Consumer Learning and Price Index Bias: How Diffusion of Product Quality Knowledge Impacts Measures of Price Change

There is a general consensus that the bias associated with the entry of new merchants has nontrivial implications for measuring inflation. However, quantifying the bias empirically has proven difficult in part because little is known about how much of the price differences in goods sold by new versus old merchants represents a pure price difference (inflation) or differences in the quality of the attendant services (quality differences). In the public transportation industry, measurement of quality is complicated by the accompanying technological change rideshare services represented. As with any completely new technology, consumers faced considerable uncertainty around the quality of rideshare services. Consequently, consumers' perceived quality of rideshare services changes over time, which makes the calculation of constant-quality price indexes even more challenging. This paper explores a new method for accounting for this bias by separately identifying changes in product price and quality over time. I estimate multiple hedonic models to recover quality adjustment factors for quality-adjusted unit value price indexes. One of these models utilizes measures of time-varying product quality that are derived from a structural demand model of endogenous consumer learning that explicitly models the diffusion of knowledge about the quality of rideshare services. I compare the measurement of quality and pure price differences across modes of transportation, and the implications they have for constant-quality price indexes and, consequently, the measurement of inflation.