Why Has Growth Not Trickled Down to the Poor? A Study of Nigeria
Bibliographic record
Abstract
Despite impressive economic growth and major economic reform policies the search for poverty-reducing growth strategies remains a perennial question in many developing countries as poverty persists unabated. This motivates the current study to investigate empirically growth-poverty nexus in Nigeria spanning between the period 1970 and 2017. The paper attempted to answer the question: why has growth not trickled down to the poor? Time series econometrics were applied to test the cointegrating, short- and long-run dynamics among the variables. The results obtained revealed that growth trickled down to the poor only when high rates of employment growth accompanied high rates of economic growth. In addition to employment, the result also revealed that the form of capital formation, rather than its absolute value, appears to matter to the question of why has growth not trickle down to the poor. Thus, economic growth policies that promote an increase in income in conjunction with a high rates of employment growth are more effective in combating poverty than those that focus only on average income levels.  
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".