Shifting Standards Due to Social Class? How Social Class Background Shapes CEO Career Outcomes
Bibliographic record
Abstract
This study investigates how social class background may lead to shifting standards of performance expectations that differentially affect the careers of CEOs from different social class backgrounds. On the one hand, we theorize that CEOs from lower-class backgrounds who have displayed considerable social mobility from positions of disadvantage are subject to greater board standards given their humble backgrounds. Thus, they will be rewarded with higher initial compensation and higher subsequent compensation when performance is exceptional. On the other hand, we posit that CEOs who come from higher-class backgrounds may be shielded from such board standards because of their similar rank to those of other executives. However, these differences in board standards may ultimately be a double-edged sword. Because stereotyped groups are subject to higher standards, instances of inability to meet this standard will be more swiftly penalized. We hypothesize that lower-class CEOs will be punished more harshly than their upper-class counterparts when performance is poor, resulting in lower compensation and a greater likelihood of dismissal. Using a novel dataset on the social class backgrounds of 790 CEOs over 4,925 firm years supplemented with board interviews, we find considerable support for our hypotheses.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".