Higher education undergraduate student loan outlay by Household Residual Income

EKOS Research Associates and the Canada Millennium Scholarship Foundation conducted a monthly national study of the finances of post-secondary students from September 2001 until May 2002. The study was designed to capture the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian post-secondary students and the adequacy of available funding. The Web based Students Financial Survey provided accurate, quantifiable results for the first time on such issues as the incidence and level of assistance, the level of debt from outstanding bank loans, personal lines of credit, and credit cards. The study also yielded up-to-date information on student assets (such as automobiles, computers, and electronics), student earnings, time usage, and types of expenses incurred. The survey featured a panel of 1,524 post-secondary students from across the country, who participated in a very brief monthly survey, either via Internet or telephone. Students were required to complete a longer baseline wave of the survey in order to participate in the study. The baseline survey asked a number of questions concerning summer income and existing debt, including credit card debt. This dataset was received from the Canada Millennium Scholarship Foundation as is. Issues with value labels and missing values were discovered and corrected as best as possible with the documentation received. The variable gasst: Do you receive any government assistance? was not corrected due to lack of documentation about this variable. Some caution should be used with this dataset. This dataset was freely received from, the Canadian Millenium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were correct as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

Loan outlay, mean loan outlay per student, number of students and proportion of students by Household Residual Income band for 2019/20

The Canadian College Student Survey was conducted by the Canada Millennium Scholarship Foundation to provide data on student finances in Canada. The primary objective of the survey was to track the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian students and the adequacy of available funding. The survey will allow the Canada Millennium Scholarship Foundation to understand the financial circumstances of students who are in a post- secondary environment on an annual basis. This research is a joint effort of the Foundation, all participating colleges and the Association of Canadian Community Colleges (ACCC). The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. The objectives of the research are to: provide national-level data on student access; time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups and/or regions; and provide each institution with top-line survey results (based on representative samples of their students); which may then be compared against the "national average".The Canada Millennium Scholarship Foundation commissioned R.A. Malatest and Associates Ltd. to conduct a comprehensive survey that provided national-level data concerning college students’ income, expenditures, levels of debt/perceptions of debt, and use of time. The 2002 Canadian College Student Survey Project was administered in March and April of 2002 in 16 colleges (representing 93,175 students). The maximum variation of the results of this survey is estimated to be ±1.2% (at a 95% confidence level). This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

Student Loan Market Outlook

According to our latest research, the global student loan market size reached USD 135.2 billion in 2024, reflecting the persistent demand for higher education financing worldwide. The market is expected to expand at a CAGR of 7.1% from 2025 to 2033, reaching an estimated USD 251.7 billion by 2033. This robust growth is driven by the increasing cost of tertiary education, rising enrollment rates, and evolving financial products tailored to diverse borrower needs. As per our latest analysis, the market is witnessing dynamic shifts in lender participation and repayment models, reflecting the changing landscape of global education finance.

One of the primary growth factors propelling the student loan market is the escalating cost of higher education across both developed and emerging economies. Tuition fees, living expenses, and ancillary costs have risen steadily, outpacing inflation and family income levels in many countries. This widening affordability gap has compelled students and their families to increasingly rely on external funding sources, particularly student loans. Simultaneously, the proliferation of private and alternative lenders has diversified borrowing options, making loans more accessible to a broader demographic. The emergence of income-driven repayment and refinancing solutions has further enhanced the market’s attractiveness, offering borrowers flexibility and financial relief over traditional rigid repayment structures.

Another significant factor impacting market growth is the ongoing digital transformation within the financial sector. Fintech innovations are streamlining loan origination, disbursement, and management, reducing operational costs for lenders and expediting the approval process for borrowers. Online lending platforms, powered by advanced analytics and AI, are enabling more personalized risk assessments and competitive interest rates, attracting tech-savvy students and parents. These platforms are also contributing to greater financial inclusion, particularly in regions where traditional banking infrastructure is limited. The integration of digital tools is not only enhancing the borrower experience but also improving portfolio performance for lenders through better risk management and customer engagement.

Demographic trends and government policies are also shaping the student loan market’s trajectory. The global surge in tertiary enrollment, especially in Asia Pacific and Africa, is expanding the borrower base. Governments in several countries are implementing supportive policies, such as interest subsidies, loan forgiveness programs, and flexible repayment schemes, to mitigate the financial burden on graduates and stimulate higher education participation. However, regulatory scrutiny around lending practices and concerns over rising student debt levels are prompting both public and private lenders to adopt more responsible lending and transparency measures. These dynamics are fostering a more balanced and sustainable growth environment for the student loan market.

Regionally, North America continues to command the largest share of the student loan market, driven by the United States’ mature lending ecosystem and high tertiary education costs. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, expanding middle-class populations, and increasing investments in higher education infrastructure. Europe, meanwhile, exhibits steady growth, supported by government-backed loan schemes and cross-border education mobility. Latin America and the Middle East & Africa are witnessing gradual expansion, with rising demand for higher education and evolving financial services infrastructure. Each region presents unique challenges and opportunities, influencing lender strategies and market dynamics.

Type Analysis

The student loan market is segmented by type into federal loans, private loans, and refinancing loans, each with distinct characteristics and growth trajectories. Federal loans, primarily offered by government agencies, remain the dominant segment in markets such as the United States and several European countries. These loans typically feature lower interest rates, flexible repayment options, and borrower protections, making them the preferred choice for undergraduate and graduate students. The stability and accessibility of federal loans are underpinned by government backing, which reduces default ri

This report summarizes the findings of the Consortium's third annual survey, which involved 25 colleges and more than 9,400 students. Participating colleges were responsible for sampling (based on a standardized procedure) and administering the survey in class. Completed questionnaires were then shipped to PRA Inc. for coding, data entry and analysis. The objectives of the research are to: provide national data on student access, time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups or regions; and provide each institution with topline survey results (based on representative samples of their students), which may then be compared against the "national average". This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

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.

The Canadian College Student Survey was conducted by the Canada Millennium Scholarship Foundation to provide data on student finances in Canada. The primary objective of the survey was to track the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian students and the adequacy of available funding. The survey will allow the Canada Millennium Scholarship Foundation to understand the financial circumstances of students who are in a post- secondary environment on an annual basis. This research is a joint effort of the Foundation, all participating colleges and the Association of Canadian Community Colleges (ACCC). The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. The objectives of the research are to: provide national-level data on student access; time use and financing for Canadian college students from participating colleges; identify issues particular to certain learner groups and/or regions; and provide each institution with top-line survey results (based on representative samples of their students); which may then be compared against the "national average". In January 2003, the Foundation engaged Prairie Research Associates (PRA) Inc. to oversee this research. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information. This dataset was freely received by the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. The y were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking documentation.

The global student loans market is projected to reach a valuation of approximately USD 2.5 trillion by 2033, growing at a compound annual growth rate (CAGR) of 5.2% from 2025 to 2033.

The University of Washington - Beyond High School (UW-BHS) project surveyed students in Washington State to examine factors impacting educational attainment and the transition to adulthood among high school seniors. The project began in 1999 in an effort to assess the impact of I-200 (the referendum that ended Affirmative Action) on minority enrollment in higher education in Washington. The research objectives of the project were: (1) to describe and explain differences in the transition from high school to college by race and ethnicity, socioeconomic origins, and other characteristics, (2) to evaluate the impact of the Washington State Achievers Program, and (3) to explore the implications of multiple race and ethnic identities. Following a successful pilot survey in the spring of 2000, the project eventually included baseline and one-year follow-up surveys (conducted in 2002, 2003, 2004, and 2005) of almost 10,000 high school seniors in five cohorts across several Washington school districts. The high school senior surveys included questions that explored students' educational aspirations and future career plans, as well as questions on family background, home life, perceptions of school and home environments, self-esteem, and participation in school related and non-school related activities. To supplement the 2000, 2002, and 2003 student surveys, parents of high school seniors were also queried to determine their expectations and aspirations for their child's education, as well as their own educational backgrounds and fields of employment. Parents were also asked to report any financial measures undertaken to prepare for their child's continued education, and whether the household received any form of financial assistance. In 2010, a ten-year follow-up with the 2000 senior cohort was conducted to assess educational, career, and familial outcomes. The ten year follow-up surveys collected information on educational attainment, early employment experiences, family and partnership, civic engagement, and health status. The baseline, parent, and follow-up surveys also collected detailed demographic information, including age, sex, ethnicity, language, religion, education level, employment, income, marital status, and parental status.

Denmark, the Netherlands, and Norway were among the European countries with most indebted households in 2023 and 2024. The debt of Dutch households amounted to *** percent their disposable income in the 2nd quarter of 2024. Meanwhile, Norwegian households' debt represented *** percent of their income in the 3rd quarter of 2023. However, households in most countries were less indebted, with that ratio amounting to ** percent in the Euro area. Less indebtedness in Western and Northern Europe There were several European countries where household's debts outweighed their disposable income. Most of those countries were North or West European. However, the indebtedness ratio in Denmark has been decreasing during the past decade. As the debt of Danish households represented nearly *** percent in the last quarter of 2014, which has fallen very significantly by 2024. Other countries with indebted households have been following similar trends. The households' debt-to-income ratio in the Netherlands has also fallen from over *** percent in 2013 to *** percent in 2024. Debt per adult in Europe In Europe, the value of debt per adult varies considerably from an average of around 10,000 U.S. dollars in Europe to a much higher level in certain countries such as Switzerland. Debts can be formed in a number of ways. The most common forms of debt include credit cards, medical debt, student loans, overdrafts, mortgages, automobile financing and personal loans.

The Survey of Consumer Finances (SCF) dataset, provided by the Federal Reserve, offers comprehensive insights into the financial condition of U.S. households. This dataset is invaluable for researchers, policymakers, and analysts interested in understanding consumer behavior, wealth distribution, and economic trends in the United States.

The SCF dataset includes detailed information on household income, assets, liabilities, and various demographic characteristics. It is collected every three years and serves as a crucial resource for analyzing the financial well-being of American families.

Key Features: Income Data: Information on various sources of income, including wages, investments, and government assistance. Asset Ownership: Detailed accounts of household assets, such as real estate, retirement accounts, stocks, and other investments. Liabilities:Comprehensive details on household debts, including mortgages, credit card debts, and student loans. Demographics: Data covering age, education, race, and family structure, allowing for nuanced analysis of financial trends across different segments of the population.

Use Cases: Economic research and analysis, Policy formulation and assessment, Understanding wealth inequality, Consumer behavior studies

Citing the Dataset:

When using this dataset in your research, please ensure to cite the Federal Reserve Board and the SCF as the original source.

Note: The dataset is intended for educational and research purposes. Users are encouraged to adhere to ethical guidelines when analyzing and interpreting the data.

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) co ntains 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.

The Canadian College Student Survey Consortium (the Consortium, CCSSC) includes the Association of Canadian Community Colleges (ACCC), individual participating colleges and the Canada Millennium Scholarship Foundation (CMSF). Established in late 2001, the Consortium conducted its first survey of college students in the spring of 2002. In 2003, it conducted a second survey, involving 27 colleges and approximately 9,900 students. This report summarizes the findings of the second annual survey. The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. Approximately 9,900 students completed the survey. Of which most students who responded to the survey are enrolled full-time in programs that take two years or longer to complete. Students' financial situations and time use vary greatly by program type as well as region. Many of the differences arise because of students' personal characteristics are correlated with the program they are enrolled in. The fact that some programs are more predominant in certain regions adds another dimension to this variation. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

Refers only to the location municipal perspective of independent schools’ results. The task is based on a regression model developed by Statistics Sweden and SKR to take into account the different socio-economic conditions of different municipalities. The explanatory variables include the level of education of the parents, the parents’ income, gender, share of newly immigrated pupils and the need for financial assistance. Municipalities whose assignments are based on fewer than 30 students have been subject to confidentiality. For more information, see the report Open Comparisons – Upper Secondary School. Refers only to the location municipal perspective of independent schools’ results. The task is based on a regression model developed by Statistics Sweden and SKR to take into account the different socio-economic conditions of different municipalities. The explanatory variables include the level of education of the parents, the parents’ income, gender, share of newly immigrated pupils and the need for financial assistance. Municipalities whose assignments are based on fewer than 30 students have been subject to confidentiality. For more information, see the report Open Comparisons – Upper Secondary School.

This refers only to the results of municipal schools. Municipal schools also include schools run by municipal associations. The task is based on a regression model developed by Statistics Sweden and SKR to take into account the different socio-economic conditions of different municipalities. The explanatory variables include the level of education of the parents, the parents’ income, gender, share of newly immigrated pupils and the need for financial assistance. Students who do not have a Swedish personal identity number at the beginning of their education (e.g. newly arrived pupils who have not yet been registered in the population register) are not included. Municipalities whose assignments are based on fewer than 30 students have been subject to confidentiality. For more information, see the report Open Comparisons – Upper Secondary School.

The Survey of Consumer Finances (SCF) is normally a triennial cross-sectional survey of U.S. families. The survey data include information on families' balance sheets, pensions, income, and demographic characteristics.

This is a development key figure, see questions and answers on kolada.se for more information. Refers only to the situational perspective of municipal schools’ results. Municipal schools also include schools run by municipal associations. The task is based on a regression model developed by Statistics Sweden and SKR to take into account the different socio-economic conditions of different municipalities. The explanatory variables include the level of education of the parents, the parents’ income, gender, share of newly immigrated pupils and the need for financial assistance. Students who do not have a Swedish personal identity number at the beginning of their education (e.g. newly arrived pupils who have not yet been registered in the population register) are not included. Municipalities whose assignments are based on fewer than 30 students have been subject to confidentiality. For more information, see the report Open Comparisons – Upper Secondary School.

Student Performance Prediction in Higher Education Dataset Description This dataset contains data representing student performance in higher education institutions across Australia. The dataset is designed to aid in the prediction of student performance based on a variety of academic, personal, and socio-economic factors. some data including university names have been removed for privacy concerns.

Dataset Summary Total Records: 100,256 Total Features: 51 Target Variable: Performance Features Student ID: Unique identifier for each student. University ID: Unique identifier for each university. University Name: Name of the university. Age: Age of the student. Gender: Gender of the student. Major: Student's major or field of study. Year of Study: Current year/level of study (e.g., freshman, sophomore). GPA: Grade Point Average. High School GPA: GPA from high school. Entrance Exam Score: Score on university entrance exams. Attendance Rate: Percentage of classes attended. Participation in Extracurricular Activities: Whether the student participates in extracurricular activities (0 = No, 1 = Yes). Part-time Job: Whether the student has a part-time job (0 = No, 1 = Yes). Hours of Study per Week: Average number of hours spent studying per week. Family Income: Family's annual income. Parental Education Level: Highest education level attained by parents. Accommodation Type: Type of accommodation (Dormitory, Off-campus, With family). Distance from Home to University: Distance between student's home and the university. Internet Access at Home: Whether the student has internet access at home (0 = No, 1 = Yes). Library Usage: Frequency of library usage (number of visits per week). Access to Academic Resources: Availability of academic resources (0 = No, 1 = Yes). Health Condition: Student's health condition (Excellent, Good, Fair, Poor). Mental Health Status: Self-reported mental health status (Excellent, Good, Fair, Poor). Scholarship: Whether the student receives a scholarship (0 = No, 1 = Yes). Financial Aid: Whether the student receives financial aid (0 = No, 1 = Yes). Tutor Support: Whether the student has access to a tutor (0 = No, 1 = Yes). Counseling Services: Whether the student uses counseling services (0 = No, 1 = Yes). Transportation Mode: Mode of transportation to university (Walking, Biking, Public Transport, Car). Hours of Sleep per Night: Average number of hours slept per night. Diet Quality: Self-reported diet quality (Excellent, Good, Fair, Poor). Exercise Frequency: Frequency of exercise per week. Social Integration: Level of social integration within the university (Excellent, Good, Fair, Poor). Peer Support: Availability of peer support (0 = No, 1 = Yes). Language Proficiency: Proficiency in the language of instruction (Excellent, Good, Fair, Poor). Use of Online Learning Platforms: Frequency of using online learning platforms. Class Participation: Level of participation in class discussions (Excellent, Good, Fair, Poor). Project/Assignment Scores: Average scores on projects and assignments. Midterm Exam Scores: Scores on midterm exams. Final Exam Scores: Scores on final exams. Attendance at Office Hours: Frequency of attending professors' office hours. Group Work Participation: Participation in group work (0 = No, 1 = Yes). Research Involvement: Involvement in research projects (0 = No, 1 = Yes). Internship Experience: Whether the student has internship experience (0 = No, 1 = Yes). Peer Reviews: Scores or feedback from peer reviews. Academic Advising: Frequency of meetings with academic advisors. Learning Style: Preferred learning style (Visual, Auditory, Kinesthetic, Reading/Writing). Study Environment: Quality of study environment (Excellent, Good, Fair, Poor). Core Course Average: Average scores in core courses. Extracurricular Participation: Level of participation in extracurricular activities (0 = No, 1 = Yes). Peer Evaluations: Peer feedback on collaborative work. Performance: Overall performance label (Excellent, Good, Satisfactory, Needs Improvement, Poor). Target Variable - Performance The target variable Performance is a categorical feature representing the overall performance of the student. The possible values are:

Excellent: Top-performing students. Good: Above-average performance. Satisfactory: Average performance. Needs Improvement: Below-average performance. Poor: Poor performance.

Usage

This dataset can be used for:

Predictive modeling to identify factors influencing student performance. Analyzing trends and patterns in student performance across different universities. Developing interventions to support students at risk of poor performance. Acknowledgements This dataset provides a rich resource for researchers and educators interested in student performance prediction and the factors that influence academic success in higher education institutions in Australia.