Every time a new technology appears that promises to transform everythingthere is a group that ends paying a high price for it. With the arrival of AI, for example, young people and white-collar jobs are the most exposed to pay that bill in the form of a lower hiring rate and salary cuts due to automation of those positions.
A published study by researchers from the London School of Economics and the Complutense University of Madrid, demonstrates with concrete data on Spain that the benefits of technological progress not only they do not reach everyone equally workers, but in many cases they are widening the gap between who else and they charge less.
The Gini does not lie: inequality has a technological name. He Gini coefficient It measures income inequality on a scale where zero equals complete equality and 100 equals absolute inequality. Spain has an index of 30.8 according to the latest data of 2023 from Eurostat, compared to the European Union average, which stood at 29.4 in 2024. Between 2000 and 2016, wage inequality in Spain grew by 6.4 Gini points, the most intense period being between 2008 and 2016, when it rose 4.7 points in just eight years.
The most striking data from the study is that without the effects of automation, wage inequality in Spain would have been 21.5% lower in 2019. To gauge the magnitude of this data, it is worth remembering that in 2000, Spain was 8.8 Gini points below the United States in terms of inequality. In 2019, that difference had narrowed to just 2.2 points.
What technology gives from above, it cuts from below. The numbers are more eloquent when broken down by salary brackets. Without the technological revolution of recent decades, the 10% of workers with the highest incomes would have received a salary quota 3.9% lower than the current one. On the other hand, the 50% of workers with the lowest salaries would have increased their salary by 0.83%, and the poorest 10% of that group would have increased it by up to 2.2%.
Automation and artificial intelligence do not act in the same way, although they both push in the same direction. While the automation of work tends to hit the salaries of the middle and lower section of the distribution, the AI raises wages at the top, thus improving the productivity and bargaining power of those employees who are already better positioned. The data from the last section of the study for the period 2015-2019 shows that, without exposure to AI, the Gini coefficient would have been 9.9% lower in 2019. That is, a smaller gap would have been generated between the highest and lowest salaries.
The educational factor: less studies, more punishment. Another of the decisive findings of the study is related to academic training either employee professional. Workers with a lower level of education have suffered a negative salary impact almost three times greater than those with university studies. Their jobs tend to focus on routine tasks or administrative management, areas very susceptible to the impact of AI and automation.
The wage gap between workers with high and low training has also skyrocketed due to greater technological implementation. In the absence of the effects of automation during the period 2000-2019, the salary difference between workers with different levels of education it would have been 43% lower. The study data shows that young people with little training are the most exposed, while older, highly skilled workers tend to integrate technology into their work rather than compete with it.
Effect of automation by studies and age group
Technology encourages wealth, but not for everyone. The authors of the study do not question technological progress itself, which has proven to be a driver of undisputed economic growth throughout history. What they do question is the idea that the benefits of this progress will end up being distributed naturally throughout society, a vision that according to the study itself does not capture the complexity of the phenomenon. Hence many of the great gurus of AI development bet on a basic income as a way to balance the imbalance which will cause the arrival of AI and the automation of more jobs.
Faced with these data, the authors’ proposals to neutralize this effect go through two fronts. The first is to reinforce investment in education and continuous training, expanding access to non-routine skills such as critical thinking, creativity or social skills, which are less susceptible to automation. In this way, access to technological skills is equalized throughout the workforce.
The second aims to review the tax treatment of capital and labor, given that in many countries taxation favors investment in machinery over hiring people, which can encourage automation processes even when productivity gains are limited.
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