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The Role of Peer Effects in Natural Resource Appropriation – The Case of Groundwater
Author(s) -
Sampson Gabriel S.,
Perry Edward D.
Publication year - 2019
Publication title -
american journal of agricultural economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.949
H-Index - 111
eISSN - 1467-8276
pISSN - 0002-9092
DOI - 10.1093/ajae/aay090
Subject(s) - groundwater , counterfactual thinking , irrigation , appropriation , extraction (chemistry) , environmental science , agriculture , resource (disambiguation) , natural resource , natural resource economics , aquifer , water resource management , environmental economics , computer science , economics , geography , political science , geology , psychology , ecology , law , social psychology , philosophy , computer network , linguistics , chemistry , archaeology , biology , chromatography , geotechnical engineering
Spatially mediated peer effects are increasingly recognized as an important driver of technology adoption. In this paper, we isolate the role of peer effects from environmental factors in the acquisition of groundwater rights for agricultural irrigation in Kansas. We find strong evidence of peer effects influencing farmers’ decisions to adopt groundwater irrigation. Using a rich dataset on groundwater rights for the period 1943–2014 and a nearest neighbor peer group definition, we find that one additional neighbor adopting groundwater for irrigation increases groundwater adoption by an average of 0.25 percentage points. We also find that the average marginal effect of one additional peer is reduced by distance and diminishes as the total number of neighbors adopting groundwater increases. Using our model estimates to simulate a counterfactual without peer effects, we find that water rights appropriation stemming from peer effects accounted for about 11 million acre‐feet of extraction from the Kansas High Plains Aquifer. This amounts to about three years of typical annual extraction. Our results provide evidence that peer effects can “speed up” resource extraction and can help inform policy makers in designing exploitation control rules.