Marginalisation and Misperception: Perceiving Gender and Racial Wage Gaps in Ego Networks

We introduce an agent-based model of localised perceptions of the gender and racial wage gap in Random Geometric Graph type networks that result from economic homophily independent of gender/race. Thereby, agents estimate inequality using a composite signal consisting of local information from their personal neighbourhood and the actual global wage gap. This can replicate the underestimation of the gender or racial wage gap that empirical studies find and the well-documented fact that the underprivileged perceive the wage gap to be higher on average with less bias. Calibration by a recent Israeli sample suggests that women place much more weight on the (correct) global signal than men, in line with the hypothesis that people who are adversely affected by a wage gap listen more carefully to global information about the issue. Hence, (educational) interventions about the global state of gender and racial inequality are promising, especially if they target the privileged.