Does immiserizing growth exist? Evidence from world’s top trading nations
Bibliographic record
Abstract
Purpose This study aims to motivate the reality that experiential investigation of immiserizing growth has not been performed at large. The key objective of the study is to analyse the empirical existence of immiserizing growth in the real world. Design/methodology/approach Theory of revealed preferences has been implemented for welfare movement by using Laspeyres and Paasche quantity index and for empirical estimations, logistic regression has been applied. The study established panel data of the world’s largest trading nations, including the USA, China, France, Germany, UK, Italy, Japan, the Netherland and Canada. Annual time series data for an extensive time period covering from 1981 till 2017 have been used. Findings Findings of the Laspeyres and Paasche index reveal that out of nine countries immiserizing growth prevails in five nations and those are Italy, Canada, the Netherland, UK and Japan. The results of panel logistic regression verify the significance of terms of trade on immiserizing growth in all included countries. Separate logistic regression has also been performed on all the five countries from which Italy, Canada, the Netherland exhibit significant results. Originality/value This study is a pioneer attempt towards the concept of immiserizing growth. Considering the fact that immiserizing growth is viewed by the majority of the scholars as a theoretical notion, this study attempts to investigate analytically the existence of immiserizing growth with real data set. The impact of terms of trade deterioration on the welfare of the world’s largest trading nations has been focused on the research which is in compliance with the concept of Bhagwati (1958).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".