Youth employment in low- and middle-income countries is in crisis, with rapid population growth outpacing job creation, leaving millions unemployed or trapped in informal, low-quality work.
Around the world, youth face disproportionately high unemployment and underemployment. The – roughly three times the adult rate (5.1%). In 2023, youth joblessness reached 20–25% in regions like the Middle East and North Africa, compared to about 9–10% in Sub-Saharan Africa and South Asia. But unemployment as an indicator understates the problem in poorer countries, where few can afford to remain jobless.
An were unemployed, underemployed, or working insecure jobs even before the COVID-19 shock. Currently, one of out five youth (~260 million) are not in employment, education or training (NEET), and is . The “youth employment crisis” cannot be explained through just a lack of jobs, but also a lack of decent work, skills mismatches, youth expectation and aspiration gaps and structural barriers that prevent young people in LMICs from securing decent livelihoods.
LMICs face a perfect storm of challenges fueling youth unemployment – a booming working-age youth population outpacing formal job growth, education and training that often fail to match labor market needs, the dominance of informal employment with poor job quality, and structural barriers like restrictive regulations and urban-rural divides. These factors combine to make the school-to-work transition difficult for millions of young people. Below we break down these key challenges:
The “youth bulge” in developing countries means millions of new jobseekers each year, but economies are not creating enough formal jobs to absorb them. Over the next decade, about will enter the global labor force - a demographic wave concentrated in Asia and Africa. In Sub-Saharan Africa alone, across the region every year in the coming decade, yet only ~3 million new formal wage jobs are currently created each year. Such explosive growth in youth labor supply, without commensurate growth in stable jobs, creates intense competition for the limited opportunities available. Realizing the potential demographic dividend of these young populations will require economies .
More than in their workforce. While this is primarily driven by young adult workers who are undereducated rather than overeducated, the latter has been steadily rising.
In many countries, this – for example, large numbers of university graduates remain unemployed even as industries report unfilled job vacancies in technical and skilled trades. , more than half of unemployed youth had a secondary school diploma and as many as 40% held a college degree. covering comparing skills mismatches across found that on average only 52% workers were in jobs well-matched to their education. Of those mismatched, 36% were over-educated while about 12% were under-educated. Another worldwide found that less than half of employees were considered well-matched. A notes that, college-educated youth unemployment coexists with shortages of IT, engineering, and other technical workers, because many students graduate in fields with limited job prospects. This mismatch stems in part from curricula and training programs that are out of touch with labor market needs, as well as limited career guidance.
Because formal jobs are scarce, the vast majority of employed youth in LMICs end up in informal employment. This often means working on family farms, in petty trade, or self-employment in micro-businesses – typically without contracts, protections, or steady salaries. These rates of informal employment among youth were much higher in developing and emerging economies. : in Sub-Saharan Africa and South Asia, 86–88% of young female workers are self-employed (mostly in informal work), far higher than for young men.
Labor mobility—both domestic and international—shapes youth unemployment in LMICs. Limited rural jobs push many young people to migrate to cities. While such rural-urban migration once improved individual prospects, it has been found to exacerbate urban joblessness – for example, than in rural areas across Africa and the Middle East. Furthermore, structural mobility restrictions can lock youth out of opportunities. This includes poor transportation infrastructure, housing costs, or even formal restrictions on internal migration in some countries, which make it difficult for youth to relocate for jobs. International mobility offers potential opportunities but is , and vulnerable to - locking out many young people from working abroad.
In many LMICs, regulatory constraints and weak business climates impede job creation. Cumbersome business registration, high taxes or compliance costs, and rigid labor regulations (like high minimum wages or strict firing rules) may discourage firms from hiring formal employees – hitting inexperienced youth the hardest. For instance, if it’s costly or risky to take on a new worker, employers are less likely to give a chance to a first-time young jobseeker. Additionally, labor market institutions (public employment services, job information systems, etc.) are often underdeveloped, which exacerbates information gaps between youth and employers (more on this in the following section).
Rapid technological change and subsequent labor demand shifts. The projects that, in the next 5 years, 170 million jobs will be created while 92 million jobs are displaced, constituting a structural labor market churn of 22% of the 1.2 billion formal jobs in the dataset being studied. This pace of technological change may worsen the skills mismatches and access to opportunities mentioned above as traditional education systems struggle to keep up with rapidly evolving labor demand, a much larger concern for resource- and budget-constrained LMIC institutions than for those more proximate to the global technological frontier driving these changes.
By connecting the dots between jobseekers and employers, good intermediation reduces information asymmetries, helps align skills supply with demand, and lowers the frictions that keep youth out of jobs. This includes better data systems, job-matching platforms, career guidance, training aligned to market needs (often with results-based incentives), targeted support for young jobseekers, and digital innovations – including open-source tools – that make job markets more transparent and efficient.
Below we delve into the different ways in which labor market intermediation addresses youth employment, followed by the promise of digital platforms and open source initiatives in shaping inclusive and accessible intermediation for the underserved.
Labor market intermediation plays a crucial role in connecting jobseekers with opportunities, especially for inexperienced young entrants who lack professional networks. Improved intermediation can close this gap by aggregating job listings, validating candidates’ skills, and streamlining matches. These efforts reduce time and friction (“search costs”) in the hiring process in mainly two ways:
By improving market transparency: In many LMICs, information asymmetry is a big problem – youth often do not know where jobs are and employers struggle to identify reliable, qualified candidates. Labor market intermediation helps improve circulation of information in the market through on one hand, aggregating and actively publicizing vacancies, and on the other hand, by guiding jobseekers in properly positioning themselves based on firm/industry expectations (career counseling, CV guidance, interview coaching)
By making information flows more targeted and efficient: Even with the rise of online job portals, the notes that obtaining the right kind of information remains a challenge for both job-seekers and employers. So now, modern job-matching platforms go beyond simple job boards: they can use advanced technology to pair candidates with suitable jobs in a more targeted way. For example, digital platforms that collect profiles of jobseekers (skills, education, and work preferences) and vacancies from employers, can algorithmically suggest good matches, alert youth to openings, and help employers find talent they might otherwise overlook.
To address the skills mismatch, many countries have been pursuing demand-driven training and education reforms as part of labor market intermediation. Demand-driven approaches start by asking: What skills are actually in demand by employers? and then working backward to shape training programs accordingly. This often involves close partnerships between training providers and industry. For example, some successful youth employment programs in Latin America have combined classroom vocational training with on-the-job internships in sectors that are hiring – essentially guaranteeing that what youth learn is immediately relevant to available jobs. Evaluations of such programs find positive effects on employment and earnings (e.g. , , , ). Key features include teaching technical skills that local employers are seeking, as well as “soft” skills and work readiness, and then placing youth into apprenticeships or short-term jobs to gain experience.
On the demand side, employer incentives can encourage firms to hire youth whom they might otherwise consider “high risk” due to lack of experience. Other demand-side measures include apprenticeship incentives (offsetting the cost for companies to train novices) and reducing regulatory burdens for hiring youth (for instance, providing a tax break or easing strict labor rules for youth apprenticeships).
Digital job-matching platforms have been transforming how workers in low- and middle-income countries (LMICs) connect with jobs across formal, informal, and high-skilled sectors. Traditionally, most workers in developing countries found jobs through personal networks or by directly approaching employers, with only a tiny fraction using employment agencies. This reliance on informal networks . Online platforms help overcome this information gap by aggregating job listings and making them easily accessible. They reduce search frictions by centralizing job information, letting workers find opportunities across locations and sectors, and even offering online tools for skills testing and verification. These efficiencies led by digital platforms have extended employment access to groups often underserved in traditional labor markets.
In the informal sector, mobile and web-based job exchanges now connect low-income and unorganized workers with work opportunities at scale. Such platforms leverage mobile technology requiring none to low internet access, using simple text-based interfaces to connect workers with employers, and dramatically increasing the visibility of informal workers to potential employers, helping even those without formal credentials find jobs.
Gig economy apps (from ride-hailing to online freelancing marketplaces) allow workers to earn income on a task or contract basis. an estimated 4.4% to 12.5% of workers worldwide (full- or part-time) Including location-based apps (like rideshare and delivery), up to 12% of the global labor market may already be gig workers. In developing countries, these platforms are opening unique avenues for youth, women, and rural populations who have been left out of traditional job markets.
by providing opportunities for young people, women, low-skilled workers, and those in areas with few local jobs. In fact, most online gig workers are youth under 30 seeking to earn or learn new skills, and women have been found to participate in the online gig economy at higher rates. that 42% of online gig workers were women, while women's participation in the general labor market in those countries was only 31.8%. In societies where cultural norms limit women's mobility or confine them to domestic responsibilities such as childcare, online gig work provides a practical solution, enabling them to earn an income while managing their household duties.
Digital platforms have also expanded pathways for high-skilled professionals in LMICs. Through online freelancing websites and remote work portals, skilled workers in developing countries can now access clients and jobs globally. This effectively “exports” skilled labor services from LMICs and brings in earnings. in Sub-Saharan Africa, job postings on a major online work platform jumped 130% between 2016 and 2020, far outpacing the 14% growth seen in North America. By reducing geographic barriers, these platforms integrate LMIC talent into international markets, creating opportunities for software developers, designers, writers, and other professionals to secure contracts that were once out of reach.
Artificial intelligence (AI) and data analytics have exponentially enhanced capabilities of digital platforms in job-matching, workforce analytics, and skills alignment. Modern online employment systems increasingly deploy machine learning algorithms to match job seekers with vacancies far more effectively than basic keyword searches. Unlike traditional job boards that rely on one-to-one keyword matching, AI-driven platforms can interpret the context and meaning of job requirements and candidate profiles. where the platform suggests the best candidates for an employer and the best jobs for a candidate, often with a “match score”.
Beyond matching, AI enables advanced jobseeker analytics and insights that were previously unattainable. , platforms can analyze profiles and employment histories to identify trends and predict outcomes. For example, Belgium’s public employment service (VDAB) uses to job-seekers’ click-activity and behavior to predict the time jobseekers are unemployed. The Austrian PES also developed a statistical model that estimates a job seeker’s probability of short-term and long-term unemployment, allowing counselors to target support to those at highest risk of staying jobless.
Gamification of psychometric assessments has also picked up speed in recent years, including among public employment services. In India, the National Skill Development Corporation (NSDC) partnered with KnackApp to develop a candidate profiling mechanism (skills, traits, and entrepreneurship potential) through cognitive games. This is used to guide students to career opportunities and jobs best suited to their interests.
AI tools are also being used to forecast labor demand – France’s “La Bonne Boîte” uses a predictive algorithm to analyze recruitments from the past 12 months, to predict those for the next 3 months. This data enables jobseekers to identify a shortlist of companies 'with high hiring potential' to help target unsolicited applications. These kinds of analytics improve decision-making for both workers and policymakers: jobseekers get data-driven guidance (for example, which industries are growing or which skills are in demand), while governments obtain real-time labor market intelligence to design better training and employment programs.
AI-driven career assistants are enhancing the personalization of guidance and training for workers on these platforms. Intelligent career assistants or “job coach” chatbots analyze user profiles, labor market data, and hiring trends to interactively provide tailored job recommendations, upskilling suggestions, and career coaching. However, the jury is still out on the effectiveness of these Ai-enabled career guidance tools. , found null effects across the board on key search and employment outcomes.
Crucially, AI is helping shift digital employment platforms toward skills-based matching and alignment. Traditional recruitment focuses heavily on formal qualifications and job titles, which can overlook candidates who have the right skills but non-linear backgrounds. AI allows platforms to parse rich data on hard and soft skills from resumes, online profiles, behavioral assessment and match those to job requirements.
Advanced job platforms now often include a competency-based matching component: rather than filtering candidates by degree or past job titles alone, the algorithm considers the full spectrum of technical skills, transferable skills, and even aptitudes. This holistic approach means a job seeker’s coding, language, or teamwork skills (even if self-taught or gained informally) can be recognized and matched to open positions, widening opportunities. It also helps employers discover talent that might be hidden in non-traditional resumes.
Many countries have have expanded traditional labor taxonomies and frameworks to include skills and competencies. The European Commission's European Skills, Competences, Qualifications and Occupations (ESCO)now defines nearly 13,500 skills mapped to the ILO's pre-existing occupational pillars. Frameworks like ESCO and O*Net, its US-equivalent, provide a holistic mapping of occupations and skills, but have been challenging to localize and apply to more developing/emerging contexts - giving rise to of leveraging big data from online job vacancies and applicants' profiles in combination with natural language processing (NLP) to extract information on skills and create local taxonomies from scratch.