Bibliographic record
Abstract
The dissertation covers two topics.The first in two essays explore the causal negative effect of professional sporting events on a hosting city.The Last essay tries to estimate the causal effect of smoking cessation on health outcome.In the first essay, I examine the impact of Major League Baseball (MLB) games on crime in a host city using the Washington Nationals case, which moved from Montreal, Canada to Washington DC in 2005, as a natural experiment.To address endogeneity concerns, I apply a synthetic control method with using 21 large cities which host an MLB team as a "donor pool" and employs a triple difference-indifference approach to estimate the change in crime before and after the Nationals coming, between MLB season and off-season, and Washington DC and the synthetic Washington.With monthly crime data from the Uniform Crime Report, only assaults increased by 7 to 7.5% annually after the Nationals moved to DC; other crimes were unchanged.This result is supported by statistical significance and in-space placebo tests, and several alternative specifications in robustness check.Little to no evidence of a causal relationship between MLB games and other types of crime.The second essay, jointly with Dr. Brad Humphreys, looked at the relationship between MLB games and traffic congestion.No empirical evidence currently exists linking sporting events to local traffic conditions.This chapter analyzes urban mobility data from 25 US metropolitan areas with MLB teams over the period 1990 to 2014 to assess the relationship between local traffic and MLB games.Instrumental variable regression results indicate MLB attendance causes increases in local vehicle-miles traveled.At the sample average attendance of 2.8 million, average daily vehicle-miles traveled increases by about 0.5% or 1.5% on a game day in cities with MLB teams.Traffic congestion increases by 2%, suggesting that MLB games generate congestion externalities.The last essay explores the causal effect of smoking cessation on health outcome.While negative impacts of smoking on health are well-known, assessing the effect of smoking cessation on health is difficult due to self-selection problems.I address this self-selection using propensity score matching.Using a rich longitudinal data set from the British Household Panel Survey, this chapter finds 5 to 6 percentage point (8%) increases in the probability of an individual reporting good health status among quitters compared to inconsistent smokers.Respiratory problems decline by 30% from the average.The estimated effect of quitting on health diminishes with the number of cigarettes smoked before quitting and smoking tenure..
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 | 0.003 | 0.009 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".