Task 4 round of ESS.VIP ADMIN grants Document Service Data ... · Task 4 Project deliverable D.4 Summary review on the main outputs and findings of the first round of ESS.VIP ADMIN

GOPA Consultants Hindenburgring 18, 61348 Bad Homburg, Germany

Phone +49 6172 930-303 Fax: +49 6172 930-130 Email: [email protected]

Task 4

Project deliverable D.4 Summary review on the main outputs and findings of the first

round of ESS.VIP ADMIN grants

Document Service Data

Quality, methodology and research

Lot 1: Methodological support

Administrative data: helpdesk and other methodological support

Framework Contract N°: 11111.2013.001-2013.251 LOT 1

Contract ESTAT no 11111.2013.2016.660

Specific contract Ref. N°: 000080

4 April 2018

D.4 Summary review on the main outputs and findings of the first round of ESS.VIP

ADMIN grants

Prepared by:

Wilfried Grossmann

Norbert Rainer

Josef Richter

mailto:[email protected]

i

CONTENTS

GOPA CONSULTANTS

D.4 Summary review on the main outputs and findings of the first round of ESS.VIP ADMIN grants

Contents

Executive summary ........................................................................................................................ 1

1 Introduction ..................................................................................................................... 4

2 Overview of the 21 grant projects .......................................................................... 7

3 Main achievements and findings: General findings ................................. 13

3.1 Summary by criteria ..................................................................................................... 13

3.1.1 Achievements .................................................................................................................. 13

3.1.2 Identified common problems ................................................................................. 14

3.1.3 Proposed solutions to identified common problems .................................. 16

3.1.4 Possible topics for knowledge transfer ............................................................... 17

3.1.5 Unsolved problems........................................................................................................ 17

3.1.6 Key problems identified and lessons learned ................................................. 18

3.1.7 Innovations yielded by the projects .................................................................... 19

3.2 Summary of basic issues with the use of administrative

data sources ..................................................................................................................... 19

4 Achievements and findings by project cluster ............................................ 28

4.1 Achievements and findings: Cluster 1 – Social statistics .......................... 28

4.1.1 Achievements ................................................................................................................. 28

4.1.2 Identified common problems ................................................................................ 30

4.1.3 Proposed solutions to identified common problems ................................... 31

4.1.4 Possible topics for knowledge transfer .............................................................. 32

4.1.5 Unsolved problems....................................................................................................... 32



4.2 Achievements and findings: Cluster 2 – Agricultural

statistics ............................................................................................................................. 33

4.2.1 Achievements ................................................................................................................. 34




ii

CONTENTS

GOPA CONSULTANTS




4.3 Achievements and findings: Cluster 3 – Methodological

issues ................................................................................................................................... 37

4.3.1 Achievements ................................................................................................................. 37







iii

TABLES

GOPA CONSULTANTS


Tables

Table 1: Project cluster 1: Social statistics, including Population

Census 2021 ...................................................................................................................................... 8

Table 2: Project cluster 2: Agricultural statistics .......................................................... 10

Table 3: Project cluster 3: Methodological issues ......................................................... 11

Table 4: Overview of projects by main areas of application .................................. 12

Table 5: Frequency of problems encountered ............................................................... 20

Table 6 : Major achievements by problem areas and countries .......................... 22

Table 7: Frequency of specific problems encountered in projects

related to social statistics, including the Population Census 2021 ...................... 31

Table 8: Frequency of specific problems encountered in projects

related to agricultural statistics ........................................................................................... 35

iv

ABBREVIATIONS

GOPA CONSULTANTS


Abbreviations

EFTA European Free Trade Association

ESS.VIP European Statistical System Vision Implementation Programme

ESS.VIP

ADMIN

European Statistical System Vision Implementation Programme (Administrative

data sources)

GOPA Gesellschaft für Organisation, Planung und Ausbildung mbH

IACS Integrated Administrative and Control System

ID Identifier

IN Innovation

IT Information Technologies

KT Knowledge transfer

MS Member States

NSIs National Statistical Institutes

1

1

Executive summary

GOPA CONSULTANTS


Executive summary

This document presents a summary of the main achievements and findings of ESS.VIP

ADMIN grants provided to Member States (MS) and EFTA countries in the years 2014 to 2016

in order to make a wider and better use of administrative data sources in producing official

statistics. This summary refers to the 21 grant projects that were performed by 16 MS and

one EFTA country.

The summary review applies the same analytical framework to all the reports provided. The

elements of this analytical framework are the following:

Achievements;

Identified common problems;

Proposed solutions to identified common problems;

Possible topics for knowledge transfer;

Unresolved problems;

Key issues identified and lessons learned;

Innovations created by the projects.

In order to facilitate the interpretation of main findings and achievements, the projects were

classified into three clusters:

Cluster 1: Projects related to the use of administrative data in the context of the

forthcoming census as well as in other social statistics.

Cluster 2: Projects related to the use of administrative data (mainly deriving from IACS,

the Integrated Administrative and Control System) for the purpose of agricultural

statistics.

Cluster 3: Projects having a strong emphasis on methodological questions.

In the 21 grant projects, the following results were achieved:

Countries were able to access the relevant administrative data, no legal problems in

assessing administrative data from the public sector were reported.

Where required, written agreements were elaborated and signed with the data owners.

Secure data transmission channels were developed and applied.

Checklists were developed, with the procedures to follow when dealing with

administrative sources, as well as to assess the quality of administrative data.

2

2

Executive summary

GOPA CONSULTANTS


Analyses of the administrative sources data and mapping to statistical requirements

were performed.

Administrative data were evaluated and tested.

Decision rules were elaborated, concerning how administrative data will or could be

utilised in the production of statistical data, in the given subject areas.

Conclusions were drawn on data deficiencies and data gaps.

Proposals were made, where the quality of administrative data should be improved.

The planned statistical registers have been developed.

Lastly and most importantly, conclusions were drawn as to the implementation

concepts that apply, when using administrative data in the statistical domains under

investigation:

In which areas will administrative data be applied and how?

In which areas can administrative data replace survey collection?

In which areas will administrative data alone be used for validation and imputation

purposes, as well as for checking data quality?

In which areas can administrative data not be used at all, due to quality deficiencies

or due to the fact that there are no adequate administrative data?

The methodological project developed two algorithms as alternatives to the repeated

weighting method.

The following issues were identified as the most problematic ones concerning the use of

administrative data:

A general issue is that of the lack of metadata pertaining to the sources of

administrative data. In some Member States, Information Standards Repositories or

similar systems were developed so that metadata on administrative systems may be

obtained regularly.

As administrative databases are geared to administrative purposes, their concepts,

units, variables and definitions are, to some extent, not in line with the requirements of

statistics. It is indispensable to check the adequacy of administrative databases,

including running basic data checks. A deep study of each source’s metadata is the

prerequisite to making adequate use of the existing sources.

Lack of timeliness of administrative data reduces their usability for statistical purposes.

Lack of unique identifiers (IDs) in administrative sources impedes the use of

administrative data for statistical purposes. The problem can be solved by

implementing extensive record-linkage procedures.

The over-coverage of the administrative population registers represents a serious

problem, given that they serve as a main basis for the population census. Persons do not

3

3

Executive summary

GOPA CONSULTANTS


de-register when they leave the country for a longer period, to work abroad or to

emigrate. The application of “sign-of-life” approaches helps to deal with this problem.

Under-coverage problems were found in databases containing information on

educational attainment and participation in educational training. It becomes necessary

for new, unexplored data channels to be used, to overcome this problem. International

cooperation in order to obtain access to information on graduations abroad might be

another option.

The place of residence recorded in the administrative register is not always correct. A

solution can be found if it is possible to combine various administrative sources.

Administrative registers often do not apply international classifications (mainly of

activities and occupations) or they do not implement a required classification at all (e.g.

in the fields of education and training).

The key lessons learned concerning the use of administrative data were:

Very close and continuous collaboration with the owners of administrative data proved

to be crucial. Signing Memoranda of Understanding or reaching other models of formal

cooperation were found to be adequate solutions.

The process has to start with an inventory of sources (or additional sources) and with the

detailed assessment of the characteristics of the sources.

The use of administrative data in the production of official statistics has a long history in

certain statistical domains. These are fields in which a number of MS already hold a lot of

experience. The results of the 21 projects put together clearly show that there is high

potential for the identification of best practices and for knowledge sharing.

4

4

CHAPTER 1

Introduction

GOPA CONSULTANTS


CHAPTER

1 Introduction

This document presents a summary of the main achievements and findings of the ESS.VIP

ADMIN grants provided to Member States (MS) and EFTA countries in the years 2014 to 2016,

in order for a wider and better use to be made of administrative data sources in the

production of official statistics. Using administrative data for the production of official

statistics is one of the requests of the European Statistics Code of Practice. The use of

administrative data will reduce the administrative burden on respondents and it can lead to

a cost-reduction for National Statistical Institutes (NSIs). Furthermore, the use of

administrative data can contribute to increasing the timeliness, coverage, frequency and

quality of statistical information disseminated by the NSIs.

There are however various challenges with respect to using administrative data. In some

areas of official statistics, it has been quite usual to use administrative data sources since the

beginning of the statistical domain. An example is population statistics, information on

births and deaths have always been derived from some kind of administrative data. Over

the past two decades, all MS have undertaken numerous efforts to introduce or to increase

the use made of administrative data in various other statistical domains.

Grants provided by Eurostat support initiatives that aim to improve the use of

administrative data. MS can benefit from those grants in order to realise appropriate

projects. Grant projects have the advantage that their results can be studied and utilised by

other MS. They thus also support the exchange of experience and contribute to capacity

building.

This summary refers to 21 grant projects performed by 16 MS and one EFTA country. These

were undertaken in the years 2014 to 2016, within the framework of the ESS.VIP ADMIN

(First round). Most of the grants either concern social statistics (including the population

census) or agricultural statistics. Any conclusions drawn from the grant projects relate to the

administrative data sources that were identified and found relevant to the needs of specific

statistical domains, in the respective MS. Nevertheless, the problems, challenges and

possibilities of using administrative data ascertained by these grant projects are, to a

considerable degree, similar to those found in other projects and statistical domains.

This summary review applies the same analytical framework to all the reports provided. The

criteria are those defined in the ESS.VIP ADMIN project, and the criteria “Potential of

knowledge sharing” and “Innovation” are directly derived from objectives that are stressed

in the Statistical Programme 2013-17. The elements of this analytical framework are the

following:

5

5

CHAPTER 1

Introduction

GOPA CONSULTANTS


Achievements: Did the projects deliver the objectives of the exercise? Also: what are

those achievements and findings?

Identified common problems: Important problems observed when using administrative

databases may be assumed also to be an issue in other countries. Are such problems

related to legal issues, to the access to administrative data sources, or to cooperation

with the owners of the administrative data? Do they relate to methodological problems,

to IT issues, etc.? Here, one may also include problems relating to statistical concepts

and procedures.

Proposed solutions to identified common problems: Do the grant projects offer

concepts, methods, procedures, etc. that enable one to deal with common problems and

to overcome them?

Possible topics for knowledge transfer: Can topics be discerned, that have a potential for

knowledge transfers between countries and/or between domains?

Unsolved problems: Issues pertaining to areas in which, for specific reasons,

administrative data (at least in their current form or without necessary improvements)

cannot be used for statistical purposes, or where further research and methodological

support is needed.

Key issues identified and lessons learned: Issues that are central to the statistical

purpose, and the related lessons that have been learned from the grant project.

Innovations created by the projects: Innovations include the implementation of new or

significantly improved statistics or statistical processes, new organisational methods or

external relations. Innovations derived from the projects´ results of course relate to the

specific project and country. They are not necessarily entire novelties to the European

Statistical System as they may already have been developed and applied in other

countries.

The summary of the main findings and achievements as presented here is solely based on

the written grant reports provided by the MS to Eurostat. As country reports are structured

differently, and focus on those issues that were seen relevant, from the country´s point of

view, comparing the reports was not easy. A given important issue may be mentioned and

covered in one report, while it is not explicitly mentioned in another report. The

interpretation made of the activities and results of the grant projects may thus not always

be 100% correct. Where the grant reports could not be interpreted exactly, issues reported

were not included in this summary. In order to avoid the incorrect description of any result,

it was aimed to apply a generic terminology as far as possible, and not to relate to the

concrete administrative source in the given country.

It should also be noted that the situation in individual statistical domains may differ

between the countries engaged in the grant projects, in particular in terms of the

methodologies applied and the experience gained. Due to that fact, making general

conclusions that apply to all countries becomes quite impossible.

6

6

CHAPTER 1

Introduction

GOPA CONSULTANTS


In this context, one should mention that in the MS significant differences are to be observed

between particular administrative sources, even if the generic name they bear is the same. It

should therefore not be concluded that the peculiarities of a certain source in a given

country are the same as in other countries. This refers both to the possibilities and the

limitations of using a given source for a specific statistical domain.

In each of the statistical domains that were the subject of grant projects, a number of MS

were found already to have some experience in using administrative data in that domain.

The summary review however only refers to the issues dealt with by the grant projects and

reported in the country grant reports.

This summary report is structured as follows: Chapter 2 provides an overview of the goals

and tasks of the 21 grant projects. As mentioned above, the projects mainly concern social

statistics, including the Population Census 2021, as well as agricultural statistics. In order to

facilitate the interpretation made of the main findings and achievements, the projects were

classified into three clusters:

Cluster 1: Projects relating to the use of administrative data, in the context of the

forthcoming census and in other social statistics.

Cluster 2: Projects relating to the use of administrative data (mainly from IACS) for

agricultural statistics.


Chapter 3 presents the main achievements and findings of all of the projects, without

differentiating between clusters. Chapter 4 presents the main achievements and findings of

the grant projects in the three separate clusters. Both chapters follow the same structure.

7

7

CHAPTER 2

Overview of the 21 grant projects

GOPA CONSULTANTS


CHAPTER

2 Overview of the 21 grant projects

As mentioned above, the 21 grant projects were classified into three clusters. The following

overview provides more detail of how the clusters are structured, as well as looking at the

allocation of grant projects to specific clusters:

Cluster 1: Projects relating to the use of administrative data in the context of the

forthcoming census and in other social statistics. The 11 grant projects classified in Cluster 1

concern the following statistical domains:

Population Census 2021: CZ, HR, HU, LV, LT, PL, SK

Education statistics: BG, SI and IS

Labour Cost Survey: BE

Cluster 2: Projects relating to the use of administrative data (mainly from IACS) for the

purpose of agricultural statistics.

Farm Structure Survey and agricultural production: AT, EL, HU, IT, LT, PL, RO

Farm register: IT


NL, SE

The project carried out in the Netherlands could also have been classified under Cluster 1. It

was allocated to Cluster 3 due to its strong emphasis on methodological aspects, and because

the procedures developed could also be applied in other fields than that of register-based

censuses.

Tables 1 to 3 provide an overview of the 21 grant projects, listing each of the projects´ main

objectives. The documentation also includes the grant agreements’ number and the dates

when the projects were finished. The links to the documents available on the CROS portal

can be found in Table 6. The reports for the projects included in Table 2 are not available on

the CROS portal.

Table 4 offers documentation of the projects by main application areas. The table cannot

provide a complete picture but should rather be seen as an overview. It needs to be taken

into account that many results of the projects can be of interest in other areas than the

originally mentioned application.

8

8

CHAPTER 2


GOPA CONSULTANTS


Table 1: Project cluster 1: Social statistics, including Population Census 2021

Country Main statistical

domain Project objectives

Czech Republic

07112.2015.002-2015.358

30.12.2016

Population census

Acquiring access to relevant administrative data

Analysis and assessment of the data; usability of selected

variables

Linking of the various administrative data

Development of an approach to correct over-coverage in the

population register

Croatia

07112.2015.002-2015.348

09.2016

Population census

Assessment of existing experience with administrative data

Analysing the administrative data sources available in

relation to the population census

Development of a concept of a unique statistical identifier

from administrative sources

Concept testing

Establishment of methodological requirements for a

statistical population register

Hungary

07112.2015.002-2015.349

08.2015

Population census

Examination of the possibilities of using administrative data

sources in order to develop a cost-effective way of executing

the 2021 Census, and producing census-type data on an

annual basis, which represents less of a burden to

respondents

Criteria for the assessment: data content, IT issues,

legislation, methodology

Latvia

07112.2015.002 - 2015.352

30.09.2016

Population census

Initial assessment of the availability and quality of relevant

administrative data

Comparison of the administrative data with other statistical

information (Labour Force Survey, Census 2011)

Lithuania

07112.2015.002-2015.351

09.2016

Population census

Identification of administrative registers that could be used

for census purposes

Assessment of coverage, reference period, definition of

variables, units in the registers and other administrative

sources

Identification of problems and solutions

Concept of the use of administrative data for the census

Poland

07112.2015.002-2015.354

30.09.2016

Population census

Obtaining access to metadata

Developing a methodology for the assessment of usability of

administrative data sources

Designing a procedure to improve quality of administrative

sources

Developing a methodology for the integration of new

administrative sources

Developing an approach to creating identifiers for linking

with administrative data

Developing algorithms to generate new variables from

administrative data

9

9

CHAPTER 2


GOPA CONSULTANTS




Slovak Republic

07112.2015.002-2015.357

30.09.2016

Population census

Access to new administrative data sources

Develop standard agreements with administrative data

owners

Access to metadata

Integration of administrative data sources into statistical

production and making better use of the sources

Bulgaria

07112.2015.002-2015.347

31.01.2017

Education statistics

Evaluation of the suitability and reliability of the Education

Ministry´s administrative register

Integration of data from the register into the statistical

demographic database

Testing a procedure for using administrative data on

education as an additional source of information for the

census and for regular social surveys

Iceland

07112.2015.002-2015.350

1.10.2016


Establishing concepts, designing and implementing the plan

for the creation of a statistical register of educational

attainment of the population

Evaluation of the new data sources, in particular coverage of

educational attainment outside of Iceland

Slovenia

07112.2015.001-2015.356

29.09.2016


Assessment of the suitability of the new database on

participation in education, for its use in the statistical

production of educational statistics

Use of the information system of data on earnings and other

payments, and the number of employees in the public sector

Belgium

07112.2015.002-2015.346

30.06.2017

Labour cost

statistics

Inventory of available and future administrative data that

can be used in the production of the labour cost data

Testing of the administrative data in order to analyse their

suitability and quality

10

10

CHAPTER 2


GOPA CONSULTANTS


Table 2: Project cluster 2: Agricultural statistics



Austria

08414.2013.001-2013.457

4.2.2016

Farm Structure

Survey

Investigating whether, in the Farms Structure Survey, it is

possible to replace the set of questions about the agricultural

labour force by administrative and/or statistical data sources

Analysis of a closer harmonisation between statistical

sources addressing similar issues

Greece

08411.2014.004-2014.670

11.10.2016

Farm register

Increasing the quality and timeliness of agricultural surveys

based on the Farm register

Improving the consistency between the Statistical Farm

register and registers held in other administrative sources

Hungary

08414.2013.001-2013.459

27.7.2016

Farm register

Developing agricultural statistics by using already available

data, thus reducing the burden both on respondents and on

the statistical system

Creating access to administrative data for statistical

purposes by adapting the information systems

Adapting statistical collection methods to take advantage of

the support systems already in place

Italy

08411.2014.004-2014.671

15.11.2016

Farm register Development of a statistical register of agricultural holdings

based on statistical and administrative sources

Italy

Not available, probably

the same as above

Not available, probably

the same as above

Agricultural

statistics

Removing the discrepancies between the IACS database and

crop statistics data produced by the NSI

Elaborating statistical models to transform the actual

administrative data into statistical ones

Lithuania

08414.2013.001-2013.460

15.2.2016

Livestock statistics;

Farm Structure

Survey

Extending the use of the IACS data to the production of

annual livestock statistics

Analysing the possibility of using the administrative register

for the Farm Structure Survey

Poland

08414.2013.001-2013.461

8.02.2016

Agricultural

statistics

Development of a methodology for the use of the IACS data

for statistical purposes

Adjusting the IT system to enable data transfers

Analysis of the differences in concepts and definitions

between the IACS and the statistical requirements

Romania

08411.2014.004-2014.672

19.05.2016

Agricultural

statistics

Establishing access to IACS data for statistical purposes

Analysis of the relations between the IACS data and the farm

register data

Possibilities of implementing a unique identifier

11

11

CHAPTER 2


GOPA CONSULTANTS


Table 3: Project cluster 3: Methodological issues



Netherlands

07112.2015.002-2015.353

03.02.2017

Population census;

Interrelated

contingency table

Methodological solutions for the estimation of large

consistent interrelated contingency tables using

different data sources

Sweden

07112.2015.002-2015.355

03.11.2016

Household Budget

Survey

Improvement of the quality of the Household Budget

Survey through the use of alternative (private) data

sources: energy data from the utilities companies and

food sales data from supermarket chains

12

CHAPTER 2


GOPA CONSULTANTS


Table 4: Overview of projects by main areas of application

Gra

nt

Co

un

try

Gra

nt

nu

mb

er

To

pic

Me

tho

d

Do

ma

ins

Sta

tistic

al r

eg

iste

rs

Su

rve

y -

LC

S

Su

rve

y -

SIL

C

Hou

seho

ld B

udge

t S

urve

y

Ce

nsu

s

Tra

nsi

tion

fro

m

a s

urv

ey

to A

DS

Sta

tistic

al R

eg

iste

r

-

ed

uca

tion

-

he

alth

-

soci

al s

yste

ms

& t

axes

-

lab

ou

r m

ark

et

P

rice

sta

tistic

s

Ag

ricu

ltura

l sta

tistic

s

2013 AT 08414.2013.001-2013.457 Replacement of a set of questions about the farm labour force by using administrative or other statistical data Analysis of various administrative and statistical sources Farm structure survey, Agricultural statisticsSpecific register-based labour market

statisticsX X X X

2015 BE 07112.2015.002-2015.346Labour Cost Survey: Replacement of the combined processing of administrative databases and of survey

data on local units by an exclusive use of administrative data

Inventory of available and future administrative data for the production of the LCS

2016 variables; comparison with 2012 resultsLabour cost statistics, Registers Add new information X X X X

2015 BG 07112.2015.002-2015.347Integration of information from administrative registers of the Ministry of Education and Science into statistical

productionInventory of available information in various administrative data; quality assessment

Education statistics, Population census 2021,

EU-SILC, Demography, RegistersImprove quality , add new variables X X X X X X X

2015 CZ 07112.2015.002-2015.358 Transition from survey-based to register-based censusAcquiring access to relevant administrative data; analysis and assessment of the

data; usability of selected variablesPopulation census 2021, Registers Improve coverage, add new information X X X X

2014 EL 08411.2014.004-2014.670 Update and complement the ELSTAT Farm Register based on administrative data Analysis of the definitions and concepts of the available administrative sources Farm register, Agricultural statistics Add new information, improve quality X X

2015 HR 07112.2015.002-2015.348 Using administrative data sources to the greatest extent possibleAnalysing the administrative data sources available in relation to the population

census Population census 2021, Registers Establishing a population register X X X X X X

2013 HU 08414.2013.001-2013.459 Improvement of agricultural statistics, no duplication of data collection, access to administrative sources Improvement of the information structures, gaining access to administrative data,

analysis of sources, testing new methodological approachesFarm register, Agricultural statistics

Setting up a farm register, integration of

new information, improv ing qualityX X X

2015 HU 07112.2015.002-2015.349 Using administrative data sources for the 2021 Census; producing census-type data on an annual basis Assessment of administrative data sources; IT issues, legislation, methodology Population census 2021, Registers Add new information X X X X X X

2015 IS 07112.2015.002-2015.350Designing and implementing a plan for the creation of a statistical register of educational attainment of the

populationEvaluation of the new data sources, testing concepts

Education statistics, Population census,

RegisterAdd new information X X X X

2014 IT 08411.2014.004-2014.671na Setting up the Italian statistical farm register (SFR) Development of a statistical register of agricultural holdings based on statistical and

administrative sourcesFarm register, Agricultural statistics Integration of new information, quality X X X

2014 IT n.a.Improving crops statistics, removing the discrepancies between the IACS database and crop statistics data

produced by the NSIElaborating statistical models to transform administrative data into statistical data Agricultural statistics, Registers Integration of additional information X X X

2013 LT 08414.2013.001-2013.460 Extended use of administrative data and already ex isting statistical sources in agricultural statisticsDetailed analysis of all sources aiming at improv ing the synergy between statistical

surveys and administrative data collection

Livestock statistics, Farm structure survey,

Agricultural statistics, RegistersIntegration of additional information X X X

2015 LT 07112.2015.002-2015.351 Identification of administrative registers that could be used for census purposesDevelopment of procedures for the usage of administrative data for the Census 2021

and after that on an annual basisPopulation census 2012, Registers Add new information X X X X X

2015 LV 07112.2015.002 - 2015.352Carry ing out the 2021 Census on the basis of administrative data sources, as well as regular statistical

sample surveys

Feasibility study on the use of administrative data and information from statistical

sample surveys in order to obtain data for Census on the economic characteristics

of population

Population census 2021, Demography, Labour

force survey, RegisterAdd new information, improve quality X X X X X

2015 NL 07112.2015.002-2015.353 Estimation of large consistent interrelated contingency tablesDeveloping a methodology to cope with inconsistencies due to differences in data

sources and compilation methodsPopulation census 2021 X

2013 PL 08414.2013.001-2013.461Development of a methodology for the use of data collected in the IACS; creating a register of agricultural

holdings

Analysis of registers kept by various institutions in terms of agricultural variables

used in the surveys Agricultural statistics, Registers Integration of additional information X X X

2015 PL 07112.2015.002-2015.354Modernisation of statistical production with respect to the Census 2021 and for arriv ing at better frames for

social surveys

Creating conditions for the use of new data sources and their integration into

statistical production

Population census 2021, Social statistics,

Frames, RegistersAdd new information, improve quality X X X X X

2014 RO 08411.2014.004-2014.672 Use of administrative data from IACS instead of surveys for agriculture statisticsIdentification of the main obstacles in using administrative data sources; carry ing out

a feasibility study for the data from IACSAgricultural statistics, Registers

Integration of additional information into

the farm register, updating the registerX X X

2015 SE 07112.2015.002-2015.355 Using administrative sources from private companiesAssessment of the quality of information on dwellings by energy prov iders, scanner

data for household budget surveys, etc.

Population census 2021, Household budget

survey

Integration of additional information,

qualityX X X X

2015 SI 07112.2015.001-2015.356Use of new ev idence on participants in education within education statistics; using administrative data for the

analysis of data on earnings and the number of all employees in the public sectorAssessment of the usability of the two additional databases for statistical purposes

Education statistics, labour cost statistics in the

public sectorAdd new information, improve quality X X X X X

2015 SK 07112.2015.002-2015.357 Integration and better utilisation of administrative data sources in statistical production Investigation and assessment of the usability of the possible administrative registersPopulation census 2021, Social statistics,

Statistical business registerAdd new information, improve quality X X X X X

13

13

CHAPTER 3

Main achievements and findings: General findings

GOPA CONSULTANTS


CHAPTER

3 Main achievements and findings:

General findings

The main achievements and findings deriving from all 21 grant projects are presented in

summary form in this chapter. Chapter 4 presents the main achievements and findings

made in each of the three clusters.

3.1 Summary by criteria

The summary provided in this chapter covers all 21 grant projects and it is structured

according to the criteria explained in the introduction. As it summarises the general

findings, more details on the different topics of the grant projects can be found in Chapter 4.

The objectives of each of the grant projects can be found in Tables 1 to 4.

3.1.1 Achievements

The objectives of the grant projects – considering that they dealt with different topics – were

the following:

Obtaining access to the respective administrative data;

Elaborating and signing of cooperation agreements (Memoranda of Understanding)

with the data owners of the administrative data sources, if needed;

Analysing these data sources with respect to their suitability and quality;

Mapping of the administrative data to statistical requirements;

Performing test calculations and elaborating compilation rules for the transformation of

the administrative data into statistical data;

Drawing conclusions with respect to the use of the administrative data investigated in

the forthcoming 2021 Census, in agricultural statistics and in some other subject areas.

Further specific objectives were:

The creation of statistical farm registers and their use for survey purposes;

The creation of a statistical register of educational attainment;

The methodological project aimed at developing alternative methods for the estimation

of large interrelated contingency tables;

One project dealt with access to and use of data from private companies.

14

14

CHAPTER 3


GOPA CONSULTANTS


The following results were achieved in all of the grant projects:

Countries could access the relevant administrative data.

Where needed, written agreements with the data owners were elaborated and signed.

Secure data transmission channels have been developed and applied.

Checklists were developed, listing the procedures to follow when dealing with

administrative sources, as well as checklists to assess the quality of administrative data.

Analysis of administrative data sources and mapping to the statistical requirements

were performed.

Administrative data were analysed, evaluated and tested.

Decision rules were elaborated concerning how administrative data will or could be

utilised for the production of statistical data, in the given subject areas.



The planned statistical registers have been developed.

Lastly and most importantly, conclusions were drawn as to the implementation

concepts that apply when using administrative data in the statistical domains under

investigation:



In which areas can administrative data only be used for validation and imputation


In which areas can administrative data not be used at all, due to deficiencies in

quality or due to the fact that no adequate administrative data exist?

The methodological project developed two algorithms as alternative methods to the

repeated-weighting method.

3.1.2 Identified common problems

This sub-chapter summarises those “problems” in the use of administrative data that were

deemed to be important, and which one can assume to be relevant to other countries and to

other statistical domains. Insofar, they can be seen as “common problems”. The focus here is

on content issues that influence the quality of the statistical process and output. These

problems are classified into two areas: problems of the administrative databases and

statistical problems.

15

15

CHAPTER 3


GOPA CONSULTANTS


Administrative databases:

No legal problems were encountered in accessing administrative data. However,

agreements needed to be elaborated and signed with the data owners.

Cooperation with data owners was partly labelled as being too bureaucratic.

A general issue is the lack of metadata with administrative data sources. It is clear that

the correct use of the data for statistical purposes is only possible if statisticians knows

the coverage, the units, the definitions of the variables, and how the administrative data

were processed.

A further general problem is the quality of the administrative data. It is indispensable to

check the quality of the administrative sources.

Lack of timeliness in administrative data reduces their usability for statistical purposes.

Lack of unique IDs in administrative sources impedes the use of administrative data for

statistical purposes and it makes it necessary to implement extensive record-linkage

procedures.

A general problem is the coverage of the various administrative registers. Statisticians

are confronted with over-coverage as well as with under-coverage (for examples, please

also see sub-chapter 4.1).


activities and occupations) or they do not implement the required classification at all

(e.g. in the field of education).

The administrative databases are aligned to administrative purposes. Therefore,

concepts, units, variables and their definitions are to some extent not in line with

statistical requirements.

Statistical problems:

The lack of unique IDs must be compensated for by comprehensive record-linkage

methods.

Given various quality problems in the administrative databases, methods and

procedures need to be elaborated, in order to ensure the high quality of statistical

results.

The lack of a register of buildings and dwellings, as well as of official address bases, leads

to quality problems in the resulting statistical data.

Within the statistical system, new basic databases need to be developed, such as

statistical population registers or educational registers.

The differences in concepts between the administrative data and the statistical needs

reduce the possibilities of replacing primary data collections by administrative data. In a

similar fashion, constraints arise due to the timeliness issue and to other quality

problems.

16

16

CHAPTER 3


GOPA CONSULTANTS


3.1.3 Proposed solutions to identified common problems

The projects resulted in a number of country-specific solutions for common problems:

Concerning cooperation with the owners of the administrative sources, it proved highly

recommendable to sign Memoranda of Understanding and to establish close

collaboration, so as to guarantee continuous data transmission, the availability of

detailed metadata and future improvements in data quality.

In order to improve the quality of the administrative data, intensive and permanent

cooperation with the data owners should be exercised, and conceptual and technical

support provided to them.

Checklists for the appropriate procedures to follow and for the elaboration of

agreements were developed.

Checklists for the assessment of administrative data – based on standards in other

countries – were also developed.

Thorough analyses and documentation of the differences between the concepts and

definitions applied by the administrative databases and the statistical requirements are

a basic prerequisite in order to understand the data and to explore their use for

statistical purposes.

Concerning issues of coverage in the administrative databases, specific methods and

procedures were elaborated to correct the under- or over-coverage problem,

respectively.

Given the issues with quality in the administrative data, the general procedure to follow

is that data from more than one administrative register must be compared, and that

decision rules should be developed. Such rules should guarantee that a characteristic is

taken into the statistical database, for which the respective source has highest

likelihood. This helps in the case where administrative data contradict each-other.

Lack of unique IDs is overcome by applying record-linkage methods.

One approach to reducing the discrepancies that are due to differing concepts could also

be that of bringing the statistical concepts more in line with administrative

circumstances. That would however require appropriate discussions at the European

level.

Some discrepancies between the national administrative databases could probably be

resolved by changing national regulations. Independently from any changes to the legal

basis, interoperability between the administrative registers would increase consistency.

This would also reduce costs as the administrative institutions concerned would not be

required to undertake the update and maintenance of basic data individually.

The new method for estimating large consistent tables represents a useful tool in

various statistical domains, one of which is the register-based census.

Lack of IT resources and expertise was overcome by engaging external resources.

17

17

CHAPTER 3


GOPA CONSULTANTS


Table 6 provides a documentation of the solutions and achievements in a condensed form

and offers links to most of the project reports.

3.1.4 Possible topics for knowledge transfer

The following project achievements appear as candidates for knowledge transfer to other

countries:

Experiences in cooperation with the owners of administrative data sources;

Concept for the evaluation of the administrative sources;

Methodology for the measurement of the quality of administrative data;

A blueprint to mapping and checking the usability of administrative data for statistical

purposes;

Methods for the integration of administrative data sources into statistical databases;

Methods to determine the usual place of residence;

Methods and experience gained in record-linkage;

Possibilities and limitations of the IACS databases;

Methodology of a statistical farm register;

The new method for estimating consistent large tables;

Exchange of experience between MS.

The column “Knowledge transfer, innovation” in Table 6 points to achievements that appear

as candidates for knowledge transfer. Entries KT in this column should be seen as hints that

in these cases the potential for knowledge sharing might be given.

3.1.5 Unsolved problems

The issues represented by unsolved problems need to be seen from the perspective of the

respective countries:

Missing unique IDs for the agricultural holdings;

Quality problems with the administrative databases, especially concerning timeliness;

Lack of data on dwellings and household structure;

Missing data in administrative registers concerning diplomas obtained abroad and the

educational status of immigrants;

No replacement by administrative or other statistical sources, of data on the agricultural

labour force, as requested by the Farm Structure Survey;

Determination of the economic activity status, especially in what concerns

unemployment;

Data integration methods and software, including data warehouse approaches;

Secure data transmission methods;

18

18

CHAPTER 3


GOPA CONSULTANTS


Data editing, outlier detection and imputation methods.

3.1.6 Key problems identified and lessons learned

The key problems identified, and the main lessons learned are:

Basic importance of a Memorandum of Understanding and of practical cooperation.

Reaching a formalised agreement with the data owners, that guarantees steady and

continuous access, together with permanent good cooperation with all stakeholders,

proved to be a conditio sine qua non.

Mutual understanding and cooperation with experts is a necessary condition to the

crossing of contextual borders, so as to discern fully, which information might be

available for statistical purposes, as well as to generate a systematic overview of the

variables that are in the administrative datasets, together with their characteristics.

When assessing, to which degree each survey concept may be covered by

administrative data, the availability of detailed metadata is crucial. A deep study of each

source’s characteristics was carried out before designing a concept for the integration of

the data.

The legal background of all administrative data needs to be known, as well as all

concepts and definitions used. In addition to such “object metadata” it is equally

important to obtain access to “process metadata”, the information on the data-

generating process (controls, editing) in the various administrations. Developing

procedures for the regular acquisition of metadata on administrative systems and

registers is highly desirable.

In the same manner, good cooperation between the IT experts of NSIs and institutions

managing administrative data sources is necessary.

On the more technical level, the presence of correct unique identification numbers is the

most important prerequisite for the combination of data from different sources. If

unique identifiers are missing, internal identifiers need to be created through applying

deterministic or stochastic methods.

The decision in favour of a variable appearing in more than one register can be

facilitated by attributing quality indicators to the various sources. That is equivalent to

setting up pre-defined rules for the prioritisation of data sources. Such decision rules

should be based on the different sources’ sound evaluation and (if possible) on tests of

past years or of sub-populations.

Conversion factors are required, in order to deal with different formats (e.g. dates,

measurement units, land size, etc.) in the sources.

If “sign-of-life” indicators can be derived from other data sources, the very common

problem of over-coverage in population registers can be solved.

19

19

CHAPTER 3


GOPA CONSULTANTS


Together with data editing, and if links can be established, imputations from other

sources can solve the problem of missing variables in the main administrative sources.

In order to identify cohabitants, algorithms need to be created. These could be based on

common children, a common declared place of residence, etc.

The incorrect classification of units in administrative sources can be corrected if links to

central registers such as the business register can be created.

3.1.7 Innovations yielded by the projects

The following innovations might be of interest to other countries:

Checklists for the appropriate procedures and the elaboration of agreements with data

owners;

Checklists for the assessment of the administrative data;

The new method for estimating large consistent tables is a useful tool in various

statistical domains, one of which is the register-based census.

Entries IN in the column “Knowledge transfer, innovation” in Table 6 indicate that the

solutions mentioned are innovative and might be of interest to other countries.

3.2 Summary of basic issues with the use of administrative data sources

Four basic issues to look at when using administrative data stand out from this chapter:

Legal issues:

No specific legal obstacles were reported to accessing administrative data. However, almost

all of the relevant reports state that Memoranda of Understanding with the various owners

of administrative data sources were needed, and that they were also successfully signed.

Such memoranda (i.e. cooperation agreements) should be seen as a great support to the NSIs.

They should include reference to all the issues relevant to data transmission and to the

correct interpretation of administrative data: structure and variables, frequency, metadata,

IT aspects of data transmission, persons responsible, feedback possibilities, the setting up of

working groups, etc.

Cooperation with the owners of administrative data:

Where Memoranda of Understanding were elaborated and signed, cooperation with the

owners of administrative data obviously worked very well, even if in some country reports

it is mentioned that the cooperation was a little “bureaucratic”.

Availability of detailed metadata: The lack of metadata is a basic problem.

20

20

CHAPTER 3


GOPA CONSULTANTS


IT issues:

One IT issue is the provision of secure data transmissions between the administrative

institution and the NSI. NSIs need to develop appropriate IT tools. Other issues are the lack of

IT resources, and the lack of expertise in dealing with large databases and their integration

into statistical systems.

Table 5 presents an overview of the frequency of the main problems encountered in all of

the projects.

Table 5: Frequency of problems encountered

Problem Frequency

Reaching an agreement with the data owner to provide data;

establishing close and permanent cooperation

All countries, in all of the projects (with the

exception of NL, due to the different nature of

that project)

Obtaining authorisation from a Privacy Commission or similar

institution Three countries; at least for some of the data

Arriving at a technical solution for data transmission and

storage

In most projects; in some countries adequate

solutions already exist, in others, such

solutions are not yet operational

Arriving at full evidence of available and potentially available

administrative data

In almost all of the projects; in some projects

however, this is restricted to certain areas of

administrative data

Obtaining detailed knowledge of the variables within the

administrative datasets, and their exact definitions

All countries, in all of the projects with the

exception of NL

Creating evidence, to which degree each survey concept may

be covered by administrative data

All countries, in all of the projects with the

exception of NL

Closely related: Availability of detailed metadata In many cases the lack of such metadata

poses serious problems

Achievements:

The following Table 6 aims to provide a summary of the solutions and the significant

progress made by countries. The focus is on selected problem areas and the table is by no

means exhaustive. It is also based on simplifications and judgements which, to some degree,

are necessarily subjective. As mentioned previously, country reports are structured

differently. They focus on those issues that were deemed relevant, and they do not cover all

the problem areas that are relevant in other countries.

A major problem lies in interpreting the entries in the table, stemming from the fact that,

what is a major achievement for a specific country might, already since many years, be

standard practice in another.

21

21

CHAPTER 3


GOPA CONSULTANTS


It also proved quite difficult to allocate some of the topics to either “Implementation of

quality standards” or “Implementation measures”. The achievements are quite interrelated

and it is not always very clear from the reports which of the findings were already

implemented.

The column “Link” offers links to the project reports available on the CROS portal. Some of

the reports are not available on the CROS portal.

As regards the statistical domains shown in one of the columns of the table, only the main

domains are mentioned. Table 4 provides a more detailed documentation.

The column “Knowledge transfer, innovation” refers to the information included in sub-

chapters 3.1.4, 3.1 7; 4.1.4, 4.1.7; 4.2.4., 4.2.7; 4.3.4 and 4.3.7 and presents the findings in a very

condensed way. Entries KT should be seen as hints that in these cases the potential for

knowledge sharing is high. Entries IN indicate that the solutions mentioned are innovative

and might be of interest to other countries.

22

CHAPTER 3


GOPA CONSULTANTS


Table 6 : Major achievements by problem areas and countries

Problem Solution Country Link Main domain(s) (*) Achievement Keywords, indicators Remarks Knowledge transfer (KT), innovation (IN)

Promotion of a culture for using administrative data

Access to administrative data

Agreement with data owner(s)

Austria not available Agricultural statistics Yes Statistical, not administrative sources

Belgium https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf Labour cost survey Yes Formal agreement KT

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Formal agreements

Hungary IACS not available Agricultural statistics Yes Formal agreements

Latvia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf Census Yes Formal agreement

Lithuania IACS not available Agricultural statistics Yes Formal agreement

Poland https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en Census, Social statistics Yes Not mentioned in the report, but full access can be assumed

Poland IACS not available Agricultural statistics Yes, not perfectly clear

Slovenia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf Education statistics, Earnings public sector Yes Formal agreement

Agreement with data owner(s), but additional progress needed

Bulgaria https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_bg.pdf Census, Education statistics Ongoing process Formal agreements

Croatia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hr_0.pdf Census For some sources yes Formal agreements

Greece not available Agricultural statistics Yes Memorandum of understanding Bureaucratic problems

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Not fully successful Bilateral agreements Bureaucratic barriers

Iceland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf Census, Education statistics Partly Formal agreements Additional agreements required

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Some agreements need to be signed

Formal agreements

Italy (two projects) not available Agricultural statistics Yes, but need to be updated

Formal agreements

Romania not available Agricultural statistics Yes, for one source Protocol of cooperation

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census For some sources yes Formal agreement

No satisfactory solution found

Sweden https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_se.pdf Census, Household budget survey No Data from private companies

(*) Only the main domains are mentioned, for additional details see Table 4

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf

https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_bg.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hr_0.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf

https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_se.pdf

23

CHAPTER 3


GOPA CONSULTANTS



Promotion of a culture for using administrative data

Privacy/confidentiality of data

Authorisation from a Privacy Commission or a similar institution

Belgium https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf Labour cost survey Yes Privacy Validation by Commission for the Protection of Privacy

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Privacy Validation by the Office for Personal Data Protection

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes, for some variables Privacy On the basis of agreements

Creating evidence of the variables available in the administrative sources

Obtaining detailed knowledge of the variables within the administrative datasets and their exact definitions

Austria not available Agricultural statistics Yes, assessment of statistical sources

Inventory, metadata

Belgium https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf Labour cost survey Yes Inventory, metadata Statistics Belgium’s Register of Administrative Datasets

KT

Bulgaria https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_bg.pdf Census, Education statistics Yes Inventory, metadata

Croatia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hr_0.pdf Census Yes, but only for few sources

Inventory, metadata

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Inventory, metadata

Greece not available Agricultural statistics Yes Inventory, metadata

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Yes Inventory, metadata KT

Hungary IACS not available Agricultural statistics Yes Inventory, metadata

Iceland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf Census, Education statistics Yes Inventory, metadata

Italy (two projects) not available Agricultural statistics Yes Inventory, metadata

Latvia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf Census Yes Inventory, metadata

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes Inventory, metadata

Lithuania IACS not available Agricultural statistics Yes Inventory, metadata

Poland https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en Census, Social statistics Yes Inventory, metadata See in particular Appendix No 3

Poland IACS not available Agricultural statistics Yes Inventory, metadata

Romania not available Agricultural statistics Yes Inventory, metadata

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Partly Inventory, metadata Detailed metadata missing

Slovenia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf Education statistics, Earnings public sector Yes Inventory, metadata

Provision of conceptual and technical support to the owners of administrative data

Bulgaria https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_bg.pdf Census, Education statistics Yes Identification numbers

Poland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl.pdf Census, Social statistics Yes Administrative registers Manuals for the administrators of administrative systems/registers

KT

Checklist for the appropriate procedures to follow

Croatia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hr_0.pdf Census In preparation Formal procedure

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Yes Framework for agreements KT, IN

Sweden https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_se.pdf Census, Household budget survey Yes for legislation Checklist For changes in legislation

















https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl.pdf


24

CHAPTER 3


GOPA CONSULTANTS



Development of an appropriate infrastructure

Data transmission

Finding an efficient IT solution

Austria not available Agricultural statistics Yes

Belgium https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf Labour cost survey Yes Datawarehouse environment KT

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Ad-hoc procedures For a sample

Greece not available Agricultural statistics Yes

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Yes Integrated data transmission system Called KA, unclear whether part of the project

Hungary IACS not available Agricultural statistics Yes Integrated data transmission system Called KA, unclear whether part of the project

Iceland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf Census, Education statistics Yes Standard csv format

Latvia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf Census Yes Datawarehouse

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Not completely clear

Lithuania IACS not available Agricultural statistics Yes Need for cooperation in the field of IT

Poland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl.pdf Census, Social statistics Not completely clear Need for a common information structure

Poland IACS not available Agricultural statistics Yes, operational?

Romania not available Agricultural statistics Yes EXCEL

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Yes Agreement for data provision KT

Slovenia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf Education statistics, Earnings public sector Yes Datawarehouse environment, formal agreements

Lack of IT resources and expertise

Engaging external resources

Greece not available Agricultural statistics Yes Individual contracts








https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl.pdf



25

CHAPTER 3


GOPA CONSULTANTS



Implementation of quality standards

Analytical investigations (selected results)

Methodology for the measurement of the quality of administrative data

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Yes Quality assessment, checklist KT, IN

Poland https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en Census, Social statistics Yes Quality assessment See Annexes 2 and 9 of the report KT, IN

Provision of a blueprint to mapping and checking the usability of administrative data for statistical purposes

Poland https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en Census, Social statistics Yes Quality assessment See Annexes 3 of the report KT

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Yes Quality assessment Summary of requirements KT

Methods for the integration of administrative data sources into statistical databases

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Yes Data integration Guidelines on the involvement of a new secondary data source for the production of official statistics

KT

Poland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl_a5.pdf Census, Social statistics Yes Data integration See Annex 5 of the report KT

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Yes Data integration Gradual integration of new sources in the existing statistical information system

KT

Dealing with over-coverage with "sign of life approaches"

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Data integration, combination of sources

KT

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes Data integration, combination of sources

KT

Development of decision rules

Latvia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf Census Yes Data integration Determination of employed persons and their occupation

KT

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes Data integration Determination of the usual place of residence, country of citizenship, etc. by combination of sources

KT


Methods and experience gained in record-linkage

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Data integration See in particular Chapter 4 KT

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Yes Data integration See in particular Chapter 9 KT



Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes Data integration Different sources available KT












https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl_a5.pdf



https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_pl_a5.pdf


26

CHAPTER 3


GOPA CONSULTANTS



Feasibility studies

Creation of evidence to which degree each survey concept may be covered by administrative data

Detailed assessment of the characteristics of the various variables

Austria not available Agricultural statistics Yes Inventory, metadata See in particular Annex 1 KT

Belgium https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf Labour cost survey Yes Inventory, metadata KT

Bulgaria https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_bg.pdf Census, Education statistics Partly Inventory, metadata

Croatia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hr_0.pdf Census Partly Inventory, metadata

Czech Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_cz.pdf Census Yes Inventory, metadata, mapping

Greece not available Agricultural statistics Yes Inventory, metadata

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Partly Inventory, metadata KT

Hungary IACS not available Agricultural statistics Yes Inventory, metadata

Iceland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf Census, Education statistics Yes Inventory, metadata

Italy farm register not available Agricultural statistics Yes Inventory, metadata Only for one crop and one region

Italy not available Agricultural statistics Yes Inventory, metadata

Latvia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf Census Yes Inventory, metadata

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes Inventory, metadata

Lithuania IACS not available Agricultural statistics Yes Inventory, metadata See in particular Annex 2

Poland https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en Census, Social statistics Yes Inventory, metadata Metadata insufficient

Poland IACS not available Agricultural statistics Partly Inventory, metadata

Romania not available Agricultural statistics Yes Inventory, metadata Detailed metadata missing

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Partly Inventory, metadata

Slovenia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf Education statistics, Earnings public sector Yes Inventory, metadata

Sweden https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_se.pdf Census, Household budget survey Partly Inventory, metadata














27

CHAPTER 3


GOPA CONSULTANTS



Implementation measures

Definition of standardised workflows

Checklists for the assessment of administrative data

Hungary https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hu.pdf Census Yes Quality assessment, checklist Based on the Dutch checklist KT, IN

Replacement of survey data by administrative data

Labour Cost Survey

Belgium https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_be.pdf Labour cost survey For the majority of variables

Substitution of variables KT

Educational statistics

Bulgaria https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_bg.pdf Education statistics Partly (about 80%) Substitution of variables

Iceland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf Census, Education statistics Partly Substitution of variables For some levels of education

Slovenia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf Education statistics, Earnings public sector Partly Substitution of variables

Agricultural statistics

Austria not available Agricultural statistics To a very limited extent Substitution of variables For labour force data only KT

Greece not available Agricultural statistics Limited possibilities Substitution of variables Focus on register

Hungary IACS not available Agricultural statistics Unclear

Italy not available Agricultural statistics Limited possibilities Substitution of variables Focus on register

Lithuania IACS not available Agricultural statistics Limited possibilities Substitution of variables Only few data can be used directly

Poland IACS not available Agricultural statistics At present not possible Substitution of variables Focus on register

Romania not available Agricultural statistics At present not possible

Household budget survey

Sweden https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_se.pdf Census, Household budget survey Not possible

Update and improvement of statistical registers

Greece not available Agricultural statistics Partly Supplementation of variables Farm register

Croatia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_hr_0.pdf Census Partly Supplementation of variables Creation of a statistical population register

Hungary IACS not available Agricultural statistics Yes Supplementation of variables Setting up a farm register

Iceland https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_is.pdf Census, Education statistics Partly Supplementation of variables Creation of a statistical register of educational attainment

Italy farm register not available Agricultural statistics Yes Supplementation of variables Developing a statistical register of agricultural holdings

KT

Latvia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lv.pdf Census Partly Supplementation of variables Creating additional registers

Lithuania https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_lt.pdf Census Yes Supplementation of variables

Lithuania IACS not available Agricultural statistics Partly Linkage Farm register

Poland https://ec.europa.eu/eurostat/cros/content/2015pl-improvement-use-administrative-sources_en Census, Social statistics Yes Supplementation of variables

Poland IACS not available Agricultural statistics Partly Feasibility study Concept for a register of agricultural holdings

Romania not available Agricultural statistics No Feasibility study Farm register

Slovak Republic https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_sk.pdf Census Partly Supplementation of variables Gradual integration of additional information

Slovenia https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_si.pdf Education statistics, Earnings public sector Yes Supplementation of variables

Improvement of the methodological knowledge

Arriving at numerical consistency of a set of interrelated tables

Divide-and-Conquer solutions

Netherlands https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_nl.pdf Census Yes Output preparation KT, IN















https://ec.europa.eu/eurostat/cros/system/files/admin_wp6_2015_nl.pdf

28

28

CHAPTER 4

Achievements and findings by project cluster

GOPA CONSULTANTS


CHAPTER

4 Achievements and findings by project cluster

In this chapter, the main achievements and findings are presented separately for each of the

three project clusters. Compared to the summary review provided by Chapter 3, the

presentation has a stronger focus on the specific statistical domains. It also describes

achievements and findings in a more detailed manner. The objectives of each of the grant

projects can be found in Tables 1 to 4.

4.1 Achievements and findings: Cluster 1 – Social statistics

This chapter deals with the main achievements and findings of the 11 grant projects of

Cluster 1. The majority of the projects (seven) deal with issues in using administrative data

for the coming Population Census 2021. Three projects focus on education statistics, which

are also closely related to the census. One project deals with the use of administrative data

sources for the purpose of Labour Cost Surveys. The presentation of main achievements and

findings of Cluster 1 projects made here is structured in the same way as in Chapter 3.

4.1.1 Achievements

As can be seen from Table 1, the projects focusing on the population census have similar

targets:

Obtaining access to the respective administrative data;

Analysing these data sources with respect to their suitability and quality;

Making comparisons between the administrative data and statistical information;

Performing test calculations and elaborating compilation rules for the transformation of

the administrative data into statistical data;

Drawing conclusions with respect to the use of the administrative data investigated for

the purpose of preparing the census.

Of course, there were differences between the projects, in their focus on these issues as well

as in the type of administrative data accessed and analysed. The three projects on education

statistics as well as the project on labour cost statistics aimed at similar targets as concerns

the use of administrative data and the probable (even partial) replacement of survey

activities. Establishing a statistical register of educational attainment was the main

objective of one of the projects.

29

29

CHAPTER 4


GOPA CONSULTANTS


In all of the Cluster 1 grant projects, the following results were achieved:

Countries were able to access the relevant administrative data.

Where required, written agreements were elaborated and signed with the data owners.

Secure data transmission channels were developed and applied.

Checklists were developed, covering the procedures to follow when dealing with

administrative sources, as well as for assessing the quality of administrative data.

Analyses of the administrative sources´ data and mapping to statistical requirements

were performed.

Administrative data were evaluated and tested.

Decision rules were elaborated, concerning how administrative data will or could be

utilised in producing census data.



The planned statistical educational register has been developed.

Lastly and most importantly, conclusions were drawn concerning the concepts to

implement in the forthcoming census:



In which areas will administrative data only be used for validation and imputation


In which areas can administrative data not be used at all, due to deficiencies in their

quality or due to the fact that no adequate administrative data exist?

The projects on education registers and statistics resulted in analogous conclusions:

whether, and in which areas, administrative data on educational attainment available

can be used for educational statistics, as well as for the census, or not, and where

problems lie?

The result of the project covering the Labour Cost Survey consisted in differentiating the

required variables into three categories: variables that could be used from available

administrative sources; variables for which the direct use of administrative sources is

not yet possible, and for which estimation methods etc. may be required; and variables

for which no administrative data are available.

Although, for some of the countries, these grant projects were just the beginning of the

analysis of administrative data with respect to the Census 2021, it can be concluded that

more countries will be making use of administrative data to support census production than

in the past. Of course, further analysis and follow-up studies will be required. Hopefully, the

quality of administrative data will be increased in coming years, based on an emerging

closer cooperation with the owners of those data.

30

30

CHAPTER 4


GOPA CONSULTANTS



This sub-chapter summarises the “problems” encountered when using administrative data,

that are deemed to be important, and for which it may be assumed that they are “common”

to other countries. The focus here is on content issues that influence the quality of the

statistical process and output. These problems are classified into two areas: problems of the

administrative databases and statistical problems.

Administrative databases:

No legal problems were encountered in accessing administrative data. However,

agreements with the data owners needed to be elaborated and to be signed.

A general issue is the lack of metadata with administrative data sources. Clearly, a

correct use of the data for statistical purposes is only possible if statisticians know the

coverage, the units, the definitions of the variables, and how the administrative data

were processed.

A further general problem is that of the quality of administrative data. Quality

deficiencies become evident, when data from different (administrative) sources are

linked, which is one of the main methods employed for register-based or register-

supported censuses. It is indispensable to check the quality of the administrative

sources.

A serious problem refers to the over-coverage of the administrative population register,

which serves as a main basis for the census. People do not de-register when they leave

the country for a longer period, to work abroad or to emigrate.

Under-coverage problems may also prevail in databases on educational participation

and status (education acquired abroad and the educational status of immigrants).

The place of residence recorded in the administrative registers does not always reflect

reality.

Some addresses in the administrative population register are, in part, insufficiently

detailed for the correct attribution of the place of residence to persons.


activities and occupations) or have not implemented a required classification at all (e.g.

in the field of education).

The lack of unique IDs in administrative sources impedes the use of the administrative

data for statistical purposes, and extensive record-linkage procedures are necessary.

31

31

CHAPTER 4


GOPA CONSULTANTS


Statistical problems:

Due to the over-coverage of the administrative population register, statistical

procedures need to be elaborated, to correct the over-coverage (applying the “sign-of-

life” method).

Some administrative data corresponding to variables of the traditional national census

are not available. Administrative data are also either not available, incomplete or

insufficient for other core variables such as the activity status of the population,

educational attainment, occupation and economic activity, for example.

The lack of unique IDs needs to be compensated for by implementing comprehensive

record-linkage methods.

The lack of an administrative or statistical register of buildings and dwellings, as well as

of an official database of addresses, leads to quality problems in the resulting statistical

data.

Within the framework of the statistical system, new basic databases may need to be

developed such as a statistical population register or a statistical education register.

Table 7: Frequency of specific problems encountered in projects related to social statistics,

including the Population Census 2021

Problem Frequency

Lack of a unique identifier Most countries

Over-coverage in administrative population registers Most countries

Missing information such as e.g. on educational attainment Most countries


The grant projects yielded country-specific solutions to some common problems:

A first issue relates to access to administrative data and the cooperation with their

owners. Checklists were developed, covering the appropriate procedures to follow as

well as the elaboration of agreements.

Checklists to use when assessing administrative data – based on standards in other

countries – were also developed.

Concerning the over-coverage problem, the sign-of-life method was specified and

tested. The method is based on the fact that a person is normally recorded in more than

one administrative register. If a person is recorded in the population register but is not

to be found in any other administrative register, it can be assumed that that person is

no longer resident in the country. This method is applied by countries that are already

producing census data on the basis of administrative registers.

32

32

CHAPTER 4


GOPA CONSULTANTS


Concerning the missing information on educational diplomas obtained abroad and

generally by immigrants, other data collections are needed. Web scraping or micro-data

exchange with other NSIs might help and should be investigated further.

Given quality issues with the administrative data, the procedure generally followed is

that data from more than one administrative register are compared and a selection is

performed, based on decision rules that ensure the characteristic with the highest

likelihood is taken into the statistical database. Such decision rules help in cases, in

which the administrative data are in contradiction to each other.

The lack of unique IDs is overcome by applying record-linkage methods.

To improve the quality of the administrative data, intensive and permanent

cooperation with the data owners should be exercised, and conceptual and technical

support provided to them.

Table 6 provides a documentation of the solutions and achievements in a condensed form

and offers links to the project reports.


Among the projects´ specific achievements, the following could be considered as candidates

for knowledge transfer to other countries:

Concept of systematically evaluating the administrative sources;

Methodology for the measurement of the quality of administrative data;

A blueprint to mapping and checking the usability of administrative data for statistical

purposes;

Methods for integrating administrative data sources into statistical databases;


Methods and experience in record-linkage.

Entries KT in the column “Knowledge transfer, innovation” in Table 6 point to achievements

that appear as candidates for knowledge transfer.


The issues of unsolved problems are to be seen from the perspective of the respective

countries:

Determination of the economic activity status, especially in what concerns

unemployment;

Lack of data on dwellings and household structure;

Lack of experience in data integration methods and software, including data-warehouse

approaches;

33

33

CHAPTER 4


GOPA CONSULTANTS


Lack of secure data-transmission methods;

Data editing, outlier detection and imputation methods;

Missing data in administrative registers, on diplomas obtained abroad and on the

educational status of immigrants.


Key problems and lessons learned are:

The lack of a register of dwellings;

The importance of analysing and understanding of the administrative databases;

In general, the quality deficiencies of the administrative data sources and missing

metadata;

Definitional differences between the administrative sources and the statistical units;

Missing data on household structure;

Missing data in administrative registers on diplomas obtained abroad and on the

educational status of immigrants.


The following issues may be regarded as being innovations that are of interest to other

countries:

Checklists for the appropriate procedures to follow when using administrative data

sources, and the elaboration of agreements with data owners.

Checklists for the assessment of the administrative data – based on standards in other

countries.

Entries IN in the column “Knowledge transfer, innovation” in Table 6 indicate that the

solutions mentioned are innovative and might be of interest to other countries.

4.2 Achievements and findings: Cluster 2 – Agricultural statistics

This chapter presents a summary review of the eight projects classified in Cluster 2. While

they all deal with agricultural statistics, these projects focus on different topics:

One project analyses whether agricultural labour force data, as requested by the Farm

Structure Survey, can be derived from other statistical or administrative sources.

Data requirements of the Farm Structure Survey are also the topic of a number of other

projects.

Three projects focus on the development and the improvement of the farm register.

34

34

CHAPTER 4


GOPA CONSULTANTS


Three projects essentially deal with the possible use of administrative data in the

production of agricultural (output) statistics.

In most of the studies, the main administrative data source is the IACS, the system that

documents the subsidies granted to farm holders.

4.2.1 Achievements

The projects belonging to Cluster 2 achieved the following results:

Cooperation agreements were signed with the owners of the administrative data

sources.

Countries could access the requested administrative data sources.

The data from the administrative sources were analysed and mapped to the statistical

requirements.

Mapping administrative registers with the statistical farm register resulted in greater

coherence.

Using other statistical data for the labour force variables, to replace survey collection,

was analysed.

The planned statistical farm registers were developed and used for survey purposes.

Proof was provided, that administrative data can be used for various agricultural

statistics concerning the structure and output of the agricultural sector.

In general, knowledge of the administrative databases was acquired, and appropriate

conceptual decisions were taken concerning their use for statistical purposes.


The problems revealed by these eight projects are conceptually quite similar to those

identified among the Cluster 1 projects:

There were no legal problems to accessing administrative data. However, agreements

with the owners of the administrative data were required, developed and signed. Such

Memoranda of Understanding are of course advantageous in cooperating with the data

owners.

Cooperation with data owners was, in part, labelled as being too bureaucratic.

The lack of metadata is also a problem in this area.

The quality of the administrative databases may be deficient, in what concerns their

coverage, the statistical units they employ, breaks in time series, etc.

Lack of unique IDs in the administrative databases makes record-matching work

necessary.

35

35

CHAPTER 4


GOPA CONSULTANTS


The administrative databases are aligned to administrative purposes (mainly grant

subsidies to the farmers). Concepts, units, variables and their definitions are therefore,

to some extent, not in line with statistical requirements. It is indispensable to check the

quality of the administrative databases. That includes basic data checks, such as

missing-value checks and outlier analysis.

Lack of timeliness of administrative data reduces their usability for statistical purposes.

Table 8: Frequency of specific problems encountered in projects related to agricultural

statistics

Problem Frequency

Lack of a unique identifier Most countries

Problems with different statistical units Most countries

Lack of timeliness of the administrative source Most countries


The grant projects developed country-specific solutions to some common problems:

Concerning cooperation with the owners of the administrative sources, the

development of Memoranda of Understanding and close collaboration are proposed, to

guarantee continuous data transmission, the availability of metadata, and future

increases in data quality.

Thorough analyses and documentation of the differences between the concepts and

definitions applied in the administrative databases and the statistical requirements are

a basic prerequisite in order to understand the data and to explore their use for

statistical purposes.

One possible approach to reducing discrepancies between differing concepts is that of

bringing the statistical concepts more in line with the administrative circumstances.

That would however require appropriate discussions at the European level.

Some of the discrepancies between the national administrative databases can be

resolved by changing national regulations.

Independently from any changes to the legal basis, interoperability between

administrative registers increases consistency and reduces costs, as the update and

maintenance of basic data no longer needs to be undertaken individually by

administrative institutions.

The lack of IT resources and expertise can be overcome by engaging external resources.

Table 6 provides a documentation of the solutions and achievements in a condensed form;

no links to project reports are available.

36

36

CHAPTER 4


GOPA CONSULTANTS



The following achievements are candidates for knowledge transfers to other countries:

Experience in cooperating with the owners of the administrative data sources;

Exchanging experience with other MS;

The results of the analysis of mapping administrative data with other statistical sources,

within the framework of the replacement of labour force data requested by the Farm

Structure Survey;

Methods of pre-treatment of data sources and their integration into statistical

databases;

Possibilities and limitations of the IACS databases;

Methodology of a statistical farm register.

Entries KT in the column “Knowledge transfer, innovation” in Table 6 point to achievements

that appear as candidates for knowledge transfer.


The list of unsolved problems needs to be seen from the perspective of the respective

countries:

The need, in the future, to survey the agricultural labour force data to the level of detail

requested by the Farm Structure Survey;

Missing unique IDs for agricultural holdings;

Quality problems in the administrative databases, especially in what concerns

timeliness.



The basic importance of Memoranda of Understanding and of practical cooperation

with the owners of the administrative data;

The importance of analysing and understanding the administrative databases;

The implementation of unique IDs;

The need to increase data quality;

The need to increase consistency between administrative data sources;

Interoperability between the registers;

Administrative and statistical concepts should be brought closer in line.

37

37

CHAPTER 4


GOPA CONSULTANTS


4.3 Achievements and findings: Cluster 3 – Methodological issues

Two projects were assigned to Cluster 3. They however deal with very different issues.

The first project aimed at developing alternative methods for the estimation of large

interrelated contingency tables, in preparation for the 2021 Census. The repeated-weighting

method - earlier developed by Statistics Netherlands - is not always successful in estimating

a consistent table set, due to the fact that it applies a sequential estimation procedure.

The topic of the second project was that of accessing and using data from private companies.

The concrete project aimed at improving the quality of the Household Budget Survey

through using energy data from the public utility companies and “scanner data” from

supermarket chains. The project is expected to establish routines and agreements that

would facilitate access to such private data.

4.3.1 Achievements

The first project developed two algorithms as alternative methods to the repeated-

weighting method. These new algorithms were applied for the consistent estimation of 42

tables from the 2011 Census, in order to test the new method´s feasibility.

In the second project a collaboration process with major network owners has already begun,

and a contract template has been under discussion. However, the legal issues still need to be

solved. The use of private data in these areas may not replace survey collections, but it will

increase the quality of results by validating the data collected and by serving for the

derivation of weights or for calibration. Not all of the expected results were achieved.


The need to estimate large contingency tables using different data sources is quite a

common problem in a register-based census.

In order to use “big data” from private companies, a general issue is the legal basis, which

usually does not exist. As mentioned above, this issue was not solved during the course of

the project.

Another common problem is the creation of a win-win situation with private companies, so

as to increase the probability of data transfers from them. Concrete win-win situations were

still open to develop.


The new method for the estimation of large consistent tables is a useful tool in various

statistical domains, one of which is the register-based census. Table 6 provides a

documentation of the achievements in a condensed form and offers links to the project

reports.

38

38

CHAPTER 4


GOPA CONSULTANTS



The new method for the estimation of large consistent tables should be shared with other

countries as well as with academia (see also the entry KT in the column “Knowledge

transfer, innovation” in Table 6).


The list of unsolved problems should be seen from the perspective of the respective

countries:

Procedures for the estimation of variances of reconciled tables apparently have not yet

been developed.

In the private database project, the legal basis and the creation of win-win situations

represent unsolved problems.



The new method overcomes the problems encountered by the repeated-weighting

procedure.

If the objective is to achieve consistency by minimal adjustment of inconsistent

estimates that have been directly derived from different data sources, the availability of

categories of variables that are contained in each table is a prerequisite. If common

variables are not available in a given table set, those variables need to be artificially

created by adding missing variables to the tables, provided that a data source is

available, from which the resulting extended tables can be estimated.

The legal basis and the establishment of win-win situations for data delivery from

private companies are key issues.

Understanding private databases and the availability of their metadata are of crucial

importance.


The following development can be regarded as an innovation:

The new method for the estimation of large consistent tables (see also the entryIN in the

column “Knowledge transfer, innovation” in Table 6).

Documents

Task 4 round of ESS.VIP ADMIN grants Document Service Data ... · Task 4 Project deliverable D.4 Summary review on the main outputs and findings of the first round of ESS.VIP ADMIN