Agricultural Economics

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    BORROWER AND LENDER DETERMINANTS INFLUENCING AGRIBUSINESS LOANS DEFAULT RATE IN AGRICULTURAL FINANCE CORPORATION, IN MOUNT KENYA REGION
    (Chuka University, 2023-10) M`MURUKU SALESIO MIRITI
    Agribusiness loans advanced by Agricultural Finance Corporation (AFC) in Mount Kenya Region have high default rate of 20.33%. This compares unfavourably with 10% benchmark for all types of loans in Kenya. This is a challenge, given the strategic importance of agribusiness credit in mainstreaming livelihoods to alleviate poverty by offering occupational and professional opportunities. This study aimed at analysing borrower and lender determinants influencing agribusiness loans default rate in agricultural finance corporation, in Mount Kenya region. The study used descriptive research design. The study is anchored by four theories: delegated monitoring, rational choice, information asymmetry and the transaction cost theories. Mount Kenya region represents a branch network of 11 branches and a population of 3,002 agribusiness borrowers as per the current AFC records. A sample comprising of 300 respondents was drawn from a combined list through systematic random sampling technique with an interval of ten borrowers. Primary data on borrower and lender determinants influencing agribusiness loans default rate was collected using a structured questionnaire. The data was analysed to find the significance of all determinants using Statistical Packages for Social Sciences (SPSS V.27). ANOVA was used to check the adequacy of the regression model. The econometric models that were used to specify the statistical relationship between the independent variables and AFC loan default includes logit, probit, stepwise and multiple linear regressions. The logit model showed that borrower socio-economic profile had the prediction power 57.15% of AFC loan default rate: Multiple borrowing and borrower-lender distance had a statistically significant effect on AFC loan default rate (dependent variable) at 5% level of significance (p-values=0.00<0.05). The use of logit for enterprise decision making showed that it explained 36.98% of AFC loan default rate: Agricultural enterprise diversification and implementation of purposed project were significant at 5% (p-values=0.00<0.05). The Probit regression model used revealed that the lender behavioural characteristics considered in the model explained 48.80% of loan default: farm visit, disbursement timeliness, political lending and adequate funding significantly affected AFC loan default rate at 5% level (p-values=0.00<0.05). The multiple regression model depicted that extraneous shocks accounts for 23.1% of the AFC loan default rate: agroclimatic extremes and market volatility positively and significantly effected AFC loans default rate at 5% level of significance (p-values=0.00<0.05). Use of step wise regression for establishment of moderation indicated that: The relationship between borrower’s socioeconomic indicators and the extraneous shocks, with introduction of the moderator, increased from 24.3% to 27.1%, which is 2.8% increase in the prediction power. The model showed that a statistically and significant interaction existed between socio-economic indicators and the extraneous shocks and the AFC loan default rate (F=37.988; p-value=0.00<0.05). In the relationship between enterprise decision making and extraneous shocks, the introduction of a moderator caused an increase 22.2% to 22.3% accounting for 0.1% increase in AFC loan default rate. The ANOVA Analysis on the overall significance indicates that in enterprise decision making, there was a statistically significant effect on AFC loan default rate (F=29.659; p-value=0.00<0.05). In the relationship between lender behavioural characteristics and extraneous shocks when the interactive term was introduced, there an increase in AFC loan default rate from 32.2% to 35.9% which is a 3.7% increase in the prediction power. This implies that the model with the interactive term is the best predictor of AFC loan default rate. ANOVA Analysis gave results that showed that vii the two models used for lender behavioural characteristics were all significant (F=72.092; p-value=0.00<0.05) and (F=56.887; p-value=0.00<0.05). This shows that there was a statistically significant interaction existing between lender behavioural characteristics, extraneous shocks and the AFC loan default rate. To determine the joint effect, multiple regression model was used and it depicted that all sixteen indicators used accounted for 61.7% of the AFC loan defaults. The model was statistically significant at 5% (p-value = 0.05 <0.05). The ten indicators which were significant at 5% level of significance are multiple borrowing, borrower-lender distance, agricultural enterprise diversification, implementation of purposed project, farm visit, disbursement timeliness, political lending, loan adequacy, agroclimatic extremes and market volatility. Mitigation of default was found to be a joint effort actioned by loan stakeholders: lender intensification of adequate funding, farm visits and disbursement timeliness while eschewing political interference. Borrowers should use good agricultural practices of enterprise diversification, use loan funds for the purpose they were applied, avoid multiple loans and manage the friction of distance. It was observed that management of extraneous shocks required all credit stakeholders to adopt coping strategies. The study recommends government interventionist policy by facilitating uptake of agricultural insurance and subsidizing input costs. Borrowers are encouraged to embrace technology, team up as farming communities to look for markets and affordable inputs, adopt agricultural insurances and adhere to lender and government directives. The lender should create politically neutral circumstances and intensify support through training and performing their lending mandate with efficiency and professionalism.