In the United States, the housing search process has largely moved online for urban residents, yet little work has examined if and how the information homeseekers are exposed to on housing websites matters for their assessments of potential destinations. We designed a unique, geographically contextual survey experiment that uses real neighborhood names and actual online housing advertisement text to test if residents’ ratings of neighborhoods as desirable are affected by novel information. We find that online housing information—descriptions of housing units, neighborhoods, and how to apply for leases—largely reproduces the existing racial-spatial hierarchy, where non-poor White neighborhoods are rated as the most desirable, and poor Black and Latinx neighborhoods are rated as the least, with Asian neighborhoods in between. Residents’ prior familiarity with neighborhoods in their metro attenuates but does not fully explain away these effects, and the effect varies by race/ethnicity, with White residents the most sensitive to novel information. We offer a sophisticated model of the digital information environment in which an ethnoracially diverse population is exposed to neighborhood options, and we detail how our methods improve survey experiment design more broadly. Our results show that relatively small amounts of seemingly race-neutral information can affect residential preferences.

In the governance of poverty and marginality, a complex array of frontline workers manages social suffering. Enabled and constrained by vertical and horizontal relations of production, these workers frequently redistribute work across organizational boundaries. Recent scholarship often reduces such redistribution to a single dynamic: “burden shuffling.” This study extends and complicates that framework. Drawing on three case studies—ambulance crews, mandated reporters of child maltreatment, and physicians treating chronic pain—we theorize how frontline workers’ practical and moral judgments structure the form and justification of redistribution. Variation in the extent to which workers feel equipped and see tasks as worthwhile produces three distinct tactics: burden shuffling (when ill-equipped workers offload tasks they do not consider worthwhile), aspirational referrals (when ill-equipped workers seek better-equipped actors for tasks they view as worthwhile), and abandonment (when well-equipped workers disengage from tasks they deem unworthy and leave them to others). By disaggregating redistribution and tracing its relational conditions, this article shows how governance is enacted—and deferred—on the frontlines, and we offer a framework for understanding how workers navigate fragmentation and uneven responsibility in contemporary labor.

A growing number of social scientists use online genealogical data as an alternative digital census of historical populations to study past demographic dynamics. However, the non-representativeness of this data source requires the development of bias-adjusting methods to obtain accurate demographic estimates. We address this challenge by proposing an indirect estimation framework to investigate fertility trends in seven European countries and the United States of America for the historical period 1751–1910, integrating data from the big genealogical database FamiLinx with more conventional data sources. The proposed methods allow for the indirect estimation of the total fertility rate using the number of women aged 15–49 and children under age 5, while accounting for child mortality, age-specific fertility patterns, and biases. Our methodological approaches demonstrate that, when combined with reliable demographic data, online genealogical data can be fruitfully used to examine fertility patterns in countries and periods lacking well-functioning national civil registration systems.

We present the results of a new approach to measuring the occupations of respondents in surveys using Large Language Models (LLMs). In our new approach, which we call SOCbot, an LLM integrated in the questionnaire scripting software is used to code the job title to the occupational classification in real-time during the interview. Where the job title does not contain sufficient information to be coded with confidence, the LLM probes for further relevant details on job tasks, industry, qualifications, and so on. SOCbot can also be used offline on already collected response data. Our results demonstrate that the approach attains rates of coder reliability comparable to trained human coders, with consistent performance across four major commercial and open-weight model families. SOCbot can also be deployed using publicly available open-weight models with only a small but measurable accuracy penalty, allowing even users with stringent data-protection constraints to use it. We also demonstrate that the approach is feasible in large-scale survey operations and has significant potential to reduce respondent burden, lower costs, and yield more timely and accurate data.

Demographers have a rich understanding of the fertility transitions that have been observed in many contexts. Yet, we lack evidence to explain whether long‐run declines in fertility levels are accompanied by simultaneous changes in reproductive variability. This is important because reproductive variability—the concentration and dispersion of childbearing—may help to better explain fertility trends and predict population change. We address this gap with a case study of Brazil, which is a well‐known example of fertility transition, using microdata on fertility by education for cohorts born 1910–1970. We contribute by using multiple measures of variability in completed fertility, studying how these measures change during an entire transition, how they relate to cohort fertility rates, and how this evidence varies by education. Reproductive variability declines across the Brazilian fertility transition—for measures of concentration and dispersion—although this is less evident for dispersion measures that adjust for levels of children ever born. We also find considerable differences by education and evidence that several measures of variability are predictive of fertility decline, highlighting a promising avenue for future research. Moreover, our findings suggest that conclusions based on one measure of variability may only provide a partial understanding of population dynamics.

Causal inference approaches often emphasize binary treatments. But in many applications, the underlying constructs are continuous. In the potential outcomes framework, a continuous treatment can take on numerous values, each corresponding to a potential outcome that may be realized. In this setting, common estimands may be intractable because of a common issue in social research, particularly research on social inequality: the exposure is highly stratified by confounders. The authors show how to avoid drawing inferences about counterfactuals where data are unlikely to exist by carefully selecting the causal estimand. The authors adopt an additive shift estimand that adds a small, fixed amount to each unit’s income. This approach is preferable to population-average dose-response curves in settings in which some treatment values rarely occur in some subgroups. The authors also show how to estimate and summarize patterns of nonlinearity and effect heterogeneity with continuous treatments. As a motivating example, the authors consider the causal effect of parental income on college attendance, a setting in which the exposure is highly stratified by confounders (e.g., parental education). This approach applies to a wide range of possible treatment conditions in sociology.

In the thriving field of network studies, there has been an emerging practice of comparing optimal modularity across social networks to evaluate the variation of network module–related substantive concepts , such as the level of consensus, polarization, or community boundary rigidity. This practice offers valuable insights and is often thoughtfully motivated, but it faces several conceptual and empirical challenges that merit careful consideration. Conceptually, the selected modularity metric may misalign with the substantive concepts researchers aim to measure. Empirically, estimated optimal modularity is highly sensitive to algorithm choice and to network characteristics unrelated to those substantive concepts, which can bias comparison results. The authors illustrate these issues with toy examples and systematic simulations and offer suggestions for more rigorous comparison practices. To show the practical significance of these lessons, the authors replicate an empirical study that examines the temporal trend of modularity scores for job mobility networks to evaluate the evolution of mobility boundary rigidity in the U.S. labor market.

Participatory-democratic organizations seek to redistribute decision-making authority, but doing so raises a practical question: how do participants learn to exercise that authority in nonhierarchical, collective ways? This paper theorizes how participatory-democratic organizations address what I term the collective-practice gap: the gap between the individualistic capacities shaped in hierarchical institutions and the relational, participatory capacities required for nonhierarchical, collective participation. Drawing on ethnographic research in two participatory-democratic organizations—a democratic school and a community-governed philanthropic project—I examine how democratic capacity is cultivated through everyday organizational practice. I identify two dilemmatic pedagogical strategies used by experienced participants: the non-exercise of influence and taking the long view. Through these relational and temporal practices, experienced members strategically withhold outcome-directing intervention and tolerate slow, inefficient, or uncertain processes, creating conditions for others to develop the skills, values, and behaviors needed to function in a democratic, collective setting. These strategies, however, carry organizational risks—including delay, conflict, and uneven participation—because the practices that support democratic learning can also strain organizational functioning. Therefore, these strategies are best understood not as fixed solutions but as partial, situational responses to the tension between cultivating egalitarian participation and preserving organizational viability.

Agroecology—a food production and consumption model that integrates ecological, social, and economic sustainability principles—underwent a period of significant growth in recent years in Argentina. Acquiring specialized knowledge about agroecology practices is a priority for farmers who pursue socio-environmental goals, but the process for building such expertise is not clearly laid out. While some consensus exists among agroecology practitioners about key guiding principles, institutionalized knowledge about agroecology is often insufficient, inadequate, and/or contested. Further, due to a long-standing history of state support for agribusiness—a model in many ways antithetical to agroecology—agroecology practitioners are wary of institutional knowledge that may not align with their socio-environmental principles. This raises the central question of this paper: how do farmers interact with institutions to generate agroecology expertise while navigating alignments between institutional knowledge and agribusiness? Based on ethnographic observations and in-depth interviews with agroecology farmers, state workers, activists, and technicians in an agricultural town in Córdoba, Argentina, this paper examines how agroecology expertise is built among farmers. The article argues by way of empirical demonstration that expertise surges through what I term a work of discernment— a dynamic process involving the evaluation and selective combination of institutional and occupational knowledge, based on perceived alignment of priorities and goals. The paper draws from literature on expertise construction and dissemination and theorizes work of discernment as a strategy employed by non-institutional actors to generate specialized knowledge.

Employer-provided benefits add to total compensation and enhance economic security. Yet an increasing share of American workers lack access to employer-provided retirement and health insurance benefits, a trend that scholars link to labor union decline. I test whether state Right to Work (RTW) laws, which weaken associational worker power by targeting union security agreements, are associated with changes in employer-provided benefit coverage. Building on organizational inequality perspectives, I theorize a novel indirect impact whereby RTW conditions the returns to market-based structural worker power. I use two-way fixed effects models that leverage variation in the timing of RTW laws to estimate effects on employer-provided retirement and health insurance, relative to a control group of workers living in states that almost implemented RTW. Using pooled cross-sectional data from the 2001 to 2019 Current Population Surveys, I find that RTW weakened the inverse associations between state unemployment and retirement and health insurance benefits. Counterfactual analyses suggest declines in benefit coverage would have been lower or reversed had Indiana not implemented RTW. The findings suggest that attention to wages alone underestimate the consequences of RTW and highlight how different forms of worker power interact to shape compensation.