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The Social Desirability Atlas

Author

Listed:
  • Leonardo Bursztyn
  • Ingar Haaland
  • Nicolas Röver
  • Christopher Roth
  • Ingar K. Haaland

Abstract

Social desirability bias (SDB) is a pervasive threat to the validity of survey and experimental data. Respondents might often misreport sensitive attitudes and behaviors to appear more socially acceptable. We begin by synthesizing empirical evidence on the prevalence and magnitude of SDB across various domains, focusing on studies with individual-level bench marks. We then critically assess commonly used strategies to mitigate SDB, highlighting how they can sometimes fail by creating confusion or inadvertently increasing perceived sensitivity. To help researchers navigate these challenges, we offer practical guidance on selecting the most suitable tools for different research contexts. Finally, we examine how SDB can distort treatment effects in experiments and discuss mitigation strategies.

Suggested Citation

  • Leonardo Bursztyn & Ingar Haaland & Nicolas Röver & Christopher Roth & Ingar K. Haaland, 2025. "The Social Desirability Atlas," CESifo Working Paper Series 11911, CESifo.
  • Handle: RePEc:ces:ceswps:_11911
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    References listed on IDEAS

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    Cited by:

    1. Felix Chopra & Ingar K. Haaland & Fabian Roeben & Christopher Roth & Vanessa Sticher, 2025. "News Customization with AI," CESifo Working Paper Series 12121, CESifo.

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    JEL classification:

    • B41 - Schools of Economic Thought and Methodology - - Economic Methodology - - - Economic Methodology
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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