Credit Constraints and Productivity in Peruvian Agriculture Steve Boucher, UC-Davis Catherine Guirkinger, Univeristy of Namur Conference on Rural Finance Research: Moving Results into Policies and Practice March 19-22, 2007 FAO, 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 & 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 – And importantly… Piura

Best Ceviche in the world…

Who are the households?

Who are the lenders?

Where do households borrow?

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?

Is the informal sector a good substitute?

How much would productivity increase if credit constraints were relaxed? 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)

How much would productivity increase if credit constraints were relaxed? 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?

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.