Piercing the Veil: Private Corporations and the Income of the Affluent
Bibliographic record
Abstract
A great deal of attention has been given recently to trends in income inequality, especially to observations that the most dramatic changes have been occurring among the top 1 percent. The key source of data in Canada for these results is individuals' income tax returns. This study extends these analyses by considering the potentially important role played by private companies. Even though individual income tax data are based on an inclusive definition of income, that definition does not include economic income received via privately owned companies. Having a private company offers a number of benefits, especially limited liability. It further offers potentially significant income-tax-planning benefits, including access to lower effective income tax rates through the small business deduction, tax deferral, and opportunities for income splitting. The omission of such economic income means that estimates of inequality levels and trends may be significantly biased. This study draws on a new anonymous linkage of income tax returns filed by Canadian-controlled private corporations (CCPCs) with a sample of their owners' individual income tax returns under the authority and protection of the Statistics Act. We first describe the conceptualization of the role of private corporations in income inequality analysis, and the methods adopted for this study. We present our initial results, including the extent of use of private corporations in various forms, and the impacts on measured income inequality, especially in the upper tail of the distribution. In sum, top income shares are significantly higher when CCPC incomes are included.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".