Panel Paper: Using Longitudinal Administrative and Ethnographic Data to Understand the Dynamics of Payday Lending

Friday, November 3, 2017
Dusable (Hyatt Regency Chicago)

*Names in bold indicate Presenter

Richard Hendra1, Kelsey Schaberg1, Stephen Nuñez1 and Lisa Servon2, (1)MDRC, (2)Russell Sage Foundation

To improve practice and policy in the field of financial inclusion we must understand the needs, characteristics, and perceptions of consumers as well as how they use financial products and services. Most of the data on these topics is locked up in proprietary administrative data sources. Our study benefits from access to a large database of financial records maintained by Clarity Services. Clarity is a credit bureau for subprime credit providers. Clarity is the subprime lending equivalent of the big three credit agencies, which report FICO scores to mainstream lending institutions. Clarity administers a very large and rapidly growing database which includes financial data for over 33 million participants.

This research is designed to better understand the needs and usage patterns of those who use small dollar credit. A first round of research, which was cited in the rule making on payday lending by the Consumer Financial Protection Board, focused on developing user profiles from a K-means cluster analysis. This analysis revealed that there is substantial policy relevant heterogeneity among the users of payday loans with some users seeming to use few loan products to get through short term events with low levels of default and many other borrowers ending up with several defaults and long spells of high interest debt.

However this first analysis also revealed that simply analyzing the administrative data was insufficient. While Clarity has good data coverage, most of the loans they covered were online and there is substantial activity that is not covered by their database. In addition (and as is usually the case with administrative data), the administrative data can not provide answers to explain the behavior seen. For these reasons, our second round of research is using a longitudinal mixed methods design in which we are combining insights from both the administrative records and longitudinal ethnographic interviews.

The core quantitative analysis analyzes changes in borrower outcomes over time and explores how those outcomes are affected by changes in the environment, policy, or personal circumstances. Using duration models, this round of analysis is designed to exploit the longitudinal data to explore quasi-experimental policy questions. In the last round, we found that medical debt was a key driver of payday lending usage. This round we plan on exploiting cross-state border variation in take-up of the Medicaid expansion in order to isolate the effects of health insurance on payday lending use. We also hope to learn more about the person level dynamics of movement into and out of debt.

In order to capture a more full bodied understanding of these behaviors we have embarked on a rich set of longitudinal ethnographic interviews led by Dr. Lisa Servon of the University of Pennsylvania. Data from the ethnographic interviews will be combined with the Clarity administrative data in order to develop borrower “timelines.” Combined with careful data visualizations the goal is to use these timelines to leverage what we learn from the administrative data to inform policy and practice in a form that is accessible to policy makers.