Essays on the industrial organisation of the international copper industry
Bibliographic record
Abstract
The aim of this thesis is to study the main drivers of the supply of copper.This thesis proposes and estimates a dynamic structural model of the operation of copper mines using a unique dataset with rich information at the mine level from 330 mines that account for more than 85% of the world production during 1992-2010.It includes several aspects of this industry that have been often neglected by previous econometric models using data at a more aggregate level.First, there is a substantial number of mines that adjust their production at the extensive margin, i.e., temporary mine closings and re-openings that may last several years.Second, there is very large heterogeneity across mines in their unit costs.This heterogeneity is mainly explained by differences across mines in ore grades (i.e., the degree of concentration of copper in the rock) though differences in capacity and input prices have also relevant contributions.Third, at the mine level, ore grade is not constant over time and it evolves endogenously.Ore grade declines with the depletion of the mine reserves, and it may increase as a result of (lumpy) investment in exploration.Fourth, for some copper mines, output from subproducts (e.g., gold, silver, nickel) represents a substantial fraction of their revenue.Fifth, there is high concentration of market shares in very few mines, and evidence of market power and strategic behavior.Finally, sunk entry and exit costs are large and a key determinant of mine turnover.This sunk costs are also an important driver of prices.The proposed and estimated structural model in this thesis helps to understand better the dynamics of prices and extraction behaviour not only for the copper industry but to all extractive industries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".