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 77 th 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/)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