Data and Code for: Using Models to Persuade

Principal Investigator(s): View help for Principal Investigator(s) Joshua Schwartzstein, Harvard Business School; Adi Sunderam, Harvard Business School

Version: View help for Version V1

Name	File Type	Size	Last Modified
Code			08/31/2020 08:56:PM
Readme.pdf	application/pdf	37.7 KB	09/29/2020 05:49:AM

Project Citation:

Schwartzstein, Joshua, and Sunderam, Adi. Data and Code for: Using Models to Persuade. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-12-17. https://doi.org/10.3886/E120507V1

Project Description

Summary: We present a framework where “model persuaders” influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model persuaders face a tradeoff: better-fitting models induce less movement in receivers’ beliefs. Consequently, a receiver exposed to the true model can be most misled by persuasion when that model fits poorly, competition between persuaders tends to neutralize the data by pushing towards better-fitting models, and a persuader facing multiple receivers is more effective when he can send tailored, private messages.

Scope of Project

JEL Classification:
      D90 Micro-Based Behavioral Economics: General
      D91 Micro-Based Behavioral Economics: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
      G41 Behavioral Finance: Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

Time Period(s): 1/1/1949 – 12/31/2018 (Good to Great); 1/8/2019 – 1/28/2019 (Technical Analysis)

Methodology

Data Source: Center for Research in Security Prices (CRSP), accessed through Wharton Research Data Services (WRDS)

Unit(s) of Observation: Firm-time

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Published Versions

V1 [2020-12-17]

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