Returns to Investment in Distance Learning: the Case of Greece
Bibliographic record
Abstract
In this paper we extent the literature on the rate of return to investment in Higher Education towards studies in distance learning Universities. In particular, we explore the difference in returns between graduates of a distance learning university (the Hellenic Open University - HOU) and applicants that were excluded by this university’s random selection process and did not study elsewhere. The data set was extracted from a database compiled from responses to a questionnaire which was part of a survey concerning HOU (the only Distance-learning University in Greece). A modified Mincer type model was estimated with fixed effects. Our findings suggest that graduates that have obtained a first degree from HOU enjoy a rate of return to education of about 8% higher than the rate of return obtained by those high school graduates that were not selected by this university. Moreover, Master’s degree graduates get about a 16.5% higher rate of return to education relatively to those applicants that were not selected for studies in HOU and did not study elsewhere. Additionally, our findings also show that the rates of return for higher education are high even after the 2008 economic crisis. These results suggest a straightforward policy implication: a distance learning University may not only be considered as a second chance to education for mature students, often facing time and budget restrictions, but, it may also be seen as a worthwhile private investment enabling a much higher private return. Moreover, from policymakers’ point of view, a distance learning university can be seen as a vehicle to reduce income inequalities and thus increase social mobility.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".