Documenting and Debiasing the Income Prediction Bias


Speaker


Abstract

Accurately predicting future income is an important part of the consumer budgeting process. However, the rise of the gig economy means that an increasing number of consumers have variable income, which may make it difficult to predict accurately. The present research tests the hypothesis that the majority of consumers with variable income display an income prediction bias in which they over-predict their future earnings. This hypothesis is supported in lab and field studies with participant samples drawn from two emblematic gigs: rideshare driving and food delivery. These studies also show that (a) people overpredict the number of hours they will work at their gig, but not the amount they will earn per hour, (b) the bias is not associated with short-term financial propensity to plan or gig experience, and (c) the bias is reduced by prompting people to consider relevant past experience when predicting their future income, but not by prompting them to consider atypical outcomes. In addition to documenting the existence and nature of a previously unidentified prediction bias with broad implications for consumer budgeting and financial decision making, these findings contribute to the vigorous social debate regarding the pros and cons of gig economy employment, and how to make gig work more equitable.