The global geopolitical-energy uncertainty index and total factor productivity: New evidence from firm-level analysis
Bibliographic record
Abstract
This paper examines the impact of the global geopolitical-energy uncertainty (GEU) on firm-level total factor productivity, considering variation across countries, industries, and firm sizes. Employing the novel GEU index proposed by Dang et al. (2024a) and firm-level annual data from 2001 to 2023, we find strong evidence that the GEU index negatively affects firm productivity. There is heterogeneity in the GEU index's impact. Firms in developed countries such as the US, UK, France, and Germany are more negatively affected, whereas Canadian firms show a positive response. Energy-intensive firms and smaller firms experience stronger negative impacts. Mechanism analysis further demonstrates that both firm level characteristics and macroeconomic energy conditions shape productivity responses to GEU. Higher profitability reduces the negative impact of GEU shocks, while higher cost intensity and higher global energy prices amplify the adverse effects, increasing productivity losses. Our baseline results remain robust under different robustness checks. The paper's findings offer guidance for firms to develop effective strategies to manage risks during periods of heightened geopolitical-energy uncertainty.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".