When Less is More: Experimental Evidence on Information Delivery during India's Demonetization

Working Paper: NBER ID: w24679

Authors: Abhijit Banerjee; Emily Breza; Arun G. Chandrasekhar; Benjamin Golub

Abstract: How should policymakers disseminate information: by broadcasting it widely (e.g., via mass media), or letting word spread from a small number of initially informed “seed” individuals? While conventional wisdom suggests delivering information more widely is better, we show theoretically and experimentally that this may not hold when people need to ask questions to fully comprehend the information they were given. In a field experiment during the chaotic 2016 Indian demonetization, we varied how information about demonetization’s official rules was delivered to villages on two dimensions: how many were initially informed (broadcasting versus seeding) and whether the identity of the initially informed was publicly disclosed (common knowledge). The quality of information aggregation is measured in three ways: the volume of conversations about demonetization, the level of knowledge about demonetization rules, and choice quality in a strongly incentivized decision dependent on understanding the rules. Our results are consistent with four predictions of a model in which people need others’ help to make the best use of announced information, but worry about signaling inability or unwillingness to correctly process the information they have access to. First, if who is informed is not publicized, broadcasting improves all three outcomes relative to seeding. Second, under seeding, publicizing who is informed improves all three outcomes. Third, when broadcasting, publicizing who is informed hurts along all three dimensions. Finally, when who is informed is made public, telling more individuals (broadcasting relative to seeding) is worse along all three dimensions.

Keywords: Information Dissemination; Social Learning; Demonetization; India

JEL Codes: D8; G41; O33


Causal Claims Network Graph

Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.


Causal Claims

CauseEffect
Broadcasting information (common knowledge vs. not) (D83)engagement in social learning (Z13)
making the identity of informed individuals common knowledge (D83)engagement in social learning (Z13)
Broadcasting with common knowledge (D83)participation in social learning (Z13)
Broadcasting to more individuals (when identity is publicized) (Z13)engagement in social learning (Z13)

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