of interest for the borrower. So far it is not possible to draw conclusions on the net value creation of a (paid) intermediary for the borrowers. In a next step we analyze the role of intermediation in the electronic marketplace in a multivariate set-up. 4.4 Empirical results
Table 8 presents our analyses on the role of intermediation in the electronic lending platform. In three different regression models we look at the influence of (1) general group membership, (2) the use of a paid intermediary, and (3) the hypothesized functions of intermediation on borrowers' credit conditions. The dependent variable in regression model (1) is Borrower Rate, borrowers' total loan cost including a potential group fee. This allows for the comparison of borrowers' credit spread with and without the use of an intermediary. In regression models (2) and (3), the dependent variable Borrower Rate Net excludes the group fee in order to evaluate the net effect of intermediation. Several interesting patterns emerge.
The results from regression model (1) regarding Group Affiliation as well as from model (3) regarding the group-specific variables confirm our fundamental hypothesis H1: the use of an intermediary in the electronic marketplace significantly lowers borrowers' loan spread. Group affiliation ceteris paribus lowers the credit spread by 25 basis points. In regression models (2) and (3) we shed more light on the function and value creation of the intermediary.
Does the choice of the intermediary matter? Should borrowers make demands on paid intermediary services? In order to be able to compare the net impact of unpaid and paid groups, we analyze Borrower Rate Net in regression model (2) and find that intermediation significantly lowers borrower's cost of credit overall. However, we document a difference in the net impact of group membership of 42 basis points: An unpaid intermediary reduces borrower's credit spread by 107 basis points, a paid intermediary by 65 basis points. It follows that the group fee can turn the case for a paid intermediary borderline. The average group fee of 110 basis points (Table 7) will more than counter the net reduction in credit spread. Taken together, intermediation has a positive net impact but the choice of intermediary matters. We hereby do not comment on the overall impact of paid groups, since this analysis does not incorporate
the intermediary's role in overall access to credit or the long-run performance of the loan thus originated.
In model (3) we analyze in greater detail how the intermediary creates value for the borrower. Again, we look at the net impact of intermediation (Borrower Rate Net) and control for the fee of a paid intermediary (Group Fee). All group-specific variables in regression model (3) have significant impact on credit spreads. The variables Certification and Group Leader Bid in regression model (3) significantly reduce borrowers' loan costs. Hypotheses H2 and H3a cannot be rejected: An important function of the intermediary is the screening of a potential borrower. The intermediary may then recommend the borrower's credit listing. There is further evidence for the hypothesized creation of value by the intermediary: We find significant lower credit spreads in groups where the group leader was committed to screening every potential borrower (Mandatory Review).
Regression model (3) also shows that \speak louder than words\leader's bid for the borrower's credit listing exerts a significant stronger impact on borrowers' credit conditions than a recommendation. Moreover, Certification is only significant at the 10-percent level. We can confirm Hypothesis H3b: the regression coefficient of Group Leader Bid exceeds Certification.
We find that a group's reputation serves as a proxy for the future diligent assessment of borrowers by the group leader. Lenders increasingly bid down the interest rate in groups with a good reputation, resulting in lower credit spreads for borrowers. Hypothesis 4 cannot be rejected as a group's reputation (Group Rating) significantly lowers borrowers' spread. Table 8 shows further evidence for the negative effect of group size on loan spreads. This finding statistically confirms our hypothesis H5. We find evidence for a perceived lower credit risk within larger groups due to more effective peer-monitoring. When comparing estimated coefficients, we see that this effect is far less important than the group leader's role in screening and monitoring of borrowers. This can be regarded as a certain restriction in the confirmation of H5 from an economist's point of view. Nonetheless, it confirms the important role of the group leader as the financial intermediary. An analysis of the effect of intermediation with
credit grade sub-samples in Table 9 confirms our main findings and yields some interesting additional insights. Controlling for borrowers' risk characteristics and the group fee we find that intermediation may significantly reduce borrowers' credit spread and that the reputation of a group has a strong impact across credit grades. Interestingly, a mandatory review process, the recommendation of a loan listing by the group leader, or a group leader's bid on a screened loan listing have a significant impact mostly for borrowers with lower credit grades \\and \These credit grades represent 57 percent of all group members. This finding highlights that the intermediary may create significant value by screening and monitoring of borrowers who represent more risky investments. We conclude that intermediation is particularly valuable for borrowers with less attractive risk characteristics.
Overall, our results show that even though the electronic P2P lending platform leads to disintermediation by enabling the direct brokerage of loans between borrowers and lenders, a new type of financial intermediary emerges. Market participants become group leaders and provide intermediary services, reducing the information asymmetries prevalent in the electronic marketplace. The intermediary primarily creates value by screening potential borrowers with lower scores, representing investments with higher risk a priori. This finding is supported by the significant reduction in borrowers' credit spread by a mandatory screening process as well as the intermediary's recommendation of a borrower (Certification). Moreover, bidding on the screened borrower's credit listing has an even stronger impact on the resulting spread. Given a mandatory screening process, the recommendation of a borrower and the group leader's bid for the recommended loan listing, the credit spread will ceteris paribus be 156 basis points lower (see model 3 in Table 8). This more than compensates for the average required fee of 110 basis points (as shown in Table 7). These results are stable when controlling for borrowers' credit history as well as transaction characteristics. In all regression models in Table 8 and 9 we find that the variables based on individual credit reports significantly influence credit conditions, and that a borrower's Credit Grade as a proxy for Probability of Default has the strongest impact. In model (1) in Table 8 for example, we find that ceteris paribus a
decline in credit grade by one grade is associated with an average of 280 basis points increase in credit spread. Increasing indebtedness (DTI Grade) which proxies for Loss Given Default or a higher loan amount (Amount) significantly increases credit spread. We cannot find a consistent significant impact of Homeownership. This result is intuitive since a house does not serve as collateral for loans on the Prosper marketplace. Hence, homeownership does not per se serve as information on borrowers' creditworthiness. There is initial evidence that this somewhat changed in the climax of the sub-prime crisis (Crowe and Ramcharan 2009), which exceeds our period of analysis.
We further control for use of the auction mechanism (Auction) and find a significant and negative impact on credit spreads. One obvious interpretation of this result is that the auction mechanism allows for competition among bidders which improves the conditions for borrowers. Another possible interpretation is that not using the auction mechanism serves as a negative signal of creditworthiness where the marketplace requires a significant risk premium for loan listings that are not auctioned. 4.5 Robustness tests
4.5.1 Tests for self-selection bias
Economic agents participating in capital markets are subject to self-selection (Alexander, Jones, and Nigro 1997). Self-selection arises if those participating in an activity are systematically different from those who do not participate (Bjorklund and Moffitt 1987). Each OLS regression analysis involving any such participation (including voluntary group memberships) can suffer from an endogeneity bias through the existence of variables that simultaneously influence the decision to choose the group membership as well as the dependent variable (Heckman 1979, Rubin 1979).
In financial transactions, self-selection in choosing an intermediary may arise from different levels of expertise as well as transaction characteristics (Alexander, Jones, and Nigro 1997, Zumpano, Elder, and Baryla 1996). Self-selection might be present in electronic marketplaces when individuals turning to intermediaries might differ significantly from those not using intermediary services. This could be the case if, for
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