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«Alejandro Drexler, Antoinette Schoar ∗ This version August, 2011 Abstract This paper provides evidence that shocks to the relationship between loan ...»

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Does Soft Information Matters? Evidence From Loan Officer


Alejandro Drexler, Antoinette Schoar ∗

This version August, 2011


This paper provides evidence that shocks to the relationship between loan officers

and their borrowers affects the credit decisions of the bank as well as customers’

repayment and borrowing behavior. When a loan officer unexpectedly has to be

absent from the job, the existing borrowers of the absent loan officer are less likely to take on a new loan from this bank and are more likely to miss a payment. The reduction in the borrowing is explained by a lower number of loan applications and a reduction in the application approval rate. This findings suggest that clients are loyal to their loan officer, that the bank reduces lending when soft information is less available, and that loan officers have an important role in monitoring the clients.

Alejandro Drexler is at the Mccombs School of Business at The University of Texas at Austin, Antoinette ∗ Schoar is at MIT, NBER, and ideas42. We are grateful for the comments and suggestions of the participant in the seminar at The University of Texas at Austin, and Universidad Catolica de Chile, specially Daniel Paravisini for a great discussion. We thank Bank Estado, especially Roxana Aravena, Jose Luis Arriagada, Pablo Coto, Enrique Errazuriz, Carlos Hernandez, Soledad Ovando, Hector Pacheco, Oliver Prostran, Marco Sambuceti, German Texido, Emilio Velez, Victor Vera, and Pamela Zalduando for providing the data and making us familiar with the internal HR processes of the bank. We also thank Manasee Desai, and Katherine Gordon for their help collecting and organizing the data. Finally we thank Rouzhna Nayeri for excellent work in editing the paper. All remaining errors are our own.

Introduction Most credit programs are based on extensive interactions between loan officers and the businesses they lend to. This relationship based approach to lending is especially widespread for small and more opaque borrowers, where formal documentation of profits and record keeping is less reliable. The loan officer has the difficult role of solving the informational gap between the bank and the borrower by gathering soft information about potential borrowers.

The relationships between loan officers and their clients often extends beyond information collection, and many times loan officers help borrowers assessing the financial needs of their business or even help ensuring that clients repay. The importance of relationship lending has been proposed in a myriad of theory papers, see for example Rajan (1992), Petersen, and Rajan (1994), Petersen, and Rajan (1995), Berger, and Udell (2002), Berger, Miller, Petersen, Rajan, and Stein (2005). However, there has been only little empirical research to document the role of loan officers in mitigating information asymmetries or moral hazard between the bank and its clients. A few notable exceptions are Herzberg, Liberti, and Paravisni (2010), and Liberti, and Mian (2009).

The novel contribution of this paper is that we study (exogenous) shocks to the loan officer-client relationship: Their impact on the credit provision to borrowers as well as the borrowers’ behavior. Specifically the shocks we rely on are loan officer absentee spells due to sickness, pregnancy, resignation or layoffs. We work with a bank in Chile, BancoEstado, which lends to small businesses in the informal sector where credit screening relies mostly

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on the loan officers but also on the entire client portfolio each loan officer manages (client characteristics and repayment borrowing behavior).

Overall we find that loan officer absenteeism leads to significant changes in the borrowing and repayment behavior of client and the credit provision of the bank. In particular, when the original loan officer is absent we observe a 0.9% reduction in the probability of taking up a new loan from the bank (13% reduction as a fraction of the unconditional probability of taking up a new loan from the bank). This reduction is explained by changes in both the client application rate, and the bank approval rate. Specifically, on the client side, the application rate decreases in 0.68% (9% reduction as a fraction of the unconditional probability of applying for a new loan), and on the bank side the approval rate per application decreases in 4.3% (5.2% reduction as a fraction of the unconditional probability of approving a loan application). This switch in credit access is particularly interesting since we do not see a change in credit terms after a loan officer leaves, e.g. interest rates and loan maturity is unchanged on average. This is of course contingent only on the borrowers who do choose to take up a new loan. The effects on repayment behavior and borrowing outside the bank are different for different type of leaves. For example while a steep increase in borrowing outside the bank is observed after a loan officer gets sick, this reaction is not significant when the absenteeism is related to a loan officer pregnancy, layoff or resignation.

The fact that reactions to different type of absentee spells are different is expectable.

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resignation might be correlated with the prior performance of the loan officer’s portfolio.

Laid off loan officers might be let go due to the particularly poor performance of their portfolio; while resigning loan officers might be poached away by competitors due to their above average skills or performance. Pregnancies differ from the other absentee spells in that there is a long lead time which allows the bank and the loan officer to prepare the clients for the loan officer’s leave in order to prevent potential problems. Therefore the most exogenous source of absenteeism in our sample are major sickness periods of loan officers. These spells are largely unexpected for both the bank and the loan officer, and are independent of the loan officers’ portfolio characteristics.

We therefore separately study the effects of the different absentee spells on the loan officer’s client portfolio. When only looking at sickness spells, we find that clients whose loan officer has to take a sickness leave are 1.2% less likely of renewing their loan with the bank during the months that the original loan officer is on leave (19% as a fraction of the unconditional probability of renewing the loan). These clients also show a 2.1% increase in the probability of borrowing outside the bank (13% increase as a fraction of the unconditional probability of borrowing outside the bank), and an increase of 1.1% in the probability of missing a payment (10% as a fraction of the unconditional probability of missing a payment). Interestingly, when looking at the credit portfolio of loan officers who were fired we see a much stronger drop in the likelihood of starting a new loan, a spike in

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that clients of loan officers who were resigned (in most cases because they were hired away) do not see a change in their loan renewal probability. But they do experience an increase in missed payment. And finally, loan officers who leave due to pregnancy see no increase in missed payments. However, these clients show a drop in loan renewals during the time of the loan officer’s leave. This drop in loan renewal rates during pregnancy leaves is almost entirely explained by a reduction in the number of loan applications, we conjecture that this decrease in the likelihood of applying for a new loan might be a form of ‘loyalty’ by the clients, who wait for the new loan until their loan officer is back from maternity leave.

Overall these results suggest that the relationship between loan officers and their clients has first order effects on the borrowing behavior and the access to credit.

We also investigate whether there is an interaction effect between the characteristics of the borrowers in the loan officer’s portfolio and the effect of loan officers leaving. In particular we are interested in client characteristics that proxy for the importance of soft information for the lending decision, such as credit score and average loan sizes of the borrower prior to the current loan, and length of the relationship between the loan officer 1 See also Hertzberg, Liberty, and Paravisini (2010), who show that incoming loan officers have strong incentives to report bad news about the portfolio of a predecessor loan officer. While in our set up the fired loan officers could not suppress information about non payment they could have manipulated default rates by renewing loans for clients that are experiencing economic distress.

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Looking at the interaction effects for sickness leaves we observe that firms offset the reduction in lending by borrowing from other banks. The only exception are big firms with poor credit score. This shows that relationship lending is particularly important for firms with low credit score, where creditworthiness is more difficult to asses. We also observe in this table that small firms with good credit score do not show a deterioration in their repayment behavior. An interesting finding is that big firms with good credit score still show a deterioration in their repayment behavior, which suggests that their quality may be lower than their actual credit score shows.

When looking at the interaction effects for pregnancy and layoff leaves we find that big companies with good credit score not only show a deterioration in their repayment behavior, but also the deterioration is steeper for these firms compared to firms with worse credit score. This finding is consistent with the findings in Hertzberg, Liberty, and Paravisini (2010). In fact loan officers will have strong incentives to suppress bad news about large companies, because disclosing these news will strongly affect their wage. Furthermore, by hiding this information, the loan officer lets big companies in financial distress to keep good credit scores. As suggested by Hertzberg, Liberty, and Paravisini, when the loan officer has to leave the bank (either permanently or temporarily) the replacing loan officer will have strong incentives to disclose the real situation about these firms.

Overall our results suggest that the relationship between loan officers and their clients is

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mit ‘soft’ information to a colleague. When loan officers have to go on leave unexpectedly, in particular due to sickness, we see that their clients are less likely to get a new loan within the bank. While small borrowers and high score borrowers are able to substitute the loss in credit access by taking on new loans outside the bank, big clients with poor credit score are not able to get outside funding. In addition borrowers show a deterioration in repayment behavior when their loan officer is absent. This might suggest that loan officers also play a role in reducing moral hazard behavior especially for small and opaque firms. Big firms show a steeper deterioration in their repayment behavior suggesting the loan officer also hide bad news from the bank by renewing loans to bad clients.

These results also shed an interesting new dimension on the pricing behavior of banks.

We observe that clients do not experience an increase in interest rates when their loan officer leaves. So it seems that loan officers do not use soft information they have on borrowers to hold them up for higher margins. We also see that the deterioration in the repayment behavior is not accompanied by an increase in interest rates. This suggest that interest rates, at least for this segment of the market, react slowly to changes in the probability of default.

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We analyze the credit characteristics and repayment behavior of micro entrepreneurs of a large local bank operating in Chile, as well as how these characteristics and repayment behaviors change when the loan officer is absent for one month or longer. We study all of the clients borrowing from the micro-credit division of the bank. The micro credit division operates independently of the rest of the bank, and has its own lending technology, specially designed for micro credit businesses. The micro credit division operates in the branches of the bank but has separate personnel and office space. Only clients with yearly sales below US$ 110,000 can borrow from the micro-credit division, clients exceeding this limit must borrow through the regular lending process of the bank. The micro credit division of the bank has 210,000 clients of which 187,000 were borrowers (had non zero debt) at some point during the period of the study.

The bank as well as its micro credit division is organized in 3 Zones: North of Chile, Metropolitan area, and South of Chile. The Metropolitan area consists of the capital city and the counties nearby. North of Chile consists of the rest of the counties located north of Santiago, and South of Chile consists of the rest of the counties located south of Santiago.

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A branch that offers micro credit services must have at least one loan officer, and may have one or more loan officer assistants. Loan officers assistants can only process pre

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officers can issue pre-approved loans as well as regular loans. In this study we will focus on loan officers, because they have decision power in the lending process.

The allocation of loan officers to clients starts when the client chooses his branch. Clients can freely choose their branch but will usually choose the branch that is closest to their business. In addition, clients rarely switch branches unless they relocate their home and/or business. However, some clients prefer to go to a bigger branch even if it is located further away from their home or business. In particular, the main branch located in downtown Santiago is very popular and has many clients that do not live particularly close to the main office. Once the client has chosen his branch the allocation of new clients to loan officers works as follows: The clients goes to the branch, new clients are serviced in a first come first serve basis and are allocated to the loan officer that becomes available. Old clients, on the contrary, wait until their already assigned loan officer becomes available.

Given this protocol, the allocation of new clients to loan officers is random within branches.

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