1. facebook.com/statisticssweden @SCB_nyheter The Swedish experience with scanner data From sampling to index calculation Paulina Jonéus

2. COICOP  COICOP divisions where we use mainly scanner data 01 (daily necessities excl. perishable fruits, vegetables and meat sold in department stores, supermarkets and hypermarkets) 02.2 (tobacco) 02.1 (alcoholic beverages) 06.1 (medicines)

3. COICOP  COICOP divisions where a combination of scanner data and traditional price collection is used 05.5 (lamps and batteries) 05.6 (household cleaning products) 09.3 (food for domestic animals, soil and nutrient for plants) 12.1 (personal hygiene products)

4. The data  Former monopoly of pharmacies Reporting to the Ministry of Finance Scanner data since 2010  Monopoly of liquor stores A government owned chain of liquor stores in Sweden Index based on scanner data. From 2016, scanner data to Statistics Sweden

5. The data  Three chains of daily necessities Account for more than 80 % of the consumer market Sample from two categories: supermarkets /smaller markets and hypermarkets Scanner data since december 2011  From five sources we get 19 percent of CPI

6. The Swedish approach  Replace the manually collected price data with scanner data for the sample of outlets and products. Annual savings 80 000 EUR per year for daily necessities A cheap way to increase (more than double) sample sizes Possible to manage substitutions in the ever changing market Quantity adjustments Homogeneous product groups

7. The Swedish approach – sampling of outlets  A stratified, sequential Poisson sampling method Stratum description Industry (NACE) Sample size Net Collection method Hypermarkets, broad assortment471117Scanner data Hypermarkets, broad assortment471119Visit Supermarkets with broad assortment4711252Scanner data Supermarkets with broad assortment4711232Visit Tobacconists472605Telephone Health food shops472915Telephone Pharmacies477309Visit Pet shops477623Telephone

8. The Swedish approach – sampling of outlets  Covering the three chains proportionally to their market shares  For scanner data from the supermarkets and hypermarkets a sample of some 60 outlets is used  For pharmacies and the liquor stores, data is used for all outlets

9. The Swedish approach – sampling of products  Since 2001, SCB produces Food Sales in the trade, based on scanner data Annual data show total annual sales values per article (GTIN) during last calendar year A comprehensive mechanical coding of the different articles into statistical product groups  The register is used as sampling frame for product sampling for the CPI

10. The Swedish approach – sampling of products  Automatic coding is made independently for all retail chains, using nomenclatures of each retail chains  A SAS script searches for parts of product names to improve the automatic coding  The files with preliminary codes for the chains are joined by GTIN, making comparisons of product group code possible.

11. The Swedish approach – sampling of products  Within these groups three annual samples of 800 very narrowly defined products are selected  A sequential Pareto πps selection within strata  About 2000 unique products  Some 100 000 price observations from scanner data are used each month

12. The Swedish approach – sampling of products  Sampling frame is based on annual sales from t-2  Updated each year with information from late autumn market analysis No over coverage. Disappearing products are removed. Possible under coverage. Sometimes hard to find replacement products.

13. The Swedish approach – temporal sampling  Scanner data is received for all weeks on a weekly basis. For the supermarkets the data for the full middle three weeks is used  The monthly average price is calculated as a quantity weighted arithmetic average  For the pharmacies an (quantity weighted) average price is calculated using all data from the 25 th of last month to the 24 th the current month

14. The Swedish approach – the production month  1 st stage: Initiating a production month  2 nd stage: Product life analysis  3 rd stage: Checking of the scanner data set  4 th stage: Select the data for the product-offer sample  5 th stage: Aggregate prices over three weeks  6 th stage: Send data to the CPI production system

15. Elementary indices  The quantity weighted arithmetic average of weekly average prices is computed in a special SAS-based module  Elementary indices is then calculated in the production system, as for all other product groups, by Jevons index

16. Summary  A reasonable amount of data at GTIN level  Take into account differences between outlets  Narrowly defined products  Quantity adjustments  Manage quality changes  Can be used in combination with manually collected prices

17. Questions?