Rethinking Escalation of Commitment: Relational Lending in Microfinance
Laura Doering Booth School of Business and Department of Sociology University of Chicago [email protected]
Social relationships are an important component of lending and investing. Nevertheless, investors who embed market transactions in social relationships run the risk of “escalating commitment” to struggling investments. When investors feel personally committed to a client, they may ignore negative feedback about that client’s performance. Previous literature on escalation of commitment argues that such behavior constitutes a judgment error and hinders actors from achieving their goals. However, I argue that escalation of commitment can be an effective strategy for achieving organizational goals. I utilize a detailed, proprietary database from a commercial microfinance institution, along with ethnographic observations from the same organization, to demonstrate the conditions under which escalation helps investors realize their goals. The qualitative data show that committed investors often have “soft” information about clients and leverage over client actions; these tools help investors evaluate and manage struggling investments. The quantitative data confirm that committed investors secure better long-term outcomes for the bank than their less-committed peers. This paper demonstrates how actions that might be labeled “decision biases” constitute rational, appropriate behavior when viewed in their social context.
1
Many scholars within both sociology and economics recognize the pervasiveness and importance of social relationships in the financial sector (Sharpe, 1990, Petersen & Rajan, 1994,
A. Berger & Udell, 1995, Abolafia, 1996, Mizruchi & Stearns, 2001). Social ties between investors and their clients can have a strong influence on how financial institutions distribute capital and overcome information asymmetries (Uzzi, 1999, A. Berger, Klapper, & Udell, 2001).
Although such ties may promote beneficial outcomes for both investors and clients (Boot, 2000,
Gibbons & Henderson, 2012), they can also expose financial institutions to certain risks. When investors feel personally committed to their clients, they may fall victim to judgment biases that promote sub-optimal decision-making. Specifically, financial institutions run the risk that investors will “escalate commitment” to poor-performing clients.
Escalation of commitment, or “escalation,” refers to the tendency to remain committed to an investment even after receiving objectively negative feedback about that investment (Staw,
1976, Kelly & Milkman, 2013). Actors who feel personally responsible for investments are more likely to escalate commitment (Staw, 1976). Indeed, actors across a variety of financial institutions escalate commitment to the struggling investments for which they feel personally responsible (Staw, Barsade, & Koput, 1997, Guler, 2007, Beshears & Milkman, 2011). In these contexts, escalation of commitment is seen as a detrimental decision bias. Otherwise-rational investors remain committed to poor-performing investments and, in doing so, act against the best interests of their organizations.
Yet findings from other streams of research suggest that organizations benefit when their agents remain committed to struggling investments. For instance, indigenous investors demonstrate greater commitment to domestic firms than their foreign counterparts; as a result, those firms experience greater stability and growth following market fluctuations (Schrank,
2 2008). Firms that make the tumultuous transition from private to public ownership under the guidance of highly committed founder CEOs have higher survival rates than firms led by less committed executives (Fischer & Pollock, 2004). Additionally, firms can suffer if they show little commitment to new practices. When they hastily reject innovations perceived as failures, firms may become trapped in fad-like waves of adoption and abandonment (Strang & Macy,
2001). These findings suggest that organizations achieve better results when their agents remain committed to tenuous investments. Paradoxically, the same mechanism—commitment in the face of objectively negative feedback—can lead to both favorable and unfavorable organizational outcomes. This paper aims to resolve that apparent inconsistency. Specifically, it asks, “Under what conditions does escalation of commitment lead to favorable organizational outcomes?”
Successfully addressing this question requires examining the social processes that parallel the development of commitment. As investors become committed to an investment, they gain tools that help them evaluate and manage struggling investments. First, investors generally vet potential investments before committing resources. Those who have personal ties with clients gain access to soft information—informal, qualitative information about an investment that supersedes the “hard facts” of the case (Petersen, 2004). Investors with soft information are better able to evaluate negative feedback because they have a more global understanding of the investment (A. Berger & Udell, 1995, Uchida, 2011). Second, the act of investing resources into a project often confers some degree of control to the investor (Bygrave & Timmons, 1992,
Campbell, 2003). Investors who have leverage over their investments can correct the problems signaled by negative feedback. For individuals with the capacity to shape their investments, negative feedback can serve as a call to action. When actors have soft information and leverage
3 over an investment, remaining committed to a faltering investment may be a wise decision, rather than a judgment error.
In this paper, I examine the consequences of escalation for loan officers in a commercial microfinance bank. This setting is particularly appropriate for two reasons. First, microfinance loan officers are encouraged to engage in relational lending practices. As officers vet new clients, they become personally familiar with clients, their families, and their communities.
However, officers also work with clients to whom they have no personal ties. Occasionally, loan officers are assigned responsibility for clients of officers who have left the bank. Since officers do not vet or approve the clients they inherit, they have no personal ties to them. Thus, officers have embedded relationships with some clients, and arms-length ties (Uzzi, 1999, Uzzi &
Lancaster, 2003) with others. Such natural variation in officer-client relationships allows for an examination of the social conditions under which escalation of commitment benefits the financial institution.
Second, this setting is appropriate because it bears striking resemblance to the financial context in which Staw, Barsade, and Koput (1997) conducted the hallmark field study of escalation of commitment. In that study, the authors demonstrated that managerial turnover in banks resulted in de-escalation to struggling loans. They showed that less committed managers who assumed control of loan portfolios were more likely to write-off poor-performing borrowers than more committed managers. The authors assumed that less committed managers acted in their organizations’ best interests when they cut ties with struggling borrowers; however, they did not demonstrate that fact empirically. Nevertheless, they argued that less committed actors handle struggling investments more effectively than their more committed peers. Many scholars
4 take this study as evidence that individuals who escalate commitment do so to the detriment of their organizations (e.g. Kelly & Milkman, 2013).
The commercial microfinance bank analyzed in this study offers a useful comparative site from which to challenge the assumption that escalation of commitment necessarily produces detrimental results. Much like Staw et al.’s managers, the loan officers at the focal microfinance bank have varying degrees of commitment to their clients. And, like those managers, they must decide whether to cut ties with poor-performing borrowers. In this setting, however, the longitudinal nature of the data provides an opportunity to observe the long-term effects of escalation of commitment on the bank’s bottom line. Moreover, because more is known about officer-client relationships in this setting, we can better understand the specific social conditions under which escalation of commitment helps investors achieve their organizational goals.
Data for this study consist of interviews and ethnographic observations with the commercial microfinance bank, as well as the bank’s proprietary database of over 100,000 loan- month observations. The qualitative data reveal that loan officers feel greater responsibility for clients whom they approve for loans. Through the vetting process, officers gain soft information about these clients and leverage over their performance. I use the proprietary database to test the hypotheses that loan officers will escalate commitment to the clients they vet and that escalating officers who have soft information and leverage will secure more favorable outcomes for the bank. I find that highly committed loan officers do escalate commitment to struggling clients more frequently, but they also achieve better long-term outcomes for the bank than less committed officers.
This paper builds our theoretical understanding of both embedded market relationships and decision biases within firms. First, it demonstrates that actors do not necessarily become
5 trapped by decision biases when they feel highly committed to market alters. Rather, by virtue of that commitment, actors can develop the tools that help them evaluate and manage under- performance. Thus, this paper contributes to a large body of work in which social ties between financial agents and their clients are seen as improving organizational outcomes (Canales &
Greenberg, forthcoming, Petersen & Rajan, 1994, A. Berger & Udell, 1995, Uzzi, 1999, Boot,
2000, Uzzi & Lancaster, 2003, Gibbons & Henderson, 2012). More broadly, this paper helps us understand how actions that are traditionally viewed as errors in judgment constitute rational, appropriate behavior when seen in their relational context.
ESCALATION OF COMMITMENT AND ORGANIZATIONAL OUTCOMES
Staw’s seminal (1976) research introduced escalation of commitment as an important concept in decision theory. He demonstrated in laboratory studies that individuals who felt personally responsible for a failing investment subsequently contributed more funds to that investment. Moreover, those who received negative feedback about their investment’s performance invested more money than those who received positive feedback. These results were surprising; they challenged the expectation that actors would disassociate from poor- performing investments. Since then, research in decision theory has aimed to uncover the psychological mechanisms that prompt escalation (Brockner, 1992). Researchers have argued that individuals escalate because they believe the situation will improve (Rubin & Brockner,
1975), because they prefer not to acknowledge their error in selecting poor investments
(Caldwell & O’Reilly, 1982, Conlon & McLean Parks, 1987), and because they become risk- seeking when they believe they have losing investments (Whyte, 1986).
6 Although the majority of the initial work on escalation took place in laboratories, scholars eventually began looking for escalation in organizational settings. Ross and Staw (1993) argued that escalation of commitment could explain the continued construction of the Shoreham nuclear power plant, even after it was decades late and millions of dollars over budget. Schoorman
(1988) found that managers escalated commitment to employees whom they had hired personally. Astebro, Jeffrey, and Adomdza (2007) showed that inventors with high-cost inventions were more likely to continue working on them, even after being told that they would fail. Beshears and Milkman (2011) found that stock analysts escalated commitment to unconventional predictions upon realizing those predictions were suboptimal. Guler (2007) demonstrated that political and institutional pressures encouraged venture capitalists to retain struggling investments.
The elegance of the initial laboratory-based studies lay in their ability to demonstrate that
(1) escalation occurred and (2) its effects were detrimental. Once studies migrated to the field, however, establishing the second component became far more difficult. With few exceptions
(e.g. Beshears & Milkman 2011), field studies stop short at documenting the existence of escalation and do not evaluate whether escalation proves detrimental. Because laboratory studies previously established the damaging effects of escalation, scholars generally assume that these effects play out in the field, as well.
Despite the lack of field-based evidence that escalation harms organizations, researchers nevertheless view escalation as a judgment error that actors would be wise to avoid. Indeed, the escalation-as-harmful perspective is expressed most clearly in the body of scholarly work that prescribes strategies for avoiding escalation (e.g. Bazerman, Giuliano, & Appelman, 1984, Ku,
2008, Sivanathan et al., 2008). Scholars in this camp suggest ways that actors can minimize their
7 commitment to struggling investments, arguing that investors will achieve more favorable outcomes by cutting ties with investments that present early signs of trouble. Such works epitomize the view that escalation is unavoidably harmful. In the following section, I present work that encourages us to think about escalation in a different light.
Escalation of Commitment, Soft Information, and Leverage
In general, individuals research potential investments prior to committing resources.
Whether investing in a stock, approving a loan, or hiring a job candidate, most decision makers gather information before making an investment. Some actors, in the process of vetting investments, have the opportunity to garner “soft information.” Soft information refers to the qualitative properties of the investment that transcend the quantitative facts (Uchida, 2011).
Compared to “hard information,” which is generally expressed numerically and is easily transmitted between agents (Petersen, 2004), soft information helps investors understand their investments not as atomistic, but as embedded within broader social, environmental, or market contexts (Canales & Greenberg, forthcoming). Investors often gain access to soft information through personal relationships with individuals associated with the investment. Indeed, sharing soft information constitutes one of the key features of embedded market relationships (Uzzi,
1997). Such information allows embedded actors to know far more about their investments than the facts documented in official reports.
Many organizations seek out soft information as part of the vetting process. Prior to investing in a firm, venture capitalists attempt to learn as much as possible about the organization and its leaders. Such vetting includes visiting the target firm, spending time with the leadership team, and learning about team members’ personal histories and backgrounds (Bygrave &
8 Timmons, 1992, Campbell, 2003). Banks that lend to small firms attempt to gather soft information when determining the firm’s creditworthiness (A. Berger, Klapper, & Udell, 2001).
Elite banking, consulting, and law firms also seek soft information about job applicants through the interview process (Rivera, 2012). These firms often base hiring decisions on applicants’ hobbies and leisure activities, rather than more objective metrics such as GPA. As investors vet potential investments, they learn more than basic facts: they develop impressions, hear stories, and gain a sense of the environmental forces that will continue to impinge upon the investment after resources are allocated. Soft information aids in assessing investment value and provides a contextual frame for interpreting and evaluating feedback (L. Ross & Nisbett, 1991).
For investors, the act of committing resources often triggers some degree of control over the investment. Venture capitalists, for instance, frequently act as management consultants or assume board membership after investing funds in a firm (Bygrave & Timmons, 1992,
Campbell, 2003). Members of an entrepreneurial team who commit resources to a start-up generally share decision-making control (Ruef, 2010). Upon hiring a new employee, employers coach the new hire, attempting to shape behavior and performance (Frazis, Gittleman, & Joyce,
2000). In each of these scenarios, investors gain leverage over their investments. That is, they gain the ability to influence those investments upon committing resources.
When individuals with leverage over their investments receive negative feedback, they can utilize that leverage to reverse problematic trends. Upon receiving news that a firm is struggling, a venture capitalist might advise changing the firm’s organizational structure. The entrepreneurial team that sees lagging sales may revisit its promotion strategies. The employer may sit down with an under-performing employee to ensure he understands company procedures
9 and goals. For actors with leverage, negative feedback can serve as a call to action: poor performance signals that previous approaches require modification.
Naturally, many investors have no leverage over their investments. No matter how loud we cheer, for instance, we are unlikely to influence the outcome of a horse race—or a stock price. Yet when an investor’s relationship to the investment allows her to modify the investment—either because she controls the investment or because she can influence those who do—negative feedback becomes a tool for addressing the problems that underlie poor results.
THE CASE: COMMERCIAL MICROFINANCE IN CENTRAL AMERICA
I examine escalation of commitment at a commercial microfinance bank in Central
America. Commercial microfinance differs in important ways from not-for-profit microfinance.
Not-for-profit microfinance organizations—popularized by Muhammad Yunus and the Grameen
Bank—generally lend small amounts to groups of borrowers. These mutual accountability groups ameliorate the risk of lending to individuals who lack credit histories (Yunus & Jolis,
1999, Armendariz de Aghion & Morduch, 2005). By comparison, commercial microfinance institutions often give larger loans to individual borrowers who pledge collateral (M. Berger,
Otero, & Schor, 2006). Under the individualized model, loan officers vet applicants extensively and assume responsibility for monitoring client repayment. While commercial microlending is practiced worldwide, Latin America has the highest concentration of commercial microfinance activity (M. Berger, 2006).
“MicroBank,”1 the focal microfinance institution in this paper, utilizes an individualized model of lending. In 2008, MicroBank held 30% of its national microfinance market. It
1 My confidentiality agreement with the bank prohibits publishing its name or country location. To that end, I refer to the bank by a pseudonym and present descriptive information in relative rather than absolute form.
10 competes with other commercial microfinance banks, not-for-profit lenders, agricultural cooperatives, and moneylenders. MicroBank’s target clients are low- to middle-income small business owners. Women make up 46% of its clientele. The bank has various branches across the country to serve its clientele, 71% of whom live in cities or municipalities. While
MicroBank offers a suite of financial services, microfinance loans account for the majority of the bank’s business.
Microfinance loans come in two forms: working capital and auto loans. Clients generally use working capital loans to purchase supplies or equipment. Auto loans are directed towards the purchase of a vehicle for the client’s business. The median principal of a working capital loan is $708.27. Among auto loans, the median principal is $8,000.00. On average, clients pay 24% annual interest—a rate that is on par with local industry standards.
The vast majority of MicroBank’s new clients have no formal credit history (93%).
Lacking objective metrics to predict future behavior, MicroBank depends heavily on loan officers to assess applicants. Officers visit applicants at their homes and small businesses to gather information about the applicant, the applicant’s family, and household’s finances. In addition to taking a full business inventory and valuing the applicant’s assets, officers gather contextual information. Does the family have running water? Do the children attend public or private school? Has the applicant ever been divorced? How many extended family members depend on the applicant financially? In addition, officers speak with community members to get an impression of the applicant’s local reputation.
Given the breadth of information that officers collect, the vetting process generally takes place over multiple hours and multiple visits. If an officer believes an applicant is creditworthy, he submits a report—with completed boilerplate forms, photos, and additional documentation—
11 to his superior. The superior then evaluates the report and makes a final decision. If the loan is approved, the officer assumes responsibility for monitoring the client’s repayment.
In a static organizational environment, officers would work exclusively with the clients they vetted. However, turnover occurs frequently among loan officer. When one officer leaves the bank—voluntarily or involuntarily—the branch manager redistributes the clients from the exiting officer’s portfolio to other loan officers. As a result, officers have a mix of “original” and “inherited” clients in their portfolios. I discuss the process of loan redistribution in more detail in the Quantitative Methods section.
Employing Mixed Methods
Together, the quantitative and qualitative data in this paper tell a unified story.
Nevertheless, I employ these data in fundamentally different ways. I use ethnographic observations and interviews to uncover the mechanisms that drive escalation of commitment. I also use these data to examine how officer-client relationships influence client performance when officers remain committed to delinquent borrowers. Additionally, I use the MicroBank database to establish the large-scale presence of escalation as well as its consequences for the organization.
Utilizing three forms of data—ethnographic observations, interviews, and the organizational database—allows me to take a complementary (Small, 2011) methodological approach to data analysis. That is, the strengths of each data source complement the weaknesses of the others (Sieber, 1973, Brewer & Hunter, 1989). For instance, ethnographic observations provide insights into loan officers’ behavior as they interact with clients, and interviews provide a platform for loan officers to reflect upon and explain their behavior. Although interviews and
12 observations reveal processes unfolding across the bank, they are poorly suited to address the frequency of these processes or their long-term, broader consequences for the organization. To explore these issues, I rely on the MicroBank database, which provides a bird’s eye view of lending and repayment behaviors over time, as well as detailed demographic information about clients.
In the following sections, I outline the data and methods employed. I first describe and analyze the qualitative data. I draw on field notes and interviews to generate hypotheses about escalation of commitment and its effects on MicroBank’s bottom line. Then, I describe and analyze the MicroBank dataset. I use these data to test the hypotheses generated through field observations and interviews. Finally, I discuss the broader theoretical implications of these findings.
QUALITATIVE METHODS
Data
Between December 2009 and January 2013, I conducted nearly 200 hours of ethnographic observations with MicroBank loan officers and administrators. My observations covered three main areas of activity: (1) client evaluations and follow-up visits; (2) loan officer attempts to recruit new clients, and (3) administrative tasks at branch offices. At two branch offices, I observed loan officers as they recruited, evaluated, and monitored clients. Since new loan officers shadow experienced officers for their first three months on the job, the presence of an additional novice was not unusual. Much like a new loan officer, I asked questions about determining creditworthiness, working with truculent clients, completing client reports, and encouraging timely repayment. Long bus or car rides offered excellent opportunities for talking
13 with loan officers about their work. On a few occasions, I accompanied collections officers on their client visits. These experiences helped me understand the consequences clients face if they repeatedly miss payments.
Beyond these activities, I sat in on staff meetings, helped officers complete client reports, and watched as branch managers dealt with loan officer departures. I also attended two weeklong training workshops for newly hired loan officers. These experiences gave me a strong grounding in the bank’s official and unofficial policies.
I wrote up field notes upon returning from branch offices or client visits. The resulting collection of field notes contains 156 pages of observations about MicroBank. I first open-coded the notes, and then organized codes according to context, dimension, range, and outcome
(Corbin & Strauss, 2008). Organizing the data in this way aided in process tracing (George &
Bennett, 2005) and generating hypotheses. To bolster the field observations, I conducted eleven targeted interviews with senior bank officials and loan officers at both branches. These interviews allowed me to explore issues that were not directly observable, or were too sensitive to discuss in public.
Responsibility for Original Clients
In this section, I discuss why loan officers feel greater responsibility for the clients whom they vet and approve, and how this sense of ownership affects their tendency to escalate commitment. Before beginning the discussion, it is useful to clarify the terminology I will use. I refer to clients and loan officers who are paired during the initial evaluation as “original”, and the subsequently reassigned officers and clients as “inherited.” I call clients who miss payments
“delinquent,” and the act of missing payments, “delinquency” or “delinquent behavior.”
14 Escalation of commitment occurs when loan officers continue working with delinquent clients instead of sending those clients to the collections department.
Loan officers’ commission structure does not encourage them to distinguish between original and inherited clients, nor does it prompt them to favor the clients they vet and approve for loans. Officers’ monthly commission is based on the number of new clients they recruit and the percentage of non-delinquent clients in their portfolio. But despite this commission structure, officers actively distinguish between the original and inherited clients in their portfolios. Most importantly, officers experience a greater sense of personal responsibility for the original clients they vetted and approved. In the words of one officer:
You feel responsible. Since I gave out the loan, the client has to pay. It’s a responsibility that falls on me. It’s not the same when a client falls behind on his payments and I’m not the one who gave him the loan. It’s my responsibility because I did the analysis. I made the visit. [I] sign the document saying everything is correct. (female, age 40; recorded interview)2
Another loan officer shared this opinion:
Recently, two months ago, I spoke with a [delinquent] client. I visited her. She’s not a really bad client, but I feel that it’s my responsibility because I did [the evaluation]. I brought her to the bank. I want all the clients who I bring to the bank to be good clients. (female, age 30; recorded interview)
As these quotations demonstrate, officers feel a heightened sense of responsibility for the clients they vetted and approved. Nonetheless, they are quick to point out that they do care about their inherited clients—just not with the same intensity:
Inherited loans are our responsibility, too […]. But… you work a little harder when it’s your [original] client because it was your job. You gave [the loan] to them. You did the analysis (male, age 19; recorded interview).
Beyond vetting new clients, officers must also deal with clients who fall behind on their payments. If officers believe that a delinquent client cannot be convinced to resume payments,
2 Interviews were conducted in Spanish. Quotations were translated by the author.
15 the loan is deemed unrecoverable and sent to MicroBank’s collections department. Loan officers, bank administrators, and even collections officers emphasize that sending clients to the collections department has negative ramifications for the bank and should not be taken lightly. If collections officers cannot convince clients to repay their loans, they repossess the goods pledged as collateral. Administrators assert that MicroBank rarely recoups its investments by collecting collateralized goods. In the words of one loan officer, “[MicroBank] is not in the business of selling people’s used refrigerators” (field notes, December 2009). For an organization that earns profit through interest, sending a client to collections may mean a loss on expected earnings.
Beyond the monetary loss, MicroBank suffers reputational damages when clients are sent to collections. Clients sent to the collections department have no further interaction with their loan officers. Instead, they work exclusively with collections officers, who take a far more aggressive interpersonal approach. The act of removing collateralized goods from clients’ homes is often an emotional and public event (Hochschild, 1983, Battilana & Dorado, 2010).
For an institution that depends on referrals for growth, repossessing clients’ goods can inflict major reputational damage.
Nevertheless, if an officer feels certain that a client will not resume payment, sending that client to collections is a reasonable course of action. The bank should attempt to recoup what it can of the investment, as well as deter other clients from missing payments. MicroBank has no hard and fast rules about when clients should be sent to collections; loan officers, in consultation with their branch managers, have discretion over this outcome. When deciding whether to send a client to collections, officers are likely to be influenced by the clients’ status as original or inherited. Indeed, officers’ relationships with their original clients present all the elements commonly associated with escalation of commitment: they feel responsible for the original
16 allocation of resources; they have repeated opportunities to act on objectively negative feedback; and the likelihood of goal attainment is uncertain (Brockner, 1992). Loan officers may resist sending original clients to collections despite evidence suggesting these clients were weak initial investments. Officers working with inherited clients, on the other hand, should be more willing to sending delinquent clients to collections because they did not vet and approve them. In accordance with existing literature on escalation of commitment, I anticipate the following:
Hypothesis 1 (H1): Loan officers will be less likely to send original clients than inherited clients to the collections department.
Some may wonder whether remaining committed to a problematic loan constitutes escalation of commitment. After all, officers do not increase clients’ capital when they miss payments; they merely resist sending them to collections. Additionally, since original officers conduct client evaluations, they have information about their clients that goes beyond the
“objectively negative feedback” of missed payments. That is, these officers may have information that makes missed payments appear less problematic. I will briefly clarify both of these important issues.
In this paper, escalation of commitment does not involve increasing an initial financial investment, as observed in Staw’s seminal (1976) experiment. However, many contemporary field studies depart from Staw’s initial definition, instead viewing escalation as the maintenance or persistence of commitment to a struggling investment. For example, previous authors have defined escalation as holding rather than writing off problematic loans (Staw, Barsade, & Koput,
1997), maintaining ties with poor-performing investments (Guler, 2007), and keeping rather than
17 trading NBA players (Staw & Hoang, 1995). In each of these examples, investors do not heighten their commitment; rather, they uphold a commitment previously made. Thus, contemporary approaches to escalation of commitment consider maintenance of commitment to a struggling investment to be a legitimate form of escalation.
Second, original officers likely have information about their clients that extends beyond objective feedback about missed payments. This information may influence their perceptions of delinquent clients, as well as the actions they take. It is important to note, however, that decision makers in the field studies described above almost certainly had information that transcended objective signs of poor performance. While such soft information has been present in previous studies, it has largely been ignored as a factor that influences escalation of commitment. One of the central contributions of the present paper is to demonstrate how information exchanged between investors and investees can fundamentally change the outcomes associated with escalation. In the following section, I examine soft information as a tool that original officers can use to evaluate and manage delinquency.
Dealing with Delinquency: Soft Information and Social Leverage
The first hypothesis postulates that loan officers are more likely to escalate commitment to their original clients. In this section, I consider the social conditions under which commitment to a struggling client constitutes an effective strategy for achieving MicroBank’s goals. First, I examine how loan officers’ soft information about original clients aids in the difficult process of evaluating delinquency. Then, I show how they leverage their relationships with original clients to change delinquent behavior.
18 When clients fall behind on their payments, loan officers must decide whether this behavior reflects a temporary or permanent problem. Officers recognize their clients have fragile household finances. As such, they see certain types of delinquency as unavoidable and, more importantly, legitimate. A death in the family, severe price fluctuations, or illness might understandably affect a client’s repayment capacity; these problems are perceived as reasonable excuses for missing payments.
Yet officers point out that a different type of delinquency exists, as well. They believe that some clients miss payments because they simply do not wish to make payments. Officers refer to these delinquent clients as “fresh”. In the words of one loan officer,
We have clients who have had problems because of personal or family issues or because they lost their business or the business slowed down. But we also have clients who we call “fresh.” For them it’s just, “I don’t want to” or “I can’t.” (male, age 42; recorded interview)
Another officer characterized fresh clients as individuals “who aren’t in the habit of being responsible” (female, age 30; recorded interview). Unlike its English usage, “fresh” in the local
Spanish vernacular denotes a durable personal characteristic.3 A fresh individual is irresponsible, disrespectful, and takes advantage of others. Indeed, loan officers’ tendency to categorize delinquent clients as either “fresh” or unfortunate victims of circumstance parallels the tendency in American politics to distinguish between the “deserving” and “undeserving” poor (Katz, 1989, Gans, 1995).
Whether loan officers evaluate delinquency as symptomatic of a temporary setback or a global characteristic has important implications for how they handle delinquent clients. When
3 Loan officers identified “fresh” behavior as embedded within the broader cultural concept of juega vivo. Juega vivo is defined—loosely—as a self-serving mentality that manifests in rude, aggressive, or arrogant public behaviors. Individuals can attribute nearly any perceived act of chicanery—from cutting in line to government embezzlement—as rooted in juega vivo culture (references omitted to avoid identifying country location).
19 officers assign a “vocabulary of motive” (Mills, 1940) to delinquent clients’ behaviors, this assignation influences their future conduct. For instance, because officers believe that fresh clients are generally irresponsible, they see little value in attempting to correct their behavior.
As one officer explained,
When I understand that it’s a client who doesn’t want to pay just because he doesn’t want to pay, and not because he can’t pay, that’s when I send that client to the collections department and I suggest that they take stronger actions. (male, 42 years old; recorded interview)
Categorizing delinquency as either legitimate or fresh guides loan officers’ strategies for working with clients. But differentiating between types of delinquency is no easy task. Loan officers must be familiar with clients’ home lives and local economies to understand why they might fall behind on their payments. It is here that original loan officers have an advantage over inherited officers. The soft information original officers gain through the vetting process helps them evaluate delinquency as legitimate or illegitimate. Officers collect a breadth of soft information when they evaluate loan applicants. They might learn, for instance, that one client has a sick child, that another is estranged from his spouse, or that a third has fallen out of favor with local politicians. Viewing delinquency in light of these factors can help loan officers correctly evaluate missed payments as stemming from (il)legitimate problems.
When officers have little contextual information about clients, they are more likely to categorize delinquency as illegitimate. When assessing others’ behavior, individuals tend to overemphasize the role of dispositional forces and underemphasize the role of situational forces
(L. Ross & Nisbett, 1991, Gilbert & Malone, 1995). That is, actors incorrectly assume others’ behavior stems from their personal preferences rather than from environmental constraints. This tendency occurs more frequently when actors cannot see the environmental forces that influence others’ behavior (Skinner, 1972, L. D. Ross, Amabile, & Steinmetz, 1977, Swann, 1984). Thus,
20 inherited officers who lack soft information about inherited clients will be more likely to incorrectly attribute missed payments to a permanent, fresh character than to temporary environmental pressures.
Original officers also have a wider range of tools at their disposal to influence delinquent clients. MicroBank encourages loan officers to take an active role in helping clients resolve the difficulties that prevent them from making payments. During training workshops for new loan officers, bank administrators advise that the first three steps to managing delinquency include (1) gathering information about the problem, (2) creating a plan of action, and (3) looking for creative solutions (field notes, March 2011).
Officers who possess soft information about delinquent clients are better able to engage in joint problem solving. Armed with soft information about clients, their families, and their communities, original officers can offer relevant action plans to resolve the problems that hinder repayment. Microfinance clients often fall behind on their payments because of personal or family emergencies. Officers can offer more relevant solutions when they have met clients’ families, visited their homes, and know their communities. During a loan officer training workshop, an officer-turned-administrator described how officers who know their clients well can help them find solutions to their problems.
Dora told a story [to the officers in the workshop] about a woman who had a loan with her husband. The husband left, taking the car and all the money. The wife was left without enough money to buy food for the children. […] In the end, Dora got the client to pay the loan by asking her to get help from her parents. She emphasized that loan officers have to help clients come up with creative solutions to their problems by asking questions like, “Could your brother help? Is there another source of income you could make use of?” Dora suggested that it might even be a good idea to call a client’s parents or his references to let them know that the client has fallen behind on his payments—this might encourage that person to pay back the loan. (field notes, March 2011)
21 Engaging in such targeted problem solving requires familiarity with clients’ lives, their families, and their communities. Inherited officers are unlikely to have such soft information. Compared to original officers, they may struggle to offer relevant solutions to the issues that prevent loan repayment.
When officers show they understand clients’ problems and make an effort to assist them in solving those problems, clients may reciprocate with improved payment. In a variety of studies, microfinance borrowers have shown improved repayment outcomes when working with others who took a collaborative approach. In Mexico, clients performed better when their loan officers took a flexible, relational style than when they adopted a strict, “letter of the law” approach (Canales, 2011). Microfinance borrowers in the United States repaid their loans at higher rates in lending circles that promoted reciprocity and collaboration (Anthony & Horne,
2003, Anthony, 2005). As loan officers demonstrate their willingness to help clients work through difficulties—rather than sending them straight to collections—clients may reciprocate by improving their repayment behavior.
The soft information that original officers gain when vetting clients helps them evaluate and manage delinquency. Unfortunately, inherited officers generally cannot “catch up” on this information when they receive clients from other officers. Much of the rich information that original officers gather during the vetting process does not end up in the official applicant report.
Even if it did, officers rarely reference these reports when dealing with delinquent clients.
Instead, officers rely on a spreadsheet that lists the name, address, phone number, and loan amount of each delinquent client in their portfolio. As the following field note excerpt demonstrates, officers often have little information about their inherited clients.
Enrique was looking over a list of clients, probably two or three pages stapled together. “Who are those clients?” I asked. Enrique responded that they were all
22 his late clients. “How many do you have?” I asked. “I don’t have the slightest idea,” he said, smiling ironically. Enrique explained that he inherited this list of late clients from other loan officers who have quit or been fired. He was quick to point out that none of the 15 or so clients who he recruited to the bank were late in their payments. I listened in on one of his phone calls to a client who was three or four days late in making her payment. “I was just calling to remind you about your loan payment…. So you’re going to make the payment on the 30th?” He told her he would put that date in the system. After the phone call, I asked what the client had said. “She’s not working at her business because she’s pregnant.” Enrique seemed entirely unperturbed. His facial expression was blank and, after hanging up the phone, he quietly returned to his list of late payers. (field notes, March 2011)
This interaction highlights the kind of formal relationship inherited officers have with their clients. Not only was Enrique unaware that his client was pregnant and not working, he was also unable to engage in joint problem solving. An officer who had more soft knowledge about the client would be better able to offer relevant suggestions for making loan payments while out of work. For instance, imagine Enrique knew this client sold empanadas on the street, and that she was active in her local church. In that case, he might suggest selling empanadas after church on
Sunday as a way of generating income during her pregnancy. As it stands, Enrique simply recorded the date on which she promised to make a payment. Given the pressures that officers like Enrique face to manage delinquency and recruit new clients, they do not have the time to develop personal relationships with inherited clients. Instead, like many organizational actors, loan officers satisfice (Simon, 1956), using the information immediately available to complete the tasks at hand.
Original officers have another—slightly more manipulative—tool at their disposal. They can leverage their relationships with clients to encourage them to repay. Clients often perceive original officers as the source of their loan, rather than MicroBank. As one officer described,
The client sees the loan officer as the one who gave him the loan. A lot of my clients will say to me, “Thanks so much for what you’ve done. Look at how the
23 money that I asked for has helped my business. I’ve been able to grow [my business] thanks to the opportunity that you gave me.” (male, age 31; recorded interview)
Original officers can leverage this position when clients fall behind on their payments.
Specifically, they can threaten to withdrawal the trust they placed in clients when they approved the loan. Officers explain the tactic in this way:
The loan officer calls a [delinquent] client and says, “No, remember, I gave you that loan. I trusted you.” And it’s like the client has a responsibility not to the bank, but to us [the loan officers] because we’re the ones who work with them. We’re the ones who gave them the opportunity to grow their business. (male, age 19; recorded interview)
When they’re your own clients [original clients], you tell them, “I explained this to you really well. You knew what this was like. I trust you. Don’t make me lose that trust I have in you.” (female, age 30; recorded interview)
Original officers leverage their relationships with clients to instill a sense of social indebtedness.
That is, officers highlight the “unequal resources brought by two parties to [a] transaction”
(Coleman, 1990: 130) to encourage client deference. Such a strategy is not available to inherited officers. Because they did not vet and approve the client, threats to withdrawal trust would ring false.
Taken together, the qualitative data strongly suggest that original officers are better equipped to evaluate and manage delinquency. The soft information that officers gain through the vetting process helps them understand why clients have missed payments, and respond appropriately. Soft information also allows them to offer clients relevant solutions to the problems that hinder repayment. Original officers’ relationships with clients, along with their status as the face of the lending institution, gives them leverage in pressuring clients to resume loan payments.
24 Given these factors, I anticipate that original officers will have greater success in correcting delinquency. Whereas H1 predicts that officers will escalate commitment to original clients, the following set of hypotheses predicts that original officers who escalate commitment to delinquent clients will secure more favorable outcomes than inherited officers who do the same.
One marker of success in correcting delinquency is client behavior after delinquency begins. Loan officers’ primary objective in working with a delinquent client is to ensure the client makes her remaining payments on time. Indeed, one outcome that would justify escalating commitment to a poor-performing client is subsequent improvement in that client’s repayment behavior. Original officers have a unique set of tools they can use to get delinquent clients back on track. Inherited officers, on the other hand, lack the relational tools to effectively evaluate and manage delinquency. Given original officers’ relational advantages over inherited officers, I anticipate the following:
Hypothesis 2 (H2): Delinquent clients working with original officers will miss fewer subsequent payments than delinquent clients working with inherited officers.
Another measure of officer success in correcting delinquency is whether or not clients repay the entirety of the loan within the term. As a lending institution, MicroBank strives to recuperate its initial investment and collect interest. Full loan repayment in the time allotted allows MicroBank to profit from its investment and re-lend capital to new clients in a timely manner. To achieve these goals, officers encourage clients to repay their loans by the deadline originally specified. To do so means delinquent clients must resume monthly payments and
25 apply additional funds to cover missed payments. Since original officers have relational tools to encourage timely repayment, I hypothesize the following:
Hypothesis 3 (H3): Delinquent clients working with original officers will be more likely to repay their loans on time than delinquent clients working with inherited officers.
MicroBank also aims to retain successful borrowers. Ideally, strong clients take out new loans upon completing existing loans. Establishing a cyclical lending relationship ensures
MicroBank a steady stream of profit from interest. And because experienced clients generally need less attention, officers who work with experienced borrowers have more time to recruit new clients. Loan officers thus encourage successful clients to take out subsequent loans.
I anticipate that delinquent clients who work with original officers will be more likely to take out subsequent loans, for two reasons. First, officers extend subsequent loans to clients whom they see as strong borrowers. Since original officers are more likely to correct delinquency, they are also more likely to transform struggling clients into clients with stronger repayment histories. Second, officers must engage in salesmanship when offering subsequent loans. They may need to show clients the advantages of a new influx of capital—given that this capital comes with the responsibility of monthly payments. Original officers can use the soft information they possess about clients’ businesses and their market environment to sell subsequent loans. Given these factors, I anticipate the following:
Hypothesis 4 (H4): Delinquent clients working with original officers will be more likely to take out subsequent loans than delinquent clients working with inherited officers.
26
The qualitative evidence suggests that original officers will be more likely to escalate commitment to their clients. Such a tendency would be unsurprising, because original officers’ relationships with their clients contain the classic triggers for escalation. However, the data also suggest original officers who escalate have the tools to correct delinquency and secure positive outcomes for MicroBank. Original officers’ soft information helps them evaluate delinquency and offer relevant solutions to their clients. Additionally, they can leverage their unique status as loan purveyors to pressure clients to repay. Compared to inherited officers, original officers may make a wise decision in remaining committed to clients who present signs of delinquency. In the following section, I use MicroBank’s company database to test whether loan officers escalate commitment to original clients, and whether such escalation helps them realize their organizational goals.
QUANTITATIVE METHODS
Data
MicroBank’s database contains monthly observations for each loan, as well as socio- demographic information for each client, financial information for each loan, and a code listing which loan officer managed the loan each month. The presence of a unique client identifier allows me to match clients to their loans, demographics, and officers. I limit the data to observations beginning in April 2009, as these are the earliest monthly observations in the database.4 To avoid censoring the data, I select only loans that a) originate on or after April 6,
4 As a result of an internal system migration, MicroBank no longer has month-by-month observations prior to April 2009. The database does, however, contain single observations for each loan prior to 2009. These observations contain client demographic information, loan information, and loan status. I utilize these data in determining clients’ previous lending history with MicroBank.
27 2009 and b) have been repaid or sent to collections by September 2012. These selection criteria ensure that only complete loans are analyzed.5 The dataset contains 121,658 complete loan- month observations. These observations correspond to 8,703 loans owned by 7,293 clients.
Table 1 reports summary statistics for the data analyzed.
[Insert Table 1 about here.]
In analyzing the effects of officer-client relationships, it is important to account for the non-random process by which loan officers exit the bank. Officers leave MicroBank either voluntarily (because they find better work opportunities) or involuntarily (because they are fired). Although officers who leave voluntarily may be highly skilled, officers who leave involuntarily may recruit weaker clients, on average. Additionally, their interactions with clients—or lack thereof—may encourage delinquency.
The departure of one officer prompts a domino effect of loan redistribution among the remaining loan officers. Branch managers have sole discretion over the redistribution process.
As the branch manager reshuffles loans among officers, he or she attempts to ensure that officers have even portfolios given (a) their level of experience and (b) their clients’ geographic locations
(field notes, January 2013). In the words of one senior loan officer,
There are no preferences in terms of the loan officer. [When the manager] divides up the old portfolio, [he or she] doesn’t say, “Oh, these are good clients so I’m going give them to this loan officer.” No. It’s divided equally. They try to ensure that the portfolios are balanced. (male, age 31; recorded interview)
Because the database does not identify officer exits as voluntary or involuntary, I include only those inherited loans where turnover occurs among officers who do not leave MicroBank. For example, imagine Officer A leaves MicroBank. The branch manager redistributes a portion of
A’s portfolio to Officer B, who has clients in a nearby community. Finding that B’s portfolio is
5 Models 3a-c are the exception to this rule. These analyses include loans repaid by September 2012 as well as those that should have been repaid by that date.
28 now too large, the manager transfers some of B’s clients to Officer C in order to ensure balanced portfolio sizes. In this example, I would examine only the inherited clients transferred from B to
C and exclude those clients transferred from A to B. This selection ensures inherited clients are not “tainted” by their experience with an exiting officer.
H2-H4 relate to the outcomes of delinquent clients. Here, the definition of delinquency is important, as officers who continue working with such clients are viewed as escalating commitment. Since MicroBank does not have an official policy that defines delinquency, I relied on quantitative and qualitative data to operationalize this concept. First, officers explained that they view loans as highly precarious after clients miss two consecutive payments. Officers shared similar views when asked to describe what kinds of clients they considered to be seriously problematic:
Once [the clients] are really late and they’ve missed two payments it becomes more difficult for them to pay [the full balance] by the end of the month. (male, age 19; recorded interview)
Speaking of lateness, once I visited a client—this was an inherited client—I visited her at home and she was like two or three payments behind. She was facing a lot of problems. (female, age 30; recorded interview)
Actually yesterday I was visiting a late client […] because now he has missed two payments. I always call the client and he says, ‘I’m almost there, I’m going to pay.’ But it isn’t true. (male, age 23; recorded interview)
Generally, a client really shouldn’t go more than two months without making a payment. (male, age 31; recorded interview)
Building on these comments, the bank’s database offers further insights into how officers respond to varying levels of missed payments. Of those loans sent to collections, one-half are written off after two or three consecutive missed payments. Two consecutive missed payments is the modal value at which loans are sent to collections, and three consecutive missed payments is the median. Thus, the data suggest that, after two missed payments, officers register clients as
29 seriously late, encourage them to resume payment, and even send some clients to collections.
Missing three payments is an even more egregious breach of contract, with many officers then sending those clients to the collections office.
Given this information, I define delinquency—and, thus, the point at which officers escalate commitment to struggling clients—as occurring after three consecutive missed payments. When a client misses three payments, he sends a clear signal that he is struggling to repay the loan. Indeed, loan officers notice these problematic trends. Consecutive events— particularly negative ones—have high cognitive salience (Wells, Hobfoll, & Lavin, 1999). In the terminology of escalation of commitment, three consecutive missed payments provide
“objectively negative feedback” about the client’s value as an investment for MicroBank. As further evidence, I include alternative measures of delinquency (four and five missed payments) in the Alternative Explanations and Sensitivity Analyses section.
Independent Variable
Original vs. Inherited Officer. The independent variable reflects whether the loan officer in charge of an account was the original vetting officer or an officer who inherited the loan. Each officer has a unique identifying code in the MicroBank database. The officer in charge of a loan in its first month is the original officer. If, at any point, a loan transitions from one officer to another, that loan is coded as “inherited.” I lag inherited status by one month to ensure both officers and clients have time to recognize the new pairing.
Dependent Variables
30 Collections Outcome (H1). The first dependent variable reflects whether a client is sent to the collections department by her loan officer. Loan officers avoid sending clients to collections because doing so produces financial and reputational losses for MicroBank. Indeed, the extremity of the collections outcome is reflected in its rarity: only 323 of 7,293 clients are sent to collections. In the Analysis section, I discuss how I account for the infrequency of this event.
Missed Payments Following Delinquency (H2). The second dependent variable captures the proportion of missed payments following the onset of delinquency. This variable reflects officers’ success in getting delinquent clients “back on track.” I measure repayment behavior following delinquency as the proportion of monthly payments a client misses between the onset of delinquency (three consecutive missed payments) and loan completion. Table 2 provides an example of how this variable is created.
[Insert Table 2 about here.]
In this example, delinquency is established in November after the third consecutive missed payment. The observation period encompasses the eight months between December and July.
During this period, the client missed three payments. Thus, the proportion of missed payments following delinquency is .375.
On-time Loan Repayment (H3). The third dependent variable measures whether a delinquent client repaid his loan within the term allotted.6
6 The MicroBank database lists the date a client opened a loan, the number of months over which the loan will amortize, and the date on which the loan was repaid. To determine if a loan was repaid on time, I first multiplied the amortization period by 30.5. This value represents the number of days a client had to repay the loan. I used this value to calculate the due date. Given that this calculation only approximates the exact due date and that MicroBank often gives clients a grace period at the end of their loans, I added 15 days to this date. I compare the date of the final payment with the date by which they should have completed the loan. Clients whose final payment exceeds the due date are coded as late.
31 Subsequent Loans (H4). The fourth dependent variable measures whether a delinquent client took out a subsequent loan following the focal loan.
Control Variables
Officer Characteristics. I controlled for four loan officer characteristics. First, I controlled for loan officers’ tenure with MicroBank. Loan officers’ experience may affect their skill in evaluating clients and encouraging them to repay. I calculated officer tenure by subtracting the first date of loan officers’ appearance in the dataset from the date of the focal monthly observation. Second, I controlled for the amount of time a loan officer had the focal loan in her portfolio. Officers may display heightened attention to clients whom they recently vetted or inherited, and this attention may wane over time. I calculated this value by subtracting the date the officer assumed ownership of the loan from the date of the focal monthly observation. I then took the natural log of this number to correct for right-skew. Third, I controlled for officers’ monthly commission. MicroBank calculates commission based on two factors: number of new clients recruited to the bank and the percentage of delinquent clients in an officer’s portfolio. Loan officers who receive higher commissions may be more skilled in evaluating clients and encouraging loan repayment. Because this variable is also right-skewed, I include its natural log in the analysis. Finally, I controlled for the MicroBank branch that issued the loan. Certain branch managers may encourage officers to take a more or less lenient approach when sending delinquent clients to collections. I account for this fact by including branch fixed effects.
32 Loan characteristics. To test H1, I controlled for five features of the loan and loan repayment status.7 First, I controlled for whether the loan was an auto or working capital loan.
Second, I controlled for the loan month, or the number of months the client had the loan at the time of the focal observation. Third, I controlled for the logged total loan amount, including both principal and interest. Fourth, I controlled for the proportion of balance remaining on the loan each month. For example, the client who has paid $500 of a $1000 loan has a remaining balance of .50. Finally, I controlled for the proportion of missed payments leading up to the focal observation.
I used a slightly different set of loan-specific controls to test H2-H4. Since these models revolve around a particular moment of delinquency (three consecutive missed payments), I control for the number of months elapsed before and after delinquency onset. Some clients become delinquent quickly; for others, delinquency occurs after many months. In Model 2, which examines post-delinquency repayment, I controlled for the proportion of missed payments during the pre-delinquency period. Some clients may have a spotless repayment record prior to falling into delinquency; others may have stumbled along the way. In Models 3 and 4, I employ a modified measure of missed payments: the proportion of missed payments over the life of the loan. Because these models predict behavior at the end of the loan, controlling for clients’ overall repayment history is more appropriate than controlling only for the pre-delinquency repayment history.
Client characteristics. I controlled for three client-specific characteristics. First, I controlled for logged household income and logged household debt. In vetting potential clients, loan officers meticulously account for total income as well as all monies owed. Then, I
7 I ran additional models that controlled for interest rate. I do not include this variable in the reported models because it has non-statistically significant effects and does not affect the significance or directionality of other variables.
33 controlled for whether clients had a previous MicroBank loan prior to the focal loan. Clients who have previous experience with MicroBank may be better able to recover from delinquency.
Moreover, officers may feel pressure to retain clients who have a history with the bank.
Analysis
H1: Logistic Regression and Rare Event Logistic Regression. To test H1, I used logistic regression to compare the log odds that loan officers will send original and inherited clients to collections. I clustered the standard errors by 7,293 clients. As mentioned above, loan officers send clients to collections infrequently. I used a rare event logistic regression to supplement the standard logistic regression. Logit estimates become biased when the unconditional probability of a binary event is low, even when the overall observation count is high (King & Zeng, 2001). In this case, the non-collections observations become statistically less informative for estimating parameters as the proportion of collections observations decreases. Rare event logistic regression corrects coefficient estimates and standard errors to account for the systematic bias attributable to the low unconditional mean of the dependent variable (King & Zeng, 2001). The limitation of this model, however, is that it does not cluster standard errors. I thus present both the standard logistic regression (Model 1d) and the rare event logistic regression models (Model 1e). Together, they offer a complementary estimation of loan officers’ tendency to escalate commitment to their original clients.
Coarsened Exact Matching. Prior to testing H2-H4, I examined how original officers manage “collections-worthy” clients. The goal of this analysis was to test whether original officers secured favorable outcomes when working with clients who are statistically similar to inherited clients who were sent to collections. This analysis responds to the question: What
34 outcomes do officers achieve when working with original clients who resemble those sent to collections by inherited officers?
I first identified the loan-month observations in which inherited clients were sent to collections. Then, I used coarsened exact matching to match original and inherited clients on five characteristics: loan type (auto or working capital), proportion of loan balance remaining, proportion of missed payments, loan size, and loan month. One can think of this analysis as a comparison between matched clients whose only difference is working with original or inherited loan officers.
H2-H4: Heckman Two-Step Correction, GLM, and Logistic Regression. The models testing H2-H4 examine a sub-population of the client universe: delinquent clients who miss three consecutive payments. Focusing on a non-random subset of the population has the potential to introduce systematic estimation biases (Heckman, 1979). To account for potential selection bias,
I ran a two-step Heckman model on each analysis. I first used a probit model to predict the likelihood that observations fall into the subset of loans that contain three consecutive missed payments. I included the Inverse Mills Ratio—generated in the first stage of the Heckman probit model—as an additional explanatory variable in the second stage analyses.
Models 2 and 4 examine the 1,677 loans that are complete, delinquent, and do not involve turnover from an exiting officer (non-exit). Model 3 examines the 1,791 loans that are delinquent, non-exit, and have been paid off or should have been paid off by the final observation. I use a generalized linear model (H2) and logistic regression (H3-H4) in the second stages. The dependent variable in H2 is the proportion of monthly payments that a client misses between delinquency onset and loan completion. Models estimating proportional dependent variables must account for the bounded distribution of the data (Baum, 2008). Following
35 previous work (e.g. Papke & Wooldridge, 1996), I use a generalized linear model in the second stage to predict the proportion of missed payments. I use the logit transformation of the dependent variable and a binomial distribution to ensure predicted values fall between 0 and 1
(Baum, 2008). Since the dependent variables in Hypotheses 3 and 4 are dichotomous, I use logistic regression to predict the odds of on-time repayment and taking out a subsequent loan.
RESULTS
Table 3 presents the correlations among variables included in the first set of models.
Unsurprisingly, loan month is highly correlated with two measures that change steadily over time: the number of months an officer had the focal loan in her portfolio (logged) and the proportion of balance remaining. Auto loans are also highly correlated with loan size (logged) because these loans tend to be larger than working capital loans. To ensure that multicollinearity did not bias the results, I entered each variable into Model 1 separately. At no point did estimation values change direction.8 As a further test, I ran Model 1 without each time-varying measure (loan month, pair months, remaining balance). The direction of the dependent variable remained unchanged across the tests of multicollinearity. Furthermore, a test of variance inflation showed that the model is unlikely to be affected by multicollinearity (Neter,
Wasserman, & Kutner, 1989). Correlation and variance inflation patterns and are similar for the data and variables used in Models 2, 3, and 4 (not reported).
[Insert Table 3 about here.]
Escalation of Commitment to Original Clients
8 The one exception is the estimation of the effect of loan officer commission (logged) in Model 2. Here, the estimation—which is very close to zero—changes from negative to positive between Model 2a and 2b.
36 H1 proposes that loan officers are more likely to escalate commitment to their original clients than their inherited clients. Table 4 presents the odds ratios associated with sending a client to the collections department. Models 1a-1d include, progressively, officer-specific controls, loan-specific controls, client-specific controls, and branch fixed effects. Model 1e is a rare event logistic regression. The complete models (Models 1d and 1e) offer similar results.
Net a variety of controls, officers are significantly less likely to send original clients to collections. Drawing from Model 1e, the odds that an officer will send an original client to collections are only 49% the odds that an officer will send an inherited client to collections. That is, clients working with their original officers have about half the odds of being sent to collections. This finding confirms H1: loan officers are more likely to remain committed to the original clients than to their inherited clients.
[Insert Table 4 about here.]
Unsurprisingly, officers are more likely to send clients to collections when they have higher remaining balances, more missed payments, and greater household debt. They are also more likely to send clients to collections later, rather than earlier, in their term. One significant effect, however, may appear particularly curious: officers are more likely to send auto-loan clients to collections. Since cars are valuable and easily moved, collections officers can repossess them relatively easily. By comparison, collections officers may need to repossess a variety of smaller collateralized items from clients who have working capital loans. Officers are more inclined to hand over responsibility for clients with auto loans because they know the collections department can more easily repossess those clients’ collateral. I examine auto loan clients more closely in the Alternative Explanations and Sensitivity Analyses section.
37 Client Matching: Original Officers and “Collections-Worthy” Clients
Having established that original officers are more likely to escalate commitment, the next question becomes: Is this a good strategy for achieving organizational goals? In an ideal world, one would compare the overall repayment outcomes of identical clients when they work with original and inherited officers. In this real-world setting, statistical matching offers the closest approximation to such a comparison. Matching allows us to estimate how inherited clients sent to collections might have fared if they had worked with original officers. To make this comparison, I matched the inherited clients who were sent to collections with clients of original loan officers who had not (yet) been sent to collections.
Descriptive statistics suggest original officers achieve far better outcomes when they work with “collections-worthy” clients. Original officers would have been justified in sending these clients to collections, just as inherited officers had. However, after continuing to work with struggling clients, original officers sent only 13% of these clients to collections. Among those who had finished repaying their loans by September 2012, 66% repaid within the term allotted and 16% even took out subsequent loans. These figures suggest escalation of commitment as practiced by original loan officers can be an effective strategy for meeting MicroBank’s goals.
When loan officers continue working with problematic clients for whom they have soft information and leverage, they are able to help the majority secure timely repayment. By comparison, when officers send clients to collection, the bank rarely recoups its initial investment and can incur serious reputational damages. Given the alternative, securing timely repayment is a far better outcome for the bank, as well as its clients.
Client Performance following Delinquency
38 In the following analyses, I compare client outcomes when original and inherited officers escalate commitment to delinquent clients. Recall that delinquent clients are those who miss three consecutive payments. These analyses test whether original loan officers who work with delinquent clients are more successful in curbing missed payments, encouraging timely repayment, and promoting sequential borrowing.
Post-Delinquency Missed Payments. H2 proposes that clients working with original officers will miss a smaller proportion of payments after delinquency onset. Table 5 presents the results of the generalized linear model predicting the proportion of missed payments following delinquency.
[Insert Table 5 about here.]
The results support H2: clients working with their original loan officers miss significantly fewer payments than clients working with inherited officers. These findings suggest original officers experience greater success in getting delinquent clients “back on track” following a spell of missed payments.
On-time Loan Repayment. H3 postulates that clients working with original officers will be more likely to repay loans on-time than clients working with inherited officers. For this analysis, I include clients who finished paying their loans by the final observation period, as well as those who should have finished repaying their loans by that point. Table 6 presents the results of the logistic regression analyses predicting clients’ odds of making on-time payments.
[Insert Table 6 about here.]
The results offer strong support for H3. After controlling for officer-specific, loan- specific, and client-specific variables, the odds that original clients will repay their loans on time and in full are 2.3 times the odds that an inherited client will do the same. These results suggest
39 that delinquent clients who work with their original officers are significantly more likely to repay loans by the contractual deadline than clients who work with inherited officers.
Subsequent Loans. H4 postulates that clients working with original officers will be more likely to take out subsequent loans. Table 7 contains the results of the logistic regression analyses predicting client odds of taking out a subsequent loan, following the focal, delinquent loan.
[Insert Table 7 about here.]
The results support H4. Controlling for officer-specific, loan-specific, and client-specific characteristics, the odds that an original client will take out a subsequent loan are 3.4 times the odds that an inherited client will do the same. The clients of original officers are not only more likely to recover from delinquency; they are also more likely to become sequential borrowers.
Results Overview. Taken together, these analyses tell a compelling story. Loan officers are significantly more likely to escalate commitment to their original clients than their inherited clients. However, such behavior does not produce the problematic outcomes that previous research would lead us to expect. Delinquent clients who work with their original officers miss fewer post-delinquency payments, are more likely to repay loans on time, and are more likely to take out subsequent loans. These results suggest that original officers who escalate commitment to struggling clients secure favorable outcomes for MicroBank.
Alternative Explanations and Sensitivity Analyses
Redefining Delinquency. I first tested the robustness of Models 2c, 3c, and 4c using alternative definitions of delinquency. In the central models, I operationalize delinquency as occurring after three consecutive missed payments. However, it is also useful to examine
40 alternative measures, since the bank has no official policy defining delinquency. I re-ran the models with delinquency defined as four and five consecutive missed payments. Table 8 lists the significance level of the independent variable (original versus inherited loan officer) under each definition of delinquency.
[Insert Table 8 about here.]
Overall, delinquent clients still tend to have greater success in getting “back on track” when they work with their original officers. With delinquency defined as four or five missed payments, original clients miss significantly fewer payments and have higher odds of taking out subsequent loans. There is no difference, however, between working with an original or inherited officer when it comes to on-time repayment. This finding suggests that the benefits of socially embedded escalation may diminish over time. At a certain point, an investment may be so weak that no amount of relational effort can bring about recovery. While the findings here still suggest generally positive outcomes for officers who remain committed to clients after four and five missed payments, future work should explore the point at which embedded escalation ceases to promote favorable outcomes.
Auto Loan Escalation. Next, I re-ran Model 1 using a subset of the client population: clients with auto loans. The value of clients’ collateral varies depending on whether the client has an auto or working capital loan. Clients with working capital loans generally pledge appliances as collateral. As discussed earlier, these goods do not fetch high value at auction.
Loan officers may resist sending working capital clients to collections because they anticipate low returns on collateral. On the other hand, clients with auto loans pledge cars as collateral. In this case, loan officers can anticipate that sending problematic clients to collections will result in higher returns on collateral. If original officers resist sending auto loan clients to collections, this
41 finding would offer even stronger evidence for the presence of escalation of commitment. That is, original officers would not resist sending clients to collections because of the low value of their collateral, but because of their commitment to the client.
To test this possibility, I re-ran Model 1 on the 524 auto-loan clients. The results (not presented) support the findings gleaned from the larger client population. Controlling for the same set of variables, along with branch fixed effects, the odds that an original, auto-loan client will be sent to collections are only 30% the odds that an inherited, auto-loan client will be sent to collections (p<.05). Thus, original officers are significantly more likely to escalate commitment to their original clients, even when those clients have valuable collateral.
DISCUSSION
This study demonstrates how escalation of commitment—commonly considered a detrimental judgment bias—functions as an effective strategy for achieving organizational goals when viewed in its social context. Utilizing a uniquely comprehensive set of quantitative and qualitative data from a Central American microfinance bank, I examine the conditions under which escalation of commitment helps loan officers realize organizational goals. I first demonstrated that loan officers feel a heightened sense of responsibility to the “original” clients they vet and approve for loans. By comparison, they feel less responsible for the clients they inherit from other officers. As previous research would lead us to expect, officers are significantly more likely to escalate commitment to their original clients by resisting passing them off to collections department.
Although previous research suggests officers who escalate will be less likely to reach their goals, the qualitative data from MicroBank give reason to anticipate that escalation does not
42 lead to uniformly negative outcomes. Data from interviews and field observations show that original officers are uniquely well equipped to evaluate and manage struggling loans. Through the client vetting process, original officers gain soft information about clients. This knowledge helps them evaluate missed payments and offer clients relevant solutions to the problems that hinder repayment. Original officers also encourage repayment by leveraging their relationships with clients. By threatening to remove interpersonal trust, these officers can pressure clients to repay their loans.
Further quantitative analyses revealed that committed loan officers with soft information and leverage outperform their less committed peers when working with delinquent clients. I first examined the outcomes of original clients who closely resemble inherited clients sent to collections. When original officers escalated commitment to these struggling clients, few were sent to collections and the majority repaid their loans on time. I then showed that original officers experience greater success in correcting client delinquency, compared to inherited officers. Original clients miss fewer payments following a spell of delinquency, are more likely to repay their loans on time, and are more likely to take out a subsequent loan. These findings suggest original officers are better equipped to help clients overcome delinquency. Rather than passing off responsibility for poor-performing clients to the collections office, original officers escalate commitment to delinquent clients. In doing so, they are more likely to correct delinquency, secure timely repayment, and even transform under-performers into clients worthy of subsequent loans. These outcomes are far superior to the alternative course of action: de- escalating commitment and sending delinquent clients to collections.
Extending Theories of Relational Lending
43 Scholars of relational lending argue that financial institutions are better able to overcome information asymmetries when their agents develop personal ties with clients. By embedding transactions in social relationships, investors gain access to soft information about their potential clients (Uzzi, 1999, Uzzi & Lancaster, 2003). Such information helps them overcome the relative dearth of information they possess about first-time clients (Petersen & Rajan, 1994, A.
Berger & Udell, 1995, A. Berger, Klapper, & Udell, 2001). Soft information transmitted via embedded relationships allows investors in financial institutions to understand their clients in ways that supersede the facts they would otherwise gain from official documents, such as lending statements (Boot, 2000). Ultimately, investors who have more complete information about applicants can allocate and price capital more appropriately.
Relational lending practices undoubtedly help financial institutions overcome information asymmetries. However, as this paper demonstrates, the benefits of social ties between lenders and their clients extend beyond simple information exchange. The present work encourages scholars to consider additional means by which financial institutions benefit from relational lending. In particular, scholars have neglected the effects of social leverage in relational lending.
Beyond gathering information, lenders who develop personal ties to their clients can use those relationships to change client outcomes. Such a tool is particularly important when clients begin to under-perform. When agents learn of slipping client performance, they can leverage their personal relationships to correct that behavior.
In the present case, loan officers leveraged their social ties by threatening to remove the trust they placed in their clients. Yet in different contexts, actors would leverage social ties in different ways. For instance, a lender who develops a personal relationship with a borrower might offer the client suggestions for improving his struggling business over dinner or at a
44 sporting event. A venture capitalist might ask an entrepreneur to add a new board member as a
“personal favor” when she sees that the business needs more guidance. When investors have relational ties with their investees, they possess subtle tools with which to shape client behavior, and by extension, secure anticipated returns on investment.
Organizational Theory for Emerging Market Development
Finally, this paper highlights the important role of organizational theory can play in understanding economic development in emerging markets. Formal organizations such as
MicroBank perform much of the “development work” that unfolds in the Global South (Escobar,
1995, Green & Matthias, 1997, Witt, 2006). As this project shows, individuals’ personal ties to agents within these organizations can exert a powerful effect on the services and opportunities available to them.
For instance, delinquency has different implications for clients working with original versus inherited officers. The client who works with her original officer receives relevant suggestions for solving problems that interfere with loan repayment. She is more likely to overcome these issues and establish a continual line of credit that helps her grow the business. In contrast, the same client, when working with an inherited officer, might be sent to collections.
Not only is her relationship with MicroBank ruined, so is her fledgling credit score. She will be unable to take out future loans that might aid in growing her small business. Here, personal ties linking individuals to organizational actors shape the type of economic change those organizations will effect. Similar processes are likely play out, for instance, among organizations that title land, distribute welfare benefits, and provide health services. Future work
45 should continue to explore how relational ties between individuals and actors in development organizations shape social and economic change in emerging markets.
CONCLUSION
Escalation of commitment is not necessarily a detrimental decision bias. Drawing on data from a commercial microfinance bank in Central America, this paper shows that financial actors who are highly committed to their clients often develop the tools to evaluate and manage under-performance. Informed, leveraged actors can evaluate the implications of negative feedback and, when possible, chart a more successful course. For such individuals, negative feedback should signal that an investment needs attention—not necessarily that the investment should be abandoned. While this paper has focused on escalation of commitment in a financial institution, future work should consider how remaining committed to struggling projects promotes favorable or detrimental outcomes in other contexts. Schools, churches, and friendship networks offer fertile grounds for examining how commitment to tenuous investments helps or hurts actors in their attempts to realize personal or organizational goals.
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54 Table 1. Officer, Client, and Loan Descriptive Statistics
55 Table 2. Example of Delinquency Observation Period Missed/made Observation Status Month payment* Period June 0 July 0 August 0 September 1 October 1 November 1 Delinquency onset December 1 x January 0 x February 0 x March 1 x April 0 x May 1 x June 0 x July 0 x Loan repaid *Missed payment=1; made payment=0.
56 Table 3. Correlation Matrix
57 Table 4. Logistic Regression Predicting Odds of Client Transfer to Collections Department (1a) (1b) (1c) (1d) (1e)† Vetting Officer (1=original) 0.367*** 0.297*** 0.331*** 0.313*** 0.489**
(.053) (.084) (0.093) (.091) (0.257) Officer Tenure 1.017*** 1.000 1.001 1.001 0.998 (.004) (.005) (0.005) (.005) (.005) Time in Portfolio (ln) 3.117*** 1.233 1.235 1.223 1.180 (.387) (.182) (0.182) (.183) (.150)
Officer Characteristics Officer Commission (ln) 1.083 1.102 1.074 1.074 1.071 (.056) (.074) (0.066) (.066) (.054) Auto Loan 14.715*** 16.040*** 18.299*** 16.869*** (5.978) (6.815) (8.080) (.424) Loan Month 1.216*** 1.230*** 1.226*** 1.194*** (.023) (0.024) (.023) (.017) Remaining Balance 1.099*** 1.099*** 1.097*** 1.063*** (.012) (0.012) (.012) (.008) Loan Amount (ln) .643** 0.551*** 0.568*** 0.894
Loan Characteristics Loan (.087) (0.086) (.087) (.141) Missed Payments 1.035*** 1.037*** 1.038*** 1.071*** (.003) (0.003) (.003) (.004)
Household Income (ln) 1.224 1.222 0.994 (.135) (.144) (.079) Household Debt (ln) 1.089*** 1.078*** 1.051** (0.019) (.020) (.016) Previous MicroBank Loan 0.917 0.957 1.187
Client Characteristics Client (0.171) (.180) (.152)
Branch Fixed Effects No No No Yes* Yes
Log pseudolikelihood -1979.71 -1324.22 -1303.72 -1287.29 ---
Number of Observations 121,658 121,658 121,658 121,658 121,658
Note: Estimates are presented as odds ratios. Robust standard errors are in parentheses. Number of client clusters=7293. Two branches have significant effects in Model 1d. † Model 1e is a rare event logistic regression. * p < .05; ** p < .01; *** p < .001
58 Table 5. Generalized Linear Model Predicting Proportion Missed Payments Following Delinquency (2a) (2b) (2c) Vetting Officer (1=original) -0.187 -0.815*** -.983***
(0.100) (0.153) (0.159) Officer Tenure 0.004* 0.004* 0.003 (0.002) (0.002) (0.002) Time in Portfolio (ln) .999*** 1.086*** 1.050*** (0.091) (0.151) (0.148)
Officer Characteristics Officer Commission (ln) -0.016 0.010 0.013 (0.023) (0.022) (0.022) Auto Loan 0.296 0.457 (0.246) (0.252) Months Pre-delinquency -0.092*** -0.113*** (0.015) (0.016) Months Post-delinquency 0.037* 0.020 (0.017) (0.017) Payments Missed, Pre-delinquency 0.006 0.005
Loan Characteristics Loan (0.003) (0.003) Loan Amount (ln) 0.056 0.174 (0.071) (0.092)
Household Income (ln) -0.040 (0.042) Household Debt (ln) 0.023 (0.011) Previous MicroBank Loan 0.174*
Client Characteristics Client (0.074) Inverse Mills Ratio 0.168 - 0.232 -0.681** (0.096) (0.185) (0.252) Log Pseduolikelihood -872.22 -824.45 -822.89 Number of Observations 1677 1677 1677 Note: Robust standard errors are in parentheses. Number of client clusters = 1643. * p < .05; ** p < .01; *** p < .001
59 Table 6. Logistic Regression Predicting Odds of On-time Loan Repayment (3a) (3b) (3c) Vetting Officer (1=original) 6.117*** 2.179** 2.278**
(1.074) (0.516) (0.554) Officer Tenure 0.995 0.999 0.998 (0.003) (0.003) (0.003) Time in Portfolio (ln) 0.186*** 0.510** 0.484** (0.028) (0.116) (0.113)
Officer Characteristics Officer Commission (ln) 0.909** 0.948 0.961 (0.032) (0.036) (0.036) Auto Loan 0.039*** 0.030*** (0.017) (0.014) Months Pre-delinquency 0.743*** 0.733*** (0.021) (0.021) Months Post-delinquency 0.929** 0.926** (0.020) (0.021) Missed Payments, Entire Loan 0.952*** 0.950***
Loan Characteristics Loan (0.005) (0.005) Loan Amount (ln) 3.660*** 4.680*** (0.423) (0.606)
Household Income (ln) 0.682*** (0.054) Household Debt (ln) 1.005 (0.017) Previous MicroBank loan 1.140
Client Characteristics Client (0.153) Log Pseudolikelihood - 1115.244 - 943.33 -929.13 Number of Observations 1791 1791 1791 Note: Robust standard errors are in parentheses. Number of client clusters=1752. Inverse Mills Ratio is excluded from the models because it has no significant effect. * p < .05; ** p < .01; ***p < .001
60 Table 7. Logistic Regression Predicting Odds of Taking out a Subsequent Loan (4a) (4b) (4c) Vetting Officer (1=original) 1.592* 2.019* 3.368**
(0.338) (0.674) (1.286) Officer Tenure 1.006 1.008 1.008 (0.004) (0.004) (0.004) Time in Portfolio (ln) 1.099 1.213 1.335 (0.213) (0.341) (0.370)
Officer Characteristics Officer Commission (ln) 1.028 1.019 1.025 (0.061) (0.061) (0.061) Auto Loan 0.967 1.149 (0.382) (0.519) Months Pre-delinquency 0.951 1.004 (0.028) (0.037) Months Post-delinquency 1.008 1.061 (0.026) (0.033) Missed Payments, Entire Loan 0.948*** 0.948***
Loan Characteristics Loan (0.006) (0.006) Loan Amount (ln) 0.563** (0.116)
Household Income (ln) 1.234* (0.115) Household Debt (ln) 0.907** (0.026) Previous MicroBank loan 1.548*
Client Characteristics Client (0.275) Inverse Mills Ratio 1.863*** 2.143* 9.023*** (0.329) (0.658) (5.197) Log Pseudolikelihood -623.81 -575.10 -564.13 Number of Observations 1677 1677 1677 Note: Robust standard errors are in parentheses. Number of client clusters=1643. * p < .05; ** p < .01; *** p < .001
61 Table 8. Significance of Original Officer in Predicting Outcomes with Alternative Measures of Delinquency % Missed On-time Subsequent Payments Repayment Loan 4 missed payments p<.001 NS p<.01 5 missed payments p<.01 NS p<.05
62