financing smallholder agriculture: an experiment with
TRANSCRIPT
Financing Smallholder Agriculture:An Experiment with Agent-Intermediated Microloans in
India
Pushkar Maitra, Sandip Mitra, Dilip Mookherjee, Alberto Motta and SujataVisaria
Presentation
June 2014
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Motivation
Financial Inclusion of Poor Farmers: The Challenge
Institutional finance does not reach smallholder agriculture owing to:
lack of collateralpoor information and enforcement capacityhigh transaction costs
Poor farmers are forced to rely on credit from informal lenders at high cost(owing either to high capital costs for informal lenders, or usury, or both)
Prevents poor farmers from escaping poverty by diversifying into high-valuecash crops
Restricts agricultural growth
Major development challenge: can financial institutions lend profitably toproductive small farmers?
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Motivation
Microcredit: Does it Solve the Problem?
Microfinance has made impressive inroads in providing credit to the poor,while attaining high repayment rates
But it has not succeeded in financing productive needs in agriculture (inIndia)
Recent RCT-based evaluations of micro finance (Banerjee et al 2011, Fischer2013, Karlan and Zinman 2011) indicate it has had very limited success inpromoting entrepreneurship, risk-taking or borrower incomes
Mainly successful in allowing the poor to smooth consumption, acquireconsumer durables (Karlan and Zinman 2011, Islam and Maitra 2012,Banerjee et al 2013)
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Motivation
Why Not?
Traditional microcredit is based on
high frequency (weekly-biweekly-monthly) repayment schedulesintensive peer-cum-MFI monitoring to limit risk-taking
Motivated by need to ensure high repayment rates
But these rule out scope for financing agriculture (crop cycles ≥ 3 months,high-risk)
Microfinance under the pump (AP crisis in India 2008-09)
Reserve Bank of India is urging development of direct lending by banks to poorborrowers in rural areas
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Motivation
The Fundamental Problem
Key ingredients of traditional MF (intensive monitoring, peer pressure, limits onrisky choices, high frequency repayments) are viewed as necessary for selectionand repayment incentives for the poor.
Is there an inevitable trade-off between adequate selection/repayment incentivesand financing productive and risky activities?
How to ensure appropriate borrower selection and repayment incentives?
Can this be fiscally sustainable for lending institutions?
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Motivation
Alternative Approaches
We design and implement a new approach to financing smallholderagriculture in two potato-growing districts of West Bengal, India
Distinctive features:
4 month repayment periods (timed according to crop cycles)built-in crop insuranceminimal monitoring by lender‘Low’ (18% p.a.) interest ratedynamic repayment incentives
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Motivation
Alternative Approaches, contd.
We compare two different methods of selection/repayment incentives:
TRAIL: appointing a local agent from within the local community,recommends borrowers to receive individual liability loans
GBL: inviting five-member borrower groups to self-form, meetmonthly with MFI officials, attain savings targets, and applyfor joint liability loan
Within each method, we randomly select from those recommend/eligible andoffer them the corresponding loans
Survey households to evaluate loan impacts and selection patterns in TRAILand GBL
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Motivation
Presentation Outline
1 Field Experiment Details
2 Theoretical Analysis and Testable Predictions
3 Impacts on Cultivation, Incomes and Rates of Return
4 Financial Performance: Take-up, Repayment rates, Administrative Costs
5 Conclusion
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Experiment
The Field Experiment
We worked with a Kolkata based MFI to implement two treatments:
TRAIL: 24 villagesGBL: 24 villages
Design loans to facilitate financing of potato cultivation: high-value high-riskcash crop, in West Bengal
Work in two potato growing districts (Hugli, West Medinipur), select 48villages randomly in these districts, divided randomly between TRAIL andGBL
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Experiment
Loan Features: Similarity and Differences
Similarity
interest rate (18%)
durations (4 month)
dynamic incentives (credit line in next cycle = 133% of current loan repaid)
index insurance
Differences:
TRAIL: loans are individual liability
GBL: joint liability and loan renewals are contingent on group repayment
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Experiment
Random Allocation of Loans:
TRAIL: 1 agent per TRAIL village, 30 borrowers recommended byagent; loans given to 10 randomly chosen from this list
GBL: Groups organized as per MFI protocol: 5 member groupsself-form, meet fortnightly with MFI officials, each borrowerrequired to save Rs. 50 per month;2 groups out of those that survived on cut-off date selectedrandomly
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Experiment
Agents’ Incentives to Select and/or Monitor Borrowers
Commission: portion of interest paid goes to agent (= 75% in experiment)
Bonus: payable at end of two years conditional on adequate repayment recordof recommended clients
Enhanced reputation within village: useful way to expand trade/market shares
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Experiment
Possible Abuse of Power by Agents
Agents could:
charge high interest rates (if permitted or through kickbacks)
select cronies
select unprofitable clients
select non-poor clients
select those willing to pay bribes
extract borrower benefits by manipulating other contractual relationships
recommend non-repayment of loans to borrowers and divide up loan funds
coerce borrowers to repay
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Experiment
Features to Limit Possible Abuse of Power
MFI lends directly to client rather than through the agent
Agent only recommends clients, MFI officials lend and collect payments
Can recommend only landless and marginal landowners (owning ≤ 1.5 acres)
Interest rate is pegged
Agent forfeits deposit he posted upfront if any client does not repay
Agent is terminated if less than 50% loans are repaid in any cycle
Not every household recommended by agent receives the loan(randomization)
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Experiment
Other Important Features
Enhanced convenience for borrowers
No savings requirements or mandated meetings with agents or MFI officials
Door-step banking, no need to open a formal bank account
MFI monitors less than in traditional microcredit, so administrative costs are lower
Allows lower interest rates
Allows payment of commission to agents
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Experiment
The Data
Household sample: 50 per village = 10 loan recipients (Treatment) + 10eligible non-recipients (Control 1) + 30 non-eligible (Control 2)
Two control groups allow separate identification of loan treatment andselection effects
Household data collected through 4-monthly surveys over 8 cycles (October2010- December 2012)
Matched to loan record data from MFI
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Theory
Theory
Use simple model based on network effects
Abstract from adverse selection, moral hazard or credit rationing
Models based on adverse selection or moral hazard generate qualitativelysimilar results
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Theory
Theoretical Framework
Segmented credit market within each village
Borrowers are classified into two categories:
Connected (C)Floating (F)
All lenders and connected borrowers are partitioned into different networks(caste/kinship/neighborhood groups with high social capital)
Each network consists of lenders and borrowers who behave in a cooperativefashion (i.e., maximize aggregate payoffs of network members)Share useful production and marketing information that raises farmproductivity: their projects have a high probability of success pc
Floating borrowers belong to groups with low social capital: behavenon-cooperatively, receive less help: projects have low probability pf ofsuccess, where pf (2− pf ) < pc
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Theory
Loans
Borrowers borrow l ≥ 0 to finance cultivation at scale l
Revenue fi (l) conditional on success with f ′i > 0, fi ” < 0 (revenue = 0 iffailure) and f ′C (l) ≥ f ′F (l) for all l .
Lenders have cost of capital ρI , no capacity constraints; can lend to anyonein the village
Limited liability and no moral hazard: loans are repaid whenever projectssucceed
Lenders from different networks compete a la Bertrand in lending toout-of-network borrowers
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Theory
Equilibrium of Informal Credit Market
No borrowing or lending across networks (since all networks are identical)
Network borrowers borrow from lenders within own-network
Select lcI cooperatively to maximize {pc fC (l)− ρI l} = pc (fC (l)− ρI
pcl)
Floating borrowers borrow at break-even interest rate ρpf
Select l fI non-cooperatively to maximize {pf fF (l)− ρI l} = pf (fF (l)− ρI
pfl)
l fI < lcI since ECC is lower for network borrowers ( ρI
pc< ρI
pf)
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Theory
Agent-Intermediated Lending: TRAIL
MFI enters, select an agent randomly from set of network lenders, who isasked to recommend n borrowers
MFI offers TRAIL loans at interest rate rT (< ρI ) to m(< n) borrowersrandomly chosen from those recommended
Assume dynamic repayment incentives suffice to ensure all borrowers repayMFI whenever projects succeed (i.e., they are patient enough)
Agent commission: proportion K ∈ (0, 1) of repaid interest
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Theory
Group-based Lending: GBL
Groups of size two self-form to apply for joint liability loan
Assume both loans are repaid iff at least one borrower’s project succeeds
Assortative matching may or may not arise, but mixed groups will be similarto homogenous groups
A type (i , i) group will select a loan l iG for each to maximizepi fi (l)− pi (2− pi )rT l
ECC in GBL is pi (2− pi )rT , higher than in TRAIL (rT ) owing to jointliability tax
Borrowers have to attend group meetings, make regular savings to qualify fora group loan: imposes additional cost γi for a borrower of type i ∈ {c , f }.
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Theory
Agent-Intermediated Lending: TRAIL
Agent prefers own-network borrower to other-network borrower
own-network borrower chooses loansize to maximize entire network profit
Agent prefers other-network borrower to floating borrower
Repayment rate is higher for any network borrower
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Theory
TRAIL Selection: Proposition
Under TRAIL, an agent who is a network lender prefers to recommend anown-network borrower, if either:
external borrowers cannot bribe the agent, or
they can bribe the agent, and the commission rate K is large enough
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Theory
TRAIL Selection Incentives: Explanation
In absence of any bribes, agent prefers to recommend own-network lenderrather than floating borrower for two reasons:
(a) Higher probability of loan repayment/commission(b) Internalization of benefits accruing to own-networkborrower
If floating borrower pays bribe, reason (b) disappears (best case scenario) but(a) remains
Agent also prefers own-network borrower to other-network borrower owing to(b)
The best he can do is to extract all possible surplus from other-networkborrower: gives him same profit as in the case of own-network borrowerIf agent’s bargaining power is any lower he cannot extract all profit and preferown-network borrower
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Theory
GBL Selection
Both (F ,F ) and (C ,C ) groups likely to gain from joining GBL, ignoringcosts of group meetings and savings targets
For (F ,F ): ECC drops from ρI
pfto (2− pf )rT
For (C ,C ): ECC drops from ρI
pcto (2− pc )rT
Cannot determine a priori which group gains more
Hence GBL likely to contain both (C ,C ) and (F ,F ) groups
No mechanism in GBL to exclude either type (unless group meetings andsavings targets are more costly for (F ,F ))
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Theory
Table: Summary of Theoretical Predictions
Treatment Composition Observed Repayment EffectiveC=connected Interest rate Rate Cost of Credit
F=floaters
Treatment C rT pc (1− K)rT
TRAIL Control 1 CρIpc
pcρIpc
Control 2 C, FρIpc
,ρIpf
pc , pfρIpc
,ρIpf
Treatment CC, FF rT pc (2− pc ), pf (2− pf ) (2− pc )rT , (2− pf )rT
GBL Control 1 CC, FFρIpc,ρIpf
pc , pfρIpc,ρIpf
Control 2 C, FρIpc,ρIpf
pc , pfρIpc,ρIpf
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Theory
Empirical Predictions
Borrower Selection: TRAIL selection patterns will be biased in favor of borrowerswho:
pay lower interest rates on the informal markethave higher productivity
Impact on Cultivation and Incomes: TRAIL borrowers will borrow andcultivate high-value crops more, earn higher increases in farmincomeGreater dispersion of loan sizes and treatment effects in GBLcompared to TRAIL
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Theory
Predictions: Repayment and Take-up rates
Repayment rate comparisons: Ambiguous (opposing effects of differentialselection and group insurance in GBL)
Loan Take-up rate: Expect higher loan take up in TRAIL owing to avoidance ofjoint liability tax, burden of group meetings and savingsrequirements
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Results
Descriptive Statistics and Randomization: Household LevelLand-Informal Loan Details
TRAIL GBL Difference
Landholding (acres) 1.00 (0.05) 1.05 (0.06) -0.05Landless .07 (0.01) 0.08 (0.01) -0.01Purchased inputs on credit 0.38 (0.02) 0.43 (0.02) -0.05*Total Borrowing 6579.42 (524.32) 6417.26 (489.24) 162.52Duration (Days) 124.88 (1.57) 122.47 (1.27) 2.40Interest Rate (Per annum) 20.48 (0.87) 20.89 (0.77) -0.40Number of Loans 2.17 (0.06) 2.24 (0.06) -0.06Collateralized 0.02 (0.00) 0.01 (0.00) 0.01*
Joint Significance: 27.07
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Results
Loan Details
Source Proportion Interest Rate Duration Proportion(APR) (days) Collateralized
Informal Lenders 0.65 27 124 0.01Family and Friends 0.05 21 169 0.02Cooperative/SJSY 0.23 15 324 0.38Government Banks 0.05 12 300 0.63
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Results
Selected Crop Characteristics
Potato Sesame Paddy Vegetables
Acreage (acres) 0.48 0.43 0.69 0.20Total cultivation cost (Rupees) 10335 695 4061 3285Family labor (Hours) 56 24 37 82Revenue (Rupees) 17782 2433 6696 8976Value-added (Rupees) 7245 1736 2843 5586Value-added per acre (Rupees) 15094.27 4037.21 4120.48 27933.95
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Treatment and Selection Effects
Estimating Treatment and Selection Effects
Report results of following regression specification:
yi = β0 + β1TRAIL + β2TRAIL× Control 1 + β3TRAIL× Treatment
+ β4GBL× Control 1 + β5GBL× Treatment + γ Xi + εi (1)
Selection effects (TRAIL, GBL): β2, β4
Treatment effects, ITT estimates (TRAIL, GBL): β3 − β2, β5 − β4
Controls include land owned, year dummy, price information dummy
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Treatment and Selection Effects
Steps
Start with treatment and selection effects on loan size and cost of borrowing
Then show corresponding effects on cultivation and incomes from variouscrops
Interpret these, comparing selection effects between TRAIL-GBL onproductivity and informal interest rate
Compare repayment rates, take-up and continuation rates
Effects on non-farm incomes; sensitivity; tests for possible extraction offarmer returns by TRAIL agent
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Treatment and Selection Effects
Impacts on Borrowing
Unit Treatment Selection Sample MeanTRAIL GBL TRAIL GBL Size Control 1
All Loans:Loan Size Rs 7126*** 6464*** -417 -919 2758 7279Cost of Borrowing Percent -0.03** -0.07*** -0.01 0.04** 2428 0.24
Non Program Loans:Loan Size Rs -495 254 -372 -930 2601 7279Cost of Borrowing Percent 0.01 -0.01 -0.01 0.04*** 2159 0.24
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Treatment and Selection Effects
Impacts on Potato Production
Unit Treatment Selection Sample MeanTRAIL GBL TRAIL GBL Size Control 1
Cultivate 0.0545 0.0492 0.0949*** 0.0614 4163 0.677Acreage Acres 0.0896*** 0.0402 0.0010 -0.0421 2718 0.432Leased-in acres Acres 0.0467** 0.0222 -0.00265 0.00447 2718 0.111Output Kg 888.0*** 278 145.4 -417.9 2718 4760Cost of production Rs 1774** 1308 372.8 -1111 2718 9538Family labour hours Hours 6.03 4.91 -0.2 4.951 2718 57.86Revenue Rs 3429*** 1637 942 -2534 2718 19137Value added Rs 1687** 271.8 555.6 -1371 2718 9498
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Treatment and Selection Effects
Impacts on Value Added of Other Crops, and on TotalFarm Income
Unit Treatment Selection Sample MeanTRAIL GBL TRAIL GBL Size Control 1
Sesame Rs 180 -158.3 -115.7 73.41 2037 2126Paddy Rs 271.6 573.6 -469.9 -759.6* 3047 2506Vegetables Rs 1255 -1955 1329 -957.5 402 8325
Total Farm Income Rs 2621*** 53.24 11466*** 10066*** 4163 10328
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Explanations
Explanation 1: Comparing Productivity of SelectedFarmers
Seek to explain preceding results by selection differences between TRAIL andGBL, as predicted by the theory
First step: test whether productivity of TRAIL-selected farmers exceeds thatof GBL-selected farmers
Estimating productivity requires strong identification assumptions
Higher productivity implies higher rate of return achieved with respect to anexogenous expansion in cultivation (costs)
Use treatment status as instrument for cultivation costs: obtain IV estimateof RoR in TRAIL and GBL, assuming Cobb-Douglas production functionspecification
Alternative approach: calculate ∆VA∆CC where ∆ denotes corresponding
treatment effect estimate, and bootstrap standard errors (with 600replications)
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Explanations
Rate of Return Estimates
Potato Total Farmincome
Bootstrapped Estimates
TRAIL 1.05*** 1.15***(0.06) (0.02)
GBL 0.09 -0.10(0.37) (0.29)
IV Estimate of CD Production Function
TRAIL 0.72** 1.03***(0.33) (0.35)
GBL 0.37 0.38(0.97) (1.23)
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Explanations
Explanation 2: Testing for Network Effects in TRAILselection
Test prediction that TRAIL agents recommend borrowers from their ownnetwork
Three alternate definitions of past transactions: purchased inputs, borrowed,worked from/for agent
Expect ‘borrowed’ to be the most relevant
Also check for caste affinity between agent and borrowers recommended(network based on caste)
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Explanations
Selection: TRAIL
(Dependent Variable: Household was recommended)
(1) (2)
Bought from agent 0.023 0.016(0.044) (0.047)
Borrow from agent 0.139*** 0.142***(0.037) (0.035)
Work for agent 0.003 -0.005(0.049) (0.055)
Non Hindu 0.030(0.143)
Non Hindu × Agent Hindu -0.098(0.132)
SC 0.544***(0.031)
SC × Agent High Caste -0.610***(0.036)
ST -0.198*(0.108)
ST × Agent High Caste 0.218(0.166)
Sample Size 1031 1031
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Explanations
Explanation 3: Comparing Informal Interest Rates Paid bySelected Farmers
Test the hypothesis that TRAIL agent recommended farmers withbelow-average default risk, while GBL invited applications from farmers withabove-average default risk
Present OLS and Heckman-selected regressions for informal interest rates(controlling for selection into borrowing, using household head occupationdummy as instrument)
Subsequently compare repayment rates between TRAIL and GBL
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Explanations
Explanation 3 (continued):
We expect:
Among the agent’s own clientele, those he recommended pay a lower interestrate
Average riskiness of GBL groups is higher (than the general population)
Average riskiness of GBL groups is higher than TRAIL recommendedhouseholds
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Explanations
Informal Interest Rate Comparisons between TRAIL andGBL Selection
TRAIL GBL TRAIL v GBLOLS Heckman OLS Heckman OLS Heckman(1) (2) (3) (4) (5) (6)
Recommend 0.022 0.022 0.053* 0.052*(0.016) (0.017) (0.027) (0.029)
Own-clientele 0.050 0.049*(0.033) (0.027)
Own-clientele × Recommend -0.071** -0.071**(0.026) (0.035)
TRAIL -0.064 -0.064**(0.046) (0.027)
High caste -0.058*** -0.059*** 0.134* 0.134*** 0.053 0.053*(0.016) (0.016) (0.071) (0.031) (0.044) (0.028)
Landholding 0.091 0.090 -0.103 -0.071 -0.047 -0.023(0.070) (0.078) (0.170) (0.142) (0.182) (0.129)
Landholding Squared -0.063 -0.062 0.065 0.050 0.053 0.042(0.044) (0.052) (0.129) (0.093) (0.136) (0.086)
Constant 0.238*** 0.240*** 0.196*** 0.151 0.271*** 0.235**
Sample Size 438 1,032 417 1,038 412 911MMMMV (June 2014) Financing Smallholder Agriculture June 2014 45 / 60
Explanations
Loan Repayment Rates
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Explanations
Loan Take-Up Rates
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Explanations
Loan Continuation Rates, conditional on eligibility
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Additional Issues
Additional Issues
Adverse treatment effects on non-farm incomes?
Sensitivity to price fluctuations
Extraction of borrower benefits by TRAIL agent?
Year Specific Effects
Effect of Prior Experience
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Additional Issues
Treatment Effect on Non-Farm Income.
Treatment Selection Sample MeanTRAIL GBL TRAIL GBL Size Control 1
Rental Income (Rupees) 153.6 784.4 -182.1 -427.9 4162 1508Income from Animal Products (Rupees) 166.8 49.18 62.66 -279.1 4162 771Labour income (year; Rupees) 393 -5642 -12729** -4941 4162 37465Wage employment (last 2 weeks; Hours) 0.615 -4.496 -6.855* 1.749 4162 40.24Self-employment (last 2 weeks; Hours) 6.884 4.294 0.215 5.914* 4162 121.8Reported profits (Rupees) 2343 2918 100.9 -1917 4162 5802Current value business (Rupees) 4917 6692 952.1 353.8 4162 10465Total Non-Farm Income (Rupees) 3056 -1890 -12748 -7565 4162 45546
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Additional Issues
Sensitivity of Treatment Effects for Potato Value Added toPrice Changes
Dependent Variable: Value added (Actual/Imputed)
Treatment Selection Sample MeanTRAIL GBL TRAIL GBL Size Control 1
1 Actual 1687** 271.8 555.6 -1371 2718 94982 2011 prices 1654*** 55.11 318 -872.7 2718 82583 2012 prices 3187*** 500 254.8 -1907 2718 143114 2007 prices -194.7 -328.5 -45.25 -2744 2718 44235 2008 prices -1913** 1653 1079 -2886** 2718 -44346 2011 market wage 1672** 217.3 463.5 -1483 2718 82197 2012 market wage 1665** 182.6 460.4 -1416 2718 8134
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Additional Issues
Treatment Effects for Input Transactions with TRAILAgent
Sample Size Mean Control 1 Treatment Effect
Ever Buy any Input from agent 12,448 0.0875 -0.00338Share of agricultural input purchased from agent 10,196 0.0760 -0.00359
Input Price (Rs/unit)
Inorganic fertilizer 1,672 13.78 -0.322Organic fertilizer 370 16.12 29.39Outside seeds 1,654 22.36 2.174Pesticide 2,691 533.5 -31.08Powertiller 1,403 195.2 -32.33***Water/irrigation 1,230 72.30 148.3
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Additional Issues
Treatment Effects for Output Transactions with TRAILAgent
Sample Size Mean Control 1 Treatment Effect
Ever sold output to agent 2,990 0.209 0.00559Share of output sold to agent 2,765 0.151 0.0152
Output Price (Rs/kg)
Potato 1,386 4.507 -0.0516Paddy 498 9.282 -0.0215Sesame 881 28.42 -1.003
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Additional Issues
Treatment Effects for Credit Transactions with TRAILAgent
Sample Size Mean Control 1 Treatment Effect
Ever borrowed from agent 1690 0.17 -0.072*Share of loan from agent 1690 0.05 -0.034**
Interest Rate (APR) 5278 0.14 -0.003
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Additional Issues
Year Specific Effects on Potato
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Additional Issues
Year Specific Effects on Overall Incomes
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Additional Issues
Effect of Prior Experience on Value-Added in PotatoCultivation
Low experience High experience(1) (2)
TRAIL Treatment 730.7 2350*GBL Treatment 1068 -121.9TRAIL Selection 620.2 431.2GBL Selection -1142 -2331Mean Control 1 6192 12864
Sample Size 1247 1451
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Discussion and Next Steps
Comparing Administrative Costs
TRAIL incurred substantially lower administrative costs: per-monthper-village costs were Rs 68, compared with Rs 1463 for GBL
Bulk of the cost savings of TRAIL came from reducing loan officers’ salariesand transport expenses, since there were no group meetings in TRAIL
These costs amounted to Rs 1125 per month (at 2012 prices) per GBL village
In contrast, loan officers visited TRAIL villages only once in four months,resulting in personnel and travel cost of only Rupees 31 per month per village
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Discussion and Next Steps
Summary
Designed and experimentally evaluated microcredit designed to financesmallholder agriculture of high-value cash crops
Key differences between TRAIL and GBL: selection mechanism, andindividual vs. joint liability loans
Evidence shows TRAIL was successful in selecting productive low-risk farmersto expand cultivation of potato, and their own incomes (RoR in excess of70% at 2011-12 prices)
GBL induced eligible farmers to expand cultivation of potato, butcorresponding effects on output, incomes were insignificant
TRAIL achieved superior repayment and take-up rates, while reducingadministrative costs significantly
No evidence of extraction of TRAIL borrower benefits by agent, but thesebenefits were sensitive to price fluctuations
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Discussion and Next Steps
Next Steps
Despite lowering administrative costs, TRAIL as implemented would notbreak even, if MFI had to borrow at commercial rates (12%)
However, rates for priority sector lending to agriculture for Indian banks hasbeen recently lowered to 4%
Banks would incur 7% costs of monitoring and capital provisions, as perexisting regulations
At this borrowing cost, TRAIL would break even only if commission rates arelowered significantly
TRAIL agents say they would be willing to participate if the program isscaled up (e.g., lend to 25 recommended borrowers per agent)
Unlikely to dilute quality of selection, but if TRAIL agents provide help andenforce loan repayments there may be adverse impacts
Need to evaluate these in trying to scale up the intervention
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