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Substantial research has documented challenges women experience building and benefiting from networks to achieve career success. Yet fundamental questions remain regarding which aspects of men’s and women’s networks differ and how differences impact their careers. To spur future research to address these questions, we present an integrative framework to clarify how and why gender and networks—in concert—may explain career inequality. We delineate two distinct, complementary explanations: (1) unequal network characteristics (UNC) asserts that men and women have different network characteristics, which account for differences in career success; (2) unequal network returns (UNR) asserts that even when men and women have the same network characteristics, they yield different degrees of career success. Further, we explain why UNC and UNR emerge by identifying mechanisms related to professional contexts, actors, and contacts. Using this framework, we review evidence of UNC and UNR for specific network characteristics. We found that men’s and women’s networks are similar in structure (i.e., size, openness, closeness, contacts’ average and structural status) but differ in composition (i.e., proportion of men, same-gender, and kin contacts). Many differences mattered for career success. We identified evidence of UNC only (same-gender contacts), UNR only (actors’ and contacts’ network openness, contacts’ relative status), neither UNC nor UNR (size), and both UNC and UNR (proportion of men contacts). Based on these initial findings, we offer guidance to organizations aiming to address inequality resulting from gender differences in network creation and utilization, and we present a research agenda for scholars to advance these efforts. 

Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.