Bibliographic record
Abstract
ABSTRACT In‐house human capital tax investment is a significant input to a firm's tax decisions. Yet, due to the lack of data on corporate in‐house tax departments, there is little empirical evidence on how tax departments are associated with tax planning and compliance outcomes. We expect the size of tax departments to be positively associated with the effectiveness of tax planning and compliance. Using hand‐collected data on the number of corporate tax employees in S&P 1500 firms over the 2009–2014 period, we find that firms with larger tax departments are associated with lower and less volatile cash effective tax rates. Furthermore, using tax employees' specialization, we identify tax departments' relative focus on planning or compliance and document a trade‐off between tax avoidance and tax risk. Specifically, tax departments with more of a tax planning focus have incrementally greater tax avoidance but higher tax risk, whereas tax departments with more of a tax compliance focus have incrementally lower tax risk but higher tax rates. Overall, this paper contributes to the literature by looking inside the “black box” of corporate tax departments and shedding light on the importance of human capital tax investment for tax outcomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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".