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.

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 statistic displays the average student support per household in the United Kingdom (UK) in 2017/18, by decile group. Households in the seventh decile received, on average, 148 British pounds in student support. This was the highest income received from student support of any decile group.

The study charted the subsistence, employment and housing of university students in the capital region in Finland. The study was funded by the Student Union of the University of Helsinki and the Student Union of Aalto University. First, the respondents were asked questions relating to student grant. The questions focused on, for example, whether the respondents had received student grant for the degree they were studying for and if not, why (e.g. because of too large income or too few study credits), as well as whether the months of student financial aid available for them had been or would be sufficient for their bachelor's and master's degrees. The respondents were also asked whether they felt it was easy or hard for them to sufficiently progress in their studies (approximately 5 study credits per month) to qualify for student financial aid as well as how they were planning to fund their studies if they could not receive student financial aid any longer (e.g. by working, with savings, with the help of their family or spouse). Next, questions relating to student loan were presented. The respondents were asked whether they had taken out student loan and why (e.g. to secure income, to improve standard of living, for investing). They were also asked whether changes such as student financial aid cuts, reduction in the maximum number of months student financial aid was available and the transfer to general housing allowance for accommodation costs had had an impact on their need to take study loan. Employment and other sources of income were examined with questions concerning the amount of time the respondents worked at present or had worked during the past school year. If the respondents had not worked, they were asked whether they would have wanted to. The respondents were asked about the most important reason for working during their studies (e.g. for work experience or networking) and how they thought working would affect the progress of their studies. Some questions focused on income by charting, for example, how much how much salary the respondents thought they would receive a year after graduation and how much their monthly income from different sources was (e.g. salary, social assistance, aid from parents). The respondents were also asked about their savings and easily disposable property (e.g. shares/stock), whether they thought their financial circumstances were good or bad and whether and how much they received financial aid from their parents or other relatives. Ways of dealing with insufficient income were examined with questions relating to, for example, whether the respondents had taken out instant loans, done undeclared work or gone without eating for a day. Next, the respondents were asked how the transfer of students to be covered under general housing allowance rather than student housing supplement had affected their subsistence and housing. Housing was examined with questions relating to, for example, accommodation type during the autumn semester, household composition and the allocation of expenses between the adults living in the household, as well as satisfaction in various aspects of the housing (e.g. rent, location, condition). The expenses related to housing were also charted and the respondents were asked how much general housing allowance they received per month. The respondents were presented with statements concerning housing and the changes in general housing allowance (e.g. whether one or two bedroom student housing had become a more interesting option and whether they thought that shared housing was no longer as cost-effective). Finally, statements concerning, for example, wellbeing, satisfaction in the physical environment of the university, and the amount of guidance received for studies were presented. Background variables included, among others, the respondent's age group, gender, parents' highest level of education, highest previous degree, faculty (University of Helsinki) or school (Aalto University), the amount of credits completed during the past school year and whether the respondent had some condition, illness or injury that hindered their studies.

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.

Financial burdens of the parental home through education of children.

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

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

The data surveys university students' incomes, employment, housing and changes in these aspects. The survey was carried out by the Research Foundation for Studies and Education (Otus) and funded by the Student Union of Tampere University (TREY), the Student Union of the University of Turku (TYY), the Student Union of the University of Eastern Finland (ISYY), the Student Union of the University of Jyväskylä (JYY) and the Student Union of Åbo Akademi University (ÅAS). First, respondents were asked about the income of carers, moving to another place to study, housing and homelessness, and sharing household expenses. Respondents were also asked to estimate their monthly expenditure. Respondents were asked whether they were receiving any student financial allowances for the studies they were currently pursuing, and how adequate the number of months of financial aid seemed at the moment. The reasons for raising student loans and other sources of income, such as support from parents or other close relatives, were also of interest. Next, they were asked about working, the reasons for working while studying and the impact of working on the progress of their studies. Respondents were asked to assess the adequacy of their own financial resources and their own employment prospects after graduation. Respondents were also asked whether they had taken out consumer credit, applied for food aid or taken any other action in the last 12 months to deal with a tight financial situation. Finally, the respondents were asked about the impact of COVID-19 pandemic on their financial situation. Background variables included year of starting studies, university, faculty, age group, gender, native language, number of children in care and minority status.

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

Using data from nearly 1.2 million Black SAT takers, we estimate the impacts of initially enrolling in an Historically Black College and University (HBCU) on educational, economic, and financial outcomes. We control for the college application portfolio and compare students with similar portfolios and levels of interest in HBCUs and non-HBCUs who ultimately make divergent enrollment decisions - often enrolling in a four-year HBCU in lieu of a two-year college or no college. We find that students initially enrolling in HBCUs are 14.6 percentage points more likely to earn a bachelor's degree and have 5 percent higher household income around age 30 than those who do not enroll in an HBCU. Initially enrolling in an HBCU also leads to $12,000 more in outstanding student loans around age 30. We find that some of these results are driven by an increased likelihood of completing a degree from relatively broad-access HBCUs and also relatively high-earning majors (e.g., STEM). We also explore new outcomes, such as credit scores, mortgages, bankruptcy, and neighborhood characteristics around age 30.

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 1979 survey included type of high school, total of expenses the students expected to receive from different sources, questions regarding the Basic Educational Opportunity Grant (BEOG) and Guaranteed Student Loan (GSL), students' life patterns, and the best estimate of students' parents' income during the past year.

Philippines SFE: Insurance & Financial Services data was reported at 2.730 % in 2023. This records a decrease from the previous number of 3.070 % for 2021. Philippines SFE: Insurance & Financial Services data is updated yearly, averaging 2.730 % from Dec 2018 (Median) to 2023, with 3 observations. The data reached an all-time high of 3.070 % in 2021 and a record low of 0.000 % in 2018. Philippines SFE: Insurance & Financial Services data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.H023: Family Income and Expenditure Survey: 2023 Master Sample: Percentage Distribution of Family Expenditure.

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.

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 Tuition Assistance Program (TAP), New York's largest student financial aid grant program, helps eligible New York residents attending in-state postsecondary institutions pay for tuition. TAP grants are based on the applicant’s and his or her family’s New York State taxable income. This data includes TAP award recipients and dollar amounts by college, sector groups, and Level of Study for academic years 2000-2011.

This table has been archived and replaced by table 36100665. The sum of the values for net worth and its components by province and region is less than the total for Canada as they exclude the territories. The income quintiles refer to the quintiles estimated at the Canada level and not at the provincial/territorial level. The Income quintiles are assigned based on the equalized household disposable income. This takes into account differences in household size and composition. The Oxford-modified equivalence scale is used; it assigns a value of 1 to the first adult, 0.5 to each additional person aged 14 and over, and 0.3 for all children under 14. The coefficients of variation from Statistics Canada's Survey of Financial Security for 2012 and 2016, which serve as indicators of the accuracy of these estimates for net worth and its components, are available in the appendix to Distributions of Household Economic Accounts, estimates of asset, liability and net worth distributions, 2010 to 2019, technical methodology and quality report for the March 2020 release. Age groups refer to the age group of the major income earner. Life insurance and pensions include the value of all life insurance and employer pension plans, termination basis. Excludes public plans administered or sponsored by governments: Old Age Security (OAS) including the Guaranteed Income Supplement (GIS) and the Spouse's Allowance (SPA), as well as the Canada and Quebec Pension Plans (CPP/QPP). Other financial assets include total currency and deposits, Canadian short-term paper, Canadian bonds and debentures, foreign investments in paper and bonds, mortgages, equity and investment funds, and other receivables. Other non-financial assets include consumer durables, machinery and equipment, and intellectual property products. Excludes accumulation of value of collectibles including coins, stamps and art work. Other liabilities include major credit cards and retail store cards, gasoline station cards, etc., vehicle loans, lines of credit, student loans, other loans from financial institutions and other money owed. Owner's equity refers to the value of the interests of an owner or partial owner in an asset, in this case real estate, divided by household real estate, which includes the value of structures (residential and non-residential) and land owned by households. Distributions of Household Economic Accounts (DHEA) estimates are benchmarked to year-end estimates for liabilities and assets from the National Balance Sheet Accounts (NBSA, Table 36-10-0580-01), and for annual household disposable income from the Provincial-Territorial Economic Accounts (Table 36-10-0224-01). DHEA ratios for debt to disposable income, real estate as a share of disposable income, and net worth as a share of disposable income differ from those included in “Financial indicators of households and non-profit institutions serving households, national balance sheet accounts” (Table 38-10-0235-01) as the latter source adjusts disposable income for the change in pension entitlements. The measure of disposable income used for the DHEA ratios is more consistent with that shown in “Household sector credit market summary table, seasonally adjusted estimates” (Table 38-10-0238), which does not adjust disposable income for the change in pension entitlements.

The College Bound Scholarship was created to provide state financial aid to low-income students who may not consider college a possibility due to the cost. The scholarship covers tuition (at comparable public college rates), some fees, and a small book allowance. This dataset contains the counts of 7th or 8th grade students whose family meets the income requirements (CBS_Eligible), those who submit and complete an application by June 30 of the student’s 8th grade year(CBS_Applications), and the Sign-Up Rate (CBS_Rate) calculated as a percentage.

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 86 households are in the fertile farmlands of western Tanzania. The sample was drawn from 2 villages in Tanzania. 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 Tanzania, the Smallholder Diaries sites included two villages located in the region of Mbeya, home to one of the largest farming populations in Tanzania. Mbeya sits within the Southern Agricultural Growth Corridor of Tanzania (SAGCOT), a region known for a productive agroecological climate and an array of crops and livestock. Farmers in the region most commonly produce maize, as well as coffee and tea, rice, potatoes, pyrethrum, and cassava. To explore the diversity within this region, Smallholder Diaries sites were selected in two different districts. The two selected villages exhibit important differences in available economic activities, climate, harvest seasons, crops, and use of agricultural inputs.

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 Tanzania, 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 Tanzania, 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.

This dataset consists of a selection of variables extracted from the U.S. Department of Education's College Scorecard 2015/2016. For the original, raw data visit the College Scorecard webpage. This dataset includes variables about institution types, proportion of degree types awarded, student enrollments and demographics, and a number of price and revenue variables. For 2005-2006 data, see here.Note: Data is not uniformly available for all schools on all variables. Variables for which there is no data (NULL), or where data is suppressed for reasons of privacy, are indicated by 999999999.

ATTRIBUTE DESCRIPTION EXAMPLE

ID2 1

UNITIDUnit ID for institution 100654

OPEID 8-digit OPE ID for institution 100200

OPEID6 6-digit OPE ID for institution 1002

State FIPS

1

State

AL

Zip

35762

City

Normal

Institution Name

Alabama A & M University

Institution Type 1 Public 2 Private nonprofit 3 Private for-profit 1

Institution Level 1 4-year 2 2-year 3 Less-than-2-year 1

In Operation 1 true 0 false 1

Main Campus 1 true 0 false 1

Branches Count of the number of branches 1

Popular Degree 1 Predominantly certificate-degree granting 2 Predominantly associate's-degree granting 3 Predominantly bachelor's-degree granting 4 Entirely graduate-degree granting 3

Highest Degree 0 Non-degree-granting 1 Certificate degree 2 Associate degree 3 Bachelor's degree 4 Graduate degree 4

PCIP01 Percentage of degrees awarded in Agriculture, Agriculture Operations, And Related Sciences. 0.0446

PCIP03 Percentage of degrees awarded in Natural Resources And Conservation. 0.0023

PCIP04 Percentage of degrees awarded in Architecture And Related Services. 0.0094

PCIP05 Percentage of degrees awarded in Area, Ethnic, Cultural, Gender, And Group Studies. 0

PCIP09 Percentage of degrees awarded in Communication, Journalism, And Related Programs. 0

PCIP10 Percentage of degrees awarded in Communications Technologies/Technicians And Support Services. 0.0164

PCIP11 Percentage of degrees awarded in Computer And Information Sciences And Support Services. 0.0634

PCIP12 Percentage of degrees awarded in Personal And Culinary Services. 0

PCIP13 Percentage of degrees awarded in Education. 0.1268

PCIP14 Percentage of degrees awarded in Engineering. 0.1432

PCIP15 Percentage of degrees awarded in Engineering Technologies And Engineering-Related Fields. 0.0587

PCIP16 Percentage of degrees awarded in Foreign Languages, Literatures, And Linguistics. 0

PCIP19 Percentage of degrees awarded in Family And Consumer Sciences/Human Sciences. 0.0188

PCIP22 Percentage of degrees awarded in Legal Professions And Studies. 0

PCIP23 Percentage of degrees awarded in English Language And Literature/Letters. 0.0235

PCIP24 Percentage of degrees awarded in Liberal Arts And Sciences, General Studies And Humanities. 0.0423

PCIP25 Percentage of degrees awarded in Library Science. 0

PCIP26 Percentage of degrees awarded in Biological And Biomedical Sciences. 0.1009

PCIP27 Percentage of degrees awarded in Mathematics And Statistics. 0.0094

PCIP29 Percentage of degrees awarded in Military Technologies And Applied Sciences. 0

PCIP30 Percentage of degrees awarded in Multi/Interdisciplinary Studies. 0

PCIP31 Percentage of degrees awarded in Parks, Recreation, Leisure, And Fitness Studies. 0

PCIP38 Percentage of degrees awarded in Philosophy And Religious Studies. 0

PCIP39 Percentage of degrees awarded in Theology And Religious Vocations. 0

PCIP40 Percentage of degrees awarded in Physical Sciences. 0.0188

PCIP41 Percentage of degrees awarded in Science Technologies/Technicians. 0

PCIP42 Percentage of degrees awarded in Psychology. 0.0282

PCIP43 Percentage of degrees awarded in Homeland Security, Law Enforcement, Firefighting And Related Protective Services. 0.0282

PCIP44 Percentage of degrees awarded in Public Administration And Social Service Professions. 0.0516

PCIP45 Percentage of degrees awarded in Social Sciences. 0.0399

PCIP46 Percentage of degrees awarded in Construction Trades. 0

PCIP47 Percentage of degrees awarded in Mechanic And Repair Technologies/Technicians. 0

PCIP48 Percentage of degrees awarded in Precision Production. 0

PCIP49 Percentage of degrees awarded in Transportation And Materials Moving. 0

PCIP50 Percentage of degrees awarded in Visual And Performing Arts. 0.0258

PCIP51 Percentage of degrees awarded in Health Professions And Related Programs. 0

PCIP52 Percentage of degrees awarded in Business, Management, Marketing, And Related Support Services. 0.1479

PCIP54 Percentage of degrees awarded in History. 0

Admission Rate

0.6538

Average RetentionRate of retention averaged between full-time and part-time students. 0.4428

Retention, Full-Time Students

0.5779

Retention, Part-Time Students

0.3077

Completion Rate

0.1104

Enrollment Number of enrolled students 4505

Male Students Percentage of the student body that is male. 0.4617

Female Students Percentage of the student body that is female. 0.5383

White Percentage of the student body that identifies as white. 0.034

Black Percentage of the student body that identifies as African American. 0.9216

Hispanic Percentage of the student body that identifies as Hispanic or Latino. 0.0058

Asian Percentage of the student body that identifies as Asian. 0.0018

American Indian and Alaskan Native Percentage of the student body that identifies as American Indian or Alaskan Native. 0.0022

Native Hawaiian and Pacific Islander Percentage of the student body that identifies as Native Hawaiian or Pacific islander. 0.0018

Two or More Races Percentage of the student body that identifies as two or more races. 0

Non-Resident Aliens Percentage of the student body that are non-resident aliens. 0.0062

Race Unknown Percentage of the student body for whom racial identity is unknown. 0.0266

Percent Parents no HS Diploma Percentage of parents of students whose highest level of education is less than high school. 0.019298937

Percent Parents HS Diploma Percentage of parents of students whose highest level of education is high school 0.369436786

Percent Parents Post-Secondary Ed. Percentage of parents of students whose highest level of education is college or above. 0.611264277

Title IV Students Percentage of student body identified as Title IV 743

HCM2 Cash Monitoring Schools identified by the Department of Ed for Higher Cash Monitoring Level 2 0

Net Price

13435

Cost of Attendance

20809

In-State Tuition and Fees

9366

Out-of-State Tuition and Fees

17136

Tuition and Fees (Program) Tuition and fees for program-year schools NULL

Tution Revenue per Full-Time Student

9657

Expenditures per Full-Time Student

7941

Average Faculty Salary

7017

Percent of Students with Federal Loan

0.8159

Share of Students with Federal Loan

0.896382157

Share of Students with Pell Grant

0.860906217

Median Loan Principal Amount upon Entering Repayment

14600

Median Debt for Completed Students Median debt for student who completed a course of study 35000

Median Debt for Incompleted Students Median debt for student who did not complete a course of study 9500

Median Debt for Family Income $0K-$30K Median debt for students of families with less thank $30,000 income 14457

Median Debt for Family Income $30K-$75K Median debt for students of families with $30,000-$75,000 income 15000

Median Debt for Family Income over $75K Median debt for students of families with over $75,000 income 14250

Median Debt Female Students

16000

Median Debt Male Students

13750

Median Debt 1st Gen. Students Median debt for first generation college student 14307.5

Median Debt Not 1st Gen. Students Median debt for not first generation college students 14953

Cumulative Loan Debt Greater than 90% of Students (90th Percentile)

48750

Cumulative Loan Debt Greater than 75% of Students (75th Percentile)

32704

Cumulative Loan Debt Greater than 25% of Students (25th Percentile)

5500

Cumulative Loan Debt Greater than 10% of Students (10th Percentile)

3935.5

Accrediting Agency

Southern Association of Colleges and Schools Commission on Colleges

Website

www.aamu.edu/

Price Calculator

www2.aamu.edu/scripts/netpricecalc/npcalc.htm

Latitude

34.783368

Longitude

-86.568502

In 2024, households in South Korea with a monthly income of eight million South Korean won or more spent an average of ******* won per month on their child's private education. The average monthly expenditure per student in South Korea for private education was approximately ******* won that year.