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Scanner Data: Advances in Methodology and New Challenges for Computing Consumer Price

Author

Listed:
  • Marie Leclair
  • Isabelle Léonard
  • Guillaume Rateau
  • Patrick Sillard
  • Gaëtan Varlet
  • Pierre Vernédal

Abstract

[eng] When consumers pay for their purchases at the store checkout, the barcodes (also known as GTINs) of the goods purchased are scanned, recording quantities and the prices linked to each barcode in the process. Scanner data present an opportunity for use in constructing consumer price indices, which could supersede the use of survey data. Based on the existing concept of consumer price indices, the volume and new types of information provided by scanner datasets raise a number of new methodological questions, in particular in relation to price aggregation to produce indices, handling quality adjustments, classifying goods by homogeneous consumption segment and dealing with product relaunches and promotions. This article looks at how these questions have been addressed in France.

Suggested Citation

  • Marie Leclair & Isabelle Léonard & Guillaume Rateau & Patrick Sillard & Gaëtan Varlet & Pierre Vernédal, 2019. "Scanner Data: Advances in Methodology and New Challenges for Computing Consumer Price," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 13-29.
    • Handle: RePEc:nse:ecosta:ecostat_2019_509_2
    DOI: https://doi.org/10.24187/ecostat.2019.509.1981
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    References listed on IDEAS

    as
    1. von der Lippe, Peter, 2012. "Notes on GEKS and RGEKS indices," MPRA Paper 42730, University Library of Munich, Germany.
    2. Diewert, W. Erwin & Fox, Kevin J., 2017. "Substitution Bias in Multilateral Methods for CPI Construction using Scanner Data," Microeconomics.ca working papers erwin_diewert-2017-3, Vancouver School of Economics, revised 23 Mar 2017.
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    Cited by:

    1. Venera Timiryanova & Irina Lakman & Vadim Prudnikov & Dina Krasnoselskaya, 2022. "Spatial Dependence of Average Prices for Product Categories and Its Change over Time: Evidence from Daily Data," Forecasting, MDPI, vol. 5(1), pages 1-25, December.
    2. Timiryanova, Venera & Krasnoselskaya, Dina, 2022. "Влияние пандемии Сovid-19 на пространственную динамику продовольственных цен [Covid-19 impact on spatial food prices dynamics]," MPRA Paper 114638, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D1 - Microeconomics - - Household Behavior

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