Data Mapping to End Child Exclusion

Save the Children Uses National Data to Create Diagnostic Toolkit
Tanzania

Background

Save the Children (SC) is the world’s leading independent organization for child advocacy. They work in more than 120 countries, saving children’s lives, fighting for their rights, and helping them live their potential. SC works to inspire breakthroughs in the way the world treats children and to achieve immediate and lasting change in their lives. In their current global campaign, “Every Last Child,” SC is combatting child exclusion, the social, economic, cultural, and environmental factors that result in some children being left behind while others thrive.

To support the global campaign and as part of the Data Revolution more broadly, SC Tanzania is undertaking a children’s exclusion mapping project to demonstrate how existing data can be used to visualize child exclusion on a national and subnational level. Specifically, the project aims to use existing national data to develop a diagnostic toolkit to identify children who are excluded and the key drivers behind their exclusion.

Problem

In spite of the impressive progress achieved by the Millennium Development Goals, there are certain groups of children who continue to be left behind. Without addressing the systemic barriers that have kept them from benefiting from rising tides around them, these children risk being forgotten again in the implementation of the Sustainable Development Goals (SDGs), including in the efforts to combat HIV infection. Using clear, data-derived evidence of the drivers of child exclusion, SC Tanzania hopes to inform decision-makers in the public and private sectors as to how their actions can help bring about an end to child exclusion.

The first critical step in combatting child exclusion is the identification of those who are at highest risk. In Tanzania, gender-based discrimination is the most common form of child exclusion. For example, Tanzania has one of the highest child marriage rates in the world, and although these rates have been dropping in recent years, the prevalence of teenage pregnancies remains alarmingly high. A number of organizations have undertaken efforts to map child exclusion and identify the most at-risk populations, including girls who are married or pregnant before they turn 18. Typically organizations use a single indicator, an approach that fails to take into account the synergistic effects of variables that include economic, education, and health factors. Merging multiple indicators, however, can lead to a more nuanced and realistic picture of where excluded children are located and who they are.

Solution

SC Tanzania is creating a platform to merge multiple indicators related to child exclusion. Starting at the local level, SC Tanzania has created a prototype diagnostic toolkit that merges a select number of indicators to create a regional performance index. Now that the pilot project has been successfully implemented, SC Tanzania is expanding their tool to include more indicators and achieve national coverage in Tanzania and is making the tool visually appealing and flexible through collaboration with data scientists at the dLab.

Process

Developing a multi-indicator toolkit for child exclusion required identifying relevant indicators and compiling related data from a variety of sources, including the Tanzanian National Survey, the Health Management Information System (HMIS), the national budget, and others. SC Tanzania sought out geographically disaggregated data in order to pin-point areas most in need of intervention. Indicators of child exclusion were identified from a Child Indicators Database and included the prevalence of child marriages, the adolescent child-bearing rate, school dropouts, wasting and stunting, HIV infections among adolescents, and others.

As an example of how these indicators can highlight areas in need of intervention, consider that the prevalence of child marriage in 2010 in Shinyanga and Tabora was greater than in Mara. An organization using only this single indicator as a measure of child exclusion might well choose one of those two districts for priority intervention. However, when merging the remaining indicators to account for other contributors to child exclusion, it becomes apparent that Mara has a higher combined child exclusion index than the other two; and in fact Lindi, which scored fifth in terms of child marriage rates, has the highest combined index.

While the toolkit already draws on multiple data sources, the scoring system will only become more robust as data become more available. SC Tanzania’s current model can be improved upon with access to more up-to-date data, as well as data disaggregated to the district or village level. In spite of current limitations in data availability and disaggregation, SC Tanzania is pushing forward with a plan to expand the project from the pilot level to a comprehensive, national-level map of child exclusion indicators.

Beyond simply creating a tool to help focus internal priorities, SC Tanzania wanted to use their new multi-indicator toolkit as a resource in advocacy. As such, they have developed a dashboard to monitor changes in child exclusion indicators and have conducted workshops and trainings to demonstrate how they have used data to map child exclusion.

Identifying child exclusion prevalence based on a single indicator such as child marriage rates (left) gives a less complete picture than one that merges multiple indicators, including child fertility, school dropouts, wasting and stunting, HIV infection rates, and others (right).

Outcomes and Impacts

SC Tanzania’s multi-indicator child exclusion toolkit will become a critical component in their design of advocacy campaign messages to address the issue of child exclusion. Using a solid foundation of data-derived evidence, SC Tanzania can increase awareness and understanding of the factors contributing to child exclusion and push for policy changes that will reduce child exclusion, early marriage, and teenage pregnancy. This evidence-based approach will combat the resource misallocation and untargeted interventions that can result from relying on a single variable to make policy decisions. The toolkit will guide policy-makers on a national level to the contributing factors and geographic areas most in need of attention, while at a regional level, administrators will be able to ascertain which indicators factor most heavily in child exclusion in their own locality.

Key Collaborators

The National Bureau of Statistics (NBS) is an autonomous public office with a mandate to provide official statistics to the government, business community, and the public at large. The Statistics Act of 2015 assigns NBS as a co-coordinating agency within the National Statistical System (NSS) to ensure the quality of official statistics. NBS provided data and approved the methodology employed in this project.