Repayment Flexibility and Risk Taking in Credit Contracts (PDF)

Review of Economic Studies (2023) 00, 1–41 doi:10.1093/restud/rdad107 © The Author(s) 2023. Published by Oxford University Press on behalf of The Review of Economic Studies Limited. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. Advance access publication 17 November 2023 Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Repayment Flexibility and Risk Taking: Experimental Evidence from Credit Contracts Marianna Battaglia University of Alicante, Spain Selim Gulesci Trinity College Dublin, Ireland and Andreas Madestam Stockholm University, Sweden First version received April 2021; Editorial decision June 2023; Accepted November 2023 (Eds.) A widely held view is that small firms in developing countries are prevented from making profitable investments by lack of access to credit and insurance markets. One solution is to provide repayment flex- ibility in credit contracts. Repayment flexibility eases both the credit constraint, as it allows for increased spending during the start-up phase, and offers insurance, in case of fluctuations in income. In a field experiment among traditional microfinance clients and larger collateralized borrowers in Bangladesh, we randomly assign the option to delay up to 2 monthly repayments at any point during a 12-month loan cycle. The flexible contract leads to substantial improvements in the traditional microfinance clients’ business outcomes, driven by borrowers in the upper tail of the distribution. In addition, we find a sig- nificant impact on socio-economic status, combined with lower default rates. We show theoretically and empirically that these effects are induced by an increase in entrepreneurial risk taking, implying that the primary mechanism is insurance provision. Repayment flexibility also attracts less risk-averse borrowers interested in business expansion. At the same time, the effects for the larger loan are much more modest. Our findings suggest that lack of insurance is an important constraint for small firms but that a sim- ple financial product that increases repayment flexibility can be an effective tool for enabling enterprise growth. Key words: Repayment flexibility, Insurance, Credit, Microfinance, Entrepreneurship JEL codes: C93, D22, D24, D25, G21, G22, O12, O14, O16 1. INTRODUCTION Starting or expanding a business often entails undertaking costly and risky investments. In devel- oping countries, where credit and insurance markets are imperfect, entrepreneurs face constraints The editor in charge of this paper was Adam Szeidl. 1 2 REVIEW OF ECONOMIC STUDIES on both fronts. It is well established that small enterprises are severely credit constrained (de Mel et al., 2008; Banerjee and Duflo, 2014) and operate under high levels of risk, having to tackle frequent aggregate and idiosyncratic shocks (Samphantharak and Townsend, 2018). While improved availability of credit and insurance ought to help aspiring entrepreneurs, exist- Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 ing evidence shows that conventional microcredit has not generated substantial firm growth, at least on average (Banerjee et al., 2015, 2019). In an environment where business growth requires access to capital and insurance against entrepreneurial risk, the ideal financial contract should cater to both of these constraints. In line with this, a large literature in corporate finance high- lights the importance of financial flexibility for businesses (Graham and Harvey, 2001; Gamba and Trianti, 2008), but evidence from developing countries is scant. In this article, we study an innovative loan product that provides credit and reduces unin- sured risk and examine which constraint is more important. To this end, we experimentally alter the debt contract terms by making the repayment obligation more flexible. Improved flexibil- ity eases the credit constraint, as it allows for increased spending during the start-up phase, and provides insurance, in case of fluctuations in income. We conduct the randomized evaluation of the flexible contract in Bangladesh together with one of the largest microfinance institutions in the world, BRAC. The regular product BRAC offers has a 12-month loan repayment cycle with monthly installments of equal size. By contrast, the flexible contract allows borrowers to delay up to 2 monthly repayments at any point during the loan cycle using repayment vouchers. On the day of their monthly repayment, borrowers can present a voucher, thereby postponing the repayment and extending the loan cycle. We study the effect of repayment flexibility on both collateral-free microfinance provided to women (Dabi), where BRAC reaches 4 million bor- rowers in Bangladesh alone, and larger collateral-backed debt (Progoti), available to female and male borrowers.1 We begin our analysis by developing a financial contracting model to illustrate how repay- ment flexibility affects credit and insurance rationing as compared to the standard credit contract. In the model, entrepreneurs either invest in a safe liquid or a risky illiquid technology. Repay- ment flexibility can alleviate uninsured risk by covering loan payments in bad times, allowing entrepreneurs to increase their investments in illiquid assets more sensitive to aggregate uncer- tainty. Flexibility can also lessen the need to save for the first repayment, thus increasing upfront investment funds. This eases the credit constraint for poorer and (assuming skills and invest- ment capital are complements) skilled entrepreneurs. If the credit-constraint channel is more important, flexibility primarily benefits the poor and skilled. By contrast, when the insurance mechanism prevails, borrowers take on more risk. If entrepreneurs have other external obliga- tions in addition to the loan payment (such as recurrent costs), our theory further predicts that repayment flexibility may not be sufficient to induce risk taking but will still allow for an increase in the safer, low-return, technology. Finally, we show that repayment flexibility has an ambigu- ous effect on the share of risk-averse clients in the resulting borrower pool, with the degree of risk aversion decreasing if the flexible contract primarily attracts borrowers willing to take risks to expand their businesses. In order to assess the effects of increased flexibility, we collaborated with BRAC to conduct a field experiment in Bangladesh. BRAC identified borrowers with good credit histories deemed to be eligible for the new contract in 50 of its branches. Following this, we surveyed a random sample of these borrowers. After our baseline survey, BRAC offered the flexible loan contract to eligible clients in 25 branches that we randomly selected. The same respondents were then 1. Both loans entail individual liability and a flat 22% annual interest rate. In the case of traditional microfinance (Dabi), borrowers attend monthly group meetings but are individually liable for their loans. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 3 resurveyed 1 and 2 years after the baseline. The experimental variation captures the relative benefit of the flexible versus the standard credit contract and allows us to study the importance of credit and insurance constraints. We find that repayment flexibility improves the business outcomes and socio-economic status Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 of traditional microfinance (Dabi) clients. In particular, the flexible contract increases borrowing, business investments, and revenues relative to the control group. The intention-to-treat (ITT) estimates reveal that treated microfinance clients increase their borrowing from BRAC by 11%, the value of their business assets is 51% higher, they generate 87% more revenues, and have 25% higher profits. In terms of their socio-economic status, they end up with higher household income (17%), more household assets (25%), and own more land (26%). A natural question is whether these improvements came at a cost to the lender in terms of default rates. If anything, we find that the likelihood of default diminishes marginally among the treated microfinance clients. When we examine the corresponding impact on larger firms with collateral-backed (Progoti) loans, there are no significant effects, on average, in terms of business or other outcomes. To understand if the treatment effects are primarily driven by credit or insurance constraints, we first test if the flexible contract increased risk taking among the eligible borrowers. Specifi- cally, we investigate four pieces of evidence. First, we examine if the flexible contract affected sales volatility, as captured by the difference between the value of sales in the best versus the worst month, and find that treated Dabi clients’ sales volatility doubled. In the same vein, we also compare the distribution of earnings in the treatment and control samples. We observe that Dabi borrowers in the left tail of the distribution experience lower revenue and lower income growth relative to the control group, while they do better in the upper quantiles. These two find- ings are consistent with flexibility leading to greater risk taking, causing some individuals in the treatment group to lose out (relative to control), while others gain. Second, we study how treated businesses are affected by demand uncertainty. Greater uncertainty should matter more for bor- rowers that take on additional risk. In particular, we find that the effects on the Dabi clients’ revenues and profits are driven by borrowers in areas where expected demand uncertainty is higher at baseline. Third, we explore quasi-experimental variation in the form of local demand shocks. In Bangladesh, excessive flooding during the growing season of the main crop (Boro rice) is particularly harmful and constitutes an important downturn in local economic activity. We find that treatment effects on business profits and revenues for Dabi borrowers are significant and positive, only in locations that experienced favourable rainfall. In locations with extreme flooding, the treatment impact is indistinguishable from zero. This implies that the flexible con- tract induced a shift to activities more sensitive to aggregate uncertainty, at least among the Dabi clients. Finally, we show that Dabi borrowers increased their holdings across a range of illiquid business assets. The findings agree with our theory’s prediction that repayment flexibility induces risk taking, establishing the importance of insurance rationing among the smaller Dabi firms. When we study the same four dimensions for the larger Progoti borrowers, we do not see evidence of any meaningful heterogeneity nor an increase in the value of the business assets used. One explanation for these results rationalized by the model is that larger firms have other external commitments such that too much risk remains even with repayment flexibility. In this case, large insurance-rationed firms refrain from taking on additional risk (explaining the much more modest treatment effects). In order to assess if the effects of the flexible contract are also driven by the credit-constraint mechanism, we examine the heterogeneity of the effects with respect to clients’ baseline eco- nomic status and skills (as proxied by their schooling level). We find no evidence of significant heterogeneity along these dimensions for the Dabi sample. While this implies that credit rationing is less important in explaining the relative benefit of the flexible over the standard 4 REVIEW OF ECONOMIC STUDIES contract for the Dabi borrowers, it does not necessarily mean that eligible Dabi clients would not be credit constrained if no external financing was available. We do, however, find that the lack of an average treatment effect on the Progoti borrowers masks important heterogeneity in the response across the skill level of the entrepreneur: treatment leads to an increase in the rev- Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 enues and profits among Progoti clients with higher skills at baseline. This is consistent with the theoretical prediction that more able Progoti borrowers might be held back under the standard contract, indicating that repayment flexibility helped alleviate the credit constraint of the larger firms. Finally, we consider how the new contract affected the selection of individuals into bor- rowing. In particular, we test if the introduction of the flexible loan attracted different types of clients in the treated branches relative to control. To do this, we conducted a census of small and medium enterprises (SMEs) operating in the 50 branches at baseline, surveying a random sample of the SMEs prior to branch randomization. We then compare, within this representative sample of SMEs, whether those borrowing from BRAC in the treatment branches at follow-up are significantly different in terms of their baseline characteristics. We find that the degree of risk aversion in the borrower pool declines as less risk-averse entrepreneurs with a desire to start a new business were more likely to become BRAC borrowers in the treated branches. According to our model, this suggests that the flexible contract primarily attracts borrowers willing to take risks to grow their businesses. In sum, the results imply that repayment flexibility benefits traditional microfinance bor- rowers mainly through the provision of insurance, enabling riskier investments at lower default rates. To the extent other contractual obligations hold back the Progoti clients, there is some evidence that the flexible contract alleviates the credit constraint faced by the larger firms, with the returns to the flexible contract being higher for more able entrepreneurs. The contract also draws in borrowers that are less averse to risk and more willing to expand their business activi- ties. The findings highlight the benefit of a novel product that simultaneously provides credit and insurance to microfinance clients, contributing to work examining the overall success of micro- finance by focusing on the inframarginal borrowers (Banerjee et al., 2015). At the same time, some caution is warranted as the effects for larger loans are less transformative. The present paper builds on and adds to three main literatures. First, it provides causal evi- dence on the joint importance of capital constraints and incomplete insurance on the growth of non-agricultural firms. While a large literature has studied the role of credit constraints for firms (see, e.g. Fafchamps et al., 2014), empirical work on insurance has mainly focused on the agri- cultural sector. Past studies show that the provision of (subsidized) access to insurance leads to higher farm investment and take up of new technologies, increasing farm profit through greater risk taking (Giné and Yang, 2009; Mobarak and Rosenzweig, 2013; Cai, 2016; Carter et al., 2016; Cole et al., 2017).2 Our paper is related to Karlan et al. (2014) who evaluate the relative importance of credit and insurance constraints by providing cash grants and rainfall insurance to farmers in Ghana. They find that the binding constraint is uninsured risk, with farmers making riskier production choices when offered insurance. Our results complement (Karlan et al., 2014) 2. Also, Groh and McKenzie (2016) evaluate an insurance against macroeconomic shocks provided to micro- finance clients in Egypt. While demand was high, there are no effects on investments or firm growth. Similarly, Lane (2018) studies the impact of an emergency loan following floods in Bangladesh, showing that it increases consumption and asset levels and reduces default in the event of flooding. By contrast, we focus on the joint provision of credit and insurance (for both aggregate and idiosyncratic shocks) via repayment flexibility for a given loan. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 5 by highlighting the role of risk taking in small firms.3 Another closely connected study is Bianchi and Bobba (2013) who find that cash transfers in Mexico increased entrepreneurship. Exploiting variation in the timing of the transfers they show that insurance as opposed to credit constraints drive this effect. While their focus is on entry into entrepreneurship, we study investments in and Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 the growth of existing businesses. Second, we link to a small but growing literature that investigates credit contract structure in microfinance, with the most notable precursor to our work being Field et al. (2013). They eval- uate the effects of giving a 2-month grace period to microfinance clients and find that this leads to an increase in short-term investments and long-run business profits, but also in default rates. Barboni and Agarwal (2018) show that 3-month blocks of repayment holidays chosen in advance attracts financially disciplined clients and leads to higher repayment rates and higher sales.4 Unlike previous work, borrowers’ complete flexibility over their voucher use allows us to eval- uate the relative importance of credit and insurance constraints. As such, the contract we study not only encompasses an early grace period or planned blocks, but also caters to unexpected shocks occurring in any given month throughout the loan cycle.5 Finally, the analysis contributes to research in corporate finance on firms’ ability to take advantage of opportunities and deal with shocks, and how this affects their capital structure. Work on financial flexibility (Gamba and Trianti, 2008; DeAngelo et al., 2011) and liquidity and risk management (see, e.g. Holmström and Tirole, 1998, 2011) emphasizes the capacity to restructure financing, hoard reserves, and hedge against risk to facilitate unexpected changes in cash flows or investment opportunities, especially in a volatile business environment.6 We provide causal evidence demonstrating that such flexibility can increase risk taking, and that this is more valuable when firms face aggregate uncertainty.7 2. THEORY Our financial contracting model illustrates how repayment flexibility affects credit and insurance constraints as compared to the standard debt contract. We also discuss how the theory extends to account for entrepreneurial ability, other contractual obligations, and selection into borrowing. Formal proofs are in Section A.1 of the Supplementary Appendix. 3. Unlike Karlan et al. (2014), we study the incremental effect of a contractual change rather than access to either credit or insurance or both for small retail and manufacturing firms, instead of farmers. While Karlan et al. (2014) experimentally investigate the relative importance of credit versus insurance constraints, the bundled nature of our treatment implies that our findings on mechanisms should be interpreted as theoretically guided suggestive evidence. 4. Czura (2015) investigates a loan targeted to dairy farmers that tailored repayments to the period when cattle produces milk, finding that it increased milk production and income as well as default rates. 5. Our findings further complement research (Attanasio et al., 2018) showing how joint as opposed individual- liability contracts in microfinance reduce the negative effect of aggregate risk on loan take up by offering implicit insurance. Moreover, by providing evidence on the selection effects of introducing a new loan product with greater repayment flexibility, we also contribute to empirical work gauging selection in developing-country credit markets (see, e.g. Karlan and Zinman, 2009; Jack et al., 2018; Ahlin et al., 2020; Beaman et al., 2020). 6. We also link to studies on the timing of repayments in consumer mortgage products, where flexibility in choosing the monthly payments have been shown to smooth consumption (Cocco, 2013) but also increase delinquency rates (Garmaise, 2013). 7. The importance of aggregate risk, and its consequences for asset illiquidity, also rationalizes why businesses in our setting prefer the flexible over the standard credit contract. Shleifer and Vishny (1992) show that asset illiquidity resulting from economy-wide shocks lowers firms’ debt capacity. With a flexible contract, borrowers avoid having to sell their assets at the same time as everyone else hit by the aggregate shock in order to cover the repayment. This may in turn increase firms’ willingness to take on risk. 6 REVIEW OF ECONOMIC STUDIES 2.1. Set-up We consider a risk-neutral entrepreneur with limited liability and assets A in a three-period economy (t = 1, 2, 3). The entrepreneur can finance a fixed investment I at date 1 using either a liquid short-term or an illiquid long-term technology. The liquid project returns ϕ at t + 1 per Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 unit invested at t, subject to aggregate uncertainty in period 2, yielding ϕ H with probability π and ϕ L with probability 1 − π. The illiquid project returns γ at date 2 and at date 3 per unit of initial investment, with > ϕ H > ϕ L > γ > 1. The period-2 return is uncertain, yielding γ or 0 with probabilities π and 1−π , respectively. If liquidated in period 2, the long-term project’s salvage value is λ < 1 per unit of initial investment. The entrepreneur saves τt at t = 1 to cover (part of) consumption and reinvestment if faced with a project-return shortfall in period 2. In periods 1 and 3, she also receives income y to meet any remaining needs. Entrepreneurs have utility U = u(c1 ) + β E[u(c2 ) + βu(c3 )], where ct is consumption at date t and β < 1 is the discount factor. An unconstrained entrepreneur prefers the riskier illiquid project over the safer liquid one, given π γ + β ≥ π ϕ H + (1 − π )ϕ L − 1 + βϕ H. However, due to insufficient wealth, A < I + τ1 , she turns to the financial market for capital. Credit is limited as repayment is imperfectly enforceable. While the investment is fully contractible, the entrepreneur may divert project returns by defaulting on the loan (ex-post moral hazard), yielding benefit φ < 1 per unit diverted.8 If she avoids diversion, she gains a net continuation value V, representing the utility of future credit access.9 The lender’s marginal cost of funds is ρ > 0. Free market entry ensures all surplus goes to the borrower, subject to incentive compatibility.10 To fit our experimental context, the lender offers two contracts: a standard two-period repayment contract and a flexible contract allowing full payment deferral to the last period via a repayment voucher. Applying the com- pound interest formula, the standard contract requires two equal payments of PS ≡ b R 2 /(1 + R) in periods 2 and 3, where b is the amount borrowed and R the gross, per-period interest rate (simplified as R = 1/β < γ ). The flexible contract demands a single payment of PF ≡ b R 2 in period 3. With the addition of a repayment burden, the entrepreneur also enters an informal risk- sharing scheme to cover consumption, reinvestment, and repayment in case of a return shortfall in period 2. The arrangement, where all project-return risk is pooled ex-post, leaves each group member the investment’s expected value plus savings. Without informal insurance, we assume savings constraints, τ1 < τ̄ , hinder the entrepreneur from fully covering consumption, reinvestment, and the repayment. 2.2. Discussion of assumptions The set-up incorporates our two main mechanisms. To capture the credit-constraint mechanism, we rely on the conventional idea that moral hazard at the repayment stage gives rise to credit rationing of poor entrepreneurs.11 To this generic model, we add the need to save in period 1 to cover consumption, reinvestment, and repayment in the next period. Repayment vouchers 8. Without diversion opportunities (φ = 0), perfect legal protection of creditors would allow even a wealthless borrower to fund the investment, eliminating credit rationing. To make our problem interesting, we assume φ > φ (defined in Supplementary Appendix A.1). 9. V reflects the common practice of microfinance institutions punishing default by denying future credit. 10. Alternatively, the lender could be a non-profit maximizing borrower welfare, subject to break-even. 11. See, e.g. Hart (1995), Shleifer and Wolfenzon (2002), and Ellingsen and Kristiansen (2011) for similar mod- els of financial contracting under imperfect enforcement and Blouin and Macchiavello (2019) for empirical work on the prevalence of strategic default in developing markets. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 7 reduce this need, allowing poor borrowers to allocate more funds for investment which relaxes the credit constraint. To capture the insurance-constraint mechanism, entrepreneurs choose between a safe liquid and a risky illiquid investment.12 Without informal risk sharing, we assume that uninsured rev- Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 enue shocks promote investment in the safe technology under the standard contract. By insuring against costly liquidation (to repay the loan), the vouchers alleviate the insurance constraint and facilitate investment in riskier illiquid assets. The key premise of the model is that entrepreneurs may need additional funds at various stages of the project: for the initial investment and/or to address earnings shortfalls later. To differentiate the credit from the insurance mechanism, we view constraints related to the ini- tial investment as distinct from those tied to managing a state-contingent shock later in the loan cycle. While credit constraints might prevent the investment from taking place, insurance con- straints could result in an inability to hedge against future income loss risks. However, a funds shortage is the common friction underlying both constraints. By assuming constrained savings, we illustrate how the standard payment obligation limits the entrepreneur when no other means of insurance is available.13 While savings can supplement self-financed entrepreneurs’ consumption and reinvestment in period 2, they cannot also cover the standard repayment. Thus, a negative period-2 shock forces the entrepreneur to either not reinvest the returns from the liquid project or liquidate if invested in the illiquid technology.14 When informal risk pooling is available, the assumption that the scheme eliminates all uninsur- able risk allows us to study the implications of binding credit constraints without a risk-based motivation for saving across periods. Figure 1 depicts the model stages and trade-offs in project choice and repayment/diversion decisions. Next, we analyse each contract (standard and flexible) to understand how credit and insurance market imperfections affect investment. The analysis, grouped by the completeness of the informal insurance market, starts with the standard contract. 2.3. Equilibrium 2.3.1. Imperfect insurance. Without informal risk pooling, a wealth-constrained entrepreneur opts for the safer technology, as the expected benefit of avoiding intermediate period liquidation exceeds the final period gain from the riskier illiquid project. We use backward induction to characterize the incentive constraint for the two-period loan, focusing on the low- return realization in t = 2, where diversion temptation is highest. In Supplementary Appendix A.1, we show that it suffices to look at the second period constraint. The entrepreneur pays the lender if the residual return after repaying exceeds the benefit from diverting all resources I ϕ L + τ1 R − PS + β (y − PS ) + β 2 V ≥ φ (I ϕ L + τ1 R + βy). (1) 12. Illiquid business assets, including special purpose tools, machinery but also certain types of inventory, are common in our setting. On average, 38% of the firms’ asset value is lost in case of a fire sale. See Section 5.4 for more details. 13. There is abundant evidence that savings constraints, caused by transaction costs, social constraints, lack of trust, regulatory practices, informational gaps, and behavioural biases, prevent the poor from smoothing over time (see, e.g. Dupas and Robinson, 2013; Karlan et al., 2014; Casaburi and Macchiavello, 2019). 14. Beyond limiting the entrepreneur’s ability to hoard liquidity across periods, we also exclude the option of period-2 refinancing. This assumption is due to our partner organization not offering this option, few viable alternatives exist for most borrowers, and the incentive incompatibility of “finance-as-you-go” as new claims dilute old ones, a concern amplified for poorer borrowers with larger debt burdens (Tirole, 2006, Chapter 5; Holmström and Tirole, 2011). 8 REVIEW OF ECONOMIC STUDIES Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 F IGURE 1 Timing of events The entrepreneur only repays in the second period if she does not plan to default in the third.15 As there is no default in equilibrium, the equilibrium interest rate ensuring zero profit is R = 1 + ρ. The following proposition summarizes our first result. Proposition 1. When ex-post risk pooling is absent and lenders offer the standard contract, there is an asset threshold Ã(φ) > 0 such that entrepreneurs with A < Ã(φ) make no investment. If A ≥ Ã(φ), then entrepreneurs borrow and invest in the safe project. A poor entrepreneur with A < Ã would need to borrow significantly to finance the invest- ment. The large repayment obligation would yield a residual return below the payoff from diverting all resources. Consequently, only entrepreneurs with A ≥ Ã secure funding, while those with A < Ã cannot obtain a loan. With the flexible contract, borrowers can use a payment voucher in t = 2 to defer the full loan payment PF to the final period. If the entrepreneur chooses the liquid project, the voucher enables reinvestment of project proceeds at date 2, even under the low-return realization. If she invests in the illiquid technology, she avoids the intermediate period liquidation risk and fully benefits from the high-return project. Given the (unconstrained) illiquid project’s higher expected value, the availability of repayment vouchers encourages the entrepreneur to undertake the riskier project. Unlike above, there is only one relevant incentive constraint to consider. In the final period, the temptation to divert all resources is resisted in favour of repaying the full loan if I + y − PF + βV ≥ φ (I + y). (2) Proposition 2. When ex-post risk pooling is absent and lenders offer the flexible contract, there ˜ is an asset threshold Ã(φ) ˜ > 0 such that entrepreneurs with A < Ã(φ) make no investment. If ˜ A ≥ Ã(φ), then entrepreneurs borrow and invest in the risky project. Since vouchers increase the discounted project value, Proposition 2 also characterizes the outcome when both credit contracts are offered simultaneously. Repayment flexibility raises the return to both technologies, but in different ways. Vouchers alleviate the insurance constraint by eliminating the liquidation risk for the illiquid project, and they free up working capital for reinvestment in the liquid technology. As the illiquid project’s expected value is higher, the 15. Diversion is an all-or-nothing decision, as we assume the lender claims all resources upon default. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 9 former effect prevails, leading entrepreneurs to invest in illiquid assets with greater sensitivity to aggregate uncertainty. ˜ vouchers also ease the credit constraint. Two opposing forces are at play. On one If Ã > Ã, hand, vouchers eliminate the need to save for the first repayment, freeing more funds for the Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 initial investment. Additionally, the gross return to honouring the contract is higher with the riskier project. Both effects increase the value of investment over diversion, making lending to poorer borrowers incentive compatible. On the other hand, the standard contract’s spread-out payments reduce the instantaneous repayment burden and the temptation to divert. Overall, the inequality holds when the savings reduction and the illiquid project’s higher return offset the larger one-time payment. Lastly, while all borrowers take up the vouchers, they are indifferent between using them and adhering to the standard contract upon a positive period-2 realization. Essentially, vouch- ers offer an option value that protects entrepreneurs against future unforeseen fluctuations. The subsequent corollary collects these additional results. Corollary 1. When ex-post risk pooling is absent and lenders offer standard and flexible con- tracts: (1) entrepreneurs prefer the flexible contract and invest in the risky project; (2) a lower ˜ (3) entrepreneurs asset level is required to obtain a loan under the flexible contract if Ã > Ã; weakly prefer using the vouchers independent of the state of nature. 2.3.2. Perfect insurance. With a complete risk market and the standard contract on offer, ex-post risk pooling eliminates the liquidation risk, leading the entrepreneur to select the illiquid project. As before, we focus on the no-diversion constraint in t = 2 given by I γ̄ + τ1 R − PS + β (I + y − PS ) + β 2 V ≥ φ I γ̄ + τ1 R + β (I + y) , (3) where I γ̄ is the expected period-2 return under risk pooling. With vouchers, entrepreneurs still invest in the riskier project but defer the full payment to t = 3, altering the incentive constraint to equation (2). Like above, the credit constraint eases if Ā > Ā¯ (the minimum incentive- compatible asset size under the standard and flexible contract, respectively). This condition holds if the savings from postponing the first repayment outweigh the increased diversion cost due to the higher repayment burden. Formally Proposition 3. Suppose Ā > Ā¯ and lenders offer standard and flexible contracts. Then there are ¯ asset thresholds Ā(φ) > Ā(φ) > 0 such that entrepreneurs with A < Ā(φ) ¯ make no investment, ¯ those with A ∈ [ Ā(φ), Ā(φ)) prefer the flexible contract and invest in the risky project, and those with A ≥ Ā(φ) are indifferent between the contracts and invest in the risky project. 2.4. Extensions In the current model, the entrepreneur’s only input is her assets. However, if ability and invest- ment capital are complements, then more able entrepreneurs will have a higher productivity for a given level of assets. Since vouchers ease the credit constraint, the return to relaxing this constraint is higher for entrepreneurs of greater ability (the formal argument is detailed in Supplementary Appendix A.1). We have so far assumed that the loan payment is the primary obligation. However, there could be other commitments (on top of the repayment), such as recurrent costs. Particularly, larger firms are often committed to periodic expenses like rent, utilities, and salaries. In Supplementary Appendix A.1, we show that even with vouchers, entrepreneurs may still face considerable risk 10 REVIEW OF ECONOMIC STUDIES under the illiquid project due to these additional obligations. While vouchers release liquid- ity that can be reinvested in the safe project, the net gain from introducing more flexibility is reduced, especially if the return to the liquid technology is low.16 In the basic model, vouchers provide an insurance mechanism, even in the context of univer- Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 sal risk neutrality. To explore how repayment flexibility affects the selection of individuals into borrowing along the risk dimension, we modify the model to incorporate risk aversion. In line with our empirical setting, we assume a smaller self-financed project is available, which appeals to less risk-averse individuals interested in business expansion and thus, in need of external credit.17 To capture that the self-funded entrepreneurs who want to expand are more prone to risk, the smaller project is a scaled-down version of the illiquid technology, now referred to as a large project. In Supplementary Appendix A.1, we demonstrate that the effect of repayment flexibility on the borrower pool’s level of risk aversion is ambiguous. While it attracts clients deterred by the risk of existing investment technologies, it also appeals to borrowers who find the large illiquid project too risky and the large liquid project too safe. If the latter group of entrepreneurs, keen on expanding their risky but smaller businesses, predominantly selects the flexible contract, the borrower pool’s risk aversion level may decrease.18 3. THEORETICAL PREDICTIONS Table 1 summarizes the theory’s main predictions, conditional on different market imperfections. We start with the case when the loan payment is the key outstanding obligation (Panel A). When insurance is imperfect and firms are credit rationed under the standard contract (row 1), repay- ment flexibility increases risk taking by enabling illiquid investments more exposed to aggregate ˜ It is especially beneficial for the least wealthy by allowing more upfront uncertainty for A ≥ Ã. investment funds, thus lowering the incentive-compatible asset level for A ∈ [ Ã, ˜ Ã). High- ˜ If only the risk market imperfection ability entrepreneurs also see greater benefits when A = Ã. is a constraint for A ≥ Ã (row 2), the flexible contract mainly boosts risk-taking, with potential positive or negative selection with respect to risk aversion (similar to row 1). When only credit is rationed (row 3), repayment flexibility improves conditions for poorer and more able borrowers ¯ Ā) and A = Ā, for A ∈ [ Ā, ¯ respectively, who now undertake risky investments. With complete credit and insurance markets (row 4), repayment vouchers have no impact on outcomes. We then consider the case when other contractual obligations are important (Panel B). The theory suggests that vouchers lead credit and risk-rationed firms to boost their safe investments 16. An alternative explanation related to recurrent costs is that larger firms, unconstrained by risk, use vouchers to smooth consumption. These firms, already undertaking risky projects, will demand more flexibility as recurrent costs rise, without affecting firm outcomes. Conversely, our extension shows that when higher recurrent costs prevent riskier projects (due to too low net income in the bad state even with vouchers), repayment flexibility can still boost low-return liquid investments. 17. In our SME sample, less risk-averse firm owners are significantly more willing to start a new business, aligning with literature dating back to Cantillon (1755), Knight (1921), and more recently Kihlstrom and Laffont (1979), where business risk bearers are less risk averse than the general population. 18. Other aspects of selection, independent of risk aversion, could affect investments. The flexible loan may increase the default temptation for present-biased borrowers (see, e.g. Bauer et al., 2012; Fischer and Ghatak, 2016; Barboni, 2017), who prefer the standard contract’s smaller, spread-out payments. Additionally, the contract’s complexity could impose a cost on financially illiterate borrowers, potentially inducing them to overconsume early in the loan cycle. If a large share of new borrowers has time-inconsistent preferences or is financially illiterate, this could result in tighter credit constraints and reduced investment in equilibrium. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 11 TABLE 1 Summary of predictions Market environment under Predicted change under the standard contract repayment flexibility Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Credit Insurance Increase in Increase in Poor benefit Able benefit Selection w.r.t. is rationed is rationed risky investment safe investment more more risk aversion Panel A: Other contractual obligations do not matter + 0 + + ≶0 + 0 0 0 ≶0 +a 0 + + 0 0 0 0 0 0 Panel B: Other contractual obligations may matter 0 + ≥0 + ≶0 0 + 0 0 ≶0 0 +b + + 0 0 0 0 0 0 Notes: The table summarizes the predictions of the theoretical model, conditional on the different market imperfections. a In contrast to row 1 of Panel A, the increase in risky investment in row 3 is confined to poor borrowers. b Contrary to row 1 of Panel B, the increase in safe investment in row 3 is limited to poor borrowers. (row 1). While flexibility still benefits able entrepreneurs, poorer individuals may not gain due to the low investment return. If insurance provision is imperfect (row 2), the theory predicts a rise in the safe investment. Similar to Panel A, risk aversion can induce borrower selection (rows 1 and 2). Vouchers benefit poor and high-ability entrepreneurs now making safe investments if only credit constraints bind (row 3). With well-functioning markets, vouchers have no effect (row 4). The model guides our understanding of small firms’ financial environment by allowing us to assess whether imperfect insurance, credit constraints, or both are binding. A key prediction is that increased risk taking is the single most important response if entrepreneurs are limited by imperfect risk markets. However, the theory also suggests that if firms have other contractual obligations beyond the loan payment, repayment flexibility alone may not increase risk tak- ing. We use this framework to structure our empirical analysis and interpret the results in the subsequent sections. 4. EXPERIMENT 4.1. Context Our study is set in Bangladesh where our partner, BRAC, is one of the main providers of micro- finance services. BRAC’s microfinance programme mainly targets two types of clients.19 The most common microfinance product is the “Dabi loan,” which is meant to finance microenter- prises, typically with no employees except for family workers (e.g. tailoring, small retail shops, poultry and livestock rearing, and carpentry). The average size of a Dabi loan is 275 nomi- nal USD (range between 100 and 1,000). Currently, BRAC has 4 million Dabi borrowers in Bangladesh. BRAC also offers “Progoti loans” for small and medium-sized enterprises. The Progoti loans are intended for working capital in shops, agricultural businesses, and small-scale manufacturers and have an average loan size of $2, 200 (range between 1,000 and 10,000). 19. BRAC also has specialized loans for sharecroppers, migrant workers’ households, and students. We do not study these products. Further details about the loan products BRAC offers are available from BRAC’s microfinance programme’s website (http://www.brac.net/program/microfinance/). 12 REVIEW OF ECONOMIC STUDIES They require collateral of equal value to the loan and a guarantor. Both types of loan products entail individual liability (with group meetings in the case of Dabi loans), a flat 22% annual interest rate, and a 12-month loan repayment cycle with monthly installments of equal size. We collaborated with BRAC to implement a pilot assessing the viability of a flexible loan Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 product. The flexible contract allowed borrowers to delay up to 2 repayments within their loan cycle through the use of repayment vouchers. BRAC decided to offer the option to borrow under the flexible contract to Dabi and Progoti clients with good credit histories. The eligible clients were selected by credit officers at the branch office level on the basis of having no defaults and few or no arrears. Under the flexible contract, borrowers had 2 vouchers that enabled them to postpone 2 monthly repayments in their loan cycle. On the day of the repayment, borrow- ers could present the voucher thereby postponing the repayment and extending the loan cycle. Specifically, by extending the cycle to 14 instead of 12 months the borrowers had 2 months dur- ing which they were not required to make any payments to BRAC. For example, if borrowers skipped the first two installments, the repayments started in month 3 and continued up to month 14 (corresponding to a contract that provides a 2-month grace period). If clients decided to use their vouchers to avoid any other installment(s), the repayment in that month would be skipped and the full loan cycle was extended by an additional month. Hence, the contract provided the borrowers with full flexibility to tailor-make their loan cycle according to their expected and unexpected cash-flow needs (they were still limited to delaying no more than 2 repayments). Moreover, if borrowers wanted, they could skip 2 repayments and pay up their remaining bal- ance within the 12th month, thus keeping the length of the loan cycle unchanged. As such, the vouchers offered considerable payment flexibility.20 No extra cost was charged for the use of the voucher(s). 4.2. Evaluation and data To evaluate the effects of the new loan contract, we randomized the introduction of the flexible loan at the BRAC branch office level. The typical branch office covers an area of a roughly 6-km radius with 200 Progoti and nearly 1,200 Dabi borrowers. BRAC selected fifty branches for the study and credit officers in each branch identified Dabi and Progoti borrowers that they deemed eligible for the flexible loan. BRAC subsequently provided us with a list of the eligible clients in each branch. From this list, we randomly sampled 2,717 eligible borrowers; 1,115 Dabi and 1,602 Progoti clients. We also obtained a list of all ineligible clients in the same 50 branches. In addition to eligible BRAC clients, we collected information on a representative sample of SMEs (independent of their borrowing status with BRAC). For this, we first conducted a census within the geographic location of each BRAC branch office by going door-to-door, capturing a comprehensive listing of all SMEs operating in selected sectors in the study branches. The objective was to identify microenterprises with fewer than 10 workers operating in light manu- facturing and retail. These characteristics were chosen to make them comparable with potential BRAC borrowers.21 This provided us with a listing of 7,270 firms. From the census, we randomly sampled and surveyed 3,504 firms at baseline (the “SME sample”).22 20. Note that while there may be some de facto flexibility in BRAC’s modus operandi, the extent of this flexibility is rather limited (see Section A.2.2 in the Supplementary Appendix). Nevertheless, the flexible contract that we evaluate should be interpreted as comparing the effects of introducing explicit flexibility (in the form of allowing 2 monthly repayments to be delayed at no cost to the borrower) relative to any de facto flexibility that BRAC already provided. 21. Manufacturing includes SMEs active in food processing, carpentry, plumbing, handicraft, and garments while retail comprises grocery, supermarkets, wholesale shops, clothing, and hardware. 22. By construction, the SME sample contains both current BRAC clients (about 10%) and non-client firms located within each study location. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 13 Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 F IGURE 2 Locations Notes: The map shows the locations of the BRAC branch offices that were part of the study. The treatment branches are represented with triangles, while the control branches are denoted with squares. The baseline survey for our two samples was conducted between January and June 2015. After the baseline, we randomly selected half of the 50 branches as treatment and the rest as control. The randomization was stratified by district (15 randomization strata), each contain- ing 2–5 of the branch offices in our study. Figure 2 shows the locations of the BRAC branches included and their randomization status. The flexible loan product was launched in mid-August 2015. By the end of September 2015, the intervention had been introduced in all branches. Immediately following the product launch, we collaborated with BRAC to implement an infor- mation campaign in the treatment branches. Its goal was to ensure that information regarding the new loan that BRAC was piloting reached the firms in the SME sample. This was achieved through: (i) phone calls, conducted by BRAC’s phone call centre, to every business owner in our SME sample. During these phone calls, the terms of the new loan product were explained; and (ii) leaflets, describing the same information, delivered by BRAC credit officers to the firms in the SME sample and to firms in the eligible-borrower sample.23 23. For most eligible Dabi clients, the information on the flexible contract was provided during their regular group meetings. At the end of the meeting, the credit officers described the new product and its features to the eligible borrowers. In order to make sure that it was well understood, they also gave them a leaflet. For eligible Progoti clients, the credit officers visited their business to provide them the same information. These meetings/visits were part of the routine operations that BRAC’s credit officers conduct for their borrowers. 14 REVIEW OF ECONOMIC STUDIES Approximately 1 year after the baseline, between May and July 2016, we implemented the first follow-up survey (the mid-line). Since the intervention was launched in August 2015, the effects at mid-line capture short-run impacts (8–10 months after treatment started). Nearly 1 year after the mid-line (and 2 years after the baseline), we conducted the endline survey.24 At Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 the end of that survey (August 2017), we received BRAC’s administrative records on its bor- rowers (eligible and ineligible borrowers at baseline, as well as the new borrowers that joined BRAC after the launch of the experiment). The records contain data on the last as well as past loans of current or past borrowers, providing us with detailed reports on borrowers’ repayment behaviour. Finally, to measure local rainfall shocks, we use monthly rainfall data at 0.25-degree reso- lution obtained from the NOAA-maintained PERSIANN-CDR dataset which covers the period 1983–2017.25 The information on precipitation is used to construct local demand shocks across the 50 branches under study. 4.3. Descriptives and validity checks Supplementary Table A.1 provides descriptive statistics on the baseline characteristics of the eligible Dabi clients, while Supplementary Table A.2 does the same for the eligible Progoti borrowers.26 The average eligible Dabi client in our sample is 38–39 years old, has 4.5 years of schooling, approximately half of them own some land, and the typical household labour income is about 7,000 USD PPP per year. In terms of business ownership, 45% of Dabi clients report having a business at baseline.27 The average Dabi borrower owns 4,300 USD PPP worth of business assets, employs 0.5 workers (excluding the owner of the business but including other family workers) and generates 4,200 USD PPP worth of annual profits.28 In contrast to the Dabi clients, Progoti borrowers are older (44 years old), more educated (7.5 years of schooling) and wealthier (83% own land and average annual household income is above 20,000 USD PPP). They are also more likely to be business owners (87%), and their businesses are larger in terms of capital (around 25,000 USD PPP), number of workers (1.9 workers on average), and profits. For all of the outcome variables we study as well as other key characteristics, Supplemen- tary Tables A.1 and A.2 report balance tests where we compare the sample means by treatment status. In particular, column (3) shows the standard difference, column (4) the randomization inference p-values, and column (5) reports the normalized difference (Imbens and Wooldridge, 2009). With the exception of two characteristics (out of 31), none of the baseline differences are statistically significant at conventional levels and the normalized differences are smaller than one-fourth of the combined sample variation. Hence, we conclude that the randomization 24. The mid- and endline surveys were planned to be in the same period of the year in order to appease concerns about seasonality in profits and other outcomes. 25. See https://www.ncei.noaa.gov/products/climate-data-records/precipitation-persiann for more details about the rainfall data. 26. Throughout the paper, all monetary values are deflated to 2015 prices, using CPI figures published by the Central Bank of Bangladesh, and converted to USD PPP terms using conversion rates published by the World Bank’s International Comparison Program database (1 USD PPP ≈ 28.25 TAKAs). 27. This is similar to the rates of business ownership among microfinance clients in other studies (see, e.g. Field et al., 2013). Among the Dabi clients in our sample, only 5% reported owning multiple businesses. In the analysis, we focus on the main household business reported by the respondent (the borrower), but the results are similar if we aggregate all business-related variables at the household level. 28. The measure of profits we use is based on a direct question on the level of profits as opposed to subtracting costs from revenues. de Mel et al. (2009) show that for small businesses, this method provides a more accurate measure of profits compared to calculations based on detailed questions on revenues and costs. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 15 was successful in achieving baseline balance in key observable characteristics. In Supplemen- tary Table A.3, we test for differential attrition at the mid- and endline surveys. At mid-line, the attrition rate was 5% among eligible Dabi clients, 9% among eligible Progoti borrowers, and 11% in the SME sample. At endline, the rates were slightly higher (8% among eligible Dabi Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 clients, 15% among eligible Progoti borrowers, and 17% in the SME sample). The attrition rates are balanced by treatment status in both follow-up surveys. Thus, it is unlikely that differential attrition drives the treatment effects we find in the empirical analysis. 5. RESULTS 5.1. Estimation To identify the effects of the flexible loan contract on eligible borrowers, we estimate an analysis of covariance (ANCOVA) model (McKenzie, 2012) of the form: 15 yit = β · Ti + λ · yi0 + E t + γs + it , (4) s=1 where yit is the outcome of interest for respondent i at mid- (t=1) or endline (t=2), Ti is a dummy variable equal to 1 if the respondent is located in a treated branch, yi0 is the baseline level of the outcome for individual i, E t is a survey-wave fixed effect, and γs are district (randomization strata) fixed effects. Since our randomization was conducted at the branch office level, we clus- ter standard errors by BRAC branch office (50 clusters). In addition, we report randomization inference p-values (Fisher’s exact test), estimating the coefficient of interest in 1,000 alternative assignments chosen randomly with replacement from the set of possible assignments given our stratified randomization procedure. The randomization inference p-values report the percentile of the coefficients found under actual treatment in the distribution of coefficients identified under the alternative treatment assignments (Young, 2018). The parameter of interest is β, the average difference between treatment and control observations at mid- and endline. Under the assump- tion that the control observations constitute a valid counterfactual for the treatment sample, this identifies the causal effect of the offer of the flexible loan contract to eligible client i. In other words, this is the ITT estimate. 5.2. The effect of repayment flexibility We first examine the treatment effects on the eligible borrowers’ credit market outcomes. Table 2 presents the results for the Dabi (Panel A) and Progoti clients (Panel B), respectively. Columns (1) and (2) show the impact on borrowing from BRAC, where the information is obtained from BRAC’s administrative records. In the control group, 57% of the eligible Dabi clients were bor- rowing from BRAC under the standard contract at mid- or endline [column (1)]. Compared to this, the introduction of repayment flexibility increased borrowing from BRAC by 6.3 percent- age points (ppt), or 11% relative to the control group. For Progoti clients, the flexible loan offer increased take up from BRAC by 2 ppt, but this effect is imprecisely estimated. We also note that 55% of the eligible clients accepted the offer. The take-up rate was slightly higher among eligible Dabi (57%) relative to Progoti borrowers (53%), but the difference is not significant at conventional levels (p-value = 0.123). On the intensive margin, column (2) of Table 2 shows that the value of BRAC borrowing increased by 302 USD PPP or 26% relative to the control 16 TABLE 2 Effects on credit market outcomes (1) (2) (3) (4) (5) (6) (7) (8) BRAC loan Non-BRAC loan Transfers Transfers or Net borrowing Aggregate Yes = 1 Value Yes = 1 Value received loans given or transfers index Panel A: Dabi Treatment 0.063** 302.413*** −0.041* −28.041 336.187 122.002*** 510.867* 0.172*** (0.024) (73.246) (0.023) (95.669) (283.589) (42.091) (272.666) (0.051) [0.044] [0.001] [0.150] [0.792] [0.366] [0.003] [0.121] [0.009] Observations 2,168 2,168 2,168 2,168 2,168 2,168 2,168 2,168 Mean in control 0.571 1, 181.671 0.234 543.632 1, 449.935 165.716 3, 009.522 0.000 Panel B: Progoti Treatment 0.024 258.669 −0.038** −306.144 −558.212 13.723 −1078.712 −0.034 (0.024) (257.204) (0.015) (509.519) (388.486) (56.182) (843.309) (0.046) [0.443] [0.422] [0.051] [0.619] [0.291] [0.848] [0.308] [0.527] Observations 3,066 3,066 3,066 3,066 3,066 3,066 3,066 3,066 Mean in control 0.522 4, 793.960 0.227 2, 681.145 3, 277.109 391.655 10, 360.559 0.000 Notes: The table presents the treatment effects on loans and transfers of eligible Dabi and Progoti borrowers. Data comes from the mid-line (2016) and endline (2017) surveys, except in columns (1)–(2) where the data comes from BRAC’s administrative records. All regressions control for the baseline (2015) value of the outcome, an indicator variable for the endline survey and district (strata) fixed effects. “Treatment” is a dummy variable equal to 1 if the respondent was based in one of the treatment branches where BRAC introduced the flexible loan contract and offered it to the eligible clients. The regressions are ordinary least squares (OLS) regressions based on specification (4). Standard errors are clustered at the BRAC branch REVIEW OF ECONOMIC STUDIES office level (∗p < 0.10,∗∗p < 0.05,∗∗∗p < 0.01). Randomization inference p-values of the null hypothesis of no effect are provided in square brackets. In column (1), the dependent variable is a dummy = 1 if the respondent had a BRAC loan at the mid-line or endline survey. In column (2), the dependent variable is the principal amount (in USD PPP) of the BRAC loan the respondent had at the mid-line or endline survey. In column (3), the dependent variable is a dummy = 1 if the respondent had a Non-BRAC loan at the mid-line or endline survey. Non-BRAC loan value is the monetary value (in USD PPP) of all formal and informal loans taken from other lenders (banks, MFIs other than BRAC, informal money-lenders or relatives and friends) during the past 12 months. Transfers received is the monetary value (in USD PPP) of any cash or in-kind informal transfers that the respondent’s household received over the last 12 months. Transfers or loans given is the total monetary value (in USD PPP) any cash or in-kind informal transfers and any loans that the respondent’s household gave to others over the last 12 months. Net borrowing or transfers is the monetary value (in USD PPP) of net borrowing (loans borrowed minus loans lent) and net tranfers (tranfers received minus transfers given) combined. “Aggregate index” is constructed by first standardizing all outcome variables in columns (1)–(7) with respect to the control group in the relevant survey wave (subtracting the mean in the control and dividing by the standard deviation of the control group), then taking their average and standardizing again with respect to the control group. Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 17 group among the Dabi clients, with a randomization inference (RI) p-value of 0.001. The corre- sponding effect for the Progoti borrowers is an insignificant 259 USD PPP (5%) increase in the value of BRAC loans. The rest of Table 2 explores other outcomes related to credit and transfers. Starting with Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Dabi, while the treatment decreased the likelihood of having a non-BRAC loan by 4 ppt [column (3)], the impact on the intensive margin is small and imprecisely estimated [column (4)], barring any definitive conclusions on substitution effects away from non-BRAC lenders toward BRAC. Eligible Dabi borrowers also receive more informal transfers from their social networks (with the point estimate similar in size to the effect on the BRAC loan), albeit insignificantly so [column (5)]. Column (6) examines transfers and loans provided to the social network. It shows that the financial outflow from the average Dabi client in the treatment group went up by 122 USD PPP or a 73% boost relative to the control sample (RI p-value < 0.01). Overall, net borrowing and transfers combined increased by 511 USD PPP or 17% relative to the control group (RI p-value = 0.121). Together, this implies that access to the flexible contract led to important changes in the Dabi clients’ credit market outcomes. The last column presents the effect on an aggregate index that combines the 7 indicators related to the credit market outcomes of the Dabi clients. We find that the aggregate index is significantly higher by 0.172 standard deviations (SDs) among the treatment group relative to control (RI p-value = 0.009). By contrast, Panel B indicates that the impact on the eligible Progoti borrowers is insignificant [with the exception of one outcome: the likelihood of having a non-BRAC loan in column (3)]. As the aggregate index in column (8) is indistinguishable from zero, we conclude that the treatment did not significantly affect the credit market outcomes of the eligible Progoti clients.29 Next, we examine the impact of repayment flexibility on a range of business outcomes. The upper panel of Table 3 shows effects for the eligible Dabi clients, starting with business own- ership in column (1).30 Eligible Dabi clients in the treatment branches are 3 ppt more likely to own a business at follow-up relative to control, but this effect is imprecisely estimated. In terms of inputs, the treated Dabi borrowers invest significantly more in their business assets but not in labour. The treatment impact on business assets (1,881 USD PPP) is equivalent to a 51% increase relative to the mean in the control group. We do not find any significant effect in terms of labour inputs (number of workers, business operating hours, and hours worked by the business owner). Column (6) shows that treatment raised revenues by 28,153 USD PPP (annually) relative to the control sample. This corresponds to a statistically and economically significant increase of 86% (RI p-value < 0.01). Eligible clients also had higher costs which is likely related to the larger investments in their business capital (e.g. cost of purchasing tools, machines, or inventories). The ITT estimate on annual business profits [column (8)] shows a sizable increase (of 25%) rel- ative to the control group, but this is imprecisely estimated at conventional levels (RI p-value = 0.171). Column (9) indicates that the effect on monthly profits (during the month preceding the survey) is similar in magnitude with the point estimate corresponding to a 26% increase relative to the control group (RI p-value = 0.182). Column (10) shows that Dabi businesses in the treat- ment group had more volatile revenues. As a proxy for volatility, we use the range of monthly revenues. The ITT estimate reveals that the treatment group had 106% higher sales volatility relative to the control group (RI p-value = 0.066). Finally, column (10) shows that the aggre- gate index is up by 0.183 SDs among the treatment group relative to control (RI p-value = 0.050). Overall, these findings suggest that the flexible contract not only led to more business 29. In Supplementary Table A.3, we test and reject the null hypothesis of equality of the treatment effects of the Dabi versus the Progoti borrowers for the aggregate index but not for most of the individual outcomes. 30. All business outcomes are coded as zero for respondents who do not own a business. 18 TABLE 3 Effects on business outcomes (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) Business Business Number Business Owner’s Revenues Costs Profits Profits Range of Aggregate owner assets of workers hours hours worked (annual) (annual) (annual) (month) revenues index Panel A: Dabi Treatment 0.026 1, 881.254** 0.172 127.789 71.219 28, 153.189*** 24, 392.605*** 1, 087.586 96.576* 2, 801.612** 0.183** (0.025) (926.570) (0.326) (83.059) (69.523) (8, 716.036) (8, 099.027) (651.456) (56.069) (1, 215.694) (0.079) [0.350] [0.064] [0.680] [0.187] [0.389] [0.006] [0.003] [0.171] [0.182] [0.066] [0.050] Observations 2,087 2,087 2,087 2,087 2,087 2,087 2,087 2,087 2,087 2,087 2,087 Mean in control 0.549 3, 685.413 1.091 1, 577.286 1, 474.800 3, 2561.844 26, 870.630 4, 275.948 358.718 2, 647.696 −0.000 Panel B: Progoti Treatment −0.004 1, 740.773 1.068** 74.965 38.695 6, 851.723 −1.33e+04 145.652 −6.950 −8073.473 0.015 (0.013) (1, 653.815) (0.438) (73.042) (55.291) (18, 148.570) (15, 979.711) (880.334) (77.065) (5, 411.127) (0.054) [0.844] [0.426] [0.035] [0.407] [0.588] [0.752] [0.486] [0.879] [0.938] [0.295] [0.812] Observations 2,854 2,854 2,854 2,854 2,854 2,854 2,854 2,854 2,854 2,854 2,854 Mean in control 0.893 20, 936.624 2.428 2, 923.813 2, 615.572 1.68e+05 1.69e+05 13, 521.567 1, 101.980 22, 956.038 0.000 Notes: The table presents the treatment effects on business outcomes of eligible Dabi and Progoti borrowers. Data comes from the mid-line (2016) and endline (2017) surveys. All regressions control for the baseline (2015) value of the outcome, an indicator variable for the endline survey and district (strata) fixed effects. “Treatment” is a dummy variable equal to 1 if the respondent was based in one of the treatment branches where BRAC introduced the flexible loan contract and offered it to the eligible clients. “Flexible loan” is a dummy variable equal to 1 if the respondent borrowed under the new, flexible loan contract and 0 otherwise. The regressions are OLS regressions based on specification (4). Standard errors are clustered at the BRAC branch office level (∗p < 0.10,∗∗p < 0.05,∗∗∗p < 0.01). Randomization inference p-values of the null hypothesis of no effect are provided in square brackets. Business owner is a dummy variable equal to one if the respondent owns a business. Business assets is the monetary value (in USD PPP) of business assets (tools, machinery, furniture, vehicle and REVIEW OF ECONOMIC STUDIES inventories) at the time of the survey. Number of workers is the number of workers (other than household members) who work in the business on a typical working day. Business hours is the number of hours that the enterprise was in operation over the last 12 months. Owner’s business hours is the number of hours that the business owner worked in the business over the last 12 months. Revenues is the monetary value (in USD PPP) of sold products or delivered services of the business over the last 12 months. Costs is the monetary value (in USD PPP) of the total amount the enterprise spent on personnel expenses, machines, tools, equipment, space, transportation, electricity, fuel for machines, and total purchase of stock over the last 12 months. Profits (annual) is profit (in USD PPP) of the business over the last 12 months. Profits (month) is profit (in USD PPP) of the business over the month preceding the survey. Range of revenues is the difference between the level of revenues during the worst month in terms of sales and the level of revenues during the best month in terms of sales during the past year. If the respondent reported that revenues did not fluctuate throughout the year, the range of revenues is set equal to zero. “Aggregate index” is constructed by first standardizing all outcome variables in columns (1)–(10) with respect to the control group in the relevant survey wave (subtracting the mean in control and dividing by the standard deviation of the control group), then taking their average and standardizing again with respect to the control group. Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 19 activity and greater business investments, but also increased the volatility of the monthly busi- ness revenues among the Dabi borrowers.31 When we study the effects on the Progoti clients, we find a strikingly different pattern. In particular, there are no significant effects on any of the business outcomes except for the number of workers, and the overall impact on the aggregate Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 index in column (11) is close to zero and insignificant.32,33 The third and final set of outcomes are related to the socio-economic status of the eligible borrowers. Panel A of Table 4 shows that eligible Dabi clients in the treatment group had higher household (labour) income, corresponding to an increase of 17% relative to the control sample. The rest of the panel indicates that, while there was no significant impact on per-capita consump- tion, the value of non-business assets owned by the respondent’s household increased by 18% compared to control (RI p-value = 0.039). Treated clients were also 8 ppt more likely to own land (RI p-value < 0.01), with land size increasing by 10 decimals (0.04 hectares) or 27% rela- tive to the control group mean (RI p-value = 0.012).34 Assessing land use reveals that most of the new, larger landholdings, were rented out (see Supplementary Table A.9). Treated borrowers are twice as likely to rent out land and hold four times as much land for this purpose, increas- ing the land rent received by about 47 USD PPP (RI p-value = 0.011)—nearly a 100% increase relative to the control group. Given that land ownership is a key indicator of socio-economic status in rural Bangladesh, this is an important sign that the status of the eligible Dabi clients improved as a result of the intervention. The aggregate index in column (6) also shows a signif- icant increase of 0.165 SDs (RI p-value = 0.026). In contrast to the Dabi borrowers, there are no significant effects on any of the outcomes nor on the aggregate index for the Progoti clients (Panel B of Table 4).35 Figure 3 provides a visual summary of the treatment impact on the eligible clients. It plots the ITT effects on standardized indicators related to the three families of outcomes we study (credit market, business, and household economic status). All the Dabi-related outcomes (shown in Figure 3A), with the exception of non-BRAC loan value and per-capita consumption expendi- ture, are positively affected, with a majority of them being statistically significant. In particular, we observe large effects on business revenues (0.24 SDs), profits (0.13 SDs), and household 31. As noted in Section 4.3 above, only 45% of the eligible Dabi clients reported having a business at baseline. In order to understand whether the effects in Table 3 are driven by business survival and growth versus starting up of new businesses, we tested for the heterogeneity of the business outcomes with respect to baseline business ownership (see Supplementary Table A.5). Overall, results show that the treatment did not have a significant impact on business ownership and most of the effects on revenues, costs, and profits are observed in households who already had a business at baseline. This suggests that the treatment effects are mainly driven by growth of existing businesses as opposed to starting up of new ones. 32. Similar to the credit market outcomes, we can reject the null hypothesis of equality of the treatment effects of the Dabi versus the Progoti borrowers for the aggregate index but not for most of the individual outcomes (see Supplementary Table A.6). 33. Firm outcomes, such as profits and revenues, are notoriously noisy. In Supplementary Tables A.7 and A.8, we assess the sensitivity of the treatment effects on all monetary business outcomes with respect to outliers. Each table reports estimates where the data is winsorized at the 99.5th (Panel A), 99th (Panel B), 98th percentile (Panel C). Qualitatively, the estimates confirm those reported in Table 3. The only outcome variable for which we lose significance is the range of revenues—when we winsorize the data at the 99th or 98th percentile, the effect on the range of monthly revenues is still positive but no longer precisely estimated for the Dabi sample. In terms of magnitude, the treatments effects on many outcomes diminish considerably when winsorizing the top 2%. This alludes to there being considerable heterogeneity in the treatment effects on Dabi clients, which we discuss in detail in Section 5.4. 34. The findings are in line with existing evidence on land ownership and land transactions in Bangladesh (see Section A.2.3 in the Supplementary Appendix). 35. In Supplementary Table A.10, we test for and reject the null hypothesis of equality of the treatment effects on household socio-economic status of the Dabi versus the Progoti borrowers for the aggregate index, household income, and land ownership, but not for the other outcomes. 20 REVIEW OF ECONOMIC STUDIES TABLE 4 Effects on household socio-economic status (1) (2) (3) (4) (5) (6) Household Consumption Non-business Land owner Size of Aggregate income per capita assets value (Yes = 1) land owned index Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Panel A: Dabi Treatment 1, 309.195* 12.417 610.540** 0.076*** 10.366*** 0.165*** (774.989) (82.422) (243.284) (0.022) (3.319) (0.056) [0.194] [0.888] [0.039] [0.002] [0.012] [0.026] Observations 2,168 2,085 2,168 2,087 2,168 2,168 Mean in control 7, 820.156 1, 613.159 3, 433.611 0.472 37.953 −0.000 Panel B: Progoti Treatment −667.980 −119.154 −392.274 −0.005 −13.853 −0.050 (918.048) (118.311) (397.728) (0.017) (14.714) (0.037) [0.576] [0.346] [0.382] [0.778] [0.438] [0.260] Observations 3,066 2,853 3,066 2,854 3,066 3,066 Mean in control 18, 641.784 2, 296.669 7, 954.081 0.820 168.575 −0.000 Notes: The table presents the treatment effects on indicators of household socio-economic status outcomes of eligi- ble Dabi and Progoti borrowers. Data comes from the mid-line (2016) and endline (2017) surveys. All regressions control for the baseline (2015) value of the outcome, an indicator variable for the endline survey and district (random- ization strata) fixed effects. “Treatment” is a dummy variable equal to 1 if the respondent was based in one of the treatment branches where BRAC introduced the flexible loan contract and offered it to the eligible clients. The regres- sions are OLS regressions based on specification (4). Standard errors are clustered at the BRAC branch office level (∗p < 0.10,∗∗p < 0.05,∗∗∗p < 0.01). Randomization inference p-values of the null hypothesis of no effect are provided in square brackets. Household income is the monetary value (in USD PPP) of the household members’ total earnings from wage-employment over the past 12 months and the profit(s) of any household business(es) operated by the house- hold. Consumption per capita is the monetary value (in USD PPP) of the total household expenditure per capita (in PPP USD) over the last 12 months divided by the household size on consumption measures). Non-business assets value is the monetary value (in USD PPP) of durable non-business assets owned by the respondent’s household at the time of the survey. Land wwner is a dummy variable = 1 if the household owns any land (excluding the homestead). Size of land wwned is the amount (in decimals) of land owned by the household (excluding the homestead). “Aggregate index” is constructed by first standardizing all outcome variables in columns (1)–(5) with respect to the control group in the relevant survey wave (subtracting the mean in control and dividing by the standard deviation of the control group), then taking their average and standardizing again with respect to the control group. income (0.14 SDs).36 The corresponding effects on the eligible Progoti clients are depicted in Figure 3B. Overall, we do not find evidence of a significant average impact on the outcomes of the Progoti clients. As noted above, one business outcome where we do observe a significant treatment effect is the number of workers employed in the Progoti clients’ businesses. The bor- rowers in the treatment group hire on average 1 additional worker, which implies a 42% increase relative to the control group (RI p-value = 0.035). Nevertheless, since the effect is observed on only 1 out of a number of business outcomes, we conclude that repayment flexibility did not have a significant impact on Progoti clients’ businesses, at least on average. A possible concern with the large treatment effects detected among the Dabi clients is whether the results are driven by some peculiarity of our context or the eligible sample itself. 36. In the Supplementary Appendix, we present the results of estimating the treatment effects at mid- and endline separately and test for the differential impact between the two surveys to shed light on the dynamics. Supplemen- tary Table A.11 shows this for the ITT estimates for Dabi and Supplementary Table A.12 for Progoti clients. Overall, the treatment impact does not appear to be significantly different for most outcome variables across the two surveys. Notably, there is no significant difference in the aggregate indices for the three families of outcomes across mid- and endline. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 21 A Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 B F IGURE 3 ITT effects: (A) effects on Dabi borrowers and (B) effects on Progoti borrowers Notes: The figures plot the standardized effect sizes and 90% confidence intervals around the treatment effects estimated using ordinary least square estimates based on specification (4). The sample includes eligible Dabi borrowers in Panel A; and eligible Progoti clients in Panel B. Data comes from the mid-line (2016) and endline (2017) surveys. All regressions control for the baseline (2015) value of the outcome, an indicator variable for the endline survey and district (randomization strata) fixed effects. Standard errors are clustered at the BRAC branch office level. 22 REVIEW OF ECONOMIC STUDIES To assess this, we compare our estimates to the treatment effects found in Field et al. (2013) who evaluate the impact of an initial 2-month grace period provided to microfinance clients in India. Even though the product we examine is quite different, allowing borrowers to manage payments freely over the loan cycle in a state-contingent manner, Field et al. (2013) is the most Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 similar study to ours that we are aware of in terms of context (traditional microfinance borrow- ers), methodology, and the type of contractual deviation analysed. The grace period increased the business assets by 81%, weekly profits by 57%, and monthly household income by 22%. Our ITT estimates correspond to a 51% increase in business assets, 26% increase in monthly profits, and 17% increase in annual household income. As the grace period was mandatory, take up was 100% by design. Considering that the take-up rate of the flexible loan product is 57% among our eligible Dabi clients, the ITT estimates are very similar to the effects found in Field et al. (2013) (assuming no spillover effects on borrowers who did not take up the flexible loan). This builds confidence in the external validity of our findings and suggests that the large treatment effects are not driven by some special feature of our context or sample. 5.3. Client retention and default rates To study the effect on the eligible borrowers’ repayment behaviour, we use BRAC’s administra- tive records. In particular, we test if the repayment rates of the eligible clients and their demand for BRAC loans are affected by the introduction of the flexible loan contract. Table 5 reports the impact on client retention and default for the eligible borrowers. Column (1) shows that treated Dabi clients are 6.8 ppt less likely to have left BRAC by August 2017, 2 years following the start of the experiment. The effect on Progoti borrowers is also negative but imprecisely estimated.37 In the remaining columns we investigate the repayment rates. We first present the official default classification used by BRAC [column (2)] and then assess how repayments change depending on the time elapsed since the start of the contract [columns (3) and (4)] or since the end of the loan cycle [columns (5)–(7)]. Specifically, column (2) reports the effect on the official default rate defined as the likelihood of not having repaid the loan by the end of the loan cycle. We find that the provision of repayment flexibility leads to a significant reduction in the rate of default for eligible Dabi borrowers (RI p-value = 0.095). In the treatment branches, they are 1.7 ppt (or 35% at the mean) less likely to default. The corresponding impact is close to zero for the Progoti clients.38 Next, we examine the likelihood that the loan was not fully paid in 12 months to quantify the proportion of borrowers who extended the loan by using at least one voucher. Treated Dabi borrowers are 8.2 ppt more likely to not repay the loan within 12 months relative to the control group, suggesting an increase in the likelihood to extend the loan by 8.2 ppt. Similarly, we see a 5.2 ppt increase for treated Progoti borrowers. Column (4) investigates the actual end of the loan cycle, defined as 12 months in the control and 14 months in the treatment branches.39 Dabi 37. We define leaving BRAC as a dummy equal to one if the borrower repaid her loan(s) and had not taken a new one by August 2017; and equal to zero if the borrower has a current loan or remain in default by August 2017. As the default rate decreased, columns (2) and (4)–(7) in Table 5, the probability of remaining with BRAC is driven by a higher likelihood of taking up a new loan. 38. The default indicator in column (2) is based on a classification entered into the system by BRAC’s credit officers. While the officers were instructed to account for the possibility of extending the loan cycle (up to 2 months) for borrowers with flexible loans, it is possible that they may not have implemented this 100% correctly. That is why we use an alternative classification in columns (5)–(7), which yields similar results. 39. Thus, in columns (4)–(7), the end of the loan cycle is computed starting 2 months after the expected last collection date in the treatment branches (to account for the extension possibility induced by the vouchers) and by the expected last collection date in the control branches. Battaglia et al. REPAYMENT FLEXIBILITY AND RISK TAKING 23 TABLE 5 Effects on repayment behaviour (1) (2) (3) (4) (5) (6) (7) Loan not fully paid Full loan not repaid within Downloaded from https://academic.oup.com/restud/advance-article/doi/10.1093/restud/rdad107/7425423 by guest on 14 August 2024 Borrower no longer Classified in 12 by the end of 2 months 6 months 12 months with BRAC as “Default” months the loan cycle after the end of the loan cycle Panel A: Dabi Treatment −0.068* −0.017** 0.082*** −0.064*** −0.018 −0.019 −0.019 (0.036) (0.008) (0.025) (0.017) (0.013) (0.013) (0.013) [0.152] [0.095] [0.007] [0.001] [0.269] [0.217] [0.218] Observations 945 945 914 914 914 914 914 Mean in control 0.371 0.048 0.109 0.109 0.046 0.042 0.040 Panel B: Progoti Treatment −0.025 −0.003 0.052*** −0.094*** 0.004 0.007 0.006 (0.028) (0.007) (0.018) (0.016) (0.009) (0.008) (0.007

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