Essays In Housing And Urban Economics
Bibliographic record
Abstract
This dissertation examines how housing and location choice decisions contribute to spatial and social inequality. The first chapter studies the financial burdens of property taxes on homeowners. Exploiting a reform in Philadelphia that generated changes in property taxes without changing the provision of public goods and services, I measure how sensitive homeowners are to increases in their property tax bills. I find that a $100 increase in property taxes increases property tax delinquency by 3.9% after one year and 7.7% after two years. Home sales also increase by 4.1% after two years. There is no effect on house prices. Further, the financial burdens of property taxes vary considerably by owner race and occupancy status. White owners are more likely to recover from delinquency and sell their homes than Black and minority owners. Owners who live in their houses are also more likely to sell than landlords. The second chapter studies how the time spent commuting to work have evolved over the last four decades for White and Black commuters. In 1980, Black commuters spent 50.3 more minutes commuting per week than White commuters; by 2019, that difference declined to 22.4 minutes. Two factors account for the majority of this decline: Black workers are more likely to commute by transit, and Black workers make up a larger share of the population in cities with long average commutes. Increases in car commuting by Black workers account for nearly one quarter of the decline in the racialized difference in commute times between 1980 and 2019. Today, commute times have mostly converged (conditional on observables) for car commuters in small- and mid-sized cities. However, persistent differences in commute times still remain today in large, segregated, congested, and---especially---expensive cities, revealing the limits of cars in overcoming entrenched racialization of other factors of commuting.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".