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Cited by 325

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

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

Hanna, Rema, Mullainathan, Sendhil, Schwartzstein, Joshua·2014·The Quarterly Journal of Economics

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.

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Metadata

Authors
Hanna, R., Mullainathan, S., & Schwartzstein, J.
Venue
The Quarterly Journal of Economics
Published
2014-06-09
Volume
129
Issue
3
Pages
1311-1353
ISSN
  • 0033-5533 (ISSN-L)
  • 1531-4650
OpenAlex
W2139860711
Cited by
325
References
6
FWCI
14.99
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Learning Through Noticing: Theory and Evidence from a Field Experiment *. https://app.cochat.ai/papers/10.1093/qje/qju015
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Hanna, R., Mullainathan, S., & Schwartzstein, J. (2014). Learning Through Noticing: Theory and Evidence from a Field Experiment *. The Quarterly Journal of Economics. https://doi.org/10.1093/qje/qju015

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.