1. The application of selective editing to the ONS Monthly Business Survey Emma Hooper Office for National Statistics emma.hooper@ons.gsi.gov.uk

2. Overview 1.Editing at the ONS 2.Monthly Business Survey (MBS) 3.Application of selective editing to MBS 4.Quality indicators 5.Implementation and post-implementation

3. Editing at the ONS 2008 project reviewed editing processes for Office for National Statistics (ONS) business surveys New selective editing methodology for ONS short-term business surveys Mix of selective editing and traditional manual micro editing was previously used

4. Surveys using selective editing Tested and implementing selective editing for the Retail Sales Inquiry –methodology developed with assistance from Pedro Silva (University of Southampton) MBS selected as second survey to test and implement selective editing on Currently investigating using Selekt for Annual Business Inquiry

5. Monthly Business Survey Launched in January 2010, it brings together existing short-term surveys that cover different sectors of the economy Old selective editing methodology used edit rules, those units that failed an edit rule would have a selective editing score calculated New selective editing methodology to run on live MBS data from summer 2010

6. Editing processes for MBS 1. Edit rule checks 2. Automatic editing 3. Selective editing 4. Macro editing Check records with unit score greater than threshold Check aggregated data Check £000s error and components Check for valid dates

7. Selective editing for MBS Target units that have significant effect on key estimates by domain (input/output group) if not edited Calculate item score for each unit and key variable –turnover, export turnover, new orders (monthly) and total employment (quarterly) Predictor for true value –previous edited value (else use register value for turnover or employment, or pseudo-imputed value for export turnover or new orders)

8. Item score

9. Unit score Combine item scores into single unit score using average of item scores Units ranked according to their unit score –if score for a unit is above threshold then that units responses are sent for manual editing –units with scores below threshold are not manually checked

10. Thresholds Thresholds set for each key domain to reduce editing costs without impacting quality Quality indicators used to compare thresholds 41 periods of data used, should ensure robustness of results

11. Absolute relative bias Absolute relative bias aims to control the residual bias left in the domain estimates after editing

12. Savings Savings measure the change in the number of units that will be manually micro edited

13. Absolute relative bias

14. Savings

15. Quality indicators Aimed to keep ARB below 1%, ARB levels showed large improvement compared to bias left after current micro editing Overall savings in the number of units being edited of around 40% in non-employment months Overall savings of 55% (MPI sectors) and 15% (MIDSS sectors) in employment months

16. Current edit rule method

17. New selective editing method

18. Implementation and limitations Selective editing is carried out via a module in the in-house built Common Software system The module is currently –restricted to 5 item scores –restricted to combining the item scores as a mean or maximum –restricted to only using variables already available in the system for use in calculating predicted values –not able to use current edit rules to calculate an edit-related score

19. Following implementation Need to monitor the thresholds, ideally through editing a small sample of those that aren’t being selectively edited This would enable us to estimate the bias left in the estimates and adjust the thresholds accordingly Continue testing these methods for other ONS business surveys, more efficient editing will result in a better quality editing process