ESTP course on LCUs

1. Statistics NL Methods and Practices ESTP course on LCUs March 20th 2018 Cyrille Pluijmen

2. Outline • March 19th: NL contact with MNE’s • March 20th: Methods and practices for ensuring the consistency

3. Structure of presentations March 19th March 20th MNE Info. / requests / Q LCU Statistics Netherlands Requests / questions

4. Content • History and Organization of LCU • Positioning • Population and Staffing • Process • Consistency Work, Tooling & Lessons learned • Consultation with End users • Balancing requests • Accountability • Wishes/ future plans • Conclusion

5. Traditional statistical chains Prod-com Int. Trade Etc SBS STS SFEG Inv Observe Analyse Publish Consistency work National Accounts

6. Traditional activities • Statistical departments verified signals; looked at results of EG’s on main variables • Problem detection was not focussed on solutions but on year t+1 data collection • Lots of explanations, very few improved data; National Accounts still got a lot of inconsistencies

7. Why a dedicated LCU ? Dual-purpose: • Provide consistent data (March 20th) • Provide additional information • On submitted financial statistics • On the way an MNE functions • Bonus: Identify problems (data gathering, manuals, etc.)

8. Positioning LCU LCU

9. Positioning LCU (2) Dutch design of the LCU • LCU stands on the sidelines of the traditional process • No need to turn your organization upside down • Cost-effective way to introduce an LCU • Important to let your colleagues participate when considering to introduce an LCU

10. Population and Staffing LCU • LCU population represents ± 50% of NL GDP • 360 Enterprise Groups; ± 2.000 Enterprises, ± 11.000 Legal Entities • Staffing: 30 FTE; • 6 account managers; account management and consistency work (post graduates in accountancy/controlling) • 4 profilers; profiling and info sharing, preparation of data collection (bachelor level)

11. Population and Staffing LCU (2) • 30 FTE; • 6 data cleaners; SBS/STS/R&D/Prodcom • 7 data cleaners; EG Balance sheet statistic • 2 specialists ITG; referencing ITG VAT data to EG data • 2 IT specialists • 2 project leaders • 1 annual reports analyst

12. A continuous process t, t-1 t t, t-1 t-1, t-2

13. Administr. sources MNE & level of detail Highly consistent, low detail Enterprise group Enterprise Legal entity Tax information –often no end to end reconciliation Very low consistency, high detail

14. Nature of inconsistencies A person with a clockknows the time, A person with more clocks is never sure. Godfried Bomans (Dutch writer)

15. Consistency work Consistency process in a nutshell: • Analyze structure; legal vs enterprises (profile if necessary) • Analyze data (primary sources) • Check secondary sources (if available) • Contact person at the enterprise group (if necessary) • Contact internal clients (if necessary) • Adjust source statistic or explain • Inform contact person at enterprise group (if applicable)

16. Consistency work (2) • After the foundation of the LCU: In collaboration with National Accounts in total 80 consistency rules were set up and programmed in an automated system  Congo-tool • The tool provides a confrontation of comparable variables from the various statistics/sources • Congo-tool is an essential supporting tool during consistency work phase

17. Consistency work (3) • One goal LCU: Consistent data for 15 variables from 11 different statistics/sources • Structural business Statistics (SBS) • Short-term statistics (STS) • International trade in services (ITGS) • International trade in goods (ITSS) • Production of industrial goods (Prodcom) • Statistic Large Enterprises • Research & Development (R&D) • Investment in fixed assets • Wage/Employee statistics • Tax (VAT, Corporate income)

18. Tooling • Consistency tool to detect inconsistencies • Tool is daily updated with data from the production systems for source statistics • Rules at Enterprise Group (EG) and Enterprise (ET) level

19. Tooling (2) • Consistency matrix on EG-level • Inconsistencies are solved or explained • Data are modified directly in the production systems • Conclusions are documented in tool

20. Example consistency rule On enterprise level: Revenue SBS should be equal to the sum of the periodic STS enquiry for one year

21. How it looks like in the Consistency tool Nace code inclsize class Enterprise_ID + Enterprise_name STS > SBS because of VAT Included in STS, but not in SBS forretail companies Inconsistencywhichcanbeexplained

22. Lessons learned • Consistency rules were evaluated over time • Overload  Loss of focus • Based on experience and staffing of the LCU number of rules and margins had to be adjusted • Now: More focus on things that do matter (after consultation with our end users)

23. Lessons learned (2) Shift in focus: • More focus on inconsistencies regarding: • Operating profit SBS vs. Statistic Large Enterprises • Export/Import SBS vs. ITGS/ITSS • Wages/Employees SBS vs. Tax info • Investment Statistic vs. Statistic Large Enterprises • Rules in Congo-tool are supportive: Analysis and background is added value of the account manager

24. Duration consistency process • Average consistency work of a MNE no smooth process • A practical example; duration 5 months • 5th July 2017: start working (sufficient data) • 7th July 2017: questions asked • 31st August 2017: first set of answers (valuation differences, capex) • 3rd November 2017: second set of answers (R&D) • 16th November 2017: third set of answers (ITGS) • Internal and external hurdles to overcome; timing of actions essential, requires coordination

25. Peer review • Evaluation of work by colleagues • Good way to check quality of work in the LCU setting Similar to health care / accountancy organizations • MNE’s for review can be submitted, but also random selections of MNE’s with label ‘finished’ • This way the LCU maintains standards of quality and improves performance  Self-regulation and learning curve

26. Consultation with end users • Consultation is an ongoing process • Growing awareness that a good alignment with end users is crucial • Continuous process: searching for best way to transfer our knowledge of the MNE to the end users

27. Consultation with end users (2) • During time, a shift from informing end users towards a data-driven approach • Explaining inconsistencies is not enough, it’s also necessary to adjust the source data when possible  a better fit with the working procedure of our end users • Maximizing added value of the LCU

28. Consultation with end users (3) • Monthly meetings with colleagues of National Accounts (i.e. end users) to discuss consistency issues from daily practice • Each account manager submits cases • Solving specific ‘inconsistencies’ • Provide insight into our activities • Agree on working standards

29. Balancing requests • Besides consistency work many requests from other departments addressed to the LCU (e.g. new enquiry)  link with the ‘outside’ world • Timing of different surveys (Partly a consequence of Eurostat regulations, timing differences) • Challenge: Stay in control over all the activities; Prioritize various requests

30. Balancing requests (2) Current LCU-issue: • Setting up a detailed timesheet  Gain better insight into which activities and which MNE’s take most of our time • The results of this information may lead to a change of the working procedures • Goal: work more effectively

31. Accountability • Congo-tool used for analysis & keeping record • Audit trail • Year end: Internal organization obtains a report per MNE • Detailed information regarding: • Changes in structure (profiling) • Major differences with t-1 • Adjusted data • Inconsistencies not be solved (input for t+1) • Inconsistencies due to different definitions • Important background information

32. Wishes/future plans • More flexibility on data collection • Timing: Better coordination with the MNE’s in advance (example of continuous interaction between yesterday’s and today’s topics) • Integration: Integrating different business surveys • Closer cooperation with the enforcement department • Creating a web portal per MNE

33. Conclusion • Contacts with MNE’s & Methods and Practices  Intertwined subjects Actions on one part have consequences for the other • Not identifying these aspects  a recipe for failure • End goal: gather quality data and gather background information on the functioning of an MNE

34. Questions? Thank you for your attention!