Higher education undergraduate student loan outlay by Household Residual Income

In 2022, the student loan default rate in the United States was highest for borrowers in the bottom ** percent of the family income bracket, at ** percent. In comparison, borrowers in the top 25 percent were least likely to default on their student loans.

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

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.

How high is the average student debt in the Netherlands? In 2016, a university (in Dutch: WO) graduate had a debt of around 10,700 euros. Newer numbers were not available, as the national system for student loans changed in 2015. In 2015-2016, the so-called basisbeurs (a conditional loan a student would receive in the Netherlands, which would turn into a gift when he/she graduated within ten years) was abolished. This currently means that if students need more money, they must loan it from the government. In 2017, the Dutch government granted 2.4 billion euros worth of loans to students.

University graduates had a higher chance of a student debt

The total student debt in the Netherlands was worth 11.2 billion euros in 2017. Roughly six out of ten research university graduates had a student debt. This was significantly higher than university of applied sciences graduates (in Dutch: HBO), of which 33 percent had a student debt.

Student debts influence house purchases in the Netherlands

In 2017, approximately 16 percent of all first-time homebuyers in the Netherlands consisted of the age group between 25 and 29 years old. This was a decrease from the approximately 25 percent in 2013. As (student) debts and personal income count towards mortgage requests and partly determine whether or not mortgage providers are willing to lend money for the purchase of a house, an increasing student debt made it more difficult for starters in the Netherlands to enter the real estate market. Mortgages are the most common way to finance real estate for households in the Netherlands.

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

The data and programs replicate tables and figures from "Insurance Versus Moral Hazard in Income-Contingent Student Loan Repayment," by Tim de Silva. Please see the README file for additional details.

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.

The main purpose of the National Student Financial Aid Scheme (NSFAS) is to enable young people from poor households to obtain a higher education. This is essential for expanding South Africa’s skills base and a powerful mechanism for breaking the intergenerational cycle of household poverty and exclusion. Tertiary education is unaffordable for most households (see Figure 1). While the average full cost of study for a single year at university was about R28 000 in 2003, this figure was three times greater than the incomes of households in the poorest 20% of the population. It was also more than 30% greater than the annual incomes of households in the second poorest quintile, and just 20% less than the total income of households in the middle 20%. In 2003, in other words, none of the poorest 50% of households could realistically afford to send a child to university.

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.

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.

Since 2013, the Federal Reserve Board has conducted the Survey of Household Economics and Decisionmaking (SHED), which measures the economic well-being of U.S. households and identifies potential risks to their finances. The survey includes modules on a range of topics of current relevance to financial well-being including credit access and behaviors, savings, retirement, economic fragility, and education and student loans. The Board's seventh annual SHED examines the economic well-being and financial lives of U.S. adults and their families. The 2019 complete survey was conducted in October 2019, offering a picture of personal finances prior to the onset of the COVID-19 pandemic. To obtain updated information in the midst of closures and stay-at-home orders, a smaller supplemental survey was conducted in April 2020, focusing on labor market effects and households' overall financial circumstances at that time. Demographic variables include age, level of education, gender, race, household income, and marital status. Users can use the industry information included in the data to obtain a perspective on financial conditions resulting from COVID-19 for individuals who work in arts and culture related fields.

Net-Interest-Income Time Series for Bright Horizons Family Solutions Inc. Bright Horizons Family Solutions Inc. provides early education and childcare, back-up care, educational advisory, and other workplace solutions services for employers and families in the United States, Puerto Rico, the United Kingdom, the Netherlands, Australia, and India. The company operates in three segments: Full Service Center-Based Child Care, Back-Up Care, and Educational Advisory services. The Full Service Center-Based Child Care segment offers traditional center-based early education and child care, preschool, and elementary education services. The Back-Up Care segment provides center-based back-up child care, in-home child and senior care, school-age programs, camps, tutoring, pet care, and self-sourced reimbursed care services, as well as sittercity, an online marketplace for families and caregivers through early education and child care centers, school-age programs and in-home care providers, the back-up care network, and other providers. The Educational Advisory services segment offers tuition assistance and student loan repayment program management, workforce education, and related educational consulting services, as well as college admissions and college financial advisory services. The company was formerly known as Bright Horizons Solutions Corp. and changed its name to Bright Horizons Family Solutions Inc. in July 2012. Bright Horizons Family Solutions Inc. was founded in 1986 and is headquartered in Newton, Massachusetts.

The purpose of the survey is to collect information from a sample of Canadian families on their assets, debts, employment, income and education. This helps in understanding how family finances change because of economic pressures. The SFS provides a comprehensive picture of the net worth of Canadians. Information is collected on the value of all major financial and non-financial assets and on the money owing on mortgages, vehicles, credit cards, student loans and other debts. A family's net worth can be thought of as the amount of money they would be left with if they sold all of their assets and paid off all of their debts. The survey data are used by government departments to help formulate policy, the private sector and by individuals and families to compare their wealth with those of similar types of families.

Debt Settlement Market size was valued at USD 4.45 Billion in 2024 and is projected to reach USD 11.22 Billion by 2032, growing at a CAGR of 14.12% during the forecast period 2026 to 2032. Global Debt Settlement Market Drivers:Household Debt Levels Globally: The demand for debt settlement services is expected to be fueled by rising consumer indebtedness driven by credit card usage, personal loans, and mortgages. According to the Federal Reserve Bank of New York, total household debt reached USD18.04 trillion in Q4 2024, representing a USD 93 billion (0.5%) increase from the previous quarter.Financial Stress Among Millennials: The adoption of debt settlement programs is anticipated to increase, supported by rising student loan burdens and limited income growth in younger demographics.

The principal purposes of this national longitudinal study of the higher education system in the United States are to describe the characteristics of new college freshmen and to explore the effects of college on students. For each wave of this survey, each student completes a questionnaire during freshman orientation or registration that asks for information on academic skills and preparation, high school activities and experiences, educational and career plans, majors and careers, student values, and financing college. Other questions elicit demographic information, including sex, age, parental education and occupation, household income, race, religious preference, and state of birth. Specific questions asked of respondents in the 1978 survey included how well the students felt that their high school had prepared them in different academic areas, information regarding the Basic Educational Opportunity Grant (BEOG) and Guaranteed Student Loan (GSL) financial programs, and whether students considered themselves to be born-again Christians. Respondents were also asked to list their probable career and their assessments of achieving certain goals during their college years, as well as their predictions about what opportunities they might have in the future.

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the "Smallholder Diaries"). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 94 households are in the Punjab province, the breadbasket of Pakistan. The sample was drawn from 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Pakistan, the Smallholder Diaries were conducted in Bahawalnagar, southern Punjab, within the country's breadbasket. Rice, wheat, and cotton are commonly grown and typically sold through a network of local commission agents (known as arthis) and village traders. Given the dominance of agricultural middlemen in Pakistan, two villages in the district of Bahawalnagar were selected as representative of an area with relatively looser connections to agricultural value chains and middlemen.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators, administered to all households in the selected villages. As a supplement to this process, village leaders and community representatives were consulted to help ensure local participation and eliminate households with large landholdings, harvests per year, use of inputs, and integration with local markets and a variety of families were chosen.

In Pakistan, the sample was selected using a traditional screener survey with questions related to household demographics, crops and livestock, main income sources, and wealth indicators. As a supplement to this process, village leaders and community representatives were consulted to help ensure local ownership and eliminate households with large landholdings.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

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.

Abstract

In order to elucidate the financial lives of smallholder households and build the evidence base on this important client group, Consultative Group to Assist the Poor (CGAP) of the World Bank launched the year-long Financial Diaries with Smallholder Families (the "Smallholder Diaries"). The study captured the financial and in-kind transactions of 270 households in Tanzania, Pakistan and Mozambique, of which 93 households are in impoverished northern Mozambique. The sample came was drawn from 3 villages in Mozambique. Villages were selected based on their involvement in agriculture, and convenience in reaching them. Between June 2014 and July 2015, enumerators visited sample families every fortnight to conduct comprehensive face-to-face interviews to track all the money flowing into and out of their households.

Geographic coverage

In Mozambique, three villages in the Rapale district of northern Nampula Province were selected based on strong recommendations from local stakeholders. While some large companies buy cash crops in the province, smallholders tend to practice the subsistence, rain-fed agriculture that is more commonly found throughout Mozambique.

Analysis unit

The main unit for data collection for transactions was the household. However, each income source and financial instrument was ascribed to a specific household member during the initial questionnaire. Thus all transactions associated with that instrument or income source are registered under its owner. Similarly, transactions related to expenses were individually attributed to the member who initiated the respective transaction.

There was a small number of cash flows where the interviewer was not able to unambiguously identify the initiating household member. In these cases, the cash flow was recorded as belonging to the entire household (in the dataset the member ID field would be blank).

Analysis can be performed at two different levels of aggregation: a) The household itself b) Individual household members

In our study the household is defined as including those who consistently share financial resources, live together, share the same cooking arrangement, and report to the same household head. This includes babies, children, people who travel for work or school during the week and consider the household to be their main residence. However, the definition does not include people who are currently spending an extended period of time away from the household, including college students, students away at boarding school, military personnel, people in prison, or people who live in the house but maintain completely separate expenses (e.g. roommates, other families).

Universe

Once the villages for the Smallholder Diaries were selected, the research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research.

In Mozambique, these eligible households were identified using a participatory rural appraisal wealth-ranking technique. Working with committees of village representatives, the research teams conducted wealth-ranking exercises to assess the relative wealth of households in village hamlets or subareas.

Kind of data

Event/Transaction data [evn]

Sampling procedure

The methodology and sample size of the Smallholder Diaries was designed to generate a rich pool of detailed information and insights on a targeted population. The Smallholder Diaries are not intended to be statistically representative of smallholder families in participating countries.

Total number of households in sample: 93 (Mozambique); 86 (Tanzania); 94 (Pakistan). The sample came was drawn from 3 villages in Mozambique, 2 villages in Tanzania, and 2 villages in Pakistan. Villages were selected based on their involvement in agriculture, and convenience in reaching them.

The research teams used a screening process to help identify a range of families with 5 acres of land or less, diverse income sources, access to agricultural inputs, wealth levels, and crops to participate in the research. In Mozambique, these eligible households were identified using a participatory rural appraisal wealth-ranking technique. Working with committees of village representatives, the research teams conducted wealth-ranking exercises to assess the relative wealth of households in village hamlets or subareas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Interviewers visited each household and conducted three initial questionnaires. They 1) collected a household roster and demographic information about household members; 2) captured a register of physical assets and income sources for each household member and 3) registered the unique financial instruments used by each household member. This baseline information was then used to generate a custom cash flows questionnaire for each household, built to collect income, expenditure, and financial transactions for each individual. This customized cash flows questionnaire was then used for the collection of cash flows data. During regular visits about every two weeks, interviewers captured a complete set of daily, individual transactions from the preceding two-week period. Households were asked only about transactions using financial instruments and income sources that they actually have, rather than going through a generic list of questions. However, the cash flows questionnaire was continuously updated as new members joined the household, members acquired new financial instruments or income sources, or as the interviewers became aware of previously undisclosed ones.

Cleaning operations

All data editing was done manually.

Response rate

The sample initially included 286 households in all three countries, and the study ended with 273 households in total – an attrition rate similar to what has been observed in the past in similar Financial Diaries exercises. Households left the study due to moving from the study villages, seasonal migration, and occasionally by the prompting of the research team due to concerns about the household’s willingness to be forthcoming about important sources of income.

Low-income Working Family Allowance Scheme - No. of approved applications based on the household size