NewIncome Inequality and Job Creation 31 May 2026 Sebastian Doerr, Thomas Drechsel, and Donggyu LeeWe propose a novel channel through which rising income inequality affects job creation and macroeconomic outcomes. High-income households save relatively more in stocks and bonds but less in bank deposits. A rising top income share thereby increases the relative financing cost for bank-dependent firms, which in turn create fewer jobs compared to other firms.
NewLiquidity Traps, Prudential Policies and International Spillovers 31 May 2026 Javier Bianchi and Louphou CoulibalyWe investigate optimal monetary and macroprudential policies in an open economy with aggregate demand externalities and an occasionally binding zero lower bound constraint. Our analysis highlights that the optimal policy balances output stabilization and capital flow management. When macroprudential policy is available, monetary policy stabilizes the output gap.
NewPigovian Transport Pricing in Practice 19 May 2026 Beat Hintermann, Beaumont Schoeman, Joseph Molloy, Thomas Götschi, Alberto Castro, Christopher Tchervenkov, Uros Tomic, and Kay W. AxhausenWe implement Pigovian transport pricing in a field experiment in urban agglomerations of Switzerland over the course of 8 weeks. Our pricing considers the external costs from climate damages, health outcomes from pollution, accidents and physical activity, and congestion. It varies across time, space and mode of transport and is deducted from a budget provided to GPS-tracked participants.
NewThe Architecture of Social Networks and the Diffusion of Innovations 19 May 2026 Bryony ReichFor many technologies and behaviors, an agent’s benefit from adopting depends on his contacts adopting, and the benefit to his contacts of adopting depends on their contacts adopting. This paper examines how the architecture of these connections shapes the success or failure of the diffusion of innovations. We start with a standard model of diffusion with the key addition that some agents can coordinate their decisions. This captures the idea that people often talk and make decisions together with friends or family to adopt technologies.
NewClosing Gender Gaps Through Workplace Diversity: The Intergenerational Effects of World War I 15 May 2026 Abhay Aneja, Silvia Farina, and Guo XuThis paper combines personnel records of the U.S. government with census data to study how exposure to greater female representation at work can persistently reduce intergenerational gender gaps in labor market outcomes. Exploiting city-by-department variation in the sudden expansion of female employment during World War I, we find that daughters of civil servants exposed to female co-workers are more likely to work later in life.
NewThe Dynamics of Internal Migration: A New Fact and its Implications 15 May 2026 Greg Howard and Hansen ShaoWe propose a new model of internal migration, based on persistent and spatially-correlated idiosyncratic utility. The model is motivated by a new fact in the data that simple moving cost models struggle to match: the t-year interstate migration rate is proportional to the square root of t.
NewTechnology Transfer and Early Industrial Development: Evidence from the Sino-Soviet Alliance 15 May 2026 Michela Giorcelli and Bo LiThis paper studies the long-term effects of technology and know-how transfers on structural transformations. In the 1950s, the Soviet Union supported the construction of the 156 Projects, which were large-scale, capital-intensive industrial clusters in China. These projects included a technology transfer, consisting of state-of-the-art Soviet machinery and equipment, and a know-how transfer, via the training of Chinese engineers, production supervisors, and high-skilled technicians by Soviet experts.
NewJackknife Standard Errors for Clustered Regression 15 May 2026 Bruce E. HansenThis paper presents a theoretical case for replacement of conventional heteroskedasticity-consistent and cluster-robust variance estimators with jackknife variance estimators, in the context of linear regression with heteroskedastic and/or cluster-dependent observations. We examine the bias of variance estimation and the coverage probabilities of confidence intervals. Concerning bias, we show that conventional variance estimators have full downward worst-case bias, while our jackknife variance estimator is never downward biased.
