Data and Code for: Fair Shares and Selective Attention

Principal Investigator(s):  View help for Principal Investigator(s) Dianna Amasino, Tilburg University (Netherlands); Davide Pace, LMU Munich; Joël van der Weele, University of Amsterdam

Version:  View help for Version V1

Name File Type Size Last Modified
  Analysis 01/05/2024 07:49:AM
  Data 01/05/2024 07:58:AM
  Experiment Materials 01/05/2024 08:12:AM
  Processed Data 01/05/2024 08:13:AM
AEJ-main-Stata.do text/plain 209 bytes 01/05/2024 02:49:AM
AnalysisAEJMicro.do text/plain 46 KB 01/05/2024 02:49:AM
Analysis_Selective_Attention.Rmd text/plain 73.5 KB 01/05/2024 02:49:AM
README.docx application/vnd.openxmlformats-officedocument.wordprocessingml.document 177.1 KB 01/09/2024 11:02:PM
README.pdf application/pdf 382.7 KB 01/09/2024 11:01:PM
Stata-preProcessing-AEJ.do text/plain 8.4 KB 01/05/2024 02:49:AM

Project Citation: 

Amasino, Dianna, Pace, Davide, and van der Weele, Joël. Data and Code for: Fair Shares and Selective Attention. Nashville, TN: American Economic Association [publisher], 2024. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2024-10-09. https://doi.org/10.3886/E196781V1

Project Description

Summary:  View help for Summary Attitudes towards fairness and redistribution differ along socio-economic lines. To under-
stand their formation, we conduct a large-scale experiment on attention to merit and luck
and the effect of attention on fairness decisions. Randomly advantaged subjects pay less
attention to information about true merit and retain more economic surplus, and this effect
persists in subsequent impartial decisions. Attention also has a causal role: encouraging
subjects to look at merit reduces the effect of an advantaged position on allocations. This
suggests that attention-based policy interventions may be effective in reducing polarized
views on inequality.

Scope of Project

Subject Terms:  View help for Subject Terms Redistribution; attention; self-serving bias; fairness; mouse-tracking
JEL Classification:  View help for JEL Classification
      C91 Design of Experiments: Laboratory, Individual
      D83 Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
      D87 Neuroeconomics


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Published Versions

V1 [2024-10-09]

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DDI 2.5

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