Child Poverty in Zanzibar Poverty in Zanzibar low res.pdf · education, information, sanitation, water and housing. Deprivation is also assessed at the indicator level. Dimension:

REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY i

Child Poverty in ZanzibarReport based on the 2014/2015 Zanzibar

Household Budget Survey

Offi ce of the Chief Statistician Offi ceand

United Nations Children’s Fund

June 2019

CHILD POVERTY IN ZANZIBARii

REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY iii

TABLE OF

CONTENTS

Abbreviations ............................................................................................................................ iv

Acknowledgements ................................................................................................................... v

Foreword .................................................................................................................................. vii

Defi nition of key concepts ....................................................................................................... ix

Key features of the MODA methodology ............................................................................... xi

Executive summary ................................................................................................................... 1

Chapter: 1 Introduction ........................................................................................................... 13

Chapter: 2 Methodology ..........................................................................................................18

Chapter: 3 Monetary and multidimensional poverty ........................................................... 29

Chapter: 4 Multiple deprivations analysis ............................................................................. 42

Chapter: 5 Single deprivation analysis .................................................................................. 58

Chapter: 6 Conclusion and recommendations ...................................................................... 64

Chapter: 7 References .............................................................................................................. 66

Annexures ................................................................................................................................ 68

CHILD POVERTY IN ZANZIBARiv

AF Alkire-Foster

CRC Convention on the Rights of the Child

EA Enumeration Area

HFIAS Household Food Insecurity Access Scale

ICPZ Integrated Child Policy Zanzibar

MPI Multidimensional Poverty Index

MLEEYWC Ministry of Labour, Empowerment, Elderly, Youth, Women and Children

MODA Multiple Overlapping Deprivation Analysis

NPS National Panel Survey

OCGS Offi ce of the Chief Government Statistician

OPM Oxford Policy Management

SDGs Sustainable Development Goals

TSh Tanzanian Shillings

UNGA United Nations General Assembly

UNICEF United Nations Children’s Fund

ZHBS Zanzibar Household Budget Survey

ABBREVIATIONS

REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY v

The Child Poverty in Zanzibar report is the product of a collaboration between the Offi ce of the Chief Government Statistician (OCGS), the United Nations Children’s Fund (UNICEF), and numerous organizations and individuals whose contributions I would like to acknowledge.

First and foremost, I wish to acknowledge the valuable support of Mr. Sebastian Silva Leander, Monet Durieux and Cora Mezger from Oxford Policy Management (OPM), who led the analysis and the preparation of this report from the inception stage. It has been very helpful to build in-house capacity for child poverty measurement.

I am also indebted to all members of the Child Poverty Technical Working Group who provided guidance on the construction of child deprivation indicators for Zanzibar. The group also provided inputs and comments during the process of preparing and fi nalizing this report. The group members are: Abdul-majid Jecha, Hashim Uzia, Fahima Mohammed, Abdalluh Othama Makame, Ahmad Hamza Mohammed, Nour Abdulwahid, Sabina Raphael, Bakar Khamis Kondo, Fadhil Ali Hassan, Khamis Ahmada Shauri, Hamisa Suleman, Mwanajuma Ali Suleiman, Ali Juma Sheha and Khalid Choum Haji of OCGS; Mwanangaza M. Hafi dh, Mbwana O. Mbwana, Maulid S. Kombo of OCGS Editorial Boards; Mashavu Khamis Omar, Mohamed M. Mrisho, Salma Hamad, Shufaa A. Khamis, Salma R. Makame of Zanzibar Planning Commission (ZPC); Zainab S. Mbarouk, Halima M. Ali and Mohamed J. Makame of MLEEYWC; Munira A. Mohamed of Department of Disability Affairs, Second Vice President’s Offi ce; Fatma K.H. Haji of Ministry of Health; Ali Juma Sheha of Ministry of Education and Vocation Training; Idrissa A. Shamte of Sumeite University; Mohamed A. Jiddawy of Tunguu-Zanzibar University; Paul Quarles van Ufford, Vu Manh Hong, Montserrat Pejuan, Jaewhi Kim and Edith Mbatia of UNICEF Tanzania.

Finally, I would like to thank UNICEF Tanzania for providing technical and fi nancial support to prepare this report, which unveils the richness of the Zanzibar Household Budget Survey 2014/15 data that was used to construct the indicators for analysis.

ACKNOWLEDGEMENTS

CHILD POVERTY IN ZANZIBARvi

REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY vii

Child Poverty in Zanzibar is the second consolidated child poverty report for Tanzania to be produced; the fi rst was published in 2016. The analysis uses data from the 2012/2013 National Panel Survey (NPS).

The Child Poverty report includes indicators of non-monetary deprivation among children aged 0–17 years living in households. This methodology for measuring deprivation captures issues of importance that affect the well-being of children during childhood and, more importantly, which impact their well-being in adulthood. The method (known as Multiple Overlapping Deprivation Analysis – MODA) was introduced by the United Nations Children’s Fund (UNICEF) and adapted by a team of in-country technicians from both governmental and non-governmental organizations through several Technical Working Group (TWG) meetings. The Offi ce of Chief Government Statistician (OCGS) led the TWG. Through meetings and work sessions with consultants and technicians from both the OCGS and MDAs, TWG members were capacitated to carry out the analysis without the need for involving an external consultant in the future.

The child poverty measurement used in this report complements the traditional method of measuring poverty through the lens of a household’s expenditure. This approach provides an avenue to continue analysing poverty in Zanzibar in a way that is relevant to all population groups, including children. It is anticipated that future analyses of poverty in Zanzibar, especially those based on the 2019/20 Household Budget Survey (HBS) data, will consider the MODA approach to paint an updated and more accurate picture of the state of child poverty in Zanzibar. This will contribute to sustain the inclusion of such a module in every household survey and enable similar analyses in the future.

The information presented in this report seeks to raise awareness and generate a deeper understanding of the multiple dimension of poverty among governmental and non-governmental stakeholders who develop plans, policies and programmes that address and aim to improve the well-being of children. Given the inclusion of child poverty in the Sustainable Development Goals (SDGs), this report is well placed to serve as a point of reference for both national and international strategies such as Zanzibar Poverty Reduction Strategies (ZPRS), aid in SDGs monitoring and poverty monitoring nationally, and inform key sector programmes and policies.

Mayasa M. Mwinyi Chief Government Statistician, Zanzibar

Offi ce of the Chief Government StatisticianJune 2019

FOREWORD

yasa M Mwi

CHILD POVERTY IN ZANZIBARviii

REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY ix

KEY CONCEPTS

Adult equivalent is a statistical adjustment used in poverty, welfare and consumption analyses because household members have different consumption needs based on their age and sex. In the ZHBS consumption analysis which was adopted in this report as provided by OCGS, the adult equivalent scale ranks males and females who are 19–59 years old as 1.0 and 0.88, respectively. Boys and girls under 10 years receive equal ranking for their age, ranging from 0.40 for 0–2 years old to 0.76 for 9–10 years old. However, male and female ranks increase from 0.80/0.88 for 11–12 years old, to 1.00/1.00 for 13–14 years old, to 1.20/1.00 for 15–18 years old, respectively.

Basic needs poverty refers to the lack of an adequate income necessary to satisfy essential non-food needs – such as clothing, energy and shelter – as well as food needs. Unless otherwise specifi ed, when the term ‘monetary poverty’ is used in this report, it refers to basic needs poverty at the household level. The ZHBS 2014/15 poverty line used for analyses is TSh 53,377 per adult per month. When this report analyses monetary poverty, it refers to children in households falling below the basic needs poverty line.

Consumption is the total value of food and non-food goods and services used, including the estimated value of non-purchased items, such as those produced by a household or received as gifts. In this report, consumption is measured in Tanzanian Shillings per month per adult equivalent.

Deprivation is the lack or denial of a basic need or right. The eight broad categories of deprivation which are examined in this report are insuffi cient nutrition, health, protection, education, information, sanitation, water and housing. Deprivation is also assessed at the indicator level.

Dimension: In this report, dimensions refer to different aspects of well-being and deprivation, such as nutrition, health, protection, education, information, sanitation, water and housing.

Food poverty refers to the lack of an adequate income necessary to satisfy basic food needs – usually defi ned on the basis of minimum calorie requirements. Often called extreme poverty, the Cost of Basic Needs method was used to estimate the food poverty line (ZHBS 2014/15). The food poverty line is set at TSh 38,071 per adult per month.

CHILD POVERTY IN ZANZIBARx

REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY xi

This report adapted the Multiple Overlapping Deprivation Analysis (MODA) methodology developed by UNICEF’s Offi ce of Research in order to assess the nature and extent of poverty and deprivation experienced by children (ages 0–17 years) in Zanzibar. MODA provides a comprehensive approach to aspects of child poverty and deprivation, employs child-specifi c indicators and produces a child deprivation scale with the particular contribution of analysing to what extent deprivations overlap. MODA has four unique features which distinguishes its approach from other poverty analysis (de Neubourg et al. 2012), namely:

that the child is the unit of analysis and not the household;

it uses a life-cycle approach based on age groups which assist in identifying changing needs across childhood;

the overlapping deprivation analysis in MODA provides insights into which aspects of child poverty can be experienced simultaneously; and

the approaches helps in identifying those children most deprived in terms of geographical location and social situation, which in turn can assist policymakers to better target poverty alleviation interventions.

The MODA approach consists of fi rst identifying a set of dimensions which align very closely with children’s rights as defi ned within the United Nations (UN) Convention on the Rights of the Child (UN 1989). Next, specifi c indicators are chosen to capture potential deprivation within each dimension. A child is considered deprived in a dimension if he or she is deprived of one or more indicators in that dimension. For example, a child is considered to be deprived of water if he or she does not have access to clean water, and/or if a household member needs to walk 30 minutes’ roundtrip to obtain water, or both. The bulk of the analysis in this report is concentrated on deprivations across multiple dimensions. These are measured by a simple count in which each dimension has an equal weight. This is in line with the principle that given that each dimension refl ects a human right, all of them are considered of equal importance (de Neubourg et al. 2014). The logic behind this approach is that one cannot trade one human right for another, which is a fundamental concept in a rights-based approach to programming and policy analysis.

This analysis employs MODA’s three multidimensional deprivation indices (de Milliano and Plavgo 2014a). The fi rst of these measures is the multidimensional deprivation headcount ratio (H), which determines the proportion of multidimensionally deprived children per given cut-off point – for example, the percentage deprived of at least three out of six dimensions. As the number of dimensions measured will vary by the age of a child, making comparisons across age groups diffi cult,

MODA METHODOLOGYKEY FEATURES OF THE

CHILD POVERTY IN ZANZIBARxii

we also analyse the headcount according to the proportion of dimensions a child is deprived in, for example, half or all relevant dimensions. The second measure determines the average intensity of deprivation among deprived children (A), showing, for example, what percentage of deprived children experience all possible deprivations. This measure is in some aspects comparable to the poverty gap when studying monetary poverty. While the poverty gap gives an idea of how far the poor are from the poverty line, the adjusted deprivation headcount gives an idea of how far below the multidimensional poverty cut-off deprived children are. The third measure combines the aspects of incidence and breadth of deprivation into one number: the adjusted multidimensional deprivation headcount ratio, M0. It is calculated by multiplying the headcount with the average Intensity (M0 = H x A), leading to a rate between 0 and 1. The M0 does not have a meaning on its own, so it is most useful in comparing populations.

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REPORT BASED ON THE 2014/2015 ZANZIBAR HOUSEHOLD BUDGET SURVEY 1

The objective of this study is to better understand the drivers of child poverty in Zanzibar across all its dimensions in order to assist policymakers in developing interventions that target the most vulnerable children. The development of child poverty measures is well advanced in Zanzibar. Monetary poverty has been analysed for many years based on the Zanzibar Household Budget Survey (ZHBS), which is conducted every fi ve years. For the United Republic of Tanzania, a Child Poverty report applying the Multidimensional Overlapping Deprivation Analysis (MODA) approach to examine multidimensional deprivations was produced in 2016, using data from the 2012/13 National Panel Survey (NPS). Shortly thereafter, a disaggregated report was produced, mapping levels of multidimensional child poverty down to the district level, using data from the 2012 Census. However, the NPS survey has a very limited sample for Zanzibar, which does not allow for tailoring the analysis to the country, including further disaggregation within Zanzibar, and the 2012 Census analysis was constrained by a limited census questionnaire.

To facilitate the institutionalization of child poverty reporting within the routine monitoring and reporting work of the OCGS, a separate report has been produced for Zanzibar, using an appropriate dataset to allow for fi ner-grained analysis of child poverty. The 2014/15 ZHBS allows for tailoring the indicators included in the analysis to the Zanzibar context, as well as disaggregating the data for the 10 districts within Zanzibar,1 providing further detail to policymakers to better develop child poverty alleviating interventions at the local level.

The UNICEF MODA is a methodology for measuring child poverty rooted in the Convention of the Rights of the Child, in which indicators are measured at the level of the child, following a life-cycle approach so as to capture age-specifi c indicators. The aim of MODA is to measure the multidimensional aspects of child deprivation and poverty. The dimensions considered in the MODA analysis include health, nutrition, water, sanitation, education, information, protection and

EXECUTIVE SUMMARY

1 When the Zanzibar HBS 2014/2015 was conducted, there were 10 districts in Zanzibar (Kaskazini A, Kakazini B, Kati, Kusini, Magharibi, Mjini, Wete, Micheweni, Chake-Chake, Mkoani). At present, Zanzibar has a total of 11 districts after the split of Magharibi district to Magharibi A and Magharibi B.

CHILD POVERTY IN ZANZIBAR2

housing. The National Multiple Overlapping Deprivation Analysis (N-MODA) allows for the selection of country-specifi c choices and the use of national datasets to provide additional indicators for the analysis.

The analysis shows that 66.1 per cent of children in Zanzibar are deprived in three or more dimensions, while only a few children are deprived in all dimensions (six dimensions for children aged 0–4 years and eight dimensions for children between the ages of 5–17 years).

Table 1: Percentage of children by number of deprivations and age

Number of dimensions deprived

0–23 months*

24–59 months*

5–13 years 14–17 years 0–17 years

At least 1 96.4 96.3 98.2 97.8 97.5

At least 2 81.6 79.2 90.6 89.3 87.0

At least 3 52.2 52.4 73.4 71.6 66.1

At least 4 11.0 8.9 48.7 48.0 35.8

At least 5 0.6 0.7 21.7 25.2 15.3

At least 6 0.0 0.0 5.3 9.4 4.3

At least 7 - - 0.9 1.6 0.8

8 - - 0.0 0.0 0.0

Source: ZHBS 2014/2015*Measured over six dimensions of well-being

Figure 1: Percentage of children deprived in three or more deprivations, by district

43.0

47.2

63.0

68.6

78.3

81.0

81.0

84.0

93.8

81.4

Magharibi

Mjini

Kusini

Kati

Wete

Mkoani

Kaskazini A

Kaskazini B

Chake Chake

Micheweni

Percentage of children

0 20 40 60 80 100

Source: ZHBS 2014/2015


Table 1 highlights that 66.1 per cent of all children were deprived in three of more dimensions, compared to 71.6 per cent of children aged 14–17 years and 52.2 per cent of children below the age of 24 months (0–23 months). There are, however, large variations by district, with 93.8 per cent of children in Micheweni deprived in three or more dimensions, compared to less than 43.0 per cent in Magharibi and 47.2 per cent in Mjini.

While one observes a clear correlation between monetary and multidimensional poverty, the overlap is not perfect. When choosing three or more dimensions as the multidimensional poverty cut-off, only 30 per cent of children are both monetarily and multidimensionally poor, 5 per cent are living in money-poor households but are not multidimensionally poor, while 36 per cent of children suffer from multidimensional poverty but are not monetarily poor. According to this defi nition, 29 per cent of children are neither monetarily or multidimensionally poor. The overlap between the two measures highlights that deprivation exists even amongst children who live in households whose consumption levels are above the basic needs poverty line. It is thus important to focus on both measures to identify vulnerable children who are deprived in aspects other than monetary poverty.

Figure 2: Overlap between monetary and multidimensional poverty (deprived in three or more dimensions)

Deprived in three or more dimensions: Poor < TSh 53,377Source: ZHBS 2014/2015

The largest gaps between monetary and multi-dimensional poverty were found for children who lived in households where the household head was below 25 years old, households where the head was an own account agricultural worker, households in rural areas and households with less than fi ve household members. In all these cases, children have higher deprivation levels than their consumption levels would predict. In contrast, children living in larger households, in urban areas, and where the household head is an employee or employer tend, on average, to have fewer deprivations than their predicated consumption levels would suggest.

The relationship between consumption levels and deprivations was also analysed using a graph which plots the relationship between the percentage of children deprived and consumption for the eight dimensions of well-being (nutrition, health, water, sanitation, education, protection,

Deprived >= 3 dimensions Money poor

36305


information and housing) by area of residence. The red line shows the relationship in rural areas, and the dotted blue line in urban areas. The steeper the line, the stronger the relationship between consumption and deprivation for that particular dimension. The vertical line in red represents the basic needs poverty line. Children to the left of this line live in money-poor households while children to the right are not money-poor.

Figure 3: Percentage of children deprived by dimension and adult equivalent consumption

Notes: Base population: all children.NUT – Nutrition; HLT – Health; PRO – Protection; WAT – Water; SAN – Sanitation; HOU – Housing; COM – Communication/information; EDU – EducationSource: ZHBS 2014/2015 (author’s calculations)

NUT100

80

60

40

20

0

0 100000 200000

Consumption

Urban Rural

Perc

en

t*

100

80

60

40

20

0

Perc

en

t*

Consumption

0 100000 200000

HLT

Urban Rural

PRO

Urban Rural

100

80

60

40

20

0

Perc

en

t*

Consumption

0 100000 200000

Urban Rural

100

80

60

40

20

Perc

en

t*

0 100000 200000

Consumption

COM

Urban Rural

0 100000 200000

Consumption

60

50

40

30

Perc

en

t*

EDU

100

80

60

40

20

0

Perc

en

t*

0 100000 200000

Consumption

WAT

Urban Rural

SAN100

80

60

40

20

0

Perc

en

t*

0 100000 200000

Consumption

Urban Rural

100

80

60

40

20

0

Perc

en

t*

0 100000 200000

Consumption

HOU

Urban Rural


While higher consumption levels were associated with lower levels of deprivations, this is not the case for all dimensions. In particular, in the case of nutrition, sanitation and housing deprivations, fi nancial barriers to access probably play a very important role in preventing money-poor children from achieving positive outcomes. In contrast, the lines for the health, protection and water dimensions are almost fl at, indicating a very weak to no link between consumption levels and deprivation. For health and protection, deprivation levels are very low across the entire consumption-level spectrum. For both dimensions, one also observes that there is virtually no difference between rural and urban areas in terms of levels of deprivation at any level of consumption. This suggests important achievements in ensuring access to healthcare, birth registration of children and protection from child labour and child marriage throughout Zanzibar. For the education and information dimensions, the steeper slope of the line suggests a strong relationship between monetary and multidimensional poverty, with the slope steepest below the poverty line.

The analysis fi nds that the highest levels of deprivation amongst children in Zanzibar are found in the areas of sanitation (84 per cent), housing (71 per cent ) and nutrition (68 per cent) while deprivation rates in water, protection and health are below 10 per cent.


Figure 4: Children deprived in at least one indicator per dimension, by age group

The percentage of children deprived in at least one indicator by dimension and age group fi nds that across all four age groups, the highest levels of deprivation are found in the area of sanitation, followed by housing and nutrition. Younger children (0–23 months) form the highest percentage deprived in sanitation (84.7 per cent), while in the area of housing, the highest percentage of children deprived (72.8 per cent) is the 5–13 years age group. For nutrition, the highest deprivation is amongst children aged 24–59 months.

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0% Health Protection Water Education Information Nutrition Housing Sanitation

Age 0-4 5-6 7-8 9-11 12-15 16-17

Perc

en

t o

f C

hil

den

3.7%8.1% 9.8%

30.4% 31.9%

67.5%71.3%

84.0%


Figure 5: Children deprived in at least one indicator per dimension, 0–23 months

Figure 7: Children deprived in at least one indicator per dimension, 5–13 years

Figure 6: Children deprived in at least one indicator per dimension, 24–59 months

Figure 8: Children deprived in at least one indicator per dimension, 14–17 years

Notes: Base population: all childrenSource: ZHBS 2014/2015 (author’s calculations)


Figure 9: Children deprived in sanitation, by district

Sanitation

Housing

Nutrition

Communication

Education

Water

0 20 40 60 80 Percentage of children deprived

84.4

69.5

69.1

9.5

2.0

3.1

Sanitation

Housing

Nutrition

Communication

Education

Water

0 20 40 60 80

Percentage of children deprived

84.7

70.8

68.7

9.5

2.1

6.1

Sanitation

Housing

Nutrition

Communication

Education

Water

Protection

Health


84.1

72.8

67.3

48.8

46.3

9.4

6.9

3.2

Sanitation

Housing

Nutrition

Communication

Education

Water

Protection

Health


82.8

69.2

65.6

11.5

45.4

43.5

22.1

2.8

Kaskazini A

Kaskazini B

Kati

Kusini

Magharibi

Mjini

Wete

Micheweni

Chake Chake

Mkoani

87.3

91.9

74.9

88.2

75.3

74.7

98.3

92.4

89.5

93.8


However, analysing the indicators used to construct the dimensions is important to identify the drivers of dimensional deprivation. Some dimensions tend to be dominated by one or two indicators. An example is the sanitation dimension, where the percentage of children without access to handwashing facilities was 80 per cent. Across the 10 districts, the overall deprivation incidence rate was also highest in sanitation, ranging from a high of 98 per cent in Micheweni to a low of 75 per cent in Magharibi.

Figure 10: Dimensional deprivation, by district

Kaskazini A

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

87.3

73.1

90.1

55.8

50.4

10.9

3.8

15.3

Kaskazini B

0 20 40 60 80 100

91.9

80.5

89.1

49.8

47.1

9.9

3.9

8.0

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Kati

0 20 40 60 80 100

74.9

72.0

83.9

47.5

47.6

9.8

2.8

14.7

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Kusini

0 20 40 60 80 100

88.2

64.3

73.1

42.2

30.9

5.7

3.4

2.8

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Magharibi

0 20 40 60 80 100

74.7

53.3

44.2

36.1

38.5

5.4

2.8

4.8

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Mjini

0 20 40 60 80 100

75.3

56.7

51.7

39.2

35.4

6.7

2.4

2.3

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health



Micheweni

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

98.3

94.0

88.3

64.1

65.1

13.1

5.9

30.9

Chake Chake

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

92.4

85.8

89.7

56.9

47.2

9.8

5.3

14.1

Mkoani

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

89.5

90.0

68.8

56.0

51.2

9.0

3.6

6.9

Wete

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

93.8

86.4

70.9

55.3

50.4

6.6

5.0

6.0

Children can suffer from several vulnerabilities simultaneously; understanding how these vulnerabilities interact can assist in targeting policies to the most vulnerable children.

The analysis fi nds that children in Zanzibar who live in rural monetarily poor, female-headed households where the number of household members exceeds eight are more vulnerable to deprivations as compared to children who do not present any of these vulnerabilities.

The report also considers the overlap in dimensions as it provides important insights for policymaking. For example, the dimensions of protection and education might be interlinked as children may be out of school as they work to contribute to the family’s income. This would require interventions to address child labour as policies addressing access to schooling may not be suffi cient. A high overlap of dimensions may point to the need for a policy which addresses structural issues simultaneously rather than sector-specifi c interventions. Understanding the correlation and overlap in specifi c dimensions can therefore also provide useful insights, in particular for the design of interventions to target specifi c groups. The analysis fi nds a high degree of overlap between nutrition, sanitation and housing, with 48 per cent of children deprived in all three dimensions. Sixteen per cent of children are deprived in both the education and information dimensions.

(contd.)

Source: ZHBS 2014/2015 (author’s calculations)


Figure 11: Share of children with three or more deprivations, by selected characteristics of household and household head

Note: Red line represents the percentage of children who are multi-dimensionally poor. Hhsizegr = household size; agehead = age of the household head; loc = location of household, either urban or rural; emphead = employment status of household head; femaleh = sex of household head.Source: ZHBS 2014/2015 (author’s calculations)

hhsizegr agehead loc emphead femaleh

Female

MaleNon-agri

Employee

AgricRural

Urban

65+>8

<5

<5

5-8

50-64

35-4925-34

80

70

60

50

40

Perc

ent

Note: A child is considered to be deprived in a given dimension if she or he is deprived in at least one indicator in that dimension.Source: ZHBS 2014/2015 (author’s calculations)

Note: A child is considered to be deprived in a given dimension if she or he is deprived in at least one indicator in that dimension.Source: ZHBS 2014/2015 (author’s calculations)

Figure 12: Overlap in dimensional deprivations: nutrition, sanitation and housing

Figure 13: Overlap in dimensional deprivations: education, information/communication and protection

Nutrition Sanitation Housing

Education Communication

Protection

10

15

4

5

3 48

12

15

2

11

12

3

13


Figure 14: Intensity of deprivations, by household characteristics

The report also provides fi ndings related to the intensity of deprivation, i.e., the share of dimensions in which a child is deprived calculated by dividing the number of deprivations experienced by a child by the total number of dimensions across which well-being is measured. Analysing the intensity of deprivation ensures that the fi gures are comparable for children under fi ve, whose well-being is only measured across six dimensions, with children over the age of fi ve, whose well-being is measured across eight dimensions.2

Children living in households where the household head is employed in agriculture have the worst outcome of any group, being deprived on average in about half of all dimensions, followed by children living in rural households (0.48). Children in large households with more than eight members are deprived in a slightly larger share of dimensions (0.44) compared to smaller households.

Based on individual mother and child characteristics, children of mothers who have more than primary education (0.36) are deprived in a smaller share of deprivations relative to children of mothers who have no education (0.51). Whether the mother is married or has no spouse also seems to make a difference, as children with married mothers experience slightly higher shares of deprivation. Moreover, children aged 0–4 years have lower deprivation levels than children in older age groups.

2 Interpretation of the results for different age groups is still problematic due to the fact that different indicators are being used for different age groups. Thus, the term ‘comparability’ here is used only to refer to direct numerical comparison, not a comparison of levels of well-being.

Note: Note: Red line represents the intensity of deprivations of children aged 0–17 years who are multidimensionally poor.Hhsizegr = number of household members; agehead = age of household head; loc = location; female = sex of household head.Source: ZHBS 2014/2015 (author’s calculations)

hhsizegr agehead loc femaleh

Female

Male

Rural

Urban

<25

>8

<5

5-8

+65

35-49

25-34

Inte

nsity

of

depr

ivat

ions

.5

.45

.4

.35

.3

50-64


Table 2 sets out a summary of the key data on the incidence of deprivation by age groups. Based on monetary poverty measures, the highest percentage of poor children can be found in the 14–17 years age group (40 per cent) compared to 35 per cent of monetary poor when considering all children (0–17 years). The percentage of children not experiencing any multidimensional deprivation is low across all age groups, while a very low percentage of children are deprived in all MODA dimensions. Based on a cut-off of three or more dimensions, 66.1 per cent of all children aged 0–17 years in Zanzibar are multidimensionally poor, ranging from a high of 73.4 per cent for children between 5–13 years to a low of 52.2 per cent for the youngest age group (0–23 months of age).

Multidimensional poverty is higher in rural areas across all age groups and amongst male children aged 0–23 months and 14–17 years compared to female children. Based on individual dimensions, deprivation incidences are highest in the sanitation and nutrition dimensions; deprivation in sanitation is highest amongst children aged 0–23 months (84.7 per cent) and 24–59 months (84.4 per cent), and similarly, in nutrition, amongst these two age groups: 0–23 months (68.7 per cent) and 24–59 months (69.1 per cent). The lowest dimension deprivation is observed for health across all age groups.

Note: Red line represents the intensity of deprivations of children who are multidimensionally poor. Analytage = age for MODA analysis; disabled = no disability or at least one disability; orphanany = child living with mother or father or neither parent; edumum = education level of mother: no education/primary/ more than primary education; marriedm = mother is married or has no spouse.Source: ZHBS 2014/2015 (author’s calculations)

Figure 15: Intensity of deprivations, by mother’s/child’s characteristics

analytage disabled orphanany edumum marriedm

no spous

married

primary

more tha

no educa

yesno

>8

<5

14-17

5-8

no disabone+ dis

.5

.45

.4

.35

Inte

nsity

of

depr

ivat

ions

5-89-3

0-4


Table 2: Incidence of dimensional deprivation by age groups (%)

Deprivations 0–23 months*

24–59 months*

5–13 years 14–17 years 0–17 years (All)

Monetary poor 27.2 30.7 36.9 40.1 34.7

Not experiencing any deprivations

3.59 3.67 1.77 2.19 2.47

All dimensions 0.0 0.0 0.0 0.0 0.0

At least one dimension

96.4 96.3 98.2 97.8 97.5

At least three dimensions

52.2 52.4 73.4 71.6 66.1

At least three dimensions (rural)

67.4 70.0 86.1 85.2 79.9

At least three dimensions (urban)

28.5 24.4 54.2 55.0 45.6

At least three dimensions (boy)

53.0 50.4 73.0 73.0 65.9

At least three dimensions (girl)

51.5 54.6 73.9 70.4 66.2

Nutrition** 68.7 69.1 67.3 65.6 67.5

Health** 6.1 3.1 3.2 2.8 3.7

Education** - - 46.3 43.5 45.5

Protection** 2.1 2.0 6.9 22.1 8.1

Information** - - 48.8 45.4 47.8

Water** 9.5 9.5 9.4 11.5 9.8

Sanitation** 84.7 84.4 84.1 82.8 84.0

Housing** 70.8 69.5 72.8 69.2 71.3

*Measured over six dimensions of well-being**Dimensional deprivations: A child is considered to be deprived in a given dimension if she or he is deprived in at least one indicator in that dimension.Source: ZHBS 2014/2015


1.1 Background and contextZanzibar is part of the United Republic of Tanzania and comprises two islands – Unguja and Pemba. The country has experienced robust economic growth averaging 6.1 per cent3 between 2007 and 2014, which resulted in social investment in a wide variety of areas, including education, healthcare and infrastructure. However, despite high economic growth rates, poverty and inequality levels remain elevated. Basic needs poverty,4 while declining from 34.9 per cent to 30.4 per cent between 2009/2010 and 2014/2015, remains high. While geographical disparities mean that rural households still face higher poverty rates relative to urban areas – especially related to food insecurity and poor access to social services in rural areas – Zanzibar has experienced a movement of the rural population to urban areas in recent years, placing a strain on infrastructure and services in these areas (ZHBS 2014/15). In addition, some of the rural areas have also become more urbanized. Children remain vulnerable due to inadequate water, hygiene and sanitation facilities, with 31.5 per cent of children in rural areas still burying stool as a means of human waste disposal (ZHBS 2014/15). Child marriages, poor schooling outcomes and violence against children both at home and in communities remain areas of concern.

However, Zanzibar has a youthful population with a median age of 18.4 years and with 49 per cent of the population below the age of 18 years (ZHBS 2014/15). This provides the country with the opportunity to capitalize on its demographic dividend if the right investment in education is made and young people are assisted to enter the labour market and become productive citizens.

The objective of this report is to provide an overview of child poverty in Zanzibar based on the Zanzibar Household Budget Survey of 2014/15 and build on the analysis undertaken by the Offi ce of the Chief Government Statistician (OCGS). Using the UNICEF Multiple Overlapping Deprivation Analysis (MODA) approach to measuring and analysing child poverty, a broad range of factors which drive poverty in the context of Zanzibar can be examined. The results will assist policymakers

INTRODUCTION CHAPTER: 1

3 Directorate of Financial Stability, Bank of Tanzania, Financial Stability Report, March 2015.4 Offi ce of the Chief Government Statistician, Government of Tanzania, 2014/15 Zanzibar Household Budget Survey Report. Basic needs poverty

based on the basic needs poverty line of TSh 53,377.


in Zanzibar in better tailoring policies to address child poverty while also aligning with the reporting requirements of the Sustainable Development Goals, in particular, Target 1.2 of Goal 1 which aims to halve poverty in all its dimensions amongst men, women and children of all ages, but also other SDGs linked to health, education, water and sanitation, and gender equality.

The MODA approach aligns well with the priorities of the government. Improving the well-being of children features strongly in the Zanzibar Strategy for Growth and Reduction of Poverty (2016/2017–2020/2021) (known as MKUZA III). In addition, the Integrated Child Policy Zanzibar (ICPZ, 2016), drafted by the Ministry of Labour, Empowerment, Elderly, Youth, Women and Children (MLEEYWC), is an overarching policy aimed at guiding all activities related to children’s well-being in Zanzibar. The policy highlights that the factors which impact children’s well-being are related to and include sociocultural and environmental, physical, health-related, psychosocial, educational, spiritual, social and protection dimensions, many of which are captured within the MODA approach to measuring child poverty. In addition, the interventions to support the implementation of the ICPZ are designed around the different stages of development of children. This life-cycle approach is also an important focus for the MODA approach, as it focuses on the aspects of well-being relevant to children at their particular life stages. It therefore allows for identifi cation of areas in which children of different ages are most deprived and assists in targeting child centred policies better.

1.2 Why focus on child poverty?Child poverty is a multidimensional phenomenon; the impact of poverty on children is not only immediate, but the effects are also prevalent throughout the child’s life and may include reduced lifetime skills, lower productivity levels, poor health and educational outcomes, and greater probability of unemployment. Thus, reducing child poverty should be an important focus for policymakers to address these defi ciencies early in a child’s life. Most existing measures for tackling monetary poverty focus on income or consumption at the household level. However, children do not control household budgets; thus, estimating their share of consumption is diffi cult. In addition, household-level indicators are not suitable for identifying intra-household dynamics which may impact children negatively, including discrimination against girl children, and the access of children with disabilities and orphaned children to household resources.

Children face a complex set of socio-economic needs which go beyond material well-being. In order to evolve into functional adults, children require, for instance, emotional nurturing, intellectual stimulation, and teaching of social skills (Gordon 2003). While these elements are often negatively affected by adverse material conditions, there are plenty of examples of local and contextual factors (e.g., family structure, social norms, cultural practices, etc.) that may moderate or mediate the relation between material resources and well-being outcomes. For these reasons, a focus on monetary means alone may provide a particularly incomplete picture of the mechanisms affecting children’s well-being. The multidimensional and non-monetary nature of child poverty has been highlighted by several in-depth studies of child poverty in recent years (see Bradshaw et al. 2006; Young Lives 2011; Wordsworth et al. 2005).

The various dimensions of well-being that are relevant to a child’s development have been captured in the Convention on the Rights of the Child (CRC), which was adopted in 1986. This document provides a framework for the programmatic interventions of UNICEF, as well as constitutes a


conceptual and legal basis for understanding child poverty. The multidimensional nature of child poverty was recognized by the UN General Assembly in 2006 when it adopted, for the fi rst time, a formal defi nition of child poverty (UNGA 2006) and confi rmed this with the adoption of the SDG 1.2 by the UN General Assembly in 2015 (UN 2015).

In 2003, UNICEF set out to develop a bespoke measure of child poverty based on the various rights and dimensions of well-being defi ned in the CRC. The measure, which came to be known as the ‘Bristol Index’ used a simple counting approach, counting deprivations in the various dimensions of well-being defi ned by the CRC, building on the sociological approach to poverty developed by Townsend (1979).

The Bristol Index was updated in 2012 to incorporate the latest methodological developments in the area of multidimensional poverty. The resulting framework is known as the Multiple Overlapping Deprivation Analysis (MODA). The MODA framework is very similar to the Bristol approach, but allows for differentiated weightings of the various dimensions in line with the Alkire-Foster (AF) methodology,5 as well as allowing for a distinction between the incidence of child poverty (i.e., the number of poor children) and its intensity (i.e., the severity of the deprivations).

Finally, the MODA adopts a life-cycle approach, focusing on age-specifi c indicators that are relevant for children at different stages of their development. The MODA methodology provides a suffi ciently broad and fl exible framework to enable differentiated applications at the global and national levels. The internationally comparable global MODA measure is known as the Cross-Country MODA (CC-MODA) whereas national variations, depending on local context, policy priorities and legal frameworks, are known as N-MODA.

1.3 Monetary vs. multidimensional povertyThe usage of a multidimensional or non-monetary poverty measure presents several advantages over conventional poverty measures, the fi rst of which is that it provides a different perspective on the nature and conditions of poverty. As such, it can provide valuable information to confi rm or challenge perspectives on issues affecting poor individuals and poor children or can simply provide an alternate view.

In particular, multidimensional measures of poverty offer the possibility of looking at the issue of intra-household allocation of resources by focusing on individual-specifi c indicators of well-being, such as immunization or literacy.

Compared to the complexity of contemporary measures of monetary poverty, which require many adjustments, transformations and imputations, multidimensional measures of poverty tend to be relatively transparent and intuitive. In their simplest form, they can be interpreted as the number of deprivations affecting a child.

Furthermore, the methodological framework is adaptable to the inclusion of different indicators of deprivation. This means that, depending on the specifi c indicators chosen, multidimensional measures may be less liable to measurement error issues related to recall periods, under-

5 The Alkire Foster (AF) method is a way of measuring multidimensional poverty developed by OPHI’s Sabina Alkire and James Foster. Building on the Foster-Greer-Thorbecke poverty measures, it involves counting the different types of deprivation that individuals experience at the same time, such as a lack of education or employment, or poor health or living standards. These deprivation profi les are analyzed to identify who is poor, and then used to construct a multidimensional index of poverty (MPI)


reporting, subjective biases, etc., which often affect conventional poverty measures. It also means that the index can be adapted and tailored to refl ect local context and to meet local needs and policy priorities.

One of the main advantages of multidimensional poverty measures over, for instance, linear composite indices, is that they allow us to look at the joint distribution of deprivations. In other words, they allow us to see not only if a country suffers from, say, high malnutrition and high illiteracy, but also whether it is the same individuals who are suffering from these various deprivations. The policy conclusions resulting from overlapping and non-overlapping deprivations can be very different. In the latter case, classic health and education interventions may be required, whereas in the case of overlapping deprivations, there may be more complex barriers at work, requiring upstream interventions before health and education policies can be made effective. These may include, for instance, legal interventions in the case of gender or ethnic discrimination, or infrastructural investments in the case of geographic isolation.

Among the various measures of multidimensional poverty, the Alkire-Foster method used in the MODA index presents the advantage that it can be decomposed in many ways that are analytically useful. First, the measure is aggregately decomposable by subgroups. This means, for instance, that the national poverty rate can be derived from the sum of sub-national poverty rates, which is very useful to understand what drives poverty dynamics at the national level.

The index can also be decomposed by dimension to see which dimensions of well-being are driving the overall result for multidimensional poverty. Furthermore, it has been shown that the fi nal multidimensional poverty score can be expressed as the product of incidence and intensity of poverty. This facilitates the interpretation of poverty fi gures as the result of breadth (or incidence) across the population as well as depth (or intensity) of deprivations at the individual level.

This being said, there is no suggestion that multidimensional poverty measures should replace monetary poverty measures. Each captures different and valuable information about the well-being of individuals. The value of these measures often lies in their comparison and in the study of their discrepancies. By studying, for instance, the characteristics of individuals who are multidimensionally poor without being monetarily poor, or vice versa, we can better understand the complex mechanisms through which resources are transformed into well-being outcomes in some cases but not others.

Furthermore, it should be noted that, at present, multidimensional measures of poverty continue to be severely constrained by data availability issues. Consequently, multidimensional poverty measures often capture only a small subset of the dimensions of well-being. There are also issues concerning the consistency and comparability of the indicators included in these indices (e.g., fl ow vs. stock indicators, input vs. output indicators, etc.).

Finally, it should be noted that multidimensional poverty measures are still in the initial stages of development. Several methodological and theoretical questions remain under discussion in the academic sphere, and some issues are yet to be resolved. Conceptually, authors have questioned whether Sen’s concept of capabilities is fully measurable (Atkinson 1999, p. 186). Methodologically, questions have been raised about the theoretical anchoring of the weights used in multidimensional poverty measures (Ravallion 2011).


1.4 Existing child poverty analysis and approachesPoverty in Zanzibar has traditionally been analysed in the context of monetary poverty measures, and without a specifi c focus on the situation of children. The Zanzibar Household Budget Survey (2014/15) data was the fi rst dataset used to analyse child poverty based on Zanzibar-specifi c basic needs poverty line and food poverty line. The basic needs poverty line is TSh 53,377 per adult equivalent per month and the food poverty line is TSh 38,071 per adult equivalent per month (Offi ce of the Chief Government Statistician Zanzibar 2016).

Based on these poverty lines, the analysis by the OCGS fi nds that 35 per cent of children in Zanzibar live in households below the poverty line, compared to 30 per cent of the population as a whole. This declines to 13 per cent of children based on the food poverty line. Poverty is higher amongst households residing in rural areas; 45 per cent of children in rural areas live in households below the poverty line, while the poverty rate is 20 per cent in urban areas. There is also a large variation in poverty rates across districts. Magharibi has the lowest (16 per cent) and Micheweni the highest child poverty rate (72 per cent).

The sex of the household head does not impact the poverty rates experienced by children living in these households. However, the education level of the household head does play an important role in determining the poverty rates experienced by children. Poverty rates are higher amongst older children relative to children under the age of fi ve years.

Some fi ndings on the prevalence and characteristics of multidimensional poverty in Zanzibar have also been included in the Child Poverty in Tanzania report (NBS and UNICEF) published in 2016. The study used data from the National Panel Survey (NPS), Wave 3, and covered the entire United Republic of Tanzania (URT). Sample size limitations meant that no geographical disaggregation could be provided for Zanzibar. Moreover, the methodology was tailored to URT as a whole and did not address the specifi cities of child poverty in Zanzibar. The current analysis was undertaken at the same time as the drafting of the Child Poverty in Tanzania report, which also presents results for Zanzibar. While the fi ndings are not directly comparable due to differences in the construction of the MODA index, the indicators and the survey on which the analysis is based (ZHBS 2014/15 vs. NPS 4), there are certain fi ndings which are aligned – for example, that the benefi ts of living in urban areas were higher for children under the age of fi ve years. This may have been driven by the relatively high rate of urbanization in Zanzibar over the last few years.

In addition to the NPS analysis, the MODA approach has also been applied to the 2012 Population and Housing Census, which allowed for an analysis down to the district level for the URT, including Zanzibar. However, given the characteristics of census questionnaires, the MODA index is based on relatively few indicators and captures fewer dimensions as nutrition and health are not included.

The analysis of the ZHBS data will provide an opportunity for an analysis that is tailored to the context of Zanzibar and makes use of a rich household survey questionnaire.


METHODOLOGYCHAPTER: 2

This report marks the fi rst time that the MODA approach was adapted to analyse child poverty in Zanzibar in particular. This follows from the adaptation of the approach in Tanzania after the 2012 Population and Housing Census and in 2015 under the guidance of the Tanzanian Child Poverty Technical Working Group (TWG), which was convened by the NBS and UNICEF Tanzania and was composed of key government ministries, NGOs and development partners. The NPS survey has a very limited sample for Zanzibar, which did not allow for further disaggregation within Zanzibar. It was agreed that a separate report would be produced for Zanzibar using an appropriate dataset to allow for a fi ner-grained analysis of child poverty in Zanzibar and its districts.

The OCGS TWG discussed and agreed on the specifi cs of each dimension and its indicators, including the cut-off point at which an essential condition was considered lacking to the extent that it was categorized as a deprivation. This sometimes involved a compromise between what was considered the best possible measure of what it means to be deprived for a child in a specifi c age group, and the availability of data. Generally, a deprivation corresponds to a violation of a child’s basic rights (UN 1989). In this regard, deprivations are different from predictors or correlates of deprivation. For example, being an orphan might make a child more vulnerable to deprivation, but it is not a deprivation, since it is not a violation of a right (de Neubourg et al. 2014). Some variables used in this study are particular to Zanzibar, such as lack of healthcare when a child is sick as an indicator for a health deprivation, or a child being two grades behind his or her scheduled grade-for-age as an indicator of educational deprivation. However, most indicators used in this study are the same as those used in similar exercises in other countries.

Data constraints also infl uenced the choice of indicators. For example, the ZHBS only collected limited data on young child health and nutrition. Similarly, measures of protection were limited to birth registration (for all children) and measures of child labour or early marriage for older children. Other critical areas of protection, such as physical violence against children in their homes, community or school are not assessed in the survey. Furthermore, as the ZHBS does not collect information on hours worked, the standard UNICEF defi nition or international defi nition of child labour cannot be calculated.

Box 1 Adaptation of the MODA index to the Zanzibar context


Table 3 provides the list of dimensions and indicators used in this report, as well as their defi nitions and age coverage. In total, deprivation is measured through a maximum of eight dimensions: nutrition, health, protection, education, information, water, sanitation and housing. Children between the ages of 0 and 4 years can be deprived in a maximum of six dimensions, as they are not yet in education and information/communication is less relevant for their well-being.

Table 3: MODA dimensions and indicators of child poverty

Dimension Indicator Age groups Defi nition – Deprived if…

0–23 months

24–59 months

5–13 years

14–17 years

0–17 years

Nutrition Meal frequency

√ √ √ √ √ Household usually consumes fewer than three meals a day

Food insecurity – Household Food Insecurity Access Scale (HFIAS)

√ √ √ √ √ Household was moderately or severely food insecure in the past 30 days according to the HFIA score

Dietary diversity

√ √ √ √ √ Household consumed fewer than three out of ten food groups, where consumption counts if a food group was consumed on four or more days the previous week

Health Sick √ √ √ √ √ Child was sick in the last four weeks and did not attend a healthcare facility, or attended a traditional healer/received traditional medicine

Malaria or diarrhoea

√ √ √ √ √ Child suffered from malaria or diarrhoea in the last four weeks

Protection Birth registration

√ √ √ √ √ Child does not have a birth certifi cate or notifi cation

Child labour √ √ √ Child missed school due to work in the last two weeks or is economically active (main activity, last week) or engaged in business activity (no reference period)

(Continued)



0–23 months

24–59 months

5–13 years

14–17 years

0–17 years

Early marriage √ √ Child is/was married or in relationship (for children aged 12 years or more)

Education Pre-school enrolment

√ Child not currently in pre-primary and did not start school before six years of age (for children aged 5–6 years)

School enrolment

√ √ Child currently not in school

Never attended secondary

√ Child currently not in school and has never attended secondary school (for children aged 16–17 years)

Literacy √ √ Child is illiterate if reported that child cannot read and write in any language OR that the child cannot read a full sentence in either language (English, Swahili) during test if tested (for children aged 9–17 years)

Grade for age √ √ More than two years over the regular age for grade (for children aged 9–17 years)

Time to school √ √ Takes more than 30 minutes to primary, and more than one hour to secondary school if travelling by foot (for children aged 7–17 years)

Information Communication √ √ √ Household does not have a landline phone, computer and mobile phone

(Continued)

(Continued)



0–23 months

24–59 months

5–13 years

14–17 years

0–17 years

Information √ √ √ Household has no TV, books and radio

Water Source √ √ √ √ Household uses unimproved and untreated drinking water source (in dry and rainy season)

Time √ √ √ √ √ Household’s drinking water source is more than 30 minutes away (in dry or rainy season) and member usually fetching water is a child younger than 15 years

Sanitation Sanitation √ √ √ √ √ Household uses unimproved or shared sanitation

Handwashing facilities

√ √ √ √ √ Household has no handwashing facilities

Housing Overcrowding √ √ √ √ √ Household has more than two adult equivalents per room

Type of roof, fl oor and walls

√ √ √ √ √ Households with fl oor made of earth/palm bamboo OR roof of mud, grass, or plastic OR walls of mud or grass

Cooking fuel √ √ √ √ √ Household uses polluting fuel and has only one room

Source: ZHBS 2014/15 *Age is measured at the time of the interview. Measuring it at the start of the school year would be more appropriate for some of the education indicators, but would generate inconsistencies in categorizing children by age when several dimensions are aggregated. Nutrition: Conceptually, there is some overlap between the HFIAS and the other two nutrition indicators; however, the correlation between the three is not high, and as such, all three indicators were included.Sanitation: Unimproved if there is no toilet/bush/fi eld, if it is an open pit without slab, pit latrine with an unwashable slab, other, or the household shares a toilet.Education: Initially, using age at the start of the school year for all attendance-type indicators was considered. However, this would mean placing the same child into two different age groups, for instance, for children coded as four years at the beginning of the school year and as fi ve years at the time of the interview. We therefore went back to using age at the time of the interview but allowed for one additional year, i.e., pre-primary at 5–6 years rather than 4–5 years as about half of the four-year-olds will have been four years at the start of the school year. Also, there are several systems in place with respect to school entry age and years at different levels as of the time of the study, and newer systems have been phased in gradually. Drinking water: Unimproved if from an unprotected spring, unprotected well, cart with small tank/drum, tanker-truck, surface water, bottled water, or any other source and not treated by boiling or chemical treatment.The maximum number of deprivations a child can be deprived in differs by age; for those aged 0–4 years, the maximum number of deprivations is six; for children aged 5–17 years, the maximum number of deprivations is eight.

(Continued)


2.1 MODA vs. MPIThe MODA index is a type of multidimensional poverty index that has been tailored to capture child poverty. Many other multidimensional poverty indices exist and have been used in Tanzania. The most well-known one is probably the Multidimensional Poverty Index (MPI) developed by United Nations Development Programme (UNDP) with support from the Oxford Poverty and Human Development Initiative (OPHI).

The MODA and MPI use very similar methodologies and are derived from similar conceptual foundations, starting from theories developed by Amartya Sen and other scholars in response to perceived shortcomings of conventional monetary measures of poverty. However, there are some key differences between the two measures, which we summarize here:

The MODA measures deprivations at the level of each individual child, whereas the MPI measures deprivations at the household level. In the MPI, a child is considered deprived in a given indicator if she or he lives in a household in which at least one household member is deprived in the specifi ed indicator. In the MODA index, a child can be deprived in one indicator and his or her sibling may not be deprived in the same indicator, depending on each child’s specifi c circumstances. Consequently, the MODA index is more adequate for identifying child-specifi c deprivations resulting, for instance, from unequal distribution of resources within the household.

The MODA uses the life-cycle approach. The indicators and dimensions on which each child is being assessed are specifi cally tailored to the needs of each child at each stage of his/her development process. For instance, a new-born baby will have very specifi c nutritional and health needs that may be less crucial for a teenager. However, the teenager may have social and cultural needs. The MPI, by contrast, measures all household members on the same set of indicators irrespective of age.

The MPI uses a dual cut-off approach: fi rst, individuals are assessed across a number of indicators to determine whether they are deprived in each indicator or not, depending on whether they meet the minimum standard set for each indicator (fi rst set of cut-offs). Secondly, the deprivations are added up to determine whether the person is multidimensionally poor or not, depending on whether she or he has the minimum number of weighted deprivations required to be considered poor (second set of cut-offs). The MODA index, by contrast, uses what is effectively a triple cut-off approach. First, children are assessed in each indicator (fi rst set of cut-offs). Secondly, the deprivations are added up within each dimension of well-being. A child is considered deprived in a given dimension if she or he is deprived in at least one indicator in that dimension (second set of cut-offs). Thirdly, dimensions rather than individual indicators are added up to determine whether a child is multidimensionally poor. To be considered poor, the child must be deprived in at least one indicator in at least three dimensions.

The MPI measures deprivations across three dimensions of well-being: health (including nutrition), education, and living standards (mostly water, sanitation and housing conditions). By contrast, the MODA measures deprivations across six dimensions of well-being for children under fi ve years (health, nutrition, child protection, housing, water, sanitation) and eight dimensions for children aged 5–17 years (same plus information and education).


Based on this comparison, we can see that the MODA and MPI indices are quite different both in their scope and approach. The indices are mutually complimentary as they measure different things and are tailored for specifi c purposes. The MPI has been crucial in highlighting the shortcomings of conventional monetary measures of poverty. For this purpose, it has put a lot of emphasis on international comparability and generalizability across different types of households. By contrast, the MODA index has been developed more for the purpose of peering into the internal dynamics of the household to identify individual pockets of child poverty that might be invisible when looking at more general or aggregated indicators of well-being.

2.2 Comparing the MODA and MPI resultsIn October 2017, the World Bank published a report entitled Zanzibar Poverty Assessment which used the Multidimensional Poverty Index (MPI) methodology to analyse poverty based on the ZHBS 2014/2015 survey.

A person is considered MPI poor if they suffer deprivations in at least 30 per cent of the indicators covering the fi ve dimensions of well-being (education, housing conditions, access to basic services, asset ownership and consumption). The current report based on the MODA index classifi es a child as deprived if they are deprived in three or more of the dimensions.

Bearing in mind that the two indexes have different approaches to measuring deprivation, including the fact that the MODA index measures deprivation at the household level across six and eight dimensions compared to the fi ve of the MPI, and thus are not directly comparable, the two reports share some similar fi ndings. In particular, both fi nd that poverty is lower in urban areas and amongst smaller households. In addition, children living in households where the household head is employed in agriculture have the worst outcome in terms of being deprived in more than half the dimensions on average, while the MPI index also fi nds poverty highest amongst agricultural workers. Both the MODA and MPI index highlight high poverty and deprivation levels on the island of Pemba and indicate similar fi ndings for different regions and for urban and rural areas.

2.3 Data sourcesThe data source on which the MODA analysis is based is the Zanzibar Household Budget Survey (ZHBS) 2014/15. Household budget surveys have been conducted since 1981/82 in Zanzibar, every fi ve years since 2004/05. The ZHBS 2014/15 survey has a sample of 4,650 households and collects information on a variety of topics. The following questionnaire modules are covered:

1. Form I: Demography, education, migration, health, disability, time use, literacy. Asked on Day 1.

2. Form II: Dwellings and household-level non-food expenditure. Asked on Day 5.

3. Form III: Non-farm household businesses. Asked on Day 10.

4. Form IV: Agriculture and livestock, food security, utilities, water, sanitation and individual income. Asked on Day 15.


5. Form V: Household diary – Household-level food expenditure and consumption.

6. Form VI: Individual diary – Daily expenditure and consumption of food. Interviewer visits every day to transfer individual information to household diary.

Household surveys have certain limitations, and as such, all datasets used for analysis have both positive and negative aspects which one should consider. Table 4 sets out the advantages and disadvantages associated with using the ZHBS 2014/15 for the MODA of child poverty.

Table 4: Advantages and disadvantages of the Zanzibar Household Based Survey 2014/15

Advantages of the ZHBS Disadvantages of the ZHBS

Large sample, allowing for disaggregation down to the district level (10 districts).

Focus on households rather than individuals (except standard household roster), which limits the number of child-level indicators that can be included in the multidimensional poverty analysis.

Can be used for estimating monetary poverty in Zanzibar, allowing for analysis of the overlap between monetary and multidimensional poverty, ensuring consistency with offi cial poverty statistics.

No specifi c focus on children, which means that some aspects included in the multidimensional measure of poverty have to be approximated. This especially affects the nutrition and health dimensions which in other MODA analyses tend to include anthropometric indicators, immunization indicators and antenatal care indicators.

2014/15 ZHBS report includes a section on child poverty, and this analysis would be a natural continuation.

Conducted over one year, it ensures that seasonal characteristics do not affect estimates.

Timeliness related to the availability of data as well as analysis, i.e., cleaned and processed.

Majority of OCGS team members are familiar with the data either due to involvement in data collection or data analysis.

The poverty data produced by the ZHBS is the offi cial poverty data of Zanzibar.


The ZHBS 2014/15 comprised a sample size of 380 clusters (12 households per cluster) and 4,650 households based on the frame provided by the most recent 2012 Tanzania Population and Housing Census (2012 TPHC). The response rate was 96.5 per cent (4,401 out of 4,650 households).

The sample design allows for disaggregation of estimates for Zanzibar – rural and urban areas as well as for the 10 districts.

Comparability between the ZHBS 2009/10 and ZHBS 2014/15

A comparison of the questionnaires in the ZHBS 2009/10 and the ZHBS 2014/15 found that some of the key questions and category options used to construct the MODA indicators had changed between the two rounds of the questionnaire.

In addition, the following changes in methodology were identifi ed:

Different stratifi cation – the subjective wealth stratum used in ZHBS 2009/10 was dropped.

Different urban-rural distribution in weighted ZHBS 2009/10 required adjustment weights for monetary poverty trend analysis and might require application of weights when comparing other indicators.

Box 2 Sample design of the ZHBS 2014/15

Table 5: Distribution of enumeration areas (EAs) and households in the ZHBS 2014/15 sample across the 10 districts

District Number of EAs Number of households

Kaskazini A 302 20,454

Kaskazini B 143 16,059

Kati 212 16,578

Kusini 129 9,008

Magharibi 749 70,765

Mjini 486 41,653

Wete 263 20,039

Micheweni 219 19,214

Chake Chake 221 17,546

Mkoani 233 18,061

Total 2,956 249,377

Source: ZHBS

(Continued)


Changes to questionnaires (e.g., in Form II).

Different method of calculating total consumption.

Based on this evaluation, a trend analysis between the survey rounds was not undertaken for this report. As such, the ZHBS 2014/15 can be viewed as a baseline and future rounds of the survey should aim for consistency in questions and variables to ensure the longitudinal comparability of child poverty.

Box 2 Sample design of the ZHBS 2014/15

Source: ZHBS 2014/15, Offi ce of the Chief Government Statistician Zanzibar, September 2016

2.4 Disaggregation criteria

Table 6: Household characteristics

Distribution of children by selected household characteristics

Disaggregation criterion

Category Obs. Percent of children

Number of children

All households 12,497 100% 725,820

Household size <5 1,658 13.0% 94,343

5-8 7,028 56.7% 411,399

>8 3,811 30.3% 220,078

Age of household head

<25 108 0.8% 6,095

25-34 1,727 14.4% 104,418

35-49 6,090 49.0% 355,721

50-64 3,517 27.8% 201,536

65+ 1,055 8.0% 58,050

District Kaskazini A 961 9.4% 68,149

Kaskazini B 688 5.2% 37,656

Kati 732 6.0% 43,538

Kusini 617 2.6% 18,619

Magharibi 2,935 29.4% 213,129

Mjini 1,592 11.4% 82,718

Wete 1,160 8.9% 64,758

(Continued)

(Continued)


Distribution of children by selected household characteristics



Number of children

Micheweni 1,300 9.6% 69,626

Chake Chake 1,262 8.5% 61,342

Mkoani 1,250 9.1% 66,285

Area of residence Rural 7,631 59.6% 432,402

Urban 4,866 40.4% 293,418

Sex of household head

Male 10,122 81.7% 593,063

Female 2,375 18.3% 132,757

Occupation of household head

Employee/employed 3,229 26.5% 192,628

Agriculture own account 3,502 28.9% 209,770

Non-agricultural worker 4,712 36.2% 262,834

Not working/family worker 1,054 8.3% 60,588

Monetary poverty Non-poor 8,011 64.1% 473,799

Status Poor 4,486 35.9% 252,020

(Continued)

(Continued)

Source: ZHBS 2014/2015N.B.: Percentages and population totals are weighted by sampling weights.Base population: All children by household characteristics.

Table 7: Children’s and mothers’ characteristics

Distribution of children by selected children’s and mothers’ characteristics



Number of children

All children 12,497 100% 725,820

Child’s sex Boy 6,275 50.4% 365,664

Girl 6,222 49.6% 360,156

Age of child 0–23 months 2,461 19.80% 143,875

24–59 months 1,660 13.50% 97,664

5–13 years 6,032 48.30% 350,462

14–17 years 2,344 18.40% 133,820

Child’s disability status

No disability 12,244 98.0% 711,291

At least one disability 253 2.0% 14,529


Source: ZHBS 2014/15 N.B.: Number of observations for mother’s characteristics will refl ect reduced sample of children living with mother.Base population: All children by individual and mother’s characteristics.

Distribution of children by selected children’s and mothers’ characteristics



Number of children

Living arrangement of child

Living with both parents 8,247 66.9% 485,759

Living with mother only 1,759 13.7% 99,286

Living with father only 386 3.1% 22,458

Living with neither parent 2,103 16.3% 118,187

Mother’s education

No education 2,459 24.9% 145,581

Primary 2,422 24.5% 141,126

More than primary 5,113 50.6% 291,460

Mother’s marital status

No partner 776 7.4% 55,976

Married 9,200 92.6% 528,726

(Continued)


This section investigates the relationship between monetary and multidimensional poverty. Monetary poverty measures the household’s lack of fi nancial resources to meet basic needs required for survival and development (UNICEF MODA in Brief 7),6 while multidimensional deprivation measures the individual status in each of the various sectors considered important for development. In general, we would expect to fi nd a close alignment between these two measures as constrained fi nancial resources also lead to constraints in accessing goods and social services required for development. However, these fi nancial barriers are not the only factors affecting child well-being, and thus, incorporating MODA child poverty measures provides further insights into how other factors related to environmental, health, legal and political areas affect child well-being. In particular, understanding the mismatch between children who may be fi nancially constrained, but perform well in other areas, and vice versa, is facilitated by the MODA poverty analysis.

3.1 Measures of monetary and multidimensional poverty

The analysis in this and subsequent sections identifi es as monetarily poor those children who were below the offi cial basic needs poverty line of TSh 53,377 per adult equivalent household member per month.7

Whether a child is regarded as multidimensionally poor or not depends on the cut-off point chosen for the headcount measure. Before examining the overlap between monetary and multidimensional poverty in Chapter 4, the different options for cut-offs are briefl y examined and some basic results for multidimensional poverty are presented. Table 8 sets out the percentage of children by number of deprivations and age group and shows that 66.1 per cent of children aged 0–17 years are deprived in three or more dimensions.

MONETARY AND MULTIDIMENSIONAL POVERTY

CHAPTER: 3

6 Multidimensional Child Deprivation and Monetary Poverty in sub-Saharan Africa-MODA In Brief 7, Innocenti Working Paper No. 2014-197 The adult equivalent poverty line adjusts the consumption poverty line for the different members of a household based on their age and

gender. For example, children require a lower caloric intake to achieve the same nutritional outcomes as an adult male.


Figure 16 shows the percentage of children by the number of dimensions they are deprived in. For instance, 2.5 per cent of children are not deprived in any of the dimensions, while 30 per cent of children are deprived in three dimensions.

Table 8: Percentage of children by number of deprivations and age

Number of dimensions deprived

0–23 months *

24–59 months *

5–13 years 14–17 years 0–17 years

At least 1 96.4 96.3 98.2 97.8 97.5

At least 2 81.6 79.2 90.6 89.3 87.0

At least 3 52.2 52.4 73.4 71.6 66.1

At least 4 11.0 8.9 48.7 48.0 35.8

At least 5 0.6 0.7 21.7 25.2 15.3

At least 6 0.1 0.0 5.3 9.4 4.3

At least 7 - - 0.9 1.6 0.8

8 - - 0.0 0.0 0.0

Source: ZHBS 2014/15 *Measured over six dimensions of well-being

Figure 16: Percentage of children by number of dimensions they are deprived in

Base population: all children Source: ZHBS 2014/15 (author’s calculations)

In addition to using this absolute headcount indicator which shows the absolute number of dimensions a child is deprived in, one can also use a relative indicator showing the share or proportion of indicators the child is deprived in. This allows for better comparison across children of different age groups, as the maximum number of possible deprivations varies (six for 0–4 year olds, eight for 5–17 year olds). As before, one can see that 2.5 per cent of children in Zanzibar are

40.0

30.0

20.0

10.0

0.0

2.5

10.5

21.0

30.3

20.4

11.0

3.5

0.7 0.0

10

Perc

en

t o

f ch

ildre

n d

ep

rived

2 3 4 5 6 7 8

Number of deprivations per child


not deprived in any dimension, while 31 per cent are deprived in half of all possible dimensions. Only very few children are deprived in all dimensions. Looking at the cumulative measure one can see that 81.5 per cent of children are deprived in half or fewer dimensions. This implies that 19.5 per cent are deprived in more than half of all dimensions.

Table 9: Percentage of children by proportion of dimensions they are deprived in

Share of deprivations per child (%) % of children % cumulative

0 2.47 2.47

13 5.28 7.74

17 5.25 12.99

25 11.52 24.51

33 9.43 33.94

38 16.27 50.21

50 31.31 81.52

63 10.81 92.34

67 3.16 95.49

75 3.53 99.02

83 0.20 99.23

88 0.74 99.97

100 0.03 100.00

Total 100


Figure 17: Percentage of children deprived in three or more dimensions, by district


Magharibi

Mjini

Kati

Kusini

Wete

Mkoani

Kaskazini A

Kaskazini B

Chake Chake

Michewni

43.0

47.2

63.0

68.6

78.3

81.0

81.0

81.4

84.0

93.8

0 20 40 60 80 100

Percent of children


Using the cut-off of deprived in three or more dimensions, Figure 17 plots the deprivation levels in 10 districts of Zanzibar. The lowest deprivation levels (less than 10 per cent) are found in Magharibi and Mjini, the two districts making up Zanzibar Urban/West, where Zanzibar Town is located. At the other end of the deprivation spectrum, one can fi nd the district of Micheweni in North Pemba, where every second child is deprived.

3.2 Relationship and overlap between monetary and multidimensional poverty

Children in money-poor households suffer, on average, from 3.8 deprivations, i.e., 1.1 more than children living in non-poor households (Table 10). The same can be observed when comparing the average share of deprivations of children who are money-poor – where, on average, children are deprived in half of all dimensions – and those who are not money-poor (children are deprived in slightly more than a third of all dimensions). This is not surprising, as fi nancial constraints remain one of the main barriers to accessing fee-based social services as well as housing infrastructure and goods such as those considered in the MODA index.


Table 10: Average number and share of deprivations by monetary poverty status

Monetary poverty status Average number of deprivations

Average share of deprivations

Non-poor 2.7 0.37

Poor 3.8 0.50

All 3.1 0.42

The relationship between monetary and multidimensional poverty is shown by looking at the correlation between household consumption and the number of dimensions children are deprived in. Figure 18 depicts this relationship for children living in rural (red line) and urban areas (dotted blue line), respectively. The vertical line in red represents the basic needs poverty line. Children to the left of this line live in money-poor households, while children to the right are not money poor. The number of deprivations decreases with increasing consumption levels, whereby the relationship is stronger below the poverty line and fl attens them out. At any level of consumption, the number of deprivations experienced by children in rural areas exceeds the corresponding number for children in urban areas.

While these fi gures show that there is a clear relationship between monetary and multidimensional poverty, they also indicate that deprivations exist even among the children who live in households whose consumption levels are above the basic needs poverty line. The following paragraphs explore the extent of overlap between monetary and multidimensional poverty further.


Figure 18: Relation between number of dimensional deprivations and consumption, by area of residence


5

4

3

2

0 50000 100000 150000 200000 250000

Nu

mb

er

of

dep

rvati

on

s p

er

child

Consumption, TzSh per adult equivalent/month

Urban Rural

Figure 19 shows the extent of overlap between children who are identifi ed as poor using monetary and multidimensional poverty measures. In 2014/ 2015 HBS, 35 per cent of children in Zanzibar were monetarily poor, meaning that they consumed less that TSh 53,377 per adult equivalent household member per month, shown by the red circle. In the fi gure, a child is defi ned as multidimensionally poor if she or he faces deprivations in three or more dimensions, the threshold chosen in the 2016 Child Poverty report based on the NPS Wave 3. These children are presented by the green circle and represent 66 per cent of all children. The fi gure shows that while there is a clear relationship, monetary and multidimensional poverty do not overlap perfectly. Based on the chosen defi nition, 30 per cent of all children are both monetarily poor and multidimensionally poor, 5 per cent are living in money-poor households, but are not multidimensionally poor, and 36 per cent of children suffer from multidimensional poverty without being considered poor according to the monetary poverty defi nition. Moreover, 29 per cent of children are neither monetarily nor multidimensionally poor.

So far, multidimensional poverty has been defi ned at the dimension level. It is also possible to use a defi nition at the indicator level in order to capture further detail in the way children are affected by deprivations. For instance, children may have a great number of deprivations across one or two dimensions, but none in other dimensions, and as such may be deprived in many indicators but few dimensions. Thus, in order to capture both the depth and breadth of deprivations across dimensions, we construct a multidimensional poverty measure based on the weighted number of indicators a child is deprived in.


The results show that slightly more than half of the children deprived in more than 40 per cent of indicators are also living in money-poor households. This pattern resembles what was observed before for the dimension-level measure of deprivation. However, the overlap between the two measures of multidimensional poverty is not perfect. Around one-third of children who are deprived in more than 40 per cent of weighted indicators are deprived in 50 per cent or fewer dimensions, i.e., their deprivations are concentrated in a relatively few dimensions. Similarly, around a third of children deprived in more than half of the dimensions are not deprived according to the indicator-level measure, i.e., they have relatively few deprivations which are spread across a relatively large number of dimensions of well-being.

Identifying the different types of multidimensional poverty is necessary to design different policy responses. In the fi rst case, targeted sectoral policies may be required to address specifi c bottlenecks in, say, health or education, whereas in the latter case, a more comprehensive response may be required to address structural or systemic constraints across many different sectors.

3.3 Discrepancies between consumption and deprivation

The previous section has shown that while there is overall a strong relationship between consumption-based poverty measures and multidimensional poverty, there are groups within which this relationship is less strong, or where the two poverty measures do not overlap. In this

Figure 19: Overlap between monetary and multidimensional poverty


Deprived in three or more dimensions; Poor < TSh 53,377

5 30 36

Neither 29

Money poor Deprived>= 3 dimensions


section, we explore further the extent to which the two measures differ, and whether discrepancies can be linked to household-level, mother or child characteristics.

Figures 20 and 21 show the difference between the actual number of deprivations children face and the predicted number of deprivations, based on their household’s monetary consumption level and their age, by household, and mother/child-level characteristics.8 A positive number on the left axis implies that the child with the given characteristic suffers from more deprivations than children of similar age and consumption levels, whereas a negative number means that the child has fewer deprivations than predicted. The dotted orange line shows the average household consumption level for the population subgroup, and its level can be read from the right axis.

Considering different household and household head characteristics, one fi nds a large positive discrepancy between actual and predicted deprivations for children in households where the household head is below 25 years old (and, to a lesser extent, between 25 and 34 years old). There were also positive, but less pronounced, discrepancies in households where the head is an own account agricultural worker, in households located in rural areas, as well as in small households with less than fi ve household members (Figure 20). Interestingly, small households and households headed by individuals younger than 25 years of age have relatively high consumption levels (in adult equivalent measures), which indicates that the multidimensional poverty is not (primarily) caused by fi nancial constraints. Children in rural households and in households where the head is an agricultural own account worker have lower consumption levels than other households but also higher than predicted deprivation levels. While fi nancial constraints are likely to be one driver of exacerbated multidimensional poverty for these groups, other factors must also contribute to raise actual deprivation levels above predicted ones. Limited access to government-provided infrastructure in rural, agricultural areas might be one reason why households face relatively higher deprivation levels at a given level of consumption. Further analysis is needed to explore the reasons behind the observed discrepancies in more detail.

By contrast, children living in households where the head is an employee or employer, in urban areas and in relatively large households, face, on average, fewer indicator deprivations than predicted given their consumption levels and age group. Employees might receive additional social benefi ts which help reduce deprivation levels, and infrastructure, goods and services may be more readily available in urban areas, even for population groups with relatively low consumption levels.

Other possible explanations may be linked to the adjustments made when computing poverty based on consumption levels. Consumption is adjusted for differences in price levels across Zanzibar by applying a price index. Moreover, consumption is adjusted for household composition by applying an adult equivalence scale. Equivalent scales are meant to correct, for instance, for the fact that small children require signifi cantly less nutritional intake (and thus, less money) to achieve the same nutritional outcomes as adults. In our case, it appears that the equivalence scales might overestimate the benefi ts of having small households. It might, for instance, be that

8 The predicted number of deprivations is obtained by regressing the weighted number of deprived indicators on total household consumption per adult equivalent and the age group of the child. Controlling for age is important here, since the index is not comparable across age groups since we use different indicators for different ages. The age group of the child is represented by a multiple categorical variable. Age groups are defi ned to match the different compositions of the index, so that this variable controls for differences in index composition due to the use of the life-cycle approach. For the consumption variable, we use a logarithmic transformation of the consumption to obtain a normally distributed variable. We also include the square value of consumption to account for possible non-linearity in the relation between consumption and deprivation. Average predicted values and actual values are then obtained for each of the characteristics, i.e., characteristics are considered one by one and no further control variables are introduced.


they underestimate economies of scale for larger households resulting from sharing common household facilities, such as the kitchen, toilets, etc. Similarly, another possibility could be that the price index correction might overestimate the negative impact of high urban prices on urban poverty. This could, for instance, be the case if poor urban households are able to adopt coping strategies (e.g., substituting with cheaper/lower quality goods) to mitigate the impact of higher prices.

Figure 21 shows the same type of analysis, examining discrepancies in predicted and actual deprivations by characteristics of a child’s mother and the child. Given the household consumption and the age of the child, deprivation levels were found to be higher than predicted for children whose mothers were relatively young when giving birth (younger than 24 years), unmarried, and uneducated. Younger and less educated mothers may have insuffi cient knowledge of childcare or may be less able to support their children in progressing through the educational system. The situation is reversed for children whose mothers have at least primary education. While

Figure 20: Difference between actual (indicator-level) deprivations and predicted deprivations based on consumption/age by household characteristics

Notes: Base population: all children** Consumption: total monthly household consumption in TSh per adult equivalentSource: ZHBS 2014/15 (author’s calculations)

Dif

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Diff.actual- predicted Average consumption

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of headSex of head

Age of head HH sizeAge

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consumption levels are also clearly higher, since education levels are correlated with higher incomes and consumption, mothers’ education impacts the well-being of children beyond the immediate fi nancial benefi ts. Educated mothers may allocate their incomes differently to support their children, and it is also possible that educated mothers have greater awareness of appropriate nutrition, for instance.

With respect to child-level characteristics, the analysis suggests that children with at least one disability face higher deprivations than children with similar consumption levels and of the same age (note that household consumption levels for children with disabilities are, on average, higher than those for children without disabilities). Given the small number of children with disabilities in the ZHBS sample, it is diffi cult to explore this issue further, but Table 18 in the Annex suggest that the main discrepancies can be found in the education, information and nutrition dimensions.

Figure 21: Difference between actual (indicator-level) deprivations and predicted deprivations based on consumption/age by mother/child characteristics

Notes: Base population: mother’s characteristics: children with biological mother living in the household and valid observations; child characteristics: all children ** Consumption: total monthly household consumption in TSh per adult equivalentSource: ZHBS 2014/15 (author’s calculations)

100000

90000

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statusEducation CHILD:

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Diff.actual- predicted Average consumption


3.4 Mismatching children This section analyses the profi les of mismatched children, that is, children who are classifi ed as poor according to one measure but not according to another measure. We compare the profi les of children who are classifi ed as multidimensionally poor only, with those who are monetarily poor only. The blue bars in Figure 22 represent the averages of selected indicators for children who are deprived in more than 50 per cent of the dimensions in the MODA index, even though their adult equivalent monthly consumption exceeds the basic needs poverty line. The red bars represent the same for children who are monetarily poor but are deprived in 50 per cent or fewer dimensions. In the graph on the right, we also represent the difference, with confi dence intervals, between the two groups, corrected for differences in index composition between age groups.9

9 The estimate is obtained by regressing the variable of interest on a dummy that takes the value 1 if the child is deprivation poor only, and 0 if the child is money poor only. A multiple categorical variable is included to describe the age group that the child is in.

Figure 22: Profi le of mismatching children

Notes: Base population: mother’s characteristics: children with the biological mother living in the household and valid observations; child and household characteristics: all childrenSource: ZHBS 2014/15 (author’s calculations)

Hhd Size

Age (year)

Female head (%)

Rural (%)

Girls (%)

Orphans (%)

Uneducated mother (%)

Single mother (%)

6.4

8.9

8.5

7.9

20.7

16.8

81.5

-2.5

-0.1

3.6

11.4

1.9

6.2

3.9

1.7

70.8

50.848.6

40.9

33.7

38.3

34.2

8.7

7.2

0 20 6040 80 -5 0 5 10

Only MODA poor

Only money poor

Mean value Age corrected difference


Children who are classifi ed as monetarily poor without being multidimensionally poor tend to live in slightly larger households than children who are only multidimensionally poor, which is consistent with the fi ndings on household size discussed above. For all other variables, we fi nd higher average values among children who are only multidimensionally poor, though differences are not or only marginally statistically signifi cant for the age of the child, having a female head of household or having an unmarried mother. Children who are only multidimensionally poor live in greater proportions in rural areas and have uneducated mothers. From this graph, it also appears that there is a large proportion of children who are either orphans or live with only one parent among those MODA deprived, but who are not considered poor based on the household’s consumption levels.

These fi ndings point to possible non-fi nancial sources of disadvantage that can negatively affect children’s well-being outcomes, even when they theoretically have enough money to achieve desirable outcomes. Orphans may, for instance, be discriminated within the household, and may, therefore, not be provided with as many resources as biological children. Single mothers may be time-constrained, and therefore, they may be unable to adequately assist their children with, say homework, as they must take care of both traditional male and female responsibilities within the households. Uneducated mothers may lack knowledge of essential practices to ensure their children’s development. Understanding precisely the mechanisms through which these non-monetary sources of disadvantage affect children is crucial to addressing child poverty in all its dimensions. This will require further research, including qualitative research. Simply providing more resource, through, for instance, a cash transfer, is not likely to resolve the problems of these children who are already above the poverty line.

3.5 Consumption vs dimensional deprivationsIn Figure 23, we investigate the relationship between the percentage of children deprived and consumption for the eight dimensions of well-being (nutrition, health, water, education, sanitation, protection, information and housing) by area of residence. The red line shows the relationship in rural areas, the dotted blue line in urban areas. The steeper the line, the stronger is the relationship between consumption and deprivation for the particular dimension. The vertical line in red represents the basic needs poverty line. Children to the left of this line live in money-poor households, while children to the right are not monetary poor.

The relationship is negative in most, but not all the dimensions. The relationship between consumption levels and nutrition deprivation is relatively steep to the left of the poverty line, but then gradually levels off. This indicates that fi nancial barriers to access probably play a very important role in preventing money-poor children from achieving positive outcomes in this dimension. For richer children, however, further increases in consumption levels do not signifi cantly decrease deprivation rates, which means that other factors (e.g., behavioural, physiological or environmental) are more important. The nutrition dimension also shows the largest gap between rural and urban areas, with deprivation levels considerably higher in rural areas despite the consumption range considered.


Figure 23: Percentage of children deprived by deprivation and adult equivalent consumption

Base population: all childrenNotes: NUT = Nutrition, HLT = Health, PRO = Protection, WAT = Water, SAN = Sanitation, HOU = HousingSource: ZHBS 2014/15 (author’s calculations)

0 100000 200000

WAT

0

20

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100

Consumption**

Perc

en

t*

Uraban Rural

0 100000 200000

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Consumption**

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Uraban Rural

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erc

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Consumption**

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Uraban Rural


Housing, and to a lesser extent, sanitation show a continued fall in deprivation with increases in consumption, at least up to the maximum consumption shown in the graph (Tsh 200,000, less than USD 100 in 2015). These are still relatively low consumption levels and increases in consumption are associated with more gradual improvements in these dimensions of well-being.

In contrast, the lines for the health, protection and water dimensions are almost fl at, indicating a very weak to no link between consumption levels and deprivation. In particular, for health and protection, deprivation levels are very low across the entire consumption spectrum. For both dimensions, one also observes that there is virtually no difference between rural and urban areas in terms of levels of deprivation, at any level of consumption. This suggests important achievements in ensuring access to healthcare, birth registration of children and protection from child labour and child marriage throughout Zanzibar. For healthcare, one should also consider the indicators used in the MODA index – access to a healthcare provider when a child is sick, and the occurrence of preventable diseases such as malaria and diarrhoea in the previous four weeks. Other aspects of health deprivation such as neonatal care, are not captured in this measure.

Levels of deprivation relating to the water dimension show a slight negative relationship to consumption only among urban households below the poverty line.


In this section, we look in more detail at the number of dimensions children are deprived in, and how these relate to selected characteristics, as well as at the overlap between different deprivations. The notion of overlap is at the heart of multidimensional poverty analysis. The fundamental intuition is that a child suffering from several deprivations at the same time will be worse off than someone suffering from just one deprivation. For this reason, it is relevant to know not only how many children are deprived in a given dimension, but also which children are deprived in different dimensions, and whether it is the same children who tend to be deprived in different dimensions, or whether different groups experience different deprivations.

The policy response required to address, for instance, a scenario where 50 per cent of children are out of school and 50 per cent of children are undernourished would be quite different if dropping out of school was an exclusively urban phenomenon and undernourishment an exclusively rural one, compared to if both problems were concentrated, say, among girls. In the fi rst case, a targeted education sector intervention might be required in urban areas, complemented by nutrition or agricultural interventions in rural areas. In the second case, it might be that undernourished children drop out because they are unable to concentrate in school, in which case a nutritional intervention might suffi ce to address both issues. Or it might be that both problems are caused by an issue that is specifi c to girls (e.g., cultural practices or discrimination), and they would not be resolved either by an educational or nutritional intervention on its own, without a broader approach to gender inequalities.

4.1 Number of deprivations We have examined the distribution of dimensional deprivations in Figure 16 at the beginning of this report. Figure 24 depicts the same graph, but this time it shows the disaggregation by age groups. The analysis by age is important as the MODA index uses a life-cycle approach with different indicators and dimensions relevant to different ages. In this index, children aged 0–4 years are

MULTIPLE DEPRIVATIONS ANALYSIS

CHAPTER: 4


only assessed in six dimensions, while children aged 5–17 years can be deprived in a maximum of eight dimensions.

The graph confi rms that the youngest age group is more likely than other age groups to experience deprivations in one, two or three dimensions of well-being. For instance, children of the 0–4 years age group represent one-third of all children, but make up half of the children deprived in one, two and three dimensions, respectively. Older children, particularly the 12–15 years age group, on the other hand, appear to be deprived in a relatively substantial number of dimensions. About 20 per cent of all children are in this age group, but they represent about a third of all children with deprivations in fi ve dimensions, and an even larger proportion among those children deprived in six dimensions. The data does not allow us to determine whether this age distribution refl ects the types of indicators chosen for specifi c age groups, or whether it refl ects a genuine disadvantage of older children. In either case, it will be important to keep this factor in mind when comparing different population subgroups, as it could bias results in cases where different groups have different demographic compositions. For instance, older children may be over-represented in the relative deprivation measure examined in the previous sections of the report, i.e., children who are deprived in more than half of the relevant dimensions.

Only very few children are deprived in seven or eight dimensions simultaneously (around 2 per cent and less than 1 per cent), and the same can be observed for children without any deprivations (less than 2 per cent).

Figure 24: Number of dimensional deprivations, by age group

Source: ZHBS 2014/15 (author’s calculations); Notes: Base population: all children.

In Figure 25, we compare the number of dimensional deprivations for children living in urban vs. rural areas. Unsurprisingly, we fi nd that children in rural area tend to experience more deprivations than children in urban areas: around 28 per cent of rural children experience deprivations in four or more dimensions, compared to just 7 per cent of urban children. Virtually all rural children

35%

0%

5%

10%

15%

20%

25%

30%

0 1 2 3 4 5 6 7 8

Age 0-4 5-6 7-8 9-11 12-15 16-17


experience at least one deprivation. There is a slight difference in the age distribution of urban and rural children, with an average age of 7.6 years in rural areas and of 8.1 years in urban areas. Given the observations made above about the number of deprivations among the younger and older age groups, this means that the disadvantage for rural children is even more pronounced than suggested in this graph if age distributions were similar.

The ZHBS 2014/15 allows for the analysis of results disaggregated to the district level in Zanzibar. Table 11 presents the proportion of children experiencing deprivations in one to eight dimensions by district. Only a small percentage of children have no deprivations or are deprived in all eight dimensions simultaneously. Over the whole of Zanzibar, only 2.5 per cent of children are not deprived in any dimension, while children in Magharibi (5.8 per cent) and Mjini (3.4 per cent) exceed the overall average. Across all children in Zanzibar, 83 per cent are deprived in between two and fi ve deprivations simultaneously; this was also the case for eight of the ten districts, with the exception of Magharibi (73 per cent) and Mjini (77 per cent), where the proportion is lower than the average across the whole of Zanzibar. Micheweni is the district where children suffer from the largest number of dimensional deprivations, with 15 per cent of children deprived in fi ve or more dimensions.

Table 12 presents the number of children who are deprived in three or more dimensions by district and by sex of the household head and highlights the variations that are prevalent between male and female-headed households. In most districts, the variation ranges between 1 and 7 percentage points. However, in the Kaskazini B district, the percentage of children with three or

Figure 25: Number of deprivations, by area of residence


0

10

20

30

0 2 4 6 8

Perc

en

t o

f ch

ildre

n

Uraban Rural


Table 11: Percentage of children by number of deprivations and age N

um

ber

of

dep

riva

tio

ns

per

ch

ild

Kas

kazi

ni A

Kas

kazi

ni B

Kat

i

Ku

sin

i

Mag

har

ibi

Mjin

i

Wet

e

Mic

he-

wen

i

Ch

ake

Ch

a

Mko

ani

Tota

l %

N

0 2.0% 0.6% 0.0% 0.7% 5.8% 3.4% 0.6% 0.0% 0.3% 0.8% 2.5% 272

1 4.0% 4.7% 8.1% 7.2% 20.3% 18.6% 4.5% 1.5% 2.6% 4.4% 10.5% 1,262

2 13.0% 13.2% 23.3% 29.0% 31.0% 30.8% 16.6% 4.7% 13.1% 13.9% 21.0% 2,535

3 36.1% 37.9% 30.0% 38.8% 25.8% 28.2% 33.3% 26.3% 32.2% 34.4% 30.3% 3,835

4 24.9% 24.5% 23.1% 17.0% 12.9% 12.4% 25.8% 27.6% 26.2% 29.2% 20.4% 2,600

5 13.8% 13.6% 11.6% 5.9% 3.5% 6.0% 14.2% 25.0% 18.4% 13.5% 11.0% 1,431

6 4.3% 5.1% 2.9% 1.2% 0.8% 0.6% 4.5% 12.1% 5.9% 3.4% 3.5% 464

7 2.0% 0.3% 1.1% 0.2% 0.1% 0.0% 0.4% 2.8% 1.3% 0.4% 0.7% 96

8 0.1% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 2

Total %

100 100 100 100 100 100 100 100 100 100 100

N 961 688 732 617 2,935 1,592 1,160 1,300 1,262 1,250 12,497


Table 12: Percentage of multidimensionally poor children (deprived in three or more dimensions) by district and sex of household head

District Male-headed household (%) Female-headed household (%) Total (%)

Kaskazini A 80.6 84.2 81.0

Kaskazini B 79.5 90.0 81.4

Kati 67.8 71.5 68.6

Kusini 59.1 76.1 63.0

Magharibi 41.6 50.1 43.0

Mjini 47.5 46.5 47.2

Wete 78.4 77.8 78.3

Micheweni 93.4 95.5 93.8

Chake Chake 84.3 82.8 84.0

Mkoani 80.6 82.3 81.0

Total 65.6 68.3 66.1


more deprivations in male-headed households is 79.5 per cent, while amongst children residing in female-headed households, the percentage is 90.0. Overall, the highest percentage of children deprived in three or more deprivations is in Micheweni at 93.8 per cent and the lowest is in Magharibi district (43 per cent).

In Figures 26 and 27, we disaggregate the intensity of deprivation for different population subgroups, based on household and child/mother characteristics. As a reminder, the intensity of deprivation is simply the number of deprivations experienced by a child, divided by the total number of dimensions across which well-being is measured. This will ensure that the fi gures are comparable for children under fi ve, whose well-being is only measured across six dimensions, with children over the age of fi ve, whose well-being is measured across eight dimensions.10

Children living in households where the household head is employed in agriculture have the worst outcome of any group, being deprived, on average, in about half of all dimensions, followed by children living in rural households (0.48). Children in large households with more than eight household members are deprived in a slightly larger share of dimensions (0.44) compared to smaller households. Based on the graph, it also appears that children in youth-headed households are more vulnerable than children living in households with older household heads. However, the differences observed are mostly not statistically signifi cant, except for the difference between under 25 years old household heads and 35–49 years old household heads.

10 Interpretation of the results for different age groups is still problematic due to the fact that different indicators are being used for different age groups. Thus, the term ‘comparability’ is used here only to refer to direct numerical comparison, not as a comparison of well-being levels.

Note: hhsizegrp=number of household members, agehead=age of household head, loc=location, femaleh=head of household is male or female; Red line represents the intensity of deprivations of children who are multidimensionally poorSource: ZHBS 2014/15 (author’s calculations)

Figure 26: Intensity of deprivations, by household

.5

.3

.35

.4

.45

hhsize gr agehead loc femaleh

Inte

nsi

ty o

f d

ep

rivati

on

s

>8

5-8

<5

rural

urban

female

male

65+

25-3450-64

35-49

<25

Agri

Employee

No workNon-agri

emphead


Figure 27: Intensity of deprivations, by household and mother/child characteristics

Note: analytage: age for MODA analysis, disabled= no disability or at least one disability, orphanany=child living with mother or father or neither parent, edumum=education level of mother: no education, primary, more than primary education, marriedm=mother is married or has no spouse; Red line represents the intensity of deprivations of children who are multidimensinally poorSource: ZHBS 2014/15 (author’s calculations)

When focusing on individual mother and child characteristics (Figure 27), children of mothers who have more than primary education (0.36) are deprived in a smaller share of deprivations relative to the children of mothers who have no education (0.51). Whether the mother is married or not also seems to make a difference although children whose mothers are married experience slightly higher shares of deprivation. Moreover, children aged 0–4 years have lower deprivation levels than children in older age groups.

Figure 28 captures three concepts in one graph. The overall poverty score (M0) is a combination of the incidence of poverty (H), that is the proportion of children deprived in at least k dimensions, and the intensity of poverty (A), that is, the share of dimensions in which a child is deprived. The poverty headcount (H) tells us how many children are affected, whereas the intensity (A) indicates how badly those children are affected. The three concepts are presented separately for children below the age of fi ve years and children aged 5–17 years in order to account for the fact that for children below the age of fi ve years, poverty is measured across only six dimensions, while for the age group 5–17 years, poverty is measured across eight dimensions. This implies that the two fi gures are not directly comparable. The graph highlights that almost all children are deprived in at least one dimension (H~100 per cent), and that, on average, children experience deprivation in close to 40 per cent of dimensions (A=0.4 for one or more dimension). The headcount drops, however, relatively quickly with fewer than 60 per cent of under fi ve years old deprived in three or more dimensions.

5

35

4

45

analytage disabled orphanany edumum mariedm

Inte

nsi

ty o

f d

ep

rivati

on

s

14-17

no disabone+ dis

yes

primary

no educa

no spous

married

more tha

no

5-89-13

0-14


Figure 28: Multi-dimensional poverty headcount, intensity, overall poverty score by age group

4

4

6

6

8

8

1

2

20

0

Perc

en

t o

f ch

ildre

n (

H)

Inte

nsi

ty (

A)

/ MO

sco

re

0

20

60

40

80

100

M = H x A

Intensity(A)Headcount(H)

4

4

6

6

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8

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Perc

en

t o

f ch

ildre

n (

H)

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nsi

ty (

A)

/ MO

sco

re

0

20

60

40

80

100

M = H x A

Intensity(A)Headcount(H)

Note: Base population: all childrenSource: ZHBS 2014/15 (author’s calculations)

4.2 Dimensions vs indicatorsThe MODA poverty index looks at deprivations across dimensions; thus, a child is considered to be deprived in a given dimension if she or he is deprived in at least one indicator in that dimension. This approach implies that one child can be deprived in the same number of dimensions as another child, but their distribution of deprived indicators can differ. Imagine a case in which Child A is deprived in four indicators in a single dimension, say education. The child may be illiterate, out of school, and have to walk more than 30 minutes on foot to school. This child is clearly very deprived in education but does not suffer from deprivations in any other dimensions (i.e., she or he is well-nourished, in good health, etc.). Child B, on the other hand, suffers only in a single indicator in education across several dimensions. In education she has never attended pre-school, but is currently enrolled in school, where she is learning to read and write, etc. At the same time, Child B is undernourished, and does not have access to either clean water or good sanitation. The total number of deprivations in terms of indicators suffered by these two children is identical, but their situations are clearly very different, and so are the responses required to address their issues. For this reason, it is relevant to look not only at the intensity of dimensional deprivations – understood as the number of indicators of deprivation suffered by a child – but also at the concentration, or the spread of those indicators of deprivation across different dimensions.

Figure 2 at the very beginning of the report illustrated the overlap between monetarily poor children and multidimensionally poor children. There was a large, but not perfect, overlap between the two concepts. Here, we explore this question further. Figure 29 presents the difference between the total number of dimensions in which a child is deprived and the predicted number of dimensions, based on the number of indicators in which the child is deprived.11 Blue bars pointing upwards indicate that children in the specifi ed groups are deprived in more dimensions than what would be expected based on the number of indicators in which they are deprived, as well as their age. In

11 The model was estimated using an ordered probity regression, using the number of dimensions as the dependent variable, and the proportion of deprived indicators as the explanatory variable. It takes into account non-linearities in the relation between the number of dimensions and indicators in which a child is deprived, by using the squared value of the proportion of deprived indicators. This is to account for the different patterns of deprivations of extreme groups, with a very large or very small number of deprivations. The model also controls for age differences between children, to account for the fact that the MODA index is non-comparable across age groups.


other words, these are children with a large spread of deprivations across dimensions. A negative number indicates that the deprivations are concentrated in fewer dimensions than expected for children of similar age and deprived indicators.

The left-hand-side axis gives the difference between the actual versus predicted number of dimensions. Most differences are less than +/- 0.03, which means that based on a selected number of household characteristics, there does not appear to be signifi cant difference between the total number of dimensions in which a child is deprived and the predicted number of dimensions based on the number of indicators the child is deprived in. The only exception is with regards to the age of the household head, where, for youth-headed households, the deprivations are concentrated in fewer dimensions than expected for children of similar age and deprived indicators who do not reside in youth-headed households. However, it should be noted that the number of observations where the household head was below the age of 25 years was small. Also, children in small households are deprived in fewer dimensions than one would expect based on the number of indicators, while the reverse is the case for large households with more than eight household members.

Figure 29: Spread of deprivations across dimensions, by household characteristics


0.1

0.05

0

-0.05

-0.1

-0.15

-0.2

-0.25

35

30

25

20

15

10

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0

Deprived indicators (weighted)Diff. actual – predicted

Dif

f. a

ctu

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red

icte

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imen

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rived

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Employment of head Age of head Household sizeSex of head Area

self-

empl

oyee

no w

ork

mal

e he

ad

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ale

head

rura

l

25-3

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5-7 >8

50-6

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urba

n

non-

agri

cultu

re s

elf e

mpl

oyee

empl

oyee

Figure 30 points to the fact that the differences between the predicted and actual values are small across both the mother and child characteristics, namely mother’s age at birth, the marital


status and education of the mother, whether the child is an orphan, with disabilities, or a boy or girl. Children of mothers who hold primary level education or children with disabilities tend to be deprived in slightly more dimensions than would be expected.

4.3 Correlation and overlap between dimensionsThis sub-section analyses the correlation and overlap across dimensions.

In Figure 31, the dimensions have been ordered in decreasing incidence rates, with those dimensions on the left having higher prevalence rates. Each bar shows what percentage of children deprived in the given dimension, e.g., sanitation, are deprived in just this one dimension, or are deprived in two, three, four and up to eight dimensions. It is expected that for dimensions which have a high rate of deprivation, for example housing and sanitation, more children will suffer in only that deprivation or in a few dimensions, as it will include a larger mix of deprived and less deprived children. For this reason, we have ordered dimensional deprivation rates in decreasing order from 84.0 per cent in sanitation, and 71.3 per cent in housing to 3.7 per cent in health.

Despite these signifi cant differences in prevalence rates, one can observe considerable constancy in the degree of overlap between dimensions; for instance, one fi nds that among children deprived in the housing, nutrition and protection dimensions, about 25¬–26 per cent are deprived in a total of four dimensions. Moreover, we fi nd that among children suffering from a health deprivation, a

Figure 30: Spread of deprivations across dimensions, by mother/child characteristics

Note: Base population: mother’s characteristics: children with biological mother living in the household and valid observations; child and household characteristics: all childrenSource: ZHBS 2014/15 (author’s calculations)

0.1

0.05

0

-0.05

-0.1

40

35

30

25

20

15

10

5

0

Mother: Age at birth Marital status Education CHILD: Orphan Disability Sex

Deprived indicators (weighted)Diff. actual – predicted

Dif

f. a

ctu

al p

red

icte

d d

imen

sio

ns

Nu

mb

er

of

dep

rived

ind

icato

rs

< 1

8

18-2

4

25-3

4

35-4

9

Mar

ried

No

par

tner

No

ed

uca

tio

n

Pri

mar

y

No

orp

han

Orp

han

No

dis

abili

ty

Dis

abili

ty

Bo

y

Gir

l

Mo

re t

han

Pri

mar

y


small percentage are affected by a larger number of other deprivations than children deprived in protection, which has a higher prevalence rate. This means that children deprived in health tend to have deprivations that are more concentrated over fewer dimensions than those deprived in protection. Among children deprived in the protection dimension, 58 per cent are deprived in more than four dimensions, while this is the case for only 38 per cent of children deprived in health, and only 18 per cent of children deprived in the sanitation dimension.

Figure 31: Percent of overlap for specifi c dimensions with other dimensions

Note: Base population: all children (children aged <5 years will have max of 6 deprivations) Source: ZHBS 2014/15 (author’s calculations)

1 dim 2 dim 3 dim 4 dim 5 dim 7 dim6 dim 8 dim

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Health

Prote

ctio

n

Wat

er

Educatio

n

Info

rmat

ion

Nutri

tion

Housing

Sanita

tion

Perc

ent o

f de

priv

ed

7 3 3 2 1 17 5

4

13

25

25

24

93

13

21

36

17

72

15

33

22

17

7

17

30

31

11

2

18

8

32

28

10

2

14

35

26

16

51

15

35

26

15

51

19

33

23

13

41

The overlap in dimensions provides important insights for the design of policies. For example, the dimensions of protection and education might be interlinked, as children may be out of school as they work to contribute to the family’s income. This would require interventions to address child labour as policies addressing access to schooling may not be suffi cient. A high overlap of dimensions may highlight the need for policies that address structural issues simultaneously rather than sector-specifi c interventions. Understanding the correlation and overlap in specifi c dimensions can therefore also provide useful insights, in particular for the design of interventions that target specifi c groups.

Table 13 shows the pairwise correlation coeffi cient between the different dimensions children are deprived in. The highest correlations can be observed between the education and protection dimensions (0.41), as well as housing and nutrition (0.38). Some of the correlations which one would expect to be relatively strong, such as between health and sanitation, or health and water, are low, which can be explained by the fact that the indicators used to measure deprivation in


health partly capture different aspects (e.g., access to healthcare services) other than those often caused a by lack of improved sanitation and water, and that overall the prevalence of health deprivation measured is very low.

Table 13: Tetrachoric correlation coeffi cients between deprivations in dimensions

Nu

mb

er o

f d

epri

vati

on

s p

er c

hild

Nu

trit

ion

Ho

usi

ng

San

itat

ion

Wat

er

Info

rmat

ion

Hea

lth

Ed

uca

tio

n

Pro

tect

ion

Nutrition 1.00

Housing 0.38 1.00

Sanitation 0.28 0.25 1.00

Water 0.15 0.16 0.26 1.00

Information 0.22 0.21 0.20 0.15 1.00

Health 0.13 0.15 0.08 0.00 0.02 1.00

Education 0.20 0.22 0.17 0.15 0.14 0.05 1.00

Protection 0.14 0.09 0.03 0.24 0.10 0.02 0.41 1.00

Note: Base population: all children (children aged <5 years will not be included for measures of education and communication)Source: ZHBS 2014/15 (author’s calculations)

Figure 32: Overlap in dimensional deprivations

Note: Base population: all children; this means that for education and communication, children aged under fi ve are considered as not deprived rather than being excluded from the sampleSource: ZHBS 2014/15 (author’s calculations)

Education Communication Protection

10

15

4

5

3 48

12

15

2

11

12

3

13

Nutrition Sanitation Housing

Overlap Nutrition, Sanitation, Housing Overlap Education, Information, Protection


Only 16 per cent of children are deprived in both, the education and information dimensions. The overlap in deprivations between all three dimensions – education, information and protection – is less pronounced with only 3.1 per cent of children deprived in all three dimensions, also due to the low prevalence of protection-related deprivations in Zanzibar.

4.4 Determinants of deprivationIn this section, we look at the factors infl uencing the number of deprivations that a child experiences, using multi-variate regression analysis.12 The advantage of using a multi-variate regression is that it takes into account the role of an explanatory variable while controlling for other characteristics of the child at the same time. We may, for instance, fi nd that children of divorced mothers tend to have better outcomes than children of married mothers. However, in actual effect, this might not be due to the benefi ts of being divorced, but due to the fact that divorce is more common in urban areas, which tend to be better off to begin with. If we look at both these factors simultaneously, we may fi nd that the net effect of divorce on children is negative.13

In addition to the variables reported in Figures 33 and 34 below, consumption was included in the regression to control for differences in income/consumption between different groups (e.g., families who are better educated tend to have a higher income, which may account for better outcomes). The results have been estimated separately for children under the age of fi ve and children aged fi ve and over. For the latter group, a variable controlling for age group has also been included.

Figure 32: Overlap in dimensional deprivations (contd.)

Health SanitationWater

72

93

Figure 32 presents the overlap across deprivations that can be considered to be related. There is a high degree of overlap between nutrition, sanitation and housing with 48 per cent of children deprived in all three dimensions. Because of the high incidence of deprivation related to sanitation, there is only a very small overlap with children being deprived in health and water (0.26 per cent of children deprived in all three dimensions), and the overlap between health and water-deprived children is surprisingly small.

Overlap Water, Health, Sanitation

12 The model was constructed using the proportion of deprived indicators as the dependent variable. In addition to the explanatory variables reported here, we included the log of consumption and its squared value to control for differences in income/consumption between various groups. We also included the age group of the respondent to control for differences in the composition of the index for different age groups. As we were interested in analysing the effect on the poorest children, we used a quantile regression, set at the 75th percentile. This means that the reported coeffi cients refer to the effect on children in the 75th percentile with most deprivations.

13 This is a hypothetical example to illustrate the value of multi-variate analysis. In our model, we have not included mother’s marital status nor the mother’s age due to the large number of missing observations for these variables. Consequently, we are not able to say anything about the impact of divorce on deprivations in this specifi c case.


Figure 33 Effects of child, mother and household characteristics on the proportion of deprived indicators (weighted) for children under fi ve years

Note: Base population: all children; this means that for education and communication, children aged under fi ve are considered as not deprived rather than being excluded from the sampleSource: ZHBS 2014/15 (author’s calculations)

A negative coeffi cient (bar pointing to the left) indicates that children satisfying a specifi ed characteristic tend to experience deprivations in fewer indicators than the reference group (indicated with “ref.”), while a positive coeffi cient (bar pointing to the right) indicates that the characteristic is associated with experiencing more deprivations.

In general, fi ndings from earlier sections that looked at each characteristic separately are confi rmed by the multi-variate analysis. Among both age groups, orphans are shown to be deprived in a larger number of indicators than children living with both biological parents. Moreover, children living in households where the household head is an own account worker in the agricultural sector or the non-agricultural sector face higher deprivation levels than those where the head of the household is an employee or employer. Among younger children, children in households where the household head is currently not working experience higher deprivation levels, though this relationship is not observed for children aged fi ve and over.

Children living in households in urban areas rather than rural areas, in larger households and where the mother has more than primary education are signifi cantly better off than children in the respective reference groups. The deprivation-reducing effect of the mother’s education is more pronounced among children aged fi ve years and older than among younger children. The benefi ts of residing in an urban area are slightly more pronounced for younger children.

-10.00 -8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00

More than primaryPrimaryNo education (ref.) M

othe

r’sed

ucat

ion

Half or full orphanNo orphan (ref.) O

rpha

nSe

x of

ch

ildGirlBoy (ref.)

No workAgric own accountNon-agric own accountEmployee/emploer (ref.) Em

ploy

men

t of

hea

d

Female headMale head (ref.) Fe

mal

ehe

ade

d H

H

Urban(Rural ref.) Re

side

nce

65+50-6435-5025-34(<25 ref.)

Age

of h

ead

>8 members5-8 members(<5 members ref.) H

H s

ize


4.5 Children cumulating multiple vulnerabilitiesUntil now, our analysis has looked at differences between groups of children, considering one criterion at a time. In other words, we only considered a single source of vulnerability each time. In real life, however, children are likely to suffer from several vulnerabilities simultaneously. Understanding how these multiple sources of vulnerability interact to reinforce or mitigate each other is crucial in understanding how we can improve the well-being of the worst-off children.

Figures 35 and 36 present the average number of dimensions in which a child is deprived, depending on the combination of vulnerabilities he or she exhibits. The set of vulnerabilities considered here has been selected through trial and error in an effort to identify interesting patterns. Thus, these are only examples of vulnerabilities, and there are many other vulnerabilities and combinations of vulnerabilities which could be considered. Figure 35 presents monetary poverty, area of residence, whether the household is male- or female-headed and whether the household has more than eight members. Starting from the left-hand side of the graph, we fi nd that on average, children in Zanzibar are deprived in 3.1 dimensions. The risk plot then splits children by monetary poverty status. The upper red line represents children living in monetarily poor households while the lower black line represents children residing in households that are not monetarily poor. The thickness of the line

Figure 34: Effects of child, mother and household characteristics on the proportion of deprived indicators (weighted) for children aged 5–¬17 years


-12.00 -10.00 -8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00

Age 16-17Age 12-15Age 9-11Age 7-8Age 5-6 (ref.)More than primaryPrimaryNo education (ref.)Half or full orphanNo orphan (ref.)GirlBoy (ref.)No workAgric own accountNon-agric own accountEmployee/emploer (ref.)

Female headMale head (ref.)Urban(Rural ref.)

>8 members5-8 members(<5 members ref.)

65+50-6435-4925-34(<25 ref.)

HH

siz

eA

ge o

f hea

dRe

side

nce

Fem

ale

head

e d

HH

Empl

oym

ent

of h

ead

Sex

of

child

Orp

han

Mot

her’s

educ

atio

nCh

ild’s

age


also decreases to show the change in the size of the sample in each of the groups.14 As expected, children living in monetarily poor households tend to suffer from more deprivations than children living in non-monetarily poor households (3.8 vs 2.7). The rural disadvantage observed earlier is also visible here. Even amongst non-poor households, the gap between rural and non-rural households in terms of number of deprivations is large (3.2 vs 2.2). If a child lives in a rural, monetarily poor, female-headed household which has more than eight members, these children will be deprived in 4.0 dimensions, compared to being deprived in 2.1 dimensions when these vulnerabilities are not present.

14 Of course, the further to the right we move on the risk plot, the smaller the sample size, as we disaggregate into smaller and smaller subgroups. For this reason, it is important to consider confi dence intervals when making comparisons. For instance, for the children experiencing all vulnerabilities (money-poor, rural, and female-headed and large household) the point estimate is 4.0 deprivations with a confi dence interval from 3.5 to 4.5 (95% confi dence). For the group with no vulnerabilities, the point estimate is 2.1 deprivations with a confi dence interval ranging from 2.0 to 2.3 deprivations.

Figure 35: Average number of deprived dimensions, by cumulated vulnerabilities (household-level)

Note: Base population: all children.Source: ZHBS 2014/15 (author’s calculations)

4

3.5

3

2.5

2

Poor Rural Femaleh Largehh

Black = No Red = Yes

Mean

nu

mb

er

dep

rived

dim

en

sio

ns

Figure 36 presents a different set of vulnerability criteria, namely: whether the mother has no education, was younger than 25 years old when the child was born, is currently unmarried and whether the child is female. The fi ndings suggest that the education level of the mother is an important determinant of the child’s well-being. A child whose mother is educated suffers from 2.8 deprivations, while a child whose mother has no education suffers from 3.8 deprivations. Children of mothers who were younger than 25 years old at the time of the birth experience more deprivations whether the mother has no education (4.0 deprivations) or some education


15 As sample sizes are relatively small once multiple vulnerabilities come together, the confi dence intervals around these estimates are relatively large. Where the maximum number of vulnerabilities come together, the point estimate is 4.2 deprivations with a 95 per cent confi dence interval ranging from 3.3 to 5.2. In the latter case, where the point estimate is 2.7, the confi dence interval is 2.4 to 3.0.

(3.0 deprivations). Female children appear to be slightly better off than male children, though the difference is negligible for children whose mother has some education. Having an unmarried mother is linked to an increase in the number of deprivations a child experiences among all combinations of vulnerabilities.

A boy whose mother has no education, was younger than 25 years at the time of his birth and currently has no spouse, has, on average, 4.2 deprivations. A girl with a mother with at least primary education, who was 25 years or older at the child’s birth and currently has a spouse, has, on average 2.7 deprivations.15

Figure 36: Average number of deprived dimensions, by cumulated vulnerabilities (mother and child characteristics)

Note: Base population: children whose mother lives in the household and has valid observation for characteristicSource: ZHBS 2014/15 (author’s calculations)

4.5

4

3.5

3

2.5

Noedm Youngmum Girl Unmarried

Black = No Red = Yes

Mean

nu

mb

er

dep

rived

dim

en

sio

ns


5.1 Dimensional deprivationsThis section presents deprivations for each dimension in order to better understand the factors that drive the results presented in the previous sections. Figure 37 shows the percentage of children who are deprived in at least one indicator for each of the eight dimensions used to construct the MODA index. The graph indicates at the same time the age distribution of deprived children in each of the dimensions. This allows to assess whether specifi c age groups are affected more than others by deprivations in particular areas of well-being and to account for the non-comparability of indicators across age groups.

Children in Zanzibar are most likely to be deprived in the sanitation and housing dimensions. In total, 84 per cent of children are deprived in terms of at least one indicator in sanitation and 71 per cent in housing, while the prevalence for water is 10 per cent, for protection 8 per cent and for health 4 per cent. However, while the percentage of children affected by a deprivation in water is small, this does not imply that the deprivation is less severe for those who suffer from inadequate access to clean water.

Children aged 0–4 years old are more likely to be affected by deprivations in nutrition, housing and sanitation, accounting for 23 per cent, 24 per cent and 28 per cent, respectively, of all children deprived in these dimensions. They also represent the largest share among children deprived in the water dimension (3.2 per cent). Compared to their share in the population, the 12–15 years old and the 16–17 years old are over-represented among those deprived in the protection dimension. The 5–6 years old face higher than average deprivations in education, which is likely to be due to the relatively low coverage of pre-school education.

SINGLE DEPRIVATION ANALYSIS

CHAPTER: 5


Figure 37: Children deprived in at least one indicator per dimension, by age group


An analysis of deprivations in single dimensions can also be analysed for the 10 districts in Zanzibar (Figure 38). In most districts, the order of deprivations across single dimensions follows the Zanzibar average discussed above, with the highest prevalence in the sanitation dimension, and the lowest in the health dimension. However, there are some exceptions. In Kaskazini A, Kaskazini B, Kati and Chake Chake, more children are deprived of adequate nutrition than they are deprived in the housing dimension. Similarly, several districts have made further progress with respect to the provision of water, as water deprivation rates in Kasakzini B, Kusini, Magharibi, Mjini, Wete and Mkoani are all lower than the deprivation rate in the protection dimension.

As the district-level analysis suggests, levels of deprivations vary considerably across Zanzibar. Children in Micheweni experience the highest levels of deprivation in almost all the dimensions, with the exception of the nutrition dimension, whereas Kaskazini A and B, as well as Chake Chake, reach similar levels. In Micheweni, the incidence rate for deprivation in water was 30.9 per cent compared to 2.3 per cent in Mjini. Gaps in other dimensions are even larger, e.g., one observes a gap of 46 percentage points between nutrition deprivation levels in Kaskazini A (90 per cent) and Magharibi (44.2 per cent).

These results can assist local policymakers in identifying vulnerable children and with prioritisation within their district to develop targeted interventions.

Age 0-4 Age 5-6 Age 7-8 Age 9-11 Age 12-15 Age 16-17

3.7%8.1% 9.8%

30.4% 31.9%

67.5%71.5%

84.0%

Health

Prote

ctio

n

Wat

er

Educatio

n

Info

rmat

ion

Nutri

tion

Housing

Sanita

tion

100%

90%

80%

70%

60%

50%

40%

30%

20%

10%

0%

Perc

ent o

f ch

ildre

n



Kaskazini A

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

87.3

73.1

90.1

55.8

50.4

10.9

3.8

15.3

Kati

0 20 40 60 80 100

74.9

72.0

83.9

47.5

47.6

9.8

2.8

14.7

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Magharibi

0 20 40 60 80 100

74.7

53.3

44.2

36.1

38.5

5.4

2.8

4.8

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Kaskazini B

0 20 40 60 80 100

91.9

80.5

89.1

49.8

47.1

9.9

3.9

8.0

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Mjini

0 20 40 60 80 100

75.3

56.7

51.7

39.2

35.4

6.7

2.4

2.3

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

Kusini

0 20 40 60 80 100

88.2

64.3

73.1

42.2

30.9

5.7

3.4

2.8

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health


Figure 38: Dimensional deprivation, by district (contd.)

Wete

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

93.8

86.4

70.9

55.3

50.4

6.6

5.0

6.0

Chake Chake

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

92.4

85.8

89.7

56.9

47.2

9.8

5.3

14.1

Micheweni

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

98.3

94.0

88.3

64.1

65.1

13.1

5.9

30.9

Mkoani

0 20 40 60 80 100

Sanitation

Housing

Nutrition

Information

Education

Water

Protection

Health

89.5

90.0

68.8

56.0

51.2

9.0

3.6

6.9

The radar graphs in Figure 39 present the dimensional deprivation rates by area of residence (urban/rural) and the monetary poverty status of the child. Children residing in rural areas and in monetarily poor households have higher deprivation rates relative to children residing in urban non-monetarily poor households in Zanzibar across most dimensions. The largest gap between urban and rural areas can be found in the nutrition and housing dimensions, followed by the education and communication dimensions. Differences in monetary and non-monetary poverty follow a largely similar pattern. No statistically signifi cant difference can be detected between poor and non-poor children with respect to deprivations in the health dimension.



Figure 39: Children deprived in at least one indicator in each dimension, by area of residence and monetary poverty status

Nutrition

Communication

Education

Water Health

Housing

Sanitation

Protection

100

80

60

40

20

RuralUrban Non-poorPoor

Nutrition

Communication

Education

Water Health

Housing

Sanitation

Protection

100

80

60

40

20

Notes: Base population: Children 5-17 for communication and education dimensions, otherwise all childrenSource: ZHBS 2014/15 (author’s calculations)

5.2 Indicators This sub-section analyses deprivations for the indicators included in the construction of each of the eight dimensions.

Figure 40 highlights variations in deprivation rates across different indicators both across and within dimensions. The MODA index considers a child deprived in a dimension if she or he is deprived in any one indicator in that dimension.

For example, in the sanitation dimension, 80 per cent of children lived in households without handwashing facilities, while the deprivation rate for unimproved sanitation was lower at 43 per cent. The indicator with the highest prevalence level will determine the overall deprivation status of a child in the dimension.

Similarly, deprivation in education overall is strongly infl uenced by low levels of pre-school attendance among 5–6 years old. Other deprivations in education show considerably lower prevalence rates, ranging from a relatively low percentage of 13 per cent of children aged 7 to 15 years old who are not attending school, to 28 per cent of 16–17 years old who have never attended secondary school. In the nutrition dimension, the deprivation rates are also spread more evenly across indicators, with 29 per cent of households having fewer than three meals a day, 34 per cent of households achieved a dietary diversity score of less than three, while 46 per cent of households were classifi ed as moderately or severely food insecure.

As observed earlier, only few children are deprived in the protection dimension. Among the three indicators which contribute to the measurement of child protection, child labour, in the sense of children being exposed to economic activities as their main job or missing school due to work, has the highest prevalence (8 per cent), while early marriage (under age 18) and lack of birth certifi cates are very rare.


Notes: Base population: as defi ned by age cut-offsColours: yellow; nutrition; brown: housing; purple: sanitation; navy: water; green: information; orange: health; blue: education; red: protection.Source: ZHBS 2014/15 (author’s calculations)

Figure 40: Children deprived by indicators within dimensions

Meal frequency

Food insecure

Dietary diversity

Polluting fuel

Overcrowding

Type of fl oor/roof/walls

Unimproved sanitation

No handwashing

Unimproved water

Time to water

Communication

Information

Health care

Malaria/diarrhoea

No preschool

No attending

Over-age

No secondary school

Time to school

Illiterate

No birth registration

Early marriage

Child labour

29.4

45.8

33.7

5.7

62.0

35.8

42.7

79.5

8.5

1.5

26.6

39.3

2.6

1.3

58.4

12.8

24.5

28.0

21.4

24.6

3.0

1.6

7.6

Overall, the indicator-specifi c analysis reveals that in most dimensions, deprivations are driven by just one or two indicators. For this reason, it is important to also consider indicator-specifi c information wherever possible, to better understand the specifi c issues and associated causes that are driving the observed deprivation patterns.


6.1 ConclusionsIn this report, the poverty situation of children in Zanzibar was analysed using the Multiple Overlapping Deprivation Analysis (MODA) methodology, which has been developed by UNICEF and adapted to the Zanzibar context together with the Offi ce of Chief Government Statistician (OCGS), using information on monetary poverty status, provided by the OCGS. The MODA approach was developed to better capture the complex and interdependent nature of vulnerabilities affecting children. Children have developmental needs that are unique and are only sometimes indirectly or not at all related to a household’s fi nancial constraints. Consequently, it is not suffi cient to look at monetary poverty to understand how well children are doing in society, and how to target and tailor policies to improve children’s well-being.

Our study has confi rmed that while there is a positive correlation between monetary and multidimensional poverty, the concordance between monetary poverty and multidimensional poverty is imperfect and uneven. In fact, less than half of the children who are among the poorest of the population in monetary terms are also classifi ed among the poorest in terms of their deprivations across MODA dimensions, and a signifi cant share of the deprivation poor are not monetary poor. This means that that if the focus is on monetary poverty alone, one risks leaving out a sensational proportion of the children who suffer the most severe deprivations.

The largest discrepancies between monetary and multidimensional poverty was found for children not living with their parents, children living in rural areas and children living in youth-headed households, who had much higher levels of deprivations than what their monetary consumption levels would predict. Our analysis showed that children who cumulate several of these vulnerabilities are in a particularly bad condition. For instance, a child who lives in a rural, monetarily poor, female-headed household which has more than eight members will be deprived in 4.0 dimensions, compared to an average of 2.1 dimensions for a child who does not present any of these vulnerabilities. Children with overlapping deprivations may require attention, and

CONCLUSION AND RECOMMENDATIONS

CHAPTER: 6


policies targeting them will need a different approach from those addressing high levels of deprivation within a given dimension.

Overall, children are deprived, on average, in less than half of the age-relevant dimensions (0.42). Also, considering the different dimensions a child can be deprived in, Zanzibar has already made far-reaching achievements in some dimensions, with low levels of deprivation in child protection and access to water and health, though there are differences across districts. Deprivation in other dimensions remains relatively high, e.g., in sanitation, nutrition and housing. Often, particular indicator-level deprivations are driving these dimension-level results, such as the lack of handwashing facilities in the sanitation dimension.

6.2 RecommendationsBased on the fi ndings of this analysis, the consultants would recommend carrying out further in-depth studies, including qualitative research, to understand the nature and drivers of disadvantage for some of the most vulnerable children identifi ed in this study. For these, and for other children, it will be crucial to understand the barriers that prevent children from achieving their full potential, if we are to effectively address their vulnerabilities. What the study suggests is that while the non-monetarily poor tend also to do better with respect to multidimensional poverty, many children who are not monetarily poor still experience deprivations in some of the dimensions, and some of these barriers therefore appear to be non-monetary. This means that the problems are unlikely to disappear by themselves if one focuses only on promoting economic growth and reducing monetary poverty.

The ZHBS is a very rich dataset that allows researchers to gain a fi ne-grained understanding of child vulnerability, covering both monetary and multidimensional aspects. However, as is always the case in these kinds of studies, the scope of the analysis and our understanding of child poverty will be limited by the availability of information on the different forms of vulnerability affecting children. For instance, as the survey focuses on the household as a unit of analysis, few variables can be constructed that vary across children within a household. Moreover, changes to the survey design and questionnaire between the last two ZHBS rounds meant that a trend analysis for the two rounds 2009/10and 2014/15 could not be included in this report. For this reason, it is suggested that comparability be maintained in future ZHBS survey rounds in order to track changes in multidimensional poverty in the coming years, and to explore improvements in data collection efforts so as to allow for further deepening our understanding of the nature and the drivers of child poverty in Zanzibar.


Atkinson, A. B., ‘The Contributions of Amartya Sen to Welfare Economics’, Scandinavian Journal of Economics, vol. 101, no. 2, 1999, pp. 173 –190.

Bradshaw, J., Petra Hoelscher and Dominic Richardson, An Index of Child Well-being in the European Union, Social Indicators Research, 2006.

Brock, K. and Carolina Knowles, Young Lives Methods Guide, Young Lives, 2011.

Brown, C., Martin Ravallion and Dominique Van De Walle, Are Poor Individuals Mainly Found in Poor Households?, 2017.

de Milliano, M. and Ilze Plavgo. ‘The Basics of the Cross-Country Multiple Overlapping

Deprivation Analysis (MODA)’, MODA In Brief 1: Innocenti Working Paper 2014–2019, UNICEF

Offi ce of Research, Florence, 2014.

de Neubourg, C., et al., ‘Child Deprivation, Multidimensional Poverty and Monetary Poverty in

Europe’, Innocenti Working Paper 2012 –02 , UNICEF Offi ce of Research, Florence, 2012.

de Neubourg, C., Marlous de Milliano and Ilze Plavgo, ‘Lost (in) Dimensions: Consolidating

Progress in Multidimensional Poverty Research’, Innocenti Working Paper 2014–04, UNICEF

Offi ce of Research, Florence, 2014.

Ministry of Education and Vocational Training, Revolutionary Government of Zanzibar, Education Policy, 2006.

Gordon, D., Child poverty in the developing world, Policy Press, 2003.

REFERENCESCHAPTER: 7


Haughton, J., and Shahidur R. Khandker, Handbook on poverty and inequality, World Bank

Publications, 2009.

Ministry of Labour, Empowerment, Elderly, Youth, Women and Children, Government of Zanzibar,

Integrated Child Policy Zanzibar, Draft version, February 2017.

Offi ce of the Chief Government Statistician, Government of Zanzibar, Household Budget Survey 2014/15, 2016.

Ravallion, M., ‘On Multidimensional Indices of Poverty’, Journal of Economic Inequality, vol. 9,

no. 2, pp. 235–248, 2011.

Revolutionary Government of Zanzibar, Zanzibar Strategy for Growth and Reduction in Poverty (ZSGRP II) 2010–2015, MKUZA II , 2010.

Townsend, P., Poverty in the United Kingdom, Allen Lane and Penguin Books, London, 1979.

Resolution adopted by the United Nations General Assembly on 25 September 2015: 70/1.

Transforming our World: the 2030 Agenda for Sustainable Development’, A/RES/70/1, 21 October

2015.

United Nations General Assembly, Promotion and Protection of the Rights of Children: Report of

the Third Committee, United Nations, New York, 2006.

United Nations, 1989. Convention on the Rights of the Child.

Wordsworth, D., Mark McPeak and Thomas Feeny, ‘Understanding Children’s Experience of

Poverty: An Introduction to the DEV Framework’, Working Paper 1, Christian Children’s Fund,

Richmond, Virginia, 2005.


Annex A: Additional statistical tables

Table 14: Deprivations and monetary poverty, by household characteristics (%)

Disaggregation criteria

Category Depr.>1/2 dim. (%)

Share dep. dim. (%)

% depr. indicators

Money poor (%)

>=1 depr. (%)

>=3 depr. (%)

>=5 depr. (%)

-All Children

18.5 0.42 26.4 34.7 97.5 66.1 15.3

Household size

<5 15.6 0.39 25.4 8.0 97.4 57.1 10.9

5-8 18.0 0.41 25.9 28.5 96.7 64.0 14.9

>8 20.6 0.44 27.8 57.9 99.2 73.7 18.1


<24 24.3 0.46 32.8 16.5 98.7 68.0 18.7

25–34 18.6 0.43 27.7 23.6 98.3 63.4 12.0

35–49 16.9 0.41 25.7 34.5 96.8 64.5 14.2

50–64 19.2 0.42 26.4 40.3 97.8 68.4 17.0

65+ 24.5 0.44 28.0 38.6 99.2 72.3 22.2

Area of residence

Rural 26.8 0.48 31.6 44.8 99.4 79.9 22.0

Urban 6.3 0.33 18.8 19.9 94.8 45.6 5.4

ANNEXURES




Share dep. dim. (%)

% depr. indicators

Money poor (%)

>=1 depr. (%)

>=3 depr. (%)

>=5 depr. (%)


Male 17.9 0.41 26.2 34.8 97.5 65.6 14.6

Female 21.2 0.43 27.6 34.5 97.7 68.3 18.7


Employee/employed

9.0 0.34 20.0 21.1 94.6 48.4 7.3

Agriculture own account

13.6 0.40 24.3 29.4 97.8 63.1 11.2

Non-agriculture own account

29.4 0.49 33.1 49.2 99.5 82.0 24.6

Not working/family worker

17.8 0.40 25.1 33.7 97.8 63.2 14.9

Monetary poverty status

Non-poor 11.3 0.37 22.3 0.0 96.3 55.4 8.8

Poor 32.0 0.50 34.1 100.0 99.8 86.1 27.6

Table 15: Deprivations and monetary poverty, by child characteristics (%)



Share dep. dim. (%)

% depr. indicators (%)

Money poor (%)

>=1 depr. (%)

>=3 depr. (%)

>=5 depr. (%)

- All children 18.5 0.42 26.4 34.7 97.5 66.1 15.3

Sex of childBoys 19.0 0.42 26.5 35.9 97.5 65.9 15.7

Girls 17.9 0.42 26.4 33.5 97.6 66.2 15.0

Age of child

0–23 months

11.0 0.40 24.1 27.2 96.4 52.2 0.6

24–59 months

8.9 0.40 23.6 30.7 96.3 52.4 0.7

5–13 years 21.7 0.42 28.7 36.9 98.2 73.4 21.7

14–17 years 25.2 0.43 25.1 40.1 97.8 71.6 25.2





Share dep. dim. (%)

% depr. indicators (%)

Money poor (%)

>=1 depr. (%)

>=3 depr. (%)

>=5 depr. (%)

Child’s disabilitystatus

No disability

18.5 0.42 26.4 34.9 97.6 66.1 15.4

At least one disability

16.2 0.41 26.0 27.0 96.0 65.0 10.8

Child living with...

Both parents

17.9 0.41 26.2 34.8 97.3 65.0 14.3

Mother/father only or neither

19.7 0.42 26.8 34.7 98.0 68.2 17.3


None 34.9 0.51 35.2 56.3 99.5 86.4 29.4

Primary 21.3 0.45 28.5 41.0 99.3 72.1 17.0

More than primary

8.6 0.36 21.2 21.0 95.6 52.1 6.3

Mother’s maritalstatus

Spouse/ partner

18.2 0.42 26.3 34.8 97.3 65.3 14.6

No spouse 19.2 0.44 28.1 33.9 99.6 69.9 16.0

Mother’s age at birth ofchild

<18 years 25.5 0.45 29.6 33.5 98.8 73.8 23.8

18 –24 years

20.0 0.43 27.6 31.2 98.2 68.0 16.3

25–34 years

17.6 0.41 25.9 33.7 97.4 63.8 14.2

35–49 years

16.6 0.41 26.0 41.6 96.7 65.6 12.9



Table 16: Percentage of children deprived by dimension, by household characteristics

Disagg-regation criteria

CategoryNutrition (%)

Housing (%)

Sanitation (%)

Water (%)

Health (%)

Protection (%)

Education (%)

Com (%)

-All Children

67.53 71.31 84.03 9.79 3.70 8.10 45.55 47.85

Household size

<5 69.84 51.91 85.11 9.29 4.84 7.51 45.28 52.99

5 –8 65.79 65.79 84.38 10.54 3.54 7.86 44.24 48.27

>8 69.79 89.94 82.90 8.61 3.52 8.81 47.96 45.59


<24 79.16 68.07 88.41 6.40 10.32 10.13 64.48 65.48

25–34 70.32 70.43 87.69 11.38 4.33 6.89 50.45 50.92

35–49 65.87 70.64 82.16 9.38 3.44 7.02 44.25 47.73

50–64 66.70 72.00 85.25 9.81 3.80 9.30 44.91 46.71

65+ 74.27 74.92 84.15 9.77 3.15 12.54 48.54 48.16

Area of residence

Rural 79.82 82.38 89.48 13.84 4.43 9.58 52.68 55.50

Urban 49.40 55.00 75.99 3.83 2.62 5.92 35.37 36.92


Male 67.00 72.04 83.82 9.92 3.66 7.68 45.66 45.92

Female 69.90 68.06 84.97 9.24 3.88 9.98 45.10 55.64


Employee/ employed

55.35 56.30 75.92 6.86 3.07 5.34 36.46 38.38

Agriculture own account

64.21 68.88 80.40 6.90 3.81 8.22 44.45 43.60

Non-agric own account

79.22 85.77 93.03 14.88 4.10 9.78 53.68 57.30

Not working

66.99 64.71 83.26 7.04 3.59 9.18 41.76 49.70

Monetary poverty status

Non-poor 60.11 61.36 79.75 7.73 3.58 6.90 39.21 40.05

Poor 81.46 90.02 92.07 13.66 3.93 10.37 56.01 60.71



Table 17: Percentage of children deprived by dimension, by mother/child characteristics

Disagg-regation Criteria

CategoryNutrition (%)

Housing (%)

Sanitation (%)

Water (%)

Health (%)

Protection (%)

Edu-cation (%)

Com (%)

All children

67.53 71.31 84.03 9.79 3.70 8.10 45.55 47.85

Sex of child

Boys 67.60 70.24 84.39 9.20 3.99 8.46 48.12 46.57

Girls 67.45 72.40 83.65 10.39 3.41 7.74 42.95 49.13

Age of child

0 –23 m 67.60 70.24 84.39 9.20 3.99 8.46 na na

24–59 m 67.45 72.40 83.65 10.39 3.41 7.74 na na

5–13 y 68.70 70.80 84.68 9.46 6.08 2.13 48.12 46.57

14–17 y 69.14 69.53 84.35 9.48 3.11 1.96 42.95 49.13

Child’s disability status

No disability

67.44 71.42 84.06 9.83 3.65 8.13 45.39 47.79

At least one disability

71.80 65.92 82.45 7.70 6.19 6.81 57.29 52.19

Child living with...

Both parents

67.17 72.23 84.68 10.43 3.88 6.78 44.86 45.96

Mother/father only or neither

68.23 69.46 82.69 8.50 3.34 10.75 46.74 51.04


None 79.34 87.67 95.49 17.75 4.45 13.68 58.05 58.57

Primary 73.54 80.66 86.65 10.87 4.19 6.75 48.80 49.67

More than primary

59.02 60.36 78.63 6.11 3.68 3.97 34.82 39.66

Mother’s marital status

Spouse/ partner

67.30 72.10 84.66 10.11 3.98 7.01 44.70 46.73

No spouse 72.32 72.83 86.64 11.16 4.26 7.89 46.96 57.60

Mother’s age at birth of child

<18 70.97 74.01 88.61 13.48 3.56 14.12 47.76 49.66

18–24 70.94 73.63 86.75 11.08 3.48 8.01 45.92 48.86

25–34 65.83 70.26 84.00 10.04 4.30 5.97 44.68 48.02

35–49 67.55 74.06 83.73 9.12 4.08 7.38 43.48 43.66



Table 18: Percentage of children deprived by indicator within dimension and age group

Disaggregationcriteria

Category

Age 0–4 years (%)

Age 5–6 years (%)

Age 7–8 years (%)

Age 9–11 years (%)



Nutrition

Fewer than 3 meals 30.28 28.66 30.08 30.18 28.52 25.94

Food insecurity status

45.49 47.66 47.06 45.30 45.83 43.65

Food diversity 35.06 33.08 34.62 32.87 32.70 32.07

Housing

Polluting fuel 11.37 4.65 3.41 2.10 2.28 3.32

Overcrowding 59.37 65.07 65.72 63.37 62.33 59.78

Floor/roof/walls 34.92 35.74 37.92 36.43 35.24 36.73

Sanitation

Unimproved or shared

44.46 43.52 43.67 42.23 41.07 38.33

No handwashing 79.06 82.16 79.61 79.81 79.14 78.23

WaterUnimproved 8.87 8.86 10.11 7.97 7.72 7.19

>30 min to water 0.62 0.70 0.39 0.66 2.18 7.45

Health

Sick and no healthcare

2.67 2.47 3.06 2.40 2.51 2.97

Malaria/diarrhoea 2.50 0.97 0.82 0.60 0.50 0.68

Protection

No birth certifi cate 2.06 2.18 2.71 3.24 4.54 3.80

Early marriage na na na na 0.79 3.48

Child labour na 2.58 2.84 3.73 10.02 23.09

Education

No preschool na 58.42 na na na na

Not enrolled 7–15 na na 22.14 6.50 12.69 na

Overage for grade na na na 11.38 25.62 48.45

Never attended sec na na na na na 27.96

>30 min by foot na na 31.77 21.82 21.24 35.76

Illiterate na na na 41.38 17.49 8.61

InformationNo comm na 26.69 26.91 27.86 25.59 26.30

No information na 37.81 42.71 40.23 37.82 38.80



Table 19: Comparison of indicators based on the NPS (2012/13) and the HBS (2014/15)

Dimension Indicators

CRC category NPS 2012 –13 Census 2012 ZHBS (2014/2015) Variables

Survival and development

NU

TRIT

ION

Meal frequency (less than 3 meals per day)

Meal frequency (Less than 3 meals per day)

F4-S2Q2

Dietary diversity score <3: Food groups consumed in past week (Form IV or diary – condition on consumption)

F4-S2Q3

Household is moderately or severely food insecure based on HFIA score

HFIAScore

constructed based on F4-S2Q4 to F4-S2Q21


HEA

LTH

Mother’s assisted delivery (traditional birth attendant, friend or relative, none, other)

none

Mother’s antenatal care (no regular visit to clinic when mother pregnant)

Child was sick or injured, but did not attend healthcare provider, also used traditional healer or medicine

F1-S1Q33, Q35, Q36

Child suffered malaria or diarrhoea in the last 4 weeks

F1-S1Q33-34

Protection

PRO

TEC

TIO

N

Birth registrationBirth registration

Birth registration (birth certifi cate or notifi cation)

F1-S1Q7

Child labour (UNICEF defi nition and hazardous act)16

Child labour (UNICEF defi nition and hazardous act)17

Child labour: Child missed school due to work, economic activity, or business activity

F1-Q1921,Q26

Early marriage (married before 18 years)

Early marriage (married before 18 years)

Child married or previously married

F1-S1Q17





EDU

CA

TIO

N

Preschool enrolment 5-6 years (or attendance?) (yes/no)

Not in pre-school at age 4 or 5 years; and also not in primary school

F1-S1Q13,Q14

Completed primary (yes/no)

Completed primary (yes/no)

School enrolment (yes/no)

School enrolment (yes/no), 5+

Not in school between ages 7–15 years

F1-S1Q13

Grade for age (2+ years behind grade for age)

Grade for age (2+ years behind grade for age)

Grade for age (2 or more years over the regular age for grade

F1-S1Q13, Q05

Time to school: Takes more than 30 min to primary, and more than 1 hour to secondary school, only by foot for deprived

F1, Q17

Not able to read and write (at age 10)

Literacy – not able to read and write in English or Swahili

F1-S1Q55-57

Never attended secondary school

F1, Q13

INFO

RM

ATI

ON

No communication devices (No computer/radio/TV/mobile phone)

No communication devices (No computer/radio/TV/mobile or fi xed phone)

No information devices – TV/books/radio

F2-S7Q1

Participation

No communication devices (computer/mobile phone/landline phone)

F2-S6Q15

16 For children aged 5–11 years: more than one hour of economic activities or more than 28 hours of chores (e.g., fetching fi rewood or water) per week. For children aged 12–14 years: more than 14 hours of economic activities, or more than 28 hours of chores, per week. For children aged 15–17 years: more than 43 hours of total activities per week. Hazardous activities: House girls/boys; miners, blasters, stone cutters, mineral processors and mining plant operators and the like; metal molders, welders and the like; metal processors and metal plant operators; chemical processors and chemical plant operators; and construction laborers and the like.

17 Ibid.





SA

NIT

ATI

ON

Unimproved sanitation (unimproved or shared)

Unimproved sanitation (without info on shared)

Households has unimproved or shared sanitation

F4-S9Q4,Q5

Disposal of stools (buried, left in the open, other)

Household has no hand washing facilities

F4-S9Q6


WA

TER

Source in rainy season (unimproved source without treatment of water)

Unimproved drinking water

Unimproved water sources in dry and rainy seasons, unless treated

F4-S9Q8,10,11

Time in dry season (30+ minutes roundtrip to fetch water)

Distance to water source: More than 30 min to water sources, regardless of season

F4-S9Q16


HO

US

ING

Overcrowding (> median people per room)

Overcrowding (> median people per room)

Overcrowding (> 2 adult equivalent per room)

FII, S2Q2

Natural fl oor Natural fl oorNatural fl oor: palm/bamboo or earth

FII, S2Q4-Q6

Natural roof Natural roofNatural roof: grass/leaves or mud and plastic

Natural walls: mud or grass

Household has one room and uses polluting fuel for cooking

FII,S2Q2, FIV S9Q03



Documents

Child Poverty in Zanzibar Poverty in Zanzibar low res.pdf · education, information, sanitation, water and housing. Deprivation is also assessed at the indicator level. Dimension: