Mexico 2021: Psychological Intimate Partner Violence Against Women and the Role of Childhood Violence Exposure -- A Machine Learning Approach
Clara Strasser Ceballos, Anna-Carolina Haensch

TL;DR
This study uses machine learning to identify key risk and protective factors for psychological intimate partner violence against women in Mexico, emphasizing the impact of childhood violence exposure and empowerment factors.
Contribution
It introduces a multidimensional dataset and applies model-based boosting with stability selection to analyze IPV risk factors across multiple societal levels.
Findings
Childhood violence exposure increases IPV risk
Women with decision-making autonomy face lower IPV risk
Living in male-only housework households reduces IPV risk
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…
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