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Trouble with Zero: The Limits of Subsidizing Technology Adoption
In this project, we use a randomized control trial to test whether paying an initial price of zero (a 100% subsidy) relative to a non-zero, partially subsidized price affects long-run technology adoption among smallholder farmers. Subsidies can encourage short-run adoption, provide farmers with the opportunity to learn from experience, and potentially lead to increased adoption in the long-run. Conversely, long-run adoption may be lower if large initial subsidies result in a lower willingness to pay (WTP) later due to behavioral phenomena such as price reference dependence or price anchoring. The price anchoring effect may be particularly salient if the initial price paid is zero. To test the impact of subsidies on long-run adoption, we randomize farmers' initial prices for improved grain storage technology. One year later, we elicit willingness-to-pay from all farmers for a second round of technology purchases. We also collect data on relevant grain storage outcomes and farmers' experience with the technology to test for differences in the usage and perceived and actual benefits across subsidy levels.
External Link(s)
Citation
Baylis, Kathy, Hemant Pullabhotla and Pallavi Shukla. 2020. "Trouble with Zero: The Limits of Subsidizing Technology Adoption." AEA RCT Registry. November 02. https://doi.org/10.1257/rct.6661-1.0.
Farmers in first group of villages received technology at a randomized subsidy level using a Becker-DeGroot-Marschak (BDM) auction. The second group received the technology through a flat-rate subsidize price. Farmers in the last group received the technology for free. In year two, farmers from all three treatment groups participated in a Becker-DeGroot-Marschak (BDM) auction and willingness-to-pay was elicited.
Experimental Design Details
Randomization Method
Randomization of villages done in office by a computer; price levels in Becker-DeGroot-Marschak (BDM) auction determined by price drawn by participant.
Randomization Unit
Clustered at village level
Was the treatment clustered?
Yes
Sample size: planned number of clusters
24 villages
Sample size: planned number of observations
1200 farmers
Sample size (or number of clusters) by treatment arms
750 farmers in BDM auction group, 200 in flat-rate group, 250 in zero price group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)