Signaling Creditworthiness in Peruvian Microfinance Markets: The Role of Information Sharing

Transcription

1 Signaling Creditworthiness in Peruvian Microfinance Markets: The Role of Information Sharing Verónica Frisancho Robles September Introduction Formal lenders usually find it too expensive to serve poor borrowers in developing countries. The lack of traditional forms of collateral and the high costs of monitoring small scale transactions translate into high interest rates that end up credit rationing the poor. Although microfinance institutions (MFIs) first emerged to tackle this market failure, they can also facilitate the development of credit histories that can be used as a creditworthiness signal with other lenders. Credit information systems that expose a personalized credit relationship with an MFI to a larger market can thus reduce screening costs for other lenders, improving borrowers access to credit. Despite the potential importance of credit information systems for the alleviation of credit constraints, too little is known about their specific effects in microfinance markets. Even less is known about one-sided increases in the credit information available. In an attempt to address these gaps in the literature, this paper evaluates the effect of an MFI s unilateral decision to increase the degree of information shared with other lenders on access to credit for its borrowers. 1

2 The specific objective of this study is to measure the differential effects of increased information sharing for heterogenous borrowers from FINCA-Peru, one of the leading MFIs in the country. Between 1999 and 2004, FINCA unilaterally increased the amount of information shared with the credit market. Before August 2004, the MFI only shared group default records but after that date, individual outstanding debt records were also exposed. This decision increased the degree of information available to other lenders in the market but did not affect the level of information FINCA had access to. As a result, some borrowers may have looked more creditworthy than before and thus become more attractive for other lenders. In particular, exclusive FINCA borrowers who used to be lumped together with individuals who were too risky to obtain a loan, could have experienced an increase in their access to credit outside the MFI. Although some borrowers might gain from the policy through better access to credit, FINCA could have been negatively affected if default rates went up in the institution. When analyzing the evolution of the percentage of borrowers with late payments to a lending group sponsored by FINCA, there is clear break in intercept and slope after the MFI increases the degree of information shared. This paper tries to show that this raise in default rates could be explained by improvements in access to credit for some borrowers initially served exclusively by FINCA. Even though the focus will be on borrower-level effects, the results presented here also shed some light on the institutional effects of information sharing. All over the developing world, MFIs have been increasingly trying to share information about their clients performance as a discipline device, but little is know about the consequences of such decisions. If information sharing facilitates client poaching and increases default rates it might not always be in the unilateral interest of the MFI to reveal its clients credit records. In this case, legal regulations requiring universal improvements in the fineness of the information set available to all lenders may be required. Previous developments in the theoretical and empirical literature have usually focused 2

3 on the average effects of a universal increase in information for all lenders. In this line, theoretical research on formal banking institutions (see [11], [16]) suggests that exchanging detailed information on outstanding debt or client characteristics can dilute the clarity of default as a negative signal. In addition, empirical cross country evidence suggests that information sharing is associated with broader credit markets and the alleviation of credit constraints (see [5], [6], and [3]). In contrast, the few theoretical, [9], and empirical, [7], studies available on microcredit markets suggest that symmetric increases in the information observed by all lenders tend to reduce default rates. Surprisingly, not a single study analyzes the effects of unilateral decisions to augment the fineness of the information set available to other lenders. Nor has anyone tried to identify the differential effects that additional information might have on heterogenous borrowers. Therefore, the contribution of this paper is threefold. First, it departs from previous efforts that concentrate exclusively on universal increases of credit information by analyzing FINCA s unilateral decision to increase the information revealed about its clients. Second, it tests the implications of a unilateral increase in the level of information using micro-level data. Finally, it identifies differential effects for borrowers who vary in terms of their credit histories and existing outstanding debt at the time their credit information is extended. The paper proceeds as follows. Section 2 briefly describes the theoretical and empirical literature on the effects of credit information systems in credit markets. Section 3 offers a general description of credit and microfinance markets in Peru, a basic profile of FINCA- Peru, as well as a detailed description of the change in FINCA s information sharing policies during the period analyzed. The data used and the estimation strategy are described in Section 4, while Section 5 presents the results. Finally, Section 6 concludes and describes the limitations of the study, as well as directions for future research. 3

4 2 Related Literature Although there is a large body of theoretical work on the effects of asymmetric information on credit markets 1, less work has been done on the effects of information sharing between lenders. Initial research by Padilla and Pagano, [11], and Vercammen, [16], on formal credit markets suggests that sharing more detailed information on borrowers characteristics and/or credit performance can reduce the disciplinary effects of a credit bureau. These studies argue that the effectiveness of default as a bad signal is reduced as banks exchange better information on their clients. When banks disclose richer information on their clientele, default is no longer a stigma because the riskiness of a borrower can now be inferred from the set of characteristics revealed by the lenders. Some empirical studies on formal banking institutions have tried to measure the effect of information on credit constraints. Cross country studies by Jappelli and Pagano, [5], Love and Mylenko, [6], and Galindo and Miller, [3], show that better developed credit information systems seem to be associated with broader credit markets, a larger volume of lending, and lower credit constraints. Among the few contributions for microfinance markets, McIntosh and Wydick, [9], propose a model of information sharing that predicts an overall reduction in default rates when all lenders exchange borrowers records on default (negative records) and outstanding debt (positive records), but no repayment information is revealed. The authors argue that sharing positive information in addition to borrowers negative records yields three effects: a screening effect, an incentive effect, and a credit expansion effect. The first two effects tend to reduce default rates through lenders increased ability to screen multiple borrowers and a reduction in the share of borrowers who engage in multiple loan contracts, respectively. In contrast, the credit expansion effect tends to improve access to credit for clean and defaulting borrowers, increasing the probability of default, but without overwhelming the first two effects. Empirical evidence provided by Luoto et. al., [7], seems to support McIntosh and 4

5 Wydick s findings. The authors evaluate the effects of the implementation of a credit bureau in the microfinance sector in Guatemala. Using branch level data for a large MFI, Luoto et. al. find that after the risk bureau was established, average default rates decreased by 3.3% among its clients. Important limitations are present in the existing theoretical and empirical literature on credit information systems exposed above. First, empirical work that evaluates the effect of information sharing is scarce, more so for microfinance markets. Second, the existing evidence, both in formal and microfinance markets, analyzes the effects of multilateral increases in information for all lenders such as the implementation of a credit bureau or simultaneous lengthening of credit histories exchanged between lenders. Moreover, all previous studies measure average effects, ignoring the possibility of differential effects on borrowers who are heterogenous in terms of their past and/or current credit histories. As mentioned above, this paper is an important contribution because i) it is the first one that analyzes the effects of an MFI s unilateral decision to increase the degree of information shared, and ii) it identifies differential effects for borrowers who are heterogenous both in terms of their credit histories and existing outstanding debt. 3 Context and Intervention Before proceeding to describe the empirical evaluation conducted, some background information on Peruvian credit markets as well as information sharing systems prevalent in the country is provided. The characteristics and credit methodology used by FINCA are also described here. 3.1 Credit Markets and Information Sharing in Peru The main suppliers of credit in Peru are institutional or formal lenders. Until the end of the last decade, commercial and government banks were the only institutions that gave formal credit in the country. By 1993, the creation of formal microfinance lenders allowed 5

6 new segments of the population to access formal credit, [8]. These formal MFIs emerged in the form of savings and loan unions (Cajas Municipales and Cajas Rurales) and financial institutions especially designed to target small and medium enterprises (EDPYMES). By December 2008, 60 formal institutions operated in the Peruvian financial market. Commercial banks represented 27% of them but had 92% of the assets in the formal credit market, [15]. Institutional lenders usually ask for collateral before granting a loan. This prerequisite usually ends up excluding poor borrowers who have no access to traditional forms of collateral such as property or tangible assets, [13]. MFIs with altruistic objectives emerged to address this market failure and provide credit to the poor. These institutions are informal in the sense that they are not supervised by Peru s Supervisory Board of Banking and Insurance (Superintendencia de Banca y Seguros, SBS) as are formal lenders. Non regulated MFIs tend to be NGOs or credit and savings cooperatives who target small local markets. By December 2006, there were already 24 informal MFIs operating in the Peruvian market. But even together, they accounted for only 2% of the credit granted to microfinance borrowers while serving 11% of the clients reached by all MFIs, [10]. Today, 1.3 millions of microfinance clients are served by 32 informal MFIs and 38 formal MFIs. Among all credit suppliers in the Peruvian credit market, banks have the highest minimum capital requirements, and offer the fullest range of financial services permitted by law. In addition, the SBS mandates that all institutional lenders, banks and formal MFIs, must share their clients default records (negative information or blacklist ) and outstanding debt records (positive information). The SBS makes these records publicly available at no cost and obtains updates from each institution on a monthly basis. In contrast, non regulated MFIs are very restricted in the type of financial services they can provide. For example, if constituted as NGOs, they are entitled to provide loans but they cannot offer savings services. However, many informal MFIs provide savings services indirectly through partnerships with other financial institutions authorized to 6

7 accept deposits. Moreover, informal MFIs are exempted from the requirement of sharing information on the performance of their clients through public credit information systems. In 1995, EQUIFAX, one of the largest private credit bureaus in the world, started its operations in Peru 2. Since records on outstanding and defaulted debt with formal lenders were available at no cost from the SBS, EQUIFAX tried to incorporate other types of lenders that were not supervised by the government into its own database. Their efforts to do so coincided with the beginning of the boom of microfinance in the country 3. Growing competition in the sector soon motivated many informal MFIs to share their clients credit records through the private credit bureau. At first, non regulated MFIs only provided information on past default. However, the growing number of lenders soon increased the probability of having hidden debt in the market. Many non regulated MFIs tried to incorporate their clients records on outstanding debt into EQUIFAX s database with the hope that other lenders would be discouraged from granting additional loans to clients already served by the MFI. Although there are other private credit bureaus operating in the country, EQUIFAX has been by far the most successful in incorporating information on microfinance borrowers into their system. By 2005, it had access to the credit records of 85% of the clients in non regulated MFIs, and more than four fifths of them had both negative and positive records in EQUIFAX s database. 3.2 FINCA-Peru Foundation for International Community Assistance (FINCA) is a non-profit but financially sustainable MFI that has been operating in Peru since It is associated with FINCA International, a US-based, non-profit organization that uses a group lending methodology based on many of the principles of the Grameen Bank model. FINCA- Peru sponsors lending groups of poor, female microentrepreneurs in three regions of the country, Lima, Ayacucho, and Huancavelica 4. In addition to providing credit, FINCA 7

8 teaches its clients saving habits by requiring savings deposits and encouraging additional voluntary savings for which they receive market interest rates 5. FINCA s methodology operates in the following way: Loan applicants self-select into groups usually consisting of members 6. The group applies for a single loan, which is initially very small, to be paid in weekly, biweekly, or monthly installments over the course of three to eight months 7. Each installment includes a fixed portion of the principal and interests owed to FINCA, as well as a fixed share of the total mandatory savings that the MFI requires for the given loan size. Once FINCA grants the loan, called External Account loan (EA), it is disbursed between members for investment in their own businesses, but all group members are jointly liable for loan repayment. Since FINCA clients have to keep savings in the institution to participate in the credit program, the lending group offers them as a non-traditional form of collateral. Thus, if someone is not able to completely repay her individual loan by the due date, her outstanding debt is first covered by her individual savings and if that is not enough, by the savings of the group, as it is responsible for the portion unpaid. After the current cycle is over, the group can choose to exclude the defaulter from further loans. Once the bank has been able to capitalize a significant amount of mandatory and voluntary savings, the clients have the option to use this capital to grant loans to themselves. These funds, known as Internal Account (IA) loans, are independently administered by the group; their members evaluate each IA loan applicant based on her credit history with the group and the investment potential of her business. These loans are usually due in periods shorter than the total duration of the cycle, so a client can get more than one IA loan per period. Although, on average, the total volume of IA loans disbursed doubles the disbursement of external account loans, legal restrictions do not allow FINCA to report these transactions to any credit bureau. Consequently, information on these loans is ignored by other lenders in the market. By 2006, FINCA had operations in Lima, the capital city, and in two Andean depart- 8

9 ments, Ayacucho and Huancavelica. FINCA reached clients in three particularly poor districts in the capital city, two districts in Ayacucho, and small rural areas in Huancavelica. As of July 2008, FINCA sponsored 584 lending groups with a total of 11,696 clients, 92% of which were women. The total savings of its client base was US$3,192,375 with US$2,339,986 in outstanding loans. FINCA clients each hold, on average, US$273 in savings whereas the average loan per cycle is US$200. The institution charges sufficient interest to be self-sustainable; between December 2004 and December 2005, FINCA charged annual nominal simple interest rates ranging between 36% and 42%. Navajas and Tejerina[10] estimate that, by 2005, FINCA served 5.5% of the clients in the Peruvian microfinance market, accounting for almost 4% of the outstanding debt in all MFIs. Recent estimates from Planet Rating, [12], a specialized microfinance rating agency, show that FINCA reaches 5.8% of the households in Ayacucho and 1.7% of the households in Lima. Considering only the pool of poor households, the MFI s participation in Ayacucho s market reaches 7.6%. 3.3 Evolution of FINCA s Information Sharing Policy FINCA started to share its blacklist with EQUIFAX in However, legal restrictions related to the group lending methodology only allow FINCA to report group default with the MFI, which means that other lenders in the market are unable to identify individual default in the institution. Since August 2004, the MFI s information sharing policy changed to include records on individual outstanding debt on EA loans in addition to the group default records already shared. This unilateral decision increased the amount of information shared with other lenders but it did not affect the degree of information that FINCA had access to. Before the intervention, borrowers with exclusive loans in FINCA were lumped together with individuals with no current access to credit because the only lender they had a contract with did not reveal the existence of that credit relationship. In contrast, the 9

10 change in the information available for FINCA borrowers with multiple credit contracts with other lenders is marginal. Before FINCA revealed their outstanding debt records, they were already observed in the credit bureau database, so the additional information available should have little effect on their access to credit. Since exclusive FINCA borrowers experience a discrete change in the information that is available on them, it is expected that they should benefit the most from the intervention. Thus, this paper will focus on the credit expansion effects for exclusive FINCA borrowers and compare them to the effects identified for multiple borrowers. Within the group of exclusive borrowers, clean and past defaulting borrowers can be identified. Before the policy was implemented, a clean client with an exclusive loan with FINCA could not be distinguished from clean individuals with no access to credit. Similarly, borrowers with past default records in any lending institution who had an exclusive credit contract with FINCA were not observably different from past defaulters with no current access to credit. After the policy was instituted, other lenders were able to differentiate between clean FINCA clients and individuals with no credit histories. Similarly, other lenders were also able to separate past defaulters who were creditworthy enough for FINCA to give them a second chance from defaulters who were too risky to obtain a loan. In sum, FINCA s decision to share outstanding debt records allowed other lenders to identify borrowers who were good enough to get a loan in the MFI. Although the increase in information shared by FINCA may be beneficial for some borrowers in terms of its access to credit, the MFI was negatively affected by the policy. Figure 1 depicts the observed evolution of the percentage of borrowers who are late in their payments to their lending group in FINCA, as well the fitted values of the series before and after the intervention took place. It is clear that there is break both in intercept and slope after the MFI increases the degree of information shared. This paper tries to show that this pattern could be explained by a credit expansion effect experience by some borrowers in FINCA. 10

11 Figure 1: Percentage of Borrowers with Late Payments to the Lending Group, FINCA Jul02 Oct02 Jan03 Apr03 Jul 03 Oct03 Jan04 Apr04 Jul04 Oct04 Jan05 Apr05 Jul05 Oct05 Jan06 Apr06 Jul06 % of Borrowers Late in Individual Repayment Fitted values before Aug04 Fitted values after Aug04 4 Data and Empirical Strategy 4.1 Data This paper draws on three different data sources. First, data on all the loans granted by FINCA between May 1999 and June 2006 is gathered from the MFI s institutional records. The unit of observation is the client, and the time dimension is given by the number of cycles in which the borrower remained as a member of a lending group. Since the timing of entry and exit decisions varies from borrower to borrower, the data provides an unbalanced panel of clients. The data contained in these records includes EA loan size, total amount disbursed and number of loans granted from the IA, interest rates charged, individual repayment balances by the end of the cycle, voluntary and mandatory savings accumulated by the end of the cycle, and basic demographics collected when the client first entered a lending group sponsored by the institution. Additionally, EQUIFAX records for individuals who were FINCA clients by December 2004 are available. In particular, the data contained in this database includes outstanding debt and past group default with FINCA, as well as past individual or group default with 11

12 other lenders at two points in time: December 2004 and December In other words, EQUIFAX s database observes FINCA clients by December 2004 and then follows them a year later, irrespective of their relationship with the MFI by then 8. Finally, district level information on the monthly balances of the credit supply offered by formal institutions has been gathered from the SBS s website. These data will allow us to control for changes in the supply of formal credit during the period analyzed. Ideally, one would want to measure the change in access to credit outside FINCA between August 2004 and December A limitation of EQUIFAX s data is that there is a gap of four months between the time in which positive information sharing started (August 2004) and the measurement of the baseline (December 2004). However, it is very likely that borrower behavior was barely affected during these months. Although the MFI provided a brief training session to inform their clients about the change, FINCA s staff point out that most borrowers still had a limited notion of the implications of positive information sharing after the training. If any, there could be a downward bias in the measurement of increased access to credit since other lenders could have used the additional information to give better loans to FINCA borrowers during these four months. In what follows, December 2004 is considered as the implementation date of the policy. This paper focuses on exclusive FINCA clients in Lima and Ayacucho. The third office in Huancavelica had very few clients by the time positive information sharing started, so this branch is excluded from the analysis. The final sample corresponds to 3320 individuals in Lima and Ayacucho who were exclusive FINCA clients according to EQUIFAX s database by December This sample covers 70% of the client base of FINCA by December 2004 in the branches analyzed. Table 1 presents some basic characteristics for exclusive FINCA borrowers, distinguishing between borrowers with a clean credit history and borrowers with past default records in FINCA or with any other lender. As expected, Table 1 shows that past defaulters are also more likely to be late in their current payments to the lending group 12

13 in FINCA. Defaulters also tend to have higher dropout rates than clean borrowers. In particular, more than half of the dropouts with past default records leave FINCA having had problems to repay its individual loan to the lending group at least in one cycle. In terms of their demographics, Table 1 reveals that defaulters tend to be more educated than clean borrowers. Moreover, defaulters seem to work more in sales activities and tend to be more concentrated in Lima than borrowers with clean credit histories. The fact that defaulters seem to be relatively more educated suggests that they may have more chances to obtain credit from other lenders than less educated individuals with low income evolutions 9. Additionally, entrepreneurs in the sales sector might face more fluctuations in their income flow which forces them to default on their loans. Table 2 shows how the sources of credit changed for exclusive borrowers in FINCA by December After the information sharing policy was implemented, both defaulters and clean exclusive FINCA borrowers increased the share of their total debt in other lending institutions, but defaulters were able to expand their debt with other lenders more than clean borrowers did. By December 2005, clean borrowers still kept 73% of their outstanding debt in FINCA while defaulters only had 62% of the value of their pending loans in the MFI. Not only do defaulters experience a greater expansion of their access to credit in other informal lenders, but they also seem to expand their share of debt in formal lenders more than clean borrowers do. 4.2 Identification Strategy Consider a market with a large number of borrowers with no wealth or collateral who request loans to finance their business projects. Borrowers are heterogenous in three dimensions: type, past default records, and current outstanding debt. In the first dimension, it is assumed that borrowers can be born either as a good (g) or a bad type (b). Define Θ = {g, b} as the set of possible types of borrowers. When a borrower has θ = g, she has a higher success probability for her project than a borrower with θ = b, so lenders find 13

14 Table 1: Characteristics of FINCA Exclusive Borrowers, by Past Default History Clean Defaulters Total Number of individuals Characteristics in FINCA Ever late in payments to lending group (%) Cumulative Savings (US$) EA Loan Size (US$) Time in a FINCA lending group (months) Dropout Rate (%) a/ Dropout With Default Records in FINCA (%) b/ Dropout With Clean Records in FINCA (%) c/ Demographic Characteristics Number of children Married (%) Educational Attainment (%) No education Primary Secondary Higher Age Lima (%) Economic Activity (%) Sales Grocery Prepared Food Services Production Other Source: FINCA-Peru historical database. a/ Dropout rate is calculated as the percentage of FINCA borrowers by December 2004 who left the institution between Dec04 and Dec05. b/ Dropout, default records in FINCA is defined as the percentage of dropouts who left the MFI between Dec04 and Dec05 and had at least one default episode before doing so. c/ Dropout, clean records in FINCA is defined as the percentage of dropouts who left the MFI between Dec04 and Dec05 with a clean credit history in the institution. 14

15 Table 2: Average Outstanding Debt in US$ and Share of Total Debt: Exclusive FINCA Borrowers, Dec04 and Dec05 Number of Outstanding Debt by Dec04 Number of Outstanding Debt by Dec05 individuals Informal Institutions individuals Informal Institutions Formal Institutions FINCA FINCA Other MFIs Banks MFIs Clean US$ Share of Total Debt Defaulter US$ Share of Total Debt All Exclusive Clients US$ Share of Total Debt Source: EQUIFAX records by December 2004 and December a/ The number of individuals changes between the two dates because 891 exclusive borrowers have no outstanding debt with any institution by December

16 the former type more attractive when giving a loan. Lenders do not observe individual types but they know the distribution of types in the population. Borrowers can also differ in terms of their past credit history and their current credit balances. In terms of past credit history, heterogeneity arises from the fact that some borrowers could have defaulted on past loans in FINCA or somewhere else, but others do not have clean credit records 10. Moreover, there could be different levels of current outstanding debt across borrowers so that current credit records may differ. Figure 2 portrays a simplified version of the setting of the experiment, focusing on exclusive FINCA borrowers by December Borrowers with current credit access through FINCA are in areas C and D 1, while individuals who are excluded from credit markets are represented by areas E and D 2. The group of past defaulters includes all borrowers who have at least one default experience with FINCA and/or with some other lender in the past. Within this group, area D 1 represents past defaulters who were able to obtain a new loan after their default, while area D 2 represents individuals with bad credit records and no current access to credit. Figure 2: Distribution of Borrowers Exclusive FINCA borrowers (C+D ) 1 C D 1 D 2 Past Defaulters (D +D ) 1 2 E Before the intervention, other lenders in the market could only distinguish between 16

17 past defaulters in group D and borrowers not in D ( D=C+E). After FINCA started to share positive records on its clients, other lenders could differentiate between groups D 1, D 2, C, and E. Past defaulters in D 1 experienced an improvement in their reputation; even though other lenders still observed their negative records, the intervention allowed them to show that they were able to obtain a new loan after the default episode. Access to credit through FINCA could be signaling that these borrowers were probably not responsible for their past default, since they were not too risky to obtain a loan from FINCA. Borrowers in C experience a similar effect; after the intervention, they are distinguished from individuals who were screened out of the MFI s pool of borrowers (E). Define α i as the proportion of type-g borrowers in group i and N i as the number of type-g borrowers in group i, where i = {C, D 1, D 2, E}. Given the differences in the success probability across types, it is reasonable to assume α C > α D1 because a higher probability of success translates into a lower default probability, all other things equal. Thus, it is more likely that type-g borrowers are more abundant in C relative to D 1. Moreover, individuals who defaulted but were given a second chance in FINCA are more likely to be type-g borrowers than individuals with past default records currently excluded from credit markets. As mentioned above, past defaulters who obtained a new loan were probably not responsible for the default. Thus, α D1 > α D2 can also be assumed. The group with the lower proportion of type-g borrowers should be the one with no current access to credit or past default records: these individuals have no credit histories which implies that most of them were screened out of the credit market. In sum, α C > α D1 > α D2 > α E. When only default records are available, lenders can only identify D and D. Therefore, the probabilities of being a type-g borrower for each group are given by: P r(θ = g D) = α D = α D 1 N D1 + α D2 N D2 N D1 + N D2 P r(θ = g D) = α D = α CN C + α E N E N C + N E 17

18 Once outstanding debt records are exposed, the lenders are able to distinguish between C and E, as well as between D 1 and D 2. Now that lenders can tell all groups apart, they can update the probabilities of being a type-g borrower to α i for each group i, where i = {C, D 1, D 2, E}. Thus, borrowers in groups C and D 1 experience an increase in the probability of being a type-g borrower. On the contrary, individuals in groups D 2 and E are worse off, because lenders assign them a lower α. Figure 3 shows how the probabilities of being a type-g borrower change for the different groups before and after outstanding debt records are shared. Figure 3: Probability of Being a Type-g Borrower, Before and After Outstanding Debt Is Revealed Only Group Default Information Shared α D Group Default and Individual Outstanding Debt Information Shared 0 α α 1 E α D2 α D1 C α D From above, it is clear that sharing outstanding debt records benefits borrowers in C and D 1 whose probabilities of being type-g increase from α D to α C and from α D to α D1, respectively. Thus, they become more attractive for other lenders who will be willing to give them more loans and better contract terms. However, one could expect that the credit expansion effect will vary depending on the level of current outstanding debt of the borrower with FINCA by the time her positive records are revealed. Even if borrower is perceived as a better client after the intervention, she could have an already high level of existing debt, which could discourage other lenders from giving her additional loans. With decreasing returns to scale for the project and a probability of default increasing in total debt, higher levels of outstanding debt tend to make the borrower a worse risk, reducing the loan size a new lender is willing to offer. Therefore, even if the borrower experiences an increase in α, it might be the case that high levels outstanding debt 18

19 discourage other lenders from giving her additional loans. Access to credit is thus mostly improved for those borrowers who experience an increase in their probability of being type-g borrowers and who have low levels of outstanding debt in FINCA. This paper focuses on the borrowers in FINCA that had an exclusive contract with the institution by December Exclusive and clean borrowers are those who, by December 2004, had not defaulted with any lender in the past and held outstanding debt exclusively with FINCA. They will be equivalent to group C in Figure 2. In contrast, exclusive and defaulting borrowers are those who have past negative credit records with at least one lender and have access to credit only through loans from FINCA. These borrowers can be represented as group D 1 in Figure 2. The main objective of this study is to empirically identify the differential effects of FINCA s unilateral decision to share its records on outstanding debt on equilibrium outcomes for its exclusive clients. From above, one would expect that the effect of the intervention will be different depending on the borrower s past credit history and her current level of pending debt by the time FINCA reveals individual positive records of its clients. In particular, the change in access to credit from other lenders between December 2004 and December 2005 will be measured, with a special focus on the change in access to credit in formal institutions 11. Within the groups of exclusive and clean borrowers and exclusive and defaulting borrowers separately, the change in the logarithm of outstanding debt outside FINCA and the logarithm of outstanding debt in formal institutions is estimated and compared to the credit expansion effects experienced by FINCA borrowers with multiple loans by December The basic identification strategy to measure the effects of the intervention on access to credit would be a before and after comparison like the one presented in Table 3. However, since all exclusive borrowers have zero outstanding debt outside FINCA in December 2004, the positive effect identified in this way is not surprising at all. Moreover, the effect for multiple borrowers is not clean. 19

20 Table 3: Percentage Change in Average Outstanding Debt Outside FINCA, Dec04-Dec05 All lenders Formal Lenders Exclusive Clean Defaulter Multiple Clean Defaulter The right way to measure the effect on access to credit is to compare exclusive borrowers in FINCA to borrowers in a similar MFI that shared information only on group default throughout the period analyzed. Unfortunately, no suitable data is available for that kind of analysis. Instead, identification will arise from the comparison of borrowers that vary in the level of outstanding debt they have with FINCA by the time the intervention takes place. However, comparing borrowers based on their observed level of outstanding debt might give us biased estimates. It might be that low debt borrowers are intrinsically different from high debt borrowers and that their access to credit evolves differently over time. For example, low debt borrowers in 2004 may be more credit constrained so that they are more likely to increase the level of their total debt outside FINCA in the future. Figure 4 exemplifies the bias that could emerge if one were to separate borrowers by their actual level of outstanding debt with FINCA in December Notice that both high and low debt groups start with zero access to credit outside FINCA because they are exclusive borrowers in the MFI. If borrowers with low levels of debt in FINCA tend to have higher access to credit over time, the double difference estimator will overestimate the effect of the treatment at time T for low debt borrowers (see panel (a) in Figure 4). If there is a variable that is uncorrelated with the change in borrower s access to credit over time but correlated with her level of outstanding debt in December 2004, the effect of the intervention can be identified. Panel (b) in Figure 4 assumes that a good proxy for the level of outstanding debt with FINCA has been found so that both groups are similar ex-ante. If borrowers do not differ in their personal attributes and credit characteristics 20

21 before T, an unbiased estimate of the effect of the intervention can be measured for low debt borrowers, because changes in access to credit after T can be attributed to the new information policy. In what follows, the proxy variable used to sort borrowers into high and low debt groups is described. Figure 4: Definition of High and Low Debt Groups Access to Credit Outside FINCA Actual Low Debt Access to Credit Outside FINCA T Actual High Debt (a) Actual Outstanding Debt Biased measure of the effect of the intervention t T Proxy Low Debt Proxy High Debt (b) Proxy for Outstanding Debt Unbiased measure of the effect of the intervention t Since all lending groups have different dates of creation and cycle durations, the identification strategy relies on the random differences in the startup date of the last ongoing cycle in December Since FINCA borrowers repay their EA debt progressively throughout the cycle, clients who are in lending groups closer to the end of their cycle are more likely to have lower levels of outstanding debt than those individuals in groups that are just starting a new cycle. It can be argued that the date chosen by FINCA to share its individual records on outstanding debt is exogenous to the date in which each group started their ongoing cycle. If so, the level of outstanding debt with FINCA in December 2004 should be independent of borrowers characteristics. Define the low debt group as those borrowers who happened to be in a lending group with a small share of their ongoing cycle left by December Correspondingly, the high debt group are those borrowers who were in lending groups that were just starting a new cycle by the time FINCA outstanding debt records are shared. In particular, the low debt group will include borrowers with less than half of their cycle left by December 21

22 2004 while the high debt group consists of borrowers who are less than half the way through their cycle by the same date. Table 6 in the Appendix shows the number of individuals included in both groups. Before estimation, it should be checked that the high and low debt groups constructed are effectively similar ex-ante so that the evolution of their access to credit can be assumed to be similar in the absence of any intervention. Tables 7 and 8 in the Appendix present t-tests on the equality of means between high and low debt groups for observed borrowers characteristics. In general, it is safe to say that our strategy produced observably similar high and low debt groups for both clean and defaulting borrowers. Some minor differences are identified. In the case of the clean group, significant differences at the 0.01 level are found for the percentage of clients in Lima and the percentage of clients with businesses in the services sector (see Table 7). But even in these cases, the magnitude of the differences between both groups is small. Among the defaulting borrowers, the low debt group has spent significantly less time working in a lending group sponsored by FINCA (see Table 8). Luckily, these small observable differences can be controlled for in estimation. 4.3 Estimation Strategy Define Y ij as the change in the level of outstanding debt outside FINCA between December 2004 and December Since all the exclusive borrowers have the same value of outstanding debt outside FINCA ex-ante (zero), Y ij is actually equivalent to the level of outstanding debt with other lenders in December Thus, a first difference estimator can capture the effect of the intervention using the following equation: Y ij = α + β 1 L ij + β 2 X ij + β 3 C j + ɛ ij (1) where Y ij is the logarithm of outstanding debt with other lenders different from FINCA in December 2005, L ij is a dummy variable that is equal to one when the borrower belongs to the low debt group and zero otherwise, X ij are additional individual controls such as 22

23 demographic and borrower characteristics, C j are contextual controls at the district level, and ɛ ij is an error term. The impact of the intervention for low debt borrowers will be given by the coefficient β 1 in equation (1). Equation (1) is estimated for clean and defaulting borrowers separately, to allow for differences in coefficients across sub-samples. Since the probability of being a type-g borrower changes equally within the clean and defaulting borrowers groups (from α D to α C and from α D to α D1, respectively), β 1 will measure the marginal effect on access to credit of having a low level of outstanding debt when the policy is implemented. 5 Results Table 4 presents estimates of β 1 in equation (1) for two outcome variables: the logarithm of outstanding debt outside FINCA and the logarithm of outstanding debt in formal institutions in December Since 27% of the exclusive FINCA borrowers had no access to credit from any lender by that date, selection bias problems may arise if individuals for whom outstanding debt is not observed are not randomly sorted out of the credit market. These issues are discussed in the next sub-section. As mentioned above, all estimates are obtained using linear models in the cross section sample in December The first column in Table 4 shows the number of individuals included in each regression. The last three columns present the estimates of β 1 with no additional covariates, adding demographic controls (X ij ), and with demographic and contextual controls (X ij and C j ), respectively. Among the demographic controls, we include number of children, civil status, educational attainment, age of the borrower, location, economic activity of the business, and time spent in a lending group sponsored by FINCA. Additionally, the two contextual controls considered are the logarithm of the credit supply provided by banks and formal MFIs, both measured at the district level. In general, exclusive borrowers closer to the end of their cycles in FINCA by December 2004 seem to have had higher credit access outside the MFI after their positive records 23

24 Table 4: Impact of Positive Information Sharing on Exclusive Borrower s Access to Credit Outside FINCA N 0 No Demographic Demographic Dependent Variable Individuals Controls Controls & Contextual Controls Log(Outstanding Debt Outside FINCA) All Exclusive Clients (0.105) (0.105) (0.104) Clean (0.109) (0.110) (0.110) Past defaulters (0.327) (0.334) (0.333) Log(Outstanding Debt in Formal Institutions) All Exclusive Clients (0.082) (0.082) (0.082) Clean (0.086) (0.086) (0.086) Past defaulters (0.254) (0.266) (0.265) Source: FINCA-Peru historical database and EQUIFAX s database. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 a/ Demographic controls included are: age in FINCA, number of children, civil status, educational attainment, age of the client, location, and economic activity of the business. b/ Contextual controls included are: log of credit supply of banks and log of credit supply of formal MFIs in the district. were exposed. This is true for both outcome variables, although the credit expansion effect is clearly weaker in formal institutions. Although these aggregate results suggest global marginal gains for exclusive borrowers in the low debt group, further analysis is conducted in the sub-samples of clean borrowers and past defaulters separately. For both outcomes, the positive effect on access to credit seems to have been driven by the gains from clean borrowers only. If we analyze estimates of β 1 obtained within the clean and past defaulters sub-samples separately, there is not a significant effect on access to credit for defaulters in the low debt group. Based on parameter estimates for 24

25 clean borrowers obtained when demographic and contextual controls are included, the value of outstanding debt outside FINCA for the low debt group is estimated to be 32% higher than it was for the high debt group. Additionally, clean borrowers with low levels of debt in FINCA seem to have had 18% more outstanding debt in regulated institutions than their high debt counterparts. In contrast, defaulters in the low debt group had, on average, 20% more debt outside FINCA and 7% more debt in regulated institutions than borrowers in the high debt group, but these effects were not significant 13. It is thus clear that clean borrowers experienced a greater credit expansion effect, particularly if they were closer to the end of their cycles when the additional information was revealed. Not only did credit access improve for them, but borrowers with lower levels of debt seem to have been the ones who obtained bigger gains from the policy: they were able to access better contract terms through their increased access to formal institutions. Table 5 presents double difference estimates of β 1 for clean and defaulting multiple borrowers 14. As it was expected, no significant effect is identified for these borrowers. Having a loan outside FINCA before the intervention was implemented implied that the borrower was already included in EQUIFAX s records when FINCA revealed individual outstanding debt records. Thus, no important effects of the policy should be expected for these borrowers. The results in Table 5 reinforce the robustness of the effects identified for exclusive borrowers because they suggest that other factors did not change during the period analyzed. To confirm that the findings presented here are linked to the increase in default rates in FINCA, a probabilistic model for default is fitted at the individual level for all borrowers who were exclusive FINCA clients by December 2004, distinguishing between clean and defaulting borrowers. First, exclusive borrowers are classified according to their status in December 2005: exclusive FINCA clients, borrowers with loans outside FINCA (either shared clients or borrowers who left FINCA and have access to credit somewhere else), and borrowers with no access to credit. The probit model estimated includes time and 25

26 Table 5: Impact of Positive Information Sharing on Multiple Borrower s Access to Credit Outside FINCA N 0 No Demographic Demographic Dependent Variable Individuals Controls Controls & Contextual Controls Log(Outstanding Debt Outside FINCA) All Multiple Borrowers (0.158) (0.156) (0.156) Clean (0.185) (0.182) (0.182) Past defaulters (0.306) (0.300) (0.300) Log(Outstanding Debt in Formal Institutions) All Multiple Borrowers (0.251) (0.245) (0.245) Clean (0.297) (0.286) (0.286) Past defaulters (0.474) (0.468) (0.467) Source: FINCA-Peru historical database and EQUIFAX s database. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 a/ Demographic controls included are: age in FINCA, number of children, civil status, educational attainment, age of the client, location, and economic activity of the business. b/ Contextual controls included are: log of credit supply of banks and log of credit supply of formal MFIs in the district. the 2005 status dummies, as well as the interactions between them. Demographic and contextual controls are also considered. The parameters are estimated in the unbalanced panel that comes from FINCA s institutional records and since each individual has one observation per cycle in which she was in the MFI, random effects at the individual level are also included. Based on the estimates of the individual default probability, Figure 5 graphs the predicted evolution of default rates for exclusive borrowers in December 2004 depending on their status in December Panel (a) shows the results for clean borrowers while panel (b) reports results for past defaulters. 26