Publications

Financial regulators in the United States have come under increased pressure to improve the economic analysis that informs their regulatory decisions. Economic theory and empirical analysis both suggest that economic analysis of financial regulations should be no more difficult than economic analysis of other types of regulations. Financial regulatory agencies can produce useful economic analysis to inform regulatory decisions if they keep three principles in mind: (1) Focus on regulatory impact analysis (RIA), not just benefit-cost analysis (BCA); (2) The analysis is not the decision; and (3) Build institutional capacity to support objective analysis.

The European economy is now recovering briskly, after an unprecedented fall in output during the second quarter of 2020. But this recovery is likely to be incomplete for some time, not least because of the substantial degree of social distancing measures still in place. The defining feature of the present situation is that the remaining demand and supply obstacles are highly sector specific. Aggregate demand management will thus be less effective. Income replacement measures, such as short-term work schemes, will be needed for some time, but should be applied flexibly to support rather than hinder structural adjustment. This also applies to the funds to be made available under the €750 bn Recovery and Resilience Facility. Money is fungible. This means that the key for success will not be the projects to be financed by the RRF, but whether member states undertake structural reforms that increase their growth potential.

To meet the environmental challenges of a warming planet and an increasingly complex, high techeconomy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that environmental agencies will need to surmount if they are to take full advantage of big data and predictive analytics. First, agencies must invest in upgrading their information technology infrastructure to take advantage of computational advances. Relatively modest technology investments, if made wisely, could support the use of algorithmic tools that could yield substantial savings in other administrative costs. Second, agencies will need to confront emerging concerns about privacy, fairness, and transparency associated with its reliance on Big Data and algorithmic analyses. Third, government agencies will need to strengthen their human capital so that they have the personnel who understand how to use machine learning responsibly. Finally, to work well, algorithms will need clearly defined objectives. Environmental officials will need to continue to engage with elected officials, members of the public, environmental groups, and industry representatives to forge clarity and consistency over how various risk and regulatory objectives should be specified in machine learning tools. Overall, with thoughtful planning, adequate resources, and responsible management, governments should be able to overcome the obstacles that stand in the way of the use of artificial intelligence to improve environmental sustainability. If policy makers and the public will recognize the need for smarter governance, they can then start to tackle obstacles that stand in its way and better position society for a more sustainable future.