The Effect of Currency Misalignment on Income Inequality
Bibliographic record
Abstract
This paper examines the relationship between currency misalignment and income inequality across 70 countries from 1998 to 2021. Currency misalignment occurs when the actual exchange rate diverges significantly from the equilibrium exchange rate. Using fixed-effects and random-effects regressions, we find that currency overvaluation is associated with higher income inequality, while undervaluation is linked to lower income inequality. These findings are strongest in emerging markets and upper-middle-income countries, where undervalued currencies may be associated with stronger tradable-sector activity and narrower income gaps. In contrast, lower-income countries experience increasing levels of inequality during the early stages of development, even with growth, which is consistent with the Kuznets hypothesis. For advanced markets and higher-income nations, currency misalignment is not statistically related to income inequality, which is likely due to the presence of stronger financial systems and more stable institutions that reduce the effects of currency misalignment. The results are robust across the two grouping methods—development level (IMF) and income level (World Bank). Overall, the study highlights that while undervaluation may be associated with equitable growth in emerging markets, its benefits likely depend on a country’s development stage and are more likely when accompanied by appropriate social and economic policies to mitigate potential risks.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".