The aim of our research is to explore the possibility of utilizing scanner data on pasta purchases to build bilateral and multilateral spatial price indexes, taking a binary approach in the latter.1 Pasta plays a major role in the Italian diet. Historically, pasta consumption was mainly concentrated in the Southern regions of the country but today pasta is perhaps the product most representative of the eating habits of the Italians. The range of pasta producers runs from firms of longstanding tradition (some of them mainly directed towards local markets, such as Mastromauro in Puglia) to well known international brands (such as Barilla and De Cecco). The marked increase in pasta prices over the last two years has aroused great interest, but with little focus on spatial price diversity. This study stems from the availability of an extremely detailed panel dataset (Nielsen data) on values and quantities of pasta purchased. This data was produced by the use of bar-code scanning at retail outlets and thus includes information which provides weights at an elementary level. The use of scanner data to construct price indexes is not new in literature and there is a widespread consensus on the advantages of this approach in achieving more representative indexes. Average prices (unit values) show a marked spatial price variability: even when only considering the five bestselling products, regional prices vary greatly. The paper is set out as follows: Sect. 2 provides a description of the pasta scanner dataset and briefly looks for price variability; in Sect. 3 the requirements of comparability and representativity in the case of pasta are discussed; Sect. 4 deals with the methods and formulas chosen to obtain indexes for the regional comparisons of prices; Sect. 5 shows empirical results; in Sect. 6 a brief conclusion and suggestions for future work are given.
Scopo della ricerca è indagare la possibilità di utilizzare scanner data sugli acquisti di pasta per costruire indici dei prezzi spaziali bilaterali e multilaterali utilizzando un approccio binario nella loro costruzione.
Carbonaro, I., Santioni, R., Carlucci, M. (2010). Price dispersion: the case of pasta. In L. Biggeri, G. Ferrari (a cura di), Price indexes in time and space: methods and practice (pp. 151-166). Berlin : Springer [10.1007/978-3-7908-2140-6_8].
Price dispersion: the case of pasta
CARBONARO, ISABELLA;
2010-01-01
Abstract
The aim of our research is to explore the possibility of utilizing scanner data on pasta purchases to build bilateral and multilateral spatial price indexes, taking a binary approach in the latter.1 Pasta plays a major role in the Italian diet. Historically, pasta consumption was mainly concentrated in the Southern regions of the country but today pasta is perhaps the product most representative of the eating habits of the Italians. The range of pasta producers runs from firms of longstanding tradition (some of them mainly directed towards local markets, such as Mastromauro in Puglia) to well known international brands (such as Barilla and De Cecco). The marked increase in pasta prices over the last two years has aroused great interest, but with little focus on spatial price diversity. This study stems from the availability of an extremely detailed panel dataset (Nielsen data) on values and quantities of pasta purchased. This data was produced by the use of bar-code scanning at retail outlets and thus includes information which provides weights at an elementary level. The use of scanner data to construct price indexes is not new in literature and there is a widespread consensus on the advantages of this approach in achieving more representative indexes. Average prices (unit values) show a marked spatial price variability: even when only considering the five bestselling products, regional prices vary greatly. The paper is set out as follows: Sect. 2 provides a description of the pasta scanner dataset and briefly looks for price variability; in Sect. 3 the requirements of comparability and representativity in the case of pasta are discussed; Sect. 4 deals with the methods and formulas chosen to obtain indexes for the regional comparisons of prices; Sect. 5 shows empirical results; in Sect. 6 a brief conclusion and suggestions for future work are given.File | Dimensione | Formato | |
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