Do managers respond to tax avoidance incentives by investing in the tax function? Evidence from tax departments
Bibliographic record
Abstract
While prior literature examines the role of certain incentives in motivating top managers (CEOs and CFOs) to engage in corporate tax avoidance, there is little evidence on the specific actions that managers take in response to these incentives. Motivated by the premise that a manager can influence a firm’s tax activities by directing resources towards the tax function, I investigate whether four specific tax avoidance incentives studied in prior literature (financial constraints, equity risk incentives, hedge fund interventions, and analyst cash flow forecasts) induce managers to make investments in hiring personnel within the firm’s tax department. Using a dataset of tax department employees collected from the professional networking website LinkedIn, I find evidence that each incentive is significantly associated with an increase in the number of individuals employed within the tax department. This association is generally stronger among higher ranked employees and employees with prior tax department experience. Overall, my findings are consistent with the premise that managers invest resources in the tax function when they are incentivized to avoid taxes. My study also provides some assurance that the association between tax avoidance incentives and effective tax rates documented in prior studies is reflective of intentional tax avoidance behavior.
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 | 0.002 | 0.017 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".