Endogeneity and the economic consequences of tax avoidance
Bibliographic record
Abstract
Abstract Academic research investigating the economic consequences of tax avoidance is almost always interested in the consequences of intentional, deliberate actions undertaken to reduce taxes relative to income. Therefore, it is crucial that such research distinguishes between intentional and incidental tax avoidance, since failure to do so can create endogeneity concerns and lead to incomplete and incorrect economic inferences. In this paper, we first develop a framework that conceptually defines and distinguishes between intentional and incidental tax avoidance. We highlight that the endogeneity problem arises because intentional tax avoidance is not directly observable. We consider two approaches to mitigating endogeneity concerns and apply these approaches by reexamining two influential studies that investigate the economic consequences of tax avoidance. We show how controlling for past accounting losses eliminates the effect of tax avoidance on credit spreads (Hasan et al. 2014, Journal of Financial Economics, 113 (1), 109–130) and how using an instrumental variables approach changes the sign of the relation between tax sheltering and stock price crash risk (Kim et al., 2011, Journal of Financial Economics, 100 (3), 639–662). Overall, our paper punctuates the importance of both (1) conceptually distinguishing between incidental and intentional tax avoidance and (2) econometrically addressing the challenges that arise when empirical differentiation between incidental and intentional tax avoidance is important to the research question.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".