House of Microdata and RDSC at the Deutsche … · (RDSC) at the Deutsche Bundesbank – a Draft Concept ... Statistics Dept cannot answer all possible questions. ... Sub Unit 2:

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House of Microdata (HoM) and Research Data and Service Centre (RDSC) at the Deutsche Bundesbank – a Draft Concept

Workshop on Integrated Management of Micro-Databases,hosted by the Banco de Portugal, Porto, June 20-22, 2013

Ulf von Kalckreuth, Deutsche Bundesbank

A mandate

Mandate for Statistics Dept in medium term strategic planning:

Enhance availability of micro data both for scientific researchand for analytical tasks

Background:

❙ New focus on financial stability – need for granular information by Banking Supervision, the new Financial Stability Dept and the ECB in its new role❙ Bundesbank Research Centre asking for support

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Problems to solve

❙ Data management in Stat Dept and elsewhere not conceived to supportmicro level analysis – micro data are intermediate products in a process ofcreating statistical aggregates.

❙ No well structured data sets ready for analysis

❙ Documentation for the use of externals incomplete or missing

❙ Use of microdata subject to complicated sets of confidentiality rules. Purpose of data use important. Level of confidentiality must besystematically assigned to groups of users

❙ Data are created in different processes which are not integrated – cannotbe analysed simultaneously

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Problems to solve

Working with new micro data extremely time intensive:

→ Data sets underused

→ Certain questions not adressed at all

→ Users liable to misinterprete data – analysis misleading

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Suggestion

Set up a House of Microdata with the following tasks:❙ Compilating, documenting and archiving micro data collections that areinformative for external research and analysis❙ Providing a platform for rapid compilation of microdata for varying purposes

- analytical (extrnal) and cross-validation (internal)❙ Enhancing existing data by record linkage – also based on external data❙ Providing the micro data collections to internal and external users, withinthe legal bounds relevant for each case❙ Methodological and substantial (data content and interrelations) support❙ Analytical work on behalf of internal clients in those cases, where clientseither do not have legal access or where the evaluation by client himself isimpossible due to time constraints or insufficient data expertise❙ Limited amount of own research -- descriptive in nature and focussed on the data provided by the unit

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Suggestion

Create a potential for

❙ Receiving and disseminating data from other Depts and external sources

❙ Exchange of research data within the ESCB

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Suggestion

❙ Statistics Dept cannot answer all possible questions. The production anddissemination of micro data collections fit for analysis of externals is a newstatistical product sui generis: The result of the statistical process is not a set of indicators but an information basis for users to work out their ownresults, answering their own questions following their own criteria.

❙ Evaluations on the behalf of externals is a classical product: Moments andquantiles of distributions are derived from the available granular information

❙ Own research needed to enhance methodological competence andknowledge on data, to communicate to researchers, understand theresearch agenda and being motivated to create highly relevant data

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1) A Research Data and Service Centre (RCDS)

❙ Changing statistical processes takes time and consumes human resources.

Data recording and formats may have to be adapted. Legal constraints have

to be overcome

❙ A first (limited) stage leaves statistical processes as they are

❙ Start providing data where there is experience with scientific usage

❙ Would be constituted as scientific use files, periodically saved (data freeze),

archived and documented.

❙ User rights and limitations, setting up an application procedure

❙ Manual record linkage on the basis of available characteristics

❙ Progressively develop new data collections

❙ Long time lags and limited linkage – in terms of flexibility, timeliness and

simultaneous analysis, this is still very much the old world

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Process databases

Recordlinkages

Analytical data bases


Sub-Unit 1: Experts for the relevant data sets, with the task of setting up and

developing the research data collections, the documentation and the interaction

with users in matters of data content. They would also do evaluations on demand

and a certain amount of own research

Sub Unit 2: Staff for the technical side of the dissemination, process, supporting

❙ Evaluation from a distance

❙ Safe computers „on site“

❙ Provision of de facto or fully anonymised scientific use files

❙ Possibly exchange with other research centers in „safe rooms“

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Sub Unit 3: Record linkage and validation, including the enhancement by

additional characteristics, such as sector information

Substantive areas eg:

❙ Company level information from balance sheets and BoP statistics

❙ Financial institutes

❙ Securities

❙ Survey data (household wealth survey, travel survey, payment survey)

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❙ The RCDS on its own will address some of the urgent needs, while leaving

other problems on the wayside

❙ Will put a new focus on the use and the information content of granular

information

❙ Manual record linking is a crutch – slow and limited

❙ Legal problems for matching data on companies (no access to NSI‘s

company register) and banks (dual purpose of bank data – supervision and

statistics).

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2) Integrated micro data management

❙ In the longer run, statistical processes can be adapted to make possible an

integrated processing of micro data.

❙ The information availably on any observational unit can be addressed

simultaneously and without delay

❙ Data repositories could be centralised or kept decentral

❙ The potential of an RDCD would increase exponentially and record linkage

does not create additional work, as far as internal informaton is concerned

❙ Evaluation and quality control – increasing informational content by cross-

checking – reduction of inconsistencies

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Important ideas from a visit to the Banco de Portugal

❙ Common reference data bases

❙ Introducing a distinct exploration level into the various stat. processes

❙ read-only

❙ Validated

❙ Subset relevant for analysis

❙ Intermediate step may be to make available common identifyers: record

linkage could then be done in a satellite environment (data repositories

within the RCDS)

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BMI reports

ZIS tim e series

„House of microdata “

BASWeboperativ e system

Four level concept within one business area

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Publication

Dissemination

File reception

Micro data fromreporting

New: Analysis

Finalisised dataused for cross

chacking in StatDept and as data

warehouse forexternal analysis

Processing

Data to be validatedand recombined, visible only for the

business unit

Gen

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busi

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unit

Exa

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lanc

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Four level concept between various business areas

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Key features of integrated micro data management

❙ Shared reference information❙ Eg by using an SDMX framework

❙ Overarching four level framework❙ Common model instead of a common platform

❙ Implemented by each business unit locally, respecting specific restrictions

Success factors as seen from the experience in Portugal

❙ „Step By Step“ instead of „Big Bang“❙ Unit by unit, beginning with the most important and the easiest cases

❙ Sequential implementation -- on the basis of existing infrastructure

❙ Integrating only where there is value added❙ Cost benefit analysis

❙ New areas need to follow a common blueprint❙ Overall, a disciplining effect

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Summary

A vision for the House of Microdata:

1. The RDCS, a separate business unit, specialised on the interchange withexternal analytics

2. Integrated microdata management as a joint feature of all businessunits

❙ Common reference data

❙ Harmonised level for data analysis in all business units for information crossing

Many thanks for listening and for discussing!

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Documents

House of Microdata and RDSC at the Deutsche … · (RDSC) at the Deutsche Bundesbank – a Draft Concept ... Statistics Dept cannot answer all possible questions. ... Sub Unit 2: