literature review on youth unemployment

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

Enter the email address you signed up with and we'll email you a reset link.

• We're Hiring!
• Help Center

Youth unemployment: a review of the literature

1985, Journal of Adolescence

Related Papers

Educational Evaluation and Policy Analysis

Henry Levin

Journal of Adolescence

Lindsey Mean

aris accornero

ABSTRACT: This paper underlines, first of all, that diverse ways to define and compute the unemployed people used by various countries produces different figures, and images too, of this phenomenon. Using overly restricted criteria means depriving oneself of useful information. Secondly, the author explains the profound differences between traditional unemployment and contemporary joblessness, which concerns many young people in Western countries. Finally, the author discusses this new feature of the unemployment phenomenon, and affirms that joblessness is not a ‘non-use‘ but rather a ‘new-use‘ of young people by modern capitalistic societies.

Scottish Journal of Political Economy

Karsten Albæk

Michael Oddy

ABSTRACT Young people are particularly vulnerable to unemployment and the consequences of this for psychosocial development and mental health are not well understood.This study is an investigation of some of these consequences.The psychological well-being and mental health of employed and unemployed school-leavers of both sexes was investigated.Those school-leavers who were unemployed were found to be more depressed and more anxious than those in work and showed a higher incidence of minor psychiatric morbidity. Unemployed young people had lower self-esteem than their employed peers and poorer subjective wellbeing. They were also found to be less well socially adjusted. Young women showed poorer psychological well-being than young men, irrespective of employment status.The psychological impact of unemployment for young people is discussed in relation to individual and sex differences and the question of whether poor mental health is a cause or a consequence of unemployment is considered.

Procedia Economics and Finance

Barbora Gontkovičová

Alexander Nabiulin

RePEc: Research Papers in Economics

Barbara Petrongolo

RELATED PAPERS

Field Environmental Philosophy

KALAIVAANAN MURTHY

BENTHAM SCIENCE PUBLISHERS eBooks

Hyperfine Interactions

Journal of Pharmacognosy and Phytochemistry

Anubhuti sharma

Journal of computational neuroscience

Khánh Linh Nguyễn

International journal of systematic bacteriology

Karel Kersters

Shipbuilding & marine infrastructure

International Journal of Applied Mathematics and Theoretical Physics

Ravo Tokiniaina Ranaivoson

Raullyan Borja Lima e Silva

a/b Autobiography Studies

Anders Høg Hansen

Francisco Javier Tinajero

International Journal of Geometric Methods in Modern Physics

Partha Guha

CENTRAL EUROPEAN SYMPOSIUM ON THERMOPHYSICS 2019 (CEST)

Vladimir Kruchinin

DOAJ (DOAJ: Directory of Open Access Journals)

Rehmat Zaman

Mastozoología neotropical

Rubén Barquez

Said Goueli

Journal of Applied Science and Technology (JAST)

Collins Nana ANDOH

Anna Fogelberg Eriksson

Immunogenetics

Eduardo Vedovetto Santos

Academic Radiology

Gregory Diette

RELATED TOPICS

•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 12 October 2020

A systematic review of employment outcomes from youth skills training programmes in agriculture in low- and middle-income countries

• W. H. Eugenie Maïga   ORCID: orcid.org/0000-0003-2735-8945 1 ,
• Mohamed Porgo   ORCID: orcid.org/0000-0002-7325-3610 2 ,
• Pam Zahonogo 2 ,
• Cocou Jaurès Amegnaglo 3 ,
• Doubahan Adeline Coulibaly 2 ,
• Justin Flynn 4 ,
• Windinkonté Seogo 5 ,
• Salimata Traoré   ORCID: orcid.org/0000-0002-8373-4995 2 ,
• Julia A. Kelly   ORCID: orcid.org/0000-0003-0796-0461 6 &
• Gracian Chimwaza 7  

Nature Food volume  1 ,  pages 605–619 ( 2020 ) Cite this article

10k Accesses

11 Citations

16 Altmetric

Metrics details

• Agriculture

A Publisher Correction to this article was published on 20 October 2020

This article has been updated

Engagement of youth in agriculture in low- and middle-income countries may offer opportunities to curb underemployment, urban migration, disillusionment of youth and social unrest, as well as to lift individuals and communities from poverty and hunger. Lack of education or skills training has been cited as a challenge to engage youth in the sector. Here we systematically interrogate the literature for the evaluation of skills training programmes for youth in low- and middle-income countries. Sixteen studies—nine quantitative, four qualitative and three mixed methods—from the research and grey literature documented the effects of programmes on outcomes relating to youth engagement, including job creation, income, productivity and entrepreneurship in agriculture. Although we find that skills training programmes report positive effects on our chosen outcomes, like previous systematic reviews we find the topic to chronically lack evaluation. Given the interest that donors and policymakers have in youth engagement in agriculture, our systematic review uncovers a gap in the knowledge of their effectiveness.

Similar content being viewed by others

Investigating the effect of vocational education and training on rural women’s empowerment

Strained agricultural farming under the stress of youths’ career selection tendencies: a case study from Hokkaido (Japan)

Conceiving of and politically responding to NEETs in Europe: a scoping review

Youth in low- and middle-income countries (LMIC) disproportionately experience working poverty. In 2019, about 21% of employed youth in LMIC were living on less than US$2 a day, compared with 16% of the overall working population 1 . In sub-Saharan Africa, nearly 70% of working youth were found to be living in poverty; in South Asia, close to 50% were living in poverty 2 . Issues of youth unemployment and underemployment are linked to greater likelihood of future unemployment, decreased future job satisfaction, lower income and poorer health in adulthood 3 . National consequences include greater costs to support public programmes (such as public work programmes that provide temporary jobs) and indirect costs of lower earnings such as loss of investment in education 4 , 5 . Furthermore, youth underemployment is linked to disillusionment and the possibility of social unrest 6 .

The working-age population in LMIC is predicted to double in the next 35 years 7 and while this presents challenges, many LMIC are currently experiencing a demographic dividend phase where there is a high ratio of working-age population to dependents. This offers unique prospects for economic development with concomitant reductions in poverty and food insecurity. Addressing unemployment and underemployment is, therefore, a major policy priority for LMIC 6 , and a key sector for the creation of employment opportunities, especially in Africa and Asia, is agriculture 6 , 8 , 9 .

Many people in LMIC rely on agriculture for their livelihoods (32% in 2019) 10 , either directly, as farmers, or indirectly in sectors that derive their existence from agricultural production 8 , 9 , 11 . Agricultural development is estimated to be up to 3.2 times more effective in alleviating poverty in low-income, resource-rich countries than any other sector 12 . Due to the close links between poverty and food insecurity 13 , 14 , 15 , agricultural development could also have positive consequences for the alleviation of hunger, particularly for women, as their empowerment in agriculture improves households’ food security and nutrition 16 , 17 , 18 .

However, there has been a declining trend of youth participation in agriculture since 2000, mainly in favour of the service sector 6 , 19 , 20 , which precipitates migration from rural to urban areas. Increased educational attainment for rural youth coupled with inability to rent or own land is a driver of urban migration 21 . In addition, the increasing ageing farmer population in rural areas exacerbates the demographic pressure on land at the expense of the youth 22 .

A further constraint on youth engagement in agriculture is a lack of education in disciplines related to agriculture or skills training 23 , 24 , 25 . A study among Thailand’s youth reported that 71% identified knowledge of farming practices as a pre-requisite to setting up a viable farm 23 . In rural Ethiopia, government initiatives to increase skills and productivity, and introduce improved and modern farming methods have generated interest among youth in joining the sector, and in Indonesia, vocational training was noted as increasing the likelihood of a successful career in agriculture 26 . A study in Zambia on rural youth aspirations, opinions and perceptions on agriculture documented high interest among youth in more productive forms of farming, such as the use of draught animals, electricity and the increased application of fertilizers 24 . Such findings challenge an assumption common in policy proposals that youth are not interested in agriculture 25 . Today, with the development of information and communication technology (ICT), young people have more opportunities to strengthen their skills and access relevant information and are therefore well positioned to understand market dynamics, and institutional and financial systems, enabling them to initiate and capitalize on processes of change in the agricultural sector 27 , 28 . Human capital theory predicts a positive correlation between human capital accumulation and labour productivity. On that basis, skills training can be used to improve agricultural employment outcomes 29 . Where governments and policy interventions support skills training for youth, there is a real possibility for entrepreneurship, a competitive economy and ultimately national growth. But, despite the implementation of skills training interventions, generally via youth employment programmes 30 , few specifically target agricultural skills training in LMIC and very little is known about the effectiveness of youth agricultural interventions 30 , 31 .

Here we systematically review skills-based training interventions that aim to increase youth engagement in agricultural employment in LMIC to better inform investment decisions made by donors and policymakers. The interventions include agriculture-related courses, on-the-job training, technical or vocational education and training in agriculture, as well as general skills training including entrepreneurship, financial literacy and life skills for engagement in agriculture. The outcomes of interest we started out with were: employment along an agricultural value chain; employment in agribusiness; engagement in contract farming; development of agricultural entrepreneurship; agricultural business performance (productivity, profit, income, marketing rate); involvement in agricultural extension service provision. After data extraction, the outcomes of interest found in the selected studies are jobs created in the agricultural sector, self-employment and entrepreneurship, provision of and employment in extension services, profit/income/earnings from an agricultural activity or job, farm productivity, and the accessibility of employment opportunities in the sector. These outcomes pertain to the categories of jobs that can be found along the agricultural value chain.

We found among the studies yielded from the systematic literature search that skills training interventions reported employment in agriculture, agribusiness or agriculture-related activities, development of agricultural entrepreneurship, agricultural business performances (productivity, profit, income) and involvement in agricultural extension service provision for young participants . However, we also found a chronic lack of evaluation of the effectiveness of interventions designed to enhance agricultural opportunities and engagement for young people in LMIC, a finding previously shown 31 .

Sixteen studies were identified for review based on a priori inclusion and exclusion criteria (Fig. 1 ) detailed in our Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol, PRISMA-P (Supplementary Material 1 , summarized in Methods and published on Open Science Framework, https://osf.io/bhegq// ).

Inclusion criteria were youth as the target population; inclusion of one or more outcome of interest (employment along an agricultural value chain; employment in agribusiness; engagement in contract farming; development of agricultural entrepreneurship; agricultural business performance (productivity, profit, income, marketing rate); involvement in agricultural extension service provision); agriculture sector as field of study; skills training as an intervention; publication in English or French between 1990 and 2019; original research or review of existing research or institutional reports; targets low- and middle-income country or countries as area(s) of study (see list of World Bank country classifications (Supplementary Table 1 ); a clear and well-accepted methodology (studies were excluded if there was no clear method on sampling, data analysis or discussion of results). Studies meeting the inclusion criteria and targeting mixed group (youth and other demographic groups) were also retained in the search strategy. A double-blind title and abstract screening were performed on 4,789 articles that were uploaded to systematic review software, Covidence, for title and abstract screening. Each article was reviewed by two independent reviewers and discrepancies were resolved by a third independent author within the team. After title and abstract screening, 261 articles remained. From title and abstract screening, 16 articles met a priori inclusion criteria.

Characteristics of selected studies

A data extraction template (Supplementary Table 2 ) was used to document all information of interest from each of the 16 studies, overviewed in Table 1 .

Eleven of the studies were based in Africa 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 and five in Asia 43 , 44 , 45 , 46 , 47 . Twelve of the studies were published in peer-reviewed journals 33 , 34 , 35 , 36 , 39 , 40 , 41 , 42 , 44 , 45 , 46 , 47 and the rest originated from the grey literature, including one dissertation 38 , one report 37 and two working papers 32 , 43 .

With regard to the study design, nine of the included studies were quantitative 32 , 33 , 34 , 35 , 36 , 37 , 43 , 44 , 45 , four were qualitative 41 , 42 , 46 , 47 and three used mixed non-experimental 38 , 39 , 40 methods. Only one study used randomized control trial (RCT) as a study design method of evaluation 32 . Quasi-experimental impact methods (difference-in-differences (DID) and propensity score matching (PSM)) and quantitative non-experimental methods (statistical and econometric methods) were used in two 33 , 43 and six 34 , 35 , 36 , 37 , 44 , 45 studies, respectively. Nine of the included studies relied on survey data 32 , 33 , 34 , 35 , 36 , 37 , 43 , 44 , 45 , one study used data from interviews 47 , one study used data from focus groups 42 and the rest of the studies used mixed sources of data 38 , 39 , 40 (Supplementary Table 3 ).

Table 2 collates information from the selected studies on the basis of types of intervention and participant characteristics. Technical education/training 35 , 41 , 42 , 46 and vocational training 37 , 40 , 44 , 45 constituted half of the interventions (four, each); youth programmes, agriculture-related courses and on-the-job training were identified as interventions in three 33 , 34 , 38 , two 39 , 47 and one 36 of the studies, respectively, and the remainder of the studies combined two types of intervention 32 , 43 . Twelve of the interventions were implemented through public policies 33 , 34 , 35 , 37 , 38 , 39 , 41 , 42 , 43 , 44 , 45 , 47 ; non-governmental organizations (NGOs) and a mix of institutions (public and private) were each identified as implementers in two 32 , 36 and one 46 of the studies, respectively, and one study reported intervention implemented by an international institution 40 .

Nine of the studies solely targeted youth 32 , 33 , 34 , 35 , 37 , 38 , 43 , 45 , 46 , and seven targeted mixed groups of youth and others 36 , 39 , 40 , 41 , 42 , 44 , 47 . In fourteen studies, the participants were from all genders. In nine of the studies, participants were a mixed group of those already and not yet engaged in agriculture 32 , 34 , 37 , 39 , 41 , 42 , 43 , 44 , 46 ; in five of the studies, participants were already engaged in agriculture before receiving skills training interventions 35 , 36 , 37 , 45 , 47 ; there was not enough information to determine whether the participants were already engaged in agriculture in two studies 33 , 40 . Six of the studies indicated that the participants resided in rural areas 33 , 34 , 35 , 36 , 46 , 47 , while participants located in urban areas and in both rural and urban areas were identified in four 32 , 38 , 40 , 45 and five 37 , 39 , 41 , 43 , 44 of the studies, respectively; there was not enough information to determine the location of the participants in one 42 study. The population targeted in the studies was both educated and non-educated youth. Among the nine studies 32 , 33 , 34 , 35 , 37 , 38 , 43 , 45 , 46 that focused exclusively on youth, two targeted youth with a secondary education background 34 , 46 , one 45 targeted youth with a university background and six 32 , 33 , 35 , 37 , 38 , 43 of the studies targeted youth with a mixed educational background.

Risk of bias assessment

We evaluated the risk of bias of the included studies based on a previous approach 48 . The domains of risk retained are (1) the sampling technique used for the study, (2) the type of intervention, (3) the choice of the area of study, (4) the population targeted, (5) the method of data collection, (6) the method of data analysis, (7) the measurement of outcome and (8) the statistical significance of the effect. For each domain of risk, the criteria evaluated were defined and rated by their relevance for assessing the effectiveness of the interventions. Supplementary Table 4 summarizes the criteria of each domain of risk and its assessment and rating.

Using this scale, 15% of our included studies are at low risk of bias, 60% at moderate risk of bias and the remaining 25% at serious risk of bias. The outcome of the risk of bias assessment of the included studies in this systematic review is presented in Table 3 .

The risk of bias assessment process highlighted differences in focus, methods used and standards of evidence across the included studies. Weaknesses in study design, survey methods and method of evaluation of the impact of the interventions were common in most of the studies (with the exception of the studies ranked at low risk of bias), leading to weak results and limited generalizability.

Effects on youth employment outcomes

The youth employment outcomes of interest to this systematic review are job creation, self-employment, engagement in entrepreneurship, provision of extension services, productivity of the farm/agriculture-related activities, profits/income, and job search or employment opportunity in agriculture-related activities. Here we elaborate on the study design and risk of bias of all studies, and highlight the effects on outcomes of interest for a selection of low and moderate risk studies.

Job creation in agriculture

Eight studies 32 , 38 , 39 , 40 , 41 , 42 , 43 , 45 looked at job creation in agriculture as an outcome. Among those studies, three are quantitative studies 32 , 43 , 45 , two are qualitative studies 41 , 42 and three are mixed-methods studies 38 , 39 , 40 .

In one quantitative study, deemed at low risk of bias (Table 3 ), 1,700 workers and 1,500 firms were followed over four years to compare the effects of offering workers vocational training and offering firms wage subsidies to train workers on-the-job (firm training) in Uganda 32 . The results showed that both interventions allowed participants to acquire sector-specific skills and firm-specific skills leading to higher employment rates post-training for each type of worker, but the effect was greater for vocational training compared with firm training (21% versus 14% post-training employment rate) and their total earnings rose by more compared with the firm-training intervention (34% versus 20%). The qualitative studies 41 , 42 , although not designed to assess the effectiveness of an intervention, highlighted a link between skills training and employment outcome. However, both studies were deemed at serious risk of bias. A mixed-methods study 38 on youth programmes in Ghana showed that about 86.4% of young people still pursued maize farming a year after exiting the Youth in Agriculture Programme (YIAP). This public intervention was implemented to address youth unemployment in Ghana with the goal of getting young people to engage in the agricultural sector. The four main components of the programme were crops/block farm, livestock and poultry, fisheries/aquaculture, and agribusiness. The study focuses on evaluating the crops/block farm component. The crops cultivated under the YIAP include maize (seed and grain), sorghum, soybean, tomato and onion. This study is ranked at moderate risk of bias.

Self-employment in agriculture

Six studies 36 , 39 , 41 , 45 , 46 , 47 indicated that skills training interventions resulted in self-employment in agriculture. Out of these studies, two studies are quantitative 36 , 45 , three are qualitative 41 , 46 , 47 and one is a mixed-methods study 39 .

In one quantitative study 36 , self-employment was stimulated by a skills training radio campaign on growing orange-fleshed sweet potatoes in Ghana, Tanzania, Burkina Faso and Uganda. A survey of the local communities where the radio campaign was run found that households that reported hearing the educational radio campaign in Ghana, Tanzania, Burkina Faso and Uganda were 8.9, 2.3, 1.7 and 1.1 times more likely, respectively, to engage in growing orange-fleshed sweet potatoes, than households that did not. This study is deemed at moderate risk of bias.

Engagement/entrepreneurship in agriculture

Five studies 34 , 38 , 39 , 41 , 42 showed that skills training interventions encourage youth engagement or entrepreneurship in agriculture. Among these studies, one is quantitative 34 , two are qualitative 41 , 42 and two are mixed-methods studies 38 , 39 . In the quantitative study, a youth programme including agriculture content (training in livestock production, crop production and dairy farming) in South Africa indicated that youth engagement or self-employment in agriculture is eight times higher when agricultural programmes that specifically target the youth are implemented compared with when agricultural programmes are not available. This study is deemed at moderate risk of bias. Regarding the mixed-methods studies, one study 38 , deemed at moderate risk of bias with youth programme (YIAP in Ghana) as intervention, showed that after exiting the programme, 86.4% of beneficiaries were still involved in farming within a year. The qualitative studies were deemed at serious risk of bias.

Productivity of the farm/agriculture

Two studies 35 , 41 found that skills training interventions lead to higher productivity of the farms. One of the studies is quantitative 35 and the other is qualitative 41 . In the quantitative study, estimated to be at moderate risk of bias, the National Agricultural Extension and Research Liaison Services (NAERLS) rural youth extension programmes (RUYEP) helped 84.2% of beneficiaries achieve yields that exceed one tonne per hectare for maize in Nigeria, compared with 66% of non-participants 35 . The qualitative study 41 , outlined in Table 1 , is deemed at serious risk of bias.

Profit/income earning of the farm

Ten studies 32 , 33 , 35 , 38 , 40 , 41 , 42 , 43 , 44 , 47 looked at profit/income earning of the farm as an outcome. Among those studies, five are quantitative 32 , 33 , 35 , 43 , 44 , three are qualitative 41 , 42 , 47 and two 38 , 40 are mixed-methoda studies. In one of the quantitative studies, the Training for Rural Economic Empowerment (TREE) programme increased beneficiaries’ income by US$787 compared with non-beneficiaries over the 2011–2014 programme implementation period 33 . This study is deemed at low risk of bias. Another quantitative study 44 , deemed at moderate risk of bias, found that the continued adopters of beekeeping and mushroom growing had increased their family income by 49% and 24%, respectively. The three qualitative studies, not described here but outlined in Table 1 , are deemed at serious risk of bias 41 , 42 , 47 . The mixed-methods study 40 showed that the creation of a company that recycled livestock by-product (bone crafts and soap production) allowed vulnerable women and youths to earn an additional US$44.6 from bone crafts and US$50.2 from soap production weekly. This study is at moderate risk of bias.

Job search or employment opportunity

Three studies 39 , 41 , 42 investigated the effect of skills training on this outcome. One study is a mixed-methods design 39 and two 41 , 42 are qualitative. All of these studies, not described here but outlined in Table 1 , are deemed at serious risk of bias.

Provision of agricultural extension service

One study 39 investigated on the effects of skills interventions on provision of agricultural extension service and found that the majority of graduates who benefited from student–farmer attachment and/or the Supervised Student Enterprise Project (SSEP) were engaged in extension work. This study, outlined in Table 1 , is deemed at serious risk of bias.

Intervention type and engagement in agriculture

Agriculture-related courses.

Two studies 39 , 47 used agriculture-related courses as interventions . One of these studies is a mixed-methods study 39 and the other is qualitative 47 . The mixed-methods study investigated several outcomes in agriculture, namely, job creation, entrepreneurship, self-employment, provision of agricultural extension service and job search opportunity, which were found to improve with the skills training interventions. The interventions consisted of introducing innovations in agricultural training curricula (community engagement and agri-enterprise development) at Gulu University in Uganda. The community engagement took the form of a one year (or less) placement of undergraduate students to work with smallholder farmers and farmer groups within a 10 km radius of the university. The agri-enterprise development consisted of having the students design business plans; the best plans were rewarded with start-up capital. The employment rate among the graduates was 84% six months after graduation and increased to 90% after one year; less than 2% of the graduates created their own businesses. The qualitative study 47 investigated two outcomes in agriculture, self-employment and income, which were found to increase after skills training on ready food mixes, maize products and mango products. The two studies are deemed to be at serious risk of bias.

Technical education/training

Four studies 35 , 41 , 42 , 46 used technical education/training as interventions. Only one of these studies is quantitative 35 ; the others are qualitative 41 , 42 , 46 . The quantitative study 35 investigated productivity and income of the farm, and found both to increase after the intervention. The NAERLS RUYEP objectives are to provide technical advisory services to boost agricultural production and raise living standards of the youth. The results showed that the intervention allowed 84.2% of beneficiaries to achieve yields that exceed one tonne per hectare for maize in Nigeria, compared with 66% of non-participants. This study is deemed at moderate risk of bias. Among the qualitative studies, one 46 looked at self-employment as an outcome and found a positive association with the intervention. The other two qualitative studies are deemed of serious risk of bias.

Youth programme

Youth programmes are programmes that target youth and train them in either specific skills (agricultural skills, ICT skills and so on) or broad skills (decision-making skills, business skills and so on) to enhance their employability. These have been used as interventions in three studies 33 , 34 , 38 . One of these studies is mixed methods 38 and the two others are quantitative 33 , 34 . The mixed-methods study 38 investigated the following outcomes in agriculture: job creation, engagement and income; a positive association was found between youth programme and both engagement and income. The results showed that about 86.4% of young people still pursued maize farming one year after exiting the programme and the mean income of GH¢758 obtained by beneficiaries was found to be greater than the national mean annual per capita income of GH¢734. Among the two quantitative studies 33 , 34 , one investigated the income of beneficiaries 33 and the other 34 looked at engagement in agriculture; both found a positive effect of the intervention on their outcome. The study that investigated the income of beneficiaries as an outcome revealed that the TREE programme increased beneficiaries’ income by US$787 compared with non-beneficiaries over the 2011–2014 programme implementation period 33 . In the other study 34 , a youth programme including agriculture content (training in livestock production, crop production and dairy farming) in South Africa indicated that youth engagement or self-employment in agriculture is eight times higher when agricultural programmes that specifically target the youth are implemented compared with when agricultural programmes are not available. Given that all three studies are at moderate or low risk of bias, we can conclude that the findings suggest that youth programmes have the potential to influence youth engagement in agriculture.

On-the-job training

Only one study 36 looked at on-the-job training as an intervention. The outcome investigated is self-employment, on which the intervention had a positive effect. The results showed that households that reported listening to an educational radio campaign in Ghana, Tanzania, Burkina Faso and Uganda were 8.9, 2.3, 1.7 and 1.1 times more likely, respectively, to engage in growing orange-fleshed sweet potatoes, than households that did not. The study was deemed at moderate risk of bias.

Vocational training

Vocational training has been used as an intervention by four studies 37 , 40 , 44 , 45 . Among these studies, three are quantitative 37 , 44 , 45 and one is a mixed-methods study 40 . One quantitative study 44 investigated income as an outcome, on which positive effects of the intervention were found in India. The findings indicated that vocational training programmes have resulted in continued adoption of beekeeping and mushroom cultivation enterprises by 20% and 51% of trained farmers, respectively, and increased their family income by 49% and 24%, respectively. The second quantitative study investigated job creation and self-employment as outcomes and found positive links with the training 45 . The results of the study highlighted that vocational training in agriculture in Iran resulted in employment of more than half of graduates. The third quantitative study found a positive effect of the intervention on job creation, the sole outcome it had investigated 37 . The study showed that vocational training for a youth employment programme in Ghana resulted in the creation of 16,383 jobs in agribusiness. All four studies are deemed at moderate risk of bias (Table 3 ); however, the use of descriptive methods in some of these studies preclude us from concluding that they are effective in improving employment outcomes for youth in the agricultural sector.

Vocational training and technical training

One study 43 investigated the combination of vocational training and technical training as an intervention. The outcomes investigated are job creation and income, on which the intervention had a positive effect. The study indicated that vocational training and technical training in agriculture (poultry technician) resulted in an increase in employment of 34.2% among the 41 beneficiaries who were trained as poultry technicians in Nepal. This study is deemed at low risk of bias, suggesting that combining vocational training and technical training may be a way of improving job prospects and income for youth in the agricultural sector.

Vocational training and on-the-job training

One study 32 investigated the combination of vocational training and on-the-job training as an intervention. The outcomes investigated are job creation and earnings, on which the intervention had a positive effect. The results showed that both interventions allowed participants to acquire sector-specific skills and firm-specific skills, leading to higher employment rates post-training for vocational-trained workers compared with firm-trained workers (21% versus 14% post-training employment rate) and their total earnings rose by more compared with the firm-trained workers (34% versus 20%). This study is deemed at low risk of bias.

Duration of training

Ten studies out of the 16 overviewed in Table 1 presented information on the duration of training. Eight of these have programmes that last one year or less. The remaining studies indicated a training duration between two and five years. This suggests that training programmes predominantly have a relatively short-term duration, which is consistent with many interventions taking the form of technical and vocational education/training. The popularity of technical and vocational/education training as a model of intervention may be due to the relatively short-term nature of the training, or due to the nature of technical and vocational training, which is well suited for out-of-school youth, which are found in large numbers in LMIC 49 .

Issues facing youth engagement in agriculture today are relatively well documented, including educational attainment, matrimonial status, gender, household size, parental income and occupation, membership in social organization, access to ICT, land tenure system and access to state-run agricultural youth programmes 50 , 51 , 52 . This present systematic review, which focused solely on interventions to engage youth in agriculture, yielded a limited set of studies—nine quantitative, four qualitative and three mixed-methods studies—so generalizable conclusions are difficult to draw. The risk of bias assessment yielded three studies 32 , 33 , 43 deemed at low risk of bias, nine studies 34 , 35 , 36 , 37 , 38 , 40 , 44 , 45 , 46 deemed at moderate risk of bias and four studies deemed at serious of risk bias 39 , 41 , 42 , 47 .

The results of our systematic review generally are in line with those found by the systematic review of Kluve et al. 53 on interventions to improve the labour market outcomes of youth. That systematic review of 107 interventions, including skills training, in 31 countries, found small positive effects for promoting entrepreneurship and skills training—especially integrated skills training programmes—but not for employment services and subsidized employment.

Our systematic review also demonstrated that in general, skills interventions seeking to motivate youth’s engagement in agriculture do not undergo a thorough evaluation for effectiveness, with hard outcomes related to employment. Our selected studies included case studies and qualitative methods, which are not adequate methods of evaluating impact and effectiveness of interventions. Only one study used an RCT 32 . The two studies relying on a quasi-experimental approach used DID and PSM methods 33 , 43 . Indeed, the results of the risk of bias assessment indicated the studies relying on RCT and quasi-experimental impact evaluation methods were at low risk of bias. However, these study designs are expensive to conduct. We found that of the studies that evaluate interventions, the majority did not use state-of-the-art impact evaluation methods. This has been corroborated by other studies 30 , 31 , showing a chronic lack of evaluation of interventions that aim to provide agricultural skills to youth.

Training on ICT is an important aspect for attracting and retaining youth in the agricultural sector 46 . ICT offers a method of delivering training to a large number of farmers, which could enhance the performance of the youth already in agriculture and attract new youth to the sector 36 . Radio campaigns have been shown to be effective in spurring adoption and consumption of orange-fleshed potatoes in Ghana, Tanzania, Burkina Faso and Uganda 36 . A study conducted in the Philippines found that ICT training helps motivate secondary school students whose parents are engaged in agriculture to work within the sector, especially when combined with offline activities such as exposure and hands-on experience as well as creative and motivational actitivites 46 .

It is important to note that heterogeneity in gender and education are not accounted for in the analysis of the impacts of education on youth participation in agriculture. Our systematic review revealed that most of the included studies failed to address the effectiveness of targeting the population of interest—educated and uneducated youth. Illiteracy and gender heterogeneity were not addressed in the included studies. Indeed, no studies assessed the effects of training interventions on illiterate youth. This calls for investigations to focus on this vulnerable group of society, which represent about 25% of youth in sub-Saharan Africa and 11% in Southern Asia 54 . Failing to account for such variation in the background of the youth participants limits the ability to assess the effectiveness of skills training interventions.

The absence of robust research and lack of effective evaluation of the available data on the effectiveness of agricultural youth employment interventions has notable consequences on potential investment. Ultimately, the commitment of policymakers is necessary to ensure the sustainability and success of interventions to boost youth’s engagement in agriculture. It is encouraging that the majority of interventions (12 studies out of 16) studied originated from public policy, compared with three originating from non-public policy programmes (NGOs, international institution) and one from mixed policies (public and non-public policies). However, to provide a compelling basis on which to convince governments and donors to fund future interventions, as well as encourage young people to partake in training, cost-effectiveness analysis and estimates of returns on investment in training programmes is necessary. Indeed, a 2018 stocktaking of the evidence on the effectiveness of youth employment interventions in Africa found that for the agricultural sector in particular, “there is very little literature and virtually no evaluation evidence to inform policymakers about what types of interventions can improve the prospects of young people in the [agricultural] sector” 31 . Our study supports this conclusion. Moreover, to ensure that the skills training provides long-term opportunities for youth, it is crucial to establish a periodic follow-up to assess how trainees are performing after completion of a training programme. This aspect was missing in most of the interventions reviewed in this systematic review, yet it is important to check that the youth who engage in agriculture after receiving skills training are still involved and thrive in their agriculture-related business in the long term.

In summary, there is a need to foster youth skills training programmes and more importantly to evaluate more rigourously these programmes so that knowledge on good practices may be generated and transferred from one developing country to another. Estimates of returns to investment of agricultural skills training programmes are warranted as they could provide governments and donors with the evidence and cost-based analysis to continue and increase support for such programmes. Interventions also need to account for heterogeneity in gender and educational background of the youth to foster sustainability in agricultural value chains, inform inclusive policy design and ultimately contribute to reducing poverty and food insecurity in LMIC.

This systematic review was prepared following guidelines from Petticrew and Roberts 55 . The approach comprises five steps: identifying the research question; identifying relevant studies; study selection; extracting and charting the data; and collating, summarizing and reporting the results. The protocol for this study was registered on the Open Science Framework before study selection and can be accessed at https://osf.io/bhegq// . The guiding question for this systematic review was: What are the effects of skills training interventions on educated and non-educated youth employment outcomes in agricultural value chains, agribusiness or contract farming in LMIC? The inclusion and exclusion criteria to identify and then select the relevant studies are shown in Table 4 .

Regarding the risk of bias assessment, each study was assessed following the criteria of the eight domains of risk of bias we considered. The maximum score a study can obtain in terms of minimizing all domains of risk of bias is 23 stars, which is 100% of the stars. A study is deemed to be at low risk of bias across all domains if its total score is in the interval 75–100%. If the total score is in the interval 50–75%, the study is said to be at moderate risk of bias across all domains. A study is at serious risk of bias if its score falls within the interval 25–50%. When the total score ranges from 0 to 25%, the study is deemed to be at critical risk of bias across all domains. See Supplementary Table 4 for details on the criteria used.

Search strategy

An exhaustive search strategy was developed and tested in CAB Abstracts to identify all available research pertaining to the effects of skills training interventions on educated and non-educated youth employment outcomes in agriculture in LMIC. Search terms were developed to address variations of the key concepts in the research question: skills training, youth, employment or engagement, and agriculture. Searches were performed on 9 May 2019 in the following electronic databases: CAB Abstracts (access via OVID); Web of Science Core Collection (access via Web of Science); EconLit (access via ProQuest); Agricola (access via OVID); and Scopus (access via Elsevier). Full search strategies for each database, including grey literature, can be accessed in their entirety at https://osf.io/xv56k/ .

A comprehensive search of grey literature sources was also conducted. A list of the resources that were searched can be found at https://osf.io/xv56k/ . The grey literature searches were performed using custom web-scraping scripts. The search strings were tested per website before initiating web-scraping. An existing Google Chrome extension was needed to scrape dynamically generated websites.

The results from the databases and the grey literature searches were combined and de-duplicated using a Python script. Duplicates were detected using title, abstract and same year of publication, where year of publication was a match, where title cosine similarity was greater than 85%, and where abstracts cosine similarity was greater than 80% or one of the abstracts (or both) was empty. When duplicates were found, the results from the databases and the grey literature searches were combined and duplicates were removed.

Following de-duplication, each citation was analysed using a machine-learning model. The model added more than 30 new metadata fields, such as identifying populations, geographies, interventions and outcomes of interest. This allowed for accelerated identification of potential articles for exclusion at the title/abstract screening stage.

Study selection and eligibility criteria

Systematic review software, Covidence, was used for both title/abstract and full-text screening decision-making with two independent reviewers evaluating each item. Citations were included in this study if they met all of the inclusion criteria noted above. Studies that did not meet all the inclusion criteria were excluded. Exclusion criteria were the inverse of the inclusion criteria. Each citation that met one of the exclusion criteria at the title, abstract or full-text screening phases were excluded. Studies included in the full-text screening stage were those that met all inclusion criteria and none of the exclusion criteria, or those whose eligibility could not be established during title/abstract screening. Reasons for exclusion were documented at the full-text screening phase.

The retrieval of hundreds of PDFs for full-text screening was done with a combination of automated and manual methods. For the automated method, a Python script was created that would handle the tasks of PDF discovery, download and file renaming using Google Scholar. The script read the bibliographic data from an Excel spreadsheet and then executed a script to retrieve the full-text PDF. If the article is spotted in the search results, the download link is clicked, and the article will be auto-renamed and marked as being downloaded. Manual methods were employed for those items that were not retrieved using the script.

A total of 245 records were identified for full-text screening. This screening process led to the identification of 16 studies that were considered adequate regarding the content and methodological rigour. The PRISMA flow diagram (Fig. 1 ) shows the steps followed during the screening process and the number of items that resulted after each step.

Data extraction

Data extraction was based on interventions and outcomes established in the research question and exclusion criteria. The data extraction focused on the outcomes of the studies, the methods used to obtain the outcomes, and the validity and reliability of those methods using a data-extraction form. To reduce risk of bias related to the extracted data, two separate researchers extracted data from each included study in the full-text review step. When disagreements occurred between researchers on data extracted from a study, a third researcher was engaged to resolve conflict by extracting data again from the study and the results were compared with those found previously. In total, 31 conflicts were solved among the 261 reviews. The critical appraisal of individual sources of evidence gave an indication of the strength of evidence provided and informed the standards followed for this systematic review.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

The code used in this study is available upon request.

Change history

20 october 2020.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

World Employment and Social Outlook 2019: Trends for Youth (International Labour Office, 2019).

World Youth Report: Youth and the 2030 Agenda for Sustainable Development (United Nations, 2018); https://www.un.org/development/desa/youth/wp-content/uploads/sites/21/2018/12/WorldYouthReport-2030Agenda.pdf

Kilimani, N. Youth employment in developing economies: evidence on policies and interventions. IDS Bull. 48 , 13–32 (2017).

Google Scholar  

Global Employment Trends for Youth: Special Issue on the Impact of the Global Economic Crisis on Youth (ILO, 2010).

Pieters, J. Youth Employment in LMIC (Institute of Labor Economics, 2013).

Yeboah, F. K. & Jayne, T. S. Africa’s evolving employment trends. J. Dev. Stud. 54 , 803–832 (2018).

Article   Google Scholar  

World Population Prospects 2019: Highlights ST/ESA/SER. A/423 (UN DESA, 2019).

Ellis, F. Rural Livelihoods and Diversity in LMIC (Oxford Univ. Press, 2000).

Pellegrini, L. & Tasciotti, L. Crop diversification, dietary diversity and agricultural income: empirical evidence from eight LMIC. Can. J. Dev. Stud. 35 , 211–227 (2014).

Employment by sex and age—ILO modelled estimates. ILOSTAT Database (International Labour Organization, accessed 14 February 2020); https://ilostat.ilo.org/data

Carletto, G. et al. Rural income generating activities in LMIC: re-assessing the evidence. Electron. J. Agric. Dev. Econ. 4 , 146–193 (2007).

Christiaensen, L., Demery, L. & Kuhl, J. The (evolving) role of agriculture in poverty reduction—an empirical perspective. J. Dev. Econ. 96 , 239–254 (2011).

Reducing Poverty and Hunger: The Critical Role of Financing for Food, Agriculture and Rural Development (FAO, 2002).

Eicher, C. K. In Strategies for African Development (eds Berg, R. J. & Whitaker, J. S.) 242–275 (Univ. California Press, 1986).

de Alan, B. & MH, S. Linkages between poverty, food security and undernutrition: evidence from China and India. China Agric. Econ. Rev. 7 , 655–667 (2015).

Sraboni, E., Malapit, H. J., Quisumbing, A. R. & Ahmed, A. U. Women’s empowerment in agriculture: what role for food security in Bangladesh? World Dev. 61 , 11–52 (2014).

Sharaunga, S., Mudhara, M. & Bogale, A. Effects of ‘women empowerment’ on household food security in rural KwaZulu‐Natal province. Dev. Policy Rev. 34 , 223–252 (2016).

Malapit, H. J. L. & Quisumbing, A. R. What dimensions of women’s empowerment in agriculture matter for nutrition in Ghana? Food Policy 52 , 54–63 (2015).

Sumberg, J., Anyidoho, N. A., Leavy, J., te Lintelo, D. J. & Wellard, K. Introduction: the young people and agriculture ‘problem’ in Africa. IDS Bull. 43 , 1–8 (2012).

Bezu, S. & Holden, S. Are rural youth in Ethiopia abandoning agriculture? World Dev. 64 , 259–272 (2014).

Tadele, G. & Gella, A. A. Becoming a Young Farmer in Ethiopia: Processes and Challenges Working Paper 83 (Future Agricultures, 2014).

Lindsjö, K., Mulwafu, W., Andersson Djurfeldt, A. & Joshua, M. K. Generational dynamics of agricultural intensification in Malawi: challenges for the youth and elderly smallholder farmers. Int. J. Agric. Sustain. https://doi.org/10.1080/14735903.2020.1721237 (2020).

Salvago, M. R., Phiboon, K., Faysse, N. & Nguyen, T. P. L. Young people’s willingness to farm under present and improved conditions in Thailand. Outlook Agric. 48 , 282–291 (2019).

Daum, T. Of bulls and bulbs: aspirations, opinions and perceptions of rural adolescents and youth in Zambia. Dev. Pract. 29 , 882–897 (2018).

Yeboah, T. et al. Hard work and hazard: young people and agricultural commercialisation in Africa. J. Rural Stud. 76 , 142–151 (2020).

Leavy, J. & Hossain, N. Who wants to farm? Youth aspirations, opportunities and rising food prices. IDS Working Papers https://doi.org/10.1111/j.2040-0209.2014.00439.x (2014).

Roser, M. & Ortiz-Ospina, E. Global education. Our World in Data https://ourworldindata.org/global-education (2016).

Gyimah-Brempong, K. & Kimenyi, M. S. Youth Policy and the Future of African Development (African Growth Initiative, 2013).

Foster, A. D. & Rosenzweig, M. R. Learning by doing and learning from others: human capital and technical change in agriculture. J. Political Econ. 103 , 1176–1209 (1995).

Eichhorst, W. & Rinne, U. An Assessment of the Youth Employment Inventory and Implications for Germany’s Development Policy (Institute of Labor Economics, 2015).

Betcherman, G. & Khan, T. Jobs for Africa’s expanding youth cohort: a stocktaking of employment prospects and policy interventions. IZA J. Dev. Migr. 8 , 13 (2018).

Alfonsi, L. et al. Tackling Youth Unemployment: Evidence from a Labour Market Experiment in Uganda (STICERD, LSE, 2017).

Lachaud, M. A., Bravo-Ureta, B. E., Fiala, N. & Gonzalez, S. P. The impact of agri-business skills training in Zimbabwe: an evaluation of the Training for Rural Economic Empowerment (TREE) programme. J. Dev. Effect. 10 , 373–391 (2018).

Cheteni, P. Youth participation in agriculture in the Nkonkobe District Municipality, South Africa. J. Hum. Ecol. 55 , 207–213 (2016).

Gambo Akpoko, J. & Kudi, T. M. Impact assessment of university-based rural youth agricultural extension out-reach program in selected villages of Kaduna-State, Nigeria. J. Apppl. Sci. 7 , 3292–3296 (2007).

Hudson, H. E., Leclair, M., Pelletier, B. & Sullivan, B. Using radio and interactive ICTs to improve food security among smallholder farmers in sub-Saharan Africa. Telecomm. Policy 41 , 670–684 (2017).

Ghana—Job Creation and Skills Development: Main Report (World Bank, 2009).

Baah, C. Assessment of the Youth in Agriculture Programme in Ejura-Sekyedumase District (Kwame Nkrumah Univ. Science and Technology, 2014).

Odongo, W., Kalule, S. W., Kule, E. K., Ndyomugyenyi, E. & Ongeng, D. Responsiveness of agricultural training curricula in African universities to labour market needs: the case of Gulu University in Uganda. African J. Rural Dev. 2 , 67–76 (2017).

Kinyanjui, W. & Noor, M. S. From waste to employment opportunities and wealth creation: a case study of utilization of livestock by-products in Hargeisa, Somaliland. Food Nutr. Sci. 4 , 1287–1292 (2013).

Latopa, A.-L. A. & Rashid, S. N. S. A. The impacts of integrated youth training farm as a capacity building center for youth agricultural empowerment in Kwara State, Nigeria. Mediterr. J. Soc. Sci. 6 , 524–532 (2015).

Shoulders, C. W., Barrick, R. K. & Myers, B. E. An assessment of the impact of internship programs in the agricultural technical schools of Egypt as perceived by participant groups. J. Int. Agric. Ext. Educ. 18 , 18–29 (2011).

Chakravarty, S., Lundberg, M., Nikolov, P. & Zenker, J. The Role of Training Programs for Youth Employment in Nepal: Impact Evaluation Report on the Employment Fund (World Bank, 2016).

Singh, K., Peshin, R. & Saini, S. K. Evaluation of the agricultural vocational training programmes conducted by the Krishi Vigyan Kendras (Farm Science Centres) in Indian Punjab. J. Agric. Rural Dev. Trop. Subtrop. 111 , 65–77 (2010).

Khosravipour, B. & Soleimanpour, M. R. Comparison of students’ entrepreneurship spirit in agricultural scientific-applied higher education centers of Iran. Am. J. Agric. Environ. Sci. 12 , 1012–1015 (2012).

Manalo, J. A. IV, Balmeo, K. P., Domingo, O. C. & Saludez, F. M. Young allies of agricultural extension: the infomediary campaign in Aurora, Philippines. Philipp. J. Crop Sci. 39 , 30–40 (2014).

Channal, G. P., Kotikal, Y. K. & Pattar, P. S. Empowering rural women as a successful entrepreneur—through Krishi Vigyan Kendra. Agric. Update 12 , 498–501 (2017).

Sterne, J. A. et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ 355 , i4919 (2016).

How Does the Short-Term Training Program Contribute to Skills Development in Bangladesh? A Tracer Study of the Short-Term Training Graduates South Asia Region, Education Global Practice Discussion Paper (World Bank, 2015).

Nnadi, F. N. & Akwiwu, C. D. Determinants of youths’ participation in rural agriculture in Imo State, Nigeria. J. Appl. Sci. 8 , 328–333 (2008).

Article   ADS   Google Scholar  

Akpan, S. B., Patrick, I. V., James, S. U. & Agom, D. I. Determinants of decision and participation of rural youth in agricultural production: a case study of youth in southern region of Nigeria. Russ. J. Agric. Socio-Econ. Sci. 7 , 35–48 (2015).

Adesugba, M. & Mavrotas, G. Youth Employment, Agricultural Transformation, and Rural Labour Dynamics in Nigeria (International Food Policy Research Institute, 2016).

Kluve, J. et al. Interventions to Improve the Labour Market Outcomes of Youth: A Systematic Review of Training, Entrepreneurship Promotion, Employment Services and Subsidized Employment Interventions (The Campbell Collaboration, 2017).

Literacy Rates Continues to Rise from One Generation to the Next (UNESCO Institute for Statistics, 2017).

Petticrew, M. & Roberts, H. Systematic Reviews in the Social Sciences: A Practical Guide (John Wiley & Sons, 2008).

Download references

Acknowledgements

We thank J.-A. Porciello and M. Eber-Rose for helpful comments on earlier drafts of this manuscript. We gratefully acknowledge funding support from Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung (Federal Ministry for Economic Cooperation and Development in Germany) and The Bill and Melinda Gates Foundation as part of Ceres2030: Sustainable Solutions to End Hunger, a project administered by Cornell University, USA.

Author information

Authors and affiliations.

Université Norbert Zongo, Koudougou, Burkina Faso

W. H. Eugenie Maïga

Université Thomas Sankara, Ouagadougou, Burkina Faso

Mohamed Porgo, Pam Zahonogo, Doubahan Adeline Coulibaly & Salimata Traoré

Université Nationale d’Agriculture de Kétou, Kétou, Benin

Cocou Jaurès Amegnaglo

Institute of Development Studies–University of Sussex, Brighton, UK

Justin Flynn

Centre Universitaire Polytechnique de Kaya, Kaya, Burkina Faso

Windinkonté Seogo

University of Minnesota, Saint Paul, MN, USA

Julia A. Kelly

Information Training and Outreach Centre for Africa, Centurion, South Africa

Gracian Chimwaza

You can also search for this author in PubMed   Google Scholar

Contributions

W.H.E.M., M.P. and P.Z. developed the research question. J.A.K. and G.C. conducted the literature search. All authors drafted the PRISMA-P protocol for this study. W.H.E.M., M.P., P.Z, C.J.A, D.A.C., J.F., W.S. and S.T. conducted the full-text reviews and drafted the paper, and all authors contributed to the writing.

Corresponding author

Correspondence to W. H. Eugenie Maïga .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Tables 1–4.

Supplementary Material 1

PRISMA protocol for the systematic review.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Maïga, W.H.E., Porgo, M., Zahonogo, P. et al. A systematic review of employment outcomes from youth skills training programmes in agriculture in low- and middle-income countries. Nat Food 1 , 605–619 (2020). https://doi.org/10.1038/s43016-020-00172-x

Download citation

Received : 23 December 2019

Accepted : 16 September 2020

Published : 12 October 2020

Issue Date : October 2020

DOI : https://doi.org/10.1038/s43016-020-00172-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

African women and young people as agriculture service providers—business models, benefits, gaps and opportunities.

• Mariam Kadzamira
• Florence Chege
• Joseph Mulema

CABI Agriculture and Bioscience (2024)

A scoping review on the impacts of smallholder agriculture production on food and nutrition security: Evidence from Ethiopia context

• Hadas Temesgen
• Chanyalew Seyoum Aweke

Agriculture & Food Security (2023)

Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning

• Jaron Porciello
• Maryia Ivanina

Nature Machine Intelligence (2020)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Anthropocene newsletter — what matters in anthropocene research, free to your inbox weekly.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?

Hila axelrad.

1 Center on Aging & Work, Boston College, Chestnut Hill, MA 02467 USA

2 The School of Social and Policy Studies, The Faculty of Social Sciences, Tel Aviv University, P.O. Box 39040, 6997801 Tel Aviv, Israel

3 Department of Public Policy & Administration, Guilford Glazer Faculty of Business & Management, Ben-Gurion University of the Negev, Beer Sheva, Israel

Israel Luski

4 Department of Economics, The Western Galilee College, Akko, Israel

In this research we show that workers aged 30–44 were significantly more likely than those aged 45–59 to find a job a year after being unemployed. The main contribution is demonstrating empirically that since older workers’ difficulties are related to their age, while for younger individuals the difficulties are more related to the business cycle, policy makers must devise different programs to address unemployment among young and older individuals. The solution to youth unemployment is the creation of more jobs, and combining differential minimum wage levels and earned income tax credits might improve the rate of employment for older individuals.

Introduction

Literature about unemployment references both the unemployment of older workers (ages 45 or 50 and over) and youth unemployment (15–24). These two phenomena differ from one another in their characteristics, scope and solutions.

Unemployment among young people begins when they are eligible to work. According to the International Labor Office (ILO), young people are increasingly having trouble when looking for their first job (ILO 2011 ). The sharp increase in youth unemployment and underemployment is rooted in long-standing structural obstacles that prevent many youngsters in both OECD countries and emerging economies from making a successful transition from school to work. Not all young people face the same difficulties in gaining access to productive and rewarding jobs, and the extent of these difficulties varies across countries. Nevertheless, in all countries, there is a core group of young people facing various combinations of high and persistent unemployment, poor quality jobs when they do find work and a high risk of social exclusion (Keese et al. 2013 ). The rate of youth unemployment is much higher than that of adults in most countries of the world (ILO 2011 ; Keese et al. 2013 ; O’Higgins 1997 ; Morsy 2012 ). Official youth unemployment rates in the early decade of the 2010s ranged from under 10% in Germany to around 50% in Spain ( http://www.indexmundi.com/g/r.aspx?v=2229 ; Pasquali 2012 ). The youngest employees, typically the newest, are more likely to be let go compared to older employees who have been in their jobs for a long time and have more job experience and job security (Furlong et al. 2012 ). However, although unemployment rates among young workers are relatively higher than those of older people, the period of time they spend unemployed is generally shorter than that of older adults (O’Higgins 2001 ).

We would like to argue that one of the most important determinants of youth unemployment is the economy’s rate of growth. When the aggregate level of economic activity and the level of adult employment are high, youth employment is also high. 1 Quantitatively, the employment of young people appears to be one of the most sensitive variables in the labor market, rising substantially during boom periods and falling substantially during less active periods (Freeman and Wise 1982 ; Bell and Blanchflower 2011 ; Dietrich and Möller 2016 ). Several explanations have been offered for this phenomenon. First, youth unemployment might be caused by insufficient skills of young workers. Another reason is a fall in aggregate demand, which leads to a decline in the demand for labor in general. Young workers are affected more strongly than older workers by such changes in aggregate demand (O’Higgins 2001 ). Thus, our first research question is whether young adults are more vulnerable to economic shocks compared to their older counterparts.

Older workers’ unemployment is mainly characterized by difficulties in finding a new job for those who have lost their jobs (Axelrad et al. et al. 2013 ). This fact seems counter-intuitive because older workers have the experience and accumulated knowledge that the younger working population lacks. The losses to society and the individuals are substantial because life expectancy is increasing, the retirement age is rising in many countries, and people are generally in good health (Axelrad et al. 2013 ; Vodopivec and Dolenc 2008 ).

The difficulty that adults have in reintegrating into the labor market after losing their jobs is more severe than that of the younger unemployed. Studies show that as workers get older, the duration of their unemployment lengthens and the chances of finding a job decline (Böheim et al. 2011 ; De Coen et al. 2010 ). Therefore, our second research question is whether older workers’ unemployment stems from their age.

In this paper, we argue that the unemployment rates of young people and older workers are often misinterpreted. Even if the data show that unemployment rates are higher among young people, such statistics do not necessarily imply that it is harder for them to find a job compared to older individuals. We maintain that youth unemployment stems mainly from the characteristics of the labor market, not from specific attributes of young people. In contrast, the unemployment of older individuals is more related to their specific characteristics, such as higher salary expectations, higher labor costs and stereotypes about being less productive (Henkens and Schippers 2008 ; Keese et al. 2006 ). To test these hypotheses, we conduct an empirical analysis using statistics from the Israeli labor market and data published by the OECD. We also discuss some policy implications stemming from our results, specifically, a differential policy of minimum wages and earned income tax credits depending on the worker’s age.

Following the introduction and literary review, the next part of our paper presents the existing data about the unemployment rates of young people and adults in the OECD countries in general and Israel in particular. Than we present the research hypotheses and theoretical model, we describe the data, variables and methods used to test our hypotheses. The regression results are presented in Sect.  4 , the model of Business Cycle is presented in Sect.  5 , and the paper concludes with some policy implications, a summary and conclusions in Sect.  6 .

Literature review

Over the past 30 years, unemployment in general and youth unemployment in particular has been a major problem in many industrial societies (Isengard 2003 ). The transition from school to work is a rather complex and turbulent period. The risk of unemployment is greater for young people than for adults, and first jobs are often unstable and rather short-lived (Jacob 2008 ). Many young people have short spells of unemployment during their transition from school to work; however, some often get trapped in unemployment and risk becoming unemployed in the long term (Kelly et al. 2012 ).

Youth unemployment leads to social problems such as a lack of orientation and hostility towards foreigners, which in turn lead to increased social expenditures. At the societal level, high youth unemployment endangers the functioning of social security systems, which depend on a sufficient number of compulsory payments from workers in order to operate (Isengard 2003 ).

Workers 45 and older who have lost their jobs often encounter difficulties in finding a new job (Axelrad et al. 2013 ; Marmora and Ritter 2015 ) although today they are more able to work longer than in years past (Johnson 2004 ). In addition to the monetary rewards, work also offers mental and psychological benefits (Axelrad et al. 2016 ; Jahoda 1982 ; Winkelmann and Winkelmann 1998 ). Working at an older age may contribute to an individual’s mental acuity and provide a sense of usefulness.

On average, throughout the OECD, the hiring rate of workers aged 50 and over is less than half the rate for workers aged 25–49. The low re-employment rates among older job seekers reflect, among other things, the reluctance of employers to hire older workers. Lahey ( 2005 ) found evidence of age discrimination against older workers in labor markets. Older job applicants (aged 50 or older), are treated differently than younger applicants. A younger worker is more than 40% more likely to be called back for an interview compared to an older worker. Age discrimination is also reflected in the time it takes for older adults to find a job. Many workers aged 45 or 50 and older who have lost their jobs often encounter difficulties in finding a new job, even if they are physically and intellectually fit (Hendels 2008 ; Malul 2009 ). Despite the fact that older workers are considered to be more reliable (McGregor and Gray 2002 ) and to have better business ethics, they are perceived as less flexible or adaptable, less productive and having higher salary expectations (Henkens and Schippers 2008 ). Employers who hesitated in hiring older workers also mentioned factors such as wages and non-wage labor costs that rise more steeply with age and the difficulties firms may face in adjusting working conditions to meet the requirements of employment protection rules (Keese et al. 2006 ).

Thus, we have a paradox. On one hand, people live longer, the retirement age is rising, and older people in good health want or need to keep working. At the same time, employers seek more and more young workers all the time. This phenomenon might marginalize skilled and experience workers, and take away their ability to make a living and accrue pension rights. Thus, employers’ reluctance to hire older workers creates a cycle of poverty and distress, burdening the already overcrowded social institutions and negatively affecting the economy’s productivity and GDP (Axelrad et al. 2013 ).

OECD countries during the post 2008 crisis

The recent global economic crisis took an outsized toll on young workers across the globe, especially in advanced economies, which were hit harder and recovered more slowly than emerging markets and developing economies. Does this fact imply that the labor market in Spain and Portugal (with relatively high youth unemployment rates) is less “friendly” toward younger individuals than the labor market in Israel and Germany (with a relatively low youth unemployment rate)? Has the market in Spain and Portugal become less “friendly” toward young people during the last 4 years? We argue that the main factor causing the increasing youth unemployment rates in Spain and Portugal is the poor state of the economy in the last 4 years in these countries rather than a change in attitudes toward hiring young people.

OECD data indicate that adult unemployment is significantly lower than youth unemployment. The global economic crisis has hit young people very hard. In 2010, there were nearly 15 million unemployed youngsters in the OECD area, about four million more than at the end of 2007 (Scarpetta et al. 2010 ).

From an international perspective, and unlike other developed countries, Israel has a young age structure, with a high birthrate and a small fraction of elderly population. Israel has a mandatory retirement age, which differs for men (67) and women (62), and the labor force participation of older workers is relatively high (Stier and Endeweld 2015 ), therefore, we believe that Israel is an interesting case for studying.

The Israeli labor market is extremely flexible (e.g. hiring and firing are relatively easy), and mobile (workers can easily move between jobs) (Peretz 2016 ). Focusing on Israel’s labor market, we want to check whether this is true for older Israeli workers as well, and whether there is a difference between young and older workers.

The problem of unemployment among young people in Israel is less severe than in most other developed countries. This low unemployment rate is a result of long-term processes that have enabled the labor market to respond relatively quickly to changes in the economic environment and have reduced structural unemployment. 2 Furthermore, responsible fiscal and monetary policies, and strong integration into the global market have also promoted employment at all ages. With regard to the differences between younger and older workers in Israel, Stier and Endeweld ( 2015 ) determined that older workers, men and women alike, are indeed less likely to leave their jobs. This finding is similar to other studies showing that older workers are less likely to move from one employer to another. According to the U.S. Bureau of Labor Statistics, the median employee tenure is generally higher among older workers than younger ones (BLS 2014 ). Movement in and out of the labor market is highest among the youngest workers. However, these young people are re-employed quickly, while older workers have the hardest time finding jobs once they become unemployed. The Bank of Israel calculated the chances of unemployed people finding work between two consecutive quarters using a panel of the Labor Force Survey for the years 1996–2011. Their calculations show that since the middle of the last decade the chances of unemployed people finding a job between two consecutive quarters increased. 3 However, as noted earlier, as workers age, the duration of their unemployment lengthens. Prolonged unemployment erodes the human capital of the unemployed (Addison et al. 2004 ), which has a particularly deleterious effect on older workers. Thus, the longer the period of unemployment of older workers, the less likely they will find a job (Axelrad and Luski 2017 ). Nevertheless, as Fig.  1 shows, the rates of youth unemployment in Israel are higher than those of older workers.

Unemployed persons and discouraged workers as percentages of the civilian labor force, by age group (Bank of Israel 2011 ). We excluded those living outside settled communities or in institutions. The percentages of discouraged workers are calculated from the civilian labor force after including them in it

(Source: Calculated by the authors by using data from the Labor Force survey of the Israeli CBS, 2011)

We argue that the main reason for this situation is the status quo in the labor market, which is general and not specific to Israel. It applies both to older workers and young workers who have a job. The status quo is evident in the situation in which adults (and young people) already in the labor market manage to keep their jobs, making the entrance of new young people into the labor market more difficult. What we are witnessing is not evidence of a preference for the old over the young, but the maintaining of the status quo.

The rate of employed Israelis covered by collective bargaining agreements increases with age: up to age 35, the rate is less than one-quarter, and between 50 and 64 the rate reaches about one-half. In effect, in each age group between 25 and 60, there are about 100,000 covered employees, and the lower coverage rate among the younger ages derives from the natural growth in the cohorts over time (Bank of Israel 2013 ). The wave of unionization in recent years is likely to change only the age profile of the unionization rate and the decline in the share of covered people over the years, to the extent that it strengthens and includes tens of thousands more employees from the younger age groups. 4

The fact that the percentage of employees covered by collective agreement increases with age implies that there is a status quo effect. Older workers are protected by collective agreements, and it is hard to dismiss them (Culpepper 2002 ; Palier and Thelen 2010 ). However, young workers enter the workforce with individual contracts and are not protected, making it is easier to change their working conditions and dismiss them.

To complete the picture, Fig.  2 shows that the number of layoffs among adults is lower, possibly due to their protection under collective bargaining agreements.

Dismissal of employees in Israel, by age. Percentage of total employed persons ages 20–75 and over including those dismissed

(Source: Israeli Central Bureau of Statistics, 2008, data processed by the authors)

In order to determine the real difference between the difficulties of older versus younger individuals in finding work, we have to eliminate the effect of the status quo in the labor market. For example, if we removed all of the workers from the labor market, what would be the difference between the difficulties of older people versus younger individuals in finding work? In the next section we will analyze the probability of younger and older individuals moving from unemployment to employment when we control for the status quo. We will do so by considering only individuals who have not been employed at least part of the previous year.

Estimating the chances of finding a job and research hypotheses

Based on the literature and the classic premise that young workers are more vulnerable to economic shocks (ILO 2011 ), we posit that:

H 1 : The unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes.

Based on the low hiring rate of older workers (OECD 2006 ) and the literature about age discrimination against older workers in labor markets (Axelrad et al. 2013 ; Lahey 2005 ), we hypothesis that:

H 2 : The difficulty face by unemployed older workers searching for a job stems mainly from their age and less from the characteristics of the labor market.

To assess the chances of younger and older workers finding a job, we used a logit regression model that has been validated in previous studies (Brander et al. 2002 ; Flug and Kassir 2001 ). Being employed was the dependent variable, and the characteristics of the respondents (age, gender, ethnicity and education) were the independent variables. The dependent variable was nominal and dichotomous with two categories: 0 or 1. We defined the unemployed as those who did not work at all during the last year or worked less than 9 months last year. The dependent variable was a dummy variable of the current employment situation, which received the value of 1 if the individual worked last week and 0 otherwise.

The regression allowed us to predict the probability of an individual finding a job. The dependent variable was the natural base log of the probability ratio P divided by (1 − P) that a particular individual would find a job. The odds ratio from the regression answers the question of how much more likely it is that an individual will find a job if he or she has certain characteristics. The importance of the probability analysis is the consideration of the marginal contribution of each feature to the probability of finding a job.

We used data gathered from the 2011 Labor Force Survey 5 of the Israeli Central Bureau of Statistics (CBS), 6 which is a major survey conducted annually among households. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. Given our focus on working age individuals, we excluded all of the respondents under the age of 18 or over the age of 59. The data sample includes only the Jewish population, because structural problems in the non-Jewish sector made it difficult to estimate this sector using the existing data only. The sample does not include the ultra-Orthodox population because of their special characteristics, particularly the limited involvement of men in this population in the labor market.

The base population is individuals who did not work at all during the past year or worked less than 9 months last year (meaning that they worked but were unemployed at least part of last year). To determine whether they managed to find work after 1 year of unemployment, we used the question on the ICBS questionnaire, “Did you work last week?” We used the answer to this question to distinguish between those who had succeeded in finding a job and those who did not. The data include individuals who were out of the labor force 7 at the time of the survey, but exclude those who were not working for medical reasons (illness, disability or other medical restrictions) or due to their mandatory military service. 8

Data and variables

The survey contains 104,055 respondents, but after omitting all of the respondents under the age of 18 or above 59, those who were outside the labor force for medical reasons or due to mandatory military service, non-Jews, the ultra-Orthodox, and those who worked more than 9 months last year, the sample includes 13,494 individuals (the base population). Of these, 9409 are individuals who had not managed to find work, and 4085 are individuals who were employed when the survey was conducted.

The participants’ ages range between 18 and 59, with the average age being 33.07 (SD 12.88) and the median age being 29. 40.8% are males; 43.5% have an academic education; 52.5% are single, and 53.5% of the respondents have no children under 17.

Dependent and independent variables

While previous studies have assessed the probability of being unemployed in the general population, our study examines a more specific case: the probability of unemployed individuals finding a job. Therefore, we use the same explanatory variables that have been used in similar studies conducted in Israel (Brander et al. 2002 ; Flug and Kassir 2001 ), which were also based on an income survey and the Labor Force Survey of the Central Bureau of Statistics.

The dependent variable—being employed

According to the definition of the CBS, employed persons are those who worked at least 1 h during a given week for pay, profit or other compensation.

Independent variables

We divided the population into sub-groups of age intervals: 18–24, 25–29, 30–44, 45–54 and 55–59, according to the sub-groups provided by the CBS. We then assigned a special dummy variable to each group—except the 30–44 sub-group, which is considered as the base group. Age is measured as a dummy variable, and is codded as 1 if the individual belongs to the age group, and 0 otherwise. Age appears in the regression results as a variable in and of itself. Its significance is the marginal contribution of each age group to the probability of finding work relative to the base group (ages 30–44), and also as an interaction variable.

This variable is codded as 1 if the individual is female and 0 otherwise. Gender also appears in the interaction with age.

Marital status

Two dummy variables are used: one for married respondents and one for those who are divorced or widowed. In accordance with the practice of the CBS, we combined the divorced and the widowed into one variable. This variable is a dummy variable that is codded as 1 if the individual belongs to the appropriate group (divorced/widowed or married) and 0 otherwise. The base group is those who are single.

This variable is codded as 1 if the individual has 13 or more years of schooling, and 0 otherwise. The variable also appears in interactions between it and the age variable.

Vocational education

This variable is codded as 1 if the individual has a secondary school diploma that is not an academic degree or another diploma, and 0 otherwise.

Academic education

This variable is codded as 1 if the individual has any university degree (bachelors, masters or Ph.D.) and 0 otherwise.

In accordance with similar studies that examined the probability of employment in Israel (Brander et al. 2002 ), we define children as those up to age 17. This variable is a dummy variable that is codded as 1 if the respondents have children under the age of 17, and 0 otherwise.

This variable is codded as 1 if the individual was born in an Arabic-speaking country, in an African country other than South Africa, or in an Asian country, or was born in Israel but had a father who was born in one of these countries. Israel generally refers to such individuals as Mizrahim. Respondents who were not Mizrahim received a value of 0. The base group in our study are men aged 30–44 who are not Mizrahim.

We also assessed the interactions between the variables. For example, the interaction between age and the number of years of schooling is the contribution of education (i.e., 13 years of schooling) to the probability of finding a job for every age group separately relative to the situation of having less education (i.e., 12 years of education). The interaction between age and gender is the contribution of gender (i.e., being a female respondent) to the probability of finding a job for each age group separately relative to being a man.

To demonstrate the differences between old and young individuals in their chances of finding a job, we computed the rates of those who managed to find a job relative to all of the respondents in the sample. Table  1 shows that the rate of those who found a job declines with age. For example, 36% of the men age 30–44 found a job, but those rates drop to 29% at the age of 45–54 and decline again to 17% at the age of 55–59. As for women, 31% of them aged 30–44 found a job, but those rates drop to 20% at the age of 45–54 and decline again to 9% at the age of 55–59.

Table 1

The rate of males and females who found a job (out of the entire group)

In an attempt to determine the role of education in finding employment, we created Model 1 and Model 2, which differ only in terms of how we defined education. In Model 1 the sample is divided into two groups: those with up to 12 years of schooling (the base group) and those with 13 or more years of schooling. In Model 2 there are three sub-groups: those with a university degree, those who have a vocational education, and the base group that has only a high school degree.

Table  2 shows that the probability of a young person (age 18–24) getting a job is larger than that of an individual aged 30–44 who belongs to the base group (the coefficient of the dummy variable “age 18–24” is significant and positive). Similarly, individuals who are older than 45 are less likely than those in the base group to find work.

Table 2

Chances of being employed—entire sample

Dependent variable: being employed

Included observations: 13,495

*  p  < 0.1, **  p  < 0.05, ***  p  < 0.01

Women aged 30–44 are less likely to be employed than men in the same age group. Additionally, when we compare women aged 18–24 to women aged 30–44, we see that the chances of the latter being employed are lower. Older women (45+) are much less likely than men of the same age group to find work. Additionally, having children under the age of 17 at home reduces the probability of finding a job.

A university education increases the probability of being employed for both men and women aged 30–44. Furthermore, for older people (55+) an academic education reduces the negative effect of age on the probability of being employed. While a vocational education increases the likelihood of finding a job for those aged 30–44, such a qualification has no significant impact on the prospects of older people.

Interestingly, being a Mizrahi Jew increases the probability of being employed.

In addition, we estimated the models separately twice—for the male and for the female population. For male and female, the probability of an unemployed individual finding a job declines with age.

Analyzing the male population (Table  3 ) reveals that those aged 18–24 are more likely than the base group (ages 30–44) to find a job. However, the significance level is relatively low, and in Model 2, this variable is not significant at all. Those 45 and older are less likely than the base group (ages 30–44) to find a job. Married men are more likely than single men to be employed. However, divorced and widowed men are less likely than single men to find a job. For men, the presence in their household of children under the age of 17 further reduces the probability of their being employed. Mizrahi men aged 18–24 are more likely to be employed than men of the same age who are from other regions.

Table 3

Chances of being employed—males and females separately

Table  3 illustrates that educated men are more likely to find work than those who are not. However, in Model 1, at the ages 18–29 and 45–54, the probability of finding a job for educated men is less than that of uneducated males. Among younger workers, this might be due to excess supply—the share of academic degree owners has risen, in contrast to almost no change in the overall share of individuals receiving some other post-secondary certificate (Fuchs 2015 ). Among older job seeking men, this might be due to the fact that the increase in employment among men during 2002–2010 occurred mainly in part-time jobs (Bank of Israel 2011 ). In Model 2, men with an academic or vocational education have a better chance of finding a job, but at the group age of 18–24, those with a vocational education are less likely to find a job compared to those without a vocational education. The reason might be the lack of experience of young workers (18–24), experience that is particularly needed in jobs that require vocational education (Salvisberg and Sacchi 2014 ).

Analyzing the female population (Table  3 ) reveals that women between 18 and 24 are more likely to be employed than those who are 30–44, and those who are 45–59 are less likely to be employed than those who are 30–44. The probability of finding a job for women at the age of 25 to 29 is not significantly different from the probability of the base group (women ages 30–44).

Married women are less likely than single women to be employed. Women who have children under the age of 17 are less likely to be employed than women who do not have dependents that age. According to Model 2, Mizrahi women are more likely to be employed compared to women from other regions. According to both models, women originally from Asia or Africa ages 25–29 have a better chance of being employed than women the same age from other regions. Future research should examine this finding in depth to understand it.

With regard to education, in Model 1 (Table  3 ), where we divided the respondents simply on the question of whether they had a post-high school education, women who were educated were more likely to find work than those who were not. However, in the 18–29 age categories, educated women were less likely to find a job compared to uneducated women, probably due to the same reason cited above for men in the same age group—the inflation of academic degrees (Fuchs 2015 ). These findings become more nuanced when we consider the results of Model 2. There, women with an academic or vocational education have a better chance of finding a job, but at the ages of 18–24 those with an academic education are less likely to find a job than those without an academic education. Finally, at the ages of 25–29, those with a vocational education have a better chance of finding a job than those without a vocational education, due to the stagnation in the overall share of individuals receiving post-secondary certificate (Fuchs 2015 ).

Thus, based on the results in Table  3 , we can draw several conclusions. First, the effect of aging on women is more severe than the impact on men. In addition, the “marriage premium” is positive for men and negative for women. Divorced or widowed men lose their “marriage premium”. Finally, having children at home has a negative effect on both men and women—almost at the same magnitude.

Unemployment as a function of the business cycle

To determine whether unemployment of young workers is caused by the business cycle, we examined the unemployment figures in 34 OECD countries in 2007–2009, years of economic crisis, and in 2009–2011, years of recovery and economic growth. For each country, we considered the data on unemployment among young workers (15–24) and older adults (55–64) and calculated the difference between 2009 and 2007 and between 2011 and 2009 for both groups. The data were taken from OECD publications and included information about the growth rates from 2007 to 2011. Our assessment of unemployment rates in 34 OECD countries reveals that the average rate of youth unemployment in 2007 was 13.4%, compared to 18.9% in 2011, so the delta of youth unemployment before and after the economic crisis was 5.55. The average rate of adult unemployment in 2007 was 4% compared to 5.8% in 2011, so the delta for adults was 1.88. Both of the differences are significantly different from zero, and the delta for young people is significantly larger than the delta for adults. These results indicate that among young people (15–24), the increase in unemployment due to the crisis was very large.

An OLS model of the reduced form was estimated to determine whether unemployment is a function of the business cycle, which is represented by the growth rate. The variables GR2007, GR2009 and GR2011 are the rate of GDP growth in 2007, 2009 and 2011 respectively ( Appendix ). The explanatory variable is either GR2009 minus GR2007 or GR2011 minus GR2009. In both periods, 2007–2009 and 2009–2011, the coefficient of the change in growth rates is negative and significant for young people, but insignificant for adults. Thus, it seems that the unemployment rates of young people are affected by the business cycle, but those of older workers are not. In a time of recession (2007–2009), unemployment among young individuals increases whereas for older individuals the increase in unemployment is not significant. In recovery periods (2009–2011), unemployment among young individuals declines, whereas the drop in unemployment among older individuals is not significant (Table  4 ).

Table 4

Unemployment rate as a function of the business cycle

Dependent variable: the increase in the unemployment rate between 2007 and 2009, and between 2009 and 2011

Summary and conclusions

The purpose of this paper was to show that while the unemployment rates of young workers are higher than those of older workers, the data alone do not necessarily tell the whole story. Our findings confirm our first hypothesis, that the high unemployment rate of young people stems mainly from the characteristics of the labor market and less from their personal attributes. Using data from Israel and 34 OECD countries, we demonstrated that a country’s growth rate is the main factor that determines youth unemployment. However, the GDP rate of growth cannot explain adult unemployment. Our results also support our second hypothesis, that the difficulties faced by unemployed older workers when searching for a job are more a function of their age than the overall business environment.

Indeed, one limitation of the study is the fact that we could not follow individuals over time and capture individual changes. We analyze a sample of those who have been unemployed in the previous year and then analyze the probability of being employed in the subsequent year but cannot take into account people could have found a job in between which they already lost again. Yet, in this sample we could isolate and analyze those who did not work last year and look at their employment status in the present. By doing so, we found out that the rate of those who found a job declines with age, and that the difficulties faced by unemployed older workers stems mainly from their age.

To solve both of these problems, youth unemployment and older workers unemployment, countries need to adopt different methods. Creating more jobs will help young people enter the labor market. Creating differential levels for the minimum wage and supplementing the income of older workers with earned income tax credits will help older people re-enter the job market.

Further research may explore the effect of structural and institutional differences which can also determine individual unemployment vs. employment among different age groups.

In addition to presenting a theory about the factors that affect the differences in employment opportunities for young people and those over 45, the main contribution of this paper is demonstrating the validity of our contention that it is age specifically that works to keep older people out of the job market, whereas it is the business cycle that has a deleterious effect on the job prospects of younger people. Given these differences, these two sectors of unemployment require different approaches for solving their employment problems. The common wisdom maintains that the high level of youth unemployment requires policy makers to focus on programs targeting younger unemployed individuals. However, we argue that given the results of our study, policy makers must adopt two different strategies to dealing with unemployment in these two groups.

Policy implications

In order to cope with the problem of youth unemployment, we must create more jobs. When the recession ends in Portugal and Spain, the problem of youth unemployment should be alleviated. Since there is no discrimination against young people—evidenced by the fact that when the aggregate level of economic activity and the level of adult employment are high, youth employment is also high—creating more jobs in general by enhancing economic growth should improve the employment rates of young workers.

In contrast, the issue of adult unemployment requires a different solution due to the fact that their chances of finding a job are related specifically to their age. One solution might be a differential minimum wage for older and younger individuals and earned income tax credits (EITC) 9 for older individuals, as Malul and Luski ( 2009 ) suggested.

According to this solution, the government should reduce the minimum wage for older individuals. As a complementary policy and in order to avoid differences in wages between older and younger individuals, the former would receive an earned income tax credit so that their minimum wage together with their EITC would be equal to the minimum wage of younger individuals. Earned income tax credits could increase employment among older workers while increasing their income. For older workers, EITCs are more effective than a minimum wage both in terms of employment and income. Such policies of a differential minimum wage plus an EITC can help older adults and constitute a kind of social safety net for them. Imposing a higher minimum wage exclusively for younger individuals may be beneficial in encouraging them to seek more education.

Young workers who face layoffs as a result of their high minimum wage (Kalenkoski and Lacombe 2008 ) may choose to increase their investment in their human capital (Nawakitphaitoon 2014 ). The ability of young workers to improve their professional level protects them against the unemployment that might result from a higher minimum wage (Malul and Luski 2009 ). For older workers, if the minimum wage is higher than their productivity, they will be unemployed. This will be true even if their productivity is higher than the value of their leisure. Such a situation might result in an inefficient allocation between work and leisure for this group. One way to fix this inefficient allocation without reducing the wages of older individuals is to use the EITC, which is actually a subsidy for this group. This social policy might prompt employers to substitute older workers with a lower minimum wage for more expensive younger workers, making it possible for traditional factories to continue their domestic production. However, a necessary condition for this suggestion to work is the availability of efficient systems of training and learning. Axelrad et al. ( 2013 ) provided another justification for subsidizing the work of older individuals. They found that stereotypes about older workers might lead to a distorted allocation of the labor force. Subsidizing the work of older workers might correct this distortion. Ultimately, however, policy makers must understand that they must implement two different approaches to dealing with the problems of unemployment among young people and in the older population.

Authors’ contributions

HA, MM and IL conceptualized and designed the study. HA collected and managed study data, HA and IL carried out statistical analyses. HA drafted the initial manuscript. MM and IL reviewed and revised the manuscript. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have any no competing interests.

Ethics approval and consent to participate

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table  5 .

Table 5

Gross domestic product, volume, annual growth rates in percentage.

Source: National Accounts at a Glance 2014, OECD, 2014. http://www.oecd-ilibrary.org/economics/national-accounts-at-a-glance-2014_na_glance-2014-en

1 For example, in the US, the UK and Portugal, we witnessed higher rates of growth during late 1990 s and lower rates of youth unemployment compared to 2011.

2 Bank of Israel Annual Report—2013, http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf .

3 Bank of Israel Annual Report—2013, http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf .

4 http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/RecentEconomicDevelopments/develop136e.pdf .

5 The Labor Force Survey is a major survey conducted by the Israeli Central Bureau of Statistics among households nationwide. The survey follows the development of the labor force in Israel, its size and characteristics, as well as the extent of unemployment and other trends. The publication contains detailed data on labor force characteristics such as their age, years of schooling, type of school last attended, and immigration status. It is also a source of information on living conditions, mobility in employment, and many other topics.

6 The survey population is the permanent (de jure) population of Israel aged 15 and over. For more details see: http://www.cbs.gov.il/publications13/1504/pdf/intro04_e.pdf .

7 When we looked at those who had not managed to find a job at the time of the survey, we included all individuals who were not working, regardless of whether they were discouraged workers, volunteers or had other reasons. As long as they are not out of the labor force due to medical reasons or their mandatory military service, we classified them as "did not manage to find a job."

8 Until 2012, active soldiers were considered outside the labor force in the samples of the CBS.

9 EITC is a refundable tax credit for low to moderate income working individuals and couples.

Contributor Information

Hila Axelrad, Phone: 972 523524746, Phone: 617 459 4116, Email: li.ca.uat.xeuat@xaalih , Email: ude.cb@hdarlexa .

Miki Malul, Phone: 972-8-6472775, Email: li.ca.ugb.mos@lulam .

Israel Luski, Phone: 972-4-9015229, Email: li.ca.lilagw@llearsi .

• Addison, J.T., Centeno, M., Portugal, P.: Reservation wages, search duration, and accepted wages in Europe (No. 1252). IZA Discussion paper series (2004)
• Axelrad H, Luski I, Malul M. Difficulties of integrating older workers into the labor market: exploring the Israeli labor market. Int. J. Soc. Econ. 2013; 40 (12):1058–1076. doi: 10.1108/IJSE-12-2011-0098. [ CrossRef ] [ Google Scholar ]
• Axelrad H, Luski I, Malul M. Behavioral biases in the labor market differences between older and younger individuals. J. Behav. Exp. Econ. 2016; 60 :23–28. doi: 10.1016/j.socec.2015.11.003. [ CrossRef ] [ Google Scholar ]
• Axelrad H, Luski I, Malul M. Reservation wages and unemployment among older workers. J. Labor Res. 2017; 38 (2):206–227. doi: 10.1007/s12122-017-9247-6. [ CrossRef ] [ Google Scholar ]
• Bank of Israel.: Bank of Israel report, Bank of Israel Research Department Jerusalem, March 2011. https://www.boi.org.il/he/NewsAndPublications/RegularPublications/Doch2010/p5.pdf (2011). Retrieved 17 Jan 2018
• Bank of Israel.: Recent economic developments 136, Bank of Israel Research Department Jerusalem, December 2013. http://www.boi.org.il/en/NewsAndPublications/RegularPublications/Research%20Department%20Publications/BankIsraelAnnualReport/Annual%20Report-2013/p5-2013e.pdf (2013). Retrieved 16 July 2014
• Bell DN, Blanchflower DG. Young people and the great recession. Oxford Rev. Econ. Pol. 2011; 27 (2):241–267. doi: 10.1093/oxrep/grr011. [ CrossRef ] [ Google Scholar ]
• Böheim R, Horvath GT, Winter-Ebmer R. Great expectations: past wages and unemployment durations. Labour Econ. 2011; 18 (6):778–785. doi: 10.1016/j.labeco.2011.06.009. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brander, A., Peled Levy, O., Kassir, N.: Government policy and labor force participation rates of the working age population Israel and the OECD countries in the 90s. Bank of Israel Survey, August 2002 (2002), 7–61 ISSN 0552-2761 (Hebrew)
• Bureau of Labor Statistics.: Employee tenure summary employee tenure in 2014, Bureau of Labor Statistics, U.S. Department of Labor. http://www.bls.gov/news.release/tenure.nr0.htm (2014). Retrieved 22 June 2015
• Culpepper PD. Powering, puzzling, and ‘pacting’: the informational logic of negotiated reforms. J. Eur. Public Policy. 2002; 9 (5):774–790. doi: 10.1080/13501760210162357. [ CrossRef ] [ Google Scholar ]
• De Coen, A., Forrier, A., Sels, L.: The impact of age on the reservation wage: the role of employability. FBE Research Report MO_1001, pp. 1–36. (2010).‏ https://ssrn.com/abstract=1620368 or http://dx.doi.org/10.2139/ssrn.1620368
• Dietrich H, Möller J. Youth unemployment in Europe—business cycle and institutional effects. Int. Econ. Econ. Policy. 2016; 13 (1):5–25. doi: 10.1007/s10368-015-0331-1. [ CrossRef ] [ Google Scholar ]
• Flug, K., Kassir, N.: On poverty, work and everything in between. Research Department Bank of Israel (2001) (Hebrow)
• Freeman, R.B., Wise, D.A.: The youth labor market problem: its nature causes and consequences. In: The youth labor market problem: Its nature, causes, and consequences, pp. 1–16. University of Chicago Press, Chicago (1982)
• Fuchs, H.: The socioeconomic situation of young adults in Israel. State of the nation report: society, economy and policy in Israel, pp. 139–181 (2015)
• Furlong, A.: Youth studies: an introduction, pp. 72–97. Routledge, New York (2012)
• Hendeles, S.: The Center for Adult Employment. National Insurance Institute, Research and Planning, Development Services Division (2008) (Hebrew)
• Henkens K, Schippers J. Labor market policies regarding older workers in the Netherlands. In: Taylor P, editor. The ageing labor force: promises and prospects. Cheltenham: Edward Elgar; 2008. pp. 141–157. [ Google Scholar ]
• International Labor Office . Global employment trends for youth: 2011 update. Geneva: International Labor Office; 2011. [ Google Scholar ]
• Isengard B. Youth unemployment: Individual risk factors and institutional determinants. A case study of Germany and the United Kingdom. J. Youth Stud. 2003; 6 (4):357–376. doi: 10.1080/1367626032000162096. [ CrossRef ] [ Google Scholar ]
• Jacob M. Unemployment benefits and parental resources: what helps the young unemployed with labor market integration? J. Youth Stud. 2008; 11 (2):147–163. doi: 10.1080/13676260701863413. [ CrossRef ] [ Google Scholar ]
• Jahoda M, editor. Employment and unemployment”. Cambridge: Cambridge University Press; 1982. [ Google Scholar ]
• Johnson RW. Trends in job demands among older workers, 1992–2002. Mon. Labor Rev. 2004; 127 :48. [ Google Scholar ]
• Kalenkoski CM, Lacombe DJ. Effects of minimum wages on youth employment: the importance of accounting for spatial correlation. J. Labor Res. 2008; 29 (4):303–317. doi: 10.1007/s12122-007-9038-6. [ CrossRef ] [ Google Scholar ]
• Keese, M., Queisser, M., Whitehouse, E.: Older workers living longer, working longer, DELSA Newsletter 2, OECD (2006)
• Keese M, Roseveare D, Giguere S. The OECD action plan for youth, giving youth a better start in the labor market. Paris: OECD; 2013. [ Google Scholar ]
• Kelly E, McGuinness S, O'Connell P. J: Transitions to long-term unemployment risk among young people: evidence from Ireland. J Youth Stud. 2012; 15 (6):780–801. doi: 10.1080/13676261.2012.678047. [ CrossRef ] [ Google Scholar ]
• Lahey, J.N.: Do older workers face discrimination? Center for Retirement Research at Boston College (2005)
• Malul M. Older workers’ employment in dynamic technology changes. J. Socio Econ. 2009; 38 (5):809–813. doi: 10.1016/j.socec.2009.05.005. [ CrossRef ] [ Google Scholar ]
• Malul M, Luski I. The optimal policy combination of the minimum wage and the earned income tax credit. BE J. Econ. Anal. Policy. 2009; 9 :1. [ Google Scholar ]
• Marmora P, Ritter M. Unemployment and the retirement decisions of older workers. J. Labor Res. 2015; 36 (3):274–290. doi: 10.1007/s12122-015-9207-y. [ CrossRef ] [ Google Scholar ]
• McGregor, J., Gray, L.: Stereotypes and older workers: The New Zealand experience. Soc. Policy J. NZ. 163–177 (2002).
• Morsy H. Scarred generation. Financ. Dev. 2012; 49 :1. [ Google Scholar ]
• Nawakitphaitoon K. Occupational human capital and wages: the role of skills transferability across occupations. J. Labor Res. 2014; 35 (1):63–87. doi: 10.1007/s12122-013-9172-2. [ CrossRef ] [ Google Scholar ]
• OECD.: Live longer, work longer—ISBN-92-64-035877 (2006)
• O’Higgins N. The challenge of youth unemployment. Int. Soc. Secur. Rev. 1997; 50 (4):63–93. doi: 10.1111/j.1468-246X.1997.tb01084.x. [ CrossRef ] [ Google Scholar ]
• O’Higgins N. Youth unemployment and employment policy: a global perspective. Geneva: International Labor Office; 2001. [ Google Scholar ]
• Palier B, Thelen K. Institutionalizing dualism: complementarities and change in France and Germany. Politics Soc. 2010; 38 (1):119–148. doi: 10.1177/0032329209357888. [ CrossRef ] [ Google Scholar ]
• Pasquali, V.: Unemployment rates in countries around the world. Global Finance. https://www.gfmag.com/global-data/economic-data/worlds-unemployment-ratescom (2012). Retrieved 4 Nov 2013
• Peretz, S.: The secret of the Israeli labor market’s flexibility. Haaretz. https://www.haaretz.com/israel-news/business/opinion/.premium-1.698583 (2016). Accessed 7 Jan 2018
• Salvisberg A, Sacchi S. Labour market prospects of Swiss career entrants after completion of vocational education and training. Eur. Soc. 2014; 16 (2):255–274. doi: 10.1080/14616696.2013.821623. [ CrossRef ] [ Google Scholar ]
• Scarpetta, S., Sonnet, A., Manfredi, T.: Rising youth unemployment during the crisis: how to prevent negative long-term consequences on a generation?, OECD Social, Employment and Migration Working Papers, No. 106, OECD Publishing (2010). 10.1787/5kmh79zb2mmv-en
• Stier H, Endeweld M. Employment transitions and labor market exits: age and gender in the Israeli labor market. Res. Soc. Stratif. Mobil. 2015; 30 (41):93–103. doi: 10.1016/j.rssm.2015.01.002. [ CrossRef ] [ Google Scholar ]
• Vodopivec M, Dolenc P. Live longer, work longer: making it happen in the labor market. Financ. Theory Pract. 2008; 32 (1):65–81. [ Google Scholar ]
• Winkelmann L, Winkelmann R. Why are the unemployed so unhappy? Evidence from panel data. Economica. 1998; 65 (257):1–15. doi: 10.1111/1468-0335.00111. [ CrossRef ] [ Google Scholar ]

An official website of the United States government, Department of Justice.

Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

NCJRS Virtual Library

Youth unemployment: a literature review, additional details.

701 St. Paul Street , Baltimore , MD 21202 , United States

1250 Eye Street NW , Washington , DC 20005 , United States

Box 6000, Dept F , Rockville , MD 20849 , United States

Box 6000 , Rockville , MD 20849-6000 , United States

Availability

• Find in a Library
• Order Photocopy

Related Topics

The Role of Youth in Achieving the SDGs: Supporting Youth-Led Solutions for Sustainable Food Systems

• First Online: 11 October 2019

Cite this chapter

• Dario Piselli 5 , 6 ,
• Siamak Sam Loni 7 ,
• Kayla Colyard 8 &
• Sienna Nordquist 9 , 10  

1436 Accesses

3 Altmetric

Young people are often identified as “the leaders of tomorrow”. In recent years, however, this mantra has slowly been replaced by a growing emphasis on the role of youth communities as critical agents of change, “leaders of today” who are already contributing to the sustainable development of their economies and societies. This holds particularly true for the challenge of food system sustainability, given that increases in agricultural productivity and broader rural transformation critically require skills and knowledge that rural youth are more likely to possess over older adults. Accordingly, this chapter analyzes the interplay between existing youth-led contributions to implement Sustainable Development Goal 2 (‘No Hunger’) and the challenges imposed upon young people by unsustainable agricultural practices and food systems. First, the chapter examines the negative impacts that unsustainable food systems have on rural youth, including in terms of rural outmigration, youth unemployment and rural poverty. Secondly, the chapter focuses on young people’s actual contributions to sustainable food system transformations, as well as on the importance of addressing the barriers facing young farmers and entrepreneurs in their countries and communities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

See https://www.barillacfn.com/en/bcfnyes2018/ . Accessed 17 September 2018.

See https://www.resolutionproject.org/issues/agriculture . Accessed 17 September 2018. The Resolution Project specifically counts 29% of fellows working directly on food and agriculture and an additional 20% working indirectly on wider food systems challenges in the areas of environmental conservation and climate change.

See http://e4impact.org/mba/meet-our-champions/ . Accessed 17 September 2018. More specifically, 221 of the 659 ventures supported by E4Impact are categorized under the Agriculture and Food sector.

AIESEC. (2016). YouthSpeak global report. Resource document . AIESEC. Retrieved September 17, 2018, from https://issuu.com/aiesecinternational/docs/report_youthspeak_2016 .

Amsler, K., Hein, C., & Klasek, G. (2016, July 15). New skills or access to resources: what do young farmers need most? CGIAR. Retrieved September 17, 2018, from https://ccafs.cgiar.org/blog/new-skills-or-access-resources-what-do-young-farmers-need-most#.W6T6TC2Q2L0 .

Atlaw, D., Estruch, E., & Termine, P. (2016). Youth mobility, food security and rural poverty reduction (RYM). Resource document . FAO. Retrieved July 26, 2018, from http://www.fao.org/3/a-i6099e.pdf .

Baumann, P. (2002). Improving access to natural resources for the rural poor. Resource document . FAO. Retrieved September 17, 2018, from http://www.fao.org/docrep/006/ad683e/ad683e00.htm#Contents .

Bertelsmann Stiftung and Sustainable Development Solutions Network. (2018). SDG Index and Dashboards Report 2018. Global Responsibilities. Resource document . Bertelsmann Stiftung and Sustainable Development Solutions Network. Retrieved September 17, 2018, from http://www.sdgindex.org/assets/files/2018/01%20SDGS%20GLOBAL%20EDITION%20WEB%20V9%20180718.pdf .

Campbell, B. M., et al. (2017). Agriculture production as a major driver of the Earth System exceeding planetary boundaries. Ecology and Society, 22 (4), 8. https://doi.org/10.5751/ES-09595-220408 .

Article   Google Scholar  

Corporate Citizenship. (2016). Advancing the sustainable development goals: Business action and millennials’ views. Resource document . Corporate Citizenship. Retrieved September 17, 2018, from https://corporate-citizenship.com/wp-content/uploads/Advancing-the-Sustainable-Development-Goals-Business-Action-and-Millennials-Views.pdf .

DeLaval and CEJA. (2017). European young farmer’s survey. Building a sustainable sector. Resource document . DeLaval and CEJA. Retrieved September 17, 2018, from http://www.ceja.eu/wp-content/uploads/2017/09/CEJA-DeLaval-Survey-European-Young-Farmers.pdf .

Deloitte. (2016). The 2016 Deloitte Millennials Survey. Winning over the next generation of leaders. Resource document . Deloitte. Retrieved September 17, 2018, from https://www2.deloitte.com/content/dam/Deloitte/global/Documents/About-Deloitte/gx-millenial-survey-2016-exec-summary.pdf .

F&BKP Office. (2016, February 17). The role of agri-entrepreneurship and farming innovations for youth in agriculture engagement . Food and Business Knowledge Platform . Retrieved December 6, 2018, from https://knowledge4food.net/role-of-agri-entrepreneurship-and-farming-innovations-for-youth-in-agriculture/ .

FAO. (2009, October 12–13). Global agriculture towards 2050 . Rome, Italy: FAO High-Level Expert Forum. Retrieved September 17, 2018, from http://www.fao.org/fileadmin/templates/wsfs/docs/Issues_papers/HLEF2050_Global_Agriculture.pdf .

FAO. (2018a). Rural youth migration, social protection and sustainable value chains in Kenya. Information material . FAO. Retrieved July 26, 2018, from http://www.fao.org/3/i9615en/I9615EN.pdf .

FAO. (2018b, January). Leveraging youth employment opportunities in agriculture in rural sectors in Africa. FAO Regional Conference for Africa. 30th Session, Khartoum, The Sudan. Retrieved September 17, 2018, from http://www.fao.org/fileadmin/user_upload/bodies/ARC_30/MV562_en.pdf .

FAO. (2018c). The Rural Youth Mobility Project: Methodology and results. Resource document . FAO. Retrieved July 26, 2018, from http://www.fao.org/3/i8740en/I8740EN.pdf .

FAO. (2018d, August 20). Digital innovations are bringing youth back to agriculture . FAO. Retrieved December 6, 2018, from http://www.fao.org/fao-stories/article/en/c/1149534/ .

FAO. (2018e). Rural migration in Tunisia. Drivers and patterns of rural youth migration and its impacts on food security and rural livelihoods. Resource document . Rome: FAO. Retrieved December 6, 2018, from http://www.fao.org/3/I9193EN/i9193en.pdf .

FAO, CTA, and IFAD. (2014). Youth and agriculture: Key challenges and concrete solutions. Resource document . FAO. Retrieved September 17, 2018, from http://www.fao.org/3/a-i3947e.pdf .

Filmer, D., & Fox, L. (2014). Youth employment in Sub-Saharan Africa. Africa development series . Washington, DC: World Bank.

Book   Google Scholar  

Fry, R., Igielnick R., & Patten, E. (2018, March 16). How millennials today compare with their grandparents 50 years ago . Pew Research Centre. Retrieved September 17, 2018, from http://www.pewresearch.org/fact-tank/2018/03/16/how-millennials-compare-with-their-grandparents/ .

G20. (2017a). Supporting the “Next Generation” in rural development, agriculture and food security in developing countries. Outcome document . G20 Initiative for Rural Youth Employment. Retrieved July 26, 2018, from https://www.consilium.europa.eu/media/23551/2017-g20-rural-youth-employment-en.pdf .

G20. (2017b). Annex to the G20 leaders declaration. G20 Africa partnership . Retrieved September 17, 2018, from https://www.g20germany.de/Content/DE/_Anlagen/G7_G20/2017-g20-annex-partnership-africa-en.pdf?__blob=publicationFile&v=6 .

Ghebru, H. et al. (2018) Role of land access in youth migration and youth employment decisions: Empirical evidence from rural Nigeria . NSSP Working Paper 68. Washington, DC: IFPRI. Retrieved December 6. 2018, from http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/132895/filename/133106.pdf .

Hendrix, C.S. (2013). Climate change, global food markets, and urban unrest. Research brief no. 7. Climate Change and African Political Stability . Retrieved July 25, 2018, from https://www.files.ethz.ch/isn/161759/CCAPS%20Research%20Brief%20No.%207_final.pdf .

IFAD. (2011, February). Feeding future generations: young rural people today – prosperous, productive farmers tomorrow. Proceedings of the governing council high-level panel and side-events , Rome, Italy. Retrieved September 17, 2018, from https://maintenance.ifad.org/documents/38714170/39150184/Feeding+future+generations+-+Young+rural+people+today+–+prosperous%2C+productive+farmers+tomorrow.pdf/1aa91e21-5271-4a9a-a54c-012ecd256e6d .

IFAD. (2016, September 13). Empowering rural youth in West and Central Africa . IFAD. Retrieved July 26, 2018, from https://www.ifad.org/web/latest/story/asset/39017536 .

IFAD. (2018a). How inclusive rural transformation can promote sustainable and resilient societies. Policy brief . IFAD. Retrieved July 26, 2018, from https://www.ifad.org/documents/38714170/40253256/policy_brief_inclusive_rural_transf.pdf/b0d43331-3408-44b6-894e-f27e1c5296ca . Accessed 26 July 2018.

IFAD. (2018b). IFAD’s engagement with rural youth. Resource document . IFAD. Retrieved July 26, 2018, from https://www.ifad.org/documents/38714170/40257372/youth_engagement.pdf/ba904804-060c-49ed-83c5-bd5d70a99335 .

ILO. (2012). Youth employment interventions in Africa. Resource document . ILO. Retrieved September 17, 2018, from https://www.ilo.org/wcmsp5/groups/public/%2D%2D-africa/documents/publication/wcms_206325.pdf .

ILO. (2015). Global employment trends for youth 2015. Resource document . ILO. Retrieved September 17, 2018, from https://www.ilo.org/wcmsp5/groups/public/%2D%2D-dgreports/%2D%2D-dcomm/%2D%2D-publ/documents/publication/wcms_412015.pdf .

Kew, J. et al., (2013). Generation entrepreneur? The state of global youth entrepreneurship. Resource document. Youth Business International and Global Entrepreneurship Monitor . Retrieved September 17, 2018, from https://www.youthbusiness.org/wp-content/uploads/2013/09/GenerationEntrepreneur.pdf .

Kew, J. et al., (2015). Africa’s young entrepreneurs: Unlocking the potential for a brighter future. Resource document. Global Entrepreneurship Monitor . Retrieved December 6, 2018, from https://www.idrc.ca/sites/default/files/sp/Documents%20EN/Africas-Young-Entrepreneurs-Unlocking-the-Potential-for-a-Brighter-Future.pdf .

Kosec, C., et al. (2018). The effect of land access on youth employment and migration decisions: Evidence from rural Ethiopia. American Journal of Agricultural Economics, 100 (3), 931. https://doi.org/10.1093/ajae/aax087 .

Lyatuu, J. (2018, July 11). How Ugandan youthful leaders shared floor with British royalty. The Observer. Retrieved September 17, 2018, from https://observer.ug/lifestyle/58152-how-ugandan-youthful-leaders-shared-floor-with-british-royalty.html .

Mungai, C. et al., (2018). Youth engagement in climate-smart agriculture in Africa. Opportunities and challenges. Resource document . CGIAR Research Program on Climate Change, Agriculture and Food Security. Retrieved September 17, 2018, from http://www.ccafs.cgiar.org .

Nink, E. (2015, July 20). 28 inspiring urban agriculture projects. Food Tank . Retrieved July 17. 2018, from https://foodtank.com/news/2015/07/urban-farms-and-gardens-are-feeding-cities-around-the-world/ .

Rossi, N.W. (2017). Reducing rural poverty: Social protection, access and decent employment. Presentation material . FAO. Retrieved July 26, 2018, from https://www.un.org/development/desa/dspd/wp-content/uploads/sites/22/2017/04/Expert-SDG-meeting-FAO-Winder.pdf .

Schwoob, M.H. et al., (2016). Agricultural transformation pathways initiative – 2016 Report. Resource document . IDDRI & Rothamsted Research. Retrieved September 17, 2018, from http://unsdsn.org/wp-content/uploads/2016/05/Agricultural-Transformation-Pathways-2016-Report.pdf

SDSN Youth. (2017a). Impacts of climate change on youth, peace and security. Resource document . SDSN Youth. Retrieved July 25, 2018, from http://sdsnyouth.org/youth-peace-security-report .

SDSN Youth. (2017b). Youth solutions report 2017. Resource document . SDSN Youth. Retrieved July 25, 2018, from http://www.youthsolutions.report/read-the-2018-report .

SDSN Youth. (2018). Youth solutions report 2018. Resource document . SDSN Youth. Retrieved July 25, 2018, from http://www.youthsolutions.report/read-the-2018-report.

Steffen, W., et al. (2015). Planetary boundaries: guiding human development on a changing planet. Science, 347 (6223), 1259855. https://doi.org/10.1126/science.1259855 .

Article   CAS   PubMed   Google Scholar  

The East African. (2017, April 29) African youth take technology to agriculture . The East African. Retrieved December 6, 2018, from https://www.theeastafrican.co.ke/scienceandhealth/African-youth-take-technology-to-agriculture-/3073694-3908328-vmrq9lz/index.html .

The Economist. (2016, January 23). The millennial generation: Young, gifted and held back. The Economist . Retrieved July 26, 2018, from https://www.economist.com/leaders/2016/01/23/young-gifted-and-held-back .

UN. (2015). Transforming our world: The 2030 agenda for sustainable development . New York: UN Publishing.

Google Scholar  

UN. (2016). World youth report on civic engagement . New York: UN Publishing.

UN. (2018). The sustainable development goals report 2018 . New York: UN Publishing.

UN Secretary-General. (2018, April 12). Remarks at “investing in youth to counter terrorism” . United Nations Secretary-General. Retrieved August 20, 2018, from https://www.un.org/sg/en/content/sg/speeches/2018-04-12/remarks-investing-youth-counter-terrorism .

UNCDF. (2014). Financial inclusion of youth factsheet. Resource document . UNCDF. Retrieved September 17, 2018, from https://www.un.org/esa/socdev/documents/youth/fact-sheets/youth-financial-inclusion.pdf .

UNDESA. (2015). Youth population trends and sustainable development. Resource document . UN. Retrieved July 26, 2018, from http://www.un.org/esa/socdev/documents/youth/fact-sheets/YouthPOP.pdf .

UNDP. (2014). Fast facts: Civic engagement and participation of youth in politics and public institutions. Resource document . UNDP. Retrieved September 17, 2018, from http://www.undp.org/content/dam/undp/library/corporate/fast-facts/english/FF-Civic-Engagement-and-Participation-of-Youth-in-Politics-and-Public-Institutions.pdf .

UNESCO. (2015a). Education for all 2000-2015: Achievements and challenges . Paris: UNESCO Publishing.

UNESCO. (2015b). Adult and youth literacy factsheet. Resource document . UNESCO. Retrieved September 17, 2018, from http://uis.unesco.org .

UNFPA. (2014). The state of world population 2014. Resource document . UNFPA. Retrieved September 17, 2018, from https://www.unfpa.org/sites/default/files/pub-pdf/EN-SWOP14-Report_FINAL-web.pdf .

UNFPA. (2016). The state of world population 2016. Resource document . UNFPA. Retrieved September 17, 2018, from https://www.unfpa.org/sites/default/files/sowp/downloads/The_State_of_World_Population_2016_-_English.pdf .

Vos, R. (2014, February 4). Is global food security jeopardised by an old age timebomb? The Guardian . Retrieved September 17, 2018, from https://www.theguardian.com/global-development-professionals-network/2014/feb/04/global-food-security-old-age-timebomb .

Wallimann-Helmer, I. (2015). Can youth quotas help avoid future disasters? In J. Tremmel, A. Mason, P. H. Godli, & I. Dimitrijoski (Eds.), Youth quotas and other efficient forms of youth participation in ageing societies . Cham: Springer.

White, B. (2012). Agriculture and the general problem: rural youth, employment and the future of farming. IDS Bulleting, 43 (6), 9. Retrieved December 6, 2018, from https://opendocs.ids.ac.uk/opendocs/bitstream/handle/123456789/7535/IDSB_43_6_10.1111-j.1759-5436.2012.00375.x.pdf?sequence=1 .

World Bank and IFAD. (2017). Rural youth employment. Input document . German Federal Ministry for Economic Cooperation and Development. Retrieved July 26, 2018, from https://www.bmz.de/de/zentrales_downloadarchiv/g20/Rural_Youth_Employment_-_WB-IFAD-Synthesis_Study_DWG.pdf .

Yifu Lin, J. (2012, May 1). Youth bulge: A demographic dividend or a demographic bomb in developing countries? World Bank Blogs . Retrieved September 17, 2018, from http://blogs.worldbank.org/developmenttalk/youth-bulge-a-demographic-dividend-or-a-demographic-bomb-in-developing-countries .

Download references

Author information

Authors and affiliations.

Centre for International Environmental Studies, Graduate Institute of International and Development Studies, Geneva, Switzerland

Dario Piselli

SDSN Youth, New York, NY, USA

Siamak Sam Loni

Kayla Colyard

Robert W. Woodruff Scholar, Emory College of Arts and Sciences, Emory University, Atlanta, GA, USA

Sienna Nordquist

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Dario Piselli .

Editor information

Editors and affiliations.

Department for Innovation on Biological, Agro-Food and Forest Systems, University of Tuscia, Viterbo, Italy, Division on Impacts on Agriculture, Forests and Ecosystem Services (IAFES), Euro-Mediterranean Center on Climate Change (CMCC), Viterbo, Italy, RUDN University, Moscow, Head of Smart Urban Nature Laboratory, Moscow, Russia

Riccardo Valentini

Faculty of Medicine, Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada, Division of Endocrinology and Metabolism, Department of Medicine, St. Michael’s Hospital, Toronto, ON, Canada, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

John L. Sievenpiper

Barilla Center for Food and Nutrition, Parma, Italy, Foundation Euro-Mediterranean Center on Climate Change, Lecce, Italy

Marta Antonelli

Barilla Center for Food and Nutrition, Parma, Italy

Katarzyna Dembska

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Piselli, D., Loni, S.S., Colyard, K., Nordquist, S. (2019). The Role of Youth in Achieving the SDGs: Supporting Youth-Led Solutions for Sustainable Food Systems. In: Valentini, R., Sievenpiper, J., Antonelli, M., Dembska, K. (eds) Achieving the Sustainable Development Goals Through Sustainable Food Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-23969-5_13

Download citation

DOI : https://doi.org/10.1007/978-3-030-23969-5_13

Published : 11 October 2019

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-23968-8

Online ISBN : 978-3-030-23969-5

eBook Packages : Biomedical and Life Sciences Biomedical and Life Sciences (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Youth unemployment: a review of the literature

• PMID: 4019874
• DOI: 10.1016/s0140-1971(85)80041-5

This paper sets out to review the studies on youth unemployment conducted in a range of English speaking countries: America, Australia and Great Britain. The studies have been divided into six sections: psychological adjustment, attributions and expectations, education about unemployment, job choice and work experience, values, and job interview training. The paucity of good studies in this area partly explains the lack of clear replicated findings or coherent theories for the causes, correlates and consequences of unemployment among young people, though this is an area of relevance to social policy. Furthermore, it was concluded that various factors such as individual differences, salient demographic variables and previous work experience have been neglected. Nevertheless, many of the studies seem to indicate the presence of a destructive vicious circle which young people experience when failing to get a job: stress and disappointment, leading to lowered self-esteem, a change in expectations, and minor psychiatric illnesses which handicap the job search and application process so making unemployment all the more likely.

• Adolescent*
• Choice Behavior
• Internal-External Control
• Self Concept
• Social Adjustment
• Social Values
• Student Dropouts / psychology
• Unemployment*
• United Kingdom
• United States
• Vocational Education

• Open access
• Published: 27 May 2024

Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada

• Meaghen Quinlan-Davidson 1 , 2 ,
• Mahalia Dixon 1 ,
• Gina Chinnery 3 ,
• Lisa D. Hawke 1 , 4 ,
• Srividya Iyer 5 , 6 ,
• Katherine Moxness 7 ,
• Matthew Prebeg 1 , 8 ,
• Lehana Thabane 9 , 10 , 11 &
• J. L. Henderson 1 , 4  

BMC Public Health volume  24 , Article number:  1402 ( 2024 ) Cite this article

287 Accesses

1 Altmetric

Metrics details

Prior research has showed the importance of providing integrated support services to prevent and reduce youth not in education, employment, or training (NEET) related challenges. There is limited evidence on NEET youth’s perspectives and preferences for employment, education, and training services. The objective of this study was to identify employment, education and training service preferences of NEET youth. We acknowledge the deficit-based lens associated with the term NEET and use ‘upcoming youth’ to refer to this population group.

Canadian youth (14–29 years) who reported Upcoming status or at-risk of Upcoming status were recruited to the study. We used a discrete choice experiment (DCE) survey, which included ten attributes with three levels each indicating service characteristics. Sawtooth software was used to design and administer the DCE. Participants also provided demographic information and completed the Global Appraisal of Individual Needs–Short Screener. We analyzed the data using hierarchical Bayesian methods to determine service attribute importance and latent class analyses to identify groups of participants with similar service preferences.

A total of n =503 youth participated in the study. 51% of participants were 24–29 years of age; 18.7% identified as having Upcoming status; 41.1% were from rural areas; and 36.0% of youth stated that they met basic needs with a little left. Participants strongly preferred services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Three latent classes were identified and included: (i) job and educational services (38.9%), or services that include career counseling and securing a work or educational placement; (ii) mental health and wellness services (34.9%), or services that offer support for mental health and wellness in the workplace and free mental health and substance use services; and (iii) holistic skills building services (26.1%), or services that endorsed skills for school and job success, and life skills.

Conclusions

This study identified employment, education, and training service preferences among Upcoming youth. The findings indicate a need to create a service model that supports holistic skills building, mental health and wellness, and long-term school and job opportunities.

Peer Review reports

Youth not in education, employment, or training (NEET) struggle to navigate school to work transitions and experience difficulties accessing jobs [ 1 ]. These youth are disconnected from school, have limited work experience [ 2 ], and experience a loss of economic, social, and human capital [ 3 ]. NEET status is associated with lower education, parental unemployment, low socioeconomic status, low self-confidence, more precarious housing, and young parenthood [ 4 , 5 , 6 , 7 , 8 ]. In Canada, the percentage of NEET youth (15–29 years) was estimated at 11% in 2022 [ 9 ]. Importantly, NEET status is not homogenous across the country, ranging from 36% in Nunavut, 20% in Northwest Territories, and 17% in Newfoundland and Labrador to 10% in Quebec, Prince Edward Island and British Columbia) [ 10 ]. Supporting and protecting these marginalized youth remains a challenge, particularly in light of the Coronavirus disease 2019 (COVID-19) pandemic, which adversely impacted the school to workforce transition for youth across the country [ 11 ]. Although the term NEET has been used to describe this population, it is considered stigmatizing and associated with a deficit-based lens[ 12 ]. As such, and in consultation with one of our youth team members, we refer to this population as ‘Upcoming youth’[ 13 ].

Upcoming status has gained attention across Canada in recent decades [ 14 ]. As an illustration of this focus, federal, provincial/territorial, and local programs exist to support Upcoming youth across the country [ 15 ]. Despite these efforts, evidence indicates program fragmentation, limited coordination across sectors and regions, and a lack of evaluation of these programs [ 16 ]. Further, these programs may be available to youth on a short-term basis and specific to youth who meet education, income, and age criteria [ 17 ]. There is a lack of knowledge of how to (re)engage Upcoming youth in general education and employment support services. Often the same limited outcomes are measured and reported (e.g., job attainment) with services focusing on these outcomes. At the same time, youth have not been asked what outcomes they prefer and accordingly what services they would like. Indeed, selective outcome reporting and lack of engagement of youth impairs the quality of evidence and contributes to research waste [ 18 ]. Given the heterogeneity of Upcoming status, this lack of evidence is particularly important for subgroups of youth (e.g., geographic location; socioeconomic status; mental health status) who face challenges in the school-to-work transition.

Prior global research has emphasized the importance of integrated, coordinated interventions that offer a range of support services (e.g., on-the-job, classroom-based, and social skills training) to prevent and reduce Upcoming status [ 19 , 20 , 21 , 22 , 23 ] [ 24 ]. Integrated youth service (IYS) models, which integrate education, employment, mental and physical health, substance use, peer support, and navigation in one, youth-friendly location have been established in Canada [ 25 ]. IYS deliver services that meet the needs, goals, and preferences of youth, and hold promise in serving vulnerable Upcoming youth through the provision of holistic services in a youth-friendly environment. Indeed, IYS models are investigating how to optimize employment, education, and training services as a critical component of supporting youth wellbeing and their successful transition to adulthood. This point is particularly important as Upcoming youth experience greater mental health and substance use (MHSU) concerns compared to youth who do not identify as Upcoming [ 26 , 27 ].

An essential component to designing and enhancing health and social services for Upcoming youth is understanding their perspectives [ 28 ]. Yet, there is a lack of evidence on Upcoming youth’s perspectives and preferences for employment, education, and training services within the Canadian context. For interventions to be relevant to the needs and experiences of youth—which will increase their chances of using the services and benefiting from them—it is important to understand what youth aim to achieve when participating in an intervention. Engaging youth in identifying service components and interventions will ensure that programs and services are relevant, feasible, and appropriate to this population group [ 29 ].

An approach that can be used to identify the demands and preferences of youth is the discrete choice experiment (DCE) [ 30 , 31 ]. The DCE is a quantitative method that requires participants to state their choice over sets of alternatives described in terms of several characteristics called attributes and the value placed on each attribute [ 30 , 31 ]. In this way, the DCE is able to identify the importance of attributes along which a variety of service options vary, as well as service preferences among subgroups. DCEs are one of the most popular methods for eliciting stated- preferences in health care [ 32 , 33 ]. They force participants to make trade-offs, identifying the importance of different service attributes [ 32 , 33 ]. Previous findings generated from DCE studies have been useful in informing service design and delivery, resource allocation, and policies, including the preferred design of IYS services [ 34 , 35 , 36 , 37 ].

Understanding service preferences from the perspective of Upcoming youth is critical for the development of interventions and policies that will help youth navigate the school-to-work transition. As such, the objective of the current study was to identify employment, education and training service preferences of Upcoming youth. As our approach to COVID-19-related impacts shifts, the need for this research is more urgent than ever, as a way to support vulnerable youth, reduce Upcoming status, prevent further exclusion, and help them on their path towards adulthood.

Discrete Choice Experiment (DCE)

A discrete choice experiment (DCE) methodology was used in this study, as described in the study protocol [ 13 ]. We followed the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guidelines on good research practices for conjoint analysis [ 38 ]. Attributes and levels were developed using the following methods. First, we reviewed the literature on relevant and preferred services for youth with Upcoming and at-risk Upcoming status [ 26 ]. An initial set of six attributes with three to four levels was developed from the literature review, highlighting components such as mental health, goals, and skills training. Second, focus groups were conducted among youth (16–29 years) with Upcoming and at-risk Upcoming status across Canada to obtain youth feedback on proposed service outcomes [ 39 ]. Thematic analysis [ 40 ] of the focus group data identified prominent attributes and levels, including skills training, mentorship, and networking. The project team included youth team members with lived/living experience of MHSU concerns and researchers; meetings were held with the team to refine the attributes and levels.

The list of attributes and levels were piloted among n  = 9 youth (16–29 years) across Canada. Pilot participants completed the DCE with a member of the project team. The aim of the pilot was to obtain youth feedback on the proposed list of attributes and levels, as well as the design and functionality of the DCE. Based on pilot feedback, a final list of attributes and levels was developed. The final DCE list included ten attributes, each with three levels. The attributes included mentorship; skills for school and job success; technical skills; life skills; basic income; networking opportunities; securing a work or educational placement; career counselling; access to free mental health and substance use services; and support for mental health and wellness in the workplace. Using a 3 × 3 partial-profile design, we used Sawtooth software (version 9.14.2) [ 41 ] to administer the 14 randomized choice tasks. This design was chosen to optimize orthogonality, minimize participant burden, and ensure data robustness [ 42 ]. Table 1 shows a sample choice task; Additional File 1 contains the full list of attributes and levels in the study.

Participants and procedure

The study was approved by the Centre for Addiction and Mental Health’s (CAMH) Research Ethics Board in Toronto, Canada. This study consisted of n  = 503 youth (14–29 years), recruited over a three-month period in late 2022 and early 2023. The sample size was based on a priori power calculations and exceeds the sample size of most DCE studies [ 13 ]. Study flyers with survey links were distributed through internal CAMH and external professional networks, as well as through social media (Facebook and Instagram).

Participants were eligible to complete the DCE if they were between the ages of 14 and 29 years; lived in Canada at the time of survey completion; and identified as Upcoming status or having ever been concerned of being at-risk of Upcoming status (self-identified). They were screened through an online survey sent via email, hosted on REDCap electronic software [ 43 ]. Participants gave informed consent and filled out anti-spam and eligibility questions. Those who were eligible were sent a link to complete the DCE through Sawtooth Software [ 41 ]. The survey was in English only. They also filled out self-report questionnaires on demographics, and mental health and substance use. Reminder emails to complete the survey were sent to participants once per week, with a maximum of three reminders sent. A total of n  = 515 participants initiated the survey and n  = 503 completed the survey, yielding a response rate of 97.7%. The median time to complete the DCE was 20.63 min. Participants received a $30 gift card as honorarium for survey completion.

Mental health and substance use measures

Participants completed the Global Appraisal of Individual Needs–Short Screener (GAIN-SS) (version 3) [ 44 ]. Internalizing disorders (depression, anxiety, somatic complaints, trauma etc.); externalizing disorders (hyperactivity, conduct problems, attention deficits, impulsivity etc.); and substance use disorders are domain subscales that are screened in the GAIN-SS [ 44 ]. The GAIN-SS also includes a crime/violence domain, however, low level of endorsement in this study precluded the inclusion of this subscale. Participants rated each administered symptom “never” to “within the past month”, indicating how recently they experienced symptom difficulties. Within each domain subscale, endorsed past month symptoms were counted and summed. Scores could range between 0–6, 0–7, and 0–5 for the Internalizing, Externalizing, and Substance Use Problems domain, respectively. Following previous literature, three or more items endorsed within the past month indicate a high likelihood of needing services and/or meeting threshold criteria for psychiatric diagnoses [ 44 , 45 ].

Demographic characteristics were collected. We included age (categorical measure), gender identity (man/boy [cis, trans]; woman/girl [cis, trans]; Gender diverse); ethnicity (White; Indigenous, Black, Asian, Mixed); region in Canada (Prairies, Western/Northern, Atlantic, Central); self-rated physical and mental health (good/very good/excellent; fair/poor) [ 46 ]; socioeconomic status (live comfortably; income meets needs with a little left; just meet basic expenses; don’t meet basic expenses); living arrangement (alone; with partner; with family; other); and area of residence (large city and suburbs of large city; small city, town, village or rural area).

Youth engagement

Following the McCain Model of Youth Engagement [ 47 ], and working with the Youth Engagement Initiative at the Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, we engaged youth throughout the study. To enhance study design, promote youth buy-in, and relevance of the study, youth were involved from project inception and implementation of study activities to interpretation of findings and manuscript development.

Statistical analysis

Statistical analyses were performed using Sawtooth Software version 9.14.2 [ 41 ] and Stata version 16.1 [ 48 ]. Descriptive statistics were calculated for all study variables overall and by latent-class grouping. Using hierarchical Bayesian methods within Sawtooth Software [ 41 ], utility estimates were calculated for each participant. Standardized zero-centered utilities were used and the average utility range of attribute levels was set to 100 (49) to calculate the estimates. Attributes with higher utility estimates indicated higher relative value compared with other attributes (Table 2 ).

To identify groups of participants with similar service preferences, we conducted latent class analyses [ 41 ]. To belong to a latent class, probabilities were assigned to each participant. Using different starting seeds, five replications for each latent class group was calculated, with log-likelihood decreases of 0.01 or less indicating convergence. Based on the analysis, we retained a three-class model. This model was determined by analyzing the Bayesian Information Criteria (BIC), Akaike Information Criteria (AIC), Consistent Akaike Information Criterion (CAIC), and Akaike’s Bayesian Information Criterion (ABIC); latent class sizes; and the interpretation of latent class groupings (Table 3. ). Team discussions with youth team members were held to review the importance scores and rankings and establish the names of the latent glass groupings (Table 4 ). Stata 16.1 [ 48 ] was used to compare latent classes on demographic characteristics and GAIN-SS scores using chi-square tests.

Table 5 presents participant demographic characteristics. The majority of participants were between 24 and 29 years of age; lived in urban areas; were engaged in employment and training only; and identified as White and girl/woman (Trans, cis). Almost two thirds of participants (65.79%) met threshold criteria for an internalizing disorder, followed by 36.62% with an externalizing disorder, and 8.65% with a substance use disorder.

Overall service preferences

The overall service preferences and importance scores of participants are presented in Table  2 . Participants positively endorsed services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Participants were least likely to endorse technical skills. Within life skills services, youth positively endorsed services that included managing finances, taxes, and skills associated with self-care and cooking. The provision of a mentor who worked within the participant’s field of interest was preferred by all youth. Participants positively endorsed basic income until having secured employment that matched the basic income level. All youth preferred services that provided support to secure long-term job positions or school placements that aligned with their career interests or long-term goals.

Table 3. illustrates the fit indices of the latent class analysis. A three-class model was retained based on fit, size of latent class grouping, and interpretation of findings. Attribute importance scores and rankings are presented in Table  4 by latent class. There were some commonalities identified across the latent classes. All latent classes positively endorsed services that offered mentorship (mentors in their field of interest, with similar backgrounds, or peer mentors), basic income, and networking. Youth preferred the provision of a mentor with work experience in their field of interest. Participants also positively endorsed receiving basic income until 25 years of age (regardless of school or job status), or until they had found a job that matched the basic income level. In addition, all participants endorsed skills to network and opportunities to network in their area of interest.

Over 60% of participants from all of the latent classes reported fair/poor mental health. In addition, over 60% of participants in each latent class grouping met threshold criteria for an internalizing disorder, compared to the other GAIN-SS disorders. Furthermore, more participants identified as having lived in large cities/suburbs compared to small cities/towns in each latent class.

Latent Class 1: Job and educational services

The first latent class endorsed services that focused on education and long-term job services, focusing on a career trajectory ( n  = 204, 38.9%). Attributes that drove these decisions included career counselling and securing a work or educational placement. Youth positively endorsed career counselling that helped to figure out career goals, create a resume, and complete job applications. Further, youth positively endorsed receiving long-term job positions or school placements that align with their career interests and long-term goals, as opposed to temporary or any job position. Participants from this latent class (Table 6 ) were more likely to be 24–29 years of age compared to other ages. Approximately 22.06% of youth in this latent class identified as Upcoming.

Latent Class 2: Mental health and wellness services

The second latent class endorsed mental health and wellness services ( n  = 171, 34.9%). This latent class preferred that services offer support for mental health and wellness in the workplace and free mental health and substance use services. Specifically, participants positively endorsed the provision of on-site in-person, individual mental health and substance use services, as opposed to the provision of virtual or in-person group services. Further, youth positively endorsed ongoing access to a support worker to help in securing accommodations in the workplace, as opposed to learning how to advocate for oneself in workplace or support during job onboarding. Participants that endorsed this latent class (Table  5 ) tended to identify as Indigenous, Black, Asian, and Mixed; girl/woman (cis, trans); both student and employed; income met needs with a little left; and rated their physical health as good/excellent. Approximately 16.96% of youth in this latent class identified as Upcoming.

Latent Class 3: Holistic skills building services

Skills building was the focus of the third latent class ( n  = 128, 26.1%). Participants positively endorsed skills for school and job success, as well as life skills. Specifically, youth were interested in learning about how to organize time; prioritize tasks; identify problems and solutions; as well as professionalism, communication and relationship building. Youth were also interested in life skills that focused on learning about how to manage finances and taxes, as well as self-care and cooking. Participants in this latent class (Table  5 ) tended to identify as White; employed only; living in an urban area; and income that just met basic expenses. Approximately 15.63% of youth in this latent class identified as Upcoming.

To our knowledge, this study was the first to identify employment, education, and training service preferences among Upcoming youth and those at-risk of Upcoming status using discrete choice experiment methods. The findings indicate that overall, youth value services that enhance their ability to deal effectively with life demands; receive advice and guidance by a mentor; and obtain financial support through basic income. In examining youth participants by latent class, the findings indicate a need to create a service model that supports long-term school and job opportunities, holistic skills building, and mental health and wellness. Job and educational services prioritized long-term job and school placements, with career counselling. Mental health and wellness services endorsed free, easily accessible and in-person support services. Meanwhile, holistic skills building focused on problem solving, communication, relationship building, and organization of time, as well as building skills to help youth manage daily life.

Participants highly endorsed services that promote life skills, mentorship, and basic income. For life skills, participants valued skills that included managing finances, taxes, and skills like self-care or cooking. Participants may have valued this service attribute because life skills empower youth. These skills are positive behaviours that give youth the knowledge, values, attitudes, and abilities necessary to effectively meet and deal with everyday challenges [ 50 , 51 ]. Prior research has shown how these skills strengthen psychosocial competencies, promote health and social relationships, and protect against risk-taking behaviours [ 51 ].

For mentorship, participants valued having a mentor who has work experience in the field they are interested in. Prior research has shown the negative associations between unemployment, exclusion, and economic hardship among Upcoming youth [ 52 , 53 ]. Participants may have chosen this service attribute as mentoring is a key component of career development. Career mentoring provides opportunities for career exploration and strengthening decision-making within this domain [ 54 , 55 , 56 , 57 ]. Research has shown the benefits of mentorship. Mentors are a positive resource, providing support and guiding youth as they navigate and succeed in their careers [ 54 , 58 , 59 ].

Youth also prioritized receiving basic income until they secured employment that matches the basic income level. Empirical evidence has shown associations between income and youth mental health outcomes [ 60 , 61 , 62 ]. Indeed, Johnson et al. [ 63 ] [ 63 ] posit that a universal basic income can positively affect health through behaviour, resources, and stress. Defined as income support to populations with minimal or no conditions [ 64 ], prior research has shown the benefits of a basic income plan in terms of poverty reduction, improvements in physical and mental health, economic growth, and human capital gains [ 65 , 66 , 67 , 68 , 69 ]. In a qualitative study in England [ 70 ], youth (14–24 years) reported that a universal basic income plan would improve their mental health through financial security, agency, greater equality, and improvements in relationships.

Differences in service preferences were observed among youth subgroups based on the identified latent classes. Youth who identify as Indigenous, Black, Asian, and Mixed prioritized mental health and wellness services compared to youth who identify as White. Previous literature has showed that Indigenous , Black, and racialized youth have experience longer wait times and poorer quality of mental health care compared to their White counterparts [ 71 , 72 , 73 ]. Prior literature has described how MHSU systems often do not consider or address the discrimination, systemic racism, economic marginalization, and intergenerational traumas that Indigenous, Black, and racialized populations experience within and outside of the service system [ 74 , 75 ]. These negative experiences adversely affect their access to and quality of MHSU care, leading to inequities in MHSU outcomes. To ensure that mental health and substance use services are culturally responsive, safe, effective, and available to Indigenous, Black, and racialized youth, services should incorporate their perspectives into service design and delivery. The finding that youth 24–29 years of age endorsed job and educational services focused on long term career planning could be attributed to being more advanced in thinking about their careers and a desire to find a career as opposed to a job [ 57 , 76 , 77 ]. It could also be due to older youth experiencing poorer labour market conditions [ 19 , 78 , 79 ]. To improve long-term job opportunities for youth, Canada’s labour standards need to be updated, ensuring protection and benefits to informal and non-standard youth workers (17).

A critical component for education, employment, and training services is raising awareness about these services and their benefits among youth. A 2019 survey among NEET youth (16–29 years) in Canada showed that 54% reported a hard time finding information on the labour market services, while 42% said the information available on these services was not easy to understand [ 80 ]. One way to address this issue is by delivering services to Upcoming youth at the local, community level. In fact, as IYS strengthen education, employment and training services, these community-based services can support youth by connecting them with local job opportunities. IYS can also work with other public, private and community organizations to change local, fragmented school and work policies [ 19 ]. Another way to address this issue would be to provide access to this information at an earlier age, as shown in a parliamentary enquiry in Victoria, Australia in which career management was recommended for incorporation into primary school curriculum[ 81 ].

All three latent classes preferred services that provided mentorship, basic income, and networking opportunities. Youth value mentorship opportunities from individuals with experience in their field of interest. Similarly, youth prioritized networking opportunities in their field of interest. Federal, provincial/territorial and local programs could harness this preference by creating mentorship and networking structures across public, private, and community organizations for youth [ 17 ]. Further, the provision of basic income would help support youth as they re-engage with school and the labour market [ 17 ]. Interestingly, technical skills were not endorsed by youth in this study. Although technical skills are endorsed as part of technical and vocational education and training programs [ 82 ], it may be that youth were not as concerned about enhancing technical skills as they were other services. Future research should investigate youth experiences of technical skill programs.

It is important to note that participants in all latent classes endorsed poor mental health, while a higher proportion of youth screened positive for internalizing disorders compared to other disorders. These findings are in line with prior literature, particularly in light of the COVID-19 pandemic [ 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. It could also reflect the positionality of the researchers. The survey was administered by CAMH, a mental health teaching hospital, and could have been seen more among youth connected with mental health services compared to those not connected to CAMH. Prior research has showed that life skills training can promote positive development, mental wellbeing, and prevent risky behaviours [ 92 , 93 ]. The prioritization of long-term school and job placements among youth with mental health concerns indicates a need to strengthen these services for this cohort.

In fact, in 2020 the Individual Placement and Support (IPS) model [ 94 , 95 ], which provides mental health service users with personalized vocational support alongside mental health support to obtain employment, education, and training opportunities was launched in Alberta, British Columbia, Nova Scotia, Ontario and Quebec to strengthen existing IYS, including ACCESS Open Minds, Foundry, and Youth Wellness Hubs Ontario [ 96 ]. The program was implemented in 12 hubs across the country and is currently being evaluated. Despite the challenges that have arisen over the course of the pandemic, COVID-19 has highlighted an opportunity to improve the education, employment and training support systems that serve these youth. Some of the core principles of implementation of the IPS model align with findings from the current study and include integration of mental health treatment teams, employment specialists to support young people as they navigate the labour market, rapid job search approaches, and tailored job supports, among other principles [ 94 ].

Indeed, in building on the services endorsed in this study, it would be important to incorporate an evaluation framework such as the Consolidated Framework for Implementation Research [ 97 ] to evaluate the effectiveness and impact of these services. Determining potential outcomes that could be measured would also be important. Following the IPS model, for job and educational placements, services could implement the Youth Employment and Education Survey [ 94 , 95 ]. Potential outcomes could include status of school or employment, job permanency, educational placement duration, and satisfaction with the program, among others. For mental health and wellness support services, outcomes could focus on the number of in-person visits, satisfaction with the services, and self-reported mental health, among others. For holistic services, potential outcomes could focus on reporting and monitoring self-reported goals for problem-solving and communication, among others. It would be important to continuously assess and match services to Upcoming youth preferences.

We would like to acknowledge some limitations. This study includes a non-randomized sample of youth across Canada. Our study included less than 20% of youth who identified as Upcoming, which limits our ability to generalize the findings to this population group. Further research is needed among youth who identify as Upcoming to determine if these education, employment, and training services represent their preferences. Further, youth without stable and consistent internet access would also have been missed. We were unable to recruit large populations of youth from specific Indigenous and racialized backgrounds, although these did account for nearly half the sample. These groups may have different needs and preferences. Future research should investigate their perspectives on employment, education, and training services. In addition to the structure of the DCE survey, along with following a rigorous process in the development of the attributes and levels, there could have been some youth service priorities not assessed. Furthermore, as some of the attributes and levels built on each other, these commonalities could have influenced preference elicitation for specific service attributes. We tried to ensure that the survey was youth-friendly for all youth, however due to the cognitive capacity required to complete the survey, some youth with greater mental health and learning challenges may have been missed.

This study identified employment, education, and training service preferences among Upcoming youth and those at-risk of Upcoming status in Canada. The findings indicate a need at the federal, provincial/territorial, and local level to create a service model that supports school and job opportunities long-term; mental health and wellness; and building holistic skills. The model also requires community-based and youth-centred approaches in the design and delivery of these services. Our findings further support the need for widespread policy support for broader-spectrum IYS for Upcoming youth and those at-risk of Upcoming status.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Akaike’s Bayesian Information Criterion

Akaike Information Criterion

Bayesian Information Criteria

Consistent Akaike Information Criterion

Centre for Addiction and Mental Health

Coronavirus disease 2019

Discrete Choice Experiment

Global Appraisal of Individual Needs Short Screener

Importance Scores

Individual Placement and Support model

International Society for Pharmacoeconomics and Outcomes Research

Integrated Youth Services

Mental health and substance use

Not in education, employment, or training

Standard Errors

Youth Wellness Hubs Ontario

Unit SE. Bridging the gap: New opportunities for 16–18 year olds not in education, employment or training. London: HMSO; 1999.

Google Scholar  

Dea B, Glozier N, Purcell R, McGorry PD, Scott J, Feilds K-L, et al. A cross-sectional exploration of the clinical characteristics of disengaged (NEET) young people in primary mental healthcare. BMJ Open. 2014;4(12):e006378.

Article   Google Scholar  

Carcillo S. NEET youth in the aftermath of the crisis: Challenges and policies. Paris: OECD; 2015.

England PH. Local action on health inequalities: Reducing the number of young people not in employment, education or training. London: UCL Institute of Health Equity; 2014.

Dorsett R, Lucchino P. Explaining patterns in the school-to-work transition: an analysis using optimal matching. Adv Life Course Res. 2014;22:1–14.

Article   PubMed   Google Scholar  

Henderson J, Hawke L, Chaim G. Not in employment, education or training: mental health, substance use, and disengagement in a multi-sectoral sample of service-seeking Canadian youth. Child Youth Serv Rev. 2017;75:138–45.

Benjet C, Hernández-Montoya D, Borges G, Méndez E, Medina-Mora ME, Aguilar-Gaxiola S. Youth who neither study nor work: mental health, education and employment. Salud Publica Mex. 2012;54(4):410–7.

Genda Y. Jobless youths and the NEET problem in Japan. SSJ. 2007;10:23–40.

Statistics Canada. 2023. Table 37–10–0196–01 Percentage of 15-to 29-year-olds in education and not in education by labour force status, highest level of education attained, age group and sex. https://doi.org/10.25318/3710019601-eng

Percentage of 15-to 29-year-olds in education and not in education by labour force status, highest level of education attained, age group and sex. Statistics Canada; 2023. Available from: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3710019601 .

Hanushek EA, Woessmann L. The economic impacts of learning losses. 2020.

Yates S, Payne M. Not so NEET? A critique of the use of ‘NEET’ in setting targets for interventions with young people. J Youth Stud. 2006;9(3):329–44.

Hawke LD, Hayes E, Iyer S, Killackey E, Chinnery G, Gariépy G, et al. Youth-oriented outcomes of education, employment and training interventions for upcoming youth: protocol for a discrete choice experiment. Early Interv Psychiatry. 2021;15(4):942–8.

Canada S. Education Indicators in Canada: an international perspective. Ottawa: Statistics Canada; 2009.

Mahboubi PHA. Lives put on hold: the impact of the COVID-19 pandemic on Canada’s Youth. Toronto: The C.D. Howe Institute; 2022.

Cukier W, Mo G, Karajovic S, Blanchette S, Hassannezhad Z, Elmi M, et al. Labour Market Implications for Racialized Youth. Toronto: The Diversity Institute & Future Skills Centre; 2023.

Government of Canada. 13 ways to modernize youth employment in Canada - strategies for a new world of work. Ottawa: Government of Canada; 2017.

Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet. 2009;374(9683):86–9.

Paabort H, Flynn P, Beilmann M, Petrescu C. Policy responses to real world challenges associated with NEET youth: a scoping review. Front Sustain Cities. 2023;5:1154464.

Quintano C, Mazzocchi P, Rocca A. The determinants of Italian NEETs and the effects of the economic crisis. Genus. 2018;74(1):5.

Article   PubMed   PubMed Central   Google Scholar  

Ruesga S, Laxe F, Picatoste X. Sustainable development, poverty, and risk of exclusion for young people in the European Union: the case of NEETs. Sustainability. 2018;10:4708.

Robert S, Romanello L, Lesieur S, Kergoat V, Dutertre J, Ibanez G, et al. Effects of a systematically offered social and preventive medicine consultation on training and health attitudes of young people not in employment, education or training (NEETs): An interventional study in France. PLoS One. 2019;14(4):e0216226.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Haikkola L. Classed and gendered transitions in youth activation: the case of Finnish youth employment services. J Youth Stud. 2021;24(2):250–66. https://doi.org/10.1080/13676261.2020.1715358 .

Mawn L, Oliver EJ, Akhter N, Bambra CL, Torgerson C, Bridle C, et al. Are we failing young people not in employment, education or training (NEETs)? A systematic review and meta-analysis of re-engagement interventions. Syst Rev. 2017;6(1):16.

Research CIoH. IYS-Net aims to build a healthy future for Canada's youth. Ottawa: CIHR; 2023. Available from: https://cihr-irsc.gc.ca/e/53552.html .

Gariépy G, Danna SM, Hawke L, Henderson J, Iyer SN. The mental health of young people who are not in education, employment, or training: a systematic review and meta-analysis. Soc Psychiatry Psychiatr Epidemiol. 2022;57(6):1107–21.

Gariépy G, Iyer S. The mental health of young Canadians who are not working or in school. Can J Psychiatry. 2019;64(5):338–44.

Brownlie E, Chaim G, Heffernan O, Herzog T, Henderson J. Youth services system review: moving from knowledge gathering to implementation through collaboration, youth engagement, and exploring local community needs. Can J Commun Ment Health. 2017;36:1–17.

Darnay K, Hawke LD, Chaim G, Henderson J. INNOVATE Research: Youth Engagement Guidebook for Researchers. Toronto: Centre for Addiction and Mental Health; 2019.

Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ. 2000;320(7248):1530–3.

Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy. 2003;2(1):55–64.

PubMed   Google Scholar  

Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32(9):883–902.

Janssen EM, Hauber AB, Bridges JFP. Conducting a discrete-choice experiment study following recommendations for good research practices: an application for eliciting patient preferences for diabetes treatments. Value Health. 2018;21(1):59–68.

Mangham LJ, Hanson K, McPake B. How to do (or not to do) … designing a discrete choice experiment for application in a low-income country. Health Policy Plan. 2009;24(2):151–8.

Hawke LD, Thabane L, Iyer SN, Jaouich A, Reaume-Zimmer P, Henderson J. Service providers endorse integrated services model for youth with mental health and substance use challenges: findings from a discrete choice experiment. BMC Health Serv Res. 2021;21(1):1035.

Hawke LD, Thabane L, Wilkins L, Mathias S, Iyer S, Henderson J. Don’t forget the caregivers! A discrete choice experiment examining caregiver views of integrated youth services. Patient. 2021;14(6):791–802.

Henderson J, Hawke LD, Iyer SN, Hayes E, Darnay K, Mathias S, et al. Youth perspectives on integrated youth services: a discrete choice conjoint experiment. Can J Psychiatry. 2022;67(7):524–33.

Bridges JF, Hauber AB, Marshall D, Lloyd A, Prosser LA, Regier DA, et al. Conjoint analysis applications in health–a checklist: a report of the ISPOR good research practices for conjoint analysis task force. Value Health. 2011;14(4):403–13.

Zhu N, Hawke LD, Prebeg M, Hayes E, Darnay K, Iyer SN, et al. Intervention outcome preferences for youth who are out of work and out of school: a qualitative study. BMC Psychol. 2022;10(1):180.

Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3(2):77–101.

Software S. Lighthouse Studio. Utah: Sawtooth Software; 2022.

Software S, The CBC. Advanced design module technical paper. Sequim: Sawtooth Software; 2008.

Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–81.

Dennis ML, Feeney T, Stevens LH. Global Appraisal of Individual Needs-Short Screener (GAIN-SS): Administration and Scoring Manual for the GAINSS Version 2.0.1. Bloomington: Chestnut Health Systems; 2006.

Dennis ML, Chan YF, Funk RR. Development and validation of the GAIN Short Screener (GSS) for internalizing, externalizing and substance use disorders and crime/violence problems among adolescents and adults. Am J Addict. 2006;15(Suppl 1):80–91.

PubMed   PubMed Central   Google Scholar  

Canada S. Table 13–10–0763–01 Health characteristics of children and youth aged 1 to 17 years, Canadian Health Survey on Children and Youth 2019. 2019.

Heffernan OS, Herzog TM, Schiralli JE, Hawke LD, Chaim G, Henderson JL. Implementation of a youth-adult partnership model in youth mental health systems research: challenges and successes. Health Expect. 2017;20(6):1183–8.

StataCorp. Stata statistical software: release 16. 16th ed. TX: College Station: StataCorp LLC; 2019.

Bk O. Getting started with conjoint analysis: strategies for product design and pricing research. Madison: Research Publishers; 2006.

Taute F. Life skills training as part of employee assistance programs in South Africa. J Work Behav Health. 2008;22(4):97–106.

Comprehensive life skills framework. rights based and life cycle approach to building skills for empowerment. New York: UNICEF; 2012.

Egdell V, Beck V. A capability approach to understand the scarring effects of unemployment and job insecurity: developing the research agenda. Work Employ Soc. 2020;34(5):937–48.

Mojsoska-Blazevski N, Petreski M, Bojadziev MI. Youth survival in the labour market: employment scarring in three transition economies. The Economic and Labour Relations Review. 2017;28(2):312–31.

Bakshi AJ, Joshi J. The Interface between Positive Youth Development and Youth Career Development: New Avenues for Career Guidance Practice. In: Arulmani G, Bakshi AJ, Leong FTL, Watts AG, editors. Handbook of Career Development: International Perspectives. New York: Springer New York; 2014. p. 173–201.

Chapter   Google Scholar  

Hamilton SF, Hamilton MA. Development in youth enterprises. New Dir Youth Dev. 2012;2012(134):65–75, 9.

Mekinda MA. Support for career development in youth: program models and evaluations. New Dir Youth Dev. 2012;2012(134):45–54, 8.

Hamilton S, Hamilton MA. School, work, and emerging adulthood. In: Arnett J, Tanner JL, editors. Emerging adults in America: Coming of age in the 21st century. Washington, DC: American Psychological Association; 2006. p. 257–77.

Halpern R. Supporting vocationally oriented learning in the high school years: rationale, tasks, challenges. New Dir Youth Dev. 2012;2012(134):85–106 ( 10 ).

Bimrose J, Brown A. Mid-career progression and development: the role for career guidance and counseling. 2014. p. 203–22.

Pitchforth J, Fahy K, Ford T, Wolpert M, Viner RM, Hargreaves DS. Mental health and well-being trends among children and young people in the UK, 1995–2014: analysis of repeated cross-sectional national health surveys. Psychol Med. 2019;49(8):1275–85.

Haula T, Vaalavuo M. Mental health problems in youth and later receipt of social assistance: do parental resources matter? J Youth Stud. 2022;25(7):877–96.

Landstedt E, Coffey J, Nygren M. Mental health in young Australians: a longitudinal study. J Youth Stud. 2015;19:1–13.

Johnson MT, Johnson EA, Nettle D, Pickett KE. Designing trials of universal basic income for health impact: identifying interdisciplinary questions to address. J Public Health. 2021;44(2):408–16.

Wispelaere J, Stirton L. The many faces of universal basic income. The Political Quarterly. 2004;75:266–74.

Fromm E. The Psychological Aspects of the Guaranteed Income. 1966.

Garfinkel I, Huang CC. The Effects of a Basic Income Guarantee on Poverty and Income Distribution. In: Ackerman B, Alstott A, van Parijs P, editors. Redesigning Distribution: Basic income and stakeholder grants as cornerstones for real egalitarian capitalism (Real Utopias Project). New York: Verso Books; 2003. p143-174

Van Parijs P. The universal basic income: why Utopian Thinking matters, and how sociologists can contribute to It*. Polit Soc. 2013;41(2):171–82.

Forget E, Marando D, Surman T, Urban MC. Pilot lessons: how to design a basic income pilot project for Ontario. Mowat Publication. 2016;123:1-30.

Gibson M, Hearty W, Craig P. The public health effects of interventions similar to basic income: a scoping review. The Lancet Public Health. 2020;5:e165–76.

Johnson EA, Webster H, Morrison J, Thorold R, Mathers A, Nettle D, et al. What role do young people believe Universal Basic Income can play in supporting their mental health? J Youth Stud. 2023;1-20. https://doi.org/10.1080/13676261.2023.2256236

Fante-Coleman T, Jackson-Best F. Barriers and facilitators to accessing mental healthcare in Canada for Black youth: a scoping review. Adolescent Research Review. 2020;5(2):115–36.

Chiu M, Amartey A, Wang X, Kurdyak P. Ethnic differences in mental health status and service utilization: a population-based study in Ontario. Canada Can J Psychiatry. 2018;63(7):481–91.

Gajaria A, Guzder J, Rasasingham R. What’s race got to do with it? A proposed framework to address racism’s impacts on child and adolescent mental health in Canada. J Can Acad Child Adolesc Psychiatry. 2021;30(2):131–7.

Smye V, Browne AJ, Josewski V, Keith B, Mussell W. Social suffering: indigenous peoples’ experiences of accessing mental health and substance use services. Int J Environ Res Public Health. 2023;20(4):3288.

Cénat JM, Kogan C, Noorishad PG, Hajizadeh S, Dalexis RD, Ndengeyingoma A, et al. Prevalence and correlates of depression among Black individuals in Canada: the major role of everyday racial discrimination. Depress Anxiety. 2021;38(9):886–95.

Arnett J. Emerging adulthood: The winding road from the late teens through the twenties. New York: Oxford University Press; 2004.

Messersmith EE, Garrett JL, Davis-Kean PE, Malanchuk O, Eccles JS. Career development from adolescence through emerging adulthood insights from information technology occupations. J Adolesc Res. 2008;23(2):206–27.

Bradley SMG, Paniagua MN. Spatial variations and clustering in the rates of youth unemployment and NEET. Lancaster: Lancaster University Management School, Economics Department; 2019.

Caroleo F, Rocca A, Mazzocchi P, Quintano C. Being NEET in Europe before and after the economic crisis: an analysis of the micro and macro determinants. Soc Indic Res. 2020;149:1–34.

The Labour Make Information Council. Finding Their Path: What Youth Not In Employment, Education or Training (NEET) Want. Ottawa: The Labour Make Information Council; 2019.

Hooley T. Career education in Primary School. Education Services Australia: Melbourne; 2021.

UNESCO. Strategy for Technical and Vocational Education and Training. Paris: UNESCO; 2016.

McMahon G, Douglas A, Casey K, Ahern E. Disruption to well-being activities and depressive symptoms during the COVID-19 pandemic: the mediational role of social connectedness and rumination. J Affect Disord. 2022;309:274–81.

McNamara L. School recess and pandemic recovery efforts: ensuring a climate that supports positive social connection and meaningful play. FACETS. 2021;6:1814–30.

Cardenas MC, Bustos SS, Chakraborty R. A “parallel pandemic”: the psychosocial burden of COVID-19 in children and adolescents. Acta Paediatr. 2020;109(11):2187–8.

Almeida ILL, Rego JF, Teixeira ACG, Moreira MR. Social isolation and its impact on child and adolescent development: a systematic review. Rev Paul Pediatr. 2021;40:e2020385.

Saurabh K, Ranjan S. Compliance and psychological impact of quarantine in children and adolescents due to covid-19 pandemic. Indian J Pediatr. 2020;87(7):532–6.

Racine N, McArthur BA, Cooke JE, Eirich R, Zhu J, Madigan S. Global Prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: a meta-analysis. JAMA Pediatr. 2021;175(11):1142–50.

Blackwell CK, Mansolf M, Sherlock P, Ganiban J, Hofheimer JA, Barone CJ, et al. Youth well-being during the COVID-19 pandemic. Pediatrics. 2022;149(4):e2021054754.

Ezeoke OM, Kanaley MK, Brown DA, Negris OR, Das R, Lombard LS, et al. The impact of COVID-19 on adolescent wellness in Chicago. Child Care Health Dev. 2022;48(6):886–90.

Tombeau Cost K, Crosbie J, Anagnostou E, Charach A, Monga S, Kelley E, et al. Mostly worse, occasionally better: impact of COVID-19 pandemic on the mental health of Canadian children and adolescents. Eur Child Adolesc Psychiatry. 2022;31:1–14.

Foxcroft DR, Tsertsvadze A. Universal school-based prevention programs for alcohol misuse in young people. Cochrane Database Syst Rev. 2011;5:Cd009113.

Botvin GJ, Baker E, Dusenbury L, Botvin EM, Diaz T. Long-term follow-up results of a randomized drug abuse prevention trial in a white middle-class population. JAMA. 1995;273(14):1106–12.

Article   CAS   PubMed   Google Scholar  

Bond GR. Principles of the individual placement and support model: empirical support. Psychiatr Rehabil J. 1998;22(1):11–23.

Rinaldi M, Perkins R, Glynn E, Montibeller T, Clenaghan M, Rutherford J. Individual placement and support: from research to practice. Adv Psychiatr Treat. 2008;14(1):50–60.

Ontario YWH. CAMH, YWHO, ACCESS Open Minds and Foundry launch first-of-its kind initiative to help young people with mental health challenges find employment: YWHO; 2021. Available from: https://youthhubs.ca/en/camh-ywho-access-open-minds-and-foundry-launch-first-of-its-kind-initiative-to-help-young-people-with-mental-health-challenges-find-employment/ .

Powell BJ, Proctor EK, Glass JE. A systematic review of strategies for implementing empirically supported mental health interventions. Res Soc Work Pract. 2014;24(2):192–212.

Download references

Acknowledgements

We would like to thank the participants for their participation in this study. We would like to thank members of the Centre for Addiction and Mental Health’s Youth Engagement Initiative for their support of this study.

This research was funded by the Social Sciences and Humanities Research Council (SSHRC) (435–2019-0393).

Author information

Authors and affiliations.

Centre for Addiction and Mental Health, 80 Workman Way, Toronto, ON, M6J 1H4, Canada

Meaghen Quinlan-Davidson, Mahalia Dixon, Lisa D. Hawke, Matthew Prebeg & J. L. Henderson

Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

Meaghen Quinlan-Davidson

Orygen, Parkville, VIC, Australia

Gina Chinnery

Department of Psychiatry, University of Toronto, Toronto, ON, Canada

Lisa D. Hawke & J. L. Henderson

Department of Psychiatry, McGill University, Montreal, QC, Canada

Srividya Iyer

Douglas Research Centre, Montreal, QC, Canada

Batshaw Youth and Family Centres, Montreal, QC, Canada

Katherine Moxness

Department of Psychiatry, The Hospital for Sick Children, Toronto, ON, Canada

Matthew Prebeg

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada

Lehana Thabane

St Joseph’s Healthcare Hamilton, Hamilton, ON, Canada

Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa

You can also search for this author in PubMed   Google Scholar

Contributions

MQD contributed to designing the research question and conducted the analysis, interpretation of the data, and drafted the manuscript. All authors read and approved the final manuscript. JLH contributed to designing the research, oversaw the conduct of the study, interpreted the data, reviewed the manuscript and provided study leadership; JLH is the overall guarantor of the work.

Corresponding author

Correspondence to J. L. Henderson .

Ethics declarations

Ethics approval and consent to participate.

The study was approved by the Research Ethics Board of the Centre for Addiction and Mental Health (124/2019). Informed consent was obtained from all participants in this study.

Consent for publication

Consent was obtained directly from participants.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary material 1. final list of discrete choice experiment attributes and levels., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Quinlan-Davidson, M., Dixon, M., Chinnery, G. et al. Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada. BMC Public Health 24 , 1402 (2024). https://doi.org/10.1186/s12889-024-18877-0

Download citation

Received : 29 November 2023

Accepted : 17 May 2024

Published : 27 May 2024

DOI : https://doi.org/10.1186/s12889-024-18877-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Youth mental health and substance use
• Youth not in education
• Or training
• Service preferences
• Discrete choice experiment

BMC Public Health

ISSN: 1471-2458

• General enquiries: [email protected]