Abstract
The purpose of this work is to form a multi-factor model of indicative diagnostics of human capital in relation to GDP per capita based on 15 aggregated global indexes. The study using regression and correlation analysis allowed us to form the innovative human capital index (IHC), focused on predicting GDP per capita, and to determine the complex optimal Predictor for IHC diagnostics. The main components of this Predictor are the Global Competitiveness Index (0.4), Mean Years of Schooling (0.3), GDP per capita (0.15), and Corruption Perception Index (0.05). The regression error of the IHC index with the optimal Predictor is 2% for a sample of 24 major economies and 5.6% is the average value for five samples of 6–72 economies.
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The reported study was funded by the RFBR, project No. 19-29-07328.
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Orekhov, V.D., Prichina, O.S., Blinnikova, A.V. (2021). Creating a Human Capital Model Based on Global Indexes. In: Ashmarina, S.I., Mantulenko, V.V. (eds) Digital Economy and the New Labor Market: Jobs, Competences and Innovative HR Technologies. IPM 2020. Lecture Notes in Networks and Systems, vol 161. Springer, Cham. https://doi.org/10.1007/978-3-030-60926-9_91
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