Marketing Seminar（2018-18）

Topic:An Empirical Bargaining Model with Digit Bias – A Study on Auto Loan Monthly Payments.

Speaker:Zhenling Jiang, the John M. Olin School of Business at Washington University

Time:Wednesday, 24 October, 13:30-15:00

Location:Room K01, Guanghua Building 2

Abstract:

This paper studies price bargaining when both parties are subject to perception biases with numbers. The empirical analysis focuses on the auto finance market in the U.S., using a large data set of 35 million auto loans. I observe that the scheduled monthly payments of auto loans bunch at $9- and $0-ending digits, especially over $100 marks. The number of loans also increases from $1- to $8-ending digits. It is especially intriguing that $9-ending loans carry a higher interest rate and $0-ending loans have a lower interest rate than loans ended at other digits. Motivated by these observations, I develop and estimate a Nash bargaining model that allows for digit bias from both consumers and finance managers of auto dealers. Results suggest that both parties perceive a discontinuity between payments ending at $99 and $00, and a steeper slope for larger ending digits, in their payoff functions. Low income and minority consumers have a lower bargaining power than the others. This model can explain the phenomena of payments bunching and differential interest rates for loans with different ending digits. I use counterfactual to show that, counter-intuitively, having digit bias is beneficial in a bargaining setting. Consumers’ payments are reduced by $203 million in total and the aggregate payments of finance managers increased by $102 million because of own digit bias. Another counterfactual quantifies the economic impact of imposing non-discretionary markup compensation policies. I find that the payments of African American consumers will be lowered by $452-473 million and that of Hispanic consumers by $275-300 million in total.

Introduction:

Zhenling Jiang is a Ph.D. candidate in quantitative marketing at the John M. Olin School of Business at Washington University in St. Louis. She applies theory-driven empirical models to analyze various consumer behaviors. Her research highlights agents’ bounded rationality in financial decision making, and investigates its impact on a large consumer purchase. She is also interested in studying consumer response to triggers or incentives using observational data. She has explored this topic in several contexts including loan uptake decisions when receiving a bonus payment, consumer retargeting from search history, and consumer retention decisions with loyalty program.

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