metadata: authors: 0: author: Arraiz,Irani
1: author: Bruhn,Miriam
2: author: Roth,Benjamin N.
3: author: Ruiz Ortega,Claudia
4: author: Stucchi,Rodolfo Mario
count: World
docna: 0: docna: Free Riding in Loan Approvals : Evidence
            from SME Lending in Peru
docty: Policy Research Working Paper
subtopic: Legal Institutions of the Market Economy,Financial Structures,Financial Sector Policy,Gender and Development
teratopic: Public Sector Development,Finance and Financial Sector Development,Gender
abstracts: cdata!: This paper provides evidence that
            commercial lenders in Peru free ride off their peers'
            screening efforts. Leveraging a discontinuity in the loan
            approval process of a large bank, the study finds that
            competing lenders responded to additional loan approvals by
            issuing approvals of their own. Competing lenders captured
            almost three-quarters of the new loans to previously
            financially excluded borrowers, greatly diminishing the
            profits accruing to the initiating bank. Lenders may
            therefore underinvest in screening new borrowers and
            expanding financial inclusion, as their competitors reap
            some of the benefit. The results highlight that information
            spillovers between lenders may operate outside credit registries.
repnme: repnme: Free Riding in Loan Approvals : Evidence
            from SME Lending in Peru
display_title: Free Riding in Loan Approvals :
            Evidence from SME Lending in Peru
listing_relative_url: /research/2019/12/31605446/free-riding-loan-approvals-evidence-sme-lending-peru
topicv3: Credit Reporting m1327250 357,Financial Inclusion m1327456 363,Gender m1327621 615,Financial Sector Development m1327681 1920,Financial Institutions m1505015 3797
publishtoextweb_dt: 2019-12-03T00:00:00Z
historic_topic: Public Sector Development,Finance and Financial Sector Development,Gender
origu: DECRG: Finance & Priv Sec Devt (DECFP)
no_of_pages: 34
author_keys: 0: author_key: 955274
1: author_key: 1174517
2: author_key: 933987
model_extractions: Policy Research Working Paper 9072 Free Riding in Loan Approvals Evidence from SME Lending in Peru _Irani Arraiz Miriam Bruhn Benjamin N. Roth Claudia Ruiz-Ortega Rodolfo Stucchi_ Development Economics Development Research Group December 2019
Policy Research Working Paper 9072

# **Abstract**

This paper provides evidence that commercial lenders in Peru free ride off their peers’ screening efforts. Leveraging a discontinuity in the loan approval process of a large bank, the study finds that competing lenders responded to additional loan approvals by issuing approvals of their own. Competing lenders captured almost three-quarters of the new loans to previously financially excluded borrowers, greatly diminishing the profits accruing to the initiating bank. Lenders may therefore underinvest in screening new borrowers and expanding financial inclusion, as their competitors reap some of the benefit. The results highlight that information spillovers between lenders may operate outside credit registries.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at mbruhn@worldbank.org and cruizortega@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Free Riding in Loan Approvals: Evidence from SME Lending in Peru**<sup>*****</sup>

Irani Arraiz<sup>⁑</sup> , Miriam Bruhn<sup>§</sup> , Benjamin N. Roth<sup>‡</sup> , Claudia Ruiz-Ortega<sup>¶</sup> , Rodolfo Stucchi<sup>†</sup>

**JEL Classification:** D82, G21, O16

**Keywords:** Asymmetric information, credit market competition, access to credit.

> * We thank the Entrepreneurial Finance Lab and our partner bank for sharing their data and information, as well as Pierre Freundt from Equifax Peru for patiently answering our questions. We are also grateful to Sumit Agarwal, Abhijit Banerjee, Vivek Bhattacharya, Jishnu Das, Ralph de Haas, Quy-Toan Do, Xavier Gine, Daniel Green, Dean Karlan, Asim Khwaja, Bailey Klinger, Michael Kremer, Nicola Limodio, Ernest Liu, Soledad Martinez-Peria, David McKenzie, and Jonathan Zinman for helpful discussions and feedback. Financial support for this project was provided by the Strategic Research Program. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, IDB Invest, or the World Bank, their Boards of Directors, or the countries they represent.

⁑ IDB Invest § World Bank and IZA ‡ Harvard Business School ¶ World Bank † IDB Invest 1
# **1 Introduction**

While the returns to credit and other benefits of financial inclusion have been of central interest in academia and policy, relatively less attention has been paid to the limits of private sector incentives to achieve financial inclusion. In deciding whether to issue credit to a new borrower, a profit-maximizing lender must consider the costs of screening the borrower and issuing the loan, how much debt the borrower can reliably service, and how long the borrower will remain a customer. This final consideration is typically framed based on whether a borrower will eventually join a competing lender after the initial lender has incurred the cost of establishing her reliability (e.g. Petersen and Rajan, 1995), but in principle this form of competition could occur even before the first loan is issued. If lenders are more likely to approve borrowers already approved by their competitors, then lenders that incur the cost of evaluating new or underserved borrowers may not reap the resulting benefits. This phenomenon whereby lenders free ride on the screening efforts of their competitors reduces the incentive to expand credit access and financial inclusion and might warrant policy intervention.

We demonstrate that free riding in loan approvals has a large impact on market outcomes. Specifically, we worked with a large Peruvian bank interested in expanding credit access to small and medium enterprises (SMEs). Our partner bank adopted a new screening technology and determined which SMEs to lend to based on a scoring rule with a strict threshold. Borrowers above the threshold were automatically granted a loan, whereas borrowers below the threshold were offered a loan only if a loan officer deemed it appropriate. Borrowers above the threshold also received more attractive loan terms. Exploiting this threshold along with administrative data from our partner bank and credit registry data from Equifax Peru, we document several findings.

While applicants _without_ prior credit histories who score above the threshold were more likely to receive a loan than those who score below it, three-quarters of the additional loans were issued by competing financial institutions rather than from our partner bank. Because the only differences between borrowers on either side of the threshold are whether they were approved for a loan from our partner bank and the resulting loan terms, this is evidence of free riding in loan approvals. We document that free riding in loan approvals is concentrated among non-bank financial institutions and is higher in regions of Peru in 2
which our partner bank faces more competition. Furthermore, we find evidence that our partner bank's loan approvals for these marginal borrowers lead to an increase in the profits of competing financial institutions but not our partner bank.

In contrast, we find that among applicants _with_ a prior credit history, nearly all additional borrowing for those above the threshold does come from loans issued by our partner bank. That is, we do not find evidence of free riding in loan approvals for these applicants. Importantly, applicants with credit histories who receive loans only because they are just above the threshold are those whose credit histories otherwise establish them to be unsuitable borrowers. So, the stamp of approval from our partner bank may have carried less weight in competitors' approval processes, as they had other signals of credit (un)worthiness on which to rely.

Taken together, these findings paint a stark picture. Though our partner bank incurred the costs of the novel screening technology, the benefits accrued largely to its competitors. The straightforward implication is that banks may underinvest in expanding the credit supply to underserved borrowers, as doing so entails a private cost but produces a public good. This may justify subsidies to private sector efforts to expand financial inclusion.

Several mechanisms may underlie this phenomenon of free riding in loan approvals. On the supply side, lenders may directly respond to the loan approvals of our partner bank by issuing loan approvals of their own. For instance, borrowers may have shared their loan approval documents with competing lenders, who were then updated about the credit worthiness of these borrowers. On the demand side, borrowers who received a loan approval from our partner bank may have updated their beliefs about their own credit worthiness and redoubled their search for credit from competing lenders. This channel is consistent with Karlan and Zinman (2009), who find that South African consumer loan applicants randomly approved for credit update positively about their selfperceived likelihood to be approved for loans from other lenders. In the case of demandside mechanisms, other lenders benefit from the loan approvals of our partner bank through the indirect channel of receiving more applicants, rather than by directly responding to the loan approvals themselves.

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Importantly, we can rule out some potential mechanisms. On the supply side, we can exclude any mechanism that operates through the credit registry, as our findings are based on loan approvals rather than loan issuances, and loan approvals are not recorded in the Peruvian credit registry. On the demand side, we can rule out complementarities in borrowing whereby an initial loan from our partner bank increases demand for credit from other lenders; few of the borrowers in our sample who received loans from competing lenders first borrowed from our partner bank.

Though we cannot pin down a single explanation, we argue that the precise mechanism by which this free riding occurs does not influence our broad conclusions; so long as a loan approval by our partner bank _causes_ (directly or indirectly) other lenders to approve the same borrowers, the private returns of identifying credit worthy borrowers and expanding the financial supply will diverge from the corresponding social returns.

Our paper relates to several literatures. First, we complement the literature examining how variation in the credit supply decisions of one lender influences the credit supply of competitors (Agarwal et al. 2018, Karlan and Zinman 2018, Azevedo et al. 2019, Burke et al. 2019). Through variation in loan terms or access to credit, each of these papers finds evidence that an initial loan from one lender causes other lenders to provide access to credit as well. Our paper is distinct in this literature in that we identify information spillovers that occur even before the initial loan is issued, and are therefore more deleterious to private incentives to expand the credit supply. Also of note, much of this focuses on consumer lending (Burke et al. 2019), or microfinance (Agarwal et al. 2018, Karlan and Zinman, Azevedo et al. 2019), while our focus is on SME lending.

Second, our paper relates to the literature on the consequences of information sharing through credit registries on bank competition and credit supply (eg. Jappelli and Pagano 2002, Djankov et al. 2007, Hertzberg et al. 2011, Liberman 2016, Dobbie et al. 2016, Foley et al. 2018, Sutherland 2018). As noted above, our principal contribution to this literature is to document that information spillovers between lenders can also occur through loan approvals rather than loan issuance. Because loan approvals are not encoded in the Peruvian credit registry, our results imply that information spillovers can occur even before information is encoded in the credit registry, and before the initial lender has derived any benefit from screening new borrowers. And our results imply that information 4
spillovers between lenders may occur even in settings without well-functioning credit registries.

The rest of the paper proceeds as follows: Section 2 discusses the context and novel screening technology and our data sources, Section 3 presents our evidence for free riding in loan approvals, and Section 4 concludes with a discussion of possible mechanisms.

# **2 Study Context and Data**

# **2.1 Study Context**

We collaborated with one of the five largest commercial banks in Peru (we refer to this bank as our “partner bank”) in an exercise that started in 2012. At the time, our partner bank had only a small SME portfolio. Among commercial banks, our partner bank had only a 5 percent market share for lending to medium enterprises and a less than 1 percent market share for lending to micro or small enterprises. Our partner bank was thus particularly interested in reaching the micro and small and medium enterprise segment. This segment was dominated by four other banks, which accounted for 90 percent of commercial bank micro and small enterprise lending. Non-bank financial institutions (NBFIs) also played an important role in the micro and small enterprise credit market. Forty-four NBFIs had a combined credit volume that was equal to 60 percent of the commercial bank credit volume for small enterprise credit and 2.4 times the commercial bank credit volume for microenterprise credit.<sup>1</sup> To better reach the SME segment, our partner bank piloted a psychometric tool for screening loan applicants, with support from the IDB. The psychometric tool was developed by the Entrepreneurial Finance Lab (EFL), a fintech company founded in 2010, and relies on a series of questions that measure personality traits, skills, knowledge, and attitudes that aim to predict the applicant’s ability and willingness to repay a loan. The IDB and EFL co-financed the implementation of the psychometric tool during the pilot with our partner bank.

SMEs that applied for a working capital loan (up to 18 months in duration with an average loan size of $3,855) between March 2012 and August 2013 were screened by the

> 1 All statistics on market share and credit volume are from the Peruvian Bank Supervisor (SBS) for February 2012.

5
EFL tool as part of the application process. Applicants who achieved a score on the EFL application higher than a threshold defined by our partner bank were automatically offered a loan, while those below the EFL threshold were offered a loan only if they were approved under the institution’s conventional screening method. Loan terms also differed across the threshold.

We note that as our identification strategy relies on comparing borrowers just above and just below an arbitrary EFL score cutoff, the predictive power of the EFL questions is not important for our study. Regardless of EFL predictive power, applicants just above and below the cutoff would have nearly identical predictions of creditworthiness. Critically, however, the borrowers and other lenders could not discern, in all cases, whether borrowers were marginally approved due to their EFL score or if they were approved because our partner bank deemed them creditworthy for substantive reasons. Therefore, both competing lenders and the borrowers themselves may rationally update about the borrowers’ creditworthiness after receiving a loan approval from our partner bank.

6
# **2.2 Data**

We obtained data on 1,883 SMEs that applied for a working capital loan with our partner bank, from two sources: administrative data from our partner bank and Equifax Peru. The administrative data include the EFL score and the date when the SME applied for the loan, as well as the applicant’s age, gender, business sales, and whether or not our partner bank approved them for a loan. The applicants in our sample were on average 39 years old and 50 percent of them were female. Average annual business revenues were about US$12,000 (see Column 1 in Table 1). In addition, for each loan applicant, the data include the national ID number (DNI) and, if their business is registered under the business name instead of the individual’s name, it also includes the business’s tax payer number (RUC).<sup>2</sup> Our second data source is Equifax Peru, the largest credit bureau in the country. For the DNIs and RUCs in the EFL data, we purchased five years of monthly information on borrowing from regulated financial institutions, covering the period from May 2011 to April 2016.<sup>3</sup> Equifax collects this information from the Peruvian Bank Supervisor’s (SBS) credit registry ( _Central de Riesgos_ ). SBS collects data directly from all regulated financial institutions monthly, covering the universe of commercial banks, as well as all regulated NBFIs. For each ID number in any given month, we obtained the total amount borrowed (current and delinquent) from each SBS supervised financial institution in Peru, for the three loan types most relevant in our sample: microloans, loans to small firms, loans to medium firms. If a borrower has more than one loan of the same type with the same institution, Equifax reports only the sum of these loans, with no information on how many loans constitute this total amount.

Our primary outcome of interest is whether loan applicants took out a new loan in the six months following their application to our partner bank. We measure loan take-up

> 2 We originally had 1,909 SMEs in the data. All provided their DNI and 1,327 also provided an RUC. However, for 20 SMEs, the DNIs and RUCs are inconsistent with each other, suggesting typos. We drop these observations from the sample to avoid using wrong information from our second data source. We also drop 6 observations where two DNIs reported the same RUC, that is, three SMEs where two co-owners seem to each have applied for a loan. In these cases, it is not possible to cleanly assign an EFL score to the SME as the unit of observation. Thus, we end up with a sample of 1,883 SMEs. The fraction of the sample dropped is not significantly different below and above the EFL threshold. 3 By law, Equifax is not allowed to provide data that are older than five years.

7
for each institution and loan type by creating a dummy equal to one if the amount outstanding of either the DNI or RUC associated with a loan applicant increases by any amount. The immediate post-application period allows six months for loans to be processed and disbursed and provides some time for applicants to potentially shop around with other financial institutions for other loan offers. For a placebo test, we define an analogous outcome measuring whether each loan applicant took out a new loan from each financial institution in the six months immediately preceding his or her loan application with our partner bank.

We also purchased Equifax credit scores for the month when the SME applied for the loan from our partner bank. Here, Equifax included a dummy variable indicating whether this score was primarily based on their credit history, i.e. a “thick file,” or on demographics and other sources, such as the Peruvian tax authority (SUNAT), i.e. a “thin file”.<sup>4</sup> Our sample includes 1,517 thick file borrowers and 366 thin file borrowers.

We utilize two measures of credit market competition: the log of per capita NBFI lending at the district level, and the per capita number of NBFI branches at the district level. We focus on NBFIs as our results indicate that the increase in loans for thin-file applicants comes from NBFIs, not banks. We note that as our partner bank operates in only eight districts, this is a fairly coarse measure of competition. Total NBFI lending for February 2012 was obtained from the Peruvian Bank Supervisor (SBS) and the 2012 population size from the Human Development Index of the UNDP.

Though we do not directly observe the profits that lenders derive from each loan applicant, we compute an imperfect measure of profits from new loans at the applicantinstitution level. If the applicant does not have a new loan with a given financial institution six months after the loan application, profits are defined to be zero. If they have a new loan within six months of the loan application, we measure the size of this loan as the sum of all loan balance increases in the six months. The size is set to zero for applicants with no new loans. We obtain the interest income by multiplying the size of the loan by its “typical” interest rate. Typical interest rates are reported by the SBS for each financial institution

> 4 For individuals with “thin files”, Equifax calculates credit scores based on several variables, including whether they have a co-signer, their income level, and recurring payments, such as tuition, rent, and electricity.

8
and each loan type. We do not observe interest rates at the loan level, but anecdotal evidence suggests that interest rates are fairly standardized within products for a given lender. To capture losses, we then subtract the amount in default 24 months after the initial loan application, which could be zero in case of no defaults. We picked 24 months to allow six months for the loans to be issued and 18 months for the termination of loans with the maximum duration.<sup>5</sup>

# **3 Results**

We now describe our identification strategy and results. As our partner bank applied a strict threshold rule based on the EFL score, across which both the likelihood of loan approval and loan terms vary discontinuously, we apply a regression discontinuity (RD) design. Specifically, letting be the borrower's EFL score, be the EFL cutoff, (1) be the outcome of interest for borrower ii conditional on being above the EFL cutoff and ii (0) ss ss̅ YY be the outcome of interest for borrower conditional on being below the EFL cutoff, we ii ii YY estimate by approximating lim ii (Hahn et al., 2001). We estimate the EE(YYii(1) −YYii(0)|ssii = ss̅) lim ssii→ss̅<sup>+ EE(YYii(1)|ssii) −lim</sup> ssii→ss̅<sup>−EE(YYii(0)|ssii)</sup> variety of bandwidths around and polynomial functions of various orders. ssii→ss̅<sup>+ EE(YYii(1)|ssii)and</sup> ss<sup>lim</sup> ii→ss̅<sup>−EE(YYii(0)|ssii)using a</sup> In our main results we estimate a linear model on either side of the threshold. ss̅ Normalizing the threshold , and letting represent the observed outcome variable for person , we estimate ii ss̅ = 0 yy 1 2 ii where is the indicator function and ii ii ii is a vector of control variables representing age, ii ii ii ii ii yy = αα+ β (ss ≥0) + γγ ββ(ss < 0)ss + γγ ββ(ss ≥0)ss + δδXX + εε gender, log business revenues, and Equifax score. Estimates from models with higher order ii ββ(⋅) XX polynomials are included in the appendix. The estimation is done in Stata using the command _rdrobust_ , described in Calonico et al. (2014a).

At the outset, in Table 1 we note that none of the demographic or business characteristics are discontinuous across the EFL threshold. Columns 3 through 5 present

> 5 If the financial institution stops reporting a loan amount for a given borrower before 24 months are over, we use the amount in default in the last month when the amount was observed (after the first six months after the loan application). If the amount in default exceeds the calculated size of the loan (which may be the case if it includes other loans from the same institution), we replace the amount in default with the size of the loan.

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the RD estimates with polynomials of order 0 through 2, and across all characteristics none exhibit a statistically significant jump at the RD cutoff. Online Appendix Tables A1 and A2 present analogous specifications restricting to the samples of borrowers with and without pre-existing credit histories (i.e. _thick_ and _thin file applicants_ ). This offers some reassurance in our research design. Figure A1 plots distribution of EFL scores in our sample and allows for visual inspection that the distribution appears continuous across the threshold. A formal test using the Stata command _rddensity_ (Cattaneo et al., 2018) confirms we cannot reject that there is no sorting around the EFL threshold.

Our primary outcomes of interest are the probability of loan approval from our partner bank and whether the applicant ultimately takes a loan from our partner bank, one of its competitors, or neither. Because there is reason to believe that the magnitude of free riding in loan approvals may differ for loan applicants with thick files and those with thin files, we separately estimate the effects of being above the EFL threshold on the full sample, and on each of the two subsamples.

We now establish our first stage; direct evidence that borrowers just above the EFL threshold are more likely to be approved for loans by our partner bank, and indirect evidence that loan terms are discontinuously more attractive above the EFL threshold. The first row of Table 2 presents our estimates of the effect of being above the EFL threshold on the likelihood an applicant is to be approved for a loan offer from our partner bank. Every point estimate in Table 2 corresponds to the coefficient of interest, , in a separate RD model. The first row of Figure 1 provides a graphical depiction of the corresponding ββ data and fitted models. All the estimates in Table 2 are from linear models and the associated optimal bandwidth. Alternate specifications with different polynomials and alternative bandwidths are presented in Online Appendix Tables A3 and A4.

The first row of Table 2 demonstrates that across all borrowers in our sample, those who are just above the EFL threshold were 23 percentage points (p-val: 0.00) more likely to be approved for a loan by our partner bank, those with thick files were 25 percentage points (p-val 0.00) more likely to be approved for a loan, and those with a thin files were 10 percentage points (p-val: 0.21) more likely to be approved for a loan. These estimates reflect that most applicants who scored below the EFL threshold were approved for a loan as well. However, while we do not directly observe the terms of loan offers, we note that 10
the second row of Table 2 indicates that across the whole sample of applicants, those just above the EFL threshold were more than twice as likely to accept a loan as those just below the EFL cutoff, compared to a mere 23 percentage points increase in the likelihood of loan approval. That additional loan approvals cannot account for most of the additional borrowing above the threshold indicates that loan offers were discontinuously more attractive at the threshold.

Rows 2 through 5 of Table 2 present our main results – applicants just above the EFL threshold are not only more likely to borrow from our partner bank but also from our partner bank’s competitors. Because the EFL score was only observed by our partner bank, these estimates reflect the competitor response to the approval and loan terms of our partner bank. The bottom two rows of Figure 1 provide a graphical depiction of the data and fitted models and alternate specifications with higher order polynomials and alternative bandwidths are presented in Online Appendix Tables A3 and A4.

The second row of Table 2 establishes that across all borrowers in our sample, those just above the threshold are 28 percentage points (p-val: 0.00) more likely to receive a loan from our partner bank in the six months following their application. The additional likelihood that they borrow from a competing lender during the same window is small and not statistically significant. The estimates are qualitatively similar for thick file borrowers.

For thin file borrowers the patterns differ markedly. The additional likelihood that a thin file applicant just above the threshold borrows from our partner bank within six months following their application is only 13 percentage points (p-val: 0.34) and not statistically significant. However, thin file borrowers just above the EFL threshold are 33 percentage points (p-val: 0.06) more likely to borrow from competing financial institutions. Taking the point estimates literally, comparing Rows 2 and 3 suggests that nearly three quarters of the new loans from thin file borrowers are issued by competing lenders. Rows 4 and 5 of Table 2 demonstrate that this effect is highly concentrated amongst non-bank financial institutions rather than traditional banks. Therefore, for borrowers without established credit histories, much of the benefit derived from our partner bank applying the EFL screening technology accrued to competing financial institutions who responded to our partner bank's loan approvals with approvals of their own. For borrowers with established credit histories, competing financial institutions do not make significantly more 11
loans as a result of the loan approval of our partner bank, as might be expected since competitors have other information with which to assess these borrowers.

While the conclusions of Table 2 are confirmed in the graphical plots in Figure 1, there is a notable additional pattern across several of the plots. To the right of the EFL cutoff, there is a downward slope in the probability of borrowing from our partner bank (for the full sample and for thick file borrowers), and in the probability of borrowing from competing financial institutions (for thin file borrowers). This pattern may be due to the fact that the EFL score is correlated with the size and profitability of a borrower’s business and may therefore also be correlated with her alternatives to borrowing from commercial lenders. Applicants with higher EFL scores may borrow at lower rates because they have superior alternatives, such as trade credit and financing investment through retained earnings. Nevertheless, for the full sample and for thick file borrowers there is a clear discontinuity at the threshold in the likelihood of borrowing from our partner bank, and similarly for thin file borrowers with competing financial institutions, providing a visual confirmation of the estimates in Table 2.

In Table 3 we present results from our primary placebo test: estimates of the same RD model but for the outcome of borrowing any time in the six months _preceding_ loan application from our partner bank. As in Table 2, every point estimate in Table 3 corresponds to the coefficient of interest, , in a separate RD model. As expected, the RD estimates for the full sample, for thick file borrowers, and for thin file borrowers are small ββ and statistically insignificant. Alternate specifications are presented in Online Appendix Tables A5 and A6.

Bolstering the evidence for free riding, in Table 4 we focus on thin file applicants and demonstrate that the free riding effects are stronger in regions where our partner bank faces more competition. Note that in contrast to Tables 2 and 3, each _column_ in Table 4 represents estimates from a separate model. We explore heterogeneous treatment effects based on log NBFI credit per capita in each locality in which our partner bank has a branch. To examine interaction effects, this table uses an OLS specification of the linear model and the bandwidth around the cutoff chosen to be consistent with the models in Table 2. The coefficient of interest is on the interaction between the degree of our partner bank branch level competition and the discontinuity around the EFL threshold. The estimates in 12
columns 1 and 2 demonstrate a negative and statistically significant relationship between the degree of competition facing our partner bank and the additional probability of a thin file applicant borrowing from our partner bank if they exceed the threshold. The estimates in column 2 imply that increasing the level of competition by one standard deviation would correspond to an additional 9.1 percentage point increase in the likelihood a thin file applicant took a loan from our partner bank as a result of being marginally over the threshold.

The pattern for borrowing from competing financial institutions is reversed, albeit less precisely estimated. The estimates in columns 3 through 6 demonstrate a positive, though not statistically significant relationship between the degree of competition facing our partner bank and the probability of a thin file applicant borrowing from a competing lender around the threshold. Table A7 presents results from our alternative measure of competition, log NBFI branches per capita. The results are qualitatively similar.

Table A8 presents the effects on our partner bank’s and on competing lenders’ profits, measured using the Inverse Hyperbolic Sine (IHS) function. Note that profits for our partner bank and its competitors vary across the threshold due to 1) the differential likelihood that borrowers are approved for a loan, and 2) the differential likelihood that they accept loans from each lender. Columns 3 and 4 demonstrate that thick file borrowers just above the cutoff are on average 242% (p-val 0.00) more profitable for our partner bank than those just below the cutoff. On the other hand, estimates of our partner bank’s additional profits from thin file borrowers are much smaller and not statistically significant. But for NBFIs, thin file borrowers just above the threshold are 301% (p-val 0.04) more profitable than those just below the threshold. Therefore, most of the additional profits resulting from screening new thin file borrowers accrued to competing financial institutions.

Table A9 presents estimates of the effect of the EFL threshold on the resulting defaulted balances for borrowers at our partner bank and its competitors. We find no evidence that marginally approved applicants that took loans from our partner bank were differentially reliable from those who took loans from competing financial institutions.

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# **4 Discussion**

In a pilot subsidized by the IDB, our partner bank in Peru adopted a novel screening technology in an effort to expand its loan portfolio among SME borrowers. While our partner bank succeeded in expanding financial inclusion, our estimates suggest that for borrowers without a pre-existing credit history, many of the new loans originated from competing financial institutions. As our partner bank followed a threshold rule whereby applicants who scored above a cutoff were automatically approved for a loan and those who scored below it were not, we can exploit exogenous variation in loan approvals; because competing financial institutions could not see the underlying scores, their additional likelihood to approve borrowers above the score threshold is a result of the loan approval of our partner bank.

A number of auxiliary tests bolster the credibility of our identification strategy and results. We note that no observable demographic or business characteristics are discontinuous at the threshold. A placebo test examining the likelihood to borrow from any financial institution in the six months prior to applying for a loan from our partner bank shows no discontinuity in the threshold. We demonstrate free-riding effects for borrowers without prior borrowing histories but not for those with prior borrowing histories, as would be expected if competing lenders and the borrowers themselves already have strong signals to infer the creditworthiness of borrowers in the latter group. Finally, we show that the freeriding effects are stronger in areas where our partner bank faces more competition from non-bank financial institutions.

This phenomenon greatly reduced the benefit accruing to our partner bank of their efforts to expand financial inclusion. Indeed, after the pilot and subsidies ended, our partner bank decided not to continue using the EFL screening technology. We argued in the introduction that this phenomenon may justify subsidies for efforts to extend loans to new borrowers, including the adoption of novel screening technologies.

We have labeled this competition effect “free riding in loan approvals,” as competing lenders benefitted from our partner bank’s investment to expand its credit supply. This free riding may operate through several channels. On the supply side, competing lenders may have responded directly to the loan approvals of our partner bank, if borrowers shared our partner bank’s loan approval as evidence of their creditworthiness.

14
On the demand side, borrowers who were approved for a loan from our partner bank may have updated about their own creditworthiness and engaged in more vigorous search for alternative sources of credit. In that case, competing financial institutions would be responding only indirectly to the loan offers of our partner bank. We view this mechanism as inconsistent with the fact that we find no effects on borrowers with an established credit history, who would presumably be similarly emboldened. But we note the borrowers with established credit histories who are approved as a result of surpassing the screening threshold are those who could not receive a loan on the basis of their credit history alone. Thus, it is possible that they did redouble their efforts to find an alternative source of credit but that these efforts were fruitless. This interpretation is consistent with the results of Karlan and Zinman (2009), who find that borrowers randomly selected to receive a loan updated their perceptions about their own creditworthiness.

On the supply side, we can rule out mechanisms in which information spillovers occur through the credit registry, as loan approvals are not recorded in the Peruvian credit registry. And on the demand side, we can rule out that increased demand for loans from competitors was driven by increased loans from our partner bank and complementarities in demand for credit. Of all thin file borrowers to the right of the EFL threshold in the bandwidth of our primary specification, only a quarter of those who took loans from competing lenders also took a loan from our partner bank.

While we cannot pin down a single mechanism, we note that the consequences of each of these are largely the same. In either case, the additional loans resulting from our partner's efforts to expand financial inclusion were primarily issued by other financial institutions. Whether this was a direct response to the loan approvals of our partner bank, or merely an indirect consequence, the justification for subsidizing the expansion of financial inclusion remains.

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