Quality Adjustment at Scale: Hedonic vs. Exact Demand-Based Price Indices
This paper explores alternative methods for adjusting price indices for quality change at scale. These methods can be applied to large-scale item-level transactions data that includes information on prices, quantities, and item attributes. The hedonic methods can take into account the changing valuations of both observable and unobservable characteristics in the presence of product turnover. The paper also considers demand-based approaches that take into account changing product quality from product turnover and changing appeal of continuing products. The paper provides evidence of substantial quality-adjustment in prices for a wide range of goods, including both high-tech consumer products and food products.
Laura Zhao worked on this project as a doctoral student and subsequently as a post doc at the University of Maryland. Luke Pardue worked on this project as a doctoral student at the University of Maryland. We acknowledge financial support of the Alfred P. Sloan Foundation and the additional support of the Michigan Institute for Data Science, the Michigan Institute for Teaching and Research in Economics and the U.S. Census Bureau. We thank David Byrne, Erwin Diewert, Robert Feenstra, Robert Martin, Ariel Pakes, Stephen Redding, Marshall Reinsdorf, David Weinstein, and participants at multiple seminars and conferences for helpful comments. Researcher(s)' own analyses calculated (or derived) based in part on data from Nielsen Consumer LLC and marketing databases provided through the NielsenIQ Datasets at the Kilts Center for Marketing Data Center at The University of Chicago Booth School of Business. The conclusions drawn from the NielsenIQ data are those of the researcher(s) and do not reflect the views of NielsenIQ. NielsenIQ is not responsible for, had no role in, and was not involved in analyzing and preparing the results reported herein. We also use the NPD data housed at the U.S. Census Bureau. All results using the NPD data have been reviewed to ensure that no confidential information has been disclosed (CBDRB-FY19-122, CBDRB-FY21-074, and CBDRB-FY23-067). Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the view of the U.S. Census Bureau. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.