INFORMATION ASYMMETRY IN RELATIONSHIP VS. TRANSACTIONAL DEBT MARKETS:

EVIDENCE FROM PEER-TO-PEER LENDING

A Dissertation Proposal

Submitted to the Faculty

of

Purdue University

by

Craig R. Everett

This version: September 7, 2010

ABSTRACT

This paper uses a unique new data source, online social lending (a.k.a. peer-to-peer lending),

to help answer the question of what impact borrower-lender information asymmetries have on

adverse selection and moral hazard. This data source has unique characteristics that allow

analysis of the public versus private debt choice without some of the endogeneity issues that

are present when using other data sources. Each loan contains detailed bidding information

from both public and private investors. Thus, a clean distinction can be drawn between

public and private debt without the potential problem of unobserved borrower risk

characteristics. The results support the idea that the hold-up problem is more severe with

private lenders than public lenders, and that personal relationships mitigate the moral hazard

problem.

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Introduction

There is growing body of evidence in the banking literature that relationship lenders use the

private soft information collected from their borrowers over time (Black 1975; Fama, 1985) to extract

monopoly economic rents in the form of higher interest rates (Rajan, 1992). Borrowers experiencing

this hold-up problem cannot easily switch lenders, since the private information proving that the

borrower is not a lemon is kept confidential by the existing relationship bank. Financial

intermediaries play an important role in mitigating these information asymmetries between borrowers

and investors. By improving selection of borrowers and providing delegated monitoring services,

financial intermediaries address the adverse-selection and moral hazard issues that characterize the

lending relationship. By addressing these issues, lending costs are reduced.

Measurement of the magnitude of the reduction in lending costs resulting from private banking

relationships, as well as measurement of the increases in interest rates due to the hold-up problem, has

proven problematic due to endogeneity problems. For example, loan terms and conditions certainly

affect the loan interest rate, but they are also jointly determined with the interest rate.

This paper uses data from Prosper.com, the largest peer-to-peer lending organization (a.k.a.

online social lending) in the United States. It is essentially an online Dutch auction for loans,

removing the need for an intermediary between borrower and investor. Peer-to-peer lending data

have unique characteristics that shed new light on adverse selection issues such as the hold-up

problem while avoiding some of the common endogeneity problems. This data source also provides

new insight into the resolution of moral hazard through monitoring.

Peer monitoring is a concept adopted from microfinance, which is most prevalent in the

developing world. In most microfinance programs, borrowers are members of a lending group. The

members of the group monitor each other and thus repayment rates remain high, even without explicit

monitoring by a financial intermediary. Groups also help improve selection through private

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information and also mitigate moral hazard via informal enforcement mechanisms such as shame and

ostracism.

Group membership is available as an optional feature in some of the online peer-to-peer lending

communities. This provides the opportunity to test whether or not the risk mitigating impacts of

membership in a lending group in the developing world have corresponding impacts in online peer-

to-peer lending. If group membership has similar effects in both microfinance and the online world,

then we should find a negative relationship between online group membership and default rate. I find

that this is indeed the case for certain types of groups. Being a member of a group with personal

relationships or a group with a high likelihood for having private information is associated with

substantially reduced default rates. In turn, lower default rates are, by definition, associated with

lower loan costs.

In this paper, I make the assumption that deals between the borrower and fellow members of the

same group are analogous to relationship banking or private debt. Both are characterized by the

potential presence of private soft information. Likewise, deals between the borrower and unaffiliated

investors in the auction are analogous to public debt auctions, where the information asymmetries are

more pronounced.

The choice between public and private debt is fundamentally an endogenous one for firms, and

thus a robust econometric analysis must somehow exogenize this choice. Just as the risk

characteristics of the borrower are a key determinant of the ultimate rate spread on the debt, the debt

terms are not exogenous and can simultaneously impact the risk profile of the borrower. The precise

measurement of the hold-up problem is thus difficult to accomplish. Previous research has either

assumed that the nature of the banking relationship is exogenous (Houston and James, 1996) or has

addressed the endogeneity issue econometrically. The latter has been done with instrumental

variables (Santos and Winton, 2008) or by exogenizing the public/private debt choice via samples that

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are matched using propensity scoring (Hale and Santos, 2009). Unfortunately, between the samples,

there still may be unobserved risk characteristics that drive the public vs. private choice.

In peer-to-peer lending, on the other hand, both insiders (private) investors, and outsiders (public)

investors actively bid on the loans, so the exercise of finding matching sets of borrowers in order to

exogenize the public vs. private debt choice is unnecessary. Each loan contains detailed bidding

records from both public and private investors. Thus, a clean distinction can be drawn between

public and private debt choice without the potential problem of the presence (between loans) of

unobserved risk characteristics that drive the decision.

This paper contributes to the literature by providing new evidence on the impact of certain

borrower characteristics on two outcomes – default rate and interest rate spread. The borrower

characteristic that I focus on is group membership. I hypothesize that group membership is related to

adverse selection and moral hazard mitigation capabilities and thus its impact on default rate and

spread can be empirically tested. The ability to distinguish between informed and uninformed

pricing within a single loan appears to be unique to this paper, allowing for relatively unambiguous

examination of the effects of private soft information. It thus provides a potentially cleaner

measurement and pricing of information monopolies. The data support the idea that group

membership is negatively and significantly associated with default rate when the group has a potential

for either personal relationships or private information. I also find evidence that the hold-up problem

prevents these cost reductions from being efficiently passed down to the borrower.

The paper proceeds as follows: Section 1 explores the relevant literature and background

information on peer-to-peer lending and the hold-up problem. In Section 2, I provide more detail

about the impact of information asymmetry on loan default and interest rate spread. Section 3

describes the data from Prosper.com. Section 4 describes the methodology for testing the various

hypotheses. Section 5 presents the empirical results, and Section 6 concludes.

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1. Background and Related Literature

A. Peer-to-peer Lending – A Primer

Since online social lending is still a relatively new phenomenon, it may be helpful to provide a

brief summary of how the online borrowing and lending processes actually function. For the

purposes of this paper, I will describe only the lending processes of Prosper.com, which is my source

of loan data. There are many social lending internet sites, each operated in a slightly different way.

Since Prosper is the largest U.S. peer-to-peer lending site, it has attracted the most media interest and

thus its description provides a relevant introduction to the basics of social lending.

The process begins when a borrower creates a listing that includes the loan amount requested, the

maximum interest rate they are willing to pay, personal data such as income and occupation, and a

detailed description of the purpose of the loan. The borrower is always an individual, even for

business loans. Loan amounts are limited to $25,000 (US), which imposes a type of credit rationing

that can lower overall default rates (Jaffee and Russell, 1976). Simply the threat of future credit

rationing can mitigate moral hazard problems (Stiglitz and Weiss, 1981).

Once the listing is submitted, Prosper.com pulls a credit report for the borrower and appends the

salient credit report information onto the loan listing. The credit report information includes credit

score, delinquency history, number of credit lines, credit line utilization and other pertinent

information. The borrower’s bank account information is also verified. When the listing is approved,

it appears on the website. Lenders, who are simply investors who have signed up on the site and have

also had their bank accounts verified, are then permitted to bid on loans. Lenders can bid as little as

$50 (US) toward any listing and can bid any interest rate less than or equal to the borrower’s

maximum rate.

The process then works like a Dutch auction. The loan stays in “unfunded” status until enough

bids are submitted to add up to the full amount of the loan requested. At that point, additional bids at

lower interest rates can continue to be submitted, crowding out the bids at higher rates. The winning

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lenders are the collection of lenders who have bid the lowest interest rates and whose cumulative bid

amounts add up to at least the loan request amount. The ultimate interest rate to the borrower is

0.05% less than the lowest rate that was bid among the losing bidders.

Borrowers have the option of joining a “group.” Group leaders can impose additional

information requirements as a condition for joining. Employment verification, for example, is a

common condition imposed by some groups, but is not required by Prosper.com itself. This is the

source of private information that can exist for group lenders. Borrowers often join a group after

they have been unsuccessful in previous individual peer-to-peer loan attempts. Thus, these borrowers

may have particularly severe information asymmetry problems.

For borrowers who have opted to become members of a “group,” the winning list of bidders will

typically consist of both members of their own group (a.k.a. insider, relationship or private investors)

as well as non-members (a.k.a. outsider, arm’s-length, transactional or public investors). This fact

becomes important in drawing the parallels between the social lending market and the traditional

public and private debt markets.

Each loan auction stays open for seven to ten days. Bidders are notified by email whenever they

have been outbid and therefore no longer one of the potential winners. Lenders then have the option

of updating their bid to a lower interest rate. At the end of the ten day period, if the loan receives

enough bids to be funded, the loan is initiated and the funds are withdrawn from the lenders’ bank

accounts, and the loan proceeds are deposited to the borrower’s account. The website charges an

initiation fee to the borrower equal to one percent of the loan principal, as well as a small monthly

servicing fee to the lenders. Prosper.com then services the three-year loan on an ongoing basis, doing

automatic withdrawals from the borrower’s account and proportional deposits to lenders’ accounts

each month.

After the loan is originated, monthly email messages are sent to the borrower a few days before

the loan payment debit is made from his/her bank account. The standard enforcement mechanisms

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for repayment are the imposition of additional fees in the case of insufficient funds, adverse credit

report postings for past due accounts, and key derogatory credit report information and referral to a

collection agency in the case of default.

If the borrower is a member of a group, then there may be additional informal enforcement

mechanisms. Lending groups are the foundation of microfinance, and the moral hazard mitigation

mechanisms available to a traditional lending group are shared risk, peer monitoring, and peer

enforcement (Brau and Woller, 2004). In online social lending, on the other hand, late payments and

defaults will affect the group’s “star” rating, which can impact the ability of groups to attract new

members, as well as the ability of existing group members to get new loans. This is the only shared

risk, since members are not responsible for the repayment of fellow members’ loans. Additionally,

group members can monitor the status of loans borrowed by other members of their group. If a late

payment is reported, they can exert pressure on the delinquent borrower via email messages. This

constitutes a peer monitoring and enforcement mechanism, albeit a potentially weak one without any

explicit sanctions.

Table 1 lists the pioneers of online social lending by their dates of inception. It is evident that this

is a rapidly developing phenomenon, with the number of new players accelerating over time to satisfy

the growing worldwide demand for unsecured business and personal loans. Returns to lenders appear

to be consistent with conventional lending. Summary statistics (Table 3) imply that return for

investors is between 9% (for loans to “AA” ranked borrowers, which is the highest credit ranking)

and 15.1% (for “D” ranked borrowers) after adjusting for default rates that range between 0.6% and

5.2%, respectively. The overall default rate, when also including “E” rated borrowers and unrated

borrowers (no credit history), is approximately 7.5%. The cumulative default rate of 7.5% is similar

to the default rates of other types of debt. For example, the average default rate on corporate bonds

between 1982 and 2001 is 4.19% (Altman et al., 2005). The default rate for small business loans

(source: SBA) during a similar time frame is approximately 11%. Market interest rates during the

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sample period (May 31, 2006 to Nov 6, 2007) were reasonably stable until the Summer of 2007,

when mortgage-backed securities began to be downgraded. Figure 1 shows a comparison of interest

rates for Prosper.com (borrowers with credit score of 720 and above), Baa rated corporate bonds, Aaa

rated corporate bonds, federal funds overnight rate, and the 3-month T-bill rate.

Figure 1 – Interest Rate Comparisons over sample period 1

B. A Brief History of Social Lending

The advent of modern social lending is attributed to the English “friendly societies” of the 18th

and 19th century that arose spontaneously during the industrial revolution as clubs that helped their

members pool resources and risk. The friendly societies allowed members to make deposits and

receive loans, and also assisted member families in the case of negative shocks such as illness. With

the Friendly Societies Act of 1793, the British Parliament formally recognized and regulated the

1 Sources of interest rate data are Prosper.com and federalreserve.gov

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burgeoning industry. Although the records of the era are informal and incomplete, it is thought that

by 1815 as many as 33% of the families in England were members of a friendly society (Gorsky,

1998). An even earlier implementation of a microfinance-style lending paradigm was instituted in

Ireland in the early 18th century, when Jonathan Swift (author of Gulliver’s Travels), with his own

resources, instituted a fund for lending to poor tradesmen. To address the adverse selection problem,

he required each borrower to obtain a personal guarantee from two neighbors. Any late payment

would result in a notice being sent to the borrower and both guarantors (to help ensure future

punctuality). There were apparently no defaults on the Swift loans (Hollis & Sweetman, 2001).

The concept caught on, and Irish microloan funds flourished for more than 100 years until they

eventually fell into decline in the mid 19th century as a result of dual death blows from the potato

famine and from punitive legislation introduced through the lobbying efforts of mainstream banks.

During their heyday, the Irish loan funds are said to have provided services for as much as 20% of the

population of Ireland. The Irish loan funds of the 19th century were quite similar in structure to those

instituted by Jonathan Swift a hundred years earlier, except that Swift did not charge interest. Like

modern microfinance efforts in lesser developed countries (LDCs), the Irish loans were intended for

enterprises that would generate enough cash flow to allow the borrower to make the required

payments and hopefully help lift him out of poverty (Hollis & Sweetman, 2001).

The concept of organized social lending was transplanted from England to the United States in

1831 with the Oxford Provident Building Association in Frankfort, PA (near Philadelphia).

Individuals could purchase up to five shares for five hundred dollars apiece. Whenever the

association accumulated at least five hundred dollars, a loan would be made to the member offering

the highest bid (Potter, 1954). This auction approach is remarkably similar to the structure of online

social lending today.

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C. Parallels to Microfinance

There is often confusion surrounding some of the common terms in microfinance. The words

microcredit, microfinance and microenterprise loans are sometimes used somewhat interchangeably,

but typically differ from each other in terms of scale. Microcredit generally refers to loans less than

$500 (US), microfinance refers to loans less than $10,000, and microenterprise loans generally refer

to a larger capital expenditure loan of less than $100,000 for a business (such as bakery or small

factory). Since microfinance has traditionally been a social welfare endeavor (often not-for-profit) in

the developing world and has mainly consisted of small loan amounts, it suffers from a lack of

perceived relevance and has therefore been largely ignored by the finance academic community (Brau

& Woller, 2004). Now that the principles of group banking have migrated to the industrialized world

and to cyberspace, they are beginning to encroach upon for-profit financial markets and therefore are

becoming increasingly of interest. In fact, the three largest social lending sites (Prosper, Zopa, and

LendingClub) report that approximately 20% of their loans are for small businesses (Farrell, 2008).

Although online social lending does not exactly fit the definition of microfinance, it shares some

key characteristics. For example, the primary challenge in microfinance is overcoming the

information asymmetry problems inherent within the non-bankable community. Microfinance has

potentially serious adverse selection and moral hazard issues (Hollis and Sweetman, 2001; Williams,

2004). Primarily, the issues are related to avoiding lending to borrowers with unacceptably high

default risk (adverse selection) and then preventing borrowers from using the loan irresponsibly after

receiving the funds (moral hazard). There is an impressive body of literature surrounding

microfinance in the developing world that can be drawn upon to make predictions about other forms

of credit (such as peer-to-peer lending). Many studies have concluded that the stronger the social

connections within group, the higher the repayment rate (Woolcock, 2001; Karlan, 2007). Karlan

also finds that higher geographic proximity of the borrowers in the group reduces default risk. This is

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consistent with Hung (2006), who demonstrates that group monitoring is more active with greater

geographic proximity.

An excellent example of the applicable theoretical issues in the developing world is illustrated by

Grameen bank in Bangladesh, as well as the concept of Rotating Savings and Credit Associations

(ROSCAs) elsewhere in the world. The Grameen Bank of Bangladesh started as an experiment in

1976 and due, to its success, became an official financial institution in 1983. It is founded on the

proposition that small loans to fund microenterprises will help lift the borrower out of poverty, as

long as the return on assets exceeds the rate of interest on the loan. Since the borrowers lack assets to

pledge as collateral, Grameen implemented a soft system of group liability. Each Grameen group

consists of five members. Unlike ROSCAs, Grameen allows multiple group members to receive

loans simultaneously. Group members are not directly responsible for the repayment of each other’s

loans, but failure to stay current on a loan prevents any other group member from obtaining future

loans (Wahid, 1994). According to information provided by Grameen, as of January 2008, it has had

a total of 7.44 million borrowers to date. There are nearly 2500 Grameen branches, covering 96% of

Bangladesh. Grameen boasts an overall default rate of approximately 2% and has been self-sufficient

(no donors) since 1998. Grameen is one of the most extreme instances of group lending, since their

enforcement model takes legal collection action completely off the table. Grameen purposefully does

not require its borrowers to sign any sort of promissory note for the loan, thereby relying entirely

upon group dynamics for loan enforcement. Similarly, Hung (2006) shows that U.S. microlenders

can reduce staff and transaction costs by transferring the screening, monitoring, and enforcement

activities from the bank to the group members themselves.

Opinion is not universally positive concerning microcredit. While most of the literature appears

to hold it in high esteem as a pathway out of poverty, others see a dark side where unsophisticated

and uneducated people are snared into a cycle of debt (Williams, 2004). The other major concern is

one of efficiency. The reasoning behind this concern is that if the principal purpose of microfinance

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is to help the poorest and most underprivileged people get a new start, then commercial microcredit

must inevitably fail since fixed transaction costs will drive lenders toward higher loan amounts to

improve profitability. Consequently, since the poorest of the poor will not be suitable risks for those

higher loan amounts, they will no longer qualify as borrowers in for-profit programs (Williams,

2004). Jaffee and Russell (1976) refer to this last effect as a type of credit rationing, which can be

viewed as the lender’s simplest way of mitigating the adverse selection problem.

Most microcredit in the world takes the more informal structure of ROSCAs. These are found on

nearly every continent and are in fact quite similar to the aforementioned Oxford Provident Building

Association of 1831 Philadelphia. In ROSCAs, all the members of the group contribute to a general

fund on a regular basis, and then the proceeds are distributed as loans, one at a time, to each member.

ROSCAs are known by many names in various parts of the world, including “kye” in Korea, “esusu”

in West Africa, and “partner” in Jamaica (Williams, 2004).

In microfinance literature, default risk is mitigated by two principal factors: joint financial

liability and personal relationships. Besley and Coate (1995) model the dynamics of group lending in

a strategic game with joint liability. They demonstrate an equilibrium in which the game value in a

joint liability situation exceeds the sum of the expected values in the corresponding individual

liability scenario. I do not address joint liability in this paper since it does not exist in any of the

major online social lending platforms. The lack of joint liability is actually helpful in this study

because it allows for easier isolation and measurement of the softer social consequences of default. A

primary result of this paper is that, even without joint liability, the screening, monitoring, and

enforcement effects performed by peers in microfinance lending groups can be shown to exist in

online social lending. In order to achieve similar group benefits as we see in microfinance, though,

online groups must be subject to the same possibility of real-life social sanctions.

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D. Information Monopolies and the Hold-Up Problem

In an ideal world that is fully transparent and without information asymmetries and transaction

costs, borrowers and investors would deal directly with each other. There would be no need for

financial intermediaries such as banks. In the presence of adverse selection and moral hazard

problems, however, it is difficult for an individual investor to effectively determine the true risk

characteristics of the borrower. Likewise, it is unrealistic to expect that individual investors will all

engage in the monitoring activities that would be required to mitigate moral hazard dangers. Even in

public debt markets, in which information asymmetries are reduced because of public disclosure

requirements, these problems still persist. Since public debt is widely held, its relatively small

investors would be inefficiently duplicating effort (thereby multiplying transaction costs) if all of

them monitored the issuing firm. Diamond (1984) proposed that delegated monitoring to a bank may

be an efficient mitigation of moral hazard. In a related paper, Diamond (1991) shows that a higher

interest rate leads to riskier project choice, but only for unmonitored borrowers, since the presence of

monitoring prevents the borrower from selecting the riskier project in the higher interest rate

environment. The downside to the borrower is that during the course of the private banking

relationship, the bank will accumulate proprietary positive information about the borrower (Fama,

1985), particularly if the borrower is also a depositor at the bank (Black, 1975). The fact that other

lenders do not have this information makes it difficult for the borrower to switch banks. This, of

course, is not a problem for the borrower if the lender passes along the cost savings (due to resolution

of adverse selection) to the borrower. The bank can instead decide to keep the proprietary positive

information private and charge a higher interest rate. Thus, the borrower is “held-up” by the bank,

finding it difficult to escape the relationship. Since the bank’s information monopoly can

theoretically either raise or lower the cost to the borrower, it is ultimately an empirical issue as to

whether or not the hold-up problem exists. This is one of the questions addressed by this paper.

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Sharpe (1990) formally models the informational hold-up problem with bank loans. In his

model, banks are unable to directly observe a firm’s quality, and thus only the bank that is currently

lending to the firm freely observes project success. Banks do not divulge this information since that

would just assist competing banks in bidding away their clients. Banks use the private information to

hold up the borrower, which in turn may distort borrower behavior. For example, the borrower may

attempt to mitigate the hold-up problem by developing multiple banking relationships, which is less

efficient and increases total lending costs (Petersen and Rajan, 1994). For the lender, the greater the

information advantage is, the greater are the rents that can be extracted (Rajan, 1992; Houston and

James, 1996).

E. The Choice between Public and Private Debt

Due to the unique nature of the online social lending data, the analyses undertaken in this paper

shed new insight into relationship versus transactional (a.k.a. arm’s-length) banking. The last twenty

years have seen tremendous growth in online banking. In 1997, less than ten percent of national banks

in the U.S. were capable of online transactions. By the end of Q3 1999, approximately twenty

percent of national banks performed web-based banking transactions, including loan applications.

(Furst, Lang and Nolle, 2000). Today, of course, that number is effectively one-hundred percent.

This phenomenon, along with widespread banking industry consolidation, has contributed to the

decline of local banks, which has been documented in a sizable body of literature regarding

relationship vs. transactional banking.

It has been long held that one of the main strengths of local savings and loan associations is the

aspect of face-to-face relationships. These relationships may reduce interest rates by reducing default

risk and also by providing more leniency in the case of temporary negative shocks to the borrower

(Potter, 1954). Agarwal and Hauswald (2008) find that the probability of obtaining a loan is

negatively and significantly related to the physical distance of the borrower from the bank branch,

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which is consistent with their proposition that physical proximity is a good indicator of information

quality. This finding supports their hypothesis that personal relationships between borrower and

lender help overcome the information asymmetries inherent in the loan transaction. They also find

that credit delinquency is lower for relationship loans (2.7%) versus transactional loans (3.2%).

Petersen and Rajan (2002) take an opposing view. Their results indicate that the impact of

geographic distance on small business access to credit declined approximately substantially from

1973 to 1993, meaning that a long physical distance between a firm and its bank is less of a barrier to

credit in 1993 as it was in 1973. They interpret this result as confirmation that improved information

and communication technology have made both geographic distance and face-to-face relationships

less important.

Degryse and Ongena (2005) find that transportation costs, not information asymmetry, are the

cause of the decrease in loan rates with geographic distance. They also show that interest rates

decrease with proximity to competing banks, which is consistent with other studies (Petersen and

Rajan (1994, 1995); Boot, 2000; Boot and Thakor, 2000). Karlan (2007) finds that geographic

concentration in groups of borrowers leads to improved repayment rates, consistent with Agarwal and

Hauswald (2008).

The long-horizon personal interaction in relationship banking provides the bank with an insider’s

view of the borrowers’ true financial condition. Therefore, any observable actions or decisions of the

insider with regard to the borrowers’ accounts (such as renewal of a line of credit) can be interpreted

by outsiders as a meaningful signal (Lummer and McConnell, 1989). Leland and Pyle (1977) and

Berger and Udell (1995), in similar lines of reasoning, show that the willingness of an entrepreneur

(the ultimate insider) to invest in his or her own company, is also a signal of true quality. With online

social lending, the only insiders that can be observed are the fellow group members of the borrower.

Since group leaders are permitted to impose additional disclosure and other reporting requirements

upon potential members before joining, it follows that group members may have private information

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that is unavailable to other investors. Therefore, the willingness of group members to lend money to

fellow group members can be viewed as a signal that reveals the nature of their private information.

Favorable private information should lower the interest rate, as long as lender competition is present

to overcome the hold-up and credit rationing problems. When competition is present, banks cannot

reasonably expect to share in the borrower’s surplus, regardless of the length and strength of the

relationship (Petersen and Rajan (1994, 1995); Berger and Udell, 1995; Boot, 2000; Boot and Thakor,

2000).

With private bank lending, the debt is more concentrated, which gives the lender greater incentive

to actively monitor (Diamond, 1984). Less worthy borrowers thus have greater access to credit, since

moral hazard associated with inferior borrowers can be mitigated via monitoring. Such borrowers

cannot easily switch lenders, since they will be identified as lemons if they do so (Rajan, 1992).

Rajan also predicts that hold-up problems should increase with firm’s risk of failure.

Santos and Winton (2008) test the proposition that hold-up power increases with firm risk. Since

firm risk is higher (for nearly all firms) in a recession, relationship banks should be able to extract

higher rents during economic downturns. They find that loan spreads do indeed rise during

recessions, but less so for firms with public debt access, consistent with hold-up theory.

It must also be considered that bank loans can, in some cases, be cheaper than public debt

because banks have the ability to mitigate adverse selection and moral hazard problems through

private information and monitoring (Houston and James, 1996). Banks, unlike public debt investors,

also have the flexibility to recontract. Since public debt is held diffusely, there is little incentive for

investors to produce private information or engage in monitoring activities. Houston and James find

that as growth opportunities increase, the share of bank debt actually decreases, which is inconsistent

with the theory that large information asymmetries (higher risk) are associated with more reliance on

bank loans (Rajan, 1992). When the firm has multiple bank relationships, however, the percentage of

bank debt is positively related to growth opportunities.

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A mitigating factor for the hold-up problem is the level of competition between banks. When

there is high competition among banks, the relationship bank cannot extract rents that are as high,

since the outside banks can observe the existence of the relationship and thus, over time, infer costless

private signals about the firm’s credit worthiness (Petersen and Rajan, 1994; Petersen and Rajan,

1995; Berger and Udell, 1995; Boot, 2000; Boot and Thakor, 2000; Dinc, 2000). This drives the

credit market toward equilibrium. Therefore, the informational rents decrease as competition

increases.

So how large are these informational rents? Hale and Santos (2009) attempt to price the hold-up

problem by comparing bank loan pricing before and after borrowers gain access to the public debt

market (bond IPO). The bond IPO reveals new information about the firm’s creditworthiness, thus

reducing the information advantage of the banks. The bond IPO is typically associated with the firm

acquiring its first credit rating, which is a significant information event, since the credit rating

agencies are believed to have access to confidential information not available to the public. Since

there is likely to be endogeneity in the public/private debt choice, they exogenize this choice by

building a matching set (using propensity scoring) of firms that do a bond IPO and firms that do not.

They find that bank loans after the bond IPO have lower spreads (loan interest rate minus LIBOR). A

high credit rating before the IPO is associated with a smaller spread (reduction of 35-50 basis points).

A lower rating, however, is associated with less of a reduction. Among safe firms, not having a

previous credit rating is associated with a larger decline in rates. This supports the hypothesis that the

revelation of firm safety to the market increases outside investors’ willingness to bid, thus driving

down the spread.

Schenone (2010), like Hale and Santos (2009), seeks evidence of hold-up problem by pricing

private loans before and after a large public information event. She does this by examining loan

pricing before and after the firm’s equity IPO. The idea is that much private information becomes

public at the IPO, thus reducing the relationship bank’s information monopoly. The central

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hypothesis is that rent extraction by the bank will be concentrated before IPO. The author develops

an intensity measure which is the frequency of new loans with the same lender. Before IPO the

relationship between interest rate and intensity is U-shaped. After IPO, the rate consistently

decreases with intensity. In general, she finds that interest rates are higher before firm IPO, as

expected.

The studies cited above reflect alternative approaches to discerning the impact of private

information monopolies on loan spread. The inherent limitation of these approaches is that they

either require an assumption that the choice between private and public debt is exogenous, or must

employ analytical techniques such as instrumental variables or propensity scoring to deal with the

endogeneity. peer-to-peer lending data, on the other hand, naturally exogenizes the private/public

choice, since the borrower never a actually makes the choice – the initiated loan is in fact simply a

collection of smaller loans (both public and private). All loans for group members have bids from

both group members (with private information) and bids from non-members (with only public

information). This allows the study of information effects on loan spread while mitigating some of

the endogeneity problems inherent in previous research.

2. Impact of Information Asymmetry on Loan Default and Interest Rate Spread

A. Adverse Selection

One of the purposes of financial intermediation is to mitigate adverse selection. Lenders are able

to improve loan selection by collecting private information about their borrower customers over time

(Fama, 1985). Using this private information to augment publicly available data, the lender is then

able to select a portfolio of loan customers with a lower average default rate than would be possible

Table 1 around here

18

using public data alone. Since loan defaults are costly to the lender, a lower average default rate for

the portfolio reduces overall lending costs.

In peer-to-peer lending, every group has a leader, typically the person who started the group. The

group leader is the final arbiter of which borrowers will be allowed to join. The group leader has the

option of requiring potential members to submit additional documentation above and beyond that

which is required by Prosper.com. This ability to collect private information in order to improve

selection is tightly analogous to the role played by traditional financial intermediation in mitigating

adverse selection.

In peer-to-peer lending, however, groups are not monolithic. There are wide ranges of purposes,

sizes and membership composition. Some group leaders may be more interested than others in

expending the additional effort required to collect private data. For example, a group named

“Quilters of Central Indiana” may be mostly interested in social networking, whereas a group named

“Debt Consolidators” is more likely to take borrower screening more seriously. Therefore, in order to

accurately capture the effects of group membership on outcomes in this lending market, it will be

necessary to categorize groups in ways that reveal their propensity to engage in screening and

monitoring efforts.

In the presence of competition between relationship lenders (group members) and arm’s-length

lenders (public bidders), the arm’s-length bidders are able to free-ride on the screening activities of

the relationship lenders. Interest rates should fall due to the arm’s-length lenders facing a reduced

threat of adverse selection (Hauswald and Marquez, 2006). An online auction is, by its very nature, a

highly competitive environment. Where competition is present, investors cannot reasonably expect to

share in the borrower’s surplus, regardless of the length and strength of the relationship (Rajan,1992;

Petersen and Rajan (1994, 1995); Berger and Udell, 1995; Boot, 2000; Boot and Thakor, 2000).

Therefore, we can reasonably expect that the lower cost of lending caused by the decreased default

19

risk in a competitive environment will be passed along to the borrower in the form of a lower interest

rate.

B. Moral Hazard

The other main purpose of financial intermediation is the mitigation of moral hazard. Once the

loan is made, the borrower may act irresponsibly with the loan proceeds or take unnecessary risks.

One way that a traditional financial intermediator addresses this issue is through monitoring of the

borrower to ensure that contractually prohibited activities are not taking place. The borrower knows

that the monitoring will happen, and thus is discouraged from engaging in those activities that would

increase the likelihood of default. Typically, the bank has contractual remedies (such as calling the

loan due) that provide teeth to its monitoring.

Like the Grameen Bank in Bangladesh, the online group lending model has no group-level

enforcement teeth to strengthen the effects of monitoring. Instead, the Grameen lending model relies

entirely upon shame and ostracism to pressure group members into compliance (Ghatak, 1999;

Williams, 2004). Effective social punishment relies heavily upon the assumption that there are social

relationships between group members outside of the lending transaction. Whether these relationships

are established before or after the formation of the group should not matter as much as the level of ex-

post importance of these relationships to the borrower. The financial impact of the relationships is

influenced by the presence of competition (or the lack thereof). Relationships should decrease the

cost of credit, since the presence of social connections has been shown to improve repayment rates

(Woolcock, 2001).

Social sanctions are one of the enforcement mechanisms that can be imposed for borrower

default. This, of course, only works if the group members actually care about the opinions of the

others. The financial impact of social sanctions in a group lending environment is addressed by De

Aghion and Morduch (2000) as part of a larger model of loan repayment incentives. In their model,

20

a borrower will repay the loan only if the perceived cost of the shame (social sanctions) is greater

than or equal to the value of the debt obligation (principal plus interest) minus the discounted project

return. They interpret this to mean that with the additional social sanctions present, lenders are able

to charge higher interest without increasing risk of default.

An alternative interpretation of the work of De Aghion and Morduch (2000) is that the presence

of social sanctions should have a negative impact on interest rates, ceteris paribus. If default risk is

exogenously reduced, then this should result in a decrease in the interest rate that the lender requires.

However, default risk is itself endogenously affected by the interest rate. Stiglitz and Weiss (1981)

demonstrate that when facing a given interest rate, if a risk-neutral borrower is indifferent between

two projects and then the interest rate is increased, the borrower will prefer the riskier project. In a

similar line of reasoning, Diamond (1991) shows that a higher interest rate leads to riskier project

choice, but only for unmonitored borrowers, since the presence of monitoring impedes the borrower

from selecting the riskier project in the higher interest rate environment.

C. Are Lending Cost Savings Passed Along to the Borrower?

The above discussion of adverse selection and moral hazard establish the basis for the first

testable hypothesis of this paper.

Hypothesis 1: Membership in a group with private information or monitoring is

associated with lower default rates.

In peer-to-peer lending, like any form of financing, it should be apparent that interest rate spread

and risk are positively associated since investors expect to be compensated for risk. In an efficient

lending market, competition should result in interest rates that are exactly equal to the lenders’

21

marginal lending costs. So if lending costs are reduced through enhanced screening and monitoring

activities, then this reduction should be reflected in lower interest rates to the borrower.

Hypothesis 2: Lower default rates are associated with lower interest rate

spreads

Alternatively, it may be the case that the lending market is not perfectly efficient, but subject to

frictions such as the hold-up problem. If this is the case, then the cost savings from improved

selection will not be passed along to the borrower, but will instead be extracted by the lender as

surplus economic rents. If the hold-up problem exists, then spread should be positively associated

with being a member of a group with an established propensity for rent extraction, since these lenders

are more likely to seek monopoly rents. If there is no association, or if the relationship is negative,

then that would be evidence against the existence of a hold-up problem.

Hypothesis 3: Group leaders with a measurable propensity to capture rents are

more likely to hold-up their borrowers and not pass along cost savings.

If the hold-up problem is indeed based on an information monopoly, then it stands to reason that

as time passes and private information leaks to the public, then monopoly rents should decrease. Hale

and Santos (2009) found evidence supporting this when comparing bank loan interest rates before and

after the bond IPO for the borrowing firm, since information about creditworthiness is released during

the first bond issue. Schenone (2010) found similar results before and after the equity IPO.

Assuming that there is information about creditworthiness released by the insider peer-to-peer lenders

as part of the process of funding the first loan, we should see a higher difference between insider and

outsider bids for the first peer-to-peer loan than for subsequent loans.

22

Hypothesis 4: The difference between group and non-group bid spreads is lower

on second loan due to a decreasing information advantage over time.

3. Data

Data are obtained from Prosper.com, which represents the largest online social lending

community in the United States. Prosper.com also has the unique feature of allowing borrowers to

join “groups” that are designed to provide risk filtering as well as peer-pressure for borrowers to

perform on the repayment of their loans. The data include detail for 13,465 loans initiated between

May 31, 2006 and November 6, 2007, borrower information including credit score (but excluding

identity), lender information, loan performance information (through May 7, 2008), group

information, and detail regarding every winning bid made for each loan listing. It is important to

recognize that these loans were the result of successful loan listings. The majority of listings (more

than 90%) never receive enough bids to become funded, and thus never become actual loans.

Although the fact that I am only including funded loans may introduce some selection bias, there

would have been significant problems with analysis of bidding behavior on the unsuccessful loan

listings due to having far fewer bid observations with higher variability and more outliers. In

successful loans, these outlier bids are self-censored as they are crowded out by competition.

The loan principal amounts range from $1,000 to $25,000 US. Total funds borrowed during this

period were $86 million (US). The historical fed funds overnight rates, which are used to calculate

the loan spread, were downloaded from the Federal Reserve website. Estimated 2007 population data

for the geographic dispersion measure was obtained from the U.S. Census Bureau.

23

There are more than 1,300 groups in the sample, which I categorized by reading each group’s

narrative self-description and then manually coding each group into the following categories: friend,

small business, company, alumni, geographic, hobby, ethnic, occupation, religious, low-risk

borrowers, high-risk borrowers, lending purpose, growth purpose, and other. Business groups (360

observations) specialize in making loans to small businesses or self-employed entrepreneurs.

Occupation groups (513 observations) consist of people that share the same profession. Depending

on the size of the profession, members may be impacted by their reputation within that profession and

thus wish to avoid default if possible. Company groups (45 observations) consist of employees of the

same company. Geographic groups (99 observations) are based in a certain geographic area (the

same city, in most cases). Alumni groups (219 observations) consist of alumni of a certain university,

who generally must prove their status as an alumnus in order to join.

Williams (2004) asserts that shame and ostracism can be a substitute for legal enforcement in the

event of default, and these effects are enhanced by geographic proximity. Karlan (2007) also finds

that geographic concentration in groups of borrowers leads to improved repayment rates, which is

consistent with Agarwal and Hauswald (2008), who also find that geographic proximity is a good

proxy for information quality. Karlan implements a measure called “geographic dispersion” which

reveals the extent to which group members are clustered by neighborhood. In this paper, I implement

a geographic dispersion measure similar to this one to determine how tightly the group members are

clustered. Since the raw data only include member locations by state, to implement Karlan’s

geographic dispersion measure I substitute states for neighborhoods. Hence, in the formula below, ��

represents the percentage of the group from a particular state, and �� is the percentage of the

aggregate total population across those represented states, that lives in each state.

Table 2 around here

24

������ � ��������

�����

Close inspection of this formula reveals that it is actually a measure of the extent to which the

geographic distribution of the group differs from the geographic distribution of the overall population

covering the same combined geographic areas. So a high value of GDSTATE indicates that some

geographic areas covered by the group are overrepresented. The pure GDSTATE calculation fails

when all group members live in the same state, since then there is no deviation from the population

distribution. GDSTATE equals zero in this case, even though the group is in fact highly

concentrated. Therefore, when all group members live in the same state, I assume perfect

concentration and substitute a value of one (1) in place of the generated value of zero.

Since shame and ostracism (or any other social sanction) can only exist in the presence of

relationships that are important to the borrower, I develop a proxy for personal connections by

reading each group’s narrative self-description and then manually coding each group into a category.

Group categories were: friend, small business, company, alumni, geographic, hobby, ethnic,

occupation, religious, low-risk borrowers, high-risk borrowers, lending purpose, and other. Personal

relationships are proxied by combining the groups: friend, company, geographic, occupation and

alumni into a single dummy variable called GRP_RELATIONSHIPS. These are the groups in which

the borrower is most likely to have direct or indirect personal relationships. Private “friend” groups

(167 observations) are the groups with descriptions such as “Family and friends of John Doe - by

invitation only.”

One of the important characteristics of this online social lending data set is that the loans have

aspects of both relationship banking as well as capital market transactions. On the one hand, like

auction-based capital markets, Prosper.com does not accept deposits – it just directly matches

borrowers with investors. On the other hand, the group membership aspect with its information

asymmetries induces behavior much like relationship banking. Each loan to a borrower that is a

25

group member has the very useful property of having both relationship lenders (investors belonging

to the same group), and arm’s-length lenders (other investors who do not belong to that group).

Because the data consists of loans that have both relationship and transactional lenders that can be

distinguished from each other within the same loan, the effects of the relationships can be measured

very cleanly with less noise and less potential for bias or endogeneity.

4. Methodology

Before being able to answer the question of what lenders do with loan cost reductions, it is

necessary to verify that membership in a group with enhanced selection or monitoring is truly

associated with lower default rates. Toward that end, I identify proxies for enhanced private

information and enhanced monitoring. During the time frame of this data set, group leaders have the

option of establishing what are called “group leader rewards.” This is an additional percentage

(ranging from zero to one percent) that would be added to the borrower’s interest rate and paid

monthly to the group leader. Therefore, I identify the groups that have an observable profit motive

as those that have their group leader rewards set at a rate greater than zero. For these groups I set the

FORPROFIT indicator to one (1). Lenders will collect private information over time (Fama, 1985)

and make lending decisions based on that information.

FORPROFIT groups have better loan selection due to private information and therefore

should have lower default rates (hence lower costs). This leads to a simple test for the

presence of a hold-up problem. If the hold-up problem exists, then spread should be

positively associated with the FORPROFIT indicator, since these lenders are more likely to

seek monopoly rents. If there is no association, or if the relationship is negative, then that

would be evidence against the existence of a hold-up problem. In this study, the association

between FORPROFIT and spread is univariate tested using t-test mean comparisons. For the

multivariate analyses, the association of private information and other loan characteristics

26

with default is tested using probit analyses. The relationship of private information with

interest rate spread is tested using OLS and tobit analyses.

Of course, as discussed previously, it is likely that there is an endogeneity problem with the

traditional choice between group and non-group loans. Fortunately, this data set has detailed bidding

data on each loan which allows the researcher to distinguish between bids from group members and

bids from outsiders. Therefore, it is possible to measure the hold-up problem without the endogeneity

problem by comparing group bids to non-group bids for the same loan.

Agarwal and Hauswald (2008) attempt to isolate the effects of private information when

comparing online eloans (transactional) to local branch loans (relationship). They do this by

orthogonalizing the internal and external (public) credit scores in order to extract the purely private

component of the overall credit screen. For the local branch loans, the internal credit score will be

impacted by proprietary information obtained by the bank over the course of their business

relationship with the borrower. They captured the proprietary information in the residual (µi) in the

following regression:

��������� �� � �� �!�� � 1�#$�% &� � &� �!��� � '�

The residuals ('�) are stored a variable called the Private Information Residual (PIR) and used as

an independent variable in subsequent regressions. XSBI is the firm’s credit score.

I employ a similar approach to extract the purely private information available to online lending

group members. The underlying assumption is that bidding behavior is based on all available

information. Public information is available to all bidders, but additional private information is only

available to insiders. Therefore, differences in insider vs. outsider bidding behavior must be driven

by differences in information. As a proxy for the internal credit score, I use the de-meaned ratio of

insider bids to outside bids on each loan. This is a reasonable proxy, since a credit score itself is

27

simply a measure of the lender’s positive opinion of the borrower. In the same sense, the ratio of

insider bids to total bids measures the insider’s relative positive opinion of the borrower as compared

to outsider opinion. The intuition is that if the private information is adverse in nature, then the

insiders will be less likely to bid (or bid a higher rate that could be crowded out by lower outsider bid

rates) resulting in a lower ratio. If the private information is positive, then insiders will bid more

aggressively, resulting in a higher ratio. Non-group loans, of course, have a ratio of zero. Thus, I

calculated the residuals (PIR1) as follows:

()�*�!*+!,���-.�

�� � ��/��+*������� � ��/��0 1)���

� 1%$%23$45#$�% &� � &�/��+*������� � &�/��0 1)���� � '�

The dependent variable RATIOBIDBYGROUP is noisy, since bidding behavior may be impacted

by factors other than soft private information about credit worthiness. When the dependent variable

is noisy, the residuals will be biased toward zero. Therefore, a significant result for PIR as an

independent variable is all the more convincing. Of course, favorable proprietary info may lead to

higher interest rates, due to information capture (Houston and James, 1987; Von Thadden, 2004).

However, in the presence of competition between relationship lenders and arm’s-length lenders,

which is certainly the case here, interest rates should fall due to the arm’s-length lenders facing a

reduced threat of adverse selection (Hauswald and Marquez, 2006).

To measure the extent to which the credit marketplace incorporates these risk factors into their

bids, I estimate the effect of these factors on the borrower interest rate by performing a tobit analysis

using interest rate spread as the dependent variable.

28

In this model, X is a vector of independent variables representing borrower characteristics, W

represents the loan characteristics and Z represents the group/investor characteristics.

The hold-up problem is about rent extraction by relationship lenders from borrowers that cannot

easily escape said relationship. Therefore, if FORPROFIT groups are excluded from the analysis,

there should be no difference in spread for relationship versus non-relationship groups.

Monitoring will only affect borrower behavior if there is some negative consequence associated

with bad behavior. Shame and ostracism only exist in the presence of relationships that are important

to the borrower, which I proxy by combining the groups: friend, company, geographic, occupation

and alumni into a single dummy variable called GRP_RELATIONSHIPS. Bertrand et al. (2004)

contend that people that share a common educational background may have personal connections that

influence financial outcomes. This is why I include alumni group membership among the proxies for

personal relationships.

The univariate effects of monitoring on default and spread are measured using t-tests of

differences in population means. Multivariate effects on default are tested using probit analysis.

Finally, the multivariate effects of monitoring on interest rate spreads are tested using OLS and tobit

analyses.

5. Results

A. Adverse Selection

Table 3 and Table 4 contain descriptive statistics from the sample of 13,465 loans. The summary

data are divided into three groups of three columns. The first three columns represent data from all

loans, regardless of group membership. The second set of three columns is restricted to only those

�� 6 � � � � &7� � 89� � :�

29

loans to borrowers who are members of a group (any group). The third set of columns represents

borrowers who are not members of any group. Table 3 reports interest rate spread means and Table

4 reports default rate means. The last column is the difference in mean between borrowers who are

group members and those who are not. On average, members of groups pay higher rates than those

who are not. However, it is also apparent that group members have higher default rates. Recall that

being a member of a group corresponds to the borrower choosing a mix of public and private funding

for their debt, whereas not being a member of a group represents the choice of purely public debt.

Although it may seem counterintuitive at first that group members pay higher rates – it begs the

question of why someone would join a group if the result is merely a more expensive loan. It is

important to note that only 7.9% of loan listings during the sample period received enough bids to

become funded. Figure 2 shows failed listings versus successful listings, broken out by credit score.

Figure 2 – Listing Success by Credit Score Category

Many borrowers must make multiple attempts before successfully initiating a loan. Successful

borrowers have an average of 2.7 unsuccessful listing attempts in their history. Joining a group may

be viewed by borrowers as a way to increase the chances of funding, even if the interest rate is not as

favorable. Figure 3 illustrates this concept by showing that group members are more than twice as

likely to have their loan funded as non-group members.

30

Figure 3 – Listing Success by Group Membership

This explanation is consistent with Petersen and Rajan (1994), who find that deeper lending

relationships do not necessarily lower the interest rate, but nevertheless have a positive impact on the

availability of credit. The fact that the inclusion of private investors seems to result in higher rates is

consistent with the hold-up problem.

Examining Table 4 in more detail, when the default rates are broken out by credit risk, it is

apparent that the higher overall default rates for group members are being driven by the extremely

risky borrowers, who may be joining groups simply to gain greater access to credit (Rajan, 1992). If

the highest risk borrowers are excluded, then there is generally no statistically significant difference

in default mean between members of groups and non-members.

Table 3 around here

31

The results in Table 5 are similar to Table 4, in that the differences between the default rates of

relationship versus non-relationship groups are negative and highly significant on average for the

entire sample. However, when broken out by credit risk, the differences in mean are generally

insignificant or of low significance, except for very high risk borrowers.

The descriptive statistics in Table 6 add to the evidence of the existence of a hold-up problem in

the peer-to-peer lending market. Like Table 3, Table 6 distinguishes between groups with and

without profit motive, but only in terms of default rates. Borrowers in FORPROFIT groups default

an average of 8.1% of the time, whereas borrowers not in FORPROFIT groups have a mean default

rate 12.6%. This difference in mean is significant at the 1% level, so the hypothesis that loans in

FORPROFIT groups default less than non-profit groups cannot be rejected. So even though the loans

are lower risk, the FORPROFIT groups do not pass those savings along to the borrower, as evidence

by the results in Table 3. In fact, those lower risk loans are charged a premium. This is consistent

with the more severe hold-up problem described by Von Thadden (2004).

Table 7 reports statistics that address the question of whether groups without enhanced selection

or monitoring have higher default rates. The lack of propensity to monitor is proxied by a dummy

Table 6 around here

Table 5 around here

Table 4 around here

32

variable that is set to one (1) if the group is neither FORPROFIT nor a relationship group. Non-

monitored groups have an average default rate of 13.8%, compared to a default rate of 8.1% for the

monitored groups. The difference in mean is significant at the 1% level and is consistent with the

idea that monitoring reduces default.

Table 8 reports descriptive statistics for interest rate spread for only those loans made to

borrowers who are group members. This time, however, the SPREAD means and medians are broken

out by whether each bid came from an group (private) versus non-group (public). The means and

medians reported represent the weighted average interest rate spreads for the loan. The weighting is

done by bid dollar amount. Pairwise t-tests of differences are performed to test the significance of the

difference between insider and outsider bids. Although the insider bids are at a fairly consistently

higher spread than the outsider bids, the difference is only about ten basis points, which is not very

economically significant. This may be due to the heterogeneity of the raison d’etre of each group.

The lowest positive difference in spread is for the very lowest risk borrowers, which is consistent with

Rajan (1992) and Hale and Santos (2009), but not very meaningful due to low economic significance.

As explained above, the groups have a very wide range of purposes. Some are largely non-

commercial, while others are clearly commercial in their focus. If there is a hold-up problem and

some groups are extracting monopoly rents, we would expect that to occur within the groups with a

commercial focus rather than a hobby group, alumni group, or a friends & family group, for example.

In this data set, group leaders have the option of setting up group leader rewards (GLREWARDS) of

Table 8 around here

Table 7 around here

33

between zero and one percent. This means that the group leader will receive that portion of the

borrowers’ interest. It is reasonable to assume that groups with a non-commercial or social

networking focus will be more likely to have a group leader reward of zero. Therefore, as a proxy

indicator of a for-profit group, I code a value of one (1) for the FORPROFIT indicator for any group

that has a non-zero group leader reward.

Table 9 reports descriptive statistics for only loans to borrowers who are group members, broken

down into for-profit and non-profit groups. If there is a hold-up problem, then the non-profit groups

should exhibit a lower spread than the for-profit groups. It can be seen in the far right column that the

groups with an explicit profit motive do indeed have higher mean interest rates, consistent with the

hold-up problem.

Table 10 takes this a step further, focusing only on groups that have a profit motive and

comparing the weighted average interest rate bids between insiders and outsiders. If there is a hold-

up problem, then insiders should be bidding higher rates on average. This is indeed the case, as

shown in the column to the far right. The difference between insiders and outsiders seems to be

greatest for high loan amounts and for borrowers with good, but not great, credit scores, consistent

with Rajan (1992).

Table 11 provides descriptive bidding statistics for groups that are excluded from Table 10

because they have no explicit profit motive. We would expect to see a lower difference in this table if

in fact the profit motive groups were extracting monopoly rents. Overall, there is virtually no

Table 10 around here

Table 9 around here

34

difference (only 8 basis points). However, if we look specifically at the risk group that had the

greatest drop between insider and outsider bid rates in Table 10 (credit score ranging from 640-679)

and hence the largest potential hold-up problem, we find in Table 11 that the reduction is fourteen

basis points less. This is consistent with the idea that insiders are extracting monopoly rents from

higher risk borrowers (Hale and Santos, 2009).

Table 12 reports the first set of multivariate probit tests performed in order to test the degree to

which the explanatory risk characteristics discussed in the univariate section are actually associated

with loan defaults. This is relevant, since rational loan pricing should, in theory, depend upon the risk

profile of the loan and the borrower. The dependent variable is DEFAULT, which is a binary

variable set to one if the loan is in default status or more than 3 months past due. The coefficients

reported are marginal effects. The relevant group characteristics are FORPROFIT, a dummy

variable that indicates that the group leader has an explicit profit motive, LNGRPLOANS, a

continuous variable that is a proxy for group activity level, GDSTATE, an index between zero and

one that represents the geographic concentration of group members, and RATIOBIDBYGROUP, a

proxy for insider confidence which is the percentage of total successful bids that came from fellow

group members.

In column (7), the coefficient on the variable RATIOBIDBYGROUP is negative, very large, and

very significant. RATIOBIDBYGROUP represents the proportion of insider bids to total bids on the

loan. FORPROFIT and GDSTATE have positive coefficients, but are either not significant or barely

so. It is reasonable to expect that attributes of the group should not have an impact on borrower

performance. RATIOBIDBYGROUP, on the other hand, is negative, economically significant and

Table 11 around here

35

statistically significant at the 1% level. If insiders, who are members of the same group as the

borrower, have positive soft information about the borrower, then it is reasonable to expect that they

will bid on the loan in higher amounts. If the insiders are correct about this positive information, then

these loans should exhibit lower default rates. The result in column (7), for example, supports this

conclusion.

Table 13 reports OLS regressions with loan spread as the dependent variable and the same

explanatory variables as Table 12. Since the relationship between risk and return is expected to be

positive, it is reasonable to expect the coefficients in Table 12 and Table 13 to generally have the

same direction and significance. Therefore, it follows that the most meaningful results are the

instances where this is not the case. LNBORROWERAGE, for example remained significant but

switched signs. Although older borrowers appear to be less risky, they actually receive higher rates

when controlling for loan, group and borrower characteristics. This age effect appears to be

inconsistent with extant literature on borrower risk characteristics. FORPROFIT kept the same sign

(+) but became highly significant (1% level) in Table 13. So although the profit motive of the group

leader was not a very significant determinant of loan default, it has a significant and positive effect on

interest rates, which is consistent with the existence of a hold-up problem (Hypothesis 3).

If the hold-up problem exists and insider lenders are indeed extracting monopoly rents, then

another empirical effects should be apparent. Rajan (1992) predicts that firms with a higher risk of

default should be more susceptible to the hold-up problem. This is because for firms with a high

probability of success (low risk), the relationship bank’s information advantage is small. This

prediction has been confirmed empirically by Hale and Santos (2009). The predicted loan default

Table 12 around here

36

probability (DEFPROB), which is generated using the significant independent variables of Table 12,

is positive and highly significant in all appearances in Table 13. This is consistent with Hypothesis 2

– the fundamental idea that risk and return are positively related.

Another principal finding from Table 13 is that the presence of relationship lenders vs. arm’s-

length lenders has a significant impact on the interest rate. In column (5), the coefficient on the

variable RATIOBIDBYGROUP is negative, very large, and very significant. RATIOBIDBYGROUP

represents the proportion of insider versus outsider bids on the loan. If insiders, who are members of

the same group as the borrower, have positive soft information about the borrower, then it is

reasonable to expect that they will bid on the loan in higher numbers and with more favorable rates

than if they were less interested, which must necessarily drive down the ultimate rate charged to the

borrower. The result in column (5) supports this conclusion. Table 13 also supports the presence of a

hold-up problem by showing that after controlling for borrower characteristics, there is not a

significant relationship between group membership and interest rate spread, even though group loans

should have better selection and monitoring.

Table 14 reports results that address a key research question of this paper. SPREAD_DIFF is the

dependent variable, which is the weighted average of insider (private) interest rate bids minus the

weighted average of outsider (public) bids on each individual loan record. Since public and private

data reside on the same loan record, all borrower, group, loan and time characteristics are fixed.

There is no need to exogenize the public/private debt choice by doing any econometric exercises such

as instrumental variables, propensity score matching, or even fixed effects. In the presence of a hold-

up problem, group characteristics should be significantly associated with SPREAD_DIFF. Indeed,

Table 13 around here

37

this is the case. In fact, the only explanatory variables with significant coefficients are group-related.

FORPROFIT is positively associated with higher insider rates at the 5% level. GDSTATE, which

measures the geographic concentration of the insiders, is positively associated with insider rates and

is significant at the 1% level. Column (7) is restricted to only non-profit groups in order to test the

idea that relationships should not be associated with differences in spread for these groups.

Consistent with this, the coefficient on GRP_RELATIONSHIPS is neither statistically nor

economically significant.

Table 15 summarizes the results of the analyses testing the effects of the soft private group

information on interest rates. PIR1 is the variable that represents the orthogonalized residuals of the

regression of outside credit score onto the ratio of insider bids to total bids, which is the proxy for

inside credit score. Due to the noisiness of the dependent variable, the residuals will be biased toward

zero. Therefore a significant result when using PIR1 as an exogenous variable is particularly

believable. It is clearly demonstrated in every analysis in Table 15 that private soft information, as

captured by PIR1, is strongly significant and has a negative relationship with interest rates, consistent

with Hauswald and Marquez (2006), and not consistent with Von Thadden’s (2004) finding that

favorable info will lead to higher rates.

It could be argued that the significant results for the PIR1 residual are due to omitted variables in

the orthogonalization regression. To address this, I calculated a new residual variable called PIR2,

which, in addition to credit score, also includes all other variables that were previously shown to have

significant effects on interest rate spreads (in Table 13), as well as their interactions. The results of

the inclusion of PIR2 as an independent variable are shown in column 6 of Panel B of Table 15.

Private positive information continues to be consistent with lower interest rates.

Table 14 around here

38

B. Moral Hazard

If the lender is extracting rents via information monopoly, then a public lending event such as a

bond IPO in the public debt markets or, in the case of peer-to-peer lending, a successful loan auction,

should result in some degree of private information dissemination. Therefore, a decline in the

difference between insider and outsider interest rate bids should be observed for subsequent loans as

the information monopoly is abated. Table 16 provides descriptive bidding statistics for insiders and

outsiders for their first loan, then subsequent loans. A narrowing of the spread is indeed observed.

Overall, it is only 7 basis points, but for the previously identified borrowers with good yet sub-

optimal credit scores, the rates narrow by 56 basis points, consistent with Hale and Santos (2009).

When a borrower is a member of a group that has personal relationships, loan default rate is

significantly reduced. This result is consistent with Abbink, Irlenbusch and Renner (2006), who find

a weakly significant result that private social contact (but not professional contact) has a positive

impact on the decision to repay. Cassar, Crowley and Wydick (2007), however, performed a series of

experimental microlending games in South Africa and Armenia and found that mere social contact

was not enough, but that reduction of default risk occurred only when those relationships included

personal trust. Both of these results are consistent with the results of this paper.

Column (2) of Table 12 tests Hypothesis 1 by adding the GRP_RELATIONSHIPS dummy to the

probit specification. The coefficient is negative and significant at the 1% level, consistent with the

Table 16 around here

Table 15 around here

39

idea that relationships decrease default risk due to increased monitoring (Diamond, 1991; De Aghion

and Murdock, 2000; Williams, 2004).

Another factor that has been shown in previous studies to have a significant effect on loan

repayment rates is geographic proximity. Karlan (2007) finds that geographic concentration in

groups of borrowers leads to improved repayment rates, which is consistent with Agarwal and

Hauswald (2008). In columns (5-7) of Table 12, the specification is modified in order to test whether

geographic proximity is negatively related to default rate. The measure for this is GDSTATE, which

is a proxy for the geographic concentration of the group. The coefficient for GDSTATE is negative,

consistent with Karlan (2007) and Agarwal and Hauswald (2008), but is not statistically significant.

GDSTATE, however, is significantly associated with a higher spreads between insider and outsider

bids in Table 13. The negative relationships between geographic concentration and both default and

interest rate is consistent with economic theory and previous empirical work. Notwithstanding, the

fact that I replaced “neighborhood” with “state” in the construction of the GDSTATE measure is

conceptually troubling, since a state would seem to be too geographically large to represent a good

proxy for borrower concentration. For example, there are many borrowers in this data set from

California, a state larger than most countries in the world. The GDSTATE measure treats all

Californians as if they are within the same community, which is clearly not the case.

Column (7) of Table 12 tests the hypothesis that group leader rewards incentivize the group

leader to monitor the loans more carefully, and thus should be associated with lower default rates.

The coefficient on GLREWARDS is indeed negative, consistent with the hypothesis, but statistically

insignificant, allowing the hypothesis to be rejected.

In Table 13, I report the results from tobit analyses using the same independent variables as Table

12, but with the borrower interest rate as the dependent variable, censured at zero. If lenders are

informed and rational, then the results should closely mirror the results in Table 12, since the lenders

should be considering the default risk effects from each of these variables when setting their

40

minimum interest rate bid. However, as previously discussed, this data set contains loans that have

both insiders and outsiders. Not surprisingly, consistent with Table 12, MEMBEROFGROUP has a

positive effect on interest rate, whereas GRP_RELATIONSHIPS has a negative impact, both at the

1% significance level. Competition is proxied by LNBIDCOUNT, which also is negative and

significant. These results support the idea that in the presence of competition, relationships will lower

the interest rate to the borrower.

Table 17 reports the results of the OLS regressions with the dependent variable defined as the

difference in insider versus outsider spreads between the first peer-to-peer loan and subsequent loans.

There is no evidence that information released during the first auction or loan monitoring process has

any significant impact on the hold-up problem. This result is inconsistent with both Hale and Santos

(2009) and Schenone (2010), putting Hypothesis 4 in question. It is difficult, however, to draw a firm

conclusion from Table 17, however, since there were so few second loans in the data set. The results,

therefore, may be driven by data limitations.

6. Conclusion

Relationship banking uses soft information gathered via proprietary means to overcome

information asymmetries relating to borrower credit-worthiness. Loans granted by relationship

lenders with accurate soft information have a lower risk of default and a lower cost. In the presence

of open lender competition, these savings should be passed along to the borrower in the form of lower

interest rates. But if lenders are able to maintain that soft information as proprietary, then they have

the ability to extract monopoly rents from the borrower. This is the hold-up problem. The choice

Table 17 around here

41

between public and private debt trades off the high transaction costs of public debt versus the high

hold-up costs of private debt.

This paper uses peer-to-peer lending data to illustrate the effects of group composition on adverse

selection and moral hazards problems in lending. This particular data set is effective for doing so

because it uniquely contains both public and private investor bidding data on the same loan record.

Thus, the typical econometric approaches for attempting to exogenize the public/private debt choice

are rendered unnecessary.

Through a series of univariate and multivariate analyses, I find that membership in a group with

private information or enhanced monitoring is indeed associated with lower default rates. In general,

lower default rates imply lower lending costs, and these are generally associated with lower interest

rate spreads in the data. The degree to which these cost reductions are passed down to the borrower

in the form of lower interest rates depends entirely upon the nature of the group to which the

borrower belongs. I find that private profit-centered investors do, in fact, “hold-up” their borrowers,

extracting higher economic rents. This is especially the case for borrowers with imperfect credit

ratings, which is consistent with extant literature. Investors that have social relationships with

borrowers, on the other hand, do not hold-up their borrowers but instead pass along the cost savings

associated with better monitoring and private information.

In recent years, the internet social lending community has grown from a small innovation to a

growing industry that now encompasses not only small personal loans, but has also begun to make

inroads into the small business loan markets. peer-to-peer lending represents a disruptive technology

to traditional financial intermediation by putting borrowers and investors together directly while

potentially mitigating moral hazard through informal peer monitoring, which presumably has lower

costs. As such, it is a phenomenon that merits continued attention.

42

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46

Table 1 The World of Online Social Lending

This table lists the principal online social lending services that have appeared since 2005, but before May 2008.

All listings are commercial for-profit enterprises unless otherwise indicated.

Lending Site Inception Description

Zopa.com (UK) Mar 2005 Peer-to-peer (P2P) lending in the UK Kiva.org Oct 2005 Non-profit. Allows individuals to make loans to entrepreneurs

in developing countries. Prosper.com Feb 2006 peer-to-peer lending in the US. Allows borrowers to list their

loan requirements and members can then bid as lenders. Prosper then services the loan for the 3-year term.

MyC4 Dec 2006 Denmark for-profit Microfinance Institution (MFI) in Africa Boober.nl Jan 2007 peer-to-peer lending in the Netherlands Virginmoneyus.com Feb 2007 Formally circelending.com. manages and services direct family

and friend lending Smava.de Mar 2007 peer-to-peer lending in Germany Fairrates.dk Apr 2007 peer-to-peer lending in Denmark LendingClub.com May 2007 peer-to-peer lending in the US, originally limited to

Facebook.com members PPdai Jun 2007 peer-to-peer lending in China Microplace.com Oct 2007 Ebay MFI investing in LDCs IGrin.com.au Oct 2007 Peer-to-peer lending in Australia Zopa.com (Italy) Nov 2007 Peer-to-peer lending in Italy Boober.it (Italy) Nov 2007 Peer-to-peer lending in Italy Loanland Dec 2007 Peer-to-peer lending in Sweden Zopa.com (US) Dec 2007 Peer-to-peer lending in the US IOUCentral.ca Feb 2008 Peer-to-peer lending in Canada Zopa.com (Japan) Mar 2008 Peer-to-peer lending in Japan CommunityLend.com Apr 2008 Peer-to-peer lending in Canada

47

Table 2 Variable Definitions

Variable Type Description

DEFAULTIND Dummy Value of 1 if the loan is default, 0 otherwise. The soonest that a loan can transition from past due to default, even if the borrower never makes a payment, is six months.

MEMBEROFGROUP Dummy Value of 1 if borrower is a member of any group, 0 otherwise. GRP_RELATIONSHIPS Dummy Value of 1 if borrower is a member of a group that has a higher likelihood

of personal relationships (group categories: private, geographic, alumni, occupation and company), value of 0 if member of group in the following categories: small business, hobby, ethnic, high-risk, low-risk or other.

GLREWARDS Continuous Percentage of loan interest in a group that is paid to the group leader. This practice has been discontinued so newer loans all have a value of zero in this field.

LISTREV Dummy Value of 1 indicates that group members in this group must have their loan listings reviewed by the group leader before being posted.

LNGROUPLOANS Continuous Natural log of the total number of loans initiated in a particular group. This represents a proxy for the effective size and experience of the group.

RATIOBIDBYGROUP Continuous Numerator is total dollar value of bids made by fellow members of the borrower’s group. Denominator is total dollar value of bids against the loan listing.

LNAMT Continuous Natural log of the original loan principal amount AGEINMONTHS Discrete Number of months from loan initiation until current date or until default. BORROWERRATE Continuous Interest rate to the borrower, expressed as a decimal. QUICKFUNDING Dummy Value of 1 if the borrower submitted loan listing with the

QUICKFUNDING option selected. This means that instead of waiting for the full bidding period to end (often resulting in the interest rate being bid down), the borrower wants the bidding to end and the loan to be originated as soon as the loan has received enough bids to be funded.

CREDITSCORE Discrete ScoreXPlus Credit Score received from credit bureau (Experian). This is an integer between 520 and 800. Prosper does not provide this numeric score directly to lenders. Instead, lenders see a credit grade corresponding to the following ranges: 7 AA 760+ 6 A 720-759 5 B 680-719 4 C 640-679 3 D 600-639 2 E 560-599 1 HR 520-559 (high risk)

LNSCORE Continuous Natural log of the 1-7 values of the CREDITSCORE HOMEOWNERIND Dummy Value of 1 if the borrower owns a home ENDORSEMENTSIND Dummy Value of 1 if the borrower has received personal endorsements from other

members of the social lending site LNBORROWERAGE Discrete Calculated as the number of years since the initiation of the borrower’s

credit report file minus 18. The assumption is that most people first receive credit in their own name at around age 18

GDSTATE Continuous Calculated value between zero and one, with one representing a very highly concentrated group membership.

SPREAD Continuous The difference between BORROWERRATE and FEDFUNDS LNDATEBIDCOUNT Continuous Natural log of the total number of bids placed on ALL loans on the

website on the last day of bidding for the loan being analyzed. This is a proxy for total website traffic.

FEDFUNDS Continuous This is the fed funds overnight rate on the last day of bidding for the loan. LNAMT * LNSCORE Continuous Interaction term FORPROFIT * LNGRPLOANS Continuous Interaction term

48

Table 3 Descriptive Statistics Concerning Loan and Borrower Characteristics Sorted by Whether

they are Members of a Lending Group

This table reports summary statistics of the full sample of loans initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into three groups: 1) all borrowers; 2) borrowers that are members of any lending group; and 3) borrowers that are not members of a group. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively for t-tests.

Total Member of Group Not a Member

Count Mean Median Count Mean Median Count Mean Median Diff in

Obs Spread Spread Obs Spread Spread Obs Spread Spread Mean

Loan Amount

1st quartile $0 - 2550 3446 0.13340 0.13715 2080 0.14192 0.14730 1366 0.12042 0.11430 0.0215 ***

2nd quartile $2551 to 4600 3253 0.14500 0.14760 1928 0.15040 0.15690 1325 0.13715 0.13250 0.0133 ***

3rd quartile $4601 to 8000 3380 0.12368 0.11755 1975 0.12820 0.12470 1405 0.11731 0.10950 0.0109 ***

4th quartile $8001 + 3386 0.11693 0.10760 1999 0.11931 0.11180 1387 0.11350 0.10190 0.0058 ***

Total 13465 0.12962 0.12480 7982 0.13491 0.13210 5483 0.12192 0.11270 0.0130 ***

Credit Score

760+ 1398 0.04449 0.03720 627 0.04469 0.03710 771 0.04433 0.03720 0.0004

720 - 759 1359 0.06592 0.05950 645 0.06471 0.05960 714 0.06701 0.05910 -0.0023

680 - 719 1752 0.09187 0.08575 901 0.09168 0.08500 851 0.09207 0.08690 -0.0004

640 - 679 2465 0.12096 0.11440 1400 0.11810 0.11025 1065 0.12472 0.11770 -0.0066 ***

600 - 639 2522 0.15141 0.14750 1497 0.14528 0.14250 1025 0.16037 0.15950 -0.0151 ***

560 - 599 1874 0.19103 0.19740 1259 0.18462 0.18750 615 0.20415 0.21780 -0.0195 ***

520 - 559 2095 0.18834 0.19760 1653 0.18708 0.19580 442 0.19306 0.23005 -0.0060 *

Total 13465 0.12962 0.12480 7982 0.13491 0.13210 5483 0.12192 0.11270 0.0130 ***

Borrower Age

1st quartile 0 - 27 2125 0.13353 0.12790 1235 0.14058 0.14080 890 0.12376 0.11575 0.0168 ***

2nd quartile 28 - 31 3062 0.13090 0.12605 1861 0.13780 0.13510 1201 0.12022 0.10760 0.0176 ***

3rd quartile 32 - 36 4163 0.13108 0.12740 2497 0.13654 0.13540 1666 0.12290 0.11425 0.0136 ***

4th quartile 37 + 4079 0.12502 0.11760 2362 0.12782 0.12225 1717 0.12117 0.11310 0.0066 ***

Total 13429 0.12959 0.12470 7955 0.13487 0.13190 5474 0.12191 0.11270 0.0130 ***

Is Borrower Homeowner?

Yes 5682 0.11251 0.10400 3152 0.11734 0.11050 2530 0.10649 0.09690 0.0109 ***

No 7783 0.14211 0.14260 4830 0.14638 0.14740 2953 0.13513 0.12960 0.0113 ***

Total 13465 0.12962 0.12480 7982 0.13491 0.13210 5483 0.12192 0.11270 0.0130 ***

First Loan?

Yes 13175 0.13019 0.12660 7830 0.13540 0.13250 5345 0.12256 0.11430 0.0128 ***

No 290 0.10372 0.09135 152 0.10989 0.09805 138 0.09693 0.07955 0.0130 *

Total 13465 0.12962 0.12480 7982 0.13491 0.13210 5483 0.12192 0.11270 0.0130 ***

49

Table 4 Descriptive Statistics Concerning Default Rates Sorted by

Whether the Borrower is in a Group or Not

This table reports summary statistics of the full sample of loans initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into three groups: 1) all borrowers; 2) borrowers that are members of a group; and 3) borrowers that do not belong to a group. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Count Mean Count Mean Count Mean Diff in

Obs Default Obs Default Obs Default Mean

Loan Amount

1st quartile $0 - 2550 3446 0.10795 2080 0.14375 1366 0.05344 0.0903 ***

2nd quartile $2551 to 4600 3253 0.09560 1928 0.12189 1325 0.05736 0.0645 ***

3rd quartile $4601 to 8000 3380 0.04704 1975 0.05570 1405 0.03488 0.0208 ***

4th quartile $8001 + 3386 0.04017 1999 0.04952 1387 0.02668 0.0228 ***

Total 13465 0.07263 7982 0.09308 5483 0.04286 0.0502 ***

Credit Score

760+ 1398 0.00572 627 0.00638 771 0.00519 0.0012

720 - 759 1359 0.01398 645 0.01860 714 0.00980 0.0088

680 - 719 1752 0.01941 901 0.01998 851 0.01880 0.0012

640 - 679 2465 0.03570 1400 0.04357 1065 0.02535 0.0182 **

600 - 639 2522 0.05234 1497 0.05812 1025 0.04390 0.0142

560 - 599 1874 0.12327 1259 0.13185 615 0.10569 0.0262 *

520 - 559 2095 0.22243 1653 0.23896 442 0.16063 0.0783 ***

Total 13465 0.07263 7982 0.09308 5483 0.04286 0.0502 ***

Borrower Age

1st quartile 0 - 27 2125 0.10306 1235 0.13603 890 0.05730 0.0787 ***

2nd quartile 28 - 31 3062 0.08589 1861 0.11231 1201 0.04496 0.0674 ***

3rd quartile 32 - 36 4163 0.06918 2497 0.08730 1666 0.04202 0.0453 ***

4th quartile 37 + 4079 0.04903 2362 0.05927 1717 0.03494 0.0243 ***

Total 13429 0.07223 7955 0.09239 5474 0.04293 0.0495 ***

Is Borrower Homeowner?

Yes 5682 0.04453 3152 0.05330 2530 0.03360 0.0197 ***

No 7783 0.09315 4830 0.11905 2953 0.05080 0.0683 ***

Total 13465 0.07263 7982 0.09308 5483 0.04286 0.0502 ***

First Loan?

Yes 13175 0.07385 7830 0.09464 5345 0.04341 0.0512 ***

No 290 0.01724 152 0.01316 138 0.02174 -0.0086

Total 13465 0.07263 7982 0.09308 5483 0.04286 0.0502 ***

Group Members Non-Group MembersTotal

50

Table 5 Descriptive Statistics Concerning Default Rates of Borrowers in a Lending Group Sorted by

whether the Group has the Potential for Personal Relationships

This table reports summary statistics of the full sample of loans initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into three groups: 1) all borrowers who belong to a group; 2) borrowers that are members of a group where there is a potential for personal relationships; and 3) borrowers that belong to all other groups. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Count Mean Count Mean Count Mean Diff in

Obs Default Obs Default Obs Default Mean

Loan Amount

1st quartile $0 - 2550 2080 0.14375 223 0.04933 1857 0.15509 -0.1058 ***

2nd quartile $2551 to 4600 1928 0.12189 215 0.06512 1713 0.12901 -0.0639 ***

3rd quartile $4601 to 8000 1975 0.05570 291 0.02405 1684 0.06116 -0.0371 ***

4th quartile $8001 + 1999 0.04952 309 0.03883 1690 0.05148 -0.0127

Total 7982 0.09308 1038 0.04239 6944 0.10066 -0.0583 ***

Credit Score

760+ 627 0.00638 166 0.00000 461 0.00868 -0.0087 **

720 - 759 645 0.01860 126 0.01587 519 0.01927 -0.0034

680 - 719 901 0.01998 158 0.00633 743 0.02288 -0.0166 **

640 - 679 1400 0.04357 176 0.02273 1224 0.04657 -0.0238 *

600 - 639 1497 0.05812 166 0.03012 1331 0.06161 -0.0315 **

560 - 599 1259 0.13185 146 0.05479 1113 0.14196 -0.0872 ***

520 - 559 1653 0.23896 100 0.24000 1553 0.23889 0.0011

Total 7982 0.09308 1038 0.04239 6944 0.10066 -0.0583 ***

Borrower Age

1st quartile 0 - 27 1235 0.13603 146 0.07534 1089 0.14417 -0.0688 ***

2nd quartile 28 - 31 1861 0.11231 235 0.05532 1626 0.12054 -0.0652 ***

3rd quartile 32 - 36 2497 0.08730 319 0.04389 2178 0.09366 -0.0498 ***

4th quartile 37 + 2362 0.05927 337 0.01780 2025 0.06617 -0.0484 ***

Total 7955 0.09239 1037 0.04243 6918 0.09988 -0.0575 ***

Is Borrower Homeowner?

Yes 3152 0.05330 495 0.01818 2657 0.05984 -0.0417 ***

No 4830 0.11905 543 0.06446 4287 0.12596 -0.0615 ***

Total 7982 0.09308 1038 0.04239 6944 0.10066 -0.0583 ***

First Loan?

Yes 7830 0.09464 1009 0.04361 6821 0.10218 -0.0586 ***

No 152 0.01316 29 0.00000 123 0.01626 -0.0163

Total 7982 0.09308 1038 0.04239 6944 0.10066 -0.0583 ***

Relationship Group Non-Relationship GroupTotal

51

Table 6 Descriptive Statistics Concerning Default Rates of Borrowers in a Lending Group Sorted by

whether the Group Leader has Observable Profit Motive

This table reports summary statistics of the full sample of loans initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into three groups: 1) all borrowers who belong to a group; 2) borrowers that are members of a group where the leader takes a percentage of the loan fee (profit motive); and 3) borrowers that belong to a group without a profit motive. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Count Mean Count Mean Count Mean Diff in

Obs Default Obs Default Obs Default Mean

Loan Amount

1st quartile $0 - 2550 2080 0.14375 1408 0.13707 672 0.15774 -0.0207

2nd quartile $2551 to 4600 1928 0.12189 1394 0.10689 534 0.16105 -0.0542 ***

3rd quartile $4601 to 8000 1975 0.05570 1485 0.04646 490 0.08367 -0.0372 ***

4th quartile $8001 + 1999 0.04952 1593 0.04269 406 0.07635 -0.0337 **

Total 7982 0.09308 5880 0.08146 2102 0.12559 -0.0441 ***

Credit Score

760+ 627 0.00638 401 0.00748 226 0.00442 0.0031

720 - 759 645 0.01860 486 0.02263 159 0.00629 0.0163 *

680 - 719 901 0.01998 701 0.01854 200 0.02500 -0.0065 *

640 - 679 1400 0.04357 1127 0.03904 273 0.06227 -0.0232

600 - 639 1497 0.05812 1162 0.05766 335 0.05970 -0.0020

560 - 599 1259 0.13185 885 0.12090 374 0.15775 -0.0369 *

520 - 559 1653 0.23896 1118 0.20930 535 0.30093 -0.0916 ***

Total 7982 0.09308 5880 0.08146 2102 0.12559 -0.0441 ***

Borrower Age

1st quartile 0 - 27 1235 0.13603 892 0.12332 343 0.16910 -0.0458 **

2nd quartile 28 - 31 1861 0.11231 1329 0.09330 532 0.15977 -0.0665 ***

3rd quartile 32 - 36 2497 0.08730 1872 0.07532 625 0.12320 -0.0479 ***

4th quartile 37 + 2362 0.05927 1765 0.05496 597 0.07203 -0.0171

Total 7955 0.09239 5858 0.08057 2097 0.12542 -0.0449 ***

Is Borrower Homeowner?

Yes 3152 0.05330 2426 0.04782 726 0.07163 -0.0238 **

No 4830 0.11905 3454 0.10510 1376 0.15407 -0.0490 **

Total 7982 0.09308 5880 0.08146 2102 0.12559 -0.0441 ***

First Loan?

Yes 7830 0.09464 5767 0.08271 2063 0.12797 -0.0453 ***

No 152 0.01316 113 0.01770 39 0.00000 0.0177

Total 7982 0.09308 5880 0.08146 2102 0.12559 -0.0441 ***

Group Profit Motive No Group Profit MotiveTotal

52

Table 7 Descriptive Statistics Concerning Default Rates of Borrowers in a Lending Group Sorted by

whether the Group has the Potential for Monitoring This table reports summary statistics of a sample of loans initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into three groups: 1) all borrowers who belong to a group; 2) borrowers that are members of a group that is neither a for-profit group nor a relationship group; and 3) borrowers that belong to all other groups. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Count Mean Count Mean Count Mean Diff in

Obs Default Obs Default Obs Default Mean

Loan Amount

1st quartile $0 - 2550 2080 0.14375 596 0.16946 1484 0.13342 0.0360 **

2nd quartile $2551 to 4600 1928 0.12189 455 0.16703 1473 0.10794 0.0591 ***

3rd quartile $4601 to 8000 1975 0.05570 406 0.09360 1569 0.04589 0.0477 ***

4th quartile $8001 + 1999 0.04952 294 0.08844 1705 0.04282 0.0456 ***

Total 7982 0.09308 1751 0.13764 6231 0.08056 0.0571 ***

Credit Score

760+ 627 0.00638 169 0.00592 458 0.00655 -0.0006

720 - 759 645 0.01860 111 0.00901 534 0.02060 -0.0116

680 - 719 901 0.01998 143 0.02797 758 0.01847 0.0095

640 - 679 1400 0.04357 224 0.05804 1176 0.04082 0.0172

600 - 639 1497 0.05812 287 0.06620 1210 0.05620 0.0100

560 - 599 1259 0.13185 330 0.17576 929 0.11625 0.0595 **

520 - 559 1653 0.23896 487 0.29774 1166 0.21441 0.0833 ***

Total 7982 0.09308 1751 0.13764 6231 0.08056 0.0571 ***

Borrower Age

1st quartile 0 - 27 1235 0.13603 297 0.17845 938 0.12260 0.0559 **

2nd quartile 28 - 31 1861 0.11231 455 0.16923 1406 0.09388 0.0754 ***

3rd quartile 32 - 36 2497 0.08730 521 0.13244 1976 0.07540 0.0570 ***

4th quartile 37 + 2362 0.05927 474 0.08650 1888 0.05244 0.0341 **

Total 7955 0.09239 1747 0.13738 6208 0.07974 0.0576 ***

Is Borrower Homeowner?

Yes 3152 0.05330 574 0.08014 2578 0.04732 0.0328 ***

No 4830 0.11905 1177 0.16568 3653 0.10402 0.0617 ***

Total 7982 0.09308 1751 0.13764 6231 0.08056 0.0571 ***

First Loan?

Yes 7830 0.09464 1719 0.14020 6111 0.08182 0.0584 ***

No 152 0.01316 32 0.00000 120 0.01667 -0.0167

Total 7982 0.09308 1751 0.13764 6231 0.08056 0.0571 ***

Non-Prof & Non-Rel All Other GroupsTotal

53

Table 8 Descriptive Statistics Concerning Weighted Average of Interest Rate

Bids for Insiders vs. Outsiders for Loans to Group Members

This table reports summary statistics of loans to borrowers that are members of a lending group. The loans were initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into two groups: 1) bids from insiders, meaning lenders who are members of the same group as the borrower, and 2) bids from outsiders, meaning bids from everyone else. The differences between the means of these two groups are reported. Statistical significance is tested using pairwise t-tests of differences. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Insider Bidders Outsider Bidders

Count Mean Median Count Mean Median Diff in

Obs Spread Spread Obs Spread Spread Mean

Loan Amount

1st quartile $0 - 2550 2080 0.12396 0.13219 2080 0.12283 0.13376 0.0011

2nd quartile $2551 to 4600 1928 0.13400 0.14605 1928 0.13372 0.14342 0.0003

3rd quartile $4601 to 8000 1975 0.12205 0.12030 1975 0.12119 0.12012 0.0009

4th quartile $8001 + 1999 0.11786 0.11290 1999 0.11640 0.11226 0.0015 ***

Total 7982 0.12326 0.12230 7982 0.12226 0.12142 0.0010 ***

Credit Score

760+ 627 0.05075 0.04505 627 0.05051 0.04536 0.0002

720 - 759 645 0.07473 0.07271 645 0.07323 0.07300 0.0015 **

680 - 719 901 0.09238 0.08700 901 0.09118 0.08660 0.0012 *

640 - 679 1400 0.11994 0.11709 1400 0.11808 0.11390 0.0019 ***

600 - 639 1497 0.14775 0.14720 1497 0.14686 0.14708 0.0009

560 - 599 1259 0.17773 0.17755 1259 0.17781 0.17852 -0.0001

520 - 559 1653 0.18315 0.18720 1653 0.18230 0.18626 0.0009

Total 7982 0.12326 0.12230 7982 0.12226 0.12142 0.0010 ***

Borrower Age

1st quartile 0 - 27 1235 0.12597 0.12806 1235 0.12529 0.12622 0.0007

2nd quartile 28 - 31 1861 0.12302 0.11818 1861 0.12206 0.12058 0.0010

3rd quartile 32 - 36 2497 0.12478 0.12504 2497 0.12438 0.12472 0.0004

4th quartile 37 + 2362 0.12043 0.11760 2362 0.11859 0.11715 0.0018 ***

Total 7955 0.12319 0.12229 7955 0.12217 0.12134 0.0010 ***

Is Borrower Homeowner?

Yes 3152 0.11045 0.10480 3152 0.10931 0.10271 0.0011 ***

No 4830 0.13388 0.13581 4830 0.13300 0.13628 0.0009 **

Total 7982 0.12326 0.12230 7982 0.12226 0.12142 0.0010 ***

First Loan?

Yes 7830 0.12377 0.12280 7830 0.12277 0.12186 0.0010 ***

No 152 0.09740 0.09337 152 0.09621 0.09199 0.0012

Total 7982 0.12326 0.12230 7982 0.12226 0.12142 0.0010 ***

54

Table 9 Descriptive Statistics Concerning Loan and Borrower Characteristics of Borrowers in a Lending Group Sorted by whether the Group Leader has Observable Profit Motive

This table reports summary statistics of the full sample of loans initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into three groups: 1) all borrowers who belong to a group; 2) borrowers that are members of a group where the leader takes a percentage of the loan fee (profit motive); and 3) borrowers that belong to a group without a profit motive. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Total Group Profit Motive No Group Profit Motive

Count Mean Median Count Mean Median Count Mean Median Diff in

Obs Spread Spread Obs Spread Spread Obs Spread Spread Mean

Loan Amount

1st quartile $0 - 2550 2080 0.14192 0.14730 1408 0.14333 0.14750 672 0.13896 0.14690 0.0044

2nd quartile $2551 to 4600 1928 0.15040 0.15690 1394 0.15193 0.15760 534 0.14641 0.14750 0.0055

3rd quartile $4601 to 8000 1975 0.12820 0.12470 1485 0.13037 0.12730 490 0.12163 0.11735 0.0087 ***

4th quartile $8001 + 1999 0.11931 0.11180 1593 0.12091 0.11260 406 0.11302 0.10550 0.0079 ***

Total 7982 0.13491 0.13210 5880 0.13602 0.13260 2102 0.13180 0.12710 0.0042 **

Credit Score

760+ 627 0.04469 0.03710 401 0.04859 0.04190 226 0.03778 0.03075 0.0108 ***

720 - 759 645 0.06471 0.05960 486 0.06766 0.06430 159 0.05567 0.04860 0.0120 ***

680 - 719 901 0.09168 0.08500 701 0.09410 0.08730 200 0.08321 0.07965 0.0109 ***

640 - 679 1400 0.11810 0.11025 1127 0.12037 0.11240 273 0.10873 0.10250 0.0116 ***

600 - 639 1497 0.14528 0.14250 1162 0.14690 0.14305 335 0.13967 0.13720 0.0072 ***

560 - 599 1259 0.18462 0.18750 885 0.18598 0.18750 374 0.18138 0.18760 0.0046 *

520 - 559 1653 0.18708 0.19580 1118 0.18831 0.19230 535 0.18451 0.19740 0.0038

Total 7982 0.13491 0.13210 5880 0.13602 0.13260 2102 0.13180 0.12710 0.0042 **

Borrower Age

1st quartile 0 - 27 1235 0.14058 0.14080 892 0.14445 0.14695 343 0.13051 0.12740 0.0139 ***

2nd quartile 28 - 31 1861 0.13780 0.13510 1329 0.13737 0.13430 532 0.13885 0.13695 -0.0015

3rd quartile 32 - 36 2497 0.13654 0.13540 1872 0.13652 0.13510 625 0.13658 0.13650 -0.0001

4th quartile 37 + 2362 0.12782 0.12225 1765 0.12996 0.12660 597 0.12148 0.11730 0.0085 ***

Total 7955 0.13487 0.13190 5858 0.13595 0.13260 2097 0.13186 0.12710 0.0041 **

Is Borrower Homeowner?

Yes 3152 0.11734 0.11050 2426 0.12001 0.11465 726 0.10842 0.09770 0.0116 ***

No 4830 0.14638 0.14740 3454 0.14727 0.14750 1376 0.14414 0.14690 0.0031

Total 7982 0.13491 0.13210 5880 0.13602 0.13260 2102 0.13180 0.12710 0.0042 **

First Loan?

Yes 7830 0.13540 0.13250 5767 0.13642 0.13370 2063 0.13253 0.12740 0.0039 **

No 152 0.10989 0.09805 113 0.11569 0.10200 39 0.09311 0.08230 0.0226 **

Total 7982 0.13491 0.13210 5880 0.13602 0.13260 2102 0.13180 0.12710 0.0042 **

55

Table 10 Descriptive Statistics Concerning Weighted Average of Interest Rate

Bids for Insiders vs. Outsiders for Loans to Borrowers who are Members of Groups that have Group Leader with Profit Motive

This table reports summary statistics of loans to borrowers that are members of a lending group. The loans were initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into two groups: 1) bids from insiders, meaning lenders who are members of the same group as the borrower, and 2) bids from outsiders, meaning bids from everyone else. The differences between the means of these two groups are reported, but significance cannot be tested since the bids from the two groups are on the same loan record. The profit motive refers only to the group leader, who makes the private information decisions. All investors are assumed to have a profit motive. Statistical significance is tested using pairwise t-tests of differences. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Insider Bidders Outsider Bidders

Count Mean Median Count Mean Median Diff in

Obs Spread Spread Obs Spread Spread Mean

Loan Amount

1st quartile $0 - 2550 1408 0.12600 0.13736 1408 0.12533 0.13957 0.0007

2nd quartile $2551 to 4600 1394 0.13656 0.14825 1394 0.13583 0.14694 0.0007

3rd quartile $4601 to 8000 1485 0.12497 0.12619 1485 0.12452 0.12420 0.0004

4th quartile $8001 + 1593 0.11962 0.11507 1593 0.11849 0.11424 0.0011 **

Total 5880 0.12513 0.12515 5880 0.12433 0.12418 0.0008 **

Credit Score

760+ 401 0.05436 0.04955 401 0.05413 0.05133 0.0002

720 - 759 486 0.07833 0.07695 486 0.07684 0.07659 0.0015 *

680 - 719 701 0.09323 0.08726 701 0.09238 0.08688 0.0008

640 - 679 1127 0.12226 0.11787 1127 0.12017 0.11729 0.0021 ***

600 - 639 1162 0.14884 0.14740 1162 0.14831 0.14787 0.0005

560 - 599 885 0.17849 0.17755 885 0.17847 0.17885 0.0000

520 - 559 1118 0.18372 0.18695 1118 0.18423 0.18928 -0.0005

Total 5880 0.12513 0.12515 5880 0.12433 0.12418 0.0008 **

Borrower Age

1st quartile 0 - 27 892 0.12885 0.13145 892 0.12743 0.12630 0.0014

2nd quartile 28 - 31 1329 0.12534 0.12644 1329 0.12438 0.12466 0.0010

3rd quartile 32 - 36 1872 0.12568 0.12540 1872 0.12572 0.12638 0.0000

4th quartile 37 + 1765 0.12245 0.12043 1765 0.12109 0.11801 0.0014 **

Total 5858 0.12503 0.12496 5858 0.12422 0.12407 0.0008 **

Is Borrower Homeowner?

Yes 2426 0.11453 0.11003 2426 0.11350 0.10710 0.0010 **

No 3454 0.13450 0.13730 3454 0.13390 0.13721 0.0006

Total 5880 0.12513 0.12515 5880 0.12433 0.12418 0.0008 **

First Loan?

Yes 5767 0.12561 0.12590 5767 0.12484 0.12552 0.0008 **

No 113 0.09993 0.09337 113 0.09745 0.09199 0.0025

Total 5880 0.12513 0.12515 5880 0.12433 0.12418 0.0008 **

56

Table 11 Descriptive Statistics Concerning Weighted Average of Interest Rate

Bids for Insiders vs. Outsiders for Loans to Borrowers who are Members of Groups without a Profit Motive

This table reports summary statistics of loans to borrowers that are members of a lending group. The loans were initiated between May 31, 2006 and November 6, 2007. It also includes performance data for these same loans through May 7, 2008. Summary results are broken down into two groups: 1) bids from insiders, meaning lenders who are members of the same group as the borrower, and 2) bids from outsiders, meaning bids from everyone else. The differences between the means of these two groups are reported, but significance cannot be tested since the bids from the two groups are on the same loan record. The profit motive refers only to the group leader, who makes the private information decisions. All investors are assumed to have a profit motive. Statistical significance is tested using pairwise t-tests of differences. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

Insider Bidders Outsider Bidders

Count Mean Median Count Mean Median Diff in

Obs Spread Spread Obs Spread Spread Mean

Loan Amount

1st quartile $0 - 2550 672 0.11894 0.11765 672 0.11669 0.11750 0.0023 *

2nd quartile $2551 to 4600 534 0.12840 0.13008 534 0.12911 0.13297 -0.0007

3rd quartile $4601 to 8000 490 0.11296 0.10305 490 0.11084 0.10114 0.0021 **

4th quartile $8001 + 406 0.10922 0.10208 406 0.10611 0.10092 0.0031 ***

Total 2102 0.11729 0.11471 2102 0.11564 0.11334 0.0017 ***

Credit Score

760+ 226 0.04398 0.03892 226 0.04375 0.03864 0.0002

720 - 759 159 0.06193 0.05558 159 0.06044 0.05501 0.0015

680 - 719 200 0.08929 0.08363 200 0.08682 0.08417 0.0025 *

640 - 679 273 0.10846 0.10146 273 0.10773 0.10117 0.0007

600 - 639 335 0.14377 0.14055 335 0.14155 0.14004 0.0022

560 - 599 374 0.17550 0.17875 374 0.17587 0.17794 -0.0004

520 - 559 535 0.18189 0.18760 535 0.17797 0.18202 0.0039 ***

Total 2102 0.11729 0.11471 2102 0.11564 0.11334 0.0017 ***

Borrower Age

1st quartile 0 - 27 343 0.11798 0.10760 343 0.11935 0.12355 -0.0014

2nd quartile 28 - 31 532 0.11489 0.10365 532 0.11393 0.10296 0.0010

3rd quartile 32 - 36 625 0.12174 0.12417 625 0.11985 0.12047 0.0019 *

4th quartile 37 + 597 0.11442 0.10770 597 0.11114 0.10561 0.0033 ***

Total 2097 0.11729 0.11471 2097 0.11564 0.11334 0.0017 ***

Is Borrower Homeowner?

Yes 726 0.09521 0.09015 726 0.09363 0.08999 0.0016 *

No 1376 0.13214 0.13230 1376 0.13045 0.13118 0.0017 **

Total 2102 0.11729 0.11471 2102 0.11564 0.11334 0.0017 ***

First Loan?

Yes 2063 0.11787 0.11513 2063 0.11613 0.11413 0.0017 ***

No 39 0.09005 0.08144 39 0.09264 0.08306 -0.0026

Total 2102 0.11729 0.11471 2102 0.11564 0.11334 0.0017 ***

57

Table 12 Determinants of Loan Default

This table reports marginal effects from probit analyses with DEFAULTIND as the dependent variable indicating the loan is in default status as of May 7, 2008. The loans were initiated between May 31, 2006 and November 6, 2007 and the data includes performance information for these same loans through May 7, 2008. z-scores from the marginal effects analyses are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

(1) (2) (3) (4) (5) (6) (7)

QUICKFUNDING 0.021*** 0.034*** 0.033*** 0.029*** 0.031***

(5.576) (5.560) (5.357) (4.813) (5.108)

LNDATEBIDCOUNT -0.040*** -0.042*** -0.041*** -0.040*** -0.043***

(-13.362) (-9.456) (-8.973) (-8.855) (-9.499)

LNSCORE -0.066*** -0.088*** -0.089*** -0.103*** -0.099*** -0.086*** -0.086***

(-23.308) (-18.144) (-18.443) (-20.745) (-20.572) (-18.155) (-18.177)

LNBORROWERAGE -0.025*** -0.045*** -0.046*** -0.043*** -0.043***

(-2.869) (-3.394) (-3.413) (-3.325) (-3.298)

ENDORSEMENTSIND -0.029*** -0.045*** -0.043*** -0.034*** -0.033***

(-8.719) (-8.340) (-7.893) (-6.150) (-6.130)

HOMEOWNERIND -0.000 -0.010* -0.010* -0.008 -0.008

(-0.059) (-1.830) (-1.836) (-1.560) (-1.592)

LNAMT 0.022*** 0.022*** 0.011*** 0.016*** 0.022*** 0.023***

(5.969) (5.817) (2.714) (4.053) (6.167) (6.286)

FORPROFIT 0.035*** 0.063*** 0.049*** 0.024*

(2.695) (4.856) (4.184) (1.952)

LNGRPLOANS 0.004** 0.011*** 0.025*** 0.017*** 0.007** 0.001

(2.242) (3.839) (7.587) (4.936) (2.473) (0.303)

FORPROFIT * LNGRPLOANS -0.009** -0.022*** -0.017*** -0.005*

(-2.507) (-5.645) (-4.542) (-1.645)

GDSTATE 0.001 0.005 0.016

(0.024) (0.122) (0.376)

LISTREV -0.044*** -0.013** -0.013**

(-7.319) (-2.303) (-2.311)

RATIOBIDBYGROUP -0.139** -0.130**

(-2.340)

GLREWARDS -0.022

(-0.848)

GLREWARDS * LNGROUPLOANS 0.008

(1.456)

GRP_RELATIONSHIPS -0.028***

(-4.494)

N 13429 7955 7955 7982 7705 7680 7680

Pseudo R^2 0.196 0.203 0.201 0.143 0.152 0.203 0.203

58

Table 13 Determinants of Loan Interest Rate Spread

This table reports ordinary least squares analyses with SPREAD as the dependent variable, indicating the difference between the interest rate on the loan and the Fed Funds rate. The loans were initiated between May 31, 2006 and November 6, 2007 and the data includes performance information for these same loans through May 7, 2008. Column (1) is restricted to non-group loans only. t-statistics from the regressions are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

(1) (2) (3) (4) (5) (6) (7)

QUICKFUNDING 0.041*** 0.036*** 0.030*** 0.030***

(33.727) (43.983) (29.164) (29.149)

LNDATEBIDCOUNT -0.003** 0.001 0.000 0.000

(-2.207) (0.984) (0.493) (0.508)

LNSCORE -0.080*** -0.070*** -0.083*** -0.080*** -0.080***

(-68.195) (-107.304) (-97.765) (-95.716) (-95.709)

LNBORROWERAGE 0.030*** 0.027*** 0.020*** 0.020***

(10.359) (13.487) (8.507) (8.509)

ENDORSEMENTSIND 0.001 0.001 -0.001 -0.001

(0.994) (1.369) (-1.380) (-1.341)

HOMEOWNERIND -0.001 -0.002*** -0.007*** -0.007***

(-1.009) (-3.079) (-6.803) (-6.807)

FORPROFIT 0.028*** 0.027*** 0.018*** 0.018***

(7.956) (9.795) (6.840) (6.828)

LNGRPLOANS 0.003*** 0.004*** 0.003*** 0.003***

(4.368) (7.726) (6.441) (6.444)

FORPROFIT * LNGRPLOANS -0.003*** -0.005*** -0.003*** -0.003***

(-3.378) (-8.129) (-5.001) (-5.003)

DEFPROB 0.334*** 0.372***

(56.308) (75.387)

GDSTATE 0.002 -0.007 -0.007

(0.361) (-1.170) (-1.181)

LISTREV -0.009*** -0.005*** -0.005***

(-9.305) (-4.895) (-4.894)

LNAMT 0.022*** 0.023*** 0.023***

(33.430) (36.533) (36.322)

RATIOBIDBYGROUP -0.103*** -0.107***

(-12.338) (-6.031)

PIR2 0.005

(0.256)

Intercept 0.125*** 0.099*** 0.083*** 0.103*** 0.016** -0.061*** -0.061***

(8.549) (10.689) (28.675) (173.975) (2.065) (-4.777) (-4.766)

N 5474 13429 7955 13429 7705 7680 7680

R^2 0.626 0.575 0.305 0.297 0.574 0.632 0.632

59

Table 14 Impact of Loan and Borrower Risk Characteristics on the Difference

Between Private and Public Bids

This table reports ordinary least squares analyses with DIFF_SPREAD as the dependent variable, indicating the difference between the weighted average of insider (private) bid interest rates and the weighted average of outsider (public) bid interest rates. The loans were initiated between May 31, 2006 and November 6, 2007 and the data includes performance information for these same loans through May 7, 2008. t-statistics from the regressions are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

(1) (2) (3) (4) (5) (6) (7)

QUICKFUNDING -0.000 -0.001 -0.002 -0.002 -0.002

(-0.059) (-0.223) (-0.295) (-0.307) (-0.216)

LNDATEBIDCOUNT 0.000 -0.000 0.000 0.000 0.002**

(0.325) (-0.029) (0.439) (0.101) (1.976)

LNSCORE 0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.002**

(0.333) (-0.796) (-0.618) (-0.466) (-0.473) (-0.541) (-1.984)

LNBORROWERAGE 0.002 0.002 0.002 0.002 0.007**

(1.245) (1.188) (1.250) (1.260) (2.408)

ENDORSEMENTSIND 0.001 -0.000 -0.000 -0.000 -0.001

(1.587) (-0.278) (-0.316) (-0.479) (-0.826)

HOMEOWNERIND -0.000 -0.000 -0.000 -0.000 -0.001

(-0.045) (-0.112) (-0.069) (-0.135) (-0.428)

LNAMT 0.001 0.001 0.001 0.001 0.001 0.001

(1.495) (1.318) (1.462) (1.421) (1.470) (1.220)

FORPROFIT 0.000 0.004* 0.004* 0.004* 0.005**

(0.266) (1.860) (1.716) (1.811) (2.050)

LNGRPLOANS -0.001*** -0.000 -0.000 -0.001* -0.001

(-4.568) (-0.865) (-1.048) (-1.842) (-1.635)

FORPROFIT * LNGRPLOANS -0.001* -0.001* -0.001* -0.001*

(-1.872) (-1.748) (-1.722) (-1.849)

GDSTATE 0.020*** 0.020*** 0.002

(3.309) (3.344) (0.170)

LISTREV 0.000 0.000 -0.000

(0.010) (0.066) (-0.103)

RATIOBIDBYGROUP 0.007 -0.004

(1.308) (-0.688)

GRP_RELATIONSHIPS -0.000

(-0.200)

Constant -0.009 -0.005 -0.011 -0.001 -0.018*** -0.027*** -0.049***

(-1.134) (-0.633) (-1.226) (-0.385) (-2.878) (-2.722) (-2.909)

N 2012 2012 2012 2016 2016 2012 480

R^2 0.003 0.015 0.016 0.015 0.021 0.023 0.033

60

Table 15 Effects of Insider Soft Information on Interest Rate Spread

This table reports tobit regressions for 13,429 peer-to-peer loans initiated between May 31, 2006 and November 6, 2007. Performance data for these same loans is included through May 7, 2008. Panel A represents the orthogonization utilized to produce the PIR residuals used in Panel B. In Panel A, the dependent variable is RATIOBIDBYGROUP, the ratio of internal bids to external bids for each loan. For PIR1, the independent variables are external credit score, arms-length dummy, and the interaction between arms-length dummy and the external credit score. PIR2 includes all other variables that may have an effect on the internal opinion of group members on the relative value of the loan application. In Panel B, rate spread (the difference between borrower interest rate and the fed funds rate) is the dependent variable, and it is censured at zero. The independent variable PIR represents the private information residual from the orthogonalization of public credit score against a proxy for internal credit score. p-values are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

PIR1 PIR2

SCORE -0.001*** -0.000

(-4.537) (-0.959)

LNAMT -0.004***

(-5.221)

INCOME 0.000

(1.319)

QUICKFUNDING -0.003**

(-2.471)

LNDATEBIDCOUNT 0.003**

(1.965)

GLREWARDS -0.026***

(-12.168)

GRP_RELATIONSHIPS 0.007***

(4.156)

LISTREV -0.000

(-0.324)

LNGRPLOANS -0.001

(-1.484)

HOMEOWNERIND -0.002

(-1.547)

ENDORSEMENTSIND 0.009***

(7.136)

ARMSLENGTH -0.003** -0.025

(-2.402) (-0.542)

A/L * SCORE 0.001*** 0.000

(3.044) (0.160)

A/L * AMOUNT 0.004

(1.492)

A/L * INCOME -0.001

(-0.451)

A/L * QUICKFUNDING 0.003

(0.698)

A/L * LNDATEBIDCOUNT -0.002

(-0.450)

A/L * GLREWARDS 0.026***

(4.248)

A/L * LISTREV 0.001

(0.275)

A/L * LNGROUPLOANS 0.001

(0.146)

A/L * HOMEOWNER 0.003

(0.652)

A/L * ENDORSEMENTS -0.009*

(-1.861)

Intercept 0.003*** 0.020

(3.976) (1.588)

Number of Observations 13465 7982.000

R^2 0.002 0.037

PANEL A: Orthogonalization

61

Table 15 – continued

(1) (2) (3) (4) (5) (6)

LNAMT 0.020*** 0.020*** 0.019*** 0.019*** 0.021*** 0.021***

(48.954) (48.733) (47.526) (47.231) (37.968) (38.240)

SCORE -0.028*** -0.028*** -0.028*** -0.028*** -0.028*** -0.028***

(-151.121) (-151.955) (-152.149) (-152.987) (-108.180) (-108.907)

QUICKFUNDING 0.034*** 0.034*** 0.033*** 0.033*** 0.029*** 0.029***

(50.530) (50.466) (49.338) (49.335) (30.858) (31.068)

LNBORROWERAGE 0.001*** 0.001***

(12.341) (12.202)

INCOME 0.001*** 0.001*** 0.001*** 0.001***

(3.386) (3.637) (3.432) (3.447)

LNDATEBIDCOUNT -0.004*** -0.004*** -0.004*** -0.004***

(-5.112) (-4.969) (-3.891) (-3.923)

GLREWARDS 0.024*** 0.024***

(15.431) (15.545)

LISTREV -0.005*** -0.005***

(-5.940) (-5.989)

LNGRPLOANS 0.000 0.000

(0.447) (0.460)

HOMEOWNERIND -0.002** -0.002**

(-2.240) (-2.264)

ENDORSEMENTSIND -0.005*** -0.005***

(-4.970) (-4.989)

GRP_RELATIONSHIPS -0.009*** -0.009***

(-7.341) (-7.378)

PIR1 -0.114*** -0.112***

(-13.138) (-13.072)

PIR2 -0.090***

(-10.355)

Constant 0.055*** 0.056*** 0.072*** 0.073*** 0.073*** 0.073***

(16.770) (17.384) (10.011) (10.211) (7.939) (7.984)

N 13465 13465 13429 13429 7982 7982

R^2 -0.449 -0.454 -0.456 -0.461 -0.424 -0.429

PANEL B: Effect of PIR on Spread

62

Table 16

Descriptive Statistics Concerning the Difference between First and Subsequent Loans for the Weighted Average of

Interest Rate Bids of Insiders vs. Outsiders for Loans to Borrowers who are Members of Groups

This table reports summary statistics of loans to borrowers that are members of a lending group. The loans were initiated between May 31, 2006 and November 6, 2007. It also includes

performance data for these same loans through May 7, 2008. Summary results are broken down into two groups: 1) bids from insiders, meaning lenders who are members of the same

group as the borrower, and 2) bids from outsiders, meaning bids from everyone else. Statistical significance is tested using pairwise t-tests of differences. *, **, and *** indicate

significance at the 10%, 5%, and 1% levels respectively. For subsequent peer-to-peer loans, none of the differences are significant due to the low number of observations.

First Peer-to-Peer Loan

Subsequent Peer-to-Peer Loans

Insider Bidders

Outsider Bidders

Insider Bidders

Outsider Bidders

Count

Mean

Median

Mean

Median

Diff in

CountMean

Median

Mean

Median

Diff in

Diff in

Obs

Spread

Spread

Spread

Spread

Mean

Obs

Spread

Spread

Spread

Spread

Mean

Diff

Loan Amount

1st quartile $0 - 2550

2057

0.12400

0.13167

0.12230

0.13320

0.00170

23

0.09887

0.10267

0.09246

0.09610

0.00641

-0.00471

2nd quartile $2551 to 4600

1907

0.13400

0.14530

0.13281

0.14275

0.00119

21

0.05855

0.05855

0.06015

0.06015

-0.00160

0.00279

3rd quartile $4601 to 8000

1936

0.12234

0.12030

0.12105

0.12116

0.00129

39

0.08255

0.08029

0.08307

0.08629

-0.00052

0.00181

4th quartile $8001 +

1930

0.11791

0.11327

0.11608

0.11218

0.00183***

69

0.10379

0.10490

0.10329

0.10283

0.00050

0.00133

Total

7830

0.12343

0.12245

0.12190

0.12142

0.00153***

152

0.09661

0.09213

0.09582

0.09171

0.00079

0.00074

Credit Score

760+

603

0.05051

0.04355

0.04999

0.04504

0.00052

24

0.05036

0.05058

0.05063

0.04772

-0.00027

0.00079

720 - 759

634

0.07431

0.07180

0.07284

0.07328

0.00147**

11

0.06531

0.04885

0.06122

0.05137

0.00409

-0.00262

680 - 719

877

0.09276

0.08750

0.09093

0.08708

0.00183*

24

0.06754

0.06880

0.07136

0.06528

-0.00382

0.00565

640 - 679

1358

0.11950

0.11750

0.11775

0.11468

0.00175**

42

0.11453

0.10733

0.11201

0.10741

0.00252

-0.00077

600 - 639

1473

0.14748

0.14715

0.14575

0.14579

0.00173

24

0.14663

0.13840

0.14924

0.14258

-0.00261

0.00434

560 - 599

1245

0.17736

0.17760

0.17688

0.17779

0.00048

14

0.15750

0.15750

0.14610

0.14610

0.01140

-0.01092

520 - 559

1640

0.18299

0.18730

0.18065

0.18445

0.00234

13

0.15730

0.15730

0.14484

0.14484

0.01246

-0.01012

Total

7830

0.12343

0.12245

0.12190

0.12142

0.00153***

152

0.09661

0.09213

0.09582

0.09171

0.00079

0.00074

Borrower Age

1st quartile 0 - 27

1207

0.12669

0.12761

0.12506

0.12468

0.00163

28

0.09073

0.09069

0.09141

0.09112

-0.00068

0.00231

2nd quartile 28 - 31

1824

0.12366

0.11842

0.12197

0.12029

0.00169*

37

0.09301

0.08320

0.09519

0.08981

-0.00218

0.00387

3rd quartile 32 - 36

2454

0.12478

0.12535

0.12372

0.12463

0.00106

43

0.11309

0.12263

0.11007

0.11232

0.00302

-0.00196

4th quartile 37 +

2318

0.12017

0.11760

0.11829

0.11750

0.00188***

44

0.08781

0.08632

0.08392

0.08615

0.00389

-0.00201

Total

7803

0.12335

0.12230

0.12181

0.12122

0.00154***

152

0.09661

0.09213

0.09582

0.09171

0.00079

0.00075

Is Borrower Homeowner?

Yes

3071

0.11035

0.10490

0.10901

0.10265

0.00134***

81

0.09461

0.07750

0.09262

0.08642

0.00199

-0.00065

No

4759

0.13416

0.13670

0.13248

0.13621

0.00168**

71

0.09948

0.09789

0.10042

0.09725

-0.00094

0.00262

Total

7830

0.12343

0.12245

0.12190

0.12142

0.00153***

152

0.09661

0.09213

0.09582

0.09171

0.00079

0.00074

63

Table 17 Impact of Loan and Borrower Risk Characteristics

on the Difference between First and Subsequent Loans for the Weighted Average of Interest Rate Bids of Insiders vs. Outsiders for Loans

to Borrowers who are Members of Groups

This table reports ordinary least squares analyses with the dependent variable defined as the difference between DIFF_SPREAD for the first loan and DIFF_SPREAD for the second loan. The loans were initiated between May 31, 2006 and November 6, 2007 and the data includes performance information for these same loans through May 7, 2008. t-statistics from the regressions are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels respectively.

(1) (2) (3) (4) (5)

QUICKFUNDING -0.006 -0.003 -0.004

(-0.849) (-0.317) (-0.398)

LNDATEBIDCOUNT 0.005 0.002 0.003

(0.521) (0.176) (0.202)

LNSCORE 0.006 0.014* 0.014* 0.012 0.013

(1.057) (1.676) (1.737) (1.552) (1.571)

LNBORROWERAGE -0.004 -0.018 -0.019

(-0.246) (-0.896) (-0.915)

ENDORSEMENTSIND -0.002 -0.000 -0.001

(-0.344) (-0.004) (-0.097)

HOMEOWNERIND -0.004 -0.004 -0.004

(-0.707) (-0.481) (-0.503)

LNAMT -0.011** -0.011** -0.011** -0.010*

(-1.982) (-2.024) (-2.116) (-1.905)

FORPROFIT 0.003 0.018 0.016 0.015

(0.287) (0.830) (0.740) (0.706)

LNGRPLOANS -0.002 0.001 0.001 -0.000

(-0.688) (0.296) (0.151) (-0.003)

FORPROFIT * LNGRPLOANS -0.004 -0.003 -0.003

(-0.786) (-0.689) (-0.677)

GDSTATE 0.045

(0.961)

LISTREV 0.005

(0.594)

Constant -0.033 0.127 0.118 0.077* 0.027

(-0.326) (0.938) (0.866) (1.720) (0.413)

N 278 147 147 147 146

R^2 0.011 0.049 0.054 .043 0.051