Tag Archives | Inequality

Ambiguous Actorhood: Twenty-First Century Firms and the Evasion of Responsibility

Carly R. Knight, Adam Goldstein

Sociological Science January 27, 2026
10.15195/v13.a4


Sociologists have long argued that the cultural construction of organizations as social actors underpins public expectations of corporate accountability. In recent decades, however, the unified bureaucratic structures that once sustained this construction have given way to increasingly fragmented and opaque organizational forms. This study considers to what extent the diffuse, often illegible nature of twenty-first century corporations undermines the ability of public audiences to demand corporate accountability. We argue that complex, fragmented organizational configurations allow firms to partially evade the negative reputational consequences of misconduct by confounding audiences and obfuscating the “actor” behind the bad organizational action. Drawing on a vignette- based survey experiment, we test whether fragmentation reduces attributions of blame following corporate wrongdoing. Consistent with our hypotheses, we find that while respondents generally attribute high levels of blame for wrongdoing, greater fragmentation decreases the blame directed at core firms and heightens audiences’ uncertainty about responsibility. Moreover, in fragmented structures, blame is not simply redistributed to auxiliary entities but is diminished overall. These findings suggest that as corporate structures grow more complex and less legible, the underlying actors behind organizational action become harder to identify and construct, and thereby harder to hold to account.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Carly R. Knight: New York University.
E-mail: carly.knight@nyu.edu.

Adam Goldstein: Princeton University.
E-mail: amg5@princeton.edu.

Acknowledgments: The authors are listed in reverse alphabetical order. For helpful com- ments, the authors wish to thank Laura Adler, Barbara Kiviat, Kim Pernell, and Claire Sieffert. This article has benefitted from presentations at the 2024 American Sociological Association Meetings and the 2025 RC17 Conference on Organizing Plurality.

Supplemental Materials

Reproducibility Package: Data and code to reproduce the results reported in this article are avail- able at OSF (https://osf.io/enpmt/). The online supplemental appendix also contains additional information about the survey data.

  • Citation: Knight, Carly R., Adam Goldstein. 2025. “Ambiguous Actorhood: Twenty-First Century Firms and the Evasion of Respon- sibility” Sociological Science 13: 63-88.
  • Received: August 26, 2025
  • Accepted: October 31, 2025
  • Editors: Arnout van de Rijt, Kieran Healy
  • DOI: 10.15195/v13.a4

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Generative AI in Sociological Research: State of the Discipline

AJ Alvero, Dustin S. Stoltz, Oscar Stuhler, Marshall A. Taylor

Sociological Science January 20, 2026
10.15195/v13.a3


Generative artificial intelligence (GenAI) has garnered considerable attention for its poten- tial utility in research and scholarship, even among those who typically do not rely on computational tools. However, early commentators have also articulated concerns about how GenAI usage comes with enormous environmental costs, serious social risks, and a tendency to produce low-quality content. In the midst of both excitement and skepticism, it is crucial to take stock of how GenAI is actually being used. Our study focuses on sociological research as our site, and here we present findings from a survey of 433 authors of articles published in 50 sociology journals in the past five years. The survey provides an overview of the state of the discipline with regard to the use of GenAI by providing answers to fundamental questions: how (much) do scholars use the technology for their research; what are their reasons for using it; and how concerned, trustful, and optimistic are they about the technology? Of the approximately one third of respondents who self-report using GenAI at least weekly, the primary uses are for writing assistance and comparatively less so in planning, data collection, or data analysis. In both use and attitudes, there are surprisingly few differences between self-identified computational and non-computational researchers. In general, respondents are very concerned about the social and environmental consequences of GenAI. Trust in GenAI outputs is low, regardless of expertise or frequency of use. Although optimism that GenAI will improve is high, scholars are divided on whether GenAI will have a positive impact on the field.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

AJ Alvero: Center for Data Science for Enterprise and Society, Cornell University.
E-mail: ajalvero@cornell.edu.

Dustin S. Stoltz: Department of Sociology and Anthropology, Lehigh University.
E-mail: dss219@lehigh.edu.

Oscar Stuhler: Department of Sociology, Northwestern University.
E-mail: oms@northwestern.edu.

Marshall A. Taylor: Department of Sociology, New Mexico State University.
E-mail: mtaylor2@nmsu.edu.

Acknowledgments: All authors contributed equally. We thank all the respondents of our survey for being generous with their time. We are indebted to Kim Weeden and Cat Dang Ton for giving us crucial comments on an early version of this article. We are also grateful for the important feedback we received at the ASA Session on Culture and Computational Social Science, the Sociological Science Conference at Cornell University, the International Network of Analytical Sociology Conference at Columbia University, the Institute for Analytical Sociology Symposium at Linköping University, and the Culture and Action Network at the University of Chicago.

Supplemental Materials

Reproducibility Package: A replication repository for this article can be found at: https://github.com/Marshall-Soc/genai_sociology. The data for this article are hosted on the Harvard Dataverse (Alvero et al. 2025) and can be accessed through: https://doi.org/10.7910/DVN/ICXIRP

  • Citation: Alvero, AJ, Dustin S. Stoltz, Oscar Stuhler, and Marshall A. Taylor. 2025. “Generative AI in Sociological Research: State of the Discipline” Sociological Science 13: 45-62.
  • Received: August 23, 2025
  • Accepted: November 8, 2025
  • Editors: Arnout van de Rijt, Cristobal Young
  • DOI: 10.15195/v13.a3

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The Forward March of Categorical Tolerance in the United States

Omar Lizardo

Sociological Science January 13, 2026
10.15195/v13.a2


This article updates the empirical picture of categorical tolerance (CT), namely, the pattern of refusing to report dislikes across cultural genres, for the third decade of the twenty-first century in the United States. Analyzing recent survey data from two platforms, I find that CT has continued its march among Americans, reaching approximately one in five respondents. The analysis confirms earlier-observed demographic trends, showing that CT is strongly associated with younger cohorts and non-white individuals. However, I also find that individuals reporting the highest educational attainment are now overrepresented among categorical tolerants, suggesting that CT may increasingly function as an elite cultural strategy consistent with contemporary forms of status display, signaling openness and refusal to refuse. Furthermore, I find that while the odds of being a CT are not strongly polarized by political ideology, the inclination toward symbolic exclusion among non-CTs is, with conservatives significantly more likely to express a greater volume of cultural dislikes than liberals.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Omar Lizardo: Department of Sociology, UCLA.
E-mail: olizardo@soc.ucla.edu.

Acknowledgments: I would like to thank the anonymous Sociological Science reviewers for insightful suggestions for revision that helped improve the article. An early version of this article was presented at the first Sociological Science Conference at Duke University in 2024, where I received useful comments and suggestions.

No supplemental materials.

Reproducibility Package: Data files and R code (in Quarto Markdown format) necessary to reproduce all of the analyses, tables, and figures reported in the article can be found at the following GitHub repo: https://github.com/olizardo/sociological-science-categorical-tolerance-followup.

  • Citation: Lizardo, Omar. 2025. “The Forward March of Categorical Tolerance in the United States” Sociological Science 13: 22-44.
  • Received: October 18, 2025
  • Accepted: November 23, 2025
  • Editors: Ari Adut, Stephen Vaisey
  • DOI: 10.15195/v13.a2

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How Do (Human) Child Welfare Workers Respond to Machine-Generated Risk Scores?

Martin Eiermann, Maria Fitzpatrick, Katharine Sadowski, Christopher Wildeman

Sociological Science January 6, 2026
10.15195/v13.a1


Algorithmic risk scoring tools have been widely incorporated into governmental decision making, yet little is known about how human decision makers interact with machine-generated risk scores at the street level. We examined such human–machine interactions in the child welfare system, a high-stakes setting where caseworkers ascertain whether government interventions in family life are warranted. Using novel data—verbatim transcripts of caseworker discussions—we found that decision makers: (1) disregarded scores in the middle of the distribution while paying attention to extremely high or low risk scores and (2) rationalized divergences between human decisions and machine-generated scores by highlighting the algorithm’s overemphasis on historical data and specific risk factors and its lack of contextual knowledge. This meant that caseworkers were unlikely to modify their decisions so that they aligned with risk scores. However, we did not find evidence of principled resistance to algorithmic tools. Our findings advance research on such tools by specifying how human perceptions of the utility and limitations of novel technologies shape discretionary decision making by state officials; and they help to explain their uneven and potentially modest impact on the bureaucratic management of social vulnerability.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Martin Eiermann: Department of Sociology, University of Wisconsin-Madison.
E-mail: meiermann@wisc.edu.
Maria Fitzpatrick: Books School of Public Policy, Cornell University; National Bureau of Economic Research.
E-mail: maria.d.fitzpatrick@cornell.edu.
Katharine Sadowski: Graduate School of Education, Stanford University.
E-mail: ksadow@stanford.edu.
Christopher Wildeman: Department of Sociology, Duke University; Sanford School of Public Policy, Duke University; ROCKWOOL Foundation Research Unit.
E-mail: christopher.wildeman@duke.edu.

Acknowledgments: The authors are grateful to Ruby Richards and Nicole Adams for feedback on earlier drafts of this manuscript and the Douglas County Department of Human Services for providing data throughout this project.

No supplemental materials.

Reproducibility Package: The terms of our Data Use Agreement with the Douglas County Department of Human Services (DCDHS) legally prohibit us from sharing the original data, which are temporarily stored on a secure Cornell University research server, cannot be shared externally, and must be destroyed at the end of the agreement period. These restrictions reflect the presence of highly sensitive child welfare data in verbatim transcripts of caseworker discussions. All analysis code and documentation of qualitative coding workflows are publicly available at OSF. Researchers with questions about Douglas County Decision Aide (DCDA) data that were generated during the randomized controlled trial may contact: Ruby Richards, Director of Human Services, Douglas County (303-688-4825).

  • Citation: Eiermann, Martin, Maria Fitzpatrick, Katharine Sadowski, and Christopher Wildeman. 2025. “How Do (Human) Child Welfare Workers Re- spond to Machine-Generated Risk Scores?” Sociological Science 13: 1-21.
  • Received: September 3, 2025
  • Accepted: November 14, 2025
  • Editors: Ari Adut, Jeremy Freese
  • DOI: 10.15195/v13.a1

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The Causal Impact of Segregation on a Disparity: A Gap-Closing Approach

Ian Lundberg

Sociological Science December 9, 2025
10.15195/v12.a35


Segregation—whether across schools, neighborhoods, or occupations—is regularly invoked as a cause of social and economic disparities. However, segregation is a complicated causal treatment: what do we mean when we appeal to a world in which segregation does not exist? One could take societal contexts as the unit of analysis and compare across societies with differing levels of segregation. In practice, it is more common for studies of segregation to take persons or households as the unit of analysis within a single societal context, focusing on what would happen if particular individuals were counterfactually assigned to social positions in a more equitable way. Taking this latter framework, this article shows how to study segregation as a cause. The first step is to theorize a counterfactual assignment rule: what would it mean to assign people to social positions equitably? The second step is to identify the causal effect of those social positions and simulate counterfactual outcomes. The third step is to interpret results as the impact of a unit-level (rather than society-level) intervention. A running example and empirical analysis illustrates the approach by studying the causal effect of occupational segregation on a racial health gap.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Ian Lundberg: Department of Sociology, UCLA.
E-mail: ianlundberg.org, ianlundberg@ucla.edu.

Acknowledgments: For helpful discussions and feedback relevant to this project, I thank Brandon Stewart, Matthew Salganik, Dalton Conley, Sara McLanahan, Rebecca Johnson, Gillian Slee, and participants in presentations at the Princeton Department of Sociology, the Cornell Center for the Study of Inequality, and the UCSF Department of Social and Behavioral Sciences, as well as the editor and anonymous reviewers. The author benefited from facilities and resources provided by the California Center for Population Research at UCLA (CCPR), which receives core support (P2C-HD041022) from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health & Human Development or the National Institutes of Health.

  • Citation: Lundberg, Ian. 2025. “The Causal Impact of Segregation on a Disparity: A Gap-Closing Approach” Sociological Science 12: 871-890.
  • Received: July 15, 2025
  • Accepted: August 31, 2025
  • Editors: Arnout van de Rijt, Maria Abascal
  • DOI: 10.15195/v12.a35

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Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity

Samuel Coavoux, Abel Aussant

Sociological Science September 4, 2025
10.15195/v12.a24


Do digital technologies affect diversity in cultural tastes? Digital sociologists have warned of “filter bubbles,” whereas sociologists of culture have shown that diversity in consumption is valued as a marker of upper-middle-class status. We estimate the effect of using streaming platforms on the diversity of cultural consumption using a matching technique applied to 2018 survey data from France. We find a statistically significant positive effect of using streaming platforms on the diversity of cultural consumption as well as on cosmopolitanism, on three domains, music, movies, and TV shows. The magnitude of this effect is much higher for TV shows. The study brings new evidence against the filter bubble thesis; it shows that platforms do reinforce cultural inequalities by increasing the social gap in consumption diversity. It further suggests that the effect of technology on cultural consumption might mainly operate through its impact on cultural markets rather than changes in cultural experience.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Samuel Coavoux: CREST, ENSAE, Institut Polytechnique de Paris, Paris, France. E-mail: samuel.coavoux@ensae.fr.
Abel Aussant: Sciences Po, CRIS, Paris, France. E-mail: abel.aussant@sciencespo.fr.

Acknowledgments: This article benefited greatly from comments by Quentin Mazel, Patrick Präg, Léa Pessin, and anonymous reviewers, as well as from the audiences of AFS 2023, ESA-RN05 Midterm 2023, ECSR 2023, Culture in a digital context conferences, and the CREST sociology seminar.

Supplemental Materials

Reproducibility Package: A replication package containing all scripts necessary to reproduce the results presented in the article is available at OSF. The data are available on demand from the Progedo-Adisp repository.

  • Citation: Coavoux, Samuel and Abel Aussant. 2025. “Streaming Platforms, Filter Bubbles, and Cultural Inequalities. How Online Services Increase Consumption Diversity” Sociological Science 12: 572-600.
  • Received: May 29, 2025
  • Accepted: July 6, 2025
  • Editors: Arnout van de Rijt, Bart Bonikowski
  • DOI: 10.15195/v12.a24

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Inequality and Social Ties: Evidence from 15 U.S. Data Sets

Cristobal Young, Benjamin Cornwell, Barum Park, Nan Feng

Sociological Science May 12, 2025
10.15195/v12.a14


What is the relationship between inequality and social ties? Do personal networks, group memberships, and connections to social resources help level the playing field, or do they reinforce economic disparities? We examine two core empirical issues: the degree of inequality in social ties and their consolidation with income. Using 142,000 person-wave observations from 15 high-quality U.S. data sets, we measure the quantity and quality of social ties and examine their distribution. Our findings show that (1) the Gini coefficient for social ties often exceeds that of income and (2) social ties are concentrated among those with the highest incomes. We introduce an overall inequality–consolidation curve, demonstrating that social ties generally reinforce economic inequality. However, we identify one key exception: there is no class gradient in the use of social ties for job search. These findings contribute to debates about the role of social ties in perpetuating or mitigating inequality.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Cristobal Young: Department of Sociology, Cornell University
E-mail: cristobal.young@cornell.edu

Benjamin Cornwell: Department of Sociology, Cornell University
E-mail: btc49@cornell.edu

Barum Park: Department of Sociology, Cornell University
E-mail: b.park@cornell.edu

Nan Feng: Institute for Public Knowledge, New York University
E-mail: nf263@cornell.edu

Acknowledgments: We received valuable comments and suggestions from Kendra Bischoff, Paul DiMaggio, Filiz Garip, Lynn Johnson, Sheela Kennedy, Edward O. Laumann, Vida Maralani, Kelly Musick, Anthony Paik, Landon Schnabel, Kim Weeden, Patricia Young, Erin York Cornwell, as well as participants at seminars at Cornell Sociology, the annual meeting of the American Sociological Association, the Sociological Science Conference, and the Future of the Social Sciences Conference. Tianyao Qu, Zhonghao Wang, and Haowen Zheng provided exceptional research assistance. We thank the Cornell Center for Social Sciences for providing computing resources and the Cornell Center of the Study of Inequality for generous funding.

Supplemental Materials

Reproducibility Package: All code, and all data that can be publicly shared, is available at OSF (https://osf.io/ky4ws/). The package also includes information about requesting access to confidential data sets, such as the Addhealth restricted-use data.

  • Citation: Young, Cristobal, Benjamin Cornwell, Barum Park, Nan Feng. 2025. “Inequality and Social Ties: Evidence from 15 U.S. Data sets” Sociological Science 12: 294-321.
  • Received: September 5, 2024
  • Accepted: March 17, 2025
  • Editors: Arnout van de Rijt, Filiz Garip
  • DOI: 10.15195/v12.a14

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Inequality and Total Effect Summary Measures for Nominal and Ordinal Variables

Trenton D. Mize, Bing Han

Sociological Science February 5, 2025
10.15195/v12.a7


Many of the topics most central to the social sciences involve nominal groupings or ordinal rankings. There are many cases in which a summary of a nominal or ordinal independent variable’s effect, or the effect on a nominal or ordinal outcome, is needed and useful for interpretation. For example, for nominal or ordinal independent variables, a single summary measure is useful to compare the effect sizes of different variables in a single model or across multiple models, as with mediation. For nominal or ordinal dependent variables, there are often an overwhelming number of effects to examine and understanding the holistic effect of an independent variable or how effect sizes compare within or across models is difficult. In this project, we propose two new summary measures using marginal effects (MEs). For nominal and ordinal independent variables, we propose ME inequality as a summary measure of a nominal or ordinal independent variable’s holistic effect. For nominal and ordinal outcome models, we propose a total ME measure that quantifies the comprehensive effect of an independent variable across all outcome categories. The added benefits of our methods are both intuitive and substantively meaningful effect size metrics and approaches that can be applied across a wide range of models, including linear, nonlinear, categorical, multilevel, longitudinal, and more.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Trenton D. Mize: Departments of Sociology & Statistics (by courtesy) and The Methodology Center at Purdue University
E-mail: tmize@purdue.edu

Bing Han: Department of Sociology, Purdue University
E-mail: han644@purdue.edu

Acknowledgements: We thank Shawn Bauldry and the audience at The Methodology Center at Purdue’s work-in-progress series for their helpful comments on this article. We also thank Jonathan Horowitz for a well-timed question that pushed us to further develop the methods for nominal and ordinal outcomes.

Reproducibility Package: All data and coding files needed to reproduce all results shown in this article are available at both www.trentonmize.com/research and OSF (osf.io/myehf/). In addition to the replication files, simplified template/example Stata and R files are also available in the same locations.

  • Citation: Mize, Trenton D., Bing Han. 2025. “Inequality and Total Effect Summary Measures for Nominal and Ordinal Variables” Sociological Science 12: 115-157.
  • Received: November 27, 2024
  • Accepted: January 7, 2025
  • Editors: Arnout van de Rijt, Kristian B. Karlson
  • DOI: 10.15195/v12.a7

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Algorithmic Risk Scoring and Welfare State Contact Among US Children

Martin Eiermann

Sociological Science August 23, 2024
10.15195/v11.a26


Predictive Risk Modeling (PRM) tools are widely used by governing institutions, yet research on their effects has yielded divergent findings with low external validity. This study examines how such tools influence child welfare governance, using a quasi-experimental design and data from more than one million maltreatment investigations in 121 US counties. It demonstrates that the adoption of PRM tools reduced maltreatment confirmations among Hispanic and Black children but increased such confirmations among high-risk and low-SES children. PRM tools did not reduce the likelihood of subsequent maltreatment confirmations; and effects were heterogeneous across counties. These findings demonstrate that the use of PRM tools can reduce the incidence of state interventions among historically over-represented minorities while increasing it among poor children more generally. However, they also illustrate that the impact of such tools depends on local contexts and that technological innovations do not meaningfully address chronic state interventions in family life that often characterize the lives of vulnerable children.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Martin Eiermann: Department of Sociology, Duke University
E-mail: martin.eiermann@duke.edu.

Acknowledgements: The author thanks Olivia Kim and Henry Zapata for invaluable research assistance, and thanks Garrett Baker, Alexandra Gibbons, Sarah Sernaker, and Christopher Wildeman for constructive feedback.

Replication Package: Access to restricted-use NCANDS data can be requested through the National Data Archive on Child Abuse and Neglect (NDACAN). Other data and replication code are available at: https://osf.io/dq3xp/.

  • Citation: Eiermann, Martin. 2024. “Algorithmic Risk Scoring and Welfare State Contact Among US Children” Sociological Science 11: 707-742.
  • Received: May 20, 2024
  • Accepted: July 2, 2024
  • Editors: Arnout van de Rijt, Maria Abascal
  • DOI: 10.15195/v11.a26


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Colorism Revisited: The Effects of Skin Color on Educational and Labor Market Outcomes in the United States

Mauricio Bucca

Sociological Science June 10, 2024
10.15195/v11.a19


Studies of colorism—the idea that racial hierarchies coexist with gradational inequalities based on skin color—consistently find that darker skin correlates with lower socioeconomic outcomes. Despite the causal nature of this debate, evidence remains predominantly associational. This study revisits the colorism literature by proposing a causal model underlying these theories. It discusses conditions under which associations may reflect contemporary causal effects of skin color and evaluates strategies for identifying these effects. Using data from the AddHealth and NLSY97 surveys and applying two identification strategies, the study estimates the causal effects of skin color on college degree attainment, personal earnings, and family income among White, Black, and Hispanic populations in the United States. Results show that darker skin correlates with poorer educational and economic outcomes within racial groups. However, evidence of contemporary causal effects of skin color is partial, limited to college attainment of Whites and family income of Hispanics. For Blacks, results suggest a generalized penalty associated with being Black rather than gradation based on skin tone. Methodologically, the article advocates using sensitivity analyses to account for unobserved confounders in models for skin color effects and uses sibling fixed-effects as a secondary complementary strategy.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Mauricio Bucca: Department of Sociology, Pontificia Universidad Católica de Chile. E-mail: mebucca@uc.cl.

Acknowledgements: I wish to thank Fabrizio Bernardi, Kendra Bischoff, Lucas Drouhot, Matias Fernandez, Vida Maralani, Mario Molina, Ben Rosche, Daniela Urbina, Sebastian Urbina, Kim Weeden as well as audiences at the 2021 Population Association of America (PAA) Annual Meeting and the Comparative Life Course and Inequality Research Centre (CLIC) at the European University Institute in Florence for helpful comments and criticisms on earlier versions of the article. I am also grateful for financial support from FONDECYT Iniciación grant project No. 11221171 and ANID Milenio Labor Market Mismatch – Causes and Consequences, LM2C2 (NCS2022045). Direct correspondence to Mauricio Bucca, mebucca@uc.cl

Data: This research uses data from Add Health, a program project directed by Kathleen Mullan Harris and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris at the University of North Carolina at Chapel Hill, and funded by grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other fed- eral agencies and foundations. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website (http://www.cpc.unc.edu/addhealth). No direct support was received from grant P01-HD31921 for this analysis. Access to restricted data through the National Institutes of Health Grant R01HD091125, led by Principal Investigator Kelly Musick.

Supplemental Material

Replication Package: The code necessary for reproducing the data manipulation, modeling, and findings is accessible at https://osf.io/vm647/?view_only=5b6477b89c284a88 9d9e3c77fc6e8fe1.

  • Citation: Bucca, Mauricio. 2024. “Colorism Revisited: The Effects of Skin Color on Educational and Labor Market Outcomes in the United States.” Sociological Science 11: 517-552.
  • Received: February 23, 2024
  • Accepted: March 26, 2024
  • Editors: Ari Adut, Maria Abascal
  • DOI: 10.15195/v11.a19


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