The Relationship between Cause and Effect Dimensions of Young People’s Being “Not in Education, Employment, or Training (NEET)” in Turkey

Abstract

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.

1. Introduction

Labor market policies that have hitherto predominantly focused on youth unemployment have been expanded to encompass young people who are not in education or employment. This concept, which is defined as “Not in Education, Employment, or Training” (NEET), includes unemployed youth, yet contains significant differences from the definition of employment [1]. The NEET status is defined as being out of the education system, training, and employment, and it represents a much wider group due to the fact that it also involves young people who have the potential to move into the labor market but remain outside the workforce [2].

The state of being NEET refers not only to the condition of being unemployed and/or out of the workforce but also to the state of not being included in any official or unofficial education and training process. Within this framework, why the young are in this state and what sort of internal and external influences affect being in such conditions need to be examined. The research by Şahin et al. (2021), titled “A Profile Research on the Youth in Turkey who are “Not in Education, Employment, or Training” (NEET)”, analyzed the reasons for and influences of the NEET status of young people and developed the scales that are related to “the causes of being NEET” and “the effects of being NEET” and whose validity and reliability coefficients are at high levels [3].

In most of the empirical studies examined in the literature review, the NEET variable is analyzed as a dependent variable using logistic models and based on the determiners of being NEET. When the research designs are taken into account, it is observed that they have not analyzed the causes or effects of being NEET as a whole. This study examines the reasons for and results of individuals’ NEET status within a holistic design by means of dimensioning. While qualitative research aimed at NEET individuals has been conducted in most studies, the determining factors and the results of being NEET were turned into a scale in this research. The causes of being NEET scale is composed of the dimensions of “education”, “individual”, “family”, “environment”, and “labor market” (29 items in total) (see Table A1). Gender [4], age [5], health status [6], personal features [7], and income level [8] factors define the individual causes for being NEET in the literature. The educational level of parents [9], family structure and household size [10], family income level [11], and marital status of individuals [5] are the factors that define the familial causes for being NEET in the literature. Educational level and academic success [12], dropping out of school early [13], and education–labor market incompatibility [14] are the factors of the educational causes for being NEET in the literature. Social environment factors [15], the disadvantages of residential areas [16], poverty [17], and economic crises [18] are the factors that define the environmental causes for being NEET in the literature. Labor market regulations [19], discrimination in the labor market [20], and unemployment and work (in)experience [21] are the factors that define the labor-market-related causes for being NEET in the literature.

The effects of being NEET scale, on the other hand, consists of “family”, “individual”, “environment”, and “political approach” (35 items in total) (see Table A2). The factors family relations [22] and a decrease in the rates of marriage and birth [23] define the family effects of being NEET in the literature. The effects on life satisfaction [24], effects on life quality [25], effects on physical and mental health [26], addiction [27], and revenue loss [28] are the factors that define the individual effects of being NEET in the literature. Social exclusion and inadaptability [29], impoverishment [22], and migration intent [30] are factors that define the environmental effects of being NEET in the literature. Low political participation and unconcern [31], and insufficient evaluation of the labor market and educational policies [1] are the factors that define the political approach effects of being NEET in the literature.

In this study, the relationship between the dimensions of “the causes of being NEET” scale and “the effects of being NETT” scale is evaluated based on a correlation test. How the dimensions of “the causes of being NEET” scale influence “the effects of being NETT” scale and its dimensions, on the other hand, is determined through a multiple regression analysis as an advanced analysis type. Within this scope of this study, the scale of “the causes of being NEET” is considered an independent variable while the scale of “the effects of being NEET” is considered a dependent variable under four different models, and the fundamental hypotheses are developed accordingly.

2. Literature Review

Empirical findings from the literature review on the causes and effects of being NEET are shown in Table 1.

Table 1. Literature review on the causes and effects of being NEET.

Author	Name of the Study	Content of the Study	Methodology	Result
Abayasekara, A., Gunasekara, N. [32].	Determinants of Youth Not in Education, Employment or Training: Evidence from Sri Lanka	Sri Lanka-2016 workforce surveys.	Dual and multiple logistic regression.	The fundamental risk factors of being NEET can include the following: being a woman, belonging to ethnic and religious minorities, being between the ages of 20 and 24, having a low or high educational level, having illiteracy in the English language, being a member of a household with a low income, the fact that the household is managed by only a male member, having a young child, and living in areas away from the center.
Alvarado, A., Conde, B., Novella, R., Repetto, A. [7].	Youths Not in Education, Employment or Training in Latin America and the Caribbean: Skills, Youths Not in Education, Employment or Training in Latin America and the Caribbean: Skills, Aspirations, and Information	Surveys were conducted on NEET individuals aged 15–24 in 7 Latin American and Caribbean countries during 2017–2018. The time periods and the number of observations vary across countries.	Probit regression model.	Strong relationships were identified between the state of being NEET and qualities like mathematical and literacy skills, core self-evaluation, extroversion, and educational expectations. In addition, intercountry heterogeneity was detected among the examined countries. In other words, in some countries, long-term target-oriented ambition and determination, emotional imbalance (neuroticism), and workforce market information biases are additional factors related to being NEET.
Avagianou, A. et al. [33].	Being NEET in Youth spaces of the EU South: A Post-recession Regional Perspective	European Union (EU) South, 15–29 age range.	ANOVA—bivariate correlation.	They found that gender, class, education, and economic growth are key sociospatial factors determining the geographically uneven spread of NEETs across the European Union (EU).
Bäckman, O., Nilsson, A. [34].	Long-Term Consequences of Being Not in Employment, Education or Training as a Young Adult. Stability and Change in Three Swedish Birth Cohorts	Sweden, 1975-, 1980-, and 1985-born NEET individuals, 2010 data.	Logistic regression model: the propensity score matching (PSM).	Being NEET at an early age poses a labor market risk for both women and men. Being NEET affects the individual’s career negatively and emerges as a cause for social exclusion.
Berlin, M. et al. [13].	Long-Term NEET Among Young Adults with Experience of Out-Of-Home Care: A Comparative Study of Three Nordic Countries	Denmark, Sweden, and Finland; 1987-born youth who were within the age range of 21–23 during 2008–2010.	Dual logistic regression.	Firstly, the rate of those who were in the status of NEET among out of home care (OHC) young adults is considerably higher than their peers who had no experience with OHC in the 21–23 age range in all three countries. The OHC experience and low educational performance are effective in reducing NEET risk. Furthermore, the OHC effect on the risk of being NEET is at the same level for Denmark, Sweden, and Finland.
Berry, C. [35].	Structured Activity and Multiple Group Memberships	In England, 16–25 age ranged between 45 NEET and 190 non-NEET young people.	Intergroup cross-sectional data analysis.	NEET individuals show symptoms of depression more than those with no NEET status.
Bonnard, C. [36].	Risk of Social Exclusion and Resources of Young NEETs	France, 5800 young people with an age range 18–24 upon a survey conducted in 2014.	Generalized serial logistic regression.	The risk of social exclusion for NEET individuals is valid in all dimensions of employment, health, education, and social relationships; however, the risk is greatest at the level of the educational dimension.
Bynner, J., Parsons, S. [37].	Social Exclusion and the Transition from School to Work: The Case of Young People Not in Education, Employment and Training (NEET)	In Britain, 16–81 age ranged NEET individuals who dropped out of school at age 16 at the least; samples of 930 people in total, with 470 males and 460 females, were included.	Logistic regression model.	The low success rate in education is the most significant determining factor of being NEET. Other important factors include the urban life conditions (for boys) and the inability of their families to give the required importance to their educational lives (for girls). Being NEET results in weakness in workforce market experience on behalf of boys and psychological effects for girls, most of whom became mothers.
Caroleo et al. [38].	Being NEET in Europe Before and After the Economic Crisis: An Analysis of the Micro and Macro Determinants	Selected EU countries, two age groups (19–24 and 25–30).	Logit model.	While the NEET youth in the 19–24 age range are under the influence of the transition from school to professional life, the NEET individuals in the 25–30 age range are affected by the operation of the workforce market and institutional factors.
de Luca et al. [39].	Going Behind the High Rates of NEETs in Italy and Spain: The Role of Early School Leavers	Italy and Spain, 2007–2017 NEET data.	Dynamic simple regression model.	The delayed effect of dropping out of school early is meaningful to being NEET in a statistical sense. In other words, dropping out early leads to being NEET. In Italy, this effect is bigger than in Spain.
Dias, T.S., Vasconcelos, A.M.N. [40].	Heterogeneity Amount NEET Young People in Brazil	Brasil, 15–29 aged NEET individuals, 2014 national household surveys.	Multiple correspondence analysis (Mca).	The multifaceted profile and heterogeneity of NEET individuals were put forth in several categories. Accordingly, NEET women were in the majority. Genderwise, while urban NEET was in abundance among men, women were more dominant in rural NEET. It was detected that the NEET rate was greater in those whose skin color was not white. An examination of risk distribution in accordance with age range indicated that the 15–17 age group was under the biggest NEET risk when compared to other age groups.
Erdoğan et al. [41].	Being a NEET in Turkey: Determinants and Consequences	Turkey, 18–29 age range 1804 NEET individuals, 2 January–10 February 2016 dated surveys conducted in 226 locations within 25 cities.	Generalized linear models (GLMs).	Primary school graduates have 2.5 times the possibility of being NEET than university graduates. The state of being NEET is three times higher in married people than in young ones. As household income rises, the rate of being NEET declines. A non-Kurdish young person’s tendency to be NEET is almost half as much as that of a Kurdish one, provided that other variables have been checked.
Everington et al. [42].	Risk Factors for Young People Not in Education, Employment or Training (NEET) Using the Scottish Longitudinal Study	Scotland, 16–19 age range NEET individuals.	Logistic regression analysis.	Unqualified labor, early pregnancy, and living in an area where the NEET rate is high are among the significant factors contributing to being NEET. It was found that while school behaviors are important in older groups, the characteristics of the household during childhood are essential in younger groups.
Gutiérrez-García, R.A. et al. [43].	Emerging Adults Not in Education, Employment or Training (NEET): Socio-Demographic Characteristics, Mental Health and Reasons for Being NEET	Mexico, 16–26 age range, 1071 young people.	World Health Organization United International Diagnosis Meeting—Depth Interview.	A total of 19% of the sample is of voluntary NEET status. Some of them have psychiatric illnesses, are alcohol- and drug-addicted, and have attempted suicide. The biggest reason for being NEET is domestic responsibilities in the first place, not seeking jobs or not being able to be admitted to school in the second, voluntariness in the third, and not knowing what to do in life in the fourth.
Hult, M., Kaarakainen, M., Moortel, D.D. [44].	Values, Health and Well-Being of Young Europeans Not in Employment, Education or Training (NEET)	European regions, 15–29 age range, 3842 young people.	Linear regression model.	The results show that there are differences in values, health, and wellbeing in different regions of Europe and between genders. They found that social judgments about employment are likely to influence this relationship.
Jakobsen, V. [45].	Non-Western Immigrants, the Transition from School to Education and to Work and NEET Status	Denmark, Individuals in the 15–39 age range.	Regression analysis—linear probability model.	The results show higher NEET rates for children of immigrants than for native Danes. Regression analysis of three-year groups suggests that unfavorable family characteristics explain the higher probability of NEET status among children of immigrants in two of these groups.
Karyda, M.; Jenkins, A. [46].	Disadvantaged Neighbourhoods and Young People Not in Education, Employment or Training at the Ages of 18 to 19 in England	England, 18–19 age range, 8887 people.	Logistic regression model.	Those who live in areas with high crime rates tend to be in the state of NEET more.
Kılıç, Y. [47].	Young People in Turkey who are Not in Education, Employment or Training (NEET)	Turkey, 15–24 age range, 78,006 people.	Relational screening model.	The NEET rate in the 15–24 age group has been identified as 26.8%, among EU countries, and in an upper-mid range. The female NEET rate is 28%, and the male NEET rate is 22.5%. Having a low education level is among the outstanding causes of being NEET.
Maguire, S., Rennsion, J. [48].	Two Years On: The Destinations of Young People who are Not in Education, Employment or Training at 16	England, age 16, 8923 people.	Descriptive analysis and interview.	It was detected that Education Maintenance Allowance (EMA) financing prevents being NEET, is successful in increasing employment, and is effective in keeping youth aged 16 and over in education.
Mussida, C. & Sciulli, D. [49].	Being poor and being NEET in Europe: Are these two sides of the same coin?	21 Europeans.	Mussida, C. & Sciulli, D.	Being poor and being NEET in Europe: Are these two sides of the same coin?
Nordenmark, M. et al. [50].	Self-Rated Health Among Young Europeans Not in Employment, Education or Training-With A Focus On The Conventionally Unemployed And The Disengaged	33 European countries, 18–30 age range, 47,354 people.	Logistic regression model.	NEET individuals have an unhealthier status than the classically unemployed. They are also at a worse level in terms of social activity and welfare. Moreover, the effect of GDP on being NEET varies among countries.
Pattisanary, I.R.I. [51].	Not in Employment, Education or Training (NEET) Among the Youth in Indonesia: The Effects of Social Activities, Access to Information, and Language Skills on NEET Youth	Indonesia, 15–24 age range NEET individuals.	Logistic regression model.	It was found that the possibility of being NEET is lower among young individuals who take part in local meetings, actively participate in religious activities and/or community and social services, have access to the internet, and have literacy in Latin and other non-Arabic alphabets.
Pemberton, S. [52].	Tackling the NEET Generation and the Ability of Policy to Generate a ‘NEET’ Solution-Evidence from the UK	The United Kingdom, 17–18 age range 21 NEET individuals.	Interview.	Peer effect and low educational level are particularly less effective in preventing NEET among men than women. Age discrimination in the workplace (low professional experience), an unrecorded economy, and a lack of appropriate opportunities in education were identified as the determiners of being NEET.
Quintano, C. et al. [53].	The Determinants of Italian NEETs and the Effects of the Economic Crisis	Italy, 15–34 age range, 12,774 youth in total, 3421 of whom are in NEET status.	Probit regression model.	The economic crisis has ruined the circumstances of youth and increased social inequalities. It was detected that the economic crisis has affected men more than women. A high correlation was observed between low educational level and being NEET. As the educational level and age increase, the possibility of being NEET decreases. Women and immigrants are more fragile in terms of being NEET.
Ralston, K. et al. [54].	Economic Inactivity, Not in Employment, Education or Training (NEET) and Scarring: The Importance of NEET as a Marker of Long-Term Disadvantage	Scotland, 8073 young people aged 16–19 years old.	Logistic regression.	The study found that NEET status leads to long-term scarring associated with economic inactivity and unemployment.
Ruesga-Benito, S.M. et al. [8].	Sustainable Development, Poverty, and Risk of Exclusion for Young People in the European Union: The Case of NEETs	The European Union, 15–29 age range, NEET individuals.	Linear regression model—structural equation model (SEM).	According to the linear regression model, the variables of economic environment are statistically meaningless (GDP, social transfers, and consumption), whereas the variables of poverty risk and social exclusion are statistically meaningful. The same situation is valid in accordance with the structural equation model results.
Salvà-Mut, F. et al. [55].	NEETs in Spain: an Analysis in a Context of Economic Crisis	Spain, 25–29 age range, 580 people for quantitative analysis and 42 people for qualitative analysis.	Probit regression model—interview.	NEET individuals are divided into 3 subgroups: job seekers, discouraged ones, and those who are under care. The determiners of being NEET were identified as low education level, immigrant status, and poor economic condition for job seekers; low education level, being a woman, lowly trained parents, being married, and having children for those who are under care; and low education level and drug addiction for those who are not under care.
Susanlı Bilgen, Z. [56].	Understanding the NEET in Turkey	Turkey, 15–24 age range, 738,386 individuals.	Probit regression model.	Higher education levels and more crowded households significantly decrease the possibility of being NEET. This result is more dominant for women. Furthermore, marriage is another important determiner of being NEET on behalf of women.
Tamesberger, D., Bacher, Z. [57].	NEET Youth in Austria: A Typology Including Socio-Demography, Labour Market Behaviour and Permanence	Austria, 16–24 age range, 16,310 people.	Cross-tabulation analysis—logistic regression analysis.	The general NEET profile predominantly consists of women, immigrants, the urban population, and those with low education levels. More often, NEET individuals have partners and/or children. The most prominent factor that increases the risk of being NEET is dropping out of school early.
Yang, Y. [58].	China’s Youth in NEET (Not in Education, Employment, or Training): Evidence from a National Survey	China, 16–35 age range, 4166 individuals.	Logistic regression model.	High education level, immigrant status, and living in an urban area are preventive factors against being NEET. Party membership and the father’s level of education are statistically unrelated to being NEET. Being a woman is the biggest NEET risk factor. The risk of being NEET is specifically greater for married women than single ones.
Zudina, A. [59].	What makes youth become NEET? Evidence from Russia	Russia, Russian LFS data, 15–24 age range.	Multinomial logit models—dynamic multinomial logit panel regression.	The study found that higher education does not provide a universal safety net against NEET status in Russia and that, generally, NEET inactivity risks are concentrated mainly among those with primary or vocational education, while in Russia, NEET unemployment is associated with higher education.

Table 1 demonstrates how the determinants of being NEET shape the majority of the empirical literature on NEET. In accordance with this, statistical method preferences are based on logistic regression analyses. In this respect, the originality of this study’s method (multiple linear regression testing for hypotheses), according to the literature, is also noteworthy.

3. Materials and Methods

3.1. Dataset and Sample

This study discusses the relationship between “the causes of being NEET” and “the effects of being NEET”, which has been developed in accordance with the results of “A Profile Research on the Youth in Turkey who are “Not in Education, Employment, or Training” (NEET)”, conducted by Şahin and friends [3] in September–December 2020. The related research was performed in 26 cities belonging to 26 subzones within the frame of Turkish Nomenclature of Units for Territorial Statistics (IBBS)—II. The target population of the study is the 15–29 age-range NEET youth throughout Turkey. The sample distribution was determined at 3300 for the purpose of acquiring strong analyses on a regional basis, and depending on the NEET rate (29.5%) announced by EUROSTAT, the number of surveys was distributed in accordance with the 15–29 age group city populations. In total, 3158 young individuals with NEET status were interviewed on the survey in question. While ethics committee approval was not required during the periods in which the field research was conducted in Turkey, research data were used with permission given by the research owner institution, the Turkish Republic Ministry of Youth and Sports, and the executive institution, Istanbul University Rectorate. In addition, with the supervision of the research institution, informed consent was obtained from all subjects involved in the study [3].

3.2. The Causes of Being NEET Scale and the Effects of Being NEET Scale

At the end of “the causes of being NEET scale” reliability analysis, the general reliability value of the data (Cronbach alpha) was identified as 0.897. This result indicates that the questions that form the reasons have reliability at a high level. The alpha numbers obtained through the split-half reliability method were detected as 0.869 and 0.776, respectively. The Spearman Brown coefficient was found to be 0.82, and the Guttmann coefficient was identified as 0.810. The fact that these values are similar to one another points to the consistency between test scores. The causes of being on the NEET scale consist of the dimensions of education, individual, family, environment, and labor market, comprising 29 items in total [3] (pp. 151–152).

In consequence of “the effects of being NEET scale” reliability analysis, the general reliability value of the data (Cronbach alpha) was detected as 0.931. This result shows that the questions that constitute the effects have a high reliability level. The alpha numbers acquired by means of the split-half reliability method were, respectively, identified as 0.882 and 0.866. The Spearman Brown coefficient as spotted as 0.926, and the Guttmann coefficient was found to be 0.925. The effects of being on the NEET scale consist of the dimensions of family, individual, environment, and political approach, comprising 35 items in total [3] (pp. 153–155).

3.3. Definition of Variables and Analysis Methodology

The relationship between the dimensions of “the causes of being NEET scale” and the dimensions of “the effects of being NEET scale” was evaluated by means of a correlation test. How the dimensions of “the causes of being NEET scale” influence the “effects of being NEET scale” and its dimensions were determined by means of multiple regression analysis as an advanced analysis type. Accordingly, in line with the methodology of our study and under four different models, the causes of being on the NEET scale are regarded as the independent variables, while the effects of being on the NEET scale are treated as the dependent variables. In order to use the Likert-type scales in a statistical model as dependent and/or independent variables in regression analysis, factor analysis was first applied to the related scales, and the items in the scales were divided into subgroups. Afterwards, the total points of each factor were calculated and transformed into a single variable. Later, regression analysis was made based on the obtained variables. The fact that the items available in the factors were included in the analysis through their total points does not indicate any difference in terms of the findings of the analysis. The fundamental hypotheses that were put to test in this study are as follows:

H1: 

The dimensions of “the causes of being NEET scale” (individual, education, family, environmental, and labor market) have no effect on the dimension of the individual effects related to “the effects of being NEET scale”.

H2: 

The dimensions of “the causes of being NEET scale” (individual, education, family, environmental, and labor market) have no effect on the dimension of the environmental effects related to “the effects of being NEET scale”.

H3: 

The dimensions of “the causes of being NEET scale” (individual, education, family, environmental, and labor market) have no effect on the dimension of familial effects related to “the effects of being NEET scale”.

H4: 

The dimensions of “the causes of being NEET scale” (individual, education, family, environmental, and labor market) have no effect on the dimension of political approach effects related to “the effects of being NEET scale”.

In accordance with these hypotheses, the literature has contributed by separately analyzing the influence of each cause dimension existing in “the causes of being NEET scale” upon each effect dimension in “the effects of being NEET scale”.

4. Results

4.1. Correlation Analysis

The relationship between the variables that were acquired after the scale had been developed was examined by means of Pearson correlation. The correlation between the variables is provided in Figure 1 below.

Figure 1. Pearson correlation analysis findings.

According to the correlation analysis given in Figure 1, except for the relationship between the educational dimension of “the causes of being NEET scale” and the political approach dimension of “the effects of being NEET scale”, a meaningful correlation was detected between all the dimensions of the causes and effects (p < 0.05). The fact that a meaningful and relatively high positive correlation was identified between the causes and effects is of particular importance on behalf of this study. Based on this correlation coefficient, the causes and effects act together by approximately 62%. Again, the statistically meaningful and positive correlation of the dimensions of “the causes of being NEET scale” with the dimensions of “the effects of being NEET scale” is attention-grabbing. From another angle, the reality that the dimensions of “the causes of being on the NEET scale” have a positive correlation with the effects is another finding of the analysis. The availability of a statistically meaningful correlation relationship between the causes, effects, and dimensions of being NEET indicates the presence of a linear relationship between the variables shown in Figure 1. The related findings obtained from the correlation analysis in this study also revealed that analyzing the relationship between the variables more deeply by using regression analysis will be appropriate.

4.2. Regression Models

In order to test the hypotheses stated above, four different regression models, in which the dimensions of “the effects of being NEET scale” are dependent variables and the dimensions of “the causes of being NEET scale” are independent variables, were formed. Regarding the regression models in Table 2, the dependent variables were defined as the Environmental Effect in Model 1, the Familial Effect in Model 2, the Individual Effect in Model 3, and the Political Approach Effect in Model 4.

Table 2. The model summaries, the ANOVA test, and the regression coefficients.

	Model 1	Model 2	Model 3	Model 4
Model Summary (R2)	0.438	0.403	0.500	0.424
Anova (F−statistics)	349.518 *	424.866 *	631.060 *	463.540 *
Regression Coefficients
(Constant) *	2.461 *	0.746 *	14.034 *	4.400 *
	(7.432)	(4.629)	(18.127)	(21.993)
Environmental *	0.344 *	0.107 *	1.325 *	0.485 *
	(14.180)	(9.032)	(23.384)	(33.147)
	[0.239]	[0.157]	[0.371]	[0.565]
Familial *	0.453 *	0.218 *	−0.053	−0.266 *
	(11.423)	(11.281)	(−0.572)	(−11.095)
	[0.221]	[0.225]	[−0.010]	[−0.218]
Individual	−0.022 *	0.022 **	−0.217 *	−0.109 *
	(−1.187)	(2.482)	(−4.997)	(−9.734)
	[−0.024]	[0.052]	[−0.096]	[−0.201]
Educational *	0.274 *	0.123 *	0.836 *	−0.042 **
	(8.679)	(8.021)	(11.314)	(−2.182)
	[0.168]	[0.160]	[0.206]	[−0.043]
Labor Market *	0.207 *	0.089 *	0.755 *	0.111 *
	(10.867)	(9.617)	(16.921)	(9.646)
	[0.229]	[0.209]	[0.337]	[0.206]

Dependent variables: Model 1 (H₁): Environmental Effect, Model 2 H₂: Familial Effect, Model 3 H₃: Individual Effect, Model 4 H₄: Political Approach Effect. Note: The parenthetical figures indicate t-statistics, and the figures in square brackets point to the standardized coefficients. * refers to 99%-confidence-level statistical meaningfulness. ** refers to 95%-confidence-level statistical meaningfulness.

The findings of the regression analysis conducted are given in Table 2. Here, when the formed models are analyzed, the coefficients of determination of the cause dimensions and their influences on the effect dimensions (R²) vary between 0.403 and 0.500. These figures point to medium-level demonstrativeness. Given the fact that the variables are the data obtained from the sum of variables that are acquired through the interval scale, this can be regarded as a high value. The model with the lowest model demonstrativeness is the familial effect model (0.403), while the highest demonstrativeness is available in the individual effect model (0.500). As for the remaining models, the model demonstrativeness value of the environmental effect model is 0.438, and that of the political approach effect model is 0.424.

According to the ANOVA test results regarding each model, the whole dimensions of “the causes of being NEET scale” (labor market, familial, environmental, educational, and individual causes) are the meaningful descriptors of the dimensions of the environmental, familial, individual, and political approach (respectively) effects of being NEET at a 95% confidence level. In line with these findings, it was detected that each regression model is meaningful in general.

The influence of the environmental, familial, individual, educational, and labor market dimensions, which are the dimensions of “the causes of being NEET scale”, on the environmental effect dimension of “the effects of being NEET scale” is examined in Model 1 by means of multivariable regression analysis. Accordingly, the dimensions of “the causes of being NEET scale” (educational, individual, familial, environmental, and labor market) explain the environmental effect dimension of being NEET, which is a dependent variable, by approximately 44% (F = 349.52; sig = 0.00). Nearly 56% of the dependent variable that signifies the environmental effect dimension of being NEET is expressed by the other independent variables, which were not included in the related model. In this model, the independent variable of education makes a positive contribution (t = 8.68; sig = 0.00 < 0.05). Accordingly, a one-unit increase in the educational dimension of “the causes of being NEET scale” leads to a 0.27-unit rise in the environmental effect dimension of “the effects of being NEET scale”. This result indicates that the increases in causes related to the educational dimension give rise to the environmental effects of being NEET. The influence of the dimension of the individual reasons within “the causes of being NEET scale” on the environmental effect dimension of “the effects of being NEET scale” was not found to be meaningful (t = −1.19; sig = 0.24 > 0.05). The independent variable of familial causes (t = 11.42; sig = 0.00 < 0.05) affects the model positively. In this context, a one-unit increase in the familial dimension of “the causes of being NEET scale” brings on a 0.45-unit rise in the environmental effect dimension of “the effects of being NEET scale”. The rise in the familial causes dimension increases the environmental effects of being NEET. The independent variable of environmental causes (t = 14.18; sig = 0.00 < 0.05) has a positive effect on the model. Accordingly, a one-unit increase in the environmental dimension of “the causes of being NEET scale” results in a 0.34-unit rise in the environmental effect dimension of “the effects of being NEET scale”. The increases in the environmental causes dimension give rise to the environmental effects of being NEET. Lastly, the variable labor market also has a positive influence on the model (t = 10.87; sig = 0.00 < 0.05). Within this scope, a one-unit rise in the labor market dimension of “the causes of being NEET scale” leads to an almost 0.21-unit increase in the environmental effect dimension of “the effects of being NEET scale”. This result demonstrates that the increases in labor market dimension-related causes give rise to the environmental effects of being NEET.

According to the coefficient of determination for Model 2 (R²), the dimensions regarding “the causes of being NEET scale” (education, individual, familial, environmental, and labor market), which are independent variables, explain the familial dimension belonging to “the effects of being NEET scale”, which is a dependent variable, by approximately 40% (F = 424.87; sig = 0.00). All variables in the model contribute positively to the model, and as can be seen in Table 2, the coefficients of the variables are statistically meaningful (p < 0.05). Accordingly, a one-unit rise in the educational dimension of “the causes of being NEET scale” leads to a nearly 0.12-unit increase in the familial effect dimension of “the effects of being NEET scale”. It is seen that the rise in educational-dimension-related reasons increases the effects of being NEET on the family. A one-unit rise in the individual dimension of “the causes of being NEET scale” results in an almost 0.02-unit increase in the familial effect dimension of “the effects of being NEET scale”. It was detected that the increases in individual dimension-related causes give rise to the effects of being NEET on the family. A one-unit increase in the familial dimension of “the causes of being NEET scale” results in a 0.22-unit rise in the familial effect dimension of “the effects of being NEET scale”. The rises in the familial-dimension-related reasons increase the effects of being NEET on family. A one-unit rise in the environmental dimension of “the causes of being NEET scale” gives way to an approximately 0.11-unit increase in the familial effect dimension of “the effects of being NEET scale”. This finding indicates that the increases in environmental-dimension-related causes lead to an increase in the effects of being NEET on family. A one-unit rise in the labor-market-related dimension of “the causes of being NEET scale” results in an almost 0.09-unit increase in the familial effect dimension of “the effects of being NEET scale”. It was identified that the rises in causes related to the labor market dimension increase the effects of being NEET on family.

In Model 3, the causes of being NEET (education, individual, familial, environmental, and labor market), which are independent variables, explain the individual effect dimension of being NEET by 50% (F = 631.06; sig = 0.00). In this model, it was found that when the family-based subdimension effect is not meaningful (p > 0.05), the individual effect has a negative influence, while the environmental, educational, and labor-market-related effects influence positively and are meaningful (p < 0.05). Within this frame, when a one-unit increase occurs in the variables, the education variable increases the individual effect dimension of being NEET by approximately 0.84 units, the individual variable decreases it by almost 0.22 units, the environment variable increases it by nearly 1.33 units, and the labor market variable increases it by more or less 0.76 units. These results demonstrate that the individual effects of being NEET are increased by the rises in the educational-dimension-based causes, decreased by the rises in the individual-dimension-related causes, increased by the rises in the environmental-dimension-oriented causes, and increased by the labor-market-dimension-related causes.

Finally, in Model 4, the causes of being NEET (education, individual, familial, environmental, and labor market) explain the political approach dimension of being NEET by 42% (F = 463.54; sig = 0.00). In the related model, the variables of family, individual, and education have negative effects, whereas the environment and labor market variables have positive effects and are meaningful (p < 0.05). To what extent the variables change the political approach effects in the case of a one-unit increase in them is given in Table 2. The findings available in the table indicate that the political approach effects of being NEET are decreased by the rises in the educational-dimension-based causes, are decreased by the rises in the individual-dimension-related causes, are decreased by the rises in the familial-dimension-oriented causes, are increased by the rises in the environmental-dimension-related causes, and are increased by the rises in the labor-market-dimension-based causes.

Based on the findings of this study, the fact that the cause dimensions are influential and statistically meaningful for the effect dimensions as a whole shows compatibility with the majority of the literature.

5. Discussion

As the coefficients standardized for Model 1 are examined, it is seen that what influences the dimension of the environmental effect of being NEET most in the sense of flexibility is the environmental dimension of “the causes of being NEET scale”. Based on the scale statements, this implies, for example, that one of the biggest reasons for feeling excluded from society, which is one of the environmental effects of being NEET, is due to social inequalities, which is one of the causes of being NEET. The following influencers on the dimension of the environmental effect of being NEET are, respectively, the labor market and family-related dimensions of “the causes of being NEET scale”, while the least influential one is the educational dimension of “the causes of being NEET scale”. When the outcomes obtained from Model 1 are compared to the literature, they show parallelism with such results that can be considered statistically meaningful environmental effects, as in the findings of Ruesga-Benito et al. [8] and Bonnard [36], who claim that NEET individuals have a high possibility of social exclusion, Nordenmark et al. [50], who argue that NEET individuals are in a less healthy condition than the standard unemployed ones and in a poor scoring level in terms of social activities and social welfare, Pattinasarany [51], who states that the participants of religious and social activities have a low possibility of being NEET, and Pemberton [52], who suggests that peer effect determines the NEET status of individuals.

When the coefficients standardized for Model 2 are analyzed, it is understood that what influences the familial dimension of “the effects of being NEET scale” most in the sense of flexibility is the familial dimension of “the causes of being NEET scale”. Based on the scale statements, this result implies that one of the primary reasons for the familial unrest, which is one of the familial effects of being NEET, stems from either the presence of a family member for whom one is obligated to care or the parents’ negative attitudes towards continuing education. The other effective dimensions, respectively, include the labor market, educational, and environmental dimensions, whereas the least influential one is the individual dimension. The majority of the analysis in the literature by Erdoğan et al. [41], Gutiérrez-Garca et al. [43], Salvà-Mut et al. [55], Tamesberger [57], and Yang [58] focuses on how family status affects being NEET. These studies state that the risk of being NEET in married individuals and those with low household income is higher. The results in Model 2, on the other hand, show that the effect of being NEET on family is statistically meaningful; therefore, a statistically meaningful relationship exists between the dimensions of “the causes of being NEET scale” and the familial dimension of “the effects of being NEET scale”, regardless of the causality aspect.

When the coefficients standardized for Model 3 are assessed, it is found that what influences the individual dimension of “the effects of being NEET scale” most is the environmental dimension of “the causes of being NEET scale”. This finding suggests that environmental causes of being NEET, such as economic crises or social inequalities, lead the NEET individual to despair, which is an individual effect. The remaining influential dimensions, respectively, involve the labor market and education-related dimensions, while the least effective one is the individual dimension in a negative sense. The findings of Model 3 bear a resemblance to those of Berry 36], who claims that NEET individuals show symptoms of depression more than those that are not in NEET status, and Gutiérrez-García et al. [43], who state that NEET individuals have a greater tendency towards psychological diseases, drug and alcohol addiction, and suicide.

When the coefficients standardized for Model 4 are studied, it is seen that what influences the political approach dimension of “the effects of being NEET scale” most in the sense of flexibility is the environmental dimension of “the causes of being NEET scale”. This result indicates that economic crises and/or social inequalities, which are environmental causes of being NEET, lead to a negative perspective towards public policies, which is one of the political effects of being NEET. The other following influential dimensions, respectively, include the familial, labor-market-related, and individual dimensions, whereas the least effective dimension is the educational one. However, it is essential to remember that this education dimension negatively affects the political approach dimension of the individual and family variables. There exists a similarity between the findings obtained from Model 4 and some studies in the literature, like that of Caroleo et al. [38], who state that NEET individuals are influenced by the operation of the workforce market and institutional factors; Maguire and Rennsion [48], who argue that the educational support provided through educational policies (Education Maintenance Allowance (EMA)) has an effect on the political perceptions of NEET individuals; and Pemberton [52], who claims that the inequality of opportunity in education increases individuals’ possibility of being NEET. Yet, it can be stated that this study shows contrast with the findings of Yang [58], who argues that party membership has a statistically meaningless association with being NEET; as in our study, a statistically meaningful relationship between being NEET and political approach was detected.

6. Conclusions

According to the correlation analysis, except for the relationship between the educational dimension of “the causes of being NEET scale” and the political approach dimension of “the effects of being NEET scale”, a meaningful correlation was detected between the all-cause and effect dimensions. The identification of a meaningful and relatively high positive correlation between the causes and effects is especially significant for the related study. In accordance with this correlation coefficient, the causes and effects of being NEET move together approximately by 62%. Moreover, four different regression models, in which the dimensions of “the effects of being NEET scale” are dependent variables while the dimensions of “the causes of being NEET scale” are independent ones, were constituted within the scope of this study. Regarding these regression models, the dependent variables were defined as the Environmental Effect in Model 1, the Familial Effect in Model 2, the Individual Effect in Model 3, and the Political Approach Effect in Model 4. At the end of each model examination within the regression analysis, the fundamental hypotheses H₁, H₂, H₃, and H₄ were rejected. Among the cause subdimensions, the factors that most affected the effect subdimensions were determined with standardized coefficients. The primary findings that require policy development are as follows:

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One of the biggest reasons for feeling excluded from society, which is one of the environmental effects of being NEET, is due to social inequalities, which is one of the causes of being NEET.

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One of the primary reasons for the familial unrest, which is one of the familial effects of being NEET, is either the presence of a family member for whom one is obligated to care or the parents’ negative attitudes towards continuing education.

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Environmental causes of being NEET, such as economic crises or social inequalities, lead the NEET individual to despair, which is an individual effect.

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Economic crises and/or social inequalities, which are environmental causes of being NEET, lead to a negative perspective towards public policies, which is one of the political effects of being NEET.

The findings of this study are compatible with the literature, and it was detected that the cause dimensions of being NEET are influential on the effect dimensions of being NEET as a whole and are statistically meaningful as well. In this context, in order to prevent the negative effects of individuals becoming NEET, it is essential to first prevent the causes of becoming NEET.