Do Analysts Matter for Corporate Tax Planning? Evidence from a Natural Experiment
Bibliographic record
Abstract
ABSTRACT We exploit an exogenous shock to analyst coverage as a result of brokerage house mergers and closures to examine whether financial analysts influence the tax‐planning activities of the firms they cover. Using a difference‐in‐differences design, we find that, on average, firms affected by broker mergers and/or closures experience a reduction in their GAAP (cash) effective tax rates (ETR) of 2.5 percent (2.6 percent), relative to control firms, translating into average tax expense (cash tax) savings of $34 ($35) million. The treatment effect is more pronounced among firms with lower pre‐event analyst coverage. To explore how analysts affect tax planning, we further document that the treatment effect is greater among firms that lose an analyst who provided an implied ETR forecast in the past, suggesting that analysts influence tax planning via their tax‐specific research efforts. In addition, we find that after merger/closure, weakly governed firms increase their use of aggressive tax strategies, and financially distressed firms experience a larger reduction of cash effective tax rates, relative to control firms. Overall, we provide evidence that a shock to analyst coverage sufficiently changes the cost‐benefit trade‐off of tax planning.
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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".