TY - JOUR
T1 - Efectos del endeudamiento de los hogares mexicanos en su ahorro y consumo
T2 - Un enfoque de Ciencia de datos
AU - Cerda-Guillén, Guillermo
AU - Cruz-Aké, Salvador
AU - Martínez-Palacios, María Teresa Verónica
N1 - Publisher Copyright:
© 2023 Russell Sage Foundation. Lewis-McCoy, R. L’Heureux, Natasha Warikoo, Stephen A. Matthews, and Nadirah Farah Foley. 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - consumption
KW - indebtedness
KW - k-means
KW - savings
UR - http://www.scopus.com/inward/record.url?scp=85159841606&partnerID=8YFLogxK
U2 - 10.21919/remef.v18i2.857
DO - 10.21919/remef.v18i2.857
M3 - Artículo
AN - SCOPUS:85159841606
SN - 1665-5346
VL - 18
JO - Revista Mexicana de Economia y Finanzas Nueva Epoca
JF - Revista Mexicana de Economia y Finanzas Nueva Epoca
IS - 2
ER -