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The importance of constructing sub-national spatial price indexes (SPIs) has been acknowledged in the literature for over two decades. However, systematic attempts to compile sub-national SPIs on a regular basis have been hampered by the labour-intensive analyses required for processing traditional price data. In the case of household consumption expenditures, the increasing availability of big data may change the current approach for estimating sub-national SPIs by considering the use of weighted index formulae. The aim of this paper is twofold: firstly, to review previous literature on sub-national SPIs and secondly to estimate Italian consumer SPIs. To this aim we use scanner data referring to grocery products sold in a random sample of approximately 1,800 Italian outlets belonging to the most important retail chains and including information on prices, quantities and quality characteristics of products at barcode level. Various weighted index formulas are used for calculating consumer SPIs at detailed territorial level and at the lowest aggregation level. Our results show an interesting territorial variability of consumer prices of products sold in large-scale retail outlets across the Italian regions. Overall, the Southern regions appear to have price levels below the national average both for food and non-food products with some interesting exceptions.

Using Scanner Data for Computing Consumer Spatial Price Indexes at Regional Level: An Empirical Application for Grocery Products in Italy