Statistics and Surveys Unit (SSU)

External Functions

In line with the Center’s strategic objectives and interests, the SSU provides a variety of research consultancy services to external clients. These constitute the external functions of the Unit, which are demand-driven and are used to give visibility to SSU and the Center.

Focus Areas

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big win

Data Mapping and Validation in Kenya Counties

Program: Statistics and Surveys Unit (SSU)

The Statistics and Surveys Unit (SSU) in conjunction with Big Win Philanthropy seeks to work with and support leaders to achieve big wins in human capital development for children and youth.  In Kenya, Big Win is seeking to support the country’s efforts in achieving demographic dividends for long-term economic growth.  Recognizing the ongoing devolution process in Kenya and the role of County Governments in driving public investment decisions, especially ‘spending and service delivery functions relevant to children and youth’, Big Win has identified the Council of Governors (CoG) as a key partner for its Kenya Investment Strategy. This strategy seeks to “improve the capacity and effectiveness of the new county governments to consolidate and achieve Big Wins in human capital investments for Kenya’s children and youth.”  The first phase of this strategy seeks to “establish exactly what data is available and relevant for current use, and its effectiveness for decision-making at the county level.”

Phase 1 of this Kenya Investment Strategy seeks to fill this gap by assessing what data is available and relevant for current use, and its effectiveness for decision-making at the county level. Three inter-related deliverables have been identified:-

1) undertaking a comprehensive mapping of county data related to human capital investments and outcomes, that are closely tied to the demographic dividend potential – their availability, quality, sources and frequency of collection at either national or county level;

2) identifying suitable options for a human capital investment tracking tool – what data is needed for the tool to be effective, how the data can be effectively presented and the accompanying systems and processes for management and making the tool operational; and

3) evidence review of indexes currently in use that are relevant to predicting domains of demographic dividends at global level, but relevant to Africa and the Kenya context.



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