The Complementarity between Tax Avoidance and Manager Diversion: Evidence from Tax Haven Firms
Bibliographic record
Abstract
ABSTRACT We investigate whether tax avoidance and manager diversion are complementary when the costs of diversion are low by comparing dividend payouts, performance, and overinvestments of tax haven firms versus other multinational firms based in countries with weak and strong investor protections. Desai and Dharmapala (2006, 2009a, b) and Desai et al. (2007) set forth a theory of tax avoidance within an agency framework (the D&D theory) based on the assumption that tax avoidance and manager diversion are complementary when the corporate governance system is “ineffective” (i.e., the manager's expected costs of diversion are low). Tax haven firms are corporate groups whose parent firms are incorporated in tax haven countries that are not the countries where the groups’ headquarters or primary operations are located (i.e., their “base” countries). We argue that tax haven incorporation potentially lowers the costs of diversion for managers of firms based in countries with weak investor protections. Using a sample from 28 base countries, we provide evidence that manager diversion and tax avoidance are complementary for tax haven firms based in countries with weak investor protections but not for tax haven firms based in countries with strong investor protections. Our results are consistent with the complementarity assumption underlying the D&D model and provide additional insights into the potential impact of the decentralization of the global firm.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".