We examine the relation between accounting quality and debt concentration in corporate capital structures (i.e., firms' tendency to rely predominantly on only a few types of debt). Motivated by theoretical and empirical research that supports a strong link between debt concentration and creditors' coordination costs and the importance of accounting quality in reducing these costs, we hypothesize that firms with higher accounting quality have less concentrated debt structures. Measuring accounting quality with a comprehensive index based on the occurrence of material internal control weaknesses, accounting restatements, SEC AAERs, and firms' reliance on small auditors, we find that higher accounting quality is indeed associated with less concentrated debt structures. This relation is stronger for firms with higher default risk, as the probability that creditors need to coordinate is higher, and for firms with lower liquidation values, as creditor coordination to avoid liquidation is more important. Data Availability: Data are available from the public sources cited in the text.

We examine how personal lending relationships between lenders and managers are affected by information and accounting environments of borrowing firms. We address this question by exploring whether, following managerial turnover, lenders migrate with the manager from the firm where a relationship developed (origin firm) to the manager's new firm (destination firm). We find that the opacity of the external information environment of the destination firm significantly increases the probability of lenders' co-migration, while accounting irregularities at both the destination and origin firms decrease it. We also show that co-migration is affected by a lender's monitoring efficiency. A lender's monitoring efficiency increases its co-migration probability when a manager moves to an opaque firm, but not when she moves to a transparent one. When the destination or origin firm experiences accounting irregularities, even lenders with strong monitoring capabilities are mostly reluctant to continue their relationship with a migrating manager.

To mitigate information asymmetry about borrowers in developing economies, digital lenders use machine-learning algorithms and nontraditional data from borrowers’ mobile devices. Consequently, digital lenders have managed to expand access to credit for millions of individuals lacking a prior credit history. However, short-term, high-interest digital loans have raised concerns about predatory lending practices. To examine how digital credit influences borrowers’ financial well-being, we use proprietary data from a digital lender in Kenya that randomly approves loan applications that would have otherwise been rejected based on the borrower’s credit profile. We find that access to digital credit improves borrowers’ financial well-being across various mobile-phone-based well-being measures, including monetary transactions and balances, mobility, and social networks as well as borrowers’ self-reported income and employment. We further show that this positive impact is more pronounced when borrowers have limited access to credit, take loans for business purposes, and obtain more credit.