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 saw growth fall by 2.2 percent, compared with an increase of 2.5 percent the year before. The last time the real GDP growth rates fell by a similar level was during the Great Recession in 2009, and the only other time since the Second World War where real GDP fell by more than one percent was in the early 1980s recession. The given records began following the Wall Street Crash in 1929, and GDP growth fluctuated greatly between the Great Depression and the 1950s, before growth became more consistent.

US Covid-19 Cases, Deaths and Mobility by State/County

Analyzing the Impact of the Pandemic on Low-Income Populations

By Liz Friedman [source]

About this dataset

Welcome to the Opportunity Insights Economic Tracker! Our goal is to provide a comprehensive, real-time look into how COVID-19 and stabilization policies are affecting the US economy. To do this, we have compiled a wide array of data points on spending and employment, gathered from several sources.

This dataset includes daily/weekly/monthly information at the state/county/city level for eight types of data: Google Mobility; Low-Income Employment and Earnings; UI Claims; Womply Merchants and Revenue; as well as weekly Math Learning from Zearn. Additionally, three files- Accounting for Geoids-State/County/City provide crosswalks between geographic areas that can be merged with other files having shared geographical levels.

Our goal here is to enable data users around the world to follow economic conditions in the US during this tumultuous period with maximum clarity and precision. We make all our datasets freely available so if you use them we kindly ask you attribute our work by linking or citing both our accompanying paper as well as this Economic Tracker at https://tracktherecoveryorg By doing so you are also agreeing to uphold our privacy & integrity standards which commit us both to individual & business confidentiality without compromising on independent nonpartisan research & policy analysis!

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How to use the dataset

This dataset provides US COVID-19 case and death data, as well as Google Community Mobility Reports, on the state/county level. Here is how to use this dataset:

• Understand the file structure: This dataset consists of three main files: 1) US Cases & Deaths by State/County, 2) Google Community Mobility Reports, and 3) Data from third-parties providing small business openings & revenue information and unemployment insurance claim data (Low Inc Earnings & Employment, UI Claims and Womply Merchants & Revenue).
• Select your Subset: If you are interested in particular types of data (e.g., mobility or employment), select the corresponding files from within each section based on your geographic area of interest – national, state or county level – as indicated in each filename.
• Review metadata variables: Become familiar with the provided variables so that you can select which ones you need to explore further in your analysis. For example, if analyzing mobility trends at a city level look for columns such as ‘Retailer_and_recreation_percent_change’ or ‘Transit Stations Percent Change’; if focusing on employment decline look for columns such pay or emp figures that align with industries of interest to you such as low-income earners (emp_{inclow},pay_{inclow}).
• Unify dateformatting across row values : Convert date formats into one common unit so that all entries have consistent formatting if necessary; for exampe some entries may display dates using YYYY/MM/DD notation while others may use MM//DD//YY format depending on their source datasets; make sure to review column labels carefully before converting units where needed..

• Merge datasets where applicable : Utilize GeoID crosswalks to combine multiple sets with same geographical coverageregionally covering ; example might be combining low income earnings figures with specific county settings by reference geo codes found in related documents like GeoIDs-County .
  6 . Visualise Data : Now that all the different measures have been reviewed can begin generating charts visualize findings . This process may include cleaning up raw figures normalizing across currency formats , mapping geospatial locations others ; once ready create bar graphs line charts maps other visual according aggregate output desired Insightful representations at this stage will help inform concrete policy decisions during outbreak recovery period..

  Remember to cite

Research Ideas

• Estimating the Impact of the COVID-19 Pandemic on Small Businesses - By comparing county-level Womply revenue and employment data with pre-COVID data, policymakers can gain an understanding of the economic impact that COVID has had on local small businesses.
• Analyzing Effects of Mobility Restrictions - The Google Mobility data provides insight into geographic areas where...

COVID-19 and attempts to slow its spread have led to a decline in economic activity unprecedented in both severity and speed. Although every part of the United States experienced dramatic decreases in activity, states in the Tenth Federal Reserve District, with lower COVID-19 cases as a percentage of the population, have fared slightly better. More recently, national and regional measures of business and consumer activity have improved but remain well below pre-pandemic levels.

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.

Business formation in the United States has been on a decline for several decades. Most prior economic recessions have accelerated this trend. However, the recent economic downturn associated with COVID-19 appears to have had the opposite effect: business formation, as measured by business applications, has actually surged since late May.

BEA has been researching the use of card transaction data as an early barometer of spending in the United States. Since the emergence of COVID-19, dramatic and fast-moving changes to the U.S. economy have increased the public and policymakers' need for more frequent and timely economic data. In response, BEA is presenting these estimates using daily payment card data to measure the effects of the pandemic on spending, updated approximately every two weeks. Note that these payment card transactions are not necessarily representative of total spending in an industry and the data have other limitations, described below. The estimates in these charts and tables are not a substitute for BEA's monthly and quarterly official data, which are grounded in well-tested and proven methodologies. An event study methodology is used to estimate the difference (in percentage points) in spending from the typical level (relative to the day of week, month, and annual trends) prior to the pandemic declared by the World Health Organization on March 11, 2020.

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.

In 2025, it was estimated that over 163 million Americans were in some form of employment, while 4.16 percent of the total workforce was unemployed. This was the lowest unemployment rate since the 1950s, although these figures are expected to rise in 2023 and beyond. 1980s-2010s Since the 1980s, the total United States labor force has generally risen as the population has grown, however, the annual average unemployment rate has fluctuated significantly, usually increasing in times of crisis, before falling more slowly during periods of recovery and economic stability. For example, unemployment peaked at 9.7 percent during the early 1980s recession, which was largely caused by the ripple effects of the Iranian Revolution on global oil prices and inflation. Other notable spikes came during the early 1990s; again, largely due to inflation caused by another oil shock, and during the early 2000s recession. The Great Recession then saw the U.S. unemployment rate soar to 9.6 percent, following the collapse of the U.S. housing market and its impact on the banking sector, and it was not until 2016 that unemployment returned to pre-recession levels. 2020s 2019 had marked a decade-long low in unemployment, before the economic impact of the Covid-19 pandemic saw the sharpest year-on-year increase in unemployment since the Great Depression, and the total number of workers fell by almost 10 million people. Despite the continuation of the pandemic in the years that followed, alongside the associated supply-chain issues and onset of the inflation crisis, unemployment reached just 3.67 percent in 2022 - current projections are for this figure to rise in 2023 and the years that follow, although these forecasts are subject to change if recent years are anything to go by.

In 2024 the real gross domestic product (GDP) of the United States increased by 2.8 percent compared to 2023.
What does GDP growth mean? Essentially, the annual GDP of the U.S. is the monetary value of all goods and services produced within the country over a given year. On the surface, an increase in GDP therefore means that more goods and services have been produced between one period than another. In the case of annualized GDP, it is compared to the previous year. In 2023, for example, the U.S. GDP grew 2.5 percent compared to 2022. Countries with highest GDP growth rate Although the United States has by far the largest GDP of any country, it does not have the highest GDP growth, nor the highest GDP at purchasing power parity. In 2021, Libya had the highest growth in GDP, growing more than 177 percent compared to 2020. Furthermore, Luxembourg had the highest GDP per capita at purchasing power parity, a better measure of living standards than nominal or real GDP.

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.

Despite an initially strong pace of recovery, the economy is a long way from pre-pandemic levels and several threats loom as Q4 approaches.

When I was searching for COVID-19 datasets online, I soon realized that there were no comprehensive datasets of the United States on a county level basis which included social, economic, and demographic factors in addition to the general case information that was already available on several sites. To quench my thirst for clean and relevant data, I proceeded to gather information from several various sources to compile the dataset I was looking for.

I started by looking for a reliable dataset that has general information such as confirmed cases, deaths, etc. I found John Hopkin's COVID-19 dataset to be the best one for this purpose as it is well organized and updated daily. Then, I set out looking for economic factors and population data for each county in the United States. I found a collection of such files compiled by the Economic Research Service branch of the USDA on their website. Finally, I had to find a dataset which had racial and demographic information for each county, which I found on the US Census Bureau's website under a page which was dedicated to county population data by several characteristics. Now that I had all the data I was looking for, I proceeded to find which counties were common in all datasets. After several hours of cleaning each dataset and extracting relevant information, I combined all the information into one CSV file with 2959 counties of clean information - exactly what I was looking for.

I hope that the Kaggle community will use this dataset to answer important questions regarding COVID-19 in the United States and the role that external economic, social, and demographic factors play in the shaping of the pandemic. I know that there are several patterns to be discovered and I sincerely hope that this helps our community understand just a little more about the pandemic than we do right now.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for Federal Net Outlays as Percent of Gross Domestic Product (FYONGDA188S) from 1929 to 2024 about outlays, Net, federal, GDP, and USA.

United States SBP: RE: Req Neg COVID Test from Employees Before Reporting: Yes data was reported at 4.700 % in 11 Apr 2022. This records an increase from the previous number of 3.700 % for 04 Apr 2022. United States SBP: RE: Req Neg COVID Test from Employees Before Reporting: Yes data is updated weekly, averaging 4.800 % from Feb 2021 (Median) to 11 Apr 2022, with 45 observations. The data reached an all-time high of 10.500 % in 10 Jan 2022 and a record low of 3.000 % in 05 Jul 2021. United States SBP: RE: Req Neg COVID Test from Employees Before Reporting: Yes data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

United States SBP: Req Neg COVID Test from Employees Before Reporting: Yes data was reported at 4.300 % in 11 Apr 2022. This records a decrease from the previous number of 5.200 % for 04 Apr 2022. United States SBP: Req Neg COVID Test from Employees Before Reporting: Yes data is updated weekly, averaging 6.500 % from Feb 2021 (Median) to 11 Apr 2022, with 45 observations. The data reached an all-time high of 13.500 % in 10 Jan 2022 and a record low of 3.200 % in 21 Jun 2021. United States SBP: Req Neg COVID Test from Employees Before Reporting: Yes data remains active status in CEIC and is reported by U.S. Census Bureau. The data is categorized under Global Database’s United States – Table US.S045: Small Business Pulse Survey: by Sector: Weekly. Beg Monday (Discontinued).

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in COVID-19 and the 2020 US presidential election: Did the pandemic cost Donald Trump reelection?, PIIE Working Paper 21-3.

If you use the data, please cite as: Noland, Marcus, and Eva (Yiwen) Zhang. (2021). COVID-19 and the 2020 US presidential election: Did the pandemic cost Donald Trump reelection?. PIIE Working Paper 21-3. Peterson Institute for International Economics

As of the first quarter of 2025, the GDP of the U.S. fell by 0.5 percent from the fourth quarter of 2024. GDP, or gross domestic product, is effectively a count of the total goods and services produced in a country over a certain period of time. It is calculated by first adding together a country’s total consumer spending, government spending, investments and exports; and then deducting the country’s imports. The values in this statistic are the change in ‘constant price’ or ‘real’ GDP, which means this basic calculation is also adjusted to factor in the regular price changes measured by the U.S. inflation rate. Because of this adjustment, U.S. real annual GDP will differ from the U.S. 'nominal' annual GDP for all years except the baseline from which inflation is calculated. What is annualized GDP? The important thing to note about the growth rates in this statistic is that the values are annualized, meaning the U.S. economy has not actually contracted or grown by the percentage shown. For example, the fall of 29.9 percent in the second quarter of 2020 did not mean GDP is suddenly one third less than a year before. In fact, it means that if the decline seen during that quarter continued at the same rate for a full year, then GDP would decline by this amount. Annualized values can therefore exaggerate the effect of short-term economic shocks, as they only look at economic output during a limited period. This effect can be seen by comparing annualized quarterly growth rates with the annual GDP growth rates for each calendar year.

The United States recorded a Government Debt to GDP of 124.30 percent of the country's Gross Domestic Product in 2024. This dataset provides - United States Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.