Are Returns to Education on the Decline in Venezuela and Does Mission Sucre Have a Role to Play
Bibliographic record
Abstract
There is anecdotal evidence that the standard of living for the educated has fallen in Venezuela over the last few years. This evidence comes as a surprise because after experiencing an economic downturn in 2002 and 2003, Venezuela's economy has boomed (gross domestic product growth has hovered between 8 and 18%) in large part due to the increase in the price of petroleum. In this paper, we provide evidence that returns to education have decreased significantly in Venezuela from 2002 to 2008. More importantly, we focus on what has led to the decrease in returns. We explore a fall in quality and a supply-demand argument for this decline. Mission Sucre was enacted in September 2003 by President Hugo Chavez to provide free mass tertiary education, in particular targeting the poor and marginalized. The implementation of this program created a sudden increase in the supply of skilled labor and had a direct impact on quality of education. Although we do not claim that 100% of the decline between 2002 and 2008 can be linked to this program, we provide ample evidence that a good part of the falling returns can be linked to Mission Sucre. Specifically, we show that for a 1% increase in the share of Mission Sucre students in the state, returns to university level of education declined by about 5.6 percentage points between 2007 and 2008.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".