1. Multiple Indicator Cluster Surveys Data Interpretation, Further Analysis and Dissemination Workshop Child Protection 1

2. Overview Preventing and responding to violence, exploitation and abuse of all children in all contexts. MICS is the largest household survey program in terms of child protection topics covered Several measurement approaches have been developed by MICS/UNICEF 2

3. Overview 18 tables:  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 3

4. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 4

5. Birth registration  CRC: Every child has the right to a name and nationality and the right to protection from being deprived of his/her identity  Birth registration is a fundamental of securing these rights – ensuring the registration of every child at or shortly after birth  MICS indicator: Percentage of children under age 5 whose birth is registered 5

6. 6 Proper customization is needed, to identify the authority in charge of official recording of births and to use the right terminology Concepts/terms might change from one country to another: Birth certificate Civil authorities Registration Overall summary MICS indicator Includes children registered with “civil authorities”

7. Expected patterns in data  Unregistered children are almost always children  From poor, marginalized or displaced families,  Living in rural areas, and  Of mothers with no/low education  Significant differences in birth registration levels may exist between regions and ethnicities within the same country  Levels of registration tend to increase with child’s age  Usually, very small differences are observed in birth registration levels between boys and girls 7

8. Things to consider  Careful analysis of the questionnaire and sample size needed before assessing trends in the level of birth registration  Questions may have been different in past surveys (e.g. due to customization differences, inclusion of deceased children) 8

9. 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 an obstacle in a child’s life for example enrolment in school  Proportion of mothers/caretakers who do not know how to register the child may be very useful for the design of programmatic interventions – however, sample sizes might be too small in some surveys 9

10. Some ideas for further analyses Explore associations in the dataset, for example:  Early childhood services may provide an access point for registration:  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  Compare with population/vital registration system  Further qualitative research to understand reasons for not registering births in those groups where non- registration was high 10

11. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 11

12. Random selection  Random selection of one child age 1-17 per household  If age 5-17, child labour module  If age 1-14, child discipline module administered  Analysis: Sample weight is multiplied by the number of children age 1-17 in each household, and the resulting “weight” normalized  The denominator is equal to the number of all children age 1-17 in the interviewed houeholds 12

13. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 13

14. “Child labour” in MICS 14 AgeEconomic activity Household choresHazardous conditions 5-11At least one hour28 hours or moreAny 12-1414 hours or more28 hours or moreAny 15-1743 hours or more Any Children who fall into any of these cells are included in the numerator of the child labour indicator

15. 15 Economic activity (paid or unpaid) is any of the following: Work on plot / farm / food garden / looking after animals Help in family / relative’s business/ran own business Produce / sell articles / handicrafts / clothes / food or agricultural products Any other activity in return for income in cash or in kind

16. 16 Household chores is any of the following: Fetch water or collect firewood for household use Shopping for household Repair household equipment Cooking / cleaning utensils /house Washing clothes Caring for children Caring for old / sick Other household tasks

17. 17 Numerator: Children age 5-17 years who were involved in economic activities or household chores above the age specific thresholds, or worked under hazardous conditions (any age) last week

18. 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 18

19. Expected patterns  Girls are more likely than boys to be engaged in household chores  Most children are engaged in some form of activity (working children – work below age-specific thresholds) but only a minority of them are engaged in child labour 19

20. Things to look for in the tables  Variations in prevalence of child labour by sex/age of the child and socio-demographic characteristics of their families  Levels of gender specialization by type of activity and intensity of involvement in labour and work by sex  Caution: Seasonality 20

21. Some ideas for further analyses  Child labour and school attendance by sex of the child and other background characteristics: assess the relative impact of child labour and sex on school participation  Relationship between school drop outs and labour  Child labour (family business/household chores) and child discipline 21

22. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 22

23. 23 Psychological aggression: shouting, yelling and screaming at the child, and addressing her or him with offensive names. Physical punishment: Cause child physical pain or discomfort but not injuries: shaking the child & slapping or hitting on hand, arm, leg or bottom, hitting child on face, head or ears, or hitting the child hard or repeatedly. Only non-violent discipline: Taking away privileges, forbidding something child likes, grounding, explaining why behaviour is wrong, giving something else to do

24. 24 …and is not necessarily a parent or caretaker of the selected child Respondent is reporting on disciplinary practices used by any adult household member (not his/her own practices)

25. Expected patterns  Non-violent discipline is more common than violent discipline.  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 25

26. Expected patterns  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 26

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 – at aggregate level 27

28. Things to look out for, things to remember  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 – needs another look at the data  Proportion of children who do not receive any discipline at all should be minimal 28

29. Ideas for further analyses  Types of specific disciplining methods, comparison of severe, moderate etc, overlaps  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  exposure to media 29

30. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 30

31. Early/Child marriage  Violation of human rights, compromising girls’ development, often resulting in early pregnancy and social isolation, with little education and poor vocational training  The right to 'free and full' consent to a marriage (Universal Declaration of Human Rights) – consent cannot be 'free and full' when one of the parties involved is not sufficiently mature to make an informed decisionUniversal Declaration of Human Rights 31

32. 32 Give an indication of the most recent situation Same table also produced for men

33. 33 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…) Same table also produced for men

34. 34 Spousal age differences : produced using the age of the current husband, even if formerly married Contributes to abuse

35. 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  Compare proportions married by 15 and 18 to calculate proportion married between 15 and 18 35

36. Things to look out for, 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 may be problematic  Sample size issues, especially women 15-19 and 20- 24 who are currently married 36

37. Some ideas for further analyses  Child marriage and attitudes towards domestic violence, early childbearing (before 15, 18), contraceptive use  Calculate means, medians 37

38. 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 Women who marry as children are more likely to justify wife-beating 38

39. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 39

40. 40 Indicator is on ANY form of GM/C : forms are, removal of flesh from the genital area, the nicking of the flesh of the genital area and sewing closed the genital area

41. 41 Information on the FGM/C status of daughters: obtained by asking the questions on FGM/C to women age 15- 49 years, on their daughters below the age of 15. May brake down by smaller age intervals, such as 0-1, 2-4 or 10-12, 13- 14, Prevalence do not represent all girls age 0-14 years in the population: Girls whose mothers are, deceased, above age 49, and living in another country are not captured. However, figures in the table very closely approximate the FGM/C status among girls age 0-14.

42. 42

43. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 43

44. 44 Standard questions – may have been customized. Important to calculate the standard indicator Each refer to a domain of gender roles Social acceptability of domestic violence, not necessarily a predictor of the prevalence

45. 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  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 45

46. 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 46

47. Some ideas for further analyses  Compare men’s and women's attitudes (both levels and patterns)  Relationship with attitudes towards violent discipline  Age at first marriage and/or spousal difference  Regular media exposure, household composition 47

48. Overview  Birth registration (1)  Child labour (3)  Child discipline (2)  Early marriage (5)  Female genital mutilation/cutting (3)  Attitudes towards domestic violence (2)  Children's living arrangements & orphanhood (2) 48

49. 49 Children who are not living with at least one biological parent, either because the parents live elsewhere or because the parents are dead

50. 50 New topic in MICS, in response to growing demand and analysis of data on “children left behind”

51. Expected patterns in data  Low prevalence of children with one or both parents dead (generally less than 10 percent)  No significant differences between males and females  Orphanhood levels increase with age  High HIV prevalence and orphanhood correlated 51

52. Some ideas for further analyses  Children's living arrangements and orphanhood might be related to birth registration, malnutrition, school attendance, child discipline or child development  Orphanhood, and the relationship to the household head  Children left behind and their primary caretakers – who is taking care?  Well-being of children left behind 52

53. 53 Thank You