By Niklas Hüther (Indiana University) and Kristoph Kleiner (Indiana University)
”The bankruptcy system is supposed to work for everyone, but in many cases it works only for the powerful.” – House Judiciary Committee Chairman Jerrold Nadler, July 28th, 2021
Researchers have long recognized that judicial outcomes are subject to the biases of the ruling judge. To alleviate concerns of fairness, courts in both the U.S. and abroad claim to assign judges to individual court cases randomly. From a policy perspective, randomization promotes public confidence in the judicial process by limiting forum shopping and the individual influence of any individual judge. From an academic perspective, recent empirical research in economics and finance exploits the random assignment of judges to causally identify of a wide range of legal outcomes.
This paper revisits the claim of randomized judicial assignment in the context of U.S. Bankruptcy Court. Our research is motivated by legal scholarship arguing that debtors in recent cases are influencing judicial assignments (Levitin, 2021), as well as renewed interest in these issues from policy makers and the public (Merle and Bernstein, 2019; Randles 2020). Despite these arguments, there are reasons to believe assignment is random. For instance, after contacting all U.S. Bankruptcy Courts, Iverson et al. (2017) found that only one court (the Eastern District of Wisconsin) reports assigning cases to judges non-randomly. In addition, a range of research including Bernstein et al. (2019) provides convincing evidence that debtor characteristics fail to predict judicial assignments. Missing from this literature is any large-scale empirical evidence of non-random assignment.
Analyzing U.S. corporate bankruptcy filings between 2010 and 2020, we provide new evidence that assignment is not random, but predicted by the lending decisions of hedge funds. By focusing on investments made before the assignment of a bankruptcy judge, our technique is not suspect to standard critiques that predictability is merely an outcome of ex-post data mining; instead, in order for investors to systemically invest in firms that are later assigned a preferred judge, it must be possible to infer future judicial assignments. In addition, we focus on hedge funds, as they routinely influence a wide range of bankruptcy outcomes including emergence and debt restructurings. The prevalence of these investors allows us to explore a new channel of activism in the distress debt market: activist influence in judicial assignment process prior to filing.
In our setting, judges can decide whether to convert a Chapter 11 bankruptcy to a Chapter 7 liquidation; while secured creditors may have a preference for liquidation, unsecured creditors recover more under reorganization. Exploiting this distinction, we confirm unsecured hedge fund creditors (relative to secured hedge funds) are significantly less likely to be assigned a judge with a tendency to convert Chapter 11 cases. We also extend our analysis to an alternate bankruptcy outcome measure: the unsecured creditor recovery rate according to the confirmed plan. We find unsecured hedge funds are far more likely to be assigned a judge with a high past unsecured recovery rate.
We next test whether these estimates differ across the filings in our sample. First, we find that unsecured hedge fund claimants are assigned a preferable judge more commonly when the hedge fund invested shortly before the bankruptcy filing, suggesting hedge funds choose to invest explicitly to influence the filing. Second, we show the effects are greatest when the hedge fund is on the board of directors of the debtor at the time of filing, providing further support for the role of communication between debtor and creditor.
Finally, we conduct three robustness tests. First, we find no evidence that a judge’s future conversion rate (after controlling for the past conversion rate) is predicted by hedge fund investment, suggesting hedge funds are explicitly influencing judicial assignment based on information regarding past information. Second, focusing on the subset of districts that explicitly state random assignment within their district, we continue to find hedge fund investments predict assignment. Third, we include district-office-year fixed effects in our analysis and continue to find a relationship between hedge fund investments and assignment.
Moving forward, we believe there are two potential policies that can alleviate these issues. The first, and simplest, is for policy makers to develop a truly randomized process. Alternatively, policy makers can instead increase the number of bankruptcy judges, leading to lower predictability even if assignment is not fully randomized. Policy makers intent on a fairer judicial system should consider both proposals.
The full article is available here.
This piece previously appeared on the Oxford Business Law Blog.