Andrew Morrison Maria Beatriz Orlando Georgina Pizzolitto September, 2008

1. Measuring the impact of intimate partner violence against women on victim’s and children’s well-being:An application of Matching Decomposition Techniques Andrew Morrison Maria Beatriz Orlando Georgina Pizzolitto September, 2008

2. Outline: IPV Impacts-MDT in Peru • MDT : non-parametric method –victims and comparison group non-victims • Prior research on impacts of IPV • Advantages of MDT to gauge impacts • DHS data for Peru: prevalence and characteristics of victims/non-victims • MDT description and results • Conclusions

3. Types of violence Psychological Physical Sexual Timing of violence Current Lifetime Prior Research Definitions of IPV

4. Methods Comparison of means Simple correlations Bivariate and multivariate logit/ probit regresions Propensity score matching Focus Groups and In-Depth interviews Data Victimization Surveys DHS WHO surveys Prior Research Methods

5. Prior research on the Impact of IPV • Women’s control of reproduction and unintended births • Use of Contraception (by type of violence) • Unintended births • STIs including HIV/AIDS • Evidence for Africa (Kishor and Johnson, 2004) –but direction of causality unknown

6. Women’s mental and physical health • Problems walking, problems carrying out daily activities, pain, memory problems, dizziness, vaginal discharge, emotional distress • Visit a doctor, be hospitalized, or undergo surgery (Nicaragua). Effects are country specific

7. Infant health -Kishor and Johnson (2004) • Lower use of antenatal health care services • Increased the probability of a non-live birth (miscarriage, abortion or stillbirth) • May produce increases in infant mortality rates

8. Advantages of using MDT: attribution, modeling, precision • Comparing women who suffer IPV with a control group of women who do not suffer IPV—but who are nearly identical over a range of measurable characteristics • MDT does not require assumptions about functional form required by multinomial logit • MDT does not assumes a causal relation between IPV and the outcome variables • More precise measurement of the explained and unexplained components of differences

9. Data • DHS Peru (2000)- violence module- nationally representative. • All women between 15 and 49 years old who are present in the household • Focused on violence by intimate partners and relatives (no questions about sexual violence)-physical violence. • The survey did not ask about the timing of the episodes -lifetime violence

10. Prevalence of domestic violence in Peru (2000)Women aged 15-49 currently married or living with a partner Decreases with age Decreases with education Increases with alcohol consumption Source: Own estimations based on DHS, Peru 2000

11. Descriptive StatisticsWomen victims and non-victims of physical violence Source: Own estimations based on DHS, Peru 2000 *** Significant at 1%, ** significant at 5%, * significant at 10%

12. Descriptive Statistics– Outcome Variables Source: Own estimations based on DHS, Peru 2000 *** Significant at 1%, ** significant at 5%, * significant at 10%

13. Descriptive Statistics– Outcome Variables Source: Own estimations based on DHS, Peru 2000

14. Matching Decomposition Technique (MDT) Nopo 2004 Using MDT women who experienced violence are matched to those who did not on the basis of their observable characteristics. The resulting matched females have exactly the same observable characteristics

15. Matching Decomposition Technique (MDT) • Step 1: Select one victim from the sample (without replacement) • Step 2: Select all the non-victims that have the same characteristics x as the victim • Step 3: With all selected in Step 2, construct a synthetic individual whose characteristics are equal to the average of all of them and “match” her to the original victim. • Step 4: Put the observations of both individuals (the synthetic non-victim and the victim) in their respective new samples

16. Unmatched non-victims (∆NV) Unmatched victims (∆V) Matched Victims and Non-victims (∆X , ∆0) The result is the generation of a partition of the dataset. Matched victims and non-victims have the same empirical probability distributions for characteristics x.

17. Variables included in control groups used in the matching decomposition Source: Own estimations based on DHS, Peru 2000

18. Results from MDT- Victims

19. Results from MDT- Children

20. Conclusions • In general, results are not robust to the use of different methods • The MD technique is our preferred methodology. • allows separating the impact of observable and unobservable factors • takes into account that women who do and do not suffer violence and female no violence have characteristics that are distributed differently in their common support (Delta X). • Naive comparisons shouldn’t be used to formulate policy

21. Based on the MD technique, IPV has: • A strong negative impact on victim’s reproductive health • Negative impact on visits to health facilities and use of contraceptives • Negative impact on children’s health with the exception of immunization • Children of women who are victims are more likely to be in school • Strong evidence of intergenerational transmission of violence