Effects of Insecurity, Terrorism and Political Instability on Foreign Direct Investment Inflows in Nigeria
Bibliographic record
Abstract
This study examines the nexus between political unrest, insurgent activities, and their detrimental impact on Nigeria's economic prospects and the viability of human livelihoods.These factors have been identified as formidable impediments to foreign direct investment (FDI), posing substantial risks to international business engagements within the nation.Utilizing a dataset spanning from 1990 to 2022, the investigation employs a Vector Auto-regression (VAR) model to dissect the influence of insecurity and political volatility on FDI inflows in Nigeria.The findings elucidate a pronounced and adverse response of FDI to the prevailing terrorism, insurgency, and political instability.Moreover, the analysis reveals inflation as a concurrent challenge that jeopardizes international investment.In response to these findings, the study underscores the imperative of democratic consolidation and the implementation of robust strategies to counteract insurgency and terrorism.Additionally, it advocates for monetary policies aimed at inflation control through judicious regulation of the money supply, alongside stabilization of prices for goods and services critical to the industrial sector and household consumption.
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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 | 0.001 | 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) | 0.002 | 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".