Trade-Off Phillips Curve, Inflation and Economic Implication: The Kenyan Case
Bibliographic record
Abstract
This paper investigated the trade-off and inflation drivers based on the Phillips curve framework to determine the relationship and their impact between inflation, unemployment and output for Kenyan case from 2006:M1 to 2016:M12 by contrasting the 2SLS on 2 different measures of marginal cost with 3 differently instrumented shocks. Results confirmed; (1) significant trade-off that reduced inflation by 2.09% and 0.08% when unemployment and output respectively increase by 1%, while, 1% increase in output demeaned unemployment by 0.02%, (2) the forward-looking inflation and unemployment significantly drive observed inflation, and (3) unlike monetary supply, oil shocks best accounts for the observed dynamics. Although laudable policies been implemented by fiscal and macro-economic planners, they have not achieved the odds to sufficiently contain the import shocks, making both unemployment and the rational expectations to significantly drive the observed inflation. However, revisiting of the incumbent fiscal policies and their tight implementation would facilitate long term price stabilities to reduce the inflationary dynamics. To the significant import shocks, the state should foster feasible macro-economic diversifications, investments policies, modern technologies in real economic activity production, and renewable energy sourcing that would facilitate robust economic growth that would curb large revenue outflows due to commodity imports and cushioning the devaluation of the Kenyan shilling.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".