Demystifying Rising Income Inequality Influence on Shadow Economy: Empirical Evidence from Nigeria
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it