Evolution of Learning in Technology Adoption: The case of the U.S. Soybean Seed Industry.
Ilin, C.; Shi, G.
Show abstract
This paper examines how the evolution of learning affects technology adoption. We use a sequential adoption model that accounts for differences between forward-looking adopters, who consider future impacts of their learning, and myopic adopters, who only consider past learning. We apply the analysis to three panels of U.S. soybean farmers representing different stages of the genetically modified (GM) seed technology diffusion path. We show that uncertainty is considerably reduced over time due to increased learning efficiency. Our results indicate that a forward-looking model fits the early adopters and early majority stages better, while both models perform equally well in the laggard stage. JEL classificationD83, Q31, Q33, Q16
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Orchard layout and plant traits influence fruit yield more strongly than pollinator behaviour and density in a dioecious crop 93%
- Reopening California : Seeking Robust, Non-Dominated COVID-19 Exit Strategies 93%
- Characterizing Two Outbreak Waves of COVID-19 in Spain Using Phenomenological Epidemic Modelling 93%
Similar papers in this journal
- Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation 93%
- The Burr distribution as a model for the delay between key events in an individual’s infection history 93%
- Modelling interference between vectors of non-persistently transmitted plant viruses to identify effective control strategies 92%
Similar papers in this journal
- YieldNet: A Convolutional Neural Network for Simultaneous Corn and Soybean Yield Prediction Based on Remote Sensing Data 93%
- Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios 92%
- Quantifying the effect of isolation and negative certification on COVID-19 transmission 92%
Similar papers in this journal
- Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans? 91%
- Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus 91%
- Impacts of vaccination and Severe Acute Respiratory Syndrome Coronavirus 2 variants Alpha and Delta on Coronavirus Disease 2019 transmission dynamics in four metropolitan areas of the United States 91%
Similar papers in this journal
- Optimal spatial monitoring of populations described by reaction-diffusion models 93%
- Uncertainty Quantification in Cost-effectiveness Analysis for Stochastic-based Infectious Disease Models: Insights from Surveillance on Lymphatic Filariasis 92%
- Density Dependent Resource Budget Model for Alternate Bearing 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.