Reference metadata describe statistical concepts and methodologies used for the collection and generation of data. They provide information on data quality and, since they are strongly content-oriented, assist users in interpreting the data. Reference metadata, unlike structural metadata, can be decoupled from the data.
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
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 survey covers private households, including persons who are temporarily absent. Students living in halls of residence are sampled via their parents living in private households. In Great Britain, an additional sample is drawn from among persons living in National Health Service/Hospital Trust accommodation. The resident population comprises persons who regard the sample address as their main address. Persons who have lived in the dwelling for more than six consecutive months are also a member of the resident population even if they do not regard this as their principal dwelling. Persons absent for more than six months are not regarded as a member of the resident population.
A household response unit is one person living alone or a group of people (not necessarily related) living at the same address who share cooking facilities and share a living room or sitting room or dining area, this also includes students who live in halls of residence in term-time and residents in National Health Service.
Include:
1) Children (aged 16 and over) working away from home in a temporary job and those under 16 attending boarding schools should be included in the parental home. On the LFS, university or college students who live in a hall of residence during term time are also included. HOWEVER this should be carefully checked because students who rent a room in a house owned by the university are excluded
2) Any respondent whose address in this country is a temporary one whilst they search for permanent accommodation. Refugees or migrant workers would be an example of this category.
Exclude:
1) Children aged 16 and over who live away from home for purposes of work or study and come home only for holidays should not be included at their parents address. This means for example that you would exclude students who are away at university or college during term time even if they are at home when you call unless they are living in a hall of residence in which case you should include them at this address (on LFS only). You should also exclude those aged 16 and over working away from home on a permanent basis and student nurses in NHS accommodation.
2) Anyone who has been away from the address continuously for 6 months or more should be excluded even if the respondent continues to think of it as their main residence. For example exclude individuals who have been in hospital or prison for 6 months or more, members of the Forces on long tours of duty and children in care for an extended period.
3) A respondent/household living at a temporary address in this country, here only for purposes of recreation, holiday, visits to friends and relatives, business, medical treatment or religious pilgrimage and who remain(s) resident abroad, should not be included.
4) Holiday homes and weekend retreats, such as addresses used only as second homes, should not be counted as a main residence and should be excluded.
16+
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)
The year is divided into quarters of 13 weeks Q1(January to March), Q2(April to June), Q3 (July to September) and Q4 (October to December).
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]
3.1. 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)
Stratified single stage systematic (single random in NI) probability sampling
For most of Great Britain, the survey base is the Royal Mail's PAF (Postcode Address File), a database of all addresses receiving mail. The list is limited to addresses receiving fewer than 50 items of post per day, so as to exclude businesses. Because of the very low population density in the far north of Scotland (north of the Caledonian Canal), interviews are carried out by telephone because face-to-face interviews would be too expensive, and telephone directories are used as sampling frames. In Northern Ireland, the Rating and Valuation Lists (which serves for the administration of land taxes) is used.
updated every six months
NA
Households
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.)
NA
In Great Britain, a systematic sample is drawn each quarter from the three sampling bases, which yields 16,640 PAF addresses, 80 telephone numbers for the north of Scotland and nine units of National Health Service housing. As the PAF is broken down geographically, the systematic sampling ensures that the sample is representative at regional level. In Northern Ireland, a simple random sample is drawn, each quarter, from each of the three strata, giving 650 addresses in all. Additionally, 260 additional (‘booster’) new addresses are added to the sample in Quarter 2 of each year; these are spread equally across the five waves. Thus, in any one quarter, a total of about 17,380 addresses are newly-selected in the UK for the main LFS (excluding the Northern Ireland boosters). Two changes were made to the sample design in 2010 that mean the LFS samples in Great Britain and also in Northern Ireland are strictly no longer equal probability samples, although the effect of the changes is relatively small. These changes relate to multiple-occupancy addresses and to households found that have only adults aged 75 plus
Most of Great Britain constitutes one stratum, while the far north of Scotland forms a separate stratum and Northern Ireland three strata: Belfast and eastern and western Northern Ireland.
The UK LFS does not have explicit stratification; as we use systematic random sampling, with the address being sorted by postcode, we have implicit stratification.
5
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)
1.3%
The number of selected UK addresses in total in the LFS 348'400 households.
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)
0.33%
87 100 households
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)
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 adjustment procedure is based on a three-stage a posteriori stratification. In each case, estimates are based on independent demographic estimates. (a) The stratification variable for the first stage is the local authority district or unitary authority. This stage makes it possible to make adjustments for different rates of non-response in the various local authority areas and ensures that the results are geographically representative. (b) The second-stage variables are sex and age group (0-15, each year of age in the 16-24 group and 25 +). This stratification is intended to ensure that the age profile of the important group of the 16-24 year olds is correct at national level. (c) The variables in the third stage are region, sex and 5-year age group. The three stages are applied by means of an iterative procedure designed to ensure that the estimates are consistent with the stratification variable sets.
Y
(We sample from the UK Postoffice Address File but we apply weights to get to the target population defined by population estimates)
NA
Y
See above
LAU
N
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
Generalised Regression methodology is used to construct Wave 1 weights using different combinations of Eurostat constraints and quarterly constraints. Yearly variables are asked of a subsample comprising Wave 1 every quarter.
Y
0-14, 15-24, 25-34, 35-44, 45-54, 55+
Local Authority Districts (population estimates) and Government Office Regions (sex and age groups)
Employment status (excluding 0-14)
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 'Integrated Weighting' method (Lemaitre and Dufour) is used. Sex, age and region specifications are applied using calibration, or generalised raking. A logit method is used, with the allowable range of weighting factors set to minimise the range, subject to avoiding significant 'bunching' of cases at the boundary.
The weighting method forces agreement with the population totals but it does not fix the weighted number of households (nor families), so these are survey estimates.
Interviews are carried out on a face-to-face basis with the help of portable computers (CAPI) for the interviews in the first wave. In the far north of Scotland (north of the Caledonian Canal) and for interviews in the second to fifth waves wherever possible, interviews are carried out by telephone (CATI). All information is obtained by interview. Results for respondents who are not contacted in waves 2 to 5 or who refuse for circumstantial reasons are carried forward from the previous wave if an interview has been carried out in the previous wave.
N
Voluntary
Final sampling unit collected by interviewing technique (%)
CAPI
CATI
PAPI
CAWI
POSTAL - OTHER
47.20
52.80
NA
NA
NA
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]
4.1. Quality assurance
[not requested for the LFS quality report]
4.2. Quality management - assessment
[not requested for the LFS quality report]
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 main purpose of the quarterly LFS is to provide information needed to develop, manage, evaluate and report on labour market policies.
Main indicators regularly published from the LFS include -
ILO unemployment, total employment, ILO unemployment rate and economic activity rate (employment and unemployment as a percentage of the total population), by age group;
employees and self-employed people, full- and part-time workers, second jobs and temporary workers, by industry and occupation;
average actual working hours and total hours worked in the economy;
redundancies;
reasons why people are economically inactive (not employed or unemployed) and whether they would like to work, including groups such as:
discouraged workers - those who say they would like to work but have not looked for work recently because they believe no jobs are available and therefore are excluded from measures of unemployment;
people (usually women) looking after the family or home;
students;
retired people;
people unable to work because they are sick or disabled.
Users of LFS data often combine it with related data from other sources to provide an overall view of the state of the labour market. One of the most important users of this sort of assessment is the Bank of England's Monetary Policy Committee, which sets interest rates in order to meet the Government's inflation target.
Other key users of LFS data are HM Treasury and the Department for Work and Pensions, because they are responsible for UK economic and labour market policy. Other users include Government departments (for example the Department for Business, Innovation and Skills (BIS) , the Department for Education, the Home Office, the Health & Safety Executive, the Scottish Government, and the Welsh Government), local authorities, the Trades Union Congress (TUC), the Employer's Association, the Confederation of British Industry, the Institute of Employment Studies, the Institute for Public Policy Research, the National Institute of Economic and Social Research, the Policy Studies Institute, the Institute for Fiscal Studies, academic researchers, the media and the general public.
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?
LAU
NUTS2 or national equivalent
NUTS3 or national equivalent
NR
5.3.1. Data completeness - rate
[not requested for the LFS quality report]
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
4000 grossed up persons
10000 grossed up persons
10000 grossed up persons
19000 grossed up 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
0.15%
0.15%
0.56%
1.58%
1.57%
2.50%
0.12%
SE
135980.08
0.12
118828.91
62085.50
0.06
0.30
0.04
CI(**)
91211400.04; 91744441.96
78.96; 79.42
20814177.34; 21279986.66
3812775.42; 4056150.58
3.74; 3.98
11.23; 12.39
36.63; 36.80
Description of the assumption underlying the denominator for the calculation of the CV for the employment rate
The denominator for the CV calculation of the employment rate for those aged 20-64 is the number of those aged 20-64 (inclusive) in employment divided by those aged 20-64 (inclusive).
Those in employment have WSTATOR = 1 or 2.
The denominator in the calculation of the standard error (SE) of the employment rate is the total number of people aged 20-64. The survey weights are calibrated by age, such that the weights of all 20-64 will always sum to the 'known' total of 20-64 in the population. This means that, in our SE calculations, the SE for 20-64 employment rate is not treated as a domain or ratio estimate, since there is no sampling variability in the denominator.
Reference on software used:
Reference on method of estimation:
STATA
Holmes, D.J. & Skinner, C.J. Variance Estimation for Labour Force Survey Estimates of Levels and Changes. GSS Methodology Series no. 21.
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
C1
C1 Tees Valley and Durham
1.97
1.66
5.28
10.64
10.97
15.81
1.08
C2
C2 Northumberland and Tyne and Wear
1.72
1.44
5.09
10.59
10.78
15.57
0.98
D1
D1 Cumbria
4.06
1.92
8.27
26.13
27.14
38.42
1.80
D3
D3 Greater Manchester
1.36
1.04
3.95
8.22
8.45
11.06
0.67
D4
D4 Lancashire
2.08
1.39
5.12
12.05
12.61
18.41
0.90
D6
D6 Cheshire
2.96
1.58
6.90
15.23
15.65
26.56
1.11
D7
D7 Merseyside
2.53
1.53
6.01
13.53
14.18
20.15
0.98
E1
E1 East Yorkshire and Northern Lincolnshire
2.75
1.92
6.35
14.53
14.84
23.61
1.43
E2
E2 North Yorkshire
2.65
1.45
5.85
17.00
17.82
26.31
1.25
E3
E3 South Yorkshire
2.24
1.54
5.32
11.87
12.19
17.97
1.06
E4
E4 West Yorkshire
1.49
1.15
3.98
8.93
9.32
13.94
0.71
F1
F1 Derbyshire and Nottinghamshire
1.75
1.09
4.14
9.38
9.73
14.51
0.77
F2
F2 Leicestershire, Rutland and Northamptonshire
1.93
1.13
4.82
9.90
10.11
16.15
0.79
F3
F3 Lincolnshire
3.74
1.96
7.91
18.58
18.27
29.29
1.43
G1
G1 Herefordshire, Worcestershire and Warwickshire
2.07
1.16
4.72
13.13
13.50
22.85
0.99
G2
G2 Shropshire and Staffordshire
1.83
1.14
5.03
11.46
11.95
15.70
0.92
G3
G3 West Midlands
1.18
1.18
3.94
7.32
7.50
12.30
0.74
H1
H1 East Anglia
1.62
0.93
3.67
10.24
10.69
16.28
0.72
H2
H2 Bedfordshire and Hertfordshire
2.03
1.04
4.79
12.31
12.64
19.00
0.79
H3
H3 Essex
2.08
1.12
4.71
11.65
12.03
23.75
0.84
I3
I3 Inner London - West
3.37
2.05
10.79
16.15
16.92
29.11
1.54
I4
I4 Inner London - East
1.63
1.10
4.85
10.22
10.65
14.52
0.82
I5
I5 Outer London - East and North East
2.28
1.31
5.32
12.84
13.11
20.15
0.92
I6
I6 Outer London - South
2.60
1.25
6.09
12.29
12.56
17.78
0.94
I7
I7 Outer London - West and North West
2.28
1.40
5.93
10.89
11.16
17.59
1.05
J1
J1 Berkshire, Buckinghamshire and Oxfordshire
1.68
0.85
4.03
10.97
11.19
17.06
0.66
J2
J2 Surrey, East and West Sussex
1.77
0.92
3.69
10.15
10.72
16.62
0.75
J3
J3 Hampshire and Isle of Wight
2.10
1.21
4.28
10.58
10.89
22.10
0.85
J4
J4 Kent
2.66
1.31
5.20
12.64
12.85
20.37
1.02
K1
K1 Gloucestershire, Wiltshire and Bath/Bristol area
1.44
0.84
3.54
9.91
10.34
15.81
0.72
K2
K2 Dorset and Somerset
2.51
1.29
5.19
16.41
16.76
24.38
1.07
K3
K3 Cornwall and Isles of Scilly
4.17
2.29
8.83
28.08
29.47
49.04
1.92
K4
K4 Devon
2.55
1.39
5.45
15.05
15.89
25.49
1.18
L1
L1 West Wales and The Valleys
1.84
1.31
4.55
10.46
11.03
17.49
0.95
L2
L2 East Wales
2.59
1.52
5.75
14.91
15.70
23.45
1.11
M5
M5 North Eastern Scotland
4.18
2.18
10.30
19.19
20.27
44.10
1.93
M6
M6 Highlands and Islands
5.39
2.49
11.83
36.20
37.94
75.38
2.04
M7
M7 Eastern Scotland
1.54
1.10
4.01
10.30
10.78
18.13
0.81
M8
M8 West Central Scotland
1.80
1.45
5.30
11.12
11.69
22.55
0.88
M9
M9 Southern Scotland
2.82
1.63
6.34
17.03
17.58
25.63
1.28
N0
N0 Northern Ireland
0.82
0.82
4.16
8.45
8.78
14.11
0.85
(*) 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
Approximately 1.5% of the total GB population
UNA
UNA
The LFS coverage omits communal establishments, excepting NHS housing and students in halls of residence. Members of the armed forces are only included if they live in private accommodation. The LFS, by not sampling from communal establishments, excludes approximately 1.5% of the total GB population
UNA
UNA
UNA
(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)?
Y
Q1 only - new questions asking if respndent provides unpaid care and number of hours of care. Q1 only - new questions to assist with the revision of the UK Socio Economic Classification. 'Othe - please spcifiy' option added to worked fewer hours and earned less than usual questions. Q4 only - additional questions to record if translater used for interview and language used. Country coding frame amended to include 'North Macedonia'. QbyQ guidance updated to clarify eligibily of students for inclusion in survey.
Y
Pilot and Internal check.
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
Y (if telephone number known)
Interviewer
Periodical training (at least 1 time per year) (Y/N)
Feedbacks from interviewer (reports, debriefings, etc.) (Y/N)
Y (regular informal on-the-job training)
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)
LFS data is weighted using population totals for each local authority area, with five-year age bands by sex.
Substitution of non-responding units (Y/N)
Substitution rate
Criteria for substitution
N
NA
NA
Other methods (Y/N)
Description of method
N
NA
Non-response rates by survey mode. Annual average (% of the theoretical yearly sample by survey mode)
Survey
CAPI
CATI
PAPI
CAWI
POSTAL
26.19
2.86
NA
NA
NA
Divisions of non-response into categories. Quarterly data and annual average
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
52.2
44.6
7.6
UNA
2
52.2
44.6
7.6
UNA
3
53.3
45.6
7.7
UNA
4
53.9
45.8
8.1
UNA
Annual
46.8
39.0
7.8
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_2019
Quarter2_2019
Quarter3_2019
Quarter4_2019
Subsample_Q1_2018
6447
Subsample_Q2_2018
5767
6418
Subsample_Q3_2018
5687
6042
5951
Subsample_Q4_2018
5331
5917
6170
6195
Subsample_Q1_2019
5564
5445
6107
6432
Subsample_Q2_2019
5298
5561
6178
Subsample_Q3_2019
5356
5362
Subsample_Q4_2019
5602
Total in absolute numbers
28796
29120
29145
29769
Total in % of theoretical quarterly sample
36.93
37.31
37.50
38.91
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_2019
Quarter2_2019
Quarter3_2019
Quarter4_2019
Subsample_Q1_2018
1129
Subsample_Q2_2018
1078
1221
Subsample_Q3_2018
1101
1231
1386
Subsample_Q4_2018
1120
1121
1140
1426
Subsample_Q1_2019
1827
945
980
1023
Subsample_Q2_2019
1820
900
1018
Subsample_Q3_2019
1975
895
Subsample_Q4_2019
1969
Total in absolute numbers
6255
6338
6381
6331
Total in % of theoretical quarterly sample
8.02
8.12
8.21
8.15
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_2019
Quarter2_2019
Quarter3_2019
Quarter4_2019
Subsample_Q1_2018
UNA
Subsample_Q2_2018
UNA
UNA
Subsample_Q3_2018
UNA
UNA
UNA
Subsample_Q4_2018
UNA
UNA
UNA
UNA
Subsample_Q1_2019
UNA
UNA
UNA
UNA
Subsample_Q2_2019
UNA
UNA
UNA
Subsample_Q3_2019
UNA
UNA
Subsample_Q4_2019
UNA
Total in absolute numbers
UNA
UNA
UNA
UNA
Total in % of theoretical quarterly sample
UNA
UNA
UNA
UNA
Non-response rates. Annual averages (% of the theoretical yearly sample)
NUTS-2 region (code + name)
Non response rate (%)
UKC1 Tees Valley and Durham
18.80
UKC2 Northumberland and Tyne and Wear
17.19
UKD1 Cumbria
14.56
UKD3 Greater Manchester
17.20
UKD4 Lancashire
16.30
UKD6 Cheshire
15.11
UKD7 Merseyside
16.79
UKE1 East Yorkshire and Northern Lincolnshire
16.24
UKE2 North Yorkshire
15.66
UKE3 South Yorkshire
16.76
UKE4 West Yorkshir
16.93
UKF1 Derbyshire and Nottinghamshire
16.73
UKF2 Leicetsershire, Rutland and Northamptonshire
15.70
UKF3 Lincolnshire
13.08
UKG1 Herefordshire, Worcestershire and Warwickshire
16.06
UKG2 Shropshire and Staffordshire
14.67
UKG3 West Midlands
13.13
UKH1 East Anglia
14.89
UKH2 Bedfordshire and Hertfordshire
14.05
UKH3 Essex
16.36
UKI3 Inner London - West
18.11
UKI4 Inner London - East
18.54
UKI5 Outer London - East and North East
16.71
UKI6 Outer London - South
17.62
UKI7 Outer London - West and North West
17.94
UKJ1 Berkshire, Buckinghamshire and Oxfordshire
17.13
UKJ2 Surrey, East and West Sussex
14.75
UKJ3 Hampshire and Isle of Wight
13.56
UKJ4 Kent
14.67
UKK1 Gloucestershire, Wiltshire and Bath/Bristol Area
15.03
UKK2 Dorset and Somerset
14.84
UKK3 Cornwall and Isles of Scilly
13.59
UKK4 Devon
14.74
UKL1 West Wales
15.73
UKL2 East Wales
15.97
UKM5 North Eastern Scotland
16.39
UKM6 Highlands and islands
14.69
UKM7 Eastern Scotland
17.30
UKM8 West Central Scotland
18.08
UKM9 Southern Scotland
16.50
UKN0 Northern Ireland
9.15
* 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
56.5
53.4
51.4
50.3
High level of non-response owing to a relatively small proportion of the employed sample in a temporary job in the reference week.
compulsory
Col_065/66
HWOVERP
90.4
90.6
90.9
.
High level of non-response owing to a relatively small proportion of the employed sample working overtime in the reference week. These observations imputed to 0 from Q4 2019 on.
compulsory
Col_067/68
HWOVERPU
85.3
85.6
86.6
.
High level of non-response owing to a relatively small proportion of the employed sample working overtime in the reference week. These observations imputed to 0 from Q4 2019 on.
compulsory
Col_073/74
HWWISH
91.4
91.3
91.1
.
Variable only computed for respondents recorded as wishing to work more hours; this group equates to only 4% of the employed sample. This variable has been imputed for persons who do wish to work the same hours (HHWISH=HWUSUAL) from Q4 2019 on.
compulsory
Col_109 - Employed
METHODG
.
C
C
C
The UK-LFS does not ask if respondents took a test, interview or examination. Therefore only 'No' responses can be calculated.
compulsory
Col_109 - Not employed
METHODG
C
C
C
C
The UK-LFS does not ask if respondents took a test, interview or examination. Therefore only 'No' responses can be calculated.
compulsory
Col_113 - Employed
METHODK
.
C
C
C
The UK-LFS does not ask if respondents are waiting for a call from a public employment office.
compulsory
Col_113 - Not employed
METHODK
C
C
C
C
The UK-LFS does not ask if respondents are waiting for a call from a public employment office.
compulsory
Col_114 - Employed
METHODL
.
C
C
C
The UK-LFS does not ask if respondents are waiting for the results of a competition for recruitment to the public sector
compulsory
Col_114 - Not employed
METHODL
C
C
C
C
The UK-LFS does not ask if respondents are waiting for the results of a competition for recruitment to the public sector
compulsory
Col_129/131
COURLEN
73.4
74.2
73.7
73.3
Not all respondents who participated in education/training are asked how many hours of instruction they have had.
compulsory
Col_197/199
HAT11LEV
11.9
12.2
12.1
12.1
Education questions are not asked of persons aged 15 or >= 70 economically inative. If fciltered for 16 - 69 then INR = 0.4%/
compulsory
Col_210
EDUCVOC
27
27.3
30
28.8
Education questions are not asked of persons aged 15. If filtered for >= 16 then INR = 0%.
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
33.3
The current calculation of TEMPREAS assigns WHYTMP6 = 5 (some other reason) to 'Blank' (no answer). The Eurostat codification allows only four values (covered by the first four response categories). It is not clear how those respondents who answer 'some other reason' should be coded.
compulsory
Col_055
TEMPAGCY
74
TEMPAGCY is derived from national q'aire variable TMPCON (contract with employment agency). TMPCON is asked only if HOWGET = 5 (private employment agency).
compulsory
Col_118 - Employed
AVAIREAS
72.6
The high INR for AVAIREAS (employed) is caused by respondents who are coded WISHMORE = 1 Yes, wish to work more hours but crucially, wish to work more hours in the current job. Consequently, because these respondents are not looking for another job then they are not asked if they could start another job within 2 weeks (START/AVAILBLE) which results in YSTART/AVAIREAS = missing or blank. I suggest the solution is to exclude respondents who are not looking for another job (even though wishing to work more hours) from AVAILBLE – consequently making them ineligible for AVAIREAS.
compulsory
Col_118 - Not employed
AVAIREAS
38.4
Calculation of variable under review.
compulsory
Col_120
NEEDCARE
45.5
Calculation of variable under review.
compulsory
Col_154/155
INCDECIL
25.1
optional
Col_122
MAINSTAT
100
Not currently included in UK-LFS.
optional
Col_132
COURPURP
78
The filter for COURPURP is COURATT. COURATT is derived from SCHM12, ED4WK, FUTUR4 and NONFORM4. COURPURP is derived solely from T4PURP which is asked only of respondents coming via NONFORM4=Yes. All respondents coded YES at COURATT are not asked T4PURP and consequently COURPUR is coded as blank.
optional
Col_136
COURWORH
78
Similar to COURPURP. COUWORH is filtered from COURATT and is derived solely from T4WORK which only applies to cases coming via NONFORM4.
(*) "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
Hard and soft checks are written into the questionnaire to identify inconsistent and/or contradictory answers. During data processing a validation stage identifies further potential errors.
3%
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, if respondents from one quarter are non-respondents in the subsequent quarter then data is carried forward.
15.0% (4Q average)
Main variables
Imputation rate
Describe method used, mentioning which auxiliary information or stratification is used
All except wave- or quarter-specific variables
15.0% (4Q average)
Data brought forward
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)
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 (*)
Y
Persons resident in NHS/Health Trust accommodation and students in institutions are included in national survey results
Identification of the main job (*)
N
NA
Employment
N
NA
Unemployment
Y
All those waiting to start a job already obtained are counted as ILO unemployed. The restriction of job starting within a period of three months is not applied to national estimates. All job search methods counted including passive methods. Differences not large.
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
The LFS provides an estimate of people in employment, whereas the workforce jobs data count the number of jobs.
The LFS is a sample survey of people. Workforce jobs are compiled quarterly from a series of surveys of employers.
In principle, the two measures should be equal. However in December 2005, the difference between the two series was 1.076,000. After taking measurable factors into account, the adjusted difference was 98,000.
See e.g.: ‘Comparisons of statistics on jobs: December 2005’,pp147-156, Labour Market Trends, May 2006. Comparisons between the two measures are now being updated quarterly.
Total employment by NACE
See above
LFS industrial classification is derived from employees' own perception of the industry they work in. WFJ industry is provided by the Interdepartmental Business Register, updated through administrative data and an annual register inquiry.
See above
See above
Number of hours worked
LFS measures hours worked. The New Earnings Survey measures hours paid.
LFS is a sample survey with significant proxy response. NES is a business survey.
A difference of ~3% is observed in the LFS hours returned from proxy responses. Hours worked is a difficult variable for respondents to recall
See 'Hours worked: a comparison of estimates from the Labour Force and New Earnings Surveys', pp429-442, Labour Market Trends, August 2002
Coherence of LFS data with registered unemployment
Description of difference in concept
Description of difference in measurement
Give references to description of differences
Difference between LFS unemployment using ILO definition and the Jobseeker's Allowance claimant count ( those registered as claiming unemployment-related benefits) arises mainly because many unemployed people are not eligible for, or do not claim, Jobseeker's Allowance. The difference is also affected, in the opposite direction, by a (usually smaller) number of claimants who are not strictly unemployed according to the LFS measure, eg they may be employed for just a few hours per week.
The claimant count is a full count from administrative records of those of working age claiming Jobseeker's Allowance on the second Thursday of each month. The LFS is a sample survey conducted on a continuing basis, and unemployment is defined in terms labour market status for those aged 16 or over: there is no upper age limit.
See ‘Comparisons between unemployment and the claimant count’,pp59-62, Labour Market Trends, February 2004.
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)
UNA
UNA
UNA
UNA
UNA
UNA
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
Employment figures in National Accounts are mainly used for calculating compensation to employees.
National Accounts employment figures are normally taken from the business survey.This is largely due to the need for industrial breakdowns.
See 8.3 on differences between business surveys and LFS.
See 8.3
Total employment by NACE
UNA
National Accounts employment figures by NACE are taken from the business survey.
See 8.3 on differences between business surveys and LFS.
UNA
Number of hours worked
UNA
Hours worked in NA productivity calculations are based on average hours from the LFS multiplied by employment estimates from the business survey. Again this is due to the need for industrial breakdowns.
UNA
UNA
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
Y
N
N
N
N
8.6. Coherence - internal
[not requested for the LFS quality report]
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
The data are widely available, generally free of charge, through a range of media. First Releases and Time Series data contained within the releases are available to download, free of charge, from the National Statistics website. Paper copies are available through subscription from the
ONS Press Office, telephone 020 7533 5707.
A highly disaggregated dataset, which covers a wealth of data for local areas, is available free from NOMIS: http://www.nomisweb.co.uk
First Releases and Labour Market Trends both contain additional textual analysis and charts that supplement the data in the tables.
Microdatasets are provided to government departments once they have signed a Data Access Agreement, which describes the conditions that apply to use of the data. The UK Data Archive at Essex University provides free access to the various LFS datasets and can be contacted via the website: http://www.data.ac.uk
The LFS Data Service provides LFS data for a fee, and can be contacted by phone (01633 812256).
The LFS Performance and Quality Monitoring (PQM) report is published quarterly. This report contains various quality indicators such as response rates and standard errors.
Customers who receive microdatasets are sent quarterly updates on questionnaire changes.
Telephone numbers for LFS contacts are published on the ONS and Nomis web sites, and on the monthly labour market first releases, so users can access the people responsible for the data and surveys.
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
Academic researchers and government organisations
1. An Approved Researcher scheme is used to grant access to data that cannot be published openly, for statistical research purposes, as permitted under the Statistics and Registration Act 2007. An individual must hold ONS Researcher Accreditation and have their research proposal approved by the ONS Microdata Release Panel.
2. A Virtual Microdata Laboratory (VML) provides secure access to sensitive detailed data for Approved Researchers working on defined and approved non-commercial projects, which serve the public good. VML data cannot be downloaded but users can access the data at their desk, if in a government organisation, or in a 'safe room' at an ONS site. Analysis results will not disclose sensitive information. VML operates within a legal framework and there are penalties for breaking these rules.
UK LFS User Guides, Vols 1 - 11
N
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
Labour Force Survey User Guide (Vol 1) - LFS Background & Methodology
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]
Restricted from publication
11.1. Confidentiality - policy
[not requested for the LFS quality report]
11.2. Confidentiality - data treatment
Restricted from publication
[not requested for the LFS quality report]
Coverage
Coverage
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 survey covers private households, including persons who are temporarily absent. Students living in halls of residence are sampled via their parents living in private households. In Great Britain, an additional sample is drawn from among persons living in National Health Service/Hospital Trust accommodation. The resident population comprises persons who regard the sample address as their main address. Persons who have lived in the dwelling for more than six consecutive months are also a member of the resident population even if they do not regard this as their principal dwelling. Persons absent for more than six months are not regarded as a member of the resident population.
A household response unit is one person living alone or a group of people (not necessarily related) living at the same address who share cooking facilities and share a living room or sitting room or dining area, this also includes students who live in halls of residence in term-time and residents in National Health Service.
Include:
1) Children (aged 16 and over) working away from home in a temporary job and those under 16 attending boarding schools should be included in the parental home. On the LFS, university or college students who live in a hall of residence during term time are also included. HOWEVER this should be carefully checked because students who rent a room in a house owned by the university are excluded
2) Any respondent whose address in this country is a temporary one whilst they search for permanent accommodation. Refugees or migrant workers would be an example of this category.
Exclude:
1) Children aged 16 and over who live away from home for purposes of work or study and come home only for holidays should not be included at their parents address. This means for example that you would exclude students who are away at university or college during term time even if they are at home when you call unless they are living in a hall of residence in which case you should include them at this address (on LFS only). You should also exclude those aged 16 and over working away from home on a permanent basis and student nurses in NHS accommodation.
2) Anyone who has been away from the address continuously for 6 months or more should be excluded even if the respondent continues to think of it as their main residence. For example exclude individuals who have been in hospital or prison for 6 months or more, members of the Forces on long tours of duty and children in care for an extended period.
3) A respondent/household living at a temporary address in this country, here only for purposes of recreation, holiday, visits to friends and relatives, business, medical treatment or religious pilgrimage and who remain(s) resident abroad, should not be included.
4) Holiday homes and weekend retreats, such as addresses used only as second homes, should not be counted as a main residence and should be excluded.
16+
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)
The year is divided into quarters of 13 weeks Q1(January to March), Q2(April to June), Q3 (July to September) and Q4 (October to December).
Not Applicable
[not requested for the LFS quality report]
[not requested for the LFS quality report]
[not requested for the LFS quality report]
[not requested for the LFS quality report]
Not Applicable
[not requested for the LFS quality report]
Not Applicable
[not requested for the LFS quality report]
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)
Stratified single stage systematic (single random in NI) probability sampling
For most of Great Britain, the survey base is the Royal Mail's PAF (Postcode Address File), a database of all addresses receiving mail. The list is limited to addresses receiving fewer than 50 items of post per day, so as to exclude businesses. Because of the very low population density in the far north of Scotland (north of the Caledonian Canal), interviews are carried out by telephone because face-to-face interviews would be too expensive, and telephone directories are used as sampling frames. In Northern Ireland, the Rating and Valuation Lists (which serves for the administration of land taxes) is used.
updated every six months
NA
Households
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.)
NA
In Great Britain, a systematic sample is drawn each quarter from the three sampling bases, which yields 16,640 PAF addresses, 80 telephone numbers for the north of Scotland and nine units of National Health Service housing. As the PAF is broken down geographically, the systematic sampling ensures that the sample is representative at regional level. In Northern Ireland, a simple random sample is drawn, each quarter, from each of the three strata, giving 650 addresses in all. Additionally, 260 additional (‘booster’) new addresses are added to the sample in Quarter 2 of each year; these are spread equally across the five waves. Thus, in any one quarter, a total of about 17,380 addresses are newly-selected in the UK for the main LFS (excluding the Northern Ireland boosters). Two changes were made to the sample design in 2010 that mean the LFS samples in Great Britain and also in Northern Ireland are strictly no longer equal probability samples, although the effect of the changes is relatively small. These changes relate to multiple-occupancy addresses and to households found that have only adults aged 75 plus
Most of Great Britain constitutes one stratum, while the far north of Scotland forms a separate stratum and Northern Ireland three strata: Belfast and eastern and western Northern Ireland.
The UK LFS does not have explicit stratification; as we use systematic random sampling, with the address being sorted by postcode, we have implicit stratification.
5
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)
1.3%
The number of selected UK addresses in total in the LFS 348'400 households.
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)
0.33%
87 100 households
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)
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 adjustment procedure is based on a three-stage a posteriori stratification. In each case, estimates are based on independent demographic estimates. (a) The stratification variable for the first stage is the local authority district or unitary authority. This stage makes it possible to make adjustments for different rates of non-response in the various local authority areas and ensures that the results are geographically representative. (b) The second-stage variables are sex and age group (0-15, each year of age in the 16-24 group and 25 +). This stratification is intended to ensure that the age profile of the important group of the 16-24 year olds is correct at national level. (c) The variables in the third stage are region, sex and 5-year age group. The three stages are applied by means of an iterative procedure designed to ensure that the estimates are consistent with the stratification variable sets.
Y
(We sample from the UK Postoffice Address File but we apply weights to get to the target population defined by population estimates)
NA
Y
See above
LAU
N
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
Generalised Regression methodology is used to construct Wave 1 weights using different combinations of Eurostat constraints and quarterly constraints. Yearly variables are asked of a subsample comprising Wave 1 every quarter.
Y
0-14, 15-24, 25-34, 35-44, 45-54, 55+
Local Authority Districts (population estimates) and Government Office Regions (sex and age groups)
Employment status (excluding 0-14)
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 'Integrated Weighting' method (Lemaitre and Dufour) is used. Sex, age and region specifications are applied using calibration, or generalised raking. A logit method is used, with the allowable range of weighting factors set to minimise the range, subject to avoiding significant 'bunching' of cases at the boundary.
The weighting method forces agreement with the population totals but it does not fix the weighted number of households (nor families), so these are survey estimates.
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 (*)
Y
Persons resident in NHS/Health Trust accommodation and students in institutions are included in national survey results
Identification of the main job (*)
N
NA
Employment
N
NA
Unemployment
Y
All those waiting to start a job already obtained are counted as ILO unemployed. The restriction of job starting within a period of three months is not applied to national estimates. All job search methods counted including passive methods. Differences not large.
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)
