Intermediated Loans - A New Approach to Microfinance Pt 2

5 Results Are the predictions regarding selection, takeup and repayment validated empirically? To do that we examine the evidence on selection/recommendation of clients and on takeup/continuation and repayment over the first year of this ongoing project. In the following discussion we focus primarily on differences between TRAIL and GBL, with analogous results for GRAIL noted in passing when the empirical results are reviewed. In our empirical analysis we use non-parametric plots and regressions which control for household characteristics and village fixed effects. TRAIL effectiveness The basic premise of the TRAIL model is that the agent has information of the type of the borrower and will use this information to recommend safe borrowers. The interest rate paid on informal loans is viewed as a measure of the innate riskiness of the household. If TRAIL is effective, then the agent will recommend own segment safe types. Whether this is indeed the case can be examined by comparing the interest rate on informal loans paid by Control 1 households to those paid by (a restricted set of) Control 2 households. If TRAIL is effective, then the interest rate paid by Control 1 households should be lower than that paid by Control 2 households. This is true irrespective of the level of landholding (see Panel A, Figure 5). This is further supported by the regression results presented in column 1 in Table 9, which shows that even after controlling for landholding, Control 1 households pay a significantly lower interest rate on informal loans compared to the (restricted set of) Control 2 households. As a further test of TRAIL effectiveness we examine whether the agents exhibit a bias towards clients with whom he has had prior interactions and belong to the same caste/religion network. Evidence presented in column 1 of Table 10 indeed supports the argument that TRAIL agents are biased towards households on whom they are likely to have more information: they are more likely to recommend households who have previously purchased from them or borrowed from them. Additionally TRAIL agents are significantly less likely to recommend households from outside their religion and caste network: Hindu agents are significantly less likely to recommend non-Hindu households; high caste agents are less likely to recommend lower caste (SC) households, though this effect is not statistically significant. TRAIL agents (the large majority of whom report Business to be their primary occupation - see Table 2) exhibit a slight bias in favour of households where the primary occupation is labour. 21 Targeting by Landholding: Selection/Recommendation Overall around 96% of households who were recommended/formed a group owned no more than 1.5 acres of land. This requirement was imposed on the agents and in the group formation process in TRAIL and GRAIL. The corresponding proportion was around 81% for the Control 2 (non-recommended/non-selected) households. Here sampling was conducted on the basis of the land distribution at the village level. There is no difference in the proportion of households that do not satisfy the landownership criterion across the three treatment groups. In analyzing selection we therefore restrict ourselves to households owning no more than 1.5 acres of land. The theoretical model predicts that TRAIL selection will be biased in favor of those paying the lowest interest rates in the informal market (subject to the constraint that the interest rate offered under TRAIL is lower than the informal rate). In contrast GBL selection will be biased in favor of landholdings exhibiting the highest average informal interest rate (i.e., the landless households). As we have argued, in the case of GRAIL however predicting targeting by landholding is difficult. Figure 6 presents the lowess plots of the likelihood of being recommended (or choosing to form a group) on landholding. There is an inverted u-shaped relationship between the likelihood of being recommended in AIL (both TRAIL and GRAIL) and landholding, with the likelihood of being recommended highest in the intermediate landholding range. The peak of the recommendation plot is attained at a higher level of landholding in the case of GRAIL. The pattern of recommendation in TRAIL is consistent with the agent recommending his own segment safe types, those who pay lower interest rates in the informal market. That said, the peak of the likelihood of recommendation is attained at a level of landholding of around 0.5 acres, slightly higher than the size (0.3 acres) associated with the lowest interest rate (Panel A in Figure 5). The likelihood of group formation (in GBL) decreases monotonically with an increase in landholding, indicating that GBL is more pro-poor. These non-parametric patterns are corroborated in the regression results presented in Table 10. Consistent with the lowess plots in Figure 6, there is an inverted u-shaped relationship between landownership and the likelihood of the household being recommended. The probability of being recommended by a TRAIL agent is highest for households owning approximately 0.5 acres of land; the corresponding probability of being recommended by a GRAIL agent is the highest for households owning around 0.64 acres of land. In an alternate specification we included a landless household dummy (as opposed to continuous landholding and landholding squared). The landless dummy was not significant in the recommendation regressions for TRAIL and GRAIL, but landless households are significantly more likely to select themselves into groups under GBL. 22 There is also evidence of biases in favor of recommending borrowers from the same religion, caste and occupation groups in TRAIL and GRAIL. This has already been reported in the test for TRAIL effectiveness (above). Hindu TRAIL agents were significantly less likely to recommend a non-Hindu household. On the other hand GRAIL agents who are cultivators were significantly more likely to recommend households where the primary occupation of the household head is cultivation. In the case of TRAIL agents who are businessmen exhibited a slight bias in favor of households where the primary occupation of the household head is labor. Finally, prior interaction with the agent (bought from agent, borrowed money from agent and worked for agent to a lesser degree) significantly affected the likelihood of being recommended. These effects were generally stronger in the case of TRAIL. The patterns of group formation under GBL reveal an interesting pattern. Remember Control 1 households in GBL villages are those who select themselves into groups but do not receive the SS loan. So the interest rate paid on informal loans by Control 1 households is a measure of the riskiness of the group members. While theoretically it is possible for groups to comprise of both risky and safe types (and this, we argue contributes to a lower repayment rate in GBL), we see that Control 1 households in TRAIL and GBL pay very similar interest rates on informal loans. This appears to suggest that there is assortative matching type in groups (as in Ghatak, 2000): risky households are excluded from groups; safe households self select into GBL and risky households borrow from the informal market at higher interest rates. See Panel B in Figure 5 and further support from the regression results presented in column 2 in Table 9. Takeup Controlling for landholding, takeup rates should be higher under TRAIL, since TRAIL clients incur a lower repayment burden and avoid the cost of attending meetings and achieving savings targets. On the other hand there is unlikely to be any difference between TRAIL and GRAIL in terms of takeup rates. Figure 7 presents the takeup rate in Cycle 1 and the likelihood of continuation in subsequent cycles (this of course depends on repayment in the previous cycle and the willingness to re-borrow). The takeup rate in cycle 1 is lower under GBL compared to TRAIL (82% compared to 87% in TRAIL) though the difference is not statistically significant (p − value = 0.12). While across the different cycles the likelihood of continuation is higher in TRAIL compared to GBL, the difference between the two actually becomes smaller. It also worth noting that the takeup rate in both GBL and TRAIL remain quite stable across the first four cycles. Table 11 presents the marginal effects from probit regressions for loan takeup in Cycle 1. While takeup rate is higher in TRAIL compared to GBL, the difference is not statistically significant. The takeup rate is significantly lower in Hugli (driven by TRAIL/GRAIL). 23 This last result is interesting as it corroborates the anecdotal evidence obtained from field visits that suggest access to microcredit is significantly higher in Hugli, which is closer to Kolkata (the state capital) and demand for additional credit is significantly lower in Hugli. This Hugli effect persists over time and indeed becomes stronger. Table 12 presents the marginal effects from the probit regressions for continuing to borrow in Cycles 2 (column 2), 3 (column 3) and 4 (column 4), conditional on eligibility. For comparison purposes we also include the results from takeup in cycle 1. Both the takeup and continuation probabilities are lower in Hugli compared to West Medinipur and this difference becomes stronger over cycles. The likelihood of takeup is 12, 15, 17 and 18 percentage points lower in Cycle 1, 2, 3 and 4 respectively. The takeup rate is always higher in TRAIL compared to GBL but the difference is never statistically significant. Takeup/continuation rates in GRAIL are very similar to those in GBL. Landholding has no effect on takeup/continuation rate in any cycle. Repayment Patterns The theoretical model predicts that if TRAIL is effective: 1. TRAIL should achieve higher repayment rates compared to GBL. 2. TRAIL should achieve higher repayment rates compared to GRAIL. Figure 8 and Table 13 present the repayment rate over the course of the first year of the credit program (comprising three successive 4-month loan cycles). At the end of the first year (end of Cycle 3), repayment rates though high are less than 100%, though all loans were been fully repaid at the end of Cycles 1 and 2. The average repayment rate after one year is around 94% across all treatments, though there is a fair amount of variation: ranging from 87% in GBL to 99% in TRAIL, with GRAIL in between (96%). The marginal effects from the probit regression on repayment in Cycle 3 essentially tells the same story. Repayment rates in TRAIL are significantly higher compared to both GRAIL and GBL, The evidence is thus consistent with the theoretical prediction. Treatment differences become statistically significant at the end of the first year. Again landholding has no effect on repayment rate. 6 Discussion The primary aim of this paper can be summarized as follows: Is it possible to design a more flexible system of microfinance that targets smallholder agriculture, without requiring collateral and without endangering financial sustainability? This system should allow 24 individual liability loans, drop savings requirements, have less rigid repayment schedules (so that recipients can invest in high return projects with longer gestation period like agriculture) and reduce/eliminate costly meetings with MFI officials. To address these questions we design and implement an intermediated loan (AIL) system in a field experiment, with group-based lending (GBL) as a control. We compare targeting (selection), takeup, repayment rates and impacts on borrowers. We build a theoretical model that addresses some of these issues relating to incentives and use the model to interpret the results. We extend the well known model of Ghatak (2000) to incorporate an informal credit market with segmentation, where lenders in particular segments have a monopoly over information about risk types of borrowers in those segments as a result of past experience from interacting with them and allow the borrowers to be heterogenous in terms of landholding (an observable). This enables us to examine targeting patterns across different landholding levels under AIL and GBL, and test the predictions of our model. The results presented in this paper suggest that TRAIL is effective (TRAIL agents recommend safe clients and there is no evidence of collusion); confirms predictions that: TRAIL agents select households with intermediate landholdings, while GBL selection is biased in favor of low landholdings; repayment rates are higher in TRAIL as are takeup rates, though the differences are not statistically significant in this case. Additionally we find that the agent intermediated lending models (both TRAIL and GRAIL) are working well at least in terms of the conventional MFI metrics of takeup and repayment rates. If anything they are doing better than GBL. Comparing TRAIL and GBL in terms of targeting is hard, because GBL is more pro-poor (more likely to select landless households) but TRAIL and GBL both appear to be able to target safe borrowers. One implication of this assortative matching on types in GBL is that it is not the presence of risky households in the groups that contributes to lower repayment rates in GBL (Table 13). We therefore need to look at alternative explanations to explain the lower repayment rates in GBL. Possibly the explanations lie in how the TRAIL and GBL households use credit, which is an issue of some importance since the GBL households appear to be poorer and more disadvantaged compared to the TRAIL households. Alternatively the excessive monitoring by peers and the inflexibility of MFIs can be contributing to lower repayment rates in GBL. At this stage we do not have an answer to this question. Interestingly GRAIL agents tend to select clients with even higher landholdings than TRAIL, and as expected achieves intermediate repayment and takeup rates. The process of targeting differed substantially between different treatments. GBL is the most pro-poor, with landless households most likely to form groups and avail of credit. Under AIL, agents tend to favor intermediate landholding groups, and targeting has been driven by the information set available to the agents. TRAIL agents in particular appear to use this information very effectively. While the information set of GRAIL agents are likely to be more diffused, they also do not appear to use their information as effectively 25 for targeting purposes. This suggests that different means of credit delivery could be used to target different segments of the population - there is no one size fits all policy. For instance, GBL and AIL could be offered at the same time, with poorest (landless, minority caste and religion) households self-selecting into GBL contracts, while small and marginal landowners are more likely to be recommended under AIL. At this stage it seems premature to comment on broader welfare implications of these different approaches or the policy implications. Impacts of the three different treatments on cultivation, profits, household incomes and assets need to be assessed, which will form the topic of our next paper. A.1 A.1.1 Appendix Proof of Lemma 1 Proof. Each lender can commit to a contract, consisting in a triple Γ = {rs (a), rr (a), r(a)} . This contract defines the interest rates respectively for own-segment safe borrowers, ownsegment risky borrowers, and other-segment borrowers, for a given autarky option a. The other-segment interest rates can be thought as the competitive market interest rate. In the competitive market lenders compete a la’ Bertrand. The lender maximizes the interest rate for the own-segment borrower, subject to the relevant constraints. In what follows, let us denote as re(a) the most competitive interest rate in the informal market. For a given autarky option a, the lender’s best reponse is ri∗ (a) = arg max ri (a) i = r, s ri (A-1) subject to ri (a) ≤e r(a) (A-2) a ri (a) ≤Ri (a) − pi ρI ri (a) ≥ , pi (A-3) (A-4) where the incentive-compatibility constraint (given by equation (A-2)) for each type of borrower requires that it is in the self-interest of a borrower to choose the own-segment lender’s contract, rather than borrowing from the competitive market. The participation constraint (equation (A-3)) of each borrower requires that the expected payoff of a borrower from the contract is at least as large as the value of her autarky option. Finally, the break-even constraint (equation (A-4)) of the lender requires that the expected repayment from each loan is at least as large as the opportunity cost of capital, ρI . As long as the 26 break-even (equation (A-4)) constraint is satisfied, the optimal interest rate can be written as   a ri∗ (a) = min re(a), Ri (a) − (A-5) pi Consider now the competitive market. Denote by α and (1 − α) respectively the fraction of risky and safe types in the competitive market. Having this schedule in place, we can show that in equilibrium α = 1 is the only possible candidate. Suppose not. If α 6= 1, a fraction of the safe borrowers borrow from the competitive market. For this to be the case, the most competitive interest rate in the informal market, re(a), must satisfy the participation constraint for the safe borrowers. Thus, re(a) ≤ Rs (a) − a . ps (A-6) The break-even constraint of the lender requires that the expected repayment from each loan in the competitive market is at least as large as the opportunity cost of capital, ρI i.e., re(a) ≥ αpr +(1−α)p . Hence, from equations (A-5) and (A-6) it follows that rs∗ (a) = n os ρI min re(a), Rs (a) − pas = re(a). Given that rs∗ (a) = re(a) ≥ αpr +(1−α)p > ρpIs for each s α ∈ (0, 1),11 the break-even constraint (equation (A-4)) is also satisfied. Hence, there is a strictly profitable deviation where the lenders offer rs∗ (a) and attract all the own-segment safe borrowers. It follows that α 6= 1 cannot be an equilibrium. QED A.1.2 Proof of Proposition 2 If an equilibrium exists, Lemma 1 entails that it must feature α = 1, i.e., the competitive market can be populated only by risky borrowers. Hence, the break-even constraint in the competitive market requires that r(a) ≥ ρpIr . Moreover, it is possible to show that in equilibrium re(a) = ρpIr . Suppose not. Then a lender could reduce r(a), attract all the risky borrowers, and make a positive profit. If Rr (a) − par < ρpIr there is no interest rate that satisfies both the break-even constraint and the risky borrowers’ participation constraint in the competitive market. For simplicity, in this case, we assume the following: Assumption 0: If Rr (a) − par < ρpIr , the lenders set r(a) = rr (a) = ρpIr and neither the own-segment nor the other-segment risky borrowers accept the contract. 11 Note that from equation (1) follows that any interest rate that satisfies the safe farmers’ participation constraint also satisfies the risky farmers’ participation constraint. Hence, α = 0 is not admissible. 27 Having this schedule in place, it is easy to see that, if an equilibrium exists, the equilibrium interest rates for the own-segment risky borrower, and the other segment borrower are ρI pr ρI ∗ rr (a) ≡ pr r∗ (a) ≡ (A-7) We are now left to study the conditions such that an equilibrium exists where α = 1. In what follows, we assume the safe borrowers’ projects are socially efficient, i.e., equation (3) holds. We want to prove that the sufficient and almost necessary conditions for the existence of an equilibrium are equation (1) and equation (2)12 and that n o the equilibrium a ρI ρI ∗ ∗ ∗ is unique and consists of the triple {rs (a), rr , r } = Rs (a) − ps , pr , pr . From equation (3), (A-1), and (A-7) follows that r∗ (a) = rr∗ (a) = n o min ρpIr , Rs (a) − pas . ρI pr , and rs∗ (a) = Proof. 1. Consider first the case where Rs (a) − pas ≥ ρpI . If this condition holds, we will show that α = 1 cannot hold in equilibrium because there is a profitable deviation where a lender can attract safe borrowers from other segments. Given that we proved that α 6= 1 cannot hold in equilibrium either, we conclude that there is no equilibrium if Rs (a) − pas ≥ ρpI . To see this point note that from ρpIr > ρpI follows that rs∗ (a) ≥ ρpI . Consider now the following sub-cases: a) Rr (a) − a pr < Rs (a) − a ps . In this case there is a profitable deviation where i   h a lender (i) offers any r(a) in the interval max Rr (a) − par , ρpIs , rs∗ (a) (ii) induces the risky borrowers from other segments to refuse the contract because r(a) > Rr (a)− par , (iii) induces the safe borrowers from other segments to accept because r(a) < rs∗ (a) (vi) and makes positive profits because the break even condition is strictly satisfied, i.e., r(a) > ρpIs . ρI a a a pr ≥ Rs (a) − ps . To begin with assume that Rs (a) − ps > p . Hence, ρI there is a profitable deviation where a lender (i) offers p . In this case   any r(a) in the interval ρpI , rs∗ (a) (ii) induces the risky borrowers from other segments to accept the contract because r(a) < rr∗ (a) = ρpIr (iii) induces the safe borrowers from other segments to accept because r(a) < rs∗ (a) and (vi) b) Rr (a) − rs∗ (a) > makes positive profits because the break even condition is strictly satisfied, i.e., r(a) > ρpI . Note that this profitable deviation exists only if Rs (a) − pas > ρpI . 12 These conditions are almost necessary. To see this point note that there case where n is a non-generic o ρI ρI ρI ρI ∗ ∗ ∗ a Rs (a) − ps = p and an equilibrium exists such that {rs (a), rr , r } = , , . The details are p pr pr provided in the proof. 28 On the other hand, in the non-generic case where Rs (a) − pas = ρpI , there n o is no profitable deviation. Indeed, rs∗ (a) = min ρpIr , ρpI = ρpI and the only profitable deviation would involve offering r(a) = ρpI and attracting both types of borrowers. This deviation yields o zero profit. Hence an equilibrium exists n ρI ρI ρI ∗ ∗ ∗ where {rs (a), rr , r } = p , pr , pr . 2. Consider now the case where Rs (a) − pas < ρpI . Given that ρpI < ρpIr , this implies that Rs (a) − pas < ρpIr and so rs∗ (a) = Rs (a) − pas < ρpI . Consider now the following sub-cases: Rr (a) − a pr < Rs (a) − a ps . a) In this case, there is a profitable deviation where a i   h lender (i) offers any r(a) in the interval max Rr (a) − par , ρpIs , Rs (a) − pas (ii) induces the risky borrowers from other segments to refuse the contract because r(a) > Rr (a)− par , (iii) induces the safe borrowers from other segments to accept because r(a) < rs∗ (a) = Rs (a) − pas (iii) makes positive profits because the break even condition is strictly satisfied, i.e., r(a) > ρpIs . b) Rr (a)− par ≥ Rs (a)− pas . In this case there is no profitable deviation. Increasing r(a) above r∗ (a) = ρpIr entails (i) losing all the risky borrowers to the competition in case Rr (a) − par ≥ ρpIr , or (ii) no effect at all if Rr (a) − par < ρpIr (i.e., the risky borrowers are not willing to borrow in the first place.) Decreasing r(a) below r∗ (a) would violate the break even condition unless the lower interest rate would manage to attract safe borrowers form other segments. Given that Rs (a) − pas < ρpI (i.e., the safe borrowers are not willing to accept the interest rate ρpI ), the lender should reduce r(a) below ρpI in order to attract the safe borrowers from other segments. Note that the risky borrowers are also willing to borrow at r(a) because (i) any interest rate that satisfies the safe borrowers’ participation constraint also satisfies the risky borrowers’ participation constraint, i.e., Rr (a) − par ≥ Rs (a) − pas , and (ii) the risky borrowers prefer r(a) to the own segment interest rate, i.e., r(a) < ρpI < rr∗ (a) = ρpIr . Hence, offering r(a) < ρpI would violate the break even constraint, i.e., r(a) ≥ ρpI . It follows o n that triple {rs∗ (a), rr∗ , r∗ } = Rs − pas , ρpIr , ρpIr is an equilibrium if Rs − pas < ρpI and Rr (a) − par ≥ Rs (a) − pas . QED A.1.3 Proof of Proposition 4 These are the options available to the lender: Proof. 29 a) By recommending the own-segment safe borrower (s, a) and setting b∗s (a) ≡ ps Rs (a) − ps rT − a the lender ensures that the safe borrower is indifferent between accepting and refusing the offer.13 The lender’s expected gain is Kp r − | {zs T} commission Π (a) | s{z } lender’s opportunity cost + b∗s (a) = | {z } bribe =Kps rT − (ps Rs (a) − ρI − a) + (ps Rs (a) − ps rT − a) =ρI − (1 − K)ps rT b) The “socially productive” own-segment risky borrower (s, a) outside option from the collusive agreement is ur (a) = pr Rr (a) − ρI . By recommending the “socially productive” own-segment risky borrower (r, a) and setting b∗r ≡ (pr Rr (a) − pr rT ) − (pr Rr (a) − ρI ) = ρI − pr rT the lender ensures that the risky borrower is indifferent between accepting and refusing the offer. The lender’s gain is Kpr rT − | {z } commission Πr (a) | {z } lender’s opportunity cost + b∗r = |{z} bribe =Kpr rT + ρI − pr rT =ρI − (1 − K)pr rT c) The “socially unproductive” own-segment risky borrower (s, a) outside option from the collusive agreement is ur (a) = a. From the definition of “socially unproductive” it follows that the lender needs to offer a negative bribe b∗ (a) = pr Rr (a) − pr rT − a < 0 in order to ensure that the risky borrower is willing to accept the offer. The lender’s gain would be Kpr rT − | {z } commission Πr (a) | {z } lender’s opportunity cost + b∗ (a) = | {z } bribe =Kpr rT + (pr Rr (a) − pr rT − a). d) If b∗s > b∗r , the lender can recommend the other-segment borrower, set the bribe to b∗s and attract only the other-segment safe borrowers. Note that the risky borrowers are not attracted by the deal because b∗s > b∗r and so they strictly prefer not be recommended. The lender’s gain is Kp r − | {zs T} commission 0 |{z} lender’s opportunity cost + b∗s = |{z} bribe =Kps rT + ps Rs (a) − ps rT − a 13 The safe farmer is indifferent because she obtains an expected payoff equal to a in both cases. Indeed, the own-segment safe farmer (s, a) outside option from the collusive agreement is us (a) = a. 30 e) If b∗r > b∗s , the lender can recommend the other-segment borrower, set the bribe to b∗r and attract only the other-segment risky borrowers. Note that the safe borrowers are not attracted by the deal because b∗r > b∗s and so they strictly prefer not be recommended (i.e., the bribe b∗r is too high.) The lender’s gain is the same as in point 2 Kpr rT − | {z } commission 0 |{z} lender’s opportunity cost + b∗r = |{z} bribe =Kpr rT + ρI − pr rT =ρI − (1 − K)pr rT f ) If the lender set the bribe to min [b∗r , b∗s ] both the other-segment safe and the risky borrowers are attracted. The lender’s gain is Kpr − | {z T} commission 0 |{z} lender’s opportunity cost + min [b∗r , b∗s ] = | {z } bribe =KprT + min [ps Rs (a) − ps rT − a, ρI − pr rT ] By assumption, option a is strictly dominated by option b. Therefore, option a is never selected. If b∗s > b∗r , the lender prefers option d; accordingly she recommends other-segment safe borrowers with a level of landholding a such that ps Rs (a) − a is maximized. If b∗r > b∗s , the optimal candidates are options b, e (which yield the same gain) and f. The trade off is between obtaining a higher expected repayment (that is, KprT under option f but only Kpr rT under option b and e), and a lower bribe (that is b∗s under option f and b∗r (> b∗s ) under option b and e). If option f is selected, then the lender targets other-segment borrowers with a level of landholding a such that ps Rs (a) − a is maximized. Otherwise, if options b or eare selected the lender targets risky borrowers with any level of landholding. 31