Learning and Technology Adoption in Developing Countries
Bibliographic record
Abstract
This thesis contains three essays that focus on understanding the learning and technology adoption in developing countries.The first essay explores network-based targeting strategies for improving technology adoption.I consider the situation where the new technology can be more beneficial to some agents than others, with this heterogeneity affecting the diffusion of information.I develop a network-based theoretical framework of information diffusion with heterogeneous agents and validate the predictions of my model using experimental secondary data from Malawi.The essay highlights the need to understand possible population heterogeneity in benefits for designing network-based interventions.The second essay investigates how experiences shape learning for index insurance products.I focus on the effect of two different types of experiences: experiencing disasters and receiving payouts.I develop a theoretical model where households learn from their experience and test the predictions of the model using data for an index insurance product in rural Kenya.My findings indicate that learning more about the index insurance product lowers its demand.Thus, contrary to common belief, I find information frictions are driving index insurance demand higher than optimal.The last essay focuses on the role of active discussion in resolving information frictions related to the relative riskiness and uncertainty of different strategies to deal with Late Blight (LB) for Peruvian potato farmers.We use primary data from an artefactual field experiment.Our results suggest the need for policy to complement active discussions with knowledge interventions for its intended effect.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".