Examining the Relationship Between Human Resource Reporting and Tax Performance: Insights from SACCOs in Kenya
Bibliographic record
Abstract
This study investigates the relationship between human resource (HR) reporting and tax performance among Savings and Credit Cooperative Organizations (SACCOs) in Kenya. HR reporting encompasses the disclosure of workforce-related practices such as employee training, diversity, remuneration, and workplace safety. It is expected to enhance tax performance by promoting compliance with labor-related tax regulations and facilitating access to government incentives. Using a descriptive and correlational research design, the study analyzed secondary data from 40 SACCOs with consistent financial and HR reporting records between 2019 and 2023. Data were sourced from financial statements and sustainability reports, with tax performance measured using indicators such as tax expense and effective tax rate (ETR). Panel regression analysis was conducted while controlling for firm size, profitability, and leverage. The results indicate that HR reporting particularly in employee training, remuneration, and workplace safety has a significant positive effect on tax performance. The findings suggest that SACCOs with more transparent HR disclosures are better positioned to achieve favorable tax outcomes. Additionally, firm-level characteristics moderated these relationships. This study contributes to the understanding of HR transparency as a driver of tax compliance and provides practical recommendations for SACCOs to enhance their reporting practices in alignment with regulatory expectations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".