Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".