Patterns of Deindustrialization: Are Countries Converging?
Bibliographic record
Abstract
During the last decades, the share of manufacturing in aggregate output (and employment) has declined in almost all advanced and emerging economies. In this paper, we investigated the patterns of deindustrialization in a sample of 117 (low-, middle-, and high-income) countries from 1995 to 2018. To this aim, we applied the nonlinear time-varying factor model, initially proposed by Phillips and Sul, to identify potential clubs wherein groups of countries converge toward a similar manufacturing share of GDP. Furthermore, we estimated an ordered logit model to assess the impact of economic globalization and technological revolution on the probability of falling into a particular club. Our results did not provide any support for the hypothesis of global convergence. However, the clustering algorithm successfully identified four strong final clubs, where the share of manufacturing on GDP ranges, on average, from 6% to 18%. Finally, the logit model indicated that as the R&D expenditures and the technological content of manufactured goods increased, so did the likelihood of belonging to a club with a high share of manufacturing value-added on GDP.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".