Específicos

The analysis applies the linear probability model and the logit model using a robust estimator to test the hypotheses. Each model, we present three regressions that differ in the dependent variable. The dependent variables include risk rationing, quantity rationing and price rationing identified based on credit rationing status.

Independent variables

We define the independent variables into several categories, each designed to capture the effects on credit rationing. The demographic variables in a model consist of sex, education and year of farming,

Measures for wealth. In literature, both financial wealth and productive wealth are

11_{They also find that less wealthy households are more sensitive to the loan price comparing to the wealthier}

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significantly associated with credit rationing. Specific variables include farm size, household income, percentage of farm income, asset value and saving. Farm size is a proxy of productive wealth and the rest are proxies of financial wealth.

Measuring for risk aversion and prudence. We asked a series of questions about risk taking, risk mitigating and precautionary saving behavior that would reflect such attributes and used data to compute a risk aversion score and prudence score. Risk aversion score is calculated based on farmers‟ willingness to take risk, risk management options use and perceptions. In the survey form farmers were asked to identify their willingness to accept greater production risks in order to increase the chance of higher profits, to take risks with new technologies, and to take risks with new management practices before seeing good results in other farms. In addition, farmers indicated how important of risk management in their farm. Risk management options that we asked include farm diversification, geographic diversification, irrigation, marketing diversification, forward contract, participation in government programs, maintaining financial reserves and investing off-farm for other sources of income. Prudence score is calculated based on the purposes of their precautionary savings. Farmers specified their level of agreement or disagreement on a five-level Likert scale for a series of statements; I save in case my automobile break down; I save for unexpected medical emergency; I save to protect job loss; and I save for unanticipated crop loss. The higher the score would indicate that the respondent is more risk averse and prudence.12

Measure of insurance market participation is represented by insurance variable. Farmers indicated whether they regularly purchase insurance for any of the following items: life

insurance, fire insurance for home and, automobile insurance, health/medical insurance, farmer‟s

12_{We also conducted a simple field experiment to estimate the partial risk aversion coefficient of the farmers based}

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minimum living standard security, rural old-age insurance, crop insurance, and livestock insurance. The higher value of insurance variable would imply the more participation in insurance markets.

To capture asymmetric information aspect, we include two binary variables; credit worthy and group guarantee variables. In the presence of asymmetric information, creditworthy borrowers may be denied credit because they are unable to meet such collateral requirements or pay such high interest rates. Loan may be disapproved if borrowers are not a member of group guarantee in which every member of a group ensure the repayment of all members. Credit worthy variable takes value 1 if a respondent indicated he is currently considered a „Credit Worthy‟ borrower by local RCC, or 0 otherwise. Group guarantee variable takes value 1 if a respondent indicated he is a member of a Group Guarantee, or 0 otherwise.

Elasticity of demand for credit. We estimate the sensitivity of the quantity demanded for credit to changes in the interest rate. Using 7% interest rate as a benchmark, farmers were asked to rank on a five-point ordinal scale (from Definitely Borrow a lot more to Definitely not borrow anymore) when interest rate decreases from 7% to 6%, 5%, 4%, and 3% (called lower interest rate) and increases from 7% to 8%, 9%, 10%, and 11% (called higher interest rate), assuming that respondents can borrow as much as they need. We create 10 binary variables to indicate characteristics of each respondent whether his credit demand is perfectly inelastic; highly inelastic, medium elastic, moderate elastic and highly elastic for lower and higher interest rate. Numerical criteria to specify each of elasticity of demand for credit variable are presented in the Appendix.

Furthermore, we include binary variables for whether the respondent held informal credit (friends and family); and/or formal credit (RCC or bank) to capture effects between formal and

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informal credit. In addition, farmers identified the willingness to borrow if they can use their land use rights as collateral for a loan on a five-point scale (from definitely borrow more to not borrow any more). The higher the value of the land use rights as collateral variable, the less likely farmers will borrow. Finally, we include a dummy variable to account for farmers‟ entrepreneurial activity. The variable “Ever started business” takes value 1 if respondents have ever started a new business and 0 otherwise. Whereas, the variable “Plan to start business” takes value 1 if respondents are planning to start a new business and 0 otherwise.