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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

mailto:[email protected]

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


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


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



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).



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


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


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


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


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



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


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:




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


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


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



WTiCPD Estimation results: PPPs for regional chief towns (Southern cities)

Pasta products and couscous




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)


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




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



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).



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)


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



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)


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)



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



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



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


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


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)


Shampoo: SPDs and scanner data


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»


High variability of item price across regional capitals within the same retail chain


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)


Average price across retail chains within the same city


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



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


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



Laureti, and Polidoro- Big data and spatial price comparisons of consumer prices

Product overlap across provinces within a region: Sugar in Calabria

RCPD



Laureti, and Polidoro- Big data and spatial price comparisons of consumer prices

Product overlap across regions: Pasta products



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