Demystifying Rising Income Inequality Influence on Shadow Economy: Empirical Evidence from Nigeria
Bibliographic record
Abstract
We investigate whether rising income disparity contributes to the proliferation of shadow economic activities in Nigeria. The study uses data from 1991 to 2018 and adopts the Auto-regressive Distributed Lags (ARDL) cointegration approach to study the effects of income inequality on the shadow economy in both the short- and the long-run. Our results show that the Nigerian shadow economy responds positively to increases in income inequality, especially in the short run. We also find that the large income disparity in Nigeria drives the poor into informal economic activity, primarily for survival, and that unemployment partly contributes to informality. Our findings suggest that unemployment may be both a result and a cause of rising income disparity in Nigeria, leading to an expansion of the shadow economy. These findings indicate that regulating the proliferation of shadow economic activities in Nigeria will necessitate, among other things, the implementation of measures to reduce income gaps, such as stronger institutional frameworks and the expansion of financial intermediation services such as credit supply to the informal 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".