Ceaseless Toil? Health and Labor Supply of the Elderly in Rural China
Bibliographic record
Abstract
Deborah Davis-Friedmann (1991) described the “retirement” pattern of the Chinese elderly in the prereform era as “ceaseless toil”: lacking sufficient means of support, the elderly had to work their entire lives. In this paper we re-cast the metaphor of ceaseless toil in a labor supply model, where we highlight the role of age and deteriorating health. The empirical focus of our paper is (1) Documenting the labor supply patterns of elderly Chinese; and (2) Estimating the extent to which failing health drives retirement. We exploit the panel dimension of the 1991-93-97 waves of the China Health and Nutrition Survey, confronting a number of econometric issues, especially the possible contamination of age by cohort effects, and the measurement error of health. In the end, it appears that “ceaseless toil” is also an accurate depiction of elderly Chinese work patterns since economic reform, but failing health only plays a small observable role in explaining declining labor supply over the life-cycle.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".