# Colombian Women's Life Patterns: A Multivariate Density Regression Approach

## TL;DR

This paper introduces a Bayesian multivariate density regression model to analyze Colombian women's socio-demographic factors and their effects on fertility, partnership, and work, revealing the negative impact of family violence.

## Contribution

The paper develops a flexible Bayesian density regression model capable of handling mixed responses and complex error structures, applied to study women's life patterns in Colombia.

## Key findings

- Family violence negatively influences women's life choices.
- The model effectively captures nonlinear relationships and complex error features.
- Insights into socio-demographic factors affecting Colombian women.

## Abstract

Women in Colombia face difficulties related to the patriarchal traits of their societies and well-known conflict afflicting the country since 1948. In this critical context, our aim is to study the relationship between baseline socio-demographic factors and variables associated to fertility, partnership patterns, and work activity. To best exploit the explanatory structure, we propose a Bayesian multivariate density regression model, which can accommodate mixed responses with censored, constrained, and binary traits. The flexible nature of the models allows for nonlinear regression functions and non-standard features in the errors, such as asymmetry or multi-modality. The model has interpretable covariate-dependent weights constructed through normalization, allowing for combinations of categorical and continuous covariates. Computational difficulties for inference are overcome through an adaptive truncation algorithm combining adaptive Metropolis-Hastings and sequential Monte Carlo to create a sequence of automatically truncated posterior mixtures. For our study on Colombian women's life patterns, a variety of quantities are visualised and described, and in particular, our findings highlight the detrimental impact of family violence on women's choices and behaviors.