Sharing private information with customers:
Strategic default and lender learning
Gerardo Pérez Cavazos∗
The University of Chicago Booth School of Business
January 12, 2015
Abstract
I use a unique data set of loans to small business owners to examine whether lenders
face negative externalities when they share private information with borrowers. When
lenders grant debt forgiveness to borrowers, borrowers communicate that information
to other borrowers, who are then more likely to strategically default on their own
obligations. This strategic default contagion is economically large. When the lender
doubles debt forgiveness, contagion causes the default rate to increase by 10.9% on
average. Using an exogenous shock to the lender’s forgiveness policy, I further show
that as the lender learns about the extent of borrower communication it tightens its
debt forgiveness and origination policies to reduce information spillovers and mitigate
the default contagion. Collectively, these results provide new evidence on the strategic
interactions between a firm and its customers in a dynamic information environment.
JEL No.: D10, D83, G21, M41
Keywords: Information transmission, communication, strategic default contagion,
learning
∗
I thank Ray Ball, Phil Berger, Andreas Bodmeier, Alejandro Cavazos, Hans Christensen, Merle Erickson,
John Gallemore, Joseph Gerakos, Christian Leuz, Mark Maffett, Mike Minnis, Adair Morse, Valeri Nikolaev,
Antonio Picca, Haresh Sapra, Andreya Silva, Doug Skinner (Chair), and Anastasia Zakolyukina for helpful
comments. I received valuable feedback from seminar participants at the University of Chicago, as well as
conference participants at the 2014 AAA/Deloitte/J. Michael Cook Doctoral Consortium. Finally, I am
extremely grateful to Financiera Ayudamos for providing their data.
1
Introduction
A large accounting literature discusses the role of proprietary costs in firms’ disclosure decisions. Because observing proprietary costs is difficult, however, empirical evidence on the
magnitude of those costs is limited. I examine how information externalities in small business
lending affect the actions of borrowers and lenders, and in particular, how private information about lenders’ debt forgiveness policies is transmitted to other borrowers, a form of
information contagion that is costly to lenders. The debt forgiveness negotiation process
creates a dynamic information environment in which lenders and borrowers both share private information. Although the information exchange can help both parties achieve a more
efficient outcome, there is a risk of sharing too much information (Crawford and Sobel, 1982).
Theoretical work shows that economic agents can strategically use a firm’s proprietary information to their advantage (Verrecchia, 1983; Dye, 1986; Wagenhofer, 1990). My research
provides evidence on the costs that firms incur when they share private information with
customers.
I present two key findings. First, I show that lenders face strategic default contagion when
granting debt forgiveness to borrowers. This unintended consequence results from borrowers
communicating the private terms of their debt forgiveness agreements to other borrowers,
who are then more likely to strategically default on their own obligations. Second, I show
that lenders learn about the extent of borrower communication and alter their operating
policies to reduce information spillovers and mitigate the default contagion.
I use a detailed data set from Financiera Ayudamos (FA), a Mexican credit institution
that grants small business and consumer loans. FA selectively grants debt forgiveness in
cases of default in an effort to reduce losses on delinquent loans by incentivizing defaulters
to continue repaying their loans.1 I use these debt forgiveness events to analyze whether
1
For example, consider a borrower who is four weeks late on his payments and is only able to pay half of
his deficiency. This borrower may not make any payments, because he knows he will remain in default. By
contrast, if the lender offers to forgive two payments, the borrower can commit to paying the remaining two
installments and become current on his loan.
1
FA incurs costs due to private information transmission between borrowers regarding its
forgiveness policy. The idea is that a borrower who is granted debt forgiveness learns private
information regarding the extent of the lender’s willingness to give a discount on the loan
repayment and can subsequently share this information with other borrowers. Financially
able borrowers can strategically use this information to their advantage to get a reduction
on their loan balance. Any information asymmetry regarding a borrower’s ability to pay
exacerbates this issue. If the lender is unable to clearly distinguish between borrowers who
can and cannot repay their loans, it is more likely that he will unknowingly offer debt
forgiveness to strategic defaulters.
A distinct feature of this setting is that lenders only share private information through
private contracts with select borrowers. Although these borrowers can communicate with
others, the extent of communication is unknown ex-ante. It is therefore unclear whether
the information externalities are substantial enough for the lender to alter its operating
decisions to reduce the amount of private information shared with borrowers. Alternatively,
borrowers may not share the details of this forgiveness event with others, because unlike a
publicly disclosed event such as foreclosure, a default and the subsequent renegotiation with
the lender are private.
To identify costs due to information externalities it is necessary to disentangle strategic
defaults from defaults that are the result of economic adversity. My empirical approach
is based on the simple premise that borrowers’ primary economic shocks occur at work,
whereas communication among borrowers occurs in the home neighborhood. I am able to
draw this important distinction because of the particularities of the Mexican setting. First,
many individuals commute over an hour to work. This allows me to separate the default rate
in the work area from that in the home neighborhood. A high default rate in the work area
indicates that borrowers suffer shocks to income, whereas a low default rate indicates stable
economic conditions. If an individual defaults but does not suffer a shock to his income, I
then consider the default to stem from his home neighborhood.
2
Defaults that originate in the home neighborhood have two possible explanations: (i) an
event that causes a shock to expenses or (ii) strategic default due to communication between
borrowers. Communication is high within Mexican neighborhoods because they comprise
tight-knit groups of families and friends who have lived there most of their lives and interact
through weekly community activities (Keefe, 1984).2 Therefore, to determine whether the
default stems from communication about the lender’s forgiveness policy, I examine the forgiveness rate within the home neighborhood. A high forgiveness rate within a neighborhood
indicates more borrowers have experienced the debt forgiveness process. As these informed
borrowers communicate about the lender’s willingness to forgive, neighborhoods with a high
forgiveness rate will have many defaults. I then control for the contemporaneous default
rate, inflation, and natural disasters to rule out any shocks to the neighborhood’s expenses.
My first set of results measures the extent of strategic default in a neighborhood after the
lender grants debt forgiveness to a borrower. I find evidence of strategic default contagion
resulting from communication among borrowers regarding the lender’s forgiveness policy. If
FA doubles its forgiveness rate in a neighborhood, the default rate increases by 1.7% in the
following month. This default contagion is equivalent to a 10.9% increase in FA’s monthly
default rate. These results are robust and valid for a wide range of econometric tests. In
addition to controlling for economic shocks, I include various fixed effects and complement
the regression analysis with an instrumental variables (IV) approach and a propensity score
method.
My findings have three critical implications. First, firms not only release information
through disclosure, but also through their operating actions. In my setting these operational
decisions are private internal policies; however, firms also make public operational decisions,
such as expanding their operations or releasing new products, which likely reveal proprietary
information to outsiders. Second, firms incur significant informational costs when negotiating
2
Keefe (1984) finds that Mexican Americans families “have relatively large kin networks with high rates
of visiting and exchange. Even immigrant Mexicans, who have experienced disruption of their kin group due
to migration, surpass Anglos in the number of relatives living nearby and their frequency of visiting kin.”
3
with customers. Although theoretical models define customers as potential opponents of
a firm, the extant empirical literature has focused almost exclusively on competitors as
users of firms’ proprietary information. Third, the evidence I provide raises questions about
whether increasing renegotiations is an optimal strategy to attenuate the high foreclosure
rate. This issue is important because of the recent foreclosure crisis. In 2008 the U.S.
government instituted the Home Affordable Modification Program (HAMP), which, in an
effort to solve the foreclosure crisis, provides lenders with incentives to renegotiate more
loans. Consistent with the conjecture of Posner and Zingales (2008), my results illustrate
that there are negative externalities associated with increasing loan renegotiations.
To provide additional evidence on the causal link between debt forgiveness and strategic
default, I exploit a plausibly exogenous event that caused a change in FA’s debt forgiveness
policy. In late August 2012, FA’s director of risk management unexpectedly resigned to
accept a position at a larger financial institution. While FA searched for a replacement,
responsibility for approving debt forgiveness was transferred to executives in the marketing
department who then granted debt forgiveness more freely. In September 2012, a borrower
in default was twice as likely to receive debt forgiveness as in the prior month. As a result,
the percentage of loans in default increased from 41.1% to 47.3% during the following six
months. Consistent with strategic default, the increase was concentrated in the group of
loans that were one to 29 days delinquent. By contrast, adverse economic conditions would
affect default across all delinquency groups.
Next, to further investigate the communication mechanism driving strategic default I
analyze borrower interconnectedness within neighborhoods. I expect that highly interconnected borrowers communicate more and thus exhibit higher strategic default contagion
after forgiveness has been granted within their home neighborhood. To test this conjecture, I construct two measures of borrower interconnectedness: (i) the fraction of borrower
referrals within a neighborhood and (ii) the geographic concentration of loans within zip
codes surrounding each branch. I find that after the change in forgiveness policy, branches
4
holding a more concentrated loan portfolio or a portfolio with a higher referral rate suffered
a higher incidence of strategic default. Taken together, my results provide evidence that
communication among borrowers results in information contagion that is costly to lenders.
Finally, I examine whether the lender learns from strategic default contagion. The motivation behind this analysis is to understand whether firms recognize that privately disclosed
information spreads between their counterparties and the extent to which they adjust their
policies to mitigate the negative externalities. Studying firm learning is challenging because
equilibrium outcomes are typically observed. However, the surge in defaults after FA’s exogenous event provides an ideal setting to examine lender learning after the surge in defaults in
September of 2012. I test whether the lender adjusts its origination and forgiveness policies
to limit the spread of its private information. I find that FA’s branches learn from strategic
default contagion and react by increasing the strictness of their forgiveness and origination
policies within nine months of the event. The policy changes are stronger for those branches
that experienced higher strategic default contagion.
These results illustrate that lenders recognize the informational trade-off they face when
making operational decisions that reveal proprietary information. As such, they alter their
policies to mitigate the negative externalities resulting from communication about their
willingness to modify loans. These findings also shed light on the observation that a low
incidence of loan renegotiations occurred during the financial crisis. Prior studies by Piskorski
et al. (2010) and Adelino et al. (2013) investigate whether institutional frictions arising from
securitizations explain the small number of loan renegotiations. I show that although the expost observation that lenders did not increasingly renegotiate during the financial crisis might
seem counterintuitive, lenders limit the number of renegotiations because they recognize the
informational costs ex-ante.
My study makes several contributions. To my knowledge, this study is the first to measure
the information externalities of sharing a lender’s private information with customers. I use
a unique approach to show that although the lender shares information privately, borrowers
5
communicate what they learn to other borrowers. This result provides empirical evidence
that supports the theory that customers can play the role of a firm’s strategic opponent.
In addition, although I study the lender-borrower relationship, my findings are applicable
to other settings where two parties transact with one another privately, such as supplier
negotiations, government-firm negotiations, or a central bank offering capital infusions to a
particular bank. Lastly, I am able to isolate the effect that a lender’s operating policies have
on borrowers’ decisions to strategically default. Disentangling this effect is a step toward
finding appropriate solutions for foreclosure contagion.
Section 2 reviews the relevant literature. Section 3 describes the data. Section 4 presents
results on the impact of forgiveness on strategic default contagion; and Section 5 further
explores communication as the mechanism driving strategic default. Section 6 examines
creditor learning. Section 7 concludes.
2
Related Literature
Customers are traditionally regarded as economic agents who contribute positively to the
firm’s bottom line; however, they can also play the role of a strategic opponent if they use
the firm’s private information to their advantage (Verrecchia, 1983; Dye, 1986; Wagenhofer,
1990). In particular, firms constantly share information with customers in negotiations
(Crawford and Sobel, 1982), and the spread of this information can impact the firms’ bargaining power and the outcome of other negotiations. For example, customers may demand
a discount if they learn that a price concession was granted to another customer. Therefore,
understanding whether information shared during private negotiations spreads and measuring the resulting costs for the firm is important.
Although offering debt forgiveness is not a disclosure choice in the traditional sense,
it is an operating decision that reveals private information to outsiders. In this respect,
my study is related to the literature on proprietary information, which analyzes whether
6
sharing information with outsiders is beneficial for the firm. In particular, the majority of
proprietary information studies extensively analyze the effect of competition on different
disclosure decisions, such as product development information (Guo et al., 2004; Jones,
2007), material contract filings (Verrecchia and Weber, 2006), sales and cost information
(Dedman and Lennox, 2009), and customer information (Ellis et al., 2012). These studies
find that despite market pressures, firms do not fully disclose all private information because
of competitors. My study is similar in that firms face a trade-off when deciding whether to
release more or less information in negotiations with customers.
A unique feature of my study is that I examine information sharing through a private
contract. Therefore, the firm cannot perfectly anticipate whether and to what degree information will spread, making it difficult to set optimal policies ex-ante. I exploit this feature to
analyze whether the lender learns about the extent of proprietary costs resulting from information transmission among its borrowers and how the lender alters its policies in response.
A recent study by Ali et al. (2014) shows that firms with high proprietary costs prefer
financing options that allow them to circumvent disclosure requirements. My study complements their findings by providing evidence that a financial institution modifies its debt
forgiveness and origination policies because of the high cost of sharing private information.
My work also relates to two recent streams within the mortgage literature. Guiso,
Sapienza, and Zingales (2013) use survey data to examine the moral and social determinants of homeowners’ attitudes toward strategic default. They find that homeowners are
more likely to default if they know of other strategic defaulters, in part because they perceive the bank is less likely to pursue them. Further work examines how foreclosure spreads
within neighborhoods, performing an analysis within Maryland (Towe and Lawley, 2013), an
analysis in Illinois (Munroe and Wilse-Samson, 2013), and a national analysis that controls
for neighborhood and zip-code (Goodstein et al., 2013). These studies find that foreclosure
is contagious and that neighbors’ behaviors influence default.
My study differs from these prior studies in three respects. First, my study is concerned
7
exclusively with how the spread of the lender’s private information affects strategic default.
I am able to identify this because I observe defaults and loan modifications that are private.
By contrast, a foreclosure is a publicly disclosed event making it difficult to disentangle
communication about the bank’s proprietary information from any learning that occurs
regarding the economic trajectory of the neighborhood or foreclosure procedures (Towe and
Lawley, 2013; Munro and Wilse-Samson, 2013).
Second, my study focuses on the period that spans a borrower’s initial default on a
loan through all renegotiations that occur until the lender sends the loan to a collection
agency. Thus, my setting closely parallels the renegotiation period prior to a foreclosure. As
such, I am able to investigate whether increasing loan modifications helps reduce foreclosure
contagion, as suggested by Munro and Wilse-Samson (2013) yet critiqued by Posner and
Zingales (2009). The paper most closely related to mine is Mayer et al. (2013), who use the
Countrywide lawsuit settlement to analyze how a publicly announced mortgage modification
policy affects strategic default. My approach differs from theirs because I focus on private
loan renegotiations and therefore communication among customers as the channel by which
learning about the lender’s policies occurs. Thus, my study more closely parallels the negotiation of a private contract, because banks and companies do not typically announce they will
grant concessions to all customers who need assistance meeting their financial obligations. In
doing so, I contribute new evidence showing that loan renegotiations cause strategic default
contagion even when renegotiations are conducted privately with only select borrowers in
default.
Lastly, I examine whether the lender identifies the informational costs associated with
renegotiating loans. Despite the importance of loan renegotiations as a tool to minimize bank
losses, there is little work that examines whether the low incidence of renegotiations occurs
because banks anticipate that loan modifications can generate information externalities.
This paucity of evidence is likely due to the difficulty of finding settings where the bank’s
renegotiation decisions vary exogenously. I circumvent this challenge by exploiting a plausible
8
exogenous change to the lender’s forgiveness policy that caused a surge in defaults.
3
Data
I use loan data from Financiera Ayudamos (FA), a subsidiary of Grupo Financiero BBVA
Bancomer, the largest commercial bank in Mexico. FA is a Sociedad Financiera de Objeto
Multiple (SOFOM), a credit institution subject to less government regulation than deposit
banks.3 SOFOMs primarily grant consumer credit, yet also provide financing for small and
medium enterprises, distributors, and intermediaries. SOFOMs are not allowed to accept
deposits; therefore, FA is fully funded by its parent company.
FA began its operations in May 2007 and currently operates 54 branches concentrated
in central Mexico. It mainly serves individuals with limited access to traditional credit.4
FA’s loans range from $1,500 to $50,000 MXN ($115 to $3,845 USD), have a 12 to 24 month
maturity, and have the same 66% interest rate across borrowers. Loans also incur a 16%
value-added tax on interest payments and a 9% origination fee or 5% renewal fee.
My main sample comprises 14,649 of FA’s loans to small business owners, granted from
January 2011 through March 2014. Panel A of Table 1 reports borrower and loan characteristics at origination. On average, the loans have a principal of $6,940 MXN ($515 USD),
an annual payment rate (APR) of 92%, a maturity of 18 months, and a weekly payment
frequency. Borrowers are 57% female and, on average, 39 years old. Nearly two-thirds of
borrowers have a credit score and, of those, the mean score is 692 of 850. According to an
executive from FA, a credit score of 692 corresponds to a medium-risk borrower. To assess
the credit-worthiness of all borrowers, FA constructs an internal credit score that aggregates
borrower characteristics. Lastly, 38% of borrowers have previously obtained a loan with FA.
My identification relies on borrowers living in a tight-knit neighborhood and commuting
3
SOFOMs do not require approval from the National Banking Regulator and are exempt from capital
requirements unless they have an economic relationship with a banking institution (Pena, 2008).
4
FA facilitates the borrowing process by accepting non-traditional documents as proof of income. For
example, FA accepts receipts from inventory purchases as proof of income, whereas most commercial banks
strictly require payroll receipts or bank statements.
9
to work. Consistent with this, on average, FA’s borrowers have spent 67% of their life at
their current home and travel 3.8 linear miles to work. Given the mountainous geography of
Mexico, the linear distance likely understates the commute to work. For example, in Mexico
City, a 3.8 linear mile can require travel of over six miles on the road, corresponding to a
commute in excess of one hour.
Panel B of Table 1 presents the monthly loan performance. FA monitors loan performance
on a weekly basis. In a given month, an average of 16% of borrowers miss a payment and
are considered to be in default. Each branch is required to pursue collection efforts, which
begin with loan officers placing telephone calls and making home visits. The branch can
also offer debt forgiveness should the first two methods become ineffective. Debt forgiveness
is contingent on the borrower (i) paying the remainder of his deficiency such that the loan
becomes fully current and (ii) committing to make the rest of his payments on time. Prior to
September 2012, FA granted forgiveness to an average of 10% of defaulters, on approximately
4% of the loan principal.5
Once delinquency surpasses 60 days, FA’s centralized collection division takes over the
collection process. This division is, on average, less than 15% successful at getting loans
back into repayment. Lastly, once the loan is more than 90 days delinquent, it is transferred
to an external collection agency that charges a percentage of any recovered portion of the
loan.
5
The average forgiveness rate over the whole sample period is 20% due to the period after August 2012,
as discussed in Section 5.
10
4
Borrower Communication and Strategic Default
Contagion
4.1
Identification Strategy
My first hypothesis is that lenders incur information externalities when they grant debt
forgiveness, because borrowers communicate with other borrowers who are then more likely
to default. To test this hypothesis, I must differentiate defaults that are due to information
spillovers from those that are purely due to adverse economic shocks. To do so, I use a novel
approach that exploits the particularities of the Mexican setting.
First, I account for economic shocks to a borrower’s income that could make him unable
to repay his loan. I exploit the fact that Mexicans typically commute to work, and separately
analyze the default rate of a borrower’s work area and home neighborhood.6 A high default
rate in a borrower’s work area indicates that local economic conditions that impact takehome pay are deteriorating. In particular, the income of small business owners, who sell
food, clothes, personal care items, repair cars, repair shoes, etc., is highly dependent on
common local factors such as a large store opening in the vicinity, a company closing down
or downsizing, nearby protests during the week, construction in the area, etc.
Next, if there are no economic shocks at the work area I consider shocks to a borrower’s
home expenses that might cause default. First, I consider a rise in housing costs. According
to the Mexican National Institute of Statistics and Geography (INEGI), individuals within
the corresponding income deciles of my sample of borrowers only spend 3.2% of their income
on rent. Consistent with this statistic, less than 1% of my sample of borrowers rent their
home. Therefore, in my setting, rent increases are unlikely to contribute to a rise in the
neighborhood’s default rate. Second, I consider a rise in the cost of consumption goods.
INEGI reports that, on average, the two greatest expenses for individuals within the corre6
A lack of urban planning, the lack of jobs in low-income communities, and government housing in the
outskirts of major cities contribute to the high number of commuters.
11
sponding income decile of my sample of borrowers are food (41%) and transportation (17%).
However, my sample period had modest annual inflation rates under 4.5%. Thus, the rise in
the cost of these goods is unlikely sufficient to cause an increase in neighborhood default.
Lastly, if a neighborhood does not experience a widespread shock that causes expenses
to increase, a borrower’s default is more likely driven by communication among neighbors
regarding the lender’s forgiveness policies. In particular, the majority of individuals in
Mexico live in the same neighborhood from infancy to adulthood, forming strong ties to their
community of large extended families and friends (Rodriguez et al., 2007). Weekly activities
within these neighborhoods also contribute to the high level of information sharing. As such,
I examine whether a borrower is more likely to default after forgiveness is granted to other
borrowers within his neighborhood.
Considering FA’s forgiveness policy, a strategic default due to communication would
occur as follows: (i) Borrower A defaults in month t, (ii) Borrower A receives forgiveness in
month t + 1, and (iii) Borrower B defaults in month t + 2. Because Borrower B’s decision
to default is separated from Borrower A’s default by two months, it is much less likely that
Borrower B’s default is due to a pervasive economic shock that impacts the entire home
neighborhood at one point in time. In sum, my identification strategy is that economic
shocks that impact the borrower’s ability to repay his loan are correlated among borrowers
that work in the same area, whereas defaults that are correlated among borrowers that live
in the same neighborhood are primarily due to information transmission.
Figure 1 illustrates the identification strategy. Borrowers A and B live in the same tightknit neighborhood and commute to their respective work areas to run small independent
businesses. Borrowers C and D live in a different neighborhood and commute to their
small businesses. I identify strategic default as follows: suppose Borrower A and Borrower
C default in t on their small consumer loans because of an economic shock that affects the
whole work area. The lender offers Borrower A debt forgiveness in t +1 as a means of getting
him out of default and back into repayment. Borrower A subsequently communicates with
12
his neighbor, Borrower B, regarding the lender’s forgiveness policy. Borrower B, who has not
suffered an economic shock, then chooses to strategically default in t + 2 with the objective
of getting a portion of his loan forgiven. Borrower D does not default, because he neither
experiences an economic shock nor has any interaction with Borrowers A or B. Moreover,
if communication were to occur among local business owners in the work area, the result
would be an overstated coefficient on economic conditions and an understated coefficient on
forgiveness. Therefore, it will be more difficult to detect contagion.
4.2
Empirical Analysis
To implement the identification strategy I estimate the following panel regression:
Def aulti,t = α0 + α1 F orgiveness Ratehome,t−1 + α2 Def ault Ratework,t +
(1)
Controls + F ixed Ef f ects.
The dependent variable, Def aulti,t , is binary and takes the value of one if a borrower’s
loan i is in default at month t, but not at month t − 1, and zero otherwise. The main
explanatory variable, F orgiveness Ratehome,t−1 , is the forgiveness rate in borrower i’s home
zip code in the prior month. Using lagged forgiveness rate overcomes the reflection problem
that arises in social interaction models (Manski, 2000; Brock and Durlauf, 2001).7
To control for other determinants of default, I include measures of economic conditions,
borrower characteristics, and loan characteristics. I construct a variable, Def ault Ratework,t ,
as a proxy for local economic conditions that impact borrowers’ ability to repay their loans.8
This variable measures the proportion of loans that are delinquent in the zip code where
borrower i works (excluding borrower i). I also include industry and time fixed effects to
7
The reflection problem arises when determining whether group behavior affects the behavior of individuals (Manski, 1993). The issue is that group behavior is merely the aggregate of individual behaviors.
8
I construct this variable because a measure of local economic conditions is unavailable at the zip-code
level. INEGI’s most granular economic measures are reported at the state level. In addition, INEGI’s state
data are reported quarterly and thus do not allow for a monthly analysis.
13
control for macroeconomic cycles and seasonality.
I control for demographic characteristics that affect a borrower’s ex-ante likelihood of
default: age, gender, education, credit score, number of years living in the current house as
a fraction of age, and number of years owning the current business as a fraction of age. I
expect that a borrower who has owned his business for many years is more likely to generate
a stable income than a borrower who recently started his business.
I control for loan characteristics: payment frequency, size of the loan relative to income,
and the portion of the loan that is repaid. I expect that borrowers are not likely to default
immediately after obtaining a loan or when a few payments are left. I also anticipate that
default is more likely to occur on loans with weekly payments, because FA grants biweekly or
monthly payments only to borrowers that demonstrate a relatively stable source of income.
Lastly, I cluster standard errors at the branch level to account for branches operating in
distinct areas and therefore the possibility that they implement collection efforts differently.
4.3
4.3.1
Empirical Results
Linear regression analysis
Table 2 presents results of the strategic default regressions using a linear probability model.9
A 1% increase in the debt forgiveness rate within a neighborhood increases the likelihood that
an additional borrower will default by 0.087% (column 1). This contagion is large in terms
of economic magnitude. If FA doubled the debt forgiveness rate within a neighborhood,
the likelihood of a borrower defaulting in a given month increases by 1.7%, equivalent to
approximately a 10.9% increase in FA’s monthly default rate. Further, the joint effect of
P ct P aid and P ct P aid2 indicates that the probability of default is a concave function that
reaches its maximum when 66% of the loan is repaid. Therefore, as predicted, defaults do
not typically occur at the beginning or near the maturity of the loan.
9
I use a linear probability model for ease of interpreting coefficients and because it allows for a larger set
of fixed effects. However, in untabulated results I run logistic specifications and find similar results.
14
In column 2, I use FA’s Internal Credit Score as a control, in lieu of borrower characteristics. The internal credit score variable aggregates borrower characteristics and can capture
nonlinearities that FA has already identified and incorporated into its scoring formula. It
also has the added benefit of increasing the sample size, because approximately 36% of FA’s
borrowers lack a credit score. I find that the coefficient of F orgiveness Ratehome,t−1 remains
positive and significant.
Next, I examine repeat defaulters to test whether the likelihood that a borrower defaults
increases after he receives debt forgiveness on his loan. An individual repeatedly defaults
for two reasons: (i) he is a bad borrower or (ii) he is taking advantage of the information
that he previously learned about FA’s willingness to offer forgiveness. Accordingly, I include
two additional explanatory variables (column 3). The first variable, N ot F orgiven, is binary
and takes the value of one for borrowers who defaulted in the past but did not receive
forgiveness. The second variable, P ast F orgiveness, is also binary and takes the value of
one for borrowers who received forgiveness in the past. The coefficient of N ot F orgiven is
positive, indicating that bad borrowers are more likely to default again. However, consistent
with borrower learning, the coefficient of P ast F orgiveness is greater than the coefficient of
N ot F orgiven. The difference in the two coefficients shows that conditional on being a bad
borrower, learning about forgiveness increases the likelihood of default by 14.3%.
In column 4, I include repeat borrowers. I am interested in this subset of borrowers
because their past borrowing history with the lender creates less information asymmetry
regarding their wealth. These borrowers are also likely interested in renewing their existing
loans or obtaining new loans, which increases the cost of taking actions that negatively affect
their credit score. In particular, when making renewal decisions, FA penalizes borrowers for
having received forgiveness on a loan. Generally, FA does not renew a loan if the borrower
has defaulted within six months or defaulted multiple times in the past. Approximately
5% of repeat borrowers received forgiveness on a previous loan. In line with my prediction, the coefficient of Repeat Borrower is negative and significant, indicating that repeat
15
borrowers default less on average. The interaction coefficient of F orgiveness Ratehome,t−1 ×
Repeat Borrower indicates that repeat borrowers do not default more when the forgiveness
rate increases in their neighborhood. This is consistent with the lender being more informed
about the repeat borrowers’ wealth, making it more difficult for this subset of borrowers to
strategically default and obtain forgiveness.
In column 4, I substitute Def ault Ratework with the interaction of work area and month
fixed effects. The purpose of the work-month fixed effects is to eliminate the effect of economic shocks that are common to all businesses in a given work area each month. Lastly, I
include home neighborhood fixed effects to control for time-invariant characteristics of the
home neighborhood that can influence the likelihood of default (column 5). The coefficients
are estimated from within-home-neighborhood variation, requiring that any potential confounding event be correlated with time-varying home neighborhood characteristics. The
coefficient of F orgiveness Ratehome,t−1 is positive and significant in all specifications.
4.3.2
Instrumental variables
As an alternative empirical method, I use instrumental variables (IV) to strengthen the causal
link between prior forgiveness and default (Table 3). The objective of the IV approach
is to exploit shocks to the home neighborhoods’ forgiveness rate that are exogenous to
each borrower. This is different from the regression analysis, which attempts to control
for all economic conditions that affect borrowers. As such, IV allows me to circumvent
potential concerns about endogeneity in FA’s decisions to grant debt forgiveness. If FA
can detect different levels of communication across neighborhoods, its best response is to
lower debt forgiveness in areas with high communication. From a statistical perspective,
this rational behavior by FA would result in a downward bias in the ordinary least squares
(OLS) estimates.
For each borrower i, I instrument the home neighborhood’s forgiveness rate by using the
fraction of neighboring borrowers that work in a different area and became delinquent in t−2,
16
N ew Def ault Ratework(−i),t−2 . The first-stage regressions show that this instrument satisfies
the relevance restriction (Table 3, Panel B). The coefficient of the instrument is positive and
significant and the F -statistics are above the critical value for a weak instrument as stated
by Stock et al. (2002). This instrument likely satisfies the exclusion restriction because
of two reasons. First, it is constructed using the default rate of borrowers that work in a
different zip code and are therefore exposed to different local economic conditions. Second,
the instrument is lagged by two periods, further separating economic shocks suffered by
the defaulters from shocks that could affect borrower i. Given that these borrowers have
minimal or no savings, it is unlikely that they will be able to withstand economic shocks for
a prolonged period of time without defaulting. As such, it is reasonable to assume that the
only channel by which N ew Def ault Ratework(−i),t−2 affects borrower i’s decision to default
is through the level of forgiveness that i observes in his home neighborhood.
In line with my previous results, the coefficient of F orgiveness Ratehome,t−1 is positive
and significant across all specifications. These coefficients are larger than those of the OLS
regressions, which is consistent with a downward bias in the OLS estimates. In terms of
economic magnitude, a one standard deviation increase in the forgiveness rate in the home
neighborhood increases the default rate by 2.6%.
Overall, the results in this Section indicate that the lender’s day-to-day operations reveal
proprietary information to customers who then communicate the terms of their agreement to
other borrowers, causing negative externalities in the form of strategic default contagion. The
lender’s private information spreads even when it is exclusively disclosed in a private contract.
Considering that all types of firms engage in a multitude of private contracts this result is
likely to apply to other settings where two companies transact with one another privately.
For example, any firm that decides to grant better delivery terms, additional warranty, or
additional services to select customers can incur costs if doing so induces additional customers
to demand the same treatment. In addition, although my setting focuses on communication
among individual borrowers, prior research has found evidence of information transmission
17
among decision makers at different firms. For instance, manufacturing executives gather
and transmit information about retailers’ payment policies through business associations
(Doner and Schneider, 2000), investment managers communicate with other local investment
managers about portfolio allocation decisions (Hong et al., 2005), and executives within the
same social networks discuss and influence each other’s managerial decisions (Cohen et al.,
2008; Shue, 2013). Therefore, the mechanism of communication among individual customers
regarding a firm’s private information is also generalizable to communication among firms’
decision makers.
Lastly, my results shed light on the information externalities associated with loan renegotiations. In particular, I provide evidence consistent with the theoretical predictions of
Posner and Zingales (2009), which state that loan renegotiations, although efficient ex-post,
can create future costs for the bank. This result is important from a policy perspective
because renegotiations have received attention as a potential solution to reduce mortgage
foreclosures. For example, the Obama administration enacted the Home Affordable Modification Program in 2008 to provide incentives for lenders to increase loan renegotiations.10
Beyond demonstrating that renegotiations have a costly consequence for lenders, my results
indicate that the foreclosure contagion problem cannot be solved by exclusively prescribing
renegotiations.
4.4
Robustness and Alternative Explanations
I perform a number of additional tests to evaluate the robustness of my main results. First,
I rule out potential shocks to expenses in the home neighborhood. Then I use a propensity
score method to evaluate the impact that past forgiveness has on default.
10
For more details on the consequences of the HAMP see Agarwal et al. (2012).
18
4.4.1
Shocks to expenses at the home
I use a series of alternative specifications to test for possible alternate explanations for strategic default contagion (Table 4). First, I include the contemporaneous default rate in the home
neighborhood as a control for potential correlated economic shocks in the home neighborhood
that drive borrowers’ default. A drawback of including this variable, Def ault Ratehome,t , is
that it is affected by the lagged forgiveness rate. As such, it will absorb a portion of the effect
associated with F orgiveness Ratehome,t−1 and thus, attenuate the coefficient. Although the
coefficient of F orgiveness Ratehome,t−1 is downwardly biased in this specification, it remains
positive and significant (column 1). Therefore, increasing debt forgiveness increases strategic
default contagion after controlling for economic conditions.
Next, I include inflation subcomponents to control for price increases in different types
of goods consumed by the borrowers. For example, according to INEGI, the average borrower in my sample spends 17% of his income on transportation. Therefore, I expect that
borrowers will be more likely to default when there is a high inflation rate with respect
to transportation goods and services (column 3). Including these controls does not affect
the coefficient of F orgiveness Ratehome,t−1 . The coefficients of inflation in transportation,
clothing, and housing are positive and significant. By contrast, the coefficient of inflation in
food prices is not statistically significant, likely due to the large portion of borrowers in my
sample that own food sales businesses.
In column 4, I analyze natural disasters because they are economic shocks that can cause
a correlation in defaults among borrowers living in the same neighborhood. I include a
binary variable, N atural Disaster, that takes the value of one for loans in states that suffered a natural disaster during the year, and the interaction term F orgiveness Ratehome,t−1 ×
N atural Disaster. The coefficients of these two variables are not statistically different from
zero. In this specification, I also include a binary variable, M ultiple Accounts, that takes
the value of one when a borrower has multiple credit accounts at origination, and the interaction term F orgiveness Ratehome,t−1 × M ultiple Accounts. A borrower that has multiple
19
outstanding loans is possibly learning from the actions of various lenders. By controlling
for borrowers with multiple credit accounts I disentangle whether the borrower is learning from FA’s actions. I find that these borrowers are less likely to default but equally
likely to learn from neighboring forgiveness and to strategically default. The coefficient
of F orgiveness Ratehome,t−1 remains largely unaffected, indicating that natural disasters or
learning about other lenders’ policies do not drive the main results.
4.4.2
Placebo test
Under my contagion hypothesis, I expect that granting forgiveness in a home neighborhood
reveals proprietary information that creates incentives for other borrowers to strategically
default. As such, randomized forgiveness rates should not explain future defaults. I conduct
placebo tests that assign a random home neighborhood to each borrower (column 5). I
find that the average coefficient of forgiveness rate in the placebo tests is not statistically
different from zero. In addition, this coefficient is positive and significant in only 3% of my
simulations.
4.4.3
Propensity score method
Next, I use a propensity score method to evaluate the impact of past forgiveness on default.
This method allows me to compare the default rate between two groups of identical borrowers who have been exposed to different levels of forgiveness in their home neighborhoods
during the previous month. Group one is exposed to high forgiveness, whereas group two
is exposed to low forgiveness. In addition, this method captures any nonlinearities in the
control variables, because it relies on logistic regressions to estimate propensity scores. I
estimate the propensity scores from the following logistic model:
Pr(High F orgivenessi,t−1 ) = α1 Def Ratework,t + α2 Def Ratehome,t +
α3 Int Score + α4 Loan Characteristics +
20
(2)
α5 Branch + M onth F.E.,
where High F orgivenessi,t−1 takes the value of one for loans located in a home neighborhood with a high forgiveness rate, and zero for loans located in a neighborhood with a low
forgiveness rate. High and low forgiveness rates are defined as the top and bottom terciles of
forgiveness granted across all neighborhoods, respectively. Following Rosembaum and Rubin
(1983), I then rank the observations by their estimated propensity score and classify them
into five strata. The average home neighborhood and work area default rates are similar
across the treated and untreated groups in each of the strata (Panel A, Table 5). Lastly,
I compare each borrower’s rate of default across the treated and untreated groups in each
stratum.
I show that after controlling for economic and loan characteristics, borrowers exposed
to high forgiveness in their home neighborhood during the previous month have a higher
likelihood of defaulting in the current month (Panel B, Table 5). Specifically, the default
rate for the high forgiveness group is between 0.9% and 4.7% higher than that of the low
forgiveness group. Taken as a whole, these findings show that my main results are not altered
by plausible economic shocks to the home neighborhood or using a propensity method as an
alternative empirical approach.
5
5.1
Communication Channel
The 2012 Event
As of 2007, FA’s debt forgiveness policy required that each branch file a formal request and
obtain approval from FA’s central risk management department before granting debt forgiveness to a borrower. The risk management department reviewed all formal requests to ensure
each loan was at least six months old and that each borrower signed a risk awareness form
21
that outlined the negative credit consequences of receiving debt forgiveness.11 On average,
only 10% of defaulters received debt forgiveness. Of these, few were repeat forgiveness cases
as FA typically declines requests for repeat forgiveness.
In August 2012, the director of the risk management department unexpectedly left FA for
a position in a much larger credit institution. Consequently, the department lacked a manager with the authority to approve debt forgiveness, and within two weeks, a large backlog of
requests accumulated. To alleviate the issue, the CEO/president of the company temporarily transferred the approval responsibilities to executives within the marketing department.
Given the marketing department’s lack of experience in assessing risk and their focus on
business growth, the executives rapidly granted most forgiveness requests as a means of getting clients to repay. The executives also eliminated the need for borrowers’ signatures on
the risk awareness form. As a result, FA’s total dollar amount forgiven increased 188% from
the previous month (Figure 2).
In light of my prior results, I anticipate that the loan performance worsened after this
event, because of a surge in strategic defaults. To examine this prediction, I compare monthly
transition matrices of delinquencies in FA’s loan portfolio during the six-month period before and after September 2012. Loans that were one to 29 days delinquent increased from
29.9% to 36.8%, whereas all delinquent loans over 30 days slightly decreased from 11.2%
to 10.5% (Table 6, Panels A and B). These results have two implications. First, because
the delinquency rate did not increase across all groups, adverse economic conditions do not
account for the high incidence of default. Second, the increase in debt forgiveness was not
an effective policy to reduce the overall number of loans in default. An effective policy would
have caused a larger decrease in delinquent loans over 30 days and little to no impact on the
rate of loans that were one to 29 days delinquent. In addition, the likelihood that, in a given
month, non-delinquent borrowers became delinquent increased by 2.24% and the likelihood
11
FA reports borrowers who received debt forgiveness to the Credit Bureau. The Credit Bureau then
factors the forgiveness into the borrowers credit score, making it more difficult for him to obtain credit in
the future.
22
that loans with delinquencies over 60 days re-entered repayment status increased by 0.51%
(0.39%+0.12% in Panel C).
In sum, the new forgiveness policy intended to help delinquent borrowers get back into repayment, yet it had the unintended consequence of incentivizing strategic default among nondelinquent borrowers. Moreover, an additional unintended consequence is a cross-subsidy between sophisticated and unsophisticated borrowers as described by Campbell (2006). Strategic defaulters are likely the higher credit quality borrowers among the group of defaulters,
and will most likely absorb the majority of the forgiveness offered by FA.12 Consistent with
this conjecture, the credit quality of borrowers that received forgiveness improved. The average internal credit score increased from 585 points to 597 points after the forgiveness policy
change.
5.2
“Neighborhood Interconnectedness”
To further analyze the mechanism driving my prior results, I test whether neighborhood
interconnectedness explains strategic default contagion in the cross-section of branches. I
expect that tight-knit neighborhoods exhibit a high level of inter-borrower communication.
Therefore, branches granting loans to borrowers residing within tight-knit neighborhoods
will experience a higher level of strategic default contagion as compared to branches that
grant loans to less inter-connected neighborhoods. A potential concern is the endogeneity of
branches’ decision to grant forgiveness, because in equilibrium, branches that observe more
communication may grant less forgiveness. To address this concern, I exploit the change
to FA’s forgiveness policy in September 2012. I construct two measures of the level of
neighborhood interconnectedness one month prior to the policy change, and test whether
they explain the level of strategic default observed during the three months after the change.
The first variable, Ref erral Ratej , captures the fraction of borrowers referred to FA by
12
Campbell (2006) explains that some financial products create a cross subsidy between sophisticated
and unsophisticated households. For example, a cross subsidy arises when unsophisticated borrowers do
not optimally refinance, thus allowing the sophisticated borrowers to obtain more attractive terms from the
financial institution.
23
a family member or a friend. The second variable, Concentrationj , measures the geographic
concentration of the loans within each branch’s portfolio through a Herfindahl measure:
Concentrationj =
X
h2j,j ,
(3)
i
where hi.j is equal to the number of loans originated by branch j in zip code i as a fraction
of the number of loans in the portfolio of branch j. I use the following linear specification
to analyze the relation between strategic default and neighborhood interconnectedness:
Strategic Def aultj = γ0 + γ1 Ref erral Ratej,Aug12 + γ2 Concentrationj,Aug12 .
(4)
In this regression, Strategic Def aultj is equal to αd
1,j × F orgiveness Ratej , the product
of the average marginal effect of the forgiveness rate and the average forgiveness rate. I
estimate αd
1,j from the following specification:
Def aulti,j,t =
X
(α1,j (Ij × F orgiveness Ratehome,t−1 )) + Controls +
(5)
j
Ij + M onth F.E.,
where Ij is an indicator variable that takes the value of one if loan i was originated in
branch j, and zero otherwise. The controls are the same as in equation 1. I use Monte
Carlo simulations to adjust the standard errors to account for the presence of the generated
13
regressor αd
1,j .
The coefficients of Ref erral Rate and Concentration are positive; however, only Ref erral
Rate is statistically significant at the 10% level (Table 7). This result suggests a positive relation between the fraction of referred customers and future contagion. By contrast,
13
I use the coefficients α
d
1,j and their joint variance-covariance matrix obtained from regression 5 to simulate
10,000 random draws of each coefficient αd
1,j,s from a multivariate normal distribution. I then estimate
regression 4 using each set of simulated coefficients α
d
1,j . I report the average coefficient and p-value obtained
after repeating the estimation of regression 4 10,000 times.
24
Concentration may not be a good predictor of future contagion, because the variable lacks
more detailed geographic and demographic data, such as zip code size and population density.14
In sum, neighborhood interconnectedness helps explain the level of strategic default observed after the lender modified its forgiveness policy. This result is consistent with borrower communication as the channel by which the lender’s private information spreads. As
additional borrowers communicate about the lender’s willingness to offer debt forgiveness,
strategic default contagion increases.
6
Lender Learning
In this Section, I examine whether the lender internalizes the costs resulting from borrowers communicating the terms of their renegotiation agreements. Specifically, I investigate
whether the lender alters its operating policies as it learns about strategic default contagion.
Although it is well understood that companies learn by doing (Arrow, 1962) and in particular
by observing financial data (Pastor and Veronesi, 2009), typically, only equilibrium outcomes
are observable. Therefore, identifying whether, how, and to what degree lenders learn from
strategic default contagion is challenging. I overcome this obstacle by further exploiting
FA’s exogenous event in 2012. I examine FA’s actions after the event and test whether
branches that experienced a high level of contagion subsequently increased the strictness of
their forgiveness and origination policies.
6.1
Forgiveness Policy
FA’s branch managers receive a bonus as a function of delinquencies in their respective loan
portfolios. As such, they have incentives to minimize the number and extent of nonperforming loans. One method to reduce defaults is to modify the forgiveness policy. Given that
14
The INEGI does not report information about the geographic size and total population at the zip code
level, thereby limiting the set of variables available to construct more sophisticated measures.
25
FA grants individual branches the autonomy to implement stricter forgiveness policies, I
expect that branches that experience high strategic default will learn about the information
externality and thus tighten their forgiveness policy. It is ultimately an empirical question
as to how long a branch would take to identify contagion; therefore, I conduct the analysis
using learning periods of three, six, and nine months (Figure 3).
To test my conjecture, I first use the regression in equation 5 to estimate the coefficient
of F orgiveness Rate for each branch j throughout each learning period. I then use the
estimates αd
1,j as a measure of the level of information transmission among the borrowers in
branch j throughout the learning period. This measure is consistent with the geographic
location of the lender’s branches, because borrowers are required to originate and service
their loans at the branch nearest to their home. In addition, because branches serve a
distinct set of neighborhoods, they are not simultaneously impacted by strategic default.
Next, I construct a measure of forgiveness policy strictness, Likelihood of F orgiveness,
to analyze the extent to which each branch uses debt forgiveness. This measure captures the
average number of loans in default that receive debt forgiveness at each branch. Branches
that offer forgiveness to a low fraction of defaulters are considered stricter.
To examine the relation between the level of information transmission and the level of
forgiveness policy strictness, I conduct the following cross-sectional test:
Likelihood of F orgivenessj,post = γ0 + γ1 Strategic Def aultj + γ2 T otal Def aultj ,
(6)
where Likelihood of F orgiveness is measured after the learning period, Strategic Def aultj
is the product αd
1,j ×F orgiveness Ratej estimated from equation 5 using the learning period,
and T otal Def aultj is the average default rate observed in branch j during the learning
period.
I find no clear relation between strategic default and likelihood of forgiveness within a
three-month learning period (Table 8, column 1). Thus, the branches are either unable to
26
detect strategic default or fail to react by tightening their forgiveness policy. Consistent
with the notion that learning occurs gradually, increasing the learning period to six months
strengthens the relation between strategic default and forgiveness policy strictness (column
3), with a larger coefficient in absolute value and smaller p-value.
In a nine-month period (column 5), the branches tighten their forgiveness policies in
response to learning from contagion. Branches that experience higher strategic default are
less likely to grant forgiveness to defaulters. In columns 2, 4, and 6, I include the average
default rate of the branch as a control for any change in overall default, which could drive
the change in forgiveness. My results are robust to including this additional control. In
addition, I use simulations in all specifications to correct standard errors for the presence of
a generated regressor.
6.2
Origination Policy
Next, I examine how FA branches adjusted their origination policy within the three, six, and
nine months after the surge in defaults in September 2012. Branches can reduce strategic
default by improving the quality of their borrowers through stricter loan origination policies.
Alternatively, branches can be less strict in origination. Although the latter strategy may
seem counterintuitive, it could prove effective if the lender credibly commits to not grant
debt forgiveness. The idea is that borrowers would refrain from strategically defaulting once
they realize that delinquent loans will not be forgiven. Relatedly, managers may choose a
less strict origination policy due to their compensation incentives. FA pays an additional
bonus that increases as a function of the number of loans originated. Managers can therefore
substitute their loan performance bonus with the origination bonus. Specifically, when the
loan performance bonus decreases due to increased default, managers can lower origination
standards to increase their origination bonus.
Traditionally, FA’s loan granting process takes less than 24 hours and relies on the borrower’s credit score and internal origination score. The credit and origination scores are
27
compared to the company’s loan granting thresholds. The branches cannot grant loans to
individuals who fail to meet the minimum requirements; however, they can choose to deny
a loan to an individual that meets the minimum requirements. If a branch sets higher standards such that it rejects candidates that meet FA’s requirements, it is considered stricter.
To examine whether the level of information transmission during the learning period
affects the strictness of the origination policy for a given branch, I conduct the following
test:
Decision to Rejectj,post = γ0 + γ1 Strategic Def aultj + γ2 T otal Def aultj .
(7)
I calculate the dependent variable, Decision to Reject, as the number of requests that
qualified for a loan and were rejected, as a fraction of all qualifying requests. This measure
is based on branches’ actual decisions, as opposed to the average quality of new borrowers.
Therefore, the measure has the advantage of circumventing concerns regarding a strictness
change occurring because of loan requests from worse candidates.
The relation between level of contagion and origination strictness after a three-month
learning period is not statistically significant (Table 9, column 1). The relation between
strategic default and origination policy strictness is stronger for six and nine-month periods
(columns 3 and 5). Therefore, as the learning period increases, the lender’s branches are
better able to detect strategic default and in turn tighten their origination policies. In
addition, branches that suffer from higher levels of strategic default are more likely to reject
loans that meet FA’s origination thresholds. After a nine-month learning period, a 1%
increase in strategic default leads to a 0.8% increase in loan rejections (column 5). This
is equivalent to a 10.1% increase in the likelihood that a branch rejects an acceptable loan
request.
In sum, I find that the branches modify their operating decisions as they learn about
strategic default contagion. On average, branches that experience a higher level of contagion
react within nine months by granting less debt forgiveness and by rejecting more borrowers
that meet FA’s loan origination thresholds. Accordingly, lenders renegotiate less often as
28
they learn that renegotiations are suboptimal because of information externalities.
7
Conclusion
This paper exploits a unique setting to examine the negative externalities companies face
when revealing private information to customers. Using a detailed data set of loans to
small business owners, I find strategic default contagion among customers within the same
neighborhood who communicate regarding the lender’s forgiveness policy. I also find that
the lender learns from strategic default contagion and attempts to mitigate it by tightening
its debt forgiveness and origination policies.
A distinct feature of my setting is that negotiations occur in a private setting. Therefore,
my findings shed light on the dynamic information environment of private negotiations and
information transfers between firms and their customers. While theoretical models demonstrate that customers can play the role of a firm’s strategic opponent, there is a paucity of
empirical evidence that quantifies the costs incurred when a firm shares private information
with customers. I am able to quantify the cost by estimating the level of strategic default
contagion after forgiveness is granted.
Customers are particularly important to study because they are a key determinant of a
firm’s success. Their relationship with firms allows them to gain access to information that
is not publicly available. As such, it could be especially damaging to a firm if customers
freely communicate the information they obtain through negotiations. It is also valuable to
understand how customers’ actions impact the firm. My results show that firms take into
account the possibility of customer communication and modify their operating decisions
accordingly.
Furthermore, my paper contributes to the foreclosure contagion literature by demonstrating the consequences of loan modifications. The objective of loan modifications is to curtail
default. However, in line with the economic predictions of Posner and Zingales (2009), I show
29
that loan modifications do not achieve an efficient outcome. Instead, due to borrower communication, loan modifications (i) only modestly reduce the likelihood that a loan in default
becomes uncollectible and (ii) cause the lender to incur expenses beyond that of granting
forgiveness to financially distressed borrowers, as financially able borrowers strategically default. Lastly, I show that lenders recognize strategic default as a cost associated with loan
modifications and therefore limit their renegotiation activities. This finding helps answer the
question of why there was a low incidence of loan renegotiations during the financial crisis.
30
Appendix
A.1 Definition of variables
Variable
Description
Age
Borrower’s age in years.
Concentration
Herfindahl index measure of the zip code concentration of loans held by
each branch on August 2012.
Credit Score
Borrower’s credit score at origination, as reported by Buró de Crédito.
Scores range from 400 to 850.
Commuting Distance
Number of miles between home and work zip codes.
Decision to Reject
Number of loans that met the minimum origination requirements but
were rejected, divided by the total number of loans that met the
minimum origination requirements.
Def aultt
One if the borrower was current on his loan in month t − 1 and
defaulted in month t − 1; zero otherwise.
Def ault Ratehome,t
Total number of nonperforming loans in month t as a fraction of all
loans in the borrower’s home zip code.
Def ault Ratework,t
Total number of nonperforming loans in month t as a fraction of all
loans in the borrower’s work zip code.
Education
The highest degree of education attained by the borrower. One if
primary school, 2 if secondary school, 3ee if high school or technical
school, 4 if undergraduate degree, 5 if graduate degree, and 0 otherwise.
F orgiven in the past
One if the borrower has previously received forgiveness on his current
loan; zero otherwise.
F orgiveness Ratehome,t−1
Total number of loans that received forgiveness in month t − 1 as a
fraction of all loans in the borrower’s home zip code.
F raction of lif e
at current home
Years at current home divided by age.
F raction of lif e
at current occupation
Years at current occupation divided by age.
Gender
One if the borrower is male, zero otherwise.
Inf lation
Monthly inflation rate, as reported by INEGI.
31
Variable
Description
Internal Credit Score
FA’s internally generated borrower loan eligibility score, which
incorporates demographic information and credit score. Score ranges
from 0 to 680.
Loan Amount
Principal amount of the loan in thousands of Mexican pesos.
M ultiple Accounts
One if the borrower has multiple credit accounts at origination, zero
otherwise.
N atural Disaster
One if the state where the borrower lives used funds from the Federal
Natural Disaster Fund (FONDEN) during the year; zero otherwise.
N ew Def ault Ratework(−i),t−2
Fraction of borrowers that become delinquent in t − 2 and satisfy the
following two conditions: (i) they live in the same home neighborhood as
borrower i, and (ii) they do not work in the same area as borrower i.
N ot F orgiven
One if the borrower has defaulted in the past, but did not receive
forgiveness; zero otherwise.
P ayment F requency
One if the loan requires monthly payments, two if it requires biweekly
payments, and three if it requires weekly payments.
P ct P aid
Percentage of the loan paid.
P ct P aid2
Square of the percentage of the loan paid.
Ref erral Rate
On August 2012, the fraction of borrowers that had been referred to FA
through a family member or friend.
Repeat Borrower
One if the borrower has previously had a loan with FA; zero otherwise.
Reported Income
Borrower’s income in thousands of Mexican Pesos as reported at
origination.
Strategic Def ault
Estimated marginal effect of forgiveness α
d
1,j times average forgiveness
rate.
T otal Def ault
Number of loans in default divided by the number of loans in the
portfolio of each branch.
32
A.2 Correlations table
This table presents the Pearson correlations for the variables used in the strategic default regressions. Variable definitions are provided in Appendix
Table A.1.
33
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
Variables
Def aultt
F orgiveness Ratehome,t−1
Def ault Ratework,t
Def ault Ratehome,t
Internal Credit Score
Age
Gender
Education
Credit Score
F raction of lif e at current home
F raction of lif e at current occupation
Loan/Income
P ayment F requency
Repeat Borrower
(1)
1.000
0.057
0.053
0.062
-0.085
-0.068
-0.004
0.013
-0.086
0.013
-0.007
-0.041
0.034
-0.038
(2)
1.000
0.133
0.351
0.015
-0.023
0.027
0.041
-0.005
0.089
0.043
0.002
0.011
0.087
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
1.000
0.279 1.000
0.006 0.010 1.000
-0.010 -0.043 0.367 1.000
-0.026 0.002 0.071 0.075 1.000
-0.010 -0.005 0.015 -0.157 0.020 1.000
0.002 0.016 0.448 0.078 -0.010 -0.069 1.000
0.012 0.034 0.019 -0.254 0.022 0.176 -0.038 1.000
-0.033 -0.008 0.230 0.018 0.082 0.063 -0.013 0.203 1.000
-0.013 -0.014 0.049 0.012 -0.002 -0.010 0.131 0.031 0.007 1.000
0.031 0.018 -0.038 -0.059 -0.048 -0.108 -0.001 0.043 0.029 -0.035
-0.004 0.001 -0.009 0.095 0.039 -0.002 -0.036 0.010 0.093 0.166
(13)
1.000
-0.014
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36
Figure 1: Identification Strategy
This figure presents a graphical exposition of the identification strategy. Borrowers A and B live in the
same tight-knit neighborhood. They commute to their respective working areas where they each run a small
independent business. Analogously, Borrowers C and D live in another neighborhood and they also commute
to the respective locations of their small businesses. I define strategic contagion in the following way: suppose
Borrower A and Borrower C default on their small consumer loans, because of an economic shock that affects
the whole work area. The lender offers Borrower A debt forgiveness as a means of getting him out of default
and back into repayment. Borrower A subsequently communicates with his neighbor, Borrower B, regarding
the lender’s forgiveness policy. Borrower B then chooses to strategically default with the objective of getting
a portion of his loan forgiven. Borrower D does not default, because he neither experiences an economic
shock nor has any interaction with Borrowers A or B.
37
Figure 2: Frequency and Total Amount of Debt Forgiveness over Time
These figures present the frequency and total amount of debt forgiveness that FA has granted over time.
Panel A plots the number of loans that received debt forgiveness during a given month. Panel B plots the
total amount of debt forgiveness in thousands of Mexican pesos (MXN) in a given month. In both graphs,
the vertical line is located on September 2012, the month when FA changed its debt-forgiveness policy.
Panel A: Frequency of forgiveness
Panel B: Total forgiveness amount
38
Figure 3: Branch Learning
This figure presents the time structure of the learning analysis conducted in section 6. Each of the three
learning periods begins on September 2012, the date that FA changed its debt-forgiveness policy. Learning
period 1 consists of three months after the policy change and the corresponding testing period begins thereafter. Learning period 2 consists of six months after the policy change and the corresponding testing period
begins thereafter. Learning period 3 consists of nine months after the policy change and the corresponding
testing period begins thereafter.
39
Table 1: Descriptive Statistics
This table presents the summary statistics for the loan sample. The loans in the sample were originated in 54
branches from January 2011 through March 2014. Panel A reports the borrower and loan characteristics at
origination for 14,649 loans. Panel B reports the performance of the loans in 110,649 loan months. Variable
definitions are provided in Appendix Table A.1.
Panel A: Borrower and loan characteristics
Variable
N
Age
14,649
Gender
14,649
Education
14,649
Credit Score
9,412
Internal Credit Score
14,649
F raction of lif e at current home
14,649
F raction of lif e at current occupation 14,649
Loan/Income
14,649
P ayment f requency
14,649
Repeat borrower
14,649
Commuting distance
14,649
Mean Std. Dev.
39.22
12.24
0.43
0.50
2.69
0.75
691.87
33.62
601.20
20.02
0.67
0.31
0.29
0.16
0.85
0.37
2.92
0.31
0.38
0.48
3.80
4.38
Panel B: Loan-performance statistics
Variable
Def ault
Def ault Ratework,t
Def ault Ratehome,t
F orgiveness Ratehome,t−1
Mean
0.16
0.29
0.29
0.20
N
110,649
110,649
110,649
110,617
40
Std. Dev.
0.36
0.11
0.12
0.12
Q1
Median
Q3
29.00
38.00
48.00
0.00
0.00
1.00
2.00
3.00
3.00
671.00 696.00 715.00
587.00 604.00 615.00
0.39
0.69
1.00
0.17
0.27
0.40
0.62
0.80
1.08
3.00
3.00
3.00
0.00
0.00
1.00
1.25
2.38
4.86
Q1
0.00
0.23
0.23
0.13
Median
0.00
0.29
0.30
0.20
Q3
0.00
0.36
0.36
0.28
Table 2: Effect of Debt Forgiveness on Defaults
This table reports the estimation results from regressions of the following form:
Def aulti,t =α0 +α1 F orgiveness Ratehome,t−1 +Controls + T ime F.E. In this model, the dependent
variable is binary and takes the value of one if a loan i is in default at month t, but not at month t − 1, and
zero otherwise. The main explanatory variable is the forgiveness rate at month t − 1 in the zip code where
borrower i lives. Columns 1 to 4 vary in the controls included in the regression. Variable definitions are
provided in Appendix Table A.1. Standard errors are clustered at the branch level and are reported below
the coefficient estimates. *, **, and *** indicate significance at the two-tailed 10%, 5%, and 1% levels,
respectively.
Variables
F orgiveness Ratehome,t−1
Predicted Sign
+
Def ault Ratework,t
+
Internal Credit Score
-
Age
?
Gender
?
Education
-
Credit Score
-
F raction of lif e at current home
?
F raction of lif e at current occupation
-
Loan/Income
+
P ct P aid
?
P ct P aid2
?
P ayment F requency
+
F orgiven in the P ast
+
N ot F orgiven
+
Repeat Borrower
-
F orgiveness Rate × Repeat Borrower
?
Month fixed effects
Industry fixed effects
Work zip code - month fixed effects
Home zip code fixed effects
Prior forgiveness
Repeat borrowers
Adj. R2
Observations
41
(1)
0.087∗∗∗
(0.023)
0.129∗∗∗
(0.031)
(2)
0.074∗∗∗
(0.019)
0.129∗∗∗
(0.021)
-0.001∗∗∗
(0.000)
(3)
0.053∗∗∗
(0.019)
0.122∗∗∗
(0.019)
-0.001∗∗∗
(0.000)
-0.002∗∗∗
(0.000)
-0.008
(0.006)
-0.002
(0.004)
-0.001∗∗∗
(0.000)
-0.018∗
(0.010)
-0.014
(0.015)
0.037∗∗∗
(0.008)
0.446∗∗∗
(0.034)
-0.337∗∗∗
(0.053)
0.035∗∗∗
(0.008)
0.051∗∗∗
(0.009)
0.458∗∗∗
(0.025)
-0.371∗∗∗
(0.035)
0.037∗∗∗
(0.007)
0.051∗∗∗
(0.008)
0.264∗∗∗
(0.024)
-0.305∗∗∗
(0.032)
0.029∗∗∗
(0.005)
0.317∗∗∗
(0.016)
0.174∗∗∗
(0.008)
Yes
Yes
No
No
No
No
0.035
32,415
Yes
Yes
No
No
No
No
0.026
64,222
Yes
Yes
No
No
Yes
No
0.068
66,339
(4)
0.095∗∗∗
(0.027)
(5)
0.086∗∗
(0.028)
-0.001∗∗∗
(0.000)
-0.001∗∗∗
(0.000)
0.036∗∗∗
(0.007)
0.227∗∗∗
(0.028)
-0.256∗∗∗
(0.037)
0.025∗∗∗
(0.006)
0.281∗∗∗
(0.014)
0.167∗∗∗
(0.007)
-0.043∗∗∗
(0.008)
-0.018
(0.036)
No
Yes
Yes
No
Yes
Yes
0.170
107,687
0.055∗∗∗
(0.008)
0.316∗∗∗
(0.027)
-0.293∗∗∗
(0.035)
0.026∗∗∗
(0.008)
0.241∗∗∗
(0.013)
0.134∗∗∗
(0.007)
-0.038∗∗∗
(0.011)
-0.013
(0.042)
No
Yes
Yes
Yes
Yes
Yes
0.179
107,687
Table 3: Instrumental Variables Regressions
This table reports the coefficients from the instrumental variables regressions.
Panel A reports
the estimation results of the second-stage regression of the following form: Def aulti,t = α0 +
d Ratehome,t−1 )+Controls. In this model, the dependent variable is binary and takes
α1 (F orgiveness
the value of one if loan i is in default at month t, but not at montht − 1, and zero otherwise.
The main explanatory variable is estimated from the following first-stage regression:
F orgivenessRatehome,t−1 = γ0 + γ1 N ew Def ault Ratework(−i),t−2 + Controls, reported in Panel B. The
instrument, N ew Def ault Ratework(−i),t−2 , is defined as the fraction of borrowers that become delinquent
in t − 2 and satisfy the following two conditions: (i) they live in the same home neighborhood as borrower
i, and (ii) they do not work in the same area as borrower i. Robust standard errors are reported below
the coefficient estimates. *, **, and *** indicate significance at the two-tailed 10%, 5%, and 1% levels,
respectively.
Panel A: Second-stage regressions
Variables
F orgiveness Ratehome,t−1
Predicted Sign
+
Def ault Ratework,t
+
Internal Credit Score
-
Loan/Income
+
P ct P aid
?
P ct P aid2
?
P ayment F requency
+
Repeat Borrower
-
F orgiveness Rate × Repeat Borrower
?
F orgiven in the P ast
+
N ot F orgiven
+
F orgiveness Rate × F orgiven in the P ast
-
Month fixed effects
Industry fixed effects
Adj.R2
Observations
Panel B: First stage regressions
Instrument
N ew Def ault Ratework(−i),t−2
Predicted Sign
+
Second stage regressors
Adj.R2
F -stat
42
(1)
2SLS
0.214∗∗
(0.108)
0.130∗∗∗
(0.017)
-0.001∗∗∗
(0.000)
0.063∗∗∗
(0.006)
0.289∗∗∗
(0.024)
-0.181∗∗∗
(0.033)
0.040∗∗∗
(0.006)
(2)
2SLS
0.264∗∗∗
(0.100)
0.116∗∗∗
(0.013)
-0.001∗∗∗
(0.000)
0.039∗∗∗
(0.004)
0.306∗∗∗
(0.017)
-0.152∗∗∗
(0.024)
0.031∗∗∗
(0.004)
-0.078∗∗∗
(0.030)
0.095
(0.138)
(3)
2SLS
0.248∗∗∗
(0.080)
0.124∗∗∗
(0.012)
-0.001∗∗∗
(0.000)
0.021∗∗∗
(0.004)
0.141∗∗∗
(0.018)
-0.175∗∗∗
(0.026)
0.027∗∗∗
(0.003)
Yes
Yes
0.015
50,817
Yes
Yes
0.026
91,462
0.349∗∗
(0.144)
0.174∗∗∗
(0.004)
-0.222
(0.557)
Yes
Yes
0.062
91,462
(1)
0.198∗∗∗
(0.008)
Yes
0.238
587.25
(2)
0.199∗∗∗
(0.008)
Yes
0.223
605.98
(3)
0.193∗∗∗
(0.006)
Yes
0.227
618.37
(4)
2SLS
0.182∗
(0.099)
0.110∗∗∗
(0.012)
-0.001∗∗∗
(0.000)
0.039∗∗∗
(0.004)
0.106∗∗∗
(0.018)
-0.135∗∗∗
(0.027)
0.022∗∗∗
(0.003)
-0.088∗∗∗
(0.029)
0.143
(0.136)
0.358∗∗
(0.144)
0.173∗∗∗
(0.004)
-0.248
(0.558)
Yes
Yes
0.067
91,462
(4)
0.198∗∗∗
(0.008)
Yes
0.228
585.68
Table 4: Robustness Analysis and Alternative Hypotheses
This table reports the estimation results from regressions of the following form: Def aulti,t = α0 +
α1 F orgiveness Ratehome,t−1 + Controls + T ime F.E. In this model, the dependent variable is binary and
takes the value of one if loan i is in default at month t, but not at month t − 1, and zero otherwise. The main
explanatory variable is the forgiveness rate at month t − 1 in the zip code where borrower i lives. Columns
1-5 vary in the controls included in the regression. Variable definitions are provided in Appendix Table A.1.
Standard errors are clustered at the branch level and are reported below the coefficient estimates. *, **, and
*** indicate significance at the two-tailed 10%, 5%, and 1% levels, respectively.
Variables
F orgiveness Ratehome,t−1
Predicted Sign
+
Def ault Ratework,t
+
Def ault Ratehome,t
+
Inf lation
+
Inf lation in F ood P rices
+
Inf lation in T ransportation
+
Inf lation in Clothes
+
Inf lation in Housing
+
N atural Disaster
+
F orgiveness Rate × N at. Disaster
?
M ultiple Accounts
-
F orgiveness Rate × M ultiple Acc.
?
(1)
0.046∗∗
(0.020)
0.109∗∗∗
(0.020)
0.097∗∗∗
(0.017)
(2)
0.109∗∗∗
(0.036)
(3)
0.074∗∗∗
(0.019)
0.129∗∗∗
(0.021)
(4)
0.057∗∗
(0.025)
0.129∗∗∗
(0.022)
Placebo
(5)
-0.003
(0.017)
0.141∗∗∗
(0.003)
0.018
(0.028)
-0.266∗∗∗
(0.088)
-0.023
(0.028)
0.088∗∗∗
(0.022)
0.147∗∗∗
(0.033)
0.080∗∗∗
(0.017)
Borrower characteristics
Loan characteristics
Month fixed effects
Industry fixed effects
Work zip code - month fixed effects
Adj.R2
Observations
Yes
Yes
Yes
Yes
No
0.027
64,222
43
Yes
Yes
No
Yes
Yes
0.154
64,222
Yes
Yes
Yes
Yes
No
0.025
64,222
0.002
(0.007)
0.016
(0.028)
-0.042∗∗∗
(0.007)
0.034
(0.029)
Yes
Yes
Yes
Yes
No
0.028
64,222
Yes
Yes
Yes
Yes
No
0.025
64,222
Table 5: Stratification on Propensity Scores
This table reports the results of stratifying the sample by using the propensity scores generated
by using the following logistic regression: P (High F orgivenesst−1 ) = α0 + α1 Def ault Ratework,t +
α2 Def ault Ratehome,t +α3 Internal Credit Score+α4 Loan Characteristics+α5 Branch+M onth F.E. Panel
A reports the summary statistics of the main control variables for the groups of high and low forgiveness
in each stratum. Panel B reports the likelihood of default for the low- and high-forgiveness groups conditional on the probability that the loan was exposed to different levels of forgiveness. *, **, and *** indicate
significance at the one-tailed 10%, 5%, and 1% levels, respectively.
Panel A: Summary statistics of main control variables
Internal Credit Score
Pr(Treatment)
Quintile
Low
2
3
4
High
Def ault Ratework,t
Low
Forgiveness
High
Forgiveness
Low
Forgiveness
High
Forgiveness
Low
Forgiveness
High
Forgiveness
600.4
596.1
598.8
598.4
595.6
602.4
599.8
595.6
597.5
593.9
21.2%
29.8%
30.9%
31.9%
33.6%
23.6%
29.7%
30.1%
30.4%
31.6%
16.0%
30.0%
27.0%
31.4%
43.3%
20.0%
31.6%
28.4%
31.2%
40.4%
Panel B: Average default rate across 5 strata ranked on propensity scores
Def aultt
Pr(Treatment)
Low
2
3
4
High
Def ault Ratehome,t
Low Forgiveness
High Forgiveness
Difference
12.0%
15.1%
14.7%
16.7%
17.6%
16.7%
17.1%
15.5%
17.7%
20.3%
4.7%∗∗∗
2.0%∗∗∗
0.8%∗∗∗
0.9%∗
2.6%∗∗∗
44
Table 6: Loan-Performance Transition Matrices, pre and post September 2012
This table presents loan-performance transition matrices. In all panels, the rows indicate the delinquency
status of the loan at month t, ranging from non-delinquent to over 90 days delinquent. The columns indicate
the status of the loan at month t + 1, ranging from non-delinquent to over 90 days delinquent. In Panel A,
the transition probabilities are calculated for the six-month period before the change in forgiveness policy. In
Panel B, the transition probabilities are calculated for the six-month period as of the change in forgiveness
policy. Panel C reports the difference between Panel B and Panel A, with *, **, and *** indicating significance
at the 10%, 5%, and 1% levels, respectively.
Panel A: March 2012 to August 2012
Fraction of
loans
Status at
month t
Non-delinquent
1-29 days
30-59 days
60-89 days
90+ days
58.9%
29.9%
3.7%
3.0%
4.5%
Status at month t + 1
Non-delinquent 1-29 days
79.9%
18.7%
1.9%
0.3%
0.0%
20.1%
69.0%
8.8%
0.8%
0.1%
30-59 days
60-89 days
90+ days
0.0%
12.3%
3.8%
0.3%
0.0%
0.0%
0.0%
85.5%
1.6%
0.1%
0.0%
0.0%
0.0%
97.1%
99.8%
30-59 days
60-89 days
90+ days
0.0%
11.6%
2.2%
0.1%
0.0%
0.0%
0.0%
81.1%
1.9%
0.0%
0.0%
0.0%
0.0%
96.6%
99.7%
30-59 days
60-89 days
90+ days
0.00%
-0.68%∗∗∗
-1.64%∗∗∗
-0.17%∗∗
-0.02%
0.00%
0.00%
-4.45%∗∗∗
0.33%
-0.03%
0.00%
0.00%
0.00%
-0.47%
-0.13%
Panel B: September 2012 to February 2013
Fraction of
loans
Status at
month t
Non-delinquent
1-29 days
30-59 days
60-89 days
90+ days
52.7%
36.8%
4.4%
3.3%
2.9%
Status at month t + 1
Non-delinquent 1-29 days
77.6%
18.4%
5.0%
0.7%
0.2%
22.4%
70.0%
11.8%
0.7%
0.2%
Panel C: Changes in delinquency after September 2012 (Panel B − Panel A)
Fraction of
loans
Status at
month t
Non-delinquent
1-29 days
30-59 days
60-89 days
90+ days
∗∗∗
-6.19%
6.86%∗∗∗
0.65%
0.29%
-1.62%∗∗
Status at month t + 1
Non-delinquent 1-29 days
∗∗∗
-2.24%
-0.35%
3.13%∗∗∗
0.39%∗∗∗
0.12%∗∗
45
∗∗∗
2.24%
1.03%∗∗∗
2.96%∗∗∗
-0.08%
0.06%
Table 7: Ex-ante Measures of Contagion
This table reports the estimation results from the following regression: Strategic Def aultj = β0 +
β1 Ref erral RateAug0 12,j + β2 ConcentrationAug0 12,j . In this model, the dependent variable is computed
as α
d
d
1,j × F orgiveness Ratej during the three-month period after the change in forgiveness policy. α
1,j is
estimated by running the strategic default regression, Def aultj,t = α0 + α1 F orgiveness Ratehome,t−1 +
Controls + T ime F.E., for branch j. The main explanatory variable in column 1 is Concentration, in column
2 is Referral Rate, and in column 3 is both Concentration and Referral Rate. Simulations are used to correct
the standard errors for the presence of a generated regressor. P-values are reported below the coefficient
estimates. *, **, and *** indicate significance at the two-tailed 10%, 5%, and 1% levels, respectively.
Variable
Predicted Sign
Constant
Ref erral Rate
+
Concentration
+
(1)
(2)
(3)
-0.073
0.009
-0.080
(0.061) (0.029) (0.072)
0.316∗
0.329∗
(0.079)
(0.073)
0.044
0.141
(0.457) (0.362)
Adj. R2
Observations
0.030
54
46
0.002
54
0.031
54
Table 8: Bank’s Adjustments to the Forgiveness Policy
This table reports the estimation results from the following regression: Likelihood of F orgivenessj = γ0 +
γ1 Strategic Def aultj + γ2 T otal Def aultj . In this model, the dependent variable is computed as the average
likelihood that a defaulter in branch j receives forgiveness. In columns 1 and 2, the analysis is performed
over a three-month learning period, in columns 3 and 4, a six-month learning period, and in columns 5 and
6, a nine-month learning period. In all Columns, the main explanatory variable is the level of strategic
default among borrowers serviced by branch j. Strategic default is measured as α
d
1,j × F orgiveness Ratej ,
estimated from: Def aultj,t = α0 + α1 F orgiveness Ratehome,t−1 + Controls + T ime F.E., columns 2, 4, and
6 include the level of total defaults in each branch as an additional control. Simulations are used to correct
the standard errors for the presence of a generated regressor. P-values are reported below the coefficient
estimates. *, **, and *** indicate significance at the two-tailed 10%, 5%, and 1% levels, respectively.
Learning period:
Prediction with learning:
Constant
Strategic Def ault
3 months
9 months
(1)
(2)
(3)
(4)
(5)
(6)
(-)
0.538∗∗∗
(<.001)
-0.165
(0.577)
(-)
1.287∗∗∗
(<.001)
-0.191
(0.612)
-7.475∗∗
(0.010)
(-)
0.509∗∗∗
(<.001)
-1.036
(0.103)
(-)
1.038∗∗∗
(<.001)
-0.891
(0.136)
-5.388∗∗
(0.034)
(-)
0.519∗∗∗
(<.001)
-1.397∗
(0.069)
(-)
1.011∗∗∗
(<.001)
-1.212∗
(0.086)
-5.191∗∗
(0.018)
1.08%
54
9.82%
54
2.15%
54
10.69%
54
2.43%
54
12.91%
54
T otal Def ault
R2
Observations:
6 months
47
Table 9: Bank’s Adjustments to the Origination Policy
This table reports the estimation results from the following regression: Decision to Rejectj = γ0 +
γ1 Strategic Def aultj + γ2 T otal Def aultj . In this model, the dependent variable is computed as the
number of loans rejected by branch j as a fraction of the number of loans that met FA origination
requirements. In columns 1 and 2, the analysis is performed over a three-month learning period, in
columns 3 and 4, a six-month learning period, and in columns 5 and 6, a nine-month learning period. In all columns, the main explanatory variable is the level of strategic default among borrowers serviced by branch j. Strategic default is measured as α
d
1,j × F orgiveness Ratej , estimated from:
Def aultj,t = α0 + α1 F orgiveness Ratehome,t−1 + Controls + T ime F.E., columns 2, 4, and 6 include the
level of total defaults in each branch as an additional control. Simulations are used to correct the standard
errors for the presence of a generated regressor. P-values are reported below the coefficient estimates. *, **,
and *** indicate significance at the two-tailed 10%, 5%, and 1% levels, respectively.
Learning period:
3 months
(1)
Prediction with learning:
Constant
Strategic Def ault
T otal Def ault
R2
Observations:
6 months
(2)
(+)
(+)
∗
0.096
0.153∗
(0.070) (0.070)
0.278
0.260
(0.357) (0.386)
-0.571
(0.492)
3.30%
54
3.93%
54
48
9 months
(3)
(4)
(5)
(6)
(+)
0.086∗∗∗
(<.001)
0.505
(0.227)
(+)
0.179∗∗
(0.013)
0.544
(0.188)
-0.945
(0.185)
(+)
0.076∗∗∗
(<.001)
0.799∗
(0.095)
(+)
0.150∗∗∗
(<.001)
0.843∗
(0.098)
-0.783
(0.175)
5.11%
54
8.56%
54
9.71%
54
13.28%
54