Entering and leaving housing assistance: Neighborhood trajectories of housing voucher recipients in the United States
Bibliographic record
Abstract
The Housing Choice Voucher (HCV) provides rental housing assistance to millions of low-income households across the United States and plays a crucial role in shaping their exposure to concentrated poverty and racial segregation. While prior research has revealed that housing voucher recipients tend to face substantial constraints in terms of neighborhood location, less is known about the direct impact of entering and exiting such programs on individual locational outcomes. Employing a unique dataset that links between housing program records and census microdata between 2000 and 2018, this article examines the impact of entering and exiting housing assistance on the neighborhood context experienced by voucher recipients. Two-way fixed-effects models show that entering the housing voucher program has no statistically significant impact on the neighborhood poverty or racial composition experienced by recipients, while exiting the voucher program results in significant decreases in neighborhood poverty rates relative to both pre-voucher and voucher locations. However, there are substantial differences in these trajectories by race: while white households experienced significant post-voucher decreases in poverty relative to both their pre-voucher and voucher locations, non-white households did not experience post-voucher changes relative to their pre-voucher locations, and Black households experienced no statistically significant post-voucher poverty decreases at all. These findings point to the continued importance of race in shaping neighborhood outcomes: even households participating in the same housing assistance program experience racially disparate outcomes, both during and after their participation in the program.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".