The source of economic shocks matters for their political outcomes
Bibliographic record
Abstract
Do different economic shocks favor different types of political leadership? Using a survey experiment conducted on 3500 American respondents, we offer causal evidence for an unexpected relation between different types of economic shocks and a demand for a radical political response. First, we show that individuals believe politicians have a role in preventing layoffs due to both offshoring and automation, compared to run-of-the-mill bankruptcies. Second, we find strong evidence that economic shocks due to offshoring lead to greater demands for leaders who display authoritarian traits, with no equivalent effect for layoffs due to automation. When presented with news of an offshoring event, respondents favor leaders who claim to be more willing to flout the rule of law, to implement divisive policies, and to employ force. By contrast, traits commonly associated with populism, such as siding with “the people” or preferring political outsiders, see no association with either offshoring or automation shocks. Our findings support the view that some economic shocks provide a greater opportunity for political candidates willing to turn to radical political solutions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".