Bibliographic record
Abstract
In this thesis I present three chapters that explore various themes in competition with a focus on collusion and spatial competition.Chapter one examines whether the availability of late-stage settlements for defendants in criminal price-fixing suits has a negative impact on the effectiveness of early stage leniency programs in the context of antitrust enforcement.Our main finding is that an appropriately designed settlement program can make collusion more difficult: in equilibrium, the adoption of an optimal settlement program by an antitrust authority (AA) reduces the occurrence of cartels by decreasing the long-run gains from collusion.However, overly generous settlement policies may undermine leniency programs and encourage the formation of more cartels.Chapter two explores the relationship between business-cycle fluctuations and collusive behavior.From a theoretical perspective, we demonstrate that the degree of antitrust enforcement (external cartel stability) directly influences the boundary that determines whether positive demand shocks are either pro-collusive or anti-collusive.We find that as cartels become increasingly unstable, they have a preference for defecting in the presence of positive demand shocks since there is riskiness associated with continuing to collude under a strong enforcement regime.From an empirical perspective, we find that the observed collusive activity is weakly procyclical, however, much of the variation is explained by enforcement and monitoring policies.Chapter three explores spatial competition in the Canadian banking industry whom I worked closely with on the second chapter of this thesis.In addition, I would like to thank the remainder of the examining committee; Gamal Atallah, Iwan Bos, and Patrick Callery.You have provided valuable comments and insights which have helped shape this thesis.I also would like to thank Thomas Ross who has been instrumental in shaping the first chapter of this thesis.I also want to thank Till Gross, our conversations altered the trajectory of the second chapter of this thesis, and as a result, it is much improved.I want to thank Patrick Coe for his constant support over the course of this thesis.Finally, I would like to thank Lynda Khalaf, Alex Maslov, and Derek Mikola for their comments, support, and conversations over the past five years.You have all played a role in shaping me as a researcher.I also owe a debt of gratitude to Marcel Voia and Kim Huynh whom trusted in my abilities and enabled me to conduct research at the Bank of Canada.This opportunity has culminated in the third chapter of this thesis.Finally, I would like to thank Heng Chen.I am incredibly lucky to have worked closely with such a dedicated researcher who introduced me to the field of spatial statistics, challenged my understanding of econometrics, and fostered my growth
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".