Saved in:
Bibliographic Details
Main Authors: Ceballos, Clara Strasser, Haensch, Anna-Carolina
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2507.22592
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866916871101480960
author Ceballos, Clara Strasser
Haensch, Anna-Carolina
author_facet Ceballos, Clara Strasser
Haensch, Anna-Carolina
contents In 2021, psychological violence was the most prevalent form of intimate partner violence (IPV) suffered by women in Mexico. The consequences of psychological IPV can include low self-esteem, depression, and even potential suicide. It is, therefore, crucial to identify the most relevant risk and protective factors of psychological IPV against women in Mexico. To this end, we adopt an ecological approach and analyze the role of a wide range of factors across four interrelated levels: Individual, relationship, community, and societal. We construct a multidimensional data set with 61,205 observations and 59 variables by integrating nationally representative data from the 2021 Mexican Survey on the Dynamics of Household Relationships with nine additional sources. For model estimation and factor selection, we combine model-based boosting with stability selection. Our findings reveal that women who were exposed to violence in childhood and whose partners were exposed to violence in childhood face a heightened risk of psychological IPV. These findings highlight the critical yet often overlooked role of childhood violence exposure for psychological IPV risk. Additionally, we confirm the role of three protective factors previously identified by Torres Munguía and Martínez-Zarzoso (2022) using 2016 data, now validated with 2021 data: women who had their first sex later in life and under consent, who have autonomy in decision-making regarding their professional life and use of economic resources, and who live in a household where housework is done only by male members face a lower risk of suffering psychological IPV.
format Preprint
id arxiv_https___arxiv_org_abs_2507_22592
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach
Ceballos, Clara Strasser
Haensch, Anna-Carolina
Applications
In 2021, psychological violence was the most prevalent form of intimate partner violence (IPV) suffered by women in Mexico. The consequences of psychological IPV can include low self-esteem, depression, and even potential suicide. It is, therefore, crucial to identify the most relevant risk and protective factors of psychological IPV against women in Mexico. To this end, we adopt an ecological approach and analyze the role of a wide range of factors across four interrelated levels: Individual, relationship, community, and societal. We construct a multidimensional data set with 61,205 observations and 59 variables by integrating nationally representative data from the 2021 Mexican Survey on the Dynamics of Household Relationships with nine additional sources. For model estimation and factor selection, we combine model-based boosting with stability selection. Our findings reveal that women who were exposed to violence in childhood and whose partners were exposed to violence in childhood face a heightened risk of psychological IPV. These findings highlight the critical yet often overlooked role of childhood violence exposure for psychological IPV risk. Additionally, we confirm the role of three protective factors previously identified by Torres Munguía and Martínez-Zarzoso (2022) using 2016 data, now validated with 2021 data: women who had their first sex later in life and under consent, who have autonomy in decision-making regarding their professional life and use of economic resources, and who live in a household where housework is done only by male members face a lower risk of suffering psychological IPV.
title Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach
topic Applications
url https://arxiv.org/abs/2507.22592