Essays in Public and Health Economics
Bibliographic record
Abstract
This dissertation studies the design and effectiveness of public policies that aim to improve population health. This dissertation is divided into three chapters. In chapter one, I study the design of an optimal paid sick leave system. To do so, I combine individual-level data on paid sick leave claims from Chile with a sick pay insurance model. First, I show that workers respond to the monetary incentives induced by the benefit scheme, and their behavior varies with the day they fall sick. I use these patterns to inform and estimate a model of sick pay insurance. In the model, risk-averse workers face health shocks and decide how many days to be on leave. Workers are insured by a risk-neutral social planner who chooses the optimal contract to maximize social welfare. I leverage the estimated model to derive the optimal sick pay contract and estimate the welfare gains from its implementation. Relative to the current system, the optimal system provides more insurance for short-term sickness and less insurance, i.e., lower replacement rates, for longer sickness spells. Workers are willing to give up 1.53\\% of their earnings to be insured under the optimal policy. The second chapter focuses on the origins and effects of the opioid crisis. Drawing on unsealed records from litigation against Purdue Pharma, I uncover rich quasi-exogenous variation in the marketing of OxyContin, to causally connect supply-side factors to the origin of this epidemic. My results indicate a strong causal link between Purdue Pharma's promotional targeting and future increases in prescription opioids. The rise in access to potent prescription opioids is responsible for a dramatic increase in opioid mortality, declines in the quality of life, increases in fertility, and deterioration of birth outcomes. The third chapter examines the effect of non-price interventions on smoking exploiting the 2011 Argentinean anti-smoking national law. I interact state-level legislation with the national law to identify the effect of incorporating graphic tobacco warnings and implementing clean indoor air policies on smoking prevalence and cigarette consumption. I explore whether alcohol and tobacco are consumed as complements or substitutes to assess the side effects of tobacco policies.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".