Relationship Between Unemployment Rate and Shadow Economy in Nigeria: A Tado-Yamamoto Approach
Bibliographic record
Abstract
This study assessed the nature of the relationship between the size of the shadow economy and unemployment rate in Nigeria using the Tado-Yamamoto approach over the period 1980Q1 to 2018Q4. The size of the shadow economy in Nigeria was determined using the parsimonious model of MIMIC (4-1-2) having four multiple causes (tax burden, self-employment, social benefits paid by the government and unemployment rate) and two indicators (index of real Gross Domestic Product and currency ratio (M1/M2)). The estimated relationship of the size of shadow economy as percentage of official GDP recorded 13.78% at the beginning of the first quarter of 1980 before fluctuating to 8.23% in the third quarter of 2009. The existence of a strong and positive association between the unemployment rate and shadow economy is affirmed by the estimated coefficient of determination (0.89) which confirmed the capacity of the shadow economy to absorb the unemployed workers from the official economy in Nigeria. Evidence exists from the Tado and Yamamoto (1995) causality test which revealed a causal relationship emanating from unemployment rate to the size of shadow economy. This was confirmed by the Modified Wald (MWald) test which demonstrated that a strong unidirectional causality running from unemployment rate to the size of shadow economy exists at 1% level of significance.
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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 | 0.001 |
| Bibliometrics | 0.002 | 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".