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Comparing Price Indices of Clothing and Footwear for Scanner Data and Web Scraped Data

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  • Antonio G. Chessa
  • Robert Griffioen

Abstract

[eng] Statistical institutes are considering web scraping of online prices of consumer goods as a feasible alternative to scanner data. The lack of transaction data generates the question whether web scraped data are suited for price index calculation. This article investigates this question by comparing price indices based on web scraped and scanner data for clothing and footwear in the same webshop. Scanner data and web scraped prices are often equal, with the latter being slightly higher on average. Numbers of web scraped product prices and products sold show remarkably high correlations. Given the high churn rates of clothing products, a multilateral method (Geary-Khamis) was used to calculate price indices. For 16 product categories, the indices show small overall differences between the two data sources, with year on year indices differing only by 0.3 percentage point at COICOP level (men’s and women's clothing). It remains to be investigated whether such promising results for web scraped data will also be found for other retailers.

Suggested Citation

  • Antonio G. Chessa & Robert Griffioen, 2019. "Comparing Price Indices of Clothing and Footwear for Scanner Data and Web Scraped Data," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 509, pages 49-68.
    • Handle: RePEc:nse:ecosta:ecostat_2019_509_4
    DOI: https://doi.org/10.24187/ecostat.2019.509.1984
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    References listed on IDEAS

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    1. 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. Patrick Bajari & Zhihao Cen & Victor Chernozhukov & Manoj Manukonda & Suhas Vijaykumar & Jin Wang & Ramon Huerta & Junbo Li & Ling Leng & George Monokroussos & Shan Wan, 2023. "Hedonic Prices and Quality Adjusted Price Indices Powered by AI," Papers 2305.00044, arXiv.org.
    2. Laureti Tiziana & Polidoro Federico, 2022. "Using Scanner Data for Computing Consumer Spatial Price Indexes at Regional Level: An Empirical Application for Grocery Products in Italy," Journal of Official Statistics, Sciendo, vol. 38(1), pages 23-56, March.

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    More about this item

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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