JavaScript is Disabled
Your browser's JavaScript functionality is disabled. It has to be enabled to use this function of ConfTool.
Here you can find information on how to enable JavaScript
If you have any problems, please contact the organizers at laccei2026@conftool.pro.

Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

Please note that all times are shown in the time zone of the conference. The current conference time is: 24th Aug 2026, 05:33:29am America, Santiago

Daily Overview

Session

27D: Humanitarian Engineering
Virtual location: VIRTUAL: Agora Meetings

Presentation 7

6:58pm - 7:10pm

Multinomial logistic regression in the detection of socioeconomic factors associated with the decision to indebted by natural persons in Colombia.

Jorge Enrique Taboada Álvarez¹, Milton Januario Rueda Varón², Jaime Eduardo Gutierrez Ascón³, Angel Custodio Navarrete Fernandez⁴, Oscar Uriel Mendoza Hernández⁴

1: Universidad Libre - Seccional Bogotá, Colombia; 2: Universidad EAN - (CO); 3: Universidad Nacional de Ingeniería - (PE); 4: Tecnológico Nacional de México/Instituto Tecnológico de San Juan del Río

Personal indebtedness is influenced by multiple socioeconomic, financial, and behavioral factors whose identification is essential for decision-making in financial education and risk management. This study aimed to determine the factors associated with individuals’ decisions to incur debt through the application of a multinomial logistic regression model. A quantitative, explanatory approach was adopted, using data collected through a structured survey administered to a sample of economically active adults. The variables analyzed included sociodemographic characteristics, income level, consumption habits, credit perception, and financial behavior. The results show that income level, employment stability, financial education, and perceived necessity significantly affect the probability of choosing different levels of indebtedness, with statistically significant differences among the analyzed groups. The estimated model demonstrated adequate explanatory capacity and consistent classification of indebtedness profiles. It is concluded that multinomial logistic regression is a robust tool for identifying determinants of indebtedness, providing useful evidence for the design of financial education strategies and policies aimed at preventing over-indebtedness.