Retooling Poverty Targeting Using Out-of-Sample Validation and Machine Learning
Bibliographic Data
| ID | 19912068 |
|---|---|
| Authors | Linden McBride (0000-0002-9475-7850), Austin Nichols (0000-0001-8076-1755), Austin Lee Nichols (0000-0003-4580-3301) |
| Year | 2016 |
| Publication date | 2016-10-01 |
| Open Access | Yes |
| Type | BOOK |
| Publisher | World Bank, Washington, DC (PUBLISHER) |
| DOI | 10.1596/1813-9450-7849 |
| OpenAlex | W3122354151 |
| Language | EN |
| Citations received | 1 |
| References cited | 2 |
Proxy means test (PMT) poverty targeting tools have become common tools for beneficiary targeting and poverty assessment where full means tests are costly. Currently popular estimation procedures for generating these tools prioritize minimization of in-sample prediction errors; however, the objective in generating such tools is out-of-sample prediction. This paper presents evidence that prioritizing minimal out-of-sample error, identified through cross-validation and stochastic ensemble methods, in PMT tool development can substantially improve the out-of-sample performance of these targeting tools. The USAID poverty assessment tool and base data are used for demonstration of these methods; however, the methods applied in this paper should be considered for PMT and other poverty-targeting tool development more broadly
Chromatography · Economic growth · Economics · Poverty · Sample (material) · Artificial Intelligence · Chemistry · Child Nutrition and Water Access · Computer Science · Income, Poverty, and Inequality · Poverty, Education, and Child Welfare
| Citations per year | 0.14 |
|---|---|
| Citation span | 2019 - 2019 (1) |
| Highly cited | No |