By Nikolay Gueorguiev and Ryota Nakatani
Careful calibration of spending and fiscal policies can reduce inequalities caused by automation.
For many observers, automation has been responsible for both strong economic growth and growing inequality in many countries over the past few decades. Automation increases productivity, but it can exacerbate inequality. This is because it replaces low-skilled workers and helps capital owners earn more monopoly rents. And with the advent of next-level automation in the form of robots, the challenge is more pressing than ever.
Fiscal policy tools can reduce inequality, generally at the cost of anticipated long-term growth.
In a recent IMF staff survey, however, we find that the right fiscal policies – public spending and fiscal policies – can improve the trade-off between economic growth and inequality. But not all fiscal policies are equally effective in this regard.
We have studied several comprehensive fiscal policy packages to address the trade-offs between growth and inequality in the age of automation. Inequality can generally be reduced by redistributing some of the automation gains from the winners (capital owners and skilled workers) to the losers (generally low-skilled, suffering from job losses and low wages). That said, redistribution policies generally require additional taxation, which can depress investment and labor supply and therefore reduce production. We discuss the pros and cons of the various policy packages and try to define the relevant trade-offs between growth and inequality for each of them.
Finding the right balance
For our analysis, we captured the hallmarks of automation: replacing low-skilled workers and increasing the productivity, profits and thus the market power of its users. We link corporate market power to the degree of automation based on empirical evidence. Specifically, we assume a positive correlation between firms’ price markup (a measure of market power) and their use of robots (a proxy for automation), calibrating the relationship using US data. Intuitively, the greater the number of robots per worker, the greater the productivity and the greater the profits. For example, large companies can take advantage of owning the platform they have created and acquire other companies in the same industry to gain high market share and high margins.
Our research examines the trade-offs between growth and inequality through the prism of three taxation and redistribution packages: a capital income tax, a corporate profit excess tax (the reload tax), and a tax on robots. All packages involve an increase in a particular tax, with the proceeds used for transfers to low-skilled workers. A fourth package directly cuts the payroll tax for unskilled workers.
We have found that the effects and trade-offs are very different in the short term versus the long term. In the short term, three policy packages (excluding capital income tax) produce modest per capita earnings and a significant reduction in inequality. However, over time, capital accumulation and productivity begin to slow down. The robot tax is the most powerful tool to reduce inequality, as it slows the replacement of low-skilled labor with robots, but the downside is the slower build-up of highly productive robots and the lack of production. Similarly, a reduction in taxes on unskilled workers reduces inequality and increases output in the short run, while the higher share of unskilled labor (less productive than robots) weighs on productivity in the long run.
Another way to tackle the problem is to compare the income dynamics of skilled and unskilled workers, a key aspect of inequality. The story is similar. Skilled workers, who work with (and thus integrate) robots into the manufacturing process, will see an initial increase in their incomes but a gradual decline over a longer period. Unskilled workers benefit from redistribution policies in a lasting way, although the improvements fade in the long term.
Three lessons learned
- Fiscal policy tools can reduce inequality, generally at the cost of anticipated long-term growth. The specific point to choose along this trade-off depends on society’s preferences for growth and inequality.
- Policy makers should consider both the benefits and costs of short and long term policies. What works best in the short term can become expensive in the long run. This does not automatically invalidate such policies – company preferences will have the last word – but it must be taken into consideration.
- Fiscal policy could more efficiently address the trade-off between equity and efficiency by taxing the excess profit of market-power firms in the automated economy.
The post-COVID era could see an acceleration in automation adoption, especially in light of the emerging labor shortage in many countries. Our analysis provides some insight into what the policy can do to improve the negative side effects of this process.
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