Vertical and horizontal mismatches in Thailand and the wage penalty
Bibliographic record
Abstract
The Thai labor market experiences increasing challenges due to disruptive technologies and demographic shifts. Furthermore, the COVID-19 pandemic outbreak has had a disruptive impact on Thailand's labor market, resulting in a loss of income and a reduction in working hours for workers. These effects are considered to be a cyclical factor of skill mismatch. This study aims to examine the incidence of vertical and horizontal mismatches and their impact on wages in Thailand before and during the COVID-19 pandemic by ordinary least squares, quantile regression, pooled ordinary least squares, and counterfactual decomposition, using data from the third quarter of 2018 to 2021 from Thailand's National Labor Force Survey. The findings suggest that the incidence of matched and overeducated workers continues to increase during the COVID-19 epidemic in comparison to the two previous years. While the proportion of undereducated workers remarkably decreases during the same period. Regarding horizontal mismatch, there is a marginal increase in field-of-study mismatch. Additionally, overeducated workers earn wage premiums, whereas undereducated and horizontally mismatched workers face wage penalties. The result also indicated that the COVID-19 pandemic has significant negative effects on overeducated workers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".