Does an economic crisis deflate education bubble and inequality? Lessons from South Korea 1997–2020
Bibliographic record
Abstract
Rapid education expansion has been a main driver of the remarkable economic growth in South Korea for last decades. However, in recent times, its excessive education credentialism is considered a hurdle against further developments. This study examined whether education bubble and inequality decreased during the Asian Financial Crisis 1997–98, the Global Financial Crisis 2008–09, and the COVID‐19 pandemic 2020. It tracked quarterly distributional changes in private education expenditure of Korean households with Changes‐in‐Changes. The findings indicate that Korean households postponed private education expenditure cut in the first quarter of the crises to prevent their children from falling behind in severe education competition. Then, they temporarily downsized it in the second quarter. During the pandemic, vulnerable students experienced higher fluctuations in private education expenditure than they did in previous crises closely related to disproportionate effects of the pandemic on household income and consumption expenditure. Therefore, this study suggests more expansionary measures for disadvantaged students to recover from a learning loss and improving the public education system as a fundamental measure to mitigate severe private education dependency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".