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.

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.

This statistic shows the share of Millennials who delayed buying a home due to student debt in the United States as of August 2017, by personal income. During the measured period, ** percent of Millennials with outstanding student loans who earned less than ****** U.S. dollars said that their debt impacted their ability to buy a home.

In recent years, the rising level of student debt has led to calls for increased government assistance in the form of partial or full student debt cancellation. In this Commentary , we use the 2019 Survey of Consumer Finances (SCF) to study the incidence of student debt and cancellation benefits along quantiles of household income, net worth, and our estimate of lifetime wealth. We show that student debt is highly concentrated among households with low net worth, but much more evenly distributed across income and lifetime wealth. We then highlight several challenges in using such statistics to draw conclusions about whether cancellation will ultimately increase or decrease inequality in lifetime wealth, and we outline open questions for future research. Note: This Economic Commentary was updated in January 2024 to correct an error in the calculation of lifetime wealth. The update corrects the figures for lifetime wealth (Figures 4, 6, and 7) and clarifies the description of the calculation of lifetime wealth in the text. The correction does not alter the conclusions drawn by the authors. Replication materials for this Economic Commentary may be found at https://github.com/tphelanECON/student_debt_SCF .

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.

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.

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

Abstract

The purpose of the Household Income and Expenditure Survey (HIES) survey is to obtain information on the income, consumption pattern, incidence of poverty, and saving propensities for different groups of people in Palau. This information will be used to guide policy makers in framing socio-economic developmental policies and in initiating financial measures for improving economic conditions of the people.

Some more specific outputs from the survey are listed below:

a) To obtain expenditure weights and other useful data for the revision of the consumer price index; b) To supplement the data available for use in compiling official estimates of household accounts in the systems of national accounts; c) To supply basic data needed for policy making in connection with social and economic planning, including producing as many of Palau's National Minimum Development Indicators (NMDI's) as possible; d) To provide data for assessing the impact on household living conditions of existing or proposed economic and social measures, particularly changes in the structure of household expenditures and in household consumption; e) To gather information on poverty lines and incidence of poverty throughout Palau.

Geographic coverage

National Coverage, excluding Sonsorol and Hatohobei. Urban and Rural.

Analysis unit

• Households;
• Individuals.

Universe

All private households and group quarters (people living in Work dormitories, as it is an important aspect of the subject matter focused on in this survey, and not addressed elsewhere).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used was the 2012 Palau census, which provided population figures for everyone living in both private households and group quarters (e.g. worker barracks, school dormitories, prison). The sampling selection was done separately in private dwellings and group quarters.

It is an accepted practice for the Household Income and Expenditure Survey (HIES) to cover all living quarters regarded as private dwellings, and the Palau 2013/14 HIES will follow this recommendation.

For group quarters it is also recommended to exclude the prison, as it is not considered appropriate to include such institutions in a survey such as HIES.

A decision as to whether the remaining group quarters should be included is based on the following criteria:

1) Ease in accessing and covering them in a survey such as HIES 2) Relevance to the subject matter of the survey 3) Whether their impact on the subject matter is mostly covered already

Under these criteria, the following recommendations are made: -School/college dormitories: Will exclude from HIES as these individuals will be covered in the households from which they came (if selected) -Work dormitories: Aim to include in the HIES as they are an important aspect of the subject matter focused on in this survey, and not addressed elsewhere -Live aboard: Will exclude due to the movement of such vehicles, and the minimal impact they may have on such a survey -Convents/religious quarters: Will exclude based on their expected minimum impact on the survey subject matter

NB: Given students in dorms are expected to have a high portion of their income and expenses covered in their original household of origin, and there were no religious group quarters identified during the census, only persons in the prison and living aboard are expected to be excluded from the survey. These people account for 81 out of 2,322 group quarters residents (only 3.6%).

Although the response rates were down in the 2006 HIES, with a smaller more experienced team working over 12 months, it is expected there will be improvements in this area. However, the expected sample loss of 10 per cent was probably too ambitious, and given the actual rate ended up at 287/1,063 = 27 per cent, it is more realistic to assume a sample loss of around 15 per cent with improvements for the 2013/14 HIES. Based on the RSEs presented in 2.3.2, it also appears that the 20 per cent desirable sample produced sound results for the survey, and with higher response rates anticipated, these results from a sample error perspective should improve. It is therefore proposed for the 2013/14 Palau HIES that a sample size of 20 per cent be adopted, which also allows for sample loss of 15 per cent.

In the 2006 Palau HIES, effort was made to design a sample which could produce results for the six domains (stratum). Whilst reasonable results were generated for each of these domains, it was felt that post survey, there was no great use of these results at that level. For the 2013 HIES it is proposed to focus on generating reliable results at the national level, with focus also being place on producing results for the urban/rural split. In the case of Palau, the urban population is considered to consist of the states of Koror and Airai.

The last phase to finalizing the sample numbers was to adjust the desirable sample numbers, so that they could be easily applied by the HIES team in a practical manner over the course of the 12 month fieldwork. This was achieved by modifying the sample counts (not too much) to enable sample sizes each round would be of a similar size, and workloads for each enumerator were the same size each round. The desirable workload for an enumerator covering the PD population was 10 households, whereas this figure was increased to 14 persons for GQs as it was envisaged the amount of time required to cover a person in a GQ would be significantly less. With this in mind, we wanted to ideally have the PD sample to be divisible by 160 so this would enable an even number of households each round, whilst maintaining a workload of 10 households for interviewers covering these areas. For the GQ sample, given the desirable number of GQs was already 225, and 16x14=224, then a simple reduction of 1 in the GQ sample would result in a nice even workload of 14 persons per round for 1 interviewer. This logic was also applied to the split between urban and rural resulting in 14 workloads in urban and 2 workloads in rural.

Mode of data collection

Face-to-face [f2f]

Research instrument

Developped in English, a questionnaire consisting of four Modules and a Weekly Diary covering 2 weeks was used for the Republic of Palau Household Income and Expenditure Survey (HIES) 2013. Each Module covers distinct but connected portion of the Household.

The Modules are as follows: -Module 1 - Demographic Information: · Demographic Profile · Labor Force Status · Health Status · Communication Status -Module 2 - Household Expenditure: · Housing Characteristics · Housing Tenure Expenditure · Utilities & Communication Details · Utilities & Communication Expenditure · Land & Home Details · Land & Home Expenditure · Household Goods & Assets Details · Household Goods & Assets Expenditures · Vehicles & Accessories Details · Vehicles & Accessories Expenditures · Private Travel Details · Private Travel Expenditures · Household Services Expenditure · Contributions to Special Occasions · Provisions of Financial Support · Loans · Household Assets Insurance & Taxes · Personal Insurance -Module 3 - Individual Expenditures: · Education grants and scholarships · Education Identifications · Education Expenditures · Health Identifications · Health Expenditures · Clothing Identification · Clothing Expenditure · Communication Identification · Communication Expenditures · Luxury Items Identification · Luxury Items Expenditures -Module 4 - Income: · Wages & Salary: In country (current) · Wages & Salary: Overseas (last 12 months) · Wages & Salary: In country (last 12 months) · Income from Non Subsistence Business · Description of Agriculture & Forestry Activities · Income from Agriculture & Forestry Activities · Description of Handicraft & Home Processed Food Activities · Income from Handicraft & Home Processed Food Activities · Description of Livestock & Aquaculture Activities · Income from Livestock & Aquaculture Activities · Description of Fishing & Hunting Activities · Income from Fishing & Hunting Activities · Property Income, Transfer Income & Other Receipts · Remittances & Other Cash Gifts -Weekly Diary - Covering 14 Days (2 weeks): · Daily expenditure of food and non-food items · Payments of service made · Gambling winning and losses · Items received for free · Home produced food and non-food items.

All questionnaires are provided as external resources in this documentation.

Cleaning operations

Program: CSPro 5.1x

Data editing took place at a number of stages throughout the processing, including:

a) Office editing and coding b) During data entry; Error report correction; Secondary editing by Quality Control Officer (QCO) c) Structure checking and completeness

Detailed documentation of the editing of data can be found in the "Data processing guidelines" document provided as an external resource.

Response rate

Some 1,145 households were selected (in private dwellings and workers quarters) to participate in the survey, and the response rate was 75.8% (i.e. 869 households responded). This response rate allows for statistically significant analysis at the national, urban and rural level.

Response rates for private households by State: -Koror: 355 households responded out of 480 selected => 73.9%; -Airai: 119 households responded out of 160 selected => 74.4%; -URBAN: 474 households responded out of 640 selected => 74.1%; -Kayangel: 0 households responded out of 10 selected => 0%; -Ngarchelong: 27 households responded out of 30 selected => 90%; -Ngaraard: 22 households responded

The Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

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 Household Market and Nonmarket Activities (HUS) project started as a joint research project between the Industrial Institute for Economic and Social Research (IUI) and Göteborg University in 1980. The ambition was to build a consistent longitudinal micro data base on the use of time, money and public services of households. The first main survey was carried out in 1984. In addition to a contact interview with the selected individuals, all designated individuals participated in a personal interview and two telephone interviews. All respondents were asked about their family background, education, marital status, labor market experience, and employment. In addition, questions about the household were asked of the head of household, concerning family composition, child care, health status, housing, possession of vacation homes, cars, boats and other consumption durables. At the end of the personal interview the household head had to fill out a questionnaire including questions about financing of current home, construction costs for building a house, house value and loans, imputation of property values and loans, additions/renovations 1983, maintenance and repairs, leasing, sale of previous home, assets and liabilities, and non-taxable benefits. All the respondents had to fill out a questionnaire including questions about tax-return information 1983, employment income, and taxes and support payments. Two telephone interviews were used primarily to collect data on the household´s time use and consumption expenditures. The 1986 HUS-survey included both a follow-up of the 1984 sample (panel study) and a supplementary sample. The 1986 sample included 1) all respondents participating in the 1984 survey, 2) the household heads, partners and third persons who should have participated in 1984 but did not (1984 nonresponse), 3) those individuals who started living together after the 1984 interview with an selected individual who participated or was supposed to participate in 1984, 4) members of the 1984 household born in 1966 or 1967. If entering a new household, for example because of leaving their parental home, the household head and his/her partner were also interviewed. Respondents participating in the 1984 survey were interviewed by telephone in 1986. Questions dealt with changes in family composition, housing, employment, wages and child care, and it was not only recorded whether a change had occurred, and what sort of change, but also when it occurred. The respondents also received a questionnaire by mail with questions mainly concerning income and assets. Respondents not participating in the earlier survey were interviewed in person and were asked approximately the same questions as in the 1984 personal interview. The 1988 HUS-survey was considerably smaller than the previous ones. It was addressed exclusively to participants in the 1986 survey, and consisted of a self-enumerated questionnaire with a nonrespondent follow-up by telephone. The questions dealt with changes in housing conditions, employment and household composition. The questionnaire also contained some questions on household income. In many respect the 1991 HUS-survey replicated the 1988 survey. The questions were basically the same in content and range, and the survey was conducted as a self-enamurated questionnaire sent out by mail. This time, however, in contrast to the 1988 survey, an attempt was made to include in the survey the new household members who had moved into sample households since 1986, as well as young people who turned 18 after the 1986 survey. Earlier respondents received a questionnaire by mail containing questions about their home, their primary occupation and weekly work hours since May 1988 (event-history data), earnings in 1989, 1990 and 1991, household composition and any changes in it that might have occurred since 1988, child care and some questions on income. New respondents were also asked about their education and labor-market experience. With respect to its design and question wording, the 1993 survey is a new version of the 1986 survey. The survey is made up of four parts: 1) the panel survey, which was addressed mainly to respondents in the 1991 survey, with certain additions; 2) the so-called supplementary survey, which focused on a new random sample of individuals; 3) the so-called nonresponse survey, which encompassed respondents who had participated in at least one of the earlier surveys but had since dropped out; 4) the time-use survey, which included the same sample of respondents as those in the panel and supplementary surveys. Individuals in the nonresponse group were not included in the time-use survey. Most of the questions in the first three surveys were the same, but certain questions sequences were targeted to the respondents in a specific survey. Thus certain retrospective questions were asked of the nonresponse group, while specific questions on social background, labor market experience etc. were addressed to new respondents. In the case of respondents who had already participated in the panel, a combined contact and main interview was conducted by telephone, after which a self-enumerated questionnaire was sent out to each respondent by mail. The panel sample also included young people in panel households who were born in 1973 or 1974 as well as certain new household members who had not previously been interviewed. These individuals, like new respondents, were not interviewed by telephone until they had been interviewed personally. Thus technically they were treated in the same manner as individuals in the supplementary sample. The new supplementary sample was first contacted by telephone and then given a fairly lengthy personal interview, at the conclusion of which each respondent was asked to fill out a written questionnaire. In this respect the survey design for the nonresponse sample was the same as for the supplementary sample. The nonresponse sample also included young people born in 1973 or 1974 as well as certain new household members. The time-use interviews were conducted by telephone. For each respondent two days were chosen at random from the period from February 15, 1993 to February 14, 1994 and the respondents were interviewed about their time use during those two days. If possible, the time-use interviews were preceded by the other parts of the survey, but this was not always feasible. In each household the household head and spouse/partner were interviewed, as well as an additional person in certain households. Questions regarding the household as a whole were asked of only one person in the household, preferably the household head. As in earlier surveys, data from the interviews was subsequently supplemented by registry data, but only for those respondents who had given their express consent. There is registry information for 75-80 percent of the sample. The telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; and cars and boats. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1992. The 1996 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and environment. The questionnaire was divided into twelve sections: sale of previous home; acquisition of current home; construction costs for building a home; house value and loans; repairs; insurance; home-related expenses; sale of previous home; assets; household income; taxes; and respondent income 1995. The 1998 telephone interview is divided into following sections: administrative data; labor market experience; employment; job-seekers; not in labor force; education; family composition; child care; health status; other household members; housing conditions; vacation homes; cars and boats; and municipal service. The questionnaire was divided into nine sections: sale of previous home; house value and loans; insurance; home-related expenses; assets; household income; inheritances and gifts; black-market work; and respondent income 1997.

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.

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 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.

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.

Korea Average: AH: Colleage Or Higher: Secured Loan data was reported at 60,060.000 KRW th in 2017. This records an increase from the previous number of 55,240.000 KRW th for 2016. Korea Average: AH: Colleage Or Higher: Secured Loan data is updated yearly, averaging 47,550.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 60,060.000 KRW th in 2017 and a record low of 42,670.000 KRW th in 2010. Korea Average: AH: Colleage Or Higher: Secured 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.

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.

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