Do financial constraints affect the CEO stock options remuneration? Evidence from a panel threshold model
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the nonlinear relationship between financial constraints and the chief executive officer (CEO) stock options compensation and to analyze whether the impact of financial constraints on the CEO stock options compensation changes at certain level of financial constraints or not. Design/methodology/approach This study is based on a sample of 90 French firms for the period extending from 2008 to 2019. To deal with the non-linearity, the authors use a panel threshold method. Findings Using different measures of financial constraints [KZ index (Baker et al., 2003), SA index (Hadlock and Pierce, 2010) and FCP index (Schauer et al., 2019)], the results reveal that the impact of the financial constraints (SA index and FCP index) is positive below the threshold value and it becomes negative above. Research limitations/implications The non-linearity between financial constraints and CEO stock options shows that the level of financial constraints can be a major determinant of the CEO compensation structure. More specifically, this study sheds light on the key role played by the level of financial constraints and how this latter influence management decisions. Originality/value This paper is the first to the best of the authors' knowledge to examine the nonlinear relationship between financial constraints and the CEO stock options compensation using a panel threshold model.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".