In this study we examine the impact of a value-based insurance design (V-BID) program implemented between 2010 and 2013 at a large public employer in the state of Oregon. The program substantially increased cost-sharing, specifically copayments and coinsurance, for several healthcare services...

This chapter develops a unified framework for the study of how network interactions can function as a mechanism for propagation and amplification of microeconomic shocks. The framework nests various classes of games over networks, models of macroeconomic risk originating from microeconomic...

We quantify and explain the firm responses and worker impacts of foreign demand shocks to domestic production networks. To capture that firms can be indirectly exposed to such shocks by buying from or selling to domestic firms that import or export, we use Belgian data with information on both...

This paper describes the response of the economy to large shocks in a nonlinear production network. While arbitrary combinations of shocks can be studied, it focuses on a sector's tail centrality, which quantifies the effect of a large negative shock to the sector - a measure of the systemic...

This paper provides evidence on child penalties in female and male earnings in different countries. The estimates are based on event studies around the birth of the first child, using the specification proposed by Kleven et al. (2018). The analysis reveals some striking similarities in the...

This note lays out the basic Susceptible-Infected-Recovered (SIR) epidemiological model of contagion, with a target audience of economists who want a framework for understanding the effects of social distancing and containment policies on the evolution of contagion and interactions with the...

Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary policy and fiscal policy. We use the general framework of sequential predictions also called online machine learning to forecast...