Essays on pension, insurance and mutual fund markets
Bibliographic record
Abstract
This thesis contains two essays on investor decisions and the role of financial intermediaries in pension and insurance markets, and one essay on the size effect in the mutual fund market. In the first chapter, my coauthor and I study how investors respond to scandals related to distinct aspects of environmental, social, and governance in their 401(k) retirement savings. We show that nearby ESG scandals correlate with increased ESG fund additions and flows, possibly through “evoking” existing sustainable preferences among investors. Investors with different characteristics respond heterogeneously to E, S, and G scandals, resulting in an overweighting of funds with higher environmental and social scores. In the second chapter, my coauthor and I study the impact of sales channels on insurance product adoption. Using novel policy-level life insurance data in China, we exploit a regulatory change that requires bank insurance agents in each quarter to sell more long-term insurance products. Exploiting a discontinuity-inslope design, we show that bank agents falling below their target qualified ratios in the first two months of a quarter make up for the shortfall in the third month. This shift in the qualified ratio in the last month of the quarter is entirely due to a product-composition change – switching from short-term unqualified life insurance products to long-term qualified annuity products. We further show that this switch is not achieved by changing the relative pricing of products or client compositions. In the third chapter, I examine the relationship between the magnitude of the negative size effect and fund sector concentration. It finds a strong correlation indicating that funds in more concentrated sectors exhibit more severe diminishing returns to scale compared to those in less concentrated sectors. The paper proposes a potential explanation: in highly concentrated sectors, fund flows are less sensitive to past returns. However, in such sectors, marketing expenses appear to positively influence flow sensitivity to good performance, while showing a neutral effect in response to poor performance. Large funds in concentrated sectors may invest more in marketing efforts, but this does not necessarily translate to better future performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".