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

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

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%**

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

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

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

Results: Multiple-model comparison

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

Spatial Patterns of Risk

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

Thank You

• Corey.sparks@utsa.edu• Lila.valencia@utsa.edu