Sürdürülebilir Kalkınma Amaçları
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- MakaleYalnız künye2022A New Approach to Human Capital: Crime Adjusted Human Capital Index on European Countries
Kabakçi Günay, Eylul; Yildiz, Umit; Günsoy, Guler; Günsoy, Bulent
Human capital is a one of the essential components in the economic growth and development. Also, the belief that education and health level, which are the main components of human capital, are main features of a happy and healthy society, is getting stronger. But there is a fact that crime is a destructive notion that affects a society???s human capital. In this context, this study aims to create an alternative social welfare measurement method that includes crime rates as a component to human capital on 35 European countries. In this regard, first, a general crime index was generated by using 13 different crime categories of the 35 European countries, and then this index was adapted to the Human Capital Index. As a result, the Crime-Adjusted Human Capital Index (CAHCI) was obtained. It was observed that the country rankings we obtained based on the CAHCI differ significantly from the rankings made based on the Human Capital Index. Significant number of countries, which ranks high in the Human Capital Index, ranks lower when crime rates are considered, indicates that the reliability of social welfare measures based on human capital should be questioned.
AtıfWoS1
İncele - MakaleYalnız künye2022Scopus SJRQ2Optimized Machine Learning Algorithms for Investigating the Relationship Between Economic Development and Human Capital
Ozden, Erdemalp; Guleryuz, Didem
In Economic Development, human capital was previously seen as production factors but gradually evolved into endogenous growth theories. Most of the previous studies have examined the relationships between economic development and human capital via econometric models. Since this relationship is usually nonlinear and machine learning (ML) models can resolve it better, this study investigates the relationships by employing ML methods to provide a new perspective. For this purpose, the optimized ML methods, namely Bayesian Tuned Support Vector Machine and Bayesian Tuned Gaussian Process Regression (BT-GPR), were performed to develop the prediction model for economic development. The hyperparameters have been optimized with the Bayes method by using different kernel functions to increase SVM and GPR methods' predictive performance. The Multiple Linear Regression model has been employed to make a comparison as an econometric model. The performance of the models is evaluated using three statistical metrics, namely, the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The BT-GPR with the exponential kernel model has superior prediction ability with the highest accuracy (R2: 0.9727, RMSE: 0.4022, MAE: 0.3728 in the testing phase). The study shows that the BT-GPR model increases the accuracy of R2 6.4%, RMSE 10.7%, and MAE 1% compared with other developed models.
AtıfScopus27WoS18OpenAlex30
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