Structural unemployment

Topic: Employment, Unemployment and Informalisation · NCERT: Beyond NCERT

Meaning

Structural unemployment is long-term unemployment caused by a mismatch between workers' skills, or where they live, and the jobs that are available. The mismatch comes from changes in technology or in the structure of the economy.

It matters because a rise in demand alone cannot cure it. The unemployed person still lacks the right skill, or is still in the wrong place. Structural unemployment is also one of the two parts of the natural rate of unemployment:

Natural rate of unemployment = Frictional unemployment + Structural unemployment

Explanation

How it arises

• The economy changes, but workers cannot change as fast.
• A new technology or a new industry creates demand for new skills.
• Old industries shrink or close.

• Workers trained for the old jobs cannot move straight into the new ones.

• There are two kinds of mismatch:

• Skill mismatch: jobs exist, but workers do not have the skills those jobs need.

• Location mismatch: jobs exist, but in a different region, and workers cannot easily move there.

• It lasts a long time. Frictional unemployment is a short gap between jobs. Structural unemployment can last for years, because learning a new skill or moving to a new city takes time and money.

• It stays even at full employment. Full employment (the level at which only frictional and structural unemployment are left) does not mean zero unemployment.

Its place in the natural rate

• The natural rate of unemployment is frictional plus structural unemployment. The economy tends to move back to this rate in the long run. This is the view of Friedman and Phelps, who developed the idea in the late 1960s.
• NAIRU (non-accelerating-inflation rate of unemployment) is the unemployment rate at which inflation stays stable.
• If demand stimulus pushes unemployment below NAIRU, labour becomes scarce, wages rise and inflation speeds up.

• This is why stimulus is the wrong tool for structural unemployment. It raises prices, but the mismatched workers still do not get jobs.

• Worked example:

• Frictional unemployment = 2%. Structural unemployment = 3%.
• Natural rate = 2 + 3 = 5%.
• If actual UR (unemployment rate) = 7%, then cyclical unemployment = 7 − 5 = 2%.
• A fiscal or monetary stimulus can, at best, remove the 2% cyclical part. The 3% structural part needs reskilling.

What makes it rise or fall

• It rises when:
• Technology changes fast. Examples are machines, automation and AI. Technological unemployment is a close cousin of structural unemployment.
• Whole industries shut down or move away.

• Hysteresis (high unemployment that stays even after the recession that caused it has ended) sets in:

  • People who stay unemployed for a long time lose skills and job contacts.
  • Employers start to see them as less employable.
  • Cyclical unemployment turns into structural unemployment, and the natural rate rises.

• It falls with:

• Reskilling and training in new skills.
• Apprenticeships, which mean learning a skill on the job.
• Help with moving (labour mobility) to places where the jobs are.

In India

• Classic Indian examples:
• Ahmedabad textile mill workers lost their jobs when the mills closed in the 1980s. Their mill skills did not fit the new jobs.

• Handloom weavers lost work when powerlooms replaced them.

• The AI threat today:

• The Economic Survey 2024-25 says AI may automate a large part of economically valuable work. This could cause large-scale labour displacement, especially of workers in the middle and lower parts of the wage distribution [4].
• India's IT-BPM sector grew on labour arbitrage (moving work to places where labour is cheaper). AI can automate routine coding and back-office tasks, and these are exactly the mid- and low-wage jobs the Survey flags [4].

• According to the Survey, India's advantage is its young, tech-savvy workforce, which can use AI to augment (add to) its productivity [4].

• The youth skill gap:

• The overall UR (usual status, 15+) was 3.2% in PLFS 2023-24 [1]. PLFS is conducted by NSO under MoSPI.

• But youth UR (15-29 years) was 10.2% in 2023-24, about three times the overall rate [2]. One reason is a mismatch between what young people are trained for and the jobs on offer.

• Policy response: Skill India-type reskilling, apprenticeships and labour mobility. Job portals such as the National Career Service (launched July 2015) mainly tackle frictional unemployment, not structural [3].

• A caution: open UR in India is low partly because most poor people cannot afford to stay unemployed. So a skill mismatch often shows up as low-paid informal work or underemployment (working fewer hours or at lower productivity than one wants), not as open unemployment.

Don't confuse with

• Frictional unemployment: a short spell of searching for or switching jobs. The worker already has usable skills. The cure is better job information, such as NCS [3]. Structural unemployment is long and needs reskilling.
• Cyclical unemployment: caused by deficient aggregate demand (too little total spending) in a recession. It goes away when the economy recovers, and fiscal or monetary stimulus cures it. Structural unemployment does not go away with a recovery.
• Technological unemployment: workers replaced specifically by machines, automation or AI. It is one cause of structural unemployment. Structural unemployment is wider, because it also covers location mismatch and the decline of whole industries.
• Disguised unemployment: more people work on a job, often a family farm, than are needed, so their marginal productivity (the extra output from one more worker) is close to zero. These people look employed. A structurally unemployed person is openly without work.

Prelims Hooks

• Natural rate of unemployment = frictional + structural. It excludes cyclical unemployment.
• Full employment ≠ zero unemployment. Structural and frictional unemployment remain even at full employment.
• Trap: reskilling cures structural unemployment, and fiscal stimulus cures cyclical unemployment. A question saying "demand stimulus removes structural unemployment" is wrong.
• Hysteresis: long spells of cyclical unemployment can turn into structural unemployment, which raises the natural rate.
• National Career Service portal (Ministry of Labour & Employment, July 2015) targets frictional unemployment, not structural [3].
• Economic Survey 2024-25: AI could cause large-scale displacement of middle- and lower-wage workers [4].

Mains Points

• Match the cure to the type: demand stimulus helps with cyclical unemployment, but it only adds inflation when the problem is structural. India needs reskilling, apprenticeships and labour mobility for structural and technological unemployment, plus job-matching platforms such as NCS [3] for frictional unemployment. A single tool cannot fix all of them.
• AI and the IT-BPM model: India's services growth was built on cheap skilled labour. AI now threatens mid- and low-wage tasks [4]. Policy should help workers use AI to raise their productivity rather than be replaced by it, with social protection for those who are displaced [5].
• The youth mismatch and scarring: a youth UR of 10.2% (2023-24) [2], against an overall 3.2% [1], points to a skill-job gap. Long spells without work can cause hysteresis, where young people lose skills and cyclical unemployment becomes structural. This supports early-career employment and skilling programmes, and tracking job quality, not just the headline UR.

Related concepts

• Voluntary unemployment
• Involuntary unemployment
• Frictional unemployment
• Cyclical unemployment
• Technological unemployment
• Natural rate of unemployment
• Hysteresis
• Okun's law
• Efficiency wage
• Labour arbitrage

Read more

• Unemployment in macroeconomic theory