Public health restrictions during the COVID-19 pandemic and the impact on international tax evasion
Bibliographic record
Abstract
The COVID-19 pandemic provides a natural experiment to examine how institutional capacity mediates tax evasion during economic shocks. Utilizing comprehensive data covering the period 2015–2020, with 9996 observations across 191 source and 38 OECD host countries, we analyze foreign portfolio investment flows to test two hypotheses about tax evasion behavior through roundtripping. We predict that tax evasion increases in non-developed markets, where economic incentives dominate, but decreases in developed markets, where regulatory constraints prevail. We find strong evidence for the incentive-driven response in MSCI non-developed market host countries, where economic distress and limited enforcement capacity outweighed operational constraints during the pandemic, leading to increased offshore tax evasion. We also confirm the constraint-driven response in MSCI-developed market host countries, where enhanced enforcement capabilities and operational barriers outweighed increased incentives, thereby reducing tax evasion activities. These results suggest that institutional capacity influences whether economic distress or regulatory constraints dominate during crisis periods.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".