Information acquisition and tax avoidance: Evidence from a natural experiment
Bibliographic record
Abstract
Abstract Analyzing the launch of high‐speed rail (HSR) services in China as a natural experiment, we identify a positive externality stemming from lower information acquisition costs: the reduction in firms’ overinvestment in tax avoidance. Specifically, we find that outsiders undertake more corporate site visits and firms engage in less tax avoidance after the opening of HSR lines in the cities where these firms are located, leading to enhanced firm value. In another result consistent with expectations, we document that the impact of the introduction of HSR lines on tax avoidance is concentrated in firms in which insiders exhibit a high propensity to extract rents through aggressive tax strategies. Our results imply that more efficient transportation facilitates site visits and the acquisition of firm‐specific information, particularly soft information. This improvement strengthens external monitoring, thereby limiting the ability of insiders to accumulate private benefits under the guise of tax avoidance that benefits all shareholders as the residual claimants.
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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 | 0.006 | 0.011 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".