Learning Through Noticing: Theory and Evidence from a Field Experiment *

Abstract

We consider a model of technological learning under which people ''learn through noticing'': they choose which input dimensions to attend to and subsequently learn about from available data. Using this model, we show how people with a great deal of experience may persistently be off the production frontier because they fail to notice important features of the data they possess. We also develop predictions on when these learning failures are likely to occur, as well as on the types of interventions that can help people learn. We test the model's predictions in a field experiment with seaweed farmers. The survey data reveal that these farmers do not attend to pod size, a particular input dimension. Experimental trials suggest that farmers are particularly far from optimizing this dimension. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, behavioral changes occur only after the farmers are presented with summaries that highlight previously unattended-to relationships in the data. JEL Codes: D03, D83, O13, O14, O30, Q16. Downloaded from2. Many other dimensions might be important. For example, the strength of the tide, the time of day, the temperature, the tightness with which pods are attached, the strain of pods used, and so on could matter. In our analysis, we largely focus on two or three dimensions for parsimony, but actual demands on attention are much greater. LEARNING THROUGH NOTICING 1313at Harvard Library on August 24, 2015 http://qje.oxfordjournals.org/ Downloaded from 4. Note that l and t appear symmetrically in equation (1). For the purpose of forming beliefs about the underlying technology (y), it does not matter whether the farmer learns through many observations across plots at a fixed time, or through many observations over time on a fixed plot. Equation (1) also reflects a simplifying assumption that the payoff function is separable across input dimensions, allowing us to separately analyze the farmer's decisions across dimensions. LEARNING THROUGH NOTICING

Lemon Technologies and Adoption: Measurement, Theory and Evidence from Agricultural Markets in Uganda*

“…If 0.5 < posterior log10 odds ratio < 1, they suggest interpreting the evidence as "positive" or "moderate". 25 Learning after one period in our model is slower than after the one harvest intervention in Hanna et al (2014). In Hanna et al (2014) farmers are presented with information on optimal seaweed pod size based on a larger number of experiments, equivalent to many more rounds or larger scale of fertilizer experimentation in our model, while in our model farmers learn from experiments one plot/period at the time.…”

“…Besley and Case (1994) and Munshi (2004) present models where farmers learn about the expected yield when applying fertilizers (of a fixed quality). Hanna et al (2014) examine a learning model in which farmers do not pay attention to certain dimensions of the production function. In contrast, the Bayesian farmer here learns about the quality of the technology itself.…”

Seeing is Believing? Evidence from an Extension Network Experiment

“…Failing to notice a gap between knowledge and actual practice, and not the information set itself, may also pose a key barrier to learning. Hanna et al (2014) find that seaweed farmers in Indonesia acted on the information received only when it included descriptions of the relationship between yield and pod size from their own plot. If the main constraint to adoption of a profitable practice such as SLM is not a lack of exposure or knowledge, but a failure to notice its benefits, then augmenting the CF model will have little effect on the pace of diffusion within the community.…”

Is a cooperative approach to seaweed farming effectual? An analysis of the seaweed cluster project (SCP), Malaysia

“…Seaweed farming is a simple activity, using cheap and recycled materials to establish the farm and individuals can learn farming skills by themselves or through observation. However, work experience using the same practises does not necessarily result in higher productivity, unless the individual switches to new technology (Hanna et al 2014). All seaweed intervention projects are about introducing new innovations, but farmers need training in such skills (Vredegoor and Pennink 2013).…”