The smile curve: Evolving sources of value added in manufacturing
Bibliographic record
Abstract
Abstract A dramatic disordering of global manufacturing has been seen in recent years. Production processes have fragmented, and many production stages have been offshored to developing nations. Organization of this new global supply chain has evolved into what are often called global value chains (GVCs). Less studied, but no less important, is the shift in the sectoral source of value added in manufactured exports. This phenomenon, often called the “smile curve,” involves a swing in the share of value added in manufactured exports that is generated in the manufacturing sector itself instead of, for example, in the pre‐ and post‐fabrication stages. Our paper presents new evidence quantifying the magnitude of the smile curve notion. Using international input–output databases, we find evidence supporting the smile curve at the aggregate level. Specifically, for almost all exporting sectors and nations, we find that the value added to exports has shifted decisively from the manufacturing sector to service sectors. We also find that developing countries reduced their own‐sourcing service value‐added share, while developed countries maintained their relatively high levels of own‐sourcing service value‐added share.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".