Determining the efficacy of consolidating municipal electric utilities in Ontario, Canada
Bibliographic record
Abstract
Purpose This paper aims to examine empirically if the encouragement by government policy of merger and acquisition activity involving municipal and provincially owned electricity distribution utilities (LDCs) in the Province of Ontario has had positive effects in terms of value creation, operating performance and economies of scale. Design/methodology/approach It was anticipated that with LDC consolidation, there will be increased operational efficiency and improvement in the cost-effectiveness of the merged electrical utility. Using matched pairs dependent t-testing and Wilcoxon signed-rank testing, the authors compared data for three years before and after the merger or acquisition of 16 municipal utilities (616 total observations) to determine if there were any statistically significant changes (positive or negative) in measures of financial, operational and service efficiency. Findings The findings indicate statistically significant increases in debt as a percentage of shareholder equity in post-merger/acquisition utilities and consequently leveraged higher returns on equity. However, there were no statistically significant changes in financial, operational or service efficiency measures (with the exception of decreased efficiency in telephone response). Research limitations/implications A total of 16 mergers or acquisitions were reviewed involving 32 of 79 LDCs, with the research implications pointing to a need for existing policy to be reviewed to determine whether a more detailed examination is required by the provincial energy regulator, including a closer examination of managerial motives, before approving mergers between municipal electricity distributors. This research involves only a quantitative approach and further research would examine these transactions using qualitative measures for a deeper examination as to managerial motives. Practical implications The results suggest that the mergers or acquisitions to date have served only to increase shareholder risk without improvement in other financial, operational or service efficiencies, a contradiction to the rationale behind the Province’s merger policy. Social implications The consolidation policy for Ontario LDCs has not resulted in any statistically significant improvement in electricity rates or service for consumers. Originality/value This paper is the first examination of the effects of Ontario’s LDC consolidation policy in terms of specific financial, operational and service efficiency measures.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".