As generative AI (gen AI) continues to advance, policymakers must proactively address the technology’s potential to disrupt labor markets and exacerbate income inequality. A recent paper by Era Dabla-Norris, Ruud de Mooij, and their colleagues offers a comprehensive analysis of this challenge and proposes fiscal policy measures to mitigate the negative effects while maximizing the benefits of AI.
At the heart of the paper’s arguments is the recognition that, like previous technological revolutions, gen AI will significantly boost productivity and economic growth. However, its transformative nature could lead to substantial job displacement across both low-skill and high-skill occupations, resulting in wage polarization and deepening income and wealth disparities.
Upgrading Social Protection Systems
To address this challenge, the authors emphasize the need to strengthen social protection systems, drawing lessons from historical evidence of how robust safety nets can cushion the adverse effects of technological transitions. They call for enhancing unemployment benefits, job retraining programs, and other forms of social insurance to support workers during periods of disruption caused by AI.
Reforming Tax Policies
The paper critically examines how existing tax policies often provide excessive incentives for firms to invest in labor-displacing technologies, thereby accelerating job losses. The authors suggest reforming these policies to reduce such incentives, including proposals for more effective taxation of capital income and the potential introduction of “automation taxes” to fund social protection measures.
Progressive Taxation and Redistribution
To address the growing inequality driven by AI, the paper advocates for progressive taxation, particularly on capital income. By enhancing the taxation of capital gains, corporate income, and wealth, governments can ensure that the benefits of AI-driven productivity gains are more evenly distributed across society. This approach is seen as crucial for generating the public revenue needed to invest in social protection and public goods.
Funding AI Innovation and Deployment
While acknowledging the significant private sector investment in AI, the authors highlight the need for public investment in areas where social returns are high but private investment is insufficient. This includes funding for fundamental research, digital infrastructure, and AI applications in public services such as education and healthcare. The goal is to ensure that AI technologies are developed and deployed in ways that maximize social benefits.
Agile Policy Frameworks
Given the uncertainty surrounding the future impacts of AI, the paper calls for agile and adaptable policy frameworks. Policymakers are urged to prepare for both business-as-usual and highly disruptive scenarios, continuously monitor technological trends, and have the capacity to implement rapid policy adjustments as needed. The authors emphasize the importance of international cooperation and the involvement of multiple stakeholders, including the private sector, academia, and civil society, in shaping AI policies.
As gen AI continues to advance, policymakers face the dual challenge of harnessing its potential for economic growth while mitigating its disruptive effects on labor markets and income inequality. The comprehensive analysis and recommendations outlined in this paper provide a roadmap for fiscal policies that can address these challenges. By strengthening social protection systems, reforming tax policies, promoting progressive taxation, investing in AI innovation and deployment, and developing agile policy frameworks, governments can ensure that the benefits of AI are broadly shared and its negative impacts are minimized.
