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Unraveling the Peltzman Effect: The Significance of Agent’s Type

  • Konrad Grabiszewski EMAIL logo and Alex Horenstein ORCID logo
Published/Copyright: November 22, 2023
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Abstract

The Peltzman effect posits that implementing safety measures incentivizes agents to reduce their effort to a degree where these measures become counterproductive. This paper emphasizes the significance of including the agent’s type (skills, attributes) when analyzing the effectiveness of safety measures. Using data from iRacing, an online racing simulator, we find that the detection of the Peltzman effect is solely attributed to the omitted variable bias; specifically, the omission of a variable capturing the agent’s type. Additionally, our data demonstrates that enhancing types (increasing skills) leads to safety improvements.

JEL Classification: C10; D80; K20; L50

Corresponding author: Konrad Grabiszewski, HEC Paris in Qatar, Doha, Qatar, E-mail:

We thank Steve Myers, Executive Vice President and Executive Producer at iRacing, for sharing the data with us. We also thank him and Dean Marsh for helping us understand online racing and the iRacing data. We thank Gil de Ferran, professional racing driver and team owner, for helping us understand the world of racing. We thank Jeffrey Segal and his team at GPX Lab for helping us understand the differences between real-life and simulation racing.


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Received: 2023-08-12
Accepted: 2023-09-19
Published Online: 2023-11-22

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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