Does nepotism run in the family? <scp>CEO</scp> pay and <scp>pay‐performance</scp> sensitivity in Indian family firms
Bibliographic record
Abstract
Abstract Research Summary Using a principal–principal agency theory lens, we examine corporate governance and compensation design in family‐owned businesses. We conceptualize how CEO pay and pay‐performance sensitivity is influenced by whether the CEO is a professional or drawn from the controlling family (family CEO). Data from a sample of 277 publicly listed Indian family firms during 2004–2013 support our argument that family CEOs get paid more than professional CEOs. This pattern is stronger in superior‐performing firms that are named after the controlling family (eponymous firms). Furthermore, family CEOs of superior‐performing firms have higher pay‐performance sensitivity compared to professional CEOs of other superior‐performing firms. Our findings reveal nuanced heterogeneity in nepotism in emerging economy family firms—CEO compensation is a mechanism for some controlling families to tunnel corporate resources. Managerial Summary We examine whether CEO compensation and its responsiveness to realized firm performance in Indian family firms in influenced by whether the CEO is a professional or drawn from the controlling family (family CEO). Data from a sample of 277 publicly listed Indian family firms during 2004–2013 suggests family CEOs get paid more than professional CEOs. This pattern is stronger in superior‐performing firms that are named after the controlling family (eponymous firms). Furthermore, family CEOs' high compensation is unaffected by poor firm performance and is disproportionately boosted by superior firm performance. These results suggest that poor corporate governance allows some family controlled Indian firms to use CEO compensation as a mechanism to tunnel corporate resources in ways that hurt minority shareholders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".