MétaCan
Menu
Back to cohort
Record W1564548668

Do Neighbourhoods Influence Long-term Labour Market Success? A Comparison of Adults Who Grew up in Different Public Housing Projects

2002· preprint· en· W1564548668 on OpenAlexaboutno aff
Philip Oreopoulos

Bibliographic record

VenueRePEc: Research Papers in Economics · 2002
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNeighbourhood (mathematics)Public housingSubsidyEarningsSubsidized housingDemographic economicsWageLabour economicsEconomicsBusinessEconomic growthFinance
DOInot available

Abstract

fetched live from OpenAlex

This paper examines whether long-run labour market outcomes depend on residential environment among adults who grew up in subsidized housing in Toronto. The housing program in Toronto provides a full spectrum of neighbourhood quality types to measure outcome differences, and offers a real-life example of large scale neighbourhood quality reform. A primary advantage with this approach is that, conditional on participation in public housing, residential choice is substantially limited. Families that applied for public housing could not specify which project they wished to be housed in and were constrained to what was offered based on availability at the time they applied and by family size. Unlike previous housing mobility experiments, the availability of administrative tax records are used to measure both short and long run outcomes. The results indicate almost no difference in educational attainment, adult earnings, income, and social assistance participation between children from different public housing types. Average outcomes, estimated wage distributions, and outcome correlations among unrelated project neighbours show no significant neighbourhood impact. In contrast, family differences seem to matter a great deal.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.372
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueRePEc: Research Papers in EconomicsSame topicUrban, Neighborhood, and Segregation StudiesFrench-language works237,207