[We compare the stability and timeliness of credit ratings produced by a traditional issuer-paid rating agency (Moody's Investors Service) and a subscriber-paid rater (Rapid Ratings). Moody's ratings exhibit less volatility but are slower to identify default risk. We control for Moody's aversion to ratings volatility and still find its ratings lag Rapid Ratings'. More importantly, accuracy ratios indicate that Rapid Ratings provides a better ordinal ranking of credit risk. We quantify the loss avoidance associated with Rapid Ratings' signals to estimate costs associated with regulatory and contractual systems based on issuer-paid ratings.]

Many financially distressed students who qualify for federal assistance plans with interest moratorium and principal forgiveness instead accrue interest over long periods of nonpayment. This loan mismanagement is associated with higher delinquency. Mismanagement varies significantly across student gender and race: it is more prominent among male and non-white students. Mismanagement also varies across loan servicers, depending on proxies for student-adverse servicer policies. We consider explanations based on student selection and servicer treatment for loan mismanagement. Student financial literacy plays an important role but variation in treatment on the part of loan servicers appears more important.

We compare the stability and timeliness of credit ratings produced by a traditional issuer-paid rating agency (Moody's Investors Service) and a subscriber-paid rater (Rapid Ratings). Moody's ratings exhibit less volatility but are slower to identify default risk. We control for Moody's aversion to ratings volatility and still find its ratings lag Rapid Ratings'. More importantly, accuracy ratios indicate that Rapid Ratings provides a better ordinal ranking of credit risk. We quantify the loss avoidance associated with Rapid Ratings'signals to estimate costs associated with regulatory and contractual systems based on issuer-paid ratings.

We examine the relationship between credit rating levels and rating agency fees in a public finance market in which rating agencies earn lower fees and face higher disclosure requirements relative to corporate bond and structured finance markets. Controlling for variation in the complexity of credit analysis at the issue level, we find evidence that rating agency conflicts of interest distort credit ratings in the municipal bond market. Unexpectedly expensive ratings are more likely downgraded, and inexpensive ratings are more likely upgraded. The relationship between credit ratings and rating agency fees is driven by issuers who lose access to AAA insurance.

A common belief held among researchers and policy makers is that regulatory reliance has inflated market demand for credit ratings, despite their decreasing informational value. Advances in information technology, coupled with reputation losses following the subprime crisis, renew the question of whether investors still rely on ratings to assess credit risk. Using Moody’s 2010 scale recalibration, which was unrelated to changing issuer fundamentals, we find that ratings still matter to investors and to issuers—apart from any regulatory implications. Our results commend improved disclosure to mitigate mechanistic reliance on ratings and inefficiencies due to rating standards that vary across asset classes. Received October 9, 2015; editorial decision June 7, 2017 by Editor Andrew Karolyi.

We study financial effects of remote product delivery in the healthcare industry. Exploiting staggered law adoption for identification, we find that telehealth provision redistributes hospital operations and access to capital away from rural communities. As urban telehealth providers acquire rural patients, rural hospitals experience decreased revenue and profit, credit rating downgrades, increased cost of capital, and ultimately risk of closure. Although telehealth reduces travel costs, some communities lose access to acute care. Overall, we conclude that remote healthcare services have financial consequences as well as real effects, and their benefits are unequally distributed.

We investigate the effects of opioid abuse on municipal finance. We employ instrumental variables, border discontinuity difference-in-differences regressions, and coarsened exact matching to identify consistent causal effects, while controlling for variation in economic conditions and demographics. Opioid abuse lowers credit ratings, increases new offer yields, and reduces bond issuance. Reversal of these effects following effective antiopioid legislation further supports causality. Differential effects due to investor heterogeneity suggest that opioid abuse affects municipal finance through a capital supply channel. Overall, we conclude that local opioid abuse impedes municipalities’ access to capital and thus hurts their ability to provide public services and infrastructure.