Burgers, Doughnuts, and Expatriations: An Analysis of the Tax Inversion Epidemic and a Solution Presented Through the Lens of the Burger King-Tim Hortons Merger
Bibliographic record
Abstract
Currently, the concept of tax inversion is a major corporate phenomenon. In the United States, companies pay taxes on all earnings, whether or not they were accumulated here. With one of the highest corporate tax rates in the world, this is a major expense for U.S. corporations competing in the world market. While most companies simply deal with the tax burden, some U.S. corporations buy foreign companies and relocate the company headquarters to the acquisitions home country. This corporate expatriation allows companies to avoid U.S. taxes on earnings in a number of ways. This Note will examine tax inversion through the lens of the 2014 Burger King-Tim Hortons merger and the resulting expatriation of the American burger purveyor from Florida to Canada. In particular, this Note will (1) examine why tax inversions have come about, (2) look at how politicians and academics have reacted to the phenomenon, (3) analyze the intricacies of the Burger King-Tim Hortons merger, and (4) propose a new solution that would actually curtail tax inversions and corporate expatriations within the United States.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".