A prototype theory of consumer expense misprediction

Abstract

The present research develops a prototype theory of consumer expense misprediction that helps explain why consumers display an expense prediction bias in which they under-predict their future spending, and how expense prediction accuracy can be improved. The logic of the prototype theory is that expense predictions are based on prototype attributes that come to mind easily when predictions are being constructed. These attributes represent a consumer’s average spending, where “average” refers to the mode of their expense distribution. This leads consumers to under-predict their expenses because, generally speaking, the distribution of expenses is positively skewed with mode < mean. Accordingly, it is proposed that prompting consumers to consider reasons why their expenses might be different than usual will increase prediction accuracy by making atypical expenses cognitively easier to retrieve. A series of studies that includes a longitudinal field experiment, data from a personal finance app, and a variety of lab paradigms provide evidence for this prototype account of the bias and the effectiveness of the proposed intervention. Evidence is also provided that consumers with variable income (e.g., Uber drivers) display a corresponding income prediction bias in which they over-predict future income.

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