Monitoring and the Portability of Soft Information Working Paper

Monitoring and the Portability of Soft Information Dennis Campbell Maria Loumioti

Working Paper 13-077 March 14, 2013

Copyright © 2013 by Dennis Campbell and Maria Loumioti Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.

Monitoring and the Portability of Soft Information

Dennis Campbell Harvard Business School Maria Loumioti USC Leventhal School of Accounting

ABSTRACT: We study the portability of soft information in a decentralized financial institution. Theories from a variety of literatures suggest that difficulties in capturing, storing, and communicating soft information can inhibit its portability over time and across individuals within the organization. Using unique data on lending decisions made by employees in a highly decentralized financial services organization, we show that a monitoring system which captures soft information for vertical communication (to superiors) purposes also facilitates the horizontal communication of soft information (across employees) for decision-making purposes. Contrary to prevailing views on the limited portability of soft information, our results provide evidence that the “stock” of soft information accumulated in this system has persistent effects on the lending decisions of employees. We show that employees rely on this information to increase access to credit for borrowers, provide more favorable pricing terms, and reduce the ex post risk of their lending decisions. These effects remain even when this information was acquired by employees other than the decision-maker, and they are not diminished by the physical separation of employees working in different business units.

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Dennis Campbell is a Professor of Business Administration at Harvard Business School, and Maria Loumioti is an Assistant Professor of Accounting at the University of Southern California. We thank Eric Allen, Randolph Beatty, Ken Merchant, Dan O’ Leary, KR Subramanyam, Marshall Vance, and the participants at the USC Accounting Research Forum and Harvard Information, Markets and Organizations conference for many helpful comments. All errors are our own. Contact emails: [email protected], [email protected]. 

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1. Introduction This study examines the “portability” of soft information within a decentralized financial institution. We define portability of information as the extent to which it can be stored for, communicated to, and used over time and by employees other than those that originally acquired or produced the information. Previous studies show that difficulties in capturing, storing, and communicating soft information can inhibit its portability across individuals within the organization (e.g., Stein, 2002; Liberti and Mian, 2009; Agarwal and Hauswald, 2010; Drexler and Schoar, 2011). However, with few exceptions, soft information is typically captured indirectly in this literature, and the specific mechanisms through which it is acquired, stored, and transmitted throughout an organization have received little attention. We address this gap using personnel, lending, and customer data from a decentralized financial institution in which employees have considerable decision-authority over the granting and structuring of consumer loans. We focus in particular on an internal monitoring information system used at this field site which, in effect, acts as a central repository of soft information collected in the course of interactions between employees and customers. Employees use this system to store and communicate soft information in text, including their opinions, ideas, assessments, and private information on borrowers’ backgrounds, without converting the text to a numeric score.1 An important feature of this process is that the content of the information collected is highly discretionary, and there are no guidelines on the type of information that employees are expected to acquire and store in the system. Using data from this system, we construct direct measures of soft information based on the definition in Berger and Udell (2002) and Petersen (2004). More specifically, soft information reflects loan officers’ assessments and feelings, and refers to borrowers’ personal                                                              1

Examples of employees’ notes on customers are reported in Appendix C.

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life (social and family background) and external interactions (professional and educational background). In contrast, hard information is readily collected and objectively verifiable, i.e. information related to borrowers’ transactions with the financial institution. We try to capture the different dimensions of soft information by coding the words in the notes that employees input in the system after their interaction with borrowers. For each note, we code the number of words that reflect soft information, versus hard information, based on available dictionaries for banking transactions and for dimensions of individuals’ personal and external interactions.2 Then, we link these proxies to employee lending decisions and outcomes by aggregating all soft information related keywords collected for borrowers over the last two years prior to loan originations, deflated by the total number of keywords related to hard information collected over the same period. Our ratio of soft over hard information captures the intensity of private soft information acquisition for a customer relative to quantitative standardized information which requires little effort on the part of the employee to generate. Because we employ direct measures of soft information, we are able to conduct empirical tests of its transmission both inter-temporally and across employees, each of which is key to measuring the portability of soft information. Contrary to the prevailing view that soft information is not readily portable, our results provide evidence that the “stock” of soft information accumulated in this system has persistent effects on the lending decisions of employees, after controlling for consumer, loan and employee characteristics. We show that employees rely on this information to increase access to credit for borrowers and provide more favorable pricing terms. Further, we find that using the “stock” of                                                              2

The list of keywords referred to soft and hard information is reported in Appendix B. Our empirical approach entices sacrifices in terms of precision. While we try to develop a comprehensive and objective list of terms to capture soft information that is recorded in text, we miss potentially important information from the rest of employees’ notes. However, if our proxy was too noisy, it would have biased against finding any significant relations with the variables of interest.

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soft information informs employees’ discretion and predicts better ex-post loan performance. These effects hold even when this information was acquired by employees other than the decision-maker, and they are not diminished by the physical separation of employees working in different business units. Also, we find that borrower characteristics strongly affect the use and portability of soft information. Employees will rely more on soft information to grant loans to customers with poor credit profile that are more information sensitive and need stronger monitoring, while they will skip relying on soft information for evaluating customers with high credit score. These findings are important for a number of reasons that also point to the relative contributions of our paper. First, a considerable body of research has demonstrated the benefits of relationship lending for both lenders and borrowers. This literature has paid special attention to the role of soft information gathered on borrowers over multiple interactions with the bank as a mechanism for facilitating relational contracting and improved access to credit at favorable terms (Pertersen and Rajan, 1994; Berger and Udell, 1995; Degryse and van Cayseele, 2000; Agarwal and Hauswald, 2010). However, considerable confusion exists on the extent to which soft information gathered in the course of relationships is separable from the employees who acquire the information (Berger and Udell, 2002; Drexler and Schoar, 2011). Even if soft information is stored and communicated in text, Petersen (2004) questions whether soft information can be processed and interpreted.3 Thus, the question of portability addressed in our paper is central to understanding                                                              3

More specifically, “Can’t text files be processed electronically? Again the answer has to be yes, conditional on what one means by processed. Whether it can be interpreted and coded into a numeric score (or scores) is a hard question […] A firm’s sales revenue for the year is an example of hard information. There is wide agreement as to what that means for a firm to have sales for ten million dollars last year. However, if we say the owner of the firm is honest, there is less agreement about what this means and why this is important. My interpretation of honest may be different than yours.” (Petersen 2004, pg. 6-7).

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whether soft information acquired in lending relationships can, in effect, constitute a strategic asset of the organization rather than remaining embedded in its employees. Indeed, one of the challenges facing empirical research on relationship lending is the difficulty to observe and measure soft information acquired by employees through their interactions with borrowers (Liberti and Mian, 2009). Our results contribute to this debate by providing empirical evidence on the portability of soft information. Second, and relatedly, our results emphasize the role that a centralized monitoring technology can play in alleviating well-documented barriers in communicating soft information and enabling its use for horizontal coordination purposes, thus, decreasing employees’ effort to produce, transmit, interpret and decide on the credibility of complex information ((Bolton and Dewatripont, 1994; Dewatripont and Tirole, 2005; Dewatripont, 2006). In this way, our results provide support for emerging economic theories on the role of information centralization as a complement to decentralized decision-making (Brynjolfsson and Hitt, 2000, Bresnahan et. al., 2002, Cremer et. al., 2007). Finally, our findings complement the literature on the role of information systems as a means of improving information processing and coordinating decentralized decision-making within financial institutions (Petersen and Rajan, 2002; Hauswald and Marquez, 2003). Petersen and Rajan (2002) find that the distance between borrowers and bankers has significantly increased over time, suggesting that lending decisions are relying more on hard information. We propose an alternative mechanism: internal centralized information systems can facilitate the transmission of soft information across employees in different branches. The rest of the paper proceeds as follows. Section 2 discusses the previous literature on relationship lending and soft information. Section 3 presents the research setting and discusses

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the data. Section 4 presents the empirical tests and results. Section 5 discusses additional analysis and section 6 concludes.

2. Prior Literature A well-established literature exists documenting the benefits of lending relationships for both lenders and borrowers (e.g., Pertersen and Rajan, 1994; 1995; Berger and Udell, 1995; Cole, 1998; Elsas and Krahnen, 1998; Degryse and van Cayseele, 2000; Berger and Udell, 2002; Agarwal and Hauswald, 2010). The primary mechanism underlying most of this research is the acquisition of soft information gained through repeated interactions with borrowers over time. In effect, soft information gained in the course of repeated interactions throughout a customer relationship can improve the precision of information available to lenders for making credit decisions on their borrowers and can provide relationship lenders with an informational advantage over competitors (Townsend, 1979; Diamond, 1984; Rajan, 1992). The soft information acquisition activities of firms can also facilitate the ex post formation of “implicit relational contracts” (Sharpe, 1990) or “social attachment” (Uzzi and Gisselspie, 1999) between borrowers and their lenders. While there is no widely accepted definition of soft information in the relationship lending literature, prior studies tend to focus on defining it with reference to a number of common attributes including “being customer-specific, often proprietary in nature” (Boot, 1999), “not easily observed, verified and transmitted by others” (Berger and Udell, 2002) and relating to “qualitative private information and loan officers’ assessments” that are difficult to communicate, collect and quantify (Petersen, 2004). Regardless of the particular definition used, the defining feature of soft information is that it is expected to have limited “portability” – it is

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difficult to store, communicate to, and use by employees other than those that originally acquire or produce the information (e.g., Berger and Udell, 2002; Petersen, 2004), whereas hard information is defined as “quantitative, easy to store and transmit in impersonal ways, and its content is independent of the collection process” (Petersen, 2004). Similarly, previous studies question whether the communicating soft information in text can improve its processing, interpretation and importance by employees other than the one generating this information (e.g., Petersen, 2004). Because of the inherent difficulties in measuring soft information that lenders collect for their borrowers, prior studies have tended to use observable outcomes of lending relationships as proxies for soft information acquisition. These include the strength and length of lending relationships (e.g., Petersen and Rajan, 1994; 1995; Berger and Udell, 1995), their scope (Cole, 1998; Degryse and van Cayseele, 2000) and their exclusivity (Ongena and Smith, 2001). Because these variables can capture other borrower characteristics such as switching costs, prior studies have found mixed results using these proxies (e.g. see Elyasiani et al., 2004). The few studies that have attempted to use more direct measures of soft information, such as loan officers’ risk assessments or subjective credit scoring, have tended to find evidence consistent with the idea that it has limited portability. Reliance on soft information has been empirically demonstrated to diminish with both hierarchical and geographic distance between employees (Liberti and Mian, 2009; Agarwal and Hauswald, 2010; Bouwens and Kroos, 2012; Qian, Strahan and Yang, 2012). Other research has provided more indirect evidence on this phenomenon by investigating the effects of loan officer turnover on lending outcomes, finding results consistent with the idea of an underlying loss of soft proprietary information due to increased turnover (Berger and Udell, 2002; Drexler and Schoar, 2011). 

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These findings raise interesting questions regarding organizations’ capabilities to engage in relationship lending. Relationship lending is a credit mechanism that depends on banks’ acquiring soft information through multiple interactions with the same customer, creating an opportunity to benefit from “inter-temporal information reusability” (Greenbaum and Thakor, 1995). If soft information is effectively not separable from the employee, then the advantages of relationship lending are diminished with either employee turnover or as customers interact with different employees within the same organization. The extent to which soft information can be made portable, then, is a key consideration in determining whether relationship lending can be developed as an organizational capability distinct from its given set of employees in a particular time period.   The existing evidence on the limited portability of soft information notwithstanding, this is still an open issue as we know little about the particular mechanisms through which soft information is acquired, stored, and communicated within organizations. Indeed, with few exceptions (Petersen and Rajan, 2002), the effect of information systems on employees’ collecting and processing private information in credit markets has been little studied (Hauswald and Marquez, 2003). Despite properties of soft information that have been posited to limit its portability, economic theories predict that the centralized storage and dissemination of soft information is one such potential mechanism (Brynjolfsson and Hitt, 2000; Bresnahan et. al., 2002; Cremer et. al., 2007). In particular, this work predicts that centralization of information can lead to delayering of hierarchies, increased horizontal integration among employees within firms, and more direct communication between peers in different functions or business units (Cremer et. al., 2007).  

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In this paper, we focus on a setting with a flat, decentralized organizational structure in which soft information is captured and stored as text in a centralized monitoring system that is transparent to all employees. While we do not test these theories directly, this setting allows us to examine the corollary that centralized soft information is a complement to decentralized decision-making. We do so by investigating the extent to which soft information captured in this system is portable both inter-temporally and across employees in ways that lead to more effective decisions and outcomes.

3. Research Setting and Data

The research site for this study is a federal credit-union with approximately $1.6 billion in assets, 140,000 customers,4 and 23 branches operating in a single state in the U.S.5 The organization offers traditional financial products and services and counts national banks, community banks, and other credit unions as major competitors. The primary data for this study come from this organization’s internal lending, personnel, and customer records during the period January 2008May 2010. Throughout this period, this organization, when compared either to a peer group of same-state credit unions or to a national peer group of similar size, has consistently ranked in the top 15 percent in productivity (revenue per employee), loan default rates (2nd lowest), and overall performance (return-on-assets). 3.1 Organizational Structure

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While our sample includes consumer loans and mortgages, the complexity of borrower and loan characteristics is similar to field settings in other papers that investigate small business relationship lending. For example, Drexel and Schoar (2011) use a sample of loans from a micro-credit division to small ventures with average annual sales of less than $110,000. 5 Credit unions are a form of depository institution that function largely like traditional banks but differ in that they are mutually owned and organized by their depositors.

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During our sample period, the organization operated with a highly decentralized structure in which employees have decision-authority over virtually any decision involving the customers with whom they interact. Under this decentralized structure, the majority of employees are considered “generalists” who are expected to be capable of handling a wide variety of tasks for customers ranging from opening new deposit accounts and processing simple banking transactions to approving, structuring, and processing consumer loans and mortgages. More specifically, there is no formal assignment of employees to customers for lending or other purposes, and any available employee, including the ones working at the back-office, are expected to assist customers in initiating the loan request and to further complete the lending decision. Indeed, for an average customer report including seven notes from employees within the last two years, only 11 percent of these notes are originated from the same employee. In recognition of this fact, by the beginning of our sample period the organization classified all customer-contact employees into one job-title called “Member Service Representative”.6 This job title encompassed roles that would traditionally be associated with specialized positions such as tellers, loan officers, and call center representatives in most banks. The organization has operated with this relatively flat decentralized structure since 2004. Prior to this period, it relied on a traditional hierarchical organizational structure in which employees had specialized tasks and decision-authority over lending, pricing, and other activities was centralized within specific job categories. Illustrating the traditional nature of management controls under the previous organizational structure, one senior executive noted: “Controls were extremely tight here. Our divisions were run as fiefdoms. You couldn’t even get a fee waived for a [customer] without going through accounting”.

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Owing to their mutual ownership structure, credit unions typically refer to customers as “members”.

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The organization’s move towards decentralization was undertaken with the general objectives of improving service and strengthening the duration and performance of customer relationships. Most salient from the perspective of this paper is the role that decentralization of decision-making authority played in the organization’s lending practices. Over our sample period, employees of this organization held virtually complete decision-authority over both the approval of loans and the structuring of loan terms including amount, rate, and duration. As one executive explained: “Employees have full latitude on rates and overriding our underwriting guidelines. Even [tellers] have this authority”. By contrast, traditional automated risk scoring methodologies were used previously to determine who could or could not get a loan with the institution. Loan approval exceptions occur when an employee overrides the organization’s guidelines for approving a potential borrower for a loan. In general, in the absence of decentralized decision-making authority at this organization, borrowers with either credit scores less than 620 or debt-to-income ratios above 45 percent would not qualify for approval. The second type of loan exception – those made in the structuring of approved loans – consists primarily of rate exceptions occurring when employees structured the loan with a different rate than was recommended on the “rate sheet” for a loan with similar characteristics. In the vast majority of cases, though not all, the actual interest rate tended to be lower than the rate-sheet rate consistent with employees using decision-making authority largely to reduce rates offered to customers. Over our sample period, employee compensation included a flat salary which was not tied to the effectiveness of loan decisions. Loan officers were annually ranked based on the revenue they generated for the credit union, and the effectiveness of their loan decisions.

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Consistently highly ranked employees were likely to be promoted, however, the timing of their promotion and salary increase were uncertain.

3.2 Lending Decisions and the Information Environment Characteristic of a prototypical “relationship lender”, its decentralized structure enabled the organization – through the discretionary decisions of its employees – to base credit decisions on broader sources of information than traditional credit scores and income based metrics. In general, the incorporation of additional information in lending decisions via employee discretion is viewed within the organization as a mechanism for improving risk assessments of potential borrowers and overcoming perceived informational inefficiencies inherent in pure credit-score based lending. One executive summarized this view as follows: “The norm in our industry is quick decision making based on hard factors that we think are reliable but miss the human element. There are plenty of people with 800 credit score that make thousands of dollars a month but could default in the blink of an eye. There are also plenty of people with scores lower than 600 that are safe bets and are seeking to legitimately rebuild their credit.” Consistent with prior literature on relationship lending, employees base their discretionary lending decisions in part on both hard and soft proprietary information gained in the course of the organization’s relationship with the customer (Petersen and Rajan, 1994; Agarwal and Hauswald, 2010; Mester et. al., 2006; Liberti and Mian, 2009). Besides credit scores and debt-to-income ratios, hard information typically consists of data captured in the organization’s transaction records and customer databases. Our interviews with employees, for example, suggested that they generally view customers with longer tenures, more products, and stronger existing loan relationships as better lending risks and thereby more eligible for favorable loan terms.

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Soft information, by contrast, is acquired by employees in the course of direct interactions with customers. Our interviews with employees suggest that more effort is typically put into soft information acquisition by employees when loan exceptions are required to extend credit to the borrower. In these instances, employees tend to expend considerable effort to identify borrowers’ specific and often idiosyncratic reasons for low credit scores or high debt-toincome ratios. Credit could be granted in these circumstances if the borrower is deemed by the employee to have legitimate reasons for poor performance on external measures of creditworthiness. For example, the employee may identify specific causes for short-term declines in these measures such as identity theft or a temporary interruption in the borrower’s employment status. As one employee noted: “I really try to get at the ‘how and the why?’ What happened that caused the credit score decline or bankruptcy? What if it is a temporary job loss, a healthcare issue, or some other issue beyond the control of the [customer]? I would consider all of these factors in making a lending decision.” In the course of their interactions, employees also appeared to seek evidence that potential borrowers accepted some level of personal responsibility for mistakes made in prior credit management decisions. Such evidence could constitute soft information in the form of particular cues picked up by the employee during conversations with the borrower. In a representative comment, for example, one employee we interviewed noted that “…I really look for accountability. Does the [customer] admit they did not handle a credit situation properly? I am really looking for a signal that the individual matured or learned. For example, I would view it very differently if the explanation was that [the customer] was young, got in over his head, and learned from those mistakes versus a customer that blames all of their problems on their previous bank.” In addition to the direct acquisition of soft information obtained interacting with a borrower upon initiation of a loan request, employees could also potentially access soft information acquired by other employees during previous interactions with the same borrower. A 12   

primary mechanism through which this could occur is a centralized monitoring system referred to internally as a “detection control” model which functions by allowing employees to deviate from formal guidelines, but requires them to explain and document their decisions when they do so. One executive captured the workings of this control system as follows: “We flag loans that are outside of our lending guidelines. If there is [an exception], then the employee should log the explanation in [our internal IT system]. If they don’t, we will have a discussion with the manager to make sure the documentation is in the system going forward. If they put the explanation in [the system], then we don’t review the exception further.” Importantly, this process emphasizes enforcing the documentation of employee rationales for deviation from guidelines rather than questioning the outcomes of individual decisions. This was a deliberate choice under the assumption that questioning individual outcomes would effectively eliminate willingness to exercise decision-making authority.7 The system does, however, maintain a record of the full history of employee explanations for any lending or other decisions that deviate from formal guidelines.8 The full archive of past rationales contained in this system is accessible by all employees at the time they make lending decisions and is searchable by customer, employee, time period, or transaction type. It is important to note that this monitoring system can contain information acquired from customers during any transaction that required an exception, such as requests for fee waivers or increased deposit rates, and is not restricted to information specific to lending transactions only. Employees could also enter notes into this system for transactions that, while not exceptions per se, might involve customer disputes or potentially require follow-up by a supervisor. In effect, the notes logged into this system by employees in one period constitute an additional source of                                                              7

This assumption is consistent with the literature on “psychological safety” in organizations (Edmondson, 1999) as well as studies on employee behavior under different monitoring regimes (Campbell et al., 2011). 8 While this monitoring system does not punish or reward employees for loan outcomes, top management does monitor trends in exception rates over time for risk management purposes. An internal risk management committee conducts periodic reviews to identify trends for employees with severe deviations in either the rate or magnitude of their exceptions. In these instances, mentorship and coaching are used to instill decision-making norms that are more aligned with the overall risk tolerance of the organization.

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soft information on customers that can potentially be used in future lending decisions by other employees. While this system was initially implemented for vertical communication purposes (monitoring), many of our interviews suggest that it has evolved in unanticipated ways to potentially enable horizontal communication and coordination of employee decisions. In particular, while not trained, required, or explicitly motivated to do so by the organization, employees often take it upon themselves to enter notes on facts they learn about customers even during routine interactions not requiring exceptions. Examples of employees’ notes are reported in Appendix C. Often, the rationale for doing so is that the information may be helpful to other employees in providing service to that customer. As one employee explained: “The notes constitute a kind of storybook about the [customer’s] life. We can use that information to start a conversation and have a more personal connection and interaction with the [customer].” Speaking directly to the question of the portability of soft information, another employee noted that the system “…ensures that the relationship is not just with one employee. I’ve been here for ten years and interacted with thousands of [customers]. Without this system, if I leave, their information goes with me.” Our measures of soft information, and our primary empirical tests of its portability across employees, are based on the employee notes contained in this system. While our primary goal in this paper is to empirically measure the portability of soft information contained in this centralized system, the qualitative evidence presented above provides initial evidence that the system is playing a primary role in coordinating the information acquisition, lending, and service activities of decentralized employees in our setting.

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3.3 Data The data used for this study are collected from the organization’s lending, personnel and customer records during the period January 2008-May 2010. During this sample period, we observe 44,110 loan applications, out of which 87 percent were approved. The sample selection process is summarized in Table 1, panel A. Our primary sample of approved loans includes 38,409 loans with size greater than $500 made to 24,033 unique borrowers in 41 unique business units.9 We eliminate loans that we cannot associate with a specific employee (4,947 loans) and for which we cannot not identify the interest rate (942 loans). We further exclude loans to members with no recorded notes three years prior to loan origination (1,820). Our final sample of approved loans includes 30,700 loans to 20,299 unique borrowers originated by 637 unique employees in 41 business units. Following similar procedures, our sample of rejected loans includes 4,435 loan applications by 3,018 unique borrowers to 367 unique employees in 38 business units. Approved loans mainly include auto loans (17,140 loans), credit cards (8,450 loans) and mortgages (1,990 loans) as shown in Table 1, panel B. Dependent Variables: We use two variables to capture employees’ loan decisions: a binary variable that equals one if a loan was approved, and zero otherwise (Approved Loan), and the difference between the loan interest rate and the standardized interest rate based on the borrower’s credit score and debt-to-income ratio (Discretionary Pricing). To investigate outcomes of lending decisions, we observe whether an outstanding loan is charged off during our

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A business unit could be a branch, call center, or even a functional unit such as accounting. Employees, even those in “back office” functions, were often required to perform customer-facing tasks including consumer lending. This arose particularly during peak periods in which the organization relied more broadly on its “generalist” employees to handle large volumes of loan inquiries and applications.

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sample period. Charge-off is a binary variable that equals one if the loan is written off of the organization’s balance sheet within two years after loan origination, and zero otherwise.10 Soft and Hard Information: We proxy for “soft” and “hard” information using content analysis of the notes that employees have collectively generated on customers during and three years prior to the origination of a particular loan or loan application. The centralized information system includes 1,953,902 notes consisting of more than ten words11 for 128,396 unique members during the period 2005-2010. This includes 394,586 notes for the borrowers in our sample, or approximately 20 notes per borrower. We identify keywords related to “soft” information using the following methodology (similar to Li, 2012). First, we read 15,000 randomly selected notes (approximately 33,000 lines) to identify repeating patterns in the words and phrases that employees use. We define as “soft” information keywords related to borrowers’ external and personal environment. External environmental characteristics are associated with borrowers’ social (“friends”, “neighbors”, “hobby”, etc.), professional (“job”, “manager”, etc.) and academic life (“education”, “degree”, etc.). Personal environmental characteristics are related to the borrowers’ personal life (“family”, “child(ren)”, “parent(s)”, etc.), feelings, and assessments. To capture keywords related to feelings and personal assessments, we employed Plutchik’s (1980) and Parrot’s (2001) lists of feelings and emotions. Traits of borrowers’ personal, social, educational and professional background shape their behavior and decisions, and processing of this information by employees signals their acquaintance with borrowers. We define as “hard” information keywords related to borrowers’ transactions (“accounts”, “credit card(s)”, etc.) and income. We use the list of Bank                                                              10

The credit union sells less than 5 percent of its loans to securitization, thus loan write-offs are considered a valid proxy of loan decision outcomes. 11 We attempt to control for notes that describe employees’ actions (for example, “called XX, no answer, left message; will call back tomorrow”) by excluding notes with fewer than eleven words.

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Transaction Codes ISO 20022 to identify transaction categories, and amplify the list adding transaction-oriented words often used by employees (e.g., “ATM”, “Visa”) and keywords related to borrowers’ income. Transaction related information is automatically stored in member’s file, is easily accessible by all employees, affects numeric scores such as credit score (e.g., the more a member uses and pays back his credit card, the better his score), and almost all customers interact with employees with a transaction-related question (overdraft, loan, paystub, atm, credit card, visa, etc.). The list of words referring to soft and hard information is reported in Appendix B.12 Our primary proxy for soft information is the number of soft information related keywords generated at and up to three years prior to loan origination deflated by the number of keywords related to hard information disclosed in the same period (Soft_Hard information). We deflate by the number of hard information related keywords in part to control for the total amount of routine information disclosed or reported in the course of employees’ and borrowers’ interactions which could be confounded with other aspects of the customer relationship such as tenure, transaction volume, or number of product relationships. Increased amounts of both hard and soft information would tend to accumulate in the centralized system for customers who interact with the organization more frequently and over longer time periods. Normalizing by the number of hard information keywords allows us to capture the intensity of private soft information acquisition for a customer relative to quantitative standardized information which requires little effort on the part of the employee to generate. For example, hard information would naturally be generated by customers interacting with the organization regarding an                                                              12   While we try to develop a comprehensive and objective list of keywords to capture soft information, we miss potentially important information from the rest of the notes. Nevertheless, if our proxy was too noisy, it would have biased against finding any significant relationship with loan decisions and outcomes.

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existing or new transaction or product (e.g., a description of the nature of the transaction or reference to the product itself), but the amount of soft information acquired and reported during the interaction highly depends on the borrower’s disclosure and the employee’s information acquisition efforts. Hence, we expect that deflating soft by hard information is likely to capture this pattern and provide a cleaner measure of the soft information content contained in the system for a given customer.13 Our primary proxy consists roughly of a “stock” component – or the amount of existing customer-specific soft information in the system at the time the employee makes a lending decision – and a “flow” component – the amount of soft information acquired and reported by the employee in the course of making the decision. The “stock” component would typically consist of information produced by several different employees over the course of their interactions with a particular borrower whereas the “flow” component would consist of information produced only by the employee making the current loan decision. Because our primary focus is on the portability of soft information, we are particularly interested in the extent to which the “stock” component of this information influences credit decisions and outcomes. In order to facilitate this, we further develop a proxy for soft information collected through loan origination, i.e. twenty days before and after loan origination, deflated by the number of hard information related keywords in the same period (Soft_Hard information at loan origination), and a proxy for soft information collected prior to loan origination, deflated by the number of hard information related keywords in the same period (Soft_Hard information prior to loan origination). Finally, we construct separate measures for                                                              13

Deflating by the total amount of information (number of words) produces similar results. However, this is a noisier measure owing to the idiosyncratic writing styles of employees. The denominator in our proxy is never zero, because for hard information originated in the past we aggregate notes originated two years prior to loan origination, and for hard information at loan origination notes include a keyword related to the transaction (“credit”, “visa”, “loan”, etc.).

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soft information collected prior to loan origination by: the same employee making the lending decision (Soft_Hard information by employee); other employees not involved in the current lending decision (Soft_Hard information by others); and for information collected within14 and outside the employee’s business unit (Soft_Hard information within branch and Soft_Hard information outside branch). Collectively, these proxies for soft information will allow us to test for the extent to which portability of soft information is influenced by temporal and physical separation of the information acquirer and ultimate decision-maker. Lending Relationships: Consistent with previous research (Berger and Udell, 1994; Petersen and Rajan, 1994; 1995; Cole, 1998; Berger and Udell, 2002) we control for a variety of different proxies associated with lending relationships that are expected to affect loan decisions and outcomes through increased precision of borrower information, implicit relational contracts, or switching costs. We use the number of products held by a customer prior to loan origination, including deposit accounts, credit cards and loans, to proxy for the scope of the customer relationship (Number of products). The length of prior lending relationships is proxied by borrower tenure (Borrower age), defined as the natural logarithm of years that the borrower has been a member of the credit union. As a proxy for the strength of prior lending relationships, we use the average size of loans made by the credit union to the customer during the two years prior to loan origination deflated by the average size of all loans outstanding at the credit union in the same period (Lending relationship). Borrower and Loan Characteristics: We further control for borrowers’ risk profile based on quantitative hard information, including credit score (Log(Credit score)), defined as the natural logarithm of borrower’s credit score, and debt-to-income ratio (Log(Debt ratio)), defined as the natural logarithm of borrower’s debt-to-income ratio. Moreover, in most of our analyses we                                                              14

We exclude information collected from the same loan officer in constructing this measure.

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control for loan size (Log(Loan amount)), defined as the natural logarithm of the loan amount, loan maturity (Log(Maturity)), defined as the natural logarithm of loan maturity in months, and the loan interest rate (Interest rate). Finally, we also include year, loan type and employee fixed effects to control for unobserved heterogeneity across employees, loan types and years.15 Employee fixed effects are particularly important since their inclusion allows us to account for potential endogenous assignment of borrowers to employees which could occur, for example, if employees are situated in locations where they are systematically more likely to interact with borrowers of high or low ex ante risk profiles. 4. Empirical Tests and Results

4.1.Validation Tests We employ several tests to validate our proxy for soft information. Table 2, Panel A, reports results from OLS regressions of our proxies for soft information acquisition at loan origination on measures of the “stock” of soft information existing on the customer prior to loan origination along with the borrower’s credit score, debt-ratio, and various relationship proxies. Employee, year, and loan type fixed effects are included in all specifications and standard errors are corrected for clustering at the employee level prior to inference. Consistent with prior theoretical arguments, the negative coefficients on credit score and our various relationship proxies for the subsample of “subprime borrowers” (e.g. credit score620) suggesting that employees generate more soft information for borrowers with higher credit scores in this sample. This finding is consistent with existing literature on the role of soft information in the presence of competing lending relationships – borrowers with higher credit scores face lower switching costs, and soft information can be used by the existing lender to provide more favorable loan terms in order to mitigate competition (Petersen and Rajan, 2002; Hauswald and Marquez, 2006; Agarwal and Hauswald, 2010). The results also show that prior soft information accumulation strongly predicts future disclosure of soft information in both the subprime and prime borrower samples – borrowers that appear to be more prone to sharing private soft information in the past are likely to maintain this pattern in the future. Prior literature also suggests that our proxy for soft information should be associated with improved credit availability and decreased loan pricing, as employees can better assess borrower risk profiles ex ante (Berger and Udell, 1994; Petersen and Rajan, 1994; 1995; Cole, 1998; Agarwal and Hauswald, 2010). We test whether our proxy for soft information is positively related to credit easing and lower interest rates. Specifically, credit union employees can discretionarily make exceptions in their loan decisions, such as applying lower interest rates, decreasing collateral requirements and increasing loan maturities, which significantly improves                                                              16

While proxies for relationship lending used in prior studies, such as the strength of prior lending relationships, borrower tenure and number of products with the lender (e.g., Berger and Udell, 1994; Petersen and Rajan, 1994; Cole, 1998; Agarwal and Hauswald, 2010), are negatively related to soft information acquisition at the time of loan origination, they are positively related to the overall accumulation of past soft information (untabulated results) in the system. Hence, our proxy for the existing “stock” of soft information is correlated to relationship proxies used in prior literature consistent with the idea that relationships allow informational advantages through the accumulation of soft information over time. The different trait of our proxy is that it allows us to disentangle prior and current soft information collected by loan officers for their borrowers.

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credit availability to subprime borrowers facing financing frictions, i.e. borrowers with credit scores lower than 620. The results in table 2, panel B and C confirm these expectations. An increase in soft information by one standard deviation (0.9 for Log(Soft Information) and 0.4 for Soft_Hard information) increases the number of loan exceptions by 3 percent for loans to subprime borrowers. Also, consistent with our expectations, this effect is lower for high quality borrowers, i.e. borrowers with credit score higher than 620, whose strong credit profile reduces the need for credit easing. Furthermore, the results suggest that our proxy for soft information is negatively related to loan pricing as expected (panel C). An increase in soft information by one standard deviation decreases the loan interest rate by 0.1 percent for subprime borrowers or by 2 percent of its standard deviation, controlling for loan, borrower and employee characteristics. The average interest rate for subprime loans is 10.25 percent. The effect of our proxy for soft information on the interest rate is similar to the effect of proxies used in prior studies to capture relationship lending (strength of prior lending relationships, borrower tenure and number of products). For example, an increase in the strength of prior lending relationships by one standard deviation decreases loan pricing by approximately 0.2 percent, and an increase in borrower tenure by one standard deviation decreases loan pricing by approximately 0.07 percent for loans to subprime borrowers. The effect of our proxy for soft information on loan pricing is consistent with the results of prior studies on relationship lending (e.g., Agarwal and Hauswald, 2010). Also, the results suggest that quantitative and easily communicated information, such as credit score, are strongly related to loan pricing. Indeed, an increase in the natural logarithm of borrower credit score by one standard deviation decreases pricing of loans to subprime borrowers by 1.5 percent. Consistent with this finding, soft information does not play a critical role in pricing of loans to

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high quality borrowers whose credit profile is considered a strong indicator of their creditworthiness. Overall, the results in this section suggest that our proxy for soft information behaves in ways consistent with predictions from the existing literature. It is relatively persistent over time consistent with lending relationships being stable in the long run; it is associated with relationship lending proxies used in prior literature; and it predicts both credit easing and favorable loan pricing. 4.2.Descriptive Statistics Table 3 reports the summary statistics for soft information, loan approvals, discretionary loan pricing, charge-offs and some loan and borrower characteristics for our sample. Eighty-seven percent of loan applications to the credit union were approved, and the mean (median) approved loan size is $14,381 ($10,000), with average (median) maturity of 5.2 (5) years. Twenty percent of approved loans were extended to borrowers with credit score 620 and lower, and employees applied lower interest rates to rate sheet interest rates by 0.66 percent on average. In terms of our main variable of interest, Table 3 shows that, on average, employees collect more hard than soft information, as acquiring soft information is relatively more costly in terms of effort. Indeed, the mean soft to hard information disclosed is 0.49, suggesting that the average note logged into the system on a borrower has relatively more hard information content. However, there is significant variation in the soft information that borrowers communicate in their interactions with employees, with a 25th percentile of 0.14, a 75th percentile of 0.60 and a standard deviation of 0.45. Also, the mean soft to hard information prior to and at loan origination is 0.47 and 0.57 respectively, thus employees appear to acquire more soft information around loan origination.

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Figure 1 documents trends in the information content of the organization’s internal communication system over time. The figure shows the average number of hard and soft information keywords per note per year along with the average discretionary component of loan interest rates. In general, Figure 1 reveals a pattern of increasing hard and soft information content over time and a corresponding increase in the magnitude of discretionary reductions in loan rates. Consistent with employees putting more effort into soft information acquisition as the implementation of the decentralized structure evolved over time, the soft relative to hard information content (Soft_Hard information) of the average note in the system is also increasing over time. The findings are consistent with prior literature showing that higher authority and accountability incentivizes employees to collect more information of high quality (Aghion and Tirole, 1997; Qian, Strahan and Yang, 2012). In Table 4, we segregate the loans in our sample based on loan approval status (panel A) and borrower credit scores (panel B). In the first column of panel A, we report loan and borrower characteristics for approved loans. The second column provides the same information for rejected loan applications. The last column reports the differences in average loan and borrower characteristics between approved and rejected loan applications. The results of the univariate tests of differences in means provide strong evidence that employees are more likely to approve a loan application if they have more soft information for their borrowers. The pattern is similar for soft information produced at loan origination and soft information collected prior to loan origination, suggesting that employees’ loan decisions also rely on past soft information available in the internal communication system. In the first column of panel B, we report loan and borrower characteristics for loans to borrowers with credit score above 620. The second column provides the same information for

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loans to subprime borrowers. Consistent with our expectations, soft information acquisition is more intense for our subsample of subprime borrowers, suggesting that employees substitute poor hard information with qualitative private information to assess borrowers’ risk profiles. Also, subprime borrowers have developed relatively weaker lending relationships with the credit union relative to prime borrowers, consistent with the recent organizational shift to decentralized and discretionary loan decisions. 4.3.Portability of Soft Information To test the extent to which soft information is portable over time and across employees, we estimate a probit model of the credit approval decision on current and past soft information, controlling for the strength of prior lending relationships, the number of borrower products with the credit union, borrower and loan characteristics and employee and year fixed effects. Results reported in Table 5, panel A demonstrate that employees make loan decisions based on both current and past soft information, and current soft information is more predictive of credit approval. An increase in current and past soft information by one standard deviation increases the probability of credit approval by approximately 6 and 3 percent respectively. The unconditional probability of credit approval is 87 percent. This effect is stronger for subprime borrowers’ loan applications, consistent with employees using soft information to exercise higher discretion for borrowers with poor external risk profiles. An increase in current and past soft information by one standard deviation increases the probability of credit approval to a borrower with credit score below 620 by approximately 11 and 6 percent respectively. The unconditional probability of granting a loan to a subprime borrower is 75 percent. Moreover, soft information does not play a significant role in lending to healthy borrowers with solid credit scores. An increase in current and past soft information by

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one standard deviation increases the probability of credit approval to a borrower with credit score below 620 by approximately 1 percent, and the unconditional probability of credit approval for borrowers with credit score above 620 is 93 percent. The coefficients on proxies for prior lending relationships are consistent with existing literature. Specifically, borrowers with stronger prior lending relationships that hold multiple product lines with the credit union are more likely to receive credit, as the significantly positive coefficients on Lending relationships and Number of products suggest. However, the significantly negative coefficient on Log(Credit score) for the subsample of borrowers with credit score below 620 is unexpected. Interviews with employees and executives at our site suggested that a potential explanation for this finding is that employees delay the granting of credit to customers that are near traditional thresholds of credit-worthiness based on credit scores – in effect encouraging customers to improve their financial health to the point where they are eligible for the most favorable lending terms. The coefficient on Log(Credit score) is significantly positive for the subsample of borrowers with credit score above 620, as credit score is a strong indicator of these borrowers’ creditworthiness. Table 5, panel B reports similar results based on an ordinary least squares regression of discretionary pricing on current and past soft information and shows that both current and past soft information is used by employees to assess the borrower’s risk profile. On the one side, collecting more soft information over borrowers may incentivize loan officers in expropriating borrower relationships by increasing interest rates (e.g., Sharpe, 1990; Hauswald and Marquez, 2003). On the other side, soft information is used to build long-term relationships thus decreasing interest rates to motivate borrowers to stay in the relationship. We find support for the latter prediction. An increase in current and past soft information by one standard deviation decreases

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the discretionary interest rate by 0.01 percent or by 1 percent of its standard deviation, controlling for borrower, loan and employee characteristics. The effect of our proxy for current and past soft information on discretionary pricing is similar to the effect of other proxies for lending relationships used in past literature. For example, an increase in Lending relationships, Log(Borrower age) and Log(Number of products) by one standard deviation decreases pricing by 0.03 percent, 0.03 percent and 0.02 percent respectively. Similar to our results for loan decisions, the effect of current and past soft information on pricing is stronger for our subsample of loans to subprime borrowers, consistent with the fact that employees are likely to exercise their discretion in lending to borrowers with weaker credit profiles. An increase by one standard deviation in current and past soft information decreases pricing of loans to subprime borrowers by 0.04 percent or by 3 percent of its standard deviation. The results further suggest that current and past soft information do not affect pricing of loans to high quality borrowers, suggesting that the net benefits of using soft information decrease in borrower’s reputation. Finally, based on the magnitude of the effects of current and past soft information on loan approval decisions and pricing, soft information appears to play a much more important role in loan approval rather than pricing decisions, particularly for borrowers with credit scores below 620. The result is consistent with the effect of soft information on loan pricing reported in several studies (e.g., Drexler and Schoar, 2011). To test the extent to which soft information reduces the ex post risk of lending decisions, we estimate a probit model of the probability that a loan is charged-off within two years after its origination on current and past soft information, controlling for the strength, scope and length of prior lending relationships and borrower, loan and employee characteristics. Results reported in Table 5, panel C shows that borrowers with higher amounts of past soft information exhibit

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significantly lower ex post risk by this measure. Increases in current or past soft information by one standard deviation have similar effects, decreasing the probability of loan charge-offs by approximately 0.1 percent with the unconditional probability of loan charge-off at only 1.2 percent in the full sample. Thus, employees use past soft information to improve discretionary decision-making and mitigate adverse selection bias. The effect of the availability of current and past soft information on future loan performance is stronger for loans to subprime borrowers: an increase in current and past soft information by one standard deviation decreases the probability of a loan charge-off by 1 percent and 0.5 percent respectively. The unconditional probability of writing off a subprime loan is 2 percent. The coefficients on proxies for prior lending relationships are consistent with past literature. Specifically, borrowers with strong prior lending relationships are less likely to default on their loans from the credit union. Also, loan pricing and credit score are strongly related to future loan outcomes. Overall, our results provide evidence that soft information is portable over time in our setting. The “stock” of soft information acquired in prior periods as captured in the centralized information system, appears to play an important role in credit approval decisions, loan pricing, and future loan performance. Thus, while the information system was developed for internal monitoring, we provide evidence that employees store valuable soft information relevant to current and future decision-making. 4.4. Portability of soft information over time We further test the extent to which soft information is portable over time by estimating a probit model of the credit approval decision on current and past soft information disaggregated into soft information collected 20 days to 6 months, 6 to 12 months, 12 to 18 months and 18 to 24 months 28   

prior to loan origination. Results reported in table 6, panel A suggest that employees assign greater weight to more recent soft information. Soft information collected within one year prior to loan origination generally appears to play a more important role in lending decisions than soft information collected prior to this period. Specifically, an increase by one standard deviation in soft information collected 6, 12 and 18 months prior to loan origination increases the probability of credit approval by approximately 6 percent, 3 percent and 2 percent respectively. The effect is stronger for approval of loans to borrowers with credit scores below 620, where an increase by one standard deviation in soft information collected 6, 12 and 18 months prior to loan origination increases the probability of credit approval by approximately 7 percent, 2 percent and 2 percent respectively. Past soft information plays a less important role for loan applications of high quality borrowers, where an increase by one standard deviation in soft information collected 6 and 12 months prior to loan origination increases the probability of credit approval by approximately 3 percent and 2 percent respectively. Table 6, panel B reports similar results based on an ordinary least squares regression of discretionary pricing on current and past soft information collected within 6, 12, 18 and 24 months prior to loan origination, and shows that the effect of past soft information on loan pricing diminishes over time. An increase by one standard deviation in past soft information collected within 6, 12 and 24 months prior to loan origination decreases discretionary interest rates by 0.02 percent, 0.01 percent and 0.01 percent respectively, controlling for borrower, loan and employee characteristics. Similar to our previous results, the effect is stronger in our subsample of loans to subprime borrowers: an increase by one standard deviation in past soft information collected within 6, 12 and 18 months prior to loan origination decreases the interest

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rate by 0.06 percent, 0.07 percent and 0.02 percent respectively. Thus, employees’ risk assessment relies more on more recent soft information. Finally, results reported in table 6, panel C show that more recent information is also a better predictor of future loan performance for our subsample of loans to borrowers with credit score below 620. An increase by one standard deviation in past soft information collected within 6, 12 and 18 months prior to loan origination decreases the probability of loan charge-off by 0.6 percent, 0.3 percent and 0.1 percent respectively. The unconditional probability of a loan chargeoff is 2 percent. Thus, the results suggest that using most recent past soft information stored in credit union’s internal communication system helps employees make better loan decisions. Taken together with the results in Section 4.3, the results in this section point to further evidence of the portability of soft information over time but also suggest that the value of soft information for decision-making purposes depreciates over time in our setting. 4.1.Portability of Soft Information Over Time and Across Employees Our tests thus far speak to the portability of soft information over time – such information acquired in prior periods appears to be useful for future lending decisions. However, we have not explicitly ruled out the possibility that employees rely primarily or even only on soft information they acquired and stored themselves via their own prior notes in the centralized information system. Whether due to geographic distance, hierarchical distance, or other factors, prior literature has pointed in particular to a lack of portability of soft information across different employees (Agarwal and Hauswald, 2010, Drexler and Schoar, 2011, Liberti and Mian, 2009, Stein, 2002, Uchida et. al., 2012). Table 7 reports the results of tests designed to address this issue more directly. For these tests, we disaggregate past soft information into soft information produced by the same employee

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that grants the loan and soft information produced by other employees. Moreover, to the extent that distance matters, employees may rely largely on soft information originated by other employees in the same business unit due to direct interaction. We proxy for the effect of the employee’s network on the portability of soft information by disaggregating past soft information into soft information produced outside and within the employee’s business unit, excluding information originated by the employee that makes the loan decision. The mean (standard deviation) of past soft information produced by the employee that makes the loan decisions versus that produced by other employees is 0.07 (0.35) and 0.59 (0.48) respectively, suggesting that borrowers usually interact with multiple employees so that our prior results are not likely due to employees relying primarily on their own prior information. The mean (standard deviation) of past soft information originated within and outside the employee’s business unit is 0.42 (0.97) and 0.61 (0.66) respectively, suggesting that employees mainly use the system to disclose and acquire soft information not readily accessible and easily available to them through direct interaction. Results reported in table 7, panel A demonstrate that while employees rely on their own soft information to make loan decisions, soft information originated by other employees also plays a significant role in the lending process. Specifically, an increase by one standard deviation in past soft information originated by the same employee and other employees increases the probability of credit approval by 10 percent and 8 percent respectively for subprime borrowers’ loan applications, and by 1 percent for high quality borrowers’ loan applications. Moreover, employees appear to rely more on soft information input into the system by employees outside their branch, suggesting that employees within the branch potentially use more direct ways of communicating their private soft information. An increase by one standard

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deviation in soft information originated within and outside employee’s branch increases the probability of loan approval by 6 and 10 percent respectively for subprime borrowers’ loan applications, and by 1 and 5 percent respectively for high quality borrowers’ loan applications. Consistent with our findings for credit approval decisions, results reported in Table 7, panel B suggest that employees’ risk assessments incorporate past soft information originated by others and outside the employee’s business unit for loans to borrowers with credit score below the 620 threshold. An increase by one standard deviation in soft information originated within and outside employee’s branch decreases loan pricing by 0.01 percent and 0.05 percent respectively, and an increase by one standard deviation of past soft information originated by the same employee and other employees decreases loan pricing by 0.06 and 0.03 respectively for subprime borrowers’ loan applications. Hence, the results suggest that soft information transmitted through the system and not otherwise easily accessible helps employees assess borrowers’ credit risk. Finally, results reported in table 7, panel C suggest that transmitting soft information though the organization helps employees make better loan decisions measured by future loan performance. An increase of one standard deviation in soft information originated within and outside the employee’s business unit decreases the probability of a loan charge-off by 1.2 and 1.3 percent respectively, and an increase by one standard deviation of past soft information originated by the same employee and other employees decreases the probability of loan chargeoff by 1.8 and 0.6 percent respectively for subprime borrowers’ loan applications. The unconditional probability of loan charge-off is 2 percent. Overall, the results in this section provide strong evidence that that soft information is not only portable across time but also across employees and business units. Collectively, the results

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in Tables 5-7 provide support for economic theories on the role of centralization of information as a complement to decentralized decision-making (Cremer et. al., 2007).

5. Additional Analysis We further investigate whether our results for the portability of soft information across employees are driven primarily by the most explicit component of soft information, i.e. information for borrowers’ professional and educational background, or by its tacit component, i.e. loan officers’ feelings and assessments and information for borrowers’ social and personal life. To test for the relative effect of the different categories of soft information, we construct the following variables: NetFeelings_Hard Information, defined as the difference between positiveminus negative-oriented words referred to loan officers’ feelings and assessments two years prior to loan origination17 deflated by words related to hard information; SocialPersonal_Hard Information, defined as the number of words related to borrower social and personal life two years prior to loan origination, deflated by words related to hard information; EducationProfession_Hard Information, defined as the number of words related to borrower professional and educational background two years prior to loan origination, deflated by words related to hard information. The mean (standard deviation) of information related to assessments and feelings, borrower social and personal life, and borrower educational and professional background is 0.04 (0.14), 0.21 (0.43), 0.14 (0.37) respectively (untabulated univariate statistics for the sample of approved loans).                                                              17

The difference is adjusted for other negative words included in the text, i.e. “no(t,n)”, “never”, “nothing”, “nobody”, “-n’t”, to capture the negative- and positive-oriented content of the text. For example, if a note includes one word related to positive feelings or a word related to loan officer’s assessment, and another negative word (“no(t,n)”, “never”, “nothing”, “nobody”, “-n’t”), then the content of the text is coded as negative.

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Results reported in table 8, panel A show that soft information related to personal assessments and feelings originated by other employees are highly weighted in credit approval decisions. Specifically, an increase by one standard deviation in soft information related to loan officers’ feelings and assessments increases the probability of a loan application approval by 7 percent for subprime borrowers, while an increase by one standard deviation in soft information on borrower personal/ social life and professional/ educational background originated by other employees increases the probability of credit approval by 5 percent and 4 percent respectively for subprime borrowers’ loan applications. Consistent with our findings for credit approval decisions, results reported in Table 8, panel B suggest that employees’ risk assessments incorporate past soft information related to employees’ assessments and borrowers’ personal life originated by others for loans to borrowers with credit score below the 620 threshold. An increase by one standard deviation in soft information related to employees’ feelings and assessments decreases loan pricing by 0.03, and an increase by one standard deviation in soft information on borrower’s personal/ social life and professional/ educational background originated by other employees decreases loan pricing by 0.03 and 0.05 respectively for loans to subprime borrowers. Finally, results reported in table 8, panel C suggest that employees’ feelings and assessments, and past information on borrowers’ personal/social and professional/educational background predicts future loan performance. An increase of one standard deviation in soft information related to other employees’ feelings and assessments decreases the probability of a loan charge-off by 0.2 percent, and an increase by one standard deviation of past soft information on borrowers’ personal/social and professional/educational background originated by other

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employees decreases the probability of loan charge-off by 0.2 percent for subprime borrowers’ loan applications. Overall, the results in this section provide evidence that that soft information is not only portable across time and employees, but also both the tacit and explicit components of soft information help loan officers in future credit decisions, and predict loan outcomes.

6. Conclusions Using a unique dataset on loans from a large credit union and employees’ notes summarizing their interactions with borrowers, we provide new insights on the portability of soft information within organizations. We focus in particular on an internal monitoring system used at this field site which, in effect, acts as a central repository of soft information gathered in the course of interactions between employees and customers. Using data from this system, we construct direct measures of soft information and link these measures to employee lending decisions and outcomes. Because we employ direct measures of soft information, we are able to conduct new empirical tests of its transmission both intertemporally and across employees, both of which are key to measuring the portability of soft information. Contrary to the prevailing view that soft information lacks portability, our results provide evidence that the “stock” of soft information accumulated in this system has persistent effects on the lending decisions of employees. We show that employees rely on this information to increase access to credit for borrowers, provide more favorable pricing terms, and reduce the ex-post risk of their lending decisions. These effects remain even when this information was acquired by employees other than the decision-maker, and they are not diminished by the physical separation of employees working in different business units.

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Overall, our findings indicate that the centralization of soft information acquired in past borrower-employee interactions can enable organizations to separate this informational asset from individual employees to facilitate future loan decisions. These results suggest that centralized information technology can alleviate the well-documented barriers of transmitting soft information consistent with economic theories on the role of centralization of information as a complement to decentralized decision-making (Cremer et. al., 2007).

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21. Elsas, R. and J. Krahnen. 1998. “Is relationship lending special? Evidence from credit-file data in Germany.” Journal of Banking and Finance, vol. 22: 1283-1316 22. Elyasiani, E. 2004. “Relationship lending: a survey of the literature,” Journal of lnternational Economics and Business, 56: 315-330 23. Graumann, C. 1990. “Perspectives Structure and Dynamics in Dialogues,” In Markova I., K. Foppa (eds.), the Dynamics of Dialogues, Harvester Wheat Sheaf, New York 24. Greenbaum, S. I., and Thakor, A. V. (1995). “Contemporary Financial Intermediation,” Dryden Press, New York 25. Hauswald, R., R. Marquez. 2003. “Information Technology and Financial Services Competition,” Review of Financial Studies, 16: 921–948 26. Hauswald, R., and R. Marquez. 2006. “Competition and Strategic Information Acquisition in Credit Markets.” Review of Financial Studies. 19: 967–1000 27. Hertzberg, A., J.M. Liberti, and D. Paravisini. 2010. “Information and Incentives Inside the Firm: Evidence from Loan Officer Rotation,” Journal of Finance. 65(3): 795-828 28. Li, F. 2012. Textual Analysis of Corporate Disclosures: A Survey of the Literature. Journal of Accounting Literature, Forthcoming 29. Liberti, J. and A. Mian. 2009. “Estimating the effect of hierarchies on information use,” Review of Financial Studies. 22(10): 4057-4090 30. Ongena, S., and D. C. Smith. 2001. “The duration of bank relationships,” Journal of Financial Economics. 61: 449-475 31. Paravisini, D., and A. Schoar.2012. “The information and agency effects of scores: randomized evidence from credit committees,” Working paper 32. Petersen, M. 2004. “Information: Hard and soft,” Working paper 33. Petersen, M. and R. Rajan. 1994. “The benefits of firm-creditor relationships: Evidence from small business data.” Journal of Finance, vol. 49: 3-37 34. Petersen, M. and R. Rajan. 1995. “The Effect of Credit Market Competition on Lending Relationships,” The Quarterly Journal of Economics, vol. 110 (2): 407-443 35. Petersen, M. and R. Rajan. 2002. “Does distance still matter? The information revolution in small business lending,” Journal of Finance, Volume 57 (6): 2533–2570 36. Qian, J., Strahan, P. E., and Z. Yang. 2012. “The impact of incentives and communication costs on information production: evidence from bank lending,” Working paper 37. Rajan, R. 1992. “Insiders and outsiders: The choice between informed and arms-length debt,” Journal of Finance, 47: 1367-1400 38. Scott, J.A. 2006. “Loan officer turnover and credit availability for small firms,” Journal of Small Business Management. 44: 544-562 39. Sharpe, S. 1990. “Asymmetric information, bank lending and implicit contracts: A stylized model of customer relationships.” Journal of Finance, 45: 1069–1087 40. Stein, J. 2002. “Information Production and Capital Allocation: Decentralized Versus Hierarchical Firms,” Journal of Finance. 57:1891-1921 41. Townsend, R. 1979. “Optimal contracts and competitive markets with costly state verification.” Journal of Economic Theory, vol. 21: 265-93 42. Uchida, G. Udell and N. Yamori, 2012. “Loan officers and relationship lending to SMEs,” Journal of Financial Intermediation, 21: 97–122 43. Uzzi, B. and J. Gillespie. 1999. “What small firms get capital and at what cost: Notes on the role of social capital and banking networks.” In Business Access to Capital and Credit (eds. J. Blanton, Williams and S. Rhine): 413-444 38   

Figure 1: Soft Information Content over Time Figure 1A: Information Content Across All Notes 0

2 1.8

-0.1

1.6

1.2

-0.3

1

-0.4

0.8

-0.5

Interest rate

Information count

-0.2 1.4

0.6

"Soft" information by note "Hard" information by note Soft_Hard information Discretionary pricing

-0.6 0.4 -0.7

0.2

-0.8

0 2005

2006

2007

2008

2009

2010

year

Figure 1B: Information Content Conditional on Any Soft Information 0

2.5

-0.1 2 -0.3

1.5

-0.4 1

-0.5 -0.6

Interest rate

Information count

-0.2

"Soft" information by note "Hard" information by note Soft_Hard information Discretionary pricing

0.5 -0.7 -0.8

0 2005

2006

2007

2008

2009

2010

year

39   

Appendix A: Variable definition Variable

Description

Approved loan

Binary variable that equals one if the loan application is accepted, and zero otherwise

Total information

The sum of total hard and soft information related words over the last two years prior to loan origination/ application

Soft information

The number of soft information related words collected for a borrower over the last two years prior to loan origination

Hard information

The number of hard information related words collected for a borrower over the last two years prior to loan origination

Soft_Hard information

The number of soft information related words collected for a borrower over the last two years prior to loan origination, deflated by the number of hard information related key words collected in the same period

Soft information at loan origination

The number of soft information related words collected for a borrower over a period of twenty days prior to or after loan origination

Hard information at loan origination

The number of hard information related words collected for a borrower over a period of twenty days prior to or after loan origination

Soft_Hard information at loan origination

The number of soft information related words collected for a borrower over a period of twenty days prior to or after loan origination, deflated by the number of hard information related key words collected in the same period

Soft information prior to loan origination

The number of soft information related words collected for a borrower over the last two years prior to loan origination

Hard information prior to loan origination

The number of hard information related words collected for a borrower over the last two years prior to loan origination

Soft_Hard information prior to loan origination

The number of soft information related words collected for a borrower over the last two years prior to loan origination, deflated by the number of hard information related key words collected in the same period

Soft_Hard information 6m prior to loan origination

The number of soft information related words collected for a borrower over the last six months prior to loan origination, deflated by the number of hard information related key words collected in the same period

Soft_Hard information 12m prior to loan origination

The number of soft information related words collected for a borrower over the last twelve to six months prior to loan origination, deflated by the number of hard information related key words collected in the same period

40   

Appendix A (continued) Variable

Description

Soft_Hard information 18m prior to loan origination

The number of soft information related words collected for a borrower over the last eighteen to twelve months prior to loan origination, deflated by the number of hard information related key words collected in the same period

Soft_Hard information 24m prior to loan origination

The number of soft information related words collected for a borrower over the last twenty four to eighteen months prior to loan origination, deflated by the number of hard information related key words collected in the same period

Discretionary pricing

The difference between interest rate applied and standardized interest rate (by management)

Interest rate

Loan interest rate

Interest rate from ratesheet

Standardized loan interest rate

Lending relationship

The size of loans raised from a member in the last two years prior to loan origination, deflated by the average loan size that members of the credit union raised in the same period

Number of products

The natural logarithm of months from borrower’s oldest trade with the credit union The number of member's credit cards, loans and deposit accounts with the credit union

Credit score

Borrower’s FICO score

Debt ratio

Borrower’s debt to income ratio

Number of exceptions

The total number of exceptions that employees make in loan origination

Charge-off

Binary variable that equals one if the loan was charged-off within two years after its origination, and zero otherwise

Maturity

The number of years when the loan will be repaid

Loan amount

The natural logarithm of loan size

Borrower age

41   

Appendix B: Keywords for soft and hard information

Soft Information Education

Profession

Personal Family-oriented Health-oriented

academi arts college conference degree educated education graduate grant phd master scholar school science seminar stud tuition universit mba undergrad

army boss business deployed employ hir job laid off military profession promot retir salary supervisor unemploy work company career venture vocation manager fired director executive chief entrepreneur

babies baby boy boyfriend break up broke up brother cousin child nephew dad daughter daycare divorce engaged family father girl girlfriend home hubby husband kid mam married marry mom

accident cancer clinic disability disabled heart attack hospital medical stroke surgery casualt health illness disease sickness therapeutic pathological Social-oriented Carnival Christmas concert Easter festival folk friend Halloween hobbies

Feelings

affectionate afraid agreeable amaze amicable anger angry annoyed anxious appeal astonish blissful bold bothered caring character cheat cheer compassionat concerned confident confus conservative contented convenient cried cry crying

feel felt fool friendly frustrated fun gloomy good faith gracious happy hard honest hopeful horrified hostil humorous integrity intimidated jealous joy kind lighthearted lively love moral nice opportunistic optimistic

stumbl stunned successful surprised suspicious tender terrified terror tight troubled trust undisturbed uneas unfriendly unhappy unhelpful unreliable unresponsive unsettled untroubled unworried upset upside down useful worried worry zeal untrusted

Assessment

Unfeeling Hesitant Timid Smil Arrogant egocentric frightened Cautious Reckless Careful Tolerant intolerant Hopeless Humble Brave Impolite

my assessment my assumption I am sure I anticipate I assess I assume I believe I conclude I consider I evaluate I expect I feel I get the impression I guess I have the impression I imagine I perceive I presume I realize I recognize I sense I suspect I take in I think my anticipation my belief my conclusion my conviction 42 

 

Soft Information Profession merchant apprentice corporation firm management administrator chief commander ceo cfo coo wealth occupation colleague

Personal mother nannies nanny parent pregnancy pregnant sister Son spouse twins wedding wife move to moved to moves to moving to fiance religion significant other mom antecedent predecessor

hobby buddy thanksgiving holidays feast celebration entertainment vacation volunteer cat dog fraud trip

Feelings decent delight disappointed discouraged discriminate displeased dissatisfied distressed disturbed doleful eager easy going embarrass encouraged energetic enjoy enthusiastic envious envy excited favor fear

overwhelmed panic passion personality pleased pride promising proud relaxed relief relieved resent responsible rude sad satisfy sentimental shame smart sorry stress struggl

Assessment Unsuccessful Calm Indifferent Devoted Considerate Responsive Respectful Polite Disagreeable Candid Outgoing Unemotional Apathetic Negligent Ignorant Faithful Dedicated Dishonest Sociable Cruel inconsiderate Courteous

my expectation my guess my impression my judgment my opinion my perception my perspective my position my sense my suspicion my thinking my thought my view point of view Viewpoint my impression

43   

Appendix B (Continued)

Hard Information account acct adjustment ATM auction bond borrow cash cd certificate charges check cheque clearing closure close collateral collection commission cd credit custody debit deposit derivative document drawdown

equity exchange fee forwards freeze futures insurance interest rate debt leas lend liquidation loan maturity mortgage bank options pay payment payroll paystub portfolio products purchase agreement payment receipt redemption

refinance refund reimbursement renewal repo reserve reversal security service settlement supply chain swap syndicate tax trade transaction transfer treasury turnaround warrant withdrawal delinquent default bankrupt overdraft visa income

44   

Appendix C: Examples of employees’ notes

“Talked with E. and it sounds like she has decided to live in Alaska permanently. Her Dad's not to cool with it but she seems really happy there. I told her to wait until the dark season arrives and we'll see then. She will be in touch with her plans.”

“B. has a son who lives in Indiana and she is helping pay for his family's visit during Thanks Giving. She has one grandson whom she really wants to spend time with. They will be here for a week. Her grandson is 22 months old and he has said "grandma" to her over the phone! Very exciting!”

“R. works as a scientist at the U of M he is helping a local resident with his Methane fuel system he put in. He is excited about green fuel in the US. His wife drives a Prius and he will be driving a VW diesel, both vehicle get over 45 miles per gallon. His computer was stolen from work and it contains his internet banking password. I reset that today and also established a codeword for his account. Please ask for this codeword whenever they come or call in.”

“C. was very upset and distraught as to what is going to happen here in the future due to action that her husband has taken. Her husband has a drinking problem is he is a recovering alcoholic and he has been clean now for about 4 years. Her husband has been to recovery a number of times as this will be his fourth relapse. He ended up taking the new truck that he had purchased in the ditch while he was drinking and member and the kids were on a short summer vacation. So when member was getting calls from the neighbors and she had not heard from him she knew something was not right. She then returned home to find this out. He is in jail right now with a 12K bail over his head which member is not going to satisfy for him… she will be pursuing a divorce. Member can't put the kids through this anymore or herself. Member and I discussed a number of items that she can list for sale as she has to move back towards family in Iowa and rent an apartment.”

“G. is closing out this account. He has brought this account current. Thelma deceased back in June. Please do not charge fees on this account since US Treasury pulled funds back for repayment.”

“S. (came) in today to complete a loan for her FIFTH organ! We’re talking organ as in the musical instrument. She has outgrown the one she currently owns and is trading it in for a bigger and better version. She absolutely loves to play the organ and the past 10+ years playing have

45   

been a great experience in her life. She’s met a whole new group of friends and is, in her words, “staying young” through her passion.”

“K. said her number one goal is to get out of debt. Right now, she has a VISA credit card with us with a balance of close to $5k. Another one of her goals was saving for vacations and holidays so when they come-up, she doesn't have to use her credit card. She also said her family is very important to her and not having to say “we can't afford it” is something she wishes she didn’t have to say to her children… She’s recently divorced, a home owner and has two children. She needs to trust me first. Trust seems to be a hard thing for her and I don't know why. However, we’re taking small steps at a time and eventually, will develop a plan for her once she's willing to open up and provide me with more information.”

“C. is looking to pay off some of the hospital bills. There is $5,500 owed to the hospital from the credit report. His parents are paying off most of it and then having him pay the rest. He said he has a heart condition that has him in and out of the hospital. This loan can help him pay off debt. I believe that his intentions are good, but his credit is not. He has the ability to pay but no stability… I think he is honest and trust him.”

46   

Table 1: Sample Selection Panel A: Sample Selection Number of loan applications

Number of approved loans

Number of rejected loans

44,110

38,409

5,701

Loans where loan officer name is missing

5,139

4,947

192

Loans where interest rate is missing

942

942

0

Loans to members with no transaction notes

2,876

1,820

1,056

Total

35,153

30,700

4,453

Primary sample of loans, 2008-2010 Less:

Panel B: Number of loans by year and type Year

Number of loan applications

Number of approved loans

Number of rejected loans

2008

13,352

11,494

1,858

2009

15,052

13,313

1,739

2010

6,749

5,893

856

Total

35,153

30,700

4,453

Loan type

Number of loan applications

Number of approved loans

Number of rejected loans

Mortgage

3,106

1,990

1,116

Auto

18,721

17,140

1,581

Credit cards

9,652

8,450

1,202

Other

3,674

3,120

554

Total

35,153

30,700

4,453

47   

Table 2: Validation Tests Panel A: Soft information origination and hard information Soft information at loan origination Credit score620

Credit scoret

Coeff.

p>t

Coeff.

0.09

0.16

0.11

-0.01 ***

0.01

0.54

0.00

0.94

-0.01

0.64

Log(Debt ratio)

0.01

Log(Soft information prior to loan origination)

0.17 *** 0.00

p>t

Credit score>620 Coeff.

p>t

0.39 *** -0.01

0.00 0.66

0.14 *** 0.00

Soft_Hard information prior to loan origination

0.13 ***

0.00

0.20 ***

0.00

Lending relationship

-0.05 *** 0.00

-0.04 *** 0.00

-0.03 ***

0.01

-0.01 ***

0.01

Log(Borrower age)

-0.07 *** 0.00

-0.07 *** 0.00

-0.01 ***

0.05

-0.04 ***

0.00

Log(Number of products)

-0.10 *** 0.00

-0.12 *** 0.00

-0.02

0.39

-0.04 **

0.02

Log(Loan amount)

0.05 *** 0.01

0.06 *** 0.00

0.01

0.53

Log(Maturity)

0.02 *** 0.36 0.56 *** 0.00

-0.01 *** 0.64 1.69 *** 0.00

Constant Fixed effects: Loan type, year, employee

YES

N= 6,447 2

R = 0.25

0.03 ** -0.09

YES

N= 24,253 2

R = 0.14

0.05 0.74

0.03 *** -0.02 *

0.06

-2.02 ***

0.00

YES

N= 6,447 2

R = 0.20

0.01

YES

N= 24,253 R2= 0.10

The dependent variable in the first specification is the natural logarithm of soft information related keywords included in employees’ notes around loan origination day (-20,+20 days), and the dependent variable in the second specification is the ratio of soft to hard information related keywords in this period. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, twotailed tests.

48   

Panel B: Soft information and loan exceptions Number of exceptions Credit scoret

Credit score>620

Coeff.

p>t

0.00

Soft_Hard information

Coeff.

p>t

0.01 * 0.04 **

0.04

Coeff.

p>t

-0.01

0.43

0.08

Lending relationship

0.00

0.57

0.01

0.21

0.00

0.29

0.00 *

0.08

Log(Borrower age)

0.01 ***

0.00

0.01 ***

0.00

0.01 ***

0.00

0.01 ***

0.00

Log(Number of products)

0.02

0.26

0.02

0.17

0.04 ***

0.00

0.04 ***

0.00

Log(Credit score)

-0.04 ***

0.00

-0.04 ***

0.00

-0.65 ***

0.00

-0.64 ***

0.00

Log(Debt ratio)

0.03 ***

0.00

0.03 ***

0.00

0.03 ***

0.00

0.03 ***

0.00

Log(Loan amount)

0.13 ***

0.00

0.13 ***

0.00

0.18 ***

0.00

0.18 ***

0.00

Log(Maturity)

0.07 ***

0.00

0.07 ***

0.00

0.07

-0.01 ***

0.01

-0.44 ***

0.00

-0.41 ***

0.00

0.00

2.84 ***

0.00

Constant Fixed effects: Loan type, year, employee

YES

N= 6,447 2

R = 0.66

-0.01 * 2.86 ***

YES

N= 6,447 2

R = 0.66

YES

N= 24,253 2

R = 0.52

YES

N= 24,253 R2= 0.52

The dependent variable is the number of exceptions (interest rate, collateral, maturity and auto-loan exception) that employees made in their credit decisions. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two  tailed tests. 

49   

Panel C: Soft information and loan pricing Loan interest rate Credit scoret

Credit score>620

Coeff.

p>t

Coeff.

p>t

0.04

0.23

0.06

Soft_Hard information

-0.22 *** 0.00

Coeff.

p>t

0.02

0.20

Lending relationship

0.14 *** 0.00

-0.15 *** 0.00

0.00

0.53

0.01 **

0.03

Log(Borrower age)

0.08 *** 0.00

-0.08 *** 0.00

0.01

0.67

0.00

0.83

Log(Number of products)

0.11

-0.11

0.25

Log(Credit score)

-0.60 *** 0.00

Log(Debt ratio)

-0.03

Log(Loan amount) Log(Maturity) Constant

0.18

-0.09 *** 0.00

-0.08 *** 0.00

-0.51 *** 0.00

-10.59 *** 0.00

-10.58 *** 0.00

0.60

0.02

0.72

-0.01

0.39

-0.01

0.54

0.81 *** 0.00

-0.72 *** 0.00

-0.28 *** 0.00

-0.29 *** 0.00

0.07 0.30 14.05 *** 0.00

0.10 * 0.08 14.61 *** 0.00

0.40 *** 0.00 80.33 *** 0.00

0.42 *** 0.00 80.33 *** 0.00

Fixed effects: Loan type, year, employee

YES

N= 6,447 2

R = 0.43

YES

N= 6,447 2

R = 0.66

YES

N= 24,253 2

R = 0.70

YES

N= 24,253 R2= 0.86

The dependent variable is the loan interest rate. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

50   

Table 3: Summary Statistics Panel A: Loan and borrower characteristics of rejected loan applications Quantiles Variable

N

Mean

S.D.

Min

25%

Median

75%

Max

Denied loan application

35,153

0.12

0.30

0.00

0.00

0.00

0.00

1.00

Total information

4,453

18.26

18.20

0.00

2.00

12.00

30.00

52.00

Hard information

4,453

11.81

12.03

0.00

1.00

8.00

20.00

34.00

Soft information

4,453

4.39

5.38

0.00

0.00

4.00

10.00

14.00

Soft_Hard information

4,453

0.39

0.60

0.00

0.00

0.15

0.42

2.80

Hard information at loan origination

4,453

3.38

3.95

0.00

0.00

2.00

6.00

12.00

Soft information at loan origination

4,453

1.70

2.52

0.00

0.00

0.00

3.00

8.00

Soft_Hard information at loan origination

4,453

0.35

0.63

0.00

0.00

0.00

0.50

3.50

Hard information prior to loan origination

4,453

8.93

11.32

0.00

0.00

4.00

15.00

32.00

Soft information prior to loan origination

4,453

3.76

4.78

0.00

0.00

0.50

7.50

12.50

Soft_Hard information prior to loan origination

4,453

0.32

0.49

0.00

0.00

0.11

0.39

2.25

Lending relationship

4,453

0.48

1.59

0.00

0.00

0.00

0.26

12.64

Borrower age

4,453

1.94

1.66

0.00

0.00

2.17

3.17

5.33

Number of products

4,453

3.63

3.68

0.00

0.00

3.00

6.00

36.00

Credit score

4,453

558.90

143.71

0.00

516.00

577.00

639.00

822.00

Credit score|z| dF/dx

0.00

p>|z| dF/dx

0.00 *

p>|z|

0.07

0.08 ***

0.00

0.15 ***

0.00

0.02 ***

0.00

0.08 ***

0.00

0.14 ***

0.00

0.02 ***

0.00

0.01 -0.04 0.20 0.00 -0.02 0.07

** *** *** *** ***

0.03 0.00 0.00 0.89 0.00 0.00

YES

N= 35,153

N= 35,153

2

2

pseudo-R = 0.17

Credit score>620

pseudo-R = 0.24

0.02 -0.21 0.25 -0.05 0.02 0.06

*** *** *** *** *** ***

0.01 0.00 0.00 0.00 0.01 0.00

YES N= 9,431

0.02 -0.24 0.32 -0.06 0.03 0.07

*** *** *** *** *** ***

0.01 0.00 0.00 0.00 0.00 0.00

YES N= 9,431

2

pseudo-R = 0.24

0.00 -0.01 0.02 0.26 -0.01 0.00

*** *** *** *** *** ***

0.00 0.00 0.00 0.00 0.00 0.00

YES N= 25,722

2

pseudo-R = 0.28

2

pseudo-R = 0.24

0.00 -0.01 0.06 0.71 -0.02 0.00

*** *** *** *** *** * YES

N= 25,722 pseudo-R2= 0.32

The dependent variable is one if the loan application was approved, and zero otherwise. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

55   

0.20 0.00 0.00 0.00 0.00 0.06

Panel B: Portability of soft information and loan pricing Discretionary pricing All loans Variable Soft_Hard information Soft_Hard information at loan origination Soft_Hard information prior to loan origination Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Log(Loan amount) Log(Maturity) Constant Fixed effects: Loan type, year, employee

Coeff.

p>t

Credit scoret

-0.05 *** 0.01

Coeff.

p>t

0.08

0.00 0.00 0.00 0.00 0.01 0.00 0.02 0.00

-0.02 -0.03 -0.04 -0.43 0.02 -0.10 -0.03 -4.28

YES

N= 30,700 2

R = 0.57

p>t

Coeff.

p>t

0.07

0.00

0.31

-0.09 *** 0.00

0.00

0.99

-0.07 *

-0.02 *** 0.01 *** *** *** *** *** *** ** ***

Coeff.

-0.19 *** 0.01 -0.02 *

-0.02 -0.03 -0.04 -0.43 0.02 -0.10 -0.03 -4.28

Credit score>620

*** *** *** *** *** *** ** ***

0.00 0.00 0.00 0.00 0.01 0.00 0.02 0.00

-0.07 -0.14 -0.05 -0.37 0.05 -0.30 -0.04 -2.86

YES

N= 30,700 2

R = 0.57

0.00 0.00 0.31 *** 0.00 * 0.10 *** 0.00 0.23 *** 0.00 *** ***

-0.07 -0.14 -0.04 -0.37 0.05 -0.30 -0.04 -2.87

YES

N= 6,447 2

R = 0.62

0.00 0.00 0.40 *** 0.00 * 0.09 *** 0.00 0.22 *** 0.00 *** ***

Coeff.

p>t

0.01

0.37

-0.01 -0.01 -0.03 -0.23 0.02 -0.04 -0.01 -1.44

YES

N= 6,447 2

R = 0.63

** *** *** *** *** *** YES

N= 24,253 2

R = 0.70

0.04 0.17 0.00 0.00 0.01 0.00 0.35 0.00

-0.01 -0.01 -0.03 -0.23 0.02 -0.04 -0.01 -1.45

** *** *** *** *** *** YES

N= 24,253 R2= 0.70

The dependent variable is the difference between loan interest rate and standardized interest rate based on borrower credit score. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

56   

0.03 0.19 0.00 0.00 0.01 0.00 0.39 0.00

Panel C: Portability of soft information and loan outcomes Charge-off=1 All loans Variable

dF/dx

Soft_Hard information

-0.002

p>|z| ***

Credit score|z|

0.01

dF/dx

p>|z|

-0.022

**

Credit score>620

dF/dx

p>|z|

0.00

dF/dx

p>|z|

0.000

0.66

dF/dx

p>|z|

Soft_Hard information at loan origination

-0.001

*

0.10

-0.011

***

0.00

0.001

0.22

Soft_Hard information prior to loan origination

-0.001

**

0.05

-0.012

**

0.05

-0.001

0.29

0.001 -0.001 -0.006 -0.001 0.001 0.001 -0.001 -0.001

***

0.00 0.45 0.00 0.01 0.78 0.00 0.03 0.09

Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Interest rate Log(Loan amount) Log(Maturity)

-0.001 0.000 -0.006 -0.005 0.001 0.001 -0.001 -0.001

*** * ** *** * *** **

0.00 0.00 0.05 0.00 0.86 0.09 0.00 0.04

*** *** *** ** *

-0.001 -0.003 -0.020 -0.004 0.001 0.000 -0.008 -0.002

*** ***

*

0.68 0.37 0.00 0.00 0.51 0.74 0.04 0.59

-0.001 0.000 -0.018 0.002 0.001 0.000 -0.005 0.000

*** ***

***

0.46 0.98 0.00 0.00 0.49 0.80 0.03 0.92

0.002 0.001 -0.006 -0.055 0.001 0.001 0.000 -0.001

*** *** *** **

0.00 0.55 0.00 0.00 0.19 0.04 0.93 0.12

-0.002 0.000 -0.006 -0.054 0.001 0.001 0.000 -0.001

*** *** *** **

Fixed effects: loan type, year, employee N= 11,027

N= 11,027

2

2

N=2,569

N=2,569 2

N=8,458 2

N= 8,458 2

pseudo-R = 0.35 pseudo-R = 0.42 pseudo-R = 0.38 pseudo-R = 0.41 pseudo-R = 0.10 pseudo-R2= 0.10 The dependent variable is one if the loan was written off credit unions’ balance sheet within two years after loan origination, and zero otherwise. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

57   

0.00 0.61 0.00 0.00 0.20 0.04 0.89 0.13

Table 6: Portability of Soft Information over Time Panel A: Portability of soft information over time and credit approval Loan application accepted=1 All loans Variable

dF/dx

Credit score|z|

dF/dx

Credit score>620 p>|z|

dF/dx

p>|z|

Soft_Hard information at loan origination

0.11 ***

0.00

0.14 ***

0.00

0.03 **

0.00

Soft_Hard information 6m prior to loan origination

0.08 ***

0.00

0.09 ***

0.00

0.04 ***

0.00

Soft_Hard information 12m prior to loan origination

0.04 ***

0.00

0.03 **

0.02

0.03 **

0.01

Soft_Hard information 18m prior to loan origination

0.03 ***

0.01

0.04 **

0.05

0.01

0.32

0.08

0.00

0.85

-0.01

0.12

0.00

0.77

0.00

0.83

Soft_Hard information 24m prior to loan origination

-0.02 *

Lending relationship

0.01

0.76

Log(Borrower age)

0.00 *

0.09

-0.09 ***

0.00

0.01 *

0.10

Log(Number of products)

0.22 ***

0.00

0.21 ***

0.00

0.10 ***

0.00

Log(Credit score)

0.00

0.26

-0.06 ***

0.00

1.22 ***

0.00

-0.03 ***

0.01

0.04 ***

0.01

-0.07 ***

0.00

0.06 ***

0.00

0.06 ***

0.00

Log(Debt ratio) Log(Loan amount)

0.00

0.87

Fixed effects: Loan type, year, employee

YES

N= 35,153 PseudoR2= 0.17

YES

N= 9,431 pseudo-R2= 0.24

YES

N= 25,722 pseudo-R2= 0.18

The dependent variable is one if the loan application was approved, and zero otherwise. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

58   

Panel B: Portability of soft information over time and loan pricing Discretionary pricing All loans Variable

Coeff.

Credit scoret

Coeff.

p>t

Coeff.

p>t

-0.06 *** 0.01

0.00

0.32

Soft_Hard information at loan origination

-0.01 **

Soft_Hard information 6m prior to loan origination

-0.02 *** 0.03

-0.06 **

0.02

0.00

0.75

Soft_Hard information 12m prior to loan origination

-0.02 **

0.02

-0.08 *** 0.01

-0.01

0.28

0.62

-0.02 **

0.05

0.01 *

0.10

0.00 *

0.06

0.00

0.80

0.00 0.00 0.52 *** 0.00 * 0.09 *** 0.00 0.23 *** 0.00

-0.01 -0.01 -0.03 -0.23 0.02 -0.04 -0.01 -1.45

Soft_Hard information 18m prior to loan origination

0.00

0.04

Credit score>620

Soft_Hard information 24m prior to loan origination

-0.01 **

0.04

Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Log(Loan amount) Log(Maturity) Constant Fixed effects: Loan type, year, employee

-0.02 -0.03 -0.03 0.43 0.02 -0.10 -0.03 -4.27

0.00 0.00 0.01 0.00 0.01 0.00 0.02 0.00

*** *** *** *** *** *** ** ***

-0.07 -0.13 -0.03 -0.37 0.05 -0.30 -0.04 -2.91

YES

N= 30,700 R2= 0.57

*** ***

YES

N= 6,447 R2= 0.62

** *** *** *** *** ***

0.03 0.16 0.00 0.00 0.01 0.00 0.40 0.00

YES

N= 24,253 R2= 0.71

The dependent variable is the difference between loan interest rate and standardized interest rate based on borrower credit score. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

59   

Panel C: Portability of soft information over time and loan outcomes

All loans dF/dx p>|z|

Charge-off=1 Credit score|z|

Credit score>620 dF/dx p>|z|

Soft_Hard information at loan origination

-0.001 ***

0.00

-0.009 ***

0.00

-0.001

0.62

Soft_Hard information 6m prior to loan origination

-0.002 *

0.10

-0.006 ***

0.01

0.000

0.87

Variable

Soft_Hard information 12m prior to loan origination

0.000

0.96

-0.004 ***

0.00

0.002

0.53

Soft_Hard information 18m prior to loan origination

0.001

0.59

-0.001 **

0.05

0.002

0.11

Soft_Hard information 24m prior to loan origination

0.001

0.19

-0.006

0.37

0.000

0.79

0.00 0.52 0.00 0.07 0.07 0.00 0.04 0.10

0.000 -0.003 -0.015 *** -0.004 *** 0.002 -0.001 -0.007 ** -0.001

0.88 0.32 0.00 0.00 0.31 0.52 0.04 0.81

0.002 0.001 -0.006 -0.053 0.001 0.001 0.000 -0.001

Lending relationship 0.001 Log(Borrower age) 0.000 Log(Number of products) -0.006 Log(Credit score) -0.001 Log(Debt ratio) 0.001 Interest rate 0.001 Log(Loan amount) -0.001 Log(Maturity) -0.001 Fixed effects: loan type, year, employee N= 11,027 2

*** *** * * *** ** *

pseudo-R = 0.35

N=2,596

*** *** *** ** ***

0.00 0.60 0.00 0.00 0.18 0.04 0.93 0.11

N=8,458 2

pseudo-R = 0.45

pseudo-R2= 0.17

The dependent variable is one if the loan was written off credit unions’ balance sheet within two years after loan origination, and zero otherwise. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

60   

Table 7: Portability of soft information, employee network and loan decisions Panel A: Portability of soft information, employee network and credit approval Loan application accepted=1 Credit score|z|

dF/dx

0.00

0.15 0.24

Credit score>620 p>|z|

dF/dx

***

0.00

0.15

***

0.01

0.23

***

***

p>|z|

dF/dx

0.00

0.02

***

p>|z|

dF/dx

0.00

0.02 0.03

***

0.00

0.01

*

0.10

0.23

***

0.00

0.02

***

0.00

0.01 -0.26 0.30 -0.07 0.03 0.07

*** *** *** *** *** ***

0.07 0.00 0.00 0.00 0.00 0.00

0.00 -0.01 0.04 0.51 -0.01 0.00

*** *** *** *** *** **

0.00 0.00 0.00 0.00 0.00 0.03

YES

** *** *** *** *** ***

0.05 0.00 0.00 0.00 0.00 0.00

0.01 -0.25 0.31 -0.07 0.03 0.07

*** *** *** *** *** ***

dF/dx

***

0.00

0.02

***

0.00

***

0.03

0.02

***

0.00

0.00 -0.01 0.04 0.51 -0.01 0.00

*** *** *** *** *** **

0.00 0.00 0.00 0.00 0.00 0.05

0.00

0.12

0.02 -0.25 0.31 -0.07 0.03 0.07

p>|z|

0.09 0.00 0.00 0.00 0.00 0.00

0.00 -0.01 0.04 0.51 -0.01 0.00

*** *** *** *** *** *

0.00 0.00 0.00 0.00 0.00 0.09

YES

p>|z|

YES YES YES YES N= 3,916 N= 3,916 N= 3,916 N= 14,160 N= 14,160 N= 14,160 pseudo-R2= 0.25 pseudo-R2= 0.27 pseudo-R2= 0.28 pseudo-R2= 0.32 pseudo-R2= 0.32 pseudo-R2= 0.28 The dependent variable is one if the loan application was approved, and zero otherwise. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

61   

Panel B: Portability of soft information, employee network and loan pricing Discretionary pricing Credit scoret

Coeff.

0.01

-0.16 -0.17

Soft_Hard information prior to loan origination by the employee

Credit score>620 p>t

Coeff.

**

0.04

-0.15

**

0.05

Soft_Hard information prior to loan origination by others

-0.14

**

***

p>t

Coeff.

p>t

Coeff.

p>t

Coeff.

p>t

0.05

0.00

0.85

0.01

0.33

0.01

0.48

-0.01

0.54 0.01

0.35

0.01

Soft_Hard information prior to loan origination by the branch

-0.01

***

0.02

-0.02

0.11

Soft_Hard information prior to loan origination outside the branch

-0.08

*

0.09

0.00

0.93

Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Log(Loan amount) Log(Maturity) Constant

-0.10 -0.15 -0.03 -0.40 0.08 -0.30 -0.05 -2.83

*** ***

0.00 0.00 0.71 0.00 0.07 0.00 0.31 0.00

Fixed effects: Loan type, year, employee

*** * *** ***

-0.06 -0.18 -0.01 -0.36 0.09 -0.32 0.02 -3.06

* *** *** * *** ***

0.10 0.00 0.93 0.00 0.08 0.00 0.73 0.00

-0.06 -0.19 -0.01 -0.36 0.08 -0.32 0.02 -2.89

*** *** * *** ***

0.11 0.00 0.90 0.00 0.10 0.00 0.73 0.00

-0.02 -0.01 -0.01 -0.15 -0.01 -0.02 0.00 -1.22

**

0.00 0.32 0.55 0.22 0.63 0.11 0.99 0.19

-0.01 -0.01 -0.02 -0.22 0.00 -0.04 -0.01 -1.49

**

*** ***

0.02 0.22 0.19 0.11 0.93 0.00 0.48 0.00

-0.01 -0.01 -0.02 -0.22 0.00 -0.04 -0.01 -1.48

YES

**

*** ***

YES YES YES YES YES N= 2,082 N= 2,082 N= 2,082 N= 9,240 N= 9,240 N= 9,240 2 2 2 2 2 R = 0.57 R = 0.69 R = 0.68 R =0.61 R = 0.73 R2= 0.73 The dependent variable is the difference between loan interest rate and standardized interest rate based on borrower credit score. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

  62   

0.02 0.32 0.21 0.12 0.92 0.00 0.49 0.00

Panel C: Portability of soft information, employee network and loan outcomes Charge-off=1 Credit score|z|

dF/dx

0.02

-0.009

-0.024

Soft_Hard information prior to loan origination by the employee

Credit score>620

p>|z|

dF/dx

*

0.06

-0.01

*

0.08

Soft_Hard information prior to loan origination by others

-0.01

**

*

p>|z|

dF/dx

p>|z|

dF/dx

p>|z|

dF/dx

p>|z|

0.02

0.000

0.37

0.001

0.28

0.001

0.53

0.002

0.61

0.000

0.95

0.09

Soft_Hard information prior to loan origination by the branch

-0.012

*

0.09

-0.002

Soft_Hard information prior to loan origination outside the branch

-0.023

**

0.05

0.000

Lending relationship

-0.014

Log(Borrower age)

-0.028

Log(Number of products)

-0.060

Log(Credit score)

0.13

-0.003

**

0.04

-0.010

**

0.02

-0.001

**

0.02

-0.005

**

0.04

-0.017

**

0.03

0.000

***

0.00

-0.022

***

0.00

-0.022

***

0.01

-0.003

*

0.10

0.78 ***

0.00

-0.003

0.67

0.001

***

0.00

-0.009

***

-0.002

0.62

-0.007

0.28

-0.002

0.39

-0.044

0.00

-0.097

Log(Debt ratio)

0.000

0.98

0.003

0.40

0.002

0.57

0.000

0.49

-0.001

Interest rate

0.006

0.08

0.000

0.10

0.006

0.00

0.000

0.21

0.002

-0.002

0.81

-0.012

0.66

-0.004

0.60

0.000

0.90

0.017

0.20

0.003

0.66

0.024

0.00

0.001

0.28

Log(Loan amount) Log(Maturity)

*

*

***

***

***

0.00

-0.001

0.70

0.001

***

0.00

-0.002

***

0.00

-0.055

**

0.75 **

0.05

***

0.01

0.51

0.001

0.38

0.08

0.001

0.23

0.000

0.74

-0.001

0.64

0.000

0.82

0.001

0.51

*

Fixed effects: loan type, year N= 841

N= 841 2

pseudo-R = 0.33

N= 841 2

pseudo-R = 0.36

N= 3,580 2

pseudo-R = 0.37

N= 3,580 2

pseudo-R = 0.27

N= 3,580 2

pseudo-R = 0.23

pseudo-R2= 0.25

The dependent variable is one if the loan was written off credit unions’ balance sheet within two years after loan origination, and zero otherwise. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

63   

0.03

Table 8: Portability of soft information, employee network and loan decisions

Panel A: Portability of soft information, soft information type and credit approval

Variable Soft_Hard information at loan origination NetFeelings_Hard information prior to loan origination by others SocialPersonal_Hard information prior to loan origination by others EducationProfession_Hard information prior to loan origination by others Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Log(Loan amount) Fixed effects: loan type, year, employee

Loan application accepted=1 Credit score620 dF/dx p>|z| dF/dx p>|z| 0.14 ***

0.00

0.01 ***

0.00

0.88 ***

0.00

0.03

0.11

0.17 ***

0.00

0.02 ***

0.00

0.25 ***

0.00

0.00

0.62

0.10 0.00 0.00 0.00 0.00 0.00

0.00 -0.02 0.03 0.49 -0.01 0.00

0.01 -0.28 0.31 -0.07 0.03 0.07 N=3,916 pseudo-R2= 0.29

* *** *** *** *** ***

*** *** *** *** *** **

0.00 0.00 0.00 0.00 0.00 0.05

N=14,160 pseudo-R2= 0.29

The dependent variable is one if the loan application was approved, and zero otherwise. NetFeelings_Hard information is the difference between positive minus negative feelings and assessments of loan officers two years prior to loan origination (adjusted for other negative words included in the text, i.e. “no(t,n)”, “never”, “nothing”, “nobody”, “-n’t”), deflated by words related to hard information. SocialPersonal_Hard is the number of words related to borrower social and personal life two years prior to loan origination, deflated by words related to hard information. EducationProfession_Hard is the number of words related to borrower professional and educational background two years prior to loan origination, deflated by words related to hard information. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

64   

Panel B: Portability of soft information, soft information type and risk assessment

Variable

Discretionary pricing Credit score620 Coeff. p>t Coeff. p>t

Soft_Hard information at loan origination NetFeelings_Hard information prior to loan origination by others SocialPersonal_Hard information prior to loan origination by others EducationProfession_Hard information prior to loan origination by others Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Number of exceptions Log(Loan amount) Log(Maturity) Constant Fixed effects: loan type, year, employee

-0.13 **

0.05

0.01

0.25

-0.15 **

0.03

0.00

0.97

-0.08 *

0.06

-0.03

0.40

-0.13 **

0.04

0.04

0.23

-0.07 -0.15 -0.01 -0.35 0.08 -1.34 -0.26 0.03 -2.83

0.11 0.00 0.79 0.00 0.12 0.00 0.00 0.58 0.00

-0.01 -0.01 -0.02 -0.37 0.00 -0.72 -0.01 -0.03 -1.18

N=2,082 2

R = 0.68

*** *** * *** *** ***

**

0.01 0.27 0.21 0.23 0.79 *** 0.00 *** 0.00 0.49 *** 0.00

N=9,240 R2= 0.73

The dependent variable is the difference between loan interest rate and standardized interest rate based on borrower credit score. NetFeelings_Hard information is the difference between positive minus negative feelings and assessments of loan officers two years prior to loan origination (adjusted for other negative words included in the text, i.e. “no(t,n)”, “never”, “nothing”, “nobody”, “-n’t”), deflated by words related to hard information. SocialPersonal_Hard is the number of words related to borrower social and personal life two years prior to loan origination, deflated by words related to hard information. EducationProfession_Hard is the number of words related to borrower professional and educational background two years prior to loan origination, deflated by words related to hard information. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

65   

Panel C: Portability of soft information, soft information type and loan outcomes

Variable

Charge-off=1 Credit score620 dF/dx p>|z| dF/dx p>|z|

Soft_Hard information at loan origination

-0.009 ***

0.00

0.000

0.29

-0.010 **

0.05

0.006

0.47

-0.005 ***

0.01

-0.001

0.77

-0.004 *

0.07

0.003

0.43

-0.008 -0.010 -0.019 -0.002 0.002 0.004 -0.006 0.025

0.04 0.00 0.01 0.35 0.37 0.00 0.18 0.05

NetFeelings_Hard information prior to loan origination by others SocialPersonal_Hard information prior to loan origination by others EducationProfession_Hard information prior to loan origination by others Lending relationship Log(Borrower age) Log(Number of products) Log(Credit score) Log(Debt ratio) Interest rate Log(Loan amount) Log(Maturity)

** ** ***

*** ***

-0.001 ** -0.001 -0.002 ** -0.035 *** 0.001 0.000 0.000 0.000

0.02 0.47 0.00 0.00 0.21 0.74 0.75 0.91

Fixed effects: loan type, year N= 841 pseudo-R2= 0.37

N= 3,580 pseudo-R2= 0.25

The dependent variable is one if the loan was written off credit unions’ balance sheet within two years after loan origination, and zero otherwise. NetFeelings_Hard information is the difference between positive minus negative feelings and assessments of loan officers two years prior to loan origination (adjusted for other negative words included in the text, i.e. “no(t,n)”, “never”, “nothing”, “nobody”, “-n’t”), deflated by words related to hard information. SocialPersonal_Hard is the number of words related to borrower social and personal life two years prior to loan origination, deflated by words related to hard information. EducationProfession_Hard is the number of words related to borrower professional and educational background two years prior to loan origination, deflated by words related to hard information. Marginal effects reported. Cluster is at the borrower level and standard errors are corrected for heteroskedasticity. All values of the continuous variables are winsorized at 5% and 95% level. Fixed effects for year, loan type and employee are included. Variables are described in Appendix A. ***Significant at 1%, ** 5% and * 10% level, two-tailed tests.

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