When measure matters: coresident sample selection bias in estimating intergenerational mobility in developing countries
Bibliographic record
Abstract
Potential biases from coresident sample selection have been a major stumbling block for research on intergenerational mobility in developing countries. We use two rich data sets from Bangladesh and India to provide evidence on the extent of coresidency bias in standard measures of intergenerational mobility: intergenerational regression coefficient (IGRC) and intergenerational correlation (IGC). Estimates for all children, father-son, and mother-daughter persistence in schooling show that the IGRC estimates are severely biased downward (average 30 percent). In contrast, the bias in IGC estimates is much lower (average less than 10 percent, in many cases less than 5 percent). Truncation due to coresidency criterion in a survey biases the IGRC estimate downward, but it also biases upward the estimate of the ratio of the standard deviations of parental to children's schooling. The IGC estimate suffers from lower bias because the upward bias in the estimate of the ratio of standard deviations partly cancels out the downward bias in the IGRC estimate. The evidence suggests that the available household surveys in developing countries can be fruitfully used to understand intergenerational mobility if one focuses on IGC. The findings have important implications for cross-country comparison of intergenerational economic mobility.
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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 | 0.071 | 0.257 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".