Essays on Crime and Social Inequality
Bibliographic record
Abstract
This dissertation examines the intersection of crime economics and discrimination through three empirical studies. Each chapter explores how structural inequality, socioeconomic shocks, or institutional responses shape criminal behavior and disparities across demographic groups. Together, the studies provide new evidence on the persistent interaction of crime and discrimination in historical, institutional, and labor market contexts. The first chapter investigates racialized enforcement of drug laws in the U.S. South during the War on Drugs. I develop a model of cultural persistence, linking punitive attitudes toward enslaved Black cotton pickers to intensified law enforcement practices a century later. Using FBI Uniform Crime Reporting data, I estimate the causal impact of historical cotton plantations on racial disparities in drug-related arrests. Counties with higher cotton production in 1860 recorded significantly higher arrest rates of Black offenders after 1982, while white arrest rates remained unchanged. A case study of Arkansas reinforces these results, showing distinct enforcement patterns between neighboring cotton and non-cotton counties. The second chapter analyzes the effect of Ecuador’s 2016 earthquake on crime. The 7.8 magnitude quake caused severe destruction, and I examine its secondary consequences for property and violent crime. Employing a difference-in-differences framework and event study design, I find a sharp but temporary increase in property crimes, alongside a slight decline in violent crimes. Evidence suggests the property crime spike reflects temporal displacement: offenders bringing forward criminal activity in response to the lowered cost of offending after the disaster. The third chapter shifts to labor market inequality in Canada, focusing on occupational task intensity and gender wage gaps. Using Labour Force Survey microdata and adapting task classifications from Autor et al. (2013), I measure the intensity of abstract, routine, contact, and manual tasks. Over the past 25 years, both men and women experienced rising abstract task intensity, with women recently surpassing men. Despite this, wage gaps persist across all task types, particularly in abstract and manual tasks. Working mothers remain underrepresented in high-abstract-task occupations, largely due to long and unpredictable hours, which intersect with the unequal division of childcare. These findings highlight structural mechanisms sustaining gender inequality in Canadian labor markets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".