Essays on Mortgage Rates, Mortgage Fees, and Merger Price Effects
Bibliographic record
Abstract
This dissertation is a series of essays that focus on studying mortgage rates, mortgage fees and merger price effects. The first essay investigates the relationship between monetary policy and mortgage rates along the business cycle. Using a large dataset of U.S. mortgage loans, we document that, following a regulatory change to payments on excess reserve, the business cycle is less related to mortgage rates, monetary policy is being transmitted more overall, and the amplification of the transmission of monetary policy to mortgage rates along the business cycle is being reduced compared to before the regulatory change. To understand the last two results, we build a theoretical banking model, where banks are subject to monetary policy through reserve requirements, and show that the empirical results could have been caused by this regulatory change. The second essay looks at heterogeneity in the fees for originating a mortgage in the U.S. Using data of millions of mortgages, I document a racial and a gender gap, for both singles and couples, in the fees for originating a mortgage. I also find that there seems to be selection into high origination fee lenders for some minority groups, male/male couples and female/female couples, which might explain some of the racial and gender gaps found. Finally, the last essay examines the price effects of mergers between cooperative firms that value both profits and a social component and standard firms that only value profits. Using a theoretical model, I show that the shape of the social component matters for the sign and magnitude of the price effects, which can include a decrease in prices.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".