Housing Subsidies and Homelessness: A Simple Idea
Bibliographic record
Abstract
Reducing homelessness is an indisputable social good, and housing subsidies offer one way to do so. However, subsidies come in many different varieties and are intricately bound up with economic and social policies. This paper, written by one of North America’s leading urban economists, cuts through the tangle and argues that the simplest approach is the best. The ideal way to deter people from harmful acts is to reward them for abstaining. Thus, to combat homelessness, governments should offer housing allowances to people for every night they are not homeless. This optimal homelessness-reducing home allowance (OHRHA) is open to adjustment to suit individual circumstances and the effects of homelessness on different demographics. It is meant to reduce homelessness by aligning individual and societal incentives, forcing people to bear the consequences or realize the benefits that their actions impose on others. The author explores methods for financing OHRHA, examines means for tailoring it to meet the diverse needs of the homeless and discusses the policy’s effect on urban housing markets, all while comparing and contrasting the proposal to existing homelessness-reduction measures in Alberta, Canada and the US.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".