1. E&I of administrative data used for producing business statistics Vera Costa, Frances Krsinich, Rudi Van der Mescht 2008 UNECE Work Session on Statistical Data Editing

2. Overview Introduction Electronic Card Transaction Data –Background –E&I Longitudinal Business Database –Background –E&I –Future work Questions

3. Introduction E&I methodologies for: –private vs government administrative data –unit record vs aggregated level data

4. Electronic Card Transaction Data Background –Concept –Data and providers –Feasibility project - comparison with Retail Trade Survey estimates –Consultation process

5. Electronic Card Transaction Data (cont.) Background (cont.) –Current situation – series published (actuals, trend and seasonally adjusted) Total Electronic Card Transactions Retail Electronic Card Transactions Core Retail Electronic Card Transactions

6. Electronic Card Transaction Data (cont.) E&I –Data characteristics and implications –Possible issue: incomplete data provided –Problem identification By provider By Statistics NZ –Problem solving Preferred approach – contact the provider Alternative approach – imputation

7. Electronic Card Transaction Data (cont.) E&I (cont.) –Imputation related issues Methodology –Pro-rating –Time series methods »Total forecasted value lower than actual value from other provider(s) »Big merchant changing providers Metadata and system requirements

8. Electronic Card Transaction Data (cont.) E&I (cont.) –Current situation Experimental series published monthly Data quality assessment –Analysis of the data received –Liaison with the providers –Media monitoring

9. Longitudinal Business Database Background –Data and providers Sales and purchases from Goods and Services Tax (GST returns) Financial performance and position variables (IR10 tax forms) Salaries and wages, and employee counts (Pay-As-You-Earn returns – PAYE)

10. Longitudinal Business Database (cont.) Background (cont.) –Data requirements –Approach used: advantages / disadvantages –Flag indicators

11. Longitudinal Business Database (cont.) Background (cont.) –Current situation Data from 2000 to 2005 Around 800,000 enterprises in the database Many related variables – auxiliary information for E&I

12. Longitudinal Business Database (cont.) Data characteristics Editing process

13. Longitudinal Business Database (cont.) Imputation process –Project’s first year Methods used –Mean –Multiple –Donor (nearest neighbour) »Number of variables to be imputed »Donors characteristics – bias introduction »Matching variables – PAYE – data issues Evaluation – simulated ‘missingness’

14. Longitudinal Business Database (cont.) Imputation process (cont.) –Project’s second year Changes introduced –More use of existing data »Interpolation »Carry forward –Data subsetted by “lifespan” –Additional matching variables (IR10 and GST)

15. Longitudinal Business Database (cont.)

16. Future work –Investigation of the most appropriate imputation method – longitudinal admin data –If donor imputation is chosen: Use of donors to impute movements rather than levels More use of past / future values in the longitudinal record Treat large non-respondents separately

17. Questions From the audience? From us: –Imputation methods when aggregated administrative data is received? –Donor imputation for movements?

18. Contacts Vera Costa –Vera.Costa@stats.govt.nzVera.Costa@stats.govt.nz Frances Krsinich –Frances.Krsinich@stats.govt.nzFrances.Krsinich@stats.govt.nz Rudi Van der Mescht –Rudi.VanderMescht@stats.govt.nz