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Network information and nudging: Experimental insights into agricultural runoff management

Danielle Roy & Tongzhe Li

Journal of Behavioral and Experimental Economics2026https://doi.org/10.1016/j.socec.2026.102522article
AJG 2ABDC A
Weight
0.50

Abstract

• Peer-effect of technology adoption in the producer network under certain information conditions. • Low information scenario with a nudge yields a socially optimal pollution level. • More information does not lend to a more socially optimal pollution outcome. We investigate the efficacy of both behavioural and policy interventions in driving the production choices which impact levels of agricultural runoff, a form of nonpoint source pollution. In an incentivized experiment, 228 participants act as producers to make input and technology adoption choices that influence group-level pollution under an ambient tax. Three scenarios with varying information available on a neighbouring producer’s adoption of the emissions-reduction technology are evaluated, orthogonal to a pro-abatement nudge. In each round, producers faced an ambient tax applied to all members of their group based on total group pollution. In addition, one randomly chosen participant received a subsidy that reduced the cost of adopting the abatement technology. Our findings show that increased information does not necessarily improve group outcomes under the ambient tax. While the nudge can help achieve socially optimal pollution levels, it is ineffective in networks with greater information flow. These findings have implications for the effectiveness of government outreach to promote uptake of best practices in more connected networks, particularly where the production decisions of other agricultural producers can be observed. (JEL Codes: C92, Q53, D83 Q18)

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https://doi.org/https://doi.org/10.1016/j.socec.2026.102522

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@article{danielle2026,
  title        = {{Network information and nudging: Experimental insights into agricultural runoff management}},
  author       = {Danielle Roy & Tongzhe Li},
  journal      = {Journal of Behavioral and Experimental Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.socec.2026.102522},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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