Countries with weak education systems face highest job automation risk

A new international study warns that countries investing the least in education are at the highest risk of job automation, signaling a looming crisis for national economies that fail to prioritize workforce readiness. The study, titled “Are Nations Ready for Digital Transformation? A Macroeconomic Perspective Through the Lens of Education Quality”, was published in Economies and provides a rigorous, data-driven analysis of how education quality directly influences a nation’s ability to cope with digital disruption.

The research introduces a novel methodology that maps OECD countries by combining three critical education indicators, education expenditure as a percentage of GDP, PISA test scores, and the UN’s Education Index, against the proportion of jobs in each country deemed at high risk of automation. The results show a stark divide between nations poised to harness the benefits of digital transformation and those vulnerable to structural economic setbacks due to a poorly prepared workforce.

Which countries are least ready for digital disruption?

The study finds that Eastern and Southern European countries are disproportionately unprepared for the wave of automation sweeping global labor markets. Among them, Slovakia, Greece, Poland, and Lithuania top the list of countries with both high job automation exposure and low levels of education investment and performance. Slovakia stands out with the highest job automation risk of 33.6% and one of the lowest education investment rates, spending just 3.87% of its GDP on education.

In contrast, countries such as Finland, Norway, Sweden, and New Zealand show high educational readiness and low automation risk. These nations invest significantly in education, Norway spends 6.48% of its GDP, and score highly in student performance and educational attainment metrics. The findings suggest that long-term education policy directly shields nations from the destabilizing effects of job displacement by automation.

This divide highlights a critical policy gap: the most resilient nations combine high education investment with low job automation risk, while countries like Slovakia and Greece remain highly vulnerable due to limited investment and underperforming education systems.

How strong is the link between education and automation risk?

Using Pearson correlation coefficients and significance testing, the authors quantitatively demonstrate that the relationship between education investment and automation risk is statistically significant and negatively correlated. Specifically:

• The correlation between education expenditure and job automation risk is −0.60, with a p-value < 0.001, confirming a robust inverse relationship.
• The Education Index also shows a statistically significant inverse correlation of −0.49, reinforcing that nations with weaker education systems face higher exposure to job automation.
• While PISA scores displayed a similar negative trend, the correlation did not reach statistical significance (p-value = 0.06), likely due to data variability across the sampled years.

These correlations suggest that education policy is not just a social issue but a core pillar of digital economic strategy. Countries that underinvest in education risk long-term economic stagnation, rising inequality, and labor market dislocation as automation replaces routine work tasks, particularly in countries with less complex job structures.

What are the policy implications for governments?

The authors argue that current national digital readiness indices overlook a critical dimension: the vulnerability of the future workforce. Most global benchmarks assess infrastructure, connectivity, and digital usage, but fail to account for the structural weaknesses in education that determine how prepared a country’s labor market is for AI-driven disruption.

The study offers specific policy guidance:

• Invest More in Education: Nations with automation-vulnerable economies must raise their public and private education spending, especially in foundational skills and digital literacy.
• Modernize Curricula: Reforms should align education with the demands of the digital economy, emphasizing STEM skills, adaptability, and lifelong learning.
• Target Structural Inequalities: Countries should address regional and demographic disparities in educational access to prevent a widening digital divide.
• Link Education to Labor Markets: Policies should bridge education and employment through dual systems, apprenticeships, and partnerships with technology firms.

The authors also categorize countries into four digital readiness types. For example:

• High Education Index and Low Automation Risk – Includes Norway, Finland, Denmark, New Zealand.
• Low Education Index and High Automation Risk – Includes Slovakia, Greece, Spain, Chile, and Turkey.

These typologies offer a strategic blueprint for tailoring policy responses based on a country’s position in the digital readiness matrix.

Overall, the findings call for urgent, systemic investment in human capital to ensure that digital transformation leads to inclusive progress, not polarized decline.