Does the Social Safety Net Improve Welfare? A Dynamic General Equilibrium Analysis
Bibliographic record
Abstract
Does the social safety net improve welfare? Conventional wisdom says that means-tested social safety net programs improve welfare because they provide partial insurance against large negative shocks by guaranteeing a minimum consumption floor, but some economists have argued that they may also discourage labor supply and reduce capital accumulation (e.g. Hubbard, Skinner, and Zeldes (1995), Moffitt (2002)). Furthermore, recent research sug-gests that the welfare gain from the insurance channel may be small since the social safety net significantly crowds out private insurance decisions (e.g. Brown and Finkelstein (2008)). In this paper, I quantitatively assess the tradeoff between these channels in a dynamic gen-eral equilibrium model with incomplete markets and endogenous health insurance decision. I find that the social safety net generates a significant welfare loss, suggesting that the insur-ance channel is dominated by the other channels. I then show that the social safety net has a large crowding out effect on private health insurance, and this crowding out is important for obtaining the welfare loss result. I show that in a model with exogenous health insurance, the social safety net can generate a small welfare gain. I also find that the model can account for
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".