Bibliographic record
Abstract
Purpose This study investigates the impact of tax planning, both independently and in conjunction with earnings management, on the persistence of earnings and its various components. Design/methodology/approach In this study, tax planning refers to corporate strategies aimed at minimizing taxes, while earnings management involves manipulating reported earnings through accounting accruals. The analysis uses a dataset of US companies from 1989 to 2016 and includes a series of regression tests. Findings The study finds that firms implementing aggressive tax strategies exhibit lower persistence in cash flows from operations and earnings. Furthermore, companies using both aggressive tax planning and earnings management techniques show the lowest persistence in total accruals, cash flows from operations and reported earnings. Research limitations/implications Our sample of US firms limits generalizability. Future research could explore the international impacts of tax planning and earnings management on earnings quality and include post-2016 data for insights on the 2018 tax cuts and COVID-19. Investigating other earnings quality measures and their influence on investors and analysts could enhance performance assessment. Practical implications This research identifies key factors influencing the interpretation of financial statements, offering valuable insights for regulators, auditors, tax authorities, financial analysts and other users with significant practical and social implications. Originality/value This study contributes to prior research by highlighting the need to investigate the real effects of tax avoidance and extends prior research by examining the impact of high levels of tax planning, along with aggressive earnings management, on earnings persistence.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".