The use of coarse categories is prevalent in various situations and has been linked to biased economic outcomes, ranging from discrimination against minorities to empirical anomalies in financial markets. In this paper we study economic rationales for categorizing coarsely. We think of the way one categorizes one's past experiences as a model of the world that is used to make predictions about unobservable attributes in new situations. We first show that coarse categorization may be optimal for making predictions in stochastic environments in which an individual has a limited number of past experiences. Building on this result, and this is a key new insight from our paper, we show formally that cases in which people have a motive to coordinate their predictions with others may provide an economic rationale for categorizing coarsely. Our analysis explains the intuition behind this rationale.