Does Exchange Rate Policy Matter for Growth?
Bibliographic record
Abstract
Previous studies on whether the nature of the exchange rate regime influences a country's medium-term growth performance have been based on a tripartite classification scheme that distinguishes between pegged, intermediate, and flexible exchange rate regimes. This classification scheme, however, leads to a situation where two of the categories (intermediate and flexible) characterize solely the exchange rate regime, whereas the third (pegged) characterizes both the exchange rate regime and the monetary policy framework. We believe that the failure to account for this discrepancy may result in an inaccurate assessment of the effects of alternative exchange rate regimes on economic growth. Our study refines this classification scheme by accounting for different monetary policy frameworks. We estimate the impact of exchange rate arrangements on growth in a panel-data set of 60 countries over the period from 1973 to 1998 using a dynamic generalized method of moments estimation technique. We find evidence that exchange rate regimes characterized by a monetary policy anchor, whether they are pegged, intermediate, or flexible, exert a positive influence on economic growth. We also find evidence that intermediate/flexible regimes without an anchor are detrimental for growth. Our results thus suggest that it is the presence of a strong monetary policy framework, rather than the type of exchange rate regime per se, that is important for economic growth. Furthermore, our work emphasizes the importance of considering the monetary policy framework that accompanies the exchange rate arrangement when assessing the macroeconomic performance of alternative exchange rate regimes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".