How student loan debt became a trillion-dollar problem for Americans | CNN Politics
- Bias Rating
-62% Very Liberal
- Reliability
80% ReliableGood
- Policy Leaning
28% Medium Conservative
- Politician Portrayal
55% Positive
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The A.I. bias rating includes policy and politician portrayal leanings based on the author’s tone found in the article using machine learning. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral.
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Politician Portrayal Analysis
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Contributing sentiments towards policy:
57% : "The Guaranteed Student Loan program is really the big original student loan program, and it was modeled off the mortgage program," said Elizabeth Shermer, a historian and associate professor at Loyola University Chicago who has researched and written about student loans.53% : "It makes it more profitable to be a part of it because you can buy and sell student debt, just like you could mortgage," Shermer said.
52% : CNN --Millions of Americans have student loan debt, amassing to more than $1.6 trillion by the end of last year, according to the Federal Reserve Bank of New York.
49% : The Federal Reserve data shows people under the age of 30 are more likely to have student loan debt compared with older adults - underscoring the crippling burden on another generation of Americans.
49% : Using the federal mortgage program as a guide allowed the government to avoid direct investment into colleges, she said, but mortgages and student loans are not the same.
48% : Student debt has not always been a crisis.
46% :1990s: Direct loansTo fix the issues caused by modeling student loans on mortgages, the William Ford Direct Student Loan program, or the Direct Loan Program, was enacted in 1992.
43% : Federal loans rose quickly due to the formation of new, private loans and the pressure to cut taxes.
39% : Another reason why private loans became more critical was pressure in Washington for Congress to cut taxes and cut spending, she said.
*Our bias meter rating uses data science including sentiment analysis, machine learning and our proprietary algorithm for determining biases in news articles. Bias scores are on a scale of -100% to 100% with higher negative scores being more liberal and higher positive scores being more conservative, and 0% being neutral. The rating is an independent analysis and is not affiliated nor sponsored by the news source or any other organization.