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Diversity is a widely pursued objective, yet current approaches often fall short or even backfire. This paper argues that treating the underrepresentation of groups such as women and ethnic minorities as separate issues oversimplifies the problem. I formally analyze how multidimensional social identities and social context interact to shape confidence and participation decisions. I show how “optimal social identification” allows agents to flexibly interpret social data to improve decision-making outcomes. However, different options to use this tool can create persistent disparities in task participation and outcomes. The framework enables a general equilibrium analysis of the interaction between social context, social identification, and task allocation. I show how one-dimensional policies, such as those focused solely on gender, neglect externalities and within-trait differences, and can have negative welfare effects. Instead, I advocate for multidimensional quotas with informational policies that nudge individuals to consider alternative traits and statistics. These interventions balance individual benefits with aggregate welfare concerns, reducing disparities while empowering individuals to reach their full potential.