The causes and effects of being “not in education, employment, or training (NEET)” differ according to countries, regions, and even individuals. In this study, the relationship between the causes of young people being NEET and the effects of being NEET is examined on young people in Turkey. The data and scales of cause and effect that have high validity and reliability coefficients were used from in-person field research conducted with 3158 NEET young people by Istanbul University between September and December 2020 in Turkey. The influence of the causes of being NEET scale on the effects of being NEET scale was analyzed by the multivariable regression method. As a result of the analysis, it was determined that the effects of the individual, familial, educational, environmental, and labor market dimensions of the causes of being NEET scale on the effects of being NEET scale were significant.

In this study, 16 countries in the MENA region (United Arab Emirates, Bahrain, Djibouti, Algeria, Egypt, Iran, Jordan, Kuwait, Lebanon, Morocco, Malta, Oman, Qatar, Saudi Arabia, Tunisia, and Yemen) were included to the sample and was analyzed, by using a panel data method Pedroni Cointegration test, that relationship between “health expenditures” and “life expectancy at birth” in 2000-2016 period these countries. As a result of the analysis, there was no relationship between health expenditures and life expectancy at birth. Although health expenditures do not have a direct impact on life expectancy at birth, If the MENA region countries take needed measures (such as; to attach importance to institutionalization and functionality in health, focusing on disadvantaged groups, and to encourage investments of hospital and personnel), the inequalities in health outcomes would be reduced. Request access from your librarian to read this chapter's full text.

This study proposes a novel Adaptive Fermatean Fuzzy Set (AFFS) structure for multi-criteria decision-making problems characterized by high levels of uncertainty and hesitation in expert opinions. Unlike the fixed-exponent structure used in classical Fermatean fuzzy sets, the proposed model dynamically captures uncertainty based on the properties of alternatives by defining an adaptive exponent parameter for each assessment. In this context, new algebraic operations, a normalization approach, ranking mechanisms, distance and similarity measures, entropy-based information measures, and weighted aggregation operators have been developed for AFFS theory. Then, based on this theoretical structure, a new decision-making framework integrating the Adaptive Fermatean Fuzzy Delphi, DEMATEL, and MARCOS methods is proposed. The proposed approach allows for the determination of criterion weights and the more reliable ranking of alternatives by simultaneously considering expert consensus, information reliability, and uncertainty levels. The applicability of the method is demonstrated with a sample problem evaluating the impact of immigration policies on investment decisions. In the illustrative application, the largest criterion weight is assigned to Harmonization in Integration Policies ($K_3$, $w_3=0.202687$). The proposed AFF-MARCOS procedure yields $A_2$ as the highest-ranked alternative with $U_2=0.722755$, followed by $A_1$, $A_4$, $A_3$, and $A_5$.