Does Multiple Borrowing in Microfinance Necessarily Mean Over-borrowing? Ratul Lahkar, IFMR Viswanath Pingali, IIMA Santadarshan Sadhu, CMF February 11, 2013

Outline Background & Motivation Data & Empirical Analysis Findings Conclusion

Background Microfinance Institutions - instrument to fight poverty Proliferation of commercial MFIs –Easy access to credit: overborrowing –Coercive/unethical collection practices Irresponsible lending? Irrational borrowing?

Background Does multiple borrowing necessarily lead to overborrowing? Irrational borrowing: Does the availability of credit, and not the necessity, that influences borrowing decisions? Alternative: –Explanation in which borrowers do not seek more loans simply because more credit sources (like MFIs) are available.

Background One such explanation that readily suggests itself is the substitution of loans –If microfinance is more preferable, then borrowers tend to substitute microfinance loans for other loans without necessarily increasing their loan burden. –However, since microcredit institutions ration the amount of loan given to an individual, multiple borrowing is inevitable for obtaining more credit.

Motivation Recent theoretical literature (Lahkar and Pingali, 2012) provides another explanation of multiple borrowing on the basis of efficient risk management

Efficient Risk Management In joint liability setting there is always an inherent risk of partner default, which increases the expected loan burden of the borrower. –In order to mitigate this risk, a borrower can divide the same total loan into several small portions, and borrow each portion with a completely different group from a different MFI This strategy enables a borrower to diversify the risk of a single partner defaulting on a big loan into several partners defaulting on smaller loans. For a risk averse individual, this is a welfare improving measure.

Motivation The theoretical framework leads to hypotheses which we can empirically investigate. –First, to rule out overborrowing, we should find that an increase in the number of formal lending agencies should not lead to more borrowing –Second, if the substitution hypothesis is true, we must observe that people prefer microfinance loans to other forms of loans available to them –Third, even if there is no overborrowing there is multiple borrowing in the form of multiple group membership

Objective Test the key hypotheses using CMF’s Access to Finance in AP data Hypotheses: –Hypothesis 1: As number of formal credit agencies in the village increases, average loan outstanding in the village remains constant –Hypothesis 2: As the number of formal credit agencies in the village increases, average loan outstanding from the formal credit agencies increases –Hypothesis 3: As the number of microcredit institutions in the village increases, average loan outstanding with the microcredit institutions increases

Sample Survey details: –8 districts (randomly selected from 22 districts of AP) –64 villages (8 villages randomly selected from each of these 8 districts) –1920 households (randomly selected from the 64 villages) Survey conducted in June to November 2009 using a rigorous random sampling methodology

Overview of Borrowing Overall indebtedness is extremely high - 93% of all rural households in AP are indebted to at least one source including: Banks (State, Private) Self Help Group (SHG) Micro Finance Institutions (MFI) Money lenders Friends and relatives (with and without interest) Employers Landlords Formal/Semi Formal Informal

Borrowing Landscape

Multiple Borrowing Multiple borrowing is extremely common –84% of households having two or more loans from any source. –Median of 4 loans outstanding per household Multiple borrowing is driven mainly by multiple loans from informal sources 13

Multiple Borrowing 14

Multiple Borrowing by Active Clients of a Given Source 15

Financing of household consumption, investment in agricultural activities major purpose of loan usage. Significant part of MFI and SHG loans is also used for repaying old debt.

Hypotheses to be tested –Hypothesis 1: As number of formal credit agencies in the village increases, average loan outstanding in the village remains constant –Hypothesis 2: As the number of formal credit agencies in the village increases, average loan outstanding from the formal credit agencies increases –Hypothesis 3: As the number of microcredit institutions in the village increases, average loan outstanding with the microcredit institutions increases

Empirical Specification: Hypothesis 1 As number of formal credit agencies in the village increases, average loan outstanding in the village remains constant Need to be able to show that as the total number of formal credit agencies in the village increases, the average total loan burden does not. Regress average loan size in a village on the number of formal credit agencies in the village and some controls that influence the amount of loan taken

Empirical Specification: Hypothesis 1 Use the following regression: Where ln(Li) represents natural log of average loan size in the ith village, and FSC represents the count of formal sources of credit in the village (including banks, MFIs, SHPIs, chit agencies and cooperative societies) and X be the vector comprising demographic & other characteristics that influences average loan size For the first hypothesis to be true we must observe that the estimated value of β 1 is insignificant

Empirical Specification: Hypothesis 1 Variables in X (controls):Several demographics characteristics that influence loan size in a village –Population –Per-capita irrigable land –Presence of Primary Health Care facility –Average number of times respondents in a given village have had to incur unexpected expenditure six months preceding the survey –Distance to the nearest town

Results: Hypothesis 1 β 1 is insignificant: NO evidence of indiscriminate borrowing –Village average loan size does not depend on the number of formal financial institutions in the village Controls having statistically significant effect : –Average number of times a household incurred non-routine expenditure in the village in six months prior to survey Controls not having significant effect: –Per-capita irrigated land, presence or absence of primary health care centres, population, distance to the nearest town

Empirical Specification: Hypothesis 2 As the number of formal credit agencies in the village increases, average loan outstanding from the formal credit agencies increases Need to be able to show that as the total number of formal credit agencies in the village increases, the average loan size from formal institutions increases Regress average loan outstanding from formal credit agencies in a village on the number of formal credit agencies in the village and other controls

Empirical Specification: Hypothesis 2 Use the following regression: Where ln(FLi) represents natural log of average loan size from formal institutions in the ith village, and FSC represents the count of formal sources of credit in the village (including banks, MFIs, SHPIs, chit agencies and cooperative societies) and X be the vector of controls For the second hypothesis to be true we must observe that the estimated value of γ 1 is positive and significant

Results: Hypothesis 2

Combining Results: Hypothsis1 & Hypothesis 2 The overall loan burden of the village is not dependent on the number of formal financial institutions; however, loan from formal financial institutions is positively and significantly dependent on number of formal institutions the village has access to. As the accessibility of credit from formal sources increases, people are tending to substitute formal sources for informal sources. In other words, people seem to prefer formal sources of credit over informal ones.

Empirical Specification: Hypothesis 3 As the number of microcredit institutions in the village increases, average loan outstanding with the microcredit institutions increases To show –As the total number of microcredit agencies (MFI+SHPI) in the village increases, the average loan outstanding with microcredit institutions increases & –Average loan outstanding with the microcredit institutions increases faster than when compared to increase in formal credit agencies Regress average loan outstanding from microcredit agencies in a village on the number of microcredit agencies in the village and other controls

Empirical Specification: Hypothesis 3 Use the following regression: Where ln(MLi) represents natural log of average loan size from microcredit institutions in the ith village, and MFI represents the count of MFIs and SHPs in the village and X be the vector comprising demographic characteristics that influences average loan size For the second hypothesis to be true we must observe that the estimated value of δ 1 is positive and significant

Results: Hypothesis 3

Combining the Results…. The overall loan size is independent of number of formal sources of credit Loan size from formal sources of credit is positively affected by number of formal sources of credit suggesting that with the increase in the number of formal sources of credit, people tend to make more use of such sources to meet their loan requirements. Loan size from microcredit institutions seems to increase faster with the increase in number of such institutions than loan size from formal credit sources with the increase in number of formal sources of credit (11% with microcredit institutions as compared to 3%) –Even within the formal sources, borrowers seem to prefer microcredit.

MFIs and Multiple Borrowing Test whether borrowers resort to multiple borrowing as the number of MFIs in a village increases –How? Measure the prevalence of multiple borrowing by the total number of joint liability groups a resident of the village is a member of Find correlation between number of MFIs in the village and average number of groups a resident of the village is a part of.

Result: Correlation of MFIs and number of group membership The number of MFIs present in a village and the number of groups a borrower is a part of are positively correlated, and that correlation is statistically significant Supports the hypothesis of the incidence of multiple borrowing in the presence of multiple MFIs in the village Correlation Co-efficient t-stat for significance of correlation Correlation between total number of MFI in the village and average number of JLG memberships of a household

MFIs and Multiple Groups Two possible explanations –Multiple group membership necessary to circumvent the credit rationing imposed by microcredit institutions –Multiple borrowing to efficient (partners default) risk management

Conclusions No evidence of indiscriminate borrowing: –Increase in number of lending agencies need not necessarily mean an increase in the amount of loan size in a village. Substitution of informal sources of credit by formal sources when access to credit from more organized sources is available. Preference for microcredit over loans from other sources available to them As the number of microcredit institutions increase in a locality, people tend to associate themselves with more and more groups.

Thank You

Non-Routine Expenditures 38 Top 5 Non-routine Expenditures Non-routine Expenditure Share of Households which Incurred Major Expenditure on Item in past 6 Months Health36% Festival or special event aside from marriage 11% Marriage11% Buy agricultural machinery or inputs 10% Home improvement/repair/construction 7% Any non-routine expenditure64%

Non-Routine Expenditure: Source of Funding 39 Top 5 Non-routine Expenditures Source of Funding Non-routine Expenditure Share of Households which Incurred Major Expenditure on Item in past 6 Months Loan from friends/relatives43% Own income or savings29% Loan from moneylender13% Loan from landlord11% Loan from MFI/SHG6%

Districts Selected for Surveying 40 District Share of poor from NSSO Poverty StratumMFI penetrationMFI stratumAdjusted MFI Stratum Final Stratum Selected for Surveying? Medak9.3Not so poor11.3High penetration 1YES Nalgonda5.4Not so poor14.5High penetration 1YES East Godavari3.3Not so poor12.5High penetration 1NO West Godavari4.4Not so poor12.3High penetration 1NO Krishna2.8Not so poor18.7High penetration 1NA Guntur3.9Not so poor13.2High penetration 1NO Vizianagaram4.7Not so poor4.7Low penetration 2YES Cuddapah5.4Not so poor9.9High penetrationLow penetration2YES Karimnagar7.2Not so poor5.5Low penetration 2NO Warangal0.9Not so poor6.1Low penetration 2NO Srikakulam6.0Not so poor4.4Low penetration 2NO Nizamabad23.1Poor9.1High penetration 3YES Visakhapatnam18.9Poor10.6High penetration 3YES Khammam13.1Poor10.1High penetration 3NO Nellore14.1Poor10.9High penetration 3NO Kurnool24.6Poor8.6Low penetrationHigh penetration3NO Mahbubnagar11.8Poor2.9Low penetration 4YES Prakasam9.9Poor7.7Low penetration 4YES Adilabad26.1Poor4.0Low penetration 4NO Rangareddi10.9Poor6.0Low penetration 4NO Anantapur20.2Poor4.1Low penetration 4NO Chittoor15.9Poor8.4Low penetration 4NO