An application of Toda-Yamamoto approach on accessing casual relationship among exchange rate, trade openness and corruption in Nigeria.
Bibliographic record
Abstract
Abstract. This study accesses the causal relationship between trade openness, corruption and exchange rate in Nigeria between. Time series data was used from first quarter of 1996 to fourth quarter of 2021 on exchange rate, trade openness and corruption. Data was extracted from World Development Indicator (WDI) of World Bank database and International Transparency database. Trade openness was constructed using Composite Trade Intensity (CTI) measurement. Toda-Yamamoto approach was estimated to examine the causality among the trade openness, corruption and exchange rate. It was exhibited uni-directional causality between exchange rate and trade openness; exchange rate and corruption, unlike trade openness and corruption which revealed bi-directional causality. Thus, the result exhibited that exchange rate granger cause trade openness. It implies that an increase in exchange rate will causes the variation on trade openness in Nigeria. More so, the result revealed that exchange rate granger causes corruption. This also implies that exchange rate fluctuation leads to variation on corruption. In addition, it was found that there is bi-directional causal relationship between trade openness and corruption. This implies that trade openness granger causes corruption and corruption also granger cause trade openness. That is, an increases in trade openness in Nigeria causes any variation on corruption on Nigerian economy and vice-versa. Base result, the study recommended that the government should makes a policy for the improvement of trade openness that will enhance naira appreciation. More efforts should be made in checking and controlling corruption whenever there is improvement on trade openness in Nigeria. Key Words: Causality, Corruption, Exchange Rate, Toda-Yamamoto. JEL Code: C1, E5, F4.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".