Left Partisanship and Top Management Pay in Affluent Capitalist Democracies
Bibliographic record
Abstract
Abstract Complementing the existing partisanship and income distribution literature that focuses on the earnings of all employees, this paper examines the effect of left government partisanship on top managers. Drawing on firm-level top executive compensation data across thirteen advanced industrialized countries, the paper shows that left government partisanship principally leads to lower CEO compensation, either through laws that enhance workers’ collective bargaining power vis-à-vis management or laws that allow shareholders to cast proxy votes on executive compensation (i.e., say-on-pay laws). Furthermore, I show that left government partisanship reduces CEO pay more heavily in those firms that more strongly favor labor’s competing stakeholders. In particular, left partisanship reduces executive compensation more heavily in firms that set aside more revenue for shareholders, managers, or creditors, that is, firms with higher asset return, stock return, or debt. These findings highlight how the macro-politics of rising top income inequality at the national level interacts with the micro-distributive conflicts at the firm level. In particular, financialization, such as the rise of the shareholder value revolution, may accentuate the impact of partisan politics against top income inequality.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".