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Notes

Labour Force Survey (LFS)

Main uses

LFS is the main government survey for analysis of the workforce, in terms of both the jobs people do and the characteristics of the people themselves.  So, for example, major uses include analyses by work status, qualifications and training received.

LFS also includes data about all the adults in a household and can therefore be used to analyse the overall work status of households (e.g. numbers of children who are in workless households).

Although LFS includes extensive data on earnings, it should only be used to analyse low pay when the desired analyses are not available from the Annual Survey of Hours and Earnings.

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Source

In summary:

The LFS datasets comes in two flavours:

From 2005 (but not before), each individual LFS dataset comes in two flavours:

For most purposes, the version with the age bands only will be sufficient, the obvious exception being those where a specific age (e.g. 19-year-olds) is required.

In addition to the dataset itself, ONS publishes various statistics from LFS on its website, for example, various time trends.

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General issues

Which software to use

Any individual LFS dataset comprises around 120,000 records.  As such, the summations should usually be done using SPSS or equivalent rather than by exporting the base data into Excel.

When to use the individual and household datasets

Most analyses will use the individual datasets, the exceptions being those concerned with workless households and/or lone parents (lone parenthood being a household variable).

For each quarter, the LFS household dataset is the same set of records as the individual dataset, with additional household variables on the end.  This means that if a household contains X people then there will be X records in the household dataset (rather than just one).  Clearly, any simple analyses on this dataset will therefore result in a count of individuals rather than a count of households.  Most times that one uses the household dataset, however, what one wants is a count of households rather than individuals.  This can be achieved by filtering the dataset to only pick up the 'Household Reference People', for whom there is one per household.

Which individual datasets to use

There a two complications here, one relating to which quarters to use and the other relating to age-based analyses.

Taking the issue of which quarters to use.  The four quarters of LFS from 2006 onwards are the calendar quarters, namely January to March, April to June, July to September and October to December.  However, for each year prior to 2006, the survey was actually undertaken using seasonal quarters, namely Winter (December to February), Spring (March-May), Summer (June-August) and Autumn (September to November).  These seasonal quarters have subsequently been re-grouped into standard calendar quarters by ONS, the net result being that there are actually eight datasets in the archive for each year (the four calendar ones and the four seasonal ones).  Clearly any research should be based on only one of these two sets, and not both. 

The obvious set to use is the calendar quarters and this is what is done in most parts of the website.  However, in their re-grouping of the quarters, ONS have deleted some of the data which makes use of these quarters problematic for certain types of analysis.  More specifically, where a variable changed definition (and name) from one seasonal quarter to another, the re-grouped calendar quarter excludes either one or both of these definitions, meaning that either one-third or all of the records for the re-grouped quarter do not have any data for that variable.  Particular variables and quarters where this occurs include:

Moving on to age-based analyses.  The standard datasets from 2007 onwards only record which five-year age band the respondent falls into rather than their age in single years.  This is because ONS has decided that age is a 'disclosive' variable which should not typically be made available to researchers.  As part of this change, they have also introduced 'special license' versions of the datasets, which are currently identical to the standard license versions except with the addition of the age variable.  These special license versions have to be applied for and this is a lengthy process with no guarantee of a successful conclusion.

Clearly, most research should use the standard license versions.  There are, however, some types of analysis where the specific age of the respondents is important and where it might therefore be worth while applying for the special license versions.

Which quarters to use

LFS is a 'semi-panel' survey, with every household being interviewed for five consecutive quarters before being dropped.  In reaction, some researchers, when producing annual results, merge the four quarters together and remove the duplicates before doing their analysis.  This is, however, a time-consuming process which makes little difference to the answers and a more practical approach is to estimate the annual figures as the average for the four relevant quarters.

It is important to average across the four quarters because there are seasonal variations across these quarters.

How to avoid making errors when using the household dataset

The first issue is the point made earlier that, although it is a household dataset, each record actually relates to an individual so any simple analyses of the dataset will result in a count of individuals rather than of households.  If a household count is what is wanted, this can be achieved by filtering to only pick up the 'Household Reference People'.

A second issue is that there is a difference between 'households' and 'families' (for a discussion of the differences between these two terms, see the page on households, families and benefit units).  So, for example, each record in the dataset is given both a 'household type' and a 'family type'.  For some purposes (e.g. analysis of workless households), the right variables to use are those that relate to the household.  For most purposes, however, the right variables to use are those that relate to the family and the two sets of variables can lead to very different results.  For example, consider a lone parent living with her parents: at a household level, the household type will be something like 'couple living with non-dependent child' and it is only at the family level that the type will be something which includes the words 'lone parent'.

A third issue is that, for some reason, LFS uses slightly different definitions than are standard for both 'families' and 'child':

These differences are important.  Consider a lone parent living with two of her children, one a dependent 11-year-old and the other a non-dependent 20-year-old: a straightforward analysis of the dataset would then result in the 20-year-old being erroneously considered to be a lone parent even though they have no children(!).  The only practical (non-ideal) way round this to to exclude such individuals from any analysis by filtering out those adults who are neither the 'head of the family' nor their spouse/partner.

Some important terms in LFS

Term Description
Household A group of people living at the same address who share common housekeeping or a living room.  In other words, everyone who lives behind the same 'front door'.
Family

NON-STANDARD.

Either a single person, or a married/cohabiting couple, or a married/cohabiting couple and their never-married children who have no children of their own living with them, or a lone parent with such children.

Working-age adult

NON-STANDARD.

Everyone aged between 16 and retirement age (65 for men and 60 for women).

Household reference person

The householder, who is the household member who owns the accommodation; or is legally responsible for the rent; or occupies the accommodation as reward of their employment, or through some relationship to its owner who is not a member of the household.

If there are joint householders, then one with the highest income. If their income is the same, then the eldest one.

Note that there is precisely one household reference person per household, so a count of household reference people is the same as a count of households.

Unemployed

'Unemployment' is the ILO definition, which is used for the official unemployment numbers.

It comprises all those with no paid work in the survey week who were available to start work in the next fortnight and who either looked for work in the last month or were waiting to start a job already obtained.

Economically active Those in paid work + the ILO unemployed.
Economically inactive

All those with no paid work in the survey week who do not meet the criteria for ILO unemployment because either they are not available to start work for some time or they are not actively seeking work.

Many lone parents and disabled people fit into this category rather than ILO unemployment.

The economically inactive + the economically active = the whole population.

Economically inactive but wanting paid work People who are economically inactive but say that they would like to be in paid work.
Lacking but wanting paid work The ILO unemployed + those who are economically inactive but want paid work.

What variables to use and what do they mean

Clearly, the variables to be used will vary according to the purpose at hand.  There are, however, a small number of variables which will be used much more than any of the others.  The table below lists some of these variables.

Variable name Variable description Comment
ages Age bands Mostly 5-year age bands
govtof Government office regions The standard regional boundaries
govtor Government office regions Somewhat more disaggregated than 'govtof'
hiqual5 Highest qualifications/Trade apprenticeship  
ilodefr Economic activity (reported) Broad categories only
inecac05 Economic activity (reported) Much more disaggregated than 'ilodefr'
pwt03 Integer weight The individual-level weights

In the LFS dataset, each variable has both a name and a description plus a description of what each of its values mean.  This material is then repeated in the user manual (in a form which is easier to read) together with more detail explanatory text where needed.  The table below lists those used in the analyses of this website, together with whether their use is major or minor.

Note that some of the detailed variable definitions change from year-to-year.  Such changes are always accompanied by a change in the variable name.  In other words changes in variable definitions always go hand in hand with changes in variable name and so, for example, one knows that if the variable name is unchanged then the variable definition is also unchanged.

Primary dataset Variable name Official variable description Extent of use on this website
Individual ages Age bands major
appr4 Recognised trade apprenticeship minor
cured Current Education received minor
discurr Current disability minor
ed13wk J R Edu & Train in last 13 weeks (in wrk) minor
ed4wk J R Edu & Train in last 4 weeks (in wrk) minor
eth01 Ethnic group minor
ethas Asian ethnic group minor
ethbl Black ethnic group minor
ethmx Mixed ethnic group minor
ethwh White ethnic group minor
ftptwk Full-time or part-time in main job minor
futur4 J R Edu & Train in last 4 weeks (unemp) minor
govtof Government office regions major
govtor Government office regions major
hiqual5 Highest qualif/Trade apprenticeship major
hourpay Gross hourly pay major
ilodefr Economic activity (reported) major
inds92m Industry SECTION (main job) minor
inecac05 Economic activity (reported) major
jobtmp Types of temporary job minor
llord Landlord of accommodation minor
numas Num (A/S level passes) already held minor
numol5 Num (O-level, GCSE etc pass) already held minor
piwt03 Integer weight - Income major
pwt03 Integer weight major
qulnow Currently working / study towards qual minor
sex Sex major
ten1 Accommodation details minor
union Trade union or staff assoc member (GB) minor
whytmp6 Reason for temporary job minor
yptjob Reason for part-time job minor
Household futype6 Type of family unit minor
hdc515 No of children in household between 5 and 15 minor
hdpch18 No dependent children in household aged 16-18 minor
hdpch4 No of children in household age 4 year or less minor
heacomb Household economic activity variable minor
hhtype6 Type of household minor
hhwt03 Household weight minor
hnftstud No in household who are full-time students minor
hnwkage No of people in Household of working age minor
relhfu Relationship to head of family unit minor
relhrp6 Relationship to HRP major

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Specific issues

Analysis by region

The 'North West' government region is split into two in LFS, namely 'North West' and 'Merseyside'.  The results for these two 'regions' therefore need to be added together to calculate the true 'North West' results.

Note that, as well as recording where someone lives, LFS also records where they work.  It can therefore be used to analyse cross-boundary flows to work.

Analysis by work status

There are around 35 possible values for work status.  Any particular analysis will need to aggregate these into a smaller number of groups, for example: working, unemployed, economically inactive but wanting work; and economically inactive and not wanting work.  The table below provides such a mapping for the 2006 datasets.

ValueWork statusGrouping
1 Employee Working
2 Self-employed Working
3 Government emp & training programmes Working
4 Unpaid family worker Working
5 ILO unemployed Unemployed
6 Inact- seeking, unavailable, student Economically inactive - wanting work
7 Inact- sking,unav,lking after fam,home Economically inactive - wanting work
8 Inact- sking,unav,temp sick or injured Economically inactive - wanting work
9 Inact-sking,unav,long-term sick,disabled Economically inactive - wanting work
10 Inact- sking, unavail, other reason Economically inactive - wanting work
11 Inact- sking, unavail, no reason given Economically inactive - wanting work
12 Inact- not sk,wld like,wait res job app Economically inactive - wanting work
13 Inact- not sking, wld like, student Economically inactive - wanting work
14 Inact- not sk,like,lking after fam,home Economically inactive - wanting work
15 Inact- not sk,like, temp sick,injured Economically inactive - wanting work
16 Inact-not sk,like,lng trm sick,disabled Economically inactive - wanting work
17 Inact-not sk,like, believes no job avail Economically inactive - wanting work
18 Inact- not sk,like, not yet looking Economically inactive - wanting work
19 Inact- not sk,wld like,doesn't need job Economically inactive - wanting work
20 Inact- not sk, like, retired Economically inactive - wanting work
21 Inact- not sk,like, other reason Economically inactive - wanting work
22 Inact- not sk,like, no reason given Economically inactive - wanting work
23 Inact- not sk,not like,wait results app Economically inactive - not wanting work
24 Inact- not sk,not like, student Economically inactive - not wanting work
25 Inact-not sk,not like,lk after fam,home Economically inactive - not wanting work
26 Inact- not sk,not like, temp sick,injur Economically inactive - not wanting work
27 Inact-not sk,not like,long-term sick,dis Economically inactive - not wanting work
28 Inact-not sk,not like,belvs no job avail Economically inactive - not wanting work
29 Inact- not sk, not like, not yet looking Economically inactive - not wanting work
30 Inact- not sk,not like,doesn't need job Economically inactive - not wanting work
31 Inact- not sk,not like, retired Economically inactive - not wanting work
32 Inact- not sk,not like, other reason Economically inactive - not wanting work
33 Inact- not sk,not like, no reason given Economically inactive - not wanting work
34 Under 16 Exclude

Analysis by highest qualification

There are around 40 possible values for highest qualification, comprising a mixture of academic (e.g. GCSEs) and vocational (e.g. NVQs) qualifications.  Any particular analysis will need to aggregate these into a smaller number of groups, and these groups can either be academic groups or vocational groups.  The Department for Education (DfE) publish 'equivalence tables' to do this grouping, a version of which is provided in the table below as it applied in 2006.

Regarding the vocational grouping, note that:

ValueHighest qualification Academic grouping Vocational grouping
1 Higher degree Higher degree NVQ5
2 NVQ level 5 Higher degree NVQ5
3 First/Foundation degree First degree NVQ4
4 Other degree First degree NVQ4
5 NVQ level 4 Other higher education NVQ4
6 Diploma in higher education Other higher education NVQ4
7 HNC, HND, BTEC etc higher Other higher education NVQ4
8 Teaching, further education Other higher education NVQ4
9 Teaching, secondary education Other higher education NVQ4
10 Teaching, primary education Other higher education NVQ4
11 Teaching foundation stage Other higher education NVQ4
12 Teaching, level not stated Other higher education NVQ4
13 Nursing etc Other higher education NVQ4
14 RSA higher diploma Other higher education NVQ4
15 Other higher education below degree Other higher education NVQ4
16 NVQ level 3 A-level or equivalent NVQ3
17 Advanced Welsh Bacheloriate A-level or equivalent NVQ3
18 International Bacheloriate A-level or equivalent NVQ3
19 GNVQ/GSVQ advanced A-level or equivalent NVQ3
20 A level or equivalent A-level or equivalent NVQ3 if 2+, otherwise NVQ2
21 RSA advanced diploma A-level or equivalent NVQ3
22 OND, ONC, BTEC etc, national A-level or equivalent NVQ3
23 City & Guilds advanced craft A-level or equivalent NVQ3
24 Scottish CSYS A-level or equivalent NVQ3
25 SCE higher or equivalent A-level or equivalent NVQ3 if 3+, otherwise NVQ2
26 Access qualification to higher education A-level or equivalent NVQ3
27 A,S level or equivalent A-level or equivalent NVQ3 if 4+, NVQ2 if 2-3 or NVQ1 if 1 only
28 Trade apprenticeship A-level or equivalent Assume 50% NVQ3 and 50% NVQ2
29 NVQ level 2 GSCEs A*-C NVQ2
30 Intermediate Welsh Bacheloriate GSCEs A*-C NVQ2
31 GNVQ/GSVQ intermediate GSCEs A*-C NVQ2
32 RSA diploma GSCEs A*-C NVQ2
33 City & Guilds craft GSCEs A*-C NVQ2
34 BTEC, SCOTVEC first or general diploma GSCEs A*-C NVQ2
35 O level, GCSE grade A-C or equivalent GSCEs A*-C NVQ2 if 5+, otherwise NVQ1
36 NVQ level 1 Other qualification NVQ1
37 GNVQ, GSVQ foundation level Other qualification NVQ1
38 CSE below grade1,GCSE below grade C Other qualification NVQ1
39 BTEC, SCOTVEC first or general certificate Other qualification NVQ1
40 SCOTVEC modules Other qualification NVQ1
41 RSA other Other qualification NVQ1
42 City & Guilds other Other qualification NVQ1
43 YT, YTP certificate Other qualification NVQ1
44 Key skills qualification Other qualification NVQ1
45 Basic skills qualification Other qualification NVQ1
46 Entry level qualification Other qualification NVQ1
47 Other qualifications Other qualification Assume 10% NVQ3, 35% NVQ2 and 55% NVQ1

Analysis by disability

LFS records two types of disability, namely 'DDA disability' and 'work-limiting disability'.  For work-related analyses, the obvious type to use is 'work-limiting disability'.

Analysis by ethnicity

LFS uses multiple fields to code ethnicity, the first field dividing people into broad groups (White, Black, Asian, etc) and the other fields subdividing each broad group into narrower groups (Pakistani, Bangladeshi, Indian, etc).

Analysis by lone parenthood

Unlike most other datasets (e.g. HBAI), adults living with their parents are classified in LFS as being in the same family as their parents.  This means that, when their parent is a lone parent, they themselves get classified as being in a lone parent family.  To ensure that they are excluded from the lone parent counts, the syntax needs to check the relationship of every adult to the head of the family and only consider them as a lone parent if they are both in a lone parent family and are the head of that family.

Analysis by trade union membership

This can only be done using the 4th quarter datasets as the other quarters do not ask this question.

Analysis of NEETs

NEETs (not in education, employment or training) are analysed in LFS by a process of exclusion rather than inclusion.  In other words, someone is not in education, employment or training if they are 'not in education' and 'not in employment' and 'not in training'.  The syntax for analysing this is complicated and should only be undertaken after discussions with DfE.

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Relevant graphs on this website

UK graphs

Indicator Dataset Graphs Comments
Children in workless households household first three

Filter on HRP to ensure that each household is only picked up once.

In line with ONS methods, children comprise all those under the age of 16 (i.e. not including dependent children aged 16 to 18).

Needs a lookup table to group household types into couples and lone parents.

The information published by ONS is only for selected quarters.

Young adults without a basic qualification individual first three  
Impact of qualifications on work individual all  
Not in education, employment or training individual first two

Syntax developed in consultation with DfE.

The information published by ONS is only for selected quarters.

Young adult unemployment individual all  
Young adult low pay individual fourth and fifth  
Wanting paid work individual all Use the variable 'durun' to distinguish between short-term and long-term unemployed.
Work and disability individual first Uses work-limiting disability rather than DDA disability.
household second, third and fourth

Uses work-limiting disability rather than DDA disability.

Define someone as a lone parent only if both family type = lone parent and relationship to family unit head = head (the latter condition being to exclude adults living with their lone parent).

individual fifth and sixth Uses work-limiting disability rather than DDA disability.
Work and lone parents household all Filter on family type = lone parent and relationship to family unit head = head to ensure that only lone parents are picked up (the latter condition being to exclude adults living with their lone parent).
Work and ethnicity individual first and second Exclude Northern Ireland because ethnic group not collected.
household fourth Filter on HRP to ensure that each household is only picked up once.
Work and gender n/a all From the ONS website.
Blue collar jobs individual third and fourth

Include self-employed workers.

Only includes the first job for those who have multiple jobs.

In workless households household first two

Filter on HRP to ensure that each household is only picked up once.

Exclude households which are entirely composed of full-time students plus households where their economic status is not known.

Exclude full-time students from the calculations to decide whether the household has one or more than one adult.

In line with ONS methods, children comprise all those under the age of 16 (i.e. not including dependent children aged 16 to 18).

Low pay by industry individual all Uses LFS rather than ASHE because of the age range.
Low pay and disability individual all

Uses LFS because analysis not possible from ASHE.

Uses work-limiting disability rather than DDA disability

Low pay by ethnicity individual all

Uses LFS because analysis not possible from ASHE.

Omit chinese because of small sample sizes.

Insecure at work individual second and third  
individual fourth Data available for the fourth quarter of each year only.
Access to training individual all  
Without educational qualifications individual first three  
Polarisation by housing tenure individual second, third and fourth  

Notes:

Scotland, Wales and Northern Ireland graphs

These are effectively a subset of the UK graphs using government region as a filter.

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