Information asymmetry and incentive contracting with the tax department
Bibliographic record
Abstract
Purpose This paper aims to examine the design of optimal incentives for a firm’s tax department in the presence of information asymmetry. Design/methodology/approach This paper provides a theoretical model to examine the design of optimal incentives. The focus is on a situation in which a risk-averse tax department has private information about its efficiency type or effort to be exerted before the firm sets the incentive schemes. Findings This paper shows that a tax department’s risk aversion leads to a decline in the fraction of the cost borne by the tax department. It also shows that the optimal contract schemes should be designed to filter out as much uncontrollable risk as possible by using third-party information relevant to a tax department’s realized cost. Social implications It contributes to a better understanding of the impact of corporate incentive plans on firms’ tax practices. This study, by designing a theoretical model, helps explain why there exist differences in tax planning across firms based on the finding that incentives for tax planning activities differ across firms. Originality/value This paper is the first study that considers the situation in which tax managers’ risk-averse and types, as well as relevant information collected by the firms, can be used to set up incentive schemes and investigates whether and how the incentive schemes will be affected when firms improve their prior information by acquiring relevant information before the tax department acts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".