Social Security Schemes, Economic Crisis, and Child Education: An Empirical Study During the <scp>COVID</scp> ‐19 Pandemic in Thailand
Bibliographic record
Abstract
ABSTRACT This study first examined the function of social security schemes in moderating the adverse effects on child education of unemployment among household members. Using data from the 2019 non‐crisis period and the 2021 crisis period from the Thailand Socio‐Economic Survey, two‐step estimation models were employed due to the presence of a large number of censored observations. In addition, the validity of the results was supported by utilizing an instrumental variable approach to address the possibility of an endogeneity problem. Of greater importance, the negative effects of unemployment were moderated when unemployed household members were covered by social security schemes. The results show that the negative impact of unemployment on educational spending was significantly larger during the 2021 crisis period compared to the 2019 non‐crisis period. To further verify the mitigating role of SSS, the robustness of the results was also checked by including an interaction term between unemployment and SSS in two‐stage decision models, along with additional control variables.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".