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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The apparently universal phenomenon suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. Our generating mechanism can simultaneously replicate the known stylised facts in the literature on biased perceptions of income inequality. Most importantly, we show analytically that homophilic graph formation implies a ‘middle class bias’, i.e., individuals perceiving themselves to be in the middle of the income distribution almost independently of their actual position.

In a second step, we augment our model to account for perceptions of gender and racial wage gaps. We show that the combination of homophilic graph formation and estimation based on locally limited model is able to replicate both the underestimation of gender and racial wage gap that empirical studies find and also the well-documented fact that the underprivileged perceive the gender wage gap to be higher on average and with less bias. In contrast to these qualitative replication, we also demonstrate that the effect of homophilic graph formation is quantitatively too strong to replicate empirical estimates. We propose a simply remedy, where agents estimate using a composite signal based on local and global information. Our calibration suggests that the underprivileged place much more weight on the (correct) global signal than the privileged, in line with the well-established psychological finding that the adversely affected part of the population is more interested in global information about the issue. Our findings thus suggest that (educational) interventions about the global state of gender equality are much more likely to succeed than information treatments about inequality but that increasing diversity might aid in improving the accuracy of perceptions in both regards.