Peer Effect on Consumer Default Decision: Evidence From Online Lending Platform

Unsecured debt issued by the consumers has been growing significantly in the world during the last 5 years with the arrival of new technology. The consumer credit outstanding has reached 4,017.9 billion USD in the US, which is 20.67% of total debt issued by consumers, while in 2013 the percent was only 18.82%.

 

Researchers have made substantial progress in studying how individual’s default decision on mortgage and credit card responds to change in risk composition, economic fundamentals, default costs and emotional considerations. Among many factors which affect the probability of being delinquent to pay interest and the due balance on the loans, changes in the behavior of individual’s social group can be a key trigger. Such a social amplification mechanism on individuals’ active choice of delinquency has aggregate implications.

 

Researchers, in general, face a central econometric challenge in identifying the causal peer effect in prior literature. It is difficult to distinguish peer influence on individual default decision from the role of correlated background factors that lead to similar individual choices. To overcome this challenge, we combine a novel dataset on individual default behavior with a unique methodology to study the peer effect.

 

We use transaction level data from individuals and their peers in connection with online cash loan providers in China, together with the detailed calling records through mobile phone between individuals and their peers 6 months before the loan approval. For each individual and his or her peers, we further collect the information related to repayment and repayment date, which allow us to disentangle the reverse causality that people with similar behavior are more likely to be peers, and to establish the causal effect from peers on individual delinquency decisions.

 

Cash loans, also known as payday loans, are short-term unsecured loans with high interest and relatively small principal amounts, targeting people with poor credit history. Compared with most of the payday loan providers through offline transactions in the US, cash loans in China are made mostly through online platforms.

 

We randomly select 19,994 borrowers with 20,340 loans, the average amount of which is approximately 300 USD with short term maturity of either 14 days or 30 days. The actual annualized interest rate is more than 300%. On average the default rate is 11.97% in our sample.

In addition, we collected all the mobile phone records between those 19,994 borrowers and their contacts within 6 months before the loan approval, together with the repayment amount and date for both borrowers and their contacts. Therefore, we separate the timing of the default decision of the peers relative to the default decision on individual borrowers, in order to distinguish the effect of default decision of the peers from other correlated background factors that lead to similar individual choices (Figure 1).

Figure 1 The Illustration of Relative Timing of Repayments

 

We find the default decisions of the peers who defaulted before the default of the borrower can predict the default choice of borrower. One more peer who defaulted before the default of the borrower significantly raises the probability of borrower default by 1.90 percent - roughly a 16% compared with baseline. However, the number of peers who defaulted after the default of borrower is not correlated with the default choice of the borrower. 

 

We further divide peers who have defaulted before the repayment date of loan into two groups. We define those peers who default before the date of application as ApplyBefore (Panel A) and those peers who default after the date of application but before the date of repayment of the borrower as RepayBefore (Panel B).

 

We find that the impact of RepayBefore peer is larger than the impact of ApplyBefore peer. The likelihood of default of the borrower increases by approximately 10% as one more ApplyBefore peer who has defaulted, which are only significant at 10% level. The likelihood of default of the borrower increases by approximately 48% as one more RepayBefore peer who has defaulted, which are significant at the 1% level. The results provide evidence on the casual effect of peers' default decision on borrower’s default decision and mitigate the effect from the similarity effect shared between borrowers and their peers.

 

The significant social network effects in individual default decision may be amplified by the development of new technology. Such social amplification mechanism on individuals’ active choice of delinquency bears aggregate implications. As the consumer credit outstanding has reached 4,017.9 billion USD in US, better understanding the determinant of default choice of borrowers is crucial.

 

The full paper can be downloaded here.

 

Emma Li, Deakin University,

Li Liao, Tsinghua University

Zhengwei Wang, Tsinghua University

Xincheng Wang, Tsinghua University

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