Intimate partner violence (IPV) is one of the most common forms of violence against women worldwide. The most recent WHO (2013) reports show that nearly one third (30%) of women reported ever experiencing either physical IPV, with regional figures as large as 38%. Risk factors exist at the individual, couple and ecological levels, but no systematic study of models at these various levels has been attempted. The goal of this analysis is to present a systematic evaluation of competing models of IPV risk. Using a Bayesian modeling framework, we compare models at various levels and evaluate their performance in terms of explaining IPV risk. In general, individual-centric models fit the data better than the couple-centric models, and the best fitting model considered woman’s and couple’s effects, plus heterogeneity at the region level. Overall, in the Peruvian setting, partner violence is largely a product of individual and couple-level dynamics.

1. Intimate Partner Violence in
Peru: An assessment of
competing models
Corey S. Sparks
Alelhie Valencia
Department of Demography
Institute for Demographic and Socioeconomic Research
The University of Texas at San Antonio

2. Introduction
• Intimate partner violence (IPV) one of most
common forms of violence against women
worldwide, with between 10% and 71% of women
reporting this experience.
• IPV as a human rights issue
• IPV as a public health issue
• Protective factors:
– Woman’s education, Rural residence, Access to
support networks
• Risk factors:
– History of IPV in family, woman’s age, lower female
status

3. Context of IPV in Peru
• The WHO (2005) found Peru to have the
highest rates of IPV experience in the world
– Between 49 – 62% of women ever experienced,
and between 17 – 25% of women experienced in
the last year**
• Highly urbanized, 77% of population
• High maternal mortality rates
• UN Development Program ranks Peru 77th in
terms of gender equality
– Low levels of women’s education ~47%
– High women’s labor force participation ~68%**

4. Current Project
• Within this context we propose to:
– Systematically compare competing models of IPV
– Focus on three levels of impact
• Women’s characteristics
• Couple’s characteristics
• Ecological/Structural characteristics
– Consider these three levels and allow for
unobserved heterogeneity in IPV risk at both
regional and local levels
• Overall goal is to apply model comparison
methodologies to assess which model(s) best
fit the data

5. Data
• Peru Continuous Demographic and Health Survey 2003-2008
– n=22,926 women responded to domestic violence questionnaire
• Peruvian 2007 Census microdata
– Form structural variables
• DV: Ever-experienced physical violence by partner
• IV:
– Woman – Age, rural residence, education, #children, IPV history
– Couple – Partner’s age, age difference, education difference,
partner’s occupation, low SES HH, decision making (purchasing
& sex)
– Structural - %Women in professional occupations, %women in
labor force, mean children/woman, %women with secondary
education, %urban, % women with purchasing decision making
power

6. Methods
• Approximate Bayesian Hierarchical
Modeling using INLA (http://www.r-
inla.org/)
• Bayesian modeling paradigm allows for
comparison of models using DIC
• Logistic Regression model
– Unstructured random effects for department
– Spatially structured random effects for PSU
• Correlated IPV Risk

7. Results: Multiple-model
comparison

8. Results: Pattern of Risk
• Risk factors:# children, IPV history,
woman’s high status job, partner’s age,
age difference, low HH SES, purchasing
decision making, %of women having
purchasing decision power
• Protective factors: Woman’s age – older,
rural residence
• Woman-level, Couple-level, structural level

9. Spatial
Patterns of
Risk

10. Conclusions
• We see Peru depart from expected patterns
of risk
– No risk factor for education, opposite effect for
women’s status
• Both at woman and couple level
• We see little role of structural level variables
– Only women’s purchasing power
• We do see considerable spatial heterogeneity
in risk
– This shows that rural areas have higher risk on
average, but certain areas of cities have high risk

11. Thank You
• Corey.sparks@utsa.edu
• Lila.valencia@utsa.edu