Poverty reduction through geographic targeting: How well does it work?

Baker, Judy L.; Grosh, Margaret

doi:10.1016/0305-750x(94)90143-0

Cited by 120 publications

(76 citation statements)

References 4 publications

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“…However, geographic targeting requires finely detailed spatial targeting to prevent leakage of benefits to non-poor areas and to ensure that aid is channeled to areas in which those that are truly poor live. Previous research has shown that geographic targeting is most effective when the geographic units are relatively small (Baker and Grosh, 1994). Unfortunately, such targeting is currently impossible since the coverage of household surveys (that is the size of the sample) that are used to measure poverty in China are too small to permit measuring poverty at fine enough level of spatial disaggregation.…”

Section: Introductionmentioning

confidence: 99%

Mapping poverty in rural China: how much does the environment matter?

Olivia¹,

Gibson²,

Rozelle³

et al. 2011

Envir. Dev. Econ.

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In this paper, we apply a recently developed small-area estimation technique to derive geographically detailed estimates of consumption-based poverty and inequality in rural Shaanxi, China. We also investigate whether using environmental variables derived mainly from satellite remote sensing improves upon traditional approaches that only use household survey and census data. According to our results, ignoring environmental variables in statistical analyses that predict small-area poverty rates leads to targeting errors. In other words, using environmental variables both helps more accurately identify poor areas (so they should be able to receive more transfers of poor area funds) and identify non-poor areas (which would allow policy makers to reduce poverty funds in these better off areas and redirect them to poor areas). Using area-based targeting may be an efficient way to reach the poor since many counties and townships in rural Shaanxi have low levels of inequality, even though, on average, there is more within-group than between-group inequality. Using information on locations that are, in fact, receiving poverty assistance, our analysis also produces evidence that official poverty policy in Shaanxi targets particular areas which in reality are no poorer than other areas that do not get targeted. KeywordsChina environment poverty small area estimation JEL Classification O15, O53, P36, Q56 AcknowledgementsWe are grateful to

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Section: Introductionmentioning

confidence: 99%

Mapping poverty in rural China: how much does the environment matter?

Olivia¹,

Gibson²,

Rozelle³

et al. 2011

Envir. Dev. Econ.

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show abstract

“…Several studies show that geographic targeting of small administrative areas improves cost effectiveness of development spending, more efficiently reaching poor or bypassed areas (Baker and Grosh, 1994, Bigman and Fofack, 2000, Elbers et al, 2004. Thus, these targeting approaches could be well suited to addressing the neglected rural areas that did not benefit from improvements in agricultural technology.…”

Section: Introductionmentioning

confidence: 99%

Methods, results and policy implications of poverty and food security mapping assessments

2005

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“…One option for focusing efforts on those at greatest risk is geographic targeting, defined as concentrating resources on groups from areas that have been ranked on some measure of risk 2 . In nutrition programming, geographic targeting is a widely used strategy and, in contrast to general subsidies, it has been shown 'to be effective at maximizing .…”

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confidence: 99%

Do childhood growth indicators in developing countries cluster? Implications for intervention strategies

Fenn

Morris

Frost

2004

Public Health Nutr.

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Objective: The effectiveness of geographic targeting in nutrition programmes depends largely on the degree to which malnutrition clusters within particular areas. This study investigates the extent to which the childhood nutrition indicators, stunting (heightfor-age Z-score ,22) and wasting (weight-for-height Z-score ,22), are spatially clustered; this information is used to determine the implications of spatial clustering for the effectiveness of geographic targeting. Design: Analysis of data from Demographic and Health Survey (DHS) results. Clustering is assessed by calculating intra-cluster correlation coefficients (ICCs). Estimating the proportion of malnourished children covered by a programme successfully targeting 10% of clusters with the highest malnutrition prevalences allows an assessment of the effectiveness of geographic targeting. Setting: Fifty-eight DHS III (1992III ( -1997 and DHS IV (1998 reports from 46 developing countries. Subjects: Pre-school children of mothers interviewed by DHS. Main results: The extent of clustering of nutritional status was surprisingly low (median ICC for national samples: stunting ¼ 0.054, wasting ¼ 0.032) and most countries were characterised by having an ICC , 0.1 -i.e. low clustering -for childhood undernutrition (91% of countries for wasting and 78% for stunting). Our assessment of the effectiveness of geographic targeting showed that coverage was better for wasting than for stunting; for wasting, 23% of countries would achieve less than 20% coverage, compared with 76% of countries achieving less than 20% coverage for stunting. Coverage is dependent on the overall prevalence of malnutrition and the ICC. Conclusions: Childhood nutritional status is determined at the household, or even individual, level; nutrition programmes that are geographically targeted may result in high levels of under-coverage and leakage, thereby compromising their costeffectiveness; the lack of clustering questions the appropriateness of current nutrition interventions.

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Poverty reduction through geographic targeting: How well does it work?

Cited by 120 publications

References 4 publications

Mapping poverty in rural China: how much does the environment matter?

Mapping poverty in rural China: how much does the environment matter?

Methods, results and policy implications of poverty and food security mapping assessments

Do childhood growth indicators in developing countries cluster? Implications for intervention strategies

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