Combining Light Monitoring Surveys with Integrated Surveys ... · Combining Light Monitoring Surveys with Integrated Surveys to Improve Targeting for ... and David Bigman for their

THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. li 195-219

Combining Light Monitoring Surveys withIntegrated Surveys to Improve Targeting for

Poverty Reduction: The Case of Ghana

Hippolyte Fofack

Policymakers use poverty maps to design and assess poverty programs. The accuracy ofthese maps, which is critical for targeting, depends largely on the nature of the instru-ment used to construct them. Recently, in response to tight budget constraints, manycountries have begun to construct poverty maps based on light monitoring surveys thatrely on short questionnaires. This article shows that poverty maps constructed fromsuch surveys are not accurate and could result in substantial leakage. Light monitoringsurveys do include large samples that can help to target poverty programs at subregionallevels. Combining these surveys with more detailed Integrated Surveys can help researchersreduce targeting errors significantly and build improved poverty maps with finer levelsof disaggregation.

Poverty analysis and the design of targeted programs traditionally have beenbased on comprehensive household surveys. Such surveys are conducted infre-quently, however, making it difficult to assess the effects of macroeconomic re-forms on poverty and income inequality in the short and medium term. Limitedbudgetary resources constrain governments from conducting these surveys moreoften.

Policymakers fully recognize the need for reliable poverty maps, and severalsurveys have been designed and proposed as short-term alternatives to comprehen-sive surveys. These short-term instruments, which include Rapid Appraisal Meth-ods (Narayan and Srinivasan 1994) and Priority Surveys (Marchant and Grootaert1991), are known as light monitoring surveys, light monitoring surveys are de-signed to quickly identify groups that interventions should target. In contrast, morecomprehensive surveys are designed to conduct integrated poverty analysis and todraw inferences on welfare (see Ravallion 1996 and Deininger and Squire 1996).These surveys include household budget surveys, Living Standards Measurement

Hippolyte Fofack is with the Africa Region at the World Bank. Hit e-mail address is hfofack®worldbank.org. Earlier versions of this article were presented at the International Workshop on PovertyMapping, organized by the United Nations Environment Programme and Global Resource InformationDatabase in Norway, and at the International Conference on Geographical Targeting, organized by JohnsHopkins University in Baltimore, Md., held in October 1998. The author would like to express his warmthanks to Lionel Demery, Jack van Hoist Pellekaan, and David Bigman for their useful comments andencouragement. He also is grateful to the article's anonymous referees for their valuable comments.

© 2000 The International Bank for Reconstruction and Development/THE WORLD BANK

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196 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 1

Study (̂ SMS) surveys (Grosh and Glewwe 1998), and Integrated Surveys (Delaineand others 1992). (For details on the design and implementation of these surveys,see Demery and others 1992 and Grosh and Munoz 1996.)

Comprehensive Integrated Surveys are broad in scope, but they take a longtime to administer and usually are based on relatively small samples in order tocontain costs. The questionnaire collects data on most household and individualcharacteristics, providing extensive information on household income, credit, andsavings; household enterprises; the value of durable, productive, and financialassets; agricultural livestock; food processing and consumption of own produce;food and nonfood consumption; and other expenditures. The survey is adminis-tered over the course of a year, with multiple visits to households to captureseasonality. Nonsampling errors are reduced by shortening the recall period. Usingintegrated surveys to target programs at the subregional level is difficult, how-ever, because the sample sizes are too small to allow sound inferences to be drawn.

Light monitoring surveys are administered very quickly. They collect socio-economic data on a smaller set of variables than Integrated Surveys, but theirsample size is typically much larger. These surveys are based on single visits tohouseholds. They thus do not capture seasonality in consumption patterns norprovide accurate estimates of consumption and income. The short questionnaireand single visit, however, reduce the time needed for data collection and allowfor a larger sample size with better representation of different geographic areas.

The limited coverage of expenditure items in light monitoring surveys has somemajor drawbacks for policy analysis, however! Short questionnaires focus on afew sets of goods; consumption aggregates based on these data are likely to pro-vide estimates of total expenditures that are lower than estimates from IntegratedSurveys (Deaton and Grosh forthcoming). Moreover, the underestimation of to-tal expenditures is not likely to be uniform, shifting the Engel curve but preserv-ing welfare rankings. Rather, the bias varies significantly across households andregions, in part because regional differences in consumption patterns and changesover time—which are determinants of the distributions of income and expendi-tures—are not taken into account The distributions that emerge from the twotypes of surveys thus differ significantly.

Despite evidence that they might generate inaccurate estimates of aggregateexpenditures, light monitoring surveys have been used extensively for policy de-sign. In Sub-Saharan Africa, for example, World Bank poverty assessments drawmajor recommendations from these surveys (World Bank 1997). This article showsthat welfare indicators estimated from light monitoring surveys are biased andthat the bias affects not only the estimated magnitude of poverty but also itsgeographic distribution. The geographic distribution of poverty indicated by lightmonitoring surveys differs significantly from that indicated by Integrated Sur-veys, in part because per capita household expenditures, which are the basis fortargeting, underestimate aggregate expenditures. Large differences in the level ofsystematic bias will yield inaccurate poverty maps; targeted income transferschemes based on light monitoring surveys may result in leakage.

Fofack 197

This article investigates how comprehensive Integrated Surveys can be com-bined with light monitoring surveys to improve geographical targeting, maketransfers for poverty alleviation.efficient, and sharpen the inferences on welfaremeasures that are drawn from light monitoring surveys. As core components ofnational statistical programs, light monitoring surveys and Integrated Surveysare both household-level surveys, with important similarities. Similarities in theirsampling frames and sampling designs, as well as the proximity in their imple-mentation make their combination extremely appealing for poverty and policyanalysis.

L LIMITATIONS OF LIGHT MONITORING SURVEYS AS

INSTRUMENTS FOR TARGETING

To understand the implications of using light monitoring surveys as the basisfor poverty analysis, I compare welfare indicators estimated from such surveyswith those derived from Integrated Surveys. To allow full comparability, I ap-proximate aggregate expenditures as subsets of consumption items from the In-tegrated Survey. The study thus can be viewed as a counterfactual experiment,since estimates of total expenditures are known.

Ideally, we would like to compare an Integrated Survey and a light monitoringsurvey that were conducted over the same period. But the surveys were neverconducted concurrently in Ghana. Moreover, in countries in which light moni-toring surveys and Integrated Surveys have been carried out consecutively, varia-tion over time and changes in the sampling frame and design make it difficult toassess the performance of these surveys from estimates of household welfare. Inthis study I make comparisons on households for which expenditure aggregateshave been suitably constructed under the assumptions of both the full IntegratedSurvey and the approximated light monitoring survey. There is no time lag indata collection between the two surveys, and errors in measurement associatedwith variation in the sampling design are completely eliminated because the com-parisons are made on the same unit of analysis.

Distribution of Expenditures from Integrated and Light Monitoring Surveys

This study is based on the third in a series of surveys of living standards inGhana (Ghana Living Standards Survey [GLSS] 3).1 The survey was administeredto a sample of about 4,500 nationally representative households over the courseof a year.2 It collected data on all dimensions of household welfare and economicbehavior, including highly disaggregated and comprehensive data on household

1. The first two surveys, conducted in 1987 and 1989, are less comprehensive than the third survey. Allthree surveys are based on the master sample of enumeration areas defined by the 1984 population census.

2. A multistage stratified random sampling was used in selecting the sample. Initially, 407 dusters wereenumerated, and households were selected with a probability proportional to the size, with 15 householdsdrawn in each urban cluster and 10 households in each rural cluster.


income and expenditures. Data were collected on 107 food items, and the rangeof nonfood items was wider than in the first two surveys (Ghana Statistical Ser-vices 1996). Estimates from the third survey are considered more accurate thanestimates from the first two, in part because estimates from the third survey arebased on much shorter bounded recall periods (8 recalls at two-day intervals inrural areas and 11 recalls at three-day intervals in urban areas).

Although Integrated Surveys collect extended information on both householdincome and expenditures, I use expenditure data as the measure of economicwelfare, partly because nonsampling errors due to underreporting of income biasreported household income. There also are strong theoretical reasons for choos-ing expenditures over income (Deaton and Muellbauer 1980). Estimates of totalhousehold expenditures on food and nonfood constructed from the GLSS 3 datause 6 aggregates constructed from 17 subaggregates. These estimates account forall household expenses, including total household expenditures on rent (includ-ing imputed rent on owner-occupied, rent-free, or subsidized dwellings); con-sumption of home-produced food; and the value of wage income received byhousehold members in the form of food. Other imputed expenditures includetotal wage income paid in-kind to household members, the value of produce ofnonfarm enterprises consumed by households, and the use value of durable goods.The value of remittances made by households, as well as all other expenses, suchas spending on education and household amenities, are also included in aggre-gate expenditures. Missing values and outliers are imputed on each variable basedon a methodology developed by a team from the Development Economics Re-search Center at the University of Warwick (see Ghana Statistical Services 1996).All expenditure data are adjusted to account for inflation, which was relativelyhigh during the survey period, using March 1992 Accra prices as the base. Noadjustment is made for seasonal effects on household expenditures.

I obtain an estimate of total household expenditures by summing across allhousehold expenditure items, subaggregates, and aggregates on food and non-food. I first sum across items and subaggregates to obtain intermediate values forexpenditure aggregates at the household level:

(1) S t -/-i

where h represents households (ranging from 1 to the total sample size), and; =(1,2,...,N) represents the total number of items. P^ and Q^ represent the priceand quantity of item;, a component of aggregate k consumed by a given house-hold h. The multiplicative factor Sfj is the frequency of purchase (recall period) ofa given item within household h; X is the "frequency factor" or frequency ofenumerator visits to households.3

3. The frequency factor X is determined from the surrey design. In the GLSS 3 survey households werevisited 8 tunes at two-day intervals in rural areas and 11 times at three-day intervals in urban areas.

Fofack 199

A final estimate of total household expenditures for Integrated Surveys is ob-tained by summing across expenditure aggregates:

(2)

where k = (1,2,...yA) is the total number of subaggregates, and Y^ is the totalhousehold expenditure aggregate from the Integrated Survey for household h.We obtain estimates of total household expenditures based on light monitoringsurveys by similar aggregation, albeit over a much smaller number of items andsubaggregates. The components of aggregate expenditures are selected followingthe guidelines of the standard Priority Survey, which recommends limiting thecollection of expenditure data to key food and nonfood items (Demery, Grootaert,and Hill 1991). Total expenditure aggregates constructed here are based on threeaggregates: two nonfood aggregates, which include expenditures on educationand health, and a food aggregate, which contains 10 key food items (corn, rice,cassava, plantains, beans, groundnuts, palm oil, sugar, salt, and meat). Theseexpenditure items, especially spending on education and food, account for a largeshare of total expenditures.

We can estimate total household expenditures from a Priority Survey by sum-ming across these three aggregates, after adjusting for inflation. The adjustmentfor inflation is necessary because light monitoring surveys are based on a singlevisit to households, and inferences on welfare are drawn assuming no seasonalvariation in consumption patterns—an assumption that could limit the value ofthese surveys for poverty analysis. An estimate of total household expendituresfrom the light monitoring survey is provided by:

a<A a<A n<N

The frequency factor, X, does not appear in equation 3 because the data arecollected in a single visit. In the light monitoring survey both the number ofaggregates and the number of items are much smaller than in the Integrated Sur-vey (a < A and n < N). As a result total household expenditures are generallyunderestimated, and Yu^ < YIS. These aggregates are adjusted for householdsize to produce household per capita expenditures.

Summary statistics on the distributions of these adjusted variables reveal im-portant differences (table 1). As expected, the level of welfare estimated from thelight monitoring survey is much lower than that estimated from the IntegratedSurvey. At the national level mean per capita expenditures estimated from thelight monitoring survey are about 8 percent of those estimated from the Inte-grated Survey. The magnitude of the difference in welfare is particularly great inrural areas. Mean per capita expenditures estimated from the light monitoring

Table 1. Distribution of Per Capita Household Expenditures across Regions

Agroclimaticregion

AccraOther urbanRural ForestRural CoastalSavannahAll urbanAll ruralNational

Mean percapita

expenditures(cedis)

260,309225,232217,835213,641188,691233,889205,910215,186

Integrated Survey

Coefficientof variation

155.69155.88147.40178.55150.81157.68166.10163.87

Share ofnational mean

per capitaexpenditures

(percent)

12101.0471.0120.9930.8771.0870.9571.000

Light monitoring survey

Mean percapita

expenditures(cedis)

38,276.1633,241.8611,628.408,769.988,674.90

34,484.269,343.80

17,678.70

Coefficientof variation

181.30175.10231.05269.8635034179.13285.52278-50

Share ofnational mean

per capitaexpenditures

(percent)

2.1651.8800.6580.4960.4911.9510.5291.000

Ratio of meanper capita

expenditures oflight monitoring

survey to IntegratedSurvey

0.14700.14760.05340.04110.04600.14740.04540.0822

Source: Author's calculation* based on Ghana Statistical Services (1995,1996).

Fofack 201

survey in urban areas are about 14 percent of those estimated from the Inte-grated Survey; in rural areas the light monitoring survey estimate is about 4 per-cent of the Integrated Survey estimate.

This bias is exacerbated by the fact that own-produced consumption, whichaccounts for a large share of consumption in rural areas, is not included in thelight monitoring survey. The different scope of sampled items also biases theresults. Increasing the number of consumption items in the light monitoring sur-vey slightly reduces the bias between expenditure aggregates in the two surveys,but the difference remains large, and the sizable urban-rural bias persists. Whenthe number of sampled items is increased to 20, aggregate mean per capita ex-penditures in the light monitoring survey rise from 8 to 10 percent of aggregatemean per capita expenditures estimated from the Integrated Survey. Increasingthe number of sampled items to 30 raises the light monitoring survey estimate to11 percent of the Integrated Survey estimate. Increasing the number of items to20 raises the light monitoring survey estimate from 14 to 20 percent of the Inte-grated Survey estimate in urban areas; in rural areas the increase is only from 4 to5 percent.4 The persistence of a large urban-rural bias despite the increase in thenumber of sampled items may reflect the importance of own-produced food inrural areas.

Estimates of total household expenditures from light monitoring surveys arebased on just a few items and generally are biased downward. The downwardbias is likely to shift the distribution of expenditures to the left. The difference inthe scope of items and subaggregates may explain the large absolute differencebetween the distributions revealed by the two surveys. The variation in the struc-ture of these distributions and the large urban-rural bias may be due largely tothe nature of the consumption items in the overall aggregate. Although consump-tion of own-production represents a large share of household consumption inrural areas, it is not accounted for in the light monitoring survey aggregate, partlybecause the rural economy has a low level of monetization and also because keyconsumption items are more tradable in urban areas, where induced transactioncosts tend to increase their relative prices.

Poverty maps constructed from the light monitoring survey reveal patternsdifferent from those constructed from the Integrated Survey. Mean per capitaexpenditures are much lower across all agroclimatic regions, and the size of thebias is not uniform across regions. Measured by mean per capita expenditures,Savannah is no longer the poorest region of Ghana, and the differences in meanper capita expenditures between Savannah and Rural Coastal decline. These fig-ures represent about 50 percent of national mean per capita expenditures as esti-mated by the light monitoring survey. Moreover, the bias toward higher ruralpoverty is greater in the light monitoring survey than in the Integrated Survey.The rural mean estimate accounts for less than 30 percent of the urban mean in

4. The national mean per capita expenditure estimates based on 20 and 30 items are 22,883 crdis and25,803 cedis. These estimates are much higher in urban areas (44,939 cedis and 50,858 cedis) and muchlower in rural areas (11,944 cedis and 13,377 cedis).

2 02 THE WORIJ) BANX ECONOMIC REVIEW, VOL 14, NO. 1

the light monitoring survey, whereas it accounts for about 90 percent of theurban mean in the Integrated Survey.

The variance in the distribution of household per capita expenditures is rela-tively high in the light monitoring survey estimates. Although the estimates ofmean per capita expenditures are uniformly smaller in the light monitoring sur-vey than in the Integrated Survey, the coefficient of variation of per capita expen-ditures is higher across all agrocUmatic regions. The dispersion around the na-tional mean is greater in the light monitoring survey, where the ratio of regionalto national estimates has a larger range. Differences in mean estimates are muchsmaller in the Integrated Survey, where the ratio oscillates around 1, suggestingthat income inequality may be much higher in the light monitoring survey. Thedifferences in variances also are measured in terms of the Gini coefficient and theLorenz curve of inequality (figure 1). Figure 1 supports the hypothesis that lightmonitoring surveys record much greater income inequality than Integrated Sur-veys. The Gini coefficient approximated from the light monitoring survey issubstantially higher (0.56) than the corresponding coefficient for the IntegratedSurvey (0.34).

The relatively large variance observed in the light monitoring survey has im-portant implications for poverty analysis. The poverty gap is proportional to theincome gap, and the severity index is proportional to the squared deviation fromthe poverty lines when a i l . And, unexpectedly, large variances are likely toincrease the estimate of the poverty gap (and therefore the cost of poverty reduc-tion, which is proportional to the income gap).

Figure 1. Lorenz Curve of Income Inequality

Cumulative income share1 T

45-degrce line

Light Monitoring Survey

Integrated Survey

02

Source: Author's calculations.

03 0.4 0.5 0.6 0.7 OS

Cumulative population share0.9

Fofack 203

These large variations are also illustrated by the distribution of per capitahousehold expenditures across expenditure quintiles, which differs significantlybetween the two surveys.5 The bottom 40 percent of the population accounts forless than 8 percent of total expenditures in the light monitoring survey and 20percent of all expenditures in the Integrated Survey. In the upper quintiles, ex-penditure shares are overestimated in the light monitoring survey (figure 2). Thesedifferences in the distribution of expenditures across quintiles suggest that pov-erty maps constructed from a light monitoring survey may not be accurate be-cause the instrument tends to overestimate the welfare of the nonpoor and un-derestimate the welfare of the poor.

Implications for Policy Analysis and Poverty Mapping

The latest Ghana poverty profile uses two measures of poverty (Boateng andothers 1990). The broader measure includes all people whose per capita expendi-tures are two-thirds or less of the national mean (equal to 132,230 cedis perperson per year).6 The narrower measure of poverty includes all people whoseper capita expenditures amount to no more than half of the national mean (equal

Figure 2. Distribution of Per Capita Household ExpendituresPer capita expenditure share

0.4-

0 .3-

0 .2 -

0.1

D Light Monitoring SurveyI Integrated Survey

PTI I Il

(Poorest)


Expenditure quintiles

5. Expenditure quintiles for the Integrated Survey are derived from the full consumption aggregateadjusted for household size; expenditure quintiles for the light monitoring survey are constructed fromconsumption aggregates adjusted for household size, but with a limited number of items.

6. The same definitions were used in the 1987-88 poverty profile. The 1988 base values were adjustedfor inflation and expressed in 1992 constant prices for the 1995 profile. The lower poverty line is equal tothe same share of national mean per capita expenditures estimated from the full GLSS 3.


to 107,188 cedis per person per year). I use the lower poverty line (zB= 107,188)as the cutoff point in the Integrated Survey, because maximum targeting is easilyachieved at the lower and upper ends of the distribution, where the within-groupvariance and the probability of household misclassification are lower than in themiddle of the distribution. Similarly, I define the relative poverty line to be half ofmean per capita expenditures as approximated by the light monitoring survey(ZLMS = 8,839). I use that measure for cross-sectional analysis, looking at varia-tions in poverty rates across regions in the two surveys during the same period. Ido so because total household expenditures aggregated from the light monitor-ing survey show a large urban-rural bias—the rural expenditure aggregate is sub-stantially lower—and because using an upper poverty line would exacerbate thescope of rural poverty.

In order to assess the performance of the light monitoring survey as a targetinginstrument, I estimate the headcount, poverty gap, and severity indexes fromdistributions in both the light monitoring survey and the Integrated Survey (table2)7 Performance is assessed by the probability of type I and type II errors, as wellas by the rate of mistargeting.

The probability of a type I error is defined formally as e; = [P(yy e P I y; e P)],where P represents the set of poor households or individuals (yy) and P representsthe set of nonpoor households or individuals. A type I error can be referred to asan error of inclusion because it indicates the probability of classifying nonpoorhouseholds or individuals as poor.

The probability of a type II error is defined as ew = [P(y; e VI yt € P)]. A type LIerror can be referred to as an error of exclusion because it gives the probability ofclassifying poor households or individuals as nonpoor. The rate of mistargetingdepends on the size of these two errors. Perfect targeting is achieved when therate of mistargeting is equal to 1.0, implying that both surveys classify the samenumber of individuals as poor. Perfect targeting occurs when the errors of inclu-sion and exclusion are both close to 0. Let £*M(£/, EH) be the rate of mistargetingexpressed as a function of the error of inclusion (e;) and the error of exclusion(en). This rate is a number between 0 and n, where n < °°, that is, 0 < £KM(EJ, %) < n.When mistargeting results largely from the error of inclusion, CRM^J. eH) > 1.When the error of exclusion is much larger than the error of inclusion, the rate ofmistargeting is confined between 0 and 1, that is, 0 < £,RM(£I> 6//) < !•

When estimates of aggregate per capita household expenditures from the lightmonitoring survey are used as the basis for constructing poverty maps, RuralCoastal and Savannah remain the poorest regions in Ghana. The magnitudes ofthe differences across regions indicated by the two surveys vary significantly,however. In GLSS 3 urban expenditures exceed rural expenditures by just 4 per-

7. The welfare indexes are selected from the Pa class of poverty indexes (Foster, Greer, and Thorbedce1984), which measures different dimensions of poverty depending on the value of a. When a = 0, the Pa

represents the headcount index; when a « 1, it measures the poverty gap index. The indexes provideestimates of the severity of poverty when a > 1.

8

Table 2. Indexes of Extreme Poverty and Rate of Mistargeting across Regions

Agroclimaticregion


Light monitoring survey

Headcountindex

0.06780.08650.55430.67310.68100.08190.65060.4621

Povertygap index

0.02740.03250.30290.37220.42360.0313037530.2612

Severityindex

0.01580.01800.20600.24930.30820.01750.26040.1799

Headcountindex

0.14290.16570.16730.18870.24710.16000.20430.1897

Integrated SuweyPoverty

gap index

0.02590.03500.03240.04090.05460.03270.04380.0401

Severityindex

0.00720.01180.00970.01350.01730.01060.01410.0129

Light monitoring surveyType I errorprobability

0.03690.04230.39780.48890.47520.04100.46480.3243

Type II errorprobability

0.11210.12140.01080.00450.04140.11910.01860.0519

Rate ofmistargeting

0.470.523.313.572.760.513.182.44

Source: Author'* calculations based on Ghana Statistical Service* (1995,1996).


centage points. In contrast, the light monitoring survey indicates a difference of55 percent. Similarly, the light monitoring survey classifies more than 68 percentof the population of the Savannah region as extremely poor (falling below thelower poverty line), while the Integrated Survey classifies just 25 percent of thepopulation as poor. The same variations are observed in other regions, with pov-erty overestimated in rural areas and underestimated in urban areas.

A poverty map constructed from the GLSS 3 data has a higher urban headcountthan a poverty map constructed from light monitoring survey data, partly be-cause the GLSS 3 accounts for consumption of own production in rural areas,which reduces the urban-rural bias and increases national mean per capita ex-penditures (and therefore the extreme poverty line). Thus the light monitoringsurvey underestimates the scope of urban poverty. However, in the comprehen-sive Integrated Survey the headcount index is much higher in urban areas, andtargeting may be justified as well.

Poverty in Sub-Saharan Africa is generally much higher in rural areas, whereopportunities for income generation are much more limited than in urban areas.Extreme differences such as those revealed by the light monitoring survey—whichclassifies more than 65 percent of the rural population and less than 8 percent ofthe urban population as Living in extreme poverty—are nevertheless unexpected.These large urban-rural differences reflect differences in the error of inclusion,which is much higher in rural areas than in urban areas (0.47 compared with0.04). The size of the error is directly proportional to the rate of misclassification;the larger is the error of inclusion, the higher is the rate of misclassification.

In urban areas the error of inclusion is low, and the error of exclusion is muchhigher (0.11). Mistargeting results from large errors of exclusion, which occur be-cause poor households are underrepresented in the sample of intended beneficia-ries. Large errors of inclusion in rural areas result from underestimating total house-hold consumption and overrepresenting the poor population in the approximatedlight monitoring surveys, which causes nonpoor households to be surveyed as in-tended beneficiaries for targeted interventions. Mistargeting is relatively high un-der the light monitoring survey design, in which the population identified for thetargeted intervention is nearly two and a half times larger than the true populationestimate (table 2). This high rate of overall mistargeting is inflated by the rural rateof misclassification, which is more than three times the true number of intendedbeneficiaries. The variations in the rates of mistargeting across other rural regions(Rural Forest, Rural Coastal, and Savannah) are not significant.

The amount of leakage is directly proportional to the rate of misclassification.It will be lower in urban areas, where differential rates are smaller. The numberof extremely poor people mistargeted by the light monitoring survey is about halfthe true targeted population in urban areas; in rural areas the number ofmisclassified people is three times the number of primary beneficiaries. The dol-lar amount of leakage that occurs as a result of poor targeting by the light moni-toring survey is more than three times the amount required to alleviate extremepoverty in rural Ghana (table 2).

Fofack 207

The estimated cost of eradicating extreme poverty (nzpi) is proportional to thepoverty gap, where px is the estimate of the poverty gap. The poverty gap esti-mated from the light monitoring survey is about six times higher than the GLSSestimate at the national level. As a result, more than six times as much moneywould be needed to eradicate extreme poverty if the light monitoring survey wereused as the basis for poverty analysis (27,998 cedis per capita per year comparedwith 4,298.5 cedis).8 The potential costs to the central government and localauthorities are considerable, because poor targeting and improper identificationof intended beneficiaries increase the amount of leakage and the amount of re-sources allocated for poverty alleviation.

n. PREDICTING CONSUMPTION EXPENDITURES TO IMPROVE TARGETING

The accuracy of light monitoring survey data can be improved by using pov-erty predictors, correlates of expenditures that are used to impute householdconsumption. Fofack (1997) proposes a methodology for deriving national pov-erty predictors that could be used to improve targeting. Poverty predictors andtheir corresponding weights are estimated from Integrated Surveys and used topredict total household expenditures. These predicted values then serve as thebasis for conducting poverty analysis and for constructing poverty maps fromlight monitoring survey data. The prediction error is low, and high rates of suc-cessful classification are achieved. Moreover, a test of stability shows that thepoverty predictors and their corresponding weights are stable over time.9

Recently, attempts have been made to exploit the wide coverage of populationcensuses by combining them with household surveys (Hentschel and others 1998).Although combining these tools is appealing—especially given the scope for geo-graphical targeting—the data requirements for capturing the large proportionalvariance in welfare could be enormous. The method proposed by Hentschel andothers uses a large number of regressors from the census to predict welfare. Here,I take a different approach, based on data reduction. The poverty correlates usedto predict welfare are reduced to a set of minimum core variables that can beeasily collected with minimal measurement error.

To model household consumption for poverty analysis, the best correlates ofwelfare are derived from the GLSS 3 survey using correlation analysis and regres-sion models. The model assumes that the conditional expectation of the response,given the covariates, £(ylxlv..pt:A), is related to the linear predictors by the re-

8. This is a hypothetical scenario included for illustrative purposes only. Direct income transfers arenot cornerstones of policy in Sub-Saharan Africa. The cost of such measures would be enormous andwould worsen fiscal deficits.

9. The stability of the poverty predictors and the corresponding weights estimated from GLSS 3 (1992)are assessed by applying the regressors from GLSS 3 to GLSS 1 (1987) and GLSS 2 (1989) to predict the levelof welfare in those years. Success rates of ranking households in the same expenditure quintiles are as highas 95 percent, despite the time lag between these surveys. For details on the implementation of the test ofstability of these regressors, see Fofack (1997).


spouse link function h(x,Q). Because the variance of total, household expendituresacross and within regions is large, a logarithmic transformation is applied to theresponse to make the relationship between y and the x's linear. This transforma-tion stabilizes the error variance, reduces asymmetry in the distribution of errorterms, and improves the prediction. The structural form of the correcting modelis specified by equation 4:

(4) Y = X'p + e

where Y is total household expenditures transformed to the log scale, X is themodel matrix containing the vector of regressors, P is the vector of estimatedparameters relative to continuous and discrete-level variables, and e are the er-ror terms, which are distributed as N ^ o 2 ) . The poverty predictors are predomi-nantly discrete-level variables. Most continuous variables with strong predictivecapabilities are dichotomized to discriminate between poor and nonpoor house-holds. These dummy regressors are constructed- and included in the model tocapture the effects of qualitative independent variables.

In order to account for selection bias in choosing predictor variables, I use aconditional maximum likelihood estimation method to select predictors. Unlikeother selection criteria, the conditional maximum likelihood method is based onthe expected overall discrepancy. Because the omission bias in the fixed modelbecomes an additional residual variation, the method produces an unbiased esti-mator of the discrepancy.10 The best poverty predictors are those that signifi-cantly increase the explanatory power of the model. That is, if (xi,X2>—>*/) is theinitial set of poverty correlates and *;+1, Xy+t for k * 1 are potential poverty pre-dictors, the variable Xy+1 will be selected over x; + k if

(5) Ify - E(y I xi,xz,...,xj,xj+1)f < I[y,- - £(y I x1,x2,...,xj,xj+k)f

Initially, I assume that all predictor variables are available for inclusion in themodel. I then proceed by elimination, using the stepwise selection method with aminimum level of significance. I remove a given independent variable from themodel only when a marginal increase in the percentage variance of the responseexplained by the model as a result of that variable's inclusion is smaller than themarginal increase attributed to the inclusion of any other independent variable:

(6) Rz

I ' ^^^ ' ' I I '/-i

ijXj

Applying this selection procedure to the model iteratively produces an optimalmodel with 10 core poverty predictors. The optimal model has very few continu-

10. Other criteria used to select the subset of predictors include the Sp and the Mallows Cp criteria. TheSf is a model selection criterion that mmimct that the response variable and the predictors are jointlynormally distributed. The Cp method, due to Mallow* (1973), assumes that the predictor variables arefixed and not random. For more details on model selection see Linhart and Zucchini (1986).

Pofack 209

ous variables; those that remain are either dichotomized or discrete-level vari-ables. This characteristic is likely to reduce errors that arise because of long recallperiods. Moreover, the accuracy of targeting increases because the poverty pre-dictors and the weighted coefficients are estimated from Integrated Survey dataand are imputed using information collected at the household level during theadministration of the light monitoring survey. The poverty indexes thus are nolonger just a function of aggregate household expenditures, but also depend onthe estimated regression coefficients:

t =f(y,z), foik = 0,1,2,

is the poverty line.

The poverty predictors are derived at the national and regional levels and areused in conjunction with the corresponding weights to predict total expendi-tures. Predicted expenditures, expressed as the weighted sum of the poverty pre-dictors, are then used as the basis for constructing poverty maps, classifying re-gions for poverty analysis, and targeting.

The poverty predictors are able to explain more than 65 percent of the pro-portional variance observed in the welfare measure reported. The proportionalvariance explained by the model is high at the national level and at the regionallevel when the model is calibrated to derive poverty predictors for each agroclimaticregion. (The appendix lists the deriyed poverty predictors at the national andregional levels. The use of different poverty predictors is intended to reflect dif-ferences in consumption patterns.) l

HI. EMPIRICAL RESULTS

To assess the accuracy of poverty maps constructed from the improved lightmonitoring survey—that is, the light monitoring survey in which total expendi-tures have been modeled using poverty predictors—I estimate the incidence ofpoverty and poverty-related indicators for different agroclimatic regions usingpredicted expenditures constructed from the model. I compare these estimateswith the poverty indicators derived from GLSS 3 using the same poverty line forthe predicted and measured consumption aggregates (since in the absence of pre-diction errors, the means of these two distributions are the same).

The differences in the poverty estimates decrease substantially in both urbanand rural areas when the poverty predictors are used to model household expen-ditures (table 3). The error of inclusion, for example, which was as high as 0.48in the approximated light monitoring survey, falls to just 0.13 or less when thepoverty predictors are the basis for poverty analysis. Moreover, the significantdecrease in this error is not accompanied by an increase in the error of exclusion,which remains low across all agroclimatic regions.

Table 3. Indexes of Extreme Poverty and Rate of Mistargeting by Region with the Improved Light Monitoring Survey

O

Agroclimaticregion


Improved light monitoring surveyHeadcount

index

0.08780.13440.09880.18680.27870.12290.19990.1743

Povertygap index

0.01630.02740.01590.03570.04610.02470.03510.0317

Severityindex

0.00400.00760.00390.01030.01160.00670.00940.0085

Headcountindex

0.14290.16570.16730.18870.24710.16000.20430.1897

Integrated SurveyPoverty

gap index

0.02590.03500.03240.04090.05460.03270.04380.0401

Severityindex

0.00720.01180.00970.01350.01730.01060.01410.0129

Improved light monitoring surveyType I errorprobability

0.02120.06230.05370.10360.12610.05220.10080.0847

Type II errorprobability

0.07630.09360.12200.10560.09460.08940.10530.1001

Rate ofmistargeting

0.6150.8110.5910.9901.1280.7680.9780.919

Source: Author's calculations based on Ghana Statistical Services (1995,1996).

Fofack 211

The poverty indicators estimated on the basis of the GLSS 3 data and the im-proved light monitoring survey data are similar (figure 3). At the national levelthe absolute relative error is less than 0.081, probably because the difference inthe headcount indexes estimated from the Integrated Survey and the predictedwelfare function is small. This relatively small difference is due largely to mea-surement errors in reported household expenditures and sample size effects. Thesample size at the subregional level is the smallest in Accra and the Rural Forestregion, where the magnitude of the difference is largest.

The prediction error is inversely proportional to the sample size, however,suggesting that the error should decrease substantially in the actual light moni-toring survey, where the large sample size and exhaustive coverage allow muchgreater representation at the regional and subregional levels. Part of the predic-tion error also could be attributed to measurement errors in reported householdexpenditures. This part of the prediction error is associated with the survey de-sign and implementation and could be large depending on sample size andnonsampling errors. In contrast, measurement errors are washed away by theinstrumented variable; targeting might even become more accurate when basedon predicted welfare. A potential increase in accuracy is another reason for usinginstrumented consumption in poverty analysis.

Figure 3. Headcount Index

Headcount index

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1 ll

1111 1ll"l IIAccra Other Rural Rural Savannah All urban All rural National

urban Forest Coastal

I Integrated Survey Q Improved light Monitoring Survey


light Monitoring Survey

212 THE WORLD RANK ECONOMIC REVIEW, VOL. 14, NO. 1

The differences between headcount indexes remain when predicted expendi-tures are used for poverty analysis. The bias toward higher rural poverty is pre-served in the regional ranking, and the ranking of the five agroclimatic regions isconsistent across the two ranking criteria (Integrated Survey data and improvedlight monitoring survey data with predicted expenditures). The magnitude of thedifferences across agroclimatic regions is also preserved in the improved lightmonitoring survey.

The incidence of poverty is highest in the Savannah and Rural Coastal re-gions, where 28 and 19 percent of the population, respectively, are extremelypoor. The largest poverty gap is in Savannah, suggesting that if the populationwere distributed uniformly across regions, the volume of transfers needed toeradicate poverty would be largest in this region. The Savannah and RuralCoastal regions also have the lowest literacy and enrollment rates, and bothdepend on food crops as their main source of income (Ghana Statistical Ser-vices 1995). In contrast, in the Rural Forest region, where income sources aremore diversified, the incidence of poverty is much lower. The poorest of thepoor are thus uneducated small farmers residing mainly in the Savannah andRural Coastal regions, where employment opportunities are Limited. To theextent that education variables (public school enrollment, proportion of school-age children enrolled in school) appear to be good proxies for income in theseregions, the placement of public infrastructure may be a good means of mak-ing transfers to the poor.

I also assess the performance of the proposed method by the size of errors ofinclusion and exclusion, as well as the rate of mistargeting. While the error ofinclusion across agroclimatic regions is generally much higher in the approximatedlight monitoring survey dian in the improved light monitoring survey (figure 4),the error of exclusion is generally much lower (figure 5). The magnitude of thedifference between the errors of exclusion in the two surveys is small, however, inpart because mistargeting is due largely to high errors of inclusion.

The rate of mistargeting falls substantially when the improved light monitor-ing survey is used (table 3). In fact, except in the Rural Forest region, targeting isalmost perfect. Perfect targeting is achieved in all Rural and Rural Coastal areaswhen predicted expenditures are used as the basis for constructing poverty maps.Compared with the light monitoring survey, the gains in accuracy are significant.Even in the Rural Forest region, where the error of inclusion is relatively high(0.12) and the rate of misclassification is slightly different from unity, the gap isless than 40 percent. Moreover, the rate of mistargeting is less than 1, implyingthat poor targeting is due largely to a high error of exclusion.

The rates of mistargeting £RM(E/J £//) are particularly high in rural areas whenthe approximated light monitoring survey is used to construct the poverty map.These high rates are attributable mosdy to large errors of inclusion. Significantimprovement is achieved when predicted expenditures are used as the basis fortargeting (in the improved light monitoring survey).

Fofack 213

Figure 4. Probability of Error of Inclusion

Probabflily

0.8

0.6

Accra RuralForest

RuralCoastal

• Light Monitoring Survey

Source: Author's calculations

Savannah All urban All rural

Improved Light Monitoring Survey

National

IV. IMPLICATIONS FOR GEOGRAPHICAL TARGETING

The method described sharpens the accuracy of poverty maps and allowspolicymakers to target beneficiaries at subregional levels. The approximated lightmonitoring survey is constructed from the more comprehensive GLSS 3 survey.The sample size corresponding to the approximated light monitoring survey isdictated by the GLSS 3 design, just as the level of disaggregation is determined bythe actual GLSS 3 sample size.

The geographic profile of poverty provides living standards indicators for thevarious agroclimatic regions. Efficiency could be improved and leakage reducedsignificantly if smaller geographical units could be targeted (Baker and Grosh1994). The Priority Survey design recommends a large sample size for targetingsmaller administrative units (Grootaert and Marchant 1992).11 Improving theaccuracy of the welfare function by predicting household expenditures shouldenable researchers to exploit the large sample size to achieve geographical target-

11. The Kenya Welfare Monitoring Survey (1994) was based on a sample of 12,000 households. TheGhana Core Welfare Indicators Survey (1997) was based on a sample of 15,000 households.

2 1 4 THE WORLD BANK ECONOMIC REVIEW, VOL. 14, NO. 1

Figure 5. Probability of Error of Exclusion

Probability

0.16

0.12

0.08

0.04

Accra RuralForest

RuralCoastal

i • Light Monitoring Survey


All urban All rural

Improved Light Monitoring Survey

National

ing with minimum leakage at a level of disaggregation well below the agroclimaticregion.

The causes and determinants of poverty, as well as the sources of large dispari-ties across agroclimatic regions, are variable. At the aggregate level differences inthe potential for income-generating activities and wage inequality may constituteimportant factors; at the regional and district levels human capital, access indica-tors, and location of infrastructure may be more critical. Light monitoring sur-veys collect good data on access indicators (location of schools, health centers,and water supply). The relatively large sample size of such surveys may provideopportunities for geo-referencing information at subregional levels, thereby im-proving the potential for analysis beyond fixed geographical boundaries. Fur-ther, overlaying improved poverty maps atop maps of local infrastructure (schools,health clinics, hospitals, water supply facilities, and roads) may improve the un-derstanding of poverty dynamics, shed more light on the possible constraints togrowth and poverty reduction, and improve priority setting, impact assessment,and policymaking.

Fofack 21S

Poverty predictors, which include food and nonfood consumption variables,are strong correlates of welfare. They can serve as the basis for targeting bycommodity and by welfare indicator, especially if indirect transfer schemes areused to reduce poverty.12 Targeting by indicator is based on the ability to easilyidentify a few key variables that are highly correlated with household incomeand expenditures. The use of regional poverty predictors, which are correlates ofwelfare, may be particularly appealing in Sub-Saharan Africa, where there areimportant differences in the determinants of welfare across agroclimatic regions.Taking these differences into account in selecting correlates might improve theefficiency of transfers and the allocation of public expenditures for povertyreduction.13

The poverty predictors also can serve as a basis for targeting by commoditybecause they include food items. This targeted scheme draws on the. differencesobserved in the consumption baskets of the poor and nonpoor. Its objective is toreduce the cost of commodities that are consumed heavily by the poor throughtargeted subsidies. Although poverty predictors are derived according toagroclimatic region rather than poverty, the methodology is flexible and could beused in multiple steps—that is, poverty predictors could first be used to predicthousehold expenditures, and expenditures in turn could be used to differentiatebetween the poor and the nonpoor. A cross-sectional analysis that focuses on thevariation in the consumption of the poor by agroclimatic region could be a start-ing point for investigating the causal link between variation in the depth of pov-erty and the nature of poverty correlates. Future research will have to explore thelinks between these correlates and poverty at the regional and district levels, anddetermine how a better understanding of those associations could be used tochannel scarce resources to the most needy.

V. CONCLUDING REMARKS

Many developing countries are confronted with widespread poverty and havelimited resources for poverty alleviation. To minimise leakage, policymakers musthave accurate arid detailed poverty maps that allow identification of the poor atfiner levels of disaggregation than the agroclimatic region.

Some developing countries have used light monitoring surveys, which havelarge sample sizes and are less expensive to administer than other types of sur-veys, as the basis for constructing disaggregated poverty maps. This study showsthat the cost of mistargeting associated with the use of such surveys is significantand can outweigh the savings generated from their lower administrative costs.Light monitoring surveys underestimate aggregate expenditures, the basis for dif-

12. Other methods of targeting include targeting by income and self-targeting. For a survey of targetingmethods with applications to developing countries, see Glewwe (1992); Kanbur, Keen, and Tuomala(1994); Bigman and Fofack (forthcoming).

13. Glewwe (1992) uses bousing indicators as the basis for targeting in Cdte d'lvoire. Ravallion (1989)uses landownership as the basis for designing land-contingent transfers for poverty alleviation in Bangladesh.


ferentiating between the poor and the nonpoor. Moreover, underestimation isnot uniform across regions. As a result, welfare indicators and poverty mapsderived from light monitoring surveys may not always be consistent with theactual distribution of poverty.

This article shows that combining more detailed surveys, which have compre-hensive income and expenditure data, with light monitoring surveys yields im-proved poverty maps that are disaggregated at a level below the agroclimaticregion. The rate of mistargeting is reduced substantially when poverty predictorsderived from more comprehensive surveys are used to model total expendituresbased on data from light monitoring surveys. These poverty predictors arehousehold-level variables that are available in both Integrated Surveys and lightmonitoring surveys.

Over the past few years demand for poverty maps that are disaggregated at alevel as low as the district has been growing in developing countries, particularlyin Sub-Saharan Africa. This demand has been prompted both by the need for amore accurate picture of the geographic distribution of poverty and by the movetoward decentralization, which increasingly channels resources to communities.As the demand for more disaggregated information continues to grow and bud-getary and resource constraints tighten, methods that optimize the use of lightmonitoring surveys while improving the accuracy of targeting will be in increas-ing demand. The method proposed here recommends that light monitoring sur-veys and more detailed comprehensive surveys be combined to improve povertymapping and geographical targeting.

The accuracy of household welfare predicted from the model depends on thebase point of the prediction, the stability of the poverty predictors, and theircorresponding weights. Although modeling consumption significantly reduceserrors of inclusion and exclusion, the level of targeting attained in the variousagroclimatic regions is imperfect because of unavoidable prediction errors. Theseerrors are attributable largely to measurement errors in reported expendituresand might be washed away when the instrumented variable is used.

The stability of the predictors over time is another important question. Fofack(1997) assesses stability using surveys conducted under the same sampling frame.It would be worth investigating how this stability is affected by variation in thesampling frame. The possible implications for poverty mapping are also worthexamining. Finally, efficiency in the transfer and allocation of resources could beimproved by combining geographical targeting with another form of targeting,such as targeting by commodity or by indicator. Poverty prediaors are correlatesof expenditures and could serve as a vector of transfers if the dynamic betweenthese correlates and poverty were better understood.

Appendix. National and Regional Poverty PredictorsNational level Urban areas Rural areas

Expenditures on soapNumber of spousesAsset scorePercentage of school-age children enrolled

in schoolExpenditures on meatOwnership of landConsumption of breadOwnership of poultryExport cropsNumber of household members per room


in schoolExpenditures on meatOwnership of landPercentage of household members employedUse of toothpastePercentage of children enrolled in public schoolPercentage of literate household members


in schoolExpenditures on meatOwnership of landOwnership of goats and sheepNumber of household members per roomOwnership of farmOwnership of cattle

Accra region Other urban Rural Forest

Asset scoreExpenditures on meatPercentage of household members employedExpenditures on riceNumber of household members who

completed secondary schoolExpenditures on soapPercentage of school-age childrenNumber of children under fiveUse of toilet paperPercentage or children enrolled in public school

Expenditures on soapNumber of household members employedExpenditures on meatAsset scorePercentage of school-age childrenExpenditures on breadPercentage of household members who

completed secondary schoolUse of toothpasteOwnership of landUse of toilet paper

Expenditures on soapConsumption of breadAsset scoreUse of toothpasteNumber of spousesExpenditures on meatPercentage of household members who completed

secondary schoolExpenditures on riceNumber of household members per roomUse of toilet paper

Rural Coastal Savannah

Expenditures on soapAsset scorePercentage of school-age childrenExpenditures on meatNumber of spousesPercentage of children enrolled in public schoolConsumption of breadUse of toilet paperOwnership of poultryNumber of children under five

Expenditures on soapNumber of spousesConsumption of breadPercentage of school-age childrenOwnership of sheep and goatsUse of toothpasteExpenditures on meatAsset scoreUse of toilet paperGender of head


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