Date of Submission

Spring 2024

Academic Program

Economics

Project Advisor 1

Gautam Sethi

Project Advisor 2

Sanjay DeSilva

Abstract/Artist's Statement

Addressing poverty in developing countries without relying on income data is becoming increasingly important, particularly since the Covid-19 pandemic has exacerbated the issue farther than anyone expected. This paper reviews literature on various poverty targeting models including Proxy Means Test, Community-Based Targeting, hybrid models, and machine learning-based models in hopes of finding the best method. The findings highlight the importance of model parameters, particularly in PMT, also revealing that the number of potential beneficiaries analyzed and number of indicators utilized can influence the targeting accuracy. CBT incorporates community involvement in the poverty targeting process at a lower cost than PMT, despite slightly lower accuracies. Hybrid models contain both benefits and problems with PMT and CBT, with many authors finding mixed results. The recent emergence of ML-based PMT models has shown promising results, particularly when applied to the feature selection phase. With targeting accuracies averaging 10% higher than PMT and cost efficiencies rivaling those from CBT, ML-based models might be the future of poverty targeting in developing countries.

Open Access Agreement

Open Access

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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