Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

Data on the average amount of OSAP debt owed by students. The data is specific to those who attended programs with typical durations. Data is for: * 4-year undergraduate university students * 2-year college diploma students * 1-year private career college students The data fields are: * academic year of completion * postsecondary sector (university, publicly-assisted college, or private career college) * program duration (1 year, 2 years or 4 years) * average repayable debt after loan forgiveness applied through the Ontario Student Opportunity Grant Debt is in nominal dollars with no adjustment for inflation. *[OSAP]: Ontario Student Assistance Program

As the pandemic accelerated calls to provide relief to millions of student borrowers, President Biden announced executive action to cancel 10,000 dollars of student debt for most federal student loan holders. Both prior to and following his announcement, policymakers have debated the merits and details of student debt relief, focusing particular attention on the perceived deservingness of student loan borrowers. But we have little systematic evidence about how the public evaluates borrower deservingness, or whether elite arguments framing support or opposition to debt relief in terms of deservingness influence public preferences for student debt cancellation. This paper employs original conjoint and framing experiments conducted just prior to Biden’s announcement to explore each query. We find that, while certain borrower characteristics indicating need (e.g., amount of debt), responsibility for debt (e.g., type of institution attended), and reciprocity (e.g., time in repayment) can influence people’s evaluations of whether borrowers deserve debt relief, those results may not translate to broader deservingness arguments for or against student debt cancellation in a clear manner. Ultimately, our results shed light on a timely policy issue, while extending scholarly understandings of deservingness for a critical, and understudied, aspect of the American welfare state.

Abstract copyright UK Data Service and data collection copyright owner.

Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.

The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.

In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.

The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.

Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).

There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.

Further information about Next Steps may be found on the CLS website.

Secure Access datasets:

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

The study includes four datasets:

Applicant: SLC data on cohort member’s application for student finance between academic years 2007 and 2020

Payments: SLC data on payment transactions made to cohort member between financial years 2007 and 2021.

Repayments: SLC data on cohort member’s repayment transactions between financial years 2009 and 2021.

Overseas: SLC data on overseas assessment for cohort member between 2007 and 2020

Content

Source ID: FL313066220.Q

For more information about the Flow of Funds tables, see: https://www.federalreserve.gov/apps/fof/Default.aspx

For a detailed description, including how this series is constructed, see: https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL313066220&t=

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

• Observation Start: 1945-10-01

• Observation End : 2019-04-01

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by Michael on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Statistics on postsecondary graduates who owed money for their education to government-sponsored student loans at graduation, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of debt paid off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

A collection of datasets including the following: Register of SLC Board Members declared interests; Register of Gifts and Hospitality offered and provided; the Freedom of Information Register (which covers the subject, requestor details and workflow event dates in responding to the requests) and Board papers, including agenda, minutes and documents for Board meetings

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside.

SN 7104 - Next Steps: Linked Education Administrative Datasets (National Pupil Database - KS2-KS5), England, 1997-2009: Secure Access includes linked National Pupil Database records on pupils’ attainment at KS2, KS3, KS4 and KS5 and data about the pupil such as free school meal eligibility and Special Education Needs (SEN) status. Information is also available about the school attended at the sampling stage.

For the sixth edition (August 2020), the study has been updated to only include the Linked Education Administrative Datasets (National Pupil Database), England, 2005-2009. The main Next Steps survey sensitive variables, previously available as part of this study, have moved to a new study (SN 8656) or are now available under EUL as part of SN 5545. The 'next_steps_redeposit_dictionary.xlsx' available under both SN 5545 and SN 8656 should be consulted for the location of specific variables.

Context

The students in the USA are seeking Loans on a regular basis for their post-secondary education through certifications or college degrees. Different types of income status students’ pursue Federal Loans and grants like PELL for low-income students to fulfill their objectives. In deciding which institution is better for the students whether it is linked with affording, education, market opportunities or repaying loans after completion, the answer is the IPEDS numeric data. IPEDS is the Integrated Postsecondary Education Data System. It is a system of interrelated surveys conducted annually by the U.S. Department of Education’s National Center for Education Statistics (NCES). IPEDS gathers information from every college, university, and technical and vocational institution that participates in the federal student financial aid programs. All the IPEDS data is based on 5.05% interest rate. We will use the respective data from Loan perspective.

Inspiration

Predicting Loan applications as "Approved or Rejected" for the applicants. The focus is mostly on Low-income students (who have earnings up to 48000 dollars per annum) to check whether they will be able to pay their loans as they face difficulties in paying their tuition fees and other related expenses.

The State Loan Repayment Program helps HRSA provide grant funding for states and territories to operate their own loan repayment programs. Through SLRP each state and territory can design programs that address the most pressing health care needs of their residents. Primary medical, mental/behavioral, and dental clinicians who receive awards through SLRP-funded programs pay off their student debt in exchange for working in areas with provider shortages.HRSA programs provide equitable health care to people who are geographically isolated and economically or medically vulnerable. This includes programs that deliver health services to people with HIV, pregnant people, mothers and their families, those with low incomes, residents of rural areas, American Indians and Alaska Natives, and those otherwise unable to access high-quality health care. HRSA programs also support health infrastructure, including through training of health professionals and distributing them to areas where they are needed most, providing financial support to health care providers, and advancing telehealth. Location and data was provided by the Health Resources and Services Administration in October 2022. Update Frequency: Annual

Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).

Secure Access versions of the Next Steps include:

• sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656.
  
• National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.
• Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.
  
• detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) are available under SN 8189, geographic indicators for Sweep 8 and 9 (2011 Census Boundaries) are available under SN 8190, and geographic indicators for Sweep 9 (2021 Census Boundaries) are available under SN 9337. The Sweep 1 geography file was previously held under SN 7104.
• Linked Health Administrative Datasets (Hospital Episode Statistics) for financial years 1997-2022 held under SN 8681.
• Linked Student Loans Company Records for years 2007-2021 held under SN 8848.

SN 5545 - Next Steps: Sweeps 1-9, 2004-2023 includes the main Next Steps survey data from Sweep 1 (age 14) to Sweep 9 (age 32).

Latest edition information
For the eighteenth edition (February 2025), the Sweep 9 Derived Variables data file has been updated with some newly derived variables categorised under the household (W9DCHNO12, W9DTOTCH, W9DTOTOWNCH) and education (W9DAQLVLH, W9DVQLVLH) sections. The Longitudinal data file have been updated with changes to the weight variables. Three out of the four weights in the previous version have been removed. W9FINWTALLB has been renamed to W9FINWT in line with previous sweeps. The user guide has been updated to reflect these changes. Furthermore, the derived variables user guide has been merged into the main user guide and can be accessed via Appendix 1.

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.

what percentage of your gross salary does the consumer financial protection bureau suggest your student loan payment be in order to be affordable and limit your risk of delinquency and default?

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.

Student Loan Meltdown? (SLM)

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.

(SLMBP) Student Loan Market: Will Series B Preferred Stock Ride the 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

Provides provisional statistics showing how applications for student support in higher education are progressing through the processing stages and showing the number of payments made to students in this cycle. These statistics cover applications assessed by Student Finance England who assess all applications for English students.

Source agency: Business, Innovation and Skills

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: student loans

Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.