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

Author

Listed:
  • Gabriel Ehrlich
  • John Haltiwanger
  • Ron Jarmin
  • David Johnson
  • Ed Olivares
  • Luke Pardue
  • Matthew D. Shapiro
  • Laura Yi Zhao
Abstract
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 in cludes information on prices, quantities, and item attributes. The hedonic methods can take into account the changing valuations of both observable and unobservable charac teristics 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.

Suggested Citation

  • Gabriel Ehrlich & John Haltiwanger & Ron Jarmin & David Johnson & Ed Olivares & Luke Pardue & Matthew D. Shapiro & Laura Yi Zhao, 2023. "Quality Adjustment at Scale: Hedonic vs. Exact Demand-Based Price Indices," Working Papers 23-26, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:23-26
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    References listed on IDEAS

    as
    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.
    2. Feenstra, Robert C, 1994. "New Product Varieties and the Measurement of International Prices," American Economic Review, American Economic Association, vol. 84(1), pages 157-177, March.
    3. C. Lanier Benkard & Patrick Bajari, 2005. "Hedonic Price Indexes With Unobserved Product Characteristics, and Application to Personal Computers," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 61-75, January.
    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.
    6. Ivancic, Lorraine & Erwin Diewert, W. & Fox, Kevin J., 2011. "Scanner data, time aggregation and the construction of price indexes," Journal of Econometrics, Elsevier, vol. 161(1), pages 24-35, March.
    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.
    9. de Haan, Jan & van der Grient, Heymerik A., 2011. "Eliminating chain drift in price indexes based on scanner data," Journal of Econometrics, Elsevier, vol. 161(1), pages 36-46, March.
    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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    12. Diewert, Erwin, 2019. "Quality Adjustment and Hedonics: A Unified Approach," Microeconomics.ca working papers erwin_diewert-2019-2, Vancouver School of Economics, revised 14 Mar 2019.
    13. Tim Erickson & Ariel Pakes, 2011. "An Experimental Component Index for the CPI: From Annual Computer Data to Monthly Data on Other Goods," American Economic Review, American Economic Association, vol. 101(5), pages 1707-1738, August.
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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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