A Natural Disaster Reshapes Prosocial Microlending

Previous studies have investigated how various factors influence prosocial microlending decisions under normal circumstances. Nevertheless, a natural disaster may result in a situation where lenders can hardly find appropriate loans from the disaster-affected region. When faced with the mismatch between the supply and demand of microloans, lenders can either simply choose not to make any contributions or divert their prosocial intentions toward a different group of beneficiaries. Drawing on the motivated information processing theory, we show that the outcome-focused nature of prosocial motivation drives lenders to seek alternative loans until they achieve the goal of helping others, and the other-focused psychological process prompts lenders to take the victims’ perspectives when deciding which loans to fund. Moreover, lenders’ attention paid to the surrounding regions is distributed in a gradient fashion associated with their geographical distance to the disaster. Using a natural experiment based on the Ebola outbreak in Africa in 2014, our research shows that a natural disaster increases (over the short-term) the average contribution size and decreases (over the long-term) the average fundraising time per dollar for prosocial microloans by borrowers from regions closer to the affected region. In contrast, a natural disaster decreases (over the long-term) the average contribution size and increases (over the long-term) the average fundraising time per dollar for prosocial microloans by borrowers from regions farther away from the affected region. This redistribution of prosocial microlending is an unintended consequence of a natural disaster that inflicts long-term economic hardship in some regions. Policymakers and researchers should closely monitor the redistribution of prosocial microlending resulting from a natural disaster so that prompt action can be taken to alleviate potential negative consequences that may arise.

History: Martin Bichler, Senior Editor; Jason Chan, Associate Editor.

Funding: Y. Ding gratefully acknowledges financial support from the Gillmore Centre for Financial Technology at Warwick Business School. H. Xu gratefully acknowledges the financial support from Shanghai Pujiang Program [Grant 21PJC072].

Supplemental Material: The web appendix is available at https://doi.org/10.1287/isre.2022.0723.