Journal article
Who should be served first? Multidimensional prioritization in a Bolivian cash transfer program
- Abstract:
- Social protection programs in developing countries often rely on broad eligibility criteria that overlook the heterogeneity of beneficiaries’ deprivation profiles. We propose an ex-ante microsimulation framework to evaluate the efficiency of conditional cash transfer (CCT) allocation rules in reducing multidimensional poverty. Applied to Bolivia’s flagship CCT, Bono Juancito Pinto, using 2022 household survey data, we compare the current, uniform allocation rule against counterfactual prioritarian rules that concentrate resources first on households with the highest burden of overlapping deprivations. BJP has the explicit aim to reduce poverty in the long run, and we show that while it is generally effective, its impact can be considerably amplified under prioritarian rules. This efficiency is driven by a poverty reduction mechanism that resolves program targeted outcomes among the most deprived and may pull some of these households out of multidimensional poverty. While our framework takes the program’s eligibility criteria and associated exclusion errors as given, we demonstrate that a multidimensional lens for resource allocation can mitigate the fiscal inefficiencies of inclusion errors.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 709.5KB, Terms of use)
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- Publisher copy:
- 10.1016/j.worlddev.2026.107434
Authors
- Publisher:
- Elsevier
- Journal:
- World Development More from this journal
- Volume:
- 204
- Article number:
- 107434
- Publication date:
- 2026-04-22
- Acceptance date:
- 2026-04-12
- DOI:
- EISSN:
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1873-5991
- ISSN:
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0305-750X
- Language:
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English
- Keywords:
- Pubs id:
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2411066
- Local pid:
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pubs:2411066
- Deposit date:
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2026-04-24
- ARK identifier:
Terms of use
- Copyright holder:
- Elsevier Ltd.
- Copyright date:
- 2026
- Rights statement:
- © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
- Notes:
- The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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