Micro-Econometric Analyses of Some Welfare Effects of Oil-Availability in the Niger Delta Region of Nigeria
Bibliographic record
Abstract
This article raises some fundamental issues in the resource-development literature, such as the effect of resource-availability and resource-related conflicts on personal and community-wide welfare, the interaction between resource-abundance and hopelessness, and the role of government presence and location of communities, using the Niger Delta region of Nigeria as a case study. Employing survey data from two sources, it shows that oil-availability has mixed effects for individuals and communities. It raises educational attainments and socio-economic access among individuals and infrastructural development in communities but also tends to generate higher episodes of violent conflicts, higher inequality, and greater feeling of despondency among individuals. It also fails to significantly affect earning levels or reduce unemployment. Government presence is beneficial as it tends to impact positively on earnings and socio-economic access among residents and reduces the likelihood of being unemployed or experiencing a longer spell of unemployment, but it is also likely to be associated with higher inequalities, higher incidences of conflict, and greater hopelessness. Individuals in more distant communities tend to have lower educational attainments and socio-economic access but they are also more likely to enjoy lower levels of inequality, greater hope, and lower incidences of unemployment. Youths tend to suffer the most from the negative effects of resource-abundance as they appear to be worse off on all measures of personal welfare employed, while geographical characteristics seem important for welfare just the same way as policy and initiatives at the local level. The paper concludes that natural resources, such as oil, can be significantly welfare-improving if its tendency to encourage violent conflict outbreaks can be addressed, but this may not be achieved by merely providing greater infrastructure or expanding educational opportunities but by matching these with significantly-expanded economic space.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".