Why Do Banks Favor Employee-Friendly Firms? A Stakeholder-Screening Perspective
Bibliographic record
Abstract
We investigate why employee-friendly firms often benefit from lower costs of debt financing. We theorize that banks use employee treatment as a screen to assess firms’ trustworthiness, which encompasses not only confidence in firms’ ability to perform well but also the belief that they will act with good intent toward their creditors. We integrate screening theory and stakeholder theory to explain the—oftentimes unintended—consequences that firms’ actions toward employees have on their relationships with other stakeholders. An analysis of U.S. firms between 2003 and 2010 shows that favorable employee treatment reduces the cost of bank loans, and this relationship is stronger when banks cannot infer firms’ intent from their relations with stakeholders other than employees. A policy-capturing study provides further support that employee treatment serves as a screen for intent. We discuss the implications of our stakeholder-screening perspective as a novel way to understand the second-order, unintended effects of a focal stakeholder relationship on firms’ relations with other stakeholders.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".