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Record W4283331653 · doi:10.1111/rode.12906

Profit sharing, industrial upgrading, and global supply chains: Theory and evidence

2022· article· en· W4283331653 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Development Economics · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsProfitability indexSupply chainEconomicsMicroeconomicsProfit (economics)Industrial organizationProfit sharingEconometricsElasticity (physics)Production (economics)Empirical researchBusinessMathematicsMarketing

Abstract

fetched live from OpenAlex

Abstract This research constructs a simple model to illustrate the global supply‐chain (GSC) profit sharing and industrial upgrading mechanism, finding that the average profitability distribution in the different supply‐chain stages is determined by three main conditions: (1) the average product of the labor in the firms at each production stage; (2) the production complexity level of each production stage in the chain; and (3) the ratio of the output elasticity of capital to the output elasticity of labor in each stage. This study also proposes a new industrial upgrading mechanism, the “smile‐curve‐driven supply‐chain upgrading,” for supply‐chain firms. Increases in production complexity and level of factor intensity in each production stage are found to be the two essential conditions for the smile‐curve‐driven supply‐chain upgrading. Our static and dynamic panel empirical models, including robustness checks, are both broadly consistent with the theoretical predictions of this paper.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.275
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it