Who Pays the Corporate Tax? Insights from the Literature and Evidence for Canadian Provinces
Bibliographic record
Abstract
Who bears the burden, or incidence, of the corporate income tax (CIT)? This is an important, if not somewhat contentious, policy issue. In this paper we provide a discussion of the existing research on the question, viewing it through a Canadian policy lens. We also use some new results from a companion technical paper, which undertakes one of the few empirical investigations of the issue using Canadian data, to discuss the implications of increases in corporate taxes for wages in Canadian provinces. While it is clear that people, not corporate entities, ultimately bear the burden of corporate taxes, a key question is which people? The answer to this question has important implications for the equity, or fairness, of the tax system. Much of the recent focus in policy discussions concerns the allocation of the burden of the CIT between owners of capital and labour. Since income from capital tends to be concentrated with wealthier individuals, if the burden of the CIT falls mostly on the owners of capital, it increases the progressivity of the tax system. On the other hand, if the tax is borne mostly by labour through lower wages, the CIT is less progressive. Much of the research into the incidence of the CIT has employed theoretical simulation models. Early models of this type, which were based on a closed economy with fixed supplies of labour and capital, suggested that most of the burden of the CIT would be borne by the owners of capital throughout the economy, and not just the shareholders of firms in the corporate sector. Subsequent extensions of those models into a small open economy setting, where capital and goods are highly mobile between jurisdictions (countries, provinces), predict that most of the burden of the CIT will be borne by other inputs that are relatively inelastic in supply, such as labour. These small open economy models are particularly relevant for Canada. Viewing the results of these models through a Canadian lens, we conclude that there is good reason to expect that much of the burden of corporate taxes in Canada, particularly those levied by provincial governments, will fall on labour through lower wages. While useful, the predictions of these simulation models should be viewed with caution, largely because of the sensitivity of the results to the underlying assumptions. A nascent empirical literature has emerged that provides econometric-based estimates of the distribution of the burden of corporate taxes. While this research is relatively new, our reading is that the evidence is mounting that corporate taxes are indeed borne to a significant extent by labour through lower wages. However, there is very little empirical work done in an explicitly Canadian context. In a companion technical paper we employ Canadian data to examine the impact of provincial corporate taxes on wages. Our results suggest that, consistent with the predictions of the open economy simulation models, provincial corporate taxes adversely affect the capital/labour ratio, which lowers the productivity of labour which, in turn, lowers wages. Accounting for the shrinkage in the corporate tax base in response to an increase in the tax rate, we calculate that for every $1 in extra tax revenue generated by an increase in the provincial CIT rate, the associated long-run decrease in aggregate wages ranges from $1.52 for Alberta to $3.85 for Prince Edward Island. Applying our estimates to the recent 2 percentage point increase in the CIT rate in Alberta we calculate that labour earnings for an average two-earner household will decline by the equivalent of approximately $830 per year, which amounts to a $1.12 billion reduction in aggregate labour earnings for the province. By way of comparison, other research has estimated the impact of the recently imposed carbon tax in Alberta – the subject of considerable scrutiny – to be approximately $525 per household.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.024 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".