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Is College Really “the” Equalizer? New Evidence Addressing Unobserved Selection

By on March 3, 2026 in Articles

Haowen Zheng, Robert Andersen, Anders Holm, Kristian Bernt Karlson

Sociological Science March 3, 2026
10.15195/v13.a10

Abstract

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Influential research shows that college graduates achieve similar labor market outcomes regardless of socioeconomic origin, leading to the view that a college degree is a “great equalizer.” Still, other evidence suggests that family background continues to shape labor market outcomes long after graduation, implying that college’s equalizing effect may largely reflect the characteristics of those who pursue higher education. However, the role of unobserved selection into college has rarely been examined. After formally illustrating how this unobserved selection can bias estimates of the college effect, we present new analyses that correct for this bias using an instrumental-variable approach on white male respondents in the 1979 cohort of the National Longitudinal Survey of Youth. The selection-corrected results suggest that intergenerational mobility is similar among college graduates and nongraduates. Although college yields substantial returns for all, these returns do not differ by family background. We conclude that for higher education to serve as a true equalizer, it must become both less selective and more accessible to students from disadvantaged backgrounds.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Haowen Zheng: Stone Center for Inequality Dynamics, University of Michigan.
E-mail: zhenghw@umich.edu

Robert Andersen: Ivey Business School, Western University.
E-mail: bob.andersen@ivey.ca

Anders Holm: Department of Sociology, Western University.
E-mail: aholm@uwo.ca

Kristian Bernt Karlson: Department of Sociology, University of Copenhagen.
E-mail: kbk@soc.ku.dk

Acknowledgments: The authors thank Richard Breen, Wenhao Jiang, Robert Manduca, Kim Weeden, Ang Yu, and participants at PAA 2025, the Sociological Science 2025 Conference, and ASA 2025 for their helpful comments and support. This research was conducted with restricted access to Bureau of Labor Statistics (BLS) data. We thank the staff at BLS and NORC at the University of Chicago for their assistance with accessing restricted data. The views expressed here do not necessarily reflect the views of the BLS.

Supplemental Materials

Reproducibility Package: A replication package is available at https://osf.io/ne23f/. It includes all code used for data cleaning and analysis as well as a cleaned data set derived from the public-use NLSY79 data. Part of the analysis relies on restricted geographic data obtained through a data contract with the BLS (see https://www.bls.gov/nls/request-restricted-data/nlsy-geocode-data.htm). This is not included in the replication package but can be accessed through a BLS application. The instrumental variables were drawn from the replication package of Carneiro, Heckman, and Vytlacil (2011) (see https://www.openicpsr.org/openicpsr/project/112467/ version/V1/view).

  • Citation: Zheng, Haowen, Robert Andersen, Anders Holm, and Kristian Bernt Karlson. 2026. “Is College Really “the” Equalizer? New Evidence Addressing Unobserved Selection” Sociological Science 13: 242-272.
  • Received: October 6, 2025
  • Accepted: December 12, 2025
  • Editors: Arnout van de Rijt, Jeremy Freese
  • DOI: 10.15195/v13.a10

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Early Childhood Investments and Women’s Work Outcomes across the Life Course

By on February 24, 2026 in Articles

Vida Maralani, Camille Portier, Berkay Özcan

Sociological Science February 24, 2026
10.15195/v13.a9

Abstract

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This study investigates variability in women’s experiences balancing work and family, focusing on the association between early childhood investments and work trajectories. Using longitudinal data and event study models, we examine work participation from two years before to 10 years after first birth across different early childhood investment levels. Although sustained intensive investment is associated with the largest reduction in paid work, the relationship between child investment and work outcomes does not follow a simple “more investment, less work” pattern. Instead, investment intensity and duration both shape work trajectories. Women with more intensive short-term practices or moderate longer-term ones work at similar levels as women making lower investments. Patterns also differ by work outcome: not working is most differentiated by sustained intensive child investment, whereas hours worked are similar across a range of investment levels. Finally, women with constrained family resources consistently work more than those married to college-educated spouses.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Vida Maralani: Cornell University.
E-mail: vida.maralani@cornell.edu.

Camille Portier: European University Institute.
E-mail: camille.portier@eui.eu.

Berkay Özcan: New York University Abu Dhabi.
E-mail: berkay.ozcan@nyu.edu.

Acknowledgments: We thank Isadora Milanez, Douglas McKee, Douglas Miller, Samuel Stabler, Kim Weeden, Kelly Musick, Patrick Ishizuka, Stephen Jenkins, Peter Rich, Lucinda Platt, Seth Sanders, Duncan Thomas, Zhipeng Zhou, and Alvaro Padilla Pozo for their valuable feedback and support on this project. We are grateful for research support from the Cornell Center on the Study of Inequality. After completing the study and drafting this manuscript, we used ChatGPT (OpenAI) to check grammar and clarity in several sections of dense prose.

Supplemental Materials

Reproducibility Package: Replication code for this article can be accessed here: https://osf.io/j8ymw/overview.

  • Citation: Maralani, Vida, Camille Portier, and Berkay Özcan. 2026. “Early Childhood Investments and Women’s Work Outcomes across the Life Course” Sociolog- ical Science 13: 214-241.
  • Received: August 31, 2025
  • Accepted: January 13, 2026
  • Editors: Ari Adut, Maria Abascal
  • DOI: 10.15195/v13.a9

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Force of Attraction and Partner Availability in the U.S. Marriage Market: A Two-Sided Matching Model

By on February 17, 2026 in Articles

Yuan Cheng, John K. Dagsvik, Xuehui Han, Zhiyang Jia

Sociological Science February 17, 2026
10.15195/v13.a8

Abstract

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This article develops and applies a stochastic two-sided matching model to analyze marriage patterns in the United States using 1 percent samples from the 2010 and 2019 American Community Survey, accessed via the Integrated Public Use Microdata Series. This approach disentangles two sources of change in marriage patterns over time: individuals’ preferences for partner characteristics (“forces of attraction”) and the numbers and composition of potential partners (“partner availability”). As illustrated by our empirical application, the model provides a flexible and unified analytical framework to address a broad range of relevant questions in marriage research, offering valuable new perspectives on marriage dynamics and facilitating future research, despite the limitation that the model does not separately identify individual-specific preferences.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Yuan Cheng: Population Research Institute, Fudan University.
E-mail: chengyuan@fudan.edu.cn.

John K. Dagsvik: Research Department, Statistics Norway.
E-mail: john.dagsvik@ssb.no.

Xuehui Han: Asia and Pacific Department, International Monetary Fund.
E-mail: XHan@imf.org.

Zhiyang Jia: Research Department, Statistics Norway.
E-mail: Zhiyang.Jia@ssb.no.

Acknowledgments: We are grateful to the editor, Professor Arnout van de Rijt, and the deputy editor for their constructive comments that significantly improved our analysis. We also thank Zhenchao Qian, participants of the sociology research seminar at The Ohio State University, and graduate students in the Labor Economics course at Fudan University for their valuable feedback. We extend special thanks to Xizhe Peng for his continued support and facilitation of this project. The views expressed in this article are those of the authors and do not necessarily represent the views or policies of the International Monetary Fund or Statistics Norway. Any remaining errors are our own.

Supplemental Materials

Reproducibility Package: https://github.com/GuoguomaFD/Matching.

  • Citation: Cheng, Yuan, John K. Dagsvik, Xuehui Han, and Zhiyang Jia. 2026. “Force of Attraction and Partner Availability in the U.S. Marriage Market: A Two-Sided Matching Model” Sociological Science 13: 178-213.
  • Received: November 22, 2025
  • Accepted: January 12, 2026
  • Editors: Arnout van de Rijt, Michael Rosenfeld
  • DOI: 10.15195/v13.a8

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The Faith Factor. How Scholars’ Religiosity Biases Research Findings on Secularization

By on February 10, 2026 in Articles

Valeria Rainero, Jörg Stolz, Ruud Luijkx

Sociological Science February 10, 2026
10.15195/v13.a7

Abstract

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Secularization is one of the most debated areas of research in current sociology of religion. Despite hundreds of empirical studies, researchers do not even agree on the very existence of secularization in different parts of the world. This article investigates whether some of the variability in findings may be attributed not to the social reality investigated but to bias in the form of researchers’ own religiosity. Specifically, we test whether researchers’ religiosity is correlated with two outcomes: their personal belief in the secularization thesis and the likelihood of supporting secularization in their published articles. To address this question, we constructed an international database of scholars working on secularization and conducted a survey measuring their religiosity and beliefs about religious decline. We then coded their publications according to whether they supported the secularization thesis and linked the two data sets. We find significant evidence of a “(non-)religious bias.” Either in their private attitudes or public writings, religious researchers find less evidence for the secularization thesis, whereas secular scholars find more. This result cannot be explained by differences in research methods, study quality, or the religious and geographic contexts under investigation.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Valeria Rainero: Department of Sociology and Social Research, University of Trento.
E-mail: valeria.rainero@unitn.it.

Jörg Stolz: Institute for Social Sciences of Religion, University of Lausanne.
E-mail: joerg.stolz@unil.ch.

Ruud Luijkx: Department of Sociology, Tilburg University & Department of Sociology and Social Research, University of Trento.
E-mail: r.luijkx@uvt.nl.

Acknowledgments: We sincerely thank everyone who provided valuable comments and suggestions during presentations of this article at the University of Milan (2023), the SSSR Conference in Pittsburgh (2024), and the Institute for Social Sciences of Religions at the University of Lausanne (2024). We also wish to thank Eduard Ponarin and Dominik Balazka for their contributions to the earlier version of the research design and Jeremy Senn for conducting the inter-coder reliability test.

Supplemental Materials

Reproducibility Package: A replication package with instructions, data, and STATA code is publicly available on the Open Science Framework (OSF): https://osf.io/vcxnk/.

  • Citation: Rainero, Valeria, Jörg Stolz, and Ruud Luijkx. 2026. “The Faith Factor. How Scholars’ Religiosity Biases Research Find- ings on Secularization” Sociological Science 13: 154-177.
  • Received: October 30, 2025
  • Accepted: December 16, 2025
  • Editors: Arnout van de Rijt, Andreas Wimmer
  • DOI: 10.15195/v13.a7

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Poor Neighborhoods, Bad Schools? A High-Dimensional Model of Place-Based Disparities in Academic Achievement

By on February 6, 2026 in Articles

Geoffrey T. Wodtke, Kailey White, Xiang Zhou

Sociological Science February 6, 2026
10.15195/v13.a6

Abstract

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Persistent disparities in academic achievement between students from high- and low- poverty neighborhoods are widely attributed to differences in school quality. Using nationally representative data from more than 18,000 students and nearly 1,000 elementary schools, we examine how the schools serving students from different neighborhoods vary across more than 160 characteristics, including detailed measures of their composition, resources, instruction, climate, and effectiveness. Our findings document significant differences in demographic composition between schools serving high- and low-poverty neighborhoods but comparatively little variation in other dimensions of the school environment. With novel machine learning methods tailored for high-dimensional data, we estimate that equalizing all these different factors would reduce the achievement gap by less than 10 percent, primarily through changes in school composition. These results suggest that the main drivers of place-based disparities in achievement lie outside of elementary schools, underscoring the need to address broader structural inequalities as part of any effort to reduce achievement gaps.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Geoffrey T. Wodtke: Department of Sociology, University of Chicago.
E-mail: wodtke@uchicago.edu.

Kailey White: Crime Lab and Education Lab, University of Chicago.
E-mail: kwhite10@uchicago.edu.

Xiang Zhou: Department of Sociology, Harvard University.
E-mail: xiang_zhou@fas.harvard.edu.

Acknowledgments: The authors thank Steve Raudenbush, Guanglei Hong, Ariel Kalil, Steven Durlauf, Eric Grodsky, and Lucienne Disch for helpful comments and discussions. This project was supported by a grant from the U.S. National Science Foundation (No. 2015613) and by the James M. and Cathleen D. Stone Foundation. We used Chat- GPT, version 4o, for help with copyediting the manuscript and debugging R scripts. Responsibility for all content rests solely with the authors.

Supplemental Materials

Reproducibility Package: Code and instructions for accessing the data necessary to reproduce the results presented in this article are available at https://doi.org/10.5281/zenodo.17634676.

  • Citation: Wodtke, T. Geoffrey, Kailey White, and Xiang Zhou. 2026. “Poor Neighborhoods, Bad Schools? A High-Dimensional Model of Place-Based Disparities in Academic Achievement” Sociological Science 13: 109-153.
  • Received: September 8, 2025
  • Accepted: December 12, 2025
  • Editors: Arnout van de Rijt, Jeremy Freese
  • DOI: 10.15195/v13.a6

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How Measurement Changes Can Exaggerate the Growth of Religious “Nones”

By on February 3, 2026 in Articles

Matthew Conrad, Conrad Hackett

Sociological Science February 3, 2026
10.15195/v13.a5

Abstract

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Academic and popular interest in nonreligion has risen in parallel with the growth of religiously unaffiliated populations. In many countries, census and survey questions used to measure religion have been modified to better capture nonreligious identities. Little attention has been given to how these changes in measures affect specific claims about the rise of the “nones.” Although there is no doubt that religiously unaffiliated populations have grown in many countries during the twenty- first century, the degree of such growth has sometimes been exaggerated due to measurement effects. We review methodological issues that affect the estimates of the size of religiously unaffiliated populations and their change over time. We call for further study to quantify the effect of these changes.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Matthew Conrad: Department of Anthropology, University of Connecticut.
E-mail: matthew.conrad@uconn.edu.

Conrad Hackett: Pew Research Center, University of Maryland.
E-mail: chackett@pewresearch.org.

Acknowledgments: We are grateful for helpful feedback from Philip Brenner, Ryan Cragun, Ariela Keysar, Courtney Kennedy, Andrew Mercer, and David Voas. Many people contributed to our broader project of measuring religious change, including Marcin Stonawski, Yunping Tong, Stephanie Kramer, Anne Shi, Alan Cooperman, Joanna Sikorska, and Caileigh Stirling. Support for this work came from The Pew Charitable Trusts and the John Templeton Foundation (grant 62287).

Reproducibility Package: A package is available on the Open Science Framework (https://osf.io/93exg/) that contains data and R code to reproduce the results in this article, as well as links to the full data sets.

  • Citation: Conrad, Matthew, and Conrad Hackett. 2025. “How Measurement Changes Can Exaggerate the Growth of Religious “Nones”” Sociological Sci- ence 13: 89-108.
  • Received: September 15, 2025
  • Accepted: November 14, 2025
  • Editors: Ari Adut, Cristobal Young
  • DOI: 10.15195/v13.a5

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

By on January 27, 2026 in Articles

Carly R. Knight, Adam Goldstein

Sociological Science January 27, 2026
10.15195/v13.a4

Abstract

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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

By on January 20, 2026 in Articles

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

Sociological Science January 20, 2026
10.15195/v13.a3

Abstract

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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

By on January 13, 2026 in Articles

Omar Lizardo

Sociological Science January 13, 2026
10.15195/v13.a2

Abstract

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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?

By on January 6, 2026 in Articles

Martin Eiermann, Maria Fitzpatrick, Katharine Sadowski, Christopher Wildeman

Sociological Science January 6, 2026
10.15195/v13.a1

Abstract

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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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How Robust Are Country Rankings in Educational Mobility?

By on December 11, 2025 in Articles

Ely Strömberg, Per Engzell

Sociological Science December 11, 2025
10.15195/v12.a36

Abstract

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We investigate the impact of analytical choices on country comparisons in intergenerational educational mobility using a multiverse approach. A literature survey gives rise to 2,880 plausible ways of measuring educational mobility, which we apply to European Social Survey data from 16 countries. Although some countries consistently appear at the top or bottom of the mobility rankings, most show substantial variation. Beyond our methodological contribution, we report two substantive findings. First, some countries often characterized as low-mobility emerge as matching or surpassing the egalitarian Nordic countries, reinforcing the view that wider mobility differences cannot be attributed solely to the education system but must be sought elsewhere, such as the labor market. Second, the choice of parameter—such as regression coefficients, correlations, or categorical measures—is the single most influential factor that shifts country rankings. As different parameters carry distinct theoretical meanings, researchers should treat parameter choice not merely as a robustness check but as an opportunity to test and refine competing theories.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Ely Strömberg: Department of Sociology, University of Amsterdam.
E-mail: e.o.stromberg@uva.nl
Per Engzell: UCL Social Research Institute, University College London; Swedish Institute for Social Research, Stockholm University.
E-mail: p.engzell@ucl.ac.uk

Acknowledgments: Per Engzell acknowledges funding from the European Research Coun- cil, grant no. 101165962 (MaMo). Earlier versions of this work were presented at the 2023 Spring Meeting of the ISA Research Committee 28 on Social Stratification and Mobility (RC28) in Paris, the 2024 Conference of the European Consortium for Sociolog- ical Research (ECSR) in Barcelona, and in seminars at the Swedish Institute for Social Research (SOFI) and the Amsterdam Institute for Social Science Research (AISSR). For comments that improved the manuscript, we thank Editor-in-Chief Arnout van de Rijt, Deputy Editor Kristian Karlson, two external reviewers, as well as Adam Altmejd, Krzysztof Czarnecki, Harry Ganzeboom, Jan Helmdag, Mike Hout, Linda Kridahl, Liliya Leopold, Silke Schneider, Edvin Syk, Max Thaning, Jens-Peter Thomsen, An- dreas Videbæk Jensen, Kim Weeden, Herman van de Werfhorst, and Daniel Wilhelm. Any errors remain our own.

Supplemental Materials

Reproducibility Package: The microdata underlying our analyses are available to download from the European Social Survey. Code necessary to reproduce the results is available at: https://doi.org/10.17605/OSF.IO/VCDSX

  • Citation: Strömberg, Ely, Per Engzell. 2025. “How Robust Are Country Rankings in Educational Mobility?” Sociological Science 12: 891-922.
  • Received: July 9, 2025
  • Accepted: October 13, 2025
  • Editors: Arnout van de Rijt, Kristian B. Karlson
  • DOI: 10.15195/v12.a36

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

By on December 6, 2025 in Articles

Ian Lundberg

Sociological Science December 9, 2025
10.15195/v12.a35

Abstract

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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.

Supplemental Materials

Reproducibility Package: doi.org/10.7910/DVN/TB5Q4N.

  • 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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What You Need to Know When Estimating Monthly Impact Functions: Comment on Hudde and Jacob, “There’s More in the Data!”

By on December 4, 2025 in Articles

Josef Brüderl, Ansgar Hudde, Marita Jacob

Sociological Science December 4, 2025
10.15195/v12.a34

Abstract

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In life course research, it is common practice to analyze the effects of life events on outcomes. This is usually done by estimating “impact functions.” To date, most studies have estimated yearly impact functions. However, Hudde and Jacob (2023) (hereafter H&J) pointed out that most panel data sets include information on the month of events. Consequently, they proposed exploiting this information by estimating monthly impact functions. In this adversarial collaboration, we address two issues regarding H&J’s work. First, H&J did not provide sufficient guidance on how to estimate monthly impact functions. We will provide a step-by-step description of how to do so. Second, the procedure H&J proposed for smoothing monthly estimates produces confidence intervals (CIs) that are likely too narrow. This can lead to misleading conclusions. Therefore, we suggest using more appropriate bootstrapped CIs.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Josef Brüderl: Department of Sociology, LMU Munich. E-mail: bruederl@lmu.de
Ansgar Hudde: Department of Sociology and Social Psychology, University of Cologne.
E-mail: hudde@wiso.uni-koeln.de
Marita Jacob: Department of Sociology and Social Psychology, University of Cologne.
E-mail: marita.jacob@uni-koeln.de

Acknowledgments: We thank Katrin Auspurg for her helpful comments. This article uses data from the German Family Panel pairfam, coordinated by Josef Brüderl, Sonja Drobniˇc, Karsten Hank, Johannes Huinink, Bernhard Nauck, Franz J. Neyer, and Sabine Walper. From 2004 to 2022, pairfam was funded as a priority program and a long-term project by the German Research Foundation (DFG).


Reproducibility Package: Stata replication code is available on the Open Science Framework (OSF), https://osf.io/kx9ne/ (file: “Monthly Impact Functions-Replication File.zip”). The replication file includes the prepared pairfam data that we used for all of our analyses. If you would like to reproduce our data preparation (also included in the replication file), you can order the pairfam data at https://www.pairfam.de/en/data/data-access/.

  • Citation: Brüderl, Josef, Ansgar Hudde, Marita Jacob. 2025. “What You Need to Know When Estimating Monthly Impact Functions: Comment on Hudde and Jacob, “There’s More in the Data!”” Sociological Science 12: 862-870.
  • Received: May 16, 2025
  • Accepted: August 31, 2025
  • Editors: Arnout van de Rijt, Kristian B. Karlson
  • DOI: 10.15195/v12.a34

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Pathways to Independence: The Dynamics of Parental Support in the Transition to Adulthood

By on November 25, 2025 in Articles

Ramina Sotoudeh, Ginevra Floridi

Sociological Science November 25, 2025
10.15195/v12.a33

Abstract

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In the United States, the financial and co-residential dependence of young adults on parents has increased for decades. This study provides the first comprehensive analysis of economic support trajectories, their contextual, family, and individual determinants, and temporal relation to other transition to adulthood milestones. Using data from the Panel Study of Income Dynamics’ Transition to Adulthood Study (2005–2021), we identify trajectories of financial and co-residential support between ages 18 and 28 and relate them to economic and partnership trajectories and events. We study how macro-economic crises (the Great Recession and COVID-19), family characteristics, and individual traits within sibships predict trajectory membership. We find three distinct pathways: first, prolonged education and financial support are more common among advantaged families and, within siblings, among those exposed to the Great Recession. Second, early employment and prolonged co-residence are the most prevalent among disadvantaged families and children. Third, economic independence through marriage is most common among white people living outside metropolitan areas.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Ramina Sotoudeh: Department of Sociology, Yale University and Department of Political
and Social Sciences, European University Institute.
E-mail: ramina.sotoudeh@yale.edu.
Ginevra Floridi: School of Social and Political Science, University of Edinburgh.
E-mail: Ginevra.Floridi@ed.ac.uk.

Acknowledgments: We acknowledge funding from British Academy/Leverhulme Small Research Grant SRG2324\240432. Versions of this article were presented at the European Population Conference in Edinburgh, Population Association of America Meeting in Washington DC, and PopDays in Cagliari. We are grateful for the helpful feedback that we received. We thank our parents for their economic and non-economic support throughout our lives and the U.S. highway system for providing us with ample time to discuss our ideas for this article.

Supplemental Materials

Reproducibility Package: Replication code for this article can be accessed here: https://github.com/raminasotoudeh/pathways_to_independence/tree/main

  • Citation: Sotoudeh, Ramina, Ginevra Floridi. 2025. “Pathways to Independence: The Dynamics of Parental Support in the Transition to Adulthood” Sociological Science 12: 833-861.
  • Received: July 9, 2025
  • Accepted: August 25, 2025
  • Editors: Arnout van de Rijt, Michael Rosenfeld
  • DOI: 10.15195/v12.a33

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Public Support for the Legalization of Undocumented Immigrants during the 2016 Presidential Campaign

By on November 21, 2025 in Articles

Mariano Sana

Sociological Science November 21, 2025
10.15195/v12.a32

Abstract

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I investigate whether the political ascent of Donald Trump, an adamant immigration restrictionist, during the 2016 presidential campaign was accompanied by decreasing support for the legalization of undocumented immigrants. Compiling survey data from 2012 to 2016, I show consistent support for legalization throughout the period. However, support was on the decline until Trump entered the presidential race in June 2015, rising thereafter. I use two Pew Research Center surveys, fielded in May 2015 and October 2016, to document that the increase in support for legalization was spearheaded by females, suburban residents, and self-identified Democrats. No demographic group, however defined, recorded a significant decline in their support for legalization. The political ascent of Donald Trump between mid-2015 and the presidential election of November 2016 was not associated with a decline in support for the legalization of undocumented immigrants but the opposite, consistent with similar trends recorded in Europe following the rise of right-wing parties. I discuss the implications of these findings for research on immigration attitudes.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Mariano Sana: Department of Sociology, Vanderbilt University.
E-mail: mariano.sana@vanderbilt.edu.

Acknowledgments: I benefited from comments and suggestions from Guy Stecklov, Jenny Trinitapoli, and Alex Weinreb as well as those of the editor and anonymous reviewers. My gratitude also goes to Yu-Ri Kim and Alyssa Davis for their research assistance.


Reproducibility Package: Data and code necessary for full replication are publicly available here: https://www.openicpsr.org/openicpsr/project/238445/version/V1/view. Original raw data were downloaded from the Roper iPoll database managed by the Public Opinion Research Archive at Cornell University (https://ropercenter.cornell.edu/ipoll/) and from the Pew Research Center (https://www.pewresearch.org/tools-and-datasets/).

  • Citation: Sana, Mariano. 2025. “Public Support for the Legalization of Undocumented Immigrants during the 2016 Presidential Campaign” Sociological Science 12: 804-832.
  • Received: August 18, 2025
  • Accepted: October 9, 2025
  • Editors: Ari Adut, Maria Abascal
  • DOI: 10.15195/v12.a32

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The Hardcore Brokers: Core-Periphery Structure and Political Representation in Denmark’s Corporate Elite Network

By on November 18, 2025 in Articles

Lasse Folke Henriksen, Jacob Aagard Lunding, Christoph Houman Ellersgaard, Anton Grau Larsen

Sociological Science November 18, 2025
10.15195/v12.a31

Abstract

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Who represents the corporate elite in democratic governance? In his seminal work on the corporate “inner circle,” Useem (1986) studied three network-related mechanisms from corporate interlocks that together shaped the ideology and political organization of American and British corporate elites during the postwar era in crucial ways: corporate brokerage, elite social cohesion, and network centrality. Subsequent research has found similar dynamics at play across a variety of democratic capitalist societies. However, all existing studies on corporate elite representation in democratic governance rest on analyses of the top ranks at very large corporations. We cast a wider net. Analyzing new population data on all members of corporate boards in the Danish economy (∼200,000 directors in ∼120,000 boards), we locate ∼1,500 directors who operate as brokers between local corporate networks and measure their network coreness using k-core detection. We find a highly connected network core of ∼275 directors, half of whom are affiliated with smaller companies or subsidiaries and then document the power of director coreness in predicting government committee attendance, a key form of political representation in Denmark’s social-corporatist model of governance. We find a large political premium for directors in very large companies but show that within the network core the gap between directors of smaller and large companies is closed, suggesting that the network core levels the playing field in corporate access to the legislative process.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Lasse Folke Henriksen: Department of Organization, Copenhagen Business School.
E-mail: lfh.ioa@cbs.dk
Jacob Aagard Lunding: Department of Social Sciences and Business, Roskilde University.
E-mail: jaagaard@ruc.dk
Christoph Houman Ellersgaard: Department of Organization, Copenhagen Business School.
E-mail: che.ioa@cbs.dk.
Anton Grau Larsen: Department of Social Sciences and Business, Roskilde University.
E-mail: agraul@ruc.dk

Acknowledgments: We would like to thank Leonard Seabrooke, Felix Bühlman, Donald Tomaskovic-Devey, Thomas Lyttelton, and Megan Neely for commenting on an earlier draft of this article. We also thank participants at the Political Economy Group Seminar at the Copenhagen Business School for engaging with an earlier draft. We are grateful to the Independent Research Fund Denmark (grant 5052-00143b), the Carlsberg Foundation (grant CF19-0175), and the Velux Foundation (grant 00048306) for generously supporting this research.

Supplemental Materials

Reproducibility Package: Because our data-use agreement prohibits direct sharing of our analytic data, we share only the analysis code here: https://github.com/JacobLunding/hardcore_brokers_replication. Interested parties may apply to Statistics Denmark (https://www.dst.dk/en/TilSalg/Forskningsservice/Dataadgang/) for access to the data (project 706264) and can run the full replication package from the folder named “/replication,” which includes all data generating steps of the analysis and the analytical code.

  • Citation: Henriksen, Lasse Folke, Jacob Aagard Lunding, Christoph Houman Ellersgaard, Anton Grau Larsen. 2025. “The Hardcore Brokers: Core-Periphery Structure and Political Representation in Denmark’s Corporate Elite Network” Sociological Science 12: 769-803.
  • Received: December 19, 2024
  • Accepted: September 24, 2025
  • Editors: Arnout van de Rijt, Michael Rosenfeld
  • DOI: 10.15195/v12.a31

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The Intergenerational Reach of Maternal Adverse Childhood Experiences: Associations with Children’s Emotional Support and Cognitive Stimulation

By on November 6, 2025 in Articles

Lawrence Stacey, Kristi Williams

Sociological Science November 6, 2025
10.15195/v12.a30

Abstract

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Adverse childhood experiences (ACEs)—such as abuse, neglect, and household dysfunction before age 18—pose substantial risks to individual health and well-being throughout life, but relatively less research has examined how ACEs are associated with parenting behaviors or children’s home environments. We use linked mother–child data from the National Longitudinal Survey of Youth 1979, a U.S. longitudinal cohort study, to investigate how maternal ACEs are associated with the emotional support and cognitive stimulation of children. Regression results demonstrate an inverse relationship between maternal ACE exposure and the degree of emotional support and cognitive stimulation in children’s home environments. Children born to mothers with four or more ACEs had, on average, 4.9 percentile-unit lower emotional support scores and 5.6 percentile-unit lower cognitive stimulation scores relative to mothers with no ACE exposure, net of maternal and child sociodemographic characteristics. Further results document the importance of emotional neglect and physical abuse, both of which were independently and negatively related to the emotional support and cognitive stimulation of children. Our article builds on a growing body of literature by documenting links between maternal ACE exposure and children’s home environments and by illuminating the lengthy intergenerational reach of parental ACEs.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Lawrence Stacey: Department of Sociology, Vanderbilt University.
E-mail: lawrence.stacey@vanderbilt.edu.
Kristi Williams: Department of Sociology, Ohio State University.
E-mail: williams.2339@osu.edu.

Acknowledgments: We wish to thank Melissa Alcaraz, John Casterline, Reanne Frank, Sarah Hayford, Jake Hays, and Alec Rhodes for feedback on an earlier draft of this article. We also wish to thank the editors of Sociological Science.

Supplemental Materials

Reproducibility Package: A replication package with instructions, data, and Stata code has been
made publicly available on the Open Science Framework (OSF): https://osf.io/gb29u/

  • Citation: Stacey, Lawrence, and Kristi Williams. 2025. “The Intergenerational Reach of Maternal
    Adverse Childhood Experiences: Associations with Children’s Emotional Support and Cognitive Stimulation” Sociological Science 12: 743-768.
  • Received: June 17, 2024
  • Accepted: April 24, 2025
  • Editors: Arnout van de Rijt, Michael Rosenfeld
  • DOI: 10.15195/v12.a30

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Wide Social Influence and the Emergence of the Unexpected: An Empirical Test Using Spotify Data

By on October 23, 2025 in Articles

Martin Arvidsson, Peter Hedström, Marc Keuschnigg

Sociological Science October 23, 2025
10.15195/v12.a29

Abstract

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Social-influence processes not only affect the rate at which behaviors spread but can also decouple adoption behavior from individual preferences, and thereby bring about unexpected collective outcomes that cannot be predicted on the basis of the initial likes and dislikes of the individuals involved. However, the conditions under which social influence can lead to such decoupling are not well understood. We identify a social-influence mechanism that widens individuals’ behavioral repertoires and breaks the link between individuals’ initial preferences and the collective outcomes they jointly bring about. We test the micro-level assumptions of the mechanism in the context of cultural choices on Spotify, combining topic modeling with traditional statistical matching to cultural change. agent-based simulation estimate peer-to-peer influence effects from digital trace data. We then use agent-based simulations to examine the macro-level consequences of “wide” social influence and its importance for explaining cultural change.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Martin Arvidsson: The Institute for Analytical Sociology, Linköping University. E-mail: martin.arvidsson@liu.se.
Peter Hedström: The Institute for Analytical Sociology, Linköping University. E-mail: peter.hedstrom@liu.se.
Marc Keuschnigg: The Institute for Analytical Sociology, Linköping University and Institute of Sociology, Leipzig University. E-mail: marc.keuschnigg@liu.se.

Acknowledgments: For helpful comments, we thank James Evans, Jacob Habinek, Mark Lutter, Arnout van de Rijt, and Duncan Watts. We are grateful for financial support from Riksbankens Jubileumsfond (M12-0301:1) and the Swedish Research Council (2013-7681, 2018-05170, 2019-00245, and 2024-01861). This research was carried out at the Swedish Excellence Center for Computational Social Science, which is also funded by the Swedish Research Council (2022-06611). Resources provided by the Swedish National Infrastructure for Computing (2024/22-1012) enabled computations.

Supplemental Materials

Reproducibility Package: A replication package has been deposited to OSF (https://osf.io/grsyt/?view_only=133867f728644ba596eb104890cb018f ) that contains code and data required to reproduce the results presented in the article.

  • Citation: Arvidsson, Martin, Peter Hedström, Marc Keuschnigg. 2025. “Wide Social Influence and the Emergence of the Unexpected: An Empirical Test Using Spotify Data.” Sociological Science 12: 715-742.
  • Received: December 16, 2024
  • Accepted: September 10, 2025
  • Editors: Ari Adut, Peter Bearman
  • DOI: 10.15195/v12.a29

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