Cited by 280Names, Identity, and Discrimination ResearchLabor market dynamics and wage inequalityIntergenerational and Educational Inequality Studies

Does it matter who answers the race question? Racial classification and income inequality in Brazil

Telles, Edward E., Lim, Nelson·1998·Demography

Abstract

Previous studies ofracial inequality have relied on official statistics that presumably use self-classification of race. Using novel data from a 1995 national survey in Brazil, we find that the estimates of racial income inequality based on self-classification are lower than those based on interviewer classification. After human capital and labor market controls, whites earn 26% more than browns with interviewer classification but earn only 17% more than browns with self-classification. Black-brown differences hardly change: Blacks earn 13% and 12% less than browns with interviewer classification and self-classification, respectively. We contend that interviewer classification of race is more appropriate because analysts of racial inequality are interested in the effects of racial discrimination, which depends on how others classify one's race.

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Crossref · 10.2307/3004014

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27 of 27 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Self-identification has been shown to be responsive to one's arrest records and to one's class position.

    “Self-identification has been shown to be responsive to one's arrest records [89] and to one's class position [115].”

    From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [115]

  • When race is utilized as a descriptive category, the choices about what categories to use and how to assign individuals to categories significantly impacts the results of analysis.

    “When race is utilized as a descriptive category, the choices about what categories to use and how to assign individuals to categories significantly impacts the results of analysis [18, 102, 114, 115].”

    From Towards a critical race methodology in algorithmic fairness (2020) · cites this paper as [115]

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Authors
Telles, E. E., & Lim, N.
Affiliations
  • Ford Foundation (US)
  • University of California, Los Angeles (US)
Venue
Demography
Published
1998-11-01
Volume
35
Issue
4
Pages
465-474
ISSN
  • 0070-3370 (ISSN-L)
  • 1533-7790
OpenAlex
W2001175042
Cited by
280
References
28
Citations by year
2026: 9 · 2025: 16 · 2024: 11 · 2023: 5 · 2022: 13 · 2021: 11 · 2020: 10 · 2019: 12 · 2018: 17 · 2017: 14 · 2016: 12 · 2015: 18 · 2014: 10 · 2013: 13 · 2012: 13
FWCI
11.60
Type
journal article
Language
en
Open access
closed
Locations
  • Demography (publishedVersion)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
  • Social Sciences › Sociology and Political Science › Intergenerational and Educational Inequality Studies
Keywords
racial classification, racial income inequality, racial discrimination, black-white income gap, Brazil, racial stratification
MeSH terms
Adolescent, Adult, Black or African American, Brazil, Data Collection, Humans, Income, Interviews as Topic, Prejudice, Random Allocation, Social Class, Urban Population, Bias, Likelihood Functions, Black People, White People, Racial Groups

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