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Influential studies of right-wing extremist violence offer evidence that such violence is motivated by grievances intensified by a perceived loss in status or by economic dislocations. This article moves away from an emphasis on grievances by turning to theories of ethno-nationalism and group conflict. Ethno-nationalism is in part driven by attitudes of dominant groups favoring ethnic exclusion, whereas group threat theories explain that ethnic diversity increases the salience of ethnic boundaries and fuels a collective response to group threat. Such threats encourage violence to contain this threat and restore dominance. Exclusionary attitudes and support for expanded gun rights in America further legitimize a culture of ethno-nationalism that encourages violent acts. I test these arguments with data from the Pew Research Center, the Southern Poverty Law Center, and the Extremist Crime Database on right-wing violence. The state-level and county-level results support the claim that rising ethnic diversity raises the rate and volume of right-wing violence significantly. State-level results also find that rising memberships in the National Rifle Association increase the rate of right-wing violence significantly.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Susan Olzak: Stanford University
E-mail: olzak@stanford.edu

Acknowledgments: The author thanks Trent Steidley, Holly Hansen, and Stephen C. Nemeth for providing some of the data used in this study. The author is grateful to Bart Bonikowski, Mike Hannan, Doug McAdam, Rory McVeigh, Sarah A. Soule, Andy Walder, and Nella Van Dyke for providing helpful suggestions and criticisms on earlier drafts.

Citation: Olzak, Susan. 2023. “Ethno-nationalism and Right-Wing Extremist Violence in the United States, 2000 through 2018.” Sociological Science 10:197-226.
Received: August 17, 2022
Accepted: December 8, 2022
Editors: Ari Adut, Peter Bearman
DOI: 10.15195/v10.a6

Dissecting the Lexis Table: Summarizing Population-Level Temporal Variability with Age–Period–Cohort Data

By Parker Webservices on March 13, 2023 in Articles

Ethan Fosse

Sociological Science March 13, 2023
10.15195/v10.a5

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Since Norman Ryder’s (1965) classic essay on cohort analysis was published more than a half century ago, scores of researchers have attempted to uncover the separate effects of age, period, and cohort (APC) on a wide range of outcomes. However, rather than disentangling period effects from those attributable to age or cohort, Ryder’s approach is based on distinguishing intra-cohort trends (or life-cycle change) from inter-cohort trends (or social change), which, together, constitute comparative cohort careers. Following Ryder’s insights, in this article I show how to formally summarize population-level temporal variability on the Lexis table. In doing so, I present a number of parametric expressions representing intra- and inter-cohort trends, intra-period differences, and Ryderian comparative cohort careers. To aid the interpretation of results, I additionally introduce a suite of novel visualizations of these model-based summaries, including 2D and 3D Lexis heat maps. Crucially, the Ryderian approach developed in this article is fully identified, complementing (but not replacing) conventional approaches that rely on theoretical assumptions to parse out unique APC effects from unidentified models. This has the potential to provide a common base of knowledge in a literature often fraught with controversy. To illustrate, I analyze trends in social trust in the U.S. General Social Survey from 1972 to 2018.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Ethan Fosse: Department of Sociology, University of Toronto
E-mail: ethan.fosse@utoronto.ca

Acknowledgments: The author thanks Jessica Harris, Sebastien Parker, and Christopher Winship.

Citation: Fosse, Ethan. 2023. “Dissecting the Lexis Table: Summarizing Population-Level Temporal Variability with Age–Period–Cohort Data.” Sociological Science 10:
150-196.
Received: April 7, 2022
Accepted: May 9, 2022
Editors: Arnout van de Rijt, Richard Breen
DOI: 10.15195/v10.a5

Testing Models of Cognition and Action Using Response Conflict and Multinomial Processing Tree Models

By Parker Webservices on March 7, 2023 in Articles

Andrew Miles, Gordon Brett, Salwa Khan, and Yagana Samim

Sociological Science March 07, 2023
10.15195/v10.a4

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Dual-process perspectives have made substantial contributions to our understanding of behavior, but fundamental questions about how and when deliberate and automatic cognition shape action continue to be debated. Among these are whether automatic or deliberate cognition is ultimately in control of behavior, how often each type of cognition controls behavior in practice, and how the answers to each of these questions depends on the individual in question. To answer these questions, sociologists need methodological tools that enable them to directly test competing claims. We argue that this aim will be advanced by (a) using a particular type of data known as response conflict data and (b) analyzing those data using multinomial processing tree models. We illustrate the utility of this approach by reanalyzing three samples of data from Miles et al. (2019) on behaviors related to politics, morality, and race.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Andrew Miles: Department of Sociology, University of Toronto
E-mail: andrew.miles@utoronto.ca

Gordon Brett: Department of Sociology, University of Toronto
E-mail: gordon.brett@alum.utoronto.ca

Salwa Khan: Department of Sociology, University of Toronto
E-mail: slw.khan@mail.utoronto.ca

Yagana Samim: Department of Sociology, University of Toronto
E-mail: yagana.samim@mail.utoronto.ca

Acknowledgments: We wish to thank the reviewers and editors at Sociological Science for their helpful comments.

Citation: Miles, Andrew, Gordon Brett, Salwa Khan, and Yagana Samim. 2023. “Testing Models of Cognition and Action Using Response Conflict and Multinomial Processing Tree Models.” Sociological Science 10: 118-149.
Received: September 23, 2022
Accepted: November 22, 2022
Editors: Arnout van de Rijt, Werner Raub
DOI: 10.15195/v10.a4

Using Machine Learning to Uncover the Semantics of Concepts: How Well Do Typicality Measures Extracted from a BERT Text Classifier Match Human Judgments of Genre Typicality?

By Parker Webservices on March 3, 2023 in Articles

Gaël Le Mens, Balázs Kovács, Michael T. Hannan, Guillem Pros

Sociological Science March 3, 2023
10.15195/v10.a3

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Social scientists have long been interested in understanding the extent to which the typicalities of an object in concepts relate to its valuations by social actors. Answering this question has proven to be challenging because precise measurement requires a feature-based description of objects. Yet, such descriptions are frequently unavailable. In this article, we introduce a method to measure typicality based on text data. Our approach involves training a deep-learning text classifier based on the BERT language representation and defining the typicality of an object in a concept in terms of the categorization probability produced by the trained classifier. Model training allows for the construction of a feature space adapted to the categorization task and of a mapping between feature combination and typicality that gives more weight to feature dimensions that matter more for categorization. We validate the approach by comparing the BERT-based typicality measure of book descriptions in literary genres with average human typicality ratings. The obtained correlation is higher than 0.85. Comparisons with other typicality measures used in prior research show that our BERT-based measure better reflects human typicality judgments.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Gaël Le Mens: Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona School of Economics, and UPF Barcelona School of Management, Barcelona, Spain
E-mail: gael.le-mens@upf.edu

Balázs Kovács: School of Management, Yale University, New Haven, CT, USA
E-mail: balazs.kovacs@yale.edu

Michael T. Hannan: Graduate School of Business, Stanford University, Stanford, CA, USA
E-mail: hannan@stanford.edu

Guillem Pros: Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Spain
E-mail: guillem.pros@upf.edu

Acknowledgments: We are grateful to Jerker Denrell, Amir Goldberg, Greta Hsu, Thorbjørn Knudsen, Cecilia Nunes, and Phanish Puranam for discussion of ideas developed in this article and for the detailed feedback we received from them on the earlier versions. We thank conference participants at the 2021 and 2022 Nagymaros Conferences for valuable feedback and discussion. G. Le Mens and G. Pros received financial support from ERC Consolidator Grant #772268 from the European Commission. G. Le Mens also received financial support from grant PID2019-105249GBI00/ AEI/10.13039/501100011033 from the Spanish Ministerio de Ciencia, Innovacion y Universidades (MCIU) and the Agencia Estatal de Investigacion (AEI) and from the BBVA Foundation Grant G999088Q. B. Kovács was supported by Yale School of Management. M. Hannan was supported by the Stanford Graduate School of Business. Data, material, and analysis code for all analyses are available online at https://osf.io/ta273/. We encourage readers to download the shared folder and use the code to compute BERT typicality on their own data sets.

Citation: Le Mens, Gaël, Balázs Kovács, Michael T. Hannan, and Guillem Pros. 2023. “Using Machine Learning to Uncover the Semantics of Concepts: How Well Do Typicality Measures Extracted from a BERT Text Classifier Match Human Judgments of Genre Typicality?” Sociological Science 10: 82-117.
Received: September 28, 2022
Accepted: November 9, 2022
Editors: Ari Adut, Filiz Garip
DOI: 10.15195/v10.a3

Do Organizational Policies Narrow Gender Inequality? Novel Evidence from Longitudinal Employer–Employee Data

By Parker Webservices on February 28, 2023 in Articles

Florian Zimmermann, Matthias Collischon

Sociological Science February 28, 2023
10.15195/v10.a2

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Scholars have long proposed that gender inequalities in wages are narrowed by organizational policies to advance gender equality. Using cross-sectional data, scarce previous research has found an association between gender wage inequalities and these organizational policies, but it remains unclear whether this correlation represents a causal effect. We provide first evidence on this topic by using longitudinal linked employer–employee data covering almost 1,500 firms and nearly one million employee observations in Germany. We investigate whether and how organizational policies affect gender gaps using firm fixed-effects regressions. Our results show that organizational policies reduce the gender wage gap by around nine percent overall. Investigating channels, we show that this effect is entirely driven by advancing women already employed at a given firm, whereas we find no effect on firms’ composition and wages of new hires. Furthermore, we show that our findings are not driven by potential sources of bias, such as reverse causality.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Florian Zimmermann: Corresponding author. Institut für Arbeitsmarkt-und Berufsforschung (IAB), Research Department PASS; FAU Erlangen-Nuremberg
E-mail: florian.zimmermann@iab.de

Matthias Collischon: Institut für Arbeitsmarkt-und Berufsforschung (IAB), Research Department PASS
E-mail: matthias.collischon2@iab.de

Acknowledgments: We thank Ann-Christin Bächmann, Dana Müller, and Michael Oberfichtner for their helpful comments.

Citation: Zimmermann, Florian, and Matthias Collischon. 2023. “Do Organizational Policies Narrow Gender Inequality? Novel Evidence from Longitudinal Employer–Employee Data.” Sociological Science 10: 47-81.
Received: September 1, 2022
Accepted: October 29, 2022
Editors: Arnout van de Rijt, Vida Maralani
DOI: 10.15195/v10.a2

Layered Legacies. How Multiple Histories Shaped the Attitudes of Contemporary Europeans

By Parker Webservices on February 23, 2023 in Articles

Andreas Wimmer

Sociological Science February 23, 2023
10.15195/v10.a1

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This article introduces the concept of multiple, layered, and interacting histories, which opens four new avenues of research. We can ask which types of institutions or events, such as states, religions, or war, are more likely to leave a historical legacy. We can also explore why only certain states, religions, or wars leave legacies. We can compare the consequences of older and newer layers of history, such as of a series of successor states. Finally, these layers may interact with each other by preserving, neutralizing, or amplifying each other’s effects. To illustrate these new research avenues, I use measurements of value orientations as well as generalized trust from the European Social Survey as dependent variables. New data on the history of states as well as the wars fought since 1500 are combined with existing data on the medieval policies of the Church, all coded at the level of 411 European regions. A series of regression models suggests that the political history of states is more consequential for contemporary attitudes than medieval religious policies or wars, that older layers of states can be as impactful as more recent ones, that interactions between layers are frequent, and that modern nation-states are more likely to leave a legacy than other types of polities.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Andreas Wimmer: Department of Sociology, Columbia University
E-mail: andreas.wimmer@columbia.edu

Acknowledgments: I thank Berenike Firestone and Jiyeon Chang for outstanding research assistance in creating the political history data set and for comments on a first draft; Thomas Soehl and Ka U Ng for assembling a merged data set with the ESS variables; Flavien Ganther for guidance on how to describe the statistical models; SangWon Han as well as Jack la Violette for creating the geocoded battlefield data set; and Sidney Hemming for encouraging my use of geological metaphors.

Citation: Wimmer, Andreas. 2023. “Layered Legacies. How Multiple Histories Shaped the Attitudes of Contemporary Europeans.” Sociological Science 10:1-46.
Received: June 26, 2022
Accepted: August 24, 2022
Editors: Ari Adut, Gabriel Rossman
DOI: 10.15195/v10.a1

Measuring Memberships in Collectives in Light of Developments in Cognitive Science and Natural-Language Processing

By Parker Webservices on December 16, 2022 in Articles

Michael T. Hannan

Sociological Science December 16, 2022
10.15195/v9.a19

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Which individuals and corporate actors belong in a collective, and who decides? Sociology has not had good analytical tools for addressing these questions. Recent work that adapts probabilistic representations of concepts and probabilistic categorization to sociological research opens opportunities for making progress on the measurement of memberships. It turns out that the probabilistic cognitive-based reformulation reveals unexpected connections to language models and natural-language processing. In particular, the leading probabilistic classifier BERT provides new and powerful ways to measure core concepts.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Michael T. Hannan: Graduate School of Business, Stanford University
E-mail: hannan@stanford.edu

Acknowledgments: I have drawn liberally from joint work with Glenn Carroll, Greta Hsu, Balázs Kovács, Gaël Le Mens, Giacomo Negro, Lászlo Pólos, Elizabeth Pontikes, and Amanda Sharkey. I thank them and Susan Olzak for their comments. They are not, of course, responsible for how I use their work here.

Citation: Hannan, Michael T. 2022. “Measuring Memberships in Collectives in Light of Developments in Cognitive Science and Natural-Language Processing.” Sociological Science 9:473-492.
Received: August 8, 2022
Accepted: September 28, 2022
Editors: Ari Adut, Ray Reagans
DOI: 10.15195/v9.a19

Racially Distinctive Names Signal Both Race/Ethnicity and Social Class

By Parker Webservices on December 12, 2022 in Articles

Charles Crabtree, S. Michael Gaddis, John B. Holbein, Edvard Nergård Larsen

Sociological Science December 12, 2022
10.15195/v9.a18

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Researchers studying discrimination and bias frequently conduct experiments that use racially distinctive names to signal race or ethnicity. The evidence that these studies provide about racial discrimination depends on the assumption that the names researchers use differ only based on perceived race and not some other factor. In this article, we assess this common assumption using data from five different studies (n = 1,004; 2,002; 1,035; 5,631; 1,858) conducted at different times across four separate survey platforms (Lucid Marketplace, Lucid Theorem, MTurk, and Prolific). We find evidence that names commonly used to signal race/ethnicity also influence perceptions about socioeconomic status and social class. Specifically, we observe that Americans tend to think that individuals with names typically used by Black and Hispanic people have lower educational attainment and income and are of a lower social class. Even when we present respondents with the educational attainment of a named individual, respondents still perceive Black people as lower social class than White people. We discuss the implications of these findings for past and future experimental work that uses names to signal race. We also articulate the importance of choosing names that best approximate the quantity that scholars want to estimate.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Charles Crabtree: Department of Government, Dartmouth College
E-mail: crabtree@dartmouth.edu; URL: charlescrabtree.com

S. Michael Gaddis: Senior Research Scientist, NWEA; Department of Sociology, University of California, Los Angeles; and California Center for Population Research
E-mail: mgaddis@soc.ucla.edu; URL: stevenmichaelgaddis.com

John B. Holbein: Frank Batten School of Leadership and Public Policy, University of Virginia
E-mail: holbein@virginia.edu; URL: sites.google.com/site/johnbholbein/

Edvard Nergård Larsen: Department of Sociology and Human Geography, University of Oslo
E-mail: e.n.larsen@sosgeo.uio.no; URL: sv.uio.no/iss/english/people/aca/edvardnl

Acknowledgments: We thank the service workers and small businesses in San Francisco’s Mission District for the bountiful supply of burritos that provided fuel for the authors’ intense writing retreat that resulted in this article. We also thank NBA League Pass.

Citation: Crabtree, Charles, S. Michael Gaddis, John B. Holbein, and Edvard Nergård Larsen. 2022. “Racially Distinctive Names Signal Both Race/Ethnicity and Social Class.” Sociological Science 9: 454-472.
Received: December 4, 2021
Accepted: February 21, 2022
Editors: Jesper Sørensen, Jeremy Freese
DOI: 10.15195/v9.a18

Perceived Social Exclusion and Loneliness: Two Distinct but Related Phenomena

By Parker Webservices on October 24, 2022 in Articles

Oliver Huxhold, Bianca Suanet, Martin Wetzel

Sociological Science October 24, 2022
10.15195/v9.a17

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Perceived social exclusion refers to the subjective feeling of not being part of the macrolevel society. Loneliness arises if existing social relationships at the micro level are either quantitatively or qualitatively perceived as deficient. Here, we conceptualize and empirically demonstrate that both experiences are distinct but related constructs and investigate how they interact over time. The data set consists of 6,002 community-dwelling adults 40 to 85 years of age living in Germany assessed at two time points in 2014 and in 2017. Structural equation modeling analyses revealed that perceived social exclusion and loneliness are highly correlated. They share risks factors (i.e., socioeconomic factors, opportunities for social participation, and social network characteristics) but display different patterns of associations. In addition, loneliness may over time induce feelings of social exclusion but not vice versa. Overall, our findings underline that people get strong cues about their worth in society from their social relationships.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Oliver Huxhold: German Centre of Gerontology, Berlin, Germany
E-mail: oliver.huxhold@dza.de1

Bianca Suanet: Faculty of Social Sciences, Sociology, Vrije Universiteit Amsterdam, Netherlands
E-mail: b.a.suanet@vu.nl

Martin Wetzel: Department of Sociology, Martin-Luther-Universität, Halle-Wittenberg, Germany
E-mail: martin.wetzel@soziologie.uni-halle.de

Citation: Huxhold, Oliver, Bianca Suanet, and Martin Wetzel. 2022. “Perceived Social Exclusion and Loneliness: Two Distinct but Related Phenomena.” Sociological Science 9: 430-453.
Received: July 13, 2022
Accepted: August 11, 2022
Editors: Ari Adut, Kristen Schilt
DOI: 10.15195/v9.a17

Local Policing and the Educational Outcomes of Undocumented College Students

By Parker Webservices on October 12, 2022 in Articles

Joscha Legewie, Amy Hsin, Niklas Harder, Linna Martén

Sociological Science October 12, 2022
10.15195/v9.a16

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A growing literature examines the impact of immigration and law enforcement on undocumented immigrants and their communities, but these studies are limited by the lack of reliable data on documentation status and their focus on federal immigration enforcement. Leveraging administrative student data from the City University of New York (CUNY) that reliably identify about 13,000 undocumented students among more than 350,000 first-year students, this article examines whether local policing practices that do not ostensibly target undocumented immigrants can affect the educational outcomes of undocumented young adults. Focusing on police stops around university campuses under the New York City Police Department’s Stop, Question, and Frisk program, our findings show a substantial negative effect of police stops around campus on course credits for undocumented men but no impact on GPA or on the likelihood of receiving zero credits in the following term (stop-out). The negative effect is larger for Black and South Asian undocumented young men, groups that experience heightened surveillance by the local police. In contrast, campus police stops have little effect on documented students or undocumented women. The results illustrate how local policing practices, even in so-called sanctuary cities, can have chilling effects on undocumented groups with important implications for the links between the criminal justice system, immigration, and social inequality.

This work is licensed under a Creative Commons Attribution 4.0 International License.

Joscha Legewie: Department of Sociology, Harvard University
E-mail: jlegewie@fas.harvard.edu

Amy Hsin: Department of Sociology, Queens College, CUNY
E-mail: hsin.amy@gmail.com

Niklas Harder: DeZIM Institute, Berlin; Immigration Policy Lab, Stanford University and ETH Zurich
E-mail: harder@dezim-institut.de

Linna Martén: Swedish Institute for Social Research, Stockholm University; Immigration Policy Lab, Stanford University and ETH Zurich
E-mail: linna.marten@sofi.su.se

Acknowledgments: This research was funded by the Russell Sage Foundation (RSF Grant# 1811-09308). Replication code is available at https://osf.io/w9yxh/.