Unravelling the NEET phenomenon: a systematic literature review and meta-analysis of risk factors for youth not in education, employment, or training

Figures & data

Table 1. Search terms used for this systematic and meta-analysis article.

Population	(youth OR adolescent OR young OR juvenile OR school student* OR NEETs OR NEET).TI, AB.
Intervention	(youth AND (engagement OR unemployment OR activism OR development OR inclusion OR community service OR after-school OR afterschool OR decision-making OR out of work OR adult partnership* OR youth-adult partnership* OR youth action OR youth involvement OR youth participation OR socially excluded OR not in work OR service learning OR not in employment OR not in education OR non-formal education OR informal education OR not in training OR participatory research)).TI, AB.
Comparison	(risk factor OR determinants OR control* OR random* OR trial* OR effectiveness OR efficacy OR comparative study OR experiment* OR impact evaluation OR impact study OR impact assessment OR outcome evaluation OR outcome study OR outcome assessment)AF.

Figure 1. PRISMA flow chart.

Figure 2. Forest plots of demographic factors.

Figure 3. Forest plots of family factors.

Figure 4. Forest plots of education and unemployment experience factors.

Figure 5. Forest plots of socio-economic factors.

Figure 6a. Forest plots of health and well-being factors.

Figure 6b. (Continued)

Table 2. Summary of findings by type of risk factors and the result of pooled OR.

Type	Number of studies	Pooled OR	95% CI	Std. Error	I^2 (%)	Q	Z
Demographic Factors
Ethnicity^a	5	1.15	(0.74, 1.55)	0.2	3.64	4.15	5.55
Immigration^a	7	1.07	(0.89, 1.25)	0.09	60.1	1.72	11.7
Marital status^b	8	1.64	(0.95, 2.33)	0.35	78.05	27.88	4.65
Family Factors
Workless Parent^a	6	1.54	(1.17, 1.91)	0.18	45.8	2.09	8.24
Parental Education level^c	10	0.87	(0.66, 1.09)	0.11	17.8	5.68	7.9
Parents’ income^d	10	1.29	(1.05, 1.54)	0.12	49.3	16.22	10.3
living with a single parent^a	11	1.03	(0.88, 1.17)	0.07	52.9	10.97	8.36
Number of siblings^e	3	1.11	(0.85, 1.36)	0.13	36.7	2.60	8.5
Have care responsibility^a	6	1.52	(1.04, 1.99)	0.24	39.09	10.72	6.24
Education and unemployment experience Factors
Level of education^c	11	0.81	(0.67, 0.95)	0.07	51.58	9.99	11.1
unemployment experience^a	4	1.98	(0.72, 3.25)	0.64	74.24	9.59	3.07
Socio-economic Factors
Living in deprivation^a	4	1.24	(0.85, 1.62)	0.19	15.2	2.90	6.25
Criminal behaviour^a	4	2.06	(1.47, 2.65)	0.30	18.8	5.78	6.8
Health and Well-being Factors
Anxiety^a	7	1.29	(0.93, 1.65)	0.18	97.7	43.88	7.01
Any psychological problem^a	9	1.28	(0.89, 1.68)	0.2	81.7	24.55	6.4
depressive disorder^a	6	1.51	(0.90, 2.12)	0.31	66.62	12.85	4.86
Suicide^a	4	2.80	(1.80, 3.80)	0.51	34.9	4.15	5.48
physical health problems^a	8	1.35	(1.02, 1.67)	0.16	21.83	13.44	8.09
Alcohol use^a	9	1.09	(0.93, 1.25)	0.08	10.23	3.46	13.5
Cannabis use^a	8	1.57	(1.07, 2.07)	0.25	59.23	16.66	6.2
Smoking^a	5	1.77	(1.26, 2.29)	0.26	32.95	6.26	6.74
Drug use^a	12	1.58	(1.24, 1.93)	0.17	38.73	21.61	8.94

^aReference category: No.

^bReference category: Single

^cReference category: lower education

^dReference category : high and average income

^eReference category: none

Table A1. Characteristics of the selected studies.

References	country	baseline year	design	N	Sample	methods	study focus	covariates
Akinyemi & Mushunje, (2017)	South Africa	2016	Cross-sectional	167	youth aged 16 to 25	Logistic regression	To determine the prevalence of ‘not in education, employment or training’ (NEET) status among rural youth in Eastern Cape Province	Gender, Age Marital status Baby, Baby number
Alcazar et al. (2020)	Peru	2015	Cross-sectional	6125	youth aged 15–29	mixed methods	development of a contextually nuanced definition of precarious young workers, and provides an estimation of their numbers with those of NEETs	Female, children, Age, Level of education, Migrant, lives with parents, family member
Alfieri et al. (2015)	Italy	2011	Cross-sectional	9087	18 and 29 years	Logistic regression	investigates the relationship between family variables (parents’ educational level, relationship quality, intrusiveness, support, and autonomy) and young Italians’ status as NEETs	parents’ educational level, relationship quality,
Baggio et al. (2015)	Switzerland	2010–2012	Cohort	4758	Young men in their early 20’s at baseline	Logistic regression	examine if NEET youths differ from other contemporaries in terms of personality, mental health and substance use, and to provide longitudinal examination of NEET status	Depressive symptoms, Binge drinking, Cannabis use disorder, General mental health
Bania et al. (2019)	Norway	2003– 2012	Cohort	3987	Youth 15–16 years old at baseline	Logistic regression	explore the prevalence of young adults NEET defined by no educational engagement, long-term recipient of sickness benefit, medical and non-medical benefit receipt or long-term unemployment in an unselected population	Ethnicity, Parent education, Depression
Basta et al. (2019)	Greece	2016	Cross-sectional	2771	Youth 15–24 years old	Logistic regression	the associations of anxiety and depressive symptoms, and substance use with NEET status in a large, randomized population-based sample in Greece.	Age, Living with parents, Being married, Financial support, Smoking, Use of cannabis or other drugs, Alcohol drinks
Benjet et al. (2012)	Mexico,	2005	Cross-sectional	3005	Youth 12–17 years old	Logistic regression	to estimate the prevalence of psychiatric disorders, substance use and suicidal behaviour in youth classified as NEET and to compare with those who study only, work only or do both.	Anxiety disorder, Substance use disorder, Behavioural disorder, Suicidal attempt, Alcohol, Tobacco, Drugs, Any substance, Any substance,
Cabral, (2018)	Senegal	2011	Cross-sectional	53494	15 and 34 years old	Logistic regression	assess the extent of the phenomenon of NEET by measuring its rate and identify the key factors of the status of NEET	Disability, deprivation, parent education, Age, Gender, parent employment, family resources
Duckworth & Schoon, (2012)	England	2004–2010	Cohort	12,744	16 and 18	Logistic regression	assesses the effect of multiple independent socioeconomic risk factors in shaping the transition from school to work	Low parent education, Workless family, Low social class, lives in a lone parent family
(Flynn & Tessier, 2011)	Canada	2006–2009	Cross-sectional	406	Youth 18–20 years	Logistic regression	risk factors would be related to less favourable educational outcomes among extended care and maintenance youth.	Gender, Age, Health-related, disability, Soft-drug use
Gariepy & Iyer, (2019)	Canada,	2014	Cross-sectional	5622	Youth 15–29 years old	Logistic regression	describe the associations between a broad range of mental and substance disorders and being NEET	Depressive disorder, Generalized anxiety disorder, Alcohol use disorder, Cannabis disorder, Drug use disorder, Suicidal ideation
Giret et al. (2020)	France	2009–2013	Cohort	30049	aged 15 to 26	Logistic regression	analyse the school-to-work transition of young people. in order to understand their risk of becoming and remaining NEET.	gender, migrant origin, highest qualification reached, type of training, field of study, reason for leaving studies, parents’ education level, parents’ status and situation, place of residence upon leaving education
Goldman-Mellor et al. (2016)	United Kingdom	2012–2013	Cohort	2232	Twins 17–18 years old at baseline	Modified Poisson and logistic regression	compare four types of work-related self-perceptions, as well as vulnerability to mental health and substance abuse problems, among youths not in education, employment or training (NEET) and among their peers	Depressive disorder, Generalized anxiety disorder, Alcohol dependence, Cannabis dependence, Conduct disorder Depressive disorder, Anxiety disorder, Harmful substance use Suicidal behaviours
Gutiérrez-García et al. (2018)	Mexico,	2005	Cross-sectional	1071	Youth 19–26 years old	Logistic regression	to examine the socio-demographic, education, employment and incident mental health outcomes in early adulthood of education and employment status in adolescence, particularly of NEET adolescents compared to their peers who study, work or both.	Mood disorder, Anxiety disorder, Behavioural disorder, Alcohol use disorder, Drug use disorder, Suicidal ideation, Suicidal plan, Suicidal attempt
Hale & Viner, (2018)	United Kingdom	2004	Cohort	8682	Youth 13 years old at baseline	Logistic regression	to examine the impact of health in early adolescence on educational attainment and NEET status at the end of secondary education	Psychological distress, Alcohol use, Smoking, Cannabis use
Henderson et al. (2017)	Canada,	2009– 2013	Cross-sectional	2576	Youth 12–24 years old seeking services	Chi-square tests	explore the characteristics ofNEET youth compared to their non-NEET peers in a large, crosssectoral sample of service-seeking youth in Canada.	Internalizing disorders,Externalizing disorders, Substance use disorder
Holloway et al. (2018)	Australia	2013–2015	cohort	57644	15–25 years	Chi-square tests	investigate participation in employment, education, or training among a national cohort of young people presenting to youth-specific mental health services across Australia	Age, Gender, Sexuality, Living Situation, Rurality,Substance use disorder, Depressive disorder, Anxiety disorder, Psychological distress
Karyda & Jenkins, (2018)	England	2004–2010	cohort	8480	ages 18 to 19	Logistic regression	investigates the effects of neighbourhood context on young people who experience NEET status at the ages 18 to 19 in one cohort born in 1989/90	Crime, area deprivation, parent education, ethnicity, Family background
Karyda, (2020)	England	2004–2010	cohort	8480	ages 16–19	Logistic regression	examines the extent to which social disorganization, understood as the level of crime experienced in the neighbourhood, can influence young people’s educational and employment outcomes.	Neighbourhood deprivation, Gender, Ethnic group, Prior educational attainment, Mother’s and father’s highest educational qualifications, Socio-economic classification (SEC), Engagement in risky behaviour
Kevelson et al. (2020)	OECD Countries	2012 and 2015	cohort	37721	aged 20–29	Logistic regression	explored the extent to which disconnection influences civic participation and well-being among NEETs with and without college degrees.	Education, Community engagement, health and disability, parent education, immigrant, age, gender, Field of study
Lallukka et al. (2019)	Finland	1987–2015	cohort	46521	25–28 years	Logistic regression	determinants of longterm unemployment in early adulthood, using a birth cohort with nearly a 30-year follow-up from birth to early adulthood.	Health-related, School performance, Marital status, unemployment rate, Parental health, Parental education,
Lee & Kim, (2012)	South Korea	1998	cohort	2557	aged 15 and above	Logistic regression	macro- and micro-level factors that produce the growing trends of inactive youth and the problem of youth joblessness.	Gender, age, Married, education level, job training, Father’s education, Residing area, Father’s occupational
Lin & Chiao, (2022)	Taiwan	2000	cohort	2903	age of 14 years until the age of 22 years	Regression and mediation analyses	examine the relationship between adverse childhood experiences (ACEs) in early adolescence and heavy smoking in emerging adulthood, focusing on the mediating role of individuals’ not in education, employment, or training (NEET) status.	Adverse childhood experiences, Abuse, parental divorce or separation, parental addiction, parental mental illness, and criminal in the household, Emotional neglect, Heavy smoking, Parent’s education, educational aspiration, substance abuse, self-esteem, and depression
Luthra & Sottie, (2019)	Sweden	1981–2010	Cross-sectional	12269	18 years and older	Logistic regression	describe and analyse the family situation for those who are NEED.	gender, age, immigrant background, married, common law, single parent, living parents
Manhica et al. (2019)	Sweden	2005–2009	Cohort	485,839	Youth 19–24 years old with secondary education	Cox regression	To investigate to what extent being outside education, employment or training after completed secondary education in Sweden might affect the risk of subsequent alcohol use disorders	Alcohol use disorder
Maraj et al. (2019)	Canada	2018	Cohort	394	14 to 35 years old	Cox regression	examine how being vocationally inactive (NEET) affects engagement in early intervention services.	age, sex, visible minority status, social and material deprivation indices, and family involvement, duration of untreated psychosis, substance use disorder, medication nonadherence, and baseline positive and negative symptoms
O’Dea, Glozier, et al. (2014)	Australia,	2011–2012	Cohort	448	Youth seeking services 15–25 years old at baseline	Multinomial logistic regression and chisquare tests	to examine NEET status and its correlates in a sample of such youth.	Age, Immigrant background, Criminal charges, Postsecondary education, Economic hardship, Alcohol risk, Tobacco risk, Cannabis risk, Anxiety score, Depression score,
Pitkänen, Remes, Moustgaard, & Martikainen, (2021)	Finland	1987–2012	Cohort	99137	age 13–18 years old	Logistic regression	assess a broad range of concurrent childhood determinants of NEET status in the context of a universal welfare state, where education is free and transitions to secondary education are strongly encouraged.	Parental unemployment, education of parents, Household income quintiles, Parental psychiatric disorder, Parental substance abuse, Parental death, Living with single parent, Placed outside home
Power et al. (2015)	Ireland,	2000– 2002	Cohort	168	Youth 15–25 years old	Logistic regression	the associations between economic inactivity and mental disorder (including suicidal behaviour) in a community sample of young people in Ireland.	depressive disorder, anxiety disorder, suicidal attempt
Ringbom et al. (2022)	Finland	1998 and 2007, 2008 and 2015	cohort	55,273	Youth 15–25 years old	Logistic regression	To carry out a comprehensive overview of the associations between different psychiatric illnesses and long-term NEET status.	Psychiatric disorder, Substance misuse, Depressive disorders, Anxiety disorders, Personality disorders, Learning disabilities, Autism spectrum disorder, hyperactivity disorder, Bipolar disorder, Eating disorders
Rodriguez-Modroño, (2019)	Spain	2013 and 2016	cohort	1,612	15–29 years old	Logistic regression	The purpose of this paper is to apply an intersectional analysis to assess the impact of structural factors on the risk of being a NEET for youth in Spain.	gender, age, household income, education, nationality or country of origin and Work experience
Rodwell et al. (2018)	Australia,	1992–1993	Cohort	1938	Youth 14–15 years old at baseline	Logistic regression	to examine independent associations between common mental disorders, substance use (i.e. alcohol and cannabis), and disruptive behaviours in adolescence and being NEET in young adulthood.	Cannabis use, Any heavy binge drinking , parental education, Parental divorce or separation
Salvà-Mut, Tugores-Ques, et al. (2018)	Spain	2010 to 2012	Cross-sectional	622	aged 25–29 years	Logistic regression	finding common characteristics of the NEET	Gender, dropout, education level, immigrants status, family immigrants status, parent education, parent work, Personal situation, economic situation, health, time usage, educational background, Work history
Schoon & Lyons-Amos, (2017)	England	2004 and 2010	cohort	9791	young people 13/14–19/20	Logistic regression	introduce a socio-ecological model of agency to address the issue of person environment interactions.	Academic expectations, Goal certainty (forethought), Academic self-concepts (self-efficacy), School engagement (self-directedness), Parental education, parent income, family composition, living area, gender ethnicity. Life satisfaction, Transition patterns
Schoon, (2014)	England	2004 and 2006, 2007 and 2010	cohort	9879	ages 16 to 20 years	Logistic regression	examine the inter-generational link between parental worklessness and a young person’s likelihood of being NEET during a time that included a major economic downturn	Parental worklessness, family structure, housing conditions, and health, as well as area deprivation Academic expectations, Goal certainty (forethought), Parental education, Long standing illness or disability in the family, ethnicity, parent income, gender
Selenko & Pils, (2019)	Austria	2007 and 2011	Cross-sectional	300	average 16.67 years of age	Logistic regression	predict adolescents’ chances of succeeding on an apprenticeship programme after having gone through a period of being NEET	Gender, Age, Nationality, number of sibling, family composition, parent employment, Cognitive abilities, Relationship quality, dropout
Stea, Abildsnes, et al. (2019)	Norway	2016	Cross-sectional	480	NEET youth from vocational services and non-NEET youth attending high school, 16–21 years old	Logistic regression	investigate differences in health-related behaviours between young people who are not in education, employment or training (NEET) and high school students in Norway.	Psychological distress, Alcohol use, Smoking, Cannabis use
Stea, Abildsnes, et al. (2019)	Norway	2016	Cross-sectional	480	NEET youth, 16–21 years old	Logistic regression	the differences in self-perceived health, mental health, and prevalence of pain between Norwegian adolescents
Su et al. (2022)	Hong Kong	2015–2020	Cross-sectional	4,726	aged 15–21	Logistic regression	to examine factor structure of the CAAS-China Form in youth with a longer spell of NEET status	Gender, Age, Educational attainment, the role of carer, Seclusion at home, Involvement in deviant activities, Having addiction, health problems, Lack of clear goals for work, study & life, Limitation of abilities
Tamesberger & Bacher, (2014)	Austria	2008–2010	Cross-sectional	59.844	16 and 24 years	Logistic regression	examine the socio-structural attributes that characterize NEET youth in Austria, the main reasons for the NEET situation	Gender, Migration, Population, Living circumstances, education level, parent education, Care responsibilities, Illness
Tayfur et al. (2022)	England	2005 and 2015	cohort	2224	15–26 years old	Logistic regression	identify longitudinal associations between adolescent psychosocial factors and subsequent NEET status among this population in a nationally representative sample of secondary school students.	Self-esteem, Locus of control, Substance use, Bullying, Behavioural problems, Educational aspirations, Attitudes to school, Job aspirations, Physical activity, Sex, Ethnicity, Socioeconomic status, Family composition, Caring responsibility
(Vancea & Utzet, 2018)	Spain	2016	Cross-sectional	1.826	ages 18–35 years	Logistic regression	investigates the main sociodemographic characteristics of Spanish young people not in education, employment or training (NEETs).	Age, gender, education level, Marital status, Caring responsibilities, Having children, parent education family income, Parental immigration background, Work experience, Social capital(friends)
(Yang, 2020)	China	2012–2020	Cross-sectional	4,978	individuals are aged 16 to 35	Logistic regression	understand the prevalence and characteristics of individuals aged 16 to 35	age, gender, ethnicity (Han versus non-Han), education level, and marital status. region of birth, father’s education, migration, respondent’s Party membership, and family income

Table A2. Quality assessment of the selected studies.

Authors, year	QAR1(5)	QAR2(5)	QAR3(5)	QAR4(5)	QAR5(5)	Average point (5)
Akinyemi & Mushunje, (2017)	4	3	3	2	4	3.2
Alcazar et al. (2020)	5	4	4	4	4	4.2
Alfieri et al. (2015)	5	4	5	4	3.5	4.3
Baggio et al. (2015)	4	3	5	4	4	4
Bania et al. (2019)	5	4	5	4	5	4.6
Basta et al. (2019)	5	4	5	4	3	4.2
Benjet et al. (2012)	5	4	5	4	5	4.6
Cabral, (2018)	4	4	5	4	4	4.2
Duckworth & Schoon, (2012)	5	5	5	4	5	4.8
Flynn & Tessier, (2011)	4	4	5	4	5	4.4
Gariepy & Iyer, (2019)	4	3	3	2	4	3.2
Giret et al., 2020)	5	5	5	1	3	3.8
Goldman-Mellor et al. 2016)	5	4	4	1	4	3.6
Gutiérrez-García et al. (2018)	3	3	2	3	2	2.6
Hale & Viner, (2018)	4	2	3	3	4	3.2
Henderson et al. (2017)	5	4	5	4	5	4.6
Holloway et al. (2018)	4	2	1	1	2	2
Karyda & Jenkins, (2018)	5	4	5	4	4	4.4
Karyda (2020)	5	5	5	4	4.5	4.7
Kevelson et al. (2020)	5	4	4	1	4	3.6
Lallukka et al., (2019)	4	4	5	4	4	4.2
Lee & Kim, (2012)	5	4	5	4	5	4.6
Lin & Chiao, (2022)	4	5	5	4.5	5	4.7
Luthra & Sottie, (2019)	4	4	5	4	5	4.4
Manhica et al. (2019)	4	2	4	2	3	3
Maraj et al., (2019)	5	4	5	3	5	4.4
O’Dea, Glozier, et al. (2014)	5	4	5	4.5	5	4.7
Pitkänen, Remes, Moustgaard, & Martikainen, (2021)	4	3	3	3	2	3
Power et al. (2015)	5	2	3	1	4	3
Ringbom et al. (2022)	2	2	1	2	2	1.8
Rodriguez-Modroño, (2019)	2	4	4	2	3	3
Rodwell et al. (2018)	4	5	5	4.5	5	4.7
Salvà-Mut, Tugores-Ques, et al. (2018)	5	4	5	4	5	4.6
Schoon & Lyons-Amos, (2017)	3	2	2	2	3	2.4
Schoon, (2014)	5	4	5	4	5	4.6
Selenko & Pils, (2019)	5	4	5	4	5	4.6
Stea, de Ridder, et al. (2019)	4	4	5	2	5	4
Stea, Abildsnes, et al. (2019)	4	5	5	2	5	4.2
Su et al., (2022)	5	4.5	5	4.5	5	4.8
Tamesberger & Bacher, (2014)	5	4.5	5	4.5	5	4.8
Tayfur et al. (2022)	4	3	4.5	3	4	3.7
Vancea & Utzet, (2018)	4	4	5	4	5	4.4
Yang, (2020)	5	4	5	4	5	4.6

Table A3. Detailed summary of findings by study.

Study	Risk factors	Results
Akinyemi & Mushunje, (2017)		Coefficients	Std. Error
	Age	0.3483	0.6951
	gender	−0.9997	0.1997
	married	−1.6468	0.9228
	children	−1.197	0.8672
Alcazar et al. (2020)		Marginal effects	Std. Error
	Female	0.0450***	−0.00936
	children	0.0126	−0.0118
	Age	0.00654***	−0.0022
	Level of education	−0.213	−0.0613
	Migrant	0.00347	−0.0125
	lives with parents	−0.0161	−0.0107
	family member	−0.00643	−0.003
Alfieri et al. (2015)		Marginal Effects	Z
	Education of the mother	−0.0874***	−3.78	Men
	Education of the father	−0.0853***	−3.65	Men
	relation with family	0.0161	1.17	Men
	Education of the mother	−0.0786**	−3.2	Women
	Education of the father	−0.0690**	−2.83	Women
	relation with family	−0.0002	−0.01	Women
Baggio et al. (2015)		Odds Ratio
	Depressive symptoms	1.11	p < 0.001
	Binge drinking	NA	p > 0.05
	Cannabis use disorder	1.09	p < 0.001
	General mental health	1.11	p < 0.001
Bania et al. (2019)		Odds Ratio	95% Cl
	ethnicity	1.58	1.02 to 2.44)	Men
	Parent education	1.75	0.91 to 3.33)	Men
	Depression	0.88	0.81 to 0.97)	Men
	ethnicity	0.74	0.46 to 1.17)	Women
	Parent education	1.06	0.63 to 1.78)	Women
	Depression	1.04	0.97 to 1.11)	Women
Basta et al. (2019)		Odds Ratio	95% Cl
	Age	1.28	1.19–1.36)	Men
	Living with parents	2.68	1.84–3.92)	Men
	Being married	2.07	0.95–4.53)	Men
	Financial support	1.56	1.01–2.43)	Men
	Smoking	1.84	1.3–2.6)	Men
	Use of cannabis or other drugs	0.99	0.67–1.48)	Men
	Alcohol drinks	1.01	0.99–1.03)	Men
	Age	1.57	1.45–1.7)	Women
	Living with parents	2.2	1.55–3.13)	Women
	Being married	2.41	1.4–4.13)	Women
	Financial support	3.4	1.92–6.01)	Women
	Smoking	1.16	0.82–1.65)	Women
	Use of cannabis or other drugs	0.518	0.28–0.94)	Women
	Alcohol drinks	0.96	0.91–1.00)	Women
Benjet et al. (2012)		Odds Ratio	95% Cl
	Anxiety disorder	1.3	0.9–1.8)
	Substance use disorder	4.4	2.6–7.6)
	Behavioural disorder	2.1	1.5–2.8)
	Suicidal attempt	1.7	1.3–2.3)
	Alcohol	3	2.4–3.9)
	Tobacco	3.9	3.0–4.9)
	Drugs	3.5	1.8–6.9)
	Any substance	3.4	2.7–4.3)
Cabral, (2018)		Coefficients
	Disability	0.774***	total
	deprivation	0.108	total
	parent education	0.157***	total
	Age	−0.0122***	total
	Gender	1.444***	total
	parent employment	0.505***	total
	family resources	−0.165***	total
	Disability	1.105***	Men
	deprivation	0.413***	Men
	parent education	0.0142	Men
	Age	0.0551***	Men
	parent employment	0.680***	Men
	family resources	−0.175***	Men
	Disability	0.505***	women
	deprivation	0.157***	women
	parent education	0.384***	women
	Age	0.00575*	women
	parent employment	0.377***	women
	family resources	−0.156***	women
Duckworth & Schoon, (2012)		Odds ratio	Effect size
	Low parent education	1.31*	0.14
	Workless family	1.32*	0.16
	Low social class	1.65***	0.23
	YP lives in a lone parent family	1.20*	0.11
Flynn & Tessier, (2011)		Odds Ratio
	Gender	1.08
	Age	0.82
	Health-related disability	1.84
	Soft-drug use	0.97
Gariepy & Iyer, (2019)		Odds Ratio	95% Cl
	Depressive disorder	1.7	1.1, 2.6
	Generalized anxiety disorder	2.6	1.4, 5.1
	Alcohol use disorder	1	0.6, 1.7
	Cannabis disorder	1	0.5, 2.0
	Drug use disorder	3.2	1.3, 7.8
	Suicidal ideation	1.8	1.0, 3.1
Giret et al. (2020)		Odds Ratio
	gender	1.165**
	Level of education	1.513***
	Field of study	0.942
	family member	1.282**
	Father not in employment	1.207**
	Mother not in employment	1.135**
Goldman-Mellor et al. (2016)		Odds Ratio	95% Cl
	Depressive disorder	1.68	1.33, 2.13
	Generalized anxiety disorder	1.84	1.19, 2.86
	Alcohol dependence	1.42	1.01, 2.01
	Cannabis dependence	3.93	2.31, 6.68
	Conduct disorder	1.46	1.12, 1.89
	Depressive disorder	2.57	1.40, 4.72
	Anxiety disorder	1.38	0.81, 2.37
	Harmful substance use	1.89	1.29, 2.77
	Suicidal behaviours	3.3	2.07, 5.27
Gutiérrez-García et al. (2018)		Odds Ratio	95% Cl
	Mood disorder	1.67	1.12, 1.90
	Anxiety disorder	0.4	0.10, 1.65
	Behavioural disorder	0.83	0.45, 1.50
	Alcohol use disorder	1.19	1.14, 1.38
	Drug use disorder	1.03	0.71, 1.50
	Suicidal ideation	1.94	1.16, 3.31
	Suicidal plan	1.18	1.07, 2.38
	Suicidal attempt	2.4	1.30, 4.31
Hale & Viner, (2018)		Odds Ratio	95% Cl
	Psychological distress	1.72	1.24, 2.41	Men
	Psychological distress	1.49	1.11, 1.99	Women
	Alcohol use	1.15	0.94 to 1.40)	Men
	Alcohol use	2.04	1.74 to 2.39)	Women
	Smoking	1.41	1.08 to 1.83)	Men
	Smoking	2.07	1.75 to 2.46)	Women
	Cannabis use	1.34	1.11 to 1.63)	Men
	Cannabis use	2.19	1.87 to 2.56)	Women
Henderson et al. (2017)		Odds Ratio	95% Cl
	Internalizing disorders	1.15	0.95, 1.38	Total
	Internalizing disorders	0.95	0.72, 1.26	Age <18
	Internalizing disorders	1.33	1.00, 1.75	Age ≥18
	Internalizing disorders	1.39	1.08, 1.78	Men
	Internalizing disorders	1.08	0.80, 1.45	Women
	Externalizing disorders	0.9	0.76, 1.08	Total
	Externalizing disorders	1.18	0.90, 1.56	Age <18
	Externalizing disorders	1.13	0.87, 1.46	Age ≥18
	Externalizing disorders	0.93	0.73, 1.19	Men
	Externalizing disorders	0.87	0.67, 1.12	Women
	Substance use disorder	1.94	1.63, 2.31	Total
	Substance use disorder	1.44	1.21, 1.88	Age <18
	Substance use disorder	2.19	1.68, 2.84	Age ≥18
	Substance use disorder	1.83	1.43, 2.34	Men
	Substance use disorder	2.05	1.58, 2.66	Women
Holloway et al. (2018)		Odds Ratio	95% Cl
	Age	1.92	1.87,1.98
	Sex	1.78	1.65,1.93
	Rurality	1.09	1.00,1.18
	Substance use disorder	1.93	1.62,2.31
	Depressive disorder	0.79	0.72,0.87
	Anxiety disorder	0.86	0.77,0.95
	Psychological distress	0.97	0.97,0.98
Karyda & Jenkins, (2018)		Coeff.	std. err.
	Highest qualification of main parent:	1.453	0.123
	Single-parent family	0.823	0.113
	Ethnic group	0.891	0.217
	person’s behaviour	1.66	0.172
Karyda, (2020)		Odds Ratio
	sex	0.774
	Ethnicity	0.844
	highest educational qualification of mother	0.928
	highest educational qualification of father	0.711
	experienced aggressive behaviour	1.288
Kevelson et al. (2020)		Odds Ratio
	Parent education	1.173
	Migrant	1.377
	Female	2.118
	Age	1.223
	Caring responsibility	0.609
	Field of study	1.234
Lallukka et al. (2019)		Odds Ratio	95% Cl
	Any mental disorder	3.22	[2.92, 3.55]	Men
	Any mental disorder	2.74	[2.40, 3.13]	Women
	Marital status	1.7	[0.96, 2.90]	Men
	Marital status	1.08	[0.69, 1.64]	Women
	Long-term unemployed	4.04	[3.38, 4.81]	Men
	Long-term unemployed	3.37	[2.47, 4.56]	Women
	Parental education	1.26	[1.07, 1.48]	Men
	Parental education	1.54	[1.22, 1.97]	Women
	level of education	1.45	[1.27, 1.65]	Men
	level of education	1.75	[1.44, 2.11]	Women
	living with single parent	1.58	[1.38, 1.82]	Men
	living with single parent	1.34	[1.10, 1.62]	Women
Lee & Kim, (2012)		Odds Ratio	B
	Age	0.27	−1.310***
	Married	0.674	−0.394
	Education	0.754	−0.283
	Father’s education	0.656	−0.422
	Father’s occupation	1.018	0.018**
Lin & Chiao, (2022)		OR	ES
	Male	1.99	0.35
	Education level	0.96	0.03
	Substance use	1.07	0.3
	Depressive symptoms	1.26	0.22
Luthra & Sottie, (2019)		Odds Ratio	95% Cl
	Gender	1.21***	1.11,1.32)
	Age	0.60***	0.53,0.69)	Men
	Age	0.56***	0.48,0.65)	Women
	Immigrant	1.46***	1.24,1.72)	Men
	Immigrant	1	0.83,1.21)	Women
	Married	1.47	0.97,2.21)	Men
	Married	3.92***	2.93,5.24)	Women
	Single parent	1.94	0.59,6.37)	Men
	Single parent	7.44***	5.39,10.3)	Women
Manhica et al. (2019)		HR	95% Cl
	Alcohol use disorder	1.30	1.11, 1.51
Maraj et al. (2019)		Odds Ratio	95% Cl
	Age	0.94	.75–1.18
	Male	2.98	.40–22.23
	Visible minority	1.34	.38–4.81
	Substance use disorder	0.54	.11–2.62
	Material deprivation	1.03	1.00–1.07
O’Dea, Glozier, et al. (2014)		Odds Ratio	95% Cl
	Age	2.42	1.64 to 3.57)
	gender	2.24	1.51 to 3.31)
	Immigrant background	1.09	0.74 to 1.61)
	Criminal charges	2.85	1.68 to 4.83
	Postsecondary education	0.84	0.51 to 1.39)
	Economic hardship	2.15	1.37 to 3.39)
	Alcohol risk	1.37	0.86 to 2.17)
	Tobacco risk	1.46	0.99 to 2.16)
	Cannabis risk	1.94	1.29 to 2.90)
	Anxiety score	1.54	0.41 to 2.68)
	Depression score	2.72	1.73 to 3.72)
Pitkänen, Remes, Moustgaard, & Martikainen, 2021)		Odds Ratio	95% Cl
	Parental education	1.61	1.47–1.76
	Parental unemployment	1.61	1.45–1.80
	Household income	1.12	0.95–1.31
	Living with a single parent	1.35	1.24–1.47
Power et al. (2015)		Odds Ratio	95% Cl
	depressive disorder	2.81	1.15–6.9)
	anxiety disorder	2.9	0.9–4.2)
	suicidal attempt	3.21	1.12–9.2)
Ringbom et al. 2022)		Odds Ratio	95% Cl
	Gender	1.8	1.6 − 2.0)
	Parental education	1.4	1.3 − 1.6)
	Any psychiatric	3.7	3.2 − 4.2)
	Substance misuse	3.1	2.4 − 3.8)
	Depressive disorders	2.8	2.4–3.3)
	Anxiety disorders	3.1	2.6–3.7)
Rodriguez-Modroño, (2019)		marginal effects	std. err.
	Age	0.00858***	3.62	men
	Age	0.00187	0.61	women
	household income	−0.00000236***	−2.91	men
	household income	−0.00000375***	−3.67	women
	household Work	−0.0380**	−2.13	men
	household Work	−0.0476**	−2.52	women
	Unemployed W 6 months	0.0449	1.54	men
	Unemployed W 6 months	0.0852***	2.79	women
Rodwell et al. (2018)		Odds Ratio	95% Cl
	Cannabis use	1.74	1.10–2.75)
	Any heavy binge drinking	0.80	0.48–1.34)
	parental education	0.59	0.40–0.87)
	Parental divorce or separation	1.75	1.26–2.43)
Salvà-Mut, Tugores-Ques, et al. (2018)		Marginal effects
	educational level	−0.1606
	female	0.0023
	Immigrant	0.3312**
	Poorly educated father	0.2297*
	Poorly educated mother	0.005
	Married	0.152
	With children	0.4798***
	economic hardship	0.1515***
	relevant illnesses	0.1707
	relevant addictions	0.2990**
Schoon & Lyons-Amos, (2017)		Coefficient	Std. Err
	Family resources	−0.015	0.015
	Urban	−0.022	0.053
	Unemployed after some education	−.627***	0.136
	Female	.094**	0.03
	Non-white	−0.069	0.022
Schoon, (2014)		Coefficient	Std. Error
	Parental worklessness	0.655	0.699
	Parental education	−0.65	0.307
	Single parent household	−0.04	0.353
	Number of siblings	0.192	0.135
	household income	0.021	0.086
	Urban/rural	0.458	0.334
	ethnicity minority	−0.683	0.318
Selenko & Pils, (2019)		Odds Ratio	95% Cl
	Gender	1.03	[0.57; 1.86]
	Nationality	2.25	[1.16; 4.35]
	Nr of siblings	1.02	[0.74; 1.40]
	Single parent household	0.55	[0.14; 2.23]
	Age	0.6	[0.43; 0.84]
Stea, de Ridder, et al. (2019)		Odds Ratio	95% Cl
	Psychological distress	1.13	0.89 to 1.44)
	Alcohol use	1.15	0.94 to 1.40)
	Smoking	1.41	1.08 to 1.83)
	Cannabis use	1.34	1.11 to 1.63)
Stea, Abildsnes, et al. (2019)			Odds Ratio
	Psychological distress		NA, p > 0.05	Men
			2.4	Women
Tamesberger & Bacher, (2014)		Coefficient
	Female	1.327*
	Migration background	1.432*
	Living with parents	0.535**
	Have child(ren)	3.919***
	Care responsibilities	5.172**
Tayfur et al. (2022)		Odds Ratio	95% Cl
	Substance use	1.02	[0.69–1.51]
	Behavioural problems	0.92	[0.61–1.38]
Vancea & Utzet, (2018)		Odds Ratio	95% Cl
	Education:	0.88	0.33; 2.44)
	Education:	0.43	0.17; 1.09)
	Caring responsibilities	0.68	0.24; 1.69)
	Caring responsibilities	2.8	1.33; 6.01)*
	Parents’ economic self-sufficiency	0.97	0.60; 1.55)
	Parents’ economic self-sufficiency	0.53	0.33; 0.83)*
	Unemployment experience	3.4	1.00; 14.97)
	Unemployment experience	4.84	1.69; 17.6)*
Yang, (2020)		Odds Ratio	Std. Error
	Age group	15.26	8.33
	Female	7.47	1.43
	Ethnicity	1.9	0.53
	Education	0.16	0.09
	Married	3.63	0.94
	Father’s education	0.93	0.13

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