Daron Acemoglu

We study the effects of testing policy on voluntary social distancing and the spread of an infection. Agents decide their social activity level, which determines a social network over which the virus spreads. Testing enables the isolation of infected individuals, slowing down the infection. However, greater testing also reduces voluntary social distancing or increases social activity, exacerbating the spread of the virus. We show that the effect of testing on infections is nonmonotone. This nonmonotonicity also implies that the optimal testing policy may leave some of the testing capacity of society unused.Funding: The authors acknowledge support from C3.DTI funding.Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2021.2220.

David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry. Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades. As EP Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy. Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors. Wages are unlikely to rise when workers cannot push for their share of productivity growth. Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. As in Ricardo’s time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages.

Despite the rapid pace of innovation in information and communications technologies (ICT) and electronics, aggregate US productivity growth has been disappointing since the 1970s. We propose and empirically explore the hypothesis that slow growth stems in part from an unbalanced sectoral distribution of innovation over the last several decades. Because an industry's success in innovation depends on complementary innovations among its input suppliers, rapid productivity growth that is concentrated in a subset of sectors may create bottlenecks and consequently fail to translate into commensurate aggregate productivity gains. Using data on input-output linkages, citation linkages, industry productivity growth and patenting, we find evidence consistent with this hypothesis: the variance of suppliers' Total Factor Productivity growth or innovation adversely affects an industry's own TFP growth and innovation. Our estimates suggest that a substantial share of the productivity slowdown in the United States (and several other industrialized economies) can be accounted for by a sizable increase in crossindustry variance of TFP growth and innovation. For example, if TFP growth variance had remained at the 1977-1987 level, US manufacturing productivity would have grown twice as rapidly in 1997-2007 as it did—yielding a counterfactual growth rate that would have been close to that of 1977-1987 and 1987-1997.

Does capital accumulation increase labor demand and wages? Neoclassical production functions, where capital and labor are q-complements, ensure that the answer is yes, so long as labor markets are competitive. This result critically depends on the assumption that capital accumulation does not change the technologies being developed and used. I adapt the theory of endogenous technological change to investigate this question when technology also responds to capital accumulation. I show that there are strong parallels between the relationship between capital and wages and existing results on the conditions under which equilibrium factor demands are upward-sloping (eg, Acemoglu, 2007). Extending this framework, I provide intuitive conditions and simple examples where a greater capital stock leads to lower wages, because it triggers more automation. I then offer an endogenous growth model with a menu of technologies where equilibrium involves choices over both the extent of automation and the rate of growth of labor-augmenting productivity. In this framework, capital accumulation and technological change in the long run are associated with wage growth, but an increase in the saving rate increases the extent of automation, and at first reduces the wage rate and subsequently depresses its long-run growth rate. JEL Classification: O30, O31, O33, C65.

In Power and progress, Daron Acemoglu and Simon Johnson argue that technological change exacerbates economic inequality, unless wise leaders take recommended actions. They further contend that artificial intelligence (AI) may pose an existential threat to democracy and capitalism. The problem is that we have seen this sort of argument before from Acemoglu, only to be proven wrong. For 25 years Acemoglu, often teamed with political scientist James Robinson, argued that decentralized democracy was the fundamental cause of national economic

Using large-scale survey data covering more than 110 countries and exploiting within-country variation across cohorts and surveys, we show that individuals with longer exposure to democracy display stronger support for democratic institutions. We bolster these baseline findings using an instrumental-variables strategy exploiting regional democratization waves and focusing on immigrants’ exposure to democracy before migration. In all cases, the timing and nature of the effects are consistent with a causal interpretation. We also establish that democracies breed their own support only when they are successful: all of the effects we estimate work through exposure to democracies that are successful in providing economic growth, peace and political stability, and public goods.

Generative artificial intelligence, including chatbots like ChatGPT, 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 probable impacts of generative AI on four critical domains: work, education, health, and information. Our goal is to warn about how generative AI could worsen existing inequalities while illuminating directions for using AI to resolve pervasive social problems. Generative AI in the workplace 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 improves diagnostics and accessibility but could deepen pre-existing inequalities. For information, it democratizes content creation and access but also dramatically expands the production and proliferation of misinformation. Each section covers a specific topic, evaluates existing research, identifies critical gaps, and recommends research directions. 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 contend that these policies should promote shared prosperity through the advancement of generative AI. We suggest several concrete policies to encourage further research and debate. This article emphasizes the …

In the mid-2010s, as computing and other digital technologies matured (), researchers began to speculate about a new era of innovation—with artificial intelligence (AI) as the standard-bearer of a “Fourth Industrial Revolution” (). The release of generative AI (Gen-AI) technologies (e.g., ChatGPT) in late 2022 reignited the discussion, prompting us to wonder: what are the barriers, risks, and potential rewards to using gen-AI for design and manufacturing? As Gen-AI has entered the mainstream, geopolitics and business practices have shifted. Covid-19 disrupted global supply chains, tensions with import partners have risen, and military conflicts introduce new uncertainties. As companies consider propositions like ‘reshoring’ or ‘nearshoring/friendshoring’ production (), we recognize other hindrances: suboptimal resource allocation, labor market volatility and trends toward an older and geographically mismatched workforce, and highly concentrated tech markets that foster anticompetitive business practices. As the United States expands domestic production capacity (e.g., semiconductors and electric vehicles), Gen-AI could help us overcome those challenges. To investigate the current and potential usefulness of Gen-AI in design and manufacturing, we interviewed industry experts—including engineers, manufacturers, tech executives, and entrepreneurs. They have identified many opportunities for the deployment of Gen-AI: (1) reducing the incidence of costly late-stage design changes when scaling production; (2) providing information to designers and engineers, including identifying suitable design spaces and material formulations and …