Geographic Targeting and Normative Frames: Revisiting the Equity of Conditional Cash Transfer Program Distribution in Bolivia, Colombia, Ecuador, and Peru
Bibliographic record
Abstract
BACKGROUND: Four Andean countries of Bolivia, Colombia, Ecuador, and Peru introduced national health-focused conditional cash transfer (CCT) programs in the 2000s. This study probes whether policymakers in these countries targeted CCT programs to subregions with the highest prevalence of ill-health or those with the lowest socioeconomic status (SES) to evaluate the equity of geographic targeting and means-testing, as well as the potential role of normative frames, bounded rationality, and clientelism as explanatory mechanisms for inequities in social spending. METHODS: The distribution of vaccination coverage, underweight, stunting, and child deaths is established both within and between subnational regions and SES quintiles from 1998 to 2012 using every available nationally representative household survey. The equity of CCT program targeting and strength of association with subregional SES and health outcomes are measured using generalized entropy index decomposition and meta-regression. Finally, simple predictive models for CCT targeting are created using lagged subregional SES, health outcomes, and concentration indices. RESULTS: Bolivia and Peru both effectively targeted at-risk subregions, but subregions in Peru with no CCT program coverage result in higher mistargeting rates for the country as a whole. Only Bolivia failed to attain CCT coverage concentration indices that are at least as large as the health inequalities they are targeting. Despite this insufficient progressivity, Bolivia has the most efficient subregional targeting, while the lowest rates of mistargeting for child deaths are found in Colombia and Ecuador. Finally, the simple predictive model performs as well or better than observed CCT coverage distribution for every country, year, and outcome. CONCLUSIONS: Both Peru and Ecuador have targeted programs to their poorest populations effectively, demonstrating that this is possible with both universal and geographic targeting. No clear evidence of clientelism was found, while the dominant normative frame underlying CCT program targeting decisions appears to be the relative SES of subregions, rather than absolute SES, prevalence of health outcomes, or health inequalities. To reduce the inequitable impacts of bounded rationality, policymakers can use simple predictive models to target CCT coverage effectively and without leaving behind the most vulnerable populations that happen to live in more affluent subregions.
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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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".