How Exits from the Labor Force of Death Impact Household Incomes: A Four Country Comparison of Public and Private Income Support
Bibliographic record
Abstract
Government policies attempt to mitigate the economic risks to households of major life transitions. This paper focuses on two such transitions that social security systems typically insure against-long term exits from the labor market (retirement, disability, unemployment insurance) and the death of a household head or spouse (survivor’s insurance). We examine labor force exits of men at various ages in four countries--Canada, Germany, Great Britain, and the United States-using data from the Cross-National Equivalent File, a matched longitudinal data set. We focus on how average net-of-tax household income changes in the years before and after the event. We find that when one measures the change in economic well-being following a labor market exit by the fraction of lost labor earnings replaced by social security income, the decline in the household’s economic well-being is substantially overstated. When we compare net-of-tax household income before and after a long term exit from the labor market, we find that such drops are much less than those implied by a social security replacement rate and that differences across countries in the average drop are much less than those based on a social security replacement rate. We find the same pattern when we focus on how net-of-tax household income changes in the years before and after the death of a head or spouse. Declines in net-of-tax household income following such a death are much lower than the decline implied by a replacement of the deceased person’s labor earnings and social security benefits by their household’s post-death social security income. But the size of the change in individualized net-of-tax income following the death of a head or spouse is greatly affected by assumptions used to adjust for changes in household size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".