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Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved confounder that affects treatment also affects the future value of the treatment. Future treatments thus proxy for the unmeasured confounder, and researchers can exploit these proxy measures productively. We establish several new results: Regarding a commonly assumed data-generating process involving future treatments, we (1) introduce a simple new approach and show that it strictly reduces bias, (2) elaborate on existing approaches and show that they can increase bias, (3) assess the relative merits of alternative approaches, and (4) analyze true state dependence and selection as key challenges. (5) Importantly, we also introduce a new nonparametric test that uses future treatments to detect hidden bias even when future-treatment estimation fails to reduce bias. We illustrate these results empirically with an analysis of the effect of parental income on children’s educational attainment.

Human mobility has become a major variable of interest during the COVID-19 pandemic and central to policy decisions all around the world. To measure individual mobility, research relies on a variety of indicators that commonly stem from two main data sources: survey self-reports and behavioral mobility data from mobile phones. However, little is known about how mobility from survey self-reports relates to popular mobility estimates using data from the Global System for Mobile Communications (GSM) and the Global Positioning System (GPS). Spanning March 2020 until April 2021, this study compares self-reported mobility from a panel survey in Austria to aggregated mobility estimates utilizing (1) GSM data and (2) Google’s GPS-based Community Mobility Reports. Our analyses show that correlations in mobility changes over time are high, both in general and when comparing subgroups by age, gender, and mobility category. However, while these trends are similar, the size of relative mobility changes over time differs substantially between different mobility estimates. Overall, while our findings suggest that these mobility estimates manage to capture similar latent variables, especially when focusing on changes in mobility over time, researchers should be aware of the specific form of mobility different data sources capture.

This paper discusses the crucial but sometimes neglected differences between unconditional quantile regression (UQR) models and quantile treatment effects (QTE) models. We argue that there is a frequent mismatch between the aim of the quantile regression analysis and the quantitative toolkit used in much of the applied literature, including the motherhood wage penalty literature. This mismatch may result in wrong conclusions being drawn from the data, and in the end, misguided theories. In this paper, we clarify the crucial conceptual distinction between influences on quantiles of the overall distributions, which we term population-level influences, and individual-level QTEs. Further, we use data simulations to illustrate that various classes of quantile regression models may, in some instances, give entirely different conclusions (to different questions). Finally, we compare quantile regression estimates using real data examples, showing that UQR and QTE models differ sometimes but not always. Still, the conceptual and empirical distinctions between quantile regression models underline the need to match the correct model to the specific research questions. We conclude the paper with a few practical guidelines for researchers.

Arbres et forêt. Le partitionnement récursif comme alternative aux modèles de régression paramétriques dans les sciences sociales. Les modèles de régression paramétrique sont devenus l’outil dominant de la sociologie quantitative. Cette domination n’est pas sans poser problème et de nombreuses critiques ont été exprimées, tant sur le plan statistique qu’épistémologique. Pourtant, le développement de la fouille de données, puis de l’apprentissage automatique, a conduit à l’émergence d’approches méthodologiques permettant de surmonter la plupart des limites des modèles de régression paramétrique, pour les différents types d’utilisation qui intéressent les sciences sociales. Nous soutenons que le partitionnement récursif, en particulier, peut être très utile pour les sciences sociales. En effet, cette approche présente un certain nombre d’avantages techniques par rapport à la régression paramétrique et, surtout, elle est cohérente avec une conception des déterminations sociales en termes de configurations de facteurs interdépendants (et non d’additions de facteurs indépendants). Dans un deuxième temps, nous passons en revue une série d’outils permettant d’interpréter les résultats obtenus par les algorithmes de partitionnement récursif. Ensemble, ils forment une boîte à outils très complète pour les sciences sociales et montrent que le partitionnement récursif n’est plus une boîte noire dès lors que les outils d’interprétation appropriés sont mobilisés. Enfin, nous illustrons les méthodes présentées à l’aide d’exemples sociologiques sur le monde du cinéma. Ce faisant, nous montrons que ces méthodes permettent de traiter différents types de problèmes qui se posent en sciences sociales lorsque des régressions paramétriques sont habituellement utilisées, en l’occurrence l’étude des effets de structure et la hiérarchisation des facteurs explicatifs.

What role did migration play in the making of modern Britain? We now have a good sense of the way ethnicity, class, religion and gender structured immigrants’ experience and what impact they had on Britain’s culture, society and economy. But as Nancy Green pointed out almost two decades ago, scholars of migration must focus on exit as well as entry. Such a call to study ‘the politics of exit’ is especially apposite in the case of the UK. For in every decade between 1850 and 1980 (with the exception of the 1930s), the UK experienced net emigration year on year. This article analyses this outflow of migrants to give a new account of the UK as an 'emigration state'. With this concept in mind, this paper offers a new account of the formation of migration policy in the UK and seeks to transform our sense of the chronological and geographical boundaries of modern Britain.

This article investigates how mail-based online panel recruitment can be facilitated through incentives. The analysis relies on two incentive experiments and their effects on panel recruitment, and the intermediate participation in the recruitment survey. The experiments were implemented in the context of the German Emigration and Remigration Panel Study and encompass two samples of randomly sampled persons. Tested incentives include a conditional lottery, conditional monetary incentives, and the combination of unconditional money-in-hand with conditional monetary incentives. For an encompassing evaluation of the link between incentives and panel recruitment, the article further assesses the incentives’ implications for demographic composition and panel recruitment unit costs. Multivariate analysis indicates that low combined incentives (€5/€5) or, where unconditional disbursement is unfeasible, high conditional incentives (€20) are most effective in enhancing panel participation. In terms of demographic bias, low combined incentives (€5/€5) and €10 conditional incentives are the favored options. The budget options from the perspective of panel recruitment include the lottery and the €10 conditional incentive which break-even at net sample sizes of 1000.

In observational studies, regression coefficients for categorical regressors are overwhelmingly presented in terms of contrasts with a reference category. For unordered regressors with many categories, however, this approach often focuses on contrasting different pairs of categories to one another with little substantive rationale for foregrounding some comparisons with others. Mean contrasts, which compare categories with the overall mean, provide an alternative to the reference category, but the magnitude of mean contrasts is conflated with the relative sizes of the categories. Instead, binary contrasts compare a category with all the other categories, allowing the familiar interpretation for dichotomous regressors. Our command binarycontrast computes binary contrasts.

The scholarship about the consequences of social revolutions contends that social revolutions boost state capacity and strengthen the state’s developmental projects. Social justice and addressing the needs of ordinary citizens also were central themes in the discourse of the Iranian revolution and the Islamic Republic that emerged as the post-revolutionary regime with the fall of the monarchy in Iran. In this essay, I assess the performance of the post-revolutionary state in Iran according to different development indicators. Specifically, I compare the record of the post-revolutionary regime with the pre-revolutionary regime. My examination of various indicators relating to health, education, poverty, income inequality, and housing presents more of a mixed result than the overall improvement that previous scholarship anticipated and that the post-revolutionary regime had promised. Furthermore, the evidence points to declines in some important areas of development and welfare provision. Based on this analysis, I propose directions for future research about the developmental outcome of revolutions.

The identification of causal effects has gained increasing attention in social sciences over the last years and this trend also has found its way into sociology, albeit on a relatively small scale. This article provides an overview of three methods to identify causal effects that are rarely used in sociology: instrumental variable (IV) regression, difference-in-differences (DiD), and regression discontinuity design (RDD). I provide intuitive introductions to these methods, discuss identifying assumptions, limitations of the methods, promising extension, and present an exemplary study for each estimation method that can serve as a benchmark when applying these estimation techniques. Furthermore, the supplemental material to this article contains Stata and R syntax that shows with simulated data how to apply these techniques in practice.

Expectations to share data underlying studies are increasing, but research on how participants, particularly those in qualitative research, respond to requests for data sharing is limited. We studied research participants’ willingness to, understanding of, and motivations for data sharing. As part of a larger qualitative study on abortion reporting, we conducted interviews with 64 cisgender women in two states in early 2020 and asked for consent to share de-identified data. At the end of interviews, we asked participants to reflect on their motivations for agreeing or declining to share their data. The vast majority of respondents consented to data sharing and reported that helping others was a primary motivation for agreeing to share their data. However, a substantial number of participants showed a limited understanding of the concept of “data sharing.” Additional research is needed on how to improve participants’ understanding of data sharing and thus ensure fully informed consent.

Why do some urban governing regimes realize a more equal distribution of public goods than others? Local government interventions in Sao Paulo, Brazil, have produced surprisingly effective redistribution of residential public goods — housing and sanitation — between 1989 and 2016. I use original interviews and archival research for a comparative-historical analysis of variation across time in Sao Paulo’s governance of housing and sanitation. I argue that sequential configurations of a) “embeddedness” of the local state in civil society and b) the “cohesion” of the institutional sphere of the local state, explain why and when urban governing regimes generate the coordinating capacity to distribute public goods on a programmatic basis. I further illustrate how these configurations can explain variation in urban governing regimes across the world.

In the late 1930s, an agency of the United States government called the “Home Owners’ Loan Corporation” (HOLC) graded thousands of urban neighborhoods on the perceived risk they posed to mortgage lenders and property investors. To make these determinations, HOLC field agents collected vast amounts of socioeconomic, demographic, and housing data about these places and presented their findings in an impressive set of maps. While these “redlining” maps have received considerable academic and media attention, the data used to assign risk grades—available for most cities in their “area description” sheets—remain virtually unusable. Correcting this issue, I convert eight of the most consequential variables from 129 cities into an accessible and analyzable tabular format. These include the Black population percentage, “foreign-born” population percentage and group, family income, occupation class, average building age, home repair status, and mortgage availability. This data product will allow researchers to gain a more complete picture of the HOLC’s City Survey program, and it will provide a valuable new source of historical socio-demographic data at the neighborhood level.

The April 2018 article of Diana Mutz “Status Threat, Not Economic Hardship, Explains the 2016 Presidential Vote,” was published in the Proceedings of the National Academy of Sciences and contradicts prior sociological research on the 2016 election. Mutz’s article received widespread media coverage because of the strength of its primary conclusion, declaimed in its title. The present article is a critical reanalysis of the models offered by Mutz, using the data files released along with her article. Contrary to her conclusions, this article demonstrates that (1) the relative importance of economic interests and status threat cannot be estimated effectively with her cross-sectional data, and (2) her panel data are consistent with the claim that economic interests are at least as important as status threat. The preexisting sociological literature has offered interpretations that incorporate economic interests and, as a result, provides a more credible explanation of the 2016 election.

Exponential random graph models (ERGM) have been widely applied in the social sciences in the past ten years. However, diagnostics for ERGM have lagged behind their use. Collinearity-type problems can emerge without detection when fitting ERGM, skewing coefficients, biasing standard errors, and yielding inconsistent model estimates. This article provides a method to detect multicollinearity in ERGM. It outlines the problem and provides a method to calculate the variance inflation factor from ERGM parameters. It then evaluates the method with a Monte Carlo simulation, fitting 216,000 ERGMs and calculating the variance inflation factors for each model. The distribution of variance inflation factors is analyzed using multilevel regression to determine what network characteristics lend themselves to collinearity-type problems. The relationship between variance inflation factors and unstable standard errors (a standard sign of collinearity) is also examined. The method is shown to effectively detect multicollinearity and guidelines for interpretation are discussed.

Racial and ethnic residential segregation has long been the central focus of stratification and inequality research, and it is a linchpin of racial stratification in the U.S. Sociologists and demographers have developed a series of spatial or aspatial measures to capture distinct aspects of segregation. Although the recent development of segregation measures, for instance, spatial exposure, accounts for spatial proximity among different groups, it is static and ignores the social connectedness dimension. This article uses population mobility across communities to correct the potential bias in spatial segregation measures. As population mobility is highly racially segregated, we modify the conventional spatial isolation index by adding an extra layer of social connectedness between communities to create a socially and spatially weighted segregation measure. We then use this spatial and social segregation measure to quantify the level of blacks' isolation with whites in the local neighboring communities. Our approach can be extended to other segregation measures and provide a new perspective to assess racial segregation in the U.S.

Policies excluding ethnoracial and religious minorities reinforce the power of political elites. This study addresses an extreme case of exclusion: urban expulsions of Jews in the medieval western Holy Roman Empire. Expulsions were official edicts proclaimed by Christian princes, lords, or town councils, whomever ruled a territory. Changing religious and political culture, in the form of new value on community righteousness and the beginning of territorialization, provided incentives for polities to expel their Jewish residents. Using a new database of Jewish settlement and city development in the Western Holy Roman Empire 1000–1520 CE, I show that the relational structure of political power between Christian elites could insulate or expose Jewish communities to political contests of the time. Jews were derided, lesser members of Christian society, but in spite of increasing focus on Christian piety and legislating community purity, most cities did not expel their Jewish residents. City rulers that did expel were attempting to solve challenges for sovereignty through their policies toward Jews.

At first glance, Bourdieu’s field theory might be considered an unlikely case for research on transnational and global phenomena. However, scholarship in the last two decades shows that field analysis can indeed be useful to study cases beyond the national realm. Reviewing this research, this contribution is organized in three sections: It starts with a discussion of the (post-)national orientation of the field theoretical research program and outlines the ontology of transnational and global processes according to this program. Second, the contribution reviews the main strands of post-national field analysis and distills from this literature the empirical foci and analytical priorities. The third section takes stock of the theoretical efforts that have recently been made to de-nationalize the field theoretical research program and identifies the theoretical and conceptual contributions made in the process. The contribution concludes with an empirical, a methodological, and a theoretical question that should be addressed by future research.

We introduce CatMapper (catmapper.org), a set of user-friendly, web-based tools designed to help researchers overcome a common bottleneck in comparative research—integrating data across diverse datasets by complex categories (e.g., ethnicities, languages, religions, archaeological artifact types) that are often encoded very differently from dataset to dataset. We illustrate CatMapper's planned architecture and capabilities with the SocioMap tool (catmapper.org/sociomap) which focuses on four inter-related domains—ethnicities (>9000), religions (>1000), districts (> 200,000), and languages, language families and dialects (>25,000). Categories in these diverse domains share commonalities that make them challenging to work with, including large numbers of categories at multiple nested scales that can also change through time. To assist users in merging data by these categories, SocioMap will include four core functions: (1) explore contextual information about specific categories, (2) translate new sets of categories from existing datasets and published studies, (3) integrate novel combinations of datasets for researchers’ custom analysis needs, including automatically generated syntax (e.g., R, Stata) to merge datasets of interest, and (4) share merging templates for public re-use and open science. We outline current progress on the development of CatMapper/SocioMap, plans for future development, and potential expansion to other domains, such as artifact types in archaeology and material goods used in asset-based wealth indices.

Organizational researchers use a variety of methods to obtain sampling frames. The utility of these methods, however, is constrained by access restrictions, limited coverage, prohibitive costs, and cumbersome formats. This article presents a new method for generating sampling frames for any type of organization that is cost-effective, uses publicly available data, and produces near-comprehensive sampling frames for any geographic area in the U.S. The Python-based program we developed systematically scans the Google Maps platform to identify organizations of interest and retrieve their contact information. We demonstrate the program’s utility by generating a sampling frame of religious congregations in the U.S. To assess Google Maps’ coverage and representativeness of such congregations, we examined two nationally representative samples of congregations and a census of every congregation in Indianapolis. We found that Google Maps contains approximately 98% of those congregations––near-complete coverage that ensures a near-perfect degree of representativeness. Using Google Maps to generate sampling frames promises to significantly improve the process for obtaining representative samples for organizational studies by reducing costs, increasing efficiency, and providing greater coverage and representativeness.

Has disease stigma declined? Our ability to answer this question has been hampered by the lack of comparable data across diseases and over time. Using word embeddings, we analyze 4.5 million news articles to create new measures of stigma for 107 health conditions. We find that in the 1980s, most diseases were marked by strong connotations of disgust, danger, impurity, and negative personality traits. Since then, stigma has declined dramatically for most physical illnesses; cancers, neurological conditions, genetic diseases, and many other conditions have shed most of their negative connotations. But this decline was uneven: mental illnesses, eating disorders, and addictions saw no stigma declines, and stigma declined more slowly for infectious diseases than for chronic conditions. Using multivariate regression, we find that patients’ activism explains some but not all of the variation in stigma. Stigma has transformed from a sea of negative connotations surrounding most diseases to a narrower set of judgments targeting conditions where the primary symptoms are aberrant behaviors.

The language used in job advertisements contains explicit and implicit cues, which signal employers’ preferences for candidates of certain ascribed characteristics, such as gender and ethnicity/race. To capture such biases in language use, existing word inventories have focused predominantly on gender and are based on general perceptions of the ‘masculine’ or ‘feminine’ orientations of specific words and socio-psychological understandings of ‘agentic’ and ‘communal’ traits. Nevertheless, these approaches are limited to gender and they do not consider the specific contexts in which the language is used. To address these limitations, we have developed the first comprehensive word inventory for work and employment diversity, (in)equality, and inclusivity that builds on a number of conceptual and methodological innovations. The BIAS Word Inventory was developed as part of our work in an international, interdisciplinary project – BIAS: Responsible AI for Labour Market Equality – in Canada and the United Kingdom (UK). Conceptually, we rely on a sociological approach that is attuned to various documented causes and correlates of inequalities related to gender, sexuality, ethnicity/race, immigration and family statuses in the labour market context. Methodologically, we rely on ‘expert’ coding of actual job advertisements in Canada and the UK, as well as iterative cycles of inter-rater verification. Our inventory is particularly suited for studying labour market inequalities, as it reflects the language used to describe job postings, and the inventory takes account of cues at various dimensions, including explicit and implicit cues associated with gender, ethnicity, citizenship and immigration statuses, role specifications, equality, equity and inclusivity policies and pledges, work-family policies, and workplace context.

Background: Abortion is a difficult-to-measure behavior with extensive survey underreporting, which compromises the ability to study and monitor abortion. The purpose of this study was to improve our understanding of how women interpret and respond to survey items asking if they have ever had an abortion. Methods: We developed multiple new questions hypothesized to improve abortion reporting, using approaches that aim to clarify which experiences to report as an induced abortion; reduce the stigma and sensitivity of induced abortion; reduce the sense of intrusiveness of asking about abortion; and increase the motivation to report. We conducted cognitive interviews with cisgender women aged 18-49 in two US states (N=64) to iteratively assess these new approaches and questions for improving abortion reporting. Results: Our findings suggest that including abortion as part of a list of other sexual and reproductive health services, asking a yes/no question about lifetime experience of abortion instead of asking about number of abortions, and developing an improved introduction to abortion questions may help to elicit more accurate survey reports. Conclusions: Opportunities exist to improve survey measurement of abortion. Reducing underreporting of abortion in surveys has the potential to improve sexual and reproductive health research that relies on pregnancy histories.

As our modern world has become increasingly digitalized, various types of data from different data domains are available that can enrich survey data. To link survey data to other sources, consent from the survey respondents is required. This article compares consent to data linkage requests for seven data domains: administrative data, smartphone usage data, bank data, biomarkers, Facebook data, health insurance data, and sensor data. We experimentally explore three factors of interest to survey designers seeking to maximize consent rates: consent question order, consent question wording, and incentives. The results of the study using a German online sample (n = 3,374) show that survey respondents have a relatively high probability of consent to share smartphone usage data, Facebook data, and biomarkers, while they are least likely to share their bank data in a survey. Of the three experimental factors, only the consent question order affected consent rates significantly. Additionally, the study investigated the interactions between the three experimental manipulations and the seven data domains, of which only the interaction between the data domains and the consent question order showed a consistent significant effect.

Across the social sciences, generalized trust has been one of the most frequently examined constructs since researchers first introduced measures of it in the 1950s. Despite its significance, there is growing consensus that conventional measures of generalized trust are prone to measurement invalidity and non-equivalence, which places sociological knowledge about generalized trust in serious doubt. In this article, I advance trust research in sociology by (1) refining two new self-report measures of generalized trust—the Stranger Face Trust scale (SFT) and the Imaginary Stranger Trust scale (IST)—and (2) assessing their empirical performance on a nationally representative probability sample (N = 1,264). I compare the reliability and validity of SFT, IST, and traditional measures of generalized trust across a number of measurement validation tests (convergent validity, discriminant validity, known-groups validity, and predictive validity). Results suggest that SFT provides the most accurate and consistent measure of generalized trust. I conclude by discussing the implications of my findings for sociological knowledge.

The application of deep learning methods to survey human development in remote areas with satellite imagery at high temporal frequency can significantly enhance our understanding of spatial and temporal patterns in human development. Current applications have focused their efforts in predicting a narrow set of asset-based measurements of human well-being within a limited group of African countries. Here, we leverage georeferenced village-level census data from across 30 percent of the landmass of India to train a deep-neural network that predicts 16 variables representing material conditions from annual composites of Landsat 7 imagery. The census-based model is used as a feature extractor to train another network that predicts an even larger set of developmental variables (over 90 variables) included in two rounds of the National Family Health Survey (NFHS) survey. The census-based model outperforms the current standard in the literature, night-time-luminosity-based models, as a feature extractor for several of these large set of variables. To extend the temporal scope of the models, we suggest a distribution-transformation procedure to estimate outcomes over time and space in India. Our procedure achieves levels of accuracy in the R-square of 0.92 to 0.60 for 21 development outcomes, 0.59 to 0.30 for 25 outcomes, and 0.29 to 0.00 for 28 outcomes, and 19 outcomes had negative R-square. Overall, the results show that combining satellite data with Indian Census data unlocks rich information for training deep learning models that track human development at an unprecedented geographical and temporal definition.

Despite established recommended standard definitions, measures, and methods by the UN Washington Group on Disability Statistics and the International Classification of Functioning, Disability and Health (ICF) to assess dimensions of disability, national censuses vary widely in the questions used to identify people with disabilities. Although many seek to conform ex-ante to ICF definitions, they also deviate from this basic framework in different ways. This complicates ex-post harmonization and standardization for cross-national comparisons of disabilityprevalence and outcomes influenced by disability status, such as labor market participation. Addressing these issues, this study uses IPUMS International Census microdata since 2,000 to examine disability mea- surement across 65 countries. We find that definitions, terminology, measurement, and instructions to both respondents and enumerators mat- ter for understanding disability prevalence cross-nationally. For instance, questions that included potentially stigmatizing language were associ- ated with lower rates of disability reporting, but questions that listed spe- cific limitations were associated with higher rates. Beyond disability, our findings also speak more broadly to ongoing challenges in survey har- monization for cross-national comparison.

Unpaid or ‘free’ labour is an important element of how precarity has been theorized. It is also an issue that is often seen as endemic to cultural and creative work, rightly attracting a range of criticism. Questions as to the role of unpaid work, for example internships, have become central to understanding the social exclusiveness of many cultural and creative jobs. This paper develops this existing analysis by comparing and contrasting the meaning of 'free' work over the life course of a range of creative occupations, historicising the impact of unpaid labour on the creative sector and showing how it has been stratified by social class, age and career stage. The paper uses two datasets drawn from the Panic! What happened to social mobility in the arts? project, to outline the differing experiences of unpaid labour in cultural and creative occupations. By demonstrating the stratification of unpaid work as a form of precariousness in cultural jobs, along with the social distribution of benign narratives of unpaid work, the paper aims to offer new empirical evidence for those seeking to resist precarious forms of labour.

Preregistrations—records made a priori about study designs and analysis plans and placed in open repositories—are thought to strengthen the credibility and transparency of research. Different authors have put forth arguments in favor of introducing this practice in qualitative research and made suggestions for what to include in a qualitative preregistration form. The goal of this study was to gauge and understand what parts of preregistration templates qualitative researchers would find helpful and informative. We used an online Delphi study design consisting of two rounds with feedback reports in between. In total, 48 researchers participated (response rate: 16%). In round 1, panelists considered 14 proposed items relevant to include in the preregistration form, but two items had relevance scores just below our predefined criterion (68%) with mixed argument and were put forth again. We combined items where possible, leading to 11 revised items. In round 2, panelists agreed on including the two remaining items. Panelists also converged on suggested terminology and elaborations, except for two terms for which they provided clear arguments. The result is an agreement-based form for the preregistration of qualitative studies that consists of 13 items. The form will be made available as a registration option on Open Science Framework (osf.io). We believe it is important to assure that the strength of qualitative research, which is its flexibility to adapt, adjust and respond, is not lost in preregistration. The preregistration should provide a systematic starting point.

This article considers the effect of criminal records on college admissions. Most colleges require criminal history information on their applications, suggesting that an applicant’s criminal history could be a significant impediment to accessing the benefits of higher education. We conducted a modified experimental audit to learn whether criminal records affect admissions decisions. Matched same-race pairs of tester applications were sent to a national sample of non-elite four-year colleges, with both testers applying as either Black or White. Within each pair, one application signaled a prior low-level felony conviction when required by the application. Consistent with research on employment, the rejection rate for applicants with felonies was 2½ times the rate of our control testers. But unlike the large racial differences observed in employment, we find smaller racial differences in admissions decisions. Nevertheless, Black applicants with criminal records who disclosed their records were particularly penalized at colleges with high campus crime rates. We address implications for reentry, racial progress, and the college “Ban the Box” movement. We suggest colleges consider narrowing the scope of such inquiries or removing the question altogether – particularly when it conflicts with the goals of these institutions, including reducing the underrepresentation of students of color.

Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in big data and other digital formats, new means of embracing the computational turn are needed. In this article, we propose a framework for data collection and case sampling using computational methods, allowing researchers to conduct thick qualitative research – analyses concerned with the particularities of a social context or phenomenon – starting from big data, which is typically associated with thinner quantitative methods and the pursuit of generalizable findings. The approach begins by using open-source web scraping algorithms to collect content from a target website, online database, or comparable online source. Next, researchers use computational techniques from the field of natural language processing to explore themes and patterns in the larger data set. Based on these initial explorations, researchers algorithmically generate a subset of data for in-depth qualitative analysis. In this computationally driven process of data collection and case sampling, the larger corpus and subset are never entirely divorced, a feature we argue has implications for traditional qualitative research techniques and tenets. To illustrate this approach, we collect, subset, and analyze three years of news releases from the Royal Canadian Mounted Police website (N = 13,637) using a mix of web scraping, natural language processing, and visual discourse analysis. To enhance the pedagogical value of our intervention and facilitate replication and secondary analysis, we make all data and code available online in the form of a detailed, step-by-step tutorial.

Chinese lineages are a relatively historically novel socio-political institution that plays a significant role in structuring communities and lives across China. Anthropologists and long noted the extensive geographic variation characterizing the relevance of lineages across communities. Using ethnographic and historical-economic evidence, I construct a theory explaining demand for lineage institutions, and thus individuals’ willingness to shoulder the costs of lineage-participation, as demand for risk-pooling institutions, which provide lineage members with access to land and its dividends. Variation in regional demand for risk-pooling likely stems from how rice-wheat agro-economic differences interact with shared Chinese inheritance practices. I test this hypothesis by examining whether lineage activity is associated with landholding size, precipitation predictability, and historically documented precipitation disasters. More so, I provide evidence that the relationship between historical lineage activity and agro-climatic factors is likely causal.

Economic and social inequalities have generated growing concerns and crises across contemporary societies. One of the mechanisms proposed by social sciences to explain the persistence of inequality is the belief in meritocracy, which would legitimize economic disparities based on differences in effort and talent. Despite its wide use as a concept, empirical research on meritocracy is relatively novel and characterized by diverse conceptualizations and measures that make the findings and their interpretation rather inconsistent. Most of the studies in the area have relied upon secondary data to operationalize meritocracy, with a wide variation in the use and interpretation of the same survey items. Taking into account the extant literature that uses measures of meritocracy, this article identifies a series of drawbacks and inconsistencies within and between studies regarding the conceptualization and operationalization of meritocracy beliefs. Based on this critical analysis, we propose a conceptual framework for measuring perceptions and preferences for meritocracy and non-meritocracy, which is then tested through confirmatory analysis using ISSP (International Social Survey Programme) data as well as a novel scale designed with this purpose (N=2,141). Our results support the conceptual framework and its operationalizarion, although with a better fit for the proposed scale than for the ISSP survey. Our discussion highlights the importance of considering different dimensions in order to advance in the study of meritocracy.

Traveling to different destinations is a big part of our lives. We visit a variety of locations both during our daily lives and when we're on vacation. How can we find the best way to navigate from one place to another? Perhaps we can test all of the different ways of traveling between two places, but another method is to use mathematics and computation to find a shortest path. We discuss how to construct a shortest path and introduce Dijkstra's algorithm to minimize the total cost of a path, where the cost may be the travel distance, travel time, or some other measurement. We also discuss how to use shortest paths in the real world to save time and increase traveling efficiency., Comment: This is an article for teens and pre-teens. Let us know if you have any comments

We re-consider Schelling’s (1971) bounded neighbourhood model as put into the form of a dynamical system by Haw and Hogan (2018). In the case of a single neighbourhood we explain the occurring bifurcation set, thereby correcting a minor scaling error. In the case of two neighbourhoods we correct a major error and derive a dynamical system that does satisfy the modeling assumptions made by Haw and Hogan (2020), staying as close as possible to their construction. We find that stable integration then is only possible if the populations in the two neighbourhoods have the option to be in neither neighbourhood. In the absence of direct movement between the neighbourhoods the problem is furthermore equivalent to independent single neighbourhood problems.

`All models are wrong, but some are useful is an often-used mantra, particularly when a models ability to capture the full complexities of social life is questioned. However, an appropriate functional form is key to valid statistical inference, and under-estimating complexity can lead to biased results. Unfortunately, it is unclear a-priori what the appropriate complexity of a functional form should be. I propose to use methods from machine learning to identify the appropriate complexity of the functional form by i) generating an estimate of the fit potential of the outcome given a set of explanatory variables, ii) comparing this potential with the fit from the functional form originally hypothesized by the researcher, and iii) in case a lack of fit is identified, using recent advances in the field of explainable AI to generate understanding into the missing complexity. I illustrate the approach with a range of simulation and real-world examples.

Social integration is known to be beneficial for mental health. However, it is not clear whether this applies to online as well as offline relationships. In this paper, we explore the association between online friendship and symptoms of depression among adolescents. We combine data from the popular social networking site with survey data on high school students (N = 144) and find that integration into the online network is a protective factor against depression. We also find that not all online connections are equally important: friendship ties with students from the same schools are stronger associated with depression than outside ties. In addition to friendship ties, we explore the effect of online interaction (“likes”). Overall, our results suggest that online relationships are associated with depression as well as offline friendship. However, the effect of more distant online connections is limited, while immediate social environment and peer relationships at school are more important.

Where do visionary ideas come from? Although the products of vision as manifested in technical innovation are readily observed, the ideas that eventually change the world are often obscured. Here we develop a novel method that uses deep learning to identify visionary ideas from the language used by individuals and groups. Quantifying vision this way unearths prescient ideas, individuals, and documents that prevailing methods would fail to detect. Applying our model to corpora spanning the disparate worlds of politics, law, and business, we demonstrate that it reliably detects vision in each domain. Moreover, counter to many prevailing intuitions, vision emanates from each domain’s periphery rather than its center. These findings suggest that vision may be as much as property of contexts as of individuals.

Desarrollamos un modelo de adopción de lenguaje basado en un modelo evolutivo genera-do por un juego con dos poblaciones y tres estrategias y encontramos dos equilibrios estables, a saber: bilingüismo en ambas poblaciones y monolingüismo basado en la lengua vernácula de cada población.

The spread and transmission dynamics of directly transmitted airborne pathogens, such as SARS-CoV-2, are fundamentally determined by in-person contact patterns. Reliable quantitative estimates of contact patterns are critical to modeling and reducing the spread of directly transmitted infectious diseases. While national-level contact data are available in many countries, including the United States, local-level estimates of age-specific contact patterns are key since disease dynamics and public health policy vary by geography. However, collecting contact data for each state would require a very large sample and be prohibitively expensive. To overcome this challenge, we develop a flexible model to estimate age-specific contact patterns at the subnational level using national-level interpersonal contact data. Our model is based on dynamic multilevel regression with poststratification. We apply this approach to a national sample of interpersonal contact data collected by the Berkeley Interpersonal Contact Study (BICS). Results illustrate important state-level variation in levels and trends of contacts across the US.

Sociological scholarship on political revolution has recently begun to embrace a process-based understanding of revolutions. Such a processual ontology opens up for understanding hitherto unaddressed processes of counterrevolution. Historians of the Spring of Nations, and The Second French Republic (1848–52) in particular, have failed to address the international aspects of the revolutions, and above all of counterrevolution in the period. This paper addresses this gap through a historical case study of the French Catholic clergy in the Second French Republic. The study applies an amalgamation of recent theoretical developments from revolution scholarship in order to dissect the empirical material and births a new framework in the process. The results demonstrate the important intersocial work of Catholic clergy on the triumph of the counterrevolution in France. The political concerns of the Papal States and Pope Pius IX spilled over into French politics and cemented the legitimacy of the counterrevolutionary turn and fuelled the rise of Louis Napoleon. From these results a new theoretical framework that addresses the intersocial nature of political agency and moves beyond a domestic understanding of political processes is developed. Further studies applying this approach across cases are encouraged in order to better understand how these processes unfold and how multiple intersocial influences can interplay.

This paper provides a comparative historical account of a contemporary phenomenon: the tendency to equate “ethical” goods with aesthetic quality. Studies of moral markets suggest that this equation of ethics and quality would reveal a process whereby these pre-existing moral sensibilities are conveyed through and fought over in the market. Yet this paper argues that we should also examine how markets forms can condition moral sensibilities in the process, not just convey or host them. Drawing on primary source archival materials from late eighteenth century abolitionists and turn-of-the-twentieth-century consumer activists, the author demonstrates that these activists participated in a recurring purity politics of consumption conditioned by the commodity form. This manifests in activists’ equation of: 1) the treatment of the laborers and 2) the quality of the labor with 3) the quality of the goods. To claim that goods were pure, in many instances, was also to claim that the laborers and the labor conditions behind those goods were as well. This purity politics, further, entails both public and private ways of arguing for the equation of ethics and quality, as well as distinct civic visions of ethical labor. It also opens up ways to explore certain racialized dimensions of the commodity form.

While evidence suggests a durable relationship between redlining and population health, we currently lack an empirical account of how this historical act of racialized violence produced contemporary inequities. In this paper, we use a mediation framework to evaluate how redlining grades influenced later life expectancy and the degree to which contemporary racial disparities in life expectancy between Black working-class neighborhoods and White professional-class neighborhoods can be explained by past Home Owners’ Loan Corporation (HOLC) mapping. Life expectancy gaps between differently graded tracts are driven by economic isolation and disparate property valuation which developed within these areas in subsequent decades. Still, only a small percent of a total disparity between contemporary Black and White neighborhoods is explained by HOLC grades. We discuss the role of HOLC maps in analyses of structural racism and health, positioning them as only one feature of a larger public–private project conflating race with financial risk. Policy implications include not only targeting resources to formerly redlined neighborhoods but also the larger project of dismantling racist theories of value that are deeply embedded in the political economy of place.

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.

Using quantile regression models to estimate quantile treatment effects is becoming increasingly popular. This paper introduces the rqr command that can be used to estimate residualized quantile regression (RQR) coefficients and the rqrplot postestimation command that can be used to effortless plot the coefficients. The main advantages of the rqr command compared to other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.

In social stratification research, the most frequently used social class schema are based on employment relations (EGP and ESEC). These schemes have been propelled to paradigms for research on social mobility and educational inequalities and applied in cross-national research for both genders. Using the European Working Conditions Survey, we examine their criterion and construct validity across 31 countries and for both genders. We investigate whether classes are welldelineated by the theoretically assumed dimensions of employment relations and we assess how several measures of occupational advantage differ across classes. We find broad similarity in the criterion validity of EGP and ESEC across genders and countries as well as satisfactory levels of construct validity. However, the salariat classes are too heterogeneous and their boundaries with the intermediate classes are blurred. To improve the measurement of social class, we propose to differentiate managerial and professional occupations within the lower and higher salariat respectively. We show that implementing these distinctions in ESEC and EGP improves their criterion validity and allows to better identify privileged positions.

Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups (THEMIS.COG) was an interdisciplinary research collaboration of computer scientists and social scientists from the University of Waterloo (Canada), Potsdam University of Applied Sciences (Germany), and Dartmouth College (USA). This white paper summarizes the results of our research at the end of the grant term. Funded by the Trans-Atlantic Platform’s Digging Into Data initiative, the project aimed at theoretical and empirical modeling of identity and sentiments in collaborative groups. Understanding the social forces behind self-organized collaboration is important because technological and social innovations are increasingly generated through informal, distributed processes of collaboration, rather than in formal organizational hierarchies or through market forces. Our work used a data-driven approach to explore the social psychological mechanisms that motivate such collaborations and determine their success or failure. We focused on the example of GitHub, the world’s current largest digital platform for open, collaborative software development. In contrast to most, purely inductive contemporary approaches leveraging computational techniques for social science, THEMIS.COG followed a deductive, theory-driven approach. We capitalized on affect control theory, a mathematically formalized theory of symbolic interaction originated by sociologist David R. Heise and further advanced in previous work by some of the THEMIS.COG collaborators, among others. Affect control theory states that people control their social behaviours by intuitively attempting to verify culturally shared feelings about identities, social roles, and behaviour settings. From this principle, implemented in computational simulation models, precise predictions about group dynamics can be derived. It was the goal of THEMIS.COG to adapt and apply this approach to study the GitHub collaboration ecosystem through a symbolic interactionist lens. The project contributed substantially to the novel endeavor of theory development in social science based on large amounts of naturally occurring digital data.

A preprint of a book review of Cold War Social Science: Transnational Entanglements, edited by Mark Solovey and Christina Dayé, to appear in the Journal of the History of the Behavioral Sciences.

The social sciences face growing demand for reproducible tools for processing massive troves of often-complex text data (political speeches, medical notes, etc.). In response, we aim toward computational literature review by developing an inductive method of applying expert-built dictionaries for automated analysis of complex texts. Our workflow begins with developing dictionaries from foundational texts and domain expertise. Next, we apply text-analytic methods of differential domain-specificity and complexity to create vector-space representations of texts. Finally, we compare the validity of these methods by using regression models to evaluate relationships between their representations and ground truth. Taking as our use case a large corpus of academic articles in organizational science, we find that domain-specific, relatively simple embeddings were most valid--while the more sophisticated models were very weak. Thus, we suggest that social science workflows for learning from complex texts incorporate embeddings that are domain-specific and straightforward--rather thanconvoluted.

Studies investigating how the number of children in a family affects the parents or the children face problems because the variable of interest is endogenous in the model. The currently accepted solution to this problem is to use instrumental variables (IVs), for example, based on twin births. In this paper, I review and add to the critique of IVs based on twin births and show that that there are so many issues—major and minor—with these IVs that results based on them are not reliable or interpretable. I also review other IVs used in the literature, for example IVs based on the sexes of the firstborn children, and conclude that there are, as of yet, no credible IVs for the number of children. We need to disregard results from studies applying these IVs, reevaluate the current state of knowledge, and develop new, more credible methods.

Fourth Century North Africa was a site of intense religious and political conflict. Emerging from a period of persecution and newly legitimized by the Roman state, the Christian Church immediately fractured into two competing camps. Now known as the Donatist schism, this fracture was the result of competing claims to religious authority between two camps of bishops, but the doctrinal debate at its core precipitated a specific form of violence: attacks on clergy and property perpetrated by roving groups of militant bandits. Known as circumcellions, these bands acquired a perverse reputation for religious zeal, a desire for martyrdom, and what their opponents described as the ‘madness’ and ‘insanity’ of their violence. Here I analyze sources produced by both Donatists and Catholics to trace patterns of circumcellion violence. I draw on borderland theory and research on non-state violence to argue that such acts were not mad, but rather the result of strategic efforts to consolidate religious and political power. In this, Donatism and the sectarian violence that accompanied it provide important insights into how banditry and peasant rebellions can se.rve as alternate sources of social and political power, avenues through which heterodox movements challenge the power state and religious hierarchies alike