Financializing the American Economy and Housing Market
Bibliographic record
Abstract
Chapter 4 explores how American policymakers expanded housing programs from the late 1960s to the early 1990s to address economic challenges such as rising inflation, unemployment, and deindustrialization. When high interest rates threatened mortgage lending and housing activity, policymakers created a government-backed mortgage-backed securities market with the quasi-public agencies Fannie Mae and Freddie Mac at its center. These actions aimed at restoring housing-based growth by attracting capital into housing and expanding mortgage lending at affordable rates. Moreover, policymakers expanded tax subsidies for homeownership, notably through the Tax Reform Act of 1986, and extended housing programs to stimulate economic activity in marginalized communities previously excluded from the benefits of housing-based growth. These programs contributed to the financialization of the American housing market and economy: They made the US mortgage market even more dependent on government support and tied the demand-led economy more closely to housing, as homeowners increasingly borrowed against their homes for consumer spending - further entrapping policymakers into supporting the housing sector as a growth strategy for decades to come.
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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 | 0.000 | 0.000 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".