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Enregistrement W4223999900 · doi:10.22215/etd/2022-14827

Essays on Social Interactions and Network Economics

2022· dissertation· en· W4223999900 sur OpenAlexaff
Nabil Afodjo

Notice bibliographique

Revuenon disponible
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueEconomic theories and models
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésBipartite graphNeighbourhood (mathematics)Social network (sociolinguistics)Cardinality (data modeling)Production (economics)Stability (learning theory)Computer scienceEconomicsMicroeconomicsMathematicsGraphSocial mediaTheoretical computer science

Résumé

récupéré en direct d'OpenAlex

This thesis is composed of three self-contained chapters which all revolve around social interactions and their effect on market stability and economic outcomes.In the first chapter, we introduce a dynamic model of an anonymous bipartite economy with heterogeneous agents, where each agent only cares about connecting with an optimal number of peers of the other type.We then explore the impact of popularity bias -the tendency to make choices that are more popular -on the efficient and long-run stability of this economy.We provide a full characterization of steady state matchings in terms of the allocation of links between the two sides of the economy.These matchings, which feature a small number of hubs on the long side (group with higher demand for connections), refine the set of matchings that form in the absence of bias, but they are not efficient in general, despite agents being rational.When irrationality (or the possibility of mistakes) in the creation and severance of links is allowed, popularity bias leads to a further refinement, as only efficient matchings remain in the long run.In addition, we uncover structural conditions under which steady state matchings are efficient in the absence of mistakes.We discuss empirical implications for competition and link our findings to the "Matthew effect", market share inequality, and to the emerging industry of "fake" views and reviews on social media.The second chapter examines the effects of network size on the coffee production of Fair Trade certified farmers in Peru.We use a unique extensive survey and administrative information from a Fairtrade cooperative to measure the number of peers within the cooperative who nominate a producer (in-degree) as well as the number of peers nominated by the same producer (out-degree).After adjusting our methodology to derive predictions for population in-degree and uncensored out-degree from observed sample values, we first find the existence of a unidirectional relationship between the i number of nominations received by individuals and coffee production.Conversely, the number of people nominated by a producer does not appear to have any effect on their output level.These findings are proved robust to different specifications.Further investigation into nominating patterns leads to the identification of an unobserved heterogeneity term, related to nominating behavior, which mitigates the strength and significance of the first set of results.Inclusion of these individual effects help show no overall effects on the size of a farmer's network on their coffee output.Looking into heterogeneous effects, we find no significant impacts, except for members of two groups: individuals who use a pest control system and those who report dissatisfaction with their current life.Producers from the former group see their production increase by 1.5% which each additional connection, whereas an extra link significantly decreases the coffee output of farmers who don't positively rate their overall life.Our study is the first to rigorously investigate network size effects within a Fairtrade environment where cooperative membership has been shown to play a significant role.The third chapter aims to investigate and quantify the neighbourhood effects in the demand for financial advice.Social interactions -effects of the group on an individual -have been found to impact a wide range of outcomes and decisions; but they have, to this point, not been taken into account in the modeling of the decision to consult a professional on financial matters.Using data from the 2009 version of the Canadian Financial Capability Survey, this study investigates the presence of group effects in the decision to seek financial counsel from a professional advisor and make use of the received advice.Significant impacts are found at all levels, especially when models are controlled for contextual effects.Moreover, endogenous effects appear to increase as we move from a macro setting (Census Metropolitan Areas) to more local partitions (Forward Sortation Areas).Investigation into heterogeneous effects by gender, age, education, and immigrant status point to homophily as the main mechanism behind these positive social interaction effects: individuals simply respond more noticeably to the actions of peers who share similar characteristics.Implications and policy relevance of these findings are discussed.ii Declaration All chapters of this thesis are self-containing research articles.I acknowledge the contribution of Roland Pongou for the research associated with the first and third chapters of this thesis.The second chapter is co-authored with Ana Dammert and Jose Galdo.In all cases, the contribution of my co-authors is equal to my own.iii I see this thesis as a culmination of my post-secondary studies journey, which started over 20 years ago.It wouldn't have been possible to reach this step without the support of various mentors, family members, colleagues, and close friends, many of whom I would like to take the time to thank.I'd like to start by acknowledging the two most important women in my life -my wife Diana and my mom Brigitte -for their unconditional support during the many ups and downs of this PhD journey, and for consistently reminding me that quitting was not option, regardless of how many family dinners I had missed, and how many weeks had passed since I last called.I dedicate this work to them.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,013
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,003
Communication savante0,0020,003
Science ouverte0,0000,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0130,002

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.

Tête enseignante Opus0,031
Tête enseignante GPT0,248
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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