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Scanner Data and Spatial Price Comparisons:Current Status and Future Implications for
International PPPs
Tiziana Laureti University of Tuscia, Viterbo, Italy ([email protected])
Member of the Governing Body of Italian National Statistical System- (COMSTAT)
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
OUTLINE OF THE PRESENTATION
Background and Aims
Scanner data and CPI computation
Scanner data and spatial comparisons Current status The Italian experience
Future Implications for International PPPs
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Over the last decade there has been a growing interest in using scanner data for constructing official price indexes thus increasing the availability of this new data source.
Almost a third of EU countries are currently using scanner data for compiling CPIs using different methods
As yet few studies have been carried out on using scanner data for compiling spatial prices indexes (Heravi, Heston and Silver, 2003; Laureti and Polidoro, 2018, Laureti and Rao, 2018)
In this context scanner data may enable countries to construct regional spatial price indexes and improve international spatial comparisons
The aims of this presentation are to: Describe the current status concerning the use of scanner data Illustrate the Italian experience Envisage future implications for international PPP computations
Background and Aims
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Country Scanner data sources Classification/linkingmethods
Norway2001
3 retail chains, gasoline stations, pharmacies
GTIN+PLU
Switzerland2008
the two largest retail store chains ( market share of about 60-70%)
In-store item numbers of the retail chain
Netherlands2010
6 supermarket chains (market share of around 50%)
EAN+item description (text mining)
Denmark2011
largest supermarket chains (60% of sales of food and beverages)
EAN + product description created by the supermarket chain
Sweden2011
3 major outlet chains in Sweden +2 foodchains for products sold by weight (from 2018)
Automatic coding +GTIN
Belgium2015
3 largest supermarket chains (75-80% of the market)
Store proprietary codes (stock keeping units – SKUs)
Iceland2016
3 largest grocery store chains Barcode (EAN) + item description
Scanner data and CPI computation
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Country Scanner data sources Classification/linking methods
Italy2018
16 largest retail store chains (95% of modern retail trade distribution)
Product key number+ GTINInformation provided by Nielsen
Luxemburg2018
retail transaction data for food products and non-alcoholic beverages
EAN
New Zealand2014 for CPI
retail transaction data for consumer electronics products
Information provided by GfK
Australia2014
retail transaction data (25% of CPI) Stock keeping unit (SKU)
Several countries are planning to use scanner data within a few years. In fact, theNSOs are still in the research phase (e.g. France, UK, Portugal, Austria, Poland,South Africa)
“secondary data source” NSOs must reclassify scanner data
Eurostat published a practical guide for Processing Supermarket Scanner Data tohelp NSOs to accelerate the process of using scanner data and to ensurecomparability among national HICPs (Eurostat, 2017).
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and CPI computation
To date, little research has been carried out on this topic
Heravi, S., Heston, A., & Silver, M. (2003). Use of scanner data for providing estimates of intercountry price parities at
the level of the basic heading. The application was based on about 1 milliontransactions for television sets over two months in three countries
Feenstra, R. C., Xu, M., & Antoniades, A. (2017). Examine the price and variety of products at barcode level in various cities in
China and the US and it was observed that , unlike the US, product prices tendto be lower in larger Chinese cities.
To my knowledge, only the Italian Statistical Institute (Istat) has started anofficial research project within the MPS framework for computing sub-national price parities using scanner data (Laureti and Polidoro, 2017, 2018;Laureti et al, 2017; Laureti and Rao, 2018)
Scanner data for spatial comparisons
Scanner data and spatial comparisons: current status
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Sub-national PPPs for Italy are required due to the high socio-economicheterogeneity across its macro-areas
Spatial price comparison in Italy
Regional values of economic indicators should be adjusted for regional price differentials
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Italy is one of the few countries that has carried out official experimental sub-national SPI estimations (using CPI data and ad-hoc surveys) referring specificallyto household consumption and considering regional capitals:
In 2008 (with reference to 2006 data): GEKS formula, three expendituredivisions (Food and Beverages, Clothing and Footwear, Furniture);In 2010 (with reference to 2009 data): all COICOP expenditure divisions;GEKS formula and CPD model for actual rents
The latest results in 2010 showed significant differences in the level of consumer prices across the regional capitals (Istat, 2010).
Consumer price levels in the Northern cities are generally higher than those in the Centre and especially in Southern Italy.
Spatial price comparison in Italy
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
However, systematic attempts to compile regional spatial price indexes on aregular basis have been hindered by laborious calculations and dataunavailability In fact, there are various drawbacks in using traditional sources of price
data (CPIs, ad-hoc survey)Using scanner data may allow for the computation of SPIs on an annual basis
Since 2014 scanner data have been regularly collected and provided by the market research company ACNielsen (Istat project on scanner data).
CPI production process has been significantly improved:
Since January 2018 Italian CPIs have been produced with scanner data
Scanner data currently replace the on-field collected price data for grocery products in supermarkets and hypermarkets (from 2019 onwards data on electronic goods will be included)
Use of scanner data for producing SPIs Experimental statistics
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
FIRST PHASE of the research project (Laureti and Polidoro, 2016; Laureti and Polidoro, 2017)
AIMS: To explore the potential advantages of the use of scanner data for constructing
sub-national PPPs (suitability of scanner data for making spatial comparisons) To deal with the empirical issues deriving from the use of this new data source
DATA: Year: 2015 Product coverage: Food products Retailers: selection based on available data
931 outlets belonging to the 6 most important retail chains (Coop Italia, Conad, Selex, Esselunga, Auchan, Carrefour) covering 57% of the market
Territorial coverage: 20 regional capitals Price and turnover information: 15,433 different products identified by GTIN
codes
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
1. GTIN/EAN codes provide detailed descriptions of the products. They are the same for each item at national level:
• Fulfil comparability requirement (like with like comparison)
2. Turnover and quantities are available for each GTIN, retail chain, outlet, and city: How to compute unit value prices?
High heterogeneity of prices , across regional capitals and chains within a city:
This suggests using the finest available classification of item (GTINs)• We computed unit value price per item according to retail chain and
outlet
Scanner data and spatial comparison: the Italian experience
GTIN/EAN Product description Brand Unit sold Volume Turnover BH City Chain Store
8000139004261 GAROFALO SEM LUNGA SPAGHETTI N.9 SEM PASTA 00500 GR 1 SACCHETTO GAROFALO 500GR 8655 8828 11.01.11.5 Turin 9 1745
8001250120120 DE CECCO SEM LUNGA SPAGHETTI N.12 SEM PASTA 00500 GR 1 SACCHETTO DE CECCO 500GR 670 677 11.01.11.5 Venice 10 1200
Potential advantages/empirical issues:
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
prdkey=114667 GTIN/EAN=8001250120113Item description=«DE CECCO SEM LUNGA SPAGHETTINI N.11 SEM PASTA 500 GR 1 SACCHETTO»
High variability of product prices across regional capitals and chains
Annual average price across Italian regional capitals
Scanner data and spatial comparison: the Italian experience
How to use the available information on turnover for each item?Assessing the representativity and importance of each item thus improving the quality of SPIs This suggests that all items under a certain BH should be included and
weighted according to their turnovers Few products may account for high percentages of the turnovers (e.g.
pasta products)• Is it possible to consider a limited number of products? One must make sure that there is a reasonable overlap in the items priced in different regions
3. Time dimensionMonthly or annual average prices
We estimated Time-interaction-Country Product Dummy models (TiCPD)• A high variability of SPIs over time
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Potential advantages/empirical issues:
0.5
10
.51
0.5
10
.51
0 10000 20000 30000 40000 0 10000 20000 30000 40000 0 10000 20000 30000 40000 0 10000 20000 30000 40000 0 10000 20000 30000 40000
AN AO AQ BA BO
CA CB CZ FI GE
MI NA PA PG PZ
RM TN TO TS VE
cum
shar
e
Pasta products
Cumulative Market Share by GTIN for Pasta products: Largest to Smallest
Scanner data and spatial comparison: the Italian experience
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
WTiCPD Estimation results: PPPs for regional chief towns (Southern cities)
Pasta products and couscous
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
SECOND PHASE of the research project (Laureti and Rao, 2018; Laureti and Polidoro, 2018)
AIMS: To explore the feasibility of implementing various aggregation methods at BH
level To estimate regional SPIs for product aggregates
DATA: This dataset is used for CPI computation
YEAR: 2017 OUTLETS:
Stratified random sample: Universe of 9,000 retailers belonging to the 16 most important retail chains
(94% of modern retail chain distribution).
Sample stratified by province, distribution chains and kind of outlets (888strata)
Outlets are selected with probabilities proportional to the 2016 turnover 1,781 outlets (510 hypermarkets and 1,271 supermarkets)
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
TERRITORIAL COVERAGE: all cities within the 107 territorial areas (provinces and metropolitan towns)
ITEMS• 487,094 different products belonging to food, beverages and personal
and home care products: five divisions of the ECOICOP (01, 02, 05, 09, 12).• Scanner data cover 55.4% of the total retail trade for this category of
products• Items were selected with probabilities proportional to the 2016 turnover
for each product aggregate (at 60% cut-off line) Chain structure in overlapping products
Price concept we compute annual averages of weekly prices (average of prices paid by
consumers) for each item and outlet using turnover as weights we compute provincial averages using sampling weights for each outlet
Expenditure weights at item level
Scanner data and spatial comparison: the Italian experience
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
We adopted a two-step procedure similar to the one used in the ICP wherebyprovinces are grouped into regions:
1. Within-regional SPIs are computed by comparing price and quantity datareferring to products sold in the various provinces within each region Several methods are used for this purpose at the lowest level of
aggregation (groups of similar products):
A. GEKS based on Jevons Index - based on products that are commonlypriced in the two areas, j and k
B. GEKS based on Fisher binary index – using price and quantity data forcommonly priced items
C. Geary-Khamis IndexD. Regional Product Dummy model (RPD)E. Weighted Regional Product Dummy model (WRPD) with expenditure
share weights and quantity weights
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
2. between-regional SPIs are computed by using prices adjusted for differenceamong provinces for each region (obtained by dividing provincial prices by theWithin-regional SPIs) and deflated expenditures
Weighted RPD model We checked if there was a reasonable overlap in the items priced in different
regions (and if overlaps exhibit a chain structure). We excluded two groups of products “Whole Milk” and “Low-Fat Milk” since there
were no reliable overlaps among regions enabling spatial price comparisons
Moreover, as in the ICP, sub-national SPIs (PPP) compilation is undertaken at two levels: For groups of similar products (Basic Heading, BH) Product aggregates (in our case Food and Non-Food products).Aggregation method: GEKS- Fisher (ICP and Eurostat-OECD) . We standardized the GEKS-Fisher based PPPs (S-GEKS).
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Food Products (Italy=100) Non-Food Products (Italy=100)Results: Regional Spatial Price Indexes
Price levels in Southern regions are below the national average both for Food and Non-Food products, with the exception of Abruzzo (101.90 and 101.33, respectively), Molise (102.90 and 101.24) and Sardinia (101.93 and 101.57)
On average, Tuscany proved to be the less expensive region for both product aggregates (96.24 and 95.17)
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experienceResults: Regional Spatial Price Indexes
Table 1: WRPD estimation results for “Pasta products” and “Non-electrical appliances” Italy=100Pasta Products (BH1) Non-electrical appliances (BH2)
Region Coef std.error p.value RPP Coef std.error p.value RPPNorth-CenterPIEMONTE 0.0028 0.0027 0.3071 100.28 -0.0550 0.0056 0.0000 94.65VALLEDAOSTA 0.0367 0.0028 0.0000 103.74 0.0528 0.0059 0.0000 105.43LIGURIA 0.0323 0.0034 0.0000 103.28 -0.0061 0.0056 0.2829 99.40LOMBARDIA 0.0104 0.0027 0.0001 101.05 -0.0402 0.0056 0.0000 96.06TRENTINO 0.0557 0.0029 0.0000 105.73 0.0268 0.0057 0.0000 102.71VENETO 0.0188 0.0027 0.0000 101.89 -0.0133 0.0056 0.0183 98.68FRIULI 0.0276 0.0026 0.0000 102.80 -0.0079 0.0057 0.1611 99.21EMILIA-ROMAGN 0.0068 0.0031 0.0270 100.68 -0.0386 0.0056 0.0000 96.22TOSCANA -0.0209 0.0028 0.0000 97.93 -0.1205 0.0057 0.0000 88.65UMBRIA -0.0254 0.0029 0.0000 97.50 0.0027 0.0056 0.6357 100.27MARCHE 0.0398 0.0031 0.0000 104.06 0.0258 0.0056 0.0000 102.61LAZIO -0.0159 0.0026 0.0000 98.42 0.0075 0.0056 0.1823 100.75South and IslandsABRUZZO 0.0401 0.0030 0.0000 104.09 0.0036 0.0057 0.5254 100.36MOLISE 0.0311 0.0031 0.0000 103.16 0.0354 0.0058 0.0000 103.60CAMPANIA -0.0256 0.0029 0.0000 97.47 0.0348 0.0057 0.0000 103.54PUGLIA -0.0547 0.0029 0.0000 94.68 -0.0071 0.0057 0.2132 99.29BASILICATA -0.0570 0.0029 0.0000 94.46 0.0236 0.0057 0.0000 102.39CALABRIA -0.0445 0.0029 0.0000 95.65 0.0270 0.0057 0.0000 102.74SICILIA -0.0758 0.0034 0.0000 92.70 0.0679 0.0057 0.0000 107.03SARDEGNA 0.0176 0.0036 0.0000 101.78 -0.0192 0.0057 0.0007 98.10
In some BHs, the usual divide between North and South is not confirmed
L=54 groups of products
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Higher price levels:• Siena (102.9)• Livorno (102.2)
Higher price levels:• Livorno (104.0)• Siena (103.2)• Grosseto (103.1)
Lower price levels:• Prato (98.3)• Firenze (98.4)
SPIs FOOD PRODUCTS (Tuscany=100)
Results: Provincial Spatial Price Indexes
SPIs NON-FOOD PRODUCTS (Tuscany=100)
Lower price levels:• Prato (97.4)• Pistoia (97.5)
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experienceResults: Provincial Spatial Price Indexes
SPIs FOOD PRODUCTS (Lombardia=100) SPIs NON-FOOD PRODUCTS (Lombardia=100)
Higher price levels:• Brescia (101.2)• Pavia (101.1)
Higher price levels:• Pavia (101.9)• Como (101.5)
Lower price levels:• Mantova (99.0)• Bergamo (99.1)
Lower price levels:• Bergamo (98.5)• Sondrio (97.4)
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Jevons GEKS Fisher GEKS GK RPD WE_RPD WQ_RPDBergamo 100.85 101.19 100.14 100.34 100.96 99.41Brescia 103.90 104.98 101.71 103.70 104.60 103.53Como 101.49 101.23 100.53 101.17 101.07 100.96Cremona 103.33 101.99 101.76 103.77 102.08 104.97Lecco 100.88 101.63 100.38 100.85 101.57 100.19Lodi 100.09 100.24 99.77 99.61 99.52 98.09Monza-Brianz 99.63 99.97 99.78 99.63 99.73 98.76Milano 100.00 100.00 100.00 100.00 100.00 100.00Mantova 102.45 103.06 100.87 102.10 102.74 101.75Pavia 101.63 101.90 100.66 101.43 102.17 100.87Sondrio 105.37 106.69 102.08 104.59 105.89 102.54Varesa 100.67 101.06 100.38 100.82 100.90 100.40
SPIs using different methods: Pasta products and coscous (Milan=100)
Results: Provincial Spatial Price Indexes
Lombardy’s low level of heterogeneity in consumer price differences is not confirmedwhen considering specific food products, i.e. Pasta
We observe lower price levels for household goods in relatively poorer provinces when we use Geary-Khamis method
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Conclusions
Scanner data enabled us to compute sub-national SPIs at local level tobe used for adjusting regional economic indicators.
The feasibility of implementing various aggregation methods has beenproved but the weighted RPD model is preferable when productoverlaps exhibit a chain structure.
Further research is underway for
Obtaining scanner data from Hard Discount, Consumer Electronicsretailers and Furniture retails (planned in 2019)
Integrating scanner data with other new data sources (i.e. web scraping)as well as traditional data collection (traditional retail trade) for clothingand footwear by using electronic devises
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Future Implications for International PPPs
IMPROVE THE QUALITY OF PRICING SAMPLES: Replacing on-field collected prices (NSOs may use scanner data to identify
products and collect prices )
Increasing the number of products priced ( and assessing their representativity using turnover as weights)
Expanding the number of cities where prices are collected (not only national capitals)
• It is easier to compute SAFs and sub-national SPIs (adjusting for rural/urban) . Thus obtaining average national prices that are more representative of the whole country
NSOs will be able to adopt probabilistic samples:
• Information on the universe of retailers, turnover and market share for each outlet
• Measures of uncertainty in price statistics
Scanner data may enhance the accuracy of international PPPs
Total L M HNot
Specified
1 60 31 20 0 0 0 20 9 0
60 31 20 0 0 0 20 9 0
No of SPD's
No of items
Spec. Brand
s
Well Known BrandsBrand less
Brand n.r.
A.12.1.3.2 - Articles for personal hygiene and wellness, esoteric products and beauty products A.12.1.3.2.01 - Articles for personal hygiene and wellness, esoteric products and beauty products
Future Implications for International PPPs
12.1.3 Non-electrical appliances and personal care products 12.1.3.1 Non-electrical appliances12.1.3.2 Articles for personal hygiene and wellness, esoteric products and beauty products
Final European list for EU group:
OECD-Eurostat Program: Istat is currently carring out (October-December 2018) price surveys for clothing and footwear and for personal care products
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
A detailed specification for each product in the product list is provided by the SPDs NSOs should establish the most important characteristics to search for in the
scanner data set The GTINs/EAN that correspond to the product specification should then be determined
The product characteristics can then be matched with the itemized information contained in the scanner data (constructing record linkage procedures)
Future Implications for International PPPs
Shampoo: SPDs and scanner data
Future Implications for International PPPs
DRAWBACKS: NSOs rely heavily on data provided by the retailers.
More IT resources are required due to the huge amount of data obtained
Not all countries may have access to this type of data
Scanner data my cover a limited number of product categories
Various EU countries (e.g. Italy, Norway, the Netherlands) are currentlyusing scanner data to produce international PPPs and following differentprocedures.
They expect EUROSTAT to establish specific guidelines
Further research is underway for
Integrating CPI and PPP computation
Exploring new methods for PPP computation using scanner data
Carrying out simulation procedures to identify “importance weights”Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Thank you for your kindattention!
Tiziana [email protected]
Feenstra, R. C., Xu, M., & Antoniades, A. (2017). What is the Price of Tea in China? Towards theRelative Cost of Living in Chinese and US Cities (No. w23161). National Bureau of EconomicResearch.Heravi, S., Heston, A., & Silver, M. (2003). Using scanner data to estimate country priceparities: A hedonic regression approach. Review of Income and Wealth, 49(1), 1-21.Laureti, T., Ferrante C. and Dramis B. (2017)Using scanner and CPI data to estimate Italian sub-national PPPs, Proceeding of 49th ScientificMeeting of the Italian Statistical Society, pp.581-588 Laureti, T., and Polidoro, F. Testing the useof scanner data for computing sub-national Purchasing Power Parities in Italy, Proceeding of61st ISI World Statistics Congress, Marrakech, (2017)Laureti, T., and Rao, D. P.(2018) Measuring Spatial Price Level Differences within a Country:Current Status and Future Developments. Estudios de economía aplicada, 36(1), pp.119-148.Laureti, T., and Polidoro, F. (2018) Big data and spatial comparisonsof consumer prices Testing the use of scanner data for computing sub-national PurchasingPower Parities in Italy, Proceeding of 49th Scientific Meeting of the Italian Statistical Society,Palermo
References
Retail chain=9, prdkey=114667 GTIN/EAN=8001250120113Item description=«DE CECCO SEM LUNGA SPAGHETTINI N.11 SEM PASTA 500 GR 1 SACCHETTO»
Scanner data and spatial comparison: the Italian experience
High variability of item price across regional capitals within the same retail chain
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Annual average price across Italian regional capitals
0.5
1av
erag
e pr
ice
9 10 18 20 31
ROMEItem=«DE CECCO SEM LUNGA SPAGHETTINI N.11 SEM PASTA 500 GR 1 SACCHETTO»
Significant differences across retail chains of annual price of the identical item (p<0.05)
Scanner data and spatial comparison: the Italian experience
Average price across retail chains within the same city
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data: % market shares (hypermarket + supermarket) – year 2016
RETAIL CHAINSPI
EMO
NTE
VALL
E D'
AOST
A
LIGU
RIA
LOM
BARD
IA
TREN
TIN
O-A
LTO
ADI
GE
VEN
ETO
FRIU
LI-V
ENEZ
IA G
IULI
A
EMIL
IA-R
OM
AGN
A
TOSC
ANA
UM
BRIA
MAR
CHE
LAZI
O
ABRU
ZZO
MO
LISE
CAM
PAN
IA
PUGL
IA
BASI
LICA
TA
CALA
BRIA
SICI
LIA
SARD
EGN
A
ITALIACOOP ITALIA 18,2 - 42,2 7,9 18,0 9,1 21,3 41,2 51,2 30,8 18,5 14,3 10,0 - 4,4 18,6 6,9 - 6,3 - 18,5
CONAD 4,3 22,3 17,0 3,3 13,8 3,6 7,7 26,5 14,8 29,9 12,6 24,5 29,8 30,9 20,5 9,6 10,3 30,2 19,5 30,6 13,3
ESSELUNGA 12,4 - 3,9 31,3 - 1,2 - 9,9 22,1 - - 0,9 - - - - - - - - 12,1
SELEX COMMERCIALE 17,9 8,6 4,8 9,9 - 32,3 9,4 6,6 1,1 22,1 18,2 3,4 2,7 23,4 7,6 29,1 6,0 3,3 4,4 12,8 11,1
GRUPPO AUCHAN 7,0 - 0,7 8,2 - 6,3 1,1 1,5 1,9 2,7 25,8 10,7 11,1 - 8,1 17,2 10,4 17,3 20,1 12,6 7,8
GRUPPO CARREFOUR ITALIA SPA 16,4 45,1 8,8 9,9 - 2,1 4,2 1,8 2,8 0,7 0,9 13,3 5,7 1,6 9,2 - 0,9 8,9 1,5 5,6 7,1
FINIPER 1,5 - - 6,4 - 1,6 2,9 1,4 - - 4,1 - 8,3 - - - - - - - 2,3
GRUPPO VEGE - - 1,5 1,1 - 6,2 - 0,2 0,1 0,2 - 0,7 2,6 5,7 20,7 1,2 5,0 4,0 19,8 13,8 3,2
GRUPPO SUN 1,4 - 3,2 2,6 - 2,0 1,2 0,3 - 2,4 9,8 14,4 18,2 27,6 - - - - - - 3,1
AGORA' NETWORK SCARL 2,5 - 13,5 6,1 34,4 0,4 - 0,2 0,2 - - - - - - - - - - - 2,8
GRUPPO PAM 3,7 - 2,7 0,9 0,6 3,1 8,0 1,8 5,4 3,1 - 8,5 0,7 - 0,2 1,4 - - - 3,8 2,7
ASPIAG - - - - 32,4 12,7 29,9 1,8 - - - - - - - - - - - - 2,7
BENNET SPA 8,7 - 1,3 5,2 - 1,2 4,0 1,9 - - - - - - - - - - - - 2,5
SIGMA 0,1 - - 1,1 - 2,8 2,6 3,0 0,3 0,3 7,0 0,8 3,2 6,4 2,8 6,9 5,3 1,6 1,1 5,0 1,8
CRAI 1,6 - 0,3 0,2 - 2,6 2,1 0,5 0,0 - 0,4 1,7 0,7 0,9 2,3 0,2 5,4 3,5 7,5 9,7 1,4
DESPAR SERVIZI - - - 0,6 - - - - - - - 0,0 - - 1,8 7,1 17,6 18,4 6,2 4,3 1,2
TOTAL 95,9 76,0 99,8 94,8 99,1 87,0 94,3 98,5 99,9 92,2 97,4 93,2 92,9 96,6 77,5 91,3 67,9 87,2 86,4 98,0 93,7
CENTER SOUTH AND ISLANDSNORTH - W NORTH - E
Scanner data and spatial comparison: the Italian experience
Scanner data and spatial comparison: the Italian experience
Product overlap across regions: Whole Milk
Outlets have been stratified according to provinces; chains; outlet-types.
Sample size – year 2016
RegionNumber of strata
Number of
outletsPIEMONTE 79 171
VALLE D'AOSTA 4 7LIGURIA 31 74
LOMBARDIA 149 325
TRENTINO-ALTO ADIGE 12 44VENETO 87 181
FRIULI-VENEZIA GIULIA 45 82EMILIA-ROMAGNA 85 190
TOSCANA 66 175UMBRIA 16 39MARCHE 44 93
LAZIO 38 127
ABRUZZO 34 67MOLISE 12 22
CAMPANIA 36 111PUGLIA 34 109
BASILICATA 6 11CALABRIA 27 65
SICILIA 49 148SARDEGNA 34 81
ITALIA 888 2122
NORTH - E
CENTER
TH AND ISLA
NORTH - W
Scanner data and spatial comparison: the Italian experience
Table 2: Gini coefficients by regional chief towns and BHsRegional chief towns Mineral or spring water Personal care products
Household Cleaning and maintenance products
N.Items Gini N.Items Gini N.Items Gini North Aosta 131 0.628 1180 0.539 709 0.468 Torino 254 0.757 2918 0.661 1459 0.604 Genova 83 0.716 1077 0.672 470 0.603 Milano 258 0.797 2930 0.76 1477 0.746 Trento 79 0.542 789 0.587 413 0.508 Venezia 197 0.724 2234 0.638 1216 0.573 Trieste 105 0.593 890 0.506 588 0.518 Bologna 204 0.771 2458 0.67 1189 0.652 Centre Firenze 147 0.827 1387 0.747 669 0.721 Ancona 189 0.727 1814 0.648 1077 0.578 Perugia 188 0.784 1412 0.731 768 0.682 Roma 270 0.752 2428 0.692 1223 0.623 South and Islands L'Aquila 149 0.698 984 0.594 564 0.467 Campobasso 117 0.666 703 0.587 456 0.455 Napoli 175 0.709 1589 0.678 877 0.622 Potenza 89 0.693 470 0.554 307 0.496 Bari 151 0.716 1611 0.673 787 0.595 Catanzaro 66 0.607 602 0.579 335 0.559 Palermo 122 0.678 1390 0.639 758 0.594 Cagliari 136 0.738 1795 0.611 887 0.565
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Scanner data and spatial comparison: the Italian experience
Laureti, and Polidoro- Big data and spatial price comparisons of consumer prices
Product overlap across provinces within a region: Sugar in Calabria
RCPD
Scanner data and spatial comparison: the Italian experience
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018
Laureti, and Polidoro- Big data and spatial price comparisons of consumer prices
Product overlap across regions: Pasta products
Scanner data and spatial comparison: the Italian experience
Fifty Years of International Comparison Program: Achievements and Moving Forward, Beijing 29-30 October, 2018