Multiple Indicator Cluster Surveys Data dissemination and further analysis workshop

1. Multiple Indicator Cluster SurveysData dissemination and further analysis workshop Child Protection MICS4 Data Dissemination and Further Analysis Workshop

2. Child Protection Issues in MICS • Birth Registration • Madagascar, Moldova, Sindh (Pakistan) • Child Labour • Belarus, Madagascar, Sindh (Pakistan) • Child Discipline • Early Marriage • Attitudes towards Domestic Violence • Above three Belarus, Madagascar, Moldova, Sindh (Pakistan)

3. Birth Registration - MICS Indicator • # 8.1 Numerator: • Number of children under age 5 whose births are reported registered Denominator: • Total number of children under age 5

4. Expected patterns in data • Unregistered children are almost always from poor, marginalized or displaced families, they live in rural areas and are from mothers with no/low education • Significant differences in birth registration levels may exist between regions within the same country • Very small differences in birth registration levels between boys and girls • Levels tend to increase with child’s age

5. Things to consider • Careful analysis of the questionnaire and sample size is needed before we can conclude that there have been changes in the level of birth registration when comparing with previous surveys • Questions may have been different in past surveys • Important to make sure the questionnaire was properly customized Concepts that might change from one country to another: • “Birth Certificate” • “Registration” • “Civil Authority” • Essential to identify the right authority at the state level in charge of the official recording of births

6. Things to look for in the tables • Proportion of children with a birth certificate (especially if “seen”) as compared to the proportion of children who are registered • If a parent does not have a certificate this may represent another obstacle in a child’s life for example enrollment in school. • Proportion of mothers who do not know how to register the child is very useful for the design of programmatic interventions

7. MICS4 Survey Design Workshop

9. MICS4 Survey Design Workshop

10. Some ideas for further analyses Explore associations in the dataset, for example: • Early childhood services may provide an access point for registration, and the likelihood that the child is registered might be related to whether the birth was assisted by a skilled attendant, or whether the child received vaccinations • Children who are registered by whether the child screen negative or positive to disability questions

11. Some ideas for further analyses Compare with other data sources and further studies, for example: • Comparison and additional analysis needed if there is a significant difference in findings compared to vital registration data. • Further qualitative research to understand reasons for not registering births in those groups where non-registration was high. MICS4 Survey Design Workshop

12. Child Labour – MICS Indicator # 8.2: Child Labour Percentage of children 5-14 years of age involved in child labour • Age 5–11 years: At least 1 hour of economic work or 28 hours of domestic work per week. • Age 12–14 years: At least 14 hours of economic work or 28 hours of domestic work per week. ‘Economic work’ is defined as any paid or unpaid work for someone who is not a member of the household, or other family work

13. Indicators and definitions # 8.3: School attendance among child labourers Percentage of children age 5-14 years involved in child labour activities who attend school # 8.4:Child labour among students Number of children age 5-14 years attending school who are involved in child labour activities

14. Things to remember • Different hour cut-off is used for economic activity, for children 5-11/12-14 in the definition of child labour • The module does not capture the most harmful types of child labour

15. Expected patterns • Children living in rural areas, children from poor families and children whose mothers have no/low education are more likely to be engaged in child labour • Significant differences or levels of child labour may exist between regions within the same country, especially in countries with high levels of economic specialization • Girls are more likely than boys to be engaged in household chores • Most children are engaged in some form of activity (working children) but only a minority of them are engaged in child labour • Different levels of schools attendance between child labourers and children who are not engaged in child labour

16. Things to look for in the tables • Variations in prevalence of child labour by sex/age of the child, as well as by socio-demographic characteristics of their families • Child labour-education relationship is important • Proportions of child labourers in school and variation in school participation by type of activity and intensity • Comparison between the proportion of working children and the proportion of children who are engaged in child labour • Levels of gender specialization by type of activity and intensity of involvement in labour and work by sex

17. MICS4 Survey Design Workshop

19. MICS4 Survey Design Workshop

20. MICS4 Survey Design Workshop

22. MICS4 Survey Design Workshop

23. Some ideas for further analyses • Association between child labour and school attendance by sex of the child and other background characteristics to assess the relative impact of child labour and sex on school participation • School participation for child labourers by the level of education of the mother also - cross tabulated with sex. • Relationship between school drop outs and labour • Child labour (family business/household chores) and child discipline • Child labour by family settings (including family size and number of children)

24. Violent Discipline Indicator: definition • Numerator: Children age 2-14 years who experienced psychological aggression or physical punishment during the 30 days preceding the survey • Denominator: Children age 2-14

25. Definition of violent discipline • Psychological aggression: shouting, yelling and screaming at the child, and addressing her or him with offensive names. • Physical (or corporal) punishment: actions intended to cause the child physical pain or discomfort but not injuries. This include: shaking the child and slapping or hitting him or her on the hand, arm, leg or bottom, hitting the child on the face, head or ears, or hitting the child hard or repeatedly.

26. Expected patterns • Non-violent discipline is more common than violent discipline. Caregivers use non-violent disciplinary practices with the overwhelming majority of children. However, the proportion of children whoaresubject tonon-violent methods only are a minority • Psychological violence is more common than physical violence However, these forms of violence are linked and occur together: most children are likely to experience both physical punishment and psychological aggression • Family wealth and levels of education of household members are significantly associated with attitudes in most countries, but not always with disciplinary practices • Larger variations in attitudes than in practices

27. Things to look for in the tables • Variations in the use of violent disciplinary practices by sex/age of the child, as well as socio-demographic characteristics of their families that may predict which children are most at risk of violent discipline • Variations in the support for physical punishment by sex, education, wealth of the respondent • Comparison between proportion of children who experience physical punishment and proportion of respondents who believe physical punishment is necessary

28. Things to look out in the tables and things to remember • Respondent is reporting on disciplinary practices used by any adult household member (not his own practices) • Previous MICS data were collected from mothers/primary caregivers; differences between surveys can be due to reporting issues • Previous MICS tables presented data on physical punishment separated for moderate and severe • Prevalence of severe punishment has to be lower than prevalence for any physical punishment • Same cases need to remain empty as they are not applicable (ex. education of the household head under the column for attitudes, respondent’s education under prevalence of disciplinary practices) • Proportion of children who do not receive any discipline (i.e. not violent nor non-violent only) should be minimal

30. Some ideas for further analyses • Experience of violent discipline by mother’s/caregiver’s attitudes towards corporal punishment (i.e. see to which extent attitudes influence practices) • Experience of violent discipline by family setting (household size and number of children, present of parents in the household, type of marital union) • Experience of violent discipline and use of alcohol in the household • Attitudes towards physical punishment and attitudes towards domestic violence • Attitudes towards physical punishment and exposure to media • Experience of violent discipline and early childhood indicators (Parent-child interaction, parental involvement with the child, materials and stimulation provided, CDI)

31. Early Marriage - MICS Indicators # 8.6:Marriage before age 15 Proportion of women age 15-49 years who were first married or in union by the exact age of 15 # 8.7:Marriage before age 18 Proportion of women age 20-49 years who were first married or in union by the exact age of 18 #8.8: Young women age 15-19 years currently married or in union

32. Marriage - MICS Indicators 8.9: Polygyny Proportion of women age 15-49 years who are in a polygynous union • Only applicable in countries where polygyny is practiced • Requires customization of questionnaire if not asked (add skips in MA2) 8.10a and 8.10b: Spousal age difference Proportion of women currently married or in union whose spouse is 10 or more years older (a) for women age 15-19 years, (b) for women age 20-24 years

33. Expected patterns • Decline in the prevalence of child marriage, particularly for marriages below age 15 • Significant differences in prevalence of child marriage between women and men • Higher levels of child marriage among the poorest women/men, women/men living in rural areas, women/men with no/low education

34. Things to look for in the tables • Trends in the proportion of women/men married/in union before age 18 and 15 can be obtained by comparing age cohorts (20-24, 25-29, 30-34…) • Percentage of women 20-24 married by 18 and percentage of women 15-19 married by 15 give an indication of the most recent situation • Comparison between the percentages of women/men married by age 15 and by age 18 for the same cohorts/groups of women/men gives you the proportions of women/men married by 15 and those married at age 15 or after but before age 18

35. Things to look out in the tables and things to remember • Some cases in the tables should be empty as they are not applicable • Some values should be the same across the tables • Proportion of women for which age of the partner is unknown • Number of unweighted cases from women 15-19 and 20-24 who are currently married • Spousal age differences are produced using the age of the current husband, even if formerly married

37. Same values as in CP 5

39. Some ideas for further analyses • Child marriage and attitudes towards domestic violence • Child marriage and early childbearing • Child marriage and contraceptive use • Child marriage and knowledge of HIV prevention • Child marriage and number of children

40. Women who marry as children are more • likely to justify wife-beating Percentage of currently married women who agree that a husband is justified in beating his wife if she goes out without telling him, by age at first marriage, DHS 2002-2009

41. Attitudes toward Domestic Violence - MICS Indicator Percentage of women aged 15-49 who state that a husband/partner is justified in hitting or beating his wife in at least one of the following circumstances: • (1) she goes out without telling him, • (2) she neglects the children, • (3) she argues with him, • (4) she refuses sex with him, • (5) she burns the food

42. Expected patterns • Women from the poorest quintiles and women with no education are more likely to justify wife-beating • High level of consistency across regions/groups of women in the pattern of agreement with reasons justifying wife beating, although the extent of agreement can vary greatly (i.e. women are thinking about gender roles and duties when answering these questions) • Neglecting the children and going out without telling the husband are the most common reasons • Women, especially girls, are more likely to justify domestic violence than their male counterparts

43. Things to look for in the tables • Disparities by place of residence/ethnicity/wealth quintile/education • Attitudes by age of the respondent • Attitudes by marital status • Main reasons for justifying wife beating

45. Some ideas for further analyses • Comparison between men’s and women's attitudes towards domestic violence (both levels and patterns) • Attitudes towards domestic violence and attitudes towards violent discipline • Attitudes towards domestic violence and age at first marriage and or spousal difference • Attitudes towards domestic violence by number of children ever born, regular media exposure, residence in an extended family