A Model of Dynamic Tax Planning with an Application to Estate Freezes
Bibliographic record
Abstract
This paper develops a model of dynamic tax planning in which the implementation of a tax plan involves exercising an option to execute an irreversible investment or financing structure transaction. The model considers four aspects of such transactions and shows that transactions are deferred if the tax savings from the transaction are lower or if the time horizon over which the transaction can be executed is longer. Deferral is also increasing in cost of a future unfavorable event to which the irreversibility of the transaction limits one's ability to respond, but may increase or decrease with a change in the probability that an unfavorable event occurs. We apply the model to a common estate freeze tax plan in Canada. Undertaking an estate freeze requires a private company's owner-manager to choose how one's business assets are to be distributed at death. In contrast to the conventional wisdom regarding the timing of this strategy, we find that waiting to implement the strategy is often optimal. We test the model using data on family-owned businesses in Canada and find strong support for the model's predictions.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".