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Purpose The purpose of this paper is to identify determinants of feasible outcome events (expired with no loss, settled for loss, still performing) and time to event of Farm Service Agency (FSA) operating and farm ownership (FO) loan guarantees. Design/methodology/approach Data on 19,126 FSA guaranteed loans, which were made by various lenders to farmers who have limited ability to obtain loans from normal sources without the Federal guarantee, were collected. Cox proportional hazards models for operating loans (OLs) and FO loans are estimated to identify borrower characteristics, loan characteristics, lender types, and farm and macroeconomic environment factors that influence guarantee outcomes. Findings Loans with different characteristics (loan amount, loan term, lender type, region originated) and assistance programs (Beginning Farmer, Interest Assistance) have differing guarantee outcomes. Contemporaneous variables, in particular delinquency status, have a significant impact on guarantee outcomes. Research limitations/implications All loans were originated in calendar years 2004 and 2005. Since FO loans may have as long as 40 year terms, results are not as robust for FO loans as for OLs. Practical implications Different loan characteristics and macroeconomic conditions significantly influence the occurrence of possible guarantee outcomes and time to the outcomes. Originality/value Guaranteed loans are the primary method of government credit assistance to US farm operators. Data on individual borrowers have been difficult to obtain for much of the life of the guaranteed program because loan applications are held privately. This study provides insight on how various factors drive guarantee performance which is useful to policy makers trying to increase guaranteed loan program efficiency.

PurposeThe purpose of this paper is to empirically investigate the influencing factors of loan demand in agriculture. With the structural changes that agriculture is undergoing and the accordingly higher financing requirements and volumes, the analysis of loan demand in agriculture is of particular interest.Design/methodology/approachDetailed actual loan data at farm level, which is provided by a major German development bank for the agricultural sector, is used for the analysis. The data set covers the period from 2010 to 2014 and consists of 68,430 observations. Due to the data structure, an ordinary least square regression is conducted with the loan amount as the dependent variable. Many explanatory variables are included, such as the interest rate, the intended use of the loan, grace periods, the gross value added (GVA) and the business climate index for agriculture.FindingsAmongst others, the authors find that interest rate, GVA, grace periods and farmers’ business expectations have significant effects on the loan demand in agriculture. According to the results, the interest rate has a significant negative effect, whereas the granted grace periods, the GVA in agriculture and farmers’ business expectations have significant positive effects on the loan demand.Originality/valueThis paper investigates the determinants of loan demand in agriculture in a developed country by using unique and comprehensive data at loan and farm level. Amongst others, elasticities of loan demand in agriculture are determined.

Purpose Evaluating a portfolio of agricultural loans has become an important issue in recent years primarily due to a large number of loan defaults. The purpose of this paper is to investigate the factors influencing credit repayment behavior of farmers in Karnataka. Design/methodology/approach The study is based on secondary data of 590 farmers collected from a private bank in the state of Karnataka, India. Binary logistic regression and multinomial regression analysis was carried out to estimate the probability of non-payment of a loan. Findings The results of the regression confirm a significant relationship between non-repayment of agricultural credit and characteristics of borrowers such as the age, years of banking relationship, yield of the crop, distance to bank branch, size and tenure of the loan, farm size and leverage and efficiency ratio. Practical implications The factors predicted by the model do certainly help in improving the decision-making process in agricultural lending. A rigorous assessment of family responsibilities, farm size, credit-to-asset ratio, interest burden on the farmers and farm income is suggested to reduce the probability of doubtful assets. Originality/value The studies that predict default risk in agricultural loan are limited in India. This is one of the few studies that estimate the determinants of substandard and doubtful categories of credit in a private sector bank.

Purpose The purpose of this paper is to investigate whether negative incentives in the pay-for-performance mechanism would trigger loan officers to strategically reject potentially good loans. If so, what is the feasible solution to alleviate the problem. Design/methodology/approach A framed field experiment was conducted to test loan decision behaviors using loan officers from Rural Credit Cooperatives in Shandong, China. A 2 by 2 between-subject design was adopted to generate variation in incentives and prior information about credit risks. Findings Results showed that loan officers did ration credit by rejecting more loans when facing risks of personal income loss. However, providing risk information about the application pool boosted the approval rate and offset the behavioral responses by a roughly same magnitude. Research limitations/implications Findings in this study suggest that certain institutional settings can result in credit rationing via strategic loan misclassification. Further, information sometimes generates similar effects as those costly incentives or mechanisms that are not implementable in practice. Originality/value This study adopted an innovative monetized experimental design that allows researchers to examine the (otherwise unobservable) trade-offs between Type I and Type II error in loan misclassification as incentives change. In addition, an anchoring prior information treatment is used to solicit the relative power of almost costless information and costly monetary incentives, and to point out a potentially feasible solution.

PurposeThe purpose of this paper is to determine if there are positive microeconomic effects from a state‐funded loan participation program on farm productivity and investment behavior.Design/methodology/approachThe authors take the approach that access to credit solves a liquidity problem. If a credit constraint exists it results in a suboptimal allocation of resources and a reduction in farm output and profitability. A two‐stage regression model approach is used to analyze farmer survey and loan application data. In the first stage, a probit regression model is used to identify the farmers who are likely to be credit rationed. In the second stage, switching regression models are used to observe the effect of credit rationing on farm productivity and on farm investment behavior.FindingsIt is found that there are liquidity effects of credit constraints for a significant share of the beginning and low‐resource farmers who participated in the state‐funded farm loan program. After controlling for various farm and farmer characteristics, the estimated productivity and investment demand equations imply that a 1 percent increase in credit received by credit constrained farmers under the state program increased their gross income by about 0.49 percent, and their investments in depreciable assets by about 0.33 percent.Originality/valueThis paper is the first to apply the switching regression model to a state‐funded farm loan program for the purpose of evaluating the financial impacts on farmer participants.

PurposeThe purpose of this paper is to estimate a three‐equation model of US commercial bank usage of the Farm Service Agency's (FSA) guaranteed operating loan and interest assistance programs. Also, to identify the key farm and banking variables that affect the decision to use loan guarantees and the volume of loans with interest assistance.Design/methodology/approachA triple hurdle, three‐equation system is estimated to model three decisions: to participate in the FSA operating loan program; whether to use interest assistance given the decision to participate in the operating loan program; and then the degree of participation in the interest assistance program. Statistical selection is modeled. Data on almost all commercial banks in the USA from 1995 to 2003 are used in the estimation sample.FindingsStatistical selection is statistically significant so selection must be included in the models. Variables reflecting state‐level characteristics such as farm debt servicing ratio, individual bank loan‐to‐asset ratio, bank size and the general guaranteed loan and interest assistance environment are significant in all three equations. Intensity of interest assistance use varies markedly across states.Originality/valueThe interest assistance program has high subsidy costs and is an important source of support for financially marginal farmers. Scant prior research has investigated this program. The present study also shows that modeling interest assistance usage must be embedded in a larger model to give a complete specification.

PurposeThe purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven‐year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives.Design/methodology/approachA systematic sample of 877 seven‐year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables.FindingsLoan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid‐in‐full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.Research limitations/implicationsThe sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors.Practical implicationsUsing length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling.Originality/valueThe study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.

This study investigates factors related to loan approval, disbursement, repayment, and loan rationing among 1,012 farmers in the Rural Farmers Scheme (RFS), Uganda, between 1987 and May 1995. Results indicate that women had a higher loan approval rate and loan repaid/loan borrowed ratio than men, but lower actual disbursement levels. Loan rationing among women and men was not statistically different, and no justification was found for microfinance institutions discriminating against women in giving loans based on repayment rates. A wide gap exists between loan amounts approved and disbursed. Strategies are outlined for improving the pool of women loan applicants.

Changes within the financial services industry have raised concerns about the ability of agricultural banks to adequately fund local rural development. To address these concerns, the Gramm‐Leach‐Bliley Act of 1999 broadened agricultural bank access to Federal Home Loan Bank (FHLB) financing. Probit analyses indicate that factors related to bank returns and risk management have been significantly associated with agricultural banks joining, and subsequently borrowing from, the Federal Home Loan Bank System.

Research on agribusiness loan success and failure has been limited, and has typically adopted the nonagricultural business approach of using financial ratios to predict loan success, with success rates generally ranging from 60 to 80%. This study uses primary loan data to test the financial and nonfinancial characteristic differences between unsuccessful and successful agribusiness loans. Previous work is augmented by accounting for nonfinancial characteristics, including lender and agribusiness manager experience that resulted in an improved model prediction success rate of 97.5%. A unique result is the identification of a significant combined experience variable comprised of the loan officer’s experience and the agribusiness manager’s experience. Findings further suggest that agribusiness lenders could benefit from incorporating experience into the loan portfolio management process.

A case loan request methodology evaluated how changes in commercial banking structure are influencing credit availability and terms for selected farm business types. Tobit and OLS regressions were used to assess bank characteristic effects on loan amount and rate, and a paired comparisons approach evaluated credit term differences among borrower types. Loan amounts were not significantly influenced by bank characteristics, and loan rates were significantly affected by bank characteristics sporadically. Some differentiation in credit terms among the demographic groups occurred. These results showed no overwhelming evidence in favor of or against the trend toward commercial bank consolidation as it impacts agricultural lending.