Essays on Industrial Organization and Health Economics
Bibliographic record
Abstract
This thesis is composed of three essays and provides empirical contributions to the Industrial Organization literature, especially in the field of transportation and health economics. It aims to understand different issues related to economics by applying various empirical methods.\t\nThe first essay (chapter 2) examines firm exit in Canadian markets, specifically the grain elevator market. There is a long line of previous literature that finds capacity, vintage, multi-plant ownership affect exit. In this paper, a choice model is used to examine a firm’s decision to shut down a grain elevator in terms of these variables, but also develops measures of spatial competition, local economic conditions and linkages to the transportation markets. In all cases, these variables are statistically important and point to results that reinforce previous studies, but also direct to new explanations on the determinants of plant exit.\n\t\nThe second essay (chapter 3) examines the effects of marijuana legislation change on the agricultural labor market. The paper uses differences-in-differences with a synthetic control methodology to identify the effects of labor market outcomes from marijuana legalization. This method aims to avoid substantial labor market spillovers in neighboring states and to construct a decent parallel trend for the pre-treatment time period with pretty varied agricultural markets in the U.S. The results show that cannabis legalization is associated with an increase in overall employments that people are flushing into the industry, but no increase in per-employee wages in both the retailer and agricultural labor market. \n \nThe third essay (chapter 4) looks into the accuracy of firms' prediction errors in the context of Medicare Advantage, where insurers receive subsidies from the government and compete to provide health insurance to seniors. The results show that on average firms overestimate future costs. Overestimation in forecast error decreases with the experience of the firm. Firms in more competitive markets (as measured by the number of other firms present) form more accurate estimates. Firms with higher costs than expected generally offer plans that feature greater patient cost sharing (i.e. higher deductibles and copays).\n\t\nThis dissertation includes both previously published/unpublished and co-authored material.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".