1. 1 The use of scanner data on non-food products Pia.Ronnevik@ssb.no Statistics Norway

2. 2 SD use in the Division of price statistics 1997 2001 2005 HICP/CPI: First contact with grocery chains SD from all grocery chains in HICP/CPI “Full” scale use of SD in HICP/CPI for pharmaceutical products First use of SD from grocery chains in PPP work PPP food survey fully based on SD 2003 2009 SD from the first pharmacy chain in HICP/CPI 2010 SD from three pharmacy chains in HICP/CPI 2012 “Full” scale use of SD in HICP/CPI for Food 2013 New calculation method at elementary level for Food SD from four petrol chains in HICP/CPI 2005 2013 SD from the first petrol chain in HICP/CPI 2003

3. SD on non-food products Non-food products are included in these COICOP groups: - 0213: Beer - 0220: Tobacco - 0454: Solid fuels - 0531: Major household appliances whether electric or not - 0540: Glassware, tableware and household utensils - 0552: Small tools and miscellaneous accessories - 0561: Non-durable household goods - 0721: Spare parts and accessories for personal transport equipment - 0722: Fuels and lubricants for personal transport equipment - 0931: Games, toys and hobbies - 0933: Gardens, plants and flowers - 0934: Pets and related products - 0952: Newspapers and periodicals - 0954: Stationery and drawing materials - 1111: Restaurants, cafes - 1112: Canteens - 1213: Other appliances, articles and products for personal care SD on non-food products is received from: - grocery and kiosk chains - petrol chains

4. Current production system of SD on non-food products Statistics Norway have build an application to deal with non-food products: –One GTIN matched to one representative item –If a GTIN is missing, then it’s replaced. –An automatically suggest of a replacement according to product group and turnover. –If the replaced GTIN is of different quality, indirect quality adjustment is made. Weakness: –If turnover is twisted towards other GTINs, re-coding/re-matching should be done. –Resource demanding to follow changing turnover figures. –If we don’t control the matching over time we may follow “unrepresentative” GTINs. Statistics Norway want to use more of the SD on non-food products.

5. ENVA-classification (EAN Norges Varegruppestandard ) GS1 Norway has developed their own classification system called ENVA (not brick): - Gives information about which product group an GTIN belongs to. - Takes into account whether the product is fresh, canned or frozen. - Is applied only by the grocery chains. Advantages with ENVA-classification: - Secure a mutual understanding about product groups. - Makes it possible to compare groups from different chains.

6. ENVA-classification Statistics Norway doesn’t receive ENVA-classification from all the chains: - The biggest chain since 2001. - One chain started in May 2015. - The last chain will be able during 2016. Statistics Norway wants to utilize the ENVA-classification to define non- food products at our unofficial COICOP-6 level. - Mostly a direct link between ENVA-classification and COICOP. - Try to reduce the use of text searches.

7. ENVA-classification

11. The new production system for SD on non-food products An automatically routine that connect the GTIN with COICOP-6 groups for non-food products each month. - Mostly directly linking between new GTIN and COICOP through the ENVA-classification. (grocery chains) - Directly inking between new GTIN and COICOP through chains’ own classification. (petrol chains) - Text searches in some COICOP groups. - Manual checks of the automatically linking. Make a basket of non-food products in December each year. Calculates unweighted Jevons indices and the percentage changes from basis month, for COICOP-6 groups. If the replaced GTIN is of different quality, indirect quality adjustment must be made. If no replacement is done, then impute missing prices.

12. The challenge with non-food products In some COICOP groups we have different data sources: - Scanner data - Questionnaires filled out by the stores (- Web scraping) How to combine different sources? - One index for each data source? How to weight these indices together? - Turnover? - Other sources?