Asymmetric impact of uncertainty in recessions: are emerging countries more vulnerable?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it