Politics and Volatility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We investigate how politics (party orientation, national elections, and strength of democratic institutions) affect stock market volatility. We hypothesize that labor-intensive industries, industries with larger exposure to foreign trade, industries whose operations require efficient contracts, and industries susceptible to government expropriation are more sensitive to changes in political environment. Using a large panel of industry-country-year observations, we show that politically-sensitive industries exhibit higher volatilities during national elections. Volatility is also higher for labor-intensive industries under leftist governments. Moreover, governance-sensitive industries and industries under a higher risk of expropriation are more volatile when democratic institutions are weak. The rise in volatility is driven largely by systematic risk rather than firm-specific risk. The results are consistent with the 'peso problem' hypothesis that uncertainty about future government policies can increase stock market volatility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 it