Tables show data on federal student aid from the Canada Student Financial Assistance Program. The data reports only on aid in the provinces and territory who take part in the program. Note: - data are from the 2009 to 2010 to the 2021 to 2022 academic year (August 1 of the current year to July 31 of the next) - figures may not add up to the total as some cells are blocked due to privacy, and due to rounding - Unless otherwise stated, tables include both full-time and part-time students

Financial burdens of the parental home through education of children.

Topics: Start of studies; length of studies; amount of money available to the student monthly; current income and burden conditions of parents; opportunities to finance studies; stay of student in semester breaks; attitude of student to work in semester breaks; readiness of parents to finance studies; degree of familiarity of the Honnef Model; detailed information on income and contributions of the student as well as the remaining children; housing situation and rent costs of respondent.

Demography: income; household income; size of household; social origins; city size; state; refugee status; possession of durable economic goods; possession of assets.

This statistic illustrates the share of the Millennial generation that had a current loan with the Student Loan company in Great Britain as of 2016. It can be seen that more than one half (52 percent) of Millennials stated that they did not have a current student loan at that time.

This data collection contains information from the first wave of High School and Beyond (HSB), a longitudinal study of American youth conducted by the National Opinion Research Center on behalf of the National Center for Education Statistics (NCES). Data were collected from 58,270 high school students (28,240 seniors and 30,030 sophomores) and 1,015 secondary schools in the spring of 1980. Many items overlap with the NCES's NATIONAL LONGITUDINAL STUDY OF THE CLASS OF 1972 (ICPSR 8085). The HSB study's data are contained in eight files. Part 1 (School Data) contains data from questionnaires completed by high school principals about various school attributes and programs. Part 2 (Student Data) contains data from surveys administered to students. Included are questionnaire responses on family and religious background, perceptions of self and others, personal values, extracurricular activities, type of high school program, and educational expectations and aspirations. Also supplied are scores on a battery of cognitive tests including vocabulary, reading, mathematics, science, writing, civics, spatial orientation, and visualization. To gather the data in Part 3 (Parent Data), a subsample of the seniors and sophomores surveyed in HSB was drawn, and questionnaires were administered to one parent of each of 3,367 sophomores and of 3,197 seniors. The questionnaires contain a number of items in common with the student questionnaires, and there are a number of items in common between the parent-of-sophomore and the parent-of-senior questionnaires. This is a revised file from the one originally released in Autumn 1981, and it includes 22 new analytically constructed variables imputed by NCES from the original survey data gathered from parents. The new data are concerned primarily with the areas of family income, liabilities, and assets. Other data in the file concentrate on financing of post-secondary education, including numerous parent opinions and projections concerning the educational future of the student, anticipated financial aid, student's plans after high school, expected ages for student's marriage and childbearing, estimated costs of post-secondary education, and government financial aid policies. Also supplied are data on family size, value of property and other assets, home financing, family income and debts, and the age, sex, marital, and employment status of parents, plus current income and expenses for the student. Part 4 (Language Data) provides information on each student who reported some non-English language experience, with data on past and current exposure to and use of languages. In Parts 5-6, there are responses from 14,103 teachers about 18,291 senior and sophomore students from 616 schools. Students were evaluated by an average of four different teachers who had the opportunity to express knowledge or opinions of HSB students whom they had taught during the 1979-1980 school year. Part 5 (Teacher Comment Data: Seniors) contains 67,053 records, and Part 6 (Teacher Comment Data: Sophomores) contains 76,560 records. Questions were asked regarding the teacher's opinions of their student's likelihood of attending college, popularity, and physical or emotional handicaps affecting school work. The sophomore file also contains questions on teacher characteristics, e.g., sex, ethnic origin, subjects taught, and time devoted to maintaining order. The data in Part 7 (Twins and Siblings Data) are from students in the HSB sample identified as twins, triplets, or other siblings. Of the 1,348 families included, 524 had twins or triplets only, 810 contained non-twin siblings only, and the remaining 14 contained both types of siblings. Finally, Part 8 (Friends Data) contained the first-, second-, and third-choice friends listed by each of the students in Part 2, along with identifying information allowing links between friendship pairs. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR as four separate datasets here:

1980: https://doi.org/10.3886/ICPSR07896.v2

1982: https://doi.org/10.3886/ICPSR08297.v3

1984: https://doi.org/10.3886/ICPSR08443.v1

1986: https://doi.org/10.3886/ICPSR08896.v3

We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

This table has been archived and replaced by table 36100664. Income quintiles are assigned based on the equalized household disposable income. This takes into account differences in household size and composition. The Oxford-modified equivalence scale is used; it assigns a value of 1 to the first adult, 0.5 to each additional person aged 14 and over, and 0.3 for all children under 14. The coefficients of variation from Statistics Canada's Survey of Financial Security for 2012 and 2016, which serve as indicators of the accuracy of these estimates for net worth and its components, are available in the appendix to Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2019, technical methodology and quality report for the March 2020 release. Age groups refer to the age group of the major income earner. This refers to the main source of income for the household, that is, wages and salaries, self-employment income, net property income, current transfers received related to pension benefits, or other current transfers received from non-pension related sources.
Self-employment income refers to mixed income related to non-farm and farm businesses. Household rental income is not included. Revenues from Current transfers received - pension benefits relate to current transfers received from corporations for employer's pension plans and current transfers received from government for the Canada and Québec pension plans (CPP/QPP) and the Old Age Security program including the Guaranteed Income Supplement (OAS/GIS). Revenues from Current transfers received - others, relate to all other current transfers received not included in Current transfers received - pensions benefits, that is, it includes current transfers from the government sector except for the Canada and Québec pension plans (CPP/QPP) and from the Old Age Security Program (OAS) and the Guaranteed Income Supplement (GIS). It also includes current transfers from Non-profit institutions serving households (NPISH) and from the non-residents sector. Owner/Renter refers to the housing tenure of a household. Households that have subsidized rents (partially or fully) are included under Renter. Distributions by generation are defined as follows and are based on the birth year of the major income earner: pre-1946 for those born before 1946, baby boom for those born between 1946 and 1964, generation X for those born between 1965 and 1980 and millennials for those born after 1980. Note that generation Z has been combined with the millennial generation as their sample size is relatively small. Life insurance and pensions include the value of all life insurance and employer pension plans, termination basis. Excludes public plans administered or sponsored by governments: Old Age Security (OAS) including the Guaranteed Income Supplement (GIS) and the Spouse's Allowance (SPA), as well as the Canada and Quebec Pension Plans (CPP/QPP). Other financial assets include total currency and deposits, Canadian short-term paper, Canadian bonds and debentures, foreign investments in paper and bonds, mortgages, equity and investment funds, and other receivables. Other non-financial assets include consumer durables, machinery and equipment, and intellectual property products. Excludes accumulation of value of collectibles including coins, stamps and art work. Other liabilities include major credit cards and retail store cards, gasoline station cards, etc., vehicle loans, lines of credit, student loans, other loans from financial institutions and other money owed. Owner's equity refers to the value of the interests of an owner or partial owner in an asset, in this case real estate, divided by household real estate, which includes the value of structures (residential and non-residential) and land owned by households. Distributions of Household Economic Accounts (DHEA) estimates are benchmarked to year-end estimates for liabilities and assets from the National Balance Sheet Accounts (NBSA, Table 36-10-0580-01), and for annual household disposable income from the Provincial-Territorial Economic Accounts (Table 36-10-0224-01). DHEA ratios for debt to disposable income, real estate as a share of disposable income, and net worth as a share of disposable income differ from those included in “Financial indicators of households and non-profit institutions serving households, national balance sheet accounts” (Table 38-10-0235-01) as the latter source adjusts disposable income for the change in pension entitlements. The measure of disposable income used for the DHEA ratios is more consistent with that shown in “Household sector credit market summary table, seasonally adjusted estimates” (Table 38-10-0238), which does not adjust disposable income for the change in pension entitlements.

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

Persons aged between 16 and 75 living in private households at the time of recruitment

According to the source, in 2022, parents planned to spend an average of 347 zloty on a school layette for one student. Nearly 60 percent of surveyed Poles finance the purchase of school supplies from their current income.