Essays on Policy Issues in Non-Pro t Markets
Bibliographic record
Abstract
This dissertation focuses on policy issues in non-profit markets using tools from the industrial organization and labour literatures. The first essay investigates the impact of hospital closures and mergers. We use data on a large wave of directed hospital mergers and closures in the province of Ontario, Canada to investigate the impact of hospital reorganization on patient welfare. We estimate a model of patient hospital choice on data collected before the reorganization, finding that both distance and hospital quality are determinants of choice. The model is then used to understand the short-run and long-run welfare impact of reorganization. Results suggest that cost savings and efficiency are not the only factors to consider when restructuring in settings where patients do not pay for services. Hospital access and quality must be considered. The second essay investigates the market for senior management in the U.S. charities sector. Using charity-level data from 990 Forms filed for the U.S. Internal Revenue Service, we document two new stylized facts not previously documented in the literature. First, we find that female managers are more likely to receive compensation than male managers. Second, conditional on receiving compensation, female managers receive 8% lower total compensation than their male counterparts. We show the gender pay gap is only present in the largest charities (top two quintiles in the revenue distribution). Both findings are robust to the inclusion of charity classification fixed effects (between estimator) and charity fixed effects (within estimator). Our exploration of mechanisms driving the gender pay gap in this context points out a mismatch between pay dispersion and performance dispersion. While pay dispersion across genders is consistent with organizations perceiving male managers as differentiated unlike female managers, and there are no differences in performance dispersion supporting this perception.
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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 | 0.028 |
| 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 | 0.007 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".