Toward a General Theory of Regulatory Arbitrage: A Marketing Systems Perspective
Bibliographic record
Abstract
Businesses and consumers frequently exploit differences in laws and policies across jurisdictions to circumvent local laws, regulations, or restrictions. This practice, known as regulatory arbitrage, can have negative consequences for both business and social welfare. Although previous research examines regulatory arbitrage in specific contexts such as financial markets and the pharmaceutical industry, a general framework remains missing. Drawing on marketing systems theory, this study proposes a conceptualization that reflects the necessary conditions for regulatory arbitrage to occur across a variety of contexts. It also derives a typology of strategies to prevent and eliminate regulatory arbitrage. Using the context of alcohol policy in Finland as an illustrative example, the study applies the conceptualization to examine a situation where regulatory arbitrage has repeatedly threatened local policy. The findings illustrate how the broader perspective offered by marketing systems theory can help to more accurately predict whether businesses and consumers will pursue regulatory arbitrage in a given situation, and to select appropriate strategies for preventing and eliminating regulatory arbitrage in situations where it has negative consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".