Employment and unemployment (Labour force survey) (employ)

National Reference Metadata in ESS Standard for Quality Reports Structure (ESQRS)

Compiling agency: Instituto Nacional de Estadística


Eurostat metadata
Reference metadata
1. Contact
2. Statistical presentation
3. Statistical processing
4. Quality management
5. Relevance
6. Accuracy and reliability
7. Timeliness and punctuality
8. Coherence and comparability
9. Accessibility and clarity
10. Cost and Burden
11. Confidentiality
12. Comment
Related Metadata
Annexes (including footnotes)
 



For any question on data and metadata, please contact: Eurostat user support

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1. Contact Top
1.1. Contact organisation

Instituto Nacional de Estadística
1.2. Contact organisation unit

Labour Force Survey Unit
1.5. Contact mail address

Avenida de Manoteras 50-52

28050 Madrid

Spain


2. Statistical presentation Top
Please take note of the abbreviations used in the report 
Abbreviation Explanation
CV Coefficient of variation (or relative standard error)
Y/N Yes / No
H/P Households/Persons
M? Member State doesn’t know
NA Not applicable/ Not relevant
UNA Information unavailable
NR Non-response: Member State doesn’t answer to Eurostat request for information. Blank is allowed only in boxes with comments
LFS Labour Force Survey
NUTS Nomenclature of territorial units for statistics or corresponding statistical regions in the EFTA and candidates countries
2.1. Data description
Coverage   
Coverage Household concept Definition of household for the LFS Inclusion/exclusion criteria for members of the household Questions relating to employment status are put to all persons aged ...
 The population residing in private households is covered, including servants; persons living in collective households and persons who are temporarily absent are sampled via the relatives living in private household. Foreign nationals are included in the resident population if they have lived or intend to live in Spain for more than one year. Household Members living regularly together in the same dwelling  According to the coverage  16 years and more

 

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 Most of the time

 

Reference week
Fixed week (data collection refers to one reference week, to which the observation unit has been assigned prior to the fieldwork) Rolling week (data collection always refers to the week before the interview)                                  
 Since 1999 the reference weeks are distributed uniformly over all the year.  
2.2. Classification system

[not requested for the LFS quality report]
2.3. Coverage - sector

[not requested for the LFS quality report]
2.4. Statistical concepts and definitions

[not requested for the LFS quality report]
2.5. Statistical unit

[not requested for the LFS quality report]
2.6. Statistical population

[not requested for the LFS quality report]
2.7. Reference area

[not requested for the LFS quality report]
2.8. Coverage - Time

[not requested for the LFS quality report]
2.9. Base period

[not requested for the LFS quality report]


3. Statistical processing Top
3.1. Source data
Sampling design & procedure
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)
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 households (dwelling units).

 

Sampling design & procedure
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 and second stage units are selected with equal probability. Based in the population size of the municipality There are 9 theoretical strata categories. In each province, the population is distributed according to these theoretical 1 to 9 strata categories, not having all of them representativeness in the 52 provincies.
At national level, considering strata and provinces, there are 271 groups.		 The sample is made up of six rotation groups. Household once selected remain in the sample for six consecutive quarters before being replaced. In any quarter, households of one wave are receiving the first interview, households of another wave are receiving the second interview, and so on.
Each quarter, the household 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.

 

Yearly sample size & Sampling rate
Overall theoretical yearly sampling rate Size of the theoretical yearly sample
(i.e. including non-response) (i.e. including non-response)
Approximately 1.6% of the dwellings are contacted.

For yearly subsampling approximately 0.27% of the dwellings are contacted.

 In 2020, about 243,300 dwellings were interviewed (each quarter the sample size of primary units was 3,822 and the sample size of the secondary units in each area was 20 on average). Initially, about 295,000 different dwellings were contacted.

In 2020, the theoretical yearly sample size for subsampling, for each quarter, was around 50,000 dwellings (1/6 of the theoretical quarterly sample size)

  

Quarterly sample size & Sampling rate
Overall theoretical quarterly sampling rate Size of the theoretical quarterly sample
(i.e. including non-response) (i.e. including non-response)
Approximately 0.39% of the dwellings are contacted. 73,750

  

Use of subsamples to survey structural variables (wave approach)
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. 430/2005, Annex I) (Y/N) If not please list deviations List of yearly variables for which the wave approach is used (Ref.: Commission Reg. 377/2008, Annex II)
 6th wave (last interview of the household)  Y  NA The variables named below are collected yearly:SUPVISOR, SIZEFIRM, WAYFOUND, TEMPREAS, TEMPAGCY, SHIFTWK, EVENWK, NIGHTWK, SATWK, SUNWK, WAYMORE, HOMEWK, LOOKREAS, LEAVREAS, STAPROPR-NACEPR2D-ISCOPR3D(when the job finished more than 12 months ago),  PRESEEK, EDUCFIELD, COURPURP, COURFILD, HATFIELD, WSTAT1Y, STAPRO1Y, NACE1Y2D, INCDECIL (from 2009 onwards), COEFFY.                                                                                   
The variables named below are collected quarterly although they are sent yearly:HHLINK, HHSPOU, HHFATH, HHMOTH, MARSTAT, FTPTREAS, SEEKREAS, AVAIREAS, NEEDCARE, REGISTER, MAINSTAT, HATYEAR, COUNTR1Y, REGION1Y

 

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 households, 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: Household 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 household aged 16 years and more has the same weight

  Y  NA  Y 0-4, 5-9, 10-15, 16-19, 20-24, ……, 65+ at NUTS 0 and NUTS2 level and 16-29, 30-49, 50+ at NUTS 3 level NUTS 2 and NUTS 3  NA

 

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. NUTS 3)? Other weighting dimensions
 The subsampling is applyed to survey yearly variables, so only households 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, 10-15, 16-19, 20-24, ……, 65+ at NUTS2 level 16-29, 30-49, 50+ at NUTS3 level and 16-24, 25-34, 35-44, 45-54, 55+ at national level

  NUTS 2 and NUTS 3  NA

 

Brief description of the method of calculating the weights for households 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.) Identical household weights for all household members? (Y/N)
The weight for each household corresponds to the one assigned to people aged 16 and over, living in the same household (all of them have the same weighting factor)

People aged 15 and less are not taken into account for the calcultation of the household weighting factors

 The same as for the calculation of the core variables  The ones used for people aged 16 and over, that were detailed above As it was said before, the same factor is calculated for all people aged 16 and over, living in the same household  N
3.2. Frequency of data collection

[not requested for the LFS quality report]
3.3. Data collection
Data collection methods: brief description Use of dependent interviewing (Y/N)? Participation is voluntary/compulsory?
 All the information is collected by interview. The first interviews are personal interviews. 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. 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 Compulsory (Law 13/1996)

 

Final sampling unit collected by interviewing technique (%)
CAPI CATI PAPI CAWI POSTAL - OTHER
 27.85*  71.35  NA  0.81  NA

* From the eleventh week of Q1 2020 onwards, the CAPI interviews were collected by phone, because of the COVID 19 pandemic.
3.4. Data validation

[not requested for the LFS quality report]
3.5. Data compilation

[not requested for the LFS quality report]
3.6. Adjustment

[not requested for the LFS quality report]


4. Quality management Top
4.1. Quality assurance

[not requested for the LFS quality report]
4.2. Quality management - assessment

[not requested for the LFS quality report]


5. Relevance Top
5.1. Relevance - User Needs
Assessment of the relevance of the main LFS statistics at national level (e.g. for policy makers, other stakeholders, media and academic research)
The last  "User Satisfaction Survey for Users of INE Statistics" , (USS 2019), states that almost 92.2% of the users gave a very positive or positive assessment to the extent INE Statistics meet their needs, in the Labour Market Statistics domain.
5.2. Relevance - User Satisfaction

[not requested for the LFS quality report]
5.3. Completeness
NUTS level of detail   
Regional level of an individual record (person) in the national data set Lowest regional level of the results published by NSI Lowest regional level of the results delivered to researchers by NSI Brief description of the method which is used to produce NUTS-3 unemployment and labour force data sent to Eurostat?
The survey can provide the main indicators at NUTS3 level. In particular, data on occupation, active population, and unemployed people by sex are published quarterly. The annual data are provided as an average of the four quarters of the year.  NUTS3  NUTS3 Annual averages from the four quarters LFS datasets; for the islands, the data are estimated through small areas techniques.
5.3.1. Data completeness - rate

[not requested for the LFS quality report]


6. Accuracy and reliability Top
6.1. Accuracy - overall

[not requested for the LFS quality report]
6.2. Sampling error
Publication thresholds   
Annual estimates Annual estimates - wave approach 
(if different from full sample thresholds) 
 Limit below which figures cannot be published  Limit below which figures must be published with warning  Limit below which figures cannot be published Limit below which figures must be published with warning
 1,000 grossed persons 5,000 grossed persons

Conventionaly, we warn about the feasibility of the estimates when it's less than 5 thousands, which is more or less equivalent to a sample of 20 units

  2,000 grossed persons  8,000 grossed persons
6.2.1. Sampling error - indicators
Coefficient of variation (CV) Annual estimates
Sampling error - indicators - Coefficient of variation (CV), Standard Error (SE) and Confidence Interval (CI)       
  Number of employed persons Employment rate as a percentage of the population Number of part-time employed persons Number of unemployed persons Unemployment rate as a percentage of labour force Youth unemployment rate as a percentage of labour force Average actual hours of work per week(*)
  Age group: 20 - 64 Age group: 20 - 64 Age group: 20 - 64 Age group: 15 -74 Age group: 15 -74 Age group: 15 -24 Age group: 20 - 64
 CV 0.28 0.28 1.25 1.38 1.27 1.88 0.13
 SE 52541.8 0.0018 32169.1 48651.1 0.0020 0.0072 0.0488
 CI(**) 18748204-18954168 0.65-0.66 2509107-2635209 3435551-3626263 0.15-0.16 0.37-0.40 36.7-36.9

 (*) The coefficient of variation for actual hours worked should be calculated for the sum of actual hours worked in 1st and 2nd jobs, and restricted to those who actually worked 1 hour or more in the reference week.

(**) The value is based on a CI of 95%. For the rates the CI should be given with 2 decimals.

 

Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
 The denominator of the employment rate (aged 20-64) is the total population (aged 20-64), which has a zero CV due to the calibration method used in the estimation process.

 

Reference on software used: Reference on method of estimation:
 Built-in program  Balanced Repeated Replication Method

 

Coefficient of variation (CV) Annual estimates at NUTS-2 Level        
NUTS-2  CV of regional (NUTS-2) annual aggregates (in %)     
Regional Code  Region Number of employed persons Employment rate as a percentage of the population Number of part-time employed persons Number of unemployed persons Unemployment rate as a percentage of labour force Youth unemployment rate as a percentage of labour force  Average actual hours of work per week(*)
    Age group: 20 - 64 Age group: 20 - 64 Age group: 20 - 64 Age group: 15 -74 Age group: 15 -74 Age group: 15 -24 Age group: 20 - 64 
ES11 Galicia 0.59 0.59 2.72 3.27 3.23 6.35 0.40
ES12 Principade de Asturias 1.40 1.40 6.83 5.22 5.03 12.61 0.86
ES13 Cantabria 1.64 1.64 5.69 5.30 5.56 10.13 0.97
ES21 Pais Vasco 0.99 0.99 3.50 5.14 5.33 8.19 0.54
ES22 Comunidad Foral de Navarra 1.50 1.50 7.30 7.74 7.68 10.50 0.77
ES23 La Rioja 1.30 1.30 5.00 6.29 6.34 10.83 1.03
ES24 Aragón 0.99 0.99 4.90 5.06 5.09 8.73 0.56
ES30 Comunidad de Madrid 0.87 0.87 5.15 3.90 3.92 5.01 0.38
ES41 Castilla y León 0.94 0.94 2.64 3.73 3.92 6.48 0.44
ES42 Castilla-la Mancha 0.96 0.96 3.64 2.86 3.00 5.02 0.45
ES43 Extremadura 1.62 1.62 5.83 4.16 4.33 7.14 0.77
ES51 Cataluña 0.64 0.64 4.16 2.51 2.54 3.77 0.35
ES52 Comunidad Valenciana 0.80 0.80 3.30 3.52 3.44 4.97 0.45
ES53 Illes Baleares 1.20 1.20 7.21 6.35 5.87 8.99 0.70
ES61 Andalucía 0.85 0.85 1.84 2.38 2.33 3.07 0.27
ES62 Region de Murcia 1.05 1.05 4.34 4.52 4.30 6.87 0.67
ES63 Ciudad Autonoma de Ceuta 9.20 9.19 24.43 15.87 18.38 12.54 1.23
ES64 Ciudad Autonoma de Melilla 7.83 7.92 16.90 19.96 20.57 10.45 2.66
ES70 Canarias 1.45 1.45 5.64 4.98 4.69 5.72 0.65

 

(*) The coefficient of variation for actual hours worked should be calculated for the sum of actual hours worked in 1st and 2nd jobs, and restricted to those who actually worked 1 hour or more in the reference week.

(**) The value is based on a CI of 95%. For the rates the CI should be given with 2 decimals.
6.3. Non-sampling error

 [not requested for the LFS quality report]
6.3.1. Coverage error
Frame quality (under-coverage, over-coverage and misclassifications(b))      
Under-coverage rate (%) Over-coverage rate (%) Misclassification rate (%)  Comments: specification and impact on estimates(a)   
 Undercoverage  Overcoverage  Misclassification(b)  Reference on frame errors
 UNA  UNA   NA  Percentage calculated as 'omitted' dwellings detected in the 'quality control survey' (see the website for reference on frame errors). Measures of impact not available  Average of the four quarter percentages of dwellings out of frame ('no encuestables') The touristic areas are more prone to higher rates  NA. The dwelling can not be 'wrong classified' and within the target population, at the same time.

 

https://www.ine.es/docutrab/eval_epa/evaluacion_epa19.pdf

(a) Mention specifically which regions / population groups are not suitably represented in the sample.

(b) Misclassification refers to statistical units having an erroneous classification where both the wrong and the correct one are within the target population.
6.3.1.1. Over-coverage - rate

[Over-coverage rate, please see concept 6.3.1 Coverage error in the LFS quality report]
6.3.1.2. Common units - proportion

[not requested for the LFS quality report]
6.3.2. Measurement error
Errors due to the medium (questionnaire)   
Was the questionnaire updated for the 2019 LFS operation? (Y/N) Synthetic description of the update Was the questionnaire tested? (Y/N) If the questionnaire has been tested, which kind of tests has been applied (pilot, cognitive, internal check)?
 N    N  

 

Main methods of reducing measurement errors 
Error source  
Respondent  Letter introducing the survey (Y/N) Phone call for booking or introducing the survey (Y/N)
 Y  N
Interviewer  Periodical training (at least 1 time per year) (Y/N)  Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
 N  Y
Fieldwork  Monitoring directly by contacting the respondents after the fieldwork (Y/N) Monitoring directly by listening the interviews (Y/N) Monitoring remotely through performance indicators (Y/N)
 Y  Y  Y
Questionnaire  Questionnaire in several languages (Y/N)  On-line checks (for computer assisted interviews (Y/N)
 Y  Y
Other / Comments  
6.3.3. Non response error

[not requested for the LFS quality report]
6.3.3.1. Unit non-response - rate

IN THIS SECTION INFORMATION REFERS TO THE FINAL SAMPLING UNITS *

Methods used for adjustments for statistical unit non-response
Adjustment via weights (Y/N) Variables used for non-response adjustment Description of method
 Y Sex, ages and Nationality (Spanish or Others), at NUTS2 and NUTS3 levels.

Household size at NUTS2 level

 Sex, ages and Nationality (Spanish or Others), at NUTS2 and NUTS3 levels.

Household size at NUTS2 level
Substitution of non-responding units (Y/N) Substitution rate Criteria for substitution
 Y 5.65%, Quarterly average (see note below)  Only refusals are substituted in the first wave, from a sampling list of households that belongs to the same PU.
Other methods (Y/N) Description of method
 Y For households that are non-response (because of absence, refusal, etc.) the information is copied from the previous quarter when it came from an effective interview. The date variables are adapted to the new time period and, in case of absence for holidays, the hours actually worked are imputed to 0, and the reason for not working in the reference week is 'holidays'.

NOTE: To calculate this rate it has been considered the number of refusals effectively substituted in the first wave of CAPI.

 

Rates of non-response by survey mode. Annual average
Survey
CAPI CATI  PAPI  CAWI  POSTAL
 20.52  8.99  NA  83.00  NA

 

Non-response rates by survey mode. Annual average (% of the theoretical yearly sample by survey mode)
Quarter Non-response rate
Total (%)             of which:
 Refusals (%)     Non-contacts (including people who migrated (or moved) internally or abroad) (%)  of which people who migrated (or moved) internally or abroad (%)
1 17.81 4.44 13  UNA
2 13.48 4.01 9.14  UNA 
3 15.15 4.02 10.77  UNA 
4 14.58 3.85 10.36  UNA 
Annual 15.26 4.08 10.82  UNA 

 

Units who refused to participate in the survey  (Please indicate the number of the units concerned in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018 588 .. .. ..
Subsample_Q1_2019 530 548 .. ..
Subsample_Q2_2019 541 501 531 ..
Subsample_Q3_2019 456 430 454 454
Subsample_Q4_2019 324 355 372 402
Subsample_Q1_2020 781 302 357 431
Subsample_Q2_2020 .. 621 363 393
Subsample_Q3_2020 .. .. 742 392
Subsample_Q4_2020 .. .. .. 674
Total in absolute numbers 3220 2757 2819 2746
Total in % of theoretical quarterly sample 4.46 4.00 4.02 3.92

 

Units who were not contacted (including people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018 1622 .. .. ..
Subsample_Q1_2019 1542 815 .. ..
Subsample_Q2_2019 1519 816 977 ..
Subsample_Q3_2019 1656 839 985 989
Subsample_Q4_2019 1840 914 1063 979
Subsample_Q1_2020 1099 1165 1238 1182
Subsample_Q2_2020 .. 1706 1801 1660
Subsample_Q3_2020 .. .. 1409 1343
Subsample_Q4_2020 .. .. .. 1083
Total in absolute numbers 72131 68907 70070 70017
Total in % of theoretical quarterly sample 12.86 9.08 10.67 10.33

 

of which people who migrated (or moved) internally or abroad) (Please indicate the number of units only in the cells where the wave is mentioned)
Subsample Quarter1_2020 Quarter2_2020 Quarter3_2020 Quarter4_2020
Subsample_Q4_2018 wave 6      
Subsample_Q1_2019 wave 5 wave 6    
Subsample_Q2_2019 wave 4 wave 5 wave 6  
Subsample_Q3_2019 wave 3 wave 4 wave 5 wave 6
Subsample_Q4_2019 wave 2 wave 3 wave 4 wave 5
Subsample_Q1_2020 wave 1 wave 2 wave 3 wave 4
Subsample_Q2_2020   wave 1 wave 2 wave 3
Subsample_Q3_2020     wave 1 wave 2
Subsample_Q4_2020       wave 1
Total in absolute numbers Total Total Total Total
Total in % of theoretical quarterly sample        

 *Data not available

 

Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)  Non response rate (%)
ES11-Galicia 10.19
ES12-Principado de de Asturias 12.8
ES13-Cantabria 15.07
ES21-Pais Vasco 21.76
ES22-Comunidad Foral de Navarra 15.61
ES23-La Rioja 9.62
ES24-Aragón 18.35
ES30-Comunidad de Madrid 11.35
ES41-Castilla y León 11.52
ES42-Castilla-la Mancha 14.34
ES43-Extremadura 24.37
ES51-Cataluña 15.57
ES52-Comunidad Valenciana 11.77
ES53-Illes Baleares 23.18
ES61-Andalucía 12.53
ES62-Region de Murcia 23.25
ES63-Ciudad Autonoma de Ceuta 9.82
ES64-Ciudad Autonoma de Melilla 19.68
ES70-Canarias 31.88

* If the final sampling unit is the household it must be considered as responding unit even in case of some household members (not all) do not answer the interview
6.3.3.2. Item non-response - rate
Item non-response (*)- Quarterly data (Compared to the variables defined by the Commission Regulation (EC) No 377/2008)       

Variable status

 Column Identifier Quarter 1 Quarter 2 Quarter 3 Quarter 4 Short comments on reasons for non-available statistics and prospects for future solutions

Compulsory / optional
compulsory Col_054 TEMPDUR 52.6 53.6 50.6 53.7 All the non response is due to "doesn't know" answers: 89,2% said that they didn't know the total duration but they worked "at least one month"; 10,8% said that they didn't know anything about the total duration (not even if they worked more or less than a month).
compulsory Col_115 - Employed METHODM . C C .  No 'other' active method in the survey
compulsory Col_115 - Not employed METHODM C C C C  No 'other' active method in the survey
compulsory Col_129/131 COURLEN 41 43.4 46.4 41.1  People aged 15 plus 'don't know' number of hours
compulsory Col_209 EDUCLEVL 11.4 11.8 12.4 11.3  People aged 15.

 

Item non-response - Annual data (Compared to the variables defined by the Commission Regulation (EC) No 377/2008)    
Variable status Column Identifier This reference year Short comments on reasons for non-available statistics and prospects for future solutions
compulsory Col_053 TEMPREAS 12.8  
compulsory Col_118 - Employed AVAIREAS 15.6  
compulsory Col_118 - Not employed AVAIREAS 13 People aged 15.
optional Col_132 COURPURP 19 People aged 15.
optional Col_133/135 COURFILD 19 People aged 15. 
optional Col_136 COURWORH 100 Not provided.

(*) "C" means all the records have the same value different from missing.
6.3.4. Processing error
Editing of statistical item non-response
Do you apply some data editing procedure to detect and correct errors? (Y/N) Overall editing rate (Observations with at least one item changed / Total Observations )
 Y 1.20%-3.01% depending on whether you take into account or not the "don't know" answer
6.3.4.1. Imputation - rate
Imputation of statistical item non-response
Are all or part of the variables with item non response imputed? (Y/N) Overall imputation rate (Observations with at least one item imputed / Total Observations )
 Y  3.01%
 Main variables Imputation rate  Describe method used, mentioning which auxiliary information or stratification is used 
 HATYEAR (V200-V203)  2.07% An automatic method of editing and imputation, based in Fellegi & Holt methodology, is used
6.3.5. Model assumption error

[not requested for the LFS quality report]
6.4. Seasonal adjustment
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. http://ec.europa.eu/eurostat/web/research-methodology/seasonal-adjustment) (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
6.5. Data revision - policy
Do you adopt a general data revision policy fully compliant with the ESS Code of Practice principles? (in particular see the 8th principle) (Y/N) Are you compliant with the ESS guidelines on revision policy for PEEIs? (ref. http://ec.europa.eu/eurostat/documents/3859598/5935517/KS-RA-13-016-EN.PDF) (Y/N)
 Y  Y
6.6. Data revision - practice

[not requested for the LFS quality report]
6.6.1. Data revision - average size

[not requested for the LFS quality report]


7. Timeliness and punctuality Top
7.1. Timeliness
Restricted from publication
7.1.1. Time lag - first result
Restricted from publication
7.1.2. Time lag - final result
Restricted from publication
7.2. Punctuality
Restricted from publication
7.2.1. Punctuality - delivery and publication
Restricted from publication


8. Coherence and comparability Top
8.1. Comparability - geographical
Divergence of national concepts from European concepts
(European concept or National proxy concept used) List all concepts where any divergences can be found    
Is there a divergence between the national and European concepts for the following characteristics? (Y/N) Give a description of difference and provide an assessment of the impact of the divergence on the statistics
Definition of resident population (*)  N  NA
Identification of the main job (*)  N  NA
Employment  N  NA
Unemployment  N  NA
8.1.1. Asymmetry for mirror flow statistics - coefficient

[not requested for the LFS quality report]
8.2. Comparability - over time
Changes at CONCEPT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes in (Y/N) Description of the impact of the changes Statistics also revised backwards (if Y: year / N) Variables affected Break in series to be flagged (if Y: year and quarter/N)  
concepts and definition  N  NA   NA   NA   NA
coverage (i.e. target population)  N   NA   NA   NA   NA
legislation  N   NA   NA   NA   NA
classifications  N   NA   NA   NA   NA
geographical boundaries  N   NA   NA   NA   NA

 

Changes at MEASUREMENT level introduced during the reference year and affecting comparability with previous reference periods (including breaks in series)
Changes to (Y/N) Description of the impact of the changes Statistics also revised backwards (if Y: year / N) Variables affected Break in series to be flagged (if Y: year and quarter/N)
sampling frame  N   NA   NA   NA    NA
sample design  N   NA   NA   NA    NA
rotation pattern  N   NA   NA   NA    NA
questionnaire  N   NA   NA   NA    NA
instruction to interviewers  N   NA   NA   NA    NA
survey mode  N   NA   NA   NA    NA
weighting scheme  N   NA   NA   NA    NA
use of auxiliary information  N   NA   NA   NA    NA
8.2.1. Length of comparable time series

[not requested for the LFS quality report]
8.3. Coherence - cross domain
Coherence of LFS data with Business statistics data    
  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. Data from Social Security Register are lower than those from LFS Work document and Comparison of Employment Data by
High Statistics Council
Total employment by NACE  Idem  Idem 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  NA  NA NA NA

 

Coherence of LFS data with registered unemployment  
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

 

Assessment of the effect of differences of LFS unemployment and registered unemployment     
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. From 2008 onwards, the LFS figures of ILO unemployment are always higher than registered unemployment. LFS unemployment data higher than registered unemployment. Young people are prone to have weaker links with employment offices LFS unemployment data higher than registered unemployment . The increase in unemployment because of the crisis could have caused weaker links with employment offices LFS unemployment data higher than registered unemployment. Young people are prone to have weaker links with employment offices LFS unemployment data higher than registered unemployment. Young people are prone to have weaker links with employment offices For 2020 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 2020 at NUTS3 level are ES530 (Illes Balears: 101.7 LFS vs 73.0 RU), ES300 (Madrid: 434.9 LFS vs 406.6 RU) and ES511 (Barcelona: 352.8 LFS vs 337.6 RU)
8.4. Coherence - sub annual and annual statistics

[not requested for the LFS quality report]
8.5. Coherence - National Accounts
Coherence of LFS data with National Accounts data    
  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 estimates have been revised in a new base 2010. The last NA data for 2020 is *19,422.2 thousands employment (first estimate). The total (head count) employment figure in the LFS is 19,202.4 (figures in thousands for 2020) For information about these differences see the following link: http://www.ine.es/en/daco/daco42/daco4211/cohe_empleo_b10_en.pdf
Total employment by NACE   Idem   Idem  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. Idem
Number of hours worked The information is calculated sector by sector and other sources are taken into account  Idem For year 2020, the National Account estimates (advanced estimate) 30,633,359.1 annual hours; the LFS based estimate gives 29,887,103 annual hours  Idem

 

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 not 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. employment 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)
8.6. Coherence - internal

[not requested for the LFS quality report]


9. Accessibility and clarity Top
9.1. Dissemination format - News release

[not requested for the LFS quality report]
9.2. Dissemination format - Publications
Please provide a list of type and frequency of publications
 Quarterly results referred to the core variables

 Annual results as average of the four quarters of each year

 Annual results referred to structural variables

 Annual results referred to the ad-hoc modules

 Annual results referred to the wage of the main job

 Annual updating of some publications as for example: Design of the survey and assessment of the quality of data

 Quarterly free microdata

9.3. Dissemination format - online database
Documentation, explanations, quality limitations, graphics etc.    
Web link to national methodological publication Conditions of access to data Accompanying information to data Further assistance available to users
National web page (national language(s))

National web page (English)

 Anonymised microdata are provided (sold) under signature of use conditions. These use conditions mainly refers to the responsability of the results obtained, the mention of INE as source of data and knowledge of limitations of estimates based in small sample size. Since June 2005, microdata files free of charge with the main variables are available in the INE web site.  Y  Y
9.3.1. Data tables - consultations

[not requested for the LFS quality report]
9.4. Dissemination format - microdata access
Accessibility to LFS national microdata (Y/N) Who is entitled to the access (researchers, firms, institutions)? Conditions of access to data Accompanying information to data Further assistance available to users
 Y There are free microdata in the website  available for all the users For these free data is not necessary any condition but some codes of variables in this case are joined.

If bigger breakdown is required in variables, there are other available microdata that are anonymized too but in this case, they are not free and are subject to confidenciality constraints.

 Registers design and explanations of variables and codes They can ask for tailor requests
9.5. Dissemination format - other

[not requested for the LFS quality report]
9.6. Documentation on methodology
References to methodological notes about the survey and its characteristics
https://www.ine.es/dyngs/INEbase/en/operacion.htm?c=Estadistica_C&cid=1254736176918&menu=metodologia&idp=1254735976595
9.7. Quality management - documentation

[not requested for the LFS quality report]
9.7.1. Metadata completeness - rate

[not requested for the LFS quality report]
9.7.2. Metadata - consultations

[not requested for the LFS quality report]


10. Cost and Burden Top
Restricted from publication


11. Confidentiality Top
11.1. Confidentiality - policy

[not requested for the LFS quality report]
11.2. Confidentiality - data treatment
Please provide information on the policy for anonymizing microdata in your country
 Instead of the variables of identification of each person, a correlative number is created


12. Comment Top

[not requested for the LFS quality report]


Related metadata Top


Annexes Top