Economic Policy Uncertainty, Industrial Intelligence, and Firms’ Labour Productivity: Empirical Evidence from China
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we empirically explore the impact of uncertainty in economic policy and industrial intelligence on firms’ labor productivity, as well as the possible methods and mechanisms of influence. After theoretical inference, we employ regression models with sample data collected from A-share companies in the manufacturing industry listed on the Shanghai and Shenzhen stock exchanges between 2007 and 2019. We find that firms’ labor productivity experiences a significant decrease under economic policy uncertainty. However, the negative effect of economic policy uncertainty shocks on labor productivity in regions with high industrial intelligence levels is effectively mitigated. These differential changes in the impact of economic policy uncertainty shock on labor productivity between areas with high and low industrial intelligence levels are found primarily for firms in high-technology and highly specialized sectors, sectors with strong financial constraints. Besides, we perform further analysis which indicates that the upgrading of human capital operates as an essential channel for economic policy uncertainty shocks and industrial intelligence to affect firms’ labor productivity. Overall, our findings illustrate that implementing economic policies in a stable and transparent way and developing intelligent technology can improve firms’ labor productivity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it