Regional Data Validation

1. Regional Data Validation Expert Group Meeting on Price Statistics and National Accounts: ICP Round 2011 Jointly organized by: UN-ECLAC, CARICOM, CARTAC and ECCB 27th-30th August 2012 Aruba

2. Summary • INTRA country data validation • INTER country data validation • Quaranta Tables • Dikanov Tables • Case Study

3. Data validation procedure • The data validation procedure has two main steps: • INTRA country validation • INTER country validation • Countries and ECLAC participate in this procedure • It is an iterative process

4. Step 2: INTER country validation The main data analysis within inter-country and global validation is carried out by using two validation tables that are • The Quaranta Tables • The Dikhanov Tables The purpose of the both tables is to • Screen the national average prices for possible errors by comparing the average prices for the same product in different countries • Possible errors are highlighted by special indices This analysis can lead to • Editing of price data for an item • Editing of metadata for an item

5. Quanranta Tablesvalidation tables The Quaranta Tables (QTs) consist of a set of tables • Table details • Summary tables for each BH • Individual tables for each item QTs can be used for the validation of • BH PPPs and PLIs • Item XR-ratios and PPP-ratios Different calculation options • EKS, EKS*, CPD, CPRD and W-CPD • Recommended method by the TAG is weighted CPD • However comparison of results for different calculation methods can be used to value given information on importance

6. Table details BH table Item table

7. Table detailsQuaranta tables

8. BH Table detailsQuaranta tables

9. Item table detailsQuaranta tables

10. PPP Price: It is the national average price for a specific product of a specific country deflated by PPP. • This price indicates how many units of currency of the base country are needed to buy the same quantity of a specific product that are bought with one unit of currency of the base country in the base country. • Since PPP are calculated based on all the products that made up the BH, the PPP price indicates how a product behaves in a specific country with respect to other products in the same country.

11. PPP ratio is the PPP price divided by the geometric mean of PPP prices in all the countries. • The value of this ratio indicates how the country behaves in comparison with other countries for a specific product. • That is to say, how the price behaves relative to the other products of the country in the BH and relative to the rest of the countries for the same product. • The average of all the PPP ratios for a specific country is 1 (or 100%) inside a BH, and at the same time, the average of PPP ratios for all the countries for a specific product is 100%

12. Suggested critical values: • CV larger than 33% • Ratios outside (80% - 125%) range. • With this tool one can detect problems with the quality of data such as: • High price variation for a certain product in each country. • Average prices which are too high (or low) in nominal terms compared to the price in other countries when converted with exchange rate. • Average prices which are too high (or low) in real terms compared to the price in other countries when converted with PPP • Behavior of average prices inside a BH. For instance when the prices for the majority of the products in a specific BH for a specific country are below (above) the regional average and for a certain product the price is above (below) regional average.

13. Dikhanov Tablesvalidation tables As the QTs, the Dikhanov Tables (DTs) consist of a set of tables • Table details • Summary table for each BH • Individual tables or rows for each item DTs can be used for the validation of • Aggregated (above BH) and BH PPPs and PLIs • Item XR-Ratios and CPD(R) residuals Different lay-out options including color scheme • Aggregate/BH tables + full item statistics • Aggregate of BH tables + simple residual rows for item Different calculation options • CPD, CPRD, CPD-W

14. DT can be used as a substitute or complement of QT in order to detect potential problems with the data. • QT analyse one BH at a time. • For certain products it is difficult to detect outliers with QT (biased averages due to lack of countries collecting data for a specific product) • The main difference between QT and DT is that the analysis in DT does not consider data grouped by BH but it considers them individually and simultaneously. • This makes easier the analyses for those products which are the only representative of a BH or when there are a few products in a BH.

15. OverviewDikhanov tables Table details BH table Item table

16. Table detailsDikhanovtables

17. BH detailsDikhanovtables

18. Item detailsDikhanovtables

19. Relation betweenQuaranta and Dikhanov Tables Both tables provides essentially the same information, but • Dikhanov Tables can be compiled for a group of BHs (an aggregate), they use color schemes to highlight potential outliers and can be collapsed to present only residuals for items • Quaranta tables presents the relations within an item potentially clearer Relation between PPP-Ratios and CPD residuals • CPD residuals are equal to the logarithms of CUP-ratios PPP-Ratios 0 to 14 14 to 47 47 to 78 78 to 128 128 to 212 212 to 739 739 to Less than -2.0 -2.0 to -0.75 -0.75 to -0.25 -0.25 to 0.25 0.25 to 0.75 0.75 to 2.0 More than 2.0 CPD residuals

20. Case Study • We will analyze a Quaranta Table and highlight the problems encountered. • We will follow the instructions provided in this presentation.

21. THANK YOU