The EU Labour Force Survey (EU-LFS) is the largest European household sample survey. Its main statistical objective is to classify the population of working age (15 years and over) into three mutually exclusive and exhaustive groups: employed persons, unemployed persons, which together represent the ‘labour force’, and the people outside the labour force.

In Spain the population working age is 16 years and over.

Population concept 

Specific population subgroups

Primary/secondary students

Tertiary students

People working out of family home for an extended period for the purpose of work

People working away from family home but returning for weekends

Children alternating two places of residence

Usual residence (12 months) 

 Family home 

Family home

Family home 

 Family home

For children in joint custody, the place where the child is found at the reference date shall be considered as his or her usual residence.

(Y/N)

Give a description of difference and provide an assessment of the impact of the divergence on the statistics

 N

 NA

 N

 NA

 N

 NA

 N

 NA

Description of difference in concept

Description of difference in measurement

Give an assessment of the effects of the differences

Give references to description of differences

Total employment

NA include employment in collective residences; CN compute the ‘domestic’ employment whereas LFS focus in the ‘national’ employment

NA is a synthesis operation that takes account not only of LFS data but also other sources

The last NA data for 2022 is *21,026.7.0 thousands employment (Advanced estimate). The total (head count) employment figure in the LFS is 20,547.5 (figures in thousands for 2022)

For information about these differences see the following link: https://www.ine.es/daco/daco42/daco4211/coherencia.pdf

Total employment by NACE

NA include employment in collective residences; CN compute the ‘domestic’ employment whereas LFS focus in the ‘national’ employment

NA is a synthesis operation that takes account not only of LFS data but also other sources

As LFS is one of the main sources of information for most of the NACE aggregates, the differences are not very important. Only Finance and Public administration sectors are based in other sources.

For information about these differences see the following link: https://www.ine.es/daco/daco42/daco4211/coherencia.pdf

Number of hours worked

The information is calculated sector by sector and other sources are taken into account

NA is a synthesis operation that takes account not only of LFS data but also other sources 

For year 2023, the National Account estimates (advanced estimate) 34,463,553 annual hours; the LFS based estimate gives 35,666,264 annual hours

For information about these differences see the following link:https://www.ine.es/daco/daco42/daco4211/coherencia.pdf

Which is the use of LFS data for National Account Data?   

Country uses LFS as the only source for employment in national accounts.

Country uses mainly LFS, but replacing it in a few industries (or labour status), on a case-by-case basis

Country doesn’t make use of LFS, or makes minimal use of it

Country combines sources for labour supply and demand giving precedence to labour supply sources (i.e. LFS)

Country combines sources for labour supply and demand not giving precedence to any labour side

Country combines sources for

labour supply and demand

giving precedence to labour

demand sources (i.e. employ-

ment registers and/or enterprise

surveys)

 N

 N

 N

It is the use for the total at national level

 N

 It is the use when it is necessary to do a break down by activity branch (in particular for those with low level of employment).

Description of difference in concept

Description of difference in measurement

Give an assessment of the effects of the differences

Give references to description of differences

Total employment

Social Security Register.- People register at the Social Security in the last day of the month or in the middle of the month

The legal concept of employment applied in the Social Security register is not exactly the same as the ILO concept. 

The employment estimates are generally consistent with the data of social security registers, once the differences between both sources are taken into account. These differences were reduced during the pandemic period, being practically nil in the second quarter of 2020. Since then, the LFS employment data continues to be higher than those of the social security registers, but the differences remain less than in the pre-pandemic period.

Work document and Comparison of Employment Data by High Statistics Council

Total employment by NACE

Social Security Register.- People register at the Social Security in the last day of the month or in the middle of the month

The legal concept of employment applied in the Social Security register is not exactly the same as the ILO concept. 

Some branches are higher in LFS and others are higher in the Social Security Register

Work document and Comparison of Employment Data by High Statistics Council

Number of hours worked

In the Spanish LFS, actual hours are considered to be hours worked during normal working hours. They do not include vacations, holidays, sick leave, and other paid absences, nor time not worked due to being affected by a labour regulation.

For the SS, the actual hours are the hours contributed corrected from suspension periods. That is, these are the quoted hours from which the hours spent on labour regulation, hours for temporary disability, and hours related to other benefits are deducted.

The origin of each statistic is different, as the Spanish LFS is a household survey, while the SS is based on social security contribution data.

In the Spanish LFS, actual hours worked come from household responses to the questionnaire, while in the SS, they come from a correction of the hours worked.

The differences in whether or not vacations are considered mean that in all third quarters (July, August, and September) the Spanish LFS reduces its average hours per worker per month, but the SS never does.

Statistics on hours worked by Social Security

Actual hours from Spanish LFS. Explanation in variable HWACTUAL (page 206)

Description of difference in concept

Description of difference in measurement

Give references to description of differences

The registered unemployment is a legal concept, which differs considerably from the ILO unemployment

 Some population groups are considered as unemployed in LFS and not in registered unemployment and viceversa. On the other hand, a new measure system was implemented in 2005 which changed the estimates of registered unemployment (around 400,000-500,000 more)

Comparison of Unemployment Data.
High Statistics Council

Give an assessment of the effects of the differences

Overall effect

Men under 25 years

Men 25 years and over

Women under 25 years

Women 25 years and over

Regional distribution (NUTS-3)

The unemployment in LFS has been, in general, higher than registered unemployment. But being different concepts occasionally the difference reversed. Results derived from a consistency analysis project at microdata level shows that ILO unemployment and registered unemployment even they are correlated, they don't measure the same object and the relationship between them are rather complex.

Since 2007 to the beginning of the pandemic in 2020, the LFS figures were higher than those of the registered unemployment, as they used to be in the past, although in the evolution in time of both series an approach between them could be appreciated. With the beginning of the pandemic the LFS unemployment data were lower although, as the pandemic period passed, the differences were reduced and specifically from the fourth quarter of 2022 the LFS unemployment figure is again higher than the registered unemployment one.

LFS unemployment data higher than registered unemployment. Young people are prone to have weaker links with employment offices

LFS unemployment data became lower than registered unemployment with the start of the pandemic, but then LFS figures returned to be higher than registered unemployment, except for the third quarter of 2022

LFS unemployment data higher than registered unemployment. Young people are prone to have weaker links with employment offices 

LFS unemployment data lower than registered unemployment. This trend was already observed in 2020 with the beginning of the pandemic 

For 2023 averages, the data are very similar for most of the NUTS3 units. It is difficult to explain in detail differences because in some territorial units one source provides higher estimates and in other NUTS3 the higher data come from the other source. Probably it depends on the availability of 'small hours jobs', possibilities of 'regional' labour markets, perceived efficiency of the employment public offices, influence of the availability condition in the LFS unemployment data, the unemployment benefits (and their relation with age/sex variables; see above), the sector predominancy in some NUT3 regions, etc. The higher discrepancies in 2023 at NUTS3 level are ES300 (Comunidad de Madrid: 367.9 LFS vs 305.3 RU), ES530 (Illes Balears: 67.8 LFS vs 31.5 RU) and ES511 (Barcelona: 278.6 LFS vs 252.3 RU) 

Sampling design (scheme; simple random sample, two stage stratified sample, etc.)

Base used for the sample (sampling frame) 

Last update of the sampling frame (continuously updated or date of the last update)

Primary sampling unit (PSU) 

 Final sampling unit (FSU)

Date of sample selection

A two-stage sampling procedure is utilised with stratification of the primary units. 

Census 2011, updated through population registers and field work routes in order to update the new dwellings 

Every quarter the sixth of the sample that enters in first interview is updated in order to include the new dwellings. The probabilities of selection of the PSU are revised, depending of the register of population figures, at least every 3-years 

First stage units are geographical areas in which all the country is split. These areas are stratified within each province, using the population size of the municipality. Within each stratum, the areas are substratified according to the socio-economic characteristics of the population.  

- Second stage units are private dwellings.

- In the first quarter of 2021 an update of the sample began and this renewal has taken place until the fourth quarter of 2023

First (and intermediate) stage sampling method

  Final stage sampling method

Stratification (variable used)

Number of strata (if strata change quarterly, refer to Q4).

Rotation scheme (2-2-2, 5, 6, etc.)

Units are selected in such a way to obtain self-weighted samples within each stratum. The first stage units are selected with proportional probability to the size.

Second stage units are selected with equal probability. 

Based in the population size of the municipality.

There are 7 theoretical strata categories. In each province, the population is distributed according to these theoretical 0 to 6 strata categories, not having all of them representativeness in the 52 provincies. 

The sample is made up of six rotation groups. Dwellings once selected remain in the sample for six consecutive quarters before being replaced. In any quarter, dwellings of one wave are receiving the first interview, dwellings of another wave are receiving the second interview, and so on.
Each quarter, the dwelling sample in one sixth of the primary unit sampled, is replaced by a new sample. Thus, there is an 83% overlap in the samples for each consecutive quarter. 

Only for countries using a subsample for yearly variables

 Wave(s) for the subsample

 Are the 30 totals for ILO labour status (employment, unemployment and inactivity) by sex (males and females) and age groups (15-24, 25-34, 35-44, 45-54, 55+) between the annual average of quarterly estimates and the yearly estimates from the subsample all consistent? (Ref.: Commission Reg. 2019/2240) (Y/N)

If not please list deviations

List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 2019/2240, Annex I)

6th wave (last interview of the dwelling).

 Y

NA

In 2021 the wave approach was used for all the variables defined in the COMMISSION DELEGATED REGULATION (EU) 2020/256 with annual, every 2 year or every eight year periodicity. 

Data collection methods: brief description

Use of dependent interviewing (Y/N)?

In case of Computer Assisted Methods adoption for data collection, could you please indicate which software is used?

All the information (except the one related to INCGROSS) is collected by interview.

The first interviews used to be personal interviews but, with the arrival of the pandemic, nearly all of them are carried out by phone.

Interviews in the second and subsequent waves are carried out by telephone, except when the family wants a personal interview or there is no telephone.

Apart from that, since 2020 the use of the CAWI system, although in a residual way, has appeared as part of the methods used to collect information.

Since the fourth quarter 1997 all interviews are done with the help of portable computers. Since 2005 telephone interviews are carried out throught CATI system that apart of the management of the telephone interviews allows to check on line the interviews.

Y

Own development software based on JAVA and ORACLE 

Are any LFS data collected from registers (Y/N)?

If Yes, please indicate which

registers.

 Y

 INCGROSS is calculated from:

-Annual Salary Declarations for Income Tax (IRPF)

-Social Security Contribution Bases

Brief description of the method of calculating the quarterly core weights

Is the sample population in private households expanded to the reference population in private households? (Y/N)

If No, please explain which population is used as reference population

Gender is used in weighting (Y/N)

Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)?

Which regional breakdown is used in the weighting (e.g. NUTS 3)?

Other weighting dimensions

The design uses Ratio Estimator and the auxiliary variable is the Population Projection, living in private dwellings, at stratum level.

Every quarter, population projections by age group (0–15 years and 16 years +) and Spanish provinces (in general corresponding to NUTS-3 regions) are made. Projections by age and region are distributed by stratum in proportion to the population of each stratum. In each stratum, age group and region, the weighting is determined by the ratio of the projection to the sample size.

Since 2002, the calibration method has been introduced regularly, in order to adjust the sample to the population distribution. The auxiliary information used is the population by sex, age at NUTS2. When the new population base was introduced in March 2005, the data for the period 1996-2004 were revised. In the calibration method was included also the variable nationality (Spanish/Non Spanish) for the total population aged 16 years and more at NUTS2 level where the sample had enough size.

In 2014, when the 2011 base was introduced, the data for the period 2002-2013 were revised. At that moment, more variables were introduced in the calibration: Dwelling size at NUTS2 level and more agregated age groups at NUTS 3 level.

A linear weighting methods is used, in which each member of the dwelling aged 16 years and more has the same weight.

In 2021 population projections by age group in which people 15 year old were taking into account with those less than this age (0–15 years and 16 years +) are changed by groups in which 15 year old people are taking into account with those related to the labour force survey, it is with 16 years and more people (groups of 0-14 years and 15 years +)

Regards to the variable nationality (Spanish/Non Spanish) from that moment the calibration was included for the total population aged 15 years and more at NUTS2 level where the sample had enough size (instead of for the population aged 16 and more as before).

           Y

 NA

      Y

0-4, 5-9, 10-14, 15-19, 20-24, …, 65+ at NUTS2 level 15-29, 30-49, 50+ at NUTS3 level and 15-24, 25-34, 35-44, 45-54, 55+ at national level

NUTS 2 and NUTS 3 

Nationality (Spanish/non Spanish) 

Brief description of the method of calculating the yearly weights (please indicate if subsampling is applied to survey yearly variables)

Gender is used in weighting (Y/N)

Which age groups are used in the weighting (e.g., 0-14, 15-19, ..., 70-74, 75+)?

Which regional breakdown is used in the weighting (e.g. NUTS3)?

Other weighting dimensions

The subsampling is applyed to survey yearly variables, so only dwellings in the 6th wave belong to the subsample.

The method of calculating the yearly weights is similar to the one used for the quarterly weights but more variables are included in the calibration to assure the consistency with annual data coming from the averages.

The additional variables are: ILO status by sex and ten year age groups and ILO status by nationality and NUTS2 as it is said in Annex I-Article 3 of the Commission Regulation No 377-2008.

       Y

0-4, 5-9, 10-14, 15-19, 20-24,…, 65+ at NUTS2 level 16-29, 30-49, 50+ at NUTS3 level and 15-24, 25-34, 35-44, 45-54, 55+ at national level.

NUTS 2 and NUTS 3 

Nationality (Spanish/non Spanish) 

Brief description of the method of calculating the weights for households

Any external reference for number of households etc.?

Which factors at household level are used in the weighting (number of households, household size, household composition, etc.)

Which factors at individual level are used in the weighting (gender, age, regional breakdown etc.)?

Are the household weights identical for all household members? (Y/N)

The weight for each dwelling corresponds to the one assigned to all the people living in the same dwelling (The weighting factor is unique for all member of the dwelling).

The same as for the calculation of the core variables.

The ones used for people that were detailed above.

As it was said before, the same factor is calculated for all people  living in the same dwelling.

         Y

Do you apply any seasonal adjustment to the LFS Series? (Y/N)

If Yes, is your adopted methodology compliant with the ESS guidelines on seasonal adjustment? (ref. ESS guidelines on seasonal adjustment - Products Manuals and Guidelines - Eurostat (europa.eu) (Y/N)

If Yes, are you compliant with the Eurostat/ECB recommendation on Jdemetra+ as software for conducting seasonal adjustment of official statistics. (ref. http://ec.europa.eu/eurostat/web/ess/-/jdemetra-officially-recommended-as-software-for-the-seasonal-adjustment-of-official-statistics) (Y/N)

If Not, please provide a description of the used methods and tools

 Y

 Y

 Y

 NA