UNIVERSITY of CALIFORNIA SANTA CRUZ ESSAYS on the EFFICIENCY of ONLINE LENDING a Dissertation Submitted in Partial Satisfaction
Total Page：16
File Type：pdf, Size：1020Kb
UNIVERSITY OF CALIFORNIA SANTA CRUZ ESSAYS ON THE EFFICIENCY OF ONLINE LENDING A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in ECONOMICS by Baizhu Chen June 2018 The Dissertation of Baizhu Chen is approved: Professor Nirvikar Singh, chair Professor Georege Bulman Professor Daniel Friedman Professor Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright © by Baizhu Chen 2018 Contents Abstract ix Acknowledgements xi 1 Peer-to-Peer Lending: A Theoretical Model and Its Empirical Implications1 1.1 Introduction .............................. 1 1.2 Growth in Peer-to-Peer Lending.................... 3 1.3 Literature Review ........................... 5 1.4 Borrowing and Lending on P2P Marketplaces............ 7 1.5 Theoretical Model ........................... 8 1.5.1 Borrower's Default Decision .................. 9 1.5.2 Lender's Funding Decision ................... 15 1.6 Empirical Strategy........................... 20 1.6.1 Research Question and Empirical Challenges......... 20 1.6.2 Testable Implications...................... 21 1.6.3 Sample Selection Issues..................... 21 1.7 Conclusion ............................... 27 Figures.................................... 28 Reference................................... 29 iii 2 Do Lenders Value the Right Characteristics? Evidence from Peer-to-Peer Lending 32 2.1 Introduction .............................. 32 2.2 Literature Review ........................... 35 2.3 Background of Renrendai and Data Description........... 37 2.3.1 The Typical Procedure of Borrowing through Renrendai .. 37 2.3.2 Data and Summary Statistics ................. 39 2.4 Effect of Information Verification on Funding and Default . 41 2.4.1 Empirical Specification..................... 41 2.4.2 Reduced Form Results ..................... 43 2.5 Identification Strategy and Instrumental Variable Results ..... 45 2.6 Bounds on the Average Treatment Effects.............. 50 2.7 Discussion ............................... 53 2.7.1 Gradual Learning by Lenders.................. 53 2.7.2 Misleading Loan Interest Rates................. 54 2.8 Conclusion ............................... 54 Figures and Tables ............................. 56 Reference................................... 69 Appendices.................................. 72 2.A Soft Information From Loan Descriptions .............. 72 2.A.1 Top 100 Most Common Words and Phrases ......... 72 2.A.2 Positive Words Among Top 100 Words and Phrases..... 72 2.B Calculation of the Standard Errors and Confidence Intervals for the Bounded Average Treatment Effects ................. 72 2.C Complementary Figures and Tables.................. 74 iv 3 Credit Insurance Impacts on Peer-to-Peer Lending Markets: Evidence from China 91 3.1 Introduction .............................. 91 3.1.1 China: The World's Largest P2P Lending Market ...... 91 3.1.2 Asymmetric Information and Credit Risk........... 94 3.2 Credit Insurance in P2P Lending Markets.............. 97 3.2.1 Principal Protection and Loan Guarantees .......... 98 3.2.2 Staggered Introduction of Loan Guarantees.......... 99 3.3 Identification Strategy and Empirical Results ............ 100 3.3.1 Quasi-Experiment and Difference-in-Difference Methods . 100 3.3.2 Empirical Results........................ 103 3.4 Conclusion ............................... 105 Figures and Tables ............................. 106 Reference................................... 143 v List of Tables 2.1 Summary Statistics.................................. 59 2.2 Self-claimed Information ............................... 60 2.3 Funding Equation: Least Squares Estimates .................... 61 2.4 Default Equation: Least Squares Estimates..................... 62 2.5 Inference on the Coefficient Estimates Comparison ................ 63 2.6 Effect of Number of Loan Posts on Funding Rate ................. 63 2.7 Relationship between Number of Loan Posts and Borrower Characteristics . 64 2.8 Funding and Default Equations: Instrument Variable Estimates......... 65 2.9 Funding and Default Equations: with Interaction Terms ............. 66 2.10 Inference on the Bounded ATET........................... 67 2.11 Bounds on the Average Treatment Effects on the Treated............. 67 2.12 Bounds on the Average Treatment Effects: Using the Entire Sample . 68 2.13 Effects of Information Verification on Funding, Default and Interest Rate . 68 2.C.1 Average Interest Rate and Number of Information Verification.......... 75 2.C.2 Average Interest Rate and Verified Information .................. 75 2.C.3 Funding Equation with Control Interaction Terms: Least Squares Estimates . 76 2.C.4 Default Equation with Control Interaction Terms: Least Squares Estimates . 77 2.C.5 Funding Equation without Control Variables: Least Squares Estimates . 78 2.C.6 Default Equations without Control Variables: Least Squares Estimates . 79 2.C.7 Correlation among the Verification Indicators ................... 80 2.C.8 Funding Equation: Least Squares Estimates of Control Variables . 81 2.C.9 Default Equation: Least Squares Estimates of Control Variables . 86 3.4.1 Waves of Ucredit Office Expansion .........................117 3.4.2 Summary Statistics of Borrower Information....................119 vi 3.4.3 Impact of Credit Insurance on Total Number of Loan Listings . 121 3.4.4 Impact of Credit Insurance on Total Number of PP Loan Listings . 125 3.4.5 Impact of Credit Insurance on Average Funding Probability . 129 3.4.6 Impacts of Credit Insurance on Average Funding Speed and Bid Amount . 135 3.4.7 Impact of Credit Insurance on Average Funding Probability (with Borroower Information)......................................139 vii List of Figures 1.1 A Typical Procedure of P2P Lending........................ 28 2.1 RRD Market Transaction............................... 56 2.2 Sample Page of Loan List on Renrendai....................... 57 2.3 Number of Loans Posted in June 2013 ....................... 58 2.C.1 Borrowing at Renrendai ............................... 74 3.4.1 Development of P2P Lending Market in China...................106 3.4.2 Geographic Distribution of P2P Lending Platforms in China . 107 3.4.3 Borrowing Procedure at Renrendai .........................108 3.4.4 Sample Webpage of a UG Loan Listing on Renrendai . 109 3.4.5 Impact of Credit Insurance on Total Number of Loan Listings . 110 3.4.6 Impact of Credit Insurance on Average Funding Probability (per loan) . 111 3.4.7 Impact of Credit Insurance on Average Funding Probability (per dollar) . 112 3.4.8 Impact of Credit Insurance on Average Funding Speed (per funded loan) . 113 3.4.9 Impact of Credit Insurance on Average Bid Amount (per funded loan) . 114 3.4.10 Impact of Credit Insurance on Average Funding Probability (per loan) . 115 3.4.11 Impact of Credit Insurance on Average Funding Probability (per PP loan) . 116 viii Abstract Essays on Efficiency of Online Lending Baizhu Chen This dissertation has three chapters, with emphasis on the efficiency of online lending using multiple identification strategies. The first chapter presents a theoretical model (1) to help illustrate the behavior of lenders and borrowers in a P2P market and (2) to derive a reduced form model for empirical analysis. In the model, borrowers decide whether or not to default. The benefit of default is that a borrower gets to increase consumption if he chooses not to repay. On the other hand, I assume that default imposes a direct utility loss on the borrower. This loss is assumed to be random and unobservable by lenders or anyone other than the borrower. Ex ante, when lenders choose whether or not fund a loan, they compare the expected return of the loan vs. their opportunity costs. The opportunity cost is also assumed to be random and unobservable by others. Also, the expected return of the loan depends on lender's subjective expectations of default risk. The subjective expectations are formulated based on the observed borrower characteristics and loan information. I present methods of testing whether the lenders' subjective expectations of default is the same as the objective ones. In the second chapter, using a unique dataset of peer-to-peer (P2P) lending with detailed loan and borrower information, I study which borrower characteristics lenders value when choosing loans to fund, and whether lenders value characteristics that minimize the probability of default. In this online context, the researcher observes everything that the lender does, enabling unbiased estimation of borrower characteristics that lenders favor. However, estimating characteristics that predict loan default is problematic due to selection at the funding stage. I implement three ix strategies to address this issue: (1) restricting attention to borrower characteristics for which there is no evidence of selection in the first stage; (2) exploiting variation in the probability of funding caused by contemporaneous competition on the platform; and (3) bounding the default estimates in the style of Lee (2009). The results imply that P2P lenders consider employment borrower characteristics as most important when making funding decisions, but disregard several characteristics that are valuable predictors of default risk. Specifically, lenders overestimate the importance of verified employment information, and underestimate the importance of verified education level and marital status. The third chapter estimates the effect of credit insurance in the peer-to-peer (P2P) lending market. Online lending is a fast growing area of finance. However, it