Macroeconomic Drivers of Foreign Direct Investment Inflows to Nigeria: Analysis of Shocks and Long-Run Causation
Bibliographic record
Abstract
This study seeks to underscore the extent to which macroeconomic vectors cause foreign direct investment inflows using cointegration, vector error correction, impulse response functions, and variance decomposition techniques against annual Nigerian data from 1986 through 2020. It also attempts to unravel how FDI inflows respond to macroeconomic shocks. The results indicate that, in the long-run, interest rate, inflation, exchange rate, level of economic activity and growth, and degree of trade openness of the economy significantly cause FDI inflows to Nigeria. Whereas the direction of causation is positive for inflation, exchange rate and trade openness of the economy, it was negative for level of economic activity and interest rate. FDI inflows responded to shocks in the above independent variables in different directions, some positively for a designated time, while some negatively at other periods. Generally, shocks in the independent variables jointly affected the shocks in FDI from the second through the tenth periods of innovations. Policy implications favor government action to lower interest rate, maintain mild inflation and moderate devaluation of the Naira against the currencies of trading partners, among others.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".