Demographic Change, Generational Accounts, and National Saving in the United States
Bibliographic record
Abstract
Abstract In recent years the role of fiscal policy in redistributing resources across generations has been subjected to intense scrutiny. That is not surprising because fiscal policy has played an increasingly important role in transferring resources between generations, especially in developed economies.1 Until recently, however, no technique was available for measuring the extent of intergenerational wealth redistribution via the public channel. This gap has now been filled by ‘generational accounting’, a method for keeping track of prospective and lifetime resource flows towards or away from specific generations as a result of a government’s tax and spending policies (Auerbach, Gokhale, and Kotlikoff 1991, 1994; Kotlikoff 1992). A generational account is the present value of taxes (net of transfers) per capita that members of a given generation may expect to pay to the government during their remaining lifetime if current fiscal policy is maintained. Generational accounting studies of the United States reveal a sizeable imbalance in US fiscal policy: the continuation of current policy for those now alive will entail the imposition of enormously larger fiscal burdens on generations to come. Similar conclusions emerge from generational accounting analyses for Italy, Germany, Norway, and Canada. (See Gokhale, Raffelhuschen, and Walliser 1995 for the case of Germany; Auerbach et al. 1992 for Norway; Oreopoulos 1995 for Canada; and Franco, et al. 1994 for Italy.)
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".