Three Essays in Energy Economics and Industrial Organization, with Applications to Electricity and Distribution Networks
Bibliographic record
Abstract
Electricity industries are experiencing upward cost pressures in many parts of the world. Chapter 1 of this thesis studies the production technology of electricity distributors. Although production and cost functions are mathematical duals, practitioners typically estimate only one or the other. This chapter proposes an approach for joint estimation of production and costs. Combining such quantity and price data has the effect of adding statistical information without introducing additional parameters into the model. We define a GMM estimator that produces internally consistent parameter estimates for both the production function and the cost function. We consider a multi-output framework, and show how to account for the presence of certain types of simultaneity and measurement error. The methodology is applied to data on 73 Ontario distributors for the period 2002-2012. As expected, the joint model results in a substantial improvement in the precision of parameter estimates.Chapter 2 focuses on productivity trends in electricity distribution. We apply two methodologies for estimating productivity growth - an index based approach, and an econometric cost based approach - to our data on the 73 Ontario distributors for the period 2002 to 2012. The resulting productivity growth estimates are approximately ‐1% per year, suggesting a reversal of the positive estimates that have generally been reported in previous periods. We implement flexible semi-parametric variants to assess the robustness of these conclusions and discuss the use of such statistical analyses for calibrating productivity and relative efficiencies within a price-cap framework. In chapter 3, I turn to the historically important problem of vertical contractual relations. While the existing literature has established that resale price maintenance is sufficient to coordinate the distribution network of a manufacturer, this chapter asks whether such vertical restraints are necessary. Specifically, I study the vertical contracting problem between an upstream manufacturer and its downstream distributors in a setting where spot market contracts fail, but resale price maintenance cannot be appealed to due to legal prohibition. I show that a bonus scheme based on retail revenues is sufficient to provide incentives to decentralized retailers to elicit the correct levels of both price and service.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".