Private Companies, Professionals, and Income Splitting -- Recent Canadian Experience
Bibliographic record
Abstract
Popular media discussions of tax policies with regard to income splitting have focused on recent changes in the individual income tax for those with pension income and families with children. However, income splitting has been an important aspect of small business taxation for many years, even though it has always been relatively obscure. In this study, we have extended earlier work by Wolfson, Veall, and Brooks on the impacts of Canadian-controlled private corporations (CCPCs) on the overall distribution of income, to develop empirical estimates of the use of CCPCs for income splitting. This new study builds on a unique record linkage, under the strict auspices of the Statistics Act, of the T2 returns for CCPCs, the T1s of their owners and these owners' immediate family members, and the relevant T4 and T5 information slips. On the basis of these data, the current study shows that while there are individuals throughout the income spectrum who own CCPCs, ownership is concentrated in upper income groups. Subject to a number of caveats with regard to data limitations, we then provide an approximate, and likely, conservative estimate of the revenue costs of income splitting via CCPCs--about half a billion dollars annually. Finally, as another indication of the benefits of using CCPCs for income-splitting purposes, we track the numbers of restaurants, law practices, and doctors' practices incorporated as CCPCs. The resulting trends reflect a form of natural experiment, since there were no substantive changes in income-splitting opportunities for restaurants, while over the period studied there was a generally more relaxed approach on the part of the Canada Revenue Agency to the sharing of the small business deduction for legal firms, and a specific facilitating change in Ontario corporate law for doctors. While the evidence on the trends in numbers of CCPCs for these two kinds of professional corporations is circumstantial, it shows a clear correlation with trends in the legal and tax treatments of such CCPCs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".