The oil price plummeted in 2014–2015: Is there an effect on Chinese firms' labour investment?
Bibliographic record
Abstract
Abstract Using the exogenous event of oil price sharp decline in 2014–2015, this paper employs the difference‐in‐difference method to establish a causal link between the oil price decline and the Chinese firms' labour investment. Data of listed companies in China from 2012 to 2016 are used to explore this relationship. We show that the employment for firms in industries with significant negative oil price risk exposure increases 16.4% after the oil price plummeted, that is, the oil price decline significantly promotes the firms' labour force. Additionally, the positive effect of oil price decline on the firms' labour force is more pronounced in firms with higher risk‐taking, financing constraints, and industry competition. Lastly, we also document that the effect of oil price decline is through sales growth channels to increase labour demand. However, firms tend to overinvest in labour after the oil price plummeted. Based on these findings we suggest that oil price fluctuation should be an important factor for the Chinese government and enterprises when they make an economic decision related to the labour force.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".