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Business Case
ESS.VIP.BUS.ADMIN
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Business Case
ESS.VIP.BUS.ADMIN
Sorina Vâju
John Allen
Sorina Vâju
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TABLE OF CONTENTS
1 PROJECT INITIATION REQUEST INFORMATION ....................................................................................4
2 CONTEXT .............................................................................................................................................5
2.1 Situation Description and Urgency ....................................................................................................5
2.2 Situation Impact ................................................................................................................................6
2.2.1 Impact on Processes and the Organization ................................................................................6
2.2.2 Impact on Stakeholders and Users .............................................................................................9
2.3 Interrelations and Interdependencies ...............................................................................................9
3 EXPECTED OUTCOMES .......................................................................................................................11
4 POSSIBLE ALTERNATIVES AND IMPACT ASSESSMENT: .......................................................................14
4.1 Alternative A: Do nothing ................................................................................................................14
4.2 Alternative B: ...................................................................................................................................14
4.3 Alternative C: implement the project as described in this business case. ......................................15
5 SOLUTION DESCRIPTION....................................................................................................................18
5.1 Legal Basis ........................................................................................................................................18
5.2 Benefits ............................................................................................................................................18
5.3 Success Criteria ................................................................................................................................20
5.4 Scope ...............................................................................................................................................20
5.5 Deliverables .....................................................................................................................................22
5.6 Assumptions ....................................................................................................................................31
5.7 Constraints.......................................................................................................................................31
5.8 Risks analysis ...................................................................................................................................32
5.9 Implementation ...............................................................................................................................34
5.10 Roadmap: ........................................................................................................................................35
5.11 Synergies and Interdependencies ...................................................................................................36
6 GOVERNANCE....................................................................................................................................38
6.1 Project Owner (PO) ..........................................................................................................................38
6.2 Solution provider (SP) ......................................................................................................................38
6.3 Approving Authority ........................................................................................................................38
6.4 Annexes ...........................................................................................................................................38
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1
PROJECT INITIATION REQUEST INFORMATION
Project Title:
Administrative data sources
Initiator:
Bettina Knauth
Date of Request:
04.09.2012
Type of Delivery:
☐In-house
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DG / Unit:
F1
Target Delivery Date:
☐Outsourced
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☒Mix
31.12.2017 (initial
target)
31.12.2019 (new
target)
☐ Not-known
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2
2.1
CONTEXT
Situation Description and Urgency
Statistical authorities are under pressure to produce data faster and at lower cost, to become more
responsive to users´ demands, while at the same time providing high quality output. One way to
fulfil this is to make more use of already available data sources, and in particular administrative
sources. Almost all Member States have been moving or are intending to move towards an increased
use of administrative data sources for statistical purposes as a substitution and/or as a complement
to information previously collected by surveys. This move is driven mainly by the need to reduce the
cost of data collection, to reduce burden on respondents and more generally to collect data only
once and use it for multiple purposes afterwards. Administrative data sources are particularly well
suited in those cases where the need for data is permanent, as the usage of administrative data
sources requires initial investments that then pay off in the continuous use of the data source.
The importance of using administrative sources in order to reduce costs and burden has been
recognised at the highest level. It was already one of the main elements of the Vision for European
Statistics1.The increased use of administrative data is also included among the five key areas of the
new ESS Vision 2020, namely new data sources: "We base our statistical products and services on
both traditional surveys and newer sources, including administrative data, geospatial and where
possible big data. New data sources complement the existing ones and help us to improve the quality
of our products. We will work together to get access to new data sources, create methods and find
suitable technology in order to use new data sources in producing European statistics in a reliable
way"2.
Eurostat actively supports this move already with the proposed revision of Regulation 223/2009 on
European Statistics, in particular through provisions aiming at facilitating the usability of
administrative records for the purposes of European statistics. However, the use of administrative
data poses multiple challenges:
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Access to data and confidentiality issues. Administrative data sources exist, however they are
not always easily accessible. NSIs need to build relationship with the data owners and develop
agreements for data provision. In some Member States national legislation is an obstacle to
using administrative data sources for statistical purposes.
Sustainability and independence of statistics. The data sources need to be stable in time in order
to guarantee some continuity of the output. Too much reliance of external data sources can put
statistical independence in danger.
Quality issues. An increased use of administrative data sources also implies the risk of impacting
negatively on several quality dimensions, in particular accuracy and comparability.
Integration of sources. An additional challenge stems from the fact that in many cases, reliance
on a single administrative data source would not be enough. Data from a variety of sources
(administrative and other) would usually need to be integrated in order to produce statistics.
Surveys and administrative sources have both particular strengths and weaknesses. Combining
them may overcome these weaknesses, provided that suitable methodology and tools are used.
European comparability. There is a lot of variety in the national administrations (depending on
legal aspects, institutional culture, and public sentiment regarding the use of data for statistical
1
Communication on the production method of EU statistics: a vision for the next decade COM(2009) 404.
2
The ESS Vision 2020.
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purposes). Producing European data on the basis of different national administrative systems is
very challenging in terms of comparability.
So far there have been several initiatives launched on the use of administrative data, most notably in
the context of the MEETS Programme3 where there was an ESSnet4 project specifically dealing with
this subject in the domain of business and trade statistics. At domain level that are various initiatives
on improving the use of administrative data. For example, the development of health status
statistics based on diagnosis-specific morbidity addresses the link of information on morbidity from
different administrative sources. There are also major country experiences, such as during the
implementation of 2011 last census exercise. Generic and harmonized ways in which administrative
data can be used efficiently in statistical production while ensuring high quality statistical outputs
should be sought. So far, there is no major project that brings together all the relevant initiatives in
order to find generic solutions both for Eurostat and for the ESS, or a common approach to
managing quality issues related to the use of administrative data in statistical production. The need
to act urgently in a coordinated way through an ESS project is based on the following arguments:

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
2.2
Not making use of data already available would mean missing the opportunity to decrease the
burden on respondents and the cost of statistical production.
Irrespectively of the existence of an EU initiative, Member States are already making greater use
of administrative sources. In the absence of coordinated actions, the large diversity of national
administrations as well as the variety of processing methods used may put in danger the quality
of national and European statistics.
The credibility of the ESS is at stake if greater reliance on administrative sources impacts
negatively the quality of statistics.
Eurostat may need to discourage greater flexibility in data production at Member State level if
Eurostat can no longer assess how this affects the different quality dimensions of European
statistics.
If Member States invest separately in national solutions, duplication of effort will arise in the
ESS.
It is important to ensure the transfer of knowledge from the more advanced Member States to
those that are currently exploring how to integrate more administrative data in their statistical
production. This project will provide the mechanisms for this transfer of knowledge.
Situation Impact
2.2.1 Impact on Processes and the Organization
A precondition for using administrative data is having access to them. Difficulties with access
prevent Member States from tapping the full potential of the existing administrative data sources.
Access depends on the legal environment (statistical laws, data protection laws, domain specific laws
that regulate the functioning of public administrations and of the administrative registers), but also
on the institutional environment, the national traditions of cooperation between public authorities
and the public acceptance regarding data access. Currently, statistical authorities are in very
different situations regarding the right to access and use administrative data: from easy and free of
charge access to heavy restrictions making access or use very cumbersome or expensive.
Even where the legal environment is favourable, the laws may set the right of access in quite general
terms; therefore their interpretation at the implementation phase can make a difference. Access can
3
Modernisation of European Enterprise and Trade Statistics' (MEETS Programme)
http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/MEETS_programme.
4
ESSnet Admin Data: http://www.cros-portal.eu/content/use-administrative-and-accounts-data-businessstatistics.
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be denied/made difficult by other administrations that do not cooperate well. In the opposite
situation, supportive public administrations allow the NSI to take full advantage of the law.
Therefore, the relationships with the owners of administrative data are crucial. When there is good
cooperation with the owners of the administrative sources, the statisticians can contribute to the
development/ changes of the registers or can make sure that some quality checks are already done
before the data reaches the NSI.
An increased use of administrative data sources entails the risk of impacting negatively on the
output quality. Of particular concern are the following quality dimensions:




The relevance of statistics might decrease, if administrative concepts replace statistical
concepts.
Depending on the type and quality of administrative sources, the accuracy of the statistics can
be affected positively or negatively. Different factors come into play and they should be taken
into account. While information from administrative data sources can be less impacted by
memory effects or effects of social desirability, the accuracy of statistics might also decrease, in
particular, when the owner of the administrative data source has little incentive for recording
and updating of some information or the incentive for registration are not good.
While administrative data sources can have the benefit of producing timelier results (e.g.
register-based censuses report results considerably earlier than traditional censuses), more
commonly the effect on the timeliness of statistics is negative, in particular, if the administrative
timetable is different from the statistical timetable (it is frequently the case for fiscal
information).
The comparability of statistics risks to be reduced considerably, whenever administrative
concepts differ from statistical concepts. Such conceptual discrepancy could reduce the
comparability of the data at national level and EU level.
It is often the case that administrative data need to be transformed before being used as input for
official statistics. Most of these transformations are related explicitly or implicitly to the use of
estimation methods. For example, one can quote data linkage and matching, data classification, data
crossing, data forecast, or data modelling techniques as possible statistical methods that enable the
use of administrative sources. The use of these methods is both a solution addressing the
deficiencies of the administrative data (different concepts, classifications, lack of timeliness etc.) and
a source of variation in the quality of the statistical output. Furthermore, processing the data
requires work on the correct data format, IT infrastructure and other technical issues. Making sure
that the right methods and tools are available and correctly applied will bring great benefits when
integrating administrative sources in statistical production. Typically, administrative variables can be
useful in more than one domain (for example, variables built from social security data can be used in
social statistics and in business statistics as a replacement for a number of survey variables or for
new outputs. Statistical offices are organised in divisions that work relatively independently. There is
a need to reach beyond the traditional approach and work together on common issues. Results from
previous projects (e.g. ESSnet Data Integration5 and ESSnet Data Warehousing6) show that methods
for integrating data in statistical production have to be applied differently when data sources
(administrative and/or survey data) are combined and re-used for several outputs.
When several sources are used in statistical production, it is not easy to quantify or describe output
quality in a simple way, as there are many ways of integrating the various sources. Both Member
States and Eurostat should be able to assess the quality of these new possible sources before
integrating them in the statistical production system, as well as the quality of the final statistical
output. Quality assessment, both at input and output level, is necessary to certify that European
5
ESSnet Data Integration http://www.cros-portal.eu/content/data-integration-finished
6
ESSnet Data Warehouse http://www.cros-portal.eu/content/data-warehouse
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Union official statistics are of sufficient quality and fit for their intended use, in particular in terms of
comparability and accuracy. However, the current set of ESS quality indicators is not always
applicable or feasible for assessing the quality of multiple source statistics. Most current indicators
are survey-oriented, while the particular quality issues typical to integrating administrative sources
in statistical production are not fully accounted for. The multiple-source integration is a new
statistical paradigm that needs to be reflected in quality standards and quality reporting and
assessment.
Frames are a particular but very important usage of administrative data where the focus is limited to
identification variables (PIN, name, address) plus a very small number of substantive variables like
Sex and Age. Whereas business statistics are based on a well-established business register, statistics
on individual and households suffer from inconsistent frames across statistical domains and a lack of
methodology to assess their quality. The Wiesbaden Memorandum acknowledged the need for highquality population frames (´social statistics should be based on reliable and up-to-date frames on
individuals and dwellings'7). The availability of correct population frames are a condition to the
implementation and evaluation of many European policies. There are some particular challenges
related to frames for social statistics that can be tackled in this project:
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Specific quality issues for frames. Good coverage8 and controlled authenticity9 are key to the
quality of any output of social statistics. A particular challenge is the assessment of cross-border
coverage problems, in particular avoiding the double enumeration of persons with bonds in
different countries. The impact of over-counts, under-counts and biases due to imperfect frames
can have a strong negative impact on the output. At the moment there is no methodology to
assess this impact. Moreover, Eurostat lacks an evidence based policy on record linkages used
for setting up frames. Up to now, Eurostat is not in a position to assess, in a transparent and
agreed manner, if a frame that has been generated by means of record linkage is of sufficient
quality in view of producing European social statistics.
Revisions. Substantial revisions of much of the output of social statistics are usually made in
order to ensure consistency with the newly available decennial census data. As these breaks in
the series are not due to any substantial development, they are annoying to the user. In intercensus periods reliability would increase if key data on the population obtained by samples
surveys could be compared with substantially equivalent data from administrative sources.
Sustainability of frames and the continuous census. Revising and improving the quality of
frames is costly and burdensome. It includes coverage assessments, possibly including mapping
activities, field checks and post-enumeration surveys, the clean-up of registers, assessment of
data on population groups that are hard to enumerate etc. To achieve economies of scale,
periodic reviews of statistical frames used in the data collections of social statistics (and the
registers on which they are built) and censuses should become two sides of the same coin. From
an organisational viewpoint, the continuity of frames' revision has the potential to flatten the
peaks in the workload while at the same time increasing the timeliness of the output.
Increasing importance of population geo-referencing. Statistical data are mostly reported for
administrative areas (with all their inherent diversity) while the causes of social and
environmental phenomena do not follow the delineations of administrative areas. However, in
many Member States geo-referenced data are not yet available on a permanent basis. The main
7
Wiesbaden Memorandum on a new conceptual design for household and social statistics (adopted by the
DGINS conference on 28 September 2011), point 5a.
8
Coverage assessment means the study of the difference between a specified target population and its
officially enumerated population.
9
Authenticity assessment means the study of the extent to which published data is based on real (neither the
whole data record nor the specific information are imputed) and valid (data record is neither conferring wrong
information nor the result of over-coverage) observations.
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problem is a lack of sustainability. Many Member States do not yet produce grid datasets from
data sources that are updated continuously and allow the production of grid datasets at short
intervals. Instead, the availability of such data is often determined by the decennial rhythm of
the population census.
2.2.2 Impact on Stakeholders and Users
The current situation affects negatively the following stakeholders:

Respondents. If the potential of the existing data sources is not fully used, the burden on
respondents is higher than necessary.
Taxpayers. Continuing to produce data on the basis of expensive surveys is costly for the
taxpayers.
National statistical authorities that are moving towards a more extensive use of administrative
sources in the statistical production face obstacles related to access to data, lack of proper tools,
methodological know how, lack of quality etc. In the current situation, many statistical
authorities have a weak position vis-à-vis the owners of administrative registers. A European
initiative could strengthen their case. Any advances in producing European statistics will spill
over into national statistics.
The administrative data holders. It is in their interest that their data is used. Although
administrative data holders collect the data for their own needs which are not necessarily
aligned with those of the NSIs, it is accepted as a rule of thumb that the overall quality of the
data will increase when there are more users and when more feed-back is give on the data and
the data structure. Moreover, the statistics produced for free by somebody else (the NSI) and
using the knowhow that the data owner lacks will most likely be of interest to the data owner
too (e.g. detailed high-quality unemployment trends for the unemployment agency).
Eurostat is confronted with the problem of not being able to assess the quality of the data
produced in different national environments through a combination of sources and methods. As
statistical production is moving towards a multisource integration paradigm, there is a real
danger that the quality of European data decreases in the absence of a commonly agreed quality
reporting methodology.
ESS. Generally, the reputation of the whole ESS will depend on how well it can integrate
administrative data sources: It might suffer if the quality of final data decreases due to an
increased reliance on administrative sources, but can also benefit if the usage of administrative
data sources increases the quality and responsiveness of the output.
Data users. On the one hand, users will suffer if the move towards using more administrative
sources impacts negatively on output quality. On the other hand, users will welcome the use of
new sources if this provides them with additional relevant information.
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2.3
Interrelations and Interdependencies
The project will build on a number of existing projects that have already addressed some of the
issues described:

10
Within social statistics, important efforts to tap previously unused administrative sources for the
purpose of producing statistics have been undertaken in several Member States in relation to
the 2011 census round. 15 Member States (AT, BE, CZ, DK, EE, FI, DE, IT, LV, LT, NL, PL, SI, ES, SE)
and all EFTA countries have used administrative registers at least partly in their census
operation. Eurostat has prepared a framework for quality reporting10 that takes into account this
development and that addresses quality reporting in a multi-source environment.
Regulation (EU) No 1151/ 2010.
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
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Within business statistics, the situation of a growing interest in the use of administrative data
sources and the respective quality concerns is similar. As a result, several work packages of the
ESSnet 'Use of administrative and accounts data in business statistics' (Admin Data)11 aimed at
supporting Member States in the use of administrative data sources (e.g. checklist to investigate
the usefulness of administrative data, checklist for the quality of administrative data inputs,
methods of estimation for business statistics variables which cannot be obtained from
administrative data, timeliness of administrative data for short term statistics, integrating data
from different sources in the production of business statistics, development of quality indicators
etc.). Additionally, the ESSnet 'Methodology for modern business statistics' (Memobust)12, which
was part of the MEETS programme, offered an overview of the methodology for business
statistics, covering all business process steps. Some parts of the work are specific for business
statistics (e.g. establishing statistical units, dealing with skewed distributions, dealing with
accounting data, the statistical business register), whereas other parts can easily be extended
beyond business statistics.
Within agriculture statistics the use of administrative registers is quite extended due to the
highly regulated character of this activity. Specific in this domain is the fact that many registers
are set up by EU legislation. Examples are the Integrated Administration and Control System
(IACS13 –system used for the single payment of subsidies under the Common Agricultural Policy),
the bovine register and the vineyard register. Taking into account their "European" design, these
registers are suitable for developing European actions aiming at their use for statistical
purposes. A task force aimed at the better use of IACS has been set up with participation of DG
AGRI, Eurostat and several national statistical offices. Within the fishery domain, DG MARE
collects data on fish catches for control purposes. These data are being analysed by Eurostat in
order to see to what extent they cover the statistical needs in this area.
In health status statistics, EU wide morbidity statistics are missing as a basis for assessing the
efficiency of health systems in view of health outcome. Pilot projects in 16 Member States on
morbidity statistics14 indicated the needs for linking information from different sources in order
to reach comparable best estimates. By 2017 results of national morbidity inventories in
Member States will form the basis for an analysis of how to overcome barriers for access and
linkage of information for going ahead at EU level.
As regards general methodology, the ESSnet "Data integration"15 has provided some
methodological work that is important for the integration of administrative sources in official
statistics by addressing issues such as deterministic and probabilistic record linking and micro
integration.
The research project BLUE-ETS in the area of business and trade statistics contains several
subtasks relevant to the use of administrative sources, especially concerning quality issues.
Several Member States are working on generic quality frameworks for register-based statistics:
Following the development for the purpose of the register-based census of a quality monitoring
system for statistics based on administrative data, Statistics Austria is currently investigating the
possibility of applying this framework also to other domains of statistics. Following progress
11
ESSnet Admin Data: http://www.cros-portal.eu/content/use-administrative-and-accounts-data-businessstatistics.
12
Handbook on Methodology of Modern Business Statistics (Memobust) http://www.crosportal.eu/content/memobust.
13
14
15
IACS (Integrated Administration and System) http://ec.europa.eu/agriculture/directsupport/iacs/index_en.htm.
http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_code=KS-TC-14003
ESSnet Data Integration http://www.cros-portal.eu/content/data-integration-finished
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
made on an approach to derive indicators for chains of statistical production processes (in the
framework of BLUE-ETS), Statistics Norway and Denmark Statistics are cooperating in order to
apply general quality indicators to the register based social statistics in Denmark.
The Statistical Network16 is running a project (MIAD-Methods for Integrated use of
Administrative Data) which aims to develop a framework for measuring quality when
administrative data is integrated with statistical surveys.
Given the important area-specific efforts that have taken place, the time seems to be ripe to bring
the various strands together, to draw on the results achieved and to turn the developments into
concrete actions for the ESS.
3
EXPECTED OUTCOMES
The project has a dual purpose: to support the EU Member States to reap the benefits (decrease
costs and burden, increase of data availability) of using administrative data sources for the
production of official statistics, and to guarantee the quality of the output produced using
administrative sources, in particular the comparability of the statistics required for European
purposes. The results of the work on the ESS. VIP.BUS ADMIN should also be put at the service of ongoing major projects such as the modernisation of social statistics or FRIBS. The work will be
organised in seven work packages (WP), whose expected high-level outcomes are described below.
The deliverables are further detailed in Chapter 5.5 Deliverables.
WP1. Access to and development of administrative data sources
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collect information on the legal/institutional obstacles that Member States face in accessing
data, group Member States according to their administrative data environment;
share best practices for cooperating with data owners;
improve communication and cooperation with data owners;
assess the need for targeted EU legislative/political actions regarding access to administrative
sources and the co-operation between their owners and statistical authorities and prepare these
actions.
WP2. Statistical methods
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take stock of the existing knowledge on relevant estimation methods for the integration of
administrative sources in statistical production;
provide up-to-date guidelines based on expert consensus on the use of estimation methods
when dealing with administrative sources;
share knowledge and experiences existing in the Member States;
promote and develop a horizontal approach applicable at individual statistical domain.
WP3. Quality measures for statistics using administrative data
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16
take stock of the existing knowledge on quality assessment and reporting and review it critically;
provide up-to-date guidelines based on expert consensus on quality assessment in statistical
production (input and output);
develop new indicators for the quality of the output based on multiple sources and a
methodology for reporting on the quality of output;
produce recommendations for updating the ESS Standard and the ESS Handbook for Quality
Reports.
The Statistical Network is a collaboration between several statistical institutes in order to improve the
management of statistical information
http://www1.unece.org/stat/platform/display/msis/Statistical+Network
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WP4. Eurostat as a (in)direct user of administrative data sources held or designed by the European
Commission
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investigate whether some data collections held by the Commission or by other institutions (but
based on EU legislation) can be used directly by Eurostat, therefore obviating the need that
Member States report twice;
investigate how administrative data sources based on EU legislation can be exploited by Eurostat
directly, or indirectly though the statistical authorities in EU Member States;
address issues arising from a direct or indirect use by Eurostat of administrative data (quality,
concepts and definitions, data confidentiality and security, accessibility of the sources by ESTAT,
working arrangements, continuity of the source, influence on the source, design of the
databases to make them suitable for statistical use, harmonising data flows, long-term impacts
etc.).
WP5. Frames for social statistics
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create a methodological framework for the assessment of the quality of frames, including
assessment of the coverage and authenticity of frames and data and the impact of record
linkage;
produce indicators relating to the quality of frames themselves and the data whose production
is supported by the frames;
draft a proposal for minimum quality requirements for sampling frames for EU social statistics;
create a monitoring system the keeps track of the kind and development of frames used in the
Member States for the production of European social statistics (including information on the
derivation of the frames from base registers; base registers on persons and buildings (dwellings)
available in the Member States, information on the statistical units, identifying variables,
identifiers, and other variables contained in the base registers, national legislation that impacts
on setting up, maintaining and using frames for the production of European social statistics);
propose a set of comparable metadata that Member States would have to provide on frames
used for EU social statistics for publication at the European level;
explore synergies between different kind of frames. The work shall focus on frames used in EU
social statistics (base registers on persons and buildings/dwellings and derived frames) because
the situation for frames on businesses is quite different as there are established business
registers. Synergies between the two areas shall be explored nonetheless;
bring out the different advantages and situations of implementation of frames on persons and
dwellings;
examine the relationship between permanent frames on persons and dwellings and multiannual census activities;
enable a follow-up to the project, e.g. improved capacity building, enhancement of established
registers and frames, and the creation or re-design of frames that shall be sustainable and of
high quality.
WP6 Pilot studies and applications
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run pilot studies applying the approaches developed in work packages 1, 2, 3 and 5 in various
statistical domains and for various administrative registers (e.g. tax register, population register,
social security registers) in several Member States;
facilitate the integration of new administrative sources in statistical production, or their use for
different purposes;
Provide case studies for work packages 2 and 3.
WP7 Centre of excellence on administrative data

ensure centralisation and continuity of knowledge as regards the use of administrative data in
statistical production;
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



provide training, coaching, and consultancy;
provide methodological help and assistance to Member States on the use of administrative
sources;
contribute to the identification of possible pilot studies and draw lessons from them;
collaborate with other related projects (projects listed in Section 5.11 Synergies and
Interdependencies).
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4
POSSIBLE ALTERNATIVES AND IMPACT ASSESSMENT:
The following alternatives are possible: A) Do nothing; B) Implement only some of the work
packages; C) Implement the project as described in the business case. Eurostat conducted a formal
impact assessment applying the methodology developed by Eurostat Task Force Impact assessment
of ESS.VIPs in order to assess the merits of the third alternative. The full details can be checked in
Annex 2.
4.1
Alternative A: Do nothing
This option means no ESS coordinated action on administrative sources. This does not exclude small
Eurostat initiatives in specific statistical domains.
SWOT Analysis
Strengths
Weaknesses
a. No direct costs
b. No additional effort requested from Eurostat
and the Member States
c. No need to reach a compromise on the best
approach
d. No need to change the institutional culture.
e. No need to gain the support of
stakeholders/financers
a. No improvement of the quality of statistics
b. No agreement on how to assess the quality
of the data that use administrative sources
c. No respondent burden reduction
d. No cost reduction of statistics
e. No improved harmonisation of the
methodology
e. No transfer of knowledge between Member
States
Opportunities
a. ESS could pursue other interesting projects
with the resources requested by ADMIN
b. Independence of statistics from outside
parties
c. Long time series based on the same
methodology
Threats
a. Member States will develop their diverging
approaches to using administrative data,
resulting in lack of harmonisation and
duplication of efforts
b. Missed opportunity to improve relationship
with data providers
c. The quality of European statistics may suffer
if Member States use more administrative data
in an uncoordinated way
d. Reputation of European statistics may be
damaged by the lack of uniform quality
Qualitative Assessment
This alternative is always possible; however it means forgoing the benefits that the project would
bring (see the analysis of alternative C).
4.2
Alternative B:
General Description
This alternative is to implement only some of the work packages sequentially. This would imply
taking a separate decision on each work package. This is a middle point between alternative A (Do
nothing) and alternative C (Implement the project as such) The SWOT analysis will show that that
the impact will be some kind of average of the impact of alternatives A and C. To avoid repetition
only the new factors that stem from a separate implementation of the work packages are mentioned
below.
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SWOT Analysis
Strengths
Weaknesses
a. The possibility to decide on each work
package individually
b. The possibility to reconsider and redesign
some work packages at a later stage
c. The possibility to spread investment over a
longer period
a. Duplication of work when different work
packages are added at a later stage
b. Higher total costs due to duplication and
overhead
c. Delivery of results will be substantially
delayed
Opportunities
Threats
a. Possibility to copy and develop further some
solutions provided outside the ESS if
international actors work meanwhile on similar
issues
a. Less priority given to the project at strategic
level
b. Heavy governance (each new task would
need to be approved separately)
c. No possibility to exploit the synergies
between work packages
Qualitative Assessment
This alternative will result in higher total costs, longer delays and more heterogeneity between
Member States. Therefore it is not the preferred option.
4.3
Alternative C: implement the project as described in this business case.
General Description
This alternative is the suggested solution, namely implementing the work packages as detailed in this
document. Eurostat prepared the SWOT table below together with the Members of the Preparatory
Group in order to assess the impact of the project.
SWOT Analysis
Strengths
Weaknesses
a. Saving of resources
b. Increased harmonisation
c. Enhanced flexibility (
d. Increased quality of statistical outputs across
the six dimensions of quality
e. Better availability or comparability of
metadata
f. Improved methodology and skills
a. Costs (of development, transition, support
and maintenance)
b. Lack of agility/flexibility of the new system
c. Heterogeneity of the starting point between
countries and partners
d. Duplication of work
e. Risk of low acceptability of the project
f. Risk of low acceptance of cultural change
Opportunities
a. Reduced burden on respondents
b. Enhance standardisation (also outside the
ESS)
c. Better communication with users and
d. New statistical products and services, and
more detail for the existing products
e. Increased visibility of NSIs and better
cooperation with other national authorities
(ONAs)
f. The ability to cope with financial and other
constraints
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Threats
a. Lack of consistency with national policies
(including legal framework)
b. Lack of support from stakeholders/financers
(including budget cuts
c. Lack of suitable skill profiles
d. Statistical independence
e. Data protection, security threat and any
judicial risks
f. Threat to availability, continuity or quality of
statistical
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A questionnaire was built on the basis of the tables and sent to Member States. The respondents
were requested, for each item, to assess the
o Relevance : Scores are from 1 to 4, where 1 is minimum Relevance and 4 is
maximum Relevance,
o Impact: Scores are from 0 to 3, where 0 is no Impact and 3 is maximum Impact;
The total score for each item was obtained by multiplying Relevance*Impact. 21 replies were
received. The average score for each item is represented in the chart below.
Image 1. Average scores (21 replies)
The graphical analysis shows that strengths outweigh weaknesses while opportunities are slightly
higher than threats. The most important strength of the project seems to be related improved
methodology and skills (score 6.9). The other strengths are in a moderate range (score between 4
and 5.8). The most important weaknesses are the cost of development (6.7) and the heterogeneity
between countries (6.2). The other weaknesses have moderate to low scores (between 2.9 and 4.7).
Opportunities are moderate (scores between 4.4 and 7), the most important ones being the
increased visibility of the NSIs (score 7) and the reduction of burden on respondents (6.6). As regards
the threats, the threat to availability, continuity or quality of statistical series (7.4) and the lack of
support from stakeholders stand out , while all the others have moderate scores (between 4 and
4.9).
The more detailed analysis of the individual answers showed that the Member States can be
grouped according to similar patterns of assessment.
1. Strong positive impact: BG, IE, ES, LT, PL, RO, CH. This group includes the countries where
most of the strengths and opportunities received scores in the medium and high range
(above 4 points) and largely offset the negative scores (weaknesses and threats).
2. Low positive impact: SE, NO. The overall impact is positive; however scores are low on most
items.
3. Balanced positive impact: BE, CY, HU, AT, PT, SK. In this groups, most items received a
medium score; with strengths and opportunities slightly offsetting on average weaknesses
and threats.
4. Mostly negative impact: DE, EE, LV, FI, SI. For this group, weaknesses and threats are much
larger than strengths and opportunities. Significant weaknesses and threats are highlighted
by this group, in particular the heterogeneity of the starting point between countries and
partners (9), the cost (7), the lack of agility of the new system (6.4), the threat to availability,
continuity or quality of statistical series (9.2), the lack of support from stakeholders (7) and
the lack of consistency with national policies (6.2).
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5. Almost no impact except for cost: NL. This group contains only one country with an
exceptional pattern. The project is deemed to have almost no impact (either positive or
negative), except for costs which receive the maximum score.
Eurostat has also produced an assessment of the project. On the whole, the project has a positive
global assessment (strengths have higher assessments than weaknesses and opportunities overcome
threats). Most strengths and opportunities are in the low to moderate range, while the threats and
weaknesses of the project are low. Image 2 presents the assessment of Eurostat.
Image 2 Assessment of Eurostat
The most important strengths from the point of view of Eurostat are the increased quality of outputs
(score 5.4) and the enhanced flexibility (score 4). Regarding weaknesses, the most relevant one
seems to be the heterogeneity between countries and partners. (3.8). The most important
opportunities are enhanced standardisation (score 5) and the increased visibility of the NSIs and
better cooperation with ONAs (4.6). Threats are considered low. The highest score was obtained by
the lack of consistency with national policies and the threat to statistical independence (both at 3.2).
Qualitative Assessment
The impact assessment shows that on the whole the project will have a positive impact at ESS level.
The following conclusions can be drawn:
 Member States have different opinions on the impact of the project. Therefore tailoring
solutions to groups of Member States facing similar issues can be very useful. This approach
is already included in the business case.
 As they do not have the same concerns, Member States can maximise their benefits by
participating in the work packages that are of most interest to them. The project does not
request all Member States to be involved.
 The concerns of the Member States that were most negative about the project (group 4)
could be alleviated by aiming at differentiated solutions for several groups of countries and
by paying special attention to the issues related to the quality and continuity of the series, as
well as the national context and policies.
 Some Member States have little room for benefiting directly from the project due to the fact
that they are already very advanced and use extensively administrative sources (e.g. NL, FI).
However, the involvement of these Member States is very important for the ESS in order to
ensure the transfer of knowledge and an increased level of harmonisation. These Member
States could be involved as advisors or “service providers” to the others. This possibility is
foreseen in the project (mainly through the Centre of Excellence).
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5
5.1
SOLUTION DESCRIPTION
Legal Basis
The use of administrative sources for the production of statistics is supported by EU legislation.
Regulation (EC) No 223/2009 on European statistics gives the NSIs and Eurostat the right to access
administrative data sources to the extent that they are necessary for the production of European
statistics. The purpose is to reduce the burden on respondents (Art 24).
Commission Decision on Eurostat (2012/504/EU) gives Eurostat the right to access administrative
data held by other Commission services and to integrate these data with statistics. Moreover,
Eurostat may be involved the design, development and discontinuation of these sources, and has the
right to propose standardisation activities concerning these sources. The use of administrative
sources is subject to EU confidentiality rules.
The proposal for amending the Regulation on European statistics (on which compromise has been
reached recently by the Council and the European Parliament) aims to reinforce the rights of the
NSIs to use administrative data. The rights proposed for the NSI in relation to their corresponding
national administrations are similar to those that Eurostat has in relation to the other Commission
services: access to data necessary for European statistics, involvement in the design, development
and discontinuation of the sources, coordination of standardisation activities. The proposal also
specifies that access to data should be free of charge, and that NSIs and national administrations
should build cooperation mechanisms.
5.2
Benefits
The project will address explicitly central goals of the Vision 2020, in particular in regard to new data
sources (‘We will exploit the potential of new data sources’; ‘We will establish alliances and
partnerships with data owners’; ‘We will consider organisational challenges in harnessing new data
sources’), quality of European statistics (‘We will enhance our quality management with quality
assurance tools that are fit for purpose’; ‘We need to assess the usability and quality of data
sources’; ‘We will promote the quality of our statistics based on sound methodology and effective
quality assurance mechanisms’), efficient and robust statistical processes (‘We will further intensify
the collaborative partnership of the ESS’; ‘We will use common methods and tools’) and cooperation
with stakeholders (‘Our strategic alliances with both public and private partners will help to respond
flexibly to users’ needs‘).
It is the main aim of the project to enable Member States to replace statistical surveys to a greater
extent with administrative data without compromising on the desired level of output quality. This
would translate into the following general benefits for the ESS:
 Reduction of costs. Statistical surveys are very costly, particularly as regards the fieldwork
operations. The statistical use of administrative data can be considerably less costly. As an
example, the last traditional census in Austria (2001) cost 72 million euro, while the first
completely register-based census (2011) cost 10 million euro to the NSI (excluding external costs
related to the maintenance of the administrative data source). While the initial costs in setting
up a production of statistical data based on administrative data sources can be considerable, the
running costs can be much lower than the respective costs of surveys, in particular when data
sources remain stable. Therefore, the extent of the savings depends on the range and quality of
the administrative data sources, as well as on their stability.
 Reduction of burden on respondents. All national statistical authorities are under pressure to
reduce burden on respondents. A stronger reliance on administrative data would reduce
considerably the burden on respondents. To quote again the example of the Austrian census,
the original burden for each citizen to fill in a questionnaire has been reduced to zero.
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


Management of field force. Field force management is an important concern for many national
statistical institutes. A stronger reliance on administrative data sources would limit the
necessary field force, thus facilitating their management (and reducing the respective costs).
Output at a low level of geographical detail / on small sub-populations without additional cost.
Given that administrative data sources (in principle) enumerate their target population
completely, the output can be produced on small geographical areas / on small sub-populations
without additional cost. In addition, this would cancel out the need for methodological and
computational efforts requested for small area estimation in surveys.
More output and more frequent output with very limited additional cost. Once a data collection
based on administrative sources has been set up, the marginal cost of additional extractions is
relatively low. In particular, this could lead to more frequent output with a marginal cost
increase.
These are well-known benefits of using administrative sources in statistical production. The
contribution of the project consists in supporting Member States to make these benefits come true.
A European project would specifically mean:
 Improved access to administrative sources. European push would facilitate the access to data
and metadata, and would improve the co-operation between the owners of the source and the
statistical office. It is particularly important to act now, when many NSIs are trying to build closer
relations with the administrative data source owners. This is a one-off opportunity to create a
long-standing cooperation between public administrations which is backed by a European policy.
 Efficiency. The methodological solutions would need to be developed only once and would in
principle be reusable for all statistical domains, which would reduce the cost of development.
 Standardisation. The application of common building blocks would foster a standardisation of
processes across Member States and fit into the upcoming Enterprise Architecture.
 Quality of statistics. The European nature of the project would ensure that sufficient attention is
devoted to methods addressing the issue of comparability. This constitutes a major challenge
from the point of view of European statistics both for Eurostat and for NSIs interested in
producing relevant international comparisons. Consequently, Eurostat would be in a position to
accept the resulting statistics as compliant with quality requirements.
 More flexibility in data collection. If Eurostat is not in a position to assess the quality of the
statistical output based on administrative sources in the long run, it might have to resist the
application of some results of statistical modernisation that become available in the Member
States in the production of European statistics. The benefits of the project are therefore broadly
identical to the benefits of enabling Member States to use multiple sources for the production of
European statistics. These benefits are hard to quantify, but they will have a strong positive
impact enabling the implementation of the Vision 2020.
 Less burden and cost on NSIs. The project also includes a work package which entails Eurostat
producing statistics on the basis of administrative data already collected by other Commission
services. If Eurostat is able to produce some data directly by using Commission administrative
records, there is no need for Member States to continue to produce the same data for European
purposes.
 Better frames impacting on social statistics as a whole.
 More credibility of the ESS due to better quality of the statistical output.
 Better relationships with data providers.
 Dynamics for change. Joining forces in a common European project would be able to generate
sufficient momentum for change to help those NSIs that otherwise face considerable resistance
nationally.
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5.3
Success Criteria
The success can be defined at two levels: immediate success of obtaining good quality deliverables
inside the foreseen time frame and budget restrictions, and longer run success indicated by the wide
use of its results impacting the statistical production process in the ESS.
By its nature, the project aims at long-term changes through the adoption of its results. The
indicators of attainment of the objectives will be evidence of progress towards:







the use of new indicators for assessing quality of statistical output based on multiple sources;
wider knowledge-sharing on using administrative data for statistical production;
better public acceptance of the need to use administrative sources in statistical production;
stronger cooperation of the statistical authorities with the administrative data owners;
improved quality of data;
increased used of administrative data in the ESS;
lower costs of statistical production.
As regards some short run indicators of success, the following are proposed:










implementation of the best practices regarding the relation with the data providers by at least 5
Member States;
guidelines on the relation with the data providers endorsed by a majority of Member States;
framework for assessing quality of statistical output based on multiple sources endorsed by a
majority of Member States;
successful application of the framework for assessing quality of statistical output based on
multiple sources in at least one statistical domain;
endorsement of the guidelines for integrating administrative sources in statistical production by
a majority of the Member States;
endorsement of the guidelines for integrating administrative sources in statistical production by
at least 5 Member States;
theoretical developments on quality and statistical methods implemented through at least 5
pilot studies or applications;
replacement of some fisheries data sent by the Member States to Eurostat by data collected by
DG MARE;
identification of at least 2 other statistical domains where data can be collected by Eurostat from
other Commission Directorates;
assistance with dealing with administrative data provided by the Centre of Excellence to at least
5 Member States.
It is important to note that some tasks in the project have an innovative aspect (e.g. research tasks
related to the production of quality indicators). For such type of tasks, there is no guarantee that a
meaningful result can be found. It can also be the case that a theoretical solution is found but it is
too complicated / costly to be applied in practice. In this sense, the project should be "allowed to
fail". As regards the pilot projects and applications, it can happen that the investigation of some
particular administrative sources proves that they cannot be used for producing statistical output of
sufficient quality. This is an important learning in itself and should not be considered an indicator of
lack of success.
5.4
Scope
The scope of the project covers all business processes of data collection and production in official
statistics that could use administrative data sources, i.e. all processes of translating raw data from
administrative sources into high quality and efficient statistical output. This includes both outputs
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produced using a combination of survey data and administrative sources and outputs based only on
administrative data (single or multiple sources).
The project will only focus on the integration of administrative data sources17 in statistical
production and will not extended to the broader issues related to ‘Big Data’18.
The project combines a theoretical dimension (integration and dissemination of statistical
methodology, quality indicators) and a practical dimension aiming to tangible results of the
theoretical work through pilot studies and applications. It is outside the scope of the project to press
Member States into changing their statistical production systems. The outputs of the project will be
adopted individually though pilots/application projects, or in a coordinated ESS manner through the
decision of the appropriate governance bodies.
The project aims to produce outcomes generally applicable or easily adaptable across Member
States or groups of Member States. It can be the case that some solutions cannot be applied to all
Member States due to the heterogeneity of their administrative environment. Therefore the project
foresees the grouping of countries according to the similarity of the national environments and aims
to offer differentiated solutions for these groups.
The project is a cross-cutting initiative. It will work on generic approaches which in principle should
be usable in various statistical domains. The project will include several pilots in different areas of
statistics that aim to develop and test general or easily generalizable solutions. The only work
package that is specific to a statistical area is WP5 Frames for social statistics. This package will also
explore the relationship between different frames, particularly on frames on persons and dwellings.
Synergies with frames for business will be explored; however the package will not be centred on
business frames. There reason for restricting the scope of this package on frames is twofold. First, it
is felt that statistics on individual and households are lagging behind business statistics in terms of
relying on consistent frames across domains. Second, the currently running ESS.VIP.BUS ESBRs
project concerns the business registers directly, therefore tackling the same issue would lead to
duplication of efforts.
The terms social statistics (focus on the domain of interest) and statistics on individuals and
households (focus on the statistical units) are used interchangeably in this document, although their
meaning is slightly different. It is important to clarify that, as there are statistics that belong to the
social domains from the point of view of the content but are collected through business surveys (e.g.
Job Vacancy statistics, Labour Cost Index, Labour Cost Survey, Structure of Earnings Survey, and
Continuous Vocational Training Survey). The latter type of statistics is outside the scope of Work
Package 5 on frames for social statistics.
The project excludes work on IT issues. There are several ESS.VIP projects that aim to develop the
infrastructure for accessing and sharing data and services, namely ESDEN, Data Warehouse, DARA
and SERV, therefore the risk of duplication would be too high. Moreover, some consensus on
methodology is needed before considering what the best IT infrastructure is.
All work packages should pay particular attention to data privacy issues. There is an increasing
concern amongst citizens, households and businesses about the use of personal data stemming from
administrative registers and large databases. At the same time, data disclosure is a serious risk when
17
A data holding containing information collected and maintained for the purpose of implementing one or
more administrative regulations. A wider definition of administrative sources is used in the Eurostat "Business
Registers Recommendations Manual": a data holding containing information which is not primarily collected
for statistical purposes. Part of the Metadata Common Vocabulary (MCV), as published in SDMX ContentOriented Guidelines (COG), annex 4 "Metadata Common Vocabulary", in 2009 (http://www.sdmx.org)
18
For an excellent overview of the comparative features of administrative data and Big Data, see Florescu et al.
"Will Big Data transform official statistics?" Q 2014 Conference.
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bringing together data from different sources with the aim to enriching individual registers and
gathering additional variables.
5.5
Deliverables
Depending on the progress of the project, work packages or tasks may be prioritised, added, deleted
or modified, subject to the decision of the Steering Committee.
All results will be made available on the CROS portal.
Work package 1: Access to and development of administrative data sources
The Vision 2020 highlights the importance of cooperation with stakeholders and of developing
strategic alliances with private and public partners (key area "identifying user needs and
cooperation with stakeholders"). This work package targets the cooperation with public and private
partners that own, collect, process or store administrative data.


Task 1.1 "Report on the legal and institutional environment in Member States" The task is to
investigate the legal/institutional obstacles that NSIs meet in practice when accessing and using
administrative data. The report will focus on issues like the legal rights of the NSIs and the laws
governing the functioning of the most important administrative counterparties, national
statistical laws, data protection legislation, situations of conflict between EU Statistical Law
(Regulation 223/2009) and the national legislation.
o
The situation can be different for access to/use of macro or microdata, as well as for data
related to business or individual/household units.
o
The investigation will inquire both the access to administrative sources and the right to use
the data for various purposes and link it.
o
The investigation will aim to compare the situation as it is in EU Member States with the
desired model articulated in the European Statistics Code of Practice and will use the related
self-assessment questionnaire of the ESS Peer reviews as a further information source.
o
The report should classify the countries in groups having similar legal and institutional
environments in order to allow the other work packages to provide common solutions for
similar environments. It is obvious that Tasks 1.2 and 1.3 required this grouping of the
countries. For other tasks, the work will start with an analysis of whether this grouping is
relevant for the requested tasks and whether a differentiated solution is useful.
o
This work should be based on already available information in the ESS Peer Reviews and the
corresponding self-assessment questionnaire.
Task 1.2: "Best practices regarding the relation with data providers" Under this task,
information will be collected on Member States practices regarding the establishment and
maintenance of relations with data owner: establishing contacts with the data owners,
framework agreements and other formal cooperation agreements, involvement of the NSI in
setting up, maintaining, changing or discontinuing the administrative registers, negotiating
arrangements of metadata exchanges, data protection arrangements and particular safeguards
in those cases where confidentiality is a major obstacle. Particular attention should be paid to:
o
the cases where the NSIs and the register owners reached some mutually beneficial
agreement: for example, reducing total cost of administration by cooperating;
communication and collaboration with administrative holders that lead to improvements of
input quality at sources (sharing of statistical knowledge, validation tools, documentation
etc.);
o
the existence of some organisations which could help to keep contacts in a structured way (a
body in which different administrations and statistical offices are represented);
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o
issues related to the internal organisation of the NIS, if they are relevant for accessing and
using data from administrative sources;
o
successful negotiations with data owners of the changes needed in their own legal
framework, in order to clearly establish the right of access of NSI to administrative data;
o
feed-back to register owners: how to balance the need to give feed-back in order to
motivate quality improvements with the need to respect confidentiality and public feelings
on sharing data;
o
overcoming confidentiality problems (safeguards and methods to protect confidentiality) so
that administrative data holders agree to share their data with statistical institutes (for the
cases where confidentiality issue is a major obstacle);
o
finding typical situations and proposing models of access to these cases, especially for cases
where sources that are very useful for producing European statistics are inaccessible due to
resistance from data owners;
o
discussing concrete examples/case studies and the learnings that can be drawn from them,
both positive and negative.
The report on best practices should also provide a simplified inventory of the main sources used at
national level. This should not be a pure enumeration of the sources. The goal is to analyse some
typical sources and the most common access/use issues related to them, as well examples of how to
overcome these obstacles. The work should build upon the results in Task 1.1 and on the
information available in the Peer Reviews.
The report should gather an inventory of national experiences and propose
recommendations/guidelines on best practices for each type of legal environment/group of
European Member States studied in Task 1. Additionally, Eurostat will facilitate the exchange of
experience of national practices through a workshop.

Task 1.3: "Preparation of long-term EU actions to improve the access and the rights to use
administrative data sources for statistical purposes" The deliverable will be a report comprising
recommendations for further EU action on the basis of a sound analysis of the current situation.
The work will include monitoring EU legislation, and will rely on the work accomplished in Task
1.1 and 1.2.
The possible options on which Eurostat could decide to proceed at a later stage are:
o
draft new legislation that refers specifically to the access to administrative data. This
could go further than the proposed revision of Regulation 223/2009, by giving stronger
rights to access data and metadata, as well as the right to link the data. It could also clarify
the role of the NSIs in the relationship with other public administration as regards the
production of administrative data: coordination, access, supervision etc., the involvement
of the NSIs in building/modifying/discontinuation of the registers, as well as the
safeguards that should be put in place in order to ensure confidentiality (rights of access
of the NSI staff, storage, processing, the use of PIN etc.). Establishment of the cooperation
mechanisms between the NSIs and the other public administrations. If the political
context is favourable, special emphasis should be put on the right to link data.
o
Commission Recommendation/guidelines on interpretation of statistical law. A strategy
which would exploit the momentum when the new statistical regulation replacing
Regulation 223/2009 will be adopted and will enter into force would take advantage of a
rare opportunity to put things in the right direction, especially for the countries in which
difficulties are to be expected.
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o
Commission Recommendation/guidelines on how to address confidentiality concerns.
NSIs would benefit from help regarding the interpretation of EU law and its applicability in
national circumstances. For instance, the legal provisions related to data protection are
stricter for NSIs than for other administrative bodies. However, data protection is very
often invoked by administrative bodies as a strong argument for denying NSIs access to
their data. Eurostat could develop a good communication strategy to explain the benefits
of data reusability while giving strong safeguards for data security.
o
Recommend preferred rules and agreements for collaboration between the statistical
world and the public administrations. The focus should be on the fact that the
administrative data owners face cost problems as well so that we could offer to support
them (e.g. share know-how, IT tools, etc.). Thus, any such obligations should highlight the
benefits for the data owners themselves in order not to create ill perceived additional
burden for them.
The work in this area will also analyse the impact of data protection legislation on accessing and
using administrative data.
Work package 2 Statistical methods
This work packages aims to contribute to the robust statistical process key area of Vision 2020
through promoting the use of sound methodology. The work package will be split into two tasks. The
more complex task is assigned an intermediate delivery date which will allow refocusing the work
during its development. The work will be continued after the intermediate delivery only if the
intermediate results identify clearly the need for continuation.

Task 2.1 "Review of relevant estimations methods and provision of guidelines":
o
Description and listing of estimation methods that could be relevant when using
administrative data. This first part of the work could be widely based upon existing
material (starting point: Memobust work on estimation methods used in the field of
business statistics, ESSNET admin data).
o
Analysis of the links between estimation methods and the different processes necessary
to use administrative sources.
Conditional to future needs:
o

Production of guidelines and/or handbook on integrating administrative data sources in
statistical production. The guidelines/handbook should also provide concrete examples of
how to apply the relevant methods (differentiated in order to allow their use for dealing
with administrative data of different quality levels).
Task 2.2 "Combining multiple sources using modelling and estimation methods"
o
Review of best practices in the Member States: combination of several administrative
registers, combination of administrative sources and surveys. Main tasks will deal with
benchmarking techniques, nowcasting, how to extend the field of application of
administrative sources, how to use survey data as auxiliary variables for administrative
data or how to combine statistical registers.
o
Identification of most relevant statistical domains of applications for combining multiple
sources according to existing practices and future needs. Particular attention should be
paid to the needs of the census.
This task has potential overlaps with the area of Big Data. For this reason, close cooperation with the
Big Data team is foreseen.
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Work package 3 Quality measures for statistics using administrative data
This work package links directly to one of the five key areas of the Vision 2020, namely the quality of
European statistics. The work package aims to develop and promote the use of suitable tools for
evaluating the quality of the administrative data (i.e. data collected by the administrative data
owners, which serve as an input to the statistical production process) and of the statistical outputs
that use administrative data. The latter includes both the outputs produced using a combination of
survey data and administrative sources and outputs based exclusively on administrative data (single
or multiple sources).

Task 3.1 "Checklists for evaluating the quality of input data"
In this area, several projects have already provided substantial contributions; what is still missing is
the expert consensus on the best approach and their consistent application. This task concerns
assessing both the usability and usefulness of a new source and assessing the quality of the
administrative data itself. The deliverable should include:
o
a critical review and testing in several statistical areas and with different types of
administrative sources of the previous results on input quality checks (ESSnet Admin Data,
BLUE-ETS,
Memobust,
national
quality
frameworks);
production
of
advice/recommendations on which approaches are more suitable; definition of quality
dimensions of possible administrative sources; metadata requirements for assessing the
quality of administrative sources;
o
testing of the preferred approach in some chosen areas in various statistical domains;
o
recommendation on whether further EU action is needed and a preliminary business case
for it.
If there is a convincing business case for more work on these topics, the new actions will be
implemented through another task.

Task 3.2 “Framework for the quality evaluation of statistical outputs using administrative
data”). This task is planned in two steps. Conditional on the success of the first step, the project
will continue with the second step. The intermediate deliverables should include:
o
critical review of existing indicators and approaches to evaluate and compare quality of
the output based on several sources (among which at least one administrative source);
o
testing of the suitability in several domains of the existing approaches;
o
proposal of new areas of investigation in order to produce a meaningful quality
assessment of outputs based on multiple sources.
If the proposal for further development of this topic is promising, the newly proposed actions will be
implemented through the final deliverables:
o
proposal of new indicators for an integrated output and a framework for quality reporting
on mixed sources statistic, as well as implementation guidelines, with a special focus on
ESS comparability. These indicators should be suitable for integration in the ESS Single
Integrated metadata Structure.
o
discussion of existing and newly developed indicators/approaches and a cost-benefit
analysis of which one should be preferred in a certain situation;
o
recommendation for updating the ESS Handbook and guidelines for quality reports in
order to include the relevant indicators.
This task should deliver a quality framework for assessing the quality of the final output based totally
or partially on administrative sources. The framework for quality assessment should be based on a
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generic model describing the possible uses of administrative sources in statistical production. The
underlying methods and processes (both at NSI level and at administrative source level) need to be
taken into account; however the focus should be on producing statistical measures for the output
quality, and the methods to compute the indicators in practice. The quality indicators should be
usable for improving the quality of the output.

Task 3.3 "Dissemination and implementation" In order that the investment pay off, it is
important that the results are implemented in statistical production. The following deliverables
should facilitate wide implementation of the results in the ESS:
o
selection of one statistical area and production of quality reports for EU-28 or at least for
a substantial number of EU Member States using the new methodology, including the
evaluation of the frames' quality;
o
promotion of results in virtual and physical for a (e.g. presentations in conferences and
workshops, online discussion groups etc.);
o
one year support for the national statistical authorities willing to implement the results.
Additionally, a workshop on best practices related to quality assessment will be organised in 2016.
This will allow Member States to benefit from the exchange of experience with the view to the
census and it will also serve as input to Tasks 3.1 and 3.2.
It is important to clarify that although this work package will produce new guidelines for quality
assessment, their adoption in the statistical process of the NSIs is outside the scope of the project.
The adoption of such guidelines should be decided upon through normal ESS governance, by the
appropriate Directors' Groups and the ESSC.
In this work package there are important synergies with the work on Big Data. Cooperation is
foreseen through mutual attendance of important meetings.
WP 4: Eurostat as a (in)direct user of administrative data sources held or designed by the
European Commission
The specific feature of this project is that is deals with administrative data sources that are either
held by other DGs of the European Commission, or are held by administrations in Member States on
the basis of EU legislation. The special nature of these data sources creates the scope for stronger EU
coordination regarding their use for statistical projects. This work package concerns the continuation
of two already existing pilot projects in the area of agriculture and fisheries statistics as well as the
identification and implementation of similar work in other statistical domains. The consequences of
discontinuation of data flows as well as the possible use in the Member States of the same
administrative source is to be considered.
 Task 4.1 "Pilot project on using catch reporting data for catch statistics". These data largely
stem from the same sources. The pilot project will investigate how feasible it is for Eurostat to
create catch statistics from DG MARE catch reporting data without having to ask MS for data
twice. Deliverables are:
o
report comparing the data collections, identifying overlaps and gaps and their reasons;
o
proposals to harmonise the collections, e.g. by aligning required inputs and legislation,
while taking account of quality, data protection and other requirements;
o
establishment of new data flows that are efficient and of sufficient quality (there will be
temporary parallel running of both collections for control purposes);
o
practical and legal discontinuation of separate ESTAT data collections;
o
report detailing challenges, benefits, risks, opportunities, lessons learnt, as well as
proposals for improvement and implementation.
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 Task 4.2 "Pilot projects on using IACS (Integrated Administration and Control System) for
agricultural statistics" The IACS register is a system of administrative registers held by Member
States with a view to implementing the Common Agricultural Policy. The registers are based by
Commission regulations initiated by DG AGRI. A common Task Force with DG AGRI has already
discussed potential adaptations of the administrative sources in order to make them more
suitable for statistical use. The work of the Task Force is being complemented by work in Member
States that target the improved use of these registers. Potential outcomes are:

o
Improved methodological documents regarding the use of IACS for statistical purposes;
o
Changes made to the information structure of the IACS databases;
o
Testing and adapting new methodological approaches for using IACS data that would
reduce the response burden by improving the synergy between statistical surveys and
administrative data collection (using input from work packages 2 and 3);
o
Improvement of the Member States’ internal data flow and data treatment;
o
Setting up statistical farm registers and developing processes for their maintenance, based
on administrative registers;
o
Introducing a unique farm identifier to be used in administrative registers and agricultural
statistics.
Task 4.3 "Other pilots" On the basis of the learning derived from Tasks 4.1 and 4.2, new pilots
will be launched on the direct or indirect use by Eurostat of data held by other DGs or collected
on the basis of EU legislation. The tasks will be similar to Tasks 4.1 and 4.2.
WP 5: Frames for social statistics

Task 5.1: "Report on the availability, kind and development of base registers on persons and
buildings (dwellings) and derived frames used in the Member States" for the production of
European social statistics in the years 2014/2015. This report shall be based on the preliminary
consultation on the target infrastructure in social statistics carried out in 2010, the quality
reporting for the 2011 censuses, information collected from staff working in Eurostat Directorate
F (e.g. related to the EU LFS and EU SILC), and on a questionnaire sent to the NSIs. The report
shall follow a methodological approach which will be practical and fit for reporting purposes.
The report shall identify best practices and include a gap analysis showing where Member States
have to invest efforts in order to have frames available that are fit for the production of
European Social Statistics.

Task 5.2 “Methodology for the assessment of the quality of frames for social statistics” This
methodological framework shall serve as a basis for a future monitoring process. The deliverable
should include:
o
indicators for the quality of frames. Geo-referencing is an important feature of frames;
o
sufficiency criteria for the quality of frames;
o
definition of metadata requirements;
o
recommendations for a future process for monitoring the quality of frames.
The methodological framework shall also focus on the relationship between different frames,
particularly the frames on persons and dwellings. Given that the situation for frames on
businesses is quite different (as there are established business registers), the project shall not
focus on those, apart from a possible analysis of the synergies between the base registers and
frames in the areas of social and business statistics.
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
Task 5.3: "Preparation of long-term EU actions to increase the availability and quality of
sustainable frames in the Member States" The deliverables will be based on the gap analysis
included in the first deliverable as well as the methodological framework on assessing quality of
frames included in Work Package 3.2. They should include:
o
draft proposal for minimum requirements for sampling frames for EU social statistics;
o
preparation of other legislative or non-legislative documents (Commission
Recommendation, Communication etc.) to support the objective of improved frames for
social statistics.
WP6. Pilots and applications
This work package will ensure the link between theoretical developments and their implementation,
which would ensure their sustainability. The pilot studies will target the application of the results of
WP 1, 2, 3 and 5 in various statistical domains and in several Member States. The applications can
include the implementation of already known approaches in new domains or different Member
States, the integration of new administrative sources in statistical production, or their use for
different purposes.
Pilots/applications in Member States would be financed through grants, subject to budgetary
availability.
It is planned to allow for pilots/applications throughout the life of the ADMIN project, with pilot
areas being identified progressively to match the developments in other work packages. For this the
input of Member States is needed – through the Steering Committee or through successive calls for
proposals. Therefore the ADMIN business case has been structured to allow for pilot areas to be
added as they are identified in order to allow for the wide differences between countries. Some
examples that can already be proposed are:

Apply quality indicators recommended in work-package 3 to specific domains, for example, in
agricultural statistics.

Practical example on how to build identifiers for linkage purposes, for example by creating
product identifiers.

Anonymisation of micro-data. When using administrative sources within a statistical process,
linking different data sources through the use of an identifier is very often required. Therefore,
the data set has to be anonymised by removing the information in the identifier in way that will
guarantee the confidentiality of the data.

Use of estimation methods in order to make use of administrative data that is not available on
time. For example, in EU-SILC, tax register data is available too late in some Member States.
Nowcasting on the basis of partial information could overcome this problem.

The currently running pilot projects in morbidity statistics provide an inventory of the sources
available in Member States. The work could be continued with further pilots applying linking
techniques and estimation methods in order to produce the variables of interest.

Using estimation techniques to produce estimates for some variables on non-monetary health
care statistics. The aim is to produce comparable outputs although Member States use different
administrative sources, surveys or a combination of surveys and administrative sources. There is
a need to develop guidelines and give best practice on how to choose the best source and how
to combine sources in order to produce comparable data.

Member States produce aggregated statistics on their educational systems where the upstream
processes include the use of data both from administrative sources and surveys. In this area
there is a need to identify best practices and to draft recommendations and guidelines related in
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in particular to estimation methods and the assessment of coverage. This is an area which could
also be used both as a test case for the development of quality measures under WP3, or for the
application of such quality measures.

Assisting some Member States in the creation of a new statistical register on the basis of the
knowledge of Member States having a register based statistical system.

Assisting some Member States in producing new statistics by combining several sources where
at least one is administrative data.

Improving the use of an administrative source that is used or can be potentially used in several
statistical domains (for example, tax register, population register, social security register etc.)
This includes linking the information in the register to other registers or surveys.

Assisting some member States with work related to the production of the census data using
administrative sources. This can include work related to geo-coding of statistical data.
WP7 Centre of excellence on administrative data
The Centre of Excellence is a rather new ESS approach for ensuring continuity of knowledge in a
certain area. An NSI or a group of NSIs are founded in order to provide support to the other
statistical authorities in the ESS on the topic of concern. The Centre of Excellence on Administrative
Data will provide the following:

Implementation of a methodological helpdesk;

Production of training material;

Provision of training;

Coaching and consultancy towards the Member State;

Study visits where more advance Member States assist other in making progress on the use of
administrative sources;

Provision of advice on the different tasks conducted by the ESS.VIP.BUS ADMIN;

Provision of advice on the identification, implementation and results of possible pilot studies;
draw lessons from pilots;

Organisation and participation to meetings, workshops promoting and sharing the results of the
ESS-VIP-BUS-ADMIN;

Exchange of relevant information with other related projects at the EU or international level (Big
Data Task Force, task force preparing the following census, Statistical network, etc.).
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Synthetic view of the deliverables
Task
Delivery
Deliverables
WP 1 Access to and development of administrative data sources
1.1
Report on the legal/institutional
12.2016 Report
environment in the Member States
1.2
Best practices regarding the
Report (including recommendations) +
06.2017
relation with data providers
workshop
1.3
Preparation of EU long-term
12.2018 Report (including recommendations)
actions on access
WP 2 Statistical methods
2.1
Review of relevant estimation
06.2018 Report + guidelines
methods; guidelines
2.2
Combining multiple sources
(administrative; surveys) using
12.2017 Report + guidelines
modelling and estimation methods
WP 3 Quality measures for statistics using administrative data
3.1
Checklist for evaluating the quality
06.2016 Guidelines
of input data
3.2
Framework for the quality
Quality indicators + quality report
evaluation of outputs using
06.2018
template + implementation guidelines
administrative data
Quality reports + workshop (2016) +
3.3
Dissemination and implementation 06.2019
support
WP 4 Eurostat as an (in)direct user of administrative sources of the European Commission
4.1
Pilot on using catch reporting data
Reports + new data flows +
12.2018
for catch statistics
discontinuation of old data flows
Implementation of the results of WP1
and WP2 in agricultural statistics +
4.2
Pilot on using IACS (Integrated
changes in the structure of the IACS
Administration Control Systems)
12.2018
data base + setting up new statistical
for agricultural statistics
farm registers + introduction of a
unique farm identifier + grant reports
Reports + data flows (creation of new,
4.3
Other pilots
12.2019
discontinuation of old) + grant reports
WP 5 Frames for social statistics
5.1
Report on existing base registers
on persons and building and
03.2016 Report
derived frames
5.2
Methodology for assessing the
Quality indicators for frames +
quality of frames for social
12.2017 sufficiency criteria for data and
statistics
metadata
5.3
Preparation of long-term EU
Minimum quality requirements for
actions to increase availability
12.2018
sampling frames
quality of frames
Implementation of the results of the
other WP in specific statistical areas +
WP 6 Pilot studies and applications
12.2019
grant reports + domain specific
guidelines
WP 7 Centre of excellence on
Centre of Excellence set-up + support,
12.2019
administrative data
training + advice
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5.6
Assumptions
No.
5.7
Assumption
1
NSIs are able to provide information about administrative data sources early in
the project.
2
Using administrative sources in statistical production is, in the long run, cheaper
than collecting data through surveys.
3
There are sufficient communalities in the administrative environments of
Member States to allow for common solutions for the ESS or at least for some
groups of similar Member States.
4
EU and national data protection legislation do no impose further restrictions on
using administrative data.
5
Public opinion and political climate in Member States will not become less
favourable to accessing and using administrative data for statistical purposes.
6
The Vision 2020 keeps being the reference strategy document during the
lifetime of the project.
Constraints
No.
Constraint
1
EU legislation (Statistical Law, data protection legislation)
2
National laws of Member States (statistics, data protection, laws governing
public administrations)
3
The principle of subsidiarity
4
Differences in national public administrations (centralised/decentralised
systems, tradition of cooperation between administrations)
5
The impact of the involvement with other public administrations on statistical
independence
6
Public feeling regarding data access and data sharing, as well as the importance
assigned to statistics
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5.8
Risks analysis
Risk
No.
Risk
L I R
Mitigation: proposals to eliminate / minimise the risk
1
Additional work for Eurostat
and Member States
Increasing response burden
for
quality
and
other
reporting
Fear of the consequences of
possible
legal/political
initiatives stemming from the
project
Lack
of
human/financial
resources
Member States are not
actively involved in steering
and implementing the project
2 1 2
Provide sufficient resources
Coordinate efforts and avoid duplications;
Frequent review of progress
Clarify in the terms of reference of the ESSnet related to Refer to the appropriate bodies the decision to implement
or not the new quality reporting features.
quality that the final indicators should be easy to use.
15
2
3
4
3 3 9
2 2 4
Foresee the evaluation and adoption of the results Communicate well that legal changes are outside the
through standard ESS procedures involving the scope of the project
appropriate bodies
2 5 10 Commit to and allocate resources as planned
1 3 3
5
No suitable contractors are 2 3 6
found (in terms of offering
adequate skills and staffing)
6
2 2 4
Duplication/repeating
previous methodological work
7
No satisfactory solution for 3 1 3
output assessment is found or
the solution is too costly
Keep contact with Member States; involve Member
States in the preparation of the project
Take into account Member States' needs in defining the
deliverables
-
Scale down the project if resources are not available
Foresee alternative instruments for implementation
(contracts); scale down the project
Scale down the project; accomplish some tasks internally
in Eurostat if resources are available
Prepare thoroughly; clarify expectations in the terms of Reject deliverables of insufficient quality
reference;
Provide references to previous work
Foresee frequent review of the progress and exchange of
information with other projects
Agreement on realistic objectives that can be adapted
during the life of the project
Frequent review of progress during the project
implementation
Accept that this work package may fail to find a solution
L=Likelihood: between 1 (very low) and 5 (very high)
I=Impact: between 1 (very low) and 5 (very high)
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Contingency; actions taken when the risk materialises
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R=Risk Level (R*I)
Risk analysis (continuation)
Risk
No.
Risk
8
Building of stovepipes/island 3 3 9
solutions
9
Resistance to change and fear
of depending on sources
outside the statistical system
Impact of legacy (more
advanced Member States
trying to impose their
approach)
More stringent provisions in
the EU data protection
legislation
Different legal environments
in Member States prevent
uniform implementation
Clashes between EU and
national legislation
Less administrative data is
available in the future due to
the reduction in global
administrative burden
No
post
project
implementation (the results
are not used)
10
11
12
13
14
L I R
2 3 6
2 2 4
Mitigation: proposals to eliminate / minimise the risk
Contingency; actions taken when the risk materialises
Request broad applicability of the results and testing in
several areas
Frequent progress review
Clarify expectations in the terms of reference
Good communication on the scope and objectives of the
project
Reject deliverables of insufficient quality
Expand the applicability of some outputs in other areas
at later stages of the project
Foresee the need for extensive cooperation and testing Mediate between the Member States involved, take all
in various national contexts
views into account
2 5 10 Monitor legislative developments
Adapt the objectives of the project to the legislative
Inform the relevant decisional actors about the needs of constraints
the statisticians
5 3 20 Consider the legal environment in the analysis
Provide different solutions for groups of MS
3 1 3
-
Maximise the use of existing data.
3 2 6
Foresee work packages on implementation
Develop convincing use cases
Intensive communication with stakeholders
Refer the results to the appropriate decision bodies with
a view to implementation
L=Likelihood: between 1 (very low) and 5 (very high); I=Impact: between 1 (very low) and 5 (very high)
R=Risk Level (R*I)
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Constant contact with Member States throughout the
lifetime of the project.
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5.9
Implementation
Instruments and operational resources
Task
Delivery
Instruments for implementation
WP1 Access to and development of administrative data sources
1.1 Report on the legal/institutional environment
12.2016
Contracts
1.2 Best practices regarding the relation with data providers 06.2017
Contracts + Workshop
1.3 Preparation of EU long-term actions on access
12.2018
ESTAT internal + TF + Contracts
WP2 Statistical methods
2.1 Review of relevant estimation methods; guidelines
06.2018
Contracts
2.2 Combining administrative sources and surveys using
12.2017
ESSnet on statistical methods for administrative data
modelling and estimation methods
WP3 Quality measures for statistics using administrative data
3.1 Checklist for evaluating the quality of input data
06.2016
ESSnet Quality of Multisource Statistics
3.2 Framework for the quality evaluation of outputs using
06.2018
ESSnet Quality of Multisource Statistics
administrative data
3.3 Dissemination and implementation
06.2019
ESSnet Quality of Multisource Statistics
WP4 Eurostat as an (in)direct user of administrative sources held or designed by the European Commission
4.1 Pilot on using catch reporting data for catch statistics
12.2018
Eurostat internal + TF + contracts
4.2 Pilot on using IACS for agricultural statistics
12.2018
ESTAT internal + grants + contracts + TF
4.3 Other pilots
12.2019
ESTAT internal + grants + contracts + TF
WP5 Frames for social statistics
5.1 Report on the availability, kind and development of base 03.2016
ESTAT internal + TF + Contracts
registers on persons and building and derived frames
5.2 Methodology for assessing the quality of frames for
12.2017
ESSnet Quality of Multisource Statistics+
social statistics
Eurostat internal +contracts
5.3 Preparation of EU actions to increase availability quality 12.2018
ESTAT internal + TF + Contracts
of frames
WP6 Pilot studies and applications
12.2019
Grants + contracts
WP7 Centre of excellence on administrative data
12.2019
Centre of excellence on administrative data
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5.10 Roadmap:
2015
Task
Q2
Q3
2016
Q4
Q1
Q2
Q3
2017
Q4
Q1
Q2
Q3
2018
Q4
Q1
Q2
Q3
2019
Q4
Q1
Q2
Q3
WP1 Access to and development of administrative data sources
1.1 Report on the legal/institutional environment
1.2 Best practices regarding the relation with data providers
1.3 Preparation of EU long-term actions on access
WP2 Estimation methods and the use of administrative data
2.1 Review of relevant estimation methods; guidelines
2.2 Combining administrative sources and surveys using modelling and
estimation methods
WP3 Quality measures for statistics using administrative data
3.1 Checklist for evaluating the quality of input data
3.2 Framework for the quality evaluation of output based on multiple
sources
3.3 Dissemination and implementation
WP4 Eurostat as a (in)direct user of administrative data sources held or designed by the European Commission
4.1 Pilot on using catch reporting for catch statistics
4.2 Pilot on using IACS for agricultural statistics
4.3 Other pilots
WP5 Frames for social statistics
5.1 Report on the availability, kind and development of base registers on
persons and building and derived frames
5.2 Methodology for assessing the quality of frames for social statistics
5.3 Preparation of EU actions to increase availability and quality of frames
WP6 Pilot studies and applications
WP7 Centre of excellence on administrative data
The hatched cells show the continuation of the tasks where there is a decision point involved (task will be continued only if there is sufficient evidence of
progress and need).
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Q4
ESS.VIP.BUS.ADMIN Business Case
5.11 Synergies and Interdependencies
Several projects/works have produced relevant useful results; therefore the ESS.VIP.BUS
ADMIN project should rely on them and continue their work:

Wallgren and Wallgren “Register-based Statistics: Statistical Methods for Administrative
Data”19 (reference methodological handbook for register-based statistics);

ESSnet AdminData20 (project aiming to improve the use of administrative data in business
statistics; provides guidelines and checklist for quality assessment);

ESSnet MEMOBUST21 (handbook for producing business statistics; includes chapter on
quality);

BLUE-ETS22 (research project aiming at reducing burden in the enterprise and trade
statistics; it includes input quality checklist);

ESSnet Data Integration23 (project developing methodologies for data integration: record
linkage, statistical matching, micro integration);

national quality frameworks for statistics based on administrative data, especially the
Austrian approach to quality reporting24;

the Statistical Network's project on an integrated use of administrative data in the
statistical process;

Eurostat framework quality reporting for the census;

ESSnet GEOSTAT 1A25 (project developing a set of methods, tools and guidelines to create
harmonised geo-referenced data sets and creating a population grid using existing
population data sources);

ESSnet GEOSTAT 1B26 (project aiming at developing guidelines for datasets and methods to
link census 2010/2011 statistics to a common harmonized grid);

The XBRL Task Force looking at the use of regulatory data;

The ESSnet Consistency27 (project aiming to improve consistency in business statistics);

The ESSnet Data Warehouse (project providing assistance in the development of more
integrated databases and data production systems for business statistics in ESS Member
States, including current best practices in integrated business data systems and
Examination of ways in which data can be combined to support new outputs
19
Wallgren and Wallgren "Register-based Statistics: Statistical Methods for Administrative Data", 2nd
Edition, Wiley 2014.
20
ESSnet AdminData: http://www.cros-portal.eu/content/use-administrative-and-accounts-data-businessstatistics; the project is part of the MEETS Programme.
21
ESSnet MEMOBUST: http://www.cros-portal.eu/content/memobust, the project is part of the MEETS
Programme.
22
BLUE-ETS: http://www.cros-portal.eu/content/blue-ets, project supporting the MEETS Programme.
23
ESSnet Data Integration http://www.cros-portal.eu/projectdetail/1393
24
Ćetcović et al. "A quality monitoring system for statistics based on administrative data" European
Conference on Quality in European Statistics 2012.
25
ESSnet GEOSTAT 1A http://www.cros-portal.eu/content/geostat-1a-finished
26
ESSnet GEOSTAT 1B http://www.cros-portal.eu/content/geostat-1b
27
ESSnet Consitency http://www.cros-portal.eu/content/consistency-0; project supporting the MEETS
Programme.
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
Peer reviews.
At the same time, there are currently running projects where interdependencies and overlaps
may be possible. The interdependencies with the following projects are taken into account:

Task Force working on the next census. One of the objectives of the Task Force is to see
which variables of interest to the census can be obtained directly from administrative
registers and which would need to be estimated. The links with WP3 Statistical methods
are very close. Therefore the project will foresee cooperation with the Task Force through
mutual participation in meetings.

Big Data Task Force. The area were overlaps are possible is the quality assessment of
multisource statistics. The project will foresee collaboration through mutual attendance of
meetings and exchange of documents.

ESS.VIP.BUS ESBRs. The purpose of the project is to obtain better business statistics and
reinforce their links, through the interoperability of SBR (statistical business registers). The
project will set up of a European SBRs business architecture, coherent with the general ESS
business architecture and will strengthen the national SBRs and their backbone role,
including the introduction of a unique identifier for legal units and measures to reinforce
NSI’s use of administrative data. ESS.VIP ADMIN will carefully monitor the progress made
by the ESS.VIP.BUS ESBRs in order to use its results and to avoid overlaps.

There are several ESS.VIP projects that aim to develop the infrastructure for accessing and
sharing of data and services, namely ESDEN, Data Warehouse, DARA and SERV.
ESS.VIP.BUS ADMIN is not directly concerned, as it does not work on IT issues. However, it
is important that the project team monitors the developments in these projects, in order
to become aware of IT issues and to give input on work that may be relevant to the use of
administrative data.

The ESS.VIP IMS aims at defining information standards at ESS level in relation with the
ESS.VIP projects, ensuring consistency across projects and providing - where relevant technical solutions for mapping project activities to existing relevant standards. The
ESS.VIP ADMIN will be a customer of this project, therefore frequent interaction between
the teams is foreseen.

The proposed ESS.VIP QUAL (Quality) will aim to enhance quality management through
defining and implementing fit-for-purpose quality assurance methods and tools; monitor
and improve the usability and quality of data sources (both existing and new ones) and
enhance the implementation of the general quality management principles. The QUAL
project is much broader in scope, however there is a risk of duplication in the work related
to the quality of new data sources. It is suggested that QUAL uses the results of ADMIN as
an input. Close cooperation between the teams is foreseen.

The ESS.VIP Validation works on a more efficient production chain in the ESS, using
commonly agreed validation steps and harmonised software solutions. ESS.VIP ADMIN will
have to look closely at the usefulness of the validation rules developed by ESS.VIP
Validation when dealing with administrative data sources.
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6
6.1
GOVERNANCE
Project Owner (PO)
Gallo Gueye, ESTAT dir. F (Social Statistics)
6.2
Solution provider (SP)
Not applicable.
6.3
Approving Authority
This business case will be submitted for consultation/approval to the following bodies:
Steering Committee (representatives of Eurostat and of Member States) –the Committee will
be created after the approval of the project by the ESSC
Directors of Methodology Group (DIME); Directors of Social Statistics Group
ESS Portfolio Management Office
Vision Implementation Group
European Statistical System Committee (ESSC)
The Steering Committee will be in charge of supervising the progress and making decision on
prioritisation of tasks. It will also decide which issues may be referred to higher level bodies.
6.4
Annexes
1. Governance structure
2. Impact assessment
3. DIME consultation
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Annex 1. Governance structure of ESS.VIP.BUS ADMIN
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