The Impact of Loan Officers on Relationship Lending in Microfinance: Empirical

evidence from PROMUJER-Mexico

Roselia Servin1 · Robert Lensink2 · Marrit Van den Berg3

(Very preliminary, please do not cite)

AbstractThe purpose of this study is to empirically evaluate the impact of relationship lending on the probability of loan default in microfinance, a topic barely studied in the literature. We statistically test this hypothesis with data compiled from field interviews conducted with PROMUJER-Mexico, a Microfinance Organization operating in Latin America through the village banking methodology. We use a unique data set consisting of 777 observations based on both quantitative and qualitative information on relationship variables, loan officer characteristics, borrower characteristics and loans to estimate a logistic random intercept model to capture the hierarchical structure of individual borrowers nested in loan officers and in branches. We find evidence that the higher the frequency of interactions between the borrower and the loan officer and the higher the rotation of loan officers within the branch, the higher the likelihood of loan default. Also, we find that borrowers having a relationship with multiple microfinance lenders does not necessarily increases the probability of default. With this results, we provide evidence and gained some insight in the theoretical literature in that, in addition to joint liability contracts, micro-lenders in Mexico appear to substantially rely on relationship driven information to succeed loan performance.

Key words: relationship lending, loan default, loan officers, microfinance, multilevel analysis.

1. Introduction1 Roselia Servin (*Corresponding Author) Wageningen University, Hollandseweg 1 6706 KN. Wageningen, The Netherlands e-mail: roselia.servin@wur.nl Colegio de Postgraduados, Carretera México-Texcoco Km 36.5, Montecillo, Edo, Mex. C.P. 56230, México e-mail: roseliasj@colpos.mx

2 Robert Lensink University of Groningen, Faculty of Economics and Business P.O. Box 800, 9700 AV, Groningen, The Netherlands e-mail: b.w.lensink@rug.nl Wageningen University, Hollandseweg 1 6706 KN. Wageningen, The Netherlands e-mail: robert.lensink@wur.nl3? Marrit Van den Berg

Wageningen University, Hollandseweg 1 6706 KN. Wageningen, The Netherlands marrit.vandenberg@wur.nl.

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Do Microfinance Institutions rely on relationship lending to improve loan performance? Although it is an interesting question for practitioners and researchers alike, there is still scarce literature addressing this topic . Opposite to this, there is a growing literature on impact assessment , financial sustainability and its trade-offs with poverty outreach and performance and corporate governance .

Among the few studies that research the role of relationship lending in microfinance are Chakravarty & Shahriar . In their study, the authors examine to what extent bank-borrower relationships impact the probability in the application and aproval of microcredits. This study was conducted in Bangladesh and target 34 villages where borrowers of the Grameen Bank were interviewed on their relationship with the bank. The results emanating from this enquiry indicate that borrowers with a longer membership with the Bank and those who have a track record of previous loans are more likely to apply for a microloan and to be approved. Another study that focuses on the impcat of relationship lending in microfinance is Schrader . In his study, the author empirically test to what extent there is competition between relationship lending and transaction lending. To test this hypothesis, the author analysed a sample of customers of the relationship lender ProCredit Ecuador combined with data about all other loans of these customers in the Ecuadorian banking system. The results emanating from this study show that ProCredit borrowers who have a transaction loan have higher default probabilities. Furthermore, the author find evidence that ProCredit customers with payment difficulties prefer to serve their relationship loan while defaulting on their transaction loan. This finding suggests that customers of a relationship bank value their banking relationship.

Besides the questions that Chakravarty & Shahriar and Schrader post on relationship lending, there are almost no study that ask to what extent relationship lending in microfinance improves loan performance by decreasing the likelihood of loan default. Seeking to fill this gap, the aim of our study is to empirically test this hypothesis in 18 branches of Promujer-Mexico which we believe are using “soft information”1 that has been accumulated by Loan Officers and Managers of branches. This hypothesis is in line with previous empirical studies which state that small credit institutions rely on relationship lending to reduce asymmetry of information with their borrowers and to achieve a Pareto-improving exchange of information between the bank and the borrower that improves loan performance, a topic that we think deserves indeed investigation.

Our study focus on the analysis of relationship lending in microfinance in Mexico with borrowers of PROMUJER-Mexico, a microfinance organization working with the village banking methodology. To this end, we measured “relationship lending” with variables related to the relationship held between the incumbent borrowers and the loan officers (e.g. number of completed contracts or credit cycles, length of the relationship (months) with the loan officers, differences in personal characteristics (age, education, civil status, religion) between the borrower and the loan officers and branch characteristics (total number of contracts held between the borrower and the branch, ratio of rotation of loan managers, length of the relationship with the branch (months), among others). In addition to this, we included a vector of borrower and loan officer characteristics and variables related with the institutional environment in which the branches are operating to account for unobserved heterogeneity. With this analysis we think that we can shed some light on the extent to which MFIs in Mexico rely on relationship lending to improve loan performance, a topic barely studied in the microfinance literature.

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In particular, we explore the role of relationship lending in the probability of loan default of microfinance borrowers of 18 branches of Promujer-Mexico. In this way, our study complements the results that so far recent models of relationship lending have formulated (e.g., Stein , Berger and Udell , Liberati and Mian 2006, Becker and Murphy 1992, Radner 1993, Bolton and Dewatripont 1994, and Garicano 2000) which state that loan officers accumulate “soft information” from their clients and can use it to make credit decisions such as extensions, renewals, renegotiations and terminations that can improve the institutional performance. Furthermore, this research is in line with previous studies on the benefits that stem from banking relationships which focus on proxies for relationship closeness such as the length and scope of the relationship (e.g. Petersen and Rajan (1994) and Berger and Udell 1995), which measures to what extent a closer relationship between borrowers and the loan officers leads to an improvement on loan repayment.

As a result, our study contributes in filling a gap in the borrower-loan officer literature in microfinance through empirically testing to what extent relationship lending impact the probability of loan default at the branch level. To do so, we use a novel approach, the Multilevel Analysis, which is suitable when analyzing hierarchical models as is the case in this empirical study. This approach has been widely used in other fields of knowledge as medicine, but scarcely in economics. In particular, we applied this technique in our study by estimating a 3-level Random-Intercept-Model to measure the probability of individual “loan default” taking into account three hierarchical levels: i. Borrower, ii. Loan Officer, and iii. Branch levels.

For this study, information was collected from a sample of 441 borrowers that were randomly selected according to the portfolio that each particular Loan Officer had at the time of the interview. This survey was conducted between December, 2010 and March, 2011 in 18 branches and contains quantitative and qualitative information on loans, borrowers and loan officer characteristics along with information on the institutional environment in which the PROMUJER branches are operating. On average, 8 borrowers per each Loan Officer were interviewed with the support of 25 trained enumerators. The information that we used for this study corresponds to the latest two credit cycles as it was easier for borrowers to recall this information than information about their complete credit history, which in some cases if of more than ten contracts.

In addition to the borrower’s interviews, information was gathered from interviews with several staff members of PROMUJER who were directly related with the operation of the microfinance program over the period of analysis. Among them are: 52 Loan Officers, 17 Bosses of Centros Focales (CFs), and 4 Regional Managers. In total, we sampled 18 branches of PROMUJER-Mexico which are located in two regions: Mexico and Puebla and in four states: Mexico, Distrito Federal, Puebla and Tlaxcala. With this data, we created a panel set of performance indicators consisting of 777 observations to empirically test our hypothesis on the borrower-loan officer relationship lending.

The remainder of this paper is organized as follows. Section 2 briefly presents a Literature Review on Relationship Lending. Section 3 shows some background information on PROMUJER-Mexico, the institution under research. In section 4 we described the data and provide the definition of variables. Section 5 presents an overview of the Study Region. Section 6 describes the Methodology. The main Results that are derived from this study and the Conclusions are presented in Sections 7 and 8 respectively.

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2 Literature review on relationship lending

The definition of “relationship banking” is not clearly defined in the financial literature. However, overall this concept is associated with “the provision of financial services made by financial intermediaries that acquire specific information on the same clients over multiple interactions”. It is generated during the screening and monitoring process of the bank (Allen, 1990; Ramakrishnan and Thakor, 1984) (Diamond, 1984; Winton, 1995). Repeated interactions with the same client creates an opportunity for the lender to benefit from inter-temporal information reusability (Greenbaum and Thakor, 1995) that may resolve Grossmann and Hart (1980)-type free-rider problems. Opposite to this, the “transaction-oriented banking” focuses on a single transaction with one or with various clients rather than on information-intensive relationship with a particular customer (Boot and Thakor,2000).

Academic research in the mid-1990s started to examine the process of commercial lending. This line of research focus on how banks provide credit and how they mitigate asymmetries of information with their clients by generating information about borrowers’ quality and behavior. In addition, this literature has focused on differences between “relationship lending” and “transactions-based lending” and in differences between “soft information” versus “hard information” (e.g. Rajan 1992, Petersen and Rajan 1995, and Stein 2002). From that date until today, there is a growing literature on this topic which has been documented (see for example Boot (2000)).

The modern literature on financial intermediation has primarily focused on the role of banks as relationship lenders. In this capacity, banks develop close relationships with borrowers over time. Such proximity between the bank and the borrower has been shown to facilitate monitoring and screening and can overcome problems of asymmetric information (Boot, 2000). According with some scholars (Diamond (1984) and Bhattacharya and Thakor (1993)), relationship banking is most directly aimed at resolving problems of asymmetric information. It allows for several special contractual features, including flexibility and discretion, the extensive use of contracts, and the inclusion of collateral requirements which may facilitate implicit long-term contracts and resolve agency and information problems .

Some authors as Carey et al. (1998) have shown that relationship lending is not unique of banks. It is important also in non-bank financial institutions as finance companies, venture capitalists, investment banks, private equity and debt markets . All this financial institutions provide a full menu of financing options for borrowers (letters of credit, deposits, check clearing, and cash management services) with varying degrees of relationships that allow them to acquire proprietary information on their clients and reduce risk through multiple interactions, which are the basis for relationship lending. According with Stein (2002), small banks rely on relationship lending to produce more soft information on their customers than large banks. In the same line, small firms that do not have audited financial statements or sufficient pledgeable collateral rely on relationship lending. In our view, the fact that small banks and firms rely on relationship lending deserves indeed research. This can be particularly well-suited in the context of microfinance organizations as the majority of institutions are small and are working with firms that lack pledgeable collateral to access credit markets.

Berger (1999) states three conditions are met when relationship banking is present: 1) The lender collects information outside freely accessible public information; 2) The collection of information takes place by means of repeated interactions with the borrower generally

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through the provision of various financial services; 3) The information remains proprietary or confidential to the lender.

The empirical literature on relationship lending does not clearly states the value added of relationship banking. Although, banks acquire confidential (or proprietary) information thought multiple interactions with their customers, little is known about how banks obtain these information, what type of information is, and the use they give to this information. In the literature, various benefits emanating from relationship banking have been documented. First, different from transaction-oriented banking, relationship lending can facilitate a Pareto-improving exchange of information between the bank and the borrower. It is because the borrower might reveal proprietary information to its bank that it would never have tell to the financial markets (Bhattacharya and Chiesa, 1995). Under this circumstances, the bank might also have better incentives to invest in information production about the borrower. While such information production is costly, it may be worthwhile due to the substantial stake that the bank has in the funding of the borrower and the valuable inter-temporal information reusability that accompanies a long relationship with the borrower. These effects can generate an improved information flow between the bank and the borrower, accentuating the value added by relationship banking .

In general, relationship banking can improve welfare by several channels: 1) It can facilitates implicit long term contracting and leaves scope for flexibility and discretion of subtle information; 2) Allows for a better control of potential conflicts of interest by conveying extensive contracts; 3) Relationship may involves collateral (e.g., as in transaction-oriented lending) that needs to be monitored and that makes the proximity of a relationship essential; 4) It permits funding loans that are not profitable for the bank in the short-term but that make possible value-enhancing inter-temporal transfers in loan pricing .

Furthermore, the bank–borrower relationship is typically less rigid than a capital market funding arrangement, making easier the renegotiation of contract terms. This greater flexibility with relationship finance can improve welfare because discretion has value (Boot et al. (1993). This is part of the important ongoing discussion in economic theory about rules versus discretion, where discretion allows decision making based on more subtle—potentially non-contractable—information. A bank–borrower relationship is in many ways a mutual commitment based on trust and respect. This may allow implicit—no enforceable—long-term contracting with a bank in circumstances in which information asymmetries and the noncontractability of various pieces of information would rule out long-term access to alternative capital market funding sources as well as explicit long-term commitments by banks. Therefore, both the bank and the borrower may realize that their relationship produces value unattainable through other means and thus should be fostered .

Some empirical studies aim to find out the value added by relationship lending. In doing so, they measured the strength of a bank–borrower relationship and use this to evaluate the link between the strength and the added value of relationship banking. In other words, does the value added by relationship banking increase with the duration of the relationship? Typically, strength is measured by the duration of the bank–lender relationship. This research has produced several interesting insights. First, the duration of the bank–borrower relationship positively affects the availability of credit (Petersen and Rajan, 1994; Berger and Udell, 1995). Second, contract terms generally improve for the borrower over the life of the relationship: interest rates and collateral requirements fall. These results are consistent with the idea that relationship banking promotes value-enhancing exchange of information and that the longer

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the duration of the relationship, the greater the information exchange. Third, there is evidence of intertemporal smoothing of contract terms that could also contribute to the increased availability of funds to “young” firms (Petersen and Rajan, 1994, 1995).

Recently, relationship banking has become an important area of scientific inquiry. It has a distinct role to play and can be a value-enhancing intermediation activity. However, much more research is needed because existing work falls short in that it has not measured the precise sources of the added value of relationship banking. In the increasingly competitive environment of banking, the differentiation of distinct costs and benefits (and the empirical verification of it) is crucial in order to predict the viability and scale of relationship banking in the future .

Understanding the role of relationship lending in microfinance adds to the new field on the literature on borrower-bank relationship. Studies examining relationship lending have been mainly conducted in the US and were based on an influential paper by Stiglitz and Weiss that correspond to a novel stream of literature led by Petersen & Rajan , Allen N. Berger & Udell , Cole , Chakravarty & Scott , although there have also been studies outside the United States as Degryse and Cayseele , Angelini, Di Salvo and Ferri , Weinstein and Yafeh and Harhoff and Korting . The main findings of these studies shows that asymmetric information and loan efficiencies can be improved by means of accumulation of “soft information” that is generated in the process of interactions between borrowers and lenders, collectively defined as “relationships”. These studies revealed that relationship measures are correlated with loan availability and with loan rates .

The costs and benefits of relationship banking have been subjected to extensive empirical academic examination. As a result, some studies we pointed out some of the benefits and shortcomings that relationship bring:

2.1 On the benefits of Relationship Lending

The creation of proprietary information. It takes place when banks are originating and pricing loans. Subsequent monitoring of borrowers yields additional private information. Proprietary information generated during the relationship produces rents for the bank later in the relationship and permits early losses to be offset. Bhattacharya and Thakor (1993) determine that informational frictions—asymmetric (and proprietary) information—“provide the most fundamental explanation for the existence of (financial) intermediaries. The access to information is inherently linked to relationship banking and may point to a comparative advantage of banks. Then, relationship lending facilitate a continuous flow information between debtor and creditor that could guarantee uninterrupted access to funding.

The extension of contractual benefits. Relationship lending is a mechanisms that allows to get more informative credit contracting decisions based on a better exchange of information, and also increase the availability of credit to information-sensitive borrowers. Bank loan contracts include extensive covenants to guide the bank–borrower relationship. Covenants help control potential conflicts of interest and reduce agency costs (Berlin and Mester, 1992; Dennis and Mullineaux, 1999). In this regard, bank loan contracts can easily accommodate collateral requirements that can mitigate moral hazard and adverse selection problems in loan contracting (see Chan and Thakor (1987) and Stiglitz andWeiss (1981)). Berlin and Mester (1998) mention that in this context, inter-temporal transfers in loan pricing is also present .

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In contrast to the accumulation of soft information, if the lender anticipate a short-term relationship with the borrower, it may lead to a reduction on their relationship-specific investments. More specifically, anticipated shorter relationships inhibit the reusability of information and thus diminish the value of information (Chan et al., 1986). Banks may then find it less worthwhile to acquire costly proprietary information, and relationships suffer.

2.2 On the Costs of Relationship Lending. According with , there are two primary costs associated with relationship banking that has been described in the literature:

The soft-budget constraint problem. It has to do with the potential lack of toughness on the bank’s part in enforcing credit contracts that may come with relationship-banking proximity. The key question is whether a bank can credibly deny additional credit when problems arise. That is, a borrower on the verge of defaulting may approach the bank for more credit to forestall default. While a new lender would not lend to this borrower, a bank that has already loaned money may well decide to extend further credit in the hope of recovering its previous loan. The problem is that borrowers who realize that they can renegotiate their contracts ex post like this may have perverse incentives ex ante (Bolton and Scharfstein, 1996; Dewatripont and Maskin, 1995). That is, if renegotiation of a loan agreement is too easy, a borrower may exert insufficient effort in preventing a bad outcome from happening .

The hold-up problem has to do with the information monopoly the bank generates in the course of lending, that may allow it to make loans at non-competitive terms in the future to the borrower. The proprietary information about borrowers that banks obtain as part of their relationships may give them an information monopoly. In this way, banks could charge (ex post) high loan interest rates (see Sharpe (1990) and Rajan (1992)). The threat of being “locked in,” or informationally captured by the bank, may make the borrower reluctant to borrow from the bank. Potentially valuable investment opportunities may then be lost. Alternatively, firms may opt for multiple bank relationships. This may reduce the information monopoly of any one bank, but possibly at a cost. Ongena and Smith (2000) show that multiple bank relationships indeed reduce the hold-up problem, but worsen the availability of credit. One explanation is that multiple relationships can reduce the value of information acquisition to any one individual bank (see Thakor (1996)) or cause too much competition ex post, which may discourage lending to “young” firms .

2.3 The role of Loan Officers in Relationship Lending

Despite the theoretical importance of the loan officer in relationship lending, there has been very little empirical research on the role that they play. The empirical literature has tended to emphasize the link between the strength of the bank-borrower relationship and specific benefits such as credit availability and credit terms (e.g., Petersen and Rajan, 1994, 1995; Berger and Udell, 1995; Cole, 1998; Elsas and Krahnen, 1998; Harhoff and Körting 1998). However, these studies have not made a clear distinction between the bank and the loan officer, and do not directly investigate information production and the quality of information (i.e. soft versus hard) .

Other studies of relationship lending have tended to focus on the association between proxies for relationship strength and borrower benefits without consideration of the role of the loan officer (e.g., Petersen and Rajan 1994, Berger and Udell 1995, Harhoff and Körting 1998). However, some studies suggests that loan officers play a critical role in relationship lending by producing soft information. Uchida et al., empirically confirm this hypothesis and examine

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whether the role of loan officers differs from small to large banks as predicted by Stein (2002). Theory suggests that if the loan officer plays an important role in relationship lending, then we would expect to see a link between loan officer attributes and loan officer underwriting activities, and the production of soft information. For example, we would expect to see an association between the production of soft information and such things as the frequency of contact between the loan officer and the borrower, and the skill level of the loan officer.

The results of Uchida et al., are consistent with prior predictions in the literature on the importance of the loan officer. The authors find that some important loan officer attributes and activities are important in producing soft information about borrowers. Specifically, more soft information tends to be accumulated when loan officer turnover is less and when loan officer contact is frequent. In this study, it is was also examined whether soft information and the role of loan officers differs from small to large banks as predicted in the theoretical literature on relationship lending (Stein 2002). Consistent with the prior literature the authors find that small banks produce more soft information (e.g., Scott 2004, Berger et al. 2005). However, they do not find clear evidence that loan officer production of soft information – and relationship lending -- is limited to small banks . The only paper that has directly linked loan officers to soft information production is Scott (2006). The results of this study shows that loan officer turnover was negatively related to credit availability.

On the other hand, Liberti and Mian ( 2006) show that decision-making at the lower level of the bank hierarchy (i.e. closer to loan officers) is likely to be more soft information-intensive. This would suggest that banks that delegate more authority to their loan officers make more relationship loans and avoid the dilution of soft information by transmitting it through layers of organizational hierarchy. Another study showed that hard public information may not be used in loan underwriting when banks have a strong relationship with the borrower, and that soft information, if available, is the driving determinant in loan underwriting (García-Appendini (2007)).

There are also a number of studies that indicate that the role of loan officers may be special. Some of these studies focus on aligning the incentives of loans officers with shareholder maximization (Udell 1989, Hertzberg, Liberti, and Paravisini 2007).

In the microfinance context, loan officers play a major role in screening potential customers. They also play the key role in the decision process of allowing the credit and are responsible for the follow up of the loans. Some of the tasks of credit officers can be best described in four categories: generating new business (identifying new customers), analysing the loans applications, monitoring and following-up the active loans and generating reports and statistics (Holtmann and Grammling, 2005, p. 53). All this activities create an opportunity to accumulate soft information that can be used to improve loan performance. Even so, the real contribution of this agents has not been documented.

According with Szafarz , Loan Officers in the context of microfinance are supposed to visit the clients, analyse their total cash-flow cycle, and make sure that the margin generated by this entrepreneur is big enough to cover the cost of credit, that the client have the right kind of collateral, and that frequent repayment will be possible.

In our study, we define the role of loan officers in the context of PROMUJER-Mexico as being versatile and including responsibilities such as:

- To carry out the promotion of the microfinance program among potential borrowers

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- To visit potential borrowers’ business and houses together with the group members or in an individual basis to see the viability of their projects

- Monitoring of repayment sessions of Communal Associations in which the village banking methodology should have a correct application

- Help the board or group members to sort out conflicts in case they emerge - Provide training to borrowers in health, education, financial issues, self-confidence,

among others that are in line with the aim of the institution.

In practice, Loan’s Officers activities requires great effort and high social commitment to do their job, which requires them to apply the policies of the institution in line with the social mission. As some Loan Officers within MFIs do not have the desirable profile or do not like to do this job opt to leave the institution in early stages, this leads to a high rotation of Loan Officers, which in turn treats the accumulation of soft information and the relationship lending with the institution. This can have a further negative impact on loan performance of particular branches depending on how staff of the organization is handling such process.

Based on the previous studies on relationship lending in banking and the role of loan officers in microfinance we expect a positive correlation between loan performance and relationship lending. In particular, we expect that Loan Officers in Promujer play a key role in the generating process of soft information from which the institution may benefit. In particular, we assume that there is an efficient transmission of soft information within the branches in PROMUJER as many of them ascend to hierarchical levels, as managers or regional managers. Then, the information about the quality and behaviors of clients with a particular branch remains among different staff members. In the following paragraphs, we briefly describe some information about the microfinance institution under study.

3 BACKGROUND INFORMATION ON PROMUJER-MEXICO

This research was carried out in collaboration with PROMUJER-Mexico, an international Microfinance Organization (MFI) working in Latin America. This organization is a an non-profit organization working with the village banking methodology that started operations in Bolivia in 1990 and later on it expanded to other countries in the region such as Nicaragua(1996), Peru (2000), Mexico (2002) and more recently in Argentina (2005).

The aim of PROMUJER to help women in initiate their own business or improve the one that they has to increase their sells and business development, have access to savings, to a continuous credits and to achieve personal development. In general, this organization is support poor entrepreneur women of modest means, lacking physical collateral and mostly with no verifiable credit histories by providing them with financial services (e.g. micro-credit, training to improve their business, health support and insurance) to progress their livelihoods. It is an organization for women’s development to reduce poverty by providing integrated financial services that help individuals to improve their well-being through leadership and self-determination, health’ support to their clients, strengthening of self-esteem, women’s

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empowerment and entailment of women and their families with services and resources in their communities.

In Mexico, PROMUJER started in 2002 in the state of Hidalgo—in the Central region. However, recently it has extended their operations to other states in the regions such as Mexico, Puebla, Distrito Federal, Tlaxcala, Veracruz and Queretaro. Currently, this organization has more than 22, 000 borrowers, 90% of which are women and has a loan portfolio of more than $5 million MEX pesos. This organization is operating through branches called Centros Focales (CFs) or agencies where the financial services are disbursed to their customers. The amount of credit and the conditions varies according to the credit cycle or contract in which the borrower is. In Table 1 we can observe some descriptive information about the different contracts and its characteristics.

Table 1. Characteristics of the credit contracts in PROMUJER-Mexico.Contract Frequency

(1=weekly, 2=biweekly)

Maturity (months)

Size(Mex. Pesos)

1 1 3 20002 1 or 2 4-5 35003 1 or 2 4- 6 50004 1 or 2 4 - 6 75005 1 or 2 4 - 6 100006 1 or 2 4 - 6 12500

Source: Authors’ compilation with information from PROMUJER.

The financial services that PROMUJER provides to their customers are: credit for business development or for starting one, obligatory savings that correspond to 10% of the amount of credit at the end of the cycle, voluntary savings within the group, education which is given in the trainings sessions of the group meetings on topics such of human development (gender, self-esteem, intra-family violence, communication, depression, women’ rights) and in financial education (business administration, accounting, etc.). In addition to this services, complementary services are offered by other socially oriented institutions which which PROMUJER has links as the National Institute for the Adult’s Education (INEA), Center of Juvenile Integration (CIJ), National Network of Refugees for Mistreated People and some Hospitals.

The methodology of PROMUJER is based in the social collateral, which is aimed at the exchange of experiences among their clients in topics in relevant topics of development such as: confidence, create social ties and networks of commercialization among their clients. In particular, this methodology is well known in the literature as Village Banking Methodology and consist in form groups between 8 to 35 members among which 1 or 2 maximum can be males. The Village Banks (or “Communal Associations”) are formed based on advertising that Loan Officers and Bosses of the Centros Focales (or branches) are doing. This advertising take place in open places such as parks, school and shops. In this promotion, can staff of the organization talk with potential borrowers who attend group meetings to receive some explanation about the rules of the program. In this meetings, the Loan Officers prepare documents to formally register the Village Bank with persons who decide participate in the microfinance program. Another way of promoting the formation of new groups is that actual clients talk with their neighbors about the institution and the criteria to become a member, which is called “from mouth to mouth”. In addition, interested people call to the branches to

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get information on the rules of the program and in how to form a group to become a client of the microfinance organization.

This research focus on the relationship lending between borrower and loan officers of 18 branches of PROMUJER-Mexico, which are operating in two regions: 1) The Mexico Region, which has three sub-regions: Mexico-North, Mexico-Northern and Distrito Federal and 2) The Puebla region. This selection is based in previous knowledge about the institution in terms of loan repayment and default rates. For instance, the Mexico region had been characterized by registering low loan default rates while the Puebla region all the opposite. As a result, we hypothesize that differences in loan performance between the two regions can be explained not only by differences in the institutional environment in which the branches are operating (e.g. competition, financial deepening, degree of marginalization, among others) but mostly because of institutional factors which are not region-specific but mostly branch-specific. In this regard, we believe that the accumulation of “soft information” that the relationship lending predicts to be attained by loan officers and other staff members of the organization about their borrower helps to mitigate asymmetry of information and improve loan efficiencies. This is very likely to occur at the branch level as interactions between borrowers and staff members of the organization (Loan Officers, Managers of Centros Focales and Operative Responsible) takes place.

Some characteristics of the portfolio at risk

The portfolio is classified from major to minor risk of recovery as function on the age of the default. As a general rule, the portfolio of microcredits should be considered in the category of maximum risk at the 90 days of default.

Table 2. Classification of the portfolio at RiskCategory Criteria of classificationI Up-to-date with default no greater to 5 daysII Is found with a default between 31 and 60 daysIII Is found with a default between 61 and 90 daysIV Greater than 90 daysSource: PROMUJER archivesThere are two types of risks for which it is necessary create previsions:

i. the specific risk of non-payment of the creditii. Generic prevision

In the occurrence of default, the internal rules of the institution indicate a particylar procedure, which is described below.

The Default Committee: It is a body that monitor and takes decisions on the portfolio at risk and is under the responsibility of staff members of the organization. This Commite is integrated in at the group level and in two bodies of revision: Commite of Default in CF (Loan Officers of AC, Bosses of CF) and Commite of Default with Regional Manager (Boss of CF, Regional Manager and Manager of Credits). The following section provides an overview of the study region.

4 THE STUDY REGION

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For the purpose of this study, we conducted the interviews in 18 branches of PROMUJER-Mexico, which are operating in four states: Mexico, Puebla, D.F. and Tlaxcala. These branches are geographically distributed in two regions (Mexico and Puebla) and in four sub-regions which are described as follows:

1. Mexico-Northern. This region includes 5 branches in total: Tonanitla, Teoloyucan, Tepotzotlan, Huehuetoca and Tultepec. All branches register very low default rates which are of less than 1% and makes this region one of the most efficient in terms of loan repayment. It is a densely populated region and nearby to Distrito Federal to the North. The Municipalities in this region have indices of marginalization very low relative to the rest of the country, which implies that they have almost all public services available (potable water, electricity, drainage, telephone, roads, etc).

2. Mexico-North. This region encompasses 5 branches: Texcoco, Teotihuacan, San Cristobal Ecatepec, Jardines de Morelos, Tecamac and Ciudad Azteca. This region is the second high-performing region with respect to loan repayment. However, recently some branches in this region are facing high rotation of personnel, mainly loan officers and therefore they are taking actions to improve their mode of operate and the quality of their portfolio.

3. Distrito Federal. This is one of the sub-regions in which PROMUJER recently started operations at the beginning of 2010. There are only two branches (Tlahuac and Xochimilco) which are working in some Municipalities of the Distrito Federal which still is living rural population with some degree of marginalization although Mexico City is almost an urban region characterized by high population density and very low degree of marginalization in the different Districts or Delegations.

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4. Puebla. This region encompasses six branches: Puebla, Coronango, Zacatlan, Huauchinango, Huamantla and Cuapiaxtla. The last two branches are located in Tlaxcala while the first four are in Puebla. A characteristic of some of this branches is that they have high default rates among clients. With exception of CF Puebla and CF Coronango, the other branches targeting rural clientele who is living in Municipalities with high degree of Marginalization. In addition to that, the competition with other MFIs is relatively high in this region and the loyalty of borrower is diminished in some branches and/or for particular Loan Officers.

5 DESCRIPTION OF THE DATA

The data used for this study is coming from field interviews with borrowers and Loan Officers in PROMUJER-Mexico from December, 2011 to March, 2011. A questionnaire was prepared with quantitative and qualitative information to collect information on the relationship between borrowers and Loan Officers, on personal characteristics, in loan characteristics and in institutional characteristics of the 18 branches that were sampled in the two regions under study (Mexico and Puebla).

13

IVTlaxcala

IIIDistrito Federal

IVPuebla

I

II

The selection of borrowers was bases on information provided by staff members of PROMUJER in the 18 branches about the portfolio of the borrowers. We ask for the total number of clients that each particular Loan manager had at the time of the interview and the distribution of clients per Communal Association. With this information, we derived a list of random numbers corresponding to the total portfolio of clients that each particular Loan Officer had at the time of the interview. From this list, we randomly selected on average 10 borrowers per Loan Officer. This number correspond to approximately 5% of the portfolio that Loan Officers has at the time of the interview. There are some cases in which only 5 borrowers were selected from Loan Officers that were entering the institution as they had a relatively small portfolio compared with Loan Managers working in the institution for more than one year. Once the list of random number was organized, we verified archives of Communal Associations that were in the random sample to identify the names of borrowers to be interviewed.

In total, we applied 441 interviews to borrowers of 18 branches of PROMUJER with the help of 26 trained enumerators. We complemented this information with interviews from 52 Loan Officers working with the borrowers in the sample at the time that the data was collected. With this information, we constructed a set of indicators consisting of 777 observations that correspond to the last two credit cycles of the borrower’s credit history. Table 1 displays the distribution of observations across regions and branches.

Table 3. Total Number of Observations per Branch and Number of Interviews

RegionBranch or

Centro Focal#

Borrowers# Loan Officers

Total Interviews* # obs.

MEXICO  

Texcoco 26 3 29 47

San Cristobal 6 2 8 7

Teotihuacan 34 4 38 64

Tecamac 28 3 31 50

Jardines de Morelos 20 3 23 40

Tonanitla 43 2 45 83

Teoloyucan 31 3 34 52

Tepotzotlan 18 2 20 33

Huehuetoca 20 2 22 35

Tultepec 30 3 33 60

Tlahuac 19 2 21 19

Xochimilco 20 2 22 33

Sub-total 295 31 326 523

PUEBLA

Puebla 28 5 33 45

Coronango 11 2 13 21

Huamantla 26 4 30 46

Cuapiaxtla 20 2 22 31

Zacatlan 30 4 34 60

Huauchinango 31 4 35 50

  Sub-total 146 21 167 253

TOTAL 441 52 493 777 Source: own elaboration with data from PROMUJER-Mexico. April (2011).

14

As we can see, the distribution of observations is unbalanced. This is because the sample of borrowers was randomly selected and some new branches (Tlahuac, Ecatepec, Coronango and Xochimilco) were included. As a result, there are some borrowers that are only in the first credit cycle and have not yet initiated a second cycle leading to only one observation per borrower. In addition, the sample was taken proportionally to the number of borrowers that the Loan Officer had at the time of the interview. It means that for some Loan Officers it was not necessary to select the same proportion of borrowers from their portfolio as they targeting only a few ones. In the following section we present the Methodology we use in our study.

6 METHODOLOGY

In order to understand the causal relationship lending bank-borrower in microfinance, we estimated a hierarchical model where units are nested in groups or clusters. In three-level models, the clusters themselves are nested in super clusters, forming hierarchical structures. For instance, we may have repeated measurement occasions (borrowers) for Loan Officers (clusters) who are clustered in branches (super clusters). This three-level design is depicted as follows:

Model 1. The Branch-Loan Manager-Borrower Relationship Hierarchical Model:

Branch Level 3: Branches k

Loan officer 1 Loan Officer 2 Loan Officer n Level 2: Loan Officers j

B1 B2 B1 B10 B1 B40 Level 1: Borrowers i

The empirical model can be defined as follows:

Where:

Yit : Outcome indicator (default) is defined as follows:

Default. This indicator takes a value of 1 if an individual borrower has a positive value in the

group meetings in one of the three Village Bank’s performance indicators: FSC, FCC or T and

0 otherwise. Here, the performance indicators are defined as:

15

1) Missing without Cuota (FSC). This indicator means that the borrower did not paid their instalment during the group meetings, and therefore she/he is incurring in “internal loan default”, which is threatening the performance of the whole group during the particular credit cycle.

2) Missing with Cuota (FCC). This measure means that the borrowers did not attended the meetings but instead, they sent their “Cuota or Instalment” with a group member or relative, which is less serious than the first case but still can be considered as “internal default” as only the members who attended the meetings are guarantying for the payments that were missing during the group meeting and they cannot easily enforce the use of “social collateral” among group members.

3) Late (T). This third indicator means that the borrower arrived 5 or 10 minutes late or even more to the group meeting, which according with the rules of the group is a serious issue and deserves a sanction. As the majority of group members arrived timely to the group meeting, borrower who arrive late are threatening the performance of the whole group and therefore they incur in “internal default”.

X1: Vector of relationship variables to measure the scope, length and quality of the relationship between borrowers, Loan Officers and the specific branch.

X2: Vector of borrower characteristics such as size of the household, dependency ratio, if the borrower belongs to a one or more microfinances organizations, if own a house.

X3: Vector of Loan Officer characteristics such as sex, education, religion, dependency ratio, if the loan officer was former client in PROMUJER, is she/he had a previous job, if being Loan Officer is the first job, and if she/he has a social or accounting career.

X4: Vector of loan characteristics such as amount, size and maturity that capture the specifics of the microloans

X5: A vector of variables related with the institutional environment in which the borrowers are living such as water, drainage, electricity and road.

For this study we use a three-level random-intercept logit model to assess the probability of

default y with different borrowers i nested in loan officers j who are nested in branches k:

16

is a vector containing all covariates

is a random intercept varying over loan officers (level 2)

Where:

The model can alternatively be written as a latent-response model:

The observed dichotomous responses are presumed to be generated from the threshold model:

The observed dichotomous response are presumed to be generated form the threshold model:

We can consider in this model different types of interclass correlations for the latent responses

of two

For the same branch k but different loan officers j and j’, we have:

Whereas for the same loan officer j (and the same branch k), we get:

17

is a random intercept varying over branches (level 3)

is assumed independent of each other and across clusters

is assumed independent across units

Where: has a logistic distribution with variance /3

If y*ijk>0

Otherwise

In a three-level model, (2)>0 and (3)>0, and it follows that (loan officer, branch)>

(branch). This makes sense since borrowers of a given loan officer are more similar than

borrowers from the same branch but with different loan officers.

In this model we can quantify the unobserved heterogeneity by considering the median odds

ratio for pairs of randomly sampled units having the same covariate values, where the unit with

larger random intercept is compared with the unit of the smaller random intercept.

In this case, if we compare borrowers of different loan officers in the same branch, give the

median odds ratio:

And when comparing borrowers of different loan officers from different branches we get:

After estimating the two different models that we aimed at this study (at the branch and

regional level), we arrived to the results that are presented in the following section.

18

7 ESTIMATION RESULTS

Table 4. Estimation Results on the Probability of Loan Default as a function of Relationship Lending

Model: 3-level Random Intercept Model (GLLAMM) at the Borrower –Loan Officer- Branch Level

Variable Definition of Variables

Model 1: Relations

hip Variables

Model 2: Relationship & Borrower

Characteristics

Model 3: Relationship &

Loan Officer Characteristics

Model 4: Pooled Model

Dependent variable: DEFAULT

1 if a borrower defaulted in group meetings, 0 Otherwise

PANEL 1. RELATIONSHIP VARIABLES

# CONTRACTS BORROWER-PROMUJER

Number of credit contracts between the Borrower-PROMUJER -0.0237 -0.0308 -0.0219 -0.0341

[0.533] [0.430] [0.562] [0.412]

ROTATION OF LOAN OFICERS

Ratio (No. Loan Officers/No. Contracts (Cycles) in the borrower's credit history) -0.743** -0.756** -0.665* -0.599

[0.047] [0.047] [0.075] [0.107]

# CONTRACTS BORROWER-LOAN OFFICER

Total credit contracts between Borrower- Loan Officer -0.145 -0.151 -0.108 -0.118

[0.159] [0.151] [0.302] [0.264]

FREQUENCY OF GROUP MEETINGS

Frequency of the group meetings (1=weekly, 2=biweekly) -0.19 -0.18 -0.2 -0.469*

[0.414] [0.454] [0.372] [0.099]

DIFFERENCE IN AGENo. of years of difference in age between Borrower-Loan Officer -0.0163* -0.0138 -0.0154 -0.0127

[0.092] [0.168] [0.120] [0.206]

DIFFERENCE IN SEX1 if differences in sex between Borrower-Loan Officer -0.483* -0.407 -0.268 0.246

[0.088] [0.200] [0.627] [0.763]

DIFFERENCE IN EDUCATION No. of years of difference in education -0.021 -0.0238 -0.0362 -0.0349

[0.392] [0.346] [0.201] [0.188]

DIFFERENCE IN RELIGION1 if the religion differ between the borrower and the Loan Manager 0.148 -0.0193

[0.532] [0.955]

BORROWER IN OTHER MFIs

1 if the borrower belongs to other MFI, 0therwise 0.0516 0.0697 0.195

[0.800] [0.723] [0.341]

19

BORROWER IN THE BOARD1 if borrower was in the Board of the group, 0 Otherwise -0.298 -0.297 -0.328

[0.133] [0.127] [0.105]

PANEL 2. BORROWER CHARACTERISTICS

SEX 1 if female, 0 Otherwise 0.0787 0.497

[0.892] [0.528]

RELIGION 1 if Catholic, 0 Otherwise -0.235 -0.185

[0.396] [0.631]

DEPENDENCY RATIO1 if the borrower own a house, 0 Otherwise 0.52 0.535

[0.121] [0.111]

INSURANCE IN PROMUJERIf the borrowers has insurance in PROMUJER, 0 Otherwise 0.332 0.342

[0.223] [0.206]

PANEL 3. LOAN OFFICER CHARACTERISTICS

SEX 1 if female, 0 if male 0.286 0.529

[0.638] [0.535]

# YEARS OF EDUCATIONNo. years of Education of the Loan Officer 0.0341 0.0900**

[0.541] [0.028]

RELIGION OF 1 if Catholic, 0 Otherwise 0.0468 0.0474

[0.308] [0.339]

SOCIAL CARREER1 if Loan officer has a social career, 0 Otherwise 0.668** 0.783***

[0.035] [0.007]

ACCOUNTING CARREER1 if the Loan Officer has an accounting career, 0 Otherwise 0.751** 0.679**

[0.029] [0.037]

DEPENDENCY RATIODependency ratio (Number of Dependents/Total Family Members) 1.485** 1.386**

[0.047] [0.024]

FORMER CLIENT IN PROMUJER

1 if the Loan Officer was former client in PROMUJER, 0 Otherwise -0.176 -0.208

[0.511] [0.390]

FIRST JOB1 if being Loan Officer is the first job, 0 Otherwise 0.323 0.376

[0.300] [0.258]

FORMER WORKER IN OTHER MFI

1 if the Loan Officer was former employee in other Microfinance Organizations -0.0966 0.0656

[0.814] [0.861] PANEL 4. LOAN CHARACTERISTICS

AMOUNTLog of the total amount of credit borrowed (MEX pesos) -0.196

[0.251]

20

MATURITY Length of the credit contract (months) 0.209**

[0.040]

# CLIENTS IN THE GROUPNo. of clients of the Communal Association 0.0204

[0.418]

PANEL 5. ENVIRONMENTAL CHARACTERISTICS

REGION1 if Mexico Region, 0 if Puebla Region -0.908*

[0.052]

WATER

1 if the borrower has potable water available in her/his house, 0 Otherwise -0.179

[0.697]

PHONE

If the borrower has fixed telephone in the household, 0 Otherwise 0.308

[0.107]

ROADIf the borrower has road in the community, 0 Otherwise -0.231

[0.317]

_cons Intercept 0.703

[0.756]

p-values in brackets

* p<0.10, ** p<0.05, *** p<0.01

As we can see from the estimation results, the probability of default the personal

characteristics of borrower and loan officers as well as the loan characteristics seem to be more

likely to impact the probability of default than the relationship variables that we think may

influence the outcome indicator.

In particular, two variables that resulted to be statistically significant with respect to

relationship lending are the ROTATION of Loan Officers and the Frequency of the Group

Meetings. In the following paragraphs we explain the individual impact that each one of this

variables has on the probability of default.

The results show that the higher the ROTATION of Loan Officers (No. Loan

Officers/No. Contracts or credit cycles), the lower the probability of default at 4% significance

level in the first two models and marginally at 10% in the pooled model. It means that if the

number of the Loan Officers is higher with respect to the total number of contracts, there is no

entire stability in the relationship between the incumbent borrower and the assigned Loan

21

Officer to the Communal Association. This higher rotation of Loan Officers can prevent a

more mature relationship and the accumulation of soft information but at the same time may

prevent collusion problems between both agents which can treat the loan performance as

expressed by a higher probability of loan default.

The other variable referring to relationship lending is the frequency of the group

meetings. This variable was statistically significant at the 9% significance level in the pooled

model. This result indicated that the higher the frequency of interactions between the borrower

an the corresponding Loan Officer in the group meetings, the lower the probability of default.

This result is in line with previous empirical studies on relationship lending in finance.

With respect to the Loan Officer characteristics, the variables SOCIAL CARREAR

which takes the value of 1 if the Loan Manager has a social career such as Psychologist, and

Social Worker resulted to be statistically significant at 1% significance level. It means that the

social orientation of a Loan Officer increases the probability of default. This result is not what

we expected as we expected a negative correlation. However, a plausible explanation for this

outcome is the fact that Loan Offices who has a non-social career may be very committed to

not only provide social services but also to achieve high repayment rates as many of them

have developed skills in management, accounting and marketing that may lead to enhance the

quality of their portfolio. In addition, the borrowers may know that in case of individual

default or a complete default of the group, social oriented Loan Officers may feel hard to

enforce the contract then leading to an ex-ante moral hazard problem on the side of the

borrower.

We also find that if Loan Offices have a career in accounting such as Accountant,

Business Administration, Managements Studies or alike, increases the likelihood of default. It

means that even when they have some skills to improve the quality of their portfolio in terms

or repayment and good administration, they still need some skill to improve the repayment

rates in the group meetings.

Finally, the MATURITY of loans increase the probability of default at 4% significance

level. It means that the lager the duration of the contract in months, the higher the probability

that a borrower default. For instance, if the group set up contracts of 8 or 9 months instead of

shorter contracts of 5 or 6 months, it leads to worsen the loan performance as the variability of

22

cash flow of the projects that borrowers undertake must be higher and it is likely that in some

meetings they default, as their projects may be related in the case that a bad state occur.

8 CONCLUSIONS

The main conclusion from this study is that relationship has an impact on the

probability of loan default in microfinance group lending in Mexico. However, the direction of

the changes are not as we expected. In particular, the individual characteristics of Loan Offices

seem to have a higher impact on the probability of default. A possible explanation for this may

be that relationship lending originates and show to have an impact not in the short-term with

only one or two contracts but mostly in the long-term where multiple interactions have taken

place already. In particular, we think that the nature of the branches that we are analysing in

our sample is quite particular. Most of the branches are new (have from 0 to 2 yearsold) and are

just starting a relationship lending with their customers. There are however some branches in

the sample (Tecamac and Tultepec in Mexico) that started since five years ago and the impact

of relationship lending can be more visible in these particular branches than in the new ones.

Then, the results of this preliminary can be complemented with more indeed research at the

bran level with the information that have been gathered from which we can get more insight

on the overall impact of relationship lending in loan performance.

END NOTES

1. “Soft information” is associated with “relationship lending” and hard information with “transactions-based lending” (e.g. Stein 2000 and Berger and Udell 2002). In contrast to hard information, soft information is not easily quantified and consists of information gathered over time through contact with the firm, the firm’s management/entrepreneur, the firm’s suppliers and customers, and other local sources (Uchida, H., et al., 2006).

2. The term microfinance encompasses a wide range of financial services to the poor that includes microcredit, savings and insurance .

3. Centro Focal (or branch). Physical places where PROMUJER delivers comprehensive services that provides to its customers.

4. Credit Information Bureau. It is a legally established institution whose purpose is to provide update information on the level of personal indebtedness, based on information generated by financial institutions and / or commercial entities.

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Berger, A. N. (2007). International comparisons of banking efficiency. Financial Markets, Institutions and Instruments, 16(3), 119-144.

Berger, A. N., & Udell, G. F. (1995). Relationship Lending and Lines of Credit in Small Firm Finance. The Journal of Business, 68(3), 351-381.

Berger, A. N., & Udell, G. F. (2002). SMALL BUSINESS CREDIT AVAILABILITY AND RELATIONSHIP LENDING: THE IMPORTANCE OF BANK ORGANISATIONAL STRUCTURE. The Economic Journal, 112(477), F32-F53.

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Copestake, J., Bhalotra, S. and Johnson, S.,. (2001). Assessing the impact of microcredit: A Zambian case study. The Journal of Development Studies, 37(4), 81-100.

Cull, R., & Demirgüç-Kunt, A. (2006). Financial Performance and Outreach: A Global Analysis of Leading Microbanks. SSRN eLibrary.

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Khandker, M. M. P. a. S. R. (1998). The impact of group-based credit programs on poor households in Bangladesh: Does the gender of participants matter? . The Journal of Political Economy, 106(5), 958-996

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McKernan, S.-M. (2002). The Impact of Microcredit Programs on Self-Employment Profits: Do Noncredit Program Aspects Matter? Review of Economics and Statistics, 84(1), 93-115.

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APPENDIX

25

Table 1. Descriptive Statistics of the Main Variables in the Model

26

Variable Description of the VariableNo.

Obs. Mean Std. Dev. Min Max

DEFAULTS1 if a borrower defaulted in group meetings, 0 Otherwise 777 0.51 0.50 0 1

PANEL 1. VECTOR OF RELATIONSHIP VARIABLES

NO_CYCLESNumber of credit contracts between the Borrower-PROMUJER 777 3.62 2.98 1 21

DURATION_PROMUJERLength of the relationship between the Borrower and PROMUJER (months) 777 20.49 18.09 0.5 96

TOTAL_LMTotal number of Loan Officers in the borrower's credit history 777 1.85 1.07 1 9

ROTATIONRatio (No. Loan Officers/No. Contracts (Cycles) in the borrower's credit history 777 0.68 0.35 0.08 3

C_CYCLES_LMTotal credit contracts between Borrower- Loan Officer 777 1.70 1.03 1 11

DURATION_LMLength of the relationship Borrower-Loan Officer (months) 777 9.12 10.01 0.5 108

DIF_AGENo. of years of difference in age between Borrower-Loan Officer 775 13.26 9.69 0.03 51.1

DIF_SEX1 if differences in sex between Borrower-Loan Officer 777 0.26 0.44 0 1

DIFF_EDUCNo. of years of difference in education between borrower and Loan Officer 777 6.67 4.15 0 20

DIF_RELIGION1 If religion between borrower and Loan Officer is different, 0 Otherwise 777 0.32 0.47 0 1

PANEL 2. VECTOR OF BORROWER CHARACTERISTICS

HHMEMBERS_BORNumber of household members for the borrower 777 4.93 2.02 1 16

DEPRAT_BORRDependency ratio (Number of Dependents/Total Family Members) 777 0.50 0.29 0 3.33

OWN_HOUSE 1 if the borrower own a house, 0 Otherwise 777 0.72 0.45 0 1

INSURANCEIf the borrowers has insurance in PROMUJER, 0 Otherwise 777 0.87 0.34 0 1

ANT_BORRNo. of years the borrower is living in the community 777 21.28 14.11 1 74

D_OMFI1 if the borrower belongs to other MFI, 0therwise 777 0.28 0.45 0 1

TOTAL_OMFITotal Number of MFIs in which the borrower has a membership 777 1.33 0.57 1 3

D_BOARD1 if borrower was in the Board of the group, 0 Otherwise 777 0.69 0.46 0 1

PANEL 3. VECTOR OF LOAN OFFICER CHARACTERISTICS

EXP_LM_PROMUJERExperience (months) of the Loan Manager in PROMUJER 776 15.30 12.69 1 54

LM_SOCIAL_CARR 1 if Loan officer has a social career, 0 Otherwise 777 0.38 0.49 0 1

DEPRAT_LMDependency ratio (Number of Dependents/Total Family Members) 776 0.37 0.20 0 0.75

LM_OMFIs1 if borrower was former Loan Officer in other Microfinance Organizations, 0 Otherwise 777 0.17 0.38 0 1

LM_CLIENT1 if the Loan Officer was former client in PROMUJER, 0 Otherwise 777 0.41 0.49 0 1

LM_PREV_JOB1 if being Loan Officer is the first job, 0 Otherwise 777 0.13 0.33 0 1

LM_COMTime the Loan Officer is living in the community (years) 777 21.19 11.20 0.5 57

PANEL 4. VECTOR OF CREDIT CHARACTERISTICS

AMOUNT Total amount of credit borrowed (MEX pesos) 7777234.2

3 4694.79 500 20000

MATURITY Length of the credit cycle (months) 777 5.09 1.16 0 8

FREQ_AFrequency of the group meetings (1=weekly, 2=biweekly) 777 1.70 0.47 1 3

CLIENTS_CA No. of clients of the Communal Association 777 12.21 4.03 8 30

PANEL 5. VECTOR OF INSTITUTIONAL ENVIRONMENET CHARCATERISTICS 777 0.00 0.00 0 0

27

D_WATER1 if the borrower has potable water available in her/his house, 0 Otherwise 777 0.94 0.23 0 1

D_DRENAJEIf the borrower has drainage available in her/his house, 0 Otherwise 777 0.86 0.34 0 1

PHONEIf the borrower has fixed telephone in the household, 0 Otherwise 777 0.45 0.50 0 1

ROADIf the borrower has road in the community, 0 Otherwise 777 0.74 0.44 0 1

Table 2. Correlation Matrix of the Outcome Indicator (Default) and the Relationship Variables

DEFAULTS NO_CYCLESDURATIONPROMUJER

TOTAL_LM ROTATION

C_CYCLES_LM

DURATION_LM

DIF_AGE

DIF_SEX

DIFF_EDUC

DIF_RELIGION

DEFAULTS 1

NO_CYCLES -0.0026 1 DURATION_PROMUJER 0.0038 0.7741 1

TOTAL_LM -0.0278 0.5355 0.4354 1

ROTATION -0.0558 -0.5923 -0.4742 0.0745 1

C_CYCLES_LM 0.0012 0.2444 0.2057 -0.1612 -0.5343 1

DURATION_LM 0.0292 0.1994 0.2619 -0.0578 -0.3283 0.3967 1

DIF_AGE -0.0876 0.0483 0.1146 0.0451 -0.0393 -0.001 0.0346 1

DIF_SEX -0.1251 -0.0616 -0.0347 -0.0247 0.0629 -0.0696 0.0198 0.0612 1

DIFF_EDUC -0.0055 0.0158 -0.0153 0.0324 0.0317 -0.0075 0.0231 0.2092 0.1011 1

DIF_RELIGION 0.0077 -0.0152 -0.0333 0.0452 0.0643 -0.1163 -0.0835 -0.0132 -0.0723 -0.0569 1

28