Are those attending preparatory classes more sensitive to wages than those attending university?
Bibliographic record
Abstract
This paper examines how students decide whether to enrol in university or attend preparatory classes after leaving high school.It is to my knowledge the first paper to investigate whether students attending preparatory classes are more sensitive to expected wages than those attending university.To tackle this question, I first provide a theoretical framework that incorporates both monetary and non-monetary elements in the value function of agents.Then, by structurally estimating the dynamic model, I find that students are sensitive to expected wages when deciding to enrol in higher education.Furthermore, my results suggest that the probability of finding a job upon graduation from business or engineering school significantly increases the likelihood of students entering higher education rather than the likelihood to enter the labour market directly after high school.Nevertheless, the choice between attending preparatory classes or university remains largely driven by intrinsic student preferences.Simulations show that changing the probability of passing the competitive exam to attend a business or engineering school changes the college decision of students and can lead to unanticipated overcrowding in university-based master's programs.This type of simulation is of particular interest as there is a considerable heterogeneity in the annual cost to the government of a student attending preparatory classes or a student attending university.* Délégation générale à l'emploi et à la formation professionnelle (DGEFP). 1Working Paper n o 31 • Are those attending preparatory classes more sensitive to wages than those attending university?La collection Working Paper publie des textes pour engager le débat avec d'autres chercheur.e.s.La publication n'engage que l'auteur.
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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 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".