One of the greatest challenges facing official statistics in the 21st century is the use of alternative sources of data about prices (scanned and scraped data) in the analysis of price dynamics, which also involves selecting the appropriate formula of the price index at the elementary group (5-digit) level. When consumer price indices of goods and services are constructed, a number of subjective decisions are made at different stages, e.g. regarding the choice of data sources and types of indices used for the purpose of estimation. All of these decisions can affect the bias of consumer price indices, i.e. the extent to which they contribute to the overall uncertainty about the resulting index values. By measuring how robust consumer price indices are, one can assess the impact that the decisions made at the different stages of index construction have on the index values. This assessment involves analysing uncertainty and sensitivity. The purpose of the study described in the article was to determine how much and in which direction the consumer price index changes when including scanner and scraped data in the analysis, in addition to the data on prices collected by enumerators. The impact of these new data sources was assessed by analysing uncertainty and sensitivity under the deterministic approach. To the best of the authors’ knowledge, it is a novel application of robustness analysis to measure inflation using new data sources. The empirical study was based on data for February and March 2021, while scanner and scraped data about selected categories of food products were obtained from one retail chain operating hundreds of points of sale in Poland and selling products online. It was found that the choice of a data source has the most significant impact on the final value of the index at the elementary group level, while the choice of the aggregation formula used to consolidate different data sources is of secondary importance.
price indices, scraped data, scanner data, robustness analysis, inflation
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