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College tuition and student debt levels have been rising at an alarming pace for at least two decades. These trends, coupled with an economy weakened by a major recession, have raised serious questions about whether we are headed for a major crisis, with borrowers defaulting on their loans in unprecedented numbers and taxpayers being forced to foot the bill. Game of Loans draws on new evidence to explain why such fears are misplaced-and how the popular myth of a looming crisis has obscured the real problems facing student lending in America.Bringing needed clarity to an issue that concerns all of us, Beth Akers and Matthew Chingos cut through the sensationalism and misleading rhetoric to make the compelling case that college remains a good investment for most students. They show how, in fact, typical borrowers face affordable debt burdens, and argue that the truly serious cases of financial hardship portrayed in the media are less common than the popular narrative would have us believe. But there are more troubling problems with student loans that don't receive the same attention. They include high rates of avoidable defaults by students who take on loans but don't finish college-the riskiest segment of borrowers-and a dysfunctional market where competition among colleges drives tuition costs up instead of down.Persuasive and compelling, Game of Loans moves beyond the emotionally charged and politicized talk surrounding student debt, and offers a set of sensible policy proposals that can solve the real problems in student lending.

Student loans --- Students --- College graduates --- Graduates, College --- University graduates --- Universities and colleges --- Pupils --- School life --- Student life and customs --- Persons --- Education --- Student loan funds --- Student loan programs --- Loans --- Student aid --- Scholarships --- Finance, Personal. --- Alumni and alumnae --- Finance, Personal --- E-books --- United States.

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Zonder onderwerpscode: economie

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Researchers and policy makers are often interested in estimating how treatments or policy interventions affect the outcomes of those most in need of help. This concern has motivated the increasingly common practice of disaggregating experimental data by groups constructed on the basis of an index of baseline characteristics that predicts the values that individual outcomes would take on in the absence of the treatment. This article shows that substantial biases may arise in practice if the index is estimated, as is often the case, by regressing the outcome variable on baseline characteristics for the full sample of experimental controls. We analyze the behavior of leave-one-out and repeated split sample estimators and show they behave well in realistic scenarios, correcting the large bias problem of the full sample estimator. We use data from the National JTPA Study and the Tennessee STAR experiment to demonstrate the performance of alternative estimators and the magnitude of their biases.