Impact Evaluation of A Grant Program for Postgraduate Studies Undertaken Abroad: Analysis of the Ecuadorian Case
Bibliographic record
Abstract
The National Secretary for Higher Education, Science, Technology and Innovation in Ecuador (locally SENESCYT) started an ambitious grants program in 2011. The main objective of the program was to send Ecuadorian students to undertake postgraduate studies at universities overseas. This article evaluates the impact of this grant policy on the labor income of the beneficiaries after they have completed their studies abroad and returned to Ecuador. Using a fixed-effects with lagged dependent variable model combined with a propensity score matching, we find a negative impact on the income of the grant holders during the first year following their return to the country. For the second year, the effect is non-significant. During the third year, the impact becomes significant and positive. The grant holders who returned having completed their postgraduate program abroad had a 9% higher employment income in the third year than those who did their postgraduate studies in Ecuador. The program's effect concentrates on women, the low-income group, and on those beneficiaries that studied in the USA.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".