Causal determinants of youth not in employment, education, or training (NEET): A global analysis with structural equation modeling

This research aims to test a potential structural model that identifies the causal conditions affecting the rates of young people not in education, employment, or training (NEET). The study focuses on human capital and labor market policies (HCLM), innovation and economic integration (IEI), labor rights (LR), and unemployment rates (UER) that have the potential to affect NEET rates. For these variables, data from world-renowned organizations such as IMF, ILO, Labour Rights, etc. and data from a total of 46 countries in the Advanced-20 and OECD countries are considered. Structural equation modeling (via SmartPLS) analysis was conducted to determine the impact of causal conditions on the outcome and to map the importance-performance for policymakers. The study attempted to identify the conditions affecting NEET using regression analyses, structural model results, and importance and performance map analyses. According to the findings of the study, it was determined that the structural model of all variables predicted for NEET had a very good level of 73.6%. However, although the effect of all variables on NEET is significant, it was found that HCLM, IEI, and LR have an inverse relationship, while UER has a parallel relationship. According to the importance-performance results, the most short-term and high-impact factor to reduce the occurrence of NEET is determined as employee rights. Thus, the results of the research play a guiding role for policymakers regarding NEET, which is considered one of the factors that disrupt the sustainable socio-economic structure.