Multidimensional Targeting and Evaluation: A General Framework with an Application to a Poverty Program in Bangladesh
Bibliographic record
Abstract
Many poverty, safety net, training, and other social programs utilize multiple screening criteria to determine eligibility. We apply recent advances in multidimensional measurement analysis to develop a straightforward method for summarizing changes in groups of eligibility (screening) indicators, which have appropriate properties. We show how this impact can differ across participants with differing numbers of initial deprivations. We also examine impacts on other specially designed multidimensional poverty measures (and their components) that address key participant deficits. We apply our methods to a BRAC ultra-poverty program in Bangladesh, and find that our measures of multidimensional poverty have fallen significantly for participants. This improvement is most associated with better food security and with acquisition of basic assets (though this does not mean that the cause of poverty reduction was program activities focused directly on these deficits). In general, we find that the BRAC program had a greater impact on reducing multidimensional poverty for those with a larger initial number of deprivations. We also showed how evaluation evidence can be used to help improve the selection of eligibility characteristics of potential participants.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.082 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".