Self-Regulation for Reputation-Sensitive Buyers: SA8000 in China
Bibliographic record
Abstract
Industries and firms have diverse motives for adopting self-regulatory institutions. This research develops and tests propositions about one motive—exploiting opportunities to do business with reputation-sensitive buyers—as distinct from self-regulation to defend against regulatory or activist threats. To study the adoption and effects of self-regulation for reputation-sensitive buyers, we study the SA8000 socially responsible employment certification among large firms in China in the early 2000s. Using official longitudinal industrial microdata, we test hypotheses generated by this assumed motive for self-regulation and find that (a) despite concerns about the corruptibility of certification bodies, SA8000 adopters in China exhibited higher precertification worker wages than comparable nonadopters, (b) self-regulation led to increased employment and sales to foreign markets, where reputation-sensitive buyers are concentrated, (c) the positive effect on exports was greater than the (insignificant, negatively signed) effect on domestic sales, and (d) there is no evidence that self-regulation increased worker wages beyond the initial high start. Contrasting these findings with prior research on industry self-regulation for other motives, this study highlights how both adoption patterns and downstream effects differ according to the audience for self-regulation. This paper was accepted by Olav Sorenson, organizations. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2020.01306 .
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".