Bibliographic record
Abstract
This thesis studies the consequences of housing unaffordability and regulation usingstructural models. In the first chapter, I consider the effect of minimum lot size regulation on welfare and urban structure. I show that minimal lots are the most expensive in the affluent, low-density neighborhoods of productive cities. Motivated by this evidence, I construct a general equilibrium model in which households of varying incomes choose cities and neighborhoods, value affluent neighbors, and are burdened differently by regulation. A counterfactual deregulation exercise shows significant and progressive welfare gains for renting households (9% of income) that offset the losses to landowners (17% of land values). Productivity gains from urban expansion are nullified by the out-migration of affluent households who prefer regulated neighborhoods. Moreover, deregulation only slightly exacerbates the externality arising from the demand for affluent neighbors. These results suggest that the most important consequence of deregulating housing markets is increasing housing affordability. In the second chapter (co-authored with Guangbin Hong), we consider the effect ofpolicy interventions designed to slow down housing redevelopment and gentrification. Using a spatial ”discontinuity-in-differences”, we estimate that a $15,000 teardown tax implemented in two Chicago neighborhoods reduced demolitions by 59%. Motivated by these findings, we develop a general equilibrium model featuring forwardlooking landlords and heterogeneous households with varying willingness to pay for housing quality. Landlords choose the optimal timing and scale of redevelopment, producing high-quality housing that depreciates and subsequently ”filters” down to low income households. The model predicts that an expanded $60,000 teardown tax shifts redevelopment and gentrification to other affordable, untreated neighborhoods, underscoring unintended policy consequences. In the third chapter, I explore how the elasticity of housing supply varies by qualitysegment. Utilizing a shift-share instrument exploiting national shocks to the worker age distribution, I estimate a supply elasticity of 1.45 for high-quality housing and nearly zero for low-quality housing. Embedding these elasticities into an equilibrium model, I analyze a large population shock in New York, showing welfare losses twice as large compared to when elasticities are uniform across quality segments. This is driven by substitution towards lower quality segments that are inelastically supplied.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".