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Federal Reserve Bank of Chicago

Perverse Incentives at the Banks?
Evidence from a Natural Experiment
Sumit Agarwal and Faye H. Wang

WP 2009-08

Perverse Incentives at the Banks?
Evidence from a Natural Experiment∗

Draft: August 20, 2009
Sumit Agarwala and Faye H. Wangb

Abstract
Incentive provision is a central question in modern economic theory. During the run up to
the financial crisis, many banks attempted to encourage loan underwriting by giving out
incentive packages to loan officers. Using a unique data set on small business loan officer
compensation from a major commercial bank, we test the model’s predictions that
incentive compensation increases loan origination, but may induce the loan officers to
book more risky loans. We find that the incentive package amounts to a 47% increase in
loan approval rate, and a 24% increase in default rate. Overall, we find that the bank loses
money by switching to incentive pay. We further test the effects of incentive pay on other
loan characteristics using a multivariate difference-in-difference analysis.
JEL Classification: D3, G2, J3
Key Words: Incentive Compensation; Small Business Lending; Loan Officers; Piece Rate
and Salaries

∗

The views expressed in this research are those of the authors and do not necessarily represent the policies
or positions of the Federal Reserve Board or the Federal Reserve Bank of Chicago. The authors wish to
thank audience members at AEA 2009 annual meeting, FIRS 2009 Prague conference, Federal Research
Bank of Chicago, Summer Research Conference at the Indian School of Business, and University of
Illinois–Chicago for comments. The authors are grateful to Bob Chirinko, Evgeny Lyandres, Rich Rosen,
and Greg Udell for helpful comments.
a
Federal Reserve Bank of Chicago, sagarwal@chi.frb.org.
b
University of Illinois–Chicago, hfwang@uic.edu.

“Despite the vast outpouring of commentary and outrage over the financial crisis, one of
its most fundamental causes has received surprisingly little attention. I refer to the
perverse incentives built into the compensation plans of many financial firms, incentives
that encourage excessive risk-taking with OPM -- Other People's Money”
Professor Alan Blinder, Wall Street Journal, May 28, 2009
1. Introduction
The current financial crisis has led to much debate about incentive provision at
financial institutions.1 While the causes of the mortgage meltdown are complex, many
would argue that perverse economic incentives are an important factor contributing to the
current mess. Specifically, many banks in recent years gave out incentive packages to
encourage loan underwriting. While such financial incentives were designed to promote
greater employee efforts, anecdotal evidence suggests that they also encouraged loan
officers to make more loans to unqualified borrowers.2 In this paper, we study the effects
of an incentive pay based on loan origination by formally modeling two tasks loan
officers perform: their efforts to assess loan quality and their loan origination decisions.
The model allows us to derive testable implications regarding the approval rates, default
rates and other characteristics of booked loans under an incentive pay. In addition, we
provide answers to two central questions in the incentive provision literature: 1) do
incentives matter, that is, do agents respond to contracts that reward performance? 2) are
these responses in the firm’s interest, or do such contracts induce perverse incentives?
We employ a unique data set from a large, national commercial bank on loan
officer compensation, which allows us to empirically study loan officers’ incentives and
loan performances. In January 2005, the management of the bank switched half of the
loan officers from fixed-wage compensation contracts to a new incentive compensation

1
2

See Fahlenbrach and Stulz (2009) and Bebchuck and Spamann (2009).
See Morgenson, Gretchen, “Was there a loan it didn’t like?,” New York Times, November 1, 2008.

package based on loan origination. We examine the status of more than 30,000 small
business loan applications received at the bank from 12 months before the compensation
change to 12 months after and the performance of more than 140 loan officers. The
effects of such incentive pay on approved loan characteristics are dramatic, and are
largely consistent with economic theory.
For this purpose, we develop a theory of loan officers’ incentives to assess loan
quality, with emphasis on the predictions that pertain to the compensation changes at this
bank. Consistent with our model’s predictions, we highlight the following empirical
results:
1) A switch to incentive pay increases the loan approval rate by 47%, the total
number of booked loans by 44%, the average dollar amount of booked loans
by 45%, and the default rate by 24%.
2) The average amount of time spent per loan applications drops by 21%.
3) The above effects of incentive pay are stronger for larger and longer maturity
loans, and loans that contain more soft-information.
4) Although the bank shares the gains in more loan origination, the large amount
of loan defaults results in a welfare loss for the bank.
These results are in line with the model we developed on loan origination, which
takes into account the loan officer’s career concerns and different loan information
regimes. The loan origination process starts when a loan officer receives a loan
application that contains observable information. The loan officer then studies the credit
risks of the borrower and investigates the loan quality by exerting costly effort. We
assume that the probability of revealing the loan quality depends on the loan officer’s

unobservable effort. She then makes a loan origination decision based on the information
she reveals. This information may be hard or soft information. One distinction between
hard information and soft information is that soft information is a signal that cannot be
verified ex post (Petersen, 2004). We study cases where the loan officer makes the loan
approval decision based on unverifiable information, or soft information, and where she
makes the decision based on verifiable information, or hard information. With soft
information lending, the loan officer may lie about the information she reveals in order to
approve the loan.
Much of the literature on incentive provision has focused on the role of outputbased contracts in solving the agency problem for the principal: if effort cannot be
monitored, output-based contracts can address the moral hazard problem induced by
agents’ hidden actions. We show that the effectiveness of such contracts in mitigating
moral hazard depends crucially on whether hard or soft information is used in the loan
origination decision. If hard information is used, incentive compensation works well to
align the loan officer’s incentive to search for good loans. High-powered incentive
compensation, in this case, motivates loan officers to work harder at assessing loan
quality and writing more and better loans. If, however, the lending decision is based on
soft information, incentive compensation may distort the loan officer’s incentive to
“overbook” risky loans. This conclusion is consistent with Inderst (2008).3
The loan officer’s career concern, on the other hand, also plays an important role
as a disciplinary device in soft-information lending. The loan officer trades off a
monetary bonus with her career concern. In this case, incentive compensation may distort
3

Inderst (2008) studies loan officers’ incentive to generate new loan applications and their loan origination
decisions under soft- and hard-information lending. The focus is more on bank competition and the optimal
lending standard.

the loan officer’s effort, depending on the strength of the incentives given. Indeed, we
find that younger loan officers respond to the incentive pay by booking more loans,
without sacrificing loan quality, compared to older loan officers whose loans have a
much higher default rates under the incentive pay. This is consistent with the view that
younger agents have more career concern at stake.
This paper offers new insights into the process of originating small business loans
by focusing on the incentives faced by loan officers and how this affects the underwriting
process. The conflict of interest between the loan officers and the bank has rarely been
studied in the literature, with the exception of Udell (1989). By surveying 140 midwestern banks, Udell finds that banks increase monitoring of their loan officers when the
bank explicitly compensates loan officers for generating more new business. Our model
provides a theoretical foundation for this result.
Our paper also relates to a broader literature on incentive provision to individuals
in organizations, which is a central issue in modern microeconomics (see Prendergast
(1999) for an extensive survey). In the context of compensation contracts, the provision
of incentives usually takes the form of pay-for-performance, or piece-rate contracts
(Lazear and Rosen (1981); Stiglitz (1981); Holmstrom (1982); Green and Stokey (1983)).
Researchers have analyzed the choice of one compensation system over another (see
Gibbons (1998), literature review). In particular, piece-rate payment has the effects of
inducing appropriate effort levels and sorting workers across jobs (Lazear (1986)).
Alternatively, economists also argue that such incentive contracts may give rise to
dysfunctional behavioral responses, where agents emphasize only those aspects of
performance that are rewarded (Baker (1992)). For example, agents may choose quantity

over quality. Following Holmstrom and Milgrom (1990) and Baker (1992), this incentive
problem has become known as multi-tasking, where agents will allocate effort toward
those activities that are directly rewarded and away from the uncompensated ones. Loan
officers’ compensation provides a perfect opportunity for studying multi-tasking.
Incentives are provided on the dimension of quantity and not on quality. We show that
distortion arises for soft-information lending in particular.
Due to the lack of data, there has been little work documenting the effect of
compensation policies on performance. Lazear (1996) studies the performance of auto
windshield workers and documents the incentive and worker selection effects of piecerate contracts. Paarsch and Shearer (1996) provide similar evidence using data on
Canadian tree planters. It is important to bear in mind that these studies document cases
in which the jobs carried out are relatively “simple,” in the sense that 1) performance is
easily measured and 2) the quality is easily observed. The loan officer’s job in our paper
is much more complicated than those in the previous papers. Most importantly, the
quality of the loan officer’s work is not easily measured due to unobservable randomness
of other factors. Our data set is richer, in the sense that it allows us to further analyze the
effects of incentive contracts on multi-tasking behavior of the agents. In addition to
providing empirical evidence for the existing theories, we add to the banking literature by
studying how loan officers’ incentives affect the process of small business loan
underwriting.
The rest of the paper proceeds as follows. Section 2 develops a model of incentive
compensation. Section 3 provides a detailed description of the data. Section 4 provides
empirical results. Section 5 concludes.

2. Model Description
The primary motivation behind the incentive scheme is to increase worker efforts.
A central role that loan officers play in the process of loan origination is to assess loan
quality. In particular, loan origination depends on a significant amount of soft and
subjective information from loan officers (Udell (1989)). In this section, we study the
loan officer’s choice of effort to detect bad loans and her loan origination decisions under
the incentive pay. We find conditions under which such incentive scheme gives rise to
loan officers’ responses that are not in the bank’s interest. 4
A loan application is characterized by (Y, T, q), where Y denotes the requested
loan amount, T is the time to maturity and q captures the ex-ante observable risk profile
of the loan. Assume that q is uniformly distributed on [0,1]. There are two types of loans:
a loan is “good” with probability q, and “bad” with probability 1-q. A good loan is repaid
with probability p—that is, with probability 1-p even a good loan may fail. A bad loan
defaults with certainty. Therefore, the higher q, the lower the probability of default. In
addition, we assume that the bank makes a profit on good loans only and loses money on
bad and questionable ones.
To focus on the loan officer’s choice of effort to assess loan quality, we model a
risk neutral loan officer’s decision to exert unobservable effort, e, and her loan
origination decision. The probability θ that the loan officer reveals the loan type depends
on her effort e. With probability 1- θ, the loan type remains uncertain. We assume that
θ’(e) >0, θ(0)=0 and θ(∞)=1. 5 We also assume that e' (θ ) ∈ [0, ∞) and e" (θ ) > 0 .6

4
5

See Inderst (2008) for an analysis of optimal compensation contracts.
Assume that θ is continuous. Then the inverse function e(.) is continuous and e’(θ) > 0.

Assume that a compensation contract is given by a + b(Y), where a is the base
salary and b(·) is the bonus based on the amount of loans originated. Assume that b(Y) ≥
0, and b’(Y) >0. For notional simplicity, we normalize a to zero.7 In the case a loan is
denied, the loan officer receives no bonus component. If a loan is booked, she receives
b(Y) – e(θ) – ρc(T),
where ρ is the probability that a loan defaults, and c(T) is the negative career
consequences of a defaulted loan. Specifically, we assume that c(T) takes the functional
form c(T ) = Ke −δτ (T ) , where K>0 is the negative career shock to the loan officer when a
default occurs, and τ(T) is the expected default time of a loan with maturity T, and is an
increasing function of T.8 δ is the discount rate.
We focus on a loan officer’s decision to assess loan quality under an incentive
scheme that rewards her for loan originations. The basic game proceeds as follows: a loan
officer reviews a loan application characterized by (Y, T, q). She exerts costly effort e,
and reveals the loan type with probability θ(e). For soft-information lending, the loan
officer has private access to information about the borrowing firm that is “hard to
quantify, verify and communicate through the normal transmission channels of a banking
organization.” (Berger and Udell (2002).) She can conceal, or lie about, the information
she reveals. On the other hand, if lending is primarily based on hard information, the loan
officer cannot conceal the information she reveals and this information is verifiable ex
post by the bank. The loan officer then makes an approval decision and receives payoffs
accordingly.

One such example of the functional form of e(.) would be e(θ)= tan(θ*π/2).
In an optimal contract, a is set such that the loan officer’s individual rationality condition is satisfied.
8
This assumption can be justified by a model with a constant Poisson intensity of default.
7

With hard-information lending, the loan officer no longer plays an active role at
the loan origination stage, since only loans revealed to be good may be booked. The
problem reduces to a pure moral hazard problem, in which the only decision factor is the
loan officer’s hidden effort. Consistent with economic theory, incentive pay promotes
greater effort without sacrificing the loan quality. We provide a formal analysis in
Appendix A.

2.1 Perverse incentives with soft-information lending
In this section, we study the loan officer’s decisions in a soft-information lending
regime. The loan officer makes a lending decision based on her privately observed soft
information, which cannot be verified by the bank. She may lie about the soft information
she reveals in order to book the loan. In this case, whether monetary incentives create
perverse incentives depend on the loan officer’s career concern and the strength of the
incentives.
We analyze the soft-information lending problem in two steps. We first focus on
the approval decision, then derive the loan officer’s optimal effort level. The loan
officer’s approval decision now depends on the information revealed and privately
observed by her: the loan type is good (G), bad (B), or no information is revealed, that is,
the loan quality remains questionable (Q). In order for the loan to be approved, the loan
officer may revise her subject input of the loan application’s risk rating downwards for a
bad or questionable loan. If a loan is booked, the loan officer gets the following payoffs
based on revealed types:

P(G ) = b(Y ) − (1 − p)c(T ) − e(θ ),
P( B) = b(Y ) − c(T ) − e(θ ),
P(Q) = b(Y ) − (q (1 − p ) + (1 − q ))c(T ) − e(θ ).
Depending on the loan officer’s career concern and the size of the monetary
bonus, which is tied to the amount of the loan requested, it may be in the loan officer’s
interest to not only book the good loans but also the questionable ones. In other words,
monetary bonus may induce her to take more risks for these loans.
If the incentives are small enough or the loan officer’s career concern is large
enough such that the she only writes good loans, we find that the effect of incentive
contract is exactly the same as with the hard-information case: incentive compensation
promotes effort without sacrificing loan quality. If the monetary bonus gets too large,
such that the loan officer will accept any loan applications even the bad ones, we show
that the loan officer has no incentive to exert effort and that incentive packages are purely
costly to the bank. We discuss these two cases in more depth in Appendix B.
We focus on the case where the loan officer approves both the good loans and the
questionable ones, but her career concern is large enough that she wants to avoid writing
bad loans. Recall that if no information is revealed, the prior assumption that the loan is
good with probability q, in which case, the bank loses money on such loans. Such
questionable loans cannot be booked with hard-information lending, nor does the loan
officer have incentives to book such loans when the monetary incentive is small relative
to her career concern. However, as the monetary incentives get large, the loan officer
may find it profitable to write such loans at the bank’s expense. This is where an
incentive pay may induce perverse incentives.

The question becomes when will the loan officer be incentized to write
questionable loans? The loan officer will revise her subjective input for the loan quality
upwards for a questionable loan when the following constraints are satisfied:
b(Y ) − (1 − p )c(T ) ≥ 0,
b(Y ) − (q (1 − p ) + (1 − q ))c(T ) ≥ 0,
b(Y ) − c(T ) < 0.

We next analyze the loan officer’s choice of effort under the above conditions.
Recall that when the loan officer exerts costly effort e, with probability θ the loan type
will be revealed, and with probability 1- θ its type remains questionable, in which case
the loan is good with probability q, and bad with probability 1-q. The loan officer has
incentive to book both the good loans and the questionable ones if the monetary incentive
is large or when her career concern is relatively weak. The loan officer gets payoff
qθ[b(Y) – (1-p)c(T)]+(1–θ)[ b(Y) – (q(1-p)+1-q)c(T)] – e(θ),
yielding a FOC:

q[b(Y ) − (1 − p)c(T )] − [b(Y ) − (q(1 − p) + 1 − q)c(T )] = e' (θ )
⇒
− (1 − q)b(Y ) + (1 − q )c(T ) = e' (θ ).
From the above FOC, we can derive the approval rate, the default rate of booked
loans, and other comparative statics. We summarize these results in the following
proposition. All proves are in the appendix.

Proposition 1 If the monetary incentive is large or the loan officer’s career concern is

small, the loan officer will book a loan unless it is revealed to be bad. In this case,

The loan officer’s effort level to investigate loan quality decreases with the

loan amount, the time to maturity, the ex-ante score of the loan and the strength of
monetary incentives, and increases with her career concern.
2.

The probability of loan origination decreases with the loan officer’s effort,

increases with the loan amount, the time to maturity, the ex-ante score of the loan, and
the strength of monetary incentives, and decreases with her career concern.
3.

The probability of defaults decreases with the loan officer’s effort, increases

with the loan amount, the time to maturity, and the strength of monetary incentives, and
decreases with her career concern.
We see that monetary incentives induce preserve incentives in these that they
reduce the loan officer’s effort to assess loan quality. The intuition is as follows. The loan
officer receives a monetary bonus only when a loan is booked. Since both the good loans
and the questionable ones are booked, her effort to investigate loan quality only affects
her booking decision of bad loans, which she will not book weighing in her career
concerns. In other words, the loan officer’s effort decreases the likelihood that she will
book a loan and receive the monetary bonus. Career concern, on the other hand, serves as
an effective disciplinary device and motivates the loan officer to exert effort to avoid
booking bad loans.
Interestingly, the ex-ante quality of a loan, q, does not predict the likelihood of
loan origination or the default probability of a booked loan. Taking the derivatives with
respect to q to the FOC, we get b(Y ) − c(T ) = e' ' (θ ) ∂∂θq* . Since c(T) > b(Y), it is easy to
see that the loan officer’s effort decreases with q. In this case, the loan officer increases
her effort to investigate lower quality loans to avoid booking a bad loan. Although these

lower quality loans are more likely to be bad and will default with greater probability, the
loan officer also spends more effort to investigate them and avoids making loan
originations. Thus, there is no direct relationship between the score and the expected
default probability.

2.2 Empirical Implications

In the previous section, we analyze whether incentive pay creates perverse
incentives for loan officers by studying their choices of effort to investigate loan quality
and their loan origination decisions. While the choice of effort is hidden, the probability
of default, which decreases with the level of effort, is observable. We thus have the
following predictions:
Prediction 1 Under incentive pay, the likelihood that a loan is booked increases with the

loan amount Y, the time to maturity T, and the ex-ante score q.
Prediction 2 Under incentive pay, the default probability of booked loans increases with

the loan amount Y and the time to maturity T, when the strength of monetary incentive is
large.
Prediction 3 The likelihood of booking a loan decreases with the loan officer’s career

concern.
Our analysis on information verifiability and compensation schemes sheds lights
on how incentives affect the subsequent loan performance based on the amount of soft
information used in the loan origination process. We show above that with hardinformation lending, incentive compensation encourages loan officers to investigate loan

quality and to avoid booking bad loans. Only when information becomes unverifiable
does a monetary bonus distort incentives.
Prediction 4 Both the likelihood of loan origination and the default probability

are higher for more informationally opaque loans with soft-information lending than with
hard-information lending.

3. Description of the Market and Data
3.1 The Loan Officer’s Job Function

Loan officers play a central role in the process of loan origination. The process
begins when the loan officers initiate contacts with the firms to determine their needs for
loans. After the initial contact has been made, loan officers assist the clients through the
process of loan application. The loan officer gathers personal and business information
about the borrower and explains the different types of loans and credit terms available to
the client. Loan officers then verify the basic information of the borrower to assess the
creditworthiness of the borrower and the probability of repayment. Specifically, loan
officers assign credit scores to the potential borrower and determine collateral
requirements. A loan that would otherwise be denied may be approved if the client can
provide the lender with appropriate collateral property pledged as security for the
repayment of a loan. For a more detailed description of the process, see Agarwal and
Hauswald (2007, 2008).
Loan officer compensation usually takes the form of fixed payment salary or
incentive plans based on loan origination. Neither of these compensation schemes is tied
to loan repayment or failure and the eventual profitability of these loans. Such

compensation contracts may distort loan officers’ incentive and encourage them to make
any loan, regardless of its quality. While bonuses based on loan profitability would have
the advantage of giving direct incentives to search out good credit risks, such
performance measure would also give the loan officers greater risk, because many things
can happen to borrowers that are essentially unknowable when a loan is written. The
additionally imposed risks on loan officers are costly to the bank through higher wages.
Baker (2002) argues that the trade-off between risk and distortion in this case is made in
favor of lower risk and higher distortion.

3.2 Data from a natural experiment

The data set used in this paper comes from a large, national commercial bank.
Starting January 2005, the management of the bank implemented a new incentive
compensation package for half of the small business credit services approval officers
(henceforth referred to as the treated group). The other half of the loan officers remained
on fixed wage (henceforth referred to as the control group). The selection of the loan
officers was quasi-random. The management had multiple legacy portfolios as a result of
earlier merger and acquisition activities over the years. They were broadly being
managed under two legacy database management systems. The portfolio of loans under
both these management systems had identical underwriting standards, geographical focus,
portfolio management practices, and loss outcomes (see Table 3a and 3b). To evaluate
the success of the incentive compensation package, the bank implemented the change on
one of the management systems while leaving the other on fixed wage compensation.

The incentive package provides a “pay for performance” bonus opportunity based
on individual results. Before that, all loan officers were paid a fixed salary. Specifically,
loan officers will receive an annual bonus based on the percentage of new money
applications booked compared to the previous year, the type of decisions made, and the
timeliness of the decision. The details of the incentive package are summarized in Table
1 and 2. The goal of this program is to “recognize and reward those associates whose
performance most aggressively contributes to the overall success of small business credit
services,” and “to attract and retain outstanding talent.”
The incentive plan comes with a quality assessment. In order for a loan officer to
be eligible to participate in the incentive program, their total of unsatisfactory
underwriting must not exceed 5% of total approvals, reviewed in a post approval review
process.
The data cover 12 months before the compensation change and 12 months after.
To study the effects of incentive compensation on loan officers’ incentives and the
implications for subsequent loan origination decisions and characteristics of the booked
loans, we employ two control groups: data on loan officers and loans of the group before
the implementation of the incentive plan, and data from the other half of the loan officers
whose salary remained fixed during the same period. Data from the control group allow
us to better control for macroeconomic fluctuations over this period. Our sample contains
data on more than 140 loan officers and the status of 15,784 loan applications in the
treated group and 14,484 loan applications in the control group. The data are summarized
in Table 3 and Table 4.

4. Empirical Evidence
4.1 Do incentives matter?
4.1.1 Loan Origination Decisions

Not surprisingly, loan officers are motivated to book more loans under the
incentive pay structure. Table 3a summarizes the status of loan applications for both
groups in year 2004 and 2005. While there is no apparent increase in the number of new
applications from year 2004 to year 2005, the number of booked loans increases by 1,132
in the treated group, a 44.4% increase. Approval rate in the treated group goes up from
32% to 47%, consistent with our model’s prediction.
Also consistent with Prediction 1, in addition to booking more loans, loan officers
in the treated group are booking larger loans and longer maturity loans. Table 3a shows
that the average dollar amount of booked loans increases by $96,470, a 44.7% increase.
Table 3d shows that loan officers are more likely to approve bigger loans than smaller
loans and longer maturity loans than shorter maturity loans. The effect is stronger after
the implementation of the incentive plan: Big loans, those with requested amount above
$700,000, have an approval rate of 55% under the incentive pay, compared to an
approval rate of 33% for small loans. Long term loans have a 52% approval rate
compared to a 37% approval rate for short term loans.

4.1.2 Are Loan Officers Booking Riskier Loans?

One potential concern of paying piece rates is that quality may deteriorate. In our
case, does a piece rate contract distort incentives in a way that results in loan officers
booking riskier loans? The model suggests that loan officers have stronger incentives to

investigate and approve loan applications with inferior ex-ante quality. One observable
key risk factor that lenders use to assess qualifying borrowers for loans is the loan-tovalue ratio (LTV). Our subsequent multivariate analysis also confirms that a higher LTV
value predicts higher loan default probability. Table 3b shows that while the average
LTV of loan applications decreased slightly from 2004 to 2005 in the treated group, the
LTV of booked loans increases from 76.24 to 84.10, a 10.32% increase, suggesting that
loan officers were booking riskier loans.
Table 3c shows a noticeable increase in the number of booked loans secured by
collaterals after year 2005 in the treated group. The average percentage of booked loans
without collateral goes down by 13%, a 55% drop compared to the average percentage
prior to the implementation of the incentive plan, whereas the pool of applicants without
collateral does not change. Berger and Udell (1990) find that collateral is associated with
ex-ante observably riskier borrowers and riskier loans. The increase in the percentage of
secured loans adds to the evidence that loan officers are approving loans from riskier
borrowers.
On the other hand, Table 3b shows that the average business scores and personal
scores of approved loans go up in year 2005, and the internal risk ratings go down. Since
the internal risk ratings reflect a large amount of soft information possessed by loan
officers, this implies that loan officers have been identifying safer borrowers since the
implementation of incentive compensation (also see Agarwal and Hauswald (2007,
2008)).

4.2 Who respond to incentives and when?

As suggested in the literature, incentive compensation may have a sorting effect
of attracting more able workers (Lazear (2000)). Table 4a shows that the treated group
attracts younger loan officers and more male loan officers after year 2005, who are likely
to be more aggressive in their career paths – the average age of the loan officers in the
treated group goes down from 41 to 37, and the percentage of males goes up from 68% to
74%. There is also evidence of higher turnover in the treated group, as it is reflected by
lower average tenure.9
Indeed, in Table 5a, we find that loan officers in the age group 25-34 are most
aggressive at approving loans, while they have the lowest default rates among all age
groups. Consistent with our model, achieving a higher approval rate without sacrificing
loan quality is possible if the loan officers put in more effort to investigate the loan
quality. Thus, the evidence above suggests that younger loan officers, who are likely to
have stronger career concerns, worker harder than older loan officers. This effect gets
amplified by the incentive pay.
We find similar evidence for male loan officers compared to female loan officers.
Table 5b reports that after the implementation of the incentive pay, the gap in the
approval rate between male and female loan officers goes up. While the female loan
officers also approve more loans under the incentive pay, the default rate among these
loans is much higher than in year 2004, and higher than their male colleagues. This
evidence is consistent with the literature that female workers have short careers, and thus
less career concerns than males. In the context of our model, such loan officers are most

Our results are both qualitatively and quantitatively very similar if we have a constant pool of loan
officers before and after the treatment period.

likely to have distorted incentives under the incentive plan, in the sense that loan quality
deteriorates the most.
We find further evidence that loan officers’ career concern becomes an important
disciplinary device under the incentive pay in our multivariate analysis.
A less studied question is the response time of the agents to incentive contracts.
Do agents respond to incentives immediately, as assumed in the theoretical literature on
incentive contracts, or is there a learning curve? In other words, if incentive contracts
leave rooms for agents to game the compensation system, will agents respond to it as
predicted by the rational theory immediately?
The analysis of this question also provides a robustness check for our results. That
is, was the information leaked to the loan officers prior to the actual implementation of
the incentive pay in January 2005 so that loan officers may hold back from approving
loans before they can receive the bonus linked to those booked loans? If there was such
an information leak and loan officers did hold back booking loans in year 2004, our
results would be weakened.
Table 6 provides a month by month break-down of the status of loan applications.
We plot the loan approval status in Figure 1 and observe a significant increase in
approval rate and a decrease in rejection rate since January 2005. Figure 2 shows that
both the average dollar amount of booked loans and the percentage of loans booked
increase since January 2005, and the structural change takes place in January 2005.
Figures 3, 4 and 5 show similar structural breaks in January 2005 for LTV, days-spentper-loan-application, and internal risk rating.

In summary, we find evidence that loan officers respond immediately as predicted
by economic theory to the incentive pay, and we find no evidence that loan officers hold
back approving loans prior to January 2005.

4.3 Does an incentive pay induce perverse incentives?

Much of the study in the literature is on whether agents’ responses to incentive
pay are in the firm’s interest. Our analysis and preliminary empirical results above show
that incentive pay may create perverse incentives to loan officers by encouraging
overbooking of inferior loans, especially for larger and longer maturity loans, whereas
loan officer’s careern concerns serve as a good disciplinary device. We provide further
evidence with a multivariate analysis.

4.3.1 Multivariate Analysis

We examine whether during the treatment period, (i) the treated loan officers are
more likely to approve or decline loan applications; and (ii) the booked loans are more
likely to default. We employ the standard logit model specification to estimate these
models.
Our results reveal that loan officers’ inputs of internal risk ratings, LTV of the
loans, loan amounts, and collateral are important for loan officers’ approval decisions.
Table 7a shows that these variables are statistically significant and marginally important
for loan approvals. Consistent with our intuition, riskier loans and larger loans are less
likely to be approved, whereas collateral requirements increase loan approval rates.
Moreover, we see that the variable, Treated Dummy*2005 Dummy is significantly

positive and marginally large, indicating that the implementation of the new incentive
package in the treated group increases loan approval rates. While larger loans possess risk
of a greater loss, the implementation of the incentive plan encourages loan officers to
book larger loans, consistent with our model’s prediction; we see in Table 7a that the
variable, Log(loan amount requested)*treated*2005 is significantly positive. Similarly,
loan maturity*treated*2005 becomes significantly positive, indicating that loan officers
in the treated group are more likely to book longer maturity loans as predicted by the
model.
We further analyze the subsequent loan performance of approved loans by
examining the default probability of the loans based on loan characteristics. The results
are reported in Table 7b. We confirm that internal risk ratings, LTV of the loan, loan
amount requested, and loan maturity are important factors in predicting loan defaults.
Collateral requirements, however, decrease the probability that a loan defaults, consistent
with the moral hazard view of collateral requirements. In addition, we also find that Days
Spent per Loan is negative, suggesting that the longer a loan officer spends on a loan
application, the less likely it will default. We can interpret the number of days spent on
the loan application as a measure of the loan officer’s effort to investigate loan quality.
The harder the loan officer works, the less likely it is that an approved loan will default.
This variable becomes especially important after the implementation of the incentive plan.
Furthermore, we see evidence that loan officers in the treated group are booking larger
and longer maturity loans that are riskier and more likely to default. The variables Loanto-Value of the Loan * treated * 2005 and Loan maturity * treated * 2005 are both
significantly positive.

We further study loan officer’s fixed effect on loan approval and default rates.
Table 8 summarizes the results from logit regressions of loan approval decisions and
defaults on loan officer’s characteristics. Consistent with our prior findings, internal risk
ratings, LTV of the loan, loan amount requested, loan terms, and collateral requirements
are the key risk factors that drive approval decisions and predict subsequent loan defaults.
Moreover, Treated Dummy*2005 Dummy is highly significant in both regressions,
suggesting that loan officers in the treated group are more likely to book loans in year
2005, and that these approved loans are more likely to default.
Our detailed micro-level information on loan officers allows us to study questions
such as how the incentive plan interacts with loan officers’ career concerns. Our model
indicates that loan officers’ career concerns serve as a powerful control mechanism that
mitigates the distortion of incentives caused by monetary bonus. A loan officer with
greater career concern will be more conservative in making loan approval decisions. We
find evidence of this from the results in Table 8. Using loan officers’ ages and number of
years on the job (tenure) as proxies for career concerns, we see that loan officers’ career
concerns become significant after the implementation of the incentive plan in year 2005.
The career concerns are insignificant on their own, but become significant after
interacting with the treated dummy and the year 2005 dummy. Following previous
literature, we argue that career concern is strongest when a person is just starting her
career, thus tenure measures the reverse strength of career concern. We find that the
marginal effects of Loan officer tenure * treated dummy * 2005 dummy and Loan officer
tenure (sq) * treated dummy * 2005 dummy are 7.24% and 3.98%, respectively. That is,
controlling for a loan officer’s age, the fewer years on the job, the less likely that she

books a loan. Interestingly, tenure does not predict default probability linearly. We see
that the marginal effect of Loan Officer Tenure * Treated Dummy * 2005 Dummy on
loan default probability is 6.77%. The positive number is consistent with our findings of
loan officers’ approval decisions that the longer the tenure, the smaller the career
concern, and thus, the more likely that the loan officer books riskier loans motivated by
monetary incentives. Loan Officer Tenure squared, however, has the opposite effect in
predicting loan defaults. Loan Officer Tenure (sq), Loan Officer Tenure (sq) * Treated
Dummy, and Loan Officer Tenure (sq) * Treated Dummy * 2005 Dummy have negative
marginal effects. In particular, the marginal effect of Loan Officer Tenure (sq) * Treated
Dummy * 2005 Dummy is -1.91%, much larger than the other two, confirming that loan
officers’ tenure is an important factor in loan approval decisions after the implementation
of the incentive package. We interpret this as a “learning-on-the-job” effect. The longer
the loan officer is on the job, the more experience she gains on detecting loan quality,
thus, the lower the likelihood of booking a bad loan. This learning effect, however,
becomes important only when the time on the job is sufficiently long.
We also observe that Days Spent Per Loan*Treated Dummy * 2005 Dummy is
marginally important for both the loan approval decision and loan default probability. We
interpret Days Spent per Loan as a proxy for the loan officer’s effort to assess loan
quality. We see that the longer the time spent reviewing the loan application, the less
likely that it will be approved, and the less likely that the loan will default. In addition,
the effect of this variable is large only after the implementation of the incentive plan
among the loan officers in the treated group.

4.3.2 Soft-information vs. hard-information lending

Our theoretical analysis suggests that whether incentive pay induces perverse
incentives for loan officers depends crucially on the type of lending regime, soft-or hardinformation lending. Hard information contained in a loan application is captured by its
observable risk factors, such as Experian scores, LTV, loan amount, loan terms, and
maturity, whereas the internal risk rating contains loan officers’ subjective input, much of
which is soft information. Following Agarwal and Hauswald (2008), we capture the
residual soft information collected by the bank by orthogonalizing the internal risk rating
with the above set of publicly available information controlling for branch, year, and
industry effects. Hence, we estimate the following regression:
IntRiskRating i = β 0 + β 1 XCI i + β 2 XPARi + u i ,
where XCI contains the Experian business score and personal score, and XPAR includes
other publicly available risk factors such as LTV, loan amount, loan terms, loan maturity,
and personal and business collateral. We refer to the residual from the above regression
as “Internal Risk Rating Residual.”
We then estimate the logit regression as in the previous sections by replacing
Internal Risk Rating with the residual from the above regression. The results are reported
in Table 9. We see that while the residual information itself is not significant in
predicting default, it is significant for the treated group in year 2005. This suggests that
under the incentive plan, loan officers book riskier loans that contain more soft
information. This observation is consistent with the model’s prediction on the interaction
between incentive compensation and soft information lending.

Furthermore, we form quintiles based on the size of the Internal Risk Rating
Residual for both the treated group and the control group in year 2004 and 2005, with the
top quintile containing loans with the largest residual, or the greatest amount of soft
information. Our theoretical analysis suggests that under the incentive pay, loan officers
are more likely to make reckless approval decisions for loans that contain more soft
information. For these loans, both the approval rate and the default rate are higher than
those with mostly hard information. Indeed, Table 10 reports the ratio of the approval
rates and the default rates of the loans in the highest quintile to those in the lowest
quintile. During the treatment period, loans that contain the greatest amount of soft
information are 2.36 more likely to be approved than those that contain the least amount
of soft information. Moreover, these loans are 3.05 times more likely to default than
those with the least amount of soft information.

4.3.3 Welfare Analysis

Finally, did the bank profit from the incentive pay? Although there are more
defaults, loan officers are indeed booking more loans, of which the bank can make a
profit from the fees. To answer this question, we carry out a simple welfare analysis.
Welfare = Volume × fees – Wages – Loss given default – other unobservables
Here the unobservables include the externalities of not making a loan, the cost of
funding for the bank, and utility loss by extending additional effort.
The marginal welfare, therefore, is
∆ welfare = increased volume × fees – increased wages – ∆ Loss given default

Table 4a shows that the average income of loan officers in the treated group
increases by $6,597 from $42,422 to $49,019 from year 2004 to 2005. Under the
incentive plan, $6,500 is amount of bonus that a loan officer gets if she reaches 100% of
the performance goal (see Table 2). This suggests that the 100% goal may create a focal
point for loan officers to aim for.
Following industry standards, fees are assumed to be 2% of the loan originated
and the loss given default is assumed to be 50% (see, Agarwal et. al. 2007).
Table 11 reports the marginal welfare of the incentive pay. We see that the bank
experiences a loss of $6,880,446 in year 2005. The program was eventually discontinued
in the first quarter of 2006 due to losses to the bank.

5. Conclusion

A central question addressed by much research on incentive compensation has
been whether incentive contracts provide the right incentives. In this paper, we propose a
model that studies two tasks that loan officers perform in the loan origination process:
their efforts to investigate loan quality and their loan origination decisions. Our model
demonstrates that monetary incentives may distort loan officers’ incentives to identify
bad loans. The distortion is greater under a soft-information lending regime. Loan
officers’ career concerns serve as a good disciplinary device to mitigate the agency
problem.
Using a unique dataset from a major national commercial bank that implemented
an incentive compensation package for half of its loan officers, we are able to test many
of the model’s predictions. We find that observable risk factors such as Experian scores,

LTV, loan amount, term, maturity, and collateral are important for loan officers’ approval
decisions and predict subsequent loan defaults. Moreover, the internal risk ratings, which
contain a large amount of soft information, also predict defaults.
Motivated by the incentive package, loan officers in the treated group book more
loans and, in particular, book larger and longer maturity loans, consistent with the
model’s predictions. These larger and longer maturity loans are more likely to default
within two years of loan origination, indicating that these are riskier loans on average.
Using loan officers’ age and tenure as proxies for their career concerns, we find that loan
officers with greater career concerns are more conservative in their approval decisions,
and their booked loans have lower default rates. Also consistent with the model, we find
evidence that loans with a greater amount of soft information are more likely to be
approved under the incentive scheme. These loans, however, are also more likely to
default.
Our research suggests that hardening the soft information used in lending
decisions will reduce distortion of incentives of piece-rate contracts. Moreover,
counteracting incentives with more stringent lending standards may also reduce some of
the agency problems.

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Table 1: Performance Metric
Metric

Weighting

Annual Goal

Pull-through yield

50%

48% of new money applications
booked based on applications
received from January 1November 30, 2005.

Decision Points

25%

1,080 points*

Application to decision time (%

25%

68.5%**

met)

*Decision points are allocated as follows: (i) Score + (all products) = 1 point ; (ii) S/L – basic
(term $500M - $1MM) = 2 points; (iii) S/L (term $1 - $3MM, lines of credit < $750M) = 3
points; (iv) S/L – complex (term > $3MM, lines of credit > $750M) = 5 points; (v) Letters of
credit (S/L) = 2 points; (vi) Commercial card (S/L) = 2 points
**Decision time guidelines are as follows: (i) Score + guideline is 3 days; (ii) S/L guideline is 5
days

Table 2: Incentive Plan
Total Score

Incentive award

Less than 80% of goal

No award

80% of goal

$4,000 + $125 per percentage point above 80% of goal

100% of goal
120% of goal

$ 6,500 + $150 per percentage point above 100% of goal
$ 9,500 + $175 per percentage point above 120% of goal

Notes: A brief description of the incentive plan that outlines the score achievement and incentive
award for each score band.

Table 3a: Descriptive Statistics of Loan Applications – Loan Status
Variable
Number of Loan Requests
Number of Loans Booked
Approval Rate
Number of Defaults
Default Rate
Avg $ of Loans Requested
Avg $ of Loans Booked
Days Spent/Loan Requested

2004 (January - December)
Control Group
Treated Group
Mean
Std.
Mean
Std.
6920
7996
2192
2548
31.68%
31.87%
91
107
4.15%
4.20%
$ 455,240
$ 336,805
$ 426,480 $ 378,698
$ 224,614
$ 279,361
$ 216,048 $ 229,403
1.38
0.85
1.35
0.70

2005 (January - December)
Control Group
Treated Group
Mean
Std.
Mean
Std.
7564
7788
2744
3680
36.28%
47.25%
119
192
4.34%
5.22%
$
454,141 $
369,635 $
444,137 $
381,829
$
253,219 $
257,801 $
312,518 $
404,976
1.32
0.75
1.06
0.53

Table 3b: Descriptive Statistics of Loan Applications – Risk Profile
Variable
Internal Risk Ratings
Business Score of Applicants
Business Score of Booked Loans
Personal Score of Applicants
Personal Score of Booked Loans
LTV of Applicants
LTV of Booked Loans

2004 (January - December)
Control Group
Treated Group
Mean
Std.
Mean
5.23
1.84
5.38
1.52
200.86
72.23
195.88
75.87
184.87
68.95
186.11
78.92
731.85
70.31
725.41
68.06
716.69
87.44
718.90
88.58
61.28
43.00
65.30
44.03
72.99
31.48
76.24
30.90

2005 (January - December)
Control Group
Treated Group
Std.
Mean
Std.
5.44
1.3
4.93
1.53
195.99
75.27
200.36
68.47
185.50
93.09
196.09
87.01
725.91
74.39
728.06
76.72
719.54
98.25
725.77
66.51
65.16
46.87
63.05
43.48
74.90
33.10
84.10
50.10

Table 3c: Descriptive Statistics of Loan Applications – Collaterals
Variable
Avg % of Applicants with Personal Collateral
Avg % of Applicants with Business Collateral
Avg % of Applicants with No Collateral
Avg % of Booked Loans with Personal Collateral
Avg % of Booked Loans with Business Collateral
Avg % of Booked Loans with No Collateral

2004 (January - December)
Control Group
Treated Group
Mean
Std.
Mean
69%
46%
68%
47%
25%
44%
26%
44%
5%
23%
6%
24%
9%
35%
7%
26%
63%
48%
68%
47%
27%
42%
25%
44%

2005 (January - December)
Control Group
Treated Group
Std.
Mean
Std.
64%
48%
70%
46%
28%
45%
24%
43%
8%
28%
6%
24%
4%
20%
19%
29%
67%
47%
69%
49%
28%
45%
11%
46%

Notes: Panels a, b, and c outline the loan statistics, risk profile, and collateral requirements for the control and treated samples during and control
and treatment time period.

Table 3d: Descriptive Statistics of Loan Applications – Loan Size and Maturity
Treated Group: Loan Approval and Performance
% approval
Loan Size / Type
Big loan (> $700K)

2004
% rejection

% walk away

% approval

2005
% rejection

% walk
away

35%

57%

55%

36%

Medium loan ($250K-$700K)

31%

56%

13%

49%

40%

11%

Small loan (< $250K)

27%

55%

18%

33%

51%

16%

Long term loan (Larger than One Year)

32%

55%

13%

52%

36%

12%

Short term loan (One Year)

29%

58%

13%

37%

51%

12%

Treated Group: Risk Profile, Collateral

Loan Size / Type
Big loan (> $700K)

Personal
Collateral

2004
Risk Score

LTV

Personal collateral

2005
Risk Score

LTV

717

713

Medium loan ($250K-$700K)

720

Small loan (< $250K)

721

725

Long term loan (Larger than One Year)

721

717

Short term loan (One Year)

717

720

Table 3d: Statistics of Loan Applications – Loan Size and Maturity (Con’t)

Control Group: Approval and Performance
% approval
Loan Size / Type
Big loan (> $700K)

2004
% rejection

% walk away

% approval

2005
% rejection

% walk
away

35%

60%

34%

59%

Medium loan ($250K-$700K)

31%

56%

13%

30%

57%

13%

Small loan (< $250K)

27%

56%

18%

26%

57%

17%

Long term loan (Larger than One Year)

32%

56%

13%

31%

57%

13%

Short term loan (One Year)

29%

59%

13%

29%

58%

13%

LTV

Control Group: Risk Profile, Collateral

Loan Size / Type
Big loan (> $700K)

Personal
Collateral

2004
Risk Score

LTV

Personal collateral

2005
Risk Score

737

724

Medium loan ($250K-$700K)

751

723

Small loan (< $250K)

722

724

Long term loan (Larger than One Year)

727

752

Short term loan (One Year)

739

730

Notes: Panels d outline the loan approval, performance, and collateral requirements for the control and treated samples during and control and
treatment time period for a given loan size and type.

Table 4a: Demographics of Loan Officers
2004
Variable
Total Number of Loan Officers
Gender – Male
Income
Age
Tenure

2005

Control Group
63
58%
$42,363
43
3.49

Treated Group
70
68%
$42,422
41
3.66

Control Group
65
59%
$42,976
43
3.58

Treated Group
65
74%
$49,019
37
2.91

Table 4b: Percentage of Loan Officers in Each Age Group
2004

2005

Loan Officer Age

Control Group

Treated Group

Control Group

Treated Group

25-34
35-44
45-55
55+

22.33
24.98
31.67
21.02

26.92
29.07
24.66
19.35

26.90
24.30
27.00
21.80

33.04
37.58
21.04
8.34

Notes: Panels a and b outline the loan officer demographics – gender, income, age, tenure, and fraction of loan officers by age groups for the
control and treated samples during and control and treatment time period for a given loan size and type.

Table 5a: Loan Status in the Treated Group for Each Age Group – 2004
Loan Officer Age

% of Loan Officers

Approval Rate

Loan Size

Credit Scores

Default Rate

25-34

26.92

34.91

142,029

5.32

3.46

35-44

29.07

33.01

166,083

5.37

4.36

45-55

24.66

29.78

211,327

5.4

4.84

55+

19.35

26.00

232,022

5.37

4.46

Table 5a: Loan Status in the Treated Group for Each Age Group – 2005
Loan Officer Age

% of Loan Officers

Approval Rate

Loan Size

Credit Scores

Default Rate

25-34

33.04

56.08

229,116

5.04

4.73

35-44

37.58

52.70

244,892

4.97

4.74

45-55

21.04

43.46

328,117

4.89

5.99

55+

8.34

40.01

387,727

4.81

6.58

Notes: Panel a outline the fraction of loan officers by age groups, the approval rates, loan sizes, credit scores, and default for the control and
treated samples during and control and treatment time period for a given loan size and type.

Table 5b: Percentage bookings in the treated group by gender groups – 2004
Loan Officer Gender

% of Loan Officers

Approval Rate

Loan Size

Credit Scores

Default Rate

Male
Female

68.40
31.60

32.28
28.41

193,092
185,029

5.27
5.39

4.51
3.71

Table 5b: Percentage bookings in the treated group by gender groups – 2005
Loan Officer Gender

% of Loan Officers

Approval Rate

Loan Size

Credit Scores

Default Rate

Male

74.30

51.19

299,101

5.03

5.21

Female

25.70

40.27

280,583

4.79

5.26

Notes: Panel b outline the fraction of loan officers by gender groups, the approval rates, loan sizes, credit scores, and default for the
control and treated samples during and control and treatment time period for a given loan size and type.

Table 6: Monthly Loan Approval Status
Number of Loans in the Treated Group
Months
Jan-04
Feb-04
Mar-04
Apr-04
May-04
Jun-04
Jul-04
Aug-04
Sep-04
Oct-04
Nov-04
Dec-04
Jan-05
Feb-05
Mar-05
Apr-05
May-05
Jun-05
Jul-05
Aug-05
Sep-05
Oct-05
Nov-05
Dec-05

Received
548
582
688
679
747
742
759
639
618
649
692
653
584
593
638
531
764
783
662
642
643
635
688
625

Rejected
380
346
354
344
342
344
370
313
401
389
411
416
262
243
204
276
316
268
249
289
255
258
297
289

69.34%
59.45%
51.45%
50.66%
45.78%
46.36%
48.75%
48.98%
64.89%
59.94%
59.39%
63.71%
44.86%
40.98%
31.97%
51.98%
41.36%
34.23%
37.61%
45.02%
39.66%
40.63%
43.17%
46.24%

Withdraw
76
133
71
92
75
83
76
88
54
107
84
99
93
74
71
73
57
66
61
74
75
75
87
96

13.87%
22.85%
10.32%
13.55%
10.04%
11.19%
10.01%
13.77%
8.74%
16.49%
12.14%
15.16%
15.92%
12.48%
11.13%
13.75%
7.46%
8.43%
9.21%
11.53%
11.66%
11.81%
12.65%
15.36%

Booked
92
103
263
243
330
315
313
238
163
153
197
138
229
276
363
182
391
449
352
279
313
302
304
240

Number of Loans in the Control Group
16.79%
17.70%
38.23%
35.79%
44.18%
42.45%
41.24%
37.25%
26.38%
23.57%
28.47%
21.13%
39.21%
46.54%
56.90%
34.27%
51.18%
57.34%
53.17%
43.46%
48.68%
47.56%
44.19%
38.40%

Received
532
531
538
520
655
644
632
570
553
568
604
573
574
599
637
645
630
636
604
591
683
639
692
634

Rejected
252
327
386
258
217
323
391
301
334
283
371
261
311
310
345
335
394
333
280
353
284
337
258
378

47.37%
61.58%
71.75%
49.62%
33.13%
50.16%
61.87%
52.81%
60.40%
49.82%
61.42%
45.55%
54.18%
51.75%
54.16%
51.94%
62.54%
52.36%
46.36%
59.73%
41.58%
52.74%
37.28%
59.62%

Withdraw
103
78
86
102
86
78
79
79
89
88
67
89
56
83
98
73
52
91
93
66
87
68
60
75

19.36%
14.69%
15.99%
19.62%
13.13%
12.11%
12.50%
13.86%
16.09%
15.49%
11.09%
15.53%
9.76%
13.86%
15.38%
11.32%
8.25%
14.31%
15.40%
11.17%
12.74%
10.64%
8.67%
11.83%

Booked
177
126
66
160
352
243
162
190
130
197
166
223
207
206
194
237
184
212
231
172
312
234
374
181

33.27%
23.73%
12.27%
30.77%
53.74%
37.73%
25.63%
33.33%
23.51%
34.68%
27.48%
38.92%
36.06%
34.39%
30.46%
36.74%
29.21%
33.33%
38.25%
29.10%
45.68%
36.62%
54.05%
28.55%

Table 7a: Loan Acceptance Decisions based on loan characteristics
Variable

Coeff Val.

t-stats

Marg Eff

Intercept

-4.0768

-2.99

Internal Risk Ratings

-0.3046

-2.92

-2.93%

Experian Business Score

0.2719

16.57

Experian Borrowers Score

0.1238

Loan-to-Value of the Loan

Marg
Eff

Coeff Val.

t-stats

-3.7241

-2.84

-0.2837

-2.97

-2.89%

0.27%

0.2641

16.82

0.25%

13.31

0.30%

0.1188

13.36

0.30%

-0.0373

-2.32

-4.06%

-0.0344

-2.25

-3.92%

log(Loan Amount Requested)

-0.0406

-2.07

-5.39%

-0.0395

-2.17

-5.15%

Loan Term

-0.0046

-5.14

-6.49%

-0.0042

-4.87

-5.99%

Loan maturity

0.6106

0.92

0.12%

0.6082

0.98

0.12%

Treated Dummy

0.6479

0.99

5.21%

0.6124

1.00

4.96%

2005 Dummy

0.7218

1.07

1.20%

0.6757

1.05

1.13%

Treated Dummy*2005 Dummy

0.7825

4.36

12.66%

0.7109

4.33

12.29%

Days Spent Per Loan

0.5733

0.87

0.25%

0.5309

0.85

0.24%

Internal Risk Ratings * treated Dummy

-0.2282

-0.50

0.13%

-0.2210

-0.52

0.13%

Experian Business Score * Treated Dummy

0.4988

1.55

-0.18%

0.4945

1.59

-0.18%

Experian Borrowers Score* Treated Dummy

0.0882

0.26

0.01%

0.0828

0.25

0.01%

Loan-to-Value of the Loan* Treated Dummy

-0.7004

-1.70

0.01%

-0.6704

-1.79

0.01%

log(Loan Amount Requested)* Treated Dummy

-0.5060

-1.34

-0.05%

-0.4795

-1.35

-0.05%

Loan Term* Treated Dummy

-1.1192

-1.83

-0.03%

-1.0905

-1.91

-0.03%

Loan maturity* Treated Dummy

0.4422

1.75

0.08%

0.4080

1.66

0.08%

Days Spent Per Loan* Treated Dummy

0.1436

1.40

0.28%

0.1390

1.44

-0.1570

-0.26

-0.11%

-0.1492

-0.24

0.28%

Internal Risk Ratings * Treated * 2005

-0.11%

Experian Business Score * Treated* 2005

0.4035

0.97

0.21%

0.3675

0.94

0.19%

Experian Borrowers Score* Treated * 2005

0.2342

0.49

0.25%

0.2110

0.45

0.23%

Loan-to-Value of the Loan* Treated * 2005

-0.4229

-1.47

-0.29%

-0.3949

-1.41

-0.28%

log(Loan Amount Requested)* Treated * 2005

0.7490

3.57

2.92%

0.7322

3.82

2.87%

Loan Term* Treated * 2005

-0.1440

-1.09

-0.08%

-0.1320

-1.09

-0.07%

Loan maturity* Treated * 2005

0.9435

3.81

5.53%

0.8917

3.75

5.19%

Days Spent Per Loan* Treated * 2005

1.7321

6.20

0.06%

1.5733

6.24

0.05%

Personal Collateral

0.5634

3.13

6.41%

0.5372

3.11

5.99%

Business Collateral

0.5669

3.59

3.76%

0.5575

3.58

3.52%

Personal Collateral*Treated Dummy

0.1743

1.41

0.30%

0.1720

1.40

0.29%

Business Collateral*Treated Dummy

0.2528

1.64

0.25%

0.2296

1.51

0.25%

Personal Collateral * Treated Dummy*2005 Dummy

0.1785

1.56

0.37%

0.1697

1.51

0.36%

Business Collateral*Bank A Dummy*2005 Dummy

0.1726

1.32

0.26%

0.1590

1.22

0.25%

SIC Dummy

Yes

Loan Officer Dummy

Yes

Number of Observations

30268

R-Square
17.28%
Notes: We report the coefficients, the Std Err, the T-stats and marginal effects for the decision to deny credit (Y = 1). We obtain the
marginal effects by simply evaluating Pr xj at the regressors’ sample means and coefficient estimates . Since the probabilities of
offering and denying credit sum to 1 the marginal effects for the decision to reject a loan application are simply the opposite of the
reported ones. The pseudo-R2 is McFadden’s likelihood ratio index.

Table 7b: Probability of Loan Default on Loan Characteristics
Variable

Coeff Val.

Std. Err.

t-stats

Marg Eff

Coeff Val.

Std. Err.

t-stats

Marg Eff

Intercept

-2.3794

0.9712

-2.45

-2.2942

0.9288

-2.47

Internal Risk Ratings

0.1784

0.0426

4.19

8.78%

0.1769

0.0395

4.47

8.72%

Experian Business Score

-0.0847

0.0110

-7.71

-0.44%

-0.0780

0.0102

-7.60

-0.40%

Experian Borrowers Score

-0.0847

0.0066

-12.41

-0.59%

-0.0809

0.0065

-12.53

-0.53%

Loan-to-Value of the Loan

0.0517

0.0120

4.30

1.28%

0.0482

0.0118

4.09

1.25%

log(Loan Amount Requested)

0.0289

0.0075

3.87

1.77%

0.0263

0.0068

3.88

1.73%

Loan Term

0.0012

0.0007

1.75

0.03%

0.0011

0.0006

1.81

0.03%

Loan maturity

0.4728

0.1168

4.05

6.79%

Treated Dummy

0.0686

0.0716

0.96

0.33%

0.4510

0.1080

4.17

6.76%

0.0623

0.0691

0.90

0.32%

2005 Dummy

0.4781

0.1385

3.45

3.38%

0.4522

0.1286

3.51

3.07%

Treated Dummy*2005Dummy

0.4274

0.1073

3.98

6.51%

0.4115

0.1059

3.88

5.87%

Days Spent Per Loan

-0.4869

0.1912

-2.55

-1.21%

-0.4723

0.1796

-2.62

-1.13%

Internal Risk Ratings * Treated

0.1269

0.0427

2.97

0.10%

Experian Business Score * Treated

-0.0235

0.0225

-1.04

-0.09%

0.1218

0.0393

3.10

0.09%

-0.0235

0.0223

-1.00

-0.08%

Experian Borrowers Score* Treated

-0.5274

0.0531

-9.94

0.02%

-0.4857

0.0479

-10.14

0.02%

Loan-to-Value of the Loan* Treated

-0.2573

0.0944

-2.27

-0.55%

-0.2544

0.0913

-2.78

-0.50%

log(Loan Amount Requested)* Treated

-0.0205

0.2780

-0.07

0.00%

-0.0186

0.2597

-0.07

0.00%

Loan Term* Treated

-0.0645

0.0780

-0.83

-0.01%

-0.0583

0.0722

-0.80

-0.01%

Loan maturity* Treated

0.6061

0.1711

3.54

3.34%

0.5781

0.1623

3.56

3.05%

Days Spent Per Loan* Treated

0.0807

0.0587

1.38

0.01%

0.0760

0.0555

1.36

0.01%

Internal Risk Ratings * Treated * 2005

0.2441

0.1755

1.39

0.08%

0.2261

0.1718

1.31

0.08%

Experian Business Score * Treated * 2005

-0.1005

0.1595

-0.63

-0.06%

-0.0942

0.1485

-0.63

-0.05%

Experian Borrowers Score* Treated * 2005

-0.3635

0.2916

-1.25

-0.94%

-0.3404

0.2706

-1.25

-0.91%

Loan-to-Value of the Loan* Treated * 2005

1.4476

0.1115

12.98

-1.82%

1.3847

0.1015

13.60

-1.68%

log(Loan Amount Requested)* Treated * 2005

0.3568

0.1045

3.41

4.05%

0.3356

0.0966

3.47

3.64%

Loan Term* Treated * 2005

0.3231

0.0986

3.28

4.40%

0.3066

0.0924

3.31

4.12%

Loan maturity* Treated * 2005

0.8975

0.1839

4.88

9.30%

0.8788

0.1808

4.86

9.26%

Days Spent Per Loan* Treated* 2005

-0.2368

0.0746

-3.17

-1.70%

-0.2330

0.0739

-3.15

-1.56%

Personal Collateral

-1.5637

0.1571

-9.95

-4.91%

-1.4954

0.1434

-10.42

-4.44%

Business Collateral

-1.8806

0.3037

6.19

-1.29%

-1.7135

0.2884

-5.94

-1.17%

Personal Collateral*Treated Dummy

-0.2745

0.2659

-1.03

-0.16%

-0.2549

0.2543

-1.00

-0.14%

Business Collateral*Treated Dummy

-0.1077

0.1029

-1.05

-0.33%

-0.1007

0.0994

-1.01

-0.30%

Personal Collateral*Treated Dummy*2005 Dummy

-0.1132

0.1702

-0.67

-0.11%

-0.1019

0.1571

-0.64

-0.10%

Business Collateral*Treated Dummy*2005 Dummy

-0.1453

0.3771

-0.39

-0.35%

-0.1439

0.3409

-0.42

-0.32%

SIC Dummy

Yes

Yes
Yes

Loan Officer Dummy

Number of Observations

11164

R-Square

7.99%

Notes: We report the coefficients, the Std Err, the T-stats and marginal effects for the decision to default on the credit (Y = 1). We
obtain the marginal effects by simply evaluating Pr xj at the regressors’ sample means and coefficient estimates . Since the
probabilities of offering and denying credit sum to 1 the marginal effects for the decision to reject a loan application are simply the
opposite of the reported ones. The pseudo-R2 is McFadden’s likelihood ratio index.

Table 8: Loan approval decisions and defaults
Acceptance
Variable

Coeff Val.

t-stats

Intercept

-4.1326

Default
Coeff
Val.

Marg Eff

t-stats

-2.5701

-2.73

-2.37

Marg Eff

Internal Risk Ratings

-0.3311

-3.15

-3.25%

0.1811

3.93

9.30%

Experian Business Score

0.2904

16.46

0.28%

-0.0932

-7.70

-0.46%

Experian Borrowers Score

0.1317

13.70

0.30%

-0.0920

-13.08

-0.64%

Loan-to-Value of the Loan

-0.0407

-2.24

-4.45%

0.0529

4.16

1.32%

log(Loan Amount Requested)

-0.0415

-2.01

-5.63%

0.0305

3.99

1.85%

Loan Term

-0.0049

-5.28

-6.92%

0.0013

1.84

0.04%

Treated Dummy

0.6606

0.94

5.34%

0.0750

0.94

0.36%

0.5172

3.38

3.69%

0.4503

3.99

6.77%

2005 Dummy

0.7966

1.14

1.24%

Treated Dummy*2005 Dummy

0.8250

4.21

13.02%

Days Spent Per Loan

0.6328

0.92

0.25%

-0.4917

-2.34

-1.23%

Loan Officer Gender (Female)

1.0382

1.44

0.14%

-0.6228

-12.00

-4.79%

Loan Officer Age

0.4458

0.68

0.58%

0.1607

2.66

0.27%

Loan Officer Age(sq)

-0.5601

-0.82

-0.21%

-0.0618

-1.36

-0.21%

Loan Officer Tenure

0.4179

0.62

0.01%

0.7065

3.18

0.15%

Loan Officer Tenure (sq)

0.9105

1.39

0.01%

-0.5330

-3.67

-0.60%

Days Spent Per Loan*Treated Dummy

-0.7350

-1.06

-0.27%

-0.0801

-0.18

-0.06%

Loan Officer Gender (Female)*Treated Dummy

-1.2078

-1.66

-0.07%

-0.2054

-1.60

-0.01%

Loan Officer Age*Treated Dummy

0.4993

0.69

0.15%

0.4904

2.78

0.76%

Loan Officer Age(sq)*Treated Dummy

-0.5918

-0.83

-0.60%

-0.1099

-0.70

-0.10%

Loan Officer Tenure*Treated Dummy

0.4399

0.56

0.79%

0.3096

2.09

0.16%

Loan Officer Tenure (sq)*Treated Dummy

1.0422

1.35

0.44%

-0.9549

-3.89

-0.41%

Days Spent Per Loan*Treated Dummy*2005 Dummy
Loan Officer Gender (Female)*Treated Dummy*2005
Dummy

-1.3716

-1.87

-4.31%

-0.5589

-3.17

-2.25%

-1.9498

-2.53

-8.09%

-0.5634

-4.83

-2.68%

Loan Officer Age*Treated Dummy*2005 Dummy

1.8456

2.62

6.13%

0.3650

2.36

2.41%

Loan Officer Age(sq)*Treated Dummy*2005 Dummy

-1.4044

-1.87

-5.26%

-0.2437

-1.15

-0.43%

Loan Officer Tenure*Treated Dummy*2005 Dummy

2.4137

3.05

7.24%

0.9385

2.73

6.77%

Loan Officer Tenure (sq)*Treated Dummy*2005 Dummy

1.9412

2.80

3.98%

-0.7134

-5.68

-1.91%

Personal Collateral

0.6172

11.71

7.00%

-1.6722

-10.52

-5.41%

Business Collateral

0.5948

14.73

3.97%

-2.0781

-65.00

-1.35%

Personal Collateral*Treated Dummy

0.1867

1.37

0.30%

-0.2997

-1.00

-0.17%

Business Collateral*Treated Dummy

0.2620

1.62

0.27%

-0.1158

-1.06

-0.33%

Personal Collateral*Treated Dummy*2005 Dummy

0.1961

1.60

0.37%

-0.1217

-0.67

-0.11%

Business Collateral*Treated Dummy*2005 Dummy

0.1857

1.35

0.28%

-0.1531

-0.39

-0.38%

SIC 2 Digit Dummies

Yes

Number of Observations

30268

R-Square

Yes

18.93%

Notes: We report the coefficients, the Std Err, the T-stats and marginal effects for the decision to deny credit (Y = 1). We obtain the
marginal effects by simply evaluating Pr xj at the regressors’ sample means and coefficient estimates . Since the probabilities of
offering and denying credit sum to 1 the marginal effects for the decision to reject a loan application are simply the opposite of the
reported ones. The pseudo-R2 is McFadden’s likelihood ratio index.

Table 9: Probability of Loan Default with Soft/hard Information
Variable

Coeff Val.

Std. Err.

t-stats

Marg Eff

Coeff Val.

Std. Err.

t-stats

-2.2298

0.9151

-2.43

0.0762

0.0384

1.92

0.0102

-7.49

Marg
Eff

Intercept

-2.3506

0.9608

-2.44

Internal Risk Ratings Residual

0.0703

0.0423

1.66

0.55%

Experian Business Score

-0.0843

0.0107

-7.87

-0.43%

-0.0763

Experian Borrowers Score

-0.0820

0.0064

-12.88

-0.57%

-0.0788

0.0065

Loan-to-Value of the Loan

0.0498

0.0116

4.31

1.24%

0.0467

0.0116

-12.22

0.39%
0.40%
0.51%

4.01

1.21%

log(Loan Amount Requested)

0.0278

0.0072

3.83

1.71%

0.0253

0.0067

3.80

1.72%

Loan Term

0.0012

0.0007

1.78

0.03%

0.0011

0.0006

1.80

0.03%

Loan maturity

0.4560

0.1148

3.97

6.75%

Treated Dummy

0.0664

0.0692

0.96

0.32%

0.4366

0.1043

4.18

6.71%

0.0607

0.0686

0.88

0.31%

2005 Dummy

0.4622

0.1375

3.36

3.27%

0.4479

0.1265

3.54

3.06%

Treated Dummy*2005Dummy

0.4220

0.1048

4.02

6.37%

0.4042

0.1042

3.87

-2.54

-1.18%

-0.4550

0.1770

-2.57

5.76%
1.12%

Days Spent Per Loan

-0.4770

0.1875

Internal Risk Ratings Residual* Treated

0.1066

0.0425

2.51

3.10%

0.1001

0.0389

2.57

Experian Business Score * Treated

-0.0228

0.0225

-1.01

-0.09%

-0.0228

0.0222

-1.02

Experian Borrowers Score* Treated

-0.5116

0.0530

-9.64

0.02%

-0.4817

0.0463

-10.39

Loan-to-Value of the Loan* Treated

-0.2480

0.0943

-2.62

-0.54%

log(Loan Amount Requested)* Treated

-0.0203

0.2680

-0.07

0.00%

Loan Term* Treated

-0.0640

0.0750

-0.85

0.5935

0.1668

3.55

3.31%

0.0895

-2.75

0.2576

-0.06

4.09%
0.08%

0.02%
0.50%

**
**

-0.0580

0.0721

-0.80

0.00%
0.01%

0.5591

0.1601

3.49

2.93%

0.0760

0.0549

1.38

0.01%

0.1985

0.0692

2.87

-0.01%

Loan maturity* Treated

-0.2465
-0.0180

Days Spent Per Loan* Treated

0.0805

0.0566

1.42

0.01%

Internal Risk Ratings Residual * Treated * 2005

0.1840

0.0532

3.46

0.08%

Experian Business Score * Treated * 2005

-0.0968

0.1560

-0.62

-0.06%

-0.0926

0.1430

-0.64

Experian Borrowers Score* Treated * 2005

-0.3560

0.2805

-1.26

-0.91%

-0.3347

0.2641

-1.26

Loan-to-Value of the Loan* Treated * 2005

1.4024

0.1074

13.06

-1.78%

1.3457

0.0981

13.71

0.08%
0.05%
0.88%
1.65%

log(Loan Amount Requested)* Treated * 2005

0.3429

0.1009

3.39

4.04%

0.3302

0.0958

3.44

3.62%

Loan Term* Treated * 2005

0.3136

0.0956

3.28

4.37%

0.2970

0.0897

3.31

4.10%

Loan maturity* Treated * 2005

0.8749

0.1828

4.78

9.18%

0.8773

0.1756

4.99

Days Spent Per Loan* Treated* 2005

-0.2274

0.0719

-3.16

-1.65%

-0.2321

0.0724

-3.20

Personal Collateral

-1.5421

0.1562

-9.87

-4.80%

-1.4719

0.1416

-10.39

Business Collateral

-1.8410

0.3001

-6.13

-1.28%

-1.6782

0.2866

-5.85

Personal Collateral*Treated Dummy

-0.2715

0.2566

-1.05

-0.15%

-0.2501

0.2538

-0.98

Business Collateral*Treated Dummy

-0.1038

0.1022

-1.01

-0.32%

-0.0979

0.0994

-0.98

Personal Collateral*Treated Dummy*2005 Dummy

-0.1099

0.1656

-0.66

-0.11%

-0.1015

0.1537

-0.66

Business Collateral*Treated Dummy*2005 Dummy

-0.1440

0.3753

-0.38

-0.34%

-0.1427

0.3400

-0.41

9.12%
1.52%
4.32%
1.12%
0.14%
0.30%
0.10%
0.31%

SIC Dummy

Yes

Loan Officer Dummy

Yes

Number of Observations

11164

R-Square

8.27%

**
**
**

Notes: We report the coefficients, the Std Err, the T-stats and marginal effects for the decision to default on credit (Y = 1). We obtain
the marginal effects by simply evaluating Pr xj at the regressors’ sample means and coefficient estimates . Since the probabilities of
offering and denying credit sum to 1 the marginal effects for the decision to reject a loan application are simply the opposite of the
reported ones. The pseudo-R2 is McFadden’s likelihood ratio index.

Table 10: Comparison of Hard- vs. Soft-information Lending
Approval rate

Default rate

Ratio of Highest/Lowest
Quintile of Soft Info

2004

2005

2004

2005

Control group

1.27

1.29

1.16

1.12

Treated group

1.24

2.36

1.19

3.05

Table 11: Welfare Analysis
2005

2004

Remarks

Average loan size

$312,518

$216,048

# of loans booked

3680

2548

Increased volume

$599,575,936

Average loan size × ∆ loans booked

∆ Fees generated

$11,991,519

2% × increased volume

Average income

$49,019

Increased wages

$428,805

# of defaults

192

107

Defaulted loans

$60,003,456

$23,117,136

∆ Loss given default

$18,443,160

$42,422
∆ Average income × # of loan officers

Average loan size × # of defaults
50% × ∆ defaulted loans

∆ Welfare = ∆ Fees generated – Increased wages – ∆ Loss given default = -$6,880,446

46
Dec-05

Nov-05

O
ct-05

Sep-05

Aug-05

Jul-05

Jun-05

M
ay-05

Apr-05

M
ar-05

Feb-05

Jan-05

Dec-04

Nov-04

O
ct-04

Sep-04

Aug-04

Jul-04

Jun-04

M
ay-04

Apr-04

M
ar-04

Feb-04

Jan-04

M
onths

Dec-05

Nov-05

O
ct-05

Sep-05

Aug-05

Jul-05

Jun-05

M
ay-05

Apr-05

M
ar-05

Feb-05

Jan-05

Dec-04

Nov-04

O
ct-04

Sep-04

Aug-04

Jul-04

Jun-04

M
ay-04

Apr-04

M
ar-04

Feb-04

Jan-04

M
onths

Figure 1: Monthly Loan Approval Status
Treated G
roup

80.00%

70.00%

60.00%

50.00%

40.00%
R
ejected
W
ithdraw

30.00%
Booked

20.00%

10.00%

0.00%

Control Group

0.8

0.7

0.6

0.5

0.4
R
ejected

0.3
W
ithdraw
Booked

0.2

0.1

Figure 2: Loan Applications Booked in the Treated Group

$500,000
$450,000
$400,000
$350,000
$300,000
$250,000
$200,000
$150,000
$100,000
$50,000
$-

Avg $ Loan Requested
Avg $ Loans Booked

Oct-04 Nov-04 Dec-04 Jan-05 Feb-05 Mar-05 Apr-05

Figure 3: Loan-to-Value Ratios
90.00
85.00
80.00
75.00

LTV of Applicants

70.00

LTV of Booked Loans

65.00
60.00
55.00
Oct-04 Nov-04 Dec-04 Jan-05 Feb-05 Mar-05 Apr-05

Figure 4: Days Spent on Loan Requested in the Treated Group
Day Spent/Loan Requested
s
1.60
1.40
1.20
1.00
D
ays S
pent/Loan
R
equested

0.80
0.60
0.40
0.20
0.00
O
ct-04

Nov
-04 Dec-04

Jan-05

Feb-05

M
ar-05

A
pr-05

Figure 5: Internal Risk Rating
Internal Risk Ratings
5.4
5.2
5
4.8
Internal Risk Ratings
4.6
4.4
4.2
4
Oct-04

Nov-04

Dec-04

Jan-05

Feb-05

Mar-05

Apr-05

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