Item III.2 Frame population EGR frame methodology Barry Coenen, Statistics Netherlands MEETS Conference June 2014

EGR 2.0 Towards an user oriented approach: providing frame populations when needed 1.Introduction EGR 2.Introduction of frame population methodology 3.EGR design: a network of statistical business registers National SBR: authentic store for national entities (enterprises, legal units, relationships) EGR: authentic store for supra national entities (global enterprise group, UCI, relationships)

The EGR is foreseen to become the platform that supports the production of micro based statistics on globalisation in Europe, both on country and European level by offering compilers access to integrated and up-to-date register data on those enterprise groups which have statistically relevant transnational operations (financial and non- financial) in at least 1 of the European countries. EGR The EGR is foreseen to become the platform that supports the production of micro based statistics on globalisation in Europe, both on country and European level by offering compilers access to integrated and up-to-date register data on those enterprise groups which have statistically relevant transnational operations (financial and non- financial) in at least 1 of the European countries.

EGR (2) The EGR will be a central business register kept at Eurostat where -Data from different sources can be processed -Users will have access to the data and will be able to assess and update the data -Users can assess what occured to their population -Users can retrieve the data needed for their national process

EGR (3) Provide data Commercial Data Provider NSI Provide data Process data and create prelimenary population Data Quality Management EGR Create frame population Statistical Activity

Frame population methodology set of rules and procedures for maintenance and common use of populations of statistical units by statistical activities Rules and procedures apply for NSI, NSA and Eurostat Maintenance is aimed at achieving a good quality of the frame population Common use is aimed at using one population frame for all national statistics on globalization in all 31 member states

Some main concepts 1.Master frame population reference period T = data set on population referring to period T to be used by statistical activities 2.Initial and intermediate frame population reference period T = data set on population referring to period T to be used for data quality management and data validation 3.Frame population error procedure = rules and procedures dealing with mistakes in the master frame population

Objectives for coming years 1.Master Outward FATS frame population of reporting units (UCI’s) referring to year T produced and disseminated in April T+1 (or T+4 months) 2. Master FATS frame population of enterprises referring to year T produced and disseminated in March T+2 (or T+14 months)

Reference year TReference year T+1Reference year T+2 Outward FATS population of reporting units FATS population of enterprises Sept year T Initial frame population Feb year T+1 Intermediate frame population Apr year T+1 Master frame population Data quality management ValidationFrame error correction procedure Apr year T Initial frame population Nov year T+1 Intermediate frame population March year T+2 Master frame population Data quality management Validation Frame error correction procedure

EGR 2.0 process reference Year T (1) September year T – February year T+1 1.EGR defines a starting list of UCI’s 2.NSA’s validate list of resident UCI’s (cooperation BR and OFATS) 3.NSI’s select on basis of this list 1.national enterprise groups which are in the OFATS population 2.national enterprise groups which are foreign owned 3.Legal units and relationships 4.Enterprises (SBS) 4.EGR processes data sets 5.NSA’s resolves issues on UCI’s in EGR

EGR 2.0 process reference Year T (2) February year T+1 – April year T+1 1.ESTAT and NSI’s validate UCI’s in EGR 2.ESTAT creates final population frame OFATS and intitial frame IFATS 3.NSI’s define the national survey populations for OFATS April year T+1 – March year T+2 1.NSA’s can use ‘frame error correction procedure’ for correcting reporting units (UCI’s) referring to year T

EGR 2.0 process reference Year T (3) April year T – November year T+1 1.EGR data quality management on legal unit structure 2.NSI’s provide update of population of intra EU enterprises (SBS) 3.NSA’s use ‘frame error procedure’ correcting UCI mistakes November year T+1 – March year T+2 1.ESTAT and NSI’s validate structure of global enterprise groups 2.ESTAT creates final population frame IFATS 3.NSA’s add the ‘country of UCI’ to the SBS frame population year T November year T+1 – March year T+2 1.November NSA’s use ‘frame error correction procedure’ for correcting country of UCI mistakes IFATS

EGR 2.0 process reference Year T (4) General 1.NSA’s can provide ‘live’ updates (EGR offers features to maintain 2 legal unit structures: topical and previous reference year) 2.EGR DQM of NSI’s focuses on: direct cross-border relationships and relationships with UCI 3.Intermediate releases possible but should be limited due to validation procedures needed 4.Maintenance of intra EU enterprises serving intra EU OFATS

ESTAT and NSA challenges 1.ESTAT: getting commitment of NSA’s on the frame population methodology (ESS and ESSnet on Consistency) 2.NSA’s: organising the synchronisation of the different national statistical activities collecting and producing data on globalisation 3.NSA’s: organising the maintenance of the datastores of the national statistical activities with the values of the ‘coordinated characteristics’ 4.NSI’s: DQM on direct cross-border relationships and relationships with UCI 5.EGR: Realisation of a by FATS accepted quality

EGR design a network of statistical business registers NBR EGR Authentic data Non authentic data Resident legal unit Resident relationships Enterprises Resident legal unit Resident relationships Enterprises CDP National statistical processes Ent. GroupCountry of UCI Link Enterprise to country of UCI Ent. GroupCountry of UCI Link Enterprise to country of UCI National statistical processes

Thank you Additional information: Harrie van der Ven, Barry Coenen,