Measuring the Differentiated Impact of New Low-Income Housing Tax Credit (LIHTC) Projects on Households’ Movements by Income Level within Urban Areas
Why this work is in the frame
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Bibliographic record
Abstract
Social mixing is one of the key objectives of the housing policy in OECD countries. The Low-Income Housing Tax Credit (LIHTC) program, the largest affordable housing construction program in the US since 1986, has recently set creating mixed-income communities as one of the standards. As a project-based program, LIHTC developments are likely to influence residential mobility; however, little is known about its empirical effects. This study investigated whether new LIHTC projects are effective at attracting heterogeneous income groups to LIHTC neighborhoods, thereby contributing to creating mixed-income communities. Using unique individual-level household movement data combined with origin–destination neighborhood characteristics, we developed zero-inflated negative binomial (ZINB) models to analyze the LIHTC’s impact on residential mobility patterns in Franklin County, Ohio, US, from 2011 to 2015. The results suggest that the LIHTC attracts low-income households while deterring higher-income families, and therefore the program is not proved to be effective at creating mixed-income neighborhoods.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it