Role of editing and imputation in integration of sources for structural business statistics Svein Gåsemyr, Statistics Norway Svein Nordbotten, University of Bergen

Contents of paper Data sources for business statistics Methods for integration of sources Use of standard models for processes The need to measure quality of linked files Work to be done

Interaction of sources and modules The statistical business register The database to coordinate samples The micro file of statistical surveys The database of available data The menu of data editing imputation and estimation

Data sources to be integrated for business statistics by ISEE

Administrative business registers that are affiliated to the Legal Unit Register Employer Register Business Enterprise Register Value Add Tax Register Tax register of business enterprises and self-employed

Enterprises and establishment of manufacturing industry Complex enterprises1086 –Establishments1 768 Single enterprises

Establishment by sources A. Census survey2 096 B. In sample and selected1 251 C. In sample and not selected1 092 D. Small complex establishment 319 E. Establishment excluded

Methods for integration Linkage at unit level Editing of a single source and linked files Estimation by mass imputation

Process errors in integrated records Errors due to incomplete registers Linking errors Observation errors Errors made during editing Imputation errors

Units identification in 2 registers Units in register 1:Not existi ng Incorrectly existing Existing with incorrect ID Existing with correct ID Sum Units in register 2: Not existing in register Incorrectly existing Existing with incorrect ID Existing correct Sum

Statistical quality indicators Process accuracy – Statistical accuracy Quality of administrative data A single source A linked file of 2 sources A linked file of a large number of sources A small evaluation sample Deviation between processes data value and the correct value

Work to be done Improve the system for unit identification Develop the standard processing system Evaluation by controlled experiments Designing suitable evaluation samples Use of process data for process design