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MODA Approach Provides Insights for Improving Child Well-Being Policy
- 1. Multiple Overlapping Deprivation Analysis (MODA) approach: Cross-Country MODA Study METHODOLOGY & TANZANIA COUNTRY OUTPUT Office of Research at Innocenti and Chris de Neubourg, Jingqing Chai, Division of Policy and Strategy Marlous de Milliano, Ize Plavgo, UNICEF Ziru Wei
- 2. MODA is a tool designed to guide equity related policy interventions • by providing a systematic procedure to identify deprived children by providing details • on the type of deprivations (incidences and depth) • on systematic coincidences of deprivations (overlap) • on the profiles of the deprived children and the families they live in • by pointing towards the mechanisms to be understood to design effective policy instruments • building on previous studies in multidimensional poverty and deprivation 4 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 3. MODA enhances • A holistic view (vs. sectorial view) • deprived children: the number and nature of single and combined deprivations from which they suffer; • Equity analysis: • comparing the severity, breadth and composition of deprivations according to geographic location and socio-economic characteristics of children and households; • Policy efficiency: • designing integrated interventions to reduce multiple deprivation simultaneously, if common inequity-generating mechanisms and common barriers and bottlenecks are identified; • Life-cycle approach, • considering children in age-groups and acknowledging that children’s needs differ depending on their age. 5 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 4. MODA can be applied at various levels • International comparative level (CC-MODA) • National specific level (N-MODA) • Sub-national level (Local L-MODA) • The basic methodology is the same, but the details and outcomes of the analysis can differ depending on the data used 6 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 5. Methodology • International standards are used as guiding principles to select dimensions of child well-being Food, nutrition (Art.24) Water (Art.24) Survival Health care (Art.24) Shelter (Art.27) Survival and Environment/Pollution (Art.24) Development Rights Education (Art.28) Leisure (Art.31) Development Cultural activities (Art.31) Information (Art.13, 17) Exploitation, Child Labor (Art.32) Protection Other forms of exploitation (Art.33-36) Cruelty, violence (Art.19, 37) Protection Protection Rights Violence at school (Art.28) Protection Social Security (Art 16, 26, 27) Protection/ Birth registration, Nationality (Art. 7, 8) Participation Participation Information (Art.13, 17) Participation Rights Freedom of expression, views, opinions; being Participation heard; freedom of association (Art.12-15) 8 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 6. CC-MODA methodology • Data limitations shape the choice of dimensions • The two main data sources used in the CC-MODA are the Demographic and Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS) 9 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 7. CC-MODA Methodology Table 2.2 Availability of relevant dimensions in DHS and MICS Indicators available in Indicators available in Dimensions DHS MICS Food, nutrition Only for children under 5 Only for children under 5 Water Yes Yes Health care Only for children under 5 Only for children under 5 Shelter Yes Yes Environment/Pollution X X Education Yes Yes Leisure X X Cultural activities X Yes Information Yes Yes Exploitation, Child Labor X Yes Other forms of exploitation X X Exposure to violence, Exposure to violence, Cruelty, violence experienced by the mother experienced by the mother Violence at school X X Social Security X X Birth registration, Nationality Only for some countries Yes Freedom of expression, views, opinions; being heard; freedom of X X association 10 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 8. Methodology • A life-cycle approach has been further employed to capture children’s age-specific needs • An inference approach can also be applied, covering all the dimensions for all children disregarding their age • Moreover, a ‘whole-child’ perspective is adopted. This means that for each age group the following is calculated: • the number of deprivations each child experiences simultaneously • the headcount of children deprived in 1, 2, 3…6 dimensions (H) • the deprivation intensity (A) • the intensity adjusted headcount (M0) 11 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 9. Figure 1 The Life-cycle approach: selection of dimensions for each age group Nutrition Education Exposure Exposure Health to Information to violence violence Age 0-4 Age 5-17 Housing Water Housing Water Sanitation Sanitation 12 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 10. Country Case: Tanzania (DHS 2010) (simple headcounts by dimensions) Figure 2.1 Deprivation headcount (%) of each MODA dimension, by age group Age 0-4 Age 5-17 Nutrition Education Domestic to 38% Health Domestic Information violence to violence 18% 23% 29% 13% 9% 48% 44% Housing 67%Water Housing 65%Water 86% 83% Sanitatio Sanitation n Note: all children included, even those with missing dimensions for consistency purpose 14 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 11. Simple headcount by indicators Figure 2.2 Deprivation headcount (%) of each MODA indicator 0% 20% 40% 60% 80% 100% Nutrition [ Height for age (0-4) 35.2% 4.4% Health [ 17.9% Mortality (0-4) 7.9% Education [ School attendance (5-17) 6.7% 11.7% Information - 29.4% Water [ 55.3% Distance to water 29.5% Sanitation - Toliet type 84.2% Housing [ Overcrowding 9.2% 40.8% Violence - Exposure to violence 10.0% 16 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 12. Cumulative deprivation in groups Figure 2.3 Deprivation pyramids based on the number of deprivations per child, urban vs. rural (D = Number of dimensions deprived in) Age 0-4 Age 5-17 D=6 D=6 D=5 D=5 D=4 D=4 D=3 D=3 D=2 D=2 D=1 D=1 D=0 D=0 40% 20% 0% 20% 40% 40% 20% 0% 20% 40% Children from rural areas Children from rural areas Children from urban areas Children from urban areas Note: All children of the reference age are included, with missing dimensions treated as non-deprived. 17 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 13. Exploiting mathematical properties allowing flexibility Table 2.1 Multi-dimensional deprivation indices, based on different cut-offs Age 0-4 Deprivation Deprivation headcount Average intensity across Adjusted deprivation headcount cut-off (H) the deprived (A) (M0=H*A) ≥ 1 dimension 95.0% 55.3% 0.458 ≥ 2 dimensions 82.8% 52.9% 0.438 ≥ 3 dimensions 59.9% 60.3% 0.362 ≥ 4 dimensions 28.5% 71.7% 0.205 ≥ 5 dimensions 7.7% 85.3% 0.066 ≥ 6 dimensions 0.9% 100.0% 0.009 Age 5-17 Deprivation Deprivation headcount Average intensity across Adjusted deprivation headcount cut-off (H) the deprived (A) (M0=H*A) ≥ 1 dimension 92.2% 54.4% 0.413 ≥ 2 dimensions 76.0% 50.8% 0.386 ≥ 3 dimensions 50.2% 59.8% 0.301 ≥ 4 dimensions 23.5% 71.0% 0.167 ≥ 5 dimensions 5.7% 84.5% 0.048 ≥ 6 dimensions 0.4% 100.0% 0.004 18 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 14. Table 3.1 Percentage of children with certain characteristics: low vs. highly deprived children Age 0-4 Low deprivation High deprivation Share of children with the following characteristics, (Deprived in 1 or 2 (Deprived in 5 or 6 as % of the total deprivation level dimensions) dimensions) Locate in rural area 71.9% 95.6% Gender of the child is girl 50.9% 50.2% No birth certificate 76.2% 95.5% Child number in the household ≥5 57.9% 64.9% Mother has no primary education 15.9% 44.2% Wealth index among the lowest 20% * 3.9% 56.9% Wealth index among the richest 20% * 21.8% 0.1% Age 5-17 Low deprivation High deprivation Share of children with the following characteristics, (Deprived in 1 or 2 (Deprived in 5 or 6 as % of the total deprivation level dimensions) dimensions) Locate in rural area 75.2% 97.1% Gender of the child is girl 50.9% 48.1% Child number in the household ≥5 69.2% 70.8% Mother has no primary education 17.0% 43.4% Wealth index among the lowest 20%* 1.5% 81.5% Wealth index among the richest 20% * 19.4% 0.0% 20 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 15. Figure 4.2 Composition of the adjusted deprivation headcount ( ≥2 dimensions), by dimension and area Age 0-4 National composite M0=0.438 (91.6%) (8.4%) Rural Urban By area M0=0.486 M0=0.210 (13%) (19%) (20%) (10%) By dimension Nutrition Housing Nutrition Housing H=38% H=48% H=30% H=14% 25 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 16. Figure 5.2a Overlapping deprivation analysis based on specific dimensions, Age 0-4 28 Outline – MODA objective – Methodology – Country Case - Conclusion
- 17. Figure 5.2a Overlapping deprivation analysis based on specific dimensions, Age 0-4 29 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 18. Figure 5.2a Overlapping deprivation analysis: profiling the children deprived in nutrition, health, and water simultaneously, based on specific characteristics, Age 0-4 30 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 19. Figure 5.2a Overlapping deprivation analysis: profiling the children deprived in nutrition, health, and water simultaneously, based on specific characteristics, Age 0-4 31 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 20. Figure 5.2b Overlapping deprivation analysis based on specific dimensions, Age 5-17 32 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 21. Table 5.1 Regional ranking based on the child deprivation headcount (H) in overlapping deprivations (whole- child approach), compared to ranking based on single sectors Age 0-4 Age 5-17 Deprived in Deprived in Deprived in Deprived in Deprived in Deprived in Deprived in Deprived all 3 Nutrition Health Water all 3 Education Housing in Water Tabora Tabora Rukwa Lindi Shinyanga Rukwa Tanga Dodoma Mara Shinyanga Dodoma Mtwara Manyara Mwanza Lindi Manyara Mwanza Mbeya Morogoro Kigoma Kagera Kagera Kigoma Singida Iringa Morogoro Zanzibar north Pwani Singida Tanga Mbeya Mara Pwani Arusha Arusha Zanzibar north Mtwara Iringa Ruvuma Ruvuma Kilimanja-ro Pemba north Pemba north Zanzibar south Town west Kilimanjaro Pemba south Zanzibar south Dar es Salaam Dar es Salaam Pemba south Town west 35 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 22. • The locational profiling of single-sector deprived child can be quite different from the locational profiling from highly multiple deprived child Deprivation headcount (H) in nutrition Adjusted Deprivation headcount (M), Deprived in Nutrition K≥4 Deprived in Nutrition & K>=4 (including nutrition) Rukwa .228 - .297 .002 - .068 .297 - .389 .068 - .124 .389 - .45 .124 - .167 .45 - .507 .167 - .254 36 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 23. • UNICEF program focuses on Immunization campaign. • To what extend the children targeted (i.e. those immunized) are also children who are experiencing higher deprivations? Age 2-4 6 deprivations Not immunized Immunized 5 deprivations 4 deprivations 3 deprivations 2 deprivations 1 deprivation 0 deprivation 40% 30% 20% 10% 0% 10% 20% 30% 40% Note: When judging whether deprived in Health, immunization indicator is excluded, therefore only mortality is used, to avoid double-counting problem. 38 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 24. CC-MODA: Global comparison Figure 6.1 Global ranking based on the adjusted deprivation headcount (M0, ≥2 dimensions) Age 0-4 Age 5-17 Tanzania 0.438 Tanzania 0.397 Kenya 0.310 Kenya 0.386 Liberia 0.286 Liberia 0.371 Malawi 0.264 Malawi 0.348 0.0 0.1 0.2 0.3 0.4 0.5 0 0.1 0.2 0.3 0.4 0.5 39 Outline – MODA objectives – Methodology – Country Case - Conclusion
- 25. Use of the outputs • A tool in the toolbox for SitAn : equity focused • A instrument for global advocacy messages (especially in the discussion of post-2015 agenda) • Impact on programming interverntions 43
- 26. Continuation of the project • Analysis for all countries with available MICS 4 and DHS V/VI (50) • Data dissemination through: • Interactive web portal • Global report • N-MODA • L-MODA 44 Outline – MODA objectives – Methodology – Country Case - Conclusion
Editor's Notes
- EQUITY in MODA: MODA can answer ‘Who’ is deprived, and ‘what’ these deprivations are (what single deprivations and what overlaps). It is then possible to answer “which groups” are more deprived (have less access to the resources they need) by comparing the worse off groups with the better off groups. “Most (or more) deprived children” – those who are worse off than the other groups of children: those suffering from more than one deprivation.This stems from the ‘equity’ definition: “Inequities are perceived as unnecessary and avoidable social differences, which lead to unfair circumstances and the deprivation of children’s rights” (UNICEF, 2011). From this follows that the worse off are those groups of children who happen to be disadvantaged due to social or other differences that block them from accessing the resources and services they need to be able to fulfill their rights. MODA can help to identify these groups that have unfair and inequitable circumstances by identifying the children deprived in no or only one sector, and the children who are deprived in several sectors simultaneously. By profiling them, that is, by investigating what are the differences between the better off and the worse off children, it is possible to discover which groups of children are suffering from inequitable deprivations. Once these groups are identified and the deprivations these children are deprived of are identified, there is a further need for qualitative research (outside the reach of MODA) to find out what are the drivers of these inequities, what are the reasons these groups of children are disadvantaged (e.g. lack of appropriate policy mechanisms or gaps in the national legislation, lack of service supply, bad quality of services, lack of information about the existing services, stigma and discrimination against certain groups, lack of integration, complexity or travel difficulties and costs to access the services, etc.)Life-cycle approach: From an equity perspective, the knowledge of individual needs is essential to understand what response is required to create an equal set of opportunities for all. Whenever possible, equity should be measured based on individual level data since children of different age and characteristics have different needs. Thus, whether they have access to the necessary resources or not should be assessed using individual level data and assessing each age-groups with their relevant needs separately.
- 1. Focusing on international comparisons implies that the data sets and the indicators used for each country in the study need to be the same to guarantee international comparability.
- The latest questionnaire waves, are used: DHS V/VI (2007-2011), and MICS4 (2009-2011). So far, we only have DHS data and only 1 country with MICS data (Bhutan). The combination of DHS V/VI and MICS4 creates a sample of 77 cases, including 27 cases with DHS datasets, 43 with MICS datasets, and 6 cases for which both questionnaires are available.Selection of countries is subject to change, since not all datasets have been released yet.
- “Whole-child approach” = multi-dimensional child-centered approach.Life-cycle approach: all children under the age 18 are divided into two age-groups: age 0-4 (infancy and early childhood), and age 5-17 (primary childhood and adolescence). The deprivation dimensions are grouped accordingly, choosing indicators relevant to the specific age-groups. Life-cycle approach from an equity perspective: the knowledge of individual needs is essential to understand what response is required to create an equal set of opportunities for all. Whenever possible, equity (deprivation) is measured based on individual level data since children of different age and characteristics have different needs. Thus, whether they have access to the necessary resources or not is assessed using individual level data and assessing each of the two age-groups with their relevant needs separately.The inference approach: all children under the age 18 are treated equally, applying all eight dimensions presented in the next slide to each child regardless of their age. For the dimensions of Nutrition, Health, and Education, information is not available for children of both age-groups, either due to data limitations (e.g. no indicators on the nutritional status, health and immunization for children above the age 5), or irrelevancy at the moment of data collection (e.g. information on education for children below the age 5). It is therefore suggested to assign an inferred status to those with missing information, based on the information of the deprivation status of other children in the same household. If at least one child of the same household who has information on the specific dimension is identified as deprived in that dimension, an inferred value of ‘deprived’ is assigned to the other children who belong to another age-group. The underlying logic is that, if the other children who have no information on a specific dimension are sharing the same household with children deprived in that dimension, it is reasonable to consider these children vulnerable in the specific dimension.
- From these figures it can be seen that vast majority of children who live in urban areas are deprived of either no deprivations (22%), or 1-2 deprivation (55%). On the contrary, vast majority of those children who live in rural areas are deprived in 3-4 dimensions (60%). Furthermore, when comparing children suffering from 5 deprivations simultaneously, almost 10% of children in rural areas are so severely deprived, while less then 2% in urban areas suffer from 5 deprivations simultaneously. situation between rural and urban areas is inequitable.
- Alkire and Foster have developed a method to adjust the headcount measure, H, by the average number of deprivations experienced by the deprived, denoted by A. M is thus the child deprivation headcount, adjusted for the number of deprivations experienced by the multi-dimensionally deprived. This measure satisfies the “dimensional monotonicity” and will change when the deprivations of the deprived change.
- *: The wealth index is obtained from DHS dataset, constructed with all household assets and utility services through Principle Component Analysis (PCA). It is to be noted that four of the CC-MODA dimensions are included in the construction of the wealth index (Housing, Water, Sanitation, and Information). Therefore, a cautionary interpretation of the statistics is required. Part of the correlation between child deprivation headcount and wealth index comes from the overlapping dimensions, while the rest shows the real correlation between child specific dimensions and wealth measured by household assets and utility. This explanation applies to elsewhere when wealth index is utilized for profiling.Interpretation of the table: Inequity can be analyzed by comparing the various social groups. Example: No birth certificate: out of all those children deprived in 1-2 deprivations, 76% do not have a birth certificate while only 24% of the deprived have a birth certificate. Even worse, when looking at those children who are deprived in 5-6 dimensions (the worse off), less than 5% of these deprived children have a birth certificate while the other 95% have not been registered. in this way, possible disadvantaged groups suffering from inequity can be identified. For example, more than 95% of those deprived in 5-6 deprivations live in rural areas, so less than 5% of the extremely deprived children live in urban areas. This can be further investigated by breaking down in regions, provided that the data sample allows. Another example: mothers’ education: for almost half of the children who are severely deprived (deprived in 5-6 deprivations), their mothers have no education, not even primary education. This can be further investigated, braking down into mothers with primary education, secondary, and higher education.This table can be further expanded, including other possible risk groups, such as geographic regions, ethnicities, orphans, and disabled children, provided that the data allows. For such analysis, better, more detailed country-specific data analysis is preferable so that more comparisons between the various social groups could be carried out to identify where inequity can be found.
- *: The wealth index: same comment as in the last slide.Comment on ‘Girls’: need to be careful interpreting the gender differences, since among the 5 deprivations, it is only possible to have 2 gender-specific dimensions since the others are based on household level data. The next slide is put in to show the gender differences, if seen as necessary to show.Interpretation of the table: While in rural areas, there are 9% of children deprived in 5 deprivations simultaneously, only 2% are so extremely deprived among those living in urban areas. Thus, the odds ratio to be severely deprived in 5 deprivations simultaneously is 4.5 times higher if a child lives in rural, compared to urban, areas. Example on mothers’ education: the probability to be deprived in 5 dimensions simultaneously is 2.2 times higher for those children whose mothers have no education, compared to those whose mothers has at least primary education (or secondary, or higher). Again, many more possible risk groups can be added and analyzed in this way to enable discovering inequity between the various social groups (geographic location, ethnicity, disability, and others, provided that the data and the sample size allow).
- The specific indicators that may show Gender differences, if seen as necessary to show.
- The adjusted deprivation headcount M is an index that is composed of both, the total number of children deprived in two or more dimensions (H), and the average intensity of deprivation each of these deprived children experiences (average number of deprivations experienced simultaneously, A).Decomposition of the adjusted headcount ratio M0 by subgroup can offer additional insights by showing the contribution of each subgroup to the national deprivation level. For instance, the contribution of two regions to the overall adjusted headcount ratio M0 can show which of the two regions contributes the most to the overall deprivation level. This decomposition can be carried out on more characteristics and social groups.
- Multiple overlapping analyses can usefully inform the discussion around cost-effectiveness of public policies. Overlaps between dimensions have implication for economic scope, by showing which deprivations could be addressed together as to make policy more integrated. Overlap analyses can help answering the question “what” the deprived children need, what resources and services they do not have access to. This figure shows the percentage of children who are deprived only in one particular dimension and do not suffer from any other deprivation analyzed, as well as the percentage of children deprived in more than one dimension.For example, 2% of all children aged 0-4 suffer from malnutrition but are not deprived in any of the other 5 dimensions, so they are deprived in only this single dimension. 32% of all the children aged 0-4, however, suffer from malnutrition and at the same time they also do not have access to other 2-5 crucial resources. For health it is even more striking: only 1% of the whole child population aged 0-4 in health and health alone. There are 21% of children out of the total number of children who are deprived in health and are also deprived in 2-5 other dimensions. This proves the hypothesis that many children experiencing lack of access to certain resources are likely to be deprived in several dimensions simultaneously. Therefore, solving problems sector by sector may not be efficient, since there may be other causes and drivers of this deprivation than a simple lack of this or that particular service (e.g. discrimination, disadvantage of certain groups). If this is the case, then the causes and drivers of inequity should be found and tackled, rather than targeting the worst-off themselves sector-by-sector.
- Multiple overlapping analyses can usefully inform the discussion around cost-effectiveness of public policies. Overlaps between dimensions have implication for economic scope, by showing which deprivations could be addressed together as to make policy more integrated. Overlap analyses can help answering the question “what” the deprived children need, what resources and services they do not have access to. This figure shows the percentage of children who are deprived only in one particular dimension and do not suffer from any other deprivation analyzed, as well as the percentage of children deprived in more than one dimension.For example, 2% of all children aged 0-4 suffer from malnutrition but are not deprived in any of the other 5 dimensions, so they are deprived in only this single dimension. 32% of all the children aged 0-4, however, suffer from malnutrition and at the same time they also do not have access to other 2-5 crucial resources. For health it is even more striking: only 1% of the whole child population aged 0-4 in health and health alone. There are 21% of children out of the total number of children who are deprived in health and are also deprived in 2-5 other dimensions. This proves the hypothesis that many children experiencing lack of access to certain resources are likely to be deprived in several dimensions simultaneously. Therefore, solving problems sector by sector may not be efficient, since there may be other causes and drivers of this deprivation than a simple lack of this or that particular service (e.g. discrimination, disadvantage of certain groups). If this is the case, then the causes and drivers of inequity should be found and tackled, rather than targeting the worst-off themselves sector-by-sector.
- Figures 5.2 further explain the simultaneous experience of deprivation by presenting the overlaps and non-overlaps between the deprivation headcount of three dimensions. For both age groups a combination of three of the essential dimensions has been chosen. Further overlap analysis between other dimensions will be available on the web portal.
- Profiling can give an insight in the characteristics of the children belonging to any of the overlap or non-overlap groups. This figure presents the deprivation headcount among children aged 0-4 deprived in nutrition, health and water simultaneously, divided into sub-groups by children’s characteristics. Interpretation: In urban areas, less than 2% of children aged 0-4 are deprived in nutrition, health, and water simultaneously, and within these urban areas, only 0.7% having a birth certificate are deprived in all three dimensions, while 2.7% of children living in urban areas and not having a birth certificate are deprived in these dimensions.
- Profiling can give an insight in the characteristics of the children belonging to any of the overlap or non-overlap groups. This figure presents the deprivation headcount among children aged 0-4 deprived in nutrition, health and water simultaneously, divided into sub-groups by children’s characteristics. Interpretation: In urban areas, less than 2% of children aged 0-4 are deprived in nutrition, health, and water simultaneously, and within these urban areas, only 0.7% having a birth certificate are deprived in all three dimensions, while 2.7% of children living in urban areas and not having a birth certificate are deprived in these dimensions.
- To have an insight in the characteristics of the children belonging to the overlap group, profiling is carried out, presented in the figure showing the deprivation headcount among children aged 5-17 deprived in education, housing and water simultaneously, divided into sub-groups by children’s characteristics. Interpretation: More children are deprived in three dimensions simultaneously when not living with their parents (8.5% deprived in all 3 dimensions out of all the children who are not living with their parents), compared to those living with parents (7.6%).
- To have an insight in the characteristics of the children belonging to the overlap group, profiling is carried out, presented in the figure showing the deprivation headcount among children aged 5-17 deprived in education, housing and water simultaneously, divided into sub-groups by children’s characteristics. Interpretation: More children are deprived in three dimensions simultaneously when not living with their parents (8.5% deprived in all 3 dimensions out of all the children who are not living with their parents), compared to those living with parents (7.6%).
- Darkest -- Top third of regions with highest deprivation headcount in the respective dimension(s), compared to the other two thirds of the regionsMiddle -- Middle third of regionsLightest -- Bottom third of regions with lowest deprivation headcount in the respective dimension(s), compared to the other two thirds of the regionsSuch ranking can facilitate between-region targeting. To enable within-region targeting, this table can be made showing the gini coefficient – the standard deviation of the number of deprivations (rather than the current deprivation level, H).However, the current trend in equity-focused targeting is that the most emphasis is put on geographic targeting rather than targeting the disadvantaged groups. Other, non-geographic strategies should also be considered, since the aim should be to find the reasons of inequity rather than only identifying and targeting the worst off group of regions.
- How to incorporate this into our story line
- One of the main inequality/inequitymeasuses – standard deviation.
- Possible comments:Nutrition dominates in urban areas since it contributes more (20%) to the total adjusted deprivation headcount than Nutrition does in the rural areas (13%).On the contrary, Housing has a higher contribution to the total M in rural areas (19%) than urban areas (10% of the total M). Such analysis can be carried out by regions, by ethnicities, and by other characteristics.