In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

The COVID-19 pandemic had severe impacts on almost every aspect of life, from health via economy to education. School closures around the world caused disruptions in learning development of children and youth. Estimates from 2022 show that globally, the annual gross domestic product (GDP) loss could amount to nearly 1,600 billion U.S. dollars annually if no counter measures are taken. The economic damage was predicted to be highest in East Asia and the Pacific, and the lowest in Sub-Saharan Africa.

Ireland DE: HU: Change in Personal Income Tax Revenue data was reported at -1,416.000 EUR mn in Dec 2021. This records an increase from the previous number of -1,712.000 EUR mn for Sep 2021. Ireland DE: HU: Change in Personal Income Tax Revenue data is updated quarterly, averaging -1,594.000 EUR mn from Mar 2021 (Median) to Dec 2021, with 4 observations. The data reached an all-time high of -1,416.000 EUR mn in Dec 2021 and a record low of -1,716.000 EUR mn in Mar 2021. Ireland DE: HU: Change in Personal Income Tax Revenue data remains active status in CEIC and is reported by Economic and Social Research Institute. The data is categorized under Global Database’s Ireland – Table IE.F013: Potential Costs and Distributional Effect: COVID-19 Related Unemployment. [COVID-19-IMPACT]

The coronavirus (COVID-19) pandemic, has had a significant impact on the global economy. In 2020, global Gross Domestic Product (GDP) decreased by 3.4 percent, while the forecast initially was 2.9 percent GDP growth. As the world's governments are working towards a fast economic recovery, the GDP increased again in 2021 by 5.8 percent. Global GDP increased by over three percent in 2022, but it is still not clear to what extent Russia's war in Ukraine will impact the global economy. Global GDP growth is expected to slow somewhat in 2023.

The purpose of the research is to examine the economic impact of COVID 19 on small and medium-sized enterprises in the short and long terms.

The impact of the coronavirus (COVID-19) pandemic had not only brought the global economy to a standstill but set the clock backwards on the developmental progress of several nations. While the rate of infection in India did not appear to be as high as in other countries, precautionary measures adopted dealt a severe blow to the country’s major industries - with the service sector bearing the largest brunt of estimated loss. Manufacturing made a swift recovery in the following months.

Impact of key industries

The loss incurred by enforcing a lockdown in the country was estimated at 26 billion U.S. dollars and a significant decline in GDP growth is also expected in the June quarter of 2020. With the imposition of restrictions on transportation worldwide, the trade sector also took a hit. Exports and imports saw a drastic decline in the country especially in the case of essential commodities such as petroleum, food crops, and coal, among others.

Effect on business in India

The growth rate of the automotive business in India was expected to be the most adversely affected followed by the power supply and IT sectors. Furthermore, many startups, small and medium enterprises in India expected to face issues of supply disruption and a decrease in demand. The effects of aid from the Narendra Modi-led government arguably did little to help in the face of a faltering economy.

The purpose of the research is to examine the economic impact of COVID 19 on small and medium-sized enterprises in the short and long terms.

As of November 2021, the U.S. goverment dedicated ***** percent of the GDP to soften the effects of the coronavirus pandemic. This translates to stimulus packages worth **** trillion U.S. dollars Economic impact of the Coronavirus pandemic The impact of the COVID-19 pandemic was felt throughout the whole world. Lockdowns forced many industries to close completely for many months and restrictions were put on almost all economic activity. In 2020, the worldwide GDP loss due to Covid was *** percent. The global unemployment rate rocketed to **** percent in 2020 and confidence in governments’ ability to deal with the crisis diminished significantly. Governmental response In order to stimulate the economies and bring them out of recession, many countries have decided to release so called stimulus packages. These are fiscal and monetary policies used to support the recovery process. Through application of lower taxes and interest rates, direct financial aid, or facilitated access to funding, the governments aim to boost the employment, investment, and demand. Stimulus packages Until November 2021, Japan has dedicated the largest share of the GDP to stimulus packages among the G20 countries, with ***** percent (*** trillion Yen or **** trillion U.S. dollars). While the first help package aimed at maintaining employment and securing businesses, the second and third ones focused more on structural changes and positive developments in the country in the post-pandemic future.

This paper explores the impact of the COVID-19 pandemic and the Korean government's fiscal measures on macroeconomic and microeconomic shifts. Utilizing the Global Trade Analysis Project (GTAP) computable general equilibrium model and database version 11, with 2017 as the base year, we aggregated 160 regions and 65 sectors into 9 regions and 18 sectors. The model projected the global economy to 2020 using variables such as real GDP, population, capital stock, and labor supply for a baseline scenario. Two policy scenarios assessed the impacts of the pandemic and a fiscal stimulus package. Results indicated a global decline in real GDP and welfare, with disruptions in supply chains and increased trade costs negatively affecting import and export volumes. Sectors such as tourism were particularly impacted. Specifically, the Korean economy faced a significant negative impact from the pandemic. Despite government fiscal measures that positively influenced real GDP, Korea's real GDP contracted by 1.7% in 2020, deviating from the pre-pandemic growth changes of approximately 2% per year. Welfare losses amounted to US$103 billion, driven by decreased consumer spending and increased unemployment. Export and import volumes fell, leading to a narrower trade deficit of US$17 billion compared to the previous year. The study underscores the need for targeted fiscal measures to mitigate adverse effects, recommending policies to stimulate private household consumption, support affected sectors, and enhance Korea's international trade competitiveness.

This paper explores the impact of the COVID-19 pandemic and fiscal policy interventions on the Korean and global economy to provide a scientific rationale for government policy interventions. We deployed a multi-region, multi-sector computable general equilibrium (CGE) model and the Global Trade Analysis Project (GTAP) database version 11A, with 2017 as the base year projected to 2020, the epitome of the COVID-19 pandemic. Two policy scenarios assessed the impacts of the pandemic and government fiscal stimulus interventions. Results indicated a global decline in real GDP and welfare, with supply chain disruptions and increased trade costs negatively affecting import and export volumes. Despite government fiscal measures boosting real GDP, Korea's economy contracted by 1.47% in 2020, deviating from its annual pre-pandemic growth of approximately 2%. Welfare losses reached US$57.38 billion, driven by decreased consumer spending and increased unemployment. Falling export and import volumes narrowed the trade deficit to US$197.04 billion. However, government fiscal measures led to a net impact of US$104.68 billion compared to the baseline scenario. Our study underscores the need for targeted budgetary measures to mitigate adverse effects, recommending policies to stimulate private household consumption, support affected sectors, and enhance Korea's international trade competitiveness.

Context

The objective of the dataset is to provide information that enables decision makers to better direct their efforts in addressing the wider effects of the COVID-19 pandemic. The dataset will track secondary impacts across a wide range of relevant themes: economy, health, migration, education to name a few.

https://data.humdata.org/dataset/global-covid-19-secondary-impacts

Content

A set of impact indicators anticipated to be impacted by COVID-19 have been identified and organised across pillars and thematic blocks. Additionally, a set of pre-COVID-19 baseline indicators have been selected for each pillar.

The data collection is conducted on a country-level and identifies the secondary impacts the COVID- 19 pandemic. Data comes from a range of available sources, including international organisations, research centres, and media analysis.

Note: These are the preliminary results of the data collection on secondary impacts. This dataset is currently in the beta-testing phase.

Acknowledgements

https://data.humdata.org/dataset/global-covid-19-secondary-impacts

Photo by Mick Haupt on Unsplash

Inspiration

Covid-19 Pandemic.

The SPIN covid19 RMRIO dataset is a time series of MRIO tables covering years from 2016-2026 on a yearly basis. The dataset covers 163 sectors in 155 countries.

This repository includes data for years from 2016 to 2019 (hist scenario) and the corresponding labels. Data for years 2020 to 2026 are stored in the corresponding repositories:

covid: 10.5281/zenodo.5713825

counterfactual: 10.5281/zenodo.5713839

Tables are generated using the SPIN method, based on the RMRIO tables for the year 2015, GDP, imports and exports data from the International Financial Statistics (IFS) and the World Economic Outlooks (WEO) of October 2019 and April 2021.

From 2020 to 2026, the dataset includes two diverging scenarios. The covid scenario is in line with April 2021 WEO's data and includes the macroeconomic effects of Covid 19. The counterfactual scenario is in line with October 2019 WEO's data and simulates the global economy without Covid 19. Tables from 2016 to 2019 are labelled as hist.

The Projections folder includes the generated tables for years from 2016 to 2019 (hist scenario) and the corresponding labels. The Sources folder contains the data records from the IFS and WEO databases. The Method data contains the data files used to generate the tables with the SPIN method and the following Python scripts:

SPIN_covid19_MRIO_files_preparation.py generates the data files from the source data.

SPIN_covid19_RMRIO runs.py is the command to run the SPIN method and generate the dataset.

figures.py is a script to produce figures reflecting the consistency of the projected tables and the evolution of macroeconomic figures in the 2016-2026 period for a selection of countries.

All tables are labelled in 2015 US$ and valued in basic prices.

Abstract

The World Bank has launched a quick-deploying high-frequency phone-monitoring survey of households to generate near real-time insights on the socio-economic impact of COVID-19 on households which hence to be used to support evidence-based response to the crisis. At a moment when all conventional modes of data collection have had to be suspended, a phone-based rapid data collection/tracking tool can generate large payoffs by helping identify affected populations across the vast archipelago as the contagion spreads, identify with a high degree of granularity the mechanisms of socio-economic impact, identify gaps in public policy response as the Government responds, generating insight that could be useful in scaling up or redirecting resources as necessary as the affected population copes and eventually regains economic footing.

Analysis unit

Household-level; Individual-level: household primary breadwinners, respondent, student, primary caregivers, and under-5 years old kids

Sampling procedure

The sampling frame of the Indonesia high-frequency phone-based monitoring of socio-economic impacts of COVID-19 on households was the list of households enumerated in three recent World Bank surveys, namely Urban Survey (US), Rural Poverty Survey (RPS), and Digital Economy Household Survey (DEHS). The US was conducted in 2018 with 3,527 sampled households living in the urban areas of 10 cities and 2 districts in 6 provinces. The RPS was conducted in 2019 with the sample size of 2,404 households living in rural areas of 12 districts in 6 provinces. The DEHS was conducted in 2020 with 3,107 sampled households, of which 2,079 households lived in urban areas and 1,028 households lived in rural areas in 26 districts and 31 cities within 27 provinces. Overall, the sampled households drawn from the three surveys across 40 districts and 35 cities in 27 provinces (out of 34 provinces). For the final sampling frame, six survey areas of the DEHS which were overlapped with the survey areas in the UPS were dropped from the sampling frame. This was done in order to avoid potential bias later on when calculating the weights (detailed below). The UPS was chosen to be kept since it had much larger samples (2,016 households) than that of the DEHS (265 households). Three stages of sampling strategies were applied. For the first stage, districts (as primary sampling unit (PSU)) were selected based on probability proportional to size (PPS) systematic sampling in each stratum, with the probability of selection was proportional to the estimated number of households based on the National Household Survey of Socio-economic (SUSENAS) 2019 data. Prior to the selection, districts were sorted by provincial code.

In the second stage, villages (as secondary sampling unit (SSU)) were selected systematically in each district, with probability of selection was proportional to the estimated number of households based on the Village Potential Census (PODES) 2018 data. Prior to the selection, villages were sorted by sub-district code. In the third stage, the number of households was selected systematically in each selected village. Prior to the selection, all households were sorted by implicit stratification, that is gender and education level of the head of households. If the primary selected households could not be contacted or refused to participate in the survey, these households were replaced by households from the same area where the non-response households were located and with the same gender and level of education of households’ head, in order to maintain the same distribution and representativeness of sampled households as in the initial design.

In the Round 8 survey where we focused on early nutrition knowledge and early child development, we introduced an additional respondent who is the primary caregiver of under 5 years old in the household. We prioritized the mother as the target of caregiver respondents. In households with multiple caregivers, one is randomly selected. Furthermore, only the under 5 children who were taken care of by the selected respondent will be listed in the early child development module.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire in English is provided for download under the Documentation section.

Response rate

The HiFy survey was initially designed as a 5-round panel survey. By end of the fifth round, it is expected that the survey can maintain around 3,000 panel households. Based on the experience of phone-based, panel survey conducted previously in other study in Indonesia, the response rates were expected to be around 60 percent to 80 percent. However, learned from other similar surveys globally, response rates of phone-based survey, moreover phone-based panel survey, are generally below 50 percent. Meanwhile, in the case of the HiFy, information on some of households’ phone numbers was from about 2 years prior the survey with a potential risk that the targeted respondents might not be contactable through that provided numbers (already inactive or the targeted respondents had changed their phone numbers). With these considerations, the estimated response rate of the first survey was set at 60 percent, while the response rates of the following rounds were expected to be 80 percent. Having these assumptions and target, the first round of the survey was expected to target 5,100 households, with 8,500 households in the lists. The actual sample of households in the first round was 4,338 households or 85 percent of the 5,100 target households. However, the response rates in the following rounds are higher than expected, making the sampled households successfully interviewed in Round 2 were 4,119 (95% of Round 1 samples), and in Rounds 3, 4, 5, 6, 7, and 8 were 4,067 (94%), 3,953 (91%), 3,686 (85%), 3,471 (80%), 3,435 (79%), 3,383 (78%) respectively. The number of balanced panel households up to Rounds 3, 4, 5, 6, 7, and 8 are 3,981 (92%), 3,794 (87%), 3,601 (83%), 3,320 (77%), 3,116 (72%), and 2,856 (66%) respectively.

This report discusses the novel coronavirus (COVID-19) outbreak worldwide. It provides valuable insights for understanding the epidemiology, mortality and morbidity ratios, current therapeutics in development, and the pandemic's impact on the global economy and healthcare industry.

The global pandemic caused by coronavirus COVID-19 could have a prolonged impact on China's economy. Manufacturing sector was estimated to drop by 3.61 percentage points form the baseline of no global coronavirus crisis. The overall impact was projected to be a decline by 3.54 percentage point.

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

The global economy is seeing significant differences in commercial vehicle activity due to the COVID-19 pandemic. The COVID-19 Mobility Impact Dataset offers insight into changes in commercial vehicle mobility and plotting its course toward recovery. Discover trends that illustrate recovery to pre-pandemic norms by industry and region. Further dive into the impact that has been felt in commercial vehicle activity surrounding airports, seaports, fuel stations, and international borders (including US/Canada and US/Mexico). These mobility changes have had an impact on the flow and transport of goods and services within cities -- peruse datasets that look at city-wide congestion changes and how they are evolving with time. For private and public sector organizations, this dataset supports critical evidence-based decision-making to inform everything from public policy, benchmarking, process optimization, and more. The data is available in BigQuery's EU and US regions: US region EU region This public dataset is hosted in Google BigQuery. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch thisto get started quickly using BigQuery What is BigQuery? This dataset is created and owned by Geotab. It has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to normal billing rates.

BackgroundThe spread of COVID-19 has been characterized by unprecedented global lock-downs. Although, the extent of containment policies cannot be explained only through epidemic data. Previous studies already focused on the relationship between the economy and healthcare, focusing on the impact of diseases in countries with a precarious economic situation. However, the pandemic caused by SARS-CoV-2 drew most countries of the world into a precarious economic situation mostly caused by the global and local lock-downs policies.MethodsA discriminant analysis performed via partial least squares procedure was applied to evaluate the impact of economic and healthcare variables on the containment measures adopted by 39 countries. To collect the input variables (macroeconomic, healthcare, and medical services), we relied on official databases of international organizations, such as The World Bank and WHO.ResultsThe stringency lock-down policies could not only be influenced by the epidemical data, but also by previous features of the selected countries, such as economic and healthcare conditions.ConclusionsIndeed, economic and healthcare variables also contributed to shaping the implemented lock-down policies.

The objective of the dataset is to provide information that enables decision makers to better direct their efforts in addressing the wider effects of the COVID-19 pandemic. The dataset will track secondary impacts across a wide range of relevant themes: economy, health, migration, education to name a few.

A set of around 80 impact indicators anticipated to be impacted by COVID-19 have been identified and organised across 4 pillars and 13 thematic blocks. Additionally, a set of around 25 pre-COVID-19 baseline indicators have been selected for each pillar.

The data collection is conducted on a country-level and identifies the secondary impacts the COVID- 19 pandemic is having in more than 190 countries. Data comes from a range of available sources, including international organisations, research centres, and media analysis.

Note: These are the preliminary results of the data collection on secondary impacts. This dataset is currently in the beta-testing phase, we will keep improving and updating in the coming weeks.

Tourism is one of the fastest growing sectors in the world economy. According to the United Nations World Tourism Organization (UNWTO), the number of tourists in 2019 on the international level reached 1.500 million. In 2018, tourism spending was at 1.700 billion US dollars, and the tourism industry accounted for 10% of the global gross domestic product (GDP). One in ten employees in the world is employed in this sector. Both internationally and in our country, the statistics related to tourism in the past decades have consistently shown positive indicators. In North Macedonia, the data related to the number of tourists and overnight stays in 2019 is as follows: the number of tourists is 1.184.963, of which 427.370 are domestic tourists, and 757.593 are foreign tourists; and the number of overnight stays in 2019 is 3.262.398, of which 1.684.627 are domestic tourists, and 1.577.771 are foreign tourists. Despite all the positive social, cultural, and economic impacts of tourism, it is still the most "sensitive" activity to implications such as military actions, terrorism, natural disasters, and catastrophes, as well as the spread of various diseases that limit or prevent tourists from traveling. The COVID-19 crisis that erupted in late 2019 led to the declaration of a global pandemic by the World Health Organization (WHO). The spread of the virus restricted movement of people in all destinations around the world. The pandemic (spread of disease in large territories) and the change in people's living habits have led to the stagnation of the economic activity in the entire economies. The main goal of this study is to explore the effects on the tourism and hospitality sector that is affected by the health-economic crisis caused by the COVID-19 virus and offer recommendations to deal with the economic consequences. To achieve the study's objective, we analyzed the country's tourism and hospitality sector; researched the international experiences in dealing with this crisis and the measures taken in the country, related to the tourism and hospitality sector. In the study, based on the conducted research (surveys and interviews), we made recommendations for the short-term and long-term measures to deal with the COVID-19 crisis.