Prediction Technology and the Welfare Benefits from Differentiated Products

Abstract

The benefit that consumers derive from experience goods depends on how well they understand attributes of the choice set prior to purchase, and pre-purchase ignorance can lead to inefficient purchases. Using novel data on individual ownership and post-purchase usage of video games, we explore both the potential benefits of full information prior to purchase as well as the efficacy of contemporary prediction technology. First, we present descriptive results on regret from current purchases and the possible benefits of better pre-purchase information. Among owned games, fully informed consumers could achieve 90 percent of their baseline playtime with 40 percent of current expenditure; and among all available games, current expenditure could buy double baseline play time. We then develop a tractable model of consumer choice among bundles based on hours of playtime relative to overall spending, which we implement using both a Cobb Douglas calibration and a logit model of bundle choice. Full information would raise CS by more than the value of initial spending while reducing spending by one half. Moreover, while sophisticated predictions can provide sizable welfare benefits, they still fall substantially short of perfect foresight.