Job Market Paper The Long-run Consequence from Living In a Poor Neighborhood
Bibliographic record
Abstract
Abstract: I examine the effect of neighborhood quality on long-run labor market outcomes among adults who grew up in substantially different public housing projects in Toronto. Subsidized families were assigned to projects throughout the city at the time they applied, with assignment based mainly on the number of bedrooms required. Unlike housing programs used in previous studies, neighborhood quality differences were not attributable to one set of families moving to better neighborhoods and another set remaining at their current residence. I match census data, longitudinal administrative records, and criminal occurrence data to public housing addresses and track participants’ outcomes, in some cases, a decade or more after leaving the program. The main finding is that differences in neighborhood quality play little or no role in determining adult earnings, education attainment, or social assistance participation, but do affect residents’ exposure to crime. Living in contrasting housing projects cannot explain large variances in labor market outcomes, but family differences, as measured by sibling outcome correlations, account for up to 30 percent of the total variance in the data. Overall, the results suggest that policies aimed at improving long-run outcomes among children from low-income households are more likely to succeed by addressing family, rather than neighborhood, circumstances. (JEL: I30, J38). I am very grateful to my advisors, Alan Auerbach, David Card, John Quigley, and Steve Raphael, for their guidance and support. I also wish to thank Miles Corak and Sophie Lefebre for assistance with Statistics Canada’s administrative data. Frances Beard and Barbara Watson from Metro Toronto Housing Corporation, Brent Donnelly and Ryner Soegtrop from Cityhome, and Monique Volpe from the Ontario Housing Corporation were instrumental in helping me compile a complete list of subsidized housing projects in Toronto. This research was written while I was employed at the Family and Labor Studies
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.003 |
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".