Investment Incentives and Effective Corporate Tax Rate for Manufacturing Firms in Kenya
Bibliographic record
Abstract
Effective corporate tax rate is a finance subject of interest to firms, policy makers and researchers. It measures level of tax burden at firm level. Thus, governments implement various investment incentives to influence effective corporate tax rate. The effective corporate tax rate in Kenya is still a problem averaging 31.3 percent for the last 10 years. Such high effective corporate tax rate militates against desired competitive corporate environment for the manufacturing sector. In the last ten years, the manufacturing sector has deteriorated to 7.4 percent contribution to gross domestic product which is less than 15 percent as envisaged in Kenya Vision 2030. This undesirable phenomenon prompted design of this study. The objective of the study was to determine the effect of investment incentives on effective corporate tax rate. The study adopted positivist philosophy and longitudinal research design. A sample of 278 firms provided secondary data for the period 2010 to 2020. Descriptive and inferential statistics were conducted using panel data regression. The study established that investment incentives are statistically significant predictors of effective corporate tax rate for manufacturing firms in Kenya. The study recommends that public policy makers should design appropriate profit based, capital investment and custom duty incentives as part of fiscal policy instruments to grow firms involved in manufacturing. The study has added to finance knowledge that fiscal policy affects corporate operations. However, there is need for further investigation on other possible investment incentives that were not covered in this study that influence effective corporate tax.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".