Since 1995, manufacturing’s share of GDP has fallen in many major economies. This ranking shows where deindustrialization was strongest and why the story differs across countries.
AI summary
- Manufacturing share of GDP can shrink even when factory output rises, because services expand faster.
- Use manufacturing value added (% of GDP) to compare structure across countries; pair it with jobs data to understand labor impacts.
- From 1995 to the latest comparable year (often 2022 in cross-country datasets), several advanced economies saw large percentage-point declines.
- Emerging economies follow different paths: some keep high shares longer, others face “premature” deindustrialization concerns.
- Supply chains, automation, and industrial policy shape outcomes, but do not guarantee a return to old manufacturing peaks.
Chart
The manufacturing shift in perspective
Why a falling share can still coexist with rising output.
Manufacturing once defined economic strength. For much of the twentieth century, a rising factory base meant jobs, exports, and growth. Since the mid-1990s, however, many economies have seen manufacturing’s share of GDP decline while services expanded.
This shift is often labeled deindustrialization. The term sounds dramatic, but the reality is more nuanced. In many cases, manufacturing output continued to grow in absolute terms, even as its share of GDP fell because services grew faster.
This article ranks where manufacturing’s share declined most from 1995 to the latest widely comparable year in major datasets (often 2022). It explains why trends differ across advanced and emerging economies and what the shift means for jobs and policy.
How to measure deindustrialization correctly
GDP share is structure; jobs data is labor impact.
Deindustrialization is commonly measured using manufacturing value added as a percentage of GDP. This indicator, published by the World Bank, captures how large manufacturing is relative to the entire economy.
A falling share does not automatically mean factories are closing. It can reflect:
- Rapid growth in services such as finance, healthcare, and digital sectors
- Productivity gains that allow the same output with fewer workers
- Global supply-chain restructuring
Employment data tell a different story. Manufacturing employment often falls faster than output because automation increases output per worker. That distinction matters when interpreting rankings.
Rankings: where manufacturing share fell the most
A percentage-point lens highlights structural change.
The chart above summarizes the percentage-point change in manufacturing value added as a share of GDP between 1995 and 2022 (a common “latest comparable” year across countries in the World Bank’s WDI series). Values are rounded to one decimal.
The United Kingdom experienced one of the largest declines among major economies. The United States and France also saw substantial drops. Germany and Japan declined more moderately, while Mexico maintained or slightly increased its share alongside deeper North American supply-chain integration.
Advanced versus emerging economies
Different starting points, different trajectories.
Advanced economies typically began deindustrializing earlier. Rising incomes increased demand for services, while productivity gains in manufacturing reduced labor intensity. As a result, the manufacturing share of GDP declined steadily.
Emerging economies followed varied paths. China maintained a high manufacturing share for decades before beginning a gradual decline. India’s manufacturing share fluctuated but did not rise sharply, reflecting structural challenges. Brazil’s decline illustrates concerns about premature deindustrialization, a concept discussed in development economics.
These patterns show that deindustrialization does not follow a single trajectory. Institutional quality, trade openness, and industrial upgrading strategies shape outcomes.
Supply chains, automation, and policy shifts
Why “bringing factories back” is harder than it sounds.
After 2000, global value chains deepened. Production stages fragmented across countries. Some economies specialized in high-value components, while others focused on assembly.
Automation also transformed output dynamics. Robotics and digital manufacturing allowed firms to increase production without proportional employment growth. This reduced manufacturing’s employment share faster than its GDP share.
Following the 2008 financial crisis and the COVID-19 shock, industrial policy regained attention. Governments introduced incentives for semiconductor production, green manufacturing, and strategic industries. Whether these policies reverse long-run trends remains uncertain.
Implications for jobs and inequality
The transition matters more than the label.
Manufacturing historically offered stable middle-income employment. When factory jobs decline, regional labor markets can struggle to adapt. Research links sharp local manufacturing declines to wage stagnation and political polarization.
However, high-income economies that retained advanced manufacturing often moved toward capital-intensive and export-oriented sectors. The challenge lies not in preserving old structures, but in upgrading skills and infrastructure.
For workers, the key issue is transition. Education systems, retraining programs, and regional investment policies shape how smoothly economies adapt to structural change.
FAQ, sources, and how to reuse the data
Definitions, quick answers, and the source list.
The manufacturing shift since 1995 reflects structural transformation rather than uniform decline. Some countries saw steep reductions in manufacturing’s GDP share, while others remained resilient or stabilized.
FAQ
- What is deindustrialization?
A decline in manufacturing’s share of GDP or employment over time. - Does a falling share mean factories are closing?
Not necessarily. Output can grow even as the share declines. - Why did the UK decline more sharply?
Rapid service-sector expansion and earlier structural shifts played a role. - Is Germany deindustrializing?
Germany’s share declined moderately but remains high by advanced-economy standards. - What about China?
China maintained a high share for decades and only recently began gradual decline. - What is premature deindustrialization?
When manufacturing peaks at lower income levels than historically observed. - Can policy reverse deindustrialization?
Policy can influence structure, but long-run trends depend on productivity and demand. - Why use GDP share instead of employment?
GDP share captures economic structure; employment captures labor impact. - Is deindustrialization harmful?
It depends on how smoothly workers transition and how competitive remaining industry is. - Where can I verify the data?
From the World Bank World Development Indicators database.
Sources
- World Bank World Development Indicators — https://data.worldbank.org/indicator/NV.IND.MANF.ZS
- UNIDO Industrial Statistics — https://stat.unido.org/
- OECD Data Portal — https://stats.oecd.org/