Sparks and Valencia PAA 2014 session107
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Transcript of Sparks and Valencia PAA 2014 session107
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