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The survey aimed to gather data on the impact of the COVID19 outbreak on the food support providers active in Greater Manchester. The lockdown created organizational hurdles to many services providing food to the most vulnerable. The survey explored more in depth the obstacles, the needs and the prospects of 55 organizations that were on the frontline in the first months of the crisis.

In the United Kingdom food banks are increasingly required to alleviate hunger and food insecurity. In Greater Manchester (GM) alone, the GM Poverty Alliance mapped 171 emergency food providers. While the renewed interest of social scientists in the topic has produced an abundance of scientific literature, there remains a lack of knowledge on the webs of influence, support, conflict and interdependence between families experiencing food poverty and the emergency food providers. Project HUNG, by embracing a relational approach, focuses on the space of relations occupied by actors and institutions engaged with one another. Thereby, it proposes a relational object of analysis: not food poverty or food banks per se, but rather the interactions and transactions involved in the process of charitable supply and food demand. The project, based on the GM metropolitan county, makes use of quantitative analysis and ethnography of the everyday life to throw light on the "hunger bonds" connecting emergency providers and their users. On the one side, by gathering original survey data on food banks and their users, it provides a descriptive analysis on the determinants of food bank use through a dataset suitable for multilevel modelling (individuals nested in food banks). On the other side, it offers an in-depth ethnography of the daily life of a small sample of families that frequently rely on food banks by shadowing their meal choices for a prolonged period of time. By doing so, HUNG creates twofold added-value for the research community and for policy makers. Scholars nterested in food inequalities will have access to a ethodological toolkit, that could be used to extend research in other metropolitan domains. Simultaneously, by describing in detail the determinants of food bank use, it will improve the capability of agencies fighting food poverty to influence public policies to end food poverty.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from http://ukdataservice.ac.uk/media/178323/secure_frs_application_guidance.pdf" style="background-color: rgb(255, 255, 255);">Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

FRS 2022-23

The impact of the coronavirus (COVID-19) pandemic on the FRS 2022-23 survey was much reduced when compared with the two previous survey years. Throughout the year, there was a gradual return to pre-pandemic fieldwork practices, with the majority of interviews being conducted in face-to-face mode. The achieved sample was just over 25,000 households. Users are advised to consult the FRS 2022-23 Background Information and Methodology document for detailed information on changes, developments and issues related to the 2022-23 FRS data set and publication. Alongside the usual topics covered, the 2022-2023 FRS also includes variables for Cost of Living support, including those on certain state benefits; energy bill support; and Council Tax support. See documentation for further details.

FRS 2021-22 and 2020-21 and the coronavirus (COVID-19) pandemic

The coronavirus (COVID-19) pandemic has impacted the FRS 2021-22 and 2020-21 data collection in the following ways:

• In 2020-21, fieldwork operations for the FRS were rapidly changed in response to the coronavirus (COVID-19) pandemic and the introduction of national lockdown restrictions. The established face-to-face interviewing approach employed on the FRS was suspended and replaced with telephone interviewing for the whole of the 2020-21 survey year.

• This change impacted both the size and composition of the achieved sample. This shift in mode of interview has been accompanied by a substantial reduction in the number of interviews achieved: just over 10,000 interviews were achieved this year, compared with 19,000 to 20,000 in a typical FRS year. While we made every effort to address additional biases identified (e.g. by altering our weighting regime), some residual bias remains. Please see the FRS 2020-21 Background Information and Methodology document for more information.
• The FRS team have published a technical report for the 2020-21 survey, which provides a full assessment of the impact of the pandemic on the statistics. In line with the Statistics Code of Practice, this is designed to assist users with interpreting the data and to aid transparency over decisions and data quality issues.

• In 2021-22, the interview mode was largely telephone, with partial return to face-to-face interviews towards end of survey year. The achieved sample was over 16,000 households. This is a return towards the number expected in a normal survey year (around 20,000 households).
• In both survey years, there remain areas where users are advised to exercise caution when making comparisons to other survey years. More details on how the results for the 2020 to 2021 and 2021-22 survey years were affected by the coronavirus (COVID-19) pandemic can be found in the FRS 2020 to 2021 Background Information and Methodology and FRS 2021 to 2022 Background Information and Methodology.

The FRS team are seeking users' feedback on the 2020-21 and 2021-22 FRS. Given the breadth of groups covered by the FRS data, it has not been possible for DWP statisticians to assess or validate every breakdown which is of interest to external researchers and users. Therefore, the FRS team are inviting users to let them know of any insights you may have relating to data quality or trends when analysing these data for your area of interest. Please send any feedback directly to the FRS Team Inbox: team.frs@dwp.gov.uk

Latest edition information

For the second edition (May 2025), the data were redeposited. The following changes have been made:

• An ONS-delivered fix to the highest level of qualification (EDUCQUAL) which for several adults had been erroneously recorded.
• For ESA (benefit 16 on the BENEFITS table) the associated VAR3 has now been populated using ESA admin data, to show whether cases are Support Group etc.
• For Pension Credit recipients (benefit 4 on the BENEFITS table) adding the low-income benefits and tax credits Cost of Living Payment as benefit 124; with its flag CLPAYIRB set on the ADULT table.

Abstract copyright UK Data Service and data collection copyright owner.

The Family Resources Survey (FRS) has been running continuously since 1992 to meet the information needs of the Department for Work and Pensions (DWP). It is almost wholly funded by DWP.

The FRS collects information from a large, and representative sample of private households in the United Kingdom (prior to 2002, it covered Great Britain only). The interview year runs from April to March.

The focus of the survey is on income, and how much comes from the many possible sources (such as employee earnings, self-employed earnings or profits from businesses, and dividends; individual pensions; state benefits, including Universal Credit and the State Pension; and other sources such as savings and investments). Specific items of expenditure, such as rent or mortgage, Council Tax and water bills, are also covered.

Many other topics are covered and the dataset has a very wide range of personal characteristics, at the adult or child, family and then household levels. These include education, caring, childcare and disability. The dataset also captures material deprivation, household food security and (new for 2021/22) household food bank usage.

The FRS is a national statistic whose results are published on the gov.uk website. It is also possible to create your own tables from FRS data, using DWP’s Stat Xplore tool. Further information can be found on the gov.uk Family Resources Survey webpage.

Secure Access FRS data
In addition to the standard End User Licence (EUL) version, Secure Access datasets, containing unrounded data and additional variables, are also available for FRS from 2005/06 onwards - see SN 9256. Prospective users of the Secure Access version of the FRS will need to fulfil additional requirements beyond those associated with the EUL datasets. Full details of the application requirements are available from Guidance on applying for the Family Resources Survey: Secure Access.

FRS, HBAI and PI
The FRS underpins the related Households Below Average Income (HBAI) dataset, which focuses on poverty in the UK, and the related Pensioners' Incomes (PI) dataset. The EUL versions of HBAI and PI are held under SNs 5828 and 8503, respectively. The Secure Access versions are held under SN 7196 and 9257 (see above).

The FRS aims to:

• support the monitoring of the social security programme
• support the costing and modelling of changes to National Insurance contributions and social security benefits
• provide better information for the forecasting of benefit expenditure

From April 2002, the FRS was extended to include Northern Ireland.

In August 2019, at the depositor's request, the Pensioners' Income (PI) dataset (pianon) previously held with the FRS was moved to a separate PI series study, SN 8503.

Main Topics:

Household characteristics (composition, tenure type); tenure and housing costs including Council Tax, mortgages, insurance, water and sewage rates; school milk and meals; educational grants and loans; children in education; informal care (given and received); childcare; occupation and employment; health restrictions on work; children's health; wage details; self-employed earnings; personal and occupational pension schemes; income and benefit receipt; income from pensions and trusts, royalties and allowances, maintenance and other sources; income tax payments and refunds; National Insurance contributions; earnings from odd jobs; children's earnings; interest and dividends; investments; National Savings products; assets; dental and medical care.

Standard Measures
Standard Occupational Classification

United Kingdom UK: Imports: % of Goods Imports: Food data was reported at 10.151 % in 2017. This records a decrease from the previous number of 10.199 % for 2016. United Kingdom UK: Imports: % of Goods Imports: Food data is updated yearly, averaging 10.828 % from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 37.108 % in 1962 and a record low of 7.837 % in 2000. United Kingdom UK: Imports: % of Goods Imports: Food data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Imports. Food comprises the commodities in SITC sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts, and oil kernels).; ; World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division.; Weighted average; Merchandise import shares may not sum to 100 percent because of unclassified trade.

United Kingdom UK: Exports: % of Goods Exports: Food data was reported at 6.868 % in 2017. This records a decrease from the previous number of 7.103 % for 2016. United Kingdom UK: Exports: % of Goods Exports: Food data is updated yearly, averaging 6.714 % from Dec 1962 (Median) to 2017, with 56 observations. The data reached an all-time high of 8.128 % in 1992 and a record low of 4.649 % in 2006. United Kingdom UK: Exports: % of Goods Exports: Food data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United Kingdom – Table UK.World Bank.WDI: Exports. Food comprises the commodities in SITC sections 0 (food and live animals), 1 (beverages and tobacco), and 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts, and oil kernels).; ; World Bank staff estimates through the WITS platform from the Comtrade database maintained by the United Nations Statistics Division.; Weighted average; Merchandise export shares may not sum to 100 percent because of unclassified trade.

This dataset supports the publication: AUTHORS: Yue Zhang, Sigrid Kusch-Brandt, Sonia Heaven and Charles J. Banks TITLE: Effect of Pasteurisation on Methane Yield from Food Waste and other Substrates in Anaerobic Digestion JOURNAL: Processes DOI: https://doi.org/10.3390/pr8111351

Good Manufacturing Practice Inspections Team (GMPIT) contribution to these assessments

The value of exotic wheat genetic resources for accelerating grain yield gains is largely unproven and unrealized. We used next-generation sequencing, together with multi-environment phenotyping, to study the contribution of exotic genomes to 984 three-way-cross-derived (exotic/elite1//elite2) pre-breeding lines (PBLs). Genomic characterization of these lines with haplotype map-based and SNP marker approaches revealed exotic specific imprints of 16.1 to 25.1%, which compares to theoretical expectation of 25%. A rare and favorable haplotype (GT) with 0.4% frequency in gene bank identified on chromosome 6D minimized grain yield (GY) loss under heat stress without GY penalty under irrigated conditions. More specifically, the ‘T’ allele of the haplotype GT originated in Aegilops tauschii and was absent in all elite lines used in study. In silico analysis of the SNP showed hits with a candidate gene coding for isoflavone reductase IRL-like protein in Ae. tauschii. Rare haplotypes were also identified on chromosomes 1A, 6A and 2B effective against abiotic/biotic stresses. Results demonstrate positive contributions of exotic germplasm to PBLs derived from crosses of exotics with CIMMYT’s best elite lines. This is a major impact-oriented pre-breeding effort at CIMMYT, resulting in large-scale development of PBLs for deployment in breeding programs addressing food security under climate change scenarios.