Last digit tendency: Lucky number and psychological rounding in mobile transactions

• Main Authors: WANG, Hai, LU, Tian, ZHANG, Yingjie, WU, Yue, SUN, Yiheng, DONG, Jingran, HUANG, Wen
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: last digit; lucky number; psychological rounding; mobile transaction; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/8539; https://ink.library.smu.edu.sg/context/sis_research/article/9542/viewcontent/LastDigitTendency_pvoa_cc_by_nc_nd.pdf
• Institution: Singapore Management University

The distribution of digits in numbers obtained from different sources reveals interesting patterns. The well-known Benford’s law states that the first digits in many real-life numerical data sets have an asymmetric, logarithmic distribution in which small digits are more common; this asymmetry diminishes for subsequent digits, and the last digit tends to be uniformly distributed. In this paper, we investigate the digit distribution of numbers in a large mobile transaction data set with 835 million mobile transactions and payments made by approximately 460,000 users in more than 300 cities. Although the first digits of the numbers in these mobile transactions follow Benford’s law, the last digit has a strong tendency to be a lucky number or be influenced by psychological rounding. This lucky number tendency is more significant in transactions that are more strongly connected to social interactions, such as money sent as gifts or as “red envelopes” (a traditional method of gift-giving during Chinese holidays), and in transactions by individuals with potentially greater emotional needs, such as during COVID-19 outbreaks and natural disasters. This psychological rounding tendency is more common in online e-commerce payments, in-store purchases, and money transfers between individuals. These findings are key for understanding the last digit tendency and its psychological and emotional mechanisms, which could be used as an indicator of public sentiment or in methods of detecting fraudulent business activity.