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.

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.

During the COVID-19 pandemic, the global economy was devastated. However, the virus indirectly impacted the environment, causing record emission reductions around the world. As global governments decide on post COVID-19 economic recovery plans, opportunities have arisen to introduce measures that will benefit the environment, and prioritize the climate crisis. In a poll with thousands of residents across ** countries, many support a green economic recovery. The highest share of support was in India, at ** percent. India was hit hard by the virus, but it also saw carbon emissions drop for the first time in **** decades.

Global COVID-19 The Road to Economic Recovery is segmented by Application (Healthcare, Government, Retail, Tourism, Technology), Type (Healthcare Solutions, Digital Transformation, Remote Work Technologies, Economic Stimulus Programs, Supply Chain Recovery) and Geography(North America, LATAM, West Europe, Central & Eastern Europe, Northern Europe, Southern Europe, East Asia, Southeast Asia, South Asia, Central Asia, Oceania, MEA)

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.

In a 2020 online survey, ** percent of small business owners in the United States said they expected the economy to not recover from the impacts of COVID-19 until beyond 2021. Only ***** percent of respondents believed that the economy would be able to recover in a few more weeks.

This contains the dataset of the 1936 household consumption survey and 1930 census data used in "Fiscal Policy and Economic Recovery: The Case of the 1936 Veterans' Bonus." The underlying household survey data come from ICPSR study 08908. The Census data come from the IPUMS 5% sample from the 1930 Census. The primary data file is urban_lprob.dta. urban_nodups.dta contains a subset of these data for programming convenience. For further documentation, see the paper, and the data and program files posted on the American Economic Review's website.

In a survey conducted in September 2020, regarding consumer perception surrounding the economic recovery after coronavirus (COVID-19) in India, ** percent of the respondents are positive that the economy will bounce back to pre-COVID levels in the next few months. Majority of the respondents disagree that COVID-19 would cause a significant recession or a major economic depression.

Using panel data for a large set of high-income, emerging market, developing, and transition countries, we find robust evidence that the large output loss from financial crises and some types of political crises is highly persistent. The results on financial crises are also highly robust to the assumption on exogeneity. Moreover, we find strong evidence of growth over optimism before financial crises. We also find a distinction between the output impact of civil wars versus other crises, in that there is a partial output rebound for civil wars but no significant rebound for financial crises or the other political crises. (JEL D72, D74, E32, E44, O17, O47)

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Filtronic (FTC): Will Economic Recovery Fuel Growth?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This data package includes the underlying data and files to replicate the calculations, charts, and tables presented in From Rapid Recovery to Slowdown: Why Recent Economic Growth in Latin America Has Been Slow, PIIE Policy Brief 15-6. If you use the data, please cite as: De Gregorio, José. (2015). From Rapid Recovery to Slowdown: Why Recent Economic Growth in Latin America Has Been Slow. PIIE Policy Brief 15-6. Peterson Institute for International Economics.

description: This spreadsheet contains Oregon Recovery and Reinvestment Act (ARRA) Data reported on the State of Oregon Recovery site (http://oregon.gov/recovery/StimulusReporting/ARRA_Projects.shtml). Please note that this data reflects the preliminary data from the most recent reporting period of the American Recovery and Reinvestment Act of 2009, including data through March 30, 2011. This data available for viewing through an interactive map site. For this, and other information on ARRA projects, please visit Oregon's Economic Recovery site: http://www.oregon.gov/recovery. For the latest recovery award data, please visit http://www.recovery.gov.; abstract: This spreadsheet contains Oregon Recovery and Reinvestment Act (ARRA) Data reported on the State of Oregon Recovery site (http://oregon.gov/recovery/StimulusReporting/ARRA_Projects.shtml). Please note that this data reflects the preliminary data from the most recent reporting period of the American Recovery and Reinvestment Act of 2009, including data through March 30, 2011. This data available for viewing through an interactive map site. For this, and other information on ARRA projects, please visit Oregon's Economic Recovery site: http://www.oregon.gov/recovery. For the latest recovery award data, please visit http://www.recovery.gov.

In a May 2020 survey, 44 percent of surveyed CIOs said that they expect a U-Shaped economic recovery from COVID-19, with declines in revenue for the second and third quarters of 2020 followed by growth in 2021.

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

The dataset provided contains records related to the impact of various economic and operational factors on businesses in three major cities in the UAE: Dubai, Abu Dhabi, and Sharjah. Each record represents a business's condition during a specific period, capturing attributes related to profitability, operational status, government support, and recovery. Below is an analysis of the dataset: Attributes in the Dataset: 1. Geographic Location: Represents the city where the business operates: Dubai, Abu Dhabi, or Sharjah. This attribute allows for a regional analysis of how economic and operational disruptions vary across different urban areas. 2. Profitability Change: Indicates whether the business experienced a change in profitability: Increase, Decrease, or No Change. Provides insight into the economic performance of businesses under varying conditions. 3. Operational Disruptions: Describes the severity of operational challenges faced by businesses: None, Mild, Moderate, or Severe. Reflects the operational resilience or vulnerability of businesses. 4. Business Closure: Specifies whether the business remained operational or had to shut down: Open or Closed. This critical indicator highlights the impact of disruptions on business continuity. 5. Government Support: Indicates whether the business received any form of support: None, Loan, or Grant. Offers insights into the role of government interventions in aiding businesses during difficult periods. 6. Sector Type: Identifies the industry to which the business belongs, such as Retail, Hospitality, Tourism, Technology, or Manufacturing. Useful for understanding sector-specific challenges and opportunities. 7. Size of Business: Categorizes businesses as Small or Medium. This attribute helps analyze how business size impacts operational resilience and revenue loss. 8. Revenue Loss (%): Quantifies the percentage of revenue lost by the business due to disruptions. Provides a measure of economic impact and vulnerability. 9. Recovery Time (Months): Indicates the estimated number of months required for the business to recover. Reflects the time needed for businesses to return to pre-crisis levels, giving insights into recovery dynamics.

Acknowledgment

Special thanks to the framework provided in the original example from Data.World, which inspired the structured analysis of this dataset. This approach aids in generating actionable insights and a detailed understanding of the underlying trends.

Filtronic's strong financial performance, growing market share, and innovative product offerings indicate a positive outlook for the company. However, risks associated with supply chain disruptions, competition from larger players, and economic downturns should be considered, potentially impacting its future growth and profitability.

Neighborhood Stabilization Program DataHUD's Neighborhood Stabilization Program (www.hud.gov/nsp) provides emergency assistance to state and local governments to acquire and redevelop foreclosed properties that might otherwise become sources of abandonment and blight within their communities. The Neighborhood Stabilization Program (NSP) provides grants to every state, certain local communities, and other organizations to purchase foreclosed or abandoned homes and to rehabilitate, resell, or redevelop these homes in order to stabilize neighborhoods and stem the decline of house values of neighboring homes. The program is authorized under Title III of the Housing and Economic Recovery Act of 2008.There have been three rounds of funding for NSP. The Housing and Economic Recovery Act of 2008 provided a first round of formula funding to States and units of general local government, and is referred to herein as NSP1. The American Recovery and Reinvestment Act provided a second round of funds in 2009 awarded by competition, and is referred to herein as NSP2. The third round of funding, NSP3, was provided in 2010 as part of the Dodd-Frank Wall Street Reform Act and was allocated by formula.LMMI Block GroupsSee the data download feature under NSP3 to obtain the low-, moderate-, and middle-income (less than 120% of area median family income).

The statistic shows the GDP of the United Kingdom between 1987 and 2024, with projections up until 2030, in US dollars.Private-sector-led economic recoveryGDP is counted among the primary indicators that are used to gauge the state of health of a national economy. GDP is the total value of all completed goods and services that have been produced within a country in a given period of time, usually a year. GDP figures allow us to gain a broader understanding of a country’s economy in a clear way. Real GDP, in a similar way, is also a rather useful indicator; this is a measurement that takes prices changes (inflation and deflation) into account, thereby acting as a key indicator for economic growth.The gross domestic product of the United Kingdom is beginning to show signs of recovery since seeing a sharp decline in the wake of the financial crisis. The decreasing unemployment rate in the United Kingdom is also indicating that the worst could be over for the country. However, some concerns have arisen about what forms of employment are being represented, how stable the jobs are, and whether or not they are simply being cited by officials in government as validation for reforms that are criticized by opponents as being ‘ideologically motivated’. Whatever the political motivation, the coalition government’s efforts to let the private sector lead the economic recovery through increasing employment in the UK in the private sector appear, for now at least, to be working.

In an online survey of ************** targeting German companies in Japan, approximately *** in ***** respondents stated that they expected the Japanese economy to recover from the COVID-19 impact to its pre-crisis level later than *********. The earliest estimated time of recovery lies within the first half of 2021 as reported by around ** percent of respondents. However, roughly the same number of companies could not assess the future of Japan's economy at the time.

Italy Exports: fob: EU27: WR: Waste Collection & Materials Recovery (WM) data was reported at 59.881 EUR mn in Aug 2018. This records a decrease from the previous number of 91.748 EUR mn for Jul 2018. Italy Exports: fob: EU27: WR: Waste Collection & Materials Recovery (WM) data is updated monthly, averaging 31.368 EUR mn from Jan 1993 (Median) to Aug 2018, with 308 observations. The data reached an all-time high of 108.851 EUR mn in Mar 2017 and a record low of 2.068 EUR mn in Aug 1993. Italy Exports: fob: EU27: WR: Waste Collection & Materials Recovery (WM) data remains active status in CEIC and is reported by National Institute of Statistics. The data is categorized under Global Database’s Italy – Table IT.JA004: Exports: By SITC: EU Countries: Nace Rev.2.

As the global economy gradually recovers from the COVID-19 pandemic, the world price of crude oil is expected to surge by 42.9% in 2021 and remain stable in 2022.