Carly R. Knight

Concealable Stigma and Occupational Segregation: Toward a Theory of Gay and Lesbian Occupations

Numerous scholars have noted the disproportionately high number of gay and lesbian workers in certain occupations, but systematic explanations for this type of occupational segregation remain elusive. Drawing on the literatures on concealable stigma and stigma management, we develop a theoretical frame-work predicting that gay men and lesbians will concentrate in occupations that provide a high degree of task independence or require a high level of social perceptiveness, or both. Using several distinct measures of sexual orientation, and controlling for potential confounds, such as education, urban location, and regional and demographic differences, we find support for these predictions across two nationally representative surveys in the United States for the period 2008-2010. Gay men are more likely to be in female-majority occupations than are heterosexual men, and lesbians are more represented in male-majority occupations than are heterosexual women, but even after accounting for this tendency, common to both gay men and lesbians is a propensity to concentrate in occupations that provide task independence or require social perceptiveness, or both. This study offers a theory of occupational segregation on the basis of minority sexual orientation and holds implications for the literatures on stigma, occupations, and labor markets.

The Causal Implications of Mechanistic Thinking: Identification Using Directed Acyclic Graphs (DAGs)

In analyzing causal claims, the most common evidentiary strategy is to use an experimental or quasi-experimental framework; holding all else constant, a treatment is varied and its effect on the outcome is determined. However, a second, quite distinct strategy is gaining prominence within the social sciences. Rather than mimic an experiment, researchers can identify causal relations by finding evidence for mechanisms that link cause and effect. In this chapter, we use Directed Acyclic Graphs (DAGs) to illustrate the power of using mechanisms. We show how mechanisms can aid in causal analysis by bringing additional variation to bear in instances where causal effects would otherwise not be identified. Specifically, we examine five generic situations where a focus on mechanisms using DAGs allows an analyst to warrant causal claims.

The Geography of Stigma Management: The Relationship between Sexual Orientation, City Size, and Self-Monitoring

This study examines whether self-monitoring—a ubiquitous social psychological construct that captures the extent to which individuals regulate their self-presentation to match the expectation of others—varies across demographic and social contexts. Building on Erving Goffman’s classic insights on stigma management, the authors expect that the propensity for self-monitoring will be greater among sexual minorities, especially in areas where the stigma surrounding minority sexual orientations is strong. The authors’ survey of U.S. adults shows that sexual minorities report significantly higher levels of self-monitoring than heterosexuals and that this difference disappears in large cities. These findings speak to sociological research on self-presentation, with implications for the literatures on identity formation, stigma management, and labor markets.

Mechanism-Based Causal Analysis

Over the past decades, ‘causal mechanisms’ have become an important part of social scientific explanation. Causal mechanisms now appear in annual review pieces, edited volumes, and occasional symposia. The publication of the Oxford Handbook of Analytic Sociology – a manifesto of the analytic paradigm that places mechanisms at the helm of its enterprise – is further evidence of this new focus. But what, precisely, are causal mechanisms? And how do they aid in explanation in the social sciences? This article investigates five major approaches to causal mechanisms toward the goal of identifying major points of consensus and contention. It further suggests that there are two distinct approaches to causal mechanisms: ‘top-down’ approaches that seek to generalize empirical events under widely instantiated causal patterns, and ‘bottom-up’ approaches that seek to disaggregate ‘average causal effects’ by opening up the ‘black-boxes.’