Becoming Part of the Solution: How Exporters from Emerging Markets Shift Toward Socially Responsible
Bibliographic record
Abstract
What explains the prevalence of socially irresponsible employment practices in emerging markets? How can private organizations – and especially multi-national corporations – drive change in the ways that their suppliers in emerging markets treat vulnerable employees? This study proposes an understanding of poor working conditions based on an emerging stream of stakeholder theory that emphasizes economic interdependencies. We categorize employment practices as either socially responsible or irresponsible based on a firm’s policies toward employees, and then model these practices as tied through incentive compatibilities to product-market strategies. We distinguish between two archetypes. First, premium strategies in which manufacturers invest in employee skills and improved working conditions. Second, efficiency strategies in which manufacturers minimize such investments and reduce costs by skirting labor regulations. We test the implications of this theory on a unique dataset linking working conditions and supplier performance in over four thousand exporters across the developing world. We first find that socially irresponsible employment practices are highly correlated with one another, suggesting they share a common cause. We then show that social irresponsibility is associated with poorer product quality, delayed order deliveries, and lower revenue per worker, consistent with the efficiency manufacturing strategy. The theory and findings suggest that interventions to change firms’ strategies of value creation may promote more socially responsible employment in emerging markets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".