The Effects of Usury Laws: Evidence from the Online Loan Market

Abstract

Usury laws cap the interest rates that lenders can charge. Using data from Prosper.com (an online lending marketplace), I show how interest rate caps affect 1) the probability that a loan is funded 2) the amount a borrower requests 3) the interest rate at which a loan is funded, and 4) loan repayments. The key to my empirical strategy is that there was initially substantial variability in states' interest rate caps, in which Prosper borrowers from different states faced different caps ranging from 6 to 36%. A behind-the-scenes change in loan origination suddenly increased the cap to 36% in all but one state. This change, which was not pre-announced, created "treatment" states whose caps rose and few control states whose caps remained unchanged. I find that higher interest rate caps increase the probability that a loan be funded, especially if its borrower is risky and had been previously * This is a revised version of chapter 1

Rational Herding in Microloan Markets

“…A Prosper credit grade is a letter grade on a seven-point scale, ranging from AA to HR ("high risk"), which Prosper assigns based on the verified Experian Scorex PLUS credit score from the borrower's credit report. (Ravina 2008), lender learning from hard versus soft information (Iyer et al 2009), perceived trustworthiness (Duarte, Siegel and Young 2010), borrowers' identity claims (Herzenstein, Sonenshein, and Dholakia 2011), taste-based discrimination (Pope and Sydnor 2011), and interest rate caps (Rigbi 2011). Another stream of research examines the social network effects of Prosper, such as how friend endorsements affect loan performance (Freedman and Jin 2008), how borrowers' group affiliations relate to loan default risk (Everett 2010), how the strength and verifiability of relational network measures influence funding outcomes and loan defaults (Lin, Viswanathan and Prabhala 2011), and how participation in online communities changes lenders' risk preferences (Zhu et al 2011 we record a set of its attributes and monitor its funding progression, including the amount of funding it has received, the number of bids, and its current interest rate.…”

Behavioral Household Finance

“…Glaeser and Scheinkman (1998) paint usury laws as a type of social insurance used to effect redistribution from the rich to the poor. 62 standard economic models of credit supply, more stringent restrictions on interest rates empirically reduce credit availability (e.g., Benmelech and Moskowitz, 2010;Rigbi, 2013).The Military Lending Act is one specific example of a recently enacted usury law in the U.S. Under this law, payday lenders can charge no more than a 36% annual percentage rate (APR) on loans to members of the U.S. armed services and their families.…”

“…Glaeser and Scheinkman (1998) paint usury laws as a type of social insurance used to effect redistribution from the rich to the poor. standard economic models of credit supply, more stringent restrictions on interest rates empirically reduce credit availability (e.g., Benmelech and Moskowitz, 2010;Rigbi, 2013).…”

Visions and challenges of payday lending in Lithuania

“…On the other hand, it is argued that usury laws actually make high-risk or low-wealth borrowers worse off because they are either excluded from regulated credit markets, deprived of the ability to build a credit history, or they are driven into the hands of loan sharks with questionable methods of providing or enforcing credit (Reifner et al, 2009;Rigbi, 2009). Moreover, the access to cheap credit gives no incentive to save and restricts development of the financial infrastructure (Hudon, Sandberg, 2011).…”