Impacts of Typhoons on Local Labor Markets based on GMM: An Empirical Study of Guangdong Province, China
Bibliographic record
Abstract
Abstract What impacts do typhoons have on local labor markets? Few empirical researches have been conducted in China. By collecting the data of 23 quarters (3-month intervals) of Guangdong province from 2009 to 2014 and using the generalized method of moments (GMM), this paper analyzes the impacts of typhoons on labor markets from the perspectives of general effect, regional effect, intensity effect, and time effect. In addition, a comparative analysis is carried out between this study and similar studies of developed countries. The results show that 1) massive typhoons resulted in a 12.5% increase in employment but did not have a significant impact on Guangdong’s per capita employee remuneration, and 2) there are periodic features to typhoons’ impacts on employment. Typhoons influence employment in a four-quarter cycle. In the quarter affected by a typhoon, the first quarter, the number of employees increased by 17.4%. The quantity of labor employed in the subsequent two quarters shows no significant change. In the last quarter, the number of employed people decreases by 17.0%, which returns to predisaster levels. Additionally, 3) the results of this study are different from those of studies involving developed countries, which may be caused by the distinctiveness of China’s labor market. Finally, conclusions and corresponding suggestions are presented.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".