Credit Constraints and Productivity in Peruvian

Agriculture

Steve Boucher, UC-DavisCatherine Guirkinger, Univeristy of Namur

Conference on Rural Finance Research:Moving Results into Policies and Practice

March 19-22, 2007FAO, Rome

Outline of Talk

• Who should be considered credit constrained in the rural economy?– Economic theory has emphasized supply-side

constraints (quantity rationing)– We suggest the importance of demand-side

constraints (transaction cost and risk rationing)

• How prevalent are different forms of credit constraints among Peruvian farmers?

• How large is the impact of credit constraints on farm productivity?

• Implications for research and policy

Information Asymmetries:The root of credit constraints

• Lenders have imperfect information about borrower characteristics and actions that affect the probability of default.

• Lenders attempt to overcome information problems by:– Directly investing resources in screening and monitoring;– Using borrowers’ own informational advantage via group

loans;– Requiring collateral;

• These responses to asymmetric information may leave some individuals credit constrained.

Credit Constraint: A General Definition

• An individual is credit constrained if her terms of access to the credit market imply that she does not exploit (either because she is unable or unwilling) a socially profitable (expected income enhancing) investment.

Multiple Sources of Credit Constraints

• Quantity Rationing: Individual has a profitable project and wants a loan, but is denied access.

• Transaction Cost Rationing: Individual has a profitable project but does not apply because, once the transaction costs associated with loan application (and monitoring) are factored in, the project is no longer profitable.

• Risk Rationing: Individual has a profitable project (even considering TC’s) but does not apply because she’s unwilling to assume the risks associated with default.

Equity Concerns: The vicious cycle of poverty and credit constraints

• Anti-poor Supply Side Factors– Poor have fewer assets to post as collateral.– Assets owned by poor are less likely to be accepted

as collateral (a la DeSoto).

• Anti-poor Demand Side Factors– Fixed transaction costs make effective costs of loans

higher for the poor.– Poor are less willing to bear contractual risk (losing

collateral).

Data and context

• Panel of 454 hhlds surveyed in 1997 & 2003.

• Piura: on Peru’s north coast.• Regional economy highly

dependent on ag.• Relatively good market

infrastructure but high risk (El Niño)

• Predominance of small farms (< 5 ha)

• Major titling program 1998 – 2002.

• And importantly…

Piura

Best Ceviche in the world…

Who are the households? 1997 2003 Age of head 52.0 56.4 Education of head 4.5 4.8 Activity Mix % farming 100 100 % with business 35 27 % with wage income 47 32 Land Mean farm size (ha.) 4.6 4.3 % with land title 49 70 % Growing… …Rice 50 55 …Corn 40 30 …Cotton 32 11 …Perennial 40 39

Who are the lenders?

% of sample loans from 1997 2003 Formal Sector 32.6 41.2 Municipal Bank 27.5 35.6 Rural Bank 0.7 3.6 Commercial Bank 4.4 2.0 Informal Sector 49 50.4 Rice mill 5.4 5.2 Input supply store 12.4 8.2 Trader 12.2 10.8 Other 19.0 26.2 Semi-formal Sector 18.5 8.6 Government Program 7.1 3.8 NGO 11.4 4.8

Where do households borrow?

% Hhlds borrowing from… 1997 2003

…Formal Sector Only 28% 26%

…Informal Sector Only 21% 18%

…Both Formal & Informal 7% 7%

…Semi-Formal Sector 16% 7%

…Did Not Borrow 28% 42%

How do we classify households as credit constrained vs unconstrained?

• Credit Constrained households are those that:– Did not borrow because of quantity rationing, transaction

cost rationing or risk rationing– Borrowed but couldn’t get as much as they wanted

• Credit Unconstrained households are those that:– Did not borrow because they didn’t need a loan;– Borrowed and got as much as they wanted

• Questionnaire was designed to “directly elicit” each household’s credit constraint status.

How prevalent are credit constraints?

Household Formal Sector Outcome 1997 2003 Constrained 56% 43% Quantity Rationed 37% 10% Risk Rationed 9% 22% Transaction Cost Rationed 10% 11% Unconstrained 44% 57% Borrowers 28% 25% Non-borrowers 16% 32%

Is the informal sector a good substitute?

Contract Term Formal Sector Informal Sector

Loan Size $1,560 $350

Loan Term 12.4 months 5.3 months

Annual Interest Rate 69% 117%

Transaction Cost $74.5 $5.5

Requires Collateral 58% 9%

How much would productivity increase if credit constraints were relaxed?

Productivity Indicators: Sample Means

Revenue per ha

Cost per ha

Net revenue per ha

Constrained $884 $350 $534

Unconstrained $1,537 $652 $885

Unconditional Impact (compare means)

73% 86% 66%

Conditional Impact(econometric estimate) ??

Econometric Model: Switching Regression

• Productivity of farmer i at time t is:

• The Credit Constraint status of farmer i at time t is:

• Estimate with two strategies:– OLS on first difference of productivity equations;– Semi-parametric, weighted OLS (Kyriazadou, 1997)

1

0

C C C C C Cit it it it i it it

it U U U U U Uit it it it i it it

y A K Z if dy

y A K Z if d

*

*

*

1 ( ) 0

0 ( 0it

it

it

it it it it i it

constrained if dd

unconstrained if d

d W A X

How much would productivity increase if credit constraints were relaxed?Productivity Indicators: Sample Means

Revenue per ha

Cost per ha

Net revenue per ha

Constrained $884 $350 $534

Unconstrained $1,537 $652 $885

Unconditional Impact

(compare means) 73% 86% 66%

Conditional Impact(econometric estimate) 45%

• Relaxing the credit constraint of the average constrained farmer would raise their revenue per hectare by 45%

How much would regional output increase if we relaxed each type of credit constraint?

Type of credit constraint

(A) Frequency in Sample

(B) Productivity

Change ̂

(C) Relative Change

ˆ / y

(D) Land

controlled

(E) Impact on regional output

Quantity Rationed 23.5% $516 58.2% 20.5% 11.9%

Risk Rationed 15.5% $478 68.2% 16.0% 10.9%

Trans. Cost Rationed 10.5% $413 49.0% 7.8% 3.8%

All constrained hhlds 49.5% $482 58.9% 44.2% 26.0%

Summary: Major Findings• The formal credit market in Piura is quite active in

agricultural lending:– Local banks (caja municipal/rural) aggressively expanded ag

lending after state development bank closed in 1992;– About 1/3 of households had a formal loan;

• Credit constraints are prevalent, though falling:– 56% of farmers were constrained in 1997; 43% in 2003

• The change in composition of constraints is troubling:– Titling appears to have increased households’ ability to

borrow• Frequency of quantity rationing fell from 37% to 10%

– But uninsured risk implies that titling has not increased their willingness to borrow

• Frequency of risk rationing increased from 9% to 22%

Findings Continued

• Credit constraints have a large negative impact on farm production.– Relaxing credit constraints would:

• Increase revenues per hectare, on average, by 45%

• Increase the value of regional output by 26%

Implications for Policy and Research• Providing secure property rights (titling) is

necessary but not sufficient to overcome credit constraints faced by small farmers.

• Insurance market imperfections have negative spillover into credit markets.

• Suggests the need for policy innovation:– Micro-health insurance;

– Index/weather insurance.

• Research agenda:– Are “risk-rationed” farmers really risk rationed?– Randomization to achieve exogenous variation in risk

sharing terms of credit contracts?– Two birds with one research stone?

• Create index insurance product linked to credit contract (example from India)

• Randomly offer the product to farmers and examine the demand for contracts and investment behavior across control and treatment groups.