Subtopic Deep Dive
Intersectional Name Discrimination
Research Guide
What is Intersectional Name Discrimination?
Intersectional Name Discrimination examines how names signaling multiple social identities like race, gender, and ethnicity interact to amplify discrimination in labor markets, as revealed through field experiments with factorial designs.
Researchers use correspondence audits sending fictitious resumes with varied name signals to measure callback disparities. Over 20 papers since 2010 document compounded effects beyond single-axis biases. Key studies include Quillian and Midtbøen (2021, 163 citations) on cross-national hiring experiments and Pedulla (2018, 77 citations) on race-unemployment interactions.
Why It Matters
Intersectional name studies reveal how biases compound, such as Black female names facing double penalties in callbacks (Pedulla 2018; Birkelund et al. 2021). These findings inform anti-discrimination policies by quantifying interactions overlooked in univariate analyses (Banaji et al. 2021). In hiring, they guide resume anonymization and AI screening reforms (Williams et al. 2018).
Key Research Challenges
Isolating Interaction Effects
Field experiments struggle to disentangle race-gender intersections from confounders like qualifications. Factorial designs help but require large samples for statistical power (Quillian and Midtbøen 2021). Pedulla (2018) shows additive vs. amplified effects demand precise controls.
Cross-National Comparability
Name signals vary culturally, complicating global comparisons of discrimination. Harmonized experiments reveal context-specific patterns (Birkelund et al. 2021, 84 citations). Quillian and Midtbøen (2021) highlight rising field experiment use for standardization.
Employer Mechanisms Unclear
Callback disparities do not reveal if discrimination stems from statistical or taste-based biases. Résumé screening tests show ethnicity strength matters (Derous and Ryan 2012). Banaji et al. (2021) call for cognitive measures to probe implicit processes.
Essential Papers
The Value of Postsecondary Credentials in the Labor Market: An Experimental Study
David Deming, Noam Yuchtman, Amira Abulafi et al. · 2016 · American Economic Review · 271 citations
We study employers' perceptions of the value of postsecondary degrees using a field experiment. We randomly assign the sector and selectivity of institutions to fictitious resumes and apply to real...
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...
How Algorithms Discriminate Based on Data They Lack: Challenges, Solutions, and Policy Implications
Betsy Anne Williams, Catherine Brooks, Yotam Shmargad · 2018 · Journal of Information Policy · 167 citations
Abstract Organizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's p...
Comparative Perspectives on Racial Discrimination in Hiring: The Rise of Field Experiments
Lincoln Quillian, Arnfinn H. Midtbøen · 2021 · Annual Review of Sociology · 163 citations
This article reviews studies of hiring discrimination against racial and ethnic minority groups in cross-national perspective. We focus on field experimental studies of hiring discrimination: studi...
Names and “Doing Gender”: How Forenames and Surnames Contribute to Gender Identities, Difference, and Inequalities
Jane Pilcher · 2017 · Sex Roles · 96 citations
Names, as proper nouns, are clearly important for the identification of individuals in everyday life. In the present article, I argue that forenames and surnames need also to be recognized as "doin...
Do Employers Prefer Fathers? Evidence from a Field Experiment Testing the Gender by Parenthood Interaction Effect on Callbacks to Job Applications
Magnus Bygren, Anni Erlandsson, Michael Gähler · 2017 · European Sociological Review · 92 citations
In research on fatherhood premiums and motherhood penalties in career-related outcomes, employers discriminatory behaviours are often argued to constitute a possible explanation for observed gender...
Reading Guide
Foundational Papers
Start with Derous and Ryan (2012) for ethnicity strength in résumés, then John Donohue (2005) on antidiscrimination economics; they establish audit basics and policy frames for intersections.
Recent Advances
Prioritize Quillian and Midtbøen (2021) for field experiment rise, Pedulla (2018) for race-unemployment, Birkelund et al. (2021) for gender harmonization.
Core Methods
Core techniques: correspondence audits with factorial name manipulations (Quillian and Midtbøen 2021), callback logit models for interactions (Pedulla 2018), harmonized cross-national designs (Birkelund et al. 2021).
How PapersFlow Helps You Research Intersectional Name Discrimination
Discover & Search
Research Agent uses searchPapers and citationGraph on 'intersectional name discrimination' to map 50+ papers from Quillian and Midtbøen (2021), revealing clusters in hiring audits. exaSearch uncovers hidden preprints on name-ethnicity interactions; findSimilarPapers links Pedulla (2018) to gender-race studies.
Analyze & Verify
Analysis Agent applies readPaperContent to Birkelund et al. (2021) for callback rates, then verifyResponse (CoVe) cross-checks claims against Derous and Ryan (2012). runPythonAnalysis computes interaction terms from audit data tables using pandas; GRADE grading scores evidence strength on compounded effects.
Synthesize & Write
Synthesis Agent detects gaps in race-gender studies post-Pedulla (2018), flagging underexplored ethnicity intersections. Writing Agent uses latexEditText for methods sections, latexSyncCitations for 20+ refs, and latexCompile for experiment tables; exportMermaid diagrams factorial designs.
Use Cases
"Analyze callback rates by race-gender name combos from recent audits"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas crosstab on Pedulla 2018 data) → matplotlib plot of interactions → researcher gets CSV of amplified effects with p-values.
"Draft LaTeX review of intersectional hiring discrimination"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (Quillian 2021, Birkelund 2021) → latexCompile → researcher gets PDF with cited tables and bibliography.
"Find code for name discrimination resume generators"
Research Agent → paperExtractUrls (Derous 2012) → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets audited factorial resume scripts with usage docs.
Automated Workflows
Deep Research workflow scans 50+ papers via searchPapers → citationGraph on Banaji et al. (2021), producing structured report on systemic name biases with GRADE scores. DeepScan's 7-step chain verifies Pedulla (2018) interactions using CoVe and runPythonAnalysis. Theorizer generates hypotheses on name-ethnicity amplification from Quillian and Midtbøen (2021) clusters.
Frequently Asked Questions
What defines Intersectional Name Discrimination?
It studies compounded biases from names signaling race, gender, and ethnicity in hiring, using factorial field experiments to isolate interactions (Pedulla 2018; Birkelund et al. 2021).
What methods are used?
Correspondence audits send fictitious resumes with varied names to real jobs, measuring callbacks (Quillian and Midtbøen 2021). Factorial designs test intersections (Derous and Ryan 2012).
What are key papers?
Quillian and Midtbøen (2021, 163 citations) reviews cross-national experiments; Pedulla (2018, 77 citations) tests race-unemployment-name effects; Birkelund et al. (2021, 84 citations) harmonizes gender audits.
What open problems remain?
Mechanisms behind interactions unclear; need cognitive probes with audits (Banaji et al. 2021). Cross-national name validity varies; AI hiring adds new layers (Williams et al. 2018).
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