Exchange Rate Misalignment and Capital Flight from Botswana: A Cointegration Approach with Risk Thresholds
Bibliographic record
Abstract
This study investigates the impact of exchange rate misalignment on outward capital flight in Botswana over the period 1980–2015. The study uses the autoregressive distributed lag (ARDL) approach to cointegration and the Toda and Yamamoto (1995) approach to Granger causality. Botswana’s currency misalignment was caused by current account imbalances. The most important determinant of capital flight from Botswana is trade openness, which indicates that exportable commodities are misinvoiced leading to net capital outflows. Our main findings show that in the long-run, when the currency is overvalued, the volume of capital flight through trade misinvoicing declines and increasing foreign reserves does not reduce outward capital flight. However, when the currency is undervalued, the volume of capital flight through trade misinvoicing increases and foreign reserves reduce outward capital flight. Investors respond more to prospects of devaluation than to inflation. Botswana should tolerate overvaluation of the pula of only up to 5%. When the pula is overvalued beyond 5%, capital flight increases substantially. The government has to formulate trade regulations and monitor imported and exported commodities. Botswana should also implement capital controls to limit capital smuggling and maintain monetary autonomy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".