[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-639240-105":59,"doc-detail-639240-en":129},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":122,"head_meta":124,"extra_data":126,"updated_unix":128},105,"en","perceived-warmth-and-competence-predict-callback-rates-in-meta-analyzed-north-american-labor-market-experiments-research-article","Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments - Research article","","Meta-analysis of North American correspondence studies examines how labor-market group discrimination is shaped when employers implicitly rely on social signals. The study draws on the Stereotype Content Model, treating stereotypes as warmth and competence perceptions. Callback disparities across 21 published studies (592 social signals) are linked to measured warmth and competence, particularly for race and gender. Effects are inconsistent for sexuality and disability, motivating further research. Findings support practical debiasing strategies and decision support for hiring systems, including frameworks for reducing bias in automated approaches such as large language models.",{"@graph":69,"@context":121},[70,84,104],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/perceived-warmth-and-competence-predict-callback-rates-in-meta-analyzed-north-american-labor-market-experiments-research-article/639240/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/perceived-warmth-and-competence-predict-callback-rates-in-meta-analyzed-north-american-labor-market-experiments-research-article/639240.png","ImageObject",300,407,{"name":92,"@type":93},"River Wang","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-22",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"How does the study measure discrimination in labor markets?","Question",{"text":111,"@type":112},"Discrimination is operationalized through employer callback disparities in correspondence studies, where resumes are matched except for social signals. The analysis links these disparities to perceived warmth and competence of the signals.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"What theoretical framework guides the warmth and competence predictions?",{"text":116,"@type":112},"The work uses the Stereotype Content Model, which categorizes social stereotypes into two dimensions: warmth and competence. These perceptions are used to predict differences in callbacks.",{"name":118,"@type":109,"acceptedAnswer":119},"Do warmth and competence perceptions predict discrimination for all identity groups?",{"text":120,"@type":112},"No. The study finds predictive relationships for race and gender signals, while influences are inconsistent for sexuality and disability signals. For example, favorable perceptions related to parenthood do not consistently increase callbacks.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},639240,1792702569,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":143},1099514067438,"https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542","PLOS ONE  \nOPEN ACCESS  \nCitation: Gallo M, Hausladen CI, Hsu M, Jenkins AC, Ona V, Camerer CF (2024) Perceived warmth and competence predict callback rates in metaanalyzed North American labor market experiments. PLoS ONE 19(7): e0304723 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0304723](doi.org/10.1371/journal.pone.0304723)  \n[Editor:](Editor: Tobias Otterbring)[ Tobias Otterbring](Editor: Tobias Otterbring), [University of Agder:](University of Agder:)[ ](University of Agder:)Universitetet i Agder, NORWAY  \nReceived: January 19, 2024  \nAccepted: May 16, 2024  \nPublished: July 10, 2024  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0304723](https://doi.org/10.1371/journal.pone.0304723)  \n[Copyright:](Copyright:) © [2024 Gallo et al](2024 Gallo et al). This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: To promote transparency and further research, all data underlying our study are openly available at the following GitHub repository: \\url{[https://github](https://github).  \nRESEARCH ARTICLE  \nPerceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments  \nMarcos Gallo1☯, Carina I. Hausladen1,2☯*, Ming Hsu3, Adrianna C. Jenkins4, Vaida Ona4, Colin F. Camerer1,5  \n1 Division of Humanities and Social Science, California Institute of Technology, Pasadena, CA, United States of America, 2 Computational Social Science, ETH Zurich, Zurich, Switzerland, 3 Haas School of Business, University of California, Berkeley, Berkeley, CA, United States of America, 4 Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America, 5 Computational and Neural Systems, California Institute of Technology, Pasadena, CA, United States of America  \n☯ These authors contributed equally to this work.  \n* [carinah@ethz.ch](carinah@ethz.ch)  \nAbstract  \nExtensive literature probes labor market discrimination through correspondence studies in which researchers send pairs of resumes to employers, which are closely matched except for social signals such as gender or ethnicity. Upon perceiving these signals, individuals quickly activate associated stereotypes. The Stereotype Content Model (SCM; Fiske 2002) categorizes these stereotypes into two dimensions: warmth and competence. Our research integrates findings from correspondence studies with theories of social psychology, asking: Can discrimination between social groups, measured through employer callback disparities, be predicted by warmth and competence perceptions of social signals? We collect callback rates from 21 published correspondence studies, varying for 592 social signals. On those social signals, we collected warmth and competence perceptions from an independent group of online raters. We found that social perception predicts callback disparities for studies varying race and gender, which are indirectly signaled by names on these resumes. Yet, for studies adjusting other categories like sexuality and disability, the influence of social perception on callbacks is inconsistent. For instance, a more favorable perception of signals like parenthood does not consistently lead to increased callbacks, underscoring the necessity for further research. Our research offers pivotal strategies to address labor market discrimination in practice. Leveraging the warmth and competence framework allows for the predictive identification of bias against specific groups without extensive correspondence studies. By distilling hiring","cbCaiq2d5IMtde2o","https://ap.wps.com/l/cbCaiq2d5IMtde2o","pdf",1755569,15,"English","# Abstract\n# Introduction\n## Background and problem statement\n# Data and methods\n## Correspondence studies and social signals\n## Warmth and competence ratings\n# Results\n## Predicting callback disparities by stereotype perceptions\n## Inconsistent effects for other identity categories\n# Discussion\n## Practical strategies for debiasing and decision support","[{\"question\":\"How does the study measure discrimination in labor markets?\",\"answer\":\"Discrimination is operationalized through employer callback disparities in correspondence studies, where resumes are matched except for social signals. The analysis links these disparities to perceived warmth and competence of the signals.\"},{\"question\":\"What theoretical framework guides the warmth and competence predictions?\",\"answer\":\"The work uses the Stereotype Content Model, which categorizes social stereotypes into two dimensions: warmth and competence. These perceptions are used to predict differences in callbacks.\"},{\"question\":\"Do warmth and competence perceptions predict discrimination for all identity groups?\",\"answer\":\"No. The study finds predictive relationships for race and gender signals, while influences are inconsistent for sexuality and disability signals. For example, favorable perceptions related to parenthood do not consistently increase callbacks.\"}]","Perceived warmth and competence predict callback rates in meta-analyzed North American labor market experiments - Research article | PDF",38]