Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».