A disaster always rings twice: Early life experiences and central bankers' reactions to natural disasters
Bibliographic record
Abstract
Abstract We analyze the impact of natural disasters experienced in the early life of central bankers to assess their reaction to present‐day similar events. We use a panel dataset covering 68 (developed and developing) countries, for the period 2000 Q1 to 2017 Q4, to examine how the very‐short‐run dynamics of inflation is affected by the (actual) natural disasters, and how past experiences affect the immediate reaction of central bankers to these shocks. Our results reveal that the effect of early‐life experiences is significant: central bankers who have been exposed to disasters in early life tend to manage inflation in a more conservative way in the very‐short‐run. The effect disappears over the course of one year, and central bankers with superior voting power have a larger influence on outcomes. Younger central bankers are more conservative than older ones, which may reveal that the early‐life disasters' impact decays over time. The European Central Bank (ECB) appears immune to such influences, in conformity with expectations given the ECB's federal architecture and the size of the area it administers. The behavior revealed by our results thus signifies that central banks tend, on average to avoid any inflationary bias, inducing that the long‐run impact of a disaster suffered in the formative years of an individual can bring socially positive consequences, when such an individual becomes governor of her country's central bank.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".