Bibliographic record
Abstract
In this paper, I develop a quantitative macroeconomic model with endogenous health and endogenous longevity and use it to study the impact of Social Security on aggregate health\nspending. I find that Social Security increases the aggregate health spending of the economy via two channels. First, Social Security transfers resources from the young with low marginal propensity to spend on health care to the elderly (age 65+) with high marginal propensity to spend on health care. Second, Social Security raises people's expected future utility and thus increases the marginal benefit from investing in health to live longer. In the calibrated version of the model, I show that the positive impact of Social Security on aggregate health spending\nis quantitatively important. The expansion of US Social Security since 1950 can account for approximately 43% of the dramatic rise in US health spending as a share of GDP over the same period (i.e. from 4% of GDP in 1950 to 13% of GDP in 2000). I also find that this positive impact of Social Security has two interesting policy implications. First, the negative effect of Social Security on capital accumulation in this model is significantly smaller than what previous studies have found, because Social Security induces extra years of life via health spending and\nthus encourages private savings for retirement. Second, Social Security has a significant spill-over effect on public health insurance programs (e.g. Medicare). As Social Security increases health spending and longevity, it also increases the insurance payments from these programs,\nthus raising their financial burden.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".