Asymmetric impact of uncertainty in recessions: are emerging countries more vulnerable?
Bibliographic record
Abstract
Abstract This paper asks two questions “ Does there exist heterogeneity in the response of macro variables to uncertainty shocks across advanced and emerging countries? and, “ How important is the state of the economy for the effects of an uncertainty shock? . I analyze the recession-specific effects of uncertainty for a sample of 8 countries – the US, UK, France, Canada, Mexico, Chile Argentina, and South Korea. The results emphasize asymmetries along two dimensions – (1) An uncertainty shock disproportionately increases the depth and duration of a recession for an emerging country vis- $\grave{a}$ <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mrow> <m:mover> <m:mi>a</m:mi> <m:mo>`</m:mo> </m:mover> </m:mrow> </m:math> -vis an advanced economy. Furthermore, I find that openness to trade exacerbates this decline and subsequently the pace of recovery in emerging countries in comparison to advanced economies. (2) Controlling for the state of the economy is crucial when quantifying the effects of an uncertainty shock. I show that a linear model – without regime differentiation – consistently underestimates the response of macroeconomic variables to uncertainty shocks when compared with the predictions from the recessionary regime of the nonlinear model. The extent of this under prediction is again disproportionately larger for emerging countries. The results in conjunction can potentially explain the excess volatility of macro variables for emerging countries during recessionary episodes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".