Can_Moneylenders_Link_with_Banks.pdf

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Can Moneylenders Link with Banks ?

Theory and Evidence from Indian Villages

Adel Varghese

The Bush School of Government and Public Service,Texas A&M University

September 2004

Bush School Working Paper # 418

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Can Moneylenders Link with Banks ? Theory

and Evidence from Indian Villages

Adel Varghese

George Bush School of Government

Texas A & M University

College Station, TX 77843-4220

U.S.A.

[email protected]

Phone: (979) 862-4779

Fax: (979) 845-4155

September 15, 2004


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Can Moneylenders Link with Banks ? Theory and Evi-

dence from Indian Villages

Abstract Yes, by providing loans to borrowers who can then repay bank

loans. This paper provides theory and evidence of a vertical link between banks

and moneylenders in rural credit markets of developing countries. Due to informa-

tional constraints, banks do not relend to borrowers who have not repaid a certain

amount. Moneylenders, with more information, will relend to these borrowers who

can then repay banks and have continuing access to bank loans. The model yields

a simple testable implication. For borrowers who can access moneylenders, re-

payments to banks will not fluctuate with their income level. An empirical test

which employs the ICRISAT panel data set confirms that moneylenders facilitate

borrowers bank repayments. (JEL D82,O12,O16,O17)

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Can Moneylenders Link with Banks ? Theory and Evi-

dence from Indian Villages

Governments in many developing countries actively sponsor the spread of

banks in rural areas. Borrowers covet the low interest rates and large sizes of

these production loans provided by banks. Unfortunately, banks performances

have fallen far short of expectations. In searching for new solutions to improve

credit disbursal, researchers has redirected themselves towards alternative credit

delivery mechanisms. As a potential option, formal creditors can enlist the help

of other active lenders, for example moneylenders. By exploiting the relative

advantages of moneylenders, banks can in effect link with these lenders in the

disbursal of credit. For example, banks can provide the initial production loan

and moneylenders provide bridge loans for borrowers to ensure continuing access.

In anecdotal evidence from Bangladesh, Sen reports that recovery agents

help borrowers roll over bank loans for a fee, after which borrowers obtain a

fresh bank loan (P.B. Ghate (1992)). In an alternative bridge role, K.G. Kar-

makar (1999) observes moneylenders in India disbursing loans to borrowers who

receive bank sanctioned loans but wait to receive them. These anecdotes imply

that linkages provide an appealing option. Immediate concerns arise on designing

an appropriate incentive structure which would induce banks, moneylenders, and

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borrowers to participate.

This paper unearths and tests a particular linkage in which formal institu-

tions (banks) and informal lenders (in this study, moneylenders) jointly operate

in disbursing loans. Similar to the difficulties banks face rural credit markets,

the model assumes that banks cannot observe borrowers income and borrowers

cannot offer collateral. Due to these constraints, banks provide contracts contin-

gent on repayments and a termination threat. Banks elicit repayments from high

income borrowers with two carrots: providing a surplus and loan renewal. This

decision by banks implies that low income borrowers do not receive further loans

even with profitable projects in the last period, . By providing funds to these low

income borrowers who could otherwise not repay banks, moneylenders link with

banks. These borrowers can now repay banks and enjoy continued access to bank

funds. Moneylenders enter in the market by exploiting their advantage in lower

information costs. From a simple gains from trade argument, moneylenders trade

in their comparative advantage information and banks in theirs lower cost of

funds.

The theoretical section yields a simple and tractable testable implication on

this particular linkage. If borrowers can access moneylenders, then regardless of

their income level they should repay banks. Using a micro data set from Indian

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villages, I test this prediction and find evidence that borrowers that borrow also

from moneylenders repay banks with greater ease. In this respect, the paper does

not propose a new linkage but exposes a system through which villagers have

obtained bank loans.

Proposals to link the formal sector with moneylenders have a long history in

development economics (S.B. Mahabel (1954) and Bell (1990)). Recent papers

have formalized the potential linkages between the formal and informal sector

(Fuentes (1995) and Jain (1999)). Fuentes explores an incentive system in which

banks can explicitly hire moneylenders as agents in disbursing credit. Jain closely

follows in spirit this paper in that banks can free ride offthe presence of money-

lenders by screening good and bad risks. These recent studies omit dynamics,

a crucial element of lending since borrowers require continuing access to a favor-

able lending source. Thus, the theory provides a novel dynamic linkage between

formal and informal lenders.

The empirical test shares some features with recent papers on credit as insur-

ance but differs in its emphasis on informational inefficiencies. In particular, with

ICRISAT data Rosenzweig (1988) and Jacoby and Skoufias (1998) find that net

debt stock (to all creditors) fluctuates inversely with full income. The test here

reflects Udry (1994) who using Nigerian data finds that state contingent loans

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and repayments allow for more efficient risk sharing. In this paper as well, the

credit is institution-specific and limited to flows but differs in studying the effect

on one creditors loans (moneylenders) on another creditors repayments (banks).

Empirically, the linkage test contributes to the dearth of studies on formal and

informal linkages. In particular in his survey on credit, Besley (1995) argues, the

idea of better appreciating links between the formal sector and informal ties is

important...There are very few good empirical studies. (Besley, p.2188)

This paper is structured as follows. The next section discusses the institutional

aspects of banks and moneylenders in the study villages. Of independent interest,

Section 2 proposes a linkage solution that serves as a conceptual framework in

addressing the institutional aspects in Section 1. Section 3 contains the empirical

work which relies on the testable implications derived in the theory and Section

4 concludes.

1. Banks and Moneylenders in the Study Villages

The ICRISAT study from India provides data for three villages for 1975 to 1984,

four villages for 1980 to 1984, and three villages for 1975 and 1976.

1

The ICRISAT1 Thomas S. Walker and James G. Ryan (1990) cover in an extensive manner the ICRISAT

data set.

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data set exhibits two particularly attractive features: it provides panel data on

detailed transactions of formal and informal creditors and second, Binswanger,

et al. (1985) offer a companion survey on the practices of formal and informal

lenders. Their survey clarifies the procedures creditors employ which helps place

restrictions on the theoretical and empirical assumptions.

The following loan shares (in 1975 rupees in parenthesis) indicate a segmented

credit market. The regulated and government sanctioned formal lenders include

government (9), commercial banks (6), and cooperatives (23) (since main financing

comes from the center). Informal lenders cover moneylenders (24), friends and

relatives (10), landlords (16), and other villagers (6). In order to sharpen its focus

on individual profit maximizing creditor behavior, this paper concerns itself with

one creditor from the formal sector, commercial banks, and one from the informal

sector, moneylenders.2

The Binswanger, et al. survey reveals the collateral requirements and informa-

tion on borrowers available to lenders. Each moneylender relies on a few customers

2 Loans from the other lenders, cooperatives and governments are rarely repaid. Villagersinterpret government and cooperative loans as grants and consequently default upon a largenumber of them. In particular 80 % of due repayments in the villages were zero. Moneylendersare pure profit-maximizing lenders while others such as friends, landlords, and shopkeepersprovide loans which are interlinked. Furthermore, except for friends (who exhibit high incomecorrelation with borrowers), other lenders represent a considerably smaller share of the informalsector.

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with whom he can maintain an informational advantage. He frequently disburses

loans without security but sends employees to monitor the borrowers activities.3

The moneylenders in ICRISAT villages do not lend strategically (as is tradition-

ally thought) in order to obtain land. Few specialize in lending and most also

farm and trade. Moneylenders usually lend from their own resources because

they cannot accept deposits due to seasonal fluctuations and covariate risk.

Banks rely on collateral such as land and third party guarantees but virtually

never succeed in their power to foreclose on land used as collateral. Officially,

banks do not lend again to borrowers who have not repaid the due amount. 4

Banks monitor loans by sending officers to examine the output of borrowers. In

particular, a Reserve Bank of India (1990) study states that:A single officer can

supervise 750 to 1700 accounts. Borrowers are scattered over a wide area so

difficult to monitor ... The officers lived in the nearest city or town (Reserve

Bank of India, pp. 100-104).

In sum, two striking differences emerge from an analysis of the constraints

banks and moneylenders face. The bank cannot observe the farmers output

effectively but moneylenders face a higher cost of lending in that he engages in

3 Moneylenders are predominantly male.4 This rule is formally written in their charters, see Reserve Bank of India (1990). In reality,

some political write-offs of debts during election years are reported to occur.

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other activities. Both also share some similarities. They do not seize collateral

the bank, because of legal difficulties and the moneylender because he does not

require collateral ex-ante. In the following section, the model describes how both

banks and moneylenders can overcome their constraints by working in unison.

2. Theory

2.1. The Environment

A simple three period model (based on Bolton and Scharfstein (1990)) captures

the dynamics with investments in periods zero and one for i.i.d.returns in periods

one and two, respectively. The timing of the model follows that of a cropping

season: with loans disbursed at the beginning, output revealed at the end, and

repayments paid before the beginning of the next cropping season. The borrower

cannot self-finance and requires an amount L for production in each period. In

periods one and two, the returns have the same probabilities: in a good state

and (1 ) in a bad state. Denote yi as income of the borrower where i = g, b

denotes good and bad states. The return in the good state (yg) covers the loan

amount but in the bad state (yb), the borrower cannot repay the loan amount.

The expected net return of the investment is positive: y = yg + (1 ) yb > L.

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Assumptions on the lenders and the borrowers follow. First, both lender and

borrower are risk-neutral. Second, the contract is written such that once both

parties agree to the contract, the lender will not renege from the original contract.

In terms of the contract literature, lenders fully commit to re-lending. For the

borrowers, the additional following assumptions hold. First, the borrower needs

to return to the bank for further financing after the first period. Second, limited

liability protects the borrowers: in both sectors, lenders cannot extract more than

the borrowers income reports since borrowers have no collateral. Third, since

borrowers have access to only one bank, the bank acts as a monopolist.

From Section 1, banks and moneylenders differ in their lending costs and

information. Define rM as the return on alternative activities for the moneylender.

Similarly, rB

denotes the banks opportunity cost of lending, where rM

r

B

,

and normalize rB to zero. Then, = 1rM+1

(0, 1] measures the moneylenders

higher discount rate relative to the bank. Additionally, as revealed in Section 1,

moneylenders can monitor borrowers incomes without incurring costs.5

5 This assumption is simple in order to yield a tractable testable implication. In this twostate model, introducing costs of monitoring would only shift the moneylenders profit function.

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2.2. The Banks Contract

Following evidence from Section 1, I assume that the bank cannot identify the

borrowers income level.6 Suppose that the periods were limited to two. The

bank would never lend because borrowers would always report the lowest output

level (yb) which is lower than the loan amount by assumption. Exchange can still

be supported by tying future rewards to previous periods performances. Due to

the informational assumptions, repayments cannot be made contingent on income.

Denote Ri1 as the repayment to the bank in the first period where i = g, b

corresponds to the good and bad reports by the borrowers, respectively in the

first period. Denote Ri2 as the repayment to the bank in the second period where

i = g, b corresponds to the good and bad outcomes, respectively, in the first

period. As a result, from the limited liability constraints, the maximum the bank

can extract in the last period is yb. The above result indicates that the last period

serves as an anchor in this simple finite period model and greater interest lies in

the first period repayments. Denote i [0, 1] as the probability of a borrower

receiving a loan in the second period conditional on the outcome being i = {g, b}

in thefi

rst period.

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6 As an equivalent assumption, the verifiable amount is yb.7 Thus, in this model the probability of re-lending is endogenous.

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The optimal contract is the solution to the following program: the bank chooses

ni, Ri1, R

i2

ofor i = g, b to maximize expected profits, subject to incentive com-

patibility and individual rationality constraints.8

The results are summarized in the following proposition:

Proposition 2.1. (Bolton-Scharfstein) The bank lends at date 0, if and only

if yb+y(1+)

> L. In this case, Rb1 = yb,b = 0, Rg1 = y,

g = 1, Rb2 = Rg2 = y

b.

Proof. Bolton-Scharfstein, p. 97; p. 105.

The above proposition provides insights into the structure of formal credit con-

tracts in developing countries. First, in a collateral constrained economy, banks

must offer a carrot to induce good borrowers to repay. If repayments were set

high, at yg

for example, then that would violate the incentive compatibility con-

straint. Good borrowers would then report a bad outcome, keep the surplus, and

not concern themselves with the additional funding. The repayments are set at

y, the expected income in the next period, where the borrowers are indifferent

8 An important point on the profit maximization assumption for banks in Indian rural creditmarkets needs to be clarified. In the long run, banks have an incentive to maximize profitsbecause their re-financing from the government depends on profitability. Furthermore, recently,

interest rates have been liberalized. Imposing interest rate regulation would simply place anadditional constraint on the problem: Rg1 r without any additional insights. As will be seen,the optimal repayment scheme exhibits features of fixed interest rate regulation. Furthemore,as will be seen in the empirical section, variation over time in repayment/debt ratios indicatethat banks accept differential repayment amounts and not a fixed amount.

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between repaying and defaulting. The optimal contract provides an endogenous

explanation for much maligned interest rate regulation. In the information con-

strained contract, the bank provides a contract such that borrowers with a critical

income level pay the fixed interest rate of y L while borrowers below a certain

income level, do not repay and cannot obtain further loans.9

Second, the optimal contract further provides an information-based argument

for the institutional rule in which banks do not re-lend to borrowers with low

repayments.10 The endogenous termination threat serves a dual purpose: it pre-

vents the bank from losing (1)(Lyb) in the last period and induces the good

borrowers to truth tell. If good borrowers were faced with repayments of y

or yb and if g = b = 1, they would prefer to repay yb. Third, due to the

limited liability constraints repayments stillfl

uctuate with income to a certain

extent, a result which will be used for the empirical section.11 Fourth, consistent

with other information models, the resulting contract indicates that separation

comes at a cost. The bad borrower does not receive additional funding even

9 This implication is clearer in the continuous version of the model: for y y , then R1 = y,g = 1. For y < y, b = g(y) and R1 = f(y), with f

0(y) > 0 and g0(y) > 0. See Bolton-Scharfstein. The model can accomodate exogenous interest rate regulation. If the set grossinterest rate were above the required repayments, then the interest rate would not bind. If thegross interest rate were below the required repayments, then the bank could not separate and acontract would only be possible with subsidies.

10 See footnote 1.11 See also footnote 2.2.

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though the expected return y is greater than the loan size L in the last period.

This inefficiency suggests a possible role for the moneylender.12

2.3. Moneylenders as Linkage Agents

Proposition 2.1 intimates that moneylenders may disburse funds to borrowers

who were excluded from banks. With these additional funds, borrowers can repay

banks and continue to access bank loans.13

The bank incorporates into its decision

the possibility that moneylenders will disburse loans at t = 1 to bad borrowers so

that bad borrowers will mimic the good and the bank cannot separate. Since the

bank moves first, it drives the moneylenders profits to zero. The moneylenders

profit constraint now replaces the incentive compatibility constraint. The solution

follows in the proposition below where R1(R2) and M2 denote repayments to the

bank and moneylender, respectively (without indicator of borrower type since now

banks cannot separate based on the income reports). For the proof, please refer

to the Appendix.

12 Notice that the solution is not subgame perfect in the last node because the bank can onlyreceive yb but it lends L < yb. Here, the bank pre-commits to the contract and will not re-negotiate because the most it can receive is yb. To avoid the end game problem, Denis Gromb(1996) extends the Bolton-Scharfstein model to multi-period and infinite periods. He findsanother problem common to infinite games, multiple equilibria.

13 I ignore the uninteresting possibility that the excluded borrowers may receive the funding Ldirectly from the moneylender at t = 1. Practically, the project may require continuing supportfrom the bank over the two periods.

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Proposition 2.2. : The bank lends at date 0 if and only if y+(2)yb

2> L. In

this case, R1 = y + (1 )yb, = 1, R2 = y

b, Mg2 = yg yb andMb2 = 0.

Through the banks repayments, the above contract links banks with money-

lenders. The banks first period repayments include the average and the bad state

incomes, weighted by the moneylenders cost of capital. The banks repayments

inversely vary with the moneylenders cost of capital (since is inversely related

to the moneylenders cost).14 This result occurs because as costs increase, the

moneylender reluctantly participates. To induce the moneylender to participate,

the bank needs to reduce its repayments. This linkage emerges as viable because

with the entrance of moneylenders, bad borrowers can complete their socially vi-

able project in the last period providing an additional surplus of (1 )y. In

turn, this additional amount transfers to the moneylender as a reward for their

services in the last period.

In this linkage, moneylenders serve a socially important role by allowing projects

to be completed and funding borrowers who would otherwise be locked out of the

formal credit market. This co-existence in effect provides another avenue for a

formal-informal link: through dynamic access. This linkage exhibits two attractive

features. First, banks do not need to explicitly hire moneylenders. As profit max-

14 Formally, R

1

> 0

R1

rM< 0.

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imizing agents, the latter serve voluntarily as implicit information agents. Both

lenders gain from trade with moneylenders trading in their comparative advan-

tage information and banks in theirs lower cost of funds. Second, the linkage

here runs counter to the belief that borrowers with access to multiple lenders

may not be disciplined in repaying (Ghatak and Guinnane). As revealed here, if

one lender (bank) holds a credible denial policy with attractive loans, borrowers

can use another lender (moneylender) to actually help loan recovery for the other

lender. In this case, multiple lenders aid in borrower discipline.

3. Empirical Section

3.1. Specification

In this section, I test whether moneylenders provide state contingent repayments

which allow borrowers continued access to bank loans. The model (through Propo-

sition 2.2) implies that for borrowers with access to moneylenders, repayments to

banks do not fluctuate with income. For all borrowers, Proposition 2.1 indicates

that bank repayments should respond to income.

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The estimated reduced form equation follows:

rit = i + 1Yit + 2(Yit Mit) + 3N Wit + uit (3.1)

rit =

(rit if r

it > 0

0, otherwise

)(3.2)

i = 1,...,N and t = 1,...,T.

where rit =Rit

Dit represents the repayments (Rit) to debt (Dit) ratio, Yit is

income, Yit Mit, is an interaction variable where Mit measures accessibility to

moneylenders, and N Wit is net worth, the difference between assets and debt

from other sources.15 From Propositions 2.2 and 2.1, I expect the following signs:

1 > 0 and 2 < 0. Additionally, households may draw on their net worth to

repay implying that the expected sign of 3 is positive.16

3.2. Data

A particular issue with credit data is aligning the flow (repayments) with the stock

(current debt). Farmers report the debt data at the beginning of the cropping

15 The testable implication relies on repayments as the dependent variable of interest. Thetheory assumes no inherited debt. To incorporate inherited debt, I normalize repayment by the

debt level. This normalization also avoids placing debt on the right hand side and correctingfor lagged dependent variables since Dit = f(Ri(t1)).

16 For households with other debt they consider more senior, this effect would be dampenedand possibly reversed.

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year (July 1). The cropping year in the ICRISAT villages is composed of basically

two seasons: Kharif (rainy) and Rabi (post-rainy). Farmers sow by July for the

Kharif season and by early October for Rabi. While farmers harvest the Rabi

crop in February, they harvest the Kharif by December. The banks rigid rules

allow for aligning of repayments and loans with the correct cropping year without

much error. As confirmation of the bank rule, banks disbursed ninety percent of

loans and received eighty- seven percent of repayments from January to July 1.

In calculating repayments to banks, I exclude all households who have never re-

ceived a bank loan and I will address the selection problems below. The ICRISAT

flow file contains loans and repayments to banks with 98 and 67 annual observa-

tions, respectively. The debt file is considerably more complete with 242 observa-

tions. Merging thefl

ow and stock data created doubts because in some villages

debt was declining but the flow data indicated no repayments. For these villages,

I relied on the more reliable debt data and imputed repayments. Farmers did not

report if they made no repayments. I recorded zero repayments if for any year I

observed zero debt, no repayments, and no decline in debt. Farmers did not report

full repayment of debt. If for a particular year, a previously indebted household

is still in sample but registers no debt, then I assumed full repayment of the debt.

The final sample consists of 165 observations and 49 households from 7 villages

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with an average of 3.3 years per household. As a check on the data, I found that

a surprising 13 % of the repayments were found to be zero which indicates that

borrowers find access to bank loans valuable. As an additional check, I found the

maximum repayment/debt ratio as a reasonable 1.6. Still, since the resulting data

set yields 165 observations from 49 households, I approach the estimation wary

of possible small sample problems and I will adopt a parsimonious specification.

As illustrated in Section 2 , the ICRISAT data offers a panel data set with both

moneylenders and formal lenders, but it has its own shortcomings. As with most

credit data, both the loan and debt files contain no information on the actual type

of debt and consequently, the owed repayment on the debt. As implied from the

theory, the assumption is that borrowers make repayments whenever their income

level is high enough and stall on repayments when their income is low regardless

of their debt: long-term or seasonal. This assumption may be justified through a

consumption smoothing approach. In light of the above, note that measurement

errors may arise, for example with missing source codes. I will try to be as careful

as I can in detailing how I arrived at the data

For a proper income measure, I must control for labor supply endogeneity.17

17 For example, Anjini Kochar (1999) finds with ICRISAT data that farmers protect themselvesfrom income shocks by increasing their hours of work.

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Full income, similar to Rosenzweig (1988), is the sum of net profits plus potential

labor income plus other income from trade and handicrafts.18 Rosenzweig defines

potential labor income as the working days (312) multiplied by the average wages

in the village multiplied by the number of adult males in the household. This

definition incorporates Beckers notion of opportunity cost of time and reflects

the total value of household assets. By construction, full income includes any

ex-post adjustments by the household. Since full income may capture permanent

features, I also employ a more conventional measure where reported labor income

substitutes for potential labor income in the full income definition above.

Good proxies for moneylender access must have two qualities: they must vary

enough to allow for precise estimation and indicate more recent and not historical

access. These qualifi

cations would rule out the small amount of variation from

village-level data. Also, these proxies should be predetermined to rule out endo-

geneity in choice. I rely on a time varying dummy variable at the household level.

If a household during the previous period has ever received a loan from a money-

lender then this variable is set to 1 (M1). Again, this variable is predetermined in

that the household decides to access moneylenders made before they repay banks.

18 Potential labor income includes income from females as well since we are not concerned asmuch here about variation in marriage. The allocation of family time to livestock and handicraftactivities is unknown but the ex post labor adjustment is unlikely to be serious.

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Furthermore, the time variant component of the access variable may be absorbed

in the fixed effect because as noted in Section 1 relationships with moneylenders

are long term and switching is uncommon. Furthermore, last years decision is

a good predictor for this years decision.19 In order to incorporate the above in

fixed effects, note that the above access variable is not time invariant.

Finally, in the theory repayments depend on income alone. In reality, borrow-

ers draw from other sources to repay banks such as liquid wealth and other lenders.

Following Udry (1994), I measure total liquid wealth by aggregating total financial

assets, livestock, durables, and stock inventory. For debt from sources other than

the creditor whom the borrower is repaying, I again exclude government loans

which villagers do not seem to treat as loans. If no debt was recorded from other

sources, this variable is set to zero. Since I am not interested in the separate esti-

mate of these stock variables but merely in controlling for them, I combine these

two into a net worth variable. Net worth is defined as the difference between

assets and debt from other sources.

19

A simple logit regression confirms the prediction, where dum0 is loan received this period anddum1 is loan received last period and t statistics are in parenthesis.

dum0 = 1.011 + 2.20dum1(4.58) (5.84)

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3.3. Estimation

Some econometric issues arise in the context of Equation 3.1. First, a sample se-

lection problem emerges in that borrowers from banks may have different charac-

teristics than those who borrow from other lenders.20 Second, borrowers different

risk-taking preferences and abilities would affect not only their choice of lender

ex-ante but also their repayments. If the above differences are fixed over time and

enter in a linear and additive manner, these can be swept out with fixed effects.

Finally, with a censored left hand side variable, the correct specification is

fixed-effect Tobit. Due to the small number of households, I employ a brute

force method and.incorporate a set of household fixed effects.21 Greene (2002)

finds that with the above method the fixed-effect Tobit is not biased but the

standard deviation is biased downwards which means that the marginal effects

are biased downwards. As a check on the results, since the censored sample is

only thirteen percent (Greenes tests employ 40-50 % censoring), I report OLS

fixed effects as well.

20 Sample selection bias over time will be addressed later.21 An alternative is Honores estimator (1995) which is consistent. Honores consistency re-

quires the error term to be independent and identically distributed over time and the sample size

to be greater than 200. I will not adopt Honores procedure for three reasons: one, the householderror term is household specific and time varying, two the sample size is small, smaller than 200and three, extensions such as endogeneity and calculating marginal effects are not well known.

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3.4. Results

3.4.1. Banks

Table 1 presents the regression results for different specifications. The likelihood

function allows for a general form of heteroscedasticity in which the variances

depend on family size. The results indicate that banks accommodate full in-

come. More significantly, the estimates suggest that moneylender access dampens

the fluctuation of these repayment-debt ratios. To obtain Tobit marginal effects,

I multiply the slopes by X

. This calculation reveals that for a ten thou-

sand rupee increase (decrease) in income, the repayment-debt ratio would rise

(fall) by twenty percent. In sharp contrast, a household with moneylender ac-

cess experiences only about half of that fall. By absorbing borrowers shocks,

moneylenders help borrowers enjoy continuing access to bank loans. As a check

on the fixed-effect Tobit results, specification (2) includes OLS fixed effects with

heteroscedastic-consistent variances. The results, similar to the Tobit marginal ef-

fects indicate that a household with moneylender access experiences virtually no

change in their repayment-debt ratio. The estimate on net worth is surprisingly

negative indicating that households with higher net worth repay less.22

22 The coefficient on net worth may be biased due to the following. The net worth position of ahousehold in period t may be influenced by the households ability to repay banks and thus affect

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A remaining concern is that the in debt sample may misrepresent households

that have access to moneylenders. Any time variant shock would not be absorbed

in the fixed effect. A priori, the direction of bias is ambiguous because moneylen-

der loans may provide households continuing access to bank loans. On the other

hand, access to moneylenders allows households to fully repay bank loans and to

drop out of the in debt sample. More formally, time varying shocks may affect

the repayment equation and the selection equation (the choice to be in debt) as

well.

Due to the complications in implementing sample selection correction in panel

data models, I only test for selection bias.23 I employ a two stage variable addition

test due to Jeff

rey M. Wooldridge (1995)). Thefi

rst step is to regress a cross-

sectional Tobit for each year for the choice to be in debt. For this step, I include

the following household-level variables: age of household head, average years of

schooling of household head, number of males, and number of females, and lagged

access to credit markets. Endogeneity was tested in a recursive simultaneous model with thefollowing instruments: various measures of rainfall, village moneylender shares, and interactionof the two with household characteristics. The explanatory power of the set of instrumentsin the first stage regressions is confirmed through F tests: the relevant F (52,111) statistic is4.95. The exogeneity test relies on measuring correlation between the residuals from (1) therepayments equation and (2) the net worth equation. The resulting t-ratio for 12 was -.039,rejecting endogeneity. The results are available upon request.

23 See Marno Verbeek and Theo Nijman (1992) for a discussion of these issues.

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full income. At the second step, regress the repayments equation augmented

by the residuals (bv). Selection bias results if the coefficient on the residuals aresignificantly different than zero.

The results shown in Table 2 indicate the presence of selection bias. The

negative coefficient suggests that in sample households have a lower repayment-

debt ratio. This suggests that households who do not have access to moneylenders

would continue to be in sample and have trouble repaying banks. Thus, the

in sample households would under-represent households that have access to

moneylenders. Even with the reduced sample size, the coefficients on M1 yield

similar results to before.

3.4.2. Moneylenders: State contingent repayments ?

In order to accommodate borrower access, Proposition 2.2 suggests that money-

lenders would offer a state contingent flexible repayment schedule. This would

need empirical confirmation. One issue with moneylenders is that the timing

cannot be properly aligned (as with banks previously). Loans and repayments are

continually disbursed. In fact moneylenders are known to disburse ex-post loans.

Due to this reason, I look at change in debt rather than actual repayments. I

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estimate the same equation as Equation 3.1, replacing the bank-specific variables

with those of the moneylender. The sample includes anyone that has received a

loan from a moneylender.24 This specification differs from that of banks in that

it does not include the interaction term and employs the net debt ratio instead

of repayments-debt ratio. The results in Table 3 are consistent with moneylender

serving as an insurance agent. The estimates suggest that households rely on

net worth also in repaying moneylenders. These results also provide a basis for

findings that households are insured against idiosyncratic shocks in the ICRISAT

villages (Kochar, Jacoby-Skoufias, and Townsend (1994)).

Researchers (Brett E. Coleman (1999)) has suggested that villagers may loan

cycle, i.e. use bank loans to repay moneylender loans, creating a vicious cycle of

debt. Since previously banks were flexible in that their repayments accommodate

income, this cycle remains a distinct possibility. Regression (2) includes a bank

interaction term (B1) similar to M1 which indicates household bank access this

year or the previous year. The non-significance ofB1 indicates that households do

not rely on banks to repay moneylenders and thus, no confirmation of the vicious

24 Since the sample size with banks is already small, reducing it further would make it muchsmaller. This result still confirms whether moneylenders offer flexible credit schedules in general.

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cycle.

The above suggestion opens the potential for other formal-informal linkages.

With formal lenders, repayments to cooperatives and governments indicated ab-

solutely no reponsiveness to full income. This result along with the large number

of zeros indicated some problems with how they function. Could other informal

lenders function as saviors bailing borrowers out ? Separate regressions with in-

teraction terms for friends and landlords did not reveal any significance as seen

in Table 4. The results indicate that moneylenders possibly due to their low

correlation with other borrowers remain the most promising of the linking agents.

4. Conclusion

Due to informational constraints, banks in developing countries face difficulties

in extending loans for borrowers continuing access. In this paper, moneylenders

provide funds which allow continuing access to bank loans. In this respect, the

linkage endorses the view that the parallel existence of both formal and informal

sectors contributes to rural financial development. Linkages serve to bridge dual-

ism and help in the process of development before developing countries can build

more solid financial institutions. From a policy perspective, banks can incorporate

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moneylenders in their policies without ignoring or discouraging their presence. 25

Practically, in the launching of new credit programs for formal lenders, moneylen-

ders can serve an invaluable role. For example, in the Philippines, as the Masagna-

99 formal credit programs expanded, repayments deteriorated under the formal

sector, borrowers were excluded and the lending shifted back to informal lenders

(P.B. Ghate). With a more proactive, inclusive attitude towards moneylenders,

the presence of moneylenders could provide early breathing room success for

new credit programs. Of course, potential loan cycling and illegal enforcement

measures would need to be monitored as well.

The linkage suggested here can be compared to other suggested credit de-

livery mechanisms. Academics and policymakers alike have recently focussed on

micro-fi

nance. The sustainability of micro-fi

nance has been questioned in a survey

article (Jonathan Morduch (1999)). In the linkage proposed here, both banks and

moneylenders satisfy the profit sustainability condition. Similarly, in the equilib-

rium identified in this paper, the bank need not hire moneylenders but rather they

participate on their own as profit maximizing agents. In contrast, in other link-

ages, Bell and Fuentes suggest that banks should explicitly hire informal lenders.

25 Careful consideration must take into account the market structure. The structure of thecredit markets would determine the division of surplus among banks, borrowers, and moneylen-ders. In a monopolistic market, the moneylender can extract the maximum amount and keepborrowers at their lowest reservation utility as in seen in the linkage here.

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In those linkages, borrowers may collude with informal lenders. One shortcoming

of the linkage here is that it begins with the premise that borrowers have access

to banks and moneylenders. Future research can address possible linkages for

borrowers in which banks and/or moneylenders initially screen out borrowers.

Alternative hypotheses on the co-existence of banks and moneylenders may be

consistent with some of the results in this paper. As suggested in the literature,

richer borrowers with collateral can access both banks and moneylenders and

the non-collateralized poor have only access to moneylenders. A formal test

would be beyond the scope of this paper but the evidence does not suggest that

possibility. The lender share does not markedly vary over land-holding class.

As another alternative hypothesis, lenders serve different purposes with banks

providing production loans and moneylenders providing consumption loans. The

model in the paper reinterprets these ex-post consumption loans as relaxing the

repayment constraints on borrowers. Borrowers do not need to explicitly state

their loan purpose, but whatever amount they borrow from moneylenders relaxes

their repayment constraint to banks.

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Appendix 1

PROOF OF PROPOSITION 2

Proof. Suppose the borrower is now in a bad state at t = 1. Consider the

moneylenders problem. Denote EM2 Mg2 + (1 )M

b2 . From t = 1, maxi-

mize the moneylenders expected profits subject to the individual rationality and

limited liability constraints of the borrower:

Maximize R1 + yb + {Mg2 + (1 )M

b2}

{Mg2 , Mb2}

subject to

y R2 + EM2

yg R2 + Mg2

y

b

R2 + Mb

2

Above the first constraint is redundant. The other two constraints bind so that

EM2 = y R2. Substituting these values into the objective function yields the

following value function R1 + yb + {yR2}. The bank then will maximize the

following objective function from t = 0 where the additional constraint now is a

participation constraint for the moneylender. Note that no incentive compatibility

constraints are now needed for the borrowers:

Maximize R1 + R2 2L

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{R1, R2}

subject to

yb + {y R2} R1

yg R2 + Mg2

yb R2 + Mb2

Again, the above three constraints bind. Thus, R1 = y + (1 )yb . To

solve for R2, the third constraint implies that R2 = yb

M

b

2 . Since Mb

2

0, the

maximum for R2 = yb. It follows that the repayments requested in the first period

are now R1 = y + (1 )yb. Finally, to ensure that the bank is making positive

profits, y+(2)yb

2> L.

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Table 1

Fixed-Effect Tobit Estimates of Fluctuations of Repayments/Debt to Banks

(1) (2) (3) (4)

Income (x104) 0.244 (3.39) 0.187 (1.98) 0.800 (4.20) 0.552 (.279)

Income x M1(x104) -0.143 (2.03) -0.142 (2.39) -0.220 (2.21) -0.764 (1.36)

Net Worth (x105) -0.351 (3.63) -0.264 (2.63) -0.222 (2.62) -0.174 (1.78)

Notes: Analysis based on 165 observations from 49 households. Absolute t-ratios

in parentheses. See text for definitions of variables. Coefficients on the full set of

regressors are not reported in this table but are available upon request. Additional

regressors include a set of household dummy variables. Regressions (1) and (3) allows

for heteroscedasticity in which the variances depend on family size. Regressions (2) and

(4) are OLS fixed effects with White heteroskedasticity-consistent standard errors.

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Table 2

Fixed-Effect Tobit Estimates with Selection

(1)

Income (x104) 0.197 (2.52)

Income x M1(x104) -0.118 (2.09)

bvi(x10

4) -0.186 (2.02)

Net Worth (x108) 0.117 (3.74)




regressors include a set of household dummy variables. . The residual

bvi calculated

from cross-sectional Tobit for each year, see text for variables.

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Table 3

Two Stage Fixed-Effect Estimates on Fluctuations of (Net Debt)/Debt to

Moneylenders

(1) (2) (3)

Income (x103) - -0.634 (3.3) -0.634 (3.38)

Income x B1(x104) 0.387 (1.43) 0.281 (2.87)

Net Worth (x105) 0.490 (1.80) 0.981 (1.89) 0.971 (2.06)




regressors include a set of household dummy variables. Regressions (1) and (2) allows

for heteroscedasticity in which the variances depend on family size.

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References

[1] Alexander, Gwendolyn. A Multi-Period Model of Microenterprise Finance:

Using Dynamic Incentives to Induce Repayment. Working Paper, University

of Maryland, College Park, November 2000.

[2] Armendariz de Aghion, Beatriz and Morduch, Jonathan. Microfinance be-

yond Group Lending. Economics of Transition, 2000, 8(2), pp. 401-420.

[3] Bell, Clive. Interactions between Institutional and Informal Credit Agencies

in India. The World Bank Economic Review September 1990, 4(3), pp. 297-

327.

[4] Besley, Timothy J. Savings, Credit, and Insurance, in Jere Behrman and

T.N. Srinivasan, eds., Handbook of Development Economics, vol. 3A. Ams-

terdam: Elsevier Science B.V., 1995, pp. 2123-2207.

[5] Binswanger, Hans P., Balaramaih, T., Bhasker Rao, V., Bhende, M. J., and

Kshirsagar, K. G. Credit Markets in Rural South India: Theoretical Issues

and Empirical Analysis. World Bank (Washington, DC) ARU Report no.

45, Agricultural Research Unit, 1995.

35


37/40

[6] Bolton, Patrick and Scharfstein, David S. A Theory of Predation Based

on Agency Problems in Financial Contracting. American Economic Review,

March 1990, 80, pp. 93-106.

[7] Coleman, Brett E. The Impact of Group Lending In Northeast Thailand.

Journal of Development Economics, October 1999, 60 (1), pp.105-141.

[8] Fuentes, Gabriel A. The Use of Village Agents in Rural Credit Delivery.

Journal of Development Studies, December 1996, 33(2), pp. 188-209.

[9] Ghate, P.B. Informal Finance: Some Findings from Asia. Hong Kong: Ox-

ford University Press, 1992.

[10] Greene, William. The Behavior of the Fixed Effects Estimator in Nonlinear

Models. Working Paper, NYU Stern School of Business, 2002.

[11] Gromb, Denis. Renegotiation in Debt Contracts. Working Paper, MIT

Sloan School of Management, 1996.

[12] Honore, Bo E. Trimmed LAD and Least Squares Estimation of Truncated

and Censored Regression Models with Fixed Effects. Econometrica, May

1992, 60 (3), pp. 533-565.

36


38/40

[13] Jacoby, Hanan G, and Skoufias, Emmanuel. Risk, Financial Markets, and

Human Capital in a Developing Country. Review of Economic Studies, July

1997, 64(3), pp. 311-335.

[14] Jain, Sanjay. Symbiosis vs Crowding-out : The Interaction of Formal and

Informal Credit Markets in Developing Countries Journal of Development

Economics, August 1999, 59(2), pp. 419-444.

[15] Karmakar, K.G. Rural Credit and Self-help Groups. Delhi: Sage Publications,

1999.

[16] Kochar, Anjini. Smoothing Consumption by Smoothing Income: Hours-of-

Work Responses to Idiosyncratic Agricultural Shocks in Rural India. Review

of Economics and Statistics, February 1999, 81(1), pp. 50-61.

[17] Mahabel, S.B. Institutionalizing the Moneylender. Indian Journal of Agri-

cultural Economics, 1954, 9, pp. 175-178.

[18] Morduch, Jonathan. The Microfinance Promise. Journal of Economic Lit-

erature, December 1999, 37 (4), pp. 1569-1614.

37


39/40

[19] Reserve Bank of India. Review of the Agricultural Credit System in India:

Report of the Agricultural Credit Review Committee. Bombay: Reserve Bank

of India, 1990.

[20] Rosenzweig, Mark R. Risk, Implicit Contracts and the Family in Rural Areas

of Low-Income Countries. The Economic Journal, December 1988, 98(393),

pp. 1148-1170.

[21] Townsend, Robert M. Risk and Insurance in Village India. Econometrica,

May 1994, 62(3), pp. 539-591.

[22] Udry, Christopher. Risk and Insurance in a Rural Credit Market: An Em-

pirical Investigation in Northern Nigeria. Review of Economic Studies, 1994,

61, pp. 495-526.

[23] Udry, Christopher. Recent Advances in Empirical Microeconomic Research

in Poor Countries. Journal of Economic Education, Winter 1997, 28(1),

pp.58-75.

[24] Verbeek, Marno and Theo Nijman, Incomplete Panels and Selection Bias,

in Laszlo Matyas and Patrick Sevestre,eds., The Econometrics of Panel Data.

Dordrecht: Kluwer Academic Publishers, 1992, pp. 262-302.

38


40/40

[25] Walker, Thomas S. and Ryan, James G. Village and Household Economies in

Indias Semi-arid Tropics. Baltimore: Johns Hopkins University Press, 1990.

[26] Wooldridge, Jeffrey M. Selection Corrections for Panel Data Models un-

der Conditional Mean independence Assumptions. Journal of Econometrics,

July 1995, 68(1), pp. 115-132.

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