The unemployment rate for Greater Manchester was *** percent as of the third quarter of 2024, compared with the UK average of *** percent in the same time period.

Graph and download economic data for Unemployment Rate in Manchester, NH (NECTA) (MANC933UR) from Jan 1990 to Dec 2024 about Manchester, NH, unemployment, rate, and USA.

Unemployment Rate in Manchester, NH (NECTA) was 2.70% in December of 2024, according to the United States Federal Reserve. Historically, Unemployment Rate in Manchester, NH (NECTA) reached a record high of 16.20 in April of 2020 and a record low of 1.70 in March of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployment Rate in Manchester, NH (NECTA) - last updated from the United States Federal Reserve on June of 2025.

The unemployment rate of the West Midlands Metropolitan County was *** percent in the twelve months to September 2024, which was the highest among England's six metropolitan counties and Greater London. By contrast, South Yorkshire, which includes the major city of Sheffield, had the lowest unemployment rate, at ***** percent.

The average weekly wage in Greater Manchester was *** British pounds in 2022, compared with *** pounds in the previous year.

Graph and download economic data for Unemployment Rate in Hillsborough County, NH (NHHILL5URN) from Jan 1990 to Apr 2025 about Hillsborough County, NH; Manchester; NH; unemployment; rate; and USA.

Graph and download economic data for Unemployment Rate in Hillsborough County, NH (LAUCN330110000000003A) from 1990 to 2024 about Hillsborough County, NH; Manchester; NH; unemployment; rate; and USA.

Graph and download economic data for Unemployed Persons in Manchester, NH (NECTA) (LAUMT337495000000004) from Jan 1990 to Dec 2024 about Manchester, NH, household survey, unemployment, persons, and USA.

In the twelve months to June 2024, the claimant count rate of the West Midlands Metropolitan County was 6.8 percent, which was the highest among Metropolitan Counties, and Greater London.

Unemployed in Manchester, NH (NECTA) was 3393.00000 Persons in December of 2024, according to the United States Federal Reserve. Historically, Unemployed in Manchester, NH (NECTA) reached a record high of 20389.00000 in April of 2020 and a record low of 1608.00000 in April of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for Unemployed in Manchester, NH (NECTA) - last updated from the United States Federal Reserve on May of 2025.

Unemployed Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in Manchester, Connecticut by age, education, race, gender, work experience and more.

Graph and download economic data for Unemployed Persons in Hillsborough County, NH (LAUCN330110000000004) from Jan 1990 to May 2025 about Hillsborough County, NH; Manchester; NH; household survey; unemployment; persons; and USA.

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.

LON:MNL MANCHESTER & LONDON INVESTMENT TRUST PLC

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.

Manchester United (MANU) Shares: Analysts Predict Growth Amidst Season Hopes

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.

Manchester United Headed for Glory? (MANU)

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.

Manchester United (MANU) Stock: Dividend Dilemma or Value Play?

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

Abstract copyright UK Data Service and data collection copyright owner.

Even after imputation of missing households, the UK’s 1991 Census counts were lower than expected. In the 1990s, the ESRC-funded Estimating with Confidence project (EwC) distributed official non-response data for local authority districts on a small area-specific basis to allow for under-enumeration, timing changes between census day and the mid-year, armed forces adjustments and the transfer of students from home to term-time addresses. The EwC-enhanced census counts became accepted as the ‘gold standard’ for mid-1991 small area populations and have been widely used in academic and government research.

Following the 2001 Census, evidence suggested that previous official upward adjustments to 1991 populations were too large. Revisions were retrospectively made to 1991 non-response in England, Wales and Scotland (GB) and to the official annual time-series of local authority (LA) mid-year population estimates.

Since data for previous populations are needed for sub-district small areas, this project has revised the original EwC non-response allowances (for small areas across GB) and converted the output to more contemporary geographies (for all of UK including Northern Ireland). As a result, the 1991 EwC small area estimates are now consistent and comparable with the 2001 Census population definition and geography and the 1981-2001 official population time-series at LA level across the whole of the UK.

These revised UK-coverage small area population estimates resource will underpin late 20th Century local area social analyses of changes in populations and, for example, changes in health and unemployment rates.

Further information can be found on the ESRC Award web page.

Main Topics:

The study provides complete mid-1991 and mid-2001 population estimates by five year age-groups and sex for Census Area Statistics (CAS) wards across England, Wales and Northern Ireland CAS postal sectors across Scotland.

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.

MANU Manchester United Ltd. Class A Ordinary Shares

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

Abstract copyright UK Data Service and data collection copyright owner.

This is a qualitative data collection. The Coventry and Liverpool Lives oral history project collected 58 oral history interviews with 21 men and women who have lived and worked in these cities since approximately 1945. The project aimed to explore how working-class men and women narrate their life histories and how social memory impacts on life stories told. By using the life history method of research change and continuity in the way people identify themselves across their life was captured. The interviews aimed to question the significance of 'affluence' amongst a group of working-class people in two economically diverse English cities after 1945. The interviews highlight the continued significance people place on class and gender to identify themselves even if at times definitions of the terms appear ambivalent. This collection contributes significantly to our understanding and knowledge of post-war everyday life. The interviews cover topics such as childhood, neighbourhood, home life, schooling, youth, leisure, first job, work history including periods of unemployment, National Service, marriage, motherhood, fatherhood and later life. Together they include people’s experiences prior to 1945, through the 1980s to the time of interviewing in 2008. Post-war migration to these cities from across the UK, Iran and the Punjab are represented. The collection fills a gap in sources of the period by focusing on Coventry and Liverpool, two cities that experienced severe bomb damage during the Second World War and subsequently significant social and economic change and redevelopment after1945. Peoples’ memories of the 1970s and 1980s when both cities experienced high unemployment and economic downturn due to the demise of industry and manufacturing are well detailed. Historians’ attention is shifting from the London centric image of the swinging sixties to consider more regional and local experiences of the post-war period. This collection will make a significant contribution to this new direction in British history.