The sources of cross-country output comovements : European and non-european linkages
Bibliographic record
Abstract
This doctoral thesis consists in three chapters investigating cross-country linkages in different samples of industrialized economies. The first chapter shows that the share of the investment cycle's variance due to common international factors has increased in the United States as well in large European countries. The second chapter estimates the impact of the liberalization and internationalization of the financial and banking sectors on real GDP growth comovements. Since the late 1970s, a common international factor has contribued to most economic growth in th EU countries, the United States, Canada and Japan. Among several financial, bank and monetary indicators, equity prices, followed by portofolio investment have been by far the main drivers of this factor. The removal of controls on domestic credit emerges as the only financial liberalization policy measure with a large and negative effect on common growth before 1995. The third chapter investigates the sources of real GDP's comovements between the founding member states of the euro area. Throughout EMU, real cyclical synchronization was robustly linked to disparities in term of fiscal policy and of total factor productivity gains. Cyclical synchronization was closely related to similarities in unit labour cost growth before 2007, but not after 2007 when long-term interest rate differentials became a major cause of cyclical divergence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".