Department of Sociology, UCLA

This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.

Disease advocacy organisations (DAOs) are critical for raising awareness about illnesses and supporting research. While most studies of DAOs focus on personally affected patient-activists, an underappreciated constituency are external allies. Building from social movement theory, we distinguish between beneficiary constituents (disease patients and their loved ones) and conscience constituents (allies) and investigate their relative fundraising effectiveness. While the former have credibility due to illness experience that should increase fundraising, the latter are more numerous. Our study is also the first to investigate where DAO supporters fundraise-through friendship- versus workplace-based networks-and how this interacts with constituent types. Our large-scale dataset includes 9372 groups (nearly 90,000 participants) active in the 'Movember' campaign, a men's health movement around testicular and prostate cancer. We find robust evidence that groups with more beneficiary constituents raise significantly greater funds per participant. Yet because conscience constituents are more numerous, they raise the majority of total aggregate funds. We also find an interaction effect: beneficiary constituents do better in friendship networks, conscience constituents in workplaces. Our findings bear implications for DAOs, indicating they may benefit by encouraging disease patient families to fundraise through friends, and for external allies to focus requests on workplace networks.

Over the past 60 years, we have witnessed a relocation of gender wage inequality. Whereas the largest wage gaps were once concentrated among lower-paid, lower-educated workers, today these wage gaps sit among the highest-paid, highly-educated workers. Given this reordering of gender wage inequality and the centrality of college graduates to total inequality trends, in this article, we assess the contribution of higher education mechanisms to top-end gender inequality. Specifically, we use Census and ACS data along with unique decomposition models to assess the extent to which two mechanisms rooted in higher education-bachelor's-level fields of study and the attainment of advanced degrees-can account for the gender wage gap across the wage distribution. Results from these decomposition models show that while these explanatory mechanisms fare well among bottom and middle wages, their explanatory power breaks down among the highest-paid college workers. We conclude that women's attainment of "different" education (via fields of study) or "more" education (via advanced degrees) would do little to close the gender wage gaps that are contributing most to contemporary wage inequality trends. We suggest some directions for future research, and we also take seriously the role of discriminatory pay-setting at the top of the wage distribution.

Sociological research on social movements and politics holds that advocacy organizations are typically trusted to be authentic agents of their constituents. At the same time, however, businesses and other outside interests often engage in covert “astroturfing” strategies in which they ventriloquize claims through apparently independent grassroots associations (but which are entirely funded and staffed to benefit the sponsor). These widespread and deceptive strategies may harm trust in advocacy groups overall, extending beyond those revealed to be involved, through a mechanism of categorical stigmatization. This study is the first to test how revealed covert patronage may “poison the well” for all advocacy groups, with implications for how social movements and other advocacy causes suffer harm from illegitimate political practices by other organizations. The authors carried out two survey-experiments in which a local advocacy organization was revealed to be operating, respectively, as a “front” for either a corporation or think tank; in each experiment, conditions varied depending upon whether the sponsor was presented as highly reputable, low reputation, or with no specified reputation. In both experiments, astroturfing led to significant declines in trust in advocacy groups overall. We highlight implications for theory and research on social movements, organizational theory, and political processes.

A large and fast-growing number of studies across the social sciences use experiments to better understand the role of race in human interactions, particularly in the American context. Researchers often use names to signal the race of individuals portrayed in these experiments. However, those names might also signal other attributes, such as socioeconomic status (e.g., education and income) and citizenship. If they do, researchers would benefit greatly from pre-tested names with data on perceptions of these attributes; such data would permit researchers to draw correct inferences about the causal effect of race in their experiments. In this paper, we provide the largest dataset of validated name perceptions to date based on three different surveys conducted in the United States. In total, our data include over 44,170 name evaluations from 4,026 respondents for 600 names. In addition to respondent perceptions of race, income, education, and citizenship from names, our data also include respondent characteristics. Our data will be broadly helpful for researchers conducting experiments on the manifold ways in which race shapes American life.

Research on shadow education-i.e., one-on-one or group learning intended to supplement children's experiences in school-has documented persistent social class and racial/ethnic inequalities. Yet, as with many things during the Covid-19 pandemic, the nature of shadow education changed dramatically. Much supplemental education shifted online, potentially increasing accessibility; many universities became testoptional, potentially reducing the demand for the shadow education industry; and a new form of shadow education-learning pods-emerged to take pandemic schooling from a more individual to a more collective experience. In this article, we use data from a sample of U.S. parents of K-12 students stratified by race/ethnicity (N = 1911) to assess social class and racial/ethnic inequalities in shadow education in 2020-21, the first full academic year of the Covid-19 pandemic. We are also the first scholars, to our knowledge, to assess high-quality data on the use of learning pods. We find that during the pandemic, African American and South Asian students were more likely than White student to use test preparation services and online supplemental education, and that African Americans, East Asians and Latinx were more likely to utilize private tutoring. We find few disparities by family income, however, thus supporting the idea that some forms of shadow education have become more accessible than they once were. Regarding learning pods, we find that pods were most common among African American families and families with parents who were less educated and worked fulltime. Thus, most learning pods were not a means of "opportunity hoarding," as some scholars originally feared, but instead provided sorely needed childcare and support during a time of social turbulence.

In many developing countries where formal institutions fail to guarantee rule of law, criminal organisations have emerged as intermediaries between citizens and the state. In some cases, these armed non-state actors adopt repressive strategies to govern their territories and local populations. This study asks under what circumstances local actors mobilise against criminal governance. Focusing on the case of Michoacán, Mexico, I argue that the emergence of vigilante groups, known as autodefensas, was prompted by the breaching of a social contract - a set of formal and informal agreements and obligations - between organised crime and civil society. As criminal governance spread in Michoacán, so did predatory behaviour against local communities, which gave way to an elite-organised social movement structured by rural inequalities.