The Economic Impact of Structural Unemployment on Global Growth

Structural unemployment affects over 250 million workers worldwide, slowing GDP growth and widening inequality as industries evolve faster than workforce skills. Automation and AI are projected to displace 85 million jobs by 2025, while creating 97 million new ones. The question remains: can global economies adapt quickly enough to bridge this gap?

Unemployment is one of the most-watched indicators of a nation’s economic health. It tells us not just how many people are out of work, but also how effectively an economy is using its available human talent. 

Within developed economies, the average unemployment rate in early 2025 was estimated at around 4.9 per cent. The world’s GDP expansion, which reached around 3.3 per cent in 2024, is expected to ease to about 2.9 per cent in 2025 and 2026. 

Structural unemployment occurs when the skills workers possess no longer match the needs of the economy. It often results from technological progress, globalisation, or long-term changes in industry structures. Unlike temporary job loss during a recession, this form of unemployment can persist even when the economy is growing, because people cannot easily shift into new roles or sectors.

Structural unemployment affects over 250 million workers worldwide, slowing GDP growth and widening inequality as industries evolve faster than workforce skills. Automation and AI are projected to displace 85 million jobs by 2025, while creating 97 million new ones. The question remains: can global economies adapt quickly enough to bridge this gap?

Understanding structural unemployment

To understand structural unemployment, it helps to first look at the broader picture of how economists classify unemployment. There are generally three main types:

• Frictional unemployment – when people are temporarily between jobs or entering the workforce.
• Cyclical unemployment – caused by downturns in the business cycle, such as recessions.
• Structural unemployment – caused by a mismatch between workers’ skills and the jobs available.

Unlike the other two, structural unemployment is often long-term. It is not about a lack of demand but about the structure of the economy itself changing. 

For example, when manufacturing moved from Europe and North America to Asia in the 1990s and 2000s, millions of skilled factory workers lost their jobs. Many could not find new work because their expertise did not fit the growing service and technology sectors.

Several key factors drive structural unemployment:

1. Technological Change: Automation and artificial intelligence are replacing repetitive or manual tasks. While this raises productivity, it also means some workers become redundant if they lack digital skills.
2. Globalisation: Production shifting across borders leads to the decline of local industries, leaving communities behind.
3. Policy and Education Gaps: When education systems fail to adapt to modern labour market needs, skill mismatches deepen.
4. Demographic Shifts: Ageing populations and migration patterns also influence which skills are in demand.

The reality is that structural unemployment does not simply resolve itself with time. Economies can grow, stock markets can rise, and yet large sections of the workforce may remain excluded. This makes it a silent but powerful drag on long-term global growth.

How structural unemployment affects economies

Structural unemployment creates long-term imbalances that weaken both national and regional economies. When industries decline or new technologies change how work is done, many workers lose jobs they cannot easily replace. This leads to lower productivity, income inequality, fiscal pressure on governments, and social challenges. 

Below are some real-world examples with statistics showing how structural unemployment affects economies.

Decline of Coal Mining in the United States (Regional Structural Unemployment)

In regions such as Appalachia, employment in coal mining has fallen dramatically, from about 140,000 workers in 1969 to only 51,000 in 2019. This steep drop left thousands without transferable skills or access to new opportunities. 

Former coal miners who managed to find other jobs earned on average 30% less per year than before. As a result, local economies in coal-dependent towns suffered significant declines in productivity and income levels, creating a persistent cycle of unemployment and underemployment.

Regional Disparities in the European Union (Geography & Industry Mismatch)

In 2023, the European Union’s average unemployment rate stood at 6.2%, but some southern regions, such as Calabria in Italy, had employment rates as low as 48.5%. These stark differences highlight how certain areas continue to struggle due to outdated industrial structures and a lack of new investment.

Automation and Technological Transformation

The rise of automation and artificial intelligence has created a major structural shift in global labour markets. Between 2015 and 2024, automation and AI technologies replaced around 85 million jobs worldwide, while simultaneously creating about 97 million new roles requiring digital and analytical skills. However, many displaced workers lack the training to fill these new roles, widening the gap between high-skilled and low-skilled workers.

Skill Erosion and Wage Decline

Workers in industries transforming often face wage stagnation or decline. In the United States, workers displaced by industrial restructuring earn, on average, 20–30% less when they shift to new sectors. This reflects the loss of skill value and the difficulty of retraining older workers. Over time, these trends reduce national productivity and lower overall consumer spending, weakening economic growth.

Fiscal Pressure and Social Costs

Long-term unemployment caused by structural changes increases government spending on welfare and retraining programmes. For example, in the U.S. and parts of Europe, social protection spending rose by over 15% in regions most affected by industrial decline.

Global growth and long-term consequences

The impact of structural unemployment extends far beyond national borders. In today’s connected economy, large-scale job losses in one region can disrupt global supply chains, trade, and investment flows.

Countries with high structural unemployment often face slower GDP growth and weaker competitiveness. According to the IMF, nations with long-term unemployment above 7% experience, on average, 1.2% lower annual GDP growth. As workers fail to adapt to new industries, productivity falls and exports decline.

A Dual Challenge for Developed and Developing Nations

In advanced economies like the United Kingdom and the United States, automation and AI are reshaping work. By 2030, up to 30% of current jobs could be automated. Older industrial sectors,  manufacturing, mining, and printing, have already shed millions of jobs, and many workers lack the digital skills needed for modern roles.

Developing economies face a different risk. Over 230 million people in Africa and Asia rely on low-skill, routine work that is highly vulnerable to automation. Without digital transformation and policy reform, the global employment divide may widen, limiting long-term growth.

Impact on Global Trade and Innovation

Persistent structural unemployment reduces consumer spending and slows demand for goods and services. The World Bank estimates that each 1% rise in long-term unemployment cuts household consumption by 0.6% globally. Lower spending discourages business investment, reducing innovation and productivity.

Moreover, regions hit hardest by job losses often experience rising protectionism and anti-globalisation sentiment, as communities left behind by automation and offshoring push for policy change. Addressing structural unemployment is therefore not just an economic priority — it’s vital for maintaining global stability and inclusive growth.

The role of technology and automation

Technology has always been a driver of economic change. Each major industrial shift has replaced certain types of work while creating new ones. 

However, the current wave of digital transformation, with advances in artificial intelligence (AI), robotics and machine learning, is different. The speed and breadth of change mean many workers are struggling to keep up, contributing to rising structural unemployment.

Automation and Job Displacement

Recent research shows that in the United States, approximately 23.2 million jobs, about 15.1% of all employment, had at least half of their tasks automated in 2025. Globally, around 14.7 million young workers aged 18-34 are expected to be displaced by automation in the coming decade, highlighting the vulnerability of routine and entry-level roles. By 2030, it is projected that up to 47.8% of jobs in China, 24.3% in India, and 14.8% in the United States could be at risk from automation.

These numbers demonstrate how automation heightens the mismatch between available jobs and workers’ skills, thereby reinforcing structural unemployment.

The Digital Divide and Inequality

A global survey of over 23,000 workers found that 76% of respondents felt unprepared for the digital future due to a lack of relevant skills. Around 87% of jobs now require some form of digital skill, yet less than half of the global adult population fully possesses them. The global cost of closing the digital-skills gap has been estimated at over US$150 billion.

Without bridging this divide, structural unemployment will remain a major obstacle to inclusive economic growth.

Adapting to the Technological Shift

Globally, around 50% of companies report difficulties in finding candidates with the right technical skills. In sectors most affected by automation, workers without AI-related training are 2.3 times more likely to lose their jobs. These figures highlight the importance of policy coordination, industry collaboration and forward-looking education systems to ensure that technology becomes a force for growth rather than exclusion.

Education, Skills, and Re-training

Education and skill development are the strongest tools against structural unemployment. In today’s fast-changing economy, workers must continually learn and adapt, yet many education systems fail to keep pace with market needs.

• Lifelong Learning- A single degree is no longer enough. Skills can become outdated within years, making lifelong learning, through online courses, vocational training, and employer-led upskilling, essential. Only 28% of workers globally take part in digital skills training, while 75% feel unprepared for future jobs and 40% believe their skills are already outdated.
• Vocational and Technical Education- Academic learning alone cannot meet modern labour demands. Technical and vocational programmes prepare workers for high-demand roles. In developing nations, just 50% of adults are digitally literate, limiting their job opportunities. Countries like Germany succeed with dual education systems combining classroom study and apprenticeships, helping reduce long-term structural unemployment.
• Reskilling for the Digital Age- Digital literacy is now as vital as reading and writing. Only 17% of employees consider themselves “advanced” in digital skills, while the global skills gap costs businesses US$1.3 trillion annually in lost productivity. Large-scale reskilling and upskilling programmes are key to building adaptable, inclusive, and future-ready workforces.

Government and policy interventions

Addressing structural unemployment requires more than short-term fixes — it demands coordinated, forward-looking policy across education, labour markets, business incentives and welfare systems. This section outlines how governments can act, supported by real-world examples and data.

Reforming Education and Labour Markets

Governments must ensure that education systems prepare people for the jobs of tomorrow, not yesterday. This means embedding digital skills, critical thinking and problem-solving into school and training programmes. Some countries have adopted “dual education” models, combining classroom learning with workplace apprenticeships, to better align workforce skills with industry demand.

 In addition, labour market policies should allow workers to transition between sectors and regions more easily; this includes recognising qualifications, reducing relocation barriers and making benefit systems adaptive.

For example, supply-side labour market reforms show that better education and training help reduce structural unemployment and improve real wages. One model illustrates that increasing the productivity of labour through improved education shifts employment up and unemployment down, thereby reducing structural unemployment.

Incentives for Businesses

Beyond training workers, policy must support businesses to absorb displaced workers and create new roles. This can include:

• Tax incentives or subsidies for firms that invest in employee training or hire retrained workers.
• Public-private partnerships to foster innovation in sectors expected to grow (such as green energy, tech or logistics).
• Regional investment to create jobs in areas left behind by industrial decline, reducing regional structural unemployment.

These approaches help convert the challenge of structural change into an opportunity for, growth of new sectors with a ready workforce.

Social Safety Nets and Inclusion

Long-term structural unemployment not only affects the economy but also has major social implications. Governments need to combine immediate support for affected workers (like unemployment benefits, retraining subsidies, relocation aid) with strategies to reintegrate them into employment. Without this dual approach, the risk is that large groups become permanently excluded from the workforce.

For instance, reports show that countries with high structural unemployment often have persistently elevated long-term jobless rates, despite economic recovery, a signal that the problem lingers without structural policy.