Determinants of debt in Mexican households: a neural network analysis
In recent years, consumer credit in Mexico has grown in significant ways. Credit cards, which represent 52% of credit in the country, grew by 19% from 2011 to 2018, while the average debt per card increased by 62%. This increase generates problems of over‑indebtedness in Mexican households....
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Problemas del Desarrollo. Revista Latinoamericana de Economía |
| Idioma: | español inglés |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/67463 |
| Acceso en línea: | https://www.probdes.iiec.unam.mx/index.php/pde/article/view/67463 |
| Access Level: | acceso abierto |
| Palabra clave: | endeudamiento financiero crédito al consumo poder adquisitivo banca comercial redes neuronales artificiales financial debt consumer credit acquiring power commercial banking artificial neural networks |
| Sumario: | In recent years, consumer credit in Mexico has grown in significant ways. Credit cards, which represent 52% of credit in the country, grew by 19% from 2011 to 2018, while the average debt per card increased by 62%. This increase generates problems of over‑indebtedness in Mexican households. Using microdata from the National Income and Expenditure Survey (NIES), this research seeks to identify the factors that affect over‑indebtedness in households, and to offer an explanation of said phenomenon using a neural network methodology. The principal determinant of over‑indebtedness in Mexican households is the existence of bank credit, given that this indicates a long‑term transfer of family income to the financial sector. |
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