Longer Table Lending: Strategizing Under Uncertainty

Abstract

Payday lending services are used by people who do not have access to mainstream, short-term, personal loans in times of financial need. However, predatory terms such as interest rates as high as 1000% and astronomical fees often leave people more financially unstable and unable to pay off their loans, thus leading to an endless cycle of compounding debt. While the federal government has not played a large role in regulating payday lenders, states and cities across the United States have taken action to reverse their negative impacts. Fifteen states have prohibited extremely high cost payday lending, and over 160 cities have passed local ordinances to control or limit the number of payday lending institutions in their communities. Outside of the policy realm, nonprofit organizations and religious institutions have attempted to meet the financial needs of community members in order to prevent the usage of predatory payday loans. In 2017, an estimated 330,000 Minnesotans took out payday loans to address short-term financial needs. Mount Olive Lutheran Church saw a need for a 0% interest, micro-lending program in their community in South Minneapolis, and started the Longer Table Lending (LTL) program. LTL’s implementation and kick-off has faced much delay due to issues obtaining an Automated Clearing House (ACH) service and countless internal decisions about operational options. With so many uncertainties, this problem required a scenario planning approach. By defining predetermined elements and uncertainties, we analyzed the factors influencing LTL and produced three future scenarios for LTL to choose from. Our approach also included a cost-benefit analysis of the LTL program so that the scenarios could be paired with a quantitative analysis of cost. We provided LTL with this cost-benefit analysis spreadsheet tool to utilize in future decision-making processes. The cost-benefit analysis rendered a large benefit-cost ratio, but the costs and benefits are modest estimates.