Curbing Unemployment Through Job Creation as Panacea to Inclusive Growth in Nigeria
Bibliographic record
Abstract
The thrust of this study is to curb unemployment rate through job creation using some key sectors of the economy specifically the manufacturing, agricultural and industrial sectors as the basis for attaining an inclusive growth in Nigeria particularly with the increasing rate of youth unemployment booming the Country. This is demonstrated by the agricultural, manufacturing and industrial policies, programmes and strategies initiated, designed and executed to retard the alarming unemployment rate. The short-run and long-run dynamics streaming from inclusive growth proxied by real gross domestic product per capita, agricultural sector proxied by real agricultural output, manufacturing sector proxied by real manufacturing output, industrial sector proxied by real industrial output and openness measured by export as percentage of real gross domestic product to unemployment rate were evaluated using Autoregressive Distributed Lag (ARDL) bounds test approach for the period 1970 to 2014. The Estimated results from the study reveals that, improvement in the agricultural, manufacturing and industrial sectors will significantly aid in reducing the problems of unemployment and poverty in Nigeria. Even though the manufacturing sector shows no contribution to reducing unemployment, this could be as a result of the use of some equipment which has taken the place of labour thereby making it redundant. Though, if the teeming unemployed populace is adequately trained in the right direction, the manufacturing sector can still absorbed them. To this effect, the study recommended Government to give utmost priority to the key indicators that are needful at a given period of time in order to ascertain the right combination of the sectors in which these scarce resources should be directed to with the intention of enhancing inclusive 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".