Estimating Poverty Rates in Target Populations: An Assessment of the Simple Poverty Scorecard and Alternative Approaches

Diamond, Alexis; Gill, Michael; Dellepiane, Miguel Angel Rebolledo; Skoufias, Emmanuel; Vinha, Katja; Xu, Yiqing

doi:10.1596/1813-9450-7793

Cited by 16 publications

(13 citation statements)

References 10 publications

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“…Schreiner (2007), Schreiner (2008)) emphasizes interpretability and ease-of-use, in addition to out-of-sample performance. However, as Diamond et al (2016) argue, his approach is not based on well-understood statistical methods and, while it does well on average, its out-of-sample performance is less satisfactory for different parts of a country. Kshirsagar et al (2017) combine penalized (regularized) regressions with sub-sampling (stability selection) to develop monetary poverty measurement tools that are both interpretable and relevant across a diverse country.…”

Section: Pragmatic Drought-contingent Targeting Strategymentioning

confidence: 99%

Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data

Báez¹,

Kshirsagar²,

Skoufias³

2019

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The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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Section: Pragmatic Drought-contingent Targeting Strategymentioning

confidence: 99%

Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data

Báez¹,

Kshirsagar²,

Skoufias³

2019

Self Cite

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“…See also Diamond et al (2016) for a careful comparison of the poverty score card-a variant of proxy means testing-and other (regressionbased) poverty imputation methods. 12 The first goal of the SDGs is to eliminate extreme poverty and reduce national poverty levels at least by half by 2030.…”

Section: Poverty Estimates For Tracking Trends At the National Lementioning

confidence: 99%

To Impute or not to Impute? A Review of Alternative Poverty Estimation Methods in the Context of Unavailable Consumption Data

Dang¹

2018

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractThere is an increasingly stronger demand for more frequent and accurate poverty estimates, despite the oftentimes unavailable household consumption data. We offer a review of alternative imputation methods that have been employed to provide poverty estimates in such contexts. These range from estimates on a nonmonetary basis, estimates for specific project targeting or tracking trends at the national level, to estimates at a more disaggregated level, as well as estimates of poverty dynamics. We provide a concise and accessible synthesis, which serves as an introduction to the literature. Our focus is on intuition and practical insights that highlight the nuanced differences between the existing methods rather than technical aspects.

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“…SPS and PAT have been applied to around 60 and 40 countries, respectively. A recent evaluation can be found in Diamond et al (2015). To the best of our knowledge, no cost-precision assessments are available for any of these initiatives.…”

Section: Introductionmentioning

confidence: 99%

Is Predicted Data a Viable Alternative to Real Data?

Fujii

Weide

2019

The World Bank Economic Review

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It is costly to collect the household- and individual-level data that underlie official estimates of poverty and health. For this reason, developing countries often do not have the budget to update estimates of poverty and health regularly, even though these estimates are most needed there. One way to reduce the financial burden is to substitute some of the real data with predicted data by means of double sampling, where the expensive outcome variable is collected for a subsample and its predictors for all. This study finds that double sampling yields only modest reductions in financial costs when imposing a statistical precision constraint in a wide range of realistic empirical settings. There are circumstances in which the gains can be more substantial, but these denote the exception rather than the rule. The recommendation is to rely on real data whenever there is a need for new data and to use prediction estimators to leverage existing data.

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Estimating Poverty Rates in Target Populations: An Assessment of the Simple Poverty Scorecard and Alternative Approaches

Cited by 16 publications

References 10 publications

Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data

Adaptive Safety Nets for Rural Africa: Drought-Sensitive Targeting with Sparse Data

To Impute or not to Impute? A Review of Alternative Poverty Estimation Methods in the Context of Unavailable Consumption Data

Is Predicted Data a Viable Alternative to Real Data?

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