Cooperation and punishment in managing social performance: Labor standards in the Gap Inc. supply chain
Bibliographic record
Abstract
Abstract Research Summary Corporate social performance depends not only on a firm's behavior but also on the behavior of its suppliers. What management strategies improve the social performance of suppliers? Scholarship on inter‐firm relations and regulatory governance debates the efficacy of threatening to penalize suppliers, compared with more cooperative approaches. This study uses a regression discontinuity design to estimate the causal effects of typical actions to manage supplier social performance, both with and without threatened penalties. Suppliers improved social performance—increasing their probability of passing labor audits by 22 percentage points—only when regulatory actions included a threatened penalty: to discontinue business. Suppliers improved most in response to threatened penalties when they faced higher levels of supply chain competition or were engaged in longer‐term commercial relationships with the buyer. Managerial Summary How can multinationals improve labor standards in their suppliers around the world? We compared two approaches at the clothing retailer Gap Inc. When Gap did not threaten to discontinue business with low‐performing suppliers, we found no improvement in labor compliance when Gap issued failing compliance grades. However, once Gap began threatening to discontinue business with its lowest‐compliance suppliers, failing suppliers showed marked improvement in labor compliance. Failing suppliers improved most when (a) they faced high competition within their product category, and (b) when they were in longer‐term commercial relationships with the buyer. Our findings suggest buyers should use a combination of both threats and cooperation with suppliers to improve labor standards in global supply chains.
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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 | 0.005 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".