Bibliographic record
Abstract
This dissertation consists of three essays linking the business models of rating agencies to the rating decisions these agencies make as market intermediaries between buyers and sellers.\nThe first study examines the link between a rating agency‟s primary revenue source and its rating decisions. Theoretically, rating payments could influence rating agency decisions or be counterbalanced by reputational rewards for rating accuracy. I explore this relationship in U.S. corporate credit ratings, where some agencies are primarily paid by bond issuers (sellers) and others by investors (buyers). Analysis of a balanced panel of 338 companies rated between 2005 and 2009 reveals that agencies produce differing ratings consistent with the preferences of their paying customers. Changes in buyer-paid ratings are more frequent and generally precede corresponding seller-paid rating changes. Seller-paid ratings are slower to incorporate negative information, particularly for rated firms in the financial services sector and firms with ratings above a critical grading cutoff.\nThe second study complements the first by estimating the gap between the rating information disclosed by sellers and the information sought by buyers, again using evidence from U.S. corporate credit ratings. While seller willingness to pay for an additional rating is highly concentrated among a subset of relatively high-quality firms, buyers demonstrate more uniform interest in additional ratings for firms at all quality levels. This finding highlights an information gap among high-risk firms that is not a major focus of existing regulation.\nThe third study focuses on rating decisions by government rating agencies, an alternative rating model to those examined in the first two studies. The empirical setting is Canadian film classification where the existence of multiple regional regulators has been justified by claims of variation in community standards. I find significant and increasing consistency in the regulatory decisions of these agencies, suggesting institutional isomorphism that brings into question the persistence of the parallel regional structure.\nOverall, these studies provide new empirical insight into the relevance of rating agency heterogeneity to firm strategy and policy. The findings may also be relevant to a variety of other settings involving information disclosure such as environmental impact and corporate social responsibility.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".