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Artificial Intelligence and Labor Market Disruptions: An Urgent Call for New Fiscal Policies

By: Dyon Elliott


Artificial intelligence can cause significant labor disruptions; therefore, governments should consider new approaches to fiscal policy and social-protection strategies, a recent IMF study advised.


The study, published on Monday, June 17th, delves into the potential and pitfalls of generative AI (gen AI) and offers a roadmap for policymakers to navigate the economic upheaval it may cause.


The IMF report, "Broadening the Gains from Generative AI: The Role of Fiscal Policies," highlights the dual nature of gen AI: while it promises to revolutionize productivity and public service delivery, it also threatens to displace a wide range of jobs, exacerbating inequality. "Generative artificial intelligence holds immense potential to boost productivity growth and advance public service delivery, but it also raises profound concerns about massive labor disruptions and rising inequality," the authors note.


The study's core message is clear: governments must adopt agile and forward-thinking fiscal policies to mitigate the adverse effects on the labor market. This involves not only updating social protection systems but also reevaluating tax structures that currently incentivize automation over employment. "Most countries have scope to broaden the coverage and generosity of unemployment insurance, improve portability of entitlements, and consider forms of wage insurance," the report suggests.


Reimagining Social Protection

One of the key recommendations is upgrading social protection systems to cushion the blow of job losses. The IMF points out that countries with more generous unemployment insurance (UI) saw less severe wage declines following automation-driven job losses. "More generous UI allows displaced workers to find jobs that better match their skills, which contributes to more efficient labor allocation," the study reveals. This approach is particularly effective for workers without a college degree, who are more reliant on UI benefits during periods of unemployment.


However, the IMF emphasizes that temporary boosts to UI, aligned with economic conditions, offer better outcomes than permanent increases, which can disincentivize job searches and increase fiscal burdens. The report illustrates this with a model showing that a temporary adjustment of UI benefits based on the unemployment rate yields the highest welfare benefits without discouraging job searches.


Tax Reforms to Balance Automation and Employment

The IMF study also explores the contentious issue of taxing automation. It acknowledges the practical challenges in implementing such taxes but argues for a reconsideration of corporate tax incentives that currently favor labor-displacing technologies. "Corporate tax systems that mimic the opposite of an automation tax and give preferential tax treatment to asset classes that are overall labor-displacing could be reconsidered to mitigate excessive labor displacement," the report advises.


Furthermore, the study underscores the importance of taxing capital income more effectively. With capital income being highly concentrated among top earners, enhancing the taxation of capital gains and addressing tax avoidance can help mitigate rising inequality and provide revenue for expanding social protection.


Preparing for the Future

Given the uncertainty surrounding the pace and impact of gen AI, the IMF urges policymakers to remain agile. This means preparing for both incremental changes and potentially disruptive shifts. "Policy should bring about conditions that steer innovation and deployment in ways that harness the benefits of gen AI and serve collective human interests, and it should be ready to cushion the transition costs for workers and households and prevent rising inequality," the authors conclude.


In addition to fiscal policy reforms, the report advocates for changes in education systems to better prepare workers for future job markets. Lifelong learning, sector-based training, and upskilling programs are essential to help workers transition to new roles as AI continues to evolve.

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