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DG ESTAT

Unit F1

Business Case

ESS.VIP.BUS.ADMIN

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ESS.VIP.BUS.ADMIN Business Case

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Document Title: Business Case

Project Title: ESS.VIP.BUS.ADMIN

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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 DG / Unit: F1

Date of Request: 04.09.2012 Target Delivery Date:

31.12.2017 (initial

target)

31.12.2019 (new

target)

Type of Delivery: ☐In-house ☐Outsourced ☒Mix ☐ Not-known


Date: 21/01/2015 5 / 39 Doc. Version: 5.1

2 CONTEXT

2.1 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:

• 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 ESSnet

4 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:

• 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.

2.2 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-business-statistics.


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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 Warehousing

6) 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 high-

quality 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:

• Specific quality issues for frames. Good coverage8 and controlled authenticity

9 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 inter-

census 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.

2.3 Interrelations and Interdependencies

The project will build on a number of existing projects that have already addressed some of the

issues described:

• 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.

10 Regulation (EU) No 1151/ 2010.


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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-business-statistics. 12 Handbook on Methodology of Modern Business Statistics (Memobust) http://www.cros-portal.eu/content/memobust. 13 IACS (Integrated Administration and System) http://ec.europa.eu/agriculture/direct-

support/iacs/index_en.htm. 14 http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_code=KS-TC-14-

003 15 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 on-

going 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

• 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

• 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

• 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.

16 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


Date: 21/01/2015 12 / 39 Doc. Version: 5.1

WP4. Eurostat as a (in)direct user of administrative data sources held or designed by the European

Commission

• 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

• 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 multi-

annual 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

• 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;


Date: 21/01/2015 13 / 39 Doc. Version: 5.1

• 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).


Date: 21/01/2015 14 / 39 Doc. Version: 5.1

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 Threats

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

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.


Date: 21/01/2015 15 / 39 Doc. Version: 5.1

SWOT Analysis


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


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


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


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


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

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).


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 SOLUTION DESCRIPTION

5.1 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 Content-

Oriented 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


Date: 21/01/2015 26 / 39 Doc. Version: 5.1

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.


Date: 21/01/2015 28 / 39 Doc. Version: 5.1

• 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


Date: 21/01/2015 29 / 39 Doc. Version: 5.1

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.


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.).


Date: 21/01/2015 30 / 39 Doc. Version: 5.1

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

environment in the Member States 12.2016 Report

1.2 Best practices regarding the

relation with data providers 06.2017

Report (including recommendations) +

workshop

1.3 Preparation of EU long-term

actions on access 12.2018 Report (including recommendations)

WP 2 Statistical methods

2.1 Review of relevant estimation

methods; guidelines 06.2018 Report + guidelines

2.2 Combining multiple sources

(administrative; surveys) using

modelling and estimation methods

12.2017 Report + guidelines

WP 3 Quality measures for statistics using administrative data

3.1 Checklist for evaluating the quality

of input data 06.2016 Guidelines

3.2 Framework for the quality

evaluation of outputs using

administrative data

06.2018 Quality indicators + quality report

template + implementation guidelines

3.3 Dissemination and implementation 06.2019 Quality reports + workshop (2016) +

support

WP 4 Eurostat as an (in)direct user of administrative sources of the European Commission

4.1 Pilot on using catch reporting data

for catch statistics 12.2018

Reports + new data flows +

discontinuation of old data flows

4.2 Pilot on using IACS (Integrated

Administration Control Systems)

for agricultural statistics

12.2018

Implementation of the results of WP1

and WP2 in agricultural statistics +

changes in the structure of the IACS

data base + setting up new statistical

farm registers + introduction of a

unique farm identifier + grant reports

4.3 Other pilots 12.2019 Reports + data flows (creation of new,

discontinuation of old) + grant reports

WP 5 Frames for social statistics

5.1 Report on existing base registers

on persons and building and

derived frames

03.2016 Report

5.2 Methodology for assessing the

quality of frames for social

statistics

12.2017

Quality indicators for frames +

sufficiency criteria for data and

metadata

5.3 Preparation of long-term EU

actions to increase availability

quality of frames

12.2018 Minimum quality requirements for

sampling frames

WP 6 Pilot studies and applications 12.2019

Implementation of the results of the

other WP in specific statistical areas +

grant reports + domain specific

guidelines

WP 7 Centre of excellence on

administrative data 12.2019

Centre of Excellence set-up + support,

training + advice


Date: 21/01/2015 31 / 39 Doc. Version: 5.1

5.6 Assumptions

No. 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.

5.7 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 Contingency; actions taken when the risk materialises

1 Additional work for Eurostat

and Member States 2 1 2 Coordinate efforts and avoid duplications;

Frequent review of progress Provide sufficient resources

15 Increasing response burden

for quality and other

reporting

3 3 9 Clarify in the terms of reference of the ESSnet related to

quality that the final indicators should be easy to use.

Refer to the appropriate bodies the decision to implement or not the new quality reporting features.

2 Fear of the consequences of

possible legal/political

initiatives stemming from the

project

2 2 4 Foresee the evaluation and adoption of the results

through standard ESS procedures involving the

appropriate bodies

Communicate well that legal changes are outside the

scope of the project

3 Lack of human/financial

resources 2 5 10 Commit to and allocate resources as planned Scale down the project if resources are not available

4 Member States are not

actively involved in steering

and implementing the project

1 3 3 Keep contact with Member States; involve Member

States in the preparation of the project

Take into account Member States' needs in defining the

deliverables

Foresee alternative instruments for implementation (contracts); scale down the project

5 No suitable contractors are

found (in terms of offering

adequate skills and staffing)

2 3 6 - Scale down the project; accomplish some tasks internally

in Eurostat if resources are available

6 Duplication/repeating

previous methodological work

2 2 4 Prepare thoroughly; clarify expectations in the terms of

reference;

Provide references to previous work

Foresee frequent review of the progress and exchange of

information with other projects

Reject deliverables of insufficient quality

7 No satisfactory solution for

output assessment is found or

the solution is too costly

3 1 3 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)


Date: 21/01/2015 33 / 39 Doc. Version: 5.1

R=Risk Level (R*I)

Risk analysis (continuation)

Risk No.

Risk L I R Mitigation: proposals to eliminate / minimise the risk Contingency; actions taken when the risk materialises

8 Building of stovepipes/island

solutions

3 3 9 Request broad applicability of the results and testing in

several areas

Frequent progress review

Clarify expectations in the terms of reference

Reject deliverables of insufficient quality

Expand the applicability of some outputs in other areas

at later stages of the project

9 Resistance to change and fear

of depending on sources

outside the statistical system

2 3 6 Good communication on the scope and objectives of the

project

Constant contact with Member States throughout the

lifetime of the project.

10 Impact of legacy (more

advanced Member States

trying to impose their

approach)

2 2 4 Foresee the need for extensive cooperation and testing

in various national contexts

Mediate between the Member States involved, take all

views into account

11 More stringent provisions in

the EU data protection

legislation

2 5 10 Monitor legislative developments

Inform the relevant decisional actors about the needs of

the statisticians

Adapt the objectives of the project to the legislative

constraints

12 Different legal environments

in Member States prevent

uniform implementation

Clashes between EU and

national legislation

5 3 20 Consider the legal environment in the analysis

Provide different solutions for groups of MS

13 Less administrative data is

available in the future due to

the reduction in global

administrative burden

3 1 3 - Maximise the use of existing data.

14 No post project

implementation (the results

are not used)

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)


Date: 21/01/2015 34 / 39 Doc. Version: 5.1

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

modelling and estimation methods

12.2017 ESSnet on statistical methods for administrative data

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

administrative data

06.2018 ESSnet Quality of Multisource Statistics

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

registers on persons and building and derived frames

03.2016 ESTAT internal + TF + Contracts

5.2 Methodology for assessing the quality of frames for

social statistics

12.2017 ESSnet Quality of Multisource Statistics+

Eurostat internal +contracts

5.3 Preparation of EU actions to increase availability quality

of frames

12.2018 ESTAT internal + TF + Contracts

WP6 Pilot studies and applications 12.2019 Grants + contracts

WP7 Centre of excellence on administrative data 12.2019 Centre of excellence on administrative data


Date: 21/01/2015 35 / 39 Doc. Version: 5.1

5.10 Roadmap: 2015 2016 2017 2018 2019

Task Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

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


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).


Date: 21/01/2015 36 / 39 Doc. Version: 5.1

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-business-

statistics; 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.


Date: 21/01/2015 37 / 39 Doc. Version: 5.1

• 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 GOVERNANCE

6.1 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


Date: 21/01/2015 39 / 39 Doc. Version: 5.1

Annex 1. Governance structure of ESS.VIP.BUS ADMIN

admin business case v5 - circabc - welcome · pdf file5.1 legal basis ... ess.vip.bus.admin...

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