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Measuring the impact of intimate partner violence against women on victims and childrens well-being: An application of Matching Decomposition Techniques.

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Presentation on theme: "Measuring the impact of intimate partner violence against women on victims and childrens well-being: An application of Matching Decomposition Techniques."— Presentation transcript:

1 Measuring the impact of intimate partner violence against women on victims and childrens 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 MDT : non-parametric method – victims and comparison group non- victims Prior research on impacts of IPV Prior research on impacts of IPV Advantages of MDT to gauge impacts Advantages of MDT to gauge impacts DHS data for Peru: prevalence and characteristics of victims/non-victims DHS data for Peru: prevalence and characteristics of victims/non-victims MDT description and results MDT description and results Conclusions Conclusions

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

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

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

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

7 Infant health -Kishor and Johnson (2004) Infant health -Kishor and Johnson (2004) Lower use of antenatal health care services Lower use of antenatal health care services Increased the probability of a non-live birth (miscarriage, abortion or stillbirth) Increased the probability of a non-live birth (miscarriage, abortion or stillbirth) May produce increases in infant mortality rates 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 IPVbut who are nearly identical over a range of measurable characteristics Comparing women who suffer IPV with a control group of women who do not suffer IPVbut 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 require assumptions about functional form required by multinomial logit MDT does not assumes a causal relation between IPV and the outcome variables MDT does not assumes a causal relation between IPV and the outcome variables More precise measurement of the explained and unexplained components of differences More precise measurement of the explained and unexplained components of differences

9 Data DHS Peru (2000)- violence module- nationally representative. DHS Peru (2000)- violence module- nationally representative. All women between 15 and 49 years old who are present in the household 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. 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 The survey did not ask about the timing of the episodes -lifetime violence

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

11 Descriptive Statistics Women victims and non-victims of physical violence Source: Own estimations based on DHS, Peru 2000 *** Significant at 1%, ** significant at 5%, * significant at 10% Difference Age* Education* Punished as Child * Partner Employed Husband Drunk *

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 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 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 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 Step 4: Put the observations of both individuals (the synthetic non-victim and the victim) in their respective new samples

16 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. Unmatched victims (V) Unmatched non-victims (NV) Matched Victims and Non-victims (X, 0)

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 In general, results are not robust to the use of different methods The MD technique is our preferred methodology. 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 shouldnt be used to formulate policy Naive comparisons shouldnt be used to formulate policy

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


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