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.

As of April 2021, Mexico's gross domestic product (GDP) was forecasted to increase by five percent during 2021. Mexico was one of the Latin American countries that faced the worst recession after the COVID-19 pandemic, as its GDP fell over eight percent in 2020. Among the biggest economies in the region, Brazil was expected to experience one of the lowest GDP growth in 2021, at around 3.7 percent.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

This dataset provides annual GDP data for 196 countries from 2005 to 2025, measured in USD billions. Data is sourced from the International Monetary Fund (IMF).

Coverage: 196 countries + header row Time span: 2005–2024 (reported), 2025 (projected) Notable trends: The data clearly shows the impact of the 2008 global financial crisis and the 2020 COVID-19 pandemic on world economies. Missing values: In some cases, GDP values are unavailable because countries did not report them.

Usability

Trend analysis — Study global and regional GDP growth patterns across two decades.

Forecasting models — Train ARIMA, Prophet, LSTM, or other models to predict future GDP.

Comparative studies — Benchmark economic performance between countries, continents, or economic blocs (e.g., G7, BRICS).

Impact assessment — Analyze the effect of global events such as the 2008 crisis and COVID-19 on GDP.

Correlation research — Combine with other datasets (population, inflation, CO₂ emissions) for cross indicator analysis.

Visualization projects — Build dashboards, choropleth maps, or interactive charts to illustrate global growth.

Educational use — Teach concepts of macroeconomics, time series data, and forecasting in classrooms.

Investment & policy insights — Support macro level decision making, financial market analysis, or policy research.

ObjectiveTo quantitatively assess the impact of COVID-19 pandemic on public health, as well as its economic and social consequences in major economies, which is an international public health concern. The objective is to provide a scientific basis for policy interventions.Subject and methodsThis study utilizes a multi-country, multi-sector CGE-COVID-19 model to analyze the repercussions of the pandemic in 2022. The re-search focuses on quantifying the effects of COVID-19 on the macroeconomy and various industry sectors within six economies: the United States, China, the EU, the United Kingdom, Japan, and South Korea.ResultsThe COVID-19 pandemic shock had the most significant impact on China and the EU, followed by notable effects observed in the United States and the United Kingdom. In contrast, South Korea and Japan experienced relatively minimal effects. The reduction in output caused by the pandemic has affected major economies in multiple sectors, including real industries such as forestry and fisheries, and the services such as hotels and restaurants.ConclusionThe overall negative macroeconomic impact of the epidemic on major economies has been significant. Strategic interventions encompassing initiatives like augmenting capital supply, diminishing corporate taxes and fees, offering individual subsidies, and nurturing international cooperation held the potential to mitigate the detrimental economic consequences and enhance the global-economic amid the pan-demic. Consequently, this study contributes to the advancement of global anti-epidemic policies targeting economic recovery. Moreover, using the CGE-COVID-19 model has enriched the exploration of general equilibrium models in PHEIC events.

The United States has had the highest economic growth in the G7 since the start of the COVID-19 pandemic, with its economy *** percent larger in the first quarter of 2023, when compared with the fourth quarter of 2019. By contrast, the United Kingdom and Germany have both seen their economies shrink by *** percent in the same time period.

The Gross Domestic Product (GDP) of countries and regions from 1960 to 2020 provides a comprehensive view of economic development over six decades. GDP measures the total value of goods and services produced in a country or region over a specific period and is an important indicator of economic health and growth. Below is a summary of the GDP trends for major regions and selected countries:

Global Overview (1960-2020) 1- 1960s-1980s: During this period, many developed economies such as the United States, Japan, and Western European countries experienced robust economic growth. This was a time of post-World War II reconstruction, technological advancement, and increasing globalization.

2- 1990s-2000s: The fall of the Soviet Union in the early 1990s marked a shift in global economic dynamics. Many former Soviet states and Eastern European countries transitioned to market economies. Asia, particularly China and India, began to emerge as major economic players due to economic reforms and rapid industrialization.

3- 2010s-2020: The 2010s were marked by steady growth in advanced economies, while emerging markets such as China, India, Brazil, and others became significant contributors to global GDP. However, the COVID-19 pandemic in 2020 led to a severe global economic downturn.

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.

This dataset provides a comprehensive view of global economic trends, combining multiple essential indicators for analysis and research. The data focuses on the period from 2020 to 2023 and includes two key components:

1. GDP Per Capita and Inflation (2020–2023)

Scope: Yearly GDP per capita (in USD) and inflation rates per countries over the four-year period.

1. Population (2023)

Scope: The total population of each country at the end of 2023.

The dataset is meticulously compiled from trusted sources:

GDP per capita and inflation data are sourced from the World Bank national accounts data and OECD National Accounts data files.

Population data is derived from the World Bank Data Catalog (Population Ranking).

Potential Applications

Analyze the impact of inflation on economic growth during and after the pandemic.

Examine relationships between GDP per capita and population size.

Compare economic indicators across countries and regions.

Key Features: Clean, structured, and ready-to-use format.

Country-level granularity for detailed comparisons.

Suitable for trend analysis, visualizations, and predictive modeling.

Licensing: This dataset is licensed under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license. You are free to copy, modify, and distribute the data for any purpose, including commercial use, as long as appropriate credit is given to the World Bank.

According to recent estimates, the most affected sectors by the coronavirus pandemic in Latin America would be wholesale and retail trade as well as services in general, such as tourism, foodservice, transport, and communications. In 2020, this group of most affected sectors was forecasted to represent more than 16 percent of Brazil’s gross domestic product (GDP). Among the countries shown in this graph, Brazil is the nation where sectors moderately affected by the pandemic could represent the highest contribution to GDP (75.8 percent).

Which Latin American economies were most vulnerable to the pandemic? In 2020, the economic sectors most affected by the coronavirus pandemic - wholesale and retail, hotels and restaurants, transport and services in general - were forecasted to account for 35.5 percent of Panama’s GDP. In addition, the moderately and most affected economic segments were estimated to contribute the most to Panama’s GDP (a combined 97.6 percent) than any other country in this region. A similar scenario was projected in Mexico, where the sectors that would least suffer the pandemic's negative effects would account for only 3.4 percent of GDP.

Did the pandemic put a stop to economic growth in Latin America? Economic growth changed dramatically after the COVID-19 outbreak. Most of the largest economies in Latin America fell under recession in 2020. Estimates predict a more optimistic scenario for 2021, with countries such as Mexico, Colombia, and Argentina growing their GDP at least five percent.

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.

BackgroundThis study aimed at examining the combined outcomes of the COVID-19 pandemic and a collapsing economy on the quality of life (QOL) of the general Lebanese population.MethodsA cross-sectional study was conducted from 10-18 May 2020, via an online-based questionnaire using the snowball sampling technique. It enrolled 502 adult participants.

This dataset covers four articles about the effects of the COVID-19 pandemic and a Collapsing Economy on Mental Health in Lebanon

The coronavirus (COVID-19) pandemic, has had a significant impact on the global economy. In 2020, global Gross Domestic Product (GDP) decreased by *** percent, while the forecast initially was *** percent GDP growth. As the world's governments are working towards a fast economic recovery, the GDP increased again in 2021 by *** percent. Global GDP increased by over ***** 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.

Abstract

The ongoing coronavirus pandemic, along with the preventive measures designed to slow its spread, are putting great stress on India's economy, and affecting the lives and livelihoods of millions of people, including refugees across the country. To determine the exact social and economic consequences of the crisis, UNDP and UNICEF, are working under the leadership of the UN Resident Coordinators, and in close collaboration with specialized UN agencies, to assess the socio-economic impacts of the COVID-19 pandemic on vulnerable communities. UNHCR led the socio economic impact assessment for refugee population in India. The assessment was conducted in collaboration with UNICEF and in partnership with BOSCO.

As of June 2020, 40,068 refugees and asylum seekers from different nationalities are registered with UNHCR in India (28,053 refugees and 12,015 asylum seekers). Approximately 51% of the population registered with UNHCR lives in Delhi NCR, the remaining population live throughout the country, with bigger groups in Hyderabad, Jammu and Mewat. Rohingya are the largest group of persons of concern to UNHCR in India with 17,772 persons, followed by Afghans (15,806 persons). Of the total population registered with UNHCR, 47% are women and girls while 16% are persons with specific needs.

The survival mechanism for most of the refugees and asylum seekers is mainly based on a daily income that is immensely challenged with the ongoing lockdown and restriction of movement introduced by the central and state governments. These restrictions make it impossible for asylum seekers and refugees to reach the location of their informal employment or daily income generating activities, or to receive customers for their goods and services. Their income and possible savings have dried up leaving them with no means to adequately provide for their families, including in the areas of food, shelter and medicine

Geographic coverage

National

Analysis unit

Individuals and households

Universe

All refugees registered by UNHCR in India.

Kind of data

Sample survey data [ssd]

Sampling procedure

Clustered random sampling, with clusters divided by region (Delhi, outside Delhi), and legal status (Asylum seekers and Refugees).

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

Questionnaires included 9 modules: 1. General information 2. Awareness of COVID outbreak 3. Current work situation and impact on household income 4. Social protection at times of lockdown 5. Life at times of lockdown 6. Scenario of work during lockdown relaxation/after lockdown 7. Protection 8. Education/Children's Protection/SGBV 9. General questions

Cleaning operations

Data was cleaned and anonymized for licensed use.

Basic descriptive statistics characterising the diagnostic features for EU-28 countries.

Analysed conditions for doing business in 3SI countries.

Source: UNECE Statistical Database, compiled from national and international (CIS, EUROSTAT, IMF, OECD, World Bank) official sources.General note: The UNECE secretariat presents time series ready for immediate analysis. When appropriate, source segments with methodological differences have been linked and rescaled to build long consistent time series.The national accounts estimates are compiled according to 2008 SNA (System of National Accounts 2008) or 1993 SNA (System of National Accounts 1993).Constant price estimates are based on data compiled by the National Statistical Offices (NSOs), which reflect various national practices (different base years, fixed base, chain, etc.). To facilitate international comparisons, the data reported by the NSOs have been scaled to the current price value of of the common reference year. The resulting chain constant price data are not additive.Common currency (US$) estimates are computed by the secretariat using purchasing power parities (PPPs), which are the rates of currency conversion that equalise the purchasing power of different currencies. PPPs, and not exchange rates, should be used in international comparisons of GDP and its components.Growth rates (per cent) are over the preceding period, unless otherwise specified.Contributions to per cent growth in GDP (in percentage points) are over the preceding period, unless otherwise specified.Regional aggregates are computed by the secretariat. For national accounts all current price aggregates are sums of national series converted into US$ at current PPPs of GDP; all constant price aggregates are calculated by summing up national series scaled to the price level of the common reference year and then converted into US$ using PPPs of GDP of the common reference year. Due to conversion and rounding the resulting aggregates and components could be non-additive.Aggregates are computed for the following regions:UNECE-52:Albania; Armenia; Austria; Azerbaijan; Belarus; Belgium; Bosnia and Herzegovina; Bulgaria; Canada; Croatia; Cyprus; Czech Republic; Denmark; Estonia; Finland; France; Georgia; Germany; Greece; Hungary; Iceland; Ireland; Israel; Italy; Kazakhstan; Kyrgyzstan; Latvia; Lithuania; Luxembourg; Malta; Montenegro; Netherlands; North Macedonia; Norway; Poland; Portugal; Republic of Moldova; Romania; Russian Federation; Serbia; Slovakia; Slovenia; Spain; Sweden; Switzerland; Tajikistan; Turkey; Turkmenistan; Ukraine; United Kingdom; United States; Uzbekistan.North America-2:Canada; United States.European Union-27 (31/12/2020):Austria; Belgium; Bulgaria; Cyprus; Croatia; Czech Republic; Denmark; Estonia; Finland; France; Germany; Greece; Hungary; Ireland; Italy; Latvia; Lithuania; Luxembourg; Malta; Netherlands; Poland; Portugal; Romania; Slovakia; Slovenia; Spain; Sweden.Euro area-20:Austria; Belgium; Croatia; Cyprus; Estonia; Finland; France; Germany; Greece; Ireland; Italy; Latvia; Lithuania; Luxembourg; Malta; Netherlands; Portugal; Slovakia; Slovenia; Spain.Eastern Europe, Caucasus and Central Asia (EECCA):Armenia; Azerbaijan; Belarus; Georgia; Kazakhstan; Kyrgyzstan; Republic of Moldova; Russian Federation; Tajikistan; Turkmenistan; Ukraine; Uzbekistan.CIS-11:Armenia; Azerbaijan; Belarus; Kazakhstan; Kyrgyzstan; Republic of Moldova; Russian Federation; Tajikistan; Turkmenistan; Ukraine; Uzbekistan.Western Balkans-6:Albania; Bosnia and Herzegovina; Croatia; Montenegro; North Macedonia; Serbia... - data not availableThe Coronavirus (COVID-19) pandemic impacts the production of statistics and may limit available resources and data sources. This may impact the quality of statistics for 2020, and could lead to later revisions.

Fault Lines Widen in the Global Recovery

Economic prospects have diverged further across countries since the April 2021 World Economic Outlook (WEO) forecast. Vaccine access has emerged as the principal fault line along which the global recovery splits into two blocs: those that can look forward to further normalization of activity later this year (almost all advanced economies) and those that will still face resurgent infections and rising COVID death tolls. The recovery, however, is not assured even in countries where infections are currently very low so long as the virus circulates elsewhere.

The global economy is projected to grow 6.0 percent in 2021 and 4.9 percent in 2022.The 2021 global forecast is unchanged from the April 2021 WEO, but with offsetting revisions. Prospects for emerging market and developing economies have been marked down for 2021, especially for Emerging Asia. By contrast, the forecast for advanced economies is revised up. These revisions reflect pandemic developments and changes in policy support. The 0.5 percentage-point upgrade for 2022 derives largely from the forecast upgrade for advanced economies, particularly the United States, reflecting the anticipated legislation of additional fiscal support in the second half of 2021 and improved health metrics more broadly across the group.

Recent price pressures for the most part reflect unusual pandemic-related developments and transitory supply-demand mismatches. Inflation is expected to return to its pre-pandemic ranges in most countries in 2022 once these disturbances work their way through prices, though uncertainty remains high. Elevated inflation is also expected in some emerging market and developing economies, related in part to high food prices. Central banks should generally look through transitory inflation pressures and avoid tightening until there is more clarity on underlying price dynamics. Clear communication from central banks on the outlook for monetary policy will be key to shaping inflation expectations and safeguarding against premature tightening of financial conditions. There is, however, a risk that transitory pressures could become more persistent and central banks may need to take preemptive action.

Risks around the global baseline are to the downside. Slower-than-anticipated vaccine rollout would allow the virus to mutate further. Financial conditions could tighten rapidly, for instance from a reassessment of the monetary policy outlook in advanced economies if inflation expectations increase more rapidly than anticipated. A double hit to emerging market and developing economies from worsening pandemic dynamics and tighter external financial conditions would severely set back their recovery and drag global growth below this outlook’s baseline.

Multilateral action has a vital role to play in diminishing divergences and strengthening global prospects. The immediate priority is to deploy vaccines equitably worldwide. A $50 billion IMF staff proposal, jointly endorsed by the World Health Organization, World Trade Organization, and World Bank, provides clear targets and pragmatic actions at a feasible cost to end the pandemic. Financially constrained economies also need unimpeded access to international liquidity. The proposed $650 billion General Allocation of Special Drawing Rights at the IMF is set to boost reserve assets of all economies and help ease liquidity constraints. Countries also need to redouble collective efforts to reduce greenhouse gas emissions. These multilateral actions can be reinforced by national-level policies tailored to the stage of the crisis that help catalyze a sustainable, inclusive recovery. Concerted, well-directed policies can make the difference between a future of durable recoveries for all economies or one with widening fault lines—as many struggle with the health crisis while a handful see conditions normalize, albeit with the constant threat of renewed flare-ups.

Title: GDP & Crises Analysis Using World Bank Indicators

**Source: **World Bank Open Data

License: This dataset is provided by the World Bank Open Data initiative and is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Description: This dataset includes key global development indicators collected from the World Bank database. It covers country-level annual data on: - Gross Domestic Product (GDP, current US$) - Population - Poverty headcount ratio at national poverty line (% of population) - Natural Resource Rents (% of GDP)

Objective: This project investigates how major global crises — specifically the 2008 Financial Crisis and the COVID-19 pandemic — influenced GDP across countries. The study also explores how structural factors like population size, poverty levels, and natural resource dependence relate to total GDP and GDP per capita.

Key Research Questions: - How did GDP respond to the 2008 and COVID-19 crises across different regions? - What is the relationship between population and GDP versus GDP per capita? - How do poverty rates correlate with national income? - Does natural resource rent (as a share of GDP) positively or negatively relate to economic performance?

Methods Used: - Descriptive statistics and trend analysis - Econometric modeling (linear regression, fixed effects models) - Interaction analysis (e.g., poverty × population) - Visualization using ggplot2

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.