Purpose The purpose of this paper is to examine whether social and/or cultural obstacles faced by African female farmers diminish their accessibility to lending opportunities provided by a commercial microfinance institution; and affect their repayment performance. Design/methodology/approach The underlying data set is comprised of information regarding 9,710 farmers from Madagascar and was provided by the AccèsBanque Madagascar. Logit and Tobit models are applied to determine gender effects on loan accessibility and repayment performance, respectively. Findings Even though female farmers are associated with a lower repayment performance, they have a higher rate of loan application approval compared to male farmers. Research limitations/implications The results are limited to Madagascar and other African countries with similar socio-economic conditions. Social implications Commercial microfinance institutions still provide access to credit for disadvantaged groups, such as female farmers. Originality/value To the best of the authors’ knowledge, this is the first study investigating gender-specific credit access and repayment performance of rural African farmers using a data set from a commercial microfinance institution without a social mission for females.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. In recent years, the application of credit scoring in urban microfinance institutions became popular, while rural microfinance institutions, which mainly lend to agricultural clients, are hesitating to adopt credit scoring. The present study aims to explore whether microfinance credit scoring models are suitable for agricultural clients, and if such models can be improved for agricultural clients by accounting for precipitation. Terms of use: Documents inDesign/Methodology/Approach: This study merges two data sets: (i) 24,219 loan and client observations provided by the AccèsBanque Madagascar and (ii) daily precipitation data made available by CelsiusPro. An in-and out-of-sample splitting separates model building from model testing. Logistic regression is employed for the scoring models. Findings:The credit scoring models perform equally well for agricultural and non-agricultural clients. Hence, credit scoring can be applied to the agricultural sector in microfinance. However, the prediction accuracy does not increase with the inclusion of precipitation in the agricultural model. Therefore, simple correlation analysis between weather events and loan repayment is insufficient for forecasting future repayment behavior.Research Limitation/Implication: The results should be verified in different countries and climate contexts to enhance the robustness.Social Implication: By applying scoring models to agricultural clients as well, all clients can benefit from an improved risk assessment (e.g. faster decision-making).Originality/Value: To the best of our knowledge, this is the first study investigating the potential of microfinance credit scoring for agricultural clients in general and for Madagascar in particular. Furthermore, this is the first study that incorporates a weather variable into a scoring model.

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Coping with asymmetric information plays a major role in successful small business lending. Our purpose is to determine if small business applicants report their income information correctly when requesting a loan. We use a randomised controlled trial bogus pipeline experiment, established during a typical cash-flow analysis of a bank for small businesses in the Philippines. The bogus pipeline approach is commonly applied in social science and aims to increase the rate of truth telling by informing participants that answers will be verified by a lie detector. The experimental data, which include 243 observations of credit clients that are mainly from the agricultural and food value chain, served to identify asymmetric information. Additionally, debtors' repayment behaviour for approved loans was observed by the bank. Our results indicate that loan applicants of the treatment group report lower incomes, an effect which is most pronounced in lower income quantile. Our analyses also reveal higher loan delinquencies in the control group.

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