research questions on youth unemployment

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• v.18(1); 2022 Mar

Interventions to increase youth employment: An evidence and gap map

Robert apunyo.

1 Africa Centre for Systematic Reviews and Knowledge Translation, Makerere University College of Health Sciences, Makerere University College of Health Sciences, Kampala Uganda

Howard White

2 Campbell Collaboration, New Delhi India

Caroline Otike

Thomas katairo, susana puerto.

3 International Labour Organization, Geneva Switzerland

Drew Gardiner

Alison annet kinengyere.

4 Sir Albert Cook Medical Library, Makerere University College of Health Sciences, Kampala Uganda

5 International Initiative for Impact Evaluation, London UK

Ashrita Saran

Ekwaro a. obuku, associated data.

Globally, 13% of the youth are not in education, employment or training (NEET). Moreover, this persistent problem has been exacerbated by the shock of Covid‐19 pandemic. More youth from disadvantaged backgrounds are likely unemployed than those from better off backgrounds. Thus, the need for increased use of evidence in the design and implementation of youth employment interventions to increase effectiveness and sustainability of interventions and outcomes. Evidence and gap maps (EGMs) can promote evidence‐based decision making by guiding policy makers, development partners and researchers to areas with good bodies of evidence and those with little or no evidence. The scope of the Youth Employment EGM is global. The map covers all youth aged 15–35 years. The three broad intervention categories included in the EGM are: strengthening training and education systems, enhancing labour market and, transforming financial sector markets. There are five outcome categories: education and skills; entrepreneurship; employment; welfare and economic outcomes. The EGM contains impact evaluations of interventions implemented to increase youth employment and systematic reviews of such single studies, published or made available between 2000 and 2019.

The primary objective was to catalogue impact evaluations and systematic reviews on youth employment interventions to improve discoverability of evidence by decision makers, development patterners and researchers, so as to promote evidence‐based decision making in programming and implementation of youth employment initiatives.

Search Methods

Twenty databases and websites were searched using a validated search strategy. Additional searches included searching within 21 systematic reviews, snowballing 20 most recent studies and citation tracking of 10 most recent studies included in the EGM.

Selection Criteria

The study selection criteria followed the PICOS approach of population, intervention, relevant comparison groups, outcomes and study design. Additional criterion is; study publication or availability period of between 2000 and 2021. Only impact evaluations and systematic reviews that included impact evaluations were selected.

Data Collection and Analysis

A total of 14,511 studies were uploaded in EPPI Reviewer 4 software, upon which 399 were selected using the criteria provided above. Coding of data took place in EPPI Reviewer basing on predefined codes. The unit of analysis for the report is individual studies where every entry represents a combination of interventions and outcomes.

Main Results

Overall, 399 studies (21 systematic reviews and 378 impact evaluations) are included in the EGM. Impact evaluations ( n  = 378) are much more than the systematic reviews ( n  = 21). Most impact evaluations are experimental studies ( n  = 177), followed by non‐experimental matching ( n  = 167) and other regression designs ( n  = 35). Experimental studies were mostly conducted in both Lower‐income countries and Lower Middle Income countries while non‐experimental study designs are the most common in both High Income and Upper Middle Income countries. Most evidence is from low quality impact evaluations (71.2%) while majority of systematic reviews (71.4% of 21) are of medium and high quality rating. The area saturated with most evidence is the intervention category of ‘training’, while the underrepresented are three main intervention sub‐categories: information services; decent work policies and; entrepreneurship promotion and financing. Older youth, youth in fragility, conflict and violence contexts, or humanitarian settings, or ethnic minorities or those with criminal backgrounds are least studied.

Conclusions

The Youth Employment EGM identifies trends in evidence notably the following:

• Most evidence is from high‐income countries, an indication of the relationship between a country's income status and research productivity.
• The most common study designs are experimental.
• Most of the evidence is of low quality. This finding serves to alert researchers, practitioners and policy makers that more rigorous work is needed to inform youth employment interventions.

Blending of interventions is practiced. While this could be an indication that blended intervention could be offering better outcomes, this remains an area with a research gap.

1. PLAIN LANGUAGE SUMMARY

1.1. the evidence for youth employment interventions is unevenly distributed by geography and population sub‐groups, and much of it is of poor quality.

There is considerable evidence on training‐based youth employment interventions across all outcomes included in the evidence and gap map (EGM), but scarce evidence in the ‘information services’ and ‘decent work policies’ categories of interventions. Much of the evidence is of low quality.

1.1.1. What is this EGM about?

Globally, approximately 13% of youth are ‘not in education, employment or training’ (NEET). This situation is aggravated by the shock of the Covid‐19 pandemic. This EGM shows the available evidence on youth employment from systematic reviews and impact evaluations.

Decisionmakers and implementers across all countries should use context‐specific evidence to increase effectiveness and sustainability of youth employment interventions and outcomes.

What is the aim of this EGM?

The aim of this EGM is to catalogue all the available evidence from impact evaluations and systematic reviews to increase youth employment across all countries.

1.1.2. What studies are included?

The EGM includes systematic reviews and impact evaluations that assess effectiveness of youth employment interventions. The studies report interventions for young people aged 15‐35 years. They also report intended or unintended or adverse outcomes. The EGM contains 399 studies: 21 systematic reviews and 378 impact evaluations. The included impact evaluations are predominantly experimental studies.

1.1.3. What are the main findings of this evidence and gap map?

There is uneven distribution of studies across intervention categories. The most frequent intervention category is ‘training’, reported by 283 out of 399 studies. It is followed by the ‘support to employment’ intervention domain with 182 out of 399 studies. There are relatively few studies for ‘information services’, ‘entrepreneurship promotion’ and ‘financing interventions’.

The most dominant outcomes are related to employment, such as ‘earnings & salary’ and ‘employment status & duration’, reported in 345 of the 399 studies. This is followed by ‘welfare’ (121) and ‘education and skills’ (97). There are few studies for ‘entrepreneurship’ outcomes.

At least 37% of the studies in the EGM combined multiple categories of interventions. The ‘training’ and the ‘support to employment’ intervention categories are most evident in different intervention combinations.

There is a general lack of high quality evidence given that the majority (73.4%) of impact evaluations have a low confidence quality rating. Three major flaws in impact evaluation reports are that:

• 1. many young people drop out of the interventions or authors fail to report that information
• 2. implementers fail to take into account other factors with the potential to affect interventions and outcomes
• 3. most studies have many variations in the characteristics of youth, like education level and age as a baseline or at the beginning of the interventions.

1.1.4. What do the findings of the map mean?

Mastercard Foundation and the Youth Futures Foundation plan to use this EGM to promote innovation and knowledge‐sharing, and to inform their funding decisions on programmes and systematic reviews.

While the evidence base is relatively large, it is weak when it comes to disadvantaged populations of youth as well as interventions under ‘decent work policies’, which include labour standards and accountability systems, and ‘information services’, such as value chain development and labour market information.

The quality of systematic reviews and impact evaluations requires improvement. More studies from low‐income countries are needed, especially on disadvantaged youth.

1.1.5. How up‐to‐date is this EGM?

The authors searched for studies published up to January 2020.

2. BACKGROUND

Globally, the population of young people is estimated at 1.2 billion, which is 16% of the world's population (United Nations,  2018 ). Worldwide, approximately 13% of young men and 30% of young women were classified as not in education, employment or training (NEET) in 2018. Youth who are NEET are almost excluded from the labour market because they are not gaining any skills to prepare them for future employment. Moreover, in the long run, a high NEET rate undermines the growth of a national economy over a sustained period (International Labour Organization [ILO],  2019a ).

In 2017, the global rate of youth unemployment was estimated at 13%, though Sub‐Saharan Africa and, Latin America and the Caribbean had the most disturbing situations. While youth unemployment in Sub‐Saharan Africa was 11.7%, some countries experienced extraordinarily high rates of youth unemployment. For instance, in South Africa youth unemployment rate was about 57.4%, and the highest in the region. In Lesotho, Mozambique and Namibia youth unemployment rates were estimated at 38.5%, 42.7% and 45.5%, respectively. Similarly, in Latin America and the Caribbean, Argentina, and Brazil registered highest youth unemployment rates of 24.7% and 30.5% respectively, pushing the regional average to 18.9%, in 2017 (United Nations,  2018 ).

Regions of the world often have some unique causes of youth unemployment. For instance, young women are generally discriminately unemployed than young men, with the situation exceedingly high in Middle East. However, this phenomenon is flipped in Western Europe and Eastern Asia where unemployment is higher among young men than young women. In the case of Eastern Asia, the situation is driven largely by China's inclusion of young women in its expanding manufacturing sector (ILO,  2019a ). In Middle East and North Africa, the higher unemployment of young women than men can be attributed to the conservative social norms that may determine opportunities considered appropriate for women. So, women may mainly seek out opportunities in the public sector and avoid jobs in industries dominated by men (ILO,  2019b ). In Sub‐Saharan Africa, one of the major causes of youth unemployment stems from the weak education systems leading to huge skills mismatch between skills provided by the education systems and labour market needs. In addition, Sub‐Saharan Africa, has experienced a bulging young population despite the small sizes of economies (The African Capacity Building Foundation,  2017 ).

Moreover, most of the world and particularly Africa is experiencing high growth in youth population, where the situation is envisaged to increase the continent's labour force to 375 million by 2030. The implication is that by 2035, there will be more young people in Africa available for the labour market each year than in the rest of the world combined (Mastercard Foundation,  2019 ). At a macro level, some drivers to youth unemployment include huge increases in labour supply, low aggregate demand for labour and, a mismatch between economic growth path and skills requirements majorly as a result of training deficits (De Lannoy et al.,  2018 ). Unfortunately, the predicament of youth has been exacerbated by the shock of Covid‐19 pandemic, which is worsening employment, livelihoods and poverty around the world.

Employment and economic empowerment are essential components of a strong foundation for all youth regardless of their gender identity and disadvantaged status. So, having decent work is crucial for young people and their future but it also has multiplier effects on local communities and the world as a whole (United Nations,  2018 ). Decent work refers to a composition of the aspirations of people in their working lives. It involves opportunities for work that are productive and deliver fair income, security in the workplace and social protection for families, better prospects for personal development and social integration (International Labour Organization,  2019a ).

Addressing youth unemployment requires investment in job creation initiatives for the ever‐increasing population and tackling the skills mismatch problem contributing to the low school‐to‐work transition situation. Clearly, efforts to stimulate youth employment require diversification of employment sector through investment in education, skills training, bolstering small and medium‐sized enterprises and, microcredit provision (United Nations,  2018 ).

The UNDP Sustainable Development Goals (SDG) 8 and SDG 10 seek to reduce youth unemployment and inequality of all forms respectively (United Nations,  2019a ). The SDG 8 sets three targets for youth employment. Target 8.5, expects countries, by 2030 to achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value. Target 8.6, expects countries, by 2020 to substantially reduce the proportion of youth not in employment, education or training and, target 8.b, by 2020, develop and operationalise a global strategy for youth employment and implement the Global Jobs Pact of the ILO (United Nations,  2019b ). The SDG 10, ‘Reduce Inequality within and among countries’ in Target 10.2, expects countries by 2030, to achieve empowerment and promotion of social, economic and political inclusion of all people irrespective of age, sex disability, race, ethnicity and economic status (United Nations,  2019c ).

Consequently, global, regional and country‐based initiatives have been put in place to deal with youth unemployment. For instance, the United Nations Youth Strategy has been developed with several priorities of which the third talks about prompting economic empowerment through decent work, by supporting young people's greater access to decent work and productive employment (United Nations, n.d.). Similarly, Mastercard Foundation through its ‘Young Africa Works Strategy’ has set out an ambitious goal to enable 30 million youth in Africa to find jobs by 2030 partly through the promotion of sharing evidence‐based knowledge and innovation with stakeholders; supporting use of technology to drive impact and scale and; empowering young women (Mastercard Foundation,  2019 ).

To respond to the above, there is an increased need to invest in making available evidence on youth employment interventions discoverable by decision makers, development partners, researchers and other stakeholders. Evidence and gap maps (EGMs) can contribute to achieving this by identifying areas in which there are good bodies of synthesised knowledge to inform policy, and those areas in which there is little or no evidence to guide commissioning of coordinated research programmes.

2.1. Intervention: All interventions that increase youth employment

All interventions or programmes or projects aimed at helping youth to find and sustain employment are the focus of this EGM. This EGM broadly categories the interventions into three domains: strengthening training and education systems, enhancing labour market and, transforming financial sector markets.

Strengthening training and education systems, includes the conventional education systems and various forms of training such as direct training often provided to the unemployed. The other types of training namely; up‐skilling and retraining/re‐skilling normally target youth who are already in employment, to ensure that their skill relevant to the changing demands of employers.

Enhancing labour markets category of interventions includes: decent work policies, support to employment services, and Information services. Decent work polices generally regulate the relationship between employers and employees in the employment environment through application of labour standards, regulations and accountability systems. Support to employment interventions are generally meant to help youth find jobs through provision of jobs via initiatives like programmes for overseas employment and public works programmes.

Transforming financial sector markets based interventions focus on entrepreneurship promotion and financing. This category of interventions tend to be popular for targeting disadvantaged youth especially those with low levels of education. As Datta et al. ( 2018 ) observe, labour market opportunities are significantly influenced by the reverence of skills for the existing job market. An intervention like self‐help or financing groups tends to be dominant among youth with less education and training. Self‐help groups are small groups that save a certain amount of money on weekly or monthly basis and issue loans to members out of their collective savings (Flynn,  2013 ).

The focus of this EGM is therefore broad, for example covering economic and welfare dimensions. So, outcomes of the various interventions go beyond the youth getting employment which is often the primary motivation of implementing youth employment programmes.

2.2. Scope of the youth employment EGM 1

Geographically, this EGM is global in coverage, considering all countries regardless of their level of development. That means all world geographical regions and the World Bank country classification by income have been covered. The geographical regions are: Sub‐Saharan Africa, Latin America and Caribbean, East Asia and Pacific, Middle East and North Africa, South Asia, Europe and Central Asia, and North America. The World Bank country classification by income includes: low‐income countries, lower‐middle income, upper middle income, and high‐income countries (World Bank,  2020 ).

By population, the map covered all young women and men aged 15–35 years from all countries. The three broad intervention categories include in the EGM are: strengthening training and education systems, enhancing labour market and, transforming financial sector markets.

The outcomes fall into five categories: education and skills, entrepreneurship, employment, welfare and economic.

In terms of evidence, the map included impact evaluations of interventions aimed at increasing youth employment and systematic reviews of such single studies, published or made available between January 2000 and December 2019.

2.3. Why it is important to develop the EGM

EGMs guide policy makers, development partners and researchers to available evidence to inform programme design and implementation of development interventions. Decision‐makers and researchers often lack awareness about the extent of evidence base, so can maps help in increasing the discoverability, and thus use of that evidence for evidence‐informed decision making in international development policy and practice. The immediate potential application of the youth employment EGM is its contribution to the implementation of the Mastercard Foundation's strategy titled, ‘Africa Works—Mastercard Foundation Strategy 2018–2030’. The goal of the strategy is to enable 30 million youth in Africa to secure dignified and fulfilling employment by 2030. Sharing of evidence‐based knowledge and innovation with stakeholders is stated as one way through which the strategy can be implemented (Mastercard Foundation,  2019 ). So, the youth employment EGM is a useful resource in that regard, as it can guide policy makers, development partners and researchers to relevant available evidence on youth employment interventions.

For researchers, the youth employment EGM has identified research gaps for new primary research and new synthesis. This can inform strategic policy‐oriented approach in commissioning relevant and coordinated research programmes (White et al.,  2020 ).

Before the production of this EGM, two pre‐existing evidence gap maps on youth employment‐related interventions were inadequate in a number of ways. For instance, each of those maps had a narrow scope (geographical, study publication period and, area/interventions and outcomes). The maps were limited to low‐ and middle‐income countries and publication period of 1990–2015. Moreover, development interventions are often best appreciated and understood in a contemporary context (Mawn et al.,  2017 ). Further, both maps did not include economic outcomes while one did not include welfare outcomes. In addition, at least one of the maps suffered methodological limitation stemming, whereby the study search strategy and screening of studies were conducted by individuals rather in pairs for validation. This is stated to have led to some studies being missed. Never the less, the two pre‐existing maps provided a basis for the development of the current youth employment EGM, with a broader focus (geographical, study publication period and, area/interventions and outcomes). Methodological limitations were also avoided by better planning for sufficient time and human resources.

2.4. Objectives

The research question guiding the production of the youth employment EGM was stated as follows: What is the nature and extent of the evidence base of impact evaluations and systematic reviews on youth employment programmes in the world?

The primary objective was to catalogue impact evaluations and systematic reviews on youth employment interventions to enhance discoverability of evidence by decision makers, development patterners and researchers, so as to promote evidence‐based decision making in programming and delivery of youth employment initiatives. This EGM was considered a primary input into the implementation of Mastercard Foundation's strategy titled ‘Africa Works: Mastercard Foundation Strategy 2018–2030’, which pointed out sharing of evidence‐based knowledge and innovation with stakeholders as a key strategy to be used (Mastercard Foundation,  2019 ). The time frame for the development of the youth EGM ran from the last quarter of 2019 to December 2020.

The five secondary objectives were:

• (i) To construct a framework for the classification of youth employment effectiveness studies.
• (ii) To identify available evidence, and clusters of evidence on effectiveness studies (impact evaluations and systematic reviews of impact evaluations) on youth employment interventions.
• (iii) To create a map of youth employment effectiveness studies equipped with an appealing user‐friendly web‐based search content visualisation using interactive mapping software.
• (iv) To produce a narrative report of the youth employment EGM.
• (v) To disseminate the EGM to users to increase awareness to support evidence‐informed decision‐making across countries.

2.5. Existing EGMs on youth employment interventions

Before the production of this EGM, there were two evidence gap maps on youth employment. The two EGMs were reviewed to inform the development of the framework for this EGM. The descriptions provided below for each of the maps pointed out associated strengthens and limitations which were of scope and methodological nature.

The first evidence gap map was the ‘Youth and Transferable Skills evidence gap map’. The map included 98 studies and is accessible at https://gapmaps.3ieimpact.org/evidence-maps/youth-transferable-skills-evidence-gap-map (Rankin et al.,  2015 ). The map included studies published or made available between 1990 and 2015. The included studies were searched from January to February of 2015. The map is restricted to low‐ and middle‐income countries. In terms of youth employment as a development area, the map has a narrow focus, covering only transferable skills interventions and associated outcomes. For, instance economic outcomes are not covered, yet these set of outcomes for example ‘cost effectiveness’ often have important bearing on the implementation of programmes. The map also suffered methodological problems due to time constraints. It was mentioned that the use of a single specialist to supervise and compile the search work as well as reliance on one person to screen studies on titles and abstracts, could have led to some studies being missed (Rankin et al.,  2015 ). The map had an accompanying published narrative report which provided detailed information on areas such as methodology and results, which is a strength. In addition, this map used an extensive study search strategy covering 34 websites and 4 research registries.

The second map was the ‘Youth employment evidence gap map’, produced by International Labour Organization. The map included 113 studies and is available at: https://gapmaps.3ieimpact.org/evidencemaps/youth-employment-evidence-gap-map (International Labour Organization,  2018 ). The map was restricted to low‐ and middle‐income countries and included studies published or made available between 1990 and 2014 which were contained in a systematic review by (Kluve et al.,  2017 ), titled ‘Interventions to improve the labour market outcomes of youth: A systematic review of training, entrepreneurship promotion, employment services and subsidised employment interventions’. The map did not include economic and welfare outcomes. In addition, a narrative report accompanying the EGM was not accessible which is an indication of its absence. Although, a narrative report is an optional product in the production of an EGM (Saran & White,  2018 ), the absence or lack of access to such a document denies users vital information.

The above maps provided a basis for the development of the current youth employment EGM, with a broader focus (geographical, study publication period and, area/interventions and outcomes).

3.1. Definition and purpose of EGMs

Saran and White ( 2018 ), define an EGM as ‘a systematic [visual] presentation of the availability of relevant evidence for a particular policy domain. The evidence is identified by a search following a prespecified, published search protocol. The map may be accompanied by a descriptive report to summarise the evidence for stakeholders such as researchers, research commissioners, policy makers, and practitioners’ (p. 11). It's important to note that EGMs summarise what evidence exists but not what the evidence says. For instance, an EGM catalogues studies in a particular policy domain in terms of outcomes and interventions but does not say the magnitude of outcomes reported by the studies.

EGMs are useful in many ways. First, they guide policy makers, development partners and researchers to relevant available evidence to inform the design and implementation of development interventions. Decision‐makers and researchers often lack awareness about the extent of evidence base, so maps help in increasing the discoverability, and thus use of that evidence for evidence‐informed decision making in international development policy and practice (White et al.,  2020 ). Second, they create awareness among implementing agencies where relevant evidence for their interventions is lacking, so that they can act accordingly by collecting evidence for the intervention they are supporting. Finally, maps identify research gaps for new primary research and new synthesis. This can inform strategic policy‐oriented approach in commissioning relevant and coordinated research programmes (White et al.,  2020 ).

3.2. Framework development and scope

Development of the framework is considered the first and most important part in the development of an evidence map (White et al.,  2020 ). The framework was therefore the first activity undertaken in the production of this EGM, in the last quarter of 2019. The framework provided the structure or layout of the EGM and was a primary resource in the development of the search strategy, screening and coding tools. A typical framework for an effectiveness EGM refers to the matrix of interventions (in rows) and outcomes (in columns), developed through a review of existing maps on a related policy domain, policy literature and consultations with stakeholders (Rankin et al.,  2015 ).

The development of the framework for this EGM was achieved through a consultative process involving authors of the map, Mastercard Foundation and stakeholders in the youth employment area. The consultative approach helped the capture of a wide range conceptual and contextual positions of Mastercard Foundation and stakeholders involved in youth employment programming and implementation. The steps followed are described below.

First, using a workshop approach in Uganda, the EGM authors constructed a draft framework by brainstorming and reviewing existing EGMs that included impact evaluations of interventions to improve youth labour market outcomes and systematic reviews of such single studies. Dr. Howard White who is an expert in development evaluation and Dr. Ekwaro A. Obuku, an expert in evidence synthesis, led this activity.

Second, the draft framework was shared with Mastercard foundation to capture their input. It's important to note that Mastercard Foundation (funder) was engaged all through the project life by ensuring that they reviewed study tools (study screening tool, coding sheet and, a dictionary of outcomes and interventions), that were developed by the EGM authors.

Finally, the EGM authors led by Dr. Saran Ashrita, a methods expert had a training workshop in Uganda which, incorporate Mastercard Foundation's feedback on the draft framework into the final framework. Additional activities that were undertaken in this stage include drafting of the coding sheet, definitions of interventions and outcomes to guide coding of studies. Training of people who coded studies using EPPI Reviewer 4, a web‐based software program for managing and analysing data in literature reviews, was also carried. The framework was also piloted with about 100 studies.

3.3. Stakeholder engagement

Meetings and workshops to engage stakeholders were planned to be conducted in Uganda, in the last quarter of 2019. The target stakeholders were relevant officials in Uganda from government ministries, departments, agencies, private sector agencies, civil society organisations, vocational training institutes, international development agencies as well as academia. Unfortunately, the COVID‐19 global lockdown constrained stakeholder engagement.

However, keeping in touch with Mastercard Foundation policy leads and the literature were informative in arriving at reasonable priority list of the interventions and outcomes for this EGM.

3.4. How youth employment interventions are supposed to work

This section provides theoretical pass‐ways in which youth employment interventions included in this EGM may put more youth into employment through both job seeking or entrepreneurship. The principal assumption of participating in youth employment interventions is that youth can acquire relevant skills for labour market and, support (financial, guidance and information access to facilitate employment. Participation in youth employment interventions is expected to address constraints to youth employment, some of which, Datta et al. ( 2018 ), identifies, as summarised in (Box  1 ).

Constraints to youth employment

• Gaps and mismatches in technical, cognitive and socioemotional skills that results from deficient education and training systems.
• Asymmetric information, whereby youth often lack information due to information gaps, little or no work experience and limited access to social networks.
• Lack of assets and limited access to credit; which excludes young people from engaging in productive self‐employment opportunities especially among rural youth and economies where agriculture is the most dominant productive activity.
• Regulatory constraints to hiring youth. Decent work policies can deter employers from hiring young new employees. For instance, employee protection legislation and mandatory social benefits may discourage hiring first‐time job seekers who may be higher risk.
• Limited access to credit and lack of assets. Young people usually have low savings, and limited assets for securing loans from formal financial institutions. These constraints exclude youth from financial inclusion and becoming entrepreneurs.

The Theory of change (ToC) provided in (Supporting Information Annex  8 ; Figure  17 ) highlights some of the pathways though which the interventions are expected to lead to outcomes. This approach identifies connections between interventions and outcomes that are contained in the Youth Employment EGM which is the subject of this report. The interventions and outcomes of the EGM later revisited in the methods section in Tables  1 and  2 , respectively, are grouped into categories and sub‐categories as well as examples. The three intervention categories are: strengthening training and education systems; enhancing labour markets and; transforming financial sector markets. The following paragraphs attempt to show the linkage between intervention sub‐categories and outcomes, with supporting literature.

Intervention categories, sub‐categories and examples/descriptions

Outcome categories and sub‐categories

First, the ‘strengthening training and education systems’ category of interventions equip youth with skills which are necessary for increasing employment opportunities in the labour market (United Nations,  2018 ). Actually globally, skills training is the most widely implemented set of labour market interventions for youth (Kluve et al.,  2017 ). Training programmes often equip youth with skills required by employers. However, training and formal education interventions often do not reach marginalised youth especially young women, indigenous groups, youth with disabilities. This leaves such youth without the skills needed to realise their potential (United Nations,  2018 ). Skills attained through training can be categorised into technical skills, business skills and life or soft skills (Kluve et al.,  2017 ). Technical skills are achieved by individuals attending training initiatives such as technical and vocational education and training (TVET) and, internship and apprenticeship. Business skills training is normally provided to increase entrepreneurial activities among youth (Kluve et al.,  2017 ). In the case of life skills training, the objective is to strengthen trainees' self‐esteem and work habits to help them achieve the goals set by employers (Lippman et al.,  2015 ). The ‘training and skills development interventions results chain’, diagnostic framework documented by Kluve et al. ( 2017 ) is a useful resource for mapping the relationship between training and education intervention and potential outcomes. Participation in training and education is expected to create mainly three categories of outcome: (i) employment outcomes (i.e., change in employment status, earning and salary); (ii) education and skills outcomes such as acquisition of technical and vocational skills.

Second, the ‘enhancing labour market’ category of interventions are grouped into three sub‐categories including: support to employment; enhancing labour market; and transforming financial sector market. Support to employment interventions are generally meant to help youth find jobs through provision of jobs via initiatives like programmes for overseas employment and public works programmes. Employment services also facilitate the youth in the process of finding jobs through career guidance and supporting employee mobility needs. Decent work polices generally regulate the relationship between employers and employees in the employment environment through application of labour standards, regulations and accountability systems. Another enhancer of labour markets, is provision of a wide range information about the labour market and associated services. Potential outcomes from this category of interventions tend to be largely associated with employment outcomes such as job quality and change in employment status.

Finally, ‘transforming financial sector’ category of interventions focus on entrepreneurship promotion and financing. The category of interventions can be considered the most popular for targeting disadvantaged youth especially those excluded from training and education programmes. Datta et al. ( 2018 ) note that labour market opportunities are significantly influenced by the reverence of skills for the existing job market. An intervention like self‐help or financing groups tends to be dominant among youth with less education and training. Self‐help groups are small groups that save a certain amount of money on weekly or monthly basis and issue loans to members out of their collective savings (Flynn,  2013 ). The category of interventions (transforming financial sector) tend to be mainly associated with welfare outcomes and employment outcomes than economic outcomes.

3.5. Dimensions

The primary dimensions of the map are interventions (in rows) and outcomes (in columns), presented in a matrix. There are three broad intervention categories, each with subcategories. The intervention categories include: Strengthening training and education systems; enhancing labour markets; and transforming financial sector markets. The outcomes are arranged in a typology of five categories: education and skills, entrepreneurship, employment, welfare, and economic. The interventions and outcomes are later described in detail in Tables  1 and  2 , respectively.

The secondary dimensions of the map are the taskbar menu (Filters, About and View records), which help a user to navigate the map. A detailed description of the taskbar menu is provided later below (Figure  4 ).

Clicking on a cell gives a list of studies. Source : https://youthfuturesfoundation.org/our-work/identify/evidence-and-gap-map/

3.6. Types of study design

Only studies with the following study designs were included in the EGM: Experimental designs, Nonexperimental matching designs, Regression‐based approaches and, Systematic reviews.

The selected study designs are the appropriate designs for estimating effectiveness of program interventions. The designs (experimental and nonexperimental) are for conducting impact evaluations of development interventions. Examples of the following non‐evaluation‐based study designs were excluded from the EGM: ethnography, case control and cross‐sectional. For instance, the focus of a cross‐sectional study is limited to data from a particular population with variables of interest, at a given point in time. Cross‐sectional studies are observational in nature and not causal implying that they are not applicable for determining the effect of an intervention.

• (a) Experimental designs

Experimental designs fall into two types, namely randomised controlled trials (RCTs) and natural experiments.

• (i) RCTs: A typical RCT design involves randomising study participants into two or groups (an experimental/treatment/intervention group and control group) in which the researcher introduces an intervention and measures its impact on the dependent variable at least two times namely pre‐ and posttest measurements (White & Sabarwal,  2014 ). A key weakness of an RCT study design is that it suffers from missing outcomes resulting from changes that occur postrandomisation of study participants. For instance, withdrawal of subjects from the study and noncompliance with established study protocols or guidelines would lead to missing outcomes (White & Sabarwal,  2014 ). Therefore, application of Intention‐to‐treat (ITT) analysis in RCTs attempts to address this problem by including in the analysis every subject who is randomised according to randomised treatment assignment and ignoring anything that happens after randomisation (Gupta,  2011 ). So, studies using ITT analysis have been included in the EGM under RCT study design.
• (ii) Natural experiments: Despite the lack of universally accepted definition of the term natural experiment, researchers are in agreement that a natural experiment happens where and/or when an intervention is implemented without the control of a researcher (Butler et al.,  2018 ). Therefore, a natural experiment is an experiential study design in which clearly defined sub‐populations are exposed to the experimental and control conditions that are determined by nature or other factors outside the control of the researchers. The allocation into experimental and control conditions allows researchers to use natural or unplanned variation in exposure to draw inferences about causality.

For example, a policy development emphasising promotion of TVET in Uganda can be seen as a natural experiment. TVET is a school‐based intervention, which makes it a commonplace intervention and thus underpinned by equity considerations. Therefore, a randomised experiment/RCT design is often considered to be politically and ethically not feasible. In this case a natural experiment is a preferred evaluation study design.

• (b) Nonexperimental matching designs

Nonexperimental designs are used where random assignment is not feasible for various reasons. For instance, where the need for evaluation arises when the program is completed or on going. Nonexperimental designs can be generally categorised as quasi‐experimental and regression‐based approaches. Quasi‐experimental methods create comparison groups by statistical methods, rather than by random assignment. These methods include difference‐in‐differences (DiD), propensity score matching (PSM), regression discontinuity designs (RDD), and synthetic controls designs (White & Raitzer,  2017 ).

• (i) PSM: In PSM, the matching enables construction of an artificial comparison group with almost similar characteristics as the treatment group. The artificially created comparison group from untreated observations is matched to treatment observations from the untreated sample, based on observable characteristics. The treated units are matched to untreated units with a similar propensity score. The matching approach is considered adequate to attain an unbiased impact estimate (White & Sabarwal,  2014 ).
• (ii) DiD: In DiD approach, impact is estimated by comparing the changes in outcome over time between treatment and comparison groups. The method is also known as controlled before and after studies or ‘double difference’ method (White & Sabarwal,  2014 ).
• (iii) RDD: This is a popular approach used in econometrics due to situations that make randomisation unfeasible to determine causal effects of interventions by assigning a cut‐off or threshold above or below which an intervention is assigned. The threshold refers to the criterion that participants must met before being included in the intervention. The threshold is usually based on a continuous variable (White & Sabarwal,  2014 ). For instance, adults above or below a particular age for enroling in a training programme. RDD approach compares observations on either side of the threshold to estimate average treatment effects of an intervention. The major limitation of RDD is that its greatly affected by confounding variables. For instance, average treatment effects of a local sanitation intervention may be affected by a regional related intervention if they were implemented at the same time.
• (c) Regression‐based approaches

All approaches based on regression models not listed above are included here. These include (but are not restricted to):

• (i) Instrumental variable (IV): ‘A statistical technique for estimating causal relationships when an RCT is not feasible or when an intervention does not reach every participant/unit in an RCT’ (White & Sabarwal,  2014 , p. i).
• (d) Systematic reviews

A systematic review summarises bodies of literature with summary statements of the findings of that literature. The term ‘systematic’ in systematic review refers to the research process that is intended to minimise the biases that may occur in a traditional literature review. Key characteristics of a systematic review include: a clear scope for the review; set of research questions; clear study inclusion and exclusion criteria; systematic search strategy used to identify the single studies that would meet the eligibility criteria; and results. Therefore, studies included in the EGM as systematic reviews had the above‐mentioned characteristics even if they did not use the term ‘systematic’ in the titles. Studies using the term ‘systematic’ but lack key features of a systematic review were excluded. For instance, scoping reviews or literature scans and Meta‐analysis of evidence were excluded on study design criterion.

3.7. Types of intervention/problem

The EGM has three broad intervention categories, each with subcategories. The intervention categories include: Strengthening training and education systems; enhancing labour markets; and transforming financial sector markets. Table  1 lists the intervention categories, subcategories as well as examples to aided study search and coding. The intervention category of ‘strengthening training and education systems’, here refers to a group of sub‐interventions covering improvements in training, upskilling and retraining or re‐skilling components of education and training systems. Specifically, up‐skilling is the process of teaching workers new skills. Retraining is a practice where employers may ensure that their workers learn new skills specially to avoid stagnant workforce. So here, some examples of interventions to be captured include TVET, business skills training and, internship and apprenticeship (Table  1 ).

A detailed compilation of definitions of the interventions are provided in Supporting Information Annex  1 , with a reference list provided as Supporting Information Annex  3 .

3.8. Types of population

The only criterion used for selecting the target population is age. So, the target population is all youth or young women and men aged 15–35 years from all countries. The diversity of varying national definitions of the term youth was acknowledged. For instance, while the United Nations (UN) defines youth as young women and men aged 15–24 years, in South Africa youth fall in the age group 14–35 years (South Africa,  2015 ), in Zimbabwe it's from 15 to 35 years of age (Zimbabwe. Ministry of Youth Development,  2000 ) and in Uganda youth are within the age bracket of 15–30 years (Uganda. Ministry of Gender Labour and Social Development,  2016 ). This EGM uses a more encompassing classification to identify youth as young women and young men aged between 15 and 35 years.

Population subgroups included: both females and males; youth with disabilities; youth in fragility, conflict and violence (FCV) contexts; youth from disadvantaged background (low‐income families or low education); criminal background; ethnic minorities; and humanitarian settings. The population sub‐groups permitted the identification of studies reporting evidence on equity.

3.9. Types of outcome

Table  2 contains outcome categories and subcategories. The outcomes are arranged in a typology of five categories: education and skills, entrepreneurship, employment, welfare, and economic. For instance, the education and skills outcomes refer to notable achievements in education, satisfaction with quality of education programs; transformative experiences, career readiness and performance.

The welfare outcomes are flagged to ensure that even welfare outcomes not directly associated with employment are captured. For instance, some welfare outcomes (criminal and delinquent behaviour as well as citizenship values) may happen as result of participating in an intervention, though such youth could still be unemployed. The protocol also provided for inclusion of adverse and unintended outcomes in the map. That was important to avoid one‐sided summaries of the evidence. An example of unintended employment outcomes include youth offending such as, increased rate of alcohol abuse due to income resulting from change in employment status. An example of an adverse outcome is accidents and disease resulting from employment hazards. Multiplier effects were also coded. These refer to effects not directly from the programme such as job displacement and youth spending earnings to improve local commerce. Job displacement in this case refers to the loss of jobs by current employees due to the recruitment of new workers for example with more relevant skills for the employer.

Definitions of outcomes are provided in Supporting Information Annex  2 , with associated reference list provided as Supporting Information Annex  3 .

3.10. Eligibility criteria

A review of 15 agencies by Saran and White ( 2018 ), established that the inclusion criteria for EGMs generally follows the PICOS approach of population, intervention, relevant comparison groups, outcomes and study design. Therefore, the study inclusion criteria for this EGM also used a similar approach. Table  3 shows a summary of PICOS. Additional criterion of study publication period was included. The document should have been published or made available between January 2000 and December 2019. The inclusion and exclusion criteria is illustrated in (Figure  1 ).

Screening tool

Summary description of PICOS elements in the EGM

Unpublished studies were included in the EGM to reduce the effects of publication bias, which refers to the failure to publish a study on the basis of the strength of the study findings.

For studies with interventions that combined youth and non‐youth populations (older adults and children), majority (over 51%) of the study population had to be youth. In addition, studies containing only subsets of eligible interventions were included in the map only when revenant outcomes were reported.

For a single study with multiple reports, it was included in the EGM multiple times only if the study employed different eligible study designs or reported different outcomes. However, if the multiple reports of a single study were for example a working paper and journal article, only one with the most detailed information (interventions and outcomes) was included in the EGM. For instance, where a journal article presented partial outcomes, a working paper was included in the EGM.

3.11. Types of settings

The coding tool provided for the following types of settings for interventions: high school, tertiary education institutions, training centres, firms, and the option for others identified when coding studies.

3.12. Search methods and sources

The Protocol which is the basis for the production of the Youth Employment EGM is published in the Campbell Systematic Reviews database (Apunyo et al.,  2021 ).

3.12.1. Search strategy

A standardised search strategy provided in Supporting Information Annex  4 was used to search over 20 databases and institutional websites, in English. The rationale for limiting the strategy to the English language documents was based on the consideration that the vast majority of studies were written in English and, translation from other languages to English would be compromised by comprehension limitations.

The search strategy was developed from the entire coding sheet (Supporting Information Annex  5 ), containing filters (i.e., population demographics and socioeconomic characteristics); selected study designs; interventions and; outcomes. For instance, impact evaluation‐based study designs were included in the search terms, since the focus of the map was effectiveness of youth employment interventions.

While scholarly databases identified peer reviewed articles, institutional websites provided mainly grey literature in the form of evaluation reports and working papers. All identified studies were screened on title and abstract as well as on full text. Completed but unpublished or studies with midterm outcomes were included in the map.

The search strategy was pre‐tested multiple times and peer reviewed by the two information science specialists. After ascertaining the effectiveness of the search strategy, the final search was performed on 29 January 2020. The studies were then uploaded into EPPI Reviewer 4 software, for screening and coding. The confinement of publication period to two decades (2000–2019) was informed by an observation that development interventions are often best appreciated and understood in a contemporary context (Mawn et al.,  2017 ).

3.12.2. Additional search methods

To ensure the more comprehensive identification of studies included in the EGM, additional three activities (searching systematic reviews, snowballing and citation tracking), described below were undertaken. These activities were carried out towards the end of the project, after coding studies identified by the search strategy.

There was a search within reference lists of the 21 systematic reviews included in the EGM and existing evidence gap maps on youth employment. There was no systematic approach of contacting individuals and organisations to access full articles that were not accessible online. Due to the big number of studies included in the map, a targeted search of reference lists of included studies was conducted through snowballing and citation tracking.

Snowballing and citation tracking were conducted after studies identified by the standardised search strategy and searching within systematic reviews had been coded. While snowballing involved reference tracking the 20 most recent publications, citation tracking was limited to the 10 most current publications. Snowballing involved searching the reference lists of included studies and identifying studies that met the eligibility criteria of the study. Citation was conducted in ‘Google Scholar’ by pasting the ‘reference text’ of each study in google scholar search area to show a list of studies which had cited that particular study.

3.13. Data collection

3.13.1. screening and study selection.

Screening of studies was carried out in EPPI‐Reviewer 4, which is a web‐based software program for managing and analysing data in literature reviews. The study references identified from databases searched, were imported into EPPI‐Reviewer 4, where duplicates were removed before screening. For studies identified through searching reference lists of systematic reviews, snowballing and citation tracking, bibliographic information was manually captured in EPPI Reviewer 4.

Studies were screened using a five‐criteria screening tool developed by Dr Howard White, an expert in impact evaluation and evidence synthesis. Included studies were those written in the English; published or made available after 1 January 2000 and by 31 December 2019; described an intervention for youth between 15 and 35 years and; should have been a systematic review or an impact evaluation (Figure  1 ).

There were two levels of screening studies, on the basis of titles and abstracts and on full texts. At first level, titles and abstracts were screened independently by each of the two reviewers against the inclusion criteria. A reconciliation report comparing the results of the two reviewers was generated from EPPI Reviewer‐4 for identification of disagreements which were resolved through discussion by the reviewers. To add rigour, where the two reviewers did not reach consensus the matter was forwarded to the third reviewer. At second level, full text papers were again screened by two reviewers independently and disagreements reconciled through discussion as in the first level of screening.

The screening tool was piloted through a number of sessions with each of the sessions using about 100 studies. In the first session reviewers were trained at Africa Centre for Systematic Reviews and Knowledge Translations, College of Health Sciences—Makerere University in Uganda, by Dr. Ashrita Saran, a methods expert.

3.13.2. Data extraction and management

The studies were coded on the basis of the information contained in the coding sheet, provided in Supporting Information Annex  5 . Guidance was provided to reviewers involved in coding the studies, through piloting coding and checklists. For instance, reviewers used the most current World Bank classification of countries by income level to code the World Bank Regions. A dictionary defining interventions and outcomes was also provided for reviewers involved in coding studies.

The coding sheet was piloted before full scale coding of studies. Five piloting sessions were conducted. In each session, each study was independently coded by a pair of reviewers. After each pilot session, the entire EGM team discussed the results of pilot coding to humanise the application of the coding sheet. Post pretest coding of studies was conducted again by pairs of reviewers who reconciled disagreements through discussion. Where the two coders did not reach consensus, the mater was forwarded to the third reviewer/tie breaker.

3.14. Quality appraisal of studies/risk of bias

Critical appraisal of each study (impact evaluations and systematic reviews) was conducted independently by a pair of reviewers who again followed the same procedure used at screening and coding phases, to reconcile disagreements.

Impact evaluation studies were assessed using the ‘Quality assessment of Impact Evaluations’ tool developed by Dr. Howard White and Dr. Saran Ashrita. The tool is a checklist of seven items with additional guidance on rating items, expressed as: high confidence, medium confidence or low confidence. However, of the seven items only four (study design (potential confounders taken into account); level of attrition or losses to follow up 2 ; definition of outcomes; and baseline balance 3 reports), were the most critical for making decisions.

A Measurement Tool to Assess systematic Reviews (AMSTAR 2) was used to conduct critical appraisal of systematic reviews. AMSTAR has been developed to facilitate the development of high‐quality reviews by guiding the conduct and evaluation of reviews. The AMSTAR 2 checklist 4 contains 16 items, each with concise sentence questions having supplementary guidance on selecting response options (expressed as: ‘yes’, partial yes and ‘no’). Overall, the AMSTAR 2 tool rates confidence in components of a systematic review as; High: no or one no‐critical weakness, Moderate: more than one noncritical weakness, Low: one critical flaw with or without noncritical weaknesses and, Critically low: more than one critical flaw with or without noncritical weaknesses (Shea et al.,  2017 ).

3.15. Methods for mapping

EPPI Reviewer 4—a web based software program for managing and analysing data in literature reviews (EPPI Centre, n.d.) was used for screening and coding studies as well conducting analysis. Screening and coding was based on predefined codes extracted from the eligibility criteria and coding sheet provide in Supporting Information Annex  5 . The study flow diagram is presented later in Figure  5 . The analysis involved generating frequency tables, cross‐tabulated tables, graphs and charts.

Flow diagram for Youth Employment Evidence and Gap Map

EPPI Mapper was used to generate the EGM. EPPI‐Mapper is a tool for visualising ‘maps’ of research evidence (EPPI Centre, n.d.). The tool is used generate an evidence and gap as follows. After coding data in EPPI Reviewer, a JSON file form is exported and uploaded to EPPI Mapping tool to generate the EGM, via: http://eppimapper.digitalsolutionfoundry.co.za/ .

3.16. Analysis and presentation

3.16.1. report structure.

The report structure provided below is an outline containing major sections with included tables, figures and boxes, in the main body of the report. Additional tables, figures and boxes are provided in the annexes as Supporting Information.

3.16.2. Dependency/unit of analysis

The unit of analysis for the report is individual studies where every entry represents a combination of interventions and outcomes. The findings are descriptive, showing the distribution of studies in terms of study design, regions of world, study quality, settings, interventions and outcomes. The aggregate map is presented in a coloured table showing well‐evidenced areas and low evidenced areas.

3.16.3. Filters and presentation

The filters are populations groups such as disadvantaged youth and age groups of youth; regions of the world by income status; countries; study settings and; implementers of interventions. The interactive EGM is presented as follows.

The youth employment EGM is a matrix of interventions (in rows) and outcomes (in columns), populated with studies that provide evidence for each cell's outcome and intervention combination. Each study was placed in each cell for which the study provides evidence. That means that majority of studies appear in the map multiple times because they have multiple outcomes and interventions. Each study has been tied to a weblink which directs the user of the map to an online database where the full text or paper of the study is uploaded. The map has primary and secondary dimensions which provide an appealing user‐friendly content visualisation.

The primary dimensions of the map are interventions (in rows) and outcomes (in columns), presented in a matrix. Figure  2 shows a snapshot of a section of Youth Employment EGM. Interventions were grouped into categories, subcategories. The outcomes are arranged in five categories: education and skills, entrepreneurship, employment, welfare and economic. For instance, the ‘economic’ category of outcomes contains the following outcome subcategories: Cost, cost benefit, cost effectiveness and multiplier effects. In the case of interventions, the ‘training’ category comprises; TVET; internship and apprenticeship; Training centre accreditation and certification; training of trainers and teachers; business skills training; and life skills training.

Snapshot of a section of the Youth Employment Evidence and Gap Map. Source : https://youthfuturesfoundation.org/our-work/identify/evidence-and-gap-map/

The map is interactive. By hovering over a cell, you can see the number of studies in that cell. At the bottom of the map, the colour‐coding of bubbles represents study quality based on critical appraisal of included studies. Study quality ratings include: low quality impact evaluation; medium and high‐quality impact evaluation; low quality systematic review; or medium and high‐quality systematic review. Pointing the cursor at the cell, identifies single studies from systematic reviews, according to study quality rating (Figure  3 ).

Hovering over a cell to get a list of studies. Source : https://youthfuturesfoundation.org/our-work/identify/evidence-and-gap-map/

By clicking on a cell, you can see a list of the studies in that cell. Clicking on a study in the list (middle panel) gives a summary of that study as well as the URL which is a gateway to that study (Journal database or website), where the uploaded paper can be assessed. Clicking on a row or column heading gives the list of studies in that row or column. By checking a filter (left hand side) you can filter studies displayed (Figure  4 ).

The secondary dimension of the map is the taskbar menu (Filters, About and View records), which help a user to navigate the map. Filters are search aids which help the user of the map to quickly find records matching criteria of interest. The filters include study design, regions of the world, country and population subgroups. The population subgroups include youth with disabilities, youth in FCV contexts and, youth from disadvantaged background (low‐income families or low education); criminal background; ethnic minorities and; humanitarian settings. Clicking on the ‘About’ tab in the taskbar menu displays the ‘about’ text which describes the map. Clicking on the ‘View Records’ tab displays a record of all studies in the map, offering the user options to export the reference list which is compatible with other reference management software such as Endnote and EPPI Reviewer.

The results section covers the following broad sections: Description of studies, Synthesis of included studies, Risk of bias in included studies and, Discussion of the results. The presentation is aided by tables and figures provided in the main document as well as those contained in the Annexes, as supporting information.

4.1. Description of studies

4.1.1. results of the search.

Evidence was mapped for the period of two decades, 2000 to 2019. More evidence was available in the last decade, with the period 2014–2019 accounting for over 38% of included studies. There was a positive trend in the number of studies made available or published over the two decades (Supporting Information Annex  8 ; Table  16 ).

There were 14,511 studies uploaded into the EPPI Reviewer‐4 management software. Majority of studies were uploaded into the software for screening in January 2020 after validation of the search strategy by the two information science specialists who developed and conducted the search strategy. These studies were majorly from 20 databases especially from Econlit: https://www.aeaweb.org/econlit/ , Web of Science: http://Thomsonreuters.Com/Social-Sciences-Citationindex/ , CAB Global Health (Ovid): https://www.ovid.com/product-details.30.html , RePEc (Research Papers in Economics)/IDEAS Economics and Finance Research: https://ideas.repec.org/and ERIC: https://eric.ed.gov/ . These altogether contributed 13,428 articles. A full list of databases searched is provided in supporting information (Supporting Information Annex  4 ).

A total of 1,809 titles and abstracts (12.5%) were included for full text screening of which only 399 (22.1%) made it to the final coding and synthesis. The review flow details are illustrated in the EGM PRISMA diagram (Figure  5 ). A list of included studies, with quality rating is provided in Supporting Information Annex  7 .

4.1.2. Excluded studies

The reasons for exclusion of studies at full text screening are captured in Table  4 . The reasons were mainly, ineligible study designs ( n  = 428), inappropriate interventions ( n  = 328) or target group ( n  = 298). Box  2 shows examples of excluded studies a reader might reasonably have expected to find in the EGM. The reasons for exclusion of these studies are: ineligible study designs and lack of interventions.

Reasons for exclusion of studies on full texts

Examples of excluded studies

4.2. Synthesis of included studies

4.2.1. types of evidence.

By types of evidence, impact evaluations designs ( n  = 378) were much more than the systematic reviews ( n  = 21). The leading impact evaluations were experimental studies ( n  = 177), closely followed by nonexperimental matching ( n  = 167) and other regression designs ( n  = 35) (Table  5 ).

Types of evidence

Experimental studies were the most conducted in Lower‐income countries and Lower Middle Income countries while nonexperimental study designs were the most common in High Income and Upper Middle Income countries (Table  6 ). Among the experimental studies, randomised controlled trials (RCTs) represent the highest quality of impact evaluations when done properly. Taken together, most RCTs were conducted in sub‐Saharan countries ( n  = 42), followed by the Americas (north and south), with n  = 38 and n  = 24, respectively. In sub‐Saharan Africa, Uganda had the leading number of RCTs ( n  = 16) (Supporting Information Annex  8 ; Table  17 ). USA ( n  = 13, 61.9%) and India ( n  = 8, 38.1%) were the countries studied most in the systematic reviews ( n  = 21) (Supporting Information Annex  8 ; Table  18 ).

Evidence types by income status based on World Bank classification

Note : Numbers in this table do not add to the actual number of respective types of studies/evidence as an impact evaluation or systematic review may be coded more than once for income regions. For instance, cross‐country studies such as systematic reviews may be coded for more than one region.

Geographically, Europe and Central Asian studies were the most predominant ( n  = 152), with the MENA region having least representation n  = 17 and therefore with most glaring evidence gaps in the literature of youth employment. There was a relationship between volumes of evidence and World Bank income regions. High‐income country studies were more (59%), with low‐income countries accounting for only 11.5% of the evidence base (Supporting Information Annex  8 ; Table  19 ).

The top 10 countries contributed about 64.2% of the studies, with the USA leading and having double the number of studies for Germany, the second ranked out of a total number of 94 countries studied. Among these Peru, India and Uganda were the countries with most studies in Latin America, Asia and sub‐Saharan Africa, respectively. Argentina, South Africa, Colombia and France were the other top 10 countries (Supporting Information Annex  8 ; Table  20 ).

In terms of authorship, studies were ranked by author, country and region of the world. There were 45 authors with two or more publications with Europe accounting for 46.7% while Africa and Asia each disproportionately contributing only 4.4% (Supporting Information Annex  8 ; Table  21 ). A total of 13 authors had three or more publications. The top 13 authors were from USA ( n  = 6), Germany ( n  = 3), France ( n  = 2) with UK and Argentina having one author each in the top 13. These top 13 authors were responsible for 55 publications of the included studies (13.9%) (Supporting Information Annex  8 ; Table  22 ).

4.2.2. Setting for interventions and, sectors of interventions

Settings for interventions were concentrated in firms ( n  = 205). Other intervention settings were training centres ( n  = 154), tertiary education ( n  = 44) and high schools ( n  = 37). Most studies did not report sectors ( n  = 307). Among those that did, the services sector was the most studied ( n  = 74) followed by the nonmanufacturing ( n  = 58) and agriculture ( n  = 33) came fourth after the manufacturing sector, n  = 43 (Table  7 ).

Setting for interventions and sectors of interventions

4.2.3. Implementers of interventions

Looking at implementers, most (85.7%) or 342 of 399 studies reported implementers of interventions. The majority of the interventions were implemented by the government (66.7%). The other implementers included: Government, Business/Industry, private enterprises, researchers, training institutions, NGOs, CBOs, Multinational organisations, Not for profit organisation and individual labour migrant (Table  8 ).

Intervention implementer

4.2.4. By population

Overall, literature was sparse about youth in fragile, conflict and violence‐affected contexts; humanitarian settings, ethnic minorities and those with criminal backgrounds.

Looking at age and gender, youth between 15 and 19 years old commanded the evidence base with an even gender distribution. Where gender was specified it was approximately 80% for both male and female (Supporting Information Annex  8 : Table  23 ). Evidence was substantially less available for ‘older youth’ groups, specifically 30‐35 years compared to those below 25 years.

Evidence gaps were also identified in terms of social factors of all the youth. There is rich literature about youth from disadvantaged backgrounds ( n  = 139) (low‐income families or low education), followed by those disabilities ( n  = 33) with hardly any literature on youth in fragile, conflict and violence context, n  = 8; humanitarian settings (n = 6), criminal background ( n  = 3), and ethnic minorities ( n  = 3) (Table  9 ).

Evidence types by social population factors

Abbreviations: Crimin., criminal background; Disadv., disadvantaged background (low‐income families or low education); Ethnic., ethnic minority; FCV, fragility, conflict and violence; Human., humanitarian settings.

There were variations in the types of evidence by population as well as country income status. Experimental evidence was concentrated around youth in urban locations ( n  = 73) and those from disadvantaged backgrounds ( n  = 77) (Supporting Information Annex  8 : Table  23 ). There were no systematic reviews (zero) capturing youth employment interventions in fragile, conflict and violent or humanitarian settings, or among ethnic minorities (Table  9 ). In terms of regions of the world by income status, there were no studies (zero) about employment interventions in youth with disability, criminal background, humanitarian setting or ethnic minority in low‐income countries. The only three studies about youth with criminal background were all in high‐income countries (Table  10 ).

Evidence about regions of the world by social population factors

4.3. Intervention categories

The five intervention sub‐categories are support to employment; decent work policies; training; information and finance and entrepreneurship. Training (in full training, up‐skilling and retraining or reskilling) was by far the most researched area ( n  = 283). This was followed by support into employment ( n  = 182). Together these two interventions are assessed in over 75% of the included studies. There are far fewer studies about the effect of information services, decent work policies, entrepreneurship promotion and financing on youth employment (Figure  6 ). The Figure  7 provides detailed coverage of intervention sub‐categories for the ‘training, upskilling and re‐training intervention category’ while Figure  8 shows intervention sub‐categories for the ‘support to employment’ intervention category.

Interventions sub‐ categories, number of studies

Intervention sub‐categories for the ‘training and up‐skilling’ category, (number of studies)

Intervention subcategory for Support to employment

Under the training and skilling category, most of the evidence was about education, technical and vocational training (TVET), n  = 193, followed by the life skills training ( n  = 105) and internship and apprenticeship ( n  = 81) intervention sub‐categories. There are few studies regarding Prior Learning Assessment and Recognition (PLAR), n  = 1, training of trainers and teachers ( n  = 2), and training centre accreditation and certification ( n  = 1) (Figure  7 ).

In support to employment intervention category, employee mobility and placement ( n  = 77) and mentoring ( n  = 67) were the leading intervention sub‐categories whilst career guidance ( n  = 46) and wage subsidies ( n  = 51) studies had little difference. There was an obvious evidence gap in the area of public works programmes ( n  = 8) (Figure  8 ).

4.3.1. Multi‐component interventions

A total of 148 (37.%) out of 399 studies included in the EGM combined multiple categories of interventions. Of the 148 studies that combined at least two interventions sub‐categories, majority (68.2%) combined the ‘training, up‐skilling and re‐training/skilling’ and, the ‘support to employment’ intervention categories. Further examination of (Table  11 ), shows that the intervention sub‐category of the ‘training, up‐skilling and re‐training/skilling’ and, the support to employment are mainly evident in the combinations of most of the other intervention categories. However, the ‘information’ intervention category was the least common in the different combinations of interventions.

Multi‐component youth employment interventions

4.4. Outcome categories

There are five outcome domains reported in the map: education and skills, entrepreneurship, employment, welfare and economic. Employment is the most reported outcome ( n  = 345) followed by welfare ( n  = 121) and, education and skills ( n  = 97) (Figure  9 ).

Outcome categories, number of studies

Among the outcome sub‐categories for employment outcomes, evidence is concentrated on employment status (including employment duration ( n  = 322), earnings and salary ( n  = 197), and hours worked ( n  = 89) (Figure  10 ). Within the outcome category of education and skills, education completion and qualification, n  = 36 (Figure  14 ); and business creation, n  = 54, under entrepreneurship category (Supporting Information Annex  8 : Figure  15 ). For the outcome category welfare, the three most studies outcomes are citizenship, value and social behaviour, n  = 62, inclusion and empowerment, n  = 59, and economic outcomes (not including earnings), n  = 54. The most reported economic outcomes are cost–benefit, n  = 41 (Supporting Information Annex  8 : Figure  16 ).

Employment outcomes domain

The clear evidence gaps in the employment outcome domain literature were about: vacancies, actively seeking employment, employment expectation and appropriate employment (Figure  10 ). Multiplier and spill over effects, digital skills and job creation sub‐categories were the obvious gaps in the evidence around economic, education and skills and entrepreneurship major outcome domains (Supporting Information Annex  8 : Figures  13 – 15 ).

4.5. Aggregate map of evidence gaps

Table  12 is the aggregate map that shows the number of studies in each intervention and outcome category.

Aggregate map of evidence gaps

Well‐evidenced areas with more than 75 studies are the employment, welfare, as well as education and skills outcomes of training interventions. This level also is employment outcomes of support to employment interventions.

Moderately evidenced areas, with between 25 and 75 studies, are largely identified for support to employment interventions across a broader range of outcomes, except employment. Training interventions also have moderate level of evidence for economic and entrepreneurship outcomes. Similarly, entrepreneurship promotion and financing interventions had moderate level of evidence for employment and welfare outcomes. The evidence base is weak (fewer than 25 studies) especially for information intervention category across all outcomes.

4.6. Risk of bias in included reviews

The results from the quality assessment of systematic reviews using the AMSTAR 2 tool and, impact evaluation studies basing on the ‘Quality assessment of Impact Evaluations’ tool are presented below. These study quality assessment tools are detailed earlier in (Section  3.7 ). A detailed risk of bias or study quality table containing a list of 399 studies is provided in Supporting Information Annex  7 .

Overall, most systematic reviews (71.4% of 21) were of medium and high quality while 28.6% were graded as low quality by AMSTAR score (Table  13 ). Across the intervention sub‐categories and outcomes categories, evidence quality was mainly rated as ‘medium/moderate and high quality’ (Table  14 ). The reviews with a low quality rating had at least one critical flaw (Table  15 ). For instance, all the reviews rated as low quality did not explicitly state if the review methods were established before the conduct of the review and did not also justify any significant deviations from the protocol. The inability of review authors accounting for risk of bias (RoB) in individual studies when interpreting/discussing the results of the review, was the second most dominant critical flaw (Table  15 ). A detailed report of RoB assessment for systematic reviews is provided in Supporting Information Annex  6 . The conduct of systematic therefore calls for improvements in a number areas such as proper design of methods and their publication for transparency, undertaking risk of bias analysis and use of comprehensive literature search strategies.

Quality of evidence/risk of bias of included studies

Note : Total percentage adds to more than 100, as an impact evaluation study may be coded for more than one design.

Quality of evidence by intervention sub‐categories and outcome categories

Critical flaws in systematic reviews rated as low quality

Looking at impact evaluations, at least 73.4% of these category studies were of low confidence quality rating (Table  13 ). In terms of Intervention sub‐categories and Outcomes categories, the bulk of the evidence was of low quality impact evaluations across the board (Table  14 ). These studies obtained a low‐quality rating on at least one of the following three items: (1) high attrition rate; (2) design (potential confounders not taken into account and; (3) weak baseline balance performance. However, attrition bias and low baseline balance rating were the two main reasons for the low‐quality rating of impact evaluations.

Results of attrition assessment presented in Figure  11 , show that most (70.4%) impact evaluations scored a low rating because either attrition was not reported or the reported measures fell outside What Works Clearinghouse (WWC) Standards acceptable combined levels. Ex‐post factor studies were not rated on this attrition parameter.

Attrition rating of impact evaluations

Regarding the ‘baseline balance parameter’, 5 still a big number of studies (43.4%) scored a low rating because they did not have baseline balance data or were reported with significant differences on more than five measures (Figure  12 ). It means that most studies with treatment and comparison groups did not have the same average characteristics of participants at baseline. Such imbalances in participants' characteristics such as in the levels of education often affect the impact of the interventions.

Baseline balance tests of impact evaluations

The ‘before versus after’ studies were not rated on this parameter as it is not applicable. The RCTs were automatically rated high on this parameter.

4.7. Discussion

4.7.1. overview of the map.

This map primarily targets policy makers and youth development practitioners as well as researchers who require evidence to inform policy and implementation of youth employment interventions. On the one hand and to the governments and international partners, this map is a useful guide to identifying existing evidence related to youth employment interventions under consideration. Researchers and research commissioners are able to identify areas of available evidence and commission syntheses where there are gaps in knowledge, they would commission new impact evaluations as appropriate.

4.7.2. Principal findings

The EGM of global literature on youth employment interventions over two decades was conducted. The EGM contains 399 eligible studies mostly (59%) from high‐income countries in Europe, Central Asia and North America. This suggested that country income persisted in research productivity, yet it is in Africa that youth unemployment will likely be more impactful in decades to come. Moreover, these low and middle‐income regions have the biggest population of youth NEET. However, the reasons for this situation are beyond the scope of this EGM. It's therefore an evidence gap for research investment. Naturally, this trend persisted among the lead authors with hardly any from Africa having two or more publications. Majority of the studies were experimental impact evaluations with few systematic reviews and interventions were located in firms or youth training centres.

It was observed that the bulk of evidence on interventions studied was from low quality impact evaluations (71.2%). Fortunately, most systematic reviews (71.4% of 21) were of medium and high quality. There were two key reasons for failings of impact evolutions. First, they had very high levels of attrition by study participants or attrition data were not reported. The second, was the large differences in the baseline characteristics between treatment groups and control groups of participants. In the case of systematic reviews, there were critical flaws characterised by uncertainties about whether such reviews had protocols before their conduct and finally, risk of bias assessment for included studies was not carried in the systematic reviews with low quality rating. This finding serves to alert researchers, practitioners and policy makers that more rigorous work is needed to inform youth employment interventions.

Overall, evidence was generally scarce in specific areas: Social impact bonds, crowd funding, micro‐franchising, overseas employment, public works, information access and accountability systems. Additional gaps were in specific population sub‐groups, interventions and outcome categories that research commissioners may consider in their agendas. These findings could possibly be a function of context‐specific capacities to conduct the research or priorities.

Older youth, youth in fragility, conflict and violence contexts, or humanitarian settings, or ethnic minorities or those with criminal backgrounds were least studied. In fact, there were no systematic reviews at all capturing youth employment interventions among these unique population groups or even impact evaluations in low‐income countries. The underrepresentation of evidence availability for youth interventions in such contexts and settings could undermine evidence‐based policymaking.

It was observed that three main intervention sub‐categories were underrepresented; information services, decent work policies and entrepreneurship promotion and financing. Despite generally available literature at main intervention category level, there were gaps at intervention sub‐categories. For example, training of trainers, and training centre accreditation and certification under training and skilling main categories. It's also finding that public works was under‐researched or simply governments engaged more the private sector. Generally, there was a clear evidence gap in the economic outcome domain literature that was really about costs and, multiplier and spill over effects. Other areas of concern were digital skills and job creation sub‐categories under education and skills and entrepreneurship major outcome domains.

Specific areas were saturated with evidence. The intervention category with most evidence is training. Within the training intervention category, the subcategory of ‘education, technical and vocational training (TVET)’, has the largest prevalence of evidence. These include literature about youth below 25 years from high‐income countries and with disadvantaged background, that is, from low‐income families or with low education. Training and skilling and, support to employment were well endowed with impact evaluations. Employee mentoring, mobility and placements were common studies in the support to employment intervention whilst life skills, education, technical and vocational training were predominant in the skilling and training literature. Literature on outcomes was concentrated around employment and welfare‐related outcomes, such as earnings, hours worked, inclusion and empowerment, family health and education and, citizenship values.

The prominence of multi‐component interventions has been observed in that many studies combined at least two youth employment interventions. Two intervention sub‐categories namely; ‘training, up‐skilling and re‐training/skilling’ and, ‘support to employment’, stood out as the most common combination of interventions. Moreover, the same two intervention sub‐categories still populate the several other combinations of interventions. While this finding could be an indication that blended interventions offer better outcomes, this remains an area with a research gap. This EGM therefore provides a basis for the establishment of a typology of youth employment interventions.

4.8. Potential biases in the mapping process

4.8.1. strengths.

The map provides more recent information. This is the most up to date EGM on youth employment interventions. It contains a total of 399 impact evaluations and systematic reviews that were published or made available between the year 2000 and 2019. In addition, at least one in five of these studies were published after the last major two evidence syntheses, from 2016 to 2019. None of the other two existing EGMs on youth employment interventions included studies published after 2015. Youth and transferable skills EGM by 3ie included 104 studies while the Youth Employment EGM by ILO was based a systematic review by Campbell Collaboration that reviewed 113 reports.

Further, there were methodological strengths including a participatory approach in protocol development involving the whole synthesis project team with African leads, a broad search strategy of 20 data bases with citation tracking and a broader age range of the definition of youth. All studies identified at different stages of study search were screened and coded by pairs of reviewers all of which improved the trust in this evidence synthesis. Not least, this map identifies key evidence gaps for future prioritisation.

4.8.2. Limitations

Due to the COVID‐19 lockdown, reviewers were unable to conduct face‐to‐face stakeholder engagements in Kampala, Uganda as stated in the study protocol. Keeping in touch with Mastercard Foundation policy leads and the literature were informative in arriving at reasonable priority list of the interventions and outcomes for this EGM.

Another key weakness in this map is that the bulk of the impact evaluations were of low quality. Attrition bias and low baseline balance measures were the two main reasons for the low quality rating of impact evaluations which generally account for a huge bulk (378 of 399) of studies that are included in the EGM. Nonetheless, as EGMs do not intend to communicate evidence of effects of interventions, this finding serves to alert researchers, practitioners and policy makers that more rigorous work is needed to inform youth employment interventions. Researchers need to improve conduct and reporting of impact evaluations. Improvements may start within postgraduate programs funded by Mastercard Foundation by linking postgraduate students to impact evaluations of youth employment interventions.

Finally, this information is time bound not earlier than 2000 for which we could have missed important information. However, the purpose of this EGM was to build on existing syntheses that captured the earlier work of youth employment interventions globally.

4.9. Conclusions

This report provides a summary of findings of the Youth Employment EGM that focuses on interventions aimed at increasing youth employment in any country of the world. The map was funded by Mastercard Foundation and Youth Futures Foundation and, developed by The African Centre for Systematic Reviews and Knowledge Translation—Makerere University of College of Health Sciences in partnership with the Campbell Collaboration Secretariat. The map is an effectiveness map. Hence included studies are impact evaluations of interventions to improve youth labour market outcomes or systematic reviews of such studies. In total 399 studies are included (21 systematic reviews and 378 impact evaluations).

The map identifies trends in evidence notably the following:

• Most evidence is from North America and Europe and as such, high‐income countries have the largest share of evidence.
• Blending of interventions is practiced. While this could be an indication blended intervention could be offering better outcomes, this remains an area with a research gap.

Inspecting the number of studies in each cell of the map shows clusters of evidence and gaps, as follows:

• Well‐evidenced areas, with more than 75 studies, are the employment, welfare, as well as education and skills outcomes of training interventions. This level also is employment outcomes of support to employment interventions.
• Moderately evidenced areas, with between 25 and 75 studies, are largely identified for support to employment interventions across a broader range of outcomes, except employment. Training interventions also have moderate level of evidence for economic and entrepreneurship outcomes. Similarly, entrepreneurship promotion and financing interventions had moderate level of evidence for employment and welfare outcomes.
• The evidence base is weak (fewer than 25 studies), for information and decent work policies.

The map reveals areas with well‐populated evidence as well as those with gaps. Hence to a great extent the map enhances discoverability of evidence by stakeholders engaged in the promotion of youth employment interventions.

CONFLICT OF INTERESTS

The authors declare that there are no conflict of interests.

The funding for the map is from by Mastercard Foundation and Youth Futures Foundation.

ROLES AND RESPONSIBILITIES

Project Director was Dr. Ekwaro Obuku, who has vast experience in management evidence synthesis teams. He provided generation administration to the project team. Ekwaro is the Director of Africa Centre for Systematic Reviews and Knowledge Translation, based at the Makarere University College of Health Sciences (Africa Centre MakCHS), Uganda. He is a coauthor of several systematic reviews and tutors Systematic Reviews at Makerere University. He recently started a course in Evidence Synthesis for Masters Students in Clinical Epidemiology and Biostatistics at Makerere University, Uganda.

Content and methods expert : Dr. Howard White is a development economist with a tremendous track record in conducting development evaluations. He has studied labour markets in Africa. Howard played a key role of quality assurance though out the project implementation, in addition to providing content and methods expertise for the project.

Project Manager was Mr. Robert Apunyo. He has vast experience in managing research projects involving multidisciplinary teams. He is a Research Fellow at Africa Centre MCHS.

Methods expert was Dr. Ashrita Saran. She has vast experience in systematic review methodology and theory‐based synthesis. She was instrumental in the development of the EGM framework and training of screeners and coders of studies.

Information retrieval was conducted by Dr. Alison Annet Kinengyere and John Eyres. Alison and John have previously supported various systematic reviews projects.

Advisors : Susana Puerto and Drew Gardiner both from ILO provided technical advice throughout the project life.

Coders : Ms. Caroline Otike and Mr Thomas Katairo did most of the coding. Caroline and Thomas are Research Fellows at Africa Centre MCHS.

PLANS FOR UPDATING THE EGM

Youth Futures Foundation and MasterCard Foundation have shown interest in updating the EGM. The Youth Futures Foundation has already availed financial support for updating the EGM with process evaluation in last quarter of 2021. The EGM will be updated on an annual basis.

DIFFERENCES BETWEEN PROTOCOL AND REVIEW

Stakeholder consultations were not conducted due to the COVID‐19 global lockdown. Meetings and workshops to engage stakeholders were planned to be conducted in Uganda in the last quarter of 2019 with relevant officials in Uganda from government ministries, departments, agencies, private sector agencies, civil society organisations, vocational training institutes, international development agencies as well as academia. However, keeping in touch with Mastercard Foundation policy leads and the literature were informative in arriving at reasonable priority list of the interventions and outcomes for this EGM.

LINK TO ONLINE INTERACTIVE EGM

https://youthfuturesfoundation.org/wp-content/uploads/2021/09/Evidence-and-Gap-Map.html .

Supporting information

Supporting information.

ACKNOWLEDGEMENTS

The review authors are grateful to Ami Bhavsar for the excellent support received during the publication of the study Protocol and this report and, Jatin from the Campbell Collaboration for providing timely logistical support to the authors. We thank Dr. Maren Duvendack from the Campbell International Development Coordinating Group (IDCG) for reviewing the protocol for this evidence and gap map. The authors are grateful to Robyn Read and Tricia Williams from Mastercard Foundation for providing technical advice, most especially during the development of the evidence and gap map framework. The authors would like to acknowledge the excellent technical support provided by EPPI Reviewer 4 Support Officers, Melissa Bond and Zak Ghouze. Financial support fromMastercard Foundation and Youth Futures Foundationis certainly appreciated.

Apunyo, R. , White, H. , Otike, C. , Katairo, T. , Puerto, S. , Gardiner, D. , Kinengyere, A. A. , Eyers, J. , Saran, A. , & Obuku, E. A. (2022). Interventions to increase youth employment: An evidence and gap map . Campbell Systematic Reviews , 18 , e1216. 10.1002/cl2.1216 [ CrossRef ] [ Google Scholar ]

Evidence and gap map

Plain language summary on the Campbell website

[Correction added on 17 March 2022, after first online publication: The fifth author name has been corrected from "Sussana Puerto" to "Susana Puerto"]

1 ‘Youth employment EGM’ is the short title adopted from the main title, ‘interventions to increase Youth employment: An evidence and gap map’.

2 Attrition or losses to follow up : can be a major source of bias in studies, especially if there is differential attrition between the treatment and comparison group (called the control group in the case of RCTs) so that the two may no longer be balanced in pre‐intervention characteristics. The US Institute of Education Sciences What Works Clearing House (WWC) has developed standards for acceptable levels of attrition, in aggregate and the differential, which are applied in this EGM.

3 Baseline balance means that the treatment and comparison groups have the same average characteristics at baseline, not only for outcomes but other factors which may affect the impact of the programme such as a prior history of parental alcohol abuse.

4 http://www.amstar.ca/Amstar_Checklist.php .

5 Baseline balance means that the treatment and comparison groups should have almost similar average characteristics at baseline, not only for outcomes but other factors which may affect the impact of the programme such as a prior history of parental alcohol abuse.

• Apunyo, R. , White, H. , Otike, C. , Katairo, T. , Puerto, S. , Gardiner, D. , Kinengyere, A. A. , Eyers, J. , Saran, A. , & Obuku, E. A. (2021). PROTOCOL: Interventions to increase youth employment: An evidence and gap map . Campbell Systematic Reviews , 17 , 800, e1196. 10.1002/cl2.1196 [ CrossRef ] [ Google Scholar ]
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Questions to Address Youth Unemployment

We need to spur fresh thinking in this field even as we test and evaluate diverse approaches that promote youth economic empowerment in developing countries.

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By Reeta Roy Sep. 14, 2010

Next week, more than 300 people will convene at the Global Youth Enterprise & Livelihoods Development Conference in Washington, DC. I will be joining several of our partners, other funders, international NGOs, and youth innovators to discuss tough questions and promising solutions related to employment and entrepreneurship for young people. 

Approximately 1.3 billion young people between the ages of 12 and 24 live in developing countries. The pace of economic growth in many of these countries will be insufficient to create the 1 billion jobs needed over the next decade as youth transition into the workforce. And while there is an increase in basic education levels, millions of young people in developing countries still face bleak employment opportunities. Young women have even higher rates of unemployment and face additional systemic, social and cultural barriers.

Thus, there is an urgent need for new approaches to create economic opportunities for young people.  If successful, the effects of youth employment and productivity will have inter-generational impact with multiplier effects from wealth creation and growth to social stability and new leadership.

This requires a continuum of interventions that equip young people to change their own lives. Access to education, knowledge, skills, social networks and capital are the building blocks of this change.  How do we enable young people to stay in school and complete their secondary education?  Is micro-franchising a potential solution to entrepreneurship and job creation? How do we expand technology applications to equip young people with employability skills and connect them to ideas, mentors and resources? What’s required to encourage financial institutions to sustainably offer youth-inclusive financial education and services to enable young people to save money, build assets and manage financial resources for their education or to start a business?

Are you enjoying this article? Read more like this, plus SSIR's full archive of content, when you subscribe .

We need to spur fresh thinking in this field even as we test and evaluate diverse approaches that promote youth economic empowerment in developing countries, particularly in Africa. We have much work ahead of us to generate approaches that work at scale.

We believe that the most compelling ideas will come from those with the greatest stake in finding solutions—young people themselves.  We’ve already seen an explosion of activities by young people around the world to tackle questions facing them, and we would like to tap into this innovation.  As funders and practitioners, we need ways to engage young people in identifying needs, developing solutions and delivering them in ways that are trusted and accessible by youth and their families. 

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The Dynamics of Youth Unemployment

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• Original Article
• Open access
• Published: 08 March 2018

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

• Hila Axelrad 1 , 2 ,
• Miki Malul 3 &
• Israel Luski 4  

Journal for Labour Market Research volume  52 , Article number:  3 ( 2018 ) Cite this article

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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.

1 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. Footnote 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 .

2 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 ).

2.1 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. Footnote 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. Footnote 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.

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

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

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. Footnote 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.

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

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

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.

3 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.

3.1 The model

i—individual i, P i —the chances that individual i will have a full or part time job (at the time of the survey). \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{\text{X}}_{\text{i}}\) —vector of explanatory variables of individual i. Each of the variables in vector \(\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{X}_{i}\) was defined as a dummy variable with the value of 1 or 0. β—vector of marginal addition to the log of the odds ratio. For example, if the explanatory variable was the log of 13 years or more of schooling, then the log odds ratio refers to the marginal addition of 13 years of education to the chances of being employed, compared with 12 years of education or less.

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.

3.2 The sample

We used data gathered from the 2011 Labor Force Survey Footnote 5 of the Israeli Central Bureau of Statistics (CBS), Footnote 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 Footnote 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. Footnote 8

3.3 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.

3.4 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.

3.5 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.

3.6 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.

3.6.2 Gender

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

3.6.3 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.

3.6.4 Education

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.

3.6.5 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.

3.6.6 Academic education

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

3.6.7 Children

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.

3.6.8 Ethnicity

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.

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.

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 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.

5 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 ).

6 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.

6.1 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) Footnote 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.

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.

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

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

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.

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 .

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."

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

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

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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.

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Hila Axelrad

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Axelrad, H., Malul, M. & Luski, I. Unemployment among younger and older individuals: does conventional data about unemployment tell us the whole story?. J Labour Market Res 52 , 3 (2018). https://doi.org/10.1186/s12651-018-0237-9

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• Unemployment
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Unemployment among young workers during COVID-19

Subscribe to the economic studies bulletin, stephanie aaronson and stephanie aaronson senior associate director, division of research and statistics - federal reserve board francisca alba francisca alba former research analyst - economic studies.

September 10, 2020

• 10 min read

On June 8, the Business Cycle Dating Committee officially declared that the United States entered a recession in February. Young workers are typically hard hit in recessions, and research suggests that entering the labor market during a recession has a negative impact on future earnings and job prospects. In this post we examine the labor market experience of young workers since the onset of the pandemic and provide some thoughts on policy implications.

While the aggregate unemployment rate increased by 11.2 percentage points between February and April of this year (the local peak), unemployment rates among young workers increased by much more. For example, over the same time period, the unemployment rate for those aged 16-19 increased by 20.9 percentage points. The result is that while young people age 16-29 make up less than a quarter of the labor force, they accounted for about a third of the rise in the unemployment rate between February and April of this year. We also find disparities among young workers by education and by race, with Black and Hispanic workers and workers with lower levels of education experiencing larger increases in unemployment rates between February and April compared to white and college-educated workers. Moreover, we find that while between April and July the unemployment rates for young white 1 , and to a lesser extent Hispanic, workers have retraced a good part of their initial rise, the unemployment rate for young Black workers remains particularly elevated and was little changed in June and July.

That young workers have experienced a greater rise in unemployment during the recession is not surprising, as this is typically the case.  However, the extent to which young workers are bearing the brunt of the downturn is unusual.  This is partly attributable to the fact that, as is typically the case, younger workers were more likely to be laid off in both April and in May 2 , within industries, compared to their older counterparts. Moreover, this pattern has been exacerbated by the fact that, prior to the pandemic, young workers were more likely to work in service industries that were heavily impacted by social distancing policies required to slow the spread of the virus and reductions in consumer spending.

LABOR FORCE STATISTICS BY AGE:

Figure 1 shows the unemployment rate for five groups by age: 16+ (the aggregate unemployment rate widely reported in the media), 16-19, 20-24, and 25-29. As shown, the aggregate unemployment rate rose 11.2 percentage points between February and April (the local peak).  Meanwhile, the unemployment rate for the young increased by about 13 percentage points on average, with the largest increases occurring among the youngest workers. Between April and July the unemployment rate for the young decreased by an average of about 7 percentage points while the aggregate unemployment rate decreased by 4.5 percentage points; although, the unemployment rate for young workers in July still remains 7 percentage points higher, on average, than the aggregate.

However, the pandemic unemployment rate has understated the extent to which workers are losing jobs , as more of those who have lost jobs have chosen to drop out of the labor force than is typically the case during a recession.  Our analysis suggests that this dynamic has been particularly prevalent among young workers. Figure 2 displays the labor force participation rate for these same age groups.  While the aggregate labor force participation rate decreased by 3.2 percentage points between February and April, the labor force participation rate for those between the ages of 16 and 29 dropped by about 6 percentage points on average. Moreover, these declines were much larger proportionally and relative to the rise in the unemployment rate, and they have been more sustained. Since April, the labor force participation rate for those between the ages of 16 and 29 has increased by an average of about 1.5 percentage points, similar to the increase in the aggregate.

LABOR FORCE STATISTICS BY RACE AND BY EDUCATION:

Overall, the young have been hit hard by the recession, but the impact also varies by race/ethnicity and education.  Figure 3 shows the unemployment rate since the start of the recession for young white, Black, and Hispanic workers. The unemployment rate rose more for young Black and Hispanic workers between February and April 3 .  Between April and July, young white and Hispanic workers started to make up ground, as their unemployment rate declined by an average of about 7 percentage points. However, unemployment rates for young Black workers only declined by about 2 percentage points.

The disparities by education are also stark.  These data, which are reported only for those over the age of 25 (by which time educational attainment is largely complete) show that the unemployment rate for those with a high school degree or less and for those with some college education rose by more than twice as much as for those with a college degree or more between February and April. Interestingly, since then, the unemployment rate for those with less than a college degree have come down—likely as the industries in which they work have recovered—while the unemployment rate for those with a college degree has been fairly flat.  That said, the unemployment rates for these lower skilled workers, especially those with a high school degree or less, remain higher than those with a college degree or more.

RECENT UNEMPLOYMENT BY INDUSTRY:

These results raise the question of why young workers have lost their jobs at much higher rates. The left panel of Figure 5 shows that between March and April, young workers were more likely to be laid off than older age workers in almost every industry, although in a few cases the differences are quite small (and the same dynamic is true between April and May). The differences are especially pronounced in mining, information, wholesale and retail trade, information, education and health services, leisure and hospitality, and other services sectors 4 . These dynamics are similar to those typically observed during a recession.  Employers may be more likely to layoff young workers for a variety of reasons, which depend on the culture of the industry, the nature of the work, and the cost structure.  For instance, firms may have policies of firing the most recent hires first, as a way to retain the morale and support of long-time workers.  In industries that require significant firm-specific knowledge, young workers with lower tenure would likely have less of this, which would make separating them from the firm less of a loss.

However, the pandemic appears to have introduced an additional economic challenge for young workers.  As the right panel of Figure 5 shows, in February, prior to the significant decline in economic activity due to the pandemic, young workers were significantly more likely to be working in many of the hardest hit industries, including leisure and hospitality (17.5 percent) and wholesale and retail trade (16.5 percent) compared to their older counterparts (7 percent and 10.8 percent respectively). These industries are not especially sensitive to economic downturns , so this is a dynamic that is unique to the pandemic and is different from a typical recession.

POLICY IMPLICATIONS & DISCUSSION:

In this post we provide evidence that the labor market prospects of young workers have been particularly hard hit by the current economic downturn, and this is especially true for young Black and Hispanic workers and young workers with lower levels of education. Our findings are consistent with research done by our colleagues in the Metropolitan Policy Program who find that the most vulnerable workers are disproportionately young and have less formal education .

The particular economic vulnerability of young workers right now points to the need for policy support.  To some extent, young workers benefit from the same policies that that aid the wider public. For example, as young workers tend to spend a higher proportion of their income on rent 5 ,they would disproportionately benefit from an extension of the federal eviction moratorium in the next relief package. However, policymakers should also take into consideration the special circumstances of young workers.  For instance, simply extending federal unemployment insurance benefits will not provide enough financial support for young workers. Congress must also continue to waive work history requirements , since many young workers have short work histories or are just entering the labor force and would therefore be ineligible for UI benefits otherwise.  Similarly, a portion of young college graduates did not receive stimulus payments 6 —a small but significant omission that policymakers should take into account, if they do another round of payments to households.

But the problems young workers currently face go beyond an immediate economic need.  The jobs young workers hold are  important stepping-stones in their careers , allowing them to learn valuable work skills and make connections, which can improve their future employment prospects.   To the extent that industries such as retail trade, and leisure and hospitality undergo significant transformations in response to the pandemic, some young workers might find that traditional pathways into the labor market are unavailable.

For those who get a college degree, research suggests that graduating during a recession can leave a lasting imprint.  For instance, nearly 1/3 rd of college graduates who entered the labor market during the Great Recession ended up in jobs that did not require a college education.  Although this is often a temporary phenomenon, it can have long-lasting implications. For example, research shows that college graduates who have the lowest predicted earnings (based on college and major) suffer the most during a typical recession: experiencing a loss of 8 percent of cumulative earnings in their first 10 years.

All this means that, as we look beyond the pandemic, young workers will need added support to make sure that they are integrated into the labor force.

Becca Portman  contributed to the graphics/data visualization for this blog.

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• We define white/Black as those whose race is white/black and whose ethnicity is not Hispanic. We define Hispanic as any race with Hispanic ethnicity. 
• Although we don’t display a figure showing these results. We did run the same analysis for May; the results were similar to April.
• Note that the unemployment rate for young Black workers was increasing before February of 2020. This increase in the Black unemployment rate is unlike the other two groups whose unemployment rates hit a low in February of 2020.
• We calculate the short-term unemployment rate from the Current Population Survey by counting the number of people in an age group and in an industry who became unemployed since the last survey and divide this number by the total amount of people employed and unemployed in that age group and industry.  An unemployed person’s industry is the industry in which they were last employed.
• Note that the young group showing up as especially rent-burdened in the linked ACS table includes a range of different household types with differing financial circumstances.
• This group consists of students whose parents claimed them as dependent on their 2019 tax returns, but who graduated in December of 2019 and started to look for work right before the downturn.

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Impacts of Early Youth Unemployment Self-Esteem and Quality of Life: Moderating Effects of Career Unemployment

• Published: 26 August 2024

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• KonShik Kim   ORCID: orcid.org/0000-0002-9817-576X 1  

This study examines the effects of early youth unemployment and career unemployment on young adults’ self-esteem and quality of life using the survey data from the Youth Panel of the Korea Employment Information Service collected from 2007 to 2020. The study found a stigma effect that increases the probability of career unemployment as the duration of early unemployment experienced by young adults in the entry into the labor market increases. The duration of unemployment negatively affects self-esteem, confirming a psychological stigma effect that alters the psychosocial status of individuals. In addition, unemployment duration harms the quality of life of young adults, confirming that joblessness leads to increased dissatisfaction and a decline in quality of life in many domains. Further, subsequent unemployment experiences of young adults later in their careers exacerbate the negative impact of early unemployment on self-esteem and quality of life. This study demonstrates that youth unemployment is not only a temporary setback for individuals who are not economically active but also a mechanism that exacerbates the subsequent significant socioeconomic costs of unemployment.

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Kim, K. Impacts of Early Youth Unemployment Self-Esteem and Quality of Life: Moderating Effects of Career Unemployment. Applied Research Quality Life (2024). https://doi.org/10.1007/s11482-024-10360-7

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DOI : https://doi.org/10.1007/s11482-024-10360-7

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