Data and Code for: Splitting the Difference in Incomplete Information Bargaining: Theory and Widespread Evidence from the Field

Principal Investigator(s): View help for Principal Investigator(s) Daniel Keniston, Louisiana State University; Bradley Larsen, Washington University in St. Louis; Shengwu Li, Harvard University; J.J. Prescott, University of Michigan Law School; Bernardo Silveira, University of California, Los Angeles; Chuan Yu, Stanford University

Version: View help for Version V2

Name	File Type	Size	Last Modified
data1.dta	application/x-stata-dta	2.3 MB	02/29/2024 10:09:AM
data2.dta	application/x-stata-dta	14.8 MB	02/29/2024 10:09:AM
data3.dta	application/x-stata-dta	42.7 KB	02/29/2024 10:09:AM
data4.dta	application/x-stata-dta	114.3 KB	02/29/2024 10:09:AM
data5_full.dta	application/x-stata-dta	24.7 KB	02/29/2024 10:09:AM
data6.dta	application/x-stata-dta	4.6 MB	02/29/2024 10:09:AM
data7.dta	application/x-stata-dta	690.7 MB	02/29/2024 10:09:AM

Project Description

Summary: This paper documents a robust pattern from diverse sequential bargaining settings: agents favor offers that split the difference between the previous two offers. Our empirical settings include used-cars, insurance claims, home sale, trade tariffs, a TV game show, eBay, and auto-rickshaws. These even-split offers are more likely to be accepted, less likely to spur exit by the opponent, and more likely to be followed by subsequent split-the-difference offers if bargaining continues. We propose several theoretical frameworks to explain this behavior, including an inference argument under which split-the-difference offers can be viewed as an equal split of the potential surplus.

Scope of Project

Subject Terms: bargaining; negotiation; fairness; split-the-difference; incomplete information; inference; alternating offers

Geographic Coverage: United States, India, Spain, International

Data Type(s): aggregate data; observational data; program source code

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Published Versions

V2 [2024-04-30]

V1 [2024-03-06]

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