Bibliographic record
Abstract
Thomas L. Steinmeier [*] Pensions and Social Security are fundamental parts of saving. They each account for about a quarter of the $500,000 in total wealth held on average by families approaching retirement age. [1] Pensions have a large effect on retirement incentives as well. A man with a defined benefit (DB) pension plan who works in the year before qualifying for early retirement on average will find his benefits increased by about 60 percent of one year's pay. For a woman, the increase amounts to about one-third of a year's pay. [2] Accordingly, it is necessary to have accurate measures of pensions and Social Security in order to measure the wealth of those entering retirement, to understand saving and retirement behavior, and to determine the true impact of policies meant to influence saving and retirement. The Health and Retirement Study To further our understanding of pensions, Social Security, and their effects, we recently helped to develop and analyze data from the Health and Retirement Study (HRS). [3] The HRS, originally fielded in 1992 as a panel survey of 12,652 individuals from households with at least one member born in 1931-41, now has added additional cohorts (age groups) so that it is representative of the U.S. population over the age of 50. Crucial to our work, the HRS has collected pension Summary Plan Descriptions (SPDs) from the employers of two-thirds of those in the original survey who were covered by a pension in 1992. The HRS also collected detailed descriptions of pensions from this group's employers on previous jobs. Also central to our work, 80 percent of the survey respondents granted permission to the HRS to obtain their earnings histories from the Social Security Administration. Social Security records were linked successfully for 95 percent of those granting permission, or 75 percent of the HRS sample. The HRS cohort is an interesting group to study: they are the group closest to retirement age and the first covered by Social Security to learn that the present value of their benefits will fall below the present value of their taxes paid. [4] Thus, the HRS data can teach us a great deal about: retirement incentives; the relationship of pensions and Social Security to total saving and retirement outcomes; complexities in behavior beyond those incorporated in the simple lifecycle model; and the relationship between pension and Social Security policies and the distribution of benefits plus behavioral outcomes. Distributions of Pensions, Social Security, Wealth, and Lifetime Earnings From the HRS data, we have learned a great deal about the distribution of total wealth and its components -- including pensions and Social Security -- and about how they vary with lifetime earnings, for households and for individuals. Contrary to the general impression, pensions are distributed widely among households. Although only half of the employed individuals in the HRS have a pension, three-fourths of HRS households were covered by a pension at one time, and two-thirds of HRS households own the rights to a pension or pension income. In 1992, pension wealth was worth $191,000 per HRS household with a claim on a pension. Moreover, less than 10 percent of pension wealth has been lost because covered respondents have cashed out their benefits after leaving a pension covered job. The share of family wealth held as pensions increases with family lifetime earnings, rising from less than 5 percent of total wealth for those in the bottom 10 percent of lifetime earners, to 30 percent of total wealth for those in the 75th to 95th percentiles of lifetime earners. [5] Although men hold pensions that are much more valuable than the pensions held by women -- for example, at age 55, men hold DB pensions worth $200,000, while women's holdings are $108,000 -- the differences are explained largely by differences in earnings. Benefit-earnings ratios are actually higher for women with DB plans than men: for example, at age 60, benefit-earnings ratios are 20 percent for women and 16 percent for men. …
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".