Impact of the NREGS on Schooling and Intellectual Human Capital
Bibliographic record
Abstract
This paper uses a quasi-experimental framework to analyze the impact of India’s largest public works program, the National Rural Employment Guarantee Scheme (NREGS), on schooling enrollment, grade progression, reading comprehension test scores, writing test scores, math test scores and Peabody Picture Vocabulary Test (PPVT) scores. The availability of pre and two rounds of post-intervention initiation data from the three rounds of the Young Lives Panel Study allow us to measure both the short- and medium-run intent-to-treat effects of the program. We find that the program has no effect on enrollment but has strong positive effects on grade progression, reading comprehension test scores, math test scores and PPVT scores. The average effect size computed over several outcomes is similar to the effects of conditional cash transfer programs implemented in Latin America. These short-run impact estimates all increased in the medium run, that is, there is no decaying of impact but instead medium-run augmentation of the estimated short-run effects. The findings reported here are robust to attrition bias, endogenous program placement, type I errors and type II errors.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".