Rolando Gonzales Martinez, Mariza Cooray
arXiv 6 Mar 2025 · Econometrics
arXiv:2503.04300 · PDF · DOI · OpenAlex · Extracted main text
This study leverages spatial machine learning (SML) to enhance the accuracy of Proxy Means Testing (PMT) for poverty targeting in Indonesia. Conventional PMT methodologies are prone to exclusion and inclusion errors due to their inability to account for spatial dependencies and regional heterogeneity. By integrating spatial contiguity matrices, SML models mitigate these limitations, facilitating a more precise identification and comparison of geographical poverty clusters. Utilizing household survey data from the Social Welfare Integrated Data Survey (DTKS) for the periods 2016 to 2020 and 2016 to 2021, this study examines spatial patterns in income distribution and delineates poverty clusters at both provincial and district levels. Empirical findings indicate that the proposed SML approach reduces exclusion errors from 28% to 20% compared to standard machine learning models, underscoring the critical role of spatial analysis in refining machine learning-based poverty targeting. These results highlight the potential of SML to inform the design of more equitable and effective social protection policies, particularly in geographically diverse contexts. Future research can explore the applicability of spatiotemporal models and assess the generalizability of SML approaches across varying socio-economic settings.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | L. McBride and A. Nichols (2018) Retooling poverty targeting using out-of-sample validation and machine learning | 0.511 | 2 | 1 | 100% |
| 2 | P. Schnitzer and Q. Stoeffler (2023) Targeting social safety nets: Evidence from nine programs in the sahel | 0.511 | 2 | 1 | 100% |
| 3 | M. Solís-Salazar and J. Madrigal-Sanabria (2022) A machine learning proposal to predict poverty | 0.511 | 2 | 1 | 100% |
| 4 | E. Aiken, S. Bellue, D. Karlan, C. Udry, and J. E. Blumenstock (2022) Machine learning and phone data can improve targeting of humanitarian aid | 0.405 | 1 | 1 | 100% |
| 5 | V. Alatas, A. Banerjee, R. Hanna, B. A. Olken, and J. Tobias (2012) Targeting the poor: evidence from a field experiment in indonesia | 0.405 | 1 | 1 | 100% |
| 6 | S. Alkire and J. Foster (2011) Counting and multidimensional poverty measurement | 0.405 | 1 | 1 | 100% |
| 7 | C. Brown, M. Ravallion, and D. Van de Walle (2018) A poor means test? econometric targeting in africa | 0.405 | 1 | 1 | 100% |
| 8 | D. Coady, M. E. Grosh, and J. Hoddinott (2004) Targeting of transfers in developing countries: Review of lessons and experience | 0.405 | 1 | 1 | 100% |
| 9 | P. Corral, H. Henderson, and S. Segovia (2025) Poverty mapping in the age of machine learning | 0.405 | 1 | 1 | 100% |
| 10 | S. Dietrich, D. Malerba, and F. Gassmann (2023) Predicting social assistance beneficiaries: On the social welfare damage of data biases | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 25 scored citations.