Reimagining Distribution in the AI Era: What Can We Learn from the Public Employment Gap Between Spain and China?
As AI and automation accelerate their integration into every industry, a fundamental question arises: once societal productivity has dramatically increased, how should the new wealth be distributed to maintain economic stability and vitality? A comparative analysis of public employment models in China, Spain, and Nordic countries offers a real-world frame of reference for this future challenge.
Public Employment Ratios: A Counterintuitive Reality
A common impression is that China has a vast public sector. However, data compiled using relatively consistent metrics shows that the proportion of China’s public sector employees directly supported by government finance, as a percentage of the total employed population, is significantly lower than in many developed European countries.
Estimates based on data from the ‘National Housing Provident Fund 2023 Annual Report’ suggest that in 2023, employees in China’s state organs and public institutions accounted for approximately 6.5% of the total employed population. In contrast, the ‘Government at a Glance 2023’ report from the Organisation for Economic Co-operation and Development (OECD) shows that the direct public sector employment share is about 15% in Spain, and as high as 27% to 30% in Nordic countries like Norway and Sweden.
This disparity reveals different models of social resource allocation. Spain and the Nordic countries channel a larger proportion of social wealth through their tax systems into public services like education, healthcare, and pensions. By hiring a corresponding workforce for these services, they achieve a secondary distribution of wealth. This indicates that the core function of their public sectors extends beyond administrative management to encompass broad social services and welfare.
AI Disrupts the Old Model: From ‘Creating Jobs’ to ‘Distributing Gains’
Previous technological revolutions have all followed the pattern of ‘old jobs disappear, new jobs emerge,’ but the rise of Artificial General Intelligence (AGI) and embodied AI robots may break this cycle. Past machines were specialized tools, whereas AI is now encroaching on domains requiring comprehensive cognition, judgment, and learning abilities, from software programming to professional analysis. Its impact is far more extensive than ever before.
Studies predict that nearly 40% of global jobs will be affected by AI, with this figure potentially reaching 60% in advanced economies. When AI handles cognitive tasks and robots perform physical labor, it will be difficult for human workers to find a new, sufficiently large industry that machines cannot enter to absorb all the displaced labor. This means the cornerstone of future social policy can no longer be the optimistic assumption that ‘there will always be enough new jobs.’ Instead, we must confront the reality that ‘jobs may no longer be plentiful.’
At that point, society’s core conflict will shift from insufficient production to poor distribution. If the immense wealth created by machines flows primarily to a few capital owners while the general public loses purchasing power due to job loss, the economy will fall into a predicament of coexisting ‘overproduction’ and ‘insufficient demand.’
Lessons from the Spanish Model: Building a ‘Second Bridge’ Beyond Employment-Income
Spain’s public employment ratio of approximately 15%, while not as high as in Nordic countries, already demonstrates a crucial social arrangement. It constructs another circular path outside the market-driven ‘employment-income’ distribution chain: wealth created by businesses and individuals is pooled through taxation and then flows back into society in the form of public services (like public healthcare and education) and salaries for public sector employees.
This model serves a dual purpose: on one hand, it reduces residents’ dependence on personal income for essential aspects of life; on the other, it creates stable employment and consumer demand for society. This system ensures, to some extent, that even if individuals do not enter high-profit, market-oriented industries, their basic livelihood and dignity are maintained.
In essence, this is a ‘second bridge’ connecting the fruits of production to the lives of ordinary people, supplementing the ‘first bridge’ of wages. It acknowledges that the distribution of social wealth should not depend entirely on whether an individual holds a market-based job.
Looking to the Future: From ‘Securing Jobs’ to ‘Securing People’
Looking ahead, as automation further penetrates public services, simply maintaining or expanding the public employment ratio is not the ultimate answer. The focus of policy needs to evolve from ‘securing jobs’ to ‘directly securing people.’ This means establishing an inclusive social security system that is not entirely dependent on employment status.
For a country like China, which is rapidly developing AI and automation technologies, proactive institutional design is particularly crucial. Potential policy directions could include:
- Establish an Inclusive Social Safety Net: Shift systems like healthcare, pensions, and unemployment benefits from being ‘work unit-centered’ to ‘individual-centered,’ ensuring that individuals can still receive basic support during periods of unemployment or career transition.
- Explore Automation Dividend-Sharing Mechanisms: Investigate ways to stably convert the surplus profits from automation into a social dividend shared by all citizens. This could be achieved through capital gains taxes, social wealth funds, or pilot programs for universal basic income (UBI) in specific regions.
- Shift Fiscal Priorities: Reallocate more public financial resources from ‘prioritizing growth,’ which often focuses on infrastructure investment, to ‘securing public welfare,’ which focuses on improving social security. This would build a more stable foundation for domestic demand by boosting residents’ income and sense of security.
Ultimately, the central question of development in the age of AI must shift from ‘how to produce more’ to ‘how to fairly distribute what is produced.’ Ensuring that the prosperity created by machines translates into security and dignity for all of society is the key to making technological progress truly serve human well-being.
