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.

Due to the coronavirus (COVID-19) outbreak in 2020, many Norwegian companies stated that they have experienced lower demand and cancellations. This is the result of a survey among member companies of the Confederation of Norwegian Enterprise (NHO), conducted regularly. In March 2020, 68 percent experienced lower demand or cancellations, which had sunk to 49 percent in January 2021. Furthermore, there was an improvement of the situation over the summer of 2021, with fewer companies in risk of bankruptcies, fewer companies experiencing liquidity problems, and fewer experiencing a lower turnover than usual. However, in December 2021, these numbers increased again after COVID-19-cases started to increase again over the winter.

The first case of COVID-19 in Norway was confirmed on February 26, 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

China removed most of its COVID-19 restrictions in November 2022 following a year of weak growth. Despite initial uncertainty about sustained COVID-19 outbreaks, the Chinese economy has begun to rebound, driven by domestic consumption. The rebound is likely to boost global growth.

https://static01.nyt.com/images/2020/11/18/nyregion/00nyblind1/merlin_179220645_b77f46ff-a503-40b6-bf2b-4922a676e61b-superJumbo.jpg" alt=""> This dataset offers a comprehensive insight into the economic trajectories of nine major economies from the onset of the COVID-19 pandemic through the beginning of 2024. It encompasses crucial economic indicators and financial market data, covering aspects such as manufacturing and services performance, consumer sentiment, monetary policies, inflation rates, unemployment rates, and overall economic output. Additionally, it includes price data for each economy, with values compared against the dollar for clarity. With data spanning this period, the dataset provides valuable insights for analysts, researchers, and stakeholders into the impact of the pandemic and other significant events on these economies, facilitating an assessment of their resilience, challenges, and opportunities.

Countries included : Australia / Canada / China / Europe / Japan / New Zealand / Switzerland / United Kingdom / United States

Column Descriptions:

• Country : The name of the country.
• Date : The date format (e.g., YYYY-MM-DD).
• Manufacturing PMI : Purchasing Managers' Index (PMI) for the manufacturing sector, indicating the economic health and activity level of the manufacturing industry.
• Services PMI : Purchasing Managers' Index (PMI) for the services sector, indicating the economic health and activity level of the services industry.
• Consumer Confidence : A measure of consumer sentiment or confidence in the economy, indicating consumers' optimism or pessimism about their financial situation and the overall state of the economy.
• Interest Rates : The prevailing interest rates set by the central bank or monetary authority, which influence borrowing costs and investment decisions.
• CPI YoY : Consumer Price Index (CPI) Year-over-Year change, indicating the percentage change in the average price level of a basket of consumer goods and services over the previous year.
• Core CPI : Core Consumer Price Index (CPI), which excludes volatile items such as food and energy prices, providing a measure of underlying inflation trends.
• Unemployment Rate : The percentage of the labor force that is unemployed and actively seeking employment, indicating the health of the labor market.
• GDP YoY : Gross Domestic Product (GDP) Year-over-Year change, indicating the percentage change in the total value of goods and services produced by a country's economy.
• Ticker: Ticker symbol for the corresponding financial asset or index.
• Open: The opening price of the financial asset or index on the specified date.
• High: The highest price of the financial asset or index during the specified date.
• Low: The lowest price of the financial asset or index during the specified date.
• Close: The closing price of the financial asset or index on the specified date.

According to a survey by McKinsey & Company on economic optimism during COVID-19 in Indonesia, ** percent of respondents stated that they were optimistic that the Indonesian economy would rebound after COVID-19 as at May 22. This figure was down from a high of ** percent as at April 12. Indonesia was one of the most countries most affected by COVID-19 in Southeast Asia.

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

Since the COVID-19 pandemic, the United States has experienced sharply rising then falling inflation alongside persistent labor market imbalances. This Economic Commentary interprets these macroeconomic dynamics, as represented by the Beveridge and Phillips curves, through the lens of a macroeconomic model. It uses the structure of the model to rationalize the debate about whether the US economy can expect a hard or soft landing. The model is surprised by the resiliency of the labor market as the US economy experienced disinflation. We suggest that the model’s limited ability to capture this resiliency is a feature of using a linear model to forecast the historically unprecedented movements seen after the pandemic among inflation, unemployment, and vacancy rates. We explain how, by adjusting the model to mimic congestion in a tight labor market and greater wage and price flexibility in a high-inflation environment, as during the post-pandemic period, the model can then capture what has been a path consistent with a soft landing.

Cambodia’s economic recovery solidified in 2022 with real growth accelerating to 5.2 percent. After shifting to “living with COVID-19” in late 2021, the economy is firmly on a path to recovery and has now returned to its pre-pandemic growth trajectory. Initially led by the strong performance of export-oriented manufacturing, growth drivers are rotating to the services and agriculture sectors. Driven by pent-up consumer demand, the overall contribution of the services sector to economic growth is returning to the 2019 levels. Underpinned by the complete removal of COVID-19-related mobility restrictions and China’s recent reopening, international arrivals have picked up, reaching 830,000 during the first two months of 2023, approaching pre-pandemic levels.

In 2025, the export value of computers and peripheral devices from South Korea is forecasted to be roughly 16.07 billion U.S. dollars. This has fluctuated throughout the survey period.

Selection of indicators for measuring and monitoring socio-economic impacts of COVID-19 pandemic on economies of the Pacific region.

Find more Pacific data on PDH.stat.

According to a survey carried out in early-2021, nearly three quarters of Mexican respondents stated that they believed that it would take between six months and *** year for their families to overcome the economic impact of the COVID-19 pandemic. Only around *** in every ten Mexicans surveyed declared that they estimated to get over their financial difficulties in less than three months.

We summarize the results from an ongoing survey that asks consumers questions related to the recent coronavirus outbreak, including their expectations for how the economy is likely to be affected by the outbreak and how their own behavior has changed in response to it. The survey began in early March, providing a window into how consumers’ responses have evolved in real time since the early days of the acknowledged spread of COVID-19 in the United States. In updating and charting the survey’s findings on the Cleveland Fed’s website going forward, we seek to inform policymakers and researchers about consumers’ beliefs during a time of high uncertainty and unprecedented policy responses.

Note: Blueprint has been retired as of June 15, 2021. This dataset will be kept up for historical purposes, but will no longer be updated. California has a new blueprint for reducing COVID-19 in the state with revised criteria for loosening and tightening restrictions on activities. Every county in California is assigned to a tier based on its test positivity and adjusted case rate for tier assignment. Additionally, a new health equity metric took effect on October 6, 2020. In order to advance to the next less restrictive tier, each county will need to meet an equity metric or demonstrate targeted investments to eliminate disparities in levels of COVID-19 transmission, depending on its size. The California Health Equity Metric is designed to help guide counties in their continuing efforts to reduce COVID-19 cases in all communities and requires more intensive efforts to prevent and mitigate the spread of COVID-19 among Californians who have been disproportionately impacted by this pandemic. Please see https://www.cdph.ca.gov/Programs/CID/DCDC/Pages/COVID-19/COVID19CountyMonitoringOverview.aspx for more information. Also, in lieu of a Data Dictionary, please refer to the detailed explanation of the data columns in Appendix 1 of the above webpage. Because this data is in machine-readable format, the merged headers at the top of the source spreadsheet have not been included: The first 8 columns are under the header "County Status as of Tier Assignment" The next 3 columns are under the header "Current Data Week Tier and Metric Tiers for Data Week" The next 4 columns are under the header "Case Rate Adjustment Factors" The next column is under the header "Small County Considerations" The last 5 columns are under the header "Health Equity Framework Parameters"

According to a survey conducted by Ipsos, ** percent of respondents from Australia stated that they strongly or somewhat disagreed that the economy would recover quickly once the lockdown was over. Compared to other countries, Australian respondents were among the least confident that the economy would recover.

Context

This data looks at the impact of COVID-19 on employment, income, ability to pay expenses, and more in Côte D'Ivoire, Kenya, Mozambique Nigeria, and South Africa. https://data.humdata.org/dataset/economic-impact-of-covid-19-in-sub-saharan-africa

Content

Data is nationally representative by age, gender, and location, and is broken down by job type and formal or informal workers.

Acknowledgements

Roxana Elliot, Dataset' s author. https://data.humdata.org/dataset/economic-impact-of-covid-19-in-sub-saharan-africa

Inspiration

Covid-19 Pandemic.

The economy of Sweden experienced a recession in 2020, following the coronavirus (COVID-19) outbreak. According to a forecast from December 2022, the gross domestic product (GDP) of Sweden then increased by over five percent in 2021. However, growth was negative in 2023 as a result of the high inflation rates.

The underemployment rate, the percent of employed people who are working part-time but prefer to be working full-time, moves closely with the unemployment rate, rising during recessions and falling during expansions. Following the Great Recession, the underemployment rate had stayed persistently elevated when compared to the unemployment rate, that is, until the COVID-19 recession. Since then, it has been consistent with its pre-2008 levels. We find that changes in relative industry size account for essentially none of the underemployment rate increase after the Great Recession nor the underemployment rate decrease after the COVID-19 recession. Based on this finding, we do not expect the underemployment rate to revert to its pre-COVID-19 levels if industry composition reverts to its pre-COVID-19 structure.

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.

Decisions to shutdown economic activities to control the spread of COVID-19 early in the pandemic remain controversial, with negative impacts including high rates of unemployment. Here we present a counterfactual scenario for the state of California in which the economy remained open and active during the pandemic’s first year. The exercise provides a baseline against which to compare actual levels of job losses. We developed an economic-epidemiological mathematical model to simulate outbreaks of COVID-19 in ten large Californian socio-economic areas. Results show that job losses are an unavoidable consequence of the pandemic, because even in an open economy, debilitating illness and death among workers drive economic downturns. Although job losses in the counterfactual scenario were predicted to be less than those actually experienced, the cost would have been the additional death or disablement of tens of thousands of workers. Furthermore, whereas an open economy would have favoured populous, services-oriented coastal areas in terms of employment, the opposite would have been true of smaller inland areas and those with relatively larger agricultural sectors. Thus, in addition to the greater cost in lives, the benefits of maintaining economic activity would have been unequally distributed, exacerbating other realized social inequities of the disease’s impact.