Efectos del endeudamiento de los hogares mexicanos en su ahorro y consumo: Un enfoque de Ciencia de datos

This research aims to group samples of indebted Mexican households that share similar socioeconomic attributes using the k-means algorithm so that nonlinear models are estimated to measure the effects of each group's debt on their savings and consumption. The algorithm was implemented on indebted households included in the ENIGH 2018. As a result, four clústers were formed where one stood out by making up 3.4% of the sample; however, its average indebtedness rate exceeds the average indebtedness rate by 53 percentage points from the rest of the clústers. Modern clústering techniques are recommended to utilize the abundance of official data and develop data-driven economic policies targeted at particular population groups. The originality of this research is based on the use of an unsupervised algorithm for the choice of the studied sample. In conclusion, the households with the highest levels of over-indebtedness are made up of those where the head has higher education, regardless of the income decile to which the household belongs.