Analyzing Upper Secondary Education Dropout in Latin America through a Cohort Approach
Bibliographic record
Abstract
This study examines recent trends and factors in school dropout at the upper secondary education level across Latin America. The methodology employs repeated cross sections of data to track the life cycle path of cohorts of individuals in 18 countries. A key finding is that while upper secondary enrollment rates increased in the region, dropout has remained persistently high, despite relatively favorable macroeconomic conditions. To explain dropout trends, the study examines the impact of three groups of factors: (i) shifts in the cohort size and socioeconomic composition of the population eligible for entering upper secondary; (b) the macroeconomic environment and labor market opportunities; and (c) the returns to schooling. We show that an important factor in persistently high dropout rates has been the higher numbers of students from poor socioeconomic backgrounds reaching upper secondary. In addition, high returns to education have been a pull factor into schooling, while, especially in countries where the majority of youth dropout prior to upper secondary, the data confirm an apparent substitution effect due to the opportunity cost of forgoing employment opportunities. The findings confirm the growing policy focus on upper secondary across Latin America and suggest implications for the policy agenda.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".