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

Author

Listed:
  • 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.

Suggested Citation

  • Abhijit Banerjee & Emily Breza & Arun G. Chandrasekhar & Benjamin Golub, 2018. "When Less is More: Experimental Evidence on Information Delivery During India's Demonetization," NBER Working Papers 24679, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24679
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    Cited by:

    1. Emerick, Kyle & Kelley, Erin & De Janvry, Alain & Sadoulet, Elisabeth, 2019. "Endogenous Information Sharing and the Gains from Using Network Information to Maximize Technology Adoption," CEPR Discussion Papers 13507, C.E.P.R. Discussion Papers.
    2. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    3. de Janvry, Alain & Sadoulet, Elisabeth, 2020. "Using agriculture for development: Supply- and demand-side approaches," World Development, Elsevier, vol. 133(C).
    4. BenYishay, Ariel & Jones, Maria & Kondylis, Florence & Mobarak, Ahmed Mushfiq, 2020. "Gender gaps in technology diffusion," Journal of Development Economics, Elsevier, vol. 143(C).
    5. Khanna, Gaurav & Mukherjee, Priya, 2023. "Political accountability for populist policies: Lessons from the world’s largest democracy," Journal of Public Economics, Elsevier, vol. 219(C).
    6. Gabriel Chodorow-Reich & Gita Gopinath & Prachi Mishra & Abhinav Narayanan, 2020. "Cash and the Economy: Evidence from India’s Demonetization," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(1), pages 57-103.
    7. Chowdhury, Shyamal & Satish, Varun & Sulaiman, Munshi & Sun, Yi, 2021. "Sooner Rather Than Later: Social Networks and Technology Adoption," IZA Discussion Papers 14307, Institute of Labor Economics (IZA).
    8. Edoardo Chiarotti & Nathalie Monnet, 2019. "Hit them in the Wallet! An Analysis of the Indian Demonetization as a Counter-Insurgency Policy," IHEID Working Papers 03-2019, Economics Section, The Graduate Institute of International Studies.
    9. Chowdhury, Shyamal & Satish, Varun & Sulaiman, Munshi & Sun, Yi, 2022. "Sooner rather than later: Social networks and technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 466-482.
    10. Rösl, Gerhard & Seitz, Franz, 2022. "On the stabilizing role of cash for societies," IMFS Working Paper Series 167, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    11. S. Nageeb Ali & Ayal Chen-Zion & Erik Lillethun, 2020. "Reselling Information," Papers 2004.01788, arXiv.org, revised Dec 2022.
    12. Islam, Asadul & Ushchev, Philip & Zenou, Yves & Zhang, Xin, 2019. "The Value of Information in Technology Adoption," IZA Discussion Papers 12672, Institute of Labor Economics (IZA).
    13. Zenou, Yves & Islam, Asad & Ushchev, Philip & Zhang, Xin, 2018. "The Value of Information in Technology Adoption: Theory and Evidence from Bangladesh," CEPR Discussion Papers 13419, C.E.P.R. Discussion Papers.
    14. Arun G. Chandrasekhar & Benjamin Golub & He Yang, 2018. "Signaling, Shame, and Silence in Social Learning," NBER Working Papers 25169, National Bureau of Economic Research, Inc.
    15. Steven Wilkins Reeves & Shane Lubold & Arun G. Chandrasekhar & Tyler H. McCormick, 2024. "Model-Based Inference and Experimental Design for Interference Using Partial Network Data," Papers 2406.11940, arXiv.org.

    More about this item

    JEL classification:

    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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