Consumer Credit Without Collateral,

Regulation, or Intermediaries∗

Filipe Correia † Antonio Martins ‡ Anthony Waikel §

March 5, 2024

Abstract

Using novel data from an online informal credit market, we investigate how consumer

credit unfolds without a fnancial system. We fnd borrowers display high rates of

default and face high credit prices. A minority of consistently successful lenders gain a

disproportionately large and proftable market share, while the average lender realizes

losses. High-skill lenders achieve better loan outcomes and provide more lenient loan

terms. Loans are more likely to be funded and repaid when acquiring information about

borrowers is easier for lenders. These fndings highlight the role of intermediaries in

consumer credit: they bring skill and internalize information acquisition.

Keywords: Consumer Credit, Financial Intermediation, Household Finance, Informal

Credit

JEL Classifcation: O33, G20, G51

∗We thank Marco Bonomo, Isaac Hacamo, Pekka Honkanen, Jef Netter, Annette Poulsen, Bryan Routledge, Rik Sen, Leigha Waikel for valuable comments. Anthony Waikel thanks the University of Georgia

Graduate College for a Summer Grant utilized in this project. All errors are our own.

†Corresponding Author. Terry College of Business, University of Georgia, 600 S Lumpkin St, Athens,

GA 30605, flipe.correia@uga.edu

‡Lisbon School of Economics and Management, Research Unit on Complexity and Economics

(UECE), Research in Economics and Mathematics (REM). Rua do Quelhas 6, 1200-781 Lisboa, acmartins@iseg.ulisboa.pt

§Terry College of Business, University of Georgia, Athens, GA, 30605, waikela@uga.edu
1 Introduction

In the United States, 4.5% of households are unbanked, with the primary reason being

“[not] hav[ing] enough money to meet minimum balance requirements” (FDIC 2022). The

lack of access to credit and considerable liquidity constraints lead these households to search

for alternative forms of consumer credit, outside the fnancial intermediation ecosystem.

The evolving debate on the role of fnancial intermediaries1 is usually centered around

frm fnancing, rather than consumer credit (e.g., Fang, Ivashina and Lerner (2015); Harris,

Jenkinson, Kaplan and Stucke (2018); Crawford, Pavanini and Schivardi (2018); Duygun,

Hashem and Tanda (2021)) and it is split into two views. On one hand, fnancial institutions extract rents via spreads and fees, have their own set of incentives, or even engage

in predatory practices. On the other hand, fnancial intermediation lowers transaction and

search costs, alleviates information asymmetry, and efciently pools and allocates capital.

We have a rich and vast literature on intermediated consumer credit, but little is known

about how it would function in the absence of intermediaries. We study the role of intermediaries in consumer credit, by directly observing a consumer credit market, operating

without intermediation.

We contribute to the debate in the context of consumer credit markets, using novel data

from an online informal credit market, operating on a Reddit forum2. Informal credit is

defned as “loans that rely on personal relationships or social sanctions as means of enforcement” (Karaivanov and Kessler 2018). These loans may be obtained from family, friends,

neighbors, or strangers with no intermediary between lender and borrower. Whether informal

loans have positive real efects for borrowers is very unclear (Banerjee, Karlan and Zinmin

2015; Angelucci, Karlan and Zinmin 2015), mostly because the inner workings of informal

1The debate dates back to Pyle (1971); Allen and Santomero (1997); Allen and Gale (1999); Scholtens

and Van Wensveen (2000), and has consistently called for more research throughout the decades, being still

the focus of the latest Nobel Lecture by Diamond (2023)

2Correia, Martins and Waikel (2022) describe the dataset in detail, and study this market from the point

of view of market efciency and fnd that loan terms refect the economic context of borrower and lenders,

with loan interest rate and requested amounts increasing after a local COVID-19 lockdown was implemented.

1
credit have not been the target object of study. The nature of non-intermediated loans

makes it very difcult to collect data that allows for in-depth studies of the informal credit

market, despite its widespread use among consumers. Our hand-collected dataset contains

information about 95,000 loans (26 million USD, 2016-2021) between about 3,000 unique

lenders and 15,000 unique borrowers, who transact in the absence of a fnancial system to

intermediate, regulate, and collateralize credit.

With surface-level descriptive evidence, we show that borrowers display high rates of

default and face high credit prices when compared to intermediated and collateralized subprime credit, such as payday loans. The absence of collateral is key, as collateral mitigates

adverse selection and moral hazard (Ioannidou, Pavanini and Peng 2022). In this market,

borrowers announce the quantity and price they want to borrow, and lenders decide whether

to fund. Thus, adverse selection is prevalent in this market, as borrowers willing to pay

such a high rate are announcing their known higher credit risk (Kawai, Onishi and Uetake

2022). Additionally, a small group of sophisticated lenders occupies a disproportionately

large and very proftable market share, with low default rates, leaving out unproftable and

riskier loans for unsophisticated lenders. This suggests that some attributes of the lender,

information of the borrower, or both determine lender choices. A survey of 100 participants

in this market shows lenders to be more fnancially sophisticated than borrowers, on average,

and a positive relationship between fnancial literacy scores and portfolio proftability.

We build a stylized framework to understand borrowers’ and lenders’ decision-making in

this market. We model decision-making in three steps, very similar to Kawai, Onishi and

Uetake (2022) but diferent from the Prosper setting, where lenders bid. First, the borrower

chooses whether to post a loan request promising a given interest rate. This key feature

matches our empirical setting. Second, given that rate, the lender chooses whether to fund

the loan. Lastly, the borrower chooses whether to repay the loan, based on whether that

repayment earns them enough reputation to allow them to borrow in the future. This feature

also matches our empirical setting, where reputation is the only device that incentivizes

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borrowers to repay in informal credit settings (Karaivanov and Kessler 2018; Xin 2023).

We assume lender unit costs of origination decrease with acquired skills, like in Besanko,

Doraszelski and Kryukov (2014), where marginal cost decreases with the stock of knowhow. The reputation gain from loan repayment decreases the lenders’ assessment of the

proportion of high-risk borrowers in the market. This reputation gain is a refection of the

lender’s ability to learn and infer from the borrower’s repayment. Our setting generates

two predictions. First, higher lender experience/skills should result in higher loan funding

likelihood, loans with a lower default rate, and lower prices. Second, less opacity between

borrowers and lenders should result in lower default incidence and higher funding likelihood.

With our dataset, we observe loan requests posted by borrowers, containing requested

amounts, promised repayment amounts, and maturity. We can use these three quantities

to compute a promised monthly interest rate. Additionally, we can match loan requests to

actual loans and obtain information on whether the request is funded (i.e., converted into

a loan), its repayment status, repayment date, and efective interest earned. We measure

lenders’ acquired skill by the number of past loans the lender funded at the date of each loan.

While the measure of acquired skill is complicated by a wide dispersion of lender activity

and endogeneity, we show results hold when changing the acquired skill measure and we use

an Instrumental Variable approach to address endogeneity issues.

Consistent with our hypotheses, we fnd that more skilled lenders are more likely to fund

a loan, face lower default, and choose loans with more lenient interest rates. This result

suggests they understand moral hazard risks and are more diversifed, allowing them to take

on more loans and handle losses. This fnding speaks to the economic importance of fnancial

intermediation in lowering search and screening costs per dollar originated. More skilled

lenders develop tools and procedures to screen borrowers at a lower cost. Intermediaries also

beneft from diversifcation via their larger funding capacity. Likewise, skilled lenders given

past successes in lending, gather larger and more diversifed portfolios.

This is also consistent with lenders learning how to screen soft information. Soft infor3
mation contained in loan funding requests enhances funding likelihood, but loans selected

based on this information underperform (Dorfeitner, Priberny, Schuster, Stoiber, Weber,

de Castro and Kammler 2016; Herzenstein, Sonenshein, Dholakia, Stoiber and Weber 2011).

There is, however, information content in narratives. Iyer, Khwaja, Luttmer and Shue (2015)

fnd that lenders achieve 87% of the predictive power of an econometrician who observes all

standard fnancial information about borrowers, and that screening on soft or nonstandard

information is relatively more important when evaluating lower-quality borrowers.

Additionally, we test the hypothesis that borrower-lender connections increase the funding likelihood for a loan request. We extend our data to measure the overlap in social

footprints of each pair of borrowers and lenders. We fnd that loans are more likely to be

funded by lenders who have more in common with the borrower. Connectedness also increases the probability that the borrower repays the loan, in line with Lin, Nagpurnanand

and Viswanathan (2012) who fnd that consumers on marketplace lending platforms with

more online friendships are associated with lower default rates. Similarly, Xin (2023) fnds

that reputation-based systems disincentivize defaults and expand credit access to borrowers.

This is consistent with the reputation gain being larger for borrower-lender pairs who

are more connected. This result holds for lenders of all skill levels, but loan terms do

not vary with connection level, also consistent with our stylized framework. Our fndings

shed light on information asymmetry in credit markets and the importance of established

lending relationships to mitigate them (Karolyi 2017). Without a fnancial system to provide

credit scores and identity verifcation, the only mechanism that enforces loan repayment is

reputation. Therefore lenders need to be able to relate and gain familiarity with borrowers,

to accurately measure their reputation and form their beliefs.

Much of the literature uses geographic proximity as a proxy for connectedness (Baily, Cao,

Kuchler, Stroebel and Wong 2018; Bayer, Mangum and Roberts 2021), Some studies, such

as Fracassi (2017) and Lin, Nagpurnanand and Viswanathan (2012) use shared education

networks, or online friendships to measure connectedness between two individuals. We add

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to the social connectedness research by measuring connectedness between people using their

observed mutual interests. This has two unique features: (i) geographic location is irrelevant,

and (ii) individuals do not necessarily know they are connected. We obtain user activity over

our entire sample period, generating variation in connectedness in the cross-section and in

the time series.

We also shed light on the importance of platforms and their design in emerging marketplace lending in increasing access to credit for risky borrowers neglected by banks. In these

markets interest rates ofer adequate returns for lenders, on a risk-adjusted basis (Kraussl,

Kraussl, Pollet and Rinne 2022). Berger and Gleisner (2009) fnd that peer-to-peer lending

platforms help reduce information asymmetry. Dinerstein, Einav, Levin and Sundaresan

(2018) document a trade-of in online platform design between reducing search frictions and

intensifying price competition among sellers, while Einav, Farronato, Levin and Sundaresan

(2018) documents a shift from auctions to posted prices in online commerce, both using

eBay data. On the credit side, Franks, Serrano-Velarde and Sussman (2021) use data on

marketplace loans from the UK to show that credit marketplaces also deviated from the

auction platform design, and in turn set prices and allocate credit on their own.

The closest paper to ours is Braggion, Manconi, Pavanini and Zhu (2022), which models

the role of platform and lender decision-making on peer-to-peer platforms moving from

direct peer-to-peer loans towards an intermediated loan portfolio model. Fitting the model

to data from the Chinese platform Renrendai, they fnd that this transition generates lender

surplus, profts for the platform, and enhances credit provision. Without platforms, peerto-peer credit would resemble our setting, with high prices, and a small share of lenders

capturing all the proftable opportunities. The inability to enforce repayment, the lack

of identity verifcation, and the absence of a platform to intermediate and equalize lending

opportunities for lenders are key in this of-bank marketplace. We are the frst to empirically

analyze a pure peer-to-peer (as compared to peer-to-platform-to-peer) market without a

platform conducting intermediation. Our fndings reconcile the transition in peer-to-peer

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lending platforms from individual lenders funding individual loans, and instead obtaining

bank charters3, or partnering with banks to originate loans.

Our unique dataset is one of the largest datasets on credit without intermediaries, collateral, or regulation collected to date. Altogether, our fndings speak to the role of the fnancial

system in consumer credit, by (i) mitigating information asymmetry: with reduced screening

costs, implementing screening systems like credit scores, and building lending relationships

(ii) improving access to afordable credit: by generating stable returns from pooling funds

and sharing risk, improving liquidity and funding capacity, internalizing the credit pricing

process, and implementing mechanisms allowing consumers to post collateral.

The remainder of the paper proceeds as follows. In Section 2 we describe the loan market

and our data collection. In Section 3 we provide descriptive evidence on the market. In Section 4 we build our stylized framework and derive hypotheses. In Section 5 we measure skill

to study how loan outcomes and terms change with lender skills, and Section 6 develops our

measures of user connectedness. We conduct robustness checks of our fndings in Section 7.

Finally, we conclude in Section 8.

2 Online Informal Credit in a Forum

The informal credit community we study has about 128 thousand users, and its sole

purpose is to be an online communication channel on Reddit where borrowers and lenders

fnd each other. The members are identifed by a username, which is very rarely identityrevealing. There are some community rules. In order to be a member of this community, an

account used must be more than 90 days old, and needs to be active in other channels on

the website. Posts requesting a loan are not allowed to be deleted by the borrower, and each

user can post a maximum of once every 24 hours. By clicking a user’s nickname others can

observe the user’s post and comment history in that and other communities. The sequential

protocol of loan origination is: (1) a borrower requests a loan, (2) a lender chooses to fund

3This makes them obey banks’ underwriting criteria (Evans 2019).

6
the loan, and (3) the borrower decides to repay the loan. We explain each step below.

Figure 1: Geographic Distribution of Loan Requests

Note: This fgure shows the geographic distribution of loan requests. On the left panel, we show how loan

requests are distributed around the world. On the right panel, given the predominance of the United States,

we show the state-level geographic distribution across the country. The size of the circles represents the

amount of loan requests in a given geography.

2.1 Borrower Requests a Loan

A potential borrower posts on the forum requesting a loan. They typically list the requested loan amount, promised repayment amount, repayment date, location, and a short

description of the loan purpose. This post requesting a loan has strict formatting requirements, which allows the community to have an automated moderation tool (or bot), for

parsing and checking posts. This bot will automatically delete requests that do not meet

the formatting requirements and requests made by users who do not satisfy the membership standards. Figure 1 shows the geographic distribution of loans. Despite being very

concentrated in the United States, loan requests are widespread across the diferent states.

2.2 Lender Funds a Loan

After a loan request is made, a potential lender will then see the post requesting a loan,

and arrange the loan with the potential borrower. The lender can ask for any information

they deem necessary to vet the borrower, while the borrower is free to choose what information they give or don’t give to the lender. The two parties negotiate privately, and if they

agree to a loan, the lender will comment on the request post stating they gave a loan to the

7
borrower in the required format. The borrower then confrms they obtained a loan from the

lender. From the moment that the two endpoints of the transaction confrm the existence

of the loan, and independently disclose matching loan amounts, the loan is outstanding in a

public ledger.

2.3 Loan Repayment is Due

Once a loan is due, the borrower is expected to repay it. The lender will return to the

forum to report whether the borrower repaid the loan or not. Borrowers are ruled to be

banned from the community in the case of no repayment. There are three main reporting

tools available for participants in this market, funded on a donation basis. First, there is a list

of users that represent scam threats. Users can search this list for a given username, and learn

whether it was verifed as a potential scammer. Second, a lender legitimacy checking tool,

where borrowers can check whether a potential lender is an active lender in the community

and whether they are in good standing. Lastly, there is a credit registry where all the loans

paid, unpaid, and outstanding are disclosed. This can be used to verify the lending history

of lenders, the credit history of borrowers, or even to infer default rates in the market as a

whole.

In this market, there is no incentive for compliance with the rules other than user reputation and the ability to access credit via the community in the future. There is also no

recourse in the event of default, beyond being banned from the community, and perhaps

being reported to the payment processing provider. Since loans do not have any collateral,

we assume a loss of 100% in the case of default. This loss given default is consistent with

anecdotal evidence from posts and comments about unpaid loans and lenders referring to

borrowers “disappearing” or “vanishing”.

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Figure 2: Example loan request

Note: This fgure summarizes an example of a loan request in the online forum. We can observe the username

of the potential borrower, the date and time of the post, the title of the post obeying strict formatting rules,

and the body of the post. In the title, we can identify a fag for loan requests, the loan request amount, the

borrower’s location, and repayment terms.

2.4 Data Collection

Strict rules on post formatting allow us to collect data and parse needed loan information

systematically. An example of a typical loan request on the forum is shown in Figure 2. The

bracketed structure of the post title gives us an indicator of a loan request, “[REQ]”, the

loan requested amount, the borrower’s self-reported location, and ofered repayment terms.

The auto-moderation bot cleans up improperly formatted postings, or posts by users who

do not meet the minimum requirements of account history, or activity in other communities.

We collect all posts from this community and flter for all loan requests. For each request,

we collect the username of the user requesting the loan, the date of the request, the requested

amount, the poster’s location, the promised repayment amount, the promised repayment

date, the system that is used to transfer money (Paypal, Venmo, etc), and the text of the

request. The latter may contain a short narrative about why the loan is needed.

Additionally, we obtain the credit registry database that stores all loans originated in the

market. From this database, we can extract the username of the borrower and lender, the

amount lent, the date of the loan, and whether a loan was marked as paid or unpaid. We

can merge the requests and loans.

Our full dataset contains 168,317 loan requests for the period 2016-2021. Out of these

requests, 111,670 are not removed due to the violation of community rules, out of which

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51,020 requests have complete information on loan amount, promised repayment amount,

and promised maturity, allowing us to compute an interest rate on the loan. Some requests

are not converted into loans. This brings our sample to 35,066 loans we can accurately price

with promised repayment date and actual repayment date. These loans occur between 1,962

unique lenders and 8,544 unique borrowers, totaling more than 7.5 million dollars in informal

credit. Compared to the entire population of loans originated, our sample can capture 65%

of the unique lenders, 56% of the unique borrowers, and 30% of the total value of loans.

While the negotiation process between borrower and lender is done in private without

any intermediary, there is no reason to believe the lender would receive less in repayment

than the ofered repayment in the loan request. Likewise, there is no reason for borrowers

to repay more than the already high price, mentioned in the request. Most posts have only

one lender commenting to let the potential borrower know that they privately messaged

them and later confrmed the loan. Therefore, we believe the information contained in loan

requests provides a good measure of the credit terms in this marketplace.

Panel A: Loan requests with complete information (N=51,020)

Mean St. Dev. p10 p50 p90

Requested Amount ($) 273 401 40 150 600

Promised Maturity (Days) 23 26 4 15 46

Promised Interest (% per month) 107 135 10 45 438

Converted Into Loan 68%

Panel B: Loans merged with requests (N=35,066)

Mean St. Dev. p10 p50 p90

Loan Principal ($) 216 300 35 125 500

Efective Maturity (Days) 25 44 3 13 52

Promised Interest (% per month) 131 160 15 57 519

Default Lower Bound 4.2%

Default Upper Bound 10.6%

Table 1: Summary Statistics. This table contains summary statistics for proposed loan terms in loan

requests (Panel A), and loan terms and default bounds for funded loans (Panel B). Default lower bound

refects the percentage of loans marked as unpaid. Default upper bound refects the complement of the

percentage of loans marked as paid.

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3 Descriptive Analysis

In Table 1, we report summary statistics for loan requests and loans. Out of 51,020

requests with complete and accurate information, we can fnd 35,066 corresponding loans.

The average amount requested is $273, with the median at $150. The mean and median

amount lent is less than the mean and median amount requested, suggesting that lenders

are not willing to grant high-dollar value loans, some credit rationing, or screening on the

extensive margin of credit. Interestingly, promised maturity is lower than realized maturity,

due to some late payments. Finally, loans have a slightly higher average and median interest

rate than requests. This corroborates screening on the extensive margin, whereby lenders

choose to lend to those most proftable to them.

We cannot observe the true default rate in this market. When a loan defaults, a lender

is expected to mark the loan as paid or unpaid. However, without an explicit incentive

for record-keeping, we do see that lenders do not always mark whether a loan results in

repayment or default. We have about 11% of our matched loans without data on whether

a loan defaulted or was repaid. To overcome this, we calculate an upper and lower bound

of default. The lower bound is defned as the percentage of loans marked as unpaid by the

lender. The upper bound of default is one minus the percentage of loans marked as paid, as

all the loans not marked as paid could potentially be unpaid loans. Figure 3 plots the upper

and lower bounds of default over the sample period and shows that these bounds generally

co-move, and the spread between them is small. The diference between the average default

rate in the upper bound and the average default rate in the lower bound is about 6%.

Appendix Table A1 shows loan summary statistics broken down by year. Overall, average

loan size and average interest rate have increased over time, but the average maturity of a

loan has stayed relatively constant. Of note, in 2019 we observe a large spike in the number

of loans in our sample. This is due to the community enforcing stricter rules on loan requests,

allowing us to successfully parse more loans beginning in 2019.

The average promised interest rate on requests is 107% per month, and the average

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Figure 3: Default Bounds. In this fgure, we plot the time series of default rates in this market. In

red, we plot the upper bound for default, which includes both those loans marked as unpaid, and those not

marked at any time as defaulted loans. In blue, we plot the lower bound for the default rate, which includes

only loans marked as unpaid.

interest rate on loans is 131% per month. These values are highly afected by extreme values

from very short-maturity loans. However, even when considering the medians of 45% and

57% per month, respectively, these interest rates are very large.

This leads us to the high default rates in this market. With an average maturity of about

25 days, and considering the midpoint of the default rate bounds of 7%, these loans are

highly risky. While this is an economically large default rate (68% when annualized), the

corresponding break-even interest (15%) rate would be substantially lower than the observed

median interest rate of more than 50% per month.

Our evidence suggests that the risk-sharing and loan-pooling capabilities of intermediaries, coupled with the monitoring role of collateral lead to less risky and more accessible

consumer credit. For example, despite being a high-seniority claim on a worker’s salary,

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payday loans are some of the most expensive and riskier types of unsecured consumer credit.

Even when compared to payday loan interest rates, which range between 300-400% (Correia,

Han and Wang 2023) per year, these loans are much more expensive and riskier. As pointed

out in Correia, Martins and Waikel (2022), interest rates in this market exhibit an inverted

term structure, usually associated with times when fnancial distress is ahead.

Mean Mean St. Dev. St. Dev. Median Median Diference Borrowers Lenders Borrowers Lenders Borrowers Lenders

A. User Statistics

Male 60% 86%

Age (Median) 25-34 25-34

College? (Median) Some Bachelors

Income (Median) $50-$60k $100k +

Employment (Median) Part Time Full Time

Has Savings Account 59% 93% t = 4.3

Has Retirement Account 37% 80% t = 4.8

Has Investment Account 22% 85% t = 8.4

Has Mortgage 10% 53% t = 4.8

Has Personal Loans 42% 14% t = 3.1

Evaluated Fin. Skills (/4) 3.56 3.94 t = 3.2 0.72 0.23 4 4

Self-Rep. Fin. Skills (/7) 3.95 5.52 t = 5.5 1.40 1.27 4 6

Financial Satisfaction (/7) 2.77 5.35 t = 7.3 1.75 1.57 2 6

Financial Attitude (/7) 5.50 5.89 t = 1.8 1.06 1.03 6 6

Financial Skills (/4) 3.01 3.43 t = 1.9 1.29 0.93 3 4

B. Loan Characteristics

Requested Amount $169 $249 t = 8.8 $283 $378 $100 $150

Promised Maturity 18.5 19 t = 0.9 22.2 27.2 11 12

Actual Maturity 25 27 t = 1.9 41.5 46.9 13 13

Promised Interest (% per month) 131% 122% t = 2.0 157% 154% 57% 52%

Table 2: Borrower and Lender Demographics. This table shows descriptive statistics from a

survey conducted on 100 participants in the informal market we study. 53 participants are lenders, and 47

participants are borrowers. We surveyed the participants on their demographics, their fnancial knowledge,

and their perceived fnancial knowledge. In the third column, we have t-tests for the diferences in mean

outcomes of continuous variables between borrowers and lenders.

With monetary incentives in place, we also survey 100 participants in this market, of

which 53 are lenders and 47 are borrowers, randomly sampled. This allows us to understand

better some prevalent demographic patterns of market participants. In Table 2, we report

the results, as well as t-tests for the diferences between borrowers and lenders.

The most predominant age range is 25-34 years old. On the lender side, the male predominance is much heavier than on the borrower side (86% vs. 60%). The educational

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attainment is high with the median achievement in both groups being some college attendance. Lenders, though, predominantly have bachelors degrees or higher. While more than

half of the lenders are employed full-time, that median is part-time employment for the borrowers. All in all, this results in the median income being signifcantly higher for lenders

($100k +) than for borrowers ($50-60k).

Household balance sheets are also statistically diferent. Most lenders have savings, retirement, and investment accounts. The same is not true for borrowers: only 6 in 10 borrowers

have a savings account, a third has a retirement account, and 1 in 4 reports having an investment account. While more than half of the lenders have a mortgage, only a sixth have

personal loans. The relationship fips for borrowers: 10% have a mortgage, and 42% have a

personal loan. Together with substantial diferences in their income and human capital, these

diferences in fnancial outcomes suggest a much higher fnancial vulnerability for borrowers

than for lenders.

That intuition is confrmed using questions from the FINRA National Financial Capability Study survey. When we compare the scores of fnancial literacy, skills, and attitudes

between borrowers and lenders, lenders score signifcantly higher. They enjoy not only higher

levels of fnancial satisfaction but also perceive themselves to be more fnancially knowledgeable. Lastly, the economically similar loan terms between the average borrower and the

average lender lend credence to the survey in that we believe the two groups are likely to

represent the individuals who interact in this market.

Beyond borrower-lender diferences, there is heterogeneity in the lender base, illustrated

in Figure 4. While the majority of lenders only fund 1 or 2 loans, there is a minority at the

upper tail of the distribution, who has originated up to thousands of loans. Moreover, in

Panel B of Figure 4, we see that lender profts bunch at zero. Assuming lower bounds of

default as actual default, the market is mechanically more proftable, than assuming upper

bounds of default. However, in both extremes, the distribution exhibits the same shape. The

online informal loan market is proftable on average, but access to those profts has barriers.

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Panel A: Loans Given Panel B: Lender Profts

Figure 4: Lender Heterogeneity. In Panel A, we plot the distribution of the number of loans (truncated

at 150) given by lenders. In Panel B, in grey, we plot the distribution of profts (truncated at $2000) for

lenders, assuming the lower bound of default as the actual default. In red, we plot the distribution of profts

(truncated at $2000) for lenders, assuming the upper bound of default as the actual default.

Only a minority of lenders has experienced large profts as shown by the truncated upper

tail.

In Figure 5, we study this segment of lenders who appear to be highly present and

successful in the market. In Panel A, for every year, we consider the top 10 lenders in the year

before, defned based on proftability. We confrm that for the average lender in the market,

it is not proftable, as the time series of cumulative returns consistently lies below zero. For

the top 10 lenders in the previous year, the cumulative return is very high, consistently

trending up, ending at around 500% in 5 years. The plot has two main takeaways: (i) lender

performance is persistent; and (ii) our inability to perfectly track default has a minor impact

when we are measuring aggregate market outcomes, since time series that assume lower or

upper bounds of default exhibit the same behavior. Performance persistence is expected,

especially since most lenders who experience a default leave the market, therefore leaving

successful lenders operating for a long period.

In Panel B, we plot the evolution in market shares of the all-time top 10 lenders throughout our sample period. Since 2018, at each point in time, the top 10 lenders represent 5-10%

15
Panel A: Yearly Top 10 Lender Performance

Panel B: All-time Top 10 Lender Market Share

Figure 5: Top 10 Lenders. In Panel A, we plot the cumulative return of the loan portfolio of the top 10

lenders in the market, and for the other lenders in the same time period. Top 10 Lenders are defned in each

year, as the 10 lenders who profted the most in the previous year. In Panel B, we establish the comparison

for market shares between all-time top 10 lenders, and other lenders.

of the lender base, as illustrated by the green dots. However, they have occupied 20-30% of

the market. Altogether the two panels even suggest that this minority of lenders is growing

steadily, and picking an extremely proftable part of the market.

The disparity in proftability between the top 10 lenders and the rest of the market can

16
Figure 6: Top 10 Lenders Default Rates. We plot the average default rates return of the loan portfolio

of the all-time top 10 lenders in the market, and for the other lenders in the same time period.

have two main drivers. One is that for the same credit risk, top lenders pick loans that

compensate them at a higher rate. Another is that they are simply better at managing risk,

and picking safer loans. In Figure 6, we plot average default rates (midpoint between the

upper bound and lower bound) for each calendar month, for all-time top 10 lenders, and

others. We fnd a clear superiority of top lenders managing default risk, or at least picking

safer investments. Towards the end of the sample, default rates are mechanically infated

because of the upper bound of defaults, which assumes that loans (not yet) market as paid

or unpaid are defaulted.

Our descriptive evidence suggests that each loan individually is very risky, but pooled,

these loans pay a rate substantially higher than the break-even rate. This opens space

for intermediation to create value, and efciently pool risk and allocate fnancial resources.

Additionally, a small and sophisticated fraction of lenders exhibits outstanding performance,

stemming from a diferential ability for credit risk management and screening. Other lenders

do not enjoy the same stability and proftability in their portfolios. The fnancial system

provides screening and risk management tools that individuals in this market do not have

17
access to. Lastly, we can speculatively elaborate on the role of collateral. Intermediaries

provide a legal and institutional framework for collateral to be posted. Collateral diminishes

losses given default, attenuating credit pricing. It also acts as a monitoring device, lowering

default.

4 Stylized Framework

In this section, we aim to understand what mechanisms are at play in this market. It has

two features in which it difers from intermediated consumer credit. First, recall that demand

is revealed and borrowers name their price. Second, there is no collateral or recourse in case

of default. Lenders choose whether to fund a loan, given the terms provided by the borrower.

We formalize below the interaction between a representative borrower and a representative

lender. We follow the loan origination steps described in Section 2.

4.1 Borrower’s Loan Request

The borrower needs an amount A0 in the current period, and anticipates needing A1 at

a future period. Without an assumed future need for credit, the borrower would have no

incentive to repay and maintain a reputation.

The borrower also faces a cost for borrowing outside of this market ab. This cost can

represent both monetary and non-monetary costs. Monetary costs can be the actual interest

charged by an outside lender, or the monetary search costs for alternatives. Non-monetary

costs can be the stigma stemming from borrowing from family and friends, entering a payday

loan shop, or even letting go of property under a title or pawn loan.

As illustrated by Figure 7, the borrower will request a loan ofering a rate rb, if borrowing

in this market is more afordable than the alternative cost. Formally, if:

rb < ab (LR)

18
ab rb

Figure 7: Borrower’s Loan Request. In the axis for the interest rates that a borrower can ofer, we

mark the threshold below which the borrower is willing to request credit in this market. In the shaded area,

below ab, the borrower is willing to request credit in this market.

4.2 Lender’s Funding Decision

Lenders face an origination cost of cl per dollar of credit originated. Like ab, this cost

goes beyond monetary opportunity costs and can represent the time spent doing credit risk

analysis, or the aversion to lending in an unsecured, non-intermediated, and unregulated

market.

In this market, borrowers are protected by a vast layer of anonymity, making the market

highly information asymmetric. We assume that lenders form their beliefs about the market,

rather than the individual borrower. They believe there are good borrowers who never

default, and bad borrowers, who default with probability d. Lenders then have their current

period perceived proportion of bad borrowers μ0.

Conditional on the amount requested A0 and an interest rate rb ofered by the borrower,

the lender decides whether to fund the loan. The lender funds the loan if they expect to

make a proft on it:

A0 + clA0 < (1 − μ0)(1 + rb)A0 + μ0(1 − d)(1 + rb)A0 (1)

Re-arranging Equation (1), the lender will fund the loan, if the rate ofered by the borrower compensates them enough for their origination costs and perceived degree of credit

risk.

cl + μ0d

rb > (LF) 1 + μ0d

As illustrated by Figure 8, a loan will exist if there is a rate that is low enough for the

19
cl+μ0d ab rb 1+μ0d

Figure 8: Lender’s Funding Decision. In the axis for the interest rates that a borrower can ofer, we

mark the threshold below which the borrower is willing to request credit in this market. In the shaded area,

below ab, the borrower is willing to request credit in this market. In blue, we add the threshold above which

the rate ofered by the borrower needs to be so that the lender is willing to fund the loan. For ofered rates

in the intersection of the two, both the borrower and the lender are willing to contract on a loan.

borrower to initiate a request, but high enough to compensate the lender. This is shown in

the fgure by the intersection of the shaded blue and orange areas. Moreover, see that if the

alternative borrowing market is very low-cost, the lenders perceive very high credit risk, or

face high origination costs, the intersection might not exist. A loan exists if a request exists,

satisfying (LR) and if it is funded (LF).

4.3 Borrower’s Repayment Decision

At maturity, the borrower needs to decide whether to repay the loan. Recall that there

is no seizure of collateral or any recourse in case of default. Reputation for future borrowing

is the only device that incentivizes the borrower to repay.

The borrower believes that repaying the loan yields a reputation gain. Such reputation

gain is refected by lowering the lender’s perception of the proportion of bad borrowers in

the market. Using the next loan as a latent reality, the borrower gauges whether repaying

the loan yields sufcient reputation gain so that the next loan is likely to exist.

The updated perceived proportion of bad borrowers in the next loan, μ ̃1 is defned in

Equation (2) as:

μ ̃1 = μ0 − bb,l (2)

where bb,l is the reputation gain upon the perception of the lender, given by borrower repayment. The borrower repays the current loan if the reputation gain creates a feasible

20
interest rate interval for a potential next loan, via lowering the perceived proportion of bad

borrowers. Formally, as illustrated in Panel A of Figure 9 the borrower repays the loan if:

cl + (μ0 − bb,l)d

< ab (RL) 1 + (μ0 − bb,l)d

Note that (RL) refers to a hypothetical future loan, where ab can change for the borrower,

and the lender can be a diferent lender, with a diferent cl. If, like in Panel B of Figure 9, they

believe that the reputational gain is not enough to lower the lender’s minimum acceptable

rate, then the likelihood of a future loan is very low, and the borrower is more likely to

default.

Panel A: Borrower Repays Panel B: Borrower Defaults

cl+(μ0−bb,l)d

1+(μ0−bb,l)d

ab rb,1 cl+(μ0−bb,l)d

1+(μ0−bb,l)d

ab rb,1

Figure 9: Borrower’s Repayment Decision. The borrower repays the current loan if it is likely that

a future loan can exist, given the update to the perceived proportion of bad borrowers (Panel A). In turn, if

the reputational gain is not enough to create a feasible set of interest rates, the borrower does not have an

incentive to repay the loan and, hence, defaults (Panel B).

Figure 9 shows that the loan is repaid (RL) if both (LR) and (LF) are likely to overlap in

a potential future loan, incorporating the reputational gain from repaying the current loan.

Using the setup of the model, we can discuss the two previous empirical results. First,

the high default rate in the market is justifed by the absence of screening and monitoring

devices beyond reputation. Similarly, the high cost of credit can be justifed simultaneously

by (i) rational expectations from lenders about high market credit risk and (ii) high-cost

alternative credit solutions for the fnancially underserved.

Second, we can discuss lender heterogeneity in light of the model. For lenders with high

origination costs, it is only worth lending if they can pick high-quality loans or have superior

monitoring abilities. As lenders diversify their loan portfolio and repeat procedures, perdollar origination costs are likely to decrease, and the marginal impact of each default in the

21
dollar value of the portfolio also decreases.

We will make two key assumptions to derive testable hypotheses in the direction discussed

above: (i) the origination burden is decreasing both with lender experience and with lender

ability, and (ii) as borrowers and lenders are more familiar, borrower loan repayment yields

larger reputational gains. We now re-arrange (RL) to relate these two quantities:

cl < [ab − (1 − ab)μ0d] + [(1 − ab)d] bb,l (3)

This inequality in Equation (3) for the origination cost is linear in the reputational gain.

Given an alternative borrowing cost, and the lender’s perception of credit risk in the market,

it creates a minimum reputational gain that justifes the borrower repaying the loan, for

each level of lender origination costs. Figure 10 illustrates this relationship. The larger the

origination cost faced by the lender, the larger the reputation gain needs to be so that the

borrower repays their loan.

bb,l

cl

Default on Current Loan

Future Loan Not Funded

Repay Current Loan

Future Loan Funded

Figure 10: Origination Costs and Reputation Gains. In this graph, we use the overlap in conditions

in Panel A of Figure 9 to relate origination costs and reputational gains from loan repayment. For each level

of lender origination costs, there is a minimum reputational gain that needs to occur for the borrower to

repay the loan. The dashed line and the shaded area under it represent that relationship.

The plot helps us formulate the key dimensions across which lenders or lender-borrower

22
pairs can difer to generate the heterogeneity observed in Section 2. With our two key

assumptions, we can derive testable hypotheses to assess whether our stylized framework

accurately describes decision-making in the absence of intermediaries:

Origination Burden Hypothesis. If lender ability or lender experience decrease unit

costs of origination, then (i) we expect to observe lower default, and higher propensity to

fund loans; and (ii) the condition (LF) would also allow for lower prices ofered by more

experienced/skilled lenders.

Borrower-Lender Opacity Hypothesis. If a larger reputation gain arises when there

is less opacity between borrowers and lenders, we expect to observe lower default and a larger

propensity to fund between pairs of more familiar borrowers and lenders.

5 Origination Costs: Experience vs. Skill

According to our frst hypothesis, lenders who experience a lower origination burden

would accept loans with lower promised interest rates, experience less default and exhibit

a higher propensity to lend, on average. The origination burden, we argue, decreases as

the lender becomes more acquainted with the workings of the market, and as they learn

the credit risk evaluation and due diligence processes. Hereafter, we refer to this as the

experience channel. It can also be a lender-specifc trait, whereby they are more skilled or

have higher abilities to obtain hard or soft information on borrowers, reducing the burden

of lending. For example, it takes them less time to analyze a potential loan. We refer to this

second channel as the skill channel.

Our approach to understanding whether and how each channel plays a role is to start with

a measure that can proxy for either mechanism and use diferent econometric specifcations

to test diferent mechanisms. Our relevant variable is the total number of loans a lender has

funded until the date of each loan they participate in, a lending activity history variable.

The underlying assumption is that each past loan contributes to lowering the origination

23
Panel A: Default (p.p.) Panel B: Interest Rate (p.p.) Panel C: Propensity to Lend (p.p.)

Figure 11: Average Outcomes by Tercile of Lending Activity. In this fgure, we plot averages and

95% confdence intervals for default rates (in Panel A), monthly interest rates (in Panel B), and propensities

to lend (in Panel C) for lenders in the frst, second, and third terciles of past lending activity.

cost of the next loan. This can happen because as a lender lends more they become more

experienced, or because more skilled lenders will lend more successfully, and in turn have

higher lending activity.

Regardless of the mechanisms at play, for our hypothesis to match empirical evidence,

we should observe a negative relationship between past lending activity and default and

interest rates, and a positive relationship between past lending activity and propensity to

lend. Figure 11 visually confrms that this pattern is present in the data. We estimate

Equation (4) to validate our measure, where we can condition on observables:

yi,l,b,t = α + βCumulativeLoansl,t + φXb,t + θXi + εi,l,b,t (4)

where yi,l,b,t is the outcome of loan i, between lender l and borrower b, originated at

time t, CumulativeLoansl,t is the measure of past lending activity for lender l until day t,

Xb,t is a vector of time-varying controls for borrower characteristics and Xi is a vector of

controls for loan proposed terms. Borrower controls include whether it is the frst loan for

this borrower, whether they have ever defaulted, whether they have ever paid their loans

late, their number of loans outstanding, and their total number of past loans. The vector

of controls for loan request terms includes maturity, the complexity of text, readability, and

misspelling measures about the loan request text, and depending on the dependent variable,

24
other controls such as requested and promised repayment amounts.

(1) (2) (3) (4)

Panel A: Default Rate (p.p.)

Cumulative Loans -1.066∗∗∗ -0.854∗∗∗ -0.683∗∗∗ -0.554∗∗∗

(0.083) (0.078) (0.074) (0.074)

N 17,558 23,864 17,558 23,864

R2 0.021 0.018 0.076 0.077

Panel B: Interest Rate (p.p.)

Cumulative Loans -3.492∗∗∗ -3.265∗∗∗ -1.961∗∗∗ -1.623∗∗∗

(0.572) (0.539) (0.442) (0.419)

N 17,558 23,864 17,558 23,864

R2 0.010 0.011 0.236 0.233

Panel C: Propensity to Lend (p.p.)

Cumulative Loans 0.047∗∗ 0.245∗∗∗ 0.122∗∗∗ 0.267∗∗∗

(0.016) (0.026) (0.010) (0.019)

N 439,119 411,561 439,119 411,561

R2 0.000 0.003 0.656 0.581

Sample Sub Full Sub Full

Controls No No Yes Yes

Table 3: Past Lender Activity. This table reports regression estimates for the number of previously

funded loans by the lender, on a default indicator in Panel A, interest rate in Panel B, and on a dummy

that fags request-to-loan conversion. In Panels A and B, the unit of observation is a loan, and in Panel

C, the unit of observation is potential loans by combining pairs of borrowers and lenders on the same day.

Controls include requested and promised repayment amounts (except Panel B), maturity, dummies for the

borrower’s frst loan, borrower default in the past, and borrower late payment in the past, as well as measures

of misspelling, readability, and complexity of the loan request text. The frst two columns exclude controls,

and odd-numbered columns do not contain loans privately prearranged between borrower and lender. All

standard errors are clustered by lender, and presented in parentheses.

In Table 3, we report estimates for the coefcient of interest, β. We run the specifcation

above with and without controls. Our preferred sample does not include loans marked as

previously arranged loans, as we do not observe private communications between lender and

borrower, which defne the loan terms. For robustness, we also present estimates on a sample

including prearranged loans. Loan terms for prearranged loans are imputed from previous

loan terms between borrower-lender pairs and are by defnition less sensitive. Panels A and

B confrm that lending activity is associated with lower default and accepting loans with

25
lower rates.

In Panel C, we analyze the propensity to fund a loan, from the lender perspective. We

build a sample with all pairs of possible borrower-lender pairs from those who borrow or

lend on any given day. For each lender, we consider the set of borrowers requesting loans

on a given day to be their choice set on that day. We then use a dummy variable equaling

one for the loans that happened on that day, and zero otherwise. We then measure how

the propensity for a request to be funded associates with lender experience, conditional on

the two vectors of borrower and loan request controls described above. Higher past lending

activity is also associated with a higher propensity to lend.

These fndings assert that our proxy for monetary and non-monetary origination costs

are associated with the three key variables in the way predicted by our stylized framework.

More interesting is to understand whether experience and learning or skill and ability are

determining this relationship. We start by adding week fxed efects to the previous specifcation. Doing so eliminates lender-specifc time variation in lending activity, shutting down the

experience acquisition channel. In this specifcation, β measures how the outcomes change

across individuals with diferent levels of past lending activity. Table 4 reports the results.

The results suggest that cross-sectional diferences in past lending activity drive our

results. Our preferred specifcation is that of column (3) since it contains controls and

eliminates some of the omitted variable bias, and it does not contain prearranged loans, for

which most of the loan term data is imputed. The cross-sectional results are all statistically

signifcant and stronger in magnitude. Economically, this means that even not allowing for

the experience channel to operate, lenders with higher past lending activity in a given week

accept lower interest rates, experience lower default, and are more likely to fund a loan, than

lenders with lower past lending activity.

Alternatively, to absorb time-invariant diferences in lender skill, we add lender fxed

efects to our baseline specifcation. Here, the variation of interest is that of the same lender

along the time series. This approach absorbs pre-existing diferences in the burden of loan

26
(1) (2) (3) (4)

Panel A: Default Rate (p.p.)

Cumulative Loans -1.241∗∗∗ -0.997∗∗∗ -0.861∗∗∗ -0.690∗∗∗

(0.090) (0.070) (0.076) (0.064)

N 17,558 23,864 17,558 23,864

R2 0.057 0.048 0.112 0.107

Panel B: Interest Rate (p.p.)

Cumulative Loans -4.249∗∗∗ -3.987∗∗∗ -2.467∗∗∗ -2.083∗∗∗

(0.600) (0.548) (0.448) (0.417)

N 17,558 23,864 17,558 23,864

R2 0.044 0.038 0.255 0.247

Panel C: Propensity to Lend (p.p.)

Cumulative Loans 0.075∗∗∗ 0.267∗∗∗ 0.134∗∗∗ 0.277∗∗∗

(0.014) (0.024) (0.008) (0.018)

N 439,119 411,561 439,119 411,561

R2 0.018 0.021 0.658 0.584

Sample Sub Full Sub Full

Controls No No Yes Yes

Week FE Yes Yes Yes Yes

Table 4: Cross-Lender Variation in Past Lending Activity. This table reports regression estimates

for the number of previously funded loans by the lender, on a default indicator in Panel A, interest rate in

Panel B, and on a dummy that fags request-to-loan conversion. In Panels A and B, the unit of observation

is a loan, and in Panel C, the unit of observation is potential loans by combining pairs of borrowers and

lenders on the same day. Controls include requested and promised repayment amounts (except Panel B),

maturity, dummies for the borrower’s frst loan, borrower default in the past, and borrower late payment

in the past, as well as measures of misspelling, readability, and complexity of the loan request text. The

frst two columns exclude controls, and odd-numbered columns do not contain loans privately prearranged

between borrower and lender. All columns were estimated using week fxed efects. All standard errors are

clustered by lender, and presented in parentheses.

origination. Table 5 reports the results with and without borrower and loan request controls,

for the sample without pre-arranged loans. If the origination costs decrease as lenders get

more experienced with lending, our hypothesis indicates that we should observe estimates

for β with the same sign as in Tables 3 and 4.

We fnd the role of experience in lowering origination burdens to be undetectable. Focusing on the even-numbered columns of Table 5, where the specifcations include controls, we

fnd that as the same individual lends more, they are more likely to encounter default. This

27
is mostly mechanical, as we are using default as the complement of loans marked as paid,

and as the pool of loans increases, the percentage of loans that go as unmarked increases.

However, it rejects a negative relation between within-lender experience and default. In

column (4), we also observe a non-result on the correlation between experience and loan

pricing. The only pattern that holds is that of propensity to lend, in column (6). Here,

it could be a mechanical relationship or an actual efect of experience acquisition through

lending activity, on the extensive margin of lending. Not only we are unable to disentangle a mechanical efect, but also, statistical signifcance is lower, and the magnitude of the

estimate is less than a ffth of the estimate allowing for skill to play a role.

(1) (2) (3) (4) (5) (6)

Default Rate (p.p.) Interest Rate (p.p.) Prop. to Lend (p.p.)

Cumulative Loans 0.171 0.286∗∗ -2.508∗∗ -1.077 -0.017 0.049∗∗

(0.095) (0.094) (0.773) (0.642) (0.041) (0.019)

N 17,558 17,558 17,558 17,558 439,119 439,119

R2 0.299 0.326 0.243 0.362 0.012 0.659

Sample Sub Sub Sub Sub Sub Sub

Controls No Yes No Yes No Yes

Lender FE Yes Yes Yes Yes Yes Yes

Table 5: Time Variation in Past Lending Activity. This table reports regression estimates for the

number of previously funded loans by the lender, on a default indicator, interest rate, and on a dummy

that fags request-to-loan conversion. In columns 1-4, the unit of observation is a loan, and in columns

5-6, the unit of observation is potential loans by combining pairs of borrowers and lenders on the same

day. Controls include requested and promised repayment amounts (except columns 3-4), maturity, dummies

for the borrower’s frst loan, borrower default in the past, and borrower late payment in the past, as well

as measures of misspelling, readability, and complexity of the loan request text. Odd-numbered columns

exclude controls, and all columns do not include loans privately prearranged between borrower and lender.

All columns were estimated using lender fxed efects. All standard errors are clustered by lender, and

presented in parentheses.

Thus far, our analysis rules out the experience channel, and highlights the role of skill in

managing credit risk, while decreasing the price of credit, and increasing likelihood of credit

origination. Skill being a key determinant of loan terms is consistent with our descriptive

results presented in Figures 5 and 6. We address the robustness of our result in terms of

measurement, sampling, and estimation in Section 7.

The results in this section speak to the role of skilled lending in decreasing default rates,

28
decreasing the price paid for credit, and increasing credit availability. An advantage of

intermediated credit, relative to a market like the one we analyze is human capital. Financial

intermediaries employ highly skilled and fnancially literate staf and use complex models to

undertake loan origination, credit pricing, and funding decisions. Therefore, another role

that intermediaries play in consumer credit is that of bringing skill to the consumer credit

market.

6 Reputation Gain: Borrower-Lender Connectedness

According to our second hypothesis, pairs of borrowers and lenders with a thinner layer

of opacity are more likely to originate a loan, and that loan is more likely to be repaid. We

argue that lenders’ ability to learn from borrower repayment leads to a larger reputation gain

from borrower repayment. Thus, it incentivizes repayment, and therefore loan origination.

This reasoning is very akin to relationship lending.

A substantial amount of literature has shown that frms, particularly small frms, beneft

tremendously from a degree of proximity to their lenders. Elyasiani and Goldberg (2004)

comprehensively review earlier literature. More recent works (Kysucky and Norden 2016;

L ́opez-Espinosa, Mayordomo and Moreno 2017; Banerjee, Gambacorta and Sette 2021) use

microdata to confrm the intuition that banking relationships reduce opacity between borrowers and lenders and allow for soft information acquisition focusing on small frms. Holmes,

Isham, Petersen and Sommers (2007) study relationship lending in consumer credit, in the

auto loan space, and posit that it is especially important for low-income and low credit score

individuals.

In our setting, we do not rely on the lending history between borrower and lender to proxy

for the lender’s ability to acquire soft information or for the asymmetry of information on

each loan. Instead, we measure how connected each pair of borrower and lender is through

their activity in other online communities of the same online platform. If the borrower and

29
lender share a lot of their interests in sports, food, or other hobbies, and coexist prominently

in other communities, then their relationship is less opaque.

We measure connectedness in two ways: in scope and intensity. Our scope measure hinges

solely on the extensive margin of whether borrower and lender post or comment in a given

community and measures the overlap. Intensity uses the percentage of their activity that

goes into each community. We exclude any posts or comments in the credit marketplace as

that would induce undesirable connectedness in our measure. In Equation (5) we express

how we construct each measure:

P P Ic,t(b, l) Ic,t(b, l) Scopeb,l,t c = Pc × P = %ScopeBorrowerb,l,t × %ScopeLenderb,l,t (5) Ic,t(b) Ic,t(l) c c

where Ic,t(X) takes the value of one if the users in the set X have ever posted or commented

in the community c as of time t. It measures the percentage of communities visited by the

borrower that are also visited by the lender, multiplied by its counterpart. It gives the same

weight to all communities, despite of how much time, energy, and participation each user

devotes to each community. Our intensity measure in Equation (6) takes that into account:

P P Nc,t(b) Nc,t(l) c∈L c∈B Intensityb,l,t = P ×P = %IntensityBorrowerb,l,t×%IntensityLenderb,l,t Nc,t(b) Nc,t(l) c∈B c∈L

(6)

where Nc,t(X) denotes the number of comments or posts the users in the set X have ever

done in the community c as of time t, the sets B and L represent the sets of communities

where the borrower and the lender, respectively, have any activity as of time t. It measures

the percentage of posts and comments by the borrower in communities where the lender is

also active, multiplied by its counterpart. Both measures can vary between zero and one.

In Figure 12, we plot the distribution of lenders’ average connectedness across all their

loans. While most lenders’ activity does not have a substantial overlap with the borrowers

they give credit to, we do see some variation in the extent to which their activity overlaps, particularly on the intensity measure. This suggests that lenders who are marginally

30
Panel A: Connectedness Scope Panel B: Connectedness Intensity

Figure 12: Histograms of Average Connectedness Measure by Lender. In this fgure, we plot

histograms of average connectedness scope (in Panel A) and intensity (in Panel B) by lender.

connected with a potential borrower, are at an advantage relative to the majority of lenders.

We use these connectedness measures to test our second hypothesis. We do so using a

similar specifcation as in Section 5.

yi,l,b,t = α + βConnectednessl,b,t + φXb,t + θXi + εi,l,b,t (7)

where we replace Connectednessl,b,t with our diferent measures of connectedness, and all else

is defned as before. The outcomes of interest, yi,l,b,t, are default rates and lender propensity

to fund a loan. Table 6 reports the results.

Due to the sensitivity of the estimates to the inclusion of controls, we opt to interpret the

most restrictive specifcation in columns (2) and (4). We neglect the magnitude of coefcients

as their interpretation would require the comparison between a zero-overlap pair of borrower

and lender, with a perfectly overlapping pair.

A qualitative assessment of our estimates suggests that while the scope of connectedness is

associated with higher chances of loan origination, the intensity of connectedness is associated

with higher loan repayment. Intuitively, it matches our reasoning: the ability of the lender

to collect soft information about the borrower by coexisting in the same online communities

31
(1) (2) (3) (4)

Panel A: Default Rate (p.p.)

Scope -20.020 -21.317

(17.442) (14.857)

Intensity -11.137 -15.672∗∗

(5.881) (4.922)

N 16,918 16,918 16,918 16,918

R2 0.000 0.067 0.000 0.068

Panel B: Propensity to Lend (p.p.)

Scope 34.996∗∗∗ 9.556∗∗

(5.164) (3.216)

Intensity 6.976∗∗∗ 1.762

(1.576) (0.945)

N 438,124 438,124 438,124 438,124

R2 0.001 0.656 0.000 0.656

Sample Sub Sub Sub Sub

Controls No Yes No Yes

Table 6: Borrower-Lender Connectedness. This table reports regression estimates for two borrowerlender connectedness measures, on a default indicator in Panel A, and on a dummy that fags request-to-loan

conversion in Panel B. In Panel A, the unit of observation is a loan, and in Panel B, the unit of observation

is potential loans by combining pairs of borrowers and lenders on the same day. Controls include requested

and promised repayment amounts, maturity, dummies for the borrower’s frst loan, borrower default in the

past, and borrower late payment in the past, as well as measures of misspelling and complexity of the loan

request text. The sample does not contain loans privately prearranged between borrower and lender. All

standard errors are clustered by lender, and presented in parentheses.

determines that the pair forms and a loan is funded. Only then, the intensity of connection

matters to increase the reputational gain of loan repayment, inducing the borrower to repay

their loan.

Our interpretation requires that the key variation is the efort the lender devotes to

activity in communities where the borrower is active. It could also be that the important

factor is that most of the borrower activity is in communities where the lender is present.

We separate the variation in our scope and intensity measures to understand which forces

are driving the associations we highlight. We re-run the regression models in Table 6, but

with the borrower and lender components as separate variables. This test clears whether

it is important that most of the lender activity is in spaces where the borrower exists, or

32
whether most of the borrower activity is in communities where the lender is active. Our

estimates are reported in Table 7.

Both panels show that the relevant variation is in the portion of lender activity where

the borrower is also active. This shows that even if the borrower has a signifcant portion of

their activity in communities where the lender is not active, the fact that the lender devotes

a signifcant portion of their activity to communities where the borrower is active is relevant.

This is true both for loan repayment and loan origination decisions and in the directions

predicted by our hypothesis.

Because credit prices are set by the borrower before a lender decides to fund the loan,

we do not expect any of the connectedness measures to be associated with credit prices. We

run that test and report results in Table 8 below.

As expected, we do not fnd an association of either connectedness scope or intensity

between a borrower-lender pair, because credit prices are set by the borrower alone. This

result also suggests that users with broader or more intense activity, hence more likely to be

connected with a lender, do not systematically promise higher or lower interest rates.

This section highlights the importance of information acquisition, monitoring, and even

social collateral (Nguyen and Dang 2020) in consumer credit. In light of the role of fnancial intermediaries, our results speak to three main aspects. First, fnancial intermediaries

internalize information asymmetries between those with excess capital and those in need of

capital, eliminating the need for any connectedness between depositors/investors and borrowers. Second, fnancial intermediaries allow collateral to be posted, lowering the cost of

credit, and raising incentives for repayment. Third, through their establishment network,

fnancial intermediaries can allocate funds from depositors/investors to borrowers active in

diferent locations or communities, without the need for them to have any proximity, geographic or other.

33
(1) (2) (3) (4) (5) (6)

Panel A: Default Rate (p.p.)

Borrower Scope

Lender Scope

Borrower Intensity

Lender Intensity

3.144

(3.319)

-9.417∗∗∗

(2.802)

3.028

(3.551)

-9.360∗∗∗

(2.819)

-0.153

(1.724)

-5.470∗∗

(1.778)

0.500

(1.832)

-5.535∗∗

(1.794)

N

R2

16,918

0.067

16,918

0.068

16,918

0.068

16,918

0.067

16,918

0.068

16,918

0.068

Panel B: Propensity to Lend (p.p.)

Borrower Scope

Lender Scope

Borrower Intensity

Lender Intensity

0.289

(0.171)

1.722∗∗∗

(0.508)

0.312

(0.198)

1.731∗∗∗

(0.506)

0.288∗

(0.138)

0.588∗

(0.298)

0.235

(0.140)

0.558

(0.294)

N

R2

438,124

0.656

438,124

0.656

438,124

0.656

438,124

0.656

438,124

0.656

438,124

0.656

Sample

Controls

Sub

Yes

Sub

Yes

Sub

Yes

Sub

Yes

Sub

Yes

Sub

Yes

Table 7: Separating Borrower and Lender Overlaps. This table reports regression estimates for

separate borrower lender community overlap measures, on a default indicator in Panel A, and on a dummy

that fags request-to-loan conversion in Panel B. In Panel A, the unit of observation is a loan, and in Panel

B, the unit of observation is potential loans by combining pairs of borrowers and lenders on the same day.

Controls include requested and promised repayment amounts, maturity, dummies for the borrower’s frst

loan, borrower default in the past, and borrower late payment in the past, as well as measures of misspelling,

readability, and complexity of the loan request text. The sample does not contain loans privately prearranged

between borrowers and lenders. All standard errors are clustered by lender, and presented in parentheses.

7 Robustness of Results

In this section, we assess whether our results are robust to diferent measurements, estimation procedures, sample designs, or conceptual approaches.

34
(1) (2) (3)

Interest Rate (p.p.)

Scope

Intensity

15.663

(88.582)

17.185

(26.896)

-17.608

(101.651)

20.646

(30.774)

N

R2

16,918

0.203

16,918

0.203

16,918

0.203

Sample

Controls

Sub

Yes

Sub

Yes

Sub

Yes

Table 8: Borrower-Lender Connectedness and Interest Rate. This table reports regression estimates

for two borrower-lender connectedness measures, on interest rates. The unit of observation is a loan. Controls

include maturity, dummies for the borrower’s frst loan, borrower default in the past, and borrower late

payment in the past, as well as measures of misspelling and complexity of the loan request text. The sample

does not contain loans privately prearranged between borrower and lender. All standard errors are clustered

by lender, and presented in parentheses.

7.1 Lender Experience

Cross-sectional variation in lender skill is less problematic than its time-series experience

counterpart. Even though our goal is to interpret our results as strong descriptive associations, rather than direct causal estimates, they can still be biased. One view of the problem

is that the lender chooses whether to lend in the past, and at every point in time, they are

choosing, again, whether to lend, what price to accept, and how to manage default.

In our best attempt to isolate some of the time variation in past lending activity, we build

an instrument that does not rely solely on lender choices. Every day, for each lender, we

compute the number of prior loans given to borrowers contained in that day’s choice set. The

choice set is comprised of all the borrowers who requested a loan on that day. While lenders

determine who to lend to, they do not determine who is part of their choice set on a given

day, as measured by our instrument. This instrument is both conceptually and statistically

correlated with past lending activity, as shown in Panel A of Table 9. It is expected that

the experience a lender acquires in the market as a whole correlates with the experience the

lender has, in a random subset of that market. The ft of the frst stage is very strong with

R2 at above 0.6 for all three outcomes.

35
(1) (2) (3)

Instrument

Panel A: Cumulative Loans - IV First Stage

3.931∗∗∗ 3.937∗∗∗ 4.737∗∗∗

(0.627) (0.630) (0.603)

N

R2

17,123

0.627

17,123

0.626

438,648

0.602

Panel B: IV Second Stage

Predicted

Cumul. Loans

Default Rate (p.p.)

0.812∗∗

(0.310)

Interest Rate (p.p.)

-2.242

(1.566)

Prop. to Lend (p.p.)

0.261∗∗∗

(0.062)

N

R2

17,123

0.324

17,123

0.365

438,648

0.659

Sample

Controls

Lender FE

Sub

Yes

Yes

Sub

Yes

Yes

Sub

Yes

Yes

Table 9: Cumulative Loans Instrumental Variable. This table reports instrumental variables

regression estimates for the number of previously funded loans by the lender, on a default indicator, interest

rate, and on a dummy that fags request-to-loan conversion. In Panel A, we present frst-stage results, and

in Panel B, we present second-stage results. In columns 1-2, the unit of observation is a loan, and in column

3, the unit of observation is potential loans by combining pairs of borrowers and lenders on the same day.

Controls include requested and promised repayment amounts (except column 2), maturity, dummies for the

borrower’s frst loan, borrower default in the past, and borrower late payment in the past, as well as measures

of misspelling, readability, and complexity of the loan request text. All columns were estimated using lender

fxed efects. All standard errors are clustered by lender, and presented in parentheses.

In Panel B, we instrument the number of past loans of each lender using the predicted

value from the estimation in Panel A, and fnd very similar results to our OLS estimation:

a weak positive association of experience with the default measure, a non-result in pricing,

and a positive association between experience and propensity to lend. Note that in the

cross-section, this approach still bears endogeneity, but in the time series, for a given lender,

the choice set available on a given day is taken as given.

An additional concern with the experience analysis is that of sample composition. As

discussed in Section 2, a small number of lenders occupies a large market share, and a large

number of lenders lend only once or twice. In such an unbalanced sample, it is hard to speak

to experience, as most loan observations will belong to highly experienced individuals with

hundreds of loans funded, using perhaps very standardized procedures. This does not leave

36
(1) (2) (3) (4) (5) (6)

Default Rate (p.p.) Interest Rate (p.p.) Prop. to Lend (p.p.)

Cumulative Loans 0.360 0.413∗∗ -0.426 0.837 0.177∗∗∗ 0.082∗∗∗

(0.200) (0.202) (0.956) (0.898) (0.016) (0.011)

N 7,020 7,020 7,020 7,020 187,697 187,697

R2 0.438 0.460 0.404 0.509 0.026 0.794

Sample First 10 First 10 First 10 First 10 First 10 First 10

Controls No Yes No Yes No Yes

Lender FE Yes Yes Yes Yes Yes Yes

Table 10: Past Lending Activity During First 10 Loans. This table reports regression estimates

for the number of previously funded loans by the lender, on a default indicator, interest rate, and a dummy

that fags request-to-loan conversion. In columns 1-4, the unit of observation is a loan, and in columns 5-6,

the unit of observation is potential loans by combining pairs of borrowers and lenders on the same day.

Controls include requested and promised repayment amounts (except columns 3-4), maturity, dummies for

the borrower’s frst loan, borrower default in the past, and borrower late payment in the past, as well as

measures of misspelling and complexity of the loan request text. All columns are ran on a subsample of the

frst 10 loans for all the lenders who funded at least 10 loans. All columns were estimated using lender fxed

efects. All standard errors are clustered by lender, and presented in parentheses.

a lot of room for experience to impact the outcomes.

To address that, we build a sample with the frst 10 loans of lenders who have at least

10 loans, a sample in which all lenders take the same weight. We report the results with

and without controls in Table 10. Our results are not only identical to those with the full

sample, but also robust to whether we consider the frst 5, 10, or 20 loans for each lender,

as shown in Appendix Table A2.

7.2 Lender Skill

To test whether our results are due to mechanical artifacts of our measure of past lending

activity, we run our baseline specifcation on an alternative measure. For each loan, instead

of using the number of loans that the lender has funded in the past, we use the number

of days since the lender’s frst loan, as our measure of lending activity. Table 11 reports

the results. In all three outcomes the results are qualitatively similar, and so is the ft

across specifcations and samples, confrming the lender skill mechanism as an important

determinant of credit risk, credit price, and loan origination.

37
(1) (2) (3) (4)

Panel A: Default Rate (p.p.)

Cumulative Days -0.150∗∗∗ -0.109∗∗∗ -0.085∗∗∗ -0.050∗∗

(0.020) (0.020) (0.016) (0.015)

N 19,842 33,137 19,842 33,137

R2 0.016 0.011 0.076 0.079

Panel B: Interest Rate (p.p.)

Cumulative Days -0.646∗∗∗ -0.572∗∗∗ -0.365∗∗∗ -0.334∗∗∗

(0.159) (0.171) (0.089) (0.096)

N 19,842 33,137 19,842 33,137

R2 0.012 0.011 0.206 0.195

Panel C: Propensity to Lend (p.p.)

Cumulative Days 0.014 0.080∗∗∗ 0.025∗∗∗ 0.080∗∗∗

(0.009) (0.019) (0.005) (0.015)

N 466,347 479,266 466,347 479,266

R2 0.000 0.007 0.625 0.501

Sample Sub Full Sub Full

Controls No No Yes Yes

Table 11: Alternative Measure of Past Lender Activity. This table reports regression estimates

for the number of days since the lender’s frst loan, on a default indicator in Panel A, interest rate in Panel

B, and on a dummy that fags request-to-loan conversion. In Panels A and B, the unit of observation is

a loan, and in Panel C, the unit of observation is potential loans by combining pairs of borrowers and

lenders on the same day. Controls include requested and promised repayment amounts (except Panel B),

maturity, dummies for the borrower’s frst loan, borrower default in the past, and borrower late payment in

the past, as well as measures of misspelling and complexity of the loan request text. The frst two columns

exclude controls, and odd-numbered columns do not contain loans privately prearranged between borrower

and lender. All standard errors are clustered by lender, and presented in parentheses.

7.3 Relevance of Connectedness 

Throughout our analysis and interpretation of results, we view connectedness in other

communities as a proxy for the acquisition of soft information, and for the ability of the

lender to monitor the borrower, leading the borrower to repay. An angle we do not directly

analyze is that of borrower reputation in other communities and the threat to that reputation. A credible threat of shaming the borrower in communities where the borrower holds a

reputation, and the lender knows this, could be the mechanism that links connectedness to

repayment, and subsequent willingness to lend.

38
To test this, we conduct an event study around borrower default, and estimate lender’s

“abnormal” activity in communities where the borrower is active, vis-`a-vis their activity in

communities where the borrower is not active. We do so by estimating Equation (8):

X

3

X

3

yl,τ,c = αl + λkI(τ = k) + γI(Overlapc) + (βkI(τ = k) × I(Overlapc)) + εl,τ,c (8)

k=−3 k=−3

where yl,τ,c denotes the number of posts or comments made by the lender in the community c,

τ weeks relative to borrower default. αl are lender fxed-efects to absorb the lender’s average

activity across all platforms, I(τ = k) are dummy variables for diferent weeks around the

default event. The coefcients {λk} absorb lender activity around default on communities

where the borrower is not active, and Overlapc is a dummy equal to one if community c is a

community where both borrower and lender are active. The coefcients of interest are {βk},

and the omitted event time is -1. We plot the coefcients in Figure 13.

Figure 13: Lender Activity Event-Study Around Default. In this fgure we plot coefcients of a

diferences-in-diferences event study around borrower default. We measure lender post and comment activity

in all communities except the credit marketplace, and separate communities based on lender and borrower

overlap. Therefore, the fgure reports estimates for abnormal changes in lender activity in communities where

the borrower is active.

39
We observe abnormal lender activity in overlapping communities in the week of the

default and in the following week. We interpret this result as a validation of our measure

of connectedness. This activity can have two purposes: (i) monitoring the borrower, and

fshing for their activity or (ii) publicly shaming the borrower, afecting their reputation.

Only in 57 out of more than 1.8 million posts and comments from lenders, the borrower’s

username is mentioned. This evidence points towards a monitoring role of connectedness,

rather than a credible threat to the borrower’s reputation.

8 Conclusion

We collect a comprehensive dataset on an online informal lending forum to study unregulated consumer credit, without any intermediaries. Our frst result is that credit prices and

default rates are both extremely high, even when compared with subprime loans. Secondly,

we fnd that a small group of lenders gains a large and very proftable market share, while

the vast majority realizes losses, on average.

We develop a stylized framework to understand the determinants of prices, default, and

loan origination. We bring the model to the data and fnd that lender skill is associated with

a higher likelihood of originating loans, lower credit prices, and lower default. Additionally,

we fnd that monitoring and acquisition of information also play a role in decreasing default

and increasing the propensity to originate.

Our study is one of the frst to study informal lending and to use that setting to understand the role of fnancial intermediaries in consumer credit. We speak to the debate on

fnancial disintermediation by showing that beyond efcient allocation of capital, risk sharing, and diversifcation, intermediaries have two more important roles in consumer credit

markets, facilitating access to credit, easing prices, and reducing risk. First, they bring in

skill, by employing skilled human capital to make origination and pricing decisions. Second,

they eliminate the need for investors to directly acquire information about borrowers.

40
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45
Time N. Requested Promised Prom. Monthly Converted Default Default

Period Obs Amount ($) Maturity (Days) Interest Rate Into Loan Upper Bound Lower Bound

Panel A: Loan Requests

Full Sample 51,020 273 23 107% 68% - -

2016 810 268 27 117% 53% - -

2017 1,627 275 25 92% 54% - -

2018 6,365 273 26 92% 53% - -

2019 15,032 276 23 107% 63% - -

2020 13,883 271 22 110% 74% - -

2021 13,303 272 20 115% 81% - -

Panel B: Funded Loans

Full Sample 35,066 216 25 131% - 4.2% 10.6%

2016 426 208 22 108% - 4.7% 14.1%

2017 880 211 19 117% - 5.3% 18.2%

2018 3,340 204 20 119% - 4.8% 15.9%

2019 9,426 201 17 135% - 3.6% 11.6%

2020 10,248 219 18 133% - 3.9% 14.4%

2021 10,746 231 18 131% - 4.7% 17.2%

Appendix Table A1: Summary Statistics by Year. This table reports yearly averages for promised

loan terms for loan requests in Panel A, and loan terms for funded loans in Panel B.

46
(1) (2) (3)

Panel A: Default Rate (p.p.)

Cumulative Loans 0.645 0.413∗ 0.064

(0.517) (0.204) (0.091)

N 2,325 2,810 2,860

R2 0.313 0.189 0.160

Sample First 5 First 10 First 20

Panel B: Interest Rate (p.p.)

Cumulative Loans 1.339 0.837 -1.354∗

(2.123) (1.042) (0.592)

N 2,325 2,810 2,860

R2 0.481 0.365 0.296

Sample First 5 First 10 First 20

Panel C: Propensity to Lend (p.p.)

Cumulative Loans 0.102∗∗∗ 0.082∗∗∗ 0.047∗∗∗

(0.025) (0.013) (0.007)

N 65,467 74,729 72,503

R2 0.799 0.759 0.717

Sample First 5 First 10 First 20

Controls Yes Yes Yes

Lender FE Yes Yes Yes

Appendix Table A2: Sample Composition Robustness. This table reports regression estimates for

the number of previously funded loans by the lender, on a default indicator in Panel A, interest rate in Panel

B, and a dummy that fags request-to-loan conversion in Panel C. In columns 1, 2 and 3, the sample contains

the frst 5, 10, and 20 loans for lenders with at least 5, 10, and 20 loans, respectively. All columns were

estimated using lender fxed efects. All standard errors are clustered by lender, and presented in parentheses.

47