| Over nine million American households borrowed on payday loans in 2002, typically paying interest rates of 18 percent for just 14 days. Despite the prevalence of these loans, little is known about why people borrow on payday loans and the effects of access to this form of short-term high-cost credit. My dissertation addresses these two questions. We use a regression-discontinuity framework, which exploits the credit-scoring process used to approve or deny loan applications, to study the causal impact of access to payday loans on borrowing activity, bankruptcy, and crime. Using personal identifying information, public records on bankruptcy and crime are matched to a four-year panel dataset of 145,000 loan applicants from a large payday and pawn lender. We find that those approved for a payday loan apply for 8.8 more payday loans on average, amounting to ;Using a unique new administrative dataset from a payday lender with two million observations, we seek to account for the prevalance of payday borrowing by estimating a structural dynamic programming model of consumption, saving, payday-loan borrowing, and default on payday loans. The model includes standard features like liquidity constraints and stochastic income, and we also incorporate institutionally-realistic payday loans and generalizations of the discount function. Method of Simulated Moments estimates of the model's key parameters are identified by evidence that the average borrower who eventually defaults first repays or services five payday loans, paying 90% of the loan's principal in interest. Procrastinating on default in this way, we find, is most consistent with partially naive quasi-hyperbolic discounting. We statistically reject the exponential discounting and quasi-hyperbolic discounting null hypotheses in most specifications. This work contributes to the literature on high-frequency consumer decision-making. |
