Optimal Adaptive Testing for Epidemic Control: Combining Molecular and Serology Tests.

Epidemic interventions based on surveillance testing programs are a fundamental tool to control the first stages of new epidemics, yet they are costly, invasive and rely on scarce resources, limiting their applicability. To overcome these challenges, we investigate two optimal control problems: (i) how testing needs can be minimized while maintaining the number of infected individuals below a desired threshold, and (ii) how peak infections can be minimized given a typically scarce testing budget. We find that in both cases the optimal testing policy for the well-known Susceptible-Infected-Recovered (SIR) model is adaptive, with testing rates that depend on the epidemic state, and leads to significant cost savings compared to non-adaptive policies. By using the concept of observability, we then show that a central planner can estimate the required unknown epidemic state by complementing molecular tests, which are highly sensitive but have a short detectability window, with serology tests, which are less sensitive but can detect past infections.

Innovation network.

Technological progress builds upon itself, with the expansion of invention in one domain propelling future work in linked fields. Our analysis uses 1.8 million US patents and their citation properties to map the innovation network and its strength. Past innovation network structures are calculated using citation patterns across technology classes during 1975-1994. The interaction of this preexisting network structure with patent growth in upstream technology fields has strong predictive power on future innovation after 1995. This pattern is consistent with the idea that when there is more past upstream innovation for a particular technology class to build on, then that technology class innovates more.

An evaluation of the FDA's analysis of the costs and benefits of the graphic warning label regulation.

The Family Smoking Prevention and Tobacco Control Act of 2009 gave the Food and Drug Administration (FDA) regulatory authority over cigarettes and smokeless tobacco products and authorised it to assert jurisdiction over other tobacco products. As with other Federal agencies, FDA is required to assess the costs and benefits of its significant regulatory actions. To date, FDA has issued economic impact analyses of one proposed and one final rule requiring graphic warning labels (GWLs) on cigarette packaging and, most recently, of a proposed rule that would assert FDA's authority over tobacco products other than cigarettes and smokeless tobacco. Given the controversy over the FDA's approach to assessing net economic benefits in its proposed and final rules on GWLs and the importance of having economic impact analyses prepared in accordance with sound economic analysis, a group of prominent economists met in early 2014 to review that approach and, where indicated, to offer suggestions for an improved analysis. We concluded that the analysis of the impact of GWLs on smoking substantially underestimated the benefits and overestimated the costs, leading the FDA to substantially underestimate the net benefits of the GWLs. We hope that the FDA will find our evaluation useful in subsequent analyses, not only of GWLs but also of other regulations regarding tobacco products. Most of what we discuss applies to all instances of evaluating the costs and benefits of tobacco product regulation and, we believe, should be considered in FDA's future analyses of proposed rules.

Political model of social evolution.

Almost all democratic societies evolved socially and politically out of authoritarian and nondemocratic regimes. These changes not only altered the allocation of economic resources in society but also the structure of political power. In this paper, we develop a framework for studying the dynamics of political and social change. The society consists of agents that care about current and future social arrangements and economic allocations; allocation of political power determines who has the capacity to implement changes in economic allocations and future allocations of power. The set of available social rules and allocations at any point in time is stochastic. We show that political and social change may happen without any stochastic shocks or as a result of a shock destabilizing an otherwise stable social arrangement. Crucially, the process of social change is contingent (and history-dependent): the timing and sequence of stochastic events determine the long-run equilibrium social arrangements. For example, the extent of democratization may depend on how early uncertainty about the set of feasible reforms in the future is resolved.

The impact of generative artificial intelligence on socioeconomic inequalities and policy making.

Generative artificial intelligence (AI) has the potential to both exacerbate and ameliorate existing socioeconomic inequalities. In this article, we provide a state-of-the-art interdisciplinary overview of the potential impacts of generative AI on (mis)information and three information-intensive domains: work, education, and healthcare. Our goal is to highlight how generative AI could worsen existing inequalities while illuminating how AI may help mitigate pervasive social problems. In the information domain, generative AI can democratize content creation and access but may dramatically expand the production and proliferation of misinformation. In the workplace, it can boost productivity and create new jobs, but the benefits will likely be distributed unevenly. In education, it offers personalized learning, but may widen the digital divide. In healthcare, it might improve diagnostics and accessibility, but could deepen pre-existing inequalities. In each section, we cover a specific topic, evaluate existing research, identify critical gaps, and recommend research directions, including explicit trade-offs that complicate the derivation of a priori hypotheses. We conclude with a section highlighting the role of policymaking to maximize generative AI's potential to reduce inequalities while mitigating its harmful effects. We discuss strengths and weaknesses of existing policy frameworks in the European Union, the United States, and the United Kingdom, observing that each fails to fully confront the socioeconomic challenges we have identified. We propose several concrete policies that could promote shared prosperity through the advancement of generative AI. This article emphasizes the need for interdisciplinary collaborations to understand and address the complex challenges of generative AI.

The Environment and Directed Technical Change.

This paper introduces endogenous and directed technical change in a growth model with environmental constraints. The final good is produced from "dirty" and "clean" inputs. We show that: (i) when inputs are sufficiently substitutable, sustainable growth can be achieved with temporary taxes/subsidies that redirect innovation toward clean inputs; (ii) optimal policy involves both "carbon taxes" and research subsidies, avoiding excessive use of carbon taxes; (iii) delay in intervention is costly, as it later necessitates a longer transition phase with slow growth; and (iv) use of an exhaustible resource in dirty input production helps the switch to clean innovation under laissez-faire. (JEL O33, O44, Q30, Q54, Q56, Q58).