Evaluating the benefits of using VAT data to improve the efficiency of editing in a multivariate annual business survey Daniel Lewis

Overview Background Creating predictors Using predictors to improve current edit rules Using predictors to improve selective editing Conclusions

Background ONS desire to improve efficiency of business statistics through greater use of admin data Investigate possibility of improving editing by incorporating admin data Use VAT data to improve editing for Annual Business Survey (ABS) Create predictors for key ABS variables based on VAT Turnover and Expenditure data Use predictors to test possible improvements to current ABS micro editing and new selective editing approach

Creating predictors Created predictors for 8 key variables Turnover, Purchases, Total taxes, Opening and Closing stocks, Net Capex, GVA, Employment costs Used multivariate linear regression to predict 8 variables based on VAT Turnover and Expenditure Additional explanatory variables from business register – Industry, Size and Region Tried combining with logistic regression to deal with zeros Developed models using previous period data and applied coefficients to current VAT data

Using predictors in current ABS editing Range of edit rules currently used for ABS, defined by industrial sector – Catering, Production Mostly based on relationships between variables Developed new rules based on VAT predictors: Fine-tuned x to get similar failures to current rules and evaluated effect on estimated bias Also tested similar rules using previous survey response as predicted value

Results for current micro editing In both sectors it is possible to greatly improve edit rule efficiency for all variables except Net Capex For Catering sector, similar improvements are possible using previous response instead of VAT data For Production sector, using VAT predictors leads to much greater improvements than simply using previous response

Using predictors in selective editing for ABS Currently investigating Selekt (Stats Sweden) selective editing method for ABS Try to improve study results for Catering and Production sectors using VAT predictors Selekt selective editing score: Try using VAT predictors as expected values in Impact part of score and test variables in Suspicion In place of previous value where available, class median where not

Results for selective editing For Catering, the VAT predictors perform less well than previous survey values in terms of estimated bias introduced (for same level of savings) For Production, it was possible to achieve the same results using VAT predictors rather than previous values, but there was no improvement May be possible to use VAT data to improve Selekt implementation in some other way as our understanding of the software deepens

Conclusions It is possible to make large improvements to current ABS micro edits by re-defining the rules For one sector tested, it is necessary to use VAT data to get the biggest benefits We have not been able to improve the application of Selekt to ABS using the predictors, but there may still be a role in Selekt for VAT data Results are for two out of seven ABS sectors – improvements may be possible in other sectors