-a-day poverty line is based in part on statistically estimated purchasing power parity (PPP) calculations, it is not immune from the poverty line sampling error described in this paper.

26

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160 The Impact of Agroforestry-Based Soil Fertility Replenishment Practices on the Poor in Western Kenya, Frank Place, Michelle Adato, Paul Hebinck, and Mary Omosa, October 2003

159 Rethinking Food Aid to Fight HIV/AIDS, Suneetha Kadiyala and Stuart Gillespie, October 2003

158 Food Aid and Child Nutrition in Rural Ethiopia, Agnes R. Quisumbing, September 2003

157 HIV/AIDS, Food Security, and Rural Livelihoods: Understanding and Responding, Michael Loevinsohn and Stuart Gillespie, September 2003

156 Public Policy, Food Markets, and Household Coping Strategies in Bangladesh: Lessons from the 1998 Floods, Carlo del Ninno, Paul A. Dorosh, and Lisa C. Smith, September 2003

155 Consumption Insurance and Vulnerability to Poverty: A Synthesis of the Evidence from Bangladesh, Ethiopia, Mali, Mexico, and Russia, Emmanuel Skoufias and Agnes R. Quisumbing, August 2003

154 Cultivating Nutrition: A Survey of Viewpoints on Integrating Agriculture and Nutrition, Carol E. Levin, Jennifer Long, Kenneth R. Simler, and Charlotte Johnson-Welch, July 2003

153 Maquiladoras and Market Mamas: Women’s Work and Childcare in Guatemala City and Accra, Agnes R. Quisumbing, Kelly Hallman, and Marie T. Ruel, June 2003

152 Income Diversification in Zimbabwe: Welfare Implications From Urban and Rural Areas, Lire Ersado, June 2003

151 Childcare and Work: Joint Decisions Among Women in Poor Neighborhoods of Guatemala City, Kelly Hallman, Agnes R. Quisumbing, Marie T. Ruel, and Bénédicte de la Brière, June 2003

150 The Impact of PROGRESA on Food Consumption, John Hoddinott and Emmanuel Skoufias, May 2003

149 Do Crowded Classrooms Crowd Out Learning? Evidence From the Food for Education Program in Bangladesh, Akhter U. Ahmed and Mary Arends-Kuenning, May 2003

148 Stunted Child-Overweight Mother Pairs: An Emerging Policy Concern? James L. Garrett and Marie T. Ruel, April 2003

147 Are Neighbors Equal? Estimating Local Inequality in Three Developing Countries, Chris Elbers, Peter Lanjouw, Johan Mistiaen, Berk Özler, and Kenneth Simler, April 2003

146 Moving Forward with Complementary Feeding: Indicators and Research Priorities, Marie T. Ruel, Kenneth H. Brown, and Laura E. Caulfield, April 2003

145 Child Labor and School Decisions in Urban and Rural Areas: Cross Country Evidence, Lire Ersado, December 2002

144 Targeting Outcomes Redux, David Coady, Margaret Grosh, and John Hoddinott, December 2002

143 Progress in Developing an Infant and Child Feeding Index: An Example Using the Ethiopia Demographic and Health Survey 2000, Mary Arimond and Marie T. Ruel, December 2002

142 Social Capital and Coping With Economic Shocks: An Analysis of Stunting of South African Children, Michael R. Carter and John A. Maluccio, December 2002

141 The Sensitivity of Calorie-Income Demand Elasticity to Price Changes: Evidence from Indonesia, Emmanuel Skoufias, November 2002

140 Is Dietary Diversity an Indicator of Food Security or Dietary Quality? A Review of Measurement Issues and Research Needs, Marie T. Ruel, November 2002

139 Can South Africa Afford to Become Africa’s First Welfare State? James Thurlow, October 2002

138 The Food for Education Program in Bangladesh: An Evaluation of its Impact on Educational Attainment and Food Security, Akhter U. Ahmed and Carlo del Ninno, September 2002

FCND DISCUSSION PAPERS

137 Reducing Child Undernutrition: How Far Does Income Growth Take Us? Lawrence Haddad, Harold Alderman, Simon Appleton, Lina Song, and Yisehac Yohannes, August 2002

136 Dietary Diversity as a Food Security Indicator, John Hoddinott and Yisehac Yohannes, June 2002

135 Trust, Membership in Groups, and Household Welfare: Evidence from KwaZulu-Natal, South Africa, Lawrence Haddad and John A. Maluccio, May 2002

134 In-Kind Transfers and Household Food Consumption: Implications for Targeted Food Programs in Bangladesh, Carlo del Ninno and Paul A. Dorosh, May 2002

133 Avoiding Chronic and Transitory Poverty: Evidence From Egypt, 1997-99, Lawrence Haddad and Akhter U. Ahmed, May 2002

132 Weighing What’s Practical: Proxy Means Tests for Targeting Food Subsidies in Egypt, Akhter U. Ahmed and Howarth E. Bouis, May 2002

131 Does Subsidized Childcare Help Poor Working Women in Urban Areas? Evaluation of a Government-Sponsored Program in Guatemala City, Marie T. Ruel, Bénédicte de la Brière, Kelly Hallman, Agnes Quisumbing, and Nora Coj, April 2002

130 Creating a Child Feeding Index Using the Demographic and Health Surveys: An Example from Latin America, Marie T. Ruel and Purnima Menon, April 2002

129 Labor Market Shocks and Their Impacts on Work and Schooling: Evidence from Urban Mexico, Emmanuel Skoufias and Susan W. Parker, March 2002

128 Assessing the Impact of Agricultural Research on Poverty Using the Sustainable Livelihoods Framework, Michelle Adato and Ruth Meinzen-Dick, March 2002

127 A Cost-Effectiveness Analysis of Demand- and Supply-Side Education Interventions: The Case of PROGRESA in Mexico, David P. Coady and Susan W. Parker, March 2002

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

125 Are the Welfare Losses from Imperfect Targeting Important?, Emmanuel Skoufias and David Coady, January 2002

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

123 Conditional Cash Transfers and Their Impact on Child Work and Schooling: Evidence from the PROGRESA Program in Mexico, Emmanuel Skoufias and Susan W. Parker, October 2001

122 Strengthening Public Safety Nets: Can the Informal Sector Show the Way?, Jonathan Morduch and Manohar Sharma, September 2001

121 Targeting Poverty Through Community-Based Public Works Programs: A Cross-Disciplinary Assessment of Recent Experience in South Africa, Michelle Adato and Lawrence Haddad, August 2001

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 Implications for Programs, Mary Arimond and Marie T. Ruel, August 2001

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

117 Evaluation of the Distributional Power of PROGRESA’s Cash Transfers in Mexico, David P. Coady, July 2001

116 A Multiple-Method Approach to Studying Childcare in an Urban Environment: The Case of Accra, Ghana, Marie T. Ruel, Margaret Armar-Klemesu, and Mary Arimond, June 2001

115 Are Women Overrepresented Among the Poor? An Analysis of Poverty in Ten Developing Countries, Agnes R. Quisumbing, Lawrence Haddad, and Christina Peña, June 2001

FCND DISCUSSION PAPERS

114 Distribution, Growth, and Performance of Microfinance Institutions in Africa, Asia, and Latin America, Cécile Lapenu and Manfred Zeller, June 2001

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

111 An Operational Tool for Evaluating Poverty Outreach of Development Policies and Projects, Manfred Zeller, Manohar Sharma, Carla Henry, and Cécile Lapenu, June 2001

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

109 Does Cash Crop Adoption Detract From Childcare Provision? Evidence From Rural Nepal, Michael J. Paolisso, Kelly Hallman, Lawrence Haddad, and Shibesh Regmi, April 2001

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

106 Strengthening Capacity to Improve Nutrition, Stuart Gillespie, March 2001

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

104 An Evaluation of the Impact of PROGRESA on Preschool Child Height, Jere R. Behrman and John Hoddinott, March 2001

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

99 Cash Transfer Programs with Income Multipliers: PROCAMPO in Mexico, Elisabeth Sadoulet, Alain de Janvry, and Benjamin Davis, January 2001

98 Participation and Poverty Reduction: Issues, Theory, and New Evidence from South Africa, John Hoddinott, Michelle Adato, Tim Besley, and Lawrence Haddad, January 2001

97 Socioeconomic Differentials in Child Stunting Are Consistently Larger in Urban Than in Rural Areas, Purnima Menon, Marie T. Ruel, and Saul S. Morris, December 2000

96 Attrition in Longitudinal Household Survey Data: Some Tests for Three Developing-Country Samples, Harold Alderman, Jere R. Behrman, Hans-Peter Kohler, John A. Maluccio, Susan Cotts Watkins, October 2000

95 Attrition in the Kwazulu Natal Income Dynamics Study 1993-1998, John Maluccio, October 2000

94 Targeting Urban Malnutrition: A Multicity Analysis of the Spatial Distribution of Childhood Nutritional Status, Saul Sutkover Morris, September 2000

93 Mother-Father Resource Control, Marriage Payments, and Girl-Boy Health in Rural Bangladesh, Kelly K. Hallman, September 2000

92 Assessing the Potential for Food-Based Strategies to Reduce Vitamin A and Iron Deficiencies: A Review of Recent Evidence, Marie T. Ruel and Carol E. Levin, July 2000

91 Comparing Village Characteristics Derived From Rapid Appraisals and Household Surveys: A Tale From Northern Mali, Luc Christiaensen, John Hoddinott, and Gilles Bergeron, July 2000

FCND DISCUSSION PAPERS

90 Empirical Measurements of Households’ Access to Credit and Credit Constraints in Developing Countries: Methodological Issues and Evidence, Aliou Diagne, Manfred Zeller, and Manohar Sharma, July 2000

89 The Role of the State in Promoting Microfinance Institutions, Cécile Lapenu, June 2000

88 The Determinants of Employment Status in Egypt, Ragui Assaad, Fatma El-Hamidi, and Akhter U. Ahmed, June 2000

87 Changes in Intrahousehold Labor Allocation to Environmental Goods Collection: A Case Study from Rural Nepal, Priscilla A. Cooke, May 2000

86 Women’s Assets and Intrahousehold Allocation in Rural Bangladesh: Testing Measures of Bargaining Power, Agnes R. Quisumbing and Bénédicte de la Brière, April 2000

85 Intrahousehold Impact of Transfer of Modern Agricultural Technology: A Gender Perspective, Ruchira Tabassum Naved, April 2000

84 Intrahousehold Allocation and Gender Relations: New Empirical Evidence from Four Developing Countries, Agnes R. Quisumbing and John A. Maluccio, April 2000

83 Quality or Quantity? The Supply-Side Determinants of Primary Schooling in Rural Mozambique, Sudhanshu Handa and Kenneth R. Simler, March 2000

82 Pathways of Rural Development in Madagascar: An Empirical Investigation of the Critical Triangle of Environmental Sustainability, Economic Growth, and Poverty Alleviation, Manfred Zeller, Cécile Lapenu, Bart Minten, Eliane Ralison, Désiré Randrianaivo, and Claude Randrianarisoa, March 2000

81 The Constraints to Good Child Care Practices in Accra: Implications for Programs, Margaret Armar-Klemesu, Marie T. Ruel, Daniel G. Maxwell, Carol E. Levin, and Saul S. Morris, February 2000

80 Nontraditional Crops and Land Accumulation Among Guatemalan Smallholders: Is the Impact Sustainable? Calogero Carletto, February 2000

79 Adult Health in the Time of Drought, John Hoddinott and Bill Kinsey, January 2000

78 Determinants of Poverty in Mozambique: 1996-97, Gaurav Datt, Kenneth Simler, Sanjukta Mukherjee, and Gabriel Dava, January 2000

77 The Political Economy of Food Subsidy Reform in Egypt, Tammi Gutner, November 1999.

76 Raising Primary School Enrolment in Developing Countries: The Relative Importance of Supply and Demand, Sudhanshu Handa, November 1999

75 Determinants of Poverty in Egypt, 1997, Gaurav Datt and Dean Jolliffe, October 1999

74 Can Cash Transfer Programs Work in Resource-Poor Countries? The Experience in Mozambique, Jan W. Low, James L. Garrett, and Vitória Ginja, October 1999

73 Social Roles, Human Capital, and the Intrahousehold Division of Labor: Evidence from Pakistan, Marcel Fafchamps and Agnes R. Quisumbing, October 1999

72 Validity of Rapid Estimates of Household Wealth and Income for Health Surveys in Rural Africa, Saul S. Morris, Calogero Carletto, John Hoddinott, and Luc J. M. Christiaensen, October 1999

71 Social Capital and Income Generation in South Africa, 1993-98, John Maluccio, Lawrence Haddad, and Julian May, September 1999

70 Child Health Care Demand in a Developing Country: Unconditional Estimates from the Philippines, Kelly Hallman, August 1999

69 Supply Response of West African Agricultural Households: Implications of Intrahousehold Preference Heterogeneity, Lisa C. Smith and Jean-Paul Chavas, July 1999

68 Early Childhood Nutrition and Academic Achievement: A Longitudinal Analysis, Paul Glewwe, Hanan Jacoby, and Elizabeth King, May 1999

67 Determinants of Household Access to and Participation in Formal and Informal Credit Markets in Malawi, Aliou Diagne, April 1999

FCND DISCUSSION PAPERS

66 Working Women in an Urban Setting: Traders, Vendors, and Food Security in Accra, Carol E. Levin, Daniel G. Maxwell, Margaret Armar-Klemesu, Marie T. Ruel, Saul S. Morris, and Clement Ahiadeke, April 1999

65 Are Determinants of Rural and Urban Food Security and Nutritional Status Different? Some Insights from Mozambique, James L. Garrett and Marie T. Ruel, April 1999

64 Some Urban Facts of Life: Implications for Research and Policy, Marie T. Ruel, Lawrence Haddad, and James L. Garrett, April 1999

63 Are Urban Poverty and Undernutrition Growing? Some Newly Assembled Evidence, Lawrence Haddad, Marie T. Ruel, and James L. Garrett, April 1999

62 Good Care Practices Can Mitigate the Negative Effects of Poverty and Low Maternal Schooling on Children's Nutritional Status: Evidence from Accra, Marie T. Ruel, Carol E. Levin, Margaret Armar-Klemesu, Daniel Maxwell, and Saul S. Morris, April 1999

61 Does Geographic Targeting of Nutrition Interventions Make Sense in Cities? Evidence from Abidjan and Accra, Saul S. Morris, Carol Levin, Margaret Armar-Klemesu, Daniel Maxwell, and Marie T. Ruel, April 1999

60 Explaining Child Malnutrition in Developing Countries: A Cross-Country Analysis, Lisa C. Smith and Lawrence Haddad, April 1999

59 Placement and Outreach of Group-Based Credit Organizations: The Cases of ASA, BRAC, and PROSHIKA in Bangladesh, Manohar Sharma and Manfred Zeller, March 1999

58 Women's Land Rights in the Transition to Individualized Ownership: Implications for the Management of Tree Resources in Western Ghana, Agnes Quisumbing, Ellen Payongayong, J. B. Aidoo, and Keijiro Otsuka, February 1999

57 The Structure of Wages During the Economic Transition in Romania, Emmanuel Skoufias, February 1999

56 How Does the Human Rights Perspective Help to Shape the Food and Nutrition Policy Research Agenda?, Lawrence Haddad and Arne Oshaug, February 1999

55 Efficiency in Intrahousehold Resource Allocation, Marcel Fafchamps, December 1998

54 Endogeneity of Schooling in the Wage Function: Evidence from the Rural Philippines, John Maluccio, November 1998

53 Agricultural Wages and Food Prices in Egypt: A Governorate-Level Analysis for 1976-1993, Gaurav Datt and Jennifer Olmsted, November 1998

52 Testing Nash Bargaining Household Models With Time-Series Data, John Hoddinott and Christopher Adam, November 1998

51 Urban Challenges to Food and Nutrition Security: A Review of Food Security, Health, and Caregiving in the Cities, Marie T. Ruel, James L. Garrett, Saul S. Morris, Daniel Maxwell, Arne Oshaug, Patrice Engle, Purnima Menon, Alison Slack, and Lawrence Haddad, October 1998

50 Computational Tools for Poverty Measurement and Analysis, Gaurav Datt, October 1998

49 A Profile of Poverty in Egypt: 1997, Gaurav Datt, Dean Jolliffe, and Manohar Sharma, August 1998.

48 Human Capital, Productivity, and Labor Allocation in Rural Pakistan, Marcel Fafchamps and Agnes R. Quisumbing, July 1998

47 Poverty in India and Indian States: An Update, Gaurav Datt, July 1998

46 Impact of Access to Credit on Income and Food Security in Malawi, Aliou Diagne, July 1998

45 Does Urban Agriculture Help Prevent Malnutrition? Evidence from Kampala, Daniel Maxwell, Carol Levin, and Joanne Csete, June 1998

44 Can FAO's Measure of Chronic Undernourishment Be Strengthened?, Lisa C. Smith, with a Response by Logan Naiken, May 1998

43 How Reliable Are Group Informant Ratings? A Test of Food Security Rating in Honduras, Gilles Bergeron, Saul Sutkover Morris, and Juan Manuel Medina Banegas, April 1998

FCND DISCUSSION PAPERS

42 Farm Productivity and Rural Poverty in India, Gaurav Datt and Martin Ravallion, March 1998

41 The Political Economy of Urban Food Security in Sub-Saharan Africa, Dan Maxwell, February 1998

40 Can Qualitative and Quantitative Methods Serve Complementary Purposes for Policy Research? Evidence from Accra, Dan Maxwell, January 1998

39 Whose Education Matters in the Determination of Household Income: Evidence from a Developing Country, Dean Jolliffe, November 1997

38 Systematic Client Consultation in Development: The Case of Food Policy Research in Ghana, India, Kenya, and Mali, Suresh Chandra Babu, Lynn R. Brown, and Bonnie McClafferty, November 1997

37 Why Do Migrants Remit? An Analysis for the Dominican Sierra, Bénédicte de la Brière, Alain de Janvry, Sylvie Lambert, and Elisabeth Sadoulet, October 1997

36 The GAPVU Cash Transfer Program in Mozambique: An assessment, Gaurav Datt, Ellen Payongayong, James L. Garrett, and Marie Ruel, October 1997

35 Market Access by Smallholder Farmers in Malawi: Implications for Technology Adoption, Agricultural Productivity, and Crop Income, Manfred Zeller, Aliou Diagne, and Charles Mataya, September 1997

34 The Impact of Changes in Common Property Resource Management on Intrahousehold Allocation, Philip Maggs and John Hoddinott, September 1997

33 Human Milk—An Invisible Food Resource, Anne Hatløy and Arne Oshaug, August 1997

32 The Determinants of Demand for Micronutrients: An Analysis of Rural Households in Bangladesh, Howarth E. Bouis and Mary Jane G. Novenario-Reese, August 1997

31 Is There an Intrahousehold 'Flypaper Effect'? Evidence from a School Feeding Program, Hanan Jacoby, August 1997

30 Plant Breeding: A Long-Term Strategy for the Control of Zinc Deficiency in Vulnerable Populations, Marie T. Ruel and Howarth E. Bouis, July 1997

29 Gender, Property Rights, and Natural Resources, Ruth Meinzen-Dick, Lynn R. Brown, Hilary Sims Feldstein, and Agnes R. Quisumbing, May 1997

28 Developing a Research and Action Agenda for Examining Urbanization and Caregiving: Examples from Southern and Eastern Africa, Patrice L. Engle, Purnima Menon, James L. Garrett, and Alison Slack, April 1997

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

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 Bangladesh: A Descriptive Analysis, Shubh K. Kumar Range, Ruchira Naved, and Saroj Bhattarai, February 1997

23 Better Rich, or Better There? Grandparent Wealth, Coresidence, and Intrahousehold Allocation, Agnes R. Quisumbing, January 1997

22 Alternative Approaches to Locating the Food Insecure: Qualitative and Quantitative Evidence from South India, Kimberly Chung, Lawrence Haddad, Jayashree Ramakrishna, and Frank Riely, January 1997

21 Livestock Income, Male/Female Animals, and Inequality in Rural Pakistan, Richard H. Adams, Jr., November 1996

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

FCND DISCUSSION PAPERS

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 Pakistan, Howarth E. Bouis and Gregory Scott, May 1996

13 Determinants of Repayment Performance in Credit Groups: The Role of Program Design, Intra-Group Risk Pooling, and Social Cohesion in Madagascar, Manfred Zeller, May 1996

12 Child Development: Vulnerability and Resilience, Patrice L. Engle, Sarah Castle, and Purnima Menon, April 1996

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

10 Women's Economic Advancement Through Agricultural Change: A Review of Donor Experience, Christine Peña, Patrick Webb, and Lawrence Haddad, February 1996

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

8 Measuring Food Insecurity: The Frequency and Severity of "Coping Strategies," Daniel G. Maxwell, December 1995

7 A Food Demand System Based on Demand for Characteristics: If There Is "Curvature" in the Slutsky Matrix, What Do the Curves Look Like and Why?, Howarth E. Bouis, December 1995

6 Gender Differentials in Farm Productivity: Implications for Household Efficiency and Agricultural Policy, Harold Alderman, John Hoddinott, Lawrence Haddad, and Christopher Udry, August 1995

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

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

3 The Extended Family and Intrahousehold Allocation: Inheritance and Investments in Children in the Rural Philippines, Agnes R. Quisumbing, March 1995

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

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