Redistributive policy

The role of fiscal policy

Careful calibration of spending and tax policies can reduce the inequalities caused by automation.

For many observers, automation has been responsible for both strong economic growth and rising inequality in many countries in recent decades. Automation increases productivity, but it can exacerbate inequalities. In effect, it replaces low-skilled workers and helps owners of capital earn higher monopoly rents. And with the advent of next-level automation in the form of robots, the challenge is more pressing than ever.

Fiscal policy instruments can reduce inequality, usually at the cost of a loss of long-term growth.

However, in recent IMF staff research, we find that good fiscal policies (public spending and tax 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 sets of fiscal policies to deal with growth-inequality trade-offs in the age of automation. Inequality can usually be reduced by redistributing some of the gains from automation from the winners (owners of capital and skilled workers) to the losers (usually low-skilled workers, who suffer job loss and low wages) . That said, redistributive policies generally require additional taxation, which can depress investment and labor supply and thus reduce output. We discuss the pros and cons of various policy sets and seek to define the relevant growth-inequality trade-offs for each.

Find the right balance

For our analysis, we have captured the defining characteristics of automation: replacing low-skilled workers and increasing the productivity, profits and therefore market power of its adopters. We link firm market power to the degree of automation based on empirical evidence. Specifically, we assume a positive correlation between firms’ markup (a measure of market power) and their use of robots (an indicator of automation), calibrating the relationship using US data. Intuitively, the higher the number of robots per worker, the higher the productivity and the higher the profits. For example, large companies can take advantage of owning the platform they have created and acquiring other companies in the same industry to gain high market shares and large margins.

Our research examines growth-inequality trade-offs through the lens of three tax and redistribution regimes: a capital income tax, a tax on corporate excess profits (the gross-up tax), and a robot tax. . All packages involve an increase in a particular tax, the proceeds of which are used for transfers to low-skilled workers. A fourth package directly reduces payroll tax for unskilled workers.

We found that the effects and trade-offs are very different in the short term compared to the long term. In the short term, three sets of measures (excluding the capital income tax) generate modest gains in per capita output and a considerable reduction in inequality. However, as time passes, capital accumulation and productivity begin to slow. The robot tax is the most powerful tool to reduce inequality because it slows the replacement of low-skilled labor by robots, but the flip side is a slower accumulation of highly productive robots and loss of production. Similarly, a tax cut on the wages of unskilled workers both reduces inequality and increases output in the short run, while the larger share of unskilled labor (less productive than robots) weighs on long-term productivity.

Another way to approach 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 therefore complement) robots in the production process, will see an initial increase in income, but a gradual decline over a longer period. Unskilled workers benefit sustainably from redistribution policies, even if the improvements falter in the long term.

Three lessons learned

  • Fiscal policy instruments can reduce inequality, usually at the cost of a loss of long-term growth. The specific point to choose in this trade-off depends on society’s preferences for growth and inequality.
  • Policy makers need to consider both the short- and long-term benefits and costs of policies. What works best in the short term can become costly in the long term. This does not automatically invalidate these policies – societal preferences will have the final say – but should be taken into account.
  • Fiscal policy could more effectively address the equity-efficiency trade-off by taxing the excess profits of firms with market power in the automated economy.

The post-COVID era could see an acceleration in the adoption of automation, especially given the emerging labor shortages in many countries. Our analysis provides insight into what politics can do to mitigate the negative side effects of this process.

Related links:

What pandemics mean for robots and inequality

Public opinion on automation

How artificial intelligence could widen the gap between rich and poor countries

Bridging the gaps: labor policies for a fairer recovery