Official Statistics

Income Dynamics: background information and methodology

Published 31 March 2022

Income Dynamics (ID) presents information on changes in income over time. It presents findings on persistent low income. Individuals are in persistent low income if they are in relative low income for at least three out of four consecutive annual interviews. ID also includes analysis of individual movements within the overall income distribution over time. This includes analysis of entries into and exits from low income, together with analysis of how different income, employment and demographic events are linked to those low income entries and exits.

1. Introduction

Context

Income Dynamics (ID) contains analysis of income movements and the persistence of low income for various population groups. Reporting on the percentage of children in the United Kingdom who live in households whose income has been less than 60% of median equivalised net household income, in at least three of the last four survey periods, is required under section 4 of the Welfare Reform and Work Act 2016, and, regarding Scotland, the Child Poverty (Scotland) Act 2017. Definitions for relevant key terms in these Acts are consistent with those given in the Glossary of this document. Data for reporting against the other three measures are available in the Households Below Average Income (HBAI) publication.

The Department for Work and Pensions’ (DWP) responsibilities include understanding and dealing with the causes of poverty rather than its symptoms, encouraging people to work and making work pay, encouraging disabled people and those with ill health to work and be independent, and providing a decent income for people of pension age and promoting saving for retirement. The extent of progress towards these responsibilities will affect these statistics.

Key strengths and purpose of the statistics

ID has been published annually alongside the HBAI series since 2017. While HBAI remains the best source of cross-sectional low income statistics, ID provides a longitudinal perspective. It fills a key gap that emerges if low income statistics are only considered on a cross-sectional basis.

By looking at low income longitudinally, we can look at durations of low income for different groups and what characteristics are associated with longer spells of low income. Longitudinal analysis also allows us to track how many individuals – and which individuals - enter and exit low income, and to calculate entry and exit rates. ID also explores the relationships between certain income, employment and demographic events, and low income entry and exit.

Potential users include:

• policy and analytical teams within the DWP
• the Devolved Administration and other government departments
• local authorities
• Parliament
• Academics
• Journalists
• and the voluntary sector

Status of these statistics and changes for this publication

Income Dynamics is Official Statistics.

Some changes have been made for this 2010 to 2020 publication. These changes have arisen mainly as a result of:

• the review of income-based poverty statistics carried out by the Office for Statistical Regulation (OSR)
• the coronavirus (COVID-19) pandemic and its implications for data collection and analysis

The OSR review

In December 2020, the Office for Statistical Regulation (OSR) announced a review of income-based poverty statistics. The review set out to answer three main questions:

• do the existing statistics help answer the key questions about income-based poverty in society today?
• do the statistics tell a coherent story about poverty in the UK and provide a comprehensive evidence base to inform decision making on poverty?
• is there sufficient quality guidance to help individuals understand how and when to use the statistics appropriately?

The review report was published in May 2021, and includes background information on why the review was commissioned as well as the findings and recommendations for statistics producers. Recommendations focussed on key areas including accessibility and guidance, understanding poverty, data gaps, data quality, and trustworthiness.

In response to OSR review recommendations regarding the accessibility of income-based poverty statistics, we have developed:

• a new ‘ID Quick Guide’ setting out key information for users on the data used and the statistics we produce
• a new ‘ID Tables Guide’ which provides users with a quick way to reference what measures and breakdowns are included in each table file, as well as guidance on interpreting persistent low income statistics.
• we have also made changes to our table files to improve guidance and navigation.

We have also updated this Background information and methodology note so that it provides more information on the representativeness of our statistics, weighting and bias, in response to OSR recommendations for clarity on these areas. Please also refer to the DWP Statistical Work Programme for further information on DWP’s response to the OSR review.

The OSR review identified some inconsistency in use of the terms ‘household’ and ‘benefit unit’/‘family’ in the reporting of income-based poverty statistics. With this in mind, we thoroughly reviewed all ID products. This resulted in a revision to one of the variables used in the analysis of events associated with low income entry and exit. This is detailed in Section 3. We have also clarified the labelling on some of our tables.

Finally, the OSR review also recommended that income-based poverty statistics should improve the way they reference other data sources via signposting. Work was taken forward by the Government Statistical Service (GSS) Coherence Team at the Office for National Statistics (ONS), who carried out a review of signposting across income and earnings statistics - including ID - and made a number of recommendations. As a result of this, we have made some changes to the signposting in Section 5 of this background note. The ONS have also developed a new interactive tool which can be used to identify sources of statistics on income and earnings, and their key features.

The coronavirus (COVID-19) pandemic

ID uses data produced by the longitudinal survey, Understanding Society (USoc), run by the University of Essex. The coronavirus pandemic and subsequent policy announcements resulted in several changes to USoc Wave 11 fieldwork, data collection, and processing. Their impact on the USoc survey has been documented by the USoc team at the University of Essex, and relevant material has been drawn on here to produce a summary of the key points and what they mean for ID:

• the first lockdown due to the coronavirus pandemic began in month 15 of the 24-month Wave 11 survey period. At this point in time, around two-thirds of the data collection for USoc was web-based, with around a third completed via face-to-face interviewing, and about 1% being conducted via telephone. Although lockdown meant a cessation of face-to-face interviewing from March 2020, USoc response rates were largely protected: data collection for all respondents was swiftly moved online in the first instance, with telephone follow-up used as necessary. Although the remainder of 2020 saw varying degrees of government restrictions regarding social mixing, USoc maintained a web-first approach. Analysis carried out by the USoc team has found that the response rate for the period April-December 2020 was just 1.5 percentage points lower than that of the same period in 2019. For ID, this has meant that we have been able to release our full set of tables with no additional detriment to sub-samples beyond the attrition that is usual for this type of longitudinal panel survey
• while USoc response rates held up well, there were small changes in the sample profile in 2020 compared to 2019, as some groups – including those in higher age groups, those living alone, those not in work, and those with lower levels of education, were less likely to respond to the survey. USoc weights were adjusted to account for these changes, and all ID analysis uses the appropriate longitudinal weights
• in terms of the USoc questionnaire itself, a new response option to capture the ‘furlough’ status of employees on the Coronavirus Job Retention Scheme was added to the economic status variable ‘jbstat’ from the end of July 2020. The variable ‘jbstat’ is only used by ID for analysis on the associations between various ‘events’ and low-income entry and exit. This is because it offers a greater degree of detail than the economic status variable ‘employ’, which ID uses elsewhere (e.g. in persistent low income breakdowns). We have amended our code to take furlough status into account in this analysis. Please see Section 3 for full details
• furlough payments for employees were received via paycheques, and are included as earnings rather than state benefits. This has not necessitated any changes to our code. ID explores changes in earnings and other income components as part of its events analysis: the uptake of furlough is likely to have had very little effect on these particular events. Please see Section 3 for full details
• a new question asking about receipt of grants via the Self-Employment Income Support Scheme (SEISS) was introduced at the end of July 2020. The derivation of income estimates for self-employed individuals is complex and relies upon the reporting of profits over an earlier timeframe, typically the previous financial year. This means that - apart from some minor exceptions - individuals who reported receipt of SEISS in 2020 will have their income derived using pre-pandemic profits, and their SEISS amount set to zero. SEISS grants will be included in estimates of 2021 income
• Section 3 discusses the degree to which the coronavirus pandemic and the changes discussed here may have affected different ID measures, such as persistent low income and low income entry and exit rates. It is important to note that while we might observe some shifts in ID estimates, both for this release and future releases, attributing any observed changes to the pandemic is beyond their scope. This is due to a number of factors including timeframes (with Wave 11 fieldwork covering the 2019 and 2020 calendar years), gaps in data (e.g. where new questions took time to be developed and implemented following policy changes), and, not least, the fact that it is not within scope to isolate such effects from other potential drivers of income and its distribution

For further information, please refer to:

• the USoc Main Survey User Guide
• USoc Wave 11 Technical Report
• USoc (2021) Understanding Society Main Study Changes due to the COVID-19 Pandemic (Wave 11 release); and
• Alvarez, Burton & Lynn (2021) Covid-19 and Mode Selection Effects in Understanding Society.

Revisions to the statistics

As noted above, ID uses data produced by the longitudinal survey, Understanding Society (USoc), run by the University of Essex. Since the first ID release, DWP have worked with the University of Essex to improve the quality of the derivation of incomes, and therefore these statistics have been subject to revisions beyond those that occur routinely in longitudinal data analysis. In particular, the derivation of housing costs was improved to bring the after housing cost (AHC) analysis into line with HBAI for example, only mortgage interest payments (not capital repayments) are included when deriving owner-occupier housing costs. This change was implemented for ID from 2010 to 2017, published in March 2019.

Figures presented in this publication are subject to future revisions because of changes made by the data providers. New information can be collected from respondents which affects their data in earlier years. For this reason it is best to refer to the most recent ID publication. Please refer to the USoc user guide for more information.

2. Source of the statistics

As noted above, these statistics are derived from the Understanding Society (USoc) survey. USoc is an initiative funded by the Economic and Social Research Council and various Government Departments, with scientific leadership by the Institute for Social and Economic Research, University of Essex, and survey delivery by NatCen Social Research and Kantar Public. The research data are distributed by the UK Data Service.

We have used the following dataset:

• University of Essex, Institute for Social and Economic Research. (2022). Understanding Society: Waves 1-11, 2009-2020 and Harmonised BHPS: Waves 1-18, 1991-2009: Special Licence Access. [data collection]. 14th Edition. UK Data Service. SN: 6931, DOI: 10.5255/UKDA-SN-6931-13

We do not publish the dataset that we derive to produce ID statistics. Please contact us if you require further information about how we process and analyse USoc data.

We would like to thank Jonathan Burton, Paul Fisher, Alita Nandi, and Raj Patel at the University of Essex for their advice and assistance, particularly in regard to helping us understand and interpret the implications of the coronavirus pandemic and in informing additional content in this note in response to recommendations made by the OSR review.

DWP bears sole responsibility for the analysis or interpretation presented here.

Population

USoc is a UK household panel survey, which has been running since 2009. Individuals in households recruited at the first round of data collection are visited each year to collect information on changes to their household and individual circumstances. These individuals and their descendants form the core sample. Household members aged 16 or older are eligible for a full adult interview each year, allowing measurement of change. Household members not part of the core sample are also eligible provided they are living with at least one core sample member.

The USoc Sample

The USoc main sample is made up of four subsamples, shown in the following table.

Subsample	Explanation
General Population Sample (GPS)	This is the largest part of the survey. The England, Scotland and Wales sample in this data release is based upon an initial sample of 47,520 addresses. In Northern Ireland, 2,395 addresses were selected in a single stage from the list of domestic addresses.
Ethnic Minority Boost Sample (EMBS)	The EMBS was designed to provide at least 1,000 adult interviews from each of five groups: Indian, Pakistani, Bangladeshi, Caribbean, and African based on sampling postal sectors with relatively high proportions of relevant ethnic minority groups, based upon 2001 Census data and more recent Annual Population Survey data. It screened in individuals living in households with at least one person with parents or grandparents from an ethnic minority group.
British Household Panel Survey (BHPS) cases	The sample issued for wave 2 (2010 to 2011) consisted of all members from the BHPS sample who were still active at Wave 18 of the BHPS and who had not refused consent to be included as part of the USoc sample. It should be noted that the BHPS sample contains different subsamples, including the original sample (first selected in 1991), boost samples in Scotland and Wales (first selected in 1999), and a Northern Ireland sample (selected in 2001).
Immigrant and Ethnic Minority Boost (IEMB)	This sample was introduced at Wave 6, and is not included in this analysis. It screened in people living in households with at least one person born outside the United Kingdom (“immigrants”) or with parents or grandparents from an ethnic minority group.

How representative is USoc?

USoc was initially designed as a representative sample. Detailed ‘following rules’ (see the next section below) set out how the sample is allowed to change over time, as sample members’ circumstances change. These rules aim to ensure that the survey remains representative of the UK population, although they do not enable the sample to capture the effect of immigration to the UK. In order to help address this gap, an Immigrant and Ethnic Minority Boost sample was introduced at Wave 6, which included around 3,200 adult immigrant respondents, sampled from private households, although it should be noted that this boost sample has not so far been integrated into ID.

The initial USoc sample only included private households living in the United Kingdom. This meant that it excluded individuals living in institutions such as nursing or retirement homes, hostels for homeless people, or prison/young offender’s institutions. As a result, analysis for particular age bands, such as older people or younger people, may not be completely representative of all individuals in those age bands where the characteristics of people who live in private households (and who are included), differ in a fundamental way from those living in institutional settings (who are not included).

USoc does collect information on the reasons why household members may be currently absent, which includes being in a range of institutions. A person who moves into an institution (except prison) is still an eligible sample member and so if they do not complete the survey online, they are followed up by an interviewer by phone where feasible. USoc is collecting more information about people who go into care homes in order to facilitate follow-up, and now asks participants to nominate a proxy if they are unable to be interviewed because of ill-health or being away from home for a long period.

For further discussion on the representativeness of the USoc sample, including information on hard to reach groups and plans to improve sub-group coverage, please refer to:

• Benzeval, Bollinger, Burton, Crossley & Lynn (2020) The Representativeness of Understanding Society and,
• Borkowska (2019) Improving population and sub-group coverage: who is missing and what can be done about it?

Sample status and “following rules”

This section sets out the three different sample ‘statuses’ as well as the ‘following rules’ that are applied when there are specific changes to individual or household circumstances.

There are three possible sample statuses:

• Original Sample Members (OSMs)
• Temporary Sample Members (TSMs)
• Permenant Sample Members (PSMs)

Original Sample Members (OSMs)

All members of USoc GPS households enumerated at Wave 1 - including absent household members and those living in institutions who would otherwise be resident - are Original Sample Members (OSMs). All ethnic minority members of an enumerated household eligible for inclusion in the EMBS are OSMs. Any child born to an OSM mother after Wave 1 and observed to be co-resident with the mother at the survey wave following the child’s birth is an OSM. In the former BHPS sample, OSMs are those who were enumerated at the first wave of the sample from which they come (Wave 1 for the original sample, Wave 9 for the Scotland and Wales boost samples, Wave 11 for Northern Ireland) or who were subsequently born to an OSM mother or father (or both). Following the incorporation into USoc from Wave 2 onward, for those in the former BHPS sample, as for all other USoc samples, only children born to an OSM mother will themselves become an OSM.

OSMs, of all ages, are followed for interview and remain eligible as long as they are resident within the UK. They remain potentially eligible sample members for the life of the survey. The case may arise where the only OSM in the household is a child. Provided they are co-resident with the child, other household members are then Temporary Sample Members (see below) and therefore eligible for interview, even if the OSM child is not yet old enough to be eligible for a youth or adult interview. If the OSM child moves house, they are followed to their new address and those living with the OSM child are eligible for interview. If the OSM child moves into an institution, where normally just the OSM/PSM would be interviewed and not co-residents, a split-off household is created containing only the OSM child and the household enumeration grid completed. The child OSM is an eligible sample member, even if they are not eligible for interview because of their age.

Temporary Sample Members (TSMs)

Any members of an enumerated household eligible for inclusion in the EMBS at Wave 1 who are not from a qualifying ethnic minority are Temporary Sample Members (TSMs) at Wave 1. This was the only category of TSM at Wave 1. In all other samples, any new person found to be co-resident with at least one OSM or PSM after Wave 1 is a TSM. This would include any child born to an OSM father after Wave 1 but not an OSM mother and observed to be co-resident with the father (or any other OSM) at the survey wave following the child’s birth. TSMs remain eligible for interview as long as co-resident in an OSM/PSM household. TSMs who are not co-resident with at least one OSM/PSM are not followed, and become ineligible for interview. TSMs are identified as re-joiners if they are subsequently found to be living with at least one OSM/PSM, and then become eligible for interview.

Permenant Sample Members (PSMs)

Any TSM father of an OSM child born after Wave 1 and observed to be co-resident with the child at the survey wave following the child’s birth is a PSM. PSMs remain potentially eligible for interview for the life of the survey.

Only OSMs have positive longitudinal weights (see Weighting section, below), and they form the basis of the analysis included here. Account is also taken of the inevitable changes of address or location that some interviewees experience; as noted above the survey follows OSMs if they move to a new household. This is to ensure that the household or family history is not lost, and that there is no significant fall off in interview numbers.

For further information on the survey, see the USoc homepage, especially the Main Survey User Guide (Waves 1-11). The Frequently Asked Questions section of the USoc website also provides useful information on all aspects of the survey.

Mode of data collection

USoc interviews were initially typically carried out face-to-face in respondents’ homes. Mixed-mode data collection introduced in Wave 8 (2016 to 2017) resulted in increasing numbers of interviews being completed online. As noted above, from March 2020 following the implementation of nationwide ‘lockdown’ measures, USoc adopted a web-first approach, with a telephone interview available for online non-responders.

Survey period

USoc fieldwork is conducted over a two-calendar-year period, with each individual being interviewed on a yearly basis. Note that the periods of waves overlap, and that individual respondents are interviewed around the same time each year on an annual basis.

For the purposes of this publication, persistent low income analysis is analysed on the basis of ‘rolling’ four-survey period datasets.

For example, the period 2010 to 2014 uses individuals present in all of the following four waves, 2010 to 2011, 2011 to 2012, 2012 to 2013 and 2013 to 2014, together with all children born to Original Sample Members before their 2010 to 2011 interview.

Usoc survey waves and samples

Survey Wave	Calendar Year	Sample Used
Wave 1	2009 to 2010	GPS and EMBS
Wave 2	2010 to 2011	BHPS, GPS and EMBS
Wave 3	2011 to 2012	BHPS, GPS and EMBS
Wave 4	2012 to 2013	BHPS, GPS and EMBS
Wave 5	2013 to 2014	BHPS, GPS and EMBS
Wave 6	2014 to 2015	BHPS, GPS, EMBS and IEMB
Wave 7	2015 to 2016	BHPS, GPS, EMBS and IEMB
Wave 8	2016 to 2017	BHPS, GPS, EMBS and IEMB
Wave 9	2017 to 2018	BHPS, GPS, EMBS and IEMB
Wave 10	2018 to 2019	BHPS, GPS, EMBS and IEMB
Wave 11	2019 to 2020	BHPS, GPS, EMBS and IEMB

Wave 1 ran in 2009 and 2010 and used the GPS and EMBS sample.

Wave 2 (2010 and 2011), Wave 3 (2011 and 2012), Wave 4 (2012 and 2013) and Wave 5 (2013 and 2014) all used the BHPS, GPS and EMBS sample.

Wave 6 (2014 and 2015) used the BHPS, GPS, EMBS samples and also the IEMB sample, which was introduced in that wave. Wave 7 (2015 and 2016), Wave 8 (2016 and 2017), Wave 9 (2017 and 2018), Wave 10 (2018 and 2019) and Wave 11 (2019 to 2020) all used the same samples as Wave 6.

Sample sizes and attrition

As with most longitudinal surveys, attrition reduces the USoc sample size over time. See Table M.1 below which shows how many individuals are available for analysis in each wave. These are Original Sample Member cases with positive longitudinal weights except where noted.

Table M.1 – single-year cases available for analysis

Wave	GPS+EMBS cases	BHPS cases	All cases
2009-2010 (not used – see limitations section)	76,240	Not included	76,240
2010-2011 (cross-sectional weights)	60,911	15,496	76,407
2011-2012	48,877	11,550	60,427
2012-2013	43,107	10,379	53,486
2013-2014	39,116	9,495	48,611
2014-2015	34,175	8,517	42,692
2015-2016	30,700	7,741	38,441
2016-2017	28,062	7,172	35,234
2017-2018	25,453	6,722	32,175
2018-2019	23,560	6,279	29,839
2019-2020	21,547	5,844	27,391

Source: Understanding Society 2010 to 2020

Attrition tends to occur among some groups more than others. Analysis of USoc data has shown, for example, that it is more likely to affect those at the lower end of the income distribution. Non-response weights take into account the differential probabilities of attrition in order to address this. For further information, including a detailed comparison of USoc income and income distribution measures with those produced by HBAI, please refer to Understanding Society and its income data as well as Tables M.8 and M.9 in the 2010 to 2020 ID methodology tables.

Persistent low income analysis in ID is based on individuals with positive longitudinal weights in the final wave being considered, meaning they are OSMs, and include individuals who were previously BHPS sample members. Any individuals who are not in the survey for all four relevant waves are excluded.

Table M.2 – Four-wave cases available for analysis

Wave	All cases	New-born children present for fewer than four waves	Cases available for analysis
2010-2011 to 2013-2014	48,611	1,537	47,074
2011-2012 to 2014-2015	42,692	1,190	41,502
2012-2013 to 2015-2016	38,441	966	37,475
2013-2014 to 2016-2017	35,234	775	34,459
2014-2015 to 2017-2018	32,175	661	31,514
2015-2016 to 2018-2019	29,839	537	29,302
2016-2017 to 2019-2020	27,391	443	26,948

Source: Understanding Society 2010 to 2020

Missing information

As well as attrition reducing the available sample, some of the variables used in analysis for ID have missing values. These may be due to item non-response or partial responses. We exclude individuals with missing data from relevant analysis, but include individuals whenever we can.

Table M.3 below shows the extent of missing data for the longitudinal analysis of incomes for individuals in each four-wave sample. The most common reason for not having income information is having individual responses but no household questionnaire. As shown in Table M.3, the percentage of individuals for whom this is the case has risen since the start of the survey, with a noticeable increase in the period 2014 to 2015 and 2017 to 2018. This may be linked to a number of factors, including a change in fieldwork procedures and the introduction of mixed- mode data collection (online reporting) from Wave 8 (2016 to 2017). The USoc team are exploring options for gathering missing household data. For further information, see the Understanding Society Main Survey User Guide (Waves 1-11).

Other reasons for missing income data include missing equivalisation factors and missing interview dates (which means we are unable to deflate incomes). Improvements to the imputation of housing costs means that the number of cases with missing AHC incomes is the same as those with missing BHC incomes. As noted above, this change was brought about for the ID 2010 to 2017 publication, released in March 2019.

Table M.3 – Cases used for income analysis

Wave	Missing equivalised BHC income	Missing equivalised AHC income
2010-2011 to 2013-2014	29 cases (<1%) out of 47,074	29 cases (<1%) out of 47,074
2011-2012 to 2014-2015	779 cases (2%) out of 41,502	779 cases (2%) out of 41,502
2012-2013 to 2015-2016	969 cases (3%) out of 37,475	969 cases (3%) out of 37,475
2013-2014 to 2016-2017	1,440 cases (4%) out of 34,459	1,440 cases (4%) out of 34,459
2014-2015 to 2017-2018	2,169 cases (7%) out of 31,514	2,169 cases (7%) out of 31,514
2015-2016 to 2018-2019	2,143 cases (7%) out of 29,302	2,143 cases (7%) out of 29,302
2016-2017 to 2019-2020	2,201 cases (8%) out of 26,948	2,201 cases (8%) out of 26,948

Source: Understanding Society 2010 to 2020

Table M.4 in the 2010 to 2020 ID methodology tables shows the extent that information is missing for classificatory variables. In many cases less than 1% of cases have missing values. For highest qualification, only those completing an individual questionnaire are included, so non-responding individuals in partially responding households will have missing values for this variable. For highest qualification, there is also a known issue in feeding forward information collected on the BHPS (i.e. pre-populating the question with what the respondent said previously) or asking individuals turning 16 about their qualifications.

In the four-wave period (between 2014 to 2015 and 2017 to 2018) there was an unusually large number of missing values for the ID derived variable which identifies whether an individual was up-to-date with their bills. This was due to missing data on the USoc variable: ‘problems paying council tax’, in Wave 6 (2014 to 2015). This variable is one of the three variables used by ID to identify whether a household is behind with their bills. From Wave 7 (2015 to 2016), the amount of missing data for this variable decreased back to levels observed during previous four-wave periods.

Weighting

USoc data is designed to be used with weights. Weights are used to ensure that any analysis removes known bias and is as representative of the population as possible, based upon what is known about the survey design (particularly how this affects selection probability) as well as non-response. Longitudinal weights also take into account differential attrition probability, as mentioned above. USoc provides cross-sectional weights (for when only one wave of the survey is being analysed), and longitudinal weights (for any analysis using data - or measures based on data - from more than one wave, such as change in income).

ID uses weights which are produced for analysing the combined GPS, EMBS and BHPS samples, and for producing population estimates by adjusting for unequal selection probabilities, differential non-response, and potential sampling error. These weights adjust for the higher sampling fraction in the Devolved Administrations and for different probabilities of selection in the EMBS, as well as for response rate differences between subgroups of the sample. USoc produces weighting factors rather than grossing factors, so we do not produce estimates of numbers of individuals in low income. Instead we focus on percentages of the population.

USoc provides detailed information on weighting in its User Guide.

3. Measures used in ID analysis

ID includes analysis of low income as well as movements within the overall income distribution. This section sets out how income measures are calculated in ID.

How is income measured

ID bases its income measure on a monthly net household income variable provided in the USoc dataset. The income measure used in ID is total weekly net (disposable) equivalised household income. This comprises total income from all sources after tax, national insurance and other deductions in the latest period before the interview. It comprises income from all household members, including dependants.

The components of the income measure used in ID are shown in the following table.

Term	Definition
Labour income	Includes usual pay and self-employment earnings. This also includes income from second jobs.
State support	Include tax credits and all state benefits, including State Pension.
Pension income	Includes occupational pensions income
Investment income	Includes private pensions/annuities, rents received, income from savings and investments.
Private benefit income	Includes trade union/friendly society payments, maintenance or alimony and sickness or accident insurance.
Miscellaneous income	Includes educational grants, payments from family members and any other regular payment.

Income is net of the following items:

• income tax payments
• National Insurance contributions
• pension contributions
• council tax (note domestic rates in Northern Ireland are not deducted)

Changes to income sources due to the coronavirus (COVID-19) pandemic

As noted above, ID derives its income measure from a monthly net household income variable produced by USoc. Policies implemented in response to the pandemic resulted in some changes to income sources and, subsequently, to the way in which information on or linked to these sources was gathered by USoc. The key changes were as follows:

Support received by employees via the Coronavirus Job Retention Scheme (CJRS)

This scheme was announced by the government in March 2020. Employers who were unable to maintain their workforce as a result of the coronavirus pandemic could put their employees on ‘furlough’ and apply for a grant. Government and employer contributions varied during the scheme to ensure that an employee received at least 80% of their monthly wage up to £2,500 a month, including National Insurance and pension contributions. The scheme was still in place at the end of Wave 11 fieldwork (December 2020).

Information on income received via the CJRS was gathered by the USoc questionnaire as part of ascertaining employee pay (amount received via employee paycheque). No additional response options or questions were included to establish whether employee pay was linked to being on furlough, and any income received via the scheme was therefore included as labour earnings.

USoc did introduce a new response option to the economic status variable ‘jbstat’, from the end of July 2020, which enabled respondents to identify if they were on furlough. This variable is used in ID’s analysis of events associated with low income entry and exit, and the implications are discussed later in this section.

Support received by self-employed individuals via the Self-Employment Income Support Scheme (SEISS)

The government introduced the Self-Employment Income Support Scheme (SEISS) to assist self-employed people whose income was affected by the coronavirus pandemic. SEISS grants were available over the period from March 2020 until April 2021.

A new question was introduced into the USoc survey at the end of July 2020 to capture receipt of SEISS grants. However, as noted in the introduction to this Background note, the derivation of income estimates for self-employed people relies upon the reporting of profits over an earlier timeframe, usually the previous financial year. This means that self-employed respondents interviewed during 2020 who were in receipt of a SEISS grant will, in the main, have had their income derived from pre-pandemic profits, and income from SEISS grants will be included in their 2021 income estimates (i.e. Wave 12 USoc data).

Universal Credit £20 uplift

From April 2020, the government increased the standard allowance in Universal Credit (UC) by £1,040.04 per year and the basic element in Working Tax Credit by £1,045 per year. Both new and existing UC claimants and existing Working Tax Credit claimants received an additional £20 per week on top of annual uprating. This uplift was still in place when Wave 11 fieldwork ended. The USoc questionnaire did not need to be changed to capture the uplift: the questionnaire asks, for each source of unearned income and state benefit, what amount was received.

The potential effect of these changes on ID estimates is discussed later in this section.

Housing costs

Income After Housing Costs (AHC) is derived by deducting housing costs from the above income measure.

Housing costs in ID include the following:

• rent (gross of housing benefit)
• water rates, community water charges and council water charges
• mortgage interest payments (net of tax relief)
• ground rent and service charges

In the case of renters, these housing costs will include service and water charges because this is how the information is requested on the USoc questionnaire. For owner-occupiers, these amounts will not be included.

The main difference between HBAI and ID in terms of housing costs data is that, for owner-occupiers, HBAI includes structural insurance payments, whereas ID does not: no information is collected on structural insurance payments in USoc.

For Northern Ireland households, water provision is funded from taxation and there are no direct water charges. Therefore, it is already taken into account in the Before Housing Costs measure.

Missing income information is imputed in the USoc dataset. See Main Survey User Guide (Waves 1-11) and ‘Understanding Society and its income data’ for more detail on the procedures used.

Incomes are adjusted for inflation so they are in real terms corresponding to the middle January of the latest USoc wave (for Wave 11, i.e. calendar years 2019 and 2020, January 2020 is used). Like HBAI, ID uses a bespoke variant of the Consumer Price Index (CPI) to adjust for inflation to look at how incomes are changing over time in real terms. After the HBAI Financial Year Ending (FYE) 2018 publication, the Office for National Statistics (ONS) made some very minor revisions to this index. Because the effect of these revisions on low income measures was negligible, no revisions were made to the deflators used by HBAI in their series, and ID followed the same approach. This means that the AHC deflators used for February and March 2019 are 0.1 percentage point higher than those published via the link above.

Equivalisation

An adjustment called ‘equivalisation’ is made to income to make it comparable across households of different size and composition. ID uses net disposable weekly household income, after adjusting for the household size and composition, as an assessment for material living standards - the level of consumption of goods and services that people could attain given the net income of the household in which they live. In order to allow comparisons of the living standards of different types of households, income is adjusted to take into account variations in the size and composition of the households in a process known as equivalisation. ID assumes that all individuals in the household benefit equally from the combined income of the household. Thus, all members of any one household will appear at the same point in the income distribution.

How household income is equivalised

Equivalence scales conventionally take an adult couple without children as the reference point, with an equivalence value of one. The process then increases relatively the income of single person households (since their incomes are divided by a value of less than one) and reduces relatively the incomes of households with three or more persons, which have an equivalence value of greater than one.

The main equivalence scales used in ID are the modified OECD scales, which take the values shown in Table M.5 below. These are in line with those used in HBAI. The equivalent values used by the McClements equivalence scales are also shown for comparison alongside modified OECD values. The McClements scales were used for the main estimates in the predecessor Low Income Dynamics publication with results based on the modified OECD equivalence scales published in an Appendix. In the modified OECD and McClements versions two separate scales are used, one for income BHC and one for income AHC.

Table M.5 – Comparison of modified OECD and McClements equivalence scales

Equivalence scales
	Modified OECD to equivalise BHC results rescaled to couple without children=1[1]	OECD ‘companion’ Scale to equivalise AHC results	McClements BHC	McClements AHC
First Adult	0.67	0.58	0.61	0.55
Spouse	0.33	0.42	0.39	0.45
Other Second Adult[2]	0.33	0.42	0.46	0.45
Third Adult	0.33	0.42	0.42	0.45
Subsequent Adults	0.33	0.42	0.36	0.40
Children aged under 14yrs[3]	0.20	0.20	0.20	0.20
Children aged 14yrs and over[3]	0.33	0.42	0.32	0.34

Notes:

[1] Presented here to two decimal places

[2] For the McClements scale, the weight for ‘Other second adult’ is used in place of the weight for ‘Spouse’ when two adults living in a household are sharing accommodation, but are not living as a couple. ‘Third adult’ and ‘Subsequent adult’ weights are used for the remaining adults in the household as appropriate. In contrast to the McClements scales, apart from for the first adult, the OECD scales do not differentiate for subsequent adults

[3] The McClements scale varies by age within these groups; appropriate average values are shown in the table

The construction of household equivalence values from these scales is quite straightforward. Consider a single person, a couple with no children, and a couple with two children aged twelve and ten, all having unadjusted weekly household incomes of £300 (BHC). The process of equivalisation, as conducted in ID, gives an equivalised income of £448 to the single person, £300 to the couple with no children, but only £214 to the couple with children.

How the low-income threshold is calculated

Relative low income sets the threshold as a proportion of the median income. The amount therefore changes each year as median income moves. It is used to measure the number and proportion of individuals who have incomes at a certain level below the median.

Changes in the rates of relative low income can be driven by changes in single year relative low income estimates or by individuals spending a longer or shorter time in relative low income.

The percentage of individuals in relative low income will increase if:

• the average income stays the same, or rises, and individuals with the lowest incomes see their income fall, or rise less, than average income; or
• the average income falls and individuals with the lowest incomes see their income fall more than the average income

The percentage of individuals in relative low income will decrease if:

• the average income stays the same, or rises, and individuals with the lowest incomes see their income rise more than average income; or
• the average income falls and individuals with the lowest incomes see their income rise, or fall less, than average income, or see no change in their income

Measuring Income Before and After Housing Costs

Before Housing Costs (BHC) measures allow an assessment of the relative standard of living of those individuals who were actually benefiting from a better quality of housing by paying more for better accommodation. On this basis, income growth over time incorporates improvements in living standards where higher costs reflected improvements in the quality of housing.

After Housing Costs (AHC) measures allow an assessment of living standards of individuals whose housing costs are high relative to the quality of their accommodation. Income growth over time may also overstate improvements in living standards for low income groups, as a rise in Housing Benefit to offset higher rents (for a given quality of accommodation) would be counted as an income rise.

For completeness, ID presents analyses on both a BHC and AHC basis. This is principally to take into account variations in housing costs that themselves do not correspond to comparable variations in the quality of housing.

Households and benefit units

ID presents information on an individual’s household income by various household and benefit unit (family) characteristics. There are important differences between households and benefit units.

A household is one person living alone or a group of people (not necessarily related) who either share living accommodation OR share one meal a day and who have the address as their only or main residence. A household will consist of one or more benefit units, which are single adults or a married or cohabiting couple and any dependent children. For example, a group of students with a shared living room would be counted as a single household even if they did not eat together, but a group of bed-sits at the same address would not be counted as a single household because they do not share living space or eat together.

A husband and wife living with their young children and an elderly parent would be one household but two benefit units. The husband, wife and children would constitute one benefit unit and the elderly parent would constitute another. It should be noted that the term ‘benefit unit’ is used as a description of groups of individuals regardless of whether they are in receipt of any benefits or tax credits. A household will consist of one or more benefit units, which in turn will consist of one or more individuals (adults and children).

Income measurements used in ID

Relative low income

There are a range of ways in which low income can be defined. ID uses a measure of relative low income. Relative low income measurements identify which individuals have income which falls below a certain threshold based upon the range of incomes in the population. In ID, we concentrate on individuals with household incomes below 60% of median income. Information on those with household incomes below 70% of the median income is available in the detailed tables published alongside this release.

Persistent low income

An individual is classified as being in persistent low income if they live in a household in relative low income for at least three of their last four consecutive interviews. It is therefore possible that some of those classed as being in persistent low income were not in relative low income at the time of their most recent interview. However, they will have experienced low income in each of the previous three years and, as a result, their long-term living standards are not anticipated to be different to other individuals in persistent low income who were in low income in the most recent interview.

This issue is relevant because the income distribution is particularly dense around the 60% and 70% of median income thresholds. In addition, some short periods of recorded high income may be due to measurement error and not reflect any real improvement in living standards.

Low income entry and exit

The methodology used here is the same as in the predecessor Low Income Dynamics publication. This analysis looks at individuals moving into and out of relative low income across a two-wave period, and uses a measure of equivalised household income consistent with that used elsewhere in ID.

When analysing transitions into and out of low income, the threshold used is the standard 60% of median income. Analysis of transitions between one wave and the next only includes what are defined as ‘clear’ transitions. For example, for an exit or entry to occur, household incomes must change such that they cross the threshold and are at least 10% higher or lower than 60% median income in the following wave. This requirement is put in place because all survey estimates - including household incomes and measures based on them such as the low income threshold - are subject to sampling and measurement error.

The exit or entry rate for individuals is calculated as, using exit rates as an example, the number of individuals in low income in one wave who exited low income in the following wave, expressed as a percentage of all those who were in low income. Roughly equal numbers of individuals move into as move out of low income across each two-wave period. Rates of entry are, however, much lower than rates of exit, because entry rates are expressed as a percentage of those who were not in low income in the first of the two waves under consideration. Exit rates are expressed as a percentage of those who were in low income in the first of the two waves under consideration (a much smaller number than those not in low income).

The unit of analysis is the individual. However, as individuals live in households and we assume that all members of the household benefit equally from the household’s income, they will be affected by changes at the household level. This could come about either through changes in income levels, or by changes in the household composition which affects incomes through the equivalisation process.

For the publication tables containing detailed breakdowns we have presented the rates as an average over three, two-wave periods as this will, to some extent, remove volatility that can be associated with smaller sample sizes, and help to create a more robust and stable series. This contrasts with the presentation given in Low Income Dynamics which was a pooled estimate from the transitions between all adjacent two-year periods.

Events associated with low income entry and exit

This analysis was first included in ID 2010 to 2019, and is based on those individuals who enter and exit low income across a two-wave period, as described above. It aims to help us understand how certain events are linked to these low income entries and exits by considering changes to income sources (including earned and non-earned income), changes in employment, and demographic changes. For AHC analysis only, we also look at changes in tenure and housing costs.

The method used is based on an approach used on the British Household Panel Survey (BHPS) previously (for example, Jenkins S.P and Rigg J.A with the assistance of Devicienti, F. (2001) ‘The dynamics of poverty in Britain’, DWP Research Report No. 157); and also in Low Income Dynamics, the predecessor of ID, based on the BHPS.

As noted in the introduction, a revision has been made to one of the variables used in the events analysis following a recommendation from the OSR review that producers use clear and accurate terminology regarding whether analysis is based on Benefit Units (families) or households. In response, we reviewed all ID products, and identified that while most events analysis was based on household changes, the variable counting the number of children was incorrectly based on a Benefit Unit measure, and was also capped at 3 children. We amended our code so that the count was based on household unit and could include any changes in the number of children. This affected the following events:

• change in the number of children in a household
• change from single person status to living as a couple – where the requirement is that there are no children in the household in either wave
• change from living as a couple to single person status – where the requirement is that there are no children in the household in either wave

We compared the statistics on these two events before and after implementing the code change. For the event which concerns a change in the number of children, changing from a BU based measure to one based on the household resulted in small increases to prevalence rates, i.e. it was more common. This is probably intuitive given the removal of the cap on what changes could be counted, and that BU has a slightly tighter definition than household. This change was also associated with slightly lower rates of exit from low income, and slightly higher rates of entry into low income.

Where the number of children in the household is used as a ‘control’ i.e. changes between single person status and living as a couple, which requires no children to be present in either wave, the variable change made little difference to the statistics presented in our tables. Sample sizes were slightly reduced when using the household-based measure, and this resulted in additional suppression in a couple of places.

As also noted in earlier sections, at the end of July 2020 USoc added a new response category to the employment status variable, ‘jbstat’, to capture whether employees were receiving support via the CJRS – or were on ‘furlough’. ID uses the ‘jbstat’ variable to count the number of workers in each wave for events which explore how changes in the number of workers in a household affect low income entry and exit. We revised our code to ensure that any individual reporting being on furlough in Wave 11 would be counted as an employee. If we had left the code unchanged, any individuals who reported being employed in Wave 10 but on furlough in Wave 11 would have been counted as a reduction in the number of workers in the household. Given that a minimum of 80% of employee earnings were protected via the scheme, the code revision was felt to be more appropriate in terms of income effects than counting individuals on furlough as no longer working.

We reviewed the numbers before and after implementation of this code change. There was very little difference (never more than a 1 percentage point change) in the statistics for events which relied upon a count of the number of workers in a household.

ID includes findings on the following events.

Event	Definition
Earnings	A change in monthly household earnings of at least 20% and a minimum of £10. No change in the number of workers in the household.
Benefit Income	A change in monthly household benefit income of at least 20% and a minimum of £10. No change in household size.
Investment Income	A change in monthly household income from investment of at least 20% and a minimum of £10. No change in household size.
Occupational pension income	A change in monthly household occupational pension income of at least 20% and a minimum of £10. No change in household size.
Other Income	A change in monthly household miscellaneous income of at least 20% and a minimum of £10. No change in household size.
Number of Workers	A change in the number of workers in a household. This is considered both for where there is an accompanying household size change and where there is no change in household size.
Number of Full-Time Workers	A change in the number of full-time workers in a household. This is considered both for where there is an accompanying household size change and where there is no change in household size.
Household Type	A change in household type.
Lone Parent Household	A change in lone parent status – either becoming a lone parent (low income entries) or changing from being a lone parent (low income exits).
Couple to Single Person Household	A change from living in a couple household to single person status – no children in either case.
Single Person to Couple Household	A change from single person status to living in a couple household – no children in either case.
Number of Children	A change in the number of children – an increase for low income entry or a decrease for low income exit.
Tenure	AHC only, a change in tenure across the four tenure types (outright ownership; buying with a mortgage; social renting or private renting).
Housing Costs	AHC only, an increase (low income entries) or decrease (low income exits) in monthly housing costs of at least 20% and a minimum of £10.

For a change in monthly income or housing costs to be considered an event, it must have changed by at least 20% and a minimum of £10. This requirement aims to ensure that only meaningful changes in income components are included. To be included in analysis of low income exits, income components must have increased, and to be included in analysis of low income entries, income components must have decreased.

Certain employment and demographic changes are controlled for when examining income and housing costs events. This is to try and remove the effect of major changes that are likely to have a bearing on the event being considered. For example, changes in household earnings are only included if the number of workers stays the same. For all other income events, the number of people in the household must not change.

For each event, we present three statistics in our ID publication tables across all two-wave periods since 2010 to 2011. To help explain the three measures, statistics from the most recent two-wave period presented in Table 9.1n, are used as an example:

	Prevalence (%)	Entry rate (%)	Share of entries (%)
Fall in earnings	12	23	37

A prevalence statistic – this tells us how common an event is among the population at risk of either entering or exiting low income. When considering the relationship between a fall in earnings and low income entry, for example, the prevalence statistic tells us what percentage of those who were not in low income in the first wave experienced a fall in earnings between the two waves. Table 9.1n tells us that over the most recent two-wave period, this was 12%.

A statistic expressing the rate of entry or exit if an individual experienced this event – the extract above shows us that 23% of those who were not in low income in 2018 to 2019 and who experienced a decrease in earnings, entered low income in 2019 to 2020.

A share statistic, indicating how common an event was among all low income entries or exits – Table 9.1n shows that 37% of all those who entered low income experienced a fall in earnings.

Because those in low income are a smaller group than those who are not, and are, by definition, closer to the low income threshold, they have a greater chance of exiting low income if they experience a particular event than those not in low income have of entering low income if they experience the same event. This explains why exit rates tend to be higher than entry rates.

While we have aimed to rule out major confounding events, this analysis does not rule out all factors which may have a bearing upon low income entry or exit, and should not be interpreted as implying causality.

How have changes related to the coronavirus pandemic affected ID measures?

Earlier sections of this Background note have set out how USoc data collection responded to policies implemented due to the coronavirus pandemic. This section considers how different ID measures may have been affected. It aims to provide relevant contextual information to help readers interpret ID measures appropriately and reach an informed view on the extent to which the statistics are able to identify and reflect the effects of the coronavirus pandemic and the policy responses to it.

Timing

The first lockdown began in March 2020, which was month 15 of the 24-month Wave 11 fieldwork schedule. A considerable amount of Wave 11 data collection was therefore carried out pre-pandemic. We have not attempted any analysis which compares the pre-pandemic to post-pandemic samples. This is beyond the scope of ID statistics. The timing will also influence the effect of the coronavirus pandemic on specific types of analysis included in ID.

Policy changes and questionnaire content

As noted earlier, policies were implemented relating to three income sources following the start of the coronavirus pandemic: the CJRS or ‘furlough’ scheme for employees; the SEISS for those who were self-employed; and the £20 uplift to UC and Working Tax Credit. The USoc questionnaire was adapted to gather information on employees being supported by the CJRS via a revision to the economic status variable ‘jbstat’ but otherwise, employees reported their income in the usual way; a new question was added on SEISS at the end of July 2020. However, in the main, SEISS grants will be factored into 2021 income estimates for the self-employed; and finally, changes to UC or Working Tax Credit will have been picked up in the usual way, by questions which ask for the most recent amount of different social benefits paid.

Implications for ID analysis

It is important to note from the outset that except for analysis on events associated with low income entry and exit, ID does not publish any analysis based on income components. This means, for example, that changes to employee or self-employed earnings cannot be identified via the statistics; and that, for example, a change in benefit income cannot be specifically allocated to, say, a change in the level of UC income.

While many employees experienced immediate changes to their earnings following the start of lockdown, it is reasonable to say that Wave 11 data collection will not have captured all changes over the period March to December 2020. Employee earnings are recorded via a question which asks about usual pay. We have not carried out any analysis to look at pre- and post- lockdown earnings, but it is plausible that some individuals in receipt of reduced earnings linked to the furlough scheme will have reported their pre-pandemic income. Attributing any changes in earnings to the CJRS is also difficult, since furlough status was not added as a response option to the economic status variable ‘jbstat’ until the end of July 2020, meaning that those individuals in receipt of this support cannot easily be identified. Furthermore, other employees not in receipt of support via the CJRS will have experienced changes to their income, some of which will have been completely unrelated to the pandemic.

In terms of self-employed individuals, and as noted earlier in this section, grants received via the SEISS will typically be factored into income calculations in 2021 (Wave 12). They are therefore not included in Wave 11 data.

Changes resulting from the UC £20 uplift were gathered via questions based on the last payment received. As such, they are likely to have been captured effectively over the period from March to December 2020.

Beyond issues associated with data collection, the way in which ID statistics are derived is also relevant.

Persistent low income measures for the four-wave period between 2016 to 2017 and 2019 to 2020 are unlikely to effectively illustrate the effects on income of the coronavirus pandemic, which occurred during the final 9 months of this period. Perhaps unsurprisingly, rates of persistent low income for the most recent four-wave period have not shown any unusual changes compared to previous four-wave periods.

Low income entry and exit rates are considered, over a two-wave period. It might be more reasonable to expect that rates over the most recent two wave period, between 2018 to 2019 and 2019 to 2020, might reflect changes to income levels associated with the coronavirus pandemic. However, these rates do not indicate any obvious changes, neither for all individuals nor for working-age adults nor children.

Analysis of the events associated with low income entry and exit is the only element of ID which considers changes in income components and changes in economic status. It looks at these changes over a two-wave period. Relevant events are considered here with findings over the most recent two-wave period (between 2018 to 2019 and 2019 to 2020):

• association between low income entry and a fall in earnings: there was a slight increase in the percentage of individuals who experienced a fall in household earnings, from 10% across all previous two-wave periods to 12%. This could be linked to employment loss or a reduction in working hours. To be counted as having experienced a fall in earnings for this analysis requires the decrease to be at least 20%. It is unlikely therefore to have captured all individuals in receipt of support via the CJRS, since workers on furlough received at least 80% of their pre-pandemic earnings. The ONS have estimated that around half of employees who were on furlough received top-up payments from their employers, although this was less likely to be the case for those in lower paid jobs

• association between low income exit and an increase in benefit income: looking at low income exits, we might anticipate being able to observe an effect linked to the £20 UC and Working Tax Credit uplift. Our low income exits analysis however, showed no increase in the prevalence of an increase in benefit income. Although Universal Credit and Working Tax Credit claimants received an additional £20 per week in benefits from April 2020, and we would expect some of those claimants to have been surveyed after this change, it is plausible that for many claimants, the £20 increase did not meet the threshold that is set by our events analysis for determining that there has been a meaningful increase in benefit income (this must be an increase of at least 20% as explained in earlier in Section 3)

• association between low income entry and reduction in the number of workers in a household: over the most recent two-wave period, there were slight increases in all events statistics which considered the association between a fall in the number of workers and low income entry, i.e. a fall in the number of workers was slightly more prevalent; it was associated with a slightly increased risk of low income entry; and it accounted for a slightly greater share of all low income entries. These changes cannot be directly attributed to the coronavirus pandemic but may be reflecting some employment loss associated with it

• association between low income entry and reduction in the number of full-time workers in a household: Events statistics on changes in the number of full-time workers only, showed no changes BHC, but slight AHC increases similar to those observed when all workers were considered (as above). Again, these changes cannot be directly attributed to the coronavirus pandemic but may be reflecting some employment loss associated with it.

Variables used for sub-group analysis

When longitudinal analysis is carried out on particular sub-groups, we allocate individuals to sub-groups according to their status in the first wave being considered, and use weights in the final wave being considered. This means that their status in any intervening waves may change, and have a bearing upon what is being measured in the final wave, but will not be taken into account.

For example, if looking at persistent low income over a four-wave period, consider a single working-age adult living alone who was working in the first interview of the period being considered and not in relative low income, but then was not working for the next three interviews and was in relative low income for each of those waves. This individual would be identified as in persistent low income, but would also be classified as in a family where all adults are working despite not having worked for the past three waves.

Variables used to classify age and pensioner status

All age-related derived variables in ID use the USoc age variable ‘age_dv’. USoc check the variable ‘age_dv’ for consistency, and make imputations when errors are found in order to improve its accuracy.

We use the USoc variable ‘pensioner_dv’ to identify if an individual is of State Pension age (SPa). ‘Pensioner_dv’ is derived using a respondent’s full date of birth (via ‘age_dv’). This allows precision in determining SPa.

State support

The Government pays money to individuals in order to support them financially under various circumstances. An individual is in receipt of state support if they receive one or more benefits, or are being paid Tax Credits. Most of these benefits are administered by DWP, with the exception of Housing Benefit and Council Tax Reduction which are administered by local authorities, and Child Benefit and Tax Credits which are administered by HM Revenue and Customs. National roll-out of UC for all new relevant claims completed in December 2018, and it is now the primary working-age benefit. It replaces income-based Jobseekers’ Allowance, income-related Employment and Support Allowance, Income Support, Working Tax Credit, Child Tax Credit and Housing Benefits. However, existing claimants with legacy benefits who have not have a change in circumstances have remained on their legacy benefits. For more information about the way in which information on state support is gathered and processed, please see the USoc Wave 11 questionnaire and ‘Understanding Society and its income data’.

Ethnicity

USoc includes detailed ethnicity classifications which are combined into the following publication splits:

White:

• British/English/Scottish/Welsh/Northern Irish
• Irish
• Gypsy or Irish Traveller resident in England, Scotland or Wales
• Any other White background

Mixed:

• White and Black Caribbean
• White and Black African
• White and Asian
• Any other mixed background

Asian:

• Asian/ Asian British: Indian
• Asian/ Asian British: Pakistani
• Asian/ Asian British: Bangladeshi
• Asian/ Asian British: Chinese
• Asian/ Asian British: Any other Asian background

Black:

• Black/ Black British: Caribbean
• Black/ Black British: African
• Black/ Black British: Any other Black Background

Other:

• Other Ethnic Group: Arab
• Other Ethnic Group: Gypsy or Irish Traveller resident in Northern Ireland
• Other Ethnic Group: Any other ethnic group

Source: Understanding Society questionnaire documentation.

To ensure ID statistics are harmonised with GSS standards Gypsy or Irish Travellers resident in Northern Ireland are included in the Other ethnic group rather than the White ethnic group. Gypsy or Irish Travellers resident in England, Scotland or Wales are still included in the White ethnic group.

Long-standing illness or disability definitions and changes to the way in which this information has been gathered by USoc

Up to and including Wave 7 (2015 to 2016)

USoc included two questions to determine the presence of a limiting or non-limiting long-standing illness or disability:

“Do you have any long-standing physical or mental impairment, illness or disability? By ‘long-standing’ I mean anything that has troubled you over a period of at least 12 months or that is likely to trouble you over a period of at least 12 months.”

If respondents have a long-standing illness or disability, a follow-up question asks:

“Does this / do these health problem(s) or disability(ies) mean that you have substantial difficulties with any of the following areas of your life?”

Twelve areas are listed:

• mobility (moving around at home and walking)
• lifting, carrying or moving objects
• manual dexterity (using your hands to carry out everyday tasks)
• continence (bladder and bowel control)
• hearing (apart from using a standard hearing aid)
• sight (apart from wearing standard glasses)
• communication or speech problems
• memory or ability to concentrate, learn or understand
• recognising when you are in physical danger
• your physical co-ordination (for example, balance)
• difficulties with own personal care (for example, getting dressed, taking a bath or shower)
• other health problem or disability

If a respondent answers that they have substantial difficulties with any of these areas, they are said to have a limiting long-standing illness or disability. If they identify none of these, then the long-standing illness or disability is said to be non-limiting. Prior to Wave 7, although the presence of a long-standing impairment, illness or disability was asked of all adult respondents, whether it was limiting or not was not asked of proxy respondents (i.e. where someone else answers interview questions on behalf of another individual). In Wave 7 (2015 to 2016), this information was also gathered via proxy interviews, resulting in a decrease in the amount of data coded as missing for this variable in 2015 to 2016.

Changes in Wave 8 (2016 and 2017)

In Wave 8, USoc made a further change to the way in which this information was captured, but only to the main questionnaire (not the proxy questionnaire). This change removed the link between the presence of a long-standing impairment, illness or disability and whether any such illness or disability was limiting or not: in Wave 8, all adult respondents were asked whether they had a limiting health problem or disability regardless of whether they reported a long-standing illness or disability.

Those who reported a long-standing impairment, illness or disability were asked:

“Do you have any health problems or disabilities that mean you have substantial difficulties with any of the following areas of your life?”

Those who did not report a long-standing impairment, illness or disability were asked:

“Even though you don’t have any long-standing health problems, do you have any health problems or disabilities that mean you have substantial difficulties with any of the following areas of your life?”

This question was followed by a list of the same twelve areas noted above.

While ID only derives its statistics on limiting and non-limiting illness or disability for those who report having a long-standing illness or disability, it should be noted therefore, that from 2016 to 2017, limiting conditions may not necessarily be linked to the long-standing illness or disability reported. Section 7 of the Summary report has been drafted to reflect these changes. See also USoc survey documentation for further details.

4. What to be aware of when interpreting ID statistics

Accurate interpretation of statistics produced using survey data relies upon understanding the way in which the data is collected. This section sets out some of the key factors that are relevant to ID.

Wave 1 (2009 to 2010) income information

There are known issues with the income information in the first USoc wave covering 2009 to 2010. See Dr Paul Fisher’s paper Does repeated measurement improve income data quality? (ISER Working Paper Series, 2016-11) for details of why income data on the first wave of USoc are not comparable with subsequent waves and are likely to be of lower quality. We have therefore excluded the first wave from any analysis presented in this publication.

Survey Data

The figures in ID come from USoc, a longitudinal survey. In 2019 to 2020, the survey gathered information on over 27,000 individuals. In addition to capturing detailed information on incomes, USoc gathers rich contextual information on household and individual circumstances, such as employment, education level and disability.

Surveys gather information from a sample rather than from the whole population. The sample is designed carefully to allow for this, and to be as accurate as possible given practical limitations, such as time and cost constraints. Results from sample surveys are always estimates, not precise figures. This means that they are subject to a margin of error which can affect how changes in the numbers should be interpreted, especially in the short-term. Year-on-year movements should be treated with caution.

In addition to sampling errors, consideration should also be given to non-sampling errors. Non-sampling errors arise from the introduction of some systematic errors in the sample compared to the population it is supposed to represent. As well as response bias, errors include inappropriate definition of the population, misleading questions, data input errors or data handling problems – in fact any factor that might lead to the survey results systematically misrepresenting the population. There is no simple control or measurement for such non-sampling errors, although the risk can be minimised through careful application of the appropriate survey techniques from the questionnaire and sample design stages through to analysis of results.

ID is based on data from a longitudinal household survey and so subject to the nuances of using a survey, including the following.

Term	Definition
Attrition	Some respondents will inevitably drop out between interviews. To minimise attrition, the USoc team maintains a database of information on respondents so they can send communications to respondents. The database builds on contact information collected during the survey interviews, and is updated throughout the year. A between-wave-mailing is also used to help maintain contact with participants and update addresses. The mailing has a report of research findings, an address confirmation slip and materials to encourage registration with the participant website.
Sampling error	Results from surveys are estimates and not precise figures. In general terms the smaller the sample size, the larger the uncertainty.
Non-response error	As with any survey, analysis based on USoc is at risk from a systematic bias due to non-response. This is when households that had been selected for interview do not respond to the survey. Individuals within households may also be non-responders even if the rest of the household does respond. In an attempt to correct for these biases, the results are weighted to adjust for non-response, taking account of previous responses.
Item non-response	Item non-response occurs where a respondent has given a full interview, but has refused or given a ‘don’t know’ answer to a particular question, which consequently leads to a missing value for that item. The USoc team, based at The Institute for Social and Economic Research (ISER) have used imputation for some USoc variables to correct for item non-response, whereby a valid value is imputed in to replace the missing value, with the aim of reducing potential bias caused by the missing values. Imputation is used to derive overall incomes where there is missing data.
Survey coverage	The initial USoc survey only sampled private households in the United Kingdom. This meant that it excluded individuals living in institutions such as nursing or retirement homes, hostels for homeless people, or prison/young offender’s institutions. As a result, analysis for particular age bands, such as older people or younger people, may not be completely representative of all individuals in those age bands where the characteristics of people who live in private households (and who were included), differ in a fundamental way from those living in institutional settings (who were not included).
Sample size	Although USoc has a large sample size for a household survey, small sample sizes for some more detailed analyses may mean results are more volatile.
Measurement error	Other social surveys underestimate incomes from certain sources when compared with administrative data. Like these surveys, it is likely that USoc also does not fully capture all income streams. However, the longitudinal nature of the survey means that this may improve over time with the use of dependent interviewing (where respondents can be reminded of previous responses) and panel conditioning (where familiarity with the questionnaire means that respondents respond more accurately to later waves of the survey). See Dr Paul Fisher’s paper Does repeated measurement improve income data quality? (ISER Working Paper Series, 2016-11) for further details.

Reporting Uncertainty

As noted above, survey results are always estimates, not precise figures and so subject to a level of uncertainty. Two different random samples from one population, for example the UK, are unlikely to give exactly the same survey results, which are likely to differ again from the results that would be obtained if the whole population was surveyed. We are unable to calculate sampling uncertainties for these statistics.

Rounding and suppression

Figures are rounded to the nearest percentage point independently and as a result, differences may not sum exactly due to rounding. Any proportions based on a sample population of 100 or less are suppressed.

Survey-based income data

The following issues need to be considered when using any survey-based income information.

Term	Definition
Lowest incomes	Comparisons of household income and expenditure suggest that those households reporting the lowest incomes may not have the lowest living standards.
Benefit receipt	Relative to administrative records, surveys tend to under-report benefit receipt.
Self-employed	Almost all analyses in the ID publication include the self-employed (the exception is the events associated with low income entry and exit which rely upon identifying the number of full-time workers in a household). A proportion of this group are believed to report incomes that do not reflect their living standards and there are also recognised difficulties in obtaining timely and accurate income information from this group. This may lead to an understatement of total income for some groups for whom this is a major income component, although this is likely to be more important for those at the top of the income distribution.
High incomes	Unlike in the HBAI series, no adjustment is made to correct for a likely undercount of ‘very rich’ households in survey data. However, this should not affect low income statistics based on median incomes.
Gender analysis	In any analysis of gender, it must be remembered that ID attempts to measure the living standards of an individual as determined by household income. This assumes that both partners in a couple benefit equally from the household’s income, and will therefore appear at the same position in the income distribution. Any difference in figures can only be driven by gender differences for single adults, which will themselves be diluted by the figures for couples. The lower-level gender disaggregation in the family type classification is therefore likely to be more informative. Research has suggested that this assumption with regard to income sharing is not always valid, as men sometimes benefit from shared household income at the expense of women. This means that it is possible that ID results broken down by gender could understate differences between the two groups. A discussion of the evidence on the distribution of money within couple households, particularly those on low incomes, is included in this research briefing published by the Women’s Budget Group.
Students	All analyses in ID includes students. Information for students should be treated with some caution because they are often dependent on irregular flows of income.
Older people	The effect of the exclusion of some of the older people who live in residential homes is likely to be small overall except for results specific to those aged 80 and above.
Ethnicity analysis	Smaller ethnic minority groups exhibit year-on-year variation which limits comparisons over time. USoc includes an ethnic minority boost which means it has larger sample sizes for non-white ethnic groups than if such a boost was not present.
Disability analysis	No adjustment is made to disposable household income to take into account any additional costs that may be incurred due to the illness or disability in question. This means that using income as a proxy for living standards for these groups may be somewhat upwardly biased.
Regional analysis	Although the USoc sample is large enough to allow some analysis to be performed at a regional level, it should be noted that no adjustment has been made for regional cost of living differences. It is therefore assumed that there is no difference in the cost of living between regions, although the After Housing Costs measure will partly take into account differences in housing costs.

5. ID and other statistics

Other measures of persistent low income

ID is a successor series to DWP’s Low Income Dynamics (LID) publication, the final issue of which was published in September 2010. LID was based on the British Household Panel Survey (BHPS). There are methodological differences between the two approaches and data sources which mean that direct comparisons are not possible.

Statistics on persistent low income have also been published by the Office for National Statistics (ONS): see Persistent Poverty in the UK and EU. These statistics were last published in 2019, and are based on the European Union Statistics on Income and Living Conditions (EU-SILC).

As well as drawing on a different data source, these statistics use slightly different definitions, and count persistent low income differently i.e. individuals who are in low income households for at least three of the last four years, including the latest year. This difference means that UK estimates published using this method tend to be a little lower than those presented in ID.

For details of SILC calculations see EU statistics on income and living conditions (EU-SILC) methodology - monetary poverty, while “The relationship between EU indicators of persistent and current poverty” by Stephen P. Jenkins and Philippe Van Kerm contains a commentary on the EU-SILC persistent low income methodology. Persistent low income data sourced from EU-SILC for other European countries is available via the eurostat website.

Other publications which use ID statistics

Poverty and Income Inequality in Scotland includes figures on persistent poverty for children and other population groups in Scotland. This year’s release will, for the first time, present findings on low income entry and exit for Scotland. These statistics are based on ID and are produced for the Scottish Government by the DWP.

Ethnicity Facts and Figures: Persistent low income published by the Race Disparity Unit at the Cabinet Office presents ID data on persistent low income and low income entry and exit, by ethnic group.

Other relevant statistics

The ID publication and tables are released alongside a number of other government statistical publications focused on income and low income.

Those listed below are the statistical releases which might be considered alongside results from ID in order to give a more complete picture of income and low income. This is not intended to be an exhaustive list. More information on income data as well as sources of data on earnings can be found in the ONS Income and earnings statistics guide.

ONS have also developed a new income and earnings interactive tool, which can be used to quickly identify sources of statistics on income and earnings, as well as information about their key features.

See also ‘Explaining income and earnings: important questions answered’, also published by the ONS.

The Households Below Average Income (HBAI) series presents information on living standards in the UK based on household income measures for the financial year ending 2021. HBAI uses data from the FRS. Estimates are provided for average incomes, and for the number and percentage of people living in low income households. Tables M.8 and M.9 in the ID publication methodology tables compare single year income statistics derived from USoc with HBAI figures, and show a good level of coherence. HBAI remains the best source of cross-sectional low income statistics.

The Family Resources Survey (FRS) is a continuous household survey. It publishes a range of annual statistics on household circumstances, including income, disability, tenure and pension participation.

The Pensioner’s Incomes Series provides a more detailed analysis of pensioners’ incomes, based on data collected by the FRS.

The Households Below Average Income Report for Northern Ireland uses data collected by the FRS to produce statistics on income and living standards for Northern Ireland.

The Households Below Average Income Reports for Wales use the headline data from HBAI and conducts further analysis on number, risk and composition using data collected by FRS.

European comparisons

Statistics on levels of low income in other European countries is available via the eurostat website.

International Comparisons

The OECD provides international comparisons on trends and levels in Gini coefficients before and after taxes and transfers, average household disposable incomes, relative poverty rates and poverty gaps, before and after taxes and transfers.

The effect of taxes and benefits on household income

These ONS statistics provide a detailed breakdown of household income, including estimates of both direct and indirect taxes, cash benefits and in-kind benefits provided by the state by decile and quintile groups, ranked by equivalised disposable income.

Personal Incomes statistics published by HMRC include data on UK taxpayers, their incomes and the income tax they are liable for, based on the annual Survey of Personal Incomes.

Information on state benefits including claims and caseload numbers on benefits administered by DWP can be found via the DWP benefits statistics collection.

Commentary on average household income is available via a separate bulletin, as is Household income inequality.

Wealth in Great Britain

The Wealth & Assets Survey (WAS) is a key source of information on how households in Great Britain are managing economically. WAS is a large-scale longitudinal survey with six rounds currently published. The most recent publication presents headline results from the sixth round (2016 to 2018), covering household debt, financial wealth, pension wealth, property wealth and physical wealth.

Personal and economic well-being in Great Britain

Based on the ONS’s Opinions and Lifestyle Survey (OPN) which has run weekly during the course of the coronavirus pandemic, as well as the Survey on Living Conditions (SLC), this publication reports on how the pandemic has affected people’s employment, income, savings and debt, as well as their well-being.

Improving Lives: Helping Workless Families indicators and evidence base

Improving Lives is a compendium of nine indicators which tracks progress in tackling the disadvantages that affect families’ and children’s outcomes. It includes measures of worklessness and problem debt.

Estimates of income and low income for small areas

We do not publish data below the level of region, due to sample sizes. However, there are some related data sources that present information at smaller geographies:

Children in Low Income Families: Local Area Statistics

These statistics were first published in March 2020. They provide estimates of the number and proportion of children living in relative and absolute (before housing costs) low income by local area across the United Kingdom. They are a joint release by the DWP and HMRC which replaced Official Statistics previously published separately by DWP (Children in out-of-work benefit households) and HMRC (Children in low-income families local measure).

Income estimates for small areas, England and Wales: Financial Year Ending 2018

ONS produce model-based estimates. of income at Middle layer Super Output Area (MSOA) level.

Other sources of information on low income

A number of non-government bodies also produce evidence on low income, including analysis of quantitative data as well as qualitative research on the experiences of people affected by low income. Such organisations include charities, campaign groups, academic research institutes, and think tanks.

6. Status of the statistics

Official statistics

ID was first published in 2017 as experimental statistics. A decision was taken, in line with official guidance published by the UK Statistics Authority, to remove the experimental label from these statistics in 2019, and ID is now official statistics. The Code of Practice for Statistics sets out the standards to which producers of official statistics should commit.

Quality Statement

We have worked closely with the University of Essex to review their income derivations. We also compare single year income distribution and low income statistics with HBAI. While we would expect differences between these sources because of different survey vehicles, timings and definitions, as can be seen in the relevant tables, there is a good level of consistency between different data sources. See Table M.8 and M.9 in the ID methodology tables for comparisons between HBAI and ID income distribution and relative low income statistics.

Feedback

We would welcome any feedback on these statistics and would also be interested in knowing how you make use of these data to inform your work and any changes or additions you would like us to consider to improve its usefulness. Feedback is always welcome. Please contact us via email: [email protected]

Useful links

See our collections page for all material on ID including the detailed tables.

7. Glossary

Adult

All individuals who are aged 16 and over, unless defined as a dependent child (see Child). All individuals aged 16 or over in the household are interviewed as part of USoc.

Benefit units or Family

A single adult or a married or cohabiting couple and any dependent children.

Child

An individual is defined as a (dependent) child for the purposes of this analysis if they are aged under 16 or aged 16 to 18 and in school or non-advanced further education, not married, and living with a parent. If an individual, aged 16 to 18 and in full-time education, did not receive an interview (to determine their educational status), they were assumed to be a dependent child. It does not include any children who have a spouse, partner or child living in the household.

Contemporary median income

The average income for the period covered by the survey. Household incomes are adjusted for inflation so they are in real terms corresponding to the middle January of the latest USoc wave.

Long-standing illness or disability

To establish whether someone has a long-standing illness or disability, USoc asks the following question:

“Do you have any long-standing physical or mental impairment, illness or disability? By ‘long-standing’ I mean anything that has troubled you over a period of at least 12 months or that is likely to trouble you over a period of at least 12 months.”

A follow-up question is used to establish whether respondents have any limiting health problems or disabilities. Up to and including Wave 7, this was only asked of those respondents who identified that they had a long-standing illness or disability:

“Does this / do these health problem(s) or disability(ies) mean that you have substantial difficulties with any of the following areas of your life?”

Twelve areas are listed (full details are in Section 3). If an individual identifies none of these, then the long-standing illness or disability is said to be non-limiting.

From Wave 8 onwards, the follow-up question was changed so that it was asked of all adult respondents, regardless of whether they had reported a long-standing illness or disability. The wording was changed accordingly, as set out in full in Section 3, although the 12 listed areas remain the same.

Economic status of the family

Families are allocated to the first applicable category:

• all adults working – benefit units where all adults regard themselves as working
• some adults working – benefit units with two adults where only one is working
• workless - benefit units where no one is working

Individuals who have retired are counted as workless. Therefore, analysis of economic status based on ‘all individuals’ will include retired people in the ‘workless’ total.

Entry and Exit rates

The standard threshold, 60% of median income Before Housing Costs (BHC) is used when analysing transitions into and out of low income.

For an entry into low income to occur, the individual must be in a household whose income is at least 10% below the low income threshold, while in the previous wave they were in a household whose income was above the relative low income threshold.

For an exit from low income to occur, the individual must be in a household whose income is at least 10% above the low income threshold in a wave, while in the previous wave they were in a household whose income was below the relative low income threshold.

Equivalisation

Income measures used in ID take into account variations in the size and composition of the households in which people live. This process is called equivalisation. Equivalisation reflects the fact that a family of several people needs a higher income than a single individual in order for them to enjoy a comparable standard of living.

Ethnicity

Ethnicity information is combined into five groups for the purposes of analysis:

• White – made up of White: British/ English/ Scottish/ Welsh/ Northern Irish; White: Irish; White: Gypsy or Irish Traveller who are resident in England, Scotland or Wales; and White: any other White background
• Mixed – made up of Mixed: White and Black Caribbean; Mixed: White and Black African; Mixed: White and Asian; and Mixed: Any other mixed background
• Asian – made up of Asian/ Asian British: Indian; Asian/ Asian British: Pakistani; Asian/ Asian British: Bangladeshi; Asian/ Asian British: Chinese; and Asian/ Asian British: Any other Asian background
• Black – made up of Black/ Black British: Caribbean; Black/ Black British: African; and Black/ Black British: Any other Black background
• Other – made up of Other Ethnic Group: Arab; Other Ethnic Group: Gypsy or Irish Traveller resident in Northern Ireland; and Other Ethnic Group: Any other ethnic group

Families/family unit

The terms ‘families’ and ‘family units’ are used interchangeably with benefit units. See Benefit unit definition.

Family type

For some analyses, individuals are classified into family type. Individuals are classified according to the status of the benefit unit in which they live. All individuals in a benefit unit (adults and children) will therefore be given the same classification. The classifications are defined below and individuals are allocated to the first applicable category.

• Pensioner couple - a couple where one or more of the adults are State Pension age or over
• Single male pensioner - single male adult of State Pension age or over
• Single female pensioner - single female adult of State Pension age or over
• Mixed-age couple – one of the couple are above State Pension age and one below
• Couple with children - a non-pensioner couple with dependent children
• Single with children - a non-pensioner single adult with dependent children
• Couple without children - a non-pensioner couple with no dependent children
• Single male without children - a non-pensioner single adult male with no dependent children
• Single female without children - a non-pensioner single adult female with no dependent children

Head of household

The head of household, also known as the household reference person, is defined as the owner or renter of the accommodation in which the household lives. If there are multiple owners or renters, the default is the eldest of them is the Household reference person. The Household reference person may change as the household composition changes.

Household

One person living alone or a group of people (not necessarily related) who either share living accommodation or share one meal a day and who have the address as their only or main residence is defined as a household. A child is classed as living in a household if their household is defined as above. A household will consist of one or more benefit units.

Housing costs

Housing costs are rent or mortgage interest payments, plus service and water charges. Consistent with HBAI, for mortgage-holders only the mortgage interest payments are included as housing costs i.e. capital repayment amounts are excluded.

Income

The Before Housing Costs (BHC) income measure used in ID is weekly net (disposable) equivalised household income. This comprises total income from all sources of all household members including dependants.

Income is adjusted for household size and composition by means of equivalence scales. Incomes are adjusted for inflation so they are in real terms corresponding to the middle January of the latest USoc wave (January 2020 in this case).

Income on ID includes:

• Labour income – usual pay and self-employment earnings. Includes income from second jobs
• State support – tax credits and all state benefits including State Pension and UC.
• Pension income – occupational pensions income
• Investment income – private pensions/annuities, rents received, income from savings and investments
• Private benefit income – includes trade union/friendly society payments, maintenance or alimony and sickness or accident insurance
• Miscellaneous income – educational grants, payments from family members and any other regular payment

Income is net of the following items:

• income tax payments
• National Insurance contributions
• council tax

Income After Housing Costs (AHC) is derived by deducting housing costs from the above BHC income measure.

Income distribution

The spread of incomes across the population.

Low income

Low income is defined using thresholds derived from percentages of median income for the whole population. Individuals are said to be in relative low income if they live in a household with an equivalised income below a percentage of median income BHC or AHC. Relative low income statistics fall if income growth at the lower end of the income distribution is greater than overall income growth.

Mean

Mean equivalised household income of individuals is found by adding up equivalised household incomes for each individual in a population and dividing the result by the number of people.

Median

Median household income divides the population, when ranked by equivalised household income, into two equal-sized groups. Contemporary median income refers to the median income in the survey period being considered.

Pensioner

Pensioners are defined as all those adults above State Pension age (SPa).

For women born on or before 5th April 1950, SPa is 60. For men born on or before this date, SPa is 65. From 6th April 2010, the SPa for women born on or after 6th April 1950 increased gradually between April 2010 and November 2018 to be the same as the SPa for men. The SPa for men remained at 65.

From December 2018, the SPa for both men and women increased, to reach 66 in October 2020. Further increases to bring the SPa to 67 are proposed to be phased in between 2026 and 2028. Read about these changes.

Pensioner couples and Mixed-age couples

In the ID pensioner tables, the “pensioner couple” category includes individuals above SPa in couples where the other partner is below SPa. Similarly, the mixed-age couple category in the Working-age adult tables covers adults below SPa in couples where the other partner is above SPa.

Persistent low income

Individuals are described as being in persistent low income if they live in a household in relative low income (below 60% or 70% of median income) in at least three out of four consecutive annual interviews. One possible measure of persistence of low incomes would be to consider only those individuals whose household income was low in each year of the period in question. However, this would exclude people who experienced slightly higher income for very short periods, but whose long-term living standards were not actually very different from those captured in low income in each year.

This issue is relevant because the income distribution is particularly dense around the 60% and 70% of median income thresholds. In addition, some short periods of recorded high income may be due to measurement error and not reflect any real improvement in living standards.

Qualifications

Highest qualification is derived as follows:

• Degree level and above – University Higher Degree (e.g. MSc, PhD); First degree level qualification including foundation degrees, graduate membership of a professional institute, PGCE
• Higher education diploma – Diploma in higher education; Teaching qualification (excluding PGCE); Nursing or other medical qualification not yet mentioned
• A-level etc – A Level; Welsh Baccalaureate; International Baccalaureate; AS Level; Higher Grade/Advanced Higher (Scotland); Certificate of sixth year studies
• GCSE etc – GCSE/O Level; Standard/Ordinary (O) Grade / Lower (Scotland)
• Other qualifications – CSE; Other school (inc. school leaving exam certificate or matriculation)
• No qualifications

Quintile values

Income values which divide the whole population, when ranked by household income, into five equal-sized groups. This helps to compare different groups of the population.

Quintile groups

Five equal-sized groups - the lowest quintile describes individuals with incomes in the bottom 20% of the income distribution. Quintile is often used as a standard shorthand term for quintile group.

Region and country

Regional classifications are based on the standard statistical geography of the former Government Office Regions: nine in England, and a single region for each of Scotland, Wales and Northern Ireland. These regions are built up of complete counties or unitary authorities. Tables also include statistics for England as a whole. For the definitive Office for National Statistics geography classifications and codes see the ONS Open Geography portal.

Although the USoc sample is large enough to allow some analysis to be performed at a regional level, it should be noted that no adjustment has been made for regional cost of living differences, as the necessary data are not available. In the analysis here it is therefore assumed that there is no difference in the cost of living between regions, although the AHC measure will partly take into account differences in housing costs.

Sampling error

The uncertainty in the estimates which arises from taking a random sample of the household population.

State support

The Government pays money to individuals in order to support them financially under various circumstances. An individual is in receipt of state support if they receive one or more benefits, or are being paid Tax Credits. Most of these benefits are administered by DWP, with the exception of Housing Benefit and Council Tax Reduction which are administered by local authorities, and Child Benefit and Tax Credits which are administered by HM Revenue and Customs. For more information regarding the way in which information on state support is gathered via USoc, please see the Wave 11 questionnaire.

Tax Credits

Working Tax Credits and Child Tax Credits are paid by HM Revenue & Customs. Tax Credits are being phased out, as they are being replaced by Universal Credit.

Tenure

A four-way split is presented:

• Owned outright
• Owned with mortgage
• Social rented – local authority rent or housing association rent
• Private rented – rented from employer, rented privately or other renting

Homes which are part-owned and part-rented are classified as being rented. A small proportion of cases have a tenure classification of ‘other’. These are included in sample sizes but not shown separately in tables.

Working-age adult

Individuals below State Pension age (SPa) who are over 16, not in further education and not classed as a dependent child.