Subtopic Deep Dive
Implicit Bias in Name Perception
Research Guide
What is Implicit Bias in Name Perception?
Implicit Bias in Name Perception examines unconscious stereotypes triggered by personal names that influence hiring and social evaluations.
Studies employ Implicit Association Test (IAT) and field experiments to measure name-based bias in employment callbacks. Quillian et al. (2017) meta-analysis of 24 field experiments found persistent racial discrimination in hiring over decades (812 citations). Neumark (2018) reviews experimental designs linking names to labor market outcomes (508 citations).
Why It Matters
Name perception bias explains stagnant racial hiring gaps despite laws like the Civil Rights Act, as shown in Quillian et al. (2017) meta-analysis revealing no decline in discrimination rates. Hainmueller and Hangartner (2013) natural experiment on Swiss passports demonstrates names signal ethnicity, reducing approval odds by 11.3% for foreign-sounding names (406 citations). Hannák et al. (2017) uncovered name-based bias in freelance platforms, where Black-identifying names receive 15% fewer gigs, informing policy for anonymous resumes (273 citations). Greenwald and Pettigrew (2014) link ingroup favoritism to implicit preferences enabling discrimination (397 citations).
Key Research Challenges
Measuring Implicit vs Explicit Bias
Distinguishing unconscious name-triggered stereotypes from overt prejudice challenges validity of self-reports. Saul (2013) argues skepticism alone fails to address implicit mechanisms (176 citations). Greenwald and Pettigrew (2014) show ingroup favoritism masks bias in traditional metrics (397 citations).
Isolating Names from Confounds
Field experiments struggle to separate name effects from resume qualifications or applicant traits. Quillian et al. (2017) highlight heterogeneity across studies complicating causal attribution (812 citations). Neumark (2018) notes resume blinding issues in correspondence audits (508 citations).
Persistence Despite Interventions
Anti-bias training shows limited long-term reduction in name perception bias. Banaji et al. (2021) describe systemic embedding of implicit biases beyond individual fixes (232 citations). Krause et al. (2012) found anonymous applications reduce but do not eliminate callbacks disparities (80 citations).
Essential Papers
Meta-analysis of field experiments shows no change in racial discrimination in hiring over time
Lincoln Quillian, Devah Pager, Ole Hexel et al. · 2017 · Proceedings of the National Academy of Sciences · 812 citations
Significance Many scholars have argued that discrimination in American society has decreased over time, while others point to persisting race and ethnic gaps and subtle forms of prejudice. The ques...
Experimental Research on Labor Market Discrimination
David Neumark · 2018 · Journal of Economic Literature · 508 citations
Understanding whether labor market discrimination explains inferior labor market outcomes for many groups has drawn the attention of labor economists for decades— at least since the publication of ...
Who Gets a Swiss Passport? A Natural Experiment in Immigrant Discrimination
Jens Hainmueller, Dominik Hangartner · 2013 · American Political Science Review · 406 citations
We study discrimination against immigrants using microlevel data from Switzerland, where, until recently, some municipalities used referendums to decide on the citizenship applications of foreign r...
With malice toward none and charity for some: Ingroup favoritism enables discrimination.
Anthony G. Greenwald, Thomas F. Pettigrew · 2014 · American Psychologist · 397 citations
Dramatic forms of discrimination, such as lynching, property destruction, and hate crimes, are widely understood to be consequences of prejudicial hostility. This article focuses on what has hereto...
Bias in Online Freelance Marketplaces
Anikó Hannák, Claudia Wagner, David García et al. · 2017 · 273 citations
Online freelancing marketplaces have grown quickly in recent years. In theory, these sites offer workers the ability to earn money without the obligations and potential social biases associated wit...
Systemic racism: individuals and interactions, institutions and society
Mahzarin R. Banaji, Susan T. Fiske, Douglas S. Massey · 2021 · Cognitive Research Principles and Implications · 232 citations
Abstract Systemic racism is a scientifically tractable phenomenon, urgent for cognitive scientists to address. This tutorial reviews the built-in systems that undermine life opportunities and outco...
Prejudice and Discrimination Toward Immigrants
Victoria M. Esses · 2020 · Annual Review of Psychology · 217 citations
Prejudice and discrimination toward immigrants, and the consequences of these negative attitudes and behavior, are key determinants of the economic, sociocultural, and civic-political future of rec...
Reading Guide
Foundational Papers
Start with Greenwald and Pettigrew (2014, 397 citations) for ingroup favoritism theory enabling implicit discrimination; Hainmueller and Hangartner (2013, 406 citations) for natural experiment design on name signals; Saul (2013, 176 citations) for philosophical skepticism on bias interventions.
Recent Advances
Quillian et al. (2017, 812 citations) meta-analysis on hiring persistence; Banaji et al. (2021, 232 citations) on systemic racism mechanisms; Neumark (2018, 508 citations) economic review of discrimination experiments.
Core Methods
IAT measures association strengths (Greenwald 2014); audit studies compare callbacks by name (Quillian 2017, Krause 2012); regression discontinuity in referendums isolates name effects (Hainmueller 2013).
How PapersFlow Helps You Research Implicit Bias in Name Perception
Discover & Search
Research Agent uses searchPapers with query 'implicit bias names hiring IAT' to retrieve Quillian et al. (2017) (812 citations), then citationGraph reveals Greenwald and Pettigrew (2014) as key implicit bias reference (397 citations), and findSimilarPapers expands to Neumark (2018) labor discrimination review.
Analyze & Verify
Analysis Agent applies readPaperContent on Hainmueller and Hangartner (2013) to extract natural experiment regression coefficients showing name effects, verifyResponse with CoVe cross-checks callback rates against Quillian meta-analysis, and runPythonAnalysis with pandas recomputes meta-effect sizes from extracted tables for GRADE A evidence verification.
Synthesize & Write
Synthesis Agent detects gaps like missing longitudinal name bias studies post-2020, flags contradictions between Quillian (2017) persistence and intervention claims, while Writing Agent uses latexEditText to draft sections, latexSyncCitations for 20+ refs, latexCompile for PDF, and exportMermaid diagrams ingroup favoritism models from Greenwald (2014).
Use Cases
"Reanalyze callback rates by name ethnicity from Quillian 2017 meta-analysis"
Research Agent → searchPapers 'Quillian 2017' → Analysis Agent → readPaperContent → runPythonAnalysis (pandas meta-regression on extracted data) → CSV export of effect sizes by decade.
"Draft LaTeX review on name bias field experiments"
Synthesis Agent → gap detection across Neumark 2018 and Hannák 2017 → Writing Agent → latexGenerateFigure (bias funnel plot) → latexSyncCitations (15 papers) → latexCompile → arXiv-ready PDF.
"Find code for IAT name perception studies"
Research Agent → paperExtractUrls from Greenwald 2014 → Code Discovery → paperFindGithubRepo (IAT-js repo) → githubRepoInspect → runnable Jupyter notebook for bias priming simulation.
Automated Workflows
Deep Research workflow scans 50+ papers via exaSearch 'name implicit bias hiring', structures report with GRADE scores prioritizing Quillian (2017). DeepScan applies 7-step CoVe to Hainmueller (2013), verifying name coefficients against Banaji (2021) systemic claims. Theorizer generates hypotheses on name-audio priming from Saul (2013) and Greenwald (2014).
Frequently Asked Questions
What defines implicit bias in name perception?
Unconscious activation of stereotypes by names, measured via IAT response latencies or hiring callback disparities, as in Greenwald and Pettigrew (2014).
What are main methods used?
Correspondence audits send identical resumes with ethnic names (Quillian et al., 2017); natural experiments like Swiss referendums (Hainmueller and Hangartner, 2013); IAT for implicit associations (Greenwald and Pettigrew, 2014).
What are key papers?
Quillian et al. (2017, 812 citations) meta-analysis shows persistent hiring discrimination; Neumark (2018, 508 citations) reviews labor experiments; Hainmueller and Hangartner (2013, 406 citations) on immigrant name bias.
What open problems remain?
Longitudinal effects of debiasing on name perception; intersection with gender/SES in names; algorithmic amplification in resume screeners (Hannák et al., 2017).
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