Corruption, Exchange Rates, and Migration Flows
Bibliographic record
Abstract
It is often argued that immigration flows depend positively upon the GDP (a migratory pull factor) and negatively upon the exchange rate depreciations (a migratory push factor) in the destination country. However, we show that both effects depend crucially on the corruption level, and, to the best of our knowledge, this is the first time that the impact of migration’s determinants depends on the level of corruption and therefore, migratory flows are found to be corruption dependent. In fact, we show that high corruption in the destination country could lead to a decoupling of the net migration flows from both effects (GDP and PPP exchange rate). The policy implications of our findings suggest that corruption, and its interactions with other migration factors, should in principle be examined in migration studies. We employ net migration flows, defined as immigrants minus emigrants, for the case of Greece as destination country where migration-flows direction has changed sign two times in the post-war era. The data are obtained from the World Bank. Our findings remain robust [1] to a series of alternative specifications with the world governance indicators (WGIs) and, [2] to the use of several estimators.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".