The structure of business preferences and Eurozone crisis policies
Bibliographic record
Abstract
Abstract What explains business views regarding policy preferences in the Eurozone crisis? Although recent literature examines the impact of the crisis on citizen views, few studies examine business preferences towards adjustment policies. We present unique data from a new representative survey of 500 high-level firm representatives from Spain to test theories about such preferences, in particular views about the euro, fiscal austerity, and wage devaluation, as well as plausible mechanisms for such preferences. We test three broad families of theories to explain such preferences, focusing on the role of structural firm characteristics, economic hardship, and political leanings of firm managers. We find that first, there is a strong conservative position regarding all of these policies. Second, we find that contra conventional approaches to explaining preferences, for the domestic policies (but not for euro views), the political leanings of firms matter much more than baseline structural characteristics. Third, we find that surprisingly economic hardship does not cause firms to demand more left-wing policies, as it might for voters; in fact, firms that have suffered are likely to be more skeptical of such measures. These findings indicate the need to better measure political orientations of firm respondents and suggest that this is a larger division among firms than previously recognized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".