Abstract copyright UK Data Service and data collection copyright owner. The COVID-19 pandemic has represented the most significant public health challenge in a century, costing tens of thousands of people in the UK their lives. The UK and devolved governments have intervened in people’s personal lives to a degree unprecedented in peace time. The UK government has also presided over a dramatic increase in public spending and borrowing both to ensure that vital public services, including the health service, can cope with the disease and to mitigate the impact of the pandemic on the labour market and the economy more generally. Previous research on pandemics, infectious disease and recession suggests that COVID-19 could have a significant impact on the public’s public policy preferences - and thus the environment in which policymakers will have to address the pandemic’s consequences. Unsurprisingly, there has been considerable speculation about the impact that this dramatic shock to people’s lives and livelihoods will have on attitudes, behaviour and public policy. This project looks at whether key political attitudes and values have changed following the pandemic. In particular, it assesses whether or not the experience has changed attitudes towards: (i) the role of government in managing the economy, in providing welfare and in addressing inequality, (ii) the relative importance of individual civil liberties versus adherence to collective social codes, and (iii) the globalisation process, including most notably immigration.

In September 2024, the global PMI amounted to 47.5 for new export orders and 48.8 for manufacturing. The manufacturing PMI was at its lowest point in August 2020. It decreased over the last months of 2022 after the effects of the Russia-Ukraine war and rising inflation hit the world economy, and remained around 50 since.

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Opinions and Lifestyle Survey (COVID-19 module), 3 to 14 November 2021

Given the outbreak of the coronavirus, SARS-CoV-2 (COVID-19), pandemic during March 2020, lockdown measures taken by governments have forced many families, especially those who have children, to re-arrange domestic and market work division. In this study, I investigate the factors associated with partnered and employed individuals’ involvement with housework during the COVID-19 lockdown in the United Kingdom. Drawing evidence from the first wave of the Covid-19 Survey from the Five National Longitudinal Studies dataset with using OLS regressions, this study found that daily working hours, socioeconomic status, and partner’s key worker status are important indicators of daily time spent on housework. Furthermore, interaction analysis showed that women living with a key worker partner not only did more housework than women whose partner was working in a regular job, but they also did more housework than men living with a key worker partner during the lockdown. Policy implications of regulating maximum daily working hours and key worker status are discussed in the context of re-arranging paid and unpaid work between couples during the first lockdown in the United Kingdom.

Citation: Sönmez, I ̇brahim. 2021. A Missed Opportunity for Men? Partnered and Employed Individuals’ Involvement with Housework during the COVID-19 Lockdown in the UK. SocialSciences10: 135. https:// doi.org/10.3390/socsci10040135

Time Use Survey data show changes in how people spent their time during coronavirus (COVID-19) restrictions in March and April 2020, September to October 2020 and March 2021, as well as before the pandemic. It also includes Opinions and Lifestyle Survey data on behaviours following vaccination in Great Britain from 19 May to 13 June 2021.

Background: COVID-19 lockdowns have reduced opportunities for physical activity (PA) and encouraged more sedentary lifestyles. A concomitant of sedentariness is compromised mental health. We investigated the effects of COVID-19 lockdown on PA, sedentary behavior, and mental health across four Western nations (USA, UK, France, and Australia).Methods: An online survey was administered in the second quarter of 2020 (N = 2,541). We measured planned and unplanned dimensions of PA using the Brunel Lifestyle Physical Activity Questionnaire and mental health using the 12-item General Health Questionnaire. Steps per day were recorded only from participants who used an electronic device for this purpose, and sedentary behavior was reported in hours per day (sitting and screen time). Results: In the USA and Australia samples, there was a significant decline in planned PA from pre- to during lockdown. Among young adults, Australians exhibited the lowest planned PA scores, while in middle-aged groups, the UK recorded the highest. Young adults exhibited the largest reduction in unplanned PA. Across nations, there was a reduction of ~2000 steps per day. Large increases in sedentary behavior emerged during lockdown, which were most acute in young adults. Lockdown was associated with a decline in mental health that was more pronounced in women.Conclusions: The findings illustrate the deleterious effects of lockdown on PA, sedentary behavior, and mental health across four Western nations. Australian young and lower middle-aged adults appeared to fare particularly badly in terms of planned PA. The reduction in steps per day is equivalent to the non-expenditure of ~100 kcal. Declines in mental health show how harmful lockdowns can be for women in particular.

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).

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...

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 2020-21 and the coronavirus (COVID-19) pandemic

The coronavirus (COVID-19) pandemic affected the FRS 2020-21 in the following ways:

• 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. It is also recognised that older, more affluent participants were over-sampled. The achieved sample was particularly small for April, and was more unbalanced across the year, with a total of 4,000 households representing the first 6 months of the survey 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 https://doc.ukdataservice.ac.uk/doc/8948/mrdoc/pdf/8948_frs_2020-21_background_information_methodology.pdf">Background Information and Methodology document for more information.
  
  
• The FRS team have published a https://doc.ukdataservice.ac.uk/doc/8948/mrdoc/pdf/8948_frs_2020-21_technical_user_guide.pdf">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.

Recreational sea angling is an important recreational activity in the United Kingdom with around 1.6% of adults participating and a total economic impact of around £1.5 billion each year. There are positive impacts of angling on physical health and mental well-being. The COVID-19 pandemic resulted in several national lockdowns in the UK, which along with additional local restrictions and personal circumstances due to the pandemic, have impacted people’s ability to fish. Angling was not allowed in the UK for some of the first lockdown (March to May 2020), and further restrictions were implemented subsequently that varied between the countries and regions. The impact of COVID-19 on the participation, effort, physical activity, and well-being of UK sea anglers remains unknown. A panel of UK sea anglers, which record their activity and catches as part of the Sea Angling Diary Project, were surveyed to assess changes in sea angling participation, physical activity, mental well-being, and expenditure between 2019 and 2020. We compared the sea angling effort and catches of the diary panel between 2019 and 2020. We found reduced sea angling effort in the panel, including sessions and catches, between 2019 and 2020, with the largest impact being in April 2020. We found that there was a significant reduction in expenditure during April 2020 with 64% of respondents spending less on sea angling than in a typical April. In total, 67% of respondents reported reduced happiness and 45% were less active due to sea angling restrictions. Using a general linear model, we found that even though anglers said that being able to go fishing has resulted in high World Health Organization Five Well-being Index scores, other factors also had significant effects. These included: age; physical and mental health status; angling activity; travel to fish during COVID-19; and whether they fished in July 2020. Of those who responded, 66% classified themselves as at either high or moderate risk to COVID-19. This work has shown that COVID-19 has negatively affected marine recreational fisheries in the UK, and not being able to go sea angling has negatively impacted participation, effort, physical activity and well-being.

Abstract copyright UK Data Service and data collection copyright owner. The UCL COVID-19 Social Study at University College London (UCL) was launched on 21 March 2020. Led by Dr Daisy Fancourt and Professor Andrew Steptoe from the Department of Behavioural Science and Health, the team designed the study to track in real-time the psychological and social impact of the virus across the UK. The study quickly became the largest in the country, growing to over 70,000 participants and providing rare and privileged insight into the effects of the pandemic on people’s daily lives. Through our participants’ remarkable two-year commitment to the study, 1.2 million surveys were collected over 105 weeks, and over 100 scientific papers and 44 public reports were published. During COVID-19, population mental health has been affected both by the intensity of the pandemic (cases and death rates), but also by lockdowns and restrictions themselves. Worsening mental health coincided with higher rates of COVID-19, tighter restrictions, and the weeks leading up to lockdowns. Mental health then generally improved during lockdowns and most people were able to adapt and manage their well-being. However, a significant proportion of the population suffered disproportionately to the rest, and stay-at-home orders harmed those who were already financially, socially, or medically vulnerable. Socioeconomic factors, including low SEP, low income, and low educational attainment, continued to be associated with worse experiences of the pandemic. Outcomes for these groups were worse throughout many measures including mental health and wellbeing; financial struggles;self-harm and suicide risk; risk of contracting COVID-19 and developing long Covid; and vaccine resistance and hesitancy. These inequalities existed before the pandemic and were further exacerbated by COVID-19, and such groups remain particularly vulnerable to the future effects of the pandemic and other national crises.Further information, including reports and publications, can be found on the UCL COVID-19 Social Study website. Main Topics: The study asked baseline questions on the following: Demographics, including year of birth, sex, ethnicity, relationship status, country of dwelling, urban/rural dwelling, type of accommodation, housing tenure, number of adults and children in the household, household income, education, employment status, pet ownership, and personality. Health and health behaviours, including pre-existing physical health conditions, diagnosed mental health conditions, pregnancy, smoking, alcohol consumption, physical activity, caring responsibilities, usual social behaviours, and social network size. It also asked repeated questions at every wave on the following: COVID-19 status, including whether the respondent had had COVID-19, whether they had come into likely contact with COVID-19, current isolation status and motivations for isolation, length of isolation, length of time not leaving the home, length of time not contacting others, trust in government, trust in the health service, adherence to health advice, and experience of adverse events due to COVID-19 (including severe illness within the family, bereavement, redundancy, or financial difficulties). Mental health, including wellbeing, depression, anxiety, which factors were causing stress, sleep quality, loneliness, social isolation, and changes in health behaviours such as smoking, drinking and exercise. How people were spending their time whilst in isolation, including questions on working, functional household activities, care, and schooling of any children in the household, hobbies, and relaxation. Certain waves of the study also included one-off modules on topics including volunteering behaviours, locus of control, frustrations and expectations, coping styles, fear of COVID-19, resilience, arts and creative engagement, life events, weight, gambling behaviours, mental health diagnosis, use of financial support, faith and religion, relationships, neighbourhood satisfaction, healthcare usage, discrimination experiences, life changes, optimism, long COVID and COVID-19 vaccination.

Abstract copyright UK Data Service and data collection copyright owner.The International Passenger Survey (IPS) aims to collect data on both credits and debits for the travel account of the Balance of Payments, provide detailed visit information on overseas visitors to the United Kingdom (UK) for tourism policy, and collect data on international migration. International Passenger Survey and COVID-19The Office for National Statistics notes that International Passenger Survey (IPS) interviewing was suspended on 16 March 2020 because of the coronavirus (COVID-19). It is not certain when it will resume.Travel and tourism estimates for Quarter 1 (Jan to Mar) 2020 have been published to make the best possible use of the available data. The ONS expect that publishable estimates for March 2020 can be produced using the data collected up to 16 March 2020. The data available from UKDS covers Quarter 1 2020 with four subject areas, termed 'Airmiles', 'Alcohol', 'Qregtown' and 'Qcontact'. These files can be joined together using the variables YEAR, SERIAL, FLOW and QUARTER.No IPS data will be collected for the period when the survey is not operational, and the usual travel and tourism outputs from the IPS will not be published for this period. However, the IPS team will publish information to help users to understand trends in total international travel, based on the available administrative data from the Civil Aviation Authority (CAA) and the Department for Transport (DfT). This will provide figures on numbers of international journeys arriving into and departing from the UK, but there will be no information about the characteristics of these passengers. Further information can be found on the ONS Travel Trends webpage. Main Topics: Each of the four subject areas covers different topics:'Airmiles': quarter; flow; serial; UK port or route; direct leg overseas port; final overseas port; distance from UK port to first port; from first to second port; from UK port to second port'Alcohol': year; quarter; month; flow; serial; money spent on spirits; wine; beer; champagne; cigarettes; hand-rolled and other tobacco'Qreg': year; quarter; month; flow; serial; towns stayed in overnight; details of type of accommodation; number of nights spent in towns; expenditure in towns; regional stay weight; regional visit weight; regional expenditure weight; various validation checks'Qcontact': year; quarter; month; flow; serial; nationality; country of visit/residence; UK counties; date visit began; purpose of visit; intended length of stay; number of people; package tour and cost; expenditure pre-, post- and during visit; flight prefix and suffix; first carrier air or shipping line; direct leg overseas port; final overseas port; long- or short-haul; type of vehicle; number travelling in vehicle; fare type and cost; class of travel; business trip; type of flight; flight origin or destination; gender; age group; UK port or route; quality of response; date of interview; money transfer, net and total expenditure; type of transport; arrivals (number of adults); departures (type of travelling group, number of adults and children); weighting variables; various validation checks Multi-stage stratified random sample Face-to-face interview

The increase in the extent of working-from-home determined by the COVID-19 health crisis has led to a substantial shift of economic activity across geographical areas; which we refer to as a Zoomshock. When a person works from home rather than at the office, their work-related consumption of goods and services provided by the locally consumed service industries will take place where they live, not where they work. Much of the clientèle of restaurants, coffee bars, pubs, hair stylists, health clubs, taxi providers and the like located near workplaces is transferred to establishment located near where people live. These data are our calculations of the Zoomshock at the MSOA level. They reflect estimats of the change in the number of people working in UK neighbourhoods due to home-working.The COVID-19 shutdown is not affecting all parts of the UK equally. Economic activity in local consumer service industries (LCSI), such as retail outlets, restaurants, hairdressers, or gardeners has all but stopped; other industries are less affected. These differences among industries and their varying importance across local economies means recovery will be sensitive to local economic conditions and will not be geographically uniform: some neighbourhoods face a higher recovery risk of not being able to return to pre-shutdown levels of economic activity. This recovery risk is the product of two variables. The first is the shock, the effect of the shutdown on local household incomes. The second is the multiplier, the effect on LCSI economic activity following a negative shock to household incomes. In neighbourhoods where many households rely on the LCSI sector as a primary source of income the multiplier may be particularly large, and these neighbourhoods are vulnerable to a vicious circle of reduced spending and reduced incomes. This project will produce data measuring the shock, the multiplier, and the COVID-19 shutdown recovery risk for UK neighbourhoods. These variables will be estimated using individual and firm level information from national surveys and administrative data. The dataset, and corresponding policy report, will be made public and proactively disseminated to guide local and national policy design. Recovery inequality is likely to be substantial: absent intervention, existing regional inequalities may be exacerbated. This research will provide a timely and necessary input into designing appropriate recovery policy. These data reflect derived variables based on the methodology described in De Fraja, Matheson and Rockey (2021) (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3752977). Variables are derived from 2011 Census data provided through the ONS Nomis website.

Tackling London’s greenhouse gas (GHG) emissions is a huge challenge. The impact of these emissions goes far beyond the city’s boundaries. From the electronics we buy and the food we eat to the clothes we wear, most are produced and transported globally.

The Mayor, together with London Councils and ReLondon, has jointly commissioned Leeds University to develop a historic trend of consumption-based emissions for London. It uses the latest available data (running from 2001-2021) on average expenditure on different types of goods and services. This methodology aligns with equivalent national government datasets at the UK level.

Findings

London’s consumption-based emissions in 2021 were around 80 MtCO 2 e. They’ve fallen by 24 per cent since 2001, despite the city’s population increasing by 1.4 million over that time. This means emissions per head have reduced by 35 per cent (from 13.9 to 8.98 tCO 2 e per person).

The biggest drop in consumption-based emissions was between 2008 and 2009 during the global financial crisis, when households’ average spending decreased. Post 2009, emissions stabilised then steadily reduced from 2014 to 2020, bar a small increase from 2017-2018. This period of emissions reduction has been mainly driven by decarbonisation of the UK electricity sector.

The national context

London’s per capita consumption-based footprint is slightly lower than the UK average. It also follows a similar trend in reduction over the same period. However, at a sector level there are some cases where the per capita emissions for Londoners are different, for example:

• Transport – London’s use of transport is unlike any other region in the UK. Private transport emissions are much lower than any other region, and public transport emissions are the highest in the country. As a result, London has the lowest per capita transport emissions of any region and is lower than the UK average. However, London also has one of the highest per capita aviation emissions. Transport emissions were still unusually low in 2021 due to the ongoing impact of the COVID-19 pandemic.
• Housing, water, electricity, gas, other fuels – Compared to other regions in the UK, Londoners spend less on heating and power. This is a function of increased household occupancy rather than lower energy bills and more efficient homes.
• Food and drink – Compared to other regions in the UK, Londoners spend less on meat, which contributes to a lower food footprint per capita.

The international context

The Mayor wants to recognise the full environmental impact of London’s consumption by publishing this data. We hope this will encourage more cities to publish their consumption-based emissions data so we can identify similarities and work together to bring these emissions down.

Abstract copyright UK Data Service and data collection copyright owner.The Opinions and Lifestyle Survey (formerly known as the ONS Opinions Survey or Omnibus) is an omnibus survey that began in 1990, collecting data on a range of subjects commissioned by both the ONS internally and external clients (limited to other government departments, charities, non-profit organisations and academia).Data are collected from one individual aged 16 or over, selected from each sampled private household. Personal data include data on the individual, their family, address, household, income and education, plus responses and opinions on a variety of subjects within commissioned modules. The questionnaire collects timely data for research and policy analysis evaluation on the social impacts of recent topics of national importance, such as the coronavirus (COVID-19) pandemic and the cost of living, on individuals and households in Great Britain. From April 2018 to November 2019, the design of the OPN changed from face-to-face to a mixed-mode design (online first with telephone interviewing where necessary). Mixed-mode collection allows respondents to complete the survey more flexibly and provides a more cost-effective service for customers. In March 2020, the OPN was adapted to become a weekly survey used to collect data on the social impacts of the coronavirus (COVID-19) pandemic on the lives of people of Great Britain. These data are held in the Secure Access study, SN 8635, ONS Opinions and Lifestyle Survey, Covid-19 Module, 2020-2022: Secure Access. From August 2021, as coronavirus (COVID-19) restrictions were lifting across Great Britain, the OPN moved to fortnightly data collection, sampling around 5,000 households in each survey wave to ensure the survey remains sustainable. The OPN has since expanded to include questions on other topics of national importance, such as health and the cost of living. For more information about the survey and its methodology, see the ONS OPN Quality and Methodology Information webpage.Secure Access Opinions and Lifestyle Survey dataOther Secure Access OPN data cover modules run at various points from 1997-2019, on Census religion (SN 8078), cervical cancer screening (SN 8080), contact after separation (SN 8089), contraception (SN 8095), disability (SNs 8680 and 8096), general lifestyle (SN 8092), illness and activity (SN 8094), and non-resident parental contact (SN 8093). See Opinions and Lifestyle Survey: Secure Access for details. Main Topics:Each month's questionnaire consists of two elements: core questions, covering demographic information, are asked each month together with non-core questions that vary from month to month. The non-core questions for this month were: Mortgage Arrears (Module 2): source of mortgage, if any, and whether behind in payments, and if so reasons for falling behind. Also question on whether bought from a Right to Buy scheme. Health Screening (Module 4): whether prefer to have a health test carried out by pharmacist or by a doctor and reasons why; attitude to self-testing kits. Contraception (Module 6): method of birth control used and reasons for choice; changes in methods used; the use of Family Planning Clinics; awareness of emergency methods for use after intercourse has taken place. Investment Income (Module 7a): ownership of shares and income from shares, bank accounts and building society accounts. Census Question and GP Visits (Module 22): census questions about number of rooms available to the household; long-term illness; state of health; recent consultations with a doctor and visits to a hospital. Sensible Drinking (Module 40): whether drinks alcohol and if so what type; whether knows how much a unit of alcohol is; how much alcohol consumed in a week; awareness of recommended units as the safe, sensible weekly limit for men and women. Smoking (Module 60): whether smokes cigarettes now or has ever smoked; how many cigarettes smoked; type of cigarettes smoked (filter, non-filter or hand-rolled). Repairs and Redecorating (Module 61): total expenditure that household paid in the last month for maintenance, decoration, repairs or replacements to a contractor or someone else outside the household. Skin Cancer (Module 62): awareness of skin cancer and risks associated with excessive exposure to the sun; views on importance of sun protection measures. Leasehold Accommodation (Module 63): home owners of leasehold property asked how long did lease have to run when property was bought; how long lease has to run now; ownership of freehold. Multi-stage stratified random sample Face-to-face interview

Analysis of the relationships between COVID-19 restrictions, homeworking and spending, comparison of these variables: percentage of homeworkers, Google Workplace Mobility Index, Oxford Stringency Index and CHAPS spending.

We advise that users familiarise themselves with the reporting requirements of the regulators on whose data we have drawn for this work. Some variables are easily understood (headline income and expenditure figures, or dates of registration and dissolution); others less so (e.g. familiarity with definitions of the components of income which charities are required to report would be desirable for work on the exposure of charities to specific income sources). We carried out work on various aspects of the financial vulnerability of charities and charitable companies, as follows: 1. patterns of registration and dissolution, as measured by the dates on which these events are recorded by the regulators. 2. the extent to which organisations held reserves prior to the onset of Covid-19. We used measures of "unrestricted reserves" which are usually provided only for larger organisations and expressed these as a proportion of the organisation's annual expenditures; 3. financial vulnerability, expressed in various ways - substantial (over 25%) fluctuations in incomes, or fluctuations in the excess of expenditure over income; 4. exposure of organisations to particular income streams. We define these in "VariableDescriptions_covid19_project.doc", attached to this deposit. Note that for time series analyses, the Charity Commission website data on the incomes and expenditures of charities only contains data for relatively recent time periods; a longer time series providing charity financials from the late 1990s to 2012 is available in the Third Sector Research Centre data collection at https://reshare.ukdataservice.ac.uk/850933/ and we recommend this is linked to the current data from the Charity Commission. Financial histories are not available for as long a time period for Scottish charities since the regulator was not established until 2006. Other data of relevance to work on this project would be a publicly-available classification of charities at https://charityclassification.org.uk/ Charitable organisations largely fall into a small number of sections of the Standard Industrial Classification and as a result scholars have developed more granular schemas. the data at the above website are publicly-available and can be linked via charity ID numbers. Project papers describing the work in more detail are available at https://www.birmingham.ac.uk/research/tsrc/research/assessing-financial-vulnerability-and-risk-in-the-uks-charities-during-and-beyond-the-covid-19-crisis.aspxThere are significant public concerns about the impact of the economic consequences of COVID-19 for UK voluntary organisations. The lockdown has caused the cessation of income generation activities involving face-to-face contacts; it will be followed by longer-term impacts depending on the scale and duration of the post-crisis recession. The impact will be highly differentiated, between organisations of different missions and size, and between communities. Central and national government, funders, voluntary organisation infrastructure bodies, and organisations themselves require analysis of these impacts if they are to make informed decisions. The immediate needs are for understandings of: 1. exactly what sorts of funding streams are at risk, and how the reduction or cessation of that funding has differentiated impacts 2. the extent to which the economic impacts of COVID-19 will differ in magnitude and character from previous shocks to voluntary sector income (there is a baseline degree of fluctuation in organisations incomes and expenditures, but we anticipate the crisis will affect far more organisations); 3. ongoing differential impacts depending on the progress of moving out from lockdown. Our work will contribute to an improved evidence base, providing actionable information on the exposure to risk of charities, drawing on a growing volume of administrative and transactional data. This will provide more granular, policy-relevant data on the impacts of economic change on charitable organisations. In turn this will provide a firmer evidential basis for interventions such as targeted financial support for strategically-significant charities. Data were downloaded regularly from the following sources. (1) The Charity Commission: the majority of charities which operate within England and Wales are legally obliged to register with the Charity Commission, whose data are now available publicly. The Charity Commission provided a comprehensive data extract which is updated regularly. Dissolutions and registrations of organisations are updated daily. Financial information is updated as and when returns are submitted by charities; there is a timelag because charities have a grace period within which to report their financial results and because there are then internal checks, which can take longer. This means that more detailed returns on which we have relied for analyses tend to take longer to appear in the publicly-downloadable files. (2) The Office of the Scottish Charity Regulator: all organisations wishing to operate as charities in Scotland must be registered with the Office of the Scottish Charity Regulator. There are differences in the characteristics of registered charities between Scotland and England / Wales, because the Scottish regulator has no income threshold above which reporting is mandatory (in England and Wales only organisations with incomes or expenditures greater than £5000 are required to do so) and because in England and Wales there are various categories of charity which do not report to the Charity Commission (because they have a different principal regulator – e.g. universities). (3) Companies House: the majority of the organisations registered with, and/or regulated by, Companies House are for-profit organisations but some are of interest to third sector researchers, such as Community Interest Companies and Companies Limited by Guarantee though the precise allocation of these to the third sector is a matter of judgement; Companies House offer, through their website, a complete list of active registered companies as a free download, updated monthly. In our work we have focussed on Companies Limited by Guarantee.

Value-based choice and compulsion theories of addiction offer distinct explanations for the persistence of alcohol use despite harms. Choice theory argues that problematic drinkers ascribe such high value to alcohol that costs are outweighed, whereas compulsion theory argues that problematic drinkers discount costs in decision making. The current study evaluated these predictions by testing whether alcohol use disorder (AUD) symptom severity (indexed by the AUDIT) was more strongly associated with the intensity item (maximum alcohol consumption if free, indexing alcohol value) compared to the breakpoint item (maximum expenditure on a single drink, indexing sensitivity to monetary costs) of the Brief Assessment of Alcohol Demand (BAAD) questionnaire, in student (n = 579) and community (n = 120) drinkers. The community sample showed greater AUD than the student sample (p = .004). In both samples, AUD severity correlated with intensity (students, r = 0.63; community, r = 0.47), but not with breakpoint (students, r = -0.01; community, r = 0.12). Similarly, multiple regression analyses indicated that AUD severity was independently associated with intensity (student, ΔR2 < 0.20, p < .001; community, ΔR2 = 0.09, p = .001) but not breakpoint (student, ΔR2 = 0.003, p = .118; community ΔR2 = 0.01, p = .294). There was no difference between samples in the strength of these associations. The value ascribed to alcohol may play a more important role in AUD severity than discounting of alcohol-associated costs (compulsivity), and there is no apparent difference between student and community drinkers in the contribution of these two mechanisms.

Assessments: Data were collated across a number of experiments and in all cases questionnaires were delivered at baseline and followed the same order. Demographic measures (age and gender) were collected. AUD severity was assessed using the ten-item Alcohol Use Disorders Identification Test (AUDIT) (Babor et al. 2001). The AUDIT total score ranges from 0-40, and can be divided into categories: low-risk (0–7), hazardous (8–15), harmful (16–19) and possibly dependent (20–40). Cronbach’s alpha for the AUDIT was .78 in the student and .81 in the community sample. The AUDIT has two subscales, measuring alcohol consumption and alcohol-related consequences (Doyle et al. 2007). Value and cost insensitivity constructs were measured with the Brief Assessment of Alcohol Demand (BAAD) questionnaire (Owens et al. 2015). The BAAD has three items. The first item indexes intensity of demand (‘If drinks were free, how many would you have in a single session?’), with possible responses ranging from 0 to 10+ drinks in increments of 1. The second item indexes Omax (‘What is the maximum total amount you would spend on drinks for yourself in a single session?’), with responses ranging from £0 to £40 in £4 increments. The final item indexes breakpoint (‘What is the maximum you would pay for a single drink?’) with responses ranging from £0 to £20 in £2 increments.

The first aim of the fellowship was to build on my PhD research to develop a novel brief intervention for hazardous drinking young people. My proposed intervention combines a number of elements with prior evidence of efficacy in hazardous alcohol use. High-risk individuals will be provided with personalised feedback regarding the specific negative emotions which trigger their drinking (for example, anger, sadness, boredom) (Blevins and Stephens 2016) and encouraged to generate individualised alternative coping strategies (Conrod et al. 2013). Crucially, individuals will also be instructed in functional imagery training, a promising technique used to encourage adoption of adaptive behaviours in high-risk scenarios. The two pilot studies proposed in clinical populations in this project are on hold due to COVID-19 restrictions. However, development of this intervention has continued and online pilot testing in student populations is ongoing. Data will be uploaded as and when these projects are complete. Additional aims of the fellowship included: 1) To publish completed research demonstrating that a brief, 6 minute mindfulness training procedure can reduce drinking under stress in students. This research has been published in the journal Addictive Behaviours and a full dataset is uploaded here. 2) To publish research validating a novel measure of negative coping motives (the Coping Motives Checklist). This measure will be used to identify specific negative triggers to alcohol use - and to provide personalised feedback on these motives - in my proposed novel intervention. Initial data linkage and analysis is ongoing and data will be uploaded when this is finalised. 3) Validation of a measure of alcohol valuation - the Brief Assessment of Alcohol Demand (BAAD) - to be used as a brief screening tool to identify those at risk of dependence. This research has been published in Addictive Behaviours and a full dataset is uploaded here.