Adaptation of the SBR to changing external conditions S

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Adaptation of the SBR to changing external
conditionsSøren Schiønning Andersen,
Statistics Denmark (ssa_at_dst.dk)
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Outline

• Background and purpose
• The three basic registers - our point of
  departure
• The integration of the SBR with the CABR
• The sources
• Recent challenges
• Pros and cons
• Key differences between SBRs and ABRs
• Overall learning points

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Purpose

• To describe
• The interface and integration between our SBR and
  the main sources, especially the CABR
• Recent challenges to our SBR and how we have
  tried to cope with these challenges
• How these changes have affected our ability to
  fulfil the objectives of the SBR (coverage,
  quality, usage, costs)
• Our main learning points from these challenges

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Our point of departure re registers
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Basic data model for our SBR
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Main sources for the SBR
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Six recent challenges to our SBR

1. Change to a key administrative source
2. Change to the underlying business model
3. Adaptation of the SBR to 1) and 2)
4. Challenges to content from political strong users
5. New supra-national requirements
6. In-house requirements for improved productivity

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Challenge 1 Change to a key source

• Cause
• e-Income from CCTA register replaces old source
• Obligatory monthly employment data at LKAU level
• Effect
• Re-design of process and IT system (1 man-year)
• Improvements to timeliness/frequency, relevance
  and accuracy
• Increased usage of SBR
• Conclusion
• Pro-active communication at an early stage is key
• Data definitions must comply with statistical
  needs
• The CCTA must have self-interest in data quality
• Follow CCTAs project all the way to
  implementation

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Challenge 2 Change to the business model

• Cause
• Web-reg due to new administrative usage of CABR
• Cost reductions in the CABR
• Change in the underlying perception re BRs
• Effect
• LLUs were no longer followed over time
  fundamental change to the business logic
• High quality potential, but also high risks. We
  will see
• So far, it has meant more work for SD
• Conclusion
• Keep statistical concepts and needs on the agenda
  try to give as much as possible in return
• Exploit the advantages and avoid the disadvantages

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Challenge 3 Adaptation of the SBR

• Cause
• Necessary response to challenge 1 and 2
• Effect
• Functionality re LKAUs had to be built-up in SBR
  instead
• Less tight relation with CABR more freedom
• Work processes changed from administrative to
  statistical units
• Extremely costly compared to available resources
• Conclusion
• Keep it (more) simple it is very hard to get
  resources to change very complex systems that
  only benefits SD
• I.e. we need to be more realistic and modest when
  we define requirements

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Challenge 4 Strong political pressure on ABR

• Cause
• Pressure to re-use data in order to reduce burden
• Digitalisation and productivity of the public
  sector
• Data must fulfil more purposes also from strong
  players
• Effect
• Data will have direct effects on data subjects
• The relative weight of statistical needs will
  diminish
• New incentives and sources of errors are
  introduced
• Net quality effects are difficult to assess
• Conclusion
• Monitoring of new initiatives
• Proactive communication and advice to admin.
  users
• The overall system must remain sustainable

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Challenge 5 New supra-national requirements

• Cause
• New EU Regulation with additional requirements
  (EGs)
• Data needs are not covered by the CABR
• No other administrative source (share holder
  register)
• Effect
• We must rely on commercial data
• Data on EGs and MNEs are already in high demand
• Conclusion
• Ensure that new supra-national requirements for
  the SBR are incorporated in a coming
  administrative register
• Avoid parallel systems becoming permanent

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Challenge 6 How to do more with less

• Cause
• Recurring cost reductions in the NSI
• Currently, our SBR do not cover all units in
  agricultural surveys - a separate farm register
  is maintained
• Effect
• Integration of missing agricultural units into
  the SBR
• Actuality and accuracy of data on farms will
  increase
• Coherence will improve
• Conclusion
• The main sources/systems must be exploited to
  maximum extent
• Redundant data and systems should be discontinued
• Traditions and cultures are difficult to change

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Pros and cons summing up
Benefits / advantages Costs / disadvantages
High coverage (depending on requirements for legal registration) High quality (if data are validated by the administrative body and updates are well coordinated) Reduces costs Reduces administrative burden on business Supports frequent statistics (if updated on a current basis) Potentially more timely data (depending on technical set-up and administrative processes) Administrative definitions can deviate from statistical needs and definitions Problems regarding match and consistency Use of different classifications (and different use of the same classifications) Limited possibilities for collecting supplementary data (because of burden considerations and division of responsibility/competence) Reluctance among enterprises regarding data exchange (necessitates assurance of confidentiality and documentation of positive cost/ benefit ratio) Dependency on providers Vulnerable to political and/or administrative changes Potentially less timely data
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Key differences between SBRs and ABRs
Aspect SBR ABR
Purpose Supports production and dissemination of aggregate statistics Supports binding legal or admini-strative decisions about individuals
Content Focus on economic phenomena at institutional and productive level Complex approach to units Focus on legality and responsibility of individual operations (More) simple approach to units
Consequences Neutral - no direct consequences Defines rights and obligations direct consequences
Relation to policies Indirect Direct
Political weight Light intangible benefits The SBR is a bi-product Heavy tangible implications The ABR is the main product
Time perspective Basis for coherence and compara-bility over time complex approach Basis for decisions with effect from date of registration simple approach
Quality philosophy Multi-dimensional approach Data are accurate when equal to a real value which is often unknown Sufficient quality is unclear More simple approach Data are accurate when equal to the self-perception of the unit Sufficient quality is (more) clear
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Key learning points for SD

• In order to better fulfil our role and objectives
  we must
• Manage our partnerships better we are not
  strong enough alone
• Communicate proactively we cannot wait for
  others to contact us
• Always be part of the solution not part of the
  problem
• Manage our risks better otherwise they seem to
  manage us
• Keep things simple balance ambitions with
  abilities

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Thank you for your attention! Any questions or
comments?