Bibliographic record
Abstract
Abstract The average expense ratio paid by Canadian mutual fund investors is 50% higher than that paid in the United States. This discrepancy is commonly thought to exist because Canadian funds do not take advantage of economies of scale and have less competition. A monopolistic competition framework is used to develop a model for the mutual fund industry. By allowing each fund to have different attributes, the model permits funds to charge different expense ratios in equilibrium and is found to strongly fit the North American mutual fund market. Empirical analysis indicates that these two common explanations and measurable fund attributes account for 24% of the discrepancy. JEL Classification: L11, L13 and G15 Les ratios de dépenses des fonds mutuels nord‐américains Le taux moyen de dépenses payées par les investisseurs canadiens dans les fonds mutuels sont de 50% plus élevées que celles qu’on paie aux Etats‐Unis. Cet écart est attribué d’habitude au fait que les fonds canadiens ne tirent pas profit des économies d’échelle et qu’il y a moins de concurrence au Canada. On utilise un modèle de concurrence monopolistique pour analyser l’industrie des fonds mutuels. En permettant à chaque fond d’avoir certains attributs, le modèle permet aux fonds de charger des taux de dépenses différents en équilibre. Il semble que cela corresponde aux caractéristiques du marché des fonds mutuels américains. Une analyse empirique montre que les deux explications usuelles et les attributs mesurables des fonds expliquent 24% de l’écart.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".