How important are spillovers from major emerging markets?
Bibliographic record
Abstract
Abstract The seven largest emerging market economies—China, India, Brazil, Russia, Mexico, Indonesia, and Turkey—constituted more than one‐quarter of global output and more than half of global output growth during 2010–2015. These emerging markets, which we call EM7, are also closely integrated with other countries, especially with other emerging and frontier markets (FMs). Given their size and integration, growth in EM7 could have significant cross‐border spillovers. We provide empirical estimates of these spillovers using a Bayesian vector autoregression model. We report three main results. First, spillovers from EM7 are sizeable: a 1 percentage point increase in EM7 growth is associated with an 0.9 percentage point increase in growth in other emerging and FMs and a 0.6 percentage point increase in world growth at the end of 3 years. Second, sizeable as they are, spillovers from EM7 are still smaller than those from G7 countries (group of seven of advanced economies). Specifically, growth in other emerging and FMs, and the global economy would increase by one‐half to three times more due to a similarly sized increase in G7 growth. Third, among the EM7, spillovers from China are the largest and permeate globally.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".