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Background Notes

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Purpose of Survey

The Labour Force Survey (LFS) replaced the Quarterly National Household Survey (QNHS) at the beginning of Q3 2017. The purpose of the survey is the production of quarterly labour force estimates and occasional reports on special social topics.

Regulatory framework

The survey meets the requirements of Regulation (EU) 2019/1700 of the European Parliament and of the Council of 10 October 2019. This is a new framework regulation governing the production of European Statistics on persons and households (Integration European Social Statistics framework regulation – IESS FR) which came into force on 1 January 2021. The IESS FR regulation replaces Council Regulation (EC) No. 577/98, adopted in March 1998 and covers various domains of social statistics including labour market statistics. It aims to ensure that social statistics based on sample surveys such as the Labour Force Survey (LFS), are produced in a more harmonised and coordinated manner across Europe.

The CSO had to introduce changes to the LFS questionnaire in Ireland from Quarter 1 (Q1) 2021 because of the IESS FR regulation. These include changes to LFS variables collected by the LFS questionnaire with some new questions added, while some questions have been removed, and others have changed in terms of response options or frequency. There have also been some changes to the order of the questions as the flow of the LFS questionnaire across Europe is now more prescribed and harmonised under the IESS regulation. Please see Information Note.

Reference Period

Information is collected continuously from households who are surveyed every week in each quarter. The QNHS operated on a seasonal quarter basis from when it started in Q4 1997 up to and including Q4 2008. In Q1 2009, the QNHS adopted a calendar quarter basis and this has been continued in the LFS.

The reference quarters for survey results are:

Q1 - January to March, Q2 - April to June, Q3 - July to September and Q4 - October to December

Data Collection

Mixed mode data collection is used for the LFS, that is, a mix of face-to-face and phone interviews. As with the QNHS, information is collected from each sample household over five successive quarters or waves. The first interview in the LFS is face-to-face by an interviewer using Computer Assisted Personal Interviewing (CAPI). If the householders agree, then Computer Assisted Telephone Interviewing (CATI), from a dedicated call centre, is used for the four follow-up interviews. Otherwise, the follow-up interviews are conducted using face-to-face interviews.

Users should note that, as referenced in the Press Statement of 20 March 2020, the CSO had to suspend direct face-to-face interviews for the LFS (and other household surveys) due to the social distancing measures introduced in Ireland because of COVID-19. Consequently, all interviews for the LFS since then have been carried out by telephone.

Sample Design

A new sample based on the 2016 Census of Population was introduced on a phased basis (over five quarters) from Q2 2019 and was fully operational in Q2 2020. As with the expiring sample below, the new sample is stratified using administrative county and the Pobal HP (Hasse and Pratschke) Deprivation Index and consists of 32,500 households per quarter.

The previous sample was based on the 2011 Census of Population and was introduced incrementally from Q1 2016 and expired in Q1 2019. The sample was stratified using administrative county and the Pobal HP (Haase and Pratschke) Deprivation Index. A two-stage sample design was used. In the first stage 1,300 blocks were selected using Probability Proportional to Size (PPS) sampling. In the second stage households were selected using Simple Random Sampling (SRS). This ensured each household in the sample frame had an equal probability of selection.

To account for the additional attrition resulting from the introduction of mixed mode data collection, the LFS sample up to Q1 2019 was increased incrementally from Q3 2017. An additional 1,300 households were included in Wave 1 for each quarter up to Q3 2018 and this has resulted in a total sample of 32,500 from Q3 2018 onwards. The actual achieved sample varies over time depending on the level of response.

The number of valid responding households for the LFS in Q1 2021 was 11,330.

Households are asked to take part in the survey for five consecutive quarters and are then replaced by other households in the same block. Thus, one fifth of the households in the survey are replaced each quarter and the QNHS/LFS sample involves an overlap of 80% between consecutive quarters and 20% between the same quarter in consecutive years. It is important to note that there is no overlap in sample between the QNHS and the LFS.

The survey results are weighted to agree with population estimates broken down by age, sex and region (the regions have changed from Q1 2018 – see below) and are also calibrated to nationality control totals. The LFS results also contain a non-response adjustment to make the results from the achieved sample representative of the target sample and the population. The population estimates for April of each year are published in a separate release.

New samples, both based on the 2011 Census of Population, were introduced incrementally for the QNHS in Q4 2012 and in Q3 2016. The former was stratified using administrative county and population density while the latter was stratified using administrative county and the Pobal HP (Haase and Pratschke) Deprivation Index. The quarterly sample in each case was 26,000 households. The actual achieved sample varied over time depending on the level of response.

Households with all persons aged 75 and over and classified as inactive

Households are not re-interviewed if they only contain people aged 75 or over who are each classified as Inactive, (i.e. not in the Labour Force). Instead, to reduce any unnecessary burden, the answers are copied forward from the last available interview.

Statistical significance

All estimates based on sample surveys are subject to error, some of which is measurable. Where an estimate is statistically significantly different from another estimate it means that we can be 95% confident that differences between those two estimates are not due to sampling error.

Usual residence and de facto population concepts

Up to and including Q1 2006 the annual population estimates were calculated using the defacto definition of population (i.e. all persons present in the state). Since Q2 2006 a new concept of usual residence has been used, i.e. all persons usually resident and present in the state plus absent persons who are usually resident in Ireland but are temporarily away from home and outside the state.

ILO Labour Force Classification

The primary classification used for the LFS results is the ILO (International Labour Office) labour force classification. Labour Force Survey data on this basis have been published since 1988. The ILO classification distinguishes the following main subgroups of the population aged 15 or over:

In Employment: Persons who worked in the week before the survey for one hour or more for payment or profit, including work on the family farm or business and all persons who had a job but were not at work because of illness, holidays etc. in the week.

Unemployed: Persons who, in the week before the survey, were without work and available for work within the next two weeks, and had taken specific steps, in the preceding four weeks, to find work. It should be noted that as per Eurostat’s operational implementation, the upper age limit for classifying a person as unemployed is 74 years.

Inactive Population (not in labour force): All other persons.

The labour force comprises persons employed plus unemployed.

Participation, Employment and Unemployment Rates

The CSO changed the calculation method of the Unemployment Rate in Q2 2015 to ensure coherence with Eurostat. Before this, the Unemployment rate was calculated as the number of unemployed as a percentage of the total labour force aged 15 and over. The change from Q2 2015 onwards uses the total labour force aged 15-74. Thus, a small number of persons aged 75 and over in employment are excluded from the total labour force used in the calculation. The overall impact of this change was very small.

Duration of Unemployment

The duration of unemployment is the length of time since a person last had a job or began looking for work, whichever is more recent. The long-term unemployment rate is the number of persons unemployed for one year or more expressed as a percentage of the total labour force aged 15 to 74 years.

Part-time Underemployment

The calculation of part-time underemployment is based on ILO and Eurostat recommendations and uses the following criteria to derive underemployment:

1. Working part-time
2. Willing to work additional hours
3. Available to work additional hours

This indicator is only available from Quarter 3 2008 onwards as estimates prior to that quarter were based on one single question which included the need for the person to be looking for additional work. From Quarter 3 2008 the indicator is derived from a series of separate questions which allow this requirement to be excluded.       

Potential Additional Labour Force

The Potential Additional Labour Force (PALF) is the sum of the two groups ‘persons seeking work but not immediately available’ and ‘persons available for work but not seeking’. Persons in the PALF are not part of the standard labour force, which encompasses only employed and unemployed people but however they have a stronger attachment to the labour market than other persons not in the labour force. The new indicators have been defined by the European statistical office (Eurostat) following extensive international discussion regarding appropriate indicators to supplement the unemployment rate.

Further background information regarding the methodology and approach adopted by Eurostat in building these new indicators can be found at the link below. European wide and individual country results are also available here:

Principal Economic Status Classification

Results are also available using the Principal Economic Status (PES) classification which is also used in the Labour Force Survey and the Census of Population. The PES classification is based on a single question in which respondents are asked what is their usual situation with regard to employment and given the following response categories:

  • At work
    • Unemployed
    • Student
    • Engaged on home duties
    • Retired
    • Other
  • Calendar effects e.g. the timing of Easter
  • Outliers, temporary changes, and level shifts in the series

NACE Industrial Classification

The LFS sectoral employment figures are based on the EU NACE Rev. 2 (Nomenclature généraledes activités économiques dans les Communauté européenne) classification as defined in Council Regulation (EC) no 1893/2006. Fourteen NACE sub-categories are distinguished in Tables 2 and 3 of this release. From Q1 2009 NACE Rev. 2 has been adopted as the primary classification of industrial sectors for use in QNHS/LFS outputs. The NACE Rev. 1.1 classification had been in use from Q4 1997 to Q4 2008.

To facilitate analysis and the running of seasonal adjustment on the time series, NACE Rev. 2 estimates have been produced from Q1 1998 onwards. As of Q2 2009 only NACE Rev. 2 estimates have been published.

Occupation Classification

The CSO was obliged to report occupational coding data to Eurostat based on the new classification ISCO-08 from Q1 2011 onwards, because of changes to the EU regulations on the Quarterly Labour Force Survey, which is implemented in Ireland using the LFS, (formerly the QNHS). The CSO changed to UKSOC2010 as the primary classification for occupations, from which ISCO-08 is then derived.

The previously used classification for publication purposes in Ireland was UK SOC1990 and this cannot be directly compared to the new UK SOC2010 classification as all occupations have been reclassified accordingly. One particular example which highlighted this change was the reclassifying of farmers from the major occupation grouping of ‘Managers and administrators’ in SOC1990 to the major occupation grouping of ‘Skilled trades’ in SOC2010.

Results for occupations coded to the new SOC2010 classification have now been recoded for historical quarters back to Q1 2007 to provide a longer and consistent time series for users.

Further information regarding SOC 2010 is available here.

NUTS2 and NUTS3 Regions

The regional classifications in this release is based on the NUTS (Nomenclature of Territorial Units) classification used by Eurostat. Until Q4 2017, the NUTS3 regions corresponded to the eight Regional Authorities established under the Local Government Act, 1991 (Regional Authorities) (Establishment) Order, 1993, which came into operation on 1 January 1994 while the NUTS2 regions, which were proposed by Government and agreed by Eurostat in 1999, were groupings of those historic NUTS3 regions.

However, the NUTS3 boundaries were amended on 21st of November 2016 under Regulation (EC) No. 2066/2016 and have come into force from Q1 2018. These new groupings are reflected in the LFS results from Q1 2012 onwards. As a result of these changes, Louth moved from the Border to the Mid-East and what was formerly South Tipperary was amalgamed with North Tipperary and moved from the South-East to the Mid-West. The new NUTS2 and NUTS3 regions are:


Northern & Western NUTS2 Region

Southern NUTS2 Region

Eastern & Midland NUTS2 Region






Dublin City






Dun Laoghaire-Rathdown












South Dublin






























































Absences from work in reference week                                     

Absences from work in the reference refer to those persons who were away from work during the interview reference week but had a job to return to after this absence. The reasons for absence would include temporary layoff due to slack work, family related leave, illness or disability and education and training.  

Actual hours worked               

Persons who are classified as employed in the survey and who worked during the reference week are asked for the number of actual hours they worked that week. The estimate of the total number of actual hours worked per week in each quarter is calculated by adding together the number of actual hours worked in the reference week for all persons in employment.

Seasonal Adjustment Methodology

To correct for typical seasonal patterns, the series presented in Table 3 have been seasonally adjusted. The seasonal adjustment of data from the QNHS between Q2 2011 and Q2 2017 was completed by applying the X-12-ARIMA model, developed by the U.S. Census Bureau. This seasonal adjustment methodology has been reviewed following the introduction of the new LFS in Q3 2017. As a result of this review, from Q3 2017 onwards, the seasonal adjustment of the LFS is conducted using the X-13ARIMA-SEATS software also developed by the U.S. Census Bureau. The adjustments are carried out by applying the X-13-ARIMA model to the unadjusted data. This methodology estimates seasonal factors while also taking into consideration factors that impact on the quality of the seasonal adjustment, such as:

For additional information on the use of X-13ARIMA-SEATS see:

Seasonal adjustment is conducted using the direct approach, where each individual series is independently adjusted. As a result of this direct seasonal adjustment approach it should be noted that the sum of any component series may not be equal to seasonally adjusted series to which these components belong, e.g. the seasonally adjusted number of males in employment and the seasonally adjusted number of females in employment will not necessarily add up to the total employment on a seasonally adjusted basis.

The X-13-ARIMA method has the X-11 moving averages process at its core, but builds on this by providing options for pre-treating the series using a regARIMA approach for prior adjustment and series extension. In essence, this methodology will estimate seasonal factors while taking account of calendar effects (e.g. timing of Easter), outliers, temporary changes and level shifts.

The seasonal adjustment is designed and implemented in full accordance with the ESS Guidelines (2015).

Users should note that the existing seasonally adjusted series may be subject to revision when data for an additional quarter is added.

Non-response adjustment

Non-response occurs when households that are sampled, and that are eligible for the survey, do not provide the requested information. This can lead to biased survey estimates if specific groups within the population are over- or under-represented and if these groups behave differently with respect to the survey variables (i.e. labour market outcomes). To correct for this, the CSO has introduced a non-response adjustment into the weighting procedure for the LFS.

The adjustment involves estimating response rates or propensities to respond as functions of characteristics available for responding and non-responding households, as well as characteristics of the areas where the households are located. Basically, the design weights have to be inflated by the inverse of the response propensities in order to compensate for the loss of units in the sample.

Linking the LFS sample with the Census of Population at household level provides a set of auxiliary variables which are available for both responding and non-responding LFS households. These include a mix of personal characteristics as well as characteristics of the dwelling and location (e.g. gender, age, marital status, education, personal employment status, dwelling type, area etc.). This allows the CSO to compare responding and non-responding households with respect to the characteristics available from the Census. This auxiliary information allows the use of “response propensities” to model non-response and adjust the grossing factors to compensate for non-response.

The response propensities are calculated using a logistic regression model where the dependent variable (Y) is an indicator variable corresponding to response (if the household responded then Y=1 and if the household did not respond then Y=0) and the independent variables are the set of auxiliary variables available from the Census. The estimated response propensities are then used to form adjustment cells or strata which are made up of respondents and non-respondents with similar estimated response propensities. Respondents within each cell/stratum are then weighted by the inverse of the observed response rate in that cell. This non-response adjusted weight is then used to inflate the original survey design weight to account for non-response. This approach is referred to as response propensity classification.

Back-casting methodology

The introduction of the LFS in Q3 2017 constituted a break in series for the labour market estimates published by the CSO. In an effort to mitigate the effect of the introduction of the LFS on the coherence of the historic data series, a back-casting exercise was carried out to link the QNHS and the LFS. The result of this is that the published QNHS series from Q1 1998 to Q2 2017 has been revised.

As part of the roll-out of the LFS, a parallel run of the two surveys was carried out. This allowed the estimation of the effect of the introduction of the new survey on the various labour market estimates. Quarter 1 of 2017 was used as the reference period to calculate scaling factors which were used to link the results from the two surveys. Labour market estimates were calculated from both surveys for a range of cohorts (age, sex, ILO status etc.) and the ratio of the two estimates provided a scaling factor which was applied to the historic QNHS series to create a back-cast series. 

In Q3 2017, separate scaling factors were calculated for ILO status by age and sex together with the fifteen economic sector categories (Table 2 of this release) by sex:

  • ILO status (Employed, Unemployed, Inactive) by sex (Male and Female) and by age (15-24 years and 25+ years)
  • NACE Rev. 2 groups (table 2 of this release) by sex (Male and Female)

In Q1 2018, additional scaling factors were calculated for the eight NUTS3 regions by Labour Force ILO status, occupation categories (Table 4 of this release) and highest level of education completed (supplementary table 8 of this release) by sex:

  • NUTS3 Regions (pre-Q1 2018 groupings) by Labour Force ILO status (Employed and Unemployed)
  • Occupation groups (Table 4 of this release)
  • Highest level of education completed (supplementary Table 8 of this release) by sex (Male and Female)

Therefore, adjustments have been made to this historic data to enable comparability with the new LFS for these indicators. However, as a result of changes to the questionnaire, the interview mode, the introduction of a new sample, data processing changes and other methodological enhancements there are changes in the levels of some series from Q3 2017 onwards.  Consequently, the series before and after the introduction of the new survey may not be directly comparable and users should therefore note this when examining annual and quarterly changes.         

 Please refer to the following information notes for further details:

Information Notice - Labour Force Survey Quarter 3 2017.

Information Notice - Labour Force Survey Quarter 1 2018

Monthly Unemployment

Monthly unemployment estimates were first introduced by the CSO in June 2015 for reference month May. The most recently published estimates were for January 2021 and these are available in a separate release from the Central Statistics Office. Please see the following link:

In an effort to reduce the scale and size of revisions to the monthly unemployment series, the CSO changed the methodology used to calculate the monthly unemployment estimates from reference month October 2019 onwards. The key change is that the latest available LFS quarterly benchmarks are now being used to calculate monthly unemployment estimates. This means that the number of revisions to the monthly series will now be reduced from 16 to 12. The CSO is satisfied that the new methodology still aligns with that of Eurostat.

Please see the following link to the detailed information note on this change which was published in conjunction with the monthly unemployment estimates for reference month October 2019.

Information Note - Monthly Unemployment October 2019

In line with Eurostat practice, the seasonally adjusted quarterly unemployment volumes and rates included in Table 3 of this LFS release are calculated as the average of the relevant three months of the quarter from the new monthly unemployment series. This approach ensures consistency between the seasonally adjusted monthly series and the seasonally adjusted quarterly series.

Reliability of Estimates Presented

Estimates for number of persons where there are less than 30 persons in a cell are too small to be considered reliable. These estimates are presented with an asterisk (*) in the relevant tables.

Where there are 30-49 persons in a cell, estimates are considered to have a wider margin of error and should be treated with caution. These cells are presented with parentheses [ ] .

In the case of rates, these limits apply to the denominator used in generating the rate. In the case of annual changes, both the current year and the preceding year are taken into account when deciding whether the estimate should be suppressed or flagged as having a wider margin of error.

Calculation of Rates and Estimates of Change

Rates and estimates of change presented in this release are calculated from whole unrounded numbers. Due to rounding, these may differ from the rates and estimates of change calculated from the rounded volumes presented in the tables.

Interpretation of volume and rate changes

There was a decline in the number of people in younger age groups in recent years because of the falling number of births through the 1980’s until a low point of 48,255 births in 1994. This results in natural changes in population in specific age groups over time. For example, there were 326,030 people born in Ireland between 1982 and 1986 who would be in the 20-24 age group by 2006. However, between 1986 and 1990 there were nearly 50,000 fewer births which would create a natural decrease in the 20-24 age group of about 50,000 between 2006 and 2010.

Net migration has been an important feature of population change in Ireland in recent years and has been most heavily concentrated in the younger age groups, in particular, those aged 20-34. Fewer births and emigration have combined to produce a drop in the number of people in younger age groups. This should be remembered when examining the changes in the number of people in younger age groups who are employed, unemployed and in the labour force.

Additional Data Series

The full series of data from the tables in this LFS release are available from PxStat, the CSO’s main data dissemination service which can be accessed through the CSO website at the following link:

Additional data series previously included in the LFS release can still be accessed through the CSO website and are available at the link below:

Implications of Census 2016 Final Results

The LFS results are weighted using population estimates which are updated every quarter. Every 5 years the Census of Population results are used to revise these population estimates, and QNHS/LFS results are revised as a consequence.

The population concept of usual residence is used for the LFS, i.e. all persons usually resident and present in the State plus absent persons who are usually resident in Ireland but are temporarily away from home and outside the State.

The total for the usually resident population enumerated on Census Night, 24 April 2016, was 4,739,597 persons. When this total was published in April 2017, the existing estimate for the usually resident population for April 2016 was 4,673,700, (see the 2016 Population and Migration Estimates release). This gave a difference of just over 65,900 or 1.4%.

The CSO has revised the population estimates for 2011 to 2016 based on this final Census count. Estimates of persons employed and unemployed have been revised in line with the higher population totals.

Participating Households

The Central Statistics Office wishes to thank the participating households for their co-operation in agreeing to take part in the survey and for facilitating the collection of the relevant data.

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