From Economic to Political Power: Economic Elites and Policymaking During Times of Crisis
Bibliographic record
Abstract
The shared crisis brought on by COVID-19 offers an opportunity to study how economic elites attempt to shape policy responses. In this article, we inquire about the conditions under which economic elites shaped containment and business support measures in Latin America. We argue that wealthier and better-organised elites are more likely to shape policies because they have increased access to policymakers. To test this, we combine regression analysis with three case studies: Chile, Mexico, and Peru. Our quantitative findings align with our expectations regarding containment measures and present mixed results for pro-business policies. Case studies illustrate how elites attempted to influence policy, highlighting the centrality of access to the Executive and the importance of distinguishing between institutionalised or personalised access. The degree to which policy responses aligned with elite preferences varied according to the nature of the ties: ranging from the most alignment in Chile to the least in Peru.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".