Knowledge that Transforms

We study learning via shared news. Each period agents receive the same quantity and quality of firsthand information and can share it with friends. Some friends (possibly few) share selectively, generating heterogeneous news diets across agents. Agents are aware of selective sharing and update beliefs by Bayes’s rule. Contrary to standard learning results, we show that beliefs can diverge in this environment, leading to polarization. This requires that (i) agents hold misperceptions (even minor) about friends’ sharing and (ii) information quality is sufficiently low. Polarization can worsen when agents’ friend networks expand. When the quantity of firsthand information becomes large, agents can hold opposite extreme beliefs, resulting in severe polarization. We find that news aggregators can curb polarization caused by news sharing. Our results hold without media bias or fake news, so eliminating these is not sufficient to reduce polarization. When fake news is included, it can lead to polarization but only through misperceived selective sharing. We apply our theory to shed light on the polarization of public opinion about climate change in the United States.

Patterns of state formation have crucial implications for comparative economic development. Diamond (1997) famously argued that “fractured land” was responsible for China’s tendency toward political unification and Europe’s protracted polycentrism. We build a dynamic model with granular geographical information in terms of topographical features and the location of productive agricultural land to quantitatively gauge the effects of fractured land on state formation in Eurasia. We find that topography alone is sufficient but not necessary to explain polycentrism in Europe and unification in China. Differences in land productivity, in particular the existence of a core region of high land productivity in northern China, deliver the same result. We discuss how our results map into observed historical outcomes, assess how robust our findings are, and analyze the differences between theory and data in Africa and the Americas.

Recent scholarship has suggested that artificial intelligence (AI) technology and autocratic regimes may be mutually reinforcing. We test for a mutually reinforcing relationship in the context of facial-recognition AI in China. To do so, we gather comprehensive data on AI firms and government procurement contracts, as well as on social unrest across China since the early 2010s. We first show that autocrats benefit from AI: local unrest leads to greater government procurement of facial-recognition AI as a new technology of political control, and increased AI procurement indeed suppresses subsequent unrest. We show that AI innovation benefits from autocrats’ suppression of unrest: the contracted AI firms innovate more both for the government and commercial markets and are more likely to export their products; noncontracted AI firms do not experience detectable negative spillovers. Taken together, these results suggest the possibility of sustained AI innovation under the Chinese regime: AI innovation entrenches the regime, and the regime’s investment in AI for political control stimulates further frontier innovation.

This article examines the relationship between ethnic-based gender norms and conflict-related sexual violence. We generate a novel dyadic data set that contains information on the ethnic identity of all the actors involved in ethnic civil conflicts around the world between 1989 and 2019 and their use of sexual violence. We exploit ethnographic information to construct a new male dominance index at the ethnicity level that captures deep-rooted gender norms. First, we find that male-dominant armed actors are more likely to be perpetrators of sexual violence. Second, we consider the cultural distance in gender norms between the combatants and show that sexual violence is driven by a specific clash of conceptions on the appropriate role of men and women in society: sexual violence increases when the perpetrator is more male dominant than the victim. Additional analyses suggest that gender norms influence both the strategic use of sexual violence for military purposes and the expressive use of sexual violence for private motivations. These patterns are specific to sexual violence and do not explain general violence in a conflict. Differences in other cultural dimensions unrelated to gender are not associated with conflict-related sexual violence.

We study time-consistent bank resolution mechanisms. The key constraint is that governments cannot avoid bailouts that are ex post efficient. Contrary to common wisdom, we show that the government may still avoid moral hazard and implement the first-best allocation by using the distribution of bailouts across banks to provide incentives. We analyze properties of credible tournament mechanisms that provide support to the best-performing banks and resolve the worst-performing ones. We extend our mechanism and show that it continues to perform well when banks are imperfect substitutes, when they are differentially interconnected as long as bailout funds can be earmarked, and when their risk-taking is driven by overoptimism instead of moral hazard.

We develop a new class of general-equilibrium models with partially unfunded debt to propose a fiscal theory of persistent inflation. In response to business cycle shocks, the monetary authority controls inflation and the fiscal authority stabilizes debt. However, the central bank accommodates unfunded fiscal shocks, causing persistent movements in inflation, output, and real interest rates. In an estimated quantitative model, fiscal inflation accounts for the bulk of inflation dynamics. In the aftermath of the pandemic, unfunded fiscal shocks sustain the recovery but also cause a persistent increase in inflation. The model is able to predict the inflationary effects of the American Rescue Plan Act fiscal stimulus out of sample and with real-time data.