Bibliographic record
Abstract
This dissertation contains three chapters in applied economics. Chapter 1 discusses the abolition fo rate bills for public schools in the United States during the 19th century and the resulting impact on primary school attendance. Until the late 19th century, families in some municipalities paid small user fees, called rate bills, for their children to attend public schools. Urban school districts gradually repealed these fees and funded public education through local taxes. States eventually abolished rate bills, forcing rural areas to provide public education without tuition requirements. Using United States Census data and a staggered adoption difference-in-differences approach, I show that state-level rate bill abolition increased rural primary school attendance by 7.2 percentage points. These results suggest that small costs can be an obstacle to school attendance and inhibit the diffusion of education.Chapter 2 discusses the effect of urbanicity on fertility. Fertility rates are significantly lower in urban areas than in rural areas. Using data from the 2016 Canadian Census, I exploit the exogenous placement of refugees to decompose the direct (causal) and indirect (compositional) effects of urbanicity on fertility. I find that these differences persist for refugee women, suggesting that causal factors are primarily responsible for rural-urban differences in fertility.Chapter 3 discusses the effect of fans on home field advantage in European soccer. We exploit exogenous variation in the level of fan attendance driven by COVID-19 mitigation policies and find that the home field advantage, as measured by home minus away (expected) goals, is reduced by more than 50\\% across the English Premier League, German Bundesliga, Italian Serie A, and Spanish La Liga. This leads to a decrease in probability for a home win, indicating that these goals are pivotal with respect to match outcomes. These results are robust to controlling for factors potentially correlated with the no-fans policies, such as changes in weather and COVID-19 prevalence by region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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; both teacher heads agree on what is shown here.
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".