The Effect of Changes in Income Shifting on Affiliate Managers' Internal Reporting Decisions*†
Bibliographic record
Abstract
ABSTRACT This study examines the interplay between tax and internal reporting incentives among affiliates of multinational corporations (MNCs). MNCs face limited information flows that may prevent affiliates' performance metrics to be responsive immediately to changes in the firm's tax planning. Using granular data of affiliates belonging to MNCs from 21 European countries, our study provides new empirical evidence of affiliate internal reporting responses induced by changing tax plans. When high‐tax‐rate countries tighten income shifting rules, we first document that income shifting is reduced and low‐tax‐rate affiliates have less income. Second, we predict and document that managers of these low‐tax‐rate affiliates offset this decrease in profits by managing upwards a key performance metric: affiliate earnings. Our results are consistent with firms not quickly adjusting the affiliate managers' incentives in the face of changing tax planning strategies, and affiliates managing reported earnings to offset the effect of changes in the tax planning of the firm. Cross‐sectional analyses provide further evidence consistent with the theory underlying the main tests. The results support the policy of tightening income shifting rules when the objective is to reduce income shifting, and firms' central management would benefit from considering the implications of changing tax plans on the assessment of local managers.
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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 | 0.022 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".