Subjective Income Expectations And Risks In Rural India
Why this work is in the frame
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Bibliographic record
Abstract
This paper analyses the pattern and determinants of income risk and expectation in rural India. It uses unique primary survey data eliciting subjective income distribution from households in twelve villages in Bihar. It finds that expected future income is significantly and positively associated with its variance. Current income is a significant predictor of expected future income and its variance. While both expected future income and its variance increase with current income, there is a significant negative association between the coefficient of variation of future income and current income, suggesting that low-income households face greater variability in their income. Upper caste households and households reliant on non-agricultural income have significantly higher expected future income and variance. Income process is highly persistent. This paper is one of the first to utilize subjective expectation data to analyse income risk in a developing country.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it