Graph and download economic data for Government consumption expenditures and gross investments: Income security: Unemployment (W609RC1A027NBEA) from 1959 to 2023 about social assistance, investment, gross, consumption expenditures, consumption, government, income, unemployment, GDP, and USA.

The Office of Policy and Management (OPM) prepares the Public Investment Community (PIC) index not later than July 15 annually, pursuant to §7-545 of the Connecticut General Statutes (CGS). The PIC index measures the relative wealth and need of Connecticut’s towns by ranking them in descending order by their cumulative point allocations for: (1) per capita income; (2) adjusted equalized net grand list per capita; (3) equalized mill rate; (4) per capita aid to children receiving Temporary Family Assistance program benefits; and (5) unemployment rate. Pursuant to CGS §7-545 the PIC index includes each town that has a cumulative point ranking in the top quartile of the PIC Index (i.e. the 42 towns with the highest number of points). When a town’s ranking falls below the top quartile in a given fiscal year, the town's designation as a Public Investment Community continues for that year and the following four fiscal years. As a result, the PIC index includes certain towns carried over from previous fiscal years. The PIC index determines eligibility for several financial assistance programs that various agencies administer, including: -Urban Action Bond Assistance -Small Town Economic Assistance Program -Community Economic Development Program -Residential Mortgage Guarantee Program -Education Cost Sharing -Malpractice Insurance Purchase Program -Connecticut Manufacturing Innovation Fund -Enterprise Corridor Zone Designation Most of the towns included on the PIC index are eligible to elect for assistance under the Small Town Economic Assistance Program (STEAP) in lieu of Urban Action Bond assistance, pursuant to CGS §4-66g(b). An eligible town’s legislative body (or its board of selectmen if the town’s legislative body is the town meeting) must vote to choose STEAP assistance and the town must notify OPM following the vote. STEAP election is valid for four years and the statute allows extensions for additional four-year periods. STEAP election is not available for Ansonia, Bridgeport, Bristol, Danbury, East Hartford, Enfield, Groton, Hartford, Killingly, Manchester, Meriden, Middletown, New Britain, New Haven, New London, Norwalk, Norwich, Stamford, Torrington, Vernon, Waterbury, West Hartford, West Haven, and Windham. Pursuant to CGS §7-545, the following municipalities are also Public Investment Communities: Groton Montville Preston Scotland Thomaston Thompson Voluntown Wethersfield

Workers’ compensation and other insurance funds businesses have experienced significant changes in recent years, largely driven by economic fluctuations and shifts in investment income. The crash of the US economy in 2020 due to pandemic-related restrictions placed immense pressure on the industry. Business formation plunged and unemployment soared, resulting in a diminished customer base for insurance funds and a steep drop in revenue. Regardless, the Federal Reserve's injection of liquidity into the financial system propelled stock prices upward, boosting investment income for insurance providers. This increase in investment income provided some relief for providers, enabling them to cover expenses and sustain profits despite revenue losses. The relaxation of COVID-19 restrictions spurred economic recovery in 2021, driving unemployment down and corporate profit up. This positive economic climate increased demand for insurance services and enhanced investment income due to robust stock market conditions. However, since 2022, inflation has wreaked havoc, causing businesses and organizations to slash investments in insurance funds amid soaring prices. More recently, rising interest rates have reduced downstream demand due to the emergence of recessionary fears, but revenue and profit have expanded because of growing returns on fixed-income products. Overall, revenue for workers’ compensation and other insurance funds has inched downward at a CAGR of 0.2% over the past five years, reaching $56.6 billion in 2025. This includes a 0.5% rise in revenue in that year. Looking ahead, providers are poised for moderate growth over the next five years. As the US economy stabilizes, with solid GDP growth and potential increases in business formation and employment, the customer base for insurance funds is likely to expand. These favorable economic conditions should bolster consumer confidence and investment in the stock market, leading to greater investment income for the industry. Nonetheless, larger players are expected to dominate, given their ability to invest in cutting-edge technologies like AI for predicting claim risks and optimizing business operations. Smaller providers may face intensified internal competition, prompting some to exit the market, while others could focus on niche offerings or invest in technological advancements to remain viable and competitive. Overall, revenue for workers’ compensation and other insurance funds is expected to expand at a CAGR of 1.3% over the next five years, reaching $60.3 billion in 2030.

Initial Jobless Claims in France increased to -11.20 thousand in May 2025 from -175.90 thousand in April 2025. This dataset provides the latest reported value for - France Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This statistic shows the unemployment rate in Greece from 1999 to 2024. In 2024, the unemployment rate in Greece was around 10.13 percent. Today, Greece reports the highest unemployment rate of all EU states. Greece's financial situation Greece is a developed country with a high-income economy, whose primary industry revolves around tourism and shipping. Agriculture also plays an important role for the country’s economy, more specifically for the EU. Greece had experienced large amounts of economic growth from the 1950s to the 1970s, however was economically devastated by the Great Recession in 2009 as well its own government debt crisis. Since the early 2000s, small increases in national debt were present within the Greek economy. These small increases turned into rather substantial surges between 2008 and 2011, which resulted in a large amount of accumulated public debt. However, financial assistance from several countries around the world as well as stimulus packages from the EU were issued to Greece, with the hopes of structural adjustments in the government and better decision making within the country in order to decrease national debt and increase productivity. The financial assistance helped stabilize Greece’s debt over the past several years, however many countries are arguing just how useful this support is, mostly because Greece has not made significant strides to improve its economy. As a result, consumers have become less optimistic about the possibility of a short term economic recovery in Greece. Additionally, investors have remained hesitant on investing into the country, generally due to an increasing debt-to-GDP ratio, which is ranked atop all countries in the European Union. The so-called debt-to-GDP ratio is an important indicator of a country’s ability to pay back its debts without incurring further debt.

The FAOSTAT monthly Food CPI and General CPI database was based on the ILO CPI data until December 2014. In 2014, IMF-ILO-FAO agreed to transfer global CPI data compilation from ILO to IMF. Upon agreement, CPIs for all items and its sub components originates from the International Monetary Fund (IMF), and the UN Statistics Division(UNSD) for countries not covered by the IMF. However, due to a limited time coverage from IMF and UNSD for a number of countries, the Organisation for Economic Co-operation and Development (OECD), Central Bank of Western African States (BCEAO), Eastern Caribbean Central Bank (ECCB), UNdata, United Nations Conference on Trade and Development (UNCTAD) and national statistical office website data are used for missing historical data from IMF and UNSD food CPI.

The FAO CPI dataset for all items(or general CPI) and the Food CPI, consists of a complete and consistent set of time series from January 2000 onwards. Data gaps on monthly Food CPI and General CPI are filled using statistical estimation procedures to have full data coverage for all countries for Food CPI and for General CPI. These indices measure the price change between the current and reference periods of the average basket of goods and services purchased by households. The General CPI is typically used to measure and monitor inflation, set monetary policy targets, index social benefits such as pensions and unemployment benefits, and to escalate thresholds and credits in the income tax systems and wages in public and private wage contracts. The FAOSTAT monthly Food CPI inflation rates are annual year-over-year inflation or percentage change over corresponding month of the previous year.

The data included in Data360 is a subset of the data available from the source. Please refer to the source for complete data and methodology details.

This collection includes only a subset of indicators from the source dataset.

Panel unit root tests for Chinese OFDI and youth unemployment in SSA.

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.

why is mutual fund investing a good idea for retirement, but not for your emergency fund or short-term savings?

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 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.

if you were looking to invest in a mutual fund focused on safety and minimal growth, what type of mutual fund would you invest in?

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