youth unemployment

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Shem Otoi Sam, Pokhariyal GP, Manene MM, Isaac C Kipchirchir. "Autoregressive distributed lag cointegration analysis of youth unemployment in Kenya." International Journal of Statistics and Applied Mathematics. 2019;4(1): 29-41. AbstractWebsite

In this paper we consider cointegration analysis in an autoregressive distributed lag (ARDL) structure. First, logarithmic transformation is performed on the series to reduce outlier effects and have elasticity interpreted in terms of percentage. Second, the variables are tested for stationarity using Augmented Dickey-Fuller test. Third, the Johansen Cointegration test is carried out to examine cointegration of the series. Fourth, cointegrated dynamic ARDL model is estimated using ordinary least squares (OLS) and effects of variables and their lags interpreted. The results indicate that Gross Domestic Product (GDP) and its two-year lag are the only ones having negative effect on youth unemployment, that is, one unit increase in GDP and GDP two-year lag reduce youth unemployment by 0.207922% and 0.2052705% respectively. Also, one unit increase in External Debt (ED) and ED two-year lag reduce youth unemployment by 0.07303% and 0.009116% respectively. Furthermore, unit increase in one-year lag of youth literacy rate is the only one which reduces youth unemployment by 0.0892691%; one-year and three-year lag of population (POP) reduce youth unemployment by 0.2590455% and 4.3093119% respectively. The Foreign Direct Investment (FDI) and Private Investment (PI) do not have significant effects on youth unemployment. In the long run, increase in GDP causes increase in youth unemployment by 0.09148447%. The long run result explains that GDP growth in the country is “jobless growth” mainly in less labour intensive sectors.

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