Bibliographic record
Abstract
Local conditions in developing countries have long played a part in determining whether their small-scale firms can benefit from deepening their participation in global value chains (GVCs). Institutional theory allows us to characterize these local conditions not simply as particularistic oddities but rather as elements of an institutional matrix that affects the livelihoods of chain participants. However, the institutional dimension of GVC analysis has been traditionally neglected in the literature, to the detriment of our understanding of the impacts of upgrading in GVCs. This study aims to remedy this failure by illustrating how institutional context mediates between value chain upgrading and the livelihoods of chain participants. It particular, it seeks to elucidate how value chain upgrading spurs a process of change in the institutions that govern the livelihoods of suppliers in developing countries. This examination sheds light on the more general question of how value chain upgrading sometimes helps, but sometimes hurts, the welfare of chain participants. This theoretical contribution to the value chain literature is based upon an institutional analysis of primary qualitative data from more than 80 small-scale tea farmers in Nepal, some of whom had upgraded from conventional to organically certified production. Our study finds that value chain upgrading launches a process of institutional change that can be summarized in a general typology. The typology highlights how rules, strategies, organizations, and informal norms affect whether a given upgrading intervention yields livelihood benefits in a particular place. Upgrading can yield positive impacts in chain-linked livelihood dimensions, such as price, and yet induce negative changes in other livelihood dimensions, such as risk, and thereby yield overall adverse livelihood implications, in a process we dub immiserizing upgrading. These findings contribute to advancing the conceptual literature on global value chains (GVCs) by suggesting a general typology for cycles of institutional change that influence livelihood outcomes. The typology provides a framework to analyze such processes that is also of use to development practitioners seeking to understand the conditions under which upgrading worsens or improves the welfare of value chain participants. The research findings provide an interesting window into how certification schemes interact with the daily lives of the rural poor.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".