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

Author

Listed:
  • Gabriel Ehrlich
  • John C. Haltiwanger
  • Ron S. Jarmin
  • David Johnson
  • Ed Olivares
  • Luke W. Pardue
  • Matthew D. Shapiro
  • Laura Zhao
Abstract
This paper explores methods for constructing price indices from item-level transactions data on prices, quantities, and product attributes. The paper evaluates approaches that are feasible at scale, i.e., across the wide range of products, disparate encoding of attributes, and rapid product turnover inherent in “big data” on economic transactions, while producing improved cost-of-living indices that reflect both substitution effects and quality change. The paper presents hedonic methods that estimate changing valuations of both observable and unobservable characteristics in the presence of product turnover. It also considers demand-based methods that account for 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 food and high-tech consumer products. The paper also shows that hedonics can be implemented with well-encoded attributes using standard econometrics and with unstructured attribute data using machine learning.

Suggested Citation

  • Gabriel Ehrlich & John C. Haltiwanger & Ron S. Jarmin & David Johnson & Ed Olivares & Luke W. Pardue & Matthew D. Shapiro & Laura Zhao, 2023. "Quality Adjustment at Scale: Hedonic vs. Exact Demand-Based Price Indices," NBER Working Papers 31309, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31309
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    References listed on IDEAS

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    1. W. Erwin Diewert & Saeed Heravi & Mick Silver, 2009. "Hedonic Imputation versus Time Dummy Hedonic Indexes," NBER Chapters, in: Price Index Concepts and Measurement, pages 161-196, National Bureau of Economic Research, Inc.
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    4. Barnett, William A. & Choi, Ki-Hong, 2008. "Operational identification of the complete class of superlative index numbers: An application of Galois theory," Journal of Mathematical Economics, Elsevier, vol. 44(7-8), pages 603-612, July.
    5. Robert C. Feenstra & Marshall B. Reinsdorf, 2007. "Should Exact Index Numbers Have Standard Errors? Theory and Application to Asian Growth," NBER Chapters, in: Hard-to-Measure Goods and Services: Essays in Honor of Zvi Griliches, pages 483-513, National Bureau of Economic Research, Inc.
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    7. Stephen J Redding & David E Weinstein, 2020. "Measuring Aggregate Price Indices with Taste Shocks: Theory and Evidence for CES Preferences," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 135(1), pages 503-560.
    8. Ana Aizcorbe & David M. Byrne & Daniel E. Sichel, 2019. "Getting Smart About Phones: New Price Indexes and the Allocation of Spending Between Devices and Services Plans in Personal Consumption Expenditures," NBER Working Papers 25645, National Bureau of Economic Research, Inc.
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    10. Sato, Kazuo, 1976. "The Ideal Log-Change Index Number," The Review of Economics and Statistics, MIT Press, vol. 58(2), pages 223-228, May.
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    Cited by:

    1. Espín, Augusto & Rojas, Christian, 2024. "Bridging the digital divide in the US," International Journal of Industrial Organization, Elsevier, vol. 93(C).
    2. Ana M. Aizcorbe & Daniel Ripperger-Suhler, 2024. "Do Price Deflators for High-Tech Goods Overstate Quality Change?," BEA Papers 0129, Bureau of Economic Analysis.

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    More about this item

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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