Estimación del impacto del programa de transferencias condicionadas “Avancemos” sobre la educación
Bibliographic record
Abstract
We evaluate the impact of the conditional cash transfer program 'Avancemos' on enrollment in formal secondary education among students aged 12 to 19 in Costa Rica. Using panel data from the National Household Survey from 2015 to 2018, we employed a fixed-effect regression model to eliminate sources of bias related to differences in students' characteristics, that do not vary over time. We find a positive impacts of 16 percentage points on attendance. These results were robust to changes in the periods of analysis and when using a random-effect model. In addition, we found greater effects in males, rural areas, and grades 7th-9th. Additionally, the effects of Avancemos in reintegrating students are higher than in retention. These results provide evidence about the positive effects of the Avancemos program on education and underscore the opportunities that exist to increase its impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".