Growth Impacts of Income Inequality: Empirical Evidence From Nigeria
Bibliographic record
Abstract
The debate on whether income inequality promotes, restricts, or is independent of economic growth has been widely studied and discussed in development economics discourse. However, a careful reading of this extensive extant and burgeoning literature suggests that, other than the ambivalent nature and the fact that the bulk of these studies relied heavily on cross-section/-country/panel econometric analysis, empirical studies examining the nexus in the context of less developed economies, particularly, African countries, has received less attention, as most of the extant studies predominantly focused on developed economies. This current study, thus, attempts to examine the impact of inequality on growth in Nigeria spanning between the period 1970 and 2018. It also examined the theoretical predictions of some of the distinct transmission channels through which inequality impacts growth. Time series econometrics were applied. The results obtained consistently revealed that inequality hurts long-run growth in Nigeria. Also, the results obtained revealed that inequality in income increases relative redistribution and fertility, but lessens investment, gross enrollment ratio, and property rights protection in Nigeria, which may in turn impede growth.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".