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

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.

This synthetic dataset simulates various demographic and socioeconomic factors that influence annual household income. It can be used for exploratory data analysis, predictive modeling, and understanding the relationships between different features and income levels.

Features:

• Age: Age of the primary household member (18 to 70 years).

• Education Level: Highest education level attained (High School, Bachelor's, Master's, Doctorate).

• Occupation: Type of occupation (Healthcare, Education, Technology, Finance, Others).

• Number of Dependents: Number of dependents in the household (0 to 5).

• Location: Residential location (Urban, Suburban, Rural).

• Work Experience: Years of work experience (0 to 50 years).

• Marital Status: Marital status of the primary household member (Single, Married, Divorced).

• Employment Status: Employment status of the primary household member (Full-time, Part-time, Self-employed).

• Household Size: Total number of individuals living in the household (1 to 7).

• Homeownership Status: Homeownership status (Own, Rent).

• Type of Housing: Type of housing (Apartment, Single-family home, Townhouse).

• Gender: Gender of the primary household member (Male, Female).

• Primary Mode of Transportation: Primary mode of transportation used by the household member (Car, Public transit, Biking, Walking).

• Annual Household Income: Actual annual household income, derived from a combination of features with added noise. Unit USD

This dataset can be used by researchers, analysts, and data scientists to explore the impact of various demographic and socioeconomic factors on household income and to develop predictive models for income estimation.

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

Drinking water affordability affects all residents including those with piped water connections and self-supply, private wells. Self-supply drinking water well users make up 12–15% of the United States population and are often overlooked during affordability studies. To the best of our knowledge, this study is the first attempt to quantify statewide water affordability costs for all residents, both utility customers and private well users. This study applies several geospatial methodologies: percent median household income (MHI), number of households beneath affordability thresholds, and hours worked at minimum wage. The greatest determinant of private well affordability was found to be initial capital costs. These costs were represented as a monthly loan payment plus operations and maintenance costs; a range of results were found. For operations and maintenance costs only, 9% of private well serviced homes exceeded the 2.5% MHI threshold while for monthly loan payments, corresponding to a $13,500 capital cost, 51% of households exceeded the same affordability metric. There are 3.17% of centralized water utility serviced households spending greater than 2.5% of their median household income on water while 16.3% of utility-serviced households fall below an income threshold representing 2.5% of the block group MHI. This range of results highlights the need for a multifaceted solution to ensure the human right to affordable water.

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 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.

Korea Average: AH: High School: Credit Loan data was reported at 7,060.000 KRW th in 2017. This records an increase from the previous number of 6,580.000 KRW th for 2016. Korea Average: AH: High School: Credit Loan data is updated yearly, averaging 6,255.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 7,060.000 KRW th in 2017 and a record low of 4,680.000 KRW th in 2010. Korea Average: AH: High School: Credit Loan data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H080: SHFLC: Household Assets, Liabilities & Income By Educational Attainments of Household Head.

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.

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 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.

In recent years, economists and policymakers have been interested in the burden of student debt across socioeconomic groups. In this Economic Commentary , we use the two most recent waves of the Survey of Consumer Finances, collected in 2019 and 2022, to study changes in the joint distribution of student debt and two measures of “ability-to-pay,” income and net worth. We find that between 2019 and 2022, both the fraction of families with student debt and real student debt per family were essentially unchanged, and aggregate student debt fell as a fraction of aggregate income and net worth. However, over the same period, the distribution of student debt shifted toward higher-income and wealthier families, with a rise in the average student debt in the highest quintile of both income and net worth. Further, this shift was not driven by changes in the distribution of debtors, but, instead, in the amount of debt per family.

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

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.

Korea Median: AH: Elementary School Or Lower: Credit Loan data was reported at 10,000.000 KRW th in 2017. This stayed constant from the previous number of 10,000.000 KRW th for 2016. Korea Median: AH: Elementary School Or Lower: Credit Loan data is updated yearly, averaging 9,250.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 10,000.000 KRW th in 2017 and a record low of 8,000.000 KRW th in 2014. Korea Median: AH: Elementary School Or Lower: Credit Loan data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H080: SHFLC: Household Assets, Liabilities & Income By Educational Attainments of Household Head.

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.

Comprehensive demographic dataset for Dallas Land and Loan, Dallas, TX, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.