actors Affecting Turnover Tax Collection Performance: A Case of West Shoa Zone Selected Districts
Bibliographic record
Abstract
Factors affecting turnover tax collection performance. A case of West Shoa Zone selected districts. In 2017/18 the targeted revenue was 9041224 birr with the actual revenue being 7888536 birr (equivalent to 87.25% or a difference of 1152688) was existence of turnover tax collection gap. This study used mixed research approach and sampling (Systematic random, purposive sampling). Sample sizes 373 respondents selected and distributed questionnaires and interview. Data analyze by SPSS version 20 and factor analysis. Findings revealed that; employee qualification and manpower, taxpayer registrations, technology and information system, management commitment level and tax knowledge affects turnover tax performance positively. It was revealed that perpetuation tax fairness affects negatively where as compliance cost has a negative statistically insignificant. They concluded that the problems facing revenue administration office while collecting turnover tax. Based on the study recommended that revenue authority need to develop strategic management commitment, recruit sufficient number of employees and continues training qualification, maintaining tax fairness and equity, improve taxpayer identification and registration, increase number of users of Electronic Tax Register, extensive tax knowledge (awareness) creation programs update and maximize frequency tax audit effective on field compromising a priority task.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".