Mergers by Choice, Not Edict: Reforming Ontario's Electricity Distribution Policy
Bibliographic record
Abstract
Provincially appointed panels have recommended that the province of Ontario force local electricity distribution companies (LDCs) to amalgamate into a handful of large, regional operations. However, just as forced consolidations of municipalities have led to few clear savings, so too it is unclear that forced amalgamation of local electricity distributors would lower costs in the sector. Instead of forcing mergers, provincial policy should be neutral towards mergers and acquisitions, permit private sector participation, and allow individual LDCs and municipalities to make their own choices. Accordingly, the government should reform tax policies that currently impose barriers on private investment, and permit individual distribution companies to pursue commercially sensible mergers and to seek the private investment they need to renew their aging capital stock. The case for forced amalgamations depends on two premises: first, that there are too many distributors in Ontario; and, second, that the government should encourage or force consolidation. These premises are, however, logically independent from each other: even if there are too many distributors in light of potential scale economies, the case for a government-directed consolidation should be examined on its own merits. Although many LDCs are smaller than the optimal size, there is no evidence to suggest that a few very large, amalgamated LDCs would be the most economically efficient outcome. A more fundamental question than scale is that of ownership. Ontario tax policy is neutral toward mergers of publicly owned LDCs, but restricts private sector participation in the sector. As a consequence, tax policy prevents any consolidations led by commercially owned companies. In our view, the province should remove barriers to private sector investment in the distribution sector, which it can achieve by eliminating taxes on sales of LDCs to private companies. To preserve the provincial tax base if Ontario were to eliminate taxes on the transfer or sale of LDCs to private companies, the federal government should enact changes to the Income Tax Act. The federal government should either allow LDCs to remain tax exempt with as much as 49 percent private ownership or, even better, introduce a transfer tax system in which it remits to the province corporate taxes it collects from privatized utilities.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".