Returns to Investment in University Education - Economics Career at Continental University
Bibliographic record
Abstract
The present work analyzes the returns to the years of superior schooling of graduates of Economics Career at Continental University within the labor market of the region Junín of the period 2019. For this purpose, the returns to education are investigated under the normal assumptions of Mincer's equation, and later the incorporation of the instrumental variable: school of origin is proposed, in order to correct the problem of endogeneity. Finally, to correct the problem of selection bias, Heckman's technique is used: two-stage regression. This consists of first analyzing the probability of accessing the labor market in the Junín region in terms of variables such as: geographic location, school of origin, age, direct costs. Subsequently, analyzing the return to years of schooling. Likewise, it is important to specify that in the modeling of the probability a second regression is estimated incorporating the variable Academic Grade in order to be able to study the Sheepskin Effect. The results obtained showed that the return to years of schooling is 0.8%, which is not significant and is not corrected for Heckman's selection bias. We also have that the R2 is 10.11% which is very low for this type of cross-sectional data. This result is explained by the degree of rootedness of the graduates in staying in the Huancayo province and the low migration to other labor markets. In addition, this means that they do not have better working conditions that can be transformed into higher income.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".