Bibliographic record
Abstract
My dissertation aims to understand how patients’ choice and utilization of healthcare are affected by health policies, including public health insurance and pharmaceutical price regulation. The first chapter of my thesis explores the determinants of health care demand for rural residents, with a focus on the introduction of a public insurance program in rural China. I develop and estimate a model of health facility choice and explore the welfare implications of counterfactual policies that would constrain rural patients’ choice of hospital, based on disease severity and care type (inpatient vs. outpatient). The second chapter examines whether health policies have the potential to improve patient and society welfare, focusing on the effects of a Canadian pharmaceutical pricing policy on drug expenditures and drug utilization among seniors. I estimate the effects of the policy with a difference-in-differences approach. To explore the mechanisms driving the effects, I also analyze the demand-side incentives and the role of the health insurance design (fixed dollar copayment vs. percentage co-insurance). To shed light on a puzzle I uncovered in my event study of chapter 2, the last chapter documents and proposes some tests of a recurring pattern with the size and evolution of cluster-robust standard errors (CRSE) in event study. The CRSE generally exhibit a decreasing trend across pre-event periods. I explore possible explanations for this issue by replicating some empirical papers and performing Monte Carlo simulations. The simulation results demonstrate that CRSE always present the decreasing pattern in pre-event periods, more so when intra-cluster correlation is high
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.011 |
| 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 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".