Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach

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.

Publication Details

Journal
ArXiv.org
Published
2025-07-30
Primary Topic
Intimate Partner and Family Violence
Type
article
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article

Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach

ArXiv.org
Intimate Partner and Family Violence
article

Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach

article en

Abstract

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.

ArXiv.org
Gender equality
Openalex Percentile: Top 42%
Intimate Partner and Family Violence
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Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach · ArXiv.org (2025) | TGRS Research Map | TGRS