First Mile of the Global Value Chain: Bringing Gender into GVC Governance
Bibliographic record
Abstract
Gender inequality has become a major challenge that has been reflected in the United Nations Sustainable Development Goals. This issue is becoming increasingly important to multinational corporation (MNC) managers responsible for the performance of their global value chains (GVC) as well as government policymakers and non-governmental organizations (NGO). One of the key ways to address gender inequality has been through various types of interventions, including public regulations, private standards, and certifications. However, it remains a theoretical and empirical question as to the conditions under which various interventions alleviate gender inequality through the empowerment of women. To address this gap, we build on institutional and social identity theories to establish a theoretical framework that allows us to understand the specific attributes of interventions intended to improve women’s empowerment. Our empirical study examines the effect of an intervention launched among artisanal mining communities—in the furthest reaches of a GVC when local institutions are weak and social relationships are fragile—using a longitudinal dataset collected from 1,777 individuals in six communities within Democratic Republic of Congo during the period 2017-2018. We find that the specific intervention (i.e., village saving and loan association) has significantly positive impact on workplace-related women’s empowerment (i.e., perception of equal payment and capabilities), but there is no significant impact on household-related women’s empowerment (i.e., equal access to household decision-making and attitudes towards domestic violence). Our analysis offers novel insights into the outcomes of interventions intended to lead to social upgrading of women within the first mile of the GVC.
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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 | 0.005 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".