{"title":"揭开佩尔茨曼效应的神秘面纱:代理人类型的重要性","authors":"Konrad Grabiszewski, A. Horenstein","doi":"10.1515/rle-2023-0072","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":44795,"journal":{"name":"Review of Law & Economics","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Unraveling the Peltzman Effect: The Significance of Agent’s Type\",\"authors\":\"Konrad Grabiszewski, A. Horenstein\",\"doi\":\"10.1515/rle-2023-0072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":44795,\"journal\":{\"name\":\"Review of Law & Economics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-11-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Review of Law & Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1515/rle-2023-0072\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"LAW\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Review of Law & Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/rle-2023-0072","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"LAW","Score":null,"Total":0}
Unraveling the Peltzman Effect: The Significance of Agent’s Type
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.