What Jobs Do Workers Want? Worker Preferences in Global Value Chains
Bibliographic record
Abstract
ABSTRACT What do workers in global value chains (GVCs) consider a good job? Research on work in GVCs largely studies compliance with standards set from the top‐down rather than worker preferences. Understanding worker preferences is crucial because workers are the intended beneficiaries of governance institutions; policies better aligned to their preferences could enjoy greater legitimacy and effectiveness. To explore what GVC workers want, we embedded an experiment in an original survey of 2500 workers in 50 formal Moroccan apparel factories integrated into GVCs. These workers highly valued jobs with characteristics that reduced their exposure to physical risks and provided job stability. They also had strong preferences for jobs with supervisors that treated workers respectfully. By contrast, worker preferences for wage increases available in the Moroccan context were weak, especially when compared with non‐wage features. Our results suggests that when wages are compressed and extended working hours is the main way to increase earnings, preferences for non‐wage attributes may dominate. We also find more similarities than differences between men and women workers; although women place greater value on respectful supervision, safety and childcare benefits, their overall preferences closely track those of men. These findings provide novel evidence to inform efforts to improve jobs in GVCs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".