The Unintended Long-Term Consequences of Mao's Mass Send-Down Movement: Marriage, Social Network, and Happiness
Bibliographic record
Abstract
This paper uses the China General Social Survey (CGSS) 2003 to evaluate the long-term consequences of a forced migration, the state's "send-down" movement (shang shan xia xiang, or up to the mountains, down to the villages) during the Chinese Cultural Revolution, on individuals' nonmaterial well-being. The send-down program resettled over 16 million urban youths to the countryside to carry out hard manual labor over the years 1968-78. Most of them were allowed to return to urban areas when the Cultural Revolution ended. To estimate the long-term impacts of the send-down experience, we compare the outcomes of individuals with send-down experience to those of individuals without send-down experience but having similar characteristics and family backgrounds during the send down period. We conduct primarily OLS estimates with a careful sample selection. We find that those who had the send-down experience have worse marriage outcomes, lower-quality social networks, and a lower level of happiness than non-send-downs. The negative effects of the forced migration are robust against regression methods and various model specifications. Our study adds to the growing literature in economics that seeks to evaluate the impact of forced migration. (C) 2016 Elsevier Ltd. All rights reserved.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".