Quality Adjustment at Scale: Hedonic versus Exact Demand-Based Price Indices

This paper implements and evaluates methods for constructing quality-adjusted price indices from item-level retail scanner data at scale — across hundreds of product categories, heterogeneously encoded product attributes, and rapid product turnover. Using proprietary NPD Group data covering five general merchandise categories (2014–2018) and NielsenIQ scanner data covering 50+ food product groups (2006–2015), the paper compares hedonic superlative indices (using the Erickson-Pakes EP-TV methodology and a novel machine-learning extension) against exact demand-based indices (the Feenstra 1994 lambda-ratio adjustment and the Redding-Weinstein 2020 CES Unified Price Index, CUPI). The central finding is that quality adjustment is quantitatively large: the hedonic Tornqvist index shows roughly 2.5–2.9 percentage points per year faster price decline than the matched-model Tornqvist in high-tech categories (headphones, memory cards) and 4–5 percentage points of cumulative additional disinflation relative to matched-model indices for food. The Feenstra index agrees closely in magnitude with the hedonic approach, but the CUPI is highly sensitive to the choice of common-goods rule (CGR) and in some specifications shows 20–40 percentage points more cumulative disinflation than the Feenstra, a gap the paper attributes to the CUPI’s extreme sensitivity to low-share goods. The paper establishes that hedonic superlative price indices are feasible to implement at scale, including with machine learning on sparse text descriptions, and recommends them as the practical benchmark for re-engineering official price statistics.

Ed Olivares | Macro Paper Warehouse

Quality Adjustment at Scale: Hedonic versus Exact Demand-Based Price Indices