Bibliographic record
Abstract
This thesis contains three chapters exploring topics related to industrial organization and banking. The first chapter studies how banks compete through interest rates and credit rationing in the U.S. mortgage market. I estimate a structural model of bank competition in interest rates and credit rationing using U.S. mortgage data. I use the estimated model to show how banks optimally trade-off interest rates and credit rationing, and illustrate its policy relevance by examining the magnitude and the form of banks' pass through -- to clients -- of a cut in funding costs. I find that banks pass through their lower funding cost by not only cutting interest rates but also relaxing credit rationing. There is substantial heterogeneity in the pass through in both margins and this is mainly explained by heterogeneity in two different types of banks' costs: funding cost of originating mortgages and cost of processing applications. The second chapter surveys recent papers using structural models to study lending markets from an industrial organization perspective. I show that the papers can be divided into two modeling and estimation frameworks: the discrete choice, differentiated product model framework and the search model framework. The discrete choice model framework is based on seminal work by Einav, Jenkins, and Levin (2012) and is used to study how issues related to adverse selection, moral hazard, and market structure affect lending market outcomes. The search model framework, on the other hand, encompasses a variety of approaches focusing on how search frictions affect lending outcomes. I compare and contrast the two different frameworks and also suggest areas for future research. The third chapter studies the factors that drive mortgage securitization in the U.S. I combine a multinomial probit model of mortgage securitization with unique application-level mortgage data to study how loan- and bank-level characteristics affect bank securitization incentives. Overall, I find that lower mortgage quality and higher bank resource constraints lead to higher mortgage securitization by banks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".