The Impact of NEET and Labor Market Indicators on Human Development: A Panel Data Analysis for EU-28 Countries

Abstract

This study aims to investigate the effect of education, unemployment and not-in-education employment or training (NEET) population on human development in the EU-28 countries during the 2004-2018 period by using panel data analysis. According to the panel data analysis results with Common Correlated Effects Mean Group (CCEMG) estimator, the variables unemployment (UNE) and education (EDU) are statistically significant in explaining Human Development Index (HDI) across the panel. In contrast, the variable NEET is found to be not statistically significant, but the obtained coefficient is in the expected direction. In this case, a 1% increase in the UNE variable decreases HDI by 0.01%, and a 1% increase in the EDU variable increases HDI by 0.30%. The model appears to be statistically significant. According to the regression estimation results based on for each country, the coefficients vary quantitatively and statistically. Still, it is noteworthy that the NEET variable, which is statistically insignificant throughout the panel, varies statistically from unit to unit. These results confirm that NEET and HDI are negatively correlated in Czechia, Denmark, Finland, and Germany, while positively correlated in France, Poland, and Portugal.

Youth not in Employment, Education, or Training (NEET) in Turkey: A Regional Analysis

“…The model results revealed that married women, those with at least one child, or those who are permanently disabled are more likely to be NEET, the risk of being NEET decreases as the education level of the individual increases, and the probability of being NEET is decreased if at least one of the parents is university. Bingöl (2020) investigated theimpact of macroeconomic indicators on NEET population in Brazil, India, Indonesia, South Africa, and Turkey accepted as Fragile Five countries and Russia 2005-2018 period by using the panel data analysis method. Gross Domestic Product Per Capita, Inflation Rate, Adjusted savings for education expenditure, Foreign Direct Investment, HDI index data were used for explaining the NEET.…”

How Far Are NEET Youth Falling Behind in Their Non-Cognitive Skills? An Econometric Analysis of Disparities

“…The reasons for the prevalence of NEET youth in society may relate either to institutional factors characterizing the effectiveness of the education system or the labor market demand for skilled labor (Amendola 2022;Bal-Doma ńska 2022;Maynou et al 2022;Bingöl 2020), or to those representing the labor supply side and describing the individual characteristics of economic agents. In addition to the basic socio-demographic parameters such as gender and age, these characteristics mainly include the highest level of education received and professional experience; existing data emphasize that early employment is an important factor in reducing the likelihood of falling into NEET (Ballo et al 2022;Berlin et al 2021).…”