Hiding in Plain Sight: The Harmful Impact of Provincial Business Property Taxes
Bibliographic record
Abstract
When governments analyze tax policies aimed at attracting investment, they typically rely on a variable called the marginal effective tax rate (METR) on capital. The METR is a measure of the effective tax burden on new business investment. Recent Ontario budgets have presented estimates of the METR, while emphasizing the economic benefit of reducing taxes included in these estimates. This Commentary makes the case that METR estimates have so far underestimated the actual tax burdens that investors face, because they exclude a major tax on businesses: provincial business property taxes. Excluding these taxes means that provinces do not adequately recognize the economic benefit of reducing them. Provincial governments in Ontario, Alberta and British Columbia, the three provinces we consider, now hold the taxing power once held by school boards. This power shift has transformed the business education tax (BET). When school boards controlled the BET, it combined – at least potentially – two separate taxes: a benefit tax financing local schools and a tax on capital investment. Provincial takeovers have since eliminated any benefit tax component. From the standpoint of investors, business education taxes – despite their obsolete name – are now simply provincial business property taxes. We find that including the BET adds substantially to METR estimates in Ontario. The impact of the BET on British Columbia’s METR appears to be somewhat less than the impact in Ontario, while the impact on Alberta’s METR appears substantially less. The BET’s substantial impact on Ontario’s METR lends strong support to the case for parity between business and residential education tax rates. We estimate that if the BET rate were reduced to parity with the residential education tax (RET) rate, its METR impact would be much smaller. Even an announcement that BET/RET rate parity is to be attained in 15 years would immediately reduce the METR impact of BET due to the effect on investor expectations. As a start, governments should include the BET in published METR estimates, such as the estimates published routinely in Ontario budgets. Leaving out the BET means missing a large part of the tax burden investors pay. It thus leads governments to underestimate the negative impacts on investment stemming from their tax systems, and it causes governments to defer – perhaps indefinitely – tax reforms needed to mitigate those negative impacts.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".