The Statistical Business Register Israel study visit April 2014 Mr Steen Eiberg Jørgensen Deputy Head of Division, Business Register

2 Our point of departure for registers The role of the SBR – most valuable player Data sources – the wider context Data model Usage of the SBR Organisation and cooperation patterns A fruitful approach Key learning points for Statistics Denmark Outline

Our point of departure for registers 3

The SBR – most valuable player … NSI management: Need for cost-efficiency – first you need to push – then users will start to pull National Accounts: Need for coverage and coherence Survey dept.: Need for “free” update of address books IT dept.: Need for standardisation of systems Methodology dept.: Need for a frame for extraction and optimisation of samples Politicians: Need for reduction of administrative burden and productivity in the public sector 4

Main sources for the SBR 5 Central ABR: the core Local legal units The SBR is a satellite in a wider system of data users/providers Selfregistration via Internet Legal units Danish Business authority CentralCustomsand TaxAdministration StatisticsDenmark DanishWorking EnvironmentAuthority DanishLabourMarket Authority Primary data providersSecondary data providers Central Registerof Persons Official Journalofthe DanishState

Basic data model for SD’s SBR 6 Establishment (LKAU) Legal unit Enterprise VAT unit 1 VAT unit 2 VAT unit 3 Enterprise group Kindofactivity unit (KAU) StatisticalunitsAdministrative units Responsibility: StatisticsDenmark Responsibility: CABR / DCCA Responsibility: CCTA Locallegal unit (LKAU) TypesofunitscomprisedbyStatisticsDenmark’sSBR

Basicdatamodel for SD’s SBR - 2 Legal formESA2010 – sectorcode in SBRActivity (NACE) codeBirthDeathStatistical information Turnover Different employment data 7

Usage of SBR for surveys in Denmark SBR is used in 70 different statistics (structural, short-term, register based, sample based) in 8 divisions / 3 directorates: Either as the statistical business units, or by Adding characteristics to other types of statistical units Approx. 165 recurring extracts each year Approx. 50 pct. of NSI staff are on-line users of SBR and extract system Examples: Expected investments in the manufacturing sector Employment in construction sector Stocks in manufacturing and whole sale Purchases of goods and services in construction sector 8

Usage of SBR in Denmark – examples - 2 Sales of goods in the in the manufacturing sector Consumer credits Financial sector companies Government finance statistics Statistics on utilities Retail trade index 9 Companies’ sales and purchases Inward FATS Business demography General accounts statistics (SBS) Structural employment statistics (obs) Tourism statistics

Patents, design and innovationBusiness services statisticsIT-expenses and IT-investmentsThe public sectors usage of ITPrivate enterprises’ usage of ITHarvest of cereals 10 Usage of SBR in Denmark – examples - 3 Forestry statisticsInternational transport of goodsNational transport of goodsForeign trade in goodsForeign trade in services Labour market stats, incl. wages and labour cost (obs)

SBR organisation – 2 Cooperation between SBR and users: Common house rules SLAs (“what, why, how, who, when”) Forum for problem-solving Cooperation with suppliers: IT (both current work and new projects): Prioritisation, methods, review, test etc. Administrative sources Statistical sources (incl. delegation) 11

Organisation – 3: SLAs 12 WHATWHYHOWWHOWHEN 1. Definition of extract Level of unit, cut-off, variables, fields/codes, format Compliance with requirements and guidelines Statistical divisions n - 1 week 2. Delivery of population extract Extract according to specifications Sample design SBR teamn 3. Selection of sample Definition of method and criteria Ensure representativity Stratification, selection etc. Methodology division n + 3 days 4. Update before sending out questionnaires Maximize unit response Update for the units in the sample SBR teamn + 3 days 5. Feedback of corrections to SBR Update SBROptimize population Statistical division n + 14 days 6. Processing of updates Max. response Correct gross-up Updates made by SBR team SBR teamn + 20 days 7. Prioritization and solving of problem cases Agreement on conflicting cases and “grey zones” Coherence be- tween different statistics Define criteriaSBR and statistician n + 20 days 8. Documentation of extract Ensure coherence and accessibility SBR teamInitially and as needed

A fruitful mindset … 1.The SBR must adapt to surveys – and vice versa … 2.… so problems must be solved together 3.Implementing a SBR is a long process – priorities and milestones 4.Grasp the low-hanging fruits 5.Not everything can be automated – keep it simple 6.Not everything can (or needs to) be checked – trust the sources and prioritize error checking 7.Focus efforts on large and complex enterprises (“90/10” rule) 8.Clear out disagreements about data – and stick to the agreement 9.The register will never be perfect – just like statistics … 10.… but everyone gets more than they give 13