Essays on worker mobility, spatial labor markets, and urban real estate markets
Bibliographic record
Abstract
This dissertation consists of three chapters. The first chapter studies the long-run effect of job displacement on workers' commuting costs to subsequent jobs. Using German employee-employer data, geo-coordinates of workers' residences and workplaces, and a matched event study design, we estimate the response of workers' commuting distances to job displacement in mass layoffs. Displaced workers take up new jobs requiring 21 percent longer commuting, and the effect persists in the subsequent 10 years. To quantify the monetary value of increased commuting, we develop and estimate an on-the-job search model for workers' willingness to pay to avoid commuting. The extra commuting cost amounts to one-fifth of the wage losses facing displaced workers, which exacerbates the total cost of job displacement. In the second chapter, we show that dockless bike sharing solves the "last-mile problem" in public transportation by reducing the commuting cost between home and subway stations. As such, shared bikes increase the attractiveness and prices of apartments distant from subways relative to those nearby. Using resale apartment data and the staggered entry of bike sharing in 10 Chinese cities, we find that bike sharing reduces the housing price premiums near the subway by 29 percent. The reduction is equivalent to 1,893-2,127 CYN (282-317 USD) of the commuting cost per household per year over 30 years of residence. It is driven by a relative increase in the listing prices of and the demand for apartments distant from vis-à-vis proximate to subways. The third chapter assesses the effects of a corporate income tax cut for small businesses on employee earnings. Following a 2014 reform in Quebec, Canada, firms receiving the tax cut significantly raised the earnings of their workers. The earnings growth is connected with firms’ increased profits, as the effects are larger in high-growth industries where firms invest more and enjoy greater productivity. We estimate that workers bear up to three-quarters of the tax burden, and 35 percent of the surplus from the tax cut is passed onto workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".