FISCAL AND TAX COMPETITIVENESS What Gets Measured Gets Managed: The Economic Burden of Business Property Taxes
Bibliographic record
Abstract
What is the total tax burden on a new investment? It’s a question businesses must ask when deciding whether to invest in a given locality. The METR, or marginal effective tax rate, measures the tax total burden on a new investment. The METR can make or break a decision to invest, with high METRs driving investment elsewhere. However, current METR estimates are incomplete. They do not include an important component of the overall tax system: business property taxes (BPTs). By analyzing provincial BPTs, we find METRs are substantially higher than previously thought, especially in New Brunswick, Prince Edward Island, Ontario, and Saskatchewan. By measuring the extent to which municipal BPT rates exceed residential rates in the largest city in each province, we find net municipal BPTs have the largest effect on METRs in Montreal, Halifax, St. John’s, and Charlottetown. Including BPTs in estimates of METRs would give jurisdictions a clearer picture of their comparative attractiveness for new investment and motivate them to lower BPTs. Governments across Canada have made reducing the marginal effective tax rate (METR) on new business investment a policy priority. Sensible policies to reduce corporate income taxes, replace retail sales taxes with a Harmonized Sales Tax and eliminate corporate capital taxes have lowered the prevailing METR estimates substantially (see Chen and Mintz 2011, for example). However, The authors thank numerous reviewers of our paper who provided helpful comments, including
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".