Does Multiple Borrowing in Microfinance Necessarily Mean Over-borrowing?

Ratul Lahkar, IFMRViswanath Pingali, IIMA

Santadarshan Sadhu, CMFFebruary 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

0%10

%15

%5%S

hare

of H

ouse

hold

s

0 2 4 6 8 10 12 14 16 18 20Total Number of Loan Outstanding

Source: Centre for Micro Finance, IFMR Research. "Access to Finance in Rural Andhra Pradesh 2010".

Multiple Borrowing by Active Clients of a Given Source

Banks Informal SHG MFI0%

10%20%30%40%50%60%70%80%90%

26%

85%

16%

30%

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 β1 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• β1 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 2As 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 γ1 is positive and significant

Results: Hypothesis 2• 1 is significant and positive:

– If the number of formal credit institutions a village has access to increases by one, then average formal loan size increases by 3%

• Controls having statistically significant effect:– Per-capita irrigated land– Average number of times a household incurred non-

routine expenditure in the village in six months prior to survey

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 3As 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 δ1 is positive and significant

Results: Hypothesis 3• 1 is significant and positive:

– As the number of microcredit institutions a village has access to increases by one, then average formal loan size increases by 11%

• Controls having statistically significant effect:– Average number of times a household incurred non-

routine expenditure in the village in six months prior to survey has a negative and statistically significant coefficient• Seems to suggest that the villages where clients

incur greater non-routine expenditure obtain lesser amount of loans through microcredit

• Effective screening of risky clients by JLG mechanism ?

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

correlationCorrelation between total number of MFI in the village and average number of JLG memberships of a household

0.6678 6.89

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

Health 36%Festival or special event aside from marriage

11%

Marriage 11%Buy agricultural machinery or inputs

10%

Home improvement/repair/construction

7%

Any non-routine expenditure 64%

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/relatives 43%Own income or savings 29%

Loan from moneylender 13%Loan from landlord 11%

Loan from MFI/SHG 6%

Districts Selected for Surveying

40

DistrictShare of poor

from NSSOPoverty Stratum MFI penetration MFI stratum Adjusted MFI Stratum

Final Stratum

Selected for Surveying?

Medak 9.3 Not so poor 11.3 High penetration High penetration 1 YESNalgonda 5.4 Not so poor 14.5 High penetration High penetration 1 YESEast Godavari 3.3 Not so poor 12.5 High penetration High penetration 1 NOWest Godavari 4.4 Not so poor 12.3 High penetration High penetration 1 NOKrishna 2.8 Not so poor 18.7 High penetration High penetration 1 NAGuntur 3.9 Not so poor 13.2 High penetration High penetration 1 NOVizianagaram 4.7 Not so poor 4.7 Low penetration Low penetration 2 YESCuddapah 5.4 Not so poor 9.9 High penetration Low penetration 2 YESKarimnagar 7.2 Not so poor 5.5 Low penetration Low penetration 2 NOWarangal 0.9 Not so poor 6.1 Low penetration Low penetration 2 NOSrikakulam 6.0 Not so poor 4.4 Low penetration Low penetration 2 NONizamabad 23.1 Poor 9.1 High penetration High penetration 3 YESVisakhapatnam 18.9 Poor 10.6 High penetration High penetration 3 YESKhammam 13.1 Poor 10.1 High penetration High penetration 3 NONellore 14.1 Poor 10.9 High penetration High penetration 3 NOKurnool 24.6 Poor 8.6 Low penetration High penetration 3 NOMahbubnagar 11.8 Poor 2.9 Low penetration Low penetration 4 YESPrakasam 9.9 Poor 7.7 Low penetration Low penetration 4 YESAdilabad 26.1 Poor 4.0 Low penetration Low penetration 4 NORangareddi 10.9 Poor 6.0 Low penetration Low penetration 4 NOAnantapur 20.2 Poor 4.1 Low penetration Low penetration 4 NOChittoor 15.9 Poor 8.4 Low penetration Low penetration 4 NO