AN ECONOMETRIC MODEL OF THE CYCLICAL SENSITIVITY OF OASDI OUTLAYS
Bibliographic record
Abstract
An econometric model of outlays for the Old Age, Survivors, and Disability Insurance (OASDI) program is constructed in this study. The model is designed to estimate the impact of changes in economic activity (chiefly changes in the unemployment rate) on OASDI outlays. The model has two components. The beneficiary component estimates flows of primary beneficiaries into and out of the program and the number of widow, widower, and child beneficiaries. The benefit payment component estimates average awards and average benefits for primary beneficiaries, average benefit payments for widow, widower, and child beneficiaries, and remaining payments as residual outlays. OASDI outlays were increased by $315.6 million (or.24 percent) from the business cycle peak in the first quarter of 1971 to the peak in the fourth quarter of 1973 and by $285.8 million (or .38 percent) during the contraction which extended from the first quarter of 1974 to the first quarter of 1975 due to departures from full employment. Previous econometric models incorrectly estimate both the short and long run impact of increased unemployment on OASDI outlays. Federal government estimates based on previous models suggest that a one percentage point increase in the unemployment rate lasting two years will increase outlays by .22 percent in the first year and .52 percent in the second year. The model developed in this study estimates that such an increase in unemployment will increase outlays by .06 percent in the first year and .23 percent in the second year. Previous models estimate that a three year increase in unemployment will continue to influence outlays for only one to two years after the unemployment rate is reduced. The model developed in this study estimates that outlays will be .37 percent higher in the third year after unemployment is reduced. Thus previous econometric models of OASDI overestimate the short run impact and underestimate the long run impact of increases in the unemployment rate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| 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 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".