Riding the Tiger: Managing Risk in U.S. Housing Finance and Health Insurance Welfare Markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article examines the political and economic dynamics of welfare markets in the USA. These marketplaces differ from other public–private welfare arrangements in that the state crafts and sustains these markets with the aim of using competition to promote cost-effective welfare provision. However, welfare markets face fundamental tensions between competition and stability that we trace to the allocation of risk between the state and private providers. Faced with the prospect of bearing potential losses, private firms often deploy instruments to reduce risk, lobby for risk protections from policymakers, or threaten to exit the market. The result is markets that are either non-competitive but stable, or competitive but unstable. In short, when policymakers create welfare markets they are riding a tiger, making themselves vulnerable to the functioning of markets over which they have imperfect control. We theorize and illustrate these dynamics through analyses of mortgage securitization, Medicare Advantage markets and the Obamacare health exchanges. This article contributes to the study of the US welfare state, speaks to the perils of marketized welfare in rich democracies and shows how market reforms can lead to unexpected state expansion.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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