As of 2024, approximately *** million people lived alone in the United Kingdom, an increase of around ****** when compared with the previous year when around **** million people were living by themselves.

Part of a series of tables relating to household expenditure categorised by Classification Of Individual Consumption by Purpose (COICOP). Estimates are drawn from the Living Costs and Food Survey

This dataset provides Census 2021 estimates that classify the Household Reference Persons aged 16 years and over in England and Wales by living arrangements and by age. The estimates are as at Census Day, 21 March 2021.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Coverage

Census 2021 statistics are published for the whole of England and Wales. Data are also available in these geographic types:

• country - for example, Wales
• region - for example, London
• local authority - for example, Cornwall
• health area – for example, Clinical Commissioning Group
• statistical area - for example, MSOA or LSOA

Living arrangements

The “living arrangements” classification combines responses to the question on marital and civil partnership status with information about whether or not a person is living in a couple. This topic is only applicable to people in households. Living arrangements differs from marital and civil partnership status because cohabiting takes priority over other categories. For example, if a person is divorced and cohabiting, then in results for living arrangements they are classified as cohabiting.

Age (C)

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age. Age is classified as follows:

• Aged 24 years and under
• Aged 25 to 34 years
• Aged 35 to 49 years
• Aged 50 to 64 years
• Aged 65 years and over

About 36 percent of homeowners in England were aged 65 and above, which contrasts sharply with younger age groups, particularly those under 35. Young adults between 25 and 35, made up 15 percent of homeowners and had a dramatically lower homeownership rate. The disparity highlights the growing challenges faced by younger generations in entering the property market, a trend that has significant implications for wealth distribution and social mobility. Barriers to homeownership for young adults The path to homeownership has become increasingly difficult for young adults in the UK. A 2023 survey revealed that mortgage affordability was the greatest obstacle to property purchase. This represents a 39 percent increase from 2021, reflecting the impact of rising house prices and mortgage rates. Despite these challenges, one in three young adults still aspire to get on the property ladder as soon as possible, though many have put their plans on hold. The need for additional financial support from family, friends, and lenders has become more prevalent, with one in five young adults acknowledging this necessity. Regional disparities and housing supply The housing market in England faces regional challenges, with North West England and the West Midlands experiencing the largest mismatch between housing supply and demand in 2023. This imbalance is evident in the discrepancy between new homes added to the housing stock and the number of new households formed. London, despite showing signs of housing shortage, has seen the largest difference between homes built and households formed. The construction of new homes has been volatile, with a significant drop in 2020, a rebound in 2021 and a gradual decline until 2024.

Information on farm household income and farm household composition. Source agency: Environment, Food and Rural Affairs Designation: National Statistics Language: English Alternative title: Farm Household Income and Household Composition, England If you require the datasets in a more accessible format, please contact fbs.queries@defra.gsi.gov.uk Background and guidance on the statistics Information on farm household income and farm household composition was collected in the Farm Business Survey (FBS) for England for the first time in 2004/05. Collection of household income data is restricted to the household of the principal farmer from each farm business. For practical reasons, data is not collected for the households of any other farmers and partners. Two-thirds of farm businesses have an input only from the principal farmer’s household (see table 5). However, details of household composition are collected for the households of all farmers and partners in the business, but not employed farm workers. Data on the income of farm households is used in conjunction with other economic information for the agricultural sector (e.g. farm business income) to help inform policy decisions and to help monitor and evaluate current policies relating to agriculture in the United Kingdom by Government. It also informs wider research into the economic performance of the agricultural industry. This release gives the main results from the income and composition of farm households and the off-farm activities of the farmer and their spouse (Including common law partners) sections of the FBS. These sections include information on the household income of the principal farmer’s household, off-farm income sources for the farmer and spouse and incomes of other members of their household and the number of working age and pensionable adults and children in each of the households on the farm (the information on household composition can be found in Appendix B). This release provides the main results from the 2013/14 FBS. The results are presented together with confidence intervals. Survey content and methodology The Farm Business Survey (FBS) is an annual survey providing information on the financial position and physical and economic performance of farm businesses in England. The sample of around 1,900 farm businesses covers all regions of England and all types of farming with the data being collected by face to face interview with the farmer. Results are weighted to represent the whole population of farm businesses that have at least 25 thousand Euros of standard output as recorded in the annual June Survey of Agriculture and Horticulture. In 2013 there were just over 58 thousand farm businesses meeting this criteria. Since 2009/10 a sub-sample of around 1,000 farms in the FBS has taken part in both the additional surveys on the income and composition of farm households and the off-farm activities of the farmer and their spouse. In previous years, the sub-sample had included over 1,600 farms. As such, caution should be taken when comparing to earlier years. The farms that responded to the additional survey on household incomes and off-farm activities of the farmer and spouse had similar characteristics to those farms in the main FBS in terms of farm type and geographical location. However, there is a smaller proportion of very large farms in the additional survey than in the main FBS. Full details of the characteristic of responding farms can be found at Appendix A of the notice. For further information about the Farm Business Survey please see: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey Data analysis The results from the FBS relate to farms which have a standard output of at least 25,000 Euros. Initial weights are applied to the FBS records based on the inverse sampling fraction for each design stratum (farm type by farm size). These weights are then adjusted (calibration weighting) so that they can produce unbiased estimators of a number of different target variables. Completion of the additional survey on household incomes and off-farm activities of the farmer and spouse was voluntary and a sample of around 1,000 farms was achieved. In order to take account of non-response, the results have been reweighted using a method that preserves marginal totals for populations according to farm type and farm size groups. As such, farm population totals for other classifications (e.g. regions) will not be in-line with results using the main FBS weights, nor will any results produced for variables derived from the rest of the FBS (e.g. farm business income). Accuracy and reliability of the results We show 95% confidence intervals against the results. These show the range of values that may apply to the figures. They mean that we are 95% confident that this range contains the true value. They are calculated as the standard errors (se) multiplied by 1.96 to give the 95% confidence interval. The standard errors only give an indication of the sampling error. They do not reflect any other sources of survey errors, such as non-response bias. For the Farm Business Survey, the confidence limits shown are appropriate for comparing groups within the same year only; they should not be used for comparing with previous years since they do not allow for the fact that many of the same farms will have contributed to the Farm Business Survey in both years. Availability of results This release contains headline results for each section. The full set of results can be found at: https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/series/farm-business-survey#publications Defra statistical notices can be viewed on the on the statistics pages of the Defra website at https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs/about/statistics. This site also shows details of future publications, with pre-announced dates. Data Uses Data from the Farm Business Survey (FBS) are provided to the EU as part of the Farm Accountancy Data Network (FADN). The data have been used to help inform policy decisions (e.g. Reform of Pillar 1 and Pillar 2 of Common Agricultural Policy) and to help monitor and evaluate current policies relating to agriculture in England (and the EU). It is also widely used by the industry for benchmarking and informs wider research into the economic performance of the agricultural industry. User engagement As part of our ongoing commitment to compliance with the Code of Practice for Official Statistics http://www.statisticsauthority.gov.uk/assessment/code-of-practice/index.html, we wish to strengthen our engagement with users of these statistics and better understand the use made of them and the types of decisions that they inform. Consequently, we invite users to make themselves known, to advise us of the use they do, or might, make of these statistics, and what their wishes are in terms of engagement. Feedback on this notice and enquiries about these statistics are also welcome. Definitions Household income of the principal farmer Principal farmer’s household income has the following components: (1) The share of farm business income (FBI) (including income from farm diversification) attributable to the principal farmer and their spouse. (2) Principal farmer’s and spouse’s off farm income from employment and self-employment, investment income, pensions and social payments. (3) Income of other household members. The share of farm business income and all employment and self-employment incomes, investment income and pension income are recorded as gross of income tax payments and National Insurance contributions, but after pension contributions. In addition, no deduction is made for council tax. Household A household is defined as a single person or group of people living at the same address as their only or main residence, who either share one meal a day together or share the living accommodation. A household must contain at least one person who received drawings from the farm business or who took a share of the profit from the business. Drawings Drawings represent the monies which the farmer takes from the business for their own personal use. The percentage of total drawings going to each household is collected and is used to calculate the total share of farm business income for the principal farmer’s household. Mean Mean household income of individuals is the ”average”, found by adding up the weighted household incomes for each individual farm in the population for analysis and dividing the result by the corresponding weighted number of farms. In this report average is usually taken to refer to the mean. Percentiles These are the values which divide the population for analysis, when ranked by an output variable (e.g. household income or net worth), into 100 equal-sized groups. E.g. twenty five per cent of the population would have incomes below the 25th percentile. Median Median household income divides the population, when ranked by an output variable, into two equal sized groups. The median of the whole population is the same as the 50th percentile. The term is also used for the midpoint of the subsets of the income distribution Quartiles Quartiles are values which divide the population, when ranked by an output variable, into four equal-sized groups. The lowest quartile is the same as the 25th percentile. The divisions of a population split by quartiles are referred to as quarters in this publication. Quintiles Quintiles are values which divide the population, when ranked by an output variable, into five equal-sized groups. The divisions of a population split by quintiles are referred to as fifths in this publication. Assets Assets include milk and livestock quotas, as well as land, buildings (including the farm house), breeding livestock, and machinery and equipment. For tenanted farmers,

In 2024, 1 was the most often registered answer to a question about the household ownership of cars owned by households in the United Kingdom, with an estimated ************* answers. Contrastingly, 3 Or More was the least often encountered response, with ************ such answers.

Generation of individual and household level population data for 2001 census output areas in the United Kingdom (UK) has been undertaken by the Modelling and Simulation for e-Social Science (MoSeS) node of the UK National Centre for e-Social Science.

The MoSeS project outlined an approach for creating an individual level demographic model, which involved selecting sets of Samples of Census 2001 Anonymised Records (SARs) to represent aggregate Census Area Statistics (CAS) populations for 2001. This initialised population would then be dynamically modelled or simulated over time.

The datasets created by integrating SARS and CAS form this study.

Further information is available from the http://www.esrc.ac.uk/my-esrc/grants/RES-149-25-0034/read" title="Modelling and Simulation for E-Social Sciences">Modelling and Simulation for E-Social Sciences ESRC Award webpage and the MoSeS project webpage.

Users should note that the Principal Investigators did not have access to non-anonymised census data and that the assignment of OAs is based on statistical analysis using published data and the SARS only.

Every ten years since 1801 the nation has set aside one day for the census - a count of all people and households. It is the most complete source of information about the population that we have. The latest census was held on Sunday 21 March 2021.

Every effort is made to include everyone, and that is why the census is so important. It is the only survey which provides a detailed picture of the entire population, and is unique because it covers everyone at the same time and asks the same core questions everywhere. This makes it easy to compare different parts of the country.

The information the census provides allows central and local government, health authorities and many other organisations to target their resources more effectively and to plan housing, education, health and transport services for years to come.

In England and Wales, the census is planned and carried out by the Office for National Statistics. Elsewhere in the UK, responsibility lies with the National Records of Scotland and the Northern Ireland Statistics and Research Agency.

A usual resident is anyone who on Census Day, 21 March 2021 was in the UK and had stayed or intended to stay in the UK for a period of 12 months or more, or had a permanent UK address and was outside the UK and intended to be outside the UK for less than 12 months.

The ONS have three processes for checking and resolving duplicate responses so that the main census data should simply be one record for each person:

1. The ONS resolve duplicates coming in for the same postcode using a process called Resolve Multiple Responses (RMR). For instance, if two people both fill in a form for their whole household, or someone from a household also submits an individual response unknown to the main submission. They have rules for checking they are duplicates, and rules for which to keep.

2. The ONS also do an over coverage check on a sample basis for duplicates across the rest of the country, and then factor the findings into their coverage estimation calculations. This sampling focuses on the types of population which are more likely to be duplicated (people who have indicated they have a second residence on the census, students aged 18-25, armed forces personnel, children, adults enumerated at a communal establishment, etc.) but also samples from the remaining population.

3. The ONS ask parents to fill in basic demographic information for any children who are away studying, and when they get to the question on their term-time address, if they answer that the term-time address is elsewhere, we then use that to filter those out-of-term students out of the main database. Then when that student does respond actually at their term-time address, they only include them there.

Please note: variables RELAT06, RELAT11, RELAT16, RELAT21, RELAT26, GENDER_IDENTITY are not available in the data.

The Research Accreditation Panel provides oversight of the framework that is used to accredit research projects, researchers and processing environments under the Digital Economy Act 2017 (DEA). Researchers are advised to liaise with SAIL support teams to understand the requirements and timelines involved with submitting a research project to the Research Accreditation Panel. https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/research-accreditation-panel/

In 2024/25, approximately 2.9 million emergency food parcels were distributed from Trussell Trust food banks in the United Kingdom, compared with 3.1 million in 2023/24. There has been a steep rise in food bank usage in the UK, with a threefold increase in the number of parcels distributed in 2023/24, compared with 2014/15. As of the most recent year, there were over 1,700 Trussell Trust food bank distribution centers in the UK, compared with 1,500 in 2018/19. Cost of Living crisis continues Since late 2021, UK households have had to grapple with a steep rise in the cost of living. This crisis appeared to have peaked in 2022, when around 90 percent of households were reporting monthly increases to their living costs, and inflation reached a 40-year high of 11.1 percent in October 2022. Although inflation subsequently came down and wages began to outpace inflation from 2023 onward, prices remain far higher than before the crisis began. Furthermore, the first half of 2025 has seen an uptick in inflation, which, although expected to subside towards the end of the year, has piled further misery on struggling UK households. Growing discontent with political mainstream After one year in power, the current Labour government is almost as unpopular as the Conservative government they replaced, which suffered one of their worst results in their history at the last election. To deal with the UK's precarious public finances without significant tax rises, Labour have attempted to make reforms to welfare, such as cutting the winter fuel allowances for all but the poorest pensioners. This cut in particular was so unpopular that Labour reinstated it for most pensioners, with further attempts at welfare reform also hitting a roadblock. These events, along with a stuttering economy, have seen Labour fall significantly at the polls, especially at the expense of the right-wing Reform Party, who have generally led the polls since the start of the year.

This dataset comprises syntax, to run in SPSS software, on the Census 2001 Household Controlled Access Microdata Sample (CAMS) Sample of Anonymised Records. The CAMS data are not included here, and prospective users will need to negotiate access to CAMS separately; further information may be found on the Office for National Statistics' Applying to use the controlled access microdata sample (CAMS) web page.

The syntax was developed as part of the research project 'Links between internal migration, commuting and inter-household relationships'. Further information about the project may be found at the ESRC award page.

The syntax runs on CAMS data to create derived variables denoting persons in households which contained one or more migrants. For each migrant, Boolean variables may be created indicating, for example, whether that person is related to anyone else in the household, is the spouse/partner of anyone else in the household, is the sibling of anyone else in the household, and so on. Separate sets of these variables may be derived for relationships to all persons in the household, relationships to other migrants, and relationships to other migrants in the same 'moving group'. Within the original project, the derived variables were constructed for migrants only (about 11% of all CAMS records).

Retirement homes depend on self-funders or local council funding that covers the retirement needs of people who satisfy financial assessment means tests. Tightening government budgets have meant publicly funded fees have failed to cover providers’ operating costs, forcing retirement homes to cross-subsidise local authority beds with fees from self-funded residents. Revenue is anticipated to climb at a compound annual rate of 3.2% over the five years through 2025-26 to £12.0 billion, and it’s set to rise by 0.8% in 2025-26. Much of this is down to care homes' fees mounting to cover costs and being paid for by self-funders, who are saw their disposable income tick upwards in 2024-25, lifting industry revenue. Although the ageing population supports revenue growth, constrained government spending, delayed reform changes and rising costs (particularly for labour) have put pressure on profit. Demand for beds far outstrips the supply, which is driving investment into the industry. Mounting demand from residents who had delayed joining a retirement home during the pandemic contributed to strong growth in revenue in 2021-22. Care homes' fees then edged up in the three years through 2024-25 to cope with enhanced staffing costs, mounting mortgage payments and heightened energy costs – these were all the result of high inflation. This has been to the dismay of many retirees whose purse strings have tightened thanks to the cost-of-living crisis, making hit harder for them to afford to move into retirement homes. Higher fees have therefore dampened some of demand for beds, but they’ve also increased the sales value of care homes, supporting revenue. Retirement home revenue is expected to rise at a compound annual rate of 1.5% over the five years through 2030-31 to £12.9 billion, driven by an ageing population. By 2036, the number of people aged 85 and over will hit 2.6 million, representing 3.5% of the UK population, according to the Office for National Statistics. However, medical advances will make an older population healthier, allowing people to live independently for longer, dampening growth. Sustainable initiatives will be incorporated into the designs of new homes, helping reduce operational costs for retirement homes and supporting profitability. As real disposable income rises, there will be greater demand for luxury retirement homes, driving sales value and supporting industry revenue growth.

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute for Statistics (RSIS), the Federal Office of Statistics (FOS) and the Agency for Statistics of Bosnia and Herzegovina (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and is also providing funding for a further two years of data collection for a panel survey, to be known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. are responsible for the management of the HSPS with technical advice and support being provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The aim of the panel survey is to provide longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and again in 2003. The LSMS constitutes wave 1 of the panel survey so there will be three years of panel data available for analysis under current funding plans. For the purposes of this document we are using the following convention to describe the different rounds of the panel survey: Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003

The panel data will allow the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey.

The panel data will provide information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation, Republic

Analysis unit

Households, Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The panel survey sample is made up of over 3,000 households drawn from the Living Standards Measurement Survey (LSMS) conducted by the World Bank in co-operation with the SIs in 2002. Approximately half the households interviewed on the LSMS were selected and carried forward into the panel survey. These households were re-interviewed in 2003 and will be interviewed for a third time in September 2004.

Sampling Frame

The 5,400 households interviewed on LSMS formed the sampling frame for the panel survey. The aim was to achieve interviews with approximately half of these (2,700) at wave 2 (1,500 in FBiH and 1,200 in RS). A response rate of 90% was anticipated (as the sample is based on households that have already co-operated with LSMS) and therefore the selected sample consisted of 3,000 households. Unlike the LSMS, the HSPS does not have a replacement element to the sample, only the original 3,000 issued addresses. This approach was new to the Supervisors and Interviewers and special training was given on how to keep non-response to a minimum.

The LSMS Sample

The LSMS sample design process experienced some difficulties which resulted in a sample with a disproportionately high number of households being selected in urban areas. Work by Peter Lynn from ISER identified the source of this problem by establishing the selection probabilities at each stage of the LSMS sampling process. Essentially, the procedures used for selecting households within municipalities would have been appropriate had municipalities been selected with equal probabilities. But in fact municipalities had been selected with probability proportional to size, and using different overall sampling fractions in each of three strata. The details are documented in a memo by Peter Lynn dated 25-3-2002. Consequently, household selection probabilities varied considerably across municipalities.

Compensating for the LSMS sample imbalance

Having established the selection probability of every LSMS household, it became possible to derive design-based weights that should provide unbiased estimates for LSMS. However, the considerable variability in these weights means that the variance of estimates (and hence standard errors and confidence intervals) is greatly increased. For the HSPS, there was an opportunity to reduce the variability in weights by constructing the subsample in a way that minimised the variability in overall selection probabilities. The overall selection probability for each household would be the product of two probabilities - the probability of being selected for LSMS, and the probability of being selected for HSPS, conditional upon having been selected for LSMS, i.e. P(HSPS) = P(LSMS) * P(HSPS)/(LSMS)

Ideally, then, we would have set the values of P(HSPS)/(LSMS) to be inversely proportional to P(LSMS). This would have resulted in each HSPS household having the same overall selection probability, P(HSPS), so that there would no longer be an increase in the variance of estimates due to variability in selection probabilities. However, this was not possible due to the very considerable variation in P(LSMS) and the limited flexibility provided by a large overall sampling fraction for HSPS (3,000 out of 5,400).

The best that could be done was to minimise the variability in sampling fractions by retaining all the LSMS households in the (mainly rural or mixed urban/rural) municipalities where LSMS household selection probabilities had been lowest and sub-sampling only in the municipalities where LSMS selection probabilities had been much higher. In 16 of the 25 LSMS municipalities, all households were retained for HSPS. In the other 9 municipalities, households were sub-sampled, with sampling fractions ranging from 83% in Travnik to just 25% in Banja Luka and Tuzla.

To select the required number of households within each municipality, every group of enumeration districts (GND) was retained from LSMS. The sub-sampling took place within the GNDs. Households were sub-sampled using systematic random sampling, with a random start and fixed interval. For example, in Novo Sarajevo, where the sampling fraction was 1 in 2, 6 households were selected out of the 12 LSMS households in each GND by selecting alternate households. In Prijedor, where the fraction was 1 in 3, 4 out of 12 were selected by taking every third LSMS household. And so on.

The total selected sample for the HSPS consists of 3,007 households (1681 in the FBIH and 1326 in the RS).

The overall design weight for the HSPS sample will be the product of the LSMS weight for the household and this extra design weight (which will of course tend to increase the size of the smallest LSMS weights).

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as used on the LSMS (see Supervisor Instructions, Annex A). While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at wave 1 (LSMS) have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules and the definition of 'out-of-scope'

The panel design means that sample members who move from their previous wave address at either wave 2 or 3 must be traced and followed to their new address for interview. The LSMS sample was clustered and over the two waves of the panel some de-clustering will occur as people move. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own.

Following rules

All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This means that a four person household at Wave 1 could generate three additional households at wave 2 if three members, either OSMs or

Abstract

In 2001, the World Bank in co-operation with the Republika Srpska Institute for Statistics (RSIS), the Federal Office of Statistics (FOS) and the Agency for Statistics of Bosnia and Herzegovina (BHAS), carried out a Living Standards Measurement Survey (LSMS).

The Living Standard Measurement Survey LSMS, in addition to collecting the information necessary to obtain a comprehensive as possible measure of the basic dimensions of household living standards, has three basic objectives, as follows: 1. To provide the public sector, government, the business community, scientific institutions, international donor organizations and social organizations with information on different indicators of the population's living conditions, as well as on available resources for satisfying basic needs. 2. To provide information for the evaluation of the results of different forms of government policy and programs developed with the aim to improve the population's living standard. The survey will enable the analysis of the relations between and among different aspects of living standards (housing, consumption, education, health, labor) at a given time, as well as within a household. 3. To provide key contributions for development of government's Poverty Reduction Strategy Paper, based on analyzed data.

The Department for International Development, UK (DFID) contributed funding to the LSMS and is also providing funding for a further two years of data collection for a panel survey, to be known as the Household Survey Panel Series (HSPS). Birks Sinclair & Associates Ltd. are responsible for the management of the HSPS with technical advice and support being provided by the Institute for Social and Economic Research (ISER), University of Essex, UK.

The aim of the panel survey is to provide longitudinal data through re-interviewing approximately half the LSMS respondents for two years following the LSMS, in the autumn of 2002 and again in 2003. The LSMS constitutes wave 1 of the panel survey so there will be three years of panel data available for analysis under current funding plans. For the purposes of this document we are using the following convention to describe the different rounds of the panel survey: Wave 1 LSMS conducted in 2001 forms the baseline survey for the panel Wave 2 Second interview of 50% of LSMS respondents in Autumn/Winter 2002 Wave 3 Third interview with sub-sample respondents in Autumn/Winter 2003

The panel data will allow the analysis of key transitions and events over this period such as labour market or geographical mobility and observe the consequent outcomes for the well-being of individuals and households in the survey.

The panel data will provide information on income and labour market dynamics within FBiH and RS. A key policy area is developing strategies for the reduction of poverty within FBiH and RS. The panel will provide information on the extent to which continuous poverty is experienced by different types of households and individuals over the three year period. And most importantly, the co-variates associated with moves into and out of poverty and the relative risks of poverty for different people can be assessed. As such, the panel aims to provide data, which will inform the policy debates within FBiH and RS at a time of social reform and rapid change.

Geographic coverage

National coverage. Domains: Urban/rural/mixed; Federation, Republic

Analysis unit

• Households
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The panel survey sample is made up of over 3,000 households drawn from the Living Standards Measurement Survey (LSMS) conducted by the World Bank in co-operation with the SIs in 2002. Approximately half the households interviewed on the LSMS were selected and carried forward into the panel survey. These households were re-interviewed in 2003 and will be interviewed for a third time in September 2004.

Sampling Frame

The 5,400 households interviewed on LSMS formed the sampling frame for the panel survey. The aim was to achieve interviews with approximately half of these (2,700) at wave 2 (1,500 in FBiH and 1,200 in RS). A response rate of 90% was anticipated (as the sample is based on households that have already co-operated with LSMS) and therefore the selected sample consisted of 3,000 households. Unlike the LSMS, the HSPS does not have a replacement element to the sample, only the original 3,000 issued addresses. This approach was new to the Supervisors and Interviewers and special training was given on how to keep non-response to a minimum.

The LSMS Sample

The LSMS sample design process experienced some difficulties which resulted in a sample with a disproportionately high number of households being selected in urban areas. Work by Peter Lynn from ISER identified the source of this problem by establishing the selection probabilities at each stage of the LSMS sampling process. Essentially, the procedures used for selecting households within municipalities would have been appropriate had municipalities been selected with equal probabilities. But in fact municipalities had been selected with probability proportional to size, and using different overall sampling fractions in each of three strata. The details are documented in a memo by Peter Lynn dated 25-3-2002. Consequently, household selection probabilities varied considerably across municipalities.

Compensating for the LSMS sample imbalance

Having established the selection probability of every LSMS household, it became possible to derive design-based weights that should provide unbiased estimates for LSMS. However, the considerable variability in these weights means that the variance of estimates (and hence standard errors and confidence intervals) is greatly increased. For the HSPS, there was an opportunity to reduce the variability in weights by constructing the subsample in a way that minimised the variability in overall selection probabilities. The overall selection probability for each household would be the product of two probabilities - the probability of being selected for LSMS, and the probability of being selected for HSPS, conditional upon having been selected for LSMS, i.e. P(HSPS) = P(LSMS) * P(HSPS)/(LSMS)

Ideally, then, we would have set the values of P(HSPS)/(LSMS) to be inversely proportional to P(LSMS). This would have resulted in each HSPS household having the same overall selection probability, P(HSPS), so that there would no longer be an increase in the variance of estimates due to variability in selection probabilities. However, this was not possible due to the very considerable variation in P(LSMS) and the limited flexibility provided by a large overall sampling fraction for HSPS (3,000 out of 5,400).

The best that could be done was to minimise the variability in sampling fractions by retaining all the LSMS households in the (mainly rural or mixed urban/rural) municipalities where LSMS household selection probabilities had been lowest and sub-sampling only in the municipalities where LSMS selection probabilities had been much higher. In 16 of the 25 LSMS municipalities, all households were retained for HSPS. In the other 9 municipalities, households were sub-sampled, with sampling fractions ranging from 83% in Travnik to just 25% in Banja Luka and Tuzla.

To select the required number of households within each municipality, every group of enumeration districts (GND) was retained from LSMS. The sub-sampling took place within the GNDs. Households were sub-sampled using systematic random sampling, with a random start and fixed interval. For example, in Novo Sarajevo, where the sampling fraction was 1 in 2, 6 households were selected out of the 12 LSMS households in each GND by selecting alternate households. In Prijedor, where the fraction was 1 in 3, 4 out of 12 were selected by taking every third LSMS household. And so on.

The total selected sample for the HSPS consists of 3,007 households (1681 in the FBIH and 1326 in the RS).

The overall design weight for the HSPS sample will be the product of the LSMS weight for the household and this extra design weight (which will of course tend to increase the size of the smallest LSMS weights).

Panel design

Eligibility for inclusion

The household and household membership definitions are the same standard definitions as used on the LSMS (see Supervisor Instructions, Annex A). While the sample membership status and eligibility for interview are as follows: i) All members of households interviewed at wave 1 (LSMS) have been designated as original sample members (OSMs). OSMs include children within households even if they are too young for interview. ii) Any new members joining a household containing at least one OSM, are eligible for inclusion and are designated as new sample members (NSMs). iii) At each wave, all OSMs and NSMs are eligible for inclusion, apart from those who move outof-scope (see discussion below). iv) All household members aged 15 or over are eligible for interview, including OSMs and NSMs.

Following rules and the definition of 'out-of-scope'

The panel design means that sample members who move from their previous wave address at either wave 2 or 3 must be traced and followed to their new address for interview. The LSMS sample was clustered and over the two waves of the panel some de-clustering will occur as people move. In some cases the whole household will move together but in others an individual member may move away from their previous wave household and form a new split-off household of their own.

Following rules

All sample members, OSMs and NSMs, are followed at each wave and an interview attempted. This means that a four person household at Wave 1 could generate

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

The Opinions and Lifestyle Survey (OPN) is an omnibus survey collecting data on a range of subjects commissioned by both internal Office for National Statistics (ONS) and external clients (limited to; other government departments, charities, non-profit organisations and academia). Data is collected from 1 adult selected from each sampled private household. Personal data include person, family, address, household, income, education plus responses and opinions on a variety of subjects within commissioned modules. The dataset includes a standard set of demographic variables and a single commissioned module.

In March 2020, the OPN was adapted to become a weekly survey used to collect data on the impact of the coronavirus pandemic on day-to-day life in Great Britain. From 25 August 2021, as COVID-19 restrictions began to be lifted across Great Britain, the OPN moved to a fortnightly data collection with the sample size at around 5,000 households in each period to help ensure the survey remains sustainable.

Prior to the changes in frequency to the OPN survey during the coronavirus pandemic, there had been on-going improvements to the OPN. In recent years, work has been undertaken to change the design of the OPN from a face-to-face survey to a mixed mode design (online first with telephone follow-up). Mixed mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers.

The questionnaire collects timely data for research and policy analysis evaluation into the impact that the coronavirus pandemic has had on individuals and households in Great Britain.