Effect of organizational status on <scp>employment‐related</scp> corporate social responsibility: Evidence from a regression discontinuity approach
Bibliographic record
Abstract
Abstract Research Summary We examine the effect of organizational status on employment‐related corporate social responsibility (CSR). As employees derive nonpecuniary benefits from both organizational status and employment‐related CSR, lower status firms may invest in nonpecuniary employment‐related CSR to compete in a status‐segmented labor market. We identify the effect using a regression discontinuity design (RDD) in the context of the Fortune 1000 rankings, as we contend that the 500th rank position marks an artificial breakpoint in status where quality follows a smooth distribution. We find that firms just failing to make the Fortune 500 perform significantly better in nonpecuniary employment‐related CSR. Our findings provide causal evidence for the labor market advantage of organizational status and a richer window into the strategic motivations behind CSR investments. Managerial Summary We examine one strategic investment that lower status firms make to compete in a status‐segmented labor market: employment‐based corporate social responsibility (CSR). We identify the effect using a regression discontinuity design (RDD) in the context of the Fortune 1000 rankings, as we argue that the 500th rank position creates a discontinuity in status at a precise location where quality differences can be assumed to follow a smooth distribution. We find that firms just failing to make it into the Fortune 500 perform significantly better in nonpecuniary employment‐related CSR as compared to firms just in the Fortune 500. The findings demonstrate that building a reputation for being socially responsible may offset differences in status and make a lower status organization more appealing to employees.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".