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

Laureti, Tiziana; Polidoro, Federico

doi:10.2478/jos-2022-0003

Cited by 4 publications

(2 citation statements)

References 43 publications

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“…Although the issue of calculating sub-national SPIs and PPPs has gained prominence over recent decades, only a few countries have produced official sub-national SPIs (for a review on previous literature regarding sub-national SPIs, see Laureti and Polidoro 2022). Here, it is worth noting that the first official measurement on sub-national price was conducted in the USA in the 1940s.…”

Section: Using Online Data For Price Statisticsmentioning

confidence: 99%

Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques

et al. 2022

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The development of Information and Communications Technology and digital economies has contributed to changes in the consumption of goods and services in various areas of life, affecting the growing expectations of users in relation to price statistics. Therefore, it is important to provide information on differences in consumer prices across space and over time in a timely manner. Web-scraped data, which is the process of collecting large amounts of data from the web, offer the potential to improve greatly the quality and efficiency of consumer price indices. In this paper, we explore the use of web-scraped data for compiling high-frequency price indexes for groups of products by using the time-interaction-region product model. We computed monthly average prices for five entry-level items according to the Consumer Price Index for All Urban Consumers (CPI-U) classification and tracked their evolution over time in 11 USA cities reported in our dataset. Even if our dataset covers a small percentage of the CPI-U index, results show how web scraping data may provide timely estimates of sub-national SPI evolution and unveil seasonal trends for specific categories.

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Section: Using Online Data For Price Statisticsmentioning

confidence: 99%

Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques

et al. 2022

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“…Italian researchers have recently conducted a series of experiments for computing subnational SPIs (Laureti and Rao 2018;Laureti and Polidoro 2018;Biggeri and Laureti 2018;Ferrante et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy

Benedetti

Biggeri

Laureti

2022

Journal of Official Statistics

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It is essential to measure within-country differences in housing costs in order to evaluate costs of living, assessing and comparing poverty levels, quantifying salaries and disposable income of families and finally for designing housing policies at local level. To the authors knowledge, no studies have yet been carried out on the computation of Space Price Indexes for Housing Rents (SPIHRs). In this article we computed preliminary estimates of sub-national SPIHRs by using hedonic regression model, which is an extension of the Country Product Dummy method, for all the Italian regions. The hedonic regression is generally used to obtain multilateral spatial indexes, thus allowing us to obtain multilateral SPIHRs for the Italian regions. The estimates have been done using 2017 data from the Real Estate Market Observatory which is a part of the Italian Agency of Revenue and Tax. This data source is the most comprehensive source of information on Italian houses price rents with a wide geographical coverage, including data for each Italian municipality. The obtained results show significant differences across the Italian regions, thus highlighting the importance of calculating SPIHR in Italy on a regular basis and the need to continue researches in this field.

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The impact of local cost-of-living differences on relative poverty incidence: an application using retail scanner data and small area estimation models

Marchetti,

Giusti,

Schirripa Spagnolo

et al. 2024

Stat Methods Appl

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Estimating economic poverty indicators at the local level is essential for well-targeted data-driven welfare policies. However, Italy is a country characterized by strong geographical heterogeneity represented by unequal price levels among different areas, and computing poverty indicators with a national monetary poverty threshold can be misleading. This work proposes a novel approach to estimate monetary poverty incidence at the provincial level in Italy considering the different price levels within national boundaries. To account for local price variation, Spatial Price Indices (SPIs) are computed using scanner data on retail prices. The SPIs are estimated in two ways, referring to the mean local prices and using the 20th percentile of the prices. These two kinds of SPIs are used to adjust the national poverty line when computing the poverty incidence at the provincial level using Small Area Estimation (SAE) models. Our findings suggest that adjusting the national poverty line using the SPIs to compute a monetary poverty index can modify the poverty mapping results from the map produced with the traditional national poverty line that ignores price differences.

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

Cited by 4 publications

References 43 publications

Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques

Computation of High-Frequency Sub-National Spatial Consumer Price Indexes Using Web Scraping Techniques

Sub-National Spatial Price Indexes for Housing: Methodological Issues and Computation for Italy

The impact of local cost-of-living differences on relative poverty incidence: an application using retail scanner data and small area estimation models

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