Borders and Nominal Exchange Rates in Risk-Sharing
Bibliographic record
Abstract
Abstract Models of risk-sharing predict that relative consumption growth rates are positively related to changes in real exchange rates. We investigate this hypothesis using a new multicountry and multiregional data set. Within countries, we find evidence for risk-sharing: episodes of high relative regional consumption growth are associated with regional real exchange rate depreciation. Across countries, however, the association is reversed: relative consumption and real exchange rates are negatively correlated. We define this reversal as a “border” effect. We find the border effect and show that it accounts for over half of the deviations from full risk-sharing. Since cross–border real exchange rates involve different currencies, it is natural to ask how much of the border effect is accounted for by movements in exchange rates. Our measures indicate that a large part of the border effect comes from nominal exchange rate fluctuations. We develop a simple open economy model that is consistent with the importance of nominal exchange rate variability in accounting for deviations from cross–country risk-sharing.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".