Monitoring and the Portability of Soft Information

Transcription

1 Monitoring and the Portability of Soft Information Dennis W. Campbell Harvard Business School David H. Erkens University of Southern California Maria Loumioti University of Southern California May 10, 2016 This study has benefited from helpful comments from conference participants at the Harvard Information, Markets and Organizations conference, workshop participants at the University of Southern California, and participants at the 2015 AAA MAS conference. Special thanks are due to Sarah Bonner, Ken Merchant, and Mark Young.

2 Monitoring and the Portability of Soft Information 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 hierarchical monitoring purposes also facilitates the horizontal communication of soft information for decisionmaking 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 lending decisions and outcomes. 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. We also document several factors that appear to enhance the portability of soft information including employee homogeneity, the use of common language, and the likelihood of hierarchical monitoring. Data Availability: All data were obtained from a confidential source. 1

3 I. 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 1 Examples of employees notes on customers are reported in Appendix C. 1

4 reflects loan officers assessments and feelings, and refers to borrowers personal 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 are 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. In particular, our measure of shared soft information is positively associated with 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. 2

5 loan approvals and negatively associated with interest rates. Moreover, consistent with the increase in loan approvals and decrease in interest rates being the result of better-informed decision making, we find that our measure of shared soft information is negatively associated with loan charge-offs. These results remain even when the collection and use of soft information are separated by employee, geography, and time period. We also explore a variety of organizational factors that prior literature suggests might enhance or limit the portability of soft information. First, we find that our results are more pronounced when employees communicate soft information using a common vocabulary that leads to less confusion and uncertainty (March 1991; Cremer, Caricano, and Prat 2007). Second, consistent with accountability pressure from superiors increasing employee effort in information collection (Lerner and Tetlock 1999; Koonce, Anderson, and Marchant 1995; Tan 1995; and Cloyd 1997), we find that our results are more pronounced when soft information is collected for decisions that are more likely to be reviewed by superiors due to either involving larger loan amounts or borrowers who do not meet traditional underwriting criteria. Finally, consistent with a common understanding of work practices facilitating the interpretation of soft information (Grant 1996, Mathieu, Heffner, Goodwin, Salas, and Cannon-Browers 2000), we find that our results are more pronounced when employees who collect the information have more similar tenures or are from similar levels in the organizational hierarchy relative to those who use the information in their lending decisions. These findings are important for a number of reasons that also point to the relative contributions of our paper to both the finance and accounting literatures. 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 3

6 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 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. In particular, 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 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). 4

7 borrowers and bankers has significantly increased over time, suggesting that lending decisions are relying more on hard information. We provide evidence on an alternative mechanism: internal centralized information systems can facilitate the transmission of soft information across employees in different geographies. Finally, to our knowledge, this study is the first to examine the extent to which unstructured information contained in reporting systems facilitates operational decision-making. Prior studies examine the use of well-structured accounting information (Bouwens and Abernethy 2000) and suggest that cognitive constraints limit the extent to which even this relatively easy to process information can be used by decision-makers (Cardinaels 2008; Cardinaels and van Veen-Dirks 2010). Our findings suggest that a common understanding of work practices, a previously overlooked aspect of employees knowledge, allows employees to process even highly unstructured information available in a firm s internal reporting systems. II. Prior Literature Soft Information in Lending Relationships 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 5

8 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 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). 6

9 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 2014 ). 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 intertemporal 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 7

10 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). 9 Organizational Factors Influencing the Portability of Soft Information Hierarchical Monitoring and Accountability Soft information in lending relationships is collected in the context of decentralized decisionmaking employees typically have some decision-rights to effect lending decisions based on information collected by themselves or others. Consequently, employees are likely to put more effort into the collection of soft information when their decisions are more likely to be monitored by hierarchical superiors. Accountability-induced motivation has been shown to increase selfcritical effort (Lerner and Tetlock 1999; Bonner 2008) that is, people are likely to engage in more complex cognitive processing by considering multiple perspectives on issues and trying to anticipate the objections that reasonable others might raise to positions that they might take. As a result, employees who feel accountable provide more valid and complete justifications than employees who do not feel accountable (Koonce et al. 1995; Tan 1995; Cloyd 1997). When 9 In this paper, we focus on a highly decentralized setting 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. 8

11 hierarchical monitoring is more likely, and hence accountability induced motivation stronger, we expect the quality of soft information collection to increase resulting in enhanced portability over time and across employees. Employee Heterogeneity Prior research finds that employees develop a common understanding of work practices when they have similar professional and social backgrounds (Schneider 1987), and work in the same context by sharing tools, work processes, and work cultures (Hinds and Mortensen 2005). A common understanding of work practices allows employees to communicate with each other on common ground (Clark 1985, 1998), permitting them to tailor communication to concepts, terms, and perspectives that employees have in common. In so doing, employees are more likely to understand the arguments others make (Zenger and Lawrence 1989; Clark 1992, 1996; Grant 1996; Krauss and Fussell 1996; Mathieu et al. 2000). We expect soft information to be less (more) portable when the employees who use soft information have less (more) similar backgrounds and experiences within the organization relative to those who collect the information. Common Code Prior research suggests that the use of a common vocabulary reduces communication costs by leading to less confusion and uncertainty (March 1991). Such common code has been shown in analytical models to increase horizontal coordination in organizations via increased efficiency in the communication and processing of information (Cremer 1993; Cremer et al. 2007). Consequently, we expect the portability of soft information to increase when decision-makers use a vocabulary that is more extensively used by other employees in the organization. 9

12 III. RESEARCH SETTING The research site for this study is a federal credit union that offered traditional financial products and services and had approximately $1.6 billion in assets, 140,000 customers, and 23 branches that operated in a single state in the U.S. 10 The primary data for this study come from the credit union s internal lending, personnel, and customer records from January 2006 through May During this period, the credit union consistently ranked in the top 15 percent with respect to productivity, low loan default rates, and overall profitability when compared either to same-state credit unions or similarly sized credit unions elsewhere. Field Setting In order to maintain a high level of customer service and strong relationships with its customers, the credit union had a relatively flat decentralized structure that gave lower-level employees decision-rights over virtually all aspects of lending decisions as long as these decisions were in both the customer s and the credit union s interest. As one executive explained, employees have full latitude on rates and overriding our underwriting guidelines. Even [tellers] have this authority. As this anecdote indicates, decision-rights were generally used to make exceptions with respect to loan approvals and interest rates. Loan approval exceptions occurred when employees approved loans that did not meet underwriting guidelines which required borrowers to have a credit score of at least 620 and a debt-to-income ratio of at most 45 percent. Rate exceptions occurred when employees structured loans with a different rate than was recommended on the rate sheet for a 10 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. 10

13 loan with similar characteristics. In the vast majority of cases, although not all, the offered interest rate was lower than the rate-sheet rate. The majority of employees were expected to handle a wide variety of tasks ranging from simple tasks such as opening new deposit accounts to complex tasks, such as approving, structuring, and processing consumer loans and mortgages. Employees were expected to perform these tasks based on their availability and, as a result, routinely processed loan applications of customers that had mostly interacted with other employees. Lending Decisions and the Information Environment Management monitored lending decisions by requiring employees to document their rationale for deviating from guidelines ( exception reports ). 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 employee to make sure the documentation is in the system going forward. The credit union deliberately emphasized the enforcement of documenting rationales for deviating from guidelines rather than monitoring the outcomes of individual actions in order to stimulate employees to use the (soft) information they acquired through their interactions with customers in their lending decisions. Nevertheless, managers held lower-employees accountable for their decisions by performing periodic reviews of exception reports. Whenever these reviews identified trends for employees with significant deviations from guidelines, mentorship and coaching were used to instill decision-making norms that were more aligned with the interests of the credit union An untabulated analysis finds that loan charge-offs are associated with dismissals. Thus, employees were potentially also punished for poor decision-making. 11

14 The credit-union believed that this approach would give it a competitive advantage over competitors that often based their lending decisions purely on standard factors such as creditscores. 12 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 an 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 620 that are safe bets and are seeking to legitimately rebuild their credit. As a result, exception reports contained borrower-specific reasons for low credit scores or high debt-to-income ratios that warranted an exception, such as identity theft or lapses in employment that are not caused by the loan applicant. 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. Exception reports contained not only qualitative characteristics of customers, but also employees personal feelings and assessments with respect to factors such as a loan applicant s attitude. For example, one employee we interviewed noted: 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. Most importantly for the purpose of our study, the credit union gave all employees access to exception reports as opposed to restricting this access to managers because it wanted lower-level employees to share their exception reports with each other. As a result, exception reports 12 Credit score-based lending is the industry standard because credit scores are strongly related to default rates (FDIC 2007). As a result, only a small fraction of industry-wide consumer loans and mortgages are issued to borrowers with credit scores below 620 (FDIC 2013). 12

15 potentially allowed employees to share soft information that helped them make better-informed decisions. Examples of exception reports are provided in Appendix C. IV. SAMPLE SELECTION AND VARIABLE DEFINITION Sample Selection As mentioned above, we use data from the credit union s internal lending, personnel, and customer records from January 2006 through May The sample consists of 113,157 unique loan applications from 54,627 unique customers that were processed by 476 unique employees from 23 branches. Variable Definitions Dependent Variables We focus on loan approvals and interest rates because decision-rights were generally used with respect to these two types of lending decisions. Loan acceptance equals one for approved loan applications, and zero for denied loan applications. For the sample of approved loan applications, Discretionary interest rate equals the difference between the offered interest rate and the rate that is recommended on the official rate-sheet for a loan with similar characteristics. If soft information shared through the internal reporting system is portable, we expect employees to use this information to increase loan approvals and reduce interest rates. Moreover, to confirm that the expected increase in loan approvals and decrease in interest rates are the result of better-informed decision making, we also examine whether this information helps employees reduce loan chargeoffs. Charge-off equals one for loans that are charged-off, and zero for loans that are not chargedoff The credit union sells less than five percent of its loans to securitization (i.e., transfers credit risk to special purpose entities), and thus, in our setting loan charge-offs are a reasonable proxy of loan decision outcomes. 13

16 Soft Information The credit union stores exception report information by customer, employee, and date. Because this information is generally entered starting from the initial review of a loan application until the closing date, we consider entries from 30 days before until 10 days after approval decisions to be an exception report. This 40 day window is based on a graphical analysis that suggests that the bulk of exception report information is generated within this period. 17 This approach results in a sample of 97,517 exception reports for 113,157 loan applications. 18 We use a content analysis on exception reports that were created by employees other than the employee making the lending decision to construct a measure of soft information that is shared through exception reports (Soft information). Specifically, we use a rule-based dictionary approach (Li 2010) that counts key words that relate either to qualitative characteristics of customers or personal feelings and assessments of employees prior to loan origination (divided by the total number of words used in exceptions reports) because this information is outside the scope of the credit union s guidelines despite being important to lending decisions (Petersen 2004; Scott 2006; Liberti and Mian 2009; Uchida et al. 2012). We read approximately 33,000 lines of exception report information to identify common patterns in words and phrases. Based on our reading, we use keywords related to customers social, professional, academic, and personal life to capture qualitative characteristics. We use Plutchik s 17 This graphical analysis is available upon request. An untabulated analysis suggests that our results are qualitatively similar when we use alternative time windows (e.g., five days before until five days after approval decisions) or simply include all entries before the approval decision date. 18 The high proportion of loan applications that is associated with an exception report is consistent with most loan applications involving decisions that deviate from guidelines. For instance, the majority of accepted loans have an interest rate that is lower than is recommended on the official rate-sheet. It is also consistent, however, with at least some of the exception reports capturing transactional information (for example, called XX, no answer, left message; will call back tomorrow ) as opposed to soft information. We attempt to reduce the number of exception reports that purely capture transactional information by excluding entries with fewer than twelve words. 14

17 (1980) and Parrot s (2001) lists of feelings and emotions to create a list of keywords that capture employees personal feelings and assessments. See Appendix B for the list of keywords that we use. 19 Control Variables We control for various factors that may influence loan approvals, interest rates, and loan charge-offs. First, we control for the collection of soft information at loan origination (Soft information at origination). We construct this measure by using the same methodology as used for our measure of soft information on exception reports that are written at loan origination. This measure captures the flow component of soft information (the amount of soft information acquired and reported by the employee in the course of making the current lending decision). Our primary measure of soft information, on the other hand, captures the stock component of soft information (the amount of customer-specific soft information already existing in the system at the time the employee makes a lending 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. Second, we control for prior lending decisions to ensure that our results are not driven by customers having stronger ties to the credit union (e.g., Petersen and Rajan 1994; Berger and Udell 19 We exclude stop words such as and and by from this vocabulary and replace words with their stems (e.g., friends is replaced with friend ). Although we try to develop a comprehensive and objective list of keywords that capture soft information, we acknowledge that we inevitably miss soft information that is important to lending decisions. To the extent that this occurs, it reduces the power of our tests of the relationship between soft information, lending decisions and outcomes. 15

18 1995). Number of products equals a logarithmic transformation of the number of products held by a customer prior to loan origination, including deposit accounts, credit cards, investment products, and loans. Borrower tenure equals a logarithmic transformation of the number of years customers have used the credit union s services. In addition, we control for the number of loan exceptions made for the same customer during the prior two years to ensure that our measure of soft information does not merely capture the incidence of previous exceptions (Previous exceptions). Third, we control for traditional underwriting criteria. Credit score equals a borrower s credit score. Debt-to-income ratio equals a borrower s debt-to-income ratio. Loan amount equals a logarithmic transformation of the loan amount. In addition to our main control variables, we add the following variables in our rate exception and charge-off analyses to control for how approved loans are structured. Loan maturity equals a logarithmic transformation of the loan maturity in months. Loan collateral equals one for collateralized loans, and zero otherwise. Loan exceptions equals the sum of four exception indicators: collateral, credit score, maturity, and auto-loan related exceptions. Finally, in our charge-off analysis we also include the loan s interest rate (Loan interest rate). Finally, we also include year, loan type, and branch fixed effects to control for unobserved heterogeneity across years, loan types, and branches. 20 Branch fixed effects are particularly important since their inclusion allows us to control for potential endogenous assignment of borrowers to branches, which could occur, for example, if branches are situated in locations where employees are systematically more likely to interact with borrowers of high or low ex ante risk profiles. 20 The primary loan types in our sample consist of mortgages, auto loans, and credit cards. Our results are substantially similar when analyses are conducted separately by loan type. For presentation purposes, we include only the pooled results that include loan type fixed effects throughout the paper. Results from analyses by loan type are available upon request. 16

19 V. DESCRIPTIVE STATISTICS AND EMPIRICAL RESULTS Descriptive Statistics Table 1 reports the summary statistics for the measures used in our analysis separately for our sample of loan applications (Panel A) and approved loans (Panel B). Panel A shows that loan applications have an unconditional probability of acceptance of 88 percent, which is consistent with either most of the credit-union s customers meeting underwriting guidelines or loan approval exceptions being common. The relatively high frequency of previous loan exceptions (1.13 per customer on average) suggests that the latter is the case. Moreover, descriptive statistics on the offered interest rate reported in Panel B suggest that rate exceptions are also very common at the credit union. On average employees approved loans that have an interest rate that is 0.33 percent lower than is recommended on the official rate-sheet. Panel A also shows considerable variation in the extent to which soft information is available to decision-makers through exception reports written by other employees at the time of loan origination. Even when customers that clearly pass underwriting guidelines are included, on average decision-makers have approximately 15 keywords (or approximately 2 percent of the total words in shared exception reports) at their disposal. [insert Table 1 about here] Figure 1 documents trends in soft information content at our field site over time. Figure 1A shows the average number of soft information keywords (scaled by total words) per note per year. Figure 1B shows the same for the two major types of soft information borrower qualitative information and employee feelings and assessments captured in our measure. Consistent with the growing importance of decentralized decision-making at our field site, Figure 1 reveals a pattern of increasing soft information content over time, both in aggregate and by type. 17

20 Main Results Table 2 reports our main findings. Consistent with the idea of portability, Soft information is positively associated with loan approvals (column 1), and negatively associated with interest rates (column 2) and loan charge-offs (column 3). Moreover, the economic impact of soft information on lending decisions and lending risk is significant. Specifically, when we focus on lending decisions that involve a significant amount of shared soft information (top quartile of sample), we find that loan applications with the greatest amount of this information are 7.41 percent more likely to be accepted, have a 0.71 percent lower interest rate, and are 0.04 percent less likely to be charged-off. Overall, the results provide evidence that soft information is portable over time in our setting. The stock of soft information acquired in prior periods by employees other than the current decision-maker appears to play an important role in credit approval decisions, loan pricing, and future loan performance. [insert Table 2 about here] Analyses of Organizational Factors Influencing the Portability of Soft Information We propose three key factors that are likely to influence the portability of soft information. First, the use of common code facilitates the sharing and use of soft information. Second, hierarchical monitoring and accountability pressure from superiors makes exception reports a particularly credible source of soft information. Third, employees can develop a common understanding of work practices that allows them to interpret soft information collected by each other. In this section, we directly test the effect of these three key factors. Common Code In this section, we attempt to directly test whether exception reports using common vocabulary enhance the portability of soft information. In order to test this conjecture, we construct a common 18

21 vocabulary measure that equals the number of words that exception reports written by decisionmakers and other employees have in common divided by the number of words decision-makers used in their reports (Common code). Thus, our measure is higher for decision-makers that use vocabularies that are used more extensively by other employees. The mean (median) of this measure equals 0.08 (0.09), suggesting that the vocabularies employees use for writing their exception reports vary significantly. Table 3 presents the results of an analysis that interacts Soft information with Common code. Because the models presented in columns (1) and (3) are nonlinear (Ai and Norton 2003), we compute the interactive effect in these columns by examining the extent to which the marginal effect of Soft information changes in response to an interquartile range increase in Common code. We find that soft information that is shared through exception reports has a significantly larger impact on lending decisions and outcomes when exception reports are written using a common vocabulary that leads to less confusion and uncertainty. [insert Table 3 about here] Monitoring, Accountability Pressure, and the Portability of Soft Information To assess whether accountability pressure from superiors increases the credibility and communication of soft information across employees and time, we examine factors that influence the probability that exception reports will be reviewed by superiors. Because we expect loan applications that involve larger loan amounts or borrowers who clearly do not meet traditional underwriting criteria to be most salient to supervisors, we focus on these two factors. In order to accomplish this, we construct a variable that equals the logarithmic transformation of a customer s previous borrowings (Large loans) and a variable that equals one when customers have a credit score below 620 or a debt-to-income ratio above 45 percent, and zero otherwise (Poor credit). We 19

22 measure both of these factors at the time soft information is collected e.g. they relate to the stock of soft information existing in the system at the time a lending decision is made. Panels A and B of Table 4 present the results of analyses that interact Soft information with respectively Large loans and Poor credit. As in our previous analysis, we compute the interactive effects presented in columns (1) and (3) of Panel A by examining the extent to which the marginal effect of Soft information changes in response to an interquartile range increase in Large loans. Because Poor credit is a dichotomous variable, we compute the interactive effects presented in columns (1) and (3) of Panel B by examining the extent to which the marginal effect of Soft information changes in response to borrowers not meeting traditional underwriting criteria. Consistent with accountability pressure increasing the quality and credibility of soft information, and hence its portability, both panels show that our results are much more pronounced when exception reports are more likely to be reviewed by superiors. For instance, the magnitude of the interactive effects presented in Panel B suggest that when soft information is collected on customers not meeting underwriting guidelines, its effect increases by 100 percent for loan acceptances, 300 percent for discretionary interest rates, and 100 percent for loan charge-offs. [insert Table 4 about here] Employee Heterogeneity and the Portability of Soft Information To assess whether a common understanding of work practices allows employees to interpret each other s exception reports, we examine how differences in tenure with the credit union between exception report writers and decision-makers influences our results. As mentioned earlier, employees from the same workplace have a common understanding of work practices that allows them to better understand the arguments others make (Zenger and Lawrence 1989; Clark 1992, 1996; Grant 1996; Krauss and Fussell 1996; Mathieu et al. 2000). Prior research suggests that this 20

23 effect is most pronounced for employees who do not only work in the same workplace, but also have similar tenures (Zenger and Lawrence 1989). This occurs, because employees of similar tenure may develop unique interpretations and understandings that stem from shared experience on projects or commonly experienced organizational events. As a result of these shared experiences, employees of similar tenure are able to effectively communicate with each other, but may be out of touch with their senior or junior colleagues. Thus, if a common understanding of work practices allows employees to interpret each other s exception reports, our results should be less pronounced when exception report writers and decision-makers have more dissimilar tenures with the credit union. To test this conjecture we create a variable that equals the absolute difference between the number of years the decision-maker and the average exception report writer have been employed by the credit union (Tenure distance). Panel A of Table 5 presents the results of an analysis that interacts this measure with our measure of soft information. As in our previous analyses, we compute the interactive effects presented in columns (1) and (3) by examining the extent to which the marginal effect of Soft information changes in response to an interquartile range increase in Tenure distance. Consistent a common understanding of work practices enhancing the portability of soft information, the panel shows that the influence of soft information on lending decisions (but not outcomes) is less pronounced when exception report writers and decision-makers have more dissimilar tenures with the credit union. Panel B shows that similar results hold when employee heterogeneity is measured instead in terms of hierarchical distance measured as the absolute value of the difference between the hierarchy levels of the decision maker and the average note report writer (hierarchy is a scale variable ranging from 1 for entry employees to 7 for senior managers). 21

24 [insert Table 5 about here] Robustness Tests 26 Measurement of Soft Information We perform three analyses (results untabulated) to assess whether our results are sensitive to how we constructed our measure of soft information. We first split our measure of soft information into information that captures qualitative customer characteristics or employees personal feelings and assessments. We find that both types of soft information are positively associated with loan acceptances, and negatively associated with discretionary interest rates and loan charge-offs, and thus, that our results are driven by both types of soft information. Next, we use an unscaled measure of soft information (we do not normalize by the total number of words) and find that also this unscaled measure is positively associated with loan acceptances, and negatively associated with discretionary interest rates and loan charge-offs. Further, recall that we group entries from 30 days before until 10 days after approval decisions together into one exception report. We take this approach because exception report information is generally entered starting from the initial review of a loan application until the closing date and the credit union stores exception report information by customer, employee, and date. Although this time window is based on a graphical analysis that suggests that the bulk of exception report information is generated within this 40 day window, our third robustness test examines whether our results are sensitive to using alternative windows that range from 40 days around approval decisions until 5 days around approval decisions. We find that our results remain qualitatively similar. Moreover, we find similar results when we simply consider all exception report 26 For brevity, we do not tabulate results of our various robustness tests. All results of these tests are available upon request. 22

25 information that is entered before approval decisions. Overall, our robustness tests suggest that our results are not sensitive to how we construct our measure of soft information. Personal Interaction versus Portability Our primary measure of soft information reflects past information collected by employees other than the current decision-maker. As such, to the extent that it reflects information collected by employees who are co-located in the same branch, it may be correlated with soft information communicated via personal interaction rather than reflecting portability enabled by the centralized reporting system. To explore this further, in untabulated analyses we find that all results are similar in magnitude, direction, and significance when we instead measure soft information based on that collected in branches other than the branch in which the current decision-maker is located. Thus, soft information in our setting appears to be portable across time, employees, and geography. These results point to an alternative to the view that geographic dispersion reduces the efficacy of soft information in lending decisions (Petersen and Rajan 2002). Endogenous Matching of Borrowers and Employees To the extent that borrowers endogenously match with employees, our measure of soft information may be correlated with unobserved characteristics of the strength of their relationship which would affect both lending decisions and outcomes. Our primary analyses control for a variety of borrower, relationship, and loan characteristics as well as business unit fixed effects to account for the potential of endogenous matching between borrowers and employees. However, in an additional attempt to rule out endogenous matching as a driver of our results, we re-ran all analyses in Tables 2-5 on the subsample of lending decisions that were made in the call center of our field site. Endogenous matching of borrowers and employees is highly unlikely in this subsample as interactions are not face to face and calls from customers are randomly routed to 23

26 employees based on availability. Untabulated results demonstrate similar signs, magnitudes, and significance of coefficients in this subsample. VI. 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 inter-temporally 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. 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 24