Education and agricultural technology adoption: Evidence from rural India
Bibliographic record
Abstract
Abstract: In previous studies, it is clearly explained that agricultural productivity is directly related to the adoption of technology. The adoption of technologies is in turn influenced by the education of individuals. Thus, this article aims to analyze education impact on the adoption of new agricultural technologies in rural India. Using data from the India Human Development Survey (IHDS) 2011-2012 (Desai and Vanneman, 2015) collected from 42,152 households across all states and union territories in India, we estimate these effects through chi-square test and binary logistics model. The results of the estimates show that when a farmer is educated, the likelihood of adopting a new farm technology increases by 3.37 %. But the effect of education is still heterogeneous. Indeed, when the farmer lives in a rural area, the probability of adopting new technology is 3.30 %. The results also show that if the farmer lives in an urban area, the probability of adopting new technology is 6.12 %. Finally, other factors are also important and enable farmers to adopt new technologies. These are farm insurance and access to farm credit, which increase the likelihood of adopting new agricultural technology by 10 % and 4.83 % respectively. Keywords: education, farmer, technology adoption, binary logistics
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".