On a smart digital gender ecosystem for managing the socio-economic development in the african contexts
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© Springer International Publishing AG, part of Springer Nature 2019. The research is aimed at investigating gender equality that affects the social and economic development in African countries. The existing data sources are examined based on the research question: “What is the role of female education and workforce on the social and economic development in African contexts, especially in East Africa?” To explore this question, we investigate the socio-economic development indicators such as employment, education, and population growth. We examine the effects of female population on impending education and employment indicators. For analysing the benchmarks, the empirical and observational research methods are deployed. Various data schemas are designed to test the gender based ecosystems and their models. Our results show the data relationship has a very strong positive connection between female education and social economic development dimensions. This is validated by qualitative research, obtained from questionnaires from one of the municipalities. We compute the polynomial regressions based on data fluctuations in large size gender ecosystems. We provide new regression models for visualization and knowledge interpretation. We conclude that female education and employment play characteristic roles in the social and economic development. Orthogonal polynomial models provide a scope of understanding the statistical inferences on equality in education, quality of life and other socio-economic insights that contribute to making knowledge-based solutions smartly and decisively.
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