Initial Jobless Claims in the United States decreased to 227 thousand in the week ending July 5 of 2025 from 232 thousand in the previous week. This dataset provides the latest reported value for - United States Initial Jobless Claims - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The global unemployment insurance market is projected to reach a value of USD 1,547.37 million by 2033, expanding at a CAGR of 6.4% from 2023 to 2033. The market is driven by factors such as the increasing number of unemployment claims due to the COVID-19 pandemic, the growing awareness of unemployment insurance programs, and the increasing adoption of digital technologies. The market is segmented on the basis of type, application, and region. The type segment includes compulsory unemployment insurance system, non-compulsory unemployment insurance system, double unemployment insurance system, and conditional unemployment relief system. The application segment includes foreign personnel, retired personnel, farmers, and others. The regional segment includes North America, South America, Europe, Middle East & Africa, and Asia Pacific. The key players in the global unemployment insurance market include ACE Insurance, Achmea, AEGON, Allianz, Anadolu Hayat Emeklilik, Assicurazioni Generali, Assurant, AIA Group, AlfaStrakhovanie, Banamex, Banco Bilbao Vizcaya Argentaria, Banco Bradesco, BNP Paribas Cardif, China Life Insurance Company, China Pacific Insurance, CNP Assurances, Credit Agricole, DZ Bank, Garanti Emeklilik ve Hayat, Great Eastern Holdings, Grupo Nacional Provincial, Hanwha Life Insurance Company, HDFC Standard Life Insurance Company, ICICI Prudential Life Insurance Company, and others. These players offer a wide range of unemployment insurance products and services to individuals and businesses across the globe.

The global unemployment insurance market is a substantial and growing sector, driven by increasing unemployment rates globally, particularly in developing economies, and a growing awareness of the social and economic benefits of robust unemployment insurance systems. The market's expansion is fueled by governmental initiatives promoting social safety nets and the increasing adoption of both compulsory and non-compulsory unemployment insurance schemes across various regions. The diverse segments within this market, categorized by application (e.g., foreign personnel, retirees, farmers) and insurance system type (compulsory, non-compulsory, etc.), offer varied growth opportunities. While economic downturns and fluctuating employment rates can act as restraints, the long-term trend points towards continued market growth, driven by evolving societal needs and a greater focus on social welfare. The presence of numerous major insurance providers highlights a competitive landscape, spurring innovation and the development of more comprehensive and tailored unemployment insurance solutions. We estimate the market size in 2025 to be around $500 billion (USD), based on extrapolation of typical insurance market growth rates and the substantial societal needs being addressed. A conservative projected CAGR of 5% over the forecast period (2025-2033) reflects a consistent yet sustainable growth trajectory. This growth is expected to be regionally diverse. While mature markets in North America and Europe may experience more moderate growth rates, developing economies in Asia-Pacific and parts of Africa are expected to witness higher growth due to increasing formalization of labor markets and governments prioritizing social security programs. The competitiveness of the market is expected to remain robust, with established players such as Allianz and AIA Group alongside regional players constantly seeking to improve their offerings and expand their market share. Future market development will likely depend on factors including regulatory changes, technological advancements in risk assessment and claim processing, and the evolving needs of different demographic segments. The adoption of innovative digital solutions will likely play a significant role in enhancing efficiency and accessibility within the unemployment insurance sector.

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.

Is There a Netflix Effect on Unemployment? A Statistical Exploration

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.

(APA) APA Stock: Riding the Energy Wave?

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

Throughout the 1920s, prices on the U.S. stock exchange rose exponentially, however, by the end of the decade, uncontrolled growth and a stock market propped up by speculation and borrowed money proved unsustainable, resulting in the Wall Street Crash of October 1929. This set a chain of events in motion that led to economic collapse - banks demanded repayment of debts, the property market crashed, and people stopped spending as unemployment rose. Within a year the country was in the midst of an economic depression, and the economy continued on a downward trend until late-1932.

It was during this time where Franklin D. Roosevelt (FDR) was elected president, and he assumed office in March 1933 - through a series of economic reforms and New Deal policies, the economy began to recover. Stock prices fluctuated at more sustainable levels over the next decades, and developments were in line with overall economic development, rather than the uncontrolled growth seen in the 1920s. Overall, it took over 25 years for the Dow Jones value to reach its pre-Crash peak.

Slovakia SK: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 9.846 % in 2009. Slovakia SK: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 9.846 % from Dec 2009 (Median) to 2009, with 1 observations. Slovakia SK: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Slovakia – Table SK.World Bank.WDI: Social Protection. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

We investigate the relationship between long-term US stock market risks and the macroeconomic environment using a two-component GARCH-MIDAS model. Our results show that macroeconomic variables are important determinants of the secular component of stock market volatility. Among the various macro variables in our dataset the term spread, housing starts, corporate profits and the unemployment rate have the highest predictive ability for long-term stock market volatility. While the term spread and housing starts are leading variables with respect to stock market volatility, for industrial production and the unemployment rate expectations data from the Survey of Professional Forecasters regarding the future development are most informative.

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.

Great Ajax: Riding the 2024 Wave (AJXA)

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

Argentina AR: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 14.566 % in 2022. This records an increase from the previous number of 8.716 % for 2021. Argentina AR: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 11.326 % from Dec 2006 (Median) to 2022, with 11 observations. The data reached an all-time high of 21.522 % in 2006 and a record low of 0.880 % in 2016. Argentina AR: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Argentina – Table AR.World Bank.WDI: Social: Social Protection and Insurance. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Belarus BY: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 1.671 % in 2016. This records an increase from the previous number of 1.574 % for 2015. Belarus BY: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 1.622 % from Dec 2008 (Median) to 2016, with 6 observations. The data reached an all-time high of 2.170 % in 2008 and a record low of 0.841 % in 2012. Belarus BY: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Belarus – Table BY.World Bank.WDI: Social: Social Protection and Insurance. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Armenia AM: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 18.432 % in 2011. This records an increase from the previous number of 17.629 % for 2010. Armenia AM: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 17.629 % from Dec 2009 (Median) to 2011, with 3 observations. The data reached an all-time high of 18.432 % in 2011 and a record low of 16.820 % in 2009. Armenia AM: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Social Protection and Insurance. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Bolivia Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 1.203 % in 2019. This records an increase from the previous number of 1.197 % for 2017. Bolivia Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 1.200 % from Dec 2017 (Median) to 2019, with 2 observations. The data reached an all-time high of 1.203 % in 2019 and a record low of 1.197 % in 2017. Bolivia Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bolivia – Table BO.World Bank.WDI: Social: Social Protection and Insurance. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Montenegro ME: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 13.181 % in 2014. This records a decrease from the previous number of 15.881 % for 2013. Montenegro ME: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 13.518 % from Dec 2011 (Median) to 2014, with 4 observations. The data reached an all-time high of 15.881 % in 2013 and a record low of 10.350 % in 2012. Montenegro ME: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Montenegro – Table ME.World Bank.WDI: Social Protection. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Lithuania LT: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 26.058 % in 2008. Lithuania LT: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 26.058 % from Dec 2008 (Median) to 2008, with 1 observations. Lithuania LT: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lithuania – Table LT.World Bank.WDI: Social Protection. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Kazakhstan KZ: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 5.035 % in 2014. Kazakhstan KZ: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 5.035 % from Dec 2014 (Median) to 2014, with 1 observations. Kazakhstan KZ: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kazakhstan – Table KZ.World Bank.WDI: Social Protection. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;

Mexico Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 15.773 % in 2022. This records an increase from the previous number of 14.729 % for 2020. Mexico Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 15.251 % from Dec 2020 (Median) to 2022, with 2 observations. The data reached an all-time high of 15.773 % in 2022 and a record low of 14.729 % in 2020. Mexico Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Social: Social Protection and Insurance. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Azerbaijan Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 15.795 % in 2008. Azerbaijan Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 15.795 % from Dec 2008 (Median) to 2008, with 1 observations. The data reached an all-time high of 15.795 % in 2008 and a record low of 15.795 % in 2008. Azerbaijan Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: Social: Social Protection and Insurance. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.;ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/);;

Senegal SN: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data was reported at 0.930 % in 2011. Senegal SN: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data is updated yearly, averaging 0.930 % from Dec 2011 (Median) to 2011, with 1 observations. Senegal SN: Adequacy: Unemployment Benefits & Active Labour Market Programs: % of Total Welfare of Beneficiary Households data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Senegal – Table SN.World Bank: Social Protection. Adequacy of unemployment benefits and active labor market programs (ALMP) is measured by the total transfer amount received by the population participating in unemployment benefits and active labor market programs as a share of their total welfare. Welfare is defined as the total income or total expenditure of beneficiary households. Unemployment benefits and active labor market programs include unemployment compensation, severance pay, and early retirement due to labor market reasons, labor market services (intermediation), training (vocational, life skills, and cash for training), job rotation and job sharing, employment incentives and wage subsidies, supported employment and rehabilitation, and employment measures for the disabled. Estimates include both direct and indirect beneficiaries.; ; ASPIRE: The Atlas of Social Protection - Indicators of Resilience and Equity, The World Bank. Data are based on national representative household surveys. (datatopics.worldbank.org/aspire/); Simple average;