The extent of inequality that people perceive in the world is often a better predictor of individual and societal outcomes than the level of inequality that actually exists. As such, scholars from across the social sciences, including economics, sociology, psychology, and political science, have recently worked to understand individuals’ (mis)perceptions of inequality. Unfortunately, many researchers treat the process underlying such perceptions as a black box, focusing predominantly on lay people’s numeric estimates of inequality, and paying less attention to how people come to form these perceptions or what these perceptions mean to participants. In the current review, we draw on research in perception, cognition, and developmental and social psychology, to introduce a novel comprehensive framework for understanding individuals’ perceptions of inequality. We argue that subjective perceptions of inequality should be viewed as a process that unfolds across five interlinked and iterative stages. To form perceptions of the scope of inequality in society, people need to (1) have access to inequality cues in the world, (2) attend to these cues, (3) comprehend these cues, (4) process these cues (often succumbing to motivational biases), and (5) summarize these cues into a meaningful representation of inequality. Our framework highlights when and why lay people may misperceive the scope of inequality in society and provides a roadmap for research to examine how the processes in people's minds affect the outcomes researchers are ultimately interested in.

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Economic inequality is notoriously difficult to quantify as reliable data on household incomes are missing for most of the world. Here, we show that a proxy for inequality based on remotely sensed nighttime light data may help fill this gap. Individual households cannot be remotely sensed. However, as households tend to segregate into richer and poorer neighborhoods, the correlation between light emission and economic thriving shown in earlier studies suggests that spatial variance of remotely sensed light per person might carry a signal of economic inequality. To test this hypothesis, we quantified Gini coefficients of the spatial variation in average nighttime light emitted per person. We found a significant relationship between the resulting light-based inequality indicator and existing estimates of net income inequality. This correlation between light-based Gini coefficients and traditional estimates exists not only across countries, but also on a smaller spatial scale comparing the 50 states within the United States. The remotely sensed character makes it possible to produce high-resolution global maps of estimated inequality. The inequality proxy is entirely independent from traditional estimates as it is based on observed light emission rather than self-reported household incomes. Both are imperfect estimates of true inequality. However, their independent nature implies that the light-based proxy could be used to constrain uncertainty in traditional estimates. More importantly, the light-based Gini maps may provide an estimate of inequality where previously no data were available at all.