Quality report of the European Union labour force survey 2021 & 2022 : 2025 edition.
Date: 2025-11-20
Institutions: Eurostat
Subjects: European Union, employment statistics, labour force, labour market, persons in work, unemployment

Quality report of the 
European Union Labour 
Force Survey 2021 & 2022

2025 edition

S TAT I S T I C A L 
R E P O R T

Quality report of the 
European Union Labour 
Force Survey 2021 & 2022

2025 edition

This document should not be considered as representative of the European Commission’s official position.

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Contents

List of abbreviations 

1.  Introduction 

2.  Overview of the EU-LFS designs and methods in 2021 and 2022 

2.1.  Coverage 

2.2.  Legal basis 

2.3.  Reference period 

2.4.  Sampling designs 
Sampling schemes 
Bases used for the sample 
Primary sampling units (PSUs) 
Final sampling units (FSUs) 
Stratification 
Achieved sample and sampling rates 

2.5.  Rotation schemes 

2.6.  Calculation of weighting factors 

2.7.  Data collection methods 

2.8.  Use of subsamples to survey structural variables 

3.  Relevance 

3.1.  Definition 

3.2.  The users 

3.3.  Completeness 

4.  Accuracy 

4.1.  Definition 

4.2.  Sampling errors 

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4.3.  Non-sampling errors 
Coverage errors 
Measurement errors 
Non-response errors 
Processing errors 

5.  Timeliness and punctuality  

5.1.  Definition 

5.2.  Post-implementation monitoring of the new regulation 

6.  Comparability 

6.1.  Definition 

6.2.  Geographical comparability of concepts 

6.3.  Comparability over time 

7.  Coherence 

7.1.  Definition 

7.2.  Coherence with population statistics 

7.3.  Coherence with other employment and unemployment estimates 

Coherence of employment for EU-LFS and Business Statistics 
Coherence of employment for the EU-LFS and National Accounts 
Coherence of unemployment for EU-LFS and registers 

8.  Accessibility and clarity 

8.1.  Definition 

8.2.  Available information 

9.  Annex 

9.1.  Tables 

9.2.  Country codes 

Geographical aggregates and country codes 
Summaries 

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Quality report of the European Union Labour Force Survey 2021 & 2022

 
List of abbreviations

CAPI 

CATI 

Computer Assisted Personal Interviewing

Computer Assisted Telephone Interviewing

CAWI 

Computer Assisted Web Interviewing

EC 

European Commission

EFTA 

European Free Trade Association

ESS 

EU 

FSU 

ILO 

European Statistical System

European Union

Final Sampling Unit

International Labour Organisation

ISCED 

International Standard Classification of Education

LFS 

NSIs 

Labour Force Survey

National Statistical Institutes

NUTS 

Nomenclature of Territorial Units for Statistics / Nomenclature des Unités Territoriales Statistiques (NUTS0-NUTS3)

LAU 

PAPI 

PSU 

Q 

Local Administrative Units

Paper and Pencil Interviewing

Primary Sampling Unit

Quarter

Quality report of the European Union Labour Force Survey 2021 & 2022

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1  Introduction

The quality concept applied to this report follows the European Statistical System Common Quality Framework. This 
framework is based on the European Statistics Code of Practice, the ESS Quality Assurance Framework, and general quality 
management principles such as continuous interaction with users, continuous improvement, integration and 
harmonisation1. The quality is assessed, and the report is structured based on the selected quality components, i.e. those 
most relevant to statistical surveys, of the 16 principles of the European Statistics Code of Practice2; relevance, accuracy and 
reliability, timeliness and punctuality, comparability and coherence, accessibility and clarity. Each chapter presents 
information regarding one quality component3 and its related sub-components.

The report summarises national reference metadata (quality reports) and is the first edition published after the entry into 
force of the updated methodology introduced by the Regulation (EU) 2019/17004 and its Implementing Regulation (EU) 
2019/22405 in the labour force domain6. The main sources are the national quality reports of the 2021 and 2022 European 
Union Labour Force Survey (EU-LFS), submitted annually to Eurostat. In addition, EU-LFS annual and quarterly microdata for 
2021 and 2022 were used. The focus is on reference year 2022, while tables also present data and metadata from 2021 and 
earlier years where relevant.

This report adheres to the standard quality report format established by Eurostat. The purpose of this common structure is 
to enable consistent reporting and summaries of statistical processes across countries. Nevertheless, in some instances the 
information available from individual countries limits the level of detail possible.

This quality report complements the statistical report describing the main characteristics of the national surveys in the EU 
Member States, EFTA and candidate countries, also available on the Eurostat website7. Eurostat wishes to thank all national 
experts for their participation in the EU-LFS and their essential contribution in providing data and metadata to compile this 
report.

1 

2 

3 

4 

5 

6 

7 

6

Details on European quality standards on Eurostat’s website 

European Statistics Code of Practice 

Quality components are explained in the ESS Handbook for Quality Reports 

REGULATION (EU) 2019/1700 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL

COMMISSION IMPLEMENTING REGULATION (EU) 2019/2240

Information about the EU-LFS legal basis and the new methodology from 2021 onwards on Eurostat’s website 

LFS in the EU, EFTA and candidate countries–Main characteristics of national surveys 2021

Quality report of the European Union Labour Force Survey 2021 & 2022

2  Overview of the EU-LFS designs 

and methods in 2021 and 2022

2.1.  Coverage

This report covers the participating countries that provided Eurostat with quarterly and annual data from their labour force 
surveys in 2021 and 2022: the 27 Member States of the European Union, three EFTA countries (Iceland8, Norway and 
Switzerland) and three candidate countries (Bosnia and Herzegovina, Serbia and Türkiye)9.

The EU-LFS covers the entire territory of the participating countries, with the exception of Cyprus, where only the areas 
under the control of the Government of the Republic of Cyprus are included. Since 2014, the French overseas departments 
(Guadeloupe, Martinique, French Guiana and Reunion Island) have also been covered, except Mayotte, which is surveyed 
separately through a specific annual survey10.

According to Regulation (EU) 2019/1700 and its Implementing Regulation (EU) 2019/2240, the statistical population of the 
EU-LFS consists of all persons who have their usual residence in a private household in each Member State. Data collection is 
therefore carried out on the basis of samples of private households or of persons belonging to such households. While 
persons living in collective households are generally excluded from this statistical population, certain groups (such as 
student or worker accommodation) may in practice be included in some national implementations, either directly through 
dedicated sampling frames or indirectly via links to private households.

Regardless of differences in sampling design, sampling frames or sampling units, the observation units in the EU-LFS are 
private households and the persons belonging to them. For instance, while dwellings may be used as a final sampling unit, 
the actual observation units are households and the persons belonging to them.

In line with the EU-LFS requirements under the Regulation (EU) 2019/2240, questions relating to employment status are put 
to all persons aged 15–89, except in Spain, Portugal, and Iceland, where interviews start at age 16.

2.2.  Legal basis

The EU-LFS has been governed by European legislation since 1973. Major methodological changes were introduced from 
2021 onwards with the entry into force of Regulation (EU) 2019/1700 and its Implementing Regulation (EU) 2019/2240, which 

8 

9 

For Iceland, quarterly and annual microdata were transmitted for 2021 and 2022, but no national quality reports were provided for these years. 
Consequently, information on survey implementation and quality documentation is only partially available.

Data for North Macedonia and Montenegro could not be included in this report due to challenges in data transmission.

10  Until 2014, the French overseas departments (Département d’outre-mer – DOM) had only partial coverage over time, as data collection only referred to Q2. 
In Mayotte this is still the case and 2022 data for this department is still not included in the standard French datasets. Data from Mayotte is nevertheless 
included in the regional tables.

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Overview of the EU-LFS designs and methods in 2021 and 2022

specifies the implementation rules, technical items and content of the EU-LFS. The purpose of these regulations is to 
enhance the quality and comparability of statistics across countries and domains in social statistics.

Alongside EU regulations, many participating countries also have national legislation governing the implementation of their 
labour force surveys. This report does not provide information on those national laws or regulations except for the following: 
in 2022, the participation, an obligation of a sampled unit to respond and provide requested information, in the EU-LFS was 
compulsory in 14 countries (Belgium, Germany, Spain, France, Italy, Cyprus, Luxembourg, Malta, Austria, Portugal, Slovakia, 
Norway, Serbia and Türkiye) and voluntary in 19 countries, (Bulgaria, Czechia, Denmark, Estonia, Ireland, Greece, Croatia, 
Latvia, Lithuania, Hungary, Netherlands, Poland, Romania, Slovenia, Finland, Sweden, Iceland, Switzerland and Bosnia and 
Herzegovina).

2.3.  Reference period

The EU-LFS collects information related to the situation of the reference week, which runs from Monday to Sunday. The 
EU-LFS is designed as a continuous quarterly survey, with interviews spread over four reference quarters. Therefore, the full 
national sample for the reference year is evenly distributed across all reference quarters. Within each reference quarter, the 
full quarterly sample is further evenly distributed across the reference weeks, except for quarters consisting of 14 weeks. In 
the latter case, the sample may first be evenly distributed in 13 reference weeks and then either one week is skipped or the 
sample of one reference week is split across two typical reference weeks, in order to cover all 14 weeks 11.

2.4.  Sampling designs

Probability sampling forms the foundation of the EU-LFS. By ensuring that each unit in the target population has a known, 
non-zero probability of selection, probability sampling enables survey results to be generalised to the population when 
appropriate weighting and estimation procedures are applied. Across countries, several different probability-based sampling 
designs are implemented, depending on national circumstances and available sampling frames.

Sampling schemes

Stratified two-stage sampling is the most common sampling approach applied in the 2022 EU-LFS, used by the majority of 
participating countries (Belgium, Bulgaria, Croatia, Czechia, Greece, Spain, Metropolitan France, Hungary, Ireland, Italy, Latvia, 
Netherlands, Poland, Romania, Slovakia, Bosnia and Herzegovina, Serbia and Türkiye). Two-stage cluster sampling is also 
applied (Portugal). Approximately one quarter of the countries apply either one-stage stratified designs (Austria, Cyprus, 
Denmark, France-Overseas and Norway), one-stage cluster (Germany) or simple random sampling (Lithuania, Luxembourg 
and Iceland). Systematic or stratified systematic sampling is employed in some of the smaller Member States (Estonia, 
Finland, Malta, Slovenia and Sweden). In addition, Switzerland applies a specific solution with two separate subsamples, both 
of which are stratified.

Bases used for the sample

A sampling frame is a set (e.g. a list or a database) that contains all sampling units of the target population from which the 
sample is drawn. Countries can broadly be divided into two groups according to the source of their sampling frame in 2022.

One group relies mainly on national registers (Belgium, Denmark, Estonia, Finland, Italy, Lithuania, Luxembourg, Hungary, 
Malta, Netherlands, Austria, Slovenia, Sweden, Iceland, Norway, Switzerland, Türkiye). Another group uses primarily census-
based frames (Bulgaria, Cyprus, Czechia, Germany, Greece, Spain, Overseas France, Croatia, Ireland, Poland, Portugal, 
Romania, Slovakia, Bosnia and Herzegovina and Serbia).

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Regulation (EU) 2019/2240 contains details on the derivation of reference periods and reference dates 

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Overview of the EU-LFS designs and methods in 2021 and 2022

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Additionally, some countries apply mixed or special solutions, such as Metropolitan France (the demographic file of 
dwellings and individuals (Fidéli), built from tax registers), and Latvia (a two-stage frame combining census areas with 
demographic and real estate-registers).

Primary sampling units (PSUs)

Primary sampling unit is the sampling unit selected in the first stage of sampling. The PSUs in the 2022 EU-LFS data 
collection in nine countries are derived either from census-based areas (Germany), census-based clusters or segments 
(Bulgaria, Czechia, Croatia, Poland, Romania, Slovakia, Bosnia and Herzegovina and Serbia).

A second group consists of 11 countries and defines PSUs as blocks of households (Türkiye) or geographical or administrative 
areas (Spain, Metropolitan France, Latvia, Netherlands, Portugal), statistical sections (Belgium), municipalities (Italy), or blocks 
of buildings (Ireland and Greece).

A PSU may also correspond to the household (Switzerland). Moreover, in 13 countries (Denmark, Estonia, France-Overseas, 
Cyprus, Lithuania, Luxembourg, Malta, Austria, Slovenia, Finland, Sweden, Iceland and Norway), no explicit PSUs are defined, 
and the sampling is directly targeted to a person, a household or household(s) in the selected dwelling from which the 
information is collected.

Final sampling units (FSUs)

The final sampling unit is the unit selected at the last stage of the sampling, which constitutes the ultimate target of 
selection. Depending on the sampling design, it may be a household, an individual person, or a dwelling, which are the 
main FSU categories 2022 in EU-LFS. The distribution across participating countries is as follows:

•  Households (Belgium, Bulgaria, Estonia, Ireland, Italy, Cyprus, Lithuania, Malta, Slovenia, Bosnia and Herzegovina and 

Serbia),

•  Dwellings (Czechia, Greece, Spain, France, Croatia, Latvia, Hungary, Austria, Poland, Portugal, Romania, Slovakia and 

Türkiye) and

•  Persons (Denmark, Luxembourg, Netherlands, Finland, Sweden, Norway and Switzerland).

Germany samples clusters of dwelling units, where all households in the sampling districts and all persons in the households 
are to be surveyed. As indicated by the list above, the FSU does not necessarily coincide with the observation unit. While the 
FSU represents the entity selected in the sampling design, the observation unit refers to the entity for which information is 
actually collected. In the EU-LFS, the observation unit is the person or household, as household-level variables are also 
recorded.

Stratification

Stratified sampling is a probability sampling method in which the population is divided into distinct, non-overlapping 
sub-groups (strata) that share certain characteristics, such as geographical region, settlement type, or demographic 
attributes. Random samples are then drawn separately from each stratum, either proportionally or with specific allocation 
rules, to ensure that all relevant groups are adequately represented in the survey.

Stratification is applied in almost all countries participating in the 2022 EU-LFS. Only a few countries do not stratify their 
samples (Lithuania, Malta and Iceland).

In total 28 of the 30 countries using stratified sampling use geographical variables such as regions at LAU12, NUTS2 or NUTS3 
level and settlement type. Only two countries (Denmark and Luxembourg) do not employ geographical variables in 
stratification.

12 

Local Administrative Units (LAUs) are the building blocks of the NUTS and comprise the municipalities and the communes of the European Union.

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Overview of the EU-LFS designs and methods in 2021 and 2022

Several countries also combine geography with other characteristics, such as building size class (Germany), settlement type 
or size or both (Bulgaria, Greece, Croatia, Italy, Cyprus, Latvia, Hungary, Poland, Romania, Slovenia, Bosnia and Herzegovina, 
Serbia and Türkiye).

Some countries include demographic or socio-economic characteristics, such as age, sex or both (Luxembourg, Finland, 
Sweden and Norway); administrative employment status, either directly or indirectly (Belgium, Denmark, Ireland and 
Norway); income distribution and household characteristics (Belgium); the administrative county Pobal HP Deprivation Index 
(Ireland); and nationality (Luxembourg).

Achieved sample and sampling rates

The achieved sample size in this report is defined as the number of observational units from which data were successfully 
collected. For the 2022 EU-LFS, the achieved quarterly sample (average over the quarters) was 1.421 million individuals for all 
participating countries (EU 1.161 million), of whom 1.216 million are in the 15–89 age group (EU 1.006 million). The achieved 
sample size for persons aged 15 to 89 in EU ranged from around 3.1 thousand persons (Malta) to 111.1 thousand persons 
(Italy).

The achieved sampling rate, defined as the ratio of the achieved sample size to the total population, allows for comparisons 
across countries and regions of different population sizes. In practice, smaller populations often achieve relatively high 
coverage despite smaller absolute samples, whereas larger populations achieve lower coverage despite larger absolute 
samples. For most countries, the rate remains below 1%, yet such sampling fractions in large scale surveys are generally 
sufficient to produce precise estimates, as precision depends more on the absolute sample size than on the proportion of 
the population covered. The achieved sampling rate for the 15–89 age group in the EU ranged from 0.16% (Germany) to 
1.06% (Malta). Overall, the 2022 EU-LFS covered approximately 0.26% (EU 0.27%) of the total population.

The differences in sample size have implications for compliance with the precision requirements laid down in Annex II to 
Regulation (EU) 2019/1700. The regulation expresses precision in terms of standard errors as functions of population size at 
national and regional (NUTS2) level, rather than fixed minimum sample sizes. Consequently, both small and large countries 
are expected to adapt their survey designs so that estimates of the key labour force indicators remain within the permissible 
error bounds. Larger countries, with higher absolute sample sizes, can achieve the required level of statistical precision 
despite lower sampling fractions, while smaller countries generally need higher relative sampling rates in order to meet the 
precision requirements set in the regulation.

2.5.  Rotation schemes

In survey design, a rotation scheme is a continuous procedure in which, in repeated surveys, a proportion of the respondents 
is dropped and replaced with new units. This rotating panel model combines characteristics of both cross-sectional studies 
(new samples) and panel studies (the same respondents over time). Rotation schemes make it possible to obtain estimates 
of both the current situation and changes over time, while keeping the variation caused by changes in the population 
structure under control.

According to the Regulation (EU) 2019/2240, sample rotation scheme means the split of the total sample into sub-samples of 
observation units similar in size and design. This is used to specify the number of times and the exact reference quarters of 
the year for which an observation unit provides information in the survey. Wave means the sub-sample of observation units 
to be interviewed for the same nth time according to the sample rotation scheme in a reference quarter.

Annex III to Regulation (EU) 2019/1700 sets the minimum requirements for the rotation design. Specifically, the samples must 
have an infra-annual rotation scheme, meaning that they are adjusted throughout the year, with a proportion of 
respondents being replaced by new participants. There must also be a minimum sample overlap of 20% between the same 
quarters in consecutive years and of 50% between consecutive quarters, without taking into account attrition.

The most prevalent panel design in 2022, implemented by 17 participating countries, is the ‘2-(2)-2’ rotation scheme. Under 
this design, sampled units are interviewed for two consecutive quarters, rotated out from the sample for the subsequent 
two quarters, and then reintroduced for an additional two quarters. Two countries apply a comparable design, in which 

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Overview of the EU-LFS designs and methods in 2021 and 2022

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sampled units are excluded for a number of quarters and subsequently reintroduced, albeit with a slightly different rotation 
pattern (‘3-(1)-2’ and ‘3-(2)-2’).

Other rotation patterns used involve panels remaining in the survey for n consecutive waves. In these designs, sampled units 
are interviewed for n successive quarters before being permanently rotated out of the sample. The corresponding rotation 
schemes are ‘6-’ (6 countries), ‘5-’ (6 countries), and ‘8-’ (2 countries).

Depending on national priorities concerning the desired precision of change estimates, cross-sectional levels or annual 
averages, the chosen number of waves and the use of rotation break patterns result in different proportions of overlapping 
populations, either between successive quarters or between the same quarters in consecutive years. All participating 
countries meet the minimum requirements of the new EU-LFS regulation, as their rotation patterns ensure the required 
overlaps and infra-annual rotation at design level (assuming equal-sized waves and excluding attrition). Specifically, the 
theoretical overlaps of the models are at least 50% between consecutive quarters and at least 20% between the same 
quarter from one year to the next.

2.6.  Calculation of weighting factors

Regulation (EU) 2019/2240 establishes common standards for calculating weighting factors. The reference population for 
weighting is defined as the (real or estimated) population usually residing in private households (Article 9(3)). Weighting 
factors must account for the probability of selection and the distribution of the target population using the best available 
external data.

At the individual level, quarterly, annual, and biennial estimates must take into account sex, age groups (with five-year 
intervals as the standard), and region at NUTS 2 level (Article 9(4a)). Consistency must also be ensured between annual totals 
of sub-samples for annual and biennial structural variables and full-sample annual averages for ILO labour status by sex 
across specified age groups (Article 9(4b)). Certain core labour market variables (WKSTAT, ABSREAS, JATTACH, SEEKWORK, 
ACTMETNE and AVAILBLE, see Annex I of (EU) 2019/2240 for descriptions) must not be used in the weighting process (Article 
9(7)). The participating countries broadly rely on post-stratification and calibration to compute final survey weights.

•  In post-stratification, sample units are stratified after data collection, using survey and auxiliary information. This adjusts 

design weights to match population control totals and can also correct for non-response.

•  In calibration, weights are adjusted through calibration equations in such way that survey estimates correspond to known 
totals of auxiliary variables to the best possible extent. The calibrated weights are then used to produce linearly weighted 
estimates of totals and other parameters. The method aims to achieve estimates that are nearly design-unbiased when 
non-response and other non-sampling errors are minimal.

Several countries complement these approaches with model-assisted estimation methods, such as generalised regression 
(GREG) and generalised linear models (GLMs). These are applied, for instance, to exploit correlations between auxiliary 
variables and key labour force indicators, to improve adjustment for non-response, or to achieve higher precision than 
standard linear calibration by modelling relationships between survey variables and explanatory factors.

In 2022 EU-LFS, all countries use sex in the weighting process. Almost all countries apply the standard five-year (or finer) age 
groups when calculating weighting factors. However, some deviations and broader age groups exist especially at the tails, 
i.e. for persons under 15 or over 74 years old. All countries with a NUTS-2 subdivision use at least NUTS-2 in weighting. 
Moreover, several countries use a more detailed regional classification (NUTS-3 or LAU).

In addition to the core dimensions of sex, age, and region, many countries incorporate a range of contextual and 
administrative auxiliary variables into their weighting procedures. Commonly used variables include place of residence or 
settlement type (urban–rural distinctions or municipal classification), and household characteristics, such as household size 
and composition. Migration- and nationality-related attributes are also employed, such as nationality, foreigner status, 
country of birth and duration of residence. Some countries additionally incorporate socioeconomic measures, such as 
education and income, possibly alongside spatial indicators such as metropolitan or major-town residence. For weighting 
annual sub-samples, the ILO labour status estimated based on the full quarterly sample is also used to ensure consistency 
between the full-sample annual average and annual estimates based on sub-samples.

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Some countries make use of labour-market registers. These include registration at employment offices or register-based 
employment status. Importantly, in all cases where labour-market information is incorporated, it is derived from 
administrative registers or through statistical modelling rather than from any of the EU-LFS survey variables used to 
construct the ILO labour status, thereby remaining in compliance with Article 9(7).

In total 19 countries (Belgium, Bulgaria, Germany, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, 
Hungary, Malta, Netherlands, Austria, Romania, Slovenia and Norway) expand private household samples to the private 
household reference population. A group of 13 countries (Czechia, Denmark, Estonia, Ireland, Poland, Portugal, Slovakia, 
Finland, Sweden, Switzerland, Bosnia and Herzegovina, Serbia and Türkiye) use broader definitions, often extending to the 
total population, specific age groups, or including collective households.

2.7.  Data collection methods

In accordance with Article 7(3) of Commission Implementing Regulation (EU) 2019/2240, interviews for the EU-LFS are to be 
conducted using computer-assisted interviewing methods, including computer-assisted personal interviewing (CAPI), 
computer-assisted telephone interviewing (CATI) and computer-assisted web interviewing (CAWI), except in duly justified 
cases. In practice, EU-LFS data collection is conducted mainly by CAPI, CATI, CAWI and pen-and-paper personal interviews 
(PAPI).

Data-collection modes vary across countries. A total of 26 countries rely solely on computer-assisted techniques (CAPI, CATI 
and CAWI), with substantial internal variation; for example, Greece and Romania use CAPI only, while Cyprus, Malta, Sweden, 
Iceland and Norway rely exclusively on CATI. Furthermore, seven countries employ a broader mix that includes paper 
questionnaires (PAPI) and other methods. Among them, five countries (Bulgaria, Czechia, Germany, Lithuania and Poland) 
continue to use PAPI to varying degrees.

Remote-only approaches, limited to CATI and CAWI, have been adopted in nine countries (Denmark, Estonia, Cyprus, 
Luxembourg, Malta, Sweden, Iceland, Norway and Switzerland). In total, 24 countries employ some form of personal 
interviewing (CAPI or PAPI), underscoring the continued role of face-to-face methods, while 13 countries make use of 
web-based interviewing (CAWI), most prominently Luxembourg, the Netherlands, Finland, Austria, Germany and 
Switzerland.

Overall, the picture reflects a strong reliance on CATI across many countries, selective but significant adoption of CAWI, a 
continuing role for CAPI, and the gradual decline, albeit not the disappearance, of PAPI in EU-LFS data collection. This is also 
evident in the aggregate figures. Table 2.1 shows that in 2022, nearly half of all interviews were conducted via CATI (49.2% for 
persons aged 15 to 74 and 47.3% for those aged 15 to 89), while about one quarter relied on CAPI (25.1% and 25.2% for 
persons aged 15 to 74 and 15 to 89, respectively).

An examination of the period 2019–2022 for the 15–74 age group reveals the following trends. CAWI has expanded in recent 
years, reaching 17.2% in 2022 compared with only 3.9% in 2019, illustrating its growing role in survey data collection. By 
contrast, PAPI has declined markedly, from 13.1% in 2019 to just 6.6% in 2022, confirming that paper-based interviewing is 
becoming increasingly marginal, though it persists in a handful of countries. Together, these trends underscore a general 
shift towards computer-assisted and remote modes of data collection, with CATI remaining the cornerstone of EU-LFS 
implementation but CAWI gaining momentum as a complementary, modern alternative.

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TABLE 2.1
EU-LFS interviews by mode of data collection in the participating countries, 2019 
– 2022 (% of interviews)

Data collection technique

CAPI

CATI

CAWI

PAPI

Imputed

Other

Persons aged 15-89

Persons aged 15-74

2022

25.2 

47.3 

15.8 

7.0 

4.1 

0.7 

2021

22.9 

52.9 

12.5 

7.5 

3.6 

0.6 

2022

25.1 

49.2 

17.2 

6.6 

1.3 

0.6 

2021

22.9 

54.5 

13.5 

7.1 

1.6 

0.5 

2020

19.5 

61.2 

7.4 

10.6 

-

-

2019

36.7 

45.9 

3.9 

13.1 

-

-

2.8.  Use of subsamples to survey structural variables

Article 2(2) of Commission Implementing Regulation (EU) 2019/2240 defines a ’structural variable’ as ‘an annual, biennial, 
eight-yearly variable or a variable collected on an ad hoc subject’. All structural variables must be transmitted for each 
quarter of the reference year. The use of sub-samples for collecting structural variables is permitted under specific 
conditions:

•  at least one complete wave per quarter and a size not smaller than 1/8 of the quarterly sample;
•  annual and biennial variables may be based either on the full quarterly samples or on a single sub-sample covering all 

weeks of the year (with the biennial sub-sample nested within the annual one);

•  eight-yearly and ad hoc topics must be based on a single sub-sample covering all weeks, nested within the annual and 

biennial sub-samples (Article 6(7)).

Member States using a sample of persons from private households may submit information on the other members of those 
households (the ‘minimum set of variables’) from a dedicated household sub-sample. This household sub-sample must 
consist of independent observations covering all weeks of the year, fully meet the distribution requirements, and include at 
least one wave per quarter or at least 15 000 households (Article 6(8)). Where a person sample, a household sub-sample, and 
a separate sub-sample for eight-yearly detailed topics are used, Member States must also transmit the minimum set of 
variables for all households containing the persons selected for those topics. This applies specifically to the eight-yearly 
topics on the labour-market situation of migrants and their descendants, pensions and labour market participation, and the 
reconciliation of work and family life (Article 6(9)). In 2022, 18 countries (Belgium, Bulgaria, Czechia, Germany, Ireland, Greece, 
Spain, France, Latvia, Luxembourg, Hungary, Malta, Netherlands, Portugal, Slovenia, Finland, Norway and Switzerland) used a 
sub-sample to survey all or some of the structural variables, taking advantage of the option to collect annual and biennial 
structural variables from an independent yearly subsample instead of the full quarterly samples, in line with the 
requirements laid down in the regulation. Under Article 9(4) of the Regulation, the estimates produced from the yearly 
subsample must be consistent with the 30 totals for ILO employment status (employment, unemployment and outside the 
labour force) by sex (males and females) and age groups (15–24, 25–34, 35–44, 45–54 and 55+) between the annual average 
of quarterly estimates and the yearly estimates from the subsample.

Among the countries applying the wave (subsample) approach for structural variables, the majority reported that annual 
estimates based on subsamples were aligned with the corresponding annual averages derived from quarterly data. 
Nevertheless, a minority of cases exhibited partial inconsistencies. These deviations typically arose from the application of 
alternative age-group classifications (for instance, five-year or broader bands in place of the prescribed groups (15–24, 25–34, 
35–44, 45–54, 55+)), from reliance on recalibration procedures, or from minor discrepancies confined to specific 
demographic subgroups.

Quality report of the European Union Labour Force Survey 2021 & 2022

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3  Relevance

3.1.  Definition

Relevance is the degree to which statistical information meets current and potential needs of the users.

Relevance refers to whether all required statistics are produced and to what extent the concepts used (definitions, 
classifications etc.) reflect user needs. It can be assessed by analysing different users, e.g. who they are, what needs they 
have and how satisfied they are.

Most EU statistics are compiled according to regulations containing a defined list of variables, which reflect the needs of 
institutional users such as the European Commission, the European Central Bank and international organisations. If certain 
variables required by the regulation are not covered, the statistics are incomplete. Accordingly, incomplete coverage of the 
mandatory variables reduces the overall relevance of the statistics.

3.2.  The users

Eurostat carries out a general User Satisfaction Survey every year as part of its Quality Assurance Framework (QAF), in order 
to collect feedback on the quality of statistics13. The survey is primarily aimed at registered Eurostat users, i.e. mainly 
students, academics, private users and affiliates of businesses, governments or international organisations. According to the 
results of the 2022 User Satisfaction Survey, 74.0% of the respondents rate the statistics on the labour market provided by 
Eurostat as ‘very good or good’.

The instrument of ad hoc modules has proven to be useful and flexible regarding major topics of interest that the standard 
EU-LFS does not cover.

Users appreciate the availability of a release calendar for the main indicators produced by the EU-LFS, although some would 
like to have more timely data releases, at least for a few of the key indicators.

3.3.  Completeness

All EU countries deliver quarterly and annual microdata.

Although countries comply with the EU regulations on the EU-LFS, they do not always provide data for all variables. This 
might be because of (temporary) inability to implement a variable in the national questionnaire or insufficient time to test a 
required change.

13 

Evaluations of the European statistical programmes on Eurostat’s website 

14

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Relevance

Furthermore, it is compliant to collect and transmit structural variables only in an annual sub-sample (see Section 2.4). 
Household data is another special case. In line with Regulation (EU) 2019/1700, Denmark, Luxembourg, Finland and Sweden 
provide data for complete households only for an annual sub-sample of their standard sample of individuals. For EFTA 
countries, the transmission of household data is subject to a general derogation, and therefore no household data are 
provided (except for Norway from 2021 onwards).

Tables 3.1 and 3.2 below summarise the completeness of the data. A more detailed analysis, by country and by variable, is 
provided in the Annex.

TABLE 3.1
Completeness of the EU-LFS variables for the EU and participating countries, 
2021-2022 (number of countries)

Number of variables with >30 % item 
non-response (1)

2022

2022

2021

2021

All countries

EU countries

All countries

EU countries

0 variables

1-4 variables

5-9 variables

over 9 variables

Total

17

12

3

1

33

17

9

1

0

27

15

11

5

2

33

15

8

3

1

27

(1)  The variable INCGROSS (gross monthly pay from the main job) and INCGROSS_F (flag on gross monthly pay from main job) are not included as these may be 
sent to Eurostat within 15 months after the end of the reference period.

In 2022, just over half of the participating countries collected and transmitted complete EU-LFS datasets, with none of the 
variables exceeding 30% of item non-response. By contrast, 12 countries (including 9 EU countries) reported between 1 and 
4 variables, while 3 countries (1 EU country) had between 5 and 9 variables above the 30% threshold (see Table 3.1). Overall, 
data completeness improved between 2021 and 2022.

Table 3.2 shows that, in total, 33 EU-LFS variables in 2022 recorded item non-response rates exceeding 30% in at least one 
country. The highest incidences were observed for variables relating to the total duration of temporary main job (TEMPDUR), 
contractual working hours in main job (CONTRHRS), the number and importance of clients of self-employed persons 
(MAINCLNT), health related aspects such as self-perceived health (GENHEALTH), work experience at a workplace (HATWORK), 
and information whether public employment service helped to find the current main job (WAYJFOUN). In most occurrences, 
the issue was limited to one country, while only six variables exceeded the 30% threshold in more than two countries.

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3

Relevance

TABLE 3.2
EU-LFS variables having one or more countries with over 30% item non-
response, 2022 (¹)

Variable 
identifier

Variable name

MODE

PROXY

Interviewing mode used

Nature of participation in the survey

CITIZENSHIP Country of main citizenship

WANTREAS Main reason for not wanting to work

TEMPDUR

Total duration of temporary main job

TEMPAGCY

Contract with a temporary employment agency for the main job

MAINCLNT

Number and importance of clients in the 12 months ending with the 
reference week

VARITIME

Decision on the start and end of working time

SUPVISOR

Supervisory responsibilities in main job

SIZEFIRM

Size of the local unit for main job

LOOKOJ

HWWISH

SEEKDUR

NEEDCARE

Looking for another job

Number of hours that the person would like to work in total in a week

Duration of search for employment

Main reason why care for children or incapacitated relatives limits 
labour market participation

MAINSTAT

Main activity status (self-defined)

HATFIELD

HATYEAR

Field of the highest level of education successfully completed

Year when the highest level of education was successfully completed

HATWORK

Work experience at a workplace as part of HATLEVEL

WAYJFOUN

Public employment service helped to find the current main job

FINDMETH

Most effective method used to find the current main job (for persons 
in employment)

NACEPR2D

Economic activity of the local unit in which person last worked

ISCOPR3D

Occupation in the last job

CONTRHRS

Contractual working hours in main job

HWACTU2J

Number of hours actually worked in second job

EDUCFED4

EDUCLEV4

Participation in formal education and training (student or apprentice) 
in the last 4 weeks

Level of the most recent formal education or training activity in the 
last 4 weeks

EDUCNFE4

Participation in non-formal education and training in the last 4 weeks

EDUCFED12

Participation in formal education and training (student or apprentice) 
in the last 12 months

Number of countries >30% 
item non-response

All countries EU countries

1 

1 

1 

1 

4 

1 

3 

2 

1 

1 

1 

2 

1 

1 

1 

1 

1 

3 

3 

1 

2 

2 

4 

1 

1 

1 

1 

1 

1 

1 

0 

0 

3 

0 

2 

1 

0 

0 

1 

0 

1 

0 

1 

0 

1 

0 

0 

0 

1 

1 

1 

1 

1 

1 

1 

0 

16

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Relevance

Variable 
identifier

EDUCLEV12

EDUCNFE12

Variable name

Level of the most recent formal education or training activity in the 
last 12 months

Participation in non-formal education and training in the last 12 
months

GENHEALTH Self-perceived general health

GALI

Limitation in activities because of health problems

REGISTER

Registration at a public employment service (PES)

Number of countries >30% 
item non-response

All countries EU countries

1 

2 

2 

3 

1 

1 

1 

1 

2 

0 

(¹)  The variable INCGROSS (gross monthly pay from the main job) and INCGROSS_F (flag on gross monthly pay from main job) are not included as these may be 
sent to Eurostat within 15 months after the end of the reference period.

Quality report of the European Union Labour Force Survey 2021 & 2022

17

4  Accuracy

4.1.  Definition

Accuracy of data is the closeness of computations or estimates to the exact or true values that the statistics were 
intended to measure.

Statistics based on samples may differ from the (unknown) true population values due to random error (sampling variability) 
or systematic error (bias). Random error causes estimates to fluctuate randomly around the true value, while bias shifts the 
expected value of the estimates systematically away from the true population values.

Several types of error, stemming from all survey processes, contribute to the total error of the statistics (bias and variance). A 
certain typology of errors is widely adopted in statistics. Sampling errors affect only surveys using samples. Errors occur because 
only a subset of the population, usually selected at random, is surveyed. Non-sampling errors affect both sample surveys and 
complete enumerations, and include coverage errors, measurement errors, non-response errors and processing errors14.

4.2.  Sampling errors

Sampling errors only affect sample surveys and occur because not all units of the target population are surveyed. The 
sampling frame defines and enables access to population units, such as lists of households with addresses. The sampling 
frame is the reference source(s) from which the sample (e.g. individuals, households, addresses and dwellings) is drawn. 
Official representative quantitative surveys, such as the EU-LFS, generally use probability sampling. This makes it possible to 
quantify sampling errors, which can be expressed, for instance, through confidence intervals (CIs).

Table 4.1 presents the annual estimates for 2022 and associated 95% CIs for three main indicators in all participating 
countries. The estimates are reported as estimated value ± CI half-width. The CIs are based on standard errors provided by the 
countries, which take into account the national sampling designs and weighting procedures. Under a normal approximation, 
the resulting intervals are symmetrical. The CI reflects the long-term reliability of the estimation method; if the same 
sampling and estimation procedures were repeated many times, approximately 95% of the intervals would cover the true 
population parameter.

A smaller CI half-width naturally means a narrower interval and therefore indicates a more precise estimate for a given level 
of confidence. As shown in Table 4.1, employment rates and unemployment-to-population ratios are consistently more 
precise than youth unemployment rate as a percentage of labour force, as reflected in the smaller values of CI half-width. 
This is expected, since the youth unemployment rate is based on a smaller subgroup of the population, which naturally 
leads to greater random variation.

14  Details about sampling and non-sampling errors are presented in the ESS handbook for Quality Reports

18

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4

Accuracy

TABLE 4.1
Confidence limits(a) for employment calculations, annual average for 2022

Country

Employment rate 

Unemployment-to-
population ratio 

Youth unemployment rate 
as a percentage of labour 
force

Age group: 15-74

Age group: 15-74

Age group: 15-24

Belgium
Bulgaria
Czechia
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal¹
Romania
Slovenia
Slovakia
Finland
Sweden
Iceland
Norway
Switzerland
Bosnia and Herzegovina
Serbia
Türkiye

(1)  Age groups 16–74 and 16–24
(a)  Calculated at the 95% confidence level

58.0 ± 0.4
59.9 ± 1.3
65.1 ± 0.6
68.2 ± 0.2
67.4 ± 0.2
69.2 ± 0.9
67.1 ± 0.8
52.8 ± 1.0
56.4 ± 0.3
58.6 ± 0.3
54.8 ± 1.0
52.2 ± 0.2
66.6 ± 0.9
63.9 ± 0.5
66.2 ± 1.2
63.5 ± 1.2
64.2 ± 0.3
67.8 ± 0.6
72.2 ± 0.2
65.4 ± 0.2
61.2 ± 0.3
63.3 ± 0.7
54.0 ± 1.1
62.6 ± 0.4
62.2 ± 0.8
63.9 ± 0.4
68.8 ± 0.3
-
70.3 ± 0.4
71.9 ± 0.3
43.3 ± 0.6
54.3 ± 0.4
49.6 ± 0.3

3.4 ± 0.2
2.6 ± 0.2
1.5 ± 0.2
3.2 ± 0.1
2.2 ± 0.04
4.1 ± 0.4
3.1 ± 0.4
7.5 ± 0.6
8.5 ± 0.2
4.6 ± 0.1
4.0 ± 0.4
4.6 ± 0.1
4.5 ± 0.4
4.7 ± 0.3
4.2 ± 0.7
3.1 ± 0.4
2.4 ± 0.1
2.4 ± 0.3
2.6 ± 0.1
3.3 ± 0.1
1.8 ± 0.1
4.2 ± 0.2
3.2 ± 0.5
2.6 ± 0.2
4.1 ± 0.4
4.6 ± 0.2
5.6 ± 0.2
-
2.4 ± 0.1
3.1 ± 0.1
7.9 ± 0.4
5.7 ± 0.2
5.8 ± 0.1

16.4 ± 1.6
10.6 ± 2.0
6.8 ± 1.8
10.6 ± 0.7
6.0 ± 0.2
18.6 ± 3.1
10.1 ± 2.0
31.4 ± 5.5
29.7 ± 1.8
17.3 ± 0.6
17.7 ± 2.7
23.7 ± 0.9
18.1 ± 3.2
15.3 ± 2.5
11.9 ± 4.7
17.6 ± 4.6
10.6 ± 0.9
9.0 ± 2.0
7.6 ± 0.3
9.5 ± 0.9
10.8 ± 1.4
19.1 ± 2.2
22.8 ± 3.6
10.1 ± 1.4
19.9 ± 3.0
14.2 ± 1.2
21.7 ± 1.2
-
10.0 ± 0.8
6.8 ± 0.6
35.1 ± 2.7
24.3 ± 2.0
19.5 ± 0.6

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Accuracy

4.3.  Non-sampling errors

Coverage errors

A coverage error (or frame error) arises when there is a discrepancy between the survey population and the target 
population due to imperfections in the sampling frame, i.e. the set of units from which the sample is drawn. There are three 
main types of coverage error:

•  Under-coverage, where the frame population does not include all units of the target population,
•  Over-coverage, where the frame population includes units that do not belong to the target population, and
•  Misclassification, where some units of the frame population that belong to the target population are wrongly classified.

TABLE 4.2
Evaluation of coverage errors, 2022

Country

Under-coverage rate (%)

Over-coverage rate (%)

Misclassification rate (%) 

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

< 0.5%

-

-

-

-

-

-

Y

-

Y

Y

 Y

1.97%

-

-

-

-

-

-

-

-

-

4.83%

N

-

N

Y

9.91%

10.6%

-

-

1.2%

-

2.5%

-

N

9.9%

1.56%

-

1.92%

0.68%

-

19%

0.53%

-

-

1.58%

4.41%

< 3%

N

-

1.06%

Y

-

-

-

-

-

-

-

-

N

-

1.36%

-

-

-

-

-

-

-

-

-

-

-

N

N

-

20

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Accuracy

Country

Under-coverage rate (%)

Over-coverage rate (%)

Misclassification rate (%) 

Sweden

Iceland

Norway

Switzerland

Bosnia Herzegovina

Serbia

Türkiye

< 1%

-

-

N

-

-

-

< 1%

-

-

N

0.56%

3.96%

11.37%

(Y) Indicates the presence of a coverage error.
(N) Indicates that there is no coverage error, or it is negligible.
(-) Indicates that the country has not been able to provide the information or does not know.

-

-

-

N

-

-

-

Under-coverage is often the most serious problem, yet it is also the most difficult to quantify and is often reported in 
qualitative terms only. On the other hand, over-coverage is often revealed in the sampling process, for example when a first 
contact with a sampling unit fails because it is no longer alive or active.

Table 4.2 summarises the information on coverage errors given by the countries participating in the EU-LFS. Belgium and 
Italy were the only countries that reported the presence of all three types of coverage error in the 2022 EU-LFS, while both 
under-coverage and over-coverage were reported by Greece, Croatia, Romania and Sweden. For more details, see Table 9.1 
in the Annex.

Measurement errors

Measurement errors arise during data collection, causing the unit-level observed values in the sample to differ from their 
true values. Such errors may be systematic or random. Systematic errors cause bias by consistently distorting the results, 
while non-systematic (random) errors may increase the variance of the estimates without necessarily affecting their 
expected value.

Measurement errors are usually categorised according to their source:

•  Survey instrument: The design of the questionnaire, form or measuring device may lead to incorrect values being 

recorded.

•  Respondent: Respondents may provide erroneous information, whether intentionally or otherwise. Errors may arise from 

memory lapses (recall error) or from social desirability pressure influencing how and what answers are given.
•  Interviewer (in interviewer-administered surveys): Interviewers may influence the responses, deliberately or 

unconsciously.

•  Situation: Contextual factors such as time, location and the presence of other people may affect the answers given by 

respondents.

In the EU-LFS, the share of proxy interviews serves as an indirect indicator of potential measurement error, since information 
is reported by someone other than the selected respondent. The proxy is usually a household member of the person whose 
information is being collected.

Table 4.3 summarises the proportion of proxy interviews and recent developments. At EU level, the proxy rate for people 
aged 15 to 89 decreased to 31.5% in 2022 from 33.0% in 2021. A similar trend was observed for the people aged 15 to 74 
over the period 2020–2022. However, there were differences between countries. Between 2021 and 2022, in the age group 
15 to 89, the proxy rate decreased in 19 countries, with reductions ranging from -0.2 up to -5.0 percentage points, and 
increased in 11 countries, with rises varying from +0.1 up to +4.8 percentage points, while three countries recorded a 0% 
proxy rate in both years.

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Accuracy

In 2022, the proxy rate of people aged 15 to 89 exceeded 50% of the interviews in two countries (Slovenia and Serbia) while 
in 10 other countries it was higher than 40% (Ireland, Greece, Spain, Croatia, Cyprus, Hungary, Malta, Portugal, Slovakia and 
Bosnia and Herzegovina). In contrast, six countries (Luxembourg, Netherlands, Sweden, Iceland and Switzerland) had a proxy 
rate under 10%.

TABLE 4.3
Share of proxy interviews, 2019-2022

Country

EU

Belgium

Bulgaria

Czech Republic

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Iceland

Norway

22

Aged 15-89

Aged 15-74

2022

2021

2022

2021

31.5 

25.1 

25.2 

38.5 

7.9 

22.9 

21.2 

45.6 

42.6 

48.8 

20.7 

46.9 

36.4 

46.0 

38.6 

34.0 

0.0 

44.3 

43.5 

0.0 

19.4 

37.6 

48.3 

27.8 

51.1 

48.1 

12.4 

2.2 

1.7 

0.0 

33.0 

25.8 

24.9 

39.4 

6.9 

25.3 

23.3 

47.9 

41.8 

49.7 

22.7 

49.0 

37.0 

45.9 

39.6 

35.0 

0.0 

43.3 

39.4 

0.0 

19.8 

39.5 

49.7 

27.1 

51.3 

50.3 

7.6 

1.9 

1.2 

0.0 

31.8 

26.6 

26.9 

40.5 

7.7 

22.1 

21.5 

48.6 

44.7 

49.3 

21.3 

49.3 

37.5 

47.1 

38.3 

33.4 

0.0 

46.3 

45.2 

0.0 

18.2 

37.9 

50.3 

28.7 

52.2 

49.4 

9.6 

2.2 

1.0 

0.0 

33.3 

26.9 

26.8 

41.3 

6.6 

24.8 

23.5 

50.8 

44.7 

50.2 

23.4 

51.3 

37.2 

47.0 

39.0 

34.5 

0.0 

45.1 

41.5 

0.0 

18.3 

39.8 

51.1 

28.1 

52.1 

51.3 

5.8 

1.9 

0.9 

0.0 

2020

34.0 

27.6 

27.0 

42.1 

6.9 

21.0 

23.4 

48.5 

39.3 

50.0 

27.1 

51.2 

32.9 

39.5 

40.2 

32.0 

26.5 

41.6 

44.7 

43.5 

22.3 

40.1 

50.3 

19.6 

54.2 

52.1 

3.9 

2.0 

0.5 

11.8 

2019

33.6 

26.1 

28.5 

41.7 

8.2 

23.8 

17.0 

47.1 

38.5 

50.3 

26.9 

50.1 

34.9 

38.2 

36.2 

34.8 

26.5 

40.9 

44.7 

44.9 

25.4 

36.2 

49.0 

17.5 

52.8 

51.3 

4.1 

2.1 

0.7 

14.6 

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Accuracy

Country

Switzerland

Bosnia and Herzegovina

Serbia

Türkiye

Aged 15-89

Aged 15-74

2022

0.7 

44.3 

52.3 

5.4 

2021

1.2 

44.6 

51.0 

10.4 

2022

0.6 

46.7 

54.3 

5.3 

2021

1.1 

46.9 

53.0 

10.4 

2020

2.1 

-

48.9 

12.1 

2019

2.4 

-

51.3 

10.6 

Table 4.4 shows the main procedures adopted by the countries to reduce measurement errors. Almost all countries provide 
interviewers with regular training (32 countries) and use interviewer feedback mechanisms (31 countries). In 32 countries, a 
cover letter introducing the survey is sent to selected units before data collection. Other procedures are less widespread but 
still applied by a significant number of countries, including advance phone calls for booking or introducing the survey (18 
countries) and supervisory monitoring remotely through performance indicators (23 countries). For detailed information 
about the procedures used in each country, see Table 9.2 in the Annex.

TABLE 4.4
Procedures used to reduce measurement errors, 2022

Procedures

Respondent

Interviewer

Fieldwork

Letter introducing the survey

Phone call for booking or introducing the survey

Periodic training (at least one time per year)

Feedback from the interviewer (reports, debriefings, etc.)

Direct monitoring by contacting the respondents after the fieldwork

Direct monitoring by listening the interviews

Remote monitoring through performance indicators

Questionnaire

Questionnaire in several languages

On-line checks (for computer assisted interviews)

Number of participating countries

All

32

18

32

31

21

16

23

20

31

EU

26

17

26

25

18

12

19

16

25

Non-response errors

Non-response errors occur when the survey fails to obtain a response to at least one of the questions.

Non-response encompasses a wide variety of reasons for missing values: for example, respondents may be impossible to 
contact, not at home, unable, or unwilling to answer, or they may not return the questionnaire. Non-response reduces the 
actual (observed) sample size and consequently increases the variance of the estimates (random error). Furthermore, 
non-response may lead to bias (systematic error), especially if the non-respondents systematically have different 
characteristics from the participants in relation to the survey variables.

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Accuracy

There are two types of non-response:

•  Unit non-response, where no data at all can be obtained from a sampled unit.
•  Item non-response, where data are missing for some, but not all, items (variables) for a sampled unit.

This section covers only the issue of unit non-response. Item non-response is presented in Chapter 3 (Table 3.2) and in detail 
for each individual country in the Annex (Table 9.3).

Table 4.5 shows unit non-response rates by country. It should be noted that these rates are not fully comparable across 
countries, as the calculation of non-response does not always use the household as the unit. In Denmark, Estonia, 
Luxembourg, Netherlands, Finland, Sweden, Norway and Switzerland, non-response is computed at individual level, in line 
with their sampling strategy where the final sampling unit is person.

Between 2021 and 2022, the unit non-response rate showed mixed developments across countries. Although the overall 
disruptions caused by the COVID-19 pandemic had already peaked in 2020, several countries still recorded notable changes 
the following year. Compared to 2021, the unit non-response rate 2022 decreased in 19 countries (with reductions between 
-0.2 and -24.6 percentage points) and increased in 13 countries (ranging from +0.2 to +7.3 percentage points).

TABLE 4.5
Unit non-response rates, 2018–2022

Country

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

2022

21.1 

22.5 

31.0 

56.9 

9.1 

30.5 

55.9 

45.9 

15.7 

24.1 

41.5 

23.1 

5.7 

36.5 

18.9 

40.4 

28.3 

28.8 

55.0 

5.3 

34.8 

39.4 

2021

20.9 

23.2 

23.7 

50.6 

15.0 

29.7 

64.8 

47.0 

15.9 

25.7 

42.6 

23.4 

6.7 

38.6 

22.0 

41.4 

28.6 

23.7 

50.0 

5.1 

35.2 

46.6 

2020

18.6 

27.4 

26.2 

46.0 

46.8 

28.2 

57.9 

35.5 

15.3 

27.2 

43.0 

20.2 

6.0 

41.6 

22.5 

37.2 

35.3 

34.6 

52.5 

5.4 

33.9 

38.3 

2019

15.5 

20.3 

21.9 

44.3 

5.8 

27.1 

51.0 

28.8 

16.1 

20.8 

43.7 

17.0 

4.7 

35.2 

22.2 

39.1 

26.9 

33.3 

50.7 

6.1 

45.9 

18.9 

2018

19.0 

20.0 

22.4 

 43

3.4 

28.1 

36.7 

26.8 

15.6 

20.3 

42.4 

14.4 

4.3 

34.8 

21.6 

44.4 

24.5 

27.8 

49.4 

7.1 

42.2 

16.4 

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Quality report of the European Union Labour Force Survey 2021 & 2022

4

Accuracy

Country

Romania

Slovenia

Slovakia

Finland

Sweden

Iceland

Norway

Switzerland

Bosnia Herzegovina

Serbia

Türkiye

2022

13.0 

26.4 

28.1 

51.9 

55.5 

-

24.9 

23.3 

22.2 

27.6 

1.9 

2021

14.1 

51.0 

23.5 

48.1 

51.7 

-

22.0 

21.0 

24.5 

27.3 

3.9 

2020

15.3 

42.2 

20.1 

40.7 

49.0 

-

14.7 

21.2 

-

29.5 

5.5 

2019

12.5 

22.3 

18.5 

37.3 

49.8 

38.1 

16.3 

22.2 

-

25.0 

4.9 

2018

12.3 

21.3 

17.6 

34.5 

47.2 

-

15.7 

20.3 

-

21.5 

4.6 

Table 4.6 shows the unit non-response rates in 2022 for each country broken down by reason. In 2 out of the 14 countries 
(Belgium, Germany, Spain, France, Italy, Cyprus, Luxembourg, Malta, Austria, Portugal, Slovakia, Norway, Serbia and Türkiye) 
where participation is mandatory, the proportion of refusals was higher than that of non-contacts. The situation is similar in 
countries where participation is voluntary. In total, 12 out of 17 countries recorded a higher proportion of non-contacts than 
refusals. It therefore appears that, although in some countries the proportion of refusals is high, a more significant problem 
in terms of non-response is that selected households or individuals cannot be reached at all.

TABLE 4.6
Non-response by category, 2022

Country

Total 

Refusals

Non-contacts

Other reasons 

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembourg

21.1 

22.5 

31.0 

56.9 

9.1 

30.5 

55.9 

45.9 

15.7 

24.1 

41.5 

23.1 

5.7 

36.5 

18.9 

40.4 

3.7 

3.6 

16.9 

10.4 

0.0 

16.7 

10.3 

8.1 

4.8 

7.6 

23.4 

4.3 

2.2 

10.3 

9.8 

2.7 

10.6 

18.2 

14.1 

46.5 

0.0 

12.7 

22.4 

26.5 

10.9 

11.3 

13.5 

17.7 

3.1 

20.7 

8.6 

37.0 

6.8 

0.8 

0.0 

0.0 

9.1 

1.2 

23.3 

11.4 

0.0 

5.2 

4.6 

1.1 

0.4 

5.5 

0.5 

0.7 

Quality report of the European Union Labour Force Survey 2021 & 2022

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4

Accuracy

Country

Total 

Refusals

Non-contacts

Other reasons 

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Iceland

Norway

Switzerland

Bosnia Herzegovina

Serbia

Türkiye

Processing errors

28.3 

28.8 

55.0 

5.3 

34.8 

39.4 

13.0 

26.4 

28.1 

51.9 

55.5 

-

24.9 

23.3 

22.2 

27.6 

1.9 

9.6 

5.5 

11.0 

3.5 

15.9 

2.1 

2.8 

13.7 

14.4 

24.0 

19.4 

-

2.9 

1.4 

5.7 

6.2 

0.1 

14.7 

23.3 

31.0 

0.7 

16.4 

33.3 

7.2 

7.7 

1.0 

27.5 

33.9 

-

18.9 

11.0 

11.0 

21.1 

1.2 

4.0 

0.0 

13.0 

1.2 

2.6 

4.0 

3.0 

5.0 

12.7 

0.3 

2.2 

-

3.1 

10.9 

5.5 

0.3 

0.6 

Before data dissemination and the production of statistics for publication, the survey undergoes several preparatory stages, 
including, e.g. data collection, coding, entry, editing, and imputation. Errors that arise during these operations are referred to 
as processing errors.

According to the information provided by the participating countries in the 2022 annual national quality reports, 17 
countries adopted data-editing procedures to detect and correct errors in EU-LFS data (Belgium, Bulgaria, Czechia, Denmark, 
Greece, Spain, Croatia, Italy, Latvia, Malta, Austria, Portugal, Romania, Slovenia, Slovakia, Bosnia and Herzegovina and Serbia), 
but for most countries, the overall editing rate remains unclear. In their quality reports, 20 countries also mention using data 
imputation to correct the non-response item (Belgium, Bulgaria, Czechia, Germany, Estonia, Greece, Spain, France, Italy, 
Latvia, Lithuania, Malta, Austria, Portugal, Romania, Slovenia, Slovakia, Finland, Bosnia and Herzegovina and Serbia).

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Quality report of the European Union Labour Force Survey 2021 & 2022

5  Timeliness and punctuality 

5.1.  Definition

Timeliness refers to the length of time between data availability and the event or phenomenon the data 
describe. In other words, it reflects how quickly after the reference period final results are disseminated.

Punctuality refers to the time between the actual delivery of the data and the deadline for delivery. In this 
report, punctuality is defined in terms of punctuality in data transmission, i.e. comparison of the transmission 
dates with the deadlines set out in the Regulation.

Under Annex V to Regulation (EU) 2019/1700 quarterly LFS data must be transmitted to Eurostat within 10 weeks of the end 
of a reference quarter during the first three years (2021-2023) of implementation of the new regulation, and within eight 
weeks of the fourth year (2024) of implementation. The EU-LFS releases calendar schedules for the dissemination of the 
EU-LFS main indicators around three weeks after the data delivery deadline. In addition, Eurostat continuously updates the 
online database with new information in connection with the quarterly or the annual data releases.

5.2.  Post-implementation monitoring of the new regulation

The figures on timeliness and punctuality (see Table 5.1) were derived by first calculating, for each quarter, the number of 
calendar days between the end of the reference period and the transmission of data to Eurostat, and between the end of 
the reference period and Eurostat publishing the data on its website. For each country-year combination, the average of 
these quarterly values was then computed, and based on these averages, countries were classified into distinct groups. The 
number of countries in each group was subsequently counted separately for the EU countries and for all EU-LFS countries, 
on an annual basis for 2021 and 2022.

To assess timeliness (the indicator is the number of days from the last day of the reference period to the day of publication of 
final results), the classifications were designed to evaluate compliance with the required transmission deadlines during the 
first three years of implementation and taking into account the three-week target for the publication of main indicators. 
Punctuality was assessed by calculating the number of days between the end of the reference period and the actual delivery 
date (first submission) of the data, and by applying class breaks reflecting the transmission time targets set in the Regulation. 
In addition, the median number of days between the end of the reference period and submission to and publication by 
Eurostat was calculated.

Quality report of the European Union Labour Force Survey 2021 & 2022

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Timeliness and punctuality

TABLE 5.1
Transmission to Eurostat and Eurostat’s dissemination of EU-LFS data, 2021-2022 
quarterly EU-LFS data

Number of calendar days from end of reference period

2022

2021

All

EU

All

EU

Number of countries

Transmission to Eurostat

< 42 (under 6 weeks)

42-55 (6-8 weeks)

56-70 (8-10 weeks)

> 70 (over 10 weeks)

Total

Median number of days

Eurostat’s dissemination of national data (website)

70-76 (10-11 weeks)

77-83 (11-12 weeks)

84-90 (12-13 weeks)

91-98 (13-14 weeks)

> 98 (over 14 weeks)

Total

Median number of days

3

16

12

2

33

53

5

25

1

0

2

33

77

3

14

10

0

27

52

5

21

1

0

0

27

77

1

7

20

5

33

63

6

11

5

4

7

33

82

1

7

18

1

27

62

6

11

4

4

2

27

80

Table 5.1 indicates that transmission periods have shortened from 2021 to 2022. The median number of days between the 
end of the reference period and the transmission to Eurostat fell from 63 days in 2021 to 53 days in 2022 (62 to 52 days for EU 
countries). It should be noted that these figures are based on the first transmission date. That is, the initial data may not 
always have been complete or fully validated, and additional deliveries may have followed. This may extend the overall 
publication delay, as Eurostat’s dissemination is based on the final validated data. In 2022, most EU-LFS countries and all EU 
countries complied with the 10-week requirement (applicable from 2021 to 2023) laid down in the Regulation, and a 
growing proportion has reached the tighter eight-week target (in force from 2024). This improved transmission performance 
has contributed directly to more timely publication.

Regarding timeliness in practice, Eurostat’s dissemination has been measured with respect to the end of the mandatory 
10-week transmission window. This approach enables assessment of how quickly data have been published in relation to 
the deadline for submission to Eurostat. The results show that in most cases they were published within one to three weeks 
of the deadline (corresponding to 10-12 weeks after the reference period). The median publication lag decreased from 82 
days in 2021 to 77 days in 2022 (80 to 77 days for EU countries). Only a few exceptional cases extended beyond 14 weeks, 
indicating longer delays in the submission of some national data.

Overall, the 2021 to 2022 results indicate that the Regulation has been effective. Countries have largely complied with the 
transmission requirements, and Eurostat has improved both the speed and predictability of dissemination.

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Quality report of the European Union Labour Force Survey 2021 & 2022

6  Comparability

6.1.  Definition

Comparability aims at measuring the impact of differences in applied statistical concepts, definitions, 
measurement tools and procedures by comparing statistics across geographical areas, non-geographical 
dimensions and sectoral domains. Cross-national comparability is ensured by reconciling different data sources 
and over a standard and reasonable period of time, in close cooperation between the reporting countries and 
Eurostat.

6.2.  Geographical comparability of concepts

The comparability of statistics between the participating countries is ensured by a common framework regulation15 and 
reference metadata16, common variable definitions17, common explanatory notes18, a common regulation regarding the 
definition of unemployment and the 12 principles of questionnaire construction19. However, this is primarily the case for the 
classification of people into groups of ILO labour status: employed, unemployed and people outside the labour force. For the 
other variables, each country is responsible for ensuring that the data provided by the national survey are compatible with 
the EU definitions and are of the same quality as for the core variables. Statistics are collected on the basis of new common 
standards for the derivation of employment status, new variables, variables with changed periodicity and the requirements 
for subsampling.

As most of the variables are defined in accordance with recommendations of the ILO and other international organisations, 
the main statistics from the EU-LFS are directly comparable to those of other industrialised countries, in particular the other 
members of the Organisation for Economic Co-operation and Development (OECD). Table 6.1 shows the main divergences 
of the national concepts from the European framework reported by the countries for the year 2022.

15 

16 

17 

18 

19 

REGULATION (EU) 2019/1700 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL

Commission Recommendation (EU) 2023/397 on reference metadata and quality reports for the ESS

COMMISSION REGULATION (EC) No 377/2008

EU Labour Force Survey Explanatory Notes from 2021 onwards on Eurostat’s website

Commission Regulation (EC) No 1897/2000

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Comparability

TABLE 6.1
Divergence between national concepts and European concepts, 2022

Belgium The Registered population definition in the sampling frame is used.

Divergences in the definition of the resident population

At the start of the interview, the interviewer verifies with the respondents whether the list of household 
members as taken from the sampling frame corresponds to the actual situation.
Interviewers are instructed to drop household members that no longer live at the given address since at least 
6 months (but in practice they sometimes drop household members earlier). Interviewers can also add new 
members in the household. Some of the most frequently added kinds of persons are newborns, a new 
partner (or a partner from another member of the household), children in co-parenting that are registered at 
the address of the other parent, needy family members.

The definition of the resident population in the Danish LFS is harmonised with the Danish population register. 
The permanent address is therefore defined as the place where you regularly sleep, when you are not abroad 
because of holidays, business trips, or the place where you have your belongings. The definition does 
therefore not explicitly include the minimum of 1 year, as stated in the Explanatory notes. This solution 
complies with art. 2(d) of EP and Council Regulation (EC) No 763/2008.

Denmark

Germany

In the German LFS only people living in dwellings are part of the frame. Homeless people and other people 
without registered residence (e.g. people living in huts, caravans) are out of the frame.

Austria

Finland

Prospective information (intention to stay at least one year) is not available.

The Finnish LFS population may include some people living in a premises resembling institution. They have 
an address registered as a private household. A person or persons living in serviced flats usually form a 
private household, like living in a boarding house. Third degree students normally form an own private 
household if their family ties are not kept. Exact information on children alternating between different homes 
are not collected. All in all, we assess that the impact in statistics or between statistics is very limited and in 
practice non-measurable.

Sweden

To belong to the Swedish resident population the residence permit is needed. This means that some 
immigrants group, i.e. persons with citizenship outside the EU, do not belong to the population even if they 
are staying or intended to stay in the country for a period longer than one year. These people cannot work or 
seek job by default and they do not exist in the populations register and thereby neither in the sample frame.

Divergences in the concept of employment

Hungary

Türkiye 

According to the ILO recommendation drafted for countries in transition in Prague in November 1995, 
persons receiving child care allowance or child care benefit during parental leave have been classified since 
1998 on the basis of their activity performed in the reference week. Therefore these persons are not classified 
as persons with a job from which he/she was absent during the reference week, if they are not working 
besides receiving childcare allowance or benefit. Questions about how long they have been out of his/her 
main job are skipped for these persons.

Three months absence criterion defined by Eurostat is not followed for unpaid family workers; they are not 
covered as employed if they did not work in the reference week even one hour. Individuals in the household 
only producing goods intended mainly for sale considered as employed.Farmers who only produce for 
own-consumption are considered as employed if the amount of this production is considerable within total 
household consumption (if the total amount ofthis product is at least 51% of total food expenditure).

Divergences in the concept of unemployment

Belgium In Belgium, unemployment rate is calculated and published for persons aged 15-64 and not 15 + or 15-74.

Türkiye 

For national calculation of unemployment, passive job search methods are covered besides the active ones.

Note: The information in the table is extracted directly from the national quality reports, without modification.

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Quality report of the European Union Labour Force Survey 2021 & 2022

6

Comparability

6.3.  Comparability over time

Each year, a number of changes are introduced in national labour force surveys. These may result from new requirements at 
European level, the need to align national practices with existing EU regulations and methodological guidelines, or from 
specific national needs. Changes may affect

•  Conceptual aspects such as the definitions used by the EU-LFS, survey coverage and geographical boundaries, target 

population, applicable legislation and the classifications employed; and

•  Measurement aspects such as the sampling strategy, data collection methods and the weighting scheme.

When these changes affect continuity, they may lead to breaks in series. Such breaks are documented in national quality 
reports and flagged in Eurostat’s dissemination database to help users interpret the time series correctly. Table 6.2 presents 
the changes reported by participating countries in their national LFSs during 202220.

TABLE 6.2
Changes in the national LFSs in 2022 compared to previous year(s)

Changes in the survey concept (legislation, definitions, coverage and classifications)

Poland

The dwelling samples surveyed in the quarters 2022 were drawn from the sampling frame subset 
comprising only dwellings with telephone numbers available.

Bosnia and 
Herzegovina

LFS 2022 is comparable with LFS 2021. For the previous period 2006-2020 LFS data are not 
comparable. A more restricted scope for employment; Different criteria in defining absence from 
work; A more simplified flowchart for the labour status module, broken down in several sub 
modules; An additional sub module on the recovery of small or occasional jobs; The introduction of 
the simplified rules for the labour status module; New LFS definitions for employment and 
unemployment A detailed flowchart and a model questionnaire for the labour status derivation; 
New concept and questions related to hours worked; Introduction of new questions related to 
current field of education; New concepts in questionnaire addressing self-employment. The Labour 
Force Survey is conducted in accordance with the standards and recommendations of the 
International Labour Organization (ILO), as well as with the regulations of the European Statistical 
Office (EUROSTAT): Regulation (EU) 2019/1700 of the European Parliament and of the Council of 10 
October 2019 and Commission Implementing Regulation (EU) 2019/2240 of 16 December 2019 
(Implementing Regulation (EU) 2019/2240 for the labour force domain), when it comes to 
methodology, organization, sampling procedure, questionnaire design and definitions of 
employment and unemployment.

Denmark

Changes in the sampling strategy (sampling frame, sample design, rotational pattern)

In 2022q1, a new stratification was introduced in the LFS. Register data on employment and register 
unemployment are utilized to a greater extent, in order to obtain a greater number of responses in 
some of the groups that suffer from low response rates. For starters, the population is divided into 
four groups: 1) in stable employment, 2) in registered unemployment, 3) neither in stable 
employment nor registered unemployment and 4) persons aged 75 to 89. In addition, groups 1 and 
3 are divided into the age groups 15-29, 30-64 and 65-74. This results in eight different strata, which 
are used in the sample selection from 2022q1 onwards. As the LFS consists of four panels, each 
appearing for six quarters, there will be a gradual implementation of the new stratification until 
2023q2. 

20 

EU Labour Force Survey–new methodology from 2021 onwards on Eurostat’s website

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6

Comparability

Poland

Portugal

Bosnia and 
Herzegovina

In each quarter 2022 one additional sample surveyed for the 5th time was used together with the 
samples foreseen by the rotation scheme in order to assure satisfactory response rate. The above 
mentioned modifications of quarterly sample design result from the changed organisation of the 
survey during the COVID – 19 pandemic

Due to the COVID-19 pandemic, the CAPI data collection mode was suspended and replaced 
exclusively by the CATI mode from the second half of March 2020 to the 2nd quarter of 2022. During 
this period the sampling frame was restricted to private dwellings as usual residence with 
telephone. From the 3rd quarter of 2022 onwards, both the CAPI mode and the usual sampling 
frame were resumed.

The Master sample frame, which is stratified by entities/district and type of settlement (urban and 
other), is used as the sampling frame. This new sample frame is introduced in 2020. The ultimate goal 
of updating the statistical master sample frame in BiH was to increase the scope and quality of 
statistical data in Bosnia and Herzegovina as well as their further harmonization with EU standards. 
The update is performed by enumerating households in a selected sample of census districts 
according to the defined methodology prepared by all three statistical institutions in BiH. The 
questionnaire contained questions about the name and surname, gender, date of birth, status of the 
facility according to residence, highest completed school and the like. From 2006 to 2019, the sample 
for the Survey was defined separately for each year, ie. there was no repeated survey or panel part of 
the sample. Since 2020, a panel component has been introduced in the sample and households are 
surveyed in two consecutive quarters, excluded from the sample for the next two quarters, and 
re-surveyed in two more consecutive quarters (rotation scheme 2- (2) -2). In this way, it is possible to 
monitor and observe changes in the situation on the labour market in quarterly and annual dynamics. 
The sample design is two-stage stratified. The sample was divided into 4 independently selected 
subsamples, rotating groups or panels. Primary sampling units (PSUs) in the Main sample frame are 
territorial units composed of one or more adjacent census districts. The sample method with 
probability proportional size (PPS) was used for their selection, whereby the number of housing units 
(inhabited and uninhabited) from the 2013 Census of Population, Households and Dwellings database 
was used as a measure of PSU size. Secondary sample selection units (SSUs)–units inhabited by private 
households were selected from the mentioned sample frame by the method of random sampling. 
Since 2020, a panel component has been introduced in the sample and households are surveyed in 
two consecutive quarters, excluded from the sample for the next two quarters, and re-surveyed in two 
more consecutive quarters (rotation scheme 2- (2) -2). In this way, it is possible to monitor and observe 
changes in the situation on the labour market in quarterly and annual dynamics.

Changes in the data collection (questionnaire, interview instructions, survey mode, use of auxiliary 
information)

Denmark

In 2022q3, we added an indicator to our survey vehicle to indicate whether the respondent receives 
old age pension. This indicator is used as display logic for WANTREAS (which is automatically coded 
as 6 if the respondent is receiving old age benefits).2022q3: Modalities on COVID-related absence 
and reasons for not seeking work were removed. From 2022 Q4 onwards, the following groups of 
people will not be re-interviewed, in accordance with Annex II.18 of EU-legislation 2019/2240: 

•  People aged 70-74, who were outside of the labour force in the previous interview
•  People aged 75-89
•  People who were outside of the labour force due to long-standing health problems in the 

previous interview

These persons will have their answers copied from the previous interview. From Q3 the variable 
REGISTER is produced only using register information. In the past, we asked everyone if they were 
registered at the unemployment center – in order to capture people that were registered and not 
receiving benefits. This was unnecessary, as only persons receiving benefits are registered at the 
unemployment office. This change leads to a better cohesion between the actual registers and the 
LFS variable REGISTER.

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Quality report of the European Union Labour Force Survey 2021 & 2022

6

Comparability

France

Some questions have been slightly modified:

•  TEMPREAS: the modality “You didn’t want a permanent job” has been changed in “It was your 

choice”

•  LEAVREAS: the modality “End of fixed-term employment” has been changed in “End of contract 
(CDD, apprenticeship, traineeship, etc.) or temporary assignment”. The modality « Because you 
were looking for a more satisfying job and didn’t find it » has been changed in « Because you had 
a new career plan »

•  EDUCNFE4 and EDUCNFE12: questions have been modified from « have you taken at least one 

non-degree course with a professional aim? » to « have you attended a course, training course or 
conference for professional purposes? » and from “have you taken at least one non-degree course 
or short or distance learning courses for leisure purposes?” to “have you attended a course, 
training course or conference for personal or leisure purposes? For example, a sports course, a 
driving lesson or a cookery workshop?

According to Commission Implementing Regulation 2029/2240, questions needed for creating 
variables (GENHEALTH, GALI, EDUCFED12, EDUCLEV12, EDUCNFE12) which should be collected in 
even years are introduced in the questionnaire for 2022. In the national questionnaire, questions 
needed for creating variable INCGROSS are introduced from the beginning of 2022. The purpose of 
these questions is to collect information on net monthly income and also additional payments from 
employer made on a higher than monthly periodicity; and then to convert net monthly pay to gross 
monthly pay. For 2021 we had a derogation for this variable. Also, regarding variable HATFIELD, 
since the beginning of 2022, a code list with all 50 narrow education fields, according to 2nd level of 
ISCED-F 2013 classification, is introduced in national questionnaire. During 2021, we had a 
derogation for implementation of this whole list of codes.

In 2022 a new survey mode that we have introduced in the beginning of 2Q_2020 because of new 
circumstances caused by COVID-19 epidemic has been still in use: CAPI interviewers were 
conducting interviews from their homes or from offices by telephone. Because it was not possible 
to go to households, our CAPI interviewers for all households for which a telephone number was 
available, conducted interview by telephone. We have sent regularly announcement letters to 
households and asked them to send us on our e-mail address their phone number, in order to make 
a contact with them and to make an interview. All interviews that were conducted by CAPI 
interviewers from their homes or offices by phone, in variable MODE we have coded (2)-CATI.

Improved questions wording and fixed issues with filters. Minor additions in instructions to 
interviewers

There was introduced a modification of the way of asking about the year of attaining the highest 
level of education aimed to improve quality of the variable (HATYEAR). Adapting the explanatory 
notes to the changes in the questionnaires. In all quarters of 2022 data were collected only by 
telephone interviews (CATI mode) except for the first wave data in the 3rd and 4th quarter, which 
were collected by face-to-face interviews (CAPI mode)

From the 3rd quarter of 2022 onwards, the CAPI mode was resumed.

New wording of adult learning variables and new adult learning variables similar to AES indicators 
improved the final results.

Croatia

Lithuania

Poland

Portugal

Slovakia

Finland

WEB data collection possible also for the last (5th) wave.

Quality report of the European Union Labour Force Survey 2021 & 2022

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6

Comparability

Bosnia and 
Herzegovina

Serbia

Poland

Bosnia and 
Herzegovina

The LFS questionnaire underwent several changes to create harmonization of indicators amongst 
EU Member States.  These changes nevertheless impacted the LFS indicators, especially those 
related to employment and unemployment. Given the complexity of the definitions to derive the 
level of employment and unemployment, the LFS respondents are not directly asked if they are 
employed, unemployed or outside the labour force. Instead, they are asked about their labour 
market behaviour in the reference week, and their labour status is derived according to the Labour 
Status Module (flow chart) and submodules. Instruction to interviewers were updated according to 
the new methodology and survey modes. With the introduction of a new, redesigned methodology 
on action, the methodological tools for data collection have been changed. The data collection is 
done through the CAPI method (Computer-assisted personal interviewing) and CATI method 
(Computer-assisted telephone interviewing).

Six missing variables (MIGREAS, SIZEFIRM, WAYFOUN, SHIFTWK, CONTRHRS, HWWISH) from 
regulation were added in the third quarter of 2022. New variables were explained to the 
interviewers.

Changes in the weighting scheme

In all quarters of 2022 an additional calibration condition has been introduced taking into account 
the structure of the population by level of education. It was introduced in order to ensure 
comparability of the survey results with the previous periods

The weighting procedure is performed to calculate estimates for the entire population. This 
compensates for the impact of sample design and size, and the impact of household non-response 
to the Survey. In the first weighting phase, the weights of unit selection to the sample from both 
sampling stages (PSU and SSU) were calculated. In the second phase, the weight correction is 
performed due to non-response of the units from the sample. In the final phase, the weights are 
calibrated to population estimates by five-year age groups and gender. 

Note: The information in the table is extracted directly from the national quality reports, without modification.

34

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7  Coherence

7.1.  Definition

Coherence indicates the adequacy of the statistics to be combined in different ways and for various uses.

The coherence of two or more statistical outputs refers to the degree to which the statistical processes by which they were 
generated have used the same concepts – classifications, definitions and target populations – and harmonised methods. 
Coherent statistical outputs have the potential to be validly combined and used jointly. However, it is generally easier to 
show cases of incoherence than to prove coherence.

The following sections assess the coherence of the EU-LFS with similar data from other sources, population statistics and 
employment data from National Accounts and Structural Business Statistics. Other comparisons are possible, such as with 
employment data from the Labour Cost Survey.

7.2.  Coherence with population statistics

The coherence with population statistics is important for users, as EU-LFS statistics often provide the most recent population 
estimates. However, the two sources are not fully comparable. Key differences to be considered include:

•  Coverage: The EU-LFS usually covers only the population living in private households, while population statistics cover 

the entire population, including those living in collective households (e.g. military conscripts).

•  Resident population definition: The rules for defining the usual resident population in the EU-LFS may differ from 

those applied in population statistics.

•  Reference period: Population statistics generally refer to a specific date (e.g. 1 January or mid-year) for population levels 

and characteristics, whereas the EU-LFS refers to the average situation over a quarter or a year.

Population censuses21 or equivalent population sources provide a key basis for updating the population estimates used in 
the LFS. In countries where a traditional census is carried out, the LFS weights, sampling frames and, where necessary, 
sampling strategies are updated on the basis of census results. In register-based or rolling census systems, the updating 
takes place on a more continuous basis, with LFS population estimates aligned with the corresponding register-based 
population figures. Table 7.1 shows the comparison between population statistics and the EU-LFS for persons aged 15–64 
years.

21 

Population and housing census 2021 – overview on Eurostat’s website

Quality report of the European Union Labour Force Survey 2021 & 2022

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Coherence

TABLE 7.1
Coherence between population statistics and EU-LFS for persons aged 15-64, 
2022

Population 15-64 (in thousands)

EU-LFS annual average 15-64 
(in thousands)

Relative difference

01/01/2022

2022

(EU-LFS − POP) / POP * 100

Total

Men

Women

Total

Men

Women

Total

Men Women

284 835(ep) 143 077(ep) 141 757(ep)

282896

141659

141237

 7 416

 4 047

 6 654

 3 732

 3 738

 2 047

 3 404

 1 886

 3 678

 2 001

 3 250

 1 846

 7 396

4022

 6 653

 3 730

 3 710

2031

 3 405

 1 882

 3 686

1992

 3 248

 1 848

 53 194

 26 996

 26 198

 53 318

 26 962

 26 356

 842

3369

6658

31418

 426

1668

3328

15779

 416

1700

3330

 833

 3 400

 6 640

 419

 1 681

 3 300

 414

 1 719

 3 340

15638 31 475(d) 15 778(d) 15 697(d)

 41 866(p)

 20 678(p)

 21 187(p)

 40 820(d)

 20 013(d)

 20 807(d)

 2 442

 37 489

 610

 1 184

 1 828

 448

6254

354

 11 353

 5 942

 1 217

 18 804

 296

 583

 897

 229

3162

190

 5 716

 2 999

 1 224

2420

1208

1212

 18 685

 37 266

 18 620

 18 646

 313

 601

 931

 218

3092

164

 5 637

 2 943

 598

 1 178

 1 836

 440

6122

 362

 11 294

 5 887

 288

 576

 898

 225

3078

 196

 5 667

 2 947

 310

 602

 938

 216

3045

 167

 5 627

 2 940

 24 014(ep)

 12 046(ep)

 11 968(ep)

 23 516

 11 782

 11 734

6602

3216

3386

6624

 12 247(e)

 6 172(e)

 6 076(e)

 12 256

 1 344

 3 617

 3 417

 6 494

 250

 3 520

 5 758

 701

 1 831

 1 742

 3 325

 130

 1 803

 2 913

 644

 1 786

 1 676

 3 170

 120

 1 717

 2 845

 1 322

 3 589

 3 400

 6 492

 239

 3 512

 5 716

3228

 6 217

 687

 1 810

 1 728

 3 320

 124

 1 794

 2 891

3397

 6 038

 634

 1 778

 1 672

 3 172

 114

 1 718

 2 826

-0.7

-0.3

-0.6

 0.0

-0.1

 0.2

-1.1

0.9

-0.3

0.2

-2.5

-0.9

-0.6

-0.7

-0.5

 0.5

-1.6

-2.1

2.2

-0.5

-0.9

-2.1

0.3

 0.1

-1.7

-0.8

-0.5

 0.0

-4.5

-0.2

-0.7

-1.0

-0.8

-0.8

 0.0

-0.2

-0.1

-1.7

0.8

-0.8

0.0

-3.2

-0.8

-1.0

-1.4

-1.4

 0.2

-2.0

-2.7

2.9

-0.9

-1.7

-2.2

0.4

 0.7

-1.9

-1.1

-0.8

-0.2

-4.5

-0.5

-0.8

-0.4

 0.2

-0.5

-0.1

 0.1

 0.6

-0.4

1.1

 0.3

0.4

-1.8

-1.0

-0.2

0.0

 0.3

 0.7

-1.2

-1.5

1.4

-0.2

-0.1

-2.0

0.3

-0.6

-1.5

-0.4

-0.2

 0.1

-4.7

 0.1

-0.7

Country

EU–27

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Iceland

Norway

Switzerland

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Coherence

Population 15-64 (in thousands)

EU-LFS annual average 15-64 
(in thousands)

Relative difference

01/01/2022

2022

(EU-LFS − POP) / POP * 100

Total

Men

Women

Total

Men

Women

Total

Men Women

:

:

:

 2 296

 1 156

 1 140

:

:

 4 375

 57 459

 2 188

 29 037

 2 187

4234

2112

2122

 28 422

 56 311

 28 293

 28 018

-3.2

-2.0

-3.5

-2.6

:

-3.0

-1.4

Country

Bosnia and 
Herzegovina

Serbia

Türkiye

Note: (p) provisional, (d) definition differs, see metadata, (e) estimated value, (:) not available
Source: Eurostat (online data codes: demo_pjan and lfsa_pganws)

7.3.  Coherence with other employment and unemployment estimates

Coherence of employment for EU-LFS and Business Statistics

Business statistics, whether Structural Business Statistics (SBS) or Short-term Business Statistics (STS), primarily focus on 
production-related variables such as output, turnover and value added. Nevertheless, they also produce employment 
estimates, which may differ from those of the EU-LFS. The main reasons for these differences are:

•  Scope: Business surveys collect information on production units operating in the territory, whereas the EU-LFS collects 

information on persons usually residing in the country. Consequently, cross-border or seasonal workers may be recorded 
in different countries depending on the source.

•  Coverage: The EU-LFS typically excludes persons living in collective households, whereas business statistics generally 
include such persons. The EU-LFS covers all economic activities and firm sizes, while business statistics often exclude 
agriculture, government, or certain service sectors. Moreover, business registers used to compile business statistics may 
omit small enterprises below a defined threshold or certain forms of employment not recorded in payrolls or accounts 
(e.g. family workers).

•  Statistical unit: Business surveys typically measure the number of jobs, while the EU-LFS counts jobholders. Furthermore, 
business surveys rarely provide information on jobholders’ individual characteristics (such as age, sex and education), for 
which the EU-LFS remains the main source.

Coherence of employment for the EU-LFS and National Accounts

National Accounts (NA) employment concepts differ from those used in the EU-LFS. Because of these conceptual and 
coverage differences, EU-LFS employment estimates are generally lower than those in NA. The main differences relate to

•  Concepts: NA measures domestic employment (all jobs within the territory), whereas the EU-LFS measures national 

employment (persons residing in the country).

•  Coverage: The EU-LFS covers only persons living in private households, while NA covers all persons, regardless of 

residence type. The EU-LFS does not classify conscripts and unpaid trainees as employed, whereas NA includes them 
explicitly or implicitly.

•  Reference period: The EU-LFS produces estimates averaged over all weeks of a quarter or year, whereas NA stock 

estimates refer to a point in time (mid-quarter or mid-year).

Table 7.2 illustrates the relative differences between the two sources in 2022, distinguishing cases where EU-LFS results are 
used directly in the compilation of NA employment. Overall, NA estimates tend to be higher, with relative differences above 
1.5% in 19 countries. The largest gaps (exceeding 5%) were reported in Romania, Greece, France, Slovenia, Bulgaria, Germany 
and Italy.

Quality report of the European Union Labour Force Survey 2021 & 2022

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Coherence

TABLE 7.2
Employment (national concept) in levels and growth rates, in two different 
datasets on the Eurostat website (LFS and NA), 2022

Country

group

Country

EU Labour 
force survey

National 
accounts

EU-LFS – NA

(EU-LFS 
– NA)/NA 
*100

EU-LFS

NA

EU-LFS – NA

2022 levels

2021-2022 growth rates

Group 1

Czechia

Estonia

Romania

Sweden

1000

1000

1000

 5 173.5

 680.8

 5 369.9

 -196.4

 684.1

 -3.3

 7 806.5

 8 742.7(p)

 -936.2

 5 257.2

 5 199.0

 58.2

Group 2

Greece

 4 140.6

 4 770.1(p)

 -629.5

Latvia

Lithuania

 891.2

 1 420.8

 891.2

 1 431.9

Group 3

Belgium

 4 990.3

 5 177.9(p)

Denmark

 2 994.4

 3 129.0

 0.0

 -11.1

 -187.6

 -134.6

28 341.1(d)

30 567.0(p)

-2 225.9

France

Cyprus

Luxembourg

Malta

Austria

Slovenia

Iceland

Norway

Bosnia and 
Herzegovina

 450.5

 312.0

 284.1

 4 442.5

 986.1

 210.4

 2 859.2

 1 162.0

 465.7(p)

 288.1

 282.8

 4 537.6

 1 090.7

 209.4(p)

 2 865.0

:

Group 4a

Spain

20 547.5(d)

20 544.6(p)

Croatia

Poland

Portugal

Türkiye

 1 707.1

 1 724.4

17 250.9

17 369.0(b)

 4 881.4

 5 063.7(p)

30 724.0

:

Group 4b Bulgaria

 2 940.6(b)

 3 446.7

Hungary

Serbia

 4 709.0

 4 695.6(p)

 2 913.5

 3 034.2(d)

 -15.2

 23.9

 1.3

 -95.1

 -104.6

 1.0

 -5.8

:

 2.9

 -17.3

 -118.1

 -182.3

:

 -506.1

 13.4

 -120.8

%

 -3.7

 -0.5

-10.7

 1.1

-13.2

 0.0

 -0.8

 -3.6

 -4.3

 -7.3

 -3.3

 8.3

 0.5

 -2.1

 -9.6

 0.5

 -0.2

:

 0.0

 -1.0

 -0.7

 -3.6

:

-14.7

 0.3

 -4.0

%

-0.8

 4.1

 0.7

 3.0

 5.4

 2.5

 3.8

 2.8

 3.0

 2.2

 4.4

 1.7

 6.7

 3.2

 1.5

 6.8

 2.2

 1.0

 3.6

 1.7

 0.5

 2.2

 6.6

-4.4

 1.4

 2.3

%

 1.1

 4.0

 0.4

 3.1

 2.5

 1.7

 4.7

 2.0

 3.8

 2.6

 2.6

 2.5

 6.0

 2.3

 2.8

 7.1

 3.5

:

 2.7

 2.2

 3.5

 1.7

:

-0.3

 1.3

 1.8

pp

-1.9

 0.1

 0.3

-0.1

 2.9

 0.8

-0.9

 0.8

-0.9

-0.4

 1.7

-0.8

 0.7

 0.8

-1.3

-0.3

-1.3

:

 0.9

-0.5

-3.0

 0.6

:

-4.1

 0.1

 0.5

38

Quality report of the European Union Labour Force Survey 2021 & 2022

7

Coherence

2022 levels

2021-2022 growth rates

Country

group

Country

EU Labour 
force survey

National 
accounts

EU-LFS – NA

(EU-LFS 
– NA)/NA 
*100

EU-LFS

NA

EU-LFS – NA

Group 4c Germany

42 465.9

45 457.0(p)

-2 991.1

1000

1000

1000

Ireland

Italy

 2 595.0

23 099.4

 2 548.8

25 015.1

Netherlands

 9 587.0

 9 937.0(p)

Slovakia

Finland

Switzerland

 2 603.9

 2 640.2

 4 712.5

 2 531.0

 2 757.3

 4 712.5

 46.2

-1 915.7

 -350.0

 72.9

 -117.1

 0.0

%

 -6.6

 1.8

 -7.7

 -3.5

 2.9

 -4.2

 0.0

%

 2.6

 6.9

 2.4

 3.3

 1.7

 2.6

 0.6

%

 1.3

 6.6

 1.8

 3.7

 1.4

 3.5

 0.6

pp

 1.3

 0.3

 0.6

-0.5

 0.3

-0.9

 0.0

Note: (p) provisional, (d) definition differs, see metadata, (b) break in time series, (:) not available
Source: Eurostat Labour Force Survey, Annual averages (online data code: lfsa_egan) and Eurostat National Accounts, national concept (online data code: 
nama_10_pe)

Countries differ in the extent to which they rely on the EU-LFS for compiling employment figures in NA. Countries are 
grouped according to the role the LFS survey has in producing employment estimates. The following classification 
distinguishes four main groups, with further sub-groups where relevant:

•  Group 1: Countries using EU-LFS as their only source for employment in NA. The EU-LFS is adjusted only for conceptual 

alignment to ESA2010

•  Group 2: Countries using mainly the EU-LFS, but replacing it in a few industries (or labour status), on a case-by-case basis
•  Group 3: Countries not using the EU-LFS, or making minimal use of it
•  Group 4: Countries combining sources for labour supply and demand, the EU-LFS being one source among others. This 

group is rather heterogeneous and is sub-divided as follows:

 ʱ 4a: Countries giving precedence to labour supply sources (i.e. EU-LFS)
 ʱ 4b: Countries not giving precedence to any labour side
 ʱ 4c: Countries giving precedence to labour demand sources (i.e. employment registers and/or enterprise surveys)

When comparing data from the EU-LFS and NA, users are generally also interested in whether or not the two sources show 
the same trend. Table 7.2 includes a comparison of data on employment growth in 2022. The results show that both sources 
are broadly comparable in terms of the direction of the employment growth.

The reasons for disparities, in either the levels or direction of employment growth, are not fully known. In general, the actual 
sources of incoherence vary considerably between countries. The issue of incoherence between the employment statistics 
of the EU-LFS and NA estimates has been addressed by a Eurostat-coordinated Task Force on the Quality of the Labour Force 
Survey22. Using reconciliation tables, a number of potential sources of incoherence on the EU-LFS side were identified. These 
concerned biased measurement of specific areas of employment (such as marginal employment, employment in hidden/
undeclared labour activities, employment in private households and illegal immigrants) or data collection, as in the case of 
non-response and proxy interviews.

National Accounts combine data from all available data sources in the country. This method allows better coverage of the 
non-observed economy. For this reason, as stated before, the NA employment estimates are frequently higher than those of 
the EU-LFS. In addition, EU-LFS estimates are subject to sampling errors, in terms of levels and changes between periods. 

22 

The Task Force on the quality of the Labour Force Survey – final report introduces recommendations to address methodological, organizational, and 
conceptual weaknesses, paving the way for improvements to both EU and national Labour Force Surveys

Quality report of the European Union Labour Force Survey 2021 & 2022

39

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Coherence

Where changes between periods are minor, this may result in diverging trends between the figures of the NAs and the 
EU-LFS, simply because for the EU-LFS the changes are within the margin of error.

Regarding NA, it is worth mentioning some indicative reasons for incoherence: NA may use different sources from those of 
the EU-LFS (or EU-LFS combined with other sources) to estimate employment; NA may introduce adjustments to achieve 
consistency between the employment reported by its sources and other related variables, such as salaries and production. 
As they compare and combine different sources, the NA approach is also more prone than the EU-LFS to identify and 
address underreporting or systematic biases.

All in all, National Accounts are deemed more suitable for measuring employment levels, employment growth and industry 
breakdowns, while the EU-LFS is more appropriate for measuring participation in the labour market (i.e. employment rates, 
participation rates, etc.) and analysing the situation of specific socio-economic groups of the population (e.g. by age, gender 
and level of education)

Coherence of unemployment for EU-LFS and registers

The main coherence issues between unemployment estimated by the EU-LFS and unemployment [statistics] derived from 
registers are the different definitions and measurement methods. According to the ILO definition adopted by the EU-LFS, the 
term ‘unemployed’ refers to persons aged 15 to 74 years who fulfil the three following conditions:

•  not employed during the reference week;
•  available to start work within two weeks of the reference week; and
•  actively seeking work in the four weeks preceding the reference week or having found a job to start within the next three 

months.

The definition of the term ‘registered unemployment’ varies from country to country. In general, the basis for being entered 
in the unemployment register is registration as a job seeker at the public employment office.

The actual sources of incoherence between the EU-LFS and register unemployment figures vary considerably across 
countries. This may be due to several reasons:

•  The EU-LFS excludes all individuals who have any kind of job (one-hour criterion) from unemployment, whereas registers 

may include individuals who have temporary jobs, jobs with less than a certain number of hours or salaries below a 
certain amount.

•  The immediate availability for a job required by EU-LFS is based on self-declaration. By contrast, registers may exclude 

individuals who miss periodical meetings or refuse a fair job offer.

•  In the EU-LFS, registration at the public employment office in order to find a job is only one possible step in actively 

looking for work.

•  Persons who are looking for a job but who have not paid any contributions and are not eligible for unemployment benefit 
may have no interest in registering for work and therefore do not appear in the registered unemployment figures; they 
may however be included in the EU-LFS.

•  Different reference periods (the EU-LFS uses continuous reference weeks and registers often use a specific day of each 

month as a reference).

•  Any delay in updating the registers.
•  In some countries, unemployed persons aged 65 and above, who are looking for a job, cannot be registered.
•  The EU-LFS often covers only people residing in private households, whereas registers cover all those who fulfil the legal 

criteria to be included in the registers, regardless of whether they live in private or institutional households.

•  Persons on lay-off (up to three months) are classified as employed (i.e. temporarily absent from work) in the EU-LFS and 
are thus not classified as unemployed, whereas they may be registered at the public employment office as unemployed.
•  Figures from registers may not include full-time students looking for employment who may be considered unemployed if 

they comply with the ILO criteria.

•  Moreover, EU-LFS estimates are subject to sampling errors, that do not affect estimates based on registers.

In 10 countries (Denmark, Spain, Cyprus, Luxembourg, Malta, Portugal, Sweden, Norway, Switzerland and Türkiye) 
unemployment estimates coming from the LFS exceed figures from registers, while in 17 countries (Belgium, Bulgaria, 

40

Quality report of the European Union Labour Force Survey 2021 & 2022

7

Coherence

Czechia, Germany, Greece, France, Lithuania, Hungary, Netherlands, Austria, Poland, Romania, Slovenia, Slovakia, Finland, 
Bosnia and Herzegovina, Serbia) it is the opposite. Six countries were unable to provide any assessment of these differences.

Quality report of the European Union Labour Force Survey 2021 & 2022

41

8  Accessibility and clarity

8.1.  Definition

Accessibility and clarity refer to the conditions and modalities by which users can access, use and interpret data.

8.2.  Available information

Eurostat publishes both quarterly and annual LFS results, and an annual compendium describing the main characteristics of 
the national labour force surveys23.

Eurostat’s public website is free of charge and includes main indicators and detailed – and constantly updated – results from 
the EU-LFS. All data on the website is supplemented by meta-data in Euro SDMX Metadata Structure (ESMS), giving basic 
information on the background and a summary of the methodology. More detailed information can be found at the 
dedicated EU-LFS web page24 and at EU-LFS Statistics Explained25.

Eurostat also produces tailor-made tables (not available online) at the request of users. Approximately 1 200 user requests for 
EU-LFS data were processed by Eurostat in 2022.

Since 2011, researchers have been able to obtain anonymised datasets26 containing microdata free of charge. Data from EU 
countries, Iceland, Norway and Switzerland are available in this format. In 2022, around 150 researchers or research groups 
worked with EU-LFS microdata (new contracts and amendments).

23 

24 

25 

Labour force survey in the EU, EFTA and candidate countries – Main characteristics of national surveys, 2021 – 2024 edition

Employment and unemployment (LFS)–Overview on Eurostat’s website 

EU-LFS Statistics Explained article on Eurostat’s website 

26  Overview of microdata and guidelines for applying access on Eurostat’s website

42

Quality report of the European Union Labour Force Survey 2021 & 2022

9  Annex

9.1.  Tables

TABLE 9.1
Comments and methodological notes about coverage errors provided by the 
countries, 2022

Country

Belgium

Czechia

Estonia

Ireland

Comments

Under-coverage: Households, all members of which are 90 years or older are excluded before draw. 
Delay between draw of household (from National Population Register, kept up to date “permanently”) 
and fieldwork: between 2 and 6 months.

Under-coverage: Problems of undercoverage are caused by the time lag in registering new residents 
or newly constructed dwellings. 
Small census enumeration units–in terms of number of residential dwellings–are currently excluded 
from the sampling frame for practical sampling reasons (about 1% of the dwellings). 
Over-coverage: Not existing or not inhabited flats remain in the Register of Census Areas.

Over-coverage: In 2022, 12589 households of 18329 households sampled for the survey, were 
interviewed. Among the households not interviewed, in 213 cases (1.2% of total number of sampled 
households) the reason was an error or inaccuracy of the frame (person deceased, person emigrated 
or left the country, person in institution etc.). By counties the share of frame error varied from 0.3% to 
2%.

Under-coverage: The entire stock of private households at the time of the most recent Census of 
Population in the country represents the full sampling frame for the LFS. This can lead to 
undercoverage because it may exclude certain segments of the population not in private households.

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Annex

Country

Greece

Spain

Croatia

Italy

Cyprus

Lithuania

Comments

Under-coverage: Population living in dwellings outside the borders of builded areas is not covered. 
Builded area is an area where there is a “city plan” and is legal to build houses for residential purposes. 
Frames are compiled at census, and sampling rates are based at census population. Frames are 
updated at the first time when the primary sampling units are selected but not at subsequent waves. 
Over-coverage: The sample in Greek LFS is a sample of dwellings. The percentage of over-coverage is 
computed as the percentage of dwellings that are either used as “secondary residence” of the 
household or they are used solely for business purposes (e.g, a doctor’s office).

Under-coverage: Percentage calculated as ‘omitted’ dwellings detected in the ‘quality control survey’ 
(see the website for reference on frame errors). Measures of impact not available. 
Over-coverage: Average of the four quarter percentages of dwellings out of frame (‘no encuestables’). 
The touristic areas are more prone to higher rates.

Under-coverage: Since the beginning of 2014, the new sample frame based on the data from the 
Census of Population, Households and Dwellings in 2011 has been in use. This sample frame includes 
addresses of private households on the whole territory of Croatia; hence, the LFS results relate to the 
whole country. As the Census database was not updated since 2011, it is becoming obsolete, and some 
problems regarding migration and/or newly built dwelings are present in a larger extent. 
Over-coverage: Overcoverage rates are actaully non-eligibility rates of addresses selected in sample.

Under-coverage: Households are selected once a year from the resident population and household 
register; they cover the whole reference population. The data might contain errors as for information 
such as addresses (due for instance to recent change of the address), wrong inclusions (recent 
emigration) and missed inclusions (recent immigration). As for the survey’s management strategies, 
Istat requires that each non-responding household be replaced with a household having similar 
characteristics of the first one, in order to maintain as much as possible the sample representativeness 
and to minimise the impact of unit non-response. No more than 3 replaces are admitted.

Under-coverage: The sample was drawn from the Census of Population household frame of 2011. In a 
post enumeration survey conducted after the census, an undercoverage of 1.97% was estimated.

Over-coverage: Among not interviewed households, in 218 cases (0,68% of total number of sampled 
households) the reason was an error or inaccuracy of the frame (imprecise address, the premises at the 
indicated address are non-residential (a hairdresser’s, shop, etc.), the building was demolished, etc).

Luxembourg Under-coverage: The LFS sample is drawn from the register around six times a year and the sampled 

persons are interviewed five times during a two year period. No additional sample selection is made in 
order to update the sample with immigrants during this two-year period. This under-coverage is 
judged to have marginal effects on the LFS-estimates. 
Over-coverage: The over coverage consists of people born abroad who left Luxembourg without 
reporting to the authorities. When these persons is included in the sample there are no information 
that they have moved out of the country. They can not be reached for interview and will be classified 
as non-response.

Under-coverage: Undercoverage may be observed in the foreign component and in households 
which we are not aware about. This is due to the fact that there is a time-lag in updates to the frame. 
Hence, the rate of undercoverage for private households cannot be worked out. 
Over-coverage: Households registered prior to 2011 are over-represented as some may no longer be 
in existence.

Under-coverage: From 2004 onwards the sample for the Austrian LFS is drawn from the Austrian 
Register of Residents. This register was set up in 2002. The sample is drawn three months before the 
start of the quarter. This results in a time lag of three to six months. Therefore dwellings where persons 
moved in after the due date for the survey are not covered. Furthermore, undercoverage of migrants 
can be observed, although the questionnaires are translated into several languages.

Malta

Austria

44

Quality report of the European Union Labour Force Survey 2021 & 2022

9

Annex

Country

Poland

Portugal

Romania

Slovakia

Finland

Norway

Comments

Under-coverage: New dwellings underrepresented in the sample-dwellings are selected once a year 
from the register of housing units and due to differences in time span there is no current information 
about addresses or flats. 
Over-coverage: Overcoverage consists of dwellings in which inhabitants are not present for a long 
time, not inhabited or inhabited seasonally, changed into uninhabitable space (for example shop), in 
liquidation, not found (incorrect address).

Under-coverage: With regard to the undercoverage rate, people living in collective households or 
institutions are excluded from the sampling frame. According to the last Census (2011), this population 
represents less than 1% of the total population. Nevertheless, calibration ensures that LFS weighted 
sample sums the estimates of resident population in Portugal. In addition, the new or renewed 
buildings and dwellings constructed after the last Census are also excluded, but it is not known the 
occupation status, that is if they are being used as usual residence or if they are vacant, and the 
percentage of the population they represent.

Under-coverage: Due to the lack of appropriate information, the new dwellings, built after 2011 
Census of the Population and Dwellings, that could possibly constitute a sampling frame of the new 
dwellings, have not been taken into account. Thus, an updates (of the addresses of dwellings) will be 
envisaged for the PSU included in EMZOT. Under-coverage rate was estimated as the ratio between 
number of new permanent dwellings, built in the period end of 2011 year (the year of the last census)- 
end of 2020 year (Source: Romanian Statistical Yearbook, 2022), and number of dwellings at the end of 
2020 year (Source: Romanian Statistical Yearbook, 2022). Thus, it was assumed that the proportion of 
the new dwellings in total dwellings should be the same in the master sample. 
Over-coverage: Over-coverage rates were estimated on the basis of the survey samples, as ratio 
between the number of not-eligible dwellings and number of sampled dwellings

Under-coverage: Undercoverage may be caused by insufficient coverage of new households, students 
living in student halls (although they are surveyed via other members of the households), members of 
the Slovak embassies, institutions and companies abroad. Even people born abroad and living in a 
closed community are not adequately represented, e.g. Chineses, Vietnamesesor part of the cross-
border commuters. 
Over-coverage: Some residents working temporarily abroad could stay to live there permanently. 
Misclassification: Misclassification by incorrect identification is negligible.

Under-coverage: The sampling frame used is the total population database maintained by Statistics 
Finland. It is based on the Population Information System of The Digital and Population Data Services 
Agency and updated regularly. Undercoverage fairly small (no large-scale immigration). 
Over-coverage: Mostly emigration in wave 1, deaths and emigration for later waves.

Over-coverage: There is some time lag in the central population register, so for instance persons who 
have died or emigrated recently might be included in it at the time of sampling to the LFS. We do not 
have any analysis to show, but the register is updated daily, and the amount of errors are generally 
assumed to be very low. It is the main source for all population statistics and population 
administration, and is very carefully monitored and run.

Switzerland Misclassification: Differing household composition: unit non-response in case the target person is not 

living in the selected household (anymore). Else no impact on estimates.

Note: The information in the table is extracted directly from the national quality reports, without modification.

Quality report of the European Union Labour Force Survey 2021 & 2022

45

9

Annex

TABLE 9.2
Procedure used by countries to reduce measurement errors, 2022

Respondent

Interviewer

Fieldwork

Questionnaire

s
w
e
i
v
r
e
t
n

i
e
h
t
g
n
n
e
t
s
i
l

i

y
b
y
l
t
c
e
r
i
d
g
n
i
r
o
t
i
n
o
M

y
l
e
t
o
m
e
r
g
n
i
r
o
t
i
n
o
M

e
c
n
a
m
r
o
f
r
e
p
h
g
u
o
r
h
t

s
r
o
t
a
c
i
d
n

i

l
a
r
e
v
e
s
n

i
e
r
i
a
n
n
o
i
t
s
e
u
Q

s
e
g
a
u
g
n
a
l

r
o
f
(
s
k
c
e
h
c
e
n

i
l
-
n
O

d
e
t
s
i
s
s
a
r
e
t
u
p
m
o
c

s
w
e
i
v
r
e
t
n

i

Country

Belgium

Bulgaria

Czechia

Denmark

Germany

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembourg

Hungary

Malta

Netherlands

Austria

e
h
t
g
n
i
c
u
d
o
r
t
n

i

r
e
t
t
e
L

y
e
v
r
u
s

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

t
s
a
e
l

t
a
(
g
n
n
i
a
r
t

i

l
a
c
i
d
o
i
r
e
P

)
r
a
e
y
r
e
p
e
m

i
t
1

r
e
w
e
i
v
r
e
t
n

i

m
o
r
f
k
c
a
b
d
e
e
F

)
.
c
t
e

,
s
g
n
fi
e
i
r
b
e
d

,
s
t
r
o
p
e
r
(

r
o
g
n
i
k
o
o
b
r
o
f

l
l
a
c
e
n
o
h
P

y
e
v
r
u
s
e
h
t
g
n
i
c
u
d
o
r
t
n

i

Y (occa-
sionally)

Y (occa-
sionally)

Y

N

Y (occa-
sionally)

Y

N

Y

N

Y (except 
in case of 
CAWI 
response)

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

N

Y

Y

Y

Y

N

Y

N

Y

Y (online 
training)

Y

Y

Y

Y

Y

Y

Y

Y

Y

y
b
y
l
t
c
e
r
i
d
g
n
i
r
o
t
i
n
o
M

s
t
n
e
d
n
o
p
s
e
r
e
h
t
g
n
i
t
c
a
t
n
o
c

N

k
r
o
w
d
l
e
fi
e
h
t

r
e
t
f
a

Y (occa-
sionally)

N

N

N

Y

Y

Y

N

Y (for 
subsam-
ple in last 
wave)

Y

Y

-

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y (annual 
debrief)

Y

Y

Y

Y

N

N

N

N

N

Y

Y

N

Y

N

Y

Y

N

N

Y

Y

N

N

Y

Y (for 
CAWI)

Y (for 
CATI-In-
terviews)

N

N

Y

N

N

Y

Y

Y

Y

N

N

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

N

Y

Y

Y

N

N

Y

N

Y

Y

Y

Y

Y

Y

N

Y

N

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

46

Quality report of the European Union Labour Force Survey 2021 & 2022

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9

Annex

Respondent

Interviewer

Fieldwork

Questionnaire

r
o
g
n
i
k
o
o
b
r
o
f

l
l
a
c
e
n
o
h
P

y
e
v
r
u
s
e
h
t
g
n
i
c
u
d
o
r
t
n

i

N

N

N

N

Y

Y

Y

-

N

Y (in case 
not 
respond-
ing in 
CAWI)

N

N

N

N

e
h
t
g
n
i
c
u
d
o
r
t
n

i

r
e
t
t
e
L

y
e
v
r
u
s

Y

Y

Y

Y

Y

Y

Y

-

Y

Y

Y

Y

Y

Y

Country

Poland

Portugal

Romania

Slovenia

Slovakia

Finland

Sweden

Iceland

Norway

Switzerland

Bosnia 
Herzegovina

North 
Macedonia

Serbia

Türkiye

t
s
a
e
l

t
a
(
g
n
n
i
a
r
t

i

l
a
c
i
d
o
i
r
e
P

)
r
a
e
y
r
e
p
e
m

i
t
1

r
e
w
e
i
v
r
e
t
n

i

m
o
r
f
k
c
a
b
d
e
e
F

)
.
c
t
e

,
s
g
n
fi
e
i
r
b
e
d

,
s
t
r
o
p
e
r
(

s
t
n
e
d
n
o
p
s
e
r
e
h
t
g
n
i
t
c
a
t
n
o
c

y
b
y
l
t
c
e
r
i
d
g
n
i
r
o
t
i
n
o
M

k
r
o
w
d
l
e
fi
e
h
t

r
e
t
f
a

s
w
e
i
v
r
e
t
n

i
e
h
t
g
n
n
e
t
s
i
l

i

y
b
y
l
t
c
e
r
i
d
g
n
i
r
o
t
i
n
o
M

y
l
e
t
o
m
e
r
g
n
i
r
o
t
i
n
o
M

e
c
n
a
m
r
o
f
r
e
p
h
g
u
o
r
h
t

s
r
o
t
a
c
i
d
n

i

l
a
r
e
v
e
s
n

i
e
r
i
a
n
n
o
i
t
s
e
u
Q

s
e
g
a
u
g
n
a
l

r
o
f
(
s
k
c
e
h
c
e
n

i
l
-
n
O

d
e
t
s
i
s
s
a
r
e
t
u
p
m
o
c

s
w
e
i
v
r
e
t
n

i

Y

Y

Y

Y

Y

Y

Y

-

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

-

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

N

-

N

N

N

Y

Y

Y

N

Y

N

Y

N

N

Y

-

N

Y

Y

Y

N

Y

Y

Y

Y

Y

N

Y

Y

-

Y

Y

N

N

Y

Y

N

N

N

N

N

Y

Y

-

Y

Y

Y

N

N

Y

Y

Y

Y

Y

Y

Y

Y

-

Y

Y

Y

Y

Y

Y

Notes: (Y) Indicates yes; (N) Indicates no; (-) Indicates that the country has not been able to provide the information or does not know; NA indicates Not 
Applicable (for example online checks with computerised questionnaire when only PAPI technique is used for interviews); UNA indicates information 
unavailable in the country.

Quality report of the European Union Labour Force Survey 2021 & 2022

47

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9

Annex

TABLE 9.3
Item non-response (%) for the variables defined by Regulation (EU) 2019/1700 
and clarifications provided by countries, 2022, Quarterly data 2022

Country

Identifier

Q1

Q2

Q3

Q4

Belgium

COBFATH

19.2 

19.1 

19.2 

19.6 

Short comments on reasons for non-available 
statistics and prospects for future solutions

Information from the National Population Register is 
used. The item non-response on COBFATH and 
COBMOTH comes from missing information in the 
National Population Register. There are three 
different reasons why a respondent can have a 
missing value on one or both of the variables. 
1) The first group is the biggest and consists of the 
elderly persons. Here the missingness is linked to the 
startup of the Population Register, which was in the 
1980ies. The older the person, the more likely that 
information on (one of) the parents is incomplete or 
missing. For the large majority of these respondents, 
their parents are most likely born in Belgium, but we 
cannot be sure. 
2) A second group is much smaller but is 
unfortunately increasing slightly over the years. It 
consists of persons who have migrated to Belgium 
and for whom information on the parents is 
incomplete. For this group, we do have information 
on the first registered citizenship of the respondent 
(usually not Belgian), meaning that it is very likely 
that one or both parents are not born in Belgium, 
but we can not say in which country. 
3) Finally, there is a third group of persons for whom 
only one parent is known (like for ‘Single mothers by 
choice’, called ‘BAM’s–Bewust Alleenstaande 
Moeders’ in Dutch).

COBMOTH

TEMPDUR

16.7 

24.8 

16.6 

30.8 

16.8 

27.9 

16.9  Same comment as Belgium COBFATH.

25.4  For persons without employment contract(main part 

of the variable non-response) the duration of job is 
oftenunclear.

Bulgaria

HWUSU2J

17.5 

22.0 

21.4 

20.3  Respondents meet difficulties to answer the 

question, especially self-employed. More than a half 
of non-responded persons were self-employed on 
the second job.

HWACTU2J

30.0 

20.0 

28.6 

28.8  Respondents meet difficulties to answer the 

question, especially self-employed. More than a half 
of non-responded persons were self-employed on 
the second job.

48

Quality report of the European Union Labour Force Survey 2021 & 2022

9

Annex

Country

Identifier

Q1

Q2

Q3

Q4

Short comments on reasons for non-available 
statistics and prospects for future solutions

Czechia

PROXY

13.8 

13.8 

13.8 

13.9  Unassigned code for children under 15. It will be 

AVAIREAS

Denmark

MODE

26.8 

34.5 

27.9 

37.1 

25.6 

38.2 

modified from 2023.

26.6  Will be improved from 2023.

37.8 

These are all persons outside the main LFS age group 
(15-89) and have not been interviewed.

PROXY

34.5 

37.1 

38.2 

37.8 

COBMOTH

14.1 

MSTARTWK

10.6 

ABSHOLID

ABSILLINJ

20.5 

20.5 

ABSOTHER

20.5 

These are all persons outside the main LFS age group 
(15-89) and have not been interviewed.

Due to an error in the register data used to create the 
filter for the COBMOTH question in Q1 of 2022 many 
respondents were erroneously not given the 
question. This error was discovered and corrected for 
Q2 and onwards.

We give respondents the option to skip to the next 
question, if they do not remember the actual month 
they started working.

12.3  The large amount of missing observations is mainly 
because persons who were absent the entire week 
will get a missing value in each of the three variables.

12.3  Due to a processing error, the missing rates for 
ABSILLINJ and ABSOTHER are higher than they 
should be. This will be fixed at a later point in time. 
However, there are still high missing rates on 
ABSILLINJ, ABSOTHER and ABSHOLID. This is mainly 
because persons who were absent the entire week 
will get a missing value in each of the three variables.

12.3  Due to a processing error, the missing rates for 
ABSILLINJ and ABSOTHER are higher than they 
should be. This will be fixed at a later point in time. 
However, there are still high missing rates on 
ABSILLINJ, ABSOTHER and ABSHOLID. This is mainly 
because persons who were absent the entire week 
will get a missing value in each of the three variables.

Germany

PROXY

CONTRHRS

21.1 

33.0 

19.3 

32.8 

19.7 

33.3 

20.0 

33.9 

In our mainly compulsory survey this variable is 
optional. High non-response comes from self-
administered modes (CAWI/PAPI). Measurements to 
reduce non-response are in focus.

Quality report of the European Union Labour Force Survey 2021 & 2022

49

 
 
 
 
 
 
 
 
 
 
 
 
 
9

Annex

Country

Identifier

Q1

Q2

Q3

Q4

Short comments on reasons for non-available 
statistics and prospects for future solutions

Ireland

TEMPDUR

29.5 

30.4 

27.7 

31.2  High item non-response believed due to difficulty in 

answering for indirect interviews.

SEEKDUR

11.0 

13.4 

14.6  High item non-response believed due to difficulty in 

MONTHPR

12.4 

answering for indirect interviews.

High item non-response believed due to difficulty in 
answering for indirect interviews.

ABSILLINJ

17.3 

20.3 

20.7 

20.8  High item non-response believed due to difficulty in 

answering for indirect interviews.

ABSOTHER

17.1 

19.9 

20.4 

20.0  High item non-response believed due to difficulty in 

answering for indirect interviews.

HWUSU2J

15.4 

11.5 

13.0 

11.8  High item non-response believed due to difficulty in 

Greece

HWACTU2J

answering for indirect interviews.

12.9 

In general, there was no high non-response in this 
variable. If the problem persist we will investigate 
the issue

Spain

PROXY

14.6 

14.9 

14.9 

14.9  The non-response correspond to people aged less 

AVAIREAS

21.0 

21.7 

21.2 

22.4 

than 16 that in the Spanish LFS don’t answer this 
variable.

It is due to a “doesn’t know” answer in the 
questionnaire for the variable AVAILBLE, which 
would imply a “not applicable” for AVAIREAS. The 
issue is that in the national quarterly file, all those 
records with “doesn’t know” in AVAILBLE are 
imputed as “not available”, which means, in tearms 
of filters and transcoding for AVAIREAS that this 
variable will take the value “blank” (in fact is a 
doesn’t know) because there is not information 
about it.

TEMPDUR

52.6 

50.9 

47.1 

52.4  The non-response correspond to “doesn’t know” 

answer

HWUSU2J

10.9 

11.7 

14.1 

12.8  The non-response correspond to “doesn’t know” 

answer

France

MODE

17.9 

17.8 

17.8 

17.9  Only people between 15 and 89 participate in the 

survey.

PROXY

17.9 

17.8 

17.8 

17.9  Only people between 15 and 89 participate in the 

survey.

COBFATH

10.6 

10.6 

10.8 

10.7  Between 22 % and 23 % of children under 15 years 

Croatia

SEEKDUR

12.8 

old live without their father in the house.

Reason for non-response rate higher than 10% 
(12.83%) is big share of proxy answers (74% or 29 
observations out of the total number of 39 non-
responses).

50

Quality report of the European Union Labour Force Survey 2021 & 2022

 
 
 
 
 
 
 
 
 
 
9

Annex

Country

Identifier

Q1

Q2

Q3

Q4

Short comments on reasons for non-available 
statistics and prospects for future solutions

Latvia

COUNTRPR

15.2 

TEMPDUR

MSTARTWK

Luxembourg NACE2J2D

MAINSTAT

EXTRAHRS

HWACTUAL

HWACTU2J

EDUCFED4

EDUCLEV4

EDUCNFE4

COUNTRPR

Malta

17.3 

15.2 

32.6 

23.6 

10.7 

53.7 

35.9 

50.2 

23.3 

19.0 

20.8 

25.3 

11.4 

32.9 

38.2 

31.2 

Netherlands

TEMPDUR

14.6 

13.7 

10.9 

27.0 

14.3 

14.9 

23.7 

12.3 

16.6 

43.9 

16.6 

11.7 

12.9 

Proxy interviews.

Proxy interviews.

28.8  Proxy interviews.

14.0  Filter error.

Filter error.

22.7  Filter error.

Filter error.

Filter error.

36.2  Filter error.

Filter error.

11.2 

12.4 

SEEKDUR

10.6 

11.0 

10.3 

10.7 

EXISTPR

14.9 

11.9 

For this variable the respondent has to recollect 
infomation about a duration, making this question 
difficult to answer.

For this variable the respondent has to recollect 
infomation about a duration, making this question 
difficult to answer.

For this variable the respondent has to recollect 
infomation about retrospective events, making this 
question difficult to answer.

Poland

PROXY

Portugal

TEMPDUR

16.7 

27.3 

16.5 

25.8 

16.2 

25.3 

15.9 

25.2  More than half of the non-responses are due to 

proxy interviews (67.2% average).

CONTRHRS

13.6 

12.4 

11.7 

11.2  More than half of the non-responses are due to 

HWACTU2J

10.1 

12.7 

Slovenia

Finland

PROXY

ABSILLINJ

13.2 

12.2 

10.1 

13.3 

21.3 

13.7 

ABSOTHER

10.6 

21.4 

proxy interviews (70.4% average).

More than half of the non-responses are due to 
proxy interviews (55.2% average).

The target group is all employed. In this variable, 
those who are absent the whole week, are included 
in non-response. This number arises during the 
holiday season.

The target group is all employed. In this variable, 
those who are absent the whole week, are included 
in non-response. This number arises during the 
holiday season.

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Country

Identifier

Q1

Q2

Q3

Q4

Short comments on reasons for non-available 
statistics and prospects for future solutions

Sweden

AVAIREAS

17.6 

20.4 

28.8 

29.4  Respondents tend to forget the end and start date of 

TEMPDUR

SEEKDUR

22.2 

31.4 

19.6 

32.5 

17.1 

22.6 

EXISTPR

YEARPR

Iceland

WANTREAS

AVAIREAS

TEMPDUR

HWUSU2J

HWACTU2J

REGISTER

TEMPDUR

Norway

25.4 

13.0 

48.1 

13.2 

15.3 

27.8 

14.2 

49.8 

13.6 

18.3 

11.0 

41.7 

35.4 

29.0 

14.9 

47.9 

11.0 

27.0 

21.3 

45.6 

35.7 

SEEKDUR

Switzerland

AVAIREAS

10.7 

29.5 

10.8 

26.3 

29.8 

NACE2J2D

21.9 

25.6 

25.1 

work.

21.6 

21.5  Respondents tend to forget how long they have 

been looking for jobs.

27.4 

15.8 

46.4 

12.0 

11.6 

24.1 

65.5 

40.2  Almost all of the non-response is ‘I don’t remember’, 
so we don’t quite know what to do in order to 
improve the situation.

11.3 

24.1  A considerable amount of employed persons wishing 
to work more (WISHMORE=2) indicate that they 
“don’t know” about the term of availability to do so. 
Because blanks are not allowed in AVAILABLE, such 
persons are coded AVAILBLE=2, which turns 
AVAIREAS applicable. However, the question on the 
reason for not being availble immediately is not 
asked in this cases.

26.9  Codification problem caused by the introduction of 
CAWI: due to the lack of assistance from an 
interviewer, there is an increasing number of 
respondents who fail to find their local unit in the 
business register. In this case, NACE information is 
only available at the 1-digit level of detail in the SLFS.

Bosnia and 
Herzegovina

Serbia

CONTRHRS

100.0 100.0 100.0 100.0 Variable not provided.

HWACTU2J

CONTRHRS

REGISTER

10.8 

81.3 

23.3 

61.7 

23.7 

24.3 

23.8 

Türkiye

CITIZENSHIP

100.0 100.0 100.0 100.0 According to the 2010 Address Based Population 

Registration System; 99,7 % of population has Turkish 
Nationality. So, it is not easy to cover non-nationals 
with a sample survey.

CONTRHRS

100.0 100.0 100.0 100.0  

Note: ‘C’ All records have the same value.
Note: The comments in the table are extracted directly from the national quality reports, without modification.

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TABLE 9.4
Annual data 2022

Country

Identifier

2022

Short comments on reasons for non-available statistics and prospects 
for future solutions

Czechia

Denmark

NEEDCARE

STAPROPR

NACEPR2D

Germany

ISCOPR3D

MAINCLNT

VARITIME

HATWORK

GENHEALTH

GALI

16.1  Not stated answers arise from respondents.

10.4 

47.7  We do not manually code ISCO and NACE for the previous job. We are 

working on better utilizing register data to collect information on NACE 
and ISCO codes, thereby reducing the amount of missing observations.

61.3 

Same comment as Denmark NACEPR2D

48.8 

25.9 

25.1 

24.6 

29.4 

In our mainly compulsory survey this variable is optional. High non-
response comes from self-administered modes (CAWI/PAPI). 
Measurements to reduce non-response are in focus.

Same comment as Germany MAINCLNT

Same comment as Germany MAINCLNT

Same comment as Germany MAINCLNT

Same comment as Germany MAINCLNT

Ireland

MAINCLNT

21.8  High item non-response believed due to difficulty in answering for 

indirect interviews.

EDUCLEV12

13.7  High item non-response believed due to difficulty in answering for 

indirect interviews.

GALI

13.0  High item non-response believed due to difficulty in answering for 

Latvia

MAINCLNT

11.6  Proxy interviews.

indirect interviews.

Lithuania

VARITIME

43.3  Had some issues with the collection of this variable in 2022. From 2023Q1 

LOOKOJ

100.0

fixed it.

WAYJFOUN

10.4  Had some issues with the collection of this variable in 2022. From 2023Q1 

fixed it.

Luxembourg EDUCLEV12

47.7 

Filter error.

Hungary

EDUCNFE12

83.1  Only asked on third waves.

GENHEALTH

83.2  Only asked on third waves.

GALI

83.2  Only asked on third waves.

Netherlands

TEMPREAS

HATYEAR

14.1 

14.1 

This question is about reasons and experiences, and is difficult to answer.

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Annex

Country

Identifier

2022

Short comments on reasons for non-available statistics and prospects 
for future solutions

Poland

MAINCLNT

44.1  Answering questions under this variable causes some problems not only 

for proxy answers but also in case of direct participation.

HATYEAR

11.0  Non responses can be caused by proxy answers and, especially in the case 

of older people, difficulties with recalling distant dates.

EDUCFED12

EDUCNFE12

GENHEALTH

GALI

HATYEAR

SIZEFIRM

NEEDCARE

HATFIELD

HATWORK

WAYJFOUN

ISCOPR3D

EDUCLEV12

EDUCNFE12

MAINCLNT

Sweden

Iceland

Norway

11.5 

11.5 

11.0 

11.4 

34.4 

Information from registers is used for HATYEAR.

14.9 

92.7 

100.0

100.0

61.2 

100.0

27.5 

60.2 

14.0 

SIZEFIRM

20.0  Coding error for dependent interviews. Will be fixed.

FINDMETH

16.1 

Switzerland

SIZEFIRM

NACEPR2D

26.8  Codification problem caused by the introduction of CAWI: due to the lack 
of assistance from an interviewer, there is an increasing number of 
respondents who fail to find their local unit in the business register. 

39.2  Codification problem caused by the introduction of CAWI: due to the lack 
of assistance from an interviewer, there is an increasing number of 
respondents who fail to find their local unit in the business register. In this 
case, NACE information is only available at the 1-digit level of detail in the 
SLFS.

HATYEAR

23.5 

Filter error: HATYEAR information not gathered for respondents having 
completed only mandatory education. Correction of the questionnaire 
planned.

Bosnia and 
Herzegovina

Serbia

HATWORK

FINDMETH

GENHEALTH

GALI

SIZEFIRM

HWWISH

WAYJFOUN

100.0

100.0

100.0

100.0

36.4 

36.4 

44.1 

Variable not provided.

Variable not provided.

Variable not provided.

Variable not provided.

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Quality report of the European Union Labour Force Survey 2021 & 2022

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Annex

Country

Identifier

2022

Short comments on reasons for non-available statistics and prospects 
for future solutions

Türkiye

TEMPAGCY

100.0

This variable is not asked since temporary working agencies are not 
common in Turkey for the moment.

MAINCLNT

VARITIME

SUPVISOR

HWWISH

HATWORK

WAYJFOUN

100.0

100.0

100.0

100.0

100.0

100.0

EDUCFED12

88.0 

Note: The comments in the table are extracted directly from the national quality reports, without modification.

9.2.  Country codes

Geographical aggregates and country codes

EU 

BE 

European Union

Belgium

BG 

Bulgaria

CZ  Czechia

DK  Denmark

DE  Germany

EE 

IE 

EL 

ES 

FR 

Estonia

Ireland

Greece

Spain

France

HR  Croatia

IT 

Italy

CY  Cyprus

LV 

LT 

Latvia

Lithuania

LU 

Luxembourg

HU  Hungary

MT  Malta

NL  Netherlands

AT  Austria

Quality report of the European Union Labour Force Survey 2021 & 2022

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Annex

PL 

PT 

Poland

Portugal

RO  Romania

SI 

SK 

FI 

SE 

Slovenia

Slovakia

Finland

Sweden

European Free Trade Asssociation (EFTA) countries

IS 

Iceland

NO  Norway

CH 

Switzerland

Candidate countries

BA 

Bosnia and Herzegovina

RS 

TR 

Serbia

Turkey

Summaries

English: This report summarises the EU Labour Force Survey methodology for 2021–2022 and is the first to reflect updates 
under the revised EU regulatory framework in the labour force domain. The main sources are EU-LFS microdata and national 
quality reports, with a core focus on reference year 2022.

French: Ce rapport résume la méthodologie de l’enquête sur les forces de travail de l’UE (EFT–UE) pour 2021–2022. C’est  le 
premier rapport reflétant le nouveau cadre réglementaire européen applicable à ce domaine. Les principales sources sont les 
micro données issues de l’EFT-UE et les rapports de qualité nationaux, en mettant l’accent sur l’année de référence 2022.

German: Dieser Bericht fasst die Methodik der EU-Arbeitskräfteerhebung für 2021–2022 zusammen und ist der erste, der 
die Aktualisierungen im überarbeiteten EU-Rechtsrahmen im Bereich der Arbeitskräfte widerspiegelt. Die Hauptquellen sind 
die Mikrodaten der EU-Arbeitskräfteerhebung (EU-AKE) und die nationalen Qualitätsberichte, mit einem Schwerpunkt auf 
das Referenzjahr 2022.

56

Quality report of the European Union Labour Force Survey 2021 & 2022

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Quality report of the European Union 
Labour Force Survey 2021 & 2022

This report summarises the EU Labour Force Survey methodology 
for 2021–2022 and is the first to reflect updates under the revised EU 
regulatory framework in the labour force domain. The main sources are EU-
LFS microdata and national quality reports, with a core focus on reference 
year 2022.

For more information
https://ec.europa.eu/eurostat/

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ISBN 978-92-68-32546-9