Socially Irresponsible Employment in Emerging-Market Manufacturers
Bibliographic record
Abstract
Are socially irresponsible employment practices, such as abusive discipline and wage theft, systematically tied to manufacturing outcomes in emerging-market countries? Drawing on a stream of stakeholder theory that emphasizes economic interdependencies and insights from the fields of industrial relations and human resource management, we argue that working conditions within a firm are facets of a systemic approach to value creation and value appropriation. Some manufacturers operate “low road” systems that rest on harmful practices. Others operate “high road” systems in which the need to develop employees’ human capital deters socially irresponsible employment practices. To test the theory, we conduct a large-scale study of labor violations and manufacturing outcomes by analyzing data on over four thousand export-oriented small manufacturers in 48 emerging-market countries. The analysis demonstrates that socially irresponsible employment practices are associated with inferior firm-level manufacturing outcomes even after controlling for the effects of firm size, industry, product mix, production processes, host country, destination markets, and buyer mix. The theory and results suggest an opportunity for multinational corporations to improve corporate social performance in global value chains by encouraging their suppliers to transition to systems of value creation that rely on the development of worker human capital. Funding: A. McGahan received funding from Social Sciences and Humanities Research Council [Grant 435-2016-0075].
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".