Capital Flight and Domestic Investment in Nigeria: Evidence From ARDL Methodology
Bibliographic record
Abstract
Capital flight is a challenge for many developing countries of the world. The problem is more severe in a nation like Nigeria where domestic investment has been terribly affected. This study undertakes an empirical investigation of the impact of capital flight on domestic investment in Nigeria between 1980 and 2017. Deploying the Auto Regressive Distributed Lag (ARDL) econometric methodology, the study finds that capital flight has negative and significant impact on domestic investment. In particular, the long run impact of capital flight on domestic investment (0.57) turns out to be more severe than its impact in the short run (0.27), implying that a continuous and persistent build-up of capital flight exerts a negative cumulative effect on domestic investment over time. The study further reveals that the quality of institutions in Nigeria is a disincentive to domestic investment. It therefore recommends the strengthening of institutions to rein in on the illegal outflow of capital from the Nigerian economy in order to guarantee the availability of investible funds. The real sector of the local economy must be grown to bolster the value of the naira. This will stem the tide of capital flight and attract investments into critical sectors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".