Due to the impact of the COVID-19 pandemic, starting on March 13, 2020, the U.S. federal government paused payments on federal student loans, moving billions of dollars of student debt into forbearance. Federal student loans are in forbearance, meaning that no payments need to be made, and the interest rate has been set to zero percent until September 30, 2021. However, despite these measures, student debt increased in all states. The amount of student debt increased by 14 percent in Alaska between 2019 and 2020, the most out of any state.

Direct combined loans, also called Stafford loans, accounted for 856 billion U.S. dollars of outstanding student loan debt in the United States in 2024. Stafford loans are a type of federal student loans offered to eligible university students at a lower interest rate than private loans. In the first quarter of 2024, outstanding student loan debt in the United States totaled over 1.75 trillion U.S. dollars.

Concept: Average interest rate of individuals registered as MEI by type of credit - Personal loan Source: Central Bank of Brazil - Department of Financial Education 27171-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---personal-loan 27171-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---personal-loan

Concept: Average interest rate of individuals registered as MEI by type of credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 27188-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---other-loans 27188-average-interest-rate-of-individuals-registered-as-mei-by-type-of-credit---other-loans

According to a survey conducted in 2022, 28 percent of adults said that vocational training or other professional certification programs were definitely worth the price, more than other higher education institutions. Undergraduate education at private universities, for-profit, was perceived by adults as the least likely to be worth the price out of the other types. The student debt crisis In the United States, the amount of outstanding student loan debt has skyrocketed in the last few years, ultimately outpacing all other forms of household debt. As of the first quarter of 2024, Americans owed over 1.75 trillion U.S. dollars in student loans, likely influenced by increasing college tuition prices at a time of rising living costs and little wage growth. By the 2020/21 academic year, the average cost of attending a four-year postsecondary institution in the U.S. reached over 31,000 U.S. dollars, a price which may triple for Americans attending private and non-profit schools. In that same year, the average student debt for a bachelor's degree in totaled almost 35,000 U.S. dollars, depicting an increase in the amount of Americans taking on larger debts to attend higher education - an agreement which ultimately leads to an even greater outstanding balance from accrued interest. Despite a three-and-a-half-year pause on monthly student loan payments during the COVID-19 pandemic which aimed to alleviate the economic burden faced by over 45 million borrowers, most Americans still struggle to afford these payments. Cutting out college costs As the cost of college - and the resulting student debt - remains on the rise in the U.S., more and more university graduates have been found to be struggling financially, often having difficulty affording bills and other living expenses. Such financial hardships have also caused significant disruption to the lives of younger Americans, with a 2022 survey showing that around a quarter of Gen Z were unable to save for retirement or emergencies and had to delay homeownership and having children due to their student debt. Consequently, debates have arisen over whether the benefits of higher education still exceed the costs in the U.S., with many beginning to doubt that getting a college degree is worth the financial risk. While tuition costs remain at an all-time high, it is probable that financing a college degree may be detrimental for those Americans who have fewer resources and are unable to fund higher education without going into a significant amount of debt.

Concept: Average interest rate of credit operations with prefixed interest rates by source of funds and type of credit - individual microentrepreneur (MEI) - nonearmarked credit - Other loans Source: Central Bank of Brazil - Department of Financial Education 26913-average-interest-rate-by-source-of-funds-and-type-of-credit---individual-microentrepreneur-me 26913-average-interest-rate-by-source-of-funds-and-type-of-credit---individual-microentrepreneur-me

In 2023, 968 billion U.S. dollars worth of student loans were in forebearance in the United States. This is due to the coronavirus (COVID-19) pandemic, where the government paused repayment of student loans and froze the accumulation of interest. This is compared to 112 billion U.S. dollars worth of student loans that were in default. As of the fourth quarter of 2022, outstanding student loan debt in the U.S. was valued at approximately 1.76 trillion U.S. dollars.

This statistic shows the percentage of U.S. Iraq and Afghanistan veterans with student loan debt in late 2019/early 2020, by the amount of the debt. During the survey, 26 percent of respondents reported having between 20,001 U.S. dollars and 50,000 U.S. dollars in student loan debt.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Student Loan Meltdown? (SLM)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract

Microenterprise sectors are a dominant feature in urban areas of low- and middle-income countries. As much as a third of the labor force in these economies is self-employed. Those involved in retail trade—street vendors and owners of small shops and restaurants—are a plurality of small scale enterprises. These vendors earn their living using their own labor and small amounts of capital. They generally lack access to loans from formal financial institutions, relying on their own savings and perhaps informal loans from family members or friends. Surveys indicate that the lack of access to finance is one of their most often mentioned complaints.

This study uses data from the Mexican National Survey of Microenterprises (ENAMIN) to estimate returns to capital. A randomized experiment was designed to generate data which allow a consistent measure of returns to capital in microenterprises. Data was collected from a panel of microenterprises in the city of Leon, in Mexico over a period of five quarters. The baseline survey was carried out in November 2005. After the first through fourth rounds of the survey, treatments were administered in the form of either cash or equipment to randomly selected enterprises in the sample. The treatments generate shocks to capital stock which are random, uncorrelated with either the ability of the enterprise owner or the prospects for the business.

An unbiased estimate of returns to capital has important policy implications in several areas. First, the returns from investment determine the interest rates which borrowers are willing to pay to microlending organizations. Higher returns imply a higher likelihood of developing financially sustainable microlenders. Second, if returns are low below some investment threshold, then these low returns may act as an entry barrier, preventing high ability entrepreneurs without access to capital from entering. If, on the other hand, returns to capital are high at very low levels of investment, then capital-constrained entrepreneurs should be able to enter and grow to a desired size by reinvesting profits earned in the enterprise. In that case, capital constraints will have short term costs, but fewer long term effects on outcomes. High returns at low very low capital stock levels suggest that credit constraints will not lead to poverty traps.

Geographic coverage

Leon, Mexico. Leon is the fifth largest city in Mexico, with a metropolitan area population of approximately 1.4 million. The city is the center of Mexico's shoe and leather industries, and is also home to an active microenterprise sector.

Analysis unit

• Microenterprise

Universe

The research team set out to select a sample of enterprises with less than 100,000 pesos (approximately US$1000) in capital stock, excluding land and buildings. The sample was limited to enterprises engaged in retail trade and owned by males aged 22-55. In order to cover only full-time work, the owners were required to be working 35 hours or more a week in the baseline period.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame was based on the 10% public use sample of the 2000 population census for the city of Leon. Data was examined at the level of the smallest geographical unit available in the public sample, the UPM (unidad primaria de muestreo). For each UPM, the research team calculated for males 22-55 years of age the average education level and the percentage self-employed in the retail sector. They also calculated the percentage of households in the UPM with a male household head present. Using these data, 20 UPMS were selected with high rates of retail self employment and modest average levels of education.

The screening survey identified enterprises owned by males 22-55 years of age in the retail sector, operating without paid employees. Enterprises with paid employees are very likely to exceed our upper limit of 100,000 pesos of capital stock, so the lack of paid employees was used as an initial screen for capital stock. Where the screening survey was administered to the owners, we also asked for the value of the capital stock excluding land and buildings, measured at replacement cost.

The sample is limited to males aged 22-55 operating in the retail sector. The average enterprise has been operating for just over five years. Only 20 percent of the enterprises were started within a year of the baseline survey. Almost 20 percent are at least ten years old. Sales average 5,700 pesos per month, and profits 3,486 pesos per month. The median levels of sales and profits are similar, 5,000 and 3,000 pesos per month, respectively. We asked owners for profits before accounting for any compensation for their own time, so the profit levels should be viewed as including the opportunity cost of the time spent in the enterprise by the owner. As a result of this, profits are never reported as being negative.

Mode of data collection

Face-to-face [f2f]

Research instrument

The study employed several questionnaires that are explained below. - Survey screen: the screening questionnaire used to determine eligibility for the study - Survey baseline: the baseline survey of enterprises - Household Survey round 1: the baseline survey of households attached to the enterprise - Round 2, Round 3, Round 4, Round 5 surveys: follow-up surveys of enterprises - Round 5 household survey: follow-up survey of the household - Digit span recall showcard: showcard of digits used for digitspan recall test

The survey instrument was modeled after the Mexican National Survey of Microenterprises (ENAMIN) survey. In the first round, detailed information was gathered on the capital invested in the enterprise, separated into tools, machinery and equipment, vehicles, real estate and buildings, and inventories and finished and unfinished goods. Operational data was also gathered on the firm--revenues, expenses and profits-for the preceding month, and personal information about the owner. In each subsequent survey, firms were asked about changes in capital stock, either purchase of new assets or sales of existing assets, and operational data for another month of the survey.

The foreclosure rate in the United States has experienced significant fluctuations over the past two decades, reaching its peak in 2010 at 2.23 percent following the financial crisis. Since then, the rate has steadily declined, with a notable drop to 0.11 percent in 2021 due to government interventions during the COVID-19 pandemic. In 2024, the rate stood slightly higher at 0.23 percent but remained well below historical averages, indicating a relatively stable housing market. Impact of economic conditions on foreclosures The foreclosure rate is closely tied to broader economic trends and housing market conditions. During the aftermath of the 2008 financial crisis, the share of non-performing mortgage loans climbed significantly, with loans 90 to 180 days past due reaching 4.6 percent. Since then, the share of seriously delinquent loans has dropped notably, demonstrating a substantial improvement in mortgage performance. Among other things, the improved mortgage performance has to do with changes in the mortgage approval process. Homebuyers are subject to much stricter lending standards, such as higher credit score requirements. These changes ensure that borrowers can meet their payment obligations and are at a lower risk of defaulting and losing their home. Challenges for potential homebuyers Despite the low foreclosure rates, potential homebuyers face significant challenges in the current market. Homebuyer sentiment worsened substantially in 2021 and remained low across all age groups through 2024, with the 45 to 64 age group expressing the most negative outlook. Factors contributing to this sentiment include high housing costs and various financial obligations. For instance, in 2023, 52 percent of non-homeowners reported that student loan expenses hindered their ability to save for a down payment.

In 2022, the value of the lending to households in Switzerland as a share of its gross domestic product (GDP) was higher than in any of the countries selected here. Australian, Canadian, and South Korean households had an amount of credit which was higher than the overall size of their economy. That year, household lending in Argentina amounted to 4 percent of its GDP, which was the lowest figure in the ranking.

What is the household debt?

Household debt, also known as family debt, includes loans taken to pay for the home or other property, education, vehicles, and other expenses. The largest component of this is mortgage debt, which is seen by many as a way to build long-term equity. As such, households are willing to take on a large amount of this debt with the goal of owning an asset that holds value and can be used as a residence in the meantime.

The cost of debt

The cost of a loan depends on a number of factors such as the interest rate, borrower’s credit risk or time period of a loan. The value of mortgage and the rate of return on assets such as real estate also depend largely on geographic location. The highest borrowers in this statistic are likely living in countries where credit is affordable and expected returns are relatively high, incentivizing heavy borrowing.