Incentivized Mergers and Cost Effciency: Evidence from the Electricity Distribution Industry
Bibliographic record
Abstract
In an effort to lower costs of provision, authorities have encouraged the consolidation of providers for a number of services such as electricity distributors, school boards, hospitals, and municipalities. In this paper we propose an endogenous merger process to evaluate the impact of government-provided incentives on consolidation patterns, and to evaluate the resulting outcomes. The process takes as input estimates from a stochastic frontier cost model, which yields an average cost curve for the industry. Policy parameters are used to simulate final configurations using offers that are the output of a Nash Bargaining problem. The efficiency of candidate merged entities is determined by a relative-influence function that measures the degree to which the combination of the involved firms' levels of efficiency results in cost-increasing amalgamations, and an interconnection cost that measures the impact of the size of the conglomerate that is formed. We calibrate parameters by applying the merger process to replicate the observed industry reconfiguration and then use these parameters to simulate the consolidation patterns that would have resulted from different policy incentives. We apply the method to the case of Ontario, where past mergers of local electricity distribution companies were incentivized by transfer tax reductions and a further round of mergers was recently proposed. Our findings suggest that the proposed tax incentive would have no impact on efficiency levels and consolidation patterns, and that even a substantial subsidy would still leave about five times as many LDCs as desired by policy makers.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".