The value of in-person banking: evidence from U.S. small businesses

Modern technology has revolutionized communication with banking institutions, offering users a high degree of autonomy and geographic liberalization. Everyday examples include online banking (Hernández-Murillo et al. 2010), ATMs in off-site locations (Magnac 2017), and mobile banking (Baptista and Oliveira 2015). Yet, small businesses have never been at the forefront of these innovations. U.S. evidence from the Federal Reserve’s Survey of Small Business Finances (SSBF) shows that only a fourth of the participant firms are willing to utilize some impersonal banking channel as a substitute for face-to-face communication with bank officers, raising two main questions. Why are certain firms more likely than others to visit the bank’s premises? How do in-person interactions compare with impersonal banking in terms of contribution to financial intermediation efficiency?

Small businesses face considerable difficulty in raising capital from stock markets. Their limited organizational footprint heightens concerns over adverse selection and moral hazard, calling for a level of information production which is disproportionately high to the available resources (Ang 1991). A preferred alternative is bank credit, mainly because it enables the transcendence of informational asymmetries through the repeated borrower-lender interactions (Rosenfeld 2014; D’Aurizio et al. 2015; Beck et al. 2018). As per substantial theoretical work (Boot 2000; Stein 2002; Berger and Udell 2002, 2006; Liberti and Petersen 2019), a distinctive feature of relationship banking is that part of the lending decision is based on soft information which, unlike hard information (e.g. accounting records, credit scores, history of payments), is neither quantifiable nor in any other manner observable by the market. Importantly, “with soft information, the context under which the information is collected and the collector of the information are part of the information. It is not possible to separate the two” (Liberti and Petersen 2019, pp. 3–4).

To disentangle the relative importance of soft and hard information over the course of the bank-firm relationship is unrealistic. However, it is equally unrealistic to assume it constant, given an environment of increasing technological automation and the vast heterogeneity of small firms’ characteristics. Relatedly, a shortcoming of the dominant empirical approach is the implicit assumption that a large number of interactions, as over a wide time frame or product range, suffice to enable the relationships effects (Petersen and Rajan 1994, 1995; Berger and Udell 1995; Hernández-Cánovas and Martínez-Solano 2010; Castelli et al. 2012; Karolyi 2018; Mi and Han 2020). By downplaying the importance of how the two parties actually communicate, this approach renders the soft information flow untraceable, obfuscating the sources of the added value of relationship banking. After all, if the main driver of the relationship is still hard data, relationship banking would not be fundamentally different from transactions-based banking technologies (e.g. financial statement or asset-based lending).

Motivated by this disconnect between theory and empirical investigation, we aim to capture more of the role of soft information on small business finance than is currently reflected in the literature. Towards this, the present paper offers, for the first time, a rigorous treatment of the determinants and implications of in-person banking by drawing evidence from a sample of 12,438 firms with less than 500 employees, spanning 9 U.S. regions, and 10 SIC divisions. Central to our approach is a previously overlooked SSBF question which sets the framework of a useful dichotomy: firms choosing to interact with their primary financial institution¹ in-person vis-à-vis firms mainly relying on impersonal communication methods (internet, post, etc.). Underlying our approach is the recognition that soft information resides within in-person contact, whereas alternative communication channels tend to suppress it. Hence, although we have no way of filtering out hard information, by choosing to focus on the communication mode, we can ensure that every firm within our sample of interest also yields substantial soft information—a condition that prior studies are unable to provide.

Exploiting this property of our dataset, we first seek to enlighten our understanding of the factors invoking face-to-face interactions. Our expectation is that this need surfaces when the hardening of information is: (1) costlier, as when the firm is informationally opaque (Petersen and Rajan 1994, 1995; Schwert 2018); and (2) of limited reusability, as within rural banking markets (Cole et al. 2004; DeYoung et al. 2012). We ascribe much of the remaining variation to owners’ characteristics which drive organisational choices to a larger extent in the context of small business (Raymond 1985). Subsequently, we seek to capture the implications. Ceteris paribus, a higher frequency of in-person communication levels the informational playing field more than is attainable via impersonal communication. In turn, a treatment of information asymmetries equates to a treatment of the common root of three major challenges in small business banking: high borrowing costs (Datta et al. 1999), tight lending horizons (Ortiz-Molina and Penas 2008), and discouraged borrowers’ propensity to self-ration (Stiglitz and Weiss 1981; Kon and Storey 2003; Han et al. 2009). Accordingly, we hypothesize that credit becomes cheaper and more available for in-person communicators, consolidating their relationship with the bank.

Our findings are in line with the above conjectures. In our determinants tests, we obtain two primary results that are new to the literature. First, banking market conditions, after controlling for potential confounding factors, emerge as a strong driver of in-person contact, which supports the theoretical conjectures of Boot and Thakor (2000) and Hauswald and Marquez (2006) about the link between banking market structure and soft information transmission. Second, we document the incremental significance of small business owners’ characteristics on determining the nature of the relationship with the main bank; the inclusion of proxies for demographics and educational attainment not only increases the explanatory power of our probit model but also reveals the high marginal effects of these variables. On this basis, we sketch the profile of owners more liable to steer their firms towards in-person banking as predominantly female, older and less educated individuals.

Next, we test for a causal effect of the communication dichotomy on a variety of banking outcomes. As we find, a preponderance of face-to-face interactions benefits both ends of the relationship: (1) the bank experiences increased loyalty from small firms; and (2) the latter gain access to cheaper credit for an extended period of time. These effects are of high economic significance. A relationship based on in-person communication, on average, lasts 17.88 months longer and is 24.46% more likely to be the firm’s sole banking relationship; the associated loans mature 3.37 months later and bear interest rates which are 11 basis points lower. Furthermore, we complement the traditional proxies for relationship strength (e.g. Berger et al. 2005) with a new and comprehensive measure, services concentration, defined as the proportion of financial services a firm purchases from the primary bank relative to the total financial services it utilizes. As an outcome variable, this ratio confirms that in-person communication drives not only the credit decision but also the totality of a small firm’s banking needs. Finally, we find that the likelihood of discouraged borrowing is smaller for in-person communicators, albeit with a caveat. Because the effect holds for borrowers of good quality only, this finding is indicative of a decrease in screening errors made by the bank rather than a window of opportunity for bad borrowers.

Endogeneity poses a valid concern in our empirical setting. This is mainly due to additional factors which might correlate with the communication decision but lie outside the SSBF scope. For example, Uchida et al. (2012) caution that a high loan officer turnover undermines the bank’s ability to act as an information repository and Schoar (2012) finds that bonding (or lack thereof) with the bank’s relationship manager explains a portion of the variability in borrowers’ delinquency. To address this concern, we conduct a battery of tests, including recursive bivariate probit estimation and treatment effect models, which jointly create a framework for inferences least distorted by selection and / or omitted variables bias.

As a new and refined lens of soft information, our in-person contact approach speaks to a longstanding deficiency in literature: “existing work falls short in that it has not measured the precise sources of the added value of relationship banking” Boot (2000 p. 21). The most important contribution of this paper, therefore, is our ability to assign the observed effects to soft information and know that soft information, rather than any other element of the bank-firm relationship, represents the actual cause. In this vein, we provide empirical grounding to the theoretical predictions of the relationship banking literature (Stein 2002; Berger and Udell 2002, 2006; Liberti and Petersen 2019) and demonstrate the salience of soft information with evidence that is both objective and measurable.

Another contribution is to shed light on a hitherto unknown aspect of soft information production. The aim to capture the underlying mechanism which generates soft information is explicit in the studies of Uchida et al. (2012) and Hattori et al. (2015) which debate whether loan officers or the branch manager, respectively, play the leading role in the process; and implicit in studies analysing the characteristics which make a bank most receptive to this type of information (Petersen and Rajan 1995, 2002; Berger et al. 2005, 2014). While this research makes inroads on the bank’s ability to capitalize on soft information, we focus on the transmitting end, showing how likely a small firm is to provide critical input in the first place.

Finally, we contribute to two evolving strands of research by offering: (1) a partial remedy, in the form of in-person contact, to the challenge of discouraged borrowing in small business banking (Kon and Storey 2003; Han et al. 2009; Chakravarty and Xiang 2013); and (2) generalizable insight which is of interest to research investigating the efficacy of communication methods in entrepreneurial settings (e.g. Casson and Giusta 2007; Sarapaivanich and Patterson 2015).

Boiled down, this paper is a note of caution for both banks and their small business customers. The former should provide adequate space for subtle, noncontractual information to emerge, or else their unremitting investment in technological automation² is likely to eliminate a key value driver. From the small business perspective, impersonal banking channels should be utilized on the understanding that they represent inferior substitutes for relationships built on face-to-face interactions.

The remainder of the paper is structured as follows. Section 2 reviews the relevant literature. We develop our hypotheses in Sect. 3 and present the dataset in Sect. 4. The empirical results are in Sect. 5. The paper concludes in Sect. 6.

Sourcing capital has always been an arduousendeavour for small businesses and indeed a financier has important reasons to shy away from this economic sector. First, small firms typically lack the managerial skills and resources to produce accounting records or other data useful to investors (Ang 1991). Hence, whether the owner has a pipeline of positive NPV projects to invest in (adverse selection problem) or whether she stands willing to channel funds towards these as opposed to subpar investments (moral hazard problem) are warranted concerns. Second and related, future prospects might be linked to owners’ characteristics which are either irreplaceable or, in part, unobservable (Bates 2005). Third, organizations of smaller size are vulnerable to environmental factors with a dramatically higher likelihood of failure (Hart and Oulton 1996).³ Consequently, with the exception of high-growth firms which might be on private equity’s radar, financing options for the vast majority of small businesses reduce to bank lending.

Banks are equipped to manage firm-specific uncertainty, the root cause of the above concerns, by their capacity to gather and integrate into the lending decision information generated from the interactions with clients. The greater the frequency of interactions, the more input becomes available and, hence, the greater the value added of the relationship banking (Petersen and Rajan 1994, 1995; Berger and Udell 1995; Hernández-Cánovas and Martínez-Solano 2010; Bharath et al. 2011; Castelli et al. 2012; Karolyi 2018; Tian and Han 2019). In spite of its vast volume, the relevant literature obtains evidence from a limited number of empirical proxies. Degryse and Van Cayseele (2000) and Iturralde et al. (2010) identify the three most popular in the following relationship dimensions: (1) duration, i.e. how long the firm has been the bank’s client; (2) breadth i.e. the range of services that the bank-firm relationship involves; and (3) concentration i.e. the firm’s total banking relationships.

Yet, unlike what such proxies imply, the informational output of relationship banking is not homogenous. This recognition becomes for first time explicit in Stein (2002) and Berger and Udell (2002) who address theoretically the interplay between organizational structure and financial intermediation, arriving at a common conclusion: flatter bank hierarchies represent better fits for small business lending. As described in Liberti and Petersen (2019), information comprises two distinct types, hard and soft. The former is quantifiable—think, for example, of financial ratios and credit scores (Udell 2008)—and, hence, transmissible by technology such as internet banking or other mediums (Petersen and Rajan 2002; Berger et al. 2005; Hertzberg et al. 2010). Conversely, soft information neither fits in numbers nor can it be evaluated separately from the physical setting which generates it; for instance, loan officers’ conviction that a certain small business owner is liable to deliver because of character and innate aversion to delinquency. Table 1 Panel A summarizes the main characteristics of each information type; Table 1 Panel B illustrates how the ability to process soft information differentiates relationship lending from all other lending technologies. The description provided explains why a clean measure of soft information is unattainable. Harder to explain is why, although soft information is exclusive to in-person communication, the empirical literature evaluates relationship effects without taking into account the mode of communication.

We note three studies attentive to the physical setting of the bank-firm interactions,⁴ two of which using survey-based evidence from Japan: Uchida et al. (2012) and Hattori et al. (2015). The former study finds that in-person contact improves firms’ perceptions about: (i) access to credit and (ii) the extent to which their idiosyncrasy and needs become apprehensible by banks. The latter study documents that face-to-face interactions with clients are among the key factors which enable branch managers to act as information repositories, more so than loan officers do. Taken together, this evidence, while subjective and of limited generalizability due to the special (keiretsu) character of the Japanese capital market, supports the capacity of in-person communication to reduce friction in the borrower-lender relationship.

From the Italian setting, Gabbi et al. (2020) document an inverse association between the cost of bank loans and the frequency of face-to-face meetings, especially when these are held at the firm’s headquarters. Our research is similar to theirs in that we relate the communication mode to actual banking outcomes rather than firms’ perceptions of them, yet different in a number of ways. First, we present the first evidence, to date, from the world’s largest economy, the U.S, using the same data that the Federal Reserve collects via a nationwide census and relies upon to form policy. Second, the time period in Gabbi et al. spans from 2009 to 2011, consistent with the study’s focus on gauging the effects of credit tightening in the aftermath of the subprime mortgage crisis. By contrast, the recurring nature of the SSBF survey enables us to capture a period extending longer than a decade, including the booming stock market of the 1990s, the subsequent crush, and the recovery in the early 2000s, a through-the-cycle approach which is not exclusively tied to a specific economic environment. Third, our interest lies in developing a symmetric understanding of both the determinants and implications of in-person banking. On the implications side, while borrowing cost is one of our outcome variables, so is an array of other important dimensions including duration, exclusivity, number of financial services purchased, loan maturity, and the phenomenon of discouraged borrowing. The next section delves into the causal mechanism explaining how the communication mode can exert a multifaceted influence on the relationship.

All data comes from the Federal Reserve’s 1993, 1998 and 2003 Survey of Small Business Finances (1993 NSSBF, 1998 SSBF and 2003 SSBF)⁵ covering the period 1993–2005. Because of its credibility and thoroughness, the SSBF continues to be a leading data source for a host of recent studies in small business (e.g. Berger et al. 2011; Cassar et al. 2015; Cole and Sokolyk 2016; Dai et al. 2017; Durguner 2017; Han et al. 2017). The survey participants comprise 12,438 enterprises (4637 from 1993 NSSBF, 3561 from 1998 and 4240 SSBF) with less than 500 employees, and represent every U.S. region and industry with the exception of agricultural businesses, non-profit organizations, government entities and subsidiaries. In addition, the SSBF provides sampling weights so that the data correctly represents the population of small businesses, overcoming bias due to disproportionate sampling and nonresponse. Echoing the call in prior literature for research designs attentive to this adjustment, we fully incorporate the sampling weights in all analysis in the study.

Table 2 defines the SSBF variables used in the study and presents key descriptive statistics. Confirming the preponderance of face-to-face interactions in small business finance, 77% of firms are shown to interact with their primary financial institution most frequently in-person. Table 3 Panel A identifies the proportion of in-person banking for each category of the independent binary variables (X) in the subsequent regressions and univariately compares the differences in means. As shown, in-person banking is most prevalent in rural areas, when the primary financial institution is a commercial bank, and for owners who are female or lack a university degree. Table 3 Panel B compares the mean value of each dependent variable (Y) based on the communication dichotomy. The two groups systematically differ along most dimensions. In-person communicators, on average, give rise to longer and more exclusive banking relationships, while they also purchase more services from the primary financial institution. Their loyalty appears to result in longer maturity loans.

We investigate our hypotheses in a multivariate regression framework using several estimation methods. Central to all subsequent analyses is the variable in-person, set equal to 1 if the firm communicates most frequently with the primary financial institution face-to-face, and zero otherwise. The variable coding is based on the respective SSBF question requesting the identification of the main (most frequent) banking method from a list of options that includes in-person and a variety of impersonal ways (e.g. by post, internet, ATM, etc.). Unfortunately, the survey requires no further clarification on the number of bank-firm interactions and it therefore becomes impossible to distinguish firms using in-person communication exclusively from firms which utilize a blend of in-person and impersonal ways of banking. Even so, our research design can ensure not only that soft information transmission has taken place but also that the method which makes this possible, i.e. face-to face interaction, has been used more times than any other mode of communication with the primary financial institution. In line with our study’s dual aim, this section proceeds in two steps. First, we use in-person as the dependent variable to gauge the key determinants. Subsequently, we place it on the right-hand side to assess its explanatory power over the banking outcomes of relationship strength (i.e. length of relationship, exclusivity, services concentration), loan contracting (i.e. interest rate, maturity of loans), and discouraged borrowing. To fully exploit our dataset, we run each regression using pooled data from 1993 NSSBF, 1998 SSBF, and 2003 SSBF as in Berger et al. (2011), and supplement these results with separate evidence drawn from each individual round as in Cole and Sokolyk (2016).

Relationship banking is distinguished by the capacity to operationalize soft information—an impossibility under alternative banking technologies. This capacity is valuable to the extent that soft information adds efficiency to financial processes and, indeed, theory suggests that it does. Absent is, however, the empirical evidence that could put the postulated benefits in perspective, with the extant studies tracing relationship banking effects at an aggregate level only. Addressing this void, our study offers a rigorous analysis of the mechanism that actually generates soft information, in-person communication.

Using data from the Federal Reserve’s 1993, 1998 and 2003 Survey of Small Business Finances, we first develop the profile of firms opting for face-to-face interactions with their main bank. This is compatible with informationally opaque organizations and firms operating in rural areas. We also find that small business owners are more likely to visit the bank premises if they are female, older, and less educated. Next, we direct our attention to the implications of in-person communication and document incremental value for both ends of the bank-firm relationship. From the small business perspective, borrowing becomes cheaper and available for a longer period of time, reducing the likelihood of self-imposed rationing among firms of good quality. From the bank perspective, not only client loyalty increases but also firms tend to purchase a wider range of financial services. In light of this evidence, we caution that technological automation in the banking industry should aim to supplement, not obviate, interpersonal communication.

Future research, among other possible directions, can blend our findings with those of previous studies to develop a more symmetrical understanding of soft information production: if loan officers and branch managers impact differently on the recipient end of the process (Berger and Udell 2002; Uchida et al. 2012; Hattori et al. 2015), it is likely that heterogeneity also resides on the transmitting end. Conditional on data availability, it would be interesting to compare the effects of soft information generated by different stakeholders communicating on the firm’s behalf (e.g. owner and family, employees, business partners, members of the local community).