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Youth Unemployment in Egypt and Determinants using Artificial Neural Network |
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PP: 1045-1055 |
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doi:10.18576/jsap/110324
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Author(s) |
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Nahla Hassan,
Mona Khalifa,
Farouk Shoieb,
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Abstract |
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| This paper explores the unemployment rates for young candidates between 15 and 29 years using a set of unemployment data captured from the Egyptian Labor Market Panel Survey (ELMPS) for 1998, 2006, 2012, and 2018 rounds to form a cross-section of the current unemployment rate and duration. We used Artificial Neural Network to investigate the determinants of unemployment depended on data derived from the 2018 Egyptian Labor Market Panel Survey (ELMPS). Our analysis demonstrated that the respondents sex, age, and education are the most important significant determinants of unemployment, whereas the marital status, the wealth index, father and mother education, and place of residence are insignificant variables. In addition, the rates and median current unemployment duration for females are significantly higher than that of males for both young people and the rest of the labor force.
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