O impacto da pandemia na inadimplência bancária de operações rurais no Estado do Maranhão
The impacts of the pandemic on agroindustry call for new management principles and practices, determining a transformation in relationships with customers, suppliers and employees themselves. Agricultural production is associated with the availability of credit to fund crops and investments in the s...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | Brasil |
| Institución: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufc.br:riufc/69593 |
| Acceso en línea: | http://www.repositorio.ufc.br/handle/riufc/69593 |
| Access Level: | acceso abierto |
| Palabra clave: | Crédito rural Logit Inadimplência Risco |
| Sumario: | The impacts of the pandemic on agroindustry call for new management principles and practices, determining a transformation in relationships with customers, suppliers and employees themselves. Agricultural production is associated with the availability of credit to fund crops and investments in the sector. Therefore, a credit risk analysis is important in the industry. This research used primary data extracted from renegotiated rural operations reports in order to estimate a default prediction logit model. According to the results obtained, there is a greater participation of male clients in rural credit, and this factor is not significant in the probability of default. The main factors identified that impact on default were the customer’s level of education, occupation and the number of registered COVID-19 cases. The estimated coefficients show that the higher the level of education of a customer, the greater the probability of default. Customers who have other types of occupation, in addition to agriculture and livestock, have a lower probability of default. The increase in the number of COVID-19 cases during the period studied had a positive impact on default. The predictive capacity of the model by the percent correctly predicted was 71.43%. The model performed better to correctly classify non-defaulting customers. |
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