Hosts: Vancouver, British Columbia, Canada June 16-18,

Transcription

1 Hosts: Vancouver, British Columbia, Canada June 16-18,

2 How flexible repayment schedules affect credit risk in microfinance Ron Weber 1,2, Oliver Musshoff 1, and Martin Petrick 3 1 Department of Agricultural Economics and Rural Development Georg-August Universität Göttingen, Germany 2 Independent Evaluation Department KfW Development Bank, Frankfurt, Germany 3 Department for Agricultural Policy Leibniz Institute of Agricultural Development in Central and Eastern Europe (IAMO), Halle (Saale), Germany 17 June 2013

3 Outline 1. Background 2. Objectives 3. Econometric Model 4. Hypotheses 5. Data 6. Results 7. Conclusions and Outlook 2

4 1. Background Financial Sector Development in Developing Countries Credit rationing of micro, small, and medium enterprises (MSMEs) led to (agricultural) development bank approach in 1970s and 1980s (old paradigm) Directed lending and interest subsidies inefficient, unsustainable Reluctance of commercial banks to lend to agricultural firms Inadequacy of the old paradigm (Adams & Graham, 1981) New paradigm: Foundation of Micro Finance Institutions (MFIs) Special credit technology for MSMEs (Krahnen & Schmidt, 1994; Maurer, 2011) Intense client assessments, including verification of enterprise and household information (Armendáriz & Morduch, 2010) 3

5 1. Background Microfinance Fixed interest rates (volume rationing vs. risk adjusted interest rates), repayment starting immediately after disbursement Consumer lending with corporate character MFI focus on (urban) MSMEs with continuous returns Standardized products, high repayment rates, growing market High repayment rates mainly attributed to product standardization (Armendáriz & Morduch, 2000) Outreach to rural areas still low (Llanto, 2007; Pellegrina, 2011) Agricultural MSME specifically affected by credit rationing (Reyes & Lensink, 2011) 4

6 1. Background Agricultural Microfinance Production and weather risks affect profitability, cash-flow volatility (Binswanger & Rosenzweig, 1986) Seasonality: mismatches between expenditures and revenues (Binswanger & Rosenzweig, 1986) affects debt obligations Standardized microfinance loans (standard loans) only adequate for agricultural MSME with continuous returns (Weber & Musshoff, 2013) Credit rationing of MSME (Beck et al., 2003; Diagne et al., 2000; Foltz, 2004; Simtowe et al., 2008) due to low product variability Stipulation of special microfinance loans for agriculture (Jain & Mansuri, 2003; Llanto, 2007; Pellegrina, 2011) 5

7 1. Background Agricultural Microfinance (cont.) Introduction of agricultural microfinance loans (flex loans) by MFIs (e.g. Azerbaijan, India, Madagascar) Flex loans for seasonal agricultural producers (crop cycle, flexibility through grace periods) Better credit access due to flex loans (Weber & Musshoff, 2013) Ambivalent experimental results (RCT) for repayment flexibility and credit risk (Czura et al., 2011; Field et al., 2011; Field & Pande, 2008) NO empirical evidence for the effects of repayment flexibility on credit risk 6

8 2. Objectives Investigation how grace periods affect the number of loan installments missed by 1, 15 and 30 days when due (credit risk indicators) (1) Do grace periods increase the credit risk for MFIs? (2) Are there different effects for farmers and non-farmers? (3) Is the lending technology applied by the MFI adequate for farmers? 7

9 3. Econometric Model 8

10 4. Hypotheses H1 Farmer Standard : The credit risk of farmers with standard loans is not different from those of non-farmers with standard loans. H2 Farmer Flex : The credit risk of farmers with flex loans without grace periods is not different from those of non-farmers with standard loans. H3 Farmer Flex Grace Period : The credit risk of farmers with flex loans and grace periods is not different from those of non-farmers with standard loans. 9

11 5. Data Provided by Accès Banque Madagascar (Antananarivo) Commercial MFI, only individual loans for (in)formal MSMEs Flex loans for agricultural producers with seasonal production schemes offered since December 2010 Solely microloans, no SME loans are analyzed (different credit technology) Time period: November May 2012 Grace period option: standard loans, flex loans Grace period: each scheduled principal repayment <50% of the average principal Average principal: Equal loan installments based on an annuity calculation 10

12 5. Data Descriptive Statistics Variable 1 Unit 2 Standard Loan Flex Loan Farmer 3 Farmer 3 Non-Farmer Mean SD Mean SD Mean SD Delinquencies (PAR 1) number 1.18*** *** Delinquencies (PAR 15) number 0.11*** *** Delinquencies (PAR 30) number 0.07*** *** Household Income ThsMGA 1,945*** 2, *** 853 3,623 6,758 Household Expenses ThsMGA 1,633*** 2, *** 694 3,268 6,483 Disbursed Loan Amount ThsMGA 1,107*** 1, *** 788 1,166 1,974 Age years 40.61*** *** Marital Status (Married) 1/0 0.88*** *** Gender (Female) 1/0 0.52*** *** Family Members number 4.77*** *** Work Experience month 86.62*** *** Repeat Client 1/0 0.35*** *** Deposit 1/0 0.68*** *** Number of Observations, thereof number 3,113 2,221 88,651 with grace period s number ,505 1 PAR, Portfolio at risk; PAR 1, PAR 15, PAR 30 indicate the number of loan installments that were missed by 1, 15 and 30 days respectively when due. 2 ThsMGA, thousand Malagasy-Ariary. Mean values for dummy variables (1/0) indicate ratios. 3 Farmer Standard Loan, farmer with standard loan; Farmer Flex Loan, farmer with flex loan; Non-Farmer, non-farmer with standard loan; ***,**,* indicate a significant mean difference between farmers with standard loans and farmers with flex loans compared to non-farmers on a 1%, 5% and 10% level respectively. Comprises only primary agricultural producers, i.e., livestock, crop as well as fruit and vegetable producers. 11

13 6. Results Tobit Estimations (highly censored data, only 5% obs. with arrears) Variable 1 PAR_1 2 PAR_15 2 PAR_30 2 Farmer Standard (H1) Flex (H2) *** * Flex Grace Period (H3) Disbursed Loan Amount *** *** Disbursed Loan Amount Square -4.57e e-08 ** -2.18e-08 *** Age * Age Square *** * * Gender (Female) Marital Status (Married) *** *** *** Family Members *** Family Members Square *** Work Experience *** ** ** Work Experience Square *** Client has deposit with bank *** *** *** Repeat Client *** *** *** Constant *** *** Number of Observations, thereof 65,535 65,535 65,535 Consored at the threshold of zero 35,605 63,377 63,377 Log-Likelihood Value -102, , , (pseudo) R-square Estimation results for the other client groups listed in equation (1), the vector of branch offices, and year dummies are not provided here. 2 Indicates the number of loan installments that were missed by > 1, >15 and >30 days respectively when due;. ***,**,* indicates significance on 1%, 5% and 10% level respectively; reference group: non-farmers with standard loans and without grace periods. 12

14 7. Conclusions and Outlook H1 Farmer Standard : accepted Farmers with standard loans have indifferent credit risk Standard loans adequate for farmers with continuous returns H2 Farmer Flex : rejected Flex loans (without grace periods) have different credit risk Agro-lending technology no lone-standing option H3 Farmer Flex Grace Period : accepted Flex loans with grace period with indifferent credit risk Flexibility seems key attribute of agricultural lending 13

15 7. Conclusions and Outlook Do grace periods increase the credit risk for MFIs? It depends. Are there different effects for farmers and non-farmers? Yes! Is the lending technology applied for farmers adequate? Not yet! Limitations: Only 1.5 years flex loan experience by the bank. Grace period decision effective? Reasons for grace periods for non-farmers? Further research: In-depth analysis of agricultural portfolio. Effects of adverse weather shocks on the repayment behavior of farmers with seasonal production types. 14

16 Thanks for listening! 15

17 References Armendáriz de Aghion, B. and Morduch, J. (2000), Microfinance beyond group lending, Economics of Transition, Vol. 8 No. 2, pp Armendáriz, B., and J. Morduch. The economics of microfinance. The MIT Press, Cambridge Adams D. W., and D. H. Graham. A critique of traditional agricultural credit projects and policies. Journal of Development Economics 8 (1981): Beck, T., A. Demirgüc-Kunt, L. Laeven, and V. Maksimovic. The determinants of financing obstacles. Journal of International Money and Finance 25(2006): Binswanger, H.P., and Mark R. Rosenzweig. Behavioural and material determinants of production relations in agriculture. Journal of Development Studies 22(1986): Diagne, A., M. Zeller, and M. Sharma. Empirical measurements of households access to credit and credit constraints in developing countries: methodological issues and evidence. FCND Discussion Paper 90. International Food Policy Research Institute, Washington Foltz, J.D. Credit market access and profitability in Tunisian agriculture. Agricultural Economics 30(2004): Structural Change in Agriculture (SiAg) Seminar, Halle (Saale) 16

18 References International Finance Corporation (IFC). Scaling-up SME access to financial services in the developing world. Financial Inclusion Experts Group, October Jain, S., and G. Mansuri. A little at a time: the use of regularly scheduled repayments in microfinance programs. Journal of Development Economics 72(2003): Krahnen, J. P., and R. H. Schmidt. Development finance as institution building. A new approach to poverty-oriented banking. Westview Press, Boulder Llanto, G.M. Overcoming obstacles to agricultural microfinance: looking at broader issues. Asian Journal of Agriculture and Development 4(2007): Maurer, K. Mobilising capital for the poor How does structured finance fit in emerging markets?. In: Köhn, D. (Ed.) Mobilizing capital for emerging markets - What can structured finance contribute?. Berlin-Heidelberg Pellegrina, L.D., Microfinance and investment: a comparison with bank and informal lending. World Development 39(2011): Reyes, A., and R. Lensink. The credit constraints of market-oriented farmers in Chile. Journal of Development Studies 47(2011): Simtowe, F., A. Diagne, and M. Zeller. Who is credit constrained? Evidence from rural Malawi. Agricultural Finance Review 68(2008): Structural Change in Agriculture (SiAg) Seminar, Halle (Saale) 17

19 References Weber, R., and O. Musshoff. Is agricultural microcredit really more risky? Evidence from Tanzania. Agricultural Finance Review 72(2012): Weber, R., and O. Musshoff. Can flexible microfinance loans improve credit access for farmers?. Agricultural Finance Review 73(2013) (forthcoming). Structural Change in Agriculture (SiAg) Seminar, Halle (Saale) 18