Moderating Effect of Political Risk on the Relationship between Capital Expenditure and Sectoral Economic Growth in Kenya
Bibliographic record
Abstract
The study sought to determine the effect of capital expenditure on Sectoral economic growth and the moderating effect of political risk on the relationship using Auto Regressive Distributed Lag model. The study targeted 11 sectors that receive government expenditure and adopted positivist philosophy and a causal research design. Secondary data for the period 2006-2015 was collected from Kenya National Bureau of Statistics Statistical Abstracts, Kenya National Audit Office reports and Political Risk Group reports. The study conducted Hausman Test, Panel Stationarity Test and Heterogeneity Test as preliminary tests. The study found that capital expenditure has a significant effect on Sectoral economic growth both in the long run and short run. The study further found that political risk has a significant moderating effect on the relationship between capital expenditure and Sectoral economic growth in the long run at the significance level of 0.05. The study concluded that capital expenditure has an effect on sectoral economic growth in Kenya both instantaneously and in the long run. As well, Political risk curbs the effect of capital expenditure in the long run. The study recommends enhancement of capital expenditure. Additionally, the government should enhance political stability to accelerate 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".