Browse
Submit Data
Databases
API
Help

Dataset Information

22 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Did the UK policy response to Covid-19 protect household incomes?

ABSTRACT: We analyse the UK policy response to Covid-19 and its impact on household incomes in the UK in April and May 2020, using microsimulation methods. We estimate that households lost a substantial share of their net income of 6.9% on average. But policies protected household incomes to a substantial degree: compared to the drop in net income, GDP per capita fell by 18.9% between the first and second quarter of 2020. Earnings subsidies (the Coronavirus Job Retention Scheme) protected household finances and provided the main insurance mechanism during the crisis. Besides subsidies, Covid-related increases to state benefits, as well as the automatic stabilisers in the tax and benefit system, played an important role in mitigating the income losses. However, analysing the impact of a near-decade of austerity on the UK safety net, we find that, compared to 2011 policies, the 2020 pre-Covid tax-benefit policies would have been less effective in insuring incomes against the shocks. We also assess the potential distributional impact of introducing a Universal Basic Income (UBI) instead of the Covid emergency measures and find that a UBI would have supported the incomes of different vulnerable groups but would have provided less protection to those hit hardest by the labour market shocks.

Supplementary information

The online version contains supplementary material available at 10.1007/s10888-021-09491-w.

SUBMITTER: Brewer M

PROVIDER: S-EPMC8345904 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

SOS incomes: simulated effects of COVID-19 and emergency benefits on individual and household income distribution in Italy

Project description:Using a static microsimulation model based on a link between survey and

| S-EPMC9353314 | biostudies-literature

COVID-19 Genomics UK (COG-UK) consortium

Project description:COVID-19 Genomics UK (COG-UK) consortium

| PRJEB37886 | ENA

Association between household size and COVID-19: A UK Biobank observational study.

Project description:ObjectiveTo assess the association between household size and risk of non-severe or severe COVID-19.DesignA longitudinal observational study.SettingThis study utilised UK Biobank linked to national SARS-CoV-2 laboratory test data.Participants401,910 individuals with available data on household size in UK Biobank.Main outcome measuresHousehold size was categorised as single occupancy, two-person households and households of three or more. Severe COVID-19 was defined as a positive SARS-CoV-2 test on hospital admission or death with COVID-19 recorded as the underlying cause; and non-severe COVID-19 as a positive test from a community setting. Logistic regression models were fitted to assess associations, adjusting for potential confounders.ResultsOf 401,910 individuals, 3612 (1%) were identified as having suffered from a severe COVID-19 infection and 11,264 (2.8%) from a non-severe infection, between 16 March 2020 and 16 March 2021. Overall, the odds of severe COVID-19 was significantly higher among individuals living alone (adjusted odds ratio: 1.24 [95% confidence interval: 1.14 to 1.36], or living in a household of three or more individuals (adjusted odds ratio: 1.28 [1.17 to 1.39], when compared to individuals living in a household of two. For non-severe COVID-19 infection, individuals living in a single-occupancy household had lower odds compared to those living in a household of two (adjusted odds ratio: 0.88 [0.82 to 0.93].ConclusionsOdds of severe or non-severe COVID-19 infection were associated with household size. Increasing understanding of why certain households are more at risk is important for limiting spread of the infection.

| S-EPMC8972956 | biostudies-literature

COVID-19 economic policy response, resilience and tourism recovery

Project description:This study investigates whether tourism sector recovery from the COVID-19 pandemic is influenced by only the size of the economic stimulus packages or whether a country's resilience plays a moderating influence in the underlying relationship. The results show that while economic stimulus packages help to enhance tourism recovery from the COVID-19 pandemic, it is contingent on the level of a country's resilience. For instance, amongst the less resilient countries, the impact of economic policy response on the tourism recovery is more pronounced, but the effect dissipates as countries become more resilient. These findings have important implications for policymakers, management teams, and relevant stakeholders in their effort to revive the tourism sector from the impact of the COVID-19 pandemic.

| S-EPMC9515351 | biostudies-literature

Jointly Modelling Economics and Epidemiology to Support Public Policy Decisions for the COVID-19 Response: A Review of UK Studies.

Project description:COVID-19 in the UK has had a profound impact on population health and other socially important outcomes, including on education and the economy. Although a range of evidence has guided policy, epidemiological models have been central. It is less clear whether models to support decision making have sought to integrate COVID-19 epidemiology with a consideration of broader health, wellbeing and economic implications. We report on a rapid review of studies seeking to integrate epidemiological and economic modelling to assess the impacts of alternative policies. Overall, our results suggest that few studies have explored broader impacts of different COVID-19 policies in the UK. Three studies looked only at health, capturing impacts on individuals with and without COVID-19, with various methods used to model the latter. Four models considered health and wider impacts on individuals' economic outcomes, such as wages. However, these models made no attempt to consider the dynamic impacts on economic outcomes of others and the wider economy. The most complex analyses sought to link epidemiological and dynamic economic models. Studies compared a wide range of policies, although most were defined in general terms with minimal consideration of their granular specifications. There was minimal exploration of uncertainty, with no consideration in half the studies. Selecting appropriate models to inform decisions requires careful thought of factors relevant to the decision options under consideration such as the outcomes of interest, sectors likely to be impacted and causal pathways. In summary, better linking epidemiological and economic modelling would help to inform COVID-19 policy.

| S-EPMC8213532 | biostudies-literature

Mega-disruptions and policy change: Lessons from the mobility sector in response to the Covid-19 pandemic in the UK.

Project description:There has been widespread interest in the potential for the significant behavioural and policy adaptations rendered necessary by Coronavirus to act as a catalyst for radical longer term policy change in transport. However, this body of work to date has been limited in its consideration of how such policy change might be brought about. Translating the lessons from the Coronavirus response to other ongoing strategic challenges such as decarbonisation requires analysis of what the pandemic has revealed about processes of policy formulation and how institutions responsible for policy implementation actually work. This paper explores the extent to which rapid policy change has actually been possible in the transport sector in England and Scotland during the pandemic, and key examples of how such change has been both achieved and blocked. Two rounds of interviews with senior stakeholders from across the transport sector were undertaken in June and November 2020 to inform the analysis. We find that the pandemic has accelerated some policy commitments that were already planned, but at a time of huge stress on the whole of government and its partner delivery organisations, the potential to deliver radical policy adaptation was limited. However, Coronavirus is recognised as being a potentially path-changing disruption to existing trajectories in terms of the adaptations to business practices, industry structures, ways of working and the public finances. Paradoxically, whilst recognising these uncertainties, decision-makers are yet to deviate from pre-pandemic planning assumptions and policy plans and this risks missing the opportunities to steer how those changes unfold.

| S-EPMC9761561 | biostudies-literature

Supporting families to protect child health: Parenting quality and household needs during the COVID-19 pandemic.

Project description:BackgroundSupportive parenting is critical for promoting healthy child development in the face of stressors, such as those occurring during COVID-19. Here, we address a knowledge gap regarding specific household risk factors associated with parenting quality during the pandemic and incorporate first-person accounts of family challenges and needs.MethodsMixed methods were applied to data collected between April 14th - 28th, 2020 from the "Parenting During the Pandemic" survey. Participants included 656 primary caregivers (e.g., mothers, fathers, foster parents) of least one child age 1.5-8 years of which 555 (84.6%) responded to at least one parenting questionnaire. Parenting quality was assessed across stressful, negative, and positive parenting dimensions. Household risk was examined across pandemic- linked (e.g., caregiver depression, unmet childcare needs) and stable factors (i.e., annual income, mental illness history). Significant correlates were examined with regressions in Mplus. Thematic analysis identified caregiver challenges and unmet needs from open-ended questions.FindingsCaregiver depression, higher child parity, unmet childcare needs, and relationship distress predicted lower-quality parenting. Caregiver depression was the most significant predictor across every parenting dimension, with analyses indicating medium effect sizes, ds = .39 - .73. Qualitative findings highlighted severe strains on parent capacities including managing psychological distress, limited social supports, and too much unstructured time.InterpretationsLower quality parenting during COVID-19 is associated with multiple household and pandemic risk factors, with caregiver depression consistently linked to parent- child relationship disruptions. Focused efforts are needed to address caregiver mental health to protect child health as part of the pandemic response.

| S-EPMC8143383 | biostudies-literature

Domesticating models: On the contingency of Covid-19 modelling in UK media and policy.

Project description:Our article traces the representation of pandemic modelling in UK print media from the emergence of Covid-19 to the early stages of implementing the first UK-wide lockdown in late March 2020. Covid modelling, it is widely assumed, has shaped policy decisions and public responses to the pandemic in unprecedented ways. We analyse how the UK print media has configured modelling as a significant evidence tool in the representation of the pandemic. Interrogating assumptions about infectious disease modelling, we ask why models became the trusted tool of choice for knowing and responding to the Covid pandemic in the UK. Our analysis has yielded four different periods in the evolution of intersecting policy and media frames. Initially, modellers, policymakers and media alike emphasized uncertainty about available data, and hence the speculative character of modelled projections, thus justifying a 'wait and see' approach to government intervention. With growing public pressure for government action, policy and media frames were adjusted to emphasize the importance of timing interventions for best effect, with modelling evidence mobilized to justify inaction. This gave way to a period of crisis, as the press increasingly questioned the reliability of the existing models and policies, leading modellers and policy makers to dramatically revise their projections. Finally, with the imposition of the first UK lockdown, policy and media frames were brought back into alignment with one another, in a process of domestication through which the language of modelling became a basic resource for the discussion of the epidemic. Our epistemological microhistory thus challenges general accounts of the impacts of pandemic modelling and instead emphasizes contingency and interpretative flexibility.

| S-EPMC9892880 | biostudies-literature

Labour Incomes in India: A Comparison of Two National Household Surveys.

Project description:The COVID-19 pandemic created a need for high-frequency employment and income data. Policy-makers and researchers of developing countries typically have not had access to such data. In India, a new private high-frequency panel dataset has recently emerged as the dataset of choice for analysis of the economic impact of COVID-19. This is the Consumer Pyramids Household Survey (CPHS) conducted by the Centre for Monitoring the Indian Economy (CMIE). But the CPHS has also been criticised for being inadequately representative nationally by missing poor and vulnerable households in its sample. We examine the comparability of monthly labour income estimates for the pre-pandemic year (2018-19) for CPHS and the official Periodic Labour Force Survey (PLFS). Across different methods and assumptions, as well as rural/urban locations, CPHS mean monthly labour earnings are anywhere between 5 percent and 50 percent higher than corresponding PLFS estimates. In addition to the sampling concerns raised in the literature, we point to differences in the way employment and income are captured in the two surveys as possible causes of these differences. While CPHS estimates are always higher, it should also be emphasised that the two surveys agree on some stylised facts regarding the Indian workforce. An individual earning ₹50,000 per month lies in the top 5 percent of the income distribution in India as per both surveys. Second, both PLFS and CPHS show that half the Indian workforce earns below the recommended National Minimum Wage.Supplementary informationThe online version contains supplementary material available at 10.1007/s41027-023-00427-8.

| S-EPMC9961298 | biostudies-literature

Response to COVID-19 in Cyprus: Policy changes and epidemic trends.

Project description:ObjectivesIn late July, Cyprus experienced the second epidemic wave of COVID-19. We present the steps taken by the government and evaluate their effect on epidemic trends.MaterialsCyprus Press and Information Office data were analysed. Using an R-based forecasting program, two models were created to predict cases up to 01/09/2020: Model 1, which utilised data up to 09/06/2020, when airports reopened to foreign travelers with COVID-19 screening; and Model 2, which utilised data until 24/06/2020, when screening for passengers from low-transmission countries was discontinued.ResultsPIO data revealed no significant policy changes between 24/06/2020 and 31/07/2020. Prediction models were robust and accurate (Model 1, R2 = 0.999, P < .001; Model 2, R2 = 0.998, P < .001). By August 30th, recorded cases exceeded those predicted by Model 1 by 24.47% and by Model 2 by 20.95%, with P values <.001 for both cases.ConclusionsThe significant difference between recorded cases and those projected by Models 1 and 2 suggests that changes in epidemic trends may have been associated with policy changes after their respective dates. Discontinuation of major restrictions such as airport reopening, can destabilise the control of the epidemic, and may concomitantly necessitate a reevaluation of the current epidemic status. In the face of an evolving situation such as the COVID-19 pandemic, states are forced to balance the imposing of restrictions against their impact on the economy.

| S-EPMC7883066 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data