College Scorecard

The College Scorecard dataset is provided by the U.S. Department of Education and contains information on nearly every college and university in the United States. The dataset includes data on student loan repayment rates, graduation rates, affordability, earnings after graduation, and more. The goal of this dataset is to help students make informed decisions about their college choice by providing them with clear and concise information about each school's performance

How to use the dataset

This dataset can help understand the cost of attending college in the United States, as well as the average debt load for students. It can also be used to compare different schools in terms of their graduation rates and repayment rates

Columns

• UNITID: Unit ID for institution
• INSTNM: Institution name
• CITY: City
• STABBR: State
• ZIP: Zip code
• OPEID: OPE ID for institution
• OPEID6: OPE ID for institution (6-digit)
• ACCREDAGENCY: Accrediting Agency
• INSTURL: Institution URL
• NPCURL: Net Price Calculator URL
• SCH_DEG: Highest degree awarded
• HCM2: Carnegie Classification 2010:** Basic
• MAIN: Carnegie Classification 2010:** Main
• NUMBRANCH: Number of branch campuses
• PREDDEG: Predominant degree awarded
• HIGHDEG: Highest degree awarded
• CONTROL: Control of institution
• ST_FIPS: State FIPS code
• REGION: Region
• LOCALE: Locale code
• LOCALE2: Locale code (multiple categories per state)
• CCBASIC: Carnegie Classification 2010:** Basic
• CCMAIN: Carnegie Classification 2010:** Main
• CCUGPROF: Carnegie Classification 2010:** Undergraduate Profile
• CCSIZSET: Carnegie Classification 2010:** Size and Setting
• HBCU: Historically Black College or University
• PBI: Predominantly Black Institution
• ANNHI: Tribal College or University
• TRIBAL: Tribal College or University (Public)
• AANAPII: Asian American and Native American Pacific Islander-Serving Institution
• HSIP: Hispanic-Serving Institution (HSI)
• NANTI: Native American-Serving Nontribal Institution
• MENONLY: Men only
• WOMENONLY: Women only
• RELAFFIL: Religious affiliation
• DISTANCEONLY: Distance-only
• CURROPER: Currently operating
• VETERAN: Veteran-supportive
• LIMDEP: Limited-degree-granting
• HIGHDEG_GRANTED: Highest degree granted
• PS: Predominantly two-year public
• UGRD_ENRL_TOTAL: Undergraduate total enrollment
• GRAD_ENRL_TOTAL: Graduate total enrollment
• UGRD_ENRL_ORIG_YR2_RT: Undergraduate, first-time, first-year retention rate (%)

Acknowledgements

This data was originally collected by the US Department of Education and made available on their website. Thank you to the US Department of Education for making this data available!

The schools that create the most student debt

The top 10 schools for student loan debt in the United States

By Andy Kriebel [source]

About this dataset

This dataset contains information on the amount of student loan debt originated by schools in the United States for the 2020-2021 academic year. The data includes the school name, city, state, zip code, school type, loan type, number of recipients, number of loans originated, amount of money loaned, and number of disbursements

How to use the dataset

There are a few things to keep in mind when using this dataset:

• The data is for the 2020-2021 academic year.
• The data is for student loan debt originated by schools in the United States.
• The data is sorted by school.
• The columns of interest are: School, City, State, Zip Code, School Type, Loan Type, Recipients, # of Loans Originated, $ of Loans Originated, # of Disbursements, and $ of Disbursements

Research Ideas

• The dataset can be used to calculate the amount of loan debt originated by each type of school.
• The dataset can be used to calculate the amount of loan debt originated by each state.
• The dataset can be used to help students estimate their future student loan debt

Acknowledgements

The data for this visualization comes from the Common Origination and Disbursement (COD) System through the Department of Education

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Student Loan Debt by School 2020-2021.csv | Column name | Description | |:--------------------------|:-------------------------------------------------| | School | The name of the school. (String) | | City | The city where the school is located. (String) | | State | The state where the school is located. (String) | | Zip Code | The zip code of the school. (String) | | School Type | The type of school. (String) | | Loan Type | The type of loan. (String) | | Recipients | The number of recipients of the loan. (Integer) | | # of Loans Originated | The number of loans originated. (Integer) | | $ of Loans Originated | The amount of money originated in loans. (Float) | | # of Disbursements | The number of disbursements. (Integer) | | $ of Disbursements | The amount of money disbursed. (Float) |

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

As the pandemic accelerated calls to provide relief to millions of student borrowers, President Biden announced executive action to cancel 10,000 dollars of student debt for most federal student loan holders. Both prior to and following his announcement, policymakers have debated the merits and details of student debt relief, focusing particular attention on the perceived deservingness of student loan borrowers. But we have little systematic evidence about how the public evaluates borrower deservingness, or whether elite arguments framing support or opposition to debt relief in terms of deservingness influence public preferences for student debt cancellation. This paper employs original conjoint and framing experiments conducted just prior to Biden’s announcement to explore each query. We find that, while certain borrower characteristics indicating need (e.g., amount of debt), responsibility for debt (e.g., type of institution attended), and reciprocity (e.g., time in repayment) can influence people’s evaluations of whether borrowers deserve debt relief, those results may not translate to broader deservingness arguments for or against student debt cancellation in a clear manner. Ultimately, our results shed light on a timely policy issue, while extending scholarly understandings of deservingness for a critical, and understudied, aspect of the American welfare state.

Over 44.7 million Americans carry student loan debt, with the total amount valued at approximately $1.31 trillion (Quarterly Report, 2019). Ergo, consumer spending, a factor of GDP, is stifled and negatively impacts the economy (Frizell, 2014, p. 22). This study examined the relationship between student loan debt and the probability of a recession in the near future, as well as the effects of proposed student loan forgiveness policies through the use of a created model. The Federal Reserve Bank of St. Louis’s website (FRED) was used to extract data regarding total GDP per quarter and student loan debt per quarter ("Federal Reserve Economic Data," 2019). Through the combination of the student loan debt per quarter and total GDP per quarter datasets, the percentage of total GDP composed of student loan debt per quarter was calculated and fitted to a logistic curve. Future quarterly values for total GDP and the percentage of total GDP composed by student loan debt per quarter were found through Long Short Term Models and Euler’s Method, respectively. Through the creation of a probability of recession index, the probability of recession per quarter was compared to the percentage of total GDP composed by student loan debt per quarter to construct an exponential regression model. Utilizing a primarily quantitative method of analysis, the percentage of total GDP composed by student loan debt per quarter was found to be strongly associated[p < 1.26696* 10-8]with the probability of recession per quarter(p(R)), with the p(R) tending to peak as the percentage of total GDP composed of student loan debt per quarter strayed away from the carrying capacity of the logistic curve. Inputting the student loan debt forgiveness policies of potential congressional bills proposed by lawmakers found that eliminating 49.7 % and 36.7% of student loan debt would reduce the recession probabilities to be 1.73545*10-29% and 9.74474*10-25%, respectively.

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.

The State Loan Repayment Program helps HRSA provide grant funding for states and territories to operate their own loan repayment programs. Through SLRP each state and territory can design programs that address the most pressing health care needs of their residents. Primary medical, mental/behavioral, and dental clinicians who receive awards through SLRP-funded programs pay off their student debt in exchange for working in areas with provider shortages.HRSA programs provide equitable health care to people who are geographically isolated and economically or medically vulnerable. This includes programs that deliver health services to people with HIV, pregnant people, mothers and their families, those with low incomes, residents of rural areas, American Indians and Alaska Natives, and those otherwise unable to access high-quality health care. HRSA programs also support health infrastructure, including through training of health professionals and distributing them to areas where they are needed most, providing financial support to health care providers, and advancing telehealth. Location and data was provided by the Health Resources and Services Administration in October 2022. Update Frequency: Annual

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

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

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

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

Citing the Dataset:

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

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

This dataset was generated from a public earning's call (press release article). And used to generate examples of the way real humans would speak regarding the matters in the article, within real world scenarios. Here they are below:

Here are the linguistic variations for each of the queries in the dataset, based on the example article provided:

Here are five examples related to strong average loan growth in US Personal Banking (#5):

1. Mortgage Loans: An increase in demand for mortgage loans contributed to the strong average loan growth in US Personal Banking. Customers taking advantage of low interest rates led to a surge in mortgage applications and approvals.

2. Auto Loans: Robust consumer spending and increased car sales led to higher demand for auto loans, contributing to the strong loan growth in US Personal Banking. Customers seeking financing options for purchasing vehicles played a significant role in this growth.

3. Personal Loans: The availability of personal loans with favorable terms and competitive interest rates attracted borrowers, resulting in strong average loan growth in US Personal Banking. Customers availed personal loans for various purposes such as home improvements, debt consolidation, or financing other personal expenses.

4. Small Business Loans: US Personal Banking also witnessed strong loan growth due to increased lending to small businesses. As entrepreneurs and small business owners sought capital for expansion, equipment purchases, or working capital, the demand for small business loans rose, contributing to the growth.

5. Student Loans: The higher education sector continued to rely on student loans to finance tuition fees and related expenses. With the increasing cost of education, a rise in student loan applications and approvals contributed to the strong average loan growth in US Personal Banking.

General Queries Query: "What was the revenue for Personal Banking and Wealth Management (PBWM) in the last quarter?"

Variation 1: "What were the PBWM revenues in the previous quarter?" Variation 2: "Can you provide the revenue figure for PBWM in the last quarter?" Variation 3: "How much revenue did PBWM generate in the last quarter?" Variation 4: "What was the total revenue for PBWM in the most recent quarter?" Variation 5: "Could you tell me the revenue earned by PBWM in the last quarter?" Query: "What were the revenue figures for different divisions under US Personal Banking?"

Variation 1: "Can you provide the revenue breakdown for various divisions within US Personal Banking?" Variation 2: "What were the revenues generated by the different divisions in US Personal Banking?" Variation 3: "How did the revenue distribution look across different divisions in US Personal Banking?" Variation 4: "What were the individual revenue figures for each division within US Personal Banking?" Variation 5: "Could you give me a breakdown of the revenues for different divisions in US Personal Banking?" Query: "How did operating expenses change for PBWM?"

Variation 1: "What was the change in operating expenses for PBWM?" Variation 2: "Were there any fluctuations in the operating expenses of PBWM?" Variation 3: "How did the operating expenses for PBWM evolve over the specified period?" Variation 4: "Can you provide insights into the changes in operating expenses for PBWM?" Variation 5: "What was the percentage change in operating expenses for PBWM?" Query: "What factors contributed to the increase in PBWM's cost of credit?"

Variation 1: "What were the drivers behind the rise in PBWM's cost of credit?" Variation 2: "Which factors influenced the increase in PBWM's cost of credit?" Variation 3: "Can you identify the elements that led to the higher cost of credit for PBWM?" Variation 4: "What were the contributing factors to the cost of credit escalation in PBWM?" Variation 5: "What were the key reasons behind the growth in PBWM's cost of credit?" Query: "What led to the decrease in PBWM's net income?"

Variation 1: "What were the factors responsible for the decline in PBWM's net income?" Variation 2: "Can you identify the causes of the reduction in PBWM's net income?" Variation 3: "What influenced the decrease in net income for PBWM?" Variation 4: "Were there specific drivers that contributed to the decline in PBWM's net income?" Variation 5: "What were the primary reasons behind the decrease in PBWM's net income?" These linguistic variations provide different ways to ask the same questions, allowing for a more diverse and robust training dataset for the chatbot.

Here are the extracted entities from the provided article:

Account Line Entities:

Revenues Operating expenses Cost of credit Net income Business Line Entities:

Personal Banking and Wealth Management (PBWM) Branded Cards Retail Services Retail Banking Global Wealth Management Markets Banking Investment Banking Corporate Lending...